Beach groundwater and nearshore hydrodynamic data were collected during a field experiment along two dissipative beach transects on Galveston Island, Texas, in the fall of 2023. The monitored beaches serve as nesting habitat for the critically endangered Kemp’s ridley sea turtle. Conditions ranged from calm to stormy, with two storms occurring during the experiment, inundating the entire beach up to the dune toe. Collected hydrodynamic data include readings from pressure loggers submerged in the foreshore and mounted in groundwater wells in the backshore, data from two wave buoys about 1.5 km offshore, and GoPro timestacks of the instantaneous waterline (wave runup). Other collected data include bathymetry and topography surveys, subsurface temperature and moisture content readings, and sediment characteristics. This comprehensive dataset can be used to (1) study relevant beach inundation and groundwater processes, including their effect on the local ecosystem (e.g., repeated flooding of sea turtle nests), (2) study the propagation of nearshore hydrodynamic processes into the beach matrix and groundwater table, and (3) validate existing beach groundwater models.

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.

Temporary permeable structures of bamboo and brushwood have been implemented for mangrove restoration along retreating coastlines worldwide. However, deriving lessons from previous studies is inhibited by their lack of morphodynamic context, with missing bathymetric data or control comparisons. In this paper, we present a low-tech, low-cost, data collection methodology to support morphodynamic system understanding and modeling of mangrove coastlines. This method was applied to monitor a mangrove restoration project featuring temporary permeable structures of bamboo and PVC, installed in late 2021 on the subsiding muddy coast of Demak, Indonesia. Seabed level changes were regularly tracked with bathymetric surveys and monitoring poles across structures and at a nearby control site. Structures were positioned landward of a chenier, at −0.7 m to −0.9 m relative to mean sea level (MSL), and 30–70 m seaward of the mangrove fringe. Measurements from August 2021 to December 2022 revealed seabed erosion (−0.33 m to −0.4 m) seaward of the structures, with mixed responses landward: two sections eroded (−0.04 m to −0.05 m), one remained stable, and a creek-adjacent section eroded by −0.43 m. At the nearby control site, chenier migration and vertical growth promoted landward accretion, though elevations remained below MSL and thus unsuitable for mangrove colonization. The bathymetric and monitoring pole measurements presented in this study constitute valuable datasets for modeling studies aiming to unravel the dominant processes driving morphodynamic changes. Such models could also inform integrated approaches to mangrove restoration in subsiding coastlines, considering sediment supply, subsidence management, and structure integrity.

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.

Beach groundwater dynamics play a critical role in coastal ecosystem functions, particularly in low-lying beach habitats used for nesting by endangered species like sea turtles. Incubating nests are susceptible to prolonged inundation below the groundwater table (GWT), as flooding duration critically affects egg viability. Understanding how oceanic processes and rain drive GWT fluctuations in the nesting area is essential for evaluating nest relocation strategies and designing nature-based solutions that mitigate nest flooding. Here, we analyze how infragravity waves, tides, storm surge, and rainfall drive short-term fluctuations (hourly to weekly) in the beach GWT on Galveston Island, Texas—a dissipative, mild-sloping barrier island system along the northwestern Gulf of Mexico coast. Applying tailored spectral analyses to field observations collected in 2023, we show that surge and rainfall dominate short-term GWT response in the nesting area, while higher-frequency wave and tidal signals are increasingly damped landward. To facilitate this analysis, we classify observed water levels into groundwater, mixed, and submerged regimes based on estimated wave runup. A flooding threshold analysis reveals multiple, prolonged nest inundation events (exceeding 12 h) across the backshore, even for the shallowest nests. This strongly suggests that Galveston Island’s beaches are currently unsuitable for turtle nesting, underscoring the need to continue the ongoing nest relocation program and further research nature-based solutions that enable sea turtle nesting (e.g., turtle-friendly nourishments).

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.

The swash zone is an important region for the coastal morphodynamics. Often, model studies of the swash zone use depth-averaged models. These models typically assume a vertically uniform velocity and sand concentration for calculating the sand transport flux. However, this assumption is not always accurate in the swash zone. In order to investigate the vertical distribution of velocity and sand, we use a depth-resolving model that is able to capture these vertical variations. We simulate the flow and suspended sediment transport induced by bichromatic waves using a 2DV depth-resolving RANS model. Our verification of the model shows that special care needs to be taken to deal with bubbles in 2DV simulations. Furthermore, we show that turning off the (Wilcox, 2006, 2008) limiter for turbulence, increases the modelled turbulent kinetic energy that is induced by wave-breaking, resulting in improved predictions of sediment concentrations. Using the depth-resolving model, we show that the vertical distribution of velocity and sand is far from uniform in the swash zone. The results show that if one assumes vertically uniform depth-averaged velocities and concentrations, one can overpredict the sediment flux by 50%.

