Case2_wflow_LISFlood_merrimack_inc_ESMValtool.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "![image](https://github.com/eWaterCycle/ewatercycle/raw/main/docs/examples/logo.png)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Case study 2: Hello worlds. Run two models on the same forcing data and compare output (discharge)\n",
    "This notebooks demonstrates how to use models in eWaterCycle by showing how to run two different models for the same region (Merrimack) and on the same forcing data (ERA5: 1990). In this notebook, in contrast with the `Case1_Marrmot_Merrimack_ESMValTool` notebook, two distributed models are used. \n",
    "\n",
    "The models used in this notebook are:\n",
    "\n",
    "- WFlow (Schellekens et. al. 2020, [https://doi.org/10.5281/ZENODO.593510](https://doi.org/10.5281/ZENODO.593510))\n",
    "- LISFLood (van der Kneijf et. al. 2010, [https://doi.org/10.1080/13658810802549154](https://doi.org/10.1080/13658810802549154))\n",
    "\n",
    "The pre-processing (ie. generating forcing data) for this experiment was already done. For information on how to generate forcing data for a given combination of model, forcing dataset and time period, see the notebook titled `Case1_Marrmot_Merrimack_ESMValTool` in this repo."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Import statements\n",
    "We'll be using the following modules"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import warnings\n",
    "warnings.filterwarnings(\"ignore\", category=UserWarning)\n",
    "\n",
    "import pandas as pd\n",
    "from cartopy.io import shapereader\n",
    "\n",
    "import ewatercycle.forcing\n",
    "import ewatercycle.models\n",
    "import ewatercycle.analysis\n",
    "import ewatercycle.observation.grdc"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Experiment settings\n",
    "Just like in `Case1_Marrmot_Merrimack_ESMValTool` the Merrimack will be used. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "experiment_start_date = \"1990-01-01T00:00:00Z\"\n",
    "experiment_end_date = \"1990-12-31T00:00:00Z\"\n",
    "\n",
    "# GRDC observation station for Merrimack river\n",
    "grdc_station_id = \"4147380\""
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Observation"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "GRDC station 4147380 is selected. The river name is: MERRIMACK RIVER.The coordinates are: (42.6459, -71.2984).The catchment area in km2 is: 12004.7. There are 0 missing values during 1990-01-01T00:00:00Z_1990-12-31T00:00:00Z at this station. See the metadata for more information.\n"
     ]
    },
    {
     "data": {
      "text/html": [
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       "\n",
       "    .dataframe thead th {\n",
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       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>GRDC</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>time</th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>1990-01-01</th>\n",
       "      <td>119.214</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1990-01-02</th>\n",
       "      <td>136.204</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1990-01-03</th>\n",
       "      <td>131.107</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1990-01-04</th>\n",
       "      <td>133.655</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1990-01-05</th>\n",
       "      <td>139.035</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1990-12-27</th>\n",
       "      <td>668.276</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1990-12-28</th>\n",
       "      <td>512.534</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1990-12-29</th>\n",
       "      <td>419.089</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1990-12-30</th>\n",
       "      <td>393.604</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1990-12-31</th>\n",
       "      <td>399.267</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>365 rows × 1 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "               GRDC\n",
       "time               \n",
       "1990-01-01  119.214\n",
       "1990-01-02  136.204\n",
       "1990-01-03  131.107\n",
       "1990-01-04  133.655\n",
       "1990-01-05  139.035\n",
       "...             ...\n",
       "1990-12-27  668.276\n",
       "1990-12-28  512.534\n",
       "1990-12-29  419.089\n",
       "1990-12-30  393.604\n",
       "1990-12-31  399.267\n",
       "\n",
       "[365 rows x 1 columns]"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Get grdc observations at 4147380 station related to Merrimack\n",
    "df, metadata = ewatercycle.observation.grdc.get_grdc_data(\n",
    "    station_id=grdc_station_id,\n",
    "    start_time=experiment_start_date,\n",
    "    end_time=experiment_end_date,\n",
    ")\n",
    "\n",
    "# Get lat/lon of the station\n",
