The Earth's landscapes are shaped by processes eroding, transporting, and depositing material over various timespans and spatial scales. To understand these surface activities and mitigate potential hazards they inflict, knowledge is needed of their occurrences and properties. Near-continuous terrestrial laser scanning (TLS) enables the acquisition of point cloud time series, constituting up to thousands of three-dimensional surface morphology representations. Exploiting the full potential of this large amount of data by extracting and characterizing different types of surface activities inside these point cloud time series is, however, challenging. This thesis addresses this challenge by developing an automated and unsupervised method for classifying 4D objects-by-change (4D-OBCs). These 4D-OBCs represent the spatiotemporal extent of individual surface activities in a point cloud time series. They are classified using a Self-organizing Map (SOM) and hierarchical clustering, grouping them into different levels of surface activity types. The workflow is tested on its ability to characterize surface activities in two study areas, a sandy beach and a snow-covered Alpine area. Application of an optimized SOM configuration on the sandy beach results in groups of 4D-OBCs physically interpretable as different types of surface activities. A validation dataset containing 51 manually labeled 4D-OBCs of various surface activity types (e.g., intertidal bar depositions, anthropogenic bulldozer depositions) is distributed over the SOM generally according to the labels provided. The SOM thus enables the identification of 4D-OBCs displaying a particular type of surface activity, as well as subtle differences between events of one surface activity. Hierarchical clustering allows us to find and characterize broader groups of surface activities, even if the same type occurs at different points in space or time. For example, the varying spatial redistribution of sand through the initiation of different types of surface activities after the destruction of an intertidal bar system under different environmental conditions is successfully studied. A nearly identical workflow configuration applied on the snow cover 4D-OBC set does not result in equal performance. Several groups of surface activity in the SOM contain a combination of 4D-OBCs representing different surface activity types. These results highlight the necessity of a study area specific workflow optimization by selecting specific features and SOM configurations. However, if optimized for the specific environment, the workflow has the potential to be used in long-term automated monitoring of surface activity in systems with complex morphological interactions, increasing the applicability of TLS for studying geomorphological change.

Sea turtles, with six of the seven species listed as endangered by the IUCN, face significant threats. Their eggs exhibit sensitivity to fluctuations in temperature, salinity, and moisture during their 6-8-week incubation period. Inundation of these eggs can substantially decrease their viability and influence sex ratios. This thesis centers on mitigating the wave runup-driven flooding of sea turtle nesting beaches. Mitigating nest flooding can involve relocating at-risk nests to higher elevations. This thesis aims to assist in responsible nesting relocation by providing flood risk information as a function of beach elevation. The study site, Ras Baridi, is a significant nesting site for green turtles ( extit{Chelonia mydas}) in the Red Sea. The research focuses on the development of a methodology for assessing flood risk in environments with limited data. The risk of sea turtle nests to flooding at Ras Baridi was assessed by employing two different metamodels, BEWARE 2.0 and HyCReWW, that were recently developed to assess wave runup in coral reef environments at low computational costs. These models employ different strategies to model wave runup. BEWARE 2.0 uses real bathymetry, whereas HyCReWW schematizes the input bathymetry. 40-year hindcast datasets of waves and water levels from 1978-2018 were used in combination with 10 m horizontal resolution bathymetry data from the Allen Coral Atlas as model inputs. HyCReWW exhibited maximum runup at an 80 m reef width (0.95 m for 1 year return period (RP), 1.95 m for 40 year RP) BEWARE 2.0’s highest runup was at an 80 m reef width (1.35 m for 1 year RP, 2.7 m for 40 year RP). Median inundation durations from BEWARE 2.0 and HyCReWW were 4 and 7 hours, respectively. Beach elevations associated with 5-year return periods were identified as suitable minimum nesting elevations to mitigate inundation risk. BEWARE 2.0 and HyCReWW results for Ras Baridi lacked direct real-world validation, prompting a methodological validation approach. Six validated XBeach 1D NH models in fringing reef environments globally were utilized for comparison against BEWARE 2.0 and HyCReWW. The comparison revealed increased median scatter indices and root-mean-square (RMS) errors for both BEWARE 2.0 (0.07 and 0.1 m increases) and HyCReWW (0.1 and 0.15 m increases) with low-resolution bathymetry (10 m horizontal). BEWARE 2.0 had lower median RMS errors and scatter indices for high-resolution (2 m horizontal) and low-resolution bathymetry than HyCReWW, as well as smaller spreads. Consequently, utilizing BEWARE 2.0 results is recommended for Ras Baridi and similar data-scarce coastal environments over HyCReWW. In summary, this thesis addresses the pressing issue of sea turtle nest flooding, providing a methodology to assist in responsible nest relocation in low data environments. The 5-year return period runup elevation of the BEWARE 2.0 results (1.25-1.75 m along the beach) has been identified as a suitable minimum nesting elevation in Ras Baridi, and the likelihood of flooding as a function of beach elevation has been provided. These results can aid coastal managers in making informed decisions for the protection of these endangered species.

