Next Article in Journal
Evaluation of Return Period and Risk in Bivariate Non-Stationary Flood Frequency Analysis
Next Article in Special Issue
A Multi-Sourced Data Retrodiction of Remotely Sensed Terrestrial Water Storage Changes for West Africa
Previous Article in Journal
Response of Aquatic Plants and Water Quality to Large-Scale Nymphoides peltata Harvest in a Shallow Lake
Previous Article in Special Issue
Variability of Arctic Sea Ice (1979–2016)
Open AccessArticle

Assimilation of Synthetic SWOT River Depths in a Regional Hydrometeorological Model

CNRM-GAME, UMR 3589, Météo-France, CNRS, 31057 Toulouse, France
Meteorologisches Institut, Bonn Universität, 53113 Bonn, Germany
IRSTEA, UR RECOVER, 13100 Aix-en-Provence, France
CECI, CERFACS-CNRS, 42 avenue G. Coriolis, 31057 Toulouse, France
CNRS, LEGOS, UMR 5566-CNRS-CNES-IRD-Université Toulouse III, 31057 Toulouse, France
Author to whom correspondence should be addressed.
Water 2019, 11(1), 78;
Received: 27 October 2018 / Revised: 20 December 2018 / Accepted: 24 December 2018 / Published: 4 January 2019
(This article belongs to the Special Issue Satellite Remote Sensing and Analyses of Climate Variability)
The 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
Keywords: overland flow; satellite altimetry; hydrological modelling; data assimilation overland flow; satellite altimetry; hydrological modelling; data assimilation
Show Figures

Figure 1

MDPI and ACS Style

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.

Show more citation formats Show less citations formats
Note that from the first issue of 2016, MDPI journals use article numbers instead of page numbers. See further details here.

Article Access Map by Country/Region

Back to TopTop