Tracking coastal sediments can provide useful information about coastal dynamics, thereby helping coastal management. However, the highly dynamic conditions of the coasts makes analyzing the trajectories of a huge number of particles challenging. To solve this limitation, the framework of coastal sediment connectivity is designed. In this framework, recent advances in graph theory are used to quantify coastal systems as complex networks. In this context, sediment sinks/sources and pathways represent the graphical nodes and links, respectively. In this work, we take the first step to evaluate the ability of this newly-developed framework in quantifying the basic processes on a sandy beach. Firstly, we used Delft3D to obtain the velocity field and bed-level changes. Then, the Eulerian results were fed into SedTRAILS to simulate the sediment pathways. We show that the current version of the model can correctly calculate the basic metrics of the sediment-connectivity network (e.g., network link strength which is a proxy for sediment fluxes). More specifically, we show that this framework is capable of exploring the initiation of the rip channel formation.

Sea turtles are key species in many coastal ecosystems worldwide, particularly coral reef and seagrass habitats. Yet, six of seven species are endangered. Their nests, which incubate in beach sand and rely on specific climatic conditions for egg viability, face significant threats from inundation, for example through wave runup. This paper examines a method to rapidly predict wave runup in low-data coral reef environments, and the implications thereof on the inundation of sea turtle nests. The study uses two metamodels, BEWARE-2 and HyCReWW, to predict wave runup at Ras Baridi, Saudi Arabia, a key nesting site of the Red Sea green turtle population. The models were used to analyze runup events and inundation durations and provide a first estimate of a safe nesting elevation. Despite data limitations, the study provides valuable insights for coastal managers to protect sea turtle nests, suggesting that a 5-year return period runup elevation could serve as a threshold for nest relocation. However, the findings also highlight the importance of more accurate hydrodynamic predictions and the need for in-situ data to validate models and improve conservation strategies.

Accurate prediction of Wave-Group-Forced (WGF) InfraGravity (IG) waves depends on resolving the corresponding phase shift, typically achieved through a coupled phase – amplitude equation. However, this approach requires a grid resolution that resolves the structure of the wave groups making it computationally expensive at regional scales. To address this limitation, an existing local expression for the phase shift of normally incident WGF-IG waves has been extended to account for directional seas. The extended formulation is verified against predictions from the coupled phase – amplitude model using bichromatic wave forcing over a uniformly sloping beach for a wide range of sea-swell conditions. Results show that the local approach performs well in the off-resonant region for obliquely incident waves. When applied outside this regime, however, its accuracy decreases, with performance varying depending on sea-swell and bathymetric conditions. The coupled and local phase shift approaches are also validated with observations obtained during the Coast3D field experiment. The total, incoming and outgoing IG waves are predicted with comparable skill and root mean square error for both methods. The good match using the local expression is attributed to the fact that the conditions during Coast3D correspond to directionally broad sea-swell spectra with relative short peak periods propagating over moderately sloping bathymetry for which the verification showed significant skill. Additional validation with field observations at other locations are necessary to firmly determine the limitations of the use of a local phase shift.

Hybrid dune-dike structures are innovative developments creating coastal defense systems which are more conveniently integrated with the natural environment. In this study, a numerical study was conducted to investigate the temporal evolution of wave overtopping, with the changing profile of the dune under extreme storm conditions with a constant water level, of two types of hybrid dune-dike structures in Katwijk (dike-in-dune type) and Raversijde (dune-in-front-of-dike type). XBeach 1DH was used to firstly calculate bed profiles for different time steps during a 10-h storm duration using the Surfbeat mode and then, in a second step, mean wave overtopping rates were modelled for each calculated bed profile using the Non-hydrostatic mode. According to the simulation results, most of the dune erosion occurs during the first two hours of the storm, and then continues at a slower rate as the sand deposits in front of the dune. Once the hybrid structure is eroding (so for t > 0), the significant wave height at the dike toe and the mean overtopping discharge increase in time for both Katwijk and Raversijde, although it quickly reaches a plateau for Raversijde. The first simulations with the original non-eroded profiles deviate from this trend. The reason for this deviation needs to be further investigated.

