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diff --git a/ecohydrology_gridhydromet/ogh_newmexico/observatory_gridmet_newmexico.ipynb b/ecohydrology_gridhydromet/ogh_newmexico/observatory_gridmet_newmexico.ipynb
new file mode 100644
index 0000000..7430971
--- /dev/null
+++ b/ecohydrology_gridhydromet/ogh_newmexico/observatory_gridmet_newmexico.ipynb
@@ -0,0 +1,9059 @@
+{
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "<img src=\"http://landlab.github.io/assets/Landlab-logo.png\"\n",
+    "style=\"float:left;width:150px;padding:0px\"> \n",
+    "<br />\n",
+    "<br />\n",
+    "<br />\n",
+    "<br />\n",
+    "<br />\n",
+    "<br />\n",
+    "<br />\n",
+    "<br />\n",
+    "\n",
+    "### Use Case 3 supplement: Downloading Gridded Hydrometeorology forcing for a Cellular Automaton Ecohydrologic Model\n",
+    "\n",
+    "* Import a shapefile for New Mexico.\n",
+    "* Set three distinct elevation bands.\n",
+    "* Calculate mean climatology for each of the three elevation bands.\n",
+    "* Save mean characteristics for each elevation band in a separate JSON file.\n",
+    "<br />\n",
+    "\n",
+    "\n",
+    "\n",
+    "**This research is actively being developed by the Watershed Dynamics Research Group in the Civil and Environmental Engineering Department at the University of Washington for the Thunder Creek basin in the Skagit Watershed, WA, USA in collaboration with the HydroShare team, CUAHSI and NCSA partners.**\n",
+    " <br /> <img src=\"https://www.washington.edu/brand/files/2014/09/W-Logo_Purple_Hex.png\" style=\"float:left;width:120px;padding:10px\">   \n",
+    "<img src=\"http://www.ncsa.illinois.edu/assets/img/logos_ncsa.png\"\n",
+    " style=\"float:right;width:120px;padding:10px\"> \n",
+    "<img src=\"https://www.cuahsi.org/assets/images/logo.png\">"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## To run this notebook:\n",
+    "\n",
+    "Click in or on each shaded code block below and use ***shift + enter*** or ***shift + return*** keys. Alternatively, you can run groups of cells by clicking ***Cell*** on the menu above and selecting among the various run options. This is also where you can clear outputs from previous runs.\n",
+    "\n",
+    "If an error occurs, click on ***Kernel*** and then select ***Restart and Clear Outputs*** in the menu above. While exiting the notebook, click ***File*** and then select ***Close and Halt***.\n",
+    "<br />For more instructions on how to run an interactive IPython notebook, click here: <a href=\"https://github.com/landlab/tutorials/blob/master/README.md\">https://github.com/landlab/tutorials/blob/master/README.md</a></small><br>\n",
+    "<small>For tutorials on learning Landlab, click here: <a href=\"https://github.com/landlab/landlab/wiki/Tutorials\">https://github.com/landlab/landlab/wiki/Tutorials</a></small>\n",
+    "<br>\n",
+    "<small>For the unexpanded version to download and run, click here: <a href=\"http://nbviewer.jupyter.org/github/landlab/tutorials/blob/master/ecohydrology/cellular_automaton_vegetation_DEM/cellular_automaton_vegetation_DEM_unexpanded.ipynb\">http://nbviewer.jupyter.org/github/landlab/tutorials/blob/master/ecohydrology/cellular_automaton_vegetation_DEM/cellular_automaton_vegetation_DEM_unexpanded.ipynb</a></small><br>"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## 1.  Notebook Setup and Preparation\n",
+    "\n",
+    "This notebook is a supplemental notebook for 'Use case 3 - Reuse_ecohydrology_gridhydromet.ipynb'. In this notebook, we will use ***ogh***, a conda package to download and process gridded hydro-meteorologic datasets, to download Livneh et al. 2015 dataset that has historical daily precipitation and temperature data for years 1950 through 2013.\n",
+    "\n",
+    "\n",
+    "**References**\n",
+    "\n",
+    "Livneh B., T.J. Bohn, D.S. Pierce, F. Munoz-Ariola, B. Nijssen, R. Vose, D. Cayan, and L.D. Brekke, 2015: A spatially comprehensive, hydrometeorological data set for Mexico, the U.S., and southern Canada 1950-2013, Nature Scientific Data, 5:150042, doi:10.1038/sdata.2015.42.\n",
+    "\n",
+    "Phuong, J., Bandaragoda C., Istanbulluoglu, E., Beveridge, C., Strauch, R., Setiawan, L., Mooney, S. D. (in-review). Automated retrieval, preprocessing, and visualization of gridded hydrometeorology data products for spatial-temporal exploratory analysis and intercomparison."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "** 1.1.1  Import libraries installed on CUAHSI JupyterHub server **"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "To run this notebook, we must import several libraries.\n",
+    "The hs_utils library provides functions for interacting with HydroShare, including resource querying, dowloading. and creation. Additional libraries support the functions of Landlab."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 1,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Adding the following system variables:\n",
+      "   HS_USR_NAME = saisiddu\n",
+      "   HS_RES_ID = 70b977e22af544f8a7e5a803935c329c\n",
+      "   HS_RES_TYPE = genericresource\n",
+      "   JUPYTER_HUB_IP = jupyter.cuahsi.org\n",
+      "\n",
+      "These can be accessed using the following command: \n",
+      "   os.environ[key]\n",
+      "\n",
+      "   (e.g.)\n",
+      "   os.environ[\"HS_USR_NAME\"]  => saisiddu\n",
+      "Successfully established a connection with HydroShare\n"
+     ]
+    }
+   ],
+   "source": [
+    "#Python libraries available on CUAHSI JupyterHub \n",
+    "import os, json\n",
+    "import numpy as np\n",
+    "import pandas as pd\n",
+    "from datetime import datetime, timedelta\n",
+    "\n",
+    "import warnings \n",
+    "warnings.filterwarnings('ignore')\n",
+    "\n",
+    "# tools for downloading & processing gridded hydro-meteorology data\n",
+    "import ogh\n",
+    "\n",
+    "#HydroShare Utilities\n",
+    "from utilities import hydroshare\n",
+    "hs=hydroshare.hydroshare()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "**1.1.2 Where are we in Hydroshare space?**"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 3,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Data will be loaded from and save to:/home/jovyan/work/notebooks/data/70b977e22af544f8a7e5a803935c329c/70b977e22af544f8a7e5a803935c329c/data/contents\n"
+     ]
+    }
+   ],
+   "source": [
+    "homedir = ogh.mapContentFolder(str(os.environ[\"HS_RES_ID\"]))\n",
+    "print('Data will be loaded from and save to:'+homedir)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## 2. Download and generate lists of gridded climate points for a watershed\n",
+    "\n",
+    "For this use case, we will explore the influence of climatology of New Mexico on vegetation pattern evolution. We will specifically be looking at the change in climatology with respect to elevation. Therefore, we need to download data for the entire state of New Mexico, parse this data and group them based on the elevation ranges, and finally calculate mean characteristics for each group and store them in JSON files.\n",
+    "\n",
+    "For this exercise, we will retrieve the shape file for New mexico from the resource in the following url: https://www.hydroshare.org/resource/5b401852758e4d49a87a788c1008b027/. The data for our processing routines can be retrieved using the *getResourceFromHydroShare* function by passing in the global identifier from the url above.  In the next cell, we will download this resource from HydroShare, and identify that the table in this resource is the 'mappingfile' variable identifying the Lat/Long points to be used for downloading hydrometeorology data.  The file must include columns with station numbers (this can be aribitrary), latitude, longitude, and elevation. The header of these columns must be FID, LAT, LONG_, and ELEV or RASTERVALU, respectively. The station numbers will be used for the remainder of the code to uniquely reference data from each climate station, as well as to identify minimum, maximum, and average elevation of all of the climate stations."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "**2.1 Download areas of interest from HydroShare Resources**"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 4,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "This resource already exists in your userspace.\n",
+      "5b401852758e4d49a87a788c1008b027/\n",
+      "|-- 5b401852758e4d49a87a788c1008b027/\n",
+      "|   |-- bagit.txt\n",
+      "|   |-- manifest-md5.txt\n",
+      "|   |-- readme.txt\n",
+      "|   |-- tagmanifest-md5.txt\n",
+      "|   |-- data/\n",
+      "|   |   |-- resourcemap.xml\n",
+      "|   |   |-- resourcemetadata.xml\n",
+      "|   |   |-- contents/\n",
+      "|   |   |   |-- New_Mexico.cpg\n",
+      "|   |   |   |-- New_Mexico.shp\n",
+      "|   |   |   |-- NewMexicoEcohydrology.jpg\n",
+      "|   |   |   |-- New_Mexico.shx\n",
+      "|   |   |   |-- New_Mexico.dbf\n",
+      "|   |   |   |-- New_Mexico.prj\n",
+      "|   |   |   |-- New_Mexico.shp.xml\n",
+      "\n",
+      "Do you want to overwrite these data [Y/n]? Y\n",
+      "Download Finished                               \n",
+      "Successfully downloaded resource 5b401852758e4d49a87a788c1008b027\n"
+     ]
+    },
+    {
+     "data": {
+      "text/html": [
+       "<b>Found the following file(s) associated with this HydroShare resource.</b>"
+      ],
+      "text/plain": [
+       "<IPython.core.display.HTML object>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "text/html": [
+       "New_Mexico.cpg<br>New_Mexico.dbf<br>NewMexicoEcohydrology.jpg<br>New_Mexico.prj<br>New_Mexico.sbn<br>New_Mexico.sbx<br>New_Mexico.shp<br>New_Mexico.shp.xml<br>New_Mexico.shx"
+      ],
+      "text/plain": [
+       "<IPython.core.display.HTML object>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "text/html": [
+       "These files are stored in a dictionary called <b>hs.content</b> for your convenience.  To access a file, simply issue the following command where MY_FILE is one of the files listed above: <pre>hs.content[\"MY_FILE\"] </pre> "
+      ],
+      "text/plain": [
+       "<IPython.core.display.HTML object>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "# This resource type is Composite Resource containing a Geographic Feature (ESRI Shapefile). \n",
+    "hs.getResourceFromHydroShare('5b401852758e4d49a87a788c1008b027')\n",
+    "download_extent_shapefile = hs.content['New_Mexico.shp']"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 5,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "This resource already exists in your userspace.\n",
+      "ef2d82bf960144b4bfb1bae6242bcc7f/\n",
+      "|-- ef2d82bf960144b4bfb1bae6242bcc7f/\n",
+      "|   |-- bagit.txt\n",
+      "|   |-- manifest-md5.txt\n",
+      "|   |-- readme.txt\n",
+      "|   |-- tagmanifest-md5.txt\n",
+      "|   |-- data/\n",
+      "|   |   |-- resourcemap.xml\n",
+      "|   |   |-- resourcemetadata.xml\n",
+      "|   |   |-- contents/\n",
+      "|   |   |   |-- NAmer_dem_list.cpg\n",
+      "|   |   |   |-- NAmer_dem_list.dbf\n",
+      "|   |   |   |-- NAmer_dem_list.prj\n",
+      "|   |   |   |-- NAmer_dem_list.sbn\n",
+      "|   |   |   |-- NAmer_dem_list.sbx\n",
+      "|   |   |   |-- NAmer_dem_list.shp\n",
+      "|   |   |   |-- NAmer_dem_list.shx\n",
+      "\n",
+      "Do you want to overwrite these data [Y/n]? Y\n",
+      "Download Finished                               \n",
+      "Successfully downloaded resource ef2d82bf960144b4bfb1bae6242bcc7f\n"
+     ]
+    },
+    {
+     "data": {
+      "text/html": [
+       "<b>Found the following file(s) associated with this HydroShare resource.</b>"
+      ],
+      "text/plain": [
+       "<IPython.core.display.HTML object>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "text/html": [
+       "NAmer_dem_list.cpg<br>NAmer_dem_list.dbf<br>NAmer_dem_list.prj<br>NAmer_dem_list.sbn<br>NAmer_dem_list.sbx<br>NAmer_dem_list.shp<br>NAmer_dem_list.shx"
+      ],
+      "text/plain": [
+       "<IPython.core.display.HTML object>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "text/html": [
+       "These files are stored in a dictionary called <b>hs.content</b> for your convenience.  To access a file, simply issue the following command where MY_FILE is one of the files listed above: <pre>hs.content[\"MY_FILE\"] </pre> "
+      ],
+      "text/plain": [
+       "<IPython.core.display.HTML object>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "### TreatGeoSelf to a mapping tool that uses your shapefile\n",
+    "# Reuse this exising point shapefile of available 1/16 degree grid centroid locations \n",
+    "# for Conterminous United States (CONUS) shared on HydroShare as a public resource\n",
+    "\n",
+    "hs.getResourceFromHydroShare('ef2d82bf960144b4bfb1bae6242bcc7f')\n",
+    "NAmer = hs.content['NAmer_dem_list.shp']"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "**2.2.1 Read in the Observatory metadata file**\n",
+    "\n",
+    "This file contains the variables, data types and metadata related to Livneh et al., 2013; 2015 and Salathe et al., 2014 gridded hydrometeorology products. "
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 6,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "['dailymet_bclivneh2013',\n",
+       " 'dailymet_livneh2013',\n",
+       " 'dailymet_livneh2015',\n",
+       " 'dailyvic_livneh2013',\n",
+       " 'dailyvic_livneh2015',\n",
+       " 'dailywrf_bcsalathe2014',\n",
+       " 'dailywrf_salathe2014']"
+      ]
+     },
+     "execution_count": 6,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "#Assuming this is pulled from Github, how can we import this from Utilities.\n",
+    "#Otherwise it needs to be in each HydroShare resource - which if fine too. \n",
+    "# initialize ogh_meta data file\n",
+    "meta_file = dict(ogh.ogh_meta())\n",
+    "sorted(meta_file.keys())"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "**2.2.2 TreatGeoSelf() to a easily generated list of lat/long points in your area of interest**"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 7,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "(7917, 4)\n",
+      "   FID       LAT      LONG_    ELEV\n",
+      "0    0  36.96875 -103.03125  1431.0\n",
+      "1    1  36.96875 -103.09375  1462.0\n",
+      "2    2  36.96875 -103.15625  1437.0\n",
+      "3    3  36.96875 -103.21875  1451.0\n",
+      "4    4  36.96875 -103.28125  1478.0\n",
+      "/home/jovyan/work/notebooks/data/70b977e22af544f8a7e5a803935c329c/70b977e22af544f8a7e5a803935c329c/data/contents/new_mexico_gridmet.csv\n",
+      "CPU times: user 23.4 s, sys: 527 ms, total: 24 s\n",
+      "Wall time: 24 s\n"
+     ]
+    }
+   ],
+   "source": [
+    "%%time\n",
+    "#New Mexico Extent is approximately 7900 gridded cells\n",
+    "mappingfile = ogh.treatgeoself(shapefile=download_extent_shapefile, \n",
+    "                               NAmer=NAmer, \n",
+    "                               buffer_distance=0,\n",
+    "                               mappingfile=os.path.join(homedir,'new_mexico_gridmet.csv'))\n",
+    "print(mappingfile)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 8,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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+      "text/plain": [
+       "<Figure size 1500x1500 with 2 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "ogh.griddedCellGradient(mappingfile=mappingfile, \n",
+    "                        shapefile=download_extent_shapefile,\n",
+    "                        outfilepath=os.path.join(homedir, 'NM_elev_gradient.png'),\n",
+    "                        plottitle='New Mexico watershed\\nGridded cell elevation gradient',\n",
+    "                        colorbar_label='Average elevation (meters)',\n",
+    "                        spatial_resolution=1/16, margin=0.25, epsg=3857, column='ELEV',\n",
+    "                        basemap_image='Elevation/World_Hillshade', cmap='terrain')"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    " ## 3. Download gridded climate data \n",
+    " \n",
+    " \n",
+    " **3.1 Get Daily Meteorologic Data from  Livneh et al. 2015 (1950-2013; North America extent)**\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 9,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# %%time\n",
+    "# # uncomment and run if the files have already been downloaded\n",
+    "# ogh.remapCatalog(homedir=homedir, \n",
+    "#                  mappingfile=mappingfile, \n",
+    "#                  subdir='livneh2015/Daily_MET_1950_2013/raw', \n",
+    "#                  catalog_label='dailymet_livneh2015')"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 10,
+   "metadata": {
+    "scrolled": true
+   },
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Meteorology_Livneh_NAmerExt_15Oct2014_36.46875_-104.09375 unzipped\n",
+      "Meteorology_Livneh_NAmerExt_15Oct2014_36.21875_-104.84375 unzipped\n",
+      "Meteorology_Livneh_NAmerExt_15Oct2014_36.71875_-103.53125 unzipped\n",
+      "Meteorology_Livneh_NAmerExt_15Oct2014_36.96875_-103.03125 unzipped\n",
+      "Meteorology_Livneh_NAmerExt_15Oct2014_35.96875_-105.59375 unzipped\n",
+      "Meteorology_Livneh_NAmerExt_15Oct2014_36.21875_-104.90625 unzipped\n",
+      "Meteorology_Livneh_NAmerExt_15Oct2014_36.46875_-104.15625 unzipped\n",
+      "Meteorology_Livneh_NAmerExt_15Oct2014_36.96875_-103.09375 unzipped\n",
+      "Meteorology_Livneh_NAmerExt_15Oct2014_35.96875_-105.65625 unzipped\n",
+      "Meteorology_Livneh_NAmerExt_15Oct2014_36.71875_-103.59375 unzipped\n",
+      "Meteorology_Livneh_NAmerExt_15Oct2014_36.46875_-104.21875 unzipped\n",
+      "Meteorology_Livneh_NAmerExt_15Oct2014_36.21875_-104.96875 unzipped\n",
+      "Meteorology_Livneh_NAmerExt_15Oct2014_36.96875_-103.15625 unzipped\n",
+      "Meteorology_Livneh_NAmerExt_15Oct2014_35.96875_-105.71875 unzipped\n",
+      "Meteorology_Livneh_NAmerExt_15Oct2014_36.71875_-103.65625 unzipped\n",
+      "Meteorology_Livneh_NAmerExt_15Oct2014_36.46875_-104.28125 unzipped\n",
+      "Meteorology_Livneh_NAmerExt_15Oct2014_35.96875_-105.78125 unzipped\n",
+      "Meteorology_Livneh_NAmerExt_15Oct2014_36.96875_-103.21875 unzipped\n",
+      "Meteorology_Livneh_NAmerExt_15Oct2014_36.21875_-105.03125 unzipped\n",
+      "Meteorology_Livneh_NAmerExt_15Oct2014_36.71875_-103.71875 unzipped\n",
+      "Meteorology_Livneh_NAmerExt_15Oct2014_36.46875_-104.34375 unzipped\n",
+      "Meteorology_Livneh_NAmerExt_15Oct2014_35.96875_-105.84375 unzipped\n",
+      "Meteorology_Livneh_NAmerExt_15Oct2014_36.21875_-105.09375 unzipped\n",
+      "Meteorology_Livneh_NAmerExt_15Oct2014_36.96875_-103.28125 unzipped\n",
+      "Meteorology_Livneh_NAmerExt_15Oct2014_36.71875_-103.78125 unzipped\n",
+      "Meteorology_Livneh_NAmerExt_15Oct2014_36.46875_-104.40625 unzipped\n",
+      "Meteorology_Livneh_NAmerExt_15Oct2014_35.96875_-105.90625 unzipped\n",
+      "Meteorology_Livneh_NAmerExt_15Oct2014_36.21875_-105.15625 unzipped\n",
+      "Meteorology_Livneh_NAmerExt_15Oct2014_36.96875_-103.34375 unzipped\n",
+      "Meteorology_Livneh_NAmerExt_15Oct2014_36.71875_-103.84375 unzipped\n",
+      "Meteorology_Livneh_NAmerExt_15Oct2014_36.46875_-104.46875 unzipped\n",
+      "Meteorology_Livneh_NAmerExt_15Oct2014_36.21875_-105.21875 unzipped\n",
+      "Meteorology_Livneh_NAmerExt_15Oct2014_35.96875_-105.96875 unzipped\n",
+      "Meteorology_Livneh_NAmerExt_15Oct2014_36.96875_-103.40625 unzipped\n",
+      "Meteorology_Livneh_NAmerExt_15Oct2014_36.71875_-103.90625 unzipped\n",
+      "Meteorology_Livneh_NAmerExt_15Oct2014_36.46875_-104.53125 unzipped\n",
+      "Meteorology_Livneh_NAmerExt_15Oct2014_35.96875_-106.03125 unzipped\n",
+      "Meteorology_Livneh_NAmerExt_15Oct2014_36.96875_-103.46875 unzipped\n",
+      "Meteorology_Livneh_NAmerExt_15Oct2014_36.71875_-103.96875 unzipped\n",
+      "Meteorology_Livneh_NAmerExt_15Oct2014_36.21875_-105.28125 unzipped\n",
+      "Meteorology_Livneh_NAmerExt_15Oct2014_36.46875_-104.59375 unzipped\n",
+      "Meteorology_Livneh_NAmerExt_15Oct2014_35.96875_-106.09375 unzipped\n",
+      "Meteorology_Livneh_NAmerExt_15Oct2014_36.96875_-103.53125 unzipped\n",
+      "Meteorology_Livneh_NAmerExt_15Oct2014_36.71875_-104.03125 unzipped\n",
+      "Meteorology_Livneh_NAmerExt_15Oct2014_36.21875_-105.34375 unzipped\n",
+      "Meteorology_Livneh_NAmerExt_15Oct2014_36.46875_-104.65625 unzipped\n",
+      "Meteorology_Livneh_NAmerExt_15Oct2014_36.21875_-105.40625 unzipped\n",
+      "Meteorology_Livneh_NAmerExt_15Oct2014_36.96875_-103.59375 unzipped\n",
+      "Meteorology_Livneh_NAmerExt_15Oct2014_36.71875_-104.09375 unzipped\n",
+      "Meteorology_Livneh_NAmerExt_15Oct2014_35.96875_-106.15625 unzipped\n",
+      "Meteorology_Livneh_NAmerExt_15Oct2014_36.46875_-104.71875 unzipped\n",
+      "Meteorology_Livneh_NAmerExt_15Oct2014_36.21875_-105.46875 unzipped\n",
+      "Meteorology_Livneh_NAmerExt_15Oct2014_36.71875_-104.15625 unzipped\n",
+      "Meteorology_Livneh_NAmerExt_15Oct2014_35.96875_-106.21875 unzipped\n",
+      "Meteorology_Livneh_NAmerExt_15Oct2014_36.96875_-103.65625 unzipped\n",
+      "Meteorology_Livneh_NAmerExt_15Oct2014_36.46875_-104.78125 unzipped\n",
+      "Meteorology_Livneh_NAmerExt_15Oct2014_36.71875_-104.21875 unzipped\n",
+      "Meteorology_Livneh_NAmerExt_15Oct2014_36.21875_-105.53125 unzipped\n",
+      "Meteorology_Livneh_NAmerExt_15Oct2014_35.96875_-106.28125 unzipped\n",
+      "Meteorology_Livneh_NAmerExt_15Oct2014_36.96875_-103.71875 unzipped\n",
+      "Meteorology_Livneh_NAmerExt_15Oct2014_36.46875_-104.84375 unzipped\n",
+      "Meteorology_Livneh_NAmerExt_15Oct2014_36.71875_-104.28125 unzipped\n",
+      "Meteorology_Livneh_NAmerExt_15Oct2014_36.21875_-105.59375 unzipped\n",
+      "Meteorology_Livneh_NAmerExt_15Oct2014_35.96875_-106.34375 unzipped\n",
+      "Meteorology_Livneh_NAmerExt_15Oct2014_36.96875_-103.78125 unzipped\n",
+      "Meteorology_Livneh_NAmerExt_15Oct2014_36.46875_-104.90625 unzipped\n",
+      "Meteorology_Livneh_NAmerExt_15Oct2014_36.71875_-104.34375 unzipped\n",
+      "Meteorology_Livneh_NAmerExt_15Oct2014_36.21875_-105.65625 unzipped\n",
+      "Meteorology_Livneh_NAmerExt_15Oct2014_36.96875_-103.84375 unzipped\n",
+      "Meteorology_Livneh_NAmerExt_15Oct2014_35.96875_-106.40625 unzipped\n",
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+    {
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+     "text": [
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+     "text": [
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+     "text": [
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+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
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+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
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+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
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+      "Meteorology_Livneh_NAmerExt_15Oct2014_32.09375_-104.78125 unzipped\n",
+      "CPU times: user 15.1 s, sys: 5.23 s, total: 20.4 s\n",
+      "Wall time: 28min 44s\n"
+     ]
+    },
+    {
+     "data": {
+      "text/plain": [
+       "'/home/jovyan/work/notebooks/data/70b977e22af544f8a7e5a803935c329c/70b977e22af544f8a7e5a803935c329c/data/contents/livneh2015/Daily_MET_1950_2013/raw'"
+      ]
+     },
+     "execution_count": 10,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "%%time\n",
+    "# From the reference mapping file, read-in, download, and unzip the data files for the longitude and latitude points\n",
+    "ogh.getDailyMET_livneh2015(homedir, mappingfile)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 11,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead tr th {\n",
+       "        text-align: left;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr>\n",
+       "      <th>Watershed</th>\n",
+       "      <th>New Mexico</th>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>Median elevation in meters [range](No. gridded cells)</th>\n",
+       "      <th>1744[891-3515] (n=7917)</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>dailymet_livneh2015</th>\n",
+       "      <td>1744[891-3515] (n=7917)</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "Watershed                                                           New Mexico\n",
+       "Median elevation in meters [range](No. gridded cells)  1744[891-3515] (n=7917)\n",
+       "dailymet_livneh2015                                    1744[891-3515] (n=7917)"
+      ]
+     },
+     "execution_count": 11,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "t1 = ogh.mappingfileSummary(listofmappingfiles = [mappingfile], \n",
+    "                            listofwatershednames = ['New Mexico'],\n",
+    "                            meta_file=meta_file)\n",
+    "\n",
+    "t1"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## 4.  Create gridded climate data dictionary"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "We have now downloaded all the files required to segregate meteorological forcings of interest (daily-- precipitation, maximum temperature and minimum temperature) for New Mexico. The ***gridclim_dict*** method from the python package ***ogh*** allows us to group the grid elements based on an elevation range. We can save these files in JSON format using ***saveDictOfDf*** method from ***ogh***."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "**Grouping data for low elevation (1200 m to 1700 m).**"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 12,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Number of data files within elevation range (1200:1700): 3145\n",
+      "PRECIP dataframe reading to start: 0:00:05.698512\n",
+      "PRECIP dataframe reading complete:0:00:48.461333\n",
+      "TMAX dataframe reading to start: 0:00:54.220242\n",
+      "TMAX dataframe reading complete:0:01:36.920118\n",
+      "TMIN dataframe reading to start: 0:01:42.628494\n",
+      "TMIN dataframe reading complete:0:02:23.288719\n",
+      "WINDSPD dataframe reading to start: 0:02:28.962937\n",
+      "WINDSPD dataframe reading complete:0:03:06.622517\n",
+      "PRECIP_dailymet_livneh2015 calculations completed in 0:00:10.336606\n",
+      "TMAX_dailymet_livneh2015 calculations completed in 0:00:10.850806\n",
+      "TMIN_dailymet_livneh2015 calculations completed in 0:00:10.861329\n",
+      "WINDSPD_dailymet_livneh2015 calculations completed in 0:00:10.820866\n"
+     ]
+    }
+   ],
+   "source": [
+    "min_elev=1200\n",
+    "max_elev=1700\n",
+    "\n",
+    "ltm_low = ogh.gridclim_dict(mappingfile=mappingfile,\n",
+    "                            dataset='dailymet_livneh2015',\n",
+    "                            metadata=meta_file,\n",
+    "                            min_elev=min_elev,\n",
+    "                            max_elev=max_elev)\n",
+    "\n",
+    "ogh.saveDictOfDf(dictionaryObject=ltm_low, outfilepath=os.path.join(homedir,'NewMexico_lowElevation.json'))\n",
+    "\n",
+    "sorted(ltm_low.keys())\n",
+    "del ltm_low"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "**Grouping data for mid elevation (1700 m to 2000 m).**"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 13,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Number of data files within elevation range (1700:2000): 1728\n",
+      "PRECIP dataframe reading to start: 0:00:03.688706\n",
+      "PRECIP dataframe reading complete:0:00:25.697995\n",
+      "TMAX dataframe reading to start: 0:00:28.917054\n",
+      "TMAX dataframe reading complete:0:00:49.981451\n",
+      "TMIN dataframe reading to start: 0:00:52.962508\n",
+      "TMIN dataframe reading complete:0:01:13.359982\n",
+      "WINDSPD dataframe reading to start: 0:01:16.554109\n",
+      "WINDSPD dataframe reading complete:0:01:38.679958\n",
+      "PRECIP_dailymet_livneh2015 calculations completed in 0:00:01.437536\n",
+      "TMAX_dailymet_livneh2015 calculations completed in 0:00:05.152022\n",
+      "TMIN_dailymet_livneh2015 calculations completed in 0:00:05.071372\n",
+      "WINDSPD_dailymet_livneh2015 calculations completed in 0:00:05.073434\n"
+     ]
+    }
+   ],
+   "source": [
+    "min_elev=1700\n",
+    "max_elev=2000\n",
+    "\n",
+    "ltm_mid = ogh.gridclim_dict(mappingfile=mappingfile,\n",
+    "                            dataset='dailymet_livneh2015',\n",
+    "                            metadata=meta_file,\n",
+    "                            min_elev=min_elev,\n",
+    "                            max_elev=max_elev)\n",
+    "\n",
+    "ogh.saveDictOfDf(dictionaryObject=ltm_mid, outfilepath=os.path.join(homedir,'NewMexico_midElevation.json'))\n",
+    "\n",
+    "sorted(ltm_mid.keys())\n",
+    "del ltm_mid"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "**Grouping data for high elevation (2000 m to 2500 m).**"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 14,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Number of data files within elevation range (2000:2500): 2041\n",
+      "PRECIP dataframe reading to start: 0:00:03.560498\n",
+      "PRECIP dataframe reading complete:0:00:30.225607\n",
+      "TMAX dataframe reading to start: 0:00:33.785031\n",
+      "TMAX dataframe reading complete:0:00:59.287196\n",
+      "TMIN dataframe reading to start: 0:01:02.890964\n",
+      "TMIN dataframe reading complete:0:01:27.736623\n",
+      "WINDSPD dataframe reading to start: 0:01:31.316397\n",
+      "WINDSPD dataframe reading complete:0:01:57.987421\n",
+      "PRECIP_dailymet_livneh2015 calculations completed in 0:00:03.525410\n",
+      "TMAX_dailymet_livneh2015 calculations completed in 0:00:06.203834\n",
+      "TMIN_dailymet_livneh2015 calculations completed in 0:00:06.275810\n",
+      "WINDSPD_dailymet_livneh2015 calculations completed in 0:00:06.251748\n"
+     ]
+    }
+   ],
+   "source": [
+    "min_elev=2000\n",
+    "max_elev=2500\n",
+    "\n",
+    "ltm_high = ogh.gridclim_dict(mappingfile=mappingfile,\n",
+    "                             dataset='dailymet_livneh2015',\n",
+    "                             metadata=meta_file,\n",
+    "                             min_elev=min_elev,\n",
+    "                             max_elev=max_elev)\n",
+    "\n",
+    "ogh.saveDictOfDf(dictionaryObject=ltm_high, outfilepath=os.path.join(homedir,'NewMexico_highElevation.json'))\n",
+    "\n",
+    "sorted(ltm_high.keys())\n",
+    "del ltm_high"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## 5. Tar the json files for storage"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 15,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "CPU times: user 60.3 ms, sys: 336 ms, total: 397 ms\n",
+      "Wall time: 3.01 s\n"
+     ]
+    }
+   ],
+   "source": [
+    "%%time\n",
+    "!tar -zcf NewMexico_lowElevation.json.tar.gz NewMexico_lowElevation.json\n",
+    "!tar -zcf NewMexico_midElevation.json.tar.gz NewMexico_midElevation.json\n",
+    "!tar -zcf NewMexico_highElevation.json.tar.gz NewMexico_highElevation.json\n",
+    "#!tar -zcf NewMexico_allElevation.json.tar.gz NewMexico_allElevation.json"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "#### Select a Dictionary and convert meteorology data from pandas dataframe into numpy arrays"
+   ]
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "Python 3",
+   "language": "python",
+   "name": "python3"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.6.5"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 1
+}
diff --git a/ecohydrology_gridhydromet/reuse_ecohydrology_gridhydromet.ipynb b/ecohydrology_gridhydromet/reuse_ecohydrology_gridhydromet.ipynb
new file mode 100644
index 0000000..94efe1b
--- /dev/null
+++ b/ecohydrology_gridhydromet/reuse_ecohydrology_gridhydromet.ipynb
@@ -0,0 +1,1687 @@
+{
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "<a href=\"http://landlab.github.io\"><img style=\"float: left\" \n",
+    "src=\"https://avatars3.githubusercontent.com/u/7543483?s=200&v=4\"></a>"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "\n",
+    "\n",
+    "## Reuse Ecohydrology Model with Gridded Hydrometeorology Forcing \n"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "* Define a geographic subset (New Mexico) within the North America and download gridded meteorological data for this region. (for brevity, this is done in a supplemental notebook)\n",
+    "* Calculate mean climatology for a range of elevation bands.\n",
+    "* Run the Landlab ecohydrology model for a watershed given various climate scenarios.<br />\n",
+    "* Observe sensitivity to climate by comparing the watershed model results. <br />\n",
+    "<hr> <small> "
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## To run this notebook:\n",
+    "\n",
+    "Click in or on each shaded code block below and use ***shift + enter*** or ***shift + return*** keys. Alternatively, you can run groups of cells by clicking ***Cell*** on the menu above and selecting among the various run options. This is also where you can clear outputs from previous runs.\n",
+    "\n",
+    "If an error occurs, click on ***Kernel*** and then select ***Restart and Clear Outputs*** in the menu above. While exiting the notebook, click ***File*** and then select ***Close and Halt***.\n",
+    "<br />For more instructions on how to run an interactive IPython notebook, click here: <a href=\"https://github.com/landlab/tutorials/blob/master/README.md\">https://github.com/landlab/tutorials/blob/master/README.md</a></small><br>\n",
+    "<small>For tutorials on learning Landlab, click here: <a href=\"https://github.com/landlab/landlab/wiki/Tutorials\">https://github.com/landlab/landlab/wiki/Tutorials</a></small>\n",
+    "<br>\n",
+    "<small>For the unexpanded version to download and run, click here: <a href=\"http://nbviewer.jupyter.org/github/landlab/tutorials/blob/master/ecohydrology/cellular_automaton_vegetation_DEM/cellular_automaton_vegetation_DEM_unexpanded.ipynb\">http://nbviewer.jupyter.org/github/landlab/tutorials/blob/master/ecohydrology/cellular_automaton_vegetation_DEM/cellular_automaton_vegetation_DEM_unexpanded.ipynb</a></small><br>"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## 1. Introduction"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### 1.1 Landlab Ecohydrology Mode\n",
+    "\n",
+    "In this tutorial you will explore the role of climatology on the spatial development of tree, shrub, and grass Plant Functional Types (PFTs) in a semiarid setting. You will run an ecohydrologic vegetation dynamics model, written using Landlab - a python toolkit for modeling earth surface processes, on a hypothetical flat watershed with varied climatology. The ecohydrologic vegetation dynamics model you will use is based on CATGraSS, a discrete time cellular automata (CA) model for spatial evolution of PFTs [Zhou et al., 2013].  Each cell in the domain can be occupied by a single PFT: Tree, Shrub, Grass or left unoccupied as bare soil. The model couples local ecohydrologic vegetation dynamics, which simulate biomass production based on local soil moisture and actual evapotranspiration, with spatial processes for plant establishment and mortality controlled by seed dispersal rules, water stress tolerance, and space availability (Figure 1). Tree and shrub plants disperse seeds to their neighbors. Grass seeds are assumed to be available at each cell. Establishment of plants in empty cells is determined probabilistically based on water stress of each PFT. Plants with lower water stress have higher probability of establishment. Plant mortality is simulated probabilistically as a result of aging and drought stress.\n",
+    "\n",
+    "![CATGraSSFlowChart](https://www.hydroshare.org/resource/70b977e22af544f8a7e5a803935c329c/data/contents/figures/CATGraSSFlowChart.jpeg)\n",
+    "**<center>Figure 1. Ecohydrologic vegetation dynamics model [Modified from Zhou et al., 2013]</center>**\n",
+    "\n",
+    "The model is driven by rainfall pulses (observed or generated), solar radiation and temperature. The latter two variables can also be used to prescribe a seasonal potential evapotranspiration input. In Landlab, the model is implemented as a set of interacting components, each describing a different element of the coupled system: PrecipitationDistribution, Radiation, PotentialEvapotranspiration, SoilMoisture, Vegetation (component for local vegetation growth), and VegCA (component for cellular automaton establishment and mortality of PFTs, i.e. spatial dynamics of vegetation pattern evolution). Each process can also operate in isolation, outside the context of this example model. Landlab components and the flow chart used to couple the components in the model driver is given below [Hobley et al., 2017]. \n",
+    "\n",
+    "\n",
+    "![CATGraSSComponents](https://www.hydroshare.org/resource/70b977e22af544f8a7e5a803935c329c/data/contents/figures/CATGraSSComponents.jpg)\n",
+    "**<center>Figure 2. Landlab components (left column) and the flow chart of their implementation in Landlab to develop the cellular automaton plant competition model [Hobley et al. 2017]</center>**\n",
+    "\n",
+    "This model is available as a function 'run_ecohydrology_model', that can be imported from python script 'supporting_files/ecohydrology_model_functions.py'. It contains [Ref. Figure 2]:\n",
+    "  * A local vegetation dynamics model that simulates storm and inter-storm water balance and ecohydrologic fluxes (ET, runoff), and plant biomass dynamics by coupling the following components:\n",
+    "    * PrecipitationDistribution\n",
+    "    * Radiation\n",
+    "    * PotentialEvapotranspiration\n",
+    "    * SoilMoisture\n",
+    "    * Vegetation\n",
+    "  * A spatially explicit probabilistic cellular automaton component that simulates plant competition by tracking establishment and mortality of plants based on soil moisture stress:\n",
+    "    * VegCA\n",
+    "    \n",
+    "\n",
+    "#### References used in this notebook\n",
+    "\n",
+    "Phuong, J., Bandaragoda C., Istanbulluoglu, E., Beveridge, C., Strauch, R., Setiawan, L., Mooney, S. D. (in-review). Automated retrieval, preprocessing, and visualization of gridded hydrometeorology data products for spatial-temporal exploratory analysis and intercomparison.\n",
+    "\n",
+    "Hobley, D. E. J., Adams, J. M., Nudurupati, S. S., Hutton, E. W. H., Gasparini, N. M., Istanbulluoglu, E., and Tucker, G. E. (2017). Creative computing with Landlab: an open-source toolkit for building, coupling, and exploring two-dimensional numerical models of Earth-surface dynamics, Earth Surf. Dynam. doi:10.5194/esurf-5-21-2017.\n",
+    "\n",
+    "Livneh B., T.J. Bohn, D.S. Pierce, F. Munoz-Ariola, B. Nijssen, R. Vose, D. Cayan, and Brekke, L.D. (2015). A spatially comprehensive, hydrometeorological data set for Mexico, the U.S., and southern Canada 1950-2013, Nature Scientific Data, 5:150042, doi:10.1038/sdata.2015.42.\n",
+    "\n",
+    "Zhou, X., Istanbulluoglu, E., & Vivoni, E. R. (2013). Modeling the ecohydrological role of aspect‐controlled radiation on tree‐grass‐shrub coexistence in a semiarid climate. Water Resources Research, 49(5), 2872-2895.\n",
+    "\n",
+    "Moore D. (2011). Meteorology Data from the Sevilleta National Wildlife Refuge, New Mexico (1988- present). Environmental Data Initiative. https://doi.org/10.6073/pasta/4d71c09b242602114fb684c843e9d6ac.\n",
+    "\n",
+    "Middleton, N. J., and Thomas, D. S. G., (1997)., World Atlas of Desertification, UNEP Edward Arnold, London, UK, 2nd edition."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### 1.2 Landscape\n",
+    "\n",
+    "In this tutorial, we are going to work with a hypothetical rectangular domain over which we will impose elevation-dependent climatology from New Mexico, USA to simulate vegetation patterns using the ecohydrologic model discussed in Figure 1 and 2. The climate in this area is semi-arid with Mean Annual Precipitation (MAP) in the range 200 - 350 mm depending on the elevation.\n",
+    "\n",
+    "The steps used for downloading gridded meteorology data and processing it are included in a separate Notebook [New Mexico Climate Data Notebook](https://www.hydroshare.org/resource/70b977e22af544f8a7e5a803935c329c/data/contents/ogh_newmexico/observatory_gridmet_newmexico.ipynb) in this Hydroshare resource, and is imported as part of this exercise.\n",
+    "\n",
+    "### 1.3 Steps to Explore the Model\n",
+    "\n",
+    "We will do the following:\n",
+    "* Import and review pre-processed gridded hydroclimatology data dictionaries for three different elevation bands. \n",
+    "* Run the coupled Ecohydrology and cellular automata plant competition model for ~250 years with a daily timestep. \n",
+    "* Visualize and examine spatio-temporal outputs.\n",
+    "\n",
+    "Start at the top by reading each block of text and sequentially running each code block (put your curser in a code block and type shift + enter OR got to the _Cell_ pulldown menu at the top and choose _Run Cells_). \n",
+    "\n",
+    "Remember that you can always go to the _Kernel_ pulldown menu at the top and choose _Restart & Clear Output_ or _Restart & Run All_ if you change things and want to start afresh. "
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## 2.0 Data Science & Cyberinfrastructure Methods\n",
+    "\n",
+    "### 2.1 Data Science Methods"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "#### 2.1.1 Import Landlab components, functions for importing data, plotting tools, and HydroShare utilities\n",
+    "\n",
+    "To run this notebook, we must import several libraries.\n",
+    "The hs_utils library provides functions for interacting with HydroShare, including resource querying, dowloading. and creation. Additional libraries support the functions of Landlab."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 2,
+   "metadata": {},
+   "outputs": [
+    {
+     "ename": "ModuleNotFoundError",
+     "evalue": "No module named 'geopandas'",
+     "output_type": "error",
+     "traceback": [
+      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
+      "\u001b[0;31mModuleNotFoundError\u001b[0m                       Traceback (most recent call last)",
+      "\u001b[0;32m<ipython-input-2-c36f5e9b593b>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m\u001b[0m\n\u001b[1;32m     11\u001b[0m \u001b[0;32mimport\u001b[0m \u001b[0mpandas\u001b[0m \u001b[0;32mas\u001b[0m \u001b[0mpd\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     12\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m---> 13\u001b[0;31m \u001b[0;32mimport\u001b[0m \u001b[0mgeopandas\u001b[0m \u001b[0;32mas\u001b[0m \u001b[0mgpd\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m     14\u001b[0m \u001b[0;32mimport\u001b[0m \u001b[0mogh\u001b[0m  \u001b[0;31m# tools for processing gridded hydro-meteorology data\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     15\u001b[0m \u001b[0;32mfrom\u001b[0m \u001b[0mlandlab\u001b[0m \u001b[0;32mimport\u001b[0m \u001b[0mRasterModelGrid\u001b[0m \u001b[0;31m# Landlab toolkit available on CUAHSI JupyterHub\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
+      "\u001b[0;31mModuleNotFoundError\u001b[0m: No module named 'geopandas'"
+     ]
+    }
+   ],
+   "source": [
+    "from __future__ import print_function\n",
+    "\n",
+    "import json\n",
+    "import os\n",
+    "import warnings\n",
+    "from datetime import datetime, timedelta\n",
+    "\n",
+    "import dask\n",
+    "import matplotlib.pyplot as plt  # plotting and shape libraries\n",
+    "import numpy as np\n",
+    "import pandas as pd\n",
+    "\n",
+    "import geopandas as gpd\n",
+    "import ogh  # tools for processing gridded hydro-meteorology data\n",
+    "from landlab import RasterModelGrid # Landlab toolkit available on CUAHSI JupyterHub\n",
+    "from utilities import hydroshare  # HydroShare Utilities\n",
+    "\n",
+    "%matplotlib inline\n",
+    "\n",
+    "# silencing warning\n",
+    "warnings.filterwarnings(\"ignore\")\n",
+    "\n",
+    "hs = hydroshare.hydroshare()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "#### 2.1.2 Where are we in Hydroshare space?"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 2,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Data will be loaded from and saved to:\n",
+      "/home/jovyan/work/notebooks/data/70b977e22af544f8a7e5a803935c329c/70b977e22af544f8a7e5a803935c329c/data/contents\n"
+     ]
+    }
+   ],
+   "source": [
+    "homedir = ogh.mapContentFolder(str(os.environ[\"HS_RES_ID\"]))\n",
+    "print(\"Data will be loaded from and saved to:\\n\" + homedir)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "#### 2.1.3 Import python functions from a subfolder in this HydroShare resource"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Import the functions 'run_ecohydrology_model' (to run the ecohydrology model) and 'plot_results' (to plot outputs) from supporting python script into this Jupyter Notebook."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 3,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "from supporting_files.ecohydrology_model_functions import (\n",
+    "    plot_results,\n",
+    "    run_ecohydrology_model,\n",
+    ")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## 3. Climate Science Methods\n",
+    "\n",
+    "**3.1.1 Read in climate data that has been processed (in a separate notebook for convenience)**  \n",
+    "\n",
+    "In this test case, we want to demonstrate how to download existing downscaled gridded national/global datasets for a region and run ecohydrology model for hypothetical flat watersheds at different elevation bands. We used the Linvneh et al.(2015) gridded hydrometeorological data in this example.  'ogh', a conda package, is used to download the data for the state of New Mexico. We then bin the data into three elevation ranges and within each bin daily precipitation (mm), minimum and maximum temperature (<sup>o</sup>C) data are spatially averaged for every day. This led to time series of daily precipitation (mm), minimum and maximum temperature in each elevation band. To keep this notebook concise (and to focus on data processing and ecohydrology model implementation), we placed the code used for this hydrometeorological data processing in a separate notebook called: 'observatory_gridmet_newmexico.ipynb', available in the sub-folder ogh_newmexico. We saved synthesized data into JSONs ('.json') files by running the notebook 'observatory_gridmet_newmexico.ipynb'. These JSONs are stored in the sub-folder 'ogh_newmexico' and are loaded into Python dicitonaries in the next code block below.\n",
+    "\n",
+    "Ranges used in the three elevation bins:\n",
+    "\n",
+    "Low elevation = 1200 - 1700 m\n",
+    "\n",
+    "Mid elevation = 1700 - 2000 m\n",
+    "\n",
+    "High elevation = 2000 - 2500 m\n",
+    "\n",
+    "![New Mexico Climate by ELevation](https://www.hydroshare.org/resource/70b977e22af544f8a7e5a803935c329c/data/contents/figures/NewMexico_elevationbands.JPG)\n",
+    " \n",
+    "<center>**Figure 3.** New Mexico climate bands by elevation correspond with high elevation (white) with trees (dark green in aerial photography).</center>"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Let's download the saved meteorological data (JSON files) into Python dictionaries. We create one dictionary for each elevation band. Each dictionary contains time series of 28 variables. These time series are stored as 'key':'value' pairs, where each variable name is a 'key' and the corresponding time series is the 'value'. "
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 4,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# These files are processed from\n",
+    "# https://www.hydroshare.org/resource/70b977e22af544f8a7e5a803935c329c/data/contents/observatory_gridmet_newmexico.ipynb\n",
+    "ltm_low = ogh.readDictOfDf(\n",
+    "    os.path.join(homedir, \"ogh_newmexico/NewMexico_lowElevation.json\")\n",
+    ")\n",
+    "ltm_mid = ogh.readDictOfDf(\n",
+    "    os.path.join(homedir, \"ogh_newmexico/NewMexico_midElevation.json\")\n",
+    ")\n",
+    "ltm_high = ogh.readDictOfDf(\n",
+    "    os.path.join(homedir, \"ogh_newmexico/NewMexico_highElevation.json\")\n",
+    ")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "#### 3.1.2 Review statistics and variables available within a dictionary, e.g. Low Elevation"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "We can look at the names of all 28 variables in each dictionary below. "
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 5,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "['anom_year_PRECIP_dailymet_livneh2015',\n",
+       " 'anom_year_TMAX_dailymet_livneh2015',\n",
+       " 'anom_year_TMIN_dailymet_livneh2015',\n",
+       " 'anom_year_WINDSPD_dailymet_livneh2015',\n",
+       " 'meanallyear_PRECIP_dailymet_livneh2015',\n",
+       " 'meanallyear_TMAX_dailymet_livneh2015',\n",
+       " 'meanallyear_TMIN_dailymet_livneh2015',\n",
+       " 'meanallyear_WINDSPD_dailymet_livneh2015',\n",
+       " 'meandaily_PRECIP_dailymet_livneh2015',\n",
+       " 'meandaily_TMAX_dailymet_livneh2015',\n",
+       " 'meandaily_TMIN_dailymet_livneh2015',\n",
+       " 'meandaily_WINDSPD_dailymet_livneh2015',\n",
+       " 'meanmonth_PRECIP_dailymet_livneh2015',\n",
+       " 'meanmonth_TMAX_dailymet_livneh2015',\n",
+       " 'meanmonth_TMIN_dailymet_livneh2015',\n",
+       " 'meanmonth_WINDSPD_dailymet_livneh2015',\n",
+       " 'meanyear_PRECIP_dailymet_livneh2015',\n",
+       " 'meanyear_TMAX_dailymet_livneh2015',\n",
+       " 'meanyear_TMIN_dailymet_livneh2015',\n",
+       " 'meanyear_WINDSPD_dailymet_livneh2015',\n",
+       " 'month_PRECIP_dailymet_livneh2015',\n",
+       " 'month_TMAX_dailymet_livneh2015',\n",
+       " 'month_TMIN_dailymet_livneh2015',\n",
+       " 'month_WINDSPD_dailymet_livneh2015',\n",
+       " 'year_PRECIP_dailymet_livneh2015',\n",
+       " 'year_TMAX_dailymet_livneh2015',\n",
+       " 'year_TMIN_dailymet_livneh2015',\n",
+       " 'year_WINDSPD_dailymet_livneh2015']"
+      ]
+     },
+     "execution_count": 5,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "sorted(ltm_low.keys())"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "#### 3.1.3 Extract selected variables from the dictionary\n",
+    "\n",
+    "The ecohydrology model uses daily precipitation, daily maximum and minimum temperature time series as spatially uniform input over the modeled domain. This model uses daily maximum and minimum temperatures to calculate Potential Evapotranspiration (PET) using 'The Priestley Taylor' method. In the code block below we assign a variable for each input time series and load data from the dictionaries listed above."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 6,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# Mean daily precipitation\n",
+    "P_met_low = ltm_low[\"meandaily_PRECIP_dailymet_livneh2015\"]  # Precipitation in mm\n",
+    "P_met_mid = ltm_mid[\"meandaily_PRECIP_dailymet_livneh2015\"]  # Precipitation in mm\n",
+    "P_met_high = ltm_high[\"meandaily_PRECIP_dailymet_livneh2015\"]  # Precipitation in mm\n",
+    "\n",
+    "# Mean daily maximum temperature\n",
+    "Tmax_met_low = ltm_low[\"meandaily_TMAX_dailymet_livneh2015\"]  # Tmax in C\n",
+    "Tmax_met_mid = ltm_mid[\"meandaily_TMAX_dailymet_livneh2015\"]  # Tmax in C\n",
+    "Tmax_met_high = ltm_high[\"meandaily_TMAX_dailymet_livneh2015\"]  # Tmax in C\n",
+    "\n",
+    "# Mean daily minimum temperature\n",
+    "Tmin_met_low = ltm_low[\"meandaily_TMIN_dailymet_livneh2015\"]  # Tmin in C\n",
+    "Tmin_met_mid = ltm_mid[\"meandaily_TMIN_dailymet_livneh2015\"]  # Tmin in C\n",
+    "Tmin_met_high = ltm_high[\"meandaily_TMIN_dailymet_livneh2015\"]  # Tmin in C"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "In the code blocks below, we calculate and print Mean Annual Precipitation (MAP) values in each elevation band."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 7,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# Check Mean Annual Precipitation\n",
+    "P_met_low_MAP = np.mean(P_met_low.values) * 365.0\n",
+    "P_met_mid_MAP = np.mean(P_met_mid.values) * 365.0\n",
+    "P_met_high_MAP = np.mean(P_met_high.values) * 365.0"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 8,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Low Elevation MAP = 315.64\n",
+      "Mid Elevation MAP = 331.06\n",
+      "High Elevation MAP = 375.84\n"
+     ]
+    }
+   ],
+   "source": [
+    "print(\"Low Elevation MAP = {:0.2f}\".format(P_met_low_MAP))\n",
+    "print(\"Mid Elevation MAP = {:0.2f}\".format(P_met_mid_MAP))\n",
+    "print(\"High Elevation MAP = {:0.2f}\".format(P_met_high_MAP))"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "#### 3.1.4 Bias Correction"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "From prior experience of working with watersheds in New Mexico, we suspect that MAP values obtained from downscaled climate data product are higher than observations, especially in low elevations, indicating a wetter bias. It is common to find biases in downscaled climate products. Therefore, we downloaded precipitation data from meteorological stations located in Sevilleta LTER, New Mexico [Moore 2011]. Table below lists these meteorological stations and MAP calculated from the precipitation data that extends over multiple years at respective stations. We can immediately note that MAP values derived from Livneh dataset, reported above, are higher than the observed MAP values at meteorological stations in Sevilleta. "
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "![TableBiasCorrection](https://www.hydroshare.org/resource/70b977e22af544f8a7e5a803935c329c/data/contents/figures/TableBiasCorrection.PNG)\n",
+    "**<center>Table 1. MAP and elevation data gathered from meteorological stations in Sevilleta LTER [Moore 2011]**"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "One approach to deal with the incoherence between the gridded hydrometeorological data and observations is to correct any biases that might exist in the gridded data. For this purpose we can use a linear relationship derived from observations. In the figure below, MAP from each meterological station is plotted with respect to elevation and the linear relationship between MAP at a location and it's elevation is obtained. "
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "![BiasCorrectionPlot](https://www.hydroshare.org/resource/70b977e22af544f8a7e5a803935c329c/data/contents/figures/BiasCorrectionPlot.PNG)\n",
+    "**<center>Figure 4. Plotting MAP at meteorological stations in Sevilleta LTER [Table 1] with respect to elevation</center>**"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "We will rearrange this model to represent **MAP at a location** in terms of the **difference between it's elevation** and **minimum elevation of all hypothetical meteorological stations (centroids of the cells in gridded dataset) of New Mexico**, and **MAP at the location with minimum elevation**."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 9,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# z_min is the minimum elevation of all hypothetical meteorological stations in New Mexico\n",
+    "z_min = 850  # in meters\n",
+    "# Calculating 'MAP_z_min': MAP at z_min\n",
+    "MAP_z_min = 0.1697 * z_min - 28.34"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 10,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "MAP_z_min = 115.91 mm\n"
+     ]
+    }
+   ],
+   "source": [
+    "# Print MAP_z_min\n",
+    "print(\"MAP_z_min = {:0.2f}\".format(MAP_z_min) + \" mm\")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Now, the model is rearranged as: *MAP_at_a_location* = (*elevation_at_a_location* - *z_min*) + *MAP_z_min*. Note that we use the mid value of each elevation band for input as *elevation_at_a_location* (i.e. for lowest elevation band, *elevation_at_a_location* = (1200.+1700.)/2. = 1450 m). We now calculate MAP for each elevation band below."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 11,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# Bias corrections to Livneh Data (ref: Caylor et. al 2005 AWR; using meteorological data from Sevilleta,NM)\n",
+    "MAP_lit_low = (\n",
+    "    (1200.0 + 1700.0) / 2.0 - 850\n",
+    ") * 0.1697 + MAP_z_min  # Mean annual precipitation in elev band 1 from literature in mm\n",
+    "MAP_lit_mid = (\n",
+    "    (1700.0 + 2000.0) / 2.0 - 850\n",
+    ") * 0.1697 + MAP_z_min  # Mean annual precipitation in elev band 1 from literature in mm\n",
+    "MAP_lit_high = (\n",
+    "    (2000.0 + 2500.0) / 2.0 - 850\n",
+    ") * 0.1697 + MAP_z_min  # Mean annual precipitation in elev band 1 from literature in mm"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "We can print calculated MAPs for each elevation band."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 12,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "MAP for Low Elevation band = 217.72\n",
+      "MAP for Mid Elevation band = 285.61\n",
+      "MAP for High Elevation band = 353.49\n"
+     ]
+    }
+   ],
+   "source": [
+    "print(\"MAP for Low Elevation band = {:0.2f}\".format(MAP_lit_low))\n",
+    "print(\"MAP for Mid Elevation band = {:0.2f}\".format(MAP_lit_mid))\n",
+    "print(\"MAP for High Elevation band = {:0.2f}\".format(MAP_lit_high))"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "We use the ratio of MAP derived from gridded dataset for each elevation band and the corresponding calculated MAP from observations above, to estimate bias correction factors. Assuming that these biases persist for daily precipitation, we will adjust the daily precipitation time series obtained from gridded dataset with the estimated bias correction factors."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 13,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# Calculate bias correction factors for each elevation band\n",
+    "bc_low = float(P_met_low_MAP) / MAP_lit_low\n",
+    "bc_mid = float(P_met_mid_MAP) / MAP_lit_mid\n",
+    "bc_high = float(P_met_high_MAP) / MAP_lit_high\n",
+    "\n",
+    "# Bias corrected precipitation:\n",
+    "P_met_low = np.array(P_met_low.values) / bc_low\n",
+    "P_met_mid = np.array(P_met_mid.values) / bc_mid\n",
+    "P_met_high = np.array(P_met_high.values) / bc_high"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "The estimated bias correction factors are:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 14,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Correction Factor - Low Elevation = 1.45\n",
+      "Correction Factor - Mid Elevation = 1.16\n",
+      "Correction Factor - High Elevation = 1.06\n"
+     ]
+    }
+   ],
+   "source": [
+    "print(\"Correction Factor - Low Elevation = {:0.2f}\".format(bc_low))\n",
+    "print(\"Correction Factor - Mid Elevation = {:0.2f}\".format(bc_mid))\n",
+    "print(\"Correction Factor - High Elevation = {:0.2f}\".format(bc_high))"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### 3.1.5 Plotting corrected MAP for each elevation band"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Below, we plot bias corrected MAP for each elevation band."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 15,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<matplotlib.figure.Figure at 0x7fe246cccdd8>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "# print annual precipitation by elevation bands\n",
+    "P_fig1 = plt.figure(figsize=(15, 5))\n",
+    "fontdict = {\"fontsize\": 14, \"weight\": \"bold\"}\n",
+    "ax4 = P_fig1.add_subplot(111)\n",
+    "ax4.plot(\n",
+    "    (ltm_low[\"meanyear_PRECIP_dailymet_livneh2015\"] * 365.0 / bc_low),\n",
+    "    \"r-*\",\n",
+    "    label=\"elev_1200_1700\",\n",
+    "    alpha=0.5,\n",
+    ")\n",
+    "ax4.plot(\n",
+    "    (ltm_mid[\"meanyear_PRECIP_dailymet_livneh2015\"] * 365.0 / bc_mid),\n",
+    "    \"b-*\",\n",
+    "    label=\"elev_1700_2000\",\n",
+    "    alpha=0.5,\n",
+    ")\n",
+    "ax4.plot(\n",
+    "    (ltm_high[\"meanyear_PRECIP_dailymet_livneh2015\"] * 365.0 / bc_high),\n",
+    "    \"k-*\",\n",
+    "    label=\"elev_2000_2500\",\n",
+    "    alpha=0.5,\n",
+    ")\n",
+    "plt.xlim([1950, 2013])\n",
+    "plt.ylabel(\"Annual Precipitation (mm)\", fontdict=fontdict)\n",
+    "plt.xlabel(\"Year\", fontdict=fontdict)\n",
+    "plt.xticks(np.arange(1950, 2013, step=10), fontsize=10, fontweight=\"bold\")\n",
+    "plt.yticks(fontsize=10, fontweight=\"bold\")\n",
+    "plt.legend(prop={\"weight\": \"bold\"})\n",
+    "plt.show()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### 3.1.6 Plotting mean monthly temperature for each elevation band"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Below, we plot mean monthly maximum and minimum temperature for each elevation band. Note that we haven't bias corrected this data."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 16,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<matplotlib.figure.Figure at 0x7fe246c40358>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "T_fig2 = plt.figure(figsize=(15, 5))\n",
+    "ax4 = T_fig2.add_subplot(111)\n",
+    "ax4.plot(\n",
+    "    ltm_low[\"meanmonth_TMAX_dailymet_livneh2015\"],\n",
+    "    \"r-*\",\n",
+    "    label=\"Tmax - low elevation\",\n",
+    "    alpha=0.5,\n",
+    ")\n",
+    "ax4.plot(\n",
+    "    ltm_mid[\"meanmonth_TMAX_dailymet_livneh2015\"],\n",
+    "    \"b-*\",\n",
+    "    label=\"Tmax - mid elevation\",\n",
+    "    alpha=0.5,\n",
+    ")\n",
+    "ax4.plot(\n",
+    "    ltm_high[\"meanmonth_TMAX_dailymet_livneh2015\"],\n",
+    "    \"k-*\",\n",
+    "    label=\"Tmax - high elevation\",\n",
+    "    alpha=0.5,\n",
+    ")\n",
+    "\n",
+    "ax4.plot(\n",
+    "    ltm_low[\"meanmonth_TMIN_dailymet_livneh2015\"],\n",
+    "    \"r--*\",\n",
+    "    label=\"Tmin - low elevation\",\n",
+    "    alpha=0.5,\n",
+    ")\n",
+    "ax4.plot(\n",
+    "    ltm_mid[\"meanmonth_TMIN_dailymet_livneh2015\"],\n",
+    "    \"b--*\",\n",
+    "    label=\"Tmin - mid elevation\",\n",
+    "    alpha=0.5,\n",
+    ")\n",
+    "ax4.plot(\n",
+    "    ltm_high[\"meanmonth_TMIN_dailymet_livneh2015\"],\n",
+    "    \"k--*\",\n",
+    "    label=\"Tmin - high elevation\",\n",
+    "    alpha=0.5,\n",
+    ")\n",
+    "\n",
+    "plt.xlim([1, 12])\n",
+    "plt.ylim([-15, 40])\n",
+    "plt.ylabel(\"Monthly Temperature (C)\", fontdict=fontdict)\n",
+    "plt.xlabel(\"Month\", fontdict=fontdict)\n",
+    "plt.xticks(np.arange(1, 13, step=1), fontsize=10, fontweight=\"bold\")\n",
+    "plt.yticks(fontsize=10, fontweight=\"bold\")\n",
+    "plt.legend(loc=1, prop={\"weight\": \"bold\"})\n",
+    "plt.show()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "We can observe that there is an inverse relationship between elevation and temperature, and a positive relationship between elevation and precipitation."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### 3.1.7 Tiling of data - Extension of meteorological records by repeating available data"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "The meteorological data downloaded from Livneh et al. (2015) extends from year 1950 through 2013, for a total of 64 years. Some shrubs in this area can live up to 600 years and trees up to 300 years. Therefore, we need a longer record to evolve natural vegetation patterns from a random initial condition using Landlab's ecohydrology model. To circumvent this issue, we extended the historical data by tiling (i.e., repeating the arrays for a set number of times) for 150 times so that we have input time series for 9600 years preserving the historical climate."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 17,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# Tiling meteorological data to extend the record to approximately 9.6k yrs.\n",
+    "P_met_low = np.tile(P_met_low, 150)\n",
+    "P_met_mid = np.tile(P_met_mid, 150)\n",
+    "P_met_high = np.tile(P_met_high, 150)\n",
+    "\n",
+    "Tmax_met_low = np.tile(Tmax_met_low.values, 150)\n",
+    "Tmax_met_mid = np.tile(Tmax_met_mid.values, 150)\n",
+    "Tmax_met_high = np.tile(Tmax_met_high.values, 150)\n",
+    "\n",
+    "Tmin_met_low = np.tile(Tmin_met_low.values, 150)\n",
+    "Tmin_met_mid = np.tile(Tmin_met_mid.values, 150)\n",
+    "Tmin_met_high = np.tile(Tmin_met_high.values, 150)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## 4. Ecohydrology Modeling using Landlab"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "With all the input forcings prepared, we can now run the ***ecohydrology model***. To run the *model*, we need the following:\n",
+    "\n",
+    " - a Landlab grid to represent the domain/watershed\n",
+    " - a collective input file ***'input_file'*** that includes inputs for all components used in this model\n",
+    " - a Python dictionary ***'input_data'*** that includes all meteorological forcings to run the model\n",
+    " - define an initial spatial organization of Plant Functional Types (PFTs) in the watershed\n",
+    " - number of days ***'n'*** for which you want to run the model"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### 4.1.1 Initialize Landlab grids"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Landlab's ecohydrology model can operate on Landlab's raster grids (RasterModelGrid objects). We have already imported ***RasterModelGrid*** (Landlab's gridding module for raster grids). We will create three ***grid*** objects, one for each elevation band. "
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 18,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "grid_low = RasterModelGrid((100, 100), spacing=(5.0, 5.0))  # Initialize grid for low-elevation band\n",
+    "grid_mid = RasterModelGrid((100, 100), spacing=(5.0, 5.0))  # Initialize grid for mid-elevation band\n",
+    "grid_high = RasterModelGrid(\n",
+    "    (100, 100), spacing=(5.0, 5.0)\n",
+    ")  # Initialize grid for high-elevation band"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### 4.1.2 Loading an input file for the model\n",
+    "\n",
+    "Each Landlab component used in this ecohydrology model accepts various input parameters as arguments. All input parameters for all the components used in the model can be collectively included in a single ***'.yaml'*** file and passed along to the ***run_ecohydrology_model*** function, where each component is initialized internally. For this exercise, we have calibrated each component of the model with respective input parameters and included those input values in the file named ***ecohyd_inputs.yaml***, which is available in the subfolder ***supporting_files***. Note that we will use the same input file for all elevation bands."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 19,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "input_file = \"supporting_files/ecohyd_inputs.yaml\""
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### 4.1.3 Prepare dictionary that includes meteorological forcings"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "For each elevation band, we will create a separate Python dictionary that includes time series of ***daily precipitation***, ***daily maximum temperature***, and ***daily minimum temperature***. "
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 20,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# Python dictionary for meteorological inputs for low-elevation band\n",
+    "input_data_low = {\n",
+    "    \"precip_met\": P_met_low,\n",
+    "    \"Tmax_met\": Tmax_met_low,\n",
+    "    \"Tmin_met\": Tmin_met_low,\n",
+    "}\n",
+    "# Python dictionary for meteorological inputs for mid-elevation band\n",
+    "input_data_mid = {\n",
+    "    \"precip_met\": P_met_mid,\n",
+    "    \"Tmax_met\": Tmax_met_mid,\n",
+    "    \"Tmin_met\": Tmin_met_mid,\n",
+    "}\n",
+    "# Python dictionary for meteorological inputs for high-elevation band\n",
+    "input_data_high = {\n",
+    "    \"precip_met\": P_met_high,\n",
+    "    \"Tmax_met\": Tmax_met_high,\n",
+    "    \"Tmin_met\": Tmin_met_high,\n",
+    "}"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### 4.1.4  Define initial spatial organization of PFTs in the watershed\n",
+    "\n",
+    "In Landlab's ecohydrologic components, each grid cell can be occupied by one PFT (grass = 0, shrub = 1, tree = 2, bare soil =3, shrub seedling = 4, or tree seedling = 5). This spatial organization is managed by a ***cellular field*** (please see documentation for ***fields*** in Landlab for more information) named ***vegetation__plant_functional_type***. We need to set the initial spatial organization. For this tutorial, we will initialize each grid with a random organization of PFTs where each plant type (grass, shrub, and tree) will each occupy 25% of watershed area. Rest of the watershed will be left unoccupied (bare soil)."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 21,
+   "metadata": {
+    "scrolled": true
+   },
+   "outputs": [],
+   "source": [
+    "# Initializing PFT organization for low-elevation band\n",
+    "grid_low.at_cell[\"vegetation__plant_functional_type\"] = np.random.randint(\n",
+    "    0, 6, size=grid_low.number_of_cells\n",
+    ")\n",
+    "# Initializing PFT organization for mid-elevation band\n",
+    "grid_mid.at_cell[\"vegetation__plant_functional_type\"] = np.random.randint(\n",
+    "    0, 6, size=grid_mid.number_of_cells\n",
+    ")\n",
+    "# Initializing PFT organization for high-elevation band\n",
+    "grid_high.at_cell[\"vegetation__plant_functional_type\"] = np.random.randint(\n",
+    "    0, 6, size=grid_high.number_of_cells\n",
+    ")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "We can plot to see the random organization of PFTs that we just initialized."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 22,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/markdown": [
+       "**Green: Grass; Red: Shrub; Black: Tree; White: Bare Soil**"
+      ],
+      "text/plain": [
+       "<IPython.core.display.Markdown object>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<matplotlib.figure.Figure at 0x7fe2469569b0>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "# Plot initial vegetation PFT state for low-elevation band\n",
+    "import matplotlib as mpl\n",
+    "from IPython.display import Markdown, display\n",
+    "\n",
+    "from landlab.plot import imshow_grid\n",
+    "\n",
+    "\n",
+    "def printmd(string):\n",
+    "    display(Markdown(string))\n",
+    "\n",
+    "\n",
+    "cmap = mpl.colors.ListedColormap([\"green\", \"red\", \"black\", \"white\", \"red\", \"black\"])\n",
+    "bounds = [-0.5, 0.5, 1.5, 2.5, 3.5, 4.5, 5.5]\n",
+    "norm = mpl.colors.BoundaryNorm(bounds, cmap.N)\n",
+    "printmd(\"**Green: Grass; Red: Shrub; Black: Tree; White: Bare Soil**\")\n",
+    "plt.figure(101)\n",
+    "imshow_grid(\n",
+    "    grid_low,\n",
+    "    \"vegetation__plant_functional_type\",\n",
+    "    values_at=\"cell\",\n",
+    "    cmap=cmap,\n",
+    "    grid_units=(\"m\", \"m\"),\n",
+    "    norm=norm,\n",
+    "    limits=[0, 5],\n",
+    "    allow_colorbar=False,\n",
+    ")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### 4.1.5  Set the *number of days* for which you want to run the model"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "In this tutorial, we will run the ecohydrology model at a daily time step. Therefore, we can set the value for ***number of days*** by approximately fixing the number of years we want to run the model. For this demonstration, we will set the value of ***number of days*** to ***550,000 days***, i.e. for approximately ***1500*** years. "
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 23,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# Setting number of storms\n",
+    "n = 550000  # approximately 1500 years"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "**Note:** If you are re-running this notebook, please note that the code blocks below that run the model (i.e. where ***run_ecohydrology_model*** is called), take approximately 15 minutes each to run. If you want to run the model for shorter times, select a smaller value for ***n (number of days)***."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### 4.1.6 Run the model!"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "We can now run the model. We will run the model thrice (once for each elevation band). Please note that, in the call to the function ***run_ecohydrology_model***, for a daily simulation ***number_of_storms*** represent the ***number of days*** for which the model is run. On a day when there is no rainfall, storm depth is set to 0. Also, note that every function run returns three outputs:\n",
+    " - VegType_elevation-band: a numpy ndarray of size [number_of_years, number_of_cells_in_the_grid] - this variable can get large in size for longer simulations.\n",
+    " - yrs_elevation-band: the total number of years this model has run (float)\n",
+    " - debug_low: ignore this output. This is only for debugging.\n",
+    "\n",
+    "**Low elevation band:**"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 24,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Elapsed time =  0  years\n",
+      "Elapsed time =  100  years\n",
+      "Elapsed time =  200  years\n",
+      "Elapsed time =  300  years\n",
+      "Elapsed time =  400  years\n",
+      "Elapsed time =  500  years\n",
+      "Elapsed time =  600  years\n",
+      "Elapsed time =  700  years\n",
+      "Elapsed time =  800  years\n",
+      "Elapsed time =  900  years\n",
+      "Elapsed time =  1000  years\n",
+      "Elapsed time =  1100  years\n",
+      "Elapsed time =  1200  years\n",
+      "Elapsed time =  1300  years\n",
+      "Elapsed time =  1400  years\n",
+      "Elapsed time =  1500  years\n"
+     ]
+    }
+   ],
+   "source": [
+    "(VegType_low, yrs_low, debug_low) = run_ecohydrology_model(\n",
+    "    grid_low,\n",
+    "    input_data=input_data_low,\n",
+    "    input_file=input_file,\n",
+    "    synthetic_storms=False,\n",
+    "    number_of_storms=n,\n",
+    "    pet_method=\"PriestleyTaylor\",\n",
+    ")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "**Mid elevation band:**"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 25,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Elapsed time =  0  years\n",
+      "Elapsed time =  100  years\n",
+      "Elapsed time =  200  years\n",
+      "Elapsed time =  300  years\n",
+      "Elapsed time =  400  years\n",
+      "Elapsed time =  500  years\n",
+      "Elapsed time =  600  years\n",
+      "Elapsed time =  700  years\n",
+      "Elapsed time =  800  years\n",
+      "Elapsed time =  900  years\n",
+      "Elapsed time =  1000  years\n",
+      "Elapsed time =  1100  years\n",
+      "Elapsed time =  1200  years\n",
+      "Elapsed time =  1300  years\n",
+      "Elapsed time =  1400  years\n",
+      "Elapsed time =  1500  years\n"
+     ]
+    }
+   ],
+   "source": [
+    "(VegType_mid, yrs_mid, debug_mid) = run_ecohydrology_model(\n",
+    "    grid_mid,\n",
+    "    input_data=input_data_mid,\n",
+    "    input_file=input_file,\n",
+    "    synthetic_storms=False,\n",
+    "    number_of_storms=n,\n",
+    "    pet_method=\"PriestleyTaylor\",\n",
+    ")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "**High elevation band**"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 26,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Elapsed time =  0  years\n",
+      "Elapsed time =  100  years\n",
+      "Elapsed time =  200  years\n",
+      "Elapsed time =  300  years\n",
+      "Elapsed time =  400  years\n",
+      "Elapsed time =  500  years\n",
+      "Elapsed time =  600  years\n",
+      "Elapsed time =  700  years\n",
+      "Elapsed time =  800  years\n",
+      "Elapsed time =  900  years\n",
+      "Elapsed time =  1000  years\n",
+      "Elapsed time =  1100  years\n",
+      "Elapsed time =  1200  years\n",
+      "Elapsed time =  1300  years\n",
+      "Elapsed time =  1400  years\n",
+      "Elapsed time =  1500  years\n"
+     ]
+    }
+   ],
+   "source": [
+    "(VegType_high, yrs_high, debug_high) = run_ecohydrology_model(\n",
+    "    grid_high,\n",
+    "    input_data=input_data_high,\n",
+    "    input_file=input_file,\n",
+    "    synthetic_storms=False,\n",
+    "    number_of_storms=n,\n",
+    "    pet_method=\"PriestleyTaylor\",\n",
+    ")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### 4.1.7  Plot and compare the results\n",
+    "\n",
+    "Now that we have run the model, we can plot results using the function ***plot_results*** for each elevation band. \n",
+    "\n",
+    "This function will first plot spatial organization of PFTs at a frequency of ***yr_step_elevation-band*** years. For the plots of spatial organization, a Green cell indicates Grass, a Red cell indicates Shrub, a Black cell indicates Tree, and a White cell indicates that it is un-occupied (bare soil). At the end, annual areal vegetation cover of each PFT is plotted with respect to time in years."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "**Low elevation band:**"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 27,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Plotting cellular field of Plant Functional Type\n",
+      "Green - Grass; Red - Shrubs; Black - Trees; White - Bare\n"
+     ]
+    },
