The growth of the offshore wind industry results in intensive usage of the sandy seabed in the North Sea, currently and in the coming decades. Large-scale bed forms are present in shallow seas with sandy beds such as the North Sea. The most dynamic bed forms are sand waves. Due to their dynamic behaviour, sand waves can interact within offshore human developments and together with their dimensions pose a threat; e.g. decrease in navigation depth, exposure of submarine cables, interaction with foundations of offshore wind turbines and destabilization of bed protections. A thorough understanding of the dynamics can result in less risks for the offshore wind sector and therefore bring down the levelized cost of electricity from offshore wind. Currently, sand wave field dynamics are investigated by data-driven analyses. These analyses are based on seabed surveys over preferable more than 10 years and are considered most reliable at the moment. However, these surveys are very costly and/or often not available. Complex numerical models may provide an approach to analyse sand wave dynamics in a cost and time efficient way, though two aspects have to be considered. Not all relevant processes regarding sand wave dynamics are yet understood. Furthermore, due to the large scale of sand wave fields in combination with the fine grid resolution required to model sand waves, large computational efforts form a difficulty for numerical modelling of sand wave fields. Previous numerical studies focused on reproducing the length and height of sand waves. The migration direction is the next step towards the full prediction of sand wave fields and the subject of this research. Recent data-driven analyses showed migration directions of sand waves in opposite direction over small spatial scale, possibly related to underlying seabed topography. Understanding the governing processes of the migration direction of sand waves including underlying seabed topography is the focus of this research using the numerical process-based model Delft3D. To this end, an idealized model is used in which underlying seabed topography (tidal sand bank) is included. For a symmetrical tidal velocity signal, it is shown that the presence of the tidal sand bank influences the hydrodynamics on the scale of sand waves. Horizontal tide-averaged flow towards the top of the tidal sand bank on both flanks is observed. This results in sediment transports and migration directions on both flanks of the tidal sand bank towards the top of the tidal sand bank. The horizontal tide-averaged flow pattern around the tidal sand bank is disturbed by the inclusion of a residual current. Sand wave migration on both flanks in the direction of the residual current is the result. Including the S4-tide constituent does not disturb the tide-averaged horizontal flow pattern around the tidal sand bank. However, the asymmetry of the tidal velocity signal enhances migration in the direction of the asymmetry. Finally, it is shown that also for a more realistic model the transition in migration direction can be explained due to the presence of the tidal sand bank. The tidal sand bank influences the hydrodynamics by creating areas in which the tide-averaged sediment transport in the ebb direction are enhanced and areas in which the tide-averaged sediment transports in the flood direction are enhanced. In this way a transition in the migration direction over the tidal sand bank is observed. The migration directions from the model results and migration direction from data-driven analyses show a comparable transition over the tidal sand bank.

Installation and maintenance strategies regarding offshore wind farm operations involve extensive logistics, since the main focus is the right temporal and spatial placement of personnel and equipment, while taking into account forecasted meteorological and hydrodynamic conditions. In order for these operations to be successful weather windows characterized by certain permissive wave, wind and current conditions is of enormous importance, whereas unforeseen events result in high cost and risk in terms of safety. For that purpose, Deltares created Meteo Dashboard, an integrated software system that collects, stores, computes and presents measured and forecasted meteorological and hydrodynamic data for decision making of maintenance or installation activities in an offshore wind farm. The wind speed, as well as the air and water temperatures, result from a meteorological model and serve as an input for the numerical modelling (e.g. SWAN or Delft3D) of waves, water levels and current related parameters. To account for the inherited uncertainty, several error modelling techniques, such as Artificial Neural Networks (ANN), Copulas, Stochastic Interpolation (SI), ARMA models, and Linear Regression (REG), already run operational on Meteo Dashboard and can be implemented in order for the numerical model forecasts to be corrected. A number of the aforementioned techniques require training using historical or present time data, while others can be incorporated forthwith. In this research, a fully automated ARIMA model and different kinds of Bayesian Network (BN) models are incorporated in order to enhance the accuracy of the significant wave height (Hs) predictions even further. Both techniques are implemented using packages provided by the free software environment of R, namely the bnlearn and forecast. The implemented BN models differ in terms of training and structure, and provide overall the most satisfying accuracy in comparison to the rest of the error correction techniques, when tested with data retrieved from stations deployed in the Irish Sea (adjacent to the Gwynt-y-Mor and Rhyl Flats offshore wind farms) corresponding to the whole year of 2017 (from January 2017 – to January 2018). Supplementary, it is also shown that the BN models illustrate even more advantages when compared to the rest of the error correction techniques, since they provide information about the incorporated variables dependence relationship through their structures, while producing estimates for the underlying uncertainty of the phenomenon, by means of 95% confidence intervals extracted by the significant wave height (Hs) conditional distribution. Finally, all error correction models are tested in operational (online) mode, with real-time data from the aforementioned locations, with the newly implemented BN models producing results of enhanced accuracy, even in the absence of measurements