Probabilistic Forecasting of Shoreline Evolution

Abstract

Sandy beach erosion is a pressing concern for coastal regions worldwide, driven by both natural processes and human-induced pressures. This study presents an ensemble modeling approach for predicting sandy shoreline dynamics using an equilibrium-based shoreline evolution model (EBSEM). A genetic algorithm (NSGA-II) was employed to calibrate multiple parameter sets, capturing the inherent uncertainty in model parameters. The method was tested with a publicly available dataset from Tairua Beach (New Zealand), spanning 14 years of high-resolution shoreline measurements. Results reveal a near-linear relationship between the slope and intercept parameters governing equilibrium wave energy, and demonstrate that the best-fit solution generally lies within the ensemble range. Comparisons of ensemble simulations with observed data indicate strong agreement in both calibration and validation phases, although certain extreme accretion and erosion events were underestimated. Overall, this ensemble framework provides a robust tool for medium- to long-term shoreline predictions, bringing coastal managers with stochastic/probabilistic estimates of shoreline change, which can be useful in the assessment of resilience of adaptive strategies for risk mitigation.