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| Titel |
The importance of parameter resampling for soil moisture data assimilation into hydrologic models using the particle filter |
| VerfasserIn |
D. A. Plaza, R. Keyser, G. J. M. Lannoy, L. Giustarini, P. Matgen, V. R. N. Pauwels |
| 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 ; 16, no. 2 ; Nr. 16, no. 2 (2012-02-08), S.375-390 |
| Datensatznummer |
250013170
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| Publikation (Nr.) |
 copernicus.org/hess-16-375-2012.pdf |
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| Zusammenfassung |
The Sequential Importance Sampling with Resampling (SISR) particle filter and
the SISR with parameter resampling particle filter (SISR-PR) are evaluated
for their performance in soil moisture assimilation and the consequent effect
on baseflow generation. With respect to the resulting soil moisture time
series, both filters perform appropriately. However, the SISR filter has a
negative effect on the baseflow due to inconsistency between the parameter
values and the states after the assimilation. In order to overcome this
inconsistency, parameter resampling is applied along with the SISR filter, to
obtain consistent parameter values with the analyzed soil moisture state.
Extreme parameter replication, which could lead to a particle collapse, is
avoided by the perturbation of the parameters with white noise. Both the
modeled soil moisture and baseflow are improved if the complementary
parameter resampling is applied. The SISR filter with parameter resampling
offers an efficient way to deal with biased observations. The robustness of
the methodology is evaluated for 3 model parameter sets and 3 assimilation
frequencies.
Overall, the results in this paper indicate that the particle filter is a
promising tool for hydrologic modeling purposes, but that an additional
parameter resampling may be necessary to consistently update all state
variables and fluxes within the model. |
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