Volume 21, Issue 1 (Spring 2017)                   JWSS 2017, 21(1): 203-216 | Back to browse issues page

DOI: 10.18869/acadpub.jstnar.21.1.203

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kouhestani S, Besalatpour A A. Projection of Climate change impacts on the Zayandeh-Rud River Basin’s temperature by using a Bayesian Soft Computing Technique. JWSS. 2017; 21 (1) :203-216
URL: http://jstnar.iut.ac.ir/article-1-3327-en.html

MS University
Abstract:   (1508 Views)

This study aims to investigate the changes of minimum and maximum temperature variables under the impact of climate change for time period of 2020-2100 in the Zayandeh-Rud River Basin. The outputs of 14 Global Climate Models (GCMs) under three green-house emission scenarios (RCP2.6, RCP4.5, and RCP8.5) are employed from the Fifth Assessment Report (CMIP5) of Intergovernmental Panel on Climate Change (IPCC). A novel statistical downscaling method using a Bayesian Relevance Vector Machine (RVM) is used to project the impact of climate change on the temperature variables at regional scale. The results of the weighting average of the GCMs show that the various models have different accuracy in the projecting the minimum and maximum temperatures in the study area. The results demonstrate that the MIROC5 and CCSM4 are the most reliable models in projecting the maximum and minimum temperatures respectively. The rise of the maximum temperature is expected in winter season. On the annual basis, the maximum temperature will increase by 0.18-0.76 °C and 0.25-1.67 °C, respectively, in the near and long-term future periods under different emission scenarios. The annual minimum temperature will increase by 0.28 to 0.82 °C and 0.24-1.56 °C, respectively, in the near and long-term future periods. In a general view, the values of the maximum temperature changes are expected to be more than the minimum temperature changes in the future in the study area.

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Type of Study: Research | Subject: Ggeneral
Received: 2016/08/28 | Accepted: 2016/11/5 | Published: 2017/06/7

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