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Reconstructing GRACE Terrestrial Water Storage Anomalies With Machine Learning for Drought Monitoring in Southwest China

Wu, Luzhen, Zhou, Xu, Shangguan, Ming, Gong, Xinghui, and Wang, Wuke, 2025. Reconstructing GRACE Terrestrial Water Storage Anomalies With Machine Learning for Drought Monitoring in Southwest China. IEEE Transactions on Geoscience and Remote Sensing, 63:TGRS.2025, doi:10.1109/TGRS.2025.3594345.

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@ARTICLE{2025ITGRS..63S4345W,
       author = {{Wu}, Luzhen and {Zhou}, Xu and {Shangguan}, Ming and {Gong}, Xinghui and {Wang}, Wuke},
        title = "{Reconstructing GRACE Terrestrial Water Storage Anomalies With Machine Learning for Drought Monitoring in Southwest China}",
      journal = {IEEE Transactions on Geoscience and Remote Sensing},
     keywords = {Drought index, Gravity Recovery and Climate Experiment (GRACE), machine learning (ML), Southwest China, terrestrial water storage anomaly (TWSA)},
         year = 2025,
        month = jan,
       volume = {63},
          eid = {TGRS.2025},
        pages = {TGRS.2025},
     abstract = "{In recent years, the southwestern region of China has experienced
        frequent drought disasters, causing severe impacts on the local
        economy and environment. The Gravity Recovery and Climate
        Experiment (GRACE)/GRACE-follow-on (FO) gravity satellites can
        invert terrestrial water storage anomalies (TWSAs), providing a
        new technological approach for drought monitoring. However, the
        data gap between the GRACE and GRACE-FO has affected the
        completeness and continuity of TWSA data, posing challenges for
        long-term drought monitoring and analysis. This study employs
        four machine learning (ML) models: extreme learning machine
        (ELM), nonlinear autoregressive with external input (NARX), long
        short-term memory (LSTM), and extreme gradient boosting
        (XGBoost), combined with the empirical orthogonal function (EOF)
        method. The key innovation of this study lies in integrating EOF
        decomposition with ML models to enhance the accuracy and
        reliability of TWSA gap filling and drought monitoring under
        limited monthly scale data conditions. The approach effectively
        bridges the data gap between GRACE and GRACE-FO and compares the
        results with reanalyzed/simulated TWSA and recently generated
        TWSA prediction products. The results indicate that all machine
        learning (ML) models can effectively reconstruct the interannual
        variations in TWSA, and the LSTM model performs the best.
        Compared with TWSA prediction products provided by recent
        studies, the LSTM model offers more accurate TWSA predictions.
        In addition, this study constructs the GRACE water storage
        deficit index (WSDI) based on the reconstructed TWSA data, and
        by comparing it with the standardized precipitation
        evapotranspiration index (SPEI), finds a correlation coefficient
        of 0.80 at a six-month timescale, indicating that GRACE WSDI can
        effectively reflect the long-term cumulative effects of drought.
        The reconstruction of GRACE WSDI successfully detected the
        extreme drought event in the southwestern region of China in
        2022, further confirming the effectiveness of GRACE WSDI in
        identifying and assessing drought severity.}",
          doi = {10.1109/TGRS.2025.3594345},
       adsurl = {https://ui.adsabs.harvard.edu/abs/2025ITGRS..63S4345W},
      adsnote = {Provided by the SAO/NASA Astrophysics Data System}
}

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