The Response of Vegetated Dunes to Wave Attack

Vegetation is believed to increase the stability of dunes during wave attack, but limited data is available. A physical model study was performed to evaluate changes in the dune stability with and without biomass, both above and belowground. The above and belowground biomass was modeled using wooden dowels and coir fibers, respectively. For both the collision and overwash storm impact regimes, the results of this study clearly demonstrate that the inclusion of biomass in the model dune reduces the erosion and overwash. The combination of both above and belowground biomass was the most effective at reducing erosion followed by belowground biomass, with aboveground biomass providing the smallest benefit regardless of the wave condition and water level. Additionally, the overwash of sediment and water was decreased with the inclusion of biomass, following the same trends as the erosion. As the dune eroded, the storm impact regime transitioned from collision to overwash. The inclusion of biomass delays this transition in storm impact regime, providing greater protection to coastal communities. This study highlights the need to consider dune vegetation for dune construction and coastal planning.

Modeling the Morphodynamics of Coastal Responses to Extreme Events: What Shape Are We In?

This review focuses on recent advances in process-based numerical models of the impact of extreme storms on sandy coasts. Driven by larger-scale models of meteorology and hydrodynamics, these models simulate morphodynamics across the Sallenger storm-impact scale, including swash, collision, overwash, and inundation. Models are becoming both wider (as more processes are added) and deeper (as detailed physics replaces earlier parameterizations). Algorithms for wave-induced flows and sediment transport under shoaling waves are among the recent developments. Community and open-source models have become the norm. Observations of initial conditions (topography, land cover, and sediment characteristics) have become more detailed, and improvements in tropical cyclone and wave models provide forcing (winds, waves, surge, and upland flow) that is better resolved and more accurate, yielding commensurate improvements in model skill. We foresee that future storm-impact models will increasingly resolve individual waves, apply data assimilation, and be used in ensemble modeling modes to predict uncertainties. Expected final online publication date for the Annual Review of Marine Science, Volume 14 is January 2022. Please see http://www.annualreviews.org/page/journal/pubdates for revised estimates.

Automatic Creation of Storm Impact Database Based on Video Monitoring and Convolutional Neural Networks

Data about storm impacts are essential for the disaster risk reduction process, but unlike data about storm characteristics, they are not routinely collected. In this paper, we demonstrate the high potential of convolutional neural networks to automatically constitute storm impact database using timestacks images provided by coastal video monitoring stations. Several convolutional neural network architectures and methods to deal with class imbalance were tested on two sites (Biarritz and Zarautz) to find the best practices for this classification task. This study shows that convolutional neural networks are well adapted for the classification of timestacks images into storm impact regimes. Overall, the most complex and deepest architectures yield better results. Indeed, the best performances are obtained with the VGG16 architecture for both sites with F-scores of 0.866 for Biarritz and 0.858 for Zarautz. For the class imbalance problem, the method of oversampling shows best classification accuracy with F-scores on average 30% higher than the ones obtained with cost sensitive learning. The transferability of the learning method between sites is also investigated and shows conclusive results. This study highlights the high potential of convolutional neural networks to enhance the value of coastal video monitoring data that are routinely recorded on many coastal sites. Furthermore, it shows that this type of deep neural network can significantly contribute to the setting up of risk databases necessary for the determination of storm risk indicators and, more broadly, for the optimization of risk-mitigation measures.

