the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
Development of a regionally consistent and fully probabilistic earthquake risk model for Central Asia
Mario A. Salgado-Gálvez
Mario Ordaz
Benjamin Huerta
Osvaldo Garay
Carlos Avelar
Ettore Fagà
Mohsen Kohrangi
Paola Ceresa
Zacharias Fasoulakis
Abstract. A fully probabilistic earthquake risk model was developed for five countries in Central Asia, providing updated earthquake loss estimates with a higher level of details on all components with respect to previous studies in the region, besides having used a regionally consistent approach that on the one hand, allows direct comparisons at different disaggregation levels (e.g., Country and Oblast), and on the other hand, is aimed to facilitate initiating a policy dialogue regarding national and regional disaster risk financing and insurance applications. This earthquake risk model made use of a regional probabilistic seismic hazard analysis, as well as a comprehensive exposure database on which different types of assets and sectors were included, and for which two scenarios (years 2020 and 2080) were modelled. For each type of asset, a unique vulnerability function was derived and later used for the convolution with the hazard data that allowed estimating the loss exceedance curve, at different disaggregation levels, from where other risk metrics such as the average annual loss (AAL) and specific return period losses, were obtained. The regional earthquake AAL for the 2020 exposure scenario has been estimated in around $2 Bn, being Kyrgyzstan and Tajikistan the countries with the highest earthquake risk levels in the region. Besides the probabilistic earthquake risk results, as-if scenarios were modelled using a pseudo-deterministic approach to assess the human and economic losses for realistic and representative earthquakes for the main cities within earthquake prone regions in the five countries within the study area.
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Mario A. Salgado-Gálvez et al.
Status: open (until 11 Oct 2023)
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RC1: 'Comment on nhess-2023-137', Anonymous Referee #1, 28 Sep 2023
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The paper is consistent with the title and the declared objectives. Nevertheless, improvements could be made to increase the study understanding, through a deeper illustration of the methodology in some parts.
The text has too many references to other papers (often the ones in the same special issue), reducing a lot the descriptions of the different steps in the present one. In many cases the references are related to important parts of the methodology, this makes less fluent the reading and sometimes it reduces the reading comprehension. Obviously, in a single paper it’s not possible to describe all the details of a complex study, but adding the essential aspects will improve it.
In the following some specific comments highlighting minor typos and integrating the general observations written before:
- Lines 85-90: it is better to remove these results from the introduction.
- Lines 103-4: there is an error in the paragraph interruption.
- Line 121: change 2090 with 2080 as reported in the other paragraphs of the paper.
- Line 123: change module with model.
- Lines 120-135: the descriptions of the exposure models are too short. It’s clear that the complete description is in other papers of the special issue, but, as example, a table with the different classes, or a resumed description, could help the comprehension of this step of your methodology, in this paper. The same for paragraph 3.1. Moreover, in the paper there isn’t a short description of the different “SSP” or a related reference
- Lines 110-118: something more has to be added about the hazard assessment
- In paragraph 3.1 the sentence “The functions collected were then harmonized and processed” doesn’t permit to understand the procedure to obtain the curves
- In paragraph 3.2 it’s not very clear the calibration process, after the comparison of the “real” and calculated data
- Line 307: change Figure 2 with Figure 3
- Line 379: change “he” with “the”
Citation: https://doi.org/10.5194/nhess-2023-137-RC1
Mario A. Salgado-Gálvez et al.
Mario A. Salgado-Gálvez et al.
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