This study analyzes laboratory data of beach response to sea-level rise (SLR), isolating shoreline changes driven by passive flooding (PF) of the beach and consequent wave-driven processes. The disequilibrium concept relates shoreline change to instantaneous and equilibrium beach states. While PF shifts the shoreline geometrically, SLR induces disequilibrium that produces wave-driven changes due to apparent profile changes. For the first time, 24 experiments from wave flumes of different scale (including new high-low energy cyclic waves experiments) are gathered into a dimensionless data set through a scaling technique to investigate SLR-induced processes. The data indicate trends (possibly linear) between relative wave power and wave-driven shoreline changes for a given SLR, highlighting the effects of changing background wave energy. Cyclic wave experiments best represent Bruun model's behavior. Wave-energy dissipation emerges as a key variable for quantifying SLR-induced disequilibrium, offering new pathways for future improvements of equilibrium shoreline models under SLR and wave-climate change.

Beaches around the world continuously adjust to daily and seasonal changes in wave and tide conditions, which are themselves changing over longer time-scales. 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.

A large-scale laboratory experiment was completed to investigate the performance of a dynamic cobble berm revetment designed to provide sustainable coastal protection under wave attack and a rising water level. The experiment demonstrated the inherent stability of the dynamic revetment, which was observed to be reshaped by every wave but retained its overall shape throughout the experiment with almost no loss of material. By comparing with a sand beach case, it was found that the revetment reduced shoreline retreat during an erosive wave condition and reduced runup excursions, thus eliminating erosion landward of the revetment. Overall the experiment provided significant new understanding of dynamic revetments and demonstrated the potential for dynamic revetments to provide low cost, robust coastal protection.