The increasing demand for offshore wind energy underscores the need for accurate wind speed estimation to support the design and operation of offshore wind farms. High-Frequency Radar (HFR), a widely used remote sensing technology in oceanographic research, offers promising potential for wind resource assessment, particularly in areas where conventional measurements are limited. This study explores the application of artificial neural networks (ANNs) for offshore wind speed prediction using HFR-derived data, addressing key challenges in model development and training. A key feature of this approach is the use of a decade-long dataset from the Celtic Sea, off the southwest UK coast, incorporating the full Doppler spectrum and sea surface radial velocity. Model performance was assessed over full-year and seasonally segmented four-month periods, with RMSE values ranging from 1.99 to 2.78 m/s and NRMSE values between 12 % and 20 %, demonstrating the feasibility of HFRinformed ANN models for supporting offshore wind applications.

In addition to external hydrodynamic forcing, the behaviour of beach-dune systems is strongly controlled by nearshore sediment availability and coastal transport pathways. Here we integrate new sedimentological observations, shoreface geomorphological analysis, and regional hydrodynamic modelling to improve understanding of the distribution, characteristics, and connectivity of mobile sediments along a macro-tidal, highenergy, sediment-limited embayed coastline in Cornwall, United Kingdom. We first map the spatial extent of mobile sand bodies along a 45-km coastline and assess headland bypassing potential to define coastal embayments. Coast-wide sediment samples (n = 348), spanning dunes to the lower shoreface, were classified using Kmeans clustering of grain-size and mineralogical data to quantify spatial variability in sediment character, refine the identification of closed/constrained embayments, and evaluate existing regional sediment transport modelling. Eight closed coastal cells and four major sediment sinks, associated with large dune systems, were identified, together with a dominant southwest-to-northeast transport pathway. Sediment-starved regions exhibited the highest calcium carbonate contents (45-57%), consistent with long-term sediment supply limitations. These results demonstrate the value of spatially comprehensive sediment resource and geomorphological datasets for constraining sediment pathway modelling and provide a scientific basis for evaluating long-term coastal evolution.

The seasonal feeding aggregations of reef manta rays (Mobula alfredi) in Hanifaru Bay, Maldives, are the largest known of their kind, yet the mechanisms facilitating this phenomenon remain poorly understood. Understanding these drivers is crucial for effective conservation and management, as protecting key foraging habitats requires knowledge of the physical processes influencing prey availability. While previous studies highlight the role of zooplankton in attracting manta, the oceanographic mechanisms responsible for concentrating prey within the bay remain hypothesised but unproven. Specifically, the influence of eddy dynamics, tidal forcing, and regional circulation on zooplankton distribution has not been thoroughly examined. A 2DH hydrodynamic model (Delft3D-FM) was used to simulate ocean circulation patterns and analyse their evolution throughout the tidal cycle. Results reveal that topographic eddies, modulated by tidal flows, create retentive zones within Hanifaru Bay capable of retaining particles. These eddies form midway through the flood tide-aligning with manta aggregation-and dissipate approximately 2 h after high tide, when the manta disperse. The combination of this timing and the ability of eddies to retain particles suggests these features are the dominant mechanism driving feeding aggregations in the bay. By identifying the physical processes that promote prey availability, this study provides critical insight into the environmental conditions underpinning manta foraging dynamics. Given the potential impacts of climate change and coastal development on local hydrodynamics, these results may help inform conservation strategies aimed at preserving key foraging habitats for Mobula alfredi.

Hanifaru Bay, in Baa Atoll, Maldives, is globally recognised for hosting the largest known feeding aggregations of reef manta rays (Mobula alfredi). These aggregations are spatially and temporally discrete, often associated with flood tides, and are thought to result from hydrodynamic processes that elevate zooplankton concentrations. This study investigates the role of flow-topography interactions, specifically around the small headland-like feature in the bay’s inlet (the ‘nodule’), in creating conditions conducive to these feeding events. A Delft3D hydrodynamic model of Baa Atoll was developed to simulate tidal flows within the bay, with high-resolution mesh focusing on Hanifaru Bay. Calibration and validation against in situ observations ensured the model reliably captured tidal dynamics. Results indicate that a retentive eddy forms behind the nodule during the flood tide, consistent with the timing of manta ray feeding events. This closed-core eddy retains zooplankton, allowing for their accumulation within the bay. By contrast, during the ebb tide, flow-through conditions prevent zooplankton aggregation.

