02-italy-drought-2022-prediction-ascat.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<img src=\"https://www.climatechangewriters.com/images/2023/03/27/CCW9893490890_large.jpg\" style=\"width:50%; display: block; margin-left: auto; margin-right: auto\">\n",
    "<table style=\"width:50%\">\n",
    "    <tr>\n",
    "        <th><img 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\"></th>\n",
    "        <th><img src=\"https://eodc.eu/wp-content/uploads/2018/10/EODC-Logo-Final.png\"></th>\n",
    "    </tr>\n",
    "</table>"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# DestinE Data Lake\n",
    "\n",
    "The Destination Earth Data Lake (DEDL) combines such disparate data sources in one large virtual database. Moreover, this virtual database comes with a set of tools that facilitates the search for relevant resources as well as the analysis of data by performing computationally expensive operations near to the data source.\n",
    "\n",
    "This Jupyter Notebook serves as an example of how the DEDL service can help design water management plans to prepare Italian socio-economic systems for prolonged periods of droughts. These conditions will become more prevalent under globally increasing temperatures."
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Use Case 2a: Italy Drought 2022 - MetOp ASCAT\n",
    "\n",
    "After an unusually dry winter, a water management expert is consulted to assess the impact to communal and agricultural water supplies in italy at the end of March 2022.\n",
    "\n",
    "In case of prolonged absence of rain the expert would require information to prepare a national drought plan. This information should encompass current and future drought intensity as well as a measure of the impact of drought on critical sectors, such as agriculture, industry, and public water. The drought impact per sector is required to estabelish sector-specific mitigating measures that depend on a range of scenarios ranging from ameliorating conditions to severe drought predicted in the forseeable future."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import xarray as xr\n",
    "from attr import dataclass\n",
    "import numpy as np\n",
    "from datetime import datetime\n",
    "\n",
    "start_drought = datetime(2022, 3, 1)\n",
    "end_drought = datetime(2022, 8, 31)\n",
    "\n",
    "xr.set_options(keep_attrs=True)\n",
    "\n",
    "@dataclass\n",
    "class Extent:\n",
    "    min_x: float\n",
    "    min_y: float\n",
    "    max_x: float\n",
    "    max_y: float\n",
    "    crs: str\n",
    "    def get_center(self):\n",
    "        return (np.mean([self.min_y, self.max_y]),\n",
    "                np.mean([self.min_x,self.max_x]))\n",
    "\n",
    "drought_roi = Extent(6.15, 46.90, 19.60, 35.64, crs='EPSG:4326')    # Italy BBOX"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Create Dask Clusters on Central Site and Lumi Bridge\n",
    "Make use of DEDL client library to create Dask cluster on each site registered in the DEDL."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\">Username: </pre>\n"
      ],
      "text/plain": [
       "Username: "
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "from dedl_stack_client.authn import DaskOIDC\n",
    "from dedl_stack_client.dask import DaskMultiCluster\n",
    "from rich.prompt import Prompt\n",
    "\n",
    "myAuth = DaskOIDC(username=Prompt.ask(prompt=\"Username\"))\n",
    "myDEDLClusters = DaskMultiCluster(auth=myAuth)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The DaskMultiCluster class provides an interface to interact with multiple Dask Gateway instances at once. However, each gateway instance can be consumed separately using the default Dask Gateway API. Gateway objects can be accessed via the gateway attribute, representing a dictionary of available gateway instances."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Create new cluster for Central Site\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/home/creimer/micromamba/envs/dedl_flood/lib/python3.11/site-packages/distributed/client.py:1401: VersionMismatchWarning: Mismatched versions found\n",
      "\n",
      "+---------+--------+-----------+---------+\n",
      "| Package | Client | Scheduler | Workers |\n",
      "+---------+--------+-----------+---------+\n",
      "| msgpack | 1.0.7  | 1.0.5     | None    |\n",
      "| toolz   | 0.12.1 | 0.12.0    | None    |\n",
      "+---------+--------+-----------+---------+\n",
      "  warnings.warn(version_module.VersionMismatchWarning(msg[0][\"warning\"]))\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Create new cluster for LUMI Bridge\n"
     ]
    }
   ],
   "source": [
    "myDEDLClusters.new_cluster()"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Access Data from DEDL Service\n",
    "\n",
    "Using the DEDL package, data can be accessed from various services. In this example, data is retrieved from the European Organisation for the Exploitation of Meteorological Satellites (EUMETSAT) Data Store, the Copernicus Land Monitoring service (CLMS), and the European Centre for Medium-Range Weather Forecasts (ECMWF).\n",
    "\n",
    "- The EUMETSAT Data Store enables access to **soil moisture data** as part of the Operational **Hydrology and water management (HSAF)** unit. The data consists of microwave images obtained by the the Advanced Scatterometer (ASCAT) satallite. Microwave imaging is a technique that is relatively unaffected by cloud cover and is especially sensitive to variations in near surface water content ($0 {-} 2\\,\\text{cm}$). This dataset is used to evaluate drought severeness at the beginning of March 2022.\n",
    "- The CLMS is queried for the **CORINE ('Coordination of information on the environment') Land Cover inventory**. The original purpose of this data set serves environmental policy development and consists of 44 classes of land cover in five main units (artificial, agriculture, forests and semi-natural, wetlands, and water). It combines a number of sources, among which Landsat-5, Landsat-7, and ESA Sentinal-2 satallite imaging data in the most recent years. This dataset will highlight which sectors will be most likely drought impacted under the predicted scenario. \n",
    "- The ECMWF catalogue is used to obtain the multi-decadal **ERA5-Land dataset**. This product is a reanalysis dataset that gives insight in the seasonal variation of land variables by averaging data over multiple decades with a 9 km spatial resolution. The ERA5-Land data is retrieved to estimate the development of drought over the months following the 30<sup>th</sup> of March 2022. This dataset is for demonstration purposes only, and will be replaced with a high resolution soil moisture prediction dataset in the future.\n",
    "\n",
    "\n",
    "Additionally, the DEDL service allows data uploads by the user. These capabilities of the DEDL services are examplified below."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Login to DEDL storage service at central site and bridge"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\">S3 Key: </pre>\n"
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       "S3 Key: "
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       "<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\">S3 Secret: </pre>\n"
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       "S3 Secret: "
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     "data": {
      "text/html": [
       "<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\">S3 Key: </pre>\n"
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       "S3 Key: "
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      "text/html": [
       "<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\">S3 Secret: </pre>\n"
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      "text/plain": [
       "S3 Secret: "
      ]
     },
     "metadata": {},
     "output_type": "display_data"
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   ],
   "source": [
    "import s3fs\n",
    "\n",
    "S3_BASE_URL = \"https://s3.{site_name}.data.destination-earth.eu\"\n",
    "\n",
    "S3_URL_CENTRAL = S3_BASE_URL.format(site_name=\"central\")\n",
    "S3_URL_LUMI = S3_BASE_URL.format(site_name=\"lumi\")\n",
    "\n",
    "s3_central = s3fs.S3FileSystem(\n",
    "    endpoint_url=S3_URL_CENTRAL,\n",
    "    key=Prompt.ask(prompt=\"S3 Key\"),\n",
    "    secret=Prompt.ask(prompt=\"S3 Secret\", password=True),\n",
    "    use_ssl=True,\n",
    ")\n",
    "\n",
    "s3_lumi = s3fs.S3FileSystem(\n",
    "    endpoint_url=S3_URL_LUMI,\n",
    "    key=Prompt.ask(prompt=\"S3 Key\"),\n",
    "    secret=Prompt.ask(prompt=\"S3 Secret\", password=True),\n",
    "    use_ssl=True,\n",
    ")"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### ASCAT Soil Moisture\n",
    "\n",
    "Datacube holding METOP ASCAT Soil Moisture observations."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
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       "  margin: 0;\n",
       "  display: grid;\n",
       "  grid-template-columns: 125px auto;\n",
       "}\n",
       "\n",
       ".xr-attrs dt,\n",
       ".xr-attrs dd {\n",
       "  padding: 0;\n",
       "  margin: 0;\n",
       "  float: left;\n",
       "  padding-right: 10px;\n",
       "  width: auto;\n",
       "}\n",
       "\n",
       ".xr-attrs dt {\n",
       "  font-weight: normal;\n",
       "  grid-column: 1;\n",
       "}\n",
       "\n",
       ".xr-attrs dt:hover span {\n",
       "  display: inline-block;\n",
       "  background: var(--xr-background-color);\n",
       "  padding-right: 10px;\n",
       "}\n",
       "\n",
       ".xr-attrs dd {\n",
       "  grid-column: 2;\n",
       "  white-space: pre-wrap;\n",
       "  word-break: break-all;\n",
       "}\n",
       "\n",
       ".xr-icon-database,\n",
       ".xr-icon-file-text2,\n",
       ".xr-no-icon {\n",
       "  display: inline-block;\n",
       "  vertical-align: middle;\n",
       "  width: 1em;\n",
       "  height: 1.5em !important;\n",
       "  stroke-width: 0;\n",
       "  stroke: currentColor;\n",
       "  fill: currentColor;\n",
       "}\n",
       "</style><pre class='xr-text-repr-fallback'>&lt;xarray.DataArray &#x27;sm&#x27; (location: 32703, time: 434)&gt;\n",
       "dask.array&lt;getitem, shape=(32703, 434), dtype=float32, chunksize=(2044, 110), chunktype=numpy.ndarray&gt;\n",
       "Coordinates:\n",
       "    lat      (location) float32 dask.array&lt;chunksize=(32703,), meta=np.ndarray&gt;\n",
       "    lon      (location) float32 dask.array&lt;chunksize=(32703,), meta=np.ndarray&gt;\n",
       "  * time     (time) datetime64[ns] 2022-03-01T09:12:00 ... 2022-08-30T21:11:00\n",
       "Dimensions without coordinates: location\n",
       "Attributes:\n",
       "    location:    central\n",
       "    resolution:  20</pre><div class='xr-wrap' style='display:none'><div class='xr-header'><div class='xr-obj-type'>xarray.DataArray</div><div class='xr-array-name'>'sm'</div><ul class='xr-dim-list'><li><span>location</span>: 32703</li><li><span class='xr-has-index'>time</span>: 434</li></ul></div><ul class='xr-sections'><li class='xr-section-item'><div class='xr-array-wrap'><input id='section-ea5b90b7-05f4-4515-be1f-a2d6b8981236' class='xr-array-in' type='checkbox' checked><label for='section-ea5b90b7-05f4-4515-be1f-a2d6b8981236' title='Show/hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-array-preview xr-preview'><span>dask.array&lt;chunksize=(2044, 67), meta=np.ndarray&gt;</span></div><div class='xr-array-data'><table>\n",
       "    <tr>\n",
       "        <td>\n",
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       "                    <tr>\n",
       "                        <td> </td>\n",
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       "                    </tr>\n",
       "                </thead>\n",
       "                <tbody>\n",
       "                    \n",
       "                    <tr>\n",
       "                        <th> Bytes </th>\n",
       "                        <td> 54.14 MiB </td>\n",
       "                        <td> 878.28 kiB </td>\n",
       "                    </tr>\n",
       "                    \n",
       "                    <tr>\n",
       "                        <th> Shape </th>\n",
       "                        <td> (32703, 434) </td>\n",
       "                        <td> (2044, 110) </td>\n",
       "                    </tr>\n",
       "                    <tr>\n",
       "                        <th> Dask graph </th>\n",
       "                        <td colspan=\"2\"> 80 chunks in 3 graph layers </td>\n",
       "                    </tr>\n",
       "                    <tr>\n",
       "                        <th> Data type </th>\n",
       "                        <td colspan=\"2\"> float32 numpy.ndarray </td>\n",
       "                    </tr>\n",
       "                </tbody>\n",
       "            </table>\n",
       "        </td>\n",
       "        <td>\n",
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       "    </tr>\n",
       "</table></div></div></li><li class='xr-section-item'><input id='section-7fa32e8e-414b-4550-b8eb-1588dcd9db96' class='xr-section-summary-in' type='checkbox'  checked><label for='section-7fa32e8e-414b-4550-b8eb-1588dcd9db96' class='xr-section-summary' >Coordinates: <span>(3)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><ul class='xr-var-list'><li class='xr-var-item'><div class='xr-var-name'><span>lat</span></div><div class='xr-var-dims'>(location)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(32703,), meta=np.ndarray&gt;</div><input id='attrs-89f7cef7-4b84-46b2-bdfe-afdfdf97abca' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-89f7cef7-4b84-46b2-bdfe-afdfdf97abca' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-877cfaba-b79e-4f98-a3bd-b3ca8cc87c9b' class='xr-var-data-in' type='checkbox'><label for='data-877cfaba-b79e-4f98-a3bd-b3ca8cc87c9b' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><table>\n",
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       "                        <th> Array </th>\n",
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       "                    </tr>\n",
       "                </thead>\n",
       "                <tbody>\n",
       "                    \n",
       "                    <tr>\n",
       "                        <th> Bytes </th>\n",
       "                        <td> 127.75 kiB </td>\n",
       "                        <td> 127.75 kiB </td>\n",
       "                    </tr>\n",
       "                    \n",
       "                    <tr>\n",
       "                        <th> Shape </th>\n",
       "                        <td> (32703,) </td>\n",
       "                        <td> (32703,) </td>\n",
       "                    </tr>\n",
       "                    <tr>\n",
       "                        <th> Dask graph </th>\n",
       "                        <td colspan=\"2\"> 1 chunks in 2 graph layers </td>\n",
       "                    </tr>\n",
       "                    <tr>\n",
       "                        <th> Data type </th>\n",
       "                        <td colspan=\"2\"> float32 numpy.ndarray </td>\n",
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       "</table></div></li><li class='xr-var-item'><div class='xr-var-name'><span>lon</span></div><div class='xr-var-dims'>(location)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(32703,), meta=np.ndarray&gt;</div><input id='attrs-ba263ae9-6973-41bf-9f18-f083c921b383' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-ba263ae9-6973-41bf-9f18-f083c921b383' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-da4cd854-32eb-4ded-ac55-bba01ff765d9' class='xr-var-data-in' type='checkbox'><label for='data-da4cd854-32eb-4ded-ac55-bba01ff765d9' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><table>\n",
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       "                        <td> 127.75 kiB </td>\n",
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       "                        <td colspan=\"2\"> 1 chunks in 2 graph layers </td>\n",
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       "</table></div></li><li class='xr-var-item'><div class='xr-var-name'><span class='xr-has-index'>time</span></div><div class='xr-var-dims'>(time)</div><div class='xr-var-dtype'>datetime64[ns]</div><div class='xr-var-preview xr-preview'>2022-03-01T09:12:00 ... 2022-08-...</div><input id='attrs-7bce92f8-6510-4992-8517-6051bfde165d' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-7bce92f8-6510-4992-8517-6051bfde165d' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-388d490e-a177-4df8-bed6-4c7ee5a8f58b' class='xr-var-data-in' type='checkbox'><label for='data-388d490e-a177-4df8-bed6-4c7ee5a8f58b' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><pre>array([&#x27;2022-03-01T09:12:00.000000000&#x27;, &#x27;2022-03-01T10:05:00.000000000&#x27;,\n",
       "       &#x27;2022-03-01T19:50:00.000000000&#x27;, ..., &#x27;2022-08-29T19:51:00.000000000&#x27;,\n",
       "       &#x27;2022-08-30T08:59:00.000000000&#x27;, &#x27;2022-08-30T21:11:00.000000000&#x27;],\n",
       "      dtype=&#x27;datetime64[ns]&#x27;)</pre></div></li></ul></div></li><li class='xr-section-item'><input id='section-44c9c275-5d52-4ed1-b8ec-526a82017ab0' class='xr-section-summary-in' type='checkbox'  ><label for='section-44c9c275-5d52-4ed1-b8ec-526a82017ab0' class='xr-section-summary' >Indexes: <span>(1)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><ul class='xr-var-list'><li class='xr-var-item'><div class='xr-index-name'><div>time</div></div><div class='xr-index-preview'>PandasIndex</div><div></div><input id='index-72e013f9-9245-495c-b47a-3aac015f906a' class='xr-index-data-in' type='checkbox'/><label for='index-72e013f9-9245-495c-b47a-3aac015f906a' title='Show/Hide index repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-index-data'><pre>PandasIndex(DatetimeIndex([&#x27;2022-03-01 09:12:00&#x27;, &#x27;2022-03-01 10:05:00&#x27;,\n",
