Analysis | Published:

Hard-adaptive measures can increase vulnerability to storm surge and tsunami hazards over time

Nature Sustainabilityvolume 1pages526530 (2018) | Download Citation

Abstract

Whether hard-adaptive measures (for example, seawalls) actually reduce vulnerability to natural hazards is the subject of considerable debate. Existing quantitative risk assessments often ignore behavioural feedbacks that some claim lead to increased development in hazardous zones. Here, we couple a tsunami model with a land-use change model and find that hard-adaptive measures can induce a false sense of security and inadvertently lead to increased vulnerability (that is, are maladaptive). We also observe that heightened hazard awareness (a type of soft-adaptation) can reduce vulnerability. Our results have two major implications: (1) they challenge existing hazard adaptation practice by quantitatively demonstrating the potential for hard-adaptive measures to be maladaptive, and (2) they highlight that ignoring the behavioural feedbacks in hazard assessment can alter the conclusions to the extent that they fail to identify maladaptive actions. In addition to the demonstrated case of tsunamis, the result may be relevant to other, repeatable natural hazards where urban growth influences exposure (for example, storm surge). Ultimately, neglecting future urban development and the temporal evolution of risk can result in incorrect conclusions regarding adaptation strategies; including these processes is therefore an essential consideration for the natural hazard and climate change impact communities.

Access optionsAccess options

Rent or Buy article

Get time limited or full article access on ReadCube.

from$8.99

All prices are NET prices.

Data availability

Data and code is available from T.M.L. upon request.

Additional information

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

References

  1. 1.

    Fazey, I. et al. Adaptation strategies for reducing vulnerability to future environmental change. Front. Ecol. Environ. 8, 414–422 (2010).

  2. 2.

    Muis, S., Güneralp, B., Jongman, B., Aerts, J. C. J. H. & Ward, P. J. Flood risk and adaptation strategies under climate change and urban expansion: a probabilistic analysis using global data. Sci. Total Environ. 538, 445–457 (2015).

  3. 3.

    Onishi, N. In Japan, seawall offered a false sense of security. The New York Times (2011).

  4. 4.

    The great wall of Japan. The Economist (2014).

  5. 5.

    Winsemius, H. C. et al. Global drivers of future river flood risk. Nat. Clim. Chang. 6, 381–385 (2016).

  6. 6.

    Nateghi, R., Bricker, J. D., Guikema, S. D. & Bessho, A. Statistical analysis of the effectiveness of seawalls and coastal forests in mitigating tsunami impacts in Iwate and Miyagi prefectures. PLoS. ONE. 11, e0158375 (2016).

  7. 7.

    Aerts, J. et al. Climate adaptation. Evaluating flood resilience strategies for coastal megacities. Science 344, 473–475 (2014).

  8. 8.

    Palmer, P. I. & Smith, M. J. Earth systems: model human adaptation to climate change. Nature 512, 365–366 (2014).

  9. 9.

    Aerts, J. C. J. H. et al. Integrating human behaviour dynamics into flood disaster risk assessment. Nat. Clim. Chang. 8, 193–199 (2018).

  10. 10.

    Newell, B. & Wasson, R. Social system vs solar system: why policy makers need history. Confl. Coop. Relat. to Int. Water Resour. Hist. Perspect. 62, 3–17 (2002).

  11. 11.

    Sovacool, B. K. Hard and soft paths for climate change adaptation. Clim. Policy 11, 1177–1183 (2011).

  12. 12.

    Magnan, A. K. et al. Addressing the risk of maladaptation to climate change. WIREs Clim. Chang. 7, 646–665 (2016).

  13. 13.

    Di Baldassarre, G. et al. Debates—perspectives on socio-hydrology: capturing feedbacks between physical and social processes. Water Resour. Res. 51, 4770–4781 (2015).

  14. 14.

    Costanza, R., Mitsch, W. J. & Day, J. W. A new vision for New Orleans and the Mississippi delta: applying ecological economics and ecological engineering. Front. Ecol. Environ. 4, 465–472 (2006).

  15. 15.

    Burby, R. J. Hurricane Katrina and the paradoxes of government disaster policy: bringing about wise governmental decisions for hazardous areas. Ann. Am. Acad. Pol. Soc. Sci. 604, 171–191 (2006).

  16. 16.

    Barnett, J., O’Neill, S. & O’Neill, S. Maladaptation. Glob. Environ. Chang. 20, 211–213 (2010).

  17. 17.

    Mileti, D. Disasters by Design: A Reassessment of Natural Hazards in the United States (Joseph Henry Press, Washington DC, 1999).

  18. 18.

    White, G. F. et al. Changes in Urban Occupance of Flood Plains in the United States, Research Paper 57 (University of Chicago, Chicago, 1958).

  19. 19.

