Coastal Engineering最新文献

Hybrid approaches to shoreline protection, where natural (“green”) features are combined with hardened (“gray”) infrastructure, are increasingly used to protect coastlines from erosion and flood-based hazards. Our understanding of hybrid systems is limited, and it is unknown whether the components of these systems interact in any meaningful sense to provide flood reduction benefits that are greater or less than “the sum of the parts.” In this study, a large-scale physical model was used to investigate the overtopping of a vertical wall protected by a hybrid system where an idealized Rhizophora mangrove forest of moderate cross-shore width fronted a rubble-mound revetment. Configurations included the wall alone, the wall with a low- or intermediate-density mangrove forest without the revetment, the wall with the revetment, and the wall with an intermediate- or high-density mangrove forest and the revetment. The study isolated the reduction in overtopping of the wall by the revetment component, the mangrove forest component, and the interaction between the components of the hybrid system. The total reduction by the hybrid system was estimated within 5% accuracy as the sum of the reduction by each component minus the product of the component reductions. Comparison of the proportional reduction in overtopping by the mangrove forest on the wall alone and the wall with the revetment indicated that the mangrove forest reduced the overtopping of the revetment by approximately the same proportion that the forest reduced the overtopping of the wall. Therefore, (1) total overtopping reduction by the hybrid system was modeled as the reduction expected from the green and gray components in series. Additional analysis showed that (2) for the same wave conditions, a mangrove forest of moderate cross-shore width can have equal or greater protective benefits than a coastal revetment, (3) there is an exponential relationship between the discharge rate and the forest density, and (4) the mangrove forest, the revetment, and the hybrid system all provided greater reduction in overtopping as wave steepness increased. The tests in this study were conducted without wave breaking, with constant freeboard and water depth, with a specific revetment geometry, and without a mangrove canopy. Therefore, these results should be interpreted with caution if used for engineering design.

To further promote the commercialization of oscillating water column (OWC) devices and expand their application to coastal protection, it is crucial to enhance their survivability as much as possible while improving the wave energy conversion efficiency. In the present study, the hydrodynamic performance of a land-fixed, breakwater-type wave energy converter combining an OWC device and a porous plate was investigated. A series of physical experiments and numerical simulations were conducted to systematically verify each other and select the proper porosity of the porous plate and the gap spacing. On this basis, the effects of wave nonlinearity on hydrodynamic efficiency and wave-induced forces were comprehensively evaluated. The results indicate that under high frequency wave conditions, the porous plate can significantly reduce horizontal forces on the front wall with limited efficiency reduction. This phenomenon is more pronounced under the strong wave nonlinearity. The maximum reduction in horizontal force can reach 52%. At low wave frequencies, the effect of the porous plate is limited as the horizontal forces on the front wall are similar to those without the porous plate. The energy conversion efficiency increases in low frequency wave conditions because the porous plate assists first-order wave energy to enter the chamber by reducing the transfer of wave energy to high-order waves. In addition, due to the dissipation of wave energy by the porous plate, the wave reflection coefficient decreases and the wave dissipation coefficient increases in all cases.

Coastal flooding events pose a critical risk in delta areas, since they are characterized by population growth and urban expansion. A better understanding of Extreme Water Levels (EWLs), the mechanisms generating them, and their components, i.e., astronomical tide and storm surge is of great importance as they drive the maintenance and design of flood protection systems. Therefore, a statistical investigation of them can provide new insights for more reliable flood risk mitigation infrastructures. In this study, we analyse these components and compare different probabilistic methods i.e., univariate extreme value analysis, copula functions, and Joint Probability Method (JPM) for the better estimation of EWLs. We use Hoek van Holland (NL) as a representative case study, since the dynamic conditions of this deltaic environment with man-made infrastructures render the area of strategic importance. The results indicate that a more accurate estimate of the declustering time between extreme events can be achieved using correlation of high surges and high wind speeds, taking into consideration also the wind direction. In the Southwest Delta this time estimated to be around 4 days. Furthermore, the EWLs components, i.e., surge and astronomical tide, show negative dependence. From the comparison between statistical approaches to model EWLs, results show that EWLs estimated via EVA and JPM do not vary significantly, while copulas’ seems to outperform the other methods. However, the selection of the proper copula to show the dependence is critical. As a conclusion, the analysis of the dependence between tides and storm surges can lead to more robust inferences of EWLs.

Concurring with high astronomical tides, storm surges have caused devastating damage in low-lying areas along the Chinese coastal regions. However, accurately calculating tropical cyclone (TC) storm tide hazards, especially those with long return periods, has proven challenging due to limited temporal and spatial information on TCs. In this study, we adopt the Synthetic Dynamic TC Method (SDTM), which enables a more robust estimation of storm tide hazards by generating a large number of synthetic TCs based on historical best track data and ocean-atmosphere environmental data. Within the SDTM framework, synthetic TCs corresponding to 10,000 years are validated using several statistical metrics, and the associated storm tides are simulated. For comparison, we employ the Historical Storm Method (HSM) to simulate storm tides for historical TCs from 1950 to 2019. Storm tide hazard curves are calculated and compared using these two methods. Our results demonstrate that the SDTM can robustly estimate storm tide hazards for both short and long return periods, whereas the HSM performs well for short return periods but struggles to reliably assess storm tide hazards for long return periods. Notably, within the SDTM, storm tide height exhibits nonlinear growth with increasing return periods in the Gumbel plot, a phenomenon not observed in the HSM due to the limited time span of TC records. With sufficient TC data, the spatial storm tide hazard maps obtained from the SDTM can serve as a robust foundation for developing disaster prevention and mitigation policies.

