Chen et al. (2025) Spatiotemporal propagation dynamics of multiple droughts in Central Asia: A three-dimensional perspective

Identification

• Journal: Journal of Hydrology
• Year: 2025
• Date: 2025-11-21
• Authors: Lu Chen, Peng Yang, Jun Xia, Heqing Huang, Yaning Chen, Kaiya Sun, Caiyuan Wang, Ping Yao, Rakhimova Matluba, Xixi Lu
• DOI: 10.1016/j.jhydrol.2025.134640

Research Groups

• Hubei Key Laboratory of Regional Ecology and Environmental Change, School of Geography and Information Engineering, China University of Geosciences, Wuhan, China
• State Key Laboratory of Water Resources and Hydropower Engineering Science, Wuhan University, Wuhan, China
• State Key Laboratory of Desert and Oasis Ecology, Xinjiang Institute of Ecology and Geography, Chinese Academy of Sciences, Urumqi, China
• Department of Geography, National University of Singapore, Singapore
• Scientific Research Institute of Irrigation and Water Problems, Tashkent, Uzbekistan

Short Summary

This study employed a three-dimensional framework integrated with extreme gradient boosting and SHAP to systematically investigate the spatiotemporal dynamics, propagation characteristics, and dominant drivers of meteorological, hydrological, and agricultural droughts in Central Asia. The findings provide a scientific basis for developing early warning systems for drought-induced disaster chains in arid and semi-arid regions.

Objective

• To systematically investigate the spatiotemporal evolution of growing-season meteorological, hydrological, and agricultural drought events, their propagation characteristics (MD-HD and MD-AD), and their dominant driving factors across Central Asia using a three-dimensional framework and machine learning.

Study Configuration

• Spatial Scale: Central Asia (CA)
• Temporal Scale: Growing season, with peak severity periods identified during 1940–1950 and 2010–2023.

Methodology and Data

• Models used: Three-dimensional (3D) framework for drought event extraction, Extreme Gradient Boosting (XGBoost) model, SHapley Additive exPlanations (SHAP).
• Data sources: Data related to meteorological, hydrological, and agricultural drought events, including variables for snow cover, vegetation (e.g., Leaf Area Index - LAI), and other meteorological characteristics.

Main Results

• Meteorological, hydrological, and agricultural drought events exhibited similar spatiotemporal patterns, with peak severity during 1940–1950 and 2010–2023, a southeast-northwest banded distribution of high-severity centroids, and a dominant east–west migration direction (>73 %).
• MD-HD and MD-AD propagation showed a spring-season concentration (>52 %), geographically distinct hotspots (MD-HD in northwestern lowlands, MD-AD in northern agricultural zones), and a prevalent east–west propagation direction (>67 %).
• Meteorological drought (MD) severity was the strongest influence on propagation, with elevated LAI facilitating and increased snow depth/snowmelt suppressing the process, further compounded by synergistic effects among MD characteristics.

Contributions

• Developed a novel three-dimensional framework combined with machine learning (XGBoost-SHAP) to comprehensively analyze drought spatiotemporal dynamics, propagation mechanisms, and the quantitative contributions of key drivers (including snow cover and vegetation) in Central Asia.
• Provided a scientific basis for the early warning of drought-induced disaster chains in arid and semi-arid regions.

Funding

• Not specified in the provided text.

Citation

@article{Chen2025Spatiotemporal,
  author = {Chen, Lu and Yang, Peng and Xia, Jun and Huang, Heqing and Chen, Yaning and Sun, Kaiya and Wang, Caiyuan and Yao, Ping and Matluba, Rakhimova and Lu, Xixi},
  title = {Spatiotemporal propagation dynamics of multiple droughts in Central Asia: A three-dimensional perspective},
  journal = {Journal of Hydrology},
  year = {2025},
  doi = {10.1016/j.jhydrol.2025.134640},
  url = {https://doi.org/10.1016/j.jhydrol.2025.134640}
}