Thu Aug 14, F. Flechtner
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Yin, Gaohong, Park, Jongmin, and Yoshimura, Kei, 2025. Spatial downscaling of GRACE terrestrial water storage anomalies for drought and flood potential assessment. Journal of Hydrology, 658:133144, doi:10.1016/j.jhydrol.2025.133144.
• from the NASA Astrophysics Data System • by the DOI System •
@ARTICLE{2025JHyd..65833144Y,
author = {{Yin}, Gaohong and {Park}, Jongmin and {Yoshimura}, Kei},
title = "{Spatial downscaling of GRACE terrestrial water storage anomalies for drought and flood potential assessment}",
journal = {Journal of Hydrology},
year = 2025,
month = sep,
volume = {658},
eid = {133144},
pages = {133144},
abstract = "{Terrestrial water storage anomaly (TWSA) from the Gravity Recovery and
Climate Experiment (GRACE) mission provides invaluable
information for quantifying changes in freshwater availability.
However, the coarse spatial resolution of GRACE TWSA limits its
application to sub-regional studies. The study proposed a
systematic framework to spatially downscale GRACE TWSA
retrievals using a long short-term memory (LSTM) model over the
Texas-Gulf Basin. A synthetic experiment was conducted to
demonstrate the robustness of the downscaling framework. The
real-world experiment revealed that the downscaled TWSA from
LSTM can represent the variation of TWSA at the basin
(R$_{LSTM}$ = 0.91) and sub-basin scales. The LSTM-based TWSA
can better represent the early recovery from extreme droughts
for the sub-basins along the coast. Moreover, the LSTM-based
TWSA outperformed model-based TWSA in characterizing groundwater
variation, especially for sub-basins with deep groundwater
levels in the west. The flood analysis showed that the
downscaled TWSA from LSTM yielded improved skill in predicting
county-level floods, providing a larger true positive rate
relative to GRACE TWSA retrievals (TPR$_{LSTM}$ = 0.36 and
TPR$_{GRACE}$ = 0.31). Additionally, the trained LSTM models
were used to predict fine-resolution TWSA without requiring
GRACE observations. Results demonstrated that the accuracy of
LSTM-based TWSA forecasts was slightly inferior to the
downscaling case, but they still provided useful information for
drought and flood predictions at sub-basin to local scales.}",
doi = {10.1016/j.jhydrol.2025.133144},
adsurl = {https://ui.adsabs.harvard.edu/abs/2025JHyd..65833144Y},
adsnote = {Provided by the SAO/NASA Astrophysics Data System}
}
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