To quantify the uncertainty in the flood forecasts a non-parametric data-based approach was developed. This approach takes into account the heteroscedastic behaviour of the forecast errors and can be applied on both water level and discharge predictions. The calculation time of the method is very limited, which is crucial for its application in an operational context.

The developed approach to quantify the uncertainties can also be used to identify the different sources of uncertainty and their contribution to the total flood forecast uncertainty. After simulating historical forecasts and eliminating individual sources of uncertainty (e.g. by making use of observations instead of forecasts), the total hydrological forecast uncertainty could be split in its sources by applying variance decomposition. Based on this improved insight in the uncertainty composition, targeted improvement actions could be defined, which aim to reduce the flood forecast uncertainty in the most efficient way.

The first improvement action studied was the hydrological model calibration. An advanced calibration methodology was applied that takes into account the performance of the hydrological model to make extrapolations beyond the range of historical conditions considered during model calibration. A novel method has been developed that studies the relationship between changes in peak flow versus changes in rainfall intensity. This relationship can be derived from the historical measurement series for different percentiles of flow and rainfall changes, and compared with the ones derived from the model simulation results. It is shown that by testing these relationships during the calibration of the hydrological model, more reliable model results are obtained for extremes.