The Role of Beach Morphology and Mid-Century Climate Change Effects on Wave Runup and Storm Impact on the Northern Yucatan Coast

Wave runup is a relevant parameter to determine the storm impact on barrier islands. Here, the role of the beach morphology on wave runup and storm impact was investigated at four coastal communities located on the northern Yucatan coast. Current wave conditions based on regional wind simulations, topo-bathymetric transects measured at each location, and a nonlinear wave transformation model were employed to reconstruct multi-year runup time series. Dune morphology features and extreme water levels (excluding storm surge contributions) were further employed to determine the storm impact at each site for different return periods. Despite the similar offshore conditions along the coast, extreme water levels (i.e., runup and setup) showed intersite differences that were mainly ascribed to subaerial and submerged morphological features. Numerical results showed that the average surf zone beach slope, sandbars, berm, and dune elevation played an important role in controlling extreme water levels and storm impact at the study sites under the present climate. Moreover, in order to assess the potential effect of climate change on coastal flooding, we analyzed wave runup and storm impact in the best-preserved site by considering wave conditions and sea level rise (SLR) projections under the RCP 8.5 scenario. Modelling results suggest no significant increase in the storm impact regime between the present and future conditions in the study area unless SLR is considered. It was found that to accurately estimate SLR contribution, it should be incorporated into mean sea level prior to performing numerical wave runup simulations, rather than simply adding it to the resulting wave-induced water levels.

A NEW STORM IMPACT MATRIX COMBINING BOTH COASTAL FLOODING AND EROSION HAZARDS

Coastal zones are often threatened by storms that elevate water levels and increase the wave energy impacting the shoreline. These storm conditions result in coastal flooding and erosion hazards for communities, threatening lives, properties and infrastructure. Coastal impact Early Warning Systems (EWSs) are currently used to alert authorities of potential impacts prior to advancing storms. Effective EWSs provide important windows of opportunity to undertake mitigating actions to minimize the damage caused by a storm.Recorded Presentation from the vICCE (YouTube Link): https://youtu.be/-U6uEHfLizA

Modeling Impact of Intertidal Foreshore Evolution on Gravel Barrier Erosion and Wave Runup with XBeach-X

This paper provides a sensitivity analysis around how characterizing sandy, intertidal foreshore evolution in XBeach-X impacts on wave runup and morphological change of a vulnerable, composite gravel beach. The study is motivated by a need for confidence in storm-impact modeling outputs to inform coastal management policy for composite beaches worldwide. First, the model is run with the sandy settings applied to capture changes in the intertidal foreshore, with the gravel barrier assigned as a non-erodible surface. Model runs were then repeated with the gravel settings applied to obtain wave runup and erosion of the barrier crest, updating the intertidal foreshore from the previous model outputs every 5, 10 and 15 min, and comparing this with a temporally static foreshore. Results show that the scenario with no foreshore evolution led to the highest wave runup and barrier erosion. The applied foreshore evolution setting update is shown to be a large control on the distribution of freeboard values indicative of overwash hazard and barrier erosion by causing an increase (with 5 min foreshore updates applied) or a decrease (with no applied foreshore updating) in the Iribarren number. Therefore, the sandy, intertidal component should not be neglected in gravel barrier modeling applications given the risk of over- or under-predicting the wave runup and barrier erosion.

Sensitivity of Storm Response to Antecedent Topography in the XBeach Model

Antecedent topography is an important aspect of coastal morphology when studying and forecasting coastal change hazards. The uncertainty in morphologic response of storm-impact models and their use in short-term hazard forecasting and decadal forecasting is important to account for when considering a coupled model framework. This study provided a methodology to investigate uncertainty of profile response within the storm impact model XBeach related to varying antecedent topographies. A parameterized island Gaussian fit (PIGF) model generated an idealized baseline profile and a suite of idealized profiles that vary specific characteristics based on collated observed LiDAR data from Dauphin Island, AL, USA. Six synthetic storm scenarios were simulated on each of the idealized profiles with XBeach in both 1- and 2-dimensional setups and analyzed to determine the morphological response and uncertainty related to the varied antecedent topographies. Profile morphologic response tends to scale with storm magnitude but among the varied profiles there is greater uncertainty in profile response to the medium range storm scenarios than to the low and high magnitude storm scenarios. XBeach can be highly sensitive to morphologic thresholds, both antecedent and time-varying, especially with regards to beach slope.