Abstract This study examines how three New Zealand estuaries with similar geomorphic features but contrasting climates respond to past and projected changes in sea‐level and riverine sediment supply, using an aggregated‐scale estuarine model. Model calibration against historical sedimentation rates demonstrated good agreement, providing confidence in the simulation settings. Results show that estuarine response depends on the interplay between accommodation space created by sea‐level rise (SLR) and the availability of riverine sediment supply, which vary locally due to regional tectonic and climate variations. Under moderate SLR scenarios, some estuaries are able to keep pace or even become shallower through sediment infilling, whereas under higher SLR projections (e.g., SSP5‐8.5), inner estuarine elements consistently deepen, changing the tidal prism and fueling ebb‐delta growth. The influence of river discharge and sediment supply fluctuations further modulates morphological trajectories, generating alternating phases of infilling and erosion. A critical SLR threshold was identified for each estuary, beyond which tidal flats cannot be maintained and the system risks drowning. These findings emphasize the need for site‐specific adaptation strategies, recognizing that estuarine systems may follow divergent evolutionary pathways under climate change.

Blue carbon ecosystems are critical for climate mitigation, yet their persistence is threatened by global coastal erosion. Conservation strategies often rely on observing shoreline retreat as an indicator of ecosystem vulnerability, implicitly assuming geomorphic stability ensures carbon retention. Here we show that the functional collapse of tidal marsh carbon sinks can occur significantly before physical retreat becomes evident. Combining field evidence from the dynamic Jiangsu coast with process-based modelling, we reveal that fringing effects (i.e., intense hydro-geomorphic disturbances that trigger cascading feedback) drive an unexpected transition from carbon sink to source where carbon losses outpace burial (up to 119%). Crucially, we identify a lag of months to a decade between marsh retreat and the sink–source transition, suggesting that apparent biogeomorphic stability masks a growing climate debt. Global carbon budgets may overestimate coastal storage and effective management requires pre-emptive action prior to development of a net local source. Functional collapse of tidal marsh carbon sinks can occur significantly before evident physical retreat, according to measured field data from Jiangsu, China, combined with process-based modelling.

Sediment total organic carbon (TOC) is a key component of marine carbon storage but increasingly disturbed by human activities such as bottom trawling. Hence, predicting TOC is a prerequisite for evaluating carbon stocks and trawling impact, yet this remains challenging across regions with distinct geophysical characteristics. Although data-driven models can predict TOC within a specific region, their transferability to other regions and the conditions controlling this translation remain poorly understood. This study evaluated four data-driven approaches: Linear Regression (LR), Non-linear Regression (NLR), Stepwise Regression (SR), and Genetic Programming (GP), using data from different coastal regions. Models were trained on two data-rich regions, the North Sea (NS) and Yellow Sea (YS), while the other regions were used for independent cross-regional validation: the Southern New England (SNE) and Hauraki Gulf (HG). Predictor variables included sediment composition, water depth, and bottom trawling intensity, represented by the Swept Area Ratio (SAR, yr-1). When sufficient training data were available, nonlinear models generally achieved high regional predictive accuracy, especially in muddy sediment. However, their performance often declined in cross-regional scenarios. In contrast, LR and SR exhibited enhanced robustness and transferability. Sediment composition, water depth, and SAR are important environmental factors influencing TOC variation but insufficiently explain the environmental differences across regions. Environmental differences across regions are one of the key factors limiting the cross-regional transferability of models. This study illustrates the trade-off between predictive accuracy and generalization capacity in data-driven TOC prediction and establishes a foundation for model selection in evaluating marine sediment carbon reservoirs.

