Tang et al. (2025) Multi-indicator comparison in characterizing spatiotemporal patterns of water disasters and corresponding agricultural applications in the Middle-and-lower Yangtze River

Identification

• Journal: Agricultural Water Management
• Year: 2025
• Date: 2025-10-14
• Authors: Rong Tang, Long Qian
• DOI: 10.1016/j.agwat.2025.109878

Research Groups

• College of Water Resource and Civil Engineering, Hunan Agricultural University, Changsha, PR China
• Changjiang Institute of Technology, Wuhan, PR China

Short Summary

This study systematically compared five hydrometeorological indicators to characterize spatiotemporal drought and flooding patterns in the Middle-and-lower Yangtze River Region, revealing increasing trends in both disasters and identifying high-risk zones for cotton and rapeseed, with rapeseed facing significantly higher risks.

Objective

• To compare the performance of different hydrometeorological indicators in characterizing the spatial-temporal characteristics of drought and flooding at interannual and crop growing scales.
• To reveal the spatial-temporal characteristics of drought and flooding (including crop-specific drought and flooding) in the Middle-and-lower Yangtze River Region (MLRYR) based on the common results of multiple indicators.
• To determine the high-risk areas of drought and flooding for cotton and oil rapeseed in the MLRYR by comprehensively considering disaster temporal trends, spatial intensity, and crop growing conditions.

Study Configuration

• Spatial Scale: Middle-and-lower Yangtze River Region (MLRYR), covering an area of over 200,000 square kilometers (28°45′N–33°25′N, 113°25′E–118°20′E), including six provinces: Hubei, Hunan, Jiangxi, Anhui, Jiangsu, and Zhejiang.
• Temporal Scale: 1961–2020 (60 years) for meteorological data; 1990–2020 for crop planting areas.

Methodology and Data

• Models used:
  • Standardized Precipitation Index (SPI)
  • Standardized Precipitation Evapotranspiration Index (SPEI)
  • Standardized Antecedent Precipitation Evapotranspiration Index (SAPEI)
  • China-Z Index (CZI)
  • Precipitation Anomaly (PA)
  • Modified Mann-Kendall (MMK) method for trend analysis.
• Data sources:
  • Daily meteorological observations (1961–2020) from 140 national standard meteorological stations in the MLRYR (China Meteorological Data Service Centre), including precipitation, mean temperature, maximum/lowest temperature, and wind speed.
  • Land cover images with a spatial resolution of 1 kilometer from the Resource and Environmental Scientific Data Centre of the Chinese Academy of Sciences.
  • Crop planting areas for oil rapeseed and cotton (1990–2020) from county-level statistical yearbooks of all counties in the MLRYR.
  • Precise timings of crop growth and development in each province from local agrometeorological stations.

Main Results

• Temporal trends of both drought and flooding intensity in the MLRYR mostly increased over the past six decades, with all significant trends being increasing. Significant increasing trends of drought intensity were greater and more extensively distributed than those of flooding intensity.
• The SPI was the most sensitive indicator, detecting the largest number of significant trends for both drought (43.4% of stations) and flooding (29.7% of stations), while the CZI was the least sensitive.
• Indicator consistency ratios were low: only 29.6% of significant drought trends and 41.7% of significant flooding trends were consistently identified by more than half of the indicators (≥3). Less than 20% of the most severe drought/flooding sites (HDA-3/HFA-3) showed multi-indicator consensus.
• Heavy drought areas (HDA-3) exhibited a "southern to northern" shift (from Hunan, Zhejiang, and Jiangsu provinces in early decades to Hubei and Jiangsu provinces in late decades).
• Heavy flooding areas (HFA-3) shifted from "northern to the northeastern" (from Hubei and Jiangsu provinces in early decades to the northeastern MLRYR in late decades).
• The eastern MLRYR (Anhui, Jiangsu, and Zhejiang provinces) was identified as a persistent hotspot for both heavy drought and flooding.
• For key crops, rapeseed faced much higher drought and flooding risks than cotton. High-risk drought areas for rapeseed were concentrated in Hunan and Hubei provinces, while high-risk flooding areas were primarily in Hubei Province.
• Cotton experienced fewer significant drought trends, with heavy drought and flooding areas mainly in the southern MLRYR (Jiangxi and Zhejiang provinces).

Contributions

• Provided a systematic, multi-indicator comparison of hydrometeorological indicators' efficacy in characterizing spatiotemporal drought and flooding patterns, addressing a critical gap in existing literature, especially concerning critical crop growing seasons.
• Revealed the evolving spatiotemporal patterns of drought and flooding in the MLRYR based on a consensus from multiple indicators, offering a more robust assessment than single-indicator studies.
• Identified high-risk areas for cotton and oil rapeseed by integrating disaster trends, spatial intensity, and crop planting conditions, providing actionable insights for precision agriculture and targeted disaster mitigation strategies in major grain-producing regions.
• Highlighted the substantial influence of indicator selection on the characterization of water disaster patterns, emphasizing the need for careful indicator choice in regional assessments.

Funding

• Hubei Provincial Natural Science Foundation of China (Grant No. 2025AFB861)
• Hunan Provincial Natural Science Foundation of China (Grant No. 2025JJ60379)
• Scientific Research project of Hunan Education Department, China (Grant No. 24B0211)

Citation

@article{Tang2025Multiindicator,
  author = {Tang, Rong and Qian, Long},
  title = {Multi-indicator comparison in characterizing spatiotemporal patterns of water disasters and corresponding agricultural applications in the Middle-and-lower Yangtze River},
  journal = {Agricultural Water Management},
  year = {2025},
  doi = {10.1016/j.agwat.2025.109878},
  url = {https://doi.org/10.1016/j.agwat.2025.109878}
}