期刊名称：Proceedings of the International Association of Hydrological Sciences
印刷版ISSN：2199-8981
电子版ISSN：2199-899X
出版年度：2016
卷号：373
页码：167-173
DOI：10.5194/piahs-373-167-2016
摘要：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.
