Thu Aug 14, F. Flechtner
• Sorted by Date • Sorted by Last Name of First Author •
Gunes, Ozge, Klos, Anna, Lenczuk, Artur, Aydin, Cuneyt, and Bogusz, Janusz, 2025. Nonlinearity in the trend of GRACE time series: improving understanding of global total water storage changes over two decades. Environmental Earth Sciences, 84(13):384, doi:10.1007/s12665-025-12389-9.
• from the NASA Astrophysics Data System • by the DOI System •
@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}
}
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