Skip to main content

Thank you for visiting You are using a browser version with limited support for CSS. To obtain the best experience, we recommend you use a more up to date browser (or turn off compatibility mode in Internet Explorer). In the meantime, to ensure continued support, we are displaying the site without styles and JavaScript.

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


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.

This is a preview of subscription content, access via your institution

Access options

Rent or buy this article

Get just this article for as long as you need it


Prices may be subject to local taxes which are calculated during checkout

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.

Data availability

The data used for this paper is available at (ref. 69). The GESLA3 data was obtained from

Code availability

The code to produce the data for this manuscript is available at (ref. 70). The automatic threshold selection code that we used employs the MultiHazard R package ( developed in ref. 71.


  1. Oppenheimer, M. et al. in IPCC Special Report on the Ocean and Cryosphere in a Changing Climate (eds Pörtner, H.-O. et al.) Ch. 4 (IPCC, Cambridge Univ. Press, 2019).

  2. Vousdoukas, M. I. et al. Global probabilistic projections of extreme sea levels show intensification of coastal flood hazard. Nat. Commun. 9, 2360 (2018).

    Article  Google Scholar 

  3. Howard, T., Palmer, M. D. & Bricheno, L. M. Contributions to 21st century projections of extreme sea-level change around the UK. Environ. Res. Lett. 1, 095002 (2019).

    Google Scholar 

  4. Muis, S. et al. A high-resolution global dataset of extreme sea levels, tides, and storm surges, including future projections. Front. Mar. Sci. 7, 263 (2020).

    Article  Google Scholar 

  5. Hunter, J. A simple technique for estimating an allowance for uncertain sea-level rise. Clim. Change 113, 239–252 (2012).

    Article  Google Scholar 

  6. Church, J. A. et al. in Climate Change 2013: The Physical Science Basis (eds Stocker, T. F. et al.) Ch. 13 (IPCC, Cambridge Univ. Press, 2013).

  7. Buchanan, M. K., Kopp, R. E., Oppenheimer, M. & Tebaldi, C. Allowances for evolving coastal flood risk under uncertain local sea-level rise. Clim. Change 137, 347–362 (2016).

    Article  Google Scholar 

  8. Wahl, T. et al. Understanding extreme sea levels for broad-scale coastal impact and adaptation analysis. Nat. Commun. 8, 16075 (2017).

    Article  CAS  Google Scholar 

  9. Frederikse, T. et al. Antarctic ice sheet and emission scenario controls on 21st-century extreme sea-level changes. Nat. Commun. 11, 390 (2020).

    Article  CAS  Google Scholar 

  10. Taherkhani, M. et al. Sea-level rise exponentially increases coastal flood frequency. Sci. Rep. 10, 6466 (2020).

    Article  CAS  Google Scholar 

  11. Fox-Kemper, B. et al. in Climate Change 2021: The Physical Science Basis (eds Masson-Delmotte, V. et al.) Ch. 9 (IPCC, Cambridge Univ. Press, 2021).

  12. Rasmussen, D. et al. Extreme sea level implications of 1.5 C, 2.0 C, and 2.5 C temperature stabilization targets in the 21st and 22nd centuries. Environ. Res. Lett. 13, 034040 (2018).

    Article  Google Scholar 

  13. Tebaldi, C. et al. Extreme sea levels at different global warming levels. Nat. Clim. Change 11, 746–751 (2021).

    Article  Google Scholar 

  14. Rasmussen, D. J., Kulp, S., Kopp, R. E., Oppenheimer, M. & Strauss, B. H. Popular extreme sea level metrics can better communicate impacts. Clim. Change 170, 30 (2022).

    Article  CAS  Google Scholar 

  15. Haasnoot, M. et al. Adaptation to uncertain sea-level rise; how uncertainty in antarctic mass-loss impacts the coastal adaptation strategy of the Netherlands. Environ. Res. Lett. 15, 034007 (2019).

    Article  Google Scholar 

  16. Haasnoot, M. et al. Long-term sea-level rise necessitates a commitment to adaptation: a first order assessment. Clim. Risk Manag. 34, 100355 (2021).

