Extreme Weather Exposure Identification for Road Networks – A

Nat. Hazards Earth Syst. Sci., 17, 515–531, 2017 www.nat-hazards-earth-syst-sci.net/17/515/2017/ doi:10.5194/nhess-17-515-2017 © Author(s) 2017. CC Attribution 3.0 License. Extreme weather exposure identification for road networks – a comparative assessment of statistical methods Matthias Schlögl1 and Gregor Laaha2 1Transportation Infrastructure Technologies, Center for Mobility Systems, Austrian Institute of Technology (AIT), Vienna, 1210, Austria 2Institute of Applied Statistics and Scientific Computing, Department of Landscape, Spatial and Infrastructure Sciences, University of Natural Resources and Life Sciences (BOKU), Vienna, 1190, Austria Correspondence to: Matthias Schlögl ([email protected]) Received: 18 November 2016 – Discussion started: 5 December 2016 Revised: 7 March 2017 – Accepted: 7 March 2017 – Published: 3 April 2017 Abstract. The assessment of road infrastructure exposure to lect the best-suited approach. This will make the analyses extreme weather events is of major importance for scientists more robust, not only in cases where threshold selection and and practitioners alike. In this study, we compare the differ- dependency introduces biases to the PDS approach but also ent extreme value approaches and fitting methods with re- in cases where the AMS contains non-extreme events that spect to their value for assessing the exposure of transport may introduce similar biases. For assessing the performance networks to extreme precipitation and temperature impacts. of extreme events we recommend the use of conditional per- Based on an Austrian data set from 25 meteorological sta- formance measures that focus on rare events only in addition tions representing diverse meteorological conditions, we as- to standardly used unconditional indicators. The findings of sess the added value of partial duration series (PDS) over the the study directly address road and traffic management but standardly used annual maxima series (AMS) in order to give can be transferred to a range of other environmental variables recommendations for performing extreme value statistics of including meteorological and hydrological quantities. 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 1 Introduction time, results question the general assumption of the threshold excess approach (employing PDS) being superior to the Reliable information about the exposure of road infrastruc- block maxima approach (employing AMS) due to informa- ture networks to extreme weather events is of major concern tion gain. For low return periods (non-extreme events) the for road authorities, governmental institutions and safety re- PDS approach tends to overestimate return levels as com- searchers all over the world (TRB, 2008; Koetse and Ri- pared to the AMS approach, whereas an opposite behavior etveld, 2009; Eisenack et al., 2011; Doll et al., 2013; UN- was found for high return levels (extreme events). In extreme ECE, 2013; Meyer et al., 2014; Michaelides, 2014; Schweik- cases, an inappropriate threshold was shown to lead to con- ert et al., 2014a, b; Matulla et al., 2017). In a changing cli- siderable biases that may outperform the possible gain of in- mate (IPCC, 2012) and due to extensive soil sealing (Ne- formation from including additional extreme events by far. stroy, 2006) the impacts of extreme weather events are likely This effect was visible from neither the square-root criterion to increase in both frequency and intensity (APCC, 2014). nor standardly used graphical diagnosis (mean residual life Against this background, the resilience of transport systems plot) but rather from a direct comparison of AMS and PDS in with respect to weather hazards has become increasingly im- combined quantile plots. We therefore recommend perform- portant. ing AMS and PDS approaches simultaneously in order to se- Published by Copernicus Publications on behalf of the European Geosciences Union. 516 M. Schlögl and G. Laaha: Extreme weather exposure identification for road networks A basic requirement for foresightful road infrastructure scale meteorological patterns, but their review did not con- management is data about both the probability and mag- tain any direct quantitative comparisons based on a common nitude of severe weather events. This information can be data set. Moreover, studies so far did not specifically assess derived from long-term records of weather quantities such the performance of methods with respect to rare events, such as precipitation and temperature by means of statistical ex- as 100-year events, which are more relevant for risk assess- treme value modeling. While extreme value theory provides a ment than events at the moderate tail of the distribution. methodological