Storm surges pose a critical threat to the Dutch North Sea coast, where low-lying areas are highly vulnerable to extreme water levels. While short-term storm surge forecasting is well established, extending predictions beyond 10 days remains a challenge due to the complexity of atmospheric dynamics and limitations in numerical models, which require extensive computational resources and lack flexibility for alternative forecasting techniques. This study explores the potential of weather pattern-based classification as a method for improving mid-term storm surge prediction. Phase I evaluates a set of predefined weather patterns (Neal, et al., 2018), originally developed by the Met Office for probabilistic forecasting in the UK, to assess their applicability for surge forecasting along the Dutch coast. The results indicate that while certain patterns show associations with high-surge events, they systematically underestimate surge magnitudes and, at longer lead times, the surge distributions associated with different patterns become increasingly similar, reducing their ability to distinguish between high- and low-surge conditions. Phase II explores an alternative self-clustered classification approach using k-means clustering with Principal Component Analysis (PCA) for dimensionality reduction. Several data selection methods, including surge thresholding, Maximum Dissimilarity Algorithm (MDA), and stratified sampling, are tested to optimise clustering. While the self-clustered patterns show slight improvements over Neal’s predefined patterns, they still underestimate surge magnitudes and lack the accuracy needed for operational forecasting. A proof-of-concept evaluation using storm Pia (December 2023) and a representative SEAS5 storm reveals that the self-clustered weather patterns struggle to capture extreme surge events. Although these methods are not yet suitable for operational forecasting, this study suggest several possible refinements, such as sequential clustering and expanding the spatial domain, to be promising avenues for enhancing predictive skill.

Addressing the impact of sea-level rise in coastal areas requires sustainable management strategies based on informed decision-making, and thus a shoreline projection is important. This research explores methods to improve shoreline change projections by obtaining a long-term shoreline change trend, empirically from \acrfull{sds} observations, and implementing a coastal impact model to project the future shoreline. We defined a heuristic approach to obtain a clean-\enquote{primary}- shoreline-change signal from all raw \acrshort{sds} observations that are available at transect. Long-term trend analysis from the primary shoreline-change signals results in a more accurate long-term shoreline-change trend. The improvement is also observed in Truc Vert, a meso-tidal case previously well known for low \acrshort{sds} accuracy, where the primary series results in accurate long-term shoreline trend regimes. Our projections in open sandy transects show that long-term trends predominantly influence shoreline change patterns and regimes compared to the retreat due to sea level rise. Using input comparable to the input of an earlier study, projections in seven out of eight regions indicate less retreat, except in North Carolina where the regional sea-level rise projections are relatively high. Compared to a hybrid, physics- and process-based, model, our projections showed higher progradation and erosion in \enquote{hot spots}, revealing limitations in linear trend extrapolation for capturing non-linear shoreline behaviour. We can conclude that future shoreline-change projections can be improved by using more accurate long-term shoreline-change trends. The framework's applicability is best suited to open sandy beaches with minimal human intervention. This highlights the need for better coastal typology classification that formalizes the concept of coastal type and coastal squeeze in the backshore of the beach. The outlook for the development of the shoreline projection framework is to explore the non-linear behaviour of shoreline change. This can be captured by decomposing the primary shoreline-change series into different signals that better represent the shoreline-change behaviour.

Under tidal currents and wave action, sediment particles show complex transport patterns for coastal systems like tidal inlets. To model the sediment transport in such coastal systems, models have been derived that can predict that transport using Eulerian (grid-based) or Lagrangian (particle-tracking) methods. The sediment transport model SedTRAILS uses a Lagrangian method to predict the pathways that sediment particles will follow. As a process-based model SedTRAILS is limited in how fast it can compute results. Here we show a methodology to make a faster probabilistic metamodel from the process-based model SedTRAILS by creating an Auto-regressive Hidden Markov Model (ARHMM). Creating a probabilistic model of the sediment transport in a coastal system using Markov chains is
a novel approach in that it builds further on the connectivity approach of SedTRAILS, which is still quite a new development in the field of sediment transport at present. For different versions of the model, correspondence with physical properties of the system were found, that the development to estimate sediment pathways accurately look promising. The method of describing sediment transport pathways of a coastal system as an ARHMM is a form of probabilistic Machine Learning allowing for faster computation times by describing the patterns the system can make. This is in contrast to a process-based model that has to compute all of the underlying processes. The autoregressive component in this approach is expected to be essential in describing any type of dynamical system that consists of trackable pathways. Taking into account the position of a particle at the current time step highly limits the position a particle can have at the next time step in favor of accurately estimating sediment pathways. The modelling approach discussed can find uses in engineering applications where particle pathways are of importance like the dispersal of nourishments, but also other dispersion events like dispersed goods from when a vessel loses a container, as long as there is a process-based model available to create the necessary Lagrangian data.

