The design of offshore and coastal infrastructures, sand nourishment and other ’soft’ coastal interventions require the analysis of environmental variables (e.g. wind, waves, rainfall) that can potentially cause the failure of such structures. Processes such as overtopping, beach erosion, and coastal flooding can result from a combined action of two or more physical processes. Traditional infrastructure design practices assume the highest load previously experienced as the design load, regardless of possible interactions between variables (or processes). This may lead to a misrepresentation of critical design loads. This thesis presents a methodology for defining infrastructure design loads accounting for their interdependence. The methodology is general and is based on regular vines. Vines are graphical tools for defining high dimensional distribution functions through pair-copula construction. With this premise in mind, the main effort was concentrated in formulating a series of steps to integrate several stages of the design: from the processing of raw data up to the choice of design loads for any specific design purpose. The vine-based methodology was applied to the design of a breakwater in Galveston Bay, Texas. This application showed that accounting for the interdependence between design variables provides a more comprehensive description of the physical system acting on the infrastructure. However, the vine-based method is computationally demanding. Hence, the applicability of this methodology should be evaluated on a case by case basis. In parallel, the possibility to define goodness of fit test for vine-copula based on the concept of tree-equivalent classes is explored. The focus is on model selection strategies based on graphical and statistical properties of the vines. The main motivation to investigate model selection strategies for vines is the considerably large computational time needed to fit all regular vines in more than 6 nodes to the data. In this thesis, a novel algorithm is developed to facilitate the implementation of vines in higher dimensions (vines with more than 6 nodes). This algorithm significantly reduces the computational effort to select a regular vine by allowing the user to test only a subgroup of vines in n-nodes constructed on specific characteristics of the vines in (n-1)-nodes.

To counteract the effect of climate change, a global agreement was put into force in 2016, aiming to limit the increase in average global warming and reduce greenhouse gas emissions. In that respect, European countries are investing in cleaner sources of energy and predominantly offshore wind, which has seen a rapid growth over the last years. The North Sea is a region suitable for developing offshore wind energy, given its strong wind climate and the relatively shallow waters. Many large-scale offshore wind farm (OWF) developments are currently ongoing in that region, while many more are planned and consented for the coming decades. The concept of large-scale OWF developments has spurred many discussions addressing its potential effect on the greater North Sea region. However, the long-term effect on the surrounding coastal areas has never been studied in detail. Especially for a low-lying country as the Netherlands, assessing the impact of large-scale OWFs is of great importance. This study aims at exploring the effect of future large-scale OWFs in the North Sea, focusing on the wave climate and the coastline response of the Dutch coast. Based on the roadmap for developing offshore wind energy until 2050, existing and future designated OWF areas are accounted in the North Sea region. The effect of OWFs on wind is introduced in a schematized way, with a constant decrease in wind speed of 20% inside the OWF areas, based on literature knowledge. Supplementary, based on the vision of creating an artificial energy island for storing and redistributing the wind farm generated electricity, a 5 km2 island is introduced, approximately 30 km away from the Dutch coast. The effect on the nearshore wave climate is studied using the numerical model SWAN, while the resulting effect on the alongshore morphology is assessed using coastline model Unibest-CL+. The impact of future OWFs on the nearshore wave climate is found to be dependent on the size, shape, orientation and distance from the coast of the individual wind farms. Results show a mean decrease in significant wave height in the order of 1 – 2%. In addition, slight changes in wave direction are observed. The effect on wave climate reduces the alongshore sediment transport at the Dutch coast, by an order of 10% with respect to present values. This results in net-induced erosion, which requires nourishment. The study shows that the areas north of Zandvoort and Petten need the greatest nourishment volumes, in the range of 1.5 – 2.5 m3/m/year. This is an additional 1% on the current annual nourishment volumes supplied along the Dutch coast. The underlying study has proved to be effective in quantifying the chain of effects of OWFs and identifying potential hot-spots along the Dutch coast. The knowledge acquired from these effects can be used to optimize future OWF planning in relation to coastline maintenance policies.