Ismail et al. (2025) Unravelling the spatiotemporal causality chain between meteorological and agricultural drought propagation in the China–Pakistan Economic Corridor

Identification

• Journal: Atmospheric Research
• Year: 2025
• Date: 2025-10-08
• Authors: Muhammad Ismail, Kadambot H. M. Siddique, Yi Li
• DOI: 10.1016/j.atmosres.2025.108532

Research Groups

• College of Water Resources and Architectural Engineering, Northwest A&F University/Key Laboratory of Agricultural Soil and Water Engineering in Arid and Semiarid Areas at Ministry of Education, Yangling, Shaanxi, China
• Northwest A&F University Shenzhen Research Institute, Shenzhen, China
• Xinjiang Research Institute of Agriculture in Arid Areas/Institute of Soil Fertilizer and Agricultural Water Saving, Xinjiang, Academy of Agricultural Sciences, Urumqi, China
• The UWA Institute of Agriculture, The University of Western Australia, Perth, WA, Australia

Short Summary

This study investigates the nonlinear spatiotemporal causality of meteorological to agricultural drought propagation in the China–Pakistan Economic Corridor (CPEC) from 1981–2022, revealing regional variations in propagation times and rates, with maximum temperature and soil moisture identified as key predictors.

Objective

• To identify and analyze nonlinear drought propagation pathways and quantify causal relationships and key influencing factors between meteorological and agricultural droughts in the China–Pakistan Economic Corridor (CPEC).

Study Configuration

• Spatial Scale: China–Pakistan Economic Corridor (CPEC)
• Temporal Scale: 1981–2022 (42 years)

Methodology and Data

• Models used: Enhanced Convergent Cross Mapping (ECCM), Artificial Neural Network (ANN), Deep Neural Network (DNN), Feed Forward Neural Network (FFNN), Explainable Artificial Intelligence (XAI).
• Data sources: Observational data from monitoring stations, including climate variables (e.g., maximum temperature, soil moisture).

Main Results

• Approximately 73 % of monitoring stations showed higher maximum Pearson Correlation Coefficient (PCC) than ECCM values, with strongest correlations and causalities in western regions (PCC: 0.48–0.79; ECCM: 0.36–0.77) and weakest in eastern areas (PCC: 0.06–0.33; ECCM: 0–0.25).
• ECCM-based propagation times ranged from 3 to 4 months in northern/southern Xinjiang and eastern Pakistan, and 1 to 2 months in eastern Xinjiang and western Pakistan. PCC-based methods extended this to 5 months in some regions.
• Southwestern Pakistan and northwestern Xinjiang exhibited the highest propagation rates (77.95 %), while eastern regions showed weaker transitions (5.64 %).
• XAI-based interpretations identified maximum temperature and soil moisture as the most significant predictors for drought propagation counts (DPCs) (R² > 0.74) and drought propagation intensity index (DIP) (R² > 0.68), with interactions among climate variables explaining regional nonlinearities.

Contributions

• The study provides valuable insights into the spatiotemporal causality chain and nonlinear propagation pathways of meteorological to agricultural droughts in the CPEC.
• It quantifies causal relationships and identifies key influencing factors using advanced methods (ECCM, XAI with deep learning models), offering critical information for drought management in arid and semiarid regions.

Funding

• Not explicitly mentioned in the provided text.

Citation

@article{Ismail2025Unravelling,
  author = {Ismail, Muhammad and Siddique, Kadambot H. M. and Li, Yi},
  title = {Unravelling the spatiotemporal causality chain between meteorological and agricultural drought propagation in the China–Pakistan Economic Corridor},
  journal = {Atmospheric Research},
  year = {2025},
  doi = {10.1016/j.atmosres.2025.108532},
  url = {https://doi.org/10.1016/j.atmosres.2025.108532}
}