Wave runup observations are important for coastal management providing data to validate predictive models of inundation frequencies and erosion rates, which are vital for assessing the vulnerability of coastal ecosystems and infrastructure. Automated algorithms to extract the instantaneous water line from video imagery struggle under dissipative conditions, where the presence of a seepage face and the lack of contrast between the sand and the swash impede proper extraction, requiring time-intensive data quality control or manual digitization. This study introduces two novel methods, based on color contrast (CC) and machine learning (ML). The CC method combines texture roughness — local entropy — with saturation. Images are first binarized using entropy values and then refined through noise reduction by binarization of the saturation channel. The ML method uses a convolutional neural network (CNN) informed by five channels: the grayscale intensity and its time gradient, the saturation channel, and the entropy and its time gradient. Both methods were validated against nine manually labeled, 80 min video time series. The CC method demonstrated strong agreement with manually digitized water lines (RMSE = 0.12 m, r=0.94 for the vertical runup time series; RMSE = 0.08 m, r=0.97 for the 2% runup exceedance (R_2%); and RMSE = 3.88 s, r=0.70 for the mean period (T_m−1,0)). The ML model compared well with the manually labeled time series (RMSE = 0.10 m, r=0.96 for the vertical runup time series; RMSE = 0.09 m, r=0.97 for R_2%; and RMSE = 3.51 s, r=0.79 for T_m−1,0). Furthermore, the computed R_2% values of both methods show a good agreement with the formula proposed by Stockdon et al. (2006) for extremely dissipative conditions, with RMSE-values lower than 0.13 m and correlations exceeding 0.70 for manual, CC, and ML estimates. While the CC method is deemed applicable for wave-by-wave analysis under similar dissipative conditions with a smooth seepage face and sufficient turbulent swash, the ML method still struggles with new, unseen data. However, it shows promise for a broader application and serves as a viable proof of concept. Together, these methods reduce the need for manual processing and enhance real-time coastal monitoring, contributing to more accurate predictive modeling of runup events and a better understanding of nearshore processes.

This study examines the importance of free infragravity (FIG) waves in the North Sea using a recent collection of wave measurements and a newly developed unstructured SWAN model. The measurements include new observations of infragravity waves at offshore (30–40 m water depth) and nearshore (10–20 m water depth) locations in the southern North Sea. These observations serve as the basis for model optimization and verification. Good agreement is obtained between model predictions and measurements during two recent storm periods, including severe storms with unusual wind directions and high wind speeds (e.g., “Storm Babet”). Model investigation along the coasts of Belgium and the Netherlands demonstrated a strong dependence between nearshore FIG conditions (i.e., energy intensity and sources) and storm characteristics (i.e., alongshore wind pattern and storm track). Specifically, several storms have demonstrated significant contributions of FIG energy originating from remote sources (e.g., the coasts of UK and Denmark). This suggests that nearshore FIG conditions in the North Sea cannot be determined based on the local sea-swell conditions alone and may be significantly underestimated if non-local contributions are ignored. Finally, modelled and measured results at nearshore locations along the Dutch coast revealed that under storm conditions FIG energy can be an order of magnitude higher than energy due to bound infragravity (BIG) waves. This result, augmented with estimated ratios of free and forced infragravity energy at the shoreline, emphasizes the necessity of considering the FIG waves as an integral part of coastal safety assessments along the coasts of the North Sea.

Climate change and human activity pose increasing challenges to endangered sea turtles, which are key species in many marine ecosystems worldwide. Among these challenges are the flooding and erosion of nesting beaches. In this perspective, we argue that existing methods and tools from coastal science and management hold significant, yet underused, potential for sea turtle conservation. We introduce a stepwise framework for integrating sea turtle ecology and coastal management to address these coastal threats. The framework follows an Observe–Understand–Predict–Intervene cycle and links ecological thresholds, coastal processes, and management interventions across scales, from Regional Management Units (RMUs) to individual beaches. We illustrate how state-of-the-art monitoring, modeling, and nature-based solutions (NBS) can be embedded within this framework to inform when and how to intervene. Increased in-situ data collection and interdisciplinary collaboration will be critical to apply and refine this approach, thereby enhancing the long-term resilience of nesting habitats.

High-resolution wave measurements at intermediate water depth are required to improve coastal impact modeling. Specifically, such data sets are desired to calibrate and validate models, and broaden the insight on the boundary conditions that force models. Here, we present a wave data set collected in the North Sea at three stations in intermediate water depth (6–14 m) during the 2021/2022 storm season as part of the RealDune/REFLEX experiments. Continuous measurements of synchronized surface elevation, velocity and pressure were recorded at 2–4 Hz by Acoustic Doppler Profilers and an Acoustic Doppler Velocimeter for a 5-month duration. Time series were quality-controlled, directional-frequency energy spectra were calculated and common bulk parameters were derived. Measured wave conditions vary from calm to energetic with 0.1–5.0 m sea-swell wave height, 5–16 s mean wave period and W-NNW direction. Nine storms, i.e., wave height beyond 2.5 m for at least six hours, were recorded including the triple storms Dudley, Eunice and Franklin. This unique data set can be used to investigate wave transformation, wave nonlinearity and wave directionality for higher and lower frequencies (e.g., sea-swell and infragravity waves) to compare with theoretical and empirical descriptions. Furthermore, the data can serve to force, calibrate and validate models during storm conditions. Dataset: https://doi.org/10.4121/233f11ff-7804-4777-8b32-92c4606e56d8 Dataset License: CC-BY 4.0.