    Article  Google Scholar 

  17. Slangen, A. B., Haasnoot, M. & Winter, G. Rethinking sea-level projections using families and timing differences. Earth’s Future 10, e2021EF002576 (2022).

    Article  Google Scholar 

  18. Cooley, S. et al. in Climate Change 2022: Impacts, Adaptation and Vulnerability (eds Pörtner H.-O. et al.) Ch. 3 (IPCC, Cambridge Univ. Press, 2022).

  19. Lambert, E., Rohmer, J., Cozannet, G. L. & van de Wal, R. S. W. Adaptation time to magnified flood hazards underestimated when derived from tide gauge records. Environ. Res. Lett. 15, 074015 (2020).

    Article  Google Scholar 

  20. Sweet, W. et al. Global and regional sea level rise scenarios for the United States: updated mean projections and extreme water level probabilities along U.S. coastlines. Technical Report (NOAA, 2022).

  21. Tiggeloven, T. et al. Global-scale benefit-cost analysis of coastal flood adaptation to different flood risk drivers using structural measures. Nat. Hazards Earth Syst. Sci. 20, 1025–1044 (2020).

    Article  Google Scholar 

  22. Mooyaart, L. F. & Jonkman, S. N. Overview and design considerations of storm surge barriers. J. Waterw. Port, Coast. Ocean Eng. (2017).

  23. Reeder, T. & Ranger, N. How Do You Adapt in an Uncertain World? Lessons from the Thames Estuary 2100 Project (World Resources Report, 2011).

  24. Kwadijk, J. C. et al. Using adaptation tipping points to prepare for climate change and sea level rise: a case study in the Netherlands. Wiley Interdiscip Rev. Clim. Change 1, 729–740 (2010).

    Article  Google Scholar 

  25. Haasnoot, M., Middelkoop, H., Offermans, A., van Beek, E. & van Deursen, W. P. Exploring pathways for sustainable water management in river deltas in a changing environment. Clim. Change 115, 795–819 (2012).

    Article  Google Scholar 

  26. Garner, G. et al. IPCC AR6 sea level projections. Zenodo (2021).

  27. Meinshausen, M. et al. The shared socio-economic pathway (SSP) greenhouse gas concentrations and their extensions to 2500. Geosci. Model Dev. 13, 3571–3605 (2020).

    Article  CAS  Google Scholar 

  28. Solari, S., Egüen, M., Polo, M. J. & Losada, M. A. Peaks over threshold (POT): a methodology for automatic threshold estimation using goodness of fit p-value. Water Resour. Res. 53, 2833–2849 (2017).

    Article  Google Scholar 

  29. Haigh, D. et al. GESLA Version 3: a major update to the global higher-frequency sea-level dataset. Geosci. Data J. 00, 1–22 (2022).

    Google Scholar 

  30. Woodworth, P. L. et al. Towards a global higher-frequency sea level data set. Geosci. Data J. 3, 50–59 (2017).

    Article  Google Scholar 

  31. Scussolini, P. et al. FLOPROS: an evolving global database of flood protection standards. Nat. Hazards Earth Syst. Sci. 16, 1049–1061 (2016).

    Article  Google Scholar 

  32. Hinkel, J. et al. Uncertainty and bias in global to regional scale assessments of current and future coastal flood risk. Earth’s Future 9, e2020EF001882 (2021).

    Article  CAS  Google Scholar 

  33. Vitousek, S. et al. Doubling of coastal flooding frequency within decades due to sea-level rise. Sci. Rep. 7, 1399 (2017).

    Article  Google Scholar 

  34. Bloemendaal, N. et al. Generation of a global synthetic tropical cyclone hazard dataset using storm. Sci. Data 7, 40 (2020).

    Article  Google Scholar 

  35. Dullaart, J. C. M. et al. Accounting for tropical cyclones more than doubles the global population exposed to low-probability coastal flooding. Commun. Earth Environ. (2021).

  36. Haigh, I. D. et al. Estimating present day extreme water level exceedance probabilities around the coastline of Australia: tropical cyclone-induced storm surges. Clim. Dyn. 42, 139–157 (2014).