Overwash influences dune resilience and sediment transport, yet the physical parameters governing this process remain poorly understood due to complex hydrodynamic and morphodynamic interactions. This study investigates how broad and narrow-band wave groups and water level variations govern the feedback between overwash dynamics and dune morphological response. Three overwash scenarios were identified: (1) early-stage overwash, where the system begins in overwash and transitions to collision; (2) intermittent overwash; and (3) persistent overwash, characterised by stronger onshore sediment transport. Results indicate that small initial water level variations (4% of the water depth) can significantly influence overwash intensity and frequency, with the highest tested water level (0.775 m) producing the most persistent overwash conditions. Longer wave groups enhance overwash initiation by maintaining elevated infragravity swash across successive waves, increasing the likelihood of crest exceedance. In contrast, shorter groups generate more frequent individual runup events that interact with the dune, increasing overwash occurrences. However, under high water levels, wave group effects become secondary. The Overwash Potential (OP) metric is assessed as an indicator of overwash occurrence. Findings show that threshold values between collision and overwash are scale-dependent, requiring calibration to reflect dune freeboard effects accurately. Additionally, OP estimation is highly sensitive to the beach slope definition; using a non-representative slope can underestimate OP exceedance, overwash frequency, and severity. Future laboratory studies should treat water level as a key design parameter and incorporate long-term morphological feedback and field-scale validation. These steps will improve predictive model accuracy and inform the development of effective coastal resilience strategies under extreme conditions.

Accurate modeling of shoreline changes is crucial for the sustainable management of coastal areas. To evaluate the state-of-the-art in shoreline modeling, a workshop, ShoreShop2.0, was initiated to benchmark existing shoreline models through blind testing. A total of 34 models were applied to predict shoreline positions at a concealed site, referred to as Beach X, and their performance was objectively evaluated against hidden data. Despite significant variations in model performance, the best-performing models provided coherent predictions regardless of the model type. The evaluation results also highlight the applicability of these models to data-poor regions. Using global, open-source datasets as inputs, the best-performing models achieved prediction accuracies comparable to the intrinsic error of the shoreline data. This collaborative benchmarking effort not only highlights the current strengths and limitations of shoreline models but also provides valuable insights and directions for future research and development in the field.

The coastal zones of New Zealand feature substantial sediment deposits accumulated over geological time scales. These sediments are reworked by tidal currents, which are funneled and amplified by the rugged coastal topography, generating eddies. This dynamic process results in the formation of complex dune fields, such as those observed in the outer Queen Charlotte Sound/To & strns;taranui. In this paper, we use a comprehensive set of meteo-oceanographic observations and numerical modeling to examine how topographically generated eddies influence the formation and behavior of these dunes. Our analysis examines long-term migration rates and short-term crest dynamics across multiple tidal cycles, revealing how crest oscillations are influenced by bed shear stress and how sediment flux phase shifts correlate with crest heights. Finally, we discuss a hypothesis based on hydrodynamic regimes to explain the discrimination between dune fields with markedly different morphologies in the northwest and southeast of the study area. This paper presents analytical methods and novel insights into the dynamics of a complex dune field, where eddy-driven processes generate diverse dune morphologies within a spatially restricted area.

This study presents an Early Warning System (EWS) for coastal flooding that integrates wind, wave, and sea level forecasts which are validated using in situ records. The system employs the SWAN spectral wave model to simulate nearshore hydrodynamics while an empirical approach is used to assess Total Watel Level (TWL) exceedances over a user-defined morphological threshold, deriving from repeated topographic surveys. This approach utilizes widely used empirical methods for wave run-up estimation and makes use of the most effective one after calibration. The performance of the EWS is assessed through seven monitored surge events of varying magnitude and hydrodynamic conditions, demonstrating strong agreement between projected TWL exceedances over predefined morphological thresholds, particularly under high-energy wave conditions. Minor discrepancies are noted during events with marginal TWL exceedances over short durations. Results underline the system's potential as a valuable tool for coastal hazard assessment and risk management, with future improvements focusing on appropriate updates of the beach morphology and the integration of suitable numerical techniques and machine learning algorithms.

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.

