.. DO NOT EDIT. .. THIS FILE WAS AUTOMATICALLY GENERATED BY SPHINX-GALLERY. .. TO MAKE CHANGES, EDIT THE SOURCE PYTHON FILE: .. "user-guide\12-imod5-model-to-modflow6.py" .. LINE NUMBERS ARE GIVEN BELOW. .. only:: html .. note:: :class: sphx-glr-download-link-note :ref:`Go to the end ` to download the full example code. .. rst-class:: sphx-glr-example-title .. _sphx_glr_user-guide_12-imod5-model-to-modflow6.py: Working with iMOD5 models in MODFLOW 6 ====================================== This example shows how to work with iMOD5 models in MODFLOW 6. It demonstrates how to convert an iMOD5 model to a MODFLOW 6 model using the `imod` package. The example fetches an iMOD5 model, converts it to a MODFLOW 6 model, regrids it to an unstructured grid, and compares differences in output between the structured and unstructured grids. .. GENERATED FROM PYTHON SOURCE LINES 12-16 .. code-block:: Python import imod .. GENERATED FROM PYTHON SOURCE LINES 18-24 Fetching an iMOD5 model ----------------------- Let's start by fetching the example data from the `imod.data` module. This will download a project file and accompanying data files to a temporary directory. .. GENERATED FROM PYTHON SOURCE LINES 25-33 .. code-block:: Python tmpdir = imod.util.temporary_directory() prj_dir = tmpdir / "prj" prj_dir.mkdir(exist_ok=True, parents=True) model_dir = imod.data.fetch_imod5_model(prj_dir) .. GENERATED FROM PYTHON SOURCE LINES 34-37 Let's view the model directory. We can use ``.glob("*")`` to view all directory contents. The directory contains the project file and accompanying model contents. .. GENERATED FROM PYTHON SOURCE LINES 38-44 .. code-block:: Python from pprint import pprint imod_dir_contents = list(model_dir.glob("*")) pprint(imod_dir_contents) .. rst-class:: sphx-glr-script-out .. code-block:: none [WindowsPath('C:/BuildAgent/temp/buildTmp/tmpvlkdapvj/prj/iMOD5_model_pooch/Database'), WindowsPath('C:/BuildAgent/temp/buildTmp/tmpvlkdapvj/prj/iMOD5_model_pooch/iMOD5_model.prj')] .. GENERATED FROM PYTHON SOURCE LINES 45-50 The directory contains a project file and a database folder. This database contains all the IDF, IPF, and GEN files that make up the spatial model input. Let's look at the projectfile contents. Read the projectfile as follows: .. GENERATED FROM PYTHON SOURCE LINES 51-56 .. code-block:: Python prj_path = model_dir / "iMOD5_model.prj" prj_content = imod.prj.read_projectfile(prj_path) pprint(prj_content) .. rst-class:: sphx-glr-script-out .. code-block:: none {'(ani)': {'active': '0', 'angle': [{'active': True, 'addition': 0.0, 'constant': -999.99, 'factor': 1.0, 'is_constant': 2, 'layer': 2, 'path': WindowsPath('C:/buildagent/temp/buildtmp/tmpvlkdapvj/prj/iMOD5_model_pooch/Database/ANI/VERSION_1/ANI_HOEK.IDF')}, {'active': True, 'addition': 0.0, 'constant': -999.99, 'factor': 1.0, 'is_constant': 2, 'layer': 4, 'path': WindowsPath('C:/buildagent/temp/buildtmp/tmpvlkdapvj/prj/iMOD5_model_pooch/Database/ANI/VERSION_1/ANI_HOEK.IDF')}, {'active': True, 'addition': 0.0, 'constant': -999.99, 'factor': 1.0, 'is_constant': 2, 'layer': 6, 'path': WindowsPath('C:/buildagent/temp/buildtmp/tmpvlkdapvj/prj/iMOD5_model_pooch/Database/ANI/VERSION_1/ANI_HOEK.IDF')}, {'active': True, 'addition': 0.0, 'constant': -999.99, 'factor': 1.0, 'is_constant': 2, 'layer': 8, 'path': WindowsPath('C:/buildagent/temp/buildtmp/tmpvlkdapvj/prj/iMOD5_model_pooch/Database/ANI/VERSION_1/ANI_HOEK.IDF')}], 'factor': [{'active': True, 'addition': 0.0, 'constant': -999.99, 'factor': 1.0, 'is_constant': 2, 'layer': 2, 'path': WindowsPath('C:/buildagent/temp/buildtmp/tmpvlkdapvj/prj/iMOD5_model_pooch/Database/ANI/VERSION_1/ANI_FACTOR.IDF')}, {'active': True, 'addition': 0.0, 'constant': -999.99, 'factor': 1.0, 'is_constant': 2, 'layer': 4, 'path': WindowsPath('C:/buildagent/temp/buildtmp/tmpvlkdapvj/prj/iMOD5_model_pooch/Database/ANI/VERSION_1/ANI_FACTOR.IDF')}, {'active': True, 'addition': 0.0, 'constant': -999.99, 'factor': 1.0, 'is_constant': 2, 'layer': 6, 'path': WindowsPath('C:/buildagent/temp/buildtmp/tmpvlkdapvj/prj/iMOD5_model_pooch/Database/ANI/VERSION_1/ANI_FACTOR.IDF')}, {'active': True, 'addition': 0.0, 'constant': -999.99, 'factor': 1.0, 'is_constant': 2, 'layer': 8, 'path': WindowsPath('C:/buildagent/temp/buildtmp/tmpvlkdapvj/prj/iMOD5_model_pooch/Database/ANI/VERSION_1/ANI_FACTOR.IDF')}], 'n_system': 4}, '(bnd)': {'active': '1', 'ibound': [{'active': True, 'addition': 0.0, 'constant': -999.99, 'factor': 1.0, 'is_constant': 2, 'layer': 1, 'path': WindowsPath('C:/buildagent/temp/buildtmp/tmpvlkdapvj/prj/iMOD5_model_pooch/Database/BND/VERSION_1/IBOUND_L1.IDF')}, {'active': True, 