The increasing frequency of extreme weather events intensifies the demand for accurate monitoring of nearshore wave dynamics, as these dynamics directly affect shoreline stability, flood risks, and coastal operations. While operational wave models provide essential forecasts, they often underestimate significant wave height (SWH) during energetic conditions and omit infragravity (IG) waves. The latter can substantially amplify coastal impacts such as flooding, dune erosion, and harbor seiching. These limitations are further exacerbated by the limited spatial coverage of in-situ buoys, making model validation in coastal waters particularly challenging.

The launch of the Surface Water and Ocean Topography (SWOT) mission presents a promising yet underutilized opportunity to improve coastal monitoring and support the validation of hydrodynamic models. Using Ka-band Radar Interferometry (KaRIn), SWOT provides high-resolution, two-dimensional measurements of sea surface height (SSH), offering spatial detail that exceeds the capabilities of buoys and traditional altimeters. While initial validations in the open ocean have confirmed SWOT’s capacity to retrieve SWH and resolve long-period waves, its performance in shallow, morphologically complex coastal seas remains largely untested.

This research presents a novel approach comprising (i) a multi-pixel match-up strategy for SWH retrieval, and (ii) the first satellite-based spectral analysis for detecting IG wave energy in the Southern North Sea. Through three targeted case studies, the study investigates SWOT’s two-dimensional SSH and SWH patterns, retrieval accuracy, and sensitivity to key processing parameters across varying sea states and bathymetric regimes. Validation is performed using in-situ buoy observations, platform-mounted radar measurements and numerical wave models to assess consistency, spatial performance, and retrieval accuracy.

By applying both single- and multi-pixel match-up strategies, our findings revealed the trade-off between statistical variance reduction through spatial averaging and the preservation of local wave variability. Similarly, for spectrally derived IG wave energy, different analysis box sizes were evaluated against platform-mounted radar observations in the Southern North Sea. Results show that SWOT-derived SWH exhibits strong agreement with buoy data (bias < 7 cm) and outperforms operational wave models during energetic events. IG wave height estimates also demonstrate good correspondence (MAE $\approx$ 0.9 cm), provided that residual noise amplification is carefully managed. This finding underscores the need for improved noise modelling in future spectral retrieval frameworks. A key uncertainty identified is the role of spatial heterogeneity, which induces representation errors due to the inherent mismatch in spatial and temporal scales between SWOT observations, in-situ buoys, and wave models.

Despite these uncertainties and other limitations, the results confirm SWOT’s capacity to observe nearshore wave dynamics with high spatial detail. The proposed configurations and filtering strategies offer a transferable framework for future applications. These insights support SWOT’s integration into coastal wave model validation, boundary condition improvement, and data assimilation schemes across coastal scales. Ultimately, this thesis advances high-resolution coastal wave monitoring by demonstrating how SWOT’s two-dimensional observations can enhance our understanding of spatial sea state variability, particularly during energetic conditions in morphologically complex environments.

This study evaluates the feasibility of applying photogrammetry techniques to reconstruct historical coastal topography and assess decadal scale coastal change from historical aerial images, focusing on the Dutch coastal of Westkapelle. The workflow was validated on two benchmark datasets, Benchmark Toronto and Benchmark Westkapelle, to verify registration accuracy under ideal acquisition conditions before being applied to the Westkapelle test dataset for change detection.

The reconstruction pipeline employed Structure from Motion and Multi-View Stereo algorithms, followed by a two stage point cloud registration. Coarse alignment was achieved through Sample Consensus Initial, and fine registration used the Iterative Closest Point algorithm. In the two benchmark datasets, registration achieved sub-metre mean C2C distances: 0.790 m for Toronto, 0.626 m for Westkapelle. In the test dataset, the mean C2C distance improved from 10.287 m before registration to 2.025 m afterwards, with 95% of points within 6 m. DEM differencing, supported by JARKUS cross-shore transect profiles, revealed systematic elevation gains of up to +10 m along foredune ridges, primarily resulting from a combination of documented coastal nourishment and natural process between 1990 and 2020.
However, limitations of the historical dataset, including sparse image coverage, strongly oblique viewing geometry, lack of vertical imagery, and poor GCP distribution, introduced geometric distortions and inconsistencies. The resulting orthophoto contained substantial voids and warped features, particularly in urban areas and low texture surfaces, underscoring the challenges of dense stereo matching under suboptimal imaging conditions.

