Integration of remote sensing data into hydrological models for reservoir management

The purpose of this paper is to present the methodology set up to derive ca tchment soil moisture from Earth Observation (EO) data using microwave spac eborne Synthetic Aperture Radar (SAR) images from ERS satellites and to stu dy the improvements brought about by an assimilation of this information in to hydrological models. The methodology used to derive EO data is based on the appropriate selection of land cover types for which the radar signal is mainly sensitive to soil moisture variations. Then a hydrological model is chosen, which can take advantage of the new information brought by remote sensing. The assimilation of soil moisture deduced from EO data into hydrol ogical models is based principally on model parameter updating. The main as sumption of this method is that the better the model simulates the current hydrological system, the better the following forecast will be. Another met hodology used is a sequential one based on Kalman filtering. These methods have been put forward for use in the European AIMWATER project on the Seine catchment upstream of Paris (France) where dams are operated to alleviate floods in the Paris area.