Predicting where floating plastic concentrates within the Great Pacific Garbage Patch is difficult because operational circulation models resolve the broad accumulation region, but not necessarily the smaller flow structures that form local hotspots. Reduced-order two-dimensional models offer an attractive way to reach finer horizontal resolution at lower computational cost, but their usefulness depends on whether they correctly retain the near-surface dynamics that control particle clustering. This thesis assesses under which dynamical conditions, and to what extent, a high-resolution 2D MITgcm framework can reproduce the surface-flow and surface-particle clustering statistics of matched 3D MITgcm benchmark simulations in a semi-idealized GPGP setting. The comparison combines Eulerian diagnostics of vorticity, strain, divergence, spectral content and submesoscale motion with Lagrangian particle-clustering metrics based on Voronoï tessellations. The results show that the 2D model can retain part of the broad mesoscale organization and surface-vorticity structure, especially under weaker energetic conditions. However, it does not reliably reproduce the connected high-strain structures, high-strain-high-vorticity asymmetry and compressive surface organization present in the 3D benchmarks. These differences limit its ability to reproduce finite-time particle clustering and local concentration extremes. The unaugmented 2D framework is therefore more defensible for qualitative broad-transport characterisation than for quantitative hotspot prediction. Future hotspot-oriented use should focus on controlled augmentation of the particle-advection kernel, using proxies for persistent filamentary attraction and unresolved surface convergence. ... Read more

The Mekong delta in Vietnam is subjected to compound flood forcing from the interaction of riverine, tidal, storm surge, and wave drivers, whose joint statistical behaviour is poorly characterised. In the absence of advanced probabilistic tools, hydraulic structures in such environments are typically designed using univariate extreme value analysis, which combines independent return values for each driver and may misrepresent the joint probability of compound loading. This research quantifies the difference in design loads on a conceptual caisson barrier in the Hàm Luông estuary, Vietnam, when comparing a traditional deterministic approach with a multivariate probabilistic framework, addressing the research question: to what extent do design loads on the barrier differ between these two approaches, considering the dominant stability failure mode of overturning. Two parallel modelling tracks are followed.

In the deterministic track, Generalised Pareto Distributions were fitted to Peak-over-Threshold exceedances of five environmental drivers (H_s, T_p, U₁₀, h_surge, h_river) to establish univariate design values for a 100-year return period in the deterministic baseline. In the probabilistic track, the joint occurrence of environmental drivers is instead captured using a vine-copula: a multivariate model that decomposes the dependence structure between many variables into a network of simpler bivariate building blocks (pair-copulas), allowing realistic joint scenarios to be simulated. A seven-dimensional regular vine-copula (environmental drivers, θ_wind, and θ_wave) was fitted to n = 77 overlapping, concomitant observations of extreme surge events, using Dißmann's algorithm to model the multivariate dependence structure between the input boundary conditions. From this vine-copula, N = 10,000 synthetic weather events were generated, of which a representative subset of M = 200 cases, selected through the Maximum Dissimilarity Algorithm (MDA), was propagated through a SFINCS hydrodynamic model. Downscaling through MDA was necessary as direct Monte Carlo propagation of all N samples through SFINCS is computationally infeasible. The subset size M = 200 was adopted following a convergence study which balanced surrogate accuracy against computational cost.

Within the probabilistic track, two methodologically distinct surrogate models were then trained on the resulting SFINCS input--output pairs to upscale the M = 200 simulated cases back to the full population of synthetic loadcases. The first is a vine-copula surrogate fitted to the joint 11-dimensional distribution of inputs and outputs, which, like the boundary vine-copula, models the barrier responses probabilistically and can therefore generate responses beyond those observed during training. The second surrogate is a Gaussian Process regression with a Radial Basis Function (RBF) kernel, a deterministic regression model that interpolates directly between the input boundary conditions and the observed barrier responses, without representing their joint probability. Both surrogates were evaluated at a newly drawn, larger Monte Carlo sample of N = 100,000 synthetic events to enable the structural reliability assessment.

