Modelling soil carbon development in Swedish coniferous forest soils—An uncertainty analysis of parameters and model estimates using the GLUE method

Boreal forest soils such as those in Sweden contain a large active carbon stock. Hence, a relatively small change in this stock can have a major impact on the Swedish national CO₂ balance. Understanding of the uncertainties in the estimations of soil carbon pools is critical for accurately assessing changes in carbon stocks in the national reports to UNFCCC and the Kyoto Protocol. Our objective was to analyse the parameter uncertainties of simulated estimates of the soil organic carbon (SOC) development between 1994 and 2002 in Swedish coniferous forests with the Q model. Both the sensitivity of model parameters and the uncertainties in simulations were assessed. Data of forests with Norway spruce, Scots pine and Lodgepole pine, from the Swedish Forest Soil Inventory (SFSI) were used. Data of 12 Swedish counties were used to calibrate parameter settings; and data from another 11 counties to validate. The “limits of acceptability” within GLUE were set at the 95% confidence interval for the annual, mean measured SOC at county scale. The calibration procedure reduced the parameter uncertainties and reshaped the distributions of the parameters county-specific. The average measured and simulated SOC amounts varied from 60 t C ha⁻¹ in northern to 140 t C ha⁻¹ in the southern Sweden. The calibrated model simulated the soil carbon pool within the limits of acceptability for all calibration counties except for one county during one year. The efficiency of the calibrated model varied strongly; for five out of 12 counties the model estimates agreed well with measurements, for two counties agreement was moderate and for five counties the agreement was poor. The lack of agreement can be explained with the high inter-annual variability of the down-scaled measured SOC estimates and changes in forest areas over time. We conclude that, although we succeed in reducing the uncertainty in the model estimates, calibrating of a regional scale process-oriented model using a national scale dataset is a sensitive balance between introducing and reducing uncertainties. Parameter distributions showed to be scale sensitive and county specific. Further analysis of uncertainties in the methods used for reporting SOC changes to the UNFCCC and Kyoto protocol is recommended.     

基于不确定性分析的土壤-水稻系统镉污染综合风险评估

从不确定性角度评估土壤-水稻系统镉(Cd)累积风险有助于风险决策的科学性和合理性.本研究应用物种敏感性分布模型(SSD)、健康风险评价模型及Monte Carlo模拟方法分析湖南省攸县土壤-水稻系统Cd富集特征,土壤Cd累积风险和稻米Cd健康风险.结果表明攸县稻米Cd富集因子(PUF)平均水平为1.86,多数Cd超标稻米样品来自土壤酸化严重的区域;土壤Cd污染负荷系数为2.4,隶属于强污染水平;在当前土壤Cd累积条件下,10年后研究区土壤Cd含量处于中度污染水平的概率达到90.4%;健康风险评价显示研究区成人经食用大米Cd平均摄入量(以BW计)为2.9μg·(kg·d)-1,有93.9%的概率高于WHO推荐标准.稻米Cd健康风险指数(HRI)主要集中在2.1~4.7之间,健康风险水平较高.当土壤pH5.5时,HRI1的概率为95.3%,土壤pH6时,HRI1的概率降为68.1%.     

Bayesian Nonstationary Frequency Analysis of Hydrological Variables1

Ouarda, T.B.M.J. and S. El‐Adlouni, 2011. Bayesian Nonstationary Frequency Analysis of Hydrological Variables. Journal of the American Water Resources Association (JAWRA) 47(3):496‐505. DOI: 10.1111/j.1752‐1688.2011.00544.x Abstract: The present paper provides a discussion of nonstationary frequency analysis models in hydrology with a focus on the Bayesian approach. The Bayesian model provides an efficient estimation framework of hydrological quantiles in the presence of nonstationarity. In nonstationary frequency analysis models, the parameters are functions of covariates, allowing for dependent parameters and trends. The use of the nonstationary Generalized Maximum Likelihood Estimation method in hydrologic frequency analysis is discussed. This model allows using prior information concerning the variables under study and considering a number of models (linear, quadratic, etc.) of the dependence of the parameters on covariates. A discussion is also provided concerning the use of the reversible jump Monte Carlo Markov Chain procedure which allows carrying out the estimation of the posterior distributions of the parameters and the selection of the Bayesian model at the same time. An application to a case study is presented to illustrate the potential of the model.     

