examples/Notebooks/flopy3_WatertableRecharge_example.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# FloPy\n",
    "\n",
    "## Simple water-table solution with recharge\n",
    "\n",
    "This problem is an unconfined system with a uniform recharge rate, a horizontal bottom, and flow between constant-head boundaries in column 1 and 100. MODFLOW models cannot match the analytical solution exactly because they do not allow recharge to constant-head cells. Constant-head cells in column 1 and 100 were made very thin (0.1 m) in the direction of flow to minimize the effect of recharge applied to them. The analytical solution for this problem can be written as:\n",
    "\n",
    "$h = \\sqrt{b_{1}^{2} - \\frac{x}{L} (b_{1}^{2} - b_{2}^{2}) + (\\frac{R x}{K}(L-x))} + z_{bottom}$\n",
    "\n",
    "where $R$ is the recharge rate, $K$ is the the hydraulic conductivity in the horizontal direction, $b_1$ is the specified saturated thickness at the left boundary, $b_2$ is the specified saturated thickness at the right boundary, $x$ is the distance from the left boundary $L$ is the length of the model domain, and $z_{bottom}$ is the elebation of the bottom of the aquifer.\n",
    "\n",
    "The model consistes of a grid of 100 columns, 1 row, and 1 layer; a bottom altitude of 0 m; constant heads of 20 and 11m in column 1 and 100, respectively; a recharge rate of 0.001 m/d; and a horizontal hydraulic conductivity of 50 m/d.  The discretization is 0.1 m in the row direction for the constant-head cells (column 1 and 100) and 50 m for all other cells."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "3.8.11 (default, Aug  6 2021, 08:56:27) \n",
      "[Clang 10.0.0 ]\n",
      "numpy version: 1.19.2\n",
      "matplotlib version: 3.4.2\n",
      "flopy version: 3.3.5\n"
     ]
    }
   ],
   "source": [
    "import os\n",
    "import sys\n",
    "import platform\n",
    "import numpy as np\n",
    "import matplotlib as mpl\n",
    "import matplotlib.pyplot as plt\n",
    "\n",
    "# run installed version of flopy or add local path\n",
    "try:\n",
    "    import flopy\n",
    "except:\n",
    "    fpth = os.path.abspath(os.path.join(\"..\", \"..\"))\n",
    "    sys.path.append(fpth)\n",
    "    import flopy\n",
    "\n",
    "print(sys.version)\n",
    "print(\"numpy version: {}\".format(np.__version__))\n",
    "print(\"matplotlib version: {}\".format(mpl.__version__))\n",
    "print(\"flopy version: {}\".format(flopy.__version__))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Set name of MODFLOW exe\n",
    "#  assumes executable is in users path statement\n",
    "exe_name = \"mfnwt\"\n",
    "if platform.system() == \"Windows\":\n",
    "    exe_name = \"MODFLOW-NWT.exe\"\n",
    "mfexe = exe_name\n",
    "\n",
    "modelpth = os.path.join(\"data\")\n",
    "modelname = \"watertable\"\n",
    "\n",
    "# make sure modelpth directory exists\n",
    "if not os.path.isdir(modelpth):\n",
    "    os.makedirs(modelpth, exist_ok=True)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Function to calculate the analytical solution at specified points in a aquifer"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "def analyticalWaterTableSolution(h1, h2, z, R, K, L, x):\n",
    "    h = np.zeros((x.shape[0]), float)\n",
    "    # dx = x[1] - x[0]\n",
    "    # x -= dx\n",
    "    b1 = h1 - z\n",
    "    b2 = h2 - z\n",
    "    h = (\n",
