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

IF 8.2 1区 环境科学与生态学 Q1 ENVIRONMENTAL SCIENCES
Omid Zabihi , Azadeh Ahmadi , Ali Torabi Haghighi
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引用次数: 0

摘要

气候变化对水文和干旱的影响通常是使用一般环流模式(GCMs)来评估的,这种模式引入了相当大的不确定性。本研究提出了一个结构化框架来评估这些不确定性,重点关注关键水文参数和干旱特征。采用多标准统计方法评估了三种选定的CMIP6 GCMs (ACCESS-CM2、CanESM5和ACCESS-ESM1-5)在SSP245和SSP585情景下的性能。采用标准化降水指数(SPI)和标准化降水蒸散发指数(SPEI)对干旱条件进行了分析,后者捕获温度驱动的蒸散发。该不确定性框架结合了估计干旱分类分布的贝叶斯概率方法和评估不确定性时间演变的多项式分解方法。将CanESM5应用于伊朗六个主要流域,SSP585下的CanESM5预测了最极端的结果,包括东部边界流域年降水量增加1.71倍,波斯湾流域减少0.87倍。里海流域的气温上升幅度最大,为2.97°C。结果表明,所有流域发生正常干旱的概率较高,其次是中度干旱和中度湿润事件。温度预估对排放情景的敏感性高于降水,而不确定性,特别是来自gcm和排放途径的不确定性,随着时间的推移而增加。贝叶斯推理和方差分解的结合使用为干旱预测中不确定性的大小和来源的量化提供了一个强有力的框架。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
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.
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来源期刊
Science of the Total Environment
Science of the Total Environment 环境科学-环境科学
CiteScore
17.60
自引率
10.20%
发文量
8726
审稿时长
2.4 months
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.
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