Note
Go to the end to download the full example code.
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.
import imod
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.
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)
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.
from pprint import pprint
imod_dir_contents = list(model_dir.glob("*"))
pprint(imod_dir_contents)
[WindowsPath('C:/BuildAgent/temp/buildTmp/tmpvlkdapvj/prj/iMOD5_model_pooch/Database'),
WindowsPath('C:/BuildAgent/temp/buildTmp/tmpvlkdapvj/prj/iMOD5_model_pooch/iMOD5_model.prj')]
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:
prj_path = model_dir / "iMOD5_model.prj"
prj_content = imod.prj.read_projectfile(prj_path)
pprint(prj_content)
{'(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': WindowsPath('C:/buildagent/temp/buildtmp/tmpvlkdapvj/prj/iMOD5_model_pooch/Database/BND/VERSION_1/IBOUND_L12.IDF')},
{'active': True,
'addition': 0.0,
'constant': -999.99,
'factor': 1.0,
'is_constant': 2,
'layer': 13,
'path': WindowsPath('C:/buildagent/temp/buildtmp/tmpvlkdapvj/prj/iMOD5_model_pooch/Database/BND/VERSION_1/IBOUND_L13.IDF')},
{'active': True,
'addition': 0.0,
'constant': -999.99,
'factor': 1.0,
'is_constant': 2,
'layer': 14,
'path': WindowsPath('C:/buildagent/temp/buildtmp/tmpvlkdapvj/prj/iMOD5_model_pooch/Database/BND/VERSION_1/IBOUND_L14.IDF')},
{'active': True,
'addition': 0.0,
'constant': -999.99,
'factor': 1.0,
'is_constant': 2,
'layer': 15,
'path': WindowsPath('C:/buildagent/temp/buildtmp/tmpvlkdapvj/prj/iMOD5_model_pooch/Database/BND/VERSION_1/IBOUND_L15.IDF')},
{'active': True,
'addition': 0.0,
'constant': -999.99,
'factor': 1.0,
'is_constant': 2,
'layer': 16,
'path': WindowsPath('C:/buildagent/temp/buildtmp/tmpvlkdapvj/prj/iMOD5_model_pooch/Database/BND/VERSION_1/IBOUND_L16.IDF')},
{'active': True,
'addition': 0.0,
'constant': -999.99,
'factor': 1.0,
'is_constant': 2,
'layer': 17,
'path': WindowsPath('C:/buildagent/temp/buildtmp/tmpvlkdapvj/prj/iMOD5_model_pooch/Database/BND/VERSION_1/IBOUND_L17.IDF')},
{'active': True,
'addition': 0.0,
'constant': -999.99,
'factor': 1.0,
'is_constant': 2,
'layer': 18,
'path': WindowsPath('C:/buildagent/temp/buildtmp/tmpvlkdapvj/prj/iMOD5_model_pooch/Database/BND/VERSION_1/IBOUND_L18.IDF')},
{'active': True,
'addition': 0.0,
'constant': -999.99,
'factor': 1.0,
'is_constant': 2,
'layer': 19,
'path': WindowsPath('C:/buildagent/temp/buildtmp/tmpvlkdapvj/prj/iMOD5_model_pooch/Database/BND/VERSION_1/IBOUND_L19.IDF')},
{'active': True,
'addition': 0.0,
'constant': -999.99,
'factor': 1.0,
'is_constant': 2,
'layer': 20,
'path': WindowsPath('C:/buildagent/temp/buildtmp/tmpvlkdapvj/prj/iMOD5_model_pooch/Database/BND/VERSION_1/IBOUND_L20.IDF')},
{'active': True,
'addition': 0.0,
'constant': -999.99,
'factor': 1.0,
'is_constant': 2,
'layer': 21,
'path': WindowsPath('C:/buildagent/temp/buildtmp/tmpvlkdapvj/prj/iMOD5_model_pooch/Database/BND/VERSION_1/IBOUND_L21.IDF')},
{'active': True,
'addition': 0.0,
'constant': -999.99,
'factor': 1.0,
'is_constant': 2,
'layer': 22,
