Li et al. (2026) A process-oriented framework to decipher drought propagation dynamics from meteorological to ecological, agricultural, hydrological, and socioeconomic drought in the Yellow River Basin

Identification

• Journal: Ecological Indicators
• Year: 2026
• Date: 2026-01-01
• Authors: Ziyan Li, Shengzhi Huang, Yimin Wang, Shuai Zhou, Qiang Huang, Dengfeng Liu, Guoyong Leng
• DOI: 10.1016/j.ecolind.2025.114574

Research Groups

• Hebei Provincial Key Laboratory of Intelligent Water Resources, Hebei University of Engineering, Handan, China
• State Key Laboratory of Eco-hydraulics in Northwest Arid Region of China, Xi'an University of Technology, Xi'an, China
• Key Laboratory of Water Cycle and Related Land Surface Processes, Institute of Geographic Sciences and Natural Resources Research, Chinese Academy of Sciences, Beijing, China

Short Summary

This study developed a novel framework to characterize high-resolution, cascading drought dynamics across five types (meteorological to socioeconomic) in the Yellow River Basin, identifying dominant propagation pathways, thresholds, and their spatiotemporal patterns using integrated hydrological models and electrical network theory.

Objective

• To establish a high-resolution (weekly-scale) framework for characterizing long-chain drought propagation dynamics (meteorological, agricultural, ecological, hydrological, and socioeconomic) in the Yellow River Basin.
• To identify dominant propagation pathways, thresholds, and controlling factors of these drought chains across different severity levels in 403 sub-basins.

Study Configuration

• Spatial Scale: Yellow River Basin (YRB), China, divided into 403 sub-basins.
• Temporal Scale: Weekly scale for drought propagation analysis (SPI 5–104 weeks, other drought indices 1 month ≈ 4 weeks). Data period for drought indicator construction: 1982–2017. General data collection period: 1960–2017.

Methodology and Data

• Models used:
  • SWAT (Soil and Water Assessment Tool) hydrological model
  • AquaCrop model (for agricultural water requirement and yield simulation)
  • Bayesian conditional probability model (for drought propagation time and thresholds)
  • Geographic Detector (for driving force analysis)
  • Electrical network-inspired “series–parallel–hybrid” theory (for drought propagation pathways and patterns)
• Data sources:
  • Meteorological data: Daily precipitation, temperature, air pressure, wind speed, relative humidity (1960–2017) from the National Meteorological Information Center of China.
  • Hydrological data: Monthly streamflow (1960–2017) from the Hydrological Yearbook in the YRB.
  • Model-simulated soil data: Monthly soil moisture content (1962–2017) from the SWAT model.
  • Vegetation data: 5 km resolution dataset of monthly NDVI product of China (1982–2020) based on NOAA CDR AVHRR NDVI product.
  • Socioeconomic information: Grain production, water conservancy projects, disaster damage, and drinking difficulties in urban areas of the YRB from Statistical Yearbook and Water Resources Bulletin.
  • Remote sensing images: Digital elevation model (DEM) dataset with a spatial resolution of 90 m (Institute of Geography, Chinese Academy of Sciences); Administrative district map in China (National Geographic Information Center of China); Land use maps for four periods (1980, 1990, 2000, 2010) at 1:100,000 scale (Resources and Environment Science Data Center of Chinese Academy of Sciences); Soil data map at 1:1,000,000 scale of Harmonized World Soil Database (HWSD) version 1.1.

Main Results

• Over 90% of secondary drought events in each sub-basin were mild or moderate in severity, with a low probability of triggering severe drought.
• Propagation time from severe meteorological drought (MD) to mild hydrological drought (HD) ranged from 30 to 60 weeks in the YRB source area, indicating a tendency for drought propagation to develop toward lower severity levels.
• Drought propagation pathways exhibited clear spatial differentiation: the series mode dominated in the middle and lower reaches (62.03% of sub-basins), while the hybrid mode concentrated in the upper reaches (37.97%).
• Series mode drought propagation across sub-basins increased under intensified MD conditions.
• When ecological drought (ED) of varying severity occurred in phase 1, the cascade consistently progressed from MD to ED, then to agricultural drought (AD), further to HD, and ultimately to socioeconomic drought (SD).
• Propagation thresholds generally decreased as drought severity increased, exhibiting clear spatial clustering patterns.
• Driving force analysis indicated that dynamic cumulative processes (propagation time) rely more on environmental drivers than static triggering conditions (propagation thresholds).
• Elevation, Gross Domestic Product (GDP), population, potential evapotranspiration, and soil type were identified as core drivers influencing drought propagation.

Contributions

• Establishes a novel, high-resolution (weekly-scale) framework for characterizing long-chain drought propagation dynamics across five types (MD, AD, HD, ED, SD), filling a critical gap in existing literature.
• Innovatively applies the "series-parallel hybrid connection" theory from electrical engineering to rigorously characterize drought propagation pathways and patterns.
• Provides a systematic investigation of propagation pathways, thresholds, and controlling factors from a severity-state perspective within natural-social systems.
• Clarifies causal relationships among multi-type droughts, supporting the development of cascade-based early warning systems, targeted regulation, and enhanced disaster prevention strategies.

Funding

• National Natural Science Foundation of China (grant number 52509012, 52279026)
• Natural Science Foundation of Hebei Province (grant number E2024402052)
• National Key Research and Development Program of China (grant Number 2024YFC3212900)
• Special Project for Key Research and Development Tasks of Xinjiang Autonomous Region (NO. 2022293120)
• Department of education of Hebei Province (grant number QN2026696, BJ2025132)

Citation

@article{Li2026processoriented,
  author = {Li, Ziyan and Huang, Shengzhi and Wang, Yimin and Zhou, Shuai and Huang, Qiang and Liu, Dengfeng and Leng, Guoyong},
  title = {A process-oriented framework to decipher drought propagation dynamics from meteorological to ecological, agricultural, hydrological, and socioeconomic drought in the Yellow River Basin},
  journal = {Ecological Indicators},
  year = {2026},
  doi = {10.1016/j.ecolind.2025.114574},
  url = {https://doi.org/10.1016/j.ecolind.2025.114574}
}