Beaches around the world continuously adjust to daily and seasonal changes in wave and tide conditions, which are themselves changing over longer timescales. 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. Quantitative prediction of beach erosion and recovery is essential to planning resilient coastal communities with robust strategies to adapt to erosion hazards. Over the last decades, research efforts to understand and predict shoreline evolution have intensified as coastal erosion is likely to be exacerbated by climatic changes 1-5. The social and economic burden of changes in shoreline position are vast, which has inspired development of a growing variety of models based on different approaches and techniques; yet current models can fail (e.g. predicting erosion in accreting conditions). The challenge for shoreline models is, therefore, to provide reliable, robust and realistic predictions of change, with a reasonable computational cost, applicability to a broad variety of systems, and some quantifiable assessment of the uncertainties.

Deltas, which are often densely populated, have become more vulnerable to flooding due to increasing rates of sea‐level rise and decreasing sediment supply. These landscapes grow through the successive stacking of delta lobes, as abrupt changes in the river's course episodically alter the river mouth's location. The dynamics of these channel avulsions determine the location and size of delta lobes. We use a newly developed coupled model of river and coastal processes to explore how changing wave climates (varying wave heights and offshore approach angles) and a range of sea‐level rise rates affect avulsion processes and large‐scale delta morphology. Although the relative impacts of the fluvial sediment flux and wave influence have long been studied and more recently quantified, we find that the sign and magnitude of wave climate diffusivity both play important roles in determining not only delta morphology but also avulsion dynamics. We also find, surprisingly, that increasing sea‐level rise rates do not always accelerate avulsions. If the river channel is prograding rapidly enough, relative to the rate of sea‐level rise, then avulsion frequency will not be significantly affected by the rate of sea‐level rise. This finding highlights important differences between wave and river‐dominated deltas, as well as between prototypical deltas and experimental deltas created in the lab. The wave climate, sea‐level rise rate, and critical superelevation required to trigger an avulsion all play important roles in determining the degree of autogenic variability operating in delta systems, which has implications for both avulsion dynamics and interpretation of delta stratigraphy.

The elevation and extent of coastal marshes are dictated by the interplay between the rate of relative sea-level rise (RRSLR), surface accretion by inorganic sediment deposition, and organic soil production by plants. These accretion processes respond to changes in local and global forcings, such as sediment delivery to the coast, nutrient concentrations, and atmospheric CO 2 , but their relative importance for marsh resilience to increasing RRSLR remains unclear. In particular, marshes up-take atmospheric CO 2 at high rates, thereby playing a major role in the global carbon cycle, but the morphologic expression of increasing atmospheric CO 2 concentration, an imminent aspect of climate change, has not yet been isolated and quantified. Using the available observational literature and a spatially explicit ecomorphodynamic model, we explore marsh responses to increased atmospheric CO 2 , relative to changes in inorganic sediment availability and elevated nitrogen levels. We find that marsh vegetation response to foreseen elevated atmospheric CO 2 is similar in magnitude to the response induced by a varying inorganic sediment concentration, and that it increases the threshold RRSLR initiating marsh submergence by up to 60% in the range of forcings explored. Furthermore, we find that marsh responses are inherently spatially dependent, and cannot be adequately captured through 0-dimensional representations of marsh dynamics. Our results imply that coastal marshes, and the major carbon sink they represent, are significantly more resilient to foreseen climatic changes than previously thought.sea-level rise | coastal marshes | coastal dynamics | atmospheric CO 2 | CO 2 fertilization C oastal marsh extent and morphology are directly controlled by rate of relative sea-level rise (RRSLR) and the soil accretion rate, the latter associated with inorganic sediment deposition and organic soil production by plants. Previous studies observed that CO 2 fertilization increases marsh plant biomass productivity through increased water use efficiency and photosynthesis (1), and hypothesized that, as a consequence, marsh resilience should increase via increased organic accretion (2, 3). However, this hypothesis has not yet been tested, and the observed increased plant productivity in response to the CO 2 fertilization effect has not been translated into its actual geomorphic effects. In fact, direct CO 2 effects on vegetation and marsh accretion (as opposed to its indirect effects, e.g., via the increase in temperature) have not yet been incorporated into marsh models, and their importance relative to other leading forcings of marsh dynamics (e.g., inorganic deposition, RRSLR, nutrient levels) remains unknown. Here we use existing data and a 1D ecomorphodynamic model to assess the direct impacts of elevated CO 2 on marsh morphology, relative to ongoing [e.g., RRSLR, and suspended sediment concentration (SSC)] and emerging [nutrient levels (4-6)] environmental change. Vegetation Responses to Changing Environmental ConditionsWe use publ...

In this study, we use a simple numerical model (the Coastline Evolution Model) to explore alongshore transport‐driven shoreline dynamics within generalized embayed beaches (neglecting cross‐shore effects). Using principal component analysis (PCA), we identify two primary orthogonal modes of shoreline behavior that describe shoreline variation about its unchanging mean position: the rotation mode, which has been previously identified and describes changes in the mean shoreline orientation, and a newly identified breathing mode, which represents changes in shoreline curvature. Wavelet analysis of the PCA mode time series reveals characteristic time scales of these modes (typically years to decades) that emerge within even a statistically constant white‐noise wave climate (without changes in external forcing), suggesting that these time scales can arise from internal system dynamics. The time scales of both modes increase linearly with shoreface depth, suggesting that the embayed beach sediment transport dynamics exhibit a diffusive scaling.