    "station_lat = [metadata[\"grdc_latitude_in_arc_degree\"]]\n",
    "station_lon = [metadata[\"grdc_longitude_in_arc_degree\"]]\n",
    "\n",
    "# Convert it to dataframe and rename it to GRDC\n",
    "observations = df.rename(columns={\"streamflow\": \"GRDC\"})\n",
    "observations"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Wflow"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Load forcing data\n",
    "Here we use pre-generated forcing data. Although data is available for 1990-2001, we will only need the first year."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Forcing data for Wflow\n",
      "----------------------\n",
      "Directory: /mnt/data/forcing/wflow_ERA5_1990-2001_merrimack\n",
      "Start time: 1990-01-01T12:00:00Z\n",
      "End time: 2001-12-31T12:00:00Z\n",
      "Shapefile: None\n",
      "Additional information for model config:\n",
      "  - netcdfinput: wflow_ERA5_Merrimack_1990_2001.nc\n",
      "  - Precipitation: /pr\n",
      "  - Temperature: /tas\n",
      "  - EvapoTranspiration: /pet\n",
      "  - Inflow: None\n"
     ]
    }
   ],
   "source": [
    "experiment_forcing_wflow = ewatercycle.forcing.load('/mnt/data/forcing/wflow_ERA5_1990-2001_merrimack')\n",
    "print(experiment_forcing_wflow)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Load parameterset"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Parameter set\n",
      "-------------\n",
      "name=wflow_merrimack_techpaper\n",
      "directory=/mnt/data/parameter-sets/wflow_merrimack_techpaper\n",
      "config=/mnt/data/parameter-sets/wflow_merrimack_techpaper/wflow_sbm_era5_test.ini\n",
      "doi=N/A\n",
      "target_model=wflow\n",
      "supported_model_versions={'2020.1.1'}\n"
     ]
    }
   ],
   "source": [
    "experiment_parameterset_wflow = ewatercycle.parameter_sets.get_parameter_set('wflow_merrimack_techpaper')\n",
    "print(experiment_parameterset_wflow)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Set up the model"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "('2020.1.1',)"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "ewatercycle.models.Wflow.available_versions"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Config file from parameter set is missing RiverRunoff option in API section, added it with value '2, m/s option'\n"
     ]
    }
   ],
   "source": [
    "model = ewatercycle.models.Wflow(version='2020.1.1', parameter_set=experiment_parameterset_wflow, \n",
    "                                 forcing=experiment_forcing_wflow)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Forcing time range is 1990-2001, but we want to run the model for the year 1990, so set `end_time` in setup() function:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Running /mnt/data/singularity-images/ewatercycle-wflow-grpc4bmi_2020.1.1.sif singularity container on port 47515\n",
      "/home/sverhoeven/technicalPaperExampleNotebooks/ewatercycle_output/wflow_20210930_120047/wflow_ewatercycle.ini\n",
      "/home/sverhoeven/technicalPaperExampleNotebooks/ewatercycle_output/wflow_20210930_120047\n"
     ]
    }
   ],
   "source": [
    "cfg_file, cfg_dir = model.setup(end_time=experiment_end_date)\n",
    "print(cfg_file)\n",
    "print(cfg_dir)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Initialize\n",
    "model.initialize(cfg_file)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "('SurfaceRunoff', 'RiverRunoff')"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Get metadata\n",
    "model.output_var_names"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Run the model\n",
    "\n",
    "We store discharge and time values. For time, only date part is kept using the function `date()`. So, we can create a dataframe joining the data on date later for the hydrograph. Wflow has steps at noon, while GRDC and lisflood uses midnight."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "1990-12-31T12:00:00Z\r"
     ]
    }
   ],
   "source": [
    "discharge = []\n",
    "time_range = []\n",
    "\n",
    "while model.time < model.end_time:\n",
    "    model.update()\n",
    "    # Get Discharge at one location\n",
    "    discharge_at_pixel = model.get_value_at_coords('RiverRunoff', lon=station_lon, lat=station_lat)[0]\n",
    "    discharge.append(discharge_at_pixel)\n",
    "    time_range.append(model.time_as_datetime.date())\n",
    "    print(model.time_as_isostr, end=\"\\r\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Stop the model\n",
    "model.finalize()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "\n",
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       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>wflow</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>1990-01-01</th>\n",
       "      <td>502.869934</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1990-01-02</th>\n",