       "               &#x27;2022-03-01 19:50:00&#x27;, &#x27;2022-03-02 10:32:00&#x27;,\n",
       "               &#x27;2022-03-02 20:16:00&#x27;, &#x27;2022-03-03 09:24:00&#x27;,\n",
       "               &#x27;2022-03-03 10:11:00&#x27;, &#x27;2022-03-03 19:55:00&#x27;,\n",
       "               &#x27;2022-03-04 09:03:00&#x27;, &#x27;2022-03-04 21:15:00&#x27;,\n",
       "               ...\n",
       "               &#x27;2022-08-26 20:53:00&#x27;, &#x27;2022-08-27 09:08:00&#x27;,\n",
       "               &#x27;2022-08-28 10:28:00&#x27;, &#x27;2022-08-28 20:12:00&#x27;,\n",
       "               &#x27;2022-08-28 21:05:00&#x27;, &#x27;2022-08-29 09:20:00&#x27;,\n",
       "               &#x27;2022-08-29 10:07:00&#x27;, &#x27;2022-08-29 19:51:00&#x27;,\n",
       "               &#x27;2022-08-30 08:59:00&#x27;, &#x27;2022-08-30 21:11:00&#x27;],\n",
       "              dtype=&#x27;datetime64[ns]&#x27;, name=&#x27;time&#x27;, length=434, freq=None))</pre></div></li></ul></div></li><li class='xr-section-item'><input id='section-e1eae1a7-9a15-4c40-8bd6-366bf9910e23' class='xr-section-summary-in' type='checkbox'  checked><label for='section-e1eae1a7-9a15-4c40-8bd6-366bf9910e23' class='xr-section-summary' >Attributes: <span>(2)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><dl class='xr-attrs'><dt><span>location :</span></dt><dd>central</dd><dt><span>resolution :</span></dt><dd>20</dd></dl></div></li></ul></div></div>"
      ],
      "text/plain": [
       "<xarray.DataArray 'sm' (location: 32703, time: 434)>\n",
       "dask.array<getitem, shape=(32703, 434), dtype=float32, chunksize=(2044, 110), chunktype=numpy.ndarray>\n",
       "Coordinates:\n",
       "    lat      (location) float32 dask.array<chunksize=(32703,), meta=np.ndarray>\n",
       "    lon      (location) float32 dask.array<chunksize=(32703,), meta=np.ndarray>\n",
       "  * time     (time) datetime64[ns] 2022-03-01T09:12:00 ... 2022-08-30T21:11:00\n",
       "Dimensions without coordinates: location\n",
       "Attributes:\n",
       "    location:    central\n",
       "    resolution:  20"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "sm_sat_italy_summer_22 = xr.open_zarr(\n",
    "    store=s3fs.S3Map(\n",
    "        root=f\"demo/dedl-drought/ascat_italy.zarr\", s3=s3_central, check=False\n",
    "    ),\n",
    "    decode_coords=\"all\",\n",
    ")[\"sm\"].sel(time=slice(start_drought, end_drought)).assign_attrs(location=\"central\", resolution=20)\n",
    "sm_sat_italy_summer_22"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### DT output\n",
    "Predicted soil moisture based on ERA-5 to simulate DT outputs."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "</style><pre class='xr-text-repr-fallback'>&lt;xarray.DataArray &#x27;swvl1&#x27; (time: 184, latitude: 1801, longitude: 3600)&gt;\n",
       "dask.array&lt;getitem, shape=(184, 1801, 3600), dtype=float32, chunksize=(100, 100, 100), chunktype=numpy.ndarray&gt;\n",
       "Coordinates:\n",
       "  * latitude     (latitude) float64 90.0 89.9 89.8 89.7 ... -89.8 -89.9 -90.0\n",
       "  * longitude    (longitude) float64 -180.0 -179.9 -179.8 ... 179.7 179.8 179.9\n",
       "    spatial_ref  int64 ...\n",
       "  * time         (time) datetime64[ns] 2022-03-01 2022-03-02 ... 2022-08-31\n",
       "Attributes:\n",
       "    location:  lumi</pre><div class='xr-wrap' style='display:none'><div class='xr-header'><div class='xr-obj-type'>xarray.DataArray</div><div class='xr-array-name'>'swvl1'</div><ul class='xr-dim-list'><li><span class='xr-has-index'>time</span>: 184</li><li><span class='xr-has-index'>latitude</span>: 1801</li><li><span class='xr-has-index'>longitude</span>: 3600</li></ul></div><ul class='xr-sections'><li class='xr-section-item'><div class='xr-array-wrap'><input id='section-6e866cfb-5da8-4fd0-abde-fe5686ea2047' class='xr-array-in' type='checkbox' checked><label for='section-6e866cfb-5da8-4fd0-abde-fe5686ea2047' title='Show/hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-array-preview xr-preview'><span>dask.array&lt;chunksize=(84, 100, 100), meta=np.ndarray&gt;</span></div><div class='xr-array-data'><table>\n",
       "    <tr>\n",
       "        <td>\n",
       "            <table style=\"border-collapse: collapse;\">\n",
       "                <thead>\n",
       "                    <tr>\n",
       "                        <td> </td>\n",
       "                        <th> Array </th>\n",
       "                        <th> Chunk </th>\n",
       "                    </tr>\n",
       "                </thead>\n",
       "                <tbody>\n",
       "                    \n",
       "                    <tr>\n",
       "                        <th> Bytes </th>\n",
       "                        <td> 4.44 GiB </td>\n",
       "                        <td> 3.81 MiB </td>\n",
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       "                        <th> Dask graph </th>\n",
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       "</table></div></div></li><li class='xr-section-item'><input id='section-4dbac4a0-1c66-4b69-8e3c-cfc16e60b5b9' class='xr-section-summary-in' type='checkbox'  checked><label for='section-4dbac4a0-1c66-4b69-8e3c-cfc16e60b5b9' class='xr-section-summary' >Coordinates: <span>(4)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><ul class='xr-var-list'><li class='xr-var-item'><div class='xr-var-name'><span class='xr-has-index'>latitude</span></div><div class='xr-var-dims'>(latitude)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>90.0 89.9 89.8 ... -89.9 -90.0</div><input id='attrs-d707845f-67fe-4285-a35e-d96ea4121f96' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-d707845f-67fe-4285-a35e-d96ea4121f96' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-c83379a6-376a-4e3a-b36f-d303ebc98d0d' class='xr-var-data-in' type='checkbox'><label for='data-c83379a6-376a-4e3a-b36f-d303ebc98d0d' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><pre>array([ 90. ,  89.9,  89.8, ..., -89.8, -89.9, -90. ])</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span class='xr-has-index'>longitude</span></div><div class='xr-var-dims'>(longitude)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>-180.0 -179.9 ... 179.8 179.9</div><input id='attrs-3c9cda54-c5cb-45a8-b01d-c8cfcccde4be' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-3c9cda54-c5cb-45a8-b01d-c8cfcccde4be' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-1b4b08ad-a634-4103-baca-d0184be90223' class='xr-var-data-in' type='checkbox'><label for='data-1b4b08ad-a634-4103-baca-d0184be90223' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><pre>array([-180. , -179.9, -179.8, ...,  179.7,  179.8,  179.9])</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>spatial_ref</span></div><div class='xr-var-dims'>()</div><div class='xr-var-dtype'>int64</div><div class='xr-var-preview xr-preview'>...</div><input id='attrs-11d63b59-deb7-414b-88e6-166bb11d3705' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-11d63b59-deb7-414b-88e6-166bb11d3705' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-f5726d55-d736-40a3-be9c-9cdb2dd4d9d8' class='xr-var-data-in' type='checkbox'><label for='data-f5726d55-d736-40a3-be9c-9cdb2dd4d9d8' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>crs_wkt :</span></dt><dd>GEOGCS[&quot;WGS 84&quot;,DATUM[&quot;WGS_1984&quot;,SPHEROID[&quot;WGS 84&quot;,6378137,298.257223563,AUTHORITY[&quot;EPSG&quot;,&quot;7030&quot;]],AUTHORITY[&quot;EPSG&quot;,&quot;6326&quot;]],PRIMEM[&quot;Greenwich&quot;,0,AUTHORITY[&quot;EPSG&quot;,&quot;8901&quot;]],UNIT[&quot;degree&quot;,0.0174532925199433,AUTHORITY[&quot;EPSG&quot;,&quot;9122&quot;]],AXIS[&quot;Latitude&quot;,NORTH],AXIS[&quot;Longitude&quot;,EAST],AUTHORITY[&quot;EPSG&quot;,&quot;4326&quot;]]</dd><dt><span>geographic_crs_name :</span></dt><dd>WGS 84</dd><dt><span>grid_mapping_name :</span></dt><dd>latitude_longitude</dd><dt><span>horizontal_datum_name :</span></dt><dd>World Geodetic System 1984</dd><dt><span>inverse_flattening :</span></dt><dd>298.257223563</dd><dt><span>longitude_of_prime_meridian :</span></dt><dd>0.0</dd><dt><span>prime_meridian_name :</span></dt><dd>Greenwich</dd><dt><span>reference_ellipsoid_name :</span></dt><dd>WGS 84</dd><dt><span>semi_major_axis :</span></dt><dd>6378137.0</dd><dt><span>semi_minor_axis :</span></dt><dd>6356752.314245179</dd><dt><span>spatial_ref :</span></dt><dd>GEOGCS[&quot;WGS 84&quot;,DATUM[&quot;WGS_1984&quot;,SPHEROID[&quot;WGS 84&quot;,6378137,298.257223563,AUTHORITY[&quot;EPSG&quot;,&quot;7030&quot;]],AUTHORITY[&quot;EPSG&quot;,&quot;6326&quot;]],PRIMEM[&quot;Greenwich&quot;,0,AUTHORITY[&quot;EPSG&quot;,&quot;8901&quot;]],UNIT[&quot;degree&quot;,0.0174532925199433,AUTHORITY[&quot;EPSG&quot;,&quot;9122&quot;]],AXIS[&quot;Latitude&quot;,NORTH],AXIS[&quot;Longitude&quot;,EAST],AUTHORITY[&quot;EPSG&quot;,&quot;4326&quot;]]</dd></dl></div><div class='xr-var-data'><pre>[1 values with dtype=int64]</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span class='xr-has-index'>time</span></div><div class='xr-var-dims'>(time)</div><div class='xr-var-dtype'>datetime64[ns]</div><div class='xr-var-preview xr-preview'>2022-03-01 ... 2022-08-31</div><input id='attrs-b1e07033-4380-42d4-8566-f42224423c70' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-b1e07033-4380-42d4-8566-f42224423c70' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-f45d9541-28c3-401b-85c9-93959b21c659' class='xr-var-data-in' type='checkbox'><label for='data-f45d9541-28c3-401b-85c9-93959b21c659' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><pre>array([&#x27;2022-03-01T00:00:00.000000000&#x27;, &#x27;2022-03-02T00:00:00.000000000&#x27;,\n",
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       "       &#x27;2022-08-06T00:00:00.000000000&#x27;, &#x27;2022-08-07T00:00:00.000000000&#x27;,\n",
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       "       &#x27;2022-08-10T00:00:00.000000000&#x27;, &#x27;2022-08-11T00:00:00.000000000&#x27;,\n",
       "       &#x27;2022-08-12T00:00:00.000000000&#x27;, &#x27;2022-08-13T00:00:00.000000000&#x27;,\n",
       "       &#x27;2022-08-14T00:00:00.000000000&#x27;, &#x27;2022-08-15T00:00:00.000000000&#x27;,\n",
       "       &#x27;2022-08-16T00:00:00.000000000&#x27;, &#x27;2022-08-17T00:00:00.000000000&#x27;,\n",
       "       &#x27;2022-08-18T00:00:00.000000000&#x27;, &#x27;2022-08-19T00:00:00.000000000&#x27;,\n",
       "       &#x27;2022-08-20T00:00:00.000000000&#x27;, &#x27;2022-08-21T00:00:00.000000000&#x27;,\n",
       "       &#x27;2022-08-22T00:00:00.000000000&#x27;, &#x27;2022-08-23T00:00:00.000000000&#x27;,\n",
       "       &#x27;2022-08-24T00:00:00.000000000&#x27;, &#x27;2022-08-25T00:00:00.000000000&#x27;,\n",
       "       &#x27;2022-08-26T00:00:00.000000000&#x27;, &#x27;2022-08-27T00:00:00.000000000&#x27;,\n",
       "       &#x27;2022-08-28T00:00:00.000000000&#x27;, &#x27;2022-08-29T00:00:00.000000000&#x27;,\n",
       "       &#x27;2022-08-30T00:00:00.000000000&#x27;, &#x27;2022-08-31T00:00:00.000000000&#x27;],\n",
       "      dtype=&#x27;datetime64[ns]&#x27;)</pre></div></li></ul></div></li><li class='xr-section-item'><input id='section-5ac7ee4a-40c0-4e1c-9067-e37ebf52c3b5' class='xr-section-summary-in' type='checkbox'  ><label for='section-5ac7ee4a-40c0-4e1c-9067-e37ebf52c3b5' class='xr-section-summary' >Indexes: <span>(3)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><ul class='xr-var-list'><li class='xr-var-item'><div class='xr-index-name'><div>latitude</div></div><div class='xr-index-preview'>PandasIndex</div><div></div><input id='index-f2f90b4c-0bf2-4571-ac70-a7c289055249' class='xr-index-data-in' type='checkbox'/><label for='index-f2f90b4c-0bf2-4571-ac70-a7c289055249' title='Show/Hide index repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-index-data'><pre>PandasIndex(Index([              90.0,               89.9,  89.80000000000001,\n",
       "        89.70000000000002,  89.60000000000002,  89.50000000000003,\n",
       "        89.40000000000003,  89.30000000000004,  89.20000000000005,\n",
       "        89.10000000000005,\n",
       "       ...\n",
       "       -89.09999999998982, -89.19999999998981, -89.29999999998981,\n",
       "        -89.3999999999898,  -89.4999999999898, -89.59999999998979,\n",
       "       -89.69999999998979, -89.79999999998978, -89.89999999998977,\n",
       "       -89.99999999998977],\n",
       "      dtype=&#x27;float64&#x27;, name=&#x27;latitude&#x27;, length=1801))</pre></div></li><li class='xr-var-item'><div class='xr-index-name'><div>longitude</div></div><div class='xr-index-preview'>PandasIndex</div><div></div><input id='index-cd027a1b-9268-4796-9aaa-6b7a726a43ff' class='xr-index-data-in' type='checkbox'/><label for='index-cd027a1b-9268-4796-9aaa-6b7a726a43ff' title='Show/Hide index repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-index-data'><pre>PandasIndex(Index([             -180.0,              -179.9,              -179.8,\n",
       "       -179.70000000000002, -179.60000000000002, -179.50000000000003,\n",
       "       -179.40000000000003, -179.30000000000004, -179.20000000000005,\n",
       "       -179.10000000000005,\n",
       "       ...\n",
       "         178.9999999999796,  179.09999999997956,  179.19999999997958,\n",
       "         179.2999999999796,  179.39999999997957,  179.49999999997954,\n",
       "        179.59999999997956,  179.69999999997958,  179.79999999997955,\n",
       "         179.8999999999795],\n",
       "      dtype=&#x27;float64&#x27;, name=&#x27;longitude&#x27;, length=3600))</pre></div></li><li class='xr-var-item'><div class='xr-index-name'><div>time</div></div><div class='xr-index-preview'>PandasIndex</div><div></div><input id='index-ad6b15c6-b74f-4b8a-9e49-14123a4a7247' class='xr-index-data-in' type='checkbox'/><label for='index-ad6b15c6-b74f-4b8a-9e49-14123a4a7247' title='Show/Hide index repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-index-data'><pre>PandasIndex(DatetimeIndex([&#x27;2022-03-01&#x27;, &#x27;2022-03-02&#x27;, &#x27;2022-03-03&#x27;, &#x27;2022-03-04&#x27;,\n",
       "               &#x27;2022-03-05&#x27;, &#x27;2022-03-06&#x27;, &#x27;2022-03-07&#x27;, &#x27;2022-03-08&#x27;,\n",
       "               &#x27;2022-03-09&#x27;, &#x27;2022-03-10&#x27;,\n",
       "               ...\n",
       "               &#x27;2022-08-22&#x27;, &#x27;2022-08-23&#x27;, &#x27;2022-08-24&#x27;, &#x27;2022-08-25&#x27;,\n",
       "               &#x27;2022-08-26&#x27;, &#x27;2022-08-27&#x27;, &#x27;2022-08-28&#x27;, &#x27;2022-08-29&#x27;,\n",
       "               &#x27;2022-08-30&#x27;, &#x27;2022-08-31&#x27;],\n",
       "              dtype=&#x27;datetime64[ns]&#x27;, name=&#x27;time&#x27;, length=184, freq=None))</pre></div></li></ul></div></li><li class='xr-section-item'><input id='section-49d8418c-6122-4ba4-9779-f45b452f671f' class='xr-section-summary-in' type='checkbox'  checked><label for='section-49d8418c-6122-4ba4-9779-f45b452f671f' class='xr-section-summary' >Attributes: <span>(1)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><dl class='xr-attrs'><dt><span>location :</span></dt><dd>lumi</dd></dl></div></li></ul></div></div>"
      ],
      "text/plain": [
       "<xarray.DataArray 'swvl1' (time: 184, latitude: 1801, longitude: 3600)>\n",
       "dask.array<getitem, shape=(184, 1801, 3600), dtype=float32, chunksize=(100, 100, 100), chunktype=numpy.ndarray>\n",
       "Coordinates:\n",
       "  * latitude     (latitude) float64 90.0 89.9 89.8 89.7 ... -89.8 -89.9 -90.0\n",
       "  * longitude    (longitude) float64 -180.0 -179.9 -179.8 ... 179.7 179.8 179.9\n",
       "    spatial_ref  int64 ...\n",
       "  * time         (time) datetime64[ns] 2022-03-01 2022-03-02 ... 2022-08-31\n",
       "Attributes:\n",
       "    location:  lumi"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "sm_mdl_22 = xr.open_zarr(\n",
    "    store=s3fs.S3Map(\n",
    "        root=f\"demo/dedl-drought/modelled_sm.zarr\", s3=s3_lumi, check=False\n",
    "    ),\n",
    "    decode_coords=\"all\",\n",
    ")[\"swvl1\"].sel(time=slice(start_drought, end_drought)).assign_attrs(location=\"lumi\")\n",
    "sm_mdl_22"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Corine LandCover\n",
    "\n",
    "Corine LandCover map of 2018 converted into a zarr datastore."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
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       "  grid-column: 1;\n",
       "}\n",
       "\n",
       ".xr-attrs dt:hover span {\n",
       "  display: inline-block;\n",
       "  background: var(--xr-background-color);\n",
       "  padding-right: 10px;\n",
       "}\n",
       "\n",
       ".xr-attrs dd {\n",
       "  grid-column: 2;\n",
       "  white-space: pre-wrap;\n",
       "  word-break: break-all;\n",
       "}\n",
       "\n",
       ".xr-icon-database,\n",
       ".xr-icon-file-text2,\n",
       ".xr-no-icon {\n",
       "  display: inline-block;\n",
       "  vertical-align: middle;\n",
       "  width: 1em;\n",
       "  height: 1.5em !important;\n",
       "  stroke-width: 0;\n",
       "  stroke: currentColor;\n",
       "  fill: currentColor;\n",
       "}\n",
       "</style><pre class='xr-text-repr-fallback'>&lt;xarray.DataArray &#x27;corine_lc&#x27; (latitude: 16981, longitude: 16878)&gt;\n",
       "dask.array&lt;getitem, shape=(16981, 16878), dtype=float32, chunksize=(1062, 1055), chunktype=numpy.ndarray&gt;\n",
       "Coordinates:\n",
       "  * latitude   (latitude) float64 49.95 49.94 49.94 49.94 ... 32.3 32.29 32.29\n",