Comparing the 11-dimensional surrogate vine-copula against the original seven-dimensional boundary vine-copula showed that several of the input dependencies weaken once the barrier responses are incorporated into the joint model, most notably between θ_wave, and θ_wind (Kendall's τ reduced from 0.65 to 0.43). This indicates that the dependence structure of the environmental drivers changes once local hydrodynamic responses are jointly modelled. The fitted vine-copula reveals that coastal drivers (storm surge, wave height, wind speed) are strongly co-dependent under NE monsoon forcing, whilst river stage is seasonally decoupled from these drivers, reducing but not eliminating compound flood risk. The vine-copula surrogate yields a probabilistic 100-year overturning moment of M_ext,100  = 2,024 kNm/m, exceeding the deterministic estimate of M_ext,det = 1,971 kNm/m by roughly 2.7%. Redesigning the caisson with the same design limits yields a required base width of B = 13.3 m, 0.8 m wider than the deterministic baseline of B = 12.5 m. This indicates an annual probability of exceeding the deterministic design margin of P_f^annual =  3.02%. The RBF surrogate did not induce failure at the deterministic design margin, and yielded a redesigned width that is 2.2 m narrower than the deterministic baseline. No samples exceeding the deterministic design margin does not mean that the structure will never fail, but rather indicates that the current set of forcing conditions does not induce failure of the structure. This happens because, beyond the support of the M = 200 training cases, the RBF prediction is governed by the decay of its Gaussian kernel: the small fitted shape parameter (c = 0.10) for all barrier responses causes the predicted responses to collapse towards zero over a short distance, preventing the surrogate from generating responses more extreme than observed in training. As the two surrogates represent methodologically distinct quantities, they are better interpreted as approximate bounds on the failure probability than as independent estimates of the same quantity.

A decomposition of the dominant-side force into its hydrostatic and wave-induced components for the vine-copula loadcases exceeding M_ext,det, shown in Figure 1, reveals a bimodal pattern: alongside cases consistent with the deterministic wave contribution, a second cluster exhibits a noticeably higher wave contribution to the total overturning moment. This is traced to a bimodal distribution of H_s at the barrier location combined with a reduced upstream hydrostatic force across nearly all exceeding cases, which lowers the restoring moment. This indicates that the increased joint load is physically driven by a shift towards more wave-dominated loading conditions.

In practical terms, the deterministic caisson design, which is designed with a design margin below the ultimate limit state, fails on rotational stability under the multivariate load distribution with a probability of 3.02%, indicating that under compound loading, the intended 100-year return level (corresponding to a failure probability of 1%) at the design margin can not be guaranteed. The results suggest that employing a multivariate probabilistic framework to quantify design loads provides a more robust basis for structural design, and that the assumption of univariate return values may underestimate the joint return load in a compound environment. This finding should be interpreted in the context of absent partial safety factors in the deterministic baseline: in code-compliant design, partial safety factors implicitly account for the joint probability of load combinations and model uncertainty, meaning that replacing the univariate assumption with a multivariate load quantification removes part of the conservatism those factors provide. ... Read more

Coastal aquaculture in data-scarce regions is increasingly exposed to climate change, yet careful calibration of hydrodynamic models in these areas is often limited by a lack of in-situ observations. This thesis examines how the Bantry Bay estuary in Ireland, a stratified coastal system supporting significant kelp and mussel cultivation, responds to 2075 climate projections under the high emission SSP5-8.5 scenario. Given the scarcity of historical tide gauge data, the model calibration was shifted from a conventional deterministic to a probabilistic modelling approach, using a three-dimensional (3D) hydrodynamic model (Delft3D-FM) to test how the bay’s interior environment reacts to plausible variations in open-boundary conditions (temperature, salinity, and sea level).

Physically bounded distributions for temperature and salinity change were built from the pooled projections of four CMIP6 models, and for sea-level rise from the NASA IPCC AR6 percentile bands. A candidate pool of 5,000 parameter combinations was generated by inverse-transform sampling from these
distributions, from which the Maximum Dissimilarity Algorithm selected a final, space-filling ensemble of 50 members. Each member was applied to the 2022 baseline simulation through the delta-change method, superimposing the sampled temperature, salinity, and sea-level offsets onto the observed
boundary forcing to represent plausible 2075 conditions.

The findings show that the bay is boundary-controlled: mean salinity, mean temperature, and mean water level are each governed almost exclusively by their own boundary offset, with no response meaningfully shaped by more than one driver. Salinity uncertainty is small and stable throughout the year,
while temperature uncertainty is seasonal, concentrated in the summer tail, where the ensemble median already exceeds the kelp thermal threshold. This extreme regime tells a more complex story than the mean: thermal stress at cultivation sites is not driven by warming alone, but by the interaction of
local freshening and sea-level rise, with the highest-risk scenarios arising from moderate to low warming compounded by strong freshening rather than warming in isolation. Spatially, the boundary-driven fields are close to uniform across the bay, yet thermal stress itself concentrates in the inner bay. Sea
level rise itself functions primarily as a mean-state shift, with no measurable change to the bay’s tidal range.

