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The timing of decreasing coastal flood protection due to sea-level rise

Nature Climate Change volume 13, pages 359–366 (2023)Cite this article

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Abstract

Sea-level rise amplifies the frequency of extreme sea levels by raising their baseline height. Amplifications are often projected for arbitrary future years and benchmark frequencies. Consequently, such projections do not indicate when flood risk thresholds may be crossed given the current degree of local coastal protection. To better support adaptation planning and comparative vulnerability analyses, we project the timing of the frequency amplification of extreme sea levels relative to estimated local flood protection standards, using sea-level rise projections of IPCC AR6 until 2150. Our central estimates indicate that those degrees of protection will be exceeded ten times as frequently within the next 30 years (the lead time that large adaptation measures may take) at 26% and 32% of the tide gauges considered, and annually at 4% and 8%, for a low- and high-emissions scenario, respectively. Adaptation planners may use our framework to assess the available lead time and useful lifetime of protective infrastructure.

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Fig. 1: Timing of the decreasing degree of protection with and without adaptation.
Fig. 2: Required SLR and FLOPROS estimates.
Fig. 3: Required SLR for AFFLOPROS = 10 and 100.
Fig. 4: Projected timing of AFFLOPROS = 10 and 100.
Fig. 5: Projected timing until annual exceedance of estimated protection standards.
Fig. 6: Projected timing in three coastal cities.

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Data availability

The data used for this paper is available at https://doi.org/10.5281/zenodo.7505441 (ref. 69). The GESLA3 data was obtained from https://gesla787883612.wordpress.com/downloads/.

Code availability

The code to produce the data for this manuscript is available at https://doi.org/10.5281/zenodo.7503090 (ref. 70). The automatic threshold selection code that we used employs the MultiHazard R package (https://doi.org/10.5281/zenodo.6600757) developed in ref. 71.

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Acknowledgements

We thank S. Solari for sharing his automatic threshold selection code and T. Tiggeloven for elucidating the FLOPROS estimates. R.E.K. and M.O. were supported by the National Science Foundation (NSF) as part of the Megalopolitan Coastal Transformation Hub (MACH) under NSF award ICER-2103754. T.H.J.H., V.M.-S. and A.B.A.S. were supported by PROTECT. This project has received funding from the European Union’s Horizon 2020 research and innovation programme under grant agreement no. 869304, PROTECT contribution number 58. T.H.J.H. also received funding from the NPP programme of NWO.

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Authors and Affiliations

  1. Department of Estuarine and Delta Systems, NIOZ Royal Netherlands Institute for Sea Research, Yerseke, the Netherlands

    Tim H. J. Hermans, Víctor Malagón-Santos & Aimée B. A. Slangen

  2. Department of Geosciences and Remote Sensing, Delft University of Technology, Delft, the Netherlands

    Tim H. J. Hermans

  3. Institute for Marine and Atmospheric Research Utrecht, Utrecht University, Utrecht, the Netherlands

    Tim H. J. Hermans

  4. Department of Hydraulic Engineering, Delft University of Technology, Delft, the Netherlands

    Caroline A. Katsman

  5. Department of Civil, Construction and Environmental Engineering, University of Central Florida, Orlando, FL, USA

    Robert A. Jane

  6. Princeton School of Public and International Affairs, Princeton University, Princeton, NJ, USA

    D. J. Rasmussen & Michael Oppenheimer

  7. Deltares, Delft, the Netherlands

    Marjolijn Haasnoot

  8. Department of Geosciences, Utrecht University, Utrecht, the Netherlands

    Marjolijn Haasnoot

  9. GRO Intelligence, New York, NY, USA

    Gregory G. Garner

  10. Department of Earth and Planetary Sciences, Rutgers University, Piscataway, NJ, USA

    Robert E. Kopp

  11. Rutgers Institute of Earth, Ocean, and Atmospheric Sciences, Rutgers University, New Brunswick, NJ, USA

    Robert E. Kopp

  12. Department of Geosciences, Princeton University, Princeton, NJ, USA

    Michael Oppenheimer

  13. High Meadows Institute, Princeton University, Princeton, NJ, USA

    Michael Oppenheimer

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Contributions

T.H.J.H. conceived the study in consultation with A.B.A.S., C.A.K., R.E.K., M.O., D.J.R., M.H. and V.M.-S. T.H.J.H. produced the results and figures and led the writing of the paper, with input from all coauthors. G.G.G. provided the sea-level projections. T.H.J.H. and V.M.-S. processed the GESLA3 observations, and V.M.-S. and R.A.J. performed the automatic threshold selection.

