Impact of Different Data Assimilation Strategies for SMOS Observations on Flood Forecasting Accuracy

Niko Verhoest1, Hans Lievens2, Brecht Martens3, Martinus Johannes van Den Berg4, Ahmad Al-Bitar5, Olivier Merlin6, Sat Kumar Tomer6, Francois Cabot6, Yann H Kerr7, Ming Pan8, Eric F Wood9, Matthias Drusch10, Harrie-Jan Hendricks Franssen11, Harry Vereecken12, Gabrielle J.M. De Lannoy13, Gift Dumedah14, Jeffrey P Walker15 and Valentijn R N Pauwels16, (1)Ghent University, Laboratory of Hydrology and Water Management, Ghent, Belgium, (2)Ghent University, Hydro-Climate Extremes Lab (H-CEL), Gent, Belgium, (3)Ghent University, Laboratory of Hydrology and Water Management, Gent, Belgium, (4)Ghent University, Ghent, Belgium, (5)CNES, UPS, Centre d'Etude Spatiales de la Biosphère (CESBIO), Toulouse, France, (6)Centre d'Etudes Spatiales de la Biosphere, Toulouse Cedex 9, France, (7)CNES French National Center for Space Studies, Toulouse Cedex 09, France, (8)Center for Western Weather and Water Extremes (CW3E), Scripps Institution of Oceanography, University of California San Diego, La Jolla, United States, (9)Princeton University, Civil and Environmental Engineering, Princeton, NJ, United States, (10)ESTEC, Noordwijk, Netherlands, (11)Forschungszentrum Juelich GmbH, Institute of Bio- and Geosciences - Agrosphere (IBG 3), Juelich, Germany, (12)Forschungszentrum Jülich GmbH, Agrosphere (IBG-3), Institute of Bio- and Geosciences, Jülich, Germany, (13)KU Leuven, Leuven, Belgium, (14)Monash University, Department of Civil Engineering, Melbourne, Australia, (15)Monash University, Department of Civil and Environmental Engineering, Melbourne, VIC, Australia, (16)Monash University, Melbourne, Australia
Abstract:
During the last decade, significant efforts have been directed towards establishing and improving flood forecasting systems for large river basins. Examples include the European Flood Alert System, and the Bureau of Meteorology Flood Warning Systems in Australia. A number of attempts have also been made to increase the accuracy of the forecasted flood volumes from these systems. One attractive way in which this can be achieved is to use remotely sensed surface soil moisture contents to constrain the hydrologic model predictions. Satellite missions such as SMOS can provide very useful information on the wetness conditions of these basins, which in many cases is an important initial condition for discharge generation. Assimilation of these satellite data is thus a logical way to proceed. We will present results from two different assimilation strategies for the Murray-Darling basin in Australia using the Variable Infiltration Capacity (VIC) model. Firstly, the SMOS soil moisture data are assimilated into the hydrologic model at their original spatial resolution. As the spatial resolution of the remote sensing data (25 km) is coarser than the spatial resolution of the model (10 km), a multiscale data assimilation algorithm needs to be implemented. Secondly, the SMOS data are downscaled to the model resolution, prior to their assimilation. In this presentation, the impact of the assimilation of both products on the accuracy of the forecasted flood volumes is assessed.
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