Efficient sediment management is crucial to maintain accessibility into the port. This thesis investigates the application of the bed leveller in conditioning pre-consolidated mud, comparing its efficiency and impact on turbidity with that of the water injection dredger. The research is conducted for pre-consolidated mud from the Botlek in the Port of Rotterdam. Instead of traditionally reallocating the dredged material, the mud’s properties are modified so that it is safe for vessels to navigate through the mud. The WID is the primary conditioning vessel at the Port of Rotterdam. Since maintenance dredging is a logistical challenge, however, it is attractive to optionally deploy other vessels for conditioning purposes. The bed leveller, a type of plough normally used to level the port’s bed, could potentially be used for condition ing. It is cheaper to deploy and more easily accessible for the Port of Rotterdam. Therefore this study investigates if the bed leveller can achieve comparable results as the WID regarding conditioning dredging. To accomplish this knowledge, the research includes the design of a conditioning plough and testing and comparing it to the conventional plough and WID on a laboratory scale. The different treatments on the pre-consolidated mud are measured on density, yield stress and turbidity so the outcomes point out the differences in conditioning effectiveness and impact on turbidity. Furthermore, experiments are executed to discover what is needed to successfully condition pre-consolidated mud and the influence that frequency in a conditioning activity has. To support the findings from the laboratory, field tests are con ducted with the bed leveller and the WID as well. This experiment also gives insight into the conditioning efficiency in terms of production rate, costs and fuel consumption. The findings are that an improved design of a new piece of bed levelling equipment, based on research and experience in the field of agriculture, can increase the ability to condition mud. Stirring and mixing, breaking up cohesive bonds within the mud and suspending it, are key for successful conditioning. Increasing frequency in a conditioning activity, increases the effectiveness of conditioning as well, however this relation stagnates. Furthermore, the bed leveller can indeed condition pre-consolidated mud like the WID can. Although in comparison, a higher dredging frequency needs to be applied, for the strength and density of the mud to be reduced just as much. Thus in terms of production rate, the bed leveller is a lot less effective than the WID, resulting in higher average costs and fuel consumption per volume of effectively conditioned pre-consolidated mud. In terms of impact on turbidity, the bed leveller has less effect than the WID and the design of a new plough specifically for conditioning, contributes to reduced environmental impact.

Compound floods, which can be attributed to different drivers (pluvial, fluvial, surge, tide, and waves), generate a larger flood hazard when drivers co-occur than when they occur in isolation of each other. Current compound flood risk assessments are affected by a curse of dimensionality, where a larger number of events need to be numerically simulated to understand the response of risk to drivers. This research aims to create a methodology that improves the quantification of compound flood risk by using a Treed Gaussian Process (TGP) for the case study of Charleston. A TGP can actively learn from the response of damages to drivers to reduce the number of events that need to be simulated. By comparing this approach with a state-of-the-art approach, the research shows a reduction of the computational cost by a factor of 4, an improvement in the root mean square error by a factor of 8, and an improvement in the estimate of Expected Annual Damages (EAD) by a factor of 20. This reduction in computational cost allows for the inclusion of random variables that are normally assumed constant such as the duration and time lag of drivers. A sensitivity analysis demonstrates these variables produce a statistically significant difference in the estimate of EAD, which increases its value from 172 to 219 Million USD. The research also shows the combination of events caused by drivers leading to extreme damage changes when including these additional random variables, although surge is always found to be dominant. By applying the TGP to multiple outputs, the research demonstrates the TGP is not only applicable to the case study, which shows a TGP can be implemented in current flood risk assessments.

Infrastructure is at risk to climate uncertainty due to a combination of long life spans, complexity of the systems it is embedded in, and the high investment costs often necessary. Current infrastructure planning approaches lose efficacy under deep uncertainty, necessitating new approaches that function better under conditions where the future cannot be predicted. The approaches that attempt to deal with this are also called Decision Making under Deep Uncertainty (DMDU). Bangladesh, the Netherlands, and New Zealand have all already adopted the use of these DMDU approaches in their delta protection guidance. One popular DMDU technique is Robust Decision Making (RDM). RDM can be seen as a computational extension of scenario planning, where proposed plans are tested against every potential combination of uncertainties. The Deep South Challenge (DSC), a New Zealand based research institute is looking into using RDM on a regional scale to discover vulnerabilities and identify robust strategies based on them. One of the test cases is in Helensville, where a Wastewater Treatment Plant (WTP) serving a small community is located in the middle of a floodplain. The WTP discharges its effluent into a strongly tidally influenced river, which drains the entire watershed and flows past large tidal flats into a dynamic estuarine environment. In order to identify potential vulnerabilities in the system, robust decision making uses a vulnerability analysis. This consists of a scenario discovery and global sensitivity analysis which sample through every combination of uncertainties to characterize the vulnerabilities of the system. In order to facilitate this, usually simple conceptual models are used due to the high number of runs necessary. However, these types of models can oversimplify complex physical processes and topography. These complicating factors are all present at the current case site selected by the DSC. This research investigates whether the added computational demand of a complex model is worth it compared to a simple conceptual model. To do this, two models are selected and forced for the same event. They are then compared on predicted system behavior, identified vulnerabilities, and potential policy advice. From a larger selection, the FLORES and SFINCS models were chosen. FLORES uses a simple hydrological balance to calculate the water level in the subbasins for each timestep. SFINCS is a reduced physics solver which only uses the Local Inertial Equations (LIE). Both models are forced by a compound rainfall and stormtide event for a storm with a 24 hour duration, for which they were calibrated and validated using the results of previous modeling efforts in the region. After the calibration and validation, a sensitivity analysis and scenario discovery were run for both models. The results of the sensitivity analysis show similar model behavior between FLORES and SFINCS. The upstream part of the model domain is only sensitive to rainfall, while the downstream part is mostly sensitive to storm surge and mean sea level, and to a lesser degree to tidal amplitude. This downstream part includes the wastewater treatment plant. Compared to SFINCS, the outcomes for FLORES on average overestimated water levels at the WTP. This is most likely due to the lack of flood attenuation taken into account by FLORES compared to SFINCS. The scenario discovery showed similar results for each model’s box describing 73% of the outcomes where failure occurs. Both models had the same three thresholds: storm surge, mean sea level, and tidal amplitude. The main difference between the boxes was the storm surge threshold being 21 centimeters lower for FLORES compared to SFINCS, indicating FLORES overestimates the water level at the WTP. The results of the scenario discovery also showed a linear relationship between these three factors. From this relationship, it is possible to see that, keeping all else similar, and with the same high tidal amplitude and storm surge, for SFINCS the plant only starts flooding when a mean sea level of at least 0.4 meters is reached, while for FLORES this is 0.25 meters. Using a RCP4.5 emissions scenario, a mean sea level of 0.25 meters will be reached in 20-30 years, and a mean sea level of 0.4 meters in 50 years. The proposed policy options for both SFINCS and FLORES would be to mitigate storm surge as long as possible, since the water level at the WTP is most sensitive to this factor. Once this is no longer possible, the WTP should be relocated. The results of SFINCS indicate this relocation is necessary later than for FLORES. These results show that while the behaviors exhibited by both models is relatively similar, the small differences in accuracy affect which are most likely due to the lack of flood attenuation taken into account for FLORES lead to a different timing of proposed adaptations. This leads to reason that while a conceptual model such as FLORES works well to identify important factors within the system, a more accurate model such as SFINCS can be more helpful once timing of adaptation becomes important. Further recommendations are to repeat this research for more models, further calibrate and validate the models, and to include scenario discovery methods that better deal with the found linear relation.

