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

Schlögl, Matthias; Laaha, Gregor

doi:https://doi.org/10.5194/nhess-17-515-2017

Articles | Volume 17, issue 4

https://doi.org/10.5194/nhess-17-515-2017

© Author(s) 2017. This work is distributed under
the Creative Commons Attribution 3.0 License.

https://doi.org/10.5194/nhess-17-515-2017

© Author(s) 2017. This work is distributed under
the Creative Commons Attribution 3.0 License.

Articles | Volume 17, issue 4

Research article

|

03 Apr 2017

Research article |

| 03 Apr 2017

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

Matthias Schlögl and Gregor Laaha

Download

Final revised paper (published on 03 Apr 2017)
Preprint (discussion started on 05 Dec 2016)

Interactive discussion

Status: closed

AC: Author comment | RC: Referee comment | SC: Short comment | EC: Editor comment

- Printer-friendly version

- Supplement

RC1: 'Comments on "Extreme weather exposure identification for road networks – a comparative assessment of statistical methods"', Anonymous Referee #1, 26 Dec 2016
RC2: 'nhess-2016-373', Dan Rosbjerg, 18 Jan 2017
AC1: 'Author response to nhess-2016-373', Matthias Schlögl, 03 Mar 2017

Peer-review completion

AR: Author's response | RR: Referee report | ED: Editor decision

ED: Publish subject to minor revisions (further review by Editor) (03 Mar 2017) by Vassiliki Kotroni

AR by Matthias Schlögl on behalf of the Authors (07 Mar 2017) Author's response Manuscript

ED: Publish as is (07 Mar 2017) by Vassiliki Kotroni

AR by Matthias Schlögl on behalf of the Authors (15 Mar 2017)

Short summary

Different extreme value analysis approaches and fitting methods are compared with respect to their value for assessing the exposure of transport networks to extreme precipitation and temperature impacts. The merits of simultaneous analysis and combined plotting of various approaches are shown. The use of conditional performance metrics is proposed as an additional measure for assessing model goodness of fit. Findings of the study can be transferred to a range of environmental variables.