    "        np.sqrt(\n",
    "            b1 ** 2 - (x / L) * (b1 ** 2 - b2 ** 2) + (R * x / K) * (L - x)\n",
    "        )\n",
    "        + z\n",
    "    )\n",
    "    return h"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Model data required to create the model files and calculate the analytical solution"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[('k', '<i8'), ('i', '<i8'), ('j', '<i8'), ('bhead', '<f4'), ('cond', '<f4')]\n",
      "stress_period_data:  [(0, 0, 0, 0., 0.) (0, 0, 0, 0., 0.)]\n",
      "type is:  <class 'numpy.recarray'>\n"
     ]
    }
   ],
   "source": [
    "# model dimensions\n",
    "nlay, nrow, ncol = 1, 1, 100\n",
    "\n",
    "# cell spacing\n",
    "delr = 50.0\n",
    "delc = 1.0\n",
    "\n",
    "# domain length\n",
    "L = 5000.0\n",
    "\n",
    "# boundary heads\n",
    "h1 = 20.0\n",
    "h2 = 11.0\n",
    "\n",
    "# ibound\n",
    "ibound = np.ones((nlay, nrow, ncol), dtype=int)\n",
    "\n",
    "# starting heads\n",
    "strt = np.zeros((nlay, nrow, ncol), dtype=float)\n",
    "strt[0, 0, 0] = h1\n",
    "strt[0, 0, -1] = h2\n",
    "\n",
    "# top of the aquifer\n",
    "top = 25.0\n",
    "\n",
    "# bottom of the aquifer\n",
    "botm = 0.0\n",
    "\n",
    "# hydraulic conductivity\n",
    "hk = 50.0\n",
    "\n",
    "# location of cell centroids\n",
    "x = np.arange(0.0, L, delr) + (delr / 2.0)\n",
    "\n",
    "# location of cell edges\n",
    "xa = np.arange(0, L + delr, delr)\n",
    "\n",
    "# recharge rate\n",
    "rchrate = 0.001\n",
    "\n",
    "# calculate the head at the cell centroids using the analytical solution function\n",
    "hac = analyticalWaterTableSolution(h1, h2, botm, rchrate, hk, L, x)\n",
    "\n",
    "# calculate the head at the cell edges using the analytical solution function\n",
    "ha = analyticalWaterTableSolution(h1, h2, botm, rchrate, hk, L, xa)\n",
    "\n",
    "# ghbs\n",
    "# ghb conductance\n",
    "b1, b2 = 0.5 * (h1 + hac[0]), 0.5 * (h2 + hac[-1])\n",
    "c1, c2 = hk * b1 * delc / (0.5 * delr), hk * b2 * delc / (0.5 * delr)\n",
    "# dtype\n",
    "ghb_dtype = flopy.modflow.ModflowGhb.get_default_dtype()\n",
    "print(ghb_dtype)\n",
    "# build ghb recarray\n",
    "stress_period_data = np.zeros((2), dtype=ghb_dtype)\n",
    "stress_period_data = stress_period_data.view(np.recarray)\n",
    "print(\"stress_period_data: \", stress_period_data)\n",
    "print(\"type is: \", type(stress_period_data))\n",
    "# fill ghb recarray\n",
    "stress_period_data[0] = (0, 0, 0, h1, c1)\n",
    "stress_period_data[1] = (0, 0, ncol - 1, h2, c2)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Create a flopy object to create and run the MODFLOW-NWT datasets for this problem"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "FloPy is using the following executable to run the model: /Users/jdhughes/.local/bin/mfnwt\n",
      "\n",
      "                                  MODFLOW-NWT-SWR1 \n",
      "    U.S. GEOLOGICAL SURVEY MODULAR FINITE-DIFFERENCE GROUNDWATER-FLOW MODEL\n",
      "                             WITH NEWTON FORMULATION\n",
      "                             Version 1.2.0 03/01/2020                        \n",
      "                    BASED ON MODFLOW-2005 Version 1.12.0 02/03/2017                       \n",
      "\n",
      "                    SWR1 Version 1.04.0 09/15/2016                       \n",
      "\n",
