Global Future Drought Layers Based on Downscaled CMIP6 Models and Multiple Socioeconomic Pathways.

IF 6.9 2区 综合性期刊 Q1 MULTIDISCIPLINARY SCIENCES Scientific Data Pub Date : 2025-02-19 DOI:10.1038/s41597-025-04612-w
Diogo S A Araujo, Brian J Enquist, Amy E Frazier, Cory Merow, Patrick R Roehrdanz, Gabriel M Moulatlet, Alex Zvoleff, Lei Song, Brian Maitner, Efthymios I Nikolopoulos
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Abstract

Droughts are a natural hazard of growing concern as they are projected to increase in frequency and severity for many regions of the world. The identification of droughts and their future characteristics is essential to building an understanding of the geography and magnitude of potential drought change trajectories, which in turn is critical information to manage drought resilience across multiple sectors and disciplines. Adding to this effort, we developed a dataset of global historical and projected future drought indices over the 1980-2100 period based on downscaled CMIP6 models across multiple shared socioeconomic pathways (SSP). The dataset is composed of two indices: the Standardized Precipitation Index (SPI) and Standardized Precipitation Evapotranspiration Index (SPEI) for 23 downscaled global climate models (GCMs) (0.25-degree resolution), including historical (1980-2014) and future projections (2015-2100) under four climate scenarios: SSP1-2.6, SSP2-4.5, SSP3-7.0, and SSP5-8.5. The drought indices were calculated for 3-, 6- and 12-month accumulation timescales and are available as gridded spatial datasets in a regular latitude-longitude format at monthly time resolution.

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基于缩小CMIP6模型和多重社会经济途径的全球未来干旱层
干旱是一种日益引起关注的自然灾害,因为预计在世界许多地区,干旱的发生频率和严重程度都将增加。确定干旱及其未来特征对于了解潜在干旱变化轨迹的地理和规模至关重要,而这反过来又是管理跨多个部门和学科的干旱复原力的关键信息。在此基础上,我们基于多个共享社会经济路径(SSP)的缩小CMIP6模型,开发了1980-2100年间全球历史干旱指数和预测未来干旱指数的数据集。该数据集由标准化降水指数(SPI)和标准化降水蒸散指数(SPEI)两项指数组成,包括历史(1980-2014)和未来(2015-2100)在4种气候情景下的预估(SSP1-2.6、SSP2-4.5、SSP3-7.0和SSP5-8.5)。干旱指数以3个月、6个月和12个月的累积时间尺度计算,并以月时间分辨率的规则经纬度格式网格化空间数据集提供。
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来源期刊
Scientific Data
Scientific Data Social Sciences-Education
CiteScore
11.20
自引率
4.10%
发文量
689
审稿时长
16 weeks
期刊介绍: Scientific Data is an open-access journal focused on data, publishing descriptions of research datasets and articles on data sharing across natural sciences, medicine, engineering, and social sciences. Its goal is to enhance the sharing and reuse of scientific data, encourage broader data sharing, and acknowledge those who share their data. The journal primarily publishes Data Descriptors, which offer detailed descriptions of research datasets, including data collection methods and technical analyses validating data quality. These descriptors aim to facilitate data reuse rather than testing hypotheses or presenting new interpretations, methods, or in-depth analyses.
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