An experimental study on geometric characteristics of beach erosion profiles

The beach profile and sediment transport are very important factors in the design of coastal structures, and the beach profile is mainly affected by a number of parameters, such as wave height and period, beach slope, and the material properties of the bed. In this study, considering wave height (H₀=6.5, 11.5, 16, 20, 23, 26 and 30 cm), wave period (T=1.46 and 2.03 s), beach slope (m=1/10 and 1/15) and mean sediment diameter (d₅₀=0.18, 0.26, 0.33 and 0.40 mm), an experimental investigation of coastal erosion profile (storm profile) was carried out in a wave flume using regular waves, and geometric characteristics of erosion profile were determined by the resultant erosion profile. Dimensional and non-dimensional equations were obtained by using linear and non-linear regression methods through the experimental data and were compared with previously developed equations in the literature. The results have shown that the experimental data fitted well to the proposed equations with respect to the previously developed equations.     

Wave climate off northern Norway

The wave climate off northern Norway is considered and the investigation is based on wave measurements made at Tromsøflaket by means of a waverider buoy during the years 1977–1981. Data quality of waverider measurements is briefly commented upon; however, more emphasis is given to an evaluation of the long-term representativity of the actual measuring period and to a procedure accounting approximately for a lack of representativity. The wave climate is presented in terms of a smoothed joint probability density function of the significant wave height, H_s, and the spectral peak period, T_p. Based on this distribution a consistent design curve in the H_s, T_p space is established.     

Joint modelling of extreme ocean environments incorporating covariate effects

Characterising the joint distribution of extremes of ocean environmental variables such as significant wave height (H_S) and spectral peak period (T_P) is important for understanding extreme ocean environments and in the design and assessment of marine and coastal structures. Many applications of multivariate extreme value analysis adopt models that assume a particular form of extremal dependence between variables without justification. Models are also typically restricted to joint regions in which all variables are extreme, but regions where only a subset of variables is extreme can be equally important for design. The conditional extremes model of Heffernan and Tawn (2004) provides one approach to overcoming these difficulties.Here, we extend the conditional extremes model to incorporate covariate effects in all of threshold selection, marginal and dependence modelling. Quantile regression is used to select appropriate covariate-dependent extreme value thresholds. Marginal and dependence modelling of extremes is performed within a penalised likelihood framework, using a Fourier parameterisation of marginal and dependence model parameters, with cross-validation to estimate suitable model parameter roughness, and bootstrapping to estimate parameter uncertainty with respect to covariate.We illustrate the approach in application to joint modelling of storm peak H_S and T_P at a Northern North Sea location with storm direction as covariate. We evaluate the impact of incorporating directional effects on estimates for return values, including those of a structure variable, similar to the structural response of a floating structure. We believe the approach offers the ocean engineer a straightforward procedure, based on sound statistics, to incorporate covariate effects in estimation of joint extreme environmental conditions.     

Prediction of safe sea-state using finite element method and artificial neural networks

This article proposes a predictive method for identifying the range of sea-states considered safe for the installation of offshore structures. A finite element dynamic analysis of the system for various sea-states characterized by significant wave heights and mean zero-up-crossing wave periods and modeled as a combination of several wave components has been performed. Using this procedure a table of safe and unsafe sea-states is generated. The significant wave height (H_s) and mean zero-up-crossing wave period (T_z) of a future sea-state in a location in the north east Pacific were predicted from the distributions whose parameters were estimated using the artificial neural networks (ANNs) trained for this purpose. The location of US National Oceanographic Data Center (NODC) Buoy 46005 is used in this study.The H_s and T_z of some future sea-states were predicted from their corresponding conditional 7-parameter distribution given some information including a number of previously measured H_s’s and T_z’s. This gives a predicted sea-state for a specific time in future. The parameters of the distributions have been estimated from the outputs of two different 7-network sets of trained ANNs. A pile-driving operation is used as a case study in which the pile configuration, including the non-linear foundation and the gap between the pile and the pile sleeve shims, has been modeled by the finite elements method and the range of sea-states suitable for safe pile-driving operation was identified.     

