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| Titel |
Assimilating SAR-derived water level data into a hydraulic model: a case study |
| VerfasserIn |
L. Giustarini, P. Matgen, R. Hostache, M. Montanari, D. Plaza, V. R. N. Pauwels, G. J. M. Lannoy, R. Keyser, L. Pfister, L. Hoffmann, H. H. G. Savenije |
| 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. 7 ; Nr. 15, no. 7 (2011-07-21), S.2349-2365 |
| Datensatznummer |
250012898
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| Publikation (Nr.) |
 copernicus.org/hess-15-2349-2011.pdf |
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| Zusammenfassung |
| Satellite-based active microwave sensors not only provide synoptic overviews
of flooded areas, but also offer an effective way to estimate spatially
distributed river water levels. If rapidly produced and processed, these
data can be used for updating hydraulic models in near real-time. The
usefulness of such approaches with real event data sets provided by
currently existing sensors has yet to be demonstrated. In this case study, a
Particle Filter-based assimilation scheme is used to integrate ERS-2 SAR and
ENVISAT ASAR-derived water level data into a one-dimensional (1-D) hydraulic
model of the Alzette River. Two variants of the Particle Filter assimilation
scheme are proposed with a global and local particle weighting procedure.
The first option finds the best water stage line across all cross sections,
while the second option finds the best solution at individual cross
sections. The variant that is to be preferred depends on the level of
confidence that is attributed to the observations or to the model. The
results show that the Particle Filter-based assimilation of remote
sensing-derived water elevation data provides a significant reduction in the
uncertainty at the analysis step. Moreover, it is shown that the periodical
updating of hydraulic models through the proposed assimilation scheme leads
to an improvement of model predictions over several time steps. However, the
performance of the assimilation depends on the skill of the hydraulic model
and the quality of the observation data. |
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