Dredging projects are capital-intensive, complex, and exposed to high uncertainty from weather, soil conditions, technical reliability, and logistics. During early tender bidding, contractors must estimate project duration, costs, and emissions with limited time, data, and engineering capacity. Detailed studies are rarely feasible, yet bidders must anticipate which uncertainties drive outcomes. Existing methods provide detailed analyses of individual aspects but require resources unavailable at this stage. What is missing is a structured, rapid way to identify and prioritize the most critical risks.

This thesis develops a rapid risk-assessment methodology that combines expert judgment with Discrete Event Simulation (DES). The approach enables contractors to focus scarce resources on uncertainties that matter most for project success. The open-source platform OpenCLSim was chosen for its layered structure (activity, sequence, asset, project), flexibility, and suitability for dredging logistics.

Inputs are expert estimates of risk likelihood and impact, grouped into four categories: workability, technical, logistical, and environmental/social. These are mapped to the correct project layer and encoded through a custom Risk & Uncertainty extension using occurrence models and impact distributions. Risks are thus represented at appropriate levels—activity variability, sequence delays, or project-wide interruptions.

Outputs are probabilistic KPIs for project duration, costs, and emissions, obtained via Monte Carlo simulation and reported at the industry-accepted P80 level. This provides risk-aware estimates under uncertainty. The method also includes mitigation evaluation, where updated risk parameters are simulated with countermeasures. Comparing mitigation costs with reductions in time, cost, and emissions highlights which measures are financially viable and where engineering capacity should be allocated.

The method shows how risks at different layers interact: delays off the critical path may be absorbed in project duration, but still cause idle costs and emissions. Thus, time buffering and cascading impacts diverge, clarifying risk importance for different KPIs.

Applied to the Malmporten case, expert elicitation identified three risks: mooring-time uncertainty, a sequence-level backhoe breakdown, and project-wide turbidity exceedance. Simulations revealed that the backhoe breakdown had the greatest impact by delaying dependent assets; turbidity exceedance mainly raised costs and emissions; mooring-time uncertainty had modest operational effects. Mitigation analysis showed sequence-critical risks offered the highest leverage: on-site spare parts sharply reduced repair durations and had the best benefit–cost ratio, while silt screens and experienced captains offered smaller, complementary benefits.

Conclusion: The methodology provides a structured, time-efficient decision-support tool for early tendering. By combining expert judgment with DES, it delivers probabilistic insights into which risks matter most and which countermeasures are justified. Though demonstrated in one case, the method can be generalized to other dredging project types and extended with advanced failure models, calibrated inputs, and broader KPI frameworks. It equips contractors to make more reliable, risk-informed decisions under the constraints of early tendering.

The Maeslant barrier is a storm surge barrier and a critical component of the Dutch coastal flood defense system. Its reliability is formally assessed through a Reliability and Availability (RA) analysis, which estimates the probability of non-closure during storm events. However, concerns have been raised regarding the completeness and transparency of this analysis, particularly the potential omission of relevant failure events. This thesis investigates whether a selected set of previously unaccounted for events can be systematically identified and quantified to improve the accuracy of the non-closure probability.

A three-stage methodology was developed. First, a structured inventory of unaccounted-for events was constructed using HAZOP, FMEA, What-If, and external event screening techniques, mapped across four analytical dimensions. Second, the list was filtered based on estimated occurrence probability and quantifiability, resulting in a shortlist of three events: epistemically uncertain events, non-stationary component degradation, and the unverified reliability of human interventions. Third, these events were quantified using structured expert judgment, research into time-dependent fault tree modeling, and human reliability assessment.

Results indicate that these unaccounted-for events can alter the estimated non-closure probability, either increasing it by an order of magnitude or reducing it by up to 50%. Moreover, the analysis revealed limitations in the current RA analysis, including outdated reliability assumptions, a fragmented integration of human interventions, and a lack of empirical data. These findings support the need for a more transparent and adaptable RA framework. The discussion highlights that while completeness in risk assessment is theoretically unattainable, similar to the limitations of physical laws, models should strive for an optimal balance between complexity, traceability, and applicability.

