Thu Dec 12, F.Flechtner
• Sorted by Date • Sorted by Last Name of First Author •
Qian, Nijia, Gao, Jingxiang, Li, Zengke, Yan, Zhaojin, Feng, Yong, Yan, Zhengwen, and Yang, Liu, 2024. Bridging the Terrestrial Water Storage Anomalies between the GRACE/GRACE-FO Gap Using BEAST + GMDH Algorithm. Remote Sensing, 16(19):3693, doi:10.3390/rs16193693.
• from the NASA Astrophysics Data System • by the DOI System •
@ARTICLE{2024RemS...16.3693Q,
author = {{Qian}, Nijia and {Gao}, Jingxiang and {Li}, Zengke and {Yan}, Zhaojin and {Feng}, Yong and {Yan}, Zhengwen and {Yang}, Liu},
title = "{Bridging the Terrestrial Water Storage Anomalies between the GRACE/GRACE-FO Gap Using BEAST + GMDH Algorithm}",
journal = {Remote Sensing},
keywords = {GRACE, GRACE-FO, gap filling, piecewise detrending, data-driven, terrestrial water storage anomalies (TWSAs)},
year = 2024,
month = oct,
volume = {16},
number = {19},
eid = {3693},
pages = {3693},
abstract = "{Regarding the terrestrial water storage anomaly (TWSA) gap between the
Gravity Recovery and Climate Experiment (GRACE) and GRACE
Follow-on (-FO) gravity satellite missions, a BEAST (Bayesian
estimator of abrupt change, seasonal change and trend)+GMDH
(group method of data handling) gap-filling scheme driven by
hydrological and meteorological data is proposed. Considering
these driving data usually cannot fully capture the trend
changes of the TWSA time series, we propose first to use the
BEAST algorithm to perform piecewise linear detrending for the
TWSA series and then fill the gap of the detrended series using
the GMDH algorithm. The complete gap-filling TWSAs can be
readily obtained after adding back the previously removed
piecewise trend. By comparing the simulated gap filled by BEAST
+ GMDH using Multiple Linear Regression and Singular Spectrum
Analysis with reference values, the results show that the BEAST
+ GMDH scheme is superior to the latter two in terms of the
correlation coefficient, Nash-efficiency coefficient, and root-
mean-square error. The real GRACE/GFO gap filled by BEAST + GMDH
is consistent with those from hydrological models, Swarm TWSAs,
and other literature regarding spatial distribution patterns.
The correlation coefficients there between are, respectively,
above 0.90, 0.80, and 0.90 in most of the global river basins.}",
doi = {10.3390/rs16193693},
adsurl = {https://ui.adsabs.harvard.edu/abs/2024RemS...16.3693Q},
adsnote = {Provided by the SAO/NASA Astrophysics Data System}
}
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