flopy/examples/Notebooks/flopy3_swi2package_ex1.ipynb

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{
"cells": [
{
"cell_type": "markdown",
"metadata": {},
"source": [
"# FloPy\n",
"\n",
"### SWI2 Example 1. Rotating Interface\n",
"\n",
"This example problem is the first example problem in the SWI2 documentation (http://pubs.usgs.gov/tm/6a46/) and simulates transient movement of a freshwater\\seawater interface separating two density zones in a two-dimensional vertical plane. The problem domain is 250 m long, 40 m high, and 1 m wide. The aquifer is confined, storage changes are not considered (all MODFLOW stress periods are steady-state), and the top and bottom of the aquifer are horizontal and impermeable.\n",
"\n",
"The domain is discretized into 50 columns, 1 row, and 1 layer, with respective cell dimensions of 5 m (`DELR`), 1 m (`DELC`), and 40 m. A constant head of 0 m is specified for column 50. The hydraulic conductivity is 2 m/d and the effective porosity (`SSZ`) is 0.2. A flow of 1 m$^3$/d of seawater is specified in column 1 and causes groundwater to flow from left to right\n",
"in the model domain.\n",
"\n",
"The domain contains one freshwater zone and one seawater zone, separated by an active `ZETA` surface between the zones (`NSRF=1`) that approximates the 50-percent seawater salinity contour. A 400-day period is simulated using a constant time step of 2 days. Fluid density is represented using the stratified option (`ISTRAT=1`) and the elevation of the interface is output every 100 days (every 50 time steps). The densities, $\\rho$, of the freshwater and saltwater are 1,000 and 1,025 kg/m$^3$, respectively. Hence, the dimensionless densities, $\\nu$, of the freshwater and saltwater are 0.0 and 0.025, respectively. The maximum slope of the toe and tip is specified as 0.2 (`TOESLOPE=TIPSLOPE=0.2`), and default tip/toe parameters are used (`ALPHA=BETA=0.1`). Initially, the interface is at a 45$^{\\circ}$ angle from (x,z) = (80,0) to (x,z) = (120,-40). The source/sink terms (`ISOURCE`) are specified to be freshwater everywhere (`ISOURCE=1`) except in cell 1 where saltwater enters the model and `ISOURCE` equals 2. A comparison of results for SWI2 and the exact Dupuit solution of Wilson and Sa Da Costa (1982) are presented below. The constant flow from left to right results in an average velocity of 0.125 m/d. The exact Dupuit solution is a rotating straight interface of which the center moves to the right with this velocity"
]
},
{
"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": [
"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": "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": [
"modelname = 'swiex1'\n",
"\n",
"#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)\n",
"\n",
"ml = flopy.modflow.Modflow(modelname, version='mf2005', exe_name=exe_name, model_ws=workspace)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Define the number of layers, rows and columns, and the cell size along the rows (`delr`) and along the columns (`delc`). Then create a discretization file. Specify the top and bottom of the aquifer. The heads are computed quasi-steady state (hence a steady MODFLOW run) while the interface will move. There is one stress period with a length of 400 days and 200 steps (so one step is 2 days). "
]
},
{
"cell_type": "code",
"execution_count": 3,
"metadata": {},
"outputs": [],
"source": [
"nlay = 1\n",
"nrow = 1\n",
"ncol = 50\n",
"delr = 5.\n",
"delc = 1.\n",
"nper, perlen, nstp = 1, 400., 200 \n",
"discret = flopy.modflow.ModflowDis(ml, nlay=nlay, nrow=nrow, ncol=ncol,\n",
" delr=delr, delc=delc,\n",
" top=0, botm=-40.0,\n",
" steady=True, nper=nper, perlen=perlen, nstp=nstp)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"All cells are active (`ibound=1`), while the last cell is fixed head (`ibound=-1`). The starting values of the head are not important, as the heads are computed every time with a steady run."
]
},
{
"cell_type": "code",
"execution_count": 4,
"metadata": {},
"outputs": [],
"source": [
"ibound = np.ones((nrow, ncol))\n",
"ibound[0, -1] = -1\n",
"bas = flopy.modflow.ModflowBas(ml, ibound=ibound, strt=0.0)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Define the hydraulic conductivity. The aquifer is confined (`laytype=0`) and the intercell hydraulic conductivity is the harmonic meand (`layavg=0`)."