On Saba, an island located in the Caribbean, a new harbour will be constructed. To protect coral reefs in the vicinity from light attenuation and sedimentation, it is important to monitor and predict the turbidity stresses caused by the dredging operation. In the early stages of such projects involving dredging operations, a common approach is to estimate the turbidity stresses in a simplified way using stationary source terms and the exclusion of important physical processes as tidal and wind-forcing. This research focused on the development of a representative approach to simulate the turbidity effects from dredging activities by backhoe dredgers in the vicinity of Caribbean islands, by building on existing methods by Becker et al. (2015) and Tuinhof (2014). The Black Rocks harbour project on Saba was used as a case study to test the effectiveness of the new method approach.

First, the local physical processes were identified and their influence on the turbidity stresses and dispersion of the sediment plumes were discussed. Insight in the wave heights and wave period should be obtained to aid in the decision for the dredging equipment to use and their workability. Key processes to include for a representative simulation for the dispersion of the plumes were identified to be the tidal and wind driven currents over the depth. Another local phenomenon to consider was the run-off from peak precipitation events, as this results in high background turbidity levels. Analysis of local sediment samples is required to obtain insight in the fines content, required for the estimation of the sediment flux, and the distribution of particle sizes and the particle density to determine the settling velocity.

Insight in the work method and duration of the dredging cycle provides information to determine the temporal distribution of the source terms to suitably simulate the loss of fines over time, making a distinction for the presence of the source between day and night cycles and during relocation. The primary source term contributing to the release of fines, identified to be the bucket drip, was spatially distributed to simulate the relocation of the backhoe. An additional method step was introduced by estimating the local fines content for each source term over the dredging volume, resulting in a more representative approach for dredging volumes exhibiting a heterogeneous distribution of the fines content compared to using a single value for the fines content. The source terms were estimated using an existing method by Becker et al. (2015) and distributed over multiple sediment fractions, to include the representation of the smaller particles, affecting the
far-field SSC in the model.

The effects of tidal and wind-forcing were incorporated using a 3D model, while running different hydrodynamic scenarios to test the effects for a variety of flow conditions. The grid resolution was chosen to ensure an accurate representation of the spatial distribution of the sediment concentration resulting from the bucket drip. The source terms were equally distributed over the depth to simulate the gradual loss of the fines over the depth by the bucket drip. The selection of an appropriate formulation for the settling velocity, to account for the local hydrodynamic conditions and sediment characteristics, increases the representation of the distribution of fines over time.

The model results indicated that for both a stationary and relocating source term, an accurate
depiction of the average SSC values over longer time periods as days and weeks is simulated, while the relocating source tends to estimate peak concentrations more accurately, as the source location and quantity is represented more precisely. Turbidity thresholds, set for the Black Rocks project, were only exceeded on one occasion during the occurrence of a current reversal for the relocating source, but not for a stationary source. This indicates the added value of applying a more detailed approach to simulate the turbidity stresses. Following the suggested additions to the methods an updated approach to simulate the turbidity stresses by a backhoe dredger was proposed. Further research into refinement of the method, focusing on the spatial distribution of the source and appropriate spatial and vertical grid resolution, can increase the suitability of the suggested method for simulating turbidity stresses induced by a backhoe dredger.

The Circular City Index (CCI) project for Barcelona, conducted in partnership with the Barcelona Supercomputing Center, aimed to assess urban circularity across multiple districts. Utilizing the City Circularity Index model as introduced by Muscillo et al. (2021), this research adapted the framework to Barcelona’s unique urban context, focusing on the four main components: Digitalization (D), Energy, Climate and Resources (ECR), Mobility (M), and Waste (W). The project benchmarks city performance against Sustainable Development Goals (SDGs) to guide strategic planning for a sustainable and resilient urban environment. Each district’s performance was measured through tailored Key Performance Indicators (KPIs) reflecting accessibility, infrastructure, and environmental data. Spatial and statistical analyses were applied to assess parameters such as waste collection accessibility, air quality, and renewable energy coverage. Data was gathered from open-source and municipal datasets, complemented by geographic analysis for a precise district-level understanding...

This report, titled Expanding Puerto Rawson: Enhancing Fishery Capacity and Project Cargo Logistics, presents a conceptual masterplan for the eco-friendly expansion of the Port of Rawson. The port’s proximity to a region abundant in marine resources makes it an attractive location for expansion. However, the port is facing strategic expansion problems and it could fail to accommodate the rise in demand from the fishery industry. This report carefully maps out the gap between the expected rise in demand and the currently existing infrastructure. After that, two models are created to transform the rise in demand that is expected by 2030 & 2040, to nautical demands for the port, like number of berths and required waterway width. The results from these model in combination with a strategic overview of the landscape, were used to come up with three conceptual designs that eventually converged into one final conceptual design by conducting an MCA. This conceptual design, together with the port waste management plan, could enable an eco-friendly and future-ready expansion of the Port of Rawson.

Sea turtle nesting beaches are under increasing pressure from climate change, rising sea levels, and human activity, making the protection of critical habitats like Ostional Beach, Costa Rica, an urgent priority. This study integrates the analysis of wave runup dynamics, groundwater behavior, stakeholder collaboration, and coastal squeeze mitigation to address the unique challenges at this vital olive ridley turtle nesting site.

Wave runup was examined using timestack imagery analysis. Two automated extraction models— entropy-only and entropy-saturation—were compared against manually digitized data. Results show that the entropy-only model is more reliable in capturing peak wave run-up values, a critical measure for understanding inundation risks. Challenges in accuracy, particularly for the entropy-saturation model, were linked to the site’s unique environmental conditions, such as dark volcanic sand. The findings fill a gap in understanding how specific extraction methods perform under unique site conditions, with implications for improving future modeling efforts.

Groundwater dynamics were studied using pressure sensors installed in custom-built wells, revealing significant interactions with tidal forces. These measurements highlighted the role of tidal cycles in influencing groundwater levels, providing crucial insights into the potential for nest inundation. These findings extend existing knowledge by combining tidal and hydrodynamic factors specific to turtle nesting sites.

Collaboration within the TURTLE project was analyzed through semi-structured interviews and structural evaluation of partner interactions. A tailored framework was developed to enhance communication and coordination among stakeholders, addressing identified gaps and leveraging existing strengths. This framework contributes to more effective project management and the application of scientific insights in conservation strategies.

To mitigate coastal squeeze—a phenomenon where natural habitats are compressed by rising sea levels and human development—the study evaluated strategies such as foreland restoration and managed retreat. Findings suggest that integrating habitat restoration with community involvement is critical to preserving the ecological and social balance at Ostional Beach.

