Publications related to the GRACE Missions (no abstracts)

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Deep learning-aided temporal downscaling of GRACE-derived terrestrial water storage anomalies across the Contiguous United States

Uz, Metehan, Akyilmaz, Orhan, and Shum, C. K., 2024. Deep learning-aided temporal downscaling of GRACE-derived terrestrial water storage anomalies across the Contiguous United States. Journal of Hydrology, 645:132194, doi:10.1016/j.jhydrol.2024.132194.

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@ARTICLE{2024JHyd..64532194U,
       author = {{Uz}, Metehan and {Akyilmaz}, Orhan and {Shum}, C.~K.},
        title = "{Deep learning-aided temporal downscaling of GRACE-derived terrestrial water storage anomalies across the Contiguous United States}",
      journal = {Journal of Hydrology},
     keywords = {GRACE, GRACE(‑FO), Deep learning neural networks, Temporal downscaling, Daily terrestrial water storage anomalies},
         year = 2024,
        month = dec,
       volume = {645},
          eid = {132194},
        pages = {132194},
     abstract = "{The Gravity Recovery And Climate Experiment (GRACE) and GRACE-FollowOn
        (GRACE(‑FO)) satellites have been monitoring Earth's changes in
        terrestrial water storage (TWS) or surficial mass changes at
        monthly sampling and a spatial scale longer than
        {\ensuremath{\sim}}330 km (half wavelength) over the past two
        decades. At monthly sampling or revisit time, the use of
        satellite gravimetry is difficult to effectively monitor abrupt
        extreme weather events which are high-frequency, including the
        climate-induced hurricanes/cyclones, flash floods and droughts.
        The majority of the contemporary studies have focused on
        satellite gravimetry spatial downscaling, and not on reducing
        the temporal resolution of Earth's mass change. Here, we
        developed a Deep Learning (DL) algorithm to downscale monthly
        GRACE/GRACE(‑FO) Mass Concentration (Mascon) TWS anomaly (TWSA)
        solutions to daily sampling over the Contiguous United States
        (CONUS), with the aim of monitoring rapidly evolving natural
        hazard episodes. The simulative performance of the DL algorithm
        is validated by comparing the modeling to an independent
        observation and the land hydrology model (LHM) predicted TWSA.
        To confirm that our daily and monthly simulations captured the
        climatic variations, we first compared our simulations with El
        Ni{\~n}o/La Ni{\~n}a Southern Oscillation (ENSO) circulation
        system index, which has a dominant climatological and
        socioeconomic impact across CONUS, and results reveal high
        correlations which are statistically significant. Next, we
        assessed the feasibilities to detect long- and short-term
        variations in the TWSA signals triggered by hydrological
        extremes, including the 2011 and 2019 Missouri River Floods, the
        August 2017 Atlantic Hurricane Harvey landfalls in Texas, the
        2012{\textendash}2017 drought in California, and the flash
        drought in the Northern Great Plains in 2017. Additional
        validation results using independent in situ observations reveal
        that our DL-aided gravimetry downscaled daily simulations are
        capable of elucidating hazards and water cycle evolutions at
        high temporal resolution.}",
          doi = {10.1016/j.jhydrol.2024.132194},
       adsurl = {https://ui.adsabs.harvard.edu/abs/2024JHyd..64532194U},
      adsnote = {Provided by the SAO/NASA Astrophysics Data System}
}

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