Letter | Published:

Increasing risk of compound flooding from storm surge and rainfall for major US cities

Nature Climate Change volume 5, pages 10931097 (2015) | Download Citation

Abstract

When storm surge and heavy precipitation co-occur, the potential for flooding in low-lying coastal areas is often much greater than from either in isolation. Knowing the probability of these compound events and understanding the processes driving them is essential to mitigate the associated high-impact risks1,2. Here we determine the likelihood of joint occurrence of these two phenomena for the contiguous United States (US) and show that the risk of compound flooding is higher for the Atlantic/Gulf coast relative to the Pacific coast. We also provide evidence that the number of compound events has increased significantly over the past century at many of the major coastal cities. Long-term sea-level rise is the main driver for accelerated flooding along the US coastline3,4; however, under otherwise stationary conditions (no trends in individual records), changes in the joint distributions of storm surge and precipitation associated with climate variability and change also augment flood potential. For New York City (NYC)—as an example—the observed increase in compound events is attributed to a shift towards storm surge weather patterns that also favour high precipitation. Our results demonstrate the importance of assessing compound flooding in a non-stationary framework and its linkages to weather and climate.

Access optionsAccess options

Rent or Buy article

Get time limited or full article access on ReadCube.

from$8.99

All prices are NET prices.

References

  1. 1.

    et al. in Managing the Risks of Extreme Events and Disasters to Advance Climate Change Adaptation (eds Field, C. B. et al.) 109–230 (IPCC, Cambridge Univ. Press, 2012).

  2. 2.

    et al. A compound event framework for understanding extreme impacts. WIREs Clim. Change 5, 113–128 (2014).

  3. 3.

    & Accelerated flooding along the U.S. East Coast: On the impact of sea level rise, tides, storms, the Gulf Stream and NAO. Earth’s Future 2, 362–382 (2014).

  4. 4.

    & From the extreme to the mean: Acceleration and tipping points of coastal inundation from sea level rise. Earth’s Future 2, 579–600 (2014).

  5. 5.

    NOAA’s State of The Coast (NOAA, accessed 2 June 2014);

  6. 6.

    Coincident Flooding in Queensland: Joint Probability and Dependence Methodologies (Department of Science, Information Technology, Innovation and the Arts, Queensland Government, 2012).

  7. 7.

    , & Joint impact of rainfall and tidal level on flood risk in a coastal city with a complex river network: A case study of Fuzhou City, China. Hydrol. Earth Syst. Sci. 17, 679–689 (2013).

  8. 8.

    & Modeling flood inundation induced by river flow and storm surges over a river basin. Water 6, 3182–3199 (2014).

  9. 9.

    , , , & Analysis of a compounding surge and precipitation event in the Netherlands. Environ. Res. Lett. 10, 035001 (2015).

  10. 10.

    & Dependence between sea surge, river flow and precipitation in south and west Britain. Hydrol. Earth Syst. Sci. 8, 973–992 (2004).

  11. 11.

    , , & The simultaneous occurrence of surge and discharge extremes for the Rhine delta. Nature Hazard Earth Sys. 13, 2017–2029 (2013).

  12. 12.

    , & Quantifying the dependence between extreme rainfall and storm surge in the coastal zone. J. Hydrol. 505, 172–187 (2013).

  13. 13.

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

  14. 14.

    Joint Archive for Sea Level (JASL) Research Quality Data Set (RQDS) (University of Hawaii Sea Level Center, accessed 17 November 2013);

  15. 15.

    NOAA Precipitation Data (National Climatic Data Center, accessed 22 January 2014);

  16. 16.

    A new measure of rank correlation. Biometrika 30, 81–89 (1938).

  17. 17.

    Lecture Notes in Statistics 2nd edn, Vol. 139 (Springer, 2006).

  18. 18.

    & Asymmetric copula in multivariate flood frequency analysis. Adv. Water Resour. 29, 1115–1167 (2006).

