Infragravity (hereafter ‘IG’) waves are ocean waves related to the presence of groups in incident shortwaves. The importance of IG waves in the nearshore and their role in beach morphodynamics have been widely documented over the last few decades (Bertin et al., 2018) while in contrast, studies on characterisation of IG waves and related impacts in tidal inlets are scarce and have only recently begun to be addressed (Williams and Stacey, 2016). Nonetheless, existing studies tend to show that IG waves in tidal inlets can play an important role in the short and long-term hydro-morphodynamics of coastal lagoons. The current study presents the first large scale field measurements and analysis of IG waves propagation and characteristics in a meso-macro tidal lagoon/inlet.@en

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.

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