Urban pluvial flood risk assessment – data resolution and spatial scale when developing screening approaches on the microscale

Löwe, Roland; Arnbjerg-Nielsen, Karsten

doi:https://doi.org/10.5194/nhess-20-981-2020

Articles | Volume 20, issue 4

https://doi.org/10.5194/nhess-20-981-2020

© Author(s) 2020. This work is distributed under
the Creative Commons Attribution 4.0 License.

https://doi.org/10.5194/nhess-20-981-2020

© Author(s) 2020. This work is distributed under
the Creative Commons Attribution 4.0 License.

Articles | Volume 20, issue 4

Research article

|

14 Apr 2020

Research article |

| 14 Apr 2020

Urban pluvial flood risk assessment – data resolution and spatial scale when developing screening approaches on the microscale

Roland Löwe and Karsten Arnbjerg-Nielsen

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Final revised paper (published on 14 Apr 2020)
Supplement to the final revised paper
Preprint (discussion started on 30 Aug 2019)
Supplement to the preprint

Interactive discussion

Status: closed

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

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- Supplement

RC1: 'Review of manuscript „Urban pluvial flood risk assessment - data resolution and spatial scale when developing screening approaches on the micro scale” by Roland Löwe and Karsten Arnbjerg-Nielsen submitted to NHESS', Anonymous Referee #1, 25 Sep 2019
- AC1: 'Reply to comments of reviewer 1', Roland Löwe, 06 Nov 2019
RC2: 'Review Report', Anonymous Referee #2, 27 Oct 2019
- AC2: 'Reply to comments of reviewer 2', Roland Löwe, 06 Nov 2019

Peer-review completion

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

ED: Publish subject to minor revisions (review by editor) (16 Dec 2019) by Thomas Glade

AR by Roland Löwe on behalf of the Authors (19 Dec 2019) Author's response

ED: Publish as is (06 Mar 2020) by Thomas Glade

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Short summary

To consider potential future urban developments in pluvial flood risk assessment, we develop empirical relationships for imperviousness and flood damage based on an analysis of existing urban characteristics. Results suggest that (1) data resolutions must be carefully selected, (2) there are lower limits for the spatial scale at which predictions can be generated, and (3) depth-dependent damage estimates are challenging to reproduce empirically and can be vulnerable to simulation artifacts.