       "    lc         int64 1\n",
       "  * longitude  (longitude) float64 1.818 1.819 1.82 1.821 ... 19.36 19.36 19.36\n",
       "Attributes:\n",
       "    AREA_OR_POINT:       Area\n",
       "    DESCRIPTION:         clc18\n",
       "    RepresentationType:  THEMATIC\n",
       "    grid_mapping:        spatial_ref\n",
       "    location:            central</pre><div class='xr-wrap' style='display:none'><div class='xr-header'><div class='xr-obj-type'>xarray.DataArray</div><div class='xr-array-name'>'corine_lc'</div><ul class='xr-dim-list'><li><span class='xr-has-index'>latitude</span>: 16981</li><li><span class='xr-has-index'>longitude</span>: 16878</li></ul></div><ul class='xr-sections'><li class='xr-section-item'><div class='xr-array-wrap'><input id='section-16d0ddc9-c45e-404b-92e4-aec540b9d688' class='xr-array-in' type='checkbox' checked><label for='section-16d0ddc9-c45e-404b-92e4-aec540b9d688' title='Show/hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-array-preview xr-preview'><span>dask.array&lt;chunksize=(1062, 1055), meta=np.ndarray&gt;</span></div><div class='xr-array-data'><table>\n",
       "    <tr>\n",
       "        <td>\n",
       "            <table style=\"border-collapse: collapse;\">\n",
       "                <thead>\n",
       "                    <tr>\n",
       "                        <td> </td>\n",
       "                        <th> Array </th>\n",
       "                        <th> Chunk </th>\n",
       "                    </tr>\n",
       "                </thead>\n",
       "                <tbody>\n",
       "                    \n",
       "                    <tr>\n",
       "                        <th> Bytes </th>\n",
       "                        <td> 1.07 GiB </td>\n",
       "                        <td> 4.27 MiB </td>\n",
       "                    </tr>\n",
       "                    \n",
       "                    <tr>\n",
       "                        <th> Shape </th>\n",
       "                        <td> (16981, 16878) </td>\n",
       "                        <td> (1062, 1055) </td>\n",
       "                    </tr>\n",
       "                    <tr>\n",
       "                        <th> Dask graph </th>\n",
       "                        <td colspan=\"2\"> 256 chunks in 3 graph layers </td>\n",
       "                    </tr>\n",
       "                    <tr>\n",
       "                        <th> Data type </th>\n",
       "                        <td colspan=\"2\"> float32 numpy.ndarray </td>\n",
       "                    </tr>\n",
       "                </tbody>\n",
       "            </table>\n",
       "        </td>\n",
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       "\n",
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       "</svg>\n",
       "        </td>\n",
       "    </tr>\n",
       "</table></div></div></li><li class='xr-section-item'><input id='section-d383d2ad-52df-4831-80bf-9eb20d154161' class='xr-section-summary-in' type='checkbox'  checked><label for='section-d383d2ad-52df-4831-80bf-9eb20d154161' class='xr-section-summary' >Coordinates: <span>(3)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><ul class='xr-var-list'><li class='xr-var-item'><div class='xr-var-name'><span class='xr-has-index'>latitude</span></div><div class='xr-var-dims'>(latitude)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>49.95 49.94 49.94 ... 32.29 32.29</div><input id='attrs-77ce08ee-851e-414b-abc0-cd34119f5540' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-77ce08ee-851e-414b-abc0-cd34119f5540' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-35edc210-a788-412d-9973-67cf4cea5957' class='xr-var-data-in' type='checkbox'><label for='data-35edc210-a788-412d-9973-67cf4cea5957' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>axis :</span></dt><dd>Y</dd><dt><span>long_name :</span></dt><dd>latitude</dd><dt><span>standard_name :</span></dt><dd>latitude</dd><dt><span>units :</span></dt><dd>degrees_north</dd></dl></div><div class='xr-var-data'><pre>array([49.945618, 49.944578, 49.943538, ..., 32.295276, 32.294236, 32.293196])</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>lc</span></div><div class='xr-var-dims'>()</div><div class='xr-var-dtype'>int64</div><div class='xr-var-preview xr-preview'>1</div><input id='attrs-d6761063-ec2b-4b63-a6f1-d5e441c83130' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-d6761063-ec2b-4b63-a6f1-d5e441c83130' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-7f903669-7923-44af-8aa6-2d2cef0c5a78' class='xr-var-data-in' type='checkbox'><label for='data-7f903669-7923-44af-8aa6-2d2cef0c5a78' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><pre>array(1)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span class='xr-has-index'>longitude</span></div><div class='xr-var-dims'>(longitude)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>1.818 1.819 1.82 ... 19.36 19.36</div><input id='attrs-a1a3c51d-67d3-429e-afb6-3c8528c05a05' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-a1a3c51d-67d3-429e-afb6-3c8528c05a05' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-fbe6129a-86e0-4935-81c4-93fc658fadb9' class='xr-var-data-in' type='checkbox'><label for='data-fbe6129a-86e0-4935-81c4-93fc658fadb9' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>axis :</span></dt><dd>X</dd><dt><span>long_name :</span></dt><dd>longitude</dd><dt><span>standard_name :</span></dt><dd>longitude</dd><dt><span>units :</span></dt><dd>degrees_east</dd></dl></div><div class='xr-var-data'><pre>array([ 1.818091,  1.81913 ,  1.82017 , ..., 19.361354, 19.362394, 19.363433])</pre></div></li></ul></div></li><li class='xr-section-item'><input id='section-59e04ace-f1c5-4bec-8b66-2fecd85723f9' class='xr-section-summary-in' type='checkbox'  ><label for='section-59e04ace-f1c5-4bec-8b66-2fecd85723f9' class='xr-section-summary' >Indexes: <span>(2)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><ul class='xr-var-list'><li class='xr-var-item'><div class='xr-index-name'><div>latitude</div></div><div class='xr-index-preview'>PandasIndex</div><div></div><input id='index-7bb39307-e3d0-44b1-98a4-a7aa67a8fb07' class='xr-index-data-in' type='checkbox'/><label for='index-7bb39307-e3d0-44b1-98a4-a7aa67a8fb07' title='Show/Hide index repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-index-data'><pre>PandasIndex(Index([ 49.94561762580621, 49.944578025030985,  49.94353842425577,\n",
       "        49.94249882348055, 49.941459222705326, 49.940419621930104,\n",
       "        49.93938002115489,  49.93834042037967, 49.937300819604445,\n",
       "        49.93626121882922,\n",
       "       ...\n",
       "       32.302552869545366,  32.30151326877015,  32.30047366799493,\n",
       "        32.29943406721971,  32.29839446644449,  32.29735486566927,\n",
       "        32.29631526489405,  32.29527566411883,  32.29423606334361,\n",
       "        32.29319646256839],\n",
       "      dtype=&#x27;float64&#x27;, name=&#x27;latitude&#x27;, length=16981))</pre></div></li><li class='xr-var-item'><div class='xr-index-name'><div>longitude</div></div><div class='xr-index-preview'>PandasIndex</div><div></div><input id='index-89b2d5b4-8ae1-42c9-8469-faf8fd2b326c' class='xr-index-data-in' type='checkbox'/><label for='index-89b2d5b4-8ae1-42c9-8469-faf8fd2b326c' title='Show/Hide index repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-index-data'><pre>PandasIndex(Index([1.8180908586712579, 1.8191304594464779, 1.8201700602216981,\n",
       "       1.8212096609969182, 1.8222492617721384, 1.8232888625473584,\n",
       "       1.8243284633225787, 1.8253680640977987,  1.826407664873019,\n",
       "        1.827447265648239,\n",
       "       ...\n",
       "       19.354076735084423,  19.35511633585964, 19.356155936634863,\n",
       "        19.35719553741008,   19.3582351381853, 19.359274738960522,\n",
       "        19.36031433973574, 19.361353940510963,  19.36239354128618,\n",
       "       19.363433142061403],\n",
       "      dtype=&#x27;float64&#x27;, name=&#x27;longitude&#x27;, length=16878))</pre></div></li></ul></div></li><li class='xr-section-item'><input id='section-0c70b414-ecd8-461a-806e-bddbedfa69eb' class='xr-section-summary-in' type='checkbox'  checked><label for='section-0c70b414-ecd8-461a-806e-bddbedfa69eb' class='xr-section-summary' >Attributes: <span>(5)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><dl class='xr-attrs'><dt><span>AREA_OR_POINT :</span></dt><dd>Area</dd><dt><span>DESCRIPTION :</span></dt><dd>clc18</dd><dt><span>RepresentationType :</span></dt><dd>THEMATIC</dd><dt><span>grid_mapping :</span></dt><dd>spatial_ref</dd><dt><span>location :</span></dt><dd>central</dd></dl></div></li></ul></div></div>"
      ],
      "text/plain": [
       "<xarray.DataArray 'corine_lc' (latitude: 16981, longitude: 16878)>\n",
       "dask.array<getitem, shape=(16981, 16878), dtype=float32, chunksize=(1062, 1055), chunktype=numpy.ndarray>\n",
       "Coordinates:\n",
       "  * latitude   (latitude) float64 49.95 49.94 49.94 49.94 ... 32.3 32.29 32.29\n",
       "    lc         int64 1\n",
       "  * longitude  (longitude) float64 1.818 1.819 1.82 1.821 ... 19.36 19.36 19.36\n",
       "Attributes:\n",
       "    AREA_OR_POINT:       Area\n",
       "    DESCRIPTION:         clc18\n",
       "    RepresentationType:  THEMATIC\n",
       "    grid_mapping:        spatial_ref\n",
       "    location:            central"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "corine_italy = xr.open_zarr(\n",
    "    store=s3fs.S3Map(\n",
    "        root=f\"demo/dedl-drought/corine_italy.zarr\", s3=s3_central, check=False\n",
    "    )\n",
    ")[\"corine_lc\"].assign_attrs(location=\"central\")\n",
    "corine_italy = corine_italy.squeeze()\n",
    "corine_italy"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Preprocess the Data\n",
    "\n",
    "The raw data requires preprocessing to be able to extract relevant information. \n",
    "\n",
    "This task is, however, computationally demanding and requires specialized IT infrastructure. The DEDL services are designed to cope with these computational limitations at the user's side by implementing distributed computation capabilities. This feature runs the task near to the source of the data. The user can therefore execute the script as if they would have performed it on their own local device (laptop, pc, or tablet). However, in reality, the computationally demanding tasks are executed at the high-performance computing facilities of DEDL. The DEDL services lower these barriers by performing these processing step close at the source of the data. Besides delegating computational intense processes to dedicated high performance clusters, this prevents redundant data transfer that cause further slowdown of the workflow. Combined, this toolchain provides an optimal user experience uninhibited by the user's hardware."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [],
   "source": [
    "from rasterio.enums import Resampling\n",
    "\n",
    "def normalize(da: xr.DataArray) -> xr.DataArray:\n",
    "        da_min = da.min()\n",
    "        return (da - da_min) / (da.max() - da_min)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### load catchment of UC from user workspace for clipping"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [],
   "source": [
    "import geopandas as gpd\n",
    "\n",
    "catchment_file = s3_central.open(\"demo/dedl-user/4dmed_catchment/catch_med.geojson\", 'rb')\n",
    "catchment_df = gpd.read_file(catchment_file)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Preprocess ASCAT soil moisture data\n",
    "\n",
    "Compute mean soil moisture and resample from swath geometry to Web Mercator projection for final visualisation."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [],
   "source": [
    "def preprocess_ascat_sm(sm: xr.DataArray, shpfile) -> xr.DataArray:\n",
    "    import numpy as np\n",
    "    from pyresample import geometry, kd_tree\n",
    "    from affine import Affine\n",
    "\n",
    "    def clip_dataset_to_extent(ds, geodf):\n",
    "        roi_extent = geodf.total_bounds\n",
    "        mask_lon = (ds.x >= roi_extent[0]) & (ds.x <= roi_extent[2])\n",
    "        mask_lat = (ds.y >= roi_extent[1]) & (ds.y <= roi_extent[3])\n",
    "        return ds.where(mask_lon & mask_lat, drop=True)\n",
    "    \n",
    "    sm.rio.write_nodata(np.nan, inplace=True)\n",
    "    sm_mm = sm.resample(time='10D').mean('time')\n",
    "    e7proj = \"+proj=aeqd +lat_0=53 +lon_0=24 +x_0=5837287.81977 +y_0=2121415.69617 +datum=WGS84 +units=m +no_defs\"\n",
    "    geo_trans = [4200000, 200000, 5500000, 1500000]\n",
    "    area_def = geometry.AreaDefinition(\"e7\", \"\", \"\", e7proj,\n",
    "                                       200, 200, geo_trans)\n",
    "    swath_def = geometry.SwathDefinition(lons=sm_mm.lon.values, lats=sm_mm.lat.values)\n",
    "    sm_area = xr.DataArray(np.full((200, 200, sm_mm.sizes['time']), np.nan, np.float32), {\n",
    "        'time': sm_mm.time.values,\n",
    "        'y': np.arange(1500000, 200000, -6500),\n",
    "        'x': np.arange(4200000, 5500000, 6500)\n",
    "    }, ['y', 'x', 'time'])\n",
    "    sm_area.values = kd_tree.resample_nearest(swath_def, sm_mm.values, area_def,\n",
    "                                              radius_of_influence=5000, fill_value=np.nan)\n",
    "    sm_area = sm_area.transpose('time', 'y', 'x')\n",
    "    sm_area = normalize(sm_area)\n",
    "    sm_area.rio.write_crs(e7proj, inplace=True)\n",
    "    sm_area.rio.write_transform(Affine.from_gdal(4200000, -6500, 0, 1500000, 0, 6500), inplace=True)\n",
    "    sm_area = sm_area.fillna(-9999.)\n",
    "    sm_area.rio.write_nodata(-9999., inplace=True)\n",
    "    sm_area = sm_area.rio.reproject('EPSG:3857', resampling=Resampling.bilinear)\n",
    "    #sm_area = clip_dataset_to_extent(sm_area, shpfile)\n",
    "    return sm_area.rio.clip_box(7, 37, 18, 46.5, crs='EPSG:4326')\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "\n",
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       "\n",
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       ".xr-attrs dd {\n",
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       "\n",
       ".xr-attrs dt {\n",
       "  font-weight: normal;\n",
       "  grid-column: 1;\n",
       "}\n",
       "\n",
       ".xr-attrs dt:hover span {\n",
       "  display: inline-block;\n",
       "  background: var(--xr-background-color);\n",
       "  padding-right: 10px;\n",
       "}\n",
       "\n",
       ".xr-attrs dd {\n",
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       "\n",
       ".xr-icon-database,\n",
       ".xr-icon-file-text2,\n",
       ".xr-no-icon {\n",
       "  display: inline-block;\n",
       "  vertical-align: middle;\n",
       "  width: 1em;\n",
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       "</style><pre class='xr-text-repr-fallback'>&lt;xarray.DataArray (time: 19, y: 170, x: 146)&gt;\n",
       "array([[[-9.9990000e+03, -9.9990000e+03, -9.9990000e+03, ...,\n",
       "          2.0342134e-01,  1.8666719e-01,  1.8731841e-01],\n",
       "        [-9.9990000e+03, -9.9990000e+03, -9.9990000e+03, ...,\n",
       "          2.4915706e-01,  2.4844261e-01,  2.4761496e-01],\n",
       "        [-9.9990000e+03, -9.9990000e+03, -9.9990000e+03, ...,\n",
       "          3.7289706e-01,  3.4265777e-01,  3.3192080e-01],\n",
       "        ...,\n",
       "        [-9.9990000e+03, -9.9990000e+03, -9.9990000e+03, ...,\n",
       "         -9.9990000e+03, -9.9990000e+03, -9.9990000e+03],\n",
       "        [-9.9990000e+03, -9.9990000e+03, -9.9990000e+03, ...,\n",
       "         -9.9990000e+03, -9.9990000e+03, -9.9990000e+03],\n",
       "        [-9.9990000e+03, -9.9990000e+03, -9.9990000e+03, ...,\n",
       "         -9.9990000e+03, -9.9990000e+03, -9.9990000e+03]],\n",
       "\n",
       "       [[-9.9990000e+03, -9.9990000e+03, -9.9990000e+03, ...,\n",
       "          1.0471817e-01,  8.0929331e-02,  7.7028669e-02],\n",
       "        [-9.9990000e+03, -9.9990000e+03, -9.9990000e+03, ...,\n",
       "          1.2938559e-01,  1.0081180e-01,  9.5860362e-02],\n",
       "        [-9.9990000e+03, -9.9990000e+03, -9.9990000e+03, ...,\n",
       "          2.2287036e-01,  1.5667798e-01,  1.4291616e-01],\n",
       "...\n",
       "        [-9.9990000e+03, -9.9990000e+03, -9.9990000e+03, ...,\n",
       "         -9.9990000e+03, -9.9990000e+03, -9.9990000e+03],\n",
       "        [-9.9990000e+03, -9.9990000e+03, -9.9990000e+03, ...,\n",
       "         -9.9990000e+03, -9.9990000e+03, -9.9990000e+03],\n",
       "        [-9.9990000e+03, -9.9990000e+03, -9.9990000e+03, ...,\n",
       "         -9.9990000e+03, -9.9990000e+03, -9.9990000e+03]],\n",
       "\n",
       "       [[-9.9990000e+03, -9.9990000e+03, -9.9990000e+03, ...,\n",
       "          2.8231806e-01,  2.7657264e-01,  2.8808773e-01],\n",
       "        [-9.9990000e+03, -9.9990000e+03, -9.9990000e+03, ...,\n",
       "          2.6861754e-01,  2.7431366e-01,  2.8206563e-01],\n",
       "        [-9.9990000e+03, -9.9990000e+03, -9.9990000e+03, ...,\n",
       "          2.7740049e-01,  2.7204302e-01,  2.6090980e-01],\n",
       "        ...,\n",
       "        [-9.9990000e+03, -9.9990000e+03, -9.9990000e+03, ...,\n",
       "         -9.9990000e+03, -9.9990000e+03, -9.9990000e+03],\n",
       "        [-9.9990000e+03, -9.9990000e+03, -9.9990000e+03, ...,\n",
       "         -9.9990000e+03, -9.9990000e+03, -9.9990000e+03],\n",
       "        [-9.9990000e+03, -9.9990000e+03, -9.9990000e+03, ...,\n",
       "         -9.9990000e+03, -9.9990000e+03, -9.9990000e+03]]], dtype=float32)\n",
       "Coordinates:\n",
