2026-10-12T21:48:32Z https://www.scidb.cn/oai

10.57760/sciencedb.41621 2026-09-28T10:48:31Z

2026-09-28 Data for: A hybrid physics–ML framework for integrating groundwater dynamics into land surface modeling doi:10.57760/sciencedb.41621 en This dataset supports the manuscript “A hybrid physics–ML framework for integrating groundwater dynamics into land surface modeling.” It includes data used for model training and evaluation; additional data for post-processing and result analysis, including physical-model outputs with free-drainage boundaries; trained model checkpoints; outputs from land-surface-model coupled tests; training log files; runnable model templates; and figure-generation scripts. The accompanying README describes the archive structure, data usage, and steps for reproducing the analyses. The associated model code and detailed documentation are available through the linked Zenodo archive (https://doi.org/10.5281/zenodo.20791904) and the actively maintained GitHub repository (https://github.com/aureliayang/ParFlow-nn)Version update (26 September 2026): A new archive, revised_supplementary_material_20260926.tar.gz, has been added. This folder contains processed data and scripts for reproducing the revised figures, statistical analyses, and computational efficiency results. integrated hydrological modeling; hybrid coupling; ParFlow; Common Land Model(CoLM); deep learning(PredRNN) Yang Chen PUBLIC https://creativecommons.org/licenses/by-nc/4.0/ dataset http://www.doi.org/10.31223/x5pr3b Science Data Bank