Despite these constraints, this study demonstrates that meaningful reconstructions of past coastal environments are achievable when supported by careful preprocessing, robust registration, and multi source validation. The proposed workflow offers a transferable approach for extracting geomorphic insights from historical imagery in other coastal settings.

Mangroves play a vital role in coastal ecosystems by dissipating wave energy, protecting against erosion and supporting biodiversity. Despite their importance, quantification of their attenuation capabilities remains challenging. This study explores the potential of a two-dimensional (2DV) OpenFOAM model to simulate wave attenuation through mangrove forests using a layered approach to capture the vertical variability in mangrove frontal area.

The model is first validated against flume measurements under a range of different hydrodynamic conditions to assess its capability in a controlled, well-measured environment. The goal is that by applying the Reynolds Averaged Navier Stokes equations and a layered approach, a more realistic bulk drag coefficient can be calibrated for different hydrodynamic conditions. One of the objectives is to explore whether this approach can yield a stronger correlation between the bulk drag coefficient (Cd) and the Keulegan-Carpenter (KC) number.

Then, the model is applied to a real-world case study for the Mandai mangrove forest on the north-west coast of Singapore. his application demonstrates the model’s ability to simulate complex coastal environments, including variable bathymetry, forest extent, and wave conditions. A sensitivity analysis is also conducted to evaluate the effect of different parameters on the modelled wave attenuation

Results confirm the model's capability to accurately represent the vertical variations in vegetation structure and their influence on wave attenuation. It showed great agreement with the measured wave attenuation of the flume after calibration. Despite its expected improvement on the Cd-KC relation, it did not succeed in this, but it does reveal the impact of depth-variable drag forces and their effect on the velocity profile and wave attenuation. Furthermore, the case study showed its applicability to real-world coasts. Despite numerical dissipation standing in the way of any quantitative analysis, the runs in combination with the sensitivity analysis provided valuable insights into the model's behaviour and wave attenuation predictions.

Accurate flood prediction is essential for effective risk management, but simulating with physics-based models is computationally intensive and time-consuming, limiting their use in operational use cases. To address this challenge, this study develops a deep learning surrogate model that replicates spatial flood depth predictions of the physics-based model SFINCS, with significantly reduced computational cost.

The surrogate model is trained with a synthetic dataset of 13,000 tracks generated by TCWiSE, that have been computed with SFINCS. Input for the surrogate model are key drivers of compound coastal flooding, including cumulative precipitation, maximum wind speed, and water depth at the river outlet, which are given to the model as scalar values. The results demonstrate that the model can reproduce flood depth patterns with a RMSE of 0.054 m on the original dataset and 0.069 m on a case study application. The CSI values of 0.829 and 0.776, respectively, are comparable to values reported in recent literature. Visual inspection of spatial predictions shows that the model performs well overall but shows localized error patterns, particularly in the western part of the domain, indicating that additional input features may be needed to better capture interactions in physical processes that occur in compound coastal flooding. From a computational perspective, the deep learning model runs approximately 100 times faster than the physics-based model SFINCS on CPU, with further gains on GPU. This computational efficiency allows for running more simulations within a given timeframe, enabling the exploration of an increased number of potential flood scenarios and providing decision-makers with more time to evaluate and respond.

Overall, the study demonstrates that deep learning surrogate models offer a promising alternative to physics-based models by providing accurate and faster predictions. Although the current set-up shows accurate results, there remains room for improvement through enhanced input representation, such as incorporating spatiotemporal information of the drivers or including additional hydrodynamic features. Further gains in performance and generalizability can also be achieved by refining the model architecture and training strategies.

