18In this paper two methodologies are investigated that contribute to better assessment of risks related to 19 extreme rainfall events. Firstly, we use one-parameter bivariate copulas to analyze rain gauge data in the 20 Netherlands. Out of three models considered, the Gumbel copula, which indicates upper tail dependence, 21 represents the data most accurately for all 33 stations in the Netherlands. We notice seasonal variability, with 22 rank correlation reaching maximum in winter and minimum in summer as well as other temporal and spatial 23 patterns. Secondly, an expert judgment elicitation was undertaken. The experts' opinions were combined using 24Cooke's classical method in order to obtain estimates of future changes in precipitation patterns. Experts 25 predicted mostly around 10% increase in rain amount, duration, intensity and the dependence between amount 26 and duration. The results were in line with official national climate change scenarios, based on numerical 27 modelling. Applicability of both methods was presented based on an example of an existing tunnel in the 28 Netherlands, contributing to better estimates of the tunnel's limit state function and therefore the probability of 29 failure. 30

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Science-based models often involve substantial uncertainty that must be quantified in a defendable way. Shortage of empirical data inevitably requires input from expert judgment. How this uncertainty is best elicited can be critical to a decision process, as differences in efficacy and robustness of the elicitation methods can be substantial. When performed rigorously, expert elicitation and pooling of experts' opinions can be powerful means for obtaining rational estimates of uncertainty.Causes of uncertainty may be interrelated and may introduce dependencies. Ignoring these dependencies may lead to large errors. Dependence modelling is an active research topic, and methods for dependence elicitation are still very much under development. Dependence measures such as rank correlations are commonly used in different types of models. Eliciting rank correlations and conditional rank correlations from experts have been proposed and used in the past. Conditional rank correlations are not elicited directly from experts, rather the experts are asked to estimate some other related quantities. In this paper two methods for eliciting conditional rank correlations via related quantities are compared in order to obtain insight about which of the two renders more accurate estimates of conditional rank correlations. Our data shows that good performance in uncertainty assessments does not automatically translates into good performance in dependence estimates. We show that, analogously to uncertainty estimates, combining experts' estimates of dependence according to their performance results in better estimates of the dependence structure.