Recommendations include developing a centralized component lifecycle database, maintaining a registry of previously unaccounted-for events, formally integrating the OPSCHEP model into the fault tree structure, and adopting structured human reliability verification. These changes can improve the accuracy, transparency, and credibility of the Maeslant barrier’s non-closure probability and serve as a blueprint for other critical infrastructure systems.

This study explores the feasibility of creating new mangrove habitats along the Arabian Peninsula coast by comparing ecological benefits with economic costs. Mangroves offer vital ecosystem services—including shoreline stabilization, carbon sequestration, and support for biodiversity and sustainable fisheries—but are under threat from coastal development and climate change. Restoration efforts are increasing, yet proactive habitat creation remains limited.

Focusing on potential coastal slope modification, this study examines whether engineering interventions can make currently unsuitable sites viable for mangroves. Suitability depends on environment’s hydrometeorological and topographical factors. Using open-access data from Deltares, Copernicus, and NASA, the study applies a multi-step geospatial and economic analysis.

First, environmental drivers of mangrove presence are analyzed, followed by modeling the relationship between Above-Ground Biomass (AGB) and environmental conditions. ABG is hypothesized to represent the cumulative influence of all environmental factors, with higher ABG indicating more suitable conditions for mangrove growth. After testing commonly used regression methods, the Random Forest (RF) machine learning algorithm proved to be the most accurate and having the least MSE in modeling existing biomass. Thus, the RF is further used to estimate potential biomass in areas without mangroves, identifying high-potential sites.

For the selected top-performing site, at Dhofar, Oman, a custom-built dredging tool estimates the required excavation volume and potential area gained by modifying coastal slopes. The economic analysis calculates costs based on dredging volume and benefits based on carbon sequestration and blue carbon credit valuation, with additional consideration of other ecosystem services such as aquaculture support, water quality improvement,
coastal protection, and eco-tourism.

Despite high AGB potential, results show that costs far exceed projected benefits. Even under optimistic carbon pricing and full valuation of ecosystem services, the best-case cost-benefit ratio remains around 10-20:1. Therefore, while it is probably ecologically and technically feasible, mangrove habitat creation under current conditions is not economically viable without significant external funding.

Limitations include reliance on existing datasets that may not capture fine-scale nvironmental variability, and exclusion of human factors like land use changes and infrastructure, which ould significantly affect project success. Additionally, while the dredging tool is grounded in geometric and vector calculations, it has not yet been validated against other established methods.

This study investigates how projected changes in tropical cyclone (TC)-induced storminess, defined by extreme wind, surge, and wave events, will affect coastal flood hazards along the Sofala coastline in Mozambique under the near-future SSP5-85 climate scenario (up to year 2050). While global studies have suggested intensified storm characteristics in the Sofala region, no study to date has assessed their implications for regional coastal flood hazard in conjunction with sea-level rise (SLR). Using the synthetic STORM dataset in combination with probabilistic extreme value analysis and a storyline-based TC Idai simulation, this study quantifies the projected increase in TC-induced hazards and evaluates the relative contribution of storminess change and SLR to future flood depth and extent. Results show a consistent upward shift in the 100-year return levels for wind, surge, and wave. These changes can be mainly statistically attributed to the exceedance frequency (i.e., how often extremes occur) rather than exceedance intensity (i.e., how extreme they are when they occur). As such, the effective return period of historical 100-year events is found to be 45–60 years on average under the near-future scenario, suggesting that extreme events will become significantly more frequent. Although the upper tail dependence between surge and wave slightly weakens, the likelihood of joint surge–wave extremes still increases, lowering the joint return period from an average of 200 years to 120 years. In the TC Idai storyline, SLR accounts for 60–90% of the increase in total water levels, with a magnitude of around 0.25 m. However, storminess change contributes up to 40% in some areas, and is shown to substantially heighten flood extent and population exposure. The findings underscore the importance of integrating TC-induced storminess change in Sofala’s future coastal hazard assessments, in addition to SLR. Recommendations include extending analyses to additional synthetic TC models and emissions scenarios (e.g. SSP2-45), improving hydrodynamic model validation, and incorporating rainfall and fluvial forcing to capture the full spectrum of compound flood hazards under a changing climate.