    Article  Google Scholar 

  37. O’Grady, J. G., Stephenson, A. G. & McInnes, K. L. Gauging mixed climate extreme value distributions in tropical cyclone regions. Sci. Rep. 12, 4626 (2022).

    Article  Google Scholar 

  38. Thames Estuary 2100 (TE2100). Technical Report (Environment Agency, 2012).

  39. Fang, J. et al. Coastal flood risks in China through the 21st century - an application of DIVA. Sci. Total Environ. 704, 135311 (2020).

    Article  CAS  Google Scholar 

  40. Brink, H. W. V. D. & Goederen, S. D. Recurrence intervals for the closure of the Dutch Maeslant surge barrier. Ocean Sci. 13, 691–701 (2017).

    Article  Google Scholar 

  41. Penning-Rowsell, E. C., Haigh, N., Lavery, S. & McFadden, L. A threatened world city: the benefits of protecting London from the sea. Nat. Hazards 66, 1383–1404 (2013).

    Article  Google Scholar 

  42. Hinkel, J. et al. Meeting user needs for sea level rise information: a decision analysis perspective. Earth’s Future 7, 320–337 (2019).

    Article  Google Scholar 

  43. Rasmussen, D. J., Buchanan, M. K., Kopp, R. E. & Oppenheimer, M. A flood damage allowance framework for coastal protection with deep uncertainty in sea level rise. Earth’s Future 8, e2019EF001340 (2020).

    Article  CAS  Google Scholar 

  44. Eijgenraam, C. et al. Economically efficient standards to protect the Netherlands against flooding. Interfaces 44, 7–21 (2014).

    Article  Google Scholar 

  45. Hall, J. W., Brown, S., Nicholls, R. J., Pidgeon, N. F. & Watson, R. T. Proportionate adaptation. Nat. Clim. Change 2, 833–834 (2012).

    Article  Google Scholar 

  46. Lionello, P., Nicholls, R. J., Umgiesser, G. & Zanchettin, D. Venice flooding and sea level: past evolution, present issues, and future projections (introduction to the special issue). Nat. Hazards Earth Syst. Sci. 21, 2633–2641 (2021).

    Article  Google Scholar 

  47. Hall, J. W., Harvey, H. & Manning, L. J. Adaptation thresholds and pathways for tidal flood risk management in london. Clim. Risk Manag. 24, 42–58 (2019).

    Article  Google Scholar 

  48. Idier, D. et al. Coastal flood: a composite method for past events characterisation providing insights in past, present and future hazards-joining historical, statistical and modelling approaches. Nat. Hazards 101, 465–501 (2020).

    Article  Google Scholar 

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

    Article  Google Scholar 

  50. Widlansky, M. J., Long, X. & Schloesser, F. Increase in sea level variability with ocean warming associated with the nonlinear thermal expansion of seawater. Commun. Earth Environ. 1, 9 (2020).

    Article  Google Scholar 

  51. Hermans, T. H. J. et al. The effect of wind stress on seasonal sea-level change on the Northwestern European Shelf. J. Clim. 35, 1745–1759 (2021).

    Article  Google Scholar 

  52. Pickering, M. D. et al. The impact of future sea-level rise on the global tides. Continental Shelf Res. 142, 50–68 (2017).

    Article  Google Scholar 

  53. Haigh, I. D. et al. The tides they are a changin’: a comprehensive review of past and future non astronomical changes in tides, their driving mechanisms and future implications. Rev. Geophysics 57, 2018RG000636 (2019).

    Google Scholar 

  54. Rashid, M. M., Wahl, T. & Chambers, D. P. Extreme sea level variability dominates coastal flood risk changes at decadal time scales. Environ. Res. Lett. 16, 024026 (2021).

    Article  Google Scholar 

  55. Ward, P. J. et al. Dependence between high sea-level and high river discharge increases flood hazard in global deltas and estuaries. Environ. Res. Lett. 13, 084012 (2018).

    Article  Google Scholar 

  56. Couasnon, A. et al. Measuring compound flood potential from river discharge and storm surge extremes at the global scale and its implications for flood hazard. Nat. Hazards Earth Syst. Sci. 20, 489–504 (2019).

