Atoll island nations (such as the Maldives, Marshall Islands, and Tuvalu) are amongst the most critically vulnerable to climate change due to rising sea levels and subsequent flooding and overwash events. To investigate the wave and water level dynamics on a coral atoll, a six-month dataset consisting of three pressure gauges was collected at three distinct sites on the island of Dhigelaabadhoo in the Republic of the Maldives along with a tide gauge located in a nearby harbor. The observed setup varied across the sites and was found to correlate with the offshore significant wave height and period, as well as tidal water level. The sites with wider reef platforms and steeper forereef slopes were observed to have higher wave setup at the shore and a slightly stronger linear correlation between setup and offshore conditions. Furthermore, the results indicate the potential influence of incident wave direction on alongshore setup gradients.

Gravel barriers are efficient absorbers of wave energy due to the high permeability of gravel sediments. They are thus highly valued as a coastal defence and have numerous other benefits including biodiversity and recreation. Given the probable increase in storm intensity under climate change, this importance will only increase in the future. However, these systems can themselves be damaged during storm events. To effectively manage and monitor them, it is necessary to understand how they respond to energetic forcing. Thus far, morphological and hydrological measurements of sufficient resolution to resolve individual swashes are rare on gravel environments, especially over a protracted period time. Hence, our current knowledge is lacking.As part of the #gravelbeach project, this study addresses this knowledge gap by investigating swash-level change at different gravel barrier typologies. Here, we focus on Borth, a drift-aligned macrotidal composite barrier in West Wales. A 6 m LiDAR tower was deployed between 28 November 2024 – 25 March 2025. Three-second scans were taken every low tide, while one-to-two hour scans were taken every high tide. The shoreline is extracted every 1 s, from which the total water level and bed profile are calculated. To extract berm location and characteristics, a novel semi-automated approach is developed.During the transition from neap to spring tide, a series of berm construction and destruction events is observed, which is in contrast to prior observations of a gradual translation. At the swash level, four different types of berm response to energetic events were identified. They could flatten, overtop, aggradate vertically, or possess an upper zone of accretion (making the local profile gentler) and a lower zone of erosion (making the local profile steeper). It is also demonstrated that a gravel barrier can change between these types rapidly; in one example, three types are displayed in a single two-hour period. This diversity in barrier slope, both temporally and spatially, is often not taken into account by modelling approaches and highlights the need for continued research into swash-level dynamics at gravel barriers.

Climate change is resulting in global changes to sea level and wave climates, which in many locations significantly increase the probability of erosion, flooding and damage to coastal infrastructure and ecosystems. Therefore, there is a pressing societal need to be able to forecast the morphological evolution of our coastlines over a broad range of timescales, spanning days-to-decades, facilitating more focused, appropriate and cost-effective management interventions and data-informed planning to support the development of coastal environments. A wide range of modelling approaches have been used with varying degrees of success to assess both the detailed morphological evolution and/or simplified indicators of coastal erosion/accretion. This paper presents an overview of these modelling approaches, covering the full range of the complexity spectrum and summarising the advantages and disadvantages of each method. A focus is given to reduced-complexity modelling approaches, including models based on equilibrium concepts, which have emerged as a particularly promising methodology for the prediction of coastal change over multi-decadal timescales. The advantages of stable, computationally-efficient, reduced-complexity models must be balanced against the requirement for good generality and skill in diverse and complex coastal settings. Significant obstacles are also identified, limiting the generic application of models at regional and global scales. Challenges include the accurate long-term prediction of model forcing time-series in a changing climate, and accounting for processes that can largely be ignored in the shorter term but increase in importance in the long term. Further complications include coastal complexities, such as the accurate assessment of the impacts of headland bypassing. Additional complexities include complex structures and geology, mixed grain size, limited sediment supply, sources and sinks. It is concluded that with present computational resources, data availability limitations and process knowledge gaps, reduced-complexity modelling approaches currently offer the most promising solution to modelling shoreline evolution on daily-to-decadal timescales.

Changing wave climates and sea levels are leading to enhanced pressures on coastal communities and infrastructure. Coastal managers require better predictive tools in order to predict current and future coastal evolution and mitigate the potential impacts of coastal erosion and flooding. This contribution utilises a longshore extension and simplification of the Forecasting Coastal Evolution (ForCE; Davidson, 2021) profile model to examine the shoreline evolution of the Collaroy-Narrabeen embayment. The resulting one-line model combines cross-shore and longshore sediment transport processes, and unlike previous one-line models of this kind, both (longshore/cross-shore) terms are derived from the same theoretical arguments. Both model components are founded on equilibrium principles, whereby sediment transport is directly related to the disequilibrium in longshore and cross-shore components of wave energy dissipation.

