A method for determining critical events during large disasters of production platforms

This article presents a calculation-based methodology to determine the dominant event class in each of the phases of disasters being analysed, and to address the question of whether different disasters have similarities at crucial times in each phase of the disaster. Our approach is based on event network analysis. Disasters can be modelled using block diagrams and multiphase process trees. We propose trees in this article can be used as a tool for modelling phases of a disaster. The starting point for developing these models was fault tree analysis used for modelling the reliability structure of complex systems. This study demonstrates the possibility of using dual fault trees to describe the process as opposed to the structure. In our analyses, we examined four major disasters of production platforms that occurred in the last 50 years: Ixtoc I, Piper Alpha, Petrobras 36 and Deep Water Horizon. The course of each of these disasters has been described, the basic events of these disasters have been isolated, and assigned to event classes. The hierarchical importance of events was determined using the Birnbaum reliability measure, Birnbaum structural measure, Fussell-Vesely measure, criticality measure and improvement potential. For each phase of the analysed disasters, event importance is ranked, and the most important events that contributed to the phase are identified. General principles on the analysed disasters and the methodology used are also discussed.     

Predicting tree distributions in an East African biodiversity hotspot: model selection, data bias and envelope uncertainty

The Eastern Arc Mountains (EAMs) of Tanzania and Kenya support some of the most ancient tropical rainforest on Earth. The forests are a global priority for biodiversity conservation and provide vital resources to the Tanzanian population. Here, we make a first attempt to predict the spatial distribution of 40 EAM tree species, using generalised additive models, plot data and environmental predictor maps at sub 1 km resolution. The results of three modelling experiments are presented, investigating predictions obtained by (1) two different procedures for the stepwise selection of predictors, (2) down-weighting absence data, and (3) incorporating an autocovariate term to describe fine-scale spatial aggregation. In response to recent concerns regarding the extrapolation of model predictions beyond the restricted environmental range of training data, we also demonstrate a novel graphical tool for quantifying envelope uncertainty in restricted range niche-based models (envelope uncertainty maps). We find that even for species with very few documented occurrences useful estimates of distribution can be achieved. Initiating selection with a null model is found to be useful for explanatory purposes, while beginning with a full predictor set can over-fit the data. We show that a simple multimodel average of these two best-model predictions yields a superior compromise between generality and precision (parsimony). Down-weighting absences shifts the balance of errors in favour of higher sensitivity, reducing the number of serious mistakes (i.e., falsely predicted absences); however, response functions are more complex, exacerbating uncertainty in larger models. Spatial autocovariates help describe fine-scale patterns of occurrence and significantly improve explained deviance, though if important environmental constraints are omitted then model stability and explanatory power can be compromised. We conclude that the best modelling practice is contingent both on the intentions of the analyst (explanation or prediction) and on the quality of distribution data; generalised additive models have potential to provide valuable information for conservation in the EAMs, but methods must be carefully considered, particularly if occurrence data are scarce. Full results and details of all species models are supplied in an online Appendix.     

基于游客支付意愿的古树名木资源保护经济价值评估——以安徽省九华山风景区为例

运用条件价值评估法中支付卡式(PC)和二分式(DC)两种问卷,对九华山风景区游客保护古树名木的支付意愿进行研究,并分析了其影响因素。结果表明：838%的游客认为应该对古树名木进行保护,仅有302%对保护现状满意。支付卡式问卷调查结果为808元/(人·月),二分式问卷调查结果为954元/(人·月);按2010年游客接待量401万人次计算,2010年游客对九华山风景区古树名木保护的总支付意愿分别为1500×108元和4589×108元。影响支付卡式问卷支付意愿值的主要指标是年龄、个人年收入、文化程度和职业的相关性;影响二分式问卷支付意愿的主要指标是个人年收入。支付卡式问卷和二分式问卷支付意愿均与第一次调查结果无显著差异,PC1和PC2、DC1和DC2支付意愿值相差分别为062和173元,两次结果重现性良好,对本次研究结果的可靠性给予了保证     