      " Using NAME file: watertable.nam \n",
      " Run start date and time (yyyy/mm/dd hh:mm:ss): 2021/10/25 16:39:34\n",
      "\n",
      " Solving:  Stress period:     1    Time step:     1    Groundwater-Flow Eqn.\n",
      " Run end date and time (yyyy/mm/dd hh:mm:ss): 2021/10/25 16:39:34\n",
      " Elapsed run time:  0.041 Seconds\n",
      "\n",
      "  Normal termination of simulation\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "(True, [])"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "mf = flopy.modflow.Modflow(\n",
    "    modelname=modelname, exe_name=mfexe, model_ws=modelpth, version=\"mfnwt\"\n",
    ")\n",
    "dis = flopy.modflow.ModflowDis(\n",
    "    mf,\n",
    "    nlay,\n",
    "    nrow,\n",
    "    ncol,\n",
    "    delr=delr,\n",
    "    delc=delc,\n",
    "    top=top,\n",
    "    botm=botm,\n",
    "    perlen=1,\n",
    "    nstp=1,\n",
    "    steady=True,\n",
    ")\n",
    "bas = flopy.modflow.ModflowBas(mf, ibound=ibound, strt=strt)\n",
    "lpf = flopy.modflow.ModflowUpw(mf, hk=hk, laytyp=1)\n",
    "ghb = flopy.modflow.ModflowGhb(mf, stress_period_data=stress_period_data)\n",
    "rch = flopy.modflow.ModflowRch(mf, rech=rchrate, nrchop=1)\n",
    "oc = flopy.modflow.ModflowOc(mf)\n",
    "nwt = flopy.modflow.ModflowNwt(mf, linmeth=2, iprnwt=1, options=\"COMPLEX\")\n",
    "mf.write_input()\n",
    "\n",
    "# remove existing heads results, if necessary\n",
    "try:\n",
    "    os.remove(os.path.join(model_ws, \"{0}.hds\".format(modelname)))\n",
    "except:\n",
    "    pass\n",
    "# run existing model\n",
    "mf.run_model()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Read the simulated MODFLOW-NWT model results"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Create the headfile object\n",
    "headfile = os.path.join(modelpth, \"{0}.hds\".format(modelname))\n",
    "headobj = flopy.utils.HeadFile(headfile, precision=\"single\")\n",
    "times = headobj.get_times()\n",
    "head = headobj.get_data(totim=times[-1])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Plot the MODFLOW-NWT results and compare to the analytical solution"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1152x432 with 3 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig = plt.figure(figsize=(16, 6))\n",
    "fig.subplots_adjust(\n",
    "    left=None, bottom=None, right=None, top=None, wspace=0.25, hspace=0.25\n",
    ")\n",
    "\n",
    "ax = fig.add_subplot(1, 3, 1)\n",
    "ax.plot(xa, ha, linewidth=8, color=\"0.5\", label=\"analytical solution\")\n",
    "ax.plot(x, head[0, 0, :], color=\"red\", label=\"MODFLOW-2015\")\n",
    "leg = ax.legend(loc=\"lower left\")\n",
    "leg.draw_frame(False)\n",
    "ax.set_xlabel(\"Horizontal distance, in m\")\n",
    "ax.set_ylabel(\"Head, in m\")\n",
    "\n",
    "ax = fig.add_subplot(1, 3, 2)\n",
    "ax.plot(x, head[0, 0, :] - hac, linewidth=1, color=\"blue\")\n",
    "ax.set_xlabel(\"Horizontal distance, in m\")\n",
    "ax.set_ylabel(\"Error, in m\")\n",
    "\n",
    "ax = fig.add_subplot(1, 3, 3)\n",
    "ax.plot(x, 100.0 * (head[0, 0, :] - hac) / hac, linewidth=1, color=\"blue\")\n",
    "ax.set_xlabel(\"Horizontal distance, in m\")\n",
    "ax.set_ylabel(\"Percent Error\");"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "anaconda-cloud": {},
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
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