Assessing the impact of climate change on rainfall and temperature variability (Case Study: Kashan and Khur and Biabank Stations)

Document Type : Research Paper

Authors

1 Ph.D. student in Watershed Management Engineering and Science, Faculty of Natural Resources and Earth Sciences, University of Kashan, Kashan, Iran

2 Associate Professor, Faculty of Natural Resources and Earth Sciences, University of Kashan, Kashan, Iran

10.29252/aridbiom.2019.1545

Abstract

General circulation models (GCMs) have been used to predict future climate change by climate agencies. GCMs outputs as local interfaces have larger spatial resolution than the simulated variables. In the present study, the Statistical Downscaling Model (SDSM) was applied to estimate the variability of rainfall and temperature in Kashan and Khur and Biabank synoptic stations in Isfahan Province, based on climate change scenario downscaled from HadCM3 Model. For this reason, firstly, the variation of mean temperature and rainfall for base period was investigated under A2 scenario of HadCM3 model using the daily long-term data from the stations.The estimation and prediction of future periods (2039-2010), (2069-2040) and (2099-2070) was then carried out. The results showed that in both stations, simulated temperature and rainfall values had a close consistence with observed values, but the performance of downscaling process in rainfall prediction was less than temperature during calibration and validation periods. Results showed that for Kashan station the mean temperature will change by 0.42, 1.08 and 2.16◦C during (2010-2039), (2040-2069) and (2070-2099) for A2 scenario as compared to the baseline period (1987-1987). The results also showed a decrease in average temperatures in January, February, March, September and December, and an increase in other months. The results of Khur and Biabank station also showed that temperature will continuously increase in the region. Furthermore, the average annual rainfall increases 1.38 mm under scenario A2 during the prediction period (2070-2099) compared to the observation period.

Keywords

References

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Journal of Arid Biome
Volume 9, Issue 1
September 2019
Pages 81-99

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