Beach nourishment — the addition of sand to increase the width or sand volume of the beach — is a widespread coastal management technique to counteract coastal erosion. Globally, rising sea levels, storms and diminishing sand supplies threaten beaches and the recreational, ecosystem, groundwater and flood protection services they provide. Consequently, beach nourishment practices have evolved from focusing on maximizing the time sand stays on the beach to also encompassing human safety and water recreation, groundwater dynamics and ecosystem impacts. In this Perspective, we present a multidisciplinary overview of beach nourishment, discussing physical aspects of beach nourishment alongside ecological and socio-economic impacts. The future of beach nourishment practices will vary depending on local vulnerability, sand availability, financial resources, government regulations and efficiencies, and societal perceptions of environmental risk, recreational uses, ecological conservation and social justice. We recommend co-located, multidisciplinary research studies on the combined impacts of nourishments, and explorations of various designs to guide these globally diverse nourishment practices.@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.

@en