PREDICTING SHORELINE EVOLUTION IN A CHANGING WAVE CLIMATE

Reliable predictions of shoreline evolution at a range time scales both now and by end the century are required for assessing coastal vulnerability in changing climate. This is particularly important given possible changes regional wave climates and/or ocean water levels due to climate variability. To this end, much work has gone into development simple efficient semi-empirical models that can be used predict over ranging from seasonal multi-decadal. An alternative use time-varying model parameters improve predictability interannual timescales. Kalman filter techniques offer framework detect (or non-stationary) adjusting them as observations become available. Ibaceta et al. (2020) implemented dual state parameter Ensemble Filter (EnKF) within ShoreFor (Davidson al., 2013), showed methodology suitable best hindcasted observed evolution. Additionally, they demonstrated non-stationarity could linked characteristics underlying forcing. The application long-term datasets enables parametrization physical interpretation function multi-year variability forcing, allowing enhanced out selected training period.