Thu Apr 10, F. Flechtner
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Khaki, M., Hamilton, F., Forootan, E., Hoteit, I., Awange, J., and Kuhn, M., 2018. Nonparametric Data Assimilation Scheme for Land Hydrological Applications. Water Resources Research, 54(7):4946–4964, doi:10.1029/2018WR022854.
• from the NASA Astrophysics Data System • by the DOI System •
@ARTICLE{2018WRR....54.4946K,
author = {{Khaki}, M. and {Hamilton}, F. and {Forootan}, E. and {Hoteit}, I. and {Awange}, J. and {Kuhn}, M.},
title = "{Nonparametric Data Assimilation Scheme for Land Hydrological Applications}",
journal = {Water Resources Research},
keywords = {nonparametric filtering, data assimilation, Kalman-Takens, adaptive unscented Kalman filtering (AUKF), hydrological modeling},
year = 2018,
month = jul,
volume = {54},
number = {7},
pages = {4946-4964},
abstract = "{Data assimilation, which relies on explicit knowledge of dynamical
models, is a well-known approach that addresses models'
limitations due to various reasons, such as errors in input and
forcing data sets. This approach, however, requires intensive
computational efforts, especially for high-dimensional systems
such as distributed hydrological models. Alternatively, data-
driven methods offer comparable solutions when the physics
underlying the models are unknown. For the first time in a
hydrological context, a nonparametric framework is implemented
here to improve model estimates using available observations.
This method uses Takens delay coordinate method to reconstruct
the dynamics of the system within a Kalman filtering framework,
called the Kalman-Takens filter. A synthetic experiment is
undertaken to fully investigate the capability of the proposed
method by comparing its performance with that of a standard
assimilation framework based on an adaptive unscented Kalman
filter (AUKF). Furthermore, using terrestrial water storage
(TWS) estimates obtained from the Gravity Recovery And Climate
Experiment mission, both filters are applied to a real case
scenario to update different water storages over Australia. In
situ groundwater and soil moisture measurements within Australia
are used to further evaluate the results. The Kalman-Takens
filter successfully improves the estimated water storages at
levels comparable to the AUKF results, with an average root-
mean-square error reduction of 37.30\% for groundwater and
12.11\% for soil moisture estimates. Additionally, the Kalman-
Takens filter, while reducing estimation complexities, requires
a fraction of the computational time, that is,
{\ensuremath{\sim}}8 times faster compared to the AUKF approach.}",
doi = {10.1029/2018WR022854},
adsurl = {https://ui.adsabs.harvard.edu/abs/2018WRR....54.4946K},
adsnote = {Provided by the SAO/NASA Astrophysics Data System}
}
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