The Nahuel Huapi National Park, in the Lake District of Northern Patagonia, Argentina, is well known for its tourism industry all year round. After COVID-19, the area saw a significant increase in the number of tourists traveling to the area. This means that the lake located in the heart of the district, Lago Nahuel Huapi, is being used more and more to explore the environmental richness of the area by boat. Now, the capacity of mooring spaces is no longer sufficient in the region, resulting in the construction of illegal private docks along the shore. To reduce this impact on the environment the authorities granted in 2024 a concession to develop one of the last not yet commercialized marina’s in the region: the marina in Bahía López.

This report provides a consult for the concessionaire of this development. The process begins with a research phase, consisting of an area study, and the mapping of environmental and hydrodynamic constraints. Subsequently, stakeholders are categorized, as the development of a marina in a national park entails complex regulations from multiple organizations. The outcomes of the research phase are translated into specific functional requirements for the marina. These functional requirements are the basis for the next phase, the design phase. This phase begins with the formulation of a design vision statement, formulating the project response to local conditions. Based on this, three different conceptual designs with various technical solutions are developed. Through a multi-criteria analysis, the concepts are tested on their robustness in order to chose a final concept. This concept is then elaborated into a preliminary design. Presenting an overview of the marina’s facilities, including structural designs, operational needs, and capital costs. Finally, suggestions for future development
are provided, outlining the next steps to advance the marina to a next phase.

Coastal erosion has caused beach loss and threatens first-row beachfront houses and other nearshore structures in Progreso, Yucatán (Mexico). This multidisciplinary project combines shoreline analysis, social research and stakeholder mapping to develop an integrated understanding of coastal erosion, its effects and the socio-environmental context in the study area. The aim is to translate this knowledge into a coordinated and sustainable approach that promotes coastal resilience.

The shoreline analysis that was performed using satellite imagery showed evidence of both accretion and erosion in the study area. A forecast of the coastline retreat showed that in the western part of the Progreso area, the number of properties that lie within 10 meters of the shoreline is expected to double within the next decade.
The findings of the social analysis and stakeholder mapping revealed a communication and knowledge gap. The communication gap occurs between neighbours, so among coastal homeowners, but also between them and the governmental institutions. There is a knowledge gap due to the need for knowledge sharing among coastal homeowners.

A solution for the coastal erosion problem in the Progreso area is only possible by first implementing a social strategy; otherwise, no physical measure will be effective. Thus, the coastal community committee (CCC) is proposed to address the identified communication and knowledge gaps, as well as the fragmented responsibilities. The CCC is a group that makes decisions about measures to improve coastal resilience and engages local residents and stakeholders. In order to physically reconstruct a resilient coast, the use of a Sandsaver is proposed for sand accretion. To achieve long-term coastal resilience, dune formation is necessary. The report includes a plan for testing this Sandsaver and also a plan for implementing the CCC.

The UNAM Sisal campus, situated in a remote and ecologically sensitive coastal region of Yucatán, faces significant challenges regarding sustainability and self-sufficiency. Its dependency on unreliable external infrastructure for energy and water, coupled with inadequate wastewater treatment and unstructured waste management, makes the campus vulnerable to environmental challenges and hinders its potential as a model for sustainable development. This multidisciplinary project aimed to address these issues by developing an integrated roadmap toward a self-sufficient and sustainable campus by 2035.

Using a methodological framework combining backcasting and design science principles, the study integrated stakeholder input (staff interviews, student surveys), technical data analysis, literature reviews, and expert consultations. Potential solutions across the domains of energy, water, and waste were systematically evaluated using Multi-Criteria Analysis (MCA) weighted by stakeholder preferences.

The findings indicate a clear pathway forward. For energy, prioritizing solar photovoltaic (PV) installations is recommended due to high local potential and scalability, contingent on initial detailed energy consumption monitoring. For water, the focus should be on implementing robust wastewater treatment to meet regulatory standards, followed by longer-term integration of small-scale desalination and supplementary rainwater/AC condensate harvesting. For waste, the primary step involves quantifying waste streams, followed by implementing an organizational strategy, such as a Zero Waste Grassroots Programme with source separation, composting, and partnerships for recycling.