This study takes an interdisciplinary approach to explore how wave runup, groundwater behavior, and collaboration strategies can be effectively combined to support conservation efforts. The results stress the need for tailored, site-specific solutions that blend engineering, ecological, and social perspectives to protect endangered species and their habitats. By bridging knowledge gaps and offering practical recommendations, the research bolsters both local and global initiatives aimed at preserving vulnerable coastal ecosystems. 

This study investigates the development of sustainable offshore fish farms in Sisal, Yucatán. Local fishermen face seasonal restrictions on fishing due to environmental regulations. Given the socioeconomic dependence of the region on fishing, the community of Sisal has been experiencing increasing instability of livelihoods. Offshore fish farming has emerged as a potential solution to this challenge, offering an alternative income source outside the traditional fishing season. However, previous industrial attempts to introduce fish cages failed due to a lack of local engagement and inadequate design, leaving Sisal residents sceptical. To address these past issues, this research seeks to design affordable, durable, and locally accepted fish cages that meet the unique environmental and social conditions of Sisal. Valuable insights were gained from fish farms in Celestún, a nearby village with successful communityled offshore aquaculture. Celestún’s approach, using smaller, manageable, and collectively funded cage, has proven to be both economically and socially beneficial. This makes it a relevant model for Sisal, though Sisal’s steeper coastal gradients and greater exposure to maritime forces require adaptations to ensure durability and long-term success. The research follows a multi-step methodology, beginning with interviews with local fishermen and experts to understand their needs and preferences for cage design and placement. These insights were integrated with environmental data on wave height, wave period, and current speeds collected through field measurements and the ERA5 reanalysis dataset. Using this input, an Multi-Criteria-Analysis (MCA) was conducted to determine the optimal offshore location for the fish farms. To determine the structural needs for fish cages under Sisal’s conditions, the research used ProteusDS simulation software [14] to model various cage dimensions, mooring tensions, and layout configurations. Key findings indicate that positioning the fish farms at 8 kilometres offshore is optimal for long term success. At closer distances to the coast, water quality decreases, resulting in higher maintenance requirements and compromised fish health. Greater distances increase installation costs and operational costs due to higher fuel demands. With the optimal location established, the research follows with the determination of key design parameters essential for the structural integrity of the fish cages near Sisal. An extreme value analysis of an ERA5 dataset was performed to estimate the 20-year return level for the wave height, resulting in a design wave height of 4.19 metres. This value was adjusted for local conditions using a scaling factor derived from the comparison between local and ERA5 data, resulting in an adjusted design wave height of 3.40 metres. A power-law regression was then applied to establish the relationship between wave height and wave period, estimating a design wave period of approximately 8.01 seconds corresponding to the adjusted wave height. For the current analysis, the 95th percentile of current speeds was examined, determining a maximum design current speed of 0.50 m/s near the surface. Furthermore, analysis of wave and current directions revealed that extreme waves predominantly come from the north to north-east directions (340° to 20°), while the strongest currents flow toward 70° and 250°, indicating eastward and westward flows. The optimal cage design determined through simulations includes a cage diameter of 12 metres and a net depth of 4.7 metres to withstand Sisal’s environmental forces. Additionally, distinct mooring-tension configurations were tested in the ProteusDS software, including Concept 1 (a single-cage setup), Concept 2 (a two-cage configuration with four mooring anchors), and Concept 3 (a three-cage arrangement with three anchors). Each concept required specific anchor weights and dimensions to endure the high wave and current forces at this location. Orientation adjustments were also incorporated to reduce tension, aligning each cage setup with different wave and current directions, thereby optimizing structural reliability. Future fish cage designs should include adaptive anchoring and precise orientation to enhance stability and involve the local community for sustainable, long-term success. The study concludes that, to achieve long-term viability, fish cages in Sisal must be affordable, easy to maintain, and capable of withstanding local environmental conditions. Future recommendations include deepening community involvement, implementing enhanced safety and resilience measures, and refining cost analysis to foster broad acceptance among local fishermen. By ensuring that the fish cages are both economically viable and environmentally sustainable, this project aims to secure a stable income for Sisal’s fishing community, thereby improving their quality of life while reducing pressure on marine ecosystems

The erosion problem along Playá Union presents significant challenges for future port expansion at Puerto Rawson. This research seeks to address the question of how to achieve a sustainable and durable port expansion, while minimizing environmental impacts, particularly concerning sediment imbalance along the coastline? Using 30 years of wave data, both normal and extreme wave conditions are simulated with SWAN, a numerical based wave model. Conceptual port expansion designs are developed, resulting in a final design with an integrated fully dimensioned breakwater. Based on visual inspections, data, and research, a new cement mixture is proposed for the breakwater armour units. A Life Cycle Assessment evaluates the environmental impact, while the effect on alongshore sediment transport is assessed using the SWAN model outcomes and the CERC formula. Visual inspection of the current breakwaters lead to a 6.3% reduction in material use in the new breakwater through reuse of armour units. Furthermore, the proposed cement mixture integrates porphyry quarry waste as the coarse aggregate, a choice also supported by prior research in sustainability. With the new End-of-Life approaches added, the total shadow costs are reduced by 22%. The hydrodynamical analysis and model result in extreme wave heights up to 3.98 m at the toe of the breakwater. By applying a neural network and a k-means algorithm on the wave data, five regular wave conditions are run in the SWAN model. The alongshore sediment transport is impacted by new breakwater concepts. Relying solely on the breakwater layout to counteract erosion north of the port, however, does not prove to be a viable approach. Based on design criteria and aspirations, the final conceptual design proposes the removal of the existing southern breakwater, while retaining sufficient space for future port expansion. The breakwater, integrated in the final design, is dimensioned based on standard design principles. To conclude, the data and model provide valuable insights into the coastal dynamics around Puerto Rawson. The proposed concrete mixture, along with the End-of-Life solutions, minimize environmental impact and enhance durability of the armour units. The sustainable breakwater design, effectively integrated into Puerto Rawson, accommodates for future port expansions

Tropical cyclones, known as hurricanes in the Atlantic and Northeast Pacific or typhoons in the Northwest Pacific, are intense storm systems characterized by strong rotating winds, heavy rainfall, and low atmospheric pressure. They form over warm tropical waters and are major drivers of coastal flooding in tropical and subtropical regions. Annually, around 50 cyclones reach hurricane strength, causing flooding through storm surges and heavy rainfall, threatening communities and ecosystems. Climate change and human activities exacerbate these risks. Accurately predicting coastal flooding due to these cyclones is challenging due to their complex features, limited historical data, and forecasting uncertainties.

This dissertation aims to enhance the reliability of coastal flood forecasts and risk analysis by improving the descriptions of tropical cyclone wind geometry and pathways. It addresses both operational (short-term) and strategic (long-term) flood risk analyses. Operational risk analysis involves forecasting days before and after a cyclone, while strategic analysis deals with climate variability over decades. Both are crucial for comprehensive climate risk management, offering different time frames for preparedness and prevention.