'addition': 0.0, 'constant': -999.99, 'factor': 1.0, 'is_constant': 2, 'layer': 2, 'path': WindowsPath('C:/buildagent/temp/buildtmp/tmpvlkdapvj/prj/iMOD5_model_pooch/Database/BND/VERSION_1/IBOUND_L2.IDF')}, {'active': True, 'addition': 0.0, 'constant': -999.99, 'factor': 1.0, 'is_constant': 2, 'layer': 3, 'path': WindowsPath('C:/buildagent/temp/buildtmp/tmpvlkdapvj/prj/iMOD5_model_pooch/Database/BND/VERSION_1/IBOUND_L3.IDF')}, {'active': True, 'addition': 0.0, 'constant': -999.99, 'factor': 1.0, 'is_constant': 2, 'layer': 4, 'path': WindowsPath('C:/buildagent/temp/buildtmp/tmpvlkdapvj/prj/iMOD5_model_pooch/Database/BND/VERSION_1/IBOUND_L4.IDF')}, {'active': True, 'addition': 0.0, 'constant': -999.99, 'factor': 1.0, 'is_constant': 2, 'layer': 5, 'path': WindowsPath('C:/buildagent/temp/buildtmp/tmpvlkdapvj/prj/iMOD5_model_pooch/Database/BND/VERSION_1/IBOUND_L5.IDF')}, {'active': True, 'addition': 0.0, 'constant': -999.99, 'factor': 1.0, 'is_constant': 2, 'layer': 6, 'path': WindowsPath('C:/buildagent/temp/buildtmp/tmpvlkdapvj/prj/iMOD5_model_pooch/Database/BND/VERSION_1/IBOUND_L6.IDF')}, {'active': True, 'addition': 0.0, 'constant': -999.99, 'factor': 1.0, 'is_constant': 2, 'layer': 7, 'path': WindowsPath('C:/buildagent/temp/buildtmp/tmpvlkdapvj/prj/iMOD5_model_pooch/Database/BND/VERSION_1/IBOUND_L7.IDF')}, {'active': True, 'addition': 0.0, 'constant': -999.99, 'factor': 1.0, 'is_constant': 2, 'layer': 8, 'path': WindowsPath('C:/buildagent/temp/buildtmp/tmpvlkdapvj/prj/iMOD5_model_pooch/Database/BND/VERSION_1/IBOUND_L8.IDF')}, {'active': True, 'addition': 0.0, 'constant': -999.99, 'factor': 1.0, 'is_constant': 2, 'layer': 9, 'path': WindowsPath('C:/buildagent/temp/buildtmp/tmpvlkdapvj/prj/iMOD5_model_pooch/Database/BND/VERSION_1/IBOUND_L9.IDF')}, {'active': True, 'addition': 0.0, 'constant': -999.99, 'factor': 1.0, 'is_constant': 2, 'layer': 10, 'path': WindowsPath('C:/buildagent/temp/buildtmp/tmpvlkdapvj/prj/iMOD5_model_pooch/Database/BND/VERSION_1/IBOUND_L10.IDF')}, {'active': True, 'addition': 0.0, 'constant': -999.99, 'factor': 1.0, 'is_constant': 2, 'layer': 11, 'path': WindowsPath('C:/buildagent/temp/buildtmp/tmpvlkdapvj/prj/iMOD5_model_pooch/Database/BND/VERSION_1/IBOUND_L11.IDF')}, {'active': True, 'addition': 0.0, 'constant': -999.99, 'factor': 1.0, 'is_constant': 2, 'layer': 12, 'path': 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WindowsPath('C:/buildagent/temp/buildtmp/tmpvlkdapvj/prj/iMOD5_model_pooch/Database/TOP/VERSION_1/TOP_L36.IDF')}, {'active': True, 'addition': 0.0, 'constant': -999.99, 'factor': 1.0, 'is_constant': 2, 'layer': 37, 'path': WindowsPath('C:/buildagent/temp/buildtmp/tmpvlkdapvj/prj/iMOD5_model_pooch/Database/TOP/VERSION_1/TOP_L37.IDF')}]}, '(wel)': defaultdict(, {'active': '1', 'ipf': [{'active': True, 'addition': 0.0, 'constant': -999.99, 'factor': 1.0, 'is_constant': 2, 'layer': 5, 'path': WindowsPath('C:/buildagent/temp/buildtmp/tmpvlkdapvj/prj/iMOD5_model_pooch/Database/WEL/VERSION_1/WELLS_L3.IPF'), 'time': 'steady-state'}, {'active': True, 'addition': 0.0, 'constant': -999.99, 'factor': 1.0, 'is_constant': 2, 'layer': 7, 'path': WindowsPath('C:/buildagent/temp/buildtmp/tmpvlkdapvj/prj/iMOD5_model_pooch/Database/WEL/VERSION_1/WELLS_L4.IPF'), 'time': 'steady-state'}, {'active': True, 'addition': 0.0, 'constant': -999.99, 'factor': 1.0, 'is_constant': 2, 'layer': 9, 'path': WindowsPath('C:/buildagent/temp/buildtmp/tmpvlkdapvj/prj/iMOD5_model_pooch/Database/WEL/VERSION_1/WELLS_L5.IPF'), 'time': 'steady-state'}], 'n_system': 3, 'time': ['steady-state']}), 'periods': {}, 'species': {1: 'benzene'}} .. GENERATED FROM PYTHON SOURCE LINES 57-61 This contains all the projectfile contents in a dictionary, which is quite a lot of information. This is too much to go through in detail. We can also open all data that the projectfile points to, using the :func:`imod.prj.open_projectfile_data` function. .. GENERATED FROM PYTHON SOURCE LINES 62-66 .. code-block:: Python imod5_data, period_data = imod.prj.open_projectfile_data(prj_path) imod5_data .. rst-class:: sphx-glr-script-out .. code-block:: none {'bnd': {'ibound': Size: 10MB dask.array Coordinates: * x (x) float64 1kB 1.947e+05 1.947e+05 ... 1.993e+05 1.993e+05 * y (y) float64 1kB 3.653e+05 3.653e+05 ... 3.607e+05 3.607e+05 dx float64 8B 25.0 dy float64 8B -25.0 * layer (layer) int64 296B 1 2 3 4 5 6 7 8 9 ... 29 30 31 32 33 34 35 36 37}, 'top': {'top': Size: 640kB dask.array Coordinates: * x (x) float64 368B 1.948e+05 1.948e+05 ... 1.992e+05 1.992e+05 * y (y) float64 376B 3.652e+05 3.652e+05 ... 