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BEWARE2 database: A meta-process model to assess wave-driven flooding hazards on morphologically diverse, coral reef-lined coasts

Published by U.S. Geological Survey | Department of the Interior | Metadata Last Checked: January 27, 2026 | Last Modified: 2024-09-25T00:00:00Z
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.

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