Four kilometers west of the Norwegian island Fedje, mercury, leaking from the U-864 wreck, has highly polluted a seabed area of approximately 40 000 m2. The bow section of the wreck is located on a stability critical slope of 15 degrees. In 2016, Van Oord used a flexible fall pipe system to install a counter fill to stabilize the wreck and simultaneously cap a portion of the contaminated area. In the future, the Norwegian government plans to cap the complete area to prevent further dispersion of mercury. This research aims to gain more knowledge about the spreading of sediments during such an operation occurring under realistic hydrodynamic conditions. A more complete understanding of the process will allow for better risk assessment of future capping operations. To this end, the unique data set gathered during the counter fill project has been analyzed. In order to predict extreme flow events, the bottom currents are decomposed. By decomposing the erratic velocity signal, tidal (25 %) and inter-tidal residual current (38 %) components are identified and understood. However, the driving force for the remaining intra-tidal part, which contains the highest current anomaly events, cannot be identified. Evidence is found for the occurrence of internal waves providing a possible explanation. Due to a lack of data, a quantitative prediction cannot be made. Consequently, the exact maximum currents at the site are unpredictable, but stayed below 0.4 m/s during the project. During the installation work, high turbidity clouds have been measured. The origin is investigated by analyzing the particle size distribution and the mercury concentration of sediment samples; said samples are drawn from the capping material, the installed capping layer and sediment traps placed around the wreck. The findings indicate that the clouds are caused by a loss of clean material and are not from the contaminated seabed. This is supported by modeling the dispersal of clean particles from the flexible fall pipe. The promising results regarding the use of a flexible fall pipe for capping layer installation are not only applicable for the U-864 area but also for other polluted offshore areas.

This thesis investigates the important physical processes related to underwater sound propagation due to offshore pile driving with the first commercial and numerical model called the Underwater Acoustic Simulator (UAS). To make adequately use of noise mitigation measures during offshore wind projects and to protect marine life the prediction of sound levels in water is important. The aim of this thesis is to assess if the UAS is suitable to give insight in the physics contributing to sound propagation and if the UAS is suitable to quantify sound levels in water for the application in future wind park projects. This is done by comparing the predicted sound levels with the UAS with the measured sound levels obtained during the construction of the Gemini wind park. The results obtained with the UAS identify the parameters related to the seabed properties as the most important for governing the attenuation of the noise levels as function of distance and frequency. In the UAS the seabed is modelled as a fluid. Especially frequencies < 800 Hz are sensitive to the choice of geo-acoustic parameters. These frequencies show a trend of underestimation of the sound levels compared to the acoustic data. An increase of the predicted sound levels is observed for increasing the coarseness of the sediment of the upper seabed layer and decreasing this layer thickness. The UAS parameters can be tuned to obtain sound levels that agree with the measured sound levels in the Gemini area. However, these parameter values do not represent the properties of a realistic seabed. Additionally, no evident choice of values exist to arrive at sound levels that agree with the acoustic data. It is concluded that the choice of values compensates for the exclusion of the shear effect in the fluid approximation of the seabed in the UAS. The exclusion of the shear effect in the representation of the seabed in the UAS is regarded as essential. Tsouvalas (2015) shows that the propagation of sound energy at low frequencies is governed by the shear of the soil. These lower frequencies propagate in the deeper parts of the seabed where shear effects play a role. Also, sound energy in the form of Scholte waves propagates at the water-seabed interface, thereby contributing to the sound levels in water. The reason the sound levels propagating at low frequencies is sought in two aspects. In a fluid seabed the geo-acoustic parameters are adjusted and account for the damping associated with shear, while the propagation of sound energy at these wave forms is not modelled. Another possible explanation is the trapping of the low frequency pressure waves in the seabed. Although the UAS is able to predict sound levels in the Gemini area that agree well with the measured sound levels, this is not due to the correct implementation of the physic regarding the seabed. As a consequence, the model is tweaked to arrive at the measured. The UAS is therefore not considered suitable for application in future wind park projects.

This thesis introduces a new algorithm for optimising shipping routes within a dredging project. Highly dynamic and time-dependent hydrodynamic features influence shipping routes. Due to the complex interactions between the horizontal tide, vertical tide, stratifying forces, wind-driven forces, and limited water-depth, shipping routes were previously only optimised for large scale routes (order of 1000 km). This study presents an algorithm that can optimise shipping routes that are influenced by these small scales (order of 10 km) hydrodynamic features. This algorithm uses graph theory to solve for the time-dependent fastest path between start and destination. Graph theory searches for the optimal path through a set of nodes that are connected with edges. This study uses the time-dependent shortest path algorithm which accounts for the FIFO-criteria (Waiting criteria) and can solve the non-convex nature of the problem The input of this algorithm is a hydrodynamic model. These models are Computational Fluid Dynamic (CFD) models that calculate currents and water levels in a specific domain. The domain is discretised into cells and nodes to calculate these hydrodynamic features. This study uses the nodes of this hydrodynamic model as the vertices of the graph. However, for some cases, the hydrodynamic model has too many nodes for the shortest path algorithm. This study presents a method for reducing the number of nodes without reducing the spatial resolution. The nodes are reduced based on a combination of the vorticity and the magnitude of the flow. This algorithm is implemented in a python software package named Hydrodynamic Algorithm for Logistic enhancement Module (HALEM). HALEM can determine the optimal shipping route for a given hydrodynamic model. Defining different cost functions results in different optimisation purposes. This thesis presents cost functions for the fastest route, shortest route, cheapest route and least polluting route. This software is then implemented in the OpenCLSim software so that this combination of software can optimise routes of entire projects. A case study simulates a beach-nourishment at Schouwen Westkop Noord to demonstrate the practical use of HALEM and OpenCLSim. For this project, 425,500 m3 sand should be dredged offshore and pumped onto the beach. Due to the narrow gullies and tidal changes in hydrodynamic features, the routes were hard to predict. The simulation with HALEM and OpenCLSim shows an increase in the production with 21 % compared to the simulation with just OpenCLSim.

