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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.

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The steepness of the beach face is a fundamental parameter for coastal morphodynamic research. Despite its importance, it remains extremely difficult to obtain reliable estimates of the beach‐face slope over large spatial scales (thousands of km of coastline). In this letter, a novel approach to estimate this slope from time series of satellite‐derived shoreline positions is presented. This new technique uses a frequency domain analysis to find the optimum slope that minimizes high‐frequency tidal fluctuations relative to lower‐frequency erosion/accretion signals. A detailed assessment of this new approach at eight locations spanning a range of tidal regimes, wave climates, and sediment grain sizes shows strong agreement (R2 = 0.93) with field measurements. The automated technique is then applied across thousands of beaches in eastern Australia and California, USA, revealing similar regional‐scale distributions along these two contrasting coastlines and highlights the potential for new global‐scale insight to beach‐face slope spatial distribution, variability, and trends.

In the Paci c Basin, El Niño/Southern Oscillation (ENSO) is the dominant mode of interannual climate variability and drives substantial changes in oceanographic forcing, likely having a signi cant impact on Paci c coastlines. Yet, how sandy coasts respond to these basin-scale changes has to date been limited to a few long-term beach monitoring sites, predominantly on developed coasts. Here we use 35 years of Landsat imagery to map shoreline variability around the Paci c Rim (72,000 beach transects) and identify coherent patterns of beach erosion and accretion controlled by ENSO. We nd that approximately one third of all beaches experience signi cant erosion during El Niño phases, with the Eastern Paci c particularly vulnerable to widespread erosion (most notably during the large 1997/1998 event). In contrast, La Niña events coincide with signi cant accretion for approximately one quarter of all beaches, although conversely drives substantial erosion in south-east Australia and other localized regions. The signi cant regional variability in coastal response to ENSO should be considered in light of future projected intensi cation and shifts in ENSO amplitudes and avors. Main TextSandy coasts comprise 31% of coastal environments worldwide 1 , of which the majority are classi ed wave-dominated 2 . These coasts are particularly vulnerable to uctuations in ocean wave energy and water levels, that drive cycles of erosion and accretion at episodic, interannual and decadal timescales, impacting adjacent infrastructure and beach habitats. The interannual timescale is of particular interest as it is closely linked to the Earth's climate and its internal modes of climate variability. In a changing climate, a likely intensi cation of these important climate patterns 3,4 , coupled with projected changes in storminess 5,6 and rising sea levels, will likely exacerbate coastal erosion 7 and threaten the future resilience of many coastal communities worldwide 8,9 .

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Wildfire and post-fire rainfall have resounding effects on hillslope processes and sediment yields of mountainous landscapes. Yet, it remains unclear how fire–flood sequences influence downstream coastal littoral systems. It is timely to examine terrestrial–coastal connections because climate change is increasing the frequency, size, and intensity of wildfires, altering precipitation rates, and accelerating sea-level rise; and these factors can be understood as contrasting accretionary and erosive agents for coastal systems. Here we provide new satellite-derived shoreline measurements of Big Sur, California and show how river sediment discharge significantly influenced shoreline positions during the past several decades. A 2016 wildfire followed by record precipitation increased sediment discharge in the Big Sur River and resulted in almost half of the total river sediment load of the past 50 years (~ 2.2 of ~ 4.8 Mt). Roughly 30% of this river sediment was inferred to be littoral-grade sand and was incorporated into the littoral cell, causing the widest beaches in the 37-year satellite record and spreading downcoast over timescales of years. Hence, the impact of fire–flood events on coastal sediment budgets may be substantial, and these impacts may increase with time considering projected intensification of wildfires and extreme rain events under global warming.