Publications related to the GRACE Missions (no abstracts)

• Sorted by Date • Sorted by Last Name of First Author •

Water Budget Closure in the Upper Chao Phraya River Basin, Thailand Using Multisource Data

Abhishek, Kinouchi, Tsuyoshi, Abolafia-Rosenzweig, Ronnie, and Ito, Megumi, 2021. Water Budget Closure in the Upper Chao Phraya River Basin, Thailand Using Multisource Data. Remote Sensing, 14(1):173, doi:10.3390/rs14010173.

Downloads

• from the NASA Astrophysics Data System  • by the DOI System  •

@ARTICLE{2021RemS...14..173A,
       author = {{Abhishek} and {Kinouchi}, Tsuyoshi and {Abolafia-Rosenzweig}, Ronnie and {Ito}, Megumi},
        title = "{Water Budget Closure in the Upper Chao Phraya River Basin, Thailand Using Multisource Data}",
      journal = {Remote Sensing},
     keywords = {GRACE-FO, multisource data, artificial neural network (ANN), water balance closure, mathematical techniques},
         year = 2021,
        month = dec,
       volume = {14},
       number = {1},
          eid = {173},
        pages = {173},
     abstract = "{Accurate quantification of the terrestrial water cycle relies on
        combinations of multisource datasets. This analysis uses data
        from remotely sensed, in-situ, and reanalysis records to
        quantify the terrestrial water budget/balance and component
        uncertainties in the upper Chao Phraya River Basin from May 2002
        to April 2020. Three closure techniques are applied to merge
        independent records of water budget components, creating up to
        72 probabilistic realizations of the monthly water budget for
        the upper Chao Phraya River Basin. An artificial neural network
        (ANN) model is used to gap-fill data in and between GRACE and
        GRACE-FO-based terrestrial water storage anomalies. The ANN
        model performed well with r {\ensuremath{\geq}} 0.95, NRMSE =
        0.24 - 0.37, and NSE {\ensuremath{\geq}} 0.89 during the
        calibration and validation phases. The cumulative residual error
        in the water budget ensemble mean accounts for
        \raisebox{-0.5ex}\textasciitilde15\% of the ensemble mean for
        both the precipitation and evapotranspiration. An increasing
        trend of 0.03 mm month$^{-1}$ in the residual errors may be
        partially attributable to increases in human activity and the
        relative redistribution of biases among other water budget
        variables. All three closure techniques show similar directions
        of constraints (i.e., wet or dry bias) in water budget variables
        with slightly different magnitudes. Our quantification of water
        budget residual errors may help benchmark regional hydroclimate
        models for understanding the past, present, and future status of
        water budget components and effectively manage regional water
        resources, especially during hydroclimate extremes.}",
          doi = {10.3390/rs14010173},
       adsurl = {https://ui.adsabs.harvard.edu/abs/2021RemS...14..173A},
      adsnote = {Provided by the SAO/NASA Astrophysics Data System}
}

Generated by bib2html_grace.pl (written by Patrick Riley modified for this page by Volker Klemann) on Mon Oct 13, 2025 16:16:51

---

Mon Oct 13, F. Flechtner