In this thesis, sensitivity analysis is used to study the influences of parameters on specific outputs in the hydrodynamic model 3D DCSM-FM of the North Sea. The sensitivity analysis is the study of how uncertainty in the outputs of a model can be divided and allocated to different sources of uncertainty in its inputs. The software Delft3D FM is used to simulate the model, which is developed by Deltares. This thesis is supported by German pilot of the UNITED project, which studies the possibilities of combing blue mussel and sugar kelp cultivation with wind energy. The investigation is conducted at FINO3 platform, 80 km off Sylt.

In this thesis project, temperature and current velocity are selected as outputs in sensitivity analysis, which are influential factors of blue mussel and sugar kelp growth. Specific parameters in the hydrodynamic model are selected as inputs respectively. Three sensitivity methods are conducted: Morris, copula-based and variance-based method. Among them, copula-based and variance-based consider the independency information, while parameters are assumed dependent in Morris method. To generate samples, parameters are transformed into a unit hypercube in Morris method and run on the contour of the grid cell, composing paths. Between two steps within the paths, only one parameter changes at a time (OAT method). The input domain is scanned with a better strategy to separate the paths maximizing the dispersion. Each parameter’s elementary effects are calculated within paths, evaluating the changes in outputs contributed by the single parameter. Then (absolute) mean and variance of elementary effects are used as sensitivity indices. Copula-based method uses similar sampling and the same measurements, but it gathers parameters into copulas before sampling to include the dependency information. In variance-based method, the variance of the outputs’ conditional expectations is used to measure the sensitivity. Only random samples are needed in this method.

Morris and copula-based method prove the temporal and spatial similarities of parameters’ sensitivity behavior. Convective and evaporative heat flux are the most influential parameters of temperature, and they also show correlations with other parameters. Air density influences current velocity the most, while Smagorinsky factor is the most correlated parameter of current velocity. Variance-based method gives similar results about rankings of influences. Correlations are proved to exist among parameters. Uniform horizontal eddy viscosity/viscosity in definition files have no impacts, as they are overwritten. Three methods are compared. Except the differences in independency, sampling and measurement, there are some other differences. Much more samples are required in variance-based method and it is used mainly to decide the existence of significant correlations and whether a parameter can be neglected. While Morris and copula-based method ranks the influences and correlations.

The need for accurate estimation of hydrodynamic and water quality model variables arises from the UNITED project, which aims to create high-resolutional forecasting systems for monitoring the cultivation of seaweed and flat oysters and operating of the Belgian pilot of UNITED in the coastal area of the North Sea. Accurate observations of physical variables are usually only known for small domains of the model, on the water surface. Therefore, data assimilation is applied on different hydrodynamic models to search for improved estimates of waterlevel, water velocity and temperature. The sequential data assimilation algorithm, the Ensemble Kalman filter is considered. The algorithm is a Monte-Carlo approximation of the Kalman filter, both using accurate observations assimilated into the model, where the model each time is shifted towards the observations when available, providing an optimal trade-off estimation between the model estimations and observations. Two models are considered: a 1D model of the Western Scheldt estuary in Python and the 2D model with depth of the Western Scheldt estuary implemented with software packages Delft3D FM and OpenDA. Model estimates and estimates of model with assimilated observations are investigated in various experiments to look for improved predictions, using twin experiments to generate artificial observations over the whole domain. The various experiments contained the search for the effect of assimilation location on the estimates, of the effect of assimilation of different physical parameters, the effect of the observational error covariance and the effect of water surface assimilation on the estimation within the water column. In most cases, the Ensemble Kalman filter improved the estimate of waterlevel and water velocity with varying results up to a decrease in rmse of a factor four, while the assimilation of temperature gave a worse prediction. Furthermore, assimilation of waterlevel gave the best improvement in estimate. For the investigated models, assimilation of observations near the boundary conditions with implied noise gave the most improved estimates. Furthermore, assimilation of water surface observations improved the estimate within the water column for water velocity with a factor of 2, while temperature assimilation did not show any improvement. The result for temperature may be due to a collapsing Ensemble Kalman filter due to small standard deviations and a not realistic model setup for temperature. Therefore, assimilation of only observations at the surface may be used to accurately improves water column estimations for water velocity. For temperature, more research is needed. Advised is an experiment with a different model setup with more realistic and longer spin-up time of temperature. The collapsing Ensemble Kalman filter can be prevented by initialization of new ensembles before the the collapse happens. In further research, data assimilation can be applied on the DCSM model, a model involving the area of the Belgian pilot.

