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Blind testing of shoreline evolution models

Abstract : 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.
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Soumis le : samedi 19 septembre 2020 - 08:11:35
Dernière modification le : jeudi 24 septembre 2020 - 14:31:08

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INSU | BRGM | EPHE | CNRS | PSL

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

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