Coastal Engineering最新文献

HySwash has been recently developed as a fast and effective hybrid method to predict nearshore wave processes under unimodal wave conditions. However, global wave climates, and especially those in the tropical regions where coral reefs are hosted, are usually exposed to multiple incoming wave systems, resulting in several energy peaks corresponding to coexisting swells and wind seas. Moreover, although the full distribution of wave runup can have a significant impact on the assessment of vulnerable low-lying tropical regions, predictive models of flooding usually synthesize wave runups to an extreme percentile value, overlooking its full distribution. To enhance the capabilities of HySwash, in the present work, the inclusion of bimodality, as well as the prediction of the complete wave runup distribution is presented. This involves adapting the sampling, selection, and interpolation algorithms together with the hydrodynamic modeling that constitutes the original HySwash methodology. The positive mathematical validation reinforces the applicability of HySwash for a variety of coastal applications. Furthermore, a comparison is conducted between extreme wave runups induced by bimodal sea states and unimodal sea states, providing insights into the impact of multimodality on wave runup extremes.

Bar and berm morphology characterize the seasonal beach evolution, and determine the protection against storm erosion as well as the touristic use of beaches. Thus, they are of particular interest for coastal management and engineering in the nearshore zone. This study used large-scale wave flume experiments to observe the transition from fully dissipative to fully reflective beach profile at a high level of detail. Starting from a post-storm profile generated under energetic waves, a very low energy wave condition caused dissipation of the outer and the inner bar, shoreline recovery, and berm accretion. Measurements revealed feedback between hydrodynamics and beach profile evolution with an onshore shift of the wave breaking location. As a result, the magnitude and cross-shore evolution of wave asymmetry-related bedload net onshore and suspended net offshore transport changed. The relative magnitudes of the two transport components and the way they shifted relative to each other caused the observed beach recovery. Additionally, a link between bar and berm morphology (surf-swash sand exchange) was observed. The shifting breakpoint enabled sustained, wave asymmetry-related onshore transport in the inner surf zone, feeding the berm accretion which occurred through advective swash zone processes including berm overwash.

Process-based nearshore morphodynamic models are commonly used tools by coastal engineers and planners to predict the nearshore morphology change of sandy beaches across various spatiotemporal scales. Accurate modeling of the morphological response on medium and long time scales is imperative for quantitative assessments of coastal infrastructure over a project’s intended life-span. However, most previous modeling applications have focused on single/sub-seasonal storm events and are often limited to an assessment of the subaerial beach (i.e. berm and dune). This not only leaves uncertainty concerning the quality of morphology predictions on extended ($>$ weeks) time scales, but also the capacity of process-based models to emulate realistic nearshore sandbar dynamics and the corresponding exchange of sediment between the nearshore-beach system. To shed light on these meso-scale dynamics, CSHORE, a 1D phase-averaged, process-based nearshore morphodynamic model, was applied on an annual scale to a multi-barred, dissipative beach in Oysterville, WA, USA. Thousands of unique sediment transport and hydrodynamic parameter combinations were executed during model calibration. A large portion of these simulations displayed physically realistic sandbar dynamics, including the growth, decay, and migration of intertidal and subtidal sandbars. To explore the model mechanisms enabling realistic bar behavior, the binary and multinomial logistic regression model were used to quantify the relationship between model parameter selection and the probability of various categorical bar configurations occurring in the final predicted profile. The results indicate the most sensitive parameters associated with barred morphology, in this study, and support the use of separate sediment transport parameters for low and high wave energy conditions. The co-utilization of numerical and statistical modeling outlined in this publication is generalizable to future exploratory modeling and/or calibration routines concerned with categorical outcomes.

Based upon previous research on a single, high-efficiency Oscillating Water Column (OWC) wave energy capture system (Mayon et al., 2021), this work further extends the numerical investigation to the layout design and performance of the wave energy convertor (WEC) array coupled with a parabolic, energy concentrating wall in both regular and irregular incident wave conditions. A heuristic method to identify the optimal siting of the component, cylindrical OWC WECs in the array, installed in the concave opening of the wall is presented. The most advantageous location of the chambers is found to lie on a parabolic curvature line which is inset from the wall. Two separate arrays composed of three and five component OWCs are investigated in a range of regular wave conditions. The hydrodynamic power and efficiency of each chamber in each of the arrays is determined, and subsequently the aggregated array performance is established. It is found that the primary OWC chamber in the array configuration can attain approximately the same hydrodynamic power output as a single, isolated OWC chamber located the parabolic wall focus, albeit with a narrower energy capture bandwidth. The secondary and tertiary component chambers in the arrays contribute a lesser, yet still considerable quantity of hydrodynamic power to the consolidated system. The cumulative hydrodynamic efficiency of the collective arrays is less than the hydrodynamic efficiency of a single OWC chamber at the wall focus, but more efficient than an isolated OWC chamber positioned in open-sea conditions. Moreover, the hydrodynamic efficiency of the arrays exhibits better stability across the range of incident wave periods investigated, denoting that the individual component chambers in the array are efficacious at different incident wave conditions. The five chamber array is comprehensively analysed in irregular incident wave conditions. The array system is demonstrated to maintain a high power output and efficient behaviour in irregular incident wave conditions-an effect attributable to the reflecting wall influence. In summary, the five-chamber array configuration yields a higher power output and improved stability in terms of efficiency performance when compared with the three-chamber array configuration in regular waves. The array maintains this exceptional performance in irregular incident wave conditions.

