This study explores the statistical dependence between wind speed and surge height along the Dutch coast using a large synthetic dataset. Storms were clustered based on wind direction, tidal offset, wind rotation, tidal peak, surge and wind exceedance duration, resulting in 16 clusters per wind direction and per location. Apart from wind direction, comparing clusters revealed a limited impact of clustering based on these storm characteristics on the choice of the best-fitting copula model, suggesting sub-clustering may not be necessary for accurately representing the statistical dependence between extreme wind speeds and surge heights. The BB8 copula generally provided the best fit to the data. However, the observed upper tail dependence did not decrease to zero, particularly for western to northern wind directions, indicating non-negligible dependence in joint extremes of wind speed and surge height. Therefore, applying the BB8 copula (or any other copula model without upper tail dependence) may lead to underestimation of the flood risk, when applied in probabilistic analyses. The findings from this study provide valuable insights for refining hydraulic load models for reliability assessments and design of flood defenses.

Sandy beach erosion is a pressing concern for coastal regions worldwide, driven by both natural processes and human-induced pressures. This study presents an ensemble modeling approach for predicting sandy shoreline dynamics using an equilibrium-based shoreline evolution model (EBSEM). A genetic algorithm (NSGA-II) was employed to calibrate multiple parameter sets, capturing the inherent uncertainty in model parameters. The method was tested with a publicly available dataset from Tairua Beach (New Zealand), spanning 14 years of high-resolution shoreline measurements. Results reveal a near-linear relationship between the slope and intercept parameters governing equilibrium wave energy, and demonstrate that the best-fit solution generally lies within the ensemble range. Comparisons of ensemble simulations with observed data indicate strong agreement in both calibration and validation phases, although certain extreme accretion and erosion events were underestimated. Overall, this ensemble framework provides a robust tool for medium- to long-term shoreline predictions, bringing coastal managers with stochastic/probabilistic estimates of shoreline change, which can be useful in the assessment of resilience of adaptive strategies for risk mitigation.

Understanding wave systems (WS) dynamics in semi-enclosed tropical basins is challenging due to interactions between remote wave generation and regional climate variability. In the Gulf of Panama, prior studies mainly relied on single-site offshore analyses, limiting characterization of wave energy distribution and its atmospheric and oceanic drivers. This gap in knowledge is particularly consequential for coastal hazard assessments and for deciphering ocean–atmosphere interactions in regions influenced by both long-period swells and low-level wind jets. To address this limitation, we present a comprehensive multi-decadal analysis of the seasonal and long-term variability of significant wave height (Hs) and peak wave period (Tp), based on WS identified applying spectral statistical techniques using the GLOSWAC-5 atlas. To understand the variability and the modulating effect of the Gulf of Panama semi-enclosed morphology, three representative sites at its entrance were selected. Monthly patterns were analyzed by classifying wave trains according to relative orientation (i.e., following, crossing, and opposing) to determine the predominant direction and multimodal activity. Long-term trends were assessed over 17 to 18-year intervals (i.e. 1969–1987, 1988–2005, 2006–2023) using exploratory analysis and quantile regression, with results reported separately for the dry season (December–April) and the wet season (May–November). The analysis revealed five dominant WS, each associated with specific generation mechanisms and geographic origins: WS1, associated with Southern Ocean swells; WS5, originating from Northern Hemisphere swells; WS2, driven by southeasterly trade winds from the subtropical Pacific; and two regionally forced systems, WS3 and WS4, generated by the Panama and Chocó Low-Level Jets, respectively. WS1 shows increases in Hs of about 5–10 cm during 1969–1987 and 1988–2005, and up to ~ 15 cm in 2006–2023, with Tp rising by ~ 0.3–0.6 s over the same intervals. WS2 exhibits small variations, with Hs ranging from − 10 to + 17 cm and Tp fluctuations within ± 0.3 s across the three intervals. WS3 displays the strongest changes, with median Hs increases of 10–30 cm in the first two intervals and extreme values reaching 50–60 cm in 1988–2005 and 2006–2023, together with Tp increments of ~ 0.3–0.5 s. WS4 remains comparatively stable in all periods, with Hs variations within ± 10 cm and Tp changes < 0.4 s. WS5 intensifies mainly in 2006–2023, showing Hs increases of 8–15 cm and Tp rises of ~ 0.2–0.4 s. Monthly mean Hs anomalies (> 0.10 m) at the 50th percentile suggest a relationship with El Niño–Southern Oscillation (ENSO), but analyses using the 95th percentile reveal that only WS2, WS3, and WS4 exhibit a stronger association with ENSO phases.