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

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

The shipping industry is responsible for almost 3% of the world's greenhouse gas emissions and is looking for possibilities to reduce their share. The demand for lowering the emissions of the maritime industry creates the need for insight in the emissions. Currently a generic method to specify emissions in space and time, as a function of vessel and waterway properties is not available. Although most of the emissions of vessels take place at sea, the most noticeable part takes place in ports since they are located close to urbanized areas. Therefore, this research focuses specifically on emissions in ports. The study investigates the impact of sea-going vessels. By gaining insight in the emissions of sea-going vessels, the big polluters can be tackled. Due to the scope of the research, not all vessel types and emission types are taken into account. Ten vessel types are selected and only CO2, NOx, SOx and PM10 emissions are estimated. These are the most relevant polluters in the shipping industry since they cause health and environmental related issues, both locally as more widely. To provide insight in the emissions, the objective of this study is to develop a generalized method to calculate and map the emission of a single vessel in space and time in ports based on reliable data. To reduce these emissions, emission reduction strategies have to be drawn up, targeting the largest emission sources and the most crucial locations. Therefore, the developed method must not only quantify the emission sources, but must also provide insight in emission patterns in ports and indicate emission hotspots. A bottom-up method is developed to estimate the emission of a single vessel in space and time. To derive the emission rate of a vessel, the fuel and energy consumption of the vessel is multiplied by a vessel-specific emission factor. The CO2 and SOx emissions follow a fuel-based approach, in which the emissions produced are directly proportional to the fuel consumption and therefore depend on the engine load. The energy-based approach is used for estimating NOx and PM10 emissions, which cannot be directly related to the fuel consumption but depend on engine characteristics. The fuel consumption is determined by multiplying the energy consumption of the engine with a fuel consumption factor specific to each vessel, meaning the fuel consumption is related to the energy consumption. The amount of energy the main engines of a vessel consume, is the energy needed to overcome the resistance a ship experiences from sailing through the water and is therefore related to the vessel's speed. This speed is derived from AIS data. AIS data represents real-life vessel tracking data and is gathered automatically, which makes it a reliable and realistic data source. It also provides the ability to make the emission estimations time dependent. Due to the global coverage, AIS data is a good data source to develop a generic method applicable to ports all around the world. However, in ports the vessel's speed alone is not a good indicator of the energy consumption, since this neglects the amount of energy the auxiliary engines consume. Their energy consumption is dependent on how much energy the electrical systems of a vessel require at that moment, which can be high when a vessel is for example at berth or manoeuvring. The energy consumption is therefore related to the operations a vessel performs. This leads to an approach which takes into account the vessel's resistance and operational modes. Four operational modes are important to distinguish in ports: 'sailing', 'manoeuvring', 'anchoring' and 'berthing'. According to the operational mode, the main engine power is either estimated with the resistance calculation from Holtrop and Mennen when the vessel is sailing or manoeuvring, or assumed zero when laying still at anchor or berth. According to the operational mode, the auxiliary engine power can be derived from values of the IMO fourth GHG research. Depending on the emission type and vessel characteristics, the emission factor is determined. The algorithm to determine the emission of a single vessel in space and time is implemented in a model. The model's input consists of AIS data providing the speed and position of the vessel, a vessel database containing vessel characteristics based on information from the Sea-web Ships database, and a FISgraph of the port network. This graph contains the