Coastal populations continue to increase globally, causing potential damage costs of coastal hazards to rise and community resiliency to become a worldwide priority. Recently, Hurricane Sandy (2012) devastated areas of New York and New Jersey and caused overwash and breaching of several urbanized barrier islands along the U.S. eastern seaboard. This study focuses on the morphological response of Bay Head, NJ, a township on a barrier island fronted with a buried seawall. The hydrodynamics and morphology of Bay Head during Hurricane Sandy are simulated with XBeach, a numerical model designed to study these processes during storm events. From the simulations, the seawall protected Bay Head by effectively dissipating wave energy during the peak of the storm and from rapidly increasing bay water levels that flood the backbarrier region of the island. When the seawall is removed from the simulation, dune heights are lowered, allowing bay side flooding to cause a devastating erosive event that completely destroys the remaining dune system. XBeach indicates severe erosion seaward of ocean

Front cover: During Hurricane Sandy, a combination of storm waves and surge cut across the barrier island at Mantoloking, NJ, eroding a wide beach, destroying houses and roads, depositing sand up the barrier island and into the back bay. SummaryIntroduction and problem description Many of the most densely populated areas are located near the coast. Climate change and population growth put more and more pressure on these coastal areas. As free space is becoming sparse, coastal disaster risk reduction plans need to be spatially efficient. In this thesis the sandy coast with hard structures, such as buildings or dune revetments, is addressed. These structures can either provide additional protection, enhance local erosion by developing a scour hole in cross-shore direction or result in extra retreat of the barrier in longshore direction. Field measurements and experimental data featuring these phenomena are scarce, but the measurements of the devastating impact of Hurricane Sandy (October 2012) on the New Jersey shore provides new model validation possibilities.XBeach (Roelvink et al., 2009) is used in this thesis as a tool to describe the morphodynamic behavior and to investigate the importance of each individual process. The main objective of this thesis is to obtain a better understanding of the effects of hard elements on the erosion process during storm surges. This will be done by validating and evaluating existing theory, calculation rules and models both in a conceptual study and against three new field cases in New Jersey, USA, during Hurricane Sandy. The secondary objective is to determine how to calibrate XBeach in order to accurately reproduce overwash conditions, since the model currently shows substantial overestimation of the erosion rates.Conceptual study and DnA calculation rules In a conceptual model the cross-shore and longshore effects of hard structures are reproduced. In cross-shore direction a structure cuts off the sediment supply to the beach. A positive effect is the prevention of erosion causing lower erosion volumes in hard cross-sections. One downside of a hard element is that less sediment can be deposited in the nearshore and therefore the efficiency of dissipating wave energy is lower (WL | Delft Hydraulics, 1987). This results in higher energetic conditions in front of the hard element, which can result in the development of a scour hole. Structures also have an impact in longshore direction. First of all, an alongshore exchange of sediment, driven by set-up differences, will result in less accretion. Second, locally higher short waves will result in more erosion. The impact of the hard element is that at the sides the barrier will retreat more (increased set-back). This increase in retreat is effective over certain length (influence length). Erosive processes are in XBeach for the collision regime responsible for 2/3 of the longshore effect.In order to describe the longshore effect of hard structures, the DnA calculation rules are developed. In addition,Deltares and Arcadis (2013)...

Wave overtopping is an important design criterion for coastal structures such as dikes, breakwaters and promenades. Hence, the prediction of the expected wave overtopping discharge is an important research topic. Existing prediction tools consist of empirical overtopping formulae, machine learning techniques like neural networks, and numerical models. In this paper, an innovative machine learning method—gradient boosting decision trees—is applied to the prediction of mean wave overtopping discharges. This new machine learning model is trained using the CLASH wave overtopping database. Optimizations to its performance are realized by using feature engineering and hyperparameter tuning. The model is shown to outperform an existing neural network model by reducing the error on the prediction of the CLASH database by a factor of 2.8. The model predictions follow physically realistic trends for variations of important features, and behave regularly in regions of the input parameter space with little or no data coverage.

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