mf6adj is a Python package for adjoint-state sensitivity analysis with MODFLOW 6. It uses the MODFLOW 6 API to access the internal solution components at run time — no modifications to MODFLOW 6 are required. Given one or more user-defined performance measures (heads, boundary fluxes, or composite objectives), mf6adj computes the sensitivity of each measure to model parameters across the full model domain.
pip
pip install mf6adjconda-forge
conda install -c conda-forge mf6adjmf6adj drives MODFLOW 6 through its shared library (libmf6). The easiest
way to get both is through the
flopy helper:
get-modflow --subset mf6,libmf6 :pythonimport flopy
import mf6adj
# locate the MF6 binary and shared library in the active conda environment
mf6_bin, lib_name = mf6adj.get_conda_mf6_paths()
# run the baseline forward model
flopy.run_model(exe_name=mf6_bin, namefile=None, model_ws="path/to/model")
# write a performance-measure file
with open("path/to/model/model.adj", "w") as f:
f.write("begin performance_measure head_obs\n")
f.write("1 1 1 5 5 head direct 1.0 -1.0e+30\n")
f.write("end performance_measure\n")
# solve forward and adjoint
adj = mf6adj.Mf6Adj("model.adj", str(lib_name), working_directory="path/to/model")
adj.solve_forward_model()
sensitivity_dfs = adj.solve_adjoint()
adj.finalize()
print(sensitivity_dfs["head_obs"])Full documentation, including API reference and example notebooks, is available at mf6adj.readthedocs.io.
If you use mf6adj in your work, please cite:
Hayek, M., White, J. T., Markovich, K. H., Hughes, J. D., & Lavenue, M. (2025). MF6-ADJ: A Non-Intrusive Adjoint Sensitivity Capability for MODFLOW 6. Groundwater, 63(6), 874–888. https://doi.org/10.1111/gwat.70025