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<matplotlib.figure.Figure at 0x7fe246bb9c18>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "image/png": 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ZJCttmTQ7+jWY0o5DGcv6nG2P2Fyv9hZNLnLTKTPl5ELrQjZDckaXTV0z9Obacg+9PYLXBHnx91IhSdGuem1lQ36NFJlKWYqZ/qSeHjnfmSY3tn4rmzyf6Oc4zkLxl4njOJOw0sOcnK0uSsKdsRSeNf3qD41cQ0hTXekqxynYU9QQZmwOpbatOnTbGDl80BIMe/plDHPGDG+yvhdLOr2gS2qlP/V+NV1Sv61KVttaxoc5juNsGlvCMhmVZGV522tiwr9NGZZ6rAhV31RvlPs9DfSoplR5Ta+4HUMod0ZcYgKdxdHcCxFLYXOl7bZsKJO1+byG5TvKsCAszu6shMjCkLOHhh3HWShbyjJpyNmvReqhe6ns8QQuoc3WdyK0kUJMkZ8qNX7Av6BueTo2XJnhI0omCObIrRT9cqYuVMki6vNMJc9NtfVnUpeOPllkPBMA5nR6t0wcx5mELWGZtJQk5eSk02tovoiMCEhJT23Zvc2C+twskYV5MeRHykj7n1wXqS1tMt8YH4zmB8mQb/mNuWXiOM6m4S8Tx3EmYWsNcxpK5pYIjF3nosjpOWIOzegZohlzSLI2ddeGIwn5g/WHmNdwJ2c4W5poOEThnKnB7VQSennSmuM4C2WlLZPkrOH2hOL4KghxZs+MTc0i1bD0bikrRtEvJ/xZ5MBW0su19WBKVpNP6tLB5EyeKCEta6OuDIssZf1ZwtJFM40T9TxpzXGchbLSlkm80lrWmFBAnQSXqDsqNb50PJ+TmDWiF7ZsHj6ZBZDhr1Hr5kzMi68VpgAki0qWSs5kwLURz9iQ+mDSHe4zcRxnwWwNyySiKAozIyCRrp0xkUtsIyfZqsCiUKM5Bh+Hmsw05MvJjcYMPct5Rn5K2i7wbWi+nF5ZQb/ksgxTJelZIlNwy8RxnAWzkK0uFo22wnkPaTxbyXKyp+2n2rb0cs04WSpU0Pv2drYT7rvd07cyWC/ReRVDZKrnJ6g66qXajvQX5VVCmViF2H+hoFm9lt+EZbfI9nnFbWllSyDhN5bhE4pxy8RxnEnwl4njOJOwJR2wWTM7JQypzqaNsHKebcoJapE7wvlmrj9B+H2qhL52fRllCoNpDdS1xFBQqpvze5kqhX+C7znLKZ+o57OGHcdZKKttmezaxevr68neUl3paqqUas3iGUqgMkwKLF5jZKhX01LQDY7IpFxL21AsiJGh3cE0fU12zvebc9+GhDltY3pLur5lPdvSZ+Pp9I7jLJTVtkzG+EykskMT1LS06Mqkclq/RoWM3qNkq1NNXiqcLOmTs/J80TMSaLUxyFGfX0/wnHxOOcmJml6pFHytbQVTOn2njFsmjuMslK1tmYxNu47Kqnu6NBh6IamHUXe0s+qZQ240J0Jd+MniF4jqW3wRbf247kzRxO/ZcI/qavexnLHWbw4Wi7Ek8mPxLcItE8dxFoy/TBzHmYStOcwJSElSUzlMU3MXshOehiid/Ro5adsqVa+kTW6Ok1GTE8tTTPhBKqlJg/O8X2m4jFa2ZEZ1VMeyfq+WkJezEZ1pbeJu2sJmD3OIaBsRfZmIvkpEVxLRueH8sUT0JSK6hog+TEQ/Fc4fGI6vCdePmZdujuNMz9wsE6pznh/MzHcT0QEAvgjgbAC/DeATzHwREb0DwFeZ+Xwi+hcAHs/MrySiMwD8GjO/QGsjKzS8V7GyG4rFVKGpSmkzYR00SI4+S5p+0XaoUd2SdPOZMprD2JBOX0RUv2lRS9O3bISuMiZFfmx6vWUKgyXEHKNYbdLvY9MtE665OxweEP4YwNMBfCycvxDAc8Pn54RjhOvPIJrof77jOHNnruuZENF+ADYAHA/g7QC+BeAOZr4/FLkRwJHh85EAbgAAZr6fiO4EcAiAWwcbKkhjlnrJQX9CRx7H7znlvRf3hGIPrUxeS+mZ1dMHuT29O9faw6bMWl+/tkxz2qJD07ZwKWctVQrrebRypHuK1/wQ9IvXdlGnEcRhfeH5DVr3lsRIifg3Ee5tJjQcy20Oq3BdSD9I1QHGJRbONZrDzP/AzE8EsAPASQAeM1YmEe0monUiWv/eaA0dx5mKhUVziOj3AfwAwL8G8NPB+ngygIqZ/wkRfSZ8/msi2h/AzQC2s6JgM9Fv1Ni3NKEoJ2ow78SnqSMWGpZ0cMM9DFokVbfarFUQWybqxLcoigf0fQ+aTyKVKCf+LDOsDtXnlpguYUqnN3w/WfsiAdj0NWCJaDsRHRQ+PxDAMwFcBeDzAE4Pxc4EcHH4fEk4Rrh+mfYicRxnuZhnNOfxqB2q+6F+aX2Emf8tER0H4CIABwP4CoAXM/N9RLQNwAcAnAjgNgBnMPO1WhtZ6fQGSyJrVfupGTNxMCBGNVKTAg25DzlRA3EKveYjqmb/7VkQnToWP9KiaDSQ/t+YVpOP61TDbRYtm6H9vnN+A7BHc+bmgGXmr6F+McTnr0XtP4nP/xDA8+alj+M488XT6R3HmYTVTqcvccBqlDhgLfISKeNDacwzp6tO04mENnVD6wLnrCQvHgpaZtpq92taS3dAT0nnUdMlMpL/ZsRY2lRm6hbJS8hvKXxWvgas4zibxmpbJpEDVg0Hxk4nS8hMcUha1uXsydf0S7UZ1dXazOotLSHxqcPRSvizrZLpHEzqOcLaMIVeLbJznPw5KQoWHQqd1glnuVsmjuMsjpW2TOgIYrxipH+gNGktg2SCUk6Cm6VMYe+WY2UlfSbKpMCc6Q2qVZmS22UTrJfB5zeVRVsphRL3olqriv9mqSb6OY6zb7G1LJOxDFg24lu+ZIwfX4eQ8FTNlineND0xmU31JwlkLbI0IslM7fmbNudtXVom5ln8Sc3lDN+YhOnZG77nXhWjdeSWieM4C2XQMglp7qcBeCqAI1BP1vsGgE8x85Vz11ChzTOJGUj9TpIax4d/xbFvfE3w3DdkjXkTOqlkpL9rbahRjQI/Q04ExLQfT0mEpVCHUau/52CwdKSpFoP+FO03q7VdMNFPTacPSy2eBuALAL4E4BYA2wA8CsCbwovmdSF13nGcfZihuTlfZua1xLW3ENEjARw9sU6O46wgW9IBOzbVea+g4fTyUUzlOMxJM1ecbqakujGUJsENhHvVMHLJc81JTIMxdBuXzQgNW57NVFuwiuFiY9KaadYwEe0C8EYAPxPqEABm5sfna+s4zlbEZJkQ0dUAfgfA1wH8pDnPzNfPT7VhxmwPqk5Qy+g1izYPLwidZk/ii64VbTReOhFRaaunX4rCiX6mshNNhpuUqa02Ta4W5m6KzssyAfA9Zr7EWNZxnH0Q68tkjYjeDeBSAPc1J5n5E3PRqpR4/KiUEVdpT5RNHnflNG5qSW58TrAWkmP9HH2V+jAkM8XnxDTr5loV6S3Ja8pUin69Bgxp+Vqdpkz0jGd0yHiW6lILmh6pdixW6hg/ZmyRdv1Jht9ze6mbNBiv+J/A+jJ5OeqV5Q/A3mEOA1iul4njOJuG2WfCzI9egD5Z9JLWtEStMX6QnBTqsUlcJZMVGxFVp0gFGeX7VnfpS+iZFUXISaqz3H9JWaG8anUY2iqayGmYiFjir8lZ59X63U2dTv9XRPQ4Y1nHcfZBrMOckwFcQUTfRu0z8dCw4zgzWF8mp8xVi1I2NgCivaadYejS0BqVmlO1yhh6CCRNzhGzaWf0s7RdxReUWaTxiVTuc0dOvHWlqINgcrfXYpO+KauY/bGzV3O0W561auYb5vi0TumwEYb6/WTMcWqfhcUBGjnqW10sTtau/BEzvofm5jyEme/W8kmaMtktO46zpVAdsER0KYArUO+6t8HM94TzxwH4FQDPB/AuZv7YAnTt0SatFYTSTFstxpSmv+c4VVPyDW1b1onNkaclM+Wk5asO7JIEMoNzsSixzeAY12butmIz9MvZhEtLtEw5U4vX4OnKmGLWMDM/g4hOBfAKAE8hokcAuB/A1QA+BeBMZr55qBHHcbY+Kz3Rb0w6fRY5iUVSGUsa81BoODctOlGmZFIakO7NLGFVS28+1E5X3l4hE32/sfxciyf1jKV7saRi5ITbM0haUDOFBEvTV1pzHGeRuGWi+A5yJseVJJmZypaklyMvkSo5WVGxsrRV6EomncUblnOOJSZFiYZ8RlamsIZKfW0Z5KwTmx2VnMIyIaJPE9ExWS07jrNPMpRn8l4AnyWiCwG8mZl/vACd7OzcCXTS6XsT/Cxp6wpq75aK/edOOovrJfQTFwBSIgNZ+SVN2UqxzJpnm5p4KFgxvbG/ZBXFOSNNXSnvYsAPBBRaJFP52FJWjOIzUf0jFv9Zw7mGMhn3F+01bGIomvNRIvofAH4PwDoRfQCz65m8xayd4zhbGksG7I8A3APgQAAPRedl4jiO0zCUAXsKgLcAuATAk5j53oVoZWTjOxugc0nfmjJm6rBxIE4PV9uSHIfREC02f2fuMDHEylqDtFLONfpJQ6vEvcwMw1Kp9loqeqhvWkMlvi59/4YhYEqeiCVUbwibx0l/plT++F66z7ppI2P4ZFodsIAhy+SNAJ632fvjOI6z/Kx0aLhdnX6t3zvGTLJquaZLlW7TsmHX3koZDroC/bJ6I22NjZxUeWPadl3UYJlYnKyWhL6huqn6MQXO3uRvQqMgPX+K5ERPWnMcZ6HMzTIhoqMAvB/AYag7lwuY+TwiOhjAhwEcA+A6AM9n5tup7pLOA3AqgHsBvIyZL9faaFZay9n2sCjZqpSSMGCsi5bKbxnHJyheTT4nIU0rk9IrSl6bqT5YWwm/F6YJFFmVlqS6SJ7oK6riEwarUkuyM+gnsQyWyf2otw59HOrFlV4VVms7B8ClzHwC6gWqzwnlnwXghPC3G8D5c9TNcZyJWZjPhIguBvC28Pc0Zt5DRIcD+AIzP5qI3hk+fyiUv7opl5SZ2NFPZUx69BzH0pPsz6L0XKpF1hSNIg2SnDFIY/OUn2tmdfQmOtRezJhyoCpkSBCsykRLcsU2LYl3lqkQBj1zVtgv2TdnIT6TkJJ/IurNzw/rvCBuRj0MAoAjAdzQqXZjOBfL2k1E60S0jqUKVDvOvs3cXyZE9BAAHwfwGma+q3uNa7Moq1th5guYeRcz78KDJlTUcZxRWNeALYKIDkD9IvnTzoZd3yWiwzvDnFvC+ZsAHNWpviOcS7JzD7BeddoLn03OVUvIUBkS9RKeLLNxlTk6Q+t9moYIUpJZSictBIv+cCdrGJYwy2fkJWYJt+c7dZND8RJnaKeNOGHOhDR3qAqXKkPbTdlYbLX3c/v8LepUg0X6ZQ1h827b1qczN8skRGf+BMBV0RyeSwCcGT6fiXpJyOb8S6nmZAB3av4Sx3GWi3mGhn8JwP/C7Gbnv4vab/IRAEcDuB51aPi28PJ5G+qV8O8F8HJmebmShnYTLku6ehUuVQblEz2empKtyDWtF5LSQSqb+s4yksMsoebiNWUT+mStRypYEqa6Gc9GTeRLIDmpk47rkQl9lvV2s0L2KSYKDc9tmMPMX0TaQnqGUJ4BvGpe+jiOM19WOp0+udJaQE3MMiTsZK2ZmZM6XtLDGBDv1yI3lVy3ltmjpuQqIVxLUtig9Sf5Q1JlhXpaSNyyZWobui75Dgu+n9GJaJl6LkPSmuM4+xCrbZnEPhNLMtNQxKZb1JLElRNR0NockjdV2n9OApQlSS/HTyPVK0gijPUUfTHNOcXSacsmom1SGXUyaaNPcyLSM5sxVkdD7m9Fel5umTiOs0hW2jLppdNrk6ZSuR6aJ99i4UyU6pykdAw8UG+0f2WC9Hqz/JyIRcKiU1PktXT6xiJp5MZ1hXNxzoxp18iJrEA1ApTxf31p0+kdx9n6+MvEcZxJmGs6/bzZecROrK+tm9KPW/MvmHptOnnVNwN7W2ZoQxhluJOcoWtJ5TeU7aVmS2ZsKrW9Gi6jYhneWYaQ8en42UtyK7nOTL1IP9XAVxyve8VFwwkt3T9uU/iNJad3DJ1LlMlJwNNWeRO3ch3WAoBbJo7jTMRKWybN6vQx4jqWVUEDOWG2nGslIT7hmmhdRWVii0zSqVdG6KnbsKchySzuHcVEssT9NWep2x/GTsUq6o27cttG85PsxFBzot6M9s3zaq5pbeYk+8V1lEBAcr1hqayiU6v7WqbOcMvEcZxmBr5SAAASH0lEQVSJWOnQcG8N2DEToixlNb9F+HfyrSoLJ2oVTS5M6YJ0slbWSvGCHkMJZJo8Nakwasekl+U5Kr+tZOr9xGF9Ub/KoG9hWN+T1hzHWShby2eSk0ZvIX6TC2/5tqUwxuy2nNpI3TKuTR5L+jVFO/JSm5GPfTbJtHItMiV9L7HfpyG1IXxXXI4lqll2Fj9K7IsQ5CX1MdzL3oYyE9uaU0P3kCs3J9IY4ZaJ4ziT4C8Tx3EmYbUdsM16JiNmSmZvRmWQ08qLzV4tPBvVz1q5zTBnoy/EUCdHbknIEwNDvqheTll1BnCqvmUIqMz/GpRfiva9pJ7NhPOs3AHrOM5CWWkHLHbuBNbVZWJbUiFIFt7yeyvNWhTSbNI2EaiS27HI7eqRSq7LDjmn0tWrpmmDlSCFwiNno+iYzEgD7yVJKXUtvW5v1fu4DtBffV9ysKfUtoSjq/73u1dAhhPUUjeRiKZNSzDhDljHcTaL1bZMNjbqt2aJ36cg1VlckTxqu2vpJHtAydKJQqVxMpK4r0qOLyG+BynM3VgbjXzBp9O20YTCDVaStldPsqe3hJoFTM+gKZMTum3qShZJpE9yEqgocKJEy7hqlb6m6pdhpcW4ZeI4ziSsdjQntW+Ogjo5bCiaYUgvnykutZEsbJgcNwZtEl9B2nfWerFSm53PvfpWpkqVj3XRmmzqTJXC31QR/HGD+/FoCFZN6jsb2qjdozmO4yyUlfaZNOn0cbq6lhLcvuWl6EHs06jCYdUvq03X7xQSy4hv/8gS6d3TTGF7unXRNAKJ6NmoadyJsb66IFOJVWTRN5bRuVayl1JSvqLXjEWW8olJll0VHUvyKsgoEcOYQSvdozmO4ywSf5k4jjMJq+2AHdgeVMWyjkRG2C4nbVt14o1JalL0szDJ1hy5aEPTIUqmEUj1RoRgVTk5cjNS+cXfbFzH4ICVfofixuxE7oB1HGdx7HOWSUlIM647U39M2xZKnaoDodGpJjhO1qtHZOknpf2HOlmr3eVYNTmWovIbE9cwruT6prV0LVMCQn3LSva1SLdMHMdZICttmbTbg2YkXcVvd8vSAaJFYZi8l9VLjvDXiAxMlV+oX8SC9jsc8iPlWkVD9XIT8IZ+fzn+EKlMgfVhwpcgcBxnGVnppLWWjDd13CNL48Z4+rbqXzHoEk+OE6s1FkQoqyWFWaaXp9YlbXpU02JOEhlW0ZgeVJoEmfI3iM/BoINpBX9LcmJqop/wPJO7MGrPKrZwhWkdoxZFkuStGX7rEW6ZOI4zCXPzmRDRewCcBuAWZv75cO5gAB8GcAyA6wA8n5lvp3rj1vMAnArgXgAvY+bLh9roLduoRSxSk6e0PJMMJL/KYPRG62EK2+xds3jsMyIdlpXhs/bCKfEVtQ1lRFbGRl+awyoUqXQRPTlD8rRcD4t/JSHfhOYjwnJEc94H4JTo3DkALmXmEwBcGo4B4FkATgh/uwGcP0e9HMeZA3N7mTDz/wRwW3T6OQAuDJ8vBPDczvn3c83/AXAQER0+L90cx5meRTtgD2PmPeHzzQAOC5+PBHBDp9yN4dweRBDRbtTWC44++mjg+uuTjc2YoorzsyPcUKgRPmtqNkfdDbfjjba19lKGqxaWjp20knMwaTJ39beEHpuZts2xsrlXb9Zs5FwGIM7ETrY9pF9miHzI+SmWbY6rhE6CPurQNf4Oq36b7W+pitrW0uk1ckLMBcOlTXPAcu2syXbYMPMFzLyLmXdt3759Dpo5jlPCoi2T7xLR4cy8JwxjbgnnbwJwVKfcjnBOp1kDNqA6x6KeNX7rA0qIUJo0lUoGk0JqI9LzxQ3RE8l0alJdzMTp79L30KqSk9LeViqcRhDroFkHiaRC044F0rmmbGx1FCat9dIDNCurEVuF81W/ydTz0+TlsGjL5BIAZ4bPZwK4uHP+pVRzMoA7O8Mhx3FWgHmGhj8E4GkADgXwXdRei/8O4CMAjgZwPerQ8G0hNPw21NGfewG8nHl4Dl9yol9Or5YTZhsIoQHQ19M0TO5K7eyXtTOdhmUau0TqWSih4eQm5zCEyy3JZpKMgQQ3sZ7B+jDtuKgkg2myi+Q04qJnm7PbobWMNZ1+bsMcZn5h4tIzhLIM4FXz0sVxnPmzNdLpA6a1QTVLYmhsr42XGxlSW02Zxm8j9TRrs/VbvaTITVNf2/clw3Nv6t1SKexCTxb7nkosCM1i1qJYPT+F9Mzj56akqyf9DN02o/vt+Wu6CWlNGxXsGKJ2PfmGZEVRlxG+NE+ndxxnEvxl4jjOJKz0eiY9B6zFtDc4QYuwOCSr2bJz23hJKD96lbcBB7bo4NTWAEmUyUJyFlaz8rUhQSsmfvZKHU2eaf5XQk8Twv/V3hB1qjB8t40lmJvjOM4+xNawTDJCaS1Cb9lg6Vnbsjk9gaZnyhEZ6yLVzwz1AXrCXNFK+93rKb1K7yEHw/32quTMara03TY6bBlPtUatSV5JCgDsoWG3TBzHmYSVtkzaNWCr6Hwlla6ZZKxeSkaiV++6FMJuL2b0qIq/xkRG8lpOElfJavKW5DVtg/kcX0ks17QOjubTKfGJCSSfQU5y4kDbbpk4jrNQVjppbeceYL3qnGjevFKyUNRDiclRJSnjVXRBk6f10EoPCsj3oloUA71N1m6CEBK6okS8WeGyr4mlHjqhj7pKXoU0KZ9EV/a5s7prCXg5aBvdt00019bSZQZXn+v8ftpPlfzbmnmOzbmM55jUUcAtE8dxJmGlfSaDeSYdivI3cvwBjbhKKFoS+7fokFM/ImsagSBPjTYl2tLWqm3FC2WSURIlxyW5DEK3fiw/J/qiULKer6jPgPyZKtF3aPIbGnGfieM4C8VfJo7jTMKWGOZYUpNzHKZZYUpLIloOQ843oaxlGJGzHapJr9T1bhkliSulV9aGZ4ahVVu0kssN6VlERnKiipb01xSJhl+WULiawCiU8WGO4zgLZbUtk127eH19cEG2GkOYLVnWIE+yDnIsnMGe2kJOurp231qZpujIdPBBJ6Xl2Wv1Yisz1Q6gOoFNDuIcEsl0JgexAdVRnHomA78Ft0wcx1koW8IyiXu54i0cM8bmY6Z+m/QbaRUNyiu0JNpL1WyZrDVWYdQ5pU+O7iXfR67fK8fPNVRHkaNNwFRD4Qmsy3C4ZeI4zkJZbcskiubE5KRdA8Pebi09vydDkJOFkiw1OimqI19DTY5qCw33rJMtRJXj/2lUkXwIA/VNVpY20a/EMjHq0yva6FcSHZLak6aOuGXiOM4i8ZeJ4ziTsNrDnMgB2yBuqdl81hyRGXMiLOt9JkPDOWawQZ9sR3Pcdo6JXKD7ZEO1Aieyuk5MTlJYNayXaeg7pAOUYXtOYprWVuY8Kh/mOI6zUFZ6PZOGoU2/Z4jfzhmJXjPv87VwTpmt2esllF69F9oz9Lqt9DHON+1aV+6IdHqpRx1Mo5fascz2jZ5FcyQ6gVPyOzJMFm2C3DVjSuhZvWtRO93wfhVONceKhTPze6wShSLcMnEcZxK2hGViCs3F5zLG22KTbaHhtOjeuF3QzxSCjPTqrX1bGoJN9bqW9H+p9zUkW7VWS3Mc96g5enZPJZIJxRT51DPWfjcS4VpsKZqsoQxU31N8LFirrTWIPKuKtG1oO7hl4jjOJKx0NKdZnb5h1IpmXRLPpHh1sqkxREsGlwHInUYwVCYz+Wow3VuRZ9pTqISchDSJoWeuXdOmLijPalREL9Fej8qjOY7jLJAt4TPp5XyYKg2n08e+CdNK9l0SvaTob0i0KQtIlLWMbQ2WRNZzNESxLCvFq5ZKpHP7PazNypi5luM/i+5Fs3QsfqmeZSG1oegSjxY06yNrXJExDcF9Jo7jbBr+MnEcZxJW2gHbW8+kwCmqbeCthTgtm5vnrCKWdCrmyM2hdNZw1LYWPlebr+T6klM1tc6p5oAdtaasYUikrps6Iu0f6OtsWT0tlmO6b2WoP/PduwPWcZxFslQOWCI6BcB5APYD8G5mftNkwlMp8t23/RpU1PR8rUyqjsUqbJK7MhPIhkKGlo3QVe0a3SXrJeGAFcXE+inJVq2DORxL244m5VqsVUsKfw4Wh24VilRK/So67hJPH7Ak4EXXumebhLZoop+JpbFMiGg/AG8H8CwAjwPwQiJ63OZq5TiOlWWyTE4CcA0zXwsARHQRgOcA+NtkjY0NgGhvKjFm37TSps09ckKkWvhXGn827RvGvu1YtbkHLYwX93g5afRKnWT6v9RmrHdXXkKOZWKZOE0hkZ6u+gUsafAlaM8vEZbWQtftM67SFkR7JIVoLQmHUdnes+5Wq2RxFpbGMgFwJIAbOsc3hnMzENFuIlonovXvLUw1x3GGWJpoDhGdDuAUZv6NcPwSAL/IzK9W6nwPwD0Abl2MlpNxKFZLZ9d3/iyzzj/DzNuHCi3TMOcmAEd1jneEc0mYeTsRrVvCVsvEquns+s6fVdQ5ZpmGOX8D4AQiOpaIfgrAGQAu2WSdHMcxsjSWCTPfT0SvBvAZ1KHh9zDzlZusluM4RpbmZQIAzPxpAJ/OrHbBPHSZM6ums+s7f1ZR5xmWxgHrOM5qs0w+E8dxVhh/mTiOMwkr/TIholOI6GoiuoaIztlsfQCAiN5DRLcQ0Tc65w4mos8R0TfDv48I54mI/jjo/zUietIm6HsUEX2eiP6WiK4korNXQOdtRPRlIvpq0PnccP5YIvpS0O3DISoIIjowHF8Trh+zaJ2DHvsR0VeI6JOroG8uK/syWeK5PO8DcEp07hwAlzLzCQAuDcdArfsJ4W83gPMXpGOX+wG8jpkfB+BkAK8Kz3GZdb4PwNOZ+QkAngjgFCI6GcAfAngrMx8P4HYAZ4XyZwG4PZx/ayi3GZwN4KrO8bLrmwczr+QfgCcD+Ezn+A0A3rDZegVdjgHwjc7x1QAOD58PB3B1+PxOAC+Uym2i7hcDeOaq6AzgQQAuB/CLqDNI949/H6jTDZ4cPu8fytGC9dyB+qX8dACfRD0Zd2n1LflbWcsExrk8S8JhzLwnfL4ZwGHh81LdQzCnTwTwJSy5zmHIcAWAWwB8DsC3ANzBzPcLerU6h+t3AjhksRrjjwC8HsBPwvEhWG59s1nll8lKwnV3s3TxeCJ6CICPA3gNM9/VvbaMOjPzPzDzE1H3+CcBeMwmq5SEiE4DcAszb2y2LvNklV8m2XN5NpHvEtHhABD+vSWcX4p7IKIDUL9I/pSZPxFOL7XODcx8B4DPox4mHERETSJmV69W53D94QC+v0A1nwLg2UR0HYCLUA91zltifYtY5ZfJKs3luQTAmeHzmaj9Es35l4YIyckA7uwMLRYCERGAPwFwFTO/pXNpmXXeTkQHhc8PRO3juQr1S+X0hM7NvZwO4LJgbS0EZn4DM+9g5mNQ/04vY+YXLau+xWy202akU+tUAP8P9Xj5jZutT9DpQwD2APgx6nHwWajHu5cC+CaAvwBwcChLqCNS3wLwdQC7NkHfX0I9hPkagCvC36lLrvPjAXwl6PwNAL8fzh8H4MsArgHwUQAHhvPbwvE14fpxm/j7eBqAT66Kvjl/nk7vOM4krPIwx3GcJcJfJo7jTIK/TBzHmQR/mTiOMwn+MnEcZxL8ZeIUEWYbf5uIDg7HjwjHxwhlH0hEfxkmZ1rlv5qI/tl0GjvzxkPDTjFE9HoAxzPzbiJ6J4DrmPk/CuVehXpC23kZsh8E4H8z84nTaezME7dMnDG8FcDJRPQa1Mlv/zlR7kUI2Z1E9LRgpVxMRNcS0ZuI6EVhfZKvE9HPAgAz3wvgOiI6aRE34ozHXyZOMcz8YwC/g/ql8ppwPEOY6nAcM1/XOf0EAK8E8FgALwHwKGY+CcC7AfxWp9w6gKfOR3tnavxl4ozlWainD/x84vqhAO6Izv0NM+9h5vtQp+V/Npz/Ouq1YBpuAXDEdKo688RfJk4xRPRE1JPsTgbw2maWccQPUM816XJf5/NPOsc/wez2K9tCfWcF8JeJU0SYbXw+6uHN3wH4TxB8Jsx8O4D9iCh+oVh4FOqJfM4K4C8Tp5TfBPB3zPy5cPxfATyWiH5ZKPtZ1A7aXJ6CehU1ZwXw0LAzd8IK9q9l5pdk1DkRwG/n1HE2F7dMnLnDzJcD+HxO0hpqx+3vzUklZw64ZeI4ziS4ZeI4ziT4y8RxnEnwl4njOJPgLxPHcSbBXyaO40zC/wer6ofjuwRpEgAAAABJRU5ErkJggg==\n",
+      "text/plain": [
+       "<matplotlib.figure.Figure at 0x7fe246866198>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "image/png": 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MchZeDRgArVrBK6/Y0z67dVNP3Fu3qsAtqCftf/4xr7Gqft54oxnwNTCevv/+2zxWtqzKFjII1HcgKgrmzFG/K4MKFcztQL7+6Gj1Om2a/XjPnur+69VzH+dCVhuCllLKRsDNwENCiFbWk8qY47ruE0IMEEIsE0IsOxJoyaTRaK5MrCqZQ4bAc8+5p1taWb4cXnjB3J87V70GyodPSzP7lbdpA59/HjzHfuZM9frss+axadMgoy0sYE7o6en2AK11YnbDmJSt7N0Lls6DAQ2BkRZq8NlnpqsJ/N1WBoFiABER7q6kIGSpIZBS7st4PQxMBa4DDgkhSgNkvB4OMHaslDJRSplY3Fq2bb8mS+47J5Cbv7smB2Ct6/n9d/VqzX93IzHRrtVjpGE2aWK/bsMGlZmzf7/9M8qVM1cBefOa/vNp0/yf4A3q1rVP4oYhOHfO/h0MKYlAhDPxGumfTpyV0DfdpFYsGanlNoNkpW1b/2NduoS+DxeyzBAIIeKFEPmNbaAjsA6YDtyTcdk9wA8X8v6xsbEkJSXlyglRSklSUhKx1swFjSYrOH5c5bbPmxf+mDNn7BLNhsZOsCwWa6bNf/4DTZua+84JtE4dJZ9gbV9rTOADBsDUqSot1FgR3Hpr4M8tX96+b7xPy5bKXWPQrJkqALOuHqwEMjThcNtt9n3DFfTzz7BjR+Cgb+3aMGiQuT9qlGqBeQFkZR1BSWCqUL+gKOBrKeVMIcRS4DshRH9gF+BSlRGacuXKsXfvXnKr2yg2NlYXoGmynoceUro3Y8cq+YRQE97OnUpHB1R+/6efmuecMgoGZ87YG7N07Qq//mruGxP67t1Ku8fA6NlburR5LDISundXhislBerXd//Mm29WBsOpJxRo0i1SxD3jx0qDBu7FZOPHBx8XFaXCvMbv1rinPHmC9zMWAt55R61c5s4N7EIKgywzBFLK7UADl+NJQPuLff/o6GgqGX9wGo3m0nLunAp8fvONeezs2dDunc2bze3rrw/PEIwdaxdyS0y0rxCMcc8+q5qxONmxw/9YdLQKUlvfF5TbZeZMJdYWE+M/ztkKsnhxZXDKlHG/dyuTJsGKFfDUU/DEE+bxu+8OPfZiMDSLArjQw+GqqCzWaDSXmEcesU/iAL/9ptwl778feGVgraC99lr7uUC6OLt2mdvbt6snW2suvjHOmpFj0Lev+4TuFrwF86k5UO9fp5pnq1bw4Yfu1zqpWlX9uElPZCVGgPwiDIFWH9VoNP5YixvbtFGvt92mqnGtuj5WFi1SE7OBU6LZuiJITlbpks2amc1dwHQrGSqeFSuahmDrVv/PDOQeDRSPMORfAgV3hYDvvzf3g/UJuNQsXQp//ZX5ccaKIBOSEk60IdBoNP5Yn96d7RCdLh5j0s4QVgRUyqfT3259z9Wr1Y/1Kf+jj8ztX3+FBx9UE3F6ur2Ji5VAhuA//zENmJVHH1WrgmAB5NtvN9NSQ7nCLiWJicowZpaHHlKvFxEj0IZAo9HYSUszRdUqVPBPX7RO6FOmqKfrlSvNY507u+v9pKerAG6fPkra2cmAAeZ2x46qkCw6WrmbAj3tOrN+DOLilBvLCLaWKKEKrGrXVlIOoeKLRu1CJoqyso3nnlPG2M1FFiY6RqDR5BZmzlRZND//rCbv++5zv65yZVUMVayYypRxqmlaVwTGhG4tnKpWTRU1gSoQ83hUhW1ammquYg1AG8yf734vUVGBz0HodozGSmL16kw1aqFnT/X9M9vtLLu4mPRVyFqtoUv146Y1pNFoMkHv3kp/pnJlU5MmEMb5555T+xs32rVsDh1Sxz//XMqCBe3nKleWMinJ/n4nT6pzUVHuWjog5a5d7vfSvLn9ur59pfz7b/97CcTIkeo6jyesX9PVBleI1pBGo7kS+PZb9Wo0YnEyYwa8/rr9ab9GDfXqDJimp6uGKPfe66/7v3atf6DWKttgxZqSGSg9s1Yt+/6nn6pK4+7d1X4ov/gzzyiTofuGB0W7hjSaq5l//jF751pxaud07qxerb79mjXVqxEjKFBAZaikpweuNM6b1/9YoCKtatVMmYhA1/TsaU9jNfzgkyape9ET/CVB/xY1mquZGjX8m65A4F6+1lROI/0zb15lTAwdoLQ0e8DY8KMHes88edz98+E0TmnfHoYN8z8eGRlYg0eTabQh0GhyC/fcY24Ha3oOKhvI6hIqWtR8Gk9NtXftMpqvWKUenDhrCkC9/zffwPTpgcdFRtqlnDVZgnYNaTRXK0Z+Pyj1TKs7yGoInEahVi33PHvDfVO7tv24kTFkzRxy8sEHKlvpKUtDwnz5oHfvwGM0lw1tCDSaq5U9e8zt4sXtgV1j8r/rLpgwwT4ukPa+049ftKga36sXHD4cfFKvW1e9r9UQ3HVX6O9gsGfPxadIagKiXUMaTU5ixw4YORLOnw9+nddrV64sUMDMAkpIMA2B1QgY0s+BDIFTv6d1a1WBGxcH//1v6OYtRiC5USOVyZOZKtpy5ex9eTWXFG0INJqcgpSq2GvYMLscsxtWITdQ7pwHH1R6NjffbBoC6+RtCLGFuyIwdHvCxWjJaDSq0VwxaEOg0eQUrKJr1paMbljz78ePV8YjIkLp2URHK5np+fPtKp9Gdy9nr18Dp8zDhejwtGkTXraQ5rKiYwQaTU5h6lRzO5AIW4cOasK3uo6cevjR0aq3gLUZDJgrgUBFWlWr2vcvpyCbJkvRhkCjySnMnatSNU+dUsJp332nFD8NvZ3kZPjjD/P6kiVVoxQngfL9//tfVeEbKOjrzNvPrGtIc8WiXUMaTU7gzz/hl19UGmixYmqC79XLPmlv2mQfc9997tINzkbs//0vrFmjCr/uuy94te7zz6u8/jZtVMxBc1WgVwQazZWO16uaqYPqi7t3r1odgAogg5KBdnYUCyTRfN119v22bcOXWx4xQr26Vftqcix6RaDRZBfGJB6KnTvN7fr17eJtxgTeqJF/S8VAhqBECbV6MPLyS5QI7z40Vy3aEGg0lxspVRP1iAhYuFBNyG5tGA1WrTK3GzSA9evN/ZiYwHIRgQwBqJoCoy5AG4JcjzYEGs3l5oUXVCtGgAceUK8zZ7pfK6Xp8vn2WxUANiqEY2KU7s+hQ+5jgxkCUGPhopqea64OtCHQaC4nGzbAiy+a+0aA1zkZb9yoFD8ff1z1CgAVHAYVH3jpJRU0Tk01Rd8M6tRRgd9Q6px//glPPnlRLQ41VwdChuunzEYSExPlsmXLsvs2NJqLY8kS1VTFjenToWtXtX3okLtss/P/aqVK9viBwYkTquAsVBtHzVWPEGK5lDIx1HV6RaDRXAjp6aqxihCquXo4rF5tbltXBWD381tjAAZuKZ158vgfe+ghVSegjYAmE2S5IRBCRAohVgohfsrYrySEWCyE2CqEmCiEcPlr1miuYMaMUYFWI8BbpQosWABPPGG/TkpV5GVglXN46CH7tYa/HtTKwcmCBf7HnCJwycn2xjIaTZiENARCiLxCiOeEEB9n7FcTQnTJxGc8Bmy07L8GvCOlrAocB/pn5oazgkd/eZS5O+dm921ocgqvvGLfP3kSWrWCN9+0Hx8zRlXfHjyo9q1CcE69ndRUZTiaNoWnn7afmzNHVRA7cbqK4uJ060bNBRHOX83nwHnA0IzdB7wczpsLIcoBnYFPMvYF0A6YlHHJF0D3TNzvJedo8lFGLRlF2y/aZudtaK4UPJ7Q+f1nzwYfb2A8nRsuoR071Ovbb/uPS0tTAeLFi9W+1bUTyM1jGBiN5iIJxxBUkVK+DqQBSCmTgXA7RLwL/BcwWiUVBU5IKY2KmL1AtoqMbzpqluWvOOCiy6K5+klOVvo9oNIznRIMTs6cMbfHjbOfM1w8Z8+agdzPPlOxhBkzoFs3GDzY/z2t1xv3YRBIh79KFfVao4bKLtJoLpBwJCZShRBxgAQQQlRBrRCCkuE+OiylXC6EaJPZGxNCDAAGAFQI1fDiAvl+/fesPmQG8NYeWkuj0kHa7TlYcWAFaZ40mpQLkAmiyRlUqKACvl9/rV6DBX89Hntgt1Ah+/m0NKXbYzR5ASUOZ2DtA7x0qbq+eXN47DH7+1j7/8bFud/LhAkwdKh61UqgmosgHEMwHJgJlBdCTABaAP3CGNcC6CaE6ATEAgWA94BCQoiojFVBOZSryQ8p5VhgLKj00VAflpyWzPA5wxneZjj58oT+T3Em9Qw9J/W0HTt1/lTIcQA3fXUTKekpzNs1D4BZ/5pFu0rtwhqruYI4dUpN2sbE36eP+3Xp6WrCjouz+/mnTfNX8kxNhY8+CvyZVonnxET76sKKm1ick2rV4IcfQl+n0YQgpGtISvk70AM1+X8DJEop54Yx7mkpZTkpZQLQG5gtpewLzAFuz7jsHuCi/5LPp58n/pV43lz0JqOXjA5rjNUllD+PekpL8wYo1Xfw67ZffUYA4P7p92fibjVXDLVqwfXX+x93Fnfdfbdqs7hypemO+eknuOUW9xWBW1onQMeOMHy4/Vh8vHtBV+HCMGuWWjVoNFlMOFlDjYCKwAFgP1BBCFFFCHGhyqVPAo8LIbaiYgafhrg+KPN3zSd2ZKxv/4W5LxCoSG7iuonsOrGLpOQkunxtJj6N7qSMR5onPEPgpES81mrJkezfr15r1rRPxlafvJRK2gGUsJuBEcC1unpAVQNPn27uGxISoGQlnNW+QvingTZvDo88Au3aqVWDRpPFhBMs/hD4G+Wm+RhYBHwPbBZCdAznQ6SUc6WUXTK2t0spr5NSVpVS3iGlDBlvCMa0TdNs++c959mctNnvun2n9tF7cm8S3ktg0K+DOHTW1GepUFDFIMJZEaR70/2OFYot5HKl5orGmt1TsyYMHGjuGwHfoUMDp2MaxqJMGeUe6p6R/Fa/Phw4YF5ntIyMjSUgxirD4M8/daN2zWUlHEOwH7hGSpkopWwMXANsBzoAr2flzYXD0eSjfsfOp/vblq3HTHXHr9Z8ZTvnMwQhVgTn089zzUdmRkmDkg1oWaElqZ7UIKP8OXz2MN+v/z5TYzRhEkiAzYk1Q6dJE3sA2DAEb70VeLzRvzcuThWK3Xmn/zUJCWb7x2B9eqdOhS+/DOeuNZosIRxDUF1K6at5l1JuAGpKKbdn3W2FT9I5/wwPtyf7YIHgMvnLECkiA64Ijp07hsfr4dt137Lu8Drf8YalGpInMk+mDUGrz1vRc1JPnp31bKbGaULw2mtKo+fPP4Nfl5pq9t8dPFhVBFsrgLduVQJvTv8/qADtnj2mln8gHnpI1Q0Y7xEsq6dSJejUSW3rgjBNNhDOX916IcQYIUTrjJ8PgQ1CiBgyaguyE+uK4JpS6mndOTEnpyWz/oiLfgvQp14fYqNiiY6MDrgiKPZ6MdqNb0e/H/rZjlcvWp2YyBjOe8Lzbo2cPxIxQvhcV68sfCXECE3YeL3w1FNqe+VKJdNw+rT9mgMHYN8+2GxxHQ4fDpGRZpaO8QT/3Xd2SYj+GQXw9eu7F3jVrm3fN/r5Nm2q6hJGjQp+/8b1//lP8Os0miwgHEPQD9gKDMr42Z5xLA3I1nLc5LRkVh806wBe76A8Vc4Jve0XbXl6lqNsHxXkndBjAgDREdGuK4Lz6eeRSObvmm873iahDfc2vDdTK4Lhc4eHvkiTeZYtU6mcBlu2KHfPvffarytTRk3igwaZx4z0z+HD4fvvoXNn988wfPzVqrmfr1tXpaIaGCuA+HjVX7hjiHBaTAwcPw7vvhv8Oo0mCwgnffSclPItKeWtGT9vSimTpZReKWWAJOjLQ+OxjW1P47FR6j+rc2Jess9FxAuIj473bbutCPac3EOrca18+9WKmJPAnHvmUDp/aWKiYlxjEm54pCf0RUGQUvLH9j/weC/ufa4q0tPh2mvhttvMY4a0g/XJ35qvP3u2//vExMDtt7uncvbpY1YeV6wY+F7q1TONTHx84OsCUaiQWp1oNJeZcNJHqwkhJgkhNgghths/l+PmgiGltNUCAOSJVPnbyWnJ/LApdHlCnRJmQw+3FcHTs562GZHtx/2/9oXECC6Ub9d9S4cvO/DpyovKuL262OzIEKtUydy2SkVs22a/rnt3lerpxCnuNmeOqtw1Kn1lPPfFAAAgAElEQVRDdfN68km49Va4777g12k0VxDhis6NAdJRrqDxwFdBR1wGJm+c7Nv+V4N/sfnhzURHqHzsR355hO4Tu7Noz6KA459o/gRf3Wp+jejIaP7a8xdtv2jryzA6knzENsbtiT7cGEGJN+y1BsNbKzeRV3rdLreR5kljwpoJPDfnOSD8CuhcgVXj/6ef7HLOhl5PWhpMmWIf16qVmfljpU0b+74ROxg+HD74QE3ywShVSn2WW6BZo7lCCccQxEkpZ6G6me2SUr6AUhTNVpKSzWyhJmWbUL1odd+KYM+pPb5z1om2eF7zaW5g44EUjDXlAaIjoll/ZD1zd87lkV8eAeDIWbshcCMqIgqP18NnKz/jly2/2M71mNiDWh+oPHKnUYmJVC6IcFYTI+aN4K6pd7Ht+Lawx+QK1qyBvn3N/aZNVTDYwDAKefL4N4KpUcP9PYsXt6eSGoYgb14VyNVZPZqrkHD+qs8LISKALUKIh4UQtwLZrnCVNzqvbzsuSolyRUfaKzTjouPYfNR0HZTObwp5OYvArGNL5VNtAp2TN0CL8i2Y388MHEdFRJHmTaP/9P50+rqT7dqpm6ay6egmktOSbcc7VulITFRwQ2Ctjl663y4z8OzsZ8MyUla2JG3h9PnToS/Mbo4dU6mZLVsqv/zrr6s4gJUff4Tt26FBA/PY7Nn2J/yCBZUhMBq9OwlkCACiouD+DNkQLeamyQWEYwgeA/ICjwKNgbtQGkHZyulUc1IzgsSGa8jA4/Ww//R+375VjM66GnCOrVpY5Zhbxxr0v6Y/11c09WmiIqJCFqLFv2IGDr+69Sum9ZrmW724BZq/WvMVES9GcOzcMU6fP81v237zu2b3yd1BP9Ngzo45/PzPz1QfXZ0nfn8i9IDs5uuv1euff6og8JNPws8/m+dPnFBSzs5q3LYZCWyLFyvRt/z54fBhuOEG989JSAh+H2PH2lcGGs1VTFBDIISIBHpJKc9IKfdKKe+VUt4mpfz7Mt2fH0nJSQz8caAtHdQwBMbkauCRHk6eN58IrYYgKsIulRQZYWZrGE/rbv77AjEFbPvREdE2o2TgDGQb3FT1JuKi43yuIbf4wgdLPwBUtlOD/2tgO/d0S/W9w3EPeaWXduPb0eUbpav00fKPrly30t69KiD7yCPmsX/+Ua9WPR+3No7WTJvrroMBA5Q7aMoUlVpqZcMGmDQpdHaOEGploNHkAoIaAimlB2h5me4lLJ6d/SxjV4z1BUxvqXELN1a9EfB3DXm8Hk6muBsCJ9aKYa/0BpwwnYbAaVBAGSsjNuCkSJySGjCMzfxd8xm12F5sZHz2+fTz7Dixw3auZYWWtmuC4Sa/MX71+JDjsoUJEwJ33DJcQ+fOwcyZ/uefdanQdlMAveUWpf1jTTXVaDRh9SNYKYSYjhKa8/Xok1JOCTwk61hzyCzaKRxbmGm9zUIiP9eQ9NgybAy5aTesT/8er4cTKSdcrwvHEEzaMMnvGKineZEhTWDca98pKtj5SBPzSfj4ueMAdJ/o38XT+PxwBPIOnD7gdyzQ93Iycd1ETp4/yYDGA8K6/qKxpnJ++aWSfjZITVVGIG9e/3Fr1qhiLidOQ7B5s1k1rNFobIRjCGKBJFSvYQMJZIshsAZenZOyn2vIa3cNWQvIguGRHt9k7CSYIWiboPzUD/78oN+4W2rcwivtX3EdZ2XbsW1+qwArxncMZ0XgFuMI1zXUe3JvAB5opGSURShtHTekDK3JAzBxor3xe5cu9vOpqfY4Qd26sC5jBVerlvtnVKtmr/StXj38+9ZochnhVBbf6/KTbdUy59LP+bbzx9if8P1cQ9LDqoOrfPvhdC4DtTqwPjlHCtOf7DQEEcL8FUoCN1Jz9ixw3qtB1VFVXY/ff839rHlwTUhDcDb1rK/yeN9pM5Xyxzt/BCAlPSXgPbpx3SfXUeqtUpkaA6jq3ogINYE/+mhgg7BlC/Tube6//LJ/16/z51WWkEFLi7cykB//nXeUjLRGowlJOJXF1YUQs4QQ6zL26wshhmX9rbljXRE4XT1O19Dbi95m6qapvn0jLbRqEffJ1sDj9XA8xVwRGP588DcEVj+8MQEXy1sMJwVj7JNboBVBIJqXb069kvV8hmDKximIEYJ/kv7xXbPv1D7y/S8f7/z9DoBNh6luibrERcVxLu0cobCmmS7bv4zDZw9n6l45fNjswduli7vg2unTKrffqu3Ts6fy9zuNxpo19sKxUqWU1EOPHoHvoXx5eOMNWLXKv/pYo9HYCCd99GPgaTKURqWUa1CtJ7OFs6m+MIXfE77TffHLVnuBV5UiVZjeezpTe00lGF7ptbmGjKwkgPg8dvfSsZRjvm2j8tgtndRa9wDuhuBMamDppgalVPaQYQi+XKP0643J/mzqWV+19bRN01iwawGjl5ptO8vmL0tcdFxYKwKjcM3Ku3+HKYa2bp29k5cVryULa9QoVa27ZYt5zNqw3cqrr5pppQA33aT6CUye7H69lQYNtFtIowlBOIYgr5TSmbPn36brMmGdyOKi44Jeaw0A/1/n/6Nr9a50rdGVuiVcgosZREVE+aWdGqmeYHcFAbYnbGNFYF21GJ/lNCBuhuDDpR+63tPewXtpVFpNrs44iFEP0fGrjjw2Uz2Fl4gvwYh5I3zXLH1gKdGR0cRGxdpca26ke9NtzXcMxi4fG3ScjzvuMKt7X3rJfu68JVV2+XL/cc89Z+4fOuSvDwQqXtCkSXj3otFowiIcQ3BUCFEFFSBGCHE7qn/xZafMW2U4l36OwrGq76tRURyKCBHBwMSBYQU8I0UkO0/s5N8//9t3LFhPYuvE6pEePF6PLaPHWAmEWhF4pdfWRc0gPjqesgXMtoVOQ2Csiv7a85fvWIGYAr6q6JjIGBLLqL63hiEYNnuYzaUEqmva/tP7/QLM5Qoo7f3i8SHE1kBVBW+y1E842y0ahuDgQfjFslorXlzp/1srg0uUUO4dK5GR/v19NRrNRROOIXgI+AioKYTYh+pJ4J8Wk8VIKTlwRtkfw9fvnFwD4Zw83Vj777X8fvfvREZE8s26b3zHR7Yb6Qu0umGsCGKjYvF4PX5P3MYqwZmx5DQEHq/HtvJoVq4Z4O/+cn4XqxSFwRerv/Cl2VrdWjGRMaw9tJaRC0bSa1Iv25iSb5ak7NtlbS6xHrV6sGrgKlqUb+G3EnLFMpHL557j072O31tKiirwKl1apYMaAm9u4m+gJv1+/cx9t/RRjUZz0YRjCHZJKW8AiqNaVLaUUu7K4vvywxqULRynVgThGgJnENmNuiXqckPlG/wmvGeuf4byBcsHGAXtKqms2qpFquKVXlsBG5h+/2CSFpCRsmoJUNcpriSynZlRbtXTwbAGuqMjozl45qDr+xhYA8OTe06maN6i5I3OGzrt1Nqw/auvGNT0BB/tdsRimjVTshEG7durV0PYzY0xY2D9+tDXaTSaCyac1JUdQoiZwETApaPH5WHDkQ2+bSOd0801tO/xfew+uZtmnzbzHctMho41VdRK52qdqVXMv1r4meuf4Z4G9/DYzMfYcmwL5d6xtzE8dk4Fk63Kp273lO5NtxW/GcbOmRnlVisRDOuKIDoi2ucyKhpneXq3rCqMILSV6MhoUlNS4dtvoXBhuPFG8+TGjcqfb4kHNF4xkBUFzlLV+c9jbRgPqi/wyZNKEgIV/7Her/oCsapW4KWXVIMYjUZzyQlnRVAT+APlItohhBgthLjsshPWBjHGU7tbsLhM/jJ+qZqBcvbdCOQC+anPT7zR8Q3X68sXLE9kRKTrpGw85Tt97FZtI1AT+tk0MyPKiIM4XUNuKwmnuqmVDzuZAWirESma1zQE1gC8nyE4cIB7Jm+jyJEzcOedKmPHSqdOSvDN0vVrZT71PbYWhe69QI4b535zCQkqxbNaNVYfXE3cyDj3hkJCwLBhULlywO+p0WgunHAKypKllN9JKXsA1wAFgHlZfmcOrFk8xuRaMr6k67XOSTacGEGgsWGPE5F+AnKtKrYi3asSrJwrAqfRmLZpGrN3mJOpb0XgcA05A94er4ctSVtwY8n9S+hc3czTtxrEIrFFfNtWl5QfZcrQc8pmfn3WXUTP+pSfcmtXSgwFafmr+qEWpLdpZR/TsSNMn24L/Bo9obtP7I4YIVwD5xqNJmsIq8uGEKK1EOJDYDlKcqJnlt6VC9an3rvrKx0a6yRnxeneCSdGEGhs2OMiIm2tLGf2ncm8fqa9NCZ2A6fR6PdDP9u+4SIJpo8EakUQSD/I+Zk7T+z0bVsNjDNTqFS+Uvx6169wJEjPgw0bIDHRdujVYz9yxLKAeamtchedL10cHnoICmQU47VsCV272sY6u659seqLwJ+t0WguKeFUFu9EZQotAOpJKXtKKUNW8gghYoUQS4QQq4UQ64UQIzKOVxJCLBZCbBVCTBRChPW4bjUEt9e+HTlcklAowfVa51P9pXANhcJpQIxAdudqnV3f188XnkGpfKV4ssWTvuBsSEPg9dhksG+sYvrvDaVTA6sInVFjcer8Kbp908123YONH6RjlY5KrtlBcs2MPgB16vjVAuyw2x1fFXaqJxVGj1Ya/wAdOvi974qDK2z7Ly942e8ajUaTNYQz69WXUt4qpfxGSnk29OU+zgPtpJQNgIbATUKIpsBrwDtSyqrAcaB/OG9m9Z+HyhYKlaoZjAt2DTnGGfGLab2ncfYZ/19bo9KN+O7273jnxndsx7tV78arN7zqSyWtWKhi0M/1SI+tItmaneSMlVjrGwyX1aCZg3xpubb3eOMN1ZqxQgV+ucMsMJPbtkERu4FJygjV7LA0fRMI/+Y7PXuqwHLTprbxu0/uZspGfw1Da4JAOAz9bSg///Nz6As1Go2NcAxBASHEVCHE4YyfyUKIcqEGSYUxQ0Vn/EiUiqnxqPkF4K+17IJ1RRBKRdSp9ZMZ19CFrggiHL9KI6MpKiIqoOG6o84dfk/8RgVy95rdGXXzKIY0GxL0cz1ej00byDr5BzNqRoxi1o5ZfucKxRaC//5X7fTsSdFjZm1EfBpw3B5T6HonTK0JKywKEW0S2vg33xFCFYo5cDbfMTAyrsIhOS2Ztxa9RZdvuoTMpNJoNHbCmfU+B6YDZTJ+fsw4FhIhRKQQYhVwGPgd2AackFIaEhV7gbIBxg4QQiwTQiw7cuSIzRCEkpZwBlQz4xoSXIDcMv5CdqHu0cA5WRuTZ8HYgjx83cOu7/Nmhzd57YbXALUisLqGmpZr6ne9k+iIaF/9gVvLy0IRFkNboQJbrlXuoBkBtPoWVYAeveFsRsnCK+1eYXLPyb4VQUp6iuvkbLinnDGO6b2nA+5tPAOx8chG3/a4VePCHqfRaMIzBMWllJ9LKdMzfsahistCIqX0SCkbAuWA61CpqGEhpRwrpUyUUiYWL17cZggy89TeqHQj3r/p/bCvdza1D5daxe01BuHKXzhjC9YCsEAMaT6EbjWUX9/j9bD+sCq42v7odtoktAk5Pl+efHi8noATbamDltabjRqxoX194p6FSbUdFxYvzvj6/uN71+1N4bjCvu/Sd0pfol6yu+f+2vMXkS9G2qQxYqNieaPDGz5JD7c2nm78sOkHm8CeWx8GjUYTmHCc50lCiLsAQ3fhTlSjmrCRUp4QQswBmgGFhBBRGauCcsC+4KMVwXLl3fi468fsOL6Dke1HZmqcMXn9r/3/uKP2HWGPM/L+DcKter7QVFfDgPSZYhZZVSpciT0n9wQcU6VwFbYd36ZqHqTHV1zmpNYb49TGH39AixbkmfsHKdGw0NHg6/yihdzzVQ2/8YY2kvFdVhxY4XfNxys+BuxCe0+3fJqhzYf6ekiEsyI4cvaIXye3K7Yvs0ZzhRLOo/V9qHTRgyixuduBe0MNEkIUF0IUytiOAzoAG4E5Ge8BcA/gUkHkz9nUs3Sq1on9j4f3tHd/o/szbQTA7DLWt15fqhSpEvY4Z75/uK4hpxKqVW8oGIH8/8EC48sGLGPXoF003e0l8uw5vz4D3TdCq51Q8I8F6kBGAxjjPbcUg9tea+y7/kghf5fbgEYDfAYg2HcxNI3c+ksY48KZ0K31Bk+3fJrYqNiwVxLrD6+3rUg0mtxKOAVlu6SU3aSUxaWUJaSU3aWU/o5lf0oDc4QQa4ClwO9Syp+AJ4HHhRBbgaLAp+HcaHJaMsXzFqd0/gCa9ZeIl9q+xD8P/xNUX8iNhqUa8u6NpmZ/uAFqQ1PIILMrAidGWmrHKh3tJ55/nkIVqlNhxzF+HH2MPh8uYNsxU+Z59M8wdSLMG2cZE6MmZOtkvb9oNLz3HrJRI8qPtlf69qnXh4+6fhTWdzHiAtbGQV2qqxaVxqosOS2ZKRunuArrGVgznqoUrkJMZEzYsYW6Y+rS4rMW7D21l01HAxTMaTS5gICGQAjxhhBioMvxgUKIV0O9sZRyjZTyGillfSllXSnlixnHt0spr5NSVpVS3iGlDPm/VkrJnlN7wna3XAzRkdFUK1ot0+MiRASPNX3Mtx9uj9/IiEjbhBlOjMAY50bhuMLMvWcu03pNUwfOnoW1a5VWz5Ej8Irqm1z4wHF6TlJ1gcXOwkNLHW9k0fy3BqM9Xg88+ij75pgLOUO3yOkec34XIzg8ZukY5u2yF6efe/ac7/du/D5enP8it313Gz9sNj8r3ZvOy/NfJik5iU1HN3Hbd7f5zlUpUoU8kXky7Roq/055an1QK2Cfao3maifYiqAd4NaN5GOgi8vxLGNz0ma80ntZDEF2cGDIAXrXVU3fLnZFANA6obVyTXk8kC8f1LdEdL//HoB/Sip3z0uzYIjTO9K6Nfz9t2/XWvVr1B/sOmEK0BrBXauQndt3MTKH/jPjP7bjMZExfnLZYFZCW+skFu5eyHNznmPgTwMZvWS07X2qFK5CTFRMSNeQx+sh3yv+/autPSg0mtxEMEMQI13W5FJKL1xgjuUFYrSnDFU/cCWw9IGljO8+PlNjisQV8bk/wo0ROKuGXVm3zty+9VYoZtZXJMV4iUmDYQvgqT8d4555xrZrNQRG2un6I+t9x4zVifOenN8l3Ztu6xpn4IyvOFcS1kyufadUbsHkjZNtbqHrK1xP2QJllWsohCE4cOaArUDRYPmB5S5XB2bFgRXc8f0drq1JNZqcRDBDcE4I4ecjyTgWugN6FpATVgSJZRK5u8HdmR5XoaBKyTGa7oQiJiqGaEvDUNeVhFUColcvOGr2dDh96ihVAnlC6ttzQnvWUS6kxqUb+57qre4aA6chcOud8P5i/1Rep4F3GhBrKq41wG2tRp5/73wiRAR5IvNwPv08by9620+4Lik5iX2n9vnVTlxTSlVOO4UBA5Gclsz8XfNpPLYxkzZMYt3hdaEHOZi6cSp7T+3N9DiNJisIZgieB34RQvQTQtTL+LkX+Dnj3GUnJxiCC+Wlti8xrdc02lZqG/iiU6cgIkI92Y8bR+rLUDmj+NY3ea5eDdu3q2Yu/S3qHZ3tAn2x6fB/Pznev3FGRlBJu6pr77q98T7vpXLhyr4VgVXAznAXWaWtAb+nf4/Xw6K9i/y+lrOfc6DmOx6vh10ng/dEiomKYfLGyQz5bYgtfgBQ7I1iVH6/Mt2/taebvt7hdbpW7xqyn7NBv2n9aD2utW+/+WfNwxpnsHD3Qnp814Py75TXcQnNFUFAQyCl/AUl/9AWGJfx0wa4TUo54zLcmx/hpmTmRGKiYril5i3BL+rcGaSEpCS4V2Xwlsvw2sRFxMCCBdCwIVSpAnUtaanHj6tYQUElP3GkeDz1DsP11gfjmBiYNw/271dSEA6EEERFRPkmfaPTGeCTuHCuCIzVjfHEne5Np1KhSr7zjUo3AvxXBG5S2wD3/3g/7y1+z//XUs00ctbVhNWzaWynelJt9RPnh53nhso3kDc6b9i1Kr9t+822b+3nEIrft/3O79t+9+2vPbw27LEaTVYRNH1USrlOSnmPlLJxxs89Usps+8sN139+1XHqFPTtCwsX+p0amqFFNPHzM9Cqld95SpSAQhk+9jVr4O+/8cTG0MzplXjzTYiPV/2EA2A03/kn6R+bDtChs4cAf7dW4bjCyOGS/teolYlHemzxhjL5VetJ54rAiWF83KQjbq99OxNvn+jbt8YXDLmNfaf2BdQzMlYf8dHxJKclk5yWHHJit/bGyAyzts+i41cdeXH+i75jmZHR0GiyigtTWMsmMqMZdFXxxRfw9ddq+7HHbKeuKVSTCC+02Rxg8vrlF3O7QgVo0oQjFRzN4qWEhx8OeRuRQlUk1xhtryY2pK+NOIcToyDN4/XYJtEy+TIMQYgkgHRvesBagttq3WYzJOfSTPeOkc306sJXXZ+8rVIleaPzcjb1LPGvxFPl/cCFhA/PsP+ehrceDhC01sHATU013OK3Hcd3+PXD1mguFTnLEGRCRfSq4tAhc3vYMNup/N5omlqf7lubvmuWLIFGjfzebnX5C/s9RopIV5G67+74jv2P7w+oAWVkFaV502wrCWMFEWpF4JEen+yEE6c7yhpDMFYSm5M2+4179vpnSfqvqZQSExXj69Tm1Cpad3gdoxaPAuCDpR/YzhmB7HDcQ1aXlKEqG844KSWV369M2y/acuD0AVs6rUZzKchRhiAzfQWuKlavVq81athSQAEKfvkdfxR8xDxQyyJ+5+ggZnDds2NY3Kw8XzSAm/uGfxuBitjyRucNWvFt1Dz0m9aPmVtn+o5b3TLBSPems+WY2Y6zdD7zs5y1C9bgq2EI3ALUtYvXtqWlBqvfaPpJUx6d+aitsQ8olVWj/uFc+jnX1Fgr1oyneiXqAeG5hoxxKw+upMzbZWj7RZCEAo3mAgjbEAghugoh5goh/hZC/Cf0iEvPVeUamjIFZs2Cp56CqgH0nQF++kn93HGH8vEDvPWW6vgFMGMGce+op1USE+GJJ8yxAaqbq9dtRZO/dtPvVpiZiSJqpyF+vtXzbH0kdG9hY9ycnXNsxztUUZ3K+jXs5zdmaLOhvqdmj9djm0StE7hzRWBtvmMEmd2eoJ1V0MEMgVFz8Pt2M8g7vfd0nr7+aZ8huO+H+4h8MXCR3+ajm/louSm/Ua9khiEIwzXkdCkt278s5BiNJjMEfMQWQjSUUlrX43ejMogEsBr40HVgFpDvGJzhKkof3bgRbrst8HmvV0lD5M9v9vZt0ADyZExWjz+uMoGcfv2lGToR//tfcONygTifvq8re11YwnzOlUTbhLYMbT6U68peR/pz6a4rjTc6vsGO4zt4a9FbpHvT+SfpH985ay9mZ8qqQXx0POnedL9eyG7vAXZD0KRsE+flAPy526y8M7SoDEPgVleRlJzED5t/4J4G91D//+y1GUag/Hz6eRbsWkDLCi1dZUn2n95Pu/Ht/I6npKcEbHeq0WSWYL6WfwshIoDnpJQHgT3AMMALXF7Bd496+mxdsXXoa690pLTp+NiOGxPBW2+pDmH7LArdRR0TXuHCqhn8qYyJLt7iXnnqqbBuZV6/eX5PxsFwts10VgQHwimHUb1odTpV66TOBemiZqwkJqydYHsat64InB3eCsYU5OT5k+SNzku6N92W5mrF+b2tQWbDpQTwwRIzJjB2ham4Ur6A3RAYeKXXFyv5v2X/x7A5w4iPjrfpHxWOLezLgHtv8XtsTtrMtF7TfOnDUkqemfUMHat0ZMYW90zt+bvm+wsLajQXSLA6goHAaOAjIcTzqCKyRcBaoFugcVlBVEQUI9qOCFvI7Yrl779VOufp0/7nUlKUMUhJgXczVEzLWpq33eHSG2HaNLU6AChTJtO306piK5+LIhxKxtsLzfLl8dfrccPpdgm3aY9hCKxGAOyGwPk3sebfa1h470JfzcPmo/6BYvBfEVizmQxDsOLACh7+xT2bynBJOQ2B1YgYQervN3zvO9b/mv7sfXyvL83VuMb6+RuPbuTVP1+l3fh2ttgIwN7BKjNgyb4lrvel0VwIoeoIVkspbwFWovoGlJFSTg9HMfRSEhVEYC1HcfPNpszDhw7PWnIyjB8PcXGqqMvK/v3+KwKAtm3h9ddh4ED4Iay2DheF0x8friGoWczemO5C23gaBFvFVChYgRYVWhAVEcWp1FN0+9b9mcX5HtYndmMybzy2MU7KFShH6rBUnwEKZAgOnD7gm8Qnb5zsO182f1nyRuf1q4kxUl3B1FMCu8tpx2M7KFugLNER0Zlu1OSV3rBSXDW5k2Ay1A8KIf4SQvwFxAM3obqL/SqEcKlcyjqumj/fM5agZaVK9nPnzsGfTvU3oGLFoEVeREbC//2fPVsoi3D648M1BE6XUmZXBE4almoY1thAbhXwTzywGgJD0sLN4JTNX9Y2NpAhKPN2Gf7e+zdO+tZXaVpOI2edpK1iegYVC1YkoVCC7zPDrWYeNnsYPSb2IPLFSOqOqRt6gCZXEmxF8B8pZXNUgPiJjH7F7wO9UdITl42r5knGk9HAvVAhSEhQ29Uy0nZOnoStlgycDiqjhor2STQ7qVG0Bm91fMu3H64hcAb5ww1yFowp6Hdswb0LuKfBPUBwgxIZEZmpfHu3FYEx8QK0rNDS7xj4r26sriEnx588TvWi1V3PWcc501TbVWrH/Hvn+/Zjo2I5l3aOX7f+6tdl7sjZI+w4vsO3P3LBSF/znw1HNvgJ8Wk0ENwQ7BNCPAM8B/jaN0kpj0spH8/yO7va2LdPxQCio5WmT82aqmHMNxmtoOvWhTkZ6ZWlS8Ojj6ptR91AdiKE4PFm5j/9hcqCZ8Y1ZI0HgJqQoyOj+ePuP1jU378+wMBtNdGhcoeA1xfLW8z3akzK1uI3o3bBmTnltiIIlKnkZtgMjLTXD5Z8wFOz7MH+z7p9ZqvajouO43jKcW6acBO3fGvXpyrxZgkqv686x7mtGh7/Vf/X1fgTLGvoFuBGIA0Yfnlux50cux6YN08FiB9+GJD1B9IAACAASURBVMqVU8e+/96Uea5bF1Id3bSGDVPdxM6fV77/IUMu7z1ngmAZP062PLKF/tP7M3/X/LBdQxC4AU/7yu2DjnMagmUPLKNmsZrsOrnLVbPqlfavUK9EPRbuWcgf2/8g3ZtuqwQ23EEFYgrYxrkZgj+2/+H3/tER0UGTHQzj4xacLpnPHqSPjYr1pdMGkrI+kXKCR3951O94oAptTe4mWNZQqpTyRynlTCkznKbZxZXqGpISZsxQWUArV8K2bfbzbdqoVM58FheK05efJ4+ZJQSmBHRMjPL9V8t828ys5rlWz9mCm+FQtUhVvrntG+6se6evG1s4JJ0zZSCCdWVz4pzsG5dpTHyeeGoXr+1a+5A3Oi/9G/UnOiIaj9dDoVcL2QKyxkRdMNb+VO+8p3Rvuq3/sSGL4hznJN2b7irfAf7GJjYqlpUHVwL2KmurC7Xwa4X5cs2Xvv0HGj1A8bzFKR4fXs8FTe4iR0hMXKFmQLlwOndWT+2NGplFXP/8A3kDFL9Vrux/7LHHoHtG2KVQIf/zVxgvtn2RQ0MPhb7QQZn8Zfj6tq9DagsFItx+zgBlC5QNfZELkSLStYOZYRScKwK3GIHVxz+wsWr77XRxOUn3prMlyUwVLZs/8P0LS4NAa0aW1ZVlZVCTQbzR4Q3aVWqndYo0ruQMQ3ClrQhGjVLuG0MM7uOP7eeffFJlAVmpWBE++QSiAnjjRo6Ea6/1ayCT23mzw5u+AGtmKmkNZdPM4nQpPdniSdb9e50vKGtUBPs+J38ZFt67kI+7qr8Bp0vJyJhyMwTtKrWjXaV2vnFWN0+gimmAbcfNlacRd5BS+gniGbx949sUjC1Ivjz5fG1fNRorOULF7QozA2Yg10liopKHWGQJYsbFKaPw8MP2jmFOatdWaqEaG0OaD6F33d6Ue6dc2FlKkDmjYcVpCFpXbE2dEnV8iqRGRbGVFhVasO+0yv13SnSXK6BiQ26B4ln/msX+0/sp+3ZZFWROM4PMRvDaDeuDkRFk/mzlZwyf6x7KM2IT8dHxnEk9w+qDqxFCUL9kfdfrNbmPHLEiuKJIt6QH1qtnr/hdtkzl9RsrhSZNVB+B8uVV83jNBVEmfxmGNBvCzL4zQ1+cQY9aPfwyfMLBGfswiuiM6mhncZyB04A0LdeUtf9e65u0A7mGjHFzd85l8K+DfceDGQK32MVv23/zu65nnZ5sftisrM4bnZeT50/S8KOGARv1GCzZt4Su33RFjBCIEYIFuxYEvV6Ts8kZK4IryTW0w8zR5r77lIIo2HV/QBWHNWmiDMMttwRUAtWERgjBmx3fzNSY6ytez9H/HqXbN91sAdVQOAPJhiGY2Xcmyw8sDxjfcBqCUvlKUbdEXV9Of9fqXYOO+2bdN7bjJeNL8r/2/6NL9S5+YyT+K4IfNvlXlrdNaGurW8hMjKXZp81sstqtxrVCDs/a/4dL9i2hZHxJhBAUiCkQMq6iuXRkmSEQQpQHxgMlUd6dsVLK94QQRYCJQAKwE+gppQzawfuKMQNHjqj8f1Ay0t27q0m+UiU4fBgmmi0TqVJFGQHQRiAbmX7n9ExdX7WIXbXVMATVilajWtHAGVzOlYRRY9GlehdWP7g6oBsmUPV0mfxleKplcPHAEvElSPOkIaV0lbN2GsBgUttWbvrqppC9FbKCJp80IT463heoP/bfY36aUJqsIStdQ+nAECllbaAp8JAQojbwFDBLSlkNmJWxf2WzeLFqFl+ihIoBAHTqpCb4SpXg/fehuCMtr0Tm0is1Vwa1i9f2KaNC6Gwfg4oF7RXgRjV1KF+8Wyzj1favMrjpYJer7RSJK0KaN83XWc2JM7BtNQROmQ6P18OO4ztYfXA1v2771XbupbYvAZDmSSMUu07sQowQfL/+e26deCtTN04NOQbwFeFZs7VeWfBKWGM1F0+WGQIp5QEp5YqM7dPARqAsqlDti4zLviAMuYrL5hqSEl55BfbsMY+lpEDTpjBunP3aGMcyu31GgVPv3kpGWq8CciSxUbH83Odn/tf+fxSOLRx20Zyz6Cvc3hl5IvP4ifk92fLJoG6cEW1GUDK+JHki8/ilq1pxdo2zGgKjaY9B78m9qfx+ZW6acJPf+xirm6RzSWw7ts3vvBWj+VDPST2ZtmkaPb7rEZYB2X58u98xTybLl/af3s/qg6szNUajuCzBYiFEAnANsBgoKaU0/nIPolxH2Y+UsGIFPPssPPCAOnbsGKxyVGK2bw+/+Qfm6N5dFZR9/bUpDa3JsTzV8imOPemel+9GhIiwSW5kRn4jmPSEG8+3fp6DQw8SHRFNqieVEfNGuF7nlA23uqGsndwAJm2YBODXv2HUzaN8Ru3mCTdTdVRVm9vIK702tVQ3oxRKMvtkykn6TO7j27+jtkrACFdhdeHuhXy5+ksqv1eZhh81ZOWBlWGN05hkuSEQQuQDJgODpJQ2ERapHvVdH/eFEAOEEMuEEMu8Wb0iyJMHIiLMHr9RUUogrmhRaNbMfu3XX5uCcE4qV9YrgVzMwaEHfS6dzHTTC1QIForCcYWZsWWGrd9Bs3Lm36tTYdVaQ+Dr57xnEWKE+99s6rBUHr7uYV+A3JCnsD7hT9owiXLvlGPOjjlM2zSNZ2Y/4/c+R5OPBv0ew2YPY+PRjb79TtU6UblwZb+ivkBc//n1/Gvav3xxkoE/DQxr3LFzx3hn0TtXVjJKNpGlhkAIEY0yAhOklFMyDh8SQpTOOF8aOOw2Vko5VkqZKKVMzNJ/pt27Ic2xdK1ZE7bYG4L4CsG0718TAGudQ2YMwf/a/4/KhVXFudHdLBxqFrWnss7oM4OZd83ky1u/ZEKPCX7XuzXfef2v1/2ua1G+BYeGHvIZEufqxqrUOm3TNADajW/HrRPtKdJvdlCZXqdT/RsxeaWXpp805YMlH7Bgtz01tXbx2uTLky+sKmg3gb+l+5eGHAfQf3p/Hv/tcd0Dmiw0BEJVsXwKbJRSvm05NR24J2P7HlTDm6B4gC1btvD2229fOuvt9apKXjeZ57Q0mDvXfmz3blit/Y+a4Dzd8mnurn83DzR+IOwx/7723ywfsBwIv1cD+GcqtU5oTYGYAtxV/y761Ovjd/3JFNMQGE/1btlBFQtVtL23M2XWcCule9M5dNZfaqR60ep81OUjX+8Ft8l667GtLN63mEd+eYTVh8z/V+O7j+faMtcSHx3P6fMunfwcuPV8qFYkPH2ujUfUKmTUklG8tvC1bMmUulLIyhVBC1TD+3ZCiFUZP52AV4EOQogtwA0Z+0FJk5J+/foxZMgQli4Nz9qHZN06VQAG9h6/ZcqoLKB//9s8Vru20hWqrysxNcEpHl+c8beOz1QVNCgNoy7Vu/BD7/A7zTmDzKGMiFGT0LJCS9+KICk5ye86pzyHc3VjGJHaH9Rm9o7ZfuNblm/JgMYDfP2kdxzfQc/ve9pcREZcwRrALpu/LHc3uBshBHHRcQGb70gp2XViF+fSzjFu1Tjf8cKxhRnQaIDrCsQNwyX35ZoveWrWU1z78bVhjbsaycqsoYVSSiGlrC+lbJjxM0NKmSSlbC+lrCalvEFKGZaDdPNmVSE5bdq0i7+5lBQYbEnPu+suVf0LcMAR7HrjDfjVnk6n0VxqIkQEP975Y0h5bSvO9NBQPb1vrHoj3ue9NCjZwPdUb52cb656s+v7urmG0r3pfv2UDVpVVA0MDQPy5qI3+X7D93y77lvfNbtO7gLw+fVvr307i+9f7DsfGxXLgTMHKPt2WZ/7yeD6z68n4b0Eqo2qZivEe7TJo+SPyR+wH4SV0+dP2zShQPWodmZTXWrWHV4XUDo8O8kxEhNJSerJZefOnYEv2rNHdfoKRffuMDvjSebzz6FOHdUkZv9++PRT+7VDh5q9BDSaK4iuNbrySddPMjVGCEFURBTHzx1n8obJtrRNY5XgTDt1cw19vNwhtJjB73f/zr8a/Mv3WdYnfkOn6bv133HvD/faxg1qMsimGBsTGcP249vZf3o/j818zHdcSsmfe1RLV0PfCVT3t2GthpEvTz6S05Jd3TxrDq3h3b/f5WjyUQq8qlRkW1ZoyYg2ZtZVOAFqKSXv/v0uR84eYdn+Zfzn5/+EbUDqjalHrQ+yvq1sZskxhsDg+PEARcg//QQVKigZ53XroF07e49gg+Rk8wn/+uuhXz+1XbCgcv/ce6/KGLr1VqUiqtFcoURFRNG/URAhwwBER0Qjkdz+/e22NFIjUB1qRZDmSWP5geW+/VtqmF3Sbqh8g21lYg0sG9IYvSb18rsnp/GxFtq1rtjatx2oeK5QbCGiIqJ849yMQb9p/Rj862A6fGlm/Q1uOpjnWz/PmM5jfONCsXjfYgb/OpiBPw3k2o+vZcyyMbY4RyCMmMeZ1DNM3TjVL1U3O8lxhmDmzJl07twZr1Hhe+4c3H47dLVoudSrp9o+OuMJW7fCH5buUYHcTBERSkLi1ZDhC40mxxFIL2lIsyHcXvt2rit7XdDrUz2pNj+8s6o6EKme1IATrbOWwmpMjB4QG45soOjrwYUEDUOQ/3/5qfiu/b6MZj7WLm11itcBTDfW/F3zeejnh4ImpRixBaMXNKiK6lBsTjIFAHt814PSb5W2BfCDsenoJmZsmcHmo5sZNntY0N7YF0KOEJ0rBlgzkWfMmMHevXupUKGC6gsweTJpwLXAnYDvOT46Wrl7evSAJ55QBsPg5EklFKfR5HAeaPRAptJVaxSt4XesQckGdKjSgQ5V/GtkCsUWYkafGRxJPsI90+5RshbnzCdzo59y8bzBu5+ledJYc2iNb79qkapsPbYV8O/gZr3OCE4/8KN/JtbD1z7M862f9+1bVxJWX7xb+1DAl7Zr/P6M1crrHV53NZhnUs/w7t/v+h0/kXLC9f2tWDvXGXy3/ruwMsyc7qRT50/x/s3vhxwXLjliReBW5L9z8WI4fpyTEyfyIFAKWI0SLtppXHTyJNx5p9IKshoB0EZAc9UwtutY3r3Jf3IKhLN724w+M1j1YPBexjdXu9knjb1ozyJ+3/6771z5gsr3XypfqaDvkepJtcUkrCmqTvE9o0I5NiqWVG8qR5OP8teev/zes01CG1v7TTftppT0FJs7CJS7SQ6XvloJv8woi8ts7aG1lHyzJPN2zqPFZy1s391wi4VjCNYfXu/bHtluJHDhPaRHLRl1QeMCkSMMgVsY5mTPnlCkCC9t385HgDX1aDvwN0CXLjB//uW4RY0mx+DsdRCs94EVIyX24V8eBqBR6UZ82u1TXxDYTTLbSpo3zVZFHWwFMf3O6QxvPZyy+cuS5kkL6E+vW6Kubd8akzB6RyzcvdBvnLN3s9MQWN/n+w3fc/jsYebtmmdbqQCMv3U8ENgQnE8/j5SS2Ttm8+qfytU8tstYnmr5FFUKV+HE+eAGRErJoJmD/I4XjnVXZT2fft41JTgUOdYQGGUsa1zOtQeaoVYINh57DFq3hh9/vIR3p9HkLJzy1MHaYlpxBpFrFqvJfdfcR9NyTVlw7wKevf5ZvzGjbx7N2x1VPWmax+5SClb41bx8c15o8wLRkUpP6a4pd9nOt0loA/j3j/j/9s48PKoia9zvyZ6QlZ0khE32RZAoopgRQcAAOiMg4jqIoqPouKCDC34qwzDqKOD4gSIgArJ8goCjjMwIIiI/ATXsgpKwRJFsJDFAku4k9fvj3u70mnQQkg6p93n64d6quveeW6TrdFWdxVFh2Abz1QdW28tsqUGbRzg741WlCGwyOy4J9WnZhy/Hf0l0aDThQeGctZ5l7cG1Tmv3hSWFRM2I4i+f/YVBiyvNgu/rex8BEkBceFy1M4l1h9Yxe/ts+/n43uO569K73HJn27j3X/fS9NWmXn0wvFEvFIFt7/9l4G3z+D6A7t05GBVlb5ee7hwZ0YILN9xgeAyPqPqXi0ZzMRMXHsevU3xLi+lIQpTzkpLNgU1EGJA0wOOa+kNXPMQ9fe4BDJ+BxXsWExwQzKvXv8qLAw2zzXax7bw+MyQwhMOnDjtZ5bSPa8/7N7/PkT8fcVtSujzecArr2LijfW/hp6LKvQJb6tDqZgS2a08Vn2J/jrGk42ixdEXCFQxIGmD0Q3A4c76Zwx9W/oEF31Wan+/J2oO1wsqcnXPsZY4WVjGhMRSUFLBi3wp+yPvB6fklZSX8UvSLU9iO5aOW82bqm0QGR1JkKeKLo1/wyQ+fOF23dM9SwNj0rgn1YrO4BVAIjAciAFtIqXmJiWTur1x3a9XK+ZdO+RVXGA5i77xjmIdeeWUtSazR+DdRoVH8vsvvWXtwrd0DuDrCg509l33NC21bh3/9/71ut/2ffNVkALb8cYvb8o7TtQHBTqaqK0ev5Lp213lVXjd0vIGCvxTw1H+fYt0hw0vb0WnO9kvfdVbkaUawJ2uP15SejgN6eFC4fcnLMXT2gZwDQKVvwtSUqU6zpojgCDYe2ci2zG0kRieS+Vhl+PvOb3bmeOFxp2eO7T4WESEqNIpTxae49r1rATxmjhu6dCi/71JthH879UIRRMXEoAoL4b33DGewGMPC4H4Hj9/BgwcTFub8h1n6yivGUpBGo3Fj+ajlnCo+Va1HsiMtI1val198VQQ2pzJHBzAb17S5xqdrbYzoNKJaC6mYsBhCAkPsg37OmUoPYtsMwmbpZMPNV6LCyqeHPefIznws0z6zAOf3cgz74Rp+u19CP6c8E47v4biUo5RyUgIxoTHMGT7H/v/kLXzJ3J1znc5dPbKrol4sDdkjfg4ZAtHRvPDCC07V69at4z//+Y/bH3RpqXv6Po1GYxAWFOa27l8dGY9k2H/V+qoIggKCnAbOmuDqaFaThD/WCiuLdi0iPd9YMm4b25ZJl0/ijl538Lu2zj8QXWc7rhZOjrgukbleB7AtcxsLdy10qrNZV9lwTC96fXvDqinnTA7N/+G8f/HQ5Q85BRH0pAg+/P5DHlz/oFe5qqN+KILoaCNxTEvDPM31l/8NN9xgVwIvvfSSXVFoRaDRnF/Cg8PtA1GTcN82maHqfYCq6N2id/WNPBAcGMxpy2l7KItpA6dx5M9H6JfYjyV/WOKmUKJCopjQZwLPXfMcYAzojktKV7e+2n5c1QzKpgiGLh3qVue6HOVoDmubKcz/br5b/gZXBeKoCGwK9uMfPraX2RwCr0z0fSm8figCFxwVQVlZGcHBlQk4pk6dys033wyAxeK2XazRaH4jf+73Z/468K88dMVDPl9T02isNnyxz/eE62ylOmc3EWH+jfPtM4Vle5ex+vtKa6NLGl8C4HE/5dCkQ2y7xxjULeUWzljO2HMpXNX6Kns7V+ss24DfLKIZpWXGj9YdJ9yzudnMc204WjTZjh2tpWye3n1a9vH+wi7US0UQauYL7tSpE4GB7u5mtno9I9Bozj/hweE8m/Ks2/p9VQxsO/CcnjWkwxD7sa/WTeA8AIO7lZA3bKEuXB22OsQZZqptYt3DaXRq0omuzQzP39KyUicPYkd7f9ekQytGraB/Yn+aNWpmD7/haV3f5v1sw1E52hSIoyKw7VP4GvoD6rkiaNHCc7pj24xh1qxZ/OiaacwDJ06cYPLkyZw541tqPI1GUzMmXzWZHyYZJpKju42upnUlQy8ZinWqFctzFk48fsLn62zOZDZ8VSKufgk2rk66mvioeF69/lWP9TalaCm3OGVciwr1bpE1pvsYtk3YRmhgKJZyC9lnnJM13trjVsKDwu2zERs2x70hHYbYZwSOy0m22YirB3lV1AurIVe6desGwLhx4zzWJyQkEBgYyM6dO+nUqRMAEyZMYP5855C9L774Iqmpqbz11lssXLiQXr16ERcXR0pKCjExNUsortFovCMidGzSkczHMqtdpnHF1VfAFxKjE+natKs9F7Kvz/SU7Cc+Kp4BSQP4+XF3qycbNkWwIX0Dnx/93On6qSlT3YLquV5rKbeQ9kuaU/mym5fxa+mvbrmne7XohfofxXObnrPHUMorrvQmvqPXHew8sdNtVlQVUh8SNycnJ6tvvnHOK3rkyBHatGlDQIDnSU1ERATFxcVOZQMGDGD+/Pl07tyZX375hfh4zxYT48ePZ+HChR7rNBpN/cBabiX679GUlJWQ82SOz7OCEctG8MmPhqPWPb3vYcFNC6q5wiD0r6FO6/cAX93zVbUDcsq7KQRIAF8c+8Je9viVj/Pa0NeqvG7aF9N4fvPzPJj8IHO+mcO1ba/lgb4PMLZHZZhvEflWKZVcnez1cmkIoF27dl6VAMCgQe6ZnrZu3crUqVMBqkx5qTeZNZr6T3BgMHf0NEJTeIvN44l1t65j5tCZgPeQ3Z5wNZFV/6N8+lUeFRrlpAQOPHigWiUAlctfc74xPJfH9RjnpARqQr1VBNWxdOlSZsyY4VYeFxfHmTNnWLRokddru3b1vwxCGo2m5swdMZf8v+QTGOAphrFnAgMC7Us9LRp53of0hKtTmq+kJKU4nVcXxdVG56bO4cRrYs7rykWrCGJiYujfv79b+bx584iMjGTNmsqkEvv27XNqo2cEGs3FQVBAELFhsTW+7t7L7mXGoBn2UBi+sGbsGuakzqm+oQuufgK+yuuqpGpiVeXKRasIABo1ql5Djxo1yr75DBASEqLNTjWaBk5IYAhTBkxxCglRHR0ad+CPvf9Y42fZAuXZ8DXkh6tJrK9RZD1xUSuC8PBKt/G7776befPmOdXv3r2bVatWISLs3LmTtWvXEhoaqhWBRqM5J2yObDVZJurYpCNbx7vnTKiOAAlg1/2ViW1+y9JQvTQf9RWbs1lqaiqLFi1i79699rpTp04RF1e5gZScnExycvI5K4Lly5fTvHlzj5vUGo2mYSAiLLxxoT1Eta9cnXQ1uU/mOkUv9YVLW17K1JSpTNsyzc30tSbUW/NRX1BKsWDBAkaPHk1sbCzHjh2jbdu29jpP2KZl7777LmlpaZSXl5OVlcXYsWMZ7ZruEigpKWHJkiVMnDixyvtqNBrNhUApRbkq9+hv4av56EWtCFxRSvHaa68xaNAg+vTxHIejqvU5W18NGjSIkydPMnDgQBYtWuTkkVwf+lOj0TQM6lwRiMhCYASQrZTqYZY1BlYCbTFyzN+ilMr3dg8b50sR+ML69esZPny4W3lcXBynTp2iuLiYiAjPoXA7d+7MwYMHPdZpNBpNbeMPDmWLgGEuZVOAjUqpjsBG89yvSE1NxWq1upXn5+fz8ssvc+mlnjMWAURHe84j6sqXX35JSEgIjz7qnpRao9FoapsLpgiUUluAUy7FNwHvmcfvAb7nUqtFgoI876FPmTLFHsSuR48eHD582G562rJlS7eQFq588MEHiAgpKSlYrVZmz56NiJCQ4HtwKI1GoznfXNA9AhFpC3zssDRUoJSKNY8FyLede7h2IjARICkpqe+xY8cumJxenm8/jo2NpaCgMvRrQEAA5eXG7n56ejqvvPIKeXl5fPbZZ07tsrOzyc3NtSsLX/YfqiM3N5fCwkI6dPAcJVGj0Whs+MPSUJUoY+TzOvoppeYppZKVUsnNmtUsWuH5JCMjg5ycypyn77zzDsePV+YT7dChA2+//TaBgYEUFhYyYMAA3n33XSIjI7n00kvp3r07JSUlpKenO913xw7nBBQ2xeIJpRRbtmxh+vTpNGvWjEsuuYSDBw8yadIkn01dv//+e+bOnUtpaSkvvfQShw8f9uk6jUbTAFBKXbAPxqbwPofzQ0Ar87gVcMiX+/Tt21fVNmlpaero0aP2cwylpbKzsz2237p1q72N6+eBBx5wOh89erQqLi5Wo0aNUvfcc48C1MiRI9XWrVvVlClTlNVqdbr34sWLvd773//+d5XvsW/fPrVmzRp7++7duytAXXXVVb+9kzQajV8DfKN8Gat9aXSuHw+K4FVgink8BXjFl/vUhSJwZdasWWrmzJlVtrnjjju8Dti2T3p6urJYLPZr5syZ49bm2WefVRkZGfY2N910k73u5Zdfdmq7adMme7vi4mI1Y8YMlZ+fr5RS6vTp017lGDZs2HnuIY1G42/UuSIAlgO/AFbgJ2AC0ATDWuhH4DOgsS/38gdF4CuNGzf2Ovh+9NFHbu2r+rW/du1a1aJFC6cyq9Xqds/CwkKnsscff1wppdT69evtZUuWLHFqM2nSpNruGo1GU8vUuSI4n5/6pAhsA+3s2bPVnj171JgxYxSgbr/9do/ts7Kyqp1FAGrUqFFqzZo1Simlxo8fr/r3768A1bdvX9WrVy+39hkZGap9+/YKUAMGDFBnzpxRrVq1UoBKSEjwKo9Go7l40IqgjrANxGfPnlVKKTV27FgFqLlz53q95r///a+65ZZbqlQEtuUeG5mZmT4pkJCQEPs1R48eVV9//bUaNGiQAlRmZqaaMWOGWrx48YXpDI1GU6f4qggu6uijdcGqVauYNGmSPfLplClTmDhxIrfddpvXawYPHuzmjGbzbh4yZAhKKWJjna1sExIS6NzZOTFFSkoKb7zxhlPZY489Zj9u06YN/fr1s8vSunVrnn76ae666y7eeustu48EGDkZLBYL06ZNQ0RYvXq1bclPo9FcbPiiLer6U59mBOfKRx99pAA1Z84c9cQTT6hNmzb5ZBWUlpamABUREaGUUur48eP22cDmzZtVeXm52zUVFRVeZxAWi0WVl5er+Ph4j/UPPPDABXl/jUZz/sHHGUGDCjrn7yilnJzOzp496zWukY3CwkJiY2OZOXMmjz76KBUVFaSmplJYWMi2bdu8OrGVlZURHBzsVt6vXz8iIyPZuHGjvaxv3758++23TnL6QmlpKYGBgV49tTUazYXFV4cy/Q31I1wH7eqUABgpOa1Wq32wDQgI4NNPP6WioqJKT2bHwfn5559n8+bNbNmyhe3btzu1e/bZZ9m1qzL5RXKy8TeVmZnJqlWrGDhwIEopezTX48eP2xP8PPDAAwwbk8d7CAAAEtdJREFUNozXXnuNnTt3cvfdd1f7PhqNpvbRM4IGjE1RKKUoLS0lLCzMXtevXz+aNGnCvHnz+Oqrrxg7dixXXnkl+fn5HDx4kNDQUKfczhaLheDg4CqVT3XKSaPRnF/8PsSEpu7ZvHkzu3fvBiA0NNQ+SJ88eZKvv/6aTz75hISEBG655RbKy8vp0aMHJ0+e5Pnnn3dSAgAjR45k27ZtTmVRUVFO52fPnr2Ab6PRaM4VPSPQ2Pn8889p1KgRV1xxhcf6d955x56JDaBt27b06dOHNWvWOLVbsGABffr04fTp06SkpNjLf/rpJx1pVaOpRfSMQFNjBg4c6FUJANx2222EhITYz7dv386HH37oZho7fvx4+vTpQ+/evUlISOC+++4D4O677+b1118nPDycadOmeX3ORx99xJgxY6ioqPiNb6TRaHzCF9Oiuv40BPPR+kJGRoZasWKFevrpp+1lM2bMUIB68cUXVVlZmds1RUVFHk1R+/fvr06dOqWUUmr//v0qODhYJSQkOLUZN26cWxA+jUbjG2jzUU1tUV5eTlpaGn379vW6GfzJJ58wYsQIj3VKKe68806WLl3qsf6rr77iqquu8vr84uJiLBYLq1evJiMjg/vvv5/4+HgCAwNr/jIazUWEXhrS1BqBgYEkJydXaRE0fPhw1q1bB8CsWbOcloZExK4EOnbs6LYklJWV5fW+27ZtIyIigtjYWCZMmMD06dNJSkoiKCiIuXPn/pbX0mgaDNqPQFNrjBw5khMnTtCqVSsA4uPjmTBhgr1+3759dOzYERHh559/5tSpU/Ts2ZM33niDbt26kZ+fT1BQEE8++SR33XUXTZo04aabbvL6vLVr1/KnP/0Jq9VKfn4+zZs399iuuLiY3bt307NnT7KzswkKCqJ169bn9+U1Gn/Gl/Wjuv7oPYKLk7S0NCUiqm/fvva9AkfKyspU06ZNqw2sl5CQoKZMmaLKy8vVF198oWbOnKmSkpK85mEoKipSFRUVatasWapfv35ubZKSkuqgNzSa8w96j0BTHzh+/Djx8fFew1Bs2rSJQYMGeb2+d+/erFu3jqSkJKfyW265hQ8++OCc5aoP3wuNpjr0HoGmXmBbz/fGddddR15eHgD33Xcf27dvp7CwEIAePXqQlpbmpgQAVq5cyWeffWY/f+GFF/jb3/7m1m769Onk5ORgsVhYs2YNd955JwD33nsvH3zwAWPGjGHcuHE899xz9muKi4vZu3cvAKdPnyY7O/sc3lyj8R/0jEBTL8jIyHBSGocOHSI+Pt7Ne9mV/fv3ExYWRocOHQB45JFHOH78OK+99hqRkZG0aNHCrX2PHj083uuhhx5i5MiRjBkzhqKiIlJTU1m/fj0AXbp0oVevXqxcufK3vqpGc97wdUagFYFG48KOHTvo169fte2Cg4OxWq1OZatXryY1NdUpbpMNi8XC4sWLOXLkCEuXLuXGG2+kQ4cOBAcHc//999coSqvVauXQoUN06NDBnvtCo3HFV0VQ5xvBvnz0ZrGmtlm/fr2aOHGiOnnypMrKylK33XabUy5ppZQqLS1VhYWFavjw4Wro0KH2+oiICDV79mzVpUsXFRERYU8L+swzz3jd8F66dKlSSimr1apKSkpUaWmpm0wVFRVq0aJF6ne/+509l/XDDz9ce52iqXegU1VqNOeP8vJy9emnn9pTkLpSVlam1q5d65OF07Jly1R2drYaPny4U13Hjh2dzqdMmaKUUio/P18NHjxYRUdHu90vMDBQff/997XZFZp6hK+KQC8NaTTnkY8//piRI0cyefJkkpOT6dq1KwMGDKCoqIjx48cza9Yst7Skixcv9pqroVu3bhw4cMB+/vDDD9OnTx927NhBUlISzzzzDAA33ngjvXv3Zvv27bRp04Y333wTEeGxxx5j//79DBw4kLy8PHJzc2natClPPvmkDgDYANB7BBpNHaCUYu/evfTq1atG13399desX7+e66+/nqCgIEJDQ+nbty+JiYnExcXxxBNPcOeddxIQUGnop5RixYoVVebDroqBAwfSsmVLWrZsSbt27Vi+fDnz5s0jJyeHsrIy0tLSyMvLY9++fXTp0oWkpCSaNm3K7bfffk7P09Q+WhFoNA2Ef/3rX+Tm5hIdHU2zZs0YN24cRUVFNG/enKeeeoqbb74ZESEvL481a9bw6aefsnnz5nN+Xps2bVixYgWxsbFs2LCBDRs2MGHCBHr27Mnu3bv58ccf2bJlC3l5eRQUFHD48GEAu/f2559/Tps2bQgKCiIxMZGAgACKioqIjo7GYrFQWFjIsWPHuOyyyzh48CDt27f3uPmuqR6tCDQajUds3/kff/yRDRs20KxZM9LT0zl48CA7duxg8ODBRERE0L59e4YMGULbtm1ZtWoVRUVFPPLIIxQXF1f7jLCwMFJSUpz8PgAiIyM5ffq0x2vCw8OxWCyUl5c7lV9zzTXMnTuXSy65xF4WFBREYGAgFRUVBAQEcPbsWbZs2UJWVpY9b3dUVJRP6V4vZvxaEYjIMGA2EAjMV0r9var2WhFoNP5BdnY2mzZt4pVXXqFx48Zcc801DB48mOnTp5OSkkJMTAzDhw8nLi6OqKgoLBYLQUFB7Nq1i2XLllFUVESTJk3Yu3cvBQUFbN26lbCwMNq3b0/Xrl05fvw43bp1A2Dnzp2cOHGCgoICj7KICCJC06ZNPTr1tW7dmhkzZmCxWNi8eTMnTpwgNzeXTp06ERISQkBAAJ06deLo0aNs376diIgIunfvTkpKCpdffjmRkZEkJCQQGBhISUmJXfHs2LGD7777jk6dOpGbm8vp06cpKyujX79+9OjRAxFh48aNNGrUiKKiIlq2bInVauXAgQOcOHGCY8eO0bhxY0pLSzlw4AC9evVixIgRXHfddfbAjdnZ2Zw8eZIuXboQEhKC1WqlvLyc0NBQ0tPTOXHiBLt27SIkJIQxY8ZQUlLCyZMnCQoK4pdffuH6668nMDDQfxWBiAQCPwDXAz8BO4FxSqkD3q7RikCjuTjJyckhOjqa0NBQr22OHj3KP//5TzIzM/nyyy+57LLLWL9+PfHx8aSmppKXl0ejRo0YNmwYl19+OT///DOTJ0/mu+++c7pPVFQURUVFHp8RExNDr1692LlzJyUlJfby4OBgwsLCvF5XU0SEmJgYrFYrZ86ccapr3LgxsbGxlJWVcfz4cbtcYWFh9gi8QUFBlJWVVfucVq1aUVxcTEFBgd8qgv7AC0qpoeb50wBKqRnertGKQKPROGK1WgkMDHTaPHelrKyMffv2kZOTw8CBA22/kMnKyqJp06YEBASQk5NDSEgIsbGxgOH0t2DBAkSEgIAA9uzZQ1paml0ZpKSkMGDAAHr06MGRI0f49ddfCQsLIyMjg5KSEsrLy8nMzCQ5OZnc3Fx69uzJnj17iI6OJiUlhVatWhEXFwcYeTzy8/NJT09nyZIlZGVloZQiJCSEuLg4rFYreXl5xMXFkZSUREFBAWFhYSQmJpKYmEjnzp1JS0vjwIED5OTk0LVrVwICAigoKGDv3r00b96cN998028VwWhgmFLqXvP8TqCfUmqSt2u0ItBoNJqa4+vSkN/mIxCRiYAtU3qpiOyrS3lqSFMgt66FqCH1Teb6Ji9omWuD+iYvXFiZ2/jSqC4Uwc+AY9aPRLPMCaXUPGAegIh844tW8xfqm7xQ/2Sub/KClrk2qG/ygn/IXBdhqHcCHUWknYiEALcCH9WBHBqNRqOhDmYESqkyEZkEbMAwH12olNpf23JoNBqNxqBO9giUUuuB9TW4ZN6FkuUCUd/khfonc32TF7TMtUF9kxf8QOZ64Vms0Wg0mguHTlWp0Wg0DRy/VgQiMkxEDonIYRGZUtfy2BCR1iLyuYgcEJH9IvJns7yxiPxXRH40/40zy0VE3jDfY4+IXFZHcgeKSJqIfGyetxOR7aZcK83Ne0Qk1Dw/bNa3rSN5Y0VklYgcFJHvRaS/P/exiDxm/j3sE5HlIhLmb30sIgtFJNvRHPtc+lRE7jbb/yginmNoX1iZXzX/LvaIyBoRiXWoe9qU+ZCIDHUor5XxxJO8DnVPiIgSkabmuV/0cZ0nnfH2wdhITgfaAyHAbqBbXctlytYKuMw8jsIImdENeAWYYpZPAV42j1OBfwMCXAlsryO5HweWAR+b5/8H3GoevwX8yTx+EHjLPL4VWFlH8r4H3GsehwCx/trHQAJwBAh36Ns/+lsfAynAZcA+h7Ia9SnQGMgw/40zj+NqWeYhQJB5/LKDzN3MsSIUaGeOIYG1OZ54ktcsb41hJHMMaOpXfVwbf3zn2Jn9gQ0O508DT9e1XF5kXYcRO+kQ0MosawUcMo/fxoinZGtvb1eLMiYCG4HrgI/NP7xchy+Tvb/NP9b+5nGQ2U5qWd4Yc2AVl3K/7GMMRZBpfnGDzD4e6o99DLR1GVRr1KfAOOBth3KndrUhs0vdH4D3zWOnccLWz7U9nniSF1gFXAocpVIR+EUf+/PSkO2LZeMns8yvMKf0fYDtQAul1C9m1UmghXnsD+8yC3gKqDDPmwAFSilbBCtHmezymvWFZvvapB2QA7xrLmfNF5FG+GkfK6V+Bv4BHAd+weizb/HvPrZR0z71h79nR+7B+FUNfiqziNwE/KyU2u1S5Rfy+rMi8HtEJBJYDTyqlPrVsU4ZatwvTLJEZASQrZT6tq5lqQFBGNPruUqpPsAZjGULO37Wx3HATRgKLB5oBAyrU6HOAX/qU18QkWeBMuD9upbFGyISATwDPF/XsnjDnxWBT6Eo6goRCcZQAu8rpT40i7NEpJVZ3wqwBUmv63e5GrhRRI4CKzCWh2YDsSJi8yVxlMkur1kfA+TVorxg/AL6SSm13TxfhaEY/LWPBwNHlFI5Sikr8CFGv/tzH9uoaZ/WdV8DICJ/BEYAt5sKDPxT5g4YPxB2m9/BROA7EWlZhVy1Kq8/KwK/DUUhIgIsAL5XSr3uUPURYNvdvxtj78BWfpdpIXAlUOgwFb/gKKWeVkolKqXaYvTjJqXU7cDnwGgv8treY7TZvlZ/JSqlTgKZItLZLBoEHMBP+xhjSehKEYkw/z5s8vptHztQ0z7dAAwRkThzJjTELKs1xEhu9RRwo1LqrEPVR8CtplVWO6AjsIM6HE+UUnuVUs2VUm3N7+BPGMYmJ/GXPr5Qmw/nacMlFcMiJx14tq7lcZBrAMb0eQ+wy/ykYqzxbgR+BD4DGpvtBfhf8z32Asl1KPu1VFoNtcf4khwGPgBCzfIw8/ywWd++jmTtDXxj9vNaDOsJv+1j4EXgILAPWIJhueJXfQwsx9jDsGIMSBPOpU8x1uUPm5/xdSDzYYw1dNv37y2H9s+aMh8CbnAor5XxxJO8LvVHqdws9os+1p7FGo1G08Dx56UhjUaj0dQCWhFoNBpNA0crAo1Go2ngaEWg0Wg0DRytCDQajaaB47fJ6zWaqhARm8kjQEugHCMkBcBZpdRV5/l5ycBdSqlHzud9NRp/QJuPauo9IvICcFop9Y+6luVCIyKBSqnyupZDc3Ghl4Y0Fx0ictr891oR+UJE1olIhoj8XURuF5EdIrJXRDqY7ZqJyGoR2Wl+rvZwz2ulMo/DC2bM+c3mfd1mCSJyj4jMcji/T0Rmmsd3mDLsEpG3RSTQLJ8rIt+IkdPgRYdrj4rIyyLyHTBGRB4RIxfGHhFZcZ67T9MA0UtDmoudS4GuwCmMmO7zlVJXiJFM6GHgUYy4SzOVUltFJAnDlb9rNfftAgzEyEdxSETmKiPGkI3/A54VkSfN8vHA/SLSFRgLXK2UsorIHOB2YDGGt+spUzFsFJFeSqk95v3ylFKXAYjICaCdUqpUHBKyaDTnilYEmoudncqMOSQi6cB/zPK9GAM5GAHjuhkhggCIFpFIpdTpKu77iVKqFCgVkWyM0M0/2SqVUqdFZBMwQkS+B4KVUntFZBLQF9hpPi+cyiBvt4jIRIzvZSuMJCs2RbDS4dl7gPdFZC1G6A2N5jehFYHmYqfU4bjC4byCyr//AOBKpVTJOd63HM/fpfkY4YcPAu+aZQK8p5R62rGhGSBtMnC5UipfRBZhxCOyccbheDhGFqyRGLOOnqoy54FGU2P0HoFGY8wSHradiEjv83FTZYTQbg3chhGIDAxLp9Ei0tx8VmMRaQNEYwz2hSLSArjB0z1FJABorZT6HPgLRvjqyPMhr6bhomcEGg08AvyviOzB+E5sAR44T/f+P6C3UiofQCl1QESeA/5jDupW4CGl1NcikoYxe8gEvvJyv0BgqYjEYMwu3lBKFZwnWTUNFG0+qtFcQExLo5lKqY3VNtZo6gi9NKTRXABEJFZEfgCKtRLQ+Dt6RqDRaDQNHD0j0Gg0mgaOVgQajUbTwNGKQKPRaBo4WhFoNBpNA0crAo1Go2ngaEWg0Wg0DZz/D7ePBp42WdZiAAAAAElFTkSuQmCC\n",
+      "text/plain": [
+       "<matplotlib.figure.Figure at 0x7fe24683b518>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "plot_results(grid_low, VegType_low, yrs_low, yr_step=yrs_low - 1)\n",
+    "plt.savefig(\"grid_low.png\")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "**Mid elevation band:**"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 28,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Plotting cellular field of Plant Functional Type\n",
+      "Green - Grass; Red - Shrubs; Black - Trees; White - Bare\n"
+     ]
+    },
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<matplotlib.figure.Figure at 0x7fe243d8a3c8>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<matplotlib.figure.Figure at 0x7fe243cb5940>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<matplotlib.figure.Figure at 0x7fe243c8b390>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "plot_results(grid_mid, VegType_mid, yrs_mid, yr_step=yrs_mid - 1)\n",
+    "plt.savefig(\"grid_mid.png\")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "**High elevation band:**"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 29,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Plotting cellular field of Plant Functional Type\n",
+      "Green - Grass; Red - Shrubs; Black - Trees; White - Bare\n"
+     ]
+    },
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<matplotlib.figure.Figure at 0x7fe243d49ef0>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<matplotlib.figure.Figure at 0x7fe243bd8e80>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<matplotlib.figure.Figure at 0x7fe243b3e5c0>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "plot_results(grid_high, VegType_high, yrs_high, yr_step=yrs_high - 1)\n",
+    "plt.savefig(\"grid_high.png\")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### 4.1.4  Discussion."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "By comparing the ***areal vegetation cover*** and ***spatial organization*** plots above, we can make some interesting observations: <br>\n",
+    "\n",
+    " - In the low elevation band, the climate is arid (Aridity Index (AI) = 7.34) [Middleton et al. (1997)]. Drought-tolerant shrubs outcompete trees, leaving few trees. Grass gradually retreats resulting in a shrub dominant ecosystem with grass becoming the secondary PFT.\n",
+    " - In the mid elevation band, the climate is cooler and wetter than low elevation band (AI = 5) with aridity in the transition zone from arid to semi-arid climate [Middleton et al. (1997)]. It is interesting to note that all three PFTs co-exist in this climate with trees dominating, grass as secondary PFT and shrubs as tertiary PFT.\n",
+    " - In the high elevation band, the climate is the coolest and wettest among all elevation bands. The climate is seim-arid (AI = 3.66) [Middleton et al. (1997)]. Trees dominate the relatively moisture abundant ecosystem. Grass gradually retreats with few shrubs left behind.\n",
+    " \n",
+    "**Things you can explore further:** <br>\n",
+    "\n",
+    " - You can re-run the simulations for longer periods to see if the trajectories we obtained above are stable. You can do this by increasing ***n***. You can check if the ecosystem responses observed in this example still hold when the model is run for longer periods of time.\n",
+    " - You can re-run the model with a different set of input parameters. You can edit ***ecohyd_inputs.yaml*** and re-run the model by changing the input parameters (e.g., soil properties, PFT properties, etc..)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## 5.0. Save the results back into HydroShare\n",
+    "<a name=\"creation\"></a>\n",
+    "\n",
+    "Using the `hs_utils` library, the results of the Geoprocessing steps above can be saved back into HydroShare.  First, define all of the required metadata for resource creation, i.e. *title*, *abstract*, *keywords*, *content files*.  In addition, we must define the type of resource that will be created, in this case *genericresource*.  \n",
+    "\n",
+    "***Note:*** Make sure you save the notebook at this point, so that all notebook changes will be saved into the new HydroShare resource.\n",
+    "\n",
+    "***Option A*** : define the resource from which this \"NEW\" content has been derived.  This is one method for tracking resource provenance."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Create list of files to save to HydroShare. Verify location and names."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 30,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "['/home/jovyan/work/notebooks/data/70b977e22af544f8a7e5a803935c329c/70b977e22af544f8a7e5a803935c329c/data/contentsreuse_ecohydrology_gridhydromet.ipynb', '/home/jovyan/work/notebooks/data/70b977e22af544f8a7e5a803935c329c/70b977e22af544f8a7e5a803935c329c/data/contentsgrid_high.png', '/home/jovyan/work/notebooks/data/70b977e22af544f8a7e5a803935c329c/70b977e22af544f8a7e5a803935c329c/data/contentsgrid_mid.png', '/home/jovyan/work/notebooks/data/70b977e22af544f8a7e5a803935c329c/70b977e22af544f8a7e5a803935c329c/data/contentsgrid_low.png']\n"
+     ]
+    }
+   ],
+   "source": [
+    "ThisNotebook = \"reuse_ecohydrology_gridhydromet.ipynb\"  # check name for consistency\n",
+    "files = [\n",
+    "    homedir + ThisNotebook,\n",
+    "    homedir + \"grid_high.png\",\n",
+    "    homedir + \"grid_mid.png\",\n",
+    "    homedir + \"grid_low.png\",\n",
+    "]\n",
+    "print(files)  # print location and names of files to save"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Create an instance of hydroshare(). If you need more information about this instance, use `help()`."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 31,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Adding the following system variables:\n",
+      "   HS_USR_NAME = saisiddu\n",
+      "   HS_RES_ID = 70b977e22af544f8a7e5a803935c329c\n",
+      "   HS_RES_TYPE = genericresource\n",
+      "   JUPYTER_HUB_IP = jupyter.cuahsi.org\n",
+      "\n",
+      "These can be accessed using the following command: \n",
+      "   os.environ[key]\n",
+      "\n",
+      "   (e.g.)\n",
+      "   os.environ[\"HS_USR_NAME\"]  => saisiddu\n",
+      "Successfully established a connection with HydroShare\n",
+      "Help on hydroshare in module utilities.hydroshare.hydroshare object:\n",
+      "\n",
+      "class hydroshare(builtins.object)\n",
+      " |  Methods defined here:\n",
+      " |  \n",
+      " |  __init__(self, username=None, password=None, cache=True)\n",
+      " |      Initialize self.  See help(type(self)) for accurate signature.\n",
+      " |  \n",
+      " |  addContentToExistingResource(self, resid, content)\n",
+      " |      Adds content files to an existing hydroshare resource.\n",
+      " |      \n",
+      " |      args:\n",
+      " |      -- resid: id of an existing hydroshare resource (str)\n",
+      " |      -- content: files paths to be added to resource (list)\n",
+      " |      \n",
+      " |      returns:\n",
+      " |      -- None\n",
+      " |  \n",
+      " |  createHydroShareResource(self, abstract, title, derivedFromId=None, keywords=[], resource_type='GenericResource', content_files=[], public=False)\n",
+      " |      Creates a hydroshare resource.\n",
+      " |      \n",
+      " |      args:\n",
+      " |      -- abstract: abstract for resource (str, required)\n",
+      " |      -- title: title of resource (str, required)\n",
+      " |      -- derivedFromId: id of parent hydroshare resource (str, default=>None)\n",
+      " |      -- keywords: list of subject keywords (list, default=>[])\n",
+      " |      -- resource_type: type of resource to create (str, default=>\n",
+      " |                                                   'GenericResource')\n",
+      " |      -- content_files: data to save as resource content (list, default=>[])\n",
+      " |      -- public: resource sharing status (bool, default=>False)\n",
+      " |      \n",
+      " |      returns:\n",
+      " |      -- None\n",
+      " |  \n",
+      " |  getContentFiles(self, resourceid)\n",
+      " |      Gets the content files for a resource that exists on the\n",
+      " |         Jupyter Server\n",
+      " |      \n",
+      " |      args:\n",
+      " |      -- resourceid: the id of the hydroshare resource\n",
+      " |      \n",
+      " |      returns:\n",
+      " |      -- {content file name: path}\n",
+      " |  \n",
+      " |  getContentPath(self, resourceid)\n",
+      " |      Gets the server path of a resources content files.\n",
+      " |      \n",
+      " |      args:\n",
+      " |      -- resourceid: the id of the hydroshare resource\n",
+      " |      \n",
+      " |      returns:\n",
+      " |      -- server path the the resource content files\n",
+      " |  \n",
+      " |  getResourceFromHydroShare(self, resourceid, destination='.')\n",
+      " |      Downloads content of a hydroshare resource.\n",
+      " |      \n",
+      " |      args:\n",
+      " |      -- resourceid: id of the hydroshare resource (str)\n",
+      " |      -- destination: path to save resource, default\n",
+      " |                      /user/[username]/notebooks/data (str)\n",
+      " |      \n",
+      " |      returns:\n",
+      " |      -- None\n",
+      " |  \n",
+      " |  getResourceMetadata(self, resid)\n",
+      " |      Gets metadata for a specified resource.\n",
+      " |      \n",
+      " |      args:\n",
+      " |      -- resid: hydroshare resource id\n",
+      " |      \n",
+      " |      returns:\n",
+      " |      -- resource metadata object\n",
+      " |  \n",
+      " |  getSecureConnection(self, username=None)\n",
+      " |      Establishes a secure connection with hydroshare.\n",
+      " |      \n",
+      " |      args:\n",
+      " |      -- email: email address associated with hydroshare\n",
+      " |      \n",
+      " |      returns:\n",
+      " |      -- hydroshare api connection\n",
+      " |  \n",
+      " |  loadResource(self, resourceid)\n",
+      " |      Loads the contents of a previously downloaded resource.\n",
+      " |      \n",
+      " |      args:\n",
+      " |      -- resourceid: the id of the resource that has been downloaded (str)\n",
+      " |      \n",
+      " |      returns:\n",
+      " |      -- {content file name: path}\n",
+      " |  \n",
+      " |  ----------------------------------------------------------------------\n",
+      " |  Data descriptors defined here:\n",
+      " |  \n",
+      " |  __dict__\n",
+      " |      dictionary for instance variables (if defined)\n",
+      " |  \n",
+      " |  __weakref__\n",
+      " |      list of weak references to the object (if defined)\n",
+      "\n"
+     ]
+    }
+   ],
+   "source": [
+    "# Creating an instance of hydroshare()\n",
+    "hs = hydroshare.hydroshare()\n",
+    "# For more information on this instance:\n",
+    "help(hs)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Create a new resource."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 32,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Resource Created Successfully                           \n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "Exception in thread Thread-5:\n",
+      "Traceback (most recent call last):\n",
+      "  File \"/opt/conda/lib/python3.6/threading.py\", line 916, in _bootstrap_inner\n",
+      "    self.run()\n",
+      "  File \"/opt/conda/lib/python3.6/threading.py\", line 864, in run\n",
+      "    self._target(*self._args, **self._kwargs)\n",
+      "  File \"/home/jovyan/work/notebooks/utilities/hydroshare/threads.py\", line 23, in f\n",
+      "    res = self.func(*args, **kwargs)\n",
+      "  File \"/opt/conda/lib/python3.6/site-packages/hs_restclient/__init__.py\", line 738, in createResource\n",
+      "    close_fd = self._prepareFileForUpload(params, resource_file, resource_filename)\n",
+      "  File \"/opt/conda/lib/python3.6/site-packages/hs_restclient/__init__.py\", line 170, in _prepareFileForUpload\n",
+      "    raise HydroShareArgumentException(\"{0} is not a file or is not readable.\".format(resource_file))\n",
+      "hs_restclient.exceptions.HydroShareArgumentException: /home/jovyan/work/notebooks/data/70b977e22af544f8a7e5a803935c329c/70b977e22af544f8a7e5a803935c329c/data/contentsreuse_ecohydrology_gridhydromet.ipynb is not a file or is not readable.\n",
+      "\n",
+      "Exception in thread Thread-6:\n",
+      "Traceback (most recent call last):\n",
+      "  File \"/opt/conda/lib/python3.6/threading.py\", line 916, in _bootstrap_inner\n",
+      "    self.run()\n",
+      "  File \"/opt/conda/lib/python3.6/threading.py\", line 864, in run\n",
+      "    self._target(*self._args, **self._kwargs)\n",
+      "  File \"/home/jovyan/work/notebooks/utilities/hydroshare/threads.py\", line 23, in f\n",
+      "    res = self.func(*args, **kwargs)\n",
+      "  File \"/home/jovyan/work/notebooks/utilities/hydroshare/hydroshare.py\", line 68, in _addContentToExistingResource\n",
+      "    self.hs.addResourceFile(resid, f)\n",
+      "  File \"/opt/conda/lib/python3.6/site-packages/hs_restclient/__init__.py\", line 833, in addResourceFile\n",
+      "    close_fd = self._prepareFileForUpload(params, resource_file, resource_filename)\n",
+      "  File \"/opt/conda/lib/python3.6/site-packages/hs_restclient/__init__.py\", line 170, in _prepareFileForUpload\n",
+      "    raise HydroShareArgumentException(\"{0} is not a file or is not readable.\".format(resource_file))\n",
+      "hs_restclient.exceptions.HydroShareArgumentException: /home/jovyan/work/notebooks/data/70b977e22af544f8a7e5a803935c329c/70b977e22af544f8a7e5a803935c329c/data/contentsgrid_high.png is not a file or is not readable.\n",
+      "\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Successfully Added Content Files                      \n"
+     ]
+    },
+    {
+     "data": {
+      "text/html": [
+       "Resource id: None"
+      ],
+      "text/plain": [
+       "<IPython.core.display.HTML object>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "text/html": [
+       "<a href=https://www.hydroshare.org/resource/None target=\"_blank\">Open Resource in HydroShare<a>"
+      ],
+      "text/plain": [
+       "<IPython.core.display.HTML object>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "# for each file downloaded onto the server folder, move to a new HydroShare Generic Resource\n",
+    "title = (\n",
+    "    \"Ecohydrology model run with climatologies of different elevation bands\"\n",
+    ")  # title for the new resource\n",
+    "abstract = (\n",
+    "    \"This is a demonstration of reuse of a Landlab ecohydrology model.\"\n",
+    ")  # abstract for the new resource\n",
+    "keywords = [\n",
+    "    \"Landlab\",\n",
+    "    \"ecohydrology\",\n",
+    "    \"CATGraSS\",\n",
+    "    \"vegetation patterns\",\n",
+    "    \"reuse\",\n",
+    "    \"elevation\",\n",
+    "]  # keywords for the new resource\n",
+    "rtype = \"genericresource\"  # Hydroshare resource type\n",
+    "\n",
+    "# create the new resource\n",
+    "resource_id = hs.createHydroShareResource(\n",
+    "    abstract,\n",
+    "    title,\n",
+    "    keywords=keywords,\n",
+    "    resource_type=rtype,\n",
+    "    content_files=files,\n",
+    "    public=False,\n",
+    ")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### Click here for more <a href=\"https://github.com/landlab/landlab/wiki/Tutorials\">Landlab tutorials</a>"
+   ]
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "Python 3",
+   "language": "python",
+   "name": "python3"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.6.5"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 2
+}
diff --git a/ecohydrology_gridhydromet/supporting_files/ecohyd_inputs.yaml b/ecohydrology_gridhydromet/supporting_files/ecohyd_inputs.yaml
new file mode 100644
index 0000000..d933cd3
--- /dev/null
+++ b/ecohydrology_gridhydromet/supporting_files/ecohyd_inputs.yaml
@@ -0,0 +1,198 @@
+### All inputs for Vegetation Cellular Automaton Model built on The Landlab
+### can be given here.
+### 14Feb2015 - Sai Nudurupati & Erkan Istanbulluoglu
+### 15Jul2016 - Updated to comply with Landlab Version 1 naming conventions.
+
+### Vegetation Cellular Automaton Model Input File:
+
+n_short: 6600 # Number of storms for short simulation that plots hydrologic parameters
+n_long_DEM: 1320 # Number of storms for long simulation that operates on single grid for sloped surface
+n_long_flat: 660000 # Number of storms for long simulation that operates on two grids - flat surface
+
+## Initial Plant Functional Types (PFT) distribution
+percent_bare_initial: 0.25 # Initial percentage of cells occupied by bare soil
+percent_grass_initial: 0.25 # Initial percentage of cells occupied by grass
+percent_shrub_initial: 0.25 # Initial percentage of cells occupied by shrubs
+percent_tree_initial: 0.25  # Initial percentage of cells occupied by trees
+
+## Precipitation:
+
+# Dry Season
+mean_storm_dry: 0.48 # Mean storm duration (hours)
+mean_interstorm_dry: 159.36 # Mean interstorm duration (hours)
+mean_storm_depth_dry: 3.07 # Mean storm depth (mm)
+
+# Wet Season
+mean_storm_wet: 0.46 # Mean storm duration (hours)
+mean_interstorm_wet: 84.24 # Mean interstorm duration (hours)
+mean_storm_depth_wet: 4.79 # Mean storm depth (mm)
+doy__start_of_monsoon: 182   # Day of the year when the monsoon starts
+doy__end_of_monsoon: 273 # Day of the year when the monsoon ends
+
+## PotentialEvapotranspiration:
+# Cosine Method
+PET_method: Cosine
+LT: 0 # Lag between peak TmaxF estimated by cosine method and solar forcing (days)
+DeltaD: 7. # Calibrated difference between
+ND: 365. # Number of days in the year (days)
+MeanTmaxF_grass: 4.96 # Mean annual rate of TmaxF (mm/d)
+MeanTmaxF_shrub: 3.77 # Mean annual rate of TmaxF (mm/d)
+MeanTmaxF_tree: 5.15 # Mean annual rate of TmaxF (mm/d)
+
+# TmaxF - Estimated maximum evapotranspiration as a function of DOY
+# using Penman Monteith method for historical weather
+
+## Soil Moisture:
+
+runon_switch: 0   # Whether to use runon or not
+f_bare: 0.7 # Fraction to partition PET for bare soil (None)
+
+# Grass
+
+VEGTYPE_grass: 0 # Integer value to infer Vegetation Type
+intercept_cap_grass: 0. # Full canopy interception capacity  (mm)
+zr_grass: 0.3 # Root depth (m)
+I_B_grass: 20. # Infiltration capacity of bare soil (mm/h)
+I_V_grass: 24. # Infiltration capacity of vegetated soil (mm/h)
+K_s_grass: 42. # Hydraulic conductivity (mm/h)
+pc_grass: 0.43 # Soil porosity (None)
+fc_grass: 0.56 # Soil saturation degree at field capacity (None)
+sc_grass: 0.33 # Soil saturation degree at stomatal closure (None)
+wp_grass: 0.13 # Soil saturation degree at wilting point (None)
+hgw_grass: 0.1 # Soil saturation degree at hygroscopic point (None)
+beta_grass: 13.8 # Deep percolation constant = 2*b+4  where b is water retention parameter
+
+# Shrub
+
+VEGTYPE_shrub: 1 # Integer value to infer Vegetation Type
+intercept_cap_shrub: 0. # Full canopy interception capacity  (mm)
+zr_shrub: 0.6 # Root depth (m)
+I_B_shrub: 20. # Infiltration capacity of bare soil (mm/h)
+I_V_shrub: 40. # Infiltration capacity of vegetated soil (mm/h)
+K_s_shrub: 42. # Hydraulic conductivity (mm/h)
+pc_shrub: 0.43 # Soil porosity (None)
+fc_shrub: 0.56 # Soil saturation degree at field capacity (None)
+sc_shrub: 0.33 # Soil saturation degree at stomatal closure (None)
+wp_shrub: 0.13 # Soil saturation degree at wilting point (None)
+hgw_shrub: 0.1 # Soil saturation degree at hygroscopic point (None)
+beta_shrub: 13.8 # Deep percolation constant = 2*b+4  where b is water retention parameter
+
+# Tree
+
+VEGTYPE_tree: 2 # Integer value to infer Vegetation Type
+intercept_cap_tree: 0. # Full canopy interception capacity  (mm)
+zr_tree: 1.3 # Root depth (m)
+I_B_tree: 20. # Infiltration capacity of bare soil (mm/h)
+I_V_tree: 40. # Infiltration capacity of vegetated soil (mm/h)
+K_s_tree: 42. # Hydraulic conductivity (mm/h)
+pc_tree: 0.43 # Soil porosity (None)
+fc_tree: 0.56 # Soil saturation degree at field capacity (None)
+sc_tree: 0.33 # Soil saturation degree at stomatal closure (None)
+wp_tree: 0.15 # Soil saturation degree at wilting point (None)
+hgw_tree: 0.1 # Soil saturation degree at hygroscopic point (None)
+beta_tree: 13.8 # Deep percolation constant = 2*b+4  where b is water retention parameter
+
+# Bare Soil
+
+VEGTYPE_bare: 3 # Integer value to infer Vegetation Type
+intercept_cap_bare: 0. # Full canopy interception capacity  (mm)
+zr_bare: 0.3 # Root depth (m)
+I_B_bare: 20. # Infiltration capacity of bare soil (mm/h)
+I_V_bare: 20. # Infiltration capacity of vegetated soil (mm/h)
+K_s_bare: 42. # Hydraulic conductivity (mm/h)
+pc_bare: 0.43 # Soil porosity (None)
+fc_bare: 0.56 # Soil saturation degree at field capacity (None)
+sc_bare: 0.33 # Soil saturation degree at stomatal closure (None)
+wp_bare: 0.13 # Soil saturation degree at wilting point (None)
+hgw_bare: 0.1 # Soil saturation degree at hygroscopic point (None)
+beta_bare: 13.8 # Deep percolation constant
+
+
+## Vegetation Dynamics:
+
+Blive_init: 102.
+Bdead_init: 450.
+PET_growth_threshold: 3.8 # PET threshold for growing season (mm/d)
+PET_dormancy_threshold: 6.8  # PET threshold for dormant season (mm/d)
+Tdmax: 10. # Constant for dead biomass loss adjustment (mm/d)
+w: 0.55 # Conversion factor of CO2 to dry biomass (Kg DM/Kg CO2)
+
+# Grass
+
+WUE_grass: 0.01 # Water use efficiency KgCO2Kg-1H2O
+cb_grass: 0.0047 # Specific leaf area for green/live biomass (m2 leaf g-1 DM)
+cd_grass: 0.009 # Specific leaf area for dead biomass (m2 leaf g-1 DM)
+ksg_grass: 0.012 # Senescence coefficient of green/live biomass (d-1)
+kdd_grass: 0.013 # Decay coefficient of aboveground dead biomass (d-1)
+kws_grass: 0.02 # Maximum drought induced foliage loss rate (d-1)
+LAI_max_grass: 2. # Maximum leaf area index (m2/m2)
+LAIR_max_grass: 2. # Reference leaf area index (m2/m2)
+
+# Shrub
+
+WUE_shrub: 0.0025 # Water use efficiency KgCO2Kg-1H2O
+cb_shrub: 0.004 # Specific leaf area for green/live biomass (m2 leaf g-1 DM)
+cd_shrub: 0.01 # Specific leaf area for dead biomass (m2 leaf g-1 DM)
+ksg_shrub: 0.002 # Senescence coefficient of green/live biomass (d-1)
+kdd_shrub: 0.013 # Decay coefficient of aboveground dead biomass (d-1)
+kws_shrub: 0.02 # Maximum drought induced foliage loss rate (d-1)
+LAI_max_shrub: 2. # Maximum leaf area index (m2/m2)
+LAIR_max_shrub: 2. # Reference leaf area index (m2/m2)
+
+# Tree
+
+WUE_tree: 0.0045 # Water use efficiency KgCO2Kg-1H2O
+cb_tree: 0.004 # Specific leaf area for green/live biomass (m2 leaf g-1 DM)
+cd_tree: 0.01 # Specific leaf area for dead biomass (m2 leaf g-1 DM)
+ksg_tree: 0.002 # Senescence coefficient of green/live biomass (d-1)
+kdd_tree: 0.013 # Decay coefficient of aboveground dead biomass (d-1)
+kws_tree: 0.01 # Maximum drought induced foliage loss rate (d-1)
+LAI_max_tree: 4. # Maximum leaf area index (m2/m2)
+LAIR_max_tree: 4. # Reference leaf area index (m2/m2)
+
+# Bare
+
+WUE_bare: 0.0001 # Water use efficiency KgCO2Kg-1H2O
+cb_bare: 0.0047 # Specific leaf area for green/live biomass (m2 leaf g-1 DM)
+cd_bare: 0.009 # Specific leaf area for dead biomass (m2 leaf g-1 DM)
+ksg_bare: 0.012 # Senescence coefficient of green/live biomass (d-1)
+kdd_bare: 0.013 # Decay coefficient of aboveground dead biomass (d-1)
+kws_bare: 0.02 # Maximum drought induced foliage loss rate (d-1)
+LAI_max_bare: 0.01 # Maximum leaf area index (m2/m2)
+LAIR_max_bare: 0.01 # Reference leaf area index (m2/m2)
+
+
+## Cellular Automaton Vegetation:
+
+# Grass
+
+Pemaxg: 0.35 # Maximal establishment probability
+ING: 2 # Parameter to define allelopathic effect on grass from cresotebush
+ThetaGrass: 0.65 # Drought resistant threshold
+PmbGrass: 0.05               # Background mortality probability
+
+# Shrub
+
+Pemaxsh: 0.16 # Maximal establishment probability
+ThetaShrub: 0.76 # Drought resistant threshold
+PmbShrub: 0.01 # Background mortality probability
+tpmaxShrub: 600 # Maximum age (yr)
+
+# Tree
+
+Pemaxtr: 0.21 # Maximal establishment probability
+ThetaTree: 0.71 # Drought resistant threshold
+PmbTree: 0.011 # Background mortality probability
+tpmaxTree: 350 # Maximum age (yr)
+
+# ShrubSeedling
+
+ThetaShrubSeedling: 0.63 # Drought resistant threshold
+PmbShrubSeedling: 0.03 # Background mortality probability
+tpmaxShrubSeedling: 18 # Maximum age (yr)
+
+# TreeSeedling
+
+ThetaTreeSeedling: 0.6 # Drought resistant threshold
+PmbTreeSeedling: 0.03 # Background mortality probability
+tpmaxTreeSeedling: 20 # Maximum age (yr)
diff --git a/ecohydrology_gridhydromet/supporting_files/ecohydrology_model_functions.py b/ecohydrology_gridhydromet/supporting_files/ecohydrology_model_functions.py
new file mode 100644
index 0000000..2c6584a
--- /dev/null
+++ b/ecohydrology_gridhydromet/supporting_files/ecohydrology_model_functions.py
@@ -0,0 +1,420 @@
+# -*- coding: utf-8 -*-
+"""
+
+"""
+
+# Authors: Sai Nudurupati & Erkan Istanbulluoglu, 21May15
+# Edited: 15Jul16 - to conform to Landlab version 1.
+import numpy as np
+import matplotlib as mpl
+import matplotlib.pyplot as plt
+from landlab.plot import imshow_grid
+from landlab import RasterModelGrid as rmg
+from landlab.components import (PrecipitationDistribution, Radiation,
+                                PotentialEvapotranspiration)
+from soil_moisture_dynamics_lb_pap import SoilMoisture
+from vegetation_dynamics_lb_pap import Vegetation
+from plant_competition_ca_lb_pap import VegCA
+from landlab import load_params
+
+GRASS = 0
+SHRUB = 1
+TREE = 2
+BARE = 3
+SHRUBSEEDLING = 4
+TREESEEDLING = 5
+
+
+# Function to compose spatially distribute PFT
+def compose_veg_grid(grid, percent_bare=0.4, percent_grass=0.2,
+                     percent_shrub=0.2, percent_tree=0.2):
+    number_cells = grid.number_of_cells
+    V = 3 * np.ones(grid.number_of_cells, dtype=int)
+    shrub_point = int(percent_bare * number_cells)
+    tree_point = int((percent_bare + percent_shrub) * number_cells)
+    grass_point = int((1 - percent_grass) * number_cells)
+    V[shrub_point:tree_point] = 1
+    V[tree_point:grass_point] = 2
+    V[grass_point:] = 0
+    np.random.shuffle(V)
+    return V
+
+
+def initialize_components(data, grid_veg=None, grid=None, pet_method='Cosine'):
+    # Plant types are defined as following:
+    # GRASS = 0; SHRUB = 1; TREE = 2; BARE = 3;
+    # SHRUBSEEDLING = 4; TREESEEDLING = 5
+    # Initialize random plant type field
+    grid.at_cell['vegetation__plant_functional_type'] = compose_veg_grid(
+                grid, percent_bare=data['percent_bare_initial'],
+                percent_grass=data['percent_grass_initial'],
+                percent_shrub=data['percent_shrub_initial'],
+                percent_tree=data['percent_tree_initial'])
+    # Assign plant type for representative ecohydrologic simulations
+    grid_veg.at_cell['vegetation__plant_functional_type'] = np.arange(0, 6)
+    grid.at_node['topographic__elevation'] = np.full(grid.number_of_nodes,
+                                                      1700.)
+    grid_veg.at_node['topographic__elevation'] = np.full(
+                        grid_veg.number_of_nodes, 1700.)
+    precip_dry = PrecipitationDistribution(
+                        mean_storm_duration=data['mean_storm_dry'],
+                        mean_interstorm_duration=data['mean_interstorm_dry'],
+                        mean_storm_depth=data['mean_storm_depth_dry'],
+                        random_seed=None)
+    precip_wet = PrecipitationDistribution(
+                        mean_storm_duration=data['mean_storm_wet'],
+                        mean_interstorm_duration=data['mean_interstorm_wet'],
+                        mean_storm_depth=data['mean_storm_depth_wet'],
+                        random_seed=None)
+    radiation = Radiation(grid_veg)
+    if pet_method=='Cosine':
+        pet_tree = PotentialEvapotranspiration(
+                        grid_veg, method=data['PET_method'],
+                        MeanTmaxF=data['MeanTmaxF_tree'],
+                        delta_d=data['DeltaD'])
+        pet_shrub = PotentialEvapotranspiration(
+                        grid_veg, method=data['PET_method'],
+                        MeanTmaxF=data['MeanTmaxF_shrub'],
+                        delta_d=data['DeltaD'])
+        pet_grass = PotentialEvapotranspiration(
+                        grid_veg, method=data['PET_method'],
+                        MeanTmaxF=data['MeanTmaxF_grass'],
+                        delta_d=data['DeltaD'])
+    elif pet_method=='PriestleyTaylor':
+        pet_met = PotentialEvapotranspiration(grid_veg,
+                                              method='PriestleyTaylor')
+        
+    soil_moisture = SoilMoisture(grid_veg, **data)   # Soil Moisture object
+    vegetation = Vegetation(grid_veg, **data)    # Vegetation object
+    vegca = VegCA(grid, **data)      # Cellular automaton object
+
+    # # Initializing inputs for Soil Moisture object
+    grid_veg.at_cell['vegetation__live_leaf_area_index'] = (
+                                    1.6 * np.ones(grid_veg.number_of_cells))
+    grid_veg.at_cell['soil_moisture__initial_saturation_fraction'] = (
+                                    0.59 * np.ones(grid_veg.number_of_cells))
+    # Initializing Soil Moisture
+    if pet_method=='Cosine':
+        return (precip_dry, precip_wet, radiation, pet_tree, pet_shrub,
+                pet_grass, soil_moisture, vegetation, vegca)
+    elif pet_method=='PriestleyTaylor':
+        return (precip_dry, precip_wet, radiation, pet_met, soil_moisture,
+                vegetation, vegca)
+
+
+def create_empty_arrays(number_of_storms, grid_veg, grid, pet_method='Cosine'):
+    P = np.empty(number_of_storms)    # Record precipitation
+    Tb = np.empty(number_of_storms)    # Record inter storm duration
+    Tr = np.empty(number_of_storms)    # Record storm duration
+    Time = np.empty(number_of_storms)  # To record time elapsed from the start of simulation
+#    CumWaterStress = np.empty([n/55, grid.number_of_cells])
+    # Cum Water Stress
+    VegType = np.empty([int(number_of_storms/55), grid.number_of_cells], dtype=int)
+    if pet_method=='Cosine':
+        pet_arr = np.zeros([365, grid_veg.number_of_cells])
+    elif pet_method=='PriestleyTaylor':
+        pet_arr = np.zeros([number_of_storms, grid_veg.number_of_cells])
+    Rad_Factor = np.empty([365, grid_veg.number_of_cells])
+    EP30 = np.empty([365, grid_veg.number_of_cells])
+    Rad_Factor_met = np.empty([number_of_storms, grid_veg.number_of_cells])
+    EP30_met = np.empty([number_of_storms, grid_veg.number_of_cells])
+    # 30 day average PET to determine season
+    pet_threshold = 0  # Initializing PET_threshold to ETThresholddown
+    return (P, Tb, Tr, Time, VegType, pet_arr, Rad_Factor,
+            Rad_Factor_met, EP30, EP30_met, pet_threshold)
+
+
+def create_pet_lookup(grid_veg, radiation=None, Rad_Factor=None,
+                      EP30=None, pet_tree=None, pet_shrub=None,
+                      pet_grass=None, pet_arr=None,
+                      Rad_Factor_met=None, number_of_storms=None,
+                      pet_met=None, Tmax_met=None, Tmin_met=None,
+                      EP30_met=None, first_day=0, pet_method='Cosine'):
+    if pet_method=='Cosine':
+        for i in range(0, 365):
+            pet_tree.update(float(i)/365.25)
+            pet_shrub.update(float(i)/365.25)
+            pet_grass.update(float(i)/365.25)
+            pet_arr[i] = [pet_grass._PET_value, pet_shrub._PET_value,
+                       pet_tree._PET_value, 0., pet_shrub._PET_value,
+                       pet_tree._PET_value]
+            radiation.update(float(i)/365.25)
+            Rad_Factor[i] = (
+                    grid_veg.at_cell['radiation__ratio_to_flat_surface'])
+            if i < 30:
+                if i == 0:
+                    EP30[0] = pet_arr[0]
+                else:
+                    EP30[i] = np.mean(pet_arr[:i], axis=0)
+            else:
+                EP30[i] = np.mean(pet_arr[i-30:i], axis=0)
+        return (pet_arr, EP30)
+
+    elif pet_method=='PriestleyTaylor':
+        for i in range(0, number_of_storms):
+            pet_met.update(float(first_day+i)/365.25, Tmax = Tmax_met[i],
+                               Tmin = Tmin_met[i],
+                               Tavg = (Tmax_met[i]+Tmin_met[i])/2.)