Sandy beaches and dunes are vital for protecting coastal communities from erosion and flooding, particularly along high-energy, sediment-limited coastlines. This rugged coastline, defined by rocky headlands and sediment-starved systems, requires a comprehensive understanding of sediment dynamics to maintain its natural defenses. This study examines sediment characteristics, dynamics, and connectivity along a 65-km stretch of coastline in southwest England using a comprehensive dataset comprising ground-truth sediment analysis, high-resolution remote sensing (bathymetric and LiDAR), and hydrodynamic modeling outputs. Four sediment clusters, distinguished by grain size and carbonate content, served as fingerprints to track sediment variability. Remote sensing data were used to create a seabed roughness map, differentiating flat sediment areas from rocky reef-dominated zones. By integrating sediment characteristics, geological features, and regional hydrodynamic models, the study identifies seven sediment cells dominated by a strong eastward-northward sediment transport pathway, with sediment settling downstream on the constraining headlands. These results provide a basis for evaluating the impacts of sediment supply and regional transport pathways on the future evolution of sand beaches and dune systems in sediment-starved embayed regions.

The adsorption of terrestrial dissolved organic carbon (tDOC) onto surfaces of suspended sediment plays a fundamental role in regulating carbon fluxes across the river-estuary-ocean continuum. It is an important process that modulates carbon transport, transformation, and long-term carbon storage, influencing regional and global carbon budgets. However, the role of suspended sediment is frequently neglected in related coastal and estuarine studies due to the complex interplay of physical and biogeochemical processes. To elucidate the relationship between suspended sediment and tDOC and quantify the adsorption process, this study developed a tDOC-adsorption-floc-population model that integrates floc behavior with tDOC adsorption processes. Taking the Changjiang Estuary as an example, the model quantified tDOC removal through adsorption and examined the key mechanisms governing this process. Results indicate that approximately 12.8 ± 1 % of DOC is removed via adsorption when passing through the turbidity maximum zone (TMZ). The dominant mechanism of tDOC adsorption is governed by floc size, with Brownian motion and differential sedimentation alternating as the primary mechanism, whereas fluid shear exerts a relatively minor influence. The adsorption process is spatially aligned with the TMZ, but its influence, driven by the hydrodynamics, can extend into adjacent areas. These findings highlight the need for incorporating suspended sediment dynamics into regional and global carbon cycle models to enhance predictions of carbon transport and transformation in estuarine and coastal systems.

This paper describes a techno-economic model for exploiting the Celtic Sea wind resource through direct production of hydrogen offshore. The model conceives a modular approach with eight 510 MW floating windfarms, each with an electrolyser system and export compressor mounted on a jacket. The model ensures an uninterrupted hydrogen supply to an industrial cluster (16.4 te.H2/h) by incorporating salt cavern hydrogen storage. During periods of no power generation, baseload power is provided from hydrogen fuel cells. The base-case model with a Discount Rate of 6% returned an Levelised Cost of Hydrogen (LCoH) of 7.25 pound per kg of hydrogen in 2023. The LCoH shows strong sensitivity to Discount Rate and electrolyser system efficiency. Electrolyser systems and wind turbine generator floating structures are relatively new technologies not yet deployed at the GW scale, and benefit significantly from learning rates, which have the potential to substantially lower the LCoH.

Laboratory investigations of beach morphology change under wave action are undertaken to gain insight into coastal processes, design coastal structures and validate the predictions of numerical models. For the results of such experiments to be reliable, it is necessary that they are repeatable. The equilibrium beach concept, that beach morphology will evolve to a quasi-static equilibrium shape for a given forcing suggests that experiments should be repeatable to some degree. However, sediment transport in turbulent breaking and broken waves is complex and highly variable and the level of repeatability at different temporal and spatial scales is challenging to measure, as such, previous work has restricted comparisons to small numbers of waves. Here we use the results of two identical, 20-h large-scale wave flume experiments to investigate the repeatability of sediment transport and beach morphology change under waves at timescales down to individual swash events. It is shown that while flow characteristics from identical swash events are very repeatable, the sediment transported can be very different in both magnitude and direction due to differences in turbulence, sediment advection and morphological feedback. Over longer periods containing multiple matching swash events however, the beach responds in a very similar manner, with the level of morphological repeatability increasing with time. The results also demonstrate that gross swash zone sediment transport remains high even as a beach profile approaches quasi-equilibrium, but the proportion of individual swash events that cause large sediment fluxes (>+/- 7.5 kg/event/m) reduces with time. The results of this laboratory study indicate that beach morphology change has a level of determinism over timescales of several minutes and longer, giving confidence in the results from physical modelling studies. However, the large differences in sediment transport from apparently identical swash events questions the value in pursuing numerical predictions of sediment transport at the wave-by-wave timescale unless the reversals in sediment transport between apparently near identical swash events can also be predicted.

