Variational assimilation of remotely sensed flood extents using a two-dimensional flood model

Abstract. A variational data assimilation (4D-Var) method is proposed to directly assimilate flood extents into a two-dimensional (2-D) dynamic flood model, to explore a novel way of utilizing the rich source of remotely sensed data available from satellite imagery for better analyzing or predicting flood routing processes. For this purpose, a new cost function is specially defined to effectively fuse the hydraulic information that is implicitly indicated in flood extents. The potential of using remotely-sensed flood extents for improving the analysis of flood routing processes is demonstrated by applying the present new data assimilation approach to both idealized and realistic numerical experiments.