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Identifying socioeconomic exposure patterns and hotspots of global tropical cyclones from 1990 to 20197.17 MB
Published VersionCC BY-NC-ND V4.0, Open Access
Journal article
Identifying socioeconomic exposure patterns and hotspots of global tropical cyclones from 1990 to 2019
Climate Services, Vol.34, pp.1-14
2024
Abstract
Tropical cyclones (TCs) are among the most devastating natural hazards. Socioeconomic exposure to TCs is essential for global disaster risk assessment. This study mapped global TC hazards using TC best track data from 1990 to 2019 and the Holland wind field model. Absolute and relative exposure indices were developed, and a matrix model was used to determine the exposure hotspots of TCs. Our results show that: (1) From 1990 to 2019, the exposed GDP to TCs increased by USD 9771.5 billion, and the population increased by 570 million people, with an average annual growth rate of 3.62 % and 1.26 %, respectively. South Asia shows a significant increasing trend in both exposed GDP and the proportion of the exposed population. In terms of income levels, lower middle income countries have the fastest growth in exposed GDP and population. (2) Countries with high absolute exposure tend to be populous and economically developed, while countries with high relative exposure are mainly small island nations. The number of countries with the highest level (level V) of exposed GDP and the population is 8 and 10, respectively. Mainly located in East Asia and the Pacific. (3) From 1990 to 2019, the total area of affected cities increased by 12.94 × 104 km2. The increase in GDP and population exposure in urban areas accounted for 45.45 % and 77.53 % of the total growth in exposure, respectively. Our study contributes to understanding the dynamics of exposure characteristics in TC regions and provides an important foundation for TC risk management.
Details
- Title
- Identifying socioeconomic exposure patterns and hotspots of global tropical cyclones from 1990 to 2019
- Authors
- Chengcheng Wan - Shanghai Normal UniversityYinwei Tian - SuperMap Software (China, Beijing)Jianli Liu - University of the Sunshine Coast, Queensland, School of Science, Technology and EngineeringYafei Yan - Shanghai Normal UniversityZhongchao Shi - Shanghai Normal UniversityJiahong Wen (Corresponding Author) - Shanghai Normal UniversityLijun Yan (Corresponding Author) - Shanghai Normal University
- Publication details
- Climate Services, Vol.34, pp.1-14
- Publisher
- Elsevier BV
- Date published
- 2024
- DOI
- 10.1016/j.cliser.2024.100494
- ISSN
- 2405-8807
- Copyright note
- © 2024 The Authors. Published by Elsevier B.V. This is an open access article under the CC BY-NC-ND license (http://creativecommons.org/licenses/bync-nd/4.0/).
- Data Availability
- The manuscript is marked with the data source.
- Grant note
- This work was supported by Key Laboratory of Land Subsidence Monitoring and Prevention, Ministry of Natural Resources (KLLSMP20230X), and the National Natural Science Foundation of China (Grants No. 42171080, 42105125).
- Organisation Unit
- School of Science, Technology and Engineering
- Language
- English
- Record Identifier
- 991043460102621
- Output Type
- Journal article
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