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Vine Copula‐Based Multi‐Dimensional Drought Risk Assessment in Central Asia

Kaiya SunHubei Key Laboratory of Regional Ecology and Environmental Change School of Geography and Information Engineering, China University of Geosciences Wuhan ChinaPeng YangHubei Key Laboratory of Regional Ecology and Environmental Change School of Geography and Information Engineering, China University of Geosciences Wuhan ChinaJun XiaState Key Laboratory of Water Resources and Hydropower Engineering Science Wuhan University Wuhan ChinaHeqing HuangHubei Key Laboratory of Regional Ecology and Environmental Change School of Geography and Information Engineering, China University of Geosciences Wuhan ChinaYaning ChenState Key Laboratory of Desert and Oasis Ecology, Xinjiang Institute of Ecology and Geography Chinese Academy of Sciences Urumqi ChinaXiang ZhangHubei Key Laboratory of Regional Ecology and Environmental Change School of Geography and Information Engineering, China University of Geosciences Wuhan ChinaCaiyuan WangHubei Key Laboratory of Regional Ecology and Environmental Change School of Geography and Information Engineering, China University of Geosciences Wuhan ChinaLu ChenHubei Key Laboratory of Regional Ecology and Environmental Change School of Geography and Information Engineering, China University of Geosciences Wuhan ChinaQian ZhangHubei Key Laboratory of Regional Ecology and Environmental Change School of Geography and Information Engineering, China University of Geosciences Wuhan ChinaHuixing RuanHubei Key Laboratory of Regional Ecology and Environmental Change School of Geography and Information Engineering, China University of Geosciences Wuhan ChinaGafforov KhusenScientific Research Institute of Irrigation and Water Problems Tashkent UzbekistanRakhimova MatlubaScientific Research Institute of Irrigation and Water Problems Tashkent UzbekistanXixi LuDepartment of Geography National University of Singapore Crescent Singapore

International Journal of Climatologyjournal2026en

ABI

Annotatsiya

ABSTRACT Under global climate change, drought frequency and severity in Central Asia (CA) have risen sharply, threatening ecological security. Despite extensive studies on drought evolution, a quantitative framework for revealing the joint mechanisms and compound risks of multiple drought types remains lacking. Therefore, this study analysed droughts in CA from 1982 to 2022 by integrating multiple indicators to characterise meteorological (Standardised Precipitation Evapotranspiration Index, SPEI), agricultural (Palmer Drought Severity Index, PDSI) and hydrological droughts (i.e., Gravity Recovery and Climate Experiment [GRACE]‐Drought Severity Index, GRACE‐DSI). A Vine Copula model was subsequently employed to construct multidimensional dependence structures among key drought characteristics. The main findings were as follows: (1) meteorological droughts were predominantly short‐term (e.g., 3 month), constituting approximately 96.7% of events, whereas hydrological and agricultural droughts exhibited substantial proportions of medium‐ to long‐term events (e.g., larger than 6 months), at 35.5% and 68.1% respectively, indicating their stronger cumulative effects and recovery lags; (2) significant time‐lagged couplings occurred among drought types, with high joint probabilities concentrated in the Tianshan Mountains and central arid core. Agricultural droughts exhibited joint probabilities above 0.8 at 3–6 month scales, while extending the timescale to 12 months substantially strengthened synchronisation across all drought categories, highlighting the importance of incorporating longer timescales in drought early warning systems; and (3) driven by increased duration, severity and intensity, the joint return periods of meteorological and hydrological droughts generally ranged between 3 and 10 months, whereas those of agricultural droughts exceeded 8 months even at short timescales. The findings can provide valuable insights into the multidimensional drought couplings in CA.

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Mavzular

Hydrology and Drought Analysis Hydrology and Watershed Management Studies Climate variability and models

Identifikatorlar

DOI: 10.1002/joc.70369

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