Dredging and dumping in-situ sediments is a fundamental operation for most coastal engineering projects and coastal defense projects, such as the construction of breakwaters, beach nourishment and land reclamation. Future projections in terms of coastal hazard suggest that coastal protection and land reclamation project will be more and more frequent. In this context, the assessment of the environmental and socio-economic impact of the risk induced by dredging is a fundamental step during both the design stage and the operational management. Most of the standard practices and available risk assessment frameworks rely on the numerical prediction of the sediment plume in the large field driven by coastal circulations forced by tides, winds and waves. In this study, we formulated a new risk assessment framework based on an unsupervised machine learning clustering algorithm, K-means clustering, for generating representative meteocean scenarios subsequently used to force a regional circulation model. Moreover, we introduced three criteria of hazard/risk based on the spatial and temporal evolution of the suspended sediment concentration that explained different environmental impacts and two new methods to synthetically present the risk values. The major improvement of the present framework is that the final probability of risk fully describes the statistics in terms of hydrodynamic and dredging conditions.This framework presents the probability analysis of risk spatial distribution based on representative hydrodynamic conditions and dredging scenarios, which is a major improvement of this study compared with previous risk assessment strategies that were unable to predict quantified dredging risk before field construction. Finally, to demonstrate the potentiality of the risk assessment framework, we applied this methodology to the Hong Kong Water and Pearl River Estuary (China) as a pilot case.

The modelling of coastal Directional Wave Spectra (DWSs) often requires downscaling techniques integrating DWSs from open ocean boundaries. Dynamic downscaling methods reliant on numerical wave models are often computationally expensive. In coastal areas, wave dynamics are strongly influenced by the bathymetry and coastal morphology, implying that once the DWSs at the open ocean boundary are known, the DWSs at various locations along the coast are almost determined. This property can be utilized for statistical downscaling of coastal DWSs. This study presents a deep learning approach to compute coastal DWSs from open ocean DWSs. The performance of the proposed downscaling model was evaluated using both numerical wave model data and buoy data in the Southern California Bight. The results show that the deep learning approach can effectively and efficiently downscale coastal DWSs without relying on any predefined spectral shapes, thereby showing potential for coastal spectral wave climate studies.

Sediment grain size is a critical parameter for sediment mobilization and transport, but often has the highest uncertainty of any coastal sediment transport model input parameter. SandSnap is an initiative to engage the public to amass a beach grain size database by taking photos of the beach sand with a coin in the image for scale and uploading the image to a web application. Images are analyzed with two deep learning convolutional neural networks one to detect the coin and the second to measure the grain size, which is trained on sediment samples within the sand regime. The results for nine gradation metrics are returned to the user within 2 min of image upload. Results from 263 test images have a mean percent error of −6.5% and median absolute error of 22.4% for the median grain size (d₅₀) with a small fine bias of −0.042 mm. The use of the database is highlighted by applying SandSnap output as an input to the AeoLiS aeolian sediment transport model to predict coastal dune growth at a nearly national scale using the full eight grain size classes (d₁₀ – d₉₀) from the SandSnap database. These outputs are used to inform the potential value of having spatially comprehensive grain size distribution information as part of coastal engineering design and planning. Education and outreach techniques for the SandSnap initiative are described in the manuscript. Though some challenges remain, the spatially and temporally robust beach grain size database being developed by SandSnap will help to improve numerous coastal engineering analyses including coastal resilience and vulnerability quantification, beach nourishment life cycle and uncertainty analysis, beach compatibility for the beneficial use of dredged sediment, and large-scale coastal morphology modeling.

