Publications related to the GRACE Missions (no abstracts)

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Unraveling the relationship between surface deformation and groundwater dynamics in karst terrains using multi-source remote sensing data

Gu, Songwei, Zhou, Yun, Jing, Yinghong, Zhang, Zhengjia, Shao, Jinshi, Shuai, Li, Chen, Jiangzhaoxia, Zhao, Yinjun, She, Xiaojun, and Li, Yao, 2025. Unraveling the relationship between surface deformation and groundwater dynamics in karst terrains using multi-source remote sensing data. Journal of Hydrology, 663:134129, doi:10.1016/j.jhydrol.2025.134129.

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@ARTICLE{2025JHyd..66334129G,
       author = {{Gu}, Songwei and {Zhou}, Yun and {Jing}, Yinghong and {Zhang}, Zhengjia and {Shao}, Jinshi and {Shuai}, Li and {Chen}, Jiangzhaoxia and {Zhao}, Yinjun and {She}, Xiaojun and {Li}, Yao},
        title = "{Unraveling the relationship between surface deformation and groundwater dynamics in karst terrains using multi-source remote sensing data}",
      journal = {Journal of Hydrology},
     keywords = {Karst region, Surface deformation, Groundwater dynamics, SBAS-InSAR, GRACE, Machine learning},
         year = 2025,
        month = dec,
       volume = {663},
          eid = {134129},
        pages = {134129},
     abstract = "{Assessing groundwater dynamics in the karst regions of Southwest China
        remains challenging due to the complex interactions among
        environmental factors that influence surface deformation.
        Groundwater depletion significantly contributes to surface
        deformation, making deformation data a promising proxy for
        estimating groundwater changes. However, the accuracy of such
        estimates is limited by the effects of additional environmental
        factors. This study investigated the relationships among
        groundwater depletion, environmental variables, and surface
        deformation to improve groundwater change modeling. Two machine
        learning algorithms{\textemdash}Random Forest (RF) and Bayesian
        Neural Network (BNN){\textemdash}were employed to assess the
        impacts of environmental factors on model accuracy. Results
        revealed a strong correlation (r = 0.84, p < 0.05) between
        surface deformation rates derived from Sentinel-1 data using the
        small baseline subset interferometric synthetic aperture radar
        (SBAS-InSAR) technique and groundwater storage changes estimated
        from Gravity Recovery and Climate Experiment (GRACE) data. Key
        environmental factors{\textemdash}such as climatic conditions,
        land use, and rocky desertification{\textemdash}were found to
        significantly influence surface deformation. Integrating these
        factors into machine learning models substantially improved
        groundwater change estimates, increasing the correlation
        coefficient from 0.03 to 0.29 and reducing the root mean square
        error (RMSE) from 8.85 to 2.56 mm compared with in situ
        observations. By combining InSAR-derived deformation data with
        multi-source environmental variables, this study presents a
        practical and innovative approach for assessing groundwater
        dynamics in karst regions, offering valuable insights for
        sustainable groundwater management in complex hydrogeological
        settings.}",
          doi = {10.1016/j.jhydrol.2025.134129},
       adsurl = {https://ui.adsabs.harvard.edu/abs/2025JHyd..66334129G},
      adsnote = {Provided by the SAO/NASA Astrophysics Data System}
}

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