农业非点源污染模拟不确定性研究进展

随着点源污染控制水平的提高,非点源污染问题日益突出.农业非点源污染由于具有涉及范围广、控制难度大、模拟过程不确定因素多等特点,已成为当前水环境污染防治研究的热点与难点.本文围绕农业非点源污染模拟的不确定性问题,介绍了国内外常用的农业非点源污染模型及其不确定性来源,着重讨论了模型结构、参数和输入信息不确定性的研究方法及其应用进展;提出了今后的研究趋势,指出定量分析非点源污染的不确定性,获取影响非点源污染负荷的关键因子,可以有针对性地进行非点源污染的预防和管理,对于提高非点源污染研究水平和制定有效的污染防治措施均具有重要意义.     

像元尺度上不确定性对空间景观直观模型模拟的影响

LANDIS模型是模拟自然和人为干扰下森林景观变化的空间直观景观模型。模型把景观概念化为由相同大小的像元或样地组成的格网。在每一个像元上,模型要求输入物种和年龄组信息。但是,由于研究区一般由成千上百万个像元构成,不可能通过实际调查获取每一个像元上的物种和年龄组信息。因此,采用了一种基于小班的随机赋值法从森林调查数据中获取每一个像元的物种和年龄组信息。该方法是一种基于概率的方法,会在LANDIS模型模拟的物种和年龄组信息的输入中引入不确定性。为了评价由基于小班的随机赋值法所引入像元尺度上的不确定性对模型模拟结果的影响,用蒙特卡罗模拟法进行不确定性分析。对LANDIS模型模拟的每一个物种,用众数年龄组发生频率来定量化单个像元上年龄组信息的不确定性,用所有像元上的众数年龄组平均发生频率来定量化年龄组信息在像元尺度上总的不确定性。平均发生频率越高,不确定性越低。为了评价基于小班的随机赋值法对景观尺度上模型模拟结果的影响,计算了每一个物种在整个研究区内的面积百分比和聚集度指数。变异系数越大,不确定性越高。对所有物种,年龄组信息不确定性在模型模拟的初期是比较低的(平均发生频率大于10)。种子传播、建群、死亡和火干扰使模型结果的不确定性随模拟时间增加而增加。最后,不确定性达到稳定状态,达到平衡状态的时间与物种寿命接近。此时,初始的物种和年龄组信息不再对模型结果有影响。在景观尺度上,物种分布面积百分比和由聚集度指数所定量化的空间格局并未受像元尺度上不确定性增加的影响。因为LANDIS模型模拟研究的目的在于预测总的景观格局变化,而不是单一的事件,所以,基于小班的随机赋值法可用于LANDIS模型的参数化。     

稻田甲烷排放模型研究——模型灵敏度分析

模型方法对区域稻田甲烷排放估计的不确定性主要源于模型参数在区域范围内的误差,这种误差导致的估计不确定性由模型灵敏度决定.采用一种动力学分析与统计分析相结合的方法对稻田甲烷模型CH4MOD进行了参数灵敏度分析,结果表明,稻田水管理方式的灵敏度最高,灵敏度指数为O.64,其次为稻田土壤的砂粒含量参数,灵敏度指数0.50,灵敏度最低的参数是水稻移栽期地上生物量.以模型灵敏度指数为基础,建立了模型估计值不确定性与模型参数区域化误差间的数量关系,利用这一量化关系得出我国2000年稻田甲烷排放的不确定性范围为3.09～10.61Tg.此外,模型灵敏度参数的大小也反映了模型要素对稻田甲烷排放影响的大小,因而分析的结果对于采取合理措施减少稻田甲烷排放具有指导意义.     

AGNPS模型机理与预测偏差影响因素

农业非点源污染已引起了严重和广泛的生态环境和水质问题,受到各种土地利用类型的潜在影响,并与污染源的扩散和防控措施的地理位置有关,涉及到复杂的生态过程和景观格局,由于监测极为困难或成本昂贵,研究方法一般采用模型模拟预测,AGNPS是应用广泛的模型之一。文章介绍了AnnAGNPS4．0模型的特点、时间空间应用范围及其局限性,主要分析了模型的不确定性和预测偏差的影响因素。通过分析认为,美国农业部农业研究局编制的模型参数手册和703号农业手册中参数取值并不适合中国大部分地区,提出了在使用模型时要根据研究地区的实际情况确定参数的取值,并对国内外模型应用结果进行了综合分析,为模型在中国的正确使用提供参考。     

