Notebooks/Module_0_WaPOR_data_bulk_download.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Module 0 - In this jupyter notebook, WaPOR data needed for Water Productivity assessment will be  downloaded in bulk and corrected to the right unit\n",
    "* Step 0a - Import modules/libraries\n",
    "* Step 0b - Read geographical extent of the study area\n",
    "* Step 0c - Bulk-download WaPOR data for the study area extent  \n",
    "\n",
    "**=====================================================================================================================**\n",
    "**Data layers for Water Productivity assessment:**\n",
    "\n",
    "| No.  | WaPOR Data | Spatial resolution | Temporal resolution | Temporal coverage |\n",
    "| :-: | :-: | :-: | :-: | :-: |\n",
    "| 1 |Actual Evapotranspiration & interception (AETI)  | 100 m | Dekadal | 2009-2019 |\n",
    "| 2  | Transpiration (T)  | 100 m | Dekadal  | 2009-2019 |\n",
    "| 3  | Net Primary Production (NPP) | 100 m | Dekadal | 2009-2019 |\n",
    "| 4  | Land cover classification (LCC)  | 100 m | Annual | 2009-2019 |\n",
    "| 5  | Precipitation (PCP)  | 5 km | Dekadal | 2009-2019 |\n",
    "| 6  | Reference Evapotranspiration (RET) | 20 km | Dekadal  | 2009-2019 |\n",
    "\n",
    "**=====================================================================================================================**"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 1 Import modules/libraries"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Insert WAPOR API Token: 821b84e4829e2627c8dfb748415b730b50857d9e5ea853ff4867c1b118f788edd115522b01db3cdf\n",
      "Loading WaPOR catalog...\n",
      "Loading WaPOR catalog...Done\n"
     ]
    },
    {
     "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>code</th>\n",
       "      <th>caption</th>\n",
       "      <th>description</th>\n",
       "      <th>additionalInfo</th>\n",
       "      <th>tags</th>\n",
       "      <th>workspaceCode</th>\n",
       "      <th>dataType</th>\n",
       "      <th>index</th>\n",
       "      <th>operation</th>\n",
       "      <th>hidden</th>\n",
       "      <th>links</th>\n",
       "      <th>measure</th>\n",
       "      <th>dimension</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>L1_GBWP_A</td>\n",
       "      <td>Gross Biomass Water Productivity</td>\n",
       "      <td>The annual Gross Biomass Water Productivity ex...</td>\n",
       "      <td>{'format': 'Raster Dataset', 'unit': 'kg/m³ is...</td>\n",
       "      <td>[{'name': 'L1', 'index': 1, 'context': 'LEVELS...</td>\n",
       "      <td>WAPOR_2</td>\n",
       "      <td>RASTER</td>\n",
       "      <td>101</td>\n",
       "      <td>{'pixelTimeSeries': True, 'areaStatsTimeSeries...</td>\n",
       "      <td>False</td>\n",
       "      <td>[{'rel': 'self', 'href': 'https://io.apps.fao....</td>\n",
       "      <td>{'code': 'WPR', 'caption': 'Water Productivity...</td>\n",
       "      <td>[{'code': 'YEAR', 'caption': 'Year', 'descript...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>L1_NBWP_A</td>\n",
       "      <td>Net Biomass Water Productivity</td>\n",
       "      <td>The annual Net Biomass Water Productivity expr...</td>\n",
       "      <td>{'format': 'Raster Dataset', 'unit': 'kg/m³ is...</td>\n",
       "      <td>[{'name': 'L1', 'index': 2, 'context': 'LEVELS...</td>\n",
       "      <td>WAPOR_2</td>\n",
       "      <td>RASTER</td>\n",
       "      <td>102</td>\n",
       "      <td>{'pixelTimeSeries': True, 'areaStatsTimeSeries...</td>\n",
       "      <td>False</td>\n",
       "      <td>[{'rel': 'self', 'href': 'https://io.apps.fao....</td>\n",
       "      <td>{'code': 'WPR', 'caption': 'Water Productivity...</td>\n",
       "      <td>[{'code': 'YEAR', 'caption': 'Year', 'descript...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>L1_AETI_A</td>\n",
       "      <td>Actual EvapoTranspiration and Interception (An...</td>\n",
       "      <td>The actual EvapoTranspiration and Interception...</td>\n",
       "      <td>{'format': 'Raster Dataset', 'unit': 'mm', 'da...</td>\n",
       "      <td>[{'name': 'L1', 'index': 3, 'context': 'LEVELS...</td>\n",
       "      <td>WAPOR_2</td>\n",
