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.
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.
the Creative Commons Attribution 4.0 License.
Data description paper
| 
09 Apr 2020
Data description paper |
| 09 Apr 2020
| 09 Apr 2020
Asset exposure data for global physical risk assessment
Institute for Environmental Decisions, ETH Zurich, Zurich, 8092,
Switzerland
Federal Office of Meteorology and Climatology MeteoSwiss,
Zurich Airport, 8058, Switzerland
Dario Stocker
Institute for Environmental Decisions, ETH Zurich, Zurich, 8092,
Switzerland
Federal Office of Meteorology and Climatology MeteoSwiss,
Zurich Airport, 8058, Switzerland
Thomas Röösli
Institute for Environmental Decisions, ETH Zurich, Zurich, 8092,
Switzerland
Federal Office of Meteorology and Climatology MeteoSwiss,
Zurich Airport, 8058, Switzerland
David N. Bresch
Institute for Environmental Decisions, ETH Zurich, Zurich, 8092,
Switzerland
Federal Office of Meteorology and Climatology MeteoSwiss,
Zurich Airport, 8058, Switzerland
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- Asset-Level Climate Physical Risk Assessment and Cascading Financial Losses G. Bressan et al. 10.2139/ssrn.4062275
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- Impact Forecasting to Support Emergency Management of Natural Hazards B. Merz et al. 10.1029/2020RG000704
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- DSCIM-Coastal v1.1: an open-source modeling platform for global impacts of sea level rise N. Depsky et al. 10.5194/gmd-16-4331-2023
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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.
The modeling of economic disaster risk on a global scale requires high-resolution maps of...
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