fairway characteristics needed to calculate the resistance. The calculated emissions will also be displayed on the FIS graph for a detailed insight in the emission distribution in space. The model provides an insight in the emissions patterns in a port. The fairway sections which are subjected to high emissions can be identified immediately and so the emission hotspots are determined. The source of the emissions can be identified by down-drilling of the emissions. The model can drill down to vessel types, operational modes and all the way down to a single vessel in space and time. The model is illustrated by means of two case studies concerning the Port of Rotterdam and the Port of Constanța. These case studies indicate that the port basins hosting the largest vessels have the highest estimated emissions. These basins are indicated as an emission hotspot by the model when the emissions are projected on the FIS graph. In ports generally, high emissions are observed at places with a high traffic intensity, such as the port entrance. Junctions of fairway sections or port basin entrances also show locally higher emissions. The rise in emissions, is probably due to the fact that vessels are slowing down when approaching a junction. This increases the emission rate of a vessel due to more inefficient engine use, but also since they spend a larger amount of time at this fairway section. By indicating the location and source of the emission hotspots, targeted emission reduction measures can be taken. Three of these measures are demonstrated on one of the case studies. The first strategy concerns installing shore power. The model is able to simulate vessels connected to shore power, by setting their emission rate at berth to zero. This simulation is compared to the original situation without using shore power. Out of the evaluated reduction measures, this seems the best strategy to reduce emissions since it shows the largest reduction of the total emissions. Besides that, it is an effective measure especially for ports, since the largest emissions reduction takes place at berth. The second evaluated strategy shows also good results and is about switching from a normal tugboat fleet to a zero-emission tugboat fleet. The model simulates this switch by eliminating all the tugboats from the fleet since their emissions will be zero. This new case is compared to the original situation with normal tugboats. The third option to reduce emissions is applying ECA limits which do not seem to have a lot of effect on reducing emissions except for the SOx emissions. This is derived from comparing the original situation without ECA limits, to a new case study where a situation with ECA limits is simulated. This means that the fuel types of the vessels are changed from the most economical fuel type to the lightest fuel type on board and that the sulphur content in the fuel is altered. However, the model has some limitations. The method does not take into account the effect of currents or variations in time of the water depth. The accuracy of the resistance calculation can be improved by adding these. Furthermore, the emission pattern of tugboats needs further examination as it could not be demonstrated that the split of emissions into operational modes is correctly. The research has also shown that the method to estimate the energy consumption is not suitable for tankers at berth, since their energy consumption pattern is different. The quantity of emissions in the case study of the Port of Rotterdam is far from the expected amount of emissions. The quantification of emissions is assumed to be unreliable and further research should focus on validating these results. Concluding, the developed model makes use of AIS data, local waterway properties, empirical emission factors and operational modes. This data is used in a physics-based method to estimate the resistance and the energy consumption. If this information is available, all this combined makes the approach in principle applicable to any port. The developed model provides an insight in the emission distribution patterns and provides the ability for down-drilling to find the source of the emission hotspots. A targeted emission reduction strategy can be proposed as a result of this and the model has the ability to evaluate specific emission reduction strategies. The strategies can be simulated with the developed model and the effect of these measures can be quantified by comparing the emission reduction strategy to a situation without these measures.