Galveston Island is crucial for sea turtle nesting but faces erosion and increasingwater levels, limiting nesting opportunities and posing dangers to nests in flood-prone areas. A numerical model was developed to estimate groundwater levels and hydrodynamics at specific locations on two onedimensional transects, located at the Tipsy Turtle and the Sea Wall. The model was used to assess the risk of turtle nest inundation at six fictitious nest locations and compared to a previous method used in a study by Ware et al. (2019), which used the 𝑅2% formulation developed by Stockdon et al. (2006).
The model was validated using data from a two-month field campaign in Galveston, Texas. Two five-day periods were simulated to represent different tidal and wind conditions. The model was also simulated for five different hydraulic conductivities.
In the nearshore, hydraulic conductivity did not influence the results. Although mean surface
elevations were captured well, the infragravity and incident wave heights were exaggerated in most cases, possibly due to wave spreading not being accounted for in 1D models and, therefore, concentrating energy transfer of incident waves to the infragravity waves.
The model demonstrated that small hydraulic conductivities performed better in estimating
groundwater levels in calmer conditions when the mean sea level was considerably lower than
the groundwater level, as in the second period. The RMSE close to the dune foot was 0.075 m for 𝐾 = 0.0001 m/s and 0.4 m for 𝐾 = 0.001 m/s in period 2 close to the dune foot at the Sea Wall. In contrast, the intermediate and higher hydraulic conductivities showed minor errors in more dynamic conditions close to the shoreline in period 1. The RMSE at TT4 (approximately halfway at the Tipsy Turtle) during period 1 was 0.164 m for 𝐾 = 0.001 m/s and 0.239 m for 𝐾 = 0.0001 m/s. In all cases, the best hydraulic conductivity decreased in landward direction.
Not a single hydraulic conductivity performed well in all conditions. Therefore, the results in a
test case assessing turtle nest inundation also varied. In most cases, the model overestimated turtle nest inundation, while the method by Ware et al. (2019) underestimated in all but one case. The run-up estimations of Stockdon et al. (2006) as used by Ware et al. (2019) were sensitive to changes in bed slope, with a steeper slope of 0.05 aligning better with modelled run-up during high tide in period 1 and a flatter slope of 0.01 in period 2. A slope of 0.0316 aligned mainly with the modelled run-up in intermediate conditions in period 1.
This report demonstrated that not one of the cases simulated is reliable enough for assessing turtle nest flooding accurately in all hydrodynamic conditions and is too conservative in most, but not all, cases. Therefore, the current model cannot be used as an alternative yet. Moreover, it concluded that the method using the empirical run-up formulation as in Ware et al. (2019) should also be used cautiously due to consistent underestimations in this experiment.

Wave runup observations are key data for coastal management, as they help validate predictive models for the inundation frequencies and erosion rates. Efforts to develop automated algorithms that effectively identify the instantaneous water line from video imagery have led to a plurality of methods. However, under dissipative conditions, the presence of a seepage face often hinders proper extraction and requires time-intensive data quality control or manual digitization. In this study, we explore traditional color contrast (CC) preprocessing techniques and develop a novel method that incorporates a measure for texture roughness — specifically, local entropy — alongside saturation. The CC-model showed good agreement with the manually digitized water line (0.12 m root-mean-square-
error (RMSE) and correlation coefficient (r) of 0.94), and with its runup statistics (0.08 m
RMSE, and r of 0.97 for the 2% runup exceedance, R_2%). The timing aspect of the method also
showed good agreement (3.88 s RMSE, and r of 0.70 for T_m−1,0). Concurrently, a convolutional
neural network (CNN) informed by CC-preprocessed images was cross-validated using nine manually labeled video time series, each lasting 1 hour and 30 minutes. The CNN model demonstrated good agreement during cross-validation with manually labeled time series (0.10 m RMSE and r of 0.96 for the full-time series, and 0.09 m RMSE and r of 0.97 for R_2%). The temporal dimension of the CNN estimate was also satisfactory (3.51 s RMSE, and r of 0.79 for Tm_−1,0). The observed R_2% values showed the best agreement with the formula for extremely dissipative conditions from Stockdon et al. (2006), with RMSE-values lower than 0.13 m and r-values that exceeded 0.70 for all three methods. When applied to other datasets, the CNN method occasionally failed to accurately capture the water line due to specific characteristics of the new timestack images. These results validate our ML method as a viable proof of concept and challenge us to enhance its adaptability and accuracy across varied environmental conditions. Despite these limitations, the CNN method can be effectively implemented for long-term runup analysis. Additionally, the CC method is anticipated
to be applicable across similar beaches along the northern Gulf of Mexico for long-term extreme value analysis and wave-by-wave analysis. Both methods demonstrate potential in reducing the time required to extract the instantaneous runup from video imagery under dissipative conditions and enhance real-time monitoring, enabling better predictive modeling of coastal processes.

Coastal areas are highly dynamic systems sensitive to natural and anthropogenic change. The range of social, economic, and environmental functions served by diverse coastal environments makes understanding their geomorphology valuable. Parametric shape functions have historically been used to represent the cross shore profile of specific coastal environments. Geometric (consequential) parameters of these functions are often related to parameters representing key morphological drivers (causal parameters) to provide first order predictive capabilities. Advances in the application of data-driven modelling techniques presents opportunity to develop a unified characterization of cross shore morphology in diverse coastal environments. However, the ability to represent diverse profile geometries using a single parametric representation has not been observed. The aim of this study is therefore to evaluate the ability of parametric expressions to effectively characterize diverse coastal profile geometries. Here, a new parametric function is presented that can effectively represent key geometric attributes of diverse cross shore profiles. This function demonstrated high performance in the representation of both theoretical profile morphotypes (mean SSE = 0.04) and measured profile data from Narrabeen-Collaroy Beach, Australia (mean RMSE = 0.05 m) compared to eight existing parametric functions. Parametric values were effectively applied to group profiles with similar geometry using manual labelling and K-means clustering. The average profile of each cluster (i.e., the cluster centroid) provided a good representation of the grouped profiles, particularly when parametric grouping was used in place of empirical grouping. Correlation analyses between causal and consequential parameters demonstrated an ability to identify expected geomorphological trends using the new parametric function. Application of cluster centroids for correlation analysis provided amplified correlation strengths for expected geomorphological relationships, particularly when clustering with fitted parametric values. These results suggest that a parametric representation of the coastal profile has potential to characterize and group diverse profile geometries, and that causal-consequential parameter relationships have potential to identify geomorphological trends. The application of these results provide a foundation for the development of a predictive coastal profile model. This model could be trained using data from diverse coastal environments to predict coastal profile response to changes in the metocean climate related to human interventions and climate change.

