Exploring conventional tidal prediction schemes for improved coastal numerical forecast modeling

This study provides a practical guide to the use of classical tidal prediction algorithms in coastal numerical forecasting models such as tide and tide-storm-surge models. Understanding tidal prediction parameter formulas and their limitations is key to successfully modifying and upgrading tidal prediction modules in order to increase the accuracy of perpetual interannual simulations and, in particular, storm-surge modeling studies for tide-dominated coastal environments. The algorithms for the fundamental prediction parameters, the five astronomical variables, used in tidal prediction are collated and tested. Comparisons between their estimation using different parameterizations shows that these methods yield essentially the same results for the period 1900–2099, revealing all are applicable for tidal forecasting simulation. Through experiments using a numerical model and a harmonic prediction program, the effects of nodal modulation correction and its update period on prediction accuracy and sensitivity are examined and discussed using a case study of the tidally-dominated coastal regime off the west coast of Korea. Results indicate that this correction needs updating within <30 days for accurate perpetual interannual tidal and mean sea-level predictions, and storm-surge model predictions requiring centimeter accuracy, for tidally-dominated coastal regimes. Otherwise, unacceptable systematic errors occur.     

Integrating Uncertainty Theories with GIS for Modeling Coastal Hazards of Climate Change

Prediction of coastal hazards due to climate change is fraught with uncertainty that stems from complexity of coastal systems, estimation of sea level rise, and limitation of available data. In-depth research on coastal modeling is hampered by lack of techniques for handling uncertainty, and the available commercial geographical information systems (GIS) packages have only limited capability of handling uncertain information. Therefore, integrating uncertainty theory with GIS is of practical and theoretical significance. This article presents a GIS-based model that integrates an existing predictive model using a differential approach, random simulation, and fuzzy set theory for predicting geomorphic hazards subject to uncertainty. Coastal hazard is modeled as the combined effects of sea-level induced recession and storm erosion, using grid modeling techniques. The method is described with a case study of Fingal Bay Beach, SE Australia, for which predicted responses to an IPCC standard sea-level rise of 0.86 m and superimposed storm erosion averaged 12 m and 90 m, respectively, with analysis of uncertainty yielding maximum of 52 m and 120 m, respectively. Paradoxically, output uncertainty reduces slightly with simulated increase in random error in the digital elevation model (DEM). This trend implies that the magnitude of modeled uncertainty is not necessarily increased with the uncertainties in the input parameters. Built as a generic tool, the model can be used not only to predict different scenarios of coastal hazard under uncertainties for coastal management, but is also applicable to other fields that involve predictive modeling under uncertainty.     

Integrating Uncertainty Theories with GIS for Modeling Coastal Hazards of Climate Change

Prediction of coastal hazards due to climate change is fraught with uncertainty that stems from complexity of coastal systems, estimation of sea level rise, and limitation of available data. In-depth research on coastal modeling is hampered by lack of techniques for handling uncertainty, and the available commercial geographical information systems (GIS) packages have only limited capability of handling uncertain information. Therefore, integrating uncertainty theory with GIS is of practical and theoretical significance. This article presents a GIS-based model that integrates an existing predictive model using a differential approach, random simulation, and fuzzy set theory for predicting geomorphic hazards subject to uncertainty. Coastal hazard is modeled as the combined effects of sea-level induced recession and storm erosion, using grid modeling techniques. The method is described with a case study of Fingal Bay Beach, SE Australia, for which predicted responses to an IPCC standard sea-level rise of 0.86 m and superimposed storm erosion averaged 12 m and 90 m, respectively, with analysis of uncertainty yielding maximum of 52 m and 120 m, respectively. Paradoxically, output uncertainty reduces slightly with simulated increase in random error in the digital elevation model (DEM). This trend implies that the magnitude of modeled uncertainty is not necessarily increased with the uncertainties in the input parameters. Built as a generic tool, the model can be used not only to predict different scenarios of coastal hazard under uncertainties for coastal management, but is also applicable to other fields that involve predictive modeling under uncertainty.     

