Extreme weather exposure identification for road networks – a comparative assessment of statistical methods
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...
Main Authors: | , |
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Format: | Article |
Language: | English |
Published: |
Copernicus Publications
2017-04-01
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Series: | Natural Hazards and Earth System Sciences |
Online Access: | http://www.nat-hazards-earth-syst-sci.net/17/515/2017/nhess-17-515-2017.pdf |
Summary: | 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 (PDS) over the standardly used annual maxima series (AMS) 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 PDS) being
superior to the block maxima approach (employing 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 behavior 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
visible from neither the square-root criterion nor standardly used
graphical diagnosis (mean residual life plot) but rather from a direct comparison
of AMS and PDS in combined 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, not only 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 the use
of 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. |
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ISSN: | 1561-8633 1684-9981 |