'path': WindowsPath('C:/buildagent/temp/buildtmp/tmpvlkdapvj/prj/iMOD5_model_pooch/Database/BND/VERSION_1/IBOUND_L22.IDF')},
{'active': True,
'addition': 0.0,
'constant': -999.99,
'factor': 1.0,
'is_constant': 2,
'layer': 23,
'path': WindowsPath('C:/buildagent/temp/buildtmp/tmpvlkdapvj/prj/iMOD5_model_pooch/Database/BND/VERSION_1/IBOUND_L23.IDF')},
{'active': True,
'addition': 0.0,
'constant': -999.99,
'factor': 1.0,
'is_constant': 2,
'layer': 24,
'path': WindowsPath('C:/buildagent/temp/buildtmp/tmpvlkdapvj/prj/iMOD5_model_pooch/Database/BND/VERSION_1/IBOUND_L24.IDF')},
{'active': True,
'addition': 0.0,
'constant': -999.99,
'factor': 1.0,
'is_constant': 2,
'layer': 25,
'path': WindowsPath('C:/buildagent/temp/buildtmp/tmpvlkdapvj/prj/iMOD5_model_pooch/Database/BND/VERSION_1/IBOUND_L25.IDF')},
{'active': True,
'addition': 0.0,
'constant': -999.99,
'factor': 1.0,
'is_constant': 2,
'layer': 26,
'path': WindowsPath('C:/buildagent/temp/buildtmp/tmpvlkdapvj/prj/iMOD5_model_pooch/Database/BND/VERSION_1/IBOUND_L26.IDF')},
{'active': True,
'addition': 0.0,
'constant': -999.99,
'factor': 1.0,
'is_constant': 2,
'layer': 27,
'path': WindowsPath('C:/buildagent/temp/buildtmp/tmpvlkdapvj/prj/iMOD5_model_pooch/Database/BND/VERSION_1/IBOUND_L27.IDF')},
{'active': True,
'addition': 0.0,
'constant': -999.99,
'factor': 1.0,
'is_constant': 2,
'layer': 28,
'path': WindowsPath('C:/buildagent/temp/buildtmp/tmpvlkdapvj/prj/iMOD5_model_pooch/Database/BND/VERSION_1/IBOUND_L28.IDF')},
{'active': True,
'addition': 0.0,
'constant': -999.99,
'factor': 1.0,
'is_constant': 2,
'layer': 29,
'path': WindowsPath('C:/buildagent/temp/buildtmp/tmpvlkdapvj/prj/iMOD5_model_pooch/Database/BND/VERSION_1/IBOUND_L29.IDF')},
{'active': True,
'addition': 0.0,
'constant': -999.99,
'factor': 1.0,
'is_constant': 2,
'layer': 30,
'path': WindowsPath('C:/buildagent/temp/buildtmp/tmpvlkdapvj/prj/iMOD5_model_pooch/Database/BND/VERSION_1/IBOUND_L30.IDF')},
{'active': True,
'addition': 0.0,
'constant': -999.99,
'factor': 1.0,
'is_constant': 2,
'layer': 31,
'path': WindowsPath('C:/buildagent/temp/buildtmp/tmpvlkdapvj/prj/iMOD5_model_pooch/Database/BND/VERSION_1/IBOUND_L31.IDF')},
{'active': True,
'addition': 0.0,
'constant': -999.99,
'factor': 1.0,
'is_constant': 2,
'layer': 32,
'path': WindowsPath('C:/buildagent/temp/buildtmp/tmpvlkdapvj/prj/iMOD5_model_pooch/Database/BND/VERSION_1/IBOUND_L32.IDF')},
{'active': True,
'addition': 0.0,
'constant': -999.99,
'factor': 1.0,
'is_constant': 2,
'layer': 33,
'path': WindowsPath('C:/buildagent/temp/buildtmp/tmpvlkdapvj/prj/iMOD5_model_pooch/Database/BND/VERSION_1/IBOUND_L33.IDF')},
{'active': True,
'addition': 0.0,
'constant': -999.99,
'factor': 1.0,
'is_constant': 2,
'layer': 34,
'path': WindowsPath('C:/buildagent/temp/buildtmp/tmpvlkdapvj/prj/iMOD5_model_pooch/Database/BND/VERSION_1/IBOUND_L34.IDF')},
{'active': True,
'addition': 0.0,
'constant': -999.99,
'factor': 1.0,
'is_constant': 2,
'layer': 35,
'path': WindowsPath('C:/buildagent/temp/buildtmp/tmpvlkdapvj/prj/iMOD5_model_pooch/Database/BND/VERSION_1/IBOUND_L35.IDF')},
{'active': True,
'addition': 0.0,
