{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Example 2: Analysis of groundwater monitoring networks using Pastas\n",
    "\n",
    "This notebook is supplementary material to the following paper in Groundwater:\n",
    "\n",
    "*Collenteur, R.A., Bakker, M., Caljé, R., Klop, S.A., Schaars, F. (2019) [Pastas: open source software for the analysis of groundwater time series](https://ngwa.onlinelibrary.wiley.com/doi/abs/10.1111/gwat.12925). Groundwater. doi: 10.1111/gwat.12925.*\n",
    "\n",
    "In this second example, it is demonstrated how scripts can be used to analyze a large number of time series. Consider a pumping well field surrounded by a number of observations wells. The pumping wells are screened in the middle aquifer of a three-aquifer system. The objective is to estimate the drawdown caused by the groundwater pumping in each observation well.\n",
    "\n",
    "\n",
    "## 1. Import the packages"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Pastas     : 2.0.0rc0\n",
      "Python     : 3.13.11\n",
      "Numpy      : 2.4.6\n",
      "Pandas     : 3.0.3\n",
      "Scipy      : 1.17.1\n",
      "Matplotlib : 3.10.9\n",
      "Numba      : 0.65.1\n",
      "\n"
     ]
    }
   ],
   "source": [
    "# Import the packages\n",
    "import os\n",
    "\n",
    "import matplotlib.pyplot as plt\n",
    "import numpy as np\n",
    "import pandas as pd\n",
    "\n",
    "import pastas as ps\n",
    "\n",
    "ps.show_versions()\n",
    "ps.set_log_level(\"ERROR\")\n",
    "\n",
    "try:\n",
    "    from timml import ModelMaq, Well\n",
    "\n",
    "    plot_timml = True\n",
    "except ImportError:\n",
    "    plot_timml = False\n",
    "\n",
    "plot_results = False"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 2. Importing the time series\n",
    "In this codeblock the time series are imported. The following time series are imported:\n",
    "\n",
    "- 44 time series with head observations [m] from the monitoring network;\n",
    "- precipitation [m/d] from KNMI station Oudenbosch;\n",
    "- potential evaporation [m/d] from KNMI station de Bilt;\n",
    "- Total pumping rate [m3/d] from well field Seppe."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Dictionary to hold all heads\n",
    "heads = {}\n",
    "\n",
    "# Load a metadata-file with xy-coordinates from the groundwater heads\n",
    "metadata_heads = pd.read_csv(\"data/metadata_heads.csv\", index_col=0)\n",
    "distances = pd.read_csv(\"data/distances.csv\", index_col=0)\n",
    "\n",
    "# Add the groundwater head observations to the database\n",
    "for fname in os.listdir(\"./data/heads/\"):\n",
    "    fname = os.path.join(\"./data/heads/\", fname)\n",
    "    obs = pd.read_csv(fname, parse_dates=True, index_col=0).squeeze()\n",
    "    heads[obs.name] = obs"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<Figure size 1000x500 with 3 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Load a metadata-file with xy-coordinates from the explanatory variables\n",
    "metadata = pd.read_csv(\"data/metadata_stresses.csv\", index_col=0)\n",
    "\n",
    "# Import the precipitation, evaporation and well time series\n",
    "rain = pd.read_csv(\"data/rain.csv\", parse_dates=True, index_col=0).squeeze()\n",
    "evap = pd.read_csv(\"data/evap.csv\", parse_dates=True, index_col=0).squeeze()\n",
    "well = pd.read_csv(\"data/well.csv\", parse_dates=True, index_col=0).squeeze()\n",
    "\n",
    "# Plot the stresses\n",
    "fig, [ax1, ax2, ax3] = plt.subplots(3, 1, figsize=(10, 5), sharex=True)\n",
    "rain.plot(ax=ax1)\n",
    "evap.plot(ax=ax2)\n",
    "well.plot(ax=ax3)\n",
    "plt.xlim(\"1960\", \"2018\");"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 3/4/5. Creating and optimizing the Time Series Model\n",
    "\n",