       "      <td>RASTER</td>\n",
       "      <td>103</td>\n",
       "      <td>{'pixelTimeSeries': True, 'areaStatsTimeSeries...</td>\n",
       "      <td>False</td>\n",
       "      <td>[{'rel': 'self', 'href': 'https://io.apps.fao....</td>\n",
       "      <td>{'code': 'WATER_MM', 'caption': 'Amount of Wat...</td>\n",
       "      <td>[{'code': 'YEAR', 'caption': 'Year', 'descript...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>L1_AETI_M</td>\n",
       "      <td>Actual EvapoTranspiration and Interception (Mo...</td>\n",
       "      <td>The actual EvapoTranspiration and Interception...</td>\n",
       "      <td>{'format': 'Raster Dataset', 'unit': 'mm', 'da...</td>\n",
       "      <td>[{'name': 'L1', 'index': 4, 'context': 'LEVELS...</td>\n",
       "      <td>WAPOR_2</td>\n",
       "      <td>RASTER</td>\n",
       "      <td>104</td>\n",
       "      <td>{'pixelTimeSeries': True, 'areaStatsTimeSeries...</td>\n",
       "      <td>False</td>\n",
       "      <td>[{'rel': 'self', 'href': 'https://io.apps.fao....</td>\n",
       "      <td>{'code': 'WATER_MM', 'caption': 'Amount of Wat...</td>\n",
       "      <td>[{'code': 'MONTH', 'caption': 'Month', 'worksp...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>L1_AETI_D</td>\n",
       "      <td>Actual EvapoTranspiration and Interception (De...</td>\n",
       "      <td>The actual EvapoTranspiration and Interception...</td>\n",
       "      <td>{'format': 'Raster Dataset', 'unit': 'mm', 'da...</td>\n",
       "      <td>[{'name': 'L1', 'index': 5, 'context': 'LEVELS...</td>\n",
       "      <td>WAPOR_2</td>\n",
       "      <td>RASTER</td>\n",
       "      <td>105</td>\n",
       "      <td>{'pixelTimeSeries': True, 'areaStatsTimeSeries...</td>\n",
       "      <td>False</td>\n",
       "      <td>[{'rel': 'self', 'href': 'https://io.apps.fao....</td>\n",
       "      <td>{'code': 'WATER_MM', 'caption': 'Amount of Wat...</td>\n",
       "      <td>[{'code': 'DEKAD', 'caption': 'Dekad (10-Days ...</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",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>193</th>\n",
       "      <td>L2_BAS_PHE_S</td>\n",
       "      <td>Phenology (Seasonal, clipped by basin)</td>\n",
       "      <td>This is a system dataset, used to let the down...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>WAPOR_2</td>\n",
       "      <td>RASTER</td>\n",
       "      <td>4151</td>\n",
       "      <td>{'pixelTimeSeries': False, 'areaStatsTimeSerie...</td>\n",
       "      <td>True</td>\n",
       "      <td>[{'rel': 'self', 'href': 'https://io.apps.fao....</td>\n",
       "      <td>{'code': 'PHE', 'caption': 'Phenology', 'descr...</td>\n",
       "      <td>[{'code': 'YEAR', 'caption': 'Year', 'descript...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>194</th>\n",
       "      <td>L2_BAS_QUAL_NDVI_D</td>\n",
       "      <td>Quality of Normalized Difference Vegetation In...</td>\n",
       "      <td>This is a system dataset, used to let the down...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>WAPOR_2</td>\n",
       "      <td>RASTER</td>\n",
       "      <td>4161</td>\n",
       "      <td>{'pixelTimeSeries': False, 'areaStatsTimeSerie...</td>\n",
       "      <td>True</td>\n",
       "      <td>[{'rel': 'self', 'href': 'https://io.apps.fao....</td>\n",
       "      <td>{'code': 'N_DAYS', 'caption': 'Number of Days'...</td>\n",
       "      <td>[{'code': 'DEKAD', 'caption': 'Dekad (10-Days ...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>195</th>\n",
       "      <td>L2_CTY_QUAL_NDVI_D</td>\n",
       "      <td>Quality of Normalized Difference Vegetation In...</td>\n",
       "      <td>This is a system dataset, used to let the down...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>WAPOR_2</td>\n",
       "      <td>RASTER</td>\n",
       "      <td>4162</td>\n",
       "      <td>{'pixelTimeSeries': False, 'areaStatsTimeSerie...</td>\n",
       "      <td>True</td>\n",
       "      <td>[{'rel': 'self', 'href': 'https://io.apps.fao....</td>\n",
       "      <td>{'code': 'N_DAYS', 'caption': 'Number of Days'...</td>\n",
