H040-0012
A global framework for SWOT discharge

Tuesday, 8 December 2020
Poster
Michael T Durand, Ohio State University Main Campus, Columbus, OH, United States, Colin J Gleason, University of Massachusetts, Amherst, MA, United States, Renato P. M. Frasson, Ohio State University Main Campus, Byrd Polar and Climate Research Center, Columbus, OH, United States, Tamlin Pavelsky, University of North Carolina at Chapel Hill, Chapel Hill, NC, United States, George H. Allen, Texas A&M University, Department of Geography, College Station, TX, United States, Paul D Bates, University of Bristol, School of Geographical Sciences, Bristol, United Kingdom, Robert W Dudley, US Geological Survey, Augusta, ME, United States, Charlotte Marie Emery, Laboratoire d’Etudes en Géophysique et Océanographie Spatiales - Observatoire Midi-Pyréenées, Toulouse, France, Luciana Fenoglio-Marc, University of Bonn, Bonn, Germany, Pierre-Andre Garambois, INSA Strasbourg, Strasbourg, France, Faisal Hossain, University of Washington, Seattle, WA, USA, Seattle, United States, Kevin Larnier, C&S corp., Toulouse, France, Peirong Lin, University of Texas, Austin, TX, United States, Pascal Matte, Environment and Climate Change Canada, Meteorological Research Division, Quebec, Canada, Jerome Monnier, University INSA & Mathematics Institute of Toulouse, Toulouse cedex 4, France, Hind Oubanas, IRSTEA, Antony Cedex, France, Ming Pan, Princeton University, Civil and Environmental Engineering, Princeton, NJ, United States, Ernesto Rodriguez, Jet Propulsion Laboratory, Pasadena, CA, United States, Jacob Schaperow, UCLA, Department of Civil and Environmental Engineering, Los Angeles, CA, United States, Angelica Tarpanelli, Research Institute for Geo-Hydrological Protection National Research Council CNR, Perugia, Italy, Mohammad J. Tourian, Institute of Geodesy, Stuttgart, Germany, Jida Wang, University of California, LA, Los Angeles, CA, United States and SWOT Discharge Algorithm Working Group
Abstract:
Remote sensing may provide the key to one of the longest-standing problems in hydrology: ungaged basins. Accurately calibrated stream gages are accurate to within 10% or better under most conditions (e.g. when flow is within banks, and rating curves are kept up to date). Publicly available stream gage data accurately capture most streamflow into the ocean, but coverage for smaller tributaries is on the order of 10% (in terms of drainage area) for basins 50,000 km2 in size. The forthcoming Surface Water and Ocean Topography (SWOT) mission will vastly expand measurements of global rivers, measuring rivers wider than 100 m and perhaps as small as 50 m, corresponding to nearly all basins 50,000 km2 in size. However, converting the SWOT measurements of height, width and slope into river discharge is non-trivial. SWOT will provide discharge for all SWOT-observed rivers, but at lower accuracy than what is possible at a gage. In this paper, we describe how the SWOT discharge data product will be produced, along with its expected accuracy. In order to address the range of possible users and uses for SWOT discharge, two branches of data products will be produced: one that incorporates a subset of globally available gage measurements in order to constrain the SWOT discharge (the gage-constrained product), and one that uses only global-model derived estimates of mean annual flow as a Bayesian prior estimate (the unconstrained product). Note that in situ gage data will be withheld in order to validate gage-constrained and unconstrained products. We present a complete discharge error budget for SWOT, constrained using a range of results from in situ and airborne measurements, hydraulic simulation, and instrument simulator experiments. We use this to construct an approximate discharge uncertainty estimate for global rivers. We show that median river discharge accuracy will be on the order of 40-50%, in terms of normalized root mean squared error (nRMSE), whereas unbiased nRMSE (which measures the accuracy of time-variations in discharge) will be on the order of 10-20%, for the unconstrained product. These results indicate that SWOT discharge will measure time variations discharge at accuracies approaching stream gages, and that mean annual flow will nonetheless improve water balances over what is currently available in ungaged basins.
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