Asset exposure data for global physical risk assessment

Eberenz, Samuel; Stocker, Dario; Röösli, Thomas; Bresch, David N.

doi:https://doi.org/10.5194/essd-12-817-2020

Articles | Volume 12, issue 2

https://doi.org/10.5194/essd-12-817-2020

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

https://doi.org/10.5194/essd-12-817-2020

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

Articles | Volume 12, issue 2

Data description paper

|

09 Apr 2020

Data description paper |

| 09 Apr 2020

Asset exposure data for global physical risk assessment

Samuel Eberenz, Dario Stocker, Thomas Röösli, and David N. Bresch

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Final revised paper (published on 09 Apr 2020)
Supplement to the final revised paper
Preprint (discussion started on 29 Oct 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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RC1: 'Review ESSD-2019-189', Anonymous Referee #1, 14 Nov 2019
RC2: 'Review comments on “Exposure data for global physical risk assessment“', Anonymous Referee #2, 26 Nov 2019
AC1: 'Final Author Response', Samuel Eberenz, 10 Feb 2020

Peer-review completion

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

AR by Samuel Eberenz on behalf of the Authors (11 Feb 2020) Author's response Manuscript

ED: Referee Nomination & Report Request started (20 Feb 2020) by David Carlson

RR by Anonymous Referee #2 (09 Mar 2020)

Suggestions for revision or reasons for rejection

Review comments for manuscript „Asset exposure for global physical risk assessment“

The revised manuscript has greatly improved in terms of readability and understanding and the authors have clarified all my comments raised. Before publishing the manuscript I have a few small comments I would like the authors to address as they will further improve the manuscript’s clarity.

Comments:
1) Line 202-204: This paragraph is slightly confusing, please clarify. What do you mean by ‘here’ and ‘in the following section’? It seems you want to say ‘I’ can stand for either assets or GDP, depending on the context.
2) Section 3.3: Please clarify what input data were used for disaggregation. Is it national data or GRP? It is not clear why the absolute values would differ so strongly if GRP were used.

Minor comments:
1) Data is a plural noun (e.g.: nightlight data are …), please correct were needed.
2) Line 92, add semicolon: A detailed overview over the input data is provided in Table 1; the disaggregation approach is illustrated in Figure 1.
3) Line 145, add clarification: For 59 of the 87 countries without produced capital data and non-financial wealth data, …
4) Please search the document again for the term validation and replace with evaluation, see e.g., line 167.
5) Line 187, correct: …zero values.
6) Line 327, add ‘beta’ to sentence to clarify that it describes the slope: … yields no information regarding the slope ‘beta’ of the linear relationship.
7) Line 250: ‘This analysis is applied…’ is not very precise as you never mention the term analysis before. Do you mean evaluation or the application of the skill metrics?
8) Line 257: replace current USD of 2014 -> current USD in 2014
9) Line 285: remove ‘compared’
10) Line 287, please be more precise: …the relatively low values (of what?). Do you mean beta or asset values?
11) Line 281-288: Circumvent using ‘economically large’ as you did before.
12) Line 334: What do you mean by 11.2% to 17.6% for Lit^m? Something like this: 11.2% (m=1) to 17.6% (m=5) for Lit^m? Please clarify. Also replace commas with points where appropriate.

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ED: Publish subject to technical corrections (09 Mar 2020) by David Carlson

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Short summary

The modeling of economic disaster risk on a global scale requires high-resolution maps of exposed asset values. We have developed a generic and scalable method to downscale national asset value estimates proportional to a combination of nightlight intensity and population data. Here, we present the methodology together with an evaluation of its performance for the subnational downscaling of GDP. The resulting exposure data for 224 countries and the open-source Python code are available online.