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A novel XGBoost-based approach for reconstruction terrestrial water storage variations with GNSS in the Northeastern Tibetan Plateau

Zhang, Tengxu, Wang, Zhuohao, Huang, Liangke, He, Lin, and Yao, Chaolong, 2025. A novel XGBoost-based approach for reconstruction terrestrial water storage variations with GNSS in the Northeastern Tibetan Plateau. Journal of Hydrology, 659:133255, doi:10.1016/j.jhydrol.2025.133255.

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@ARTICLE{2025JHyd..65933255Z,
       author = {{Zhang}, Tengxu and {Wang}, Zhuohao and {Huang}, Liangke and {He}, Lin and {Yao}, Chaolong},
        title = "{A novel XGBoost-based approach for reconstruction terrestrial water storage variations with GNSS in the Northeastern Tibetan Plateau}",
      journal = {Journal of Hydrology},
     keywords = {GNSS, XGBML, Terrestrial water storage, Hydrological drought, Northeastern Tibetan plateau},
         year = 2025,
        month = oct,
       volume = {659},
          eid = {133255},
        pages = {133255},
     abstract = "{Accurately estimating terrestrial water storage (TWS) variations is
        essential for ensuring the sustainable management of global
        water resources. The Global Navigation Satellite System (GNSS)
        offers a promising approach for monitoring TWS changes with high
        spatial and temporal resolution. However, its application is
        significantly constrained by the sparse and uneven distribution
        of GNSS stations. In this study, we build upon traditional GNSS
        inversion techniques by employing the Extreme Gradient Boosting
        Machine Learning (XGBML) model to simulate crustal deformation
        caused by hydrological loading. The simulation is conducted on a
        <mml:math><mml:mrow><mml:mn>0</mml:mn><mml:mo>.</mml:mo><mml:msu
        p><mml:mn>5</mml:mn><mml:mo>{\textdegree}</mml:mo></mml:msup><mm
        l:mo>{\texttimes}</mml:mo><mml:mn>0</mml:mn><mml:mo>.</mml:mo><m
        ml:msup><mml:mn>5</mml:mn><mml:mo>{\textdegree}</mml:mo></mml:ms
        up></mml:mrow></mml:math> grid across the Northeastern Tibetan
        Plateau (NETP). This study compared TWS variations derived from
        the XGBML simulations and traditional inversion methods with
        data from the Gravity Recovery and Climate Experiment (GRACE)
        satellite and the Global Land Data Assimilation System (GLDAS).
        The Pearson Correlation Coefficients (PCC) between TWS changes
        derived from the XGBML inversion technique and those from GRACE
        and GLDAS data were 0.72 and 0.50, respectively, representing
        improvements of 8.82 \% and 11.10 \% compared to the
        conventional inversion approach. Furthermore, GNSS-DSI, GRACE-
        DSI, and SPEI were integrated to analyze hydrological drought
        events in the study area, revealing that precipitation and
        temperature are important drivers of hydrological drought in the
        NETP. These findings highlight the effectiveness of the XGBML
        model in simulating GNSS vertical displacements induced by
        hydrological loading and demonstrate its potential as a novel
        tool for identifying water storage variations in regions with
        uneven GNSS station distribution.}",
          doi = {10.1016/j.jhydrol.2025.133255},
       adsurl = {https://ui.adsabs.harvard.edu/abs/2025JHyd..65933255Z},
      adsnote = {Provided by the SAO/NASA Astrophysics Data System}
}

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