Process-based design flood estimation in ungauged basins by conditioning model parameters on regional hydrological signatures

The use of rainfall–runoff models constitutes an alternative to statistical approaches (such as at-site or regional flood frequency analysis) for design flood estimation and represents an answer to the increasing need for synthetic design hydrographs associated with a specific return period. Nevertheless, the lack of streamflow observations and the consequent high uncertainty associated with parameters estimation usually pose serious limitations to the use of process-based approaches in ungauged catchments, which in contrast represent the majority in practical applications. This work presents a Bayesian procedure that, for a predefined rainfall–runoff model, allows for the assessment of posterior parameters distribution, using limited and uncertain information available about the response of ungauged catchments, i.e. the regionalized first three L-moments of annual streamflow maxima. The methodology is tested for a catchment located in southern Italy and used within a Monte Carlo scheme to obtain design flood values and simulation uncertainty bands through both event-based and continuous simulation approaches. The obtained results highlight the relevant reduction in uncertainty bands associated with simulated peak discharges compared to those obtained considering a prior uniform distribution for model parameters. A direct impact of uncertainty in regional estimates of hydrological signatures on posterior parameters distribution is also evident. For the selected case study, continuous simulation, generally, better matches the estimates of the statistical flood frequency analysis.