Luo et al. (2025) Identification of Spatiotemporal Variations and Influencing Factors of Groundwater Drought Based on GRACE Satellite

Identification

• Journal: Agriculture
• Year: 2025
• Date: 2025-12-21
• Authors: Weiran Luo, Fei Wang, Guo Jianzhong, Ziwei Li, Ning Li, Mengting Du, Ruyi Men, Rong Li, Hexin Lai, Qian Xu, Kai Feng, Yanbin Li, Shengzhi Huang, Qingqing Tian
• DOI: 10.3390/agriculture16010020

Research Groups

• State Key Laboratory of Spatial Datum, College of Remote Sensing and Geoinformatics Engineering, Faculty of Geographical Science and Engineering, Henan University, Zhengzhou 450046, China
• Henan Industrial Technology Academy of Spatiotemporal Big Data, Henan University, Zhengzhou 450046, China
• School of Water Conservancy, North China University of Water Resources and Electric Power, Zhengzhou 450046, China

Short Summary

This study analyzed the spatiotemporal variations and influencing factors of groundwater drought in the Yangtze River Basin (YRB) from 2003 to 2022 using GRACE satellite data and hydrological models, identifying key climatic and atmospheric circulation drivers and their nonlinear relationships.

Objective

• To assess the temporal evolution and spatial pattern of groundwater drought in the Yangtze River Basin (YRB) and its sub-basins from 2003 to 2022.
• To determine the change points of hidden seasonal and trend components in groundwater drought.
• To identify the direct/indirect driving contributions of main climatic and atmospheric circulation factors to groundwater drought.

Study Configuration

• Spatial Scale: Yangtze River Basin (YRB) and its 9 secondary sub-basins (HanJiang Water System, Main Stream of the Yangtze Water System, Tai Lake Water System, Poyang Lake Water System, Dongting Lake Water System, Wujiang Water System, Jialing Water System, Minjiang Water System, and Yalong Water System).
• Temporal Scale: 2003–2022 (20 years), analyzed at monthly and seasonal scales.

Methodology and Data

• Models used:
  • Groundwater Drought Index (GDI) construction.
  • Bayesian Estimator of Abrupt Seasonal and Trend Change (BEAST) algorithm.
  • Gridded Mann–Kendall Trend Test Method (GMK).
  • Wavelet Coherence Analysis (using Morlet wavelet).
  • Shapley Additive explanations (SHAP) model combined with XGBoost.
• Data sources:
  • GRACE satellite data (RL06 mascon model, 0.25° resolution).
  • GLDAS Noah land surface process model (for soil moisture, surface water, plant canopy water, snow water equivalent).
  • Atmospheric circulation factors (10 selected factors including NPI, PNA, SSI, NAO, AMO, ENSO, AO, IPO, DMI, SOI, PDO, ONI).
  • Digital Elevation Model (DEM) (30 m accuracy).

Main Results

• The minimum GDI value of -1.66 (severe drought) occurred in July 2020. The average GDI in the YRB ranged from -1.66 to 0.52, with summer experiencing the most severe drought (average GDI -1.28, 94.48% arid area).
• Bayesian analysis identified a seasonal change point in June 2022 (99.53% probability) and a trend change point in February 2020 (88.91% probability) at the basin scale, with sub-basins showing varied change points.
• The Gridded Mann–Kendall test indicated a worsening groundwater drought trend in most areas of the YRB, with 49.48% (September) to 73.11% (February) of areas showing aggravation monthly, and 89.70% in autumn.
• Partial wavelet coherence analysis revealed precipitation as the strongest dominant factor influencing groundwater drought dynamics.
• SHAP analysis identified the North Pacific Index (NPI), Pacific/North American Index (PNA), and Sunspot Index (SSI) as the main atmospheric circulation predictors for groundwater drought changes in the YRB. An increase in NPI exacerbates drought, while increases in SSI, IPO, and DMI help alleviate it.

Contributions

• Provides a systematic investigation into the nonlinear driving mechanisms and characteristics of groundwater drought changes in the YRB, addressing a gap in existing literature.
• Introduces the BEAST algorithm for comprehensive time series decomposition of groundwater drought, offering probability distributions and confidence intervals for abrupt changes.
• Explores the conjoint effects and nonlinear coupling relationships between multiple teleconnection factors on groundwater drought using SHAP theory, moving beyond single-factor analyses.
• Demonstrates the effectiveness of integrating GRACE satellite data with hydrological models for large-scale, continuous groundwater drought monitoring and assessment, which is crucial for developing early warning systems.

Funding

• Henan Provincial Youth Science Foundation (grant number 252300420828)
• National Natural Science Foundation of China (grant number 42401022)
• State Key Laboratory of Spatial Datum Open Project (grant number SKLGIE2024-ZZ-8, SKLGIE2024-Z-4-1, SKLGIE2023-ZZ-9)
• Open Research Fund of Key Laboratory of River Basin Digital Twinning of Ministry of Water Resources (grant number Z0202042022)
• Key Research Projects of Higher Education Institutions in Henan Province (grant number 24A570005)
• Scientific and Technological Research Projects in Henan Province (grant number 242102321005)
• Key Research and Development Special Project of Henan Province (grant number 251111210700)
• Key Science Foundation Project of Henan Provincial Natural Science Foundation (grant number 252300421259)
• Henan Province University Science and Technology Innovation Team Support Plan (grant number 26IRTSTHN022)

Citation

@article{Luo2025Identification,
  author = {Luo, Weiran and Wang, Fei and Jianzhong, Guo and Li, Ziwei and Li, Ning and Du, Mengting and Men, Ruyi and Li, Rong and Lai, Hexin and Xu, Qian and Feng, Kai and Li, Yanbin and Huang, Shengzhi and Tian, Qingqing},
  title = {Identification of Spatiotemporal Variations and Influencing Factors of Groundwater Drought Based on GRACE Satellite},
  journal = {Agriculture},
  year = {2025},
  doi = {10.3390/agriculture16010020},
  url = {https://doi.org/10.3390/agriculture16010020}
}