Assessment of a Bayesian Belief Network-GIS framework as a practical tool to support marine planning

For the UK continental shelf we developed a Bayesian Belief Network-GIS framework to visualise relationships between cumulative human pressures, sensitive marine landscapes and landscape vulnerability, to assess the consequences of potential marine planning objectives, and to map uncertainty-related changes in management measures. Results revealed that the spatial assessment of footprints and intensities of human activities had more influence on landscape vulnerabilities than the type of landscape sensitivity measure used. We addressed questions regarding consequences of potential planning targets, and necessary management measures with spatially-explicit assessment of their consequences. We conclude that the BN-GIS framework is a practical tool allowing for the visualisation of relationships, the spatial assessment of uncertainty related to spatial management scenarios, the engagement of different stakeholder views, and enables a quick update of new spatial data and relationships. Ultimately, such BN-GIS based tools can support the decision-making process used in adaptive marine management.     

广西灌江流域岩溶生态环境敏感性分析

灌江流域作为一个典型的岩溶环境单元,具有地表崎岖破碎、植被生长困难、生态系统脆弱、环境承载量低、环境变化敏感的特点。本文根据地表水系将流域划分为22个子流域,并利用层次分析法对生态环境敏感度进行分析评价,目的是为有关政府部门提供宏观依据,以便合理而有效地进行流域开发与环境整治。从整体上看,流域的生态环境为一般敏感,但在流域的不同区段,生态环境的敏感度仍存在着较大差异。白宝以南,两河以北地区的生态环境最为敏感,枧塘、文市、水车和新圩等地区次之,西山、洞井以北等地生态环境的敏感程度相对较低。      

A low-order model for the response of the Atlantic thermohaline circulation to climate change

Concern has been expressed that anthropogenic climate change may lead to a slowdown or even collapse of the Atlantic thermohaline circulation (THC). Because of the possibly severe consequences that such an event could have on the northern North Atlantic and northwestern Europe, integrated assessment models (IAMs) are needed to explore the associated political and socioeconomic implications. State-of-the-art climate models representing the THC are, however, often too complex to be incorporated into an integrated assessment framework. In this paper we present a low-order model of the Atlantic THC which meets the main requirements of IAMs: it (1) is physically based, (2) is computationally highly efficient, (3) allows for comprehensive uncertainty analysis and (4) can be linked to globally aggregated climate models that are mostly used in IAMs. The model is an interhemispheric extension of the seminal Stommel model. Its parameters are determined by a least-squares fit to the output of a coupled climate model of intermediate complexity. Results of a number of transient global warming simulations indicate that the model is able to reproduce many features of the behaviour of coupled ocean–atmosphere circulation models such as the sensitivity of the THC to the amount, regional distribution and rate of climate change.Responsible Editor: Richard Greatbatch     

杨家岭1~#滑坡稳定性影响因素及敏感性分析

杨家岭滑坡区地质条件复杂 ,且严重影响了 2 0 9国道的正常使用 ,本文研究了影响该滑坡稳定性的各类因素及滑坡稳态对这些因素的敏感性问题。研究表明 ,滑坡的稳定性受多种因素影响 ,主要包括滑坡岩土体容重、滑带的c、值、滑体的c、值、滑坡内地下水的动态和地震作用等等 ,其中滑带的抗剪强度指标c、值、地下水作用和地震作用是滑坡稳态的最敏感因素 ,甚至成为诱发因素。文中还通过研究滑坡坡面加固力的大小、方位与稳定系数的关系 ,确立了滑坡整治方案的最佳加固角 ,为滑坡的综合防治提供了可靠依据     

An insight into the formation of severe ozone episodes: modeling the 21/03/01 event in the ESCOMPTE region

High ozone concentrations are observed more and more frequently in the lower troposphere. The development of such polluted episodes is linked to a complex set of chemical, physical and dynamical parameters that interact with each other. To improve air quality, it is necessary to understand and quantify the role of all these processes on the intensity of ozone formation. The ESCOMPTE program, especially dedicated to the numerical simulation of photochemical episodes, offers an ideal frame to investigate details of the roles of many of these processes through 3D modeling. This paper presents the analysis, with a 3D eulerian model, of a severe and local episode of ozone pollution that occurred on the 21st of March 2001 in the ESCOMPTE region. This episode is particularly interesting due to the intensity of the observed ozone peaks (450 μg/m³ during 15 mn) but also because it did not occur in summer but at the beginning of spring. As part of the premodeling work of the ESCOMPTE program, this study focuses on the sensitivity of the simulated ozone peaks to various chemical and physical phenomena (long-range transport, industrial emissions, local dynamic phenomena…) to determine their influence on the rise of high local photooxidant concentrations and to better picture the photochemistry of the ESCOMPTE region. Through sensitivity tests to dynamical calculation resolution and emissions, this paper shows how the combination of sea and pond breezes with emissions of reactive VOCs can generate local intense ozone peaks.     

