examples/Notebooks/flopy3_swi2package_ex4.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# FloPy\n",
    "\n",
    "### SWI2 Example 4. Upconing Below a Pumping Well in a Two-Aquifer Island System\n",
    "\n",
    "This example problem is the fourth example problem in the SWI2 documentation (http://pubs.usgs.gov/tm/6a46/) and simulates transient movement of the freshwater-seawater interface beneath an island in response to recharge and groundwater withdrawals. The island is 2,050$\\times$2,050 m and consists of two 20-m thick aquifers that extend below sea level. The aquifers are confined, storage changes are not considered (all MODFLOW stress periods are steady-state), and the top and bottom of each aquifer is horizontal. The top of the upper aquifer and the bottom of the lower aquifer are impermeable.\n",
    "\n",
    "The domain is discretized into 61 columns, 61 rows, and 2 layers, with respective cell dimensions of 50 m (`DELR`), 50 m (`DELC`), and 20 m. A total of 230 years is simulated using three stress periods with lengths of 200, 12, and 18 years, with constant time steps of 0.2, 0.1, and 0.1 years, respectively. \n",
    "\n",
    "The horizontal and vertical hydraulic conductivity of both aquifers are 10 m/d and 0.2 m/d, respectively. The effective porosity is 0.2 for both aquifers. The model is extended 500 m offshore along all sides and the ocean boundary is represented as a general head boundary condition (GHB) in model layer 1. A freshwater head of 0 m is specified at the ocean bottom in all general head boundaries. The GHB conductance that controls outflow from the aquifer into the ocean is 62.5 m$^{2}$/d and corresponds to a leakance of 0.025 d$^{-1}$ (or a resistance of 40 days).\n",
    "\n",
    "The groundwater is divided into a freshwater zone and a seawater zone, separated by an active ZETA surface between the zones (`NSRF=1`) that approximates the 50-percent seawater salinity contour. Fluid density is represented using the stratified density option (`ISTRAT=1`). The dimensionless density difference ($\\nu$) between freshwater and saltwater is 0.025. The tip and toe tracking parameters are a `TOESLOPE` and `TIPSLOPE` of 0.005, a default `ALPHA` of 0.1, and a default `BETA` of 0.1. Initially, the interface between freshwater and saltwater is 1 m below land surface on the island and at the top of the upper aquifer offshore. The SWI2 `ISOURCE` parameter is set to -2 in cells having GHBs so that water that infiltrates into the aquifer from the GHB cells is saltwater (zone 2), whereas water that flows out of the model at the GHB cells is identical to water at the top of the aquifer. `ISOURCE` in layer 2, row 31, column 36 is set to 2 so that a saltwater well may be simulated in the third stress period of simulation 2. In all other cells, the SWI2 `ISOURCE` parameter is set to 0, indicating boundary conditions have water that is identical to water at the top of the aquifer and can be either freshwater or saltwater, depending on the elevation of the active `ZETA` surface in the cell.\n",
    "\n",
    "A constant recharge rate of 0.4 millimeters per day (mm/d) is used in all three stress periods. The development of the freshwater lens is simulated for 200 years, after which a pumping well having a withdrawal rate of 250 m$^3$/d is started in layer 1, row 31, column 36. For the first simulation (simulation 1), the well pumps for 30 years, after which the interface almost reaches the top of the upper aquifer layer. In the second simulation (simulation 2), an additional well withdrawing\n",
    "saltwater at a rate of 25 m$^3$/d is simulated below the freshwater well in layer 2 , row 31, column 36, 12 years after the freshwater groundwater withdrawal begins in the well in layer 1. The saltwater well is intended to prevent the interface from\n",
    "upconing into the upper aquifer (model layer)."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Import `numpy` and `matplotlib`, set all figures to be inline, import `flopy.modflow` and `flopy.utils`."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "flopy is installed in /Users/jdhughes/Documents/Development/flopy_git/flopy_us/flopy\n",
      "3.7.3 (default, Mar 27 2019, 16:54:48) \n",
      "[Clang 4.0.1 (tags/RELEASE_401/final)]\n",
      "numpy version: 1.16.2\n",
      "matplotlib version: 3.0.3\n",
      "flopy version: 3.2.12\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": "markdown",
   "metadata": {},
   "source": [
    "Define model name of your model and the location of MODFLOW executable. All MODFLOW files and output will be stored in the subdirectory defined by the workspace. Create a model named `ml` and specify that this is a MODFLOW-2005 model."