       "      <td>501.310394</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1990-01-03</th>\n",
       "      <td>467.357941</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1990-01-04</th>\n",
       "      <td>422.030151</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1990-01-05</th>\n",
       "      <td>379.955841</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1990-12-27</th>\n",
       "      <td>590.317139</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1990-12-28</th>\n",
       "      <td>511.656464</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1990-12-29</th>\n",
       "      <td>450.715698</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1990-12-30</th>\n",
       "      <td>447.656281</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1990-12-31</th>\n",
       "      <td>475.045166</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>365 rows × 1 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "                 wflow\n",
       "1990-01-01  502.869934\n",
       "1990-01-02  501.310394\n",
       "1990-01-03  467.357941\n",
       "1990-01-04  422.030151\n",
       "1990-01-05  379.955841\n",
       "...                ...\n",
       "1990-12-27  590.317139\n",
       "1990-12-28  511.656464\n",
       "1990-12-29  450.715698\n",
       "1990-12-30  447.656281\n",
       "1990-12-31  475.045166\n",
       "\n",
       "[365 rows x 1 columns]"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Concatenate timeseries\n",
    "wflow = pd.DataFrame({'wflow': discharge}, index=pd.to_datetime(time_range))\n",
    "wflow"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Lisflood"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Load forcing data\n",
    "Pre-generated lisflood forcing data is available for the year 1990. It is a global file, where all but the Merrimack catchment (and a few others that are irrelevant for this example) are masked."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "eWaterCycle forcing\n",
      "-------------------\n",
      "start_time=1990-01-01T12:00:00Z\n",
      "end_time=1990-12-31T12:00:00Z\n",
      "shape=None\n",
      "PrefixPrecipitation=lisflood_ERA5_pr_1990_1990.nc\n",
      "PrefixTavg=lisflood_ERA5_tas_1990_1990.nc\n",
      "PrefixE0=lisflood_ERA5_e0_1990_1990.nc\n",
      "PrefixES0=lisflood_ERA5_es0_1990_1990.nc\n",
      "PrefixET0=lisflood_ERA5_et0_1990_1990.nc\n",
      "directory=/mnt/data/forcing/lisflood_ERA5_1990_global-masked\n"
     ]
    }
   ],
   "source": [
    "experiment_forcing_lisflood = ewatercycle.forcing.load('/mnt/data/forcing/lisflood_ERA5_1990_global-masked')\n",
    "print(experiment_forcing_lisflood)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Load parameter set"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Parameter set\n",
      "-------------\n",
      "name=lisflood_global-masked_01degree\n",
      "directory=/mnt/data/parameter-sets/lisflood_global-masked_01degree\n",
      "config=/mnt/data/parameter-sets/lisflood_global-masked_01degree/settings_lisflood_ERA5.xml\n",
      "doi=N/A\n",
      "target_model=lisflood\n",
      "supported_model_versions={'20.10'}\n"
     ]
    }
   ],
   "source": [
    "experiment_parameterset_lisflood = ewatercycle.parameter_sets.get_parameter_set('lisflood_global-masked_01degree')\n",
    "print(experiment_parameterset_lisflood)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Set up the model"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "('20.10',)"
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "ewatercycle.models.Lisflood.available_versions"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "eWaterCycle Lisflood\n",
      "-------------------\n",
      "Version = 20.10\n",
      "Parameter set = \n",
      "  Parameter set\n",
      "  -------------\n",
      "  name=lisflood_global-masked_01degree\n",
      "  directory=/mnt/data/parameter-sets/lisflood_global-masked_01degree\n",
      "  config=/mnt/data/parameter-sets/lisflood_global-masked_01degree/settings_lisflood_ERA5.xml\n",
      "  doi=N/A\n",
      "  target_model=lisflood\n",
      "  supported_model_versions={'20.10'}\n",
      "Forcing = \n",
      "  eWaterCycle forcing\n",
      "  -------------------\n",
      "  start_time=1990-01-01T12:00:00Z\n",
      "  end_time=1990-12-31T12:00:00Z\n",
      "  shape=None\n",
      "  PrefixPrecipitation=lisflood_ERA5_pr_1990_1990.nc\n",
      "  PrefixTavg=lisflood_ERA5_tas_1990_1990.nc\n",
      "  PrefixE0=lisflood_ERA5_e0_1990_1990.nc\n",
      "  PrefixES0=lisflood_ERA5_es0_1990_1990.nc\n",
      "  PrefixET0=lisflood_ERA5_et0_1990_1990.nc\n",
      "  directory=/mnt/data/forcing/lisflood_ERA5_1990_global-masked\n"
     ]
    }
   ],
   "source": [
    "model = ewatercycle.models.Lisflood(version='20.10', parameter_set=experiment_parameterset_lisflood, \n",
    "                                    forcing=experiment_forcing_lisflood)\n",
