flopy_lake_example.py

import os
import sys

import matplotlib.pyplot as plt
import numpy as np

import flopy


def run():
    workspace = os.path.join("lake")
    # make sure workspace directory exists
    if not os.path.exists(workspace):
        os.makedirs(workspace)

    fext = "png"
    narg = len(sys.argv)
    iarg = 0
    if narg > 1:
        while iarg < narg - 1:
            iarg += 1
            basearg = sys.argv[iarg].lower()
            if basearg == "--pdf":
                fext = "pdf"

    # save the starting path
    cwdpth = os.getcwd()

    # change to the working directory
    os.chdir(workspace)

    # We are creating a square model with a specified head equal to `h1` along all boundaries.
    # The head at the cell in the center in the top layer is fixed to `h2`. First, set the name
    # of the model and the parameters of the model: the number of layers `Nlay`, the number of rows
    # and columns `N`, lengths of the sides of the model `L`, aquifer thickness `H`, hydraulic
    # conductivity `Kh`
    name = "lake_example"
    h1 = 100
    h2 = 90
    Nlay = 10
    N = 101
    L = 400.0
    H = 50.0
    Kh = 1.0

    # Create a MODFLOW model and store it (in this case in the variable `ml`, but you can call it
    # whatever you want). The modelname will be the name given to all MODFLOW files (input and output).
    # The exe_name should be the full path to your MODFLOW executable. The version is either 'mf2k'
    # for MODFLOW2000 or 'mf2005'for MODFLOW2005.
    ml = flopy.modflow.Modflow(
        modelname=name, exe_name="mf2005", version="mf2005"
    )

    # Define the discretization of the model. All layers are given equal thickness. The `bot` array
    # is build from the `Hlay` values to indicate top and bottom of each layer, and `delrow` and
    # `delcol` are computed from model size `L` and number of cells `N`. Once these are all computed,
    # the Discretization file is built.
    bot = np.linspace(-H / Nlay, -H, Nlay)
    delrow = delcol = L / (N - 1)
    dis = flopy.modflow.ModflowDis(
        ml,
        nlay=Nlay,
        nrow=N,
        ncol=N,
        delr=delrow,
        delc=delcol,
        top=0.0,
        botm=bot,
        laycbd=0,
    )

    # Next we specify the boundary conditions and starting heads with the Basic package. The `ibound`
    # array will be `1` in all cells in all layers, except for along the boundary and in the cell at
    # the center in the top layer where it is set to `-1` to indicate fixed heads. The starting heads
    # are used to define the heads in the fixed head cells (this is a steady simulation, so none of
    # the other starting values matter). So we set the starting heads to `h1` everywhere, except for
    # the head at the center of the model in the top layer.
    Nhalf = int((N - 1) / 2)
    ibound = np.ones((Nlay, N, N), dtype=int)
    ibound[:, 0, :] = -1
    ibound[:, -1, :] = -1
    ibound[:, :, 0] = -1
    ibound[:, :, -1] = -1
    ibound[0, Nhalf, Nhalf] = -1

    start = h1 * np.ones((N, N))
    start[Nhalf, Nhalf] = h2

    # create external ibound array and starting head files
    files = []
    hfile = f"{name}_strt.ref"
    np.savetxt(hfile, start)
    hfiles = []
    for kdx in range(Nlay):
        file = f"{name}_ib{kdx + 1:02d}.ref"
        files.append(file)
        hfiles.append(hfile)
        np.savetxt(file, ibound[kdx, :, :], fmt="%5d")

    bas = flopy.modflow.ModflowBas(ml, ibound=files, strt=hfiles)

    # The aquifer properties (really only the hydraulic conductivity) are defined with the
    # LPF package.
    lpf = flopy.modflow.ModflowLpf(ml, hk=Kh)

    # Finally, we need to specify the solver we want to use (PCG with default values), and the
    # output control (using the default values). Then we are ready to write all MODFLOW input
    # files and run MODFLOW.
    pcg = flopy.modflow.ModflowPcg(ml)
    oc = flopy.modflow.ModflowOc(ml)
    ml.write_input()
    ml.run_model()

    # change back to the starting directory
    os.chdir(cwdpth)

    # Once the model has terminated normally, we can read the heads file. First, a link to the heads
    # file is created with `HeadFile`. The link can then be accessed with the `get_data` function, by
    # specifying, in this case, the step number and period number for which we want to retrieve data.
    # A three-dimensional array is returned of size `nlay, nrow, ncol`. Matplotlib contouring functions
    # are used to make contours of the layers or a cross-section.
    hds = flopy.utils.HeadFile(os.path.join(workspace, f"{name}.hds"))
    h = hds.get_data(kstpkper=(0, 0))
    x = y = np.linspace(0, L, N)
    c = plt.contour(x, y, h[0], np.arange(90, 100.1, 0.2))
    plt.clabel(c, fmt="%2.1f")
    plt.axis("scaled")

    outfig = os.path.join(workspace, f"lake1.{fext}")
    fig = plt.gcf()
    fig.savefig(outfig, dpi=300)
    print("created...", outfig)

    x = y = np.linspace(0, L, N)
    c = plt.contour(x, y, h[-1], np.arange(90, 100.1, 0.2))
    plt.clabel(c, fmt="%1.1f")
    plt.axis("scaled")

    outfig = os.path.join(workspace, f"lake2.{fext}")
    fig = plt.gcf()
    fig.savefig(outfig, dpi=300)
    print("created...", outfig)

    z = np.linspace(-H / Nlay / 2, -H + H / Nlay / 2, Nlay)
    c = plt.contour(x, z, h[:, 50, :], np.arange(90, 100.1, 0.2))
    plt.axis("scaled")

    outfig = os.path.join(workspace, f"lake3.{fext}")
    fig = plt.gcf()
    fig.savefig(outfig, dpi=300)
    print("created...", outfig)

    return 0


if __name__ == "__main__":
    success = run()