The research ends with a phased roadmap outlining concrete short-, medium-, and long-term actions across all three domains. Successful implementation can transform the UNAM Sisal campus into a resilient, self-sufficient facility and a valuable example for sustainable practices in other coastal communities, though success depends on institutional commitment, securing funding, and establishing continuous monitoring.

Future research should focus on collecting reliable on-site data, testing pilot projects, and strengthening institutional frameworks to ensure long-term implementation, funding, and monitoring.

The Port of Santa Fe was once a major hub for both domestic and international trade, but changing river dynamics have reduced its accessibility and economic importance. As a result, the port now faces the challenge of redefining its role and exploring new functions that reconnect the port with the public. The Dyke 2 waterfront in the Port of Santa Fe, is currently in a deteriorated and underdeveloped state, lacking essential public facilities, accessible green spaces, and safe access to the river. Most importantly, the site faces severe riverbank instability, confirmed by a calculated Safety Factor (SF) of 0.67.

This report presents an integrated vision and technical design for the sustainable redevelopment of the project site area, commissioned as an advisory document for the Ente Administrador del Puerto de Santa Fe (EAPSF). The project employed a strategic track, guided by four pillars, and a slope protection track, using a Multi-Criteria Decision Analysis (MCDA) to select a solution, resulting in a design containing both technical stability and a public urban concept.

The resulting urban concept, The Santa Fe Riverside Park, serves as a project embodying the strategic vision. The design integrates adaptive infrastructure, including stepped terraces and docking places, engineered to accommodate significant seasonal river fluctuations. This concept is supported by the delivery of a 15-year long-term roadmap. The unstable slope is protected using an ecosystem-friendly Articulated Concrete Block mattress system, improving the calculated sliding SF from 0.67 to 1.9, and achieving an erosion SF of 2.10.
Finally, the report provides the Port Authority with a strategic foundation of recommendations to realise the project.

Sandy beaches provide a wide range of ecosystem services, including flood protection, sediment and water storage, recreational values, and habitat for diverse flora and fauna. Over the past centuries, humans have increasingly developed settlements and infrastructure on the landward side, while waves, storm surges, and sea level rise encroach from the ocean side. These stressors may lead to ecological impacts across varying temporal and spatial scales, threatening the ecosystem services for humans and animals alike.
Among the species particularly vulnerable to the degradation of sandy beaches are sea turtles, who rely on these habitats for nesting. These endangered reptiles play key ecological roles in coastal and marine ecosystems worldwide, for instance by maintaining healthy coral reefs and sea grass meadows. Unfortunately, climate change and human activity severely threaten their populations. Among the challenges they face are the flooding and erosion of their nesting beaches. Incubating nests require a narrow temperature and moisture window to develop, making them susceptible to inundation. Episodic erosion can destroy nests and change beach morphology over several seasons. Long-term, chronic erosion and coastal squeeze may gradually diminish suitable nesting beaches worldwide. Although both flooding and erosion are recognized as significant threats, they remain under-represented in conservation management and research. Nature-based solutions—such as turtle-friendly sand nourishments or restoration of coastal vegetation and reefs—may offer promising opportunities to preserve existing nesting habitats, and potentially enable sea turtles to expand to currently unused beaches. However, we first need to understand the coastal processes that enable and threaten sea turtle nesting to effectively design such solutions.
This thesis identifies coastal processes that govern the vulnerability of sea turtle nesting beaches, and assesses their implications for global habitat suitability and conservation. Specifically, it investigates processes related to nest flooding and long-term erosion, while also examining how regional coastal characteristics influence global nesting habitat suitability. Employing detailed local case studies and global analyses, this thesis integrates diverse methods—including field experiments, numerical modeling, remote sensing, statistical analyses, global datasets, and machine learning—to illustrate the broad potential of coastal science tools for sea turtle conservation, which are essential for developing an integrative approach to assess nesting beach vulnerability and inform targeted interventions...

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.