Quantifying sensitivity of local site response models to statistical variations in soil properties

We perform a combined stochastic-deterministic analysis of local site response using two computer codes, an equivalent linear analysis program SHAKE and a fully nonlinear finite element code SPECTRA. Our goal is to compare the relative sensitivity of the two codes to statistical variations in soil properties. For the case studies, we re-analyze two ground motion records in Lotung, Taiwan, and one ground motion record in Gilroy, California, utilizing the recorded ground motions at the site deterministically as input into the two codes while treating the uncertain soil parameters as random variables. We then obtain empirical cumulative distribution functions of the Arias intensity and acceleration spectrum intensity, two measures of cumulative damage, to compare the relative sensitivity of the two codes to variations in model parameters. We show that the two codes exhibit comparable sensitivities to statistical parameter variations, indicating that even in the presence of fluctuations in the soil parameter values it is possible to pursue a fully nonlinear site response analysis with SPECTRA and benefit from its superior accuracy.     

A sensitivity study of the WRF model in offshore wind modeling over the Baltic Sea

Accurate wind modeling is important for wind resources assessment and wind power forecasting.To improve the WRF model configuration for the offshore wind modeling over the Baltic Sea,this study per-formed a sensitivity study of the WRF model to multiple model configurations,including domain setup,grid resolution,sea surface temperature,land surface data,and atmosphere-wave coupling.The simu-lated offshore wind was evaluated against LiDAR observations under different wind directions,atmo-spheric stabilities,and sea status.Generally,the simulated wind profiles matched observations,despite systematic underestimations.Strengthening the forcing from the reanalysis data through reducing the number of nested domains played the largest role in improving wind modeling.Atmosphere-wave cou-pling further improved the simulated wind,especially under the growing and mature sea conditions.Increasing the vertical resolution,and updating the sea surface temperature and the land surface infor-mation only had a slight impact,mainly visible during very stable conditions.Increasing the horizontal resolution also only had a slight impact,most visible during unstable conditions.Our study can help to improve the wind resources assessment and wind power forecasting over the Baltic Sea.     

某拟建铁矿裂隙-岩溶矿区开采涌水量数值模拟研究

矿坑涌水是矿产安全开发的主要威胁,如何精确地预测开采涌水量直接关系到矿山开发远景和生产安全。以某拟建铁矿为例,建立地下水系统水文地质概念模型和数学模型,并对地下水流模型的参数进行识别校正和敏感性分析,采用有限差分数值模拟方法,评价矿体-400、-500、-600m共3个开采水平的矿坑开采涌水量。计算结果表明,数值模拟技术可以系统地描述矿区地下水系统,评价涌水量的主要来源,并对后期矿产开采和水量的优化配置具有指导意义。     

ICP-MS法测定地球化学样品中痕量金

用电感偶合等离子体质谱法(ICP-MS)测定矿石中痕量金,测定速度快,检出限低,灵敏度高,分析重现性好的特点。方法经国家一级标准物质验证,金的测定结果与标准值相符。     

Support vector machines applied to uniaxial compressive strength prediction of jet grouting columns

Learning from data is a very attractive alternative to “manually” learning. Therefore, in the last decade the use of machine learning has spread rapidly throughout computer science and beyond. This approach, supported on advanced statistics analysis, is usually known as Data Mining (DM) and has been applied successfully in different knowledge domains. In the present study, we show that DM can make a great contribution in solving complex problems in civil engineering, namely in the field of geotechnical engineering. Particularly, the high learning capabilities of Support Vector Machines (SVMs) algorithm, characterized by it flexibility and non-linear capabilities, were applied in the prediction of the Uniaxial Compressive Strength (UCS) of Jet Grouting (JG) samples directly extracted from JG columns, usually known as soilcrete. JG technology is a soft-soil improvement method worldwide applied, extremely versatile and economically attractive when compared with other methods. However, even after many years of experience still lacks of accurate methods for JG columns design. Accordingly, in the present paper a novel approach (based on SVM algorithm) for UCS prediction of soilcrete mixtures is proposed supported on 472 results collected from different geotechnical works. Furthermore, a global sensitivity analysis is applied in order to explain and extract understandable knowledge from the proposed model. Such analysis allows one to identify the key variables in UCS prediction and to measure its effect. Finally, a tentative step toward a development of UCS prediction based on laboratory studies is presented and discussed.