A note on the joint distribution of wave height and period during the growth phase of a storm

In this note the effect of changes in sea-state, as measured by the significant wave heigh H_s, on the joint distribution of individual wave height and period are considered. Wave data, obtained from a Waverider buoy during the growth phase of a storm, are used in the analysis. It is found that, by correctly scaling the individual heights and periods, the form of the joint distribution does not depend on H_s, but is dependent on the bandwidth of the spectrum. The results obtained also give some indication of the period of individual, high zero-upcrossing waves.     

Long-Term Joint Distribution of One-Tenth Large (or Significant) Wave Height with Average Period

- In this paper, by using the wave data from a few oceanographic observation stations in the coastal zone of the Yellow Sea, the East China Sea and the South China Sea, the long-term joint distribution of the one-tenth large (or significant) wave height with average period is studied. The statistical data demonstrate that the long- term distribution of the one- tenth wave height or average period fits the log-normal distribution, thus the joint distribution also fits the two-dimensional log-normal distribution. Then the conditional probability distribution of the average period is derived, and the range as well as the mode of the average wave period corresponding to a certain return period of wave height can be calculated easily.     

A maximum-entropy compound distribution model for extreme wave heights of typhoon-affected sea areas

A new compound distribution model for extreme wave heights of typhoon-affected sea areas is proposed on the basis of the maximum-entropy principle.The new model is formed by nesting a discrete distribution in a continuous one,having eight parameters which can be determined in terms of observed data of typhoon occurrence-frequency and extreme wave heights by numerically solving two sets of equations derived in this paper.The model is examined by using it to predict the N-year return-period wave height at two hydrology stations in the Yellow Sea,and the predicted results are compared with those predicted by use of some other compound distribution models.Examinations and comparisons show that the model has some advantages for predicting the N-year return-period wave height in typhoon-affected sea areas.     

Joint modelling of wave spectral parameters for extreme sea states

Characterising the dependence between extremes of wave spectral parameters such as significant wave height (H_S) and spectral peak period (T_P) is important in understanding extreme ocean environments and in the design and assessment of marine structures. For example, it is known that mean values of wave periods tend to increase with increasing storm intensity. Here we seek to characterise joint dependence in a straightforward manner, accessible to the ocean engineering community, using a statistically sound approach.Many methods of multivariate extreme value analyses are based on models which assume implicitly that in some joint tail region each parameter is either independent of or asymptotically dependent on other parameters; yet in reality the dependence structure in general is neither of these. The underpinning assumption of multivariate regular variation restricts these methods to estimation of joint regions in which all parameters are extreme; but regions where only a subset of parameters are extreme can be equally important for design. The conditional approach of Heffernan and Tawn (2004), similar in spirit to that of Haver (1985) but with better theoretical foundation, overcomes these difficulties.We use the conditional approach to characterise the dependence structure of H_S and T_P. The key elements of the procedure are: (1) marginal modelling for all parameters, (2) transformation of data to a common standard Gumbel marginal form, (3) modelling dependence between data for extremes of pairs of parameters using a form of regression, (4) simulation of long return periods to estimate joint extremes. We demonstrate the approach in application to measured and hindcast data from the Northern North Sea, the Gulf of Mexico and the North West Shelf of Australia. We also illustrate the use of data re-sampling techniques such as bootstrapping to estimate the uncertainty in marginal and dependence models and accommodate this uncertainty in extreme quantile estimation.We discuss the current approach in the context of other approaches to multivariate extreme value estimation popular in the ocean engineering community.     

The distribution of zero-crossing wave heights and periods in a stationary sea state

Existing theoretical distributions of wave height and period do not reflect measured joint distributions from field data. A simulation methodology is introduced to retain the essential features of the theoretical background in Gaussian random noise but to avoid further compromising assumptions in the interpretation of height and period in the amplitude domain. A joint distribution can be associated directly with an empirical or measured variance spectrum. Spectral shape appears to dominate the detail of predicted joint distributions. There is generally a much sharper decay in probability levels at higher periods than is predicted by theoretical models. For Jonswap spectra, there is a dominant central ridge and a distinct bimodal structure in the joint distribution, features that are not evident in symmetric Gaussian spectral forms. The wave height distributions for Jonswap spectra differ little from the Rayleigh distribution, except at extreme wave heights where Rayleigh overpredicts. The period distributions are strongly sensitive to spectral shape. In the conditional distribution of periods, given the height, the asymptotic median period at extreme wave heights is significantly longer than the mean period for Jonswap spectra, but not for symmetric Gaussian forms.     