   ]
  },
  {
   "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 = 'mf2005'\n",
    "if platform.system() == 'Windows':\n",
    "    exe_name = 'mf2005.exe'\n",
    "\n",
    "workspace = os.path.join('data')\n",
    "#make sure workspace directory exists\n",
    "if not os.path.exists(workspace):\n",
    "    os.makedirs(workspace)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Define the number of layers, rows and columns. The heads are computed quasi-steady state (hence a steady MODFLOW run) while the interface will move. There are three stress periods with a length of 200, 12, and 18 years and 1,000, 120, and 180 steps. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "ncol = 61\n",
    "nrow = 61\n",
    "nlay = 2\n",
    "\n",
    "nper = 3\n",
    "perlen = [365.25 * 200., 365.25 * 12., 365.25 * 18.]\n",
    "nstp = [1000, 120, 180]\n",
    "save_head = [200, 60, 60]\n",
    "steady = True"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Specify the cell size along the rows (`delr`) and along the columns (`delc`) and the top and bottom of the aquifer for the `DIS` package. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [],
   "source": [
    "# dis data\n",
    "delr, delc = 50.0, 50.0\n",
    "botm = np.array([-10., -30., -50.])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Define the `IBOUND` array and starting heads for the `BAS` package. The corners of the model are defined to be inactive."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [],
   "source": [
    "# bas data\n",
    "# ibound - active except for the corners\n",
    "ibound = np.ones((nlay, nrow, ncol), dtype= np.int)\n",
    "ibound[:, 0, 0] = 0\n",
    "ibound[:, 0, -1] = 0\n",
    "ibound[:, -1, 0] = 0\n",
    "ibound[:, -1, -1] = 0\n",
    "# initial head data\n",
    "ihead = np.zeros((nlay, nrow, ncol), dtype=np.float)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Define the layers to be confined and define the horizontal and vertical hydraulic conductivity of the aquifer for the `LPF` package."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [],
   "source": [
    "# lpf data\n",
    "laytyp = 0\n",
    "hk = 10.\n",
    "vka = 0.2"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Define the boundary condition data for the model"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [],
   "source": [
    "# boundary condition data\n",
    "# ghb data\n",
    "colcell, rowcell = np.meshgrid(np.arange(0, ncol), np.arange(0, nrow))\n",
    "index = np.zeros((nrow, ncol), dtype=np.int)\n",
    "index[:, :10] = 1\n",
    "index[:, -10:] = 1\n",
    "index[:10, :] = 1\n",
    "index[-10:, :] = 1\n",
    "nghb = np.sum(index)\n",
    "lrchc = np.zeros((nghb, 5))\n",
    "lrchc[:, 0] = 0\n",
    "lrchc[:, 1] = rowcell[index == 1]\n",
    "lrchc[:, 2] = colcell[index == 1]\n",
    "lrchc[:, 3] = 0.\n",
    "lrchc[:, 4] = 50.0 * 50.0 / 40.0\n",
    "# create ghb dictionary\n",
    "ghb_data = {0:lrchc}\n",
    "\n",
    "# recharge data\n",
    "rch = np.zeros((nrow, ncol), dtype=np.float)\n",
    "rch[index == 0] = 0.0004\n",
    "# create recharge dictionary\n",
    "rch_data = {0: rch}\n",
    "\n",
    "# well data\n",
    "nwells = 2\n",
    "lrcq = np.zeros((nwells, 4))\n",
    "lrcq[0, :] = np.array((0, 30, 35, 0))\n",
    "lrcq[1, :] = np.array([1, 30, 35, 0])\n",
    "lrcqw = lrcq.copy()\n",
    "lrcqw[0, 3] = -250\n",
    "lrcqsw = lrcq.copy()\n",
    "lrcqsw[0, 3] = -250.\n",
    "lrcqsw[1, 3] = -25.\n",
    "# create well dictionary\n",