A key element in both types of analyses is accurately representing tropical cyclone conditions in computational models. By examining historical best-track data, empirical relationships for two tropical cyclone geometry parameters—the radius of maximum winds and the radius of gale-force winds—were derived, improving estimates by up to 25%. This improvement is significant, particularly for cyclones outside the United States. These parameters, either observed or derived, are essential for computing surface wind distributions using the Holland wind model, which is critical for coastal flood evaluations.

Strategic risk analyses often suffer from a lack of sufficient historical tracks for reliable flood hazard assessment. To address this, an empirical track model based on Markov chains was introduced, capable of simulating thousands of synthetic storm pathways. The Tropical Cyclone Wind Statistical Estimation Tool (TCWiSE) generates these tracks, showing good agreement with historical data and extreme wind speeds. This methodology enhances the estimation of extreme cyclone conditions for strategic risk analysis.

The combined data-driven and physics-based methods were used to quantify coastal flooding in the Southeast Atlantic Coastal Zone of the United States. Comparing cyclone-induced flooding to non-cyclonic flooding revealed that extratropical cyclones are responsible for frequent flooding, while tropical cyclones cause the majority of infrequent but severe floods. For example, with current sea levels, extratropical cyclones contributed to half the flooded area, but tropical cyclones accounted for ~96% of the flooded area for 100-year events, affecting significantly more people. At higher sea levels, tropical cyclone-specific flood risk diminished as areas became uniformly susceptible to flooding. This analysis highlights the importance of considering both cyclone and non-cyclone flood factors in future research.

Operational risk assessments, critical for protecting lives and minimizing economic impacts, involve simulating numerous ensemble members to account for uncertainties in cyclone track, speed, and intensity. The Tropical Cyclone Forecasting Framework (TC-FF) integrates major physical drivers such as tide, surge, and rainfall, using Gaussian error distributions and autoregressive techniques. A case study of Cyclone Idai in Mozambique demonstrated the need for a large number of ensemble members for reliable forecasts. TC-FF showed less than 10% difference from operational ensembles, suggesting its utility in data-scarce environments.

This dissertation provides new insights into tropical cyclone wind geometry, pathways, and their role in compound flooding. Future research should enhance data collection, particularly from satellites, to validate models and understand storm characteristics better. Incorporating overlooked processes, leveraging data assimilation, and exploring more efficient methods, including Deep Learning, are essential for advancing flood risk assessment and capturing tropical cyclone variability.

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Worldwide, estuaries are centres of life. Their connection with the open seas while providing fertile lands and freshwater has resulted in these regions becoming major hubs of human settlement and activities, which is expected to continue growing in magnitude. However economically useful, the open connection to the seas also poses a challenge: freshwater availability. Via this open connection saline water can enter the estuary and contaminate the freshwater reserves - so-called (estuarine) salt intrusion. This may cause freshwater shortages and damage to all living beings that depend on this invaluable resource.

This research aims to develop mitigation measures to this estuarine salt intrusion following the Building with Nature-philosophy. Thus, the goal of this research is to develop nature-based solutions to mitigate salt intrusion. This is achieved by (1) exploring the potential of estuary-scale interventions to affect salt intrusion; (2) conceptualising various nature-based solutions that mitigate salt intrusion; (3) evaluating nature-based solutions in a multidisciplinary context; and, to conclude, (4) reflecting on the role of nature-based solutions.

The exploration of potential estuary-scale modifications is a computationally expensive endeavour for which a novel simulation strategy is developed. This strategy is presented in Chapter 2 and proposes the use of machine learning techniques to determine the input space - i.e., which model simulations to execute, and which to exclude. The aim of the strategy is to put more focus on exploring the output space instead of exploring the input space.

Subsequently, the model simulations are analysed in Chapter 3. Thus, Chapters 2 and 3 present respectively the method and the results of an extensive sensitivity analysis of estuarine salt intrusion to estuary-scale modifications. The end-result of Chapter 3 includes a shortlist of potential nature-based solutions to mitigate salt intrusion, including a ranking based on the sensitivity analysis.

The conceptualisation of nature-based solutions focuses mainly on two potential options: (1) a (temporary) sill, or submerged dam (Ch. 4); and (2) enhancement of intertidal area (Ch. 5). In addition to these novel nature-based solutions, Chapter 6 evaluates a third nature-based solution: shallowing of an estuary. From a physical perspective, this mitigation measure is well-known but mainly poses challenges in the socio-economic domain, which is why it is not extensively covered in the conceptualisation-phase of this dissertation but the evaluation-phase instead.

Both the sill and the intertidal area show a dependency on estuary class in how effectively the mitigation measures are. For the sill it holds that the weaker the tide, the more effective the sill mitigates landward salt transport - i.e., salt intrusion. When the tide is limited, salt intrusion is largely (if not fully) driven by gravitational circulation, for which the sill functions as a wall beyond which the formed salt wedge can hardly penetrate. However, with tidal energy increasing, the tidal momentum to push the saline water over the sill also increases resulting in more salt intrusion - the sill functions more like a speed-bump than a wall. All in all, a sill is most effective for estuaries with little tidal influence.

Enhancement of intertidal area increases the mixing in the estuary. When the dominant salt transport mechanism is related to the estuarine circulation, this enhanced mixing by increasing the intertidal area reduces the salt intrusion. However, the opposite holds for estuaries in which the salt transport is dominated by the tidal oscillation. This means that in case of salt wedge and partially mixed estuaries, enhancement of the intertidal area reduces salt intrusion; and in case of well-mixed estuaries, the intertidal area increase promotes salt intrusion. Thus, the additional (vertical) mixing caused by the intertidal areas reduces salt intrusion as long as there is something to mix - i.e., as long as there is a (sufficient) vertical salinity gradient.

The evaluation of nature-based solutions is inherently multidisciplinary. In this dissertation,
two different perspectives are considered: (1) socio-economy (Ch. 6), and (2) socio-ecology (Ch. 7). In both cases, the Rhine-Meuse Delta is considered as case study.

The socio-economic evaluation addresses the effect of water depth on two major stakeholders in an estuary: a port, and water boards. These stakeholders have opposing interests regarding the water depth: a port benefits from enhanced water depth to facilitate larger vessels, but the resulting contamination of freshwater reserves via salt intrusion is negatively affecting water intakes, and everyone that depends on them. Chapter 6 presents a multidisciplinary evaluation method based on Pareto-fronts, which are to inform decision- and policy-makers. The Pareto-front in Chapter 6 shows that the port performance remains relatively unaffected for shallowing until two metres with the current bed levels, while the freshwater availability improves. However, beyond this level of shallowing, port performance drops substantially against limited gains in freshwater availability.

