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|dc.contributor.author||BRAKENRIDGE G. Robert||en_GB|
|dc.contributor.author||DE GROEVE Tom||en_GB|
|dc.description.abstract||The availability of in-situ data has been a constraining issue in hydrological prediction, especially in those regions that are only sparsely monitored or completely ungauged. The application of remote-sensing data, without conventional in-situ hydrological measurements, to force, calibrate and update a hydrologic model is a major contribution of this study. First, a rainfall-runoff hydrological model called CREST, coupled with an Ensemble Square Root Filter (EnSRF), is used for exceedance probability-based flood prediction. Then, this advanced flood prediction framework, with different experimental designs, is forced by TRMM precipitation while Aqua AMSR-E microwave brightness temperature signals are used for model calibration and data assimilation for progressively improved river discharge prediction. Results indicate that solely relying on remote-sensing data for model forcing, parameter calibration, and state updating with EnSRF, the designed framework can adequately predict flooding events. A high flow threshold was applied and has further improved modeling performance, particularly in the flooding seasons, with a flood warning lead-time of one day. Given the anticipated global availability of satellite-based precipitation (i.e. GPM) and AMSR-E like passive microwave signal information (i.e. SMAP) in near real-time, this proposed research framework could potentially contribute to the exceedance probability-based flood prediction in the vast sparsely gauged or ungauged basins around the world.||en_GB|
|dc.description.sponsorship||JRC.G.2-Global security and crisis management||en_GB|
|dc.publisher||John Wiley & Sons, Inc||en_GB|
|dc.title||Impact of assimilating spaceborne microwave signals for improving hydrological prediction in ungauged basins||en_GB|
|dc.type||Articles in periodicals and books||en_GB|
|JRC Directorate:||Space, Security and Migration|
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