Sand waves, large scale dynamic bedforms, which are found on sandy, shallow seabeds worldwide, present an immediate risk to offshore structures, raising a pressing need for predicting related bed level dynamics on decadal timescales. Numerical models can help us understand and predict sand wave dynamics, but have shown difficulties with preserving sand wave shapes. Using the process-based Delft3D Flexible Mesh model, we have found that the choice of sediment transport formulation has a significant effect on the stability of sand wave shapes. The widely used Van Rijn (1993) sediment transport formulation predicts relatively high bed load transport rates, thereby raising a need for more dominant slope-induced transport. The simulations revealed that the Van Rijn (2007) formulation, which predicts relatively lower transport rates, and thus allows for lower bed slope-induced transports, is better capable of preserving the steep slopes of sand waves, while limiting sand wave growth. By considering various shape characteristics in our model assessment, more insight is gained about the improvements as well as adverse effects of changes in the parameterization of physical processes. These characteristics show that only with the less dominant bed slope-induced transport the crest levels are stable, while trough levels still lower slowly over time. This indicates that local processes are responsible for limiting the growth of sand waves and the importance of slope-induced transport has been overstated in previous works. With the adapted, non-upscaled set-up, the evolution of sand waves over multiyear timescales is represented well in the model compared to bathymetric field data for two contrasting sand wave field sites.

Coastal regions face increasing pressure from climate change, sea-level rise, and growing coastal populations. This “coastal squeeze” threatens both the systems’ sustainability and their ecosystem services. Coastal changes depend on the distribution of sediment throughout the system, which evolves continuously through complex transport processes. While we can quantify net morphological changes, this alone provides incomplete understanding of coastal evolution as similar morphological states can result from vastly different sediment movement patterns. Coastline perturbations-deviations from straight coastlines ranging from beach cusps to headlands, deltas, and artificial nourishments-exemplify this challenge. Although their diffusive morphological evolution is well understood, we have limited knowledge of the underlying sediment movement patterns driving this change. This study reveals how coastline perturbations alter sediment transport by tracing particles from origin to destination using Lagrangian tracking at the Sand Engine mega-nourishment. Our results demonstrate that perturbations alter both sediment dispersal and accumulation. During initial stages, the longshore dispersal of sediment is strongly restricted by rapid deposition and burial on both sides of the perturbation. A backward-tracing approach reveals that sediment deposition not only originates directly from the protruding part of the coastline, but also from updrift sources. As coastline perturbations diffuse over time, sediment movement patterns gradually converge toward those of an undisturbed coast. At locations with oblique wave incidence this evolution manifests itself with predominant downdrift dispersal and updrift trapping of sediment from adjacent beaches. The successful application of our Lagrangian approach to this multi-year evolution demonstrates the potential of sediment particle tracking for understanding more complex coastal environments. Increased understanding of sediment pathways enhances our ability to predict and communicate coastal response to interventions, supporting more effective management strategies.

Rising sea levels and anthropogenic activities are intensifying pressure on coastal zones. Process-based coastal morphodynamic models are increasingly used to forecast natural and anthropogenic beach morphology changes at various spatio-temporal scales. Such predictions are crucial for the sustainable management of coasts. However, process-based morphodynamic models contain numerous free model parameters, introducing uncertainty in predictions. Systematically exploring the parameter space has remained a challenge due to the high computational demands of these morphodynamic models. Here, for the first time we quantify parameter uncertainty of a state-of-the-art morphodynamic (2DH) coastal area model (Delft3D) by systematically varying key model parameters, utilizing the Dutch national supercomputer: SurfSara. We simulate the initial (14-month) response of the Sand Engine, an innovative mega-nourishment placed along the Holland coast with 1024 strategically chosen parameter sets. The resulting simulations are analysed using Generalised Likelihood Uncertainty Estimation (GLUE) to attain probability distributions of morphological evolution and its sensitivity to parameter settings. The model simulations all show an alongshore redistribution of sediment resembling what is observed. However, even simulations with similar skill reveal substantial differences in predicted morphologies (same order of magnitude as the predictions’ 90% confidence interval). Our findings suggest that identifying a single optimal parameter set for coastal numerical models might be unrealistic, even for well-defined cases like large-scale coastal interventions, and that an ensemble modeling approach that quantifies parameter uncertainty is likely better suited for studies relying on morphodynamic predictions. Furthermore, we find that the magnitude of the uncertainty induced by the free model parameters is comparable to that resulting from year-to-year variations in wave climate, underscoring the importance of including both sources in uncertainty assessments.

