Abstract. Accurate estimation of extreme discharges in rivers, such as the Meuse, is crucial for effective flood risk assessment. However, existing statistical and hydrological models that estimate these discharges often lack transparency regarding the uncertainty of their predictions, as evidenced by the devastating flood event that occurred in July 2021 which was not captured by the existing model for estimating design discharges. This article proposes an alternative approach with a central role for expert judgment, using Cooke’s method. A simple statistical model was developed for the river basin, consisting of correlated GEV-distributions for discharges in upstream sub-catchments. The model was fitted to expert judgments, measurements, and the combination of both, using Markov chain Monte Carlo. Results from the model fitted only to measurements were accurate for more frequent events, but less certain for extreme events. Using expert judgment reduced uncertainty for these extremes but was less accurate for more frequent events. The combined approach provided the most plausible results, with Cooke's method reducing the uncertainty by appointing most weight to two of the seven experts. The study demonstrates that utilizing hydrological experts in this manner can provide plausible results with a relatively limited effort, even in situations where measurements are scarce or unavailable.

No abstract

No abstract

No abstract