    "base_well_data = {0:lrcq, 1:lrcqw}\n",
    "swwells_well_data = {0:lrcq, 1:lrcqw, 2:lrcqsw}"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [],
   "source": [
    "# swi2 data\n",
    "nadptmx = 10\n",
    "nadptmn = 1\n",
    "nu = [0, 0.025]\n",
    "numult = 5.0\n",
    "toeslope = nu[1] / numult  #0.005\n",
    "tipslope = nu[1] / numult  #0.005\n",
    "z1 = -10.0 * np.ones((nrow, ncol))\n",
    "z1[index == 0] = -11.0\n",
    "z = np.array([[z1, z1]])\n",
    "iso = np.zeros((nlay, nrow, ncol), dtype=np.int)\n",
    "iso[0, :, :][index == 0] = 1\n",
    "iso[0, :, :][index == 1] = -2\n",
    "iso[1, 30, 35] = 2\n",
    "ssz=0.2\n",
    "# swi2 observations\n",
    "obsnam = ['layer1_', 'layer2_']\n",
    "obslrc=[[0, 30, 35], [1, 30, 35]]\n",
    "nobs = len(obsnam)\n",
    "iswiobs = 1051"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Create output control (OC) data using words "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [],
   "source": [
    "# oc data\n",
    "spd = {(0,199): ['print budget', 'save head'],\n",
    "       (0,200): [],\n",
    "       (0,399): ['print budget', 'save head'],\n",
    "       (0,400): [],\n",
    "       (0,599): ['print budget', 'save head'],\n",
    "       (0,600): [],\n",
    "       (0,799): ['print budget', 'save head'],\n",
    "       (0,800): [],\n",
    "       (0,999): ['print budget', 'save head'],\n",
    "       (1,0): [],\n",
    "       (1,59): ['print budget', 'save head'],\n",
    "       (1,60): [],\n",
    "       (1,119): ['print budget', 'save head'],\n",
    "       (1,120): [],\n",
    "       (2,0): [],\n",
    "       (2,59): ['print budget', 'save head'],\n",
    "       (2,60): [],\n",
    "       (2,119): ['print budget', 'save head'],\n",
    "       (2,120): [],\n",
    "       (2,179): ['print budget', 'save head']}"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Create the model with the freshwater well (Simulation 1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "ModflowSwi2: specification of nobs is deprecated.\n"
     ]
    }
   ],
   "source": [
    "modelname = 'swiex4_s1'\n",
    "ml = flopy.modflow.Modflow(modelname, version='mf2005', exe_name=exe_name, model_ws=workspace)\n",
    "\n",
    "discret = flopy.modflow.ModflowDis(ml, nlay=nlay, nrow=nrow, ncol=ncol, laycbd=0,\n",
    "                                   delr=delr, delc=delc, top=botm[0], botm=botm[1:],\n",
    "                                   nper=nper, perlen=perlen, nstp=nstp)\n",
    "bas = flopy.modflow.ModflowBas(ml, ibound=ibound, strt=ihead)\n",
    "lpf = flopy.modflow.ModflowLpf(ml, laytyp=laytyp, hk=hk, vka=vka)\n",
    "wel = flopy.modflow.ModflowWel(ml, stress_period_data=base_well_data)\n",
    "ghb = flopy.modflow.ModflowGhb(ml, stress_period_data=ghb_data)\n",
    "rch = flopy.modflow.ModflowRch(ml, rech=rch_data)\n",
    "swi = flopy.modflow.ModflowSwi2(ml, nsrf=1, istrat=1, toeslope=toeslope, tipslope=tipslope, nu=nu,\n",
    "                                zeta=z, ssz=ssz, isource=iso, nsolver=1,\n",
    "                                nadptmx=nadptmx, nadptmn=nadptmn, \n",
    "                                nobs=nobs, iswiobs=iswiobs, obsnam=obsnam, obslrc=obslrc, iswizt=55)\n",
    "oc = flopy.modflow.ModflowOc(ml, stress_period_data=spd)\n",
    "pcg = flopy.modflow.ModflowPcg(ml, hclose=1.0e-6, rclose=3.0e-3, mxiter=100, iter1=50)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Write the simulation 1 MODFLOW input files and run the model"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(True, [])"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "ml.write_input()\n",
