HyCReWW: A Hybrid Coral Reef Wave and Water level metamodel

Rueda, Ana; Cagigal, Laura; Pearson, Stuart; Antolínez, José A. Á.; Storlazzi, Curt D.; Dongeren, Ap van; Camus, Paula; Méndez, Fernando J.

doi:10.1016/j.cageo.2019.03.004

Cited by 29 publications

(21 citation statements)

References 20 publications

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“…Although XBeach is a useful tool to examine single events, it is less suitable as an operational forecast system due to the high computational cost. However, recent advances in hybrid-downscaling of coastal flood models (e.g., Camus et al, 2011;Rueda et al, 2019Rueda et al, , 2016 could provide an adequate solution for operational forecast systems in the region. In this study, the model slightly underpredicted H s,SW and the maximum TWL.…”

Section: Discussionmentioning

confidence: 99%

“…Long-and cross-shore currents as well as wave-current interaction in the wave boundary layer and resulting changes in wave-averaged bed shear stress are also accounted for. Despite being originally developed for mild sloping beaches, XBeach has since been successfully adapted and applied to fringing reef environments (Pomeroy et al, 2012;Van Dongeren et al, 2013;Buckley et al, 2014;Quataert et al, 2015;Rueda et al, 2019). This has been achieved by including a bottom friction dissipation term in the short-wave energy balance and by increasing the bottom friction coefficient for flow cf , which is associated with currents and IG wave action, and the incident short wave dissipation fw (Van Dongeren et al, 2013;Quataert et al, 2015).…”

Section: Numerical Model (Xbeach)mentioning

confidence: 99%

“…The bathymetry on the reef platform (shallower than ∼10-15 m) and the topography data were derived from satellite imagery. The dissipation of waves across a reef is extremely sensitive to the geomorphology (i.e., reef slope, water depth, reef width, or bed friction) (Rueda et al, 2019). Initial attempts to validate the model failed due to inaccuracies in the satellite-derived bathymetry on the reef flat.…”

Section: Numerical Model (Xbeach)mentioning

confidence: 99%

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Distant-Source Swells Cause Coastal Inundation on Fiji’s Coral Coast

et al. 2020

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Distant-source swells are known to regularly inundate low-lying Pacific Island communities. Here we examine extreme total water level (TWL) and inundation driven by a distant-source swell on Fiji's Coral Coast using observations and a phase-resolving wave model (XBeach). The objective of this study is to increase understanding of swell-driven hazards in fringing reef environments to identify the contribution of wave setup and infragravity waves to extreme TWL and to investigate coastal flooding during present and future sea levels. The maximum TWL near the shore was caused by compounding mechanisms, where tides, wave setup, infragravity waves, and waves in the sea swell frequencies contributed to the TWL. Waves and wave setup on the reef were modulated by offshore wave heights and tides with increased setup during low tide and increased wave heights during high tide. Numerical simulations were able to reproduce the mechanisms contributing to the extreme TWL and allowed an estimation of the inundation extent. Simulations of the same swell under the RCP8.5 sea-level rise scenario suggest the area of inundation would increase by 97% by 2100. A comparison between the numerical model, a multiple linear regression model, and two commonly used parametric models reveals that both XBeach and the linear regression model are better suited to reproduce the nearshore wave setup and TWL than the empirical equations. The results highlight the need for customized, site-specific coastal hazard assessments and inundation forecast systems in the South Pacific.

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Section: Discussionmentioning

confidence: 99%

Section: Numerical Model (Xbeach)mentioning

confidence: 99%

Section: Numerical Model (Xbeach)mentioning

confidence: 99%

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Distant-Source Swells Cause Coastal Inundation on Fiji’s Coral Coast

et al. 2020

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“…The output of the offshore wave module is fed into the nearshore module to estimate extreme water levels at the shoreline, for example using the parameterization by Merrifield et al (2014). As coastal flooding can also be related to wave run-up and overtopping rather than only mean water levels at the shoreline, an improved nearshore wave module would predict wave run-up levels from offshore wave data (e.g., from WW3), for example, using probabilistic or deterministic models such as BEWARE (Pearson et al, 2017) or HyCReWW (Rueda et al, 2019). These models of run-up are parameter-based and require most importantly characteristic reef dimensions such as reef width, depth, slope, and hydrodynamic roughness (reef complexity and/or coral cover) that can be extracted from satellite imagery or automated remote sensing algorithms (e.g., Dekker et al, 2011;Chirayath and Earle, 2016).…”

