Xiao et al. (2025) Quantitative identification of drought dominant periods and driving factors in China: integrating from TVDI and pixel-wise EMD

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Identification

• Journal: Geomatics Natural Hazards and Risk
• Year: 2025
• Date: 2025-10-29
• Authors: Dacheng Xiao, Shuyang Wu, Zhihao Zhu, Liujie He, Zhijian Wu, Zijian Wan, Jinqi Zhu, Bofu Zheng, Wei Wan
• DOI: 10.1080/19475705.2025.2577180

Research Groups

Not explicitly mentioned in the provided text.

Short Summary

This research quantifies the multi-scale driving mechanisms of drought in China from 2000 to 2022 using the Temperature-Vegetation Drought Index (TVDI) and pixel-wise Empirical Mode Decomposition (EMD), revealing that precipitation drives seasonal drought, potential evapotranspiration dominates interannual drought in arid regions, and maximum temperature is crucial for interdecadal drought, with its influence increasing for longer drought periods.

Objective

• To quantitatively identify the dominant periods and driving factors of drought in China across multiple spatio-temporal scales.

Study Configuration

• Spatial Scale: Continental (China), pixel-wise analysis.
• Temporal Scale: 2000 to 2022; analysis across seasonal, interannual, and interdecadal scales.

Methodology and Data

• Models used: Temperature-Vegetation Drought Index (TVDI), Mann–Kendall test, Hurst trend analysis, pixel-wise Empirical Mode Decomposition (EMD).
• Data sources: Satellite-derived temperature and vegetation (for TVDI), meteorological observations (for precipitation, maximum temperature, potential evapotranspiration), Normalized Difference Vegetation Index (NDVI).

Main Results

• Precipitation (PRE) is identified as the primary driver of seasonal drought.
• Normalized Difference Vegetation Index (NDVI) shows high sensitivity to drought in ecologically vulnerable regions.
• Potential evapotranspiration dominates interannual drought dynamics in China's arid northwest.
• Maximum temperature (Tmax) significantly drives interdecadal drought patterns in northern China.
• As the dominant drought period lengthens, the influence of PRE gradually diminishes, while the role of Tmax becomes increasingly prominent.

Contributions

• Provides a quantitative understanding of drought driving mechanisms by dominant factors across multiple spatio-temporal scales in China, addressing a current research gap.
• Introduces and applies a pixel-wise Empirical Mode Decomposition (EMD) method to accurately quantify multi-scale drought risk and identify dominant drivers.

Funding

Not explicitly mentioned in the provided text.

Citation

@article{Xiao2025Quantitative,
  author = {Xiao, Dacheng and Wu, Shuyang and Zhu, Zhihao and He, Liujie and Wu, Zhijian and Wan, Zijian and Zhu, Jinqi and Zheng, Bofu and Wan, Wei},
  title = {Quantitative identification of drought dominant periods and driving factors in China: integrating from TVDI and pixel-wise EMD},
  journal = {Geomatics Natural Hazards and Risk},
  year = {2025},
  doi = {10.1080/19475705.2025.2577180},
  url = {https://doi.org/10.1080/19475705.2025.2577180}
}

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