Zou et al. (2026) Linking agricultural drought to compound hot–dry risk: A process-based compound index for crop and water management in the North China Plain

Identification

• Journal: Agricultural Water Management
• Year: 2026
• Date: 2026-09-22
• Authors: Yi Zou, Ruiyuan Pan, Shaowei Ning, Xiangyu Li, Yuliang Zhou, Xiaoyan Xu, Zihao Zheng, Le Chen, Kaixuan Zhang, Lichang Xu
• DOI: 10.1016/j.agwat.2026.110796

Research Groups

• College of Civil Engineering, Hefei University of Technology
• Joint Laboratory of Artificial Intelligence for Water Security, Hefei University of Technology
• Aerospace Information Research Institute, Chinese Academy of Sciences
• School of Geography and Planning, Sun Yat-sen University

Short Summary

This study develops a Copula-based Compound Hot-Dry Index (CHDI) to characterize compound hot-dry events in the North China Plain. The CHDI integrates meteorological drought, agricultural drought, and high-temperature anomalies into a unified probabilistic framework.

Objective

• Investigate the spatiotemporal evolution and phase-dependent driving mechanisms of compound hot-dry events during 1980–2022.
• Characterize the dependence structure among meteorological drought, agricultural drought, and high-temperature anomalies using Copula-based joint modeling.

Study Configuration

• Spatial Scale: The study area is located in the North China Plain, covering an area of approximately 300,000 km².
• Temporal Scale: The temporal scale ranges from 1980 to 2022, with a focus on the period 2000–2022 for detailed analysis.

Methodology and Data

• Models used: Copula-based joint modeling (Gaussian Copula, t Copula, Frank Copula, Clayton Copula, Gumbel Copula) was used to model the multivariate dependence structure among SPEI, SSI, and STI.
• Data sources:
  • Meteorological variables (temperature, precipitation, surface pressure, relative humidity, wind speed, potential evapotranspiration) from the National Cryosphere Desert Data Center.
  • Hydrothermal indicators (SPEI, SSI, STI) derived from Zhang et al. (2025a).
  • NDVI data obtained from the GIMMS NDVI global long-term vegetation dataset archived by the ORNL DAAC.
  • Root-zone soil moisture data obtained from the GLEAM4 dataset.
  • Runoff data derived from the ERA5-Land dataset acquired through the Google Earth Engine platform.
  • Solar radiation data obtained from the National Tibetan Plateau Data Center.

Main Results

• The CHDI was found to accurately identify the occurrence timing, spatial extent, and month-to-month evolution of typical events.
• The results show that CHDI exhibits stronger consistency with anomalies in agricultural vegetation productivity compared to existing indices.
• Over the past four decades, compound hot-dry conditions in the North China Plain have intensified overall, with CHDI decreasing at a rate of approximately −0.111 per decade.

Contributions

• This study extends the characterization framework of compound events by integrating meteorological drought, agricultural drought, and high-temperature anomalies into a unified probabilistic framework.
• The CHDI provides a more comprehensive representation of the integrity and continuity of compound hot-dry processes.

Funding

• National Natural Science Foundation of China (Grant No. 51979017)
• National Key Research and Development Program of China (Grant No. 2018YFC1504204)

Citation

@article{Zou2026Linking,
  author = {Zou, Yi and Pan, Ruiyuan and Ning, Shaowei and Li, Xiangyu and Zhou, Yuliang and Xu, Xiaoyan and Zheng, Zihao and Chen, Le and Zhang, Kaixuan and Xu, Lichang},
  title = {Linking agricultural drought to compound hot–dry risk: A process-based compound index for crop and water management in the North China Plain},
  journal = {Agricultural Water Management},
  year = {2026},
  doi = {10.1016/j.agwat.2026.110796},
  url = {https://doi.org/10.1016/j.agwat.2026.110796}
}