Yu et al. (2025) Irrigated agriculture expansion drives groundwater storage decline in Black Soil Region of Northeast China

Identification

• Journal: Agricultural Water Management
• Year: 2025
• Date: 2025-09-22
• Authors: Yexiang Yu, Guangxin Zhang, Peng Qi, Jingxuan Sun, Qingsong Zhang, Boting Hu, Y. Jun Xu
• DOI: 10.1016/j.agwat.2025.109813

Research Groups

• Northeast Institute of Geography and Agroecology, Chinese Academy of Sciences, Changchun, China
• University of Chinese Academy of Sciences, Beijing, China
• School of Renewable Natural Resources, Louisiana State University Agricultural Center, Baton Rouge, USA

Short Summary

This study analyzed the spatiotemporal distribution and drivers of groundwater storage changes in the Black Soil Region of Northeast China using high-resolution GRACE data and a random forest model. It revealed a significant overall decline in groundwater storage, primarily driven by the expansion of irrigated agriculture, particularly in long-term trends.

Objective

• To analyze the spatiotemporal distribution and trends of groundwater storage in the Black Soil Region of Northeast China and to quantify the relative contributions of natural and anthropogenic factors to these changes across long-term and seasonal scales.

Study Configuration

• Spatial Scale: Black Soil Region of Northeast China (total area of 1.249 million km²), focusing on four key irrigated agricultural areas: Sanjiang Plain (SJP), Songnen Plain (SNP), Liaohe Plain (LHP), and West Liao River Basin (WLRB). Data resolutions varied from 30 meters to 0.25 degrees.
• Temporal Scale: 2003–2022 for GRACE data, 2000–2022 for climatic data, and 2000–2020 for anthropogenic data in the regression model. Monthly temporal resolution for most datasets.

Methodology and Data

• Models used:
  • Water balance equation (for Groundwater Storage Anomalies - GWSA calculation)
  • Theil-Sen trend analysis
  • Mann-Kendall trend test
  • Seasonal-Trend decomposition using Loess (STL)
  • Random Forest regression model
  • Global Land Data Assimilation System (GLDAS) NOAH land surface model (for soil moisture storage, snow water equivalent, and canopy water storage)
• Data sources:
  • Satellite/Reanalysis:
    • High-resolution GRACE data: "Global high-resolution water storage anomaly dataset (2002–2022)" from National Tibetan Plateau Data Center (0.05° × 0.05°).
    • GLDAS NOAH land surface model product (0.25° × 0.25°): Soil moisture storage (SMS), snow water equivalent (SWE), canopy water storage (CWS).
    • GLEAM (v4.1a) (0.1° × 0.1°): Actual evapotranspiration (ET).
    • ERA5-Land dataset (0.1° × 0.1°): Runoff (RO), snowmelt (SMLT).
  • Observation/Other:
    • In-situ groundwater level measurement data (from local hydrological bureaus).
    • "China 1 km resolution monthly precipitation dataset" (1 km): Precipitation (PRE).
    • "Landscan Population Density Dataset" (1 km): Population density (POP).
    • "CIrrMap250" (250 m): Irrigated area (ICA).
    • "Annual China Land Cover Dataset (CLCD)" (30 m): Forestland area (FLA), Grassland area (GLA), Wetland area (WLA).
    • "China Industrial Water Withdrawal Dataset (CIWW)" (0.1° × 0.1°): Industrial water use (IWW).
    • Songliao River Basin Water Resources Bulletin: Water supply and consumption structure (WSC).

Main Results

• Groundwater storage anomalies (GWSA) in the Black Soil Region of Northeast China showed a significant overall decline from 2003–2022, with an average annual rate of decrease of 3.72 millimeters per year (approximately 4.65 cubic kilometers per year, p < 0.01).
• Spatially, GWSA exhibited heterogeneous patterns, with the highest decline rate in the West Liao River Basin (8.13 millimeters per year) and the lowest in the Songnen Plain (2.68 millimeters per year). The general spatial pattern was "more in the north and less in the south, more in the east and less in the west."
• Significant seasonal fluctuations were observed, with GWSA reaching a maximum in July (−11.65 millimeters) and a minimum in January (−34.56 millimeters). Spring snowmelt and monsoon precipitation were major contributors to increased groundwater storage.
• Long-term GWSA trends were primarily driven by anthropogenic factors, with the expansion of irrigated cropland contributing 31.2% in the Liaohe Plain, 26.6% in the Sanjiang Plain, and 31.6% in the West Liao River Basin. In the Songnen Plain, changes in wetland area were the dominant driver (26.2%).
• Seasonal GWSA trends were mainly controlled by natural conditions. Precipitation was the main driver in the West Liao River Basin (21.1%), while actual evapotranspiration dominated in the Sanjiang Plain (41.3%), Songnen Plain (40.8%), and Liaohe Plain (25.8%).

Contributions

• Utilized high spatial resolution GRACE data (0.05° × 0.05°) to analyze groundwater storage, offering improved accuracy and better capture of spatial heterogeneity compared to previous studies.
• Applied STL decomposition and Random Forest regression to quantitatively assess the distinct contributions of natural and anthropogenic factors to both long-term trends and seasonal variations of groundwater storage, addressing a gap in single-scale attribution analyses.
• Provided scientific evidence to support sustainable groundwater management and inform planning strategies for irrigated agriculture in the Black Soil Region of Northeast China.

Funding

• Strategic Priority Research Program of the Chinese Academy of Sciences, China (XDA28020501)
• National Key Research and Development Program of China (2021YFC3200203)
• Strategic Research and Consulting Program of the Chinese Academy of Engineering (JL2023-17)

Citation

@article{Yu2025Irrigated,
  author = {Yu, Yexiang and Zhang, Guangxin and Qi, Peng and Sun, Jingxuan and Zhang, Qingsong and Hu, Boting and Xu, Y. Jun},
  title = {Irrigated agriculture expansion drives groundwater storage decline in Black Soil Region of Northeast China},
  journal = {Agricultural Water Management},
  year = {2025},
  doi = {10.1016/j.agwat.2025.109813},
  url = {https://doi.org/10.1016/j.agwat.2025.109813}
}