Measuring drivers of shoreline and subaerial beach change using limited datasets in a temperate, wave-dominated sandy system: Inverloch, Australia

Abstract

Understanding the drivers of coastal change is important for establishing how a system behaves and responds under different conditions, which is then used for informing management interventions. At seasonal to decadal scales this ideally requires long periods of repeat and frequent monitoring to detect changes in, for instance, shoreline position and beach volume. In this study, we analysed just two years of unmanned aerial vehicle (UAV) surveys over a single eroding beach-dune system (Victoria, Australia), in order to assess its utility in understanding the drivers of change. This morphological data was combined with a WAVEWATCH III (CAWCR) model hindcast, that was assessed against a newly installed, portable, wave buoy. Between August 2018–2020, net shoreline recession, at a rate of 3.1 m/year and net erosion volume change of 1736 m3 occurred along Main Beach in Inverloch. Negative correlations were found between coastal change metrics and wave height, wave direction and rougher sea states. Stronger correlations were found between oceanic conditions and volumetric change than with shoreline change. Beachface changes at this scale have a more instantaneous response to driving conditions, whereas the shoreline reflects longer-term patterns and the impact of extreme events. Spatial and temporal variations were evident, resulting from increasing wave exposure from west to east along the coast, geological controls, human interventions, and variable wave and storm patterns. Strong seasonal patterns were identified in rates of coastal change; however, these changes were offset by the most energetic wave and storm seasons due to morphological hysteresis and the relative change in magnitude of waves or frequency of storms between seasons. The data prove that studies over short period and spatial scales can be highly effective in understanding the drivers of beach change. These findings allow managers to have confidence that small datasets can be highly useful for sound planning strategies.