Predictions of coastal morphology evolution are necessary to assess engineering solutions as well as understand coastal systems behaviors. Among the tools used to predict morphological evolution are the process-based models that make use of physical laws and empirical knowledge. Such models account for a considerable range of coastal processes and are rather complex, hence demanding substantial computational time. Usually, when using complex process-based models, reducing the size of the input parameters, named input reduction, is made necessary in order to reduce the computational effort. The scope of the present study is to understand the influence of reduced wave climate on simulated morphological evolution. Input reduction algorithms, sequencing methods, number of cases and duration of the reduced wave climate were investigated and evaluated with a 1D (cross-shore) brute force simulation of 3.3 years in Noordwijk, Netherlands. Noordwijk is a wave-dominated sandy beach characterized by a double sandbar system that has an inter-annual net offshore migration. The assessment of the methods was carried out through cumulative skill scores, temporal evolution of profile perturbations (bars and troughs) and profiles at the end of the simulation. Furthermore, the findings on the Dutch coast were validated with a 1 year-long, 1D (cross-shore) brute force simulation in Anmok beach, South Korea. Anmok is a wave-dominated sandy beach with crescentic bars in the nearshore morphological evolution. One of the conclusions of the present study is that input reduction methods that have more control on the definition of bins such as pre-definition of wave height and wave direction bins perform better than methods that are based fully on the statistical properties of the wave dataset. Also, the order of the wave cases in the reduced wave climate must resemble at its best the natural variability of the full wave climate. Finally, a less robust reduced wave climate (with less representative wave conditions) applied in a smaller timescale performs better in terms of morphology than a more robust reduced wave climate (with more representative wave conditions) applied in longer time-scales.

Evolution of coastline position under the influence of natural and anthropogenic processes is directly linked to the development of seaside societies. In the context of coastal zone management, process-based morphodynamic models are often used topredict coastline evolution and support the decision-making process for adaptation/mitigation strategies. Frequently, the processes driving themorphodynamic evolution transcend the applicability limits of a single model. In those cases, model ensembles can be used to estimate coastline change under the joint effect of the relevant processes. However, model output and in extent the aggregated result are characterised by uncertainty originating among others from forcing variability and parameter imprecision. The increasing exposure ofc oastal societies to coastal recession risks and emergence of risk-informed coastal zone management create the need for aggregated coastal recessionestimates with quantified uncertainty. This study investigates different statistical methods for forcing and parameter uncertainty quantification around coastline change estimates from process-based morphodynamic models. Subsequently a numerical convolution approach for the aggregation of the probabilistic coastal recession estimates from multiple models was formulated to account for the combined uncertain effect of processes acting on different timescales. The methods of this study were applied on Anmok beach, South Korea, a coastal stretch experiencing erosion caused by long, intermediate and short timescale processes. Available UNIBEST-CL+ and Delft3D model schematizations from theCoMIDAS research program, capable of simulating the relevant processes, were utilised. Following a literature review, two methods were considered applicable for process-based morphodynamic models: Standard Monte Carlo (SMC) and Latin Hypercube Sampling (LHS). The application of both methods on the UNIBEST-CL+model schematisation enabled the evaluation of their relative performance based on the precision of the different coastline change estimates achieved for the different sample sizes. Only LHS was applied on the Delft3D model schematisation due to computational demands limitations. Subsequently,the scenario-based approach currently used for the aggregation of multi-modelcoastline change outputs was extended to explicitly account for the uncertainties quantified in the individual model outputs. A numerical convolution approach, using Monte Carlo sampling, was suggested for linear super position of the contributing probability distribution functions. The advantages of this approach include speed, ease of implementation,comprehensibility and high resolution even at the tails of the aggregated distributions. Utilising this approach, the effect of alternative interventions (combinations of various breakwater designs with a small-scale nourishment) on the coastline change probabilities was quantified. The results showed that both methods (i.e., SMC and LHS) with adequate sampling can produce probability distribution outputs for coastline change when applied to the process-based models. SMC remains the most suitable method for coastline change uncertainty quantification for models with small simulation durations. The method gives quantified estimates of the precision, enabling the achievement specific target precisions, with the respective computational cost. For the smaller sample sizes used, LHS gave better precision results, proving more suitable for models with longer computational time. On the downside, without extra iterations of the procedure only upper estimates of the achieved precision for a specific sample size can be obtained. The probabilistic aggregation framework presented in this thesis has several advantages compared to the scenario-based approach currently used. It allowsfor quantified coastline change uncertainty estimates with the respective precision estimates and provides the distribution of the uncertainty across its range, information that could not be derived using the scenario-based approach. Different coastline change percentile estimates or confidence intervals with practical use to decision makers and the likelihood of any coastline change realisation of interest can be evaluated. The probabilistic uncertainty quantification and aggregation framework is believed to be useful for intervention assessment and comparison. It allows for the assessment of uncertainty around the morphological response under the combined effect of the processes acting on the coast with/without the intervention and thus the evaluation of the probabilistic intervention impact. Different interventions can be compared in terms of the probability of inducing desired/undesired morphodynamic realisations as well as in terms of the uncertainty range in the coastline change estimates.