framework that is commonly used in various In this study, we compare the different extreme value scientific disciplines, such as hydrology (Katz et al., 2002), approaches and fitting methods with respect to their value finance (Embrechts et al., 2003), engineering (Castillo et al., for assessing the exposure of transport networks to ex- 2005) and climate sciences (Katz, 2010; Cheng et al., 2014), treme weather impacts. Based on an Austrian data set from the application of these tools for road network exposure anal- 25 meteorological stations representing diverse meteorolog- ysis is a relatively uncharted area. In particular, formal com- ical conditions, we assess the added value of partial duration parative assessments of the various statistical methods that series over the standardly used annual maxima series in or- can be applied for estimating return levels of extreme events der to give recommendations for performing extreme value are rare. statistics of meteorological hazards. Two basic approaches have been proposed for deriving extreme value series (Coles, 2001), which are both widely applied in studying extreme meteorological events (e.g., Smith, 2 Materials and methods 1989; Davison and Smith, 1990; Parey et al., 2010; Vil- larini, 2011; Papalexiou and Koutsoyiannis, 2013). On the 2.1 Data – meteorological indicators one hand, the maximum value per year can be used in the block maxima approach, resulting in an annual maxima se- This study focuses on several meteorological indicators that ries (AMS). On the other hand, all values exceeding a certain can be used to assess the exposure of road networks to two threshold can be considered extreme, leading to the thresh- main meteorological quantities: precipitation and tempera- old excess approach based on partial duration series (PDS). ture. These two variables are considered to have the most Once the extreme value series has been derived, an appro- serious influence on damage to infrastructure (Matulla et al., priate distribution function is fitted to these observations by 2017). They are measured by meteorological services on a using different parameter estimation methods, such as maxi- regular basis so the data quality is usually high. Neverthe- mum likelihood estimation, method of moments or Bayesian less, the methodology presented in this paper is applicable to methods for parameter estimation. Clearly, there are a num- various other meteorological quantities (e.g., maximum wind ber of possible combinations of the approaches that may lead speed) when time series of about 30 years or more are avail- to different, often equally plausible results. able. Several efforts have been made to compare the perfor- Four meteorological indices are used in this study. Tem- mance of block maxima and threshold excess approaches. perature impacts are considered by daily minimum (Tmin/ While some studies only provide a qualitative description and daily maximum temperature (Tmax/. In addition, maxi- of resulting parameter estimates and estimated return lev- mum daily temperature difference (T1 D Tmax − Tmin/ is an- els for both methods (Jarušková and Hanek, 2006), more alyzed, with all temperature indices in ◦C. Regarding pre- formal assessment approaches are based on the asymptotic cipitation impacts, the daily precipitation sum mm day−1 has variance of the T -year event estimator (Cunnane, 1973) or been chosen. on various goodness-of-fit tests and model performance met- In order to identify suitable meteorological stations that rics (Madsen et al., 1997a, b; Bezak et al., 2014). Controver- represent the main climate features of the highway network sial conclusions have been drawn. For instance, Madsen et in Austria, all monitoring stations operated by the national al. (1997a) found for extreme discharges that the most suit- weather service Zentralanstalt für Meteorologie und Geody- able approach depends on the sample size and the shape pa- namik (ZAMG) served as a starting point. The selection of rameter of the fitted functions. However, Ben-Zvi (2009) and suitable stations was carried out in a stepwise procedure with Bezak et al. (2014) argue that a generalized Pareto (GP) dis- respect to the following considerations. Firstly, the spatial tribution fitted to partial duration series yields the best re- proximity of available measuring stations to the highway net- sults for modeling rainfall and discharge extremes. Mkhandi work was considered by excluding stations with a distance et al. (2005), again, found that AMS and PDS methods result greater than 10 km from the data set. Secondly, data avail- in similar predictions of flood magnitudes. All of these stud- ability and data quality were considered. As

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