For the physical model testing of wave energy converters (WECs) in the wave basin, it is necessary to test the models in a small number of sea states. Previously, the H – T binning method was widely used to determine the sea states that are representative of an ocean area. However, it omitted much useful information such as the wave directionality. In this paper, a novel method, the K-means clustering technique is used in combination with High Frequency (HF) radar measured data from Wave Hub, UK. The results show that K-means clustering method better preserves the characteristics of the ocean area than the binning method. Furthermore, the impact of different regrouping methods on assessing the annual energy output of the model is investigated, by applying the K-means clustering method to a 1:25 two-body hinged raft WEC. It is found that although non-linear performance can be clearly observed in the model both physically and numerically. Due to the fact that most sea states from Wave Hub are out of the non-linearity range of the model, the non-linear effect on the overall performance of the WEC model in this ocean area is limited. It allows the annual energy output to be accurately predicted by using only a small number of representative sea states (defined as K) ≤15, based on K-means clustering method.

Robust and accurate long-term mapping of the shoreline is a fundamental requirement for effective coastal management and policy making.Earth observation (EO) coupled with novel image analysis techniques have demonstrated the potential of EO for long-term, local, regional, and even global scale investigations of shoreline change.However, satellitederived shoreline (SDS) data is associated with large uncertainties relating to environmental factors, tidal range, and wave action among other factors.This contribution investigates the impacts of morphological and hydrodynamic setting on the accuracy of SDS in macrotidal, high-energy coasts.Results revealed significant differences in SDS accuracy between the two morphologically contrasting sites in terms of the level of error and the optimal water level correction.We show that SDS accuracy at macrotidal sites can be greatly improved by applying appropriate water level corrections and that a different approach is required depending on beach type (dissipative/reflective).

The present study aims at providing more data and insights for the hinged type WEC, especially focusing on the two-body hinged raft WECs. Two WECs are considered: a well-studied generic hinged raft WEC (G-HRWEC) and a 1:25 scale designed hinged raft WEC (D-HRWEC). The open-source tool WEC-Sim is employed in numerical studies. Referring to the published numerical data for G-HRWEC, corrections are proposed to the WEC-Sim model which are shown to realise the ability of WEC-Sim to model the two-body hinged WEC. For the 1:25 scale D-HRWEC, the physical results show that the relative hinge motion between the rafts is predominately linear under small waves, but significant nonlinearities like viscosity, submergence and overtopping exist under large oscillations. The updated WEC-Sim model is used to simulate this D-HRWEC and is validated with experiment data, showing good representation of the nonlinear behaviour observed in physical experiments with low computation expense. Annual average power of similar to 83 kW is predicted for the D-HRWEC in full scale at EMEC site, using the validated WEC-Sim model. The power performance of D-HRWEC is shown to be dependent on the wave steepness. The updated WEC-Sim is provided and can be directly used to model any two-body hinged WECs dominated by linear hydrodynamics. For realistic devices, representative nonlinear terms need to be carefully identified via physical data or computational fluid dynamic simulations and then added into the updated WEC-Sim to improve the modelling accuracy. Following the theory and method given in this work, the updated WEC-Sim for two body hinged raft WEC can be re-developed to extend the WEC-Sim application for WECs or platforms with numbers of hinge connections.

Earth observation coupled with novel image analysis techniques now present a unique and powerful tool for the historical study of shoreline change at local to global scale. However, satellite-derived shoreline (SDS) data is limited in certain areas and is associated with large uncertainties relating to environmental factors, tidal range, and wave action. We use 14 years of monthly topographic surveys at two macrotidal sites in the UK representing end members of beach type (reflective, dissipative) to investigate the influence of tidal elevation and wave action on SDS accuracy. We find that applying appropriate water level corrections can significantly improve SDS accuracy. Results show that a different approach is required for water level definition depending on beach type and reveal that ultimately SDS accuracy is primarily controlled by beach state (beach profile shape). Accounting for tidal elevation led to substantial accuracy improvement at both sites and formed the optimal SDS strategy for the reflective site (Slapton). At the dissipative site (Perranporth) considering wave-induced water level fluctuations (wave setup and/or runup), including wave shoaling, was critical for reducing the tidally corrected SDS RMSE by a third and the mean bias by three quarters. An important realization for areas with high cloud cover such as the UK, and/or low satellite coverage, was that critically low image availability restricts temporally the type of phenomenon that can be detected (e.g., seasonal/interannual variability) and may compromise computed long-term trends. Our results suggest that the optimal approach is site-specific and depends on the shoreline translation method used and is therefore different depending on the application. We propose optimal SDS strategies to increase confidence in SDS extraction in meso-macrotidal environments with potentially low satellite useability (i.e., high cloud cover and/or low satellite coverage) depending on the spatial scale of the intended application. Long-term trends derived using this approach can reproduce trends from ground-based surveys and therefore enable more accurate projections of future shoreline position to be made.