    "print(model)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Running /mnt/data/singularity-images/ewatercycle-lisflood-grpc4bmi_20.10.sif singularity container on port 45457\n",
      "/home/sverhoeven/technicalPaperExampleNotebooks/ewatercycle_output/lisflood_20210930_120434/lisflood_setting.xml\n",
      "/home/sverhoeven/technicalPaperExampleNotebooks/ewatercycle_output/lisflood_20210930_120434\n"
     ]
    }
   ],
   "source": [
    "# Lisflood requires a mask map, in this case only the Merrimack is unmasked\n",
    "model_mask = 'settingFiles/lisflood_model_mask.nc'\n",
    "\n",
    "config_file, config_dir = model.setup(end_time=experiment_end_date, MaskMap=model_mask)\n",
    "print(config_file)\n",
    "print(config_dir)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "('Discharge',)"
      ]
     },
     "execution_count": 19,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Initialize \n",
    "model.initialize(config_file)\n",
    "\n",
    "# Get metadata\n",
    "model.output_var_names"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Run the model\n",
    "\n",
    "The model is masked by the Merrimack catchment shapefile. However, GRDC station (-71.2984, 42.6459) is located outside of the catchment boundary. Therefore, the model returns none value at these coordinates. Here, we use the coordinates (-71.35, 42.65) inside the catchment given by lisflood modelers that is close to the GRDC station."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "1990-12-30T00:00:00Z\r"
     ]
    }
   ],
   "source": [
    "model_lon = [-71.35]\n",
    "model_lat = [42.65]\n",
    "discharge = []\n",
    "time_range = []\n",
    "\n",
    "while model.time < model.end_time:\n",
    "    model.update()\n",
    "    # Get Discharge at one location\n",
    "    discharge_at_pixel = model.get_value_at_coords('Discharge', lon=model_lon, lat=model_lat)[0]\n",
    "    discharge.append(discharge_at_pixel)\n",
    "    time_range.append(model.time_as_datetime.date())\n",
    "    print(model.time_as_isostr, end=\"\\r\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Stop the model\n",
    "model.finalize()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>lisflood</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>1990-01-03</th>\n",
       "      <td>8.438896</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1990-01-04</th>\n",
       "      <td>16.816620</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1990-01-05</th>\n",
       "      <td>41.966252</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1990-01-06</th>\n",
       "      <td>67.418683</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1990-01-07</th>\n",
       "      <td>95.661038</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1990-12-26</th>\n",
       "      <td>301.802257</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1990-12-27</th>\n",
       "      <td>393.529669</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1990-12-28</th>\n",
       "      <td>620.846660</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1990-12-29</th>\n",
       "      <td>753.768539</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1990-12-30</th>\n",
       "      <td>696.610750</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>362 rows × 1 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "              lisflood\n",
       "1990-01-03    8.438896\n",
       "1990-01-04   16.816620\n",
       "1990-01-05   41.966252\n",
       "1990-01-06   67.418683\n",
       "1990-01-07   95.661038\n",
       "...                ...\n",
       "1990-12-26  301.802257\n",
       "1990-12-27  393.529669\n",
       "1990-12-28  620.846660\n",
       "1990-12-29  753.768539\n",
       "1990-12-30  696.610750\n",
       "\n",
       "[362 rows x 1 columns]"
      ]
     },
     "execution_count": 22,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "lisflood = pd.DataFrame({'lisflood': discharge}, index=pd.to_datetime(time_range))\n",
    "lisflood"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Hydrograph"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(<Figure size 720x720 with 2 Axes>,\n",
       " (<AxesSubplot:title={'center':'Hydrograph'}, ylabel='Discharge (m$^3$ s$^{-1}$)'>,\n",
       "  <AxesSubplot:>))"
      ]
     },
     "execution_count": 23,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 720x720 with 2 Axes>"
      ]
     },
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   ],
   "source": [
    "# Combine simulated and observed values to one dataframe according to time axis\n",
    "hydro_data = pd.concat([wflow, lisflood, observations], axis=1)\n",
    "\n",
    "# Plot hydrograph and show metrics\n",
    "ewatercycle.analysis.hydrograph(hydro_data, reference='GRDC', filename = 'figures/case2_HelloWorldsHydrograph.png')"
   ]
  }
 ],
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