Statistical prediction of peak surge height from tropical cyclones under idealized coastal bathymetry

Coastal regions face increasing disaster risk from tropical cyclones (TCs), intensified by climate change. Accurate and rapid prediction of peak surge height (PSH) is essential for coastal disaster prevention and sustainable development. However, numerical models are computationally intensive, while empirical regression and machine learning approaches require large datasets and are often site-specific, limiting their applicability for real-time prediction. This study, considering a wide range of tropical cyclone scenarios under idealized coastal bathymetry, aims to address limitations by developing a statistical formula to predict PSH in nearshore areas. A total of 1080 numerical experiments were conducted using a dynamic storm surge model at a simplified bathymetry under TC parameters of central pressure deficit, maximum wind radius, and translation speed, as well as additional parameters of incidence angle, distance from the typhoon center, and initial water depth. Based on these results, a PSH prediction formula was derived using log-transformed linear regression analysis for sites below 23 m water depth. The study showed that the proposed formula provided reliable PSH predictions with R2 of 0.91. The formula demonstrates potential for computational efficiency and accuracy within the confines of the idealized conditions, providing a foundational approach to address the increasing risks of coastal disasters.