Liu et al. (2025) Drought risk assessment and future scenario prediction in agricultural cropping zones of China

Identification

• Journal: Journal of Arid Land
• Year: 2025
• Date: 2025-12-01
• Authors: Xiaohong Liu, Chunhui Liu, Jiejie Fan, Chunxia Qiu
• DOI: 10.1007/s40333-025-0113-8

Research Groups

• College of Geomatics, Xi’an University of Science and Technology, Xi’an, 710054, China
• Key Laboratory of Pollution Processes and Environmental Criteria (Ministry of Education), Carbon Neutrality Interdisciplinary Science Centre, College of Environmental Science and Engineering, Nankai University, Tianjin, 300350, China

Short Summary

This study developed a novel drought risk assessment framework based on agricultural cropping zones in China, coupling a Geographical and Temporal Neural Network Weighted Regression (GTNNWR) model with the Standardized Precipitation Evapotranspiration Index (SPEI). The framework accurately assessed historical drought patterns and predicted a substantial intensification of drought risk across different cropping zones under various future climate change scenarios.

Objective

• To develop and validate a novel drought risk assessment framework based on agricultural cropping zones (single-, double-, and triple-cropping zones) to overcome limitations of traditional assessment frameworks that overlook variations in vulnerability due to agronomic practices.

Study Configuration

• Spatial Scale: Agricultural cropping zones across China (single-, double-, and triple-cropping zones).
• Temporal Scale: Historical period (2001–2020) and projected future scenarios (2021–2100).

Methodology and Data

• Models used: Geographical and Temporal Neural Network Weighted Regression (GTNNWR) model, Standardized Precipitation Evapotranspiration Index (SPEI), Shared Socioeconomic Pathway (SSP) scenarios (SSP126, SSP245, SSP585).
• Data sources: Normalized Difference Vegetation Index (NDVI) for GTNNWR prediction, precipitation and evapotranspiration data for SPEI calculation.

Main Results

• The GTNNWR model demonstrated high accuracy for NDVI prediction, achieving R² values ranging from 0.72 to 0.82 and Root Mean Square Error (RMSE) values between 0.11 and 0.14, significantly outperforming conventional models.
• Historical drought risk assessment (2001–2020) revealed that drought events were most frequent during summer and predominantly concentrated in single-cropping and double-cropping zones.
• Future projections (2021–2100) indicate a substantial intensification of drought risk across China's agricultural zones.
  • Under the SSP126 scenario, drought risk is projected to increase in the triple-cropping zones of the middle and lower reaches of the Yangtze River Plain.
  • Under the SSP245 scenario, the frequency of spring and winter droughts is anticipated to rise markedly.
  • Under the SSP585 scenario, drought intensity is projected to intensify in central–eastern single-cropping zones and southwestern double-cropping zones.

Contributions

• Developed and validated a novel drought risk assessment framework that integrates agricultural cropping zones, providing a more precise and agriculturally relevant assessment compared to traditional administrative or macro-climatic zoning.
• Introduced the coupling of a GTNNWR model for forecasting crop vegetation dynamics with the SPEI for comprehensive drought risk assessment.
• Provided detailed historical and future drought risk predictions under different SSP scenarios, offering critical insights for targeted adaptation strategies in agricultural management, such as optimizing irrigation systems and adjusting crop structures in China.

Funding

Not explicitly stated in the provided text.

Citation

@article{Liu2025Drought,
  author = {Liu, Xiaohong and Liu, Chunhui and Fan, Jiejie and Qiu, Chunxia},
  title = {Drought risk assessment and future scenario prediction in agricultural cropping zones of China},
  journal = {Journal of Arid Land},
  year = {2025},
  doi = {10.1007/s40333-025-0113-8},
  url = {https://doi.org/10.1007/s40333-025-0113-8}
}