Maximum Entropy Distribution Function and Uncertainty Evaluation Criteria

Marine environmental design parameter extrapolation has important applications in marine engineering and coastal disaster prevention.The distribution models used for environmental design parameter usually pass the hypothesis tests in statistical analysis,but the calculation results of different distribution models often vary largely.In this paper,based on the information entropy,the overall uncertainty test criteria were studied for commonly used distributions including Gumbel,Weibull,and Pearson-III distribution.An improved method for parameter estimation of the maximum entropy distribution model is proposed on the basis of moment estimation.The study in this paper shows that the number of sample data and the degree of dispersion are proportional to the information entropy,and the overall uncertainty of the maximum entropy distribution model is minimal compared with other models.     

基于多态模糊贝叶斯网络的半潜平台安装风险分析

针对半潜式平台安装作业风险评估中存在的风险多态性和模糊性问题,构建了一种多态模糊贝叶斯网络风险分析模型。根据行业规范推荐标准定义语言性评价模糊集,描述根节点的事故状态发生概率,克服了传统方法中确定性概率难以获取的困难。利用相似性聚合法结合置信度指标融合专家意见,引入改进的去模糊化转换方法,提高了专家知识经验转化为定量数据的合理性和可靠性。基于贝叶斯网络的双向推理和敏感性分析技术,实现了工程作业全过程的风险评估。通过对陵水17-2项目半潜平台整体吊装过程进行风险分析,验证该模型的合理性与有效性,为半潜平台安装作业风险管理与防控策略制定提供指导。     

应用贝叶斯方法对有效信息较少的渔业资源进行资源评估-以印度洋长鳍金枪鱼的资源评估为例

众所周知,对有效信息较少的渔业资源进行资源评估面临很大的挑战,而贝叶斯方法在数据数量较少、质量较差的情况下能利用其它种群高质量的数据或已知的先验信息提高资源评估结果的可靠性。由于印度洋长鳍金枪鱼的数据质量较差而数据量有限,长鳍金枪鱼的资源评估结果存在很大的不确定性,为此,本文以印度洋长鳍金枪鱼的资源评估为例,以调查贝叶斯方法在有效信息较少的资源评估中的优势。本文根据不同的先验假设与捕捞数据系列,共构建了8个贝叶斯动态产量模型,以评估长鳍金枪鱼资源。结果表明:(1)分析参数的后验分布能提高捕捞数据系列选择与参数假设的合理性; (2) 利用种群统计学方法为内禀增长率(r)构建有信息先验,能提高资源评估结果的可靠性。与传统方法相比,当基于贝叶斯框架时,能将已知的知识表示为先验信息并能分析参数的后验分布,从而在数据较少或数据质量较差的情况下,能利用各种信息提高参数估计的合理性与资源评估的可靠性。因此,对数据量较少或数据质量较差情况下的渔业资源评估而言,贝叶斯方法非常有效,如本文所示的印度洋长鳍金枪鱼的资源评估。     

Susceptibility assessment to different types of landslides in the coastal cliffs of Lourinhã (Central Portugal)

The coastal zone of Lourinhã (located in Central Portugal) is characterized by beach–cliff systems, where beaches are narrow and cliffs have notorious slope instability. These cliffs evolve by different types of landslides, which are one of the main sources of natural hazard and risk in this coastal region.In this work, aerial photo interpretation and a systematic field survey were performed in order to obtain an inventory of landslides of the following types: rotational slides, translational slides and debris flows. The entire coast was then split into 261 terrain mapping units. For each unit, landslide predisposing factors were derived and classified: cliff elevation, slope angle (maximum, mean and standard deviation), potential solar radiation, slope curvature (profile and plan), lithological units and geologic structure. The predictive susceptibility models were computed for each type of landslide using a bi-variate statistical method — the Information Value Method. The degree of fit and the predictive capacity of the models were assessed using the Effectiveness Ratio, the standard Receiver Operator Characteristic curves and the respective Area Under Curve.Results show that each landslide typology occurs in particular terrain conditions. Individual susceptibility models evidence better predictive capacity than susceptibility model for total landslides.     

