Benchmarking shoreline prediction models over multi-decadal timescales
Yongjing Mao (University of New South Wales)
Giovanni Coco (The University of Auckland)
Sean Vitousek (United States Geological Survey )
Jose A.A. Antolinez (TU Delft - Coastal Engineering)
Georgios Azorakos (Université de Bordeaux)
Masayuki Banno (Port and Airport Research Institute)
Clément Bouvier (Bureau de Recherches Géologiques et Minières )
Karin R. Bryan (The University of Auckland)
Ahmed Elghandour (IHE Delft Institute for Water Education, TU Delft - Coastal Engineering, Port Said University)
Dano Roelvink (TU Delft - Coastal Engineering, IHE Delft Institute for Water Education, Deltares)
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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.
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