用贝叶斯方法推算设计波高

采用长期概率预报提供波浪要素设计值是海洋工程中不可缺少的内容之一，文中介绍了一种基於贝叶斯原理的新计算方法，它较传统统计方法有明显的优越性，不仅可以通过补充新信息不断修正原有的估算结果，而且可以避免传统方法带来诸多不确定性，文中以北海中部百年一遇设计波高为例进行了计算。     

Uncertainty in adaptive capacity

The capacity to adapt is a critical element of the process of adaptation: it is the vector of resources that represent the asset base from which adaptation actions can be made. Adaptive capacity can in theory be identified and measured at various scales, from the individual to the nation. The assessment of uncertainty within such measures comes from the contested knowledge domain and theories surrounding the nature of the determinants of adaptive capacity and the human action of adaptation. While generic adaptive capacity at the national level, for example, is often postulated as being dependent on health, governance and political rights, and literacy, and economic well-being, the determinants of these variables at national levels are not widely understood. We outline the nature of this uncertainty for the major elements of adaptive capacity and illustrate these issues with the example of a social vulnerability index for countries in Africa. To cite this article: W.N. Adger, K. Vincent, C. R. Geoscience 337 (2005).     

Uncertainties associated with Ra and Ra measurements in water via a Delayed Coincidence Counter (RaDeCC)

The short-lived radium isotopes, ²²³Ra (T_1/2 = 11.4 days) and ²²⁴Ra (T_1/2 = 3.66 days), have been successfully used as tracers of several environmental processes, e.g., submarine groundwater discharge, coastal mixing processes, and water residence times. In this paper, the uncertainties associated with ²²³Ra and ²²⁴Ra measurements using a Radium Delayed Coincidence Counter are determined on a detailed error propagation basis with a confidence interval of 1σ. From the data analyses of several groups of coastal water samples, the calculated relative uncertainties averaged 12% for the ²²³Ra and 7% for the ²²⁴Ra. These uncertainties can decrease for radium-enriched groundwater samples although asymptotic limits have been found at 7% relative uncertainty for ²²³Ra and 4% for ²²⁴Ra. In this paper, the influence of sampling and measurement parameters on the final radium uncertainties is evaluated in order to provide guidance to optimize these factors and obtain more reliable results.     

Inferred ground motions on Guadeloupe during the 2004 Les Saintes earthquake

Accurate estimates of the ground motions that occurred during damaging earthquakes are a vital part of many aspects of earthquake engineering, such as the study of the size and cause of the uncertainties within earthquake risk assessments. This article compares a number of methods to estimate the ground shaking that occurred on Guadeloupe (French Antilles) during the 21st November 2004 (M _w 6.3) Les Saintes earthquake, with the aim of providing more accurate shaking estimates for the investigation of the sources of uncertainties within loss evaluations, based on damage data from this event. The various techniques make differing use of the available ground-motion recordings of this earthquake and by consequence the estimates obtained by the different approaches are associated with differing uncertainties. Ground motions on the French Antilles are affected by strong local site effects, which have been extensively investigated in previous studies. In this article, use is made of these studies in order to improve the shaking estimates. It is shown that the simple methods neglecting the spatial correlation of earthquake shaking lead to uncertainties similar to those predicted by empirical ground-motion models and that these are uniform across the whole of Guadeloupe. In contrast, methods (such as the ShakeMap approach) that take account of the spatial correlation in motions demonstrate that shaking within roughly 10 km of a recording station (covering a significant portion of the investigated area) can be defined with reasonable accuracy but that motions at more distant points are not well constrained.     

Model and parameter uncertainty in IDF relationships under climate change

Quantifying distributional behavior of extreme events is crucial in hydrologic designs. Intensity Duration Frequency (IDF) relationships are used extensively in engineering especially in urban hydrology, to obtain return level of extreme rainfall event for a specified return period and duration. Major sources of uncertainty in the IDF relationships are due to insufficient quantity and quality of data leading to parameter uncertainty due to the distribution fitted to the data and uncertainty as a result of using multiple GCMs. It is important to study these uncertainties and propagate them to future for accurate assessment of return levels for future. The objective of this study is to quantify the uncertainties arising from parameters of the distribution fitted to data and the multiple GCM models using Bayesian approach. Posterior distribution of parameters is obtained from Bayes rule and the parameters are transformed to obtain return levels for a specified return period. Markov Chain Monte Carlo (MCMC) method using Metropolis Hastings algorithm is used to obtain the posterior distribution of parameters. Twenty six CMIP5 GCMs along with four RCP scenarios are considered for studying the effects of climate change and to obtain projected IDF relationships for the case study of Bangalore city in India. GCM uncertainty due to the use of multiple GCMs is treated using Reliability Ensemble Averaging (REA) technique along with the parameter uncertainty. Scale invariance theory is employed for obtaining short duration return levels from daily data. It is observed that the uncertainty in short duration rainfall return levels is high when compared to the longer durations. Further it is observed that parameter uncertainty is large compared to the model uncertainty.     