Wave nonlinearity plays an important role in cross-shore beach morphodynamics and is often parameterized in engineering-type morphodynamic models through a nonlinear relationship with the Ursell number. It is not evident that the relationship established in previous studies also holds for sheltered sites with fetch-limited seas as they are more prone to effects of local winds and currents, the waves are generally steeper, and the beaches are typically reflective. This study investigates near-bed orbital velocity nonlinearity from wave records collected at two sheltered beaches in The Netherlands and contrasts them to earlier observations made along the exposed, wave-dominated North Sea coast. Our observations at sheltered beaches show that the Ursell number has comparable skill in predicting wave nonlinearity as it has on previously studied exposed coasts. However, the orbital velocities at sheltered coasts are more asymmetric for the same Ursell number than on exposed coasts. When exposed coast data were examined for moments with comparable high-steepness waves, a similar effect on asymmetry was observed. In addition, following and opposing winds were found to have a clear relationship with total nonlinearity, while they did not affect the phase between skewness and asymmetry at the sheltered beaches. Refitting the free parameters of an Ursell-based predictor improved the bias for the asymmetry parameterization. Whether this has implications for modeling of the magnitude of wave-nonlinearity-driven sediment transport using engineering type models is strongly dependent on the sediment transport formulation used, as these formulations depend on additional calibration coefficients too.

The South China Sea (SCS), located at the intersection of two major tectonic plates and near the Manila Fault Zone, is a region highly susceptible to earthquakes and tsunami activities. To develop a more comprehensive and reliable understanding of tsunami behaviours over coral reefs, this study employs the actual topography of a coral reef in the SCS and N-wave theory for the numerical simulation, encompassing the entire tsunami life cycle. Utilizing the open-source solver OlaFlow, driven by the Reynolds-averaged Navier-Stokes (RANS) equations, this study performs a series of numerical simulations of N-wave tsunamis considering the measured topography of the coral reef, as well as the real dimension of an engineering defence structure on the top of the coral reef. The adopted tsunami parameters are equivalent to an earthquake with a moment magnitude of 7.1. The simulations focus on the impact of wave profiles and initial static water levels on the propagation and evolution of tsunamis. Numerical simulations reveal that tsunami profiles, water depth, and topography significantly influence the tsunami dynamics, notably in the waveform transformation, the relationship between wave height and trough-to-peak ratio, and the topographic effects on the wave energy dissipation. These results highlight the critical need to incorporate factors such as tsunami profiles, dispersion, and realistic topography into tsunami predictive models for the purpose of more reliable hazard evaluation and the development of effective coastal defences.

Coastal imaging systems have been developed to measure wave runup and total water level (TWL) at the shoreline, which is a key metric for assessing coastal flooding and erosion. However, extracting quantitative measurements from coastal images has typically been done through the laborious task of hand-digitization of wave runup timestacks. Timestacks are images created by sampling a cross-shore array of pixels from an image through time as waves propagate towards and run up a beach. We utilize over 7000 hand-digitized timestacks from six diverse locations to train and validate machine learning models to automate the process of TWL extraction. Using these data, we evaluate two deep learning model architectures for the task of runup detection. One is based on a fully convolutional architecture trained from scratch, and the other is a transformer-based architecture trained using transfer learning. The deep learning models provide a probability of each pixel being either wet or dry. When contoured at the 50% level (equal chance of being wet or dry), the deep learning models more accurately identified TWL maxima than minima at all sites. This resulted in accurate predictions of 2% exceedance runup, but under predictions of significant swash and over predictions of wave setup. Improved agreement with the complete TWL time series was obtained through post-processing by utilizing the wet/dry probability of each pixel to weight the contouring toward lower dryness probabilities for runup minima (maxima agreed well with observations without tuning). Overall, a transformer-based model using transfer learning provided the best agreement with wave runup statistics, including a) the 2% exceedance runup, b) significant swash, and c) wave setup at the shoreline. For a random subset of images, the model was found to be within the uncertainty range of hand-digitization. The relative success of the transfer learning model suggests that fine-tuning a large model has advantages compared to training a smaller model from scratch. Models provide per-pixel probabilistic estimates in less than 10 s per timestack on a single computational unit, versus the more than 5 min required for hand-digitization. The model is therefore well-suited for near real-time applications, allowing for the development of early warning systems for difficult to forecast events. Real-time wave runup and total water level observations can also be incorporated into coastal hazards forecasts for data assimilation and continual model validation and improvement.