Section: Ews Methodology (Short-term Predictions On the Order Of Days)mentioning

confidence: 99%

Steps to Develop Early Warning Systems and Future Scenarios of Storm Wave-Driven Flooding Along Coral Reef-Lined Coasts

et al. 2020

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Tropical coral reef-lined coasts are exposed to storm wave-driven flooding. In the future, flood events during storms are expected to occur more frequently and to be more severe due to sea-level rise, changes in wind and weather patterns, and the deterioration of coral reefs. Hence, disaster managers and coastal planners are in urgent need of decision-support tools. In the short-term, these tools can be applied in Early Warning Systems (EWS) that can help to prepare for and respond to impending storm-driven flood events. In the long-term, future scenarios of flooding events enable coastal communities and managers to plan and implement adequate risk-reduction strategies. Modeling tools that are used in currently available coastal flood EWS and future scenarios have been developed for open-coast sandy shorelines, which have only limited applicability for coral reef-lined shorelines. The tools need to be able to predict local sea levels, offshore waves, as well as their nearshore transformation over the reefs, and translate this information to onshore flood levels. In addition, future scenarios require long-term projections of coral reef growth, reef composition, and shoreline change. To address these challenges, we have formed the UFORiC (Understanding Flooding of Reef-lined Coasts) working group that outlines its perspectives on data and model

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“…After training the Bayesian Network (BN) with the synthetic database, it acts as an emulator or surrogate for the numerical model and provides a probabilistic estimate of wave runup for real-world coral reef profiles based on the similarity of morphology and wave conditions to that stored in the database. The database has been useful for other reanalysis studies (Pearson et al, 2018;Rueda et al, 2019b), but there are limitations that come with it. Only simplified cross-shore profiles characteristic of a fringing reef can be used (e.g., Figure 1A), and it is not representative of the global diversity of reef morphology.…”

Section: Introductionmentioning

confidence: 99%

Hydro-Morphological Characterization of Coral Reefs for Wave Runup Prediction

et al. 2020

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Many coral reef-lined coasts are low-lying with elevations <4 m above mean sea level. Climate-change-driven sea-level rise, coral reef degradation, and changes in storm wave climate will lead to greater occurrence and impacts of wave-driven flooding. This poses a significant threat to their coastal communities. While greatly at risk, the complex hydrodynamics and bathymetry of reef-lined coasts make flood risk assessment and prediction costly and difficult. Here we use a large (>30,000) dataset of measured coral reef topobathymetric cross-shore profiles, statistics, machine learning, and numerical modeling to develop a set of representative cluster profiles (RCPs) that 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 large dataset is 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, including setup at the shoreline and swash separated into infragravity and sea-swell components, of the entire dataset. The predictive capability of the RCPs is tested against 5,000 profiles from the dataset. The wave runup is predicted with a mean error of 9.7-13.1%, depending on the number of cluster profiles used, ranging from 312 to 50. 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. Such a tool can be used for climate change impact assessments and studying the effectiveness of reef restoration projects, as well as for the provision of coastal flood predictions in a simplified (global) early warning system.

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HyCReWW: A Hybrid Coral Reef Wave and Water level metamodel

Cited by 29 publications

References 20 publications

Distant-Source Swells Cause Coastal Inundation on Fiji’s Coral Coast

Distant-Source Swells Cause Coastal Inundation on Fiji’s Coral Coast

Steps to Develop Early Warning Systems and Future Scenarios of Storm Wave-Driven Flooding Along Coral Reef-Lined Coasts

Hydro-Morphological Characterization of Coral Reefs for Wave Runup Prediction

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