Articles | Volume 373
12 May 2016
 | 12 May 2016

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

Fabio Castelli and Giulia Ercolani

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

Short summary
Improving flood forecasting can strengthen the reduction of floods impacts through early warning systems. Our study presents improvements obtained with the integration of a data assimilation system into a hydrological model that is part of the operational forecasting chain for Arno river (central Italy). The system effectively combines the model with observations of river flow at multiple locations and satellite data, leading to more accurate predictions of flood peak flow.