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

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Type:	Resource
Storage:	The size of this resource is 161.3 GB
Created:	Sep 03, 2025 at 2:51 a.m. (UTC)
Last updated:	Oct 20, 2025 at 2:24 p.m. (UTC)
Citation:	See how to cite this resource
Content types:	Multidimensional Content

Sharing Status:	Public
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Abstract

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

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Data Services

The following web services are available for data contained in this resource. Geospatial Feature and Raster data are made available via Open Geospatial Consortium Web Services. The provided links can be copied and pasted into GIS software to access these data. Multidimensional NetCDF data are made available via a THREDDS Data Server using remote data access protocols such as OPeNDAP. Other data services may be made available in the future to support additional data types.

THREDDS Data Service

https://thredds.hydroshare.org/thredds/catalog/hydroshare/resources/6c8191d3613c4477b717be41c81a4372/data/contents/catalog.html

THREDDS Documentation OPeNDAP Documentation

How to Cite

Ji, H. (2025). High-Resolution Global Streamflow Dataset from 1980 - 2020 for 2.94 Million Rivers Using the Physics-Embedded δHBV2–δMC2 Model, HydroShare, http://www.hydroshare.org/resource/6c8191d3613c4477b717be41c81a4372

This resource is shared under the Creative Commons Attribution CC BY.

http://creativecommons.org/licenses/by/4.0/

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