3.608e+05 3.606e+05 dx float64 8B 100.0 dy float64 8B -100.0 * layer (layer) int64 296B 1 2 3 4 5 6 7 8 9 ... 29 30 31 32 33 34 35 36 37}, 'bot': {'bottom': Size: 640kB dask.array Coordinates: * x (x) float64 368B 1.948e+05 1.948e+05 ... 1.992e+05 1.992e+05 * y (y) float64 376B 3.652e+05 3.652e+05 ... 3.608e+05 3.606e+05 dx float64 8B 100.0 dy float64 8B -100.0 * layer (layer) int64 296B 1 2 3 4 5 6 7 8 9 ... 29 30 31 32 33 34 35 36 37}, 'khv': {'kh': Size: 640kB array([[[1.00000000e+00, 1.00000000e+00, 1.00000000e+00, ..., 1.00000000e+00, 1.00000000e+00, 1.00000000e+00], [1.00000000e+00, 1.00000000e+00, 1.00000000e+00, ..., 1.00000000e+00, 1.00000000e+00, 1.00000000e+00], [1.00000000e+00, 1.00000000e+00, 1.00000000e+00, ..., 1.00000000e+00, 1.00000000e+00, 1.00000000e+00], ..., [1.00000000e+00, 1.00000000e+00, 1.00000000e+00, ..., 1.00000000e+00, 1.00000000e+00, 1.00000000e+00], [1.00000000e+00, 1.00000000e+00, 1.00000000e+00, ..., 1.00000000e+00, 1.00000000e+00, 1.00000000e+00], [1.00000000e+00, 1.00000000e+00, 1.00000000e+00, ..., 1.00000000e+00, 1.00000000e+00, 1.00000000e+00]], [[0.00000000e+00, 0.00000000e+00, 6.95740432e-03, ..., 0.00000000e+00, 0.00000000e+00, 0.00000000e+00], [0.00000000e+00, 7.01683480e-03, 7.00508710e-03, ..., 0.00000000e+00, 0.00000000e+00, 0.00000000e+00], [0.00000000e+00, 7.09291873e-03, 7.07956450e-03, ..., 0.00000000e+00, 5.12321472e-01, 1.93920404e-01], ... 0.00000000e+00, 0.00000000e+00, 0.00000000e+00], [1.98880007e-05, 1.98470461e-05, 1.97940062e-05, ..., 0.00000000e+00, 0.00000000e+00, 0.00000000e+00], [1.98590096e-05, 1.98099970e-05, 1.97449954e-05, ..., 0.00000000e+00, 0.00000000e+00, 0.00000000e+00]], [[0.00000000e+00, 0.00000000e+00, 0.00000000e+00, ..., 0.00000000e+00, 0.00000000e+00, 0.00000000e+00], [0.00000000e+00, 0.00000000e+00, 0.00000000e+00, ..., 0.00000000e+00, 0.00000000e+00, 0.00000000e+00], [0.00000000e+00, 0.00000000e+00, 0.00000000e+00, ..., 0.00000000e+00, 0.00000000e+00, 0.00000000e+00], ..., [2.22650909e+00, 2.22723508e+00, 2.22795606e+00, ..., 0.00000000e+00, 0.00000000e+00, 0.00000000e+00], [2.22167802e+00, 2.22234702e+00, 2.22300601e+00, ..., 0.00000000e+00, 0.00000000e+00, 0.00000000e+00], [2.21684098e+00, 2.21745610e+00, 2.21805811e+00, ..., 0.00000000e+00, 0.00000000e+00, 0.00000000e+00]]], shape=(37, 47, 46)) Coordinates: * x (x) float64 368B 1.948e+05 1.948e+05 ... 1.992e+05 1.992e+05 * y (y) float64 376B 3.652e+05 3.652e+05 ... 3.608e+05 3.606e+05 * layer (layer) int64 296B 1 2 3 4 5 6 7 8 9 ... 29 30 31 32 33 34 35 36 37 dx float64 8B 100.0 dy float64 8B -100.0}, 'kva': {'vertical_anisotropy': Size: 296B array([0.3, 0.3, 0.3, 0.3, 0.3, 0.3, 0.3, 0.3, 0.3, 0.3, 0.3, 0.3, 0.3, 0.3, 0.3, 0.3, 0.3, 0.3, 0.3, 0.3, 0.3, 0.3, 0.3, 0.3, 0.3, 0.3, 0.3, 0.3, 0.3, 0.3, 0.3, 0.3, 0.3, 0.3, 0.3, 0.3, 0.3]) Coordinates: * layer (layer) int64 296B 1 2 3 4 5 6 7 8 9 ... 29 30 31 32 33 34 35 36 37}, 'shd': {'head': Size: 10MB array([[[25.16032219, 25.14756393, 25.13460541, ..., 19.49370575, 19.3827076 , 19.27472115], [25.12084389, 25.10797691, 25.09492683, ..., 19.38327789, 19.27821541, 19.17650032], [25.08100891, 25.06808472, 25.05514908, ..., 19.27186775, 19.17440605, 19.07987785], ..., [19.95754242, 19.92946815, 19.90377617, ..., 17.02923965, 17.04399109, 17.02148819], [19.93486214, 19.91115379, 19.89091682, ..., 17.01935768, 17.03422737, 17.0165863 ], [19.91596413, 19.89661026, 19.88234901, ..., 17.00821877, 17.02318382, 17.00966835]], [[25.15736008, 25.14456367, 25.13166237, ..., 19.49052811, 19.38079643, 19.27355576], [25.11772537, 25.10481644, 25.09185791, ..., 19.38098717, 19.27675056, 19.17549133], [25.0778141 , 25.06483841, 25.05184746, ..., 19.27054214, 19.17319489, 19.07863617], ... 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float64 1kB 3.653e+05 3.653e+05 ... 3.607e+05 3.607e+05 dx float64 8B 25.0 dy float64 8B -25.0 * layer (layer) int64 8B 34}, 'chd-35': {'head': Size: 272kB dask.array Coordinates: * x (x) float64 1kB 1.947e+05 1.947e+05 ... 1.993e+05 1.993e+05 * y (y) float64 1kB 3.653e+05 3.653e+05 ... 3.607e+05 3.607e+05 dx float64 8B 25.0 dy float64 8B -25.0 * layer (layer) int64 8B 35}, 'chd-36': {'head': Size: 272kB dask.array Coordinates: * x (x) float64 1kB 1.947e+05 1.947e+05 ... 1.993e+05 1.993e+05 * y (y) float64 1kB 3.653e+05 3.653e+05 ... 3.607e+05 3.607e+05 dx float64 8B 25.0 dy float64 8B -25.0 * layer (layer) int64 8B 36}, 'chd-37': {'head': Size: 272kB dask.array Coordinates: * x (x) float64 1kB 1.947e+05 1.947e+05 ... 1.993e+05 1.993e+05 * y (y) float64 1kB 3.653e+05 3.653e+05 ... 3.607e+05 3.607e+05 dx float64 8B 25.0 dy float64 8B -25.0 * layer (layer) int64 8B 37}, 'pcg': {'mxiter': 5000, 'iter1': 20, 'hclose': 0.001, 'rclose': 0.1, 'relax': 0.98, 'npcond': 1, 'iprpcg': 1, 'mutpcg': 0, 'damppcg': 1.0, 'damppcgt': 1.0, 'iqerror': 0, 'qerror': 0.1, 'active': '1'}} .. GENERATED FROM PYTHON SOURCE LINES 67-69 This groups all data per package in the projectfile into a dictionary with DataArrays per variable. .. GENERATED FROM PYTHON SOURCE LINES 70-73 .. code-block:: Python imod5_data["riv-1"]["stage"] .. raw:: html 
