Enhanced coastal shoreline modelling using an Ensemble Kalman Filter to
include non-stationarity in future wave climates
Abstract
A novel approach to improve seasonal to interannual sandy shoreline
predictions is presented, whereby model free parameters can vary in
time, adjusting to potential non-stationarity in the underlying model
forcing. This is achieved by adopting a suitable data assimilation
technique (Dual State-Parameter Ensemble Kalman Filter) within the
established shoreline evolution model, ShoreFor. The method is first
tested and evaluated using synthetic scenarios, specifically designed to
emulate a broad range of natural sandy shoreline behavior. This approach
is then applied to a real-world shoreline dataset, revealing that
time-varying model free parameters are linked through physical processes
to changing characteristics of the wave forcing. Greater accuracy of
shoreline predictions is achieved, compared to existing stationary
modelling approaches. It is anticipated that the wider application of
this method can improve our understanding and prediction of future beach
erosion patterns and trends in a changing wave climate.