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| Titel |
Utility of different data types for calibrating flood inundation models within a GLUE framework |
| VerfasserIn |
N. M. Hunter, P. D. Bates, M. S. Horritt, A. P. J. Roo, M. G. F. Werner |
| 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 ; 9, no. 4 ; Nr. 9, no. 4 (2005-10-07), S.412-430 |
| Datensatznummer |
250006972
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| Publikation (Nr.) |
 copernicus.org/hess-9-412-2005.pdf |
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| Zusammenfassung |
| To translate a point hydrograph forecast into products for use by
environmental agencies and civil protection authorities, a hydraulic model is
necessary. Typical one- and two-dimensional hydraulic models are able to
predict dynamically varying inundation extent, water depth and velocity for
river and floodplain reaches up to 100 km in length. However, because of
uncertainties over appropriate surface friction parameters, calibration of
hydraulic models against observed data is a necessity. The value of different
types of data is explored in constraining the predictions of a simple
two-dimensional hydraulic model, LISFLOOD-FP. For the January 1995 flooding
on the River Meuse, The Netherlands, a flow observation data set has been
assembled for the 35-km reach between Borgharen and Maaseik, consisting of
Synthetic Aperture Radar and air photo images of inundation extent,
downstream stage and discharge hydrographs, two stage hydrographs internal to
the model domain and 84 point observations of maximum free surface elevation.
The data set thus contains examples of all the types of data that potentially
can be used to calibrate flood inundation models. 500 realisations of the
model have been conducted with different friction parameterisations and the
performance of each realisation has been evaluated against each observed data
set. Implementation of the Generalised Likelihood Uncertainty Estimation
(GLUE) methodology is then used to determine the value of each data set in
constraining the model predictions as well as the reduction in parameter
uncertainty resulting from the updating of generalised likelihoods based on
multiple data sources. |
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