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Radar imagery and saturated areas: decreasing model equifinality

Remote sensing data not only permit the local measurement of a phenomenon within a catchment, but also permit the examination of internal fine features and heterogeneity to obtain better knowledge of elementary hydrological processes and compare observed and predicted elementary hydrological processes. The images also enable running the models more effectively when ground truth given by the images fits with the model results. An application is shown for a small catchment in French Brittany (Coët-Dan) through several series of radar images from European Remote Sensing Satellite 1 (ERS-1) and ground observations of saturated areas. ERS radar signals are related not only to soil moisture but also to vegetation and roughness, so these images seem incapable of providing reliable soil moisture mapping directly. Nevertheless, a time series over a short period may yield useful information on soil moisture variations and saturation within a catchment, allowing a comparison of the saturated ground areas with model predictions at the catchment scale. We used Topmodel modelling, which computes the saturated areas at each time step during a rain event. In fact, one ERS-1 image does not permit the detection of the saturated areas, but a time series reveals the wettest areas, which appear to provide valuable assistance for better modelling. Using the global likelihood uncertainty estimation (GLUE) methodology opens an interesting research domain: indeed, models often accept numerous sets of parameters that give quite acceptable flow-rate simulations (equifinality), and the radar observations may help to choose within all the different parameter sets those with internal behaviour close to that of the physical modelling assumptions. Radar data thus help to retain only numerical solutions that are physically consistent and consequently to reduce predictive uncertainty caused by equifinality. This considerably improves our knowledge of elementary hydrological processes.