    "For each time series of groundwater head observations a TFN model is constructed with the following model components:\n",
    "- A Constant\n",
    "- A NoiseModel\n",
    "- A RechargeModel object to simulate the effect of recharge\n",
    "- A StressModel object to simulate the effect of groundwater extraction\n",
    "\n",
    "\n",
    "***Calibrating all models can take a couple of minutes!!***"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/Users/raoulcollenteur/Github/pastas/pastas/pastas/decorators.py:70: FutureWarning: ml is deprecated and will not be available from Pastas version >= 2.4.0. Use 'solver.model' instead.\n",
      "  warn(message=msg, category=FutureWarning)\n",
      "/Users/raoulcollenteur/Github/pastas/pastas/pastas/decorators.py:70: FutureWarning: ml is deprecated and will not be available from Pastas version >= 2.4.0. Use 'solver.model' instead.\n",
      "  warn(message=msg, category=FutureWarning)\n",
      "/Users/raoulcollenteur/Github/pastas/pastas/pastas/decorators.py:70: FutureWarning: ml is deprecated and will not be available from Pastas version >= 2.4.0. Use 'solver.model' instead.\n",
      "  warn(message=msg, category=FutureWarning)\n",
      "/Users/raoulcollenteur/Github/pastas/pastas/pastas/decorators.py:70: FutureWarning: ml is deprecated and will not be available from Pastas version >= 2.4.0. Use 'solver.model' instead.\n",
      "  warn(message=msg, category=FutureWarning)\n",
      "/Users/raoulcollenteur/Github/pastas/pastas/pastas/decorators.py:70: FutureWarning: ml is deprecated and will not be available from Pastas version >= 2.4.0. Use 'solver.model' instead.\n",
      "  warn(message=msg, category=FutureWarning)\n",
      "/Users/raoulcollenteur/Github/pastas/pastas/pastas/decorators.py:70: FutureWarning: ml is deprecated and will not be available from Pastas version >= 2.4.0. Use 'solver.model' instead.\n",
      "  warn(message=msg, category=FutureWarning)\n",
      "/Users/raoulcollenteur/Github/pastas/pastas/pastas/decorators.py:70: FutureWarning: ml is deprecated and will not be available from Pastas version >= 2.4.0. Use 'solver.model' instead.\n",
      "  warn(message=msg, category=FutureWarning)\n",
      "/Users/raoulcollenteur/Github/pastas/pastas/pastas/decorators.py:70: FutureWarning: ml is deprecated and will not be available from Pastas version >= 2.4.0. Use 'solver.model' instead.\n",
      "  warn(message=msg, category=FutureWarning)\n",
      "/Users/raoulcollenteur/Github/pastas/pastas/pastas/decorators.py:70: FutureWarning: ml is deprecated and will not be available from Pastas version >= 2.4.0. Use 'solver.model' instead.\n",
      "  warn(message=msg, category=FutureWarning)\n",
      "/Users/raoulcollenteur/Github/pastas/pastas/pastas/decorators.py:70: FutureWarning: ml is deprecated and will not be available from Pastas version >= 2.4.0. Use 'solver.model' instead.\n",
      "  warn(message=msg, category=FutureWarning)\n",
      "/Users/raoulcollenteur/Github/pastas/pastas/pastas/decorators.py:70: FutureWarning: ml is deprecated and will not be available from Pastas version >= 2.4.0. Use 'solver.model' instead.\n",
      "  warn(message=msg, category=FutureWarning)\n",
      "/Users/raoulcollenteur/Github/pastas/pastas/pastas/decorators.py:70: FutureWarning: ml is deprecated and will not be available from Pastas version >= 2.4.0. Use 'solver.model' instead.\n",
      "  warn(message=msg, category=FutureWarning)\n",
      "/Users/raoulcollenteur/Github/pastas/pastas/pastas/decorators.py:70: FutureWarning: ml is deprecated and will not be available from Pastas version >= 2.4.0. Use 'solver.model' instead.\n",
      "  warn(message=msg, category=FutureWarning)\n",
      "/Users/raoulcollenteur/Github/pastas/pastas/pastas/decorators.py:70: FutureWarning: ml is deprecated and will not be available from Pastas version >= 2.4.0. Use 'solver.model' instead.\n",
