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Global spread of local cyclone damages through urban trade networks

Abstract

Geophysical hazards stress urban social, economic and political systems, but many studies focus on single locations over short periods. The manner in which a natural disaster propagates across cities globally through urban trade networks remains unexplored. Starting from a novel empirical baseline model for global production and trade, here we develop a dynamical model for the spread of individual cyclone impacts across the world’s cities. We find that cities are vulnerable to economic harm even if they are geographically distant from the location of direct impacts of cyclones. These adverse secondary impacts are responsible for up to three-fourths of the effects of the largest storms and are generated primarily by cyclone exposure in North America and East Asia, in part because of the roles of these regions as principal purchasers and suppliers, respectively, of industrial materials. Vulnerability to adverse secondary impacts of cyclones is highest in cities that are strongly dependent on the global trade network but have relatively few suppliers. Our results suggest that, in addition to improvements in protective infrastructure, urban adaptation to storm damage and climate change might require modifications to trade network linkages.

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Fig. 1: Secondary impacts exported between world regions across 1,209 historical storm simulations (100 million displaced units of production).
Fig. 2: Secondary impacts from a hypothetical cyclone in Mysore, India, propagate through the network over 48 months.
Fig. 3: Ratio of secondary impacts to direct impacts versus direct impacts for 1,209 simulated storms originating in region indicated with colour.
Fig. 4: Mean fractional secondary impacts per storm of cities versus number of strong supply relationships that connect that city to the network, averaged over 1,209 storm simulations.

Data availability

The data that support the findings of this study are available from the corresponding author upon request.

Code availability

Simulation code can be accessed at https://github.com/chrisshughrue/GlobalUrbanCycloneImpactSimulation.

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Acknowledgements

Comments and suggestions from E. Lazarus helped us improve the manuscript.

Author information

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Authors

Contributions

C.S., B.W. and K.C.S. conceived the study and developed model dynamics. C.S. designed the simulation and performed the analyses. B.W. and K.C.S. supervised and provided extensive feedback on the analysis and text. All authors discussed the results and contributed to the final manuscript.

Corresponding author

Correspondence to Chris Shughrue.

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The authors declare no competing interests.

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Extended data

Extended Data Fig. 1 Impacts over time for four cities.

Direct, secondary, and total impacts versus time following a single large storm striking Seoul, New York City, Colombo, and Mysore.

Extended Data Fig. 2 Production change over time at city, national, and global scales.

Fractional change in production versus time at the city, country, and global scale by industrial sector for a single large storm striking Mysore, India.

Extended Data Fig. 3 Industrial sector price over time at city, national, and global scales.

Fractional change in unitary industrial input price versus time at the city, country, and global scale by industrial sector for a single large storm striking Mysore, India.

Extended Data Fig. 4 Impacts over time following earthquake scenarios.

Direct (orange), secondary (blue), and total impacts (grey) to the economy of Japan in value added USD versus time for 2011 earthquake (A) and Nankai earthquake scenarios (B).

Extended Data Fig. 5 Comparison of impacts among models for 2011 and Nankai earthquake scenarios.

Fractional total impacts on economy of Japan versus time for 2011 earthquake (blue) and Nankai earthquake (red) scenarios, and measured fractional loss (from ref. 1) following 2011 earthquake (black). Findings from1 reproduced as circles with ±1 s.d. error bars.

Extended Data Fig. 6 Map of historical cyclone tracks.

Cyclone tracks (purple polygons) between 1968 and 20093.

Extended Data Fig. 7 Sensitivity of results to parameters.

Root-mean square deviation of supply output normalized by baseline parameter output. Parameters are varied between 50% and 150% of the baseline value, with 100% representing the baseline parameter. Parameter sensitivity is calculated among randomly selected storms from the dataset.

Supplementary information

Supplementary Information

Supplementary Table 1, Discussion and Figs. 1–7.

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Shughrue, C., Werner, B. & Seto, K.C. Global spread of local cyclone damages through urban trade networks. Nat Sustain 3, 606–613 (2020). https://doi.org/10.1038/s41893-020-0523-8

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