Publications related to the GRACE Missions (no abstracts)

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Interpreting Global Terrestrial Water Storage Dynamics and Drivers with Explainable Deep Learning

Huang, Haijun, Cai, Xitian, Li, Lu, Wu, Xiaolu, Zhao, Zichun, and Tan, Xuezhi, 2025. Interpreting Global Terrestrial Water Storage Dynamics and Drivers with Explainable Deep Learning. Remote Sensing, 17(13):2118, doi:10.3390/rs17132118.

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@ARTICLE{2025RemS...17.2118H,
       author = {{Huang}, Haijun and {Cai}, Xitian and {Li}, Lu and {Wu}, Xiaolu and {Zhao}, Zichun and {Tan}, Xuezhi},
        title = "{Interpreting Global Terrestrial Water Storage Dynamics and Drivers with Explainable Deep Learning}",
      journal = {Remote Sensing},
     keywords = {global terrestrial water storage, spatio-temporal pattern analysis, driving factor analysis, explainable deep learning},
         year = 2025,
        month = jun,
       volume = {17},
       number = {13},
          eid = {2118},
        pages = {2118},
     abstract = "{Sustained reductions in terrestrial water storage (TWS) have been
        observed globally using Gravity Recovery and Climate Experiment
        (GRACE) satellite data since 2002. However, the underlying
        mechanisms remain incompletely understood due to limited record
        lengths and data discontinuity. Recently, explainable artificial
        intelligence (XAI) has provided robust tools for unveiling
        dynamics in complex Earth systems. In this study, we employed a
        deep learning technique (Long Short-Term Memory network, LSTM)
        to reconstruct global TWS dynamics, filling gaps in the GRACE
        record. We then utilized the Local Interpretable Model-agnostic
        Explanations (LIME) method to uncover the underlying mechanisms
        driving observed TWS reductions. Our results reveal a consistent
        decline in the global mean TWS over the past 22 years
        (2002{\textendash}2024), primarily influenced by precipitation
        (17.7\%), temperature (16.0\%), and evapotranspiration (10.8\%).
        Seasonally, the global average of TWS peaks in April and reaches
        a minimum in October, mirroring the pattern of snow water
        equivalent with approximately a one-month lag. Furthermore, TWS
        variations exhibit significant differences across latitudes and
        are driven by distinct factors. The largest declines in TWS
        occur predominantly in high latitudes, driven by rising
        temperatures and significant snow/ice variability. Mid-latitude
        regions have experienced considerable TWS losses, influenced by
        a combination of precipitation, temperature, air pressure, and
        runoff. In contrast, most low-latitude regions show an increase
        in TWS, which the model attributes mainly to increased
        precipitation. Notably, TWS losses are concentrated in coastal
        areas, snow- and ice-covered regions, and areas experiencing
        rapid temperature increases, highlighting climate change
        impacts. This study offers a comprehensive framework for
        exploring TWS variations using XAI and provides valuable
        insights into the mechanisms driving TWS changes on a global
        scale.}",
          doi = {10.3390/rs17132118},
       adsurl = {https://ui.adsabs.harvard.edu/abs/2025RemS...17.2118H},
      adsnote = {Provided by the SAO/NASA Astrophysics Data System}
}

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