    "ml.run_model(silent=True)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Create the model with the saltwater well (Simulation 2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "ModflowSwi2: specification of nobs is deprecated.\n"
     ]
    }
   ],
   "source": [
    "modelname2 = 'swiex4_s2'\n",
    "ml2 = flopy.modflow.Modflow(modelname2, version='mf2005', exe_name=exe_name, model_ws=workspace)\n",
    "\n",
    "discret = flopy.modflow.ModflowDis(ml2, nlay=nlay, nrow=nrow, ncol=ncol, laycbd=0,\n",
    "                                   delr=delr, delc=delc, top=botm[0], botm=botm[1:],\n",
    "                                   nper=nper, perlen=perlen, nstp=nstp)\n",
    "bas = flopy.modflow.ModflowBas(ml2, ibound=ibound, strt=ihead)\n",
    "lpf = flopy.modflow.ModflowLpf(ml2, laytyp=laytyp, hk=hk, vka=vka)\n",
    "wel = flopy.modflow.ModflowWel(ml2, stress_period_data=swwells_well_data)\n",
    "ghb = flopy.modflow.ModflowGhb(ml2, stress_period_data=ghb_data)\n",
    "rch = flopy.modflow.ModflowRch(ml2, rech=rch_data)\n",
    "swi = flopy.modflow.ModflowSwi2(ml2, nsrf=1, istrat=1, \n",
    "                                toeslope=toeslope, tipslope=tipslope, nu=nu,\n",
    "                                zeta=z, ssz=ssz, isource=iso, nsolver=1,\n",
    "                                nadptmx=nadptmx, nadptmn=nadptmn,\n",
    "                                nobs=nobs, iswiobs=iswiobs, obsnam=obsnam, obslrc=obslrc, iswizt=55)\n",
    "oc = flopy.modflow.ModflowOc(ml2, stress_period_data=spd)\n",
    "pcg = flopy.modflow.ModflowPcg(ml2, hclose=1.0e-6, rclose=3.0e-3, mxiter=100, iter1=50)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Write the simulation 2 MODFLOW input files and run the model"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(True, [])"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "ml2.write_input()\n",
    "ml2.run_model(silent=True)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Load the simulation 1 `ZETA` data and `ZETA` observations."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [],
   "source": [
    "# read base model zeta\n",
    "zfile = flopy.utils.CellBudgetFile(os.path.join(ml.model_ws, modelname+'.zta'))\n",
    "kstpkper = zfile.get_kstpkper()\n",
    "zeta = []\n",
    "for kk in kstpkper:\n",
    "    zeta.append(zfile.get_data(kstpkper=kk, text='ZETASRF  1')[0])\n",
    "zeta = np.array(zeta)\n",
    "# read swi obs\n",
    "zobs = np.genfromtxt(os.path.join(ml.model_ws, modelname+'.zobs.out'), names=True)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Load the simulation 2 `ZETA` data and `ZETA` observations."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [],
   "source": [
    "# read saltwater well model zeta\n",
    "zfile2 = flopy.utils.CellBudgetFile(os.path.join(ml2.model_ws, modelname2+'.zta'))\n",
    "kstpkper = zfile2.get_kstpkper()\n",
    "zeta2 = []\n",
    "for kk in kstpkper:\n",
    "    zeta2.append(zfile2.get_data(kstpkper=kk, text='ZETASRF  1')[0])\n",
    "zeta2 = np.array(zeta2)\n",
    "# read swi obs\n",
    "zobs2 = np.genfromtxt(os.path.join(ml2.model_ws, modelname2+'.zobs.out'), names=True)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Create arrays for the x-coordinates and the output years"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [],
   "source": [
    "x = np.linspace(-1500, 1500, 61)\n",
    "xcell = np.linspace(-1500, 1500, 61) + delr / 2.\n",
