Enhanced Coastal Shoreline Modeling Using an Ensemble Kalman Filter to Include Nonstationarity in Future Wave Climates. Issue 22 (9th November 2020) - Ibaceta, Raimundo; Splinter, Kristen D.; Harley, Mitchell D.; Turner, Ian L.
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 nonstationarity 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 data set, 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 modeling 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. Plain Language Summary: Understanding and predicting future changes along sandy coastlines worldwide is highly relevant for coastal management in the context of climate change. In the future, the changing occurrence of storms—and over longer time scales, rising sea levels—are expected to result in new patterns of shoreline erosion. It is very common for shoreline change models to use past records of measured shorelines and waves to match mathematical equations to these existing observations. However, the validity of these types of shoreline models to predict the future is questionable, when waves and storm patterns around the world in coming decades are expected to be different to those observed in the past. A new methodology is presented to address this issue by exploring how a mathematical shoreline model can self‐adjust to wave climates that vary through time. The proposed methodology is shown to be successful at improving shoreline predictions. Key Points: A data‐assimilation dual state‐parameter ensemble Kalman filter (EnKF) methodology is integrated within an established shoreline model Nonstationary model parameters are obtained, with the accuracy and sampling frequency of shoreline data critical to overall EnKF skill Time‐varying model parametrizations are physically linked to nonstationary wave forcing, resulting in more accurate shoreline predictions