DOI: 10.5281/zenodo.15161569
title: THIS IS AN OLD VERSION - CLICK LATEST VERSION TO RIGHT OF SCREEN ---> Dataset for article: "Morphologically adaptive modelling of sea level rise induced coastal erosion impact for south-east Australia"

description: AN UPDATED VERSION IS AVAILABLE --> INCLUDING ALL DATA FILES AND TUTORIAL - 10.5281/zenodo.15161568

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This dataset contains files and code associated with an article submitted for review on 6/42025. Information below is in draft form and may be revised.

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TITLE: Morphologically adaptive modelling of sea level rise induced coastal erosion impact for south-east Australia

AUTHORS: Robert Jak McCarroll1, David M. Kennedy1, Daniel Ierodiaconou2

1 School of Geography, Earth and Atmospheric Sciences, The University of Melbourne, Parkville, Victoria, Australia

2 School of Life and Environmental Sciences, Deakin University, Warrnambool, Victoria, Australia

ABSTRACT

Sea level rise induced coastal erosion represents an impending threat to the world’s coastlines. A critical control on coastal recession is the onshore accommodation space available for a receding beach to occupy. Despite the importance of morphologic controls, coastal change models applied at regional-scale over ~100-year time-frames typically address a limited range of coastal morphologies.

ShoreTrans is a shoreface translation model that kinematically projects profile change, based on inputs of sea level rise and sediment budget imbalances. This work presents updates to enable broad-scale model application (1000’s km), automating classification and adapting  to the morphology of individual profiles, including: (1) dunes; (2) cliffs; (3) bluffs and ridges; (4) inter-subtidal rock outcrops; (5) protection structures; (6) low-energy environments; and (7) short-term dune erosion. The model was applied to Victoria, Australia (2000 km coastline, 30 m spaced transects), for a scenario of 1 m sea level rise from 2010 to 2100, using a single time step and simplified treatment of uncertainty and sediment budget. Shoreline trends and variability were determined from satellite extracted shorelines.

Modelled rates of shoreline recession were 20% lower than for a simple parameterization (uncertainty range 58% lower to 8% higher), due to sediment transfer from the backshore to the active shoreface (e.g., dune encroachment) and hard backshores restricting shoreline movements (e.g., cliffs, seawalls). Low dunes exhibited the highest recession rates, due to rollover (68 m long-term recession). Total setback extent, including short-term variability, is projected to exceed 182 m in 5% of low dune areas. High rates of beach loss were associated with beaches fronting hard cliffs (55% beach loss) and seawalls (80%). The worst impacts are expected for rocky, sediment poor coastlines, such as the Great Ocean Road Surf Coast, where a loss of 30% to 50% of beaches is projected, not accounting for infrastructure and potential management interventions.

Automated morphological adaptation represents a step-change for regional scale coastal change assessment. The method also allows for coupling of future erosion to inundation hazards, by interpolating a 3D surface of future morphology. At local-scale, ShoreTrans is suitable to add to hybrid models, providing a means to improve future coastal change projections.

 

Keywords: coastal recession, cliff recession, coastal hazards, shoreline modelling, shoreface translation