流域水文模型在面源污染模拟与管控中的应用研究进展

面源污染是影响流域水环境和水安全的重要污染来源,对其进行有效防控需要对其负荷以及防控措施效果进行科学高效精准的预测。流域水文模型(Hydrological Simulation Program-FORTRAN,HSPF)具有突出的综合性和灵活性,是面源污染模型的典范。近年来,HSPF模型应用于我国流域面源污染相关的研究和实践有了飞速发展,但同样也面临着模型机理和参数本地化、模型构建精细化、模型结构不确定性较大等方面的挑战。围绕该模型在面源污染模拟与管控中的研究进展,对其在变化环境下的模拟方法和成果,以及应对参数识别、不确定性分析、措施效果评估和总量控制的思路和方法等方面进行了总结,并分析了现代化环境模拟形势下HSPF模型的延伸发展。结合模型相关研究的总结,强调了面向我国流域特色的本地化模型改进、服务河长制精细监管的大尺度精细化模拟、以及模型与大数据统计及人工智能耦合的互馈集合模拟等后续研究是需要重点关注的发展动向。     

干旱地区经济-生态环境系统规划方法与应用

从干旱地区经济－生态环境系统的动态性,综合性,多目标性和不确定性等特征出发,提出用不确定性多目标规划（IMOP）模型来解决干旱地区以水资源为核心的生态环境规划优化问题,建立了干旱地区经济与生态环境系统不确定性多目标规划模型（IMOPMEES）,应用模型算法退化形式进行多目标规划的求解,并以新疆和墨洛地区为具体研究对象,采用IMOPMEES进行可持续发展规划,应用交互式调整和情景分析的方法得到模型在两种情景下的优化方案,进而对两种情景进行综合分析比较,为决策者提供科学合理的决策依据,研究表明IMOP模型可以充分反映干旱地区经济－生态环境系统的不确定性和动态性,并有效地协调目标之间的冲突,为干旱地区经济－生态环境规划工作提供了一种较为有力的工具。     

通用选择指数的通径分析化模型

通过对各种选择指数的通径分析化模型进行系统化的研究,建立了通用选择指数的通径分析化模型,并给出了各种选择指数及其通径分析化的统一表示形式．用该模型可以充分利用各种信息来源,特别是在对各种信息来源需作各种约束时,为选种和评定优种提供了行之有效的计算方法和统一程序,避免了在实际应用中因计算体系不同而导致的混乱．     

森林生物量估算中模型不确定性分析

单木生物量估算是区域森林生物量估算的基础。量化单木生物量模型中各种不确定性来源,分析各不确定性来源对森林生物量估算的影响,可为提高森林生物量估算精度提供理论依据。基于52株杉木地上部分生物量实测数据,建立杉木单木地上部分生物量一元与二元模型。在两种模型形式下,根据临安市2009年森林资源连续清查数据中杉木实测数据,分析单木生物量模型中所包含的2种不确定性,即模型参数不确定性和模型残差变异引起的不确定性。最后利用误差传播定律计算单木生物量模型总不确定性。结果表明,基于一元生物量模型的临安市杉木生物量估计均值为6.94 Mg/hm~2,由一元模型残差变异引起的生物量不确定性约为11.1%,模型参数误差引起的生物量不确定性约为14.4%,一元生物量模型估算合成不确定性为18.18%。基于二元生物量模型的临安市杉木生物量估计均值为7.71 Mg/hm~2,模型残差变异引起的不确定性约为7.0%,模型参数误差引起的不确定性约为8.53%,二元生物量模型估算合成不确定性为11.03%。研究表明模型参数不确定性随建模样本的增加逐渐降低,当建模样本由30增加到40再增加到52时,一元生物量模型模型参数不确定性分别为20.26%、16.19%、14.4%,二元生物量模型分别为13.09%、9.4%、8.53%。此外,建模样本的增加对残差变异不确定性也有一定影响,当建模样本由30增加到42再增加到48时,一元模型残差变异不确定性分别为15.2%,12.3%和11.7%;二元模型残差变异不确定性分别为13.3%,9.4%和8.3%。在2种不确定性来源中模型参数不确定性对估计结果影响最大,其次为模型残差变异。由于模型残差变异、参数不确定性与建模样本有关,因此可以通过增加建模样本来减小模型参数不确定性。二元生物量模型总的不确定性要低于一元生物量模型。     