       "      <td>[{'code': 'DEKAD', 'caption': 'Dekad (10-Days ...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>196</th>\n",
       "      <td>L2_BAS_QUAL_LST_D</td>\n",
       "      <td>Quality Land Surface Temperature (Dekadal, cli...</td>\n",
       "      <td>This is a system dataset, used to let the down...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>WAPOR_2</td>\n",
       "      <td>RASTER</td>\n",
       "      <td>4171</td>\n",
       "      <td>{'pixelTimeSeries': False, 'areaStatsTimeSerie...</td>\n",
       "      <td>True</td>\n",
       "      <td>[{'rel': 'self', 'href': 'https://io.apps.fao....</td>\n",
       "      <td>{'code': 'N_DAYS', 'caption': 'Number of Days'...</td>\n",
       "      <td>[{'code': 'DEKAD', 'caption': 'Dekad (10-Days ...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>197</th>\n",
       "      <td>L2_CTY_QUAL_LST_D</td>\n",
       "      <td>Quality Land Surface Temperature (Dekadal, cli...</td>\n",
       "      <td>This is a system dataset, used to let the down...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>WAPOR_2</td>\n",
       "      <td>RASTER</td>\n",
       "      <td>4172</td>\n",
       "      <td>{'pixelTimeSeries': False, 'areaStatsTimeSerie...</td>\n",
       "      <td>True</td>\n",
       "      <td>[{'rel': 'self', 'href': 'https://io.apps.fao....</td>\n",
       "      <td>{'code': 'N_DAYS', 'caption': 'Number of Days'...</td>\n",
       "      <td>[{'code': 'DEKAD', 'caption': 'Dekad (10-Days ...</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>198 rows × 13 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "                   code                                            caption  \\\n",
       "0             L1_GBWP_A                   Gross Biomass Water Productivity   \n",
       "1             L1_NBWP_A                     Net Biomass Water Productivity   \n",
       "2             L1_AETI_A  Actual EvapoTranspiration and Interception (An...   \n",
       "3             L1_AETI_M  Actual EvapoTranspiration and Interception (Mo...   \n",
       "4             L1_AETI_D  Actual EvapoTranspiration and Interception (De...   \n",
       "..                  ...                                                ...   \n",
       "193        L2_BAS_PHE_S             Phenology (Seasonal, clipped by basin)   \n",
       "194  L2_BAS_QUAL_NDVI_D  Quality of Normalized Difference Vegetation In...   \n",
       "195  L2_CTY_QUAL_NDVI_D  Quality of Normalized Difference Vegetation In...   \n",
       "196   L2_BAS_QUAL_LST_D  Quality Land Surface Temperature (Dekadal, cli...   \n",
       "197   L2_CTY_QUAL_LST_D  Quality Land Surface Temperature (Dekadal, cli...   \n",
       "\n",
       "                                           description  \\\n",
       "0    The annual Gross Biomass Water Productivity ex...   \n",
       "1    The annual Net Biomass Water Productivity expr...   \n",
       "2    The actual EvapoTranspiration and Interception...   \n",
       "3    The actual EvapoTranspiration and Interception...   \n",
       "4    The actual EvapoTranspiration and Interception...   \n",
       "..                                                 ...   \n",
       "193  This is a system dataset, used to let the down...   \n",
       "194  This is a system dataset, used to let the down...   \n",
       "195  This is a system dataset, used to let the down...   \n",
       "196  This is a system dataset, used to let the down...   \n",
       "197  This is a system dataset, used to let the down...   \n",
       "\n",
       "                                        additionalInfo  \\\n",
       "0    {'format': 'Raster Dataset', 'unit': 'kg/m³ is...   \n",
       "1    {'format': 'Raster Dataset', 'unit': 'kg/m³ is...   \n",
       "2    {'format': 'Raster Dataset', 'unit': 'mm', 'da...   \n",
       "3    {'format': 'Raster Dataset', 'unit': 'mm', 'da...   \n",
       "4    {'format': 'Raster Dataset', 'unit': 'mm', 'da...   \n",
       "..                                                 ...   \n",
       "193                                                NaN   \n",
       "194                                                NaN   \n",
       "195                                                NaN   \n",