This thesis reviewed current methods for monetarily quantifying the sustainable aspects of coastal engineering solutions. It developed a holistic monetary valuation framework that integrates the Ecosystem Services Assessment (ESA) and Life Cycle Assessment (LCA) to evaluate the impacts and benefits of coastal engineering interventions. The framework offers a comprehensive approach to assessing the economic, social, and environmental impacts and benefits of coastal solutions, thereby enabling informed decision-making.

Applying the framework to a case study in the Netherlands demonstrated its effectiveness in providing valuable insights into the sustainability of coastal protection measures. The sensitivity analysis underscored the importance of accurately valuing ecosystem services and environmental impacts, as well as the influence of economic parameters, materials, and modes of operation on project viability. Additionally, the framework’s potential for broader application was illustrated through a feasibility study in Suriname.

The study’s findings highlight the need for improved data collection, stakeholder engagement, methodological refinements, and consideration of policy environments to enhance the framework’s applicability and effectiveness. Future research should address these areas to further improve the informed decision-making process regarding the sustainability of coastal engineering alternatives.

Maintenance dredging plays a crucial role in maintaining navigable waterways. While traditional methods using Trailing Suction Hopper Dredgers (TSHDs) are well-established, Water Injection Dredgers (WIDs) are gaining popularity due to their energy efficiency. WIDs utilize hydrodynamic forces to mobilize sediment, eliminating the need for physical transport, thereby reducing energy consumption. However, a comprehensive comparison between WID and TSHD-based dredging methods has been lacking.

This research aimed to address this gap by developing a model to simulate WID operations, estimate production rates, and assess energy consumption. The model was integrated into the discrete-event simulation package OpenCLSim, enabling the comparison of different dredging strategies.

The model incorporates various factors, including sediment characteristics, hydrodynamic conditions, and equipment specifications. Production rates were estimated using empirical, theoretical, and data-driven approaches. The energy footprint was calculated by considering the power consumption of key components such as engines, jet pumps, and thrusters. A case study was conducted to validate the model and compare WID and TSHD performance. Results demonstrated that WIDs were significantly more energy-efficient per cubic meter of dredged material compared to TSHDs. However, the energy footprint is significantly effected by the operational circumstances.

The model provides valuable insights for dredging contractors and port authorities. By simulating various scenarios, stakeholders can optimize dredging strategies, minimizing environmental impact and maximizing efficiency. Future research should focus on refining the model's accuracy, expanding its applicability to diverse scenarios, and exploring the potential for optimizing dredging operations based on energy consumption. This research contributes to a better understanding of sustainable dredging practices. By promoting the adoption of energy-efficient technologies like WIDs, the dredging industry can play a vital role in mitigating its environmental impact and contributing to a more sustainable future.

Maintaining the channels and infrastructure for large commercial ports is costly and complex. Port authorities traditionally rely on bathymetry surveys to guide their dredging operations, which limits their ability to be proactive. This research aims to develop machine learning (ML) methods to predict sedimentation rates (SR) by analysing patterns in hydrological and meteorological (hydro-meteo) conditions and dredging data. By investigating the possibility of SR prediction models, this research can contribute to efficient maintenance operations.

The study focuses on the Botlek, a harbour in the Port of Rotterdam situated in the Rhine-Meuse estuary. Three data types are integrated to capture the dynamic interplay of saline and riverine factors within the estuary. The data consists of Multibeam bathymetry surveys, hydro-meteo variables (such as salinity, river discharge, and tidal variation), and dredging logs. The surveys provide the net sediment accumulation, which is assumed to result from a specific period of hydro-meteo conditions and dredging. Due to the limited availability of surveys, the number of features had to be managed. To avoid having more features than samples, the hydro-meteo variables are aggregated into daily, weekly, and
monthly means.

The ML algorithms evaluated in this research are Linear Regression (LR), Random Forest Regression (RFR), and Support Vector Regression (SVR). The feature importance scores from the RFR and the accuracy on small datasets of the SVR were decisive in this selection. All algorithms were tested and refined over multiple development phases to determine their predictive accuracy and robustness across different data configurations. It was found that the ML algorithms can reasonably predict SR. In addition to dredging data, incorporating hydro-meteo variables enhanced the predictive accuracy and consistency. Specifically, a feature set of dredging volumes, salinity, discharge, and tidal variation improved model performance. Training the model on dredging data only resulted in an inferior performance. The monthly aggregation of these hydro-meteo data was the most beneficial configuration. Among the tested algorithms, SVR consistently outperformed both LR and RFR, with its best configuration achieving a mean R2 score of 0.69 over four different dataset splits.

The ML models do not have to be able to predict sediment volumes with a small confidence interval to be practical for the port authorities. Instead, the models should be able to predict trends in sediment accumulation and provide actionable insights to enable proactive dredging. The research shows promising results, as most predictions fall within the acceptable range. However, predictive accuracy strongly varied across the different dredging areas in Botlek. Further development is required to provide real-time, reliable SR predictions before the models can be integrated into the maintenance operations of ports.

The Earth's landscapes are shaped by processes eroding, transporting, and depositing material over various timespans and spatial scales. To understand these surface activities and mitigate potential hazards they inflict, knowledge is needed of their occurrences and properties. Near-continuous terrestrial laser scanning (TLS) enables the acquisition of point cloud time series, constituting up to thousands of three-dimensional surface morphology representations. Exploiting the full potential of this large amount of data by extracting and characterizing different types of surface activities inside these point cloud time series is, however, challenging.