'constant': -999.99,
'factor': 1.0,
'is_constant': 2,
'layer': 36,
'path': WindowsPath('C:/buildagent/temp/buildtmp/tmpvlkdapvj/prj/iMOD5_model_pooch/Database/BND/VERSION_1/IBOUND_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/BND/VERSION_1/IBOUND_L37.IDF')}],
'n_system': 37},
'(bot)': {'active': '1',
'bottom': [{'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/BOT/VERSION_1/BOT_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/BOT/VERSION_1/BOT_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/BOT/VERSION_1/BOT_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/BOT/VERSION_1/BOT_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/BOT/VERSION_1/BOT_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/BOT/VERSION_1/BOT_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/BOT/VERSION_1/BOT_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/BOT/VERSION_1/BOT_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/BOT/VERSION_1/BOT_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/BOT/VERSION_1/BOT_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/BOT/VERSION_1/BOT_L11.IDF')},
{'active': True,
'addition': 0.0,
'constant': -999.99,
'factor': 1.0,
'is_constant': 2,
'layer': 12,
'path': WindowsPath('C:/buildagent/temp/buildtmp/tmpvlkdapvj/prj/iMOD5_model_pooch/Database/BOT/VERSION_1/BOT_L12.IDF')},
{'active': True,
'addition': 0.0,
'constant': -999.99,
'factor': 1.0,
'is_constant': 2,
'layer': 13,
'path': WindowsPath('C:/buildagent/temp/buildtmp/tmpvlkdapvj/prj/iMOD5_model_pooch/Database/BOT/VERSION_1/BOT_L13.IDF')},
{'active': True,
'addition': 0.0,
'constant': -999.99,
'factor': 1.0,
'is_constant': 2,
'layer': 14,
'path': WindowsPath('C:/buildagent/temp/buildtmp/tmpvlkdapvj/prj/iMOD5_model_pooch/Database/BOT/VERSION_1/BOT_L14.IDF')},
{'active': True,
'addition': 0.0,
'constant': -999.99,
'factor': 1.0,
'is_constant': 2,
'layer': 15,
'path': WindowsPath('C:/buildagent/temp/buildtmp/tmpvlkdapvj/prj/iMOD5_model_pooch/Database/BOT/VERSION_1/BOT_L15.IDF')},
{'active': True,
'addition': 0.0,
'constant': -999.99,
'factor': 1.0,
'is_constant': 2,
'layer': 16,
'path': WindowsPath('C:/buildagent/temp/buildtmp/tmpvlkdapvj/prj/iMOD5_model_pooch/Database/BOT/VERSION_1/BOT_L16.IDF')},
{'active': True,
'addition': 0.0,
'constant': -999.99,
'factor': 1.0,
'is_constant': 2,
'layer': 17,
'path': WindowsPath('C:/buildagent/temp/buildtmp/tmpvlkdapvj/prj/iMOD5_model_pooch/Database/BOT/VERSION_1/BOT_L17.IDF')},
{'active': True,
'addition': 0.0,
'constant': -999.99,
'factor': 1.0,
'is_constant': 2,
'layer': 18,
'path': WindowsPath('C:/buildagent/temp/buildtmp/tmpvlkdapvj/prj/iMOD5_model_pooch/Database/BOT/VERSION_1/BOT_L18.IDF')},
{'active': True,
'addition': 0.0,
'constant': -999.99,
'factor': 1.0,
'is_constant': 2,
'layer': 19,
'path': WindowsPath('C:/buildagent/temp/buildtmp/tmpvlkdapvj/prj/iMOD5_model_pooch/Database/BOT/VERSION_1/BOT_L19.IDF')},
{'active': True,
'addition': 0.0,
'constant': -999.99,
'factor': 1.0,
'is_constant': 2,
'layer': 20,