Progressive coastal retreat has been an issue exacerbated in recent years due to climate change. Sand is eroded from beaches during the winter and partially recovered during summer by slow accretion processes. The development of new working with nature techniques that produce enhanced beach accretion could help recover most of the sand lost during winter and thus reduce the impact of climate change on beaches. The presence of bedforms contribute to increasing onshore sediment transport, but few studies have been performed to quantify their effect. In this study, the evolution and effect of artificially created bedforms on onshore sediment transport were analysed in prototype-scale laboratory experiments. The tested bedforms mimicked a beach ploughing of the intertidal area, with a wavelength of 1.6 m and height of 0.25 m, corresponding to the ploughing dimensions that a tractor can perform. Two tests were performed with the same initial morphology, medium sand (D₅₀ = 0.318 mm), sea state conditions (H_s = 0.3 m, T_p = 7 s) that produced accretion, and different water levels that represent two tidal states. The experimental flume was longitudinally split into two equal channels of 1 m wide, allowing the simultaneous simulation of a natural control geometry and a ploughed geometry, facilitating the comparison and assuring the very same sea conditions. The presence of ploughed bedforms produced two effects: (1) an acceleration of natural accretion rates reaching 40%, and (2) onshore sediment transport due to the migration of the bedforms. The acceleration of natural accretion was explained by the extra bottom roughness induced by the bedforms, which produced more wave dissipation through bottom friction and thus more accretive conditions. The ploughed height decreased exponentially as waves broke over the crest of the ridges, which almost disappeared after 2–3 h of wave action. As a result, the extra bottom roughness also decreased as time passed. Consequently, the nature-assisted beach enhancement technique of ploughing should be applied at each low tide to produce a cumulative effect. Plough bedforms migrated onshore at a rate of approximately 0.2 m/h during the first hour, mobilizing onshore up to 61 kg m⁻¹ h⁻¹ of sediment. Ripples appeared on the tops of the ridge crests and migrated faster onshore, contributing to the migration of the ploughed bedforms. These results demonstrated the importance of considering bedforms while studying accretion processes and the potential of ploughing as an innovative strategy of working with nature to enhance beach recovery.

Estimation of nearshore bathymetry is important for accurate prediction of nearshore wave conditions. However, direct bathymetry data collection is expensive and time-consuming while accurate airborne lidar-based survey is limited by breaking waves and decreased light penetration affected by water turbidity. Instead, tower-based platforms or Unmanned Aircraft System (UAS) can provide indirect video-based observations such as time-series (or videos) and time-averaged (Timex) or variance enhanced (Var) images. The time-series imagery can provide wave celerity information for bathymetry estimation through the well-known dispersion relationship, for example the cBathy algorithm, or physics-based models. However, wave celerities and associated inverted water depths are sensitive to noise during image collection and processing stages or may not even be available over the entire area of interest. Timex or Var images can be used to identify persistent regions of wave breaking (for example over the sand bar and at the shoreline) so that one can create bathymetry profiles using simplified approximations based on parametric forms. However, the accuracy of this approach highly depends on the assumption of the chosen parametric form as well as the accuracy of detecting sandbars and shoreline.

In this work, we propose a rapid and improved bathymetry estimation method that takes advantage of image-derived wave celerity from cBathy and a first-order bathymetry estimate from Parameter Beach Tool (PBT), software that fits parameterized sandbar and slope forms to the nearshore imagery. Two different sources of the data, PBT and wave celerity, are combined or blended optimally based on their assumed accuracy in a statistical (i.e., Bayesian) framework. The PBT-derived bathymetry serves as “prior” coarse-scale background information and then is updated and corrected with the cBathy-derived wave data through the dispersion relationship, which results in a better bathymetry estimate that is consistent with imagery-based wave data. To illustrate the accuracy of our proposed method, imagery data sets collected in 2017 at the US Army Engineer Research and Development Center’s (ERDC) Field Research Facility (FRF) in Duck, North Carolina under different weather and wave height conditions are tested. Estimated bathymetry profiles are remarkably close to the direct survey data due to the optimal fusion of two data sets. The computational time for the estimation from PBT-based bathymetry and CBathy-derived wave celerity is only about five minutes on a free Google Cloud node with one CPU core. These promising results indicate the feasibility of reliable real-time bathymetry imaging during a single flight of UAS.

This paper presents a comprehensive investigation of the storm damage at a commercial port located in the Southwestern Black Sea Region that occurred on January 18–19, 2018. One week after the event, a field survey was conducted at the port focusing on significantly damaged mound breakwaters and protection structures that failed at several sections. A numerical wave modeling study is carried out to estimate the wave characteristics at deep sea, nearshore, and inside the port to assess the observed damage during the field survey. Widely used numerical models WAVEWATCH III, SWAN, and SWASH are utilized using nested computational domains and calibrated based on satellite measurements. As a result, the significant wave height of the storm is estimated as 7.8 m with a peak period of 12.4 s near the port area, approaching mainly from the northwest direction. The damage mechanisms of the mound structures are discussed based on the field observations and the wave modeling studies. The insufficient seaside armor unit sizes and the orientation of the breakwaters are found to be the main reasons for the damage.