Coral reefs are effective natural coastal flood barriers that protect adjacent communities. Coral degradation compromises the coastal protection value of reefs while also reducing their other ecosystem services, making them a target for restoration. Here we provide a physics-based evaluation of how coral restoration can reduce coastal flooding for various types of reefs. Wave-driven flooding reduction is greatest for broader, shallower restorations on the upper fore reef and between the middle of the reef flat and the shoreline than for deeper locations on the fore reef or at the reef crest. These results indicate that to increase the coastal hazard risk reduction potential of reef restoration, more physically robust species of coral need to be outplanted to shallower, more energetic locations than more fragile, faster-growing species primarily being grown in coral nurseries. The optimization and quantification of coral reef restoration efforts to reduce coastal flooding may open hazard risk reduction funding for conservation purposes.
<p>The impact of extreme weather events on coastal areas around the world is set to increase in the future, both through sea level rise, climate change (increasing storm intensity, rainfall and droughts) and continued development and investment in hazard-prone deltaic and coastal environments. Given the changing natural and socio-economic environment, accurate predictions of current and future risk are becoming increasingly important world-wide to mitigate risks. Recent advances in computational power (e.g., cloud-computing) and data availability (e.g., growth of satellite-derived products) are enabling, for the first time, the development of global scale flood risk models for application in areas where local models are less well developed or prohibitively expensive, or for applications where a synoptic global coverage is important. Despite the increasing granularity of these global models and datasets however, they often still lack the resolution and accuracy to be &#8220;locally relevant&#8221;, especially where inundation and impact assessments are considered. While a solution to this problem is to downscale global models and datasets to the local scale, setting up local models is hampered by inconsistency between underlying datasets, and the required manual effort to generate downscaled integrated risk models inhibits their global application. To address these issues, we are developing a generalized risk assessment framework, called GHIRAF (Globally-applicable High-resolution Integrated Risk Assessment Framework), which couples data and models to quickly provide locally-actionable information on impact of historic, current- and future world-wide extreme weather events (e.g., storms, extreme rainfall, drought). The framework is designed to support world-wide efforts to reduce and mitigate risks associated with extreme weather events by aiding prevention (scenario-testing, design) and preparation (Early Warning) for extreme events, as well as support response (targeted relief efforts) and recovery (build-back-better) efforts. In this work we discuss application of the framework to study hurricane impacts on the eastern coast of the USA, as well as in data-poor, small island state environments.</p>
<p>Many coral reef islands are low-lying, which in combination with population growth, sea level rise and possibly more frequent extreme weather events is likely to result in increased coastal risk (e.g. Storlazzi et al., 2015). On smaller scales of O(10 km) wave-driven coastal inundation can be accurately predicted with advanced models such as XBeach (Roelvink et al., 2009), at already high computational costs. For larger scales, larger number of islands, for scenario modelling, and for implementation in early warning systems, computationally faster methods are needed. Reduced physics models, which neglect some of the processes (e.g. non-hydrostatic pressure gradient term and viscosity), are a potential solution. However, their accuracy and the best method to force them has not been established.</p><p>In this research we propose a new methodology to model wave-driven flooding on coral reef-lined coasts. A look-up-table (LUT), composed of XBeach model runs, is combined with a reduced-physics model, SFINCS (Leijnse et al., 2021), to achieve high accuracy predictions at limited computational expense. The LUT consists of pre-run 1D XBeach simulations for several reef profiles from Scott et al. (2020), forced with different offshore wave and water level conditions. Wave conditions close to the shore as predicted by the LUT are used to force SFINCS which then simulates the wave runup, overtopping and flooding. These are forced in SFINCS using random wave timeseries from an interpolated parameterized wave spectrum following Athif (2020).</p><p>The accuracy of the method is investigated for 6 distinctive cross-shore profiles from Scott et al. (2020), for two wave scenarios (gentle swell and stormy conditions). Results of complete XBeach simulations are compared to LUT-SFINCS simulations with different boundary forcing locations. The sensitivity analysis shows that the preferred boundary location to initialize the SFINCS model is at a water depth between 0.5 m and 2.5 m, preferably shoreward of the reef edge. Errors introduced by the generated parameterized spectra lead to runup estimation errors of up to around 40% depending on reef geometry. The developed methodology will be applied to a case study of Majuro Island, the Republic of Marshall Islands, as proof of concept.</p><p>&#160;</p><p><strong>References</strong></p><p>Athif, A. A. (2020). Computationally efficient modelling of wave driven flooding in Atoll Islands: Investigation on the use of a reduced-physics model solver SFINCS. Master&#8217;s thesis, IHE, the Netherlands.</p><p>Leijnse, T., van Ormondt, M., Nederhoff, K., and van Dongeren, A. (2021). Modeling compound flooding in coastal systems using a computationally efficient reduced-physics solver: Including fluvial, pluvial, tidal, wind-and wave- driven processes. <em>Coastal Engineering</em>, 163:103796.</p><p>Roelvink, D., Reniers, A., Van Dongeren, A. P., De Vries, J. V. T., McCall, R., and Lescinski, J. (2009). Modelling storm impacts on beaches, dunes and barrier islands. <em>Coastal engineering</em>, 56(11-12), 1133-1152.</p><p>Scott, F., Antolinez, J. A. A., Mccall, R., Storlazzi, C., Reniers, A., and Pearson, S. (2020). Hydro-Morphological Characterization of Coral Reefs for Wave Runup Prediction. <em>Frontiers in Marine Science</em>, 7(May):1&#8211;20.</p><p>Storlazzi, C. D., Elias, E. P., and Berkowitz, P. (2015). Many atolls may be uninhabitable within decades due to climate change. <em>Scientific reports</em>, 5:14546.</p>
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