2015
DOI: 10.1016/j.coastaleng.2014.11.009
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Moving from deterministic towards probabilistic coastal hazard and risk assessment: Development of a modelling framework and application to Narrabeen Beach, New South Wales, Australia

Abstract: Traditional methods for assessing coastal hazards have not typically incorporated a rigorous treatment of uncertainty. Such treatment is necessary to enable risk assessments which are now required by emerging risk based coastal zone management/planning frameworks. While unresolved issues remain, relating to the availability of sufficient data for comprehensive uncertainty assessments, this will hopefully improve in coming decades. Here, we present a modelling framework which integrates geological, engineering … Show more

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Cited by 48 publications
(38 citation statements)
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“…For example, the currently used methods in the Netherlands to determine the probability of dike failure due to wave overtopping only include a few stochastic variables and use a model to determine the wave hydrodynamics that does not include infragravity waves or morphological changes. By using a full-probabilistic approach, the risks could potentially be further reduced and the designs further optimized, see e.g., Wainwright et al [3].…”
Section: Introductionmentioning
confidence: 99%
“…For example, the currently used methods in the Netherlands to determine the probability of dike failure due to wave overtopping only include a few stochastic variables and use a model to determine the wave hydrodynamics that does not include infragravity waves or morphological changes. By using a full-probabilistic approach, the risks could potentially be further reduced and the designs further optimized, see e.g., Wainwright et al [3].…”
Section: Introductionmentioning
confidence: 99%
“…For process-based simulation models that are used to predict short-term beach response to storms, model boundary conditions (e.g., waves and water levels) are relatively well known to high spatial and temporal resolutions, and so statistical techniques to manage the not insignificant uncertainty that is introduced by the selected parameterisation of complex models are of particular importance [77]. For behaviour-based models that are typically used to predict long-term shoreline change, similar techniques have been demonstrated to manage uncertainty in boundary conditions and model parameterisation [78][79][80][81]. Awareness and demand for uncertainty management in beach erosion and shoreline change predictions is growing within the coastal management community [58,81].…”
Section: Uncertainty Managementmentioning
confidence: 99%
“…Section 2 provided empirical evidence that this specific difficulty has not prevented the development of CCS in practice, as it presents examples where end-users have been able to include sea level information and coastal impact studies in their workflows. For example, coastal impacts studies are commonly used to justify "low-regret" strategies, such as maintaining ecological services and quality in coastal zones [115], relocating some buildings or activities, or to limit further urbanization in low lying or erodible coastal areas [116]. They are also used to anticipate the upgrade of defence works in coastal areas, which will be necessary to control safety levels despite sea level rise, demographic growth and land use pressure [32,36,89,90].…”
Section: Lack Of Formalized Requirements From End-usersmentioning
confidence: 99%
“…Most of them rely on equilibrium profile assumptions, such as the Bruun rule, to model the impacts of SLR [108,109,[129][130][131]. While flexible probabilistic modelling approaches for estimating setback lines from erosion are increasingly being developed [106,116,132,133], care is required in interpreting the uncertainties reported in IPCC SLR scenarios, particularly when representing the uncertainties by probability distributions (see Section 3.4.3 below). Similar cautions apply to the calculation of sea level allowances [32,[90][91][92].…”
Section: Lack Of Formalized Requirements From Translators Of Climate mentioning
confidence: 99%