Integrating a multivariate extreme value method within a system flood risk analysis model

Effective management of flooding requires models that are capable of quantifying flood risk. Quantification of flood risk involves both the quantification of probabilities of flooding and the associated consequences. Modern flood risk models account for the probabilities of extreme hydraulic loading events and also include a probabilistic representation of the performance of flood defence infrastructure and its associated reliability. The spatial and temporal variability of flood events makes probabilistic representation of the hydraulic loading conditions on the flood defences complex. In the system method used widely within England and Wales, simplifying assumptions relating to the spatial dependence of flood events are made. Recent research has shown the benefits of using improved multivariate extreme value methods to define the hydraulic loading conditions for flood risk analysis models. This paper describes the development of an improved modelling system that enhances the systems-based risk analysis model currently applied in practice, through the incorporation of a multivariate extreme value model. The improved system has been presented on a case study site in the North West of England

Adaptive flood risk management under climate change uncertainty using real options and optimisation

It is well recognised that adaptive and flexible flood risk strategies are required to account for future uncertainties. Development of such strategies is however, a challenge. Climate change alone is a significant complication but in addition complexities exist trying to identify the most appropriate set of mitigation measures, or interventions. There are a range of economic and environmental performance measures that require consideration and the spatial and temporal aspects of evaluating the performance of these is complex. All of these elements pose severe difficulties to decision makers. This paper describes a decision support methodology that has the capability to assess the most appropriate set of interventions to make in a flood system and the opportune time to make these interventions, given the future uncertainties. The flood risk strategies have been explicitly designed to allow for flexible adaptive measures by capturing the concepts of Real Options to evaluate potential flood risk management opportunities. A state of the art flood risk analysis tool is employed to evaluate the risk associated to each strategy over future points in time and a multi-objective genetic algorithm is utilised to search for the optimal adaptive strategies. The modelling system has been applied to a reach on the Thames Estuary (London, England), and initial results show the inclusion of flexibility is advantageous while the outputs provide decision makers with supplementary knowledge which previously has not been considered

Multiobjective Optimisation for Improved Management of Flood Risk

Effective flood risk management requires consideration of a range of different mitigation measures. Depending on the location, these could include structural or non-structural measures as well as maintenance regimes for existing levee systems. Risk analysis models are used to quantify the benefits, in terms of risk reduction, when introducing different measures; further investigation is required to identify the most appropriate solution to implement. Effective flood risk management decision making requires consideration of a range of performance criteria. Determining the better performing strategies, according to multiple criteria can be a challenge. This paper describes the development of a decision support system that couples a multi-objective optimisation algorithm with a flood risk analysis model and an automated cost model. The system has the ability to generate potential mitigation measures that are implemented at different points in time. It then optimises the performance of the mitigation measures against multiple criteria. The decision support system is applied to an area of the Thames Estuary and the results obtained demonstrate the benefits multiobjective optimisation can bring to flood risk management

A generic and practical wave overtopping model that includes uncertainty

Mean wave overtopping discharge is generally accepted to be a primary design criterion for assessing the performance of coastal structures. It is a boundary condition for many coastal flood risk assessments. Modern methods for assessing wave overtopping discharges and their consequences are well documented and reported. Among the various tools available for assessing wave overtopping, the use of artificial neural networks has become increasingly popular. This paper introduces the next stage in the development of these models. Using the same source data, the new generic meta-modelling overtopping model reduces uncertainties and gives clear guidance on the range and validity of the outputs

Multivariate extreme value modelling of sea conditions around the coast of England

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

FRMRC presentations to practitioners workshop

Practitioners workshop introduction - Infrastructure management Channels and their management Estimating blockage potential at culvert trash screens Fragility curves Predicting breach Simplified tools for risk assessment 2nd generation asset inspection techniques Use of non-invasive measuring techniques in asset inspection Asset deterioration - Assessment and measurement Attributing risk to assets - Examples from pilot projects Practitioner workshop on asset managment Multi-objective optimisation of flood risk mitigation measures, including real options Next steps to implementation and future research need

Briefing: Reliability and optimised maintenance for sea defences

This briefing article aims to provide a broader, explanatory commentary for non-specialists on the paper that follows in this issue on optimum repair intervals for sea defences

Staged uncertainty and sensitivity analysis within flood risk analysis

Modelling flood risk is complex and associated with many sources of uncertainty. Models that are unable to capture the full physical processes that they are intended to represent are widely used and some physical processes, like breach formation, for example, are poorly understood. Furthermore, statistical modelling of extreme events is often based on relatively short periods of observed data and knowledge of basic parameters within the flood system, such as defence crest level or floodplain property, is subject to inaccuracies. Uncertainty analysis is intrinsically linked to flood risk analysis and it is increasingly becoming acknowledged as an important component to explicitly include within the decision making process. Whilst methods for uncertainty analysis have been available for many years, these typically become computationally intensive and impractical when applied to flood system risk analysis