<xarray.DataArray 'stage' (layer: 1, y: 185, x: 184)> Size: 136kB
    dask.array<add, shape=(1, 185, 184), dtype=float32, chunksize=(1, 185, 184), chunktype=numpy.ndarray>
    Coordinates:
      * x        (x) float64 1kB 1.947e+05 1.947e+05 ... 1.993e+05 1.993e+05
      * y        (y) float64 1kB 3.653e+05 3.653e+05 ... 3.607e+05 3.607e+05
        dx       float64 8B 25.0
        dy       float64 8B -25.0
      * layer    (layer) int64 8B 0


.. GENERATED FROM PYTHON SOURCE LINES 74-75 Let's plot the stage data of the first river package. .. GENERATED FROM PYTHON SOURCE LINES 76-79 .. code-block:: Python imod5_data["riv-1"]["stage"].isel(layer=0, drop=True).plot.imshow() .. image-sg:: /user-guide/images/sphx_glr_12-imod5-model-to-modflow6_001.png :alt: dx = 25.0, dy = -25.0 :srcset: /user-guide/images/sphx_glr_12-imod5-model-to-modflow6_001.png :class: sphx-glr-single-img .. rst-class:: sphx-glr-script-out .. code-block:: none .. GENERATED FROM PYTHON SOURCE LINES 80-95 Converting iMOD5 model to MODFLOW 6 ----------------------------------- This is nice enough, but we want to convert this iMOD5 model to a MODFLOW 6 model. We can do this using :meth:`imod.mf6.Modflow6Simulation.from_imod5_data` method. Next to the iMOD5 data and period data, we also need to provide the times. These will be used to resample the asynchronous well timeseries data to these times. For instance, well 1 in the iMOD5 database can have rates specified on a daily basis, whereas well 2 is specified on a few days in the year. Say the user wants to run a model on a monthly basis, this will require resampling these rate timeseries to make them consistent with the simulation timesteps. Let's therefore first create a list of times which will be the simulation's timesteps, we can use pandas for this. "MS" stands for "month start", meaning the first day of each month. .. GENERATED FROM PYTHON SOURCE LINES 96-102 .. code-block:: Python import pandas as pd times = pd.date_range(start="2020-01-01", periods=10, freq="MS") times .. rst-class:: sphx-glr-script-out .. code-block:: none DatetimeIndex(['2020-01-01', '2020-02-01', '2020-03-01', '2020-04-01', '2020-05-01', '2020-06-01', '2020-07-01', '2020-08-01', '2020-09-01', '2020-10-01'], dtype='datetime64[ns]', freq='MS') .. GENERATED FROM PYTHON SOURCE LINES 103-109 Now that we have a list of times, we can import the iMOD5 data into a MODFLOW 6 simulation. This might require some time, as it will convert all the iMOD5 data to be compatible with MODFLOW . For example, the river systems with infiltration factors are transformed into a separate Drain and River package (if necessary) to get the same behavior as iMOD5's infiltration factors. .. GENERATED FROM PYTHON SOURCE LINES 110-114 .. code-block:: Python mf6_sim = imod.mf6.Modflow6Simulation.from_imod5_data(imod5_data, period_data, times) mf6_sim .. rst-class:: sphx-glr-script-out .. code-block:: none Modflow6Simulation( name='imported_simulation', directory=None ){ 'imported_model': GroundwaterFlowModel, 'ims': Solution, 'time_discretization': TimeDiscretization, } .. GENERATED FROM PYTHON SOURCE LINES 115-121 Improving the solver settings ----------------------------- At the moment the MODFLOW 6 simulation has quite loose solver settings. Most notably, the ``inner_dvclose`` is set to 0.01, which means that the solver allows a numerical error of 1 cm in the head values. This is quite loose. .. GENERATED FROM PYTHON SOURCE LINES 122-125 .. code-block:: Python mf6_sim["ims"] .. raw:: html
Solution
<xarray.Dataset> Size: 320B
    Dimensions:                        (model: 1)
    Dimensions without coordinates: model
    Data variables: (12/28)
        outer_dvclose                  float64 8B 0.01
        outer_maximum                  int64 8B 50
        under_relaxation               <U3 12B 'dbd'
        under_relaxation_theta         float64 8B 0.9
        under_relaxation_kappa         float64 8B 0.0001
        under_relaxation_gamma         float64 8B 0.0
        ...                             ...
        print_option                   <U3 12B 'all'
        outer_csvfile                  object 8B None