    Article  Google Scholar 

  57. Wahl, T. & Chambers, D. P. Evidence for multidecadal variability in US extreme sea level records. J. Geophys. Res. Oceans 120, 1527–1544 (2015).

    Article  Google Scholar 

  58. Kirezci, E. et al. Projections of global-scale extreme sea levels and resulting episodic coastal flooding over the 21st century. Sci. Rep. 10, 11629 (2020).

    Article  CAS  Google Scholar 

  59. Thompson, P. R. et al. Rapid increases and extreme months in projections of United States high-tide flooding. Nat. Clim. Change 11, 584–590 (2021).

    Article  Google Scholar 

  60. Rotzoll, K. & Fletcher, C. H. Assessment of groundwater inundation as a consequence of sea-level rise. Nat. Clim. Change 3, 477–481 (2013).

    Article  Google Scholar 

  61. Hallegatte, S. Strategies to adapt to an uncertain climate change. Glob. Environ. Change 19, 240–247 (2009).

    Article  Google Scholar 

  62. Rasmussen, D. J., Kopp, R. E., Shwom, R. & Oppenheimer, M. The political complexity of coastal flood risk reduction: lessons for climate adaptation public works in the U.S. Earth’s Future 9, e2020EF001575 (2021).

    Article  Google Scholar 

  63. Haasnoot, M., Kwakkel, J. H., Walker, W. E. & ter Maat, J. Dynamic adaptive policy pathways: a method for crafting robust decisions for a deeply uncertain world. Glob. Environ. Change 23, 485–498 (2013).

    Article  Google Scholar 

  64. Barnett, J. et al. A local coastal adaptation pathway. Nat. Clim. Change 4, 1103–1108 (2014).

    Article  Google Scholar 

  65. Haigh, I. D. et al. Spatial and temporal analysis of extreme sea level and storm surge events around the coastline of the UK. Sci. Data 3, 160107 (2016).

    Article  Google Scholar 

  66. Coles, S. An Introduction to Statistical Modeling of Extreme Values (Springer, 2001).

  67. Ghanbari, M., Arabi, M., Obeysekera, J. & Sweet, W. A coherent statistical model for coastal flood frequency analysis under nonstationary sea level conditions. Earth’s Future 7, 162–177 (2019).

    Article  Google Scholar 

  68. Cozannet, G. L., Manceau, J. & Rohmer, J. Bounding sea level projections within the framework of the possibility theory. Environ. Res. Lett. 12, 014012 (2017).

    Article  Google Scholar 

  69. Hermans, T. H. J. et al. Projections of the timing of decreasing coastal flood protection. Zenodo (2023).

  70. Hermans, T. H. J. & Malagon-Santos, V. Timingafs: v1.0.0. Zenodo (2023).

  71. Jane, R., Cadavid, L., Obeysekera, J. & Wahl, T. Multivariate statistical modelling of the drivers of compound flood events in south Florida. Nat. Hazards Earth Syst. Sci. 20, 2681–2699 (2020).

    Article  Google Scholar 

Download references


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.

Author information

Authors and Affiliations



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.

Corresponding author

Correspondence to Tim H. J. Hermans.

Ethics declarations

Competing interests

The authors declare no competing interests.

Peer review

Peer review information

Nature Climate Change thanks Kristina Hill, David Johnson, Goneri LeCozannet and the other, anonymous, reviewer(s) for their contribution to the peer review of this work.

Additional information

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

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.

Rights and permissions

Springer Nature or its licensor (e.g. a society or other partner) holds exclusive rights to this article under a publishing agreement with the author(s) or other rightsholder(s); author self-archiving of the accepted manuscript version of this article is solely governed by the terms of such publishing agreement and applicable law.

Reprints and Permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

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).

Download citation

  • Received:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI:

This article is cited by


Quick links

Nature Briefing

Sign up for the Nature Briefing newsletter — what matters in science, free to your inbox daily.

Get the most important science stories of the day, free in your inbox. Sign up for Nature Briefing