Blind testing of shoreline evolution models

Jennifer Montaño , Giovanni Coco , Jose Antolínez , Tomas Beuzen , Karin Bryan , Laura Cagigal , Bruno Castelle (1) , Mark Davidson (2) , Evan Goldstein , Raimundo Ibaceta , Déborah Idier (3) , Bonnie Ludka , Sina Masoud-Ansari , Fernando Méndez (4) , A. Brad Murray (5) , Nathaniel Plant , Katherine Ratliff , Arthur Robinet (3) , Ana Rueda , Nadia Senechal (6) , Joshua Simmons , Kristen D Splinter (7) , Scott Stephens , Ian Townend , Sean Vitousek , Kilian Vos

1 CNRS - Centre National de la Recherche Scientifique
2 School of Biological and Marine Sciences
3 BRGM - Bureau de Recherches Géologiques et Minières (BRGM)
4 Marketing Department
5 Division of Earth and Ocean Sciences, Nicholas School of the Environment and Earth Sciences, Center for Nonlinear and Complex Systems
6 EPOC - UMR 5805 Environnements et Paléoenvironnements Océaniques et Continentaux
7 WRL - Water Research Laboratory

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.

Domains: Sciences of the Universe [physics]; Earth Sciences; Oceanography

Jennifer Montaño, Giovanni Coco, Jose Antolínez, Tomas Beuzen, Karin Bryan, et al.. Blind testing of shoreline evolution models. Scientific Reports, 2020, 10 (1), ⟨10.1038/s41598-020-59018-y⟩. ⟨hal-02506235⟩