      "  warn(message=msg, category=FutureWarning)\n",
      "/Users/raoulcollenteur/Github/pastas/pastas/pastas/decorators.py:70: FutureWarning: ml is deprecated and will not be available from Pastas version >= 2.4.0. Use 'solver.model' instead.\n",
      "  warn(message=msg, category=FutureWarning)\n",
      "/Users/raoulcollenteur/Github/pastas/pastas/pastas/decorators.py:70: FutureWarning: ml is deprecated and will not be available from Pastas version >= 2.4.0. Use 'solver.model' instead.\n",
      "  warn(message=msg, category=FutureWarning)\n",
      "/Users/raoulcollenteur/Github/pastas/pastas/pastas/decorators.py:70: FutureWarning: ml is deprecated and will not be available from Pastas version >= 2.4.0. Use 'solver.model' instead.\n",
      "  warn(message=msg, category=FutureWarning)\n",
      "/Users/raoulcollenteur/Github/pastas/pastas/pastas/decorators.py:70: FutureWarning: ml is deprecated and will not be available from Pastas version >= 2.4.0. Use 'solver.model' instead.\n",
      "  warn(message=msg, category=FutureWarning)\n",
      "/Users/raoulcollenteur/Github/pastas/pastas/pastas/decorators.py:70: FutureWarning: ml is deprecated and will not be available from Pastas version >= 2.4.0. Use 'solver.model' instead.\n",
      "  warn(message=msg, category=FutureWarning)\n",
      "/Users/raoulcollenteur/Github/pastas/pastas/pastas/decorators.py:70: FutureWarning: ml is deprecated and will not be available from Pastas version >= 2.4.0. Use 'solver.model' instead.\n",
      "  warn(message=msg, category=FutureWarning)\n",
      "/Users/raoulcollenteur/Github/pastas/pastas/pastas/decorators.py:70: FutureWarning: ml is deprecated and will not be available from Pastas version >= 2.4.0. Use 'solver.model' instead.\n",
      "  warn(message=msg, category=FutureWarning)\n",
      "/Users/raoulcollenteur/Github/pastas/pastas/pastas/decorators.py:70: FutureWarning: ml is deprecated and will not be available from Pastas version >= 2.4.0. Use 'solver.model' instead.\n",
      "  warn(message=msg, category=FutureWarning)\n",
      "/Users/raoulcollenteur/Github/pastas/pastas/pastas/decorators.py:70: FutureWarning: ml is deprecated and will not be available from Pastas version >= 2.4.0. Use 'solver.model' instead.\n",
      "  warn(message=msg, category=FutureWarning)\n",
      "/Users/raoulcollenteur/Github/pastas/pastas/pastas/decorators.py:70: FutureWarning: ml is deprecated and will not be available from Pastas version >= 2.4.0. Use 'solver.model' instead.\n",
      "  warn(message=msg, category=FutureWarning)\n",
      "/Users/raoulcollenteur/Github/pastas/pastas/pastas/decorators.py:70: FutureWarning: ml is deprecated and will not be available from Pastas version >= 2.4.0. Use 'solver.model' instead.\n",
      "  warn(message=msg, category=FutureWarning)\n",
      "/Users/raoulcollenteur/Github/pastas/pastas/pastas/decorators.py:70: FutureWarning: ml is deprecated and will not be available from Pastas version >= 2.4.0. Use 'solver.model' instead.\n",
      "  warn(message=msg, category=FutureWarning)\n",
      "/Users/raoulcollenteur/Github/pastas/pastas/pastas/decorators.py:70: FutureWarning: ml is deprecated and will not be available from Pastas version >= 2.4.0. Use 'solver.model' instead.\n",
      "  warn(message=msg, category=FutureWarning)\n",
      "/Users/raoulcollenteur/Github/pastas/pastas/pastas/decorators.py:70: FutureWarning: ml is deprecated and will not be available from Pastas version >= 2.4.0. Use 'solver.model' instead.\n",
      "  warn(message=msg, category=FutureWarning)\n"
     ]
    }
   ],
   "source": [
    "# Create folder to save the model figures\n",
    "mls = {}\n",
    "mlpath = \"models\"\n",
    "if not os.path.exists(mlpath):\n",
    "    os.mkdir(mlpath)\n",
    "\n",
    "# Choose the calibration period\n",
    "tmin = \"1970\"\n",
    "tmax = \"2017-09\"\n",