Climate change and human activity threaten sea turtle nesting beaches through increased flooding and erosion. Understanding the environmental characteristics that enable nesting can aid to preserve and expand these habitats. While numerous local studies exist, a comprehensive global analysis of environmental influences on the distribution of sea turtle nesting habitats remains largely unexplored. Here, we relate the distribution of global sea turtle nesting to 22 coastal indicators, spanning hydrodynamic, atmospheric, geophysical, habitat, and human processes. Using state-of-the-art global datasets and a novel 50-km-resolution hexagonal coastline grid (Coastgons), we employ machine learning to identify spatially homogeneous patterns in the indicators and correlate these to the occurrence of nesting grounds. Our findings suggest sea surface temperature, tidal range, extreme surges, and proximity to coral and seagrass habitats significantly influence global nesting distribution. Low tidal ranges and low extreme surges appear to be particularly favorable for individual species, likely due to reduced nest flooding. Other indicators, previously reported as influential (e.g., precipitation and wind speed), were not as important in our global-scale analysis. Finally, we identify new, potentially suitable nesting regions for each species. On average, 23 % of global coastal regions between - 39 ^∘ and 48 ^∘ latitude could be suitable for nesting, while only 7 % is currently used by turtles, showing that the realized niche is significantly smaller than the fundamental niche, and that there is potential for sea turtles to expand their nesting habitat. Our results help identify suitable nesting conditions, quantify potential hazards to global nesting habitats, and lay a foundation for nature-based solutions to preserve and potentially expand these habitats.

Accelerated sea level rise prompts the upscaling of nourishment strategies, either through larger individual nourishment volumes or increased frequency of implementation. In such strategies, the nourished sand may lack time to effectively redistribute in the designated timeframe, leading to significant deformation of the profile over multiple nourishment cycles. This study quantifies subsequent effects, focusing on profile steepening, nourishment lifetimes, and the feasibility of operational objectives. We simulated two common nourishment strategies at a Dutch case study location using the cross-shore morphological model Crocodile over a 50-year timespan under sea level rise rates of 2–32 mm/year. The choice of strategy led to a variation of up to 75% in the total amount of sand used. Our results show increasing profile deformation with nourishment volume applied and duration of the nourishment strategy, with sand accumulating in the nourished section and little dissipation to the lower shoreface. The consequent profile steepening leads to reduced nourishment lifetimes by up to 30%. Additionally, under high sea level rise rates, more erosive coasts experience a reduction in nourishment lifetimes to annual intervals, while less erosive areas require up to four times more sand than currently needed. These findings illustrate key dilemmas in the formulation of future nourishment strategies and highlight the importance of optimizing these strategies to account for sea level rise.

Quantitative predictions of marine and aeolian sediment transport in the nearshore–beach–dune system are important for designing Nature-Based Solutions (NBS) in coastal environments. To quantify the impact of the marine-aeolian interactions on shaping NBS, we present a framework coupling three existing process-based models: Delft3D Flexible Mesh, SWAN and AeoLiS. This framework facilitates the continuous exchange of bed levels, water levels and wave properties between numerical models focussing on the aeolian and marine domain. The coupled model is used to simulate the morphodynamic evolution of the Sand Engine mega-nourishment. Results display good agreement with the observed aeolian and marine volumetric developments, showing similar marine-driven erosion from the main peninsula and aeolian-driven infilling of the dune lake. To estimate the magnitude of the interactions between aeolian and marine processes, a comparison between the simulated morphological development by the coupled and stand-alone models was made. This comparison shows that aeolian sediment transport to the foredune, i.e. 214,000 m³ over 5 years, extracts sediment from the marine domain. As a result, the alongshore redistribution of sediment from the main peninsula by marine-driven processes decreased by 70,000 m³, representing 1.7% of the total marine-driven dispersion. From the aeolian perspective, marine-driven deposition and erosion reshape the cross-shore profile, controlling the supply-limited aeolian sediment transport and the magnitude of sediment deposition in the foredunes. In the region with persistent accretion along the Sand Engine's southern flank, a higher than average foredune deposition was predicted due to morphological development of the region where sediment is picked up by aeolian transport. Including these marine processes in the coupled model resulted in an increase of 1.3% in foredune growth in year 1 and up to 6.7% in year 5 along this accretive section. At the northern flank, where the developing lagoon and tidal channel provided increased shelter to the supratidal beach, predicted foredune deposition reduced up to −11.5% over the evaluation period. Our findings show that both aeolian and marine transports impact reshaping the nourished sand, where developments in one domain affect the other. The study findings echo that the interplay between aeolian- and marine-driven morphodynamics could play a relevant role when predicting sandy NBS.