  19. 19.

    & A Generalized Pareto intensity-duration model of storm rainfall exploiting 2-copulas. J. Geophys. Res. 108, 4067 (2003).

  20. 20.

    & Nonparametric estimation of tail dependence. Scand. J. Stat. 33, 307–335 (2006).

  21. 21.

    & Evidence for multidecadal variability in US extreme sea level records. J. Geophys. Res. 120, 1527–1544 (2015).

  22. 22.

    , & Methods for the estimation of loss of life due to floods: A literature review and a proposal for a new method. Nature Hazards 46, 353–389 (2008).

  23. 23.

    & Modeling multivariate distributions with continuous margins using the copula R package. J. Stat. Softw. 34, 1–20 (2010).

  24. 24.

    HURDAT Best Track Data (NOAA Hurricane Research Division of AOML, 2014);

  25. 25.

    & On the impact angle of Hurricane Sandy’s New Jersey landfall. Geophys. Res. Lett. 40, 2312–2315 (2013).

  26. 26.

    et al. The twentieth century reanalysis project. Q. J. R. Meteorol. Soc. 137, 1–28 (2011).

  27. 27.

    , & Correlation methods in fingerprint detection studies. Clim. Dynam. 8, 265–276 (1993).

  28. 28.

    et al. New York City’s vulnerability to coastal flooding. Bull. Amer. Meteorol. Soc. 89, 829–841 (2008).

  29. 29.

    et al. Multivariate return periods in hydrology: A critical and practical review focusing on synthetic design hydrograph estimation. Hydrol. Earth Syst. Sci. 17, 1281–1296 (2013).

  30. 30.

    et al. Heavier summer downpours with climate change revealed by weather forecast resolution model. Nature Clim. Change 4, 570–576 (2014).

  31. 31.

    , & Classical tidal harmonic analysis including error estimates in MATLAB using T_TIDE. Comput. Geosci. 28, 929–937 (2002).

  32. 32.

    & A modified Mann–Kendall trend test for autocorrelated data. J. Hydrol. 204, 182–196 (1998).

Download references

Acknowledgements

T.W. was supported by a fellowship within the postdoctoral programme of the German Academic Exchange Service (DAAD). I. D. Haigh assisted in editing the manuscript.

Author information

Affiliations

  1. College of Marine Science, University of South Florida, 140 7th Avenue South, St Petersburg, Florida 33701, USA

    • Thomas Wahl
    • , Steven D. Meyers
    •  & Mark E. Luther
  2. Research Centre Siegen, University of Siegen, Weidenauer Str 169, 57076 Siegen, Germany

    • Thomas Wahl
    •  & Jens Bender
  3. Civil and Environmental Engineering, Climate Change Institute & Mitchell Center for Sustainability Solutions, University of Maine, 5711 Boardman Hall Orono, Maine 04469-5711, USA

    • Shaleen Jain
  4. Research Institute for Water and Environment, University of Siegen, Paul-Bonatz Str 9-11, 57076 Siegen, Germany

    • Jens Bender

Authors

  1. Search for Thomas Wahl in:

  2. Search for Shaleen Jain in:

  3. Search for Jens Bender in:

  4. Search for Steven D. Meyers in:

  5. Search for Mark E. Luther in:

Contributions

T.W. conceived the idea of the study, performed the analyses and wrote the paper; J.B. assisted in writing MATLAB code, participated in technical discussions and co-wrote the paper; S.J. provided guidance on selecting the precipitation data, assisted in performing the statistical analyses and co-wrote the paper; S.D.M. and M.E.L. participated in technical discussions at an early stage when the idea for the study evolved and co-wrote the paper.

Competing interests

The authors declare no competing financial interests.

Corresponding author

Correspondence to Thomas Wahl.

Supplementary information

About this article

Publication history

Received

Accepted

Published

DOI

https://doi.org/10.1038/nclimate2736

Further reading