多尺度下的不确定性空间方向锥形模型研究

利用质心作为参考点,并在空间方向锥形模型中添加了扩展不确定度的参数,用区间分析法,