       "  * x            (x) float64 7.8e+05 7.884e+05 7.968e+05 ... 1.991e+06 2e+06\n",
       "  * y            (y) float64 5.859e+06 5.85e+06 ... 4.446e+06 4.437e+06\n",
       "  * time         (time) datetime64[ns] 2022-03-01 2022-03-11 ... 2022-08-28\n",
       "    spatial_ref  int64 0\n",
       "Attributes:\n",
       "    _FillValue:  -9999.0</pre><div class='xr-wrap' style='display:none'><div class='xr-header'><div class='xr-obj-type'>xarray.DataArray</div><div class='xr-array-name'></div><ul class='xr-dim-list'><li><span class='xr-has-index'>time</span>: 19</li><li><span class='xr-has-index'>y</span>: 170</li><li><span class='xr-has-index'>x</span>: 146</li></ul></div><ul class='xr-sections'><li class='xr-section-item'><div class='xr-array-wrap'><input id='section-c72bdfe1-f2c1-4014-bde2-59ee90df137b' class='xr-array-in' type='checkbox' checked><label for='section-c72bdfe1-f2c1-4014-bde2-59ee90df137b' title='Show/hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-array-preview xr-preview'><span>-9.999e+03 -9.999e+03 -9.999e+03 ... -9.999e+03 -9.999e+03 -9.999e+03</span></div><div class='xr-array-data'><pre>array([[[-9.9990000e+03, -9.9990000e+03, -9.9990000e+03, ...,\n",
       "          2.0342134e-01,  1.8666719e-01,  1.8731841e-01],\n",
       "        [-9.9990000e+03, -9.9990000e+03, -9.9990000e+03, ...,\n",
       "          2.4915706e-01,  2.4844261e-01,  2.4761496e-01],\n",
       "        [-9.9990000e+03, -9.9990000e+03, -9.9990000e+03, ...,\n",
       "          3.7289706e-01,  3.4265777e-01,  3.3192080e-01],\n",
       "        ...,\n",
       "        [-9.9990000e+03, -9.9990000e+03, -9.9990000e+03, ...,\n",
       "         -9.9990000e+03, -9.9990000e+03, -9.9990000e+03],\n",
       "        [-9.9990000e+03, -9.9990000e+03, -9.9990000e+03, ...,\n",
       "         -9.9990000e+03, -9.9990000e+03, -9.9990000e+03],\n",
       "        [-9.9990000e+03, -9.9990000e+03, -9.9990000e+03, ...,\n",
       "         -9.9990000e+03, -9.9990000e+03, -9.9990000e+03]],\n",
       "\n",
       "       [[-9.9990000e+03, -9.9990000e+03, -9.9990000e+03, ...,\n",
       "          1.0471817e-01,  8.0929331e-02,  7.7028669e-02],\n",
       "        [-9.9990000e+03, -9.9990000e+03, -9.9990000e+03, ...,\n",
       "          1.2938559e-01,  1.0081180e-01,  9.5860362e-02],\n",
       "        [-9.9990000e+03, -9.9990000e+03, -9.9990000e+03, ...,\n",
       "          2.2287036e-01,  1.5667798e-01,  1.4291616e-01],\n",
       "...\n",
       "        [-9.9990000e+03, -9.9990000e+03, -9.9990000e+03, ...,\n",
       "         -9.9990000e+03, -9.9990000e+03, -9.9990000e+03],\n",
       "        [-9.9990000e+03, -9.9990000e+03, -9.9990000e+03, ...,\n",
       "         -9.9990000e+03, -9.9990000e+03, -9.9990000e+03],\n",
       "        [-9.9990000e+03, -9.9990000e+03, -9.9990000e+03, ...,\n",
       "         -9.9990000e+03, -9.9990000e+03, -9.9990000e+03]],\n",
       "\n",
       "       [[-9.9990000e+03, -9.9990000e+03, -9.9990000e+03, ...,\n",
       "          2.8231806e-01,  2.7657264e-01,  2.8808773e-01],\n",
       "        [-9.9990000e+03, -9.9990000e+03, -9.9990000e+03, ...,\n",
       "          2.6861754e-01,  2.7431366e-01,  2.8206563e-01],\n",
       "        [-9.9990000e+03, -9.9990000e+03, -9.9990000e+03, ...,\n",
       "          2.7740049e-01,  2.7204302e-01,  2.6090980e-01],\n",
       "        ...,\n",
       "        [-9.9990000e+03, -9.9990000e+03, -9.9990000e+03, ...,\n",
       "         -9.9990000e+03, -9.9990000e+03, -9.9990000e+03],\n",
       "        [-9.9990000e+03, -9.9990000e+03, -9.9990000e+03, ...,\n",
       "         -9.9990000e+03, -9.9990000e+03, -9.9990000e+03],\n",
       "        [-9.9990000e+03, -9.9990000e+03, -9.9990000e+03, ...,\n",
       "         -9.9990000e+03, -9.9990000e+03, -9.9990000e+03]]], dtype=float32)</pre></div></div></li><li class='xr-section-item'><input id='section-77f868aa-1b2c-4f86-830d-9baa447f1abd' class='xr-section-summary-in' type='checkbox'  checked><label for='section-77f868aa-1b2c-4f86-830d-9baa447f1abd' class='xr-section-summary' >Coordinates: <span>(4)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><ul class='xr-var-list'><li class='xr-var-item'><div class='xr-var-name'><span class='xr-has-index'>x</span></div><div class='xr-var-dims'>(x)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>7.8e+05 7.884e+05 ... 2e+06</div><input id='attrs-bbd00066-5176-4370-8851-0eab2d55be57' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-bbd00066-5176-4370-8851-0eab2d55be57' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-33f766f8-1540-4c15-b6f1-d885acd9a1fb' class='xr-var-data-in' type='checkbox'><label for='data-33f766f8-1540-4c15-b6f1-d885acd9a1fb' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>axis :</span></dt><dd>X</dd><dt><span>long_name :</span></dt><dd>x coordinate of projection</dd><dt><span>standard_name :</span></dt><dd>projection_x_coordinate</dd><dt><span>units :</span></dt><dd>metre</dd></dl></div><div class='xr-var-data'><pre>array([ 779990.855904,  788401.583394,  796812.310884,  805223.038374,\n",
       "        813633.765864,  822044.493354,  830455.220844,  838865.948334,\n",
       "        847276.675824,  855687.403314,  864098.130804,  872508.858294,\n",
       "        880919.585784,  889330.313274,  897741.040764,  906151.768254,\n",
       "        914562.495744,  922973.223234,  931383.950724,  939794.678214,\n",
       "        948205.405704,  956616.133194,  965026.860684,  973437.588174,\n",
       "        981848.315664,  990259.043154,  998669.770644, 1007080.498134,\n",
       "       1015491.225624, 1023901.953114, 1032312.680604, 1040723.408094,\n",
       "       1049134.135584, 1057544.863074, 1065955.590564, 1074366.318054,\n",
       "       1082777.045544, 1091187.773034, 1099598.500524, 1108009.228014,\n",
       "       1116419.955504, 1124830.682994, 1133241.410484, 1141652.137975,\n",
       "       1150062.865465, 1158473.592955, 1166884.320445, 1175295.047935,\n",
       "       1183705.775425, 1192116.502915, 1200527.230405, 1208937.957895,\n",
       "       1217348.685385, 1225759.412875, 1234170.140365, 1242580.867855,\n",
       "       1250991.595345, 1259402.322835, 1267813.050325, 1276223.777815,\n",
       "       1284634.505305, 1293045.232795, 1301455.960285, 1309866.687775,\n",
       "       1318277.415265, 1326688.142755, 1335098.870245, 1343509.597735,\n",
       "       1351920.325225, 1360331.052715, 1368741.780205, 1377152.507695,\n",
       "       1385563.235185, 1393973.962675, 1402384.690165, 1410795.417655,\n",
       "       1419206.145145, 1427616.872635, 1436027.600125, 1444438.327615,\n",
       "       1452849.055105, 1461259.782595, 1469670.510085, 1478081.237575,\n",
       "       1486491.965065, 1494902.692555, 1503313.420045, 1511724.147535,\n",
       "       1520134.875025, 1528545.602515, 1536956.330005, 1545367.057495,\n",
       "       1553777.784985, 1562188.512475, 1570599.239965, 1579009.967455,\n",
       "       1587420.694945, 1595831.422435, 1604242.149925, 1612652.877415,\n",
       "       1621063.604905, 1629474.332395, 1637885.059885, 1646295.787375,\n",
       "       1654706.514865, 1663117.242355, 1671527.969845, 1679938.697336,\n",
       "       1688349.424826, 1696760.152316, 1705170.879806, 1713581.607296,\n",
       "       1721992.334786, 1730403.062276, 1738813.789766, 1747224.517256,\n",
       "       1755635.244746, 1764045.972236, 1772456.699726, 1780867.427216,\n",
       "       1789278.154706, 1797688.882196, 1806099.609686, 1814510.337176,\n",
       "       1822921.064666, 1831331.792156, 1839742.519646, 1848153.247136,\n",
       "       1856563.974626, 1864974.702116, 1873385.429606, 1881796.157096,\n",
       "       1890206.884586, 1898617.612076, 1907028.339566, 1915439.067056,\n",
       "       1923849.794546, 1932260.522036, 1940671.249526, 1949081.977016,\n",
       "       1957492.704506, 1965903.431996, 1974314.159486, 1982724.886976,\n",
       "       1991135.614466, 1999546.341956])</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span class='xr-has-index'>y</span></div><div class='xr-var-dims'>(y)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>5.859e+06 5.85e+06 ... 4.437e+06</div><input id='attrs-126d5d33-3f30-4edb-b904-c811b3f83358' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-126d5d33-3f30-4edb-b904-c811b3f83358' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-1622e000-07f7-4fa0-8e59-4954f1ed38b0' class='xr-var-data-in' type='checkbox'><label for='data-1622e000-07f7-4fa0-8e59-4954f1ed38b0' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>axis :</span></dt><dd>Y</dd><dt><span>long_name :</span></dt><dd>y coordinate of projection</dd><dt><span>standard_name :</span></dt><dd>projection_y_coordinate</dd><dt><span>units :</span></dt><dd>metre</dd></dl></div><div class='xr-var-data'><pre>array([5858663.05655 , 5850252.32906 , 5841841.60157 , 5833430.87408 ,\n",
       "       5825020.14659 , 5816609.4191  , 5808198.69161 , 5799787.96412 ,\n",
       "       5791377.23663 , 5782966.50914 , 5774555.78165 , 5766145.05416 ,\n",
       "       5757734.32667 , 5749323.59918 , 5740912.87169 , 5732502.1442  ,\n",
       "       5724091.41671 , 5715680.68922 , 5707269.96173 , 5698859.23424 ,\n",
       "       5690448.50675 , 5682037.77926 , 5673627.05177 , 5665216.32428 ,\n",
       "       5656805.59679 , 5648394.8693  , 5639984.14181 , 5631573.41432 ,\n",
       "       5623162.68683 , 5614751.95934 , 5606341.23185 , 5597930.50436 ,\n",
       "       5589519.77687 , 5581109.04938 , 5572698.32189 , 5564287.5944  ,\n",
       "       5555876.86691 , 5547466.13942 , 5539055.41193 , 5530644.68444 ,\n",
       "       5522233.95695 , 5513823.229459, 5505412.501969, 5497001.774479,\n",
       "       5488591.046989, 5480180.319499, 5471769.592009, 5463358.864519,\n",
       "       5454948.137029, 5446537.409539, 5438126.682049, 5429715.954559,\n",
       "       5421305.227069, 5412894.499579, 5404483.772089, 5396073.044599,\n",
       "       5387662.317109, 5379251.589619, 5370840.862129, 5362430.134639,\n",
       "       5354019.407149, 5345608.679659, 5337197.952169, 5328787.224679,\n",
       "       5320376.497189, 5311965.769699, 5303555.042209, 5295144.314719,\n",
       "       5286733.587229, 5278322.859739, 5269912.132249, 5261501.404759,\n",
       "       5253090.677269, 5244679.949779, 5236269.222289, 5227858.494799,\n",
       "       5219447.767309, 5211037.039819, 5202626.312329, 5194215.584839,\n",
       "       5185804.857349, 5177394.129859, 5168983.402369, 5160572.674879,\n",
       "       5152161.947389, 5143751.219899, 5135340.492409, 5126929.764919,\n",
       "       5118519.037429, 5110108.309939, 5101697.582449, 5093286.854959,\n",
       "       5084876.127469, 5076465.399979, 5068054.672489, 5059643.944999,\n",
       "       5051233.217509, 5042822.490019, 5034411.762529, 5026001.035039,\n",
       "       5017590.307549, 5009179.580059, 5000768.852569, 4992358.125079,\n",
       "       4983947.397589, 4975536.670098, 4967125.942608, 4958715.215118,\n",
       "       4950304.487628, 4941893.760138, 4933483.032648, 4925072.305158,\n",
       "       4916661.577668, 4908250.850178, 4899840.122688, 4891429.395198,\n",
       "       4883018.667708, 4874607.940218, 4866197.212728, 4857786.485238,\n",
       "       4849375.757748, 4840965.030258, 4832554.302768, 4824143.575278,\n",
       "       4815732.847788, 4807322.120298, 4798911.392808, 4790500.665318,\n",
       "       4782089.937828, 4773679.210338, 4765268.482848, 4756857.755358,\n",
       "       4748447.027868, 4740036.300378, 4731625.572888, 4723214.845398,\n",
       "       4714804.117908, 4706393.390418, 4697982.662928, 4689571.935438,\n",
       "       4681161.207948, 4672750.480458, 4664339.752968, 4655929.025478,\n",
       "       4647518.297988, 4639107.570498, 4630696.843008, 4622286.115518,\n",
       "       4613875.388028, 4605464.660538, 4597053.933048, 4588643.205558,\n",
       "       4580232.478068, 4571821.750578, 4563411.023088, 4555000.295598,\n",
       "       4546589.568108, 4538178.840618, 4529768.113128, 4521357.385638,\n",
       "       4512946.658148, 4504535.930658, 4496125.203168, 4487714.475678,\n",
       "       4479303.748188, 4470893.020698, 4462482.293208, 4454071.565718,\n",
       "       4445660.838228, 4437250.110738])</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span class='xr-has-index'>time</span></div><div class='xr-var-dims'>(time)</div><div class='xr-var-dtype'>datetime64[ns]</div><div class='xr-var-preview xr-preview'>2022-03-01 ... 2022-08-28</div><input id='attrs-17489866-0234-4ba1-b25f-74164a27d4d2' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-17489866-0234-4ba1-b25f-74164a27d4d2' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-05a66c76-bc18-4de6-b3d6-8efdf718a9fa' class='xr-var-data-in' type='checkbox'><label for='data-05a66c76-bc18-4de6-b3d6-8efdf718a9fa' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><pre>array([&#x27;2022-03-01T00:00:00.000000000&#x27;, &#x27;2022-03-11T00:00:00.000000000&#x27;,\n",
       "       &#x27;2022-03-21T00:00:00.000000000&#x27;, &#x27;2022-03-31T00:00:00.000000000&#x27;,\n",
       "       &#x27;2022-04-10T00:00:00.000000000&#x27;, &#x27;2022-04-20T00:00:00.000000000&#x27;,\n",
       "       &#x27;2022-04-30T00:00:00.000000000&#x27;, &#x27;2022-05-10T00:00:00.000000000&#x27;,\n",
       "       &#x27;2022-05-20T00:00:00.000000000&#x27;, &#x27;2022-05-30T00:00:00.000000000&#x27;,\n",
       "       &#x27;2022-06-09T00:00:00.000000000&#x27;, &#x27;2022-06-19T00:00:00.000000000&#x27;,\n",
       "       &#x27;2022-06-29T00:00:00.000000000&#x27;, &#x27;2022-07-09T00:00:00.000000000&#x27;,\n",
       "       &#x27;2022-07-19T00:00:00.000000000&#x27;, &#x27;2022-07-29T00:00:00.000000000&#x27;,\n",
       "       &#x27;2022-08-08T00:00:00.000000000&#x27;, &#x27;2022-08-18T00:00:00.000000000&#x27;,\n",
       "       &#x27;2022-08-28T00:00:00.000000000&#x27;], dtype=&#x27;datetime64[ns]&#x27;)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>spatial_ref</span></div><div class='xr-var-dims'>()</div><div class='xr-var-dtype'>int64</div><div class='xr-var-preview xr-preview'>0</div><input id='attrs-aedcf2ae-0fca-4683-97df-1b84c870210c' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-aedcf2ae-0fca-4683-97df-1b84c870210c' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-8188111f-e6c8-41f4-8915-89fc9512f025' class='xr-var-data-in' type='checkbox'><label for='data-8188111f-e6c8-41f4-8915-89fc9512f025' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>crs_wkt :</span></dt><dd>PROJCS[&quot;WGS 84 / Pseudo-Mercator&quot;,GEOGCS[&quot;WGS 84&quot;,DATUM[&quot;WGS_1984&quot;,SPHEROID[&quot;WGS 84&quot;,6378137,298.257223563,AUTHORITY[&quot;EPSG&quot;,&quot;7030&quot;]],AUTHORITY[&quot;EPSG&quot;,&quot;6326&quot;]],PRIMEM[&quot;Greenwich&quot;,0,AUTHORITY[&quot;EPSG&quot;,&quot;8901&quot;]],UNIT[&quot;degree&quot;,0.0174532925199433,AUTHORITY[&quot;EPSG&quot;,&quot;9122&quot;]],AUTHORITY[&quot;EPSG&quot;,&quot;4326&quot;]],PROJECTION[&quot;Mercator_1SP&quot;],PARAMETER[&quot;central_meridian&quot;,0],PARAMETER[&quot;scale_factor&quot;,1],PARAMETER[&quot;false_easting&quot;,0],PARAMETER[&quot;false_northing&quot;,0],UNIT[&quot;metre&quot;,1,AUTHORITY[&quot;EPSG&quot;,&quot;9001&quot;]],AXIS[&quot;Easting&quot;,EAST],AXIS[&quot;Northing&quot;,NORTH],EXTENSION[&quot;PROJ4&quot;,&quot;+proj=merc +a=6378137 +b=6378137 +lat_ts=0 +lon_0=0 +x_0=0 +y_0=0 +k=1 +units=m +nadgrids=@null +wktext +no_defs&quot;],AUTHORITY[&quot;EPSG&quot;,&quot;3857&quot;]]</dd><dt><span>spatial_ref :</span></dt><dd>PROJCS[&quot;WGS 84 / Pseudo-Mercator&quot;,GEOGCS[&quot;WGS 84&quot;,DATUM[&quot;WGS_1984&quot;,SPHEROID[&quot;WGS 84&quot;,6378137,298.257223563,AUTHORITY[&quot;EPSG&quot;,&quot;7030&quot;]],AUTHORITY[&quot;EPSG&quot;,&quot;6326&quot;]],PRIMEM[&quot;Greenwich&quot;,0,AUTHORITY[&quot;EPSG&quot;,&quot;8901&quot;]],UNIT[&quot;degree&quot;,0.0174532925199433,AUTHORITY[&quot;EPSG&quot;,&quot;9122&quot;]],AUTHORITY[&quot;EPSG&quot;,&quot;4326&quot;]],PROJECTION[&quot;Mercator_1SP&quot;],PARAMETER[&quot;central_meridian&quot;,0],PARAMETER[&quot;scale_factor&quot;,1],PARAMETER[&quot;false_easting&quot;,0],PARAMETER[&quot;false_northing&quot;,0],UNIT[&quot;metre&quot;,1,AUTHORITY[&quot;EPSG&quot;,&quot;9001&quot;]],AXIS[&quot;Easting&quot;,EAST],AXIS[&quot;Northing&quot;,NORTH],EXTENSION[&quot;PROJ4&quot;,&quot;+proj=merc +a=6378137 +b=6378137 +lat_ts=0 +lon_0=0 +x_0=0 +y_0=0 +k=1 +units=m +nadgrids=@null +wktext +no_defs&quot;],AUTHORITY[&quot;EPSG&quot;,&quot;3857&quot;]]</dd><dt><span>GeoTransform :</span></dt><dd>775785.4921588254 8410.727490015583 0.0 5862868.420295143 0.0 -8410.72749001558</dd></dl></div><div class='xr-var-data'><pre>array(0)</pre></div></li></ul></div></li><li class='xr-section-item'><input id='section-0fc3767c-955b-4391-954f-4ddfa82eb5a2' class='xr-section-summary-in' type='checkbox'  ><label for='section-0fc3767c-955b-4391-954f-4ddfa82eb5a2' class='xr-section-summary' >Indexes: <span>(3)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><ul class='xr-var-list'><li class='xr-var-item'><div class='xr-index-name'><div>x</div></div><div class='xr-index-preview'>PandasIndex</div><div></div><input id='index-21add048-5e92-4b2b-aab4-12c69f9d2049' class='xr-index-data-in' type='checkbox'/><label for='index-21add048-5e92-4b2b-aab4-12c69f9d2049' title='Show/Hide index repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-index-data'><pre>PandasIndex(Index([ 779990.8559038332,  788401.5833938487,  796812.3108838643,\n",
       "        805223.0383738799,  813633.7658638954,   822044.493353911,\n",
       "        830455.2208439265,  838865.9483339421,  847276.6758239578,\n",
       "        855687.4033139733,\n",
       "       ...\n",
