Mon Oct 13, F. Flechtner
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Li, Fupeng, Springer, Anne, Kusche, Jürgen, Gutknecht, Benjamin D., and Ewerdwalbesloh, Yorck, 2025. Reanalysis and Forecasting of Total Water Storage and Hydrological States by Combining Machine Learning With CLM Model Simulations and GRACE Data Assimilation. Water Resources Research, 61(2):2024WR037926, doi:10.1029/2024WR037926.
• from the NASA Astrophysics Data System • by the DOI System •
@ARTICLE{2025WRR....6137926L,
author = {{Li}, Fupeng and {Springer}, Anne and {Kusche}, J{\"u}rgen and {Gutknecht}, Benjamin D. and {Ewerdwalbesloh}, Yorck},
title = "{Reanalysis and Forecasting of Total Water Storage and Hydrological States by Combining Machine Learning With CLM Model Simulations and GRACE Data Assimilation}",
journal = {Water Resources Research},
year = 2025,
month = feb,
volume = {61},
number = {2},
pages = {2024WR037926},
abstract = "{Hydrological Models face limitations in simulating the water cycle due
to deficiencies in process representation and such problems also
weaken their forecasting skills. Here, we use Machine Learning
(ML) to forecast the Gravity Recovery and Climate Experiment
(GRACE) derived total water storage anomaly (TWSA) up to 1 year
ahead over Europe with near real-time meteorological
observations as predictors. Subsequently, we assimilate the
forecasted and GRACE TWSA into the Community Land Model (CLM) to
enhance its performance in both reanalysis and forecast. As
found in five hindcast experiments, ML forecasted TWSA for the
following year fits quite well to the actual GRACE observations
over Europe, with an average correlation of 0.91, 0.92, and 0.94
in the Iberian peninsula, Danube, and Volga basins. Validation
by observations and reanalysis data suggests that assimilating
forecasted TWSA can improve CLM's capacity to forecast not only
hydrological states but also hydrological droughts.
Additionally, ML forecasted TWSA is a viable alternative to
GRACE data in terms of enhancing hydrological forecasting on
seasonal to annual scales through Data assimilation (DA). We
also highlight the contribution of GRACE DA for generating a CLM
based TWSA reanalysis that overcomes deficiencies of purely
model-based TWSA. This study suggests that seasonal drought or
water resource forecasting services might not only consider to
integrate GRACE TWSA but would also benefit from constraining
models with ML-forecasted TWSA. At shorter timescales, such
forecasts could also be useful in the quick-look analysis of
near real-time TWSA processing as is suggested for upcoming
satellite gravity missions.}",
doi = {10.1029/2024WR037926},
adsurl = {https://ui.adsabs.harvard.edu/abs/2025WRR....6137926L},
adsnote = {Provided by the SAO/NASA Astrophysics Data System}
}
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Mon Oct 13, F. Flechtner