黄渤海海域波浪时空变化特征分析

本文利用欧洲中期预报中心(ECMWF)第五代再分析数据集(ECMWF Reanalysis v5 ERA5,ERA5),对中国黄渤海海域2000-2019年的波浪进行了统计分析。得到如下的结论：1.黄渤海海区波浪具有明显的季节性,渤海区域有效波高呈现出周边小,中间大的特点;黄海海域有效波高Hs呈现由南向北降低的趋势;研究区域冬季有效波高均值大于其他季节。2.平均周期T的季节分布类似于有效波高的季节均值分布。渤海仅秋冬季T的均值存在大于4s的区域;黄海海域T的季节分布也呈现由南向北递减的趋势,其中长江口外海区域秋冬季T的季节均值可达6s。3.有效波高距平场EOF分解结果显示,第一模态表明了波浪变化具有明显的季节性特征;第二模态反映了季风的季节转换对有效波高的影响;第三模态代表的可能是地形的变化对有效波高变化的影响。4．代表点统计结果显示：整个渤海地区的常浪向为 NNE~NE,强浪向以 NE和 NNE 为主;黄海海域的常浪向为SSE-SE向,强浪向以 N和 SSE 为主。     

Characteristics of a breaking wind-wave field in the light of the individual wind-wave concept

The relation between the intensity of breaking of individual wind-wave crests and parameters of wave size and wave form (e. g., height, period, steepness and skewness) is examined, and the process of change of these parameters is studied in a wind-wave tank (reference wind speed 15 m sec⁻¹, fetch 16 m). Distributions of the wave form parameters are different for breaking and nonbreaking waves. Fully breaking waves seem to hold the relationH ∞T ², whereH is the individual wave height andT is the period. The condition of breaking is not simply determined by the simple criterion of Stokes' limit. Wave height and steepness of a breaking wave are not always larger than those of a nonbreaking wave. This suggests the existence of an overshooting phenomenon in the breaking wave. The wave form parameters are found to change cyclically in a statistical sense during the wave propagation. The period of the cycle in the present case is estimated to be longer than four wave periods. An intermittency of wave breaking is associated with this cyclic process. Roughly speaking, two or three succeeding breaking-waves sporadically exist among a series of nonbreaking waves along the fetch.     

Determination of bar parameters caused by cross-shore sediment movement

Waves, topographic features and material properties are known as the most important factors affecting the sediment movement and coastal profiles. In this study, considering wave height (H=6.5, 17, 16, 20, 23, 26 and 30 cm) and period (T=1.46 and 2.03 s), bed slope (m=1/10, 1/15 and 1/25) and sediment diameter (d₅₀=0.18, 0.26, 0.33 and 0.40 mm), cross-shore sediment movement was investigated using a physical model and various offshore bar geometric parameters were determined by the resultant erosion profile. The offshore bar geometric characteristics are the distance between the bar crest and the shoreline, the depth from bar crest to the still-water level, the distance between the equilibrium point and the shoreline, the distance between the closure point and the shoreline, and the bar volume. Dimensional and non-dimensional equations were obtained by using non-linear regression methods through the experimental data and compared with those of previously developed equations. The results have indicated that the proposed equations fit to experimental data better than previously developed equations.     

Effective fetch and non-linear four-wave interactions during wave growth in slanting fetch conditions

Wave growth in slanting fetch (with wind blowing obliquely off a coast) is investigated with 7 years worth of routine wave measurements in Lake IJssel in The Netherlands and with the SWAN wave model. Two aspects are considered in particular for this case: the validity of the concept of effective fetch and the role of the non-linear four-wave interactions. For slanting and parallel fetch conditions, we found some significant deviations from the effective fetch assumption, leading to 20–35% mismatch in either the peak period T_p or the significant wave height H_m0 respectively. However, the effect of discrepancies between various widely accepted wave growth formulas turned out to be even more important. The wave directions during slanting fetch are significantly ‘steered’ by the coastline, especially in the first kilometre(s) off the coast. The role of the non-linear four-wave interactions is investigated by running the SWAN (version 40.41) wave model with three different quadruplet formulations. Exact quadruplet methods (Xnl) yielded relatively strong wave steering, despite the four-wave interactions being relatively weak. Application of Xnl did not lead to better overall agreement with measurements — improvements for the mean wave period T_m01 were offset by some deterioration for the wave height H_m0.     

