Yu et al. (2025) Research on the reciprocal feedback relationship and influencing factors between meteorological and agricultural drought in Northeast China

Identification

• Journal: Agricultural Water Management
• Year: 2025
• Date: 2025-10-16
• Authors: Tongxi Yu, Tianxiao Li, Qiang Fu, Zhaoqiang Zhou, Mo Li, Dong Liu, Renjie Hou, Xuechen Yang
• DOI: 10.1016/j.agwat.2025.109893

Research Groups

• School of Water Conservancy and Civil Engineering, Northeast Agricultural University, Harbin, Heilongjiang, China
• Joint Laboratory for International Cooperation on Cold Region Black Soil Habitat Health of the Ministry of Education, Harbin, Heilongjiang, China
• Key Laboratory of Effective Utilization of Agricultural Water Resources of the Ministry of Agriculture, Northeast Agricultural University, Harbin, Heilongjiang, China
• Heilongjiang Provincial Key Laboratory of Water Resources and Water Conservancy Engineering in Cold Region, Northeast Agricultural University, Harbin, Heilongjiang, China

Short Summary

This study quantifies meteorological and agricultural drought dynamics and their reciprocal feedback mechanisms in Northeast China (2000–2023) using advanced statistical and machine learning methods, revealing significant spatiotemporal variations and key influencing factors like precipitation and temperature.

Objective

• To characterize meteorological and agricultural drought events in Northeast China.
• To examine meteorological–agricultural drought reciprocal feedback mechanisms in Northeast China from 2000 to 2023.
• To identify the principal influencing factors governing the meteorological–agricultural drought reciprocal feedback in the region.

Study Configuration

• Spatial Scale: Northeast China, specifically Heilongjiang, Jilin, and Liaoning Provinces (38°43′N to 53°33′N, 118°53′E to 135°05′E).
• Temporal Scale: 2000–2023.

Methodology and Data

• Models used:
  • Standardized Precipitation Index (SPI) for meteorological drought.
  • Standardized Soil Moisture Index (SSI) for agricultural drought (both at 1-month scale).
  • Three-threshold run theory for drought event identification (thresholds: R0=0, R1=-0.3, R2=-0.5).
  • Pearson correlation analysis for drought correlation.
  • Copula functions (Gumbel, Clayton, Frank, Gaussian, T) and Bayesian framework for reciprocal feedback analysis (Clayton Copula selected as optimal).
  • Random Forest method and SHAP value theory (TreeExplainer) for identifying influencing factors.
• Data sources:
  • Precipitation (Pre) and Temperature (Tmp): Loess Plateau Subcenter of the National Earth System Science Data Center (http://loess.geodata.cn), 1 km resolution, 1901–2023 (analysis period 2000–2023).
  • Soil Moisture (SM): ERA5-Land reanalysis product (https://cds.climate.copernicus.eu/cdsapp#!/dataset/reanalysis-era5-land), 0.1° × 0.1° spatial resolution, 1982–2023 (analysis period 2000–2023), topsoil (2–5 cm).
  • Normalized Difference Vegetation Index (NDVI): MOD13A3 dataset from NASA (https://www.earthdata.nasa.gov), 1 km resolution, 2000–2023.
  • Potential Evapotranspiration (ETp): NASA’s Earth Science Data System (https://disc.gsfc.nasa.gov), 0.1° × 0.1° spatial resolution, 1901–2023 (analysis period 2000–2023).
  • All datasets were standardized to a consistent 0.1° × 0.1° spatial resolution.

Main Results

• The monthly scale correlation between the SPI and the SSI in Northeast China exhibits significant seasonal differences, with the strongest positive correlation in summer (e.g., 92.18% positive correlation in August, peak Pearson correlation coefficient of 0.872 in July) and the weakest in winter.
• The meteorological–agricultural drought reciprocal feedback relationship shows significant spatiotemporal heterogeneity. Spatially, positive feedback dominates in the southwestern Songnen Plain from April to August, while negative feedback prevails in the Liaodong hills region (except October). Temporally, August has the highest proportion (37.11%) of positive feedback thresholds (within the [0.5–2] range), indicating a stable positive feedback mechanism, whereas negative feedback thresholds (within the [-2, −0.5) range) dominate from December to March.
• Low precipitation, high temperature, low soil moisture, and high potential evapotranspiration (ETp) significantly strengthen the negative feedback effect of agricultural drought on meteorological drought. The synergistic effect of low precipitation and high temperature is the most prominent. NDVI has a relatively weak influence, with feedback transitioning from positive to weak negative as NDVI values exceed 0.4.

Contributions

• Provides a comprehensive analysis of meteorological–agricultural drought interconnections and their influencing factors, moving beyond static correlation limitations.
• Establishes methodological references for cross-regional drought feedback analysis by integrating Copula functions, Random Forest, and SHAP value theory.
• Offers a clearer and more detailed understanding of the spatiotemporal differentiation of reciprocal feedback mechanisms between meteorological and agricultural droughts in Northeast China.
• Provides practical insights for optimizing agricultural water allocation, designing targeted drought prevention systems, and supporting ecological protection policies to strengthen food security and improve drought resilience.

Funding

• National Natural Science Foundation of China Key Program (52539003)
• The National Natural Science Foundation of China General Program (52579033, 42577373)
• The National Natural Science Foundation of China Young Scientists Fund (52409012)
• Heilongjiang Provincial Postdoctoral Research Funding Program (LBH-Z24007)
• New Era Heilongjiang Outstanding Master’s/Doctoral Dissertation Grant Program

Citation

@article{Yu2025Research,
  author = {Yu, Tongxi and Li, Tianxiao and Fu, Qiang and Zhou, Zhaoqiang and Li, Mo and Liu, Dong and Hou, Renjie and Yang, Xuechen},
  title = {Research on the reciprocal feedback relationship and influencing factors between meteorological and agricultural drought in Northeast China},
  journal = {Agricultural Water Management},
  year = {2025},
  doi = {10.1016/j.agwat.2025.109893},
  url = {https://doi.org/10.1016/j.agwat.2025.109893}
}