The socio-ecological evaluation focuses on the effects of reopening a closed-off estuary on two opposing interests as well: freshwater availability, and estuarine ecosystem functioning. Reopening is beneficial for the ecological diversity in the former estuary, but comes at the costs of returning saline influences into a freshwater lake - i.e., freshwater availability is hampered. The ecological implications are mapped by translating hydrodynamic (model) data to an ecotopes-potential map due to which the ecological impact of interventions can be quantified. As in Chapter 6, Pareto-fronts are used as method to inform decision- and policy-makers. The Pareto-front in Chapter 7 shows an increased ecological diversity in the former-estuary without impeding the freshwater availability when partially opening the gates. However, once the salt intrusion reaches the most western water intakes, the freshwater availability drops without major gains in diversity.

This dissertation has shown the complexity of developing future-proof nature-based solutions to mitigate salt intrusion. Building on the lessons learned, the reflection of this dissertation proposes a next step for nature-based solutions: DARE (diverse, adaptive, and robust engineering; Ch. 8). DARE is based on nature's own approach to dealing with uncertainties. The three axes of DARE show how to deal with uncertainties in the three dimensions of engineering: (1) a diverse set of solutions to deal with uncertainty in forcing conditions; (2) an adaptive approach with room to change course in response to the uncertainty that comes with time; and (3) robust - or even antifragile - solutions regarding the uncertainty in performance. With DARE, the focus shifts from the input to the output: Which part of the output is desirable, and what are the options to get there?

The nature-based solutions presented in this dissertation form a starting point for further explorations, with work in this dissertation already being used as a stepping stone for other studies. Besides further exploring the opportunities of nature-based solutions to mitigate salt intrusion, next steps also include bringing these findings into practice. This includes reassessing past estuarine modifications from a new perspective, one that is less susceptible to the challenges that the future may have in store.
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This research focuses on a new system for nourishing the Dutch coast with sand, which utilizes semi-autonomous sailing barges and an electrical crawler to continuously replenish the beach. Yearly, the Dutch government grants contracts to nourish the Dutch coast with volumes around 10.000.000 m$^3$. The Dutch dune coast protects the low part of the Netherlands against inundation from the North sea. This sand is dredged by Trailer Suction Hopper Dredgers (TSHD) on a depth of 20 m and transported to the coast. This transport causes a CO2 emission of about 190 kilo tonne a year. As emissions are expected to grow with increasing sea levels the Dutch government looks for ways to improve the high-energy demanding strategy that is currently used. Sweco introduced the Slow Sailing, and believes in the energy reducing concept and therefore initiates this further research. The general idea of this concept is to reduce the energy demand and emissions associated with traditional sand nourishment practices by using multiple barges that sail slowly and continuously nourish the Dutch coast. The aim of this research is to concretize this concept, in which feasibility of the system compared with energy, emission and cost calculations are central. To achieve this goal, the Slow Sailing system is compared to the traditional trailer suction hopper dredger, currently used for sand nourishment in the Netherlands. The comparison is based on a scenario in the Northsea. This leads to the main research question: What is the gain in energy consumption, emissions and cost for the Slow Sailing concept compared to classical nourishment strategies? In order to achieve this goal a review of current sand nourishment strategies was conducted. Next, the OpenTNSim model was adapted to perform energy calculations and enable a comparison of energy demands and emissions between the new system and the traditional trailer suction hopper dredger. Finally, a cost evaluation was conducted to compare the cost per cubic meter of sand for a 1.000.000 cubic meter nourishment project at the Egmond aan Zee nearshore in the Netherlands.

Compared to the traditional trailer suction hopper dredger, the Slow Sailing system showed a significant factor 15 decrease in energy demand in the sailing component of the dredging cycle. The Slow Sailing system with barges of 1500 m$^3$ was found to be the most energy-efficient and cost-effective option, with an estimated project cost of 3.00 euro/m$^3$, while the TSHD with 4280 m$^3$ was found to be the cheapest option, with an estimated project cost of 2.55 euro/m$^3$. In terms of CO2 emissions, the Slow Sailing system emitted significantly less CO2 compared to the TSHD, with a difference of 1000 tonnes CO2+ emission in a one million cubic meter dredging project. Overall, the results suggest that the Slow Sailing system is a more energy-efficient and environmentally-friendly option for sand nourishment of the Dutch coast.

This study integrates strategic decisions and operational control systems in autonomous shipping. By providing ships with situational information and adding a virtual operator, we show that vessels can make informed choices regarding their route and engine settings. To demonstrate this integration, we developed new components that were tested in three lab experiments.
The 'green routing' experiment showed the bridge between the control system of the autonomous vessel, operated via Robot Operating System (ROS), to the simulation environment of OpenCLSim. We developed a real-time variant of OpenCLSim and a communication component that could expose the state of the OpenCLSim simulation with the ROS system. This experiment showed that the path provided by the simulation was followed by an autonomous vessel.
The 'green steaming' experiment showed that the ship could also adapt its speed based on information from the simulations. We developed an additional communication component capable of advising the vessel about its velocity. Together with the green routing capability, this forms the basis for more complex experiments.
The 'port call' experiment showed a potential use case of the green routing and green steaming capabilities. We created a waypoint layout similar to the port. While a ship is sailing, every five seconds, twelve simulations are computed. The scenarios vary in engine order, route choices, resulting in varying emissions, fuel, and cost. We evaluated the impact of different tactics such as green routing, green steaming, and full-speed sailing on operational behavior like steering and engine order. Our approach, using a real-time version of a Vessel in the OpenCLSim simulation software, enabled predictive simulations to facilitate the chosen tactic based on a given strategy. Integrating simulations to evaluate the options with the control systems can develop into a valuable tool for optimizing vessel performance and reducing environmental impact in autonomous shipping operations.

To reduce computational efforts, surrogate models have been developed for dune erosion prediction. Current surrogate models can describe the relationship between the XBeach input and output (Athanasiou, 2022) and provides a prediction of a morphological indicator based on a parameterized input (profile shape parameters and hydrodynamics). In this research, first steps are taken to set-up a surrogate model that is able to deal with spatial input and the prediction of actual profile shapes along the Holland Coast. Using a convolutional neural network structure (U-Net), several model set-ups are explored and scaled-up to a realistic storm scenario along the Holland Coast.

