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A Novel Framework for Heterogeneity Decomposition and Mechanism Inference in Spatiotemporal Evolution of Groundwater Storage: Case Study in the North China Plain

Zhao, Xiaowei, Yu, Ying, Cheng, Jianmei, Ding, Kuiyuan, Luo, Yiming, Zheng, Kun, Xian, Yang, and Lin, Yihang, 2024. A Novel Framework for Heterogeneity Decomposition and Mechanism Inference in Spatiotemporal Evolution of Groundwater Storage: Case Study in the North China Plain. Water Resources Research, 60(12):2023WR036102, doi:10.1029/2023WR036102.

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@ARTICLE{2024WRR....6036102Z,
       author = {{Zhao}, Xiaowei and {Yu}, Ying and {Cheng}, Jianmei and {Ding}, Kuiyuan and {Luo}, Yiming and {Zheng}, Kun and {Xian}, Yang and {Lin}, Yihang},
        title = "{A Novel Framework for Heterogeneity Decomposition and Mechanism Inference in Spatiotemporal Evolution of Groundwater Storage: Case Study in the North China Plain}",
      journal = {Water Resources Research},
     keywords = {GRACE, groundwater storage, spatiotemporal evolution, heterogeneity decomposition, driving factors, North China Plain},
         year = 2024,
        month = dec,
       volume = {60},
       number = {12},
        pages = {2023WR036102},
     abstract = "{Properly understanding the evolution mechanisms of groundwater storage
        anomaly (GWSA) is the basis of making effective groundwater
        management strategies. However, current analysis methods cannot
        objectively capture the spatiotemporal evolution characteristics
        of GWSA, which might lead to erroneous inferences of the
        evolution mechanisms. Here, we developed a new framework to
        address the challenge of spatiotemporal heterogeneity in the
        GWSA evolution analysis. It is achieved by integrating the
        Bayesian Estimator of Abrupt change, Seasonal change, and Trend
        (BEAST), the Balanced Iterative Reducing and Clustering using
        Hierarchies (BIRCH), and the Optimal Parameters-based
        Geographical Detector (OPGD). In the case study of the North
        China Plain (NCP), the GWSA time series is divided into four
        stages by three trend change points in BEAST. An increasing
        trend of GWSA is observed at Stage IV, and the third trend
        change point occurs before the third seasonal change point. This
        distinguishes the positive feedback of anthropogenic
        interventions and the effects of seasonal precipitations for the
        first time. Moreover, the spatial distribution of GWSA in the
        NCP is classified into two clusters by BIRCH in each stage. The
        differences in GWSA trends and responses to environmental
        changes between Cluster-1 and Cluster-2 are significant. Then
        the driving effects of 16 factors on the evolution of GWSA are
        identified using OPGD, in which the contributions of topographic
        and aquifer characteristics are highlighted by quantitative
        analysis. This framework provides a novel method for examining
        the spatiotemporal heterogeneity of GWSA, which can be extended
        to analyze spatiotemporal trends in GWSA at diverse scales.}",
          doi = {10.1029/2023WR036102},
       adsurl = {https://ui.adsabs.harvard.edu/abs/2024WRR....6036102Z},
      adsnote = {Provided by the SAO/NASA Astrophysics Data System}
}

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