基于不确定性的城市扩展用地生态适宜性评价

针对土地适宜性评价中采用定性和单因素方法而产生的主观、片面和精度低等缺点,分析了土地适宜性评价中的不确定性因素,并提出基于不确定性和灰色系统关联度的土地生态适宜性评价模型。结合长沙市生态规划,在野外生态调查的基础上,依据稳定性、独立性、主导性和综合性原则选取坡度、地基承载力、土壤生产力、植被、土壤渗透性、地表水、居民点用地程度、景观价值等土地生态适宜性评价因子。在地理信息系统（GIS）和遥感（Rs）的支持下,定量地获取各评价因子信息;采用AHP法确定各评价因子的权重;并运用改进的土地生态适宜性评价模型对长沙市城市扩展用地进行定性定量的评价,最终得到长沙市最适宜用地、适宜用地、基本适宜用地、不适宜用地、不可用地分别占总用地的14．77％、16．64％、24．07％、30．91％、13．61％,说明长沙市适宜开发的土地较多（最适宜、适宜和基本适宜用地占55．48％）。根据评价结果提出的相应对策,对城市用地的可持续性发展具有指导意义。     

面向土地整治的耕地质量评价优化

探讨面向土地整治的耕地质量评价方法和指标体系,进行土地整治成效分析,是当前土地整治的现实需求.借鉴已有耕地质量评价相关成果,从地学特征、土壤特性、耕作条件、健康状况和生物特性等五个方面改进评价方法和指标体系.以河南省温县土地整治工程进行实证研究,采用综合算法,其中耕地地形特征、土壤性状和耕作条件指数采用加权求和法,健康状况系数和生物特性系数利用“1+X”模型,将耕地质量划分为5个等级,评价项目区土地整治前和整治后耕地质量指数的变化.结果表明:相较于《农用地质量分等规程》,新的耕地质量评价体系应用于土地整治工程实施前后耕地质量评价具有一定的优势,可以更精确地分析土地整治成效.研究结果可为面向土地整治的耕地质量评价工作提供一定参考.     

Combining control measures for more effective management of fisheries under uncertainty: quotas, effort limitation and protected areas

We consider combinations of three types of control measures for the management of fisheries when the input information for policy decisions is uncertain. The methods considered include effort controls, catch quotas and area closures. We simulated a hypothetical fishery loosely based on the Icelandic cod fishery, using a simple spatially explicit dynamic model. We compared the performance with respect to conserving the resource and economic return for each type of control measure alone and in combination. In general, combining more than one type of primary direct control on fishing provides a greater buffer to uncertainty than any single form of fishery control alone. Combining catch quota control with a large closed area is a most effective system for reducing the risk of stock collapse and maintaining both short and long-term economic performance. Effort controls can also be improved by adding closed areas to the management scheme. We recommend that multiple control methods be used wherever possible and that closed areas should be used to buffer uncertainty. To be effective, these closed areas must be large and exclude all principal gears to provide real protection from fishing mortality.     

Adaptive Management and the Value of Information: Learning Via Intervention in Epidemiology

Optimal intervention for disease outbreaks is often impeded by severe scientific uncertainty. Adaptive management (AM), long-used in natural resource management, is a structured decision-making approach to solving dynamic problems that accounts for the value of resolving uncertainty via real-time evaluation of alternative models. We propose an AM approach to design and evaluate intervention strategies in epidemiology, using real-time surveillance to resolve model uncertainty as management proceeds, with foot-and-mouth disease (FMD) culling and measles vaccination as case studies. We use simulations of alternative intervention strategies under competing models to quantify the effect of model uncertainty on decision making, in terms of the value of information, and quantify the benefit of adaptive versus static intervention strategies. Culling decisions during the 2001 UK FMD outbreak were contentious due to uncertainty about the spatial scale of transmission. The expected benefit of resolving this uncertainty prior to a new outbreak on a UK-like landscape would be £45–£60 million relative to the strategy that minimizes livestock losses averaged over alternate transmission models. AM during the outbreak would be expected to recover up to £20.1 million of this expected benefit. AM would also recommend a more conservative initial approach (culling of infected premises and dangerous contact farms) than would a fixed strategy (which would additionally require culling of contiguous premises). For optimal targeting of measles vaccination, based on an outbreak in Malawi in 2010, AM allows better distribution of resources across the affected region; its utility depends on uncertainty about both the at-risk population and logistical capacity. When daily vaccination rates are highly constrained, the optimal initial strategy is to conduct a small, quick campaign; a reduction in expected burden of approximately 10,000 cases could result if campaign targets can be updated on the basis of the true susceptible population. Formal incorporation of a policy to update future management actions in response to information gained in the course of an outbreak can change the optimal initial response and result in significant cost savings. AM provides a framework for using multiple models to facilitate public-health decision making and an objective basis for updating management actions in response to improved scientific understanding.     