Channel refraction, a process in which obliquely incident waves are turned by navigation channel slopes and effectively reflected away from the channel, can significantly influence nearshore hydrodynamics and sediment transport. This thesis investigates how well phase-averaged and phase-resolving numerical models capture these effects. A comparative study was conducted using SWAN-FINEL (phase-averaged) and XBeach non-hydrostatic (phase-resolving). First, model performance in simulating wave transformation was assessed using a physical model of Taman Port. Both models reproduced general wave patterns and showed comparable performance based on the wave measurements. XBeach offered slightly improved accuracy in energetic conditions and on the lee side of the channel. Second, an idealized numerical experiment was used to explore sediment transport differences. Offline sediment transport calculations using an intra-wave and phase-averaged model based on the Meyer-Peter-Müller formulations revealed very similar longshore transport gradients and infilling patterns, but notable differences in cross-shore transport due to the inclusion of wave skewness, asymmetry, and swash-zone processes in XBeach. The findings highlight that while SWAN-FINEL performs well in longshore-dominated systems at low computational cost, phase-resolving models offer added value for short-term cross-shore-dominated settings, albeit at higher computational demand. Model choice should therefore depend on the dominant transport direction and project scope.

The Lagos State coastline is increasingly vulnerable to erosion, driven by both a dominant eastward longshore sediment transport and ongoing human interventions. Climate change, through rising sea levels and a changing wave climate, is expected to further influence shoreline dynamics in the coming decades. Understanding how these factors interact is crucial for supporting long-term coastal management along this coastline of major economic and social significance. This study investigates the impact of climate change on shoreline evolution over a 50-year timescale, focusing on sea level rise, changes in significant wave height, and changes in wave direction. The onedimensional shoreline evolution model ShorelineS was applied and calibrated using satellite derived historical shoreline data. Simulations were performed for a range of climate scenarios, allowing for a separate evaluation of the individual contributions of sea level rise, changing wave height, and changing wave direction to coastal erosion and accretion patterns. Longshore sediment transport is projected to increase by up to 20% by 2075 under extreme RCP8.5 scenarios. This intensification directly drives changes in shoreline dynamics, leading to amplified erosion in some areas and enhanced accretion in others. Projected shoreline retreat can reach up to 700 meters locally under baseline conditions (i.e., without climate change) by 2075. Climate change can further amplify these patterns, with shoreline retreat increasing by up to 10% in erosion-prone areas near coastal structures. In contrast, accretive zones show a more moderated response due to counteracting effects between sea level rise and wave-driven accretion. Wave direction changes were found to have the strongest localized impact on shoreline retreat near coastal structures, while the influence of wave height changes was less consistent and more variable along the coast. Sea level rise causes relatively uniform retreat along the entire coastline. The results provide spatially explicit projections of shoreline retreat and accretion, offering insights into areas most at risk. These findings support the development of targeted, adaptive coastal management strategies for Lagos State in the face of a changing climate.

With increasing threats from climate change, the vulnerability of coastal areas is heightened. One major challenge is the degradation of beaches due to flooding and erosion. Tropical and subtropical beaches are crucial nesting sites of sea turtles who are essential to marine ecosystems by supporting the preservation of coral reef and transporting nutrients from the ocean to coastal environments. Developing engineering maintenance or designing nature-based solutions can help mitigate the effects of flooding and erosion, preserving or expanding sea turtle nesting beaches. To effectively develop nature-based solutions that enhance sea turtle beaches by improving nesting suitability and reducing flooding and erosion, an understanding of the natural processes on small-scale environmental variables, such as sediment characteristics, are essential. The sediment characteristics are important for the survival of sea turtles because they influence the nesting behavior, egg survival and hatching success. Unfortunately, current data on sediment characteristics are scarce, especially at a global scale, which makes it difficult to identify global patterns of these characteristics across sea turtle nesting beaches.