       "196                                                NaN   \n",
       "197                                                NaN   \n",
       "\n",
       "                                                  tags workspaceCode dataType  \\\n",
       "0    [{'name': 'L1', 'index': 1, 'context': 'LEVELS...       WAPOR_2   RASTER   \n",
       "1    [{'name': 'L1', 'index': 2, 'context': 'LEVELS...       WAPOR_2   RASTER   \n",
       "2    [{'name': 'L1', 'index': 3, 'context': 'LEVELS...       WAPOR_2   RASTER   \n",
       "3    [{'name': 'L1', 'index': 4, 'context': 'LEVELS...       WAPOR_2   RASTER   \n",
       "4    [{'name': 'L1', 'index': 5, 'context': 'LEVELS...       WAPOR_2   RASTER   \n",
       "..                                                 ...           ...      ...   \n",
       "193                                                NaN       WAPOR_2   RASTER   \n",
       "194                                                NaN       WAPOR_2   RASTER   \n",
       "195                                                NaN       WAPOR_2   RASTER   \n",
       "196                                                NaN       WAPOR_2   RASTER   \n",
       "197                                                NaN       WAPOR_2   RASTER   \n",
       "\n",
       "     index                                          operation  hidden  \\\n",
       "0      101  {'pixelTimeSeries': True, 'areaStatsTimeSeries...   False   \n",
       "1      102  {'pixelTimeSeries': True, 'areaStatsTimeSeries...   False   \n",
       "2      103  {'pixelTimeSeries': True, 'areaStatsTimeSeries...   False   \n",
       "3      104  {'pixelTimeSeries': True, 'areaStatsTimeSeries...   False   \n",
       "4      105  {'pixelTimeSeries': True, 'areaStatsTimeSeries...   False   \n",
       "..     ...                                                ...     ...   \n",
       "193   4151  {'pixelTimeSeries': False, 'areaStatsTimeSerie...    True   \n",
       "194   4161  {'pixelTimeSeries': False, 'areaStatsTimeSerie...    True   \n",
       "195   4162  {'pixelTimeSeries': False, 'areaStatsTimeSerie...    True   \n",
       "196   4171  {'pixelTimeSeries': False, 'areaStatsTimeSerie...    True   \n",
       "197   4172  {'pixelTimeSeries': False, 'areaStatsTimeSerie...    True   \n",
       "\n",
       "                                                 links  \\\n",
       "0    [{'rel': 'self', 'href': 'https://io.apps.fao....   \n",
       "1    [{'rel': 'self', 'href': 'https://io.apps.fao....   \n",
       "2    [{'rel': 'self', 'href': 'https://io.apps.fao....   \n",
       "3    [{'rel': 'self', 'href': 'https://io.apps.fao....   \n",
       "4    [{'rel': 'self', 'href': 'https://io.apps.fao....   \n",
       "..                                                 ...   \n",
       "193  [{'rel': 'self', 'href': 'https://io.apps.fao....   \n",
       "194  [{'rel': 'self', 'href': 'https://io.apps.fao....   \n",
       "195  [{'rel': 'self', 'href': 'https://io.apps.fao....   \n",
       "196  [{'rel': 'self', 'href': 'https://io.apps.fao....   \n",
       "197  [{'rel': 'self', 'href': 'https://io.apps.fao....   \n",
       "\n",
       "                                               measure  \\\n",
       "0    {'code': 'WPR', 'caption': 'Water Productivity...   \n",
       "1    {'code': 'WPR', 'caption': 'Water Productivity...   \n",
       "2    {'code': 'WATER_MM', 'caption': 'Amount of Wat...   \n",
       "3    {'code': 'WATER_MM', 'caption': 'Amount of Wat...   \n",
       "4    {'code': 'WATER_MM', 'caption': 'Amount of Wat...   \n",
       "..                                                 ...   \n",
       "193  {'code': 'PHE', 'caption': 'Phenology', 'descr...   \n",
       "194  {'code': 'N_DAYS', 'caption': 'Number of Days'...   \n",
       "195  {'code': 'N_DAYS', 'caption': 'Number of Days'...   \n",
       "196  {'code': 'N_DAYS', 'caption': 'Number of Days'...   \n",
       "197  {'code': 'N_DAYS', 'caption': 'Number of Days'...   \n",
       "\n",
       "                                             dimension  \n",
       "0    [{'code': 'YEAR', 'caption': 'Year', 'descript...  \n",
       "1    [{'code': 'YEAR', 'caption': 'Year', 'descript...  \n",