Dutch beaches are increasingly urbanized with both permanent beach pavilions and seasonal sheds and holiday houses. The effect of these buildings on long term dune development between 1999 and 2024 is studied in this paper along ~ 100 km of coast on the outer delta in the south western part of the Netherlands. A total of ~ 7000 beach buildings have been manually identified in this period based on satellite images and the time line function of Google earth desktop. The effect of the buildings is determined and analyzed at 477 cross-shore profiles with dune volumes and properties like dune toe, top and heel based on airborne lidar datasets of 1999 and 2024. On natural beaches the dune toe position is derived from profile information, whereas on urbanized beaches near buildings the dune toe is based on the location of the buildings. Yearly volume changes at the profile locations vary between -10 m³/m/y and up to 40 m³/m/y. The results indicate that smaller and standalone buildings allow for larger variations in dune volume changes and suggest that larger buildings and connected buildings impede natural dune dynamics which could impact coastal resilience in the long run.

Four-dimensional (4D) topographic datasets are increasingly available at high spatial and temporal resolution, particularly from permanent terrestrial laser scanning (PLS) time series. These data offer unprecedented opportunities to analyse rapid and complex morphological processes occurring in sandy coastal environments, such as sandbar welding or bulldozer activity, as well as their longer-term impacts on sandy beaches. However, studying these processes requires the extraction and recognition of recurrent topographical surface dynamics across time, which in turn demands novel, automated methods. This study presents a novel workflow that combines 4D objects-by-change (4D-OBCs) with unsupervised classification using Self-Organizing Maps (SOMs) and hierarchical clustering. Applied to a three-year PLS time series comprising 21 194 hourly point clouds, the method identifies 4412 instances of short-term surface dynamics. These are organized into two SOMs (64 nodes each) and further grouped into 31 clusters representing distinct dynamic types, such as berm deposition, large-scale backshore erosion, and human interventions (e.g., bulldozer activity). The classification results enable detailed spatiotemporal analyses of coastal morphodynamics. The SOM topology reveals seasonal patterns in surface activity, where, for example, winter is dominated by erosional activity over the whole beach but depositional activity mainly occurs in the intertidal area. The broader clusters facilitate interpretation of environmental responses and identification of changes in cross-shore zonation of types of dynamics, like berm formation. This approach demonstrates the potential of integrating PLS and unsupervised learning to characterize complex surface dynamics, through a fully automated extraction and classification workflow. While the interpretation of clusters and their relation to environmental variables in this study is performed through expert-based analysis, the methods provide a framework for targeted, data-driven investigation and prediction of morphodynamic processes in high-resolution 4D remote sensing datasets.

Sea turtles are key species in many coastal ecosystems worldwide, particularly coral reef and seagrass habitats. Yet, six of seven species are endangered. Their nests, which incubate in beach sand and rely on specific climatic conditions for egg viability, face significant threats from inundation, for example through wave runup. This paper examines a method to rapidly predict wave runup in low-data coral reef environments, and the implications thereof on the inundation of sea turtle nests. The study uses two metamodels, BEWARE-2 and HyCReWW, to predict wave runup at Ras Baridi, Saudi Arabia, a key nesting site of the Red Sea green turtle population. The models were used to analyze runup events and inundation durations and provide a first estimate of a safe nesting elevation. Despite data limitations, the study provides valuable insights for coastal managers to protect sea turtle nests, suggesting that a 5-year return period runup elevation could serve as a threshold for nest relocation. However, the findings also highlight the importance of more accurate hydrodynamic predictions and the need for in-situ data to validate models and improve conservation strategies.

