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The assessment of road infrastructure exposure to extremeweather events is of major importance for scientists and practitionersalike. In this study, we compare the different extreme value approaches andfitting methods with respect to their value for assessing the exposure oftransport networks to extreme precipitation and temperature impacts. Basedon an Austrian data set from 25 meteorological stations representing diversemeteorological conditions, we assess the added value of partial durationseries (PDS) over the standardly used annual maxima series (AMS) in order to giverecommendations for performing extreme value statistics of meteorologicalhazards. Results show the merits of the robust  L-moment estimation, whichyielded better results than maximum likelihood estimation in 62 % of allcases. At the same time, results question the general assumption of thethreshold excess approach (employing PDS) beingsuperior to the block maxima approach (employing AMS)due to information gain. For low return periods (non-extreme events) the PDSapproach tends to overestimate return levels as compared to the AMSapproach, whereas an opposite behavior was found for high return levels(extreme events). In extreme cases, an inappropriate threshold was shown tolead to considerable biases that may outperform the possible gain ofinformation from including additional extreme events by far. This effect wasvisible from neither the square-root criterion nor  standardly usedgraphical diagnosis (mean residual life plot) but rather from a direct comparisonof AMS and PDS in combined quantile plots. We therefore recommend performingAMS and PDS approaches simultaneously in order to select the best-suitedapproach. This will make the analyses more robust, not only in cases where thresholdselection and dependency introduces biases to the PDS approach but also incases where the AMS contains non-extreme events that may introduce similarbiases. For assessing the performance of extreme events we recommend the useof conditional performance measures that focus on rare events only inaddition to standardly used unconditional indicators. The findings of thestudy directly address road and traffic management but can be transferredto a range of other environmental variables including meteorological andhydrological quantities

Extreme weather exposure identification for road networks – a comparative assessment of statistical methods