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Climate change risk to global port operations

Abstract

The ports sector is critical to global transport and trade. Climate change may compromise port operations, resulting in an increase in operational shutdowns and subsequent economic losses. Here, we present an analysis of historical global risk across the operations of 2,013 ports worldwide and the impacts under a high-end warming scenario, considering atmospheric and marine hazards, industry established operational thresholds, exposure and vulnerability. Increased coastal flooding and overtopping due to sea level rise, as well as the heat stress impacts of higher temperatures, are the main contributors to amplified risk. Ports located in the Pacific Islands, Caribbean Sea and Indian Ocean appear to be at extremely high risk by 2100, whereas those in the African Mediterranean and the Arabian Peninsula (Persian Gulf and Red Sea) are expected to experience very high risk. Estimating risks at the global scale cannot capture site-level details, but these results provide a benchmark for further research and decision-making.

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Fig. 1: Present multi-hazard conditions in world port operations.
Fig. 2: Multi-hazard severity across world port operations.
Fig. 3: Climate risk for the world port sector in the year 2100 under RCP8.5.

Data availability

The atmospheric and marine frequency impact indicators that support the findings of this study are available from the IFC and were used under licence for the current study. Data are available from the authors upon request by contacting ihdata@ihcantabria.es, if permission from the IFC is granted.

Code availability

Computer codes or algorithms used to generate results that are reported in the paper and central to its main claims are available at: https://doi.org/10.5281/zenodo.3987516.

References

  1. 1.

    International Marine Organization (IMO). International Shipping Facts and Figures – Information Resources on Trade, Safety, Security, Environment (IMO Maritime Knowledge Center, 2012).

  2. 2.

    IPCC: Summary for Policymakers. In Climate Change 2014: Impacts, Adaptation and Vulnerability (eds Field, C. B. et al.) (Cambridge Univ. Press, 2014).

  3. 3.

    Ng, A. K. Y. et al. Port decision maker perceptions on the effectiveness of climate adaptation actions. Coast. Manag. 46, 148–175 (2018).

    Google Scholar 

  4. 4.

    Smythe, T. C. Assessing the Impacts of Hurricane Sandy on the Port of New York and New Jersey’s Maritime Responders and Response Infrastructure. Quick Response Report No. 238: Final Report to the University of Colorado Natural Hazards Center (2013).

  5. 5.

    Becker, A., Ng, A. K. Y., McEvoy, D. & Mullet, J. Implications of climate change for shipping: ports and supply chains. Wiley Interdiscip. Rev. Clim. Change 9, e508 (2018).

    Google Scholar 

  6. 6.

    Caribbean Development and Cooperation Committee. Irma and Maria by Numbers. Focus: ECLAC in the Caribbean (Economic Commission for Latin America and the Caribbean (ECLAC), 2018).

  7. 7.

    Becker, A., Inoue, S., Fischer, M. & Schwegler, B. Climate Change impacts on international seaports: knowledge, perceptions, and planning efforts among port administrators. Climatic Change 110, 5–29 (2012).

    Google Scholar 

  8. 8.

    Hanson, S. et al. A global ranking of port cities with high exposure to climate extremes. Climatic Change 104, 89–111 (2011).

    Google Scholar 

  9. 9.

    McIntosh, R. D. & Becker, A. Expert evaluation of open-data indicators of seaport vulnerability to climate and extreme weather impacts for U.S. North Atlantic ports. Ocean Coast. Manag. 180, 104911 (2019).

    Google Scholar 

  10. 10.

    Mutombo, K. & Ölçer, A. Towards port infrastructure adaptation: a global port climate risk analysis. WMU J. Marit. Aff. 16, 161–173 (2017).

    Google Scholar 

  11. 11.

    O’Keeffe, J. M., Cummins, V., Devoy, R. J. N., Lyons, D. & Gault, J. Stakeholder awareness of climate adaptation in the commercial seaport sector: a case study from Ireland. Mar. Policy 111, 102404 (2020).