Increasing pressures on coastal environments induced by sea level rise and coastal squeeze has meant that tracking the morphological evolution of sedimentary coasts from the last known survey, pre-empting storm impacts and forecasting potential beach recovery following extreme events is of substantial and increasing societal importance. Equilibrium models for forecasting coastal evolution have figured prominently in the literature in the past two decades and show a strong potential for fulfilling this societal need. In particular some very skilful shoreline evolution models have been proposed based on equilibrium concepts. These models are stable, simple and permit long-term (O(10) years) predictions of coastal change. However, equilibrium models are typically highly empirical and in many cases do not consider explicitly the impact of dynamic sea level, which is modulated by tides, surge and global sea level rise. Equilibrium-based models of shoreline evolution have shown particular promise, but these models generally do not consider the role of the sub- and supra-tidal morphology on coastal evolution (e.g. the importance of coastal dune systems). This contribution presents a new model for Forecasting Coastal Evolution (ForCE), which addresses these issues. The model algorithm adopts a reduced complexity but fundamentally physics-based approach, whilst maintaining equilibrium principles. Unlike, most prior models, the sub- and supra-tidal areas are represented explicitly in the model, as are sea level variations. Sediment transport is equated directly with the disequilibrium in wave energy dissipation flux, leading to a sediment transport formulation that negates the normal intermediate step of computing surfzone currents, generally required in process models. Two components of sediment transport are considered: The first is forced by the turbulent kinetic energy associated with wave breaking and the second diffusive term, is related to a sea bed-slope disequilibrium. The first component perturbs the equilibrium profile and dominates in the surfzone, whilst that latter component plays an important role in beach recovery. Equations are developed for a depth-averaged, beach profile model, assuming longshore uniformity. These computational efficient and stable equations facilitate long forecasts (>decade) and easy comparisons with a field data at cross-shore transport dominated field sites. At the test field site, the model is capable of reproducing qualitative observations of nearshore sand-bar dynamics and quantitative comparisons with measured coastal state indicators including both the shoreline displacement (r = 0.90, N.M.S.E. = 0.145) and intertidal beach volume (r = 0.87).

In this paper, a new approach to model wave-driven, cross-shore shoreline change incorporating multiple timescales is introduced. As a base, we use the equilibrium shoreline prediction model ShoreFor that accounts for a single timescale only. High-resolution shoreline data collected at three distinctly different study sites is used to train the new data-driven model. In addition to the direct forcing approach used in most models, here two additional terms are introduced: a time-upscaling and a time-downscaling term. The upscaling term accounts for the persistent effect of short-term events, such as storms, on the shoreline position. The downscaling term accounts for the effect of long-term shoreline modulations, caused by, for example, climate variability, on shorter event impacts. The multi-timescale model shows improvement compared to the original ShoreFor model (a normalized mean square error improvement during validation of 18 to 59%) at the three contrasted sandy beaches. Moreover, it gains insight in the various timescales (storms to inter-annual) and reveals their interactions that cause shoreline change. We find that extreme forcing events have a persistent shoreline impact and cause 57–73% of the shoreline variability at the three sites. Moreover, long-term shoreline trends affect short-term forcing event impacts and determine 20–27% of the shoreline variability.

Implementation of multiple shoreline response factors to consider different timescales and their interplay in equilibrium shoreline models.• Extreme forcing events can have a persistent impact on the longer term state of the beach.• The long term shoreline location can modulate extreme event impacts.

