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Prospects for Reconstructing Daily Runoff from Individual Upstream Remotely-Sensed Climatic Variables

Fok, Hok Sum, Chen, Yutong, and Zhou, Linghao, 2022. Prospects for Reconstructing Daily Runoff from Individual Upstream Remotely-Sensed Climatic Variables. Remote Sensing, 14(4):999, doi:10.3390/rs14040999.

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@ARTICLE{2022RemS...14..999F,
       author = {{Fok}, Hok Sum and {Chen}, Yutong and {Zhou}, Linghao},
        title = "{Prospects for Reconstructing Daily Runoff from Individual Upstream Remotely-Sensed Climatic Variables}",
      journal = {Remote Sensing},
     keywords = {daily runoff forecast, Mekong Basin, GRACE gravimetry, TRMM precipitation, ENSO},
         year = 2022,
        month = feb,
       volume = {14},
       number = {4},
          eid = {999},
        pages = {999},
     abstract = "{Basin water supply, planning, and its allocation requires runoff
        measurements near an estuary mouth. However, insufficient
        financial budget results in no further runoff measurements at
        critical in situ stations. This has recently promoted the runoff
        reconstruction via regression between the runoff and nearby
        remotely-sensed variables on a monthly scale. Nonetheless,
        reconstructing daily runoff from individual basin-upstream
        remotely-sensed climatic variables is yet to be explored. This
        study investigates standardized data regression approach to
        reconstruct daily runoff from the individual remotely-sensed
        climatic variables at the Mekong Basin's upstream. Compared to
        simple linear regression, the daily runoff reconstructed and
        forecasted from the presented approach were improved by at most
        5\% and 10\%, respectively. Reconstructed runoffs using neural
        network models yielded \raisebox{-0.5ex}\textasciitilde0.5\%
        further improvement. The improvement was largely a function of
        the reduced discrepancy during dry and wet seasons. The best
        forecasted runoff obtained from the basin-upstream standardized
        precipitation index, yielded the lowest normalized root-mean-
        square error of 0.093.}",
          doi = {10.3390/rs14040999},
       adsurl = {https://ui.adsabs.harvard.edu/abs/2022RemS...14..999F},
      adsnote = {Provided by the SAO/NASA Astrophysics Data System}
}

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