Spatiotemporal characteristics and comparison of global multi-type droughts under climate warming

doi:10.11755/j.issn.1006-7639-2026-04-0525

Abstract

Abstract:

Understanding the spatiotemporal evolution of multi-type drought and its responses to temperature and precipitation is crucial for drought risk assessment and water resources management under a warming climate. This study is based on global multi-source observational and reanalysis datasets covering the period 1982 to 2022. The Self-Calibrating Palmer Drought Severity Index (scPDSI), Standardized Soil Moisture Index (SSMI), Standardized Runoff Index (SRI) and Standardized Vegetation Health Index (SVHI) are constructed to characterize meteorological, agricultural, hydrological, and ecological droughts, respectively. Using linear trend analysis and multiple regression methods, this study investigates the long-term trends and variability of multi-type droughts across global climate zones and quantifies the contributions of temperature and precipitation to these changes. The results indicate a significant global intensification of meteorological, hydrological, and ecological droughts, whereas agricultural drought shows a contrasting hemispheric pattern, with alleviation in the Northern Hemisphere and intensification in the Southern Hemisphere. Meteorological drought variability decreased markedly, whereas the variability of agricultural, hydrological droughts generally increased. In arid regions, the responses of different drought indexes are inconsistent. The mitigation trend in agricultural drought is opposite to the aggravation trends in meteorological, hydrological, and ecological droughts. Moreover, the changes in variability of meteorological and ecological droughts are more pronounced in arid regions compared with other regions. In humid regions, multi-type droughts intensified consistently, with hydrological drought showing particularly pronounced aggravation. Temperature contributes significantly more than precipitation to multi-type droughts. Warming generally exacerbates these droughts, with its contribution being particularly pronounced for meteorological, hydrological, and ecological droughts in arid regions. By contrast, increased precipitation tends to alleviate meteorological, agricultural, and hydrological droughts.

Key words: multi-type droughts, global warming, standardized drought index, climate response

摘要：

全球变暖背景下，分析多种类型干旱时空演变特征及其对温度、降水的响应，对干旱风险评估及水资源管理具有重要意义。基于1982—2022年全球多源观测与再分析数据的自校正帕尔默干旱指数、标准化土壤湿度指数、标准化径流指数及标准化植被健康指数分别表征气象干旱、农业干旱、水文干旱和生态干旱，结合线性趋势分析及多元回归方法，揭示全球不同气候区多种类型干旱的长期趋势、变率以及温度与降水对其的贡献。结果表明：全球气象、水文和生态干旱呈显著加剧趋势，农业干旱呈现北半球缓解、南半球加剧特征；气象干旱变率显著减小但农业、水文干旱变率普遍增加。干旱区各干旱指数响应不一致，农业干旱呈缓解趋势，与气象、水文及生态干旱的加剧趋势相反，且气象与生态干旱变率增幅明显高于其他气候区；湿润区多类型干旱一致加剧，水文干旱加剧更显著。温度对多类型干旱的贡献均显著大于降水，升温加剧干旱，其中干旱区升温对气象、水文与生态干旱加剧的贡献尤为突出；降水量增加对气象、农业、水文干旱均具缓解作用。

关键词: 多类型干旱, 全球变暖, 标准化干旱指数, 气候响应

CLC Number:

P467

WANG Yinqi, ZHANG Yanting, CHEN Hui, ZENG Zhanran. Spatiotemporal characteristics and comparison of global multi-type droughts under climate warming[J]. Journal of Arid Meteorology, 2026, 44(4): 525-539.

王胤淇, 张艳婷, 陈慧, 曾展然. 气候变暖下全球多类型干旱时空演变特征分析及比较[J]. 干旱气象, 2026, 44(4): 525-539.

Figures/Tables 12

Fig.1 Global drought zoning （a） and spatial distribution of soil moisture （b， Unit： mm）， runoff （c， Unit： m）， and vegetation health index （VHI）（d） averaged during 1982-2022

Fig.2 Spatial distribution of long-term trends of scPDSI （a）， SSMI （b）， SRI （c） and SVHI （d） during 1982-2022 （the dotted areas passing the significance test （P<0.05），the same as below）

Tab.1 Temporal trends of scPDSI， SSMI， SRI， and SVHI at different spatial scales during 1982-2022

指数	全球	北半球	南半球
scPDSI	-0.37*	0.04	-1.46*
SSMI	-0.03	0.64*	-1.88 *
SRI	-1.17*	-1.15*	-1.25 *
SVHI	-2.24*	-2.22*	-2.30*

Fig.3 Variation of trends of scPDSI， SSMI， SRI and SVHI （a） and the proportion of areas with significant trends （b） during 1982-2022 in different arid and humid areas

Fig.4 The spatial distribution of variability of scPDSI （a）， soil moisture （b， Unit： mm）， runoff （c， Unit： m） and VHI （d） averaged during 1982-2022

Fig.5 Spatial distribution of long-term trends in the variability of scPDSI （a）， SSMI （b）， SRI （c） and SVHI （d） during 1982-2022 （Unit：（10 a）-1）

Tab.2 Temporal change trends of the variability of scPDSI， SSMI， SRI， and SVHI at different spatial scales during 1982-2022

指数	全球	北半球	南半球
scPDSI	-0.76*	-0.58*	-1.27*
SSMI	0.07	0.09*	0.02
SRI	0.13*	0.15 *	0.08
SVHI	0.07	0.08	0.05

Fig.6 Variation of trends of variability of scPDSI， SSMI， SRI， and SVHI with the aridity index （AI） during 1982-2022

Fig.7 Spatial distribution of longterm trends （a， Unit： ℃·（10 a）-1； b， Unit： mm·（10 a）-1） and time series （c， d） of temperature （a， c） and precipitation （b， d） during 1982-2022 （Solid lines for five-year moving average， dashed lines for linear trend， the slope （k-value） marked with “*” passing the significance test （P<0.05））

Fig.8 The spatial distribution of the multiple linear regression coefficients of temperature （a， c， e， g） and precipitation （b， d， f， h） on scPDSI （a， b）， SSMI （c， d）， SRI （e， f）， and SVHI （g， h） from 1982 to 2022

Fig.9 Spatial distribution of contributions of temperature （a， c， e， g） and precipitation （b， d， f， h） to the trends of scPDSI （a， b）， SSMI （c， d）， SRI （e， f）， and SVHI （g， h） during 1982-2022 （The positive or negative contributions indicate that the factor change increases or decreases the index value）

Fig.10 The variation of the contribution values of temperature （a） and precipitation （b） to scPDSI， SSMI， SRI， and SVHI with the aridity index during 1982-2022

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