In this paper we present a methodology to estimate the probability of future coastal flooding given uncertainty over possible sea level rise. We take as an example the range of sea level rise magnitudes for 2100 contained in the IPCC Third Assessment Report [Church,., 2001. Changes in sea level. In Houghton, J.T. et al. (eds), Climate Change 2001: The Scientific Basis. Contribution of Working Group I to the Third Assessment Report of the Intergovernmental Panel on Climate Change. Cambridge University Press, Cambridge, United Kingdom, 881pp.] and infer a plausible probability distribution for this range. We then use a Monte Carlo procedure to sample from this distribution and use the resulting values as an additional boundary forcing for a two-dimensional model of coastal inundation used to simulate a 1 in 200 year extreme water level event. This yields an ensemble of simulations for an event of this magnitude occurring in 2100, where each member represents a different possible scenario of sea level rise by this time. We then develop a methodology to approximate the probability of flooding in each model grid cell over the ensemble and by combining these hazards maps with maps of land use values (consequence) we are able to estimate spatial contributions to flood risk that can aid planning and investment decisions. The method is then applied to a 32 km section of the UK coast in Somerset, South-West England and used to estimate the monetary losses and risk due a 1 in 200 year recurrence interval event under: (a) current conditions; (b) with the IPCC's most plausible value for sea level rise by 2100 (0.48 m) and (c) using the above methodology to fully account for uncertainty over possible sea level rise. The analysis shows that undertaking a risk assessment using the most plausible sea level rise value may significantly underestimate monetary losses as it fails to account for the impact of low probability, high consequence events. The developed method provides an objective basis for decisions regarding future defence spending and can be easily extended to consider other sources of uncertainty such as changing event frequency–magnitude distribution, changing storm surge conditions or model structural uncertainty, either singly or in combination as joint probabilities.