Abstract. Mountain hazard risk analysis for transport infrastructure is regularly based on deterministic approaches. Standard risk assessment approaches for roads need a variety of variables and data for risk computation, however without considering potential uncertainty in the input data. Consequently, input data needed for risk assessment are normally processed as discrete mean values without scatter or as an individual deterministic value from expert judgement if no statistical data are available. To overcome this gap, we used a probabilistic approach to analyse the effect of input data uncertainty on the results, taking a mountain road in the Eastern European Alps as a case study. The uncertainty of the input data are expressed with potential bandwidths using two different distribution functions. The risk assessment included risk for persons, property risk and risk for non-operational availability exposed to a multi-hazard environment (torrent processes, snow avalanches and rockfall). The study focuses on the epistemic uncertainty of the risk terms (exposure situations, vulnerability factors and monetary values), ignoring potential sources of variation in the hazard analysis. As a result, reliable quantiles of the calculated probability density distributions attributed to the aggregated road risk due to the impact of multiple mountain hazards were compared to the deterministic outcome from the standard guidelines on road safety. The results based on our case study demonstrate that with common deterministic approaches risk might be underestimated in comparison to a probabilistic risk modelling setup, mainly due to epistemic uncertainties of the input data. The study provides added value to further develop standardized road safety guidelines and may therefore be of particular importance for road authorities and political decision-makers.

Abstract. Mountain hazard risk analysis for transport infrastructure is regularly based on deterministic approaches. Due to a variety of variables and data needed for risk computation, a considerable degree of epistemic uncertainty results. Consequently, input data needed for risk assessment is normally processed as mean values with or without scatter, or as an individual deterministic value from expert judgement if no statistical data is available. To overcome this gap, we used a probabilistic approach to express the potential bandwidth of input data with two different distribution functions, taking a mountain road in the Eastern European Alps as case study. The risk assessment included the damage potential of road infrastructure and traffic exposed to a multi-hazard environment (torrent processes, snow avalanches, rock fall). Reliable quantiles of the calculated probability density distributions attributed to the aggregated road risk due to the impact of multiple-mountain hazards were compared to the deterministic results from the standard guidelines on road safety. The results clearly show that with common deterministic approaches risk is significantly underestimated in comparison to a probabilistic risk modelling setup, mainly due to epistemic uncertainties of the input data. The study provides added value to further develop standardized road safety guidelines and may therefore be of particular importance for road authorities and political decision-makers.