Knowledge gaps in the physics of shoreline response to the combined action of waves and sea-level rise (SLR) make long-term shoreline projections uncertain. The lack of sufficiently long-term shoreline data partly hinders a better understanding of shoreline change driven by SLR. Thereby, existing formulations related to the equilibrium approach, which states that a beach profile shape equilibrates with its local wave and sea-level conditions, have serious limitations. Recent physical modelling studies provided data of beach profile evolution under changing sea levels in controlled laboratory settings. Here, we bring together and analyze laboratory data from three different physical modelling studies to better understand and predict shoreline response to SLR in the context of equilibrium concept. The data indicate a clear relationship among the analyzed variables, highlighting the importance of accounting for interactions between SLR and incident wave power. To further understand these interactions, and given the limited range of conditions tested in the laboratory, we implement a quasi-2D shoreline change model to generate additional synthetic data. We first calibrate and validate the model to emulate the existing laboratory experiments. The model reproduces multiple laboratory experiments that cover a range of settings, providing confidence in its accuracy. Further work will include the generation of synthetic data obtained by forcing the model with new combinations of SLR and wave conditions to better capture the dependency of shoreline recession on SLR and wave conditions.

Abstract The collapse of channel banks in tidal environments has typically been interpreted using fluvial concepts that prioritize hydraulic (flow‐driven) erosion. Yet daily tidal fluctuations trap pore water in channel banks, potentially producing sustained seepage flows capable of triggering collapse even without strong currents. To assess the relative roles of hydraulic and seepage erosion, we performed scaled laboratory experiments spanning a range of tidal‐current and seepage conditions. We identify two distinct failure modes: fast, toppling failures driven by tidal currents and slow, progressive pop‐out failures caused by seepage flows. Numerical simulations further show that seepage becomes the dominant driver of bank collapse under ebb‐dominant tides as tidal range increases. Integrating experimental and numerical results, we derive dimensionless predictors that unify hydraulic and seepage controls within a common scaling framework. These findings reveal seepage as a critical but previously underappreciated mechanism governing bank collapse in tidal systems.

Satellite derived shoreline position data from “CoastSat” has provided novel insights on the underlying climatic patterns driving cross-shore shoreline movement and shoreline rotation at many sites around the globe. Here we use CoastSat shoreline and wavelet coherence analysis to identify common scales between wave components and cross-shore shoreline change/alongshore shoreline rotation along the New Zealand coast. Wavelet-based decomposition was performed on two contrasting wave-climate regimes, comprising 250 km of west coast shoreline exposed to the Southern Ocean and 260 km of east coast shoreline influenced by the Southeast Pacific. A key challenge inherent to wavelet analysis of highly noisy satellite-derived data is determining statistical significance. Our approach estimates null hypothesis empirically using Monte Carlo red-noise simulations using the same effective number of degrees of freedom as presented in the real data. Thresholds change when scales or results from different sections (transects) of the coast are combined. Transect-averaged wavelet results indicate that, along the west coast, changes in wave height are consistently accompanied by changes in wave direction over the analysed period (1999-2024), implying that higher waves are associated with a single dominant direction. In contrast, the east coast exhibits multiple coherent signals, indicating that similar wave heights can occur under different directional regimes. Global coherence for the east coast (0.66 to 0.76) shows a high coherence in the seasonal band and the dominance of the alongshore component of the wave radiation stress in explaining beach rotation.

Alluvial rivers have long been described by hydraulic geometry theory, which links equilibrium channel dimensions to flow discharge. Yet natural rivers are inherently dynamic, with planforms evolving over time and widths fluctuating around an equilibrium state. Despite increasingly refined datasets, the mechanisms underlying river width variability remain poorly understood. Here, we analyze a globally distributed set of alluvial rivers using high-resolution satellite imagery to examine spatial patterns of width variability. When normalized by mean channel width, we identify three characteristic wavelengths of width variability, each associated with a distinct geomorphic feature: meander bends, mid-channel bars, and localized bank-line incisions linked to intermittent bank collapse. Fourier analysis reveals a strong inverse relation between intermittent collapse-driven width variability and bend-average curvature, suggesting that intermittent bank collapse plays a prominent geomorphic role in mildly curved rivers. Numerical modeling further demonstrates that intermittent bank collapse affects the overall river morphodynamics, accelerating lateral migration and enhancing floodplain reworking. By illustrating intermittent bank collapse as a significant mechanism of river width adjustment, our findings refine classical fluvial geomorphology theory and hold implications for river restoration and organic carbon flux estimation in a warming era.

