Zhang et al. (2026) Nonstationary agricultural drought risk under compound meteorological drought and hot conditions

Identification

• Journal: Journal of Hydrology
• Year: 2026
• Date: 2026-01-29
• Authors: Quan Zhang, Ziyang Lu, Jiaolong Ying, Yue Qiu, P. Wang, Zhipeng Xu, Dongxiao Xu, Yong Li
• DOI: 10.1016/j.jhydrol.2026.135059

Research Groups

• Hebei Province Collaborative Innovation Center for Sustainable Utilization of Water Resources and Optimization of Industrial Structure, Hebei Center for Ecological and Environmental Geology Research, Hebei GEO University, Shijiazhuang 050031, China
• Department of Earth System Science, Ministry of Education Key Laboratory for Earth System Modelling, Institute for Global Change Studies, Tsinghua University, Beijing 100084, China
• State Key Laboratory of Regional Environment and Sustainability, School of Environment, Beijing Normal University, Beijing 100875, China
• Joint International Research Laboratory of Catastrophe Simulation and Systemic Risk Governance, School of National Safety and Emergency Management, Beijing Normal University, Zhuhai 519087, China

Short Summary

This study develops a novel nonstationary vine copula conditional probability model (NVCCP) to quantify nonstationary agricultural drought risk under compound dry-hot conditions. It demonstrates NVCCP's superior performance in the Aral Sea Basin, revealing significant temporal variability and regional trends in agricultural drought risk over the past 70 years.

Objective

• Develop and apply a novel nonstationary vine copula conditional probability model (NVCCP) to accurately quantify nonstationary conditional probability of agricultural drought under compound meteorological drought and hot conditions, addressing limitations of existing stationary approaches.

Study Configuration

• Spatial Scale: Aral Sea Basin
• Temporal Scale: Past 70 years (ending around 2025-2026)

Methodology and Data

• Models used: Nonstationary vine copula conditional probability model (NVCCP)
• Data sources: Not explicitly detailed in the provided text, but involves meteorological drought and hot event data.

Main Results

• The NVCCP model outperforms other stationary multivariate dependence models in capturing the complex relationship among agricultural drought, hot events, and meteorological drought.
• Agricultural drought risks are increased from March to May, reflecting the seasonal evolution of land-surface hydrological conditions.
• Over the past 70 years, decreasing trends account for 33% (normal agricultural drought) and 32% (severe agricultural drought) of the Aral Sea Basin.
• Over the past 70 years, increasing trends account for 27% (normal agricultural drought) and 28% (severe agricultural drought) of the Aral Sea Basin.
• Agricultural drought in April exhibits more pronounced temporal variability compared to March and May, with an average absolute trend of 0.0007 per year.

Contributions

• Development of a novel nonstationary vine copula conditional probability model (NVCCP) for quantifying agricultural drought risk under compound dry-hot conditions.
• Addresses the limitation of existing approaches that generally assume stationarity in assessing agricultural drought risk.
• Provides critical insights for nonstationary research and offers an effective method applicable to other sectors.

Funding

• Not explicitly detailed in the provided text.

Citation

@article{Zhang2026Nonstationary,
  author = {Zhang, Quan and Lu, Ziyang and Ying, Jiaolong and Qiu, Yue and Wang, P. and Xu, Zhipeng and Xu, Dongxiao and Li, Yong},
  title = {Nonstationary agricultural drought risk under compound meteorological drought and hot conditions},
  journal = {Journal of Hydrology},
  year = {2026},
  doi = {10.1016/j.jhydrol.2026.135059},
  url = {https://doi.org/10.1016/j.jhydrol.2026.135059}
}