Coastal habitats shield people and property from sea-level rise and storms

Journal name:
Nature Climate Change
Volume:
3,
Pages:
913–918
Year published:
DOI:
doi:10.1038/nclimate1944
Received
Accepted
Published online
Corrected online

Extreme weather, sea-level rise and degraded coastal ecosystems are placing people and property at greater risk of damage from coastal hazards1, 2, 3, 4, 5. The likelihood and magnitude of losses may be reduced by intact reefs and coastal vegetation1, especially when those habitats fringe vulnerable communities and infrastructure. Using five sea-level-rise scenarios, we calculate a hazard index for every 1km2 of the United States coastline. We use this index to identify the most vulnerable people and property as indicated by being in the upper quartile of hazard for the nation’s coastline. The number of people, poor families, elderly and total value of residential property that are most exposed to hazards can be reduced by half if existing coastal habitats remain fully intact. Coastal habitats defend the greatest number of people and total property value in Florida, New York and California. Our analyses deliver the first national map of risk reduction owing to natural habitats and indicates where conservation and restoration of reefs and vegetation have the greatest potential to protect coastal communities.

At a glance

Figures

  1. Coastal habitats reduce by approximately 50% the proportion of people and property along the US coastline that are most exposed to storms and sea-level rise.
    Figure 1: Coastal habitats reduce by approximately 50% the proportion of people and property along the US coastline that are most exposed to storms and sea-level rise.

    We estimate people and property exposed to hazards with (black bars) and without (white bars) habitats using four metrics: total population, elderly people, poor families (three left y axes) and residential property values (right y axis). Results are represented using the same set of bars for all metrics because on the national scale these variables are highly correlated. The correlation breaks down on more local scales (Figs 3, 4). Data are for highest hazard segments (index>3.36).

  2. Exposure of the US coastline and coastal population to sea-level rise in 2100 (A2 scenario) and storms.
    Figure 2: Exposure of the US coastline and coastal population to sea-level rise in 2100 (A2 scenario) and storms.

    Warmer colours indicate regions with more exposure to coastal hazards (index >3.36). The bar graph shows the population living in areas most exposed to hazards (red 1km2 coastal segments in the map) with protection provided by habitats (black bars) and the increase in population exposed to hazards if habitats were lost owing to climate change or human impacts (white bars). Letters on the x axis represent US state abbreviations. Data depicted in the inset maps are magnified views of the nationwide analysis.

  3. Nature/'s shield for total residential property value.
    Figure 3: Nature’s shield for total residential property value.

    a,b, Total property value for which habitats reduce exposure to storms and sea-level rise in each coastal county of the United States for the current (a) and future A2 (b) sea-level-rise scenarios. Insets show Monroe County in Florida, Georgetown and Horry counties in South Carolina and Brunswick and Pender counties in North Carolina. Reduction in the total value of property exposed to coastal hazards is the difference in the total value of property exposed to coastal hazards with and without habitats included in the hazard index. Estimates for each 1km2 segment in the highest hazard category (index >3.36) are summed by county.

  4. Nature/'s shield for socially vulnerable counties.
    Figure 4: Nature’s shield for socially vulnerable counties.

    ad, Proportion of poor families (a,b) and elderly people (c,d), relative to the total population in each country that are protected by habitats from exposure to current (a,c) and future A2 (b,d) sea-level rise and storms. Cut-offs for high (upper 25%), medium (centre 50%) and low (lower 25%) proportions are based on the quantiles of the two distributions (ratio of poor or elderly to total population) across the two sea-level-rise scenarios.

Change history

Corrected online 01 August 2013
In the version of this Letter originally published online, the second sentence of the Acknowledgements section should have read "We thank Zillow and the many individuals and institutions that provided data (see Supplementary Information for full details)". This error has now been corrected in all versions of the Letter.

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Author information

Affiliations

  1. The Natural Capital Project, Stanford University, 8907 25th Ave NE Seattle, Washington 98115, USA

    • Katie K. Arkema
  2. The Natural Capital Project, Stanford University, 371 Serra Mall, Stanford, California 94305-5020, USA

    • Greg Guannel,
    • Spencer A. Wood,
    • Anne Guerry,
    • Mary Ruckelshaus,
    • Martin Lacayo &
    • Jessica M. Silver
  3. The Natural Capital Project, Stanford University, c/o Conservation Science Program World Wildlife Fund—US, 1250 24th Street NW, Washington DC 20037-1193, USA

    • Gregory Verutes
  4. The Nature Conservancy, 4722 Latona Ave NE, Seattle, Washington 98105, USA

    • Peter Kareiva

Contributions

P.K., M.R., K.K.A., G.G., A.G., S.A.W. and G.V. conceived the research. G.G. and G.V. developed the coastal hazard index. K.A., G.V. and S.W. carried out analyses. K.K.A., G.G., G.V. and S.A.W. collected the data. M.L. and J.M.S. helped with data collection and analyses. K.K.A. wrote the paper with contributions from A.G., G.G, P.K., M.R., J.M.S., G.V. and S.A.W.

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The authors declare no competing financial interests.

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