城市生命线系统的地震网络可靠性研究

震后生命线系统的网络可靠性对于灾后的应急救援和恢复至关重要,因此,评价城市生命线系统网络在一定地震强度下,网络系统保持连通的能力已成为研究生命线地震网络可靠性的重要内容。以经典的系统单元可靠性模型为基础,在众多的网络可靠性计算方法中根据方法实现的难易程度和计算效率,选择采用了不交化穷举集合法和有序二分决策图方法,比较研究了网络系统的可靠性问题,并与Monte Carlo方法进行了对照。此外,针对淮南市配水管网的实际案例,采用上述方法计算了相应的可靠度,计算结果表明,所提方法能够高效地应用于一般的工程实践,具有显著的实用意义。     

Quantifying parameter uncertainty in a coral reef model using Metropolis-Coupled Markov Chain Monte Carlo

Coral reefs are threatened ecosystems, so it is important to have predictive models of their dynamics. Most current models of coral reefs fall into two categories. The first is simple heuristic models which provide an abstract understanding of the possible behaviour of reefs in general, but do not describe real reefs. The second is complex simulations whose parameters are obtained from a range of sources such as literature estimates. We cannot estimate the parameters of these models from a single data set, and we have little idea of the uncertainty in their predictions.We have developed a compromise between these two extremes, which is complex enough to describe real reef data, but simple enough that we can estimate parameters for a specific reef from a time series. In previous work, we fitted this model to a long-term data set from Heron Island, Australia, using maximum likelihood methods. To evaluate predictions from this model, we need estimates of the uncertainty in our parameters. Here, we obtain such estimates using Bayesian Metropolis-Coupled Markov Chain Monte Carlo. We do this for versions of the model in which corals are aggregated into a single state variable (the three-state model), and in which corals are separated into four state variables (the six-state model), in order to determine the appropriate level of aggregation. We also estimate the posterior distribution of predicted trajectories in each case.In both cases, the fitted trajectories were close to the observed data, but we had doubts about the biological plausibility of some parameter estimates. We suggest that informative prior distributions incorporating expert knowledge may resolve this problem. In the six-state model, the posterior distribution of state frequencies after 40 years contained two divergent community types, one dominated by free space and soft corals, and one dominated by acroporid, pocilloporid, and massive corals. The three-state model predicts only a single community type. We conclude that the three-state model hides too much biological heterogeneity, but we need more data if we are to obtain reliable predictions from the six-state model. It is likely that there will be similarly large, but currently unevaluated, uncertainty in the predictions of other coral reef models, many of which are much more complex and harder to fit to real data.     

基于蒙特卡洛法的储罐爆炸碎片冲击失效模型研究

为定量研究相邻储罐间爆炸碎片冲击的多米诺效应,基于蒙特卡洛方法建立爆炸碎片冲击失效模型。该模型共包括爆炸能量与碎片初始速度、考虑风速及碎片初始位置的碎片三维抛射轨迹、空气阻力、碎片冲击穿透等4个分步模型。基于上述模型,研究储罐爆炸后碎片的初始状态、抛射轨迹以及对相邻储罐的冲击效应。在数值模拟结果的基础上,用储罐最高允许工作压力代替泄放装置的泄压压力来计算爆炸压力,绘制碎片质量及初始速度的直方图,定量分析储罐间距对击中概率的影响。结果表明,热辐射、超压和碎片冲击3种能量作用方式均可能导致储罐间火灾爆炸事故多米诺现象发生,但爆炸碎片冲击导致相邻罐失效的概率较低。     

FREEDOM II: An improved methodology to assess domino effect frequency using simulation techniques

