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   "source": [
    "# Potential Evapotranspiration from ZAMG data\n",
    "*A. Kokimova, November 2021, University of Graz*\n",
    "\n",
    "Data source: ZAMG - https://data.hub.zamg.ac.at\n",
    "\n",
    "What is done:\n",
    "\n",
    "- load the station data from ZAMG\n",
    "- estimate potential evapotranspiration\n",
    "- plot and store results"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "7a2ae568-3c3b-44f5-8b82-ab5a99d35d2d",
   "metadata": {
    "pycharm": {
     "name": "#%%\n"
    }
   },
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "import matplotlib.pyplot as plt\n",
    "import numpy as np\n",
    "import pyet\n",
    "pyet.show_versions()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "da6f3e98-0073-4db9-b763-03beb2611e78",
   "metadata": {
    "pycharm": {
     "name": "#%% md\n"
    }
   },
   "source": [
    "## Loading daily data from ZAMG (Messstationen Tagesdaten)\n",
    "\n",
    "station: Graz Universität 16412\n",
    "\n",
    "Selected variables:\n",
    "- globalstrahlung (global radiation), J/cm2 needs to be in MJ/m3d, ZAMG abbreviation - strahl\n",
    "- arithmetische windgeschwindigkeit (wind speed), m/s, ZAMG abbreviation - vv\n",
    "- relative feuchte (relative humidity), %, ZAMG abbreviation - rel\n",
    "- lufttemparatur (air temperature) in 2 m, C, ZAMG abbreviation - t\n",
    "- lufttemperatur (air temperature) max in 2 m, C, ZAMG abbreviation - tmax\n",
    "- lufttemperatur (air temperature) min in 2 m, C, ZAMG abbreviation - tmin\n",
    "- latitute and elevation of a station"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "236434b2-3c33-4772-93ec-de0cb7317209",
   "metadata": {
    "pycharm": {
     "name": "#%%\n"
    }
   },
   "outputs": [],
   "source": [
    "#read data\n",
    "data_16412 = pd.read_csv('data/example_1/klima_daily.csv', index_col=1, parse_dates=True)\n",
    "data_16412"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "22ebf3bc-6b9a-4f81-a455-7ca1b51879dd",
   "metadata": {
    "pycharm": {
     "name": "#%% md\n"
    }
   },
   "source": [
    "## Calculate PET for Graz Universität - 16412"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "bc5ba933-22c2-4c5b-9ca6-43a4bcdad344",
   "metadata": {
    "pycharm": {
     "name": "#%%\n"
    }
   },
   "outputs": [],
   "source": [
    "# Convert Glabalstrahlung J/cm2 to MJ/m2 by dividing to 100\n",
    "\n",
    "meteo = pd.DataFrame({\"time\":data_16412.index, \"tmean\":data_16412.t, \"tmax\":data_16412.tmax, \"tmin\":data_16412.tmin, \"rh\":data_16412.rel, \n",
    "                      \"wind\":data_16412.vv, \"rs\":data_16412.strahl/100})\n",
    "time, tmean, tmax, tmin, rh, wind, rs = [meteo[col] for col in meteo.columns]\n",
    "\n",
    "lat = 47.077778*np.pi/180  # Latitude of the meteorological station, converting from degrees to radians\n",
    "elevation = 367  # meters above sea-level\n",
    "\n",
    "# Estimate evapotranspiration with four different methods and create a dataframe\n",
    "pet_df = pyet.calculate_all(tmean, wind, rs, elevation, lat, tmax=tmax,\n",
    "                                tmin=tmin, rh=rh)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "e6e2564c-48ed-46b7-a275-fd17db592456",
   "metadata": {
    "pycharm": {
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    }
   },
   "source": [
    "## Plot results"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "c9a4582f-219b-47f8-9ed2-dac80720cf55",
   "metadata": {
    "pycharm": {
     "name": "#%%\n"
    }
   },
   "outputs": [],
   "source": [
    "fig, axs = plt.subplots(figsize=(13,4), ncols=2)\n",
    "pet_df.plot(ax=axs[0])\n",
    "pet_df.cumsum().plot(ax=axs[1], legend=False)\n",
    "\n",
    "axs[0].set_ylabel(\"PET [mm/day]\", fontsize=12)\n",
    "axs[1].set_ylabel(\"Cumulative PET [mm]\", fontsize=12)\n",
    "axs[0].legend(ncol=6, loc=[0,1.])\n",
    "for i in (0,1):\n",
    "    axs[i].set_xlabel(\"Date\", fontsize=12)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "1fe2a55b-838a-4e3a-a621-c5fe55db399a",
   "metadata": {
    "pycharm": {
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   },
   "source": [
    "## Store results"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "3c5e47fb-3ec7-4bc9-ad6c-be0edc67ba09",
   "metadata": {
    "pycharm": {
     "name": "#%%\n"
    }
   },
   "outputs": [],
   "source": [
    "#plt.savefig(\"PET_methods.png\", dpi=300)\n",
    "#pet_u.to_csv('../evap_16412.csv')"
   ]
  }
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