Accounting for uncertainty in model-based prevalence estimation: paratuberculosis control in dairy herds

ABSTRACT: BACKGROUND: A common approach to the application of epidemiological models is to determine a single (point estimate) parameterisation using the information available in the literature. However, in many cases there is considerable uncertainty about parameter values, reflecting both the incomplete nature of current knowledge and natural variation, for example between farms. Furthermore model outcomes may be highly sensitive to different parameter values. Paratuberculosis is an infection for which many of the key parameter values are poorly understood and highly variable, and for such infections there is a need to develop and apply statistical techniques which make maximal use of available data. RESULTS: A technique based on Latin hypercube sampling combined with a novel reweighting method was developed which enables parameter uncertainty and variability to be incorporated into a model-based framework for estimation of prevalence. The method was evaluated by applying it to a simulation of paratuberculosis in dairy herds which combines a continuous time stochastic algorithm with model features such as within herd variability in disease development and shedding, which have not been previously explored in paratuberculosis models. Generated sample parameter combinations were assigned a weight, determined by quantifying the model's resultant ability to reproduce prevalence data. Once these weights are generated the model can be used to evaluate other scenarios such as control options. To illustrate the utility of this approach these reweighted model outputs were used to compare standard test and cull control strategies both individually and in combination with simple husbandry practices that aim to reduce infection rates. CONCLUSIONS: The technique developed has been shown to be applicable to a complex model incorporating realistic control options. For models where parameters are not well known or subject to significant variability, the reweighting scheme allowed estimated distributions of parameter values to be combined with additional sources of information, such as that available from prevalence distributions, resulting in outputs which implicitly handle variation and uncertainty. This methodology allows for more robust predictions from modelling approaches by allowing for parameter uncertainty and combining different sources of information, and is thus expected to be useful in application to a large number of disease systems.     

Multi-attribute mate choice decisions and uncertainty in the decision process: a generalized sequential search strategy

The behavior of females in search of a mate determines the likelihood that high quality males are encountered and adaptive search strategies rely on the effective use of available information on the quality of prospective mates. The sequential search strategy was formulated, like most models of search behavior, on the assumption that females obtain perfect information on the quality of encountered males. In this paper, we modify the strategy to allow for uncertainty of male quality and we determine how the magnitude of this uncertainty and the ability of females to inspect multiple male attributes to reduce uncertainty influence mate choice decisions. In general, searchers are sensitive to search costs and higher costs lower acceptance criteria under all versions of the model. The choosiness of searchers increases with the variability of the quality of prospective mates under conditions of the original model, but under conditions of uncertainty the choosiness of searchers may increase or decrease with the variability of inspected male attributes. The behavioral response depends on the functional relationship between observed male attributes and the fitness return to searchers and on costs associated with the search process. Higher uncertainty often induces searchers to pay more for information and under conditions of uncertainty the fitness return to searchers is never higher than under conditions of the original model. Further studies of the performance of alternative search strategies under conditions of uncertainty may consequently be necessary to identify search strategies likely to be used under natural conditions.     

The Value of the Value of Information

An important application of decision analysis is determining the value that information has to a decision maker. The expected value of information (EVOI) is the expected increase in the value (or decrease in the loss) associated with obtaining more information about quantities relevant to the decision process. The EVOI can be thought of as a measure of the importance of the uncertainty about a quantity in terms of the expected improvement in the decision that might be obtained from having additional information about it. Examples of EVOI quantities useful in risk management situations include the expected value of including uncertainty (EVIU), the expected value of perfect information (EVPI), and the expected value of sample information (EVSI). Value of information (VOI) analysis is useful because it makes the losses associated with decision errors explicit, balances competing probabilities and costs, helps identify the decision alternative that minimizes the expected loss, prioritizes spending on research, quantifies the value of the research to the decision maker, and provides an upper bound on what should be spent on getting information.     