This thesis addresses this challenge by developing an automated and unsupervised method for classifying 4D objects-by-change (4D-OBCs). These 4D-OBCs represent the spatiotemporal extent of individual surface activities in a point cloud time series. They are classified using a Self-organizing Map (SOM) and hierarchical clustering, grouping them into different levels of surface activity types. The workflow is tested on its ability to characterize surface activities in two study areas, a sandy beach and a snow-covered Alpine area.

Application of an optimized SOM configuration on the sandy beach results in groups of 4D-OBCs physically interpretable as different types of surface activities. A validation dataset containing 51 manually labeled 4D-OBCs of various surface activity types (e.g., intertidal bar depositions, anthropogenic bulldozer depositions) is distributed over the SOM generally according to the labels provided. The SOM thus enables the identification of 4D-OBCs displaying a particular type of surface activity, as well as subtle differences between events of one surface activity. Hierarchical clustering allows us to find and characterize broader groups of surface activities, even if the same type occurs at different points in space or time. For example, the varying spatial redistribution of sand through the initiation of different types of surface activities after the destruction of an intertidal bar system under different environmental conditions is successfully studied.

A nearly identical workflow configuration applied on the snow cover 4D-OBC set does not result in equal performance. Several groups of surface activity in the SOM contain a combination of 4D-OBCs representing different surface activity types. These results highlight the necessity of a study area specific workflow optimization by selecting specific features and SOM configurations. However, if optimized for the specific environment, the workflow has the potential to be used in long-term automated monitoring of surface activity in systems with complex morphological interactions, increasing the applicability of TLS for studying geomorphological change.

Playa Unión and Puerto Rawson are facing severe coastal erosion and increasingly the negative effects. Since the first half of the 20th century there have been signs of erosion, but also of sedimentation. However, the coastline in the project area is retreating over the years. The situation has recently been declared an emergency. Furthermore, there are several expansion plans for the port consisting of new quay walls and dredging. It is unsure if these plans will influence the erosion. In this report, the following research question is formed: ”What are valued, preferably nature-based, interventions that can mitigate the coastal erosion in Playa Unión and Puerto Rawson considering the planned expansion of the port?” Due to the limited information and data available, assumptions have been made during the research. A site analysis and a CoastSat analysis are conducted to research the morphology of the coastline and its drivers. The coast has been shaped into a steep upper section of coarse granular material and a gentle lower slope with finer material. The main driver of longshore sediment transport in the area are swell waves, with predominant directions SSE and E. This transport is directed from south to north, resulting in the inflow and outflow of sediments. Furthermore, there is a sediment flow from the Chubut river. Due to the construction of the port’s breakwaters, the longshore sediment transport is interrupted, which causes an imbalance in the sediment flow in the system. More sediment flows out of the system than enters, causing coastal erosion. The development of the port contains public as well as private expansion plans and maintenance dredging. The expansion plans will have little effect on its surroundings. The private plan include a parallel breakwater, which alters the natural balance in the area and dredging works, for which the stability of the existing breakwaters has to be figured out. A stakeholder analysis was done to get insights in the opinions and visions of the stakeholders. This was done by interviewing stakeholders and by doing a questionnaire, resulting in a power-interest grid and overview of interests and attitudes. The boundary conditions for the interventions and criteria for the multi-criteria analysis are partly formulated as a result of the stakeholder analysis. The following interventions were considered in this report: • Permeable pile groynes and low crested groynes
• Opening the northern breakwater: opening and reshaping with a curve, constructing tunnels underneath the breakwater and a sediment bypass
• Port expansion and a sediment bypass with power supply southern of the port
• Dredging and moving sediment
• Sediment trap
• Plant vegetation with beach nourishment
• Gravel engine
• Temporary longitudinal flood barrier as a short term intervention.
A conceptual multi-criteria analysis in combination with a nature-based assessment has been conducted to distinguish the most promising interventions in the conceptual design phase. The criteria formulated were effectiveness, easiness of implementation, maintenance, environmental impact and the benefits for recreation. From this, it can be concluded that the gravel engine and the plant vegetation with beach nourishment score the best.

Sea turtles, with six of the seven species listed as endangered by the IUCN, face significant threats. Their eggs exhibit sensitivity to fluctuations in temperature, salinity, and moisture during their 6-8-week incubation period. Inundation of these eggs can substantially decrease their viability and influence sex ratios. This thesis centers on mitigating the wave runup-driven flooding of sea turtle nesting beaches. Mitigating nest flooding can involve relocating at-risk nests to higher elevations. This thesis aims to assist in responsible nesting relocation by providing flood risk information as a function of beach elevation. The study site, Ras Baridi, is a significant nesting site for green turtles (\textit{Chelonia mydas}) in the Red Sea. The research focuses on the development of a methodology for assessing flood risk in environments with limited data.

The risk of sea turtle nests to flooding at Ras Baridi was assessed by employing two different metamodels, BEWARE 2.0 and HyCReWW, that were recently developed to assess wave runup in coral reef environments at low computational costs. These models employ different strategies to model wave runup. BEWARE 2.0 uses real bathymetry, whereas HyCReWW schematizes the input bathymetry. 40-year hindcast datasets of waves and water levels from 1978-2018 were used in combination with 10 m horizontal resolution bathymetry data from the Allen Coral Atlas as model inputs. HyCReWW exhibited maximum runup at an 80 m reef width (0.95 m for 1 year return period (RP), 1.95 m for 40 year RP) BEWARE 2.0’s highest runup was at an 80 m reef width (1.35 m for 1 year RP, 2.7 m for 40 year RP). Median inundation durations from BEWARE 2.0 and HyCReWW were 4 and 7 hours, respectively. Beach elevations associated with 5-year return periods were identified as suitable minimum nesting elevations to mitigate inundation risk.

