Coastal flood risk is a function of the probability of coastal flooding and the consequential damage. Scenarios of potential changes in coastal flood risk due to changes in climate, society and the economy over the twenty-first century have been analysed using a national-scale quantified flood risk analysis methodology. If it is assumed that there will be no adaptation to increasing coastal flood risk, the expected annual damage in England and Wales due to coastal flooding is predicted to increase from the current 0.5 billion pounds to between 1.0 pound and 13.5 billion pounds, depending on the scenario of climate and socio-economic change. The proportion of national flood risk that is attributable to coastal flooding is projected to increase from roughly 50% to between 60 and 70%. Scenarios of adaptation to increasing risk, by construction of coastal dikes or retreat from coastal floodplains, are analysed. These adaptations are shown to be able to reduce coastal flood risk to between 0.2 pounds and 0.8 billion pounds. The capital cost of the associated coastal engineering works is estimated to be between 12 pounds and 40 billion pounds. Non-structural measures to reduce risk can make a major contribution to reducing the cost and environmental impact of engineering measures.

It is widely recognised that coastal flood events can arise from combinations of extreme waves and sea levels. For flood risk analysis and the design of coastal structures it is therefore necessary to assess the joint probability of the occurrence of these variables. Traditional methods have involved the application of joint probability contours, defined in terms of extremes of sea conditions that can, if applied without correction factors, lead to the underestimation of flood risk and under-design of coastal structures. This paper describes the application of a robust multivariate statistical model to analyse extreme offshore waves, wind and sea levels around the coast of England. The approach described here is risk based in that it seeks to define extremes of response variables directly, rather than the joint extremes of sea conditions. The output of the statistical model comprises a Monte Carlo simulation of extreme events. These distributions of extreme events have been transformed from offshore to nearshore using a statistical emulator of a wave transformation model. The resulting nearshore extreme sea condition distributions have the potential to be applied for a range of purposes. The application is demonstrated using two structures located on the south coast of England. Notation

Increasingly, an understanding of flood risk across regions and nations, and an ability to explore how these might change in time, is seen as a prerequisite to effective and efficient flood risk management. In response, specific flood risk analysis methods have been developed that are both accurate and fast to run. Although widely acknowledged as desirable, it has not previously been possible to quantify the uncertainty associated with the assessed flood probability, consequence, or risk. To help overcome this deficiency, an efficient method for the propagation of epistemic uncertainties through large-scale flood risk system models has been developed and trialed for three pilot catchments. The approach is allied to an efficient sensitivity analysis that enables the influence of individual uncertainties on the output quantity of risk to be isolated, enabling future research, development, and data-gathering efforts to be focused.

Abstract. The National Flood Risk Assessment (NaFRA) for England and Wales was initially undertaken in 2002 with frequent updates since. NaFRA has become a key source of information on flood risk, informing policy and investment decisions as well as communicating risk to the public and insurers. To make well informed decisions based on these data, users rightfully demand to know the confidence they can place in them. The probability of inundation and associated damage however cannot be validated in the traditional sense, due the rare and random nature of damaging floods and the lack of a long (and widespread) stationary observational record (reflecting not only changes in climate but also the significant changes in land use and flood defence infrastructure that are likely to have occurred). To explore the validity of NaFRA this paper therefore provides a bottom-up qualitative exploration of the potential errors within the supporting methods and data. The paper concludes by underlining the need for further research to understand how to robustly validate probabilistic risk models.

Risk analysis models of fluvial and coastal flood systems have been in use for over a decade. They have been applied to support a wide range of flood risk management decisions, including long term strategic planning and shorter term asset management. Models that are currently applied in practice make a number of simplifying assumptions. The development of a new model that offers a major improvement over these methods is described. The new model incorporates: a unique dynamic 2D inundation model that captures sub-mesh element topography (RFSM EDA); a new computationally efficient model of embankment breach growth (AREBA) and extends the range of consequences considered to include the loss of life. The model has been applied on a pilot site to demonstrate its capabilities. A flood system risk analysis model with dynamic sub-element 2D inundation model, dynamic breach growth and life-loss

Traditional flood-risk assessment considers coastal defences to be static features with foreshores represented simply with an assigned elevation and slope. However, beach elevations can vary rapidly over time, perhaps fluctuating seasonally, and often losing or gaining volume over a longer time period. Their dynamic nature ultimately influences the risk of coastal erosion and flooding. In a regional flood-risk assessment, in which beaches offer protection to a variety of backshore features such as seawalls, soft cliffs, and dunes, the ability to represent the beach dynamics fronting these defences is important. At regional scale, it is also necessary to consider the various backshore environments that may be encountered over a long stretch of coastline, and how their individual processes affect flood risk, particularly if they are erodible. An integrated framework of dynamically linked numerical models of coastal processes and statistical analysis methods is being developed to enhance regional flood-risk assessment via consideration of coastal evolution and foreshore morphodynamics. The framework is run entirely from within commercially available geographical information system software where model operation and numerical output is managed; standard geographical information system analysis and database capabilities are therefore also available within the modelling system. This paper outlines the basis of the modular framework, and demonstrates how flood-risk assessment is enhanced.

Rapid deterministic modelling of shoreline evolution at regional and coastal-scheme scale enables Monte-Carlo simulations by which long-term shoreline statistics can be estimated. This paper describes UnaLinea, a fast, accurate finite difference solver of 1 Corresponding author the one-line sediment continuity equation. The model is verified for the evolution of an initially straight shoreline of a plane beach subject to regular breaking waves at constant angle of incidence in the presence of either a groyne or a continuous singlepoint feed of sediment. Grid convergence and stability tests are used to obtain accurate, stable results, with satisfactory computational efficiency. Influences of wave input filtering and event-based sediment loading are considered. The rapid deterministic model is applied to Monte-Carlo simulations of the evolution of the west coast of Calabria, Italy for different scenarios including increased sediment load from a river and selected beach nourishment. The potential role of probabilistic shoreline evolution in regional coastal flood-risk assessment is explored through application to an idealised stretch of the Holderness coastline, U.K., where flood depths and expected damage are estimated for a 1000 year return period event.

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