Wind-wave generation of ocean current: on model identification

It is shown how stochastic models based on inertial fluctuations, forced by Stokes drift and wind stress, give apparently accurate predictions of sea surface current. A parameter estimation procedure that gives subjectively reasonable results may therefore also be found. However, objective model identification turns out to be difficult and an estimation model capable of following large-scale model errors is necessary for reasonably accurate parameter estimates. Such a model is proposed and simulation results are presented and discussed. In this mode the inertial oscillation damping, is easily overestimated and the Stokes drift effect is seen to be smaller than the wind stress effect. The latter appears to be uncertain     

贝叶斯统计方法在海浪方向谱研究中的应用

较详细介绍了贝叶斯统计方法在海浪方向谱估计中的应用,指出先验分布的不同对估计结果没有影响,由于均匀分布与最大熵原则相对应,使其具有某种特殊性.数值模拟表明贝叶斯方法对双峰方向分布估计的有效性,将其应用于黑海实测资料,证明双峰方向分布的出现依赖于估计方法分辨力的大小,而用最大似然法得到的双峰方向分布值得怀疑.最后给出出现双峰方向分布的一种可能物理解释.     

Uncertainty analysis on extreme value analysis of significant wave height at eastern coast of Korea

In this study, we considered the problem of estimating long-term predictions of design wave height based on the observation data collected over 10–15 years along the eastern-coast of the Korean peninsula. We adopted a method that combines Bayesian method and extreme value theory. The conventional frequency analysis methods must be reconsidered in two ways. First, the conventional probability distributions used in the frequency analysis should be evaluated to determine whether they can accurately model the variation in extreme values. Second, the uncertainty in the frequency analysis should also be quantified. Therefore, we performed a comparative study of the Gumbel distribution and GEV distribution to show the higher efficiency of the latter. Further, we compared the Bayesian MCMC (Markov Chain Monte Carlo) scheme and the MLE (Maximum Likelihood Estimation) with asymptotic normal approximation for parameter estimation to confirm the advantage of the Bayesian MCMC with respect to uncertainty analysis.     

Budget of sediments and forecast of long-term coastal changes

A method for predicting the coast evolution based on the calculated estimates of the components of the sediment budget is discussed. The approaches outlined in a series of previous publications of the author [9, 10, 11] are further developed. The prerequisites and concepts used as the basis of the suggested method for forecasting are characterized. The sediment budget parameters under typical conditions are presented. The contributions of natural processes and the anthropogenic impact are compared. Different approaches for calculating the principal sediment budget components, including the cross-shore flux through the lower boundary of the coastal zone, the eolian flux of sand material through the upper limit of the coastal zone, and the alongshore sediment flux gradients, are considered. The examples of forecasting the development of coasts in the Baltic and Kara seas and the Sea of Okhotsk are given for the period from 100 to 500 years. The results obtained show that, in the case of a balanced budget of the sediments, the future behavior of the coast would be mainly governed by the variations in the sea level. This factor is capable of determining the changes in the coastline, whose recession and advancing would depend on the rate of the sea level rise. Under specific conditions, an enhanced sea level rise can trigger destructive processes (for example, the erosion of a coastal bar or the thermal abrasion of a cliff). In the case of a strong imbalance in the sediment budget, sea-level changes play a subordinate role.     

