Schematic atoll models with varying theoretical morphologies were used to evaluate the relative control of individual morphological parameters on alongshore transport gradients. Here we present physics-based numerical SWAN model results of incident wave transformations for a range of atoll and island morphologies and sea-level rise scenarios. Model results are presented in NetCDF format, accompanied by a README text file that lists the parameters used in each model run. These data accompany the following publication: Shope, J.B., and Storlazzi, C.D., 2019, Assessing morphologic controls on atoll island alongshore sediment transport gradients due to future sea-level rise: Frontiers in Marine Science, doi:10.3389/fmars.2019.00245.

Projected wave climate trends from WAVEWATCH3 model output were used as input for nearshore wave models (for example, SWAN) for the main Hawaiian Islands to derive data and statistical measures (mean and top 5 percent values) of wave height, wave period, and wave direction for the recent past (1996-2005) and future projections (2026-2045 and 2085-2100). Three-hourly global climate model (GCM) wind speed and wind direction output from four different GCMs provided by the Coupled Model Inter-Comparison Project, phase 5 (CMIP5), were used as boundary conditions to the physics-based WAVEWATCH3 numerical wave model for the area encompassing the main Hawaiian islands. Two climate change scenarios for each of the four GCMs were run: the representative concentration pathway (RCP)-4.5 and RCP-8.5, representing a medium mitigation and a high emissions scenario, respectively. Simulation timeframes were limited to the years 2026-2045 and 2085-2100, as prescribed by the CMIP5 modeling framework. The WAVEWATCH3 modeled deep-water wave heights, wave periods, and wave directions, with current bathymetry were used as boundary conditions to drive simulations of mean and top 5 percent wave conditions at higher resolution over the insular shelves of the main Hawaiian islands using the 3rd-generation SWAN wave model. For each scenario, 12 simulations were made representing the month-averaged or top 5 percent conditions. The SWAN model is based on discrete spectral action balance equations, computing the evolution of random, short-crested waves. Physical processes such as bottom friction and depth induced breaking, and, non-linear quadruplet and triad wave-wave interactions are included. Wave propagation, growth, and decay are solved periodically throughout the model grid. The SWAN model has been shown to accurately model the propagation and breaking of waves over Pacific coral reefs.

Projected wave climate trends from WAVEWATCH3 model output were used as input for nearshore wave models (for example, SWAN) for the main Hawaiian Islands to derive data and statistical measures (mean and top 5 percent values) of wave height, wave period, and wave direction for the recent past (1996-2005) and future projections (2026-2045 and 2085-2100). Three-hourly global climate model (GCM) wind speed and wind direction output from four different GCMs provided by the Coupled Model Inter-Comparison Project, phase 5 (CMIP5), were used as boundary conditions to the physics-based WAVEWATCH3 numerical wave model for the area encompassing the main Hawaiian islands. Two climate change scenarios for each of the four GCMs were run: the representative concentration pathway (RCP)-4.5 and RCP-8.5, representing a medium mitigation and a high emissions scenario, respectively. Simulation timeframes were limited to the years 2026-2045 and 2085-2100, as prescribed by the CMIP5 modeling framework. The WAVEWATCH3 modeled deep-water wave heights, wave periods, and wave directions, with current bathymetry were used as boundary conditions to drive simulations of mean and top 5 percent wave conditions at higher resolution over the insular shelves of the main Hawaiian islands using the 3rd-generation SWAN wave model. For each scenario, 12 simulations were made representing the month-averaged or top 5 percent conditions. The SWAN model is based on discrete spectral action balance equations, computing the evolution of random, short-crested waves. Physical processes such as bottom friction and depth induced breaking, and, non-linear quadruplet and triad wave-wave interactions are included. Wave propagation, growth, and decay are solved periodically throughout the model grid. The SWAN model has been shown to accurately model the propagation and breaking of waves over Pacific coral reefs.

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.

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.

