The Effect of Climate change on the Zayandeh-Rud River Basin’s temperature using a Bayesian machine learning Soft Computing Technique

• Main Authors: Sh. Kouhestani, S, Eslamian, A. Besalatpour
• Format: Article
• Language: fas
• Published: Isfahan University of Technology 2017-06-01
• Series: علوم آب و خاک
• Subjects: climate change; downscaling; emission scenarios; temperature; zayandeh-rud river basin
• Online Access: http://jstnar.iut.ac.ir/article-1-3327-en.html
• ISSN: 2476-3594; 2476-5554

This study aims to investigate the changes of minimum and maximum temperature variables under the impact of climate change for time period of 2015-2100 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 highest increase for both maximum and minimum temperatures was obtained in winter.     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, changes in maximum temperature will be slightly higher than minimum temperature changes in the future.