A Bayesian approach for real-time flood forecasting

The paper describes a methodology based on the Bayesian Forecasting System (BFS), aimed at evaluating total uncertainty in real-time forecasting of flood events. The system was adapted for a small basin in the Calabria region (Southern Italy), assuming a stochastic model as rainfall predictor and a distributed rainfall–runoff model for simulating the hydrological response. Through two separate processors, the system evaluates the input uncertainty and the hydrological uncertainty, associated with all other possible sources of error. To discriminate adequately the effect of more intense rainfall in the hydrological response, the total uncertainty consists of a mixture of two components, conditioned on forecast rainfall, with respect to an assigned threshold, and on discharge observed at the forecast time. The results highlight the role of each BFS component in the real-time forecasting of a flood event.