Continuous sea level rise and growing environmental awareness have led to increasing implementation of nature-based solutions to counter coastal erosion. An example in the Netherlands is the Sand Engine---a mega-scale sand nourishment designed to feed the Dutch coast over a period of 20 years. For such designs to work, a good understanding of the governing natural processes is paramount. The behaviour of the sandy coast, however, is subject to natural variability in future weather and uncertainties in the interactions between sand dynamics and hydrodynamic forcing. To predict the evolution of coastal systems, engineers apply numerical models. Next to uncertainty due to variability in natural forcing, such models introduce a series of model-related uncertainties, which are often not consistently included in predictions. With limited knowledge of the magnitude of these uncertainties, the long-term strategy development and design of projects are impeded. Parameter uncertainty denotes our limited knowledge of the values of free model parameters and is an important source of overall model uncertainty. This study aims to quantify parameter uncertainty in process-based coastal area predictions by analysing uncertainty bounds for morphodynamic predictions of the Sand Engine. The analysis of parameter uncertainty is carried out for a study period of 14 months, from August 2011 (directly after construction of the Sand Engine) until October 2012. Using advanced numerical acceleration techniques, a synthetic dataset of 1024 morphological Delft3D predictions is generated, each with an identical hindcast period but different model parameter settings. First, a sensitivity analysis (elementary effects method) is performed to find the most influential parameters on three morphological indicators: cumulative volume change, shoreline position, and bed level change. Based on the sensitivity analysis, five parameters are selected for an uncertainty analysis. Subsequently, parameter uncertainty is quantified by the Generalised Likelihood Uncertainty Estimation (GLUE). To increase the convergence speed of the samples, quasi-random Sobol sampling is applied. To validate the applied parameter ranges and assess model performance, the predictions are compared to observations. Using GLUE, uncertainty bounds for the morphological indicators and posterior likelihood distributions of the five parameters are derived from the 1024 model runs. Finally, through a simplified uncertainty comparison, the relative influence of parameter uncertainty and wave climate variability is examined. A first key conclusion is that uncertainty in input parameters translates to significant uncertainty in predictions: cumulative erosion volumes for the peninsula show a spread of 1.3 -0.4/+0.7 million m3 after 14 months. Extended to bed level changes in 2D, the uncertainty bounds result in spatial uncertainty maps, which reveal that uncertainty is highest (5--6 $m$ spread) in the northern area of the peninsula, where a sand spit develops. Most uncertainty (90% for volume changes) develops in the first seven months, during which uncertainty growth correlates strongly to periods of high morphological activity (r > 0.95 for volume changes). Further, posterior likelihood distributions of the parameters indicate that, of the five selected parameters based on the sensitivity analysis, only three (f_sus, gamma, and d_50) significantly contribute to parameter induced uncertainty, while the other two (alpha_rol and theta_sd) are considerably less influential. This contrast in results is attributed to a resolution problem in the sensitivity analysis. Optimised parameter sets derived from the posterior likelihood distributions result in similar model skill as the best GLUE simulations (BSS = 0.8), implying that they may be close to the maximum achievable model skill within the examined parameter ranges. Finally, a simplified estimate of the variation due to wave climate variability is in the same order of magnitude as the parameter induced uncertainty bounds, implying that both uncertainty sources form significant contributions to overall prediction uncertainty. The presented results have an impact on two key levels. First, they can be used to communicate and address uncertainty in predictions of coastal change. For example, the spatial uncertainty maps can let stakeholders understand the potential range of outcomes for a certain design. Second, the results, combined with the created dataset, can provide valuable information for future morphodynamic studies. This work contributes to justifying the need for stochastic simulations, which are expected to be increasingly used for many coastal engineering and management purposes in the years to come.

The Hondsbossche Dunes is a seven-kilometre-long dune, beach and shoreface nourishment that serves as a primary flood defence since 2015. In addition to its protective function, the Hondsbossche Dunes stimulate ecological habitat development in the coastal area, in line with Ecoshape's 'Building with Nature'-approach. Its morphologic development is monitored to simulate the morphological response to marine processes and aeolian sediment transport, and to determine the optimal profile for dune growth and nature development.The monitoring program of Ecoshape studied the volume change of the beach and the dunes. The decrease of sediment volume on the beach is not equal to the increase of sediment volume on the dunes. The hypothesis is that the remaining sediment must stem from the intertidal zone and the shoreface. The research objective of this study is to investigate the contribution of sediment supply and marine processes in the intertidal zone to changes in morphology and bed composition in the beach and dune. The results of this data analysis show that sediment is transported from the intertidal zone to the shoreface in the north and south of the Hondsbossche Dunes. This behaviour is different from the middle of the domain, where sediment is transported from the shoreface to the intertidal zone. The direction of cross-shore sediment transport between the shoreface and the intertidal zone may depend on the crest level of sandbars in the shoreface: The sandbars in the middle of the Hondsbossche Dunes break more short waves than those in the north and south, which causes 'freeing' of long waves and net landward sediment transport. The modelling study suggests that wave runup and the presence of soil moisture reduce aeolian sediment transport from the intertidal zone to the beach, because grains in the intertidal zone are immobile when they are moist. This reduction results in increased erosion at the dry part of the beach, because the wind picks up sediment from the beach instead of the intertidal zone. The soil moisture content also significantly coarsens the bed composition in the intertidal zone, because the difference in threshold for aeolian transport between fine and coarse grains becomes larger. Hydraulic mixing does not contribute to significant morphological changes in the intertidal zone, but redistributes grains on the bed of the intertidal zone toward its initial distribution by stirring up the bed of the intertidal zone. These marine processes only affect dune growth if aeolian sediment transport is supply limited, which is mainly the case for Profile 2 north and Profile 4. Aeolian sediment transport in Profile 3 south is mainly transport limited, because the dune growth volume in most simulations is equal to the dune growth capacity following the formulation of Bagnold (1941). It seems that dune growth in this profile is less affected by marine processes. These results are likely to be related to the finer bed composition and wider beach at Profile 3 south.

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.