Document Type : Original Article
Authors
1 Water Science and Engineering, Faculty of Agriculture, Birjand University, Birjand, Iran
2 Department of Water Science and Engineering, Faculty of Agriculture, Birjand University, Birjand, Iran
3 M.Sc. Student in Water Resources Engineering, University of Birjand, Iran
4 Associate Professor, Department of Water Engineering, Faculty of Agriculture, University of Birjand, Birjand, Iran.
Abstract
Climate change, as one of the most significant environmental challenges of the 21st century, has extensive impacts on global temperature. In the present study, in order to reproduce the maximum temperature variable, monthly maximum temperature data from the Tabas meteorological station for the period 1990–2014 were used. The years 1990–2007 were considered for calibration and 2008–2014 for validation. To analyze the maximum temperature, a bias correction method based on quantile mapping was applied using coding in the R software environment with the help of the Qmap package. The performance of the models was evaluated using the statistical indices RMSE and KGE, and the superior model was selected through a weighting approach. In this regard, the outputs of two global climate models, IPSL-CM6A-LR and MPI-ESM1-2-HR, from the CMIP6 project (raw data) and the ISIMIP project (bias-corrected data) were analyzed, and their performance in reproducing maximum temperature was assessed and compared. The results indicated that ISIMIP models performed better than those of CMIP6. Accordingly, the superior ISIMIP model was employed to simulate changes in maximum temperature for the future period 2030–2050 under the SSP5-8.5 scenario. The results of these simulations revealed a continuing increasing trend in maximum temperature in the region, leading to serious consequences such as intensified heat stress, increased evapotranspiration, reduced aquifer recharge, declining groundwater levels, and more severe droughts. This study highlights the importance of utilizing accurate models in local climate studies, especially in vulnerable regions
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