+            pet_arr[i] = [pet_met._PET_value, pet_met._PET_value,
+                          pet_met._PET_value, 0., pet_met._PET_value,
+                          pet_met._PET_value]
+            radiation.update(float(i)/365.25)
+            Rad_Factor_met[i] = (
+                    grid_veg.at_cell['radiation__ratio_to_flat_surface'])
+            if i < 30:
+                if i == 0:
+                    EP30_met[0] = pet_arr[0]
+                else:
+                    EP30_met[i] = np.mean(pet_arr[:i], axis=0)
+            else:
+                EP30_met[i] = np.mean(pet_arr[i-30:i], axis=0)
+        return(pet_arr, EP30_met)
+
+
+def save_data(sim, Tb, Tr, P, VegType, yrs, Time_Consumed, Time):
+    np.save(sim+'Tb', Tb)
+    np.save(sim+'Tr', Tr)
+    np.save(sim+'P', P)
+    np.save(sim+'VegType', VegType)
+#    np.save(sim+'CumWaterStress', CumWaterStress)
+    np.save(sim+'Years', yrs)
+    np.save(sim+'Time_Consumed_minutes', Time_Consumed)
+    np.save(sim+'CurrentTime', Time)
+
+
+def plot_results(grid, VegType, yrs, yr_step=10):
+    # # Plotting
+    pic = 0
+    years = range(0, yrs)
+    cmap = mpl.colors.ListedColormap(
+                        ['green', 'red', 'black', 'white', 'red', 'black'])
+    bounds = [-0.5, 0.5, 1.5, 2.5, 3.5, 4.5, 5.5]
+    norm = mpl.colors.BoundaryNorm(bounds, cmap.N)
+    print('Plotting cellular field of Plant Functional Type')
+    print('Green - Grass; Red - Shrubs; Black - Trees; White - Bare')
+    # # Plot images to make gif.
+    for year in range(0, yrs, yr_step):
+        filename = 'Year = ' + "%05d" % year
+        pic += 1
+        plt.figure(pic)
+        imshow_grid(grid, VegType[year], values_at='cell', cmap=cmap,
+                    grid_units=('m', 'm'), norm=norm, limits=[0, 5],
+                    allow_colorbar=False)
+        plt.title(filename)
+        plt.savefig(filename)
+    grass_cov = np.empty(yrs)
+    shrub_cov = np.empty(yrs)
+    tree_cov = np.empty(yrs)
+    grid_size = float(VegType.shape[1])
+    for x in range(0, yrs):
+        grass_cov[x] = (VegType[x][VegType[x] == GRASS].size/grid_size) * 100
+        shrub_cov[x] = ((VegType[x][VegType[x] == SHRUB].size/grid_size) *
+                        100 + (VegType[x][VegType[x] == SHRUBSEEDLING].size /
+                        grid_size) * 100)
+        tree_cov[x] = ((VegType[x][VegType[x] == TREE].size/grid_size) *
+                       100 + (VegType[x][VegType[x] == TREESEEDLING].size /
+                       grid_size) * 100)
+    pic += 1
+    plt.figure(pic)
+    plt.plot(years, grass_cov, '-g', label='Grass')
+    plt.hold(True)
+    plt.plot(years, shrub_cov, '-r', label='Shrub')
+    plt.hold(True)
+    plt.plot(years, tree_cov, '-k', label='Tree')
+    plt.xlim(xmin=0, xmax=years[-1])
+    plt.ylim(ymin=0, ymax=(max(np.max(grass_cov),
+                               np.max(shrub_cov),
+                               np.max(tree_cov))+10))
+    plt.ylabel(' % Coverage ')
+    plt.xlabel('Time in years')
+    plt.legend(loc=0)
+    plt.savefig('PercentageCover_PFTs')
+    # plt.show()
+
+def run_ecohydrology_model(grid, input_data, input_file,
+                           synthetic_storms=True, number_of_storms=None,
+                           number_of_years=None,
+                           first_julian_day_of_observations=0,
+                           pet_method='Cosine',
+                           save_files=False, sim_name='Trial'):
+    # Create data object by reading in the input_file
+    data = load_params(input_file)
+    # Create a grid that can hold enough cells to represent all individual
+    # vegetation types 
+    grid_veg = rmg((5, 4), spacing=(5., 5.))
+    if pet_method == 'Cosine':
+        (precip_dry, precip_wet, radiation, pet_tree, pet_shrub, pet_grass,
+         soil_moisture, vegetation, vegca) = initialize_components(data,
+                                         grid_veg, grid, pet_method='Cosine')
+    elif pet_method == 'PriestleyTaylor':
+        (precip_dry, precip_wet, radiation, pet_met,
+         soil_moisture, vegetation, vegca) = initialize_components(data,
+                                grid_veg, grid, pet_method='PriestleyTaylor')        
+
+    if number_of_years!=None:
+        # Calculate approximate number of storms per year
+        fraction_wet = ((data['doy__end_of_monsoon']-
+                         data['doy__start_of_monsoon'])/365.)
+        fraction_dry = (1 - fraction_wet)
+        number_of_storms_wet = (8760 * (fraction_wet)/
+                                (data['mean_interstorm_wet'] +
+                                 data['mean_storm_wet']))
+        number_of_storms_dry = (8760 * (fraction_dry)/
+                                (data['mean_interstorm_dry'] +
+                                 data['mean_storm_dry']))
+        number_of_storms = int(number_of_years *
+                               (number_of_storms_wet + number_of_storms_dry))
+
+    (precip, inter_storm_dt, storm_dt, Time, VegType, pet_arr, Rad_Factor,
+     Rad_Factor_met, EP30, EP30_met, pet_threshold) = create_empty_arrays(
+                                                     number_of_storms,
+                                                     grid_veg, grid,
+                                                     pet_method=pet_method)
+    
+    if pet_method == 'Cosine':
+        (pet_arr, EP30) = create_pet_lookup(grid_veg, radiation=radiation, Rad_Factor=Rad_Factor,
+                              EP30=EP30, pet_tree=pet_tree, pet_shrub=pet_shrub,
+                              pet_grass=pet_grass, pet_arr=pet_arr)
+    if pet_method == 'PriestleyTaylor':
+        (pet_arr, EP30_met) = create_pet_lookup(grid_veg, radiation=radiation,
+                              Rad_Factor_met=Rad_Factor_met,
+                              number_of_storms=number_of_storms,
+                              pet_met=pet_met, Tmax_met=input_data['Tmax_met'],
+                              Tmin_met=input_data['Tmin_met'], EP30_met=EP30_met,
+                              first_day=first_julian_day_of_observations,
+                              pet_method=pet_method, pet_arr=pet_arr)
+
+    # declaring few variables that will be used in the storm loop
+    current_time = first_julian_day_of_observations/365.25  # Initial time
+    time_check = 0.     # Buffer to store current_time at previous storm
+    yrs = 0             # Keep track of number of years passed
+    water_stress = 0.             # Buffer for Water Stress
+    Tg = 0            # Growing season in days
+    
+    # # Run storm Loop
+    for i in range(0, number_of_storms):
+        # Update objects
+    
+        # Calculate Day of Year (DOY)
+        julian = np.int(np.floor((current_time - np.floor(current_time)) *
+                                 365.))
+        if synthetic_storms:
+            # Generate seasonal storms
+            # for Dry season
+            if julian < data['doy__start_of_monsoon'] or julian > data[
+                                'doy__end_of_monsoon']:
+                precip_dry.update()
+                precip[i] = precip_dry.storm_depth
+                storm_dt[i] = precip_dry.storm_duration
+                inter_storm_dt[i] = precip_dry.interstorm_duration
+            # Wet Season - Jul to Sep - NA Monsoon
+            else:
+                precip_wet.update()
+                precip[i] = precip_wet.storm_depth
+                storm_dt[i] = precip_wet.storm_duration
+                inter_storm_dt[i] = precip_wet.interstorm_duration
+        else:
+            precip[i] = input_data['precip_met'][i]
+            storm_dt[i] = 0.
+            inter_storm_dt[i] = 24.
+    
+        # Spatially distribute PET and its 30-day-mean (analogous to degree day)
+        if pet_method == 'Cosine':
+            grid_veg.at_cell['surface__potential_evapotranspiration_rate'] = (
+                    pet_arr[julian])
+            grid_veg.at_cell['surface__potential_evapotranspiration_30day_mean'] = (
+                    EP30[julian])
+            grid_veg.at_cell['surface__potential_evapotranspiration_rate__grass'] = (
+                    np.full(grid_veg.number_of_cells, pet_arr[julian, 0]))
+        elif pet_method=='PriestleyTaylor':
+            grid_veg.at_cell['surface__potential_evapotranspiration_rate'] = (
+                    pet_arr[i])
+            grid_veg.at_cell['surface__potential_evapotranspiration_30day_mean'] = (
+                    EP30_met[i])
+            grid_veg.at_cell['surface__potential_evapotranspiration_rate__grass'] = (
+                    np.full(grid_veg.number_of_cells, pet_arr[i, 0]))
+    
+        # Assign spatial rainfall data
+        grid_veg.at_cell['rainfall__daily_depth'] = (
+                np.full(grid_veg.number_of_cells, precip[i]))
+    
+        # Update soil moisture component
+        current_time = soil_moisture.update(current_time, Tr=storm_dt[i],
+                                            Tb=inter_storm_dt[i])
+    
+        # Decide whether its growing season or not
+        if pet_method == 'Cosine':
+            if julian != 364:
+                if EP30[julian+1, 0] > EP30[julian, 0]:
+                    pet_threshold = 1
+                    # 1 corresponds to ETThresholdup (begin growing season)
+                    if EP30[julian, 0] > vegetation._ETthresholdup:
+                        growing_season = True
+                    else:
+                        growing_season = False
+                else:
+                    pet_threshold = 0
+                    # 0 corresponds to ETThresholddown (end growing season)
+                    if EP30[julian, 0] > vegetation._ETthresholddown:
+                        growing_season = True
+                    else:
+                        growing_season = False
+        elif pet_method=='PriestleyTaylor':
+            if i != number_of_storms-1:
+                if EP30_met[i+1, 0] > EP30_met[i, 0]:
+                    pet_threshold = 1
+                    # 1 corresponds to ETThresholdup (begin growing season)
+                    if EP30_met[i, 0] > vegetation._ETthresholdup:
+                        growing_season = True
+                    else:
+                        growing_season = False
+                else:
+                    pet_threshold = 0
+                    # 0 corresponds to ETThresholddown (end growing season)
+                    if EP30_met[i, 0] > vegetation._ETthresholddown:
+                        growing_season = True
+                    else:
+                        growing_season = False                            
+
+        # Update vegetation component
+        vegetation.update(PETThreshold_switch=pet_threshold,
+                          Tb=inter_storm_dt[i], Tr=storm_dt[i])
+
+        if growing_season:
+            # Update yearly cumulative water stress data
+            Tg += (storm_dt[i]+inter_storm_dt[i])/24.    # Incrementing growing season storm count
+            water_stress += ((grid_veg.at_cell['vegetation__water_stress']) *
+                             inter_storm_dt[i]/24.)
+
+        # Record time (optional)
+        Time[i] = current_time
+    
+        # Update spatial PFTs with Cellular Automata rules
+        if (current_time - time_check) >= 1.:
+            if yrs % 100 == 0:
+                print('Elapsed time = ', yrs, ' years')
+            VegType[yrs] = grid.at_cell['vegetation__plant_functional_type']
+            WS_ = np.choose(VegType[yrs], water_stress)
+            grid.at_cell['vegetation__cumulative_water_stress'] = WS_/Tg
+            vegca.update()
+            time_check = np.floor(current_time)
+            water_stress = 0
+            yrs += 1
+            Tg = 0
+    
+    VegType[yrs] = grid.at_cell['vegetation__plant_functional_type']
+    if save_files:
+        save_data(sim_name, inter_storm_dt, storm_dt, precip,
+                  VegType, yrs, 0, Time)
+    if pet_method == 'Cosine':
+        returns_debug = [grid_veg, precip_dry, precip_wet,
+                         radiation, pet_tree, pet_shrub,
+                         pet_grass, soil_moisture, vegetation,
+                         vegca]
+
+    elif pet_method == 'PriestleyTaylor':
+        returns_debug = [grid_veg, precip_dry, precip_wet,
+                         radiation, pet_met, soil_moisture,
+                         vegetation, vegca]
+    return (VegType, yrs-1, returns_debug)
+        
diff --git a/ecohydrology_gridhydromet/supporting_files/plant_competition_ca_lb_pap.py b/ecohydrology_gridhydromet/supporting_files/plant_competition_ca_lb_pap.py
new file mode 100644
index 0000000..0a94988
--- /dev/null
+++ b/ecohydrology_gridhydromet/supporting_files/plant_competition_ca_lb_pap.py
@@ -0,0 +1,414 @@
+
+from landlab import Component
+import numpy as np
+from landlab.utils.decorators import use_file_name_or_kwds
+
+_VALID_METHODS = set(['Grid'])
+GRASS = 0
+SHRUB = 1
+TREE = 2
+BARE = 3
+SHRUBSEEDLING = 4
+TREESEEDLING = 5
+
+
+def assert_method_is_valid(method):
+    if method not in _VALID_METHODS:
+        raise ValueError('%s: Invalid method name' % method)
+
+
+class VegCA(Component):
+    """
+    Landlab component that simulates inter-species plant competition using
+    a 2D cellular automata model.
+
+    .. codeauthor:: Sai Nudurupati and Erkan Istanbulluoglu
+
+    Construction::
+
+        VegCA(grid, Pemaxg=0.35, ING=2., ThetaGrass=0.62, PmbGrass=0.05,
+            Pemaxsh=0.2, ThetaShrub=0.8, PmbShrub=0.01, tpmaxShrub=600,
+            Pemaxtr=0.25, ThetaTree=0.72, PmbTree=0.01, tpmaxTree=350,
+            ThetaShrubSeedling=0.64, PmbShrubSeedling=0.03,
+            tpmaxShrubSeedling=18, ThetaTreeSeedling=0.64,
+            PmbTreeSeedling=0.03, tpmaxTreeSeedling=18)
+
+    Parameters
+    ----------
+    grid: RasterModelGrid
+        A grid.
+    Pemaxg: float, optional
+        Maximal establishment probability of grass.
+    ING: float, optional
+        Parameter to define allelopathic effect of creosote on grass.
+    ThetaGrass: float, optional
+        Drought resistance threshold of grass.
+    PmbGrass: float, optional
+        Background mortality probability of grass.
+    Pemaxsh: float, optional
+        Maximal establishment probability of shrub.
+    ThetaShrub: float, optional
+        Drought resistance threshold of shrub.
+    PmbShrub: float, optional
+        Background mortality probability of shrub.
+    tpmaxShrub: float, optional
+        Maximum age of shrub (years).
+    Pemaxtr: float, optional
+        Maximal establishment probability of tree.
+    Thetatree: float, optional
+        Drought resistance threshold of tree.
+    PmbTree: float, optional
+        Background mortality probability of tree.
+    tpmaxTree: float, optional
+        Maximum age of tree (years).
+    ThetaShrubSeedling: float, optional
+        Drought resistance threshold of shrub seedling.
+    PmbShrubSeedling: float, optional
+        Background mortality probability of shrub seedling.
+    tpmaxShrubSeedling: float, optional
+        Maximum age of shrub seedling (years).
+    ThetaTreeSeedling: float, optional
+        Drought resistance threshold of tree seedling.
+    PmbTreeSeedling: float, optional
+        Background mortality probability of tree seedling.
+    tpmaxTreeSeedling: float, optional
+        Maximum age of tree seedling (years).
+
+    Examples
+    --------
+    >>> from landlab import RasterModelGrid
+    >>> from landlab.components import VegCA
+    >>> grid = RasterModelGrid((5, 4), spacing=(0.2, 0.2))
+    >>> VegCA.name
+    'Cellular Automata Plant Competition'
+    >>> sorted(VegCA.output_var_names)
+    ['plant__age', 'plant__live_index']
+    >>> sorted(VegCA.units) # doctest: +NORMALIZE_WHITESPACE
+    [('plant__age', 'Years'),
+     ('plant__live_index', 'None'),
+     ('vegetation__cumulative_water_stress', 'None'),
+     ('vegetation__plant_functional_type', 'None')]
+    >>> grid['cell']['vegetation__plant_functional_type']= np.arange(
+    ...        0, grid.number_of_cells, dtype=int)
+    >>> grid['cell']['vegetation__cumulative_water_stress'] = np.ones(
+    ...        grid.number_of_cells)
+    >>> ca_veg = VegCA(grid)
+    >>> ca_veg.grid.number_of_cell_rows
+    3
+    >>> ca_veg.grid.number_of_cell_columns
+    2
+    >>> ca_veg.grid is grid
+    True
+    >>> import numpy as np
+    >>> A = np.copy(grid['cell']['plant__age'])
+    >>> ca_veg.update()
+    >>> np.alltrue(grid['cell']['plant__age'] == A)
+    False
+    """
+    _name = 'Cellular Automata Plant Competition'
+
+    _input_var_names = (
+        'vegetation__cumulative_water_stress',
+        'vegetation__plant_functional_type',
+    )
+
+    _output_var_names = (
+        'plant__live_index',
+        'plant__age',
+    )
+
+    _var_units = {
+        'vegetation__cumulative_water_stress': 'None',
+        'vegetation__plant_functional_type': 'None',
+        'plant__live_index': 'None',
+        'plant__age': 'Years',
+    }
+
+    _var_mapping = {
+        'vegetation__cumulative_water_stress': 'cell',
+        'vegetation__plant_functional_type': 'cell',
+        'plant__live_index': 'cell',
+        'plant__age': 'cell',
+    }
+
+    _var_doc = {
+        'vegetation__cumulative_water_stress':
+            'cumulative vegetation__water_stress over the growing season',
+        'vegetation__plant_functional_type':
+            'classification of plants (int), grass=0, shrub=1, tree=2, \
+             bare=3, shrub_seedling=4, tree_seedling=5',
+        'plant__live_index':
+            '1 - vegetation__cumulative_water_stress',
+        'plant__age':
+            'Age of plant',
+    }
+
+    @use_file_name_or_kwds
+    def __init__(self, grid,
+                 Pemaxg=0.35, ING=2., ThetaGrass=0.62, PmbGrass=0.05,
+                 Pemaxsh=0.2, ThetaShrub=0.8, PmbShrub=0.01, tpmaxShrub=600,
+                 Pemaxtr=0.25, ThetaTree=0.72, PmbTree=0.01, tpmaxTree=350,
+                 ThetaShrubSeedling=0.64, PmbShrubSeedling=0.03,
+                 tpmaxShrubSeedling=18, ThetaTreeSeedling=0.64,
+                 PmbTreeSeedling=0.03, tpmaxTreeSeedling=18,
+                 **kwds):
+        """
+        Parameters
+        ----------
+        grid: RasterModelGrid
+            A grid.
+        Pemaxg: float, optional
+            Maximal establishment probability of grass.
+        ING: float, optional
+            Parameter to define allelopathic effect of creosote on grass.
+        ThetaGrass: float, optional
+            Drought resistance threshold of grass.
+        PmbGrass: float, optional
+            Background mortality probability of grass.
+        Pemaxsh: float, optional
+            Maximal establishment probability of shrub.
+        ThetaShrub: float, optional
+            Drought resistance threshold of shrub.
+        PmbShrub: float, optional
+            Background mortality probability of shrub.
+        tpmaxShrub: float, optional
+            Maximum age of shrub (years).
+        Pemaxtr: float, optional
+            Maximal establishment probability of tree.
+        Thetatree: float, optional
+            Drought resistance threshold of tree.
+        PmbTree: float, optional
+            Background mortality probability of tree.
+        tpmaxTree: float, optional
+            Maximum age of tree (years).
+        ThetaShrubSeedling: float, optional
+            Drought resistance threshold of shrub seedling.
+        PmbShrubSeedling: float, optional
+            Background mortality probability of shrub seedling.
+        tpmaxShrubSeedling: float, optional
+            Maximum age of shrub seedling (years).
+        ThetaTreeSeedling: float, optional
+            Drought resistance threshold of tree seedling.
+        PmbTreeSeedling: float, optional
+            Background mortality probability of tree seedling.
+        tpmaxTreeSeedling: float, optional
+            Maximum age of tree seedling (years).
+        """
+        self._method = kwds.pop('method', 'Grid')
+        self._Pemaxg = Pemaxg   # Pe-max-grass - max probability
+        self._Pemaxsh = Pemaxsh    # Pe-max-shrub
+        self._Pemaxtr = Pemaxtr   # Pe-max-tree
+        self._INg = ING  # Allelopathic effect on grass from creosotebush
+        self._th_g = ThetaGrass  # grass
+        self._th_sh = ThetaShrub  # shrub - Creosote
+        self._th_tr = ThetaTree  # Juniper pine
+        self._th_sh_s = ThetaShrubSeedling  # shrub seedling
+        self._th_tr_s = ThetaTreeSeedling  # Juniper pine seedling
+        self._Pmb_g = PmbGrass  # Background mortality probability - grass
+        self._Pmb_sh = PmbShrub  # shrub
+        self._Pmb_tr = PmbTree  # tree
+        self._Pmb_sh_s = PmbShrubSeedling  # shrub seedling
+        self._Pmb_tr_s = PmbTreeSeedling  # tree seedling
+        self._tpmax_sh = tpmaxShrub  # Maximum age - shrub
+        self._tpmax_tr = tpmaxTree  # Maximum age - tree
+        self._tpmax_sh_s = tpmaxShrubSeedling  # Maximum age - shrub seedling
+        self._tpmax_tr_s = tpmaxTreeSeedling  # Maximum age - tree seedling
+
+        assert_method_is_valid(self._method)
+
+        super(VegCA, self).__init__(grid)
+        """
+        Note: If the field 'vegetation__plant_functional_type' is not
+        input, a random field will be generated.
+        """
+        if 'vegetation__plant_functional_type' not in self.grid.at_cell:
+            grid['cell']['vegetation__plant_functional_type'] = (
+                    np.random.randint(0, 6, grid.number_of_cells))
+        """
+        Note: If the field 'plant__age' is not input,
+        a random field will be generated. Also note that the
+        field 'vegetation__plant_functional_type' will be altered
+        if the age of the shrubs and trees randomnly generated are
+        below the maximum age of their respective seedlings.
+        i.e. if the field 'vegetation__plant_functional_type'
+        originally has a shrub at a location 'x' and the field
+        'plant__age' is not provided, 'plant__age' will be generated.
+        If the value of this field at location 'x' is less than
+        shrub_seedling's maximum age, the field 
+        'vegetation__plant_functional_type' will be altered to
+        show SHRUBSEEDLING instead of SHRUB.
+        """
+        if 'plant__age' not in self.grid.at_cell:
+            VegType = grid['cell']['vegetation__plant_functional_type']
+            tp = np.zeros(grid.number_of_cells, dtype=int)
+            tp[VegType == TREE] = np.random.randint(
+                0, self._tpmax_tr, np.where(VegType == TREE)[0].shape)
+            tp[VegType == SHRUB] = np.random.randint(
+                0, self._tpmax_sh, np.where(VegType == SHRUB)[0].shape)
+            locs_trees = np.where(VegType == TREE)[0]
+            locs_shrubs = np.where(VegType == SHRUB)[0]
+            VegType[locs_trees[tp[locs_trees] < self._tpmax_tr_s]] = TREESEEDLING
+            VegType[locs_shrubs[tp[
+                locs_shrubs] < self._tpmax_sh_s]] = SHRUBSEEDLING
+            grid.at_cell['plant__age'] = tp
+
+        for name in self._input_var_names:
+            if name not in self.grid.at_cell:
+                self.grid.add_zeros('cell', name, units=self._var_units[name])
+
+        for name in self._output_var_names:
+            if name not in self.grid.at_cell:
+                self.grid.add_zeros('cell', name, units=self._var_units[name])
+
+        self._cell_values = self.grid['cell']
+
+    def update(self, time_elapsed=1, edit_vegcov=False):
+        """
+        Update fields with current loading conditions.
+
+        Parameters
+        ----------
+        time_elapsed: int, optional
+            Time elapsed - time step (years).
+        edit_vegcov: bool, optional
+            If edit_vegcov=True, an optional field 'vegetation__boolean_vegetated'
+            will be output, (i.e.) if a cell is vegetated the corresponding
+            cell of the field will be 1, otherwise it will be 0.
+        """
+        self._VegType = self._cell_values['vegetation__plant_functional_type']
+        self._CumWS = self._cell_values[
+            'vegetation__cumulative_water_stress']
+        self._live_index = self._cell_values['plant__live_index']
+        self._tp = self._cell_values['plant__age'] + time_elapsed
+#        self._tp_check_1 = np.copy(self._tp)
+
+        # Check if shrub and tree seedlings have matured
+        shrub_seedlings = np.where(self._VegType == SHRUBSEEDLING)[0]
+        tree_seedlings = np.where(self._VegType == TREESEEDLING)[0]
+        self._shrub_seedlings = np.copy(shrub_seedlings)
+        self._tree_seedlings = np.copy(tree_seedlings)
+        matured_shrubs = np.where(self._tp[shrub_seedlings] >
+                                  self._tpmax_sh_s)[0]
+        self._matured_shrubs = np.copy(matured_shrubs)
+        matured_trees = np.where(self._tp[tree_seedlings] >
+                                 self._tpmax_tr_s)[0]
+        self._matured_trees = np.copy(matured_trees)
+        self._VegType[shrub_seedlings[matured_shrubs]] = SHRUB
+        self._VegType[tree_seedlings[matured_trees]] = TREE
+        self._tp[shrub_seedlings[matured_shrubs]] = 0
+        self._tp[tree_seedlings[matured_trees]] = 0
+#        self._tp_check_2 = np.copy(self._tp)
+
+        # Identifying bare_cells and vegetated_cells before executing establishment
+        # and mortality.
+        bare_cells = np.where(self._VegType == BARE)[0]
+        plant_cells = np.where(self._VegType != BARE)[0]        
+
+        # Establishment
+        self._live_index = 1 - self._CumWS      # Plant live index = 1 - WS
+        n_bare = len(bare_cells)
+        first_ring = self.grid.looped_neighbors_at_cell[bare_cells]
+        second_ring =                                                   \
+            self.grid.second_ring_looped_neighbors_at_cell[bare_cells]
+        veg_type_fr = self._VegType[first_ring]
+        veg_type_sr = self._VegType[second_ring]
+        sh_phi_fr = phi_pft(veg_type_fr, SHRUB, self._live_index[first_ring])
+        tr_phi_fr = phi_pft(veg_type_fr, TREE, self._live_index[first_ring])
+        tr_phi_sr = phi_pft(veg_type_sr, TREE, self._live_index[second_ring])
+
+        n = count(veg_type_fr, SHRUB)
+        n[n==0] = 1./self._INg    # to avoid divide by zero errors for Peg calculation
+        Phi_sh = sh_phi_fr/8.
+        Phi_tr = (tr_phi_fr + tr_phi_sr/2.)/8.
+        Phi_g = np.mean(self._live_index[np.where(self._VegType == GRASS)])   # Need to check how to deal with empty veg plot
+        Pemaxg = self._Pemaxg * np.ones(n_bare)
+        Pemaxsh = self._Pemaxsh * np.ones(n_bare)
+        Pemaxtr = self._Pemaxtr * np.ones(n_bare)
+        Peg = np.amin(np.vstack((Phi_g/(n*self._INg), Pemaxg)), axis=0)
+        Pesh = np.amin(np.vstack((Phi_sh, Pemaxsh)), axis=0)
+        Petr = np.amin(np.vstack((Phi_tr, Pemaxtr)), axis=0)
+        Select_PFT_E = np.random.choice([GRASS, SHRUBSEEDLING, TREESEEDLING],
+                                        n_bare)
+        # Grass - 0; Shrub Seedling - 4; Tree Seedling - 5
+        Pest = np.choose(Select_PFT_E, [Peg, 0, 0, 0, Pesh, Petr])
+        # Probability of establishment
+        R_Est = np.random.rand(n_bare)
+        # Random number for comparison to establish
+        Establish = np.int32(np.where(
+                             np.greater_equal(Pest, R_Est) == True)[0])
+        self._VegType[bare_cells[Establish]] = Select_PFT_E[Establish]
+        self._tp[bare_cells[Establish]] = 0
+#        self._tp_check_3 = np.copy(self._tp)
+
+        # Mortality
+        n_plant = len(plant_cells)
+        Theta = np.choose(self._VegType[plant_cells], [
+                          self._th_g, self._th_sh, self._th_tr,
+                          0, self._th_sh_s, self._th_tr_s])        
+        PMd = self._CumWS[plant_cells] - Theta
+        PMd[PMd < 0.] = 0.
+        # Multiplied 2 below to not kill seedlings
+        tpmax = np.choose(self._VegType[plant_cells], [
+                          200000, self._tpmax_sh, self._tpmax_tr,
+                          0, 2 * self._tpmax_sh_s, 2 * self._tpmax_tr_s])        
+        PMa = np.zeros(n_plant)
+        tp_plant = self._tp[plant_cells]
+        tp_greater = np.where(tp_plant > 0.5*tpmax)[0]        
+        PMa[tp_greater] = ((tp_plant[tp_greater] - 0.5 * tpmax[tp_greater]) /
+                           (0.5 * tpmax[tp_greater]))        
+        PMb = np.choose(self._VegType[plant_cells], [
+                        self._Pmb_g, self._Pmb_sh, self._Pmb_tr, 0,
+                        self._Pmb_sh_s, self._Pmb_tr_s])        
+        PM = PMd + PMa + PMb
+        PM[PM > 1.] = 1.        
+        R_Mor = np.random.rand(n_plant)  # Random number for comparison to kill        
+        Mortality = np.int32(np.where(np.greater_equal(PM, R_Mor) == True)[0])
+        self._VegType[plant_cells[Mortality]] = BARE
+        self._tp[plant_cells[Mortality]] = 0
+        self._cell_values['plant__age'] = self._tp
+
+        if edit_vegcov:
+            self.grid['cell']['vegetation__boolean_vegetated'] =             \
+                        np.zeros(self.grid.number_of_cells, dtype=int)
+            self.grid['cell']['vegetation__boolean_vegetated'][
+                        self._VegType != BARE] = 1
+
+        # For debugging purposes
+#        self._n = n
+#        self._phi_g = Phi_g
+#        self._Peg = Peg
+#        self._Pesh = Pesh
+#        self._Petr = Petr
+#        self._selected_pft_e = Select_PFT_E
+#        self._theta = Theta  # shape = plant_cells
+#        self._PMd = PMd
+#        self._tpmax = tpmax
+#        self._tp_greater = tp_greater
+#        self._PMa = PMa
+#        self._PMb = PMb
+#        self._PM = PM
+#        self._R_Mor = R_Mor
+        self._bare_cells = bare_cells
+        self._Established = bare_cells[Establish]
+        self._plant_cells = plant_cells
+        self._Mortified = plant_cells[Mortality]
+
+
+def count(Arr, value):
+    Res = np.zeros(Arr.shape[0])
+    x, y = Arr.shape
+    for i in range(0, x):
+        for j in range(0, y):
+            if Arr[i][j] == value:
+                Res[i] += 1
+    return Res
+
+
+def phi_pft(VegType, PlantType, phi_value):
+    Phi = np.zeros(phi_value.shape[0])
+    x, y = phi_value.shape
+    for i in range(0, x):
+        for j in range(0, y):
+            if VegType[i][j] == PlantType:
+                Phi[i] += phi_value[i][j]
+    return Phi
diff --git a/ecohydrology_gridhydromet/supporting_files/soil_moisture_dynamics_lb_pap.py b/ecohydrology_gridhydromet/supporting_files/soil_moisture_dynamics_lb_pap.py
new file mode 100644
index 0000000..1875e78
--- /dev/null
+++ b/ecohydrology_gridhydromet/supporting_files/soil_moisture_dynamics_lb_pap.py
@@ -0,0 +1,676 @@
+
+from landlab import Component
+from landlab.utils.decorators import use_file_name_or_kwds
+import numpy as np
+import six
+
+_VALID_METHODS = set(['Grid', 'Multi'])
+
+
+def assert_method_is_valid(method):
+    if method not in _VALID_METHODS:
+        raise ValueError('%s: Invalid method name' % method)
+
+
+class SoilMoisture(Component):
+    """
+    Landlab component that simulates root-zone average soil moisture at each
+    cell using inputs of potential evapotranspiration, live leaf area index,
+    and vegetation cover.
+
+    .. codeauthor:: Sai Nudurupati and Erkan Istanbulluoglu
+
+    Construction::
+
+        SoilMoisture(grid, runon=0., f_bare=0.7, soil_ew=0.1,
+           intercept_cap_grass= 1., zr_grass=0.3, I_B_grass=20.,
+           I_V_grass=24., K_s_grass=42., pc_grass=0.43, fc_grass=0.56,
+           sc_grass=0.33, wp_grass=0.13, hgw_grass=0.1, beta_grass=13.8,
+           LAI_max_grass=2., LAIR_max_grass=2.88,
+           intercept_cap_shrub=1.5, zr_shrub=0.5, I_B_shrub=20.,
+           I_V_shrub=40., K_s_shrub=42., pc_shrub=0.43, fc_shrub=0.56,
+           sc_shrub=0.24, wp_shrub=0.13, hgw_shrub=0.1, beta_shrub=13.8,
+           LAI_max_shrub=2., LAIR_max_shrub=2.,
+           intercept_cap_tree=2., zr_tree=1.3, I_B_tree=20.,
+           I_V_tree=40., K_s_tree=42., pc_tree=0.43, fc_tree=0.56,
+           sc_tree=0.22, wp_tree=0.15, hgw_tree=0.1, beta_tree=13.8,
+           LAI_max_tree=4., LAIR_max_tree=4.,
+           intercept_cap_bare=1., zr_bare=0.15, I_B_bare=20.,
+           I_V_bare=20., K_s_bare=42., pc_bare=0.43, fc_bare=0.56, sc_bare=0.33,
+           wp_bare=0.13, hgw_bare=0.1, beta_bare=13.8,
+           LAI_max_bare=0.01, LAIR_max_bare=0.01)
+
+    Parameters
+    ----------
+    grid: RasterModelGrid
+        A grid.
+    runon: float, optional
+        Runon from higher elevation (mm).
+    f_bare: float, optional
+        Fraction to partition PET for bare soil (None).
+    soil_ew: float, optional
+        Residual Evaporation after wilting (mm/day).
+    intercept_cap: float, optional
+        Plant Functional Type (PFT) specific full canopy interception
+        capacity.
+    zr: float, optional
+        Root depth (m).
+    I_B: float, optional
+        Infiltration capacity of bare soil (mm/h).
+    I_V: float, optional
+        Infiltration capacity of vegetated soil (mm/h).
+    K_s: float, optional
+        Hydraulic conductivity of soil (mm/h).
+    pc: float, optional
+        Soil porosity (None).
+    fc: float, optional
+        Soil saturation degree at field capacity (None).
+    sc: float, optional
+        Soil saturation degree at stomatal closure (None).
+    wp: float, optional
+        Soil saturation degree at wilting point (None).
+    hgw: float, optional
+        Soil saturation degree at hygroscopic point (None).
+    beta: float, optional
+        Deep percolation constant = 2*b+3 where b is
+        water retention (None).
+    LAI_max: float, optional
+        Maximum leaf area index (m^2/m^2).
+    LAIR_max: float, optional
+        Reference leaf area index (m^2/m^2).
+
+    Examples
+    --------
+    >>> from landlab import RasterModelGrid
+    >>> from landlab.components.soil_moisture import SoilMoisture
+    >>> grid = RasterModelGrid((5, 4), spacing=(0.2, 0.2))
+    >>> SoilMoisture.name
+    'Soil Moisture'
+    >>> sorted(SoilMoisture.output_var_names) # doctest: +NORMALIZE_WHITESPACE
+    ['soil_moisture__root_zone_leakage',
+     'soil_moisture__saturation_fraction',
+     'surface__evapotranspiration',
+     'surface__runoff',
+     'vegetation__water_stress']
+    >>> sorted(SoilMoisture.units) # doctest: +NORMALIZE_WHITESPACE
+    [('rainfall__daily_depth', 'mm'),
+     ('soil_moisture__initial_saturation_fraction', 'None'),
+     ('soil_moisture__root_zone_leakage', 'mm'),
+     ('soil_moisture__saturation_fraction', 'None'),
+     ('surface__evapotranspiration', 'mm'),
+     ('surface__potential_evapotranspiration_rate', 'mm'),
+     ('surface__runoff', 'mm'),
+     ('vegetation__cover_fraction', 'None'),
+     ('vegetation__live_leaf_area_index', 'None'),
+     ('vegetation__plant_functional_type', 'None'),
+     ('vegetation__water_stress', 'None')]
+    >>> grid['cell']['vegetation__plant_functional_type']= (
+    ...            np.zeros(grid.number_of_cells, dtype=int))
+    >>> SM = SoilMoisture(grid)
+    >>> SM.grid.number_of_cell_rows
+    3
+    >>> SM.grid.number_of_cell_columns
+    2
+    >>> SM.grid is grid
+    True
+    >>> import numpy as np
+    >>> np.allclose(grid.at_cell['soil_moisture__saturation_fraction'], 0.)
+    True
+    >>> grid['cell']['surface__potential_evapotranspiration_rate']= np.array([
+    ...            0.2554777, 0.2554777 , 0.22110221, 0.22110221,
+    ...            0.24813062, 0.24813062])
+    >>> grid['cell']['soil_moisture__initial_saturation_fraction']= (
+    ...        0.75 * np.ones(grid.number_of_cells))
+    >>> grid['cell']['vegetation__live_leaf_area_index']= (
+    ...        2. * np.ones(grid.number_of_cells))
+    >>> grid['cell']['vegetation__cover_fraction']= (
+    ...        np.ones(grid.number_of_cells))
+    >>> current_time = 0.5
+    >>> grid['cell']['rainfall__daily_depth'] = (
+    ...        25. * np.ones(grid.number_of_cells))
+    >>> current_time = SM.update(current_time)
+    >>> np.allclose(grid.at_cell['soil_moisture__saturation_fraction'], 0.)
+    False
+    """
+    _name = 'Soil Moisture'
+
+    _input_var_names = (
+        'vegetation__cover_fraction',
+        'vegetation__live_leaf_area_index',
+        'surface__potential_evapotranspiration_rate',
+        'surface__potential_evapotranspiration_rate__grass',
+        'soil_moisture__initial_saturation_fraction',
+        'vegetation__plant_functional_type',
+        'rainfall__daily_depth',
+    )
+
+    _output_var_names = (
+        'vegetation__water_stress',
+        'soil_moisture__saturation_fraction',
+        'soil_moisture__root_zone_leakage',
+        'surface__runoff',
+        'surface__runon',
+        'surface__evapotranspiration',
+    )
+
+    _var_units = {
+        'vegetation__cover_fraction': 'None',
+        'vegetation__live_leaf_area_index': 'None',
+        'surface__potential_evapotranspiration_rate': 'mm',
+        'surface__potential_evapotranspiration_rate__grass': 'mm',
+        'vegetation__plant_functional_type': 'None',
+        'vegetation__water_stress': 'None',
+        'soil_moisture__saturation_fraction': 'None',
+        'soil_moisture__initial_saturation_fraction': 'None',
+        'soil_moisture__root_zone_leakage': 'mm',
+        'surface__runoff': 'mm',
+        'surface__runon': 'mm',
+        'surface__evapotranspiration': 'mm',
+        'rainfall__daily_depth': 'mm',
+    }
+
+    _var_mapping = {
+        'vegetation__cover_fraction': 'cell',
+        'vegetation__live_leaf_area_index': 'cell',
+        'surface__potential_evapotranspiration_rate': 'cell',
+        'surface__potential_evapotranspiration_rate__grass': 'cell',
+        'vegetation__plant_functional_type': 'cell',
+        'vegetation__water_stress': 'cell',
+        'soil_moisture__saturation_fraction': 'cell',
+        'soil_moisture__initial_saturation_fraction': 'cell',
+        'soil_moisture__root_zone_leakage': 'cell',
+        'surface__runoff': 'cell',
+        'surface__runon': 'cell',
+        'surface__evapotranspiration': 'cell',
+        'rainfall__daily_depth': 'cell',
+    }
+
+    _var_doc = {
+        'vegetation__cover_fraction':
+            'fraction of land covered by vegetation',
+        'vegetation__live_leaf_area_index':
+            'one-sided green leaf area per unit ground surface area',
+        'surface__potential_evapotranspiration_rate':
+            'potential sum of evaporation and plant transpiration',
+        'surface__potential_evapotranspiration_rate__grass':
+            'potential sum of evaporation and grass transpiration, \
+             for partitioning bare soil evapotranspiration rate',
+        'vegetation__plant_functional_type':
+            'classification of plants (int), grass=0, shrub=1, tree=2, \
+             bare=3, shrub_seedling=4, tree_seedling=5',
+        'vegetation__water_stress':
+            'parameter that represents nonlinear effects of water deficit \
+             on plants',
+        'soil_moisture__saturation_fraction':
+            'relative volumetric water content (theta) - limits=[0,1]',
+        'soil_moisture__initial_saturation_fraction':
+            'initial soil_moisture__saturation_fraction',
+        'soil_moisture__root_zone_leakage':
+            'leakage of water into deeper portions of the soil not accessible \
+             to the plant',
+        'surface__runoff':
+            'infiltration excess runoff from ground surface',
+        'surface__runon':
+            'infiltration excess runon',
+        'surface__evapotranspiration':
+            'actual sum of evaporation and plant transpiration',
+        'rainfall__daily_depth':
+            'Rain in (mm) as a field, allowing spatio-temporal soil moisture \
+             saturation analysis.',
+    }
+
+    @use_file_name_or_kwds
+    def __init__(self, grid, ordered_cells=None, runon_switch=0,
+                 f_bare=0.7, soil_ew=0.1,
+                 intercept_cap_grass=1., zr_grass=0.3, I_B_grass=20.,
+                 I_V_grass=24., K_s_grass=42., pc_grass=0.43, fc_grass=0.56,
+                 sc_grass=0.33, wp_grass=0.13, hgw_grass=0.1, beta_grass=13.8,
+                 LAI_max_grass=2., LAIR_max_grass=2.88,
+                 intercept_cap_shrub=1.5, zr_shrub=0.5, I_B_shrub=20.,
+                 I_V_shrub=40., K_s_shrub=42., pc_shrub=0.43, fc_shrub=0.56,
+                 sc_shrub=0.24, wp_shrub=0.13, hgw_shrub=0.1, beta_shrub=13.8,
+                 LAI_max_shrub=2., LAIR_max_shrub=2.,
+                 intercept_cap_tree=2., zr_tree=1.3, I_B_tree=20.,
+                 I_V_tree=40., K_s_tree=42., pc_tree=0.43, fc_tree=0.56,
+                 sc_tree=0.22, wp_tree=0.15, hgw_tree=0.1, beta_tree=13.8,
+                 LAI_max_tree=4., LAIR_max_tree=4.,
+                 intercept_cap_bare=1., zr_bare=0.15, I_B_bare=20.,
+                 I_V_bare=20., K_s_bare=42., pc_bare=0.43, fc_bare=0.56,
+                 sc_bare=0.33, wp_bare=0.13, hgw_bare=0.1, beta_bare=13.8,
+                 LAI_max_bare=0.01, LAIR_max_bare=0.01, **kwds):
+        """
+        Parameters
+        ----------
+        grid: RasterModelGrid
+            A grid.
+        runon_switch: int, optional
+            To indicate whether runon needs to considered (mm);
+            0 - No runon, 1 - runon.
+        ordered_cells: numpy.array, required if runon_switch = 1
+            ordered_cells has the grid cells sorted in an order of descending
+            channel length in a delineated watershed
+         f_bare: float, optional
+            Fraction to partition PET for bare soil (None).
+        soil_ew: float, optional
+            Residual Evaporation after wilting (mm/day).
+        intercept_cap: float, optional
+            Plant Functional Type (PFT) specific full canopy interception
+            capacity.
+        zr: float, optional
+            Root depth (m).
+        I_B: float, optional
+            Infiltration capacity of bare soil (mm/h).
+        I_V: float, optional
+            Infiltration capacity of vegetated soil (mm/h).
+        K_s: float, optional
+            Hydraulic conductivity of soil (mm/h).
+        pc: float, optional
+            Soil porosity (None).
+        fc: float, optional
+            Soil saturation degree at field capacity (None).
+        sc: float, optional
+            Soil saturation degree at stomatal closure (None).
+        wp: float, optional
+            Soil saturation degree at wilting point (None).
+        hgw: float, optional
+            Soil saturation degree at hygroscopic point (None).
+        beta: float, optional
+            Deep percolation constant = 2*b+4 where b is
+            water retention (None).
+        LAI_max: float, optional
+            Maximum leaf area index (m^2/m^2).
+        LAIR_max: float, optional
+            Reference leaf area index (m^2/m^2).
+        """
+        self._method = kwds.pop('method', 'Grid')
+
+        assert_method_is_valid(self._method)
+
+        super(SoilMoisture, self).__init__(grid)
+
+        self.initialize(ordered_cells=ordered_cells, runon_switch=runon_switch,
+                        f_bare=f_bare, soil_ew=soil_ew,
+                        intercept_cap_grass=intercept_cap_grass,
+                        zr_grass=zr_grass, I_B_grass=I_B_grass,
+                        I_V_grass=I_V_grass, K_s_grass=K_s_grass,
+                        pc_grass=pc_grass, fc_grass=fc_grass,
+                        sc_grass=sc_grass, wp_grass=wp_grass,
+                        hgw_grass=hgw_grass, beta_grass=beta_grass,
+                        LAI_max_grass=LAI_max_grass,
+                        LAIR_max_grass=LAIR_max_grass,
+                        intercept_cap_shrub=intercept_cap_shrub,
+                        zr_shrub=zr_shrub, I_B_shrub=I_B_shrub,
+                        I_V_shrub=I_V_shrub, K_s_shrub=K_s_shrub,
+                        pc_shrub=pc_shrub, fc_shrub=fc_shrub,
+                        sc_shrub=sc_shrub, wp_shrub=wp_shrub,
+                        hgw_shrub=hgw_shrub, beta_shrub=beta_shrub,
+                        LAI_max_shrub=LAI_max_shrub,
+                        LAIR_max_shrub=LAIR_max_shrub,
+                        intercept_cap_tree=intercept_cap_tree, zr_tree=zr_tree,
+                        I_B_tree=I_B_tree, I_V_tree=I_V_tree,
+                        K_s_tree=K_s_tree, pc_tree=pc_tree,
+                        fc_tree=fc_tree, sc_tree=sc_tree, wp_tree=wp_tree,
+                        hgw_tree=hgw_tree, beta_tree=beta_tree,
+                        LAI_max_tree=LAI_max_tree, LAIR_max_tree=LAIR_max_tree,
+                        intercept_cap_bare=intercept_cap_bare, zr_bare=zr_bare,
+                        I_B_bare=I_B_bare, I_V_bare=I_V_bare,
+                        K_s_bare=K_s_bare, pc_bare=pc_bare,
+                        fc_bare=fc_bare, sc_bare=sc_bare, wp_bare=wp_bare,
+                        hgw_bare=hgw_bare, beta_bare=beta_bare,
+                        LAI_max_bare=LAI_max_bare,
+                        LAIR_max_bare=LAIR_max_bare, **kwds)
+
+        for name in self._input_var_names:
+            if name not in self.grid.at_cell:
+                self.grid.add_zeros('cell', name, units=self._var_units[name])
+
+        for name in self._output_var_names:
+            if name not in self.grid.at_cell:
+                self.grid.add_zeros('cell', name, units=self._var_units[name])
+
+        self._nodal_values = self.grid['node']
+
+        self._cell_values = self.grid['cell']
+
+    def initialize(self, ordered_cells=None, runon_switch=0, f_bare=0.7,
+                   soil_ew=0.1, intercept_cap_grass=1., zr_grass=0.3,
+                   I_B_grass=20., I_V_grass=24., K_s_grass=42.,
+                   pc_grass=0.43, fc_grass=0.56,
+                   sc_grass=0.33, wp_grass=0.13, hgw_grass=0.1,
+                   beta_grass=13.8, LAI_max_grass=2., LAIR_max_grass=2.88,
+                   intercept_cap_shrub=1.5, zr_shrub=0.5, I_B_shrub=20.,
+                   I_V_shrub=40., K_s_shrub=42., pc_shrub=0.43,
+                   fc_shrub=0.56, sc_shrub=0.24,
+                   wp_shrub=0.13, hgw_shrub=0.1, beta_shrub=13.8,
+                   LAI_max_shrub=2., LAIR_max_shrub=2.,
+                   intercept_cap_tree=2., zr_tree=1.3, I_B_tree=20.,
+                   I_V_tree=40., K_s_tree=42., pc_tree=0.43,
+                   fc_tree=0.56, sc_tree=0.22,
+                   wp_tree=0.15, hgw_tree=0.1, beta_tree=13.8,
+                   LAI_max_tree=4., LAIR_max_tree=4.,
+                   intercept_cap_bare=1., zr_bare=0.15, I_B_bare=20.,
+                   I_V_bare=20., K_s_bare=42., pc_bare=0.43,
+                   fc_bare=0.56, sc_bare=0.33,
+                   wp_bare=0.13, hgw_bare=0.1, beta_bare=13.8,
+                   LAI_max_bare=0.01, LAIR_max_bare=0.01, **kwds):
+        # GRASS = 0; SHRUB = 1; TREE = 2; BARE = 3;
+        # SHRUBSEEDLING = 4; TREESEEDLING = 5
+        """
+        Parameters
+        ----------
+        grid: RasterModelGrid
+            A grid.
+        runon_switch: int, optional
+            To indicate whether runon needs to considered (mm);
+            0 - No runon, 1 - runon.
+        ordered_cells: numpy.array, required if runon_switch = 1
+            ordered_cells has the grid cells sorted in an order of descending
+            channel length in a delineated watershed
+        f_bare: float, optional
+            Fraction to partition PET for bare soil (None).
+        soil_ew: float, optional
+            Residual Evaporation after wilting (mm/day).
+        intercept_cap: float, optional
+            Plant Functional Type (PFT) specific full canopy interception
+            capacity.
+        zr: float, optional
+            Root depth (m).
+        I_B: float, optional
+            Infiltration capacity of bare soil (mm/h).
+        I_V: float, optional
+            Infiltration capacity of vegetated soil (mm/h).
+        K_s: float, optional
+            Hydraulic conductivity of soil (mm/h).
+        pc: float, optional
+            Soil porosity (None).
+        fc: float, optional
+            Soil saturation degree at field capacity (None).
+        sc: float, optional
+            Soil saturation degree at stomatal closure (None).
+        wp: float, optional
+            Soil saturation degree at wilting point (None).
+        hgw: float, optional
+            Soil saturation degree at hygroscopic point (None).
+        beta: float, optional
+            Deep percolation constant = 2*b+4 where b is
+            water retention (None).
+        parameter (None)
+        LAI_max: float, optional
+            Maximum leaf area index (m^2/m^2).
+        LAIR_max: float, optional
+            Reference leaf area index (m^2/m^2).
+        """
+
+        self._vegtype = self.grid['cell']['vegetation__plant_functional_type']
+        self._runon_switch = runon_switch
+        self._fbare = f_bare
+        self.ordered_cells = ordered_cells
+
+        self._interception_cap = np.choose(self._vegtype, [
+             intercept_cap_grass, intercept_cap_shrub, intercept_cap_tree,
+             intercept_cap_bare, intercept_cap_shrub, intercept_cap_tree])
+
+        self._zr = np.choose(self._vegtype, [
+             zr_grass, zr_shrub, zr_tree, zr_bare, zr_shrub, zr_tree])
+
+        self._soil_Ib = np.choose(self._vegtype, [
+             I_B_grass, I_B_shrub, I_B_tree, I_B_bare, I_B_shrub, I_B_tree])
+
+        self._soil_Iv = np.choose(self._vegtype, [
+             I_V_grass, I_V_shrub, I_V_tree, I_V_bare, I_V_shrub, I_V_tree])
+
+        self._soil_Ks = np.choose(self._vegtype, [
+             K_s_grass, K_s_shrub, K_s_tree, K_s_bare, K_s_shrub, K_s_tree])
+
+        self._soil_Ew = soil_ew
+
+        self._soil_pc = np.choose(self._vegtype, [
+             pc_grass, pc_shrub, pc_tree, pc_bare, pc_shrub, pc_tree])
+
+        self._soil_fc = np.choose(self._vegtype, [
+             fc_grass, fc_shrub, fc_tree, fc_bare, fc_shrub, fc_tree])
+
+        self._soil_sc = np.choose(self._vegtype, [
+             sc_grass, sc_shrub, sc_tree, sc_bare, sc_shrub, sc_tree])
+
+        self._soil_wp = np.choose(self._vegtype, [
+             wp_grass, wp_shrub, wp_tree, wp_bare, wp_shrub, wp_tree])
+
+        self._soil_hgw = np.choose(self._vegtype, [
+             hgw_grass, hgw_shrub, hgw_tree, hgw_bare, hgw_shrub, hgw_tree])
+
+        self._soil_beta = np.choose(self._vegtype, [
+             beta_grass, beta_shrub, beta_tree,
+             beta_bare, beta_shrub, beta_tree])
+
+        self._LAI_max = np.choose(self._vegtype, [
+             LAI_max_grass, LAI_max_shrub, LAI_max_tree,
+             LAI_max_bare, LAI_max_shrub, LAI_max_tree])
+
+        self._LAIR_max = np.choose(self._vegtype, [
+             LAIR_max_grass, LAIR_max_shrub, LAIR_max_tree,
+             LAIR_max_bare, LAIR_max_shrub, LAIR_max_tree])
+
+
+    def update(self, current_time, Tb=24., Tr=0., **kwds):
+        """
+        Update fields with current loading conditions.
+
+        Parameters
+        ----------
+        current_time: float
+            Current time (years).
+        Tr: float, optional
+            Storm duration (hours).
+        Tb: float, optional
+            Inter-storm duration (hours).
+        """
+        P_ = self._cell_values['rainfall__daily_depth']
+        self._PET = (
+            self._cell_values['surface__potential_evapotranspiration_rate'])
+        self._pet_g = (
+            self._cell_values['surface__potential_evapotranspiration_rate__grass'])
+        self._SO = (
+            self._cell_values['soil_moisture__initial_saturation_fraction'])
+        self._vegcover = self._cell_values['vegetation__cover_fraction']
+        self._water_stress = self._cell_values['vegetation__water_stress']
+        self._S = self._cell_values['soil_moisture__saturation_fraction']
+        self._D = self._cell_values['soil_moisture__root_zone_leakage']
+        self._ETA = self._cell_values['surface__evapotranspiration']
+        self._fr = (self._cell_values['vegetation__live_leaf_area_index'] /
+                    self._LAIR_max)
+        self._runoff = self._cell_values['surface__runoff']
+        self._runon = self._cell_values['surface__runon']
+        self._runoff[:] = 0.   # Initializing runoff to zero
+        self._runon[:] = 0.    # Initializing runon to zero
+        # LAIl = self._cell_values['vegetation__live_leaf_area_index']
+        # LAIt = LAIl+self._cell_values['DeadLeafAreaIndex']
+        # if LAIt.all() == 0.:
+        #     self._fr = np.zeros(self.grid.number_of_cells)
+        # else:
+        #     self._fr = (self._vegcover[0]*LAIl/LAIt)
+        self._fr[self._fr > 1.] = 1.
+        self._Sini = np.zeros(self._SO.shape)
+        self._ETmax = np.zeros(self._SO.shape)
+        self._ts = np.zeros(self._SO.shape)  # record Time to Saturation
+        self._precip_int = np.zeros(self._SO.shape)
+        # Adding routine to add runon & runoff
+        if self._runon_switch:
+            # Make sure that flow_router has been called before
+            r_cell = (self.grid.cell_at_node[
+                self.grid.at_node['flow__receiver_node']])
+            r_cell = r_cell[r_cell != -1]
+            # finding cells that aren't part of
+            # ordered cells (delineated watershed)
+            diff_cells = (np.setdiff1d(
+                range(0, self.grid.number_of_cells), self.ordered_cells))
+            # trvrsl_order has ordered cells computed first and then the rest
+            # of the cells
+            trvrsl_order = np.concatenate((self.ordered_cells, diff_cells),
+                                          axis=0)
+        else:
+            # trvrsl_order is just regular 'all cells' if no runon is computed
+            trvrsl_order = range(0, self.grid.number_of_cells)
+
+        for cell in trvrsl_order:
+            # Routine to calculate runon
+            if self._runon_switch:
+                if cell in self.ordered_cells:
+                    donors = []
+                    donors = list(np.where(r_cell == cell)[0])
+                    if len(donors) != 0:
+                        for k in range(0, len(donors)):
+                            self._runon[cell] += self._runoff[donors[k]]
+
+            P = P_[cell]
+            runon = self._runon[cell]
+            if runon < 0:
+                six._print('Runon < 0!')
+            # print cell
+            s = self._SO[cell]
+            fbare = self._fbare
+            ZR = self._zr[cell]
+            pc = self._soil_pc[cell]
+            fc = self._soil_fc[cell]
+            scc = self._soil_sc[cell]
+            wp = self._soil_wp[cell]
+            hgw = self._soil_hgw[cell]
+            beta = self._soil_beta[cell]
+            Ks = self._soil_Ks[cell]
+            if self._vegtype[cell] == 0:   # 0 - GRASS
+                sc = scc*self._fr[cell]+(1-self._fr[cell])*fc
+            else:
+                sc = scc
+
+            # Infiltration capacity
+            Inf_cap = (self._soil_Ib[cell]*(1-self._vegcover[cell]) +
+                       self._soil_Iv[cell]*self._vegcover[cell])
+            # Interception capacity
+            Int_cap = min(self._vegcover[cell]*self._interception_cap[cell],
+                          P*self._vegcover[cell])
+            # Effective precipitation depth
+            Peff = max((P + max(runon, 0.) - Int_cap), 0.)
+            mu = (Ks/1000.0)/(pc*ZR*(np.exp(beta*(1.-fc))-1.))
+
+            if self._vegtype[cell] == 3:
+                Ep = max((fbare*self._pet_g[cell]), 0.0001)
+            else:
+                Ep = max((self._PET[cell]*self._fr[cell] +
+                         fbare*self._pet_g[cell]*(1.-self._fr[cell])) -
+                         Int_cap, 0.0001)  # mm/d
+            self._ETmax[cell] = Ep
+            nu = ((Ep / 24.) / 1000.) / (pc*ZR)   # Loss function parameter
+            # Loss function parameter
+            nuw = ((self._soil_Ew/24.)/1000.)/(pc*ZR)
+            # Precipitation Intensity
+            precip_int = Peff/Tr
+            self._precip_int[cell] = precip_int
+            # Time to saturation Ts
+            if precip_int <= 0.:
+                Ts = np.inf
+            else:
+                Ts = (((1 - self._SO[cell]) * (pc * ZR * 1000.)) /
+                      (precip_int*(1-np.exp((-1)*Inf_cap/precip_int))))
+            self._ts[cell] = Ts
+            # Computing runoff
+            # If using Poisson storms with Tr = 0, (Precip_int * Tr = Precip)
+            if Tr == 0.:
+                self._runoff[cell] = max((Peff - Inf_cap), 0.)
+                sini = min(self._SO[cell] + ((Peff -
+                           self._runoff[cell])/(pc*ZR*1000.)), 1.)
+            # If using regular storms with (Tr != 0.)
+            elif Tr < Ts:
+                self._runoff[cell] = max((precip_int - Inf_cap)*Tr, 0.)
+                sini = min(self._SO[cell] + ((precip_int * Tr -
+                           self._runoff[cell])/(pc*ZR*1000.)), 1.)
+            else:
+                sini = 1
+                self._runoff[cell] = max(((precip_int-Inf_cap)*Ts +
+                                          (precip_int*(Tr-Ts))), 0.)
+
+            if sini >= fc:
+                tfc = (1./(beta*(mu-nu)))*(beta*(fc-sini) + np.log((
+                       nu-mu+mu*np.exp(beta*(sini-fc)))/nu))
+                tsc = ((fc-sc)/nu)+tfc
+                twp = ((sc-wp)/(nu-nuw))*np.log(nu/nuw)+tsc
+
+                if Tb < tfc:
+                    s = abs(sini-(1./beta)*np.log(((nu-mu+mu *
+                            np.exp(beta*(sini-fc)))*np.exp(beta*(nu-mu)*Tb) -
+                            mu*np.exp(beta*(sini-fc)))/(nu-mu)))
+
+                    self._D[cell] = ((pc*ZR*1000.)*(sini-s))-(Tb*(Ep/24.))
+                    self._ETA[cell] = (Tb*(Ep/24.))
+
+                elif Tb >= tfc and Tb < tsc:
+                    s = fc-(nu*(Tb-tfc))
+                    self._D[cell] = ((pc*ZR*1000.)*(sini-fc))-((tfc)*(Ep/24.))
+                    self._ETA[cell] = (Tb*(Ep/24.))
+
+                elif Tb >= tsc and Tb < twp:
+                    s = (wp+(sc-wp)*((nu/(nu-nuw))*np.exp((-1)*((nu-nuw) /
+                         (sc-wp))*(Tb-tsc))-(nuw/(nu-nuw))))
+                    self._D[cell] = ((pc*ZR*1000.)*(sini-fc))-(tfc*Ep/24.)
+                    self._ETA[cell] = (1000.*ZR*pc*(sini-s))-self._D[cell]
+
+                else:
+                    s = (hgw+(wp-hgw)*np.exp((-1)*(nuw/(wp-hgw)) *
+                         max(Tb-twp, 0.)))
+                    self._D[cell] = ((pc*ZR*1000.)*(sini-fc))-(tfc*Ep/24.)
+                    self._ETA[cell] = (1000.*ZR*pc*(sini-s))-self._D[cell]
+
+            elif sini < fc and sini >= sc:
+                tfc = 0.
+                tsc = (sini-sc)/nu
+                twp = ((sc-wp)/(nu-nuw))*np.log(nu/nuw)+tsc
+
+                if Tb < tsc:
+                    s = sini - nu*Tb
+                    self._D[cell] = 0.
+                    self._ETA[cell] = 1000.*ZR*pc*(sini-s)
+
+                elif Tb >= tsc and Tb < twp:
+                    s = (wp+(sc-wp)*((nu/(nu-nuw))*np.exp((-1) *
+                         ((nu-nuw)/(sc-wp))*(Tb-tsc))-(nuw/(nu-nuw))))
+                    self._D[cell] = 0
+                    self._ETA[cell] = (1000.*ZR*pc*(sini-s))
+
+                else:
+                    s = hgw+(wp-hgw)*np.exp((-1)*(nuw/(wp-hgw))*(Tb-twp))
+                    self._D[cell] = 0.
+                    self._ETA[cell] = (1000.*ZR*pc*(sini-s))
+
+            elif sini < sc and sini >= wp:
+                tfc = 0
+                tsc = 0
+                twp = (((sc-wp)/(nu-nuw))*np.log(1+(nu-nuw)*(sini-wp) /
+                       (nuw*(sc-wp))))
+
+                if Tb < twp:
+                    s = (wp+((sc-wp)/(nu-nuw))*((np.exp((-1)*((nu-nuw) /
+                         (sc-wp))*Tb))*(nuw+((nu-nuw)/(sc-wp))*(sini-wp))-nuw))
+                    self._D[cell] = 0.
+                    self._ETA[cell] = (1000.*ZR*pc*(sini-s))
+
+                else:
+                    s = hgw+(wp-hgw)*np.exp((-1)*(nuw/(wp-hgw))*(Tb-twp))
+                    self._D[cell] = 0.
+                    self._ETA[cell] = (1000.*ZR*pc*(sini-s))
+
+            else:
+                tfc = 0.
+                tsc = 0.
+                twp = 0.
+
+                s = hgw+(sini-hgw)*np.exp((-1)*(nuw/(wp-hgw))*Tb)
+                self._D[cell] = 0.
+                self._ETA[cell] = (1000.*ZR*pc*(sini-s))
+
+            self._water_stress[cell] = min(((max(((sc - (s+sini)/2.) /
+                                           (sc - wp)), 0.))**4.), 1.0)
+            self._S[cell] = s
+            self._SO[cell] = s
+            self._Sini[cell] = sini
+
+        current_time += (Tb+Tr)/(24.*365.25)
+        return current_time
diff --git a/ecohydrology_gridhydromet/supporting_files/vegetation_dynamics_lb_pap.py b/ecohydrology_gridhydromet/supporting_files/vegetation_dynamics_lb_pap.py
new file mode 100644
index 0000000..ceea4c9
--- /dev/null
+++ b/ecohydrology_gridhydromet/supporting_files/vegetation_dynamics_lb_pap.py
@@ -0,0 +1,473 @@
+
+from landlab import Component
+from landlab.utils.decorators import use_file_name_or_kwds
+import numpy as np
+
+_VALID_METHODS = set(['Grid'])
+
+
+def assert_method_is_valid(method):
+    if method not in _VALID_METHODS:
+        raise ValueError('%s: Invalid method name' % method)
+
+
+class Vegetation(Component):
+    """
+    Landlab component that simulates net primary productivity, biomass
+    and leaf area index at each cell based on inputs of root-zone
+    average soil moisture.
+
+    .. codeauthor:: Sai Nudurupati and Erkan Istanbulluoglu
+
+    Construction::
+        Vegetation(grid, Blive_init=102., Bdead_init=450.,
+            ETthreshold_up=3.8, ETthreshold_down=6.8, Tdmax=10., w=0.55,
+            WUE_grass=0.01, LAI_max_grass=2., cb_grass=0.0047, cd_grass=0.009,
+            ksg_grass=0.012, kdd_grass=0.013, kws_grass=0.02,
+            WUE_shrub=0.0025, LAI_max_shrub=2., cb_shrub=0.004, cd_shrub=0.01,
+            ksg_shrub=0.002, kdd_shrub=0.013, kws_shrub=0.02,
+            WUE_tree=0.0045, LAI_max_tree=4., cb_tree=0.004, cd_tree=0.01,
+            ksg_tree=0.002, kdd_tree=0.013, kws_tree=0.01,
+            WUE_bare=0.01, LAI_max_bare=0.01, cb_bare=0.0047, cd_bare=0.009,
+            ksg_bare=0.012, kdd_bare=0.013, kws_bare=0.02)
+
+    Parameters
+    ----------
+    grid: RasterModelGrid
+        A grid.
+    Blive_init: float, optional
+        Initial value for vegetation__live_biomass. Converted to field.
+    Bdead_init: float, optional
+        Initial value for vegetation__dead_biomass. Coverted to field.
+    ETthreshold_up: float, optional
+        Potential Evapotranspiration (PET) threshold for
+        growing season (mm/d).
+    ETthreshold_down: float, optional
+        PET threshold for dormant season (mm/d).
+    Tdmax: float, optional
+        Constant for dead biomass loss adjustment (mm/d).
+    w: float, optional
+        Conversion factor of CO2 to dry biomass (Kg DM/Kg CO2).
+    WUE: float, optional
+        Water Use Efficiency - ratio of water used in plant water
+        lost by the plant through transpiration (KgCO2Kg-1H2O).
+    LAI_max: float, optional
+        Maximum leaf area index (m2/m2).
+    cb: float, optional
+        Specific leaf area for green/live biomass (m2 leaf g-1 DM).
+    cd: float, optional
+        Specific leaf area for dead biomass (m2 leaf g-1 DM).
+    ksg: float, optional
+        Senescence coefficient of green/live biomass (d-1).
+    kdd: float, optional
+        Decay coefficient of aboveground dead biomass (d-1).
+    kws: float, optional
+        Maximum drought induced foliage loss rate (d-1).
+
+    Examples
+    --------
+    >>> from landlab import RasterModelGrid
+    >>> from landlab.components import Vegetation
+
+    Create a grid on which to simulate vegetation dynamics.
+
+    >>> grid = RasterModelGrid((5,4), spacing=(0.2, 0.2))
+
+    The grid will need some input data. To check the names of the fields
+    that provide the input to this component, use the *input_var_names*
+    class property.
+
+    >>> sorted(Vegetation.input_var_names)  # doctest: +NORMALIZE_WHITESPACE
+    ['surface__evapotranspiration',
+     'surface__potential_evapotranspiration_30day_mean',
+     'surface__potential_evapotranspiration_rate',
+     'vegetation__plant_functional_type',
+     'vegetation__water_stress']
+
+    >>> sorted(Vegetation.units) # doctest: +NORMALIZE_WHITESPACE
+    [('surface__evapotranspiration', 'mm'),
+     ('surface__potential_evapotranspiration_30day_mean', 'mm'),
+     ('surface__potential_evapotranspiration_rate', 'mm'),
+     ('vegetation__cover_fraction', 'None'),
+     ('vegetation__dead_biomass', 'g m^-2 d^-1'),
+     ('vegetation__dead_leaf_area_index', 'None'),
+     ('vegetation__live_biomass', 'g m^-2 d^-1'),
+     ('vegetation__live_leaf_area_index', 'None'),
+     ('vegetation__plant_functional_type', 'None'),
+     ('vegetation__water_stress', 'None')]
+
+    Provide all the input fields.
+
+    >>> grid['cell']['vegetation__plant_functional_type']= (
+    ...         np.zeros(grid.number_of_cells, dtype=int))
+    >>> grid['cell']['surface__evapotranspiration'] = (
+    ...         0.2 * np.ones(grid.number_of_cells))
+    >>> grid['cell']['surface__potential_evapotranspiration_rate']= np.array([
+    ...         0.25547770, 0.25547770, 0.22110221,
+    ...         0.22110221, 0.24813062, 0.24813062])
+    >>> grid['cell']['surface__potential_evapotranspiration_30day_mean']= (
+    ...        np.array([0.25547770, 0.25547770, 0.22110221,
+    ...                  0.22110221, 0.24813062, 0.24813062]))
+    >>> grid['cell']['vegetation__water_stress'] = (
+    ...        0.01 * np.ones(grid.number_of_cells))
+
+    Instantiate the 'Vegetation' component.
+
+    >>> Veg = Vegetation(grid)
+
+    >>> Veg.grid.number_of_cell_rows
+    3
+    >>> Veg.grid.number_of_cell_columns
+    2
+    >>> Veg.grid is grid
+    True
+    >>> import numpy as np
+    >>> sorted(Vegetation.output_var_names)  # doctest: +NORMALIZE_WHITESPACE
+    ['vegetation__cover_fraction',
+     'vegetation__dead_biomass',
+     'vegetation__dead_leaf_area_index',
+     'vegetation__live_biomass',
+     'vegetation__live_leaf_area_index']
+
+    >>> np.all(grid.at_cell['vegetation__live_leaf_area_index'] == 0.)
+    True
+
+    >>> Veg.update()
+
+    >>> np.all(grid.at_cell['vegetation__live_leaf_area_index'] == 0.)
+    False
+    """
+    _name = 'Vegetation'
+
+    _input_var_names = (
+        'surface__evapotranspiration',
+        'vegetation__water_stress',
+        'surface__potential_evapotranspiration_rate',
+        'surface__potential_evapotranspiration_30day_mean',
+        'vegetation__plant_functional_type',
+    )
+
+    _output_var_names = (
+        'vegetation__live_leaf_area_index',
+        'vegetation__dead_leaf_area_index',
+        'vegetation__cover_fraction',
+        'vegetation__live_biomass',
+        'vegetation__dead_biomass',
+    )
+
+    _var_units = {
+        'vegetation__live_leaf_area_index': 'None',
+        'vegetation__dead_leaf_area_index': 'None',
+        'vegetation__cover_fraction': 'None',
+        'surface__evapotranspiration': 'mm',
+        'surface__potential_evapotranspiration_rate': 'mm',
+        'surface__potential_evapotranspiration_30day_mean': 'mm',
+        'vegetation__water_stress': 'None',
+        'vegetation__live_biomass': 'g m^-2 d^-1',
+        'vegetation__dead_biomass': 'g m^-2 d^-1',
+        'vegetation__plant_functional_type': 'None',
+    }
+
+    _var_mapping = {
+        'vegetation__live_leaf_area_index': 'cell',
+        'vegetation__dead_leaf_area_index': 'cell',
+        'vegetation__cover_fraction': 'cell',
+        'surface__evapotranspiration': 'cell',
+        'surface__potential_evapotranspiration_rate': 'cell',
+        'surface__potential_evapotranspiration_30day_mean': 'cell',
+        'vegetation__water_stress': 'cell',
+        'vegetation__live_biomass': 'cell',
+        'vegetation__dead_biomass': 'cell',
+        'vegetation__plant_functional_type': 'cell',
+    }
+
+    _var_doc = {
+        'vegetation__live_leaf_area_index':
+            'one-sided green leaf area per unit ground surface area',
+        'vegetation__dead_leaf_area_index':
+            'one-sided dead leaf area per unit ground surface area',
+        'vegetation__cover_fraction':
+            'fraction of land covered by vegetation',
+        'surface__evapotranspiration':
+            'actual sum of evaporation and plant transpiration',
+        'surface__potential_evapotranspiration_rate':
+            'potential sum of evaporation and platnt transpiration',
+        'surface__potential_evapotranspiration_30day_mean':
+            '30 day mean of surface__potential_evapotranspiration',
+        'vegetation__water_stress':
+            'parameter that represents nonlinear effects of water defecit \
+             on plants',
+        'vegetation__live_biomass':
+            'weight of green organic mass per unit area - measured in terms \
+             of dry matter',
+        'vegetation__dead_biomass':
+            'weight of dead organic mass per unit area - measured in terms \
+             of dry matter',
+        'vegetation__plant_functional_type':
+            'classification of plants (int), grass=0, shrub=1, tree=2, \
+             bare=3, shrub_seedling=4, tree_seedling=5',
+    }
+
+    @use_file_name_or_kwds
+    def __init__(self, grid, Blive_init=102., Bdead_init=450.,
+                 ETthreshold_up=3.8, ETthreshold_down=6.8, Tdmax=10., w=0.55,
+                 WUE_grass=0.01, LAI_max_grass=2., cb_grass=0.0047,
+                 cd_grass=0.009, ksg_grass=0.012, kdd_grass=0.013,
+                 kws_grass=0.02,
+                 WUE_shrub=0.0025, LAI_max_shrub=2., cb_shrub=0.004,
+                 cd_shrub=0.01, ksg_shrub=0.002, kdd_shrub=0.013,
+                 kws_shrub=0.02,
+                 WUE_tree=0.0045, LAI_max_tree=4., cb_tree=0.004, cd_tree=0.01,
+                 ksg_tree=0.002, kdd_tree=0.013, kws_tree=0.01,
+                 WUE_bare=0.01, LAI_max_bare=0.01, cb_bare=0.0047,
+                 cd_bare=0.009, ksg_bare=0.012, kdd_bare=0.013, kws_bare=0.02,
+                 **kwds):
+        """
+        Parameters
+        ----------
+        grid: RasterModelGrid
+            A grid.
+        Blive_init: float, optional
+            Initial value for vegetation__live_biomass. Converted to field.
+        Bdead_init: float, optional
+            Initial value for vegetation__dead_biomass. Coverted to field.
+        ETthreshold_up: float, optional
+            Potential Evapotranspiration (PET) threshold for
+            growing season (mm/d).
+        ETthreshold_down: float, optional
+            PET threshold for dormant season (mm/d).
+        Tdmax: float, optional
+            Constant for dead biomass loss adjustment (mm/d).
+        w: float, optional
+            Conversion factor of CO2 to dry biomass (Kg DM/Kg CO2).
+        WUE: float, optional
+            Water Use Efficiency - ratio of water used in plant water
+            lost by the plant through transpiration (KgCO2Kg-1H2O).
+        LAI_max: float, optional
+            Maximum leaf area index (m2/m2).
+        cb: float, optional
+            Specific leaf area for green/live biomass (m2 leaf g-1 DM).
+        cd: float, optional
+            Specific leaf area for dead biomass (m2 leaf g-1 DM).
+        ksg: float, optional
+            Senescence coefficient of green/live biomass (d-1).
+        kdd: float, optional
+            Decay coefficient of aboveground dead biomass (d-1).
+        kws: float, optional
+            Maximum drought induced foliage loss rate (d-1).
+        """
+        self._method = kwds.pop('method', 'Grid')
+
+        assert_method_is_valid(self._method)
+
+        super(Vegetation, self).__init__(grid)
+
+        self.initialize(Blive_init=Blive_init, Bdead_init=Bdead_init,
+                        ETthreshold_up=ETthreshold_up,
+                        ETthreshold_down=ETthreshold_down, Tdmax=Tdmax, w=w,
+                        WUE_grass=WUE_grass, LAI_max_grass=LAI_max_grass,
+                        cb_grass=cb_grass, cd_grass=cd_grass,
+                        ksg_grass=ksg_grass, kdd_grass=kdd_grass,
+                        kws_grass=kws_grass, WUE_shrub=WUE_shrub,
+                        LAI_max_shrub=LAI_max_shrub, cb_shrub=cb_shrub,
+                        cd_shrub=cd_shrub, ksg_shrub=ksg_shrub,
+                        kdd_shrub=kdd_shrub, kws_shrub=kws_shrub,
+                        WUE_tree=WUE_tree, LAI_max_tree=LAI_max_tree,
+                        cb_tree=cb_tree, cd_tree=cd_tree, ksg_tree=ksg_tree,
+                        kdd_tree=kdd_tree, kws_tree=kws_tree,
+                        WUE_bare=WUE_bare, LAI_max_bare=LAI_max_bare,
+                        cb_bare=cb_bare, cd_bare=cd_bare, ksg_bare=ksg_bare,
+                        kdd_bare=kdd_bare, kws_bare=kws_bare, **kwds)
+
+        for name in self._input_var_names:
+            if name not in self.grid.at_cell:
+                self.grid.add_zeros('cell', name, units=self._var_units[name])
+
+        for name in self._output_var_names:
+            if name not in self.grid.at_cell:
+                self.grid.add_zeros('cell', name, units=self._var_units[name])
+
+        self._cell_values = self.grid['cell']
+
+        self._Blive_ini = self._Blive_init * np.ones(self.grid.number_of_cells)
+        self._Bdead_ini = self._Bdead_init * np.ones(self.grid.number_of_cells)
+
+    def initialize(self, Blive_init=102., Bdead_init=450., ETthreshold_up=3.8,
+                   ETthreshold_down=6.8, Tdmax=10., w=0.55,
+                   WUE_grass=0.01, LAI_max_grass=2., cb_grass=0.0047,
+                   cd_grass=0.009, ksg_grass=0.012, kdd_grass=0.013,
+                   kws_grass=0.02,
+                   WUE_shrub=0.0025, LAI_max_shrub=2., cb_shrub=0.004,
+                   cd_shrub=0.01, ksg_shrub=0.002, kdd_shrub=0.013,
+                   kws_shrub=0.02,
+                   WUE_tree=0.0045, LAI_max_tree=4., cb_tree=0.004,
+                   cd_tree=0.01, ksg_tree=0.002, kdd_tree=0.013, kws_tree=0.01,
+                   WUE_bare=0.01, LAI_max_bare=0.01, cb_bare=0.0047,
+                   cd_bare=0.009, ksg_bare=0.012, kdd_bare=0.013,
+                   kws_bare=0.02, **kwds):
+        # GRASS = 0; SHRUB = 1; TREE = 2; BARE = 3;
+        # SHRUBSEEDLING = 4; TREESEEDLING = 5
+        """
+        Parameters
+        ----------
+        grid: RasterModelGrid
+            A grid.
+        Blive_init: float, optional
+            Initial value for vegetation__live_biomass. Converted to field.
+        Bdead_init: float, optional
+            Initial value for vegetation__dead_biomass. Coverted to field.
+        ETthreshold_up: float, optional
+            Potential Evapotranspiration (PET) threshold for
+            growing season (mm/d).
+        ETthreshold_down: float, optional
+            PET threshold for dormant season (mm/d).
+        Tdmax: float, optional
+            Constant for dead biomass loss adjustment (mm/d).
+        w: float, optional
+            Conversion factor of CO2 to dry biomass (Kg DM/Kg CO2).
+        WUE: float, optional
+            Water Use Efficiency - ratio of water used in plant water
+            lost by the plant through transpiration (KgCO2Kg-1H2O).
+        LAI_max: float, optional
+            Maximum leaf area index (m2/m2).
+        cb: float, optional
+            Specific leaf area for green/live biomass (m2 leaf g-1 DM).
+        cd: float, optional
+            Specific leaf area for dead biomass (m2 leaf g-1 DM).
+        ksg: float, optional
+            Senescence coefficient of green/live biomass (d-1).
+        kdd: float, optional
+            Decay coefficient of aboveground dead biomass (d-1).
+        kws: float, optional
+            Maximum drought induced foliage loss rate (d-1).
+        """
+        self._vegtype = self.grid['cell']['vegetation__plant_functional_type']
+        self._WUE = np.choose(self._vegtype, [
+            WUE_grass, WUE_shrub, WUE_tree, WUE_bare, WUE_shrub, WUE_tree])
+        # Water Use Efficiency  KgCO2kg-1H2O
+        self._LAI_max = np.choose(self._vegtype, [
+            LAI_max_grass, LAI_max_shrub, LAI_max_tree,
+            LAI_max_bare, LAI_max_shrub, LAI_max_tree])
+        # Maximum leaf area index (m2/m2)
+        self._cb = np.choose(self._vegtype, [
+            cb_grass, cb_shrub, cb_tree, cb_bare, cb_shrub, cb_tree])
+        # Specific leaf area for green/live biomass (m2 leaf g-1 DM)
+        self._cd = np.choose(self._vegtype, [
+            cd_grass, cd_shrub, cd_tree, cd_bare, cd_shrub, cd_tree])
+        # Specific leaf area for dead biomass (m2 leaf g-1 DM)
+        self._ksg = np.choose(self._vegtype, [
+            ksg_grass, ksg_shrub, ksg_tree, ksg_bare, ksg_shrub, ksg_tree])
+        # Senescence coefficient of green/live biomass (d-1)
+        self._kdd = np.choose(self._vegtype, [
+            kdd_grass, kdd_shrub, kdd_tree, kdd_bare, kdd_shrub, kdd_tree])
+        # Decay coefficient of aboveground dead biomass (d-1)
+        self._kws = np.choose(self._vegtype, [
+            kws_grass, kws_shrub, kws_tree, kws_bare, kws_shrub, kws_tree])
+        # Maximum drought induced foliage loss rates (d-1)
+        self._Blive_init = Blive_init
+        self._Bdead_init = Bdead_init
+        self._ETthresholdup = ETthreshold_up    # Growth threshold (mm/d)
+        self._ETthresholddown = ETthreshold_down   # Dormancy threshold (mm/d)
+        self._Tdmax = Tdmax         # Constant for dead biomass loss adjustment
+        self._w = w                  # Conversion factor of CO2 to dry biomass
+
+        self._Blive_ini = self._Blive_init * np.ones(self.grid.number_of_cells)
+        self._Bdead_ini = self._Bdead_init * np.ones(self.grid.number_of_cells)
+
+    def update(self, PETthreshold_switch=0, Tb=24., Tr=0.01, **kwds):
+        """
+        Update fields with current loading conditions.
+
+        Parameters
+        ----------
+        Tr: float, optional
+            Storm duration (hours).
+        Tb: float, optional
+            Inter-storm duration (hours).
+        """
+        PETthreshold_ = PETthreshold_switch
+        PET = self._cell_values['surface__potential_evapotranspiration_rate']
+        PET30_ = self._cell_values[
+            'surface__potential_evapotranspiration_30day_mean']
+        ActualET = self._cell_values['surface__evapotranspiration']
+        Water_stress = self._cell_values['vegetation__water_stress']
+
+        self._LAIlive = self._cell_values['vegetation__live_leaf_area_index']
+        self._LAIdead = self._cell_values['vegetation__dead_leaf_area_index']
+        self._Blive = self._cell_values['vegetation__live_biomass']
+        self._Bdead = self._cell_values['vegetation__dead_biomass']
+        self._VegCov = self._cell_values['vegetation__cover_fraction']
+
+        if PETthreshold_ == 1:
+            PETthreshold = self._ETthresholdup
+        else:
+            PETthreshold = self._ETthresholddown
+
+        for cell in range(0, self.grid.number_of_cells):
+
+            WUE = self._WUE[cell]
+            LAImax = self._LAI_max[cell]
+            cb = self._cb[cell]
+            cd = self._cd[cell]
+            ksg = self._ksg[cell]
+            kdd = self._kdd[cell]
+            kws = self._kws[cell]
+            # ETdmax = self._ETdmax[cell]
+            LAIlive = min(cb*self._Blive_ini[cell], LAImax)
+            LAIdead = min(cd * self._Bdead_ini[cell], (LAImax -
+                          LAIlive))
+            NPP = max((ActualET[cell]/(Tb+Tr)) *
+                      WUE*24.*self._w*1000, 0.001)
+
+            if self._vegtype[cell] == 0:
+                if PET30_[cell] > PETthreshold:
+                                # Growing Season
+                    Bmax = (LAImax - LAIdead)/cb
+                    Yconst = (1/((1/Bmax)+(((kws*Water_stress[cell]) +
+                              ksg)/NPP)))
+                    Blive = ((self._Blive_ini[cell] - Yconst) *
+                             np.exp(-(NPP/Yconst) * ((Tb+Tr)/24.)) + Yconst)
+                    Bdead = ((self._Bdead_ini[cell] + (Blive - max(Blive *
+                             np.exp(-1 * ksg * Tb/24.), 0.00001))) *
+                             np.exp(-1 * kdd *
+                             min(PET[cell]/self._Tdmax, 1.) * Tb/24.))
+                else:                                 # Senescense
+                    Blive = max(self._Blive_ini[cell] * np.exp((-2) * ksg *
+                                Tb/24.), 1)
+                    Bdead = max((self._Bdead_ini[cell]+(self._Blive_ini[cell] -
+                                (max(self._Blive_ini[cell]*np.exp((-2) *
+                                 ksg*Tb/24.), 0.000001))))*np.exp((-1)*kdd *
+                                 min(PET[cell]/self._Tdmax, 1.) * Tb/24.), 0.)
+
+            elif self._vegtype[cell] == 3:
+                Blive = 0.
+                Bdead = 0.
+
+            else:
+                Bmax = LAImax/cb
+                Yconst = (1./((1./Bmax)+(((kws*Water_stress[cell]) +
+                          ksg)/NPP)))
+                Blive = ((self._Blive_ini[cell] - Yconst) *
+                         np.exp(-(NPP/Yconst) * ((Tb+Tr)/24.)) + Yconst)
+                Bdead = ((self._Bdead_ini[cell] + (Blive - max(Blive *
+                         np.exp(-ksg * Tb/24.), 0.00001))) *
+                         np.exp(-kdd * min(PET[cell]/self._Tdmax, 1.) *
+                         Tb/24.))
+
+            LAIlive = min(cb * (Blive + self._Blive_ini[cell])/2., LAImax)
+            LAIdead = min(cd * (Bdead + self._Bdead_ini[cell])/2.,
+                          (LAImax - LAIlive))
+            if self._vegtype[cell] == 0:
+                Vt = 1. - np.exp(-0.75 * (LAIlive + LAIdead))
+            else:
+                # Vt = 1 - np.exp(-0.75 * LAIlive)
+                Vt = 1.
