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dc.contributor.authorYoshimoto, Shuheien_US
dc.contributor.authorAmarnath, Girirajen_US
dc.date.accessioned2017-11-09T08:09:37Zen_US
dc.date.available2017-11-09T08:09:37Zen_US
dc.identifier.urihttps://hdl.handle.net/10568/89283en_US
dc.titleApplications of satellite-based rainfall estimates in flood inundation modeling: a case study in Mundeni Aru River Basin, Sri Lankaen_US
dcterms.abstractThe performance of Satellite Rainfall Estimate (SRE) products applied to flood inundation modelling was tested for the Mundeni Aru River Basin in eastern Sri Lanka. Three SREs (PERSIANN, TRMM, and GSMaP) were tested, with the Rainfall-Runoff-Inundation (RRI) model used as the flood inundation model. All the SREs were found to be suitable for applying to the RRI model. The simulations created by applying the SREs were generally accurate, although there were some discrepancies in discharge due to differing precipitation volumes. The volumes of precipitation of the SREs tended to be smaller than those of the gauged data, but using a scale factor to correct this improved the simulations. In particular, the SRE, i.e., the GSMaP yielding the best simulation that correlated most closely with the flood inundation extent from the satellite data, was considered the most appropriate to apply to the model calculation. The application procedures and suggestions shown in this study could help authorities to make better-informed decisions when giving early flood warnings and making rapid flood forecasts, especially in areas where in-situ observations are limited.en_US
dcterms.accessRightsOpen Accessen_US
dcterms.bibliographicCitationYoshimoto, Shuhei; Amarnath, Giriraj. 2017. Applications of satellite-based rainfall estimates in flood inundation modeling: a case study in Mundeni Aru River Basin, Sri Lanka. Remote Sensing, 9(10):1-16. doi: 10.3390/rs9100998en_US
dcterms.extent1-16en_US
dcterms.issued2017-09-27en_US
dcterms.languageenen_US
dcterms.licenseCC-BY-4.0en_US
dcterms.publisherMDPI AGen_US
dcterms.subjectsatellite observationen_US
dcterms.subjectrainfall patternsen_US
dcterms.subjectfloodingen_US
dcterms.subjectmonitoringen_US
dcterms.subjectrainfall-runoff relationshipsen_US
dcterms.subjectmodelsen_US
dcterms.subjectriver basinsen_US
dcterms.subjectprecipitationen_US
dcterms.subjectforecastingen_US
dcterms.subjectcase studiesen_US
dcterms.typeJournal Articleen_US
cg.subject.ccafsCLIMATE SERVICES AND SAFETY NETSen_US
cg.contributor.affiliationInternational Water Management Instituteen_US
cg.identifier.doihttps://doi.org/10.3390/rs9100998en_US
cg.coverage.regionAsiaen_US
cg.coverage.regionSouthern Asiaen_US
cg.contributor.crpClimate Change, Agriculture and Food Securityen_US
cg.contributor.crpWater, Land and Ecosystemsen_US
cg.identifier.ccafsprojectpiiPII-FP4_CSRDen_US
cg.reviewStatusPeer Reviewen_US
cg.volume9en_US
cg.issue10en_US


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