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.
Complete Metadata
| accessLevel | public |
|---|---|
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[
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|
| description | 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. |
| distribution |
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| identifier | http://datainventory.doi.gov/id/dataset/USGS_5e596bf5e4b01d50924c66f5 |
| keyword |
[
"CMHRP",
"Coastal and Marine Hazards and Resources Program",
"Commonwealth of the Northern Mariana Islands",
"PCMSC",
"Pacific Coastal and Marine Science Center",
"Puerto Rico",
"State of Florida",
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|
| modified | 2021-10-13T00:00:00Z |
| publisher |
{
"name": "U.S. Geological Survey",
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| spatial | -180.00000000, -90.00000000, 180.00000000, 90.00000000 |
| theme |
[
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|
| title | Coral reef profiles for wave-runup prediction |
