Laboratory wave flume experiments in coastal engineering and physical oceanography are widely used to provide an improved understanding of morphodynamic processes. Wave flume facilities around the world vary greatly in their physical dimensions and differences in the resulting distortion of the modelled processes are reconciled using scaling laws. However, it is known that perfect model-prototype scaling of all hydro and morphodynamic processes is rarely possible and there is a lack of understanding to what extent distorted models can be used for direct morphological comparison. To address this issue, distorted scale laboratory flume experiments were undertaken in three different facilities, with the aim to measure and compare beach profile evolution under erosive waves and increasing water levels. A novel approach was developed to transform and scale the different experimental geometries into dimensionless coordinates, which enabled a direct quantitative comparison of the beach profile evolution and sediment transport rates between the differing distorted experimental scales. Comparing results from the three experiments revealed that the dimensionless scaled morphological behaviour was similar after the same number of waves – despite very different degrees of model distortion. The distorted profiles appeared to be suitable for comparison as long as a modified version of the Dean number is maintained between them. The new method was then validated with two further published datasets, and showed good agreement for both dimensionless profile shape, dimensionless sediment transport and morphodynamics parameters. The new approach scales the sediment transport by the square of the runup, proportional to HL, rather than H2, and yields good agreement between the datasets. It is further shown that the new scaling method is also applicable for comparing distorted profile evolution under water level increase, as long as the water level is raised in a similar way between the experiments and by the same total increment relative to the significant wave height (Δh/Hs).@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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