A Bayesian-Based System to Assess Wave-Driven Flooding Hazards on Coral Reef-Lined Coasts

42Citations
Citations of this article
94Readers
Mendeley users who have this article in their library.

This article is free to access.

Abstract

Many low-elevation, coral reef-lined, tropical coasts are vulnerable to the effects of climate change, sea level rise, and wave-induced flooding. The considerable morphological diversity of these coasts and the variability of the hydrodynamic forcing that they are exposed to make predicting wave-induced flooding a challenge. 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. It was found that, in order to accurately predict flooding hazards, water depth over the reef flat, incident wave conditions, and reef flat width are the most essential factors, whereas other factors such as beach slope and bed friction due to the presence or absence of corals are less important. 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.

Cite

CITATION STYLE

APA

Pearson, S. G., Storlazzi, C. D., van Dongeren, A. R., Tissier, M. F. S., & 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, 122(12), 10099–10117. https://doi.org/10.1002/2017JC013204

Register to see more suggestions

Mendeley helps you to discover research relevant for your work.

Already have an account?

Save time finding and organizing research with Mendeley

Sign up for free