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dc.contributor.authorZHANG Yuen_GB
dc.contributor.authorHONG Yangen_GB
dc.contributor.authorGOURLEY Jonathanen_GB
dc.contributor.authorWANG Xuguangen_GB
dc.contributor.authorBRAKENRIDGE G. Roberten_GB
dc.contributor.authorDE GROEVE Tomen_GB
dc.contributor.authorVERGARA Humbertoen_GB
dc.date.accessioned2014-12-02T01:07:37Z-
dc.date.available2014-12-01en_GB
dc.date.available2014-12-02T01:07:37Z-
dc.date.created2014-11-28en_GB
dc.date.issued2014en_GB
dc.date.submitted2014-03-11en_GB
dc.identifier.isbn978-1-118-87203-1en_GB
dc.identifier.urihttp://onlinelibrary.wiley.com/doi/10.1002/9781118872086.ch27/summaryen_GB
dc.identifier.urihttp://publications.jrc.ec.europa.eu/repository/handle/JRC89445-
dc.description.abstractThe 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.sponsorshipJRC.G.2-Global security and crisis managementen_GB
dc.format.mediumOnlineen_GB
dc.languageENGen_GB
dc.publisherJohn Wiley & Sons, Incen_GB
dc.relation.ispartofseriesJRC89445en_GB
dc.titleImpact of assimilating spaceborne microwave signals for improving hydrological prediction in ungauged basinsen_GB
dc.typeArticles in periodicals and booksen_GB
dc.identifier.doi10.1002/9781118872086.ch27en_GB
JRC Directorate:Space, Security and Migration

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