    Google Scholar 

  12. 12.

    Yang, Z. et al. Risk and cost evaluation of port adaptation measures to climate change impacts. Transportation Res. D 61B, 444–458 (2018).

    Google Scholar 

  13. 13.

    Esteban, M., Webersik, C. & Shibayama, T. Methodology for the estimation of the increase in time loss due to future increase in tropical cyclone intensity in Japan. Climatic Change 102, 555–578 (2010).

    Google Scholar 

  14. 14.

    Esteban, M., Thao, N., Takagi, H. & Shibayama, T. Increase in port downtime and damage in Vietnam due to a potential increase in tropical cyclone intensity. In Climate Change and the Sustainable Use of Water Resources (ed. Leal Filho, W.) 101–125 (Springer, 2012).

  15. 15.

    Lam, J. S. L., Liu, C. & Gou, X. Cyclone risk mapping for critical coastal infrastructure: cases of East Asian seaports. Ocean Coast. Manag. 141, 43–54 (2017).

    Google Scholar 

  16. 16.

    Jian, W., Liu, C. & Lam, J. S. L. Cyclone risk model and assessment for East Asian container ports. Ocean Coast. Manag. 178, 104796 (2019).

    Google Scholar 

  17. 17.

    Zhang, Y. & Lam, J. S. L. Estimating the economic losses of port disruption due to extreme wind events. Ocean Coast. Manag. 116, 300–310 (2015).

    Google Scholar 

  18. 18.

    Sierra, J. P., Casas-Prat, M., Virgili, M., Mösso, C. & Sánchez-Arcilla, A. Impacts on wave-driven harbour agitation due to climate change in Catalan ports. Nat. Hazards Earth Syst. Sci. 15, 1695–1709 (2015).

    Google Scholar 

  19. 19.

    Sierra, J. P., Casanovas, I., Mösso, C., Mestres, M. & Sánchez-Arcilla, A. Vulnerability of Catalan (NW Mediterranean) ports to wave overtopping due to different scenarios of sea level rise. Reg. Environ. Change 16, 1457–1468 (2015).

    Google Scholar 

  20. 20.

    Becker, A., Chase, N. T. L., Fischer, M., Schwegler, B. & Mosher, K. A method to estimate climate-critical construction materials applied to seaport protection. Glob. Environ. Change 40, 125–136 (2016).

    Google Scholar 

  21. 21.

    Gracia, V. et al. Assessing the impact of sea level rise on port operability using LiDAR-derived digital elevation models. Remote Sens. Environ. 232, 111318 (2019).

    Google Scholar 

  22. 22.

    Christodoulou, A., Christidis, P. & Demirel, H. Sea-level rise in ports: a wider focus on impacts. Marit. Econ. Logist. 21, 482–496 (2019).

    Google Scholar 

  23. 23.

    Monioudi, I. N. et al. Climate change impacts on critical international transportation assets of Caribbean Small Island Developing States (SIDS): the case of Jamaica and Saint Lucia. Reg. Environ. Change 18, 2211–2225 (2018).

    Google Scholar 

  24. 24.

    Stenek, V. et al. Climate Risk and Business Ports: Terminal Marítimo Muelles el Bosque, Cartagena, Colombia (International Finance Corporation, 2011).

  25. 25.

    Chhetri, P., Jayatilleke, G., Gekara, V., Manzoni, A. & Corbitt, B. J. Container terminal operations simulator (CTOS) – simulating the impact of extreme weather events on port operation. Eur. J. Transp. Infrastruct. Res. 16, 195–213 (2016).

    Google Scholar 

  26. 26.

    Morris, L. L. & Sempier, T. Ports Resilience Index: A Port Management Self-Assessment (U.S. Department of Commerce, 2016).

  27. 27.