The causal drivers of short-term changes (days to months) in human-, wind-, and wave-driven sand transport on a sandy beach are not often considered in an integral and data-driven approach. However, improving current knowledge on (urban) sandy beach topographical change requires the incorporation of multi-scale, cross-sectional and human factors. In this research we process a time series of 21,194 hourly point clouds, obtained in a Permanent Terrestrial Laser Scanning setup. From this 3D time series we extract 5,102 short-term temporary surface dynamics, through a method called 4D objects-by-change (4D-OBCs). The causal drivers of two of these 4D-OBCs are investigated in detail. One is interpreted as an aeolian depositional surface dynamic (1), and one as a bulldozer deposit, that consecutively eroded under high wave energy conditions (2). The dynamics show clear correlation to a particular combination of wind direction and intensity (1), and wave height and wave period (2), indicating that point cloud time series derived 4D-OBCs are useful data to study causality of short-term surface dynamics of different origins. However, to study these surface dynamics systematically and derive statistical proof of causal relations we must consider multivariate correlations, as well as spatiotemporal dependence between sediment dynamics and larger scale morphological changes on the beach.

Coastal communities worldwide are facing accelerating pressures from rising sea levels, changing storm patterns, and shifting sediment dynamics. These trends demand reliable tools for predicting morphological evolution. Despite advances in this area, existing engineering tools still struggle to predict morphological evolution, especially in settings shaped by interventions such as nature-based solutions aimed at fostering climate-resilient coastal development. Modelling morphological change, which unfolds over much longer timescales than hydrodynamic processes, remains a challenge due to the complexity of sediment transport and related interactions, including flow–vegetation effects and human interventions. These challenges are further heightened by uncertainties in long-term climate projections. Therefore, developing and testing engineering tools capable of delivering reliable predictions is essential for planning effective short- and long-term responses to coastal erosion and flooding.

This Research Topic seeks to advance the existing coastal engineering toolbox, enabling reliable long-term predictions of coastal morphological evolution and flooding in the context of climate change and multi-biophysical phenomena, such as sediment transport and flow-vegetation interactions. Six studies contributed to this Research Topic, spanning riverine sediment supply, estuarine and dyke breach dynamics, vegetated coastal defences, and probabilistic methods for projecting flooding and erosion. These contributions, outlined below in no particular order, illustrate the scientific and methodological advancements that are reshaping our ability to anticipate coastal change and support informed, climate-resilient coastal planning.

Coastal zones are highly dynamic environments shaped by various environmental forcing agents such as waves and nearshore currents operating across diverse spatio-temporal scales. For effective decision-making, coastal managers require simplified, computationally efficient models to predict future shoreline morphodynamics. Among the models developed over the years, equilibrium-based shoreline evolution models (EBSEMs) have garnered significant attention for their computational efficiency. However, their application has mainly been limited to microtidal sandy beaches when simulating shoreline orientation, necessitating further evaluation across broader coastal settings. This study investigates the applicability of EBSEMs in predicting shoreline rotational variability at two morphologically distinct sites: Narrabeen Beach, Australia, and Moncofa Beach, Spain. These sites differ in sediment size, tidal regimes, data sources, observation periods, and monitoring frequencies, providing a robust framework for model evaluation. Results demonstrate that the EBSEM successfully replicates the general trends of shoreline orientation variability on both sites, qualitatively and quantitatively. Seasonal rotation trends were accurately captured, emphasizing the model’s capability to operate across varying spatial and temporal scales. These findings further reinforced the capabilities of EBSEMs as practical tools for coastal management, particularly for predicting shoreline orientation changes under diverse environmental conditions.

