Journal of Waterway, Port, Coastal, and Ocean Engineering

As scientific understanding of barrier morphodynamics has improved, so has the ability to reproduce observed phenomena and predict future barrier states using mathematical models. To use existing models effectively and improve them, it is important to understand the current state of morphodynamic modeling and the progress that has been made in the field. This manuscript offers a review of the literature regarding advancements in morphodynamic modeling of coastal barrier systems and summarizes current modeling abilities and limitations. Broadly, this review covers both event-scale and long-term morphodynamics. Each of these sections begins with an overview of commonly modeled phenomena and processes, followed by a review of modeling developments. After summarizing the advancements toward the stated modeling goals, we identify research gaps and suggestions for future research under the broad categories of improving our abilities to acquire and access data, furthering our scientific understanding of relevant processes, and advancing our modeling frameworks and approaches.

The large amount of debris generated in the aftermath of hurricane and storm events can cause severe financial and logistical burdens to coastal communities. Existing debris estimation models mainly focus on wind-induced debris and produce estimates with errors of nearly 50%, highlighting the importance of developing more comprehensive models that can account for other types of debris while improving accuracy. Therefore, the objective of this study is to develop a probabilistic framework to estimate the presence and amount of waterborne debris following a severe storm using machine learning (ML) techniques as a function of relevant storm and landcover parameters. Machine learning techniques are leveraged to generate debris presence and volume models, employing pre- and post-event aerial and satellite imagery and a debris removal database for Hurricane Ike, respectively. The results show that the ensemble learning algorithms perform the best for both tasks, with a misclassification error of 5.56% for the debris presence predictive model, and a normalized root mean squared error (RMSE) value of 11.98 for the debris volume model, the lowest RMSE of the tested algorithms. Dual-layer ML models are also investigated, incorporating the debris presence as a predictor in the debris volume model. The results show a percent error of 11.29% for the dual-layer model and an approximately 5.4% increase in performance with respect to the model that does not incorporate debris presence. The generated debris volume and presence models will provide useful tools to inform decision-making, evaluate mitigation strategies, facilitate recovery efforts, and improve resource allocation following a storm event.

In this study, rectangular and triangular bottom waver makers were developed to generate linear regular waves in a flume using both analytical solutions and extended mild-slope equations. The bottom wave makers were found to more efficiently generate wave heights with operable strokes in shallow water than conventional piston and flap wave makers. For the bottom wave makers, the ratios of generated wave amplitude to bottom movement amplitude varied depending on the ratio of wave maker length to wavelength and also relative water depth. The triangular bottom wave makers can generate more correct forms of linear sinusoidal waves than the rectangular bottom wave maker. Linear bichromatic and irregular waves were successfully generated with the bottom wave makers using the extended mild-slope equations. Numerical simulations of the extended mild-slope equations showed that waves reflected from structures pass over the bottom wave makers with negligibly small reflection. The bottom wave makers can be used to efficiently generate linear long waves in a flume without disturbances of reflected waves from structures.

Some areas of the California coast have physical conditions—such as local bathymetry, topography, shoreline orientation, and other factors—that increase the risk for damage from tsunami inundation compared with other regions. Technical tsunami hazard analyses can inform decision-makers on regional planning and policy decisions. In this paper, we focus on recent efforts in California to delineate various tsunami hazard zones along the coastline under the Seismic Hazard Mapping Act, wherein structural mitigation measures will be recommended. To define the zone wherein structural mitigation measures are recommended, we present a state-wide tsunami reliability analysis that combines existing probabilistic hazard information and structure fragility curves derived from recent events. We examine design-life chances of significant structural damage for various structure types along all developed coastlines throughout California and use this analysis to show how hazard and risk have different correlations throughout the State. Finally, we outline an approach for creating mitigation zones, which provides state-wide tsunami risk consistency for all structures in the tsunami inundation zone.

Coastal resilience is the ability of a system to prepare, resist, recover, and adapt to achieve functional performance under adverse events such as storms and sea-level changes. Usually, previously developed metrics are applied to quantify the resistance portion of coastal resilience, which supports the preparation and adaptation portions. Recent research produced a GIS-based tool that provides a quantitative coastal resilience metric to inform decisions related to protection from coastal storm impacts and to evaluate the success of past management actions. Other research developed a forward-looking metric that uses Beach-fx results to calculate future resilience based on nourishment alternatives. This study, for the first time, combines the Coastal Engineering Resilience Index (CERI) and Buffer Width (BW) metrics to better understand the historic, current, and future resilience of the coastal system at Panama City Beach, Florida. After the construction of the US Army Corps of Engineers Coastal Storm Risk Management (CSRM) project at Panama City Beach, the CERI resilience metric has increased up to 21.3%, while negative storm impacts in the same have been less than 8%. The frequency of nourishment efforts moving forward is justified by a 24.3% increase in the BW metric when comparing cases that are nourished frequently with cases that are not nourished frequently. Moreover, there is a 129.2% increase in the BW metric when comparing the frequently nourished cases with the cases that are nourished only on an emergency basis. While the CERI and BW metrics have both been considered previously, their combined application provides an understanding of a broader temporal view of how storm events, CSRM projects, and nourishments have played a part in the resilience of the system at Panama City Beach over the last two decades and how they may play a role in the next half century.