This dataset contains the reef profiles and resulting hydrodynamic outputs of the "Broad-range Estimator of Wave Attack in Reef Environments" (BEWARE-2) meta-process modeling system. A process-based, wave-resolving hydrodynamic model (XBeach Non-Hydrostatic+, "XBNH+") was used to create a large synthetic database for use in BEWARE-2, relating incident hydrodynamics and coral reef geomorphology to coastal flooding hazards on reef-lined coasts. Building on previous work, BEWARE-2 improves system understanding of reef hydrodynamics by examining the intrinsic reef and extrinsic forcing factors controlling runup and flooding on reef-lined coasts. We developed this meta-process model using a training dataset of hydrodynamics and wave runup computed by XBNH+ for 440 combinations of water level, wave height, and wave period on 195 morphologically diverse representative reef profiles. In validation, the BEWARE-2 modeling system produced runup results that had a root-mean square error of 0.63 m and bias of 0.26 m, relative to runup of 0.17 to 20.9 m simulated by XBNH+ for a large range of oceanographic forcing conditions and for a diverse reef morphologies. This relatively accurate solution is provided by the BEWARE-2 modeling system 4 to 5 orders of magnitude faster than the full, process-based hydrodynamic model and could therefore be integrated in large-scale early-warning systems for tropical, reef-lined coasts, as well as used for large-scale flood risk assessments. These data accompany the following publication: McCall, R.T., Storlazzi, C.D., Roelvink, F.E., Pearson, S.G., de Goede, R., Antolinez, J., 2024, Rapid simulation of wave runup on morphologically-diverse, reef-lined coasts with the BEWARE-2 meta-process model: Natural Hazards and Earth System Sciences, https://doi.org/10.5194/nhess-2024-28.

A process-based wave-resolving hydrodynamic model (XBeach Non-Hydrostatic, ‘XBNH’) was used to create a large synthetic database for use in a “Bayesian Estimator for Wave Attack in Reef Environments” (BEWARE), relating incident hydrodynamics and coral reef geomorphology to coastal flooding hazards on reef-lined coasts. Building on previous work, BEWARE improves system understanding of reef hydrodynamics by examining the intrinsic reef and extrinsic forcing factors controlling runup and flooding on reef-lined coasts. The Bayesian estimator has high predictive skill for the XBNH model outputs that are flooding indicators, and was validated for a number of available field cases. BEWARE is a potentially powerful tool for use in early warning systems or risk assessment studies, and can be used to make projections about how wave-induced flooding on coral reef-lined coasts may change due to climate change. These data accompany the following publication: Pearson, S.G., Storlazzi, C.D., van Dongeren, A.R., Tissier, M.F.S., and Reniers, A.J.H.M., 2017, A Bayesian-based system to assess wave-driven flooding hazards on coral reef-lined coasts: Journal of Geophysical Research—Oceans, https://doi.org/10.1002/2017JC013204.

A process-based wave-resolving hydrodynamic model (XBeach Non-Hydrostatic, ‘XBNH’) was used to create a large synthetic database for use in a “Bayesian Estimator for Wave Attack in Reef Environments” (BEWARE), relating incident hydrodynamics and coral reef geomorphology to coastal flooding hazards on reef-lined coasts. Building on previous work, BEWARE improves system understanding of reef hydrodynamics by examining the intrinsic reef and extrinsic forcing factors controlling runup and flooding on reef-lined coasts. The Bayesian estimator has high predictive skill for the XBNH model outputs that are flooding indicators, and was validated for a number of available field cases. BEWARE is a potentially powerful tool for use in early warning systems or risk assessment studies, and can be used to make projections about how wave-induced flooding on coral reef-lined coasts may change due to climate change. These data accompany the following publication: Pearson, S.G., Storlazzi, C.D., van Dongeren, A.R., Tissier, M.F.S., and Reniers, A.J.H.M., 2017, A Bayesian-based system to assess wave-driven flooding hazards on coral reef-lined coasts: Journal of Geophysical Research—Oceans, https://doi.org/10.1002/2017JC013204.

We developed the HyCReWW metamodel to predict wave run-up under a wide range of coral reef morphometric and offshore forcing characteristics. Due to the complexity and high dimensionality of the problem, we assumed an idealized one-dimensional reef profile, characterized by seven primary parameters. XBeach Non-Hydrostatic was chosen to create the synthetic dataset and Radial Basis Functions implemented in Matlab were chosen for interpolation. Results demonstrate the applicability of the metamodel to obtain fast and accurate results of wave run-up for a large range of intrinsic coral reef morphologic and extrinsic hydrodynamic forcing parameters, offering a useful tool for risk management and early warning systems. These data accompany the following publication: Rueda, A., Cagigal, L., Pearson, S., Antolinez J.A.A., Storlazzi, C., van Dongeren, A., Camus, P., Mendez, F.J., 2019, HyCReWW: A hybrid coral reef waves and water level metamodel: Computers & Geosciences, https://doi.org/10.1016/j.cageo.2019.03.004.

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.

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.

This data release includes the XBeach input data files used to evaluate the importance of explicitly modeling sea-swell waves for runup. This was examined using a 2D XBeach short wave-averaged (surfbeat, XB-SB) and a wave-resolving (non-hydrostatic, XB-NH) model of Roi-Namur Island on Kwajalein Atoll in the Republic of Marshall Islands. Results show that explicitly modelling the sea-swell component (using XB-NH) provides a better approximation of the observed runup than XB-SB (which only models the time-variation of the sea-swell wave height), despite good model performance of both models on reef flat water levels and wave heights. However, both models under-predict runup peaks. The difference between XB-SB and XB-NH increases for more extreme wave events and higher sea levels, as XB-NH resolves individual waves and therefore captures SS-wave motions in runup. However, for even larger forcing conditions with offshore wave heights of 6 m, the island is flooded in both XB-SB and XB-NH computations, regardless of the sea-swell wave energy contribution. In such cases, XB-SB would be adequate to model flooding depths and extents on the island while requiring 4-5 times less computational effort. These input files accompany the modeling for following publication: Quataert, E., Storlazzi, C., van Dongeren, A., and McCall, R., 2020, The importance of explicitly modeling sea-swell waves for runup on reef-lined coasts: Coastal Engineering, https://doi.org/10.1016/j.coastaleng.2020.103704