       "       1923849.7945459525,  1932260.522035968, 1940671.2495259836,\n",
       "       1949081.9770159991, 1957492.7045060147, 1965903.4319960303,\n",
       "       1974314.1594860458, 1982724.8869760614,  1991135.614466077,\n",
       "       1999546.3419560927],\n",
       "      dtype=&#x27;float64&#x27;, name=&#x27;x&#x27;, length=146))</pre></div></li><li class='xr-var-item'><div class='xr-index-name'><div>y</div></div><div class='xr-index-preview'>PandasIndex</div><div></div><input id='index-cffc1b9b-1f22-461a-9577-d04e35edf09e' class='xr-index-data-in' type='checkbox'/><label for='index-cffc1b9b-1f22-461a-9577-d04e35edf09e' title='Show/Hide index repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-index-data'><pre>PandasIndex(Index([5858663.056550135,  5850252.32906012, 5841841.601570103,\n",
       "       5833430.874080088, 5825020.146590073, 5816609.419100057,\n",
       "       5808198.691610041, 5799787.964120026,  5791377.23663001,\n",
       "       5782966.509139994,\n",
       "       ...\n",
       "       4512946.658147641, 4504535.930657626,  4496125.20316761,\n",
       "       4487714.475677595, 4479303.748187579, 4470893.020697564,\n",
       "       4462482.293207549, 4454071.565717532, 4445660.838227517,\n",
       "       4437250.110737502],\n",
       "      dtype=&#x27;float64&#x27;, name=&#x27;y&#x27;, length=170))</pre></div></li><li class='xr-var-item'><div class='xr-index-name'><div>time</div></div><div class='xr-index-preview'>PandasIndex</div><div></div><input id='index-f87700d5-357e-4f44-869a-38a91e24efe1' class='xr-index-data-in' type='checkbox'/><label for='index-f87700d5-357e-4f44-869a-38a91e24efe1' title='Show/Hide index repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-index-data'><pre>PandasIndex(DatetimeIndex([&#x27;2022-03-01&#x27;, &#x27;2022-03-11&#x27;, &#x27;2022-03-21&#x27;, &#x27;2022-03-31&#x27;,\n",
       "               &#x27;2022-04-10&#x27;, &#x27;2022-04-20&#x27;, &#x27;2022-04-30&#x27;, &#x27;2022-05-10&#x27;,\n",
       "               &#x27;2022-05-20&#x27;, &#x27;2022-05-30&#x27;, &#x27;2022-06-09&#x27;, &#x27;2022-06-19&#x27;,\n",
       "               &#x27;2022-06-29&#x27;, &#x27;2022-07-09&#x27;, &#x27;2022-07-19&#x27;, &#x27;2022-07-29&#x27;,\n",
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   ],
   "source": [
    "sm_sat_ = preprocess_ascat_sm(sm_sat_italy_summer_22, catchment_df)\n",
    "sm_sat_"
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  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Compute predicted soil moisture of simluated DT outputs (ERA5)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
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       "                    \n",
       "                    <tr>\n",
       "                        <th> Bytes </th>\n",
       "                        <td> 4.26 MiB </td>\n",
       "                        <td> 39.06 kiB </td>\n",
       "                    </tr>\n",
       "                    \n",
       "                    <tr>\n",
       "                        <th> Shape </th>\n",
       "                        <td> (13, 199, 432) </td>\n",
       "                        <td> (1, 100, 100) </td>\n",
       "                    </tr>\n",
       "                    <tr>\n",
       "                        <th> Dask graph </th>\n",
       "                        <td colspan=\"2\"> 195 chunks in 63 graph layers </td>\n",
       "                    </tr>\n",
       "                    <tr>\n",
       "                        <th> Data type </th>\n",
       "                        <td colspan=\"2\"> float32 numpy.ndarray </td>\n",
       "                    </tr>\n",
       "                </tbody>\n",
       "            </table>\n",
       "        </td>\n",
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       "</table></div></div></li><li class='xr-section-item'><input id='section-b296ac1e-e802-4307-a466-9c0df2af401b' class='xr-section-summary-in' type='checkbox'  checked><label for='section-b296ac1e-e802-4307-a466-9c0df2af401b' class='xr-section-summary' >Coordinates: <span>(4)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><ul class='xr-var-list'><li class='xr-var-item'><div class='xr-var-name'><span class='xr-has-index'>latitude</span></div><div class='xr-var-dims'>(latitude)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>48.2 48.1 48.0 ... 28.6 28.5 28.4</div><input id='attrs-8aeca476-42a9-44f6-8a73-5f8c5298baa5' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-8aeca476-42a9-44f6-8a73-5f8c5298baa5' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-0201fe8e-8234-4e51-b939-9981aba769e8' class='xr-var-data-in' type='checkbox'><label for='data-0201fe8e-8234-4e51-b939-9981aba769e8' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><pre>array([48.2, 48.1, 48. , 47.9, 47.8, 47.7, 47.6, 47.5, 47.4, 47.3, 47.2, 47.1,\n",
       "       47. , 46.9, 46.8, 46.7, 46.6, 46.5, 46.4, 46.3, 46.2, 46.1, 46. , 45.9,\n",
       "       45.8, 45.7, 45.6, 45.5, 45.4, 45.3, 45.2, 45.1, 45. , 44.9, 44.8, 44.7,\n",
       "       44.6, 44.5, 44.4, 44.3, 44.2, 44.1, 44. , 43.9, 43.8, 43.7, 43.6, 43.5,\n",
       "       43.4, 43.3, 43.2, 43.1, 43. , 42.9, 42.8, 42.7, 42.6, 42.5, 42.4, 42.3,\n",
       "       42.2, 42.1, 42. , 41.9, 41.8, 41.7, 41.6, 41.5, 41.4, 41.3, 41.2, 41.1,\n",
       "       41. , 40.9, 40.8, 40.7, 40.6, 40.5, 40.4, 40.3, 40.2, 40.1, 40. , 39.9,\n",
       "       39.8, 39.7, 39.6, 39.5, 39.4, 39.3, 39.2, 39.1, 39. , 38.9, 38.8, 38.7,\n",
       "       38.6, 38.5, 38.4, 38.3, 38.2, 38.1, 38. , 37.9, 37.8, 37.7, 37.6, 37.5,\n",
       "       37.4, 37.3, 37.2, 37.1, 37. , 36.9, 36.8, 36.7, 36.6, 36.5, 36.4, 36.3,\n",
       "       36.2, 36.1, 36. , 35.9, 35.8, 35.7, 35.6, 35.5, 35.4, 35.3, 35.2, 35.1,\n",
       "       35. , 34.9, 34.8, 34.7, 34.6, 34.5, 34.4, 34.3, 34.2, 34.1, 34. , 33.9,\n",
       "       33.8, 33.7, 33.6, 33.5, 33.4, 33.3, 33.2, 33.1, 33. , 32.9, 32.8, 32.7,\n",
       "       32.6, 32.5, 32.4, 32.3, 32.2, 32.1, 32. , 31.9, 31.8, 31.7, 31.6, 31.5,\n",
       "       31.4, 31.3, 31.2, 31.1, 31. , 30.9, 30.8, 30.7, 30.6, 30.5, 30.4, 30.3,\n",
       "       30.2, 30.1, 30. , 29.9, 29.8, 29.7, 29.6, 29.5, 29.4, 29.3, 29.2, 29.1,\n",
       "       29. , 28.9, 28.8, 28.7, 28.6, 28.5, 28.4])</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span class='xr-has-index'>longitude</span></div><div class='xr-var-dims'>(longitude)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>-5.6 -5.5 -5.4 ... 37.3 37.4 37.5</div><input id='attrs-aef2249c-be88-40b5-9c3d-63f699178e92' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-aef2249c-be88-40b5-9c3d-63f699178e92' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-de5cd5b9-18be-4190-993f-54aea6ec813b' class='xr-var-data-in' type='checkbox'><label for='data-de5cd5b9-18be-4190-993f-54aea6ec813b' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><pre>array([-5.6, -5.5, -5.4, ..., 37.3, 37.4, 37.5])</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>spatial_ref</span></div><div class='xr-var-dims'>()</div><div class='xr-var-dtype'>int64</div><div class='xr-var-preview xr-preview'>0</div><input id='attrs-c9c760fa-4bc9-4208-8308-3e7a66bc07ad' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-c9c760fa-4bc9-4208-8308-3e7a66bc07ad' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-ae211150-0684-4514-a634-2f09ee1b2ae9' class='xr-var-data-in' type='checkbox'><label for='data-ae211150-0684-4514-a634-2f09ee1b2ae9' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>crs_wkt :</span></dt><dd>GEOGCS[&quot;WGS 84&quot;,DATUM[&quot;WGS_1984&quot;,SPHEROID[&quot;WGS 84&quot;,6378137,298.257223563,AUTHORITY[&quot;EPSG&quot;,&quot;7030&quot;]],AUTHORITY[&quot;EPSG&quot;,&quot;6326&quot;]],PRIMEM[&quot;Greenwich&quot;,0,AUTHORITY[&quot;EPSG&quot;,&quot;8901&quot;]],UNIT[&quot;degree&quot;,0.0174532925199433,AUTHORITY[&quot;EPSG&quot;,&quot;9122&quot;]],AXIS[&quot;Latitude&quot;,NORTH],AXIS[&quot;Longitude&quot;,EAST],AUTHORITY[&quot;EPSG&quot;,&quot;4326&quot;]]</dd><dt><span>geographic_crs_name :</span></dt><dd>WGS 84</dd><dt><span>grid_mapping_name :</span></dt><dd>latitude_longitude</dd><dt><span>horizontal_datum_name :</span></dt><dd>World Geodetic System 1984</dd><dt><span>inverse_flattening :</span></dt><dd>298.257223563</dd><dt><span>longitude_of_prime_meridian :</span></dt><dd>0.0</dd><dt><span>prime_meridian_name :</span></dt><dd>Greenwich</dd><dt><span>reference_ellipsoid_name :</span></dt><dd>WGS 84</dd><dt><span>semi_major_axis :</span></dt><dd>6378137.0</dd><dt><span>semi_minor_axis :</span></dt><dd>6356752.314245179</dd><dt><span>spatial_ref :</span></dt><dd>GEOGCS[&quot;WGS 84&quot;,DATUM[&quot;WGS_1984&quot;,SPHEROID[&quot;WGS 84&quot;,6378137,298.257223563,AUTHORITY[&quot;EPSG&quot;,&quot;7030&quot;]],AUTHORITY[&quot;EPSG&quot;,&quot;6326&quot;]],PRIMEM[&quot;Greenwich&quot;,0,AUTHORITY[&quot;EPSG&quot;,&quot;8901&quot;]],UNIT[&quot;degree&quot;,0.0174532925199433,AUTHORITY[&quot;EPSG&quot;,&quot;9122&quot;]],AXIS[&quot;Latitude&quot;,NORTH],AXIS[&quot;Longitude&quot;,EAST],AUTHORITY[&quot;EPSG&quot;,&quot;4326&quot;]]</dd></dl></div><div class='xr-var-data'><pre>array(0)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span class='xr-has-index'>time</span></div><div class='xr-var-dims'>(time)</div><div class='xr-var-dtype'>datetime64[ns]</div><div class='xr-var-preview xr-preview'>2022-03-01 ... 2022-08-28</div><input id='attrs-7e2e0d46-0293-492d-82c5-ce39ba2e9960' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-7e2e0d46-0293-492d-82c5-ce39ba2e9960' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-1bbcc97e-ce95-4420-b8dc-01b63bf0d075' class='xr-var-data-in' type='checkbox'><label for='data-1bbcc97e-ce95-4420-b8dc-01b63bf0d075' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><pre>array([&#x27;2022-03-01T00:00:00.000000000&#x27;, &#x27;2022-03-16T00:00:00.000000000&#x27;,\n",
       "       &#x27;2022-03-31T00:00:00.000000000&#x27;, &#x27;2022-04-15T00:00:00.000000000&#x27;,\n",
       "       &#x27;2022-04-30T00:00:00.000000000&#x27;, &#x27;2022-05-15T00:00:00.000000000&#x27;,\n",
       "       &#x27;2022-05-30T00:00:00.000000000&#x27;, &#x27;2022-06-14T00:00:00.000000000&#x27;,\n",
       "       &#x27;2022-06-29T00:00:00.000000000&#x27;, &#x27;2022-07-14T00:00:00.000000000&#x27;,\n",
       "       &#x27;2022-07-29T00:00:00.000000000&#x27;, &#x27;2022-08-13T00:00:00.000000000&#x27;,\n",
       "       &#x27;2022-08-28T00:00:00.000000000&#x27;], dtype=&#x27;datetime64[ns]&#x27;)</pre></div></li></ul></div></li><li class='xr-section-item'><input id='section-ce2b27bb-0f17-4747-ae6e-f48009d4b1ff' class='xr-section-summary-in' type='checkbox'  ><label for='section-ce2b27bb-0f17-4747-ae6e-f48009d4b1ff' class='xr-section-summary' >Indexes: <span>(3)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><ul class='xr-var-list'><li class='xr-var-item'><div class='xr-index-name'><div>latitude</div></div><div class='xr-index-preview'>PandasIndex</div><div></div><input id='index-a3e130fe-154e-4e6e-903e-f92a55e69de3' class='xr-index-data-in' type='checkbox'/><label for='index-a3e130fe-154e-4e6e-903e-f92a55e69de3' title='Show/Hide index repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-index-data'><pre>PandasIndex(Index([48.200000000002376,  48.10000000000238,  48.00000000000239,\n",
       "        47.90000000000239,   47.8000000000024, 47.700000000002404,\n",
       "        47.60000000000241, 47.500000000002416,  47.40000000000242,\n",
       "        47.30000000000243,\n",
       "       ...\n",
       "        29.30000000000345, 29.200000000003456,  29.10000000000346,\n",
       "       29.000000000003467, 28.900000000003473,  28.80000000000348,\n",
       "       28.700000000003485,  28.60000000000349, 28.500000000003496,\n",
       "         28.4000000000035],\n",
       "      dtype=&#x27;float64&#x27;, name=&#x27;latitude&#x27;, length=199))</pre></div></li><li class='xr-var-item'><div class='xr-index-name'><div>longitude</div></div><div class='xr-index-preview'>PandasIndex</div><div></div><input id='index-685fbe61-ed7f-4742-ac32-d58294bffe7d' class='xr-index-data-in' type='checkbox'/><label for='index-685fbe61-ed7f-4742-ac32-d58294bffe7d' title='Show/Hide index repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-index-data'><pre>PandasIndex(Index([-5.6000000000099135,  -5.500000000009919,  -5.400000000009925,\n",
       "       -5.3000000000099305,  -5.200000000009936,  -5.100000000009942,\n",
       "        -5.000000000009948,  -4.900000000009953,  -4.800000000009959,\n",
       "        -4.700000000009965,\n",
       "       ...\n",
       "         36.59999999998769,   36.69999999998768,  36.799999999987676,\n",
       "         36.89999999998767,  36.999999999987665,   37.09999999998766,\n",
       "        37.199999999987654,   37.29999999998765,   37.39999999998764,\n",
       "         37.49999999998764],\n",
       "      dtype=&#x27;float64&#x27;, name=&#x27;longitude&#x27;, length=432))</pre></div></li><li class='xr-var-item'><div class='xr-index-name'><div>time</div></div><div class='xr-index-preview'>PandasIndex</div><div></div><input id='index-4cbd1516-54f7-4fac-9c20-50ab0a13c2da' class='xr-index-data-in' type='checkbox'/><label for='index-4cbd1516-54f7-4fac-9c20-50ab0a13c2da' title='Show/Hide index repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-index-data'><pre>PandasIndex(DatetimeIndex([&#x27;2022-03-01&#x27;, &#x27;2022-03-16&#x27;, &#x27;2022-03-31&#x27;, &#x27;2022-04-15&#x27;,\n",
       "               &#x27;2022-04-30&#x27;, &#x27;2022-05-15&#x27;, &#x27;2022-05-30&#x27;, &#x27;2022-06-14&#x27;,\n",
       "               &#x27;2022-06-29&#x27;, &#x27;2022-07-14&#x27;, &#x27;2022-07-29&#x27;, &#x27;2022-08-13&#x27;,\n",
       "               &#x27;2022-08-28&#x27;],\n",
       "              dtype=&#x27;datetime64[ns]&#x27;, name=&#x27;time&#x27;, freq=&#x27;15D&#x27;))</pre></div></li></ul></div></li><li class='xr-section-item'><input id='section-da26d9d4-9aac-41f0-bf9d-125d63604987' class='xr-section-summary-in' type='checkbox'  checked><label for='section-da26d9d4-9aac-41f0-bf9d-125d63604987' class='xr-section-summary' >Attributes: <span>(1)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><dl class='xr-attrs'><dt><span>location :</span></dt><dd>lumi</dd></dl></div></li></ul></div></div>"
      ],
      "text/plain": [
       "<xarray.DataArray 'swvl1' (time: 13, latitude: 199, longitude: 432)>\n",
       "dask.array<truediv, shape=(13, 199, 432), dtype=float32, chunksize=(1, 100, 100), chunktype=numpy.ndarray>\n",
       "Coordinates:\n",
       "  * latitude     (latitude) float64 48.2 48.1 48.0 47.9 ... 28.7 28.6 28.5 28.4\n",
       "  * longitude    (longitude) float64 -5.6 -5.5 -5.4 -5.3 ... 37.2 37.3 37.4 37.5\n",
       "    spatial_ref  int64 0\n",
       "  * time         (time) datetime64[ns] 2022-03-01 2022-03-16 ... 2022-08-28\n",
       "Attributes:\n",
       "    location:  lumi"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "def clip_dataset_to_extent(ds, geodf):\n",
    "    roi_extent = geodf.total_bounds\n",
    "    mask_lon = (ds.longitude >= roi_extent[0]) & (ds.longitude <= roi_extent[2])\n",
    "    mask_lat = (ds.latitude >= roi_extent[1]) & (ds.latitude <= roi_extent[3])\n",
    "    return ds.where(mask_lon & mask_lat, drop=True)\n",
    "\n",
    "sm_mdl_italy_summer_22 = clip_dataset_to_extent(sm_mdl_22, catchment_df)\n",
    "sm_mdl_mm = normalize(sm_mdl_italy_summer_22.resample(time='15D').mean('time'))\n",
    "sm_mdl_mm"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### LUMI Bridge Dask Dashboard"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "\n",
       "        <iframe\n",
       "            width=\"100%\"\n",
       "            height=\"800\"\n",
       "            src=\"http://dask.lumi.data.destination-earth.eu/clusters/dask-gateway.630d89ea89fe41b7b3179bae1c8d7833/status\"\n",
       "            frameborder=\"0\"\n",
       "            allowfullscreen\n",
       "            \n",
       "        ></iframe>\n",
       "        "
      ],
      "text/plain": [
       "<IPython.lib.display.IFrame at 0x7fe66f234d50>"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from IPython import display\n",
    "LUMI_DASHBOARD_URL = myDEDLClusters.cluster[\"lumi\"].dashboard_link\n",
    "display.IFrame(LUMI_DASHBOARD_URL, width=\"100%\", height=800)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
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       "\n",
       ".xr-attrs dt {\n",
       "  font-weight: normal;\n",
       "  grid-column: 1;\n",
       "}\n",
       "\n",
       ".xr-attrs dt:hover span {\n",
       "  display: inline-block;\n",
       "  background: var(--xr-background-color);\n",
       "  padding-right: 10px;\n",
       "}\n",
       "\n",
       ".xr-attrs dd {\n",
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       "\n",
       ".xr-icon-database,\n",
       ".xr-icon-file-text2,\n",
       ".xr-no-icon {\n",
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       "</style><pre class='xr-text-repr-fallback'>&lt;xarray.DataArray &#x27;swvl1&#x27; (time: 13, y: 199, x: 432)&gt;\n",
       "array([[[           nan,            nan,            nan, ...,\n",
       "         4.95624989e-01, 5.05377710e-01, 5.05289257e-01],\n",
       "        [           nan,            nan,            nan, ...,\n",
       "         4.98278648e-01, 5.05719900e-01, 5.04702032e-01],\n",
       "        [           nan,            nan,            nan, ...,\n",
       "         4.98845220e-01, 4.99287874e-01, 5.01554728e-01],\n",
       "        ...,\n",
       "        [1.45745631e-02, 1.39832608e-02, 1.33316610e-02, ...,\n",
       "         2.67294161e-02, 2.56185140e-02, 2.19189320e-02],\n",