Predicting change to shorelines globally presents an increasing challenge as sea level rise (SLR) accelerates. Many shoreline prediction models use the simplistic 'Bruun rule' for dealing with SLR profile translation, in-part due to alternative approaches being too complex and time-consuming to implement. To address this, we introduce ShoreTrans: a simple, rules-based, user-input driven, shoreface translation and sediment budgeting model, that applies the surveyed 2D-profile (not a parameterization), for estimating change to realistic coastlines, resulting from SLR and variations in sediment supply, while accounting for armouring, hard-rock cliffs and outcropping rocks. The tool can be applied to sand, gravel, rock and engineered coasts at a temporal scale of 10-100 years, accounting for shoreline trends as well as variability. The method accounts for: (1) dune encroachment/accretion; (2) barrier rollback; (3) non-erodible layers; (4) seawalls; (5) lower shoreface transport; (6) alongshore rotation; and (7) other sources and sinks. Uncertainty is accounted for using a probabilistic distribution for inputs and Monte Carlo simulations. We provide a first-pass assessment of two macrotidal UK embayments: Perranporth (sandy, dissipative, cross-shore dominant transport) and Start Bay (gravel, reflective, bi-directional alongshore dominant), then use idealised profiles to investigate the relative importance of forcing controls on shoreline recession and beach width. For the dissipative sandy site, the primary modes of coastal change are predicted to be short-term storm erosion and SLR translation while long-term trends may be important but are highly uncertain. For the reflective gravel site, the primary mode is multi-decadal longshore sediment flux, while short-term alongshore rotation and SLR translation are secondary. Relative to the ShoreTrans approach, the Bruun rule under-predicts shoreline recession in front of cliffs, seawalls and for low barriers that rollback, and over-predicts where large erodible dunes are present. ShoreTrans directly addresses change in beach width, with beaches in front of seawalls and cliffs predicted to shrink, such that narrow beaches (<50 m width) may disappear under 1-m SLR. As a standalone tool, ShoreTrans is transferable to many coast types and will provide coastal practitioners with a simple first-pass estimate of how the 2D appearance of a complex profile may change under SLR. A future benefit will be to combine this approach with existing hybrid modelling techniques to augment SLR translation predictions.

With sea level rise accelerating and coastal populations increasing, the requirement of coastal managers and scientists to produce accurate predictions of shoreline change is becoming ever more urgent. Waves are the primary driver of coastal evolution, and much of the interannual variability of the wave conditions in the Northeast Atlantic can be explained by broadscale patterns in atmospheric circulation. Two of the dominant climate indices that capture the wave climate in western Europe's coastal regions are the 'Western Europe Pressure Anomaly' (WEPA) and 'North Atlantic Oscillation' (NAO). This study utilises a shoreline prediction model (ShoreFor) which is forced by synthetic waves to investigate whether forecasts can be improved when the synthetic wave generation algorithm is informed by relevant climate indices. The climate index-informed predictions were tested against a baseline case where no climate indices were considered over eight winter periods at Perranporth, UK. A simple adaption to the synthetic wave-generating process has allowed for monthly climate index values to be considered before producing the 10(3)random waves used to force the model. The results show that improved seasonal predictions of shoreline change are possible if climate indices are known a priori. For NAO, modest gains were made over the uninformed ShoreFor model, with a reduction in average root mean square error (RMSE) of 7% but an unchanged skill score. For WEPA, the gains were more significant, with the average RMSE 12% lower and skill score 5% higher. Highlighted is the importance of selecting an appropriate index for the site location. This work suggests that better forecasts of shoreline change could be gained from consideration of a priori knowledge of climatic indices in the generation of synthetic waves.

Infragravity waves (frequency = 0.005 - 0.05 Hz) play a key role in coastal storm impacts such as flooding and beach/ dune erosion. They are known to dominate the inner surf zone on low-sloping sandy beaches during storms. However, in large wave conditions, their importance on different beach types, of variable swell and wind-waves dominance, is largely unknown. Here, a new dataset is presented comprising in-situ observations during storm wave conditions (significant wave height of 3.3 m, peak periods of 18 s and return periods up to 1 in 60 years) from two contrasting sites: a low-sloping sandy beach and a steep gravel beach. Wave measurements were collected seaward of the breakpoint by wave buoys and bed-mounted acoustic Doppler current profilers, and through the surf zone using arrays of pressure transducers. Wave spectra showed contrasting evolution from the shoaling zone to the inner surf zone at the two sites. At the sandy beach, gravity band energy dissipated gradually as depth reduced, while infragravity band energy simultaneously increased, resulting in strongly infragravity-dominated wave spectra in the inner surf zone. At the steep gravel site, a rapid drop in short wave energy was observed, with limited growth of infragravity energy so that inner surf zone spectra showed a low energy peak in the infragravity band. The normalized bed slope parameter indicated whether infragravity waves were generated by bound long wave release or breakpoint forcing, showing that the former (latter) was dominant on the sandy (gravel) beach. In spite of these differences, the shoreline wave spectra under storm wave conditions were infragravity-dominated on both the sandy and gravel beaches.