In spectral wave models, the nonlinear triad source term accounts for the transfer of energy to the bound higher harmonics. This paper presents an extension to commonly used spectral models that resolves the evolution of the bound wave energy by keeping track of the energy that has been bound by the triad interactions. This extension is referred to as the bound wave evolution (BWE) model. From this, the spatial evolution of the bound wave height is obtained, which serves as a proxy for the nonlinear wave shape. The accuracy of these bound wave heights, and thus wave shape predictions, is highly dependent on the accuracy of the triad source term. Therefore, in this study, the capability of the LTA and SPB triad formulations to capture the growth of the bound wave height is evaluated. For both of these formulations, it is found that slope dependent calibration parameters are required. Overall, despite being computationally more expensive, the SPB method proves to be significantly more accurate in predicting the bound wave evolution. In the shoaling zone, where the bound wave energy is dominated by triads, the BWE model is well capable of predicting the nonlinear wave’s shape. In the surf zone, however, where a combination of triads and wave breaking control the spectral evolution, the BWE model over-predicts the bound wave height. Nevertheless, this paper shows the promising capabilities of spectral models to predict the nonlinear wave shape.

Infragravity (IG) waves are relatively long waves with typical periods of several tens of seconds to several minutes. The energy at the IG band plays an important role in nearshore areas. For example, IG waves can significantly contribute to dune erosion and sediment transport (e.g., Roelvink et al., 2009), and may excite harbor oscillations (e.g., Bowers, 1977). Furthermore, IG waves may result in destructive inundation events (e.g., Roeber and Bricker, 2015). These documentations of IG waves' impacts emphasise the necessity to account for IG contributions as part of coastal hazard assessments, especially under storm conditions.

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.

A field campaign was carried out at a sheltered sandy beach with the aim of gaining new insights into the driving processes behind sheltered beach morphodynamics. Detailed measurements of the local hydrodynamics, bed-level changes and sediment composition were collected at a man-made beach on the leeside of the barrier island Texel, bordering the Marsdiep basin that is part of the Dutch Wadden Sea. The dataset consists of (1) current, wave and turbidity measurements from a dense cross-shore array and a 3 km alongshore array; (2) sediment composition data from beach surface samples; (3) high-temporal-resolution RTK-GNSS beach profile measurements; (4) a pre-campaign spatially covering topobathy map; and (5) meteorological data. This paper outlines how these measurements were set up and how the data have been processed, stored and can be accessed. The novelty of this dataset lies in the detailed approach to resolve forcing conditions on a sheltered beach, where morphological evolution is governed by a subtle interplay between tidal and wind-driven currents, waves and bed composition, primarily due to the low-energy (near-threshold) forcing. The data are publicly available at 4TU Centre for Research Data at: https://doi.org/10.4121/19c5676c-9cea-49d0-b7a3-7c627e436541 (Van der Lugt et al., 2023).

Nearshore hydro- and morphodynamic data were collected during a field experiment under calm conditions, moderate conditions, and storm conditions with dune erosion in the collision regime. The experiment was conducted on the Sand Engine near Kijkduin, the Netherlands, from October 18, 2021, to January 7, 2022. Two artificial unvegetated dunes were constructed just above the high water line to measure storm erosion and dune impacts from higher water levels and waves. During the experiment, three storms occurred that resulted in significant erosion of both dunes. The collected hydrodynamic data include pressure sensor and velocimeter data along two cross-shore transects. The collected morphodynamic data include bathymetry and topography surveys, optical backscatter sensor data in the inner surf zone, and a continuous cross-shore line-scanning lidar data set of the dune face. This comprehensive data set can be used to (1) study relevant nearshore hydrodynamic and morphodynamic processes that occur during calm conditions, moderate conditions, and storm conditions with dune erosion in the collision regime, and (2) validate existing dune erosion models.

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.

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.

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.

During extreme conditions, the transport of the wave-averaged suspended sediment concentrations in the inner surf zone affects dune erosion. Although large-scale laboratory experiments have provided insight in what drives these sediment concentrations, corresponding field data are lacking. To fill this gap, novel field observations of suspended sediment concentrations are compared to drivers that govern sediment suspension during storm conditions known from literature. A total of 128 time intervals of 20 min are analysed, spread over 10 different high water events with different hydrodynamic conditions. For each time interval, the wave-averaged (i.e. 20 min mean) suspended sediment concentration is computed and compared to three suspension drivers. The studied drivers are (1) bed shear due to near bed velocities that originate from mean currents in combination with wave-induced orbital flow, (2) the horizontal pressure gradients under steep wave fronts that increase the forces on the bed material, and (3) bore-induced turbulence that is generated at the free surface and reaches the bed. The derived bore-induced turbulence generates the greatest correlation with the mean suspended sediment concentrations (r = 0.74, p = 4.47E-23). Samples that deviate from this correlation correspond to time intervals with lower values of derived bore turbulence, less wave energy saturation in the inner surf zone, and stronger mean currents. The correlation with the mean suspended sediment concentrations increases when the shear stress originating from mean currents is used for these time intervals (r = 0.83, p = 1.63E-33). For time intervals during which more energetic conditions persist and the wave energy is saturated in the nearshore, bore turbulence was the dominant mechanism in stirring up sediment. The outcome of this study suggests that, based on the events analysed, dune erosion models may achieve more accurate results if computations of suspended sediment concentrations include a bore-induced turbulence term, or if already included, properly address the relative importance of bore-induced turbulence when compared to bed shearing.

