A staggered approach to flash flood forecasting a case study in the Cevennes region

Alfieri, L. and Smith, P. J. and Thielen-del Pozo, J. and Beven, K. J. (2011) A staggered approach to flash flood forecasting a case study in the Cevennes region. Advances in Geosciences, 29. pp. 13-20. ISSN 1680-7359

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Abstract

A staggered approach to flash flood forecasting is developed within the IMPRINTS project (FP7-ENV-2008-1-226555). Instead of a single solution system, a chain of different models and input data is being proposed that act in sequence and provide decision makers with information of increasing accuracy in localization and magnitude as the events approach. The first system in the chain is developed by adapting methodologies of the European Flood Alert System (EFAS) to forecast flash floods and has the potential to provide early indication for probability of flash floods at the European scale. The last system in the chain is an adaptation of the data based mechanistic model (DBM) to probabilistic numerical weather predictions (NWP) and observed rainfall, with the capability to forecast river levels up to 12 h ahead. The potential of both systems to provide complementary information is illustrated for a flash flood event occurred on 2 November 2008 in the Cévennes region in France. Results show that the uncertainty in meteorological forecasts largely affects the outcomes. However, at an early stage, uncertain results are still valuable to decision makers, as they raise preparedness towards prompt actions to be taken.

Item Type:
Journal Article
Journal or Publication Title:
Advances in Geosciences
Additional Information:
© Author(s) 2011. This work is distributed under the Creative Commons Attribution 3.0 License.
Uncontrolled Keywords:
/dk/atira/pure/core/keywords/environmentalscience
Subjects:
?? environmental scienceearth and planetary sciences (miscellaneous)space and planetary science ??
ID Code:
56759
Deposited By:
Deposited On:
15 Aug 2012 09:32
Refereed?:
Yes
Published?:
Published
Last Modified:
31 Dec 2023 00:25