        inner_csvfile                  object 8B None
        ats_outer_maximum_fraction     object 8B None
        no_ptc                         object 8B None
        modelnames                     (model) <U14 56B 'imported_model'


.. GENERATED FROM PYTHON SOURCE LINES 126-131 This is because by default an iMOD5 model is imported with a SolutionPresetModerate, which is quite loose. Let's set a stricter solver setting preset, by setting it to SolutionPresetSimple. This has a ``inner_dvclose`` of 0.001, which allows a numerical error of 1 mm in the head values. .. GENERATED FROM PYTHON SOURCE LINES 132-136 .. code-block:: Python mf6_sim["ims"] = imod.mf6.SolutionPresetSimple(["imported_model"]) mf6_sim["ims"] .. raw:: html
Solution
<xarray.Dataset> Size: 308B
    Dimensions:                        (model: 1)
    Dimensions without coordinates: model
    Data variables: (12/28)
        outer_dvclose                  float64 8B 0.001
        outer_maximum                  int64 8B 25
        under_relaxation               object 8B None
        under_relaxation_theta         float64 8B 0.0
        under_relaxation_kappa         float64 8B 0.0
        under_relaxation_gamma         float64 8B 0.0
        ...                             ...
        print_option                   <U7 28B 'summary'
        outer_csvfile                  object 8B None
        inner_csvfile                  object 8B None
        ats_outer_maximum_fraction     object 8B None
        no_ptc                         object 8B None
        modelnames                     (model) <U14 56B 'imported_model'


.. GENERATED FROM PYTHON SOURCE LINES 137-152 A note on performance --------------------- By default, the iMOD5 model rasters will not be directly loaded into memory, but instead will be lazily loaded. Read more about this in the :doc:`06-lazy-evaluation` documentation. By default the data is chunked per raster file, which is a chunk per layer, per timestep. Usually this is not optimal, as this creates many small chunks. Writing the structured model: in fits and starts ------------------------------------------------ Let's try to write this simulation. **spoiler alert**: this will fail, because we still have to configure some packages. .. GENERATED FROM PYTHON SOURCE LINES 153-161 .. code-block:: Python mf6_dir = tmpdir / "mf6_structured" # Ignore this "with" statement, it is to catch the error and render the # documentation without error. with imod.util.print_if_error(ValueError): mf6_sim.write(mf6_dir) # Attention: this will fail! .. rst-class:: sphx-glr-script-out .. code-block:: none No oc package found in model imported_model .. GENERATED FROM PYTHON SOURCE LINES 162-164 We are still missing output control, as the projectfile does not contain this information. For this example, we'll only save the last head of each stress period. .. GENERATED FROM PYTHON SOURCE LINES 165-176 .. code-block:: Python gwf_model = mf6_sim["imported_model"] gwf_model["oc"] = imod.mf6.OutputControl( save_head="last", ) from imod.schemata import ValidationError with imod.util.print_if_error(ValidationError): mf6_sim.write(mf6_dir) # Attention: this will fail! .. rst-class:: sphx-glr-script-out .. code-block:: none Simulation validation status: - imported_model model: - model options package: - npf package: - k: - nodata is not aligned with idomain - k33: - nodata is not aligned with idomain - ic package: - start: - nodata is not aligned with idomain .. GENERATED FROM PYTHON SOURCE LINES 177-184 Argh! The simulation still fails to write. In general, iMOD Python is a lot stricter with writing model data than iMOD5. iMOD Python forces users to conciously clean up their models, whereas iMOD5 cleaned data under the hood. The error message states that the nodata is not aligned with idomain. This means there are k values and ic values specified at inactive locations, or vice versa. Let's try if masking the data works. This will remove all inactive locations (``idomain > 0``) from the data. .. GENERATED FROM PYTHON SOURCE LINES 185-191 .. code-block:: Python idomain = gwf_model["dis"]["idomain"] mf6_sim.mask_all_models(idomain) mf6_sim.write(mf6_dir) .. GENERATED FROM PYTHON SOURCE LINES 192-196 Running the structured model ---------------------------- Let's run the simulation and open the head data. .. GENERATED FROM PYTHON SOURCE LINES 197-203 .. code-block:: Python mf6_sim.run() head_structured = mf6_sim.open_head() # Plot the head of the last stress period at layer 5. head_structured.isel(time=-1).sel(layer=5).plot.imshow() .. image-sg:: /user-guide/images/sphx_glr_12-imod5-model-to-modflow6_002.png :alt: dx = 25.0, dy = -25.0, layer = 5, time = 274.0 :srcset: /user-guide/images/sphx_glr_12-imod5-model-to-modflow6_002.png :class: sphx-glr-single-img .. rst-class:: sphx-glr-script-out .. code-block:: none .. GENERATED FROM PYTHON SOURCE LINES 204-209 Regridding the structured model to an unstructured grid ------------------------------------------------------- Now that we have a MODFLOW 6 simulation, we can regrid it to an unstructured grid. Let's first load a triangular grid. .. GENERATED FROM PYTHON SOURCE LINES 210-214 .. code-block:: Python triangular_grid = imod.data.lhm_clip_triangular_grid() triangular_grid.plot() .. image-sg:: /user-guide/images/sphx_glr_12-imod5-model-to-modflow6_003.png :alt: 12 imod5 model to modflow6 :srcset: /user-guide/images/sphx_glr_12-imod5-model-to-modflow6_003.png :class: sphx-glr-single-img .. rst-class:: sphx-glr-script-out .. code-block:: none .. GENERATED FROM PYTHON SOURCE LINES 215-219 That looks more exciting than the rectangular grid we had before. You can see there is refinement around some of the streams and especially around horizontal flow barriers. We haven't looked at horizontal flow barriers yet, so let's plot them on top of the triangular mesh. .. GENERATED FROM PYTHON SOURCE LINES 220-228 .. code-block:: Python import matplotlib.pyplot as plt fig, ax = plt.subplots() triangular_grid.plot(ax=ax, color="lightgrey", edgecolor="black") gwf_model["hfb-25"].line_data.plot(ax=ax, color="blue", linewidth=2) gwf_model["hfb-26"].line_data.plot(ax=ax, color="blue", linewidth=2) .. image-sg:: /user-guide/images/sphx_glr_12-imod5-model-to-modflow6_004.png :alt: 12 imod5 model to modflow6 :srcset: /user-guide/images/sphx_glr_12-imod5-model-to-modflow6_004.png :class: sphx-glr-single-img .. rst-class:: sphx-glr-script-out .. code-block:: none .. GENERATED FROM PYTHON SOURCE LINES 229-234 However, this grid is triangular, which has the disadvantage that the connections between cell centers are not orthogonal to the cell edges, which can lead to mass balance errors. xugrid has a method to convert this triangular grid to a Voronoi grid, which has orthogonal connections between cell centers and edges. .. GENERATED FROM PYTHON SOURCE LINES 235-245 .. code-block:: Python voronoi_grid = triangular_grid.tesselate_centroidal_voronoi() voronoi_grid.plot() # iMOD Python regridding functionality requires a UgridDataArray instead of a # Ugrid2d, so we create a UgridDataArray with the voronoi grid. from imod.util import ones_like_ugrid voronoi_uda = ones_like_ugrid(voronoi_grid) .. image-sg:: /user-guide/images/sphx_glr_12-imod5-model-to-modflow6_005.png :alt: 12 imod5 model to modflow6 :srcset: /user-guide/images/sphx_glr_12-imod5-model-to-modflow6_005.png :class: sphx-glr-single-img .. GENERATED FROM PYTHON SOURCE LINES 246-248 Now that we have a Voronoi grid, we can regrid the MODFLOW 6 simulation to this grid. .. GENERATED FROM PYTHON SOURCE LINES 249-255 .. code-block:: Python mf6_unstructured = mf6_sim.regrid_like( "unstructured_example", voronoi_uda, validate=False ) mf6_unstructured .. rst-class:: sphx-glr-script-out .. code-block:: none Modflow6Simulation( name='unstructured_example', directory=None ){ 'imported_model': GroundwaterFlowModel, 'ims': Solution, 'time_discretization': TimeDiscretization, } .. GENERATED FROM PYTHON SOURCE LINES 256-257 Let's take a gander at how the river data is regridded. .. GENERATED FROM PYTHON SOURCE LINES 258-261 .. code-block:: Python mf6_unstructured["imported_model"]["riv-1riv"]["stage"].isel(layer=0).ugrid.plot() .. image-sg:: /user-guide/images/sphx_glr_12-imod5-model-to-modflow6_006.png :alt: layer = 1 :srcset: /user-guide/images/sphx_glr_12-imod5-model-to-modflow6_006.png :class: sphx-glr-single-img .. rst-class:: sphx-glr-script-out .. code-block:: none .. GENERATED FROM PYTHON SOURCE LINES 262-267 Writing the unstructured model: in more fits and starts ------------------------------------------------------- Let's try to write this to a temporary directory. **Spoiler alert**: Like before, this will fail. .. GENERATED FROM PYTHON SOURCE LINES 268-276 .. code-block:: Python mf6_dir = tmpdir / "mf6_unstructured" # Ignore this "with" statement, it is to catch the error and render the # documentation without error. with imod.util.print_if_error(ValidationError): mf6_unstructured.write(mf6_dir) # Attention: this will fail! .. rst-class:: sphx-glr-script-out .. code-block:: none Simulation validation status: - imported_model model: - model options package: - riv-1riv package: - bottom_elevation: - not all values comply with criterion: >= bottom -> You might fix this