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HyCReWW database: A hybrid coral reef wave and water level metamodel

Dates

Publication Date
Time Period
2018

Citation

Rueda, A., Cagigal, L., Pearson, S., Antolinez, J.A.A., Storlazzi, C.D., van Dongeren, A.R., Camus, P., and Mendez, F.J., 2019, HyCReWW database: A hybrid coral reef wave and water level metamodel: Computers & Geosciences, https://doi.org/10.5066/F7SX6CFQ.

Summary

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 [...]

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HyCreWW code.zip 785.93 KB
HyCreWW_code_metadata.xml
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Purpose

Low-lying tropical coasts fronted by coral reefs are threatened by the effects of climate change, sea-level rise, and flooding caused by waves. However, the reefs on these coasts differ widely in their shape, size, and physical characteristics; the wave and water level conditions affecting these coastlines also vary in space and time. These factors make it difficult to predict flooding caused by waves along coral reef-lined coasts. We created a metamodel ("HyCReWW") that estimates how different wave, water level, and reef combinations can lead to flooding. HyCReWW can be used to make short-term predictions of flooding in early warning systems, or long-term predictions of how climate change will affect flooding caused by waves on coral reef-lined coasts.

Additional Information

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DOI https://www.sciencebase.gov/vocab/category/item/identifier doi:10.5066/F7SX6CFQ

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