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@ARTICLE{2026RemS...18.2206M,
       author = {{Ma}, Chongya and {Liu}, Jiping and {Fu}, Guobin},
        title = "{Spatial and Temporal Variability of Terrestrial Water Storage and Their Relationship with Groundwater Level with GRACE, GLDAS and Observations: A Case Study of Murray--Darling Basin}",
      journal = {Remote Sensing},
     keywords = {GLDAS, GRACE, groundwater level, hierarchical cluster analysis (HCA), Murray--Darling Basin (MDB), terrestrial water storage (TWS)},
         year = 2026,
        month = jul,
       volume = {18},
       number = {13},
          eid = {2206},
        pages = {2206},
     abstract = "{What are the main findings? A clear temporal variability of terrestrial
        water storage (TWS) with positive and negative anomalies, as
        well as declining and increasing trends. Strong correlation
        between TWS and rainfall, evaporation and runoff, and some
        similarities and differences between TWS-derived groundwater
        storage changes and in situ groundwater-level observation
        changes. A clear temporal variability of terrestrial water
        storage (TWS) with positive and negative anomalies, as well as
        declining and increasing trends. Strong correlation between TWS
        and rainfall, evaporation and runoff, and some similarities and
        differences between TWS-derived groundwater storage changes and
        in situ groundwater-level observation changes. What are the
        implications of the main findings? The temporal variability
        highlights the caveats and limitations of existing TWS trend
        analysis based on relatively short time periods. The
        correlations can support sustainable groundwater management by
        assessing the impacts of future climate change and variability
        and by identifying regions where groundwater extraction is
        larger than natural recharge to serve water allocation planning.
        The temporal variability highlights the caveats and limitations
        of existing TWS trend analysis based on relatively short time
        periods. The correlations can support sustainable groundwater
        management by assessing the impacts of future climate change and
        variability and by identifying regions where groundwater
        extraction is larger than natural recharge to serve water
        allocation planning. Spatial and temporal patterns of
        terrestrial water storage (TWS), and their relationship with
        groundwater levels, were investigated with the Gravity Recovery
        and Climate Experiment (GRACE) satellite data, the Global Land
        Data Assimilation System (GLDAS) land surface model results, and
        climate observations for the Murray--Darling Basin (MDB). The
        results show that: (1) TWS displays a clear temporal
        variability: a negative TWS anomaly with a declining trend
        during 2002--2009, a positive TWS anomaly with a decreasing trend
        during 2010--2017, and a period of mixed positive and negative
        TWS anomalies being accompanied by an increasing trend from 2018
        to 2025; (2) five dominant cluster patterns were identified that
        explain the spatial variability of temporal TWS across the MDB;
        (3) overall, TWS temporal variability is strongly correlated
        with rainfall, although it is weak at certain locations; (4) TWS
        is also influenced by evaporation (both actual and potential
        evapotranspiration, AET and PET) and runoff, and a combined
        model significantly improves the overall performance in
        explaining TWS temporal variability; and (5) TWS-derived
        groundwater storage changes show both similarities and
        differences in comparison with groundwater level observation
        changes, reflecting complex hydrogeological processes and the
        influence of human activities such as groundwater extraction.
        These findings provide valuable insights to support improved
        groundwater resource management with GRACE satellite information
        and land surface models.}",
          doi = {10.3390/rs18132206},
       adsurl = {https://ui.adsabs.harvard.edu/abs/2026RemS...18.2206M},
      adsnote = {Provided by the SAO/NASA Astrophysics Data System}
}