]
},
{
"cell_type": "code",
"execution_count": 5,
"metadata": {},
"outputs": [],
"source": [
"lpf = flopy.modflow.ModflowLpf(ml, hk=2., laytyp=0, layavg=0)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Inflow on the right side of the model is 1 m$^3$/d (layer 0, row 0, column 0, discharge 1)"
]
},
{
"cell_type": "code",
"execution_count": 6,
"metadata": {},
"outputs": [],
"source": [
"wel = flopy.modflow.ModflowWel(ml, stress_period_data = {0:[[0, 0, 0, 1]]} )"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Define the output control to save heads and interface every 50 steps, and define the pcg solver with default arguments."
]
},
{
"cell_type": "code",
"execution_count": 7,
"metadata": {},
"outputs": [],
"source": [
"spd = {}\n",
"for istp in range(49, nstp+1, 50):\n",
" spd[(0, istp)] = ['save head', 'print budget']\n",
" spd[(0, istp+1)] = []\n",
"\n",
"oc = flopy.modflow.ModflowOc(ml, stress_period_data=spd)\n",
"pcg = flopy.modflow.ModflowPcg(ml)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"The intial interface is straight. The isource is one everywhere (inflow and outflow is fresh (zone 1)) except for the first cell (index=0) which has saltwater inflow (zone 2)."
]
},
{
"cell_type": "code",
"execution_count": 8,
"metadata": {},
"outputs": [],
"source": [
"z = np.zeros((nrow, ncol), float)\n",
"z[0, 16:24] = np.arange(-2.5, -40, -5)\n",
"z[0, 24:] = -40\n",
"z = [z] # zeta needs to be \n",
"isource = np.ones((nrow, ncol), int)\n",
"isource[0, 0] = 2\n",
"#\n",
"swi = flopy.modflow.ModflowSwi2(ml, nsrf=1, istrat=1, \n",
" toeslope=0.2, tipslope=0.2, nu=[0, 0.025],\n",
" zeta=z, ssz=0.2, isource=isource, \n",
" nsolver=1, iswizt=55)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Write the MODFLOW input files and run the model"
]
},
{
"cell_type": "code",
"execution_count": 9,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"(True, [])"
]
},
"execution_count": 9,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"ml.write_input()\n",
"ml.run_model(silent=True)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Load the head and zeta data from the file"
]
},
{
"cell_type": "code",
"execution_count": 10,
"metadata": {},
"outputs": [],
"source": [
"# read model heads\n",
"hfile = flopy.utils.HeadFile(os.path.join(ml.model_ws, modelname+'.hds'))\n",
"head = hfile.get_alldata()\n",
"# read 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)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Make a graph and add the solution of Wilson and Sa da Costa"
]
},
{
"cell_type": "code",
"execution_count": 11,
"metadata": {},
"outputs": [
{
"data": {
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\n",
"text/plain": [
"<Figure size 1152x432 with 1 Axes>"
]
},
"metadata": {
"needs_background": "light"
},
"output_type": "display_data"
}
],
"source": [
"plt.figure(figsize=(16,6))\n",
"# define x-values of xcells and plot interface\n",
"x = np.arange(0, ncol*delr, delr) + delr/2.\n",
"label = ['SWI2','_','_','_'] # labels with an underscore are not added to legend\n",
"for i in range(4):\n",
" zt = np.ma.masked_outside(zeta[i,0,0,:], -39.99999, -0.00001)\n",
" plt.plot(x, zt, 'r-', lw=1, \n",
" zorder=10, label=label[i])\n",
"# Data for the Wilson - Sa da Costa solution\n",
"k = 2.0\n",
"n = 0.2\n",
"nu = 0.025\n",
"H = 40.0\n",
"tzero = H * n / (k * nu) / 4.0\n",
"Ltoe = np.zeros(4)\n",
"v = 0.125\n",
"t = np.arange(100, 500, 100)\n",
"label = ['Wilson and Sa Da Costa (1982)','_','_','_'] # labels with an underscore are not added to legend\n",
"for i in range(4):\n",
" Ltoe[i] = H * np.sqrt(k * nu * (t[i] + tzero) / n / H)\n",
" plt.plot([100 - Ltoe[i] + v * t[i], 100 + Ltoe[i] + v * t[i]], [0, -40], '0.75', \n",
" lw=8, zorder=0, label=label[i])\n",
"# Scale figure and add legend\n",
"plt.axis('scaled')\n",
"plt.xlim(0, 250)\n",
"plt.ylim(-40, 0)\n",
"plt.legend(loc='best');"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Use ModelCrossSection plotting class and plot_surface() method to plot zeta surfaces."