A two-stage inflation method in parameter estimation to com-pensate for constant parameter evolution in Community Earth System Model

Parameter estimation is defined as the process to adjust or optimize the model parameter using observations. A long-term problem in ensemble-based parameter estimation methods is that the parameters are assumed to be constant during model integration. This assumption will cause underestimation of parameter ensemble spread,such that the parameter ensemble tends to collapse before an optimal solution is found. In this work, a two-stage inflation method is developed for parameter estimation, which ...     

河口海岸三维双σ坐标斜压水流数值模型研究

在海底地形陡变、垂向密度分层明显的水域,三维σ坐标模式中会出现一种"伪"水平斜压梯度力,并会引起"伪"密度流,以至于影响模拟的精度。垂向上引入双σ坐标变换,建立河口海岸水域三维斜压水流数值模型。数值试验结果表明,在海底地形陡变水域,双σ坐标模式可以减小水平斜压梯度力处理引起的误差。     

Quadrature-based approach for the efficient evaluation of surge hazard

The Joint Probability Method (JPM) has been used for hurricane surge frequency analysis for over three decades, and remains the method of choice owing to the limitations of more direct historical methods. However, use of the JPM approach in conjunction with the modern generation of complex high-resolution numerical models (used to describe winds, waves, and surge) has become highly inefficient, owing to the large number of costly storm simulations that are typically required. This paper describes a new approach to the selection of the storm simulation set that permits reduction of the JPM computational effort by about an order of magnitude (compared to a more conventional approach) while maintaining good accuracy. The method uses an integration scheme called Bayesian or Gaussian-process quadrature (together with conventional integration methods) to evaluate the multi-dimensional joint probability integral over the space of storm parameters (pressure, radius, speed, heading, and any others found to be important) as a weighted summation over a relatively small set of optimally selected nodes (synthetic storms). Examples of an application of the method are shown, drawn from the recent post-Katrina study of coastal Mississippi.     

波高的长期极值统计分布

通过对国内外常用的４种极值波高分布模式的拟合与比较,得到了以下结论：（１）由于地区差异,港口工程技术规范给出的一单一模式并非具有普遍性,对同一工程应该采用多种理论分布进行计算比较,从中选择最佳模式。（２）应用麦奎尔特法拟合Ｗｅｉｂｕｌｌ分布实现了对未知参数的一举寻优,解的收敛速度快,结果稳定且精度高。同时对其它几种分布实现了资料的微机化处理;（３）本文算例用４种极值分布对不同重视值波高进行了比较,     

Bayesian geoacoustic inversion for the Inversion Techniques 2001 Workshop

This paper applies a Bayesian formulation to range-dependent geoacoustic inverse problems. Two inversion methods, a hybrid optimization algorithm and a Bayesian sampling algorithm, are applied to some of the 2001 Inversion Techniques Workshop benchmark data. The hybrid inversion combines the local (gradient-based) method of downhill simplex with the global search method of simulated annealing in an adaptive algorithm. The Bayesian inversion algorithm uses a Gibbs sampler to estimate properties of the posterior probability density, such as mean and maximum a posteriori parameter estimates, marginal probability distributions, highest-probability density intervals, and the model covariance matrix. The methods are applied to noise-free and noisy benchmark data from shallow ocean environments with range-dependent geophysical and geometric properties. An under-parameterized approach is applied to determine the optimal model parameterization consistent with the resolving power of the acoustic data. The Bayesian inversion method provides a complete solution including quantitative uncertainty estimates and correlations, while the hybrid inversion method provides parameter estimates in a fraction of the computation time.     

