GRACE and GRACE-FO Related Publications (no abstracts)

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A deep learning model for reconstructing centenary water storage changes in the Yangtze River Basin

Wang, Jielong, Shen, Yunzhong, Awange, Joseph L., and Yang, Ling, 2023. A deep learning model for reconstructing centenary water storage changes in the Yangtze River Basin. Science of the Total Environment, 905:167030, doi:10.1016/j.scitotenv.2023.167030.

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@ARTICLE{2023ScTEn.90567030W,
       author = {{Wang}, Jielong and {Shen}, Yunzhong and {Awange}, Joseph L. and {Yang}, Ling},
        title = "{A deep learning model for reconstructing centenary water storage changes in the Yangtze River Basin}",
      journal = {Science of the Total Environment},
     keywords = {GRACE, Total water storage anomalies, Convolutional neural network, Climate indices, Extreme hydrological events},
         year = 2023,
        month = dec,
       volume = {905},
          eid = {167030},
        pages = {167030},
     abstract = "{Since 2002, the Gravity Recovery and Climate Experiment (GRACE) and its
        Follow-On mission (GRACE-FO) have facilitated highly accurate
        observations of changes in total water storage anomalies (TWSA).
        However, limited observations of TWSA derived from GRACE in the
        Yangtze River Basin (YRB) have hindered our understanding of its
        long-term variability. In this paper, we present a deep learning
        model called RecNet to reconstruct the climate-driven TWSA in
        the YRB from 1923 to 2022. The RecNet model is trained on
        precipitation, temperature, and GRACE observations with a
        weighted mean square error (WMSE) loss function. The performance
        of the RecNet model is validated and compared against GRACE
        data, water budget estimates, hydrological models, drought
        indices, and existing reconstruction datasets. The results
        indicate that the RecNet model can successfully reconstruct
        historical water storage changes, surpassing the performance of
        previous studies. In addition, the reconstructed datasets are
        utilized to assess the frequency of extreme hydrological
        conditions and their teleconnections with major climate
        patterns, including the El Ni{\~n}o-Southern Oscillation, Indian
        Ocean Dipole, Pacific Decadal Oscillation, and North Atlantic
        Oscillation. Independent component analysis is employed to
        investigate individual climate patterns' unique or combined
        influence on TWSA. We show that the YRB exhibits a notable
        vulnerability to extreme events, characterized by a recurrent
        occurrence of diverse extreme dry/wet conditions throughout the
        past century. Wavelet coherence analysis reveals significant
        coherence between the climate patterns and TWSA across the
        entire basin. The reconstructed datasets provide valuable
        information for studying long-term climate variability and
        projecting future droughts and floods in the YRB, which can
        inform effective water resource management and climate change
        adaptation strategies.}",
          doi = {10.1016/j.scitotenv.2023.167030},
       adsurl = {https://ui.adsabs.harvard.edu/abs/2023ScTEn.90567030W},
      adsnote = {Provided by the SAO/NASA Astrophysics Data System}
}

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