Mangrove is a coastal vegetation type primarily located in the tropical regions between 5 North and 5 South. This coastal vegetation is capable of reducing the force of incoming waves. This is a result of the obstruction created by the roots, stems and canopies against waves to propagate through. Because of this capability, mangrove vegetation offers coastal protection against extreme annual flood events for 15 million people. Recent studies identified that in 2050, the present-day 100-year extreme sea level will occur annually in a large part of the tropical region. The increase of these extreme events is primarily driven by the projected sea level rise. The hypothesis is that before this chronic flooding is observed, storm-induced flooding might already be observed. The expected increase in the probability of these extreme events is defined as non-stationarity of extreme events. The hypothesis therefore is that the non-stationarity of extreme events could already be observed in historical data. This study examined non-stationary extreme events between 1987 and 2018 for a total of 5809 hydrodynamic environments distributed around the global mangrove coastline. These mangrove environments defined the hydrodynamic wave characteristics and water levels. The results showed that 87.5% of the mangrove environments have a positive trend in the location parameter of the Generalized Pareto Distribution for the extreme events. The shift of the location parameter indicates an increase of the impact of the extreme events. These extreme events were defined as multivariate extreme events consisting of the significant wave height, the mean wave period and the skew-storm surge, respectively the parameters Hs, Tm and S. This combination is based on the fact that the coastal protection offered by mangroves is depending on the water level and the energy within the long-period waves. The methodology proposed is capable to perform a non-stationary multivariate extreme value analysis and observe the evolution of the extreme events in these three dimensions between 1987 and 2018. The study showed that the average of the extreme events has been increasing at 70.6%, 72.6% and 64.6% of the mangrove environments in the discussed three dimensions. The average multivariate extreme event in mangrove environments increased by 0.06 m, 0.16 s, and 0.7 cm in three dimensions respectively Hs, Tm, and S. Furthermore, the number of extreme events increased, on average, between the first and second half of the time period, from 4.16 to 4.32 extreme events per year. The global impact assessment of the non-stationarity of these multivariate extreme events was translated to the 27440 mangrove locations along the global coastlines. Statistical upscaling methods allowed the application of a numerical expensive hydrodynamic model to propagate the offshore conditions to onshore. The impact of the non-stationarity of extreme events was applied to a theoretical framework, introducing the possibility of defining the vegetation width as an optimization parameter. To meet the same safety standard for the 1/40 year design condition in 2018 concerning 1987, the vegetation width of the theoretical framework had to increase on average by 16.8 m. After post-processing the hydrodynamic runs, the results showed that the coastal safety offered by mangrove vegetation is depending on the combined impact of these three parameters, emphasizing the importance of expanding the mangrove vegetation to withstand the non-stationary multivariate extreme event. The main recommendations from this study are based on the results. First, the study showed that the required vegetation width to ensure coastal safety has increased. It is therefore highly questionable if a stationary extreme value analysis is still valid in these environments, especially when the currently neglecting sea level rise is taken into account. Second, the observed increase in multivariate extreme events and the number of extreme events may reduce the persistence of mangroves and therewith their ability as coastal protection. It is therefore recommended to monitor the condition of the global mangrove forests more closely and to take action when mangrove vegetation is decreasing.

