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Precipitation projection using a CMIP5 GCM ensemble model: a regional investigation of Syria
Department of Water and Environmental Engineering, School of Civil Engineering, Faculty of Engineering, Universiti Teknologi Malaysia (UTM), Johor Bahru, Malaysia.
Department of Water and Environmental Engineering, School of Civil Engineering, Faculty of Engineering, Universiti Teknologi Malaysia (UTM), Johor Bahru, Malaysia. Department of Environmental Sciences, Faculty of Science, Federal University Dutse, Dutse, Nigeria.
Department of Water and Environmental Engineering, School of Civil Engineering, Faculty of Engineering, Universiti Teknologi Malaysia (UTM), Johor Bahru, Malaysia.
Department of Water and Environmental Engineering, School of Civil Engineering, Faculty of Engineering, Universiti Teknologi Malaysia (UTM), Johor Bahru, Malaysia.
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2020 (English)In: Engineering Applications of Computational Fluid Mechanics, ISSN 1994-2060, E-ISSN 1997-003X, Vol. 14, no 1, p. 90-106Article in journal (Refereed) Published
Abstract [en]

The 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.

Place, publisher, year, edition, pages
UK: Taylor & Francis Group, 2020. Vol. 14, no 1, p. 90-106
Keywords [en]
precipitation projection, general circulation model, random forest, symmetrical uncertainty, Syria
National Category
Geotechnical Engineering
Research subject
Soil Mechanics
Identifiers
URN: urn:nbn:se:ltu:diva-76647DOI: 10.1080/19942060.2019.1683076ISI: 000495143600001Scopus ID: 2-s2.0-85075007804OAI: oai:DiVA.org:ltu-76647DiVA, id: diva2:1368690
Note

Validerad;2019;Nivå 2;2019-11-11 (johcin)

Available from: 2019-11-08 Created: 2019-11-08 Last updated: 2019-11-25Bibliographically approved

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