<?xml version='1.0' encoding='utf-8'?>
<oai_dc:dc xmlns:dc="http://purl.org/dc/elements/1.1/" xmlns:oai_dc="http://www.openarchives.org/OAI/2.0/oai_dc/" xmlns:xsi="http://www.w3.org/2001/XMLSchema-instance" xsi:schemaLocation="http://www.openarchives.org/OAI/2.0/oai_dc/ http://www.openarchives.org/OAI/2.0/oai_dc.xsd">
  <dc:contributor>Jose A.A. Antolinez</dc:contributor>
  <dc:contributor>Robert T. McCall</dc:contributor>
  <dc:contributor>Curt D. Storlazzi</dc:contributor>
  <dc:contributor>Ad Reiners</dc:contributor>
  <dc:contributor>Stuart Pearson</dc:contributor>
  <dc:creator>Fred Scott</dc:creator>
  <dc:date>2020</dc:date>
  <dc:description>&lt;p&gt;&lt;span&gt;Many coral reef-lined coasts are low-lying with elevations &amp;lt;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 (&amp;gt;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.&lt;/span&gt;&lt;/p&gt;</dc:description>
  <dc:format>application/pdf</dc:format>
  <dc:identifier>10.3389/fmars.2020.00361</dc:identifier>
  <dc:language>en</dc:language>
  <dc:publisher>Frontiers</dc:publisher>
  <dc:title>Hydro-morphological characterization of coral reefs for wave runup prediction</dc:title>
  <dc:type>article</dc:type>
</oai_dc:dc>