Abstract
Tropical cyclones have far-reaching impacts on livelihoods and population health that often persist years after the event1,2,3,4. Characterizing the demographic and socioeconomic profile and the vulnerabilities of exposed populations is essential to assess health and other risks associated with future tropical cyclone events5. Estimates of exposure to tropical cyclones are often regional rather than global6 and do not consider population vulnerabilities7. Here we combine spatially resolved annual demographic estimates with tropical cyclone wind fields estimates to construct a global profile of the populations exposed to tropical cyclones between 2002 and 2019. We find that approximately 560 million people are exposed yearly and that the number of people exposed has increased across all cyclone intensities over the study period. The age distribution of those exposed has shifted away from children (less than 5 years old) and towards older people (more than 60 years old) in recent years compared with the early 2000s. Populations exposed to tropical cyclones are more socioeconomically deprived than those unexposed within the same country, and this relationship is more pronounced for people exposed to higher-intensity storms. By characterizing the patterns and vulnerabilities of exposed populations, our results can help identify mitigation strategies and assess the global burden and future risks of tropical cyclones.
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Data availability
Replication data and codes for this study have been deposited at https://doi.org/10.5061/dryad.76hdr7t30.
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Acknowledgements
E.B., R.J., S.H.-N., A.C.-G., D.G.-S., Z. Wang and Z. Wagner disclose support for the research and publication of this work from the NIH grant R01HD104835.
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E.B., Z. Wagner and R.J. conceived the idea. R.J. generated tropical cyclone wind data. M.G. processed population data. R.J. designed the study, performed the analysis and interpreted the results. R.J., S.H.-N., D.R.C., M.G., Z. Wang, A.C.-G., D.G.-S., E.B. and Z. Wagner drafted and revised the manuscript.
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Extended data figures and tables
Extended Data Fig. 1 Population exposure to tropical cyclones in 2002–2019, by continent.
Curves represent exposure to tropical storms or higher winds (>63 km h−1 maximum wind speed), assuming 12-h duration of sustained wind over land. The overall increase in global exposure to tropical cyclones is primarily driven by Asia.
Extended Data Fig. 2 Global person-days exposure to tropical cyclones in 2002–2019.
Similar to Fig. 2, the person-days exposure has increased between 2002 and 2019. The top, middle and bottom curves represent person-days exposure to tropical storms, Category 1 or more intense and Category 3 or more intense storms, respectively. The solid, dashed and dotted lines represent exposure assuming up to 12 h, 6 h and no limit on duration of sustained wind over land. Point estimates represent raw data and the curves represent locally estimated scatterplot smoothing (LOESS) best fit. Increasing trends in person-days exposure are observed for all wind levels.
Extended Data Fig. 3 Comparison of population exposure levels with different wind modelling approaches.
The figure compares the population exposure estimates calculated using three different wind modelling approaches (the primary approach Quad-by-Quad with two other approaches, Quad-by-Quad-85 and C15+LC12). Similarly to the solid curves in Fig. 2, the coloured curves represent exposures to tropical storms or higher winds (>63 km h−1 maximum wind speed), assuming a 12-h duration of sustained wind over land. The estimates are further compared with population exposure estimates in previous studies, that is, Geiger et al.7 and Peduzzi et al.22. Peduzzi et al. estimate population exposure based on a decade-long average, with the two red triangles representing estimates for the two decades of 2000–2020. This comparison indicates that: (1) the increasing trend from 2002 to 2019 is robust to model choice, and we observe this pattern with all three modelling approaches; (2) by correcting the outer radius through a reduction factor of 0.85, the population exposure reduces by approximately 15%, indicating that the impact of the reduction factor on estimated population exposure is approximately linear; (3) the uncertainty in population exposure to stronger storms is greater than that to weaker storms, and the disparities in the estimates of exposure to Category 3 or more intense storms can be as much as threefold in certain years; (4) despite the uncertainties in the exposure estimates obtained from the three different approaches, the difference is very small compared with previous studies. The discrepancy between our estimates and Geiger et al.7 can be partially attributed to differences in the wind parametric method. Geiger et al. used a parametric wind model from Holland57, which may potentially overestimate tropical cyclone surface winds.
Extended Data Fig. 4 Sensitivity analysis for age profile, as in Fig. 3.
a, Age profile of unexposed population, indicating that the overall shift in the age distribution of exposed population is largely driven by the general population ageing patterns in Asia, North America and Europe. b,c, Age profiles for the exposed population identified by Quad-by-Quad-85 (b) and C15+LC12 (c), suggesting that the overall change in the age distribution of the exposed population is robust to model choice.
Extended Data Fig. 5 Variations in the RDI of exposed countries/regions.