+
+            self._LAIlive[cell] = LAIlive
+            self._LAIdead[cell] = LAIdead
+            self._VegCov[cell] = Vt
+            self._Blive[cell] = Blive
+            self._Bdead[cell] = Bdead
+
+        self._Blive_ini = self._Blive
+        self._Bdead_ini = self._Bdead
diff --git a/explore_flow_routing/explore_routing_tutorial.ipynb b/explore_flow_routing/explore_routing_tutorial.ipynb
new file mode 100644
index 0000000..b33a518
--- /dev/null
+++ b/explore_flow_routing/explore_routing_tutorial.ipynb
@@ -0,0 +1,1224 @@
+{
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "<img src=\"http://landlab.github.io/assets/Landlab-logo.png\"\n",
+    "style=\"float:left;width:150px;padding:0px\">  "
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "# Exploring overland flow routing with Landlab\n",
+    "\n",
+    "## Objectives\n",
+    "* Introduce the shallow water equation, modeling domains, model steps, and curriculum questions.\n",
+    "* Use Landlab to set up a comparative model experiment.\n",
+    "* Run Landlab flow routing components for different conditions.\n",
+    "* Explore flow sensitivity to model selection and parameter values using model outputs.\n",
+    "* Save results to a new HydroShare resource."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## About this notebook\n",
+    "\n",
+    "### Data Science and Cyberinfrastructure\n",
+    "\n",
+    "This notebook is a computational narrative using the [Landlab](http://landlab.github.io) Python-based modeling toolkit that allows scientists and students to build numerical models for earth surface processes. This notebook also utilizes the following open source research software libraries: [NumPy](http://www.numpy.org), [Matplotlib](https://matplotlib.org), [Pandas](https://pandas.pydata.org), and [HydroShare](https://www.hydroshare.org). The software environment is provided by the [CUAHSI JupyterHub server](https://jupyter.cuahsi.org). \n",
+    "<hr>\n",
+    "\n",
+    "**[Click here for additional Landlab tutorials](https://github.com/landlab/landlab/wiki/Tutorials)**\n",
+    "<hr>\n",
+    "\n",
+    "### How to use this notebook\n",
+    "\n",
+    "#### Navigation\n",
+    "\n",
+    "This notebook is made of text cells, including the cell you are reading now, and code cells. The order in which you run the cells matters. The notebook cells are generally designed to be run top to bottom.\n",
+    "\n",
+    "Code cells have the following characters to their left:\n",
+    "* `In [ ]:` is a cell yet to be run.\n",
+    "* `In [*]:` is a cell that is running.\n",
+    "* `In [n]:` where `n` is an integer, is a cell that has run. The number indicates the sequence the cell was run, i.e. the first cell run is `n`=1.\n",
+    "\n",
+    "The code in a notebook is ran one cell at a time. Place your cursor in a code cell, hold down `shift` and then also press `enter`. You can even experiment with typing your own code into a cell and running it.\n",
+    "\n",
+    "Alternatively, you can run groups of cells by clicking `Cell` on the menu above and then selecting among the various run options. This is also where you can clear outputs from previous runs.\n",
+    "\n",
+    "You can also `shift` + `enter` in a text cell to advance your place in the notebook.\n",
+    "\n",
+    "See [this page](https://github.com/landlab/tutorials/blob/master/README.md) for more instructions on how to use an interactive Jupyter notebook.\n",
+    "\n",
+    "#### Output\n",
+    "\n",
+    "Every time you run this notebook the model outputs you generate are saved in your personal user space in the virtual computer through Hydroshare. These outputs are available for you to plot and explore using the plotting functions. We will plot model output at the end of this tutorial. \n",
+    "\n",
+    "Your final results can be saved to Hydroshare as a resource (see section 9.0 below). Unwanted model output can be deleted at anytime from your space, including by running the final code block of this tutorial. \n",
+    "\n",
+    "If an error occurs, click on the upper menu bar item, `Kernel` and then select `Restart and Clear Outputs` in the drop down menu.\n",
+    "\n",
+    "### Acknowledgements\n",
+    "\n",
+    "This notebook was developed from code written by Jordan Adams as part of her Ph.D. disseration at Tulane University and Amanda Manaster as a tutorial for a Landlab workshop at the Geophysical Society of America Annual Meeting, Seattle, WA, 2018. Nathan Lyons provided code and technical support on animations.   \n",
+    "\n",
+    "Use or citation of this notebook should also reference:\n",
+    "\n",
+    "Adams, J. M., Gasparini, N. M., Hobley, D. E. J., Tucker, G. E., Hutton, E. W. H., Nudurupati, S. S., and Istanbulluoglu, E.: The Landlab v1.0 OverlandFlow component: a Python tool for computing shallow-water flow across watersheds, Geosci. Model Dev., 10, 1645-1663, https://doi.org/10.5194/gmd-10-1645-2017, 2017."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## 1.0. Introduction\n",
+    "\n",
+    "### 1.1. Theory\n",
+    "\n",
+    "The 1-D Saint Venant equation for transient shallow water flow is in the core of most hydrodynamics models:\n",
+    "\n",
+    "$$ \\frac{\\partial Q}{\\partial t} + \\frac{\\partial}{\\partial x}\\left(\\frac{Q^2}{A_{xs}}\\right) + gA_{xs} \\frac{\\partial (h+z)}{\\partial x} + \\frac{gn^2 \\lvert{Q}\\rvert Q}{R^{4/3}A_{xs}} = 0 \\space\\space\\space\\space\\space\\space\\space\\space\\space\\space\\space\\space\\space\\space\\space\\space\\space\\space\\space\\space(1)$$\n",
+    "    \n",
+    "\n",
+    "\n",
+    "where      \n",
+    "&nbsp;&nbsp;&nbsp;&nbsp;$Q = discharge \\left[L^3{T^{-1}}\\right]$  \n",
+    "&nbsp;&nbsp;&nbsp;&nbsp;$t = time \\left[T\\right]$  \n",
+    "&nbsp;&nbsp;&nbsp;&nbsp;$x = location \\space in \\space space \\left[L\\right]$   \n",
+    "&nbsp;&nbsp;&nbsp;&nbsp;$g = acceleration \\space due \\space to \\space gravity \\left[L{T^{-2}}\\right]$  \n",
+    "&nbsp;&nbsp;&nbsp;&nbsp;$h = water \\space depth \\left[L\\right]$  \n",
+    "&nbsp;&nbsp;&nbsp;&nbsp;$z = bed \\space elevation \\left[L\\right]$  \n",
+    "&nbsp;&nbsp;&nbsp;&nbsp;$n = Manning's \\space roughness \\space coeffiecient \\left[T{L^{{-1}/{3}}}\\right]$  \n",
+    "&nbsp;&nbsp;&nbsp;&nbsp;$R = hydraulic \\space radius \\left[L\\right]$  \n",
+    "&nbsp;&nbsp;&nbsp;&nbsp;$A_{xs} = cross-sectional \\space area \\left[L^2\\right]$ \n",
+    "\n",
+    "From left to right, the terms in Equation (1) represent local acceleration, convective acceleration, gradients of fluid pressure and bed elevation, and friction. Because this equation is difficult (i.e., almost impossible) to solve explicitly, approximations are commonly used. The simplest approximation, the Kinematic Wave model, only retains the friction term, making it the simplest approximation one can use. The Landlab `KinwaveImplicitOverlandFlow` component provides a 2-D locally implicit kinematic wave solution in which energy slope is assumed to be equal to the bed slope. \n",
+    "\n",
+    "The diffusive wave model developed from Equation (1) neglects the first two terms. The Landlab `OverlandFlow` component (Adams et al. 2017) adapts a 2D hydrodynamic algorithm similar to the diffusive approximation with negligible advection term (de Almeida et al., 2012) used in LISFLOOD-FP. The flow geometry is assumed to be rectangular and of constant flow width. The approximation used in `OverlandFlow` is more accurate but also more computationally expensive than the approximation used in `KinwaveImplicitOverlandFlow`.  \n",
+    "\n",
+    "<small>Click here for the OverlandFlow Component Users Manual: https://github.com/landlab/landlab/wiki/OverlandFlow-Component-Users-Manual</a></small><br>\n",
+    "\n",
+    "This tutorial illustrates the use of both the `KinwaveImplicitOverlandFlow` and the `OverlandFlow` components to map overland flow depth across two modeling domains and plot a flow hydrograph at the outlet and several internal nodes. We will use two domains:\n",
+    "\n",
+    "* Spring Creek watershed, CO, USA \n",
+    "* A synthetic landscape modeled with Landlab   \n",
+    "\n",
+    "We will investigate the roles of watershed shape and runoff intensity and duration on the characteristics of hydrographs. Runoff is assumed to be generated at a spatially constant rate on the landscape and flows downhill, driving overland flow and a hydrograph at every location on the landscape. "
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "**Terms:** runoff intensity, runoff duration, peak discharge, hydrograph time to peak, rising limb, falling limb. "
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### 1.2. Model Domains \n",
+    "\n",
+    "The natural catchment used in this tutorial and studied in Adams et al. (2017) is the Spring Creek watershed, a steep 27 km$^2$ watershed located in central Colorado. The digital elevation model (DEM) we will use has a resolution of 30 m. To clearly contrast landscape shape, this notebook uses a modeled \"synthetic\" landscape produced using a model developed in Landlab by coupling hillslope diffusion and stream-power erosion rules. The landscape evolution model was run on a 36 km$^2$ area represented by regular grids with a node spacing of 30 m. \n",
+    "\n",
+    "These watershed domains are available at the Landlab Github site and as Hydroshare resources, and will be imported as part of this exercise.\n",
+    "\n",
+    "### 1.3. Steps to Explore the Model\n",
+    "\n",
+    "We will run the two overland flow models for 6 hours. A storm begins at the onset of the simulation and lasts for 2 hours. We will visualize water depths across the watershed over the course of the simulation and plot hydrographs at the outlet and two internal nodes.\n",
+    "\n",
+    "In repeated model experiments you can investigate the influence of runoff intensity, runoff duration, and surface resistance on streamflow hydrographs. You can organize your approach by running the two models and saving results from each watershed domain before running the models for the other domain.  \n",
+    "\n",
+    "Revisit the 'How to use this notebook' section at the top of the page as needed.\n",
+    "\n",
+    "### 1.4. Questions to consider before running this notebook\n",
+    "\n",
+    "We will focus on the hydrograph characteristics, time to peak, peak discharge, and hydrograph shape. \n",
+    "\n",
+    "1. How do watershed shape and drainage area affect hydrograph characteristics? \n",
+    "2. How does runoff intensity impact the time to peak and peak discharge across the channel network? \n",
+    "3. How does runoff duration impact the time to peak and peak discharge across the channel network? \n",
+    "4. How does surface roughness due to soil and land cover influence hydrograph peak discharge and shape?"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## 2.0 Data Science & Cyberinfrastructure Methods\n",
+    "\n",
+    "### 2.1 Import Landlab components and additional modules"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "from landlab import imshow_grid\n",
+    "from landlab.components import KinwaveImplicitOverlandFlow, OverlandFlow\n",
+    "from landlab.components.flow_accum import (\n",
+    "    FlowAccumulator,\n",
+    "    find_drainage_area_and_discharge,\n",
+    "    flow_accumulation,\n",
+    ")\n",
+    "from landlab.io import read_esri_ascii"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### 2.2 Import standard,  plotting, and numeric modules"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "import copy\n",
+    "import os\n",
+    "\n",
+    "import matplotlib.animation as animation\n",
+    "import matplotlib.image as mgimg\n",
+    "import numpy as np\n",
+    "import pandas as pd\n",
+    "from IPython.display import HTML\n",
+    "from matplotlib import pyplot as plt\n",
+    "\n",
+    "# Set the environment to place plots in Jupyter cells.\n",
+    "%matplotlib inline"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### 2.3 Import HydroShare tools\n",
+    "\n",
+    "The results of the Geoprocessing steps below will be saved in the HydroShare environment using the `hydroshare` library. These are needed to import data from HydroShare and create new HydroShare resources with your results."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "from utilities import hydroshare\n",
+    "\n",
+    "hs = hydroshare.hydroshare()\n",
+    "print(\"This is the list of folders in your directory of this HydroShare resource.\")\n",
+    "os.getcwd()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## 3.0 Landlab Methods\n",
+    "\n",
+    "### 3.1. Use Landlab to read in the ASCII DEM and set boundary conditions "
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# Square Basin\n",
+    "watershed_dem_sq = \"supporting_files/Square_TestBasin.asc\"\n",
+    "(rmg_sq, z_sq) = read_esri_ascii(watershed_dem_sq, name=\"topographic__elevation\")\n",
+    "rmg_sq.set_watershed_boundary_condition(z_sq)\n",
+    "\n",
+    "# Spring Creek Basin\n",
+    "watershed_dem_sc = \"supporting_files/SpringCreek_DEM.asc\"\n",
+    "(rmg_sc, z_sc) = read_esri_ascii(watershed_dem_sc, name=\"topographic__elevation\")\n",
+    "rmg_sc.set_watershed_boundary_condition(z_sc)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### 3.2. Use Landlab to calculate flow accumulation and drainage area  \n",
+    "\n",
+    "Drainage areas derived in this step will be used in selecting internal points for plotting hydrographs. "
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# Square Basin\n",
+    "fa_sq = FlowAccumulator(rmg_sq)\n",
+    "fa_sq.run_one_step()\n",
+    "(da_sq, q_sq) = fa_sq.accumulate_flow()\n",
+    "\n",
+    "# Spring Creek\n",
+    "fa_sc = FlowAccumulator(rmg_sc)\n",
+    "fa_sc.run_one_step()\n",
+    "(da_sc, q_sc) = fa_sc.accumulate_flow()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### 3.3. List and select the nodes with a drainage area greater than a threshold  \n",
+    "\n",
+    "Below we show how you can identify internal nodes in the two domains for plotting streamflow hydrographs. We selected nodes using drainge area thresholds such that the watershed areas above midstream and upstream nodes of the two watersheds are close to each other. This gives us the opportunity to compare hydrographs in relation to watershed shape and watershed relief above each node, given the similar watershed sizes.\n",
+    "\n",
+    "The midstream and upstream node drainage area thresholds can be identified by drainage area ranges in km$^2$. The range used to identify a midstream location should be larger than the range used to identify an upstream location. These internal nodes can be identified by trial using different drainage area values, and plotting the locations selected. After we select the drainage area ranges we will print the node IDs, drainge areas, and elevations of the locations we select and map the locations on the elevation map of the watershed. To continue your project with the Square domain you can skip the steps in 3.3.2 that pertains to Spring Creek and move to 4.0. After modeling flows in the Square domain you will need to come back to 3.3.2. and identify the nodes you would like to include for hydrograph analysis in Spring Creek. "
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "#### 3.3.1 Square Basin: a) set thresholds and b) select output nodes\n",
+    "\n",
+    "We will identify the nodes for the outlet, midstream, and upstream locations for plotting hydrographs using drainage area thresholds for the latter two. Code is written to set the outlet at the node with the largest drainage area. Below we will first identify the outlet node ID, its elevation and drainage area.  "
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "outlet_node_to_sample_sq = np.argmax(rmg_sq.at_node[\"drainage_area\"])\n",
+    "\n",
+    "print(\n",
+    "    \"Outlet Node = \"\n",
+    "    + str(outlet_node_to_sample_sq)\n",
+    "    + \"; Drainage Area = \"\n",
+    "    + str(da_sq[outlet_node_to_sample_sq] / 1000000)\n",
+    "    + \" km^2; Elev = \"\n",
+    "    + str(round(z_sq[outlet_node_to_sample_sq], 1))\n",
+    "    + \" m\"\n",
+    ")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "The midstream and upstream node drainge area thresholds can be identified by drainge area ranges in km$^2$. The range used to identify a midstream location should be larger than the range used to identify an upstream location. These internal nodes can be identified by trial using different drainge area values, and plotting the locations selected. After we select the drainage area ranges we will print the node IDs, drainge areas, and elevations of the locations we select and map the locations on the elevation map of the watershed."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# Set drainge area threshold ranges in km^2 to identify nodes that fall into these ranges.\n",
+    "# Values can be changed to set different locations on the landscape for plotting hydrographs.\n",
+    "midstream_da_upperbound = 20\n",
+    "midstream_da_lowerbound = 12\n",
+    "upstream_da_upperbound = 4\n",
+    "upstream_da_lowerbound = 3\n",
+    "\n",
+    "midstream_node_to_sample_sq = np.where(\n",
+    "    np.logical_and(\n",
+    "        rmg_sq.at_node[\"drainage_area\"] > midstream_da_lowerbound * 1000000,\n",
+    "        rmg_sq.at_node[\"drainage_area\"] < midstream_da_upperbound * 1000000,\n",
+    "    )\n",
+    ")[0][-1]\n",
+    "upstream_node_to_sample_sq = np.where(\n",
+    "    np.logical_and(\n",
+    "        rmg_sq.at_node[\"drainage_area\"] > upstream_da_lowerbound * 1000000,\n",
+    "        rmg_sq.at_node[\"drainage_area\"] < upstream_da_upperbound * 1000000,\n",
+    "    )\n",
+    ")[0][-1]\n",
+    "\n",
+    "print(\n",
+    "    \"Outlet Node = \"\n",
+    "    + str(outlet_node_to_sample_sq)\n",
+    "    + \"; Drainage Area= \"\n",
+    "    + str(da_sq[outlet_node_to_sample_sq] / 1000000)\n",
+    "    + \" km^2; Elev = \"\n",
+    "    + str(round(z_sq[outlet_node_to_sample_sq], 1))\n",
+    "    + \" m\"\n",
+    ")\n",
+    "print(\n",
+    "    \"Midstream Node = \"\n",
+    "    + str(midstream_node_to_sample_sq)\n",
+    "    + \"; Drainage Area= \"\n",
+    "    + str(da_sq[midstream_node_to_sample_sq] / 1000000)\n",
+    "    + \" km^2; Elev = \"\n",
+    "    + str(round(z_sq[midstream_node_to_sample_sq], 1))\n",
+    "    + \" m\"\n",
+    ")\n",
+    "print(\n",
+    "    \"Upstream Node = \"\n",
+    "    + str(upstream_node_to_sample_sq)\n",
+    "    + \"; Drainage Area= \"\n",
+    "    + str(da_sq[upstream_node_to_sample_sq] / 1000000)\n",
+    "    + \" km^2; Elev = \"\n",
+    "    + str(round(z_sq[upstream_node_to_sample_sq], 1))\n",
+    "    + \" m\"\n",
+    ")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# Plot Square Basin\n",
+    "\n",
+    "# set up the figure\n",
+    "fig, ax = plt.subplots(nrows=1, ncols=1, figsize=(6, 6))\n",
+    "ax.xaxis.set_visible(False)\n",
+    "\n",
+    "# plot the DEM\n",
+    "imshow_grid(\n",
+    "    rmg_sq,\n",
+    "    z_sq,\n",
+    "    plot_name=\"Square\",\n",
+    "    color_for_closed=\"white\",\n",
+    "    colorbar_label=\"Elevation (m)\",\n",
+    ")\n",
+    "\n",
+    "# plot the sample nodes\n",
+    "ax.plot(\n",
+    "    rmg_sq.node_x[outlet_node_to_sample_sq],\n",
+    "    rmg_sq.node_y[outlet_node_to_sample_sq],\n",
+    "    \"ro\",\n",
+    "    label=\"outlet\",\n",
+    ")\n",
+    "ax.plot(\n",
+    "    rmg_sq.node_x[midstream_node_to_sample_sq],\n",
+    "    rmg_sq.node_y[midstream_node_to_sample_sq],\n",
+    "    \"go\",\n",
+    "    label=\"midstream\",\n",
+    ")\n",
+    "ax.plot(\n",
+    "    rmg_sq.node_x[upstream_node_to_sample_sq],\n",
+    "    rmg_sq.node_y[upstream_node_to_sample_sq],\n",
+    "    \"bo\",\n",
+    "    label=\"upstream\",\n",
+    ")\n",
+    "\n",
+    "_ = ax.legend()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "#### 3.3.2 Spring Creek: set thresholds and select the output nodes listed by threshold\n",
+    "\n",
+    "We will identify the nodes for outlet, midstream, and upstream locations for plotting hydrographs using drainge area thresholds for the latter two. Code is written to set the outlet at the node with the largest drainage area. Below we will first identify the outlet node ID, its elevation and drainage area.\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "outlet_node_to_sample_sc = np.argmax(rmg_sc.at_node[\"drainage_area\"])\n",
+    "print(\n",
+    "    \"Outlet Node = \"\n",
+    "    + str(outlet_node_to_sample_sc)\n",
+    "    + \"; Drainage Area= \"\n",
+    "    + str(da_sc[outlet_node_to_sample_sc] / 1000000)\n",
+    "    + \" km^2; Elev = \"\n",
+    "    + str(round(z_sc[outlet_node_to_sample_sc], 1))\n",
+    "    + \" m\"\n",
+    ")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "The midstream and upstream node drainge area thresholds can be identified by drainge area ranges in km$^2$. The range used to identify a midstream location should be larger than the range used to identify an upstream location. These internal nodes can be identified by trial using different drainge area values, and plotting the locations selected. After we select the drainage area ranges we will print the node IDs, drainge areas, and elevations of the locations we select and map the locations on the elevation map of the watershed. "
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# These values can be change to set drainage area thresholds in km^2\n",
+    "midstream_da_upperbound = 20\n",
+    "midstream_da_lowerbound = 17.5\n",
+    "upstream_da_upperbound = 5\n",
+    "upstream_da_lowerbound = 3.2\n",
+    "\n",
+    "# Identify sample nodes.\n",
+    "outlet_node_to_sample_sc = np.argmax(rmg_sc.at_node[\"drainage_area\"])\n",
+    "midstream_node_to_sample_sc = np.where(\n",
+    "    np.logical_and(\n",
+    "        rmg_sc.at_node[\"drainage_area\"] > midstream_da_lowerbound * 1000000,\n",
+    "        rmg_sc.at_node[\"drainage_area\"] < midstream_da_upperbound * 1000000,\n",
+    "    )\n",
+    ")[0][0]\n",
+    "upstream_node_to_sample_sc = np.where(\n",
+    "    np.logical_and(\n",
+    "        rmg_sc.at_node[\"drainage_area\"] > upstream_da_lowerbound * 1000000,\n",
+    "        rmg_sc.at_node[\"drainage_area\"] < upstream_da_upperbound * 1000000,\n",
+    "    )\n",
+    ")[0][0]\n",
+    "\n",
+    "print(\n",
+    "    \"Outlet Node = \"\n",
+    "    + str(outlet_node_to_sample_sc)\n",
+    "    + \"; Drainage Area= \"\n",
+    "    + str(da_sc[outlet_node_to_sample_sc] / 1000000)\n",
+    "    + \" km^2; Elev = \"\n",
+    "    + str(round(z_sc[outlet_node_to_sample_sc], 1))\n",
+    "    + \" m\"\n",
+    ")\n",
+    "print(\n",
+    "    \"Midstream Node = \"\n",
+    "    + str(midstream_node_to_sample_sc)\n",
+    "    + \"; Drainage Area= \"\n",
+    "    + str(da_sc[midstream_node_to_sample_sc] / 1000000)\n",
+    "    + \" km^2; Elev = \"\n",
+    "    + str(round(z_sc[midstream_node_to_sample_sc], 1))\n",
+    "    + \" m\"\n",
+    ")\n",
+    "print(\n",
+    "    \"Upstream Node = \"\n",
+    "    + str(upstream_node_to_sample_sc)\n",
+    "    + \"; Drainage Area= \"\n",
+    "    + str(da_sc[upstream_node_to_sample_sc] / 1000000)\n",
+    "    + \" km^2; Elev = \"\n",
+    "    + str(round(z_sc[upstream_node_to_sample_sc], 1))\n",
+    "    + \" m\"\n",
+    ")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "## Plot Spring Creek\n",
+    "\n",
+    "# Set up the figure.\n",
+    "fig, ax = plt.subplots(nrows=1, ncols=1, figsize=(6, 6))\n",
+    "ax.xaxis.set_visible(False)\n",
+    "\n",
+    "# Plot the DEM.\n",
+    "imshow_grid(\n",
+    "    rmg_sc,\n",
+    "    z_sc,\n",
+    "    plot_name=\"Spring Creek\",\n",
+    "    var_name=\"Elevation\",\n",
+    "    var_units=\"m\",\n",
+    "    grid_units=(\"m\", \"m\"),\n",
+    "    cmap=\"terrain\",\n",
+    "    limits=(1875, 2615),\n",
+    "    color_for_closed=\"white\",\n",
+    ")\n",
+    "ax.set_facecolor(\"white\")\n",
+    "\n",
+    "# Plot the sample nodes.\n",
+    "ax.plot(\n",
+    "    rmg_sc.node_x[outlet_node_to_sample_sc],\n",
+    "    rmg_sc.node_y[outlet_node_to_sample_sc],\n",
+    "    \"ro\",\n",
+    "    label=\"outlet\",\n",
+    ")\n",
+    "ax.plot(\n",
+    "    rmg_sc.node_x[midstream_node_to_sample_sc],\n",
+    "    rmg_sc.node_y[midstream_node_to_sample_sc],\n",
+    "    \"go\",\n",
+    "    label=\"midstream\",\n",
+    ")\n",
+    "ax.plot(\n",
+    "    rmg_sc.node_x[upstream_node_to_sample_sc],\n",
+    "    rmg_sc.node_y[upstream_node_to_sample_sc],\n",
+    "    \"bo\",\n",
+    "    label=\"upstream\",\n",
+    ")\n",
+    "\n",
+    "_ = ax.legend(loc=\"lower right\")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "<hr>\n",
+    "\n",
+    "# 4.0. Make Model Decisions \n",
+    "\n",
+    "We used the following runoff categories for this exercise, which can be changed by the user: \n",
+    "  * `Base` has an intensity of 5.0 mm/hr, with a duration of 2 hr.\n",
+    "  * `HigherIntensity` has an intensity of 10.0 mm/hr, with a duration of 2 hr.\n",
+    "  * `LongerDuration` has an intensity of 5.0 mm/hr, with a duration of 4 hr. Longer duration is hardcoded as twice the storm duration of the base case. However it can be changed.\n",
+    " \n",
+    " Below you will indicate the domain, storm category, and algorithm you would like to use. You can change the runoff generation intesity and duration. Note that time is given in hours, and the model is run in *seconds*\n",
+    " \n",
+    "**Change your model with the options below**"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "basin_flag = \"Spring Creek\"  # 'Square' or 'Spring Creek'\n",
+    "storm_flag = \"HigherIntensity\"  # 'Base' or'HigherIntensity' or 'LongerDuration'\n",
+    "routing_method = (\n",
+    "    \"KinwaveImplicitOverlandFlow\"\n",
+    ")  # use 'OverlandFlow' OR 'KinwaveImplicitOverlandFlow'"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Advanced users only: if you know what you are doing, change the options below. "
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "image_save = 1  # 1: save image; 0: don't save\n",
+    "hours = 6  # hours of model run time\n",
+    "Number_frames = 6  # number of frames to plot\n",
+    "n = 0.03  # Manning's roughness coefficient, (s/m^(1/3))\n",
+    "Base_runoff_rate = 10  # Base runoff rate (mm/h)\n",
+    "HigherIntesity_runoff_rate = 20  # High intensity runoff rate (mm/h)\n",
+    "Storm_duration = 2  # Storm duration (hours)\n",
+    "dt = 600  # time step for `KinwaveImplicitOverlandFlow`, (s) [i.e., 10 minutes]"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "<hr>\n",
+    "\n",
+    "## 5.0  Model Computations\n",
+    "\n",
+    "### 5.1. Initialize model run time and output variables used for plotting and animation \n",
+    "\n",
+    "The cell below is designed to set up the model with the decisions made in the cell above. Do not modify the cell unless necessary."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# Convert time.\n",
+    "elapsed_time = 1.0  # seconds\n",
+    "model_run_time = hours * 3600  # converted to seconds\n",
+    "\n",
+    "# Set up the number of frames to be recorded for creating the animation.\n",
+    "ani_inc = np.arange(\n",
+    "    0, hours, np.true_divide(hours, Number_frames)\n",
+    ")  # list of timesteps for animation\n",
+    "snap = 0  # first snapshot for animation\n",
+    "\n",
+    "# Create lists for saving data.\n",
+    "discharge_at_outlet = []\n",
+    "discharge_upstream = []\n",
+    "discharge_midstream = []\n",
+    "hydrograph_time = []\n",
+    "\n",
+    "if basin_flag == \"Square\":\n",
+    "    # watershed_dem = 'Square_TestBasin.asc'\n",
+    "    outlet_node_to_sample = outlet_node_to_sample_sq\n",
+    "    midstream_node_to_sample = midstream_node_to_sample_sq\n",
+    "    upstream_node_to_sample = upstream_node_to_sample_sq\n",
+    "\n",
+    "    # Read in the DEM given the filename from above.\n",
+    "    (rmg, z) = read_esri_ascii(watershed_dem_sq, name=\"topographic__elevation\")\n",
+    "\n",
+    "    # Set initial fields.\n",
+    "    rmg[\"node\"][\"surface_water__discharge\"] = np.zeros(rmg.number_of_nodes)\n",
+    "    rmg_sq.set_watershed_boundary_condition_outlet_id(\n",
+    "        outlet_node_to_sample_sq, z_sq, -9999.0\n",
+    "    )\n",
+    "\n",
+    "else:\n",
+    "    # watershed_dem = 'SpringCreek_DEM.asc'\n",
+    "    outlet_node_to_sample = outlet_node_to_sample_sc\n",
+    "    midstream_node_to_sample = midstream_node_to_sample_sc\n",
+    "    upstream_node_to_sample = upstream_node_to_sample_sc\n",
+    "\n",
+    "    # Read in the DEM given the filename from above.\n",
+    "    (rmg, z) = read_esri_ascii(watershed_dem_sc, name=\"topographic__elevation\")\n",
+    "\n",
+    "    # Set initial fields.\n",
+    "    rmg[\"node\"][\"surface_water__discharge\"] = np.zeros(rmg.number_of_nodes)\n",
+    "\n",
+    "    # Set boundary coditions on the grid.\n",
+    "    rmg.set_watershed_boundary_condition(z)\n",
+    "\n",
+    "# Instantiate OverlandFlow object.\n",
+    "if routing_method == \"OverlandFlow\":\n",
+    "    of = OverlandFlow(rmg, alpha=0.15, mannings_n=n, steep_slopes=True)\n",
+    "else:\n",
+    "    kw = KinwaveImplicitOverlandFlow(rmg, runoff_rate=0.0, roughness=n, depth_exp=5 / 3)\n",
+    "\n",
+    "# Assign storm conditions based on flag in code cell above.\n",
+    "if storm_flag == \"Base\":\n",
+    "    runoff_mmhr = Base_runoff_rate\n",
+    "    runoff_ms = runoff_mmhr * (2.77778 * 10 ** -7)\n",
+    "    storm_duration = Storm_duration * 3600.0\n",
+    "elif storm_flag == \"HigherIntensity\":\n",
+    "    runoff_mmhr = HigherIntesity_runoff_rate\n",
+    "    runoff_ms = runoff_mmhr * (2.77778 * 10 ** -7)\n",
+    "    storm_duration = Storm_duration * 3600.0\n",
+    "elif storm_flag == \"LongerDuration\":\n",
+    "    runoff_mmhr = Base_runoff_rate\n",
+    "    runoff_ms = runoff_mmhr * (2.77778 * 10 ** -7)\n",
+    "    storm_duration = Storm_duration * 2 * 3600.0"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### 5.2. Run Overland Flow Model \n",
+    "\n",
+    "For each timestep, discharge is calculated at each node.  Using the depth slope product, shear stress is calculated at every node.  Outputs for each node include: water depth, discharge and shear stress values through time at every point in the input grid.  Read more on the [Landlab component description](https://landlab.readthedocs.io/en/release/landlab.components.overland_flow.html) by Jordan Adams.\n",
+    "\n",
+    "This make take up to 10 minutes to run for a six hour storm. The elapsed time (the simulated time, not run time) will print immediately below the code cell."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "animate_fig = plt.figure()\n",
+    "animation_file_name = (\n",
+    "    str(basin_flag) + \"_\" + str(storm_flag) + \"_\" + str(routing_method) + \".mp4\"\n",
+    ")\n",
+    "writer = animation.FFMpegWriter(fps=1)\n",
+    "writer.setup(animate_fig, \"animation.mp4\")\n",
+    "\n",
+    "while elapsed_time < model_run_time:\n",
+    "    # Set the adaptive time step.\n",
+    "    if routing_method == \"OverlandFlow\":\n",
+    "        of.dt = of.calc_time_step()\n",
+    "\n",
+    "    # The storm starts when the model starts. While the elapsed time is less\n",
+    "    # than the storm duration, we add water to the system as runoff.\n",
+    "    if routing_method == \"OverlandFlow\":\n",
+    "\n",
+    "        if elapsed_time < (storm_duration):\n",
+    "            of.rainfall_intensity = runoff_ms\n",
+    "        else:\n",
+    "            # elapsed time exceeds the storm duration, rainfall ceases.\n",
+    "            of.rainfall_intensity = 0.0\n",
+    "\n",
+    "    if routing_method == \"OverlandFlow\":\n",
+    "        of.overland_flow()  # Generating overland flow based on the deAlmeida solution.\n",
+    "        rmg.at_node[\"surface_water__discharge\"] = of.discharge_mapper(\n",
+    "            of.q, convert_to_volume=True\n",
+    "        )\n",
+    "    else:\n",
+    "        if elapsed_time < storm_duration:\n",
+    "            kw.run_one_step(dt, current_time=elapsed_time, runoff_rate=runoff_ms)\n",
+    "        else:\n",
+    "            kw.run_one_step(dt, current_time=elapsed_time, runoff_rate=0.0)\n",
+    "\n",
+    "    if routing_method == \"OverlandFlow\":\n",
+    "        discharge_at_outlet.append(\n",
+    "            rmg.at_node[\"surface_water__discharge\"][outlet_node_to_sample]\n",
+    "        )\n",
+    "        discharge_midstream.append(\n",
+    "            rmg.at_node[\"surface_water__discharge\"][midstream_node_to_sample]\n",
+    "        )\n",
+    "        discharge_upstream.append(\n",
+    "            rmg.at_node[\"surface_water__discharge\"][upstream_node_to_sample]\n",
+    "        )\n",
+    "    else:\n",
+    "        discharge_at_outlet.append(\n",
+    "            rmg.at_node[\"surface_water_inflow__discharge\"][outlet_node_to_sample]\n",
+    "        )\n",
+    "        discharge_midstream.append(\n",
+    "            rmg.at_node[\"surface_water_inflow__discharge\"][midstream_node_to_sample]\n",
+    "        )\n",
+    "        discharge_upstream.append(\n",
+    "            rmg.at_node[\"surface_water_inflow__discharge\"][upstream_node_to_sample]\n",
+    "        )\n",
+    "\n",
+    "    # Append time and discharge to their lists to save data and for plotting.\n",
+    "    hydrograph_time.append(round(elapsed_time / 3600, 2))  # saved in hours\n",
+    "\n",
+    "    # Output time every now and then so that you know the code is actually running\n",
+    "    # each time loop a new image is mapped. This sequence will build the animation.\n",
+    "\n",
+    "    if snap < len(ani_inc):\n",
+    "        if (elapsed_time / 3600) > ani_inc[snap]:\n",
+    "\n",
+    "            print(\"elapsed time = \" + str(round(elapsed_time / 3600, 2)) + \" hours\")\n",
+    "\n",
+    "            imshow_grid(\n",
+    "                rmg,\n",
+    "                \"surface_water__depth\",\n",
+    "                colorbar_label=\"Depth (m)\",\n",
+    "                plot_name=\"Surface Water Depth\",\n",
+    "                var_name=\"Water Depth\",\n",
+    "                var_units=\"m\",\n",
+    "                grid_units=(\"m\", \"m\"),\n",
+    "                cmap=\"Blues\",\n",
+    "                limits=(0, 0.5),\n",
+    "            )\n",
+    "            plt.title(\n",
+    "                \"{} hours\".format(str(round(elapsed_time / 3600, 2))), fontsize=14\n",
+    "            )\n",
+    "\n",
+    "            if image_save == 1:\n",
+    "                plt.savefig(\n",
+    "                    str(basin_flag)\n",
+    "                    + \"_\"\n",
+    "                    + str(storm_flag)\n",
+    "                    + \"_\"\n",
+    "                    + str(routing_method)\n",
+    "                    + \"_\"\n",
+    "                    + str(ani_inc[snap])\n",
+    "                    + \"hr_map.png\",\n",
+    "                    format=\"png\",\n",
+    "                    dpi=2500,\n",
+    "                )\n",
+    "\n",
+    "            writer.grab_frame()\n",
+    "\n",
+    "            snap += 1\n",
+    "\n",
+    "            # Remove the colorbar to reset the figure for the next animation timestep.\n",
+    "            plt.gci().colorbar.remove()\n",
+    "\n",
+    "    # Update elapsed_time.\n",
+    "    if routing_method == \"OverlandFlow\":\n",
+    "        elapsed_time += of.dt\n",
+    "    else:\n",
+    "        elapsed_time += dt\n",
+    "\n",
+    "writer.finish()\n",
+    "plt.close(animate_fig)\n",
+    "\n",
+    "# Display animation.\n",
+    "HTML(\n",
+    "    \"\"\"<div align=\"middle\"> <video width=\"80%\" controls loop><source src=\"animation.mp4\" type=\"video/mp4\"> </video></div>\"\"\"\n",
+    ")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "See [this Landlab animation tutorial](https://github.com/landlab/tutorials/blob/master/plotting/animate-landlab-output.ipynb) for more on animating model output."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## 6.0 Results\n",
+    "\n",
+    "In this section you will create figures of the model simulations you ran above. You will plot hydrographs from modeled data at each location in the same figure for the selected study domain. In this plot you will notice differences in the time to peak and peak discharge magnitudes at different locations. You may also notice differences in the results generated by the two methods."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### 6.1. Plot hydrograph for one model result "
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "hydrograph_fig = plt.figure(figsize=(12, 8))\n",
+    "\n",
+    "# Plot hydrograph series.\n",
+    "plt.plot(hydrograph_time, discharge_at_outlet, \"r-\", label=\"outlet\", linewidth=4)\n",
+    "plt.plot(hydrograph_time, discharge_midstream, \"g-\", label=\"midstream\", linewidth=3)\n",
+    "plt.plot(hydrograph_time, discharge_upstream, \"b-\", label=\"upstream\", linewidth=2)\n",
+    "\n",
+    "# Configure plot elements.\n",
+    "plt.ylabel(\"Discharge (cms)\", fontsize=18)\n",
+    "plt.xlabel(\"Time (hours)\", fontsize=18)\n",
+    "plt.legend(loc=\"upper right\", fontsize=18)\n",
+    "title_text = (\n",
+    "    \"Model=\" + str(basin_flag) + \" + \" + str(storm_flag) + \" + \" + routing_method\n",
+    ")\n",
+    "plt.title(title_text, fontsize=18)\n",
+    "plt.ylim(0, 150)\n",
+    "\n",
+    "\n",
+    "plt.savefig(\n",
+    "    basin_flag + \"_\" + storm_flag + \"_\" + routing_method + \"_hydrograph.png\",\n",
+    "    format=\"png\",\n",
+    "    dpi=1000,\n",
+    ")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### 6.2. Show map for one model result at the time step of interest to you \n",
+    "\n",
+    "The code you ran above produced image files of model output. \n",
+    "\n",
+    "List the available image files printed in each model run. The files have a naming structure that includes the model time. Note that you will need to change the last line of the code block below get the file names for a given model.\n",
+    "\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "print(\"Image file names:\\n\")\n",
+    "\n",
+    "# Change the shell script below to list all file names for a png based on your model (e.g., *png, Spring*png, or Square*png)\n",
+    "#!ls Spring*png\n",
+    "!ls Spring*png"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Choose and visualize an image file by changing the name of the image input to be consistent with the name of a image listed above. "
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "scrolled": false
+   },
+   "outputs": [],
+   "source": [
+    "img = mgimg.imread(\n",
+    "    fname=\"Spring Creek_HigherIntensity_KinwaveImplicitOverlandFlow_2.0hr_map.png\"\n",
+    ")\n",
+    "fig, ax = plt.subplots(figsize=(15, 15))\n",
+    "ax.imshow(img)\n",
+    "_ = plt.axis(\"off\")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### 6.3. Save model results into a text file at the end of each model run \n",
+    "\n",
+    "If you want to explore the hydrographs created in this application in another software environment, you will run the code below to create a single .csv file that contains modeled hydrograps at each location in the watershed. Please run this code for each run you completed separately. The .csv file can be used in Excel, Matlab or any other software. The name of the new file will be printed.  "
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "modeldata = pd.DataFrame()\n",
+    "modeldata[\"Timestep\"] = hydrograph_time\n",
+    "modeldata[\"outlet\"] = discharge_at_outlet\n",
+    "modeldata[\"midstream\"] = discharge_midstream\n",
+    "modeldata[\"upstream\"] = discharge_upstream\n",
+    "header = {\"Timestep\", \"Outlet_cms\", \"Midstream_cms\", \"Upstream_cms\"}\n",
+    "modeldata.to_csv(\n",
+    "    str(basin_flag + \"_\" + storm_flag + \"_\" + routing_method + \"_flow.csv\"),\n",
+    "    index=False,\n",
+    "    header=header,\n",
+    "    sep=\",\",\n",
+    ")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Now we will explore where your data is saved. First execute \"pwd\" (as below) to identify your working directory and copy the HydroShare resource ID (alpha numeric code) for future use. Next we will locate this directory. Go to the top left corner of your browser and find the CUAHSI logo. Right-click on the logo; this opens the file browser of your work space. Go to notebooks/data directory. There find your resource ID (alpha numeric code) and click on it. Follow the link until you get to data/contents. You can download these files to your own computer as you wish."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "pwd"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "print(\"The code you ran above produced image files of model output.\")\n",
+    "print(\"The files have a naming structure that includes the model time. The files:\")\n",
+    "\n",
+    "# Change the shell script below to list all the file names for a png based on your model (e.g., *png, Spring*png, or Square*png).\n",
+    "!ls *csv\n",
+    "\n",
+    "print(\"To download this file, go to the File Dashbard and browse the JupyterHub server\")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "\n",
+    "**You completed the tutorial to run the model. You can now explore different routing methods and model parameters  by editing and re-executing sections by making model decisions (4.0), running the model (5.0) and saving results (6.0). Repeat as many model runs as you like and save all of your results before you start comparing results.**  \n",
+    "\n"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## 7.0. Discussion \n",
+    "\n",
+    "In this section you will visualize results from your model runs to compare results obtained in the two study watersheds and begin writing your observations from model runs. In this section you may also generate more research questions to explore with the two models. You will also see how the two methods can yield different results."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### 7.1. Plot maps of water depth for two model runs  \n",
+    "\n",
+    "Choose two image files printed in each animation increment **from two different models** to compare at a timestep that is most interesting. \n",
+    "\n",
+    "**Note:** You will get the error `No such file or directory` following the code block below if you have not run the `OverlandFlow` option of the `routing_method`. In summary, you must the routing method, rerun the model, then the code block below can be run successfully."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "hydrograph_fig = plt.figure(figsize=(20, 18))\n",
+    "ax1 = hydrograph_fig.add_subplot(221)\n",
+    "fname1 = \"Square_HigherIntensity_OverlandFlow_3.0hr_map.png\"\n",
+    "img1 = mgimg.imread(fname1)\n",
+    "imgplot = plt.imshow(img1)\n",
+    "ax1.axis(\"off\")\n",
+    "\n",
+    "# Plotting the hydrographs from the Spring Creek DEM at the outlet\n",
+    "ax2 = hydrograph_fig.add_subplot(222)\n",
+    "fname2 = \"Spring Creek_HigherIntensity_OverlandFlow_3.0hr_map.png\"\n",
+    "img2 = mgimg.imread(fname2)\n",
+    "imgplot = plt.imshow(img2)\n",
+    "ax2.axis(\"off\")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### 7.2. Plot hydrographs for two model runs  "
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# Here\n",
+    "hydrograph_fig = plt.figure(figsize=(20, 20))\n",
+    "ax1 = hydrograph_fig.add_subplot(221)\n",
+    "fname1 = \"Spring Creek_HigherIntensity_KinwaveImplicitOverlandFlow_hydrograph.png\"\n",
+    "img1 = mgimg.imread(fname1)\n",
+    "imgplot = plt.imshow(img1)\n",
+    "ax1.axis(\"off\")\n",
+    "\n",
+    "# Plot the hydrographs from the Spring Creek DEM at the outlet.\n",
+    "ax2 = hydrograph_fig.add_subplot(222)\n",
+    "fname2 = \"Spring Creek_HigherIntensity_OverlandFlow_hydrograph.png\"\n",
+    "img2 = mgimg.imread(fname2)\n",
+    "imgplot = plt.imshow(img2)\n",
+    "ax2.axis(\"off\")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## 8.0. Conclusions\n",
+    "\n",
+    "At this point in the notebook, you should have produced a plot of three hydrographs from different points in the Spring Creek watershed or in the Square domain, produced from overland flow driven by a select storm.\n",
+    "\n",
+    "There are six scenarios to explore: two different watersheds and three different storms, if you use the default parameters. If you explore different Manning's roughness values, run all six scenarios by systematically changing the `basin_flag` and `storm_flag` in Code Block 2 and rerunning all of the code blocks sequentially. Save the hydrograph plots for each scenario. \n",
+    "\n",
+    "### Directions if you are using this as a laboratory exercise\n",
+    "\n",
+    "Include the plots of hydrographs and mapped flow depths in a document with your typed answers to each of the questions below with complete sentences. Try to be as specific and as quantitative as you can. For example, you can compare times to peak discharge and peak discharge values among the scenarios. You are encouraged to discuss the results of the models with your classmates, but the text you turn in must be your own thoughts and words.\n",
+    "\n",
+    "1. What aspects of the hydrograph change at the outlet as the storm gets longer or more intense? Are there aspects of the outlet hydrograph that are not sensitive to the storm duration or intensity? Do the midstream and upstream hydrographs exhibit the same sensitivity to storm duration and intensity? Explain why.  \n",
+    "\n",
+    "2. What are the differences in the hydrograph response in the outlets of the two watersheds? How does the watershed shape influence the hydrograph shape?\n",
+    "\n",
+    "3. Now compare the hydrograph shapes across the three different locations in the watersheds. Compare only between similar points (e.g. mid or upstream locations) and between the same storm characteristics. How does watershed shape affect hydrograph shape? Does it impact all locations in the same manner? Do different storm characteristics exaggerate the differences between the different watersheds?\n",
+    "\n",
+    "4. Now compare the results between the two different watershed. How different are hydrograph characteristics between the square and natural watershed (Spring Creek)?\n",
+    "\n",
+    "5. Please compare your answers to the questions you answered before running the models. Do the model results match your intuition? If not, do you think your intuition was wrong, or the model was wrong, or both? Remember, models are helpful for learning but they are highly simplified representations of the real world. Without knowing the details of the model, does it seem like something is missing from your model results?"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## 9.0. Save the results in HydroShare\n",
+    "<a name=\"creation\"></a>\n",
+    "\n",
+    "Using the HydroShare utilities library, the results of the Geoprocessing steps above can be saved in HydroShare.  First, define all of the required metadata for resource creation, i.e. *title*, *abstract*, *keywords*, *content files*.  In addition, we must define the type of resource that will be created, in this case *genericresource*."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### 9.1. List files to export to new HydroShare resource\n",
+    "The name of files listed below need to be consistent with what exists in your file directory"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "ThisNotebook = \"explore_routing_tutorial.ipynb\"  # check name for consistency\n",
+    "modelresults = \"Square_HigherIntensity_OverlandFlow_flow.csv\"\n",
+    "animation = \"animation.mp4\"\n",
+    "waterdepth = \"Square_HigherIntensity_OverlandFlow_3.0hr_map.png\"\n",
+    "\n",
+    "files = [ThisNotebook, modelresults, animation, waterdepth]"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### 9.2. Generate a new HydroShare resource with Title, Abstract, Keyword and files (listed above)\n",
+    "\n",
+    "The metadata can be edited from HydroShare once the new resource is created.  "
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# For each file downloaded onto the server folder, move to a new HydroShare Generic Resource.\n",
+    "title = \"Test Delete\"\n",
+    "abstract = \"abstract text \"\n",
+    "keywords = [\"Landlab\", \"routing\"]\n",
+    "rtype = \"genericresource\"\n",
+    "\n",
+    "# Create the new resource.\n",
+    "resource_id = hs.createHydroShareResource(\n",
+    "    abstract,\n",
+    "    title,\n",
+    "    keywords=keywords,\n",
+    "    resource_type=rtype,\n",
+    "    content_files=files,\n",
+    "    public=False,\n",
+    ")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## 10.0 Clean up—be a good community data steward—thank you!\n",
+    "Check that all of the files you want are saved on HydroShare. Then use shell scripting from the Notebook to view and delete data.  \n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# Print current working directory and make sure you are in the directory you want to clean up.\n",
+    "!pwd\n",
+    "\n",
+    "# List files in directory with date of creation. Check that you do not need any of these files.\n",
+    "!ls -lt"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# Remove all files you created. This will not remove the Notebook you are using now.\n",
+    "!rm *png\n",
+    "!rm *mp4\n",
+    "!rm *csv\n",
+    "\n",
+    "# List everything remaining in the folder - this should be only what you downloaded from\n",
+    "# HydroShare. Note that all of this will be overwritten the next time you `Open With`\n",
+    "# from the HydroShare webpage for this resource, execute the Welcome page, and answer\n",
+    "# ‘Yes’ to download the data again from HydroShare.\n",
+    "!ls -lt"
+   ]
+  }
+ ],
+ "metadata": {
+  "anaconda-cloud": {},
+  "kernelspec": {
+   "display_name": "Python 3",
+   "language": "python",
+   "name": "python3"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.6.5"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 2
+}
diff --git a/explore_flow_routing/supporting_files/SpringCreek_DEM.asc b/explore_flow_routing/supporting_files/SpringCreek_DEM.asc
new file mode 100644
index 0000000..69376a0
--- /dev/null
+++ b/explore_flow_routing/supporting_files/SpringCreek_DEM.asc
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diff --git a/explore_flow_routing/supporting_files/Square_TestBasin.asc b/explore_flow_routing/supporting_files/Square_TestBasin.asc
new file mode 100644
index 0000000..30898ab
--- /dev/null
+++ b/explore_flow_routing/supporting_files/Square_TestBasin.asc
@@ -0,0 +1,205 @@
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diff --git a/landslide_model_for_fire/replicate_landslide_model_for_fire.ipynb b/landslide_model_for_fire/replicate_landslide_model_for_fire.ipynb
new file mode 100644
index 0000000..82805ba
--- /dev/null
+++ b/landslide_model_for_fire/replicate_landslide_model_for_fire.ipynb
@@ -0,0 +1,2109 @@
+{
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "<img src=\"http://landlab.github.io/assets/Landlab-logo.png\"\n",
+    "style=\"float:left;width:150px;padding:0px\">  \n",
+    "\n",
+    "<br /> \n",
+    "\n",
+    "# Reuse a published regional Landlab shallow landslide model to explore changes in forest cover at a subcatchment within the study area<br /> \n",
+    "### You will explore how to reuse model code and data in a subregion within a larger region where the model was developed \n",
+    "* Load data from a regional Landlab landslide model (Strauch et al., 2018) developed for the North Cascades National Park, WA USA, published on HydroShare. <br />\n",
+    "* Define a geographic subset (Thunder Creek watershed) within the study region.\n",
+    "* Explore landslide probability sensitivity to fire by adjusting the cohesion parameter for Thunder Creek.\n",
+    "* Save results to a new HydroShare resource.  <br />\n",
+    "\n",
+    "The shallow landslide model you will is based on a spatially distributed Monte Carlo solution of the infinite slope stability model. Detailes of the model and the study site are described in Strauch et al. (2018).\n",
+    "\n",
+    "Strauch R., Istanbulluoglu E., Nudurupati S.S., Bandaragoda C., Gasparini N.M., and G.E. Tucker (2018). A hydro-climatological approach to predicting regional landslide probability using Landlab. Earth Surf. Dynam., 6, 1–26. \n",
+    "https://www.earth-surf-dynam.net/6/1/2018/\n",
+    "\n",
+    " <br /> <img src=\"https://www.washington.edu/brand/files/2014/09/W-Logo_Purple_Hex.png\" style=\"float:right;width:120px;padding:10px\">   "
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## To run this notebook:\n",
+    "\n",
+    "Click in each shaded cell below and \"shift + enter\" to run each code block. Alternatively, you can run groups of cells by clicking \"Cell\" on the menu above and selecting your run options from the pull-down menu. This is also where you can clear outputs from previous runs.\n",
+    "\n",
+    "If an error occurs, click on *Kernal* and *Restart and Clear Outputs* in the menu above."
+   ]
+  },
+  {
+   "attachments": {
+    "image.png": {
+     "image/png": 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"
+    }
+   },
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## 1. Introduction\n",
+    "\n",
+    "### 1.1 Infinite Slope Factor of Safety Equation\n",
+    "\n",
+    "This equation predicts the ratio of stabilizing to destabilizing forces on a hillslope plane. The properties are assumed to represent an infinte plane, neglecting the boundary conditions around the landslide location. When FS<1, the slope is instable.\n",
+    "\n",
+    "![image.png](attachment:image.png)"
+   ]
+  },
+  {
+   "attachments": {
+    "image.png": {
+     "image/png": 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"
+    }
+   },
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "![image.png](attachment:image.png)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "C* can be calculated by the ratio of the sum of root cohesion, Cr, and soil cohesion, Cs, to the product of soil depth, density, and gravity."
+   ]
+  },
+  {
+   "attachments": {
+    "image.png": {
+     "image/png": "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"
+    }
+   },
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "![image.png](attachment:image.png)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Relative wetness is the ratio of depth of water subsurface flow above an impervious layer to the depth of soil. "
+   ]
+  },
+  {
+   "attachments": {
+    "image.png": {
+     "image/png": 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"
+    }
+   },
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "![image.png](attachment:image.png)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "R: recharge rate to water table (m/d)\n",
+    "T: soil transmissivity (m^2/d)\n",
+    "a: specific catchment area (m)."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### 1.2 Monte Carlo solution of the FS equation\n",
+    "\n",
+    "Below the Monte Carlo approach used by Strauch et al. (2018) is illustrated in a figure. At each node of the model grid variables for soil and vegetation are generated from triangular distributions. Recharge to water table is obtained from hydrologic model simulations by selecting the largest daily value for each year. Probability of shallow landslide initiation is defined as the ratio of number of times FS<1 to the total sample size of FS calculations."
+   ]
+  },
+  {
+   "attachments": {
+    "image.png": {
+     "image/png": 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"
+    }
+   },
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "![image.png](attachment:image.png)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### Module overview for a Landlab Modeling Toolkit Landslide Introduction\n",
+    "\n",
+    "This Jupyter Notebook runs the Landlab LandslideProbability component on a 30-m digital elevation model (DEM) for Thunder Creek, a subwaterhsed in the larger study domain developed by Strauch et al. (2018), using _data driven spatial_ recharge distribution as described in the paper (https://www.earth-surf-dynam.net/6/1/2018/).\n",
+    "\n",
+    "\n",
+    "To run a landslide demonstration using the paper data and approach we will:\n",
+    "1. Import data from North Cascades National Park (NOCA: study area)\n",
+    "1. Review data needed as input for the landslide model\n",
+    "1. Create a RasterModelGrid based on a 30-m DEM - subset to Thunder Creek watershed\n",
+    "1. Assign data fields used to calculate landslide probability\n",
+    "1. Specify recharge option as _data driven spatial_ and access Python dictionaries to generate recharge distributions\n",
+    "1. Set Number of iterations to run Monte Carlo simulation\n",
+    "1. Run Landlab LandslideProbability component\n",
+    "1. Run the model again to simulate post-fire conditions\n",
+    "1. Display and visualize results of stability analysis\n",
+    "1. Save Notebook and Results back to HydroShare\n",
+    "\n",
+    "The estimated time to run this Notebook is 30-60 minutes with 20 minutes of computation time. \n"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## 2.0 Methods"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## 2.1 Notebook Setup and Preparation\n",
+    "\n",
+    "To run this notebook, we must import several libraries.\n",
+    "The hs_utils library provides functions for interacting with HydroShare, including resource querying, dowloading, and creation. Additional libraries support the functions of Landlab."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### 2.1.1 Install Python packages \n",
+    "The CUAHSI JupyterHub server provides many Python packages and libraries, but to add additional libraries to your personal user space, use the cell below.  To request an Installation to the server, visit https://github.com/hydroshare/hydroshare-jupyterhub, create a New Issue, and add the label 'Installation Request'. Uncomment the lines below to install the library. Python2 and Python3 kernels are both available. If you are a new Landlab user on Hydroshare you are advised to run the code block below in your first run of this tutorial, then comment them out."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 1,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "#!python2 -m pip install xarray\n",
+    "#!python2 -m pip install plotly\n",
+    "#!python2 -m pip install geopandas\n",
+    "#!python2 -m pip install rasterstats\n",
+    "#!python2 -m pip install affine\n",
+    "#!python2 -m pip install seaborn\n",
+    "# from __future__ import absolute_import, division, print_function, unicode_literals"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### 2.2.2 Import Python libraries"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 2,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# Import standard Python utilities for calculating and plotting\n",
+    "import os\n",
+    "import pickle as pickle\n",
+    "\n",
+    "# Import general tools\n",
+    "import time\n",
+    "import warnings\n",
+    "from collections import defaultdict\n",
+    "from datetime import datetime, timedelta\n",
+    "\n",
+    "import geopandas as gpd\n",
+    "import matplotlib as mpl\n",
+    "import matplotlib.pyplot as plt\n",
+    "import numpy as np\n",
+    "import pandas as pd\n",
+    "import six\n",
+    "from landlab import CLOSED_BOUNDARY, CORE_NODE, imshow_grid_at_node\n",
+    "\n",
+    "# Import Landlab libraries\n",
+    "from landlab.components import LandslideProbability\n",
+    "from landlab.io import read_esri_ascii, write_esri_ascii\n",
+    "from landlab.plot import imshow_grid\n",
+    "\n",
+    "# Import utilities for importing and exporting to HydroShare\n",
+    "from utilities import hydroshare\n",
+    "\n",
+    "mpl.use(\"agg\")\n",
+    "%matplotlib inline\n",
+    "warnings.filterwarnings(\"ignore\")\n",
+    "\n",
+    "\n",
+    "st = time.time()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### 2.1.3 Connection with HydroShare\n",
+    "After importing libraires, we now establish a secure connection with HydroShare by instantiating the hydroshare class that is defined within hs_utils. In addition to connecting with HydroShare, this command also sets and prints environment variables for several parameters that will be useful for saving work back to HydroShare."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 3,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Adding the following system variables:\n",
+      "   HS_USR_NAME = ChristinaBandaragoda\n",
+      "   HS_RES_ID = 70b977e22af544f8a7e5a803935c329c\n",
+      "   HS_RES_TYPE = genericresource\n",
+      "   JUPYTER_HUB_IP = jupyter.cuahsi.org\n",
+      "\n",
+      "These can be accessed using the following command: \n",
+      "   os.environ[key]\n",
+      "\n",
+      "   (e.g.)\n",
+      "   os.environ[\"HS_USR_NAME\"]  => ChristinaBandaragoda\n",
+      "Successfully established a connection with HydroShare\n",
+      "This is a basic Unix folder structure.\n",
+      "Data will be loaded from and saved to:/home/jovyan/work/notebooks/data/70b977e22af544f8a7e5a803935c329c/70b977e22af544f8a7e5a803935c329c/data/contents/\n"
+     ]
+    }
+   ],
+   "source": [
+    "hs = hydroshare.hydroshare()\n",
+    "homeresid = str(os.environ[\"HS_RES_ID\"])\n",
+    "homedir = os.path.join(\n",
+    "    \"/home/jovyan/work/notebooks/data\", str(homeresid), str(homeresid), \"data/contents/\"\n",
+    ")\n",
+    "print(\"This is a basic Unix folder structure.\")\n",
+    "print(\"Data will be loaded from and saved to:\" + homedir)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "If you are curious about where the data is being downloaded, click on the Jupyter Notebook dashboard icon in upper rigth corner to see a File System view.  The homedir directory location printed above is where you can find the data and contents you will download to a HydroShare JupyterHub server.  At the end of this work session, you can migrate this data to the HydroShare iRods server as a Generic Resource. "
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### 2.2. Import spatial data \n",
+    "Strauch et al (2018) pre-processed the data for the North Cascades National Park Complex case study and is on HydroShare as [Regional landslide hazard using Landlab - NOCA Data](https://www.hydroshare.org/resource/a5b52c0e1493401a815f4e77b09d352b/). Here the first task is find this resource on Hydrohsare. We will click on the link to see the published data repository on HydroShare and collect the resource ID of the data. The resource ID can be found in the \"How to cite\" box, and it will be the series of numbers and letters following \"hs.\". Here's the copied resource: http://dx.doi.org/10.4211/hs.a5b52c0e1493401a815f4e77b09d352b citation. Now we copy this ID and introduce it as \"Data_ResourceID=\" in the code below. \n",
+    "\n",
+    "#### 2.2.1 Set DEM data download variable name\n",
+    "To learn more about this data visit [Regional landslide hazard using Landlab - NOCA Data](https://www.hydroshare.org/resource/a5b52c0e1493401a815f4e77b09d352b/)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 4,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "Data_ResourceID = \"a5b52c0e1493401a815f4e77b09d352b\""
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "#### 2.2.2 Download Data\n",
+    "We will execute the next cell to download data from HydroShare iRods database to your personal user space - this may take a few minutes."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 5,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "This resource already exists in your userspace.\n",
+      "a5b52c0e1493401a815f4e77b09d352b/\n",
+      "|-- a5b52c0e1493401a815f4e77b09d352b/\n",
+      "|   |-- bagit.txt\n",
+      "|   |-- manifest-md5.txt\n",
+      "|   |-- readme.txt\n",
+      "|   |-- tagmanifest-md5.txt\n",
+      "|   |-- data/\n",
+      "|   |   |-- resourcemap.xml\n",
+      "|   |   |-- resourcemetadata.xml\n",
+      "|   |   |-- contents/\n",
+      "|   |   |   |-- Data files/\n",
+      "|   |   |   |   |-- ASCII_header.txt\n",
+      "|   |   |   |   |-- cohesion_max.txt\n",
+      "|   |   |   |   |-- cohesion_min.txt\n",
+      "|   |   |   |   |-- cohesion_mode.txt\n",
+      "|   |   |   |   |-- cont_area.txt\n",
+      "|   |   |   |   |-- dict_coeff.p\n",
+      "|   |   |   |   |-- dict_uniq_ids.p\n",
+      "|   |   |   |   |-- elevation.txt\n",
+      "|   |   |   |   |-- exclud_mask.txt\n",
+      "|   |   |   |   |-- frict_angle.txt\n",
+      "|   |   |   |   |-- HSD_dict.p\n",
+      "|   |   |   |   |-- ksat_mpd.txt\n",
+      "|   |   |   |   |-- landslide_type.txt\n",
+      "|   |   |   |   |-- Re_annualMax_ex.txt\n",
+      "|   |   |   |   |-- sample_area.txt\n",
+      "|   |   |   |   |-- slope_tang17d.txt\n",
+      "|   |   |   |   |-- soil_depth_model.txt\n",
+      "|   |   |   |   |-- soil_depth.txt\n",
+      "|   |   |   |   |-- transmis_model.txt\n",
+      "|   |   |   |   |-- transmis.txt\n",
+      "|   |   |   |   |-- vic_idsnoca.txt\n",
+      "|   |   |   |   |-- VIC_latlon_key.txt\n",
+      "|   |   |   |   |-- wetness.txt\n",
+      "|   |   |   |-- Driver and related codes/\n",
+      "|   |   |   |   |-- __init__.py\n",
+      "|   |   |   |   |-- landslide_probability9Jun17.py\n",
+      "|   |   |   |   |-- NOCA_runPaper_LandlabLandslide.ipynb\n",
+      "|   |   |   |   |-- vicDataProc.py\n",
+      "|   |   |   |-- Results/\n",
+      "|   |   |   |   |-- prob_fail3000_Model_SD_LT.txt\n",
+      "|   |   |   |   |-- prob_fail3000_Model_SD.txt\n",
+      "|   |   |   |   |-- prob_fail3000_SSURGO_SD.txt\n",
+      "\n",
+      "Do you want to overwrite these data [Y/n]? n\n"
+     ]
+    },
+    {
+     "data": {
+      "text/html": [
+       "<b>Found the following file(s) associated with this HydroShare resource.</b>"
+      ],
+      "text/plain": [
+       "<IPython.core.display.HTML object>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "text/html": [
+       "Data files<br>Driver and related codes<br>Results"
+      ],
+      "text/plain": [
+       "<IPython.core.display.HTML object>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "text/html": [
+       "These files are stored in a dictionary called <b>hs.content</b> for your convenience.  To access a file, simply issue the following command where MY_FILE is one of the files listed above: <pre>hs.content[\"MY_FILE\"] </pre> "
+      ],
+      "text/plain": [
+       "<IPython.core.display.HTML object>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "This is the location on the HydroShare JupyterHub server where the data has just been downloaded:\n",
+      "/home/jovyan/work/notebooks/data/a5b52c0e1493401a815f4e77b09d352b/a5b52c0e1493401a815f4e77b09d352b/data/contents/Data files/\n"
+     ]
+    }
+   ],
+   "source": [
+    "hs.getResourceFromHydroShare(Data_ResourceID)\n",
+    "data_folder = os.path.join(\n",
+    "    \"/home/jovyan/work/notebooks/data\",\n",
+    "    str(Data_ResourceID),\n",
+    "    str(Data_ResourceID),\n",
+    "    \"data/contents/Data files/\",\n",
+    ")\n",
+    "print(\n",
+    "    \"This is the location on the HydroShare JupyterHub server where the data has just been downloaded:\"\n",
+    ")\n",
+    "print(data_folder)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    " ### 2.3. Review data needed as input for the landslide model\n",
+    " This section loads metadata associated with the Landlab component.  \n",
+    " To view the code source for this component, visit [Landlab on Github](https://github.com/landlab/) or [Download the landslide_probability.py python file](https://github.com/landlab/landlab/blob/master/landlab/components/landslides/landslide_probability.py)\n"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Check the list of data inputs that the component needs."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 6,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "['soil__density',\n",
+       " 'soil__internal_friction_angle',\n",
+       " 'soil__maximum_total_cohesion',\n",
+       " 'soil__minimum_total_cohesion',\n",
+       " 'soil__mode_total_cohesion',\n",
+       " 'soil__saturated_hydraulic_conductivity',\n",
+       " 'soil__thickness',\n",
+       " 'soil__transmissivity',\n",
+       " 'topographic__slope',\n",
+       " 'topographic__specific_contributing_area']"
+      ]
+     },
+     "execution_count": 6,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "sorted(LandslideProbability.input_var_names)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Review the details of what each variable represents."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 7,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "{'topographic__specific_contributing_area': 'specific contributing (upslope area/cell face ) that drains to node',\n",
+       " 'topographic__slope': 'slope of surface at node represented by tan theta',\n",
+       " 'soil__transmissivity': 'mode rate of water transmitted through a unit width of saturated soil - either provided or calculated with Ksat and soil depth',\n",
+       " 'soil__saturated_hydraulic_conductivity': 'mode rate of water transmitted through soil - provided if transmissivity is NOT provided to calculate tranmissivity  with soil depth',\n",
+       " 'soil__mode_total_cohesion': 'mode of combined root and soil cohesion at node',\n",
+       " 'soil__minimum_total_cohesion': 'minimum of combined root and soil cohesion at node',\n",
+       " 'soil__maximum_total_cohesion': 'maximum of combined root and soil cohesion at node',\n",
+       " 'soil__internal_friction_angle': 'critical angle just before failure due to friction between particles',\n",
+       " 'soil__density': 'wet bulk density of soil',\n",
+       " 'soil__thickness': 'soil depth to restrictive layer',\n",
+       " 'soil__mean_relative_wetness': 'Indicator of soil wetness; relative depth perched water table within the soil layer',\n",
+       " 'landslide__probability_of_failure': 'number of times FS is <=1 out of number of iterations user selected',\n",
+       " 'soil__probability_of_saturation': 'number of times relative wetness is >=1 out of number of iterations user selected'}"
+      ]
+     },
+     "execution_count": 7,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "LandslideProbability._var_doc"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Check the units of each variable."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 8,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "{'topographic__specific_contributing_area': 'm',\n",
+       " 'topographic__slope': 'tan theta',\n",
+       " 'soil__transmissivity': 'm2/day',\n",
+       " 'soil__saturated_hydraulic_conductivity': 'm/day',\n",
+       " 'soil__mode_total_cohesion': 'Pa or kg/m-s2',\n",
+       " 'soil__minimum_total_cohesion': 'Pa or kg/m-s2',\n",
+       " 'soil__maximum_total_cohesion': 'Pa or kg/m-s2',\n",
+       " 'soil__internal_friction_angle': 'degrees',\n",
+       " 'soil__density': 'kg/m3',\n",
+       " 'soil__thickness': 'm',\n",
+       " 'soil__mean_relative_wetness': 'None',\n",
+       " 'landslide__probability_of_failure': 'None',\n",
+       " 'soil__probability_of_saturation': 'None'}"
+      ]
+     },
+     "execution_count": 8,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "LandslideProbability._var_units"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Now we will establish a RasterModelGrid based on a DEM for assigning our variables to.\n",
+    "Nodes are the center point of grid cells or pixels that are 30 m by 30 m in this example. "
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### 2.4. Create a watershed subset using a regional RasterModelGrid"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "The shapefile that you will download is a GIS point file with a table containing a grid_code for each 30m DEM cell in the Thunder Creek watershed. We import this table to generate a list of locations we want to keep \"Open\" as active nodes in this Landlab application for Thunder Creek.   \n",
+    "\n",
+    "Here we will establish the watershed domain for our modeling study. In step 2.2.2, we have downloaded the DEM of the entire NOCA study region of Strauch et al. (2018) as well as other input data. This includes NoData resource that was created using a mask of Thunder Creek will be downladed and used to set no data nodes as inactive nodes (e.g., -9999). This step will establish boundary conditions. In your final run when you output modeled probability of landslide initiation, you will notice gaps in the model results at some ridge tops and peaks. This results from excluding glaciated areas and bedrock. <br />\n",
+    "This might take a few minutes as the park is large (2,757 km2).\n",
+    "\n",
+    "This shapefile resource was uploaded to HydroShare to generate an interoperable Notebook.  To learn more about this data visit [Thunder Creek Landlab Node ID Point Shapefile\n",
+    "](https://www.hydroshare.org/resource/8bf8de77227c4dcba4816dbe15e55687/)\n",
+    "\n",
+    "#### 2.4.1 Get shapefile of ThunderCreek watershed\n",
+    "The shapefile table contains Landlab Gridcode value and Albers Conical X and Y values, spatial projection is WGS84.  "
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 9,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "This resource already exists in your userspace.\n",
+      "8bf8de77227c4dcba4816dbe15e55687/\n",
+      "|-- 8bf8de77227c4dcba4816dbe15e55687/\n",
+      "|   |-- bagit.txt\n",
+      "|   |-- manifest-md5.txt\n",
+      "|   |-- readme.txt\n",
+      "|   |-- tagmanifest-md5.txt\n",
+      "|   |-- data/\n",
+      "|   |   |-- resourcemap.xml\n",
+      "|   |   |-- resourcemetadata.xml\n",
+      "|   |   |-- contents/\n",
+      "|   |   |   |-- Thunder_node_id_WGS.cpg\n",
+      "|   |   |   |-- Thunder_node_id_WGS.dbf\n",
+      "|   |   |   |-- Thunder_node_id_WGS.prj\n",
+      "|   |   |   |-- Thunder_node_id_WGS.sbn\n",
+      "|   |   |   |-- Thunder_node_id_WGS.sbx\n",
+      "|   |   |   |-- Thunder_node_id_WGS.shp\n",
+      "|   |   |   |-- Thunder_node_id_WGS.shx\n",
+      "\n",
+      "Do you want to overwrite these data [Y/n]? n\n"
+     ]
+    },
+    {
+     "data": {
+      "text/html": [
+       "<b>Found the following file(s) associated with this HydroShare resource.</b>"
+      ],
+      "text/plain": [
+       "<IPython.core.display.HTML object>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "text/html": [
+       "Thunder_node_id_WGS.cpg<br>Thunder_node_id_WGS.dbf<br>Thunder_node_id_WGS.prj<br>Thunder_node_id_WGS.sbn<br>Thunder_node_id_WGS.sbx<br>Thunder_node_id_WGS.shp<br>Thunder_node_id_WGS.shx"
+      ],
+      "text/plain": [
+       "<IPython.core.display.HTML object>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "text/html": [
+       "These files are stored in a dictionary called <b>hs.content</b> for your convenience.  To access a file, simply issue the following command where MY_FILE is one of the files listed above: <pre>hs.content[\"MY_FILE\"] </pre> "
+      ],
+      "text/plain": [
+       "<IPython.core.display.HTML object>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "Data_ResourceID = \"8bf8de77227c4dcba4816dbe15e55687\"\n",
+    "hs.getResourceFromHydroShare(Data_ResourceID)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 10,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "/home/jovyan/work/notebooks/data/8bf8de77227c4dcba4816dbe15e55687/8bf8de77227c4dcba4816dbe15e55687/data/contents/Thunder_node_id_WGS.shp\n"
+     ]
+    }
+   ],
+   "source": [
+    "Node_path = os.path.join(\n",
+    "    \"/home/jovyan/work/notebooks/data\",\n",
+    "    str(Data_ResourceID),\n",
+    "    str(Data_ResourceID),\n",
+    "    \"data/contents/\",\n",
+    "    \"Thunder_node_id_WGS.shp\",\n",
+    ")\n",
+    "print(Node_path)\n",
+    "NodeID_shpfile = gpd.GeoDataFrame.from_file(Node_path)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Print a sample portion (bottom) of the table\n",
+    "* Col 1 = FID or POINTID\n",
+    "* Col 2 = landlab grid code for NOCA region\n",
+    "* Col 3 Point_X - the Alber Conical Latitude (original dataset)\n",
+    "* Col 4 Point_Y -  the Alber Conical Longitude (original dataset)\n",
+    "* Geometry of lat/lon in WGS coordinate system of this GIS point file for launching in HydroShareGIS App"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 11,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>POINTID</th>\n",
+       "      <th>GRID_CODE</th>\n",
+       "      <th>POINT_X</th>\n",
+       "      <th>POINT_Y</th>\n",
+       "      <th>geometry</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>306808</th>\n",
+       "      <td>0</td>\n",
+       "      <td>2348058</td>\n",
+       "      <td>-1841730.0</td>\n",
+       "      <td>3076320.0</td>\n",
+       "      <td>POINT (-120.9517781910061 48.49903226527576)</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>306809</th>\n",
+       "      <td>0</td>\n",
+       "      <td>2348059</td>\n",
+       "      <td>-1841700.0</td>\n",
+       "      <td>3076320.0</td>\n",
+       "      <td>POINT (-120.9513901735801 48.49910301141747)</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>306810</th>\n",
+       "      <td>0</td>\n",
+       "      <td>2348060</td>\n",
+       "      <td>-1841670.0</td>\n",
+       "      <td>3076320.0</td>\n",
+       "      <td>POINT (-120.9510021553025 48.49917375650436)</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>306811</th>\n",
+       "      <td>0</td>\n",
+       "      <td>2348061</td>\n",
+       "      <td>-1841640.0</td>\n",
+       "      <td>3076320.0</td>\n",
+       "      <td>POINT (-120.9506141361734 48.49924450053641)</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>306812</th>\n",
+       "      <td>0</td>\n",
+       "      <td>2345876</td>\n",
+       "      <td>-1841640.0</td>\n",
+       "      <td>3076290.0</td>\n",
+       "      <td>POINT (-120.9505098563902 48.49898126887325)</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "        POINTID  GRID_CODE    POINT_X    POINT_Y  \\\n",
+       "306808        0    2348058 -1841730.0  3076320.0   \n",
+       "306809        0    2348059 -1841700.0  3076320.0   \n",
+       "306810        0    2348060 -1841670.0  3076320.0   \n",
+       "306811        0    2348061 -1841640.0  3076320.0   \n",
+       "306812        0    2345876 -1841640.0  3076290.0   \n",
+       "\n",
+       "                                            geometry  \n",
+       "306808  POINT (-120.9517781910061 48.49903226527576)  \n",
+       "306809  POINT (-120.9513901735801 48.49910301141747)  \n",
+       "306810  POINT (-120.9510021553025 48.49917375650436)  \n",
+       "306811  POINT (-120.9506141361734 48.49924450053641)  \n",
+       "306812  POINT (-120.9505098563902 48.49898126887325)  "
+      ]
+     },
+     "execution_count": 11,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "NodeID_shpfile.tail()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Make an array using Grid_Code column to develop a node based mask. No output from this command."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 12,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "filtercriteria = np.array(NodeID_shpfile.GRID_CODE)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "#### Load  spatial landslide model inputs from ASCII textfile (ArcGIS raster conversion) into Landlab grid\n",
+    "\n",
+    "Create Landlab RasterModelGrid using DEM grid with elevation - this takes approximately 60 sec for the North Cascades National Park (NOCA). "
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 13,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "dict_keys(['topographic__elevation'])"
+      ]
+     },
+     "execution_count": 13,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "(grid, z) = read_esri_ascii(\n",
+    "    data_folder + \"/elevation.txt\", name=\"topographic__elevation\"\n",
+    ")\n",
+    "grid.at_node.keys()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "The original NOCA dem dataset has nodes closed outside of NOCA.\n",
+    "All nodes insides of NOCA are open. To close all all nodes except those inside Thunder Creek: \n",
+    "* Close all nodes in the landlab RasterModelGrid.\n",
+    "* Open the Landlab RasterModelGrid only within the Thunder Creek watershed.\n",
+    "* Assign Core Nodes (these are the only nodes where computation will be executed)."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Plot the elevation grid of NOCA"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 14,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<matplotlib.figure.Figure at 0x7f06b9466780>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "imshow_grid(grid, \"topographic__elevation\", limits=(0, 3000))"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Plot where the watershed is witin NOCA and zoom in."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 15,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "grid.status_at_node[grid.nodes.flatten()] = CLOSED_BOUNDARY\n",
+    "grid.status_at_node[filtercriteria] = CORE_NODE"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 16,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# In case the DEM has no data values inside the subset area, set boundary conditions closed.\n",
+    "grid.set_nodata_nodes_to_closed(grid.at_node[\"topographic__elevation\"], -9999)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 17,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "(30000, 55000)"
+      ]
+     },
+     "execution_count": 17,
+     "metadata": {},
+     "output_type": "execute_result"
+    },
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<matplotlib.figure.Figure at 0x7f063b149a20>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "fig = plt.figure(\"Limit NOCA computational nodes to Thunder Creek extent\")\n",
+    "xticks = np.arange(-0.1, 0.8, 0.4)\n",
+    "ax1 = fig.add_subplot(221)\n",
+    "ax1.xaxis.set_visible(False)\n",
+    "imshow_grid(\n",
+    "    grid,\n",
+    "    \"topographic__elevation\",\n",
+    "    limits=(0, 3000),\n",
+    "    plot_name=\"NOCA extent\",\n",
+    "    allow_colorbar=False,\n",
+    "    color_for_closed=\"white\",\n",
+    ")\n",
+    "ax2 = fig.add_subplot(222)\n",
+    "ax2.xaxis.set_visible(False)\n",
+    "imshow_grid(\n",
+    "    grid,\n",
+    "    \"topographic__elevation\",\n",
+    "    limits=(0, 3000),\n",
+    "    plot_name=\"Thunder Creek extent\",\n",
+    "    color_for_closed=\"white\",\n",
+    ")\n",
+    "plt.xlim(25000, 55000)\n",
+    "plt.ylim(30000, 55000)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Confirm the size of the grid, nodes located every 30 m."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 18,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "7029145"
+      ]
+     },
+     "execution_count": 18,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "grid.number_of_nodes"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Confirm the size of the core nodes where we'll run our model, a subset of the nodes for the watershed."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 19,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "306798"
+      ]
+     },
+     "execution_count": 19,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "grid.number_of_core_nodes"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### 2.5. Attach data (e.g., soil and vegetation variables) to the Landlab rastermodelgrid\n",
+    "This will be used to calculate shallow landslide probability and set boundary conditions. THe data we attach in this step was downloaded in Step 2.2.2. \n",
+    "\n",
+    "#### For each input below\n",
+    "1. Load data from ascii text file\n",
+    "2. Add this data as node variable to the Thunder Creek grid\n",
+    "3. Set boundary conditions\n",
+    "\n",
+    "For the entire NOCA extent, this takes ~60 sec to load each file using the CUAHSI JupyterHub server. "
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "#### 2.5.1 Load slope\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 20,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "(grid1, slope) = read_esri_ascii(data_folder + \"/slope_tang17d.txt\")\n",
+    "grid.add_field(\"node\", \"topographic__slope\", slope)\n",
+    "grid.set_nodata_nodes_to_closed(grid.at_node[\"topographic__slope\"], -9999)\n",
+    "grid.set_nodata_nodes_to_closed(grid.at_node[\"topographic__slope\"], 0.0)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 21,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "4.430756\n",
+      "0.3057313\n"
+     ]
+    }
+   ],
+   "source": [
+    "print(np.max(grid.at_node[\"topographic__slope\"][grid.core_nodes]))\n",
+    "print(np.min(grid.at_node[\"topographic__slope\"][grid.core_nodes]))"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "#### 2.5.2 Load contributing area"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "(grid1, ca) = read_esri_ascii(data_folder + \"/cont_area.txt\")\n",
+    "grid.add_field(\"node\", \"topographic__specific_contributing_area\", ca)\n",
+    "grid.set_nodata_nodes_to_closed(\n",
+    "    grid.at_node[\"topographic__specific_contributing_area\"], -9999\n",
+    ")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "#### 2.5.3 Load transmissivity"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "(grid1, T) = read_esri_ascii(data_folder + \"/transmis.txt\")\n",
+    "grid.add_field(\"node\", \"soil__transmissivity\", T)\n",
+    "grid.set_nodata_nodes_to_closed(grid.at_node[\"soil__transmissivity\"], -9999)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "#### 2.5.4 Load cohesion (mode, min, and max) \n",
+    "This takes ~3 minutes because 3 cohesion fields are provided to create more flexibility in how cohesion is distributed on the landscape with different vegetation."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "(grid1, C) = read_esri_ascii(data_folder + \"/cohesion_mode.txt\")\n",
+    "C[C == 0.0] = 1.0  # ensure minimum is >0 Pa for use in distributions generation\n",
+    "grid.add_field(\"node\", \"soil__mode_total_cohesion\", C)\n",
+    "grid.set_nodata_nodes_to_closed(grid.at_node[\"soil__mode_total_cohesion\"], -9999)\n",
+    "\n",
+    "(grid1, C_min) = read_esri_ascii(data_folder + \"/cohesion_min.txt\")\n",
+    "grid.add_field(\"node\", \"soil__minimum_total_cohesion\", C_min)\n",
+    "grid.set_nodata_nodes_to_closed(grid.at_node[\"soil__minimum_total_cohesion\"], -9999)\n",
+    "\n",
+    "(grid1, C_max) = read_esri_ascii(data_folder + \"/cohesion_max.txt\")\n",
+    "grid.add_field(\"node\", \"soil__maximum_total_cohesion\", C_max)\n",
+    "grid.set_nodata_nodes_to_closed(grid.at_node[\"soil__maximum_total_cohesion\"], -9999)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "#### 2.5.5 Load internal angle of friction"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "(grid1, phi) = read_esri_ascii(data_folder + \"/frict_angle.txt\")\n",
+    "grid.add_field(\"node\", \"soil__internal_friction_angle\", phi)\n",
+    "grid.set_nodata_nodes_to_closed(grid.at_node[\"soil__internal_friction_angle\"], -9999)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "#### 2.5.6 Set soil density values\n",
+    "In this example, we assign all nodes a constant value."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "grid[\"node\"][\"soil__density\"] = 2000 * np.ones(grid.number_of_nodes)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "#### 2.5.7 Load soil thickness"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "(grid1, hs) = read_esri_ascii(data_folder + \"/soil_depth.txt\")\n",
+    "grid.add_field(\"node\", \"soil__thickness\", hs)\n",
+    "grid.set_nodata_nodes_to_closed(grid.at_node[\"soil__thickness\"], -9999)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "#### 2.5.8 Load observed landslide inventory. Class 1-5 are landslides, 8 is no landslide mapped for later plotting"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "(grid1, slides) = read_esri_ascii(data_folder + \"/landslide_type.txt\")\n",
+    "grid.add_field(\"node\", \"landslides\", slides)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### 2.6. Specify recharge option as _data driven spatial_ and access Python dictionaries to generate recharge distributions \n",
+    "Recharge in this model represents the annual maximum recharge in mm/day generated within the upslope contributing area of each model element. This corresponds to the wettest conditions expected annually, which would provide the highest pore-water pressure in a year. Details of this approach can be found in Strauch et al. (2018).\n",
+    "\n",
+    "#### 2.6.1 Select recharge method from the component"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "distribution = \"data_driven_spatial\""
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "#### 2.6.2 Load pre-processed routed flows dictionaries \n",
+    "These contain HSD_id and fractional drainage at each node and recharge dictionaries.  HSD is the Hydrologic Source Domain, which is the VIC hydrologic model data in this case study at ~5x6 km2 grid size.  The 'pickle' utility loads existing dictionaries."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# dict of node id (key) and HSD_ids (values)\n",
+    "HSD_id_dict = pickle.load(\n",
+    "    open(data_folder + \"/dict_uniq_ids.p\", \"rb\"), encoding=\"latin1\"\n",
+    ")\n",
+    "# dict of node id (key) and fractions (values)\n",
+    "fract_dict = pickle.load(open(data_folder + \"/dict_coeff.p\", \"rb\"), encoding=\"latin1\")\n",
+    "# dict of HSD id (key) with arrays of recharge (values)\n",
+    "HSD_dict = pickle.load(open(data_folder + \"/HSD_dict.p\", \"rb\"), encoding=\"latin1\")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "#### 2.6.3 Combine dictionaries into __ordered__ parameters\n",
+    "This sequence of parameters is required for _data driven spatial_ distribution in the component.  Recharge is this model is unique to each node represented by an array. "
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "HSD_inputs = [HSD_dict, HSD_id_dict, fract_dict]"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### 2.7. Set Number of iterations to run Monte Carlo simulation"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "The landslide component employes the infinite slope model to calculate factor-of-safety index values using a Monte Carlo simulation, which randomly selects input values from parameter distributions. You can specify the number of Monte Carlo samples, but the default is 250. The larger the Monte Carlos sample size, the longer the program runs, but the more precise the probability of failure results become. Strauch et al. (2018) sampled 3,000 times for each parameter in each model grid."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "iterations = 250"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## 3. Results"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### 3.1. Run the Landlab LandslideProbability Component in Thunder Creek for pre-fire conditions"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "#### 3.1.1 Initialize Pre-fire\n",
+    "To run the landslide model, we first instantiate the LandslideProbability component with the above parameters, as well as the grid and number of samples we specified before. Instantiation creates an instance of a class called LS_prob. \n",
+    "\n",
+    "No outputs are generated by this command as it is setting up the recharge and instantiating the component."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "LS_prob = LandslideProbability(\n",
+    "    grid,\n",
+    "    number_of_iterations=iterations,\n",
+    "    groudwater__recharge_distribution=distribution,\n",
+    "    groudwater__recharge_HSD_inputs=HSD_inputs,\n",
+    ")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "#### 3.1.2 Run the Pre-fire model\n",
+    "Once the component has been instantiated, we generate outputs from running the component by calling the component's 'calculate_landslide_probability' method using the class instance (e.g., LS_prob). The cell below runs the model; in the following section we will assessing the results. These calculations will take a few minutes given the size of the modeling domain represented by core nodes."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "scrolled": true
+   },
+   "outputs": [],
+   "source": [
+    "LS_prob.calculate_landslide_probability()\n",
+    "print(\"Landslide probability successfully calculated\")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "What is the maximum probabilty of failure we found?"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "np.max(grid.at_node[\"landslide__probability_of_failure\"][grid.core_nodes])"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "The outputs of landslide model simulation are:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "sorted(LS_prob.output_var_names)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "This simulation generates a probability value for each core node. "
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "grid.at_node[\"landslide__probability_of_failure\"]"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "This simulation generates a probability of saturation value for each core node as well."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "grid.at_node[\"soil__probability_of_saturation\"]"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "-9999 means there is no data for that cell, it is a closed node."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### 3.2. Run the Landlab LandslideProbability Component in Thunder Creek for fire conditions"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Make a 'grid_fire' version of 'grid' for which we will give post-fire cohesion parameter values 30% of the original cohesion. This is a crude estimation of the lowest root cohesion after tree removal based on a combined decay and regrowth model (e.g., Sidle, 1992).\n",
+    "\n",
+    "Sidle, R. C. (1992), A theoretical model of the effects of timber harvesting\n",
+    "on slope stability, Water Resour. Res., 28(7), 1897–1910.\n",
+    "\n",
+    "#### 3.2.1 Make a Post-fire copy of the grid"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "import copy\n",
+    "\n",
+    "grid_fire = copy.deepcopy(grid)\n",
+    "grid_fire.at_node[\"soil__mode_total_cohesion\"] = (\n",
+    "    grid.at_node[\"soil__mode_total_cohesion\"] * 0.3\n",
+    ")\n",
+    "grid_fire.at_node[\"soil__minimum_total_cohesion\"] = (\n",
+    "    grid.at_node[\"soil__minimum_total_cohesion\"] * 0.3\n",
+    ")\n",
+    "grid_fire.at_node[\"soil__maximum_total_cohesion\"] = (\n",
+    "    grid.at_node[\"soil__maximum_total_cohesion\"] * 0.3\n",
+    ")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "#### 3.2.2 Change the Post-fire cohesion values "
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "scrolled": true
+   },
+   "outputs": [],
+   "source": [
+    "print(\"this is the highest mode value for coehsion before fire across the domain\")\n",
+    "print(np.max(grid.at_node[\"soil__mode_total_cohesion\"][grid.core_nodes]))\n",
+    "print(\"this is the lowest mode value for coehsion before fire across the domain\")\n",
+    "print(np.min(grid.at_node[\"soil__mode_total_cohesion\"][grid.core_nodes]))\n",
+    "print(\"this is the highest mode value for coehsion after fire across the domain\")\n",
+    "print(np.max(grid_fire.at_node[\"soil__mode_total_cohesion\"][grid.core_nodes]))\n",
+    "print(\"this is the lowest mode value for coehsion after fire across the domain\")\n",
+    "print(np.min(grid_fire.at_node[\"soil__mode_total_cohesion\"][grid.core_nodes]))"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "#### 3.2.3  Initialize the Post-fire model\n",
+    "Now we'll run the landslide component with the adjusted cohesion, everything else kept constant."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "scrolled": true
+   },
+   "outputs": [],
+   "source": [
+    "LS_probFire = LandslideProbability(\n",
+    "    grid_fire,\n",
+    "    number_of_iterations=iterations,\n",
+    "    groudwater__recharge_distribution=distribution,\n",
+    "    groudwater__recharge_HSD_inputs=HSD_inputs,\n",
+    ")\n",
+    "print(\"Post-fire cohesion successfully instantiated\")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "#### 3.2.4 Run the Post-fire model"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "scrolled": true
+   },
+   "outputs": [],
+   "source": [
+    "LS_probFire.calculate_landslide_probability()\n",
+    "print(\"Landslide probability successfully calculated\")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "np.max(grid_fire.at_node[\"landslide__probability_of_failure\"][grid.core_nodes])"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "The outputs of landslide model simulation are:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "sorted(LS_probFire.output_var_names)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "\n",
+    "This simulation generates a probability value for each core node. "
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "grid_fire.at_node[\"landslide__probability_of_failure\"]"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### 3.3. Display and visualize results of stability analysis"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Set plotting parameters"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "mpl.rcParams[\"xtick.labelsize\"] = 15\n",
+    "mpl.rcParams[\"ytick.labelsize\"] = 15\n",
+    "mpl.rcParams[\"lines.linewidth\"] = 1\n",
+    "mpl.rcParams[\"axes.labelsize\"] = 18\n",
+    "mpl.rcParams[\"legend.fontsize\"] = 15"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Plot elevation"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "plt.figure(\"Elevations from the DEM [m]\")\n",
+    "imshow_grid_at_node(\n",
+    "    grid,\n",
+    "    \"topographic__elevation\",\n",
+    "    cmap=\"terrain\",\n",
+    "    grid_units=(\"coordinates\", \"coordinates\"),\n",
+    "    shrink=0.75,\n",
+    "    var_name=\"Elevation\",\n",
+    "    var_units=\"m\",\n",
+    "    color_for_closed=\"white\",\n",
+    ")\n",
+    "plt.xlim(25000, 55000)\n",
+    "plt.ylim(30000, 55000)\n",
+    "plt.savefig(\"NOCA_elevation.png\")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Excluded areas from the analysis are shown in white, including outside the park and inside the park areas that are water bodies, snow, glaciers, wetlands, exposed bedrock, and slopes <= 17 degrees. "
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Plot slope overlaid with mapped landslide types. Takes about a few minutes."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "plt.figure(\"Landslides\")\n",
+    "ls_mask1 = grid.at_node[\"landslides\"] != 1.0\n",
+    "ls_mask2 = grid.at_node[\"landslides\"] != 2.0\n",
+    "ls_mask3 = grid.at_node[\"landslides\"] != 3.0\n",
+    "ls_mask4 = grid.at_node[\"landslides\"] != 4.0\n",
+    "overlay_landslide1 = np.ma.array(grid.at_node[\"landslides\"], mask=ls_mask1)\n",
+    "overlay_landslide2 = np.ma.array(grid.at_node[\"landslides\"], mask=ls_mask2)\n",
+    "overlay_landslide3 = np.ma.array(grid.at_node[\"landslides\"], mask=ls_mask3)\n",
+    "overlay_landslide4 = np.ma.array(grid.at_node[\"landslides\"], mask=ls_mask4)\n",
+    "imshow_grid_at_node(\n",
+    "    grid,\n",
+    "    \"topographic__slope\",\n",
+    "    cmap=\"pink\",\n",
+    "    grid_units=(\"coordinates\", \"coordinates\"),\n",
+    "    vmax=2.0,\n",
+    "    shrink=0.75,\n",
+    "    var_name=\"Slope\",\n",
+    "    var_units=\"m/m\",\n",
+    "    color_for_closed=\"white\",\n",
+    ")\n",
+    "imshow_grid_at_node(\n",
+    "    grid, overlay_landslide1, color_for_closed=\"None\", allow_colorbar=False, cmap=\"cool\"\n",
+    ")\n",
+    "imshow_grid_at_node(\n",
+    "    grid,\n",
+    "    overlay_landslide2,\n",
+    "    color_for_closed=\"None\",\n",
+    "    allow_colorbar=False,\n",
+    "    cmap=\"autumn\",\n",
+    ")\n",
+    "imshow_grid_at_node(\n",
+    "    grid,\n",
+    "    overlay_landslide3,\n",
+    "    color_for_closed=\"None\",\n",
+    "    allow_colorbar=False,\n",
+    "    cmap=\"winter\",\n",
+    ")\n",
+    "imshow_grid_at_node(\n",
+    "    grid,\n",
+    "    overlay_landslide4,\n",
+    "    color_for_closed=\"None\",\n",
+    "    allow_colorbar=False,\n",
+    "    cmap=\"summer\",\n",
+    ")\n",
+    "# plt.savefig('NOCA_Landslides_on_Slope.png')\n",
+    "plt.xlim(25000, 55000)\n",
+    "plt.ylim(30000, 55000)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Legend to mapped landslides: blue - debris avalanches, cyan - falls/topples, red - debris torrents, and green - slumps/creeps"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Plot of soil depth (m)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "plt.figure(\"Soil Thickness\")\n",
+    "imshow_grid_at_node(\n",
+    "    grid,\n",
+    "    \"soil__thickness\",\n",
+    "    cmap=\"copper_r\",\n",
+    "    grid_units=(\"coordinates\", \"coordinates\"),\n",
+    "    shrink=0.75,\n",
+    "    var_name=\"Soil Thickness\",\n",
+    "    var_units=\"m\",\n",
+    "    color_for_closed=\"white\",\n",
+    ")\n",
+    "plt.xlim(25000, 55000)\n",
+    "plt.ylim(30000, 55000)\n",
+    "# plt.savefig('NOCA_SoilDepth.png')"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Plot probability of saturation "
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "fig = plt.figure(\"Probability of Saturation\")\n",
+    "xticks = np.arange(-0.1, 0.8, 0.4)\n",
+    "ax1 = fig.add_subplot(221)\n",
+    "ax1.xaxis.set_visible(True)\n",
+    "imshow_grid(\n",
+    "    grid,\n",
+    "    \"soil__probability_of_saturation\",\n",
+    "    cmap=\"YlGnBu\",\n",
+    "    limits=((0), (1)),\n",
+    "    plot_name=\"Pre-Fire\",\n",
+    "    allow_colorbar=False,\n",
+    "    grid_units=(\"coordinates\", \"coordinates\"),\n",
+    "    color_for_closed=\"white\",\n",
+    ")\n",
+    "plt.xlim(25000, 55000)\n",
+    "plt.ylim(30000, 55000)\n",
+    "ax2 = fig.add_subplot(222)\n",
+    "ax2.xaxis.set_visible(True)\n",
+    "ax2.yaxis.set_visible(False)\n",
+    "imshow_grid(\n",
+    "    grid_fire,\n",
+    "    \"soil__probability_of_saturation\",\n",
+    "    cmap=\"YlGnBu\",\n",
+    "    limits=((0), (1)),\n",
+    "    plot_name=\"Post-Fire\",\n",
+    "    grid_units=(\"coordinates\", \"coordinates\"),\n",
+    "    color_for_closed=\"white\",\n",
+    ")\n",
+    "plt.xlim(25000, 55000)\n",
+    "plt.ylim(30000, 55000)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "This map shows the probability of saturation as high throughout much of the area because we modeled the annual maximum recharge, which is esssentially the worst case conditions that might lead to instability."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Plot probability of failure; Compare this with the elevation and slope maps."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "fig = plt.figure(\"Probability of Failure\")\n",
+    "xticks = np.arange(-0.1, 0.8, 0.4)\n",
+    "ax1 = fig.add_subplot(221)\n",
+    "# ax1.xaxis.set_visible(False)\n",
+    "imshow_grid(\n",
+    "    grid,\n",
+    "    \"landslide__probability_of_failure\",\n",
+    "    cmap=\"OrRd\",\n",
+    "    limits=((0), (1)),\n",
+    "    plot_name=\"Pre-fire\",\n",
+    "    allow_colorbar=False,\n",
+    "    grid_units=(\"coordinates\", \"coordinates\"),\n",
+    "    color_for_closed=\"white\",\n",
+    ")\n",
+    "plt.xlim(25000, 55000)\n",
+    "plt.ylim(30000, 55000)\n",
+    "ax2 = fig.add_subplot(222)\n",
+    "ax2.yaxis.set_visible(False)\n",
+    "imshow_grid(\n",
+    "    grid_fire,\n",
+    "    \"landslide__probability_of_failure\",\n",
+    "    cmap=\"OrRd\",\n",
+    "    limits=((0), (1)),\n",
+    "    plot_name=\"Post-fire\",\n",
+    "    grid_units=(\"coordinates\", \"coordinates\"),\n",
+    "    color_for_closed=\"white\",\n",
+    ")\n",
+    "plt.xlim(25000, 55000)\n",
+    "plt.ylim(30000, 55000)\n",
+    "\n",
+    "# plt.savefig('Probability_of_Failure_Original_Fire.png')"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "The map of probability of failure shows higher probabilities in a post-fire scenario with reduced cohesion."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "We can compare the range of probabilities before and after the simulated fire using a cumulative distribution plot.   "
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "X1 = np.sort(\n",
+    "    np.array(grid.at_node[\"landslide__probability_of_failure\"][grid.core_nodes])\n",
+    ")\n",
+    "Y1 = np.arange(1, len(X1) + 1) / len(X1)\n",
+    "X2 = np.sort(\n",
+    "    np.array(grid_fire.at_node[\"landslide__probability_of_failure\"][grid.core_nodes])\n",
+    ")\n",
+    "Y2 = np.arange(1, len(X2) + 1) / len(X2)\n",
+    "fig = plt.figure(\"Empirical CDF of Probability of Failure\")\n",
+    "plt.plot(X1, Y1, marker=\" \", color=\"black\", linewidth=2, label=\"Pre-fire\")\n",
+    "plt.plot(\n",
+    "    X2, Y2, marker=\" \", color=\"red\", linewidth=2, linestyle=\"dashed\", label=\"Post-fire\"\n",
+    ")\n",
+    "plt.xlabel(\"Probability of Failure\")\n",
+    "plt.ylabel(\"Fraction of Area\")\n",
+    "plt.legend()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "We can interpret this figure as a higher fraction of the landscape has a higher probability of failure (red) after fire than before fire (black). "
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "To review the fields assigned to the grid, simply execute the following command."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "grid.at_node"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Export data from model run: FS probability, mean Reletive wetness, probability of saturation"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "import pandas as pd\n",
+    "\n",
+    "core_nodes = grid.core_nodes\n",
+    "data_extracted = {\n",
+    "    \"Prob_fail_std\": np.array(\n",
+    "        grid.at_node[\"landslide__probability_of_failure\"][grid.core_nodes]\n",
+    "    ),\n",
+    "    \"mean_RW_std\": np.array(\n",
+    "        grid.at_node[\"soil__mean_relative_wetness\"][grid.core_nodes]\n",
+    "    ),\n",
+    "    \"prob_sat_std\": np.array(\n",
+    "        grid.at_node[\"soil__probability_of_saturation\"][grid.core_nodes]\n",
+    "    ),\n",
+    "    \"Prob_fail_fire\": np.array(\n",
+    "        grid_fire.at_node[\"landslide__probability_of_failure\"][grid_fire.core_nodes]\n",
+    "    ),\n",
+    "    \"mean_RW_fire\": np.array(\n",
+    "        grid_fire.at_node[\"soil__mean_relative_wetness\"][grid_fire.core_nodes]\n",
+    "    ),\n",
+    "    \"prob_sat_fire\": np.array(\n",
+    "        grid_fire.at_node[\"soil__probability_of_saturation\"][grid_fire.core_nodes]\n",
+    "    ),\n",
+    "}\n",
+    "headers = [\n",
+    "    \"Prob_fail_std\",\n",
+    "    \"mean_RW_std\",\n",
+    "    \"prob_sat_std\",\n",
+    "    \"Prob_fail_fire\",\n",
+    "    \"mean_RW_fire\",\n",
+    "    \"prob_sat_fire\",\n",
+    "]\n",
+    "df = pd.DataFrame(data_extracted, index=core_nodes, columns=(headers))\n",
+    "df.to_csv(\"Landslide_std_fire.csv\")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Make ascii files for raster creation in GIS"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "scrolled": true
+   },
+   "outputs": [],
+   "source": [
+    "write_esri_ascii(\"prob_fail_std.txt\", grid, names=\"landslide__probability_of_failure\")\n",
+    "write_esri_ascii(\"mean_RW_std.txt\", grid, names=\"soil__mean_relative_wetness\")\n",
+    "write_esri_ascii(\"prob_sat_std.txt\", grid, names=\"soil__probability_of_saturation\")\n",
+    "write_esri_ascii(\n",
+    "    \"prob_fail_fire.txt\", grid_fire, names=\"landslide__probability_of_failure\"\n",
+    ")\n",
+    "write_esri_ascii(\"mean_RW_fire.txt\", grid_fire, names=\"soil__mean_relative_wetness\")\n",
+    "write_esri_ascii(\n",
+    "    \"prob_sat_fire.txt\", grid_fire, names=\"soil__probability_of_saturation\"\n",
+    ")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "How long did the code above take to run?"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "print(\"Elapsed time is %3.2f seconds\" % (time.time() - st))"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## 4.0. Save the results back into HydroShare\n",
+    "<a name=\"creation\"></a>\n",
+    "\n",
+    "Using the `hs_utils` library, the results of the Geoprocessing steps above can be saved back into HydroShare.  First, define all of the required metadata for resource creation, i.e. *title*, *abstract*, *keywords*, *content files*.  In addition, we must define the type of resource that will be created, in this case *genericresource*.  \n",
+    "\n",
+    "***Note:*** Make sure you save the notebook at this point, so that all notebook changes will be saved into the new HydroShare resource.\n",
+    "\n",
+    "\n",
+    "***Option A*** : define the resource from which this \"NEW\" content has been derived.  This is one method for tracking resource provenance."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Create list of files to save to HydroShare. Verify location and names."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "scrolled": true
+   },
+   "outputs": [],
+   "source": [
+    "ThisNotebook = \"replicate_landslide_model_for_fire.ipynb\"  # check name for consistency\n",
+    "files = [\n",
+    "    homedir + ThisNotebook,\n",
+    "    homedir + \"Landslide_std_fire.csv\",\n",
+    "    homedir + \"prob_fail_std.txt\",\n",
+    "    homedir + \"mean_RW_std.txt\",\n",
+    "    homedir + \"prob_sat_std.txt\",\n",
+    "    homedir + \"prob_fail_fire.txt\",\n",
+    "    homedir + \"mean_RW_fire.txt\",\n",
+    "    homedir + \"prob_sat_fire.txt\",\n",
+    "]\n",
+    "print(files)  # print location and names of files to save"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "scrolled": false
+   },
+   "outputs": [],
+   "source": [
+    "hs = hydroshare.hydroshare()\n",
+    "help(hs)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "scrolled": false
+   },
+   "outputs": [],
+   "source": [
+    "# for each file downloaded onto the server folder, move to a new HydroShare Generic Resource\n",
+    "title = \"Landslide Model run with Monte Carlo from NOCA Observatory - Thunder Creek with Fire\"  # title for the new resource\n",
+    "abstract = \"This a reproducible demonstration of the landslide modeling results from eSurf paper: Strauch et al. (2018) \"  # abstract for the new resource\n",
+    "keywords = [\n",
+    "    \"landslide\",\n",
+    "    \"climate\",\n",
+    "    \"VIC\",\n",
+    "    \"saturation\",\n",
+    "    \"relative wetness\",\n",
+    "    \"fire\",\n",
+    "    \"Geohackweek\",\n",
+    "]  # keywords for the new resource\n",
+    "rtype = \"genericresource\"  # Hydroshare resource type\n",
+    "\n",
+    "# create the new resource\n",
+    "resource_id = hs.createHydroShareResource(\n",
+    "    abstract,\n",
+    "    title,\n",
+    "    keywords=keywords,\n",
+    "    resource_type=rtype,\n",
+    "    content_files=files,\n",
+    "    public=False,\n",
+    ")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  }
+ ],
+ "metadata": {
+  "anaconda-cloud": {},
+  "kernelspec": {
+   "display_name": "Python 3",
+   "language": "python",
+   "name": "python3"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.6.5"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 2
+}
diff --git a/landslide_model_for_fire/replicate_landslide_model_for_fire_fisher_creek.ipynb b/landslide_model_for_fire/replicate_landslide_model_for_fire_fisher_creek.ipynb
new file mode 100644
index 0000000..cbb1a0e
--- /dev/null
+++ b/landslide_model_for_fire/replicate_landslide_model_for_fire_fisher_creek.ipynb
@@ -0,0 +1,2482 @@
+{
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "<img src=\"http://landlab.github.io/assets/Landlab-logo.png\"\n",
+    "style=\"float:left;width:150px;padding:0px\">  \n",
+    "\n",
+    "<br /> \n",
+    "\n",
+    "# Reuse a published regional Landlab shallow landslide model to explore changes in forest cover at a subcatchment within the study area<br /> \n",
+    "### You will explore how to reuse model code and data in a subregion within a larger region where the model was developed \n",
+    "* Load data from a regional Landlab landslide model (Strauch et al., 2018) developed for the North Cascades National Park, WA USA, published on HydroShare. <br />\n",
+    "* Define a geographic subset (Fisher Creek watershed) within the study region.\n",
+    "* Explore landslide probability sensitivity to fire by adjusting the cohesion parameter for Fisher Creek.\n",
+    "* Save results to a new HydroShare resource.  <br />\n",
+    "\n",
+    "The shallow landslide model you will is based on a spatially distributed Monte Carlo solution of the infinite slope stability model. Detailes of the model and the study site are described in Strauch et al. (2018).\n",
+    "\n",
+    "Strauch R., Istanbulluoglu E., Nudurupati S.S., Bandaragoda C., Gasparini N.M., and G.E. Tucker (2018). A hydro-climatological approach to predicting regional landslide probability using Landlab. Earth Surf. Dynam., 6, 1–26, 2018. \n",
+    "https://www.earth-surf-dynam.net/6/1/2018/\n",
+    "\n",
+    " <br /> <img src=\"https://www.washington.edu/brand/files/2014/09/W-Logo_Purple_Hex.png\" style=\"float:left;width:120px;padding:10px\">   \n",
+    "<img src=\"http://www.ncsa.illinois.edu/assets/img/logos_ncsa.png\"\n",
+    " style=\"float:right;width:120px;padding:10px\"> \n",
+    "<img src=\"https://www.cuahsi.org/assets/images/logo.png\">"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## To run this notebook:\n",
+    "\n",
+    "Click in each shaded cell below and \"shift + enter\" to run each code block. Alternatively, you can run groups of cells by clicking \"Cell\" on the menu above and selecting your run options from the pull-down menu. This is also where you can clear outputs from previous runs.\n",
+    "\n",
+    "If an error occurs, click on *Kernal* and *Restart and Clear Outputs* in the menu above."
+   ]
+  },
+  {
+   "attachments": {
+    "image.png": {
+     "image/png": 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"
+    }
+   },
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## 1. Introduction\n",
+    "\n",
+    "### 1.1 Infinite Slope Factor of Safety Equation\n",
+    "\n",
+    "This equation predicts the ratio of stabilizing to destabilizing forces on a hillslope plane. The properties are assumed to represent an infinte plane, neglecting the boundary conditions around the landslide location. When FS<1, the slope is instable.\n",
+    "\n",
+    "![image.png](attachment:image.png)"
+   ]
+  },
+  {
+   "attachments": {
+    "image.png": {
+     "image/png": 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"
+    }
+   },
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "![image.png](attachment:image.png)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "C* can be calculated by the ratio of the sum of root cohesion, Cr, and soil cohesion, Cs, to the product of soil depth, density, and gravity."
+   ]
+  },
+  {
+   "attachments": {
+    "image.png": {
+     "image/png": "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"
+    }
+   },
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "![image.png](attachment:image.png)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Relative wetness is the ratio of depth of water subsurface flow above an impervious bedrock to the depth of soil. "
+   ]
+  },
+  {
+   "attachments": {
+    "image.png": {
+     "image/png": 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"
+    }
+   },
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "![image.png](attachment:image.png)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "R: recharge rate to water table (m/d)\n",
+    "T: soil transmissivity (m^2/d)\n",
+    "a: specific catchment area."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### 1.2 Monte Carlo solution of the FS equation\n",
+    "\n",
+    "Below the Monte Carlo approach used by Strauch et al. (2018) is illustrated in a figure. At each node of the model grid variables for soil and vegetation are generated from triangular distributions. Recharge to water table is obtained from hydrologic model simulations by selecting the largest daily value for each year. Probability of shallow landslide initiation is defined as the ratio of number of times FS<1 to the total sample size of FS calculations."
