Articles | Volume 30, issue 11
https://doi.org/10.5194/hess-30-3647-2026
https://doi.org/10.5194/hess-30-3647-2026
Research article
 | 
16 Jun 2026
Research article |  | 16 Jun 2026

Continental-scale prediction of hydrologic signatures and processes

Ryoko Araki, Anne Holt, John C. Hammond, Admin Husic, Gemma Coxon, and Hilary K. McMillan

Data sets

Dataset and source codes for Araki et al., (2026) "Continental-scale prediction of hydrologic signatures and processes" Ryoko Araki et al. https://doi.org/10.5281/zenodo.20185650

Caravan - A global community dataset for large-sample hydrology Version 1.4 F. Kratzert et al. https://doi.org/10.5281/zenodo.10968468

GAGES-II: Geospatial Attributes of Gages for Evaluating Streamflow J. Falcone https://doi.org/10.5066/P96CPHOT

Data-Driven Drought Prediction Project Model Inputs for Upper and Lower Colorado Portions of the National Hydrologic Geo-Spatial Fabric version 1.1 and Select U.S. Geological Survey Streamgage Basins (ver. 2.0, July 2025) M. E. Wieczorek et al. https://doi.org/10.5066/P98IG8LO

Daily time series of surface water input from rainfall, rain on snow, and snowmelt for the Conterminous United States from 1990 to 2023, as well as annual series of input seasonality, precipitation seasonality, and average rainfall, rain on snow, and snow J. C. Hammond https://doi.org/10.5066/P9JWJPNC

CAMELSH: A Large-Sample Hourly Hydrometeorological Dataset and Attributes at Watershed-Scale for Contiguous United States V. N. Tran and T. Kim https://doi.org/10.5281/zenodo.15066778

Model code and software

Caravan - A global community dataset for large-sample hydrology (https://github.com/kratzert/Caravan) F. Kratzert et al. https://doi.org/10.1038/s41597-023-01975-w

A Toolbox for Streamflow Signatures in Hydrology (https://github.com/TOSSHtoolbox/TOSSH) S. J. Gnann et al. https://doi.org/10.1016/j.envsoft.2021.104983

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Short summary
We mapped dominant hydrologic processes across the United States by analyzing observed streamflow dynamics. Using random forest models and interpretable machine learning techniques, we predicted processes in data-scarce regions and identified key drivers such as climate, soil and geology, land cover, topography, and human influence. The resulting maps of dominant processes and their drivers reveal strong regional patterns that guide hydrologic model selection and water resource management.
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