]
},
{
"cell_type": "code",
"execution_count": 12,
"metadata": {},
"outputs": [
{
"data": {
"image/png": 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\n",
"text/plain": [
"<Figure size 1152x216 with 1 Axes>"
]
},
"metadata": {
"needs_background": "light"
},
"output_type": "display_data"
}
],
"source": [
"fig = plt.figure(figsize=(16, 3))\n",
"ax = fig.add_subplot(1, 1, 1)\n",
"modelxsect = flopy.plot.PlotCrossSection(model=ml, line={'Row': 0})\n",
"label = ['SWI2','_','_','_']\n",
"for k in range(zeta.shape[0]):\n",
" modelxsect.plot_surface(zeta[k, :, :, :], masked_values=[0, -40.], \n",
" color='red', lw=1, label=label[k])\n",
"linecollection = modelxsect.plot_grid()\n",
"ax.set_title('ModelCrossSection.plot_surface()')\n",
"# Data for the Wilson - Sa da Costa solution\n",
"k = 2.0\n",
"n = 0.2\n",
"nu = 0.025\n",
"H = 40.0\n",
"tzero = H * n / (k * nu) / 4.0\n",
"Ltoe = np.zeros(4)\n",
"v = 0.125\n",
"t = np.arange(100, 500, 100)\n",
"label = ['Wilson and Sa Da Costa (1982)','_','_','_'] # labels with an underscore are not added to legend\n",
"for i in range(4):\n",
" Ltoe[i] = H * np.sqrt(k * nu * (t[i] + tzero) / n / H)\n",
" ax.plot([100 - Ltoe[i] + v * t[i], 100 + Ltoe[i] + v * t[i]], [0, -40], 'blue',\n",
" lw=1, zorder=0, label=label[i])\n",
"# Scale figure and add legend\n",
"ax.axis('scaled')\n",
"ax.set_xlim(0, 250)\n",
"ax.set_ylim(-40, 0)\n",
"ax.legend(loc='best');"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Use ModelCrossSection plotting class and plot_fill_between() method to fill between zeta surfaces."
]
},
{
"cell_type": "code",
"execution_count": 13,
"metadata": {},
"outputs": [
{
"data": {
"image/png": 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\n",
"text/plain": [
"<Figure size 1152x216 with 1 Axes>"
]
},
"metadata": {
"needs_background": "light"
},
"output_type": "display_data"
}
],
"source": [
"fig = plt.figure(figsize=(16, 3))\n",
"ax = fig.add_subplot(1, 1, 1)\n",
"modelxsect = flopy.plot.PlotCrossSection(model=ml, line={'Row': 0})\n",
"modelxsect.plot_fill_between(zeta[3, :, :, :])\n",
"linecollection = modelxsect.plot_grid()\n",
"ax.set_title('ModelCrossSection.plot_fill_between()');"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Convert zeta surfaces to relative seawater concentrations"
]
},
{
"cell_type": "code",
"execution_count": 14,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"[1.00000001 1.00000001 1.00000001 1.00000001 1.00000001 1.00000001\n",
" 1.00000001 1.00000001 1.00000001 1.00000001 1.00000001 1.00000001\n",
" 1.00000001 1.00000001 1.00000001 1.00000001 1.00000001 0.97957368\n",
" 0.93777831 0.89900599 0.8606122 0.82240776 0.78431243 0.74628681\n",
" 0.70830902 0.6703652 0.63244558 0.59454289 0.55665098 0.5187645\n",
" 0.4808785 0.44298783 0.40508729 0.36717053 0.32923027 0.29125705\n",
" 0.25323816 0.21515508 0.17697897 0.13865538 0.10004826 0.06099987\n",
" 0.02483263 0. 0. 0. 0. 0.\n",
" 0. 0. ]\n"
]
},
{
"data": {
"image/png": 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\n",
"text/plain": [
"<Figure size 432x288 with 2 Axes>"
]
},
"metadata": {
"needs_background": "light"
},
"output_type": "display_data"
}
],
"source": [
"X, Y = np.meshgrid(x, [0, -40])\n",
"zc = flopy.plot.SwiConcentration(model=ml)\n",
"conc = zc.calc_conc(zeta={0:zeta[3,:,:,:]}) / 0.025\n",
"print(conc[0, 0, :])\n",
"v = np.vstack((conc[0, 0, :], conc[0, 0, :]))\n",
"plt.imshow(v, extent=[0, 250, -40, 0], cmap='Reds')\n",
"cb = plt.colorbar(orientation='horizontal')\n",
"cb.set_label('percent seawater');\n",
"plt.contour(X, Y, v, [0.25, 0.5, 0.75], linewidths=[2, 1.5, 1], colors='black');"
]
},
{
"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",
"mimetype": "text/x-python",
"name": "python",
"nbconvert_exporter": "python",
"pygments_lexer": "ipython3",
"version": "3.8.6"
}
},
"nbformat": 4,
"nbformat_minor": 1
}