+   ]
+  },
+  {
+   "attachments": {
+    "image.png": {
+     "image/png": 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"
+    }
+   },
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "![image.png](attachment:image.png)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### Module overview for a Landlab Modeling Toolkit Landslide Introduction\n",
+    "\n",
+    "This Jupyter Notebook runs the Landlab LandslideProbability component on a 30-m digital elevation model (DEM) for Fisher Creek, a subwaterhsed in the larger study domain developed by Strauch et al. (2018), using _data driven spatial_ recharge distribution as described in the paper (https://www.earth-surf-dynam.net/6/1/2018/).\n",
+    "\n",
+    "\n",
+    "To run a landslide demonstration using the paper data and approach we will:<br >\n",
+    "1) Import data from North Cascades National Park (NOCA: study area)<br />\n",
+    "2) Review data needed as input for the landslide model<br />\n",
+    "3) Create a RasterModelGrid based on a 30-m DEM - subset to Fisher Creek watershed<br />\n",
+    "4) Assign data fields used to calculate landslide probability<br />\n",
+    "5) Specify recharge option as _data driven spatial_ and access Python dictionaries to generate recharge distributions<br />\n",
+    "6) Set Number of iterations to run Monte Carlo simulation<br />\n",
+    "7) Run Landlab LandslideProbability component<br /> \n",
+    "8) Run the model again to simulate post-fire conditions,<br />\n",
+    "9) Display and visualize results of stability analysis<br /> \n",
+    "10) Save Notebook and Results back to HydroShare<br /> \n",
+    "<br /> \n",
+    "\n",
+    "The estimated time to run this Notebook is 30-60 minutes with 20 minutes of computation time. \n"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## 2.0 Methods"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## 2.1 Notebook Setup and Preparation\n",
+    "\n",
+    "To run this notebook, we must import several libraries.\n",
+    "The hs_utils library provides functions for interacting with HydroShare, including resource querying, dowloading, and creation. Additional libraries support the functions of Landlab."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### 2.1.1 Install Python packages \n",
+    "The CUAHSI JupyterHub server provides many Python packages and libraries, but to add additional libraries to your personal user space, use the cell below.  To request an Installation to the server, visit https://github.com/hydroshare/hydroshare-jupyterhub, create a New Issue, and add the label 'Installation Request'. Uncomment the lines below to install the library. Python2 and Python3 kernels are both available. If you are a new Landlab user on Hydroshare you are advised to run the code block below in your first run of this tutorial, then comment them out."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "#!python2 -m pip install xarray\n",
+    "#!python2 -m pip install plotly\n",
+    "#!python2 -m pip install geopandas\n",
+    "#!python2 -m pip install rasterstats\n",
+    "#!python2 -m pip install affine\n",
+    "#!python2 -m pip install seaborn\n",
+    "# from __future__ import absolute_import, division, print_function, unicode_literals"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### 2.2.2 Import Python libraries"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 1,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# Import standard Python utilities for calculating and plotting\n",
+    "import os\n",
+    "import pickle as pickle\n",
+    "\n",
+    "# Import general tools\n",
+    "import time\n",
+    "import warnings\n",
+    "from collections import defaultdict\n",
+    "from datetime import datetime, timedelta\n",
+    "\n",
+    "import geopandas as gpd\n",
+    "import matplotlib as mpl\n",
+    "import matplotlib.pyplot as plt\n",
+    "import numpy as np\n",
+    "import pandas as pd\n",
+    "import six\n",
+    "from landlab import CLOSED_BOUNDARY, CORE_NODE, imshow_grid_at_node\n",
+    "from landlab.io import read_esri_ascii, write_esri_ascii\n",
+    "from landlab.plot import imshow_grid\n",
+    "\n",
+    "# Import Landlab libraries\n",
+    "import landslide_probability\n",
+    "from landslide_probability import LandslideProbability\n",
+    "\n",
+    "# Import utilities for importing and exporting to HydroShare\n",
+    "from utilities import hydroshare\n",
+    "\n",
+    "mpl.use(\"agg\")\n",
+    "%matplotlib inline\n",
+    "warnings.filterwarnings(\"ignore\")\n",
+    "\n",
+    "\n",
+    "st = time.time()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### 2.1.3 Connection with HydroShare\n",
+    "After importing libraires, we now establish a secure connection with HydroShare by instantiating the hydroshare class that is defined within hs_utils. In addition to connecting with HydroShare, this command also sets and prints environment variables for several parameters that will be useful for saving work back to HydroShare."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 2,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Adding the following system variables:\n",
+      "   HS_USR_NAME = swalt826\n",
+      "   HS_RES_ID = 70b977e22af544f8a7e5a803935c329c\n",
+      "   HS_RES_TYPE = genericresource\n",
+      "   JUPYTER_HUB_IP = jupyter.cuahsi.org\n",
+      "\n",
+      "These can be accessed using the following command: \n",
+      "   os.environ[key]\n",
+      "\n",
+      "   (e.g.)\n",
+      "   os.environ[\"HS_USR_NAME\"]  => swalt826\n",
+      "\n",
+      "The hs_utils library requires a secure connection to your HydroShare account.\n",
+      "Enter the HydroShare password for user 'swalt826': ········\n",
+      "Successfully established a connection with HydroShare\n",
+      "This is a basic Unix folder structure.\n",
+      "Data will be loaded from and saved to:/home/jovyan/work/notebooks/data/70b977e22af544f8a7e5a803935c329c/70b977e22af544f8a7e5a803935c329c/data/contents/\n"
+     ]
+    }
+   ],
+   "source": [
+    "hs = hydroshare.hydroshare()\n",
+    "homeresid = str(os.environ[\"HS_RES_ID\"])\n",
+    "homedir = os.path.join(\n",
+    "    \"/home/jovyan/work/notebooks/data\", str(homeresid), str(homeresid), \"data/contents/\"\n",
+    ")\n",
+    "print(\"This is a basic Unix folder structure.\")\n",
+    "print(\"Data will be loaded from and saved to:\" + homedir)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "If you are curious about where the data is being downloaded, click on the Jupyter Notebook dashboard icon in upper rigth corner to see a File System view.  The homedir directory location printed above is where you can find the data and contents you will download to a HydroShare JupyterHub server.  At the end of this work session, you can migrate this data to the HydroShare iRods server as a Generic Resource. "
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### 2.2. Import spatial data \n",
+    "Strauch et al (2018) pre-processed the data for the North Cascades National Park Complex case study and is on HydroShare as [Regional landslide hazard using Landlab - NOCA Data](https://www.hydroshare.org/resource/a5b52c0e1493401a815f4e77b09d352b/). Here the first task is find this resource on Hydrohsare. We will click on the link to see the published data repository on HydroShare and collect the resource ID of the data. The resource ID can be found in the \"How to cite\" box, and it will be the series of numbers and letters following \"hs.\". Here's the copied resource: http://dx.doi.org/10.4211/hs.a5b52c0e1493401a815f4e77b09d352b citation. Now we copy this ID and introduce it as \"Data_ResourceID=\" in the code below. \n",
+    "\n",
+    "#### 2.2.1 Set DEM data download variable name\n",
+    "To learn more about this data visit [Regional landslide hazard using Landlab - NOCA Data](https://www.hydroshare.org/resource/a5b52c0e1493401a815f4e77b09d352b/)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 3,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "Data_ResourceID = \"a5b52c0e1493401a815f4e77b09d352b\""
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "#### 2.2.2 Download Data\n",
+    "We will execute the next cell to download data from HydroShare iRods database to your personal user space - this may take a few minutes."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 4,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "This resource already exists in your userspace.\n",
+      "a5b52c0e1493401a815f4e77b09d352b/\n",
+      "|-- a5b52c0e1493401a815f4e77b09d352b/\n",
+      "|   |-- bagit.txt\n",
+      "|   |-- manifest-md5.txt\n",
+      "|   |-- readme.txt\n",
+      "|   |-- tagmanifest-md5.txt\n",
+      "|   |-- data/\n",
+      "|   |   |-- resourcemap.xml\n",
+      "|   |   |-- resourcemetadata.xml\n",
+      "|   |   |-- contents/\n",
+      "|   |   |   |-- Data files/\n",
+      "|   |   |   |   |-- ASCII_header.txt\n",
+      "|   |   |   |   |-- cohesion_max.txt\n",
+      "|   |   |   |   |-- cohesion_min.txt\n",
+      "|   |   |   |   |-- cohesion_mode.txt\n",
+      "|   |   |   |   |-- cont_area.txt\n",
+      "|   |   |   |   |-- dict_coeff.p\n",
+      "|   |   |   |   |-- dict_uniq_ids.p\n",
+      "|   |   |   |   |-- elevation.txt\n",
+      "|   |   |   |   |-- exclud_mask.txt\n",
+      "|   |   |   |   |-- frict_angle.txt\n",
+      "|   |   |   |   |-- HSD_dict.p\n",
+      "|   |   |   |   |-- ksat_mpd.txt\n",
+      "|   |   |   |   |-- landslide_type.txt\n",
+      "|   |   |   |   |-- Re_annualMax_ex.txt\n",
+      "|   |   |   |   |-- sample_area.txt\n",
+      "|   |   |   |   |-- slope_tang17d.txt\n",
+      "|   |   |   |   |-- soil_depth_model.txt\n",
+      "|   |   |   |   |-- soil_depth.txt\n",
+      "|   |   |   |   |-- transmis_model.txt\n",
+      "|   |   |   |   |-- transmis.txt\n",
+      "|   |   |   |   |-- vic_idsnoca.txt\n",
+      "|   |   |   |   |-- VIC_latlon_key.txt\n",
+      "|   |   |   |   |-- wetness.txt\n",
+      "|   |   |   |-- Driver and related codes/\n",
+      "|   |   |   |   |-- __init__.py\n",
+      "|   |   |   |   |-- landslide_probability9Jun17.py\n",
+      "|   |   |   |   |-- NOCA_runPaper_LandlabLandslide.ipynb\n",
+      "|   |   |   |   |-- vicDataProc.py\n",
+      "|   |   |   |-- Results/\n",
+      "|   |   |   |   |-- prob_fail3000_Model_SD_LT.txt\n",
+      "|   |   |   |   |-- prob_fail3000_Model_SD.txt\n",
+      "|   |   |   |   |-- prob_fail3000_SSURGO_SD.txt\n",
+      "\n",
+      "Do you want to overwrite these data [Y/n]? y\n",
+      "Download Finished                               \n",
+      "Successfully downloaded resource a5b52c0e1493401a815f4e77b09d352b\n"
+     ]
+    },
+    {
+     "data": {
+      "text/html": [
+       "<b>Found the following file(s) associated with this HydroShare resource.</b>"
+      ],
+      "text/plain": [
+       "<IPython.core.display.HTML object>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "text/html": [
+       "Data files<br>Driver and related codes<br>Results"
+      ],
+      "text/plain": [
+       "<IPython.core.display.HTML object>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "text/html": [
+       "These files are stored in a dictionary called <b>hs.content</b> for your convenience.  To access a file, simply issue the following command where MY_FILE is one of the files listed above: <pre>hs.content[\"MY_FILE\"] </pre> "
+      ],
+      "text/plain": [
+       "<IPython.core.display.HTML object>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "This is the location on the HydroShare JupyterHub server where the data has just been downloaded:\n",
+      "/home/jovyan/work/notebooks/data/a5b52c0e1493401a815f4e77b09d352b/a5b52c0e1493401a815f4e77b09d352b/data/contents/Data files/\n"
+     ]
+    }
+   ],
+   "source": [
+    "hs.getResourceFromHydroShare(Data_ResourceID)\n",
+    "data_folder = os.path.join(\n",
+    "    \"/home/jovyan/work/notebooks/data\",\n",
+    "    str(Data_ResourceID),\n",
+    "    str(Data_ResourceID),\n",
+    "    \"data/contents/Data files/\",\n",
+    ")\n",
+    "print(\n",
+    "    \"This is the location on the HydroShare JupyterHub server where the data has just been downloaded:\"\n",
+    ")\n",
+    "print(data_folder)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    " ### 2.3. Review data needed as input for the landslide model\n",
+    " This section loads metadata associated with the Landlab component.  \n",
+    " To view the code source for this component, visit [Landlab on Github](https://github.com/landlab/) or [Download the landslide_probability.py python file](https://github.com/landlab/landlab/blob/master/landlab/components/landslides/landslide_probability.py)\n"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Check the list of data inputs that the component needs."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 5,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "['soil__density',\n",
+       " 'soil__internal_friction_angle',\n",
+       " 'soil__maximum_total_cohesion',\n",
+       " 'soil__minimum_total_cohesion',\n",
+       " 'soil__mode_total_cohesion',\n",
+       " 'soil__saturated_hydraulic_conductivity',\n",
+       " 'soil__thickness',\n",
+       " 'soil__transmissivity',\n",
+       " 'topographic__slope',\n",
+       " 'topographic__specific_contributing_area']"
+      ]
+     },
+     "execution_count": 5,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "sorted(LandslideProbability.input_var_names)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Review the details of what each variable represents."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 6,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "{'topographic__specific_contributing_area': 'specific contributing (upslope area/cell face ) that drains to node',\n",
+       " 'topographic__slope': 'slope of surface at node represented by tan theta',\n",
+       " 'soil__transmissivity': 'mode rate of water transmitted through a unit width of saturated soil - either provided or calculated with Ksat and soil depth',\n",
+       " 'soil__saturated_hydraulic_conductivity': 'mode rate of water transmitted through soil - provided if transmissivity is NOT provided to calculate tranmissivity  with soil depth',\n",
+       " 'soil__mode_total_cohesion': 'mode of combined root and soil cohesion at node',\n",
+       " 'soil__minimum_total_cohesion': 'minimum of combined root and soil cohesion at node',\n",
+       " 'soil__maximum_total_cohesion': 'maximum of combined root and soil cohesion at node',\n",
+       " 'soil__internal_friction_angle': 'critical angle just before failure due to friction between particles',\n",
+       " 'soil__density': 'wet bulk density of soil',\n",
+       " 'soil__thickness': 'soil depth to restrictive layer',\n",
+       " 'soil__mean_relative_wetness': 'Indicator of soil wetness; relative depth perched water table within the soil layer',\n",
+       " 'landslide__probability_of_failure': 'number of times FS is <=1 out of number of iterations user selected',\n",
+       " 'soil__probability_of_saturation': 'number of times relative wetness is >=1 out of number of iterations user selected'}"
+      ]
+     },
+     "execution_count": 6,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "LandslideProbability._var_doc"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Check the units of each variable."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 7,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "{'topographic__specific_contributing_area': 'm',\n",
+       " 'topographic__slope': 'tan theta',\n",
+       " 'soil__transmissivity': 'm2/day',\n",
+       " 'soil__saturated_hydraulic_conductivity': 'm/day',\n",
+       " 'soil__mode_total_cohesion': 'Pa or kg/m-s2',\n",
+       " 'soil__minimum_total_cohesion': 'Pa or kg/m-s2',\n",
+       " 'soil__maximum_total_cohesion': 'Pa or kg/m-s2',\n",
+       " 'soil__internal_friction_angle': 'degrees',\n",
+       " 'soil__density': 'kg/m3',\n",
+       " 'soil__thickness': 'm',\n",
+       " 'soil__mean_relative_wetness': 'None',\n",
+       " 'landslide__probability_of_failure': 'None',\n",
+       " 'soil__probability_of_saturation': 'None'}"
+      ]
+     },
+     "execution_count": 7,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "LandslideProbability._var_units"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Now we will establish a RasterModelGrid based on a DEM for assigning our variables to.\n",
+    "Nodes are the center point of grid cells or pixels that are 30 m by 30 m in this example. "
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### 2.4. Create a watershed subset using a regional RasterModelGrid"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "The shapefile that you will download is a GIS point file with a table containing a grid_code for each 30m DEM cell in the Fisher Creek watershed. We import this table to generate a list of locations we want to keep \"Open\" as active nodes in this Landlab application for Fisher Creek.   \n",
+    "\n",
+    "Here we will establish the watershed domain for our modeling study. In step 2.2.2, we have downloaded the DEM of the entire NOCA study region of Strauch et al. (2018) as well as other input data. This includes NoData resource that was created using a mask of Fisher Creek will be downladed and used to set no data nodes as inactive nodes (e.g., -9999). This step will establish boundary conditions. In your final run when you output modeled probability of landslide initiation, you will notice gaps in the model results at some ridge tops and peaks. This results from excluding glaciated areas and bedrock. <br />\n",
+    "This might take a few minutes as the park is large (2,757 km2).\n",
+    "\n",
+    "This shapefile resource was uploaded to HydroShare to generate an interoperable Notebook.  To learn more about this data visit [Boundary and Nodes for Fisher Creek\n",
+    "](http://www.hydroshare.org/resource/e2e3a56690354b20af292ff8db519ee5)\n",
+    "\n",
+    "#### 2.4.1 Get shapefile of Fisher Creek watershed\n",
+    "The shapefile table contains Landlab Gridcode value and Albers Conical X and Y values, spatial projection is WGS84.  "
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 8,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Download Finished                               \n",
+      "Successfully downloaded resource e2e3a56690354b20af292ff8db519ee5\n"
+     ]
+    },
+    {
+     "data": {
+      "text/html": [
+       "<b>Found the following file(s) associated with this HydroShare resource.</b>"
+      ],
+      "text/plain": [
+       "<IPython.core.display.HTML object>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "text/html": [
+       "Fisher_HUC12_watershed_WGS.cpg<br>Fisher_HUC12_watershed_WGS.dbf<br>Fisher_HUC12_watershed_WGS.prj<br>Fisher_HUC12_watershed_WGS.sbn<br>Fisher_HUC12_watershed_WGS.sbx<br>Fisher_HUC12_watershed_WGS.shp<br>Fisher_HUC12_watershed_WGS.shx<br>Fisher_node_id_WGS.cpg<br>Fisher_node_id_WGS.dbf<br>Fisher_node_id_WGS.prj<br>Fisher_node_id_WGS.sbn<br>Fisher_node_id_WGS.sbx<br>Fisher_node_id_WGS.shp<br>Fisher_node_id_WGS.shx"
+      ],
+      "text/plain": [
+       "<IPython.core.display.HTML object>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "text/html": [
+       "These files are stored in a dictionary called <b>hs.content</b> for your convenience.  To access a file, simply issue the following command where MY_FILE is one of the files listed above: <pre>hs.content[\"MY_FILE\"] </pre> "
+      ],
+      "text/plain": [
+       "<IPython.core.display.HTML object>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "Data_ResourceID = \"e2e3a56690354b20af292ff8db519ee5\"\n",
+    "hs.getResourceFromHydroShare(Data_ResourceID)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 9,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "/home/jovyan/work/notebooks/data/e2e3a56690354b20af292ff8db519ee5/e2e3a56690354b20af292ff8db519ee5/data/contents/Fisher_node_id_WGS.shp\n"
+     ]
+    }
+   ],
+   "source": [
+    "Node_path = os.path.join(\n",
+    "    \"/home/jovyan/work/notebooks/data\",\n",
+    "    str(Data_ResourceID),\n",
+    "    str(Data_ResourceID),\n",
+    "    \"data/contents/\",\n",
+    "    \"Fisher_node_id_WGS.shp\",\n",
+    ")\n",
+    "print(Node_path)\n",
+    "NodeID_shpfile = gpd.GeoDataFrame.from_file(Node_path)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Print a sample portion (bottom) of the table\n",
+    "* Col 1 = FID or POINTID\n",
+    "* Col 2 = landlab grid code for NOCA region\n",
+    "* Col 3 Point_X - the Alber Conical Latitude (original dataset)\n",
+    "* Col 4 Point_Y -  the Alber Conical Longitude (original dataset)\n",
+    "* Geometry of lat/lon in WGS coordinate system of this GIS point file for launching in HydroShareGIS App"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 10,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>POINTID</th>\n",
+       "      <th>GRID_CODE</th>\n",
+       "      <th>POINT_X</th>\n",
+       "      <th>POINT_Y</th>\n",
+       "      <th>geometry</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>81211</th>\n",
+       "      <td>0</td>\n",
+       "      <td>2466288</td>\n",
+       "      <td>-1834530.0</td>\n",
+       "      <td>3077940.0</td>\n",
+       "      <td>POINT (-120.8642431518081 48.53020052341004)</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>81212</th>\n",
+       "      <td>0</td>\n",
+       "      <td>2466289</td>\n",
+       "      <td>-1834500.0</td>\n",
+       "      <td>3077940.0</td>\n",
+       "      <td>POINT (-120.863854850779 48.53027103583014)</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>81213</th>\n",
+       "      <td>0</td>\n",
+       "      <td>2466290</td>\n",
+       "      <td>-1834470.0</td>\n",
+       "      <td>3077940.0</td>\n",
+       "      <td>POINT (-120.8634665489001 48.53034154719415)</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>81214</th>\n",
+       "      <td>0</td>\n",
+       "      <td>2464104</td>\n",
+       "      <td>-1834500.0</td>\n",
+       "      <td>3077910.0</td>\n",
+       "      <td>POINT (-120.8637508752399 48.53000770708789)</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>81215</th>\n",
+       "      <td>0</td>\n",
+       "      <td>2464105</td>\n",
+       "      <td>-1834470.0</td>\n",
+       "      <td>3077910.0</td>\n",
+       "      <td>POINT (-120.8633625748339 48.53007821808954)</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "       POINTID  GRID_CODE    POINT_X    POINT_Y  \\\n",
+       "81211        0    2466288 -1834530.0  3077940.0   \n",
+       "81212        0    2466289 -1834500.0  3077940.0   \n",
+       "81213        0    2466290 -1834470.0  3077940.0   \n",
+       "81214        0    2464104 -1834500.0  3077910.0   \n",
+       "81215        0    2464105 -1834470.0  3077910.0   \n",
+       "\n",
+       "                                           geometry  \n",
+       "81211  POINT (-120.8642431518081 48.53020052341004)  \n",
+       "81212   POINT (-120.863854850779 48.53027103583014)  \n",
+       "81213  POINT (-120.8634665489001 48.53034154719415)  \n",
+       "81214  POINT (-120.8637508752399 48.53000770708789)  \n",
+       "81215  POINT (-120.8633625748339 48.53007821808954)  "
+      ]
+     },
+     "execution_count": 10,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "NodeID_shpfile.tail()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Make an array using Grid_Code column to develop a node based mask. No output from this command."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 11,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "filtercriteria = np.array(NodeID_shpfile.GRID_CODE)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "#### Load  spatial landslide model inputs from ASCII textfile (ArcGIS raster conversion) into Landlab grid\n",
+    "\n",
+    "Create Landlab RasterModelGrid using DEM grid with elevation - this takes approximately 60 sec for the North Cascades National Park (NOCA). "
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 12,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "dict_keys(['topographic__elevation'])"
+      ]
+     },
+     "execution_count": 12,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "(grid, z) = read_esri_ascii(\n",
+    "    data_folder + \"/elevation.txt\", name=\"topographic__elevation\"\n",
+    ")\n",
+    "grid.at_node.keys()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Plot the elevation grid of NOCA"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 13,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 432x288 with 2 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "imshow_grid(grid, \"topographic__elevation\", limits=(0, 3000))"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "The original NOCA dem dataset has nodes closed outside of NOCA.\n",
+    "All nodes insides of NOCA are open. To close all all nodes except those inside Fisher Creek: \n",
+    "* Close all nodes in the landlab RasterModelGrid.\n",
+    "* Open the Landlab RasterModelGrid only within the Fisher Creek watershed.\n",
+    "* Assign Core Nodes (these are the only nodes where computation will be executed)."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 14,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "grid.status_at_node[grid.nodes.flatten()] = CLOSED_BOUNDARY\n",
+    "grid.status_at_node[filtercriteria] = CORE_NODE"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 15,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# In case the DEM has no data values inside the subset area, set boundary conditions closed.\n",
+    "grid.set_nodata_nodes_to_closed(grid.at_node[\"topographic__elevation\"], -9999)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Plot where the watershed is witin NOCA and zoom in."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 53,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "(32000, 47000)"
+      ]
+     },
+     "execution_count": 53,
+     "metadata": {},
+     "output_type": "execute_result"
+    },
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 432x288 with 3 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "fig = plt.figure(\"Limit NOCA computational nodes to Fisher Creek extent\")\n",
+    "xticks = np.arange(-0.1, 0.8, 0.4)\n",
+    "ax1 = fig.add_subplot(221)\n",
+    "ax1.xaxis.set_visible(False)\n",
+    "imshow_grid(\n",
+    "    grid,\n",
+    "    \"topographic__elevation\",\n",
+    "    limits=(0, 3000),\n",
+    "    plot_name=\"NOCA extent\",\n",
+    "    allow_colorbar=False,\n",
+    ")\n",
+    "ax2 = fig.add_subplot(222)\n",
+    "ax2.xaxis.set_visible(False)\n",
+    "imshow_grid(\n",
+    "    grid, \"topographic__elevation\", limits=(0, 3000), plot_name=\"Fisher Creek extent\"\n",
+    ")\n",
+    "plt.xlim(36000, 53000)\n",
+    "plt.ylim(32000, 47000)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Confirm the size of the grid, nodes located every 30 m."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 21,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "7029145"
+      ]
+     },
+     "execution_count": 21,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "grid.number_of_nodes"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Confirm the size of the core nodes where we'll run our model, a subset of the nodes for the watershed."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 54,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "69434"
+      ]
+     },
+     "execution_count": 54,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "grid.number_of_core_nodes"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### 2.5. Attach data (e.g., soil and vegetation variables) to the Landlab rastermodelgrid\n",
+    "This will be used to calculate shallow landslide probability and set boundary conditions. THe data we attach in this step was downloaded in Step 2.2.2. \n",
+    "\n",
+    "#### For each input below\n",
+    "1. Load data from ascii text file\n",
+    "2. Add this data as node variable to the Fisher Creek grid\n",
+    "3. Set boundary conditions\n",
+    "\n",
+    "For the entire NOCA extent, this takes ~60 sec to load each file using the CUAHSI JupyterHub server. "
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "#### 2.5.1 Load slope\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 23,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "(grid1, slope) = read_esri_ascii(data_folder + \"/slope_tang17d.txt\")\n",
+    "grid.add_field(\"node\", \"topographic__slope\", slope)\n",
+    "grid.set_nodata_nodes_to_closed(grid.at_node[\"topographic__slope\"], -9999)\n",
+    "grid.set_nodata_nodes_to_closed(grid.at_node[\"topographic__slope\"], 0.0)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 24,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "3.768809\n",
+      "0.3057313\n"
+     ]
+    }
+   ],
+   "source": [
+    "print(np.max(grid.at_node[\"topographic__slope\"][grid.core_nodes]))\n",
+    "print(np.min(grid.at_node[\"topographic__slope\"][grid.core_nodes]))"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "#### 2.5.2 Load contributing area"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 25,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "(grid1, ca) = read_esri_ascii(data_folder + \"/cont_area.txt\")\n",
+    "grid.add_field(\"node\", \"topographic__specific_contributing_area\", ca)\n",
+    "grid.set_nodata_nodes_to_closed(\n",
+    "    grid.at_node[\"topographic__specific_contributing_area\"], -9999\n",
+    ")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "#### 2.5.3 Load transmissivity"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 26,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "(grid1, T) = read_esri_ascii(data_folder + \"/transmis.txt\")\n",
+    "grid.add_field(\"node\", \"soil__transmissivity\", T)\n",
+    "grid.set_nodata_nodes_to_closed(grid.at_node[\"soil__transmissivity\"], -9999)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "#### 2.5.4 Load cohesion (mode, min, and max) \n",
+    "This takes ~4 minutes because 3 cohesion fields are provided to create more flexibility in how cohesion is distributed on the landscape with different vegetation."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 27,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "(grid1, C) = read_esri_ascii(data_folder + \"/cohesion_mode.txt\")\n",
+    "C[C == 0.0] = 1.0  # ensure minimum is >0 Pa for use in distributions generation\n",
+    "grid.add_field(\"node\", \"soil__mode_total_cohesion\", C)\n",
+    "grid.set_nodata_nodes_to_closed(grid.at_node[\"soil__mode_total_cohesion\"], -9999)\n",
+    "\n",
+    "(grid1, C_min) = read_esri_ascii(data_folder + \"/cohesion_min.txt\")\n",
+    "grid.add_field(\"node\", \"soil__minimum_total_cohesion\", C_min)\n",
+    "grid.set_nodata_nodes_to_closed(grid.at_node[\"soil__minimum_total_cohesion\"], -9999)\n",
+    "\n",
+    "(grid1, C_max) = read_esri_ascii(data_folder + \"/cohesion_max.txt\")\n",
+    "grid.add_field(\"node\", \"soil__maximum_total_cohesion\", C_max)\n",
+    "grid.set_nodata_nodes_to_closed(grid.at_node[\"soil__maximum_total_cohesion\"], -9999)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "#### 2.5.5 Load internal angle of friction"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 28,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "(grid1, phi) = read_esri_ascii(data_folder + \"/frict_angle.txt\")\n",
+    "grid.add_field(\"node\", \"soil__internal_friction_angle\", phi)\n",
+    "grid.set_nodata_nodes_to_closed(grid.at_node[\"soil__internal_friction_angle\"], -9999)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "#### 2.5.6 Set soil density values\n",
+    "In this example, we assign all nodes a constant value."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 29,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "grid[\"node\"][\"soil__density\"] = 2000 * np.ones(grid.number_of_nodes)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "#### 2.5.7 Load soil thickness"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 30,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "(grid1, hs) = read_esri_ascii(data_folder + \"/soil_depth.txt\")\n",
+    "grid.add_field(\"node\", \"soil__thickness\", hs)\n",
+    "grid.set_nodata_nodes_to_closed(grid.at_node[\"soil__thickness\"], -9999)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "#### 2.5.8 Load observed landslide inventory. Class 1-5 are landslides, 8 is no landslide mapped for later plotting"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 31,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "array([-9999., -9999., -9999., ..., -9999., -9999., -9999.])"
+      ]
+     },
+     "execution_count": 31,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "(grid1, slides) = read_esri_ascii(data_folder + \"/landslide_type.txt\")\n",
+    "grid.add_field(\"node\", \"landslides\", slides)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### 2.6. Specify recharge option as _data driven spatial_ and access Python dictionaries to generate recharge distributions \n",
+    "Recharge in this model represents the annual maximum recharge in mm/day generated within the upslope contributing area of each model element. This corresponds to the wettest conditions expected annually, which would provide the highest pore-water pressure in a year. Details of this approach can be found in Strauch et al. (2018).\n",
+    "\n",
+    "#### 2.6.1 Select recharge method from the component"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 32,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "distribution = \"data_driven_spatial\""
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "#### 2.6.2 Load pre-processed routed flows dictionaries \n",
+    "These contain HSD_id and fractional drainage at each node and recharge dictionaries.  HSD is the Hydrologic Source Domain, which is the VIC hydrologic model data in this case study at ~5x6 km2 grid size.  The 'pickle' utility loads existing dictionaries."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 33,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# dict of node id (key) and HSD_ids (values)\n",
+    "HSD_id_dict = pickle.load(\n",
+    "    open(data_folder + \"/dict_uniq_ids.p\", \"rb\"), encoding=\"latin1\"\n",
+    ")\n",
+    "# dict of node id (key) and fractions (values)\n",
+    "fract_dict = pickle.load(open(data_folder + \"/dict_coeff.p\", \"rb\"), encoding=\"latin1\")\n",
+    "# dict of HSD id (key) with arrays of recharge (values)\n",
+    "HSD_dict = pickle.load(open(data_folder + \"/HSD_dict.p\", \"rb\"), encoding=\"latin1\")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "#### 2.6.3 Combine dictionaries into __ordered__ parameters\n",
+    "This sequence of parameters is required for _data driven spatial_ distribution in the component.  Recharge is this model is unique to each node represented by an array. "
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 34,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "HSD_inputs = [HSD_dict, HSD_id_dict, fract_dict]"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### 2.7. Set Number of iterations to run Monte Carlo simulation"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "The landslide component employes the infinite slope model to calculate factor-of-safety index values using a Monte Carlo simulation, which randomly selects input values from parameter distributions. You can specify the number of Monte Carlo samples, but the default is 250. The larger the Monte Carlos sample size, the longer the program runs, but the more precise the probability of failure results become.Strauch et al. (2018) sapmple 3,000 times for each parameter in each model grid."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 35,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "iterations = 100"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## 3. Results"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### 3.1. Run the Landlab LandslideProbability Component in Fisher Creek for pre-fire conditions"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "#### 3.1.1 Initialize Pre-fire\n",
+    "To run the landslide model, we first instantiate the LandslideProbability component with the above parameters, as well as the grid and number of samples we specified before. Instantiation creates an instance of a class called LS_prob. \n",
+    "\n",
+    "No outputs are generated by this command as it is setting up the recharge and instantiating the component."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 36,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "LS_prob = LandslideProbability(\n",
+    "    grid,\n",
+    "    number_of_iterations=iterations,\n",
+    "    groudwater__recharge_distribution=distribution,\n",
+    "    groudwater__recharge_HSD_inputs=HSD_inputs,\n",
+    ")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "#### 3.1.2 Run the Pre-fire model\n",
+    "Once the component has been instantiated, we generate outputs from running the component by calling the component's 'calculate_landslide_probability' method using the class instance (e.g., LS_prob). The cell below runs the model; in the following section we will assessing the results. These calculations will take a few minutes given the size of the modeling domain represented by core nodes."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 37,
+   "metadata": {
+    "scrolled": true
+   },
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Landslide probability successfully calculated\n"
+     ]
+    }
+   ],
+   "source": [
+    "LS_prob.calculate_landslide_probability()\n",
+    "print(\"Landslide probability successfully calculated\")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "What is the maximum probabilty of failure we found?"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 38,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "1.0"
+      ]
+     },
+     "execution_count": 38,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "np.max(grid.at_node[\"landslide__probability_of_failure\"][grid.core_nodes])"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "The outputs of landslide model simulation are:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 39,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "['landslide__probability_of_failure',\n",
+       " 'soil__mean_relative_wetness',\n",
+       " 'soil__probability_of_saturation']"
+      ]
+     },
+     "execution_count": 39,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "sorted(LS_prob.output_var_names)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "This simulation generates a probability value for each core node. "
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 40,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "array([ 0.,  0.,  0., ...,  0.,  0.,  0.])"
+      ]
+     },
+     "execution_count": 40,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "grid.at_node[\"landslide__probability_of_failure\"]"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "This simulation generates a probability of saturation value for each core node as well."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 41,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "array([-9999., -9999., -9999., ..., -9999., -9999., -9999.])"
+      ]
+     },
+     "execution_count": 41,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "grid.at_node[\"soil__probability_of_saturation\"]"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "-9999 means there is no data for that cell, it is a closed node."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### 3.2. Run the Landlab LandslideProbability Component in Fisher Creek for fire conditions"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Make a 'grid_fire' version of 'grid' for which we will give post-fire cohesion parameter values 30% of the original cohesion. This is a crude estimation of the lowest root cohesion after tree removal based on a combined decay and regroth model (e.g., Sidle, 1992).\n",
+    "\n",
+    "Sidle, R. C. (1992), A theoretical model of the effects of timber harvesting\n",
+    "on slope stability, Water Resour. Res., 28(7), 1897–1910.\n",
+    "\n",
+    "#### 3.2.1 Make a Post-fire copy of the grid"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 42,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "import copy\n",
+    "\n",
+    "grid_fire = copy.deepcopy(grid)\n",
+    "grid_fire.at_node[\"soil__mode_total_cohesion\"] = (\n",
+    "    grid.at_node[\"soil__mode_total_cohesion\"] * 0.3\n",
+    ")\n",
+    "grid_fire.at_node[\"soil__minimum_total_cohesion\"] = (\n",
+    "    grid.at_node[\"soil__minimum_total_cohesion\"] * 0.3\n",
+    ")\n",
+    "grid_fire.at_node[\"soil__maximum_total_cohesion\"] = (\n",
+    "    grid.at_node[\"soil__maximum_total_cohesion\"] * 0.3\n",
+    ")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "#### 3.2.2 Change the Post-fire cohesion values "
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 43,
+   "metadata": {
+    "scrolled": true
+   },
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "this is the highest mode value for coehsion before fire across the domain\n",
+      "10000.0\n",
+      "this is the lowest mode value for coehsion before fire across the domain\n",
+      "100.0\n",
+      "this is the highest mode value for coehsion after fire across the domain\n",
+      "3000.0\n",
+      "this is the lowest mode value for coehsion after fire across the domain\n",
+      "30.0\n"
+     ]
+    }
+   ],
+   "source": [
+    "print(\"this is the highest mode value for coehsion before fire across the domain\")\n",
+    "print(np.max(grid.at_node[\"soil__mode_total_cohesion\"][grid.core_nodes]))\n",
+    "print(\"this is the lowest mode value for coehsion before fire across the domain\")\n",
+    "print(np.min(grid.at_node[\"soil__mode_total_cohesion\"][grid.core_nodes]))\n",
+    "print(\"this is the highest mode value for coehsion after fire across the domain\")\n",
+    "print(np.max(grid_fire.at_node[\"soil__mode_total_cohesion\"][grid.core_nodes]))\n",
+    "print(\"this is the lowest mode value for coehsion after fire across the domain\")\n",
+    "print(np.min(grid_fire.at_node[\"soil__mode_total_cohesion\"][grid.core_nodes]))"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "#### 3.2.3  Initialize the Post-fire model\n",
+    "Now we'll run the landslide component with the adjusted cohesion, everything else kept constant."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 44,
+   "metadata": {
+    "scrolled": true
+   },
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Post-fire cohesion successfully instantiated\n"
+     ]
+    }
+   ],
+   "source": [
+    "LS_probFire = LandslideProbability(\n",
+    "    grid_fire,\n",
+    "    number_of_iterations=iterations,\n",
+    "    groudwater__recharge_distribution=distribution,\n",
+    "    groudwater__recharge_HSD_inputs=HSD_inputs,\n",
+    ")\n",
+    "print(\"Post-fire cohesion successfully instantiated\")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "#### 3.2.4 Run the Post-fire model"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 45,
+   "metadata": {
+    "scrolled": true
+   },
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Landslide probability successfully calculated\n"
+     ]
+    }
+   ],
+   "source": [
+    "LS_probFire.calculate_landslide_probability()\n",
+    "print(\"Landslide probability successfully calculated\")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 46,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "1.0"
+      ]
+     },
+     "execution_count": 46,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "np.max(grid_fire.at_node[\"landslide__probability_of_failure\"][grid.core_nodes])"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "The outputs of landslide model simulation are:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 47,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "['landslide__probability_of_failure',\n",
+       " 'soil__mean_relative_wetness',\n",
+       " 'soil__probability_of_saturation']"
+      ]
+     },
+     "execution_count": 47,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "sorted(LS_probFire.output_var_names)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Save the arrays as variables by 'attaching the fields to the grid' and view the outputs. **component already does this**\n",
+    "\n",
+    "This simulation generates a probability value for each core node. "
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 48,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "array([ 0.,  0.,  0., ...,  0.,  0.,  0.])"
+      ]
+     },
+     "execution_count": 48,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "grid_fire.at_node[\"landslide__probability_of_failure\"]"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### 3.3. Display and visualize results of stability analysis"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Set plotting parameters"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 49,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "mpl.rcParams[\"xtick.labelsize\"] = 15\n",
+    "mpl.rcParams[\"ytick.labelsize\"] = 15\n",
+    "mpl.rcParams[\"lines.linewidth\"] = 1\n",
+    "mpl.rcParams[\"axes.labelsize\"] = 18\n",
+    "mpl.rcParams[\"legend.fontsize\"] = 15"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Plot elevation"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 52,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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OnrNO7n30I58lFKFaiLfHqlTimCAI6Pd94npKmhqPKpvP8jyP2rD5RVkbrlsvy/yzRrWYqFZbJ+N1ODZkVJW4vuaeFvAW+3jOOOeejRFiAHifffzhOO3AZEwfhlHWKAP3YwzcmF+Sqnq5VSc6CTgFo4jxdjtFs97YEFLeJ+JRzMK5szHzTE8B/wN8umhAVHW5Tf88H5OSPgQswhibImdhjNSngM2B24E3q+oTa9DXlLL04tewzUdu51kH3sRDl76ebT+8hNG+vnUSJnz3/SdS8X2iNAXrYWENV+h5xJg4QqRKX+gXwoBhbqQA4kZKPfIJSilByaPcY9rWo2jc+zocGzWqa+Vpqeq8DtudjfnObHf+XFoM1ARtLwIu6qTtVLFBGS2bunlxYf8uoCOBRlW9B6OzNVEbxWQsfnFt+5pq5h57D3GaknoezzrwJh7+9m7MOfF+4knOInzD346nav+xIhFCVQIR+n2fCsbTilWpJgmhPR7ODAjKJqcnTVJrmBLKoU/YCEiTlLC3RJoocSNlLcL6Dse0xSRiuD/+tWV9p7w7OmTg7OcThyHLLnwpq7bYgm0/vIQgivIQ4dqy/V0fZ6e7j2Zpvc6yRoOhJCFWJaD5yyZKU6ppSjVJqKYpQ0nC0nqd5UlCYOe2ymGA5wueJ2YOa9RscSPNN89zf3aOTQ9FJyXlfVNng/K0HBMThyH9Jz9ImqaM9vVRrtWIwxC/0aDnyyvyNsnRPZ13umQ+paBGNUmIRJhr56oqnkfoeXz32Wcy/9FTiNKUQITAGq7YXj43MH9CI6qUQ5/6aILnCUHZy0OC9cgZKYcDm/LuWDuc0ZpGJKUSqefl67XiNGXwSzua0GEYEvX3r37R8VW3QbgMyoNmi+o0gipDPU+wXblMYFVJYkwo8AP//FRu0CJVImvMKtZbGohjIs9jbhDQ5zfXZGUJGpl3BSab0POF0RH3j+vY9FDFeVSTgPsJPI1YdcpWVBdsQ2PmTOq9vUT9/cw58X7Scpmhs55jFiFnE70XtTEM730F7LMP1OfAG+ZDXAGvTiDSDP+lKZHdqknCQBybFHfbd6yaHwOTnFHxfWrDDeJGM5sQIE3VeFyhT1Ay3leaun9cx6bHZCwudjhPa1pS+/Rcyl+rIklCvdeIiPSf/CDYxccAHD6OSO2Vf4agCl4dvEG4frHxtjCGKMsSzJItBoCK5xGIUPF9YrWp7TaMGNjngQixKmFvQJooaRIQN1J6R0vNzMIVZgLa8wRNUxYeeSgnXfjddfk2ORwbFi48OCk4T2uakpRKjJ4wO1fIaMycydBZzxmbAn9hm/Rarw577wtp2Xhc9Tno6FYM18Pcg1rWaHD3rucwEMf5ZaENDfb7vpn3snNfFc9jhghRLc6TLrJMweIarnKPyTIMyr7753VscihKmiZtt9UhIu8VkZ+JyL9slfc7RGT/lja/HafSu9p6gsV224rIT2w/AyJyvhVbaL3n4SLyDxGJ7P06yuZel6yRpyUiPZgMcpe/uZ5Q30goxsf24p83ilcynpUkCcHZNeJje+HIlt8k+71o7P6bDx+7f8tJKFBlFWEQsMtfjqFiZZsqnkd/EOSeV+ZdZR5aCIS9zT+nuJFS7glyT6seGe8rS86Iau5Px7FpoWvvaR0H/BOzKHgAo2RxmYjMVdWiQMKNwKdbrh3Nnlg5qOswahj7YdbAfs0+HlRo936M2MICjILRocDPReQV63OBcUdGS0RegpE/2gPYFajY41XgLxhli6ut5L1jCkiPsh/dhSnJkR7+16pUFjxG9ZStKH+t2v7CK/8MSRkO2AWuuc6EC/fe14QKeYRSUCP0fJ588QVU7j6afqCapmZRsSpRmubrtAIRlsWxkXdKEuaWAyqlci6SmyaK5wtpopTDgNB6YL19CWmqnHrw+/j899ot2nc4NjLWPhFjn5ayITdYyabjGKvqM6iq7WugmO/yXYAdVfWfACLSAK4QkVNVNas5eCpwiaqeZtvcBLwEUwF+XHX5qWDC8KCIvF1ElmCUI04Gtgb+FyPaeLV9vg1GYeJ2EVlipZccU4X1ppJSicbMme3bfWc5XPKE8bb8OvzoD+a41OGmhdD7CFJaTsVmAj777qMBuGWXRVSThCEbNqwWkjWqNjkjm+uq+L5Zh+UL5R4fz5c8CaPcY7ag5OXruByOTYm1TcRoMVgZd2JUg7rhbcBtmcGy/BTjee0JICLPAZ7L2ErvKea7v1jpfcpp62mJyPUYz+q3wIeBX7RTFRaRrTDVLw8EfiYiN6rqf076aB1tSUqlPN3dq9cpf61K/bhKs8GHbP3KSx+A/XaAn9xovKxS0yvL0t0Dmuuv5t4738xh2dBgxfMIMAkaxTmtft9H45R6lJi5rWiscG4x/T1NdEyGocOxaaDrIuX9NcA9LcfeYiutg6lOfIJVF8rYufUaVa2LyAM0K1xkj62VLe7FiPVusb7Kk0zkaa0EXqKqb1LViyeSwVfVJ1T126r6RuBlwHC7to5J5lu21MeRHur79J4xgN9oIEmCf97o09sfuIN5fPcbTCJGhldvzlv5PqH1uOaVy3kpkn7fN49BYJIy7NxWIELoedQjk4QR1RpUV9RZORhRXTFKbdj8ikxTzSWe6lFCuSfkvBOOWcdvkMOxYdCBpzVXRG4vbBNWQ7dJEe8EFhcO34RRgX8rppr6MzGV3ucV2nRa6Z1x2i1vOT/ltPW0VPXda9Khqv4JWKNrHWuAlsfsJqUSozYlviPScm68AhFCu2WKGPmW6Q9mmYPW0+q3Bi6jKNcUrTLZhMb7auSelzFeKZ7fWo/T4dh46UB7cKDT6ufWCF0GXGM1W7N7fL7Q7GYR+Q3GWzrWbnnT8bod53jr/niV3qcUl/I+3Smsx0qO7smzCjtSxsjw6uDV82xAYIyxCu06rdb9SkEdY4YI9dEk96ayEGA9SqgN16muqFMbblCr1pvGK0mc4rtj08GqvLfbOkVE5mDq/j3CahIiVHUZphZWJ5Xe+xlb6Z1x2mX7U1rFvUjHRktENheRXVqOPVtEzhORS0XkrZM/PEe3pNbryUKEbXnvK552KJvTAnKx3Mx4BYU2uUdW8MRyD6tuDZc1Xvm4EiVuJMR1888ZlH1GR2pEtRpfPXrCKIjDsVGQ1dNqt3WCXUv1c0wdrL1VtdNS4Kur9F7G1A4sVnqntZ3dH1xf81nQ3TqtczDZJK8EsMXBbsZkDwLsJyJvVNXfTe4QHd0QH9tLz5dXUDthdvsQ4aUPmCQMHsm9LJEGgZTGhgStZ5WFAouGKvPKMlWM0boxWvXRJPe0Mq3BLIMwM2JZva1MBb427KZAHZsGa1NPy66vugpTPXj3ifIMCtdsBewOfKdw+JfAASLyLFV92B57B9CDqVeIqj4oIn/HpMdfZ/vy7P4vuxjzczHLpLbEGM4ngb8U0uq7phuj9WrgB4X9/TAGay/gT8CvgRMBZ7Q2AMKzBonblQA5cAe47N7mfFZaRrUZZhzPk8qSLcJC4kUmmhur5mnsWSp7th+UjAKG58XWgPl4nmefxwQlj5WNUc48/GA+ddH31vn74nCsLzRN17ae1gWY79tPYDL4XlU4dyfwPOBMjGF7GJOE8SkgZWxRyKuBz2Cqs58CzMYUur2sxZgsAH4gIg9hQowfxBjMAyYapI3IHYkxcFtlh+2j2jZPYNLpv6Gq93b06i3dGK2tMDHUjLcBt6vq/9hBXIxZ5OZYj/jnjTJ6dE9TFaMd2eLipFxIxmj+Q2VGqVKc2xrnecX38ROlXvCicoNV9mz9LHOsHPqUw2byhedJLqjbGHWyTo6NG2WtFxe/xT6eM865Z2MquwvGcG2OyeL+LfAuVc2/u1W1ISJ7Yqqz/xCjlnEFcMKY8apebiNqJwGnAH8F3t5ODUNEdgAWYhLxRjCRuFuABwpjmwPsCLwKOAw4WkR+DJykqg928iZ0Y7QawIzC/uspVBnGTMxt3kV/jnVAlojhdRKGSMsm+zAtg1dvagpazwrGel1ZKLBo0HyrMQit3lXTywtKHpXZzRpfni9Eqxp2nisg7E2A2ClkODZu1lLGSVXnddBsrw77Wgq8q4N2FwEXddInZu3X3cAhwI9XN98mIjOB9wDH2GvDidpndGO0/g7sKyKLgX0wFvP6wvntgbUvoetYK7LkizELi9uRlo0iRlCl5MUEYv4ccrmmwtwVNJMwsjbVNCUWoRL6hJhUduNhmUrGAEGpTj1K8mxCzzNZhp7vNdUyRv1cYNfh2FiZBO3BDZ33qeo1nTa2Ru0S4BIReWen13VjtBZjPKvlQC/wIGON1uswVtaxHumqarFXH/OYGarIGqpYlbjlkihNGQKqSZKL6caqlETyMGBQ8kgTLxfQDUrJmEzCuBE1kzNSpRwFTiXDsdGjuk4UMTYYujFYa3Ntx0ZLVb8nIikmXrkCOENVG2DS4TGTeRd0OVbHOiA42yi4TDinBWMUMQJbmbhqw4pV3ydIEpMpiDFSQ0mSSzz1B8EYL0xF8pBgHiIs+YS9wZjqxbXhRj6vlYUTjZJGgueJCxE6Nl42fk9rSuiqNImq/oCxGYTZ8acw8k2ODQD1fZKje/AuiJtq8OORzWnZhcVRmlK1IcDc67LCuLFqXktrTFkSe02sSljyKFtvK8NUKlarkNHA84Wwt5SHAz3feFz1yCcoJfiB8MUPH8Rnvv20PzOHY1qjsElW7RaRN2OyDjenmUWYoZmKfKesaT2tHTHZhH9R1RVr0odj3ZEc3YN/3ijJUYEpBNlaVysjyxqMKzTiKpEXEaQpkYgpR2K3gTjOjVW2vWnWJwH4c+3s3KBV7GM4I0CsB5aF/aJanIcOs9BgVl8LyLMLw96AqNZwhsux0bEJzGmNQUR2Bn6CWd/brqyDAl0Zra5knGypkgeAv2HWY73MHt9SRO4Xkfd0019L39vaKppq0ywRkWeIyJdF5M/23KMicomtIVO8do821TrPGuc+q63E2WlVzw2ZtnNb31leaGQMFnEF0jJRmhrPyW5RmjJkvSwg97CiNOW6FV/m+pVfYciGDcdsccxwmlKaERDODAh7AzxPTE0tO8+Vq2bYlPewN8jPh70BSeyKRDo2MuycVrttI+QbmAS9YzEyUs8eZ3tOt5127GmJyB4Yq/knTMbHguycqv7bGrP3YxaurQlfBqpAsSjUyzBzaN8ClmC8uwXAH0XkBaraKvlwICZBJONfLa9htZU4O63qOR3wLoiRRoMx/w4fKogzH7CL0SAcnQNBlYZXZ8hfRUzTQG0NzUXEkHtckTVksWqeFp+VL8nWcg0liUmhD0wdrfpoks9tZRWMs8XI5Z7EJnCoyyR0bJSsraclIu8FPoD5XpyNcR6+oqqX2/OzgOMxa2ifh1krdQtmDdTfC/3Mw1RAbuVKVX1/yz3fCZyOCe89CJyqqld2OORXAme1VFVea7oJD34O+DOwG0aWfkHL+VuAg9dkECLyWkzxsTMwxivj98DOqhoX2v4f5sPaF2M8i9y1mjLQnVTi7LSq5wZPelQAFxX+ScYLFTYq0JgD8SDU56BBmSrLje6gCEM2NFgkCwdmzwOR/A8pKojoVjyPyPPIku89zxSHDGdmWYU+Uc2Mr1YFbzQZI/204KD3sOAHa/obyOHYsOhA5X11HIcxNvOBAcyarMtEZK41DM8EDge+jVG86MUoYiwRkReq6qMt/X0So3SRMabIpIj8P+BHmAS7Y+z9LheR5ar6qw7G+1Rrn5NBN0br5cDnVTUVGTc8uRRT2bgrRMTHlIr+Ai3Kwar6NCVhVf27LXDWVbXOQiXOTxT6SkXkquIxVl/Vc9oYLcCowF/UwGsI6ZGeqb91WMGZPWAXuPLPJu09rkB5EI0rVHuXMhDHzfkqz8vDhpm3lRmuTFg3SBKCNCW0xqq/p6cptGvnsYB8LqseGR+wNtzIU+KLOoXlMHCGy7ERYUryrAX7tFQvvsFOlRyH+Q79J7CDqo5kDUTkZoyS0YcwP9qL/E1Vb53gfqcAv1PVrOjdjSKyK8aB6cRoXYFZwLx4dQ27oRuj5WPkPtoxl6IOUOcciVkJvRgT3psQEXkh5hdEa7VOMB/i5sCjmJDimaqaRcc6rcTZSVXP6cXhJVIwnlaM8b6k3jTJeap4AAAgAElEQVRe+73IPF75ZwiXQWmQBvBkeZBqaYRIdUz9rKJqRlHWaSCOCWzdra1LJappahYjxzH9lZLJErRhwJWDkU2FTwqGKh2rDG/nu07/0H589judRiQcjg2TtfW0WgxWxp2YQpDZYt3WawZF5GG6/5HfA7wB42EVuQL4rojM7iAJ7zPA1SLyE+Bc4CHgaZN3RYmpTujGaN0LvJb2a7Hejgkfdow1MKcBB1k9rNW19zC6W/9grKVfAZyF0bqq27GcCmxB04vqpBLnk3RW1XN6EoyM9bJa2e9FRkh3RhlKVUjLjKSDDLCC2PeJMqUMVRP2s2HD2GYbgpnzwh6vJkkusDsQx1Q8j57QJ6rFYzyquNE0VsWyJkUD5gyXY9qja6fy3obXMP4PeABEZAuM1t9439vftbW5/g1cDnym4KXtAJQY/0e+h4la3cbENDB6hSdgVOTb0VU12G6M1reBc20lzJ/ZY2qz6s7CqMB3O6f1RWCJqv6iw/Zn2vu8PlvYDKCqd2J+cWT8RkRGgeNE5LSWXyidVOLstKqnOWHKYm/4RaEOm/n08GArB9iSaddem6fEj3h1Yq3lc1QAse/nCRuBKkN2IXJo13ZlWYhBkuSeWijCDN8qvJd9yj2pTYkv5YuRq0OjVFfUGWJkjAFziRmO6U5WT2sC5orI7YX9b6rqN9s1tpnP78SE/trxVUyC2xWFY6OYyNavgJXAHhhR3B1sf9DZj/zVsRATurwTk5+wfOLmndGNIsbXRWR3jHjiVzFf4JdjFoz5wHdV9dJO+7Ox0Q8BrxORrBpmllY+W0SSltjsURiLvb+qLungFldjSqW8ELiBsZU4i25tayXOTqp6jsH+YX3TjnPD1iI6bCZc8oRJwMiM1yVPQGmwabAAas/M13CRlmkEVZaXB5HS8lx3cG5WUytL2AiCvLJxFjrMMgjnBkGedRj2l+jbrMwWnkdjJKYeJUS1mKjWsKnxZr3WY/9cmQ/H84Wzjtifk79x+RS/YQ7H5KDo6jytAVV9eSd92QzAy4BrVPXiNm0+ikkw29cKQJhxqD4OfLzQ9Le2VMgFIvJiVf3TmGG3dNvm+HgcDPxEVdd4KdR4dKuIcZCI/AjzRuyMeQFLgO+p6o+6vPdOGPfzlnHOLcV4docBiMi+mInGE7tIt8yHbR+LlTgfLpxvrcQ5UVXPC7u894bJB7cyhuqSJ8zzD9qSN5fd2zRc+73IpMMDRFubopFpGU3LDPDEGAmnbO1WaHULsy2TfQpECNMU4jhP5MjmwfrLPjP85tqtTI8wTZXKbCMzZbIOTfLG1445iLiRcuLXL5vKd8zhWHvWPnsQABvS+yUmwWLcZTgi8g7Md+ZJqvqTDrq9GhNCfClmWVPxR36R1h/5E9FLZwkbXdG1IoZ9Azp5E1bH7zETfUX2xLipe2HXW9n1YZcC56vqV7rof19M2sFd0FUlztVW9dwoyAzVpQ/kpUk4YIexbd77irH7v7gc4goKLC8PEjVXIhCrMjcIctmn0M5PZl5XNdMxVKWaJXRY76uvFIxRyshCgr19xmjFvSlRzdzL84W4nnLGYe/n09+6AodjOrGaafsOrpde4OdAGdh7vOQLEXkNJhx4oap+ufV8G7Tl8QHMnNTOwE2Fdjtjikr+ndVzK6Zq8aTSzeLiG4Avqur1bc6/AThFVd/YSX92num3LX3Ms09vVtWqrYD5U4z3c2VLpc4nVfUBe93XMUkUt2ESMfbCuL9nF91iOqvE2WlVz+nNlX823tSB1lBddRv86A8mHJhlE7ay1/7m8frFEFcYCZcxgIngVmwoEJolTPLQYTbfVUiTB/OLIgAaQUBQ9nJlDDDyT2FvYIxUI6UcBrYOl0dUa1Ae9Z3hckw7irqc3WKFD67CfGftrqr/HqfNrhij9j88PfNvIrIQ3h0AqjoqIjdiftR/o9BuP+CWDuX7jgd+JSI3qeqPuxjLhHTjae2BSSNvx5aYwpCTyW4Yo/Eixi6CA7Ow+BD7/F5MKHE+5hfI/Zg37NziBZ1U4uy0que05ZrrjFfl3W+ep2V49xuMV/XfPxqj/D7mmne+tbn/po+ZxxsXMVIehN1PI7r9I2MMVRY6BHKh3Wy9V1EWqmo9s82sIjyA53t5mZNoVUx9NCZaFZMmzSrIAPWehLPnfyBP1HAhQ8eGjIjkWptryAWYH+SfwCzTKf6IvxPzXfk/mMSLc4FXFjKyV6rqPXYcC4A+zHfqSkxZqRMwhRvvKvR5Gma+62yM87CX3fbscLxnY6onXyUi/8KsI2tNeVdVfZqU3kSIamd5A7YsyUGqOu43g4gcASxS1Wml0TfZbPCJGADXXQJv/SDctLCpPRhXYO99TQgwqcA++3TW182nwWtPgVtOYkbPinwdV1Ysst/38/Vd2Vqv7UqlfC3X1qUS25XL9Ps+JaAeJWYbTRh6coSoFtv9mMEnRohWNYxivD2eTWwXr3PJGo51gaquVXBvuy0310+8p/33/Ylfv+yOiRIxbIToWW1OPxuYB9zY5vxNqrqH7ef9GDWMnTDV6B/BJHV8UVXHrMUVkXfRlHH6J7BAVTsKb9jxrvb7UFWf3Ul/GRN6WnYh74sLh15rXdRW5gBHMcF6AccGRHnQGJvysmaGYH2OObfX/sZw/eRG44Gtjswze/VCRpbMJ9aaUYgXaa7jUm0qY2RlTWgmcERWWT4CwtAntCHBbI4r86wqs8u2/lacyz0BxPXUljmRXMvQ4djQEGGtPC1VnbeaJg/RXk292M8VjE2Bn6jtTzFeVtd0MN41YnXhwXcDn8/GABxht/EYprsYqmN90fsIM0ojhCLERETpEI3yoPG8Xn+SMVzXXvv0sGArv74IUqtZ/IdTwBuk8bJv0MD8MfCno4CCPqHImLIn2TquMI4ZiGNCz2NuEDC3HNjikWXKYZInYaRpamtvmWNhb0KapHnZk3pPQs2r5yHD4851pU0cGw7C2s1pOQyrM1oXY5IlBLPW6Qzg1y1tFBNDvUdVo0ken2NdsNsiRoCt7z7aZPMBUWmEEZY1Q4Zv2sfMcU1kuN58ePN5UIVXjJUYi1QhScD3c0NV8f3cw8rKnmT6hnmyhggzQ5+wEVAflabyez0AYjOvZcV3s0KSni9Wr9DPn589/wN55eQ0UY756vfW1TvqcKyetZ/TcrAao2VTvh8GEJFDMXHRh6ZgXI4poGLVK/B9SBIifxVaHmyG/Pbed/wLf3F5M5MQci+rlShNwfPArteqeB7VJKGayTylqRmDVcyYGwT5Gq9K4NPbV7Jrt4J87RaY9Vyz5oTUhhu5dmHcSAjKHuU0yA1VUDJ9ZHNiZ8//AMcu+v5kvoUOR8esbXhwQ8eK835GVX/X5XVvBL6gqv+vk/bdKGK0lgFxTHNCz+PuXc8BYPu7Pk6kykhQNV6TTWvnrR9sZhVmyRlFgwXNCsiW0h1H0EgDGi9bTOO2jxF5ca4/CM2U+KwGlzkYECQJ/XaNV5SmbFcu5wUks3IlmXc1Z6teqitG8Txh4PEaaap4nhDVYnr7yrmadlD2iVY1qI8GeJ7H+Sd+0Ar1mrCiph69fX0cf15btRyHY1IQ2ejDg49hsg3vxGR3/7LdMiEReT4mE/EDwAuAjkUjul5cLCIvp1lTq/Vng2a1qhzTj0CEkhcTz3gU9eomOeP6xRBXTZXj8bh+MWC8M7n9I+jL7Zd/ZsReYQxXUfE9VqViswlzBXmbxVpNU+JGg7lBQDVJqMwI8n90Y7w86lFMb18pH0J5+ajxyhLF8718fZfZz5Iz6lT6m6+hPmoMaG24QVSrcdohxhCfcrHLPHSsI0QISl1pw04rVHU/ETkHkwexCFgkIiswQhGDmGmmORiNwz7M1NJ1wBGrKZEyhm4WF88Afgy8haZ4bFGHKjvmjNY04bZdFvGSe4412XsFUdtYlaGeJ4zhim0JR6/89LAgmDVbNy2EVy/Mc1sbaTB2vdcrFjN828eAJF+jFalStZ7WskZjzBqvLEyYzXltVvIph/6YTMLSjID+0Kc6VKfc49NbKRPXTQZhucfPa3cBRCWbqBEleJ6QpkrcCKhHiQ0fxvTM8EhT5YzDTOFWt2jZMdkIbPTZrar6R+CtIrIDZmHy64DnYwrrKkYE4mZMrsSP1mS6qZsA6+cwBuuLGPklwShKvM0O4jY7OMc0IlOpCEXydVTZuqoZPSuQGY+aGltB1ZQr+cU4nkjrguRXLIZXL3xam0z5fShJmpvNHFxar1PN1OGTxGy2/bDNGpTQzFH1VkoMxTHLkyQX1w3KHr19JpxoDFzArDlhfqwyu2y2/h4qs3sIewN6+0r5Y9Zv9vwrH19taTeHoyvEE6P80mZb7fUi7xWRn4nIv0SkKiJ3iMj+47Q7XET+ISKRbfO0xbsisq2I/MT2MyAi51uJqK77Gg9VfUBVz1LVvVR1nqrOVNWKqj5bVfdR1a+uaX5EN+HB9wBXqernbB0sgH+p6g0icj3GaB2CKe/smCbcsssi3vz3TwLkHldsva7YJkUMeE8S9TyBNjYzIcNscXLGG+aP7TRbcFxkt0U0lsyn4dWJ/YTQhgujgucV2fVccamU3ztLFqn6fi7KO7e/zKOjowzFMbuFvYQzTaiwWB057A2o9PcQ15MxFZOjVbGZC7Phw6gW51mH2VxXVlX5ws8cSlSLXfKGY9JYS0/rOMwC3/mYMvZ7AZeJyFxVPQ/yhcMXYiTrfg8cCvxcRF6RKf/YtbbXYSTv9sOI4H7NPuYCvJ30tT7oxmhtj3lh0JTiKAOoaiwilwMfxRmtacevn/sV3n3/iSYBwhqtOFNst4oTUZpS9VYw4tWNZ9VquMCECQHicea/bloI9ToEVUaoEtvkjEg1F9UFuC+KGEoS5vX0MNfeNzNgsU2hz7yxpY0GL5ih9FZK+ZeBySRMjQdVKZGmxnjVo8RUS66neUJHb185b9/bV6YexdRHm5WU61FCUPK54FOHENdT0lSpRwlxI3XhQ0fXTEL24D4ttQFvEJFtMMbsPHvsVOCSLLdARG4CXgKcTNMgvRcTrttRVf9p2zWAK0Tk1ELyRCd9TTndGK3hQvthjNLvNoXzK4CtJ2lcjinmJzt+if0fPJkK5GunMqMV2lT1ME0ZYISRcJnRL/z1Rc21Wr++CF5/ePsbxBVzjQ0lNrw6DUD8VQxYgxUXkjGGkoQde3qo2DVe/QXj+RBGHmooSbh/dJQXzJxBiEmFLwrshjObmYdDTzaXEGYVkyFTjTdzW8ZYpbkXVu+JqYcJ0SovL0JZD2PiespXPn5gnn0YzpxJb6WPjy1cxHdP/xyHfvYLk/WxODYi1lZ7sMVgZdyJLdwoIs/BVBT+ROGaVESuKh7DTOnclhksy08xnteewD+66GvK6cZoPYB5EahqIiJ/xYQMvyNGlfG/gEcnf4iOqSIr1AgFw5WmDMQxke/nJUeWyUqGg+pYw/XmCQwWNEV2AW5clIcU9ebTeLI8yFBSM8UibdHITN5p65IJ/WWlTLK5ru1KJe6PIvrtmq8dZ/Qwq9xDtMoka5RDnxEfBuIG2/ll0lTzjEPPF3r7ytSG6/nC47C3RFRr5MbMJGuUiGox5Z5GM2w4mhCtiokbaS7kW6sOE61axZmHH8zgE8tYfNJ8PN8nTRLKYUhldj+z5piI+t6HHDZpn5djmrFuUt5fQ1M+L6sDeF9Lm3sxArtb2LqBO9MiuaeqdRF5oNBHp31NOd0Yrd8AHxKRY1U1wcjVn29fqGIEGz+9DsbomCLOe+bpnPKvz+f7sSqRLSUSFaSYqmkKQcpwaLULr1881iitjuIc2GtPgZsW0igPMlBa3rxvYa4rVs0NWlY1GYxHtrRez8V3w1JAT3+ZWJUnk4RqI2Egjun3fXorJco9xsBlnlYWGsyyCIOSl9fyStM0zzbMMg6Dkke0qpFfVxuW3MgZIzZqFzw38pcX1EpEtVXUqsOUe0Iu/coZHPhJ92+yKWLCg5OX8m6TIt6JqQAPZhkSPL1A4/LC+Sft43hFHJcX+ui0rymnG6N1FvB9bJq7ql4gIiEmtpkAFwFfmvQROqaU07Y9lYWPn0oMfOYZn+fUxxYQQC6CO2SNlqlKHLO8qKCxprz+JLhxERpXGJjxqEnAyIpJWnmnLByYhSoz43VfFLF1qcT9o6MsazTyDMihxBisoThmaaPBdmGJvnKzPtfKwYjK7LL1pGwGojVGWakTzxrsbK1XUPbsY0I9igshxka+JqweJYyOrCokekBQK1OPotzrcmyqyOo8rbkicnth/5uqOu6qd1t78DLgGlW9uOV0q7K6jHN8PPV1Ged4J31NKd0oYlSBv7Uc+xrN5AzHRsJJzzDe1jlPmCV32SLgLFzY7/ssbTRYKkKkK4yKxh9OMZmFabk7rysjCxfeuIjhoMqwV0d6niD0PAKbjp/V4wpE8rVdWThzXk8PWwcBW5dKZmGyDWtWkyRP4Q98n8qsMiMr6zYdvkR1qE59NKG3UqI+2jRGtarxljLjE5SaaclxPSWqNbMU66NmYfPKwYh6T5J7ZcZjM8kbq1YOkaazqMzu57pLL+GtB35wnDfBsTHTQSLGwESlSZr9yBxMhfVHGJsQkXlB/ZgcAwr70PSalheO0dKu2KaTvqacrhUxHJsOFetpxJAnaGTZfNU0ZcDqBQ5JQlVaFiO3o5i8MR5Z6PD6xWhaNgbRqzPs1ekLYiJrvMD88WbPh5KErUsldrTjy45la74eqtfZrlQyXlwlYIYII2lKpb9MtCo2SRtWKipupLksVFbypNzjWwNUolo3JYfC3qY2YlRr0NtXxsxlN8+liVJllPpoQpok1KOIwSeW8f2FpwPwgZM+2/Xn4pieiKz94mK7lurnmMztvVV1VeF0Nv+0M1YztrA/WJiDuo/mnFXWbxl4DibFvZu+Oh3zPGBzximd0q1WYVdGyyZc/CemINh4A3AyThsRmZeTGYFMsSKq1wlEmFc2YcFMR3BIhmh49WaJk1ZuXGSEddudL9Lqrd18GsPhMmI/IbChytDziAqhwix5IxvnQBxTTdOmEK9N7Mi8s7lBQFjyqfSXGVHNpaCyUiie1zBeWF+Zco/P0MCIUZe3674yAd+sNAo0PbOsCnNUiymP+pQjnyROTNJGrWbet1KJ7y883RmuTYW1zB6066uuwnz/7q6q/y6eV9UHReTvmJT26+w1nt3/ZaHpL4EDRORZVhQd4B1AD6bycTd9TTTeXkwk7lDGtzVZOLKrib5uZJx2wqRF7kz7QmNOxmkj4sDNP8U1yxeCCLE9FsWxqXllM/kyA5HLP/mr0KDazBC8ufDnUFSCH28B8kTYtiNL5oNdoJwtRq4mCaH1qDJR3orvMxTHecp8Ng+XK8unKUEYEtn9oSRhbhAQo8zORHo9oRymVGaXqY8mZu5rplHfqA4ZbysomfmwepTYfRMWDGeWiOsJni/Uhuv5y1g5+JRtVybs7aW3r48rz/kK+33ik919OI5pxySs07oAs6D4E5gMvlcVzt1pqw4vAH5gqwb/AaNatBNwQKHt1cBngB+LyCnAbIxW4GUtAred9DUR5wAfBn6BKW31VIfXTYiodjafJiL/A+wBfHaiARQs9yaJiKy3Ccp1yfUrv0IgwlAc58ceqte5vVZjoCDFNBDHNOr9JkyYlo1HtWQ+7LYIwIjqaqlZMTmurN7rGg9rvADTl1en5MV50kalkMCxXblsDJLNQszKoMwNAvqDIDe42ZzZ3CCg4vuUABWhmiTMSKAeJVRX1M0iZLvIOPsSqo8mVIdGC16aUB9NqA03GHyilqfXA7nC/OhIwpyttqZ/7paUw9AZrg0cVV2r2N4uOz1Dv3Puh9qef81eZ9wx0ZyWNR7PanP62ZkskogcDpyEEYT4K3CCql7f0td2wPmYyNkoppLxCapaa2m32r4mGO+TwK9UdVI10boxWlXgfFU9eTIHsLGxsRotgDtWLRqz8Hhpvc7vq1Uj9RTHPGQz+KppSiPubWYVvnrhWAV4CsYrrpgEDptB+DRJqIn4wynGcGU6h3Z/Rs+KPIswFDGGyiZjVDyPrUslti6VxrSJITdY/VnihlXsqFovrM/zrCp8bFLfy74xTpFZt1VdUTRMSd524LFV+dyYSeIwx1c8NcqW2z2TwK5FC3tnUg5DymFIb6XPJWtsYKy10XruNvq9Cz7c9vwr33z6hEZrumFtxnxVvWgy++1mTquO0b1ybKK8bKYxKHesWsRQo0GkyryeHgbimAAYsnNIMUbxAq+OSAOFMQYLzGLhmJTIs/NgN59mwod/OKVZnystT2zEdj9t3P34jiNM6DBNia0XVbUp9P02szALB84NAqOzCERWb7FYTTm2avMABAEz7HxWscZXtug4S7wAco8LaC5qtt5Xmiq14QYiENVWETcaBKVSnhY/a87mlHtClvzql8yaM4ddXr7bGn1ejg2LTaCeViu3Y8KJk0o3Rus6YHfMomLHJkyWap55LgFGXqni+4TWgPUFmeySwB1HjMn4C0R48sUXsP1dHyf2PIZkhDhYCphkjzx8mJaNEcvCiBOl0he8tEbcS+yvMvNWhYKT2fxWNoe1rNHIPbAssWOuLZWShQhDm9AxZJU3+n2filWCV8n0DuPceGWsHIzM2i1v7BzGysFRGx5skKZKY3QFaaqMVBNmVGbh+T5xo07cqBPVVpEmCXf+7kZ6K30876UbzY/wTZLJXlw8DTgZuFZErlLV2yar026M1nHA70TkeOA8Va2v7gLHxsHfRs7heTOacmPZF/x+s0/gmuUL84KOYDIO+4OAKE15/EWL2eJPR+XXZQYkEKH/7qPNQRn7y9MI9KZEOkKsNVOby3ptQHv1jaJHttsi9JaTjL6h9fbizIvKJKKs2kcW/svms7IClTFQsbqLQ3FMDCyz3mW/TT7JmDujmS7v+UJ1hRlrVgMsbhjh3bhhPLa4kdBbKVPuCaiPGi1DzxPiep00SUgTUwizNjxs9tMkN14ved0b1uxDdGwAyEZfT6uFjwBLgVtE5BZMMcikpY2qavuY6Th0Y7T+AMzEqF6cJSKPtRnADt0MwLFhk+g3WFaQJcoIMy+j4MVUCl/8sT3/5IsvyK/Z6e6jc4WL7A8vBiqZgjtPF+uNPBtCzBYws6yzlPlsnuuWk1BgJKgSSYPYjj2mKRcVYLINMyPWb1Pns2SObGyBHW+WMZmVcAmAMPAISgEloBz5Nk0+sRqGmQivn0tD9faV7Lovn5WDRsw3TdPc06pHUf7cG/aZNSelHIbdfXiODQrx6Khu1kbEIYXnu9utFcVkGHZMN0brEdahdIeIbItR3JgJ9FkFjmxt2KcwZU/mYup2HaOqf2q5/vkYef5XY1Zrfws41eokMtl9bSr4cgRpeiFPNBabeR4RI+FkjVJU0AKs+D5h0lxHlbH9XR/n0Reen4fiMmHe7Is/K38SFOafsoKRuVeUpgz5TxKr0khb5r4mMmCFYpQKDC+ZT9VflYcJc8UN61WFVqswM8LZ+azeV6TKUBznxqzfhhyzsW9dKrFFf5lwZkC0ysxrhb1mTqu6wqTJx42USr+Z1xpqjOShxdpwxNCT/yacOZOwt0a5OkxQKlHuCYlqq+jtmzVpn6tj6pmElPdphaqukxfbjYzTHutiAAW+DFQxRqvIycApwAmYVdrHAb8RkReo6jIAEdkMI+h7D0ZAcgfgq5jKzJ9dR31tMkSrYuZWSlStsQrslzQ0M+6gqVCRfdnvdPfR/OM/ziMU4SX3HJt7YlnmXtZHtbAguJokueeVyTDFkJdIyYxZVZ5ozn3dfJqZ8+ok89CGDke8OpRGxnhNob1H7unZ1xGK5PN22WvGvheZkc3m+Ko2aWNuOSCkWZgym8fyfGHWnJCVg1FurMphYD2yMlEtIm40qEcRQamUZxT2pn2T/Kk6pp5NLjy4TtggZJxE5LWYOi5nYIxXdjzEGJozVfV8e+wW4CHg4zSNyJHADOC/VHUl8GsRmQUsEJEvqerKyexrHb0NGyyZDt+MskdQknxx7p9rZzNkS4kMWO8jFMlLiFRs8kKmHZipsefp5nYeLPOoAKq270zlPStTUk1TwkxCKkkYEiFWM/c1wqCZ87r5tNV7XpB7XyN267vzo8RJQiRCmGUPQp7I0W/nuPp9PzecYEOKBS9ru3LZhBXt+dAX4iShEvp4Vr9w1mY9Y2SjMoHejExRI00jVq1clWcU1qOI3r6IH11wDvsetV7LGTnWkMnwtERkR8yP7lcBLwBuLjoUIrIHcGOby3+lqm+17Q4BvjtOm4+qaibl1HF0ajVjFkzxyOfYQw9iFkOvUeRuvRstEfExobgv8HQRxtcAs4AfZgdUdZWIXIspZJYZmrcB17UYlCuAhcDrgWsnua9NirnPmM+yR75KUPJMGfs+o+FHkuQGLDNCWbisWF4ksIkbYcG7yhI2AowCZ+ahDFmVjYE4JrTrl/LwYFa12GYp5hqIjBD5q5rrvjLjBas3YLd9jGrSyMdUnFsrjhfMP8uANcCtZLJR2bwYNOfO5nkevi9UZveQpkYmqjbs4XkNWxolprevxOAT5GVQwHi49dEGI6tGqUdm3svzN6nss42LyUl53xWjinErtnJ8C/+HmdYo8kzgSsaXX3oj5rdbxoMt51cbnZoIEdkTo+TRuij6IRE5SlWvW10frbQ1WiLyT0x14p1VtSEirS9mPNYkEeNIIAQWA60rp3fGJHv8o+X4vcB+Le1uaBnIIyJSs+euneS+Njm2fubxPHTvWaSJMqvksfWMEgMiDI2OGr0/bGjQ94ns3Ff2x1XJQmzWeFU8b4wie2Z8YtV8cXJW/DHzapY2GsyzihyZgntmyDIV+mqSEAUrqSbL0WRmrsIxIa9YnE/UjiyZz8hui1gObPGnowgLWYLZGLP5vMw4Z2TFKKueR78dSyDC1qWS8RLLQlzy6UtNeLAc+tSjUi7MWw793GBl3qDgkzYAACAASURBVJdRnU+oDdfNIuXH/0VUW8V5JxzD0V8+dxI+VcdUsraViy3Xquo1tr+rMd5Pjv2xfWvLfV+L+S7/IU/ntix/YJzxdhqdGhcR2R34GbAKOBf4iz21KyZJ42ci8gZV/eNE/bQykaf1MGbuOvufnvREDBHZHKNVeJA1jK1NNgOq4yRALAd6RaRsU+87LWo2WX21vo6PYNI7N2rm7XIyD917Fr19JUozmpl+QG6UiuVCAhFefe/83Jhl66G2K5eZ19PDvHK5OYdkDR/QXMwLude1XalEfxAwFMcss6HITFIqEDEemF1/BRBJjcaS+WMXKq9O63C3RblyR6QKWYKIHUfFjrM47wXGKGf/9ZVCpmGxRAp2P/NWg5JHzavTWykT9pZyRflMsLc2XM+/4Dyvh3qUIAK1YSO4u/DIQznpwvGiO44NGc9fO6OlqunqWz2N/YGbVPWxLq/rNDrVjs8By4DdVPXx4gkR+TKwxLbZs5tBtTVarYkX6ygR44vAElX9xQRt2hUraz3XSVGzyeyr2dgUavsmbNwyTkDuCST1hP7A6Ppl2XV56E8k/8Lu9336rTRSMTU+W+vVGDaVgGdi4/09PSyt15uJGdZIZPNiA1a9Apop69m6sGqaEjQauSElqNFI681QYbZQeYKQYabcMfySrzOcHfzTUVRoLqDOyNZ0RenY75Hs+FAhiWSuHeOI5zFzsx5GVtapjxqF+WJIMEvQ6N9iRl7Ha+XyUTzfpMkbmaiE0ZE6Zx5+MLPmbM7HFnbgUTrWO+sje9AKnb+E9j+qH7DOwwPA11S1KB7RaXSqHbsBX2k1WACq+riIXAQc30E/Y1hvc1oisiumTPTrRCQrLNZrH2eLSILxbvpExG/xkPqBmqpmC4jaFTWbzdiiZpPV1ybLc3b9FI/8faGt7CtsHvpsN2tmnjAB5Lp9ADuG4dMEaTNDVgJWRQkDj5mSQOXQp3/uDF4UhqzAhOK2K5fz9PlYNdcGvC+Kcl3BLBFiab1upJoKCRMm7DjCyEsvNF5Utt6rE8/L8uSLLyC686Nj1ncF1iBn676CNCXyvDyTcK7NPhyI4zzUCU0Dtlmf8a56K6WmQkaiY8qeDC6rWVHeBnO26i2EDWNWDo4yOhJRqw7zjc+eyBGnu6LhGzqTFB7slv2BBvCjluOPY+aq/hdTGmR/4EIR6VXV7FdQp9GpdpSh+dtvHFYy/rzchKzPRQM7ASXgFsybsBwzrwVmFfV5mIk/H9ix5dqdaRYpg/GLmm2PSZ+/r9BmsvrapHnmc09i6MkRhgZGGHyixmi1kWcFZkYlE6PNBWiz49aghGJK06ep5skJ9ShhaGCE6oo6m9lrN8ejNJLSU1dm01Rn37pUyrMRP7/NAoAx82qZJxfaxc/Yc6WgBuVBs/2h89Iowy/5ujFehbpdmZ5hcV1ZVngyk4mKCnNhyxoNljYaDCUJy5OEnkqJmZv1mCKTYcCsOSGz5vQwa05PXgYl7A2ozO6h10pHzZrTY+WhhFLZI67XiWo1Fp/UhdCwY72QFYFstwFzReT2wjYZUw7vx2QNDhYPqup1qnq6qv5KVX+pqgdjwoCftXWz8qbjvZQJzhW5F3i/rQM2tgNzbD/bpismSsToJPGilW4SMX4PtGrS7ImRwd8Lk8XyMMYavxc43Y6rF9gHG46z/BI4QUT6VDWz7PthsmJusvt/nMS+Nnlqww1euPvnefCvZxLVYmZXZuRrtrK5piwMWPH9PNyXeVqh55Ekpl1Wlr66ok7YCAhKRjlihi9EUWxDZqZuVW9fKZ8fi2xxx3OeOI1ljUbueeUKFmmmf5jmclKxKpEkxF6NXOfjlpOM59UqwDsORRWN2NbrAogLIdKK55lzngdWizG2nle/7+cKI1n4NBPUTROlHiVEtZiglOB5HuFM8y9qFOITPN8j7C1RDn2GnoxIYqiuGKIyu5/vnv45Zs3Z3KXEb6isPjw4MJkq7yLyImAXzDRMJ1wNvA9TZfhBOo9OtePrmO/W60XkS5i1r2ASMU7AhA+7NswThQfHS7zYDrPYdiXmRQnwbMxk3QMYD6kjVHUA+G3xmIjMs09vLihinAWcIiLLaaZcehhPLONC4BhMUbOFmPUACzAx2pX2ftFk9eUw3H/XGUauKPQRVRMKrNdZZsN1H97iM1z61JkmKcGGyDKjJY3UZsfFuTp6VqOqmBZc+//svXucJGV59v996tQ1Pd0zvcMuu8iioKgoKB7iKYdfTIzx1TfJ+yZqlEgUPHB0gRVBcDkfReQMIooREw1q1DcGDyHxJfoSowYFPIGKyHnZZYfZmemenurqqnp+f9zP83R178zuDCyw7M71+fRnprurq6qrZuqu+76v+7qaKdMTHYJIZpwAaqOho8xbqrz1zgpKpUjL4GtBHwvQ9plaXkLy48OBSRHq/aHJVrbCOizLUq24/SjJpErMyFgpEitnZUqmVsewZsqHGTBuAt7qMHRBfVUcEg9QoltTqRtC9nxF0s5Y/owq0xMdqnU5XlOPGnHdIidNEj5zzmkEYUSt0eAvD3//4znFS9iOUKgnW+X97cjN9tcW+Tl73S9Xp35Ven+wOjX3SrS+1vTUPgj8/hyLXKi1/vQi923hRAyl1MsQpYjjgE/YWqZSKgKOQuqjC2nOLRYfQQLLycBuiNz967XWG0v7ulkp9TrE1OwGpPd0CRJsnqh17dIIImHBRRXfBZw46mU6raLgmk3n9hiFwO/Vj+dXs5dR8326aeaGaq3Nh1yUpbeTdQtxBO7kpJ2MovCdKG35TsoGqLgoRBQXGfoNisIpyjs2I/TUNczriZWS0ppMtSV4LfAYlAPYPj9bI2xDzyMrETMyzyMwgdUGTHs8rIdXWRar4fvUG5EZPM6Ju1aM1/S6ck1cDV1/K+sWZN2CdlMGkbNul6gSE1erRHHMVz5+GUEYUmss43VvPWiRZ3kJ2xNPARHjbQhFfk5K+xx4MzCOVLhg4dWpeaG1/pBS6tOIutA+SKJzN/AvWutfL3C/+rAYE8ibgF9rrY+Y5/1rgH211q97LDuys2BnZw9a/PLH5zAyVuEZ+5zA+MOXEFcDwrpIPW3IMiazjN+rH89XNl/Am5d9iO82L3Llw73CkGQmEzPFdpd2s0vayc1rXWNXLz2eZCajNdUhroY849kjjK0coulp1xuylPjJPHczXLavlCEeX2VmoxPkNcvZQNLKcwlcWjOb+/CKq7b6/beGF/3iWCdlNeikbBVDrNGk9fKyz2MzwzaKx8TGWWcwaft+yUzGIw+2RBLKU0xv7jA9kZClkrnOTEvQGhnbjXh4WALWaEMejQZBGPGqP33jdvkb2NXweE0gf+d3nqVvueXD877veUds0wTSBI03mafHI1Wu083zb1rnYaXUqxG+wF9qrf95jvV8BSFh/BTJpt4GHIyoXVxRWu5kthwufhWwf/lm/8nEYtiDr2Tu4TSL24C/eXy7s4SnA35006nOMh5wd/tRtyAOeqUxoI8ObgOHVr3MyvM9odGnBVlXhmkBM1TbJU2M6OxwwMiyCjr0SLKMA6vH8cPWxS5bcTBKGbZMmGnthoTLAr2J+UxW2sfYiffmJANOywvFPj9b05N2spqK5r3ECOza42KzPMCJEVvfMUtxD0LPqWYIk1Ay3LgakLQzqrXCHS9rfTLb6pJ2EtJOQrVWJwhDp6QRxTG3fPtGXvEnb1j0d1vC44MGttUEWgB2B/5p4DX7fB9k8BekNDjF3CoYIOW+dwN7IdnPHcA7tdb/MLDcNqtTTzYWE7RmkQj7iXnefw2QPO49WsIOjyLXHPDq07jjlrOF/p7mtFK5YNZ3i2X4NstclmVRdgW2tf0i7y9zlR1/i7xD2smJq/JnGg8HbDLMPIBX1T4AwA9bF4vYrWEK2rmxTPfPjpUFblslCSqrptEygSRQipZSZLceQVIUiwpe97xIblL3+dka6W+VtmmDq7VzmTQ+XjbDsgHUfr/GSERcteU/jyDyCcIUz1e0m8I0DkKPeDgkmemStDMzipDR7UzRmdUUeU7W7ZK022RpSmPF7iTtNt//1tcJwpAgipY8up4kFLDFTN9iobW+lx57b2vLHYe0cuZ7/8PA/GlfbzmNEDm2SeZQSv0dEpsP01rn5vlCNvGEWZP8M/BuI+90cYkoUUPS1IOBhezkEp7meOXrz+HW75zOC19xKr++/Vye95J1/PYX55MmOTqT4GGZg2VYskIItA3povfIe2Wwmd79aBB6LvvSRvkiGyhpO81Ay+SzPSqbYZW2nWntekhBnotWoFWtzzIJbGa9ielRzYJTylgokqIgKwVRq65hM0GbVa0Kwz4RYIxti+t/AY2hgCpWTLcgTXKqdel7pUne19wfGYvxvI4puRojyaKgKHKqtTrt5jRJe0b6XsPDeJ7PT/7zuxz4+3+44O+2hMcGrdnib3cnwyFI0DoSGUo+ZAGfeUL9tE4ADkQICacopR42G3yGWc+tZpkl7AJoN1Nu/c7ppEnOr28/15Ww0iQnHg4cLR3gxqkLHR295nmOJWhLXvaRJsImTDs5SbvNyFjs5pNqo5H7hz+w2n8D6Zh79EqQVmTXltusoG9SEsINzHK2Sx0YFQ+gxzjMc4LbjpQgtMDAVb/tSB4+UHpie/30/dz1wksBeMWda10wsr2sVomkYQOmNZe03w0kcHm+cnJPlmUZVzVF3tM7TTu5YXQGckw7OcnMDEEY0pqaJO0keJ5PrdEg63ap1utMT0zwvW8IwSzrdvnD//0WAO76yW0EYUiaJDz/ZduNiQ3AZ88/k3edfPq2F9yJoOlJf+2MGPTPeqL8tBZMxAA3EPZuhAnybHpMkK8Bn1kAb3+nx65CxAD49hdPkEHYOOAlf3AG9975EcZWVhkZO4ZcX8O9nQ4bul1qZgi45nkMKcX0RMfp6yXtjMlNs+Z5l+mJhHazS60R8ez9x4hi0elrLI9Z9aw6G3XOntHcNO67k8t75T8z8FueD1sdRUzmOeNZ5oR5y+QNWyrcYEgeLUPUcC7KWtN86dWP6VhZ12a7L2VShp1lW16Su7Kv28FsS2KpFEKDLwd4OZ5dWpOpuxloTaVMjs8aIkuKUr4rB0aVmFqj4fpdRdFzRfY8j7g67EgcYkDZpjU1SZHnFEXuSo6D82DXX3yBW58wGVPazSbtVlM+awJ0EEV4nkcUx9RGGxx6ylmP6Zg+2Xi8RIwXv/yZ+uvfP3He959VWbNNIsYSFinjpLXOEKrj4jvUS9jp8Cdvu5DvfLXXs7L+UO3mleRp7hTc9zUXRKV1KavKzIU3dwHLMgkBodPHgfnpE0QycFwb8vjV7GU8f6j/gvmL2ctcxmV/loOWLcWVSRuW+m6ll/pU44vClRvtvFes9WPqcwFOgPeuF13BS+84TjIuQ4fPSnYmSUkeys2dGZuWpCiIjehueUzAKmR4nqI1lTrCRvkBms5sm7STkKWpEGDynCAMybpdR9awz9NOQlwdJmm3KfLclRWLvNeT+dTpJ5MmCUWR43k+0xOPuoBkjSzbrSbJzAx51iWIZDyiM9vGD2Q7uxK2R0/r6QQjUHGc1vpf5nn/z4DLtdbPnuv9+bCgoGX6VlPAGVrrbcsGLGGXQdLOiGKf228+oxeQOmbgdzigEQTkaS6qF0b5wpIGLOXdZg2tyZSk3aU2KkPEdnmALBXJp5rvUysKfjF7GfuXAtf+Q8dyd3I5z6qsYWP3KidwmxSFy1pCEKX4PCcOQwlQvs+k6WVZJQ/7eo0eq89Zn5gMKbvtSFp5vuDg9cCLr2Sfn63huT9bw20vvJSX3nEcFAWTCGuxZoahbQnUqm7Y8qAlbWRaO+agndUqCt1jYw4444pTskg/JYaJ2E0T2k0RewnCyASjHM/3iSox1boYTyYzMy6QtZtN2s1piqLAs+adU5PSLzNZVBTHbvkiL0g7CcnMDH6gqQxFjglpRZdnW81dTqk+2/YiOxP2BmpbeX+YLX22tokFBS2tdUspNQk8stgNLGHnhlWzABkStgoXAGHVZ4XvkyQZ1bqU9MYfvoTWVNpXHkzaMqeVdQui2HeisVk3J0s9srAw684JwoxGRd4fzLgSrbmvcwUrw6MBmMmllBdmmtZkykShqdZC9ooC/IqoVYxnGeMls0crgLvaKHiUFTSsO7Olzbc8j9ZtRy64ZGiZhSDZ223m+R/88gM9er7J8myZMAMyM7A9meesDkOeE0SSoc50abe6tCY7jnnZmuyYgeycIPSJKgFB5JGlBfFw5ubjZk1A8ryE1tQk2mQAfiC9ryAMCcKojy4/sXEDWTclCEXjtNtJiGLptfmBojM77f4eirzA8z2i2KdaE9UUSxyxXmFZ6HH2IQdx6nXXL/Cv7emNwo1ULMFgJdBe7IcWUx78D8S595ptLbiEXQc2YBW5Zu8XnOQo8Fno0fCPJGlf1RewbFCzbEFLwLCDxXG1p4guQUrKgmUCQjX2aZmS393J5SRas//QsS7bKqNSQGKEedMkc3YfUSxahjXPo1XqMQXgBHehd2dsCRNl2jwmM+O2I2Uo+WXzTYNsibtedAXP/dka7nrRFdy838UAvPGuE8hK23HeYEYZfrUpF4rArk88bK1KcD8tASOq+EQV32VfSVuOexSbTK6dkSYJnuehFFSGQ5elFUVCc3MT5Xl4nm+Cm4fWXeqNiLQjgceaWfbGF3o3MHZ/QDLuIPR76zfZobBCn1RZo6cU26M8qJTaFyG8vRo4AJG8e+3AMveyZQazUWu9amC5FyISdq9BlH+uBc4s6wwqMTk8GWEELgduQQaQb59n//4/oLw/f2X2eRBjyCzZnOvZGhbLHvyuUupM4KIlHb4lWNjM6sHffNQ9Lwczt1zec+UtP087Oe1WSmsqJYr9XknQUOFtr8wOGhe5pjYaOikk8txlXZnWPJRe2ZOPCo+g3bzSXTCFsSj+VUHoEUSK/YeO5XvNi3p6hiXWoUXZeTkpCmJrEGn6XUlREN92JJvnybrqc2Rkd73oir7nlu2Y2LKhzbqCgOX0Zrg25zn12CfuBjLnVgyRzEhQak12iGLfzbvZ7LdaD915sczDIs8NIUICXDkA9SxTMoKwAAqGRyoEkUe1HjqWqA1G9hzZ42zPHSAml6XSpSwn5zXfxepl2+Hr7o8oYvyArdt6/CP9mqp9FiJKqWWILN8dCLHuOcBFyCBx2dzxJLZUxPi2UuoArfWGObb7R/QUOjTwV+YxF34DLNqeYDFB6/8CMfKFTlFKbWLL1G4xKu9L2Amxet8TWX/PhX2vTY5f5hQbLN09mclcaWvDfU0nP9RudokqQR8V3vNzsq7nsoWi0DRin8ZQ4O5cbSZULhdOZR8n617tLtKWfGAv3CAZzd3J5cJyNGXCySyjEfT/a5TtTfqClnnNDifPFZyABZUQ3Qya70u/y4jqWtsTSk7N+1Yqbuja8z2iipAahIzR6ett2SDmNbuOuFHkGq1zsi5UhpRR2gidviPIzUFrSgwoo0pgKPayTRu0bMAahJxnKSv29lO5EmHW9U3GvTU7pp0Lhd4uRIwbtNZfA1BKfRnJfubCw1rrH2xlPUcAQ8BfmQTk35VSI8AZSqmPaq2nlVIxErTO11pfabb5fUR14/3M7Vx8KXAdkkL/FhlwHhTs1YhP1wSPAYsJWnOpvi9hCYD0nywBI4qD0usF7WZKu9kl6xZMbGzz4G+mHFvQEjIAPN+nNdV22Va1HpXUzbtufbb8FeeaMPLYzfOohR4PpVfSynOeP3QsD6VXMhocRaf4BF4hd/mrnnk86++50JUIPV8RGv+tmslyWkVBEkW9cmGJCLHKkDesX1bN85j0fVFsN8K3cVFQ+8nRfZT7hZI1XIZnGIXQuzMPul3HxgQJXKvCgMZuMXE1I62FtFvS46qNVvDGzLyaocLL8ZXjYAeTbWnWMTRDsUJ5+3EiBPzVq9c4t+ReSbUnLWV7jxYug3aq/SbIRr5zZ85SW3mK3P585PCDSGa6nPG5Ly/oOD1dodGPe7hYa729mmJvBG4cqJh9AbgAaQPdAPwuom3o5Pu01jNKqRvM57cIWlrrKYS0h1Lqj4A7tNabttM+A4sIWoN10yUsAXqq1ZYRtuH+i1i+x1omNl7a93rSzpwafJntFsU+1Tw0wWuGIIwchTtLc9KO5+7krTJGEHouQHodufAFocfy4cAFGTvLlRQFwwPZQLkH4xkFj1WGUh7kOUkQ9AWt2POomSznQUPTLusY2mASm8Dl+l8IOaQ3+rt1/Gz/y/qev/GuEwhMqdCyC+13sgoaGdCIfTxTmosq0quzfSz7fYPQkx6XycDKNwLxcEBUCdwxAfjnTx7jsigJWIHTPLQ3FeUsy2autvdY5JqsGrhSrw1aCRBVCnce7Q0IwNmHHEQUx0RxzNpLH9s83I4MDU/mcPG7lVLHIPJ7/w4cr7W+r/T+fsBNffun9f1KqbZ57wbzMwfuGlj3nSzA0UNr/YT4Dy5qTmsJSyjjunMP6ys3Weo6wNjKnmpFj0SRu7v2ItfmQpm5i6DcvcdMT7SIYp+0I6aHgAtk9kKXzHTdXbto8nlUgXiovxeVaY0K5P0N919Eu5maLKtwF9rh0CPwenJPtrdkpZbKklRWMX4yy5wnlqXGW03BlsmSJoFAa7j1iEWRNCzKpSQ7SxaYubKWKVFaavxoNTAEh0pfjymqBIbQ4lFrVFxpVHpVsp9xNSSuBi7z+pdrj3XHFHA2MTZgVetRX/Czywz2MdNOT1DZvm4fURw44k1vf1NmzSDyzgjNkybj9DWk5/UgYgJ5OnCzUupFJhMCWIb8iQ5is3nPLtMaMIC0y1SVUpG1qJoPRpDifyO6tcuQnlkZT6j2oN2J59BTxACpW35Na333Yte1hKc30k7Wd6Gy/aqJjZe6oFXurdiynGUIDjLfZGjWY6i2jA33PeqYg1KiCpwh4tjKIYqi4tYbVXyjUZgxMhbTKT5BxRMHnd3Co8n1NRS5ZnoiYXoioVqPqAFtc7F01vaR9MgSo/9nhWx1kjMahsyongxPpjWrwrAv45o0ZUPL+rP9L7LMSUF1X75w8m2r1DezA86BUoyX5smWmxk0fGgsq7jMyGoSxsPSi6qNatJOJnYnTasY71MbrRDF8tOW9ap1CWK2l2Vns2qNyBE2rL9XWVpqcKbOnlebXWdpT6XeZcqeot3qCj0/9k2vs7PgY/R0wgJ6WsuVUj8qPf+k1nrRQg5a6/Lk/c1Kqf9CWHqHIj0nt+gcH1cDr8+3zHzv9RZSagxhnR9QWm/5s/a1Jy5oKaXORhpz/sBbH1VKnae1Pm0x61vC0xtlsgRAUdifvb9ld8cf+yZo5U7Rwa4jTaQflqU59WUi7ZMmCZObZhlbWZV1Gpp0tR65PpnneS7Ts1lBaypl93rITH61y37yNHdKEc7SPszcHX6Rm3JlWjBkdBNBgo7qFsyarKReD52GYctqKZplJ80Ml9M+NJnRpPHpIs/B8xZlTXHLCy7hNXeuddlVkOdC/DDrrZksq6xTOFwNyH2FZ/t+1YC0IjcT7VbqVOHHTIYUxVJKtD0teyzLJUGQGwqxSvEd27B87Oz5kZ9B3+BzEEqQteVKCXiyL9MTlAKt7/5+PnL4QRS55sPXfmERR2zHxgJ6WuNPhIyT1vrnSqlfAS8rvbwZaMyx+Ci9DGwzUFdK+QPZVgNoL0C27xykxPhexKX+buANCD/iVOC55vmisOCgpZR6N7AOcbO8EPi5eWt/hA65Til1j9Z61xpx34Vx1PnX8Y8XHUmRi0BrPBz0lYkAaqNrWH/PhRLcUjM83PHJugFeq0SuSHOGanVqow2iOGbVs/bmgbvuoFrvOsZgXA1ceVCQ97IBO+DsK9rNrpSzAh+lNe0kI+uWWYNSksy6BXTpBS0TSBvD0qPyc02aFm4gNor9PpmoVSaAWQHc8SwT8oVh/IFRnLekiixjaJGlwkRrAmOZ0rI0/tIsV8sMPdvn+D5ZXjBsyoEAQZg7I02QzDTrhu54AW4I2N5klGfZPE8RRL5zqg4iCWq6ZKxZMRlnSK9UaH9GlV4PLAg9I9UVkXYyk0VL5tb2UlcGtvJe57337TtN4HqSe1rz7YLFL5GA4qCU2gtRqfhlaRkf2Bfx37LYr7TM1vA/gb/XWn9GKbWbeS3XWv8KOFgp9R3gfGQGbMFYTKZ1NPBD4LVGg9DibqXUN4GbERrkUtDahdCaklJObGjl9i59cvwyGsulSlGtyzhJmfJc5KJOMT2ROBZamog6Q7UYIa5WGRoeZXK8SW00Ik08lyXF1YBao9LrcaUF8XDggluRa2qNimvwl7NBz1c9wdlGxfVp7HtpJyc2Hl6pVW7o2uHknHi45zBsB5GtmC5IdvZgaryujBCuzbiszuH4rUeQab2gUqET7DXZWllLMSkKKR2aEqT17FoVhjSGQsKhAJ0VtL2uC8ppJ3SEFugn0kQVX+a5SjcAlsFpg9is1sxqTWYySFsedetTisCDwFcElYCKUtSQEmFU8Wm3uu5nmvglpRPZjyj2aU15JDMZ1XpEu5ny0SP/hqLQnHTN01s5YztR3hcNpdQBwPPpF4b4FnCCUqqutW6a196GEDcsgeK/gGngrUjWZJ2T/5yF6c+uQoaRoUeEjUvv/zOS8DxhQesFwMkDAQsQIV2l1BeQqLmEXQg2INhgYWWCyooIjeXH8siDFxOEvTt4kRnyGBmLjWuxEDWStoz+FXlOXB1mZnqStJKbC2nqFB9kMLg3iJzMZE7tAWwGIRdcq28IvR5bu9V17EUrKyTvSZnSlskscSDrZiShx7KRiERrlg/MbU3muRO4TYLAlQet4aQV4C0raiykVJhpzS0vuIQ/+OUHwPMIjNyT7XGBDDhnWlMzwaOV52wA15er1sNSGTVzJT/oBS0LS2m3mZGV1dJK0TSzaHa/yq7Lbn02Gxww36ybweWi0KSGSeh5Gn1vSwAAIABJREFUvfJiEMl+tJtQG41KfbLA/Z093bE9iBgmaLzJPN0TGFFKvcU8/yYy3Hsw8HVgPZIVnYKU5K4rreoTwDHAV5VSFyAchTMQr8RpAK11opT6CHCqUmozveFij/7B5fkwgWRuAE3EuHmv0vtdeqSPBWMxQStl6+KHdQamrpew8+Oo86/junMP65vRKbMIy+hRqG1mY6zjTVkRoN2cNsuK8KpSEuDKKhm2mR90CzcTVu6jASRhRhQHLN9jba88WaJlu97WjOynNbQE3PbK65YMT5OnOStCv0cymOkyFPsEQW/QueH7ZGamC8MwBMmILH0905omC8fN+13MH/3qeEf6sCzHAGiVzDbLfTTb64qVol43pUGbVQ4I67pz5CuGRiJ0Jt9FBR6zRUFinZ2Lwt0yzxW07D44GSxTQp3VmiFDu3fbssGrNDMmahuy3zYjh50laG0XP63dgX8aeM0+3wd4wCxzKdJ7ehT4V+DD5ZksrfVmpdTrgCsRevskcAkSuMr4CBKkTgZ2A34EvF5rvXEB+/pr4IVme4VS6jbgEKXUdUjZ8Z0IkW9RWEzQugU4XCl17eAOK6V2Bw5DyodL2MVgmWI9maC8rwRVhi3pWYp11s1d+TCZyQydfQaA2miD3VfvxfjD652Nhc1+bGCRbcoFrVqP3EWwyANHiX/GPifwm5+e58p8ZTX6tNOjfpf32QY1NyPW6Sl6WJakfcTdgJGxCo0gcCaO5Qv6qiBgstSLchf5249i00s+vtVje+cBl/OCnx/DnQdc7sR77TZskLSZTVbOvmxvzQSvTGuhxZsMRwVe374ESqG0RislmSFC8iArnFml9RWzn3lV7QPz7vePZy5xjs3OudnzGBrumVnaY2tnyuLS/s1F5nm6Y3tkWlrre9m2YOPrFriuO4A/3sYyGjjXPBaLfwM+qJR6v9a6A1yMDDBPIIdjCIkbi8JigtbZiJTTnUqpTyOaVSBEjEORTOsdi92BJewcKOvaAVtQ36Xk5BMVEnCi2MdrKqNE7iOlPLkLT9oZs61pPM+jWh/hGfs8m0cefICk3XZDsq3JDkHou3KjZBApWVfIGmlFGIJWUmpwhkgCUObo8hYHvPo0fnTTqc5+w5bKilwznrbdOlxG2ckZWSbzTytWxMRKCQ09TcH0tsolxHtL/S6AZAEq8XceICLANlCV+yIZvUFnu86W6aXZC2TNqH60TMaX5ZoskwBoB6ApracccJ36vXk90ZrXjXyQG6f6pboG8fJhkZT78cwl8nnjGZZ5HnHsU4t7Qsi2F2otV6xkVBJ6eF7XnYOnO56qntZTiPOAj5mAhdb6S0qpDClf5sCXtdZfXOxKF6OI8f+UUn+FpJPHD7x9P/AurfXNi92BJTz98e5TP2VKhL1sy1qNWIytPI7xhy+hyI2BoaOryzIHn/AJrjv3MILIIyrk4tpuNp0/U220wfjDM27wVO7Wcyfy2rt7L/oyqSj2eebzxKjy9pvPcE1/e3dfZjse8GqZ2PidPxbLuB/cuI6i8PqGZdvNnvCszbhsf63dlFJhPZQAYZU1WqVAs9oob1hWYazUVsuEe/zkaGqe1yeu21ceND8xxJA+KSgTdKx2YWueod1yIXdrmUA5oL1h9ISt7HUPLx9ey0/al5KZUqbNujKlGDL9RAlauRPYFUJM5vqJnl/wZ4desqDt7cjQ7Fp+WiZL6wy89lXgq49nvYPTydvaiRuQuumrEFn5g4BXAs/WWn99MetSSr1FKfVfSqlHlVKJUupXSqlTlFKRef+1Sik9z+PG0noOmWeZIwa2p5RSH1ZKPaCUmlVK/T+l1Evm2K8XKqX+r1KqrZRar5Q6Syk1OJe2hAEcsu6TrvRm2Xn2ImRR5Jpn7HOC0/2TQVXJjL5w6VEcsu6TfVmP1jntZpPpiUcBiCqx9LgGynP9ZBAJDpZ8YWeDADNn5LlZpXLgKuO//10k1V79hnNdr8W6K0+Oz7oSYdoRO5VkRnysWlMpk5sSkpmM5UHA8iBgVRiyyvze8H1WhSHLg8DNe8WeR/22+clTDx94FQAvveM4vv+CS/iP51/EDc+90PWWLHPRkj3so2UyOyvkO5nn4h2WZW4A2v2eZU7hozwg3Sr9bOU5bxg9gf/ZmN8ufj4cWD2O/YeOdWXGSbOvzUJYnz21jcANNlsPLvveN/9+/lLk0wUa3XeOBh87G5RSxyil5hP0fcxYtCKGEWy8hR6V8bFiN2Ra+kKkCfhKpAm4CqHO34r4vJTxTOCLCF1zEH8MfTJvgw2+bUrsL0KufwlzwDbOhaWWUK2HrL/nQjzfcxYZv/3F+bQmU9dfsnj7cR/nC5ceRbvZJQh7c1FFntHcPEHSbjtduiCMaE5uck39wAwKp54EyclNs0RxQLvZpdaIuOOWsx0b7ZWvP4dbv3O6Y6u1Jju0m11+dNOpBKHPS/7gDF75+nP4738/hVe+/hxe/YZz+dfPHY+1RilyTVztyRPZR2uq4xQ7knaF3VfX2HcoduaRVmB33FDTbdAZz7J5s629fvp+x7677YWX8po714p9iu/zf/b9qFvuffd+WDIqUz50s2Ql7UToKdUHSvGu5R8G4LPj57n12NcWgq9svsCtv1xChPmzMOs0/avZy1wWmGlNzZQLk5nM3ch4nqh5tJtd0kQYqTd9+USybsGfHvSxBe/njoTt4af1NMOlwIVmJOqzwDcWMJC8TTxlMk5a68Ehlf8w0vhHK6XWGKZLn7S+UuoPkHP/JbbELVrr1jz7vFCJ/W3K9S/mO+5K+MS6Q500D2S0mzJEamnTMiPUIzE4ixLjWvyFS4/i7cd9nL//yOGu5Gd7GTJQnDlmYW20gVIhWTdzTXprPwKWldYvDVTO4F722jP50U2nOjKIzaLiqubW75zOy157Jq98/TluebufSTsj7eRUa6EEYkMjt9p8kFKtR0653pYOl1cCF0SsVqCd87IX/PkQKOV6Wt9/wSWOQVjGp/Y+j0PvOVmCgBHRjZVyJUNL0MhKNH2Aazadu6hABRKs7P6X9RnLfbAfti52pBGAP6z3dxOeXwpegVKQ58SeR6VU6rX0fCixDA3j8Zt//wHe9M6LF7XfOwJ2tfIgogT/TiRe/AWwWSl1PTJw/JiTnkWVB42M0y+BjwFHmcfHgF8ppc56rDtRwqNs3djsIOC7Wuv1i1zvnBL7CNXzjaXl5pPrH0Lk+pcwD6y2nO1lpUnP+r2ckdiyWprk5i46d9Tzz10oFd3Dzvo7okpAtR5RrYfUGhEjYxXqjYih4YDm5gmCMCRNsl4QNOu05AhLu5cA0iXtSBC59TviT2f7VnaOa2Jj2wSljB/cuI4f3LiO//qmXNDLCvFpkjO9ucP0REIy0zW28j3xWOnX9YJZkWsnuusyEtgiO5kLNot5zZ1rZU4L+I/nX0SiNX9+V38285l9zheig8m2zl99livrWYFdm+G18pzLNp7N4SvWcdnGs7ls49nbPL+fHT+Pr2y+wJE6aibjs2XPcjl0eRDQMK83fJ/vNS+ac53WtDMxc2sdD0eqkVGIkGotpFoPiSqBU+IIQo9/ufbYOde5I0MbIsauUh7UWt+otX4HUj17L/AzJGb8QCl1h1LqRKXUnotd71Mu42T6RRVEF+sY4GrTwBtc7rnAS5mfInm3kQq5GxmQK2dyC5XYX4hc/xLmwPREp1TSk5mgpC2Bq8g1B7z6NO645WwXuNqtsp9WVxruHcVhZ/0dAO8949qtbu+sd/01Q7U6aZIYb64Mz0spCs3yPYb7sjWA2LAKAReMykSMyckEwPXYBinZcTUkqvhuCNm+Z9XQrddUebvDyyps6HZJOh0yrfvEdO3vGcxJe19x+1E9lXnfJ1aKN5pA9a3nzs3c+4d9zueg357E9c/+CAAX7nU2a+4/hSueeQ5rHzi1r5xng+axK0/d1qmV42cCbzlorY4i/BJjtMg1QeThV3rGlZnWxEoJGUNrxyq0eFZljfv9ofRKEqWIA8/1H0XMWMgz1jTUlqC/evUa/urIhcy47hjYHn5axrr+BODViBDtzWXbKKXUHkjr40+R9sZm5Jp2cvlmXyn1WqQ9M4gLtNYnDWzzfcCJyGDwL4ATtdb/d6H7bCpgnwE+o5R6JvC3CIPwfERpY2uJyhbYEWScZpCgBfD3yAmZCwchE9RfGXj9YaRX9d/IwNpBwCeUUlWttaUcLVRifyFy/VtAKXUYj2HeYGfCSddcz8XHHNxXrrOkDEeDTwuKonBag3YZG6g+se7QBW8vrgbMzsxQrY/Qbk6jiwI/CPEDLUGwEBXz6c2dngZe3POTsqSRNBE35LST0ZpKiU1JT8qaATd9+cSStUqB53uOSGKDl6XNW9FZ+9wGqg1mxmyQ9NDayh227UvZEmKZwv6XvzmRTGtumCN42YBlccUzpcx5yV6STZ3wwKl9QevUh07n7D3PnHMfrtl0bq8vppRkV0HAqiBgme8zsXHWHNved7BBPwg9lsU+fhQwbma/Mq35SftSDqweN+f2Mq3ZLXo/j3avIg4Corgn/VUmyzgj0DjgHy86kr85/unhvVWwXbQH90cUMX7A3Bf7lwN/CVyLXK9XIlyB/zL9+8EWyjvo7/8/VH5TKfV2RD3jDOA/kfGmryulXqG1/jmLhEkCPo/EnrXIqNSisCPIOP0uUEWIGKchlPqj5lju7cC/DVo0a61vBG4svfQtpVQFOEUpdVnJ6XOhEvsLkevvg7EP+CSAUurpP1DyGGE9moqKzDEl7W6fEgX05qSsT1YZc9m2z4cTr/5Hznvv20lmZpzIbpEXbH5kA+2m8esysk6SYckMV28/evR8W8prN2WGqme/4fHHbxHCw01fPhGvUAR4fQGqx4QMnKp6tR4RREZJoih4ME1dGWwyz/lNpyNBK88dO7CMPX5ydM980mRZVt3CDRhrzVvvFir/Pz3nAvfZcqZVxtoHTuWSvc7mwr36S4HlgHXBw2cSex7HrjyVT286l/esWMfXNl/ggqYt+Q13YXqqw/j6mZ72ZLUnwFtmA0YVn93qIXGgHMtxW1nXbuHRTGUfJ/Y9Il8y17h07uwQuu1jXnfuYRyybtEOHk86tpOf1g1a668BKKW+DAyy8/4T2K98nVZK3YoI3r4ZIUSU8dNtBJ8zgc9qrc826/ouUvE6CcmWFgTDDfhrpMf1e+bln8+xP9vEUy7jpLW+1fz6n0qpceCzSqmLysQOpdSBSNBc6FT2l5EDtDdyF7FQif2FyPUvYR58+Nov8JHDDxJZprQwg6LaBaMX/97p/Pe/n9IjYAzQ4svq8Avd3lnv+mtA6PBBFJG0Z0g7ZgjYqV/k1EYrfaW98iC07cc5W3qv4oRiQcqJNluzxBJ7UbZByw7HVusRcTVAhx7jaeoklTZ0u44+vqHb7RPZLWPF7Ue5zMY+an5PXd7OSs118fvbe04mVopD7zkZkD6XxVyZ1tl7nsm5D5/psrlAKRewYs+THpYpBcaex6ogYBSPyamE1mTHqJdkTG/uuAA+eByi2CdpZ4yMVagN9S43mdb8avYyR8oYxGhwFFPZx8mUIo5853Zc5IVIPZkepj3+nzzt3S5j31Fhe1qPbx16qyvQWm9xndJa/9q0OHZfzLaUUs8Gnge4k2TkmP6p/NpWPu8B/wMJVH+BiOVuAi5HAuHti9kfix1NxskGsH2Q3pTF2xE6+9cWuT77n71Qif2FyPUvYSs46Zrr+cjhB9FYHvf6PqHnCBBpR/pctpfVmkw5+5CD8ANNayrlyhPf5T4DcMS5W682C7OwS9JuU4silj9jTx554H46s13azVn8ICSslO1Heqy/ouh5PiXtzGVeZbsOgN9903n84MZ1rrQos0UhtdHI9LA80/cSHUUdevym02FDt+seD6apo77buakHXnzlFt+nHCSWB+LtZee7Yms4WZJwyrRmzf29aYxtsRHLmdapD8k5sUzAw1es4/OPnu+2b+n1lnQxlGjGN7eZ3DRLu9ml3UxJOznTEwmtyZR4WC4ntdHImUsGoSc3DIloQY6OVWjEkQvadyciTzVX8BoNjmJj9yoyrakb+ScQMky1FonVTcUqayyKU/aUoNg+2oOLhlLqxUg164453r7JcAEeQEqK55du7O21cPDadycwppRaobXetJVNrwdWIG2dG5D2zzfnaNMsCos502cDeyAyThcqpQ41j48hX2IVRr7+ccCmjfcMvP42JC2ek9I+B94MjAP3medliX2gT2K/PPP1LeANSqlynXVQrn8J28BJ11zfxwy02c7LXnvmFnJK9kIUVmrkWcTExln32W0FrDKOvfhK0iQhCENGxnajWq8zVKsTxTGd2R5TMWnLxVZcjGVGy5abbFCyFh1B6HH7zWdw+81nEIQev//n5xsGm2G4DYfURivmIh1Ra0Rs9jTjWdYXrO41AcwGrKQo5gxY+/xsTa93ZAwmLQOvzMxbXhpWbgw+N4+a53Hyg6LwccIDc5Mtzt7zTNbtcTrHrjyVw1es49ObeoWMwOyHXfcoHq2plGSmS7vZZXoiYXJcnKDTRPy6HnmgxcSGNuvvmWZyfJbpCZldm55IaE2ltKY6TE906Eym1EvfMfY87k4un3MfV4ZHkxg7FGs+KcdesjhLhPF85W54dlTY8uB8jycCJtu5DCGh/VvprSlEDPcQxIjxn5FSYHmWwPbxB7O3zQPvz4f7EJ7DHlrrt2qtb3i8AQueQhknpdS/IoO8v0CYfb9n1vvFgdLgq5HMa86ReKXUVxASxk+RbOpt5nGMTaUXIbG/Tbn+JSwM7VbXWb2XrdfLzrieJz/zrCvlvVD6IrMzM49Jay5NEqI4ZmzlKpL2DO1m0wntTk80nSlikWu6qUeRF1SGFCNjFaOSERCEviNZlEkFaaJctmiJGvZiWa2HzGrNJku0MCXAe9PUBatywCpLMkGph2VKgTbbqpk5qHIwsrYmVgrKyjaV56TcAzj34TPJtHZZ1XykC4D3rFjHZ8fP6xPkdUPAqbWOyZxDtT2WNqNqtVNaU70OwchY7GxGpicSETTONWki83G1RkQj9MXgcitlM/t9tOf1bhoqYhxpy732fOzIWEB5cLlS6kel5580/fLHg/MRkYY/LA/2aq1vA24rLfdtpVQH+IBS6myt9Xh51wfWORcXYAtorV/12Hd7fixquFhrfYNS6hsIQ2UfZOfvBm7dVq11DtyCRPm9kdGV3yLy94O2rm9H7grmUsEAKfe9G6FjKiQFfqfW+h8GltumxP4i5PqXsA2c8ndf5GPvf4dh3oV9ZIwoDoiN7FLWzYmHQ9rNaWqNBrXRBp7vk8zM8JHDDyIIPT545efn3c6Hr/0C577nYC457kiCMKTIc6I4ZmRsNzeMPDm+iXZ1mKQ9g+f75FlKniXsvnrYEAdCGit6xIrG8iFqjZ6MUNko0WaIliVojRHHs4x705RWnvNgt8u9hmxhsy7b27pnjoBVppOX55tWRxGrzMzT6jB0fl22vGi1B8tU9nsN6aNlBnb7ghlw2cazSYqCD+1x+pzHM9OaNy/7kCNgtIqCuChYPhS4DLMRDjnfrenNHXNOfaJELFuUCmluVjzywDhjK6ssf8Yw0xMJtYZkpXE1pNaQjLexYojhakAcBGzsXsXK8Ogt9mnP6P1s7F4FeU7NOCinSeayrSjpeYZ9Yt2hi8rQn0wsgIgxrrX+ne21PaXUUQgb+yCt9UJaN19GqO0vRmjyNqNqINdgSs9hgX1+pdQ+iPL8SuDzWut7jVzfKmCDYW4vGE+ZjJPW+lSEqr6t5Y4D5ubIyvsfBrY51r9Qif2FyPUvYWEolwJt0Pr9Pz+ff7v+g6WmfUi1ljOxsU08PMzI2AhFIRWEZGaGrCuW68CCbdeLPMfzfcZW7UEyM2No9l2S9gxZmtJNE0bGKm6uyg6y2iAk+nfmecVn9b4n8pufnifMSE+7AVh7h28zAUuy2ELDz/RvthawykxBG7RWlYZ0bfnPBiI7SFzzPOqmn9M0d/HjWeYMIO1F0vaQEqVIPI+LNpxFojXrBoLXe1asA3rKDU7LMM+pjUbGlXq2p/dobWJsuddTdNOUuFpldPkKZmcS7r1zM7XRiLFVVdJkiNqoHTCXzLdal9JqLfDmDVwrw6N51PS3xLwzIKr0ZJ+sKsk7T7qGvzv7fWTdfIcjZjyZPS2l1JuRKtKJj0FJvcwFAOlt3Vd6fz9gYhv9LLsfFyBVLd+s1yoRxUiCcQoi97RgLDifVkq9Tik1L6VdKXW+UuqPFrPxJezccH5Xxsbjpi+L2KrnKYLId8SFeFjYZq1JuXFrLN+dkWVjVOt1fL9Cnim6acEZB7+FMw5+C2e+UxiDZx9yEOe+52A8z3OBqgzP8yiKnDRJyLopSXuGPO84UVbL+itnTfJ74AJTEMk6LY1b1Bl8Z4uigv4gYkkS5TmspCj6AtYeP5GLclk015YC+wgQpTKhLRuGmSacLagnmnqroLupw29++ii//PEmAmOLYntie0cR+1Yq7B1FrI6ivr6XXfcFD89fLizrAya6Z9ZoWZT2+EWV3kB1FPuEkUe72TTncgWju+1Bc7Lg/l9NsuG+JuMPz5h+V4fpzfKzNZkyZPpoG7tbjgFAz0fMnTNzjux5s/3Rd5/6KaJKwKVr/3axf7JPOJ6MnpYZHP48cKXWejFCjW9GDvNPAbTWv0WMHMtcAM88n6/yVd6Pw5FM7ypk2NmxhEy75V8QXsGisJhM60P0p4iD2McsM9eU9RJ2QZx0zfV87P3vEKmkUTFo/LfrP8ifvO1C/u36D5KlthchFz2tM5KZGaq1OquetTfTExO0piZd0LH9KYALjjgUz/cNG9DH8yS4RHFMtT5CEIZEcUyyfkZo8EmCLgqCMHTlPju46n43fRErF2TnjCbHLwOg1qg4tqENcpuzjA1mYHhDlvGbTocHTS/LBjDbw9rrp+935bzGz4+hZrIpW9pbHUU0TOC1RAgbxCwtvZMWTG/u0G6mtCaF5FAUmt1X1+huTtlzNCKoiB3J3pUKeZqDgm5kyoommLqS5Tw9ljcvkxmw7xoJpkApdgsDFyyyrlDPLUY6OVElM/YtXaYnOiQzM3ieT2PFCsZWrqLdajK+/iEmNk6YsmGVIteikm+G0BsrYmqBx32dK/rUMkCyrans44Slm424Gsp8YCRqJZ+78AhH/gHkRudzX37sf8TbEdvDT8sQyN5knu4JjCil3mKefxN4FkKq+CXwRcMJsNhk+QJKqasR+vktyKjSmxDSxKVa60dLnzkD+JxS6l7ge8C7gOcCf7OA3T0K+D9a6+MMQ3EQPzXbXBQWE7QOBD66lfd/iNRDl7AEh7I6huf1iEN/etDH+Ppn1vbp9Xm+cn2nKB5i99V7Ua3XXeCyd++DCMLQ+W55nu9+j6vDLvsqisINNJezBPndGD5GXt/+lGePygHO0uKbRdEXsDZ0u84ixGZZVuzWMgPt0HAALA8CVkciahB7HqvDkJrvO0HauVAewLZivyCD3ePrZ0iTnBUrYrqhT3c2c1qM1VrIiuGA2PSpLPFhElG+ONyUBS2+tvmCvn3oleV8oo6U46IkcMPZtvc3PaEJwtxkZV2ybupYnbXRBmmSMD3xKK2pDrVG5CS44nbgVPtHxirUwrkDlz1/ct79/vNoNB+lZNmznLlozWEcf8VTP3y8nRQxdgf+aeA1+9zaRo0i1+vvDSz3WYRHAML4fi+iShEBv0GIcH00Tq319UqpGpKQnIoQ5/5sgWoYzwO2JleyiS2Ho7eJxQStUURyaT7Msm0K5BJ2QVjDRKCPFViW/3H9oW6X6YlHqdbrrHrm3i5zSpNZNtx3bx9zzUKyp9AEO7EviavD7nebkUHvgmd/LxtIymte32v2Qm3LgJnWEChmioJ7O5JRPWiClZ3FahUFt72wv0zvhoRtCVApVoUhe0eRC2S2tOe+V4lkMdcxtWg3U8bXy0+771HFp2OMMNtNoaqPjMUMDQfgy7YyranNcxG12d8f1o/nJ+1L3Tb7gnnkEXR9origNlqh3UzNMLEyZIncWcoAVOt1GstXUOQ5j254iLGVVVpTHaJYgp9lHnq+Ymj32M1xPSc+xu2XBM/eOSorkxSFdqomto+qTCb++Y+dxzs+uDhF++2Px18G1FrfS6nMNgeuM49tredyBgLUVpb9FPCphSw7gASZcZ0Pz+IxiDYsJmg9hLAG58PLgQ2L3YEl7Nw46Zrrufz4d7qgBfDPn5SLkJNRKno9kiLPXTlvevMEI8vGDBOwyfTEBFk3NbqCYcnF2DeBK5KyYCWmWq8TV4cleFVis5zJsAxtWu7SfacV6GjsJTq7/TlrGHtZqWdlKe3jJZq7JV4MoswQtCSJ2PNYZbKrcu8KcC7DNsANBi4XWH3JFJN2RhT7xo/MI+sGFLnRf5zsBTOAoeHABa1AKbKsX5ntG5Mfdfv745lL+rZZdnq2r1kVDHtOoRc0uh0JXPY8xdUqtUaDdqvJ5CYx1GysiPG8Cklb9r3dTGksj529Shn2OPRuMnrfy24/men5nUWVmKgS95WWnypoFN1i0dy3pzP+G9FB3ELm39hF/S1bZoPbxGKO4DeAI5RSX9Raf3tgB16H1Dq3Ls29hF0S7VZX1N9L81r2QpOlBdMTotReFJo8S0iThMnxTaze93mMjO1GPDxMa3LSkCnatCY34/m+uxCJMaSUBoMwpChy4uow1XqdIAxdAOumzb4mfrn8Jw39oNTcD1zfqlly/h382RpgCd6835Y+Ty+947g+xfbygLBzNw5DF9AyQ6FPtGb/oWO5r9PPOrSlsCj2qY1Gbmh6YmPbBYuRsUrPw8z0mSSYFYwUMcvqIXEUsaHbJdOab0x+VBTZB5TclwcBQ7moUExOJX3nTo69b5T9e9udHJfl4mpohrgnzc1Im2qt7jKuNEl48DcPkHZqUiKsBkSVwDhEJyxbVaWV5zyUXsn0SuFWAAAgAElEQVSeRkh3NDiKrHu1Ow5l4oz93Q6LF7lGqYK0k/Cuk+em+D+p0AqKRQmaP91xIXCjUuofAEvlXKWUegMyyLyahfXG+rCYoHUuwi65USn1LeB2hML4UsSHagOimrGEJfQhmenSrvhGmdt3gcteRK07LYAfhEJRT1M23HcPRZ5TrddZ/gyx3Wk3p/E8j7ST9GVYlpBhS4VFkdNuNl2JcGzlKqYefbivPOgGiX3Vl1WVy1+zZoDXBqayhmCfV5XJwuaCGxoe8KAaVLAY1tKTiQ1j0dqYlNGXYQ305tKOWL70VOplPZ4nx3lyXIy9ra29LQHGSvG6hrSjv9u8yAWuhu8zrBVJ0hOotar4RS5klH1f3Cu5/fr2c90xlHJfh6xrzT5nsIenKHJGxnYjac8QRH6faLE4WsscVp7mBEEvy9xtDip871x6Zv4roihEE1GyroS1n/7cNv5CnyRoH7KtybfuXNBaf1spdSSiyGGDk52fTYH3aa2/v9j1LkYRY6NS6neRxtob6TFYNEJ/fL/W+uHF7sASdn4s230Vmx/ZQLUeOWaXcyY2Fys/CF3/ySpjWIyM7cbI2G4UeU5R5MIOjCJHc7cBK6rExMPD1EYbBGHI5PgmHnnwAVqTk1TrdfKs18B3DELXoylnXvK7H/k0SzNXrdJPG6yueOY5HPRbsR+aK2i94s61LA8C17OyWZVlCVoESpHOCmkiCHPq9dA17Z9VWcNU1vPcKveVoopl9Mm/cruZmgyxVw6z4sRWpy9NMinxBf0lx+82L3IyUjYrbE+mTpcxS3sGm0WheeEr+scsn/eSdfz69nMpck3NsEXHVvaGsaW/JjcdMoPX7ct0LbEkCH3SmkhvjYxVtigRzgenGVkJnCzXjsIcFOxymRZa608qpf4Focnvh/Tj7gK+pLV+aKsfngeLVcS4D3iTUmoZIj6rgLu01pu3/skl7MqoNRqMjI3Rmprk0YfXozxP6OfmLnuoNuICVbVWJ4hMbyoecq8nxjsrabcZGZPZK+mRDAtTcHiYLE155MEHmJ54lCLPaU1NsuG+exlf/xB51qUyVCUzflnlOZ9BLyw7k+VKf2UPLKNskWnNFc88hyPvE9ad7Tu9/tcfpGXs7e28lQ1S9rF3peICg82yhpRi0phStpspaZKzfPchGahFxGNn8qsZCnt0fNsPtNTzotC0W11HRpCyYUbayXtzaJHnWHY2g/m9+vHuO8RGlaPmeeQzGRvubzI9kTjzTsAwBX1+/oOzOODVp7nz/Ovbz3U3JNV6JD2wNGdkLCaKA5J218xnSe+93WwyVLMEim6PpTnA3BwamftC3zeuMCDlZM/pRw4/iJOuuX77/CE/Xmhvl8q0LLTWG+iXy3tceExdQROkHpcixhJ2HbznNNFR/vRZp5AmiaMfi7q7ZEprL72aqz601jH/ylR1O3MVxTHt5nQpw/Kp1uvURqWx35qaJOt2aTebFLmUByc3PYLWubEaKVx2ZzM8GwBc4IpEjDX3FZPdzJUGraqFlT86+cHTWPvAqY6enhSFU5Eo6wgOitmWn5fVMNJZCS7WHNPSyJcNB8xqzUzeYw4PlgatIoQtu9q5JUtwsUoWtk8XRBK8mgMlTTcbphSVAiZbPdHjyfGEZEayt2o9MplUwE+/dyZR7LPfy0/heS9Zx89/cFavzJdr2WbHEGY80TAUNmli+pK5sxgRqxPbp/RpTYkosS1zDmIu8WVbFpXvni3a7uYJxS5WHnyiMG/QUkote6wZ1OP57BJ2blTrI1xwxKEDzL+Iqz60Fs/32X31MwHIuim10QarnrU3UTzkglQQRo5CbWex2q2mBLOiIOumtJvTItnU