Anthropogenic activities induced global warming have caused visible consequences in the increase of atmosphere and ocean temperatures. During the 20th century, the global sea level rise behaves a non-uniformed rising rate. In most of the 20th century, the global mean sea level (GMSL) has risen to around 1.8 mm/yr. However, since the early 90s, this rate has increased beyond 3 mm/yr, and from 2006 – 2018 the rate of rise is 3.7 mm/yr. Many researchers have suggested that relatively moderate changes in sea level can cause significant shoreline changes. One of the most significant impacts of sea level rise can be attributed to the retreat of the shoreline, owing to the permanent passive submersion of lands and coastal erosion. To comprehensively understand the effect of sea level rise on different coastal areas, multiple site-specific data-driven models are developed based on the collected global sandy beach datasets. Based on the long-term evolution of available beach indicators, this research are dedicated to studying spatial and temporal topographic morphological variation as a response to relative regional rising sea level.

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 existing hydrodynamic models consider full physics approaches to calculate storm surge at coastal regions. However, due to the complexity of the equations that model these processes, the computational time and power required to run them can be large, compared to models that consider simplified equations. By contrast, simplified hydraulic models lack physical background, what leads to a minor accuracy in the surge estimations with respect to hydrodynamic models. In this project, a stochastic model has been developed with the objective to estimate surge at the coast of Mississippi (United States) at a reasonable accuracy and time, without solving equations that represent complex physical processes. The stochastic model needs to be trained by means of hurricane data, including surge levels. This information must be generated beforehand, by simulating a limited number of hurricanes with an hydrodynamic model. In this project, the hydrodynamic model Delft3D Flexible Mesh has been used for this purpose. The approach followed to build the stochastic model has been based on three main steps. The first step has been setting up and validating the hydrodynamic model in Delft3D FM. Hurricane Katrina (2005) has been simulated to calibrate the input parameters of the model, by comparing the maximum simulated water levels at 41 stations along the shoreline of Mississippi to the high water marks observed at the same locations during the event. Tide, surge and wave setup have been considered in the validation. The results of the validation show a line best fit slope from the origin of 0.912 and an R-squared of 0.996. At Gulfport, the absolute error of the surge estimation is 19 centimeters, equivalent to a relative error of 2.5%. The second step in the construction of the stochastic model has been the generation of a historical hurricane data base. The hurricane best tracks have been retrieved from the HURDAT2 data base. The variables considered have been the forward speed and the forward direction of the hurricane at landfall, the wind speed at landfall, the distance from landfall location to a reference point (Galveston Bay) and the maximum storm surge during the hurricane. In this case, only the hurricane forcing is considered as external action. The storm surge has been recorded at Gulfport Harbour (central coast of Mississippi). The values of the surge have been obtained by using the validated model to simulate the historical hurricanes making landfall in a rectangular domain of 600 kilometers, being Gulfport Harbour the center of the rectangle. Due to the scarce number of hurricanes making landfall in this region, the tracks of the hurricanes making landfall in the North of the Gulf of Mexico but outside the rectangular domain have been shifted inside the domain, in order to generate a sufficiently large data base to train the stochastic model. A data base with 140 hurricanes has been built, from which the 85% (119 hurricanes) have been used for the training of the stochastic model and the other 15% (21 hurricanes) have been used for the validation of the stochastic model. The third and last step has been the setup and validation of the stochastic model, by comparing the storm surge obtained from the stochastic model to the surge obtained from the hydrodynamic simulations. The stochastic model used to estimate storm surge has been a Bayesian Network that assumes normal copulas to represent the joint distribution between nodes of the network. The calculated slope of the best fit line for the mean surge values has been 0.861, with an R-squared of 0.885. Moreover, the average standard deviation of the estimations is 1.16 meters. These results indicate a reasonable estimation of the surge by means of the Bayesian Network. This estimation can be made in the order of seconds.

