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

Wednesday, 17 December 2014: 12:05 PM
Niko Verhoest1, Hans Lievens1, Brecht Martens1, Martinus Johannes van Den Berg1, Ahmad Al Bitar2, Olivier Merlin3, Sat Kumar Tomer2, Francois Cabot2, Yann H Kerr4, Ming Pan5, Eric F Wood5, Matthias Drusch6, Harrie-Jan Hendricks Franssen7, Harry Vereecken8, Gabrielle J.M. De Lannoy9, Gift Dumedah10, Jeffrey P Walker11 and Valentijn R N Pauwels12, (1)Ghent University, Ghent, Belgium, (2)Centre d'Etudes Spatiales de la Biosphere, Toulouse Cedex 9, France, (3)Centre d'Etudes Spatiales de la Biosphere, Toulouse, France, (4)CNES French National Center for Space Studies, Toulouse, France, (5)Princeton University, Princeton, NJ, United States, (6)ESTEC, Noordwijk, Netherlands, (7)Forschungszentrum Jülich GmbH, Julich, Germany, (8)Agrosphere Institute (IBG-3), Forschungszentrum Jülich, Deutschland, Germany, (9)NASA/GSFC (USRA), Greenbelt, MD, United States, (10)Monash University, Department of Civil Engineering, Melbourne, Australia, (11)Monash University, Clayton, VIC, Australia, (12)Monash University, Melbourne, Australia
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.