GRACE and GRACE-FO Related Publications (no abstracts)

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Generating Long-Term Grace-Like Total Water Storage Change Products Using Conditional Generative Adversarial Networks

Wang, Jielong, Shen, Yunzhong, and Awange, Joseph, 2024. Generating Long-Term Grace-Like Total Water Storage Change Products Using Conditional Generative Adversarial Networks. ISPRS Annals of Photogrammetry, Remote Sensing and Spatial Information Sciences, X:389–394, doi:10.5194/isprs-annals-X-4-2024-389-2024.

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@ARTICLE{2024ISPAn...X..389W,
       author = {{Wang}, Jielong and {Shen}, Yunzhong and {Awange}, Joseph},
        title = "{Generating Long-Term Grace-Like Total Water Storage Change Products Using Conditional Generative Adversarial Networks}",
      journal = {ISPRS Annals of Photogrammetry, Remote Sensing and Spatial Information Sciences},
         year = 2024,
        month = oct,
       volume = {X},
        pages = {389-394},
     abstract = "{Since 2002, the Gravity Recovery And Climate Experiment (GRACE) and its
        Follow-On (GRACE-FO, hereafter GRACE) missions have offered
        global observations of total water storage (TWS). However, the
        relatively short record of GRACE data poses a significant
        challenge for researchers to investigate the full range and
        long-term variability in TWS. In this study, we present RecGAN,
        a novel Conditional Generative Adversarial Network (CGAN)
        comprising a RecNet generator and pixel discriminator. Our
        approach aims to generate long-term GRACE-like TWS observations
        by calibrating the WaterGAP Global Hydrology Model (WGHM). The
        generator is trained to produce observations conforming to the
        distribution of GRACE data, while the discriminator is trained
        to assess whether each generated pixel resembles GRACE data. Our
        results show that RecGAN effectively enhances the consistency
        between GRACE observations and WGHM-derived TWS changes,
        achieving improved correlation coefficients, Nash-Sutcliffe
        Efficiency, and Normalized Root-Mean-Square Error. In addition,
        RecGAN is robust to different GRACE mascon data, crop sizes used
        during the training period, and hydrological models targeted for
        calibration. This study illustrates a promising application of
        employing CGANs to fine-tune the WGHM output to match GRACE
        observations. This approach enables the generation of longterm
        TWS change datasets, which are invaluable for evaluating long-
        term water storage fluctuations, allocating water resources, and
        forecasting future hydrological extremes.}",
          doi = {10.5194/isprs-annals-X-4-2024-389-2024},
       adsurl = {https://ui.adsabs.harvard.edu/abs/2024ISPAn...X..389W},
      adsnote = {Provided by the SAO/NASA Astrophysics Data System}
}

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