Publications related to the GRACE Missions (no abstracts)

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Discharge Estimation Using Integrated Satellite Data and Hybrid Model in the Midstream Yangtze River

Xiong, Jinghua, Guo, Shenglian, and Yin, Jiabo, 2021. Discharge Estimation Using Integrated Satellite Data and Hybrid Model in the Midstream Yangtze River. Remote Sensing, 13(12):2272, doi:10.3390/rs13122272.

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@ARTICLE{2021RemS...13.2272X,
       author = {{Xiong}, Jinghua and {Guo}, Shenglian and {Yin}, Jiabo},
        title = "{Discharge Estimation Using Integrated Satellite Data and Hybrid Model in the Midstream Yangtze River}",
      journal = {Remote Sensing},
     keywords = {satellite altimetry, GRACE, machine learning, hydrological simulation},
         year = 2021,
        month = jun,
       volume = {13},
       number = {12},
          eid = {2272},
        pages = {2272},
     abstract = "{Remotely sensing data have advantages in filling spatiotemporal gaps of
        in situ observation networks, showing potential application for
        monitoring floods in data-sparse regions. By using the water
        level retrievals of Jason-2/3 altimetry satellites, this study
        estimates discharge at a 10-day timescale for the virtual
        station (VS) 012 and 077 across the midstream Yangtze River
        Basin during 2009-2016 based on the developed Manning formula.
        Moreover, we calibrate a hybrid model combined with Gravity
        Recovery and Climate Experiment (GRACE) data, by coupling the
        GR6J hydrological model with a machine learning model to
        simulate discharge. To physically capture the flood processes,
        the random forest (RF) model is employed to downscale the 10-day
        discharge into a daily scale. The results show that: (1)
        discharge estimates from the developed Manning formula show good
        accuracy for the VS012 and VS077 based on the improved Multi-
        subwaveform Multi-weight Threshold Retracker; (2) the
        combination of the GR6J and the LSTM models substantially
        improves the performance of the discharge estimates solely from
        either the GR6J or LSTM models; (3) RF-downscaled daily
        discharge demonstrates a general consistency with in situ data,
        where NSE/KGE between them are as high as 0.69/0.83. Our
        approach, based on multi-source remotely sensing data and
        machine learning techniques, may benefit flood monitoring in
        poorly gauged areas.}",
          doi = {10.3390/rs13122272},
       adsurl = {https://ui.adsabs.harvard.edu/abs/2021RemS...13.2272X},
      adsnote = {Provided by the SAO/NASA Astrophysics Data System}
}

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