7ZJ1u/3STM4LS7K8pJ2JjYaRA7LzRg8b6rotBQ4OhZ6/+ixA7D1s4GkVBQEiKVDOpGw2VRvIvPxck7ZzZotMMqIk6xuM9XzFSBE7Zl7ZFdqy9LaYJzMuy8lM13la2czIfjdb+kzMALRFXCoLzmzukMx03bhCMtPlkQdbxNXQDAJXiduBEwy+45az8TzFfi8/hR/ddKqbD7OwrM14ODCO1gl+ELmsz2Zykm157nm7mbrvX4ZjKRpyTzKT9Y1V2HUuVPrrycHOXR5USj0W3SyttX7PYj6wtUzrXqXURcBVAxPS80IptQJRSV/D3GaKS9iF8Z7TzuHTZ51CXB2mKHInbmup6p7v025OEw8PU62P4Pkek5secQoXdvbKSjPZIDW+/iFaU5MkMzO0W00eefABE7ja+IF2wcrKN9mL+chYbLyx+k0LZ43DsFWPsNnI0bufssV3sqrpF204i9VRtIWIraO1DwQsneS0jaWH7RHJoK1kN0WumRyfJa6GLN+jSq1RcVliY/mxPPLgxX0ZhvThehmLZULajMyJy5qgbYkk5WBsVTe6JutLO8JKtAPMnq945MGWe31kTAKqBNycaj3k1u+cLj5lRa+X9ZajxYblq1fLoLDd79ZUKpl2qbRniTJ92oZJTrt5pRE3PpogPJLW1BUuk7SQYJiagJnxgct3EAKGxXbItJRS+yLSSK8GDgBu1lq/dmAZhQiDH4kM796CuF7cPrDcC5Gy3WuQealrgTPL9iELXZfBIY/hK2lguwWtkxAJjw8btuA3Ed793dbXyvhOPRc5gG9C9KUmkOnpJSxhC9hS4RUnHAOG+RfFsXv9n664uKRqETmh2/H1DzkF+Kzbpd1qCovQ0ONbk5tpN5u0W00j+yRqDHWj1l5mCkJ/H6Tvua+YKgWqbeGiDWdx/KrTOH7VaVz1yDlkhrZeVl4vGynGnkeIUKdsRiAEha7rZZUFaIMwdb0ra2k//vAl7jWbzdhAUZaoklKgZ7QZ7fqM/JRRfbf4Yetip+1X5LiAUV6nJUr4QcT4+rZ5LSJoZy64uHmtXPPHbxEBnX/93PGkScZfvPcyvnT50cTV0GlSltX/yxmkNem0wTMIPbxC0W5eSbUuyj/O/6ykPWhdsdvNp34ua0tsl0xrf+Ra+wNEyWIunISoV5xAz4bp20qpA0x/CcNL+DYiWvu/gOcg81QeImK74HVZaK2fFG+YeTeitb4aCUhnIpIg1yDOwlNKqY5SKkWi8y2Ilcd+wDrguVrra57oHV/C0xtrLrzcZVg2YIGUCx0j0PMIwpAXveb3ef7LfsdlWNMTj5ImicxsTU0yvv4hsR9pNcnSlKLIybpd5jLxtRfi+eB5yg322ov4O3Y7ed7ly0O/ZdPERrl3ZTIuzPp0aUDWmRjOoc7xPw6+iKxbMLGxzfj6tsnKMpKZrusZ9fa7l6UI1bsr5bhqyMtee2b/AG7eE2l9VU1s6uzPQCnHFCwHLpCZurgakHVT/GCIiY2zTGxoG0PIWdnPh2doTaW0W12+/pm1fP0za8m6BX/xXtFv/OtjrnLOz9V6KFT9PrPQgiIvenN83cLtS210jQtYvb+Xfu1I+5lyaXKHgSVizPdYGG7QWu+ltX4rIqnUBzO0exLiQHylmal9K5LRlA/eEcAQ8Fda63/XWn8CudZ/QCk1ssh1PanYKhHDKPGebwwUXwn8IfBCxEJZI9pRPwe+o7X+8RO8r0vYyWBnqsqwBAwbvAB+8cPvs/+rXkPSbsuQ6syME9G1JcIizw0lvliQ+sGgskP597IBosV7BrT5AK565BwCpfjs+HmuFGgDVKCU62/1bdese8j01Jzwq7GqD8JM6OFJzr9+7nj+x8EiJnDTl090yzl/L9O3eeErTuX2m89w6hh2Zqv8vexAshxbxaB/3w9bF/ddDGxPqdxji2IRpdWmpDg0vBubNz3CUFsGeq2s0shYTBCKSoc1ifzS5TJjVRSatx8n9P2/O/t9ZGlhXI1TV8Kd61wNBqHa6Bomxy/rUy8Z7PPteHj8mdYCfAt/FxgBvlT6zIxS6gZkVMlmUW8Ebhwwt/0CcAFynb9hEet6TDDiv6uMmvyCsSD2oPGi+qF5LGEJ2wXvO/N8Pnt+vzWGzbAsgjBi/1e9hrt/9hOSGZG+lLJRQpampB1RX8i6Xd535vlc9aG1fesrGzdCiXlXykp621aowIOsp703H67ZdG5Phd0sa58HSnFgdW4LuLuTy6VPphS1Wmi+o0cUZ0SVLqkRo7U6gt/+4gkuA5meSGi3PKq1iCCSY+TIB+Wg2y1Imznj69s0lsf84MZ1rudUvpiXv1950Nj22Gz5rt1MnWJJWc9vZGyM5c94hlMiWX/Pb9l4/6MyExb7TE90KHLN2MqqCywW1517GIes+ySfWHcoQdujm3pMT3TcjJwlZUSVwpUHH3nwYseYbCw/dgvWYFmlv9zr2mHw5LAH90Oc4O8aeP1OxNG9vNxN5QW01vcrpdrmvRsWsS4HU4F7p9b6C+Z5HbFJWae1/tnA4n8J/D3itbVg7Ii3I0vYhTAor9MrD/YyrUFEceyyKstCDMKQa0450RlIQr8gb/82+vXq5PP9F3ObKQVK8ZXNF7j3rtkkHqLlUmC5JGj7V7+avWzObWdaszI82tmWVGpSJquNRlTrEdVaaBh//UxHEOJCu9k1Boo2C8r50U2nunJhkVtjxozWVIcN9zd55IEW0xNJ//c13+/HM5fw45lLetmh6nlkObNLw/ADqNZCKkNCmJnYuEHGC4qel1kUxzy6YZZHHpxhtlWQZwHTE4khdAjJ5Noz3ssh62Ts4YhzP0MQeuRZ6ggetq+XtDOSdrdPjSNNeurtzq+tFLyg1+s6/33vnPMcPGXYdnlwuVLqR6XHYY9hK8uAVplMYbAZqBrHYLvcXGK1m+kJny90XWUE9MeVCPgzpDq3XbBLqTcuYcfHfMHqOS86kEc3PEy7Oe2YhoVhEJax5sLLufDo97rng3M6c92BC/VdhltjIyLrMg8zg/X5R8/nHbudzOEr1vHZ8fP6ZqxstmVtRYA5FcoBnj90LCDeUBuNE28QKFcWE9Hbnvt40u7i+UqYhTMidyUEC1F8cKLDhtCRmAHlIPQYXz9DEHqMray6cl9R7z8eLx9eyy9mL+vru1lYIdzGiiGXwaRJThB5tJtdNyQspdmCWqNBtV4nac84nUHP95mZfpiknZkenmRBHz/5EED6ZEI8KYjisER/z9y5ceopjhSS9bEn+86vvwNmWA7bLA+Oa61/ZztsaK6mrZrjvfmWW8gy8733hGMpaC1hh4T0Zvovoja78jyRbGpNbhbqe14Y9lzBJccduUVPS5htGbXRyD0vs9bKLL6sW1AzlvaZ55EZdYua5/GVzRe4PpctCb58eC13J5dT831W+L7TvButBnRCn4fSK2nluQtWZZRt5R/tXkVjJEJnhcgtdXKiSod2y3eq5eI3ljH+8IwbFC5/B8u+qzXke6ZJ7rJKyVoyWpMd4xjtu/KgzSjtoPSssbN31PlEhrOLQtP2ZHYrSwvaSubgPN+n1mjQWL7CWctY3cc0Sdh4/70Ekd+nh1g+D2mSURmqksy0DYW+63qOnq+YnohZ9aw6tdEKQeQZVX65+bfnDOSGJK4GLphZ4d4dBoUP3Se8PLgZqCul/IEMqQG0tdbd0nJzjSWN0svAFrquJxVL5cEl7FDoMQctIUP+RO/+2U/kuQlkSXvGZVl/+6FTHBFj7aVXU+Q56z79OZTn9V3M7QXOau/ZUpSFvRjbAduyMnvZPuRtYye5z1ivKbeOQgaWpyc6hJkWjyzf5+5k69ZFu4VHM5lldDzc7Fg8HEq5cFjmyOycGUBrsmMU1BMJSDOSidlh4mrNlBxrMlRt+0RWoNiWA0GyPxuMldZOn9AJGxsnZzkfPXsX0QuUwJWlKX/05rfx+rcfzJuPOpbdV+9FbVSuiZWhKnmm6aYFrcmOe8zOZGgdMFSrM7ZyFUO1uskoMyP5lDCxsc3keMLkeML4wzOMr58xJdKcvV9wEs97yTo3p2bLqtV6SLUeEVV8znrXXy/q7++Jhcm05ntsH/wS6RHtO/D6fua98nL79e2dUnsh/le/LC2zkHU9qVjKtJawQ+GNf3voFq/d9ZPbZAg5ivA6SZ/Ce5okfPqsU8jSdIvPCcvNyEGVpJvsPI80+6VsFVV8PF9U0huxT1Yql1m7EBC79O81L3L6gwdWj5PymtZ922k3U9EFrIU0qj4tpbivcwWZ1luUDC0SrcmKAjyPeCggLmeDuSbr5n0Eknar634HDC28sgXpwWY4znm50FsQTexzS3G3jL2ySkfvkTtDyKSTUOQ5b13zgb7v8oZ3vAuAL172MeLhYadiEsXib9VuidSWtZA59JSzuOKEY2g3oTUlavRaiyfYpDdLkYvGomUmluEsSkrsQ+soELR3oPty7UE29ERv5b+AaYSafg44lt6fA2X75m8BJyil6lprawn+NsTM97uLXNeTCqW3MkSplPoScPiSJNPCoZTagcTOdk7c+aMfMj0xwav+9I3utas+tJajL7iEi9Yc5gaSlbLirkIVr41WHLHBltfs82pdhpCjSu93O0fk+YoucmEfzzISo/C+f6nkd1/nCvb0AtPr6TA90TFBJGJkLKbWiNCh52Sh5rKRt3govdJZlSRt8cKa2Nh267a/W+UHz+tdsMdWVvtkn9z8l+e5TG1kLGZ4Rcx4lrFn9H63zYbvE2aaiY2zTE8ktCZTJja2nWqHJYH0JJa61JftzpoLt22A+7VPfZz/9b6j+MZ115J1U6YnJgBxM37zUVuWTsv4/9s78zi5qirxf08tnU6TpbOwjOggKBIQ+bCJCKgRRx1wdAYFBFkCyJIFYpA1QCBsAQJDcExIWH7soiwKDv5ARAUJIIwQEYMQJBBlM5Otk3Q63bWd+eO+++pVdXV3dXelq6r7fD+f+6nUfefd9+p25Z06957l8hOOZuQYd9/N4xtp3no4I0Y30NiUZI8D8448S566xFVpbsu4as4ftNHa0kFba7oiSXNVtV8bZrLDvho7v2sH7NzUxEs97WkFSsNX2DgL55LuJ+FRVW0TkZl0Dgj+DPBJVV0ZjDMGF1i8FOfmvhNwPXCDqoau7OWMVXR/OeBeXEwvQBMu/utmOnsh7gMcpaq98h7sydL6JnCQiJymqo/0ZmDD2FLsuu9nOvVNu8ZV2PXJeGcfezjxhjjnLryXG848LpSLOi74996C2H3/i3nzlTlk0nk38mjmcY0L45IxOhIxGnM5Xt38A1c4MWKxRL3ZUu1ZNmQ7whiiEaMbaB6egEwmLGxYiu0bTmdlegEjEvlyKb50R6LNJb7Nu6V7r7248zgMvP72PfhyXl48GyB0gIhmtS8mXx2582+uvJNHOlxezOWUYcNHlaWwAP79lKkAfO0E5yTzyG03EYvFw/fdkctlaWgcFipj79QR5eXFs9nzc7N57tELwni1mnN/1xyxEisCnp4CsAK2AR4o6vPvdwRWAFfjtn5mAuOAF4EvR5WMqq4LivfOx7m3twDzcFmQovQ4Vgm+Q+fijqd1IdvrH/k9Ka2JwO3AwyJyJ/C9iCnZL0TkcJzW3gW3jvo3XIGwuaqaCmRWADsUnbpSVbcrGqtiObTKGcuofWbf8yBzTj6K604/pqA/v/yVJZdzAb4kKVBi7W0ZGoY5hZDqyEasFQkf/OMbggdoYDW9l5rfqew7OO+/1vX5NEojgObhLvffyvSCAmeMKNsmp9GRWxSpmxUP458amxJkUtmCxLDRUiv7HuxqsXqFu9unZ/HmK3PCpMCJpHM0KVaaCRHS2cJ8fi5vYbIgg3zbxhQjx4wNfyD0ha+f1NUzrDPJQPFEa7H5v8mLv51FLBZjz8/NBvLLs6n2TJgWq7sMKAOJqBLvJvC9OBC9FKq6grz3XlcyClwZtO7k/gIcXImxInyxTLk+01NGjGdEZA9gLjAVOFhEvquqv6nAtccBT+JKMrfgMm7MBrajMEXIvRTWYin4qVLJHFq9GMuoAy649SdcP/3YMPC0QEElCQsFOnfqHC8vnk0uq+y050zeevWqgmzr/iHprbLGJqW5MU5rcK3tG04n1XFjJ/lch9vfSiRjYdmNEcDIxjgJ1W4VV0aVZOj4kC8nn0imSTTEaRgWlCNJl3blj9a6io4RiwsdJbYFMqqhVehz+EWdMqIP//4orN7i96n8UqhP4OtpaIzz/OMXhvkPPb6CcrSvqihIiWrUgwlV/V3PUv2jR0cMVd0MnCEiPwVuA34lIjfhEjaWkr+rnAuXyE/4ZJDzapqInKH5zbYPVLXktQKiObQ2AE8E48wWkbmquqE4hxaAiPweZ0qfTl4h9ThWOZ/NqB1icWH6f97FogtPDINRj5y+gAcXnE4sli1IeZRocJ6FS566hNaWVLi0NGGfi3jr1aucTNI5ImTSOUbFh9HckGBTdiFbxacUVEGOZmlo25jutBzpl/G2HpmkI7eI9qBeV7ECU5FOnnsNjXEa087a8srYVxRub8vwP09cRCIZZ++J+WwjuSILqjjbh/cmTGdd3kF/vVxOQwvHjzHQ1YBnzHMV2m8487ggNi1Ha4vbo8ukG/MB0H7pN8wwn19erQV6srSM8ijbe1BVnxKRg4CXceuTxfa9XxAvS2l1wRq6zlzcFZXMoVXOWEYd4R94ACfNuiX8t1cc/qHsH3r+NZMWEmmnfJY+f1nBPoqLH4qRSTlLrTERoyO3iBgU1LZKNMRIpOOAc6ZobEpGChfmS28kkjG2aoyTiAlr0gsK7l80X+LDLwHGNvp8e+69Dyh2nn6J8D5fefZSGrdK8Ik9LyzIV5jLKo2xWKhs12duJCGCZryLu1uCTLUnaWxKh0osk24gFq/eQ9f/LedO+U6Q2zAdlGLJKy3IL/V6q7JmamqpDnpLayAo+yeIiByMc4EcD9wCnFTUTgxee4WIxEWkKVCI04GFESsL4CQRSYnIehF5UESK97g6xQyo6t8Bn0PLy3SVQysaq1DOWEYdMvnK2wveh8HEQQZxb6mEr4Fnnnf5LshCHsmCnmrPkk1liUcClsOyIBEFBtCyenMQW9URLru1taZpa03Tuj7FsBw0JxI0xmKMS05jq/iUgvv1hK7rWXf9k2bdEnrNRVuqI0P7pgyvv3RF3lU9sDY1kwsV1+jE1Hx298iSaWNTosCbMlrvqpqcu/BeUh2umvKGtR1BHbJM6Gnp/w1w+tw7q3y3ebyl1VUzyqNHS0tEhuP2tKYA7wNfVdUnKngPm4Bhwb/vwu05eX6OW4Z8F9gV59q5WEQ+parrA5mK5NAKnD/KGasTQY6wvuQJMwaYRReeSCIZDx9qPuuCj4Hy1gsQ7kv5ulS+CGOiIRac1xE6Nvjii6HFE9TE8qmHvJJsWd0e1s4aNbaRCftcxPtvXwskaF3vqvQOj2SNiMUml/W5Tr3stjA1UttG9xlS7XkX/0TSeR82jcxnt29ojDMsJuRyi8hlcgVODF5RQ96pJNWe7aT8q4V3Yb/ipG/TPN4vERb+Bj959q3VuLVuMEurEnSrtETkAOAOXET03Thvu/XdndMHDsD58u8HXIxzwZwKoKrRAI7FIvIcbnnyRCCaiqCSObTKGasAVb2ZINjO4rRqm8lX3s4NZx5XsA/i94XcPpSv55R3dYdCV3bvZu4dM7zi8g97X4XYWzd+rynVkeXQ46/nVz8+2103Lqx47erAQsuQSDbQ2FTaKaMcpl51BwB3XX1aeF8eZyHm8tZUJLms/3f0Pn32kLCmVlCU8frpx9ZUReB4fBgtq9sZMbohXA70ZVJqDtvTqgg9LQ8+jdsLOkxVJ20BhYWqLlHVZ1T1etzy4BQR+VgXskuBZcDeke5e5dAqkulLPi6jzpkx727WrtwcFBjMhcuD0Qe2zygetTqimdUzqVx+GS5wr/Z1oVpbUuHyX/umSOn6wHvwK0dfx8GHzw2Pebf15vHdB9mWy/Hn3+S8/zalCxRQqj1La0sHLavaWf3+pqDUSTqynBjxFIwsR6ba/efMdvLcqzYzb7mLZEMTLas2h3O/YW0769d0VPvWOtHf5UEReUpEtIv22UBmRYlj/ygx1m4i8hsRaROR90XkshLPx5qkp+XBh4Apqrp6IG6GfBT1jsDybuSi1kxvc2gti4j2JR+XMQi46Lb7mH/upNDbDwLLKZULs4oXZhePFywBtm1MBxkzYuE+kLOuCi0UX6K+bWOKI6cXOlkc9PWrQjf7CftUNqLi+PNv4p5rJ4f7b4mGOJlUltb1Lr1U83iXVaJ56+E0j28MLZN//sR5ALzxsgvLcc4fCRJtmXA+ao3zFrklywXnncmGtWsYPmJY2FdT9N/lfSrOiIhyGbAXLu7UU6kwoZqkpzitIwbqRgIODF7fLnVQRHbHBSNH3eUrmUOrnLGMQYILIo6HwbmkfYxVLFwu9DgF5LKf+4BjcMtrmXSWxqYkmXQ2rNbrHTZ8Yt5oVvkoPih2S5BJ5UjFnCVH4KTgY51akx155RwETUcVZxhQHY/k9RvhHHuvO/0Yzp7/oy12331l2jXzuOWSmWGNtVqjvy7vQTBwfjxXz2pf4D5VjZrA/Q4T6vNNDgBVS5grIr/EaftXcZ59B+Jyad2nqstF5GvAscAvcA4gE3C/Av6O22fzLMItK/5MRHwOrdnA9X7yVbVdRK4GZonIOvLBxTEKf5H0OJYxeDh7/o+Yc/JRgYNCsiDxai6XCy2wgppO2XS4D+b3vLxCKk4s65PcVmtZzRdavHX2yeRyPig6WVDYMdWRpa01RSad4M1X5vDxPS4Izy/wWKwBr8Fy2ObDHwlTRtUeFXfE+Fecg1hvEyvWdWhPNW39PwAn4PJm3Y+zemYCPlHcO7g8WzcAv8J5Dj4BHBSd7CCZ75dwy3+P4JIzziOfRNJzNS4VyUycIhxFiXxcZY5lDBIuuPUnXHzn/agOZ/Mmwj2efNxTfi8r6lbt94zCOlXrU+G5qXbnSl+YIb16v/5Pnn0rbRvdL/x8AHRh3BbQqcaYP16Q4T3YA6y5qsABtauwtojL+1HAe8Diov5KhAnVLFWztFR1Fi6tUlfHX3C7q38AABNwSURBVMEpkHLGqlgOrXLGMgYf5yy4lXkzptDa0kIsWFJz1lWuYB/He95Fy5wkkjFiG/Npnrycf8gX1+2qBjPm3c2ts/PJaRsaE2Guwlhc2Hvipbzy7KW8/tIVBemqfMn7ltXtYbb39k1pxmwzroqfpk7peU9rvIi8GHl/c+CZ3Ino9kZRXGulwoRqFqunZRgBZ96wEHCxP36fK9zvgmB/KhMsJeaXCX26J++04fPd+cS8Pmi52vjijdH9uFhM2P+r7ndc1NLyuQd9MLT3jhRJsvX2H2FEcyknW6Nbet7TWt1TaZIIX8elsSxYGqxwmFBNYkrLMIpINjTR3rapQHH5oNtYPBZaIL7QYCZdGNMFLrlr8dJatUm1ZyKKNR+z5dl74qW8+NtZoXu8rxrsa1INGz6SUWPGMmrsOI7+/nlV/CT1ifRQmqSXHAW8qaovdiekqktFpC9hQjWLKS3DKMK7S1877WRy2Syp9nZi8QSjx49hw9o1tG1Mhw4brvpxPqGtf/V7Pz4GLOrUUC0mX3k7108/llFjG0MHkqaRDTz98/NDhdvakgqXBH3mjpbV7QwfMYqx227HqDFjO1UpNspEIZbr/48XERmNc6aY27urh9R1aI8pLcPognMWuDRA82ZMIRaL09DYSKrdlZdPdbR38iKEfFmSaHHJbEZJJHubB3rLsGFte5haqnGrRJgA1yveaEkSX1vs4jvv725Io0xEtVKW1mG41Hc9eg32I0yoZjGlZRg94Pe6AH54znQy6TSxeJxUezvpTBbN+US5sUKllcoSTyRpGJYk0VAbSis5rJFM2rm5+3RTiWQHo8YOI5GMh0l4Ux1Z2jamajIeq37RilhauKXBP6nqa9HOSoYJ1TKmtAyjFxSXl583w2Vij8XiJJJJNqxdQy6XQwSGj2gikUwSi8eJxWojQ86F/+8erpl8Ii2rNpNIOsWVzcQZNryVsds20diUKMizaFSQXP8tLREZj/OqLuV5HQ0TasaVevolcEFxmJCIfAmX5/UR3D7WPJziqnlMaRlGP4haYeAssVR7exCcnCSRTHLGtf/FwgvOrtIddua8Rbdz00Xn0t7WRiweo23jRto2biIW2+z2u2ox2ewgQOj/nlaQUi/ZxbGKhgnVKqIlym4bfceyvBsLLzibTDpFItlAIpkkl81y2hW92TMfWObNmML61asY0TwsLMPi8ZnjDVDVfmnzphEf1V0+1XWegpefP+mlXri8D1lqL/ulYdQ5U+ZcR9PIUYwY3UzTyJE1rbDAWYtbjW6mY7OwYV0Ha1duZu3KzaawKowVgawMZmlVGLO0jHpnwXlnMu2aedW+jZqjv5ZWkG91fDciq1X1X/tzjaGAKa0KY0rLMAYn/VVaRmWw5UHDMAyjbjClZRiGYdQNprQMwzCMusGUlmEYhlE3mNIyDMMw6gZTWoZhGEbdYErLMAzDqBtMaRmGYRh1gyktwzAMo24wpWUYhmHUDaa0DMMwjLrBlJZhGIZRN1RNaYnI4SLynIisEZF2EVkmIheJSENw/J9E5FoR+ZOItIrIOyJyp4h8qGiciSKiJdrVJa55ioj8NbjeS0H1zmKZ7UXkoeCaq0Vkvog0bbmZMAzDMMqlmpWLxwFPAtfiyj3vhyv3vB1wOrAPcBhwK/ACsG1w/DkR2V1VW4vGOwZ4K/L+vehBETkKWBSM8QxwIvALEfm0qi4NZBLA40AK+DauZPX1weux/f/IhmEYRr9Q1ZppwJU4BSY4RZEoOv4JQIFJkb6JQd/uPYy9DLgt8j4G/Bm4J9J3NJAFdoz0HQnkgJ3L/AxqzZq1wdeq/Xy05lqt7WmtARoAVLVFVTPRg6r6BtAGbNObQUVkJ5zCuz8yVg54ADgkInoI8AdVfTvS9zDO8rLibIZhGFWm6kpLROIi0iQiBwHTgYUamCwlZPcAmoC/lDj8WxHJisiKYG8sHjk2IXh9veic14CxIrJ1RK5ARlVTwPLIGIZhGEaVqOaelmcTMCz4913AOaWERCQG/AD4K/CryKH1wNXAYpxF9G/ApcDWwPcCmTHBa0vRsOsix1cFr8UyXm5MiX7DMAxjAKkFpXUAznraD7gYmA9MLSF3FfBZ4AuqmvadqvpH4I8RuV+LSAfwfRG5XFVXR44VW3BSor+UlSdd9LuDIqcCp3Z13DAMw6gMVV8eVNUlqvqMql6PWx6cIiIfi8qIyFScBTZJVV8oY9gHcQp5j+C9t6iai+T8+5aIXLGMlytlgfnPcLOq7quq+5Zxb4ZhGEYfqbrSKmJJ8Lqj7xCRbwE/BM5V1ft6OZ63jvw+VfG+1ARgraquisgVyARxYzvReT/MMAzDGGBqTWkdGLy+DS5wGPgRMF9Vr+vFON8CMsArAKr6FvAGcIQXCPbIjgAei5z3GPBpEdkh0vcN3J7bL3vzQQzDMIzKU7U9LRH5JfBr4FVcbNSBwFnAfaq6XER2xbmbvw7cJyL7R05fparLg3EW4pwo/oBzxDgUF5x8g6quiZwzG7hHRFYAzwKTgJ2B70RkHgQuBH4mIrOA0cA84F5V/WvlPr1hGIbRJ6oVIAZcDiwFWnH7RUuAM4BkcPwEug70uyMyznScRbUR6MApwRlArMQ1TwHeDOSWAF8qIfNhnLJsxcWNLQCaevG5qh4Eac2atcq3agfVWnNNggetUSFExCbUMAYhqio9SxlbmlpweR9srAb+Vu2bGADG4z6rURqbn56ppznaodo3YDjM0jL6hIi8qObi3yU2Pz1jc2T0hVrzHjQMwzCMLjGlZRiGYdQNprSMvnJztW+gxrH56RmbI6PX2J6WYRiGUTeYpWUYhmHUDaa0DMMwjLrBlNYQQkS2F5FWEVERGRHpFxG5QETeEZHNIvK0iOxZ4vzdROQ3ItImIu+LyGVFxTYrOtZA0838rAj6ou0fJc4fdPMjIieU+OwqIpMjMvb9MQaOaqfksDZwDbgX+AcuLc2ISP9MYDMuZ+O/AI/igj63i8iMAd7H5Yv8MjAZV8DziqJrVGysGpqfFbjEzftH2t5F5w7K+SGfTu2LRZ9/G/v+WKtGq/oNWBugPzR8DlgLnB19KAONuOrPF0dkt8IlIb4i0jcTV29sVKTvXKDN91VyrFqZn+DYCuC6Hs4flPMTUVojujhu3x9rA9pseXAIECyb/BC4jM5pcw4ARgH3+w5V3QQ8AhwSkTsEeFxVN0T6fgIMB76wBcYaMHqYn3IZtPPTA0P++2MMLKa0hgaTcb9iF5Q4NgFXGqa49MprFBbEnEBRIUxV/Tvu1+2EiEylxhpIupsfz0kikhKR9SLyYFHNNRjc8wOwXEQyIrJMRE6L9Nv3xxhQLGHuIEdExuHKwByrqmmRTomqxwCtqpot6l8HNIlIg6qmArmWEpdYFxyr9FgDQhnzA/Bz4HngXWBX4BJgsYh8SlXXBzKDcn6AD4BZwP8AceBoYJGINKnqPIb498cYeExpDX6uBF5Q1Ue7kSkVYS4ljnUlV45MX8YaCHqcH1X9XuTtYhF5DngZOBG4ISpa4vS6nh9VfRx4PNL1mIgMAy4SkR94sRKnDpXvjzHAmNIaxIjIJ4GTgM+LSHPQ3RS8jhaRLO7X6UgRiRf9wm0G2lQ1HbxfF/QVM5r8r95KjrXFKWd+VHVz8XmqulRElgF7R7oH3fx0w4PAkcBHGcLfH6M62J7W4GZnIAn8HvcffR35fZt3cc4Hr+OWfT5edG7xvsHrFO0XiMhHcN5dr0dkKjXWQFDO/HRH9Ff9YJyfnlCG9vfHqAKmtAY3z+Dia6LtmuDYocC1wHPABuAIf5KINAFfBx6LjPUY8FURGRnp+zYupuZ3wftKjjUQlDM/nRCR3YFdgJci3YNxfrriW+SLnQ7l749RDartc29tYBsl4m5wcS9twDTgS8D/xz2Uto3IjMFtyj+BC/o8FWildHBoRcaqhfkBvgb8GDgGp9SmAO8Bb1EYJzQo5wf4KXAezs3834C7g/k5w74/1qrRqn4D1gb4D15aaQlwIW5JbDOwGNirxLm7Ab8NZD7Aed3Fi2QqNlYtzA+wB/AbXIBrGpcx4w7gQ0NhfoA5wLJAkWzGWZfHbanPVG/zY23gm5UmMQzDMOoG29MyDMMw6gZTWoZhGEbdYErLMAzDqBtMaRmGYRh1gyktwzAMo24wpWUYhmHUDaa0jKohItuIyAYROaXa99JfImXpJ3bXN1gQkf8ISrXsXO17MYYWprSMshGROcFD+MQSx0REficiHUGao3K4Avhf4PaK3ugQRURmiMgJA3EtVX0Y+DP5tFeGMSCY0jJ6w2xgKTAvSFAaZQbweeASVV3a00Ai8mFchvUfqmqm0jdaI9yNq6b79ABdbwYuo8dA8QPgsCBbvmEMCKa0jLJRV4BvEi6b9q2+X0R2IahLRRdJZktwGi5d0o8rfJsVR0TiQeLWXqGqWVVtV9XclrivGuBnuPROk6t9I8bQwZSW0StUdQlwFfAVETlVROLAXbiccZO0c9XZrjgCeFFV/7f4QLDUeIqIvCAirUH7s4hcViQ3XkQWiMg7wf7KO8H7cSXGLEs2sg/1LyIyS0SWA+24+lFe5mQReT1YCn1TRL5HvlBhqbEmlug7WETOFpHlwThviMikEmN8W0T+W0T+HsitFpGHRWSPIjkFdgC+EIzv20cjMvuKyEPBGB0iskxELhSRRNFYnxSRB0TkvUDuHyLypIh8LSqnqq243IBHYBgDhBWBNPrC5bhyEdcBewL7Ad9X1WXlnCwi2+JKe/xXFyJ347Kqv4Cz4Fpw9ZMOBy4OxhiNK2XxceA2YAmwFy4L+8Eisp+qbuytbITrcLW2bsGVy1gWjDUDmAf8CbgAVzTyHNzeXG+Yg1s6vAnoCO7lDhF5U1WfjcidDqwFbsYl6/0YLqv5syKyt6r+NZA7Lriv1cGceVYF930o8BDwJvCfwZifBS7D/Q2PCOTG4RLRAizClR8ZD+wLfAaXdT3K73FlQiaoqtWyMrY81c7Ya60+Gy77eQq3xLcYiPXi3C8G500vcezI4NjdxWNG3+MezApMLZKZFvRf3kfZE4K+ZUBTkXwzsAn4S/QY8GFcaQwFJpYYq1TfH4GGSP/2OOX146JrblVijnYNZG8s6l8BPFVCvhGn8J4GEkXHzozeI/CN4P2RZf4tjw3kv1Xt76S1odFsedDoKxtwD06AR7V3+zZbB69rSxw7Jng9u3jMoveH4ayIm4vOvwlnbRzWR1nPQlVtK+r7Cs6yWhA9pqrvAj8qMUZ33Khuj9CP8R7wBq6aMpH+TRAumY4SkfHBZ1mGs3zK4cvAtjgvzeZgqXR8MNajkc8GsD54PURERpUx9prgdZsy78Uw+oUpLaPXiIjgHoANwGvARSLysV4M4evhdNoHwj20P1DVlT2MsSOwTIs8D4P3y4Cd+ijreaNEn5crtQz2lx7ut5i3SvStAYr32PYSkV8AG3EKZVXQPoUrhlgOuwavt0XO981/lm0BVPV3uD3KE4DVIvKsiFwqIrt1Mbb/G1qNI2NAsD0toy+cAUzEFev7OW6P6DYRmaiq5Ty8VgWvY0scE2rjAVhsZUH3D+hSCrg7unJYCccRkX/GLeltwO0jLsMtTypwAzCizGv5Mc8BXu5C5n3/D1WdJCLXAocCBwFnAReKyAxVnV90nv8brsIwBgBTWkavCDIgXAX8AbhGVbMicilu3+gMunauiPIq7sFbKpvCMuDfRWTbHqytt4BdRCQRtaACT7hPUGjJ9Ea2O5YHr7uSd1Yg0ldpDsMppm+o6pPRA4HDREeRfFfK3jtrbFLVX5dzYXWxdkuBuSLSjHOKuVpEFhT9MPl48NpjbJ5hVAJbHjTKRkRiuFLzcQrd268BXgSuKmeZUFVX4ZbT9i9x2O8NzQ2uF71+1Jp5GLc3dnLR+acE/Q/1UbY7nsCVd58WjdsKAqW/U+YYvcHPb4EVJy7t1XYl5Fspbb0+jvNuPF9EOh0XkeEiMjL499jieVfVFuBt3H5eY9Hp+wMrtUzPUcPoL2ZpGb3hLOAA4DxVfc13BtbWCfRumfABYJaI/JOqfhAZ6wERuQ84HthZRP4bWIeziL4K+BRRc3Fu2gtEZG+cN95ewHdx1trcyLV6I9slqrpORGbh3OGfE5G7cA/yyThrZq9yxukFj+GWKe8Wkfm4eTgQt2y3nM7/f58Hvisil+P2GnPAI6q6SUSOxynvZSJyG871vRkXSvBNnFX3FG7ezxQR7x6fBr6Am/v7VXWzv5iIjAA+h9srM4yBodrui9bqo+GWvzbj4nLiXcjMpAtX9hKyH8I9EM8qcSyGc0dfgntobwRewaWIisptDdwIvBuM9S6wABhfYsyyZCnhpl5irNNwyq4D92CfAZxYfF6psbobH6c0VhT1fR54JpiDFlyc1O5dyG4D/BTnlZkLrvPRyPHdgXuA93DhCitx8WuzgLGBzJ7AncHn2oTbT/sT7gfLsKLrTQqusXu1v5/Whk4T1VrY8zaGIiKyCOdqvYuqpqt9P0bvEJGXgL+p6jerfS/G0MH2tIxqcjHOxbtT1nijthGR/8C53Z9X7XsxhhZmaRmGYRh1g1lahmEYRt1gSsswDMOoG0xpGYZhGHWDKS3DMAyjbjClZRiGYdQNprQMwzCMusGUlmEYhlE3/B/1yf7SLMxskAAAAABJRU5ErkJggg==\n",
+      "text/plain": [
+       "<Figure size 432x288 with 2 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "plt.figure(\"Elevations from the DEM [m]\")\n",
+    "imshow_grid_at_node(\n",
+    "    grid,\n",
+    "    \"topographic__elevation\",\n",
+    "    cmap=\"terrain\",\n",
+    "    grid_units=(\"coordinates\", \"coordinates\"),\n",
+    "    shrink=0.75,\n",
+    "    var_name=\"Elevation\",\n",
+    "    var_units=\"m\",\n",
+    ")\n",
+    "plt.xlim(36000, 53000)\n",
+    "plt.ylim(32000, 47000)\n",
+    "plt.savefig(\"NOCA_elevation.png\")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Excluded areas from the analysis are shown in black, including outside the park and inside the park areas that are water bodies, snow, glaciers, wetlands, exposed bedrock, and slopes <= 17 degrees. "
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Plot slope overlaid with mapped landslide types. Takes about a few minutes."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 55,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "(32000, 47000)"
+      ]
+     },
+     "execution_count": 55,
+     "metadata": {},
+     "output_type": "execute_result"
+    },
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 432x288 with 2 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "plt.figure(\"Landslides\")\n",
+    "ls_mask1 = grid.at_node[\"landslides\"] != 1.0\n",
+    "ls_mask2 = grid.at_node[\"landslides\"] != 2.0\n",
+    "ls_mask3 = grid.at_node[\"landslides\"] != 3.0\n",
+    "ls_mask4 = grid.at_node[\"landslides\"] != 4.0\n",
+    "overlay_landslide1 = np.ma.array(grid.at_node[\"landslides\"], mask=ls_mask1)\n",
+    "overlay_landslide2 = np.ma.array(grid.at_node[\"landslides\"], mask=ls_mask2)\n",
+    "overlay_landslide3 = np.ma.array(grid.at_node[\"landslides\"], mask=ls_mask3)\n",
+    "overlay_landslide4 = np.ma.array(grid.at_node[\"landslides\"], mask=ls_mask4)\n",
+    "imshow_grid_at_node(\n",
+    "    grid,\n",
+    "    \"topographic__slope\",\n",
+    "    cmap=\"pink\",\n",
+    "    grid_units=(\"coordinates\", \"coordinates\"),\n",
+    "    vmax=2.0,\n",
+    "    shrink=0.75,\n",
+    "    var_name=\"Slope\",\n",
+    "    var_units=\"m/m\",\n",
+    ")\n",
+    "imshow_grid_at_node(\n",
+    "    grid, overlay_landslide1, color_for_closed=\"None\", allow_colorbar=False, cmap=\"cool\"\n",
+    ")\n",
+    "imshow_grid_at_node(\n",
+    "    grid,\n",
+    "    overlay_landslide2,\n",
+    "    color_for_closed=\"None\",\n",
+    "    allow_colorbar=False,\n",
+    "    cmap=\"autumn\",\n",
+    ")\n",
+    "imshow_grid_at_node(\n",
+    "    grid,\n",
+    "    overlay_landslide3,\n",
+    "    color_for_closed=\"None\",\n",
+    "    allow_colorbar=False,\n",
+    "    cmap=\"winter\",\n",
+    ")\n",
+    "imshow_grid_at_node(\n",
+    "    grid,\n",
+    "    overlay_landslide4,\n",
+    "    color_for_closed=\"None\",\n",
+    "    allow_colorbar=False,\n",
+    "    cmap=\"summer\",\n",
+    ")\n",
+    "# plt.savefig('NOCA_Landslides_on_Slope.png')\n",
+    "plt.xlim(36000, 53000)\n",
+    "plt.ylim(32000, 47000)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Legend to mapped landslides: blue - debris avalanches, cyan - falls/topples, red - debris torrents, and green - slumps/creeps"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Plot of soil depth (m)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 56,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "(32000, 47000)"
+      ]
+     },
+     "execution_count": 56,
+     "metadata": {},
+     "output_type": "execute_result"
+    },
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 432x288 with 2 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "plt.figure(\"Soil Thickness\")\n",
+    "imshow_grid_at_node(\n",
+    "    grid,\n",
+    "    \"soil__thickness\",\n",
+    "    cmap=\"copper_r\",\n",
+    "    grid_units=(\"coordinates\", \"coordinates\"),\n",
+    "    shrink=0.75,\n",
+    "    var_name=\"Soil Thickness\",\n",
+    "    var_units=\"m\",\n",
+    ")\n",
+    "plt.xlim(36000, 53000)\n",
+    "plt.ylim(32000, 47000)\n",
+    "# plt.savefig('NOCA_SoilDepth.png')"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Plot probability of saturation "
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 57,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "(32000, 47000)"
+      ]
+     },
+     "execution_count": 57,
+     "metadata": {},
+     "output_type": "execute_result"
+    },
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 432x288 with 3 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "fig = plt.figure(\"Probability of Saturation\")\n",
+    "xticks = np.arange(-0.1, 0.8, 0.4)\n",
+    "ax1 = fig.add_subplot(221)\n",
+    "ax1.xaxis.set_visible(True)\n",
+    "imshow_grid(\n",
+    "    grid,\n",
+    "    \"soil__probability_of_saturation\",\n",
+    "    cmap=\"YlGnBu\",\n",
+    "    limits=((0), (1)),\n",
+    "    plot_name=\"Pre-Fire\",\n",
+    "    allow_colorbar=False,\n",
+    "    grid_units=(\"coordinates\", \"coordinates\"),\n",
+    ")\n",
+    "plt.xlim(36000, 53000)\n",
+    "plt.ylim(32000, 47000)\n",
+    "ax2 = fig.add_subplot(222)\n",
+    "ax2.xaxis.set_visible(True)\n",
+    "ax2.yaxis.set_visible(False)\n",
+    "imshow_grid(\n",
+    "    grid_fire,\n",
+    "    \"soil__probability_of_saturation\",\n",
+    "    cmap=\"YlGnBu\",\n",
+    "    limits=((0), (1)),\n",
+    "    plot_name=\"Post-Fire\",\n",
+    "    grid_units=(\"coordinates\", \"coordinates\"),\n",
+    ")\n",
+    "plt.xlim(36000, 53000)\n",
+    "plt.ylim(32000, 47000)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "This map shows the probability of saturation as high throughout much of the area because we modeled the annual maximum recharge, which is esssentially the worst case conditions that might lead to instability."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Plot probability of failure; Compare this with the elevation and slope maps."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 59,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "(32000, 47000)"
+      ]
+     },
+     "execution_count": 59,
+     "metadata": {},
+     "output_type": "execute_result"
+    },
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 432x288 with 3 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "fig = plt.figure(\"Probability of Failure\")\n",
+    "xticks = np.arange(-0.1, 0.8, 0.4)\n",
+    "ax1 = fig.add_subplot(221)\n",
+    "# ax1.xaxis.set_visible(False)\n",
+    "imshow_grid(\n",
+    "    grid,\n",
+    "    \"landslide__probability_of_failure\",\n",
+    "    cmap=\"OrRd\",\n",
+    "    limits=((0), (1)),\n",
+    "    plot_name=\"NOCA cohesion\",\n",
+    "    allow_colorbar=False,\n",
+    "    grid_units=(\"coordinates\", \"coordinates\"),\n",
+    ")\n",
+    "plt.xlim(36000, 53000)\n",
+    "plt.ylim(32000, 47000)\n",
+    "ax2 = fig.add_subplot(222)\n",
+    "ax2.yaxis.set_visible(False)\n",
+    "imshow_grid(\n",
+    "    grid_fire,\n",
+    "    \"landslide__probability_of_failure\",\n",
+    "    cmap=\"OrRd\",\n",
+    "    limits=((0), (1)),\n",
+    "    plot_name=\"Post-Fire\",\n",
+    "    grid_units=(\"coordinates\", \"coordinates\"),\n",
+    ")\n",
+    "plt.xlim(36000, 53000)\n",
+    "plt.ylim(32000, 47000)\n",
+    "\n",
+    "# plt.savefig('Probability_of_Failure_Original_Fire.png')"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "The map of probability of failure shows higher probabilities in a post-fire scenario with reduced cohesion."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "To review the fields assigned to the grid, simply execute the following command."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 60,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "{'topographic__elevation': array([-9999., -9999., -9999., ..., -9999., -9999., -9999.]),\n",
+       " 'topographic__slope': array([-9999., -9999., -9999., ..., -9999., -9999., -9999.]),\n",
+       " 'topographic__specific_contributing_area': array([-9999., -9999., -9999., ..., -9999., -9999., -9999.]),\n",
+       " 'soil__transmissivity': array([-9999., -9999., -9999., ..., -9999., -9999., -9999.]),\n",
+       " 'soil__mode_total_cohesion': array([-9999., -9999., -9999., ..., -9999., -9999., -9999.]),\n",
+       " 'soil__minimum_total_cohesion': array([-9999., -9999., -9999., ..., -9999., -9999., -9999.]),\n",
+       " 'soil__maximum_total_cohesion': array([-9999., -9999., -9999., ..., -9999., -9999., -9999.]),\n",
+       " 'soil__internal_friction_angle': array([-9999., -9999., -9999., ..., -9999., -9999., -9999.]),\n",
+       " 'soil__density': array([ 2000.,  2000.,  2000., ...,  2000.,  2000.,  2000.]),\n",
+       " 'soil__thickness': array([-9999., -9999., -9999., ..., -9999., -9999., -9999.]),\n",
+       " 'landslides': array([-9999., -9999., -9999., ..., -9999., -9999., -9999.]),\n",
+       " 'soil__saturated_hydraulic_conductivity': array([ 0.,  0.,  0., ...,  0.,  0.,  0.]),\n",
+       " 'soil__mean_relative_wetness': array([ 0.,  0.,  0., ...,  0.,  0.,  0.]),\n",
+       " 'landslide__probability_of_failure': array([ 0.,  0.,  0., ...,  0.,  0.,  0.]),\n",
+       " 'soil__probability_of_saturation': array([-9999., -9999., -9999., ..., -9999., -9999., -9999.])}"
+      ]
+     },
+     "execution_count": 60,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "grid.at_node"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Export data from model run: FS probability, mean Reletive wetness, probability of saturation"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 61,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "import pandas as pd\n",
+    "\n",
+    "core_nodes = grid.core_nodes\n",
+    "data_extracted = {\n",
+    "    \"Prob_fail_std\": np.array(\n",
+    "        grid.at_node[\"landslide__probability_of_failure\"][grid.core_nodes]\n",
+    "    ),\n",
+    "    \"mean_RW_std\": np.array(\n",
+    "        grid.at_node[\"soil__mean_relative_wetness\"][grid.core_nodes]\n",
+    "    ),\n",
+    "    \"prob_sat_std\": np.array(\n",
+    "        grid.at_node[\"soil__probability_of_saturation\"][grid.core_nodes]\n",
+    "    ),\n",
+    "    \"Prob_fail_fire\": np.array(\n",
+    "        grid_fire.at_node[\"landslide__probability_of_failure\"][grid_fire.core_nodes]\n",
+    "    ),\n",
+    "    \"mean_RW_fire\": np.array(\n",
+    "        grid_fire.at_node[\"soil__mean_relative_wetness\"][grid_fire.core_nodes]\n",
+    "    ),\n",
+    "    \"prob_sat_fire\": np.array(\n",
+    "        grid_fire.at_node[\"soil__probability_of_saturation\"][grid_fire.core_nodes]\n",
+    "    ),\n",
+    "}\n",
+    "headers = [\n",
+    "    \"Prob_fail_std\",\n",
+    "    \"mean_RW_std\",\n",
+    "    \"prob_sat_std\",\n",
+    "    \"Prob_fail_fire\",\n",
+    "    \"mean_RW_fire\",\n",
+    "    \"prob_sat_fire\",\n",
+    "]\n",
+    "df = pd.DataFrame(data_extracted, index=core_nodes, columns=(headers))\n",
+    "df.to_csv(\"Landslide_std_fire.csv\")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Make ascii files for raster creation in GIS"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 62,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "['prob_sat_fire.txt']"
+      ]
+     },
+     "execution_count": 62,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "write_esri_ascii(\"prob_fail_std.txt\", grid, names=\"landslide__probability_of_failure\")\n",
+    "write_esri_ascii(\"mean_RW_std.txt\", grid, names=\"soil__mean_relative_wetness\")\n",
+    "write_esri_ascii(\"prob_sat_std.txt\", grid, names=\"soil__probability_of_saturation\")\n",
+    "write_esri_ascii(\n",
+    "    \"prob_fail_fire.txt\", grid_fire, names=\"landslide__probability_of_failure\"\n",
+    ")\n",
+    "write_esri_ascii(\"mean_RW_fire.txt\", grid_fire, names=\"soil__mean_relative_wetness\")\n",
+    "write_esri_ascii(\n",
+    "    \"prob_sat_fire.txt\", grid_fire, names=\"soil__probability_of_saturation\"\n",
+    ")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "How long did the code above take to run?"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 63,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Elapsed time is 3703.20 seconds\n"
+     ]
+    }
+   ],
+   "source": [
+    "print(\"Elapsed time is %3.2f seconds\" % (time.time() - st))"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## 4.0. Save the results back into HydroShare\n",
+    "<a name=\"creation\"></a>\n",
+    "\n",
+    "Using the `hs_utils` library, the results of the Geoprocessing steps above can be saved back into HydroShare.  First, define all of the required metadata for resource creation, i.e. *title*, *abstract*, *keywords*, *content files*.  In addition, we must define the type of resource that will be created, in this case *genericresource*.  \n",
+    "\n",
+    "***Note:*** Make sure you save the notebook at this point, so that all notebook changes will be saved into the new HydroShare resource.\n",
+    "\n",
+    "\n",
+    "***Option A*** : define the resource from which this \"NEW\" content has been derived.  This is one method for tracking resource provenance."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Create list of files to save to HydroShare. Verify location and names."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 67,
+   "metadata": {
+    "scrolled": false
+   },
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "['/home/jovyan/work/notebooks/data/70b977e22af544f8a7e5a803935c329c/70b977e22af544f8a7e5a803935c329c/data/contents/Replicate_Landslide_model_for_fire_Fisher_Creek.ipynb', '/home/jovyan/work/notebooks/data/70b977e22af544f8a7e5a803935c329c/70b977e22af544f8a7e5a803935c329c/data/contents/Landslide_std_fire.csv', '/home/jovyan/work/notebooks/data/70b977e22af544f8a7e5a803935c329c/70b977e22af544f8a7e5a803935c329c/data/contents/prob_fail_std.txt', '/home/jovyan/work/notebooks/data/70b977e22af544f8a7e5a803935c329c/70b977e22af544f8a7e5a803935c329c/data/contents/mean_RW_std.txt', '/home/jovyan/work/notebooks/data/70b977e22af544f8a7e5a803935c329c/70b977e22af544f8a7e5a803935c329c/data/contents/prob_sat_std.txt', '/home/jovyan/work/notebooks/data/70b977e22af544f8a7e5a803935c329c/70b977e22af544f8a7e5a803935c329c/data/contents/prob_fail_fire.txt', '/home/jovyan/work/notebooks/data/70b977e22af544f8a7e5a803935c329c/70b977e22af544f8a7e5a803935c329c/data/contents/mean_RW_fire.txt', '/home/jovyan/work/notebooks/data/70b977e22af544f8a7e5a803935c329c/70b977e22af544f8a7e5a803935c329c/data/contents/prob_sat_fire.txt']\n"
+     ]
+    }
+   ],
+   "source": [
+    "ThisNotebook = (\n",
+    "    \"Replicate_Landslide_model_for_fire_Fisher_Creek.ipynb\"\n",
+    ")  # check name for consistency\n",
+    "files = [\n",
+    "    homedir + ThisNotebook,\n",
+    "    homedir + \"Landslide_std_fire.csv\",\n",
+    "    homedir + \"prob_fail_std.txt\",\n",
+    "    homedir + \"mean_RW_std.txt\",\n",
+    "    homedir + \"prob_sat_std.txt\",\n",
+    "    homedir + \"prob_fail_fire.txt\",\n",
+    "    homedir + \"mean_RW_fire.txt\",\n",
+    "    homedir + \"prob_sat_fire.txt\",\n",
+    "]\n",
+    "print(files)  # print location and names of files to save"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 68,
+   "metadata": {
+    "scrolled": false
+   },
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Adding the following system variables:\n",
+      "   HS_USR_NAME = swalt826\n",
+      "   HS_RES_ID = 70b977e22af544f8a7e5a803935c329c\n",
+      "   HS_RES_TYPE = genericresource\n",
+      "   JUPYTER_HUB_IP = jupyter.cuahsi.org\n",
+      "\n",
+      "These can be accessed using the following command: \n",
+      "   os.environ[key]\n",
+      "\n",
+      "   (e.g.)\n",
+      "   os.environ[\"HS_USR_NAME\"]  => swalt826\n",
+      "Successfully established a connection with HydroShare\n",
+      "Help on hydroshare in module utilities.hydroshare.hydroshare object:\n",
+      "\n",
+      "class hydroshare(builtins.object)\n",
+      " |  Methods defined here:\n",
+      " |  \n",
+      " |  __init__(self, username=None, password=None, cache=True)\n",
+      " |      Initialize self.  See help(type(self)) for accurate signature.\n",
+      " |  \n",
+      " |  addContentToExistingResource(self, resid, content)\n",
+      " |      Adds content files to an existing hydroshare resource.\n",
+      " |      \n",
+      " |      args:\n",
+      " |      -- resid: id of an existing hydroshare resource (str)\n",
+      " |      -- content: files paths to be added to resource (list)\n",
+      " |      \n",
+      " |      returns:\n",
+      " |      -- None\n",
+      " |  \n",
+      " |  createHydroShareResource(self, abstract, title, derivedFromId=None, keywords=[], resource_type='GenericResource', content_files=[], public=False)\n",
+      " |      Creates a hydroshare resource.\n",
+      " |      \n",
+      " |      args:\n",
+      " |      -- abstract: abstract for resource (str, required)\n",
+      " |      -- title: title of resource (str, required)\n",
+      " |      -- derivedFromId: id of parent hydroshare resource (str, default=>None)\n",
+      " |      -- keywords: list of subject keywords (list, default=>[])\n",
+      " |      -- resource_type: type of resource to create (str, default=>\n",
+      " |                                                   'GenericResource')\n",
+      " |      -- content_files: data to save as resource content (list, default=>[])\n",
+      " |      -- public: resource sharing status (bool, default=>False)\n",
+      " |      \n",
+      " |      returns:\n",
+      " |      -- None\n",
+      " |  \n",
+      " |  getContentFiles(self, resourceid)\n",
+      " |      Gets the content files for a resource that exists on the\n",
+      " |         Jupyter Server\n",
+      " |      \n",
+      " |      args:\n",
+      " |      -- resourceid: the id of the hydroshare resource\n",
+      " |      \n",
+      " |      returns:\n",
+      " |      -- {content file name: path}\n",
+      " |  \n",
+      " |  getContentPath(self, resourceid)\n",
+      " |      Gets the server path of a resources content files.\n",
+      " |      \n",
+      " |      args:\n",
+      " |      -- resourceid: the id of the hydroshare resource\n",
+      " |      \n",
+      " |      returns:\n",
+      " |      -- server path the the resource content files\n",
+      " |  \n",
+      " |  getResourceFromHydroShare(self, resourceid, destination='.')\n",
+      " |      Downloads content of a hydroshare resource.\n",
+      " |      \n",
+      " |      args:\n",
+      " |      -- resourceid: id of the hydroshare resource (str)\n",
+      " |      -- destination: path to save resource, default\n",
+      " |                      /user/[username]/notebooks/data (str)\n",
+      " |      \n",
+      " |      returns:\n",
+      " |      -- None\n",
+      " |  \n",
+      " |  getResourceMetadata(self, resid)\n",
+      " |      Gets metadata for a specified resource.\n",
+      " |      \n",
+      " |      args:\n",
+      " |      -- resid: hydroshare resource id\n",
+      " |      \n",
+      " |      returns:\n",
+      " |      -- resource metadata object\n",
+      " |  \n",
+      " |  getSecureConnection(self, username=None)\n",
+      " |      Establishes a secure connection with hydroshare.\n",
+      " |      \n",
+      " |      args:\n",
+      " |      -- email: email address associated with hydroshare\n",
+      " |      \n",
+      " |      returns:\n",
+      " |      -- hydroshare api connection\n",
+      " |  \n",
+      " |  loadResource(self, resourceid)\n",
+      " |      Loads the contents of a previously downloaded resource.\n",
+      " |      \n",
+      " |      args:\n",
+      " |      -- resourceid: the id of the resource that has been downloaded (str)\n",
+      " |      \n",
+      " |      returns:\n",
+      " |      -- {content file name: path}\n",
+      " |  \n",
+      " |  ----------------------------------------------------------------------\n",
+      " |  Data descriptors defined here:\n",
+      " |  \n",
+      " |  __dict__\n",
+      " |      dictionary for instance variables (if defined)\n",
+      " |  \n",
+      " |  __weakref__\n",
+      " |      list of weak references to the object (if defined)\n",
+      "\n"
+     ]
+    }
+   ],
+   "source": [
+    "hs = hydroshare.hydroshare()\n",
+    "help(hs)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 69,
+   "metadata": {
+    "scrolled": false
+   },
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Resource Created Successfully                           \n",
+      "Successfully Added Content Files                      \n"
+     ]
+    },
+    {
+     "data": {
+      "text/html": [
+       "Resource id: 1c4f83e308f340f4ae16997b90899cca"
+      ],
+      "text/plain": [
+       "<IPython.core.display.HTML object>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "text/html": [
+       "<a href=https://www.hydroshare.org/resource/1c4f83e308f340f4ae16997b90899cca target=\"_blank\">Open Resource in HydroShare<a>"
+      ],
+      "text/plain": [
+       "<IPython.core.display.HTML object>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "# for each file downloaded onto the server folder, move to a new HydroShare Generic Resource\n",
+    "title = \"Landslide Model run with Monte Carlo n=100 for eSurf paper from NOCA Observatory - Fisher Creek with Fire\"  # title for the new resource\n",
+    "abstract = \"This a reproducible demonstration of the landslide modeling results from eSurf paper: Strauch et al. (2018) \"  # abstract for the new resource\n",
+    "keywords = [\n",
+    "    \"landslide\",\n",
+    "    \"climate\",\n",
+    "    \"VIC\",\n",
+    "    \"saturation\",\n",
+    "    \"relative wetness\",\n",
+    "    \"fire\",\n",
+    "    \"Geohackweek\",\n",
+    "]  # keywords for the new resource\n",
+    "rtype = \"genericresource\"  # Hydroshare resource type\n",
+    "\n",
+    "# create the new resource\n",
+    "resource_id = hs.createHydroShareResource(\n",
+    "    abstract,\n",
+    "    title,\n",
+    "    keywords=keywords,\n",
+    "    resource_type=rtype,\n",
+    "    content_files=files,\n",
+    "    public=False,\n",
+    ")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  }
+ ],
+ "metadata": {
+  "anaconda-cloud": {},
+  "kernelspec": {
+   "display_name": "Python 3",
+   "language": "python",
+   "name": "python3"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.6.5"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 2
+}
diff --git a/observatory_gridmet/observatory_gridmet_newmexico.ipynb b/observatory_gridmet/observatory_gridmet_newmexico.ipynb
new file mode 100644
index 0000000..273a47b
--- /dev/null
+++ b/observatory_gridmet/observatory_gridmet_newmexico.ipynb
@@ -0,0 +1,9111 @@
+{
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "<img src=\"http://landlab.github.io/assets/Landlab-logo.png\"\n",
+    "style=\"float:left;width:150px;padding:0px\">  \n",
+    "\n",
+    "# Lowering the Barriers of Advanced Cyberinfrastructure for Enabling Collaborative Numerical Modeling in Earth Science <br /> \n",
+    "<br /> \n",
+    "###  Gridded Hydrometeorology forcing a Cellular Automaton Vegetation-Ecohydrologic Model\n",
+    "* Load data from a Landlab model published on HydroShare. <br />\n",
+    "* Define a geographic subset (New Mexico) within the North America.\n",
+    "* Calculate mean climatology for a range of elevation bands.\n",
+    "* Run the Landlab ecohydrology model for a watershed given various climate scenarios.<br />\n",
+    "* Explore sensitivity to climate by comparing the watershed model results.\n",
+    "* Save results to a new HydroShare resource.  <br />\n",
+    "\n",
+    "## Sai- we need a citation here and to update the list below.  <br />\n",
+    "\n"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "<hr>\n",
+    "<small> For instructions on how to run an interactive iPython notebook, click here: <a href=\"https://github.com/landlab/tutorials/blob/master/README.md\">https://github.com/landlab/tutorials/blob/master/README.md</a></small><br>\n",
+    "<small>For the unexpanded version to download and run, click here: <a href=\"http://nbviewer.jupyter.org/github/landlab/tutorials/blob/master/ecohydrology/cellular_automaton_vegetation_DEM/cellular_automaton_vegetation_DEM_unexpanded.ipynb\">http://nbviewer.jupyter.org/github/landlab/tutorials/blob/master/ecohydrology/cellular_automaton_vegetation_DEM/cellular_automaton_vegetation_DEM_unexpanded.ipynb</a></small><br>\n",
+    "<small>For more Landlab tutorials, click here: <a href=\"https://github.com/landlab/landlab/wiki/Tutorials\">https://github.com/landlab/landlab/wiki/Tutorials</a></small>\n",
+    "<hr>"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "This tutorial demonstrates implementation of the Cellular Automaton Tree-GRass-Shrub Simulator (CATGRaSS) [Zhou et al., 2013] on a flat domain. This model is built using components from the Landlab component library. CATGRaSS is spatially explicit model of plant coexistence. It simulates local ecohydrologic dynamics (soil moisture, transpiration, biomass) and spatial evolution of tree, grass, and shrub Plant Functional Types (PFT) driven by rainfall and solar radiation. \n",
+    "\n",
+    "Each cell in the model grid can hold a single PFT or remain empty. Tree and shrub plants disperse seeds to their neighbors. Grass seeds are assumed to be available at each cell. Establishment of plants in empty cells is determined probabilistically based on water stress of each PFT. Plants with lower water stress have higher probability of establishment. Plant mortality is simulated probabilistically as a result of aging and drought stress. Fires and grazing will be added to this model soon.  \n",
+    "\n",
+    "This model (driver) contains:\n",
+    "  - A local vegetation dynamics model that simulates storm and inter-storm water balance and ecohydrologic fluxes (ET, runoff), and plant biomass dynamics by coupling the following components:\n",
+    "        - PrecipitationDistribution\n",
+    "        - Radiation\n",
+    "        - PotentialEvapotranspiration\n",
+    "        - SoilMoisture\n",
+    "        - Vegetation\n",
+    "\n",
+    "  - A spatially explicit probabilistic cellular automaton component that simulates plant competition by tracking establishment and mortality of plants based on soil moisture stress:\n",
+    "        - VegCA\n",
+    "    \n",
+    "To run this Jupyter notebook, please make sure that the following files are in the same folder:\n",
+    "        - cellular_automaton_vegetation_flat_domain.ipynb (this notebook)\n",
+    "        - Inputs_Vegetation_CA.txt (Input parameters for the model)\n",
+    "        - Ecohyd_functions_flat.py (Utility functions)\n",
+    "\n",
+    "[Ref: Zhou, X, E. Istanbulluoglu, and E.R. Vivoni. \"Modeling the ecohydrological role of aspect-controlled radiation on tree-grass-shrub coexistence in a semiarid climate.\" Water Resources Research 49.5 (2013): 2872-2895]\n"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "In this tutorial, we are going to work with a landscape in central New Mexico, USA, where aspect controls the organization of PFTs. The climate in this area is semi-arid with Mean Annual Precipitation (MAP) of 254 mm [Zhou et. al 2013]. \n",
+    "We will do the following: \n",
+    "- Import a landscape \n",
+    "- Initialize the landscape with random distribution of PFTs\n",
+    "- Run the coupled Ecohydrology and cellular automata plant competition model for 50 years\n",
+    "- Visualize and examine outputs"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## 1.  Notebook Setup and Preparation\n",
+    "\n",
+    "To run this notebook, we must import several libraries.\n",
+    "The hs_utils library provides functions for interacting with HydroShare, including resource querying, dowloading. and creation. Additional libraries support the functions of Landlab.\n",
+    "### 1a.  Import libraries installed on CUAHSI JupyterHub server"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 1,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Adding the following system variables:\n",
+      "   HS_USR_NAME = saisiddu\n",
+      "   HS_RES_ID = 70b977e22af544f8a7e5a803935c329c\n",
+      "   HS_RES_TYPE = genericresource\n",
+      "   JUPYTER_HUB_IP = jupyter.cuahsi.org\n",
+      "\n",
+      "These can be accessed using the following command: \n",
+      "   os.environ[key]\n",
+      "\n",
+      "   (e.g.)\n",
+      "   os.environ[\"HS_USR_NAME\"]  => saisiddu\n",
+      "Successfully established a connection with HydroShare\n"
+     ]
+    }
+   ],
+   "source": [
+    "# Python libraries available on CUAHSI JupyterHub\n",
+    "import json\n",
+    "import os\n",
+    "import warnings\n",
+    "from datetime import datetime, timedelta\n",
+    "\n",
+    "import numpy as np\n",
+    "import pandas as pd\n",
+    "\n",
+    "# HydroShare Utilities\n",
+    "from utilities import hydroshare\n",
+    "\n",
+    "warnings.filterwarnings(\"ignore\")\n",
+    "\n",
+    "hs = hydroshare.hydroshare()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### Set variables for interacting with HydroShare from this notebook\n",
+    "<br />\n",
+    "The home directory on the JupyterHub server is printed. You can navigate to see the data using a terminal or folders. "
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### 1b.  Import python scripts from a Github repository"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "#### Please see the [Observatory]( https://github.com/ChristinaB/Observatory/blob/master/README.md) repository on Github with a Readme instructions on how to use Git and the JupyterHub server terminal to push/pull changes.   After completing the steps to get the observatory_gridded_hydromet.py script into your HydroShare Utilities folder, execute the next cell. "
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 2,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "import ogh"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 3,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Data will be loaded from and save to:/home/jovyan/work/notebooks/data/70b977e22af544f8a7e5a803935c329c/70b977e22af544f8a7e5a803935c329c/data/contents\n"
+     ]
+    }
+   ],
+   "source": [
+    "homedir = ogh.mapContentFolder(str(os.environ[\"HS_RES_ID\"]))\n",
+    "print(\"Data will be loaded from and save to:\" + homedir)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## 2. Download and generate lists of gridded climate points for a watershed\n",
+    "Retrieve a list of grid points and configuration file from a HydroShare resource\n",
+    "This example uses a ascii text that is stored in HydroShare at the following url: https://www.hydroshare.org/resource/d90289409f904017831d308642c1eb30/ . The data for our processing routines can be retrieved using the getResourceFromHydroShare function by passing in the global identifier from the url above.  In the next cell, we download this resource from HydroShare, and identify that the table in this resource is the 'mappingfile' variable identifying the Lat/Long points to be used for downloading hydrometeorology data.  The file must include columns with station numbers (this can be aribitrary), latitude, longitude, and elevation. The header of these columns must be FID, LAT, LONG_, and ELEV or RASTERVALU, respectively. The station numbers will be used for the remainder of the code to uniquely reference data from each climate station, as well as to identify minimum, maximum, and average elevation of all of the climate stations."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### Download areas of interest from HydroShare Resources"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 4,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "This resource already exists in your userspace.\n",
+      "5b401852758e4d49a87a788c1008b027/\n",
+      "|-- 5b401852758e4d49a87a788c1008b027/\n",
+      "|   |-- bagit.txt\n",
+      "|   |-- manifest-md5.txt\n",
+      "|   |-- readme.txt\n",
+      "|   |-- tagmanifest-md5.txt\n",
+      "|   |-- data/\n",
+      "|   |   |-- resourcemap.xml\n",
+      "|   |   |-- resourcemetadata.xml\n",
+      "|   |   |-- contents/\n",
+      "|   |   |   |-- New_Mexico.cpg\n",
+      "|   |   |   |-- New_Mexico.shp\n",
+      "|   |   |   |-- NewMexicoEcohydrology.jpg\n",
+      "|   |   |   |-- New_Mexico.shx\n",
+      "|   |   |   |-- New_Mexico.dbf\n",
+      "|   |   |   |-- New_Mexico.prj\n",
+      "|   |   |   |-- New_Mexico.shp.xml\n",
+      "\n",
+      "Do you want to overwrite these data [Y/n]? Y\n",
+      "Download Finished                               \n",
+      "Successfully downloaded resource 5b401852758e4d49a87a788c1008b027\n"
+     ]
+    },
+    {
+     "data": {
+      "text/html": [
+       "<b>Found the following file(s) associated with this HydroShare resource.</b>"
+      ],
+      "text/plain": [
+       "<IPython.core.display.HTML object>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "text/html": [
+       "New_Mexico.cpg<br>New_Mexico.dbf<br>NewMexicoEcohydrology.jpg<br>New_Mexico.prj<br>New_Mexico.sbn<br>New_Mexico.sbx<br>New_Mexico.shp<br>New_Mexico.shp.xml<br>New_Mexico.shx"
+      ],
+      "text/plain": [
+       "<IPython.core.display.HTML object>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "text/html": [
+       "These files are stored in a dictionary called <b>hs.content</b> for your convenience.  To access a file, simply issue the following command where MY_FILE is one of the files listed above: <pre>hs.content[\"MY_FILE\"] </pre> "
+      ],
+      "text/plain": [
+       "<IPython.core.display.HTML object>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "# This resource type is Composite Resource containing a Geographic Feature (ESRI Shapefile).\n",
+    "hs.getResourceFromHydroShare(\"5b401852758e4d49a87a788c1008b027\")\n",
+    "download_extent_shapefile = hs.content[\"New_Mexico.shp\"]"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 5,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "This resource already exists in your userspace.\n",
+      "ef2d82bf960144b4bfb1bae6242bcc7f/\n",
+      "|-- ef2d82bf960144b4bfb1bae6242bcc7f/\n",
+      "|   |-- bagit.txt\n",
+      "|   |-- manifest-md5.txt\n",
+      "|   |-- readme.txt\n",
+      "|   |-- tagmanifest-md5.txt\n",
+      "|   |-- data/\n",
+      "|   |   |-- resourcemap.xml\n",
+      "|   |   |-- resourcemetadata.xml\n",
+      "|   |   |-- contents/\n",
+      "|   |   |   |-- NAmer_dem_list.cpg\n",
+      "|   |   |   |-- NAmer_dem_list.dbf\n",
+      "|   |   |   |-- NAmer_dem_list.prj\n",
+      "|   |   |   |-- NAmer_dem_list.sbn\n",
+      "|   |   |   |-- NAmer_dem_list.sbx\n",
+      "|   |   |   |-- NAmer_dem_list.shp\n",
+      "|   |   |   |-- NAmer_dem_list.shx\n",
+      "\n",
+      "Do you want to overwrite these data [Y/n]? Y\n",
+      "Download Finished                               \n",
+      "Successfully downloaded resource ef2d82bf960144b4bfb1bae6242bcc7f\n"
+     ]
+    },
+    {
+     "data": {
+      "text/html": [
+       "<b>Found the following file(s) associated with this HydroShare resource.</b>"
+      ],
+      "text/plain": [
+       "<IPython.core.display.HTML object>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "text/html": [
+       "NAmer_dem_list.cpg<br>NAmer_dem_list.dbf<br>NAmer_dem_list.prj<br>NAmer_dem_list.sbn<br>NAmer_dem_list.sbx<br>NAmer_dem_list.shp<br>NAmer_dem_list.shx"
+      ],
+      "text/plain": [
+       "<IPython.core.display.HTML object>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "text/html": [
+       "These files are stored in a dictionary called <b>hs.content</b> for your convenience.  To access a file, simply issue the following command where MY_FILE is one of the files listed above: <pre>hs.content[\"MY_FILE\"] </pre> "
+      ],
+      "text/plain": [
+       "<IPython.core.display.HTML object>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "### TreatGeoSelf to a mapping tool that uses your shapefile\n",
+    "# Reuse this exising point shapefile of available 1/16 degree grid centroid locations\n",
+    "# shared on HydroShare as a public resource\n",
+    "hs.getResourceFromHydroShare(\"ef2d82bf960144b4bfb1bae6242bcc7f\")\n",
+    "NAmer = hs.content[\"NAmer_dem_list.shp\"]"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "# Read in the Observatory metadata file \n",
+    "This file contains the variables, data types and metadata related to Livneh et al., 2013; 2015 and Salathe et al., 2014 gridded hydrometeorology products. "
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 6,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "['dailymet_bclivneh2013',\n",
+       " 'dailymet_livneh2013',\n",
+       " 'dailymet_livneh2015',\n",
+       " 'dailyvic_livneh2013',\n",
+       " 'dailyvic_livneh2015',\n",
+       " 'dailywrf_bcsalathe2014',\n",
+       " 'dailywrf_salathe2014']"
+      ]
+     },
+     "execution_count": 6,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "# Assuming this is pulled from Github, how can we import this from Utilities.\n",
+    "# Otherwise it needs to be in each HydroShare resource - which if fine too.\n",
+    "# initialize ogh_meta data file\n",
+    "meta_file = dict(ogh.ogh_meta())\n",
+    "sorted(meta_file.keys())"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## TreatGeoSelf() to a easily generated list of lat/long points in your area of interest"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 7,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "(7917, 4)\n",
+      "   FID       LAT      LONG_    ELEV\n",
+      "0    0  36.96875 -103.03125  1431.0\n",
+      "1    1  36.96875 -103.09375  1462.0\n",
+      "2    2  36.96875 -103.15625  1437.0\n",
+      "3    3  36.96875 -103.21875  1451.0\n",
+      "4    4  36.96875 -103.28125  1478.0\n",
+      "/home/jovyan/work/notebooks/data/70b977e22af544f8a7e5a803935c329c/70b977e22af544f8a7e5a803935c329c/data/contents/new_mexico_gridmet.csv\n",
+      "CPU times: user 23.4 s, sys: 527 ms, total: 24 s\n",
+      "Wall time: 24 s\n"
+     ]
+    }
+   ],
+   "source": [
+    "%%time\n",
+    "# New Mexico Extent is approximately 7900 gridded cells\n",
+    "mappingfile = ogh.treatgeoself(\n",
+    "    shapefile=download_extent_shapefile,\n",
+    "    NAmer=NAmer,\n",
+    "    buffer_distance=0,\n",
+    "    mappingfile=os.path.join(homedir, \"new_mexico_gridmet.csv\"),\n",
+    ")\n",
+    "print(mappingfile)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 8,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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+      "text/plain": [
+       "<Figure size 1500x1500 with 2 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "ogh.griddedCellGradient(\n",
+    "    mappingfile=mappingfile,\n",
+    "    shapefile=download_extent_shapefile,\n",
+    "    outfilepath=os.path.join(homedir, \"NM_elev_gradient.png\"),\n",
+    "    plottitle=\"New Mexico watershed\\nGridded cell elevation gradient\",\n",
+    "    colorbar_label=\"Average elevation (meters)\",\n",
+    "    spatial_resolution=1 / 16,\n",
+    "    margin=0.25,\n",
+    "    epsg=3857,\n",
+    "    column=\"ELEV\",\n",
+    "    basemap_image=\"Elevation/World_Hillshade\",\n",
+    "    cmap=\"terrain\",\n",
+    ")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    " ## 3. Download gridded climate data \n",
+    " ### Get Daily Meteorologic Data from  Livneh et al. 2015 (1950-2013; North America extent)\n",
+    "\n",
+    "Please cite 2015 data with: <br/>\n",
+    "Livneh B., T.J. Bohn, D.S. Pierce, F. Munoz-Ariola, B. Nijssen, R. Vose, D. Cayan, and L.D. Brekke, 2015: A spatially comprehensive, hydrometeorological data set for Mexico, the U.S., and southern Canada 1950-2013, Nature Scientific Data, 5:150042, doi:10.1038/sdata.2015.42."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 9,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# %%time\n",
+    "# # uncomment and run if the files have already been downloaded\n",
+    "# ogh.remapCatalog(homedir=homedir,\n",
+    "#                  mappingfile=mappingfile,\n",
+    "#                  subdir='livneh2015/Daily_MET_1950_2013/raw',\n",
+    "#                  catalog_label='dailymet_livneh2015')"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 10,
+   "metadata": {
+    "scrolled": true
+   },
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Meteorology_Livneh_NAmerExt_15Oct2014_36.46875_-104.09375 unzipped\n",
+      "Meteorology_Livneh_NAmerExt_15Oct2014_36.21875_-104.84375 unzipped\n",
+      "Meteorology_Livneh_NAmerExt_15Oct2014_36.71875_-103.53125 unzipped\n",
+      "Meteorology_Livneh_NAmerExt_15Oct2014_36.96875_-103.03125 unzipped\n",
+      "Meteorology_Livneh_NAmerExt_15Oct2014_35.96875_-105.59375 unzipped\n",
+      "Meteorology_Livneh_NAmerExt_15Oct2014_36.21875_-104.90625 unzipped\n",
+      "Meteorology_Livneh_NAmerExt_15Oct2014_36.46875_-104.15625 unzipped\n",
+      "Meteorology_Livneh_NAmerExt_15Oct2014_36.96875_-103.09375 unzipped\n",
+      "Meteorology_Livneh_NAmerExt_15Oct2014_35.96875_-105.65625 unzipped\n",
+      "Meteorology_Livneh_NAmerExt_15Oct2014_36.71875_-103.59375 unzipped\n",
+      "Meteorology_Livneh_NAmerExt_15Oct2014_36.46875_-104.21875 unzipped\n",
+      "Meteorology_Livneh_NAmerExt_15Oct2014_36.21875_-104.96875 unzipped\n",
+      "Meteorology_Livneh_NAmerExt_15Oct2014_36.96875_-103.15625 unzipped\n",
+      "Meteorology_Livneh_NAmerExt_15Oct2014_35.96875_-105.71875 unzipped\n",
+      "Meteorology_Livneh_NAmerExt_15Oct2014_36.71875_-103.65625 unzipped\n",
+      "Meteorology_Livneh_NAmerExt_15Oct2014_36.46875_-104.28125 unzipped\n",
+      "Meteorology_Livneh_NAmerExt_15Oct2014_35.96875_-105.78125 unzipped\n",
+      "Meteorology_Livneh_NAmerExt_15Oct2014_36.96875_-103.21875 unzipped\n",
+      "Meteorology_Livneh_NAmerExt_15Oct2014_36.21875_-105.03125 unzipped\n",
+      "Meteorology_Livneh_NAmerExt_15Oct2014_36.71875_-103.71875 unzipped\n",
+      "Meteorology_Livneh_NAmerExt_15Oct2014_36.46875_-104.34375 unzipped\n",
+      "Meteorology_Livneh_NAmerExt_15Oct2014_35.96875_-105.84375 unzipped\n",
+      "Meteorology_Livneh_NAmerExt_15Oct2014_36.21875_-105.09375 unzipped\n",
+      "Meteorology_Livneh_NAmerExt_15Oct2014_36.96875_-103.28125 unzipped\n",
+      "Meteorology_Livneh_NAmerExt_15Oct2014_36.71875_-103.78125 unzipped\n",
+      "Meteorology_Livneh_NAmerExt_15Oct2014_36.46875_-104.40625 unzipped\n",
+      "Meteorology_Livneh_NAmerExt_15Oct2014_35.96875_-105.90625 unzipped\n",
+      "Meteorology_Livneh_NAmerExt_15Oct2014_36.21875_-105.15625 unzipped\n",
+      "Meteorology_Livneh_NAmerExt_15Oct2014_36.96875_-103.34375 unzipped\n",
+      "Meteorology_Livneh_NAmerExt_15Oct2014_36.71875_-103.84375 unzipped\n",
+      "Meteorology_Livneh_NAmerExt_15Oct2014_36.46875_-104.46875 unzipped\n",
+      "Meteorology_Livneh_NAmerExt_15Oct2014_36.21875_-105.21875 unzipped\n",
+      "Meteorology_Livneh_NAmerExt_15Oct2014_35.96875_-105.96875 unzipped\n",
+      "Meteorology_Livneh_NAmerExt_15Oct2014_36.96875_-103.40625 unzipped\n",
+      "Meteorology_Livneh_NAmerExt_15Oct2014_36.71875_-103.90625 unzipped\n",
+      "Meteorology_Livneh_NAmerExt_15Oct2014_36.46875_-104.53125 unzipped\n",
+      "Meteorology_Livneh_NAmerExt_15Oct2014_35.96875_-106.03125 unzipped\n",
+      "Meteorology_Livneh_NAmerExt_15Oct2014_36.96875_-103.46875 unzipped\n",
+      "Meteorology_Livneh_NAmerExt_15Oct2014_36.71875_-103.96875 unzipped\n",
+      "Meteorology_Livneh_NAmerExt_15Oct2014_36.21875_-105.28125 unzipped\n",
+      "Meteorology_Livneh_NAmerExt_15Oct2014_36.46875_-104.59375 unzipped\n",
+      "Meteorology_Livneh_NAmerExt_15Oct2014_35.96875_-106.09375 unzipped\n",
+      "Meteorology_Livneh_NAmerExt_15Oct2014_36.96875_-103.53125 unzipped\n",
+      "Meteorology_Livneh_NAmerExt_15Oct2014_36.71875_-104.03125 unzipped\n",
+      "Meteorology_Livneh_NAmerExt_15Oct2014_36.21875_-105.34375 unzipped\n",
+      "Meteorology_Livneh_NAmerExt_15Oct2014_36.46875_-104.65625 unzipped\n",
+      "Meteorology_Livneh_NAmerExt_15Oct2014_36.21875_-105.40625 unzipped\n",
+      "Meteorology_Livneh_NAmerExt_15Oct2014_36.96875_-103.59375 unzipped\n",
+      "Meteorology_Livneh_NAmerExt_15Oct2014_36.71875_-104.09375 unzipped\n",
+      "Meteorology_Livneh_NAmerExt_15Oct2014_35.96875_-106.15625 unzipped\n",
+      "Meteorology_Livneh_NAmerExt_15Oct2014_36.46875_-104.71875 unzipped\n",
+      "Meteorology_Livneh_NAmerExt_15Oct2014_36.21875_-105.46875 unzipped\n",
+      "Meteorology_Livneh_NAmerExt_15Oct2014_36.71875_-104.15625 unzipped\n",
+      "Meteorology_Livneh_NAmerExt_15Oct2014_35.96875_-106.21875 unzipped\n",
+      "Meteorology_Livneh_NAmerExt_15Oct2014_36.96875_-103.65625 unzipped\n",
+      "Meteorology_Livneh_NAmerExt_15Oct2014_36.46875_-104.78125 unzipped\n",
+      "Meteorology_Livneh_NAmerExt_15Oct2014_36.71875_-104.21875 unzipped\n",
+      "Meteorology_Livneh_NAmerExt_15Oct2014_36.21875_-105.53125 unzipped\n",
+      "Meteorology_Livneh_NAmerExt_15Oct2014_35.96875_-106.28125 unzipped\n",
+      "Meteorology_Livneh_NAmerExt_15Oct2014_36.96875_-103.71875 unzipped\n",
+      "Meteorology_Livneh_NAmerExt_15Oct2014_36.46875_-104.84375 unzipped\n",
+      "Meteorology_Livneh_NAmerExt_15Oct2014_36.71875_-104.28125 unzipped\n",
+      "Meteorology_Livneh_NAmerExt_15Oct2014_36.21875_-105.59375 unzipped\n",
+      "Meteorology_Livneh_NAmerExt_15Oct2014_35.96875_-106.34375 unzipped\n",
+      "Meteorology_Livneh_NAmerExt_15Oct2014_36.96875_-103.78125 unzipped\n",
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+    {
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+     "output_type": "stream",
+     "text": [
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+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
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+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
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+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
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+      "Meteorology_Livneh_NAmerExt_15Oct2014_32.09375_-104.78125 unzipped\n",
+      "CPU times: user 15.1 s, sys: 5.23 s, total: 20.4 s\n",
+      "Wall time: 28min 44s\n"
+     ]
+    },
+    {
+     "data": {
+      "text/plain": [
+       "'/home/jovyan/work/notebooks/data/70b977e22af544f8a7e5a803935c329c/70b977e22af544f8a7e5a803935c329c/data/contents/livneh2015/Daily_MET_1950_2013/raw'"
+      ]
+     },
+     "execution_count": 10,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "%%time\n",
+    "# From the reference mapping file, read-in, download, and unzip the data files for the longitude and latitude points\n",
+    "ogh.getDailyMET_livneh2015(homedir, mappingfile)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 11,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead tr th {\n",
+       "        text-align: left;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr>\n",
+       "      <th>Watershed</th>\n",
+       "      <th>New Mexico</th>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>Median elevation in meters [range](No. gridded cells)</th>\n",
+       "      <th>1744[891-3515] (n=7917)</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>dailymet_livneh2015</th>\n",
+       "      <td>1744[891-3515] (n=7917)</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "Watershed                                                           New Mexico\n",
+       "Median elevation in meters [range](No. gridded cells)  1744[891-3515] (n=7917)\n",
+       "dailymet_livneh2015                                    1744[891-3515] (n=7917)"
+      ]
+     },
+     "execution_count": 11,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "t1 = ogh.mappingfileSummary(\n",
+    "    listofmappingfiles=[mappingfile],\n",
+    "    listofwatershednames=[\"New Mexico\"],\n",
+    "    meta_file=meta_file,\n",
+    ")\n",
+    "\n",
+    "t1"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## 4.  Create gridded climate data dictionary"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### Make a dictionary of the original time series from Livneh and WRF, the monthly average statistics for each grid cell will be used to calculate differences between the two datasets."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 12,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Number of data files within elevation range (1200:1700): 3145\n",
+      "PRECIP dataframe reading to start: 0:00:05.698512\n",
+      "PRECIP dataframe reading complete:0:00:48.461333\n",
+      "TMAX dataframe reading to start: 0:00:54.220242\n",
+      "TMAX dataframe reading complete:0:01:36.920118\n",
+      "TMIN dataframe reading to start: 0:01:42.628494\n",
+      "TMIN dataframe reading complete:0:02:23.288719\n",
+      "WINDSPD dataframe reading to start: 0:02:28.962937\n",
+      "WINDSPD dataframe reading complete:0:03:06.622517\n",
+      "PRECIP_dailymet_livneh2015 calculations completed in 0:00:10.336606\n",
+      "TMAX_dailymet_livneh2015 calculations completed in 0:00:10.850806\n",
+      "TMIN_dailymet_livneh2015 calculations completed in 0:00:10.861329\n",
+      "WINDSPD_dailymet_livneh2015 calculations completed in 0:00:10.820866\n"
+     ]
+    }
+   ],
+   "source": [
+    "min_elev = 1200\n",
+    "max_elev = 1700\n",
+    "\n",
+    "ltm_low = ogh.gridclim_dict(\n",
+    "    mappingfile=mappingfile,\n",
+    "    dataset=\"dailymet_livneh2015\",\n",
+    "    metadata=meta_file,\n",
+    "    min_elev=min_elev,\n",
+    "    max_elev=max_elev,\n",
+    ")\n",
+    "\n",
+    "ogh.saveDictOfDf(\n",
+    "    dictionaryObject=ltm_low,\n",
+    "    outfilepath=os.path.join(homedir, \"NewMexico_lowElevation.json\"),\n",
+    ")\n",
+    "\n",
+    "sorted(ltm_low.keys())\n",
+    "del ltm_low"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 13,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Number of data files within elevation range (1700:2000): 1728\n",
+      "PRECIP dataframe reading to start: 0:00:03.688706\n",
+      "PRECIP dataframe reading complete:0:00:25.697995\n",
+      "TMAX dataframe reading to start: 0:00:28.917054\n",
+      "TMAX dataframe reading complete:0:00:49.981451\n",
+      "TMIN dataframe reading to start: 0:00:52.962508\n",
+      "TMIN dataframe reading complete:0:01:13.359982\n",
+      "WINDSPD dataframe reading to start: 0:01:16.554109\n",
+      "WINDSPD dataframe reading complete:0:01:38.679958\n",
+      "PRECIP_dailymet_livneh2015 calculations completed in 0:00:01.437536\n",
+      "TMAX_dailymet_livneh2015 calculations completed in 0:00:05.152022\n",
+      "TMIN_dailymet_livneh2015 calculations completed in 0:00:05.071372\n",
+      "WINDSPD_dailymet_livneh2015 calculations completed in 0:00:05.073434\n"
+     ]
+    }
+   ],
+   "source": [
+    "min_elev = 1700\n",
+    "max_elev = 2000\n",
+    "\n",
+    "ltm_mid = ogh.gridclim_dict(\n",
+    "    mappingfile=mappingfile,\n",
+    "    dataset=\"dailymet_livneh2015\",\n",
+    "    metadata=meta_file,\n",
+    "    min_elev=min_elev,\n",
+    "    max_elev=max_elev,\n",
+    ")\n",
+    "\n",
+    "ogh.saveDictOfDf(\n",
+    "    dictionaryObject=ltm_mid,\n",
+    "    outfilepath=os.path.join(homedir, \"NewMexico_midElevation.json\"),\n",
+    ")\n",
+    "\n",
+    "sorted(ltm_mid.keys())\n",
+    "del ltm_mid"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 14,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Number of data files within elevation range (2000:2500): 2041\n",
+      "PRECIP dataframe reading to start: 0:00:03.560498\n",
+      "PRECIP dataframe reading complete:0:00:30.225607\n",
+      "TMAX dataframe reading to start: 0:00:33.785031\n",
+      "TMAX dataframe reading complete:0:00:59.287196\n",
+      "TMIN dataframe reading to start: 0:01:02.890964\n",
+      "TMIN dataframe reading complete:0:01:27.736623\n",
+      "WINDSPD dataframe reading to start: 0:01:31.316397\n",
+      "WINDSPD dataframe reading complete:0:01:57.987421\n",
+      "PRECIP_dailymet_livneh2015 calculations completed in 0:00:03.525410\n",
+      "TMAX_dailymet_livneh2015 calculations completed in 0:00:06.203834\n",
+      "TMIN_dailymet_livneh2015 calculations completed in 0:00:06.275810\n",
+      "WINDSPD_dailymet_livneh2015 calculations completed in 0:00:06.251748\n"
+     ]
+    }
+   ],
+   "source": [
+    "min_elev = 2000\n",
+    "max_elev = 2500\n",
+    "\n",
+    "ltm_high = ogh.gridclim_dict(\n",
+    "    mappingfile=mappingfile,\n",
+    "    dataset=\"dailymet_livneh2015\",\n",
+    "    metadata=meta_file,\n",
+    "    min_elev=min_elev,\n",
+    "    max_elev=max_elev,\n",
+    ")\n",
+    "\n",
+    "ogh.saveDictOfDf(\n",
+    "    dictionaryObject=ltm_high,\n",
+    "    outfilepath=os.path.join(homedir, \"NewMexico_highElevation.json\"),\n",
+    ")\n",
+    "\n",
+    "sorted(ltm_high.keys())\n",
+    "del ltm_high"
+   ]
+  },
+  {
+   "cell_type": "raw",
+   "metadata": {},
+   "source": [
+    "min_elev=1200\n",
+    "max_elev=2500\n",
+    "\n",
+    "ltm_all = ogh.gridclim_dict(mappingfile=mappingfile,\n",
+    "                            dataset='dailymet_livneh2015',\n",
+    "                            metadata=meta_file,\n",
+    "                            min_elev=min_elev,\n",
+    "                            max_elev=max_elev)\n",
+    "\n",
+    "ogh.saveDictOfDf(dictionaryObject=ltm_all, outfilepath=os.path.join(homedir,'NewMexico_allElevation.json'))\n",
+    "\n",
+    "sorted(ltm_all.keys())\n",
+    "del ltm_all"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## 5. Tar the json files for storage"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 15,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "CPU times: user 60.3 ms, sys: 336 ms, total: 397 ms\n",
+      "Wall time: 3.01 s\n"
+     ]
+    }
+   ],
+   "source": [
+    "%%time\n",
+    "!tar -zcf NewMexico_lowElevation.json.tar.gz NewMexico_lowElevation.json\n",
+    "!tar -zcf NewMexico_midElevation.json.tar.gz NewMexico_midElevation.json\n",
+    "!tar -zcf NewMexico_highElevation.json.tar.gz NewMexico_highElevation.json\n",
+    "#!tar -zcf NewMexico_allElevation.json.tar.gz NewMexico_allElevation.json"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "#### This research is actively being developed by the Watershed Dynamics Research Group in the Civil and Environmental Engineering Department at the University of Washington for the Thunder Creek basin in the Skagit Watershed, WA, USA in collaboration with the HydroShare team, CUAHSI and NCSA partners.\n",
+    "<br /> <img src=\"https://www.washington.edu/brand/files/2014/09/W-Logo_Purple_Hex.png\" style=\"float:left;width:120px;padding:10px\">   \n",
+    "<img src=\"http://www.ncsa.illinois.edu/assets/img/logos_ncsa.png\" style=\"float:right;width:120px;padding:10px\"> \n",
+    "<img src=\"https://www.cuahsi.org/assets/images/logo.png\">"
+   ]
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "Python 3",
+   "language": "python",
+   "name": "python3"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.6.5"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 1
+}
diff --git a/ogh_xarray_landlab/Observatory_usecase7_xmapLandlab.ipynb b/ogh_xarray_landlab/Observatory_usecase7_xmapLandlab.ipynb
new file mode 100755
index 0000000..bf7edde
--- /dev/null
+++ b/ogh_xarray_landlab/Observatory_usecase7_xmapLandlab.ipynb
@@ -0,0 +1,2431 @@
+{
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "# Retrieve NetCDF and model gridded climate time-series for a watershed\n",
+    "\n",
+    "### Case study:  the Sauk-Suiattle Watershed\n",
+    "<img src=\"http://www.sauk-suiattle.com/images/Elliott.jpg\" \n",
+    "style=\"float:right;width:150px;padding:20px\">\n",
+    "\n",
+    "### Use this Jupyter Notebook to:\n",
+    "    1. HydroShare setup and preparation\n",
+    "    2. Re-establish the paths to the mapping file\n",
+    "    3. Compute daily, monthly, and annual temperature and precipitation statistics\n",
+    "    4. Visualize precipitation results relative to the forcing data\n",
+    "    5. Visualize the time-series trends\n",
+    "    6. Save results back into HydroShare\n",
+    "\n",
+    "<br/><br/><br/>\n",
+    "<img src=\"https://www.washington.edu/brand/files/2014/09/W-Logo_Purple_Hex.png\"\n",
+    "style=\"float:right;width:150px;padding:20px\">\n",
+    "\n",
+    "<br/><br/>\n",
+    "#### This data is compiled to digitally observe the watersheds, powered by HydroShare. <br/>Provided by the Watershed Dynamics Group, Dept. of Civil and Environmental Engineering, University of Washington"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## 1.  Prepare HydroShare Setup and Preparation\n",
+    "\n",
+    "To run this notebook, we must import several libaries. These are listed in order of 1) Python standard libraries, 2) hs_utils library provides functions for interacting with HydroShare, including resource querying, dowloading and creation, and 3) the observatory_gridded_hydromet library that is downloaded with this notebook. "
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 1,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# silencing warning\n",
+    "# data processing\n",
+    "import os\n",
+    "import warnings\n",
+    "\n",
+    "import dask\n",
+    "\n",
+    "# plotting and shape libraries\n",
+    "import matplotlib.pyplot as plt\n",
+    "import numpy as np\n",
+    "\n",
+    "# data migration library\n",
+    "import ogh\n",
+    "import pandas as pd\n",
+    "from landlab import CLOSED_BOUNDARY, imshow_grid\n",
+    "\n",
+    "import ogh_xarray_landlab as oxl\n",
+    "from ecohydrology_model_functions import plot_results, run_ecohydrology_model\n",
+    "from utilities import hydroshare\n",
+    "\n",
+    "warnings.filterwarnings(\"ignore\")\n",
+    "\n",
+    "\n",
+    "input_file = \"ecohyd_inputs.yaml\"\n",
+    "\n",
+    "%matplotlib inline"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 2,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "['dailymet_bclivneh2013',\n",
+       " 'dailymet_livneh2013',\n",
+       " 'dailymet_livneh2015',\n",
+       " 'dailyvic_livneh2013',\n",
+       " 'dailyvic_livneh2015',\n",
+       " 'dailywrf_bcsalathe2014',\n",
+       " 'dailywrf_salathe2014']"
+      ]
+     },
+     "execution_count": 2,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "# initialize ogh_meta\n",
+    "meta_file = dict(ogh.ogh_meta())\n",
+    "sorted(meta_file.keys())"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Establish a secure connection with HydroShare by instantiating the hydroshare class that is defined within hs_utils. In addition to connecting with HydroShare, this command also sets and prints environment variables for several parameters that will be useful for saving work back to HydroShare. "
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 4,
+   "metadata": {
+    "scrolled": false
+   },
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Adding the following system variables:\n",
+      "   HS_USR_NAME = jphuong\n",
+      "   HS_RES_ID = 70b977e22af544f8a7e5a803935c329c\n",
+      "   HS_RES_TYPE = genericresource\n",
+      "   JUPYTER_HUB_IP = jupyter.cuahsi.org\n",
+      "\n",
+      "These can be accessed using the following command: \n",
+      "   os.environ[key]\n",
+      "\n",
+      "   (e.g.)\n",
+      "   os.environ[\"HS_USR_NAME\"]  => jphuong\n",
+      "Successfully established a connection with HydroShare\n"
+     ]
+    }
+   ],
+   "source": [
+    "notebookdir = os.getcwd()\n",
+    "\n",
+    "hs = hydroshare.hydroshare()\n",
+    "homedir = hs.getContentPath(os.environ[\"HS_RES_ID\"])\n",
+    "os.chdir(homedir)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "If you are curious about where the data is being downloaded, click on the Jupyter Notebook dashboard icon to return to the File System view.  The homedir directory location printed above is where you can find the data and contents you will download to a HydroShare JupyterHub server.  At the end of this work session, you can migrate this data to the HydroShare iRods server as a Generic Resource. "
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## 2. Get list of gridded climate points for the watershed\n",
+    "\n",
+    "For visualization purposes, we will also remap the study site shapefile, which is stored in HydroShare at the following url: https://www.hydroshare.org/resource/c532e0578e974201a0bc40a37ef2d284/. Since the shapefile was previously migrated, we can select 'N' for no overwriting.\n",
+    "\n",
+    "In the usecase1 notebook, the treatgeoself function identified the gridded cell centroid coordinates that overlap with our study site. These coordinates were documented within the mapping file, which will be remapped here. In the usecase2 notebook, the downloaded files were cataloged within the mapping file, so we will use the mappingfileSummary function to characterize the files available for Sauk-Suiattle for each gridded data product."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 5,
+   "metadata": {
+    "scrolled": true
+   },
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "This resource already exists in your userspace.\n",
+      "ef2d82bf960144b4bfb1bae6242bcc7f/\n",
+      "|-- ef2d82bf960144b4bfb1bae6242bcc7f/\n",
+      "|   |-- bagit.txt\n",
+      "|   |-- manifest-md5.txt\n",
+      "|   |-- readme.txt\n",
+      "|   |-- tagmanifest-md5.txt\n",
+      "|   |-- data/\n",
+      "|   |   |-- resourcemap.xml\n",
+      "|   |   |-- resourcemetadata.xml\n",
+      "|   |   |-- contents/\n",
+      "|   |   |   |-- NAmer_dem_list.cpg\n",
+      "|   |   |   |-- NAmer_dem_list.dbf\n",
+      "|   |   |   |-- NAmer_dem_list.prj\n",
+      "|   |   |   |-- NAmer_dem_list.sbn\n",
+      "|   |   |   |-- NAmer_dem_list.sbx\n",
+      "|   |   |   |-- NAmer_dem_list.shp\n",
+      "|   |   |   |-- NAmer_dem_list.shx\n",
+      "\n",
+      "Do you want to overwrite these data [Y/n]? n\n"
+     ]
+    },
+    {
+     "data": {
+      "text/html": [
+       "<b>Found the following file(s) associated with this HydroShare resource.</b>"
+      ],
+      "text/plain": [
+       "<IPython.core.display.HTML object>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "text/html": [
+       "NAmer_dem_list.cpg<br>NAmer_dem_list.dbf<br>NAmer_dem_list.prj<br>NAmer_dem_list.sbn<br>NAmer_dem_list.sbx<br>NAmer_dem_list.shp<br>NAmer_dem_list.shx"
+      ],
+      "text/plain": [
+       "<IPython.core.display.HTML object>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "text/html": [
+       "These files are stored in a dictionary called <b>hs.content</b> for your convenience.  To access a file, simply issue the following command where MY_FILE is one of the files listed above: <pre>hs.content[\"MY_FILE\"] </pre> "
+      ],
+      "text/plain": [
+       "<IPython.core.display.HTML object>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "This resource already exists in your userspace.\n",
+      "c532e0578e974201a0bc40a37ef2d284/\n",
+      "|-- c532e0578e974201a0bc40a37ef2d284/\n",
+      "|   |-- bagit.txt\n",
+      "|   |-- manifest-md5.txt\n",
+      "|   |-- readme.txt\n",
+      "|   |-- tagmanifest-md5.txt\n",
+      "|   |-- data/\n",
+      "|   |   |-- resourcemap.xml\n",
+      "|   |   |-- resourcemetadata.xml\n",
+      "|   |   |-- contents/\n",
+      "|   |   |   |-- wbdhub12_17110006_WGS84_Basin.cpg\n",
+      "|   |   |   |-- wbdhub12_17110006_WGS84_Basin.shp\n",
+      "|   |   |   |-- wbdhub12_17110006_WGS84_Basin.shx\n",
+      "|   |   |   |-- wbdhub12_17110006_WGS84_Basin.dbf\n",
+      "|   |   |   |-- wbdhub12_17110006_WGS84_Basin.prj\n",
+      "\n",
+      "Do you want to overwrite these data [Y/n]? n\n"
+     ]
+    },
+    {
+     "data": {
+      "text/html": [
+       "<b>Found the following file(s) associated with this HydroShare resource.</b>"
+      ],
+      "text/plain": [
+       "<IPython.core.display.HTML object>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "text/html": [
+       "wbdhub12_17110006_WGS84_Basin.cpg<br>wbdhub12_17110006_WGS84_Basin.shp<br>wbdhub12_17110006_WGS84_Basin.shx<br>wbdhub12_17110006_WGS84_Basin.dbf<br>wbdhub12_17110006_WGS84_Basin.prj"
+      ],
+      "text/plain": [
+       "<IPython.core.display.HTML object>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "text/html": [
+       "These files are stored in a dictionary called <b>hs.content</b> for your convenience.  To access a file, simply issue the following command where MY_FILE is one of the files listed above: <pre>hs.content[\"MY_FILE\"] </pre> "
+      ],
+      "text/plain": [
+       "<IPython.core.display.HTML object>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "\"\"\"\n",
+    "1/16-degree Gridded cell centroids\n",
+    "\"\"\"\n",
+    "# List of available data\n",
+    "hs.getResourceFromHydroShare(\"ef2d82bf960144b4bfb1bae6242bcc7f\")\n",
+    "NAmer = hs.content[\"NAmer_dem_list.shp\"]\n",
+    "\n",
+    "\n",
+    "\"\"\"\n",
+    "Sauk\n",
+    "\"\"\"\n",
+    "# Watershed extent\n",
+    "hs.getResourceFromHydroShare(\"c532e0578e974201a0bc40a37ef2d284\")\n",
+    "sauk = hs.content[\"wbdhub12_17110006_WGS84_Basin.shp\"]\n",
+    "\n",
+    "# reproject the shapefile into WGS84\n",
+    "ogh.reprojShapefile(sourcepath=sauk)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### Summarize the file availability from each watershed mapping file"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 6,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "(99, 4)\n",
+      "   FID       LAT      LONG_    ELEV\n",
+      "0    0  48.53125 -121.59375  1113.0\n",
+      "1    1  48.46875 -121.46875   646.0\n",
+      "2    2  48.46875 -121.53125   321.0\n",
+      "3    3  48.46875 -121.59375   164.0\n",
+      "4    4  48.46875 -121.65625   369.0\n",
+      "CPU times: user 17.1 s, sys: 479 ms, total: 17.6 s\n",
+      "Wall time: 17.7 s\n"
+     ]
+    }
+   ],
+   "source": [
+    "%%time\n",
+    "\n",
+    "# map the mappingfiles from usecase1\n",
+    "mappingfile1 = ogh.treatgeoself(\n",
+    "    shapefile=sauk,\n",
+    "    NAmer=NAmer,\n",
+    "    buffer_distance=0.06,\n",
+    "    mappingfile=os.path.join(homedir, \"Sauk_mappingfile.csv\"),\n",
+    ")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## 3.  Compare Hydrometeorology \n",
+    "\n",
+    "This section performs computations and generates plots of the Livneh 2013 and Salathe 2014 mean temperature and mean total monthly precipitation in order to compare them with each other. The generated plots are automatically downloaded and saved as .png files within the \"homedir\" directory.\n",
+    "\n",
+    "Let's compare the Livneh 2013 and Salathe 2014 using the period of overlapping history."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 7,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Help on function getDailyMET_livneh2013 in module ogh.ogh:\n",
+      "\n",
+      "getDailyMET_livneh2013(homedir, mappingfile, subdir='livneh2013/Daily_MET_1915_2011/raw', catalog_label='dailymet_livneh2013')\n",
+      "    Get the Livneh el al., 2013 Daily Meteorology files of interest using the reference mapping file\n",
+      "    \n",
+      "    homedir: (dir) the home directory to be used for establishing subdirectories\n",
+      "    mappingfile: (dir) the file path to the mappingfile, which contains the LAT, LONG_, and ELEV coordinates of interest\n",
+      "    subdir: (dir) the subdirectory to be established under homedir\n",
+      "    catalog_label: (str) the preferred name for the series of catalogged filepaths\n",
+      "\n"
+     ]
+    }
+   ],
+   "source": [
+    "help(ogh.getDailyMET_livneh2013)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 8,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Help on function get_x_dailymet_Livneh2013_raw in module ogh_xarray_landlab:\n",
+      "\n",
+      "get_x_dailymet_Livneh2013_raw(homedir, spatialbounds, subdir='livneh2013/Daily_MET_1915_2011/raw_netcdf', nworkers=4, start_date='1915-01-01', end_date='2011-12-31', rename_timelatlong_names={'lat': 'LAT', 'lon': 'LON', 'time': 'TIME'}, file_prefix='sp_', replace_file=True)\n",
+      "    get Daily MET data from Livneh et al. (2013) using xarray on netcdf files\n",
+      "\n"
+     ]
+    }
+   ],
+   "source": [
+    "help(oxl.get_x_dailymet_Livneh2013_raw)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## NetCDF retrieval and clipping to a spatial extent\n",
+    "\n",
+    "The function get_x_dailywrf_salathe2014 retrieves and clips NetCDF files archived within the UW Rocinante NNRP repository. This archive contains daily data from January 1970 through December 1979 (10 years). Each netcdf file is comprised of meteorologic and VIC hydrologic outputs for a calendar month. The expected number of files would be 360 files (12 months for 30 years). \n",
+    "\n",
+    "In the code chunk below, 40 parallel workers will be initialized to distribute file retrieval and spatial clipping tasks. For each worker, they will wget the requested file, clip the netcdf file to gridded cell centroids within the the provided bounding box, then return the location of the spatially clipped output files.\n",
+    "\n",
+    "Provide the home and subdirectory where the cropped NetCDF files will be stored. Also provide the spatial bounds (in WGS84) to crop the NetCDF files upon download. Finally, provide the number of workers to carry out the download tasks, and the start and end date of the files of interest."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 9,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Number of gridded data files:99\n",
+      "Minimum elevation: 164.0m\n",
+      "Mean elevation: 1151.040404040404m\n",
+      "Maximum elevation: 2216.0m\n",
+      "[                                        ] | 0% Completed | 19.7sMeteorology_Livneh_CONUSExt_v.1.2_2013.197008.nc unzipped\n",
+      "[                                        ] | 0% Completed | 24.8sMeteorology_Livneh_CONUSExt_v.1.2_2013.197009.nc unzipped\n",
+      "Meteorology_Livneh_CONUSExt_v.1.2_2013.197005.nc unzipped\n",
+      "Meteorology_Livneh_CONUSExt_v.1.2_2013.197004.nc unzipped\n",
+      "Meteorology_Livneh_CONUSExt_v.1.2_2013.197012.nc unzipped\n",
+      "Meteorology_Livneh_CONUSExt_v.1.2_2013.197007.nc unzipped\n",
+      "[####################                    ] | 50% Completed | 48.6sMeteorology_Livneh_CONUSExt_v.1.2_2013.197002.nc unzipped\n",
+      "Meteorology_Livneh_CONUSExt_v.1.2_2013.197010.nc unzipped\n",
