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Modified approach to reduce GCM bias in downscaled precipitation: A study in Ganga River Basin

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dc.citedby4
dc.contributor.authorSharma C.en_US
dc.contributor.authorOjha C.S.P.en_US
dc.contributor.authorShukla A.K.en_US
dc.contributor.authorPham Q.B.en_US
dc.contributor.authorLinh N.T.T.en_US
dc.contributor.authorFai C.M.en_US
dc.contributor.authorLoc H.H.en_US
dc.contributor.authorDung T.D.en_US
dc.contributor.authorid57207884881en_US
dc.contributor.authorid7004206177en_US
dc.contributor.authorid56447884900en_US
dc.contributor.authorid57208495034en_US
dc.contributor.authorid57211268069en_US
dc.contributor.authorid57214146115en_US
dc.contributor.authorid57189027363en_US
dc.contributor.authorid57200870280en_US
dc.date.accessioned2023-05-29T07:23:28Z
dc.date.available2023-05-29T07:23:28Z
dc.date.issued2019
dc.descriptionClimate models; Precipitation (chemical); Rivers; Watersheds; Down-scaling; GCM bias; General circulation model; Model performance; River basins; River basin projects; CMIP; computer simulation; downscaling; general circulation model; performance assessment; precipitation (climatology); prediction; spatial resolution; Ganges Basinen_US
dc.description.abstractReanalysis data is widely used to develop predictor-predictand models, which are further used to downscale coarse gridded general circulation models (GCM) data at a local scale. However, large variability in the downscaled product using different GCMs is still a big challenge. The first objective of this study was to assess the performance of reanalysis data to downscale precipitation using different GCMs. High bias in downscaled precipitation was observed using different GCMs, so a different downscaling approach is proposed in which historical data of GCM was used to develop a predictor-predictand model. The earlier approach is termed "Re-Obs" and the proposed approach as "GCM-Obs". Both models were assessed using mathematical derivation and generated synthetic series. The intermodal bias in different GCMs downscaled precipitation using Re-Obs and GCM-Obs model was also checked. Coupled Model Inter-comparison Project-5 (CMIP5) data of ten different GCMs was used to downscale precipitation in different urbanized, rural, and forest regions in the Ganga river basin. Different measures were used to represent the relative performances of one downscaling approach over other approach in terms of closeness of downscaled precipitation with observed precipitation and reduction of bias using different GCMs. The effect of GCM spatial resolution in downscaling was also checked. The model performance, convergence, and skill score were computed to assess the ability of GCM-Obs and Re-Obs models. The proposed GCM-Obs model was found better than Re-Obs model to statistically downscale GCM. It was observed that GCM-Obs model was able to reduce GCM-Observed and GCM-GCM bias in the downscaled precipitation in the Ganga river basin. � 2019 by the authors.en_US
dc.description.natureFinalen_US
dc.identifier.ArtNo2097
dc.identifier.doi10.3390/w11102097
dc.identifier.issue10
dc.identifier.scopus2-s2.0-85073193920
dc.identifier.urihttps://www.scopus.com/inward/record.uri?eid=2-s2.0-85073193920&doi=10.3390%2fw11102097&partnerID=40&md5=84285f72e10b447f0d7cf8d9b710e6ae
dc.identifier.urihttps://irepository.uniten.edu.my/handle/123456789/24432
dc.identifier.volume11
dc.publisherMDPI AGen_US
dc.relation.ispartofAll Open Access, Gold
dc.sourceScopus
dc.sourcetitleWater (Switzerland)
dc.titleModified approach to reduce GCM bias in downscaled precipitation: A study in Ganga River Basinen_US
dc.typeArticleen_US
dspace.entity.typePublication
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