'path': WindowsPath('C:/buildagent/temp/buildtmp/tmpvlkdapvj/prj/iMOD5_model_pooch/Database/BOT/VERSION_1/BOT_L20.IDF')},
{'active': True,
'addition': 0.0,
'constant': -999.99,
'factor': 1.0,
'is_constant': 2,
'layer': 21,
'path': WindowsPath('C:/buildagent/temp/buildtmp/tmpvlkdapvj/prj/iMOD5_model_pooch/Database/BOT/VERSION_1/BOT_L21.IDF')},
{'active': True,
'addition': 0.0,
'constant': -999.99,
'factor': 1.0,
'is_constant': 2,
'layer': 22,
'path': WindowsPath('C:/buildagent/temp/buildtmp/tmpvlkdapvj/prj/iMOD5_model_pooch/Database/BOT/VERSION_1/BOT_L22.IDF')},
{'active': True,
'addition': 0.0,
'constant': -999.99,
'factor': 1.0,
'is_constant': 2,
'layer': 23,
'path': WindowsPath('C:/buildagent/temp/buildtmp/tmpvlkdapvj/prj/iMOD5_model_pooch/Database/BOT/VERSION_1/BOT_L23.IDF')},
{'active': True,
'addition': 0.0,
'constant': -999.99,
'factor': 1.0,
'is_constant': 2,
'layer': 24,
'path': WindowsPath('C:/buildagent/temp/buildtmp/tmpvlkdapvj/prj/iMOD5_model_pooch/Database/BOT/VERSION_1/BOT_L24.IDF')},
{'active': True,
'addition': 0.0,
'constant': -999.99,
'factor': 1.0,
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'path': WindowsPath('C:/buildagent/temp/buildtmp/tmpvlkdapvj/prj/iMOD5_model_pooch/Database/TOP/VERSION_1/TOP_L13.IDF')},
{'active': True,
'addition': 0.0,
'constant': -999.99,
'factor': 1.0,
'is_constant': 2,
'layer': 14,
'path': WindowsPath('C:/buildagent/temp/buildtmp/tmpvlkdapvj/prj/iMOD5_model_pooch/Database/TOP/VERSION_1/TOP_L14.IDF')},
{'active': True,
'addition': 0.0,
'constant': -999.99,
'factor': 1.0,
'is_constant': 2,
'layer': 15,
'path': WindowsPath('C:/buildagent/temp/buildtmp/tmpvlkdapvj/prj/iMOD5_model_pooch/Database/TOP/VERSION_1/TOP_L15.IDF')},
{'active': True,
'addition': 0.0,
'constant': -999.99,
'factor': 1.0,
'is_constant': 2,
'layer': 16,
'path': WindowsPath('C:/buildagent/temp/buildtmp/tmpvlkdapvj/prj/iMOD5_model_pooch/Database/TOP/VERSION_1/TOP_L16.IDF')},
{'active': True,
'addition': 0.0,
'constant': -999.99,
'factor': 1.0,
'is_constant': 2,
'layer': 17,
'path': WindowsPath('C:/buildagent/temp/buildtmp/tmpvlkdapvj/prj/iMOD5_model_pooch/Database/TOP/VERSION_1/TOP_L17.IDF')},
{'active': True,
'addition': 0.0,
'constant': -999.99,
'factor': 1.0,
'is_constant': 2,
'layer': 18,
'path': WindowsPath('C:/buildagent/temp/buildtmp/tmpvlkdapvj/prj/iMOD5_model_pooch/Database/TOP/VERSION_1/TOP_L18.IDF')},
{'active': True,
'addition': 0.0,
'constant': -999.99,
'factor': 1.0,
'is_constant': 2,
'layer': 19,
'path': WindowsPath('C:/buildagent/temp/buildtmp/tmpvlkdapvj/prj/iMOD5_model_pooch/Database/TOP/VERSION_1/TOP_L19.IDF')},
{'active': True,
'addition': 0.0,
'constant': -999.99,
'factor': 1.0,
'is_constant': 2,
'layer': 20,
'path': WindowsPath('C:/buildagent/temp/buildtmp/tmpvlkdapvj/prj/iMOD5_model_pooch/Database/TOP/VERSION_1/TOP_L20.IDF')},
{'active': True,
'addition': 0.0,
'constant': -999.99,
'factor': 1.0,
'is_constant': 2,
'layer': 21,
'path': WindowsPath('C:/buildagent/temp/buildtmp/tmpvlkdapvj/prj/iMOD5_model_pooch/Database/TOP/VERSION_1/TOP_L21.IDF')},
{'active': True,
'addition': 0.0,
'constant': -999.99,
'factor': 1.0,
