Code and data for "Machine learning surrogates for efficient hydrologic modeling: Insights from stochastic simulations of managed aquifer recharge"
| Authors: | |
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| Owners: | Timothy Dai |
| Type: | Resource |
| Storage: | The size of this resource is 3.9 GB |
| Created: | Dec 16, 2024 at 2:24 a.m. |
| Last updated: | Jan 09, 2025 at 2:09 p.m. |
| Published date: | Jan 09, 2025 at 2:09 p.m. |
| DOI: | 10.4211/hs.f0a31fbc3de148a98deb36795b4fac53 |
| Citation: | See how to cite this resource |
| Sharing Status: | Published |
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| Views: | 252 |
| Downloads: | 76 |
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Abstract
This repository contains the data and code associated with the paper "Machine Learning Surrogates for Efficient Hydrologic Modeling: Insights from Stochastic Simulations of Managed Aquifer Recharge" by Dai et al. (2025) in the Journal of Hydrology (https://doi.org/10.1016/j.jhydrol.2024.132606) The study evaluates a hybrid modeling framework that combines process-based hydrologic simulations (with the integrated hydrologic code ParFlow-CLM) and machine learning (ML) surrogates to efficiently simulate managed aquifer recharge. This repository includes:
1) Sample ParFlow-CLM output for all three simulation stages
2) PyTorch dataset modules and utility functions that construct PyTorch tensors from raw ParFlow-CLM outputs
3) PyTorch modules to implement each of the eight ML architectures described in the paper (CNN3d, CNN4d, U-FNO3d, U-FNO4d, ViT3d, ViT4d, PredRNN++, and a CNN autoencoder)
4) PyTorch modules for custom layers implemented in each architecture
5) A PyTorch module that implements a normalized L2 loss function
6) Scripts to train and evaluate each surrogate architecture, including the autoencoder
Though this repository only contains sample ParFlow-CLM simulation output, complete ParFlow output files for all simulations used in the paper are available to the public in a separate repository (https://doi.org/10.25740/hj302gv2126)
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Related Resources
| This resource is referenced by | Timothy Dai, Kate Maher, Zach Perzan, Machine learning surrogates for efficient hydrologic modeling: Insights from stochastic simulations of managed aquifer recharge, Journal of Hydrology, Volume 652, 2025, 132606, ISSN 0022-1694, https://doi.org/10.1016/j.jhydrol.2024.132606. |
How to Cite
This resource is shared under the Creative Commons Attribution CC BY.
http://creativecommons.org/licenses/by/4.0/
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