Increased climate impacts threaten coastal functions globally, highlighting the need for multifunctional coastal climate adaptation. Sand nourishment can adapt sandy coasts to sea level rise, mitigate erosion, increase flood safety, enhance ecological habitats and expand recreational space. Therefore, sand nourishment is increasingly regarded as a promising nature-based strategy for coastal climate adaptation. However, despite this growing recognition, the assessment of how sand nourishment design impacts multifunctional adaptation remains limited. In this perspective article, we argue for three key lessons for researchers to optimise assessing multifunctional coastal climate adaptation by sand nourishment. We conducted stakeholder workshops to scope and inform our perspective, performed semi-structured literature reviews to concretise and validate this for international applications, built a qualitative model to visualise our interdisciplinary overview of how nourishments impact coastal multifunctionality, reflected on this in expert workshops, and identified implications for researchers. In this manner, we assessed the effects of nourishment design on coastal morphology, ecology, socio-economics and ecosystem services in realising the key policy goals of flood safety, nature and recreation. We found that sand nourishment design can result in conflicts between policy goals, generate ambiguous outcomes and lead to system-wide feedback effects. As such, we identified three key lessons: (1) conflicts between policy goals require informing political decision-making on prioritisation between coastal functions, (2) concreteness is needed on otherwise ambiguous functions, and (3) ongoing, multidisciplinary system-wide monitoring is essential. We thus call for a holistic approach to sand nourishment design and encourage researchers from diverse expertise and localities to expand on and adapt our findings to optimise informing sand nourishment design for delivering multifunctional coastal climate adaptation worldwide.

Coastal ecosystems provide vital services, but human disturbance causes massive losses. Remaining ecosystems are squeezed between rising seas and human infrastructure development. While shoreline retreat is intensively studied, coastal congestion through infrastructure remains unquantified. Here we analyse 235,469 transects worldwide to show that infrastructure occurs at a median distance of 392 meter from sandy shorelines. Moreover, we find that 33% of sandy shores harbour less than 100 m of infrastructure-free space, and that 23–30% of this space may be lost by 2100 due to rising sea levels. Further analyses show that population density and gross domestic product explain 35–39% of observed squeeze variation, emphasizing the intensifying pressure imposed as countries develop and populations grow. Encouragingly, we find that nature reserves relieve squeezing by 4–7 times. Yet, at present only 16% of world’s sandy shores have a protected status. We therefore advocate the incorporation of nature protection into spatial planning policies.

Coastal erosion and flooding are projected to increase during the 21^st century due to sea-level rise (SLR). To prevent adverse impacts of unmanaged coastal development, national organizations can apply a land protection policy, which consists of acquiring coastal land to avoid further development. Yet, these reserved areas remain exposed to flooding and erosion enhanced by SLR. Here, we quantify the exposure of the coastal land heritage portfolio of the French Conservatoire du littoral (Cdl). We find that 30% (~40%) of the Cdl lands owned (projected to be owned) are located below the contemporary highest tide level. Nearly 10% additional surface exposure is projected by 2100 under the high greenhouse gas emissions scenario (SSP5-8.5) and 2150 for the moderate scenario (SSP2-4.5). The increase in exposure is largest along the West Mediterranean coast of France. We also find that Cdl land exposure increases more rapidly for SLR in the range of 0–1 m than for SLR in the range 2–4 m. Thus, near-future uncertainty on SLR has the largest impact on Cdl land exposure evolution and related land acquisition planning. Concerning erosion, we find that nearly 1% of Cdl land could be lost in 2100 if observed historical trends continue. Adding the SLR effect could lead to more than 3% land loss. Our study confirms previous findings that Cdl needs to consider land losses due to SLR in its land acquisition strategy and start acquiring land farther from the coast.

