A Bayesian approach to decision-making under uncertainty: An application to real-time forecasting in the river Rhine

Enhanced ability to forecast peak discharges remains the most relevant non-structural measure for flood protection. Extended forecasting lead times are desirable as they facilitate mitigating action and response in case of extreme discharges. Forecasts remain however affected by uncertainty as an exact prognosis of water levels is inherently impossible. Here, we implement a dedicated uncertainty processor, that can be used within operational flood forecasting systems. The processor is designed to support decision-making under conditions of uncertainty. The scientific approach at the basis of the uncertainty processor is general and independent of the deterministic models used. It is based on Bayesian revision of prior knowledge on the basis of past evidence on model performance against observations. The revision of the prior distributions on water levels and/or flow rates leads to posterior probability distributions that are translated into an effective decision support under uncertainty. The processor is validated on the operational real-time river Rhine flood forecasting system.