OSPAR's Commission has been battling eutrophication since the problem was first established in the 1950s. To battle eutrophication, an important factor is to monitor it. Five indicators are used together to assess the status of eutrophication, determined by the Common Procedure. These are the chlorophyll-a concentration, the turbidity, the nitrate and phosphorus concentration, the oxygen levels and the biological water quality. All five indicators need to be known to obtain the final eutrophication status. However, just looking at the chlorophyll-a concentration on its own is also a good measure. This thesis focuses only on the chlorophyll-a concentration as an indicator for eutrophication.

To monitor the North Sea, the OSPAR's Commission has established eutrophication monitoring zones. The aim of this study is to determine eutrophication monitoring zones based on available satellite data of the chlorophyll-a concentration in the Dutch part of the North Sea. The zones are defined using four clustering algorithms: K-means clustering, Hierarchical clustering, Random Forest clustering and HDBSCAN. The results from these clustering algorithms are compared to both each other and to the previously defined eutrophication zones.

First, the case study region is split into two areas: the coastal area, which lies closer to the shore, and the offshore area, which lies farther away from the shore. The best result for this separation was generated by K-means clustering with two clusters.

Afterwards, the eutrophication zones are determined separately in the offshore area and the coastal area. The clustering results are ranked based on four criteria. The first criterion is correspondence to OSPAR's previously defined eutrophication monitoring zones. The second criterion is the similarity of the clusters to the zones that are visible in the data. The third criterion is the performance determined by validation metrics. This criterion was considered less important because of the lack of ability to capture the goals of the research. The last criterion is confirmation through the HDBSCAN clustering. This was added later during the study when it was found that HDBSCAN yielded very accurate results. Due to how HDBSCAN works these accurate results were not usable directly, as the number of clusters this yields it too high, but they were usable for verification. The best results were found through random forest clustering with respectively nine and five clusters for the offshore and coastal areas.

Subsequently, the zones derived from clustering were compared to other data to see whether the determined monitoring zones also hold over time. This appeared to be the case.
Moreover, the distribution of the chlorophyll-a concentration for each zone is determined. Additionally, the trend of the chlorophyll-a concentration of one determined monitoring zone is analysed over time. Lastly, the defined eutrophication monitoring zones are compared to other defined zones within the Dutch North Sea coast. These other zones were fishery policies, marine protected areas, spatial planning, and bathymetry. The comparison validated the defined monitoring zones.

Eutrophication processes in coastal waters are becoming more prominent as a result of high nutrient discharges from intensive agriculture and increased urban waste. These processes can be devastating for local ecosystems and lead to dissolved oxygen depletion, which applies considerable stress on aquatic organisms. For ecosystems to preserve their status, stop and reverse the negative effects of eutrophication, regular estimation of corresponding indicators has to take place. In this direction, mostly process-driven models have been used, but the presented project argues that freely available remote sensing data can also provide useful insights for the oxygen saturation of the water. The proposed methodology uses Sea Surface Temperature and Chlorophyll-a estimations from AQUA and ENVISAT satellite sensors for the period 2003-2011 to predict the dissolved oxygen content in the Dutch coastal waters. It does so by implementing various Machine Learning models, namely Random Forest, Artificial Neural Network and Gradient Boosting Regressors, with the latter demonstrating the best results. After extensive data pre-processing, the results show that dissolved oxygen can be predicted with an average Root-Mean-Squared error of 0.8 g/m3. Important steps towards a lower error include the use of gap-filled variables and their decomposition into their temporal components as inputs for the model. Furthermore, the effect of the Sea Surface Temperature on the dissolved oxygen is documented through its contribution in the estimation of the latter’s seasonal variability, while the estimation of the maximum dissolved oxygen values is attributed to Chlorophyll-a. Further feature engineering and model development can possibly improve the estimation of the minimum dissolved oxygen values in the coast and the overall prediction in more complex intertidal areas, like the Wadden Sea.