GRACE and GRACE-FO Related Publications (no abstracts)

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A novel generative adversarial network and downscaling scheme for GRACE/GRACE-FO products: Exemplified by the Yangtze and Nile River Basins

Wang, Jielong, Shen, Yunzhong, Awange, Joseph, Tabatabaeiasl, Maryam, Song, Yongze, and Liu, Chang, 2025. A novel generative adversarial network and downscaling scheme for GRACE/GRACE-FO products: Exemplified by the Yangtze and Nile River Basins. Science of the Total Environment, 969:178874, doi:10.1016/j.scitotenv.2025.178874.

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@ARTICLE{2025ScTEn.96978874W,
       author = {{Wang}, Jielong and {Shen}, Yunzhong and {Awange}, Joseph and {Tabatabaeiasl}, Maryam and {Song}, Yongze and {Liu}, Chang},
        title = "{A novel generative adversarial network and downscaling scheme for GRACE/GRACE-FO products: Exemplified by the Yangtze and Nile River Basins}",
      journal = {Science of the Total Environment},
     keywords = {Generative adversarial network, Downscale, Total water storage, Deep learning},
         year = 2025,
        month = mar,
       volume = {969},
          eid = {178874},
        pages = {178874},
     abstract = "{The coarse spatial resolution of about 300 km in Total Water Storage
        Anomalies (TWSA) data from the Gravity Recovery And Climate
        Experiment (GRACE) and its follow-on (GRACE-FO, hereafter GRACE)
        missions presents significant challenges for local water
        resource management. Previous approaches to addressing this
        issue through statistical downscaling have been limited by the
        reliance on the scale-invariance assumption, residual
        correction, hydrological models, and a lack of consideration for
        spatial correlations among the TWSA grids. This study introduces
        the DownGAN generative adversarial network, which downscales
        GRACE TWSA to 25 km, as exemplified in the Yangtze River Basin
        (YRB) and the Nile River Basin (NRB). Additionally, we propose a
        novel downscaling scheme to address the above limitations.
        DownGAN receives static and dynamic variables as inputs while
        considering their potential time-delay effects. The downscaled
        TWSA is validated using a synthetic example, in-situ runoff,
        groundwater levels, and two hydrological models. The potential
        benefits of the downscaled TWSA in closing the water balance
        budget and monitoring hydrological droughts in the YRB and NRB
        are explored. The synthetic example indicates that DownGAN
        trained using the proposed downscaling scheme can downscale the
        YRB and NRB's TWSA from 1{\textdegree} to 0.5{\textdegree} and
        0.25{\textdegree}, respectively. DownGAN outperforms RecNet, a
        fully convolutional neural network, producing continuous,
        consistent, and realistic downscaled TWSA. The downscaled TWSA
        exhibits high correlations with the runoff and groundwater
        levels in the YRB and NRB, respectively. In addition, DownGAN
        demonstrates better performance in closing the water balance
        budget and monitoring drought events in both the YRB and NRB
        than HR GRACE mascon products, as evidenced by its higher
        correlations with the total water storage changes derived from
        the water balance equation and two drought indices,
        respectively. DownGAN is adaptable to other downscaling tasks
        and regions, offering a flexible downscaling factor, minimal
        assumptions, cost-effectiveness, and realistic predictions.}",
          doi = {10.1016/j.scitotenv.2025.178874},
       adsurl = {https://ui.adsabs.harvard.edu/abs/2025ScTEn.96978874W},
      adsnote = {Provided by the SAO/NASA Astrophysics Data System}
}

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