Sandy shorelines shift in response to variations in wave conditions over multiple timescales, from storm events to interannual and multi-decadal wave climate variability. As weekly wave forecasts are readily available, morphodynamic models that rely on time series of waves are being used to develop early warning systems to estimate erosion impacts ahead of forecast storms, but this approach cannot be used to predict shoreline behaviour over seasonal timescales and longer. However, future hydrodynamic conditions, and therefore shoreline behaviour, can be inferred from typical seasonal patterns as well as climate drivers, such as the El Niño–Southern Oscillation and the Indian Ocean Dipole, using a data-driven approach.
Thirty-eight years of satellite-derived shorelines were used to explore the influence of the seasonal cycle and climate drivers on monthly shoreline variability across the Gold Coast. A multilinear regression model was developed between shoreline anomalies and climate driver indices, allowing average shoreline behaviour to be estimated around six months in advance using climate driver outlooks from the Bureau of Meteorology. Although short-term shoreline variability is dominated by individual storms, the seasonality pattern and climate drivers can indicate broader shoreline tendencies over the coming months, enabling coastal managers to anticipate periods of increased erosion risk.