'is_constant': 2,
'layer': 22,
'path': WindowsPath('C:/buildagent/temp/buildtmp/tmpvlkdapvj/prj/iMOD5_model_pooch/Database/TOP/VERSION_1/TOP_L22.IDF')},
{'active': True,
'addition': 0.0,
'constant': -999.99,
'factor': 1.0,
'is_constant': 2,
'layer': 23,
'path': WindowsPath('C:/buildagent/temp/buildtmp/tmpvlkdapvj/prj/iMOD5_model_pooch/Database/TOP/VERSION_1/TOP_L23.IDF')},
{'active': True,
'addition': 0.0,
'constant': -999.99,
'factor': 1.0,
'is_constant': 2,
'layer': 24,
'path': WindowsPath('C:/buildagent/temp/buildtmp/tmpvlkdapvj/prj/iMOD5_model_pooch/Database/TOP/VERSION_1/TOP_L24.IDF')},
{'active': True,
'addition': 0.0,
'constant': -999.99,
'factor': 1.0,
'is_constant': 2,
'layer': 25,
'path': WindowsPath('C:/buildagent/temp/buildtmp/tmpvlkdapvj/prj/iMOD5_model_pooch/Database/TOP/VERSION_1/TOP_L25.IDF')},
{'active': True,
'addition': 0.0,
'constant': -999.99,
'factor': 1.0,
'is_constant': 2,
'layer': 26,
'path': WindowsPath('C:/buildagent/temp/buildtmp/tmpvlkdapvj/prj/iMOD5_model_pooch/Database/TOP/VERSION_1/TOP_L26.IDF')},
{'active': True,
'addition': 0.0,
'constant': -999.99,
'factor': 1.0,
'is_constant': 2,
'layer': 27,
'path': WindowsPath('C:/buildagent/temp/buildtmp/tmpvlkdapvj/prj/iMOD5_model_pooch/Database/TOP/VERSION_1/TOP_L27.IDF')},
{'active': True,
'addition': 0.0,
'constant': -999.99,
'factor': 1.0,
'is_constant': 2,
'layer': 28,
'path': WindowsPath('C:/buildagent/temp/buildtmp/tmpvlkdapvj/prj/iMOD5_model_pooch/Database/TOP/VERSION_1/TOP_L28.IDF')},
{'active': True,
'addition': 0.0,
'constant': -999.99,
'factor': 1.0,
'is_constant': 2,
'layer': 29,
'path': WindowsPath('C:/buildagent/temp/buildtmp/tmpvlkdapvj/prj/iMOD5_model_pooch/Database/TOP/VERSION_1/TOP_L29.IDF')},
{'active': True,
'addition': 0.0,
'constant': -999.99,
'factor': 1.0,
'is_constant': 2,
'layer': 30,
'path': WindowsPath('C:/buildagent/temp/buildtmp/tmpvlkdapvj/prj/iMOD5_model_pooch/Database/TOP/VERSION_1/TOP_L30.IDF')},
{'active': True,
'addition': 0.0,
'constant': -999.99,
'factor': 1.0,
'is_constant': 2,
'layer': 31,
'path': WindowsPath('C:/buildagent/temp/buildtmp/tmpvlkdapvj/prj/iMOD5_model_pooch/Database/TOP/VERSION_1/TOP_L31.IDF')},
{'active': True,
'addition': 0.0,
'constant': -999.99,
'factor': 1.0,
'is_constant': 2,
'layer': 32,
'path': WindowsPath('C:/buildagent/temp/buildtmp/tmpvlkdapvj/prj/iMOD5_model_pooch/Database/TOP/VERSION_1/TOP_L32.IDF')},
{'active': True,
'addition': 0.0,
'constant': -999.99,
'factor': 1.0,
'is_constant': 2,
'layer': 33,
'path': WindowsPath('C:/buildagent/temp/buildtmp/tmpvlkdapvj/prj/iMOD5_model_pooch/Database/TOP/VERSION_1/TOP_L33.IDF')},
{'active': True,
'addition': 0.0,
'constant': -999.99,
'factor': 1.0,
'is_constant': 2,
'layer': 34,
'path': WindowsPath('C:/buildagent/temp/buildtmp/tmpvlkdapvj/prj/iMOD5_model_pooch/Database/TOP/VERSION_1/TOP_L34.IDF')},
{'active': True,
'addition': 0.0,
'constant': -999.99,
'factor': 1.0,
'is_constant': 2,
'layer': 35,
'path': WindowsPath('C:/buildagent/temp/buildtmp/tmpvlkdapvj/prj/iMOD5_model_pooch/Database/TOP/VERSION_1/TOP_L35.IDF')},
{'active': True,