by calling the package's ``.cleanup()`` method. - riv-2riv package: - bottom_elevation: - not all values comply with criterion: >= bottom -> You might fix this by calling the package's ``.cleanup()`` method. .. GENERATED FROM PYTHON SOURCE LINES 277-284 The error message states that the iMOD5 model has a river package that has its river bottom elevation below the model bottom. The averaging when regridding can cause this: The model bottom has a continuous surface, whereas the rivers usually are located in a local valley. Upscaling both with a mean causes the river bottom elevation to have the tendency to be lower than the model bottom. We therefore need to reallocate the river data to the new model layer schematization. .. GENERATED FROM PYTHON SOURCE LINES 285-299 .. code-block:: Python gwf_unstructured = mf6_unstructured["imported_model"] dis = gwf_unstructured["dis"] npf = gwf_unstructured["npf"] gwf_unstructured["riv-1riv"] = gwf_unstructured["riv-1riv"].reallocate(dis, npf) gwf_unstructured["riv-2riv"] = gwf_unstructured["riv-2riv"].reallocate(dis, npf) gwf_unstructured["riv-1drn"] = gwf_unstructured["riv-1drn"].reallocate(dis, npf) gwf_unstructured["riv-2drn"] = gwf_unstructured["riv-2drn"].reallocate(dis, npf) gwf_unstructured["riv-1riv"].cleanup(dis) gwf_unstructured["riv-2riv"].cleanup(dis) gwf_unstructured["riv-1drn"].cleanup(dis) gwf_unstructured["riv-2drn"].cleanup(dis) .. GENERATED FROM PYTHON SOURCE LINES 300-305 Finally, we need to set the HFB validation settings to less strict. Otherwise, we'll get errors about hfb's being connected to inactive cells. Normally, you would set this when creating a new Modflow6Simulation, but since we created one from an iMOD5 model, these settings cannot be set upon creation. We can however set the validation settings directly by changing this attribute: .. GENERATED FROM PYTHON SOURCE LINES 306-311 .. code-block:: Python from imod.mf6 import ValidationSettings mf6_unstructured._validation_context = ValidationSettings(strict_hfb_validation=False) .. GENERATED FROM PYTHON SOURCE LINES 312-313 We'll now be able to finally write the unstructured model. .. GENERATED FROM PYTHON SOURCE LINES 314-317 .. code-block:: Python mf6_unstructured.write(mf6_dir) .. GENERATED FROM PYTHON SOURCE LINES 318-322 Running the unstructured model ------------------------------ Let's run the unstructured model and open the head data. .. GENERATED FROM PYTHON SOURCE LINES 323-329 .. code-block:: Python mf6_unstructured.run() head_unstructured = mf6_unstructured.open_head() # Plot the head of the last stress period at layer 5. head_unstructured.isel(time=-1).sel(layer=5).ugrid.plot() .. image-sg:: /user-guide/images/sphx_glr_12-imod5-model-to-modflow6_007.png :alt: layer = 5, time = 274.0 :srcset: /user-guide/images/sphx_glr_12-imod5-model-to-modflow6_007.png :class: sphx-glr-single-img .. rst-class:: sphx-glr-script-out .. code-block:: none .. GENERATED FROM PYTHON SOURCE LINES 330-343 Comparing differences in output ------------------------------- In this section, we will compare the output of the structured and unstructured models. We fill first plot the structured and unstructured results side by side. Next, we will regrid the structured head data to the unstructured grid and compare the differences in output. This will show how the regridding affects the output. Side-by-side comparison *********************** Let's first plot the structured and unstructured head data side by side. .. GENERATED FROM PYTHON SOURCE LINES 344-365 .. code-block:: Python head_structured_end_l5 = head_structured.isel(time=-1).sel(layer=5) head_unstructured_end_l5 = head_unstructured.isel(time=-1).sel(layer=5) import matplotlib.pyplot as plt # Get the data range from both datasets vmin = min(head_structured_end_l5.min().values, head_unstructured_end_l5.min().values) vmax = max(head_structured_end_l5.max().values, head_unstructured_end_l5.max().values) fig, axes = plt.subplots(nrows=1, ncols=3, figsize=(12, 6), width_ratios=(1, 1, 0.1)) head_structured_end_l5.plot.imshow(ax=axes[0], add_colorbar=False, vmin=vmin, vmax=vmax) axes[0].set_title(f"Structured grid (ncells = {head_structured_end_l5.size})") head_unstructured_end_l5.ugrid.plot(ax=axes[1], cbar_ax=axes[2], vmin=vmin, vmax=vmax) axes[1].set_title(f"Unstructured grid (ncells = {head_unstructured_end_l5.size})") axes[1].get_yaxis().set_visible(False) # Get the y-limits from the first axis and apply to both ylim = axes[0].get_ylim() axes[1].set_ylim(ylim) .. image-sg:: /user-guide/images/sphx_glr_12-imod5-model-to-modflow6_008.png :alt: Structured grid (ncells = 33856), Unstructured