On the runup parameterisation for reef-lined coasts

Abstract

The degradation of coastal ecosystems in recent years, combined with more intense storms and greater sea levels associated with climate change, are likely to increase vulnerability to coastal flooding along reef-lined coasts. Therefore, there is a need to accurately predict extreme water levels to identify areas with high vulnerability and implement mitigation measures. Runup parameterisations allow a rapid assessment of coastal vulnerability at a regional to global scale, however these formulations are primarily developed for beaches. Hydrodynamic forcing and reef geometry are key parameters for the estimation of coastal flooding in reef environments. The present study aims to develop runup parameterisations for an idealised 2DV reef-lined coast profile using a widely validated nonlinear non-hydrostatic numerical model (SWASH). The numerical model is employed to simulate different combinations of wave conditions, water levels, and reef geometries. A machine learning (ML) approach, in the form of genetic programming, is used to identify the most suitable predictors for wave runup based on the numerical results. Analysis of runup results suggests that runup parameterisations can be improved for reef environments by incorporating the crest elevation, lagoon width, reef flat depth, and forereef slope. A dimensional and non-dimensional parameterisation that include reef geometry are presented. Further research efforts should be devoted to incorporate the effects of bed roughness and three-dimensional processes in this framework that were not taken into account in the present work.