Assimilation of Synthetic SWOT River Depths in a Regional Hydrometeorological Model
AbstractThe SWOT (Surface Water and Ocean Topography) mission, to be launched in 2021, will provide water surface elevations, slopes, and river width measurements for rivers wider than 100 m. In this study, synthetic SWOT data are assimilated in a regional hydrometeorological model in order to improve the dynamics of continental waters over the Garonne catchment, one of the major French catchments. The aim of this paper is to demonstrate that the sequential assimilation of SWOT-like river depths allows the correction of river bed roughness coefficients and thus simulated river depths. An extended Kalman filter is implemented and the data assimilation strategy was applied to four experiments of gradually increasing complexity regarding observation and model error over the 1995–2000 period. With respect to a “true” river state, assimilating river depths allows the proper retrieval of constant and spatially distributed roughness coefficients with a root mean square error of 1 m1/3 s−1, and the estimation of associated river depths. It was also shown that river depth differences can be assimilated, resulting in a higher root mean square error for roughness coefficients with respect to the true river state. Finally, the last experiment shows how one can take into account more realistic sources of SWOT error measurements, in particular the importance of the estimation of the tropospheric water content in the process. View Full-Text
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Häfliger, V.; Martin, E.; Boone, A.; Ricci, S.; Biancamaria, S. Assimilation of Synthetic SWOT River Depths in a Regional Hydrometeorological Model. Water 2019, 11, 78.
Häfliger V, Martin E, Boone A, Ricci S, Biancamaria S. Assimilation of Synthetic SWOT River Depths in a Regional Hydrometeorological Model. Water. 2019; 11(1):78.Chicago/Turabian Style
Häfliger, Vincent; Martin, Eric; Boone, Aaron; Ricci, Sophie; Biancamaria, Sylvain. 2019. "Assimilation of Synthetic SWOT River Depths in a Regional Hydrometeorological Model." Water 11, no. 1: 78.
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