grid (ncells = 5910) :srcset: /user-guide/images/sphx_glr_12-imod5-model-to-modflow6_008.png :class: sphx-glr-single-img .. rst-class:: sphx-glr-script-out .. code-block:: none (360700.0, 365300.0) .. GENERATED FROM PYTHON SOURCE LINES 366-375 Computing differences ********************* Next, we will compute the differences between the structured and unstructured head data. This will show how the regridding affects the output in more detail. For that we first need to upscale the structured head data to the unstructured grid. This is done using the `OverlapRegridder from the xugrid package `_, .. GENERATED FROM PYTHON SOURCE LINES 376-382 .. code-block:: Python import xugrid as xu regridder = xu.OverlapRegridder(head_structured, head_unstructured.ugrid.grid) head_structured_upscaled = regridder.regrid(head_structured) .. GENERATED FROM PYTHON SOURCE LINES 383-387 Compute the difference between the upscaled structured head and the unstructured head. A zero difference means the regridding didn't result in any differences. We can see around the western fault that the regridding caused differences. .. GENERATED FROM PYTHON SOURCE LINES 388-392 .. code-block:: Python diff = (head_structured_upscaled - head_unstructured).isel(time=-1).compute() diff.mean(dim="layer").ugrid.plot() .. image-sg:: /user-guide/images/sphx_glr_12-imod5-model-to-modflow6_009.png :alt: dx = 25.0, dy = -25.0, time = 274.0 :srcset: /user-guide/images/sphx_glr_12-imod5-model-to-modflow6_009.png :class: sphx-glr-single-img .. rst-class:: sphx-glr-script-out .. code-block:: none .. GENERATED FROM PYTHON SOURCE LINES 393-395 Let's also plot the standard deviation of the difference. This shows that variations in difference are also mostly around the western fault. .. GENERATED FROM PYTHON SOURCE LINES 396-399 .. code-block:: Python diff.std(dim="layer").ugrid.plot() .. image-sg:: /user-guide/images/sphx_glr_12-imod5-model-to-modflow6_010.png :alt: dx = 25.0, dy = -25.0, time = 274.0 :srcset: /user-guide/images/sphx_glr_12-imod5-model-to-modflow6_010.png :class: sphx-glr-single-img .. rst-class:: sphx-glr-script-out .. code-block:: none .. GENERATED FROM PYTHON SOURCE LINES 400-408 Differences in detail ********************* This is a good example of how regridding can lead to differences in output: The line representing the fault has to be snapped to the cell edges. This is strongly grid dependent. And can lead to local differences in output. Let's visualize how faults are snapped to the grid edges in the structured and unstructured grid. .. GENERATED FROM PYTHON SOURCE LINES 409-422 .. code-block:: Python structured_snapped = gwf_model["hfb-5"].snap_to_grid(gwf_model["dis"]) unstructured_snapped = gwf_unstructured["hfb-5"].snap_to_grid(gwf_unstructured["dis"]) import matplotlib.pyplot as plt fig, ax = plt.subplots() structured_snapped["resistance"].ugrid.plot(add_colorbar=False, ax=ax) unstructured_snapped["resistance"].ugrid.plot(add_colorbar=False, ax=ax) diff.mean(dim="layer").ugrid.plot(ax=ax) ax.set_xlim(197500, 198500) ax.set_ylim(361000, 363000) .. image-sg:: /user-guide/images/sphx_glr_12-imod5-model-to-modflow6_011.png :alt: dx = 25.0, dy = -25.0, time = 274.0 :srcset: /user-guide/images/sphx_glr_12-imod5-model-to-modflow6_011.png :class: sphx-glr-single-img .. rst-class:: sphx-glr-script-out .. code-block:: none (361000.0, 363000.0) .. GENERATED FROM PYTHON SOURCE LINES 423-429 EXERCISE: Download this file as a script or Jupyter notebook, remove all HFB packages and re-run the example. Investigate if differences are still as large as they were. You can remove the HFB packages by using the ``pop`` method on the groundwater flow model. There are 26 HFB packages in the model, so you can use a for loop to remove them all. The HFB packages are named "hfb-1", "hfb-2", ..., "hfb-26". .. rst-class:: sphx-glr-timing **Total running time of the script:** (3 minutes 0.054 seconds) .. _sphx_glr_download_user-guide_12-imod5-model-to-modflow6.py: .. only:: html .. container:: sphx-glr-footer sphx-glr-footer-example .. container:: sphx-glr-download sphx-glr-download-jupyter :download:`Download Jupyter notebook: 12-imod5-model-to-modflow6.ipynb <12-imod5-model-to-modflow6.ipynb>` .. container:: sphx-glr-download sphx-glr-download-python :download:`Download Python source code: 12-imod5-model-to-modflow6.py <12-imod5-model-to-modflow6.py>` .. container:: sphx-glr-download sphx-glr-download-zip :download:`Download zipped: 12-imod5-model-to-modflow6.zip <12-imod5-model-to-modflow6.zip>` .. only:: html .. rst-class:: sphx-glr-signature `Gallery generated by Sphinx-Gallery `_