    Zhang, H. & Ng, A. The regional efforts of Port Metro Vancouver in adapting to potential impacts posed by climate change. In Proc. Annual Conference of the International Association of Maritime Economists (IAME) (2016).

  28. 28.

    Connell, R. et al. Port of Manzanillo: Climate Risk Management (Inter-American Development Bank, 2015).

  29. 29.

    Campos, Á. et al. Addressing long-term operational risk management in port docks under climate change scenarios – a Spanish case study. Water 11, 2153 (2019).

    Google Scholar 

  30. 30.

    Messner, S., Moran, L., Reub, G. & Campbell, J. Climate change and sea level rise impacts at ports and a consistent methodology to evaluate vulnerability and risk. WIT Trans. Ecol. Environ. 169, 141–153 (2013).

    Google Scholar 

  31. 31.

    Sierra, J. P. et al. Modelling the impact of climate change on harbour operability: the Barcelona port case study. Ocean Eng. 141, 64–78 (2017).

    Google Scholar 

  32. 32.

    Camus, P. et al. Probabilistic assessment of port operation downtimes under climate change. Coast. Eng. 147, 12–24 (2019).

    Google Scholar 

  33. 33.

    World Port Index (National Geospatial-Intelligence Agency, 2017).

  34. 34.

    Riahi, K. et al. RCP 8.5—a scenario of comparatively high greenhouse gas emissions. Climatic Change 109, 33–57 (2011).

    CAS  Google Scholar 

  35. 35.

    Grenzeback, L. R. & Lukmann, A. T. Case Study of the Transportation Sector’s Response to and Recovery from Hurricanes Katrina and Rita (Cambridge Systematics, 2008).

  36. 36.

    Izaguirre, C., Losada, I. J., Camus, P., González-Lamuño, P. & Stenek, V. Seaport climate change impact assessment using a multi-level methodology. Marit. Policy Manag. 47, 544–557 (2020).

    Google Scholar 

  37. 37.

    Calil, J., Reguero, B. G., Zamora, A. R., Losada, I. J. & Méndez, F. J. Comparative Coastal Risk Index (CCRI): a multidisciplinary risk index for Latin America and the Caribbean. PLoS ONE 12, e0187011 (2017).

    Google Scholar 

  38. 38.

    Alexandrakis, G. & Poulos, S. E. An holistic approach to beach erosion vulnerability assessment. Sci. Rep. 4, 6078 (2014).

    Google Scholar 

  39. 39.

    Balica, S. F., Wright, N. G. & van der Meulen, F. A flood vulnerability index for coastal cities and its use assessing climate change impacts. Nat. Hazards 64, 73–105 (2012).

    Google Scholar 

  40. 40.

    Camus, P. et al. Statistical wave climate projections for coastal impact assessments. Earths Future 5, 918–933 (2017).

    Google Scholar 

  41. 41.

    Morim, J. et al. Robustness and uncertainties in global multivariate wind-wave climate projections. Nat. Clim. Change 9, 711–718 (2019).

    Google Scholar 

  42. 42.

    Sánchez-Arcilla, A. et al. A review of potential physical impacts on harbours in the Mediterranean Sea under climate change. Reg. Environ. Change 16, 2471–2484 (2016).

    Google Scholar 

  43. 43.

    Sierra, J. P. & Casas-Prat, M. Analysis of potential impacts on coastal areas due to changes in wave conditions. Climatic Change 124, 861–876 (2014).

    Google Scholar 

  44. 44.

    Mase, H., Tsujio, D., Yasuda, T. & Mori, N. Stability analysis of composite breakwater with wave-dissipating blocks considering increase in sea levels, surges and waves due to climate change. Ocean Eng. 71, 58–65 (2013).

    Google Scholar 

  45. 45.

    Suh, K.-D., Kim, S.-W., Mori, N. & Mase, H. Effect of climate change on performance-based design of caisson breakwaters. J. Waterw. Port. Coast. Ocean Eng. 138, 215–225 (2012).