BEWARE 2.0 and HyCReWW results for Ras Baridi lacked direct real-world validation, prompting a methodological validation approach. Six validated XBeach 1D NH models in fringing reef environments globally were utilized for comparison against BEWARE 2.0 and HyCReWW. The comparison revealed increased median scatter indices and root-mean-square (RMS) errors for both BEWARE 2.0 (0.07 and 0.1 m increases) and HyCReWW (0.1 and 0.15 m increases) with low-resolution bathymetry (10 m horizontal). BEWARE 2.0 had lower median RMS errors and scatter indices for high-resolution (2 m horizontal) and low-resolution bathymetry than HyCReWW, as well as smaller spreads. Consequently, utilizing BEWARE 2.0 results is recommended for Ras Baridi and similar data-scarce coastal environments over HyCReWW.

In summary, this thesis addresses the pressing issue of sea turtle nest flooding, providing a methodology to assist in responsible nest relocation in low data environments. The 5-year return period runup elevation of the BEWARE 2.0 results (1.25-1.75 m along the beach) has been identified as a suitable minimum nesting elevation in Ras Baridi, and the likelihood of flooding as a function of beach elevation has been provided. These results can aid coastal managers in making informed decisions for the protection of these endangered species.

The shipping industry is responsible for almost 3% of the world's greenhouse gas emissions and is looking for possibilities to reduce their share. The demand for lowering the emissions of the maritime industry creates the need for insight in the emissions. Currently a generic method to specify emissions in space and time, as a function of vessel and waterway properties is not available.
Although most of the emissions of vessels take place at sea, the most noticeable part takes place in ports since they are located close to urbanized areas. Therefore, this research focuses specifically on emissions in ports. The study investigates the impact of sea-going vessels. By gaining insight in the emissions of sea-going vessels, the big polluters can be tackled. Due to the scope of the research, not all vessel types and emission types are taken into account. Ten vessel types are selected and only CO2, NOx, SOx and PM10 emissions are estimated. These are the most relevant polluters in the shipping industry since they cause health and environmental related issues, both locally as more widely.
To provide insight in the emissions, the objective of this study is to develop a generalized method to calculate and map the emission of a single vessel in space and time in ports based on reliable data. To reduce these emissions, emission reduction strategies have to be drawn up, targeting the largest emission sources and the most crucial locations. Therefore, the developed method must not only quantify the emission sources, but must also provide insight in emission patterns in ports and indicate emission hotspots.
A bottom-up method is developed to estimate the emission of a single vessel in space and time. To derive the emission rate of a vessel, the fuel and energy consumption of the vessel is multiplied by a vessel-specific emission factor. The CO2 and SOx emissions follow a fuel-based approach, in which the emissions produced are directly proportional to the fuel consumption and therefore depend on the engine load. The energy-based approach is used for estimating NOx and PM10 emissions, which cannot be directly related to the fuel consumption but depend on engine characteristics. The fuel consumption is determined by multiplying the energy consumption of the engine with a fuel consumption factor specific to each vessel, meaning the fuel consumption is related to the energy consumption.
The amount of energy the main engines of a vessel consume, is the energy needed to overcome the resistance a ship experiences from sailing through the water and is therefore related to the vessel's speed. This speed is derived from AIS data. AIS data represents real-life vessel tracking data and is gathered automatically, which makes it a reliable and realistic data source. It also provides the ability to make the emission estimations time dependent. Due to the global coverage, AIS data is a good data source to develop a generic method applicable to ports all around the world.