Grain size affects the rates of aeolian sediment transport on beaches. Sediment in coastal environments typically consists of multiple grain-size fractions and exhibits spatiotemporal variations. Still, conceptual and numerical aeolian transport models are simplified and often only include a single fraction that is constant over the model domain. It is unclear to what extent this simplification is valid and if the inclusion of multi-fraction transport and spatial grain-size variations affects aeolian sediment transport simulations and predictions of coastal dune development. This study applies the numerical aeolian sediment transport model AeoLiS to compare single-fraction to multi-fraction approaches for a range of grain-size distributions and spatial grain-size scenarios. The results show that on timescales of days to years, single-fraction simulations with the median grain size, D₅₀, often give similar results to multi-fraction simulations, provided the wind is able to mobilize all fractions within that time frame. On these timescales, vertical variability in grain size has a limited effect on total transport rates, but it does influence the simulation results on minute timescales. Horizontal grain-size variability influences both the total transport rates and the downwind bed grain-size composition. The results provide new insights into the influence of beach sediment composition and spatial variability on total transport rates toward the dunes. The findings of this study can guide the implementation of grain-size variability in numerical aeolian sediment transport models.

Currents can affect the evolution of waves in nearshore regions through altering their wavenumber and amplitude. Including the effect of ambient currents (e.g., tidal and wind-driven) on waves in phase-resolving wave models is not straightforward as it requires appropriate boundary conditions in combination with a large domain size and long simulation duration. In this paper, we extended the non-hydrostatic wave-flow model SWASH with additional terms that account for the influence of a depth-uniform ambient current on the wave dynamics, in which the current field can be taken from an external source (e.g., from observations or a circulation model). We verified the model ability by comparing predictions to results from linear theory, laboratory experiments and a spectral wave model that accounts for wave interference effects. With this extension, the model was able to account for current-induced changes to the wave field (i.e., changes to the wave amplitude, length and direction) due to following and opposing currents, and two classical examples of sheared currents (a jet-like current and vortex ring). Furthermore, the model captured the wave dynamics in the presence of strong opposing currents. This includes reflections of relatively small amplitude waves at the theoretical blocking point, and transmission of breaking waves beyond the theoretical blocking point for larger wave amplitudes. The proposed model extension allows phase-resolving models to more accurately and efficiently simulate the wave dynamics in coastal regions with tidal and/or wind-driven flows.

Weyl rule of association, proposed by Hermann Weyl for quantum mechanics applications (Weyl, 1931), can be used to associate between the dispersion relation of water waves and a non-local pseudo-differential operator. The central result of this study is that this operator correctly approximates the Dirichlet-to-Neumann operator derived for linear waves over a slowly varying bathymetry. This opens the door to a formal use of Weyl's operational calculus, and consequently, allowing straightforward derivations and generalizations of water waves’ models over mild slopes. Specifically, within the framework of linear wave theory, the formulation based on Weyl rule of association provides a generalized mild-slope model which does not impose a limit on the spectral width. Most significantly, the mild-slope formulation based on Weyl rule of association allows to derive a general linear kinetic equation for which the widely used energy balance equation (the central equation of forecasting models such as SWAN and WAVEWATCH) serves as a special case. This result not only provides a formal link between the deterministic description (i.e., Euler equations) and the stochastic description (i.e., the energy balance equation), but also establishes the theoretical foundations for the statistical description of bathymetry-induced wave interferences. Such a statistical description is especially important over coastal waters, where through the interaction with the bathymetry, waves are rapidly scattered and tend to form focal zones and associated interference patterns.

Dune erosion during storm surges can lead to excessive damage to the dune system with devastating floods as a potential consequence. A risk assessment of areas protected by dunes can be facilitated by an understanding and description of the physical processes that take place. Field measurements, knowledge of underlying processes and numerical modelling have developed with time, which enabled a more comprehensive description and new predictive techniques. This review concerns dune erosion in the collision regime, and summarises relevant observations, describes underlying processes and explains existing models predicting dune erosion. Observations of dune erosion consist of field observations, laboratory experiments and manipulative field campaigns. The underlying physical processes that contribute to dune erosion are divided into processes that contribute to sediment transport due to hydrodynamic forcing, which occurs in the surf and swash zone, and sediment transport due to avalanching, which occurs in the swash zone, on the dune face and on the dune crest. The existing dune erosion models that are discussed here contain (empirical) equilibrium profile models and process-based models, which can both be a valuable tool for the risk assessment of storm surges. However, model uncertainties still remain, as specific processes are not yet fully understood and described. Examples are the influences of wave obliquity, sediment grain size, and vegetation on the dune face. By improving our knowledge through research and reducing these uncertainties, we can further improve our predictive models. This could eventually lead to more accurate predictions, more complete risk assessments, and sandy coastlines which are more resilient to excessive dune erosion and possible floods.