Thu Aug 14, F. Flechtner
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Retegui-Schiettekatte, Leire, Schumacher, Maike, Madsen, Henrik, and Forootan, Ehsan, 2025. Assessing daily GRACE Data Assimilation during flood events of the Brahmaputra River Basin. Science of the Total Environment, 975:179181, doi:10.1016/j.scitotenv.2025.179181.
• from the NASA Astrophysics Data System • by the DOI System •
@ARTICLE{2025ScTEn.97579181R,
author = {{Retegui-Schiettekatte}, Leire and {Schumacher}, Maike and {Madsen}, Henrik and {Forootan}, Ehsan},
title = "{Assessing daily GRACE Data Assimilation during flood events of the Brahmaputra River Basin}",
journal = {Science of the Total Environment},
keywords = {Floods, GRACE, Data Assimilation, Covariance localization, Brahmaputra River Basin, Fast evolving storage anomalies},
year = 2025,
month = may,
volume = {975},
eid = {179181},
pages = {179181},
abstract = "{The integration of satellite-based observations into hydrological models
contributes to achieving more precise simulations, thus
supporting hazard mitigation and policy-making especially in
poorly gauged basins. Sub-monthly Terrestrial Water Storage
(TWS) observations derived from the Gravity Recovery and Climate
Experiment (GRACE) mission have been shown to contain useful
information for the prediction and monitoring of sub-monthly
water storage anomalies such as floods. This study assesses, for
the first time, the benefits and challenges of integrating sub-
monthly TWS into a large-scale hydrological model during flood
events. The experiment is carried out for the Brahmaputra River
Basin and the integration is performed through the state-of-the-
art of sequential Data Assimilation (DA) with the aim of
improving model water storage estimates. The results indicate
that the daily TWS DA, based on the Ensemble Kalman Filter
(EnKF), successfully introduces the observed sub-monthly TWS
variability into the model (differences below 10 mm with daily
GRACE TWS). The daily TWS DA spatially and vertically downscales
storage updates with precise timing and distribution.
Especially, it modifies the river storage compartment, where
sub-monthly variations are expected during floods. In contrast,
the updates of monthly TWS DA, implemented through both an EnKF
and an Ensemble Kalman Smoother (EnKS), introduce undesired
peaks in the TWS time series. Choosing an adequate model
covariance localization is found to be crucial for daily TWS DA.
Finally, the statistical characteristics of the daily TWS DA and
the translation of water storage updates into river discharge
are investigated, and recommendations for future developments
are provided.}",
doi = {10.1016/j.scitotenv.2025.179181},
adsurl = {https://ui.adsabs.harvard.edu/abs/2025ScTEn.97579181R},
adsnote = {Provided by the SAO/NASA Astrophysics Data System}
}
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