@COMMENT This file was generated by bib2html_grace.pl <https://sourceforge.net/projects/bib2html/> version 0.94
@COMMENT written by Patrick Riley <https://sourceforge.net/users/patstg/>
@COMMENT This file was prepared using the NASA Astrophysics Data System (ADS)
@COMMENT https://ui.adsabs.harvard.edu/
@ARTICLE{2025EES....84..384G,
       author = {{Gunes}, Ozge and {Klos}, Anna and {Lenczuk}, Artur and {Aydin}, Cuneyt and {Bogusz}, Janusz},
        title = "{Nonlinearity in the trend of GRACE time series: improving understanding of global total water storage changes over two decades}",
      journal = {Environmental Earth Sciences},
         year = 2025,
        month = jul,
       volume = {84},
       number = {13},
          eid = {384},
        pages = {384},
     abstract = "{The Gravity Recovery and Climate Experiment (GRACE) mission and its
        successor, the GRACE Follow-On mission, have been continuously
        observing changes in Total Water Storage (TWS) since 2002. These
        global, monthly, two-decade changes are conventionally modelled
        using a harmonic regression function, assuming a linear trend
        and seasonal signals; the former is extremely important because
        it indicates the long-term loss or gain of water masses.
        However, current climate change, sudden, unpredictable floods,
        or prolonged droughts, increased human water withdrawals due to
        increased demand in drought-affected areas or excessive
        population growth, make the actual long-term changes occurring
        in the TWS time series significantly different from the linear
        trend. For the first time, we parameterize these deviations
        globally, supplementing the conventional model with a polynomial
        function of the third, fourth and fifth degree; the optimal
        degree is chosen separately for each region. We demonstrate that
        the new parameterized augmented deterministic model of the TWS
        has advantages, as the previously unparameterized nonlinearities
        lead to improvements in the root-mean-square (RMS) values of up
        to 50\%, especially for areas where nonlinearity is most
        pronounced. Furthermore, it allows for the assessment of the
        nature of the residuals of the TWS series, previously considered
        white noise, and leads to more reliable interpretations of
        short-term events such as droughts or floods.}",
          doi = {10.1007/s12665-025-12389-9},
       adsurl = {https://ui.adsabs.harvard.edu/abs/2025EES....84..384G},
      adsnote = {Provided by the SAO/NASA Astrophysics Data System}
}