This study presents a comprehensive analysis of an extensive dataset comprising 63 time series of swash flow velocity extracted from 15 previous studies, including both laboratory experiments and field observations. A consistent pattern in the swash flow time series is identified across the beachface profile, characterized by an approximately linear decrease around the flow reversal point, followed by a deviation point and subsequent deceleration. By defining two key transitional times as functions of the relative location along the beachface, a general form of swash flow is established. The swash flow behavior during the early uprush and late backwash phases, the influencing factors affecting the characteristics of swash flow velocity time series, and the cross-shore distribution of initial uprush velocity are further discussed. Based on this analysis, a general representation of the temporal-spatial evolution of swash flow velocity is proposed. These findings provide new insights into swash hydrodynamics and offer a foundation for future quantitative parameterization of cross-shore swash flow processes.

Coral reefs are complex biological structures that provide critical ecosystem services, such as provision of habitat for marine organisms, fisheries supply, recreational space for tourism industry, and coastal protection. Due to climate change, coral reefs have been undergoing, and will continue to experience, alterations in their capacity to deliver essential ecosystem services. To comprehend these existing alterations and forecast reef responses to future climate change scenarios, it is imperative to employ dynamic modeling approaches that encompass both abiotic and biotic factors. This study aims to build an eco-morphodynamic point model (also known as a Zero-Dimensional – 0D - Model) that incorporates the key variables responsible for driving changes within reef systems, ultimately affecting their capacity to mitigate wave impacts and facilitate sediment production. For the development of this model, we chose the Great Barrier Reef (GBR) as a testing ground, due to its extensive reef network, offering a wide range of scenarios, and its ample and long-term data availability.

Subaqueous sand dunes are found in many natural environments and pose significant operational challenges. However, classic dune predictors found in the literature fail at predicting equilibrium dune dimensions. In this study, we first investigated the potential of using genetic programming to derive predictive equations of dune wavelength and height. The predictors outperformed existing relationships, yet the equations remain complex due to the intricate physics governing dune evolution. We carried out a global sensitivity analysis to evaluate the most influential parameters of the GP predictors. Finally, we proposed a set of robust predictors, for equilibrium dune heights and wavelengths, relying on basic environmental parameters.

Ripples are ubiquitous in sandy beds and their geometry plays an important role in determining seabed roughness and intensifying near-bed turbulence. Ripple geometry in natural settings often deviates from equilibrium configurations. To understand how such nonequilibrium geometry and structures impact near-bottom hydrodynamics and hydraulic roughness, we performed laboratory experiments examining the effect of two different types of ripple configurations. We employed two distinct fixed 3D-printed ripple morphologies, uniform ripples and ripples with superimposed secondary crests, and replicated natural conditions by adhering sand grains, matching in size to the ripple scale, onto their surfaces. Our results show that the introduction of secondary crest disrupts the flow over not only the modified ripple but also over its neighbors. Secondary crests induce a thicker boundary layer than regular ripples. Velocities over the upstream side of secondary crest show substantial deviation from the regular ripple baseline case, while the downstream side experiences a lower effect. The shear velocity at the crest of ripples with a secondary feature is significantly higher, indicating an increased capacity for sediment transport and bedform evolution. The turbulent kinetic energy over ripples with secondary crests is twice as high as that over regular ripples. Our results further affirm that the hydraulic roughness is a function of not only the height and wavelength of the ripples, but also of specific structures and ripple geometry.

