A framework for assessing uncertainties in drought projections under climate change: Insights from CMIP6 models

被引：1
作者：
Zabihi, Omid [1 ]
Ahmadi, Azadeh [2 ]
Haghighi, Ali Torabi [3 ]
机构：
[1] Department of Civil Engineering, Isfahan University of Technology, Isfahan
[2] Faculty of Civil, Water and Environmental Engineering, Shahid Beheshti University, Tehran
[3] Water, Energy, and Environmental Engineering Research Unit, University of Oulu, Oulu
来源：
Science of the Total Environment | 2025年 / 982卷
关键词：
Bayesian approach; Climate change; CMIP6; Drought projection; Iran; Uncertainty;
D O I：
10.1016/j.scitotenv.2025.179679
中图分类号：
学科分类号：
摘要：
The impact of climate change on hydrology and drought is commonly assessed using General Circulation Models (GCMs), which introduce considerable uncertainty. This study presents a structured framework to evaluate these uncertainties, focusing on key hydrological parameters and drought characteristics. A multi-criteria statistical approach was used to assess the performance of three selected CMIP6 GCMs- ACCESS-CM2, CanESM5, and ACCESS-ESM1–5- under SSP245 and SSP585 scenarios. Drought conditions were analyzed using the Standardized Precipitation Index (SPI) and the Standardized Precipitation Evapotranspiration Index (SPEI), the latter capturing temperature-driven evapotranspiration. The uncertainty framework integrates a Bayesian probabilistic method for estimating the distribution of drought classifications and a polynomial-based decomposition approach to evaluate the temporal evolution of uncertainty. Applied to six major Iranian watersheds, CanESM5 under SSP585 projected the most extreme outcomes, including a 1.71-fold increase in annual precipitation in the Eastern border watershed and a 0.87-fold decrease in the Persian Gulf watershed. The highest temperature increase, 2.97 °C, was observed in the Caspian Sea watershed. Results indicate a higher probability of normal drought conditions across all watersheds, followed by moderately dry and moderately wet events. Temperature projections showed greater sensitivity to emission scenarios than precipitation, and uncertainties, particularly from GCMs and emission pathways, increased over time. The combined use of Bayesian inference and variance decomposition provides a robust framework for quantifying both the magnitude and sources of uncertainty in drought projections. © 2025 Elsevier B.V.
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