Intensive case finding and isoniazid preventative therapy in HIV infected individuals in Africa: economic model and value of information analysis

BACKGROUND: Tuberculosis (TB) accounts of much of the morbidity and mortality associated with HIV. We evaluate the cost-effectiveness of different strategies to actively screen for TB disease in HIV positive individuals, where isoniazid preventative therapy (IPT) is given to those screening negative, and use value of information analysis (VOI) to identify future research priorities. METHODOLOGY/ PRINCIPAL FINDINGS: We built an individual sampling model to investigate the costs (2010 US Dollars) and consequences of screening for TB, and providing TB treatment or IPT in adults testing HIV positive in Sub-Saharan Africa. A systematic review and meta-analysis was conducted to assess performance of the nine different TB screening strategies evaluated. Probabilistic sensitivity analysis was conducted to incorporate decision uncertainty, and expected value of perfect information for the entire model and for groups of parameters was calculated. Screening all HIV infected individuals with sputum microscopy was the least costly strategy, with other strategies not cost-effective at WHO recommended thresholds. Screening those with TB symptoms with sputum microscopy and CXR would be cost-effective at a threshold ICER of $7,800 per quality-adjusted life year (QALY), but associated with significant uncertainty. VOI analysis suggests further information would be of value. CONCLUSIONS/ SIGNIFICANCE: Resource-constrained countries in sub-Saharan Africa wishing to scale up TB preventative services in their HIV infected populations should consider expanding laboratory facilities to enable increased screening for TB with sputum microscopy, whilst improved estimates of the TB prevalence in the population to be screened are needed, as it may influence the optimal strategy.     

Metabolic engineering under uncertainty. I: framework development

Standard bioprocess conditions have been widely applied for the microbial conversion of raw material to essential industrial products. Successful metabolic engineering (ME) strategies require a comprehensive framework to manage the complexity embedded in cellular metabolism, to explore the impacts of bioprocess conditions on the cellular responses, and to deal with the uncertainty of the physiochemical parameters. We have recently developed a computational and statistical framework that is based on Metabolic Control Analysis and uses a Monte Carlo method to simulate the uncertainty in the values of the system parameters [Wang, L., Birol, I., Hatzimanikatis, V., 2004. Metabolic control analysis under uncertainty: framework development and case studies. Biophys. J. 87(6), 3750-3763]. In this work, we generalize this framework to incorporate the central cellular processes, such as cell growth, and different bioprocess conditions, such as different types of bioreactors. The framework provides the mathematical basis for the quantification of the interactions between intracellular metabolism and extracellular conditions, and it is readily applicable to the identification of optimal ME targets for the improvement of industrial processes [Wang, L., Hatzimanikatis, V., 2005. Metabolic engineering under uncertainty. II: analysis of yeast metabolism. Submitted].     

Task Uncertainty Can Account for Mixing and Switch Costs in Task-Switching

Cognitive control is required in situations that involve uncertainty or change, such as when resolving conflict, selecting responses and switching tasks. Recently, it has been suggested that cognitive control can be conceptualised as a mechanism which prioritises goal-relevant information to deal with uncertainty. This hypothesis has been supported using a paradigm that requires conflict resolution. In this study, we examine whether cognitive control during task switching is also consistent with this notion. We used information theory to quantify the level of uncertainty in different trial types during a cued task-switching paradigm. We test the hypothesis that differences in uncertainty between task repeat and task switch trials can account for typical behavioural effects in task-switching. Increasing uncertainty was associated with less efficient performance (i.e., slower and less accurate), particularly on switch trials and trials that afford little opportunity for advance preparation. Interestingly, both mixing and switch costs were associated with a common episodic control process. These results support the notion that cognitive control may be conceptualised as an information processor that serves to resolve uncertainty in the environment.     

Managing uncertainty: information and insurance under the risk of starvation

In an uncertain world, animals face both unexpected opportunities and danger. Such outcomes can select for two potential strategies: collecting information to reduce uncertainty, or insuring against it. We investigate the relative value of information and insurance (energy reserves) under starvation risk by offering model foragers a choice between constant and varying food sources over finite foraging bouts. We show that sampling the variable option (choosing it when it is not expected to be good) should decline both with lower reserves and late in foraging bouts; in order to be able to reap the reduction in uncertainty associated with exploiting a variable resource effectively, foragers must be able to afford and compensate for an initial increase in the risk of an energetic shortfall associated with choosing the option when it is bad. Consequently, expected exploitation of the varying option increases as it becomes less variable, and when the overall risk of energetic shortfall is reduced. In addition, little activity on the variable alternative is expected until reserves are built up early in a foraging bout. This indicates that gathering information is a luxury while insurance is a necessity, at least when foraging on stochastic and variable food under the risk of starvation.