the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
A new regionally consistent exposure database for Central Asia: population and residential buildings
Chiara Scaini
Alberto Tamaro
Baurzhan Adilkhan
Satbek Sarzhanov
Vakhitkhan Ismailov
Ruslan Umaraliev
Mustafo Safarov
Vladimir Belikov
Japar Karayev
Ettore Fagà
Abstract. Central Asia is highly exposed to a broad range of hazardous phenomena including earthquakes, floods and landslides, which have cause substantial damages in the past. However, disaster risk reduction strategies are still under development in the area. We provide a regional-scale exposure database for population and residential buildings based on existing information from previous exposure development efforts at regional and national scale. Such datasets are complemented with country-based data (e.g. building census, national statistics) collected by national representatives in each Central Asia country (Kazakhstan, Kyrgyz Republic, Tajikistan, Turkmenistan, Uzbekistan). We also develop population and residential buildings exposure layers for the year 2080, which support the definition of disaster risk reduction strategies in the region.
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Chiara Scaini et al.
Status: final response (author comments only)
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RC1: 'Comment on nhess-2023-94', Anonymous Referee #1, 15 Aug 2023
This manuscript proposes a regional exposure database featuring the population and residential building stock for the countries of Central Asia. This new dataset has a regional scale, with the appropriate resolution to be used in multi-hazard and risk assessment. Moreover, the authors provide estimates for exposure for the year 2080 that can support current and future risk mitigation strategies. To achieve this, the authors use high-resolution population and building datasets to generate gridded exposure datasets.
The introduction clearly states the research goals and main novelties. The methodological aspects of the research are very interesting. The authors rely on local data and expert knowledge to provide reliable building characterization and replacement costs. The manuscript presents relevant information for other risk scientists. This information is clearly laid out in maps, figures, and tables, making the paper useful, appealing, and easy to read. The exposure layers for 2080 are a significant addition to the research outputs. These rely on future scenarios of population and urbanization to propose different possibilities for future exposure in Central Asia. The results section is brief, clear, and very well written. I praise the authors for presenting the results of their work so well.
The manuscript contains a couple of typos, which can be easily corrected, but it is also missing several references, some of which are essential to the research. I strongly suggest thorough proof-read by the authors. Regarding the methodological aspects of the research, there are only a couple of points that need to be explicit in the manuscript. One is regarding the SSPs, specifically the motivation for the scenarios chosen and the uncertainties that they account for in this research (i.e., it seems that future population and urbanization are being accounted for, but not future sustainability, resilience, or vulnerability of the residential building stock). The other point that needs to be clearly addressed is the expected reduction in the future number of buildings, which is most likely due to the fact the future abandoned or unoccupied buildings are not being accounted for in the 2080 exposure dataset.
Based on this assessment, I believe the manuscript would be an excellent contribution to the scientific community, and that its main contents are up to the standards of NHESS. I also want to extend my congratulations to the authors for their hard work. My recommendation is to accept the manuscript, subject to minor revisions. Please find all the comments line to line in the attached supplement, which I hope can help the authors improve the quality of the final publication.
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RC2: 'Comment on nhess-2023-94', Massimiliano Pittore, 05 Sep 2023
The paper presents a new exposure model providing information on population and residential buildings in five Central Asian Countries, namely Kazakhstan, the Kyrgyz Republic, Tajikistan, Turkmenistan and Uzbekistan. The model, mainly aiming at supporting seismic risk assessment, is updating and further advancing a former model developed within the framework of the EMCA (Earthquake Model Central Asia) in several aspects. At regional and transnational scale the authors improved the spatial resolution of the model by leveraging high-resolution proxies obtained by open-source data. At national scale up-to-date authoritative have been also considered and integrated by expert consultations. This is already a significant and useful achievement considering the challenges related with collecting and integrating information on exposure in data-scarce regions. Furthermore, the authors projected the updated model to a relatively far future (2080) to investigate the possible changes in exposure in the region based on different SSPs and related urbanization models. Although such projection is likely affected by strong uncertainties, it would still be very useful for risk assessment under non-stationary conditions. In fact, although seismic hazard can be considered stationary over the considered time-frame, the dynamics of exposure already proved to be instrumental in driving the expected risk over the next decades and a better consideration of such dynamics might improve both short- and longer-term risk mitigation and climate change adaptation efforts. The authors in particular estimate the expected relative differences in building replacement cost (considering no variation in usd/m2) between current and future, which provides useful insights on the possible change in seismic risk, but fall a bit short in exploring the interplay between the population change and the urbanization process, which would perhaps allow further considerations on the possible spatial pattern of future risk in the region. Overall the paper is well designed and written and would provide a interesting and useful contribution to the topic of exposure modelling.
Further comments and notes are included in the accompanying file.
Chiara Scaini et al.
Chiara Scaini et al.
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