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Blind testing of shoreline evolution models

Authors
  • Montaño, Jennifer1
  • Coco, Giovanni1
  • Antolínez, Jose A. A.2
  • Beuzen, Tomas3
  • Bryan, Karin R.4
  • Cagigal, Laura1, 2
  • Castelle, Bruno5
  • Davidson, Mark A.6
  • Goldstein, Evan B.7
  • Ibaceta, Raimundo3
  • Idier, Déborah8
  • Ludka, Bonnie C.9
  • Masoud-Ansari, Sina1
  • Méndez, Fernando J.2
  • Murray, A. Brad10
  • Plant, Nathaniel G.11
  • Ratliff, Katherine M.10
  • Robinet, Arthur5, 8
  • Rueda, Ana2
  • Sénéchal, Nadia5
  • And 6 more
  • 1 Faculty of Science, University of Auckland, Auckland, 1010, New Zealand , Auckland (New Zealand)
  • 2 Universidad de Cantabria, Santander, Spain , Santander (Spain)
  • 3 School of Civil and Environmental Engineering, UNSW, Sydney, 2052, Australia , Sydney (Australia)
  • 4 University of Waikato, Hamilton, New Zealand , Hamilton (New Zealand)
  • 5 University of Bordeaux/CNRS, Bordeaux, France , Bordeaux (France)
  • 6 School of Biological and Marine Sciences, Plymouth University, Drake Circus, Plymouth, PL4 8AA, UK , Plymouth (United Kingdom)
  • 7 Environment, and Sustainability, University of North Carolina, Greensboro, NC, 27412, USA , Greensboro (United States)
  • 8 BRGM, 3 avenue Claude Guillemin, Orléans cédex, 45060, France , Orléans cédex (France)
  • 9 University of California, San Diego, United States , San Diego (United States)
  • 10 Nicholas School of the Environment, Center for Nonlinear and Complex Systems, Duke University, Durham, NC, USA , Durham (United States)
  • 11 U.S. Geological Survey St. Petersburg Coastal and Marine Science Center, 600 4th Street South, St. Petersburg, FL, USA , St. Petersburg (United States)
  • 12 National Institute of Water and Atmospheric Research, Hamilton, New Zealand , Hamilton (New Zealand)
  • 13 University of Southampton, Southampton, SO17 1BJ, UK , Southampton (United Kingdom)
  • 14 Pacific Coastal and Marine Science Center, U.S. Geological Survey, Santa Cruz, CA, USA , Santa Cruz (United States)
  • 15 University of Illinois, Chicago, IL, USA , Chicago (United States)
Type
Published Article
Journal
Scientific Reports
Publisher
Springer Nature
Publication Date
Feb 07, 2020
Volume
10
Issue
1
Identifiers
DOI: 10.1038/s41598-020-59018-y
Source
Springer Nature
License
Green

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.

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