Print Email Facebook Twitter Hydro-Morphological Characterization of Coral Reefs for Wave Runup Prediction Title Hydro-Morphological Characterization of Coral Reefs for Wave Runup Prediction Author Scott, Fred (Deltares; W.F. Baird Associates) Antolinez, Jose A.A. (Deltares) McCall, Robert (Deltares) Storlazzi, Curt (North Central Climate Science Centre) Reniers, A.J.H.M. (TU Delft Environmental Fluid Mechanics) Pearson, S.G. (TU Delft Coastal Engineering; Deltares) Date 2020 Abstract Many coral reef-lined coasts are low-lying with elevations <4 m 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 modeling 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 studying the effectiveness of reef restoration projects, as well as for the provision of coastal flood predictions in a simplified (global) early warning system. Subject cluster analysiscoral reefsdata miningK-meanswave runupXBeach To reference this document use: http://resolver.tudelft.nl/uuid:4ad2d7d7-df5a-43b2-a05b-ab8401173fc4 DOI https://doi.org/10.3389/fmars.2020.00361 Source Frontiers in Marine Science, 7 Part of collection Institutional Repository Document type journal article Rights © 2020 Fred Scott, Jose A.A. Antolinez, Robert McCall, Curt Storlazzi, A.J.H.M. Reniers, S.G. Pearson Files PDF fmars_07_00361.pdf 2.43 MB Close viewer /islandora/object/uuid:4ad2d7d7-df5a-43b2-a05b-ab8401173fc4/datastream/OBJ/view