DOI: 10.5281/zenodo.14995332
title: Dataset for article: "A framework to combine ongoing beach monitoring data with modular hazard and erosion risk modelling to inform operational coastal management"

description: Details below for article submitted for review on 9/3/2025.

Article title: A framework to combine ongoing beach monitoring data with modular hazard and erosion risk modelling to inform operational coastal management

Robert J. McCarroll1, David M. Kennedy1, Jin Liu2, Blake Allan3, Elisa Zavadil3, Daniel Ierodiaconou4

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

2 School of Biosciences, The University of Melbourne, Parkville, Victoria, Australia

3 Department of Energy Environment and Climate Action, Melbourne, Victoria, Australia

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

 

Abstract

Access to information on short and long-term coastal erosion risk is essential for effective coastal management. Ideally, risk assessments should be regularly updated with ongoing monitoring data, allowing coastal managers to identify local-scale, time-varying threats. Yet, due to resource requirements, this approach is rare.  

This work presents on a collaboration between a provincial (state) government and a regional authority to establish cost-effective methods for dynamic reporting on coastal risk, covering 16 sites along the Great Ocean Road region of Victoria, Australia, incorporating ongoing monitoring data (>300 drone surveys and 35 years of satellite-derived shorelines). A transect-based coastal database, maintained at the state level, provides a framework for modular analyses that scale in complexity according to site priority. Tools built into the framework include: (1) coastal erosion warning indicators; (2) simplified estimation of potential impact; and (3) numerical modelling of coastal risk levels and beach management scenarios. 

Dynamic assessments were designed to support operational management, providing updates on short and long-term shoreline change. As part of the final pilot program assessment, 3 sites (of 16) had high long-term erosion trends, 4 experienced moderate short-term erosion (last 2 years), and a high risk of erosion to assets was predicted for 2 sites. More detailed risk modelling is demonstrated for a high-priority site (Marengo) where a major national asset, the Great Ocean Road, is located on the narrow foredune. After a severe storm erosion event, followed by rapid-response beach nourishment and a period of natural recovery, model updates of alongshore varying risk levels were used to inform whether (and where) further intervention was required. Additional decision-support capacity is added through beach nourishment scenario modelling, in this case projecting an order-of-magnitude estimate for additional nourishment volume (c. 105 m3/yr) required to maintain the present shoreline over the medium term (1 to 10 years).

Use of a modular, scalable framework capable of incorporating ongoing monitoring data is beneficial for identifying, quantifying and comparing coastal erosion risk, to inform time-sensitive management options. We suggest wide-reaching management benefits could be achieved by provision of large-scale coastal databases by national and provincial authorities, allowing resource-limited coastal managers to benefit through cost-efficient local-scale application of various hazard and risk assessment tools.

 

Keywords: coastal hazards, shoreline change, drone, UAV, satellite derived shorelines, beach nourishment