refugees from Brazil’s Grande Seca drought of 1878
TBD: Ground water hydrology, surface water hydrology, coastal water hydrology…
Tools
Simulation
MODFLOW is the USGS's modular hydrologic model. MODFLOW is considered an international standard for simulating and predicting groundwater conditions and groundwater/surface-water interactions. MODFLOW 6 is presently the core MODFLOW version distributed by the USGS. The previous core version, MODFLOW-2005, is actively maintained and supported as well.
The FloPy package consists of a set of Python scripts to run MODFLOW, MT3D, SEAWAT and other MODFLOW-related groundwater programs. FloPy enables you to run all these programs with Python scripts.
But how do you install MODFLOW? It's one of those US government software packages which is perfectly respectable and functional but some versions use a “classic”, which is to say, antiquated, mode of development. (I mean, it does date to 1988.)
For the version of a… distinguished vintage, there is no source code repository (not for us punters anyway), just giant (like, 400MB) zip files full of compiled Windows binaries with big chunks of fortran code alongside for compilation by the adventurous.
There are various versions which are incompatible with each other to some unexplained extent.
These have version numbers like NWT, 2005 and 6.
Thankfully the community have made modflow pymake. The documentation was a little confusing (it suggests you use certain scripts that are not installed per default), but they all turn out to be one-liners. Here is how to install MODFLOW-NWT:
pip install pymake
pythonThen
import pymake
pymake.build_apps("mfnwt")This gets you a version that runs on linux, I think, but does not work on macos.
On macos, the easiest way seems to be to use docker files.
Here are some miscellaneous ones I am auditioning: lucasaugustomcc/modflow-nwt, flopymetascript, mintproject/modflow-2005 (the most popular one).
Or you can install MODFLOW 6 (but I think only that version) via conda:
conda install -c conda-forge modflow6MODFLOW 6 has a source code repository and everything, compiles and runs everywhere etc, but it does not implement all the features of historical MODFLOW version
Bakker, M., V. Post, C. D. Langevin, J. D. Hughes, J. T. White, J. J. Starn, and M. N. Fienen. 2016. “Scripting MODFLOW Model Development Using Python and FloPy.” Groundwater 54 (5): 733–39. https://doi.org/10.1111/gwat.12413.
Cai, Yuzhi, and Dominic E. Reeve. 2013. “Extreme Value Prediction via a Quantile Function Model.” Coastal Engineering 77 (July): 91–98. https://doi.org/10.1016/j.coastaleng.2013.02.003.
Cranmer, Miles, Sam Greydanus, Stephan Hoyer, Peter Battaglia, David Spergel, and Shirley Ho. 2020. “Lagrangian Neural Networks,” July. http://arxiv.org/abs/2003.04630.
Cui, Tao, Luk Peeters, Dan Pagendam, Trevor Pickett, Huidong Jin, Russell S. Crosbie, Matthias Raiber, David W. Rassam, and Mat Gilfedder. 2018. “Emulator-Enabled Approximate Bayesian Computation (ABC) and Uncertainty Analysis for Computationally Expensive Groundwater Models.” Journal of Hydrology 564 (September): 191–207. https://doi.org/10.1016/j.jhydrol.2018.07.005.
Gladish, Daniel W., Daniel E. Pagendam, Luk J. M. Peeters, Petra M. Kuhnert, and Jai Vaze. 2018. “Emulation Engines: Choice and Quantification of Uncertainty for Complex Hydrological Models.” Journal of Agricultural, Biological and Environmental Statistics 23 (1): 39–62. https://doi.org/10.1007/s13253-017-0308-3.
He, QiZhi, David Barajas-Solano, Guzel Tartakovsky, and Alexandre M. Tartakovsky. 2020. “Physics-Informed Neural Networks for Multiphysics Data Assimilation with Application to Subsurface Transport.” Advances in Water Resources 141 (July): 103610. https://doi.org/10.1016/j.advwatres.2020.103610.
Kimura, Nobuaki, Ikuo Yoshinaga, Kenji Sekijima, Issaku Azechi, and Daichi Baba. 2020. “Convolutional Neural Network Coupled with a Transfer-Learning Approach for Time-Series Flood Predictions.” Water 12 (1, 1): 96. https://doi.org/10.3390/w12010096.
Lutter, Michael, Christian Ritter, and Jan Peters. 2019. “Deep Lagrangian Networks: Using Physics as Model Prior for Deep Learning,” July. http://arxiv.org/abs/1907.04490.
Raissi, Maziar, Alireza Yazdani, and George Em Karniadakis. 2020. “Hidden Fluid Mechanics: Learning Velocity and Pressure Fields from Flow Visualizations.” Science 367 (6481): 1026–30. https://doi.org/10.1126/science.aaw4741.
Siade, Adam J., Tao Cui, Robert N. Karelse, and Clive Hampton. 2020. “Reduced‐Dimensional Gaussian Process Machine Learning for Groundwater Allocation Planning Using Swarm Theory.” Water Resources Research 56 (3). https://doi.org/10.1029/2019WR026061.
Tait, Daniel J., and Theodoros Damoulas. 2020. “Variational Autoencoding of PDE Inverse Problems,” June. http://arxiv.org/abs/2006.15641.
Tartakovsky, Alexandre M., Carlos Ortiz Marrero, Paris Perdikaris, Guzel D. Tartakovsky, and David Barajas-Solano. 2018. “Learning Parameters and Constitutive Relationships with Physics Informed Deep Neural Networks,” August. https://arxiv.org/abs/1808.03398v2.
Yu, Xiayang, Tao Cui, J. Sreekanth, Stephane Mangeon, Rebecca Doble, Pei Xin, David Rassam, and Mat Gilfedder. 2020. “Deep Learning Emulators for Groundwater Contaminant Transport Modelling.” Journal of Hydrology, August, 125351. https://doi.org/10.1016/j.jhydrol.2020.125351.