       "2    [{'code': 'YEAR', 'caption': 'Year', 'descript...  \n",
       "3    [{'code': 'MONTH', 'caption': 'Month', 'worksp...  \n",
       "4    [{'code': 'DEKAD', 'caption': 'Dekad (10-Days ...  \n",
       "..                                                 ...  \n",
       "193  [{'code': 'YEAR', 'caption': 'Year', 'descript...  \n",
       "194  [{'code': 'DEKAD', 'caption': 'Dekad (10-Days ...  \n",
       "195  [{'code': 'DEKAD', 'caption': 'Dekad (10-Days ...  \n",
       "196  [{'code': 'DEKAD', 'caption': 'Dekad (10-Days ...  \n",
       "197  [{'code': 'DEKAD', 'caption': 'Dekad (10-Days ...  \n",
       "\n",
       "[198 rows x 13 columns]"
      ]
     },
     "execution_count": 1,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "import os                     # module for interacting with the operating system\n",
    "import shapefile             # module foe reading shapefile\n",
    "import matplotlib.pyplot as plt # module for plotting\n",
    "import sys\n",
    "\n",
    "folder=r\"..\\Modules\"\n",
    "sys.path.append(folder) #add folder with local modules to system paths  #change working directory to 'Modules' folder\n",
    "import WaPOR                  # Import local module in 'Modules' folder\n",
    "WaPOR.API.version=2 #select WaPOR version\n",
    "WaPOR.API.getCatalog()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**Get WaPOR API Token: https://wapor.apps.fao.org/profile**\n",
    "![title](img/Fig0.gif)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 2 Read geographical extent of the study area\n",
    "\n",
    "**Study area: Xinavane, Mozambique**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Extent of study area: lonlim = [32.65692516077674,32.90420244933813], latlim = [-25.16412729789142,-25.01450956754679]\n"
     ]
    }
   ],
   "source": [
    "roi_shapefile=r\"..\\Data\\1Boundary\\Shapefile\\Xinavane_1.shp\" #path to the shapefile of study area\n",
    "shape=shapefile.Reader(roi_shapefile) #read shapefile\n",
    "xmin,ymin,xmax,ymax=shape.bbox #read shapefile extent\n",
    "print('Extent of study area: lonlim = [{0},{2}], latlim = [{1},{3}]'.format(xmin,ymin,xmax,ymax))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "##plot to check shapefile\n",
    "plt.figure()\n",
    "sf=shape\n",
    "for shape in sf.shapeRecords(): #loop over all features in shapefile\n",
    "    for i in range(len(shape.shape.parts)): #loop over all points in feature\n",
    "        i_start = shape.shape.parts[i]\n",
    "        if i==len(shape.shape.parts)-1:\n",
    "            i_end = len(shape.shape.points)\n",
    "        else:\n",
    "            i_end = shape.shape.parts[i+1]\n",
    "        x = [i[0] for i in shape.shape.points[i_start:i_end]]\n",
    "        y = [i[1] for i in shape.shape.points[i_start:i_end]]\n",
    "        plt.plot(x,y)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 3 Bulk-download WaPOR data for the study area extent"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [],
   "source": [
    "output_dir=r'..\\Data' # folder to save data"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Land cover classification (Annual)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "Download WaPOR Level 2 yearly LCC data for the period 2009-01-01 till 2019-12-31\n",
      "Loading WaPOR catalog...\n",
      "Loading WaPOR catalog...Done\n",
      "Progress: |██████████████████████████████████████████████████| 100.0% Complete\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "'..\\\\Data\\\\WAPOR.v2_yearly_L2_LCC_A'"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "WaPOR.download_yearly(output_dir, \n",
    "                       data='LCC',\n",
    "                       Startdate='2009-01-01', \n",
    "                       Enddate='2019-12-31',\n",
    "                       latlim=[ymin-0.1, ymax+0.1], \n",
    "                       lonlim=[xmin-0.1, xmax+0.1],\n",
    "                       level=2, \n",
    "                       )"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Reference Evapotranspiration (Dekadal)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "Download WaPOR Level 1 dekadal RET data for the period 2009-01-01 till 2019-12-31\n",