'addition': 0.0,
'constant': -999.99,
'factor': 1.0,
'is_constant': 2,
'layer': 36,
'path': 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(<class 'list'>,
{'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'}}
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
imod.prj.open_projectfile_data() function.
imod5_data, period_data = imod.prj.open_projectfile_data(prj_path)
imod5_data
{'bnd': {'ibound': <xarray.DataArray (layer: 37, y: 184, x: 184)> Size: 10MB
dask.array<add, shape=(37, 184, 184), dtype=float64, chunksize=(1, 184, 184), chunktype=numpy.ndarray>
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dx float64 8B 25.0
dy float64 8B -25.0
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dx float64 8B 100.0
dy float64 8B -100.0
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array([[[1.00000000e+00, 1.00000000e+00, 1.00000000e+00, ...,
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...,
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1.00000000e+00, 1.00000000e+00, 1.00000000e+00],
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1.00000000e+00, 1.00000000e+00, 1.00000000e+00],
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1.00000000e+00, 1.00000000e+00, 1.00000000e+00]],
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0.00000000e+00, 0.00000000e+00, 0.00000000e+00],
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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, ...,
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Coordinates:
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* y (y) float64 376B 3.652e+05 3.652e+05 ... 3.608e+05 3.606e+05
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...,
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...
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Coordinates:
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* y (y) float64 1kB 3.653e+05 3.653e+05 ... 3.607e+05 3.607e+05
* layer (layer) int64 296B 1 2 3 4 5 6 7 8 9 ... 29 30 31 32 33 34 35 36 37
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Coordinates:
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Coordinates:
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* y (y) float64 376B 3.652e+05 3.652e+05 ... 3.608e+05 3.606e+05
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1 52,hoofdbreuken_br_2_line ... 10.0
2 50,hoofdbreuken_br_3_line ... 10.0
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.. ... ... ...
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63 79,shape2 ... 1000.0
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1 52,hoofdbreuken_br_2_line ... 1000.0
2 50,hoofdbreuken_br_3_line ... 1000.0
3 53,hoofdbreuken_br_4_line ... 1000.0
4 54,hoofdbreuken_br_5_line ... 1000.0
.. ... ... ...
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63 79,shape2 ... 1000.0
64 80,shape3 ... 1000.0
65 81,shape4 ... 1000.0
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2 50,hoofdbreuken_br_3_line ... 1000.0
3 53,hoofdbreuken_br_4_line ... 1000.0
4 54,hoofdbreuken_br_5_line ... 1000.0
.. ... ... ...
