Publications related to the GRACE Missions (no abstracts)

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Improving the Resolution and Accuracy of Groundwater Level Anomalies Using the Machine Learning-Based Fusion Model in the North China Plain

Zhang, Gangqiang, Zheng, Wei, Yin, Wenjie, and Lei, Weiwei, 2020. Improving the Resolution and Accuracy of Groundwater Level Anomalies Using the Machine Learning-Based Fusion Model in the North China Plain. Sensors, 21(1):46, doi:10.3390/s21010046.

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@ARTICLE{2020Senso..21...46Z,
       author = {{Zhang}, Gangqiang and {Zheng}, Wei and {Yin}, Wenjie and {Lei}, Weiwei},
        title = "{Improving the Resolution and Accuracy of Groundwater Level Anomalies Using the Machine Learning-Based Fusion Model in the North China Plain}",
      journal = {Sensors},
     keywords = {machine learning-based fusion model, GRACE, gradient boosting decision tree, groundwater level anomalies, statistical downscaling, North China Plain},
         year = 2020,
        month = dec,
       volume = {21},
       number = {1},
          eid = {46},
        pages = {46},
     abstract = "{The launch of GRACE satellites has provided a new avenue for studying
        the terrestrial water storage anomalies (TWSA) with
        unprecedented accuracy. However, the coarse spatial resolution
        greatly limits its application in hydrology researches on local
        scales. To overcome this limitation, this study develops a
        machine learning-based fusion model to obtain high-resolution
        (0.25{\textdegree}) groundwater level anomalies (GWLA) by
        integrating GRACE observations in the North China Plain.
        Specifically, the fusion model consists of three modules, namely
        the downscaling module, the data fusion module, and the
        prediction module, respectively. In terms of the downscaling
        module, the GRACE-Noah model outperforms traditional data-driven
        models (multiple linear regression and gradient boosting
        decision tree (GBDT)) with the correlation coefficient (CC)
        values from 0.24 to 0.78. With respect to the data fusion
        module, the groundwater level from 12 monitoring wells is
        incorporated with climate variables (precipitation, runoff, and
        evapotranspiration) using the GBDT algorithm, achieving
        satisfactory performance (mean values: CC: 0.97, RMSE: 1.10 m,
        and MAE: 0.87 m). By merging the downscaled TWSA and fused
        groundwater level based on the GBDT algorithm, the prediction
        module can predict the water level in specified pixels. The
        predicted groundwater level is validated against 6 in-situ
        groundwater level data sets in the study area. Compare to the
        downscaling module, there is a significant improvement in terms
        of CC metrics, on average, from 0.43 to 0.71. This study
        provides a feasible and accurate fusion model for downscaling
        GRACE observations and predicting groundwater level with
        improved accuracy.}",
          doi = {10.3390/s21010046},
       adsurl = {https://ui.adsabs.harvard.edu/abs/2020Senso..21...46Z},
      adsnote = {Provided by the SAO/NASA Astrophysics Data System}
}

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