Please use this identifier to cite or link to this item: https://hdl.handle.net/10356/138556
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dc.contributor.authorVallam, Pramodhen_US
dc.contributor.authorQin, Xiao Shengen_US
dc.date.accessioned2020-05-08T04:32:00Z-
dc.date.available2020-05-08T04:32:00Z-
dc.date.issued2017-
dc.identifier.citationVallam, P., & Qin, X. S. (2018). Projecting future precipitation and temperature at sites with diverse climate through multiple statistical downscaling schemes. Theoretical and Applied Climatology, 134(1-2), 669-688. doi:10.1007/s00704-017-2299-yen_US
dc.identifier.issn0177-798Xen_US
dc.identifier.urihttps://hdl.handle.net/10356/138556-
dc.description.abstractAnthropogenic-driven climate change would affect the global ecosystem and is becoming a world-wide concern. Numerous studies have been undertaken to determine the future trends of meteorological variables at different scales. Despite these studies, there remains significant uncertainty in the prediction of future climates. To examine the uncertainty arising from using different schemes to downscale the meteorological variables for the future horizons, projections from different statistical downscaling schemes were examined. These schemes included statistical downscaling method (SDSM), change factor incorporated with LARS-WG, and bias corrected disaggregation (BCD) method. Global circulation models (GCMs) based on CMIP3 (HadCM3) and CMIP5 (CanESM2) were utilized to perturb the changes in the future climate. Five study sites (i.e., Alice Springs, Edmonton, Frankfurt, Miami, and Singapore) with diverse climatic conditions were chosen for examining the spatial variability of applying various statistical downscaling schemes. The study results indicated that the regions experiencing heavy precipitation intensities were most likely to demonstrate the divergence between the predictions from various statistical downscaling methods. Also, the variance computed in projecting the weather extremes indicated the uncertainty derived from selection of downscaling tools and climate models. This study could help gain an improved understanding about the features of different downscaling approaches and the overall downscaling uncertainty.en_US
dc.description.sponsorshipMOE (Min. of Education, S’pore)en_US
dc.language.isoenen_US
dc.relation.ispartofTheoretical and Applied Climatologyen_US
dc.rights© 2017 Springer-Verlag GmbH Austria. All rights reserved.en_US
dc.subjectEngineering::Environmental engineeringen_US
dc.titleProjecting future precipitation and temperature at sites with diverse climate through multiple statistical downscaling schemesen_US
dc.typeJournal Articleen
dc.contributor.schoolSchool of Civil and Environmental Engineeringen_US
dc.contributor.organizationEnvironmental Process Modelling Centreen_US
dc.contributor.researchNanyang Environment and Water Research Instituteen_US
dc.identifier.doi10.1007/s00704-017-2299-y-
dc.identifier.scopus2-s2.0-85031912818-
dc.identifier.issue1-2en_US
dc.identifier.volume134en_US
dc.identifier.spage669en_US
dc.identifier.epage688en_US
dc.subject.keywordsClimate Changeen_US
dc.subject.keywordsBias Corrected Disaggregationen_US
item.fulltextNo Fulltext-
item.grantfulltextnone-
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