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Volume 8, issue 2 | Copyright

Special issue: Propagation of uncertainty in advanced meteo-hydrological...

Nat. Hazards Earth Syst. Sci., 8, 281-291, 2008
https://doi.org/10.5194/nhess-8-281-2008
© Author(s) 2008. This work is licensed under
the Creative Commons Attribution-NonCommercial-ShareAlike 2.5 License.

  02 Apr 2008

02 Apr 2008

A probabilistic view on the August 2005 floods in the upper Rhine catchment

S. Jaun1, B. Ahrens2, A. Walser3, T. Ewen1, and C. Schär1 S. Jaun et al.
  • 1Institute for Atmospheric and Climate Science, ETH Zurich, Switzerland
  • 2Institute for Atmosphere and Environment, Goethe-University Frankfurt a.M., Germany
  • 3MeteoSwiss, Zurich, Switzerland

Abstract. Appropriate precautions in the case of flood occurrence often require long lead times (several days) in hydrological forecasting. This in turn implies large uncertainties that are mainly inherited from the meteorological precipitation forecast. Here we present a case study of the extreme flood event of August 2005 in the Swiss part of the Rhine catchment (total area 34 550 km2). This event caused tremendous damage and was associated with precipitation amounts and flood peaks with return periods beyond 10 to 100 years. To deal with the underlying intrinsic predictability limitations, a probabilistic forecasting system is tested, which is based on a hydrological-meteorological ensemble prediction system. The meteorological component of the system is the operational limited-area COSMO-LEPS that downscales the ECMWF ensemble prediction system to a horizontal resolution of 10 km, while the hydrological component is based on the semi-distributed hydrological model PREVAH with a spatial resolution of 500 m. We document the setup of the coupled system and assess its performance for the flood event under consideration.

We show that the probabilistic meteorological-hydrological ensemble prediction chain is quite effective and provides additional guidance for extreme event forecasting, in comparison to a purely deterministic forecasting system. For the case studied, it is also shown that most of the benefits of the probabilistic approach may be realized with a comparatively small ensemble size of 10 members.

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