Coastal flood risk is expected to increase over the 21st century as a result of climate change and economic growth, which makes low-lying regions especially vulnerable. Global screening techniques are needed for a more widespread use of NBS in these flood prone coastal regions. This research expands on the current assessments done by developing a quantitative global screening method that evaluates the costs and benefits for two defence approaches; 1) increasing the dike height, 2) a hybrid solution that includes increasing of the dike height in combination with restoring mangroves and/or corals. The screening method is based on Van Oord’s Climate Risk Overview tool, in which, globally, coastal hotspots are indicated that have a predefined risk of flooding in the 21st century. The steps added by my screening method include; 1) determining which NBS can be applied depending on the local physical conditions, 2) determining the costs for both NBS and conventional hard solutions, 3) determining the increase/decrease in flood risk of the different interventions for current and future conditions, 4) monetizing additional benefits that NBS provide, 5) assessing the benefits and costs to determine if NBS are the most optimal solution. The results of this global method are inherently limited by several simplifying assumptions and by the lack of high resolution local data, which influences the cost/risk estimates and corresponding site identification. For 2.6-3.3% of the coastal hotspots, NBS can reduce the investment costs in addition to being cost-beneficial. There is potential for expanding this work by adding sea grasses, salt marshes and oyster reefs as vegetated foreshore systems, and by including more thresholds to make the criterion for potential sites to apply NBS more strict.

Knowledge of nearshore bathymetry is crucial for every aspect of the blue economy. Currently, it is expensive to obtain nearshore bathymetry at regional scales, and therefore this data is not available for many coasts. The data scarcity occurs especially in the global south and big ocean states that are at the highest risk from climate change. Currently the only global bathymetric dataset, GEBCO, provides depth data at approximately a 500 m resolution. This data is of limited accuracy in the nearshore zone because the source data is sparse in nearshore areas. Recent research has found that NASA’s ICESat-2, launched in 2018 to study the cryosphere, can incidentally capture bathymetric data. Some studies have been done in small sites, but the potential for a global product has not yet been investigated. This thesis proposes a method of using GEBCO data as a starting point and incorporating ICESat-2 data via a Kalman filter. This results in a product with a downscaled spatial resolution and improved accuracy without requiring any in-situ data. If other local bathymetry data is available, it can be added as input to the Kalman filter or used for validation of the method. Recent studies in lidar remote sensing have shown that ICESat-2 can capture nearshore bathymetry at depths of up to 20 me in tracks 0–3 km apart, provided atmospheric conditions are good and the water is sufficiently clear. Hence, this data could provide a source of high-resolution bathymetric depth profiles in tropical areas and possibly at higher latitudes. This research project proposes an automated processing chain, written in python, for extracting bathymetry points from the lidar data based on the density of the photon returns in the underwater zone. This method can reliably identify points containing bathymetric signal. To downscale the data using a Kalman filter, first global data from GEBCO is clipped to the area of interest, and then resampled bilinearly to 50 m resolution. Then, the ICESat-2 photon data for the area is processed to generate point estimates of bathymetric depth. To fill in the gaps between these point estimates, the bathymetric points are subsampled and interpolated to the same resolution as the GEBCO data using a universal kriging interpolator. This interpolator results in a raster of the estimated depth, and a raster of the estimated uncertainty. To update the interpolated GEBCO grid, the Kalman gain is calculated for each raster cell and using the Kalman state equation a new bathymetry grid is produced. If other data is available, the process can be applied recursively with other depth and uncertainty grids, allowing the Bayesian combination of any number of bathymetry datasets for the site. To validate the method, the improvement in RMSE is calculated between the resulting bathymetry grid and previously validated, high-accuracy survey data. The validation has been applied at several global test sites to verify that the method is generalizable to other regions. Validation sites are chosen based on the availability of validation data from either USGS survey or survey data from Van Oord projects. The approach was found to improve RMSE compared to validation data by up to 34% in some test sites. However, other sites showed marginal improvement, or increased RMS error. The results of this research could allow easier methods of characterizing nearshore bathymetry in remote areas. It could also be extended to include temporal variation – as more bathymetric data becomes available, it could be used to measure the dynamic changes of coastal systems. However, the method is limited by strict water clarity requirements and missing data due to atmospheric conditions. Despite these limitations, it could potentially be expanded to create a downscaled global bathymetry dataset of clearwater areas.