Assessing uncertainties in flood forecasts for decision making: prototype of an operational flood management system integrating ensemble predictions
- 1Inst. of Hydrology, Water Resources Management and Environmental Engineering, Ruhr Univ. Bochum, Bochum, Germany
- 2DHI-WASY GmbH, Dresden, Germany
- 3Deutscher Wetterdienst DWD (German National Weather Service), Offenbach, Germany
- 4Büro für Angewandte Hydrologie, Berlin, Germany
- 5Sächsisches Landesamt für Umwelt, Landwirtschaft und Geologie, Dresden, Germany
- *now at: Institute of Water Resources Management, Hydrology and Agricultural Hydraulic Engineering, Leibniz University, Hanover, Germany
Abstract. 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.