An experimental study on the reshaping of berm breakwaters under irregular wave attacks

The main idea concerned with the design of berm breakwaters is to construct a less expensive structure with reshaping berm. An experimental study on the front slope stability of homogeneous berm breakwaters has been carried out in a large number of 2D model tests at Tarbiat Modares University. In this paper, the results of this experimental study are presented conjointly with a formula for estimation of berm recession as the most important parameter for describing the reshaping. This includes the influence of wave height and period, storm duration, berm width and elevation variations on the stability of berm breakwater with different armor stone sizes. A total of 222 tests have been performed to cover the impact of these parameters. According to the present research, one can observe that considering different armor stone sizes, berm width is a significant parameter concerning reshaping of a berm breakwater that has not been covered in previous works, so that as the berm width increases the amount of berm recession decreases. To assess the validity of the present formula, comparisons are made between the estimated berm recessions by this formula and formulae given by other researchers, showing that the estimation procedure foretells berm recession well according to the present data. It is observed that the recession estimated by the present formula has comparatively better correlation with the present experimental data, and also with other experimental results within the range of parameters tested.     

Hazard assessment in rock cliffs at Central Algarve (Portugal): A tool for coastal management

Coastal hazards are in the interface of human activities with natural coastal processes. The conflicts arising from this relationship require new approaches suitable for coastal management that consider the dynamic of coastal areas. A method to assess hazard in rock cliffs is presented, combining cliff evolution forcing mechanisms along with protection factors, according to a weighted factors system. This method provides a rapid evaluation of vulnerability for cliffed areas, supporting coastal management and hazard mitigation. The method was applied to the rocky cliffs of the densely populated coastal zone between Galé and Olhos de Água (Southern Portugal), where high and very high hazard values were found to be dominant. A method validation was made using the vulnerability areas and the recorded mass movements over a 45 year period in the same area.     

Forecasting ocean waves: Comparing a physics-based model with statistical models

The literature on ocean wave forecasting falls into two categories, physics-based models and statistical methods. Since these two approaches have evolved independently, it is of interest to determine which approach can predict more accurately, and over what time horizons. This paper runs a comparative analysis of a well-known physics-based model for simulating waves near shore, SWAN, and two statistical techniques, time-varying parameter regression and a frequency domain algorithm. Forecasts are run for the significant wave height, over horizons ranging from the current period (i.e., the analysis time) to 15 h. Seven data sets, four from the Pacific Ocean and three from the Gulf of Mexico, are used to evaluate the forecasts. The statistical models do extremely well at short horizons, producing more accurate forecasts in the 1–5 hour range. The SWAN model is superior at longer horizons. The crossover point, at which the forecast error from the two methods converges, is in the area of 6 h. Based on these results, the choice of statistical versus physics-based models will depend on the uses to which the forecasts will be put. Utilities operating wave farms, which need to forecast at very short horizons, may prefer statistical techniques. Navies or shipping companies interested in oceanic conditions over longer horizons will prefer physics-based models.     

Prediction and assimilation of surf-zone processes using a Bayesian network: Part I: Forward models

Prediction of coastal processes, including waves, currents, and sediment transport, can be obtained from a variety of detailed geophysical-process models with many simulations showing significant skill. This capability supports a wide range of research and applied efforts that can benefit from accurate numerical predictions. However, the predictions are only as accurate as the data used to drive the models and, given the large temporal and spatial variability of the surf zone, inaccuracies in data are unavoidable such that useful predictions require corresponding estimates of uncertainty. We demonstrate how a Bayesian-network model can be used to provide accurate predictions of wave-height evolution in the surf zone given very sparse and/or inaccurate boundary-condition data. The approach is based on a formal treatment of a data-assimilation problem that takes advantage of significant reduction of the dimensionality of the model system. We demonstrate that predictions of a detailed geophysical model of the wave evolution are reproduced accurately using a Bayesian approach. In this surf-zone application, forward prediction skill was 83%, and uncertainties in the model inputs were accurately transferred to uncertainty in output variables. We also demonstrate that if modeling uncertainties were not conveyed to the Bayesian network (i.e., perfect data or model were assumed), then overly optimistic prediction uncertainties were computed. More consistent predictions and uncertainties were obtained by including model-parameter errors as a source of input uncertainty. Improved predictions (skill of 90%) were achieved because the Bayesian network simultaneously estimated optimal parameters while predicting wave heights.