Coral reef profiles for wave-runup prediction

This data release includes representative cluster profiles (RCPs) from a large (>24,000) selection of coral reef topobathymetric cross-shore profiles (Scott and others, 2020). We used statistics, machine learning, and numerical modelling to develop the set of RCPs, which 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 data were 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. 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. These data accompany the following publication: Scott, F., Antolinez, J.A., McCall, R.T., Storlazzi, C.D., Reniers, A., and Pearson, S., 2020, Hydro-morphological characterization of coral reefs for wave runup prediction: Frontiers in Marine Science, https://doi.org/10.3389/fmars.2020.000361.

Data e Risorse

Campo Valore
accessLevel public
bureauCode {010:12}
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identifier http://datainventory.doi.gov/id/dataset/usgs-5e596bf5e4b01d50924c66f5
metadata_type geospatial
modified 2021-10-13T00:00:00Z
old-spatial -180.00000000, -90.00000000, 180.00000000, 90.00000000
publisher U.S. Geological Survey
resource-type Dataset
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spatial {"type": "Polygon", "coordinates": [[[-180.00000000, -90.00000000], [-180.00000000, 90.00000000], [ 180.00000000, 90.00000000], [ 180.00000000, -90.00000000], [-180.00000000, -90.00000000]]]}
theme {geospatial}
Gruppi
  • AmeriGEOSS
  • National Provider
  • North America
Tag
  • AmeriGEO
  • AmeriGEOSS
  • CKAN
  • GEO
  • GEOSS
  • National
  • North America
  • United States
  • cmhrp
  • coastal-and-marine-hazards-and-resources-program
  • coastal-processes
  • commonwealth-of-the-northern-mariana-islands
  • coral-reefs
  • flooding
  • geoscientificinformation
  • hazards
  • numerical-modeling
  • oceans
  • pacific-coastal-and-marine-science-center
  • pcmsc
  • puerto-rico
  • reef
  • state-of-florida
  • state-of-hawaii
  • territory-of-american-samoa
  • territory-of-guam
  • u-s-geological-survey
  • united-states-virgin-islands
  • usgs
  • usgs-5e596bf5e4b01d50924c66f5
  • water-column-features
  • water-level
  • waves
isopen False
license_id notspecified
license_title License not specified
maintainer PCMSC Science Data Coordinator
maintainer_email pcmsc_data@usgs.gov
metadata_created 2025-09-23T14:33:07.139470
metadata_modified 2025-09-23T14:33:07.139478
notes This data release includes representative cluster profiles (RCPs) from a large (>24,000) selection of coral reef topobathymetric cross-shore profiles (Scott and others, 2020). We used statistics, machine learning, and numerical modelling to develop the set of RCPs, which 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 data were 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. 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. These data accompany the following publication: Scott, F., Antolinez, J.A., McCall, R.T., Storlazzi, C.D., Reniers, A., and Pearson, S., 2020, Hydro-morphological characterization of coral reefs for wave runup prediction: Frontiers in Marine Science, https://doi.org/10.3389/fmars.2020.000361.
num_resources 2
num_tags 33
title Coral reef profiles for wave-runup prediction