       "        [1.52864046e-02, 1.41106825e-02, 1.29277352e-02, ...,\n",
       "         1.92238614e-02, 1.73848439e-02, 7.18897860e-03],\n",
       "        [1.53837875e-02, 1.38954492e-02, 1.26969125e-02, ...,\n",
       "         9.20688827e-03, 8.99268687e-03, 1.60973500e-02]],\n",
       "\n",
       "       [[           nan,            nan,            nan, ...,\n",
       "         4.69979793e-01, 4.85048234e-01, 4.85448718e-01],\n",
       "        [           nan,            nan,            nan, ...,\n",
       "         4.72934455e-01, 4.82996464e-01, 4.84338403e-01],\n",
       "        [           nan,            nan,            nan, ...,\n",
       "         4.70457137e-01, 4.73388165e-01, 4.77756202e-01],\n",
       "...\n",
       "        [2.17066234e-04, 1.27476989e-04, 1.26961109e-04, ...,\n",
       "         6.95615029e-03, 5.53349871e-03, 4.58475715e-03],\n",
       "        [1.88292208e-04, 1.13663154e-04, 6.00127714e-05, ...,\n",
       "         5.79985883e-03, 4.64792270e-03, 2.26913579e-03],\n",
       "        [1.89209313e-04, 4.94661072e-05, 1.07759315e-05, ...,\n",
       "         2.73456424e-03, 2.71364325e-03, 4.87215351e-03]],\n",
       "\n",
       "       [[           nan,            nan,            nan, ...,\n",
       "         2.49551028e-01, 2.57777184e-01, 2.49325544e-01],\n",
       "        [           nan,            nan,            nan, ...,\n",
       "         2.71968335e-01, 2.52792597e-01, 2.49995947e-01],\n",
       "        [           nan,            nan,            nan, ...,\n",
       "         2.72561997e-01, 2.56124914e-01, 2.37403139e-01],\n",
       "        ...,\n",
       "        [9.22438316e-03, 8.41876771e-03, 7.54329516e-03, ...,\n",
       "         2.11397820e-21, 2.11397820e-21, 2.11397820e-21],\n",
       "        [9.69425496e-03, 8.29839893e-03, 5.21006295e-03, ...,\n",
       "         2.11397820e-21, 2.11397820e-21, 2.11397820e-21],\n",
       "        [9.53669939e-03, 7.30277132e-03, 1.85025041e-03, ...,\n",
       "         2.11397820e-21, 2.11397820e-21, 2.11397820e-21]]], dtype=float32)\n",
       "Coordinates:\n",
       "  * y            (y) float64 48.2 48.1 48.0 47.9 47.8 ... 28.7 28.6 28.5 28.4\n",
       "  * x            (x) float64 -5.6 -5.5 -5.4 -5.3 -5.2 ... 37.2 37.3 37.4 37.5\n",
       "    spatial_ref  int64 0\n",
       "  * time         (time) datetime64[ns] 2022-03-01 2022-03-16 ... 2022-08-28\n",
       "Attributes:\n",
       "    location:  lumi</pre><div class='xr-wrap' style='display:none'><div class='xr-header'><div class='xr-obj-type'>xarray.DataArray</div><div class='xr-array-name'>'swvl1'</div><ul class='xr-dim-list'><li><span class='xr-has-index'>time</span>: 13</li><li><span class='xr-has-index'>y</span>: 199</li><li><span class='xr-has-index'>x</span>: 432</li></ul></div><ul class='xr-sections'><li class='xr-section-item'><div class='xr-array-wrap'><input id='section-9f3c8b06-038d-4b63-816c-7c1918ceb6e8' class='xr-array-in' type='checkbox' checked><label for='section-9f3c8b06-038d-4b63-816c-7c1918ceb6e8' title='Show/hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-array-preview xr-preview'><span>nan nan nan nan nan ... 4.944e-05 2.114e-21 2.114e-21 2.114e-21</span></div><div class='xr-array-data'><pre>array([[[           nan,            nan,            nan, ...,\n",
       "         4.95624989e-01, 5.05377710e-01, 5.05289257e-01],\n",
       "        [           nan,            nan,            nan, ...,\n",
       "         4.98278648e-01, 5.05719900e-01, 5.04702032e-01],\n",
       "        [           nan,            nan,            nan, ...,\n",
       "         4.98845220e-01, 4.99287874e-01, 5.01554728e-01],\n",
       "        ...,\n",
       "        [1.45745631e-02, 1.39832608e-02, 1.33316610e-02, ...,\n",
       "         2.67294161e-02, 2.56185140e-02, 2.19189320e-02],\n",
       "        [1.52864046e-02, 1.41106825e-02, 1.29277352e-02, ...,\n",
       "         1.92238614e-02, 1.73848439e-02, 7.18897860e-03],\n",
       "        [1.53837875e-02, 1.38954492e-02, 1.26969125e-02, ...,\n",
       "         9.20688827e-03, 8.99268687e-03, 1.60973500e-02]],\n",
       "\n",
       "       [[           nan,            nan,            nan, ...,\n",
       "         4.69979793e-01, 4.85048234e-01, 4.85448718e-01],\n",
       "        [           nan,            nan,            nan, ...,\n",
       "         4.72934455e-01, 4.82996464e-01, 4.84338403e-01],\n",
       "        [           nan,            nan,            nan, ...,\n",
       "         4.70457137e-01, 4.73388165e-01, 4.77756202e-01],\n",
       "...\n",
       "        [2.17066234e-04, 1.27476989e-04, 1.26961109e-04, ...,\n",
       "         6.95615029e-03, 5.53349871e-03, 4.58475715e-03],\n",
       "        [1.88292208e-04, 1.13663154e-04, 6.00127714e-05, ...,\n",
       "         5.79985883e-03, 4.64792270e-03, 2.26913579e-03],\n",
       "        [1.89209313e-04, 4.94661072e-05, 1.07759315e-05, ...,\n",
       "         2.73456424e-03, 2.71364325e-03, 4.87215351e-03]],\n",
       "\n",
       "       [[           nan,            nan,            nan, ...,\n",
       "         2.49551028e-01, 2.57777184e-01, 2.49325544e-01],\n",
       "        [           nan,            nan,            nan, ...,\n",
       "         2.71968335e-01, 2.52792597e-01, 2.49995947e-01],\n",
       "        [           nan,            nan,            nan, ...,\n",
       "         2.72561997e-01, 2.56124914e-01, 2.37403139e-01],\n",
       "        ...,\n",
       "        [9.22438316e-03, 8.41876771e-03, 7.54329516e-03, ...,\n",
       "         2.11397820e-21, 2.11397820e-21, 2.11397820e-21],\n",
       "        [9.69425496e-03, 8.29839893e-03, 5.21006295e-03, ...,\n",
       "         2.11397820e-21, 2.11397820e-21, 2.11397820e-21],\n",
       "        [9.53669939e-03, 7.30277132e-03, 1.85025041e-03, ...,\n",
       "         2.11397820e-21, 2.11397820e-21, 2.11397820e-21]]], dtype=float32)</pre></div></div></li><li class='xr-section-item'><input id='section-c0610b7e-b708-4192-a60a-c1c2858f9a85' class='xr-section-summary-in' type='checkbox'  checked><label for='section-c0610b7e-b708-4192-a60a-c1c2858f9a85' class='xr-section-summary' >Coordinates: <span>(4)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><ul class='xr-var-list'><li class='xr-var-item'><div class='xr-var-name'><span class='xr-has-index'>y</span></div><div class='xr-var-dims'>(y)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>48.2 48.1 48.0 ... 28.6 28.5 28.4</div><input id='attrs-6c9b357f-361a-4ff0-a86f-2caa3b0f8252' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-6c9b357f-361a-4ff0-a86f-2caa3b0f8252' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-68fe74ca-f0f5-4ebb-93f0-fc7850bdd1ab' class='xr-var-data-in' type='checkbox'><label for='data-68fe74ca-f0f5-4ebb-93f0-fc7850bdd1ab' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><pre>array([48.2, 48.1, 48. , 47.9, 47.8, 47.7, 47.6, 47.5, 47.4, 47.3, 47.2, 47.1,\n",
       "       47. , 46.9, 46.8, 46.7, 46.6, 46.5, 46.4, 46.3, 46.2, 46.1, 46. , 45.9,\n",
       "       45.8, 45.7, 45.6, 45.5, 45.4, 45.3, 45.2, 45.1, 45. , 44.9, 44.8, 44.7,\n",
       "       44.6, 44.5, 44.4, 44.3, 44.2, 44.1, 44. , 43.9, 43.8, 43.7, 43.6, 43.5,\n",
       "       43.4, 43.3, 43.2, 43.1, 43. , 42.9, 42.8, 42.7, 42.6, 42.5, 42.4, 42.3,\n",
       "       42.2, 42.1, 42. , 41.9, 41.8, 41.7, 41.6, 41.5, 41.4, 41.3, 41.2, 41.1,\n",
       "       41. , 40.9, 40.8, 40.7, 40.6, 40.5, 40.4, 40.3, 40.2, 40.1, 40. , 39.9,\n",
       "       39.8, 39.7, 39.6, 39.5, 39.4, 39.3, 39.2, 39.1, 39. , 38.9, 38.8, 38.7,\n",
       "       38.6, 38.5, 38.4, 38.3, 38.2, 38.1, 38. , 37.9, 37.8, 37.7, 37.6, 37.5,\n",
       "       37.4, 37.3, 37.2, 37.1, 37. , 36.9, 36.8, 36.7, 36.6, 36.5, 36.4, 36.3,\n",
       "       36.2, 36.1, 36. , 35.9, 35.8, 35.7, 35.6, 35.5, 35.4, 35.3, 35.2, 35.1,\n",
       "       35. , 34.9, 34.8, 34.7, 34.6, 34.5, 34.4, 34.3, 34.2, 34.1, 34. , 33.9,\n",
       "       33.8, 33.7, 33.6, 33.5, 33.4, 33.3, 33.2, 33.1, 33. , 32.9, 32.8, 32.7,\n",
       "       32.6, 32.5, 32.4, 32.3, 32.2, 32.1, 32. , 31.9, 31.8, 31.7, 31.6, 31.5,\n",
       "       31.4, 31.3, 31.2, 31.1, 31. , 30.9, 30.8, 30.7, 30.6, 30.5, 30.4, 30.3,\n",
       "       30.2, 30.1, 30. , 29.9, 29.8, 29.7, 29.6, 29.5, 29.4, 29.3, 29.2, 29.1,\n",
       "       29. , 28.9, 28.8, 28.7, 28.6, 28.5, 28.4])</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span class='xr-has-index'>x</span></div><div class='xr-var-dims'>(x)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>-5.6 -5.5 -5.4 ... 37.3 37.4 37.5</div><input id='attrs-4425b87d-86ac-4c40-ab77-8b924477ea42' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-4425b87d-86ac-4c40-ab77-8b924477ea42' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-411bc63b-ac08-465d-83ca-2b99be562cc7' class='xr-var-data-in' type='checkbox'><label for='data-411bc63b-ac08-465d-83ca-2b99be562cc7' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><pre>array([-5.6, -5.5, -5.4, ..., 37.3, 37.4, 37.5])</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>spatial_ref</span></div><div class='xr-var-dims'>()</div><div class='xr-var-dtype'>int64</div><div class='xr-var-preview xr-preview'>0</div><input id='attrs-9fbcdaa4-5e43-4575-8964-a49102c2c57a' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-9fbcdaa4-5e43-4575-8964-a49102c2c57a' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-065d6033-b4fe-4d04-a1a3-5d7af0614a33' class='xr-var-data-in' type='checkbox'><label for='data-065d6033-b4fe-4d04-a1a3-5d7af0614a33' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>crs_wkt :</span></dt><dd>GEOGCS[&quot;WGS 84&quot;,DATUM[&quot;WGS_1984&quot;,SPHEROID[&quot;WGS 84&quot;,6378137,298.257223563,AUTHORITY[&quot;EPSG&quot;,&quot;7030&quot;]],AUTHORITY[&quot;EPSG&quot;,&quot;6326&quot;]],PRIMEM[&quot;Greenwich&quot;,0,AUTHORITY[&quot;EPSG&quot;,&quot;8901&quot;]],UNIT[&quot;degree&quot;,0.0174532925199433,AUTHORITY[&quot;EPSG&quot;,&quot;9122&quot;]],AXIS[&quot;Latitude&quot;,NORTH],AXIS[&quot;Longitude&quot;,EAST],AUTHORITY[&quot;EPSG&quot;,&quot;4326&quot;]]</dd><dt><span>geographic_crs_name :</span></dt><dd>WGS 84</dd><dt><span>grid_mapping_name :</span></dt><dd>latitude_longitude</dd><dt><span>horizontal_datum_name :</span></dt><dd>World Geodetic System 1984</dd><dt><span>inverse_flattening :</span></dt><dd>298.257223563</dd><dt><span>longitude_of_prime_meridian :</span></dt><dd>0.0</dd><dt><span>prime_meridian_name :</span></dt><dd>Greenwich</dd><dt><span>reference_ellipsoid_name :</span></dt><dd>WGS 84</dd><dt><span>semi_major_axis :</span></dt><dd>6378137.0</dd><dt><span>semi_minor_axis :</span></dt><dd>6356752.314245179</dd><dt><span>spatial_ref :</span></dt><dd>GEOGCS[&quot;WGS 84&quot;,DATUM[&quot;WGS_1984&quot;,SPHEROID[&quot;WGS 84&quot;,6378137,298.257223563,AUTHORITY[&quot;EPSG&quot;,&quot;7030&quot;]],AUTHORITY[&quot;EPSG&quot;,&quot;6326&quot;]],PRIMEM[&quot;Greenwich&quot;,0,AUTHORITY[&quot;EPSG&quot;,&quot;8901&quot;]],UNIT[&quot;degree&quot;,0.0174532925199433,AUTHORITY[&quot;EPSG&quot;,&quot;9122&quot;]],AXIS[&quot;Latitude&quot;,NORTH],AXIS[&quot;Longitude&quot;,EAST],AUTHORITY[&quot;EPSG&quot;,&quot;4326&quot;]]</dd></dl></div><div class='xr-var-data'><pre>array(0)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span class='xr-has-index'>time</span></div><div class='xr-var-dims'>(time)</div><div class='xr-var-dtype'>datetime64[ns]</div><div class='xr-var-preview xr-preview'>2022-03-01 ... 2022-08-28</div><input id='attrs-8462c75f-14f1-446a-8d71-89d3b0b4f67b' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-8462c75f-14f1-446a-8d71-89d3b0b4f67b' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-faa6e2dd-ff3f-4258-aed5-3f39120183aa' class='xr-var-data-in' type='checkbox'><label for='data-faa6e2dd-ff3f-4258-aed5-3f39120183aa' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><pre>array([&#x27;2022-03-01T00:00:00.000000000&#x27;, &#x27;2022-03-16T00:00:00.000000000&#x27;,\n",
       "       &#x27;2022-03-31T00:00:00.000000000&#x27;, &#x27;2022-04-15T00:00:00.000000000&#x27;,\n",
       "       &#x27;2022-04-30T00:00:00.000000000&#x27;, &#x27;2022-05-15T00:00:00.000000000&#x27;,\n",
       "       &#x27;2022-05-30T00:00:00.000000000&#x27;, &#x27;2022-06-14T00:00:00.000000000&#x27;,\n",
       "       &#x27;2022-06-29T00:00:00.000000000&#x27;, &#x27;2022-07-14T00:00:00.000000000&#x27;,\n",
       "       &#x27;2022-07-29T00:00:00.000000000&#x27;, &#x27;2022-08-13T00:00:00.000000000&#x27;,\n",
       "       &#x27;2022-08-28T00:00:00.000000000&#x27;], dtype=&#x27;datetime64[ns]&#x27;)</pre></div></li></ul></div></li><li class='xr-section-item'><input id='section-8cf48659-3fde-4717-a82e-cba37af1295f' class='xr-section-summary-in' type='checkbox'  ><label for='section-8cf48659-3fde-4717-a82e-cba37af1295f' class='xr-section-summary' >Indexes: <span>(3)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><ul class='xr-var-list'><li class='xr-var-item'><div class='xr-index-name'><div>y</div></div><div class='xr-index-preview'>PandasIndex</div><div></div><input id='index-9712e193-13b1-42e7-bb29-81122b9eaf16' class='xr-index-data-in' type='checkbox'/><label for='index-9712e193-13b1-42e7-bb29-81122b9eaf16' title='Show/Hide index repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-index-data'><pre>PandasIndex(Index([48.200000000002376,  48.10000000000238,  48.00000000000239,\n",
       "        47.90000000000239,   47.8000000000024, 47.700000000002404,\n",
       "        47.60000000000241, 47.500000000002416,  47.40000000000242,\n",
       "        47.30000000000243,\n",
       "       ...\n",
       "        29.30000000000345, 29.200000000003456,  29.10000000000346,\n",
       "       29.000000000003467, 28.900000000003473,  28.80000000000348,\n",
       "       28.700000000003485,  28.60000000000349, 28.500000000003496,\n",
       "         28.4000000000035],\n",
       "      dtype=&#x27;float64&#x27;, name=&#x27;y&#x27;, length=199))</pre></div></li><li class='xr-var-item'><div class='xr-index-name'><div>x</div></div><div class='xr-index-preview'>PandasIndex</div><div></div><input id='index-c8763ae0-0a97-4460-9212-8a0ad1bf67da' class='xr-index-data-in' type='checkbox'/><label for='index-c8763ae0-0a97-4460-9212-8a0ad1bf67da' title='Show/Hide index repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-index-data'><pre>PandasIndex(Index([-5.6000000000099135,  -5.500000000009919,  -5.400000000009925,\n",
       "       -5.3000000000099305,  -5.200000000009936,  -5.100000000009942,\n",
       "        -5.000000000009948,  -4.900000000009953,  -4.800000000009959,\n",
       "        -4.700000000009965,\n",
       "       ...\n",
       "         36.59999999998769,   36.69999999998768,  36.799999999987676,\n",
       "         36.89999999998767,  36.999999999987665,   37.09999999998766,\n",
       "        37.199999999987654,   37.29999999998765,   37.39999999998764,\n",
       "         37.49999999998764],\n",
       "      dtype=&#x27;float64&#x27;, name=&#x27;x&#x27;, length=432))</pre></div></li><li class='xr-var-item'><div class='xr-index-name'><div>time</div></div><div class='xr-index-preview'>PandasIndex</div><div></div><input id='index-53268267-b659-480f-9a03-5006a641a2ee' class='xr-index-data-in' type='checkbox'/><label for='index-53268267-b659-480f-9a03-5006a641a2ee' title='Show/Hide index repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-index-data'><pre>PandasIndex(DatetimeIndex([&#x27;2022-03-01&#x27;, &#x27;2022-03-16&#x27;, &#x27;2022-03-31&#x27;, &#x27;2022-04-15&#x27;,\n",
       "               &#x27;2022-04-30&#x27;, &#x27;2022-05-15&#x27;, &#x27;2022-05-30&#x27;, &#x27;2022-06-14&#x27;,\n",
       "               &#x27;2022-06-29&#x27;, &#x27;2022-07-14&#x27;, &#x27;2022-07-29&#x27;, &#x27;2022-08-13&#x27;,\n",
       "               &#x27;2022-08-28&#x27;],\n",
       "              dtype=&#x27;datetime64[ns]&#x27;, name=&#x27;time&#x27;, freq=&#x27;15D&#x27;))</pre></div></li></ul></div></li><li class='xr-section-item'><input id='section-bf56743e-5d24-4e4e-af59-8ec0de5958d5' class='xr-section-summary-in' type='checkbox'  checked><label for='section-bf56743e-5d24-4e4e-af59-8ec0de5958d5' class='xr-section-summary' >Attributes: <span>(1)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><dl class='xr-attrs'><dt><span>location :</span></dt><dd>lumi</dd></dl></div></li></ul></div></div>"
      ],
      "text/plain": [
       "<xarray.DataArray 'swvl1' (time: 13, y: 199, x: 432)>\n",
       "array([[[           nan,            nan,            nan, ...,\n",
       "         4.95624989e-01, 5.05377710e-01, 5.05289257e-01],\n",
       "        [           nan,            nan,            nan, ...,\n",
       "         4.98278648e-01, 5.05719900e-01, 5.04702032e-01],\n",
       "        [           nan,            nan,            nan, ...,\n",
       "         4.98845220e-01, 4.99287874e-01, 5.01554728e-01],\n",
       "        ...,\n",
       "        [1.45745631e-02, 1.39832608e-02, 1.33316610e-02, ...,\n",
       "         2.67294161e-02, 2.56185140e-02, 2.19189320e-02],\n",
       "        [1.52864046e-02, 1.41106825e-02, 1.29277352e-02, ...,\n",
       "         1.92238614e-02, 1.73848439e-02, 7.18897860e-03],\n",
       "        [1.53837875e-02, 1.38954492e-02, 1.26969125e-02, ...,\n",
       "         9.20688827e-03, 8.99268687e-03, 1.60973500e-02]],\n",
       "\n",
       "       [[           nan,            nan,            nan, ...,\n",
       "         4.69979793e-01, 4.85048234e-01, 4.85448718e-01],\n",
       "        [           nan,            nan,            nan, ...,\n",
       "         4.72934455e-01, 4.82996464e-01, 4.84338403e-01],\n",
       "        [           nan,            nan,            nan, ...,\n",
       "         4.70457137e-01, 4.73388165e-01, 4.77756202e-01],\n",
       "...\n",