+      "Meteorology_Livneh_CONUSExt_v.1.2_2013.197006.nc unzipped\n",
+      "Meteorology_Livneh_CONUSExt_v.1.2_2013.197001.nc unzipped\n",
+      "Meteorology_Livneh_CONUSExt_v.1.2_2013.197011.nc unzipped\n",
+      "Meteorology_Livneh_CONUSExt_v.1.2_2013.197003.nc unzipped\n",
+      "[########################################] | 100% Completed | 49.0s\n"
+     ]
+    }
+   ],
+   "source": [
+    "maptable, nstations = ogh.mappingfileToDF(mappingfile1)\n",
+    "spatialbounds = {\n",
+    "    \"minx\": maptable.LONG_.min(),\n",
+    "    \"maxx\": maptable.LONG_.max(),\n",
+    "    \"miny\": maptable.LAT.min(),\n",
+    "    \"maxy\": maptable.LAT.max(),\n",
+    "}\n",
+    "\n",
+    "outputfiles = oxl.get_x_dailymet_Livneh2013_raw(\n",
+    "    homedir=homedir,\n",
+    "    subdir=\"livneh2013/Daily_MET_1970_1970/raw_netcdf\",\n",
+    "    spatialbounds=spatialbounds,\n",
+    "    nworkers=6,\n",
+    "    start_date=\"1970-01-01\",\n",
+    "    end_date=\"1970-12-31\",\n",
+    ")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### Convert collection of NetCDF files into a collection of ASCII files\n",
+    "\n",
+    "Provide the home and subdirectory where the ASCII files will be stored, the source_directory of netCDF files, and the mapping file to which the resulting ASCII files will be cataloged. Also, provide the Pandas Datetime code for the frequency of the time steps. Finally, provide the catalog label that will be used for the mapping file catalog and the metadata file label."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 10,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "[########################################] | 100% Completed |  0.1s\n",
+      "[########################################] | 100% Completed |  0.1s\n",
+      "[########################################] | 100% Completed |  0.1s\n",
+      "[########################################] | 100% Completed |  0.1s\n",
+      "[########################################] | 100% Completed |  1.0s\n",
+      "[########################################] | 100% Completed |  1.1s\n",
+      "CPU times: user 1.75 s, sys: 398 ms, total: 2.15 s\n",
+      "Wall time: 2.52 s\n"
+     ]
+    }
+   ],
+   "source": [
+    "%%time\n",
+    "# convert the netCDF files into daily ascii time-series files for each gridded location\n",
+    "outfilelist = oxl.netcdf_to_ascii(\n",
+    "    homedir=homedir,\n",
+    "    subdir=\"livneh2013/Daily_MET_1970_1970/raw_ascii\",\n",
+    "    source_directory=os.path.join(homedir, \"livneh2013/Daily_MET_1970_1970/raw_netcdf\"),\n",
+    "    mappingfile=mappingfile1,\n",
+    "    temporal_resolution=\"D\",\n",
+    "    meta_file=meta_file,\n",
+    "    catalog_label=\"sp_dailymet_livneh_1970_1970\",\n",
+    ")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 11,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead tr th {\n",
+       "        text-align: left;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr>\n",
+       "      <th>Watershed</th>\n",
+       "      <th>Sauk-Suiattle river</th>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>Median elevation in meters [range](Number of gridded cells)</th>\n",
+       "      <th>1171[164-2216] (n=99)</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>sp_dailymet_livneh_1970_1970</th>\n",
+       "      <td>1171[164-2216] (n=99)</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "Watershed                                                      Sauk-Suiattle river\n",
+       "Median elevation in meters [range](Number of gridded cells)  1171[164-2216] (n=99)\n",
+       "sp_dailymet_livneh_1970_1970                                 1171[164-2216] (n=99)"
+      ]
+     },
+     "execution_count": 11,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "t1 = ogh.mappingfileSummary(\n",
+    "    listofmappingfiles=[mappingfile1],\n",
+    "    listofwatershednames=[\"Sauk-Suiattle river\"],\n",
+    "    meta_file=meta_file,\n",
+    ")\n",
+    "\n",
+    "t1"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 12,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# Save the metadata\n",
+    "ogh.saveDictOfDf(dictionaryObject=meta_file, outfilepath=\"test.json\")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### Create a dictionary of climate variables for the long-term mean (ltm).\n",
+    "#### INPUT: gridded meteorology ASCII files located from the Sauk-Suiattle Mapping file. The inputs to gridclim_dict() include the folder location and name of the hydrometeorology data, the file start and end, the analysis start and end, and the elevation band to be included in the analsyis (max and min elevation). <br/>OUTPUT: dictionary of dataframes where rows are temporal summaries and columns are spatial summaries"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 13,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "['Prec', 'Tmax', 'Tmin', 'Wind']"
+      ]
+     },
+     "execution_count": 13,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "meta_file[\"sp_dailymet_livneh_1970_1970\"][\"variable_list\"]"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 14,
+   "metadata": {
+    "scrolled": true
+   },
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Number of data files within elevation range (164.0-2216.0 m): 99\n",
+      "[########################################] | 100% Completed |  1.4s\n",
+      "[########################################] | 100% Completed |  1.5s\n",
+      "Prec dataframe reading complete:0:00:01.655280\n",
+      "[########################################] | 100% Completed |  1.0s\n",
+      "[########################################] | 100% Completed |  1.0s\n",
+      "Tmax dataframe reading complete:0:00:02.790228\n",
+      "[########################################] | 100% Completed |  1.0s\n",
+      "[########################################] | 100% Completed |  1.1s\n",
+      "Tmin dataframe reading complete:0:00:04.015945\n",
+      "[########################################] | 100% Completed |  0.1s\n",
+      "[########################################] | 100% Completed |  0.1s\n",
+      "[########################################] | 100% Completed |  0.1s\n",
+      "[########################################] | 100% Completed |  0.2s\n",
+      "Prec_sp_dailymet_livneh_1970_1970 calculations completed in 0:00:00.326512\n",
+      "[########################################] | 100% Completed |  0.1s\n",
+      "[########################################] | 100% Completed |  0.2s\n",
+      "[########################################] | 100% Completed |  0.1s\n",
+      "[########################################] | 100% Completed |  0.2s\n",
+      "Tmax_sp_dailymet_livneh_1970_1970 calculations completed in 0:00:00.422493\n",
+      "[########################################] | 100% Completed |  0.1s\n",
+      "[########################################] | 100% Completed |  0.2s\n",
+      "[########################################] | 100% Completed |  0.1s\n",
+      "[########################################] | 100% Completed |  0.2s\n",
+      "Tmin_sp_dailymet_livneh_1970_1970 calculations completed in 0:00:00.424068\n",
+      "CPU times: user 3.94 s, sys: 822 ms, total: 4.76 s\n",
+      "Wall time: 5.2 s\n"
+     ]
+    }
+   ],
+   "source": [
+    "%%time\n",
+    "\n",
+    "ltm = ogh.gridclim_dict(\n",
+    "    mappingfile=mappingfile1,\n",
+    "    metadata=meta_file,\n",
+    "    dataset=\"sp_dailymet_livneh_1970_1970\",\n",
+    "    variable_list=[\"Prec\", \"Tmax\", \"Tmin\"],\n",
+    ")\n",
+    "\n",
+    "sorted(ltm.keys())"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### Compute the total monthly and yearly precipitation, as well as the mean values across time and across stations\n",
+    "#### INPUT: daily precipitation for each station from the long-term mean dictionary (ltm) <br/>OUTPUT: Append the computed dataframes and values into the ltm dictionary"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 15,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "[########################################] | 100% Completed |  0.1s\n",
+      "[########################################] | 100% Completed |  0.2s\n",
+      "[########################################] | 100% Completed |  0.1s\n",
+      "[########################################] | 100% Completed |  0.2s\n",
+      "Prec_sp_dailymet_livneh_1970_1970 calculations completed in 0:00:00.428293\n"
+     ]
+    }
+   ],
+   "source": [
+    "# extract metadata\n",
+    "dr = meta_file[\"sp_dailymet_livneh_1970_1970\"]\n",
+    "\n",
+    "# compute sums and mean monthly an yearly sums\n",
+    "ltm = ogh.aggregate_space_time_sum(\n",
+    "    df_dict=ltm,\n",
+    "    suffix=\"Prec_sp_dailymet_livneh_1970_1970\",\n",
+    "    start_date=dr[\"start_date\"],\n",
+    "    end_date=dr[\"end_date\"],\n",
+    ")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 16,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "['Prec_sp_dailymet_livneh_1970_1970',\n",
+       " 'Tmax_sp_dailymet_livneh_1970_1970',\n",
+       " 'Tmin_sp_dailymet_livneh_1970_1970',\n",
+       " 'anomyear_Prec_sp_dailymet_livneh_1970_1970',\n",
+       " 'anomyear_Tmax_sp_dailymet_livneh_1970_1970',\n",
+       " 'anomyear_Tmin_sp_dailymet_livneh_1970_1970',\n",
+       " 'meanalldailysum_Prec_sp_dailymet_livneh_1970_1970',\n",
+       " 'meanallmonthsum_Prec_sp_dailymet_livneh_1970_1970',\n",
+       " 'meanallyear_Prec_sp_dailymet_livneh_1970_1970',\n",
+       " 'meanallyear_Tmax_sp_dailymet_livneh_1970_1970',\n",
+       " 'meanallyear_Tmin_sp_dailymet_livneh_1970_1970',\n",
+       " 'meanallyearsum_Prec_sp_dailymet_livneh_1970_1970',\n",
+       " 'meanbydaily_Prec_sp_dailymet_livneh_1970_1970',\n",
+       " 'meanbydaily_Tmax_sp_dailymet_livneh_1970_1970',\n",
+       " 'meanbydaily_Tmin_sp_dailymet_livneh_1970_1970',\n",
+       " 'meanbymonth_Prec_sp_dailymet_livneh_1970_1970',\n",
+       " 'meanbymonth_Tmax_sp_dailymet_livneh_1970_1970',\n",
+       " 'meanbymonth_Tmin_sp_dailymet_livneh_1970_1970',\n",
+       " 'meanbymonthsum_Prec_sp_dailymet_livneh_1970_1970',\n",
+       " 'meanbyyear_Prec_sp_dailymet_livneh_1970_1970',\n",
+       " 'meanbyyear_Tmax_sp_dailymet_livneh_1970_1970',\n",
+       " 'meanbyyear_Tmin_sp_dailymet_livneh_1970_1970',\n",
+       " 'meanbyyearsum_Prec_sp_dailymet_livneh_1970_1970',\n",
+       " 'meandaily_Prec_sp_dailymet_livneh_1970_1970',\n",
+       " 'meandaily_Tmax_sp_dailymet_livneh_1970_1970',\n",
+       " 'meandaily_Tmin_sp_dailymet_livneh_1970_1970',\n",
+       " 'meanmonth_Prec_sp_dailymet_livneh_1970_1970',\n",
+       " 'meanmonth_Tmax_sp_dailymet_livneh_1970_1970',\n",
+       " 'meanmonth_Tmin_sp_dailymet_livneh_1970_1970',\n",
+       " 'meanmonthsum_Prec_sp_dailymet_livneh_1970_1970',\n",
+       " 'meanyear_Prec_sp_dailymet_livneh_1970_1970',\n",
+       " 'meanyear_Tmax_sp_dailymet_livneh_1970_1970',\n",
+       " 'meanyear_Tmin_sp_dailymet_livneh_1970_1970',\n",
+       " 'meanyearsum_Prec_sp_dailymet_livneh_1970_1970',\n",
+       " 'monthsum_Prec_sp_dailymet_livneh_1970_1970',\n",
+       " 'yearsum_Prec_sp_dailymet_livneh_1970_1970']"
+      ]
+     },
+     "execution_count": 16,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "# print the name of the analytical dataframes and values within ltm\n",
+    "sorted(ltm.keys())"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 17,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# initialize list of outputs\n",
+    "files = []\n",
+    "\n",
+    "# create the destination path for the dictionary of dataframes\n",
+    "ltm_sauk = os.path.join(homedir, \"ltm_1970_1970_sauk.json\")\n",
+    "ogh.saveDictOfDf(dictionaryObject=ltm, outfilepath=ltm_sauk)\n",
+    "files.append(ltm_sauk)\n",
+    "\n",
+    "# append the mapping file for Sauk-Suiattle gridded cell centroids\n",
+    "files.append(mappingfile1)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### Visualize the \"average monthly total precipitations\"\n",
+    "\n",
+    "#### INPUT: dataframe with each month as a row and each station as a column. <br/>OUTPUT: A png file that represents the distribution across stations (in Wateryear order)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 18,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "[(69, 48.09375, -121.09375),\n",
+       " (3, 48.46875, -121.59375),\n",
+       " (24, 48.34375, -121.53125)]"
+      ]
+     },
+     "execution_count": 18,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "# # two lowest elevation locations\n",
+    "lowE_ref = ogh.findCentroidCode(mappingfile=mappingfile1, colvar=\"ELEV\", colvalue=164)\n",
+    "\n",
+    "# one highest elevation location\n",
+    "highE_ref = ogh.findCentroidCode(mappingfile=mappingfile1, colvar=\"ELEV\", colvalue=2216)\n",
+    "\n",
+    "# combine references together\n",
+    "reference_lines = highE_ref + lowE_ref\n",
+    "reference_lines"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 19,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "No handles with labels found to put in legend.\n"
+     ]
+    },
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 432x432 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "text/plain": [
+       "<matplotlib.axes._subplots.AxesSubplot at 0x7f78c7d1eb38>"
+      ]
+     },
+     "execution_count": 19,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "ogh.renderValueInBoxplot(\n",
+    "    ltm[\"meanbymonthsum_Prec_sp_dailymet_livneh_1970_1970\"],\n",
+    "    outfilepath=\"totalMonthlyRainfall.png\",\n",
+    "    plottitle=\"Total monthly rainfall\",\n",
+    "    time_steps=\"month\",\n",
+    "    wateryear=True,\n",
+    "    reference_lines=reference_lines,\n",
+    "    ref_legend=True,\n",
+    "    value_name=\"Total daily precipitation (mm)\",\n",
+    "    cmap=\"seismic_r\",\n",
+    "    figsize=(6, 6),\n",
+    ")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 20,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 750x750 with 2 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "ogh.renderValuesInPoints(\n",
+    "    ltm[\"meanbymonthsum_Prec_sp_dailymet_livneh_1970_1970\"],\n",
+    "    vardf_dateindex=12,\n",
+    "    shapefile=sauk,\n",
+    "    cmap=\"seismic_r\",\n",
+    "    outfilepath=\"test.png\",\n",
+    "    plottitle=\"December total rainfall\",\n",
+    "    colorbar_label=\"Total monthly rainfall (mm)\",\n",
+    "    figsize=(1.5, 1.5),\n",
+    ")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 21,
+   "metadata": {
+    "scrolled": true
+   },
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Number of gridded data files:99\n",
+      "Minimum elevation: 164.0m\n",
+      "Mean elevation: 1151.040404040404m\n",
+      "Maximum elevation: 2216.0m\n",
+      "115.26040113876661 -87.99363603095117 65333.46979869377 76402.0986115007\n"
+     ]
+    },
+    {
+     "data": {
+      "text/plain": [
+       "<Figure size 5000x5000 with 0 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "minx2, miny2, maxx2, maxy2 = oxl.calculateUTMbounds(\n",
+    "    mappingfile=mappingfile1,\n",
+    "    mappingfile_crs={\"init\": \"epsg:4326\"},\n",
+    "    spatial_resolution=0.06250,\n",
+    ")\n",
+    "\n",
+    "print(minx2, miny2, maxx2, maxy2)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "# generate a raster"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 22,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Help on function rasterDimensions in module ogh_xarray_landlab:\n",
+      "\n",
+      "rasterDimensions(maxx, maxy, minx=0, miny=0, dy=100, dx=100)\n",
+      "\n"
+     ]
+    }
+   ],
+   "source": [
+    "help(oxl.rasterDimensions)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 23,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "(78, 68)"
+      ]
+     },
+     "execution_count": 23,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "# generate a raster\n",
+    "raster, row_list, col_list = oxl.rasterDimensions(\n",
+    "    minx=minx2, miny=miny2, maxx=maxx2, maxy=maxy2, dx=1000, dy=1000\n",
+    ")\n",
+    "raster.shape"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "# Higher resolution children of gridded cells \n",
+    "### get data from Lower resolution parent grid cells to the children"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 24,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Help on function mappingfileToRaster in module ogh_xarray_landlab:\n",
+      "\n",
+      "mappingfileToRaster(mappingfile, maxx, maxy, minx=0, miny=0, dx=100, dy=100, spatial_resolution=0.0625, mappingfile_crs={'init': 'epsg:4326'}, raster_crs={'init': 'epsg:3857'})\n",
+      "\n"
+     ]
+    }
+   ],
+   "source": [
+    "help(oxl.mappingfileToRaster)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 25,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Number of gridded data files:99\n",
+      "Minimum elevation: 164.0m\n",
+      "Mean elevation: 1151.040404040404m\n",
+      "Maximum elevation: 2216.0m\n",
+      "Warning: CRS does not match!\n",
+      "CPU times: user 18.7 s, sys: 91.7 ms, total: 18.8 s\n",
+      "Wall time: 18.7 s\n"
+     ]
+    },
+    {
+     "data": {
+      "text/plain": [
+       "<Figure size 5000x5000 with 0 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "%%time\n",
+    "\n",
+    "# landlab raster node crossmap to gridded cell id\n",
+    "nodeXmap, raster, m = oxl.mappingfileToRaster(\n",
+    "    mappingfile=mappingfile1,\n",
+    "    spatial_resolution=0.06250,\n",
+    "    minx=minx2,\n",
+    "    miny=miny2,\n",
+    "    maxx=maxx2,\n",
+    "    maxy=maxy2,\n",
+    "    dx=1000,\n",
+    "    dy=1000,\n",
+    ")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 26,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "(78, 68)"
+      ]
+     },
+     "execution_count": 26,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "# print the raster dimensions\n",
+    "raster.shape"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 27,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "CPU times: user 2.5 s, sys: 228 ms, total: 2.73 s\n",
+      "Wall time: 2.44 s\n"
+     ]
+    },
+    {
+     "data": {
+      "text/plain": [
+       "<matplotlib.axes._subplots.AxesSubplot at 0x7f78a5cd4358>"
+      ]
+     },
+     "execution_count": 27,
+     "metadata": {},
+     "output_type": "execute_result"
+    },
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 720x720 with 2 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "%%time\n",
+    "nodeXmap.plot(column=\"ELEV\", figsize=(10, 10), legend=True)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 28,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 720x720 with 2 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "# generate vector array of December monthly precipitation\n",
+    "prec_vector = ogh.rasterVector(\n",
+    "    vardf=ltm[\"meanbymonthsum_Prec_sp_dailymet_livneh_1970_1970\"],\n",
+    "    vardf_dateindex=12,\n",
+    "    crossmap=nodeXmap,\n",
+    "    nodata=-9999,\n",
+    ")\n",
+    "\n",
+    "# close-off areas without data\n",
+    "raster.status_at_node[prec_vector == -9999] = CLOSED_BOUNDARY\n",
+    "\n",
+    "fig = plt.figure(figsize=(10, 10))\n",
+    "imshow_grid(\n",
+    "    raster,\n",
+    "    prec_vector,\n",
+    "    var_name=\"Daily precipitation\",\n",
+    "    var_units=meta_file[\"sp_dailymet_livneh_1970_1970\"][\"variable_info\"][\"Prec\"].attrs[\n",
+    "        \"units\"\n",
+    "    ],\n",
+    "    color_for_closed=\"black\",\n",
+    "    cmap=\"seismic_r\",\n",
+    ")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 29,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 720x720 with 2 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "tmax_vector = ogh.rasterVector(\n",
+    "    vardf=ltm[\"meanbymonth_Tmax_sp_dailymet_livneh_1970_1970\"],\n",
+    "    vardf_dateindex=12,\n",
+    "    crossmap=nodeXmap,\n",
+    "    nodata=-9999,\n",
+    ")\n",
+    "\n",
+    "fig = plt.figure(figsize=(10, 10))\n",
+    "imshow_grid(\n",
+    "    raster,\n",
+    "    tmax_vector,\n",
+    "    var_name=\"Daily maximum temperature\",\n",
+    "    var_units=meta_file[\"sp_dailymet_livneh_1970_1970\"][\"variable_info\"][\"Tmax\"].attrs[\n",
+    "        \"units\"\n",
+    "    ],\n",
+    "    color_for_closed=\"black\",\n",
+    "    symmetric_cbar=False,\n",
+    "    cmap=\"magma\",\n",
+    ")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 30,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 720x720 with 2 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "tmin_vector = ogh.rasterVector(\n",
+    "    vardf=ltm[\"meanbymonth_Tmin_sp_dailymet_livneh_1970_1970\"],\n",
+    "    vardf_dateindex=12,\n",
+    "    crossmap=nodeXmap,\n",
+    "    nodata=-9999,\n",
+    ")\n",
+    "\n",
+    "fig = plt.figure(figsize=(10, 10))\n",
+    "imshow_grid(\n",
+    "    raster,\n",
+    "    tmin_vector,\n",
+    "    var_name=\"Daily minimum temperature\",\n",
+    "    var_units=meta_file[\"sp_dailymet_livneh_1970_1970\"][\"variable_info\"][\"Tmin\"].attrs[\n",
+    "        \"units\"\n",
+    "    ],\n",
+    "    color_for_closed=\"black\",\n",
+    "    symmetric_cbar=False,\n",
+    "    cmap=\"magma\",\n",
+    ")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 33,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>nodeid</th>\n",
+       "      <th>value</th>\n",
+       "      <th>raster_geom</th>\n",
+       "      <th>index_right</th>\n",
+       "      <th>FID</th>\n",
+       "      <th>LAT</th>\n",
+       "      <th>LONG_</th>\n",
+       "      <th>ELEV</th>\n",
+       "      <th>sp_dailymet_livneh_1970_1970</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>0</th>\n",
+       "      <td>24</td>\n",
+       "      <td>426.699999</td>\n",
+       "      <td>POLYGON ((24115 -87, 24115 913, 23115 913, 231...</td>\n",
+       "      <td>98.0</td>\n",
+       "      <td>98.0</td>\n",
+       "      <td>47.90625</td>\n",
+       "      <td>-121.40625</td>\n",
+       "      <td>866.0</td>\n",
+       "      <td>/home/jovyan/work/notebooks/data/70b977e22af54...</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1</th>\n",
+       "      <td>25</td>\n",
+       "      <td>426.699999</td>\n",
+       "      <td>POLYGON ((25115 -87, 25115 913, 24115 913, 241...</td>\n",
+       "      <td>98.0</td>\n",
+       "      <td>98.0</td>\n",
+       "      <td>47.90625</td>\n",
+       "      <td>-121.40625</td>\n",
+       "      <td>866.0</td>\n",
+       "      <td>/home/jovyan/work/notebooks/data/70b977e22af54...</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2</th>\n",
+       "      <td>26</td>\n",
+       "      <td>426.699999</td>\n",
+       "      <td>POLYGON ((26115 -87, 26115 913, 25115 913, 251...</td>\n",
+       "      <td>98.0</td>\n",
+       "      <td>98.0</td>\n",
+       "      <td>47.90625</td>\n",
+       "      <td>-121.40625</td>\n",
+       "      <td>866.0</td>\n",
+       "      <td>/home/jovyan/work/notebooks/data/70b977e22af54...</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3</th>\n",
+       "      <td>27</td>\n",
+       "      <td>426.699999</td>\n",
+       "      <td>POLYGON ((27115 -87, 27115 913, 26115 913, 261...</td>\n",
+       "      <td>98.0</td>\n",
+       "      <td>98.0</td>\n",
+       "      <td>47.90625</td>\n",
+       "      <td>-121.40625</td>\n",
+       "      <td>866.0</td>\n",
+       "      <td>/home/jovyan/work/notebooks/data/70b977e22af54...</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>4</th>\n",
+       "      <td>28</td>\n",
+       "      <td>426.699999</td>\n",
+       "      <td>POLYGON ((28115 -87, 28115 913, 27115 913, 271...</td>\n",
+       "      <td>98.0</td>\n",
+       "      <td>98.0</td>\n",
+       "      <td>47.90625</td>\n",
+       "      <td>-121.40625</td>\n",
+       "      <td>866.0</td>\n",
+       "      <td>/home/jovyan/work/notebooks/data/70b977e22af54...</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>5</th>\n",
+       "      <td>29</td>\n",
+       "      <td>377.899997</td>\n",
+       "      <td>POLYGON ((29115 -87, 29115 913, 28115 913, 281...</td>\n",
+       "      <td>97.0</td>\n",
+       "      <td>97.0</td>\n",
+       "      <td>47.90625</td>\n",
+       "      <td>-121.34375</td>\n",
+       "      <td>934.0</td>\n",
+       "      <td>/home/jovyan/work/notebooks/data/70b977e22af54...</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>6</th>\n",
+       "      <td>30</td>\n",
+       "      <td>377.899997</td>\n",
+       "      <td>POLYGON ((30115 -87, 30115 913, 29115 913, 291...</td>\n",
+       "      <td>97.0</td>\n",
+       "      <td>97.0</td>\n",
+       "      <td>47.90625</td>\n",
+       "      <td>-121.34375</td>\n",
+       "      <td>934.0</td>\n",
+       "      <td>/home/jovyan/work/notebooks/data/70b977e22af54...</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>7</th>\n",
+       "      <td>31</td>\n",
+       "      <td>377.899997</td>\n",
+       "      <td>POLYGON ((31115 -87, 31115 913, 30115 913, 301...</td>\n",
+       "      <td>97.0</td>\n",
+       "      <td>97.0</td>\n",
+       "      <td>47.90625</td>\n",
+       "      <td>-121.34375</td>\n",
+       "      <td>934.0</td>\n",
+       "      <td>/home/jovyan/work/notebooks/data/70b977e22af54...</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>8</th>\n",
+       "      <td>32</td>\n",
+       "      <td>377.899997</td>\n",
+       "      <td>POLYGON ((32115 -87, 32115 913, 31115 913, 311...</td>\n",
+       "      <td>97.0</td>\n",
+       "      <td>97.0</td>\n",
+       "      <td>47.90625</td>\n",
+       "      <td>-121.34375</td>\n",
+       "      <td>934.0</td>\n",
+       "      <td>/home/jovyan/work/notebooks/data/70b977e22af54...</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>9</th>\n",
+       "      <td>33</td>\n",
+       "      <td>377.899997</td>\n",
+       "      <td>POLYGON ((33115 -87, 33115 913, 32115 913, 321...</td>\n",
+       "      <td>97.0</td>\n",
+       "      <td>97.0</td>\n",
+       "      <td>47.90625</td>\n",
+       "      <td>-121.34375</td>\n",
+       "      <td>934.0</td>\n",
+       "      <td>/home/jovyan/work/notebooks/data/70b977e22af54...</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>10</th>\n",
+       "      <td>34</td>\n",
+       "      <td>368.675006</td>\n",
+       "      <td>POLYGON ((34115 -87, 34115 913, 33115 913, 331...</td>\n",
+       "      <td>96.0</td>\n",
+       "      <td>96.0</td>\n",
+       "      <td>47.90625</td>\n",
+       "      <td>-121.28125</td>\n",
+       "      <td>1061.0</td>\n",
+       "      <td>/home/jovyan/work/notebooks/data/70b977e22af54...</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>11</th>\n",
+       "      <td>35</td>\n",
+       "      <td>368.675006</td>\n",
+       "      <td>POLYGON ((35115 -87, 35115 913, 34115 913, 341...</td>\n",
+       "      <td>96.0</td>\n",
+       "      <td>96.0</td>\n",
+       "      <td>47.90625</td>\n",
+       "      <td>-121.28125</td>\n",
+       "      <td>1061.0</td>\n",
+       "      <td>/home/jovyan/work/notebooks/data/70b977e22af54...</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>12</th>\n",
+       "      <td>36</td>\n",
+       "      <td>368.675006</td>\n",
+       "      <td>POLYGON ((36115 -87, 36115 913, 35115 913, 351...</td>\n",
+       "      <td>96.0</td>\n",
+       "      <td>96.0</td>\n",
+       "      <td>47.90625</td>\n",
+       "      <td>-121.28125</td>\n",
+       "      <td>1061.0</td>\n",
+       "      <td>/home/jovyan/work/notebooks/data/70b977e22af54...</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>13</th>\n",
+       "      <td>37</td>\n",
+       "      <td>368.675006</td>\n",
+       "      <td>POLYGON ((37115 -87, 37115 913, 36115 913, 361...</td>\n",
+       "      <td>96.0</td>\n",
+       "      <td>96.0</td>\n",
+       "      <td>47.90625</td>\n",
+       "      <td>-121.28125</td>\n",
+       "      <td>1061.0</td>\n",
+       "      <td>/home/jovyan/work/notebooks/data/70b977e22af54...</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>14</th>\n",
+       "      <td>92</td>\n",
+       "      <td>426.699999</td>\n",
+       "      <td>POLYGON ((24115 913, 24115 1913, 23115 1913, 2...</td>\n",
+       "      <td>98.0</td>\n",
+       "      <td>98.0</td>\n",
+       "      <td>47.90625</td>\n",
+       "      <td>-121.40625</td>\n",
+       "      <td>866.0</td>\n",
+       "      <td>/home/jovyan/work/notebooks/data/70b977e22af54...</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>15</th>\n",
+       "      <td>93</td>\n",
+       "      <td>426.699999</td>\n",
+       "      <td>POLYGON ((25115 913, 25115 1913, 24115 1913, 2...</td>\n",
+       "      <td>98.0</td>\n",
+       "      <td>98.0</td>\n",
+       "      <td>47.90625</td>\n",
+       "      <td>-121.40625</td>\n",
+       "      <td>866.0</td>\n",
+       "      <td>/home/jovyan/work/notebooks/data/70b977e22af54...</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>16</th>\n",
+       "      <td>94</td>\n",
+       "      <td>426.699999</td>\n",
+       "      <td>POLYGON ((26115 913, 26115 1913, 25115 1913, 2...</td>\n",
+       "      <td>98.0</td>\n",
+       "      <td>98.0</td>\n",
+       "      <td>47.90625</td>\n",
+       "      <td>-121.40625</td>\n",
+       "      <td>866.0</td>\n",
+       "      <td>/home/jovyan/work/notebooks/data/70b977e22af54...</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>17</th>\n",
+       "      <td>95</td>\n",
+       "      <td>426.699999</td>\n",
+       "      <td>POLYGON ((27115 913, 27115 1913, 26115 1913, 2...</td>\n",
+       "      <td>98.0</td>\n",
+       "      <td>98.0</td>\n",
+       "      <td>47.90625</td>\n",
+       "      <td>-121.40625</td>\n",
+       "      <td>866.0</td>\n",
+       "      <td>/home/jovyan/work/notebooks/data/70b977e22af54...</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>18</th>\n",
+       "      <td>96</td>\n",
+       "      <td>426.699999</td>\n",
+       "      <td>POLYGON ((28115 913, 28115 1913, 27115 1913, 2...</td>\n",
+       "      <td>98.0</td>\n",
+       "      <td>98.0</td>\n",
+       "      <td>47.90625</td>\n",
+       "      <td>-121.40625</td>\n",
+       "      <td>866.0</td>\n",
+       "      <td>/home/jovyan/work/notebooks/data/70b977e22af54...</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>19</th>\n",
+       "      <td>97</td>\n",
+       "      <td>377.899997</td>\n",
+       "      <td>POLYGON ((29115 913, 29115 1913, 28115 1913, 2...</td>\n",
+       "      <td>97.0</td>\n",
+       "      <td>97.0</td>\n",
+       "      <td>47.90625</td>\n",
+       "      <td>-121.34375</td>\n",
+       "      <td>934.0</td>\n",
+       "      <td>/home/jovyan/work/notebooks/data/70b977e22af54...</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>20</th>\n",
+       "      <td>98</td>\n",
+       "      <td>377.899997</td>\n",
+       "      <td>POLYGON ((30115 913, 30115 1913, 29115 1913, 2...</td>\n",
+       "      <td>97.0</td>\n",
+       "      <td>97.0</td>\n",
+       "      <td>47.90625</td>\n",
+       "      <td>-121.34375</td>\n",
+       "      <td>934.0</td>\n",
+       "      <td>/home/jovyan/work/notebooks/data/70b977e22af54...</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>21</th>\n",
+       "      <td>99</td>\n",
+       "      <td>377.899997</td>\n",
+       "      <td>POLYGON ((31115 913, 31115 1913, 30115 1913, 3...</td>\n",
+       "      <td>97.0</td>\n",
+       "      <td>97.0</td>\n",
+       "      <td>47.90625</td>\n",
+       "      <td>-121.34375</td>\n",
+       "      <td>934.0</td>\n",
+       "      <td>/home/jovyan/work/notebooks/data/70b977e22af54...</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>22</th>\n",
+       "      <td>100</td>\n",
+       "      <td>377.899997</td>\n",
+       "      <td>POLYGON ((32115 913, 32115 1913, 31115 1913, 3...</td>\n",
+       "      <td>97.0</td>\n",
+       "      <td>97.0</td>\n",
+       "      <td>47.90625</td>\n",
+       "      <td>-121.34375</td>\n",
+       "      <td>934.0</td>\n",
+       "      <td>/home/jovyan/work/notebooks/data/70b977e22af54...</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>23</th>\n",
+       "      <td>101</td>\n",
+       "      <td>377.899997</td>\n",
+       "      <td>POLYGON ((33115 913, 33115 1913, 32115 1913, 3...</td>\n",
+       "      <td>97.0</td>\n",
+       "      <td>97.0</td>\n",
+       "      <td>47.90625</td>\n",
+       "      <td>-121.34375</td>\n",
+       "      <td>934.0</td>\n",
+       "      <td>/home/jovyan/work/notebooks/data/70b977e22af54...</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>24</th>\n",
+       "      <td>102</td>\n",
+       "      <td>368.675006</td>\n",
+       "      <td>POLYGON ((34115 913, 34115 1913, 33115 1913, 3...</td>\n",
+       "      <td>96.0</td>\n",
+       "      <td>96.0</td>\n",
+       "      <td>47.90625</td>\n",
+       "      <td>-121.28125</td>\n",
+       "      <td>1061.0</td>\n",
+       "      <td>/home/jovyan/work/notebooks/data/70b977e22af54...</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>25</th>\n",
+       "      <td>103</td>\n",
+       "      <td>368.675006</td>\n",
+       "      <td>POLYGON ((35115 913, 35115 1913, 34115 1913, 3...</td>\n",
+       "      <td>96.0</td>\n",
+       "      <td>96.0</td>\n",
+       "      <td>47.90625</td>\n",
+       "      <td>-121.28125</td>\n",
+       "      <td>1061.0</td>\n",
+       "      <td>/home/jovyan/work/notebooks/data/70b977e22af54...</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>26</th>\n",
+       "      <td>104</td>\n",
+       "      <td>368.675006</td>\n",
+       "      <td>POLYGON ((36115 913, 36115 1913, 35115 1913, 3...</td>\n",
+       "      <td>96.0</td>\n",
+       "      <td>96.0</td>\n",
+       "      <td>47.90625</td>\n",
+       "      <td>-121.28125</td>\n",
+       "      <td>1061.0</td>\n",
+       "      <td>/home/jovyan/work/notebooks/data/70b977e22af54...</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>27</th>\n",
+       "      <td>105</td>\n",
+       "      <td>368.675006</td>\n",
+       "      <td>POLYGON ((37115 913, 37115 1913, 36115 1913, 3...</td>\n",
+       "      <td>96.0</td>\n",
+       "      <td>96.0</td>\n",
+       "      <td>47.90625</td>\n",
+       "      <td>-121.28125</td>\n",
+       "      <td>1061.0</td>\n",
+       "      <td>/home/jovyan/work/notebooks/data/70b977e22af54...</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>28</th>\n",
+       "      <td>160</td>\n",
+       "      <td>426.699999</td>\n",
+       "      <td>POLYGON ((24115 1913, 24115 2913, 23115 2913, ...</td>\n",
+       "      <td>98.0</td>\n",
+       "      <td>98.0</td>\n",
+       "      <td>47.90625</td>\n",
+       "      <td>-121.40625</td>\n",
+       "      <td>866.0</td>\n",
+       "      <td>/home/jovyan/work/notebooks/data/70b977e22af54...</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>29</th>\n",
+       "      <td>161</td>\n",
+       "      <td>426.699999</td>\n",
+       "      <td>POLYGON ((25115 1913, 25115 2913, 24115 2913, ...</td>\n",
+       "      <td>98.0</td>\n",
+       "      <td>98.0</td>\n",
+       "      <td>47.90625</td>\n",
+       "      <td>-121.40625</td>\n",
+       "      <td>866.0</td>\n",
+       "      <td>/home/jovyan/work/notebooks/data/70b977e22af54...</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>...</th>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3172</th>\n",
+       "      <td>4710</td>\n",
+       "      <td>308.424996</td>\n",
+       "      <td>POLYGON ((18115 68913, 18115 69913, 17115 6991...</td>\n",
+       "      <td>2.0</td>\n",
+       "      <td>2.0</td>\n",
+       "      <td>48.46875</td>\n",
+       "      <td>-121.53125</td>\n",
+       "      <td>321.0</td>\n",
+       "      <td>/home/jovyan/work/notebooks/data/70b977e22af54...</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3173</th>\n",
+       "      <td>4711</td>\n",
+       "      <td>308.424996</td>\n",
+       "      <td>POLYGON ((19115 68913, 19115 69913, 18115 6991...</td>\n",
+       "      <td>2.0</td>\n",
+       "      <td>2.0</td>\n",
+       "      <td>48.46875</td>\n",
+       "      <td>-121.53125</td>\n",
+       "      <td>321.0</td>\n",
+       "      <td>/home/jovyan/work/notebooks/data/70b977e22af54...</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3174</th>\n",
+       "      <td>4712</td>\n",
+       "      <td>329.999997</td>\n",
+       "      <td>POLYGON ((20115 68913, 20115 69913, 19115 6991...</td>\n",
+       "      <td>1.0</td>\n",
+       "      <td>1.0</td>\n",
+       "      <td>48.46875</td>\n",
+       "      <td>-121.46875</td>\n",
+       "      <td>646.0</td>\n",
+       "      <td>/home/jovyan/work/notebooks/data/70b977e22af54...</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3175</th>\n",
+       "      <td>4713</td>\n",
+       "      <td>329.999997</td>\n",
+       "      <td>POLYGON ((21115 68913, 21115 69913, 20115 6991...</td>\n",
+       "      <td>1.0</td>\n",
+       "      <td>1.0</td>\n",
+       "      <td>48.46875</td>\n",
+       "      <td>-121.46875</td>\n",
+       "      <td>646.0</td>\n",
+       "      <td>/home/jovyan/work/notebooks/data/70b977e22af54...</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3176</th>\n",
+       "      <td>4714</td>\n",
+       "      <td>329.999997</td>\n",
+       "      <td>POLYGON ((22115 68913, 22115 69913, 21115 6991...</td>\n",
+       "      <td>1.0</td>\n",
+       "      <td>1.0</td>\n",
+       "      <td>48.46875</td>\n",
+       "      <td>-121.46875</td>\n",
+       "      <td>646.0</td>\n",
+       "      <td>/home/jovyan/work/notebooks/data/70b977e22af54...</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3177</th>\n",
+       "      <td>4715</td>\n",
+       "      <td>329.999997</td>\n",
+       "      <td>POLYGON ((23115 68913, 23115 69913, 22115 6991...</td>\n",
+       "      <td>1.0</td>\n",
+       "      <td>1.0</td>\n",
+       "      <td>48.46875</td>\n",
+       "      <td>-121.46875</td>\n",
+       "      <td>646.0</td>\n",
+       "      <td>/home/jovyan/work/notebooks/data/70b977e22af54...</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3178</th>\n",
+       "      <td>4771</td>\n",
+       "      <td>348.925004</td>\n",
+       "      <td>POLYGON ((11115 69913, 11115 70913, 10115 7091...</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>48.53125</td>\n",
+       "      <td>-121.59375</td>\n",
+       "      <td>1113.0</td>\n",
+       "      <td>/home/jovyan/work/notebooks/data/70b977e22af54...</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3179</th>\n",
+       "      <td>4772</td>\n",
+       "      <td>348.925004</td>\n",
+       "      <td>POLYGON ((12115 69913, 12115 70913, 11115 7091...</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>48.53125</td>\n",
+       "      <td>-121.59375</td>\n",
+       "      <td>1113.0</td>\n",
+       "      <td>/home/jovyan/work/notebooks/data/70b977e22af54...</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3180</th>\n",
+       "      <td>4773</td>\n",
+       "      <td>348.925004</td>\n",
+       "      <td>POLYGON ((13115 69913, 13115 70913, 12115 7091...</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>48.53125</td>\n",
+       "      <td>-121.59375</td>\n",
+       "      <td>1113.0</td>\n",
+       "      <td>/home/jovyan/work/notebooks/data/70b977e22af54...</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3181</th>\n",
+       "      <td>4774</td>\n",
+       "      <td>348.925004</td>\n",
+       "      <td>POLYGON ((14115 69913, 14115 70913, 13115 7091...</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>48.53125</td>\n",
+       "      <td>-121.59375</td>\n",
+       "      <td>1113.0</td>\n",
+       "      <td>/home/jovyan/work/notebooks/data/70b977e22af54...</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3182</th>\n",
+       "      <td>4839</td>\n",
+       "      <td>348.925004</td>\n",
+       "      <td>POLYGON ((11115 70913, 11115 71913, 10115 7191...</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>48.53125</td>\n",
+       "      <td>-121.59375</td>\n",
+       "      <td>1113.0</td>\n",
+       "      <td>/home/jovyan/work/notebooks/data/70b977e22af54...</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3183</th>\n",
+       "      <td>4840</td>\n",
+       "      <td>348.925004</td>\n",
+       "      <td>POLYGON ((12115 70913, 12115 71913, 11115 7191...</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>48.53125</td>\n",
+       "      <td>-121.59375</td>\n",
+       "      <td>1113.0</td>\n",
+       "      <td>/home/jovyan/work/notebooks/data/70b977e22af54...</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3184</th>\n",
+       "      <td>4841</td>\n",
+       "      <td>348.925004</td>\n",
+       "      <td>POLYGON ((13115 70913, 13115 71913, 12115 7191...</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>48.53125</td>\n",
+       "      <td>-121.59375</td>\n",
+       "      <td>1113.0</td>\n",
+       "      <td>/home/jovyan/work/notebooks/data/70b977e22af54...</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3185</th>\n",
+       "      <td>4842</td>\n",
+       "      <td>348.925004</td>\n",
+       "      <td>POLYGON ((14115 70913, 14115 71913, 13115 7191...</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>48.53125</td>\n",
+       "      <td>-121.59375</td>\n",
+       "      <td>1113.0</td>\n",
+       "      <td>/home/jovyan/work/notebooks/data/70b977e22af54...</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3186</th>\n",
+       "      <td>4907</td>\n",
+       "      <td>348.925004</td>\n",
+       "      <td>POLYGON ((11115 71913, 11115 72913, 10115 7291...</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>48.53125</td>\n",
+       "      <td>-121.59375</td>\n",
+       "      <td>1113.0</td>\n",
+       "      <td>/home/jovyan/work/notebooks/data/70b977e22af54...</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3187</th>\n",
+       "      <td>4908</td>\n",
+       "      <td>348.925004</td>\n",
+       "      <td>POLYGON ((12115 71913, 12115 72913, 11115 7291...</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>48.53125</td>\n",
+       "      <td>-121.59375</td>\n",
+       "      <td>1113.0</td>\n",
+       "      <td>/home/jovyan/work/notebooks/data/70b977e22af54...</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3188</th>\n",
+       "      <td>4909</td>\n",
+       "      <td>348.925004</td>\n",
+       "      <td>POLYGON ((13115 71913, 13115 72913, 12115 7291...</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>48.53125</td>\n",
+       "      <td>-121.59375</td>\n",
+       "      <td>1113.0</td>\n",
+       "      <td>/home/jovyan/work/notebooks/data/70b977e22af54...</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3189</th>\n",
+       "      <td>4910</td>\n",
+       "      <td>348.925004</td>\n",
+       "      <td>POLYGON ((14115 71913, 14115 72913, 13115 7291...</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>48.53125</td>\n",
+       "      <td>-121.59375</td>\n",
+       "      <td>1113.0</td>\n",
+       "      <td>/home/jovyan/work/notebooks/data/70b977e22af54...</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3190</th>\n",
+       "      <td>4975</td>\n",
+       "      <td>348.925004</td>\n",
+       "      <td>POLYGON ((11115 72913, 11115 73913, 10115 7391...</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>48.53125</td>\n",
+       "      <td>-121.59375</td>\n",
+       "      <td>1113.0</td>\n",
+       "      <td>/home/jovyan/work/notebooks/data/70b977e22af54...</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3191</th>\n",
+       "      <td>4976</td>\n",
+       "      <td>348.925004</td>\n",
+       "      <td>POLYGON ((12115 72913, 12115 73913, 11115 7391...</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>48.53125</td>\n",
+       "      <td>-121.59375</td>\n",
+       "      <td>1113.0</td>\n",
+       "      <td>/home/jovyan/work/notebooks/data/70b977e22af54...</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3192</th>\n",
+       "      <td>4977</td>\n",
+       "      <td>348.925004</td>\n",
+       "      <td>POLYGON ((13115 72913, 13115 73913, 12115 7391...</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>48.53125</td>\n",
+       "      <td>-121.59375</td>\n",
+       "      <td>1113.0</td>\n",
+       "      <td>/home/jovyan/work/notebooks/data/70b977e22af54...</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3193</th>\n",
+       "      <td>4978</td>\n",
+       "      <td>348.925004</td>\n",
+       "      <td>POLYGON ((14115 72913, 14115 73913, 13115 7391...</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>48.53125</td>\n",
+       "      <td>-121.59375</td>\n",
+       "      <td>1113.0</td>\n",
+       "      <td>/home/jovyan/work/notebooks/data/70b977e22af54...</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3194</th>\n",
+       "      <td>5043</td>\n",
+       "      <td>348.925004</td>\n",
+       "      <td>POLYGON ((11115 73913, 11115 74913, 10115 7491...</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>48.53125</td>\n",
+       "      <td>-121.59375</td>\n",
+       "      <td>1113.0</td>\n",
+       "      <td>/home/jovyan/work/notebooks/data/70b977e22af54...</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3195</th>\n",
+       "      <td>5044</td>\n",
+       "      <td>348.925004</td>\n",
+       "      <td>POLYGON ((12115 73913, 12115 74913, 11115 7491...</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>48.53125</td>\n",
+       "      <td>-121.59375</td>\n",
+       "      <td>1113.0</td>\n",
+       "      <td>/home/jovyan/work/notebooks/data/70b977e22af54...</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3196</th>\n",
+       "      <td>5045</td>\n",
+       "      <td>348.925004</td>\n",
+       "      <td>POLYGON ((13115 73913, 13115 74913, 12115 7491...</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>48.53125</td>\n",
+       "      <td>-121.59375</td>\n",
+       "      <td>1113.0</td>\n",
+       "      <td>/home/jovyan/work/notebooks/data/70b977e22af54...</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3197</th>\n",
+       "      <td>5046</td>\n",
+       "      <td>348.925004</td>\n",
+       "      <td>POLYGON ((14115 73913, 14115 74913, 13115 7491...</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>48.53125</td>\n",
+       "      <td>-121.59375</td>\n",
+       "      <td>1113.0</td>\n",
+       "      <td>/home/jovyan/work/notebooks/data/70b977e22af54...</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3198</th>\n",
+       "      <td>5111</td>\n",
+       "      <td>348.925004</td>\n",
+       "      <td>POLYGON ((11115 74913, 11115 75913, 10115 7591...</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>48.53125</td>\n",
+       "      <td>-121.59375</td>\n",
+       "      <td>1113.0</td>\n",
+       "      <td>/home/jovyan/work/notebooks/data/70b977e22af54...</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3199</th>\n",
+       "      <td>5112</td>\n",
+       "      <td>348.925004</td>\n",
+       "      <td>POLYGON ((12115 74913, 12115 75913, 11115 7591...</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>48.53125</td>\n",
+       "      <td>-121.59375</td>\n",
+       "      <td>1113.0</td>\n",
+       "      <td>/home/jovyan/work/notebooks/data/70b977e22af54...</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3200</th>\n",
+       "      <td>5113</td>\n",
+       "      <td>348.925004</td>\n",
+       "      <td>POLYGON ((13115 74913, 13115 75913, 12115 7591...</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>48.53125</td>\n",
+       "      <td>-121.59375</td>\n",
+       "      <td>1113.0</td>\n",
+       "      <td>/home/jovyan/work/notebooks/data/70b977e22af54...</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3201</th>\n",
+       "      <td>5114</td>\n",
+       "      <td>348.925004</td>\n",
+       "      <td>POLYGON ((14115 74913, 14115 75913, 13115 7591...</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>48.53125</td>\n",
+       "      <td>-121.59375</td>\n",
+       "      <td>1113.0</td>\n",
+       "      <td>/home/jovyan/work/notebooks/data/70b977e22af54...</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "<p>3202 rows × 9 columns</p>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "      nodeid       value                                        raster_geom  \\\n",
+       "0         24  426.699999  POLYGON ((24115 -87, 24115 913, 23115 913, 231...   \n",
+       "1         25  426.699999  POLYGON ((25115 -87, 25115 913, 24115 913, 241...   \n",
+       "2         26  426.699999  POLYGON ((26115 -87, 26115 913, 25115 913, 251...   \n",
+       "3         27  426.699999  POLYGON ((27115 -87, 27115 913, 26115 913, 261...   \n",
+       "4         28  426.699999  POLYGON ((28115 -87, 28115 913, 27115 913, 271...   \n",
+       "5         29  377.899997  POLYGON ((29115 -87, 29115 913, 28115 913, 281...   \n",
+       "6         30  377.899997  POLYGON ((30115 -87, 30115 913, 29115 913, 291...   \n",
+       "7         31  377.899997  POLYGON ((31115 -87, 31115 913, 30115 913, 301...   \n",
+       "8         32  377.899997  POLYGON ((32115 -87, 32115 913, 31115 913, 311...   \n",
+       "9         33  377.899997  POLYGON ((33115 -87, 33115 913, 32115 913, 321...   \n",
+       "10        34  368.675006  POLYGON ((34115 -87, 34115 913, 33115 913, 331...   \n",
+       "11        35  368.675006  POLYGON ((35115 -87, 35115 913, 34115 913, 341...   \n",
+       "12        36  368.675006  POLYGON ((36115 -87, 36115 913, 35115 913, 351...   \n",
+       "13        37  368.675006  POLYGON ((37115 -87, 37115 913, 36115 913, 361...   \n",
+       "14        92  426.699999  POLYGON ((24115 913, 24115 1913, 23115 1913, 2...   \n",
+       "15        93  426.699999  POLYGON ((25115 913, 25115 1913, 24115 1913, 2...   \n",
+       "16        94  426.699999  POLYGON ((26115 913, 26115 1913, 25115 1913, 2...   \n",
+       "17        95  426.699999  POLYGON ((27115 913, 27115 1913, 26115 1913, 2...   \n",
+       "18        96  426.699999  POLYGON ((28115 913, 28115 1913, 27115 1913, 2...   \n",
+       "19        97  377.899997  POLYGON ((29115 913, 29115 1913, 28115 1913, 2...   \n",
+       "20        98  377.899997  POLYGON ((30115 913, 30115 1913, 29115 1913, 2...   \n",
+       "21        99  377.899997  POLYGON ((31115 913, 31115 1913, 30115 1913, 3...   \n",
+       "22       100  377.899997  POLYGON ((32115 913, 32115 1913, 31115 1913, 3...   \n",
+       "23       101  377.899997  POLYGON ((33115 913, 33115 1913, 32115 1913, 3...   \n",
+       "24       102  368.675006  POLYGON ((34115 913, 34115 1913, 33115 1913, 3...   \n",
+       "25       103  368.675006  POLYGON ((35115 913, 35115 1913, 34115 1913, 3...   \n",
+       "26       104  368.675006  POLYGON ((36115 913, 36115 1913, 35115 1913, 3...   \n",
+       "27       105  368.675006  POLYGON ((37115 913, 37115 1913, 36115 1913, 3...   \n",
+       "28       160  426.699999  POLYGON ((24115 1913, 24115 2913, 23115 2913, ...   \n",
+       "29       161  426.699999  POLYGON ((25115 1913, 25115 2913, 24115 2913, ...   \n",
+       "...      ...         ...                                                ...   \n",
+       "3172    4710  308.424996  POLYGON ((18115 68913, 18115 69913, 17115 6991...   \n",
+       "3173    4711  308.424996  POLYGON ((19115 68913, 19115 69913, 18115 6991...   \n",
+       "3174    4712  329.999997  POLYGON ((20115 68913, 20115 69913, 19115 6991...   \n",
+       "3175    4713  329.999997  POLYGON ((21115 68913, 21115 69913, 20115 6991...   \n",
+       "3176    4714  329.999997  POLYGON ((22115 68913, 22115 69913, 21115 6991...   \n",
+       "3177    4715  329.999997  POLYGON ((23115 68913, 23115 69913, 22115 6991...   \n",
+       "3178    4771  348.925004  POLYGON ((11115 69913, 11115 70913, 10115 7091...   \n",
+       "3179    4772  348.925004  POLYGON ((12115 69913, 12115 70913, 11115 7091...   \n",
+       "3180    4773  348.925004  POLYGON ((13115 69913, 13115 70913, 12115 7091...   \n",
+       "3181    4774  348.925004  POLYGON ((14115 69913, 14115 70913, 13115 7091...   \n",
+       "3182    4839  348.925004  POLYGON ((11115 70913, 11115 71913, 10115 7191...   \n",
+       "3183    4840  348.925004  POLYGON ((12115 70913, 12115 71913, 11115 7191...   \n",
+       "3184    4841  348.925004  POLYGON ((13115 70913, 13115 71913, 12115 7191...   \n",
+       "3185    4842  348.925004  POLYGON ((14115 70913, 14115 71913, 13115 7191...   \n",
+       "3186    4907  348.925004  POLYGON ((11115 71913, 11115 72913, 10115 7291...   \n",
+       "3187    4908  348.925004  POLYGON ((12115 71913, 12115 72913, 11115 7291...   \n",
+       "3188    4909  348.925004  POLYGON ((13115 71913, 13115 72913, 12115 7291...   \n",
+       "3189    4910  348.925004  POLYGON ((14115 71913, 14115 72913, 13115 7291...   \n",
+       "3190    4975  348.925004  POLYGON ((11115 72913, 11115 73913, 10115 7391...   \n",
+       "3191    4976  348.925004  POLYGON ((12115 72913, 12115 73913, 11115 7391...   \n",
+       "3192    4977  348.925004  POLYGON ((13115 72913, 13115 73913, 12115 7391...   \n",
+       "3193    4978  348.925004  POLYGON ((14115 72913, 14115 73913, 13115 7391...   \n",
+       "3194    5043  348.925004  POLYGON ((11115 73913, 11115 74913, 10115 7491...   \n",
+       "3195    5044  348.925004  POLYGON ((12115 73913, 12115 74913, 11115 7491...   \n",
+       "3196    5045  348.925004  POLYGON ((13115 73913, 13115 74913, 12115 7491...   \n",
+       "3197    5046  348.925004  POLYGON ((14115 73913, 14115 74913, 13115 7491...   \n",
+       "3198    5111  348.925004  POLYGON ((11115 74913, 11115 75913, 10115 7591...   \n",
+       "3199    5112  348.925004  POLYGON ((12115 74913, 12115 75913, 11115 7591...   \n",
+       "3200    5113  348.925004  POLYGON ((13115 74913, 13115 75913, 12115 7591...   \n",
+       "3201    5114  348.925004  POLYGON ((14115 74913, 14115 75913, 13115 7591...   \n",
+       "\n",
+       "      index_right   FID       LAT      LONG_    ELEV  \\\n",
+       "0            98.0  98.0  47.90625 -121.40625   866.0   \n",
+       "1            98.0  98.0  47.90625 -121.40625   866.0   \n",
+       "2            98.0  98.0  47.90625 -121.40625   866.0   \n",
+       "3            98.0  98.0  47.90625 -121.40625   866.0   \n",
+       "4            98.0  98.0  47.90625 -121.40625   866.0   \n",
+       "5            97.0  97.0  47.90625 -121.34375   934.0   \n",
+       "6            97.0  97.0  47.90625 -121.34375   934.0   \n",
+       "7            97.0  97.0  47.90625 -121.34375   934.0   \n",
+       "8            97.0  97.0  47.90625 -121.34375   934.0   \n",
+       "9            97.0  97.0  47.90625 -121.34375   934.0   \n",
+       "10           96.0  96.0  47.90625 -121.28125  1061.0   \n",
+       "11           96.0  96.0  47.90625 -121.28125  1061.0   \n",
+       "12           96.0  96.0  47.90625 -121.28125  1061.0   \n",
+       "13           96.0  96.0  47.90625 -121.28125  1061.0   \n",
+       "14           98.0  98.0  47.90625 -121.40625   866.0   \n",
+       "15           98.0  98.0  47.90625 -121.40625   866.0   \n",
+       "16           98.0  98.0  47.90625 -121.40625   866.0   \n",
+       "17           98.0  98.0  47.90625 -121.40625   866.0   \n",
+       "18           98.0  98.0  47.90625 -121.40625   866.0   \n",
+       "19           97.0  97.0  47.90625 -121.34375   934.0   \n",
+       "20           97.0  97.0  47.90625 -121.34375   934.0   \n",
+       "21           97.0  97.0  47.90625 -121.34375   934.0   \n",
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+       "23           97.0  97.0  47.90625 -121.34375   934.0   \n",
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+       "26           96.0  96.0  47.90625 -121.28125  1061.0   \n",
+       "27           96.0  96.0  47.90625 -121.28125  1061.0   \n",
+       "28           98.0  98.0  47.90625 -121.40625   866.0   \n",
+       "29           98.0  98.0  47.90625 -121.40625   866.0   \n",
+       "...           ...   ...       ...        ...     ...   \n",
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+       "3175          1.0   1.0  48.46875 -121.46875   646.0   \n",
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+       "3177          1.0   1.0  48.46875 -121.46875   646.0   \n",
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+       "3201          0.0   0.0  48.53125 -121.59375  1113.0   \n",
+       "\n",
+       "                           sp_dailymet_livneh_1970_1970  \n",
+       "0     /home/jovyan/work/notebooks/data/70b977e22af54...  \n",
+       "1     /home/jovyan/work/notebooks/data/70b977e22af54...  \n",
+       "2     /home/jovyan/work/notebooks/data/70b977e22af54...  \n",
+       "3     /home/jovyan/work/notebooks/data/70b977e22af54...  \n",
+       "4     /home/jovyan/work/notebooks/data/70b977e22af54...  \n",
+       "5     /home/jovyan/work/notebooks/data/70b977e22af54...  \n",
+       "6     /home/jovyan/work/notebooks/data/70b977e22af54...  \n",
+       "7     /home/jovyan/work/notebooks/data/70b977e22af54...  \n",
+       "8     /home/jovyan/work/notebooks/data/70b977e22af54...  \n",
+       "9     /home/jovyan/work/notebooks/data/70b977e22af54...  \n",
+       "10    /home/jovyan/work/notebooks/data/70b977e22af54...  \n",
+       "11    /home/jovyan/work/notebooks/data/70b977e22af54...  \n",
+       "12    /home/jovyan/work/notebooks/data/70b977e22af54...  \n",
+       "13    /home/jovyan/work/notebooks/data/70b977e22af54...  \n",
+       "14    /home/jovyan/work/notebooks/data/70b977e22af54...  \n",
+       "15    /home/jovyan/work/notebooks/data/70b977e22af54...  \n",
+       "16    /home/jovyan/work/notebooks/data/70b977e22af54...  \n",
+       "17    /home/jovyan/work/notebooks/data/70b977e22af54...  \n",
+       "18    /home/jovyan/work/notebooks/data/70b977e22af54...  \n",
+       "19    /home/jovyan/work/notebooks/data/70b977e22af54...  \n",
+       "20    /home/jovyan/work/notebooks/data/70b977e22af54...  \n",
+       "21    /home/jovyan/work/notebooks/data/70b977e22af54...  \n",
+       "22    /home/jovyan/work/notebooks/data/70b977e22af54...  \n",
+       "23    /home/jovyan/work/notebooks/data/70b977e22af54...  \n",
+       "24    /home/jovyan/work/notebooks/data/70b977e22af54...  \n",
+       "25    /home/jovyan/work/notebooks/data/70b977e22af54...  \n",
+       "26    /home/jovyan/work/notebooks/data/70b977e22af54...  \n",
+       "27    /home/jovyan/work/notebooks/data/70b977e22af54...  \n",
+       "28    /home/jovyan/work/notebooks/data/70b977e22af54...  \n",
+       "29    /home/jovyan/work/notebooks/data/70b977e22af54...  \n",
+       "...                                                 ...  \n",
+       "3172  /home/jovyan/work/notebooks/data/70b977e22af54...  \n",
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+       "3179  /home/jovyan/work/notebooks/data/70b977e22af54...  \n",
+       "3180  /home/jovyan/work/notebooks/data/70b977e22af54...  \n",
+       "3181  /home/jovyan/work/notebooks/data/70b977e22af54...  \n",
+       "3182  /home/jovyan/work/notebooks/data/70b977e22af54...  \n",
+       "3183  /home/jovyan/work/notebooks/data/70b977e22af54...  \n",
+       "3184  /home/jovyan/work/notebooks/data/70b977e22af54...  \n",
+       "3185  /home/jovyan/work/notebooks/data/70b977e22af54...  \n",
+       "3186  /home/jovyan/work/notebooks/data/70b977e22af54...  \n",
+       "3187  /home/jovyan/work/notebooks/data/70b977e22af54...  \n",
+       "3188  /home/jovyan/work/notebooks/data/70b977e22af54...  \n",
+       "3189  /home/jovyan/work/notebooks/data/70b977e22af54...  \n",
+       "3190  /home/jovyan/work/notebooks/data/70b977e22af54...  \n",
+       "3191  /home/jovyan/work/notebooks/data/70b977e22af54...  \n",
+       "3192  /home/jovyan/work/notebooks/data/70b977e22af54...  \n",
+       "3193  /home/jovyan/work/notebooks/data/70b977e22af54...  \n",
+       "3194  /home/jovyan/work/notebooks/data/70b977e22af54...  \n",
+       "3195  /home/jovyan/work/notebooks/data/70b977e22af54...  \n",
+       "3196  /home/jovyan/work/notebooks/data/70b977e22af54...  \n",
+       "3197  /home/jovyan/work/notebooks/data/70b977e22af54...  \n",
+       "3198  /home/jovyan/work/notebooks/data/70b977e22af54...  \n",
+       "3199  /home/jovyan/work/notebooks/data/70b977e22af54...  \n",
+       "3200  /home/jovyan/work/notebooks/data/70b977e22af54...  \n",
+       "3201  /home/jovyan/work/notebooks/data/70b977e22af54...  \n",
+       "\n",
+       "[3202 rows x 9 columns]"
+      ]
+     },
+     "execution_count": 33,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "# convert a raster vector back to geospatial presentation\n",
+    "t4, t5 = rasterVectorToWGS(prec_vector, nodeXmap=nodeXmap, UTM_transformer=m)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 35,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "<matplotlib.axes._subplots.AxesSubplot at 0x7f78a5aeb320>"
+      ]
+     },
+     "execution_count": 35,
+     "metadata": {},
+     "output_type": "execute_result"
+    },
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 720x720 with 2 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "t4.plot(column=\"value\", figsize=(10, 10), legend=True)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 39,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# this is one decade\n",
+    "inputvectors = {\n",
+    "    \"precip_met\": np.tile(ltm[\"meandaily_Prec_sp_dailymet_livneh_1970_1970\"], 15000),\n",
+    "    \"Tmax_met\": np.tile(ltm[\"meandaily_Tmax_sp_dailymet_livneh_1970_1970\"], 15000),\n",
+    "    \"Tmin_met\": np.tile(ltm[\"meandaily_Tmin_sp_dailymet_livneh_1970_1970\"], 15000),\n",
+    "}"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 40,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Elapsed time =  0  years\n",
+      "Elapsed time =  100  years\n",
+      "Elapsed time =  200  years\n",
+      "CPU times: user 1min 58s, sys: 3.94 s, total: 2min 2s\n",
+      "Wall time: 1min 58s\n"
+     ]
+    }
+   ],
+   "source": [
+    "%%time\n",
+    "(VegType_low, yrs_low, debug_low) = run_ecohydrology_model(\n",
+    "    raster,\n",
+    "    input_data=inputvectors,\n",
+    "    input_file=input_file,\n",
+    "    synthetic_storms=False,\n",
+    "    number_of_storms=100000,\n",
+    "    pet_method=\"PriestleyTaylor\",\n",
+    ")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 41,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Plotting cellular field of Plant Functional Type\n",
+      "Green - Grass; Red - Shrubs; Black - Trees; White - Bare\n"
+     ]
+    },
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 432x288 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 432x288 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 432x288 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "text/plain": [
+       "<Figure size 432x288 with 0 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "plot_results(raster, VegType_low, yrs_low, yr_step=yrs_low - 1)\n",
+    "plt.show()\n",
+    "plt.savefig(\"grid_low.png\")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### Visualize the \"average monthly total precipitation\""
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## 5. Save the results back into HydroShare\n",
+    "<a name=\"creation\"></a>\n",
+    "\n",
+    "Using the `hs_utils` library, the results of the Geoprocessing steps above can be saved back into HydroShare.  First, define all of the required metadata for resource creation, i.e. *title*, *abstract*, *keywords*, *content files*.  In addition, we must define the type of resource that will be created, in this case *genericresource*.  \n",
+    "\n",
+    "***Note:*** Make sure you save the notebook at this point, so that all notebook changes will be saved into the new HydroShare resource."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### Total files and image to migrate"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "len(files)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# for each file downloaded onto the server folder, move to a new HydroShare Generic Resource\n",
+    "title = \"Computed spatial-temporal summaries of two gridded data product data sets for Sauk-Suiattle\"\n",
+    "abstract = \"This resource contains the computed summaries for the Meteorology data from Livneh et al. 2013 and the WRF data from Salathe et al. 2014.\"\n",
+    "keywords = [\n",
+    "    \"Sauk-Suiattle\",\n",
+    "    \"Livneh 2013\",\n",
+    "    \"Salathe 2014\",\n",
+    "    \"climate\",\n",
+    "    \"hydromet\",\n",
+    "    \"watershed\",\n",
+    "    \"visualizations and summaries\",\n",
+    "]\n",
+    "rtype = \"genericresource\"\n",
+    "\n",
+    "# create the new resource\n",
+    "resource_id = hs.createHydroShareResource(\n",
+    "    abstract,\n",
+    "    title,\n",
+    "    keywords=keywords,\n",
+    "    resource_type=rtype,\n",
+    "    content_files=files,\n",
+    "    public=False,\n",
+    ")"
+   ]
+  }
+ ],
+ "metadata": {
+  "anaconda-cloud": {},
+  "kernelspec": {
+   "display_name": "Python 3",
+   "language": "python",
+   "name": "python3"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.6.5"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 2
+}
diff --git a/ogh_xarray_landlab/ecohyd_inputs.yaml b/ogh_xarray_landlab/ecohyd_inputs.yaml
new file mode 100755
index 0000000..6af50be
--- /dev/null
+++ b/ogh_xarray_landlab/ecohyd_inputs.yaml
@@ -0,0 +1,198 @@
+### All inputs for Vegetation Cellular Automaton Model built on The Landlab
+### can be given here.
+### 14Feb2015 - Sai Nudurupati & Erkan Istanbulluoglu
+### 15Jul2016 - Updated to comply with Landlab Version 1 naming conventions.
+
+### Vegetation Cellular Automaton Model Input File:
+
+n_short: 6600 # Number of storms for short simulation that plots hydrologic parameters
+n_long_DEM: 1320 # Number of storms for long simulation that operates on single grid for sloped surface
+n_long_flat: 660000 # Number of storms for long simulation that operates on two grids - flat surface
+
+## Initial Plant Functional Types (PFT) distribution
+percent_bare_initial: 0.25 # Initial percentage of cells occupied by bare soil
+percent_grass_initial: 0.25 # Initial percentage of cells occupied by grass
+percent_shrub_initial: 0.25 # Initial percentage of cells occupied by shrubs
+percent_tree_initial: 0.25  # Initial percentage of cells occupied by trees
+
+## Precipitation:
+
+# Dry Season
+mean_storm_dry: 0.48 # Mean storm duration (hours)
+mean_interstorm_dry: 159.36 # Mean interstorm duration (hours)
+mean_storm_depth_dry: 3.07 # Mean storm depth (mm)
+
+# Wet Season
+mean_storm_wet: 0.46 # Mean storm duration (hours)
+mean_interstorm_wet: 84.24 # Mean interstorm duration (hours)
+mean_storm_depth_wet: 4.79 # Mean storm depth (mm)
+doy__start_of_monsoon: 182   # Day of the year when the monsoon starts
+doy__end_of_monsoon: 273 # Day of the year when the monsoon ends
+
+## PotentialEvapotranspiration:
+# Cosine Method
+PET_method: Cosine
+LT: 0 # Lag between peak TmaxF estimated by cosine method and solar forcing (days)
+DeltaD: 7. # Calibrated difference between
+ND: 365. # Number of days in the year (days)
+MeanTmaxF_grass: 4.96 # Mean annual rate of TmaxF (mm/d)
+MeanTmaxF_shrub: 3.77 # Mean annual rate of TmaxF (mm/d)
+MeanTmaxF_tree: 5.15 # Mean annual rate of TmaxF (mm/d)
+
+# TmaxF - Estimated maximum evapotranspiration as a function of DOY
+# using Penman Monteith method for historical weather
+
+## Soil Moisture:
+
+runon_switch: 0   # Whether to use runon or not
+f_bare: 0.7 # Fraction to partition PET for bare soil (None)
+
+# Grass
+
+VEGTYPE_grass: 0 # Integer value to infer Vegetation Type
+intercept_cap_grass: 0. # Full canopy interception capacity  (mm)
+zr_grass: 0.3 # Root depth (m)
+I_B_grass: 20. # Infiltration capacity of bare soil (mm/h)
+I_V_grass: 24. # Infiltration capacity of vegetated soil (mm/h)
+K_s_grass: 42. # Hydraulic conductivity (mm/h)
+pc_grass: 0.43 # Soil porosity (None)
+fc_grass: 0.56 # Soil saturation degree at field capacity (None)
+sc_grass: 0.33 # Soil saturation degree at stomatal closure (None)
+wp_grass: 0.13 # Soil saturation degree at wilting point (None)
+hgw_grass: 0.1 # Soil saturation degree at hygroscopic point (None)
+beta_grass: 13.8 # Deep percolation constant = 2*b+4  where b is water retention parameter
+
+# Shrub
+
+VEGTYPE_shrub: 1 # Integer value to infer Vegetation Type
+intercept_cap_shrub: 0. # Full canopy interception capacity  (mm)
+zr_shrub: 0.6 # Root depth (m)
+I_B_shrub: 20. # Infiltration capacity of bare soil (mm/h)
+I_V_shrub: 40. # Infiltration capacity of vegetated soil (mm/h)
+K_s_shrub: 42. # Hydraulic conductivity (mm/h)
+pc_shrub: 0.43 # Soil porosity (None)
+fc_shrub: 0.56 # Soil saturation degree at field capacity (None)
+sc_shrub: 0.33 # Soil saturation degree at stomatal closure (None)
+wp_shrub: 0.13 # Soil saturation degree at wilting point (None)
+hgw_shrub: 0.1 # Soil saturation degree at hygroscopic point (None)
+beta_shrub: 13.8 # Deep percolation constant = 2*b+4  where b is water retention parameter
+
+# Tree
+
+VEGTYPE_tree: 2 # Integer value to infer Vegetation Type
+intercept_cap_tree: 0. # Full canopy interception capacity  (mm)
+zr_tree: 1.3 # Root depth (m)
+I_B_tree: 20. # Infiltration capacity of bare soil (mm/h)
+I_V_tree: 40. # Infiltration capacity of vegetated soil (mm/h)
+K_s_tree: 42. # Hydraulic conductivity (mm/h)
+pc_tree: 0.43 # Soil porosity (None)
+fc_tree: 0.56 # Soil saturation degree at field capacity (None)
+sc_tree: 0.33 # Soil saturation degree at stomatal closure (None)
+wp_tree: 0.15 # Soil saturation degree at wilting point (None)
+hgw_tree: 0.1 # Soil saturation degree at hygroscopic point (None)
+beta_tree: 13.8 # Deep percolation constant = 2*b+4  where b is water retention parameter
+
+# Bare Soil
+
+VEGTYPE_bare: 3 # Integer value to infer Vegetation Type
+intercept_cap_bare: 0. # Full canopy interception capacity  (mm)
+zr_bare: 0.3 # Root depth (m)
+I_B_bare: 20. # Infiltration capacity of bare soil (mm/h)
+I_V_bare: 20. # Infiltration capacity of vegetated soil (mm/h)
+K_s_bare: 42. # Hydraulic conductivity (mm/h)
+pc_bare: 0.43 # Soil porosity (None)
+fc_bare: 0.56 # Soil saturation degree at field capacity (None)
+sc_bare: 0.33 # Soil saturation degree at stomatal closure (None)
+wp_bare: 0.13 # Soil saturation degree at wilting point (None)
+hgw_bare: 0.1 # Soil saturation degree at hygroscopic point (None)
+beta_bare: 13.8 # Deep percolation constant
+
+
+## Vegetation Dynamics:
+
+Blive_init: 102.
+Bdead_init: 450.
+PET_growth_threshold: 3.8 # PET threshold for growing season (mm/d)
+PET_dormancy_threshold: 6.8  # PET threshold for dormant season (mm/d)
+Tdmax: 10. # Constant for dead biomass loss adjustment (mm/d)
+w: 0.55 # Conversion factor of CO2 to dry biomass (Kg DM/Kg CO2)
+
+# Grass
+
+WUE_grass: 0.01 # Water use efficiency KgCO2Kg-1H2O
+cb_grass: 0.0047 # Specific leaf area for green/live biomass (m2 leaf g-1 DM)
+cd_grass: 0.009 # Specific leaf area for dead biomass (m2 leaf g-1 DM)
+ksg_grass: 0.012 # Senescence coefficient of green/live biomass (d-1)
+kdd_grass: 0.013 # Decay coefficient of aboveground dead biomass (d-1)
+kws_grass: 0.02 # Maximum drought induced foliage loss rate (d-1)
+LAI_max_grass: 2. # Maximum leaf area index (m2/m2)
+LAIR_max_grass: 2. # Reference leaf area index (m2/m2)
+
+# Shrub
+
+WUE_shrub: 0.0025 # Water use efficiency KgCO2Kg-1H2O
+cb_shrub: 0.004 # Specific leaf area for green/live biomass (m2 leaf g-1 DM)
+cd_shrub: 0.01 # Specific leaf area for dead biomass (m2 leaf g-1 DM)
+ksg_shrub: 0.002 # Senescence coefficient of green/live biomass (d-1)
+kdd_shrub: 0.013 # Decay coefficient of aboveground dead biomass (d-1)
+kws_shrub: 0.02 # Maximum drought induced foliage loss rate (d-1)
+LAI_max_shrub: 2. # Maximum leaf area index (m2/m2)
+LAIR_max_shrub: 2. # Reference leaf area index (m2/m2)
+
+# Tree
+
+WUE_tree: 0.0045 # Water use efficiency KgCO2Kg-1H2O
+cb_tree: 0.004 # Specific leaf area for green/live biomass (m2 leaf g-1 DM)
+cd_tree: 0.01 # Specific leaf area for dead biomass (m2 leaf g-1 DM)
+ksg_tree: 0.002 # Senescence coefficient of green/live biomass (d-1)
+kdd_tree: 0.013 # Decay coefficient of aboveground dead biomass (d-1)
+kws_tree: 0.01 # Maximum drought induced foliage loss rate (d-1)
+LAI_max_tree: 4. # Maximum leaf area index (m2/m2)
+LAIR_max_tree: 4. # Reference leaf area index (m2/m2)
+
+# Bare
+
+WUE_bare: 0.0001 # Water use efficiency KgCO2Kg-1H2O
+cb_bare: 0.0047 # Specific leaf area for green/live biomass (m2 leaf g-1 DM)
+cd_bare: 0.009 # Specific leaf area for dead biomass (m2 leaf g-1 DM)
+ksg_bare: 0.012 # Senescence coefficient of green/live biomass (d-1)
+kdd_bare: 0.013 # Decay coefficient of aboveground dead biomass (d-1)
+kws_bare: 0.02 # Maximum drought induced foliage loss rate (d-1)
+LAI_max_bare: 0.01 # Maximum leaf area index (m2/m2)
+LAIR_max_bare: 0.01 # Reference leaf area index (m2/m2)
+
+
+## Cellular Automaton Vegetation:
+
+# Grass
+
+Pemaxg: 0.35 # Maximal establishment probability
+ING: 2 # Parameter to define allelopathic effect on grass from cresotebush
+ThetaGrass: 0.65 # Drought resistant threshold
+PmbGrass: 0.05               # Background mortality probability
+
+# Shrub
+
+Pemaxsh: 0.16 # Maximal establishment probability
+ThetaShrub: 0.76 # Drought resistant threshold
+PmbShrub: 0.01 # Background mortality probability
+tpmaxShrub: 600 # Maximum age (yr)
+
+# Tree
+
+Pemaxtr: 0.21 # Maximal establishment probability
+ThetaTree: 0.71 # Drought resistant threshold
+PmbTree: 0.011 # Background mortality probability
+tpmaxTree: 350 # Maximum age (yr)
+
+# ShrubSeedling
+
+ThetaShrubSeedling: 0.63 # Drought resistant threshold
+PmbShrubSeedling: 0.03 # Background mortality probability
+tpmaxShrubSeedling: 18 # Maximum age (yr)
+
+# TreeSeedling
+
+ThetaTreeSeedling: 0.6 # Drought resistant threshold
+PmbTreeSeedling: 0.03 # Background mortality probability
+tpmaxTreeSeedling: 20 # Maximum age (yr)
diff --git a/ogh_xarray_landlab/ecohydrology_model_functions.py b/ogh_xarray_landlab/ecohydrology_model_functions.py
new file mode 100755
index 0000000..2c6584a
--- /dev/null
+++ b/ogh_xarray_landlab/ecohydrology_model_functions.py
@@ -0,0 +1,420 @@
+# -*- coding: utf-8 -*-
+"""
+
+"""
+
+# Authors: Sai Nudurupati & Erkan Istanbulluoglu, 21May15
+# Edited: 15Jul16 - to conform to Landlab version 1.
+import numpy as np
+import matplotlib as mpl
+import matplotlib.pyplot as plt
+from landlab.plot import imshow_grid
+from landlab import RasterModelGrid as rmg
+from landlab.components import (PrecipitationDistribution, Radiation,
+                                PotentialEvapotranspiration)
+from soil_moisture_dynamics_lb_pap import SoilMoisture
+from vegetation_dynamics_lb_pap import Vegetation
+from plant_competition_ca_lb_pap import VegCA
+from landlab import load_params
+
+GRASS = 0
+SHRUB = 1
+TREE = 2
+BARE = 3
+SHRUBSEEDLING = 4
+TREESEEDLING = 5
+
+
+# Function to compose spatially distribute PFT
+def compose_veg_grid(grid, percent_bare=0.4, percent_grass=0.2,
+                     percent_shrub=0.2, percent_tree=0.2):
+    number_cells = grid.number_of_cells
+    V = 3 * np.ones(grid.number_of_cells, dtype=int)
+    shrub_point = int(percent_bare * number_cells)
+    tree_point = int((percent_bare + percent_shrub) * number_cells)
+    grass_point = int((1 - percent_grass) * number_cells)
+    V[shrub_point:tree_point] = 1
+    V[tree_point:grass_point] = 2
+    V[grass_point:] = 0
+    np.random.shuffle(V)
+    return V
+
+
+def initialize_components(data, grid_veg=None, grid=None, pet_method='Cosine'):
+    # Plant types are defined as following:
+    # GRASS = 0; SHRUB = 1; TREE = 2; BARE = 3;
+    # SHRUBSEEDLING = 4; TREESEEDLING = 5
+    # Initialize random plant type field
+    grid.at_cell['vegetation__plant_functional_type'] = compose_veg_grid(
+                grid, percent_bare=data['percent_bare_initial'],
+                percent_grass=data['percent_grass_initial'],
+                percent_shrub=data['percent_shrub_initial'],
+                percent_tree=data['percent_tree_initial'])
+    # Assign plant type for representative ecohydrologic simulations
+    grid_veg.at_cell['vegetation__plant_functional_type'] = np.arange(0, 6)
+    grid.at_node['topographic__elevation'] = np.full(grid.number_of_nodes,
+                                                      1700.)
+    grid_veg.at_node['topographic__elevation'] = np.full(
+                        grid_veg.number_of_nodes, 1700.)
+    precip_dry = PrecipitationDistribution(
+                        mean_storm_duration=data['mean_storm_dry'],
+                        mean_interstorm_duration=data['mean_interstorm_dry'],
+                        mean_storm_depth=data['mean_storm_depth_dry'],
+                        random_seed=None)
+    precip_wet = PrecipitationDistribution(
+                        mean_storm_duration=data['mean_storm_wet'],
+                        mean_interstorm_duration=data['mean_interstorm_wet'],
+                        mean_storm_depth=data['mean_storm_depth_wet'],
+                        random_seed=None)
+    radiation = Radiation(grid_veg)
+    if pet_method=='Cosine':
+        pet_tree = PotentialEvapotranspiration(
+                        grid_veg, method=data['PET_method'],
+                        MeanTmaxF=data['MeanTmaxF_tree'],
+                        delta_d=data['DeltaD'])
+        pet_shrub = PotentialEvapotranspiration(
+                        grid_veg, method=data['PET_method'],
+                        MeanTmaxF=data['MeanTmaxF_shrub'],
+                        delta_d=data['DeltaD'])
+        pet_grass = PotentialEvapotranspiration(
+                        grid_veg, method=data['PET_method'],
+                        MeanTmaxF=data['MeanTmaxF_grass'],
+                        delta_d=data['DeltaD'])
+    elif pet_method=='PriestleyTaylor':
+        pet_met = PotentialEvapotranspiration(grid_veg,
+                                              method='PriestleyTaylor')
+        
+    soil_moisture = SoilMoisture(grid_veg, **data)   # Soil Moisture object
+    vegetation = Vegetation(grid_veg, **data)    # Vegetation object
+    vegca = VegCA(grid, **data)      # Cellular automaton object
+
+    # # Initializing inputs for Soil Moisture object
+    grid_veg.at_cell['vegetation__live_leaf_area_index'] = (
+                                    1.6 * np.ones(grid_veg.number_of_cells))
+    grid_veg.at_cell['soil_moisture__initial_saturation_fraction'] = (
+                                    0.59 * np.ones(grid_veg.number_of_cells))
+    # Initializing Soil Moisture
+    if pet_method=='Cosine':
+        return (precip_dry, precip_wet, radiation, pet_tree, pet_shrub,
+                pet_grass, soil_moisture, vegetation, vegca)
+    elif pet_method=='PriestleyTaylor':
+        return (precip_dry, precip_wet, radiation, pet_met, soil_moisture,
+                vegetation, vegca)
+
+
+def create_empty_arrays(number_of_storms, grid_veg, grid, pet_method='Cosine'):
+    P = np.empty(number_of_storms)    # Record precipitation
+    Tb = np.empty(number_of_storms)    # Record inter storm duration
+    Tr = np.empty(number_of_storms)    # Record storm duration
+    Time = np.empty(number_of_storms)  # To record time elapsed from the start of simulation
+#    CumWaterStress = np.empty([n/55, grid.number_of_cells])
+    # Cum Water Stress
+    VegType = np.empty([int(number_of_storms/55), grid.number_of_cells], dtype=int)
+    if pet_method=='Cosine':
+        pet_arr = np.zeros([365, grid_veg.number_of_cells])
+    elif pet_method=='PriestleyTaylor':
+        pet_arr = np.zeros([number_of_storms, grid_veg.number_of_cells])
+    Rad_Factor = np.empty([365, grid_veg.number_of_cells])
+    EP30 = np.empty([365, grid_veg.number_of_cells])
+    Rad_Factor_met = np.empty([number_of_storms, grid_veg.number_of_cells])
+    EP30_met = np.empty([number_of_storms, grid_veg.number_of_cells])
+    # 30 day average PET to determine season
+    pet_threshold = 0  # Initializing PET_threshold to ETThresholddown
+    return (P, Tb, Tr, Time, VegType, pet_arr, Rad_Factor,
+            Rad_Factor_met, EP30, EP30_met, pet_threshold)
+
+
+def create_pet_lookup(grid_veg, radiation=None, Rad_Factor=None,
+                      EP30=None, pet_tree=None, pet_shrub=None,
+                      pet_grass=None, pet_arr=None,
+                      Rad_Factor_met=None, number_of_storms=None,
+                      pet_met=None, Tmax_met=None, Tmin_met=None,
+                      EP30_met=None, first_day=0, pet_method='Cosine'):
+    if pet_method=='Cosine':
+        for i in range(0, 365):
+            pet_tree.update(float(i)/365.25)
+            pet_shrub.update(float(i)/365.25)
+            pet_grass.update(float(i)/365.25)
+            pet_arr[i] = [pet_grass._PET_value, pet_shrub._PET_value,
+                       pet_tree._PET_value, 0., pet_shrub._PET_value,
+                       pet_tree._PET_value]
+            radiation.update(float(i)/365.25)
+            Rad_Factor[i] = (
+                    grid_veg.at_cell['radiation__ratio_to_flat_surface'])
+            if i < 30:
+                if i == 0:
+                    EP30[0] = pet_arr[0]
+                else:
+                    EP30[i] = np.mean(pet_arr[:i], axis=0)
+            else:
+                EP30[i] = np.mean(pet_arr[i-30:i], axis=0)
+        return (pet_arr, EP30)
+
+    elif pet_method=='PriestleyTaylor':
+        for i in range(0, number_of_storms):
+            pet_met.update(float(first_day+i)/365.25, Tmax = Tmax_met[i],
+                               Tmin = Tmin_met[i],
+                               Tavg = (Tmax_met[i]+Tmin_met[i])/2.)
+            pet_arr[i] = [pet_met._PET_value, pet_met._PET_value,
+                          pet_met._PET_value, 0., pet_met._PET_value,
+                          pet_met._PET_value]
+            radiation.update(float(i)/365.25)
+            Rad_Factor_met[i] = (
+                    grid_veg.at_cell['radiation__ratio_to_flat_surface'])
+            if i < 30:
+                if i == 0:
+                    EP30_met[0] = pet_arr[0]
+                else:
+                    EP30_met[i] = np.mean(pet_arr[:i], axis=0)
+            else:
+                EP30_met[i] = np.mean(pet_arr[i-30:i], axis=0)
+        return(pet_arr, EP30_met)
+
+
+def save_data(sim, Tb, Tr, P, VegType, yrs, Time_Consumed, Time):
+    np.save(sim+'Tb', Tb)
+    np.save(sim+'Tr', Tr)
+    np.save(sim+'P', P)
+    np.save(sim+'VegType', VegType)
+#    np.save(sim+'CumWaterStress', CumWaterStress)
+    np.save(sim+'Years', yrs)
+    np.save(sim+'Time_Consumed_minutes', Time_Consumed)
+    np.save(sim+'CurrentTime', Time)
+
+
+def plot_results(grid, VegType, yrs, yr_step=10):
+    # # Plotting
+    pic = 0
+    years = range(0, yrs)
+    cmap = mpl.colors.ListedColormap(
+                        ['green', 'red', 'black', 'white', 'red', 'black'])
+    bounds = [-0.5, 0.5, 1.5, 2.5, 3.5, 4.5, 5.5]
+    norm = mpl.colors.BoundaryNorm(bounds, cmap.N)
+    print('Plotting cellular field of Plant Functional Type')
+    print('Green - Grass; Red - Shrubs; Black - Trees; White - Bare')
+    # # Plot images to make gif.
+    for year in range(0, yrs, yr_step):
+        filename = 'Year = ' + "%05d" % year
+        pic += 1
+        plt.figure(pic)
+        imshow_grid(grid, VegType[year], values_at='cell', cmap=cmap,
+                    grid_units=('m', 'm'), norm=norm, limits=[0, 5],
+                    allow_colorbar=False)
+        plt.title(filename)
+        plt.savefig(filename)
+    grass_cov = np.empty(yrs)
+    shrub_cov = np.empty(yrs)
+    tree_cov = np.empty(yrs)
+    grid_size = float(VegType.shape[1])
+    for x in range(0, yrs):
+        grass_cov[x] = (VegType[x][VegType[x] == GRASS].size/grid_size) * 100
+        shrub_cov[x] = ((VegType[x][VegType[x] == SHRUB].size/grid_size) *
+                        100 + (VegType[x][VegType[x] == SHRUBSEEDLING].size /
+                        grid_size) * 100)
+        tree_cov[x] = ((VegType[x][VegType[x] == TREE].size/grid_size) *
+                       100 + (VegType[x][VegType[x] == TREESEEDLING].size /
+                       grid_size) * 100)
+    pic += 1
+    plt.figure(pic)
+    plt.plot(years, grass_cov, '-g', label='Grass')
+    plt.hold(True)
+    plt.plot(years, shrub_cov, '-r', label='Shrub')
+    plt.hold(True)
+    plt.plot(years, tree_cov, '-k', label='Tree')
+    plt.xlim(xmin=0, xmax=years[-1])
+    plt.ylim(ymin=0, ymax=(max(np.max(grass_cov),
+                               np.max(shrub_cov),
+                               np.max(tree_cov))+10))
+    plt.ylabel(' % Coverage ')
+    plt.xlabel('Time in years')
+    plt.legend(loc=0)
+    plt.savefig('PercentageCover_PFTs')
+    # plt.show()
+
+def run_ecohydrology_model(grid, input_data, input_file,
+                           synthetic_storms=True, number_of_storms=None,
+                           number_of_years=None,
+                           first_julian_day_of_observations=0,
+                           pet_method='Cosine',
+                           save_files=False, sim_name='Trial'):
+    # Create data object by reading in the input_file
+    data = load_params(input_file)
+    # Create a grid that can hold enough cells to represent all individual
+    # vegetation types 
+    grid_veg = rmg((5, 4), spacing=(5., 5.))
+    if pet_method == 'Cosine':
+        (precip_dry, precip_wet, radiation, pet_tree, pet_shrub, pet_grass,
+         soil_moisture, vegetation, vegca) = initialize_components(data,
+                                         grid_veg, grid, pet_method='Cosine')
+    elif pet_method == 'PriestleyTaylor':
+        (precip_dry, precip_wet, radiation, pet_met,
+         soil_moisture, vegetation, vegca) = initialize_components(data,
+                                grid_veg, grid, pet_method='PriestleyTaylor')        
+
+    if number_of_years!=None:
+        # Calculate approximate number of storms per year
+        fraction_wet = ((data['doy__end_of_monsoon']-
+                         data['doy__start_of_monsoon'])/365.)
