Towards an increased performance of flood forecasting through assimilation of remotely sensed soil saturation levels in conceptual rainfall-runoff models

* Corresponding author ** This study is supported by the ‘Ministère Luxembourgeois de la Culture, de l’Enseignement Supérieur et de la Recherche’ and the French Space Agency (CNES) Abstract –Owing to the non-linearity of the rainfallinfiltration-runoff relationship, soil water content in the river basin represents a key parameter to be monitored for flood management purposes. Remote sensing observations can be used in hydrologic models as a source of time varying hydrologic state data that allows constraining model predictions. The analysis of a series of ERS-1 SAR images showed that the mean backscattering coefficient of selected soil parcels is strongly correlated with a ground-based wetness index the so-called soil saturation index (SSI). This paper shows that SSI values obtained via remote sensing can be used to update the internal saturation states of rainfall-runoff models through the sequential assimilation of the soil moisture information. The assimilation procedure is based on an extended Kalman filter as both simulated and observed saturation states are prone to errors. The magnitude of the correction thus depends on the ratio of errors on the observations and the model. Further research is needed to reduce the uncertainties that remain over the reliability of SAR to provide soil moisture information with a sufficient level of accuracy.