Assimilation of remote sensing observations into a continuous distributed hydrological model: Impacts on the hydrologic cycle

A reliable estimation of soil moisture conditions is fundamental for discharges prediction and, consequently, for flood risk mitigation. Microwave remote sensing can be exploited to estimate soil moisture at large scale. These estimates can be used to enhance the predictions of hydrological models using Data Assimilation techniques and to reduce model uncertainties. This research tested the effects of the assimilation of three different satellite-derived soil moisture products (obtained from ASCAT acquisitions) in a distributed, physically based, hydrological model applied to three small Italian catchments. The products were firstly preprocessed, in order to be to be comparable with the state variables of the model. Subsequently they were assimilated by using different techniques: a simple Nudging applied at both model and satellite scale and the Ensemble Kalman Filter. Finally, observed discharges were compared with the modelled ones. The reanalysis was executed for a multi-year period ranging from July 2012 to June 2014.