GRACE and GRACE-FO Related Publications (no abstracts)

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Methodological evaluation of river discharges derived from remote sensing and land surface models

Duvvuri, Bhavya, Gehring, Jacyln, and Beighley, Edward, 2024. Methodological evaluation of river discharges derived from remote sensing and land surface models. Scientific Reports, 14(1):25653, doi:10.1038/s41598-024-75361-w.

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@ARTICLE{2024NatSR..1425653D,
       author = {{Duvvuri}, Bhavya and {Gehring}, Jacyln and {Beighley}, Edward},
        title = "{Methodological evaluation of river discharges derived from remote sensing and land surface models}",
      journal = {Scientific Reports},
     keywords = {GLDAS, CLSM, NOAH, KGE, GRACE-FO, Saturation-excess},
         year = 2024,
        month = oct,
       volume = {14},
       number = {1},
          eid = {25653},
        pages = {25653},
     abstract = "{This study assesses river discharges derived using remote sensing and
        hydrologic modeling approaches throughout the CONUS. The remote
        sensing methods rely on total water storage anomalies (TWSA)
        from the GRACE satellite mission and water surface elevations
        from altimetry satellites (JASON-2/3, Sentinel-3). Surface and
        subsurface runoff from two Land Surface Models (NOAH, CLSM) are
        routed using the Hillslope River Routing model to determine
        discharge. The LSMs are part of NASA's Global Land Data
        Assimilation System (GLDAS). Differences in key physical
        processes represented in each model, model forcings, and use of
        data assimilation provide an intriguing basis for comparison.
        Evaluation is performed using the Kling Gupta Efficiency and
        USGS stream gauges. Results highlight the effectiveness of both
        satellite-derived discharge methods, with altimetry generally
        performing well over a range of discharges and TWSA capturing
        mean flows. LSM-derived discharge performance varies based on
        hydroclimatic conditions and drainage areas, with NOAH generally
        outperforming CLSM. CLSM-derived discharges may be impacted by
        the use of data assimilation (GLDAS v2.2). Low correlation and
        high variability contribute to lower KGE values. GLDAS models
        tend to perform poorly in snow dominated, semi-arid and water-
        regulated systems where both the timing and magnitude of the
        simulated results are early and overestimated.}",
          doi = {10.1038/s41598-024-75361-w},
       adsurl = {https://ui.adsabs.harvard.edu/abs/2024NatSR..1425653D},
      adsnote = {Provided by the SAO/NASA Astrophysics Data System}
}

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