期刊
JOURNAL OF HYDROLOGY
卷 377, 期 1-2, 页码 1-11出版社
ELSEVIER SCIENCE BV
DOI: 10.1016/j.jhydrol.2009.07.058
关键词
Shallow water equations; Flood model; Variational data assimilation (4D-var); Satellite image; Parameter identification; Temporal strategy
资金
- Region Rhone-Alpes, France
- National Natural Science Foundation of China [50709034]
Recent applications of remote sensing techniques produce rich spatially distributed observations for flood monitoring. In order to improve numerical flood prediction, we have developed a variational data assimilation method (4D-var) that combines remote sensing data (spatially distributed water levels extracted from spatial images) and a 2D shallow water model. In the present paper (part I), we demonstrate the efficiency of the method with a test case First, we assimilated a single fully observed water level image to identify time-independent parameters (eg. Manning coefficients and initial conditions) and time-dependent parameters (e.g. inflow). Second, we combined incomplete observations (a time series of water elevations at certain points and one partial image). This last configuration was very similar to the real case we analyze in a forthcoming paper (part II) In addition, a temporal strategy with time overlapping is suggested to decrease the amount of memory required for long-duration Simulation (C) 2009 Elsevier B.V. All rights reserved
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