A novel environmental indicator: Compound wind droughts and heat waves for assessing climate-driven ecological and energy sustainability

Jiewen You, Fangxu Yin, Boen Zhang, Mo Zhou, Yamin Qing, Ying Chen, Lu Gao

Ecological Indicators · 25 August 2025 · University of Oxford

In one sentence. XGBoost with SHAP maps compound wind droughts and heat waves across China (2000-2022), finding Tmax and VPD as main drivers (R2 0.84 on test data).

Abstract

Compound extremes, specifically concurrent low wind power (wind droughts) and heat waves, threaten ecological stability and renewable energy. However, their dynamics and impacts remain poorly understood. This study introduces compound wind droughts and heat waves (WDHW) indicator to assess their patterns in mainland China from 2000 to 2022. Using observational data and explainable machine learning (XGBoost and SHAP), we analyzed the spatiotemporal distributions, underlying drivers, and ecological implications of WDHW. Results reveal spatial heterogeneity, with high-frequency WDHW (>70 cumulative days) concentrated in northwestern China and a national increase in event frequency within affected regions (0.042 d yr –1 ). The XGBoost model performed well, with R 2 values of 0.88, 0.83, and 0.84 for training, cross-validation, and test datasets, respectively. SHAP analysis highlights maximum temperature (Tmax; SHAP = 0.722) and vapor pressure deficit (VPD; SHAP = 0.698) as primary drivers, with their interaction (SHAP = 0.321) demonstrating how heat and dryness link with 100-m hub-height winds. Ecological analysis shows peak WDHW frequencies in Half Protected ecoregions (28.8 days) and Deserts & Xeric Shrublands biomes (28.75 days), indicating dual vulnerabilities to biodiversity and energy systems. This study advances understanding of concurrent wind droughts and heat waves, providing implications for sustainable ecological and energy adaptation strategies.

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