Blind testing of shoreline evolution models

Jennifer Montaño; Giovanni Coco; Jose Antolinez; Tomas Beuzen; Karin Bryan; Laura Cagigal; Bruno Castelle; Mark Davidson; Evan B. Goldstein; Raimundo Ibaceta; Déborah Idier; Bonnie C. Ludka; Sina Masoud-Ansari; Fernando Mendez; A. Brad Murray; Nathaniel G. Plant; Katherine Ratlif; Arthur Robinet; Ana Rueda; Nadia Sénéchal; Joshua Simmons; Kristen Splinter; Scott Stephens; Ian Townend; Sean Vitousek; Kilian Vos

doi:10.1038/s41598-020-59018-y

Blind testing of shoreline evolution models

Scientific Reports

By: Jennifer Montaño, Giovanni Coco, Jose Antolinez, Tomas Beuzen, Karin Bryan, Laura Cagigal, Bruno Castelle, Mark Davidson, Evan B. Goldstein, Raimundo Ibaceta, Déborah Idier, Bonnie C. Ludka, Sina Masoud-Ansari, Fernando Mendez, A. Brad Murray, Nathaniel G. Plant, Katherine Ratlif, Arthur Robinet, Ana Rueda, Nadia Sénéchal, Joshua Simmons, Kristen Splinter, Scott Stephens, Ian Townend, Sean Vitousek, and Kilian Vos

https://doi.org/10.1038/s41598-020-59018-y

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Abstract

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.

Additional publication details
Publication type	Article
Publication Subtype	Journal Article
Title	Blind testing of shoreline evolution models
Series title	Scientific Reports
DOI	10.1038/s41598-020-59018-y
Volume	10
Year Published	2020
Language	English
Publisher	Nature
Contributing office(s)	Pacific Coastal and Marine Science Center
Description	2137, 10 p.
Google Analytic Metrics	Metrics page