At the time of initial submission to EarthArXiv Preprints (4th June 2020), this manuscript had been submitted to the JOURNAL OF COASTAL ENGINEERING and was undergoing peer review. Subsequent versions may have different content depending on the outcome of the review process. If accepted, the final version of the manuscript will be available via the ‘Peer-reviewed Publication DOI’ on the right-hand side of this webpage. Please free to contact the corresponding author with any feedback:

Predicting changes to global shorelines presents a challenge that will become increasingly urgent over coming years as sea-level rise (SLR) accelerates. Current shoreline prediction models typically estimate the impact of SLR using variations of the ‘Bruun Rule’, which fails to account for many relevant processes, potentially producing erroneous results. To address this shortcoming, we introduce a simple rule-based model that predicts change across a wide variety of sand, gravel, rock and engineered (anthropogenic) coastal environments, at the scale of years to centuries, accounting for trend rates of change as well as natural short-term variability. Applying recent findings of laboratory and field-based research, the model translates 2D cross-sections of the shoreface, then integrates these changes across multiple alongshore profiles (into pseudo-3D). Uncertainty is accounted for using a probability distribution for inputs (e.g., rate of SLR, depth of closure, depth to bedrock). The model accounts for: (1) dune erosion and slumping [for large dunes]; (2) barrier rollback and overwash [for low barriers]; (3) aeolian dune accretion; (4) non-erodible bedrock layers, including those below ‘perched’ dunes; (5) seawall and revetment backed profiles; (6) onshore transport from the lower shoreface; (7) cross-shore variability due to storm erosion; (8) alongshore variability due to beach rotation; (9) alongshore re-distribution of dune erosion across the shoreface of a closed embayment; and (10) other sources and sinks (e.g., estuary infill, longshore flux, headland bypassing, biogenic production). We apply the model to two extensively monitored macrotidal embayments in the UK: Perranporth (sandy, dissipative, cross-shore dominant transport) and Start Bay (gravel, reflective, bi-directional alongshore dominant). For the dissipative sandy site, the primary modes of coastal change are predicted to be: (1) sea-level rise profile translation; and (2) extreme event cross-shore fluctuations. By contrast, for the reflective gravel site, the primary modes are: (1) short-term fluctuations in alongshore rotation; and (2) multi-decadal trends in longshore flux. For the steep gravel barrier, sea-level rise profile translation is important but secondary. Relative to the new model, the Bruun Rule underpredicts shoreline recession in front of cliffs and seawalls, and overpredicts where large erodible dunes are present. This new shoreface translation model is easily transferable to many coastal environments and will provide a useful tool for coastal practitioners to make rapid assessments of future coastal change.

Beaches around the world continuously adjust to daily and seasonal changes in wave and tide conditions, which are themselves changing over longer time-scales. Different approaches to predict multi-year shoreline evolution have been implemented; however, robust and reliable predictions of shoreline evolution are still problematic even in short-term scenarios (shorter than decadal). Here we show results of a modelling competition, where 19 numerical models (a mix of established shoreline models and machine learning techniques) were tested using data collected for Tairua beach, New Zealand with 18 years of daily averaged alongshore shoreline position and beach rotation (orientation) data obtained from a camera system. In general, traditional shoreline models and machine learning techniques were able to reproduce shoreline changes during the calibration period (1999–2014) for normal conditions but some of the model struggled to predict extreme and fast oscillations. During the forecast period (unseen data, 2014–2017), both approaches showed a decrease in models’ capability to predict the shoreline position. This was more evident for some of the machine learning algorithms. A model ensemble performed better than individual models and enables assessment of uncertainties in model architecture. Research-coordinated approaches (e.g., modelling competitions) can fuel advances in predictive capabilities and provide a forum for the discussion about the advantages/disadvantages of available models.

Accurate forecasts of coastal erosion are essential for the effective management (operation and protection) of critical infrastructure such as gas terminals and shallow-buried nearshore pipelines, preventing the costly losses of production associated with storm damage or exposure. Traditionally, these predictions were the preserve of computationally-expensive, morphodynamic simulations of the three-dimensional structure of the beach surface, however recent developments in reduced-complexity ‘equilibrium’ models have been shown to skilfully hindcast coastal change in cross-shore and long-shore transport dominated environments more accurately, over much longer time-scales. The simplicity and stability of these models – expressed as a function of the incident wave power and the relative equilibrium in dimensionless fall velocity – make them particularly appropriate for assessing the current ‘health’ of the coastline in actionable terms, while unlocking their potential use in forecast mode. Here, we present such a system, forced by data from the Met Office Wave Ensemble Prediction System, capable of providing real-time probabilistic forecasts of important coastal indices (e.g. beach volume and shoreline position) out to seven days ahead. The system is calibrated using an extended Kalman Filter and becomes more accurate over time as it assimilates more observational measurements. Once calibrated, tests on unseen data from the University of Plymouth coastal monitoring station at Perranporth, UK, during Winter 2017/18 confirm it can accurately predict the impact of an extreme storm sequence on coastal erosion and subsequent recovery. This promises the potential for a new coastal management tool, able to be applied to other vulnerable locations.

