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| Titel |
Applying sequential Monte Carlo methods into a distributed hydrologic model: lagged particle filtering approach with regularization |
| VerfasserIn |
S. J. Noh, Y. Tachikawa, M. Shiiba, S. Kim |
| Medientyp |
Artikel
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| Sprache |
Englisch
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| ISSN |
1027-5606
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| Digitales Dokument |
URL |
| Erschienen |
In: Hydrology and Earth System Sciences ; 15, no. 10 ; Nr. 15, no. 10 (2011-10-25), S.3237-3251 |
| Datensatznummer |
250012999
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| Publikation (Nr.) |
 copernicus.org/hess-15-3237-2011.pdf |
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| Zusammenfassung |
| Data assimilation techniques have received growing attention due to their
capability to improve prediction. Among various data assimilation
techniques, sequential Monte Carlo (SMC) methods, known as "particle
filters", are a Bayesian learning process that has the capability to handle
non-linear and non-Gaussian state-space models. In this paper, we propose an
improved particle filtering approach to consider different response times of
internal state variables in a hydrologic model. The proposed method adopts a
lagged filtering approach to aggregate model response until the uncertainty
of each hydrologic process is propagated. The regularization with an
additional move step based on the Markov chain Monte Carlo (MCMC) methods is
also implemented to preserve sample diversity under the lagged filtering
approach. A distributed hydrologic model, water and energy transfer
processes (WEP), is implemented for the sequential data assimilation through
the updating of state variables. The lagged regularized particle filter
(LRPF) and the sequential importance resampling (SIR) particle filter are
implemented for hindcasting of streamflow at the Katsura catchment, Japan.
Control state variables for filtering are soil moisture content and overland
flow. Streamflow measurements are used for data assimilation. LRPF shows
consistent forecasts regardless of the process noise assumption, while SIR
has different values of optimal process noise and shows sensitive variation
of confidential intervals, depending on the process noise. Improvement of
LRPF forecasts compared to SIR is particularly found for rapidly varied high
flows due to preservation of sample diversity from the kernel, even if
particle impoverishment takes place. |
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