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| Titel |
Assessing uncertainties in flood forecasts for decision making: prototype of an operational flood management system integrating ensemble predictions |
| VerfasserIn |
J. Dietrich, A. H. Schumann, M. Redetzky, J. Walther, M. Denhard, Y. Wang, B. Pfützner, U. Büttner |
| Medientyp |
Artikel
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| Sprache |
Englisch
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| ISSN |
1561-8633
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| Digitales Dokument |
URL |
| Erschienen |
In: Natural Hazards and Earth System Science ; 9, no. 4 ; Nr. 9, no. 4 (2009-08-31), S.1529-1540 |
| Datensatznummer |
250006912
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| Publikation (Nr.) |
 copernicus.org/nhess-9-1529-2009.pdf |
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| Zusammenfassung |
| Ensemble forecasts aim at framing the uncertainties of the potential future
development of the hydro-meteorological situation. A probabilistic
evaluation can be used to communicate forecast uncertainty to decision
makers. Here an operational system for ensemble based flood forecasting is
presented, which combines forecasts from the European COSMO-LEPS, SRNWP-PEPS
and COSMO-DE prediction systems. A multi-model lagged average super-ensemble
is generated by recombining members from different runs of these
meteorological forecast systems. A subset of the super-ensemble is selected
based on a priori model weights, which are obtained from ensemble
calibration. Flood forecasts are simulated by the conceptual
rainfall-runoff-model ArcEGMO. Parameter uncertainty of the model is
represented by a parameter ensemble, which is a priori generated from a
comprehensive uncertainty analysis during model calibration. The use of a
computationally efficient hydrological model within a flood management
system allows us to compute the hydro-meteorological model chain for all
members of the sub-ensemble. The model chain is not re-computed before new
ensemble forecasts are available, but the probabilistic assessment of the
output is updated when new information from deterministic short range
forecasts or from assimilation of measured data becomes available. For
hydraulic modelling, with the desired result of a probabilistic inundation
map with high spatial resolution, a replacement model can help to overcome
computational limitations. A prototype of the developed framework has been
applied for a case study in the Mulde river basin. However these techniques,
in particular the probabilistic assessment and the derivation of decision
rules are still in their infancy. Further research is necessary and
promising. |
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