High-Resolution Global Streamflow Dataset from 1980 - 2020 for 2.94 Million Rivers Using the Physics-Embedded δHBV2–δMC2 Model

This repo provides a complete set of global-scale streamflow simulations generated with the physics-embedded, high-resolution δHBV2–δMC2 model. Due to storage limitations on Zenodo, the complete global streamflow simulations are archived in HydroShare. For citation purposes, please reference the Zenodo record: [10.5281/zenodo.17042358].

This dataset is a direct result of Ji et al., 2025 described below, which built upon the work in Song et al., 2025. Please cite these two papers if you find the data to be of use (* indicates MHPI group members):

Ji, Haoyu*, Yalan Song*, Tadd Bindas*, Chaopeng Shen*, Yuan Yang, Ming Pan, Jiangtao Liu*, Farshid Rahmani*, Ather Abbas, Hylke Beck, Kathryn Lawson* and Yoshihide Wada. Distinct hydrologic response patterns and trends worldwide revealed by physics-embedded learning. Nature Communications. https://doi.org/10.1038/s41467-025-64367-1

Song, Yalan*, Tadd Bindas*, Chaopeng Shen*, Haoyu Ji*, Wouter J. M. Knoben, Leo Lonzarich*, Martyn P. Clark, Jiangtao Liu*, Katie van Werkhoven, Sam Lemont, Matthew Denno, Ming Pan, Yuan Yang, Jeremy Rapp, Mukesh Kumar, Farshid Rahmani*, Cyril Thébault, Richard Adkins, James Halgren, Trupesh Patel, Arpita Patel, Kamlesh Sawadekar*, and Kathryn Lawson* (2025). High-resolution national-scale water modeling is enhanced by multiscale differentiable physics-informed machine learning. Water Resources Research, doi: 10.1029/2024WR038928