Blind testing of shoreline evolution models.

Journal Article (Journal Article)

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.

Full Text

Duke Authors

Cited Authors

  • Montaño, J; Coco, G; Antolínez, JAA; Beuzen, T; Bryan, KR; Cagigal, L; Castelle, B; Davidson, MA; Goldstein, EB; Ibaceta, R; Idier, D; Ludka, BC; Masoud-Ansari, S; Méndez, FJ; Murray, AB; Plant, NG; Ratliff, KM; Robinet, A; Rueda, A; Sénéchal, N; Simmons, JA; Splinter, KD; Stephens, S; Townend, I; Vitousek, S; Vos, K

Published Date

  • February 2020

Published In

Volume / Issue

  • 10 / 1

Start / End Page

  • 2137 -

PubMed ID

  • 32034246

Pubmed Central ID

  • PMC7005834

Electronic International Standard Serial Number (EISSN)

  • 2045-2322

International Standard Serial Number (ISSN)

  • 2045-2322

Digital Object Identifier (DOI)

  • 10.1038/s41598-020-59018-y


  • eng