IEEE Access (Jan 2024)

Analysis and Prediction of Temporal and Spatial Evolution of Groundwater Storage by Combined SAR-GRACE Satellite Data

  • Yan An,
  • Fan Yang,
  • Jia Xu,
  • Chuang Ren,
  • Jin Hu,
  • Guona Luo

DOI
https://doi.org/10.1109/ACCESS.2024.3368423
Journal volume & issue
Vol. 12
pp. 33671 – 33686

Abstract

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Regional surface deformation resulting from the development and utilization of underground space resources poses a significant threat to the safety of urban areas, and the combination of Synthetic Aperture Radar and Gravity Recovery and Climate Experiment (GRACE) satellite data has become a new means to study the impact of underground space evolution on surface deformation. We combine the Interferometric Synthetic Aperture Radar (InSAR) technology and gravity satellite data to extract information on regional surface deformation and groundwater storage changes in Shanxi Province, to explore the patterns of their temporal and spatial variations, to discover their links with seasonal climate change, to re-conceptualize the laws of the regional water cycle, and to quantify the contribution of multiple fields to the evolution of the surface. Furthermore, we propose a novel multi-source neural network prediction model (LSTM/BP) based on signal decomposition (VMD) and algorithm optimization to handle the complex time series characteristics of groundwater storage. Our findings reveal that groundwater storage in Shanxi Province has been consistently declining, with a monthly deficit rate of approximately 1.05 mm. Additionally, there is a notable spatial variation in the annual rate of change, ranging from −21 to 4 mm/year from north to south. Furthermore, we observe a close relationship between inter-annual and seasonal groundwater storage changes and local rainfall patterns, and we find that regional surface deformation is influenced by these groundwater storage changes. The new prediction model outperforms other models, with a root mean square error of 1.56 mm and a correlation coefficient of more than 0.98 on the test set. The model improves the prediction accuracy of the groundwater reserves in the basin, and it can be used to provide a reference for the comprehensive management of the groundwater in Shanxi Province, the rational development of mineral resources, and other major national needs.

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