However, in ports the vessel's speed alone is not a good indicator of the energy consumption, since this neglects the amount of energy the auxiliary engines consume. Their energy consumption is dependent on how much energy the electrical systems of a vessel require at that moment, which can be high when a vessel is for example at berth or manoeuvring. The energy consumption is therefore related to the operations a vessel performs. This leads to an approach which takes into account the vessel's resistance and operational modes.
Four operational modes are important to distinguish in ports: 'sailing', 'manoeuvring', 'anchoring' and 'berthing'. According to the operational mode, the main engine power is either estimated with the resistance calculation from Holtrop and Mennen when the vessel is sailing or manoeuvring, or assumed zero when laying still at anchor or berth. According to the operational mode, the auxiliary engine power can be derived from values of the IMO fourth GHG research. Depending on the emission type and vessel characteristics, the emission factor is determined.
The algorithm to determine the emission of a single vessel in space and time is implemented in a model. The model's input consists of AIS data providing the speed and position of the vessel, a vessel database containing vessel characteristics based on information from the Sea-web Ships database, and a FISgraph of the port network. This graph contains the fairway characteristics needed to calculate the resistance. The calculated emissions will also be displayed on the FIS graph for a detailed insight in the emission distribution in space.
The model provides an insight in the emissions patterns in a port. The fairway sections which are subjected to high emissions can be identified immediately and so the emission hotspots are determined. The source of the emissions can be identified by down-drilling of the emissions. The model can drill down to vessel types, operational modes and all the way down to a single vessel in space and time.
The model is illustrated by means of two case studies concerning the Port of Rotterdam and the Port of Constanța. These case studies indicate that the port basins hosting the largest vessels have the highest estimated emissions. These basins are indicated as an emission hotspot by the model when the emissions are projected on the FIS graph. In ports generally, high emissions are observed at places with a high traffic intensity, such as the port entrance. Junctions of fairway sections or port basin entrances also show locally higher emissions. The rise in emissions, is probably due to the fact that vessels are slowing down when approaching a junction. This increases the emission rate of a vessel due to more inefficient engine use, but also since they spend a larger amount of time at this fairway section.
By indicating the location and source of the emission hotspots, targeted emission reduction measures can be taken. Three of these measures are demonstrated on one of the case studies. The first strategy concerns installing shore power. The model is able to simulate vessels connected to shore power, by setting their emission rate at berth to zero. This simulation is compared to the original situation without using shore power. Out of the evaluated reduction measures, this seems the best strategy to reduce emissions since it shows the largest reduction of the total emissions. Besides that, it is an effective measure especially for ports, since the largest emissions reduction takes place at berth. The second evaluated strategy shows also good results and is about switching from a normal tugboat fleet to a zero-emission tugboat fleet. The model simulates this switch by eliminating all the tugboats from the fleet since their emissions will be zero. This new case is compared to the original situation with normal tugboats. The third option to reduce emissions is applying ECA limits which do not seem to have a lot of effect on reducing emissions except for the SOx emissions. This is derived from comparing the original situation without ECA limits, to a new case study where a situation with ECA limits is simulated. This means that the fuel types of the vessels are changed from the most economical fuel type to the lightest fuel type on board and that the sulphur content in the fuel is altered.
However, the model has some limitations. The method does not take into account the effect of currents or variations in time of the water depth. The accuracy of the resistance calculation can be improved by adding these. Furthermore, the emission pattern of tugboats needs further examination as it could not be demonstrated that the split of emissions into operational modes is correctly. The research has also shown that the method to estimate the energy consumption is not suitable for tankers at berth, since their energy consumption pattern is different. The quantity of emissions in the case study of the Port of Rotterdam is far from the expected amount of emissions. The quantification of emissions is assumed to be unreliable and further research should focus on validating these results.
Concluding, the developed model makes use of AIS data, local waterway properties, empirical emission factors and operational modes. This data is used in a physics-based method to estimate the resistance and the energy consumption. If this information is available, all this combined makes the approach in principle applicable to any port. The developed model provides an insight in the emission distribution patterns and provides the ability for down-drilling to find the source of the emission hotspots. A targeted emission reduction strategy can be proposed as a result of this and the model has the ability to evaluate specific emission reduction strategies. The strategies can be simulated with the developed model and the effect of these measures can be quantified by comparing the emission reduction strategy to a situation without these measures.

Dunes are the primary sea defence along the Dutch coast. This research investigated the development between 1965 and 2021 of the beaches and dunes of the Hoogheemraadschap Hollands Noorderkwartier (HHNK) area which stretches from IJmuiden to northern Texel. This research used an advanced data analysis on the annual coastal elevation data. The coastline of the HHNK area is maintained by several different measures including nourishments, planting and removal of grass, placing reed fences and building regulations. The aim of this research was to relate the investigated developments of the beaches and dunes to these measures and natural processes.
A literature study was performed first, to get acquainted with the research area and the processes that drive the beach and dune development in the area. In four phases, the developments and their relationships with the drivers were investigated for several subsections of the research area that showed similar behaviour. In phase 1, the coastal profile data was collected for multiple transects spanning the research area. Characteristic parameters describing features of the dune and beach were derived from the coastal profile data. In phase 2, the collected profile data was decomposed into spatial and temporal patterns by a principal component analysis. In phase 3, the transects of the research area were categorised on the temporal development of their coastal profile using the results of the spatial and temporal decomposition. This created subsections of the coast that showed a similar morphological behaviour. In phase 4, the morphological behaviour of several subsections was investigated and related to the natural and human drivers. The relationships between the drivers and the developments were investigated for several subsections, varying in morphological behaviour and drivers, to gain insight into these relationships for the entire research area.
The coastal categorisation resulted in 36 subsections which were mainly continuous in space with the exception of four clusters. Seven subsections were studied in more detail, by investigating the development of several characteristic parameters derived from the coastal profiles, like dune volume, beach slope and shoreline location among others. The shoreline location and beach width showed a strong correlation with the nourishments. The beach slope and width were expected to influence the dune volume changes, which was not supported by the results. The beach width is assumed to be larger than the critical fetch length and the variations in beach slope had a marginal effect on the transport capacity. The presence of beach pavilions limited the dune growth in both height and volume, but regularly moving the pavilions did allow for a seaward migration of the dune front. The effect of small beach houses on the dune volume change was found negligible. Maintenance works of HHNK influenced the dune development locally. The placement of reed fences caused seaward migration of the dune front and the creation of blow-outs increased the dune volume behind the most seaward dune. The strongest dune volume increase was found at coastal areas behind shoals that reduced the incoming wave energy.
The coastal categorisation succeeded in grouping transects with a similar development of their modified coastal profiles. Several modifications were proposed to improve the categorisation of the original profiles and to take into account smaller scale processes. Nourishment was distinguished as most important driver for the development of the coastal area, which has maintained the shoreline location and caused a trend break in several other characteristic parameters. Human influences like buildings, coastal structures and reed fences have a strong local effect on the development of the beaches and dunes. Further research could take into account wave and wind climate, water level variations and grain size quantitatively to get a more comprehensive study of the relationships between the development and the drivers.

Sea turtles are a popular tourist attraction that offers travelers a unique nature experience. As a part of Saudi Arabia’s 2030 vision, an unprecedented amount of tourism development is being carried out under the ‘Red Sea Development Project’. The aim is to develop hyper-luxury islands in a sustainable and ecologically friendly way by closely following ‘Building with Nature’ principles. The tourism development site at Al Wajh Bank encompasses an archipelago of more than 90 islands and some of the sandy islands provide nesting grounds for Hawksbill and Green turtles. The thesis is a component of a broad study on investigating the preferential conditions for sea turtle nesting and conservation where TU Delft is collaborating with NIOZ and KAUST. The present study makes a preliminary assessment of the preferential conditions for sea turtle nesting of wave hydrodynamics and geomorphological conditions based on available data and numerical wave model simulations. The study is expected to provide information and guidance for future research for the conservation of sea turtle nesting at Al Wajh Bank.