    "num = 0\n",
    "\n",
    "for name, head in heads.items():\n",
    "    # Create a Model for each time series and add a StressModel2 for the recharge\n",
    "    ml = ps.Model(head, name=name)\n",
    "\n",
    "    # Add the RechargeModel to simulate the effect of rainfall and evaporation\n",
    "    ps.RechargeModel(ml, rain, evap, rfunc=ps.Gamma(), name=\"recharge\")\n",
    "\n",
    "    # Add a StressModel to simulate the effect of the groundwater extractions\n",
    "    sm = ps.StressModel(\n",
    "        ml, well / 1e6, rfunc=ps.Hantush(), name=\"well\", settings=\"well\", up=False\n",
    "    )\n",
    "\n",
    "    # Add a NoiseModel (explicitly required since Pastas 1.5)\n",
    "    nm = ps.ArNoiseModel(ml)\n",
    "\n",
    "    # Estimate the model parameters\n",
    "    ps.solver.Lmfit(ml)\n",
    "    ml.solve(tmin=tmin, tmax=tmax, report=False)\n",
    "\n",
    "    # Check if the estimated effect of the groundwater extraction is significant.\n",
    "    # If not, delete the stressmodel and calibrate the model again.\n",
    "    gain, stderr = ml.parameters.loc[\"well_A\", [\"optimal\", \"stderr\"]]\n",
    "    if stderr is None:\n",
    "        stderr = 10.0\n",
    "    if 1.96 * stderr > -gain:\n",
    "        num += 1\n",
    "        ml.del_stressmodel(\"well\")\n",
    "        ml.solve(tmin=tmin, tmax=tmax, report=False)\n",
    "\n",
    "    # Plot the results and store the plot\n",
    "    mls[name] = ml\n",
    "    if plot_results:\n",
    "        ml.plots.results()\n",
    "        path = os.path.join(mlpath, name + \".png\")\n",
    "        plt.savefig(path, bbox_inches=\"tight\")\n",
    "        plt.close()\n",
    "\n",
    "print(f\"The number of models where the well is dropped from the model is {num}\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Make plots for publication\n",
    "In the next codeblocks the Figures used in the Pastas paper are created. The following figures are created:\n",
    "\n",
    "- Figure of the drawdown estimated for each observations well; \n",
    "- Figure of the decomposition of the different contributions;\n",
    "- Figure of the pumping rate of the well field."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Figure of the drawdown estimated for each observations well"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "x = np.linspace(100, 5000, 100)\n",
    "\n",
    "if plot_timml:\n",
    "    # Values from REGIS II v2.2 (Site id B49F0240)\n",
    "    z = [9, -25, -83, -115, -190]  # Reference to NAP\n",
    "    kv = np.array(\n",
    "        [\n",
    "            1e-3,\n",
    "            5e-3,\n",
    "        ]\n",
    "    )  # Min-Max of Vertical hydraulic conductivity for both leaky layer\n",
    "    D1 = z[0] - z[1]  # Estimated thickness of leaky layer\n",
    "    c1 = D1 / kv  # Estimated resistance\n",
    "    D2 = z[2] - z[3]\n",
    "    c2 = D2 / kv\n",
    "\n",
    "    kh1 = np.array(\n",
    "        [\n",
    "            1e0,\n",
    "            2.5e0,\n",
    "        ]\n",
    "    )  # Min-Max of Horizontal hydraulic conductivity for aquifer 1\n",
    "    kh2 = np.array(\n",
    "        [\n",
    "            1e1,\n",
    "            2.5e1,\n",
    "        ]\n",
    "    )  # Min-Max of Horizontal hydraulic conductivity for aquifer 2\n",
    "\n",
    "    mlm = ModelMaq(\n",
    "        kaq=[kh1.mean(), 35], z=z, c=[c1.max(), c2.mean()], topboundary=\"semi\", hstar=0\n",
    "    )\n",
    "    w = Well(mlm, 0, 0, 34791, layers=1)\n",
    "    mlm.solve()\n",
    "    h = mlm.headalongline(x, 0)\n",
    "    np.savetxt(\"head_timml.out\", h)\n",
    "else:\n",
    "    h = np.loadtxt(\"head_timml.out\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Get the parameters and distances to plot\n",
    "params = pd.DataFrame(\n",
