# Circle#

This example illustrates how to setup a very simple unstructured groundwater model using the `imod` package and associated packages.

In overview, we’ll set the following steps:

• Create a triangular mesh for a disk geometry.

• Create the xugrid UgridDataArrays containg the MODFLOW6 parameters.

• Feed these arrays into the imod mf6 classes.

• Write to modflow6 files.

• Run the model.

• Open the results back into UgridDataArrays.

• Visualize the results.

```
```

```import matplotlib.pyplot as plt
import meshzoo
import numpy as np
import xarray as xr
import xugrid as xu

import imod
```

## Create a mesh#

The first steps consists of generating a mesh. In this example, our geometry is so simple that we don’t need complex mesh generators. Instead we will use `meshzoo`, which can generate a number of meshes for simple domains such as triangles, rectangles, regular polygons, or disks.

We can use the nodes and the cell node connectivity directly to initialize a xugrid Ugrid2d object, and we’ll create a quick plot to take a look at the grid.

```nodes, triangles = meshzoo.disk(6, 6)
nodes *= 1000.0
grid = xu.Ugrid2d(*nodes.T, -1, triangles)

fig, ax = plt.subplots()
xu.plot.line(grid, ax=ax)
ax.set_aspect(1)
``` ## Create UgridDataArray#

Now that we have defined the grid, we can start defining the model parameter data.

Our goal here is to define a steady-state model with:

• Uniform conductivities of 1.0 m/d;

• Two layers of 5.0 m thick;

• Uniform recharge of 0.001 m/d on the top layer;

• Constant heads of 1.0 m along the exterior edges of the mesh.

From these boundary conditions, we would expect circular mounding of the groundwater; with small flows in the center and larger flows as the recharge accumulates while the groundwater flows towards the exterior boundary.

```nface = len(triangles)
nlayer = 2

idomain = xu.UgridDataArray(
xr.DataArray(
np.ones((nlayer, nface), dtype=np.int32),
coords={"layer": [1, 2]},
dims=["layer", grid.face_dimension],
),
grid=grid,
)
icelltype = xu.full_like(idomain, 0)
k = xu.full_like(idomain, 1.0, dtype=float)
k33 = k.copy()
rch_rate = xu.full_like(idomain.sel(layer=1), 0.001, dtype=float)
bottom = idomain * xr.DataArray([5.0, 0.0], dims=["layer"])
```

All the data above have been constants over the grid. For the constant head boundary, we’d like to only set values on the external border. We can py:method:xugrid.UgridDataset.binary_dilation to easily find these cells:

```chd_location = xu.zeros_like(idomain.sel(layer=2), dtype=bool).ugrid.binary_dilation(
border_value=True
)
constant_head = xu.full_like(idomain.sel(layer=2), 1.0, dtype=float).where(chd_location)

fig, ax = plt.subplots()
xu.plot.line(grid, ax=ax, color="black")
ax.set_aspect(1)
``` ## Write the model#

The first step is to define an empty model, the parameters and boundary conditions are added in the form of the familiar MODFLOW packages.

```gwf_model = imod.mf6.GroundwaterFlowModel()
gwf_model["disv"] = imod.mf6.VerticesDiscretization(
top=10.0, bottom=bottom, idomain=idomain
)
constant_head, print_input=True, print_flows=True, save_flows=True
)
gwf_model["npf"] = imod.mf6.NodePropertyFlow(
icelltype=icelltype,
k=k,
k33=k33,
save_flows=True,
)
gwf_model["sto"] = imod.mf6.SpecificStorage(
specific_storage=1.0e-5,
specific_yield=0.15,
transient=False,
convertible=0,
)
gwf_model["oc"] = imod.mf6.OutputControl(save_head="all", save_budget="all")
gwf_model["rch"] = imod.mf6.Recharge(rch_rate)

simulation = imod.mf6.Modflow6Simulation("circle")
simulation["GWF_1"] = gwf_model
simulation["solver"] = imod.mf6.Solution(
modelnames=["GWF_1"],
print_option="summary",
csv_output=False,
no_ptc=True,
outer_dvclose=1.0e-4,
outer_maximum=500,
under_relaxation=None,
inner_dvclose=1.0e-4,
inner_rclose=0.001,
inner_maximum=100,
linear_acceleration="cg",
scaling_method=None,
reordering_method=None,
relaxation_factor=0.97,
)
```

We’ll create a new directory in which we will write and run the model.

```modeldir = imod.util.temporary_directory()
simulation.write(modeldir)
```
```/builds/deltares/imod/imod-python/imod/mf6/disv.py:91: FutureWarning: the 'line_terminator'' keyword is deprecated, use 'lineterminator' instead.
self._verts_dataframe().to_csv(
/builds/deltares/imod/imod-python/imod/mf6/disv.py:97: FutureWarning: the 'line_terminator'' keyword is deprecated, use 'lineterminator' instead.
self._cell2d_dataframe().to_csv(
```

## Run the model#

Note

The following lines assume the `mf6` executable is available on your PATH. The Modflow 6 examples introduction shortly describes how to add it to yours.

```simulation.run()
```

## Open the results#

First, we’ll open the heads (.hds) file.

```head = imod.mf6.out.open_hds(
modeldir / "GWF_1/GWF_1.hds",
modeldir / "GWF_1/disv.disv.grb",
)
```
```<xarray.DataArray 'head' (time: 1, layer: 2, mesh2d_nFaces: 216)>
dask.array<stack, shape=(1, 2, 216), dtype=float64, chunksize=(1, 2, 216), chunktype=numpy.ndarray>
Coordinates:
* layer    (layer) int64 1 2
* time     (time) float64 1.0
Dimensions without coordinates: mesh2d_nFaces```

For a DISV MODFLOW6 model, the heads are returned as a UgridDataArray. While all layers are timesteps are available, they are only loaded into memory as needed.

We may also open the cell-by-cell flows (.cbc) file.

```cbc = imod.mf6.open_cbc(
modeldir / "GWF_1/GWF_1.cbc",
modeldir / "GWF_1/disv.disv.grb",
)
print(cbc.keys())
```
```dict_keys(['flow-horizontal-face', 'flow-horizontal-face-x', 'flow-horizontal-face-y', 'flow-lower-face', 'chd'])
```

The flows are returned as a dictionary of UgridDataArrays. This dictionary contains all entries that are stored in the CBC file, but like for the heads file the data are only loaded into memory when needed.

The horizontal flows are stored on the edges of the UgridDataArray topology. The other flows are generally stored on the faces; this includes the flow-lower-face.

We’ll create a dataset for the horizontal flows for further analysis.

```ds = xu.UgridDataset(grids=grid)
ds["u"] = cbc["flow-horizontal-face-x"]
ds["v"] = cbc["flow-horizontal-face-y"]
```

## Visualize the results#

We can quickly and easily visualize the output with the plotting functions provided by xarray and xugrid. We’ll add some some edge coordinates to the dataset so that they can be used to place the arrows in the quiver plot.

```ds = ds.ugrid.assign_edge_coords()
fig, ax = plt.subplots() 