Integrating Information from Hydrologic Models and SWOT Remote Sensing Measurements to Estimate Discharge across River Networks
Year: 2025
Type: preprint
Abstract: The Surface Water and Ocean Topography (SWOT, launched in December 2022) satellite has the potential to improve knowledge of river discharge globally, but preliminary SWOT discharge estimates are prone to timeseries bias, mainly induced by globally available prior information. To address this issue, we present a new algorithm designed to improve SWOT discharge. SFOI analytically integrates basin-scale information by applying mass conservation to time averaged SWOT discharge and estimates of regionalized runoff. We test the algorithm on three river basins with drainage areas on the order of 105 km2. We find that when the time averaged SWOT discharge observations are spatially unbiased (i.e. their spatially averaged error is close to zero) the algorithm significantly improves SWOT discharge: the median (interquartile range) absolute value of the normalized SFOI error is 15% (10-19%) for the Willamette River basin, and 12% (7-48%) for the upper Ohio River basin, significant improvements over SWOT discharge error (median values of 30% and 31%, respectively). However, for the Loire River basin, which has high (~60%) spatial bias, the algorithm does not improve SWOT discharge. These results imply that understanding and reducing spatial bias at the basin scale is key for reducing temporal bias in river discharge timeseries. Assessing spatial bias globally, we find that SWOT discharge should achieve its target accuracy (30% discharge error, with most of it temporal bias) even in ungaged basins, enabling SWOT discharge to provide significant improvements to the global water budget.
Authors: Michael Durand, Demián D Gómez, Tamlin M. Pavelsky, Stephen Paul Coss, Paul D Bates, David Michael Bjerklie, Cédric Hervé David, Robert W. Dudley, Renato Prata de Moraes Frasson, Pierre-André Garambois, Colin Joseph Gleason, Kevin Larnier, Pierre-Olivier Malaterre, Jérôme Monnier, Hind Oubanas, Travis Thomas Simmons, Laurence Smith, Nikki Tebaldi, Mohammad J. Tourian, Yushan Zhang
Institutions: The Ohio State University, University of North Carolina at Chapel Hill, Byrd Polar and Climate Research Center, University of Bristol, United States Geological Survey, Jet Propulsion Laboratory, Aix-Marseille Université, Institut National de Recherche pour l'Agriculture, l'Alimentation et l'Environnement, Risques, écosystèmes, vulnérabilité, environnement, résilience, University of Massachusetts Amherst, Institut National des Sciences Appliquées de Toulouse, Services déconcentrés d'appui à la recherche Occitanie-Montpellier, Brown University, University of Stuttgart
Language: English
Subfield: Water Science and Technology
Field: Environmental Science
Domain: Physical Sciences
Open Access status: gold
Awards: G2676722403, G3648211085, G830101876
OpenAlex ID: W4414629506
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