Discharge assimilation in a distributed flood forecasting model

In the field of operational flood forecasting, uncertainties linked to hydrological forecast are often crucial. In this work, data assimilation techniques are employed to improve hydrological variable estimates coming from numerical simulations using all the available real-time water level measurements. The proposed assimilation scheme, a classical Kalman filter extension to non-linear systems, is applied in a rainfall-runoff distributed model based on the SCS-CN approach. The complex hydrological system of the Toce river basin is studied, a mountainous catchment of about 1500 km2 in the Italian alps, through the development of a prototype available for operational use. For the considered flood event, the assimilation scheme is stable, even when available observations show gaps or outliers. It allows significant improvements in the simulation results, in particular when the focus is addressed to the peak.