    Votsis, A. Utilizing a cellular automaton model to explore the influence of coastal flood adaptation strategies on Helsinki’s urbanization patterns. Comput. Environ. Urban. Syst. 64, 344–355 (2017).

  20. 20.

    Sivapalan, M. & Blöschl, G. Time scale interactions and the coevolution of humans and water. Water Resour. Res. 51, 6988–7022 (2015).

  21. 21.

    Aldrich, D. P. & Sawada, Y. The physical and social determinants of mortality in the 3.11 tsunami. Soc. Sci. Med. 124, 66–75 (2015).

  22. 22.

    Batty, M. The size, scale, and shape of cities. Science (80-.). 319, 769–771 (2008).

  23. 23.

    White, R., Engelen, G. & Uljee, I. Modeling Cities and Regions as Complex Systems (MIT Press, Cambridge, MA, 2015).

  24. 24.

    Society for Risk Analysis Glossary. Society for Risk Analysis http://sra.org/resources (2015).

  25. 25.

    Birkmann, J. et al. Framing vulnerability, risk and societal responses: the {MOVE} framework. Nat. Hazards 67, 193–211 (2013).

  26. 26.

    Turner, B. L. et al. A framework for vulnerability analysis in sustainability science. Proc. Natl Acad. Sci. USA. 100, 8074–8079 (2003).

  27. 27.

    Suppasri, A. et al. Developing tsunami fragility curves from the surveyed data of the 2011 great east Japan tsunami in Sendai and Ishinomaki plains. Coast. Eng. J. 54, 1250008–1 (2012).

  28. 28.

    Mori, N., Takahashi, T., Yasuda, T. & Yanagisawa, H. Survey of 2011 Tohoku earthquake tsunami inundation and run‐up. Geophys. Res. Lett. 38 (2011).

  29. 29.

    Tani, K. Past and present map comparison website. Saitama University (2015). Available at: http://ktgis.net/kjmapw/ (accessed 1 August 2016).

  30. 30.

    Esteban, M. et al. Storm surge awareness in the Philippines prior to typhoon Haiyan: a comparative analysis with tsunami awareness in recent times. Coast. Eng. J. 58, 1640009 (2016).

  31. 31.

    Aven, T. On the meaning of a black swan in a risk context. Saf. Sci. 57, 44–51 (2013).

  32. 32.

    Haque, C. E. & Burton, I. Adaptation options strategies for hazards and vulnerability mitigation: an international perspective. Mitig. Adapt. Strateg. Glob. Chang. 10, 335–353 (2005).

  33. 33.

    Fuchs, S. et al. Flood risk perception and adaptation capacity: a contribution to the socio-hydrology debate. Hydrol. Earth Syst. Sci. Katlenburg-Lindau 21, 3183–3198 (2017).

  34. 34.

    Smith, K. & Petley, D. Environmental Hazards: Assessing Risk and Reducing Disasters (Routledge, New York, 2009).

  35. 35.

    Pawson, E. Environmental hazards and natural disasters. N. Z. Geog. 67, 143–147 (2011).

Download references

Acknowledgements

We thank A. Judge, who assisted T.M.L. to optimize the simulation code, and T.M.L.’s colleagues in the Michigan University-Wide Sustainability and Environment (MUSE) Initiative for their feedback on the manuscript. We also thank A. Suppasri, who helped us find data on the historical tsunami sources. Funding was provided by the University of Michigan, US National Science Foundation (grant numbers CMMI-1621116 and SEES-1631409), the Japanese Society for the Promotion of Science (NSF cooperative programme for interdisciplinary joint research projects in Hazards and Disasters, project titled ‘Evolution of Urban Regions in Response to Recurring Disasters’) and TU Delft’s Delta Infrastructure and Mobility Initiative (DIMI). This support is gratefully acknowledged. The opinions expressed in this paper are those of the authors and do not necessarily reflect the views of the sponsoring agencies.

Author information

Affiliations

  1. Department of Industrial and Operations Engineering, University of Michigan, Ann Arbor, MI, USA

    • T. M. Logan
    •  & S. D. Guikema
  2. Department of Hydraulic Engineering, Delft University of Technology, Delft, the Netherlands

    • J. D. Bricker

Authors

  1. Search for T. M. Logan in:

  2. Search for S. D. Guikema in:

  3. Search for J. D. Bricker in:

Contributions

T.M.L. designed and performed the research. S.D.G. guided the research process. J.D.B. advised and assisted with the tsunami model.

Competing interests

The authors declare no competing interests.

Corresponding author

Correspondence to T. M. Logan.

Supplementary information

  1. Supplementary Information

    Supplementary Methods, Supplementary Figures 1–19, Supplementary Tables 1–5, Supplementary References 1–65

About this article

Publication history

Received

Accepted

Published

DOI

https://doi.org/10.1038/s41893-018-0137-6