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@ARTICLE{2026ITGRS..64S0800Z,
       author = {{Zhong}, Yulong and {Wang}, Yingying and {Kusche}, J{\"u}rgen and {Tian}, Baoming and {Wu}, Yunlong},
        title = "{A Novel Metric for Quantifying Precipitation-Driven Variability in Global Terrestrial Water Storage}",
      journal = {IEEE Transactions on Geoscience and Remote Sensing},
     keywords = {Gravity Recovery and Climate Experiment (GRACE), nonstationarity, precipitation variability, standard deviation (STD), terrestrial water storage},
         year = 2026,
        month = jan,
       volume = {64},
          eid = {TGRS.2026},
        pages = {TGRS.2026},
     abstract = "{Under global climate change and human intervention, an increased
        frequency of alternating drought and flood events exists, making
        such extreme conditions seemingly the new normal. These frequent
        events result in greater variability in terrestrial water
        storage anomalies (TWSA) (i.e., nonstationary behavior). In this
        study, we introduce a novel and straightforward metric, i.e.,
        the standard deviation of TWSA (STD), to detect regions with
        nonstationary in TWSA and gain insight into the impact of global
        change on regional TWSA. TWSA estimates from the Center for
        Space Research (CSR) Mascon are used to calculate STDTWSA and
        estimated in a rolling window approach, and the results are
        compared with STD in precipitation anomalies (PAs) and
        temperature anomalies (TAs). Our results reveal that 63.8\% of
        global land areas show significant prevalence of increasing TWSA
        variability with nonstationary, while only 3.5\% exhibit a
        decrease. Among the 40 large basins analyzed, 11 show
        significant upward trends in STDTWSA, with only one basin
        showing a significant decline from 2002 to 2022. In 34 of these
        basins, precipitation accounts for over 50\% of TWSA
        variability, suggesting that variabilities in PAs can be used to
        model and explain the nonstationary behavior of TWSA in these
        regions. In addition, in three of the remaining six basins, we
        observed significant correlations between TWSA and PA
        variability at a five-year window. Furthermore, WaterGAP v2.2e
        hydrological model (WGHM)-modeled STDTWSA estimates align with
        the STDTWSA from CSR and show consistent trends in most basins.
        By linking precipitation-driven dynamics to TWSA variability,
        this study demonstrates the utility of STDTWSA as a practical
        tool for identifying regions vulnerable to hydrological
        extremes. This study provides valuable insights into global TWSA
        nonstationarity and enhances our understanding of the impacts of
        global climate change and human intervention on water storage
        changes.}",
          doi = {10.1109/TGRS.2026.3650800},
       adsurl = {https://ui.adsabs.harvard.edu/abs/2026ITGRS..64S0800Z},
      adsnote = {Provided by the SAO/NASA Astrophysics Data System}
}