The aim of this study was to analyze key sediment characteristics of sixteen globally significant sea turtle nesting beaches and to evaluate different experimental methods to investigate these sediment characteristics as a pilot study. The key sediment characteristics in this pilot study included color, particle size, particle grading, particle shape, bulk density, solid particle density, angle of repose, thermal retention, and porosity. This research served as a pilot study for a more extensive analysis that will be applied to approximately 2000 sediment samples from 209 sea turtle nesting beaches worldwide.
To achieve this, 32 sediment samples were examined from sixteen beaches, with one sample taken from the nesting line and one from the shoreline at each location. The nesting line was identified by visible nests and body pits, while the shoreline was defined as the highest point of debris left by the retreating tide. The sediment samples were analyzed using both qualitative and quantitative methods in different laboratories (TU Delft, Boskalis and Deltares). Qualitative methods included the Munsell color chart, Power scale, classification of sorting (well-sorted vs. poorly sorted), sand ruler and LEICA digital microscope.
Quantitative methods included dry sieving, Dynamic Image Analysis (DIA), Static Light Scattering (SLS), free-fall \& vibration table, volume of water displacement, helium gas displacement pycnometer, image-based angle of repose analysis and thermal retention analysis.

The results showed that all sixteen nesting beaches in this study were composed of well-sorted, medium-sized sand. These beaches may also be characterized by particles with moderate roundness and high circularity. Variations in bulk density and solid particle density suggested that sea turtles nest in diverse beach conditions, without a clear preference for specific density values. Results for angle of repose and thermal retention did not yield meaningful patterns related to sea turtle nesting preferences. Porosity values were consistent with those typically found in sandy environments. However, definitive conclusions about sea turtle nesting preferences can not be drawn at this stage, given the limited sample size and the lack of comparative data from non-nesting beaches. The findings should be considered suggestive rather than conclusive, and their main purpose is to guide future research directions.

This study also identified differences between key sediment characteristics at the nesting- and shoreline. While some variation was observed, no general statically significant differences were found between these two environments. Based on these findings, future large-scale studies may consider initially focusing on the sediment samples at the nesting line. No clear relationships were observed between sediment characteristics and latitude, and sea turtle species did not show distinct preferences for specific sediment characteristics. However, these findings should be further investigated with a large number of sampling sites.

For large-scale sediment analysis, DIA would be the most efficient alternative to analyze particle size and it also provides information on particle shape. Although dry sieving remained the most reliable method, it might be time-consuming for analyzing a large number of sediment samples. Regarding solid particle density analysis, the helium gas pycnometer seemed to be the most accurate option. However, it required professional expertise. Nevertheless, no significant differences were revealed between results obtained from the gas pycnometer and the volume of water displacement. The reliability of the water displacement method remained uncertain, and further testing was necessary to validate its accuracy or consider alternative methods.

Based on the outcomes of this pilot study, future research is recommended to pursue a more comprehensive and globally representative understanding of sediment characteristics at sea turtle nesting beaches. This will involve analyzing sediment samples from 209 beaches, focusing on the nesting line. The extended study will asses key characteristics, including color, particle size, particle shape, bulk density, solid particle density and porosity. The resulting dataset will provide essential information for researchers, ecologists and coastal engineers, and may contribute to the development of nature-based solutions, such as beach nourishment, to protect sea turtle nesting areas.

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.

This research develops a surrogate modeling framework to efficiently analyze and optimize a proposed pump and gate system designed to mitigate compound flooding in the Clear Lake region, Texas. Traditional numerical hydraulic models are often computationally expensive for large number of simulations. As probabilistic assessments can require $O(10^3)$ to O(10^5) runs, a cheaper alternative would be necessary for robust probabilistic assessment. This study addresses this limitation by developing a deep-learning surrogate to approximate the complex hydrodynamic behavior.

The methodology involved three main stages. First, a 1D HEC-RAS model of the Clear Lake system was adapted to serve as the physics-based "ground truth" generator. Second, this model was used to generate a training dataset of 2,400 simulations. This was achieved by systematically sampling key infrastructure design parameters (gate width $W_g$, number of pumps $n_p$, and activation levels $h_{on}$) alongside a wide range of synthetic compound flood forcings (inflow hydrographs and downstream storm surge boundaries).