Sand waves are found on shallow, sandy seabeds throughout the world and their dynamics may pose an imminent threat to offshore construction. Therefore, there is a pressing need to understand bed level dynamics in sand wave areas. These bed level dynamics lead to variations in sand wave shape and migration rate over time. However, these variations cannot be explained with the present-day process-based sand wave models, which all include a purely periodic tidal forcing. To explain these fluctuations a more intricate description of the hydrodynamics is necessary. The aim of this study is to explore the importance of time-varying, non-tidal currents for sand wave dynamics in the North Sea. We adopted the three-dimensional Delft3D-Flexible Mesh model, and were able to reconstruct time-varying, non-tidal currents on top of the periodic tidal forcing, while significantly reducing computation times. The simulated currents and water levels showed a good agreement with in-situ measurements. Compared to the situation with only tidal forcing, the simulated sedimentation and erosion rates were amplified up to 15 times due to time-varying, non-tidal currents. Additionally, periods of net erosion were found at locations in the sand wave transect where tidally forced models only showed net-sedimentation. It is therefore important to consider time-varying, non-tidal currents when predicting future sand wave dynamics in the field.

In this study, we present a novel modeling framework that provides a stylized representation of coastal adaptation and migration dynamics under sea level rise (SLR). We develop an agent-based model that simulates household and government agents adapting to shoreline change and increasing coastal flood risk. This model is coupled to a gravity-based model of migration to simulate coastward migration. Household characteristics are derived from local census data from 2015, and household decisions are calibrated based on empirical survey data on household adaptation in France. We integrate projections of shoreline retreat and flood inundation levels under two Representative Concentration Pathways (RCPs) and account for socioeconomic development under two Shared Socioeconomic Pathways (SSPs). The model is then applied to simulate coastal adaptation and migration between 2015 and 2080. Our results indicate that without coastal adaptation, SLR could drive the cumulative net outmigration of 13,100 up to as many as 21,700 coastal inhabitants between 2015 and 2080 under SSP2–RCP4.5 and SSP5–RCP8.5, respectively. This amounts to between 3.0 %–3.7 % of the coastal population residing in the 1/100-year flood zone in 2080 under a scenario of SLR. We find that SLR-induced migration is largely dependent on the adaptation strategies pursued by households and governments. Household implementation of floodproofing measures combined with beach renourishment reduces the projected SLR-induced migration by 31 %–36 % when compared to a migration under a scenario of no adaptation. A sensitivity analysis indicates that the effect of beach renourishment on SLR-induced migration largely depends on the level of coastal flood protection offered by sandy beaches. By explicitly modeling household behavior combined with governmental protection strategies under increasing coastal risks, the framework presented in this study allows for a comparison of climate change impacts on coastal communities under different adaptation strategies.

Projections of high rates of sea level rise have stimulated proposals for adaptation strategies with increasingly high nourishment volumes along sandy beaches. An underlying assumption is that coastal profiles respond rapidly to nourishments by redistributing sediments towards a (new) equilibrium shape. However, this perception may not be valid when high volumes of nourishment are applied, as the profile shape may then undergo significant deformation. Current state-of-the-art modelling techniques often concentrate on a single spatio-temporal scale, either lacking the necessary temporal horizon or failing to provide the required level of cross-shore detail. This article introduces Crocodile, a diffusion based cross-shore model designed to bridge the gap between short- and long-term nourishment modelling. The model simulates the effects of nourishment strategies on coastal volume, coastline position and beach width over a decadal timeframe. It incorporates different elements which compute cross-shore diffusion, sediment exchange with the dune and longshore sediment losses. To test the model performance, a series of idealized nourishment scenarios are examined, along with three case studies along the Dutch coast with different nourishment strategies over the past few decades. The modelled coastal volume, shoreline position and beach width strongly resemble the observations with only a 12% overestimation in profile volume and 13% underestimation in beach width. Averaged over selected periods of nourishment, trends and trend reversals between different strategies are well replicated with slight overestimation for coastal volume trends by 1.5m³/m/yr(10%), while beach width trends are underestimated by 0.2m/yr (15%). Given that the added nourishment volumes are typically in the order of 100m³/m, these model errors are considered sufficiently low to conclude that Crocodile effectively simulates variations in coastal volume, coastline position and beach width over a decadal timeframe in response to different nourishment strategies. Therefore, Crocodile can facilitate the evaluation of future nourishment strategies.