Effects of chronic elevated ozone exposure on gas exchange responses of adult beech trees (Fagus sylvatica) as related to the within-canopy light gradient

The effects of elevated O₃ on photosynthetic properties in adult beech trees (Fagus sylvatica) were investigated in relation to leaf mass per area as a measure of the gradually changing, within-canopy light availability. Leaves under elevated O₃ showed decreased stomatal conductance at unchanged carboxylation capacity of Rubisco, which was consistent with enhanced δ¹³C of leaf organic matter, regardless of the light environment during growth. In parallel, increased energy demand for O₃ detoxification and repair was suggested under elevated O₃ owing to enhanced dark respiration. Only in shade-grown leaves, light-limited photosynthesis was reduced under elevated O₃, this effect being accompanied by lowered F_v/F_m. These results suggest that chronic O₃ exposure primarily caused stomatal closure to adult beech trees in the field regardless of the within-canopy light gradient. However, light limitation apparently raised the O₃ sensitivity of photosynthesis and accelerated senescence in shade leaves.     

基于遥感影像的峻河流域高寒灌丛决策树提取方法

本文选择青海湖流域内一个具有代表性的小流域—峻河流域为研究对象,通过该区域 IKNOS-2 高分辨率影像的分析,发现该区域高寒灌丛的分布与海拔、坡度、坡向等因素密切相关。 据此,将研究区1:5 万DEM 数据融入TM 影像植被分类过程中,建立一种新的决策树分类方法, 结果将分类总体精度提高到89.37%,Kappa 系数提高到0.7875,达到一般分类结果的精度要求。 这说明,加入多源数据,尤其是地形数据,能够显著提高高寒灌丛植被的分类精度。     

江西九连山常绿阔叶林下土壤的物理性质、水分状况及水源涵养能力的初步研究

我们在江西省九连山自然保护区对山地常绿阔叶林下的土壤进行了土壤物理性质和土壤水分季节动态的测定(一年连续),并据此对其水源涵养能力进行了初步分析。这种土壤具有良好的物理性质,质地主要为壤质粘土和粘壤土,容重小(0.88—1.29),孔隙度大(51.7—65.4％),透水速度快(17.4—142.8ml/min),最大持水量高(占容积50.6—58.2％)。土壤排水良好,水分充足。水分含量一年中绝大部分时间在30％以上。变动范围:0—15cm土层,26—56％;15—30cm土层,26—45％;30—50cm土层,26—45％。我们用透水速度和最大持水量来分析其水源涵养能力,并与丘陵红壤进行比较。结果是:土壤的透水能力为丘陵红壤的3—5倍;贮水能力为丘陵红壤的138—145％;吸收雨水能力为丘陵红壤的219％。     

城市道路绿化带不同植物叶片附尘对大气污染的磁学响应

对上海市金沙江路两侧绿化带内24个常绿植物叶片样品进行了磁性与重金属测试,以探讨城市道路绿化带不同植物叶片附尘对大气污染的磁学响应.结果表明,χ、SIRM值分别在（4～59）×10-8m3.kg-1和（496～6 114）×10-6Am2.kg-1之间变化,S-300 mT在89%～98%之间变动.所有植物样品中χARM/χ〈4,χARM/SIRM〈30×10-5mA-1.磁性参数表明,植物叶片附尘以亚铁磁性矿物为主,磁性矿物颗粒以假单畴（PSD）-多畴（MD）为主.重金属元素Zn、Cu、Pb含量与反映亚铁磁性矿物含量的χ、SIRM、χARM呈显著正相关,可以将磁性参数SIRM作为叶片重金属元素的替代指标.推荐在上海地区广泛种植的广玉兰作为道路植物污染的指示植物。     

