Spatio-temporal variation of precipitation projection based on bias-adjusted CORDEX-SA regional climate model simulations for arid and semi-arid region

Abstract

Global warming significantly affects the hydrological cycle that influences hydrological systems worldwide. Knowledge of precipitation variation under a changing climate can provide effective ways to deal with its impacts on water resource management. Hence, in this study, spatiotemporal variability of precipitation under future climatic scenarios was studied using suitable regional climate models (RCMs) extracted from the South Asia Coordinated Regional Climate Downscaling Experiments (CORDEX-SA) based on the control period (1976-2005). Additionally, this research attempted to evaluate the changes in precipitation patterns in Iran by integrating output from a set of RCMs extracted from CORDEX-SA with selected bias-correction (BC) methods, namely quantile ranking-based bias correction (QRBC), linear scaling (LS), empirical quantile mapping (EQM), Gamma quantile mapping (GQM), and Gamma-Pareto quantile mapping (GPQM). Regarding the findings, the performance of RCMs varied based on the climates of regions. Moreover, QRBC played an essential role in improving the output of RCMs to simulate precipitation values. Under RCP4.5 and RCP8.5, the projection of seasonal precipitation indicated that a slight wetting pattern might occur across the northern area in all seasons, especially in the southern regions. Whereas the southern area exhibited a strong wetting tendency in autumn, and the maximum percentage changes (PC) of precipitation were projected to increase by 35% for the mid (2040-2070) and far future (2070-2100) periods, based on RCP4.5 and RCP8.5, respectively. This study will be helpful in understanding the uncertainties in CORDEX-SA RCM projections of climate changes in Iran. Using the outcomes of this study, strategies can be adopted to better manage water resources for various regions of Iran.