Please use this identifier to cite or link to this item: http://hdl.handle.net/10397/81732
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dc.contributorDepartment of Civil and Environmental Engineering-
dc.creatorHomsi, Ren_US
dc.creatorShiru, MSen_US
dc.creatorShahid, Sen_US
dc.creatorIsmail, Ten_US
dc.creatorBin Harun, Sen_US
dc.creatorAl-Ansari, Nen_US
dc.creatorChau, KWen_US
dc.creatorYaseen, ZMen_US
dc.date.accessioned2020-02-10T12:28:53Z-
dc.date.available2020-02-10T12:28:53Z-
dc.identifier.issn1994-2060en_US
dc.identifier.urihttp://hdl.handle.net/10397/81732-
dc.language.isoenen_US
dc.publisherTaylor & Francisen_US
dc.rights© 2019 The Author(s). Published by Informa UK Limited, trading as Taylor & Francis Groupen_US
dc.rightsThis is an Open Access article distributed under the terms of the Creative Commons Attribution License (http://creativecommons.org/licenses/by/4.0/), which permits unrestricted use,distribution, and reproduction in any medium, provided the original work is properly cite.en_US
dc.rightsThe following publication Rajab Homsi, Mohammed Sanusi Shiru, Shamsuddin Shahid, TarmiziIsmail, Sobri Bin Harun, Nadhir Al-Ansari, Kwok-Wing Chau & Zaher Mundher Yaseen(2020) Precipitation projection using a CMIP5 GCM ensemble model: a regional investigationof Syria, Engineering Applications of Computational Fluid Mechanics, 14:1, 90-106 is available at https://dx.doi.org/10.1080/19942060.2019.1683076en_US
dc.subjectPrecipitation projectionen_US
dc.subjectGeneral circulation modelen_US
dc.subjectRandom foresten_US
dc.subjectSymmetrical uncertaintyen_US
dc.subjectSyriaen_US
dc.titlePrecipitation projection using a CMIP5 GCM ensemble model : a regional investigation of Syriaen_US
dc.typeJournal/Magazine Articleen_US
dc.identifier.spage90en_US
dc.identifier.epage106en_US
dc.identifier.volume14en_US
dc.identifier.issue1en_US
dc.identifier.doi10.1080/19942060.2019.1683076en_US
dcterms.abstractThe possible changes in precipitation of Syrian due to climate change are projected in this study. The symmetrical uncertainty (SU) and multi-criteria decision-analysis (MCDA) methods are used to identify the best general circulation models (GCMs) for precipitation projections. The effectiveness of four bias correction methods, linear scaling (LS), power transformation (PT), general quantile mapping (GEQM), and gamma quantile mapping (GAQM) is assessed in downscaling GCM simulated precipitation. A random forest (RF) model is performed to generate the multi model ensemble (MME) of precipitation projections for four representative concentration pathways (RCPs) 2.6, 4.5, 6.0, and 8.5. The results showed that the best suited GCMs for climate projection of Syria are HadGEM2-AO, CSIRO-Mk3-6-0, NorESM1-M, and CESM1-CAM5. The LS demonstrated the highest capability for precipitation downscaling. Annual changes in precipitation is projected to decrease by -30 to -85.2% for RCPs 4.5, 6.0, and 8.5, while by < 0.0 to -30% for RCP 2.6. The precipitation is projected to decrease in the entire country for RCP 6.0, while increase in some parts for other RCPs during wet season. The dry season of precipitation is simulated to decrease by -12 to -93%, which indicated a drier climate for the country in the future.-
dcterms.accessRightsopen accessen_US
dcterms.bibliographicCitationEngineering applications of computational fluid mechanics, 2020, v. 14, no. 1, p. 90-106en_US
dcterms.isPartOfEngineering applications of computational fluid mechanicsen_US
dcterms.issued2020-
dc.identifier.isiWOS:000495143600001-
dc.identifier.scopus2-s2.0-85075007804-
dc.identifier.eissn1997-003Xen_US
dc.description.validate202002 bcrc-
dc.description.oaVersion of Recorden_US
dc.identifier.FolderNumberOA_Scopus/WOSen_US
dc.description.pubStatusPublisheden_US
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