The coral reefs provide breeding grounds for sea turtles in the gently sloping sandy beaches along the raised islands where the sand has ideal conditions of moisture, temperature, and distribution of sediment size for successful nesting and hatching of eggs at an optimum distance from the highwater line to avoid inundation by wave runup. In general, the foreshore at the turtle nesting site has a fore reef with a steep slope followed by a wide and shallow reef flat having high bottom roughness for dissipation of wave energy. The Al Wajh Bank spread over an area of 2880 km^2 has a very large and complex barrier reef system at the outer edge in the deep sea enclosing a massive lagoon with several islands. These islands have sandy beaches on the up wave (windward) side and mudflats with sand and vegetation in the sheltered zones. The vegetation is dominated by mangroves in the mudflats and seagrass in the shallow lagoon beds.

The study used the Delft3D WAVE stand-alone phase averaging spectral wave model for transforming waves from offshore to nearshore and to examine the energy dissipation characteristics of the reef system. The high-resolution bathymetry data for the Al Wajh Banks was obtained from GCC and relatively coarse resolution data for the offshore from GEBCO. The model simulated offshore wind and wave data validated using satellite altimetry, SAR, and Buoy measurements was obtained from BMT ARGOSS online services website “waveclimate.com”. The normal and extreme wave conditions were derived using this data. The data on GPS coordinates at the location of turtle nesting sites was provided by a survey undertaken by KAUST. A limited data on sediment size distribution at four islands south of Al Wajh Bank was made available by KAUST collected by ALS Arabia. The data on beach slopes, the distance of nests from HWL, geomorphology, vegetation was derived from secondary sources or in an indirect manner using bathymetry data.

The wave model results were analyzed for the nearshore wave heights and distribution of energy density at the fore reef and inside the lagoon. The wave model results revealed that the extensive barrier reef on the seaward side of the Al Wajh Bank is able to completely prevent and dissipate the energy of the offshore waves providing ideal conditions for turtle nesting at seaside islands. Further, the results show that the offshore waves do not have any role in the production and dissipation of wave energy inside the lagoon. The study confirmed that the waves inside the lagoon are exclusively local wind-generated waves. The wave climate inside the lagoon during storm conditions is only influenced by the prevailing strong winds and not dependent on the storm-generated extreme waves from offshore.

Most of the sea turtle nests have been found on the up wave or windward side of beaches with flat and wide reefs or fringing reefs between the reef crest and the high-water line. The wave model demonstrated the large energy dissipation rates under these geo-morphological conditions. In the absence of relevant data on sediments for the nesting and non-nesting beaches, no specific conclusions could be drawn. The beach slopes where sea turtle nests were abundant had slopes in the range of 1:10 and 1:20. The study indicated that the non-nesting sites inside the lagoon are in the sheltered zones of inner reef shelfs and behind the islands where mudflats with mangroves and other vegetation are abundant.

The wave runup is estimated using the HyCReWW metamodel at the nesting and non-nesting sites. The metamodel estimates indicated that the wave runup is of comparable magnitude both for the nesting and non-nesting sites. The comparison of runup distance along the beach indicated that the turtle nesting sites are located sufficiently away from the runup computed for 1 in 100-year wave height

A satellite imagery-based global shoreline data source (Shoreline Monitor, Deltares/TUDelft) was used to examine the erosion and accretion trends at Al Wajh Bank. The data identified two of the islands as having chronic erosion which have a small percentage of nesting sites. The analysis of the data identified that the turtle nesting sites are located on beaches with stable erosion/accretion rates.

The study confirmed the limitations of the Delft3D WAVE (SWAN) model used in simulating IG waves. Due to the very large domain and the course grid adopted in the present study the wave setup aspects have not been covered. The present study was useful in providing a range of information and guidance for further hydrodynamic and geomorphological data inputs at a regional scale and application of models like X-Beach or SWASH for a more comprehensive and detailed analysis on the influence of the surf and swash zone dynamics including IG waves on the sea turtle nesting sites. A systematic data acquisition on geomorphological and sediment characteristics will be useful for logistic regression analysis to study the dominant factors influencing the turtle nesting and non-nesting sites.

Climate change causes cities to deal with increased temperatures and more frequent weather extremes. Heat waves will occur more often, becoming a more prevalent issue in especially urban areas. The quantification of heat stress is a first step to define mitigation measures. For that purpose, a standardised method to assess the spatial influence of surfaces on the Physiological Equivalent Temperature (PET) was developed. This study aims to reshape this model into a statistical dependence model which is more flexible regarding missing data. To this end, we used a Non-Parametric Bayesian Network (NBPN). We created a model driven by both data and expert knowledge, that is capable of dealing with input data layers with a grid resolution up to 20 m. Results show that training the model with only 20 sample points did not affect the performance considerably, compared to using 2,000 data points. Inclusion of a layer with sky view factor mainly improves the estimation of observations in the tails of the distribution. The model predicts the PET with a Mean Absolute Error (MAE) of 1 to 2 ºC, dealing adequately with missing data layers. With this limited amount of necessary input, the NPBN in our study helps in standardising the assessment of heat stress outside the borders of the Netherlands. Also, our model offers a framework to make a first assessment regarding the effect of NBSs on heat stress.