    "xedge = np.linspace(-1525, 1525, 62)\n",
    "years = [40, 80, 120, 160, 200, 6, 12, 18, 24, 30]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Define figure dimensions and colors used for plotting `ZETA` surfaces"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [],
   "source": [
    "# figure dimensions\n",
    "fwid, fhgt = 8.00, 5.50\n",
    "flft, frgt, fbot, ftop = 0.125, 0.95, 0.125, 0.925\n",
    "\n",
    "# line color definition\n",
    "icolor = 5\n",
    "colormap = plt.cm.jet  #winter\n",
    "cc = []\n",
    "cr = np.linspace(0.9, 0.0, icolor)\n",
    "for idx in cr:\n",
    "    cc.append(colormap(idx))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Recreate **Figure 9** from the SWI2 documentation (http://pubs.usgs.gov/tm/6a46/)."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 576x396 with 4 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.rcParams.update({'legend.fontsize': 6, 'legend.frameon' : False})\n",
    "fig = plt.figure(figsize=(fwid, fhgt), facecolor='w')\n",
    "fig.subplots_adjust(wspace=0.25, hspace=0.25, left=flft, right=frgt, bottom=fbot, top=ftop)\n",
    "# first plot\n",
    "ax = fig.add_subplot(2, 2, 1)\n",
    "# axes limits\n",
    "ax.set_xlim(-1500, 1500)\n",
    "ax.set_ylim(-50, -10)\n",
    "for idx in range(5):\n",
    "    # layer 1\n",
    "    ax.plot(xcell, zeta[idx, 0, 30, :], drawstyle='steps-mid', \n",
    "            linewidth=0.5, color=cc[idx], label='{:2d} years'.format(years[idx]))\n",
    "    # layer 2\n",
    "    ax.plot(xcell, zeta[idx, 1, 30, :], drawstyle='steps-mid',\n",
    "            linewidth=0.5, color=cc[idx], label='_None')\n",
    "ax.plot([-1500, 1500], [-30, -30], color='k', linewidth=1.0)\n",
    "# legend\n",
    "plt.legend(loc='lower left')\n",
    "# axes labels and text\n",
    "ax.set_xlabel('Horizontal distance, in meters')\n",
    "ax.set_ylabel('Elevation, in meters')\n",
    "ax.text(0.025, .55, 'Layer 1', transform=ax.transAxes, va='center', ha='left', size='7')\n",
    "ax.text(0.025, .45, 'Layer 2', transform=ax.transAxes, va='center', ha='left', size='7')\n",
    "ax.text(0.975, .1, 'Recharge conditions', transform=ax.transAxes, va='center', ha='right', size='8')\n",
    "\n",
    "# second plot\n",
    "ax = fig.add_subplot(2, 2, 2)\n",
    "# axes limits\n",
    "ax.set_xlim(-1500, 1500)\n",
    "ax.set_ylim(-50, -10)\n",
    "for idx in range(5, len(years)):\n",
    "    # layer 1\n",
    "    ax.plot(xcell, zeta[idx, 0, 30, :], drawstyle='steps-mid', \n",
    "            linewidth=0.5, color=cc[idx-5], label='{:2d} years'.format(years[idx]))\n",
    "    # layer 2\n",
    "    ax.plot(xcell, zeta[idx, 1, 30, :], drawstyle='steps-mid',\n",
    "            linewidth=0.5, color=cc[idx-5], label='_None')\n",
    "ax.plot([-1500, 1500], [-30, -30], color='k', linewidth=1.0)\n",
    "# legend\n",
    "plt.legend(loc='lower left')\n",
    "# axes labels and text\n",
    "ax.set_xlabel('Horizontal distance, in meters')\n",
    "ax.set_ylabel('Elevation, in meters')\n",
    "ax.text(0.025, .55, 'Layer 1', transform=ax.transAxes, va='center', ha='left', size='7')\n",
    "ax.text(0.025, .45, 'Layer 2', transform=ax.transAxes, va='center', ha='left', size='7')\n",
    "ax.text(0.975, .1, 'Freshwater well withdrawal', transform=ax.transAxes, va='center', ha='right', size='8')\n",
    "\n",
    "# third plot\n",
    "ax = fig.add_subplot(2, 2, 3)\n",
    "# axes limits\n",
    "ax.set_xlim(-1500, 1500)\n",
    "ax.set_ylim(-50, -10)\n",
