Li, F., van Gelder, P.H.A.J.M., Callaghan, D.P., Jongejan, R.B., den Hijer, C. and Ranasinghe, R., 2013. Probabilistic modelling of wave climate and predicting dune erosion considering sea level rise Knowledge about future oceanographic events will assist governments to better manage risk in coastal zones, a crucial task in the light of projected sea level rise, population growth and economic development. In this study, a 31-year data set of deep water wave climate parameters and bathymetry measurements (yearly cross-shore transect surveys) at Noordwijk, the Netherlands, were analyzed (1) to jointly estimate storm events variates of deep water wave conditions, and (2) to probabilistically compute dune erosion volume and the resulting coastal retreat distance with the simulated wave climate and plausible local sea level rise scenarios by 2100. The probabilistic coastline retreat models were applied and adjusted to the study site. Based on the outcomes of this application, a modeling technique can be established to propose a framework for probabilistically describing the coastal risk along the Dutch coast.

This paper presents an application of the Bayesian belief network for coastal erosion management at the regional scale. A “Bayesian ERosion Management Network” (BERM-N) is developed and trained based on yearly cross-shore profile data available along the Holland coast. Profiles collected for over 50 years and at 604 locations were combined with information on different sand nourishment types (i.e., beach, dune, and shoreface) and volumes implemented during the analyzed time period. The network was used to assess the effectiveness of nourishments in mitigating coastal erosion. The effectiveness of nourishments was verified using two coastal state indicators, namely the momentary coastline position and the dune foot position. The network shows how the current nourishment policy is effective in mitigating the past erosive trends. While the effect of beach nourishment was immediately visible after implementation, the effect of shoreface nourishment reached its maximum only 5–10 years after implementation of the nourishments. The network can also be used as a predictive tool to estimate the required nourishment volume in order to achieve a predefined coastal erosion management objective. The network is interactive and flexible and can be trained with any data type derived from measurements as well as numerical models.

This paper describes an investigation on the usefulness of Bayesian Networks in the safety assessment of dune coasts. A network has been created that predicts the erosion volume based on hydraulic boundary conditions and a number of cross-shore profile indicators. Field measurement data along a large part of the Dutch coast has been used to train the network. Corresponding storm impact on the dunes was calculated with an empirical dune erosion model named duros+. Comparison between the Bayesian Network predictions and the original duros+ results, here considered as observations, results in a skill up to 0.88, provided that the training data covers the range of predictions. Hence, the predictions from a deterministic model (duros+) can be captured in a probabilistic model (Bayesian Network) such that both the process knowledge and uncertainties can be included in impact and vulnerability assessments.