Threatened sea turtles rely on sandy beaches for nesting, linking their long-term survival to global beach availability. However, beaches worldwide are increasingly threatened by anthropogenic stressors and sea level rise (SLR). Reliable vulnerability assessments require understanding beach dynamics across multiple time scales, informed by long-term coastal change records. While many nesting beaches lie in remote, data-poor environments, recent advances in coastal remote sensing now allow us to monitor coastal change worldwide. Here, we combine satellite-derived shorelines (CoastSat), shoreline modeling (CoSMoS-COAST), and global data sets to investigate shoreline evolution and future vulnerability at nine globally important sea turtle nesting sites. We investigate seasonal and long-term shoreline change, hindcast (1980–2024) and forecast (2025–2100) shoreline positions under various SLR scenarios, and quantify available accommodation space based on backbeach elevation and infrastructure footprints. We find that shoreline evolution and vulnerability vary considerably, with three sites showing historical accretion trends and four sites showing erosion. This demonstrates that the previously widely applied bathtub approach—adding SLR to a static beach profile—is not suitable to assess the vulnerability of sea turtle nesting beaches to erosion. Three eroding beaches emerge as particularly vulnerable due to projected shoreline retreat coupled with limited accommodation space. Despite significant uncertainties arising from long-term shoreline projections, our results provide important insights into seasonal and long-term morphodynamics, identify vulnerable nesting sites, and offer a comprehensive, transferable framework for assessing shoreline evolution and relative erosion vulnerability at other sites. Understanding these dynamics is crucial to inform conservation and management strategies to future-proof these critical nesting habitats.

Plain Language Summary
Sea turtles depend on sandy beaches for nesting, which means their survival is closely linked to how these beaches change over time. Today, many beaches are increasingly pressured by human activity and rising sea levels, putting turtle nesting habitats at risk. To better understand which beaches are most vulnerable, we used satellite images, computer models, and global data to study nine of the world's most important nesting sites. We looked at how the shoreline has moved since 1980, how it might change through 2100 under different sea level rise (SLR) scenarios, and how much space may remain for turtles to nest given local terrain and development. Our results show that some beaches are naturally building up while others are eroding, and that vulnerability is not the same everywhere. In particular, three beaches appear especially at risk because they are eroding and have little room for turtles to nest further inland. These findings highlight the importance of moving beyond simple “bathtub” estimates of SLR, and instead considering the complex, long-term behavior of beaches. This approach can help identify priority sites for conservation and guide strategies to protect sea turtle nesting habitats in a changing world.

Extreme sea level events pose significant risks to coastal regions, with non-tidal residuals (NTRs) being a primary driver in low-lying areas like the Netherlands, where shallow seas amplify their impact. This study investigates the spatial patterns of NTRs along the Dutch coast using time series clustering on historical NTR hydrographs. The design of hydraulic boundary conditions divides the Netherlands into three coastal regions. To evaluate whether this division sufficiently captures regional variability, three clustering scenarios (k = 3, k = 4, and k = 5) were explored. The analysis identified k = 5 as the optimal configuration based on the Davies-Bouldin index. This result emphasized the importance of fine-scale approaches to understanding regional spatial variations in NTR dynamics. Regional bathymetry and tide-surge interactions were explored as drivers of these spatial patterns. Southern stations near river systems and deeper waters displayed characteristics distinct from northern stations in the Wadden Sea, which are influenced by shallow tidal flats. Analysis of the M2 tidal constituent and the timing of NTR maxima relative to high tides underscored the role of tidal dynamics in shaping spatial clusters. Future research will focus on integrating spatio-temporal patterns and environmental drivers into clustering methodologies, providing deeper insights for coastal risk management and adaptation strategies.

Addressing global challenges such as coastal hazards and climate change requires innovative tools capable of analyzing complex environmental drivers, including waves, storm surges, and cyclones, across varying scales. These tools are vital for predicting floods, assessing risks, and planning adaptive responses. BlueMath-Hub has been developed as a global collaborative initiative to provide accessible, customizable solutions for both researchers and practitioners. It aims to simplify the use of advanced statistical and numerical models, fostering creative and scalable approaches in coastal science and engineering. BlueMath, the core of this platform, is an open-source repository of Python tools accessible via a cloud-based Jupyter Hub environment. It integrates statistical methods and numerical model wrappers within a modular framework. The system includes: (a) BlueMath-Toolkit, providing tools for data mining, interpolation, and model integration; (b) BlueMath-Statistical Downscaling, focusing on extreme events and generalized models; (c) BlueMath-Hybrid Downscaling, combining statistical and numerical approaches for optimized solutions; and (d) BlueMath-Climate Services, supporting integrated applications such as compound flooding assessments. BlueMath is continuously evolving, with its tools already applied in research, publications, and training. By lowering barriers to entry and enabling collaborative workflows, BlueMath-Hub supports the development of innovative solutions to mitigate the impacts of a changing climate.

