Benchmarking shoreline prediction models over multi-decadal timescales

Benchmarking shoreline prediction models over multi-decadal timescales

Abstract Robust predictions of shoreline change are critical for sustainable coastal management. Despite advancements in shoreline models, objective benchmarking remains limited. Here we present results from ShoreShop2.0, an international collaborative benchmarking workshop, where 34 groups submitte...

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Main Authors: Yongjing Mao, Giovanni Coco, Sean Vitousek, Jose A. A. Antolinez, Georgios Azorakos, Masayuki Banno, Clément Bouvier, Karin R. Bryan, Laura Cagigal, Kit Calcraft, Bruno Castelle, Xinyu Chen, Maurizio D’Anna, Lucas de Freitas Pereira, Iñaki de Santiago, Aditya N. Deshmukh, Bixuan Dong, Ahmed Elghandour, Amirmahdi Gohari, Eduardo Gomez-de la Peña, Mitchell D. Harley, Michael Ibrahim, Déborah Idier, Camilo Jaramillo Cardona, Changbin Lim, Ivana Mingo, Julian O’Grady, Daniel Pais, Oxana Repina, Arthur Robinet, Dano Roelvink, Joshua Simmons, Erdinc Sogut, Katie Wilson, Kristen D. Splinter
Format: Article
Language:English
Published: Nature Portfolio 2025-07-01
Series:Communications Earth & Environment
Online Access:https://doi.org/10.1038/s43247-025-02550-4
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Summary:Abstract Robust predictions of shoreline change are critical for sustainable coastal management. Despite advancements in shoreline models, objective benchmarking remains limited. Here we present results from ShoreShop2.0, an international collaborative benchmarking workshop, where 34 groups submitted shoreline change predictions in a blind competition. Subsets of shoreline observations at an undisclosed site (BeachX) over short (5-year) and medium (50-year) periods were withheld from modelers and used for model benchmarking. Using satellite-derived shoreline datasets for calibration and evaluation, the best performing models achieved prediction accuracies on the order of 10 m, comparable to the accuracy of the satellite shoreline data, indicating that certain beaches can be modelled nearly as well as they can be remotely observed. The outcomes from this collaborative benchmarking competition critically review the present state-of-the-art in shoreline change prediction as well as reveal model limitations, facilitate improvements, and offer insights for advancing shoreline-prediction capabilities.
ISSN:2662-4435

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