    "    index=list(mls.keys()), columns=[\"optimal\", \"stderr\"], dtype=float\n",
    ")\n",
    "for name, ml in mls.items():\n",
    "    if \"well\" in ml.stressmodels:\n",
    "        params.loc[name] = (\n",
    "            ml.parameters.loc[\"well_A\", [\"optimal\", \"stderr\"]]\n",
    "            * well.loc[\"2007\":].mean()\n",
    "            / 1e6\n",
    "        )\n",
    "\n",
    "# Select model per aquifer\n",
    "shallow = metadata_heads.z.loc[(metadata_heads.z < 96)].index\n",
    "aquifer = metadata_heads.z.loc[(metadata_heads.z < 186) & (metadata_heads.z > 96)].index\n",
    "\n",
    "# Make the plot\n",
    "fig = plt.figure(figsize=(8, 5))\n",
    "plt.grid(zorder=-10)\n",
    "\n",
    "display_error_bars = True\n",
    "\n",
    "if display_error_bars:\n",
    "    plt.errorbar(\n",
    "        distances.loc[shallow, \"Seppe\"],\n",
    "        params.loc[shallow, \"optimal\"],\n",
    "        yerr=1.96 * params.loc[shallow, \"stderr\"],\n",
    "        linestyle=\"\",\n",
    "        elinewidth=2,\n",
    "        marker=\"\",\n",
    "        markersize=10,\n",
    "        capsize=4,\n",
    "    )\n",
    "    plt.errorbar(\n",
    "        distances.loc[aquifer, \"Seppe\"],\n",
    "        params.loc[aquifer, \"optimal\"],\n",
    "        yerr=1.96 * params.loc[aquifer, \"stderr\"],\n",
    "        linestyle=\"\",\n",
    "        elinewidth=2,\n",
    "        marker=\"\",\n",
    "        capsize=4,\n",
    "    )\n",
    "\n",
    "plt.scatter(\n",
    "    distances.loc[shallow],\n",
    "    params.loc[shallow, \"optimal\"],\n",
    "    marker=\"^\",\n",
    "    s=80,\n",
    "    label=\"aquifer 1\",\n",
    ")\n",
    "plt.scatter(\n",
    "    distances.loc[aquifer],\n",
    "    params.loc[aquifer, \"optimal\"],\n",
    "    marker=\"s\",\n",
    "    s=80,\n",
    "    label=\"aquifer 2\",\n",
    ")\n",
    "\n",
    "# Plot two-layer TimML model for comparison\n",
    "plt.plot(x, h[0], color=\"C0\", linestyle=\"--\", label=\"TimML L1\")\n",
    "plt.plot(x, h[1], color=\"C1\", linestyle=\"--\", label=\"TimML L2\")\n",
    "\n",
    "plt.ylabel(\"steady drawdown (m)\")\n",
    "plt.xlabel(\"radial distance from the center of the well field (m)\")\n",
    "plt.xlim(0, 4501)\n",
    "plt.legend(loc=4)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Example figure of a TFN model "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Select a model to plot\n",
    "ml = mls[\"B49F0232_5\"]\n",
    "\n",
    "# Create the figure\n",
    "[ax1, ax2, ax3] = ml.plots.decomposition(\n",
    "    split_contributions=False, figsize=(7, 6), ytick_base=1, tmin=\"1985\"\n",
    ")\n",
    "plt.xticks(rotation=0)\n",
    "ax1.set_yticks([2, 0, -2])\n",
    "ax1.set_ylabel(\"head (m)\")\n",
    "ax1.legend().set_visible(False)\n",
    "ax3.set_yticks([-4, -6])\n",
    "ax2.set_ylabel(\n",
    "    \"contributions (m)                 \"\n",
    ")  # Little trick to get the label right\n",
    "ax3.set_xlabel(\"year\")\n",
    "ax3.set_ylabel(\"\")\n",
    "ax3.set_title(\"pumping well\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Figure of the pumping rate of the well field"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "fig, ax = plt.subplots(1, 1, figsize=(8, 2.5), sharex=True)\n",
    "ax.plot(well, color=\"k\")\n",
    "ax.set_ylabel(\"pumping rate\\n[m$^3$/day]\")\n",
    "ax.set_xlabel(\"year\")\n",
    "ax.set_xlim(pd.Timestamp(\"1951\"), pd.Timestamp(\"2018\"))"
   ]
  }
 ],
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   "codemirror_mode": {
    "name": "ipython",
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   "file_extension": ".py",
   "mimetype": "text/x-python",
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   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
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