The goal of achieving net-zero emissions by 2050 requires innovative ways to reduce energy consumption in all sectors. This thesis presents an in-depth analysis on the effect of coastal currents on energy consumption of sailing dredging vessels and the potential to make use of these currents to minimize energy consumption in dredging projects. To model the highly dynamic and time-dependent currents of the North Sea, an analytical solution for the tidal-induced currents and residual density-driven currents was used to create a synthetic 3D flow field, respectively derived by Prandle and Heaps. To quantify the energy consumption of dredging vessels sailing through a 3D flow field, a python-based model was developed. This model utilizes the Holtrop and Mennen method to calculate the sailing speed corresponding to a desired engine power, with a modification to account for current-induced drag resistance on the rudder caused by currents perpendicular to the sailing direction. The model was validated with sensory vessel data of Van Oord containing information of a dredging project in the North Sea, in combination with measured data for the currents at the location of the project. The validated model was used to explore a dredging strategy for a sand nourishment project that minimizes energy consumption by waiting for favourable marine currents before sailing. A sand nourishment project was chosen because the primary cross-shore movement of the vessel interacts with the bi-weekly occurrence of alternating cross-shore currents. Two cases were simulated, a hypothetical case in which the vessel does not consume energy when waiting, and a realistic case in which the vessel's energy consumption continues due to utilities such a lighting and heating of on-board accommodations. The hypothetical case showed a reduction of energy consumption of 3\% when sailing with the cross-shore currents as opposed to neglecting these currents, which is at the lower boundary of the energy reduction due to voyage optimization predicted by IMO (1\% - 10\%). However, the energy reduction was outweighed by energy consumption during waiting in the realistic case, making this strategy unsuitable for conventional dredging vessels. The model developed in this thesis can be of significant value for both dredging companies as well as researchers. These stakeholders can use the model to plan and optimize dredging activities, reduce environmental impact, and identify areas for innovation and improvement in the dredging industry.

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.

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.

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.

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.

Van Oord is currently building the Gemini Wind farm in the North Sea. It is located 80km North of Schiermonnikoog. Before execution started some wave and current analysing buoys have been deployed to investigate the currents on the location of the wind farm for workability and insurance purposes. In this report the ADCP-current data will be analysed.
The major finding is that maximum tidal currents do not occur in winter but in summer, since the tidal currents are influenced more by stratification than by wind influences.

This report has been written as part of my internship at Van Oord DMC, in Rotterdam.