Sea level rise (SLR) threatens estuaries with growing risks of flooding, erosion and loss of biodiversity. Macrobenthic bioturbators can destabilize sediments and potentially increase local erosion. Simultaneously, SLR may change macrobenthic habitat when estuarine morphology adjusts, affecting bioturbator abundance and bioturbation effects. This results in a feedback loop between changing hydrodynamics, morphology and bioturbation that to date is not well understood, but determines the long term evolution of coasts and their ecosystems in a changing climate. To shed light on these feedbacks, we use a novel eco-morphodynamic model that couples bioturbation effects with hydro-morphodynamic computations in an exploratory temperate climate estuary model. Bioturbation is hereby parameterized based on the characteristics of two macrobenthic species for which flume experiments and data on habitat preferences and abundance exist. In addition, we test whether an increase in fluvial mud supply caused by increased hinterland erosion can mitigate net estuarine erosion and habitat changes for the species. Our exploratory model shows that both SLR and bioturbation, individually and in conjunction, reduce mud content and increase estuary volume. Intertidal bed profiles become smoother through erosion of the higher elevations, which leads to larger intertidal habitat in the inner estuary. These changes result in a species-dependent response to SLR: the bioturbator that prefers sandy habitat adapts to an increasingly dynamic morphology, whereas the model species that prefers calm and muddy habitat declines. However, with increasing SLR rates, both model species decline and relative bioturbation effects reduce, leading to a morphology mainly controlled by tides. Hinterland erosion can potentially counteract drowning, but, if habitat and hence bioturbation rates are increased, this positive effect might be neutralized. Our findings show that, while bioturbation can drive estuarine response and resilience under lower rates of SLR, morphological change depends largely on the physical processes under high rates of SLR. This nonlinear modification depends on species-specific bioturbation effects, their habitat preferences, and also the number of species, which all control the vulnerability of species to SLR and hence their potential to induce morphological change.

Sand dunes are ubiquitous in natural subaqueous environments and may pose a significant risk in coastal setups for many domains such as the offshore industry or marine renewable energies (Vantorre et al., 2013 ; Barrie and Conway, 2014). Observations of bedform development show that an initially flatbed evolves through different phases: an incipient bedform phase, a growing phase and a stabilizing phase leading to a fully developed dune field. Finite-amplitude dune equilibrium is essentially controlled by the flow depth, the bed shear stress, and the inertia length of the sediment transport in suspension and for bedload at smaller depths (Dore et al., 2023). Existing predictors fail to describe dune dimensions at equilibrium (height and wavelength) (Bradley and Venditti, 2017). Data availability and the poor description of complex morphodynamic phenomena has led to an increasing use of Machine learning in coastal science (Goldstein et al., 2019). In the present work we use Genetic algorithms (GAs) to perform regression analysis on dune laboratory data available in the literature. Our results showed that GAs outperform usual predictors against performance metrics.

Robust and reliable models are needed to understand how coastlines will evolve over the coming decades, driven by both natural variability and climate change. This study evaluated how accurately five popular ‘reduced-complexity’ models replicate multi-decadal shoreline change at Narrabeen-Collaroy Beach, a sandy embayment in Sydney, Australia. Measured shoreline positions derived from approximately monthly field surveys were used for 20-year calibration and 20-year validation periods. The models performed similarly on average but with large variability between transects. The set-up of several models was modified to compensate for their sensitivity to imperfect input wave data, and further site-specific improvements were identified. Capturing interannual to decadal-scale variability in cross-shore and longshore dynamics at this site was challenging for all five models. Models appeared to aggregate key processes at this timescale into parameter values rather than representing them directly. This suggests time-varying parameters or changes to model structure may be necessary for decadal-scale simulations.

The Pearl River Delta (PRD) is one of the world's most complex deltaic systems, shaped by the dynamic interaction between river discharge and tidal forces. However, the mechanisms governing river-tide connectivity within this system remain unclear, particularly with respect to the nonlinear feedback processes and spatiotemporal lag effects. This study employs an information-theoretic framework to investigate process connectivity in the PRD, integrating relative mutual information and relative transfer entropy to quantify synchrony, causality, and directional information flow among river discharge, tides, and water levels. The results reveal that river discharge predominantly governs water level synchrony in the upper PRD, while tidal dynamics exert stronger causal effects downstream water levels. Since the 1990s, human interventions have weakened the influence of river discharge, while tidal impacts have remained relatively stable. Furthermore, water level connectivity is modulated by seasonal and tidal cycles, with discharge effects dominating during flood seasons and tidal forces prevailing during dry seasons, particularly under spring tide conditions. By integrating time-lag effects, our framework reveals delayed yet physically consistent driver-response pathways and refines the spatial structure of hydrodynamic connectivity. This work presents the first lag-aware, information-theoretic quantification of river-tide connectivity in a complex deltaic system. These insights, constituting the first lag-aware, information-theoretic quantification of river-tide connectivity in a complex delta, enhance our understanding of deltaic hydrodynamics and provide a stronger basis for hydrodynamic modeling, adaptive management, and resilience planning in deltas.