    Google Scholar 

  46. 46.

    Esteban, M., Takagi, H. & Shibayama, T. Sea level rise and the increase in rubble mound breakwater damage. In Proc. 6th International Conference on Coastal Structures 130–140 (World Scientific, 2013).

  47. 47.

    Takagi, H., Kashihara, H., Esteban, M. & Shibayama, T. Assessment of future stability of breakwaters under climate change. Coast. Eng. J. 53, 21–39 (2011).

    Google Scholar 

  48. 48.

    Hallegate, S., Green, C., Nicholls, R. J. & Corfee-Morlot, J. Future flood losses in major coastal cities. Nat. Clim. Change 3, 802–806 (2013).

    Google Scholar 

  49. 49.

    Owen, M. W. Design of Seawalls Allowing for Wave Overtopping, Report No. EX924 (Hydraulics Research Wallingford, 1982).

  50. 50.

    Perez, J., Menendez, M. & Losada, I. J. GOW2: a global wave hindcast for coastal applications. Coast. Eng. 124, 1–11 (2017).

    Google Scholar 

  51. 51.

    Egbert, G. D. & Erofeeva, S. Y. Efficient inverse modeling of barotropic ocean tides. J. Atmos. Ocean. Technol. 19, 183–204 (2002).

    Google Scholar 

  52. 52.

    Church, J. A., White, N. J., Coleman, R., Lambeck, K. & Mitrovica, J. X. Estimates of the regional distribution of sea level rise over the 1950–2000 period. J. Clim. 17, 2609–2625 (2004).

    Google Scholar 

  53. 53.

    Slangen, A. B. A. et al. Projecting twenty-first century regional sea-level changes. Climatic Change 124, 317–332 (2014).

    CAS  Google Scholar 

  54. 54.

    Taylor, K. E., Stouffer, R. J. & Meehl, G. A. An overview of CMIP5 and the experiment design. Bull. Am. Meteorol. Soc. 93, 485–498 (2012).

    Google Scholar 

  55. 55.

    Villarini, G. & Vecchi, G. A. Projected increases in North Atlantic tropical cyclone intensity from CMIP5 models. J. Clim. 26, 3231–3240 (2013).

    Google Scholar 

  56. 56.

    Camus, P., Méndez, F. J., Medina, R. & Cofiño, A. S. Analysis of clustering and selection algorithms for the study of multivariate wave climate. Coast. Eng. 58, 453–462 (2011).

    Google Scholar 

  57. 57.

    Koks, E. E. et al. A global multi-hazard risk analysis of road and railway infrastructure assets. Nat. Commun. 10, 2677 (2019).

    CAS  Google Scholar 

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Acknowledgements

We acknowledge the support from the Spanish Ministerio de Economía y Competitividad (MINECO) under the RISKOADAPT project (grant no. BIA2017-89401-R). We are grateful to C. Amman for his work in the development of atmospheric frequency impact indicators and A. Espejo for his useful comments. The atmospheric and marine frequency impact indicators have been developed jointly by NCAR and IHCantabria with the financial support of the IFC. This material is based in part on work supported by the NCAR, which is a major facility sponsored by the National Science Foundation under cooperative agreement no. 1852977. Computing resources were provided by the Climate Simulation Laboratory at the NCAR Computational and Information Systems Laboratory. We acknowledge the Working Group on Coupled Modelling of the World Climate Research Programme, which is responsible for the CMIP, and we thank the climate modelling groups for producing and making available their model output.

Author information

Affiliations

Authors

Contributions

I.J.L. conceived the study and designed it with C.I. and P.C. jointly. C.I. and P.C. performed the analysis and developed the marine frequency indicators. J.L.V. developed the atmospheric frequency indicators. C.I., I.J.L. and V.S. wrote the manuscript. All of the authors contributed to make substantial improvements to the manuscript.

Corresponding author

Correspondence to I. J. Losada.

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Competing interests

The authors declare no competing interests.