Coastal evolution occurs on a wide range of time-scales, from storms, seasonal and inter-annual time-scales to longer-term adaptation to changing environmental conditions. Measuring campaigns typically either measure morphological evolution on a short-time scale (days) with high frequency (hourly) or long-time scales (years) but intermittently (monthly). This leaves an important observational gap that limits morphological variability assessments. Traditional echo sounding measurements on this long time-scale and high-frequency sampling require a significant financial injection. Shore-based video systems with high spatiotemporal resolution can bridge this gap. For the first time, hourly Kalman filtered video-derived bathymetries covering 1.5 years of morphological evolution with an hourly resolution obtained at Porhtowan, UK are presented. Here, the long-term hourly dataset is used and aims to show its added value for, and provide an in-depth, morphological analyses with unprecedented temporal resolution. The time-frame includes calm and extreme (storm) wave conditions in a macro-tidal environment. The video-derived bathymetries allow hourly beach state classification while before this was not possible due to the dependence on foam patterns of wave breaking (e.g., saturation during storms). The study period covers extreme storm erosion during the most energetic winter season in 60 years (2013–2014). Recovery of the beach takes place on several time-scales: (1) an immediate initial recovery after the storm season (first 2 months), (2) limited recovery during low energetic summer conditions and (3) accelerated recovery as the wave conditions picked up in the subsequent fall—under wave conditions that are typically erosive. The video-derived bathymetries are shown to be effective in determining bar-positions, outer-bar three-dimensionality and volume analyses with an unprecedented hourly temporal resolution.

The storm sequence of the 2013–14 winter left many beaches along the Atlantic coast of Europe in their most eroded state for decades. Understanding how beaches recover from such extreme events is essential for coastal managers, especially in light of potential regional increases in storminess due to climate change. Here we analyse a unique dataset of decadal beach morphological changes along the west coast of Europe to investigate the post‐2013–14 winter recovery. We show that the recovery signature is site specific and multi‐annual, with one studied beach fully recovered after 2 years, and the others only partially recovered after 4 years. During the recovery phase, winter waves primarily control the timescales of beach recovery, as energetic winter conditions stall the recovery process whereas moderate winter conditions accelerate it. This inter‐annual variability is well correlated with climate indices. On exposed beaches, an equilibrium model showed significant skill in reproducing the post‐storm recovery and thus can be used to investigate the recovery process in more detail. © 2018 John Wiley & Sons, Ltd.

Coastal Sediments 2019, pp. 1385-1399 (2019) No AccessOPERATIONAL FORECASTING OF COASTAL RESILIENCEMARK DAVIDSON, EDWARD STEELE, and ANDREW SAULTERMARK DAVIDSONSchool of Biology and Marine Science, Plymouth University, Plymouth, Devon, PL4 8AA, UK, EDWARD STEELEMet Office, FitzRoy Road Exeter, Devon, EX1 3PB, UK, and ANDREW SAULTERMet Office, FitzRoy Road Exeter, Devon, EX1 3PB, UKhttps://doi.org/10.1142/9789811204487_0121Cited by:2 PreviousNext AboutSectionsPDF/EPUB ToolsAdd to favoritesDownload CitationsTrack CitationsRecommend to Library ShareShare onFacebookTwitterLinked InRedditEmail Abstract: Mobile sediments form the first line of coastal defense against inundation and damage to infrastructure along approximately 30% of the world's coastlines. These mobile sediments are responsible for both dissipating wave energy and providing sufficient freeboard to prevent flooding. Thus, the intertidal beach volume (V) is a useful state indicator of the health and resilience of the coastline. This parameter is usually measured directly by surveys. However, these surveys are costly, and even when conducted, are rarely frequent enough to keep adequate track of the current resilience of the coast. The recent emergence of simple and skillful equilibrium models (Davidson et al., 2011, Yates et al., 2011, Davidson et al., 2013, Splinter et al., 2013) now presents an opportunity to predict the evolution of V, bridging the gap between expensive and relatively infrequent surveys by keeping track of the resilience of the coastline and providing the initial conditions for subsequent probabilistic forecasts (Davidson et al., 2018). This contribution describes such an operational forecasting system – where an equilibrium model is forced using wave data provided by the Met Office – capable of both tracking and predicting future beach volume changes on both short (e.g. 7-day) and long (e.g. multi-annual) time-scales. FiguresReferencesRelatedDetailsCited By 2Enhanced Coastal Shoreline Modeling Using an Ensemble Kalman Filter to Include Nonstationarity in Future Wave ClimatesRaimundo Ibaceta, Kristen D. Splinter, Mitchell D. Harley and Ian L. Turner9 November 2020 | Geophysical Research Letters, Vol. 47, No. 22Seasonal Predictions of Shoreline Change, Informed by Climate IndicesDan Hilton, Mark Davidson and Tim Scott17 August 2020 | Journal of Marine Science and Engineering, Vol. 8, No. 8 Coastal Sediments 2019Metrics History PDF download

