Publications related to the GRACE Missions (no abstracts)

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Revealing groundwater depletion and seasonal dynamics in Northwest China by integrating GRACE with physically based and data–driven modeling

Li, Mingyue, Xie, Yueqing, Song, Jinxi, Wu, Jichun, and Zhang, Jun, 2026. Revealing groundwater depletion and seasonal dynamics in Northwest China by integrating GRACE with physically based and data–driven modeling. Journal of Hydrology, 666:134735, doi:10.1016/j.jhydrol.2025.134735.

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@ARTICLE{2026JHyd..66634735L,
       author = {{Li}, Mingyue and {Xie}, Yueqing and {Song}, Jinxi and {Wu}, Jichun and {Zhang}, Jun},
        title = "{Revealing groundwater depletion and seasonal dynamics in Northwest China by integrating GRACE with physically based and data-driven modeling}",
      journal = {Journal of Hydrology},
     keywords = {Groundwater storage, GRACE, Artificial Neural Network, Groundwater dynamics, Seasonal variability},
         year = 2026,
        month = feb,
       volume = {666},
          eid = {134735},
        pages = {134735},
     abstract = "{Monitoring and understanding of groundwater variability is critical for
        water resource management but remains highly challenging in
        vulnerable arid regions with limited ground observations. This
        study explores improved characterization for groundwater storage
        anomalies (GWSA) and recharge dynamics by leveraging GRACE
        observations, land surface models, key climate variables, and
        in-situ well records, combined with complementary modeling
        strategies. Multiple GRACE-derived terrestrial water storage
        anomaly (TWSA) solutions were examined to support a reliable
        observational basis for the analysis. The GRACE-Noah model
        indicated that the long-term and seasonal trends in GWSA are
        highly synchronized with TWSA, emphasizing groundwater's central
        role in terrestrial water storage, while snowmelt was also found
        to influence groundwater dynamics through its impact on surface
        water and soil moisture. The well-configured artificial neural
        network (ANN) model demonstrated superior performance, proving
        to be effective for capturing connections between key
        hydroclimate factors and groundwater. Results revealed a
        significant decline averaging {\ensuremath{-}}4.34 mm/yr in
        groundwater storage since 2002 ({\ensuremath{-}}4.51 mm/yr from
        GRACE-Noah and {\ensuremath{-}}4.17 mm/yr from GRACE-ANN),
        highlighting ongoing stress on groundwater resources. The
        spatiotemporal variations in groundwater recharge and depletion
        across basins are characterized, revealing the depletion
        patterns around the Tianshan Mountains and seasonal dynamics
        driven by snowmelt and evapotranspiration. By integrating GRACE
        observations with complementary modeling strategies, this study
        contributes to improving the characterization of groundwater
        depletion and recharge dynamics in Northwest China, advancing
        knowledge of groundwater system behavior under changing climatic
        conditions, further providing new insights for effective
        groundwater resource assessment in arid regions.}",
          doi = {10.1016/j.jhydrol.2025.134735},
       adsurl = {https://ui.adsabs.harvard.edu/abs/2026JHyd..66634735L},
      adsnote = {Provided by the SAO/NASA Astrophysics Data System}
}

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