Low-lying tropical coasts fronted by coral reefs are threatened by the effects of climate change, sea-level rise, and flooding caused by waves. However, the reefs on these coasts differ widely in their shape, size, and physical characteristics; the wave and water level conditions affecting these coastlines also vary in space and time. These factors make it difficult to predict flooding caused by waves along coral reef-lined coasts. Here we use a large (>24,000) 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. Such a tool can be used for longer term assessments of 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.
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Use_Constraints:USGS-authored or produced data and information are in the public domain from the U.S. Government and are freely redistributable with proper metadata and source attribution. Please recognize and acknowledge Deltares, the U.S. Geological Survey, and TU Delft as the originators of the dataset and in products derived from these data.
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