       "        [2.17066234e-04, 1.27476989e-04, 1.26961109e-04, ...,\n",
       "         6.95615029e-03, 5.53349871e-03, 4.58475715e-03],\n",
       "        [1.88292208e-04, 1.13663154e-04, 6.00127714e-05, ...,\n",
       "         5.79985883e-03, 4.64792270e-03, 2.26913579e-03],\n",
       "        [1.89209313e-04, 4.94661072e-05, 1.07759315e-05, ...,\n",
       "         2.73456424e-03, 2.71364325e-03, 4.87215351e-03]],\n",
       "\n",
       "       [[           nan,            nan,            nan, ...,\n",
       "         2.49551028e-01, 2.57777184e-01, 2.49325544e-01],\n",
       "        [           nan,            nan,            nan, ...,\n",
       "         2.71968335e-01, 2.52792597e-01, 2.49995947e-01],\n",
       "        [           nan,            nan,            nan, ...,\n",
       "         2.72561997e-01, 2.56124914e-01, 2.37403139e-01],\n",
       "        ...,\n",
       "        [9.22438316e-03, 8.41876771e-03, 7.54329516e-03, ...,\n",
       "         2.11397820e-21, 2.11397820e-21, 2.11397820e-21],\n",
       "        [9.69425496e-03, 8.29839893e-03, 5.21006295e-03, ...,\n",
       "         2.11397820e-21, 2.11397820e-21, 2.11397820e-21],\n",
       "        [9.53669939e-03, 7.30277132e-03, 1.85025041e-03, ...,\n",
       "         2.11397820e-21, 2.11397820e-21, 2.11397820e-21]]], dtype=float32)\n",
       "Coordinates:\n",
       "  * y            (y) float64 48.2 48.1 48.0 47.9 47.8 ... 28.7 28.6 28.5 28.4\n",
       "  * x            (x) float64 -5.6 -5.5 -5.4 -5.3 -5.2 ... 37.2 37.3 37.4 37.5\n",
       "    spatial_ref  int64 0\n",
       "  * time         (time) datetime64[ns] 2022-03-01 2022-03-16 ... 2022-08-28\n",
       "Attributes:\n",
       "    location:  lumi"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "sm_mdl_mm_ = myDEDLClusters.compute(sm_mdl_mm, sync=True)\n",
    "sm_mdl_mm_ = sm_mdl_mm_.rename({\"longitude\":\"x\", \"latitude\":\"y\"})\n",
    "sm_mdl_mm_"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Corine Land Cover\n",
    "\n",
    "Resample data to coarser resolution and provide only major LC classes for visulisation."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [],
   "source": [
    "def preprocess_corine(lc: xr.DataArray) -> xr.DataArray:\n",
    "    steps = 500 // 100\n",
    "    downsampled = lc.coarsen({'latitude': steps, 'longitude': steps}, boundary='trim').median()\n",
    "    downsampled = xr.apply_ufunc(\n",
    "        np.floor,\n",
    "        downsampled/100.,\n",
    "        dask=\"allowed\",\n",
    "    )\n",
    "    return downsampled.transpose('latitude', 'longitude')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2023-05-22T19:03:22.614088006Z",
     "start_time": "2023-05-22T19:03:07.251266791Z"
    }
   },
   "outputs": [
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       "</table></div></div></li><li class='xr-section-item'><input id='section-43d2c2d8-05f1-42fc-b304-64e3751a3e78' class='xr-section-summary-in' type='checkbox'  checked><label for='section-43d2c2d8-05f1-42fc-b304-64e3751a3e78' class='xr-section-summary' >Coordinates: <span>(3)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><ul class='xr-var-list'><li class='xr-var-item'><div class='xr-var-name'><span class='xr-has-index'>y</span></div><div class='xr-var-dims'>(y)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>49.94 49.94 49.93 ... 32.3 32.3</div><input id='attrs-30d3b46a-1657-4348-91da-713c7d681248' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-30d3b46a-1657-4348-91da-713c7d681248' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-cdf03b1d-d302-4092-9251-e9c1fa484ff7' class='xr-var-data-in' type='checkbox'><label for='data-cdf03b1d-d302-4092-9251-e9c1fa484ff7' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>axis :</span></dt><dd>Y</dd><dt><span>long_name :</span></dt><dd>latitude</dd><dt><span>standard_name :</span></dt><dd>latitude</dd><dt><span>units :</span></dt><dd>degrees_north</dd></dl></div><div class='xr-var-data'><pre>array([49.943538, 49.93834 , 49.933142, ..., 32.306711, 32.301513, 32.296315])</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>lc</span></div><div class='xr-var-dims'>()</div><div class='xr-var-dtype'>int64</div><div class='xr-var-preview xr-preview'>1</div><input id='attrs-dce18fe9-bd88-4aaa-97ff-b6302f75e9d8' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-dce18fe9-bd88-4aaa-97ff-b6302f75e9d8' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-5953498b-e838-48f8-a1fe-439040541a72' class='xr-var-data-in' type='checkbox'><label for='data-5953498b-e838-48f8-a1fe-439040541a72' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><pre>array(1)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span class='xr-has-index'>x</span></div><div class='xr-var-dims'>(x)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>1.82 1.825 1.831 ... 19.35 19.36</div><input id='attrs-a8efdaae-892c-4682-978a-73edfa05ed38' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-a8efdaae-892c-4682-978a-73edfa05ed38' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-e8003507-2487-4995-8d80-897dcb84bba8' class='xr-var-data-in' type='checkbox'><label for='data-e8003507-2487-4995-8d80-897dcb84bba8' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>axis :</span></dt><dd>X</dd><dt><span>long_name :</span></dt><dd>longitude</dd><dt><span>standard_name :</span></dt><dd>longitude</dd><dt><span>units :</span></dt><dd>degrees_east</dd></dl></div><div class='xr-var-data'><pre>array([ 1.82017 ,  1.825368,  1.830566, ..., 19.347839, 19.353037, 19.358235])</pre></div></li></ul></div></li><li class='xr-section-item'><input id='section-3d34e4f2-727d-48d9-b18d-a50b04b585d1' class='xr-section-summary-in' type='checkbox'  ><label for='section-3d34e4f2-727d-48d9-b18d-a50b04b585d1' class='xr-section-summary' >Indexes: <span>(2)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><ul class='xr-var-list'><li class='xr-var-item'><div class='xr-index-name'><div>y</div></div><div class='xr-index-preview'>PandasIndex</div><div></div><input id='index-4838a340-8e2d-4387-936f-6107251a4d36' class='xr-index-data-in' type='checkbox'/><label for='index-4838a340-8e2d-4387-936f-6107251a4d36' title='Show/Hide index repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-index-data'><pre>PandasIndex(Index([ 49.94353842425577,  49.93834042037967, 49.933142416503564,\n",
       "        49.92794441262747, 49.922746408751365,  49.91754840487526,\n",
       "       49.912350400999166,  49.90715239712306,  49.90195439324697,\n",
       "       49.896756389370864,\n",
       "       ...\n",
       "        32.34309729977896,  32.33789929590286, 32.332701292026755,\n",
       "        32.32750328815065,  32.32230528427455,  32.31710728039845,\n",
       "        32.31190927652235,  32.30671127264625, 32.301513268770144,\n",
       "       32.296315264894055],\n",
       "      dtype=&#x27;float64&#x27;, name=&#x27;y&#x27;, length=3396))</pre></div></li><li class='xr-var-item'><div class='xr-index-name'><div>x</div></div><div class='xr-index-preview'>PandasIndex</div><div></div><input id='index-c56fed89-e015-4cb0-a667-1bd02af31aaa' class='xr-index-data-in' type='checkbox'/><label for='index-c56fed89-e015-4cb0-a667-1bd02af31aaa' title='Show/Hide index repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-index-data'><pre>PandasIndex(Index([1.8201700602216981, 1.8253680640977987, 1.8305660679738995,\n",
       "       1.8357640718500001, 1.8409620757261007, 1.8461600796022015,\n",
       "        1.851358083478302, 1.8565560873544027, 1.8617540912305035,\n",
       "        1.866952095106604,\n",
       "       ...\n",
       "       19.311453103300398, 19.316651107176497, 19.321849111052597,\n",
       "       19.327047114928696,   19.3322451188048, 19.337443122680902,\n",
       "          19.342641126557,   19.3478391304331, 19.353037134309197,\n",
       "         19.3582351381853],\n",
       "      dtype=&#x27;float64&#x27;, name=&#x27;x&#x27;, length=3375))</pre></div></li></ul></div></li><li class='xr-section-item'><input id='section-dfee4143-749f-4cf6-a411-b3bee10e5004' class='xr-section-summary-in' type='checkbox'  checked><label for='section-dfee4143-749f-4cf6-a411-b3bee10e5004' class='xr-section-summary' >Attributes: <span>(5)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><dl class='xr-attrs'><dt><span>AREA_OR_POINT :</span></dt><dd>Area</dd><dt><span>DESCRIPTION :</span></dt><dd>clc18</dd><dt><span>RepresentationType :</span></dt><dd>THEMATIC</dd><dt><span>grid_mapping :</span></dt><dd>spatial_ref</dd><dt><span>location :</span></dt><dd>central</dd></dl></div></li></ul></div></div>"
      ],
      "text/plain": [
       "<xarray.DataArray 'corine_lc' (y: 3396, x: 3375)>\n",
       "dask.array<floor, shape=(3396, 3375), dtype=float32, chunksize=(213, 211), chunktype=numpy.ndarray>\n",
       "Coordinates:\n",
       "  * y        (y) float64 49.94 49.94 49.93 49.93 49.92 ... 32.31 32.31 32.3 32.3\n",
       "    lc       int64 1\n",
       "  * x        (x) float64 1.82 1.825 1.831 1.836 ... 19.34 19.35 19.35 19.36\n",
       "Attributes:\n",
       "    AREA_OR_POINT:       Area\n",
       "    DESCRIPTION:         clc18\n",
       "    RepresentationType:  THEMATIC\n",
       "    grid_mapping:        spatial_ref\n",
       "    location:            central"
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "lc = preprocess_corine(corine_italy)\n",
    "lc = lc.rename({\"longitude\":\"x\", \"latitude\":\"y\"})\n",
    "lc"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Central Site Dask Dashboard"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "\n",
       "        <iframe\n",
       "            width=\"100%\"\n",
       "            height=\"800\"\n",
       "            src=\"http://dask.central.data.destination-earth.eu/clusters/dask-gateway.69a06f421add43a9b6edd51f2b7160c1/status\"\n",
       "            frameborder=\"0\"\n",
       "            allowfullscreen\n",
       "            \n",
       "        ></iframe>\n",
       "        "
      ],
      "text/plain": [
       "<IPython.lib.display.IFrame at 0x7fe66e46e3d0>"
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from IPython import display\n",
    "CENTRAL_DASHBOARD_URL = myDEDLClusters.cluster[\"central\"].dashboard_link\n",
    "display.IFrame(CENTRAL_DASHBOARD_URL, width=\"100%\", height=800)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [],
   "source": [
    "lc_ = myDEDLClusters.compute(lc, sync=True)\n",
    "lc_ = lc_.fillna(-9999.)"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Inspect Data\n",
    "\n",
    "The DEDL service provides functionality to quickly visualize the previously processed data. These features not only help to validate the queried data, and the applied data transformations, but also allows an assessment of the current situation of soil moisture with only minimal effort.  More so, it marks the starting point of exploratory data analysis signified by the formulation of hypotheses that can be quickly tested. With the available Sentinel-1 soil moisture data, the expert can easily spot that water stocks are already severely reduced in some regions, such as the Po Valley (Emilia-Romagna and Veneto) at the end of March 2022. More so, the CORINE land cover data suggests that reduced water tables are especially prevalent in areas with an extensive agricultural component. This raises the question how agricultural yield will fair if drought conditions prevail over the coming months.\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Reproject Corine LC data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [
    {
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       "  grid-column: 4;\n",
       "}\n",
       "\n",
       ".xr-index-preview {\n",
       "  grid-column: 2 / 5;\n",
       "  color: var(--xr-font-color2);\n",
       "}\n",
       "\n",
       ".xr-var-name,\n",
       ".xr-var-dims,\n",
       ".xr-var-dtype,\n",
       ".xr-preview,\n",
       ".xr-attrs dt {\n",
       "  white-space: nowrap;\n",
       "  overflow: hidden;\n",
       "  text-overflow: ellipsis;\n",
       "  padding-right: 10px;\n",
       "}\n",
       "\n",
       ".xr-var-name:hover,\n",
       ".xr-var-dims:hover,\n",
       ".xr-var-dtype:hover,\n",
       ".xr-attrs dt:hover {\n",
       "  overflow: visible;\n",
       "  width: auto;\n",
       "  z-index: 1;\n",
       "}\n",
       "\n",
       ".xr-var-attrs,\n",
       ".xr-var-data,\n",
       ".xr-index-data {\n",
       "  display: none;\n",
       "  background-color: var(--xr-background-color) !important;\n",
       "  padding-bottom: 5px !important;\n",
       "}\n",
       "\n",
       ".xr-var-attrs-in:checked ~ .xr-var-attrs,\n",
       ".xr-var-data-in:checked ~ .xr-var-data,\n",
       ".xr-index-data-in:checked ~ .xr-index-data {\n",
       "  display: block;\n",
       "}\n",
       "\n",
       ".xr-var-data > table {\n",
       "  float: right;\n",
       "}\n",
       "\n",
       ".xr-var-name span,\n",
       ".xr-var-data,\n",
       ".xr-index-name div,\n",
       ".xr-index-data,\n",
       ".xr-attrs {\n",
       "  padding-left: 25px !important;\n",
       "}\n",
       "\n",
       ".xr-attrs,\n",
       ".xr-var-attrs,\n",
       ".xr-var-data,\n",
       ".xr-index-data {\n",
       "  grid-column: 1 / -1;\n",
       "}\n",
       "\n",
       "dl.xr-attrs {\n",
       "  padding: 0;\n",
       "  margin: 0;\n",
       "  display: grid;\n",
       "  grid-template-columns: 125px auto;\n",
       "}\n",
       "\n",
       ".xr-attrs dt,\n",
       ".xr-attrs dd {\n",
       "  padding: 0;\n",
       "  margin: 0;\n",
       "  float: left;\n",
       "  padding-right: 10px;\n",
       "  width: auto;\n",
       "}\n",
       "\n",
       ".xr-attrs dt {\n",
       "  font-weight: normal;\n",
       "  grid-column: 1;\n",
       "}\n",
       "\n",
       ".xr-attrs dt:hover span {\n",
       "  display: inline-block;\n",
       "  background: var(--xr-background-color);\n",
       "  padding-right: 10px;\n",
       "}\n",
       "\n",
       ".xr-attrs dd {\n",
       "  grid-column: 2;\n",
       "  white-space: pre-wrap;\n",
       "  word-break: break-all;\n",
       "}\n",
       "\n",
       ".xr-icon-database,\n",
       ".xr-icon-file-text2,\n",
       ".xr-no-icon {\n",
       "  display: inline-block;\n",
       "  vertical-align: middle;\n",
       "  width: 1em;\n",
       "  height: 1.5em !important;\n",
       "  stroke-width: 0;\n",
       "  stroke: currentColor;\n",
       "  fill: currentColor;\n",
       "}\n",
       "</style><pre class='xr-text-repr-fallback'>&lt;xarray.DataArray &#x27;corine_lc&#x27; (y: 3847, x: 2851)&gt;\n",
       "array([[-9999., -9999., -9999., ..., -9999., -9999., -9999.],\n",
       "       [-9999., -9999., -9999., ..., -9999., -9999., -9999.],\n",
       "       [-9999., -9999., -9999., ..., -9999., -9999., -9999.],\n",
       "       ...,\n",
       "       [-9999., -9999., -9999., ..., -9999., -9999., -9999.],\n",
       "       [-9999., -9999., -9999., ..., -9999., -9999., -9999.],\n",
       "       [-9999., -9999., -9999., ..., -9999., -9999., -9999.]],\n",
       "      dtype=float32)\n",
       "Coordinates:\n",
       "  * x            (x) float64 2.027e+05 2.034e+05 ... 2.154e+06 2.155e+06\n",
       "  * y            (y) float64 6.437e+06 6.436e+06 ... 3.803e+06 3.802e+06\n",
       "    lc           int64 1\n",
       "    spatial_ref  int64 0\n",
       "Attributes:\n",
       "    AREA_OR_POINT:       Area\n",
       "    DESCRIPTION:         clc18\n",
       "    RepresentationType:  THEMATIC\n",
       "    grid_mapping:        spatial_ref\n",
       "    location:            central\n",
       "    _FillValue:          -9999.0</pre><div class='xr-wrap' style='display:none'><div class='xr-header'><div class='xr-obj-type'>xarray.DataArray</div><div class='xr-array-name'>'corine_lc'</div><ul class='xr-dim-list'><li><span class='xr-has-index'>y</span>: 3847</li><li><span class='xr-has-index'>x</span>: 2851</li></ul></div><ul class='xr-sections'><li class='xr-section-item'><div class='xr-array-wrap'><input id='section-596ce280-1062-47a6-9f9e-e50ef226c53b' class='xr-array-in' type='checkbox' checked><label for='section-596ce280-1062-47a6-9f9e-e50ef226c53b' title='Show/hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-array-preview xr-preview'><span>-9.999e+03 -9.999e+03 -9.999e+03 ... -9.999e+03 -9.999e+03 -9.999e+03</span></div><div class='xr-array-data'><pre>array([[-9999., -9999., -9999., ..., -9999., -9999., -9999.],\n",
       "       [-9999., -9999., -9999., ..., -9999., -9999., -9999.],\n",
       "       [-9999., -9999., -9999., ..., -9999., -9999., -9999.],\n",
       "       ...,\n",
       "       [-9999., -9999., -9999., ..., -9999., -9999., -9999.],\n",
       "       [-9999., -9999., -9999., ..., -9999., -9999., -9999.],\n",
       "       [-9999., -9999., -9999., ..., -9999., -9999., -9999.]],\n",
       "      dtype=float32)</pre></div></div></li><li class='xr-section-item'><input id='section-199f3cd4-2025-4ea5-a3ed-dd2bd4b1ca9a' class='xr-section-summary-in' type='checkbox'  checked><label for='section-199f3cd4-2025-4ea5-a3ed-dd2bd4b1ca9a' class='xr-section-summary' >Coordinates: <span>(4)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><ul class='xr-var-list'><li class='xr-var-item'><div class='xr-var-name'><span class='xr-has-index'>x</span></div><div class='xr-var-dims'>(x)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>2.027e+05 2.034e+05 ... 2.155e+06</div><input id='attrs-02a5dc27-d4ad-4716-bf9a-63bbc35ecc69' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-02a5dc27-d4ad-4716-bf9a-63bbc35ecc69' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-d610d5ca-d1c7-4949-85d2-ce9a61722475' class='xr-var-data-in' type='checkbox'><label for='data-d610d5ca-d1c7-4949-85d2-ce9a61722475' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>axis :</span></dt><dd>X</dd><dt><span>long_name :</span></dt><dd>x coordinate of projection</dd><dt><span>standard_name :</span></dt><dd>projection_x_coordinate</dd><dt><span>units :</span></dt><dd>metre</dd></dl></div><div class='xr-var-data'><pre>array([ 202673.599877,  203358.630255,  204043.660632, ..., 2153640.114124,\n",