Additional information

Peer review information Nature Climate Change thanks Austin Becker, Robert Nicholls, Joan Pau Sierra and the other, anonymous, reviewer(s) for their contribution to the peer review of this work.

Publisher’s note Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Extended data

Extended Data Fig. 1 Methodological overview of study.

Green colour represents vulnerability variables/indicators used and produced, orange represents exposure characteristics, and blue represents hazard variables/indicators. Grey boxes and arrows indicate the method/approach used for analysing information.

Extended Data Fig. 2 Future change in multihazard severity.

Multihazard change severity for each port in the year 2100 under RCP8.5 in terms of the improvement and worsening of present multihazard conditions.

Extended Data Fig. 3 Present risk level.

Present-day risk levels shown for all ports.

Extended Data Fig. 4 Present vulnerability level.

Present vulnerability level shown for each port.

Extended Data Fig. 5 Future risk levels.

Climate risk in the year 2100 under RCP8.5 across the operations of the largest OECD port cities. Marker size indicates the change in risk level.

Extended Data Fig. 6 Present exposure level.

Present-day exposure level shown for each port.

Extended Data Fig. 7 Dominant climatic drivers of port disruptions.

Spatial representation of dominant climatic drivers of port disruptions in 30 locations around the world.

Extended Data Fig. 8 Multihazard change conditions in world port operations.

a, Representative clusters of multihazard change conditions for each port in the year 2100 under RCP8.5. b, Characteristics of the clusters. Each panel contains the % of ports included in the cluster, the number of the cluster, the representation of the centroid (cross), the 5 and 95 percentiles of the data of the cluster (points) and the 25 and 75 percentiles of the data of the cluster (coloured line) on a normalized (0-1) scale for each frequency indicator. DaysW15 mph = average number of days per year with daily mean wind speed ≥ 15 m/s; DaysT40Deg = average number of days per year with daily maximum temperature ≥ 40°C; DaysP20 mm = average number of days per year with heavy precipitation (>20 mm); DaysHs2.5m = average number of days per year with significant wave height > 2.5 m in the navigation zone; DaysOv0.1 l = number of days per year with overtopping flow > 0.1 l/m/s; DaysCF = average number of days per year with coastal flooding; ProbTC = the annual exceedance probability of experiencing a Saffir-Simpson Hurricane Scale Category 1 or higher; and ProbMajorTC = the annual exceedance probability of experiencing a Saffir-Simpson Hurricane Scale Category 3 or higher (major hurricanes).

Extended Data Fig. 9 Present vulnerability conditions across clusters.

Present-day vulnerability conditions are shown for each port, categorized by cluster. a, Representative clusters of vulnerability conditions for each port. b, Characteristics of the clusters of vulnerability. Each panel contains the % of ports included in the cluster, the number of the cluster, the representation of the centroid (cross), the 5 and 95 percentiles of the data of the cluster (points) and the 25 and 75 percentiles of the data of the cluster (coloured line) on a normalized (0-1) scale for each vulnerability dimension: Tech. Cap = technological capacity; Resilience and Recov. Cap. = recovery capacity.

Extended Data Fig. 10 Present risk conditions across clusters.

Present-day risk conditions are shown for each port, categorized by cluster. a, Representative cluster of present risk conditions for each port. b, Characteristics of the clusters. Each panel contains the % of ports included in the cluster, the number of the cluster, the representation of the centroid (cross), the 5 and 95 percentiles of the data of the cluster (points) and the 25 and 75 percentiles of the data of the cluster (coloured line) on a normalized (0-1) scale for each frequency indicator.

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Supplementary Information

Supplementary Figs. 1–2, Discussions 1–7 and Tables 1–18.

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Izaguirre, C., Losada, I.J., Camus, P. et al. Climate change risk to global port operations. Nat. Clim. Chang. 11, 14–20 (2021). https://doi.org/10.1038/s41558-020-00937-z

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