华南中尺度暴雨数值预报的不确定性与集合预报试验

利用非静力MM5模式,分析了不同积云对流参数化方案对华南暖区暴雨数值预报的不确定性影响,进行了中尺度暴雨模式扰动集合预报试验。不同对流参数化方案的对流凝结加热引起不同的局地温度扰动,通过大气内部的热力动力过程,导致垂直速度的差异,进而影响网格尺度和次网格尺度降水时间、地点和强度。后续降水再通过凝结潜热释放形成新的扰动源。不同积云对流参数化方案还可引起扰动源能量传播方式不同,最终使模拟大气的动力和热力结构有差异。针对物理过程的不确定性,使用两种模式扰动方法构造集合预报扰动模式,第一种方法是随机组合不同积云对流参数化方案和边界层方案,第二种方法是扰动Grell积云对流参数化方案中主要参数振幅。集合预报结果表明,第一种方法的集合预报效果优于第二种方法,仅扰动参数振幅值似乎还不足以反映华南暴雨预报的不确定性。单一的确定性预报在暴雨落区和强度方面的可信度不稳定,集合产品能给华南暴雨过程提供更有用价值的指导预报,具有较高的应用价值。     

Taking the uncertainty in climate-change vulnerability assessment seriously

Climate-change vulnerability assessment has become a frequently employed tool, with the purpose of informing policy-makers attempting to adapt to global change conditions. However, we suggest that there are three reasons to suspect that vulnerability assessment often promises more certainty, and more useful results, than it can deliver. First, the complexity of the system it purports to describe is greater than that described by other types of assessment. Second, it is difficult, if not impossible, to obtain data to test proposed interactions between different vulnerability drivers. Third, the time scale of analysis is too long to be able to make robust projections about future adaptive capacity. We analyze the results from a stakeholder workshop in a European vulnerability assessment, and find evidence to support these arguments. To cite this article: A. Patt et al., C. R. Geoscience 337 (2005).     

Seismic reliability analysis of earth slopes under short term stability conditions

Different models were developed for evaluating the probabilistic three-dimensional (3-D) stability analysis of earth slopes and embankments under earthquake loading. The 3-D slope stability model assumed is that of a simple cylindrical failure surface. The probabilistic models evaluate the probability of failure under seismic loading considering the randomness of earthquake occurrence, and earthquake induced acceleration and uncertainties stemming from the discrepancies between laboratory-measured and in-situ values of shear strength parameters. The models also takes into consideration the spatial variabilities and correlations of soil properties. The probabilistic analysis and design approach is capable of obtaining the 2-D and 3-D static and dynamic safety factors, the probability of slope failure, the earthquake induced acceleration coefficient, the yield acceleration coefficient, the earthquake induced displacement, and the probability of allowable displacement exceedance taking into account the local site effect. The approach is applied to a well known landslide case: Congress Street Landslide in Chicago. A sensitivity analysis was conducted on the different parameters involved in the models by applying those models to the Congress Street landslide considering different levels of seismic hazard. Also, a sensitivity analysis was carried out to study the sensitivity of computed results to input parameters of undrained shear strength, and corrective factors. A comparison was made between the different models of failure. The parametric study revealed that the hypocentral distance and earthquake magnitude have major influence on the earthquake induced displacement, probability of failure and dynamic 2-D and 3-D safety factors.     

Statistical Characterization of a Random Velocity Field Using Stacking Velocity Profiles

In petroleum exploration and production, it is important to have good estimations of the uncertainties on the reserves. Uncertainties in the velocity model used during the data processing are of major importance in this estimation. The use of geo-statistical tools can help in dealing with these uncertainties. Up to now, a strong limitation has been the inability to properly merge velocity functions measured in the wells with seismic velocity data. This was due to the different “supports” among the two, i.e. the well velocity may be regarded as a direct measurement of the instantaneous velocity field, while the seismic velocities correspond to an “average along the travelled paths” of this field. The problem is that, apart from the well positions, the instantaneous velocity field is out of reach. Luckily, for many practical applications, it is enough to know just its covariance model. However, no algorithmic method is available in the literature to yield the covariance model, and geologists are forced to use arbitrary distributions. The present paper proposes an original method to obtain a good estimate of this covariance model, using widely available information, mainly seismic stacking velocities. This method was first developed in a simple one-layer case with constant velocity, and then extended to more realistic situations. Finally, a real data application is performed, which highlights the robustness of the resulting estimation.