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Projection the long-term ungauged rainfall using integrated Statistical Downscaling Model and Geographic Information System (SDSM-GIS) model

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dc.citedby6
dc.contributor.authorTukimat N.N.A.en_US
dc.contributor.authorAhmad Syukri N.A.en_US
dc.contributor.authorMalek M.A.en_US
dc.contributor.authorid55531417400en_US
dc.contributor.authorid57210995689en_US
dc.contributor.authorid55636320055en_US
dc.date.accessioned2023-05-29T07:23:52Z
dc.date.available2023-05-29T07:23:52Z
dc.date.issued2019
dc.description.abstractAn accuracy in the hydrological modelling will be affected when having limited data sources especially at ungauged areas. Due to this matter, it will not receiving any significant attention especially on the potential hydrologic extremes. Thus, the objective was to analyse the accuracy of the long-term projected rainfall at ungauged rainfall station using integrated Statistical Downscaling Model and Geographic Information System (SDSM-GIS) model. The SDSM was used as a climate agent to predict the changes of the climate trend in ?2030s by gauged and ungauged stations. There were five predictors set have been selected to form the local climate at the region which provided by NCEP (validated) and CanESM2-RCP4.5 (projected). According to the statistical analyses, the SDSM was controlled to produce reliable validated results with lesser %MAE (<23%) and higher R. The projected rainfall was suspected to decrease 14% in ?2030s. All the RCPs agreed the long term rainfall pattern was consistent to the historical with lower annual rainfall intensity. The RCP8.5 shows the least rainfall changes. These findings then used to compare the accuracy of monthly rainfall at control station (Stn 2). The GIS-Kriging method being as an interpolation agent was successfully to produce similar rainfall trend with the control station. The accuracy was estimated to reach 84%. Comparing between ungauged and gauged stations, the small %MAE in the projected monthly results between gauged and ungauged stations as a proved the integrated SDSM-GIS model can producing a reliable long-term rainfall generation at ungauged station. � 2019 The Author(s)en_US
dc.description.natureFinalen_US
dc.identifier.ArtNoe02456
dc.identifier.doi10.1016/j.heliyon.2019.e02456
dc.identifier.issue9
dc.identifier.scopus2-s2.0-85072188012
dc.identifier.urihttps://www.scopus.com/inward/record.uri?eid=2-s2.0-85072188012&doi=10.1016%2fj.heliyon.2019.e02456&partnerID=40&md5=09fa10f25a1c9d132ecce715dd1cb9bb
dc.identifier.urihttps://irepository.uniten.edu.my/handle/123456789/24479
dc.identifier.volume5
dc.publisherElsevier Ltden_US
dc.relation.ispartofAll Open Access, Gold, Green
dc.sourceScopus
dc.sourcetitleHeliyon
dc.titleProjection the long-term ungauged rainfall using integrated Statistical Downscaling Model and Geographic Information System (SDSM-GIS) modelen_US
dc.typeArticleen_US
dspace.entity.typePublication
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