{"title":"气候变化下干旱预估不确定性评估框架:来自CMIP6模型的见解","authors":"Omid Zabihi , Azadeh Ahmadi , Ali Torabi Haghighi","doi":"10.1016/j.scitotenv.2025.179679","DOIUrl":null,"url":null,"abstract":"<div><div>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.</div></div>","PeriodicalId":422,"journal":{"name":"Science of the Total Environment","volume":"982 ","pages":"Article 179679"},"PeriodicalIF":8.2000,"publicationDate":"2025-05-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":"{\"title\":\"A framework for assessing uncertainties in drought projections under climate change: Insights from CMIP6 models\",\"authors\":\"Omid Zabihi , Azadeh Ahmadi , Ali Torabi Haghighi\",\"doi\":\"10.1016/j.scitotenv.2025.179679\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"<div><div>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.</div></div>\",\"PeriodicalId\":422,\"journal\":{\"name\":\"Science of the Total Environment\",\"volume\":\"982 \",\"pages\":\"Article 179679\"},\"PeriodicalIF\":8.2000,\"publicationDate\":\"2025-05-17\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"0\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"Science of the Total Environment\",\"FirstCategoryId\":\"93\",\"ListUrlMain\":\"https://www.sciencedirect.com/science/article/pii/S0048969725013208\",\"RegionNum\":1,\"RegionCategory\":\"环境科学与生态学\",\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"Q1\",\"JCRName\":\"ENVIRONMENTAL SCIENCES\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"Science of the Total Environment","FirstCategoryId":"93","ListUrlMain":"https://www.sciencedirect.com/science/article/pii/S0048969725013208","RegionNum":1,"RegionCategory":"环境科学与生态学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q1","JCRName":"ENVIRONMENTAL SCIENCES","Score":null,"Total":0}
A framework for assessing uncertainties in drought projections under climate change: Insights from CMIP6 models
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.
期刊介绍:
The Science of the Total Environment is an international journal dedicated to scientific research on the environment and its interaction with humanity. It covers a wide range of disciplines and seeks to publish innovative, hypothesis-driven, and impactful research that explores the entire environment, including the atmosphere, lithosphere, hydrosphere, biosphere, and anthroposphere.
The journal's updated Aims & Scope emphasizes the importance of interdisciplinary environmental research with broad impact. Priority is given to studies that advance fundamental understanding and explore the interconnectedness of multiple environmental spheres. Field studies are preferred, while laboratory experiments must demonstrate significant methodological advancements or mechanistic insights with direct relevance to the environment.