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.

The erosion impact of large coastal storm events typically occurs across broad (100s of km) sections of coastline and may include significant variability both alongshore and vertically between the berm and dunes. Identifying controls of variability in storm erosion is critical to understanding the response of coastlines to present and changing storminess. This contribution analyses immediate pre‐ and post‐storm Lidar data of over 1700 cross‐shore profile transects, determined at every 100 m alongshore and spanning 400km of the southeast Australian coastline. This unique dataset allowed for a data‐driven Bayesian network analysis of the key relationships between the measured storm erosion response and a range of variables describing the antecedent morphology and hydrodynamic forcing at the coastline. It was found that while erosion of the dune and berm was observed to increase with increased exposure of the local profile to incident storm waves, additional erosion controls were found to be different for these two different sections of the beach. Erosion of the berm was specifically linked to the pre‐storm berm volume, with more accreted berms experiencing a greater proportion of erosion of the overall berm, regardless of variability in forcing conditions. In contrast, dune erosion was equally controlled by the exceedance of wave runup above the antecedent dune toe elevation and the width of the beach immediately fronting the dune, with wider beaches resulting in reduced dune erosion. The results of this large, data‐driven analysis provide important affirmation and insights into the primary controls of berm and dune storm erosion.

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