Wave runup observations are key data for coastal management, as they help validate predictive models for the inundation frequencies and erosion rates. Efforts to develop automated algorithms that effectively identify the instantaneous water line from video imagery have led to a plurality of methods. However, under dissipative conditions, the presence of a seepage face often hinders proper extraction and requires time-intensive data quality control or manual digitization. In this study, we explore traditional color contrast (CC) preprocessing techniques and develop a novel method that incorporates a measure for texture roughness — specifically, local entropy — alongside saturation. The CC-model showed good agreement with the manually digitized water line (0.12 m root-mean-square-
error (RMSE) and correlation coefficient (r) of 0.94), and with its runup statistics (0.08 m
RMSE, and r of 0.97 for the 2% runup exceedance, R_2%). The timing aspect of the method also
showed good agreement (3.88 s RMSE, and r of 0.70 for T_m−1,0). Concurrently, a convolutional
neural network (CNN) informed by CC-preprocessed images was cross-validated using nine manually labeled video time series, each lasting 1 hour and 30 minutes. The CNN model demonstrated good agreement during cross-validation with manually labeled time series (0.10 m RMSE and r of 0.96 for the full-time series, and 0.09 m RMSE and r of 0.97 for R_2%). The temporal dimension of the CNN estimate was also satisfactory (3.51 s RMSE, and r of 0.79 for Tm_−1,0). The observed R_2% values showed the best agreement with the formula for extremely dissipative conditions from Stockdon et al. (2006), with RMSE-values lower than 0.13 m and r-values that exceeded 0.70 for all three methods. When applied to other datasets, the CNN method occasionally failed to accurately capture the water line due to specific characteristics of the new timestack images. These results validate our ML method as a viable proof of concept and challenge us to enhance its adaptability and accuracy across varied environmental conditions. Despite these limitations, the CNN method can be effectively implemented for long-term runup analysis. Additionally, the CC method is anticipated
to be applicable across similar beaches along the northern Gulf of Mexico for long-term extreme value analysis and wave-by-wave analysis. Both methods demonstrate potential in reducing the time required to extract the instantaneous runup from video imagery under dissipative conditions and enhance real-time monitoring, enabling better predictive modeling of coastal processes.

Galveston Island is crucial for sea turtle nesting but faces erosion and increasingwater levels, limiting nesting opportunities and posing dangers to nests in flood-prone areas. A numerical model was developed to estimate groundwater levels and hydrodynamics at specific locations on two onedimensional transects, located at the Tipsy Turtle and the Sea Wall. The model was used to assess the risk of turtle nest inundation at six fictitious nest locations and compared to a previous method used in a study by Ware et al. (2019), which used the 𝑅2% formulation developed by Stockdon et al. (2006).
The model was validated using data from a two-month field campaign in Galveston, Texas. Two five-day periods were simulated to represent different tidal and wind conditions. The model was also simulated for five different hydraulic conductivities.
In the nearshore, hydraulic conductivity did not influence the results. Although mean surface
elevations were captured well, the infragravity and incident wave heights were exaggerated in most cases, possibly due to wave spreading not being accounted for in 1D models and, therefore, concentrating energy transfer of incident waves to the infragravity waves.
The model demonstrated that small hydraulic conductivities performed better in estimating
groundwater levels in calmer conditions when the mean sea level was considerably lower than
the groundwater level, as in the second period. The RMSE close to the dune foot was 0.075 m for 𝐾 = 0.0001 m/s and 0.4 m for 𝐾 = 0.001 m/s in period 2 close to the dune foot at the Sea Wall. In contrast, the intermediate and higher hydraulic conductivities showed minor errors in more dynamic conditions close to the shoreline in period 1. The RMSE at TT4 (approximately halfway at the Tipsy Turtle) during period 1 was 0.164 m for 𝐾 = 0.001 m/s and 0.239 m for 𝐾 = 0.0001 m/s. In all cases, the best hydraulic conductivity decreased in landward direction.
Not a single hydraulic conductivity performed well in all conditions. Therefore, the results in a
test case assessing turtle nest inundation also varied. In most cases, the model overestimated turtle nest inundation, while the method by Ware et al. (2019) underestimated in all but one case. The run-up estimations of Stockdon et al. (2006) as used by Ware et al. (2019) were sensitive to changes in bed slope, with a steeper slope of 0.05 aligning better with modelled run-up during high tide in period 1 and a flatter slope of 0.01 in period 2. A slope of 0.0316 aligned mainly with the modelled run-up in intermediate conditions in period 1.
This report demonstrated that not one of the cases simulated is reliable enough for assessing turtle nest flooding accurately in all hydrodynamic conditions and is too conservative in most, but not all, cases. Therefore, the current model cannot be used as an alternative yet. Moreover, it concluded that the method using the empirical run-up formulation as in Ware et al. (2019) should also be used cautiously due to consistent underestimations in this experiment.

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 (\textit{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.

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.