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.

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.

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.

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 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.

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.

Provided here are the required input files to run a standalone wave model (Simulating Waves Nearshore [SWAN]; Booij and others, 1999) on eleven model domains from the Canada-U.S. border to Norton Sound, Alaska. The model runs create a downscaled wave database (DWDB) which, can be used to reconstruct hindcast, historical, or projected time series at each point in the model domains (see Engelstad and others, 2023 for further information on reconstruction of time-series). The model forcing files consist of reduced sets of binned wind and wave parameter combinations, hereafter termed ‘sea states’. The use of representative sea states allows for lower computational costs and follows modified methods outlined in for example Camus and others, 2011, Reguero and others, 2013, and Lucero and others, 2017. Wind and wave parameters were extracted from the ERA5 reanalysis (Hersbach and others, 2020; https://cds.climate.copernicus.eu/) for the hindcast period (1979–2019) and for the historical (1979-2014) and projected (2020-2050) time periods from WAVEWATCHIII wave model runs (Erikson and others, 2022) driven by winds and sea ice fields from the 6th generation Coupled Model Inter-comparison Projects (CMIP6 Haarsma and others, 2016 The extent of each model domain can be inferred from the browse graphic. Model input files are described in the Entity and Attribute Overview section.

Provided here are the required input files to run a standalone wave model (Simulating Waves Nearshore [SWAN]; Booij and others, 1999) on eleven model domains from the Canada-U.S. border to Norton Sound, Alaska. The model runs create a downscaled wave database (DWDB) which, can be used to reconstruct hindcast, historical, or projected time series at each point in the model domains (see Engelstad and others, 2023 for further information on reconstruction of time-series). The model forcing files consist of reduced sets of binned wind and wave parameter combinations, hereafter termed ‘sea states’. The use of representative sea states allows for lower computational costs and follows modified methods outlined in for example Camus and others, 2011, Reguero and others, 2013, and Lucero and others, 2017. Wind and wave parameters were extracted from the ERA5 reanalysis (Hersbach and others, 2020; https://cds.climate.copernicus.eu/) for the hindcast period (1979–2019) and for the historical (1979-2014) and projected (2020-2050) time periods from WAVEWATCHIII wave model runs (Erikson and others, 2022) driven by winds and sea ice fields from the 6th generation Coupled Model Inter-comparison Projects (CMIP6 Haarsma and others, 2016 The extent of each model domain can be inferred from the browse graphic. Model input files are described in the Entity and Attribute Overview section.

Provided here are the required input files to run a standalone wave model (Simulating Waves Nearshore [SWAN]; Booij and others, 1999) on eleven model domains from the Canada-U.S. border to Norton Sound, Alaska. The model runs create a downscaled wave database (DWDB) which, can be used to reconstruct hindcast, historical, or projected time series at each point in the model domains (see Engelstad and others, 2023 for further information on reconstruction of time-series). The model forcing files consist of reduced sets of binned wind and wave parameter combinations, hereafter termed ‘sea states’. The use of representative sea states allows for lower computational costs and follows modified methods outlined in for example Camus and others, 2011, Reguero and others, 2013, and Lucero and others, 2017. Wind and wave parameters were extracted from the ERA5 reanalysis (Hersbach and others, 2020; https://cds.climate.copernicus.eu/) for the hindcast period (1979–2019) and for the historical (1979-2014) and projected (2020-2050) time periods from WAVEWATCHIII wave model runs (Erikson and others, 2022) driven by winds and sea ice fields from the 6th generation Coupled Model Inter-comparison Projects (CMIP6 Haarsma and others, 2016 The extent of each model domain can be inferred from the browse graphic. Model input files are described in the Entity and Attribute Overview section.