Shoreline change is affected by a multitude of complex processes operating at various spatiotemporal scales. Comprehensive multi-year simulations of shoreline changes and forecasts are feasible with process-based models. However, these detailed and computationally expensive numerical simulations do not always lead to increased predictive skill in comparison to simpler shoreline models (Davidson et al., 2013). ShoreFor (Davidson et al., 2013) employs the concept of (dis-) equilibrium of shoreline location following Wright and Short (1985). In this research, the current ShoreFor model (Splinter et al., 2014) is used as baseline. ShoreFor seeks for an optimum decay factor that best describes the morphological response of a coastal system to the corresponding hydrodynamic forcing. This parameter is measured in days and effectively controls the shoreline response timescale. Currently, the ShoreFor model provides a single value for φ, representing a single dominant shoreline response timescale. As morphological systems can contain multiple dominant timescale responses, a new approach to multi-timescale shoreline change modelling is proposed. Three video-derived datasets are used to improve the model towards a generally applicable one which incorporates multiple temporal scales: Narrabeen (Australia), Nha Trang (Vietnam) and Grand Popo (Benin). Each dataset has different hydrodynamic- and morphological characteristics. The storm timescale is a dominant mode of shoreline response for Narrabeen, whereas for Nha Trang and Grand Popo the seasonal timescale is the most dominant. Furthermore, all sites are subjected to more modes of shoreline response such that the application of a single memory decay factor will hamper shoreline modelling. The existing model is improved using 3 steps.In the first step, the raw wave- and shoreline signals are filtered to distinguish temporal scales. Then filtered temporal scales in shoreline position are forced with the corresponding scales in the wave signals. For each temporal scale, a distinct memory decay factor φ is found. In the second step, the effect of small temporal scales in wave forcing on larger temporal scales in shoreline position is accounted for. The improved model takes this effect into account using the envelope of the filtered wave signals. The envelope is used to force the model and to calculate shoreline change with the same timescale. In the third and final step, the effect of large temporal scales in shoreline position on smaller scales in shoreline response is accounted for. The efficiency with which waves induce cross-shore sediment transport can be dependent on the large scale shoreline variation. A time varying response factor c is introduced that controls the efficiency with which waves induce cross-shore sediment transport. The dynamic response factor varies over time with the shape of the larger scale shoreline signal: it represents the effect of the large scale shoreline variation on the smaller scale shoreline response.

Observations of the depth integrated and time averaged sediment transport on a mixed sand and gravel (MSG) beach are presented and analysed to examine the performance of a new portable streamer trap. Measurement of the longshore sediment transport rate in the surf zone remains one of the great challenges in coastal engineering and coastal sciences. Sediment traps for sand beaches have proven useful in the past, but are not suitable for MSG beaches. This paper describes a portable depth-integrated streamer trap designed to measure the depth-integrated combined bed load and suspended longshore sediment transport on MSG beaches. The device consists of a polyester sieve cloth mounted into a rectangular holding frame. The stability of the device is achieved by gravity: the combined weight of the device and the operator, who is standing on and down-current of the device. The device has been tested in the field under moderate wave conditions at Minsmere, UK. We show that the observed suspended and bed load sediment transport are proportional to the wave energy flux, as formulated in the standard theoretical model, CSHORE. The data suggest that the empirical efficiency of wave breaking and bed load parameter are several orders of magnitude larger than that previously observed for uniform fine sand values.