+        fraction_dry = (1 - fraction_wet)
+        number_of_storms_wet = (8760 * (fraction_wet)/
+                                (data['mean_interstorm_wet'] +
+                                 data['mean_storm_wet']))
+        number_of_storms_dry = (8760 * (fraction_dry)/
+                                (data['mean_interstorm_dry'] +
+                                 data['mean_storm_dry']))
+        number_of_storms = int(number_of_years *
+                               (number_of_storms_wet + number_of_storms_dry))
+
+    (precip, inter_storm_dt, storm_dt, Time, VegType, pet_arr, Rad_Factor,
+     Rad_Factor_met, EP30, EP30_met, pet_threshold) = create_empty_arrays(
+                                                     number_of_storms,
+                                                     grid_veg, grid,
+                                                     pet_method=pet_method)
+    
+    if pet_method == 'Cosine':
+        (pet_arr, EP30) = create_pet_lookup(grid_veg, radiation=radiation, Rad_Factor=Rad_Factor,
+                              EP30=EP30, pet_tree=pet_tree, pet_shrub=pet_shrub,
+                              pet_grass=pet_grass, pet_arr=pet_arr)
+    if pet_method == 'PriestleyTaylor':
+        (pet_arr, EP30_met) = create_pet_lookup(grid_veg, radiation=radiation,
+                              Rad_Factor_met=Rad_Factor_met,
+                              number_of_storms=number_of_storms,
+                              pet_met=pet_met, Tmax_met=input_data['Tmax_met'],
+                              Tmin_met=input_data['Tmin_met'], EP30_met=EP30_met,
+                              first_day=first_julian_day_of_observations,
+                              pet_method=pet_method, pet_arr=pet_arr)
+
+    # declaring few variables that will be used in the storm loop
+    current_time = first_julian_day_of_observations/365.25  # Initial time
+    time_check = 0.     # Buffer to store current_time at previous storm
+    yrs = 0             # Keep track of number of years passed
+    water_stress = 0.             # Buffer for Water Stress
+    Tg = 0            # Growing season in days
+    
+    # # Run storm Loop
+    for i in range(0, number_of_storms):
+        # Update objects
+    
+        # Calculate Day of Year (DOY)
+        julian = np.int(np.floor((current_time - np.floor(current_time)) *
+                                 365.))
+        if synthetic_storms:
+            # Generate seasonal storms
+            # for Dry season
+            if julian < data['doy__start_of_monsoon'] or julian > data[
+                                'doy__end_of_monsoon']:
+                precip_dry.update()
+                precip[i] = precip_dry.storm_depth
+                storm_dt[i] = precip_dry.storm_duration
+                inter_storm_dt[i] = precip_dry.interstorm_duration
+            # Wet Season - Jul to Sep - NA Monsoon
+            else:
+                precip_wet.update()
+                precip[i] = precip_wet.storm_depth
+                storm_dt[i] = precip_wet.storm_duration
+                inter_storm_dt[i] = precip_wet.interstorm_duration
+        else:
+            precip[i] = input_data['precip_met'][i]
+            storm_dt[i] = 0.
+            inter_storm_dt[i] = 24.
+    
+        # Spatially distribute PET and its 30-day-mean (analogous to degree day)
+        if pet_method == 'Cosine':
+            grid_veg.at_cell['surface__potential_evapotranspiration_rate'] = (
+                    pet_arr[julian])
+            grid_veg.at_cell['surface__potential_evapotranspiration_30day_mean'] = (
+                    EP30[julian])
+            grid_veg.at_cell['surface__potential_evapotranspiration_rate__grass'] = (
+                    np.full(grid_veg.number_of_cells, pet_arr[julian, 0]))
+        elif pet_method=='PriestleyTaylor':
+            grid_veg.at_cell['surface__potential_evapotranspiration_rate'] = (
+                    pet_arr[i])
+            grid_veg.at_cell['surface__potential_evapotranspiration_30day_mean'] = (
+                    EP30_met[i])
+            grid_veg.at_cell['surface__potential_evapotranspiration_rate__grass'] = (
+                    np.full(grid_veg.number_of_cells, pet_arr[i, 0]))
+    
+        # Assign spatial rainfall data
+        grid_veg.at_cell['rainfall__daily_depth'] = (
+                np.full(grid_veg.number_of_cells, precip[i]))
+    
+        # Update soil moisture component
+        current_time = soil_moisture.update(current_time, Tr=storm_dt[i],
+                                            Tb=inter_storm_dt[i])
+    
+        # Decide whether its growing season or not
+        if pet_method == 'Cosine':
+            if julian != 364:
+                if EP30[julian+1, 0] > EP30[julian, 0]:
+                    pet_threshold = 1
+                    # 1 corresponds to ETThresholdup (begin growing season)
+                    if EP30[julian, 0] > vegetation._ETthresholdup:
+                        growing_season = True
+                    else:
+                        growing_season = False
+                else:
+                    pet_threshold = 0
+                    # 0 corresponds to ETThresholddown (end growing season)
+                    if EP30[julian, 0] > vegetation._ETthresholddown:
+                        growing_season = True
+                    else:
+                        growing_season = False
+        elif pet_method=='PriestleyTaylor':
+            if i != number_of_storms-1:
+                if EP30_met[i+1, 0] > EP30_met[i, 0]:
+                    pet_threshold = 1
+                    # 1 corresponds to ETThresholdup (begin growing season)
+                    if EP30_met[i, 0] > vegetation._ETthresholdup:
+                        growing_season = True
+                    else:
+                        growing_season = False
+                else:
+                    pet_threshold = 0
+                    # 0 corresponds to ETThresholddown (end growing season)
+                    if EP30_met[i, 0] > vegetation._ETthresholddown:
+                        growing_season = True
+                    else:
+                        growing_season = False                            
+
+        # Update vegetation component
+        vegetation.update(PETThreshold_switch=pet_threshold,
+                          Tb=inter_storm_dt[i], Tr=storm_dt[i])
+
+        if growing_season:
+            # Update yearly cumulative water stress data
+            Tg += (storm_dt[i]+inter_storm_dt[i])/24.    # Incrementing growing season storm count
+            water_stress += ((grid_veg.at_cell['vegetation__water_stress']) *
+                             inter_storm_dt[i]/24.)
+
+        # Record time (optional)
+        Time[i] = current_time
+    
+        # Update spatial PFTs with Cellular Automata rules
+        if (current_time - time_check) >= 1.:
+            if yrs % 100 == 0:
+                print('Elapsed time = ', yrs, ' years')
+            VegType[yrs] = grid.at_cell['vegetation__plant_functional_type']
+            WS_ = np.choose(VegType[yrs], water_stress)
+            grid.at_cell['vegetation__cumulative_water_stress'] = WS_/Tg
+            vegca.update()
+            time_check = np.floor(current_time)
+            water_stress = 0
+            yrs += 1
+            Tg = 0
+    
+    VegType[yrs] = grid.at_cell['vegetation__plant_functional_type']
+    if save_files:
+        save_data(sim_name, inter_storm_dt, storm_dt, precip,
+                  VegType, yrs, 0, Time)
+    if pet_method == 'Cosine':
+        returns_debug = [grid_veg, precip_dry, precip_wet,
+                         radiation, pet_tree, pet_shrub,
+                         pet_grass, soil_moisture, vegetation,
+                         vegca]
+
+    elif pet_method == 'PriestleyTaylor':
+        returns_debug = [grid_veg, precip_dry, precip_wet,
+                         radiation, pet_met, soil_moisture,
+                         vegetation, vegca]
+    return (VegType, yrs-1, returns_debug)
+        
diff --git a/ogh_xarray_landlab/ogh_xarray_landlab.pdf b/ogh_xarray_landlab/ogh_xarray_landlab.pdf
new file mode 100644
index 0000000..d39a731
Binary files /dev/null and b/ogh_xarray_landlab/ogh_xarray_landlab.pdf differ
diff --git a/ogh_xarray_landlab/ogh_xarray_landlab.py b/ogh_xarray_landlab/ogh_xarray_landlab.py
new file mode 100755
index 0000000..3f3c381
--- /dev/null
+++ b/ogh_xarray_landlab/ogh_xarray_landlab.py
@@ -0,0 +1,585 @@
+# import libraries
+import os
+import numpy as np
+import pandas as pd
+import xarray as xray
+import ftplib, wget, bz2, urllib
+import dask as da
+from dask.diagnostics import ProgressBar
+from multiprocessing.pool import ThreadPool
+
+import ogh
+import landlab.grid.raster as r
+
+import matplotlib as mpl
+import matplotlib.pyplot as plt
+mpl.style.use('tableau-colorblind10')
+import shapely.ops
+from shapely.geometry import MultiPolygon, shape, point, box, Polygon
+from mpl_toolkits.basemap import Basemap
+import geopandas as gpd
+
+
+def compile_x_wrfnnrp_raw_Salathe2014_locations(time_increments):
+    """
+    Compile a list of file URLs for Salathe et al., 2014 raw WRF NNRP data
+    
+    time_increments: (list) a list of dates that identify each netcdf file
+    """
+    locations=[]
+    domain='http://cses.washington.edu'
+    subdomain='/rocinante/WRF/NNRP/vic_16d/PNW_1970_1999/WRF_NNRP_noBC/netcdf_daily'
+
+    for ind, yearmo in enumerate(time_increments):
+        basename='/WRF_NNRP_noBC.{0}.nc'.format(yearmo)
+        url='{0}{1}{2}'.format(domain, subdomain, basename)
+        locations.append(url)
+    return(locations)
+
+
+def compile_x_dailymet_Livneh2013_raw_locations(time_increments):
+    """
+    Compile a list of file URLs for Livneh et al., 2013 raw MET data
+    
+    time_increments: (list) a list of dates that identify each netcdf file
+    """
+    locations=[]
+    domain='ftp://livnehpublicstorage.colorado.edu'
+    subdomain='/public/Livneh.2013.CONUS.Dataset/Meteorology.nc.v.1.2.1915.2011.bz2'
+
+    for ind, yearmo in enumerate(time_increments):
+        if yearmo.startswith('1915') or (yearmo=='191601'):
+            basename='/Meteorology_Livneh_CONUSExt_v.1.2_2013.{0}.nc'.format(yearmo)
+        else:
+            basename='/Meteorology_Livneh_CONUSExt_v.1.2_2013.{0}.nc.bz2'.format(yearmo)
+        url='{0}{1}{2}'.format(domain, subdomain, basename)
+        locations.append(url)
+    return(locations)
+
+
+def compile_x_wrfpnnl2018_raw_locations(time_increments):
+    """
+    Compile a list of file URLs for PNNL 2018 raw WRF data
+    time_increments: (list) a list of dates that identify each netcdf file
+    """
+    locations=[]
+    domain='http://cses.washington.edu'
+    subdomain='/rocinante/WRF/PNNL_NARR_6km'
+
+    for ind, ymd in enumerate(time_increments):
+        subfolder = '/{0}'.format(ymd[0:4])
+        basename='/data.{0}-{1}-{2}.nc'.format(ymd[0:4], ymd[4:6], ymd[6:8])
+        url='{0}{1}{2}{3}'.format(domain, subdomain, subfolder, basename)
+        locations.append(url)
+    return(locations)
+
+
+def wget_x_download_spSubset(fileurl, 
+                             spatialbounds, 
+                             file_prefix='sp_',
+                             rename_timelatlong_names={'LAT':'LAT','LON':'LON'}, 
+                             replace_file=True):
+    """
+    Download files from an http domain
+    
+    fileurl: (str) a urls to request a netcdf file
+    spatialbounds: (dict) dictionary providing the minx, miny, maxx, and maxy of the spatial region
+    file_prefix: (str) a string to mark the output file as a spatial subset
+    rename_timelatlong_names: (dict) a dictionary to standardize latitude/longitude synonyms to LAT/LON, respectively
+    replace_file: (logic) If True, the existing file will be replaced; if False, the file download is skipped
+    """
+    
+    # check if the file path already exists; if so, apply replace_file logic
+    basename = os.path.basename(fileurl)
+    if os.path.isfile(basename):
+        os.remove(basename)
+        
+    if os.path.isfile(file_prefix+basename) and replace_file:
+        os.remove(file_prefix+basename)
+    elif os.path.isfile(file_prefix+basename) and not replace_file:
+        # replace_file is False; return file path and skip
+        return(os.path.join(os.getcwd(), file_prefix+basename))
+        
+    # try the file connection
+    try:
+        ping = urllib.request.urlopen(fileurl)
+        
+        # if the file exists, download it
+        if ping.getcode() != 404:
+            ping.close()
+            wget.download(fileurl)
+
+            # open the parent netcdf file
+            ds = xray.open_dataset(basename, engine = 'netcdf4')
+            
+            # rename latlong if they are not LAT and LON, respectively
+            if not isinstance(rename_timelatlong_names, type(None)):
+                ds = ds.rename(rename_timelatlong_names)
+                
+            # slice by the bounding box
+            spSubset = ds.sel(LON=slice(spatialbounds['minx'], spatialbounds['maxx']),
+                              LAT=slice(spatialbounds['miny'], spatialbounds['maxy']))
+
+            # print the spatial subset
+            spSubset.to_netcdf(file_prefix+basename)
+            
+            # remove the parent
+            ds.close()
+            os.remove(basename)
+            return(os.path.join(os.getcwd(), file_prefix+basename))
+
+        else:
+            ping.close()
+    except:
+        print('File does not exist at this URL: ' + basename)
+
+
+def ftp_x_download_spSubset(fileurl,
+                            spatialbounds,
+                            file_prefix='sp_',
+                            rename_timelatlong_names={'LAT':'LAT','LON':'LON','TIME':'TIME'},
+                            replace_file=True):
+    """
+    Download files from an http domain
+    
+    fileurl: (str) a urls to request a netcdf file
+    spatialbounds: (dict) dictionary providing the minx, miny, maxx, and maxy of the spatial region
+    file_prefix: (str) a string to mark the output file as a spatial subset
+    rename_timelatlong_names: (dict) a dictionary to standardize latitude/longitude synonyms to LAT/LON, respectively
+    replace_file: (logic) If True, the existing file will be replaced; if False, the file download is skipped
+    """
+    # establish path info
+    fileurl=fileurl.replace('ftp://', '') # loci is already the url with the domain already appended
+    ipaddress=fileurl.split('/', 1)[0] # ip address
+    path=os.path.dirname(fileurl.split('/', 1)[1]) # folder path
+    filename = os.path.basename(fileurl)
+    
+    # check if the file path already exists; if so, apply replace_file logic
+    if os.path.isfile(filename):
+        os.remove(filename)
+        
+    if os.path.isfile(file_prefix+filename) and replace_file:
+        os.remove(file_prefix+filename)
+    elif os.path.isfile(file_prefix+filename) and not replace_file:
+        # replace_file is False; return file path and skip
+        return(os.path.join(os.getcwd(), file_prefix+filename))
+        
+    # download the file from the ftp server
+    ftp=ftplib.FTP(ipaddress)
+    ftp.login()
+    ftp.cwd(path)
+    try:
+        # try the file connection
+        ftp.retrbinary('RETR ' + filename , open(filename, 'wb').write)
+        ftp.close()
+        
+        # decompress the file
+        if filename.endswith('.bz2'):
+            ogh.decompbz2(filename)
+            filename = filename.replace('.bz2','')
+            
+        # open the parent netcdf file
+        ds = xray.open_dataset(filename, engine = 'netcdf4')
+        
+        if not isinstance(rename_timelatlong_names, type(None)):
+            ds = ds.rename(rename_timelatlong_names)
+
+        # slice by the bounding box
+        spSubset = ds.sel(LON=slice(spatialbounds['minx'], spatialbounds['maxx']),
+                          LAT=slice(spatialbounds['miny'], spatialbounds['maxy']))
+
+        # print the spatial subset
+        spSubset.to_netcdf(file_prefix+filename)
+
+        # remove the parent
+        ds.close()
+        os.remove(filename)
+        return(os.path.join(os.getcwd(), file_prefix+filename))
+    except:
+        #os.remove(filename)
+        print('File does not exist at this URL: '+fileurl)
+        
+        
+def get_x_dailywrf_Salathe2014(homedir,
+                               spatialbounds,
+                               subdir='salathe2014/Daily_WRF_1970_1999/noBC',
+                               nworkers=4,
+                               start_date='1970-01-01',
+                               end_date='1989-12-31',
+                               rename_timelatlong_names={'LAT':'LAT','LON':'LON','TIME':'TIME'},
+                               file_prefix='sp_',
+                               replace_file=True):
+    """
+    get Daily WRF data from Salathe et al. (2014) using xarray on netcdf files
+    """
+    # check and generate DailyMET livneh 2013 data directory
+    filedir=os.path.join(homedir, subdir)
+    ogh.ensure_dir(filedir)
+    
+    # modify each month between start_date and end_date to year-month
+    dates = [x.strftime('%Y%m') for x in pd.date_range(start=start_date, end=end_date, freq='M')]
+
+    # initialize parallel workers
+    da.set_options(pool=ThreadPool(nworkers))
+    ProgressBar().register()
+    
+    # generate the list of files to download
+    filelist = compile_x_wrfnnrp_raw_Salathe2014_locations(dates)
+
+    # download files of interest
+    NetCDFs=[]
+    for url in filelist:
+        NetCDFs.append(da.delayed(wget_x_download_spSubset)(fileurl=url,
+                                                            spatialbounds=spatialbounds,
+                                                            file_prefix=file_prefix,
+                                                            rename_timelatlong_names=rename_timelatlong_names,
+                                                            replace_file=replace_file))
+
+    # run operations
+    outputfiles = da.compute(NetCDFs)[0]
+
+    # reset working directory
+    os.chdir(homedir)
+    return(outputfiles)
+
+
+def get_x_hourlywrf_PNNL2018(homedir,
+                             spatialbounds,
+                             subdir='PNNL2018/Hourly_WRF_1981_2015/noBC',
+                             nworkers=4,
+                             start_date='1981-01-01',
+                             end_date='2015-12-31',
+                             rename_timelatlong_names={'LAT':'LAT','LON':'LON','TIME':'TIME'},
+                             file_prefix='sp_',
+                             replace_file=True):
+    """
+    get hourly WRF data from a 2018 PNNL WRF run using xarray on netcdf files
+    """
+    # check and generate data directory
+    filedir=os.path.join(homedir, subdir)
+    ogh.ensure_dir(filedir)
+    
+    # modify each month between start_date and end_date to year-month
+    dates = [x.strftime('%Y%m%d') for x in pd.date_range(start=start_date, end=end_date, freq='D')]
+    
+    # initialize parallel workers
+    da.set_options(pool=ThreadPool(nworkers))
+    ProgressBar().register()
+    
+    # generate the list of files to download
+    filelist = compile_x_wrfpnnl2018_raw_locations(dates)
+    
+    # download files of interest
+    NetCDFs=[]
+    for url in filelist:
+        NetCDFs.append(da.delayed(wget_x_download_spSubset)(fileurl=url,
+                                                            spatialbounds=spatialbounds,
+                                                            file_prefix=file_prefix,
+                                                            rename_latlong_names=rename_timelatlong_names,
+                                                            replace_file=replace_file))
+    
+    # run operations
+    outputfiles = da.compute(NetCDFs)[0]
+    
+    # reset working directory
+    os.chdir(homedir)
+    return(outputfiles)
+
+
+def get_x_dailymet_Livneh2013_raw(homedir,
+                                  spatialbounds,
+                                  subdir='livneh2013/Daily_MET_1915_2011/raw_netcdf',
+                                  nworkers=4,
+                                  start_date='1915-01-01',
+                                  end_date='2011-12-31',
+                                  rename_timelatlong_names={'lat':'LAT','lon':'LON','time':'TIME'},
+                                  file_prefix='sp_',
+                                  replace_file=True):
+    """
+    get Daily MET data from Livneh et al. (2013) using xarray on netcdf files
+    """
+    # check and generate DailyMET livneh 2013 data directory
+    filedir=os.path.join(homedir, subdir)
+    ogh.ensure_dir(filedir)
+    
+    # modify each month between start_date and end_date to year-month
+    dates = [x.strftime('%Y%m') for x in pd.date_range(start=start_date, end=end_date, freq='M')]
+
+    # initialize parallel workers
+    da.set_options(pool=ThreadPool(nworkers))
+    ProgressBar().register()
+    
+    # generate the list of files to download
+    filelist = compile_x_dailymet_Livneh2013_raw_locations(dates)
+
+    # download files of interest
+    NetCDFs=[]
+    for url in filelist:
+        NetCDFs.append(da.delayed(ftp_x_download_spSubset)(fileurl=url,
+                                                           spatialbounds=spatialbounds,
+                                                           file_prefix=file_prefix,
+                                                           rename_timelatlong_names=rename_timelatlong_names,
+                                                           replace_file=replace_file))
+
+    # run operations
+    outputfiles = da.compute(NetCDFs)[0]
+
+    # reset working directory
+    os.chdir(homedir)
+    return(outputfiles)
+
+
+def netcdf_to_ascii(homedir, subdir, source_directory, mappingfile, catalog_label, meta_file, 
+                    temporal_resolution='D', netcdfs=None, variable_list=None):
+    # initialize list of dataframe outputs
+    outfiledict = {}
+    
+    # generate destination folder
+    filedir=os.path.join(homedir, subdir)
+    ogh.ensure_dir(filedir)
+
+    # connect with collection of netcdfs
+    if isinstance(netcdfs, type(None)):
+        netcdfs=[os.path.join(source_directory,file) for file in os.listdir(source_directory) if file.endswith('.nc')]
+    ds_mf = xray.open_mfdataset(netcdfs, engine = 'netcdf4').sortby('TIME')
+
+    # generate list of variables
+    if not isinstance(variable_list, type(None)):
+        ds_vars = variable_list.copy()
+    else:
+        ds_vars = [ds_var for ds_var in dict(ds_mf.variables).keys() 
+                   if ds_var not in ['YEAR','MONTH','DAY','TIME','LAT','LON']]
+
+    # convert netcdfs to pandas.Panel API
+    ds_pan = ds_mf.to_dataframe()[ds_vars]
+
+    # read in gridded cells of interest
+    maptable, nstation = ogh.mappingfileToDF(mappingfile, colvar=None, summary=False)
+
+    # at each latlong of interest
+    for ind, eachrow in maptable.iterrows():
+
+        # generate ASCII time-series
+        ds_df = ds_pan.loc[eachrow['LAT'],eachrow['LONG_'],:].reset_index(drop=True, level=[0,1])
+
+        # create file name
+        outfilename = os.path.join(filedir,'data_{0}_{1}'.format(eachrow['LAT'],eachrow['LONG_']))
+
+        # save ds_df
+        outfiledict[outfilename] = da.delayed(ds_df.to_csv)(path_or_buf=outfilename, sep='\t', header=False, index=False)
+    
+    # compute ASCII time-series files
+    ProgressBar().register()
+    outfiledict = da.compute(outfiledict)[0]
+    
+    # annotate metadata file
+    meta_file[catalog_label] = dict(ds_mf.attrs)
+    meta_file[catalog_label]['variable_list']=list(np.array(ds_vars))
+    meta_file[catalog_label]['delimiter']='\t'
+    meta_file[catalog_label]['start_date']=pd.Series(ds_mf.TIME).sort_values().iloc[0].strftime('%Y-%m-%d %H:%M:%S')
+    meta_file[catalog_label]['end_date']=pd.Series(ds_mf.TIME).sort_values().iloc[-1].strftime('%Y-%m-%d %H:%M:%S')
+    meta_file[catalog_label]['temporal_resolution']=temporal_resolution
+    meta_file[catalog_label]['variable_info'] = dict(ds_mf.variables)
+    
+    # catalog the output files
+    ogh.addCatalogToMap(outfilepath=mappingfile, maptable=maptable, folderpath=filedir, catalog_label=catalog_label)
+    os.chdir(homedir)
+    return(list(outfiledict.keys()))
+
+
+def calculateUTMbounds(mappingfile, mappingfile_crs={'init':'epsg:4326'}, spatial_resolution=0.06250):
+    # read in the mappingfile
+    map_df, nstation = ogh.mappingfileToDF(mappingfile)
+
+    # loop though each LAT/LONG_ +/-0.06250 centroid into gridded cells
+    geom=[]
+    midpt = spatial_resolution/2
+    for ind in map_df.index:
+        mid = map_df.loc[ind]
+        geom.append(box(mid.LONG_- midpt, mid.LAT - midpt, mid.LONG_ + midpt, mid.LAT + midpt, ccw=True))
+
+    # generate the GeoDataFrame
+    test = gpd.GeoDataFrame(map_df, crs=mappingfile_crs, geometry=geom)
+
+    # compile gridded cells to extract bounding box
+    test['shapeName'] = 1
+
+    # dissolve shape into new shapefile
+    newShape = test.dissolve(by='shapeName').reset_index()
+    newShape.bounds
+
+    # take the minx and miny, and centroid_x and centroid_y
+    minx, miny, maxx, maxy = newShape.bounds.loc[0]
+    lon0, lat0 = np.array(newShape.centroid[0])
+
+    # generate the basemap raster
+    fig = plt.figure(figsize=(10,10), dpi=500)
+    ax1 = plt.subplot2grid((1,1),(0,0))
+    m = Basemap(projection='tmerc', resolution='h', ax=ax1, lat_0=lat0, lon_0=lon0,
+                llcrnrlon=minx, llcrnrlat=miny, urcrnrlon=maxx, urcrnrlat=maxy)
+
+    # transform each polygon to the utm basemap projection
+    for ind in newShape.index:
+        eachpol = newShape.loc[ind]
+        newShape.loc[ind,'g2'] = shapely.ops.transform(m, eachpol['geometry'])
+
+    # transform each polygon to the utm basemap projection
+    newShape['g2'] = newShape.apply(lambda x: shapely.ops.transform(m, x['geometry']), axis=1)
+    
+    # remove the plot
+    plt.gcf().clear()
+
+    # establish the UTM basemap bounding box dimensions
+    minx2, miny2, maxx2, maxy2 = newShape['g2'].iloc[0].bounds
+    return(minx2, miny2, maxx2, maxy2)
+
+
+def calculateUTMcells(mappingfile, mappingfile_crs={'init':'epsg:4326'}, spatial_resolution=0.06250):
+    # read in the mappingfile
+    map_df, nstation = ogh.mappingfileToDF(mappingfile)
+
+    # loop though each LAT/LONG_ +/-0.06250 centroid into gridded cells
+    geom=[]
+    midpt = spatial_resolution/2
+    for ind in map_df.index:
+        mid = map_df.loc[ind]
+        geom.append(box(mid.LONG_- midpt, mid.LAT - midpt, mid.LONG_ + midpt, mid.LAT + midpt, ccw=True))
+
+    # generate the GeoDataFrame
+    test = gpd.GeoDataFrame(map_df, crs=mappingfile_crs, geometry=geom)
+
+    # compile gridded cells to extract bounding box
+    test['shapeName'] = 1
+
+    # dissolve shape into new shapefile
+    newShape = test.dissolve(by='shapeName').reset_index()
+
+    # take the minx and miny, and centroid_x and centroid_y
+    minx, miny, maxx, maxy = newShape.bounds.loc[0]
+    lon0, lat0 = np.array(newShape.centroid[0])
+
+    # generate the basemap raster
+    fig = plt.figure(figsize=(10,10), dpi=500)
+    ax1 = plt.subplot2grid((1,1),(0,0))
+    m = Basemap(projection='tmerc', resolution='h', ax=ax1, lat_0=lat0, lon_0=lon0,
+                llcrnrlon=minx, llcrnrlat=miny, urcrnrlon=maxx, urcrnrlat=maxy)
+
+    # transform each polygon to the utm basemap projection
+    test['geometry'] = test.apply(lambda x: shapely.ops.transform(m, x['geometry']), axis=1)
+    test = test.drop('shapeName', axis=1)
+    
+    # remove the plot
+    plt.gcf().clear()
+
+    # return the geodataframe and the spatial transformation from WGS84
+    return(test, m)
+
+
+def rasterDimensions (maxx, maxy, minx=0, miny=0, dy=100, dx=100):
+    # construct the range
+    x = pd.Series(range(int(minx),int(maxx)+1,1))
+    y = pd.Series(range(int(miny),int(maxy)+1,1))
+    
+    # filter for values that meet the increment or is the last value
+    cols = pd.Series(x.index).apply(lambda x1: x[x1] if x1 % dx == 0 or x1==x[0] or x1==x.index[-1] else None)
+    rows = pd.Series(y.index).apply(lambda y1: y[y1] if y1 % dy == 0 or y1==y[0] or y1==y.index[-1] else None)
+    
+    # construct the indices
+    row_list = np.array(rows.loc[pd.notnull(rows)])
+    col_list = np.array(cols.loc[pd.notnull(cols)])
+    
+    # construct the raster
+    raster = r.RasterModelGrid((len(row_list), len(col_list)), spacing=(dy, dx))
+    raster.add_zeros
+    return(raster, row_list, col_list)
+
+
+def mappingfileToRaster(mappingfile, maxx, maxy, minx=0, miny=0, dx=100, dy=100, 
+                        spatial_resolution=0.06250, mappingfile_crs={'init':'epsg:4326'}, raster_crs={'init':'epsg:3857'}):
+    
+    # generate the mappingfile with UTM cells
+    UTMmappingfile, m = calculateUTMcells(mappingfile=mappingfile,
+                                          mappingfile_crs=mappingfile_crs,
+                                          spatial_resolution=spatial_resolution)
+    
+    # construct the raster
+    raster, row_list, col_list = rasterDimensions(maxx, maxy, minx=minx, miny=miny, dy=dy, dx=dx)
+    
+    # initialize node list
+    df_list=[]
+    
+    # loop through the raster nodes (bottom to top arrays)
+    for row_index, nodelist in enumerate(raster.nodes):
+
+        # index bottom to top arrays with ordered Latitude
+        lat = row_list[row_index]
+
+        # index left to right with ordered Longitude
+        for nodeid, long_ in zip(nodelist, col_list):
+            df_list.append([nodeid, 
+                            box(long_, lat, long_+dx, lat+dy, ccw=True),
+                            box(long_, lat, long_+dx, lat+dy, ccw=True).centroid])
+            
+    # convert to dataframe
+    df = pd.DataFrame.from_records(df_list).rename(columns={0:'nodeid',1:'raster_geom', 2:'raster_centroid'})
+    raster_map = gpd.GeoDataFrame(df, geometry='raster_centroid', crs=raster_crs)
+
+    # identify raster nodeid and equivalent mappingfile FID
+    raster_df = gpd.sjoin(raster_map, UTMmappingfile, how='left', op='intersects')
+    raster_df = raster_df.drop('raster_centroid', axis=1).set_geometry('raster_geom')
+    
+    # return the raster node to mappingfile FID cross-map, and the rastermodelgrid
+    return(raster_df, raster, m)
+
+
+def temporalSlice(vardf, vardf_dateindex):
+    values = vardf.loc[vardf_dateindex, :].reset_index(level=0)
+    values = values.rename(columns={'level_0': 'FID', vardf_dateindex: 'value'}).reset_index(drop=True)
+    return(values)
+
+
+def rasterVector(vardf, vardf_dateindex, crossmap, nodata=-9999):
+    values = temporalSlice(vardf=vardf, vardf_dateindex=vardf_dateindex)
+    vector = crossmap.merge(values, on='FID', how='left').fillna(nodata)['value']
+    return(vector)
+
+    
+def valueRange(listOfDf):
+    all_values = pd.concat(listOfDf, axis=1).as_matrix()
+    return(all_values)
+
+
+def rasterVectorToWGS(value_vector, nodeXmap, UTM_transformer):
+    # name the vector column
+    t1 = value_vector.reset_index().rename(columns={'index':'nodeid'})
+
+    # reduce the nodeXmap
+    t2 = nodeXmap[pd.notnull(nodeXmap.FID)]
+
+    # merge the node vector information with the crossmap
+    t3 = pd.merge(t1, t2, how='right', on='nodeid')
+
+    # transform raster_geom into WGS84
+    ids=[]
+    newpol = []
+
+    for ind, eachpoly in t3.iterrows():
+        # reverse polygon centroid mapping to WGS84
+        ras_x, ras_y = np.array(eachpoly['raster_geom'].centroid)
+        newcent = UTM_transformer(ras_x, ras_y, inverse=True)
+
+        # indexed by nodeid, LAT, LON
+        ids.append(tuple([eachpoly['nodeid'],newcent[1], newcent[0]]))
+
+        # reverse polygon mapping to WGS84
+        newpol.append(Polygon([UTM_transformer(x, y, inverse=True) 
+                               for x, y in eachpoly['raster_geom'].__geo_interface__['coordinates'][0]]))
+
+    # index each raster node by nodeid, LAT, LON
+    t4 = t3.set_index(pd.MultiIndex.from_tuples(ids, names=['','','']))
+    t4['wgs_raster'] = newpol
+    t4 = t4.set_geometry('wgs_raster')
+
+    # assimilate t5 as wide table
+    t5 = t4[['value']].T.reset_index(drop=True)
+    return(t4, t5)
diff --git a/ogh_xarray_landlab/plant_competition_ca_lb_pap.py b/ogh_xarray_landlab/plant_competition_ca_lb_pap.py
new file mode 100755
index 0000000..e073de7
--- /dev/null
+++ b/ogh_xarray_landlab/plant_competition_ca_lb_pap.py
@@ -0,0 +1,414 @@
+
+from landlab import Component
+import numpy as np
+from landlab.utils.decorators import use_file_name_or_kwds
+
+_VALID_METHODS = set(['Grid'])
+GRASS = 0
+SHRUB = 1
+TREE = 2
+BARE = 3
+SHRUBSEEDLING = 4
+TREESEEDLING = 5
+
+
+def assert_method_is_valid(method):
+    if method not in _VALID_METHODS:
+        raise ValueError('%s: Invalid method name' % method)
+
+
+class VegCA(Component):
+    """
+    Landlab component that simulates inter-species plant competition using
+    a 2D cellular automata model.
+
+    .. codeauthor:: Sai Nudurupati and Erkan Istanbulluoglu
+
+    Construction::
+
+        VegCA(grid, Pemaxg=0.35, ING=2., ThetaGrass=0.62, PmbGrass=0.05,
+            Pemaxsh=0.2, ThetaShrub=0.8, PmbShrub=0.01, tpmaxShrub=600,
+            Pemaxtr=0.25, ThetaTree=0.72, PmbTree=0.01, tpmaxTree=350,
+            ThetaShrubSeedling=0.64, PmbShrubSeedling=0.03,
+            tpmaxShrubSeedling=18, ThetaTreeSeedling=0.64,
+            PmbTreeSeedling=0.03, tpmaxTreeSeedling=18)
+
+    Parameters
+    ----------
+    grid: RasterModelGrid
+        A grid.
+    Pemaxg: float, optional
+        Maximal establishment probability of grass.
+    ING: float, optional
+        Parameter to define allelopathic effect of creosote on grass.
+    ThetaGrass: float, optional
+        Drought resistance threshold of grass.
+    PmbGrass: float, optional
+        Background mortality probability of grass.
+    Pemaxsh: float, optional
+        Maximal establishment probability of shrub.
+    ThetaShrub: float, optional
+        Drought resistance threshold of shrub.
+    PmbShrub: float, optional
+        Background mortality probability of shrub.
+    tpmaxShrub: float, optional
+        Maximum age of shrub (years).
+    Pemaxtr: float, optional
+        Maximal establishment probability of tree.
+    Thetatree: float, optional
+        Drought resistance threshold of tree.
+    PmbTree: float, optional
+        Background mortality probability of tree.
+    tpmaxTree: float, optional
+        Maximum age of tree (years).
+    ThetaShrubSeedling: float, optional
+        Drought resistance threshold of shrub seedling.
+    PmbShrubSeedling: float, optional
+        Background mortality probability of shrub seedling.
+    tpmaxShrubSeedling: float, optional
+        Maximum age of shrub seedling (years).
+    ThetaTreeSeedling: float, optional
+        Drought resistance threshold of tree seedling.
+    PmbTreeSeedling: float, optional
+        Background mortality probability of tree seedling.
+    tpmaxTreeSeedling: float, optional
+        Maximum age of tree seedling (years).
+
+    Examples
+    --------
+    >>> from landlab import RasterModelGrid
+    >>> from landlab.components import VegCA
+    >>> grid = RasterModelGrid((5, 4), spacing=(0.2, 0.2))
+    >>> VegCA.name
+    'Cellular Automata Plant Competition'
+    >>> sorted(VegCA.output_var_names)
+    ['plant__age', 'plant__live_index']
+    >>> sorted(VegCA.units) # doctest: +NORMALIZE_WHITESPACE
+    [('plant__age', 'Years'),
+     ('plant__live_index', 'None'),
+     ('vegetation__cumulative_water_stress', 'None'),
+     ('vegetation__plant_functional_type', 'None')]
+    >>> grid['cell']['vegetation__plant_functional_type']= np.arange(
+    ...        0, grid.number_of_cells, dtype=int)
+    >>> grid['cell']['vegetation__cumulative_water_stress'] = np.ones(
+    ...        grid.number_of_cells)
+    >>> ca_veg = VegCA(grid)
+    >>> ca_veg.grid.number_of_cell_rows
+    3
+    >>> ca_veg.grid.number_of_cell_columns
+    2
+    >>> ca_veg.grid is grid
+    True
+    >>> import numpy as np
+    >>> A = np.copy(grid['cell']['plant__age'])
+    >>> ca_veg.update()
+    >>> np.alltrue(grid['cell']['plant__age'] == A)
+    False
+    """
+    _name = 'Cellular Automata Plant Competition'
+
+    _input_var_names = (
+        'vegetation__cumulative_water_stress',
+        'vegetation__plant_functional_type',
+    )
+
+    _output_var_names = (
+        'plant__live_index',
+        'plant__age',
+    )
+
+    _var_units = {
+        'vegetation__cumulative_water_stress': 'None',
+        'vegetation__plant_functional_type': 'None',
+        'plant__live_index': 'None',
+        'plant__age': 'Years',
+    }
+
+    _var_mapping = {
+        'vegetation__cumulative_water_stress': 'cell',
+        'vegetation__plant_functional_type': 'cell',
+        'plant__live_index': 'cell',
+        'plant__age': 'cell',
+    }
+
+    _var_doc = {
+        'vegetation__cumulative_water_stress':
+            'cumulative vegetation__water_stress over the growing season',
+        'vegetation__plant_functional_type':
+            'classification of plants (int), grass=0, shrub=1, tree=2, \
+             bare=3, shrub_seedling=4, tree_seedling=5',
+        'plant__live_index':
+            '1 - vegetation__cumulative_water_stress',
+        'plant__age':
+            'Age of plant',
+    }
+
+    @use_file_name_or_kwds
+    def __init__(self, grid,
+                 Pemaxg=0.35, ING=2., ThetaGrass=0.62, PmbGrass=0.05,
+                 Pemaxsh=0.2, ThetaShrub=0.8, PmbShrub=0.01, tpmaxShrub=600,
+                 Pemaxtr=0.25, ThetaTree=0.72, PmbTree=0.01, tpmaxTree=350,
+                 ThetaShrubSeedling=0.64, PmbShrubSeedling=0.03,
+                 tpmaxShrubSeedling=18, ThetaTreeSeedling=0.64,
+                 PmbTreeSeedling=0.03, tpmaxTreeSeedling=18,
+                 **kwds):
+        """
+        Parameters
+        ----------
+        grid: RasterModelGrid
+            A grid.
+        Pemaxg: float, optional
+            Maximal establishment probability of grass.
+        ING: float, optional
+            Parameter to define allelopathic effect of creosote on grass.
+        ThetaGrass: float, optional
+            Drought resistance threshold of grass.
+        PmbGrass: float, optional
+            Background mortality probability of grass.
+        Pemaxsh: float, optional
+            Maximal establishment probability of shrub.
+        ThetaShrub: float, optional
+            Drought resistance threshold of shrub.
+        PmbShrub: float, optional
+            Background mortality probability of shrub.
+        tpmaxShrub: float, optional
+            Maximum age of shrub (years).
+        Pemaxtr: float, optional
+            Maximal establishment probability of tree.
+        Thetatree: float, optional
+            Drought resistance threshold of tree.
+        PmbTree: float, optional
+            Background mortality probability of tree.
+        tpmaxTree: float, optional
+            Maximum age of tree (years).
+        ThetaShrubSeedling: float, optional
+            Drought resistance threshold of shrub seedling.
+        PmbShrubSeedling: float, optional
+            Background mortality probability of shrub seedling.
+        tpmaxShrubSeedling: float, optional
+            Maximum age of shrub seedling (years).
+        ThetaTreeSeedling: float, optional
+            Drought resistance threshold of tree seedling.
+        PmbTreeSeedling: float, optional
+            Background mortality probability of tree seedling.
+        tpmaxTreeSeedling: float, optional
+            Maximum age of tree seedling (years).
+        """
+        self._method = kwds.pop('method', 'Grid')
+        self._Pemaxg = Pemaxg   # Pe-max-grass - max probability
+        self._Pemaxsh = Pemaxsh    # Pe-max-shrub
+        self._Pemaxtr = Pemaxtr   # Pe-max-tree
+        self._INg = ING  # Allelopathic effect on grass from creosotebush
+        self._th_g = ThetaGrass  # grass
+        self._th_sh = ThetaShrub  # shrub - Creosote
+        self._th_tr = ThetaTree  # Juniper pine
+        self._th_sh_s = ThetaShrubSeedling  # shrub seedling
+        self._th_tr_s = ThetaTreeSeedling  # Juniper pine seedling
+        self._Pmb_g = PmbGrass  # Background mortality probability - grass
+        self._Pmb_sh = PmbShrub  # shrub
+        self._Pmb_tr = PmbTree  # tree
+        self._Pmb_sh_s = PmbShrubSeedling  # shrub seedling
+        self._Pmb_tr_s = PmbTreeSeedling  # tree seedling
+        self._tpmax_sh = tpmaxShrub  # Maximum age - shrub
+        self._tpmax_tr = tpmaxTree  # Maximum age - tree
+        self._tpmax_sh_s = tpmaxShrubSeedling  # Maximum age - shrub seedling
+        self._tpmax_tr_s = tpmaxTreeSeedling  # Maximum age - tree seedling
+
+        assert_method_is_valid(self._method)
+
+        super(VegCA, self).__init__(grid)
+        """
+        Note: If the field 'vegetation__plant_functional_type' is not
+        input, a random field will be generated.
+        """
+        if 'vegetation__plant_functional_type' not in self.grid.at_cell:
+            grid['cell']['vegetation__plant_functional_type'] = (
+                    np.random.randint(0, 6, grid.number_of_cells))
+        """
+        Note: If the field 'plant__age' is not input,
+        a random field will be generated. Also note that the
+        field 'vegetation__plant_functional_type' will be altered
+        if the age of the shrubs and trees randomnly generated are
+        below the maximum age of their respective seedlings.
+        i.e. if the field 'vegetation__plant_functional_type'
+        originally has a shrub at a location 'x' and the field
+        'plant__age' is not provided, 'plant__age' will be generated.
+        If the value of this field at location 'x' is less than
+        shrub_seedling's maximum age, the field 
+        'vegetation__plant_functional_type' will be altered to
+        show SHRUBSEEDLING instead of SHRUB.
+        """
+        if 'plant__age' not in self.grid.at_cell:
+            VegType = grid['cell']['vegetation__plant_functional_type']
+            tp = np.zeros(grid.number_of_cells, dtype=int)
+            tp[VegType == TREE] = np.random.randint(
+                0, self._tpmax_tr, np.where(VegType == TREE)[0].shape)
+            tp[VegType == SHRUB] = np.random.randint(
+                0, self._tpmax_sh, np.where(VegType == SHRUB)[0].shape)
+            locs_trees = np.where(VegType == TREE)[0]
+            locs_shrubs = np.where(VegType == SHRUB)[0]
+            VegType[locs_trees[tp[locs_trees] < self._tpmax_tr_s]] = TREESEEDLING
+            VegType[locs_shrubs[tp[
+                locs_shrubs] < self._tpmax_sh_s]] = SHRUBSEEDLING
+            grid.at_cell['plant__age'] = tp
+
+        for name in self._input_var_names:
+            if name not in self.grid.at_cell:
+                self.grid.add_zeros('cell', name, units=self._var_units[name])
+
+        for name in self._output_var_names:
+            if name not in self.grid.at_cell:
+                self.grid.add_zeros('cell', name, units=self._var_units[name])
+
+        self._cell_values = self.grid['cell']
+
+    def update(self, time_elapsed=1, edit_vegcov=False):
+        """
+        Update fields with current loading conditions.
+
+        Parameters
+        ----------
+        time_elapsed: int, optional
+            Time elapsed - time step (years).
+        edit_vegcov: bool, optional
+            If edit_vegcov=True, an optional field 'vegetation__boolean_vegetated'
+            will be output, (i.e.) if a cell is vegetated the corresponding
+            cell of the field will be 1, otherwise it will be 0.
+        """
+        self._VegType = self._cell_values['vegetation__plant_functional_type']
+        self._CumWS = self._cell_values[
+            'vegetation__cumulative_water_stress']
+        self._live_index = self._cell_values['plant__live_index']
+        self._tp = self._cell_values['plant__age'] + time_elapsed
+#        self._tp_check_1 = np.copy(self._tp)
+
+        # Check if shrub and tree seedlings have matured
+        shrub_seedlings = np.where(self._VegType == SHRUBSEEDLING)[0]
+        tree_seedlings = np.where(self._VegType == TREESEEDLING)[0]
+        self._shrub_seedlings = np.copy(shrub_seedlings)
+        self._tree_seedlings = np.copy(tree_seedlings)
+        matured_shrubs = np.where(self._tp[shrub_seedlings] >
+                                  self._tpmax_sh_s)[0]
+        self._matured_shrubs = np.copy(matured_shrubs)
+        matured_trees = np.where(self._tp[tree_seedlings] >
+                                 self._tpmax_tr_s)[0]
+        self._matured_trees = np.copy(matured_trees)
+        self._VegType[shrub_seedlings[matured_shrubs]] = SHRUB
+        self._VegType[tree_seedlings[matured_trees]] = TREE
+        self._tp[shrub_seedlings[matured_shrubs]] = 0
+        self._tp[tree_seedlings[matured_trees]] = 0
+#        self._tp_check_2 = np.copy(self._tp)
+
+        # Identifying bare_cells and vegetated_cells before executing establishment
+        # and mortality.
+        bare_cells = np.where(self._VegType == BARE)[0]
+        plant_cells = np.where(self._VegType != BARE)[0]        
+
+        # Establishment
+        self._live_index = 1 - self._CumWS      # Plant live index = 1 - WS
+        n_bare = len(bare_cells)
+        first_ring = self.grid.looped_neighbors_at_cell[bare_cells]
+        second_ring =                                                   \
+            self.grid.second_ring_looped_neighbors_at_cell[bare_cells]
+        veg_type_fr = self._VegType[first_ring]
+        veg_type_sr = self._VegType[second_ring]
+        sh_phi_fr = phi_pft(veg_type_fr, SHRUB, self._live_index[first_ring])
+        tr_phi_fr = phi_pft(veg_type_fr, TREE, self._live_index[first_ring])
+        tr_phi_sr = phi_pft(veg_type_sr, TREE, self._live_index[second_ring])
+
+        n = count(veg_type_fr, SHRUB)
+        n[n==0] = 1./self._INg    # to avoid divide by zero errors for Peg calculation
+        Phi_sh = sh_phi_fr/8.
+        Phi_tr = (tr_phi_fr + tr_phi_sr/2.)/8.
+        Phi_g = np.mean(self._live_index[np.where(self._VegType == GRASS)])   # Need to check how to deal with empty veg plot
+        Pemaxg = self._Pemaxg * np.ones(n_bare)
+        Pemaxsh = self._Pemaxsh * np.ones(n_bare)
+        Pemaxtr = self._Pemaxtr * np.ones(n_bare)
+        Peg = np.amin(np.vstack((Phi_g/(n*self._INg), Pemaxg)), axis=0)
+        Pesh = np.amin(np.vstack((Phi_sh, Pemaxsh)), axis=0)
+        Petr = np.amin(np.vstack((Phi_tr, Pemaxtr)), axis=0)
+        Select_PFT_E = np.random.choice([GRASS, SHRUBSEEDLING, TREESEEDLING],
+                                        n_bare)
+        # Grass - 0; Shrub Seedling - 4; Tree Seedling - 5
+        Pest = np.choose(Select_PFT_E, [Peg, 0, 0, 0, Pesh, Petr])
+        # Probability of establishment
+        R_Est = np.random.rand(n_bare)
+        # Random number for comparison to establish
+        Establish = np.int32(np.where(
+                             np.greater_equal(Pest, R_Est) == True)[0])
+        self._VegType[bare_cells[Establish]] = Select_PFT_E[Establish]
+        self._tp[bare_cells[Establish]] = 0
+#        self._tp_check_3 = np.copy(self._tp)
+
+        # Mortality
+        n_plant = len(plant_cells)
+        Theta = np.choose(self._VegType[plant_cells], [
+                          self._th_g, self._th_sh, self._th_tr,
+                          0, self._th_sh_s, self._th_tr_s])        
+        PMd = self._CumWS[plant_cells] - Theta
+        PMd[PMd < 0.] = 0.
+        # Multiplied 2 below to not kill seedlings
+        tpmax = np.choose(self._VegType[plant_cells], [
+                          200000, self._tpmax_sh, self._tpmax_tr,
+                          0, 2 * self._tpmax_sh_s, 2 * self._tpmax_tr_s])        
+        PMa = np.zeros(n_plant)
+        tp_plant = self._tp[plant_cells]
+        tp_greater = np.where(tp_plant > 0.5*tpmax)[0]        
+        PMa[tp_greater] = ((tp_plant[tp_greater] - 0.5 * tpmax[tp_greater]) /
+                           (0.5 * tpmax[tp_greater]))        
+        PMb = np.choose(self._VegType[plant_cells], [
+                        self._Pmb_g, self._Pmb_sh, self._Pmb_tr, 0,
+                        self._Pmb_sh_s, self._Pmb_tr_s])        
+        PM = PMd + PMa + PMb
+        PM[PM > 1.] = 1.        
+        R_Mor = np.random.rand(n_plant)  # Random number for comparison to kill        
+        Mortality = np.int32(np.where(np.greater_equal(PM, R_Mor) == True)[0])
+        self._VegType[plant_cells[Mortality]] = BARE
+        self._tp[plant_cells[Mortality]] = 0
+        self._cell_values['plant__age'] = self._tp
+
+        if edit_vegcov:
+            self.grid['cell']['vegetation__boolean_vegetated'] =             \
+                        np.zeros(self.grid.number_of_cells, dtype=int)
+            self.grid['cell']['vegetation__boolean_vegetated'][
+                        self._VegType != BARE] = 1
+
+        # For debugging purposes
+#        self._n = n
+#        self._phi_g = Phi_g
+#        self._Peg = Peg
+#        self._Pesh = Pesh
+#        self._Petr = Petr
+#        self._selected_pft_e = Select_PFT_E
+#        self._theta = Theta  # shape = plant_cells
+#        self._PMd = PMd
+#        self._tpmax = tpmax
+#        self._tp_greater = tp_greater
+#        self._PMa = PMa
+#        self._PMb = PMb
+#        self._PM = PM
+#        self._R_Mor = R_Mor
+        self._bare_cells = bare_cells
+        self._Established = bare_cells[Establish]
+        self._plant_cells = plant_cells
+        self._Mortified = plant_cells[Mortality]
+
+
+def count(Arr, value):
+    Res = np.zeros(Arr.shape[0])
+    x, y = Arr.shape
+    for i in range(0, x):
+        for j in range(0, y):
+            if Arr[i][j] == value:
+                Res[i] += 1
+    return Res
+
+
+def phi_pft(VegType, PlantType, phi_value):
+    Phi = np.zeros(phi_value.shape[0])
+    x, y = phi_value.shape
+    for i in range(0, x):
+        for j in range(0, y):
+            if VegType[i][j] == PlantType:
+                Phi[i] += phi_value[i][j]
+    return Phi
diff --git a/ogh_xarray_landlab/soil_moisture_dynamics_lb_pap.py b/ogh_xarray_landlab/soil_moisture_dynamics_lb_pap.py
new file mode 100755
index 0000000..fe8c1f8
--- /dev/null
+++ b/ogh_xarray_landlab/soil_moisture_dynamics_lb_pap.py
@@ -0,0 +1,676 @@
+
+from landlab import Component
+from landlab.utils.decorators import use_file_name_or_kwds
+import numpy as np
+import six
+
+_VALID_METHODS = set(['Grid', 'Multi'])
+
+
+def assert_method_is_valid(method):
+    if method not in _VALID_METHODS:
+        raise ValueError('%s: Invalid method name' % method)
+
+
+class SoilMoisture(Component):
+    """
+    Landlab component that simulates root-zone average soil moisture at each
+    cell using inputs of potential evapotranspiration, live leaf area index,
+    and vegetation cover.
+
+    .. codeauthor:: Sai Nudurupati and Erkan Istanbulluoglu
+
+    Construction::
+
+        SoilMoisture(grid, runon=0., f_bare=0.7, soil_ew=0.1,
+           intercept_cap_grass= 1., zr_grass=0.3, I_B_grass=20.,
+           I_V_grass=24., K_s_grass=42., pc_grass=0.43, fc_grass=0.56,
+           sc_grass=0.33, wp_grass=0.13, hgw_grass=0.1, beta_grass=13.8,
+           LAI_max_grass=2., LAIR_max_grass=2.88,
+           intercept_cap_shrub=1.5, zr_shrub=0.5, I_B_shrub=20.,
+           I_V_shrub=40., K_s_shrub=42., pc_shrub=0.43, fc_shrub=0.56,
+           sc_shrub=0.24, wp_shrub=0.13, hgw_shrub=0.1, beta_shrub=13.8,
+           LAI_max_shrub=2., LAIR_max_shrub=2.,
+           intercept_cap_tree=2., zr_tree=1.3, I_B_tree=20.,
+           I_V_tree=40., K_s_tree=42., pc_tree=0.43, fc_tree=0.56,
+           sc_tree=0.22, wp_tree=0.15, hgw_tree=0.1, beta_tree=13.8,
+           LAI_max_tree=4., LAIR_max_tree=4.,
+           intercept_cap_bare=1., zr_bare=0.15, I_B_bare=20.,
+           I_V_bare=20., K_s_bare=42., pc_bare=0.43, fc_bare=0.56, sc_bare=0.33,
+           wp_bare=0.13, hgw_bare=0.1, beta_bare=13.8,
+           LAI_max_bare=0.01, LAIR_max_bare=0.01)
+
+    Parameters
+    ----------
+    grid: RasterModelGrid
+        A grid.
+    runon: float, optional
+        Runon from higher elevation (mm).
+    f_bare: float, optional
+        Fraction to partition PET for bare soil (None).
+    soil_ew: float, optional
+        Residual Evaporation after wilting (mm/day).
+    intercept_cap: float, optional
+        Plant Functional Type (PFT) specific full canopy interception
+        capacity.
+    zr: float, optional
+        Root depth (m).
+    I_B: float, optional
+        Infiltration capacity of bare soil (mm/h).
+    I_V: float, optional
+        Infiltration capacity of vegetated soil (mm/h).
+    K_s: float, optional
+        Hydraulic conductivity of soil (mm/h).
+    pc: float, optional
+        Soil porosity (None).
+    fc: float, optional
+        Soil saturation degree at field capacity (None).
+    sc: float, optional
+        Soil saturation degree at stomatal closure (None).
+    wp: float, optional
+        Soil saturation degree at wilting point (None).
+    hgw: float, optional
+        Soil saturation degree at hygroscopic point (None).
+    beta: float, optional
+        Deep percolation constant = 2*b+3 where b is
+        water retention (None).
+    LAI_max: float, optional
+        Maximum leaf area index (m^2/m^2).
+    LAIR_max: float, optional
+        Reference leaf area index (m^2/m^2).
+
+    Examples
+    --------
+    >>> from landlab import RasterModelGrid
+    >>> from landlab.components.soil_moisture import SoilMoisture
+    >>> grid = RasterModelGrid((5, 4), spacing=(0.2, 0.2))
+    >>> SoilMoisture.name
+    'Soil Moisture'
+    >>> sorted(SoilMoisture.output_var_names) # doctest: +NORMALIZE_WHITESPACE
+    ['soil_moisture__root_zone_leakage',
+     'soil_moisture__saturation_fraction',
+     'surface__evapotranspiration',
+     'surface__runoff',
+     'vegetation__water_stress']
+    >>> sorted(SoilMoisture.units) # doctest: +NORMALIZE_WHITESPACE
+    [('rainfall__daily_depth', 'mm'),
+     ('soil_moisture__initial_saturation_fraction', 'None'),
+     ('soil_moisture__root_zone_leakage', 'mm'),
+     ('soil_moisture__saturation_fraction', 'None'),
+     ('surface__evapotranspiration', 'mm'),
+     ('surface__potential_evapotranspiration_rate', 'mm'),
+     ('surface__runoff', 'mm'),
+     ('vegetation__cover_fraction', 'None'),
+     ('vegetation__live_leaf_area_index', 'None'),
+     ('vegetation__plant_functional_type', 'None'),
+     ('vegetation__water_stress', 'None')]
+    >>> grid['cell']['vegetation__plant_functional_type']= (
+    ...            np.zeros(grid.number_of_cells, dtype=int))
+    >>> SM = SoilMoisture(grid)
+    >>> SM.grid.number_of_cell_rows
+    3
+    >>> SM.grid.number_of_cell_columns
+    2
+    >>> SM.grid is grid
+    True
+    >>> import numpy as np
+    >>> np.allclose(grid.at_cell['soil_moisture__saturation_fraction'], 0.)
+    True
+    >>> grid['cell']['surface__potential_evapotranspiration_rate']= np.array([
+    ...            0.2554777, 0.2554777 , 0.22110221, 0.22110221,
+    ...            0.24813062, 0.24813062])
+    >>> grid['cell']['soil_moisture__initial_saturation_fraction']= (
+    ...        0.75 * np.ones(grid.number_of_cells))
+    >>> grid['cell']['vegetation__live_leaf_area_index']= (
+    ...        2. * np.ones(grid.number_of_cells))
+    >>> grid['cell']['vegetation__cover_fraction']= (
+    ...        np.ones(grid.number_of_cells))
+    >>> current_time = 0.5
+    >>> grid['cell']['rainfall__daily_depth'] = (
+    ...        25. * np.ones(grid.number_of_cells))
+    >>> current_time = SM.update(current_time)
+    >>> np.allclose(grid.at_cell['soil_moisture__saturation_fraction'], 0.)
+    False
+    """
+    _name = 'Soil Moisture'
+
+    _input_var_names = (
+        'vegetation__cover_fraction',
+        'vegetation__live_leaf_area_index',
+        'surface__potential_evapotranspiration_rate',
+        'surface__potential_evapotranspiration_rate__grass',
+        'soil_moisture__initial_saturation_fraction',
+        'vegetation__plant_functional_type',
+        'rainfall__daily_depth',
+    )
+
+    _output_var_names = (
+        'vegetation__water_stress',
+        'soil_moisture__saturation_fraction',
+        'soil_moisture__root_zone_leakage',
+        'surface__runoff',
+        'surface__runon',
+        'surface__evapotranspiration',
+    )
+
+    _var_units = {
+        'vegetation__cover_fraction': 'None',
+        'vegetation__live_leaf_area_index': 'None',
+        'surface__potential_evapotranspiration_rate': 'mm',
+        'surface__potential_evapotranspiration_rate__grass': 'mm',
+        'vegetation__plant_functional_type': 'None',
+        'vegetation__water_stress': 'None',
+        'soil_moisture__saturation_fraction': 'None',
+        'soil_moisture__initial_saturation_fraction': 'None',
+        'soil_moisture__root_zone_leakage': 'mm',
+        'surface__runoff': 'mm',
+        'surface__runon': 'mm',
+        'surface__evapotranspiration': 'mm',
+        'rainfall__daily_depth': 'mm',
+    }
+
+    _var_mapping = {
+        'vegetation__cover_fraction': 'cell',
+        'vegetation__live_leaf_area_index': 'cell',
+        'surface__potential_evapotranspiration_rate': 'cell',
+        'surface__potential_evapotranspiration_rate__grass': 'cell',
+        'vegetation__plant_functional_type': 'cell',
+        'vegetation__water_stress': 'cell',
+        'soil_moisture__saturation_fraction': 'cell',
+        'soil_moisture__initial_saturation_fraction': 'cell',
+        'soil_moisture__root_zone_leakage': 'cell',
+        'surface__runoff': 'cell',
+        'surface__runon': 'cell',
+        'surface__evapotranspiration': 'cell',
+        'rainfall__daily_depth': 'cell',
+    }
+
+    _var_doc = {
+        'vegetation__cover_fraction':
+            'fraction of land covered by vegetation',
+        'vegetation__live_leaf_area_index':
+            'one-sided green leaf area per unit ground surface area',
+        'surface__potential_evapotranspiration_rate':
+            'potential sum of evaporation and plant transpiration',
+        'surface__potential_evapotranspiration_rate__grass':
+            'potential sum of evaporation and grass transpiration, \
+             for partitioning bare soil evapotranspiration rate',
+        'vegetation__plant_functional_type':
+            'classification of plants (int), grass=0, shrub=1, tree=2, \
+             bare=3, shrub_seedling=4, tree_seedling=5',
+        'vegetation__water_stress':
+            'parameter that represents nonlinear effects of water deficit \
+             on plants',
+        'soil_moisture__saturation_fraction':
+            'relative volumetric water content (theta) - limits=[0,1]',
+        'soil_moisture__initial_saturation_fraction':
+            'initial soil_moisture__saturation_fraction',
+        'soil_moisture__root_zone_leakage':
+            'leakage of water into deeper portions of the soil not accessible \
+             to the plant',
+        'surface__runoff':
+            'infiltration excess runoff from ground surface',
+        'surface__runon':
+            'infiltration excess runon',
+        'surface__evapotranspiration':
+            'actual sum of evaporation and plant transpiration',
+        'rainfall__daily_depth':
+            'Rain in (mm) as a field, allowing spatio-temporal soil moisture \
+             saturation analysis.',
+    }
+
+    @use_file_name_or_kwds
+    def __init__(self, grid, ordered_cells=None, runon_switch=0,
+                 f_bare=0.7, soil_ew=0.1,
+                 intercept_cap_grass=1., zr_grass=0.3, I_B_grass=20.,
+                 I_V_grass=24., K_s_grass=42., pc_grass=0.43, fc_grass=0.56,
+                 sc_grass=0.33, wp_grass=0.13, hgw_grass=0.1, beta_grass=13.8,
+                 LAI_max_grass=2., LAIR_max_grass=2.88,
+                 intercept_cap_shrub=1.5, zr_shrub=0.5, I_B_shrub=20.,
+                 I_V_shrub=40., K_s_shrub=42., pc_shrub=0.43, fc_shrub=0.56,
+                 sc_shrub=0.24, wp_shrub=0.13, hgw_shrub=0.1, beta_shrub=13.8,
+                 LAI_max_shrub=2., LAIR_max_shrub=2.,
+                 intercept_cap_tree=2., zr_tree=1.3, I_B_tree=20.,
+                 I_V_tree=40., K_s_tree=42., pc_tree=0.43, fc_tree=0.56,
+                 sc_tree=0.22, wp_tree=0.15, hgw_tree=0.1, beta_tree=13.8,
+                 LAI_max_tree=4., LAIR_max_tree=4.,
+                 intercept_cap_bare=1., zr_bare=0.15, I_B_bare=20.,
+                 I_V_bare=20., K_s_bare=42., pc_bare=0.43, fc_bare=0.56,
+                 sc_bare=0.33, wp_bare=0.13, hgw_bare=0.1, beta_bare=13.8,
+                 LAI_max_bare=0.01, LAIR_max_bare=0.01, **kwds):
+        """
+        Parameters
+        ----------
+        grid: RasterModelGrid
+            A grid.
+        runon_switch: int, optional
+            To indicate whether runon needs to considered (mm);
+            0 - No runon, 1 - runon.
+        ordered_cells: numpy.array, required if runon_switch = 1
+            ordered_cells has the grid cells sorted in an order of descending
+            channel length in a delineated watershed
+         f_bare: float, optional
+            Fraction to partition PET for bare soil (None).
+        soil_ew: float, optional
+            Residual Evaporation after wilting (mm/day).
+        intercept_cap: float, optional
+            Plant Functional Type (PFT) specific full canopy interception
+            capacity.
+        zr: float, optional
+            Root depth (m).
+        I_B: float, optional
+            Infiltration capacity of bare soil (mm/h).
+        I_V: float, optional
+            Infiltration capacity of vegetated soil (mm/h).
+        K_s: float, optional
+            Hydraulic conductivity of soil (mm/h).
+        pc: float, optional
+            Soil porosity (None).
+        fc: float, optional
+            Soil saturation degree at field capacity (None).
+        sc: float, optional
+            Soil saturation degree at stomatal closure (None).
+        wp: float, optional
+            Soil saturation degree at wilting point (None).
+        hgw: float, optional
+            Soil saturation degree at hygroscopic point (None).
+        beta: float, optional
+            Deep percolation constant = 2*b+4 where b is
+            water retention (None).
+        LAI_max: float, optional
+            Maximum leaf area index (m^2/m^2).
+        LAIR_max: float, optional
+            Reference leaf area index (m^2/m^2).
+        """
+        self._method = kwds.pop('method', 'Grid')
+
+        assert_method_is_valid(self._method)
+
+        super(SoilMoisture, self).__init__(grid)
+
+        self.initialize(ordered_cells=ordered_cells, runon_switch=runon_switch,
+                        f_bare=f_bare, soil_ew=soil_ew,
+                        intercept_cap_grass=intercept_cap_grass,
+                        zr_grass=zr_grass, I_B_grass=I_B_grass,
+                        I_V_grass=I_V_grass, K_s_grass=K_s_grass,
+                        pc_grass=pc_grass, fc_grass=fc_grass,
+                        sc_grass=sc_grass, wp_grass=wp_grass,
+                        hgw_grass=hgw_grass, beta_grass=beta_grass,
+                        LAI_max_grass=LAI_max_grass,
+                        LAIR_max_grass=LAIR_max_grass,
+                        intercept_cap_shrub=intercept_cap_shrub,
+                        zr_shrub=zr_shrub, I_B_shrub=I_B_shrub,
+                        I_V_shrub=I_V_shrub, K_s_shrub=K_s_shrub,
+                        pc_shrub=pc_shrub, fc_shrub=fc_shrub,
+                        sc_shrub=sc_shrub, wp_shrub=wp_shrub,
+                        hgw_shrub=hgw_shrub, beta_shrub=beta_shrub,
+                        LAI_max_shrub=LAI_max_shrub,
+                        LAIR_max_shrub=LAIR_max_shrub,
+                        intercept_cap_tree=intercept_cap_tree, zr_tree=zr_tree,
+                        I_B_tree=I_B_tree, I_V_tree=I_V_tree,
+                        K_s_tree=K_s_tree, pc_tree=pc_tree,
+                        fc_tree=fc_tree, sc_tree=sc_tree, wp_tree=wp_tree,
+                        hgw_tree=hgw_tree, beta_tree=beta_tree,
+                        LAI_max_tree=LAI_max_tree, LAIR_max_tree=LAIR_max_tree,
+                        intercept_cap_bare=intercept_cap_bare, zr_bare=zr_bare,
+                        I_B_bare=I_B_bare, I_V_bare=I_V_bare,
+                        K_s_bare=K_s_bare, pc_bare=pc_bare,
+                        fc_bare=fc_bare, sc_bare=sc_bare, wp_bare=wp_bare,
+                        hgw_bare=hgw_bare, beta_bare=beta_bare,
+                        LAI_max_bare=LAI_max_bare,
+                        LAIR_max_bare=LAIR_max_bare, **kwds)
+
+        for name in self._input_var_names:
+            if name not in self.grid.at_cell:
+                self.grid.add_zeros('cell', name, units=self._var_units[name])
+
+        for name in self._output_var_names:
+            if name not in self.grid.at_cell:
+                self.grid.add_zeros('cell', name, units=self._var_units[name])
+
+        self._nodal_values = self.grid['node']
+
+        self._cell_values = self.grid['cell']
+
+    def initialize(self, ordered_cells=None, runon_switch=0, f_bare=0.7,
+                   soil_ew=0.1, intercept_cap_grass=1., zr_grass=0.3,
+                   I_B_grass=20., I_V_grass=24., K_s_grass=42.,
+                   pc_grass=0.43, fc_grass=0.56,
+                   sc_grass=0.33, wp_grass=0.13, hgw_grass=0.1,
+                   beta_grass=13.8, LAI_max_grass=2., LAIR_max_grass=2.88,
+                   intercept_cap_shrub=1.5, zr_shrub=0.5, I_B_shrub=20.,
+                   I_V_shrub=40., K_s_shrub=42., pc_shrub=0.43,
+                   fc_shrub=0.56, sc_shrub=0.24,
+                   wp_shrub=0.13, hgw_shrub=0.1, beta_shrub=13.8,
+                   LAI_max_shrub=2., LAIR_max_shrub=2.,
+                   intercept_cap_tree=2., zr_tree=1.3, I_B_tree=20.,
+                   I_V_tree=40., K_s_tree=42., pc_tree=0.43,
+                   fc_tree=0.56, sc_tree=0.22,
+                   wp_tree=0.15, hgw_tree=0.1, beta_tree=13.8,
+                   LAI_max_tree=4., LAIR_max_tree=4.,
+                   intercept_cap_bare=1., zr_bare=0.15, I_B_bare=20.,
+                   I_V_bare=20., K_s_bare=42., pc_bare=0.43,
+                   fc_bare=0.56, sc_bare=0.33,
+                   wp_bare=0.13, hgw_bare=0.1, beta_bare=13.8,
+                   LAI_max_bare=0.01, LAIR_max_bare=0.01, **kwds):
+        # GRASS = 0; SHRUB = 1; TREE = 2; BARE = 3;
+        # SHRUBSEEDLING = 4; TREESEEDLING = 5
+        """
+        Parameters
+        ----------
+        grid: RasterModelGrid
+            A grid.
+        runon_switch: int, optional
+            To indicate whether runon needs to considered (mm);
+            0 - No runon, 1 - runon.
+        ordered_cells: numpy.array, required if runon_switch = 1
+            ordered_cells has the grid cells sorted in an order of descending
+            channel length in a delineated watershed
+        f_bare: float, optional
+            Fraction to partition PET for bare soil (None).
+        soil_ew: float, optional
+            Residual Evaporation after wilting (mm/day).
+        intercept_cap: float, optional
+            Plant Functional Type (PFT) specific full canopy interception
+            capacity.
+        zr: float, optional
+            Root depth (m).
+        I_B: float, optional
+            Infiltration capacity of bare soil (mm/h).
+        I_V: float, optional
+            Infiltration capacity of vegetated soil (mm/h).
+        K_s: float, optional
+            Hydraulic conductivity of soil (mm/h).
+        pc: float, optional
+            Soil porosity (None).
+        fc: float, optional
+            Soil saturation degree at field capacity (None).
+        sc: float, optional
+            Soil saturation degree at stomatal closure (None).
+        wp: float, optional
+            Soil saturation degree at wilting point (None).
+        hgw: float, optional
+            Soil saturation degree at hygroscopic point (None).
+        beta: float, optional
+            Deep percolation constant = 2*b+4 where b is
+            water retention (None).
+        parameter (None)
+        LAI_max: float, optional
+            Maximum leaf area index (m^2/m^2).
+        LAIR_max: float, optional
+            Reference leaf area index (m^2/m^2).
+        """
+
+        self._vegtype = self.grid['cell']['vegetation__plant_functional_type']
+        self._runon_switch = runon_switch
+        self._fbare = f_bare
+        self.ordered_cells = ordered_cells
+
+        self._interception_cap = np.choose(self._vegtype, [
+             intercept_cap_grass, intercept_cap_shrub, intercept_cap_tree,
+             intercept_cap_bare, intercept_cap_shrub, intercept_cap_tree])
+
+        self._zr = np.choose(self._vegtype, [
+             zr_grass, zr_shrub, zr_tree, zr_bare, zr_shrub, zr_tree])
+
+        self._soil_Ib = np.choose(self._vegtype, [
+             I_B_grass, I_B_shrub, I_B_tree, I_B_bare, I_B_shrub, I_B_tree])
+
+        self._soil_Iv = np.choose(self._vegtype, [
+             I_V_grass, I_V_shrub, I_V_tree, I_V_bare, I_V_shrub, I_V_tree])
+
+        self._soil_Ks = np.choose(self._vegtype, [
+             K_s_grass, K_s_shrub, K_s_tree, K_s_bare, K_s_shrub, K_s_tree])
+
+        self._soil_Ew = soil_ew
+
+        self._soil_pc = np.choose(self._vegtype, [
+             pc_grass, pc_shrub, pc_tree, pc_bare, pc_shrub, pc_tree])
+
+        self._soil_fc = np.choose(self._vegtype, [
+             fc_grass, fc_shrub, fc_tree, fc_bare, fc_shrub, fc_tree])
+
+        self._soil_sc = np.choose(self._vegtype, [
+             sc_grass, sc_shrub, sc_tree, sc_bare, sc_shrub, sc_tree])
+
+        self._soil_wp = np.choose(self._vegtype, [
+             wp_grass, wp_shrub, wp_tree, wp_bare, wp_shrub, wp_tree])
+
+        self._soil_hgw = np.choose(self._vegtype, [
+             hgw_grass, hgw_shrub, hgw_tree, hgw_bare, hgw_shrub, hgw_tree])
+
+        self._soil_beta = np.choose(self._vegtype, [
+             beta_grass, beta_shrub, beta_tree,
+             beta_bare, beta_shrub, beta_tree])
+
+        self._LAI_max = np.choose(self._vegtype, [
+             LAI_max_grass, LAI_max_shrub, LAI_max_tree,
+             LAI_max_bare, LAI_max_shrub, LAI_max_tree])
+
+        self._LAIR_max = np.choose(self._vegtype, [
+             LAIR_max_grass, LAIR_max_shrub, LAIR_max_tree,
+             LAIR_max_bare, LAIR_max_shrub, LAIR_max_tree])
+
+
+    def update(self, current_time, Tb=24., Tr=0., **kwds):
+        """
+        Update fields with current loading conditions.
+
+        Parameters
+        ----------
+        current_time: float
+            Current time (years).
+        Tr: float, optional
+            Storm duration (hours).
+        Tb: float, optional
+            Inter-storm duration (hours).
+        """
+        P_ = self._cell_values['rainfall__daily_depth']
+        self._PET = (
+            self._cell_values['surface__potential_evapotranspiration_rate'])
+        self._pet_g = (
+            self._cell_values['surface__potential_evapotranspiration_rate__grass'])
+        self._SO = (
+            self._cell_values['soil_moisture__initial_saturation_fraction'])
+        self._vegcover = self._cell_values['vegetation__cover_fraction']
+        self._water_stress = self._cell_values['vegetation__water_stress']
+        self._S = self._cell_values['soil_moisture__saturation_fraction']
+        self._D = self._cell_values['soil_moisture__root_zone_leakage']
+        self._ETA = self._cell_values['surface__evapotranspiration']
+        self._fr = (self._cell_values['vegetation__live_leaf_area_index'] /
+                    self._LAIR_max)
+        self._runoff = self._cell_values['surface__runoff']
+        self._runon = self._cell_values['surface__runon']
+        self._runoff[:] = 0.   # Initializing runoff to zero
+        self._runon[:] = 0.    # Initializing runon to zero
+        # LAIl = self._cell_values['vegetation__live_leaf_area_index']
+        # LAIt = LAIl+self._cell_values['DeadLeafAreaIndex']
+        # if LAIt.all() == 0.:
+        #     self._fr = np.zeros(self.grid.number_of_cells)
+        # else:
+        #     self._fr = (self._vegcover[0]*LAIl/LAIt)
+        self._fr[self._fr > 1.] = 1.
+        self._Sini = np.zeros(self._SO.shape)
+        self._ETmax = np.zeros(self._SO.shape)
+        self._ts = np.zeros(self._SO.shape)  # record Time to Saturation
+        self._precip_int = np.zeros(self._SO.shape)
+        # Adding routine to add runon & runoff
+        if self._runon_switch:
+            # Make sure that flow_router has been called before
+            r_cell = (self.grid.cell_at_node[
+                self.grid.at_node['flow__receiver_node']])
+            r_cell = r_cell[r_cell != -1]
+            # finding cells that aren't part of
+            # ordered cells (delineated watershed)
+            diff_cells = (np.setdiff1d(
+                range(0, self.grid.number_of_cells), self.ordered_cells))
+            # trvrsl_order has ordered cells computed first and then the rest
+            # of the cells
+            trvrsl_order = np.concatenate((self.ordered_cells, diff_cells),
+                                          axis=0)
+        else:
+            # trvrsl_order is just regular 'all cells' if no runon is computed
+            trvrsl_order = range(0, self.grid.number_of_cells)
+
+        for cell in trvrsl_order:
+            # Routine to calculate runon
+            if self._runon_switch:
+                if cell in self.ordered_cells:
+                    donors = []
+                    donors = list(np.where(r_cell == cell)[0])
+                    if len(donors) != 0:
+                        for k in range(0, len(donors)):
+                            self._runon[cell] += self._runoff[donors[k]]
+
+            P = P_[cell]
+            runon = self._runon[cell]
+            if runon < 0:
+                six._print('Runon < 0!')
+            # print cell
+            s = self._SO[cell]
+            fbare = self._fbare
+            ZR = self._zr[cell]
+            pc = self._soil_pc[cell]
+            fc = self._soil_fc[cell]
+            scc = self._soil_sc[cell]
+            wp = self._soil_wp[cell]
+            hgw = self._soil_hgw[cell]
+            beta = self._soil_beta[cell]
+            Ks = self._soil_Ks[cell]
+            if self._vegtype[cell] == 0:   # 0 - GRASS
+                sc = scc*self._fr[cell]+(1-self._fr[cell])*fc
+            else:
+                sc = scc
+
+            # Infiltration capacity
+            Inf_cap = (self._soil_Ib[cell]*(1-self._vegcover[cell]) +
+                       self._soil_Iv[cell]*self._vegcover[cell])
+            # Interception capacity
+            Int_cap = min(self._vegcover[cell]*self._interception_cap[cell],
+                          P*self._vegcover[cell])
+            # Effective precipitation depth
+            Peff = max((P + max(runon, 0.) - Int_cap), 0.)
+            mu = (Ks/1000.0)/(pc*ZR*(np.exp(beta*(1.-fc))-1.))
+
+            if self._vegtype[cell] == 3:
+                Ep = max((fbare*self._pet_g[cell]), 0.0001)
+            else:
+                Ep = max((self._PET[cell]*self._fr[cell] +
+                         fbare*self._pet_g[cell]*(1.-self._fr[cell])) -
+                         Int_cap, 0.0001)  # mm/d
+            self._ETmax[cell] = Ep
+            nu = ((Ep / 24.) / 1000.) / (pc*ZR)   # Loss function parameter
+            # Loss function parameter
+            nuw = ((self._soil_Ew/24.)/1000.)/(pc*ZR)
+            # Precipitation Intensity
+            precip_int = Peff/Tr
+            self._precip_int[cell] = precip_int
+            # Time to saturation Ts
+            if precip_int <= 0.:
+                Ts = np.inf
+            else:
+                Ts = (((1 - self._SO[cell]) * (pc * ZR * 1000.)) /
+                      (precip_int*(1-np.exp((-1)*Inf_cap/precip_int))))
+            self._ts[cell] = Ts
+            # Computing runoff
+            # If using Poisson storms with Tr = 0, (Precip_int * Tr = Precip)
+            if Tr == 0.:
+                self._runoff[cell] = max((Peff - Inf_cap), 0.)
+                sini = min(self._SO[cell] + ((Peff -
+                           self._runoff[cell])/(pc*ZR*1000.)), 1.)
+            # If using regular storms with (Tr != 0.)
+            elif Tr < Ts:
+                self._runoff[cell] = max((precip_int - Inf_cap)*Tr, 0.)
+                sini = min(self._SO[cell] + ((precip_int * Tr -
+                           self._runoff[cell])/(pc*ZR*1000.)), 1.)
+            else:
+                sini = 1
+                self._runoff[cell] = max(((precip_int-Inf_cap)*Ts +
+                                          (precip_int*(Tr-Ts))), 0.)
+
+            if sini >= fc:
+                tfc = (1./(beta*(mu-nu)))*(beta*(fc-sini) + np.log((
+                       nu-mu+mu*np.exp(beta*(sini-fc)))/nu))
+                tsc = ((fc-sc)/nu)+tfc
+                twp = ((sc-wp)/(nu-nuw))*np.log(nu/nuw)+tsc
+
+                if Tb < tfc:
+                    s = abs(sini-(1./beta)*np.log(((nu-mu+mu *
+                            np.exp(beta*(sini-fc)))*np.exp(beta*(nu-mu)*Tb) -
+                            mu*np.exp(beta*(sini-fc)))/(nu-mu)))
+
+                    self._D[cell] = ((pc*ZR*1000.)*(sini-s))-(Tb*(Ep/24.))
+                    self._ETA[cell] = (Tb*(Ep/24.))
+
+                elif Tb >= tfc and Tb < tsc:
+                    s = fc-(nu*(Tb-tfc))
+                    self._D[cell] = ((pc*ZR*1000.)*(sini-fc))-((tfc)*(Ep/24.))
+                    self._ETA[cell] = (Tb*(Ep/24.))
+
+                elif Tb >= tsc and Tb < twp:
+                    s = (wp+(sc-wp)*((nu/(nu-nuw))*np.exp((-1)*((nu-nuw) /
+                         (sc-wp))*(Tb-tsc))-(nuw/(nu-nuw))))
+                    self._D[cell] = ((pc*ZR*1000.)*(sini-fc))-(tfc*Ep/24.)
+                    self._ETA[cell] = (1000.*ZR*pc*(sini-s))-self._D[cell]
+
+                else:
+                    s = (hgw+(wp-hgw)*np.exp((-1)*(nuw/(wp-hgw)) *
+                         max(Tb-twp, 0.)))
+                    self._D[cell] = ((pc*ZR*1000.)*(sini-fc))-(tfc*Ep/24.)
+                    self._ETA[cell] = (1000.*ZR*pc*(sini-s))-self._D[cell]
+
+            elif sini < fc and sini >= sc:
+                tfc = 0.
+                tsc = (sini-sc)/nu
+                twp = ((sc-wp)/(nu-nuw))*np.log(nu/nuw)+tsc
+
+                if Tb < tsc:
+                    s = sini - nu*Tb
+                    self._D[cell] = 0.
+                    self._ETA[cell] = 1000.*ZR*pc*(sini-s)
+
+                elif Tb >= tsc and Tb < twp:
+                    s = (wp+(sc-wp)*((nu/(nu-nuw))*np.exp((-1) *
+                         ((nu-nuw)/(sc-wp))*(Tb-tsc))-(nuw/(nu-nuw))))
+                    self._D[cell] = 0
+                    self._ETA[cell] = (1000.*ZR*pc*(sini-s))
+
+                else:
+                    s = hgw+(wp-hgw)*np.exp((-1)*(nuw/(wp-hgw))*(Tb-twp))
+                    self._D[cell] = 0.
+                    self._ETA[cell] = (1000.*ZR*pc*(sini-s))
+
+            elif sini < sc and sini >= wp:
+                tfc = 0
+                tsc = 0
+                twp = (((sc-wp)/(nu-nuw))*np.log(1+(nu-nuw)*(sini-wp) /
+                       (nuw*(sc-wp))))
+
+                if Tb < twp:
+                    s = (wp+((sc-wp)/(nu-nuw))*((np.exp((-1)*((nu-nuw) /
+                         (sc-wp))*Tb))*(nuw+((nu-nuw)/(sc-wp))*(sini-wp))-nuw))
+                    self._D[cell] = 0.
+                    self._ETA[cell] = (1000.*ZR*pc*(sini-s))
+
+                else:
+                    s = hgw+(wp-hgw)*np.exp((-1)*(nuw/(wp-hgw))*(Tb-twp))
+                    self._D[cell] = 0.
+                    self._ETA[cell] = (1000.*ZR*pc*(sini-s))
+
+            else:
+                tfc = 0.
+                tsc = 0.
+                twp = 0.
+
+                s = hgw+(sini-hgw)*np.exp((-1)*(nuw/(wp-hgw))*Tb)
+                self._D[cell] = 0.
+                self._ETA[cell] = (1000.*ZR*pc*(sini-s))
+
+            self._water_stress[cell] = min(((max(((sc - (s+sini)/2.) /
+                                           (sc - wp)), 0.))**4.), 1.0)
+            self._S[cell] = s
+            self._SO[cell] = s
+            self._Sini[cell] = sini
+
+        current_time += (Tb+Tr)/(24.*365.25)
+        return current_time
diff --git a/ogh_xarray_landlab/vegetation_dynamics_lb_pap.py b/ogh_xarray_landlab/vegetation_dynamics_lb_pap.py
new file mode 100755
index 0000000..7f6d21e
--- /dev/null
+++ b/ogh_xarray_landlab/vegetation_dynamics_lb_pap.py
@@ -0,0 +1,473 @@
+
+from landlab import Component
+from landlab.utils.decorators import use_file_name_or_kwds
+import numpy as np
+
+_VALID_METHODS = set(['Grid'])
+
+
+def assert_method_is_valid(method):
+    if method not in _VALID_METHODS:
+        raise ValueError('%s: Invalid method name' % method)
+
+
+class Vegetation(Component):
+    """
+    Landlab component that simulates net primary productivity, biomass
+    and leaf area index at each cell based on inputs of root-zone
+    average soil moisture.
+
+    .. codeauthor:: Sai Nudurupati and Erkan Istanbulluoglu
+
+    Construction::
+        Vegetation(grid, Blive_init=102., Bdead_init=450.,
+            ETthreshold_up=3.8, ETthreshold_down=6.8, Tdmax=10., w=0.55,
+            WUE_grass=0.01, LAI_max_grass=2., cb_grass=0.0047, cd_grass=0.009,
+            ksg_grass=0.012, kdd_grass=0.013, kws_grass=0.02,
+            WUE_shrub=0.0025, LAI_max_shrub=2., cb_shrub=0.004, cd_shrub=0.01,
+            ksg_shrub=0.002, kdd_shrub=0.013, kws_shrub=0.02,
+            WUE_tree=0.0045, LAI_max_tree=4., cb_tree=0.004, cd_tree=0.01,
+            ksg_tree=0.002, kdd_tree=0.013, kws_tree=0.01,
+            WUE_bare=0.01, LAI_max_bare=0.01, cb_bare=0.0047, cd_bare=0.009,
+            ksg_bare=0.012, kdd_bare=0.013, kws_bare=0.02)
+
+    Parameters
+    ----------
+    grid: RasterModelGrid
+        A grid.
+    Blive_init: float, optional
+        Initial value for vegetation__live_biomass. Converted to field.
+    Bdead_init: float, optional
+        Initial value for vegetation__dead_biomass. Coverted to field.
+    ETthreshold_up: float, optional
+        Potential Evapotranspiration (PET) threshold for
+        growing season (mm/d).
+    ETthreshold_down: float, optional
+        PET threshold for dormant season (mm/d).
+    Tdmax: float, optional
+        Constant for dead biomass loss adjustment (mm/d).
+    w: float, optional
+        Conversion factor of CO2 to dry biomass (Kg DM/Kg CO2).
+    WUE: float, optional
+        Water Use Efficiency - ratio of water used in plant water
+        lost by the plant through transpiration (KgCO2Kg-1H2O).
+    LAI_max: float, optional
+        Maximum leaf area index (m2/m2).
+    cb: float, optional
+        Specific leaf area for green/live biomass (m2 leaf g-1 DM).
+    cd: float, optional
+        Specific leaf area for dead biomass (m2 leaf g-1 DM).
+    ksg: float, optional
+        Senescence coefficient of green/live biomass (d-1).
+    kdd: float, optional
+        Decay coefficient of aboveground dead biomass (d-1).
+    kws: float, optional
+        Maximum drought induced foliage loss rate (d-1).
+
+    Examples
+    --------
+    >>> from landlab import RasterModelGrid
+    >>> from landlab.components import Vegetation
+
+    Create a grid on which to simulate vegetation dynamics.
+
+    >>> grid = RasterModelGrid((5,4), spacing=(0.2, 0.2))
+
+    The grid will need some input data. To check the names of the fields
+    that provide the input to this component, use the *input_var_names*
+    class property.
+
+    >>> sorted(Vegetation.input_var_names)  # doctest: +NORMALIZE_WHITESPACE
+    ['surface__evapotranspiration',
+     'surface__potential_evapotranspiration_30day_mean',
+     'surface__potential_evapotranspiration_rate',
+     'vegetation__plant_functional_type',
+     'vegetation__water_stress']
+
+    >>> sorted(Vegetation.units) # doctest: +NORMALIZE_WHITESPACE
+    [('surface__evapotranspiration', 'mm'),
+     ('surface__potential_evapotranspiration_30day_mean', 'mm'),
+     ('surface__potential_evapotranspiration_rate', 'mm'),
+     ('vegetation__cover_fraction', 'None'),
+     ('vegetation__dead_biomass', 'g m^-2 d^-1'),
+     ('vegetation__dead_leaf_area_index', 'None'),
+     ('vegetation__live_biomass', 'g m^-2 d^-1'),
+     ('vegetation__live_leaf_area_index', 'None'),
+     ('vegetation__plant_functional_type', 'None'),
+     ('vegetation__water_stress', 'None')]
+
+    Provide all the input fields.
+
+    >>> grid['cell']['vegetation__plant_functional_type']= (
+    ...         np.zeros(grid.number_of_cells, dtype=int))
+    >>> grid['cell']['surface__evapotranspiration'] = (
+    ...         0.2 * np.ones(grid.number_of_cells))
+    >>> grid['cell']['surface__potential_evapotranspiration_rate']= np.array([
+    ...         0.25547770, 0.25547770, 0.22110221,
+    ...         0.22110221, 0.24813062, 0.24813062])
+    >>> grid['cell']['surface__potential_evapotranspiration_30day_mean']= (
+    ...        np.array([0.25547770, 0.25547770, 0.22110221,
+    ...                  0.22110221, 0.24813062, 0.24813062]))
+    >>> grid['cell']['vegetation__water_stress'] = (
+    ...        0.01 * np.ones(grid.number_of_cells))
+
+    Instantiate the 'Vegetation' component.
+
+    >>> Veg = Vegetation(grid)
+
+    >>> Veg.grid.number_of_cell_rows
+    3
+    >>> Veg.grid.number_of_cell_columns
+    2
+    >>> Veg.grid is grid
+    True
+    >>> import numpy as np
+    >>> sorted(Vegetation.output_var_names)  # doctest: +NORMALIZE_WHITESPACE
+    ['vegetation__cover_fraction',
+     'vegetation__dead_biomass',
+     'vegetation__dead_leaf_area_index',
+     'vegetation__live_biomass',
+     'vegetation__live_leaf_area_index']
+
+    >>> np.all(grid.at_cell['vegetation__live_leaf_area_index'] == 0.)
+    True
+
+    >>> Veg.update()
+
+    >>> np.all(grid.at_cell['vegetation__live_leaf_area_index'] == 0.)
+    False
+    """
+    _name = 'Vegetation'
+
+    _input_var_names = (
+        'surface__evapotranspiration',
+        'vegetation__water_stress',
+        'surface__potential_evapotranspiration_rate',
+        'surface__potential_evapotranspiration_30day_mean',
+        'vegetation__plant_functional_type',
+    )
+
+    _output_var_names = (
+        'vegetation__live_leaf_area_index',
+        'vegetation__dead_leaf_area_index',
+        'vegetation__cover_fraction',
+        'vegetation__live_biomass',
+        'vegetation__dead_biomass',
+    )
+
+    _var_units = {
+        'vegetation__live_leaf_area_index': 'None',
+        'vegetation__dead_leaf_area_index': 'None',
+        'vegetation__cover_fraction': 'None',
+        'surface__evapotranspiration': 'mm',
+        'surface__potential_evapotranspiration_rate': 'mm',
+        'surface__potential_evapotranspiration_30day_mean': 'mm',
+        'vegetation__water_stress': 'None',
+        'vegetation__live_biomass': 'g m^-2 d^-1',
+        'vegetation__dead_biomass': 'g m^-2 d^-1',
+        'vegetation__plant_functional_type': 'None',
+    }
+
+    _var_mapping = {
+        'vegetation__live_leaf_area_index': 'cell',
+        'vegetation__dead_leaf_area_index': 'cell',
+        'vegetation__cover_fraction': 'cell',
+        'surface__evapotranspiration': 'cell',
+        'surface__potential_evapotranspiration_rate': 'cell',
+        'surface__potential_evapotranspiration_30day_mean': 'cell',
+        'vegetation__water_stress': 'cell',
+        'vegetation__live_biomass': 'cell',
+        'vegetation__dead_biomass': 'cell',
+        'vegetation__plant_functional_type': 'cell',
+    }
+
+    _var_doc = {
+        'vegetation__live_leaf_area_index':
+            'one-sided green leaf area per unit ground surface area',
+        'vegetation__dead_leaf_area_index':
+            'one-sided dead leaf area per unit ground surface area',
+        'vegetation__cover_fraction':
+            'fraction of land covered by vegetation',
+        'surface__evapotranspiration':
+            'actual sum of evaporation and plant transpiration',
+        'surface__potential_evapotranspiration_rate':
+            'potential sum of evaporation and platnt transpiration',
+        'surface__potential_evapotranspiration_30day_mean':
+            '30 day mean of surface__potential_evapotranspiration',
+        'vegetation__water_stress':
+            'parameter that represents nonlinear effects of water defecit \
+             on plants',
+        'vegetation__live_biomass':
+            'weight of green organic mass per unit area - measured in terms \
+             of dry matter',
+        'vegetation__dead_biomass':
+            'weight of dead organic mass per unit area - measured in terms \
+             of dry matter',
+        'vegetation__plant_functional_type':
+            'classification of plants (int), grass=0, shrub=1, tree=2, \
+             bare=3, shrub_seedling=4, tree_seedling=5',
+    }
+
+    @use_file_name_or_kwds
+    def __init__(self, grid, Blive_init=102., Bdead_init=450.,
+                 ETthreshold_up=3.8, ETthreshold_down=6.8, Tdmax=10., w=0.55,
+                 WUE_grass=0.01, LAI_max_grass=2., cb_grass=0.0047,
+                 cd_grass=0.009, ksg_grass=0.012, kdd_grass=0.013,
+                 kws_grass=0.02,
+                 WUE_shrub=0.0025, LAI_max_shrub=2., cb_shrub=0.004,
+                 cd_shrub=0.01, ksg_shrub=0.002, kdd_shrub=0.013,
+                 kws_shrub=0.02,
+                 WUE_tree=0.0045, LAI_max_tree=4., cb_tree=0.004, cd_tree=0.01,
+                 ksg_tree=0.002, kdd_tree=0.013, kws_tree=0.01,
+                 WUE_bare=0.01, LAI_max_bare=0.01, cb_bare=0.0047,
+                 cd_bare=0.009, ksg_bare=0.012, kdd_bare=0.013, kws_bare=0.02,
+                 **kwds):
+        """
+        Parameters
+        ----------
+        grid: RasterModelGrid
+            A grid.
+        Blive_init: float, optional
+            Initial value for vegetation__live_biomass. Converted to field.
+        Bdead_init: float, optional
+            Initial value for vegetation__dead_biomass. Coverted to field.
+        ETthreshold_up: float, optional
+            Potential Evapotranspiration (PET) threshold for
+            growing season (mm/d).
+        ETthreshold_down: float, optional
+            PET threshold for dormant season (mm/d).
+        Tdmax: float, optional
+            Constant for dead biomass loss adjustment (mm/d).
+        w: float, optional
+            Conversion factor of CO2 to dry biomass (Kg DM/Kg CO2).
+        WUE: float, optional
+            Water Use Efficiency - ratio of water used in plant water
+            lost by the plant through transpiration (KgCO2Kg-1H2O).
+        LAI_max: float, optional
+            Maximum leaf area index (m2/m2).
+        cb: float, optional
+            Specific leaf area for green/live biomass (m2 leaf g-1 DM).
+        cd: float, optional
+            Specific leaf area for dead biomass (m2 leaf g-1 DM).
+        ksg: float, optional
+            Senescence coefficient of green/live biomass (d-1).
+        kdd: float, optional
+            Decay coefficient of aboveground dead biomass (d-1).
+        kws: float, optional
+            Maximum drought induced foliage loss rate (d-1).
+        """
+        self._method = kwds.pop('method', 'Grid')
+
+        assert_method_is_valid(self._method)
+
+        super(Vegetation, self).__init__(grid)
+
+        self.initialize(Blive_init=Blive_init, Bdead_init=Bdead_init,
+                        ETthreshold_up=ETthreshold_up,
+                        ETthreshold_down=ETthreshold_down, Tdmax=Tdmax, w=w,
+                        WUE_grass=WUE_grass, LAI_max_grass=LAI_max_grass,
+                        cb_grass=cb_grass, cd_grass=cd_grass,
+                        ksg_grass=ksg_grass, kdd_grass=kdd_grass,
+                        kws_grass=kws_grass, WUE_shrub=WUE_shrub,
+                        LAI_max_shrub=LAI_max_shrub, cb_shrub=cb_shrub,
+                        cd_shrub=cd_shrub, ksg_shrub=ksg_shrub,
+                        kdd_shrub=kdd_shrub, kws_shrub=kws_shrub,
+                        WUE_tree=WUE_tree, LAI_max_tree=LAI_max_tree,
+                        cb_tree=cb_tree, cd_tree=cd_tree, ksg_tree=ksg_tree,
+                        kdd_tree=kdd_tree, kws_tree=kws_tree,
+                        WUE_bare=WUE_bare, LAI_max_bare=LAI_max_bare,
+                        cb_bare=cb_bare, cd_bare=cd_bare, ksg_bare=ksg_bare,
+                        kdd_bare=kdd_bare, kws_bare=kws_bare, **kwds)
+
+        for name in self._input_var_names:
+            if name not in self.grid.at_cell:
+                self.grid.add_zeros('cell', name, units=self._var_units[name])
+
+        for name in self._output_var_names:
+            if name not in self.grid.at_cell:
+                self.grid.add_zeros('cell', name, units=self._var_units[name])
+
+        self._cell_values = self.grid['cell']
+
+        self._Blive_ini = self._Blive_init * np.ones(self.grid.number_of_cells)
+        self._Bdead_ini = self._Bdead_init * np.ones(self.grid.number_of_cells)
+
+    def initialize(self, Blive_init=102., Bdead_init=450., ETthreshold_up=3.8,
+                   ETthreshold_down=6.8, Tdmax=10., w=0.55,
+                   WUE_grass=0.01, LAI_max_grass=2., cb_grass=0.0047,
+                   cd_grass=0.009, ksg_grass=0.012, kdd_grass=0.013,
+                   kws_grass=0.02,
+                   WUE_shrub=0.0025, LAI_max_shrub=2., cb_shrub=0.004,
+                   cd_shrub=0.01, ksg_shrub=0.002, kdd_shrub=0.013,
+                   kws_shrub=0.02,
+                   WUE_tree=0.0045, LAI_max_tree=4., cb_tree=0.004,
+                   cd_tree=0.01, ksg_tree=0.002, kdd_tree=0.013, kws_tree=0.01,
+                   WUE_bare=0.01, LAI_max_bare=0.01, cb_bare=0.0047,
+                   cd_bare=0.009, ksg_bare=0.012, kdd_bare=0.013,
+                   kws_bare=0.02, **kwds):
+        # GRASS = 0; SHRUB = 1; TREE = 2; BARE = 3;
+        # SHRUBSEEDLING = 4; TREESEEDLING = 5
+        """
+        Parameters
+        ----------
+        grid: RasterModelGrid
+            A grid.
+        Blive_init: float, optional
+            Initial value for vegetation__live_biomass. Converted to field.
+        Bdead_init: float, optional
+            Initial value for vegetation__dead_biomass. Coverted to field.
+        ETthreshold_up: float, optional
+            Potential Evapotranspiration (PET) threshold for
+            growing season (mm/d).
+        ETthreshold_down: float, optional
+            PET threshold for dormant season (mm/d).
+        Tdmax: float, optional
+            Constant for dead biomass loss adjustment (mm/d).
+        w: float, optional
+            Conversion factor of CO2 to dry biomass (Kg DM/Kg CO2).
+        WUE: float, optional
+            Water Use Efficiency - ratio of water used in plant water
+            lost by the plant through transpiration (KgCO2Kg-1H2O).
+        LAI_max: float, optional
+            Maximum leaf area index (m2/m2).
+        cb: float, optional
+            Specific leaf area for green/live biomass (m2 leaf g-1 DM).
+        cd: float, optional
+            Specific leaf area for dead biomass (m2 leaf g-1 DM).
+        ksg: float, optional
+            Senescence coefficient of green/live biomass (d-1).
+        kdd: float, optional
+            Decay coefficient of aboveground dead biomass (d-1).
+        kws: float, optional
+            Maximum drought induced foliage loss rate (d-1).
+        """
+        self._vegtype = self.grid['cell']['vegetation__plant_functional_type']
+        self._WUE = np.choose(self._vegtype, [
+            WUE_grass, WUE_shrub, WUE_tree, WUE_bare, WUE_shrub, WUE_tree])
+        # Water Use Efficiency  KgCO2kg-1H2O
+        self._LAI_max = np.choose(self._vegtype, [
+            LAI_max_grass, LAI_max_shrub, LAI_max_tree,
+            LAI_max_bare, LAI_max_shrub, LAI_max_tree])
+        # Maximum leaf area index (m2/m2)
+        self._cb = np.choose(self._vegtype, [
+            cb_grass, cb_shrub, cb_tree, cb_bare, cb_shrub, cb_tree])
+        # Specific leaf area for green/live biomass (m2 leaf g-1 DM)
+        self._cd = np.choose(self._vegtype, [
+            cd_grass, cd_shrub, cd_tree, cd_bare, cd_shrub, cd_tree])
+        # Specific leaf area for dead biomass (m2 leaf g-1 DM)
+        self._ksg = np.choose(self._vegtype, [
+            ksg_grass, ksg_shrub, ksg_tree, ksg_bare, ksg_shrub, ksg_tree])
+        # Senescence coefficient of green/live biomass (d-1)
+        self._kdd = np.choose(self._vegtype, [
+            kdd_grass, kdd_shrub, kdd_tree, kdd_bare, kdd_shrub, kdd_tree])
+        # Decay coefficient of aboveground dead biomass (d-1)
+        self._kws = np.choose(self._vegtype, [
+            kws_grass, kws_shrub, kws_tree, kws_bare, kws_shrub, kws_tree])
+        # Maximum drought induced foliage loss rates (d-1)
+        self._Blive_init = Blive_init
+        self._Bdead_init = Bdead_init
+        self._ETthresholdup = ETthreshold_up    # Growth threshold (mm/d)
+        self._ETthresholddown = ETthreshold_down   # Dormancy threshold (mm/d)
+        self._Tdmax = Tdmax         # Constant for dead biomass loss adjustment
+        self._w = w                  # Conversion factor of CO2 to dry biomass
+
+        self._Blive_ini = self._Blive_init * np.ones(self.grid.number_of_cells)
+        self._Bdead_ini = self._Bdead_init * np.ones(self.grid.number_of_cells)
+
+    def update(self, PETthreshold_switch=0, Tb=24., Tr=0.01, **kwds):
+        """
+        Update fields with current loading conditions.
+
+        Parameters
+        ----------
+        Tr: float, optional
+            Storm duration (hours).
+        Tb: float, optional
+            Inter-storm duration (hours).
+        """
+        PETthreshold_ = PETthreshold_switch
+        PET = self._cell_values['surface__potential_evapotranspiration_rate']
+        PET30_ = self._cell_values[
+            'surface__potential_evapotranspiration_30day_mean']
+        ActualET = self._cell_values['surface__evapotranspiration']
+        Water_stress = self._cell_values['vegetation__water_stress']
+
+        self._LAIlive = self._cell_values['vegetation__live_leaf_area_index']
+        self._LAIdead = self._cell_values['vegetation__dead_leaf_area_index']
+        self._Blive = self._cell_values['vegetation__live_biomass']
+        self._Bdead = self._cell_values['vegetation__dead_biomass']
+        self._VegCov = self._cell_values['vegetation__cover_fraction']
+
+        if PETthreshold_ == 1:
+            PETthreshold = self._ETthresholdup
+        else:
+            PETthreshold = self._ETthresholddown
+
+        for cell in range(0, self.grid.number_of_cells):
+
+            WUE = self._WUE[cell]
+            LAImax = self._LAI_max[cell]
+            cb = self._cb[cell]
+            cd = self._cd[cell]
+            ksg = self._ksg[cell]
+            kdd = self._kdd[cell]
+            kws = self._kws[cell]
+            # ETdmax = self._ETdmax[cell]
+            LAIlive = min(cb*self._Blive_ini[cell], LAImax)
+            LAIdead = min(cd * self._Bdead_ini[cell], (LAImax -
+                          LAIlive))
+            NPP = max((ActualET[cell]/(Tb+Tr)) *
+                      WUE*24.*self._w*1000, 0.001)
+
+            if self._vegtype[cell] == 0:
+                if PET30_[cell] > PETthreshold:
+                                # Growing Season
+                    Bmax = (LAImax - LAIdead)/cb
+                    Yconst = (1/((1/Bmax)+(((kws*Water_stress[cell]) +
+                              ksg)/NPP)))
+                    Blive = ((self._Blive_ini[cell] - Yconst) *
+                             np.exp(-(NPP/Yconst) * ((Tb+Tr)/24.)) + Yconst)
+                    Bdead = ((self._Bdead_ini[cell] + (Blive - max(Blive *
+                             np.exp(-1 * ksg * Tb/24.), 0.00001))) *
+                             np.exp(-1 * kdd *
+                             min(PET[cell]/self._Tdmax, 1.) * Tb/24.))
+                else:                                 # Senescense
+                    Blive = max(self._Blive_ini[cell] * np.exp((-2) * ksg *
+                                Tb/24.), 1)
+                    Bdead = max((self._Bdead_ini[cell]+(self._Blive_ini[cell] -
+                                (max(self._Blive_ini[cell]*np.exp((-2) *
+                                 ksg*Tb/24.), 0.000001))))*np.exp((-1)*kdd *
+                                 min(PET[cell]/self._Tdmax, 1.) * Tb/24.), 0.)
+
+            elif self._vegtype[cell] == 3:
+                Blive = 0.
+                Bdead = 0.
+
+            else:
+                Bmax = LAImax/cb
+                Yconst = (1./((1./Bmax)+(((kws*Water_stress[cell]) +
+                          ksg)/NPP)))
+                Blive = ((self._Blive_ini[cell] - Yconst) *
+                         np.exp(-(NPP/Yconst) * ((Tb+Tr)/24.)) + Yconst)
+                Bdead = ((self._Bdead_ini[cell] + (Blive - max(Blive *
+                         np.exp(-ksg * Tb/24.), 0.00001))) *
+                         np.exp(-kdd * min(PET[cell]/self._Tdmax, 1.) *
+                         Tb/24.))
+
+            LAIlive = min(cb * (Blive + self._Blive_ini[cell])/2., LAImax)
+            LAIdead = min(cd * (Bdead + self._Bdead_ini[cell])/2.,
+                          (LAImax - LAIlive))
+            if self._vegtype[cell] == 0:
+                Vt = 1. - np.exp(-0.75 * (LAIlive + LAIdead))
+            else:
+                # Vt = 1 - np.exp(-0.75 * LAIlive)
+                Vt = 1.
+
+            self._LAIlive[cell] = LAIlive
+            self._LAIdead[cell] = LAIdead
+            self._VegCov[cell] = Vt
+            self._Blive[cell] = Blive
+            self._Bdead[cell] = Bdead
+
+        self._Blive_ini = self._Blive
+        self._Bdead_ini = self._Bdead