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Data assimilation has the potential to improve flood forecasting. However, it\udis rarely employed in distributed hydrologic models for operational\udpredictions. In this study, we present variational assimilation of river flow\uddata at multiple locations and of land surface temperature (LST) from\udsatellite in a distributed hydrologic model that is part of the operational\udforecasting chain for the Arno river, in central Italy. LST is used to\udestimate initial condition of soil moisture through a coupled surface\udenergy/water balance scheme. We present here several hindcast experiments to\udassess the performances of the assimilation system. The results show that\udassimilation can significantly improve flood forecasting, although in the\udlimit of data error and model structure

Improvement of operational flood forecasting through the assimilation of satellite observations and multiple river flow data