Monitoring sandy shoreline evolution from years to decades is critical to understand the past and predict the future of our coasts. Optical satellite imagery can now infer such datasets globally, but sometimes with large uncertainties, poor spatial resolution, and thus debatable outcomes. Here we validate and analyse satellite-derived-shoreline positions (1984–2021) along the Atlantic coast of Europe using a moving-averaged approach based on coastline characteristics, indicating conservative uncertainties of long-term trends around 0.4 m/year and a potential bias towards accretion. We show that west-facing open coasts are more prone to long-term erosion, whereas relatively closed coasts favor accretion, although most of computed trends fall within the range of uncertainty. Interannual shoreline variability is influenced by regionally dominant atmospheric climate indices. Quasi-straight open coastlines typically show the strongest and more alongshore-uniform links, while embayed coastlines, especially those not exposed to the dominant wave climate, show weaker and more variable correlation with the indices. Our results provide a spatial continuum between previous local-scale studies, while emphasizing the necessity to further reduce satellite-derived shoreline trend uncertainties. They also call for applications based on a relevant averaging approach and the inclusion of coastal setting parameters to unravel the forcing-response spectrum of sandy shorelines globally.

Marine plastic pollution poses a potential threat to the ecosystem, but the sources and their magnitudes remain largely unclear. Existing bottom-up emission inventories vary among studies for two to three orders of magnitudes (OMs). Here, we adopt a top-down approach that uses observed dataset of sea surface plastic concentrations and an ensemble of ocean transport models to reduce the uncertainty of global plastic discharge. The optimal estimation of plastic emissions in this study varies about 1.5 OMs: 0.70 (0.13–3.8 as a 95% confidence interval) million metric tons yr⁻¹ at the present day. We find that the variability of surface plastic abundance caused by different emission inventories is higher than that caused by model parameters. We suggest that more accurate emission inventories, more data for the abundance in the seawater and other compartments, and more accurate model parameters are required to further reduce the uncertainty of our estimate.

Correction to: Nature Communications, published online 13 March 2023 The original version of this Article contained an error in Fig. 2, in which c was incorrectly described as “middle scenario” where it should have been described as “low scenario”. The correct version of Fig. 2 is: (Figure presented.) which replaces the previous incorrect version: (Figure presented.) This has been corrected in both the PDF and HTML versions of the Article.

Satellite remote sensing is becoming a widely used monitoring technique in coastal sciences. Yet, no benchmarking studies exist that compare the performance of popular satellite-derived shoreline mapping algorithms against standardized sets of inputs and validation data. Here we present a new benchmarking framework to evaluate the accuracy of shoreline change observations extracted from publicly available satellite imagery (Landsat and Sentinel-2). Accuracy and precision of five established shoreline mapping algorithms are evaluated at four sandy beaches with varying geologic and oceanographic conditions. Comparisons against long-term in situ beach surveys reveal that all algorithms provide horizontal accuracy on the order of 10 m at microtidal sites. However, accuracy deteriorates as the tidal range increases, to more than 20 m for a high-energy macrotidal beach (Truc Vert, France) with complex foreshore morphology. The goal of this open-source, collaborative benchmarking framework is to identify areas of improvement for present algorithms, while providing a stepping stone for testing future developments, and ensuring reproducibility of methods across various research groups and applications.

A common inference in research studies of observed and projected changes in global ocean wave height and storm surge, is that such changes are potentially important for long-term coastal management. Despite numerous studies of the impacts of anthropogenic climate change on trends in global wind and waves, a clear link to impacts on sandy coastlines, at global scale, is yet to be demonstrated. This study presents a first-pass assessment of the potential link between historical trends in global wave and storm surge values and recession/progradation rates of sandy coastlines since the 1980s. Global datasets of waves, surge and shoreline change rate are used for this purpose. Over the past 30 + years, we show that there have been clear changes in waves and storm surge at global scale. The data, however, does not show an unequivocal linkage between trends in wave and storm surge climate and sandy shoreline recession/progradation. We conclude that these long-term changes in oceanographic parameters may still be too small to have a measurable impact on shoreline recession/progradation and that primary drivers such as ambient imbalances in the coastal sediment budget may be masking any such linkages.