Over the recent years, flood risks and losses have been increasing for coastal cities due to climate change, subsidence, population and economic growth. Hong Kong and Macau are two cities located in the Pearl River Delta that experience a significant flood risk due to storm surges. The increased losses and risks has sparked interest around the world for efficient and accurate flood forecasting. At the moment coastal flooding events are often simulated with difficult hydrodynamic models that reproduce the physical phenoms. Over the last decades there has been more interest in other methods to forecast storm surges, namely neural networks. Other than hydrodynamic models a neural network is capable is making predictions in seconds, while the model can take hours to finish simulation. fast and accurate storm surge forecasting is of importance for disaster and evacuations management strategies and will only become more important in the future. During this research the main goal is to develop a neural network capable of prediction maximum water levels due to storm surges in case of an approaching tropical cyclone. The neural network is trained with data that is obtained from hydrodynamic simulations. A synthetic storm database is used to provide the necessary data to conduct 1000 simulations of which the results are used in the neural network. The first focusses on developing a hydrodynamic model capable of accurately simulation tropical cyclone induced storm surges in Hong Kong and Macau. The model is calibrated in a way to reproduce the real world as close as possible. It accounts for the real life bathymetry, topography, astronomical tides and wind forcing. The storm surge model is validated extensively based on three historical tropical cyclones. The errors between the observed and simulated water level are below 20 cm, after calibrations of different physical parameters within the model. The second step of this research is to use the validated storm surge model to run synthetic storm simulations. Instead of using historical storms who only have been recorded for 40 years, a synthetic storm database is used containing 10000 years worth of data. From that storm data, 1000 synthetic tropical cyclones are selected that come close to Hong Kong and Macau. With the data obtained from the previous two steps, it is finally possible to training the neural network. In this network a total of seven input parameters (tropical cyclone track parameters) are used to estimate the maximum water level that will occur during the tropical cyclone. The input parameters considered are: latitude, longitude of TC eye, maximum wind speed, minimum eye pressure, radius of maximum winds, forward speed, forward propagation direction. During the development of the network, three different types of configurations are tested. Extensive validation and calibration shows that neural networks are capable of making maximum water level predictions for a large number of cases. However, variety in the quality of the prediction is observed. Improvements still can be made for more accurate predictions.

Crescentic sandbars are a commonly observed nearshore feature in coastal zones that strongly influence the surfzone circulations and are connected with the occurrence of rip currents. Furthermore, their spatial characteristics have been associated with the shoreline position in the form of shoreline perturbations, indicating morphological coupling between them and in turn affecting the beach width. As changes in the beach width affect coastal infrastructure and user functions such as recreation, understanding the bar dynamics is crucial for coastal zone management. The present work intends to improve this understanding by means of a case study at Anmok beach located at the South Korean East coast. Data analysis is used to estimate the long-term changes of the sandbar characteristics in time, under the influence of the ambient environmental conditions and human interventions. Additionally, this study investigates how to account for these features in modelling frameworks.
Available in-situ bathymetric surveys and high resolution aerial photographs are complemented with freely available satellite images in order to extract an estimate of the horizontal position of the sandbar crest line at Anmok beach. The introduction of satellite imagery observations dramatically increases both temporal coverage and frequency of the dataset resulting in 27 years of bar observations. The sandbar characteristics are found to change mainly in response to high wave energy events, while the initial sandbar position seems to be an important factor e.g. the closer the sandbar to the shore the more prone to changes it is. Furthermore, the results indicate that the alongshore migration of the sandbar features and the alongshore component of the wave energy flux show an agreement between their long-term (5-10 years) trends. This highlights the potential of the alongshore wave generated current to migrate the sandbar patterns in the alongshore direction. Moreover, the magnitude of the crescentic length and amplitude in an area 600-700m away from the port decreased after its construction.
The use of coastline models that assume alongshore uniform bathymetry can be questioned in cases of pronounced alongshore morphological variability. To this end, the impact of the alongshore variability of the sandbar on net annual sediment transports is investigated with the process based morel Delft3D for a selection of schematized bathymetries, created based on the natural variability of the sandbar at Anmok. It appears that for the symmetrical shaped crescentic sandbars, which are mostly present at Anmok beach, the influence of the alongshore variability on the net sediment transports is not large (~10%). The shoreline response to different sandbar configurations is investigated by computing alongshore incoming and outgoing sand volumes in cells specified close to the shoreline. It is found that the cross-shore distance between the sandbar and the shoreline is critical for the intensity of the erosion and accretion patterns formed at the coastline. Moreover, according to the predominant direction of the wave climate the alongshore location of these shoreline patterns can vary.
After highlighting the importance of the sandbar location for the beach width, an attempt to model the sandbar mean cross-shore location in response to the wave forcing is realized in pursuit of predictive capability for future changing wave conditions. As process based numerical models have shown limitations in producing any predictive skill, two empirical models selected from literature are used. The calibration and validation of the models is unsatisfactory, possibly connected with the spatial accuracy and varying temporal resolution of the dataset or the simplification of the models themselves.
From the present work, the use of satellite imagery for studying of long-terms sandbar dynamics and the derivation of its characteristics is proven to be quite promising. It allowed us to increase the number of observations and hence, study the sandbar dynamics beyond the time-frame of in-situ surveys and aerial photography. Therefore, we could study the effects of human intervention on the long-term sandbar characteristics. This technique is believed to be useful for other sites with crescentic sandbars around the world.