The Wadden Sea is a large system of tidal flats and barrier islands. Its individual features have been heavily researched, but sufficient knowledge on the sediment transport patterns on the scale of the Wadden Sea is not yet available. This thesis aims to indicate the dominant sediment transport patterns in the Dutch Wadden Sea and the hydrodynamic forcing mechanism the patterns can be attributed to. The Delft3D-FM model used to indicate these patterns is first adapted by implementing an improved Manning’s roughness field. To assess the patterns for each contributing hydrodynamic forcing mechanism, the tide, wind and waves are added to the model one by one. The results show that the Dutch Wadden Sea is a highly interconnected system, with the influence of each hydrodynamic forcing mechanism depending on the feature and inlet system.

Coral reefs are vital to the prosperity of the world and the local communities by providing food and coastal protection. Coral reefs are home to 25% of marine life and have therefore gained the nickname ’rainforest of the sea’. However, the reefs are being damaged by climate change and human intervention, resulting in an alarming rate of degradation. The combination of higher water levels on reef flats and the reduced friction due to reef degradation lead to greater risks of flooding and overwash on low-lying islands.

These aforementioned effects already present a threat to small islands that are naturally fronted by coral reefs. However, there is one more threat of damage to these islands: resonance. The characteristic bathymetry of the coral reefs can induce this type of threat, which is likely to be enhanced through climate change. Fringing reefs can display harbour-like resonance under the right conditions, because of their generally steep fore reefs and shallow horizontal reef flats. This has been shown to occur during various occasions, but the conditions that lead to are still unclear. What drives the occurrence and magnitude of resonance?

Resonance has been observed during high-energy events on various pacific islands, and though it occurs only 3.6% of the time, its damaging potential is larger than any other form of infragravity (IG) wave propagation. Various numerical models have been set up to study the behaviour and drivers of resonance, yet the defining characteristics and drivers of the phenomenon remain enigmatic.

To get a better understanding of the parameters that influence the occurrence and magnitude of resonance in fringing reef environments, a SWASH model is used. The amplification of the incoming IG wave is determined
by comparing the incoming IG wave energy at the beach toe for a beach boundary to an absorbing sponge layer boundary. If the ratio of incoming IG energy is greater than unity, resonance is expected to occur.

The magnitude of the AR is largely dependent on the characteristics of the incoming SS wave field and coral reef bathymetry: a great modulation and a gentle fore reef slope lead to a large amplification. Additionally, a lot of incoming SS wave energy corresponds to a limited amplification of the incoming IG wave, likely due to the interaction of the released bound long wave and the IG wave generated through break point forcing, which are 180° out of phase. Therefore, the largest amplification occurs for a strongly modulated wave field without being too energetic. There is no clear relationship between the incoming SS wave periods and the amplification, the height of the incoming IG wave is similar for various situations. The amplification appears to depend on the combination of the fore reef slope and incoming SS wave periods. Different SS wave periods
lead to different amplifications depending on the slope of the fore reef they pass over. The height of the incoming IG wave is largest for the steepest slopes under consideration. Thus, for these conditions, the risk of flooding is largest. However, the greatest amplification occurs for the gentler slopes
under the considered conditions. It should be noted that this relationship is heavily dependent on the combination of incoming SS periods and the steepness of the fore reef slopes.

The period for which resonance occurs is always higher than the theoretical fundamental eigenperiod, which is found through water depth integration from the location of minimum IG wave height to the beach toe. Integrating the water depth from the most offshore point of break point forcing leads to a smaller deviation of the theoretical fundamental eigenperiod from the observed period, yet this method needs further exploration.

The insights acquired by this research can be used as a base for site-specific modelling, which can provide circumstances under which certain areas are at risk of flooding due to IG wave resonance.

Embankments are one of the key aspects of flood protection programs. As the sea levels are expected to rise the coming decades coastal protection is becoming increasingly important. To better prepare for the future, increased understanding of embankment breaching is desired. An area of embankment breaching that has been relatively left untouched is the impact of a foreland during a breaching event. This study examines the different effects caused by the presence of a foreland, namely reduced flow rate through the breach, limited breach growth and an elliptical erosion pattern in the foreland.
Embankments are generally more erodible than forelands, and erosion of the foreland occurs after the adjacent embankment has been breached to its base level. As water continuous to flow over the foreland, a plunging jet will form, falling from the high foreland into the breach bottom. This causes undermining in the foreland and subsequent headcut erosion. A distinction is made between cohesive and non-cohesive forelands. Cohesive soil layers experience rotational failure and non-cohesive layers experience sliding failure.
A model is presented that calculates headcut erosion in a foreland, flow characteristics over the foreland and erosion shape of the foreland. The erosion shape of the foreland affects the discharge through the breach and is captured in the non-dimensional constant m. The presented model is implemented as a module into the existing breaching model BRES. The added functionality will increase the accuracy BRES model results.
Two experiments were performed to verify the model. The first experiment examined soil failure of a foreland in dry conditions by pushing an extracted foreland soil layer from a table to observe the failure type and the effects of vegetation on the strength of the soil. The second experiment was performed in collaboration with the University of Antwerp. A soil layer was extracted from a foreland and placed in a large flume. The foreland was subjected to a plunging jet to simulate a breaching event. The outcomes of the model were in line with observations made during both experiments.
The presented model serves as a significant first step towards implementing forelands in flood protection programs, but further research is suggested. The uncertainties stemming from assumptions in the model and the underlying sediment transport relations are significant. Most importantly uniformity of the soil is assumed, whereas in reality root systems in fertile forelands can lead to significant local strengthening. The assumptions in this model are conservative, but additional studies can further increase the accuracy of the foreland- and BRES model.