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| Titel |
Towards the sequential assimilation of SAR-derived water stages into hydraulic models using the Particle Filter: proof of concept |
| VerfasserIn |
P. Matgen, M. Montanari, R. Hostache, L. Pfister, L. Hoffmann, D. Plaza, V. R. N. Pauwels, G. J. M. Lannoy, R. Keyser, 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 ; 14, no. 9 ; Nr. 14, no. 9 (2010-09-27), S.1773-1785 |
| Datensatznummer |
250012424
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| Publikation (Nr.) |
 copernicus.org/hess-14-1773-2010.pdf |
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| Zusammenfassung |
| With the onset of new satellite radar constellations (e.g. Sentinel-1) and
advances in computational science (e.g. grid computing) enabling the supply
and processing of multi-mission satellite data at a temporal frequency that
is compatible with real-time flood forecasting requirements, this study
presents a new concept for the sequential assimilation of Synthetic Aperture
Radar (SAR)-derived water stages into coupled hydrologic-hydraulic models.
The proposed methodology consists of adjusting storages and fluxes simulated
by a coupled hydrologic-hydraulic model using a Particle Filter-based data
assimilation scheme. Synthetic observations of water levels, representing
satellite measurements, are assimilated into the coupled model in order to
investigate the performance of the proposed assimilation scheme as a function
of both accuracy and frequency of water level observations. The use of the
Particle Filter provides flexibility regarding the form of the probability
densities of both model simulations and remote sensing observations. We
illustrate the potential of the proposed methodology using a twin experiment
over a widely studied river reach located in the Grand-Duchy of Luxembourg.
The study demonstrates that the Particle Filter algorithm leads to
significant uncertainty reduction of water level and discharge at the time
step of assimilation. However, updating the storages of the model only
improves the model forecast over a very short time horizon. A more effective
way of updating thus consists in adjusting both states and inputs. The
proposed methodology, which consists in updating the biased forcing of the
hydraulic model using information on model errors that is inferred from
satellite observations, enables persistent model improvement. The present
schedule of satellite radar missions is such that it is likely that there
will be continuity for SAR-based operational water management services. This
research contributes to evolve reactive flood management into systematic or
quasi-systematic SAR-based flood monitoring services. |
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