    "for idx in range(5, len(years)):\n",
    "    # layer 1\n",
    "    ax.plot(xcell, zeta2[idx, 0, 30, :], drawstyle='steps-mid', \n",
    "            linewidth=0.5, color=cc[idx-5], label='{:2d} years'.format(years[idx]))\n",
    "    # layer 2\n",
    "    ax.plot(xcell, zeta2[idx, 1, 30, :], drawstyle='steps-mid',\n",
    "            linewidth=0.5, color=cc[idx-5], label='_None')\n",
    "ax.plot([-1500, 1500], [-30, -30], color='k', linewidth=1.0)\n",
    "# legend\n",
    "plt.legend(loc='lower left')\n",
    "# axes labels and text\n",
    "ax.set_xlabel('Horizontal distance, in meters')\n",
    "ax.set_ylabel('Elevation, in meters')\n",
    "ax.text(0.025, .55, 'Layer 1', transform=ax.transAxes, va='center', ha='left', size='7')\n",
    "ax.text(0.025, .45, 'Layer 2', transform=ax.transAxes, va='center', ha='left', size='7')\n",
    "ax.text(0.975, .1, 'Freshwater and saltwater\\nwell withdrawals', transform=ax.transAxes,\n",
    "        va='center', ha='right', size='8')\n",
    "\n",
    "# fourth plot\n",
    "ax = fig.add_subplot(2, 2, 4)\n",
    "# axes limits\n",
    "ax.set_xlim(0, 30)\n",
    "ax.set_ylim(-50, -10)\n",
    "t = zobs['TOTIM'][999:] / 365 - 200.\n",
    "tz2 = zobs['layer1_001'][999:]\n",
    "tz3 = zobs2['layer1_001'][999:]\n",
    "for i in range(len(t)):\n",
    "    if zobs['layer2_001'][i+999] < -30. - 0.1:\n",
    "        tz2[i] = zobs['layer2_001'][i+999]\n",
    "    if zobs2['layer2_001'][i+999] < 20. - 0.1:\n",
    "        tz3[i] = zobs2['layer2_001'][i+999]\n",
    "ax.plot(t, tz2, linestyle='solid', color='r', linewidth=0.75, label='Freshwater well')\n",
    "ax.plot(t, tz3, linestyle='dotted', color='r', linewidth=0.75, label='Freshwater and saltwater well')\n",
    "ax.plot([0, 30], [-30, -30], 'k', linewidth=1.0, label='_None')\n",
    "# legend\n",
    "leg = plt.legend(loc='lower right', numpoints=1)\n",
    "# axes labels and text\n",
    "ax.set_xlabel('Time, in years')\n",
    "ax.set_ylabel('Elevation, in meters')\n",
    "ax.text(0.025, .55, 'Layer 1', transform=ax.transAxes, va='center', ha='left', size='7')\n",
    "ax.text(0.025, .45, 'Layer 2', transform=ax.transAxes, va='center', ha='left', size='7');"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Use `ModelCrossSection` plotting class and `plot_fill_between()` method to fill between zeta surfaces."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 576x198 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig = plt.figure(figsize=(fwid, fhgt/2))\n",
    "fig.subplots_adjust(wspace=0.25, hspace=0.25, left=flft, right=frgt, bottom=fbot, top=ftop)\n",
    "\n",
    "colors = ['#40d3f7', '#F76541']\n",
    "\n",
    "ax = fig.add_subplot(1, 2, 1)\n",
    "modelxsect = flopy.plot.PlotCrossSection(model=ml, line={'Row': 30}, \n",
    "                                         extent=(0, 3050, -50, -10))\n",
    "modelxsect.plot_fill_between(zeta[4, :, :, :], colors=colors, ax=ax,\n",
    "                              edgecolors='none')\n",
    "linecollection = modelxsect.plot_grid(ax=ax)\n",
    "ax.set_title('Recharge year {}'.format(years[4]));\n",
    "\n",
    "ax = fig.add_subplot(1, 2, 2)\n",
    "ax.set_xlim(0, 3050)\n",
    "ax.set_ylim(-50, -10)\n",
    "modelxsect.plot_fill_between(zeta[-1, :, :, :], colors=colors, ax=ax)\n",
    "linecollection = modelxsect.plot_grid(ax=ax)\n",
    "ax.set_title('Scenario year {}'.format(years[-1]));"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
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