A Multiscale Approach to Shoreline Prediction

Shorelines respond to a number of drivers operating on a variety of time-scales. For some time-scales (e.g., seasonal), the driver-shoreline relationship is often evident; however, at longer timescales (e.g., multiannual), the shoreline changes may be superimposed on changes at shorter time-scales and thus are difficult to identify. Here, we predict shoreline evolution from storm events to decadal timescales, using a novel approach based on the Complete Ensemble Empirical Mode Decomposition. This approach identifies and links the primary time-scales in the model drivers (large-scale sea level pressure [SLP] and/or waves) with the same time-scales in the shoreline position. The multiscale approach reproduced shoreline changes at two beaches more skillfully than a common shoreline model when SLP and wave information were used in combination. In addition, the analysis can be applied to climate indices, providing the opportunity to link longer time-scales with climate patterns (e.g., El Nino Southern Oscillation). Plain Language Summary Beaches are changing constantly, advancing or retreating depending for instance, on the climate and ocean conditions. Beach retreat and advance may occur in cycles (seasonally, annually, or over several decades) or because of particular events such as storms. All these changes are superimposed and difficult to disentangle. Therefore, the same beach can look completely different in summer or winter, and the changes are not the same year after year. Therefore, predicting the beach state over the following months, years, or decades is a daunting task. Here, we introduce a new approach to the prediction of shoreline changes and test it at two beaches (one in New Zealand and the other in Australia). The new approach relates changes in shoreline position with drivers (waves and atmospheric patterns) decomposed into time-scales (e.g., seasonal, annual, and biannual) and uses these connections to predict shoreline changes.

Automated summary

Shorelines respond to various drivers on different time-scales, with changes at longer time-scales often superimposed on shorter ones. A new approach using Complete Ensemble Empirical Mode Decomposition successfully predicted shoreline evolution from storm events to decadal timescales. By linking model drivers with shoreline position on multiple time-scales, the approach outperformed common shoreline models when using SLP and wave information. Prediction of shoreline changes over months, years, or decades remains a challenging task due to the complex interplay of different factors influencing beach dynamics.