Journal cover Journal topic
Nonlinear Processes in Geophysics An interactive open-access journal of the European Geosciences Union
Journal topic

Journal metrics

Journal metrics

  • IF value: 1.699 IF 1.699
  • IF 5-year value: 1.559 IF 5-year
    1.559
  • CiteScore value: 1.61 CiteScore
    1.61
  • SNIP value: 0.884 SNIP 0.884
  • IPP value: 1.49 IPP 1.49
  • SJR value: 0.648 SJR 0.648
  • Scimago H <br class='hide-on-tablet hide-on-mobile'>index value: 52 Scimago H
    index 52
  • h5-index value: 21 h5-index 21
Volume 15, issue 2 | Copyright

Special issue: Predictability in Earth Sciences

Nonlin. Processes Geophys., 15, 275-286, 2008
https://doi.org/10.5194/npg-15-275-2008
© Author(s) 2008. This work is licensed under
the Creative Commons Attribution-NonCommercial-ShareAlike 2.5 License.

  19 Mar 2008

19 Mar 2008

Combination of different types of ensembles for the adaptive simulation of probabilistic flood forecasts: hindcasts for the Mulde 2002 extreme event

J. Dietrich1, S. Trepte2, Y. Wang1, A. H. Schumann1, F. Voß1,*, F. B. Hesser1,**, and M. Denhard2 J. Dietrich et al.
  • 1Institute of Hydrology, Water Resources Management and Environmental Engineering, Ruhr University Bochum, Bochum, Germany
  • 2Deutscher Wetterdienst DWD (German National Weather Service), Offenbach, Germany
  • *now at: Center for Environmental Systems Research, University of Kassel, Kassel, Germany
  • **now at: Bundesanstalt für Wasserbau (Federal Waterways Engineering and Research Institute), Hamburg, Germany

Abstract. Flood forecasts are essential to issue reliable flood warnings and to initiate flood control measures on time. The accuracy and the lead time of the predictions for head waters primarily depend on the meteorological forecasts. Ensemble forecasts are a means of framing the uncertainty of the potential future development of the hydro-meteorological situation.

This contribution presents a flood management strategy based on probabilistic hydrological forecasts driven by operational meteorological ensemble prediction systems. The meteorological ensemble forecasts are transformed into discharge ensemble forecasts by a rainfall-runoff model. Exceedance probabilities for critical discharge values and probabilistic maps of inundation areas can be computed and presented to decision makers. These results can support decision makers in issuing flood alerts. The flood management system integrates ensemble forecasts with different spatial resolution and different lead times. The hydrological models are controlled in an adaptive way, mainly depending on the lead time of the forecast, the expected magnitude of the flood event and the availability of measured data.

The aforementioned flood forecast techniques have been applied to a case study. The Mulde River Basin (South-Eastern Germany, Czech Republic) has often been affected by severe flood events including local flash floods. Hindcasts for the large scale extreme flood in August 2002 have been computed using meteorological predictions from both the COSMO-LEPS ensemble prediction system and the deterministic COSMO-DE local model. The temporal evolution of a) the meteorological forecast uncertainty and b) the probability of exceeding flood alert levels is discussed. Results from the hindcast simulations demonstrate, that the systems would have predicted a high probability of an extreme flood event, if they would already have been operational in 2002. COSMO-LEPS showed a reasonably good performance within a lead time of 2 to 3 days. Some of the deterministic very short-range forecast initializations were able to predict the dynamics of the event, but others underpredicted rainfall. Thus a lagged average ensemble approach is suggested. The findings from the case study support the often proposed added value of ensemble forecasts and their probabilistic evaluation for flood management decisions.

Publications Copernicus
Special issue
Download
Citation
Share