Articles | Volume 12, issue 2
Earth Syst. Sci. Data, 12, 817–833, 2020
https://doi.org/10.5194/essd-12-817-2020
Earth Syst. Sci. Data, 12, 817–833, 2020
https://doi.org/10.5194/essd-12-817-2020

Data description paper 09 Apr 2020

Data description paper | 09 Apr 2020

Asset exposure data for global physical risk assessment

Samuel Eberenz et al.

Related authors

Regional tropical cyclone impact functions for globally consistent risk assessments
Samuel Eberenz, Samuel Lüthi, and David N. Bresch
Nat. Hazards Earth Syst. Sci., 21, 393–415, https://doi.org/10.5194/nhess-21-393-2021,https://doi.org/10.5194/nhess-21-393-2021, 2021
Short summary

Related subject area

Data, Algorithms, and Models
An all-sky 1 km daily land surface air temperature product over mainland China for 2003–2019 from MODIS and ancillary data
Yan Chen, Shunlin Liang, Han Ma, Bing Li, Tao He, and Qian Wang
Earth Syst. Sci. Data, 13, 4241–4261, https://doi.org/10.5194/essd-13-4241-2021,https://doi.org/10.5194/essd-13-4241-2021, 2021
Short summary
100 years of lake evolution over the Qinghai–Tibet Plateau
Guoqing Zhang, Youhua Ran, Wei Wan, Wei Luo, Wenfeng Chen, Fenglin Xu, and Xin Li
Earth Syst. Sci. Data, 13, 3951–3966, https://doi.org/10.5194/essd-13-3951-2021,https://doi.org/10.5194/essd-13-3951-2021, 2021
Short summary
The 30 m annual land cover dataset and its dynamics in China from 1990 to 2019
Jie Yang and Xin Huang
Earth Syst. Sci. Data, 13, 3907–3925, https://doi.org/10.5194/essd-13-3907-2021,https://doi.org/10.5194/essd-13-3907-2021, 2021
Short summary
Coastal complexity of the Antarctic continent
Richard Porter-Smith, John McKinlay, Alexander D. Fraser, and Robert A. Massom
Earth Syst. Sci. Data, 13, 3103–3114, https://doi.org/10.5194/essd-13-3103-2021,https://doi.org/10.5194/essd-13-3103-2021, 2021
Short summary
UAV-based very high resolution point cloud, digital surface model and orthomosaic of the Chã das Caldeiras lava fields (Fogo, Cabo Verde)
Gonçalo Vieira, Carla Mora, Pedro Pina, Ricardo Ramalho, and Rui Fernandes
Earth Syst. Sci. Data, 13, 3179–3201, https://doi.org/10.5194/essd-13-3179-2021,https://doi.org/10.5194/essd-13-3179-2021, 2021
Short summary

Cited articles

Aznar-Siguan, G. and Bresch, D. N.: CLIMADA v1: a global weather and climate risk assessment platform, Geosci. Model Dev., 12, 3085–3097, https://doi.org/10.5194/gmd-12-3085-2019, 2019. 
Aznar-Siguan, G., Bresch, D. N., and Eberenz, S.: CLIMADA-papers repository – github.com/CLIMADA-project/climada_papers, available at: https://github.com/CLIMADA-project/climada_papers, last access: 20 March 2019. 
Bresch, D. N., Aznar-Siguan, G., Eberenz, S., Röösli, T., Stocker, D., Hartman, J., Pérus, M., and Bozzini, V.: CLIMADA repository, available at: https://github.com/CLIMADA-project/climada_python last access: 20 March 2019a. 
Bresch, D. N., Aznar-Siguan, G., Eberenz, S., Röösli, T., Stocker, D., Hartman, J., Pérus, M,. and Bozzini, V.: CLIMADA v.1.2.0, ETH Data Archive, https://doi.org/10.5905/ethz-1007-226, 2019b. 
Brönnimann, S. and Wintzer, J.: Climate data empathy, Wires. Clim. Change, 10, 1–8, https://doi.org/10.1002/wcc.559, 2018. 
Download

The requested paper has a corresponding corrigendum published. Please read the corrigendum first before downloading the article.

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.