Arribadas, a phenomenon of mass nesting behavior of sea turtles, attract millions of olive ridley turtles (Lepidochelys olivacea) to Playa del Ostional, a nesting beach in Costa Rica. The timing and size of these arribadas are influenced by various environmental factors, including temperature, tides, and moon phase [1]. The sea turtles are threatened by a variety of factors, like amongst others climate change.
Rising temperatures and sea levels, changes in ocean currents, and more frequent and intense storms are all likely to have negative impacts on sea turtles [2]. Without intervention, climate change could lead to the disappearance or flooding of sea turtle nesting beaches, resulting in a loss of critical habitat for these creatures. To prevent such an outcome, it is imperative to gain a deeper understanding of the morphological and hydrodynamic characteristics of nesting beaches, as well as identify the factors that influence sea turtle nesting behavior.
This study aimed to identify and map the critical factors that must be considered to ensure persistence of the olive ridley sea turtle and arribadas at Playa del Ostional, Costa Rica. The objectives of this research were, therefore, (1) to map the seasonal morphological and hydrodynamical differences of the arribada nesting beach, (2) to identify the environmental parameters that have the greatest influence on the occurrence of an arribada, and (3) to map out the stakeholders involved. The study site is the beach that ranges from the northernmost part of Playa del Ostional down to the southernmost part of Playa Nosara, which is located on the northern peninsula at the west coast of Costa Rica. The part at Playa del Ostional where most turtles nest is called ‘Main Nesting Beach’ (MNB). A field investigation was carried out to determine the seasonal morphological and hydrodynamical differences of the nesting beach. This field study comprised of two distinct components: (1) a characterization of the morpho- and hydrodynamics of Playa del Ostional in the dry season, and (2) a comparative analysis of these conditions during the wet and dry season. The morpho- and hydrodynamic beach characteristics consisted of the beach profile, sediment composition, hydrodynamic properties and other general environmental characteristics, such as vegetation and nearby rivers. The beach profile was measured by walking transects perpendicular to the shoreline using RTK-GPS equipment. Moreover, a drone was flown that made an orthophoto and collected 30 million data points. The difference in sediment composition was analyzed by obtaining sediment samples in the dry season, sieving these and comparing the obtained particle size distributions and D50 values of the dry and wet season. The hydrodynamical properties and the other general environmental characteristics are analyzed by means of literature review, observations and photography. In order to identify the environmental parameters that have the greatest influence on the occurrence on an arribada, an autoregressive logistic regression model was used. The model that was made the previous research of 2022, was updated and automated. Also, design choices of the model were made and new data was added.
To map out the stakeholders, interviews have been conducted and a stakeholder map was created. Through the use of GPS transects the beach profiles taken in dry season (February 2023) were compared to wet season (October 2022). To tackle normal spacial variance the comparison is done through the calculation of averages on three beach stretches with equal characteristics. Main findings were that beach width is equal in both seasons, slopes are more gradual in dry season, beach plateaus are on average 3.0m wider in wet season. Crossing rivers do not influence the beach profile below waterline in the dry season. For more river characteristic more offshore research is needed. The sediment composition of the beach turned out to show significant differences between the dry and wet season. A significant difference is present in D50 values between the dry and wet season for almost all sediment samples. Moreover, during the wet season, the sediment tends to be coarser compared to the dry season. Additionally, during the dry season, coarser sediment tends to accumulate at the top of the slope, whereas during the wet season, coarser sediment accumulates near the waterline. These observations suggest that coarse sediment may move from areas close to the waterline to the submerged part of the slope over time. This behavior implies that sediment transportation is affected by the seasonal fluctuations in wave energy. The findings altogether indicate that the sediment composition at Playa del Ostional, particularly at Main Nesting Beach, is notably affected by seasonal changes. The impact is more pronounced from the low tide waterline to the high waterline’s end at the top of the slope, with a particular emphasis on the low tide waterline. The wave climate surrounding Playa del Ostional is expected to be less turbulent, with lower wave energy during the dry season. However, the exact distinctions in both wave climate and tidal surroundings between the two seasons cannot be ascertained due to inadequate data availability.
The different stretches of Playa del Ostional demonstrate notable differences in environmental characteristics during the wet and dry seasons. The majority of rivers that flow out during the wet season are absent during the dry season. In addition, a beach scarp appeared during the dry season and not during the wet season, and an estuary that was observed in the dry season was not reported during the wet season research. On the other hand, the beach is mostly surrounded by vegetation in both seasons, with comparable grass and trees. Moreover, no significant difference in wildlife presence was observed between the dry and wet seasons at Playa del Ostional.
The autoregressive logistic regression model was trained on five year of arribada data and 116 individual environmental parameters. The weights of the parameters were plotted and analysed in multiple groups. This resulted in six parameters with the biggest influence: pdTIDE_P1, pdTIDE_mf, pdVELOCITY_IHC_rho, pdVELOCITY_IHC_rho, Mooncycle_third and Moon_v. The maximum probability of an arribada occurring during a certain day was 80%.
By conducting interviews and conducting a stakeholder analysis, the degree of awareness about climate change is assessed and mapped out, which appears to be quite high. The residents of Ostional are aware of the changes and willing to work in new projects. Moreover, the analysis showed that it is important to engage with two key stakeholders: the Refugio Nacional de Vida Silvestre Ostional and CITES.

The dredging industry is a contributor to the greenhouse gas (GHG) emissions due to the energy-intensive process of dredging and transporting sediment. This industry has to reduce the GHG emissions due to future possible restricting regulations on GHG emissions. The dredging industry is shifting towards more environmentally friendly fuels, but designing new vessels which can run on these fuels will be a time consuming transformation. Though, the demand of dredging activities is growing, think of construction of offshore wind farms and creating flood defences against sea level rise. To minimize the costs due to the high GHG footprint in the short term, Van Oord should be able to estimate the energy consumption (GHG emissions) based on different dredging strategies.

Van Oord uses a model to estimate energy consumption that accurately describes the majority of dredging projects, based on the power installed, dredging time, and a power coefficient term. This power coefficient term is based on empirical data and may result in a less accurate estimation of more complex (new) projects such as offshore wind farms and cable dredging, which are the new opportunities arising for dredging companies. To better estimate energy consumption during these complex dredging operations, a physics-based and semi-empirical method is developed that can more accurately estimate energy consumption based on certain dredging strategies. Previous research already looked at the sailing phases of the dredging cycle, therefore the main objective of this thesis is:

To quantify the energy consumption of different TSHD dredging strategies based on physics for the loading phase.

To achieve this research objective, first a literature study is conducted to list the different physics-based methods available to estimate the energy consumption. The energy consumption of a TSHD is divided into five main energy consumers (propulsion system, dredge pumps, jet pumps, bow thrusters and board net) in which the energy of the individual components is described by a power and a time term. The time component is represented by the duration of the loading cycle, which in this study is limited to the filling process of the hopper until overflow. The power term of the jet- and dredge pumps have been estimated by the (adjusted) in-house physics-based methods of Van Oord. The board net and the bow thrusters have been estimated by data analysis of a case study. The goal of this research is to develop a method to estimate the required propulsion power and a tool to bring all methods together to simulate the total energy consumption during the loading phase based on different dredging strategies.

A model is developed to estimate the required propulsion power during loading. The model development is divided into three phases; The first phase describes the development of a semi-empirical physics based model. In this, the various resistance forces acting on the vessel, suction pipe, and draghead are calculated based on stationary parameters such as vessel dimensions, and project parameters. The project parameters, including water depth, trailing speed, and visor angle, are extracted by filtering the actual data from the case study. In this way the estimation model is set equal to a reference case. Finally, the output of the estimation model is calibrated by comparison with the actual data of the case study. This calibration phase is an iterative process in which the accuracy of the model is described and increased.