      "Progress: |██████████████████████████████████████████████████| 100.0% Complete\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "'..\\\\Data\\\\WAPOR.v2_dekadal_L1_RET_D'"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "WaPOR.download_dekadal(output_dir, \n",
    "                       data='RET',\n",
    "                       Startdate='2009-01-01', \n",
    "                       Enddate='2019-12-31',\n",
    "                       latlim=[ymin-0.1, ymax+0.1], \n",
    "                       lonlim=[xmin-0.1, xmax+0.1],\n",
    "                       level=1, \n",
    "                       )"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Precipitation (Dekadal) "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "Download WaPOR Level 1 dekadal PCP data for the period 2009-01-01 till 2019-12-31\n",
      "Progress: |██████████████████████████████████████████████████| 100.0% Complete\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "'..\\\\Data\\\\WAPOR.v2_dekadal_L1_PCP_D'"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "WaPOR.download_dekadal(output_dir, \n",
    "                       data='PCP',\n",
    "                       Startdate='2009-01-01', \n",
    "                       Enddate='2019-12-31',\n",
    "                       latlim=[ymin-0.1, ymax+0.1], \n",
    "                       lonlim=[xmin-0.1, xmax+0.1],\n",
    "                       level=1, \n",
    "                       )"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Actual evapotranspiration & interception (Dekadal)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "Download WaPOR Level 2 dekadal AETI data for the period 2009-01-01 till 2019-12-31\n",
      "Progress: |██████████████████████████████████████████████████| 100.0% Complete\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "'..\\\\Data\\\\WAPOR.v2_dekadal_L2_AETI_D'"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "WaPOR.download_dekadal(output_dir, \n",
    "                       data='AETI',\n",
    "                       Startdate='2009-01-01', \n",
    "                       Enddate='2019-12-31',\n",
    "                       latlim=[ymin-0.1, ymax+0.1], \n",
    "                       lonlim=[xmin-0.1, xmax+0.1],\n",
    "                       level=2, \n",
    "                       )"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Transpiration (Dekadal)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "Download WaPOR Level 2 dekadal T data for the period 2009-01-01 till 2019-12-31\n",
      "Progress: |██████████████████████████████████████████████████| 100.0% Complete\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "'..\\\\Data\\\\WAPOR.v2_dekadal_L2_T_D'"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "WaPOR.download_dekadal(output_dir, \n",
    "                       data='T',\n",
    "                       Startdate='2009-01-01', \n",
    "                       Enddate='2019-12-31',\n",
    "                       latlim=[ymin-0.1, ymax+0.1], \n",
    "                       lonlim=[xmin-0.1, xmax+0.1],\n",
    "                       level=2, \n",
    "                       )"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Net Primary Production (Dekadal)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "Download WaPOR Level 2 dekadal NPP data for the period 2009-01-01 till 2019-12-31\n",
      "Progress: |██████████████████████████████████████████████████| 100.0% Complete\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "'..\\\\Data\\\\WAPOR.v2_dekadal_L2_NPP_D'"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "WaPOR.download_dekadal(output_dir, \n",
    "                       data='NPP',\n",
    "                       Startdate='2009-01-01', \n",
    "                       Enddate='2019-12-31',\n",
    "                       latlim=[ymin-0.1, ymax+0.1], \n",
    "                       lonlim=[xmin-0.1, xmax+0.1],\n",
    "                       level=2, \n",
    "                       )"
   ]
  }
 ],
 "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": "3.7.3"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}