SWOT, Empiricism, and River Modeling

Abstract Given the insurmountable challenge of measuring all rivers in situ, global river models serve as the foundation of freshwater knowledge past, present, and future. We adopt an inductive empirical framework based on Surface Water and Ocean Topography (SWOT) satellite measurements to assess these models. After controlling for SWOT data quality, we examine 68,347 individual river reaches representing ∼38% of global discharge. We find river models currently struggle in areas of heavy economic development, multi‐channel rivers, arid areas, and many Arctic rivers. After controlling for these expected errors, we find better skill as rivers get wider and that parts of Siberia and China are particularly difficult to model. We also find large variability and spatial heterogeneity to model performance, resisting oversimplification. Our results suggest that leveraging SWOT observations within river models will improve them, but river models must adapt their structure to represent realistic hydraulics to do so.

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Publication Details

Journal
Geophysical Research Letters
Published
2026-08-24
DOI
https://doi.org/10.1029/2026gl124323
Primary Topic
Flood Risk Assessment and Management
Type
article
Field-Weighted Citation Impact
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article

SWOT, Empiricism, and River Modeling

Mohammad J. Tourian, Dung Trung Vu, Peyman Saemian, Dai Yamazaki et al.
Geophysical Research Letters
Flood Risk Assessment and Management
article

SWOT, Empiricism, and River Modeling

Mohammad J. Tourian, Dung Trung Vu, Peyman Saemian, Dai Yamazaki, Jida Wang, Sreelekha Jarugula, Yuan Yang, George H. Allen, Merritt E. Harlan, Ming Pan, Casey Brown, Stephen Coss, Chenqi Fang, Paul Bates, Konstantinos M. Andreadis, Tamlin M. Pavelsky, Cédric Hervé David, Peirong Lin, Sylvain Biancamaria, Heejin An, Michael Durand, Di Long, Chaopeng Shen, Fiona B Bennitt, Augusto Getirana, Colin J. Gleason, LAURENCE C. SMITH, Stephen Chuter, Theodore Langhorst, Elisa Friedmann, Hind Oubanas, Jeffrey Neal, Jonathan A. Flores, Haoyu Ji, Dongmei Feng, Ziyun Yin, Travis T. Simmons, Nikki Tebaldi
article en

Abstract

Abstract Given the insurmountable challenge of measuring all rivers in situ, global river models serve as the foundation of freshwater knowledge past, present, and future. We adopt an inductive empirical framework based on Surface Water and Ocean Topography (SWOT) satellite measurements to assess these models. After controlling for SWOT data quality, we examine 68,347 individual river reaches representing ∼38% of global discharge. We find river models currently struggle in areas of heavy economic development, multi‐channel rivers, arid areas, and many Arctic rivers. After controlling for these expected errors, we find better skill as rivers get wider and that parts of Siberia and China are particularly difficult to model. We also find large variability and spatial heterogeneity to model performance, resisting oversimplification. Our results suggest that leveraging SWOT observations within river models will improve them, but river models must adapt their structure to represent realistic hydraulics to do so.

Geophysical Research LettersVol. 53(16)
University of Stuttgart (DE), University of North Carolina at Chapel Hill (US), United States Geological Survey (US), Goddard Space Flight Center (US), Pennsylvania State University (US), Centre de Coopération Internationale en Recherche Agronomique pour le Développement (FR), Jet Propulsion Laboratory (US), Scripps Institution of Oceanography (US), University of Illinois Urbana-Champaign (US), Peking University (CN), AgroParisTech (FR), University of Massachusetts Amherst (US), Brown University (US), China Geological Survey (CN), At Bristol (GB), University of Bristol (GB), University of California San Diego (US), University of Chicago (US), Institut National de Recherche pour l'Agriculture, l'Alimentation et l'Environnement (FR), Laboratoire d’Études en Géophysique et Océanographie Spatiales (FR), Institut Agro Montpelier (FR), Science Systems and Applications (United States) (US), Bureau de Recherches Géologiques et Minières (FR), Institut de Recherche pour le Développement (FR), The Ohio State University (US), University of Cincinnati (US), The University of Tokyo (JP), Virginia Tech (US), Tsinghua University (CN)
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Flood Risk Assessment and Management
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