Hydro-Morphological Characterization of Coral Reefs for Wave Runup Prediction - Scott, Fred; Antolinez, Jose A. A.; McCall, Robert; Storlazzi, Curt; Reniers, Ad; Pearson, Stuart (Frontiers in Marine Science, 2020)
Many coral reef-lined coasts are low-lying with elevations less than four meters above mean sea level. Climate-change-driven sea-level rise, coral reef degradation, and changes in storm wave climate will lead to greater occurrence and impacts of wave-driven flooding. This poses a significant threat to their coastal communities. While greatly at risk, the complex hydrodynamics and bathymetry of reef-lined coasts make flood risk assessment and prediction costly and difficult. Here we use a large ($>$30,000) dataset of measured coral reef topobathymetric cross-shore profiles, statistics, machine learning, and numerical modelling to develop a set of representative cluster profiles (RCPs) that can be used to accurately represent the shoreline hydrodynamics of a large variety of coral reef-lined coasts around the globe. In two stages, the large dataset is reduced by clustering cross-shore profiles based on morphology and hydrodynamic response to typical wind and swell wave conditions. By representing a large variety of coral reef morphologies with a reduced number of RCPs, a computationally feasible number of numerical model simulations can be done to obtain wave runup estimates, including setup at the shoreline and swash separated into infragravity and sea-swell components, of the entire dataset. The predictive capability of the RCPs is tested against 5,000 profiles from the dataset. The wave runup is predicted with a mean error of 9.7% -- 13.1%, depending on the number of cluster profiles used, ranging from 312 to 50. The RCPs identified here can be combined with probabilistic tools that can provide an enhanced prediction given a multivariate wave and water level climate and reef ecology state. Such a tool can be used for climate change impact assessments and effectiveness of reef restoration projects, as well as for the provision of coastal flood potential predictions in a simplified (global) early warning system.