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": [
      "3.8.6 | packaged by conda-forge | (default, Oct  7 2020, 18:42:56) \n",
      "[Clang 10.0.1 ]\n",
      "numpy version: 1.18.5\n",
      "matplotlib version: 3.2.2\n",
      "flopy version: 3.3.3\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.exists(modelpth):\n",
    "    os.makedirs(modelpth)"
   ]
  },
  {
   "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 = np.sqrt(b1**2 - (x/L)*(b1**2 - b2**2) + (R * x / K) * (L - x)) + z\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.\n",
    "delc = 1.\n",
    "\n",
    "# domain length\n",
    "L = 5000.\n",
    "\n",
    "# boundary heads\n",
    "h1 = 20.\n",
    "h2 = 11.\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.\n",
    "\n",
    "# bottom of the aquifer\n",
    "botm = 0.\n",
    "\n",
    "# hydraulic conductivity\n",
    "hk = 50.\n",
    "\n",
    "# location of cell centroids\n",
    "x = np.arange(0.0, L, delr) + (delr / 2.)\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): 2021/02/18 11:30:05\n",
      "\n",
      " Solving:  Stress period:     1    Time step:     1    Groundwater-Flow Eqn.\n",
      " Run end date and time (yyyy/mm/dd hh:mm:ss): 2021/02/18 11:30:05\n",
      " Elapsed run time:  0.023 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(modelname=modelname, exe_name=mfexe, model_ws=modelpth, version='mfnwt')\n",
    "dis = flopy.modflow.ModflowDis(mf, nlay, nrow, ncol, \n",
    "                               delr=delr, delc=delc, \n",
    "                               top=top, botm=botm, \n",
    "                               perlen=1, nstp=1, steady=True)\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(left=None, bottom=None, right=None, top=None,\n",
    "                    wspace=0.25, hspace=0.25)\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.*(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",
    "version": 3
   },
   "file_extension": ".py",
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