Detecting the spatiotemporal evolution, mutation characteristics and dominant drivers of vegetation drought in the Yangtze River Basin, China

The Yangtze River Basin (YRB), serving as a crucial ecological shield and economic lifeline in China, has witnessed a gradual increase in the frequency of drought disasters, posing a significant ecological security challenge to the region. Addressing the typical characteristics of “high temperature-high evapotranspiration” droughts in the YRB, this study employs the Standardized Vegetation Water-deficit Index (SVWI) (1982–2022) in the basin, which overcomes the limitations of traditional greenness indices in distinguishing between water stress and abiotic stress. The aim is to precisely identify typical drought events, capture trend mutations, and quantify the statistical associations and relative importance of meteorological and teleconnection factors with vegetation drought. The results show that: (1) from 1982 to 2022, drought with the highest intensity (6.73) in the YRB occurred from August 2019 to June 2020; (2) the grid-based trend characteristic value Z s was –0.06 in summer and –0.86 in winter; (3) the most severe vegetation drought occurred in January 2014 (SVWI=–1.33). The mutation point of SVWI in the YRB appeared in December 2002, showing a monotonically decreasing trend; (4) there are 4 main mutation types of vegetation drought in the YRB, including interrupted decline, increase to decrease, decrease to increase, and monotonic decrease, with most mutation points occurring around 2002; (5) among circulation factors, the Atlantic Multidecadal Oscillation (AMO), Sunspot Index (SSI), and El Niño-Southern Oscillation (ENSO) have the most significant teleconnection driving effects on vegetation drought; and (6) the trivariate combination of evapotranspiration-soil moisture-precipitation is the optimal multi-factor combination for explaining the dynamic changes of vegetation drought. The results help to clarify the evolutionary characteristics of vegetation drought and dominant driving factors, and provide a scientific basis for the adaptive management of ecosystems and the prevention of drought risks. • The most severe vegetation drought in the Yangtze River Basin (YRB) occurred in February 2020. • The Standardized Vegetation Water-deffcit Index (SVWI) experienced a sudden change around 2002 in the YRB. • Evapotranspiration, soil moisture, and precipitation are the optimal factors for explaining vegetation drought. • Atlantic Multidecadal Oscillation (AMO) had the greatest impact on vegetation drought in the YRB.

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Publication Details

Journal
Agricultural Water Management
Published
2026-04-25
DOI
https://doi.org/10.1016/j.agwat.2026.110388
Primary Topic
Hydrology and Drought Analysis
Type
article
Field-Weighted Citation Impact
0.00

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article

Detecting the spatiotemporal evolution, mutation characteristics and dominant drivers of vegetation drought in the Yangtze River Basin, China

Hexin Lai, Mengting Du, Ruyi Men, Yan Li et al.
Agricultural Water Management
Hydrology and Drought Analysis
article

Detecting the spatiotemporal evolution, mutation characteristics and dominant drivers of vegetation drought in the Yangtze River Basin, China

Hexin Lai, Mengting Du, Ruyi Men, Yan Li, Haoyu Li, Qian Xu, Shengzhi Huang, Ruirui Qi, Yanbin Li, Fei Wang, Qingqing Tian, Rong Li, Kai Feng, Haibo Yang
article en

Abstract

The Yangtze River Basin (YRB), serving as a crucial ecological shield and economic lifeline in China, has witnessed a gradual increase in the frequency of drought disasters, posing a significant ecological security challenge to the region. Addressing the typical characteristics of “high temperature-high evapotranspiration” droughts in the YRB, this study employs the Standardized Vegetation Water-deficit Index (SVWI) (1982–2022) in the basin, which overcomes the limitations of traditional greenness indices in distinguishing between water stress and abiotic stress. The aim is to precisely identify typical drought events, capture trend mutations, and quantify the statistical associations and relative importance of meteorological and teleconnection factors with vegetation drought. The results show that: (1) from 1982 to 2022, drought with the highest intensity (6.73) in the YRB occurred from August 2019 to June 2020; (2) the grid-based trend characteristic value Z s was –0.06 in summer and –0.86 in winter; (3) the most severe vegetation drought occurred in January 2014 (SVWI=–1.33). The mutation point of SVWI in the YRB appeared in December 2002, showing a monotonically decreasing trend; (4) there are 4 main mutation types of vegetation drought in the YRB, including interrupted decline, increase to decrease, decrease to increase, and monotonic decrease, with most mutation points occurring around 2002; (5) among circulation factors, the Atlantic Multidecadal Oscillation (AMO), Sunspot Index (SSI), and El Niño-Southern Oscillation (ENSO) have the most significant teleconnection driving effects on vegetation drought; and (6) the trivariate combination of evapotranspiration-soil moisture-precipitation is the optimal multi-factor combination for explaining the dynamic changes of vegetation drought. The results help to clarify the evolutionary characteristics of vegetation drought and dominant driving factors, and provide a scientific basis for the adaptive management of ecosystems and the prevention of drought risks. • The most severe vegetation drought in the Yangtze River Basin (YRB) occurred in February 2020. • The Standardized Vegetation Water-deffcit Index (SVWI) experienced a sudden change around 2002 in the YRB. • Evapotranspiration, soil moisture, and precipitation are the optimal factors for explaining vegetation drought. • Atlantic Multidecadal Oscillation (AMO) had the greatest impact on vegetation drought in the YRB.

Agricultural Water ManagementVol. 329
Xiamen University (CN), North China University of Water Resources and Electric Power (CN), Zhengzhou University (CN)
Key Technologies Research and Development Program
Openalex Percentile: Top 5%
Hydrology and Drought Analysis
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