Extreme storms over the North Sea drive coastal flood risk in the Netherlands, causing high waves and extreme sea levels. Designing flood defenses requires accurate statistical extrapolation of hydraulic load conditions with return periods of 1,000 years or more. This is a challenging task given limited observational data. This study uses a large, simulated dataset (~9,000 years) to explore the statistical dependence between extreme wind speed u and surge height s. Storms were clustered using several techniques. Self-organizing maps (SOM) effectively captured physical relationships, such as the influence of wind direction and tidal offset on storm dynamics, however variability in statistical dependence between u and s for different clusters was better represented using manual clusters. Copula models were fitted to the cluster data, with the BB8 copula outperforming others. This study illustrates the potential of machine learning to identify patterns in large datasets while emphasizing the relevance of manual clustering approaches for revealing nuanced statistical dependencies critical to flood risk assessment.

Understanding and predicting shoreline variability at various temporal and spatial scales is vital for effective, data-driven coastal management. Shoreline position, a reliable indicator of beach morphological changes, has been assessed using complex numerical models. Recently, equilibrium-based shoreline evolution models (EBSEMs) have gained traction for their efficiency in simulating shoreline orientation, including cross-shore and rotational (longshore) changes. However, existing EBSEMs for shoreline rotation have been applied predominantly to microtidal beaches, with limited validation across diverse coastal environments. This study evaluates the performance and scalability of the EBSEM proposed by Jaramillo et al. (2021) in modelling shoreline rotational variability at seven embayed beaches: Narrabeen Beach (Australia), Tairua Beach (New Zealand), Blackpool Beach (United Kingdom), Poniente Beach, Llevant Beach, Cala Millor Beach, and Moncofa Beach (Spain). These sites represent diverse environmental conditions in terms of sediment size, tidal regimes, monitoring frequency, and data types. The model was tested across full monitoring periods, elevation contours, and temporal resolutions. Results show that EBSEM performs well across contrasting beach types, effectively capturing short-term and seasonal shoreline rotation patterns. However, reduced accuracy was observed in environments with high-energy events or human interventions, such as Poniente, Llevant, and Cala Millor beaches. Sensitivity analyses highlight the importance of temporal resolution and intertidal elevation in model performance. While the EBSEM shows significant potential for broader application, further refinement is needed to better capture storm-driven and anthropogenic variability. These improvements would enhance its utility for coastal adaptation planning, hazard mitigation, and long-term shoreline management in the face of climate change.

Correction to: Communications Earth & Environmenthttps://doi.org/10.1038/s43247-025-02178-4, published online 3 April 2025 In the version of the article initially published, the title and legend for Fig. 5 was duplicated from Fig. 4; the colour descriptions in the legends to Figs. 3 and 4 were incorrect; the zenodo link in the Data Availability section (https://doi.org/10.5281/zenodo.14872179 (2025)) was missing; and the legend to Supplementary Fig. 1 was missing data source citations. The changes are made in the HTML and PDF versions of the article.

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

Flooding is the natural hazard most likely to affect individuals and can be driven by rainfall, river discharge, storm surge, tides, and waves. Compound floods result from their co-occurrence and can generate a larger flood hazard when compared to the synthetic flood hazard generated by the respective flood drivers occurring in isolation from one another. Current state-of-the-art stochastic compound flood risk assessments are based on statistical, hydrodynamic, and impact simulations. However, the stochastic nature of some key variables in the flooding process is often not accounted for as adding stochastic variables exponentially increases the computational costs (i.e., the curse of dimensionality). These simplifications (e.g., a constant flood driver duration or a constant time lag between flood drivers) may lead to a mis-quantification of the flood risk. This study develops a conceptual framework that allows for a better representation of compound flood risk while limiting the increase in the overall computational time. After generating synthetic events from a statistical model fitted to the selected flood drivers, the proposed framework applies a treed Gaussian process (TGP). A TGP uses active learning to explore the uncertainty associated with the response of damages to synthetic events. Thereby, it informs regarding the best choice of hydrodynamic and impact simulations to run to reduce uncertainty in the damages. Once the TGP predicts the damage of all synthetic events within a tolerated uncertainty range, the flood risk is calculated. As a proof of concept, the proposed framework was applied to the case study of Charleston County (South Carolina, USA) and compared with a state-of-the-art stochastic compound flood risk model, which used equidistant sampling with linear scatter interpolation. The proposed framework decreased the overall computational time by a factor of 4 and decreased the root mean square error in damages by a factor of 8. With a reduction in overall computational time and errors, additional stochastic variables such as the drivers' duration and time lag were included in the compound flood risk assessment. Not accounting for these resulted in an underestimation of 11.6 % (USD 25.47 million) in the expected annual damage (EAD). Thus, by accelerating compound flood risk assessments with active learning, the framework presented here allows for more comprehensive assessments as it loosens constraints imposed by the curse of dimensionality.