Spectral information of coastal waves and the associated statistical parameters (e.g., the significant wave height and mean wave period) over large spatial scales is essential for many applications (e.g., coastal safety assessments, coastal management and developments, etc.). This demand explains the necessity for accurate yet effective models. A well-known efficient modelling approach is the quadratic approach (often referred to as frequency-domain models, weakly nonlinear mild-slope models, amplitude models, etc.). The efficiency of this approach is achieved through modelling reduction of the original governing equations (e.g., Euler equations). Most significantly, wave nonlinearity is described solely by a single quadratic mode-coupling term. Therefore, doubts arise with regard to the predictive capabilities of the quadratic approach to reliably describe the nonlinear development of waves in the coastal environment where nonlinearity is typically significant. This study attempts to push the limit of the prediction capabilities of nonlinear coastal waves based on the quadratic approach. To this end, an optimization process is proposed, striving to extract the quadratic formulation which describes most adequately nonlinear wave developments over water depths and bathymetrical structures which characterize the coastal environment. The outcome is the model QuadWave1D: a fully dispersive quadratic model for coastal wave prediction in one-dimension. Based on a wide set of examples (including monochromatic, bichromatic and irregular wave conditions) and comparing to other representative quadratic formulations, it is found that QuadWave1D presents superior predictive capabilities of both the sea-swell components and the infragravity field.

Wave nonlinearity plays an important role in cross-shore beach morphodynamics and is often parameterized in engineering-type morphodynamic models through a nonlinear relationship with the Ursell number. It is not evident that the relationship established in previous studies also holds for sheltered sites with fetch-limited seas as they are more prone to effects of local winds and currents, the waves are generally steeper, and the beaches are typically reflective. This study investigates near-bed orbital velocity nonlinearity from wave records collected at two sheltered beaches in The Netherlands and contrasts them to earlier observations made along the exposed, wave-dominated North Sea coast. Our observations at sheltered beaches show that the Ursell number has comparable skill in predicting wave nonlinearity as it has on previously studied exposed coasts. However, the orbital velocities at sheltered coasts are more asymmetric for the same Ursell number than on exposed coasts. When exposed coast data were examined for moments with comparable high-steepness waves, a similar effect on asymmetry was observed. In addition, following and opposing winds were found to have a clear relationship with total nonlinearity, while they did not affect the phase between skewness and asymmetry at the sheltered beaches. Refitting the free parameters of an Ursell-based predictor improved the bias for the asymmetry parameterization. Whether this has implications for modeling of the magnitude of wave-nonlinearity-driven sediment transport using engineering type models is strongly dependent on the sediment transport formulation used, as these formulations depend on additional calibration coefficients too.

Coastal wave forecasting over large spatial scales is essential for many applications (e.g., coastal safety assessments, coastal management and developments, etc.). This demand explains the necessity for accurate yet effective models. A well-known efficient modelling approach is the quadratic approach (often referred to as frequency-domain models, nonlinear mild-slope models, amplitude models, etc.). The efficiency of this approach stems from a significant modelling reduction of the original governing equations (e.g., Euler equations). Most significantly, the description of wave nonlinearity essentially collapses into a single mode coupling term determined by the quadratic interaction coefficients. As a result, it is expected that the efficiency achieved by the quadratic approach is accompanied by a decrease in prediction accuracy. In order to gain further insight into the predictive capabilities of this modelling approach, this study examines six different quadratic formulations, three of which are of the Boussinesq type and the other three are referred to as fully dispersive. It is found that while the Boussinesq formulations reliably predict the evolution of coastal waves, the predictions by the fully dispersive formulations tend to be affected by false developments of modulational instability. Consequently, the predicted wave fields by the fully dispersive formulations are characterized by unexpectedly strong modulations of the sea-swell part and associated unexpected infragravity response. The impact of the modulational instability on wave prediction based on the quadratic approach is further demonstrated using existing laboratory results of bichromatic and irregular wave conditions.