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63 79,shape2 ... 1000.0
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0 51,hoofdbreuken_br_1_line ... 101000.0
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3 53,hoofdbreuken_br_4_line ... 101000.0
4 54,hoofdbreuken_br_5_line ... 101000.0
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4 1990-01-01 -327.0 197910 362860 extractions_2 11.0 6.0
.. ... ... ... ... ... ... ...
89 2012-01-01 -528.0 197910 362860 extractions_2 11.0 6.0
90 2012-04-01 -633.0 197910 362860 extractions_2 11.0 6.0
91 2012-07-01 -617.0 197910 362860 extractions_2 11.0 6.0
92 2012-10-01 -528.0 197910 362860 extractions_2 11.0 6.0
93 2013-01-01 0.0 197910 362860 extractions_2 11.0 6.0
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* 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'}}
This groups all data per package in the projectfile into a dictionary with DataArrays per variable.
imod5_data["riv-1"]["stage"]
Let’s plot the stage data of the first river package.
imod5_data["riv-1"]["stage"].isel(layer=0, drop=True).plot.imshow()
<matplotlib.image.AxesImage object at 0x00000251C8FAF920>
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
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.
import pandas as pd
times = pd.date_range(start="2020-01-01", periods=10, freq="MS")
times
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')
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.
mf6_sim = imod.mf6.Modflow6Simulation.from_imod5_data(imod5_data, period_data, times)
mf6_sim
Modflow6Simulation(
name='imported_simulation',
directory=None
){
'imported_model': GroundwaterFlowModel,
'ims': Solution,
'time_discretization': TimeDiscretization,
}
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.
mf6_sim["ims"]
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.
mf6_sim["ims"] = imod.mf6.SolutionPresetSimple(["imported_model"])
mf6_sim["ims"]
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 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.
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!
No oc package found in model imported_model
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.
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!
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
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.
idomain = gwf_model["dis"]["idomain"]
mf6_sim.mask_all_models(idomain)
mf6_sim.write(mf6_dir)
Running the structured model#
Let’s run the simulation and open the head data.
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()
<matplotlib.image.AxesImage object at 0x00000251C91AE000>
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.
triangular_grid = imod.data.lhm_clip_triangular_grid()
triangular_grid.plot()
<matplotlib.collections.LineCollection object at 0x00000251CFD91760>
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.
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)
<Axes: >
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.
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)
Now that we have a Voronoi grid, we can regrid the MODFLOW 6 simulation to this grid.
mf6_unstructured = mf6_sim.regrid_like(
"unstructured_example", voronoi_uda, validate=False
)
mf6_unstructured
Modflow6Simulation(
name='unstructured_example',
directory=None
){
'imported_model': GroundwaterFlowModel,
'ims': Solution,
'time_discretization': TimeDiscretization,
}
Let’s take a gander at how the river data is regridded.
mf6_unstructured["imported_model"]["riv-1riv"]["stage"].isel(layer=0).ugrid.plot()
<matplotlib.collections.PolyCollection object at 0x00000251C53D09B0>
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.
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!
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.
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.
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)
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:
from imod.mf6 import ValidationSettings
mf6_unstructured._validation_context = ValidationSettings(strict_hfb_validation=False)
We’ll now be able to finally write the unstructured model.
mf6_unstructured.write(mf6_dir)
Running the unstructured model#
Let’s run the unstructured model and open the head data.
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()
<matplotlib.collections.PolyCollection object at 0x00000251D22C6480>
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.
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)
(360700.0, 365300.0)
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,
import xugrid as xu
regridder = xu.OverlapRegridder(head_structured, head_unstructured.ugrid.grid)
head_structured_upscaled = regridder.regrid(head_structured)
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.
diff = (head_structured_upscaled - head_unstructured).isel(time=-1).compute()
diff.mean(dim="layer").ugrid.plot()
<matplotlib.collections.PolyCollection object at 0x00000251D1A9EE40>
Let’s also plot the standard deviation of the difference. This shows that variations in difference are also mostly around the western fault.
diff.std(dim="layer").ugrid.plot()
<matplotlib.collections.PolyCollection object at 0x00000251D257FE90>
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.
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)
(361000.0, 363000.0)
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”.
Total running time of the script: (3 minutes 0.054 seconds)