The model shows that it is possible to calculate the required propulsion power based on operational parameters, such as trailing speed. Furthermore, the model provides insight into the amount of resistance on the three components (draghead, suction pipe and vessel). The power curve follows a quadratic pattern for increasing trailing speed, which appears to be a reasonable estimate. When comparing the model to the actual data it can be seen that the model underestimates at lower trailing speed and over estimates at higher trailing speed. By finding correlations between visor angle and trailing speed the model is calibrated and seems to better fit the dataset, however the slope of the curve still has a large deviation compared to the regression line based on the actual dataset. Therefore, the model needs to be further developed before it can be included as an addition to the existing models within Van Oord. The static friction term is not included, which could compensate the underestimation of the model and the high cutting forces are the potential reason for the overestimation at high Van Oord Marine Ingenuity iii Delft University of Technology trailing speed. For now, the current developed model falls within the reference dataset for the trailing speed between 1.6 [kn] and 1.9 [kn], thus making it a reasonable first estimate of the required propulsion power.

The OpenCLSim python package, which can be used to simulate discrete events, is used to simulate the dredging processes of the loading phase. A plugin for this python package is developed to calculate the total energy consumption during these processes. The propulsion model is integrated (with the set limitations) in this plugin together with the four other power estimation methods (dredge pumps, jet pumps, bow thrusters and board net). The simulation tool runs based on four input characteristics: vessel parameters (TSHD), site characteristics, dredging strategy, and data describing the bow thrusters and board net. The output of the simulation includes required power, duration, and consumed energy. Additionally, the simulation estimates the fuel usage,
emissions, and project costs associated with energy consumption.

The main ability of the simulation tool is that it can visualize the energy consumption (and emissions) based on dredging strategies and location. This enables the prediction and potential reduction of emissions in sensitive areas, such as fine dust emissions near cities. By using this simulation tool, a dredging plan can be created based on dredging strategies (trailing speed) to reduce emissions in these sensitive areas. To better demonstrate the other capabilities of the developed plugin within the OpenCLSim Python package, the simulation tool is applied to an imaginary project called Barachi. This project has strict regulations that prohibit overflow and apply emission taxes. Two dredging strategies are compared based on the limitations of the developed propulsion model: trailing speeds of 1.6 [kn] and 1.9 [kn]. The results show that, first of all, the project duration will decrease with an increasing trail speed. Secondly, the increase in power has a greater effect on energy consumption than the decrease in project duration, meaning that energy consumption will increase. Since fuel use and corresponding emissions are linked to energy consumption, they will also increase. However, the cost analysis provides a different view. In this case, because the ship’s operating costs are governing, faster trailing is the most cost-efficient, even if an emission tax of 200 euros/ton CO2e is applied.

To conclude, by creating a model to estimate the propulsion power and by integrating this model in a simulation tool, it is possible to quantify the energy consumption of different TSHD dredging strategies based on physics for the loading phase. Subsequently, the simulation also provides insight into the fuel usage, carbon emissions, and costs of the project. Further development and validation of the propulsion model is needed to give a more accurate estimation of the required power. Firstly, the static friction component should be included in the estimation model which could potentially solve the underestimation at lower trailing speed. Secondly, a maximum trail speed limit should be set based on the available propulsion power with respect to the total power distribution of the vessel. This limit should be included in the simulation tool, since it is linked to the other power consumers and the operational parameters. Thirdly, the propulsion model can only be used in saturated sand projects. Other cutting models based on cutting of silt, clay and rock should be added in the propulsion model to be able to apply the model in these soil conditions. Finally, the use of the tool is limited to dredging projects where overflow is not allowed. The suction production and the settling process within the hopper should be added to expand the capabilities of the tool to projects where overflow is possible.

Kelp Blue is a company whose top priority is the well-being of the planet. Through the cultivation of giant kelp on offshore farms, they create several sustainable products, new job opportunities in regions where they are needed, enhance biodiversity in the water, and above all, sequester tons of CO2 from the air. The start-up is still in its research and development phase, but plans to be building farms on a large scale in just a few years. Despite their knowledge in engineering, the company still needs consulting on certain elements. Therefore the company invited a group of students from the Delft University of Technology to Lüderitz, Namibia for a consultancy project. The project involved creating a procedure for the company to scale up in a sustainable manner. The students decided that this complex problem should be divided into sub-problems. One workstream focused on reducing the carbon emissions during upscaling, while the other workstream focused on analyzing and improving the company’s current design and installation of the farms. Following, both parts of the project are shortly summarized: Part I: Improving the company’s current design Kelp Blue is currently in the pilot phase, in which they’re installing their first large giant kelp farms. Before, they were focusing on the complete installation, including planting the kelp on the submerged netting structure, and the review of this. For the company’s commercial phase, where they want to be able to place farms daily on a large scale, designs were still developed and analyzed. For the commercial phase, this workstream made a new design and installation method. It required an installation where buoys would need to be submerged, the structure locked in place at the desired depth, without the use of scuba divers or remote operated vehicles. Despite the fact that these requirements were challenging, an outstanding result was achieved. The main problem was divided into subproblems, and for each of those a suitable solution was created. Hopes are that the company will consider the given advice as helpful and maybe implement some parts of (or the whole) new design. Part II: Reducing carbon emissions during upscaling First, interviews and desk research were conducted to get a good idea of the challenge of sustainability. This included reading reports, speaking with employees, policy makers and experts with experience within the area. With this information, the challenge could be mapped out and the solution space became clear in terms of legislation and technical possibilities. Climate information was also requested that could later be used to run simulations. During the determination of the possible solutions, research was done on the realistic possibilities, where eventually the use of either solar or wind energy was most appropriate. After conducting a multi-criteria analysis that was put together with the management of Kelp Blue, investing in a solar plant proved to be the most appropriate solution.

This thesis analyses the importance and application of considering multiple coexisting wave trains in sediment transport predictions.

The wave trains at offshore and nearshore locations are analyzed by wave spectral partitions (Portilla at el., 2009). The temporal variability of consistently occurring wave trains can be analyzed through so-called wave families (Portilla et al., 2015). The wave partitions show that a unimodal wave spectrum occurs 35% of the time in front of the coast of the province of North Holland in the Netherlands. This means an error is introduced 65% of the time as unimodal spectra are assumed in traditional methods that use generalized wave parameters to estimate sediment transport. When the wave field is not unimodal, the angle between wave trains is considerable. Only 39% of the time is the angle between a wave train and the most energetic wave train smaller than 60°. For 43% of the time, the angle is between 60° and 120°. The remaining 17% are wave trains propagating in opposite directions. The difference in wave angle is half of the time larger than 50° when a threshold of one meter of deepwater significant wave height over the whole spectrum, defined by Holthuijsen (2007), is used.

Unsupervised machine learning techniques Principal Component Analysis, Maximum Dissimilarity Analysis, and the kmean clustering algorithm are used to group similar wave spectrums over time, spatially group wave families, analyze the wave climate's spatial-temporal variability, relate wave - and weather conditions, and assess their impact on wave-driven potential sediment transport. The Principal Component Analysis is used as a dimensionality reduction technique. This study shows the physical meaning of the Principal Components and their temporal variability in the wave frequency-directional spectrums, where Principal Components describe wave energy in each frequency-directional bin. The kmean clustering algorithm groups similar wave spectrums over time and gives insight into wave conditions, like calm and stormy conditions, called Sea States. The Maximum Dissimilarity Technique changes the focus between common conditions or extreme events. The wave partitions, wave families, and Principal Components give additional insight into the Sea States.