Prediction of berm geometry using a set of laboratory tests combined with teaching–learning-based optimization and artificial bee colony algorithms

Understanding sediment movement in coastal areas is crucial in planning the stability of coastal structures, the recovery of coastal areas, and the formation of new coast. Accretion or erosion profiles form as a result of sediment movement. The characteristics of these profiles depend on the bed slope, wave conditions, and sediment properties. Here, experimental studies were performed in a wave flume with regular waves, considering different values for the wave height (H₀), wave period (T), bed slope (m), and mean sediment diameter (d₅₀). Accretion profiles developed in these experiments, and the geometric parameters of the resulting berms were determined. Teaching–learning-based optimization (TLBO) and artificial bee colony (ABC) algorithms were applied to regression functions of the data from the physical model. Dimensional and dimensionless equations were found for each parameter. These equations were compared to data from the physical model, to determine the best equation for each parameter and to evaluate the performances of the TLBO and ABC algorithms in the estimation of the berm parameters. Compared to the ABC algorithm, the TLBO algorithm provided better accuracy in estimating the berm parameters. Overall, the equations successfully determined the berm parameters.     

On spectral and statistical characteristics of shallow water waves

Spectral, zero up-crossing and Tucker's methods of analysis are examined for shallow water wave records. Among the wave height parameters H_s and are most reliable and consistent irrespective of the analysis technique. Tucker analysis, though simple, seems versatile for estimating these wave height parameters. The standard period parameters are less consistent. For practical purposes every period that might reasonably occur has to be considered along with their corresponding height estimate. Joint distribution of heights and periods is in agreement with the function proposed by CNEXO.     

广东阳西近岸海域波浪的分布特征

通过对广东阳西近岸测站1a实测波浪资料及岸边同期风资料的整理与分析,探讨了该海域的波浪特性,得出研究海域不分方向H1/10年平均值为1.00 m,常浪向为SE向,出现的频率为38.35%,强浪向为SE向,观测期间的最大波高出现在0814号"黑格比"台风期间,Hmax值为8.31 m.用已有的理论分布函数对实测统计数据进行拟合,筛选出研究海域的波高分布、周期分布及波高与周期联合分布的特征,结果表明双参数威布尔理论波高分布、杨正己威布尔周期分布、朗格-赫金斯83模式或者孙孚模式较为适用于本海区的波高分布、周期分布、波高周期联合分布;结合相应的风速风向资料,运用回归分析方法,建立了该地区的波高与风速之间、波高与波周期之间的关系.     

Stochastic bivariate time series models of waves in the North Sea and their application in simulation-based design

In this paper, we present and evaluate three long-term wave models for application in simulation-based design of ships and marine structures. Designers and researchers often rely on historical weather data as a source for ocean area characteristics based on hindcast datasets or in-situ measurements. The limited access and size of historical datasets reduces repeatability of simulations and analyses, making it difficult to assess the sampling variability of performance and loads on marine vessels and structures. Markov, VAR and VARMA wave models, producing independent long-term time series of significant wave height (H_s) and spectral peak period (T_p), is presented as possible solutions to this problem. The models are tested and compared by addressing how the models affect interpretation of design concepts and the ability to replicate statistical and physical characteristics of the wave process. Our results show that the VAR and VARMA models perform sufficiently in describing design performance, but does not capture the physical process fully. The Markov model is found to perform worst of the tested models in the applied tests, especially for measures covering several consecutive sea states.     

Prediction of Height and Period Joint Distributions for Stochastic Ocean Waves

This article proposes a new methodology to predict the wave height and period joint distributions by utilizing a transformed linear simulation method. The proposed transformed linear simulation method is based on a Hermite transformation model where the transformation is chosen to be a monotonic cubic polynomial, calibrated such that the first four moments of the transformed model match the moments of the true process. The proposed new approach is applied for calculating the wave height and period joint distributions of a sea state with the surface elevation data measured at an offshore site, and its accuracy and efficiency are favorably validated by using comparisons with the results from an empirical joint distribution model, from a linear simulation model and from a second-order nonlinear simulation model.