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Correspondence to Tim H. J. Hermans.

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Extended data

Extended Data Fig. 1 Generalized Pareto distribution parameters.

a, selected threshold percentiles, b, central estimate of scale parameters, c, central estimate of shape parameters. The map insets zoom in on three regions densely covered with tide gauges (US East Coast, Europe and Southeast Asia).

Extended Data Fig. 2 Schematic influence of distribution parameters and fRef on required SLR.

Schematic illustration of how the required SLR for an amplification factor of 100 (blue, vertical lines) depends on the form of the return curve (black) and on the reference return frequency fRef, for different scale (σ) and shape (ξ) parameters: a, σ = 0.05, ξ = 0.2; b, σ = 0.05, ξ = − 0.2; c, σ = 0.1, ξ = 0.2. The y-axis in c was extended to accommodate the steep return curve and large required SLR.

Extended Data Fig. 3 Sensitivity of required SLR to fRef.

a, central estimate of required SLR [m] for AFFLOPROS = 10 minus that for AF0.01 = 10. b, central estimate of required SLR [m] for AFFLOPROS = 100 minus that for AF0.01 = 100.

Extended Data Fig. 4 Projected timing of AFFLOPROS = 10 and 100.

a, c, e & g, central estimate of the timing [yr] of AFFLOPROS = 10 at GESLA3 tide gauges (SSP1-2.6-lowconf, SSP2-4.5, SSP5-8.5 & SSP5-8.5-lowconf). b, d, f & h, as in a, c, e & g, but for AFFLOPROS = 100. White indicates where the projected timing evaluates to later than 2150 and cyan indicates where a relative sea-level fall is projected. The map insets zoom in on three regions densely covered with tide gauges (US East Coast, Europe and Southeast Asia).

Extended Data Fig. 5 Relative sea-level change projected for 2150.

Median sea-level projections of IPCC AR611,26 at GESLA3 tide gauges, in 2150 relative to 2022, for a, SSP1-2.6, and b, SSP3-7.0. The median at each location is derived from the probability box bounding the distributions of the workflows extending to 2150 (see Methods). The map insets in the other subpanels zoom in on three regions densely covered with tide gauges (US East Coast, Europe and Southeast Asia).

Extended Data Fig. 6 Projected timing percentiles of AFFLOPROS = 10 and 100.

a, & b, 5th and c, & d, 95th percentiles of the probability box of the projected timing [yr] of AFFLOPROS = 10 and 100 (SSP1-2.6). e, f, g & h, as in a, b, c & d, but for SSP3-7.0. White indicates where the projected timing evaluates to later than 2150 and cyan indicates where a relative sea-level fall is projected. The map insets zoom in on three regions densely covered with tide gauges (US East Coast, Europe and Southeast Asia).

Extended Data Fig. 7 Sensitivity of projected timing.

a-b, central timing estimates of AFFLOPROS = 100 derived using locally selected thresholds (Extended Data Fig. 1a) minus those derived using the median selected threshold percentile 98.8% at each tide gauge [yr], under SSP1-2.6 (blue) and SSP3-7.0 (orange). c-d, central timing estimates of AFFLOPROS = 100 minus those of AF0.01 = 100 [yr], under SSP1-2.6 (blue) and SSP3-7.0 (orange). Timing differences are only displayed where both compared estimates evaluate to before 2150. The map insets in the other subpanels zoom in on three regions densely covered with tide gauges (US East Coast, Europe and Southeast Asia).

Extended Data Fig. 8 Projected and required SLR at three coastal cities.

a, b, & c, projected relative SLR of IPCC AR611,26 nearest to tide gauges ‘Sheerness’, ‘Xiamen’ and ‘Hoek_van_Holland’ under SSP1-2.6 (blue) and SSP3-7.0 (orange), relative to 2022. The solid lines indicate the medians and the shading the 5-95% ranges of the probability boxes. d, e, & f, required SLR for the amplification of fRef (see Fig. 6) at these locations. The solid lines indicate the central estimates and the shading the 5-95% ranges. Combining the projected and required SLR samples shaping the distributions shown in this figure results in the projected timing in Fig. 6.

Extended Data Fig. 9 Methodology flowchart.

Methodology used to project the timing of amplification factors. Grey: input data; lightblue: steps taken; yellow: output obtained.

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Hermans, T.H.J., Malagón-Santos, V., Katsman, C.A. et al. The timing of decreasing coastal flood protection due to sea-level rise. Nat. Clim. Chang. 13, 359–366 (2023). https://doi.org/10.1038/s41558-023-01616-5

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