The RDI is suitable for this analysis as it captures the variations in the socioeconomic profiles across different regions within the same country. Here we show the RDI distribution for the most heavily exposed nine countries/regions in a as an example. In Fig. 4, we exclude countries/regions with populations less than 100,000 or areas smaller than 8,000 km2 for clarity. There are two reasons for removing these areas: first, in regions with small areas or low populations, everyone is affected by tropical cyclones, making the RDI ratio meaningless; second, in regions with small areas, there is insufficient variation in the RDI. In the two aforementioned circumstances, the RDI is not a suitable indicator, as we show in b. With this criterion, countries/regions removed in the RDI analyses are: Turks and Caicos Is., St. Kitts and Nevis, Virgin Is., Samoa, Vanuatu, St. Vincent and the Grenadines, Guadeloupe, British Virgin Is., Martinique, Micronesia, Mayotte, Wallis and Futuna, Reunion, American Samoa (Eastern Samoa), French Polynesia, Fiji, Macau (China), Hong Kong (China), New Caledonia and Northern Mariana Is. We show the RDI ratio of these countries/regions in Extended Data Fig. 6. In Fig. 5, we only exclude countries/regions with populations less than 100,000, so that the most intense wind category (Category 5) still has five countries/regions remaining. In Extended Data Fig. 7, we also confirm that the primary findings on the socioeconomic characteristics of the exposed population in this study remain valid, regardless of whether the countries/regions are included or excluded.
Extended Data Fig. 6 Sensitivity analysis for the RDI ratio of exposed countries/regions, as in Fig. 4.
Subplots a and b indicate that the main conclusion about the RDI ratio of exposed countries/regions is not sensitive to model choice. Here we present the RDI ratios of each affected country/region, identified by the Quad-by-Quad-85 modelling approach (a) and the C15+LC12 modelling approach (b). The raw results are listed in Extended Data Table 2. Our results show that 70% and 73% of the countries/regions have RDI ratios greater than 1, respectively. It is important to note that the RDI ratio for Portugal experiences substantial fluctuations when different wind modelling approaches are used. This is primarily because of the location of Portugal in a higher-latitude region, in which tropical cyclones have already undergone extratropical transition, which greatly affected the symmetric structure of the storm. In this scenario, the C15+LC12 approach is less suitable, and only the Quad-by-Quad method can capture this substantial asymmetry. When comparing the Quad-by-Quad-85 and Quad-by-Quad methods, the change in the RDI ratio for Portugal is minimal. c, The RDI ratio for all countries/regions exposed to Category 1 or more intense storms, including those with population less than 100,000 or area smaller than 8,000 km2.
Extended Data Fig. 7 Sensitivity analysis for the trends in the RDI ratio with increasing storm severity, as in Fig. 5.
Subplots a and b indicate that the main conclusion about the socioeconomic profiles of the exposed population is not sensitive to model choice. Here we show results of the analysis similar to those shown in Fig. 5, but with exposed population identified with the Quad-by-Quad-85 modelling approach (a) and the C15+LC12 modelling approach (b). They both show similar patterns and the conclusion is robust. c, Sensitivity test based on our primary modelling approach Quad-by-Quad but using three different countries/regions subsets. The top plot includes the raw 93 countries/regions that were exposed to tropical cyclones between 2010 and 2019, the middle plot removes areas with populations less than 100,000 and the bottom plot removes countries/regions with populations less than 100,000 or areas smaller than 8,000 km2. In all three subplots, we show that the exposed populations are more socioeconomically deprived than unexposed populations within the same country, and this relationship is more pronounced for higher-intensity storms. Our results indicate that this conclusion is robust and unaffected by the removal of small countries/regions.
Extended Data Fig. 8 Relative contributions of population growth and changes in tropical cyclone climatology to the change in tropical cyclone exposure.
For each tropical cyclone wind intensity, we hold population size constant at the 2002 level and re-estimate population exposure over time, as shown by the light-coloured curve in each panel. We do this exercise for both population exposure (a) and person-days exposure (b). We show that around 67%, 71% and 29% of the change in total population exposure is attributable to the changes in tropical cyclone climatology at the tropical storms, Category 1 and Category 3 wind levels, respectively. Similarly, 72%, 74% and 27% of the change in person-days exposure is attributable to changes in tropical cyclone climatology at the tropical storms, Category 1 and Category 3 wind levels, respectively. The reasons behind the differences in the distribution between weak and intense storms remain uncertain and require further study.
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This file includes one supplementary table, which shows the full list of 117 countries/regions that were exposed to tropical cyclones from 2002 to 2019.
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Jing, R., Heft-Neal, S., Chavas, D.R. et al. Global population profile of tropical cyclone exposure from 2002 to 2019. Nature 626, 549–554 (2024). https://doi.org/10.1038/s41586-023-06963-z
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DOI: https://doi.org/10.1038/s41586-023-06963-z
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