Potential sediment transport estimations are made by traditional methods that use a representative wave frequency and direction and through vectorizing and superpositioning the estimations by wave partitions and wave families. The outcomes show that it is challenging to predict sediment transport for multimodal wave conditions with state-of-the-art sediment transport formulations and models. The results show that the wave period significantly impacts sediment transport. The sediment transport for coexisting wave trains causes about 58% of the time more sediment transport than traditional methods. Here the not considered interaction between wave trains on bedload and sediment suspension will enhance sediment transport. Furthermore, the sediment transport estimations give insight into the wavefield and weather conditions that contribute most to the yearly wind-wave induced potential sediment transport caused by high energetic or consistently occurring conditions.

Mangrove is a coastal vegetation type primarily located in the tropical regions between 5 North and 5 South. This coastal vegetation is capable of reducing the force of incoming waves. This is a result of the obstruction created by the roots, stems and canopies against waves to propagate through. Because of this capability, mangrove vegetation offers coastal protection against extreme annual flood events for 15 million people. Recent studies identified that in 2050, the present-day 100-year extreme sea level will occur annually in a large part of the tropical region. The increase of these extreme events is primarily driven by the projected sea level rise. The hypothesis is that before this chronic flooding is observed, storm-induced flooding might already be observed. The expected increase in the probability of these extreme events is defined as non-stationarity of extreme events. The hypothesis therefore is that the non-stationarity of extreme events could already be observed in historical data.

This study examined non-stationary extreme events between 1987 and 2018 for a total of 5809 hydrodynamic environments distributed around the global mangrove coastline. These mangrove environments defined the hydrodynamic wave characteristics and water levels. The results showed that 87.5% of the mangrove environments have a positive trend in the location parameter of the Generalized Pareto Distribution for the extreme events. The shift of the location parameter indicates an increase of the impact of the extreme events. These extreme events were defined as multivariate extreme events consisting of the significant wave height, the mean wave period and the skew-storm surge, respectively the parameters H_s, T_m and S. This combination is based on the fact that the coastal protection offered by mangroves is depending on the water level and the energy within the long-period waves.

The methodology proposed is capable to perform a non-stationary multivariate extreme value analysis and observe the evolution of the extreme events in these three dimensions between 1987 and 2018. The study showed that the average of the extreme events has been increasing at 70.6%, 72.6% and 64.6% of the mangrove environments in the discussed three dimensions. The average multivariate extreme event in mangrove environments increased by 0.06 m, 0.16 s, and 0.7 cm in three dimensions respectively H_s, T_m, and S. Furthermore, the number of extreme events increased, on average, between the first and second half of the time period, from 4.16 to 4.32 extreme events per year.

The global impact assessment of the non-stationarity of these multivariate extreme events was translated to the 27440 mangrove locations along the global coastlines. Statistical upscaling methods allowed the application of a numerical expensive hydrodynamic model to propagate the offshore conditions to onshore. The impact of the non-stationarity of extreme events was applied to a theoretical framework, introducing the possibility of defining the vegetation width as an optimization parameter. To meet the same safety standard for the 1/40 year design condition in 2018 concerning 1987, the vegetation width of the theoretical framework had to increase on average by 16.8 m. After post-processing the hydrodynamic runs, the results showed that the coastal safety offered by mangrove vegetation is depending on the combined impact of these three parameters, emphasizing the importance of expanding the mangrove vegetation to withstand the non-stationary multivariate extreme event.

The main recommendations from this study are based on the results. First, the study showed that the required vegetation width to ensure coastal safety has increased. It is therefore highly questionable if a stationary extreme value analysis is still valid in these environments, especially when the currently neglecting sea level rise is taken into account. Second, the observed increase in multivariate extreme events and the number of extreme events may reduce the persistence of mangroves and therewith their ability as coastal protection. It is therefore recommended to monitor the condition of the global mangrove forests more closely and to take action when mangrove vegetation is decreasing.

Coastal areas around the world have always been densely populated areas. However, sea-level-rise and an increase in single extreme events due to climate change, threaten the coastal areas and their inhabitants. Governmental organizations, coastal managers and various private parties thrive for better insights into long-term shoreline behavior for sustainable decision-making. Currently, these insights are gathered by process-driven models that are often time expensive, require local input and calibration, and are often limited by equations. However, satellite imagery proved to be a promising data source to derive historic shoreline behavior on a global scale. Machine Learning (ML) algorithms are suggested to create an extra in-depth understanding of these satellite images. The increase in the availability of both satellite data and ML algorithms opens possibilities for shoreline forecasting on a global scale. This research aims to improve forecasting of shorelines by creating a global model in which cross-time series information can be used. To encourage a meaningful exchange of time series information, a clustering forecasting approach is proposed.This research builds upon the yearly SDS data behind the Shoreline Monitor of Deltares. The SDS dataset captures shoreline evolution and behavior on a global scale by quantifying it in time series. The SDS dataset consists of transects every 500 m along the global coast and contains 33 years (1984-2016) of shoreline evolution. First, in order to cluster transects, the features for clustering were defined. Here, clustering features were divided into time series features and hydraulic and geomorphic features. Whether transect clustering could improve shoreline forecasting, based on these time series and hydraulic and geomorphic features, was explored. For this thesis, it was decided to focus on time series features for further clustering usage. Eventually, a shape-based transect representation was chosen as feature for input of the clustering algorithm. Around 350,000 globally distributed sandy transects were assigned to nine different main clusters, with a semi-unsupervised clustering approach. These nine clusters captured global sandy shoreline behavior ranging from extreme erosion to extreme accretion and proved to be a practical tool to quantify the (distribution of) shoreline behavior on different spatial scales. Subsequently, a second supervised step was developed to create sub-clusters to gain more insights into the trends of the nine clusters. With this supervised sub-clustering step, three sub-clusters were generated for each of the five largest clusters, allowing for the distinction between accelerating and decelerating behavior in the last decade of time series per cluster. Global, country-level and local case studies showed the potential of these sub-cluster refinements. Hereafter, the nine main clusters were separately used as input for four different forecasting algorithms. This resulted in predictions per cluster with a more reliable and more accurate seven-year forecast for >95 % of the transects, sometimes improving accuracy up to factor 15, compared to recently published work. Furthermore, a multi-method approach was created to determine the most reliable forecast, on a local scale by combining overall accuracy and information gained during clustering. Time series clustering enhances the forecasting of transects based on overall accuracy and local reliability. The multi-method approach for selecting the appropriate prediction on a local level, could be a practical tool for governments to apply. Besides, a vulnerability assessment explores the possible application of the predictions and underlines the need for global databases regarding the coastal zone. However, further research should consider incorporating hydraulic and geomorphic features to strengthen the clustering of transects. Hence, shoreline forecasting based on historical data should be considered as a valuable tool for sustainable decision-making regarding coastal zones.