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Natural Hazards and Earth System Sciences An interactive open-access journal of the European Geosciences Union
© Author(s) 2016. This work is distributed
under the Creative Commons Attribution 3.0 License.
Research article
05 Dec 2016
Review status
A revision of this discussion paper was accepted for the journal Natural Hazards and Earth System Sciences (NHESS) and is expected to appear here in due course.
Extreme weather exposure identification for road networks – a comparative assessment of statistical methods
Matthias Schlögl1 and Gregor Laaha2 1Mobility Department – Transportation Infrastructure Technologies, Austrian Institute of Technology, Vienna, 1210, Austria
2Institute of Applied Statistics and Scientific Computing, University of Natural Resources and Life Sciences, Vienna, 1190, Austria
Abstract. The assessment of road infrastructure exposure to extreme weather events is of major importance for scientists and practitioners alike. In this study, we compare the different extreme value approaches and fitting methods with respect to their value for assessing the exposure of transport networks to extreme precipitation and temperature impacts. Based on an Austrian data set from 25 meteorological stations representing diverse meteorological conditions, we assess the added value of partial duration series over the standardly used annual maxima series in order to give recommendations for performing extreme value statistics of meteorological hazards. Results show the merits of the robust L-moment estimation, which yielded better results than maximum likelihood estimation in 62 % of all cases. At the same time, results question the general assumption of the threshold excess approach (employing partial duration series, PDS) being superior to the block maxima approach (employing annual maxima series, AMS) due to information gain. For low return periods (non-extreme events) the PDS approach tends to overestimate return levels as compared to the AMS approach, whereas an opposite behaviour was found for high return levels (extreme events). In extreme cases, an inappropriate threshold was shown to lead to considerable biases that may outperform the possible gain of information from including additional extreme events by far. This effect was neither visible from the square-root criterion, nor from standardly used graphical diagnosis (mean residual life plot), but from a direct comparison of AMS and PDS in synoptic quantile plots. We therefore recommend performing AMS and PDS approaches simultaneously in order to select the best suited approach. This will make the analyses more robust, in cases where threshold selection and dependency introduces biases to the PDS approach, but also in cases where the AMS contains non-extreme events that may introduce similar biases. For assessing the performance of extreme events we recommend conditional performance measures that focus on rare events only in addition to standardly used unconditional indicators. The findings of the study directly address road and traffic management, but can be transferred to a range of other environmental variables including meteorological and hydrological quantities.

Citation: Schlögl, M. and Laaha, G.: Extreme weather exposure identification for road networks – a comparative assessment of statistical methods, Nat. Hazards Earth Syst. Sci. Discuss., doi:10.5194/nhess-2016-373, in review, 2016.
Matthias Schlögl and Gregor Laaha
Interactive discussionStatus: closed
AC: Author comment | RC: Referee comment | SC: Short comment | EC: Editor comment
Printer-friendly Version - Printer-friendly version      Supplement - Supplement
RC1: 'Comments on "Extreme weather exposure identification for road networks – a comparative assessment of statistical methods"', Anonymous Referee #1, 26 Dec 2016 Printer-friendly Version 
RC2: 'nhess-2016-373', Dan Rosbjerg, 18 Jan 2017 Printer-friendly Version Supplement 
AC1: 'Author response to nhess-2016-373', Matthias Schlögl, 03 Mar 2017 Printer-friendly Version Supplement 
Matthias Schlögl and Gregor Laaha
Matthias Schlögl and Gregor Laaha


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