| 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. |