A statistical model to estimate the local vulnerability to severe weather

Pardowitz, Tobias

doi:https://doi.org/10.5194/nhess-18-1617-2018

Articles | Volume 18, issue 6

https://doi.org/10.5194/nhess-18-1617-2018

© Author(s) 2018. This work is distributed under
the Creative Commons Attribution 4.0 License.

https://doi.org/10.5194/nhess-18-1617-2018

© Author(s) 2018. This work is distributed under
the Creative Commons Attribution 4.0 License.

Articles | Volume 18, issue 6

Research article

|

13 Jun 2018

Research article |

| 13 Jun 2018

A statistical model to estimate the local vulnerability to severe weather

Tobias Pardowitz

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Final revised paper (published on 13 Jun 2018)
Preprint (discussion started on 12 Sep 2017)

Interactive discussion

Status: closed

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

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- Supplement

RC1: 'Review', Anonymous Referee #1, 11 Oct 2017
- AC1: 'Answers on RC1 by Anonymus reviewer #1', Tobias Pardowitz, 18 Apr 2018
RC2: 'nhess-2017-326', Olga Petrucci, 09 Feb 2018
- AC2: 'Answers on RC of O. Petrucci', Tobias Pardowitz, 18 Apr 2018

Peer-review completion

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

ED: Reconsider after major revisions (further review by editor and referees) (27 Apr 2018) by Vassiliki Kotroni

AR by Tobias Pardowitz on behalf of the Authors (27 Apr 2018) Manuscript

ED: Referee Nomination & Report Request started (27 Apr 2018) by Vassiliki Kotroni

RR by Anonymous Referee #1 (03 May 2018)

ED: Publish subject to technical corrections (03 May 2018) by Vassiliki Kotroni

AR by Tobias Pardowitz on behalf of the Authors (10 May 2018) Manuscript

Short summary

The paper presents a statistical analysis of socioeconomic factors influencing vulnerability and exposure to severe weather. By means of statistical modelling, the risks of weather impacts can be predicted at very high spatial resolutions. Such models can serve as a basis for a broad range of tools or applications in emergency management and planning and thus might help to enhance resilience to severe weather.