Hydro-Morphological Characterization of Coral Reefs for Wave Runup Prediction

Fred Scott,Robert Mccall,Curt Storlazzi,Ad Reniers,Stuart Pearson,Jose A A Antolinez

doi:10.3389/fmars.2020.00361

Fred Scott, Robert Mccall + Show 4 more

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https://doi.org/10.3389/fmars.2020.00361

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Abstract

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.

Highlights

Flooding of coral reef-lined coasts affects thousands of vulnerable communities around the world, and climate change induced sea level rise (SLR) and coral degradation are going to continue to intensify the magnitude and frequency of hazardous flooding events (Ferrario et al, 2014; Quataert et al, 2015; Storlazzi et al, 2015; Vitousek et al, 2017).The low elevations of most coral reef-lined coasts increase the relative influence of the incoming swell waves and make them extremely susceptible to flooding, especially during tropicalHydro-Morphological Characterization of Coral Reefs cyclones or “blue-sky” events
The median of each group is used as the representative profile and is known as the initial representative cluster profile (RCP) (iRCPs)
Data mining techniques were used to reduce an extensive dataset of coral reef topobathymetric cross-shore profiles to a subset of RCPs

Summary

Introduction

Flooding of coral reef-lined coasts affects thousands of vulnerable communities around the world, and climate change induced sea level rise (SLR) and coral degradation are going to continue to intensify the magnitude and frequency of hazardous flooding events (Ferrario et al, 2014; Quataert et al, 2015; Storlazzi et al, 2015; Vitousek et al, 2017).The low elevations of most coral reef-lined coasts increase the relative influence of the incoming swell waves and make them extremely susceptible to flooding, especially during tropicalHydro-Morphological Characterization of Coral Reefs cyclones or “blue-sky” events. For millions of people living in areas at risk of coastal flooding (UNFPA, 2014), the vast majority have no early warning system (EWS) in place due to high cost and/or the required technology. This problem has gained worldwide attention, and as a result the United Nations-endorsed Sendai Framework for Disaster Risk Reduction has called for the improved access to early warning systems and disaster risk assessments by 2030 (UNISDR, 2015). A simple, globally applicable tool to better understand and forecast wave runup will enhance access to early warnings of flood events and will, in turn, increase coastal resilience for numerous communities around the globe

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