A First Look at River Discharge Estimation From SWOT Satellite Observations

Abstract The Surface Water and Ocean Topography (SWOT) satellite has the potential to transform global hydrologic science by offering simultaneous and synoptic estimates of river discharge and other hydraulic variables. Discharge is estimated from SWOT observations of water surface elevation, width, and slope. A first assessment using just the highest quality SWOT measurements, over the first 15 months (March 2023–July 2024) of the mission evaluated at 65 gauged reaches shows results consistent with pre‐launch expectations. SWOT estimates track discharge dynamics without relying on any gauge information: median correlation is 0.73, with a correlation interquartile range of 0.51–0.89. SWOT estimates capture discharge magnitude correctly in some cases but are biased (median bias is 50%) in others. There are already a total of 11,274 ungauged global locations with highest quality SWOT measurements where SWOT discharge is expected to accurately track discharge variations: this value will increase as SWOT data record length grows, algorithms are refined and SWOT measurements are reprocessed. This first look indicates that SWOT discharge is performing as expected for SWOT data that achieve performance requirements, providing observed information on discharge variations in ungauged basins globally.

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Institutions

Publication Details

Journal
Geophysical Research Letters
Published
2025-05-03
DOI
https://doi.org/10.1029/2024gl114185
Citations
32
Primary Topic
Flood Risk Assessment and Management
Type
article
Field-Weighted Citation Impact
35.37

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A First Look at River Discharge Estimation From SWOT Satellite Observations

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Geophysical Research Letters
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article

A First Look at River Discharge Estimation From SWOT Satellite Observations

Mohammad J. Tourian, Bidhyananda Yadav, Ernesto Rodríguez, Omid Elmi, Igor Gejadze, Jida Wang, Md. Safat Sikder, Luciana Fenoglio-Marc, J. Toby Minear, Alessio Domeneghetti, C. M. Stuurman, Nikki Tebaldi, George H. Allen, Pascal Matte, Kévin Larnier, Travis Simmons, Robert W. Dudley, Stephen Coss, Paul Bates, Konstantinos M. Andreadis, Tamlin M. Pavelsky, Cédric Hervé David, Jérôme Monnier, David M. Bjerklie, Michael Durand, Ryan Riggs, Augusto Getirana, Pierre‐Olivier Malaterre, Colin J. Gleason, LAURENCE C. SMITH, Craig Brinkerhoff, Jay Taneja, Aggrey Muhebwa, Elisa Friedmann, Hind Oubanas, Jaclyn Gehring, Angelica Tarpanelli, Renato Prata de Moraes Frasson, Pierre‐André Garambois, Jonghyun Lee, Marissa Hughes, Brent A. Williams
article en
32 citations

Abstract

Abstract The Surface Water and Ocean Topography (SWOT) satellite has the potential to transform global hydrologic science by offering simultaneous and synoptic estimates of river discharge and other hydraulic variables. Discharge is estimated from SWOT observations of water surface elevation, width, and slope. A first assessment using just the highest quality SWOT measurements, over the first 15 months (March 2023–July 2024) of the mission evaluated at 65 gauged reaches shows results consistent with pre‐launch expectations. SWOT estimates track discharge dynamics without relying on any gauge information: median correlation is 0.73, with a correlation interquartile range of 0.51–0.89. SWOT estimates capture discharge magnitude correctly in some cases but are biased (median bias is 50%) in others. There are already a total of 11,274 ungauged global locations with highest quality SWOT measurements where SWOT discharge is expected to accurately track discharge variations: this value will increase as SWOT data record length grows, algorithms are refined and SWOT measurements are reprocessed. This first look indicates that SWOT discharge is performing as expected for SWOT data that achieve performance requirements, providing observed information on discharge variations in ungauged basins globally.

Geophysical Research LettersVol. 52(9)
University of Stuttgart (DE), University of North Carolina at Chapel Hill (US), University of Hawaiʻi at Mānoa (US), California Institute of Technology (US), United States Geological Survey (US), Northeastern University (US), Goddard Space Flight Center (US), Centre de Coopération Internationale en Recherche Agronomique pour le Développement (FR), Jet Propulsion Laboratory (US), Environment and Climate Change Canada (CA), University of Bonn (DE), University of Illinois Urbana-Champaign (US), Cooperative Institute for Research in Environmental Sciences (US), Amherst College (US), University of Colorado Boulder (US), Institut National des Sciences Appliquées de Toulouse (FR), Aix-Marseille Université (FR), AgroParisTech (FR), University of Massachusetts Amherst (US), Institut Agro Montpellier (FR), Brown University (US), Yale University (US), University of Bristol (GB), Institut National de Recherche pour l'Agriculture, l'Alimentation et l'Environnement (FR), Research Institute for Geo-Hydrological Protection (IT), Gestion de l'Eau, Acteurs, Usages (FR), Université Bourgogne Franche-Comté (FR), Columbus Center (US), National Aeronautics and Space Administration (US), National Research Council (IT), Bureau de Recherches Géologiques et Minières (FR), Institut de Recherche pour le Développement (FR), New England Water Science Center, The Ohio State University (US), Institut de Mathématiques de Toulouse (FR), Virginia Tech (US), University of Bologna (IT)
California Institute of Technology, Jet Propulsion Laboratory
Openalex Percentile: Top 0%
Flood Risk Assessment and Management
35.37
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