The swash zone is a highly dynamic region of the nearshore in terms of both hydro- and sediment dynamics. Previous work has demonstrated that the majority of swash events transport only small amounts of sediment and net beachface volume change over several hours tends to be small. However, a small number of individual swash events can deposit or remove hundreds of kilograms of sediment per metre width of beach. These events are typically associated with swash flows that involve one or more highly turbulent swash-swash interactions, causing enhanced suspension and transport of sediment (Blenkinsopp et al. 2011). The timing and location of these interactions is complex and small changes in either can lead to very different local flow conditions. The complexity of these flows make sediment transport prediction on a swash-by-swash basis very challenging, and raises the question whether deterministic physical and numerical modelling of swash sediment transport is warranted.

Wave tank model testing has been widely used to assess the performance of Wave Energy Converters (WEC) in different technology readiness levels (TRL). At early stage the use of simple wave conditions such as regular waves and parametric wave spectrum JONSWAP or Pierson-Moskowitz spectrum is acceptable. However at later stages there is a need to use site specific complex wave conditions representative of potential prototype deployment sites. In previous research, 10 different regrouping methods on HF radar measured wave spectrum were tested to find out the most representative sea states for tank testing. It has been shown that by using the K-means clustering technique, the characteristics of wave conditions can be well preserved. In order to assess the power capture performance of a typical WEC in these representative sea states, the RM3 point absorber has been simulated. By analysing how well the average power output predicted from different representative sea-state selection methods compares with the total power output prediction, it is shown that the non-directional wave spectrum K-means clustering method provides the most representative sea states and, for a point absorber, with a very accurate estimation of the total power output, which is not the case by using a traditional binning method. The importance of using the complex site-specific sea states rather than simplified parametric JONSWAP sea states to obtain the accurate total power estimation has also been shown.

Many human activities rely on accurate knowledge of the sea surface dynamics. This is especially true during storm events, when wave-current interactions might represent a leading order process of the upper ocean. In this study, we assess and analyze the impact of including three wave-dependent processes in the ocean momentum equation of the Met Office North European Shelf ocean-wave forecasting system on the accuracy of the simulated surface circulation. The analysis is conducted using ocean currents and Stokes drift data produced by different implementations of the coupled forecasting systems to simulate the trajectories of surface (iSphere) and 15 m drogued (SVP) drifters affected by four storms selected from winter 2016. Ocean and wave simulations differ only in the degree of coupling and the skills of the Lagrangian simulations are evaluated by comparing model results against the observed drifter tracks. Results show that, during extreme events, ocean-wave coupling improves the accuracy of the surface dynamics by 4%. Improvements are larger for ocean currents on the shelf (8%) than in the open ocean (4%): this is thought to be due to the synergy between strong tidal currents and more mature decaying waves. We found that the Coriolis-Stokes forcing is the dominant wave-current interaction for both type of drifters; for iSpheres the secondary wave effect is the wave-dependent sea surface roughness while for SVPs the wave-modulated water-side stress is more important. Our results indicate that coupled ocean-wave systems may play a key role for improving the accuracy of particle transport simulations. the

Embayed beaches separated by irregular rocky headlands represent 50% of global shorelines. Quantification of inputs and outflows via headland bypassing is necessary for evaluating long-term coastal change. Bypassing rates are predictable for idealized headland morphologies; however, it remains to test the predictability for realistic morphologies, and to quantify the influence of variable morphology, sediment availability, tides and waves-tide interactions. Here we show that headland bypassing rates can be predicted for wave-dominated conditions, and depend upon headland cross-shore length normalised by surf zone width, headland toe depth and spatial sediment coverage. Numerically modeled bypassing rates are quantified for 29 headlands under variable wave, tide and sediment conditions along 75 km of macrotidal, embayed coast. Bypassing is predominantly wave-driven and nearly ubiquitous under energetic waves. Tidal elevations modulate bypassing rates, with greatest impact at lower wave energies. Tidal currents mainly influence bypassing through wave-current interactions, which can dominate bypassing in median wave conditions. Limited sand availability off the headland apex can reduce bypassing by an order of magnitude. Bypassing rates are minimal when cross-shore length >5 surf zone widths. Headland toe depth is an important secondary control, moderating wave impacts off the headland apex. Parameterisations were tested against modeled bypassing rates, and new terms are proposed to include headland toe depth and sand coverage. Wave-forced bypassing rates are predicted with mean absolute error of a factor 4.6. This work demonstrates wave-dominated headland bypassing is amenable to parameterization and highlights the extent to which headland bypassing occurs with implications for embayed coasts worldwide.

