examples/Notebooks/swiex4.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": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "%matplotlib inline\n",
    "import os\n",
    "import platform\n",
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "\n",
    "import flopy.modflow as mf\n",
    "import flopy.utils as fu\n",
    "import flopy.plot as fp"
   ]
  },
  {
   "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": {
    "collapsed": false
   },
   "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": {
    "collapsed": false
   },
   "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": {
    "collapsed": false
   },
   "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": {
    "collapsed": false
   },
   "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": {
    "collapsed": false
   },
   "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": {
    "collapsed": false
   },
   "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": {
    "collapsed": false
   },
   "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": {
    "collapsed": false
   },
   "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": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "2 6 (2, 3)\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/Users/jdhughes/Documents/Development/flopy_git/flopy/modflow/mfdis.py:164: UserWarning: ModflowDis.sr is deprecated. use Modflow.sr\n",
      "  warnings.warn(\"ModflowDis.sr is deprecated. use Modflow.sr\")\n"
     ]
    }
   ],
   "source": [
    "modelname = 'swiex4_s1'\n",
    "ml = mf.Modflow(modelname, version='mf2005', exe_name=exe_name, model_ws=workspace)\n",
    "\n",
    "discret = mf.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 = mf.ModflowBas(ml, ibound=ibound, strt=ihead)\n",
    "lpf = mf.ModflowLpf(ml, laytyp=laytyp, hk=hk, vka=vka)\n",
    "wel = mf.ModflowWel(ml, stress_period_data=base_well_data)\n",
    "ghb = mf.ModflowGhb(ml, stress_period_data=ghb_data)\n",
    "rch = mf.ModflowRch(ml, rech=rch_data)\n",
    "swi = mf.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)\n",
    "oc = mf.ModflowOc(ml, stress_period_data=spd)\n",
    "pcg = mf.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": {
    "collapsed": false
   },
   "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": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "2 6 (2, 3)\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/Users/jdhughes/Documents/Development/flopy_git/flopy/modflow/mfdis.py:164: UserWarning: ModflowDis.sr is deprecated. use Modflow.sr\n",
      "  warnings.warn(\"ModflowDis.sr is deprecated. use Modflow.sr\")\n"
     ]
    }
   ],
   "source": [
    "modelname2 = 'swiex4_s2'\n",
    "ml2 = mf.Modflow(modelname2, version='mf2005', exe_name=exe_name, model_ws=workspace)\n",
    "\n",
    "discret = mf.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 = mf.ModflowBas(ml2, ibound=ibound, strt=ihead)\n",
    "lpf = mf.ModflowLpf(ml2, laytyp=laytyp, hk=hk, vka=vka)\n",
    "wel = mf.ModflowWel(ml2, stress_period_data=swwells_well_data)\n",
    "ghb = mf.ModflowGhb(ml2, stress_period_data=ghb_data)\n",
    "rch = mf.ModflowRch(ml2, rech=rch_data)\n",
    "swi = mf.ModflowSwi2(ml2, 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)\n",
    "oc = mf.ModflowOc(ml2, stress_period_data=spd)\n",
    "pcg = mf.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": {
    "collapsed": false
   },
   "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": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "# read base model zeta\n",
    "zfile = fu.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'), names=True)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Load the simulation 2 `ZETA` data and `ZETA` observations."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "# read saltwater well model zeta\n",
    "zfile2 = fu.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'), names=True)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Create arrays for the x-coordinates and the output years"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {
    "collapsed": false
   },
   "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": {
    "collapsed": false
   },
   "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": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "image/png": 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S6aqJdRXSB/1Sl6ua6qpSqbWFxIkAAGu3/ggdIX+/iPjK/QDgrkUu9v47TNey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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x1090e04a8>"
      ]
     },
     "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": {
    "collapsed": false
   },
   "source": [
    "Use `ModelCrossSection` plotting class and `plot_fill_between()` method to fill between zeta surfaces."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/Users/jdhughes/Documents/Development/flopy_git/flopy/modflow/mfdis.py:164: UserWarning: ModflowDis.sr is deprecated. use Modflow.sr\n",
      "  warnings.warn(\"ModflowDis.sr is deprecated. use Modflow.sr\")\n"
     ]
    },
    {
     "data": {
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REZGMsn341GFm9oKZtZjZxNj47cxsvZnVRa+Z2YcqIqVE+UGkvGR7WeUC4OvAH9OUveLu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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x10947a198>"
      ]
     },
     "metadata": {},
     "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 = fp.ModelCrossSection(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": {
    "collapsed": true
   },
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
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