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Discussion papers
https://doi.org/10.5194/essd-2019-189
© Author(s) 2019. This work is distributed under
the Creative Commons Attribution 4.0 License.
https://doi.org/10.5194/essd-2019-189
© Author(s) 2019. This work is distributed under
the Creative Commons Attribution 4.0 License.

Submitted as: data description paper 29 Oct 2019

Submitted as: data description paper | 29 Oct 2019

Review status
This discussion paper is a preprint. It is a manuscript under review for the journal Earth System Science Data (ESSD).

Exposure data for global physical risk assessment

Samuel Eberenz1,2, Dario Stocker1,2, Thomas Röösli1,2, and David N. Bresch1,2 Samuel Eberenz et al.
  • 1Institute for Environmental Decisions, ETH Zurich, Zurich, 8092, Switzerland
  • 2Federal Office of Meteorology and Climatology MeteoSwiss, Zurich-Airport, 8058, Switzerland

Abstract. One of the challenges in the globally consistent assessment of physical climate risks is that exposure data are either unavailable or restricted to single countries or regions. Here, we introduce lit population (LitPop), a globally consistent methodology to estimate spatially explicit exposure data proportional to a combination of nightlight intensity and geographical population data. By multiplying nightlight and population data, unwanted artefacts such as blooming, saturation, and lack of resolution are mitigated. Thus, the combination of both data types improves the spatial distribution of macroeconomic indicators. To evaluate the predictive skill of the downscaling approach, GDP distributed proportional to LitPop to subnational administrative regions is compared to reference values. The results for 14 countries show that the predictive skill of LitPop is higher than using nightlights or population data alone. The advantages of this approach are: high predictive skill, global consistency, scalability, openness, replicability, and low entry threshold. The flexibility of the open-source LitPop exposure data and methodology offers value for manifold use cases for economic disaster risk assessments and climate change adaptation studies. Code is published on GitHub as part of the open-source software CLIMADA (CLIMate ADAptation) and archived in the ETH Data Archive with link: http://doi.org/10.5905/ethz-1007-226 (Bresch et al., 2019b). The resulting exposure dataset for 227 countries is archived in the ETH Research Repository with link: https://doi.org/10.3929/ethz-b-000331316 (Eberenz et al., 2019).

Samuel Eberenz et al.
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Samuel Eberenz et al.
Data sets

LitPop: Global Exposure Data for Disaster Risk Assessment S. Eberenz, D. Stocker, T. Röösli, and D. N. Bresch https://doi.org/10.3929/ethz-b-000331316

Model code and software

CLIMADA v.1.2.0 D. N. Bresch, G. Aznar Siguan, S. Eberenz, T. Röösli, D. Stocker, J. Hartman, M. Pérus, and V. Bozzini https://doi.org/10.5905/ethz-1007-226

Samuel Eberenz et al.
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Short summary
The modelling of direct economic disaster risk on a global scale requires consistent high-resolution exposure data. We have developed a generic and scalable method to downscale exposure based on national produced capital, nightlights, and population data. Here, we present the methodology together with an evaluation of its performance for the sub-national downscaling of GDP. The resulting exposure data for 227 countries and the open-source Python code are available online.
The modelling of direct economic disaster risk on a global scale requires consistent...
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