Lai et al. (2025) Identification of spatiotemporal changes and driving factors of ecological drought during 1982–2024 across the mainland China

Identification

• Journal: Agricultural Water Management
• Year: 2025
• Date: 2025-12-12
• Authors: Hexin Lai, Shaofeng Yan, Shikai Gao, Ruyi Men, Fei Wang, Mengting Du, Kai Feng, Yanbin Li, Wenxian Guo, Haibo Yang
• DOI: 10.1016/j.agwat.2025.110079

Research Groups

• School of Water Conservancy, North China University of Water Resources and Electric Power, Zhengzhou, China.
• Hubei Institute of Water Resources Survey and Design Co., Ltd., Wuhan, China.
• School of Water Conservancy and Transportation, Zhengzhou University, Zhengzhou, China.

Short Summary

This study utilizes the Standardized Ecological Water Deficit Index (SEWDI) to assess ecological drought across mainland China from 1982 to 2024, revealing a general increasing trend in drought severity since 2000. The research identifies evapotranspiration, soil moisture, and irrigation water as the primary drivers of these spatiotemporal changes.

Objective

• To identify the spatiotemporal evolution, mutation points, and driving factors (climatic and anthropogenic) of ecological drought across mainland China using a standardized index based on vegetation water supply and demand imbalance.

Study Configuration

• Spatial Scale: Mainland China, divided into nine major zones (NAR, NCP, HPR, LPR, QTP, SBR, YGP, MYP, and SCR), with data resampled to a 8 km × 8 km grid resolution.
• Temporal Scale: 1982–2024, analyzed at a monthly resolution.

Methodology and Data

• Models used: Standardized Ecological Water Deficit Index (SEWDI), Bayesian Estimator of Abrupt Seasonal and Trend Change (BEAST), Modified Mann-Kendall (MMK) test, Improved Run Theory, and Wavelet Coherence (Partial Wavelet Coherency - PWC and Multiple Wavelet Coherence - MWC).
• Data sources:
  • Famine Early Warning Systems Network Land Data Assimilation System (FLDAS) for meteorological variables.
  • High-resolution Sectoral Water Use Dataset (HSWUD) for irrigation water (IW).
  • Remote sensing data: NDVI (National Earth System Science Data Center), Potential Evapotranspiration (ET0 from National Tibetan Plateau Data Center), and Actual Evapotranspiration (ETc act from Harvard University and USGS).

Main Results

• General Trends: Ecological drought generally increased across China, except in the Huang-Huai-Hai Plain (HPR) and Middle-Lower Yangtze Plain (MYP). Aridification significantly worsened after the year 2000.
• Mutation Points: Two seasonal mutation points were detected in January 2003 and April 2017. A major trend mutation occurred in 2003.
• Extreme Events: The most severe ecological drought event was recorded from July 2019 to April 2020, lasting 10 months with an intensity of 9.15 and a peak SEWDI of -1.21 in February 2020.
• Seasonal Dynamics: Drought intensity decreased in summer (mean Zs = 0.13) but increased during spring, autumn, and winter, with winter showing the most significant increase (mean Zs = -1.12).
• Driving Factors: The combination of evapotranspiration (ET), soil moisture (SM), and irrigation water (IW) provided the highest explanatory power for ecological drought variations (Average Wavelet Coherence = 0.97).

Contributions

• Early Warning Capability: SEWDI captures water supply-demand imbalances earlier than traditional greenness-based vegetation indices (like VCI), which only respond after physical wilting occurs.
• Methodological Integration: Combines Bayesian frameworks for mutation detection with multivariate wavelet analysis to disentangle the nonlinear interactions between climate change and human activities (irrigation).
• Refined Spatial Analysis: Provides a high-resolution (8 km) assessment of ecological drought across diverse Chinese ecosystems, offering a scientific basis for regional water resource management and ecological protection.

Funding

• National Key R&D Program of China (2023YFC3006603).
• National Natural Science Foundation of China (42401022, 42301024).
• Innovation Fund for doctoral of North China University of Water Resources and Electric Power (BCJJ202404).
• Hubei Province Water Conservancy Research Project (HBSLKY202310).
• Open Research Fund of Key Laboratory of River Basin Digital Twinning of Ministry of Water Resources (Z0202042022).
• Key Research Projects of Higher Education Institutions in Henan Province (24A570005).
• Scientific and Technological Research Projects in Henan Province (242102321005).

Citation

@article{Lai2025Identification,
  author = {Lai, Hexin and Yan, Shaofeng and Gao, Shikai and Men, Ruyi and Wang, Fei and Du, Mengting and Feng, Kai and Li, Yanbin and Guo, Wenxian and Yang, Haibo},
  title = {Identification of spatiotemporal changes and driving factors of ecological drought during 1982–2024 across the mainland China},
  journal = {Agricultural Water Management},
  year = {2025},
  doi = {10.1016/j.agwat.2025.110079},
  url = {https://doi.org/10.1016/j.agwat.2025.110079}
}

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