Evaluating the impact of climate change on precipitation in the future period using CMIP6 models (case study: Siminehrood River Basin, Iran)

Abstract

The quantity and quality of water resources are significantly impacted by climate change. In this research, the performance of 26 climate models used for predicting precipitation in the baseline period (1988–2018) was evaluated, and the models were ranked and weighted. Then, an analysis was conducted on the trend of precipitation changes during the period of 2031 to 2050 under the SSP1-2.6, SSP2-4.5, and SSP5-8.5 scenarios. The LARS-WG 7.0 model was implemented for the downscaling of precipitation data of the GCM models. Additionally, a comparison was made between the scenarios and models of the Sixth Assessment Report (AR6) in estimating future precipitation. Furthermore, besides investigating the certainty of model results regarding precipitation, the uncertainty of precipitation in different months of the year was examined. Six models with the best performance—MIROC6, INM-CM5-0, FGOALS-G3, KACE-1–0-G, BCC-CSM2-MR, and INM-CM4-8—were selected after evaluating their performance and ranking in the baseline period using four evaluation criteria: R2, RMSE, NSE, and CRM. Consequently, the prediction results from these six models were utilized to monitor precipitation changes during the future period (2031–2050). The KACE-1–0-G model under the SSP5-8.5 scenario is predicted to have the highest increase in precipitation compared to the baseline period, with a difference of 34.7 mm occurring in April. Similarly, the MIROC6 model under the SSP5-8.5 scenario is projected to have the highest decrease in precipitation compared to the baseline period, with a difference of 26 mm in May. The results show that annual precipitation will decrease under all three scenarios compared to the baseline period. Under the SSP5-8.5 scenario, annual precipitation is estimated at 261.3 mm, while under the SSP2-4.5 scenario, it is 264.6 mm, and under the SSP1-2.6 scenario, it is 290.9 mm. When comparing uncertainty, February has the lowest uncertainty in estimating precipitation under all three scenarios due to having a higher bandwidth range for predictions. August experiences the lowest precipitation change, while February has the highest precipitation change. In terms of model uncertainty comparison across all three scenarios, it was found that the MIROC6 model exhibits less uncertainty due to having a lowest bandwidth range for predictions.