Abstract
We perform the first global analysis of storm surge seasonality using surge data from a global hydrodynamic model with full coverage of coastal areas, providing valuable insights for regions not represented in alternative observational data sources. We apply directional statistics based on the mixture model of the von Mises‐Fisher distribution to identify surge seasons and their characteristics. Results reveal that nearly half of the global coastal stations, predominantly in tropical and subtropical regions, either lack a distinct surge season or experience heightened surge activity across multiple periods. Furthermore, the seasonality of storm surges follows a consistent large‐scale spatial pattern tied to regional atmospheric variables. Spatial variability in the length of surge seasons is minimal in regions with bimodal surge seasons; however, the variability of surge peaks differs. Lastly, the seasonal distribution of storm surges differs regionally due to the underlying storm regime. These results provide valuable insights into the seasonality of storm surges on a global scale, which is useful for coastal risk management.

Plain Language Summary
Storm surges, the abrupt rise in sea levels above tidal elevations during a storm, are among the most devastating causes of coastal flooding globally. However, they are influenced by seasonal variations in atmospheric processes, which can amplify their peaks. In this study, we analyzed when these peaks typically occur and how the seasons vary spatially over a consistent period globally. By using surge data from a global hydrodynamic model with comprehensive geographical coverage, we provide insights for regions that lack observations from alternative data sources such as tide gauges. We show that the seasonality of surge peaks is not confined to a single well‐defined season globally. Some regions lack clear seasons, while others have well‐defined seasons with varying numbers. This means that some regions have more pronounced seasonal cycles than others. Despite this variation, the seasonal patterns are regionally coherent and are tied to underlying atmospheric patterns. Understanding these seasonal patterns could help improve coastal management plans, especially in places with extended surge risk windows

Climate change and human activity pose increasing challenges to endangered sea turtles, which are key species in many marine ecosystems worldwide. Among these challenges are the flooding and erosion of nesting beaches. In this perspective, we argue that existing methods and tools from coastal science and management hold significant, yet underused, potential for sea turtle conservation. We introduce a stepwise framework for integrating sea turtle ecology and coastal management to address these coastal threats. The framework follows an Observe–Understand–Predict–Intervene cycle and links ecological thresholds, coastal processes, and management interventions across scales, from Regional Management Units (RMUs) to individual beaches. We illustrate how state-of-the-art monitoring, modeling, and nature-based solutions (NBS) can be embedded within this framework to inform when and how to intervene. Increased in-situ data collection and interdisciplinary collaboration will be critical to apply and refine this approach, thereby enhancing the long-term resilience of nesting habitats.

Robust predictions of shoreline change are critical for sustainable coastal management. Despite advancements in shoreline models, objective benchmarking remains limited. Here we present results from ShoreShop2.0, an international collaborative benchmarking workshop, where 34 groups submitted shoreline change predictions in a blind competition. Subsets of shoreline observations at an undisclosed site (BeachX) over short (5-year) and medium (50-year) periods were withheld from modelers and used for model benchmarking. Using satellite-derived shoreline datasets for calibration and evaluation, the best performing models achieved prediction accuracies on the order of 10 m, comparable to the accuracy of the satellite shoreline data, indicating that certain beaches can be modelled nearly as well as they can be remotely observed. The outcomes from this collaborative benchmarking competition critically review the present state-of-the-art in shoreline change prediction as well as reveal model limitations, facilitate improvements, and offer insights for advancing shoreline-prediction capabilities.