This data set consists of physics-based Delft3D-FLOW and SWAN hydrodynamic models input files used to study the wave-induced 3D flow over spur-and-groove (SAG) formations. SAG are a common and impressive characteristic of coral reefs. They are composed of a series of submerged shore-normal coral ridges (spurs) separated by shore-normal patches of sediment (grooves) on the fore reef of coral reef environments. Although their existence and geometrical properties are well documented, the literature concerning the hydrodynamics around them is sparse. Here, the three-dimensional flow patterns over SAG formations, and a sensitivity of those patterns to waves, currents, and SAG geometry were examined. Shore-normal shoaling waves over SAG formations were shown to drive two circulation cells: 1) a cell on the lower fore reef with offshore flow over the spur and onshore flow over the groove, except near the seabed where velocities were always onshore; and 2) a cell on the upper fore reef with offshore surface velocities and onshore bottom currents, which result in depth-averaged onshore and offshore flow over the spurs and grooves, respectively. These input files accompany the modeling conducted for the following publication: da Silva, R.F., Storlazzi, C.D., Rogers, J.S., Reyns, J., and McCall, R., 2020, Modeling three-dimensional flow over spur-and-groove morphology: Coral Reefs, https://doi.org/10.1007/s00338-020-02011-8.

This data set consists of physics-based Delft3D-FLOW and SWAN hydrodynamic models input files used to study the wave-induced 3D flow over spur-and-groove (SAG) formations. SAG are a common and impressive characteristic of coral reefs. They are composed of a series of submerged shore-normal coral ridges (spurs) separated by shore-normal patches of sediment (grooves) on the fore reef of coral reef environments. Although their existence and geometrical properties are well documented, the literature concerning the hydrodynamics around them is sparse. Here, the three-dimensional flow patterns over SAG formations, and a sensitivity of those patterns to waves, currents, and SAG geometry were examined. Shore-normal shoaling waves over SAG formations were shown to drive two circulation cells: 1) a cell on the lower fore reef with offshore flow over the spur and onshore flow over the groove, except near the seabed where velocities were always onshore; and 2) a cell on the upper fore reef with offshore surface velocities and onshore bottom currents, which result in depth-averaged onshore and offshore flow over the spurs and grooves, respectively. These input files accompany the modeling conducted for the following publication: da Silva, R.F., Storlazzi, C.D., Rogers, J.S., Reyns, J., and McCall, R., 2020, Modeling three-dimensional flow over spur-and-groove morphology: Coral Reefs, https://doi.org/10.1007/s00338-020-02011-8.

An extensive set of physics-based XBeach Non-hydrostatic hydrodynamic model simulations (with input files here included) were used to evaluate the influence of shore-normal reef channels on flooding along fringing reef-lined coasts, specifically during extreme wave conditions when the risk for coastal flooding and the resulting impact to coastal communities is greatest. These input files accompany the modeling conducted for the following publication: Storlazzi, C.D., Rey, A.E., and van Dongeren, A.R., 2022, A numerical study of geomorphic and oceanographic controls on wave-driven runup on fringing reefs with shore-normal channels: Journal of Marine Science and Engineering, 10(6), 828, https://doi.org/10.3390/jmse10060828.

An extensive set of physics-based XBeach Non-hydrostatic hydrodynamic model simulations (with input files here included) were used to evaluate the influence of shore-normal reef channels on flooding along fringing reef-lined coasts, specifically during extreme wave conditions when the risk for coastal flooding and the resulting impact to coastal communities is greatest. These input files accompany the modeling conducted for the following publication: Storlazzi, C.D., Rey, A.E., and van Dongeren, A.R., 2022, A numerical study of geomorphic and oceanographic controls on wave-driven runup on fringing reefs with shore-normal channels: Journal of Marine Science and Engineering, 10(6), 828, https://doi.org/10.3390/jmse10060828.

This portion of the USGS data release contains simulated nearshore wave parameters derived from a stand-alone spectral wave model of the Columbia River littoral cell, Washington and Oregon. The model output includes significant wave heights, peak wave periods, mean wave directions, and water depths for a series of 221 shore normal transects that extended from the coastline to the -15 m NAVD88 elevation (about 16.5 m average water depth). Data are provided at the seaward extent of each transect as well as at the location of the break point, or location just outside the surf zone, which varied dynamically based on the local bathymetry and wave conditions. Additional data are provided at four locations corresponding to the locations of buoy observations used to validate the model application. The data are derived from two hindcasts solved at hourly intervals between 1) August 2014 to September 2023 (h1), and 2) July 2010 to August 2011 (h2). The data from both hindcasts were compiled into netCDF files for the nearshore and buoy locations for distribution.