An extensive record of current velocities at all levels in the water column is an indispensable requirement for a tidal resource assessment and is fully necessary for accurate determination of available energy throughout the water column as well as estimating likely energy capture for any particular device. Traditional tidal prediction using the least squares method requires a large number of harmonic parameters calculated from lengthy acoustic Doppler current profiler (ADCP) measurements, while long-term in situ ADCPs have the advantage of measuring the real current but are logistically expensive. This study aims to show how these issues can be overcome with the use of a neural network to predict current velocities throughout the water column, using surface currents measured by a high-frequency radar. Various structured neural networks were trained with the aim of finding the network which could best simulate unseen subsurface current velocities, compared to ADCP data. This study shows that a recurrent neural network, trained by the Bayesian regularisation algorithm, produces current velocities highly correlated with measured values: r2 (0.98), mean absolute error (0.05 ms−1), and the Nash–Sutcliffe efficiency (0.98). The method demonstrates its high prediction ability using only 2 weeks of training data to predict subsurface currents up to 6 months in the future, whilst a constant surface current input is available. The resulting current predictions can be used to calculate flow power, with only a 0.4% mean error. The method is shown to be as accurate as harmonic analysis whilst requiring comparatively few input data and outperforms harmonics by identifying non-celestial influences; however, the model remains site specific.

A Correction to this paper has been published: https://doi.org/10.1038/s41597-021-00874-2.

The EU H2020 MaRINET2 project has a goal to improve the quality, robustness and accuracy of physical modelling and associated testing practices for the offshore renewable energy sector. To support this aim, a round robin scale physical modelling test programme was conducted to deploy a common wave energy converter at four wave basins operated by MaRINET2 partners. Test campaigns were conducted at each facility to a common specification and test matrix, providing the unique opportunity for intercomparison between facilities and working practices. A nonproprietary hinged raft, with a nominal scale of 1:25, was tested under a set of 12 irregular sea states. This allowed for an assessment of power output, hinge angles, mooring loads, and six-degree-of-freedom motions. The key outcome to be concluded from the results is that the facilities performed consistently, with the majority of variation linked to differences in sea state calibration. A variation of 5–10% in mean power was typical and was consistent with the variability observed in the measured significant wave heights. The tank depth (which varied from 2–5 m) showed remarkably little influence on the results, although it is noted that these tests used an aerial mooring system with the geometry unaffected by the tank depth. Similar good agreement was seen in the heave, surge, pitch and hinge angle responses. In order to maintain and improve the consistency across laboratories, we make recommendations on characterising and calibrating the tank environment and stress the importance of the device–facility physical interface (the aerial mooring in this case).

Waves and tidal currents resuspend and transport shelf sediments, influencing sediment distributions and bedform morphology with implications for various disciplines including benthic habitats, marine operations, and marine spatial planning. Shelf-scale assessments of wave-tide-dominance of sand transport tend not to fully include wave-tide interactions, which nonlinearly enhance bed shear stress and apparent roughness, change the current profile, modulate wave forcing, and can dominate net sand transport. Assessment of the contribution of wave-tide interactions to net sand transport requires computationally/labor intensive coupled numerical modeling, making comparison between regions or climate conditions challenging. Using the Northwest European Shelf, we show the dominant forcing mode and potential magnitude of net sand transport is predictable from readily available, uncoupled wave, tide, and morphological data in a computationally efficient manner using a k-Nearest Neighbor algorithm. Shelf areas exhibit different dominant forcing modes for similar wave exceedance conditions, related to differences in depth, grain size, tide range, and wave exposure. Wave-tide interactions dominate across most areas in energetic combined conditions. Meso-macrotidal areas exhibit tide-dominance while shallow, fine-grained, microtidal regions show wave-dominance over a statistically representative year, with wave-tide interactions dominating extensively >30 m depth. Sediment transport mode strongly affects seabed morphology. Sand wave geometry varies significantly between predicted dominance classes with increased wave length and asymmetry, and decreased height, for increasing wave-dominance. This approach efficiently indicates where simple noninteractive wave and tide processes may be sufficient for modeling sediment transport, and enables efficient interregional comparisons and sensitivity testing to changing climate conditions with applications globally.