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| Titel |
Cascading model uncertainty from medium range weather forecasts (10 days) through a rainfall-runoff model to flood inundation predictions within the European Flood Forecasting System (EFFS) |
| VerfasserIn |
F. Pappenberger, K. J. Beven  , N. M. Hunter, P. D. Bates, B. T. Gouweleeuw, J. Thielen, A. P. J. Roo |
| 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.381-393 |
| Datensatznummer |
250006970
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| Publikation (Nr.) |
 copernicus.org/hess-9-381-2005.pdf |
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| Zusammenfassung |
| The political pressure on the scientific community to provide medium to long
term flood forecasts has increased in the light of recent flooding events in
Europe. Such demands can be met by a system consisting of three different
model components (weather forecast, rainfall-runoff forecast and flood
inundation forecast) which are all liable to considerable uncertainty in the
input, output and model parameters. Thus, an understanding of cascaded
uncertainties is a necessary requirement to provide robust predictions. In
this paper, 10-day ahead rainfall forecasts, consisting of one deterministic,
one control and 50 ensemble forecasts, are fed into a rainfall-runoff model
(LisFlood) for which parameter uncertainty is represented by six different
parameter sets identified through a Generalised Likelihood Uncertainty
Estimation (GLUE) analysis and functional hydrograph classification. The
runoff of these 52 * 6 realisations form the input to a flood inundation
model (LisFlood-FP) which acknowledges uncertainty by utilising ten different
sets of roughness coefficients identified using the same GLUE methodology.
Likelihood measures for each parameter set computed on historical data are
used to give uncertain predictions of flow hydrographs as well as spatial
inundation extent. This analysis demonstrates that a full uncertainty
analysis of such an integrated system is limited mainly by computer power as
well as by how well the rainfall predictions represent potential future
conditions. However, these restrictions may be overcome or lessened in the
future and this paper establishes a computationally feasible methodological
approach to the uncertainty cascade problem. |
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