Zhu et al. (2025) Assessing the Use of the Standardized GRACE Satellite Groundwater Storage Change Index for Quantifying Groundwater Drought in the Mu Us Sandy Land

Identification

• Journal: Remote Sensing
• Year: 2025
• Date: 2025-12-12
• Authors: Yonghua Zhu, Longfei Zhou, Qi Zhang, Zhiming Han, Jiamin Li, Yan Chao, Xiaohan Wang, Hui Yuan, Jie Zhang, Bisheng Xia
• DOI: 10.3390/rs17244015

Research Groups

• College of Civil Engineering and Architecture, Yan’an University, Yan’an, China.
• College of Mathematics and Computer Science, Yan’an University, Yan’an, China.
• College of Resources and Environment, Northwest A&F University, Xi’an, China.

Short Summary

This study evaluates the effectiveness of a GRACE satellite-derived standardized groundwater index (GRACE_SGI) for monitoring groundwater drought in the Mu Us Sandy Land. The research identifies a significant intensifying trend in groundwater depletion and a temporal lag of up to 12 months between meteorological and groundwater drought at annual scales.

Objective

• To assess the suitability of the GRACE_SGI for quantifying groundwater drought in a data-scarce semiarid region.
• To identify the optimal probability density function for fitting groundwater storage anomaly (GWA) data and clarify the hysteresis effect between precipitation and groundwater response.

Study Configuration

• Spatial Scale: Mu Us Sandy Land, China (approximately 40,000 km²).
• Temporal Scale: April 2002 to April 2021 (228 months).

Methodology and Data

• Models used: GLDAS Noah v2.1 (to isolate soil moisture anomalies); GRACE RL06 mascon solutions (CSR) for total water storage anomalies.
• Data sources: GRACE satellite data, GLDAS land surface model, in situ groundwater level data from 3 observation wells (2010–2018), and daily precipitation data from 8 meteorological stations.
• Statistical Methods: Anderson–Darling (AD) test to evaluate four fitting functions (Gamma, Normal, Beta, and Pearson III); Standardized Precipitation Index (SPI) for meteorological drought; cross-correlation analysis for lag determination.

Main Results

• Optimal Fitting: The Pearson III distribution function was identified as the best-fitting model for calculating the GRACE_SGI across 1, 3, 6, and 12-month scales.
• Drought Trends: The GRACE_SGI showed a consistent linear downward trend across all scales (slopes ranging from -0.4 × 10⁻³ to -0.5 × 10⁻³ cm/month), indicating intensifying groundwater drought, particularly after 2015.
• Seasonal Variation: Persistent negative SGI values (drought conditions) have been observed in spring and autumn seasons since 2011.
• Hysteresis Effect: Correlation analysis revealed no lag between SPI and SGI at 1-month and 3-month scales, but significant lags of 11 months (at the 12-month scale) and 12 months (at the 6-month scale) were identified.
• Quantitative Correlation: Maximum absolute correlation coefficients between SPI and SGI ranged from 0.1296 (1-month) to 0.5224 (12-month).

Contributions

• Establishes a satellite-based framework for quantitative groundwater drought monitoring in regions where ground-based observation networks are sparse.
• Demonstrates that the selection of the probability density function (specifically Pearson III) significantly improves the accuracy of the SGI compared to traditional Gamma or Normal distributions.
• Quantifies the propagation time from meteorological drought to groundwater drought in a semiarid sandy environment, highlighting the impact of human activities (mining and irrigation) on groundwater storage.

Funding

• Shaanxi Provincial Department of Science and Technology (2023JCYB449).
• Yan’an Science and Technology Bureau’s List System Project (2023SLJBZ002).
• Shaanxi Province College Student Innovation and Entrepreneurship Training Program Project (S202310719123).
• Yan’an University Project (YDBK2019-35).

Citation

@article{Zhu2025Assessing,
  author = {Zhu, Yonghua and Zhou, Longfei and Zhang, Qi and Han, Zhiming and Li, Jiamin and Chao, Yan and Wang, Xiaohan and Yuan, Hui and Zhang, Jie and Xia, Bisheng},
  title = {Assessing the Use of the Standardized GRACE Satellite Groundwater Storage Change Index for Quantifying Groundwater Drought in the Mu Us Sandy Land},
  journal = {Remote Sensing},
  year = {2025},
  doi = {10.3390/rs17244015},
  url = {https://doi.org/10.3390/rs17244015}
}

Generated by BiblioAssistant using gemini-3-flash-preview (Google API)