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Blind testing of shoreline evolution models

Blind testing of shoreline evolution models
Blind testing of shoreline evolution models
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.
2045-2322
Montaño, Jennifer
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Coco, Giovanni
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Antolínez, Jose A. A.
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Beuzen, Tomas
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Bryan, Karin R.
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Cagigal, Laura
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Castelle, Bruno
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Davidson, Mark A.
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Goldstein, Evan B.
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Ibaceta, Raimundo
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Idier, Déborah
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Ludka, Bonnie C.
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Masoud-ansari, Sina
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Méndez, Fernando J.
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Murray, A. Brad
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Plant, Nathaniel G.
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Ratliff, Katherine M.
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Robinet, Arthur
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Rueda, Ana
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Sénéchal, Nadia
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Simmons, Joshua A.
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Splinter, Kristen D.
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Stephens, Scott
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Townend, Ian
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Vitousek, Sean
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Vos, Kilian
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Montaño, Jennifer
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Coco, Giovanni
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Antolínez, Jose A. A.
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Beuzen, Tomas
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Bryan, Karin R.
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Cagigal, Laura
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Castelle, Bruno
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Davidson, Mark A.
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Goldstein, Evan B.
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Ibaceta, Raimundo
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Idier, Déborah
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Ludka, Bonnie C.
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Masoud-ansari, Sina
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Méndez, Fernando J.
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Murray, A. Brad
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Plant, Nathaniel G.
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Ratliff, Katherine M.
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Robinet, Arthur
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Rueda, Ana
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Sénéchal, Nadia
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Simmons, Joshua A.
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Splinter, Kristen D.
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Stephens, Scott
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Townend, Ian
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Vitousek, Sean
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Vos, Kilian
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Montaño, Jennifer, Coco, Giovanni, Antolínez, Jose A. A., Beuzen, Tomas, Bryan, Karin R., Cagigal, Laura, Castelle, Bruno, Davidson, Mark A., Goldstein, Evan B., Ibaceta, Raimundo, Idier, Déborah, Ludka, Bonnie C., Masoud-ansari, Sina, Méndez, Fernando J., Murray, A. Brad, Plant, Nathaniel G., Ratliff, Katherine M., Robinet, Arthur, Rueda, Ana, Sénéchal, Nadia, Simmons, Joshua A., Splinter, Kristen D., Stephens, Scott, Townend, Ian, Vitousek, Sean and Vos, Kilian (2020) Blind testing of shoreline evolution models. Scientific Reports, 10 (1), [2137]. (doi:10.1038/s41598-020-59018-y).

Record type: Article

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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More information

Accepted/In Press date: 22 January 2020
e-pub ahead of print date: 7 February 2020
Published date: 7 February 2020

Identifiers

Local EPrints ID: 438320
URI: http://eprints.soton.ac.uk/id/eprint/438320
ISSN: 2045-2322
PURE UUID: cea8b0e0-21f5-456b-bffa-f005e70e2ab0
ORCID for Ian Townend: ORCID iD orcid.org/0000-0003-2101-3858

Catalogue record

Date deposited: 05 Mar 2020 17:30
Last modified: 26 Nov 2021 02:45

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Contributors

Author: Jennifer Montaño
Author: Giovanni Coco
Author: Jose A. A. Antolínez
Author: Tomas Beuzen
Author: Karin R. Bryan
Author: Laura Cagigal
Author: Bruno Castelle
Author: Mark A. Davidson
Author: Evan B. Goldstein
Author: Raimundo Ibaceta
Author: Déborah Idier
Author: Bonnie C. Ludka
Author: Sina Masoud-ansari
Author: Fernando J. Méndez
Author: A. Brad Murray
Author: Nathaniel G. Plant
Author: Katherine M. Ratliff
Author: Arthur Robinet
Author: Ana Rueda
Author: Nadia Sénéchal
Author: Joshua A. Simmons
Author: Kristen D. Splinter
Author: Scott Stephens
Author: Ian Townend ORCID iD
Author: Sean Vitousek
Author: Kilian Vos

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