       "       2154325.144502, 2155010.174879])</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span class='xr-has-index'>y</span></div><div class='xr-var-dims'>(y)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>6.437e+06 6.436e+06 ... 3.802e+06</div><input id='attrs-5154ca52-8344-4554-b1d0-b2dcfc163c43' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-5154ca52-8344-4554-b1d0-b2dcfc163c43' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-087ef33d-cbcf-49c7-96b3-be4fa8d39d5d' class='xr-var-data-in' type='checkbox'><label for='data-087ef33d-cbcf-49c7-96b3-be4fa8d39d5d' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>axis :</span></dt><dd>Y</dd><dt><span>long_name :</span></dt><dd>y coordinate of projection</dd><dt><span>standard_name :</span></dt><dd>projection_y_coordinate</dd><dt><span>units :</span></dt><dd>metre</dd></dl></div><div class='xr-var-data'><pre>array([6436610.496911, 6435925.466534, 6435240.436157, ..., 3803353.72698 ,\n",
       "       3802668.696603, 3801983.666225])</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>lc</span></div><div class='xr-var-dims'>()</div><div class='xr-var-dtype'>int64</div><div class='xr-var-preview xr-preview'>1</div><input id='attrs-c18753c6-de94-45f3-af3d-1071284cf9ff' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-c18753c6-de94-45f3-af3d-1071284cf9ff' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-251c09e6-064c-4691-af83-ff8c7be636ac' class='xr-var-data-in' type='checkbox'><label for='data-251c09e6-064c-4691-af83-ff8c7be636ac' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><pre>array(1)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>spatial_ref</span></div><div class='xr-var-dims'>()</div><div class='xr-var-dtype'>int64</div><div class='xr-var-preview xr-preview'>0</div><input id='attrs-f301d66f-1788-4dca-b3de-26c6b080cfb6' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-f301d66f-1788-4dca-b3de-26c6b080cfb6' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-d91b836c-7e91-4fee-a2d2-4030b0f9a1c8' class='xr-var-data-in' type='checkbox'><label for='data-d91b836c-7e91-4fee-a2d2-4030b0f9a1c8' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>crs_wkt :</span></dt><dd>PROJCS[&quot;WGS 84 / Pseudo-Mercator&quot;,GEOGCS[&quot;WGS 84&quot;,DATUM[&quot;WGS_1984&quot;,SPHEROID[&quot;WGS 84&quot;,6378137,298.257223563,AUTHORITY[&quot;EPSG&quot;,&quot;7030&quot;]],AUTHORITY[&quot;EPSG&quot;,&quot;6326&quot;]],PRIMEM[&quot;Greenwich&quot;,0,AUTHORITY[&quot;EPSG&quot;,&quot;8901&quot;]],UNIT[&quot;degree&quot;,0.0174532925199433,AUTHORITY[&quot;EPSG&quot;,&quot;9122&quot;]],AUTHORITY[&quot;EPSG&quot;,&quot;4326&quot;]],PROJECTION[&quot;Mercator_1SP&quot;],PARAMETER[&quot;central_meridian&quot;,0],PARAMETER[&quot;scale_factor&quot;,1],PARAMETER[&quot;false_easting&quot;,0],PARAMETER[&quot;false_northing&quot;,0],UNIT[&quot;metre&quot;,1,AUTHORITY[&quot;EPSG&quot;,&quot;9001&quot;]],AXIS[&quot;Easting&quot;,EAST],AXIS[&quot;Northing&quot;,NORTH],EXTENSION[&quot;PROJ4&quot;,&quot;+proj=merc +a=6378137 +b=6378137 +lat_ts=0 +lon_0=0 +x_0=0 +y_0=0 +k=1 +units=m +nadgrids=@null +wktext +no_defs&quot;],AUTHORITY[&quot;EPSG&quot;,&quot;3857&quot;]]</dd><dt><span>spatial_ref :</span></dt><dd>PROJCS[&quot;WGS 84 / Pseudo-Mercator&quot;,GEOGCS[&quot;WGS 84&quot;,DATUM[&quot;WGS_1984&quot;,SPHEROID[&quot;WGS 84&quot;,6378137,298.257223563,AUTHORITY[&quot;EPSG&quot;,&quot;7030&quot;]],AUTHORITY[&quot;EPSG&quot;,&quot;6326&quot;]],PRIMEM[&quot;Greenwich&quot;,0,AUTHORITY[&quot;EPSG&quot;,&quot;8901&quot;]],UNIT[&quot;degree&quot;,0.0174532925199433,AUTHORITY[&quot;EPSG&quot;,&quot;9122&quot;]],AUTHORITY[&quot;EPSG&quot;,&quot;4326&quot;]],PROJECTION[&quot;Mercator_1SP&quot;],PARAMETER[&quot;central_meridian&quot;,0],PARAMETER[&quot;scale_factor&quot;,1],PARAMETER[&quot;false_easting&quot;,0],PARAMETER[&quot;false_northing&quot;,0],UNIT[&quot;metre&quot;,1,AUTHORITY[&quot;EPSG&quot;,&quot;9001&quot;]],AXIS[&quot;Easting&quot;,EAST],AXIS[&quot;Northing&quot;,NORTH],EXTENSION[&quot;PROJ4&quot;,&quot;+proj=merc +a=6378137 +b=6378137 +lat_ts=0 +lon_0=0 +x_0=0 +y_0=0 +k=1 +units=m +nadgrids=@null +wktext +no_defs&quot;],AUTHORITY[&quot;EPSG&quot;,&quot;3857&quot;]]</dd><dt><span>GeoTransform :</span></dt><dd>202331.08468872702 685.0303771934631 0.0 6436953.012100007 0.0 -685.0303771934631</dd></dl></div><div class='xr-var-data'><pre>array(0)</pre></div></li></ul></div></li><li class='xr-section-item'><input id='section-14b5ccfd-e14c-42e4-8af1-310f23e37122' class='xr-section-summary-in' type='checkbox'  ><label for='section-14b5ccfd-e14c-42e4-8af1-310f23e37122' class='xr-section-summary' >Indexes: <span>(2)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><ul class='xr-var-list'><li class='xr-var-item'><div class='xr-index-name'><div>x</div></div><div class='xr-index-preview'>PandasIndex</div><div></div><input id='index-6ee752fe-a14a-42f4-896b-7ba8ae0173fa' class='xr-index-data-in' type='checkbox'/><label for='index-6ee752fe-a14a-42f4-896b-7ba8ae0173fa' title='Show/Hide index repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-index-data'><pre>PandasIndex(Index([202673.59987732375, 203358.63025451722, 204043.66063171066,\n",
       "       204728.69100890413,  205413.7213860976, 206098.75176329107,\n",
       "       206783.78214048452,   207468.812517678, 208153.84289487146,\n",
       "       208838.87327206493,\n",
       "       ...\n",
       "       2148844.9014839525,  2149529.931861146, 2150214.9622383392,\n",
       "        2150899.992615533, 2151585.0229927264,   2152270.05336992,\n",
       "        2152955.083747113,  2153640.114124307, 2154325.1445015003,\n",
       "       2155010.1748786937],\n",
       "      dtype=&#x27;float64&#x27;, name=&#x27;x&#x27;, length=2851))</pre></div></li><li class='xr-var-item'><div class='xr-index-name'><div>y</div></div><div class='xr-index-preview'>PandasIndex</div><div></div><input id='index-b71b5c97-11dc-4aff-a453-4e0fe0819ec2' class='xr-index-data-in' type='checkbox'/><label for='index-b71b5c97-11dc-4aff-a453-4e0fe0819ec2' title='Show/Hide index repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-index-data'><pre>PandasIndex(Index([  6436610.49691141,  6435925.466534217, 6435240.4361570235,\n",
       "         6434555.40577983,  6433870.375402637,  6433185.345025443,\n",
       "        6432500.314648249,  6431815.284271056, 6431130.2538938625,\n",
       "        6430445.223516669,\n",
       "       ...\n",
       "       3808148.9396200925, 3807463.9092428987, 3806778.8788657053,\n",
       "        3806093.848488512, 3805408.8181113186,  3804723.787734125,\n",
       "       3804038.7573569315,  3803353.726979738, 3802668.6966025447,\n",
       "       3801983.6662253514],\n",
       "      dtype=&#x27;float64&#x27;, name=&#x27;y&#x27;, length=3847))</pre></div></li></ul></div></li><li class='xr-section-item'><input id='section-66e401eb-5683-4c0e-9ee6-49d962966676' class='xr-section-summary-in' type='checkbox'  checked><label for='section-66e401eb-5683-4c0e-9ee6-49d962966676' class='xr-section-summary' >Attributes: <span>(6)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><dl class='xr-attrs'><dt><span>AREA_OR_POINT :</span></dt><dd>Area</dd><dt><span>DESCRIPTION :</span></dt><dd>clc18</dd><dt><span>RepresentationType :</span></dt><dd>THEMATIC</dd><dt><span>grid_mapping :</span></dt><dd>spatial_ref</dd><dt><span>location :</span></dt><dd>central</dd><dt><span>_FillValue :</span></dt><dd>-9999.0</dd></dl></div></li></ul></div></div>"
      ],
      "text/plain": [
       "<xarray.DataArray 'corine_lc' (y: 3847, x: 2851)>\n",
       "array([[-9999., -9999., -9999., ..., -9999., -9999., -9999.],\n",
       "       [-9999., -9999., -9999., ..., -9999., -9999., -9999.],\n",
       "       [-9999., -9999., -9999., ..., -9999., -9999., -9999.],\n",
       "       ...,\n",
       "       [-9999., -9999., -9999., ..., -9999., -9999., -9999.],\n",
       "       [-9999., -9999., -9999., ..., -9999., -9999., -9999.],\n",
       "       [-9999., -9999., -9999., ..., -9999., -9999., -9999.]],\n",
       "      dtype=float32)\n",
       "Coordinates:\n",
       "  * x            (x) float64 2.027e+05 2.034e+05 ... 2.154e+06 2.155e+06\n",
       "  * y            (y) float64 6.437e+06 6.436e+06 ... 3.803e+06 3.802e+06\n",
       "    lc           int64 1\n",
       "    spatial_ref  int64 0\n",
       "Attributes:\n",
       "    AREA_OR_POINT:       Area\n",
       "    DESCRIPTION:         clc18\n",
       "    RepresentationType:  THEMATIC\n",
       "    grid_mapping:        spatial_ref\n",
       "    location:            central\n",
       "    _FillValue:          -9999.0"
      ]
     },
     "execution_count": 19,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "lc_reprojected = lc_.rio.write_crs('EPSG:4326').rio.reproject('EPSG:3857', nodata=-9999.)\n",
    "lc_reprojected"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Convert relative soil moisture to percentage."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "b4541a4a464945e592a3bd84a8a05aaf",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "Map(center=[41.269999999999996, 12.875], controls=(ZoomControl(options=['position', 'zoom_in_text', 'zoom_in_t…"
      ]
     },
     "execution_count": 32,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "import leafmap\n",
    "import matplotlib as mpl\n",
    "\n",
    "m = leafmap.Map(center=drought_roi.get_center(),\n",
    "                zoom=6, height=600)\n",
    "\n",
    "norm = mpl.colors.Normalize(1,10)\n",
    "norm_index = norm([1,2,3,4,5])\n",
    "cmap = mpl.cm.get_cmap('tab10')\n",
    "leg_cols = [mpl.colors.rgb2hex(cmap(c)) for c in norm_index]\n",
    "\n",
    "sm_vis = sm_sat_.sel(time=start_drought)*100.\n",
    "\n",
    "m.split_map(\n",
    "    lc_reprojected,\n",
    "    sm_vis,\n",
    "    left_args={\n",
    "        \"layer_name\": \"CorineLC\",\n",
    "        \"colormap\": \"tab10\",\n",
    "        \"vmin\": 1,\n",
    "        \"vmax\": 10,\n",
    "        \"nodata\": -9999.,\n",
    "    },\n",
    "    left_label=\"Corine LandCover 2018\",\n",
    "    right_args={\n",
    "        \"layer_name\": \"ASCAT SSM\",\n",
    "        \"colormap\": \"RdBu\",\n",
    "        \"nodata\": -999900.,\n",
    "        \"vmin\": 0,\n",
    "        \"vmax\": 100,\n",
    "    },\n",
    "    right_label=\"Soil Moisture (ASCAT)\"\n",
    ")\n",
    "m.add_legend(title=\"Corine LC\",\n",
    "             position='bottomleft',\n",
    "             layer_name=\"CorineLC\",\n",
    "             colors=leg_cols,\n",
    "             labels=['Artificial surfaces',\n",
    "                     'Agricultural areas',\n",
    "                     'Forest and seminatural areas',\n",
    "                     'Wetlands',\n",
    "                     'Water bodies'])\n",
    "m"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Predicting Drought\n",
    "\n",
    "The DEDL services provide also generic modelling components to enable an user-specific predictive assessment of future conditions. In this regard the water management expert is interested in the soil moisture content over the months starting at April and ending in August. "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Reproject data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [],
   "source": [
    "sm_mdl_mm_reprojected = sm_mdl_mm_.rio.reproject('EPSG:3857').rio.write_nodata(-9999., inplace=True)\n",
    "sm_mdl_mm_reprojected = sm_mdl_mm_reprojected.fillna(-9999.)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {},
   "outputs": [],
   "source": [
    "time_dim_len = sm_mdl_mm_reprojected.shape[0]\n",
    "for day in range(0, time_dim_len):\n",
    "    fpath_str = f\"./{day}.tif\"\n",
    "    sm_mdl_mm_reprojected[day,:].rio.to_raster(fpath_str,\n",
    "                                  driver=\"COG\",\n",
    "                                  overview_count=10)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "93d0bc1ea9274955ba5d5c9e7786a2b9",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "Map(center=[41.269999999999996, 12.875], controls=(ZoomControl(options=['position', 'zoom_in_text', 'zoom_in_t…"
      ]
     },
     "execution_count": 35,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "import leafmap\n",
    "from localtileserver import get_leaflet_tile_layer\n",
    "\n",
    "m = leafmap.Map(center=drought_roi.get_center(),\n",
    "                zoom=5, height=600)\n",
    "\n",
    "layer_dict = {}\n",
    "date_vals = np.datetime_as_string(sm_mdl_mm_reprojected[\"time\"].values, unit='D')\n",
    "for day in range(0, time_dim_len):\n",
    "    layer_dict[date_vals[day]]= get_leaflet_tile_layer(f\"./{day}.tif\",\n",
    "                                                       colormap=\"RdBu\",\n",
    "                                                       indexes=[1],\n",
    "                                                       nodata=-9999.,\n",
    "                                                       vmin=0,\n",
    "                                                       vmax=1,\n",
    "                                                       opacitiy=0.5)\n",
    "m.add_time_slider(layer_dict,\n",
    "                  layer=\"Accumluated Rainfall\",\n",
    "                  time_interval=2)\n",
    "m"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The simulated DT outputs indicate that intensified drought can be expected for the six months following the beginning of this survey. Moreover the simulated results suggest that especially agricultural areas will be inflicted by drought.The water management expert uses this scenario to preemptively call on water preserving measures and estabelish rationing plans whilst identifying the most critical systems. Preserving measures can consist of changing to crops that are more well adapted to prolonged drought, which include drought adapted varieties of corn, wheat, and rice and already drough-tolerant soy. Alternatively, a reduction of hydro-electrical energy production could reduce consumption of water. Whereas proactive rationing can reduce the overall pressure on harvests by more effective spreading of water consumption restrictions over longer periods, which reduces the risk of total crop failure."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Retrospective Analysis of the DEDL Services\n",
    "\n",
    "The Italy drought event of 2022 severely affected water supply in the regions Emilia-Romagna and Veneto along the river Po. These regions are known for their high agricultural yields and a significant proportion of the European crop production takes place here. Estimates suggest that ~6 billion euro of produce were damaged during this particular drought event. Besides dwindling water supplies, low ground water levels caused saltwater intrusion into fresh water aquifers in some of the coastal regions. This compounding of effects resulted in water shortages and required water rationing at local administrative levels in Italy. Predicted future warming will likely exacerbate the frequency and severity of drought effect in the coming years and clearly stipulates the need for early warning systems as demonstrated here."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "metadata": {},
   "outputs": [],
   "source": [
    "sm_vis = sm_sat_*100\n",
    "time_dim_len = sm_vis.shape[0]\n",
    "for day in range(0, time_dim_len):\n",
    "    fpath_str = f\"./sm_{day}.tif\"\n",
    "    sm_vis[day,:].rio.to_raster(fpath_str,\n",
    "                                driver=\"COG\",\n",
    "                                overview_count=10)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "cd7f52722c9040b5bc46e3b74a74a582",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "Map(center=[41.269999999999996, 12.875], controls=(ZoomControl(options=['position', 'zoom_in_text', 'zoom_in_t…"
      ]
     },
     "execution_count": 38,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "import leafmap\n",
    "from localtileserver import get_leaflet_tile_layer\n",
    "\n",
    "m = leafmap.Map(center=drought_roi.get_center(),\n",
    "                zoom=6, height=600)\n",
    "\n",
    "layer_dict = {}\n",
    "date_vals = np.datetime_as_string(sm_vis[\"time\"].values, unit='D')\n",
    "for day in range(0, time_dim_len):\n",
    "    layer_dict[date_vals[day]]= get_leaflet_tile_layer(f\"./sm_{day}.tif\",\n",
    "                                                       colormap=\"RdBu\",\n",
    "                                                       indexes=[1],\n",
    "                                                       nodata=-999900.,\n",
    "                                                       vmin=0,\n",
    "                                                       vmax=100,\n",
    "                                                       opacitiy=0.5)\n",
    "m.add_time_slider(layer_dict,\n",
    "                  layer=\"DT - Soil Moisture\",\n",
    "                  time_interval=2)\n",
    "m"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Shutdown Dask Cluster to free up resources"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "metadata": {},
   "outputs": [],
   "source": [
    "myDEDLClusters.shutdown()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
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