Abstract
Abstract. A variational data assimilation (4D-Var) method is proposed to directly assimilate flood extents into a 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.
Highlights
Flooding poses a significant threat to human society
Models are not perfect, and uncertainties and computational errors may arise from various sources, including the uncertainties associated with hydrological parameters, initial and boundary conditions, as well as numerical errors as a result of numerical discretization and mathematical approximations (Le Dimet et al, 2009; Pappenberger et al, 2007a)
A 4D-Var method incorporated with a new cost function is introduced to advance this research topic
Summary
Flooding poses a significant threat to human society. Nowadays, floods are becoming more frequent as a result of intensive regional human activities and environmental change. In 2-D river hydraulic modeling, 4D-Var methods have been developed to assimilate spatially distributed water stage (Lai and Monnier, 2009) and Lagrangian-type observations; e.g., remotely sensed surface velocity (Honnorat et al, 2009, 2010). We attempt to use a 4D-Var method to assimilate remotely sensed flood-extent data into a dynamic flood model based on the numerical solution to the 2-D shallow water equations (SWEs).
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