The uncertainty and the complexity associated with the domino effect is a barrier to assessing the frequency of such accidents analytically. The use of simulation techniques, such as Monte Carlo, to examine the domino effect instead of analytical techniques has shown great promise. In this paper, a new method to assess the frequency of domino accidents is proposed—FREEDOM II—which is an improvement on the recent algorithm proposed by the authors (FREEDOM). The modifications on FREEDOM were carried out to overcome a limitation of the method and to extend its capabilities. A key shortcoming of the earlier method was its inability to handle multiple failure scenarios. This shortcoming has been overcome in FREEDOM II. A new and improved algorithm has been developed that carries out the simulation in a significantly shorter run time. The applicability of the new model is shown by performing a multi-scenario case study.     

我国36个重点城市饮用水中多环芳烃健康风险评价

分别用2种基于不确定性的风险评价方法(蒙特卡洛法和三角模糊数法)和1种基于确定性的风险评价方法(美国EPA终身致癌风险)对我国36个重点城市饮用水中多环芳烃的终生致癌风险进行评价。所研究的98个水厂出水中多环芳烃浓度范围为17.5~408.3 ng·L-1,致癌性多环芳烃(苯并[a]蒽,屈,苯并[b]荧蒽,苯并[k]荧蒽,苯并[a]芘,茚并[1,2,3-cd]芘)的总量浓度为nd~94.7 ng·L-1。所有水厂出水中苯并[a]芘浓度均小于10 ng·L-1。假设出厂水即为最终饮用水,对16种PAHs浓度用毒性当量因子法转化为相对于苯并[a]芘等效浓度(TEQ BaP)。使用概率风险评价方法计算,结果显示在95%的概率区间我国居民通过饮水途径暴露多环芳烃的终生致癌风险小于5.45×10-6(蒙特卡洛法)和7.56×10-6(三角模糊数法)。而采用确定性风险评价方法,计算得到的最大风险为7.12×10-6。两种计算方法得到的我国饮用水中多环芳烃的终生致癌风险都处于可接受水平。比较不同的评价方法后发现,不同方法获得的信息并不完全重合,相对于通常的基于确定性的非概率健康风险评价方法,基于不确定性的概率风险评价方法获得的结果更为保守。     

基于Landsim的填埋场长期渗漏的污染风险评价

介绍了Landsim模型的基本理论及其填埋场防渗系统、导排系统长期性能变化的表征方式,在此基础上提出了填埋场长期渗漏风险的表征方式..通过Landsim和HELP模型的耦合,弥补了Landsim模型中堆体入渗计算过于简单的缺陷.运用耦合的Landsim-HELP模型评价了西南地区某危险废物填埋场的长期渗漏的地下水污染风险.结果表明,该耦合模型可以准确的评价填埋场性能变化条件下的渗漏量及其对应概率;该填埋场在短期内(1~3a)地下水被污染的概率风险较小(￡0.33),而在长期内(34a)被污染的风险较大(30.68).建议在制定填埋场的设计和运行标准时需考虑防渗膜、导排管等重要单元长期性能的变化,从而减小其长期渗漏造成的地下水污染风险.     

金属埋地管道腐蚀泄漏及大气扩散过程推理模型研究

为了定量评估埋地金属管道腐蚀泄漏的风险,将贝叶斯网络和蒙特卡罗模拟方法用于计算管道腐蚀泄漏及扩散范围概率。建立了埋地金属管道腐蚀泄漏贝叶斯网络,量化了各事件之间的关联。在已知先验概率的基础上,运用贝叶斯概率计算得到管道腐蚀泄漏的后验概率值。利用蒙特卡罗模拟方法对泄漏后气体扩散范围进行了数值模拟,给出了主要模型变量的概率密度函数,得到了扩散区域范围概率分布特征。研究结果表明,管道腐蚀泄漏过程受输送介质和土壤腐蚀性、防腐措施有效性和大气扩散条件的影响,具有较大的不确定性。分析方法考虑了模型参数随机性对计算结果的影响,评估结果可以用于比较不同条件下埋地金属管道腐蚀泄漏扩散的风险。