Probabilistic online flood forecasting for flash flood prone catchments in lower mountain ranges

• Applicant: Professor Dr. Clemens Simmer
• Subject Area: Hydrogeology, Hydrology, Limnology, Urban Water Management, Water Chemistry, Integrated Water Resources Management
• Term: from 2007 to 2010
• Project identifier: Deutsche Forschungsgemeinschaft (DFG) - Project number 36862337

The questionable reliability and brief validity of flood forecasting for fast responding catchments remains one of the most challenging problems in hydrometeorology. We tackle this problem by considering the hydrological and meteorological model uncertainties using rigorous meteorological/hydrological/hydraulic modelling together with computationally highly efficient artificial intelligence techniques. The meteorological part develops a suite of ensemble precipitation forecasts from days to minutes with increasing accuracy by (a) a novel approach to improve operational ensembles from the national weather service for lead times from 18 hours down to 6 hours, (b) extending the validity of best ensemble members using physical initialisation for lead times down to 3 hours and (c) nowcasting based on ensemble forecasts from radar feature tracking. Quantifying the uncertainties from the ensembles is a major task of the meteorological project part. The hydrologic part processes the meteorological forecast uncertainty basically by Monte Carlo rainfall-runoff and flood routing simulations. The inclusion of the hydrologic model uncertainties employs a perturbation approach for setting up a physically based stochastic catchment model. This is subsequently fully portrayed by a quasi-stochastic artificial neural network (ANN-S), which originates from an extensive training on the basis of uncertain soil data together with simulations of all flood relevant rainstorm scenarios using the stochastic catchment model. Combining the resulting hydrologic model uncertainty with the meteorological uncertainty allows coupling the ANN-S to a flood routing ANN (ANN-F), which itself is trained by a hydrodynamic model. The Monte Carlo simulations of the coupled ANN-S and ANN-F thus allow a relatively simple and fast prediction of the probability of exceeding critical water levels also in rivers with backwater effects.

• DFG Programme: Research Grants