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dc.contributor.advisorValdes, Juan B.en_US
dc.contributor.authorMerino, Manuel*
dc.creatorMerino, Manuelen_US
dc.date.accessioned2013-06-10T17:40:47Z
dc.date.available2013-06-10T17:40:47Z
dc.date.issued2013
dc.identifier.urihttp://hdl.handle.net/10150/293730
dc.description.abstractIn the context of the SERVIR-Africa project, the SERVIR Arizona Team is developing streamflow forecast systems on African basins using Satellite Precipitation Products (SPP) to drive the models. These products have errors that need to be addressed before using them to drive hydrologic models. An analysis of the errors of the Satellite Precipitation Products TMPA-3B42RT, CMORPH, and PERSIANN over Africa is presented, followed by bias correction and error reduction methods to improve the remote sensed estimates. The GPCP 1-degree-day reanalysis product was used as the rainfall truth dataset. The Bias Correction Spatial Downscaling (BCSD) method developed by Wood et al., was used successfully to reduce the errors of SPP. The original and bias corrected estimates from the three SPP are used to calibrate and simulate three catchments of the Senegal River basin using HYMOD, finding that the use of bias corrected estimates produces a significant improvement in streamflow simulation.
dc.language.isoenen_US
dc.publisherThe University of Arizona.en_US
dc.rightsCopyright © is held by the author. Digital access to this material is made possible by the University Libraries, University of Arizona. Further transmission, reproduction or presentation (such as public display or performance) of protected items is prohibited except with permission of the author.en_US
dc.subjectbias correctionen_US
dc.subjecterroren_US
dc.subjectrainfallen_US
dc.subjectremote sensingen_US
dc.subjectHydrologyen_US
dc.subjectafricaen_US
dc.titleEvaluation of the Performance of Satellite Precipitation Products over Africaen_US
dc.typetexten_US
dc.typeElectronic Thesisen_US
thesis.degree.grantorUniversity of Arizonaen_US
thesis.degree.levelmastersen_US
dc.contributor.committeememberGupta, Hoshin V.en_US
dc.contributor.committeememberSerrat-Capdevila, Aleixen_US
thesis.degree.disciplineGraduate Collegeen_US
thesis.degree.disciplineHydrologyen_US
thesis.degree.nameM.S.en_US
refterms.dateFOA2018-07-14T11:30:24Z
html.description.abstractIn the context of the SERVIR-Africa project, the SERVIR Arizona Team is developing streamflow forecast systems on African basins using Satellite Precipitation Products (SPP) to drive the models. These products have errors that need to be addressed before using them to drive hydrologic models. An analysis of the errors of the Satellite Precipitation Products TMPA-3B42RT, CMORPH, and PERSIANN over Africa is presented, followed by bias correction and error reduction methods to improve the remote sensed estimates. The GPCP 1-degree-day reanalysis product was used as the rainfall truth dataset. The Bias Correction Spatial Downscaling (BCSD) method developed by Wood et al., was used successfully to reduce the errors of SPP. The original and bias corrected estimates from the three SPP are used to calibrate and simulate three catchments of the Senegal River basin using HYMOD, finding that the use of bias corrected estimates produces a significant improvement in streamflow simulation.


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