Tidal mudflats and salt marshes adapt to sea level rise (SLR) through vertical growth and inland retreat. However, in areas of coastal squeeze where retreat is constrained, SLR may exceed vertical accretion due to limited sediment supply or unfavourable hydrodynamics. In Northern Europe, brushwood groyne fields, historically used for land reclamation, have been constructed to enhance sedimentation and foreshore resilience. These structures, comprising rows of wooden posts with compacted brushwood bundles, create hydrodynamically quiescent zones that facilitate suspended sediment deposition and reduce sediment remobilization by dampening waves and currents. Scholarly and practical interest in optimizing brushwood groyne arrangements continues to rise in the framework of nature-based solutions. Their biodegradable material supports ecological integration but limits structural lifespan and alters flow resistance over time. Thus, a detailed assessment of their flow-retentive properties, accounting for deterioration, is essential for optimization. This study addresses the knowledge gap regarding the evolution of flow resistance in European brushwood groynes. The study assesses current- and wave-structure interactions together with a quantification of material parameters for the confined brushwood along the vertical structure using two laboratory campaigns. Parametrizations are provided through Darcy–Forchheimer and wave transformation coefficients. A Forchheimer coefficient of β = 1 . 44 was found to best represent a range of groynes with varying porosities and compositions. Parametrized hyperbolic fits described both wave reflection and transmission behaviour. All parametrizations enable improved design and longevity of brushwood groynes, enhancing their ability to support mudflat and salt marsh resilience, thereby preserving both their ecological and coastal protection functions. • Investigation of the flow retention of brushwood groynes for waves and currents. • Darcy–Forchheimer coefficients are derived from the current structure interaction. • Wave transformation coefficients are derived from the wave structure interaction. • Flow retention is correlated with the material parameters of the constructed groynes. • First-time provision of bulk and vertical varying brushwood material parameters.

Nonlinear surface waves interacting with rippled seabed can significantly influence sediment transport processes, particularly in coastal environments where the shoreline is covered with bedforms. In such conditions, the transport dynamics of plastic debris become complex, leading to distinctive displacement patterns that are crucial for understanding coastal morphodynamics and environmental impacts. The interaction between waves and bottom ripples increases turbulence, promotes the saltation or suspension of plastic particles and generates ripple vortices, which influence the flow structure within a layer exceeding twice the height of the bottom ripples. In this study, a broad range of regular surface wave cases was analyzed through new experiments conducted in a wave flume with fixed rough beds. The presence of nonlinear surface waves was identified through spectral analysis. Some cases revealed a significant second harmonic in the wave signal, whose intensity was amplified in some tests when passing over the rippled seabed. This nonlinear behavior indicates strong interactions between surface waves and the seabed, which enhance sediment transport mechanisms and, under certain tested wave conditions, lead to a reversal in sediment transport direction. The analysis of nonlinear waves – whose components can significantly influence sediment transport dynamics by modifying orbital velocities near the seabed – helps explain the observed patterns of plastic debris transport during the experimental campaign. Furthermore, spectral analysis suggests that in cases where the transport direction reverses, there is a strong presence of nonlinear surface waves. In some instances, higher harmonic amplitudes are enhanced, resulting in a second harmonic that surpasses the fundamental frequency. • Denser pellets move offshore in velocity-skewed, onshore in accel-skewed waves • Second harmonic in L/h > 10.9 waves linked to onshore transport of pellets • Pellet transport reverses direction when L/h goes above 10.9 • Rippled beds reduce velocity skewness but increase acceleration asymmetry • Full time series Sk and As better describe wave nonlinearity

When collecting coastal monitoring data, it is common practice to survey down to spring low tide to capture the maximum extent of the exposed subaerial beach. However, collecting topographic beach data is possible for only a few days per month. By reducing the seaward extent of the survey, the incurred costs and risks to the survey schedule could be greatly reduced. However, this would result in information loss at the lowest extremes of the subaerial beach. This study assesses the feasibility of predicting this part of the beach using deep learning neural networks based on partial beach profile data. A range of network architectures were tested alongside linear extrapolation, which was used as a baseline model. Each model was tested on three beaches with varying morphology, ranging from steep (reflective) to mildly sloping (dissipative). The presence of morphological features was found to play a dominant role in the accuracy of the predicted profiles; profiles with more pronounced cross-shore morphological features, such as sandbars, produced the highest error. While local connectivity of each network architecture was found to be the key factor in producing realistic profiles, the 1D Convolutional Neural Network was found to be the most effective with an average RMSE of between 0.026-0.119 m. This RMSE is not substantially larger than the vertical accuracy of current survey techniques (0.03 m), and the study found that errors of this magnitude have negligible effects when the survey data is used to calculate beach volumes and conduct numerical wave runup analysis to assess coastal flood risk. • Neural networks can reconstruct lower beach profile extents from partial data. • 1D CNN achieved a comparable accuracy to standard survey methods. • Enables optimisation of monitoring without compromising coastal hazard assessments.