Third, three distinct Long Short-Term Memory (LSTM) network architectures (Models A, B, and C) were developed to compare different data encoding strategies. Model A, a direct sequence-to-sequence (seq2seq) model, was provided with all dynamic inputs, including the known pump discharge time series ($Q_{pump}$). Model B tested the model's ability to infer dynamics by replacing the $Q_{pump}$ time series with static design parameters ($n_p$, $h_{on}$). Model C used an autoregressive structure, feeding its own past water level predictions back as inputs to dynamically infer the pump response.

The results demonstrate that the fully-informed LSTM (Model A) can successfully learn and reproduce the governing hydrodynamic processes with very high accuracy. However, models that attempted to infer dynamic behavior from static design parameters (Models B and C) show reduced performance. These models particularly struggled to capture the sharp, transient effects of pump (de)activation, leading to overly smoothed predictions. This study concludes that while LSTMs are capable of learning the physical patterns of the system. The main challenge lies in feature encoding, specifically, enabling the model to capture complex, dynamic responses from static inputs. The framework demonstrates the potential of LSTMs, but emphasizes that how the data is represented is the key factor in developing a surrogate model suitable for design optimization.

The North Sea is a highly dynamic environment where accurate wave modeling is essential for coastal safety, offshore operations, and long-term climate adaptation. Recently, policy in the Netherlands has shifted toward reliance on SEAS5-based datasets to estimate storm loads, creating a demand for computationally efficient wave models capable of producing long climatologies with sufficient accuracy. HurryWave, a new third-generation spectral wave model developed by Deltares, promises an order-of-magnitude faster performance than SWAN due to simplified physics and numerics. This thesis investigates whether such efficiency compromises wave modeling accuracy in the North Sea.

HurryWave was evaluated against buoy measurements and ERA5 reanalysis data from 1950–2023. Model performance was assessed for significant wave height (Hm0), peak period (Tp), and mean wave direction (θm), with additional analyses on storm events, and spectral representation. Results show that HurryWave reproduces significant wave height with skill comparable to ERA5, achieving root mean square error (RMSE) values of 0.35-0.55 m and scatter index values of 0.12-0.20 across stations in shallower water near the Netherlands. However, in deeper areas such as at North Cormorant, HurryWave without boundary conditions underestimates wave height by a large amount with a scatter index of 0.36. This is mitigated by including a larger domain or by adding boundary conditions. Additionally, using this model setup, large wave heights are underestimated, even at stations in shallower water. This is mainly due to the general underestimation of high wind speeds in the ERA5 input compared to KNMI observations. Peak period (Tp) was consistently underestimated using this setup, with RMSE values of 1.5-2.5 s and negative biases of -0.5 to -1.5 s. Wave direction was the weakest-performing parameter, with RMSE frequently exceeding 30° and large directional scatter during storms. This is due in part to both, the absence of triad modeling which would have an effect on energy distribution to lower frequencies and in transferring energy to other directions, and the inaccuracies in the ERA5 wind data. Additionally the DIA approximation used in modeling quadruplets can lead to errors in fast turning wave fields, contributing to the error in direction. During the 2013 “Sinterklaas storm”, HurryWave captured overall storm evolution but underestimated offshore peak Hm0 by up to 1.0 m and showed narrower spectral energy distributions compared to buoy observations. The analysis of other spectral wave parameters revealed that HurryWave performed better when modeling mean period than the peak period with it performing best in modeling the mean period of the negative first order spectral moment Tm-10 with an RMSE of up to 0.24s compared to the 1.5 to 2.5s RMSE observed for Tp during the Sinterklaas storm.

The findings highlight HurryWave’s strength in reproducing significant wave height, making it suitable for applications such as long-term SEAS5-based climatologies. However, limitations in extreme wave heights, peak period, wave direction, and spectral realism caution against its sole use in operational forecasting or safety assessments that depend on extreme storm accuracy. This study concludes that HurryWave is a promising complement, not replacement, to established models, aligning well with the SEAS5-driven shift toward risk-based, long-horizon coastal policy, while still requiring refinement for storm-scale hazard analysis.

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.

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.

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.

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.