Sea turtles are a popular tourist attraction that offers travelers a unique nature experience. As a part of Saudi Arabia’s 2030 vision, an unprecedented amount of tourism development is being carried out under the ‘Red Sea Development Project’. The aim is to develop hyper-luxury islands in a sustainable and ecologically friendly way by closely following ‘Building with Nature’ principles. The tourism development site at Al Wajh Bank encompasses an archipelago of more than 90 islands and some of the sandy islands provide nesting grounds for Hawksbill and Green turtles. The thesis is a component of a broad study on investigating the preferential conditions for sea turtle nesting and conservation where TU Delft is collaborating with NIOZ and KAUST. The present study makes a preliminary assessment of the preferential conditions for sea turtle nesting of wave hydrodynamics and geomorphological conditions based on available data and numerical wave model simulations. The study is expected to provide information and guidance for future research for the conservation of sea turtle nesting at Al Wajh Bank. The coral reefs provide breeding grounds for sea turtles in the gently sloping sandy beaches along the raised islands where the sand has ideal conditions of moisture, temperature, and distribution of sediment size for successful nesting and hatching of eggs at an optimum distance from the highwater line to avoid inundation by wave runup. In general, the foreshore at the turtle nesting site has a fore reef with a steep slope followed by a wide and shallow reef flat having high bottom roughness for dissipation of wave energy. The Al Wajh Bank spread over an area of 2880 km^2 has a very large and complex barrier reef system at the outer edge in the deep sea enclosing a massive lagoon with several islands. These islands have sandy beaches on the up wave (windward) side and mudflats with sand and vegetation in the sheltered zones. The vegetation is dominated by mangroves in the mudflats and seagrass in the shallow lagoon beds. The study used the Delft3D WAVE stand-alone phase averaging spectral wave model for transforming waves from offshore to nearshore and to examine the energy dissipation characteristics of the reef system. The high-resolution bathymetry data for the Al Wajh Banks was obtained from GCC and relatively coarse resolution data for the offshore from GEBCO. The model simulated offshore wind and wave data validated using satellite altimetry, SAR, and Buoy measurements was obtained from BMT ARGOSS online services website “waveclimate.com”. The normal and extreme wave conditions were derived using this data. The data on GPS coordinates at the location of turtle nesting sites was provided by a survey undertaken by KAUST. A limited data on sediment size distribution at four islands south of Al Wajh Bank was made available by KAUST collected by ALS Arabia. The data on beach slopes, the distance of nests from HWL, geomorphology, vegetation was derived from secondary sources or in an indirect manner using bathymetry data. The wave model results were analyzed for the nearshore wave heights and distribution of energy density at the fore reef and inside the lagoon. The wave model results revealed that the extensive barrier reef on the seaward side of the Al Wajh Bank is able to completely prevent and dissipate the energy of the offshore waves providing ideal conditions for turtle nesting at seaside islands. Further, the results show that the offshore waves do not have any role in the production and dissipation of wave energy inside the lagoon. The study confirmed that the waves inside the lagoon are exclusively local wind-generated waves. The wave climate inside the lagoon during storm conditions is only influenced by the prevailing strong winds and not dependent on the storm-generated extreme waves from offshore. Most of the sea turtle nests have been found on the up wave or windward side of beaches with flat and wide reefs or fringing reefs between the reef crest and the high-water line. The wave model demonstrated the large energy dissipation rates under these geo-morphological conditions. In the absence of relevant data on sediments for the nesting and non-nesting beaches, no specific conclusions could be drawn. The beach slopes where sea turtle nests were abundant had slopes in the range of 1:10 and 1:20. The study indicated that the non-nesting sites inside the lagoon are in the sheltered zones of inner reef shelfs and behind the islands where mudflats with mangroves and other vegetation are abundant. The wave runup is estimated using the HyCReWW metamodel at the nesting and non-nesting sites. The metamodel estimates indicated that the wave runup is of comparable magnitude both for the nesting and non-nesting sites. The comparison of runup distance along the beach indicated that the turtle nesting sites are located sufficiently away from the runup computed for 1 in 100-year wave height A satellite imagery-based global shoreline data source (Shoreline Monitor, Deltares/TUDelft) was used to examine the erosion and accretion trends at Al Wajh Bank. The data identified two of the islands as having chronic erosion which have a small percentage of nesting sites. The analysis of the data identified that the turtle nesting sites are located on beaches with stable erosion/accretion rates. The study confirmed the limitations of the Delft3D WAVE (SWAN) model used in simulating IG waves. Due to the very large domain and the course grid adopted in the present study the wave setup aspects have not been covered. The present study was useful in providing a range of information and guidance for further hydrodynamic and geomorphological data inputs at a regional scale and application of models like X-Beach or SWASH for a more comprehensive and detailed analysis on the influence of the surf and swash zone dynamics including IG waves on the sea turtle nesting sites. A systematic data acquisition on geomorphological and sediment characteristics will be useful for logistic regression analysis to study the dominant factors influencing the turtle nesting and non-nesting sites.

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

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

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