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Exploring the factors controlling the error characteristics of the Surface Water and Ocean Topography mission discharge estimates

May 12, 2021

The Surface Water and Ocean Topography (SWOT) satellite mission will measure river width, water surface elevation, and slope for rivers wider than 50-100 m. SWOT observations will enable estimation of river discharge by using simple flow laws such as the Manning-Strickler equation, complementing in-situ streamgages. Several discharge inversion algorithms designed to compute unobserved flow law parameters (e.g. friction coefficient, bathymetry) have been proposed, but to date, a systematic assessment of factors controlling algorithm performance has not been conducted. Here, we assess the performance of the five algorithms that are expected to be used in the construction of the SWOT product. To perform this assessment, we used synthetic SWOT observations created with hydraulic model output corrupted with SWOT-like error. Prior information provided to the algorithms was purposefully limited to an estimate of mean annual flow (MAF), designed to produce a “worst case” benchmark. Prior MAF error was an important control on algorithm performance, but discharge estimates produced by the algorithms are less biased than the MAF; thus, the discharge algorithms improve on the prior. We show for the first time that accuracy and frequency of remote sensing observations are less important than prior bias, hydraulic variability among reaches, and flow law accuracy in governing discharge algorithm performance. The discharge errors and error sensitivities reported herein are a bounding benchmark, representing worst possible expected errors and error sensitivities. This study lays the groundwork to develop predictive power of algorithm performance, and thus map the global distribution of worst-case SWOT discharge accuracy.

Citation Information

Publication Year 2021
Title Exploring the factors controlling the error characteristics of the Surface Water and Ocean Topography mission discharge estimates
DOI 10.1029/2020WR028519
Authors Renato Frasson, Michael T. Durand, Kevin Lanier, Colin Gleason, Konstantinos Andreadis, Mark Hageman, Robert W. Dudley, David M. Bjerklie, Hind Oubanas, Pierre-André Garambois, Pierre-Olivier Malaterre, Peirong Lin, Tamlin M. Pavelsky, Jerome Monnier, Craig Binkerhoff, Cedric H. David
Publication Type Article
Publication Subtype Journal Article
Series Title Water Resources Research
Series Number
Index ID 70220886
Record Source USGS Publications Warehouse
USGS Organization New England Water Science Center

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