This work demonstrates that in data-limited coastal systems, boundary uncertainty, rather than internal model complexity, is what dominates the predictive picture, and that resolving it requires more than a simple temperature projection. Climate-risk assessments for surface aquaculture must account for the complex interplay of multiple physical drivers, as the conditions most likely to cause biological stress arise from their interaction rather than from any single driver in isolation. This probabilistic workflow is designed to be directly transferable to other demonstration sites within the PHAROS project network
and beyond. ... Read more

Meteotsunamis are long ocean waves generated by fast-moving atmospheric disturbances that transfer energy to the ocean surface through resonance mechanisms. One of the primary amplification mechanisms, Proudman resonance, leads this displaced wave to grow over space and time as long as the speeds of the ocean wave and pressure wave are similar to each other; other resonance mechanisms can also lead to further growth of a meteotsunami wave. Due to the complexity of the processes involved, predicting them can present a challenge, particularly in regions where the conditions leading to their generation and amplification are not yet fully understood. Although meteotsunamis have been observed along the Dutch coast, the mechanisms governing their occurrence and amplification remain insufficiently investigated.
The meteotsunami of 29 May 2017 provides an opportunity to investigate the extent to which current available models can approximate the reproduce observed meteotsunami characteristics, as well as improve the understanding of meteotsunami dynamics along the Dutch coast. To achieve this, sea level and atmospheric pressure records from predominantly Dutch monitoring stations were analyzed using wavelet and spectral analysis to identify key characteristics of the event, including its dominant periods. These findings were subsequently used to reconstruct the 2017 meteotsunami through coupled atmospheric pressure forcing and numerical ocean modelling. The reconstructed model was evaluated through comparisons with available observations and was found to reproduce the timing and order of magnitude of the observed meteotsunami response with reasonable accuracy. Following the reconstruction, a series of scenario experiments was performed in which atmospheric disturbance speed and propagation direction were systematically varied.
The scenario experiments identified a favorable atmospheric disturbance speed range of ca. 60-70 km hr⁻¹, in which the strongest wave amplification occurred across most stations. In contrast, directional sensitivity varied considerably along the Dutch coast. Stations located along the southern Dutch coast exhibited the strongest amplification for disturbances arriving from the north, whereas stations further north were generally more sensitive to disturbances arriving from the southwest. The results further indicate a gradual transition in directional sensitivity from the southern to the northern Dutch coast, highlighting the influence of local coastal geometry and exposure on meteotsunami amplification.
Overall, the results confirm that meteotsunami amplification along the Dutch coast is governed by a combination of regional resonance conditions and local coastal characteristics. Atmospheric disturbance speed acts as the primary regional control on amplification, while propagation direction and local geometry determine where the strongest responses occur. These findings improve the understanding of meteotsunami dynamics along the Dutch coast and provide a basis for future meteotsunami prediction and hazard assessment efforts.
... Read more

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. ... Read more

In planning offshore renewable energy systems—such as offshore wind farms, wave energy devices, or hybrid installations—engineers and policymakers intervene in marine environments governed by coupled wind and wave processes. These processes respond to atmospheric forcing on short time scales (days, seasons) as well as on longer climate time scales (years, decades). For instance, a change in large-scale atmospheric circulation can immediately alter regional wind fields, which in turn modifies wave generation and propagation. Over longer periods, such changes can reorganise spatial patterns of wind–wave variability and affect the persistence and stability of offshore energy resources. The success of climate-informed offshore development therefore depends, at least partially, on our ability to predict how coupled wind–wave systems respond to changes in atmospheric forcing. We essentially aim to answer questions such as: how stable are wind–wave conditions across seasons and years? Which patterns persist, and which reorganise under climate variability? How reliably can climate models represent the wind–wave regimes that underpin offshore wind, wave, and hybrid energy potential, and how may these regimes reorganise under near-term climate forcing in the coming decades? .... ... Read more

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. ... Read more

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. ... Read more

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.
... Read more

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.
... Read more

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.
... Read more

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. ... Read more

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.
... Read more

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... ... Read more

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. ... Read more

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. ... Read more

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. ... Read more

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. ... Read more

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... ... Read more