西双版纳热带森林挥发性有机物的观测研究

2002年7月中旬利用异戊二烯通量测定系统对云南西双版纳热带森林异戊二烯的排放通量进行了连续测量,同时对光合有效辐射(PAR)、气温、湿度、风等参数进行了观测.结果表明,热带地区人工橡胶林区域湿季异戊二烯的排放有明显的日变化规律,中午前后为一天中的最大值,异戊二烯排放通量的日平均值在0.85～0.24mgC/(m²h)之间.对小时值和日平均值而言,异戊二烯的排放与PAR和气温的变化规律比较一致,但它对PAR比对气温更加敏感.不同的天气状况对异戊二烯的排放有非常明显的影响.橡胶林是一个单萜烯排放者,其单萜烯的排放速率明显依赖于PAR.橡胶林和热带雨林冠层上空异戊二烯和单萜烯的浓度也有明显的日变化规律,而且橡胶林区域的异戊二烯和单萜烯的浓度均明显高于热带雨林.     

冠层结构对亚热带常绿林光能利用效率估算的影响

利用遥感方法可以使用光化学植被指数（PRI）在叶片尺度表征光能利用效率（LUE）的动态变化,但在冠层尺度上,森林植被冠层结构是影响LUE估算精度的关键因素之一。利用2014-2015年中国科学院广东省鼎湖山森林生态试验站自动多角度高光谱观测系统的光谱反射数据,分别计算常绿阔叶林PRI、归一化植被指数（NDVI）、增强型植被指数（EVI）和优化比值植被指数（MSR）。基于通量观测计算的LUE,分析不同表征冠层结构的植被指数对于LUE与PRI拟合精度的影响,并利用不同类型植被指数的组合,构建多元线性回归模型。研究结果表明：（1）亚热带常绿阔叶林冠层结构型植被指数与冠层尺度PRI具有显著的相关性,其中MSR与PRI相关性较为显著（R²=0.40,P<0.01）;（2）在植被冠层密度较大、LAI较高（即高NDVI和MSR）时,PRI对于表征LUE的动态变化更具优势;（3）利用NDVI、EVI、MSR和PRI所构建的估算LUE的多元回归模型,能将LUE估算精度提高18.14%,对于冠层结构变化活跃期（1-5月）,能将LUE估算精度提高54%。研究认为利用冠层结构参数能够进一步改进PRI对LUE的估算精度,提升遥感精确评估亚热带常绿林生产力的能力。     

井冈山重要森林生态系统碳密度对比

采用生物量模型与实际测量相结合的方法,从植被层(包括乔木与林下植被)、枯落层和土壤层(表层1 m)比较了井冈山5种重要森林生态系统碳密度. 结果表明:①森林生态系统平均碳密度为29.047 kg/m2,常绿阔叶林>针阔混交林>人工杉木林>落叶阔叶林>毛竹林;②土壤碳密度平均值为22.453 kg/m2,占森林总碳密度的77.3%,5种森林类型土壤碳密度排序与总碳密度相同,且差异较小;③植被层碳密度差异最大,针阔混交林碳密度最大(12.039 kg/m2),是碳密度最小的落叶阔叶林(1.322 kg/m2)的9.1倍;④乔木层碳密度排序为针阔混交林>常绿阔叶林>人工杉木林>毛竹林>落叶阔叶林,乔木地上碳密度占乔木总碳密度的61.4%(人工杉木林)～75.8%(落叶阔叶林);⑤灌木层总碳密度差异大,常绿阔叶林和落叶阔叶林的灌木总碳密度分别为最大(0.153 kg/m2)和最小(0.027 kg/m2),前者是后者的5.6倍,灌木地上碳密度占灌木总碳密度的78.3%(针阔混交林)～81.0%(常绿阔叶林);⑥草本层总碳密度差异较小,在0.074 kg/m2(人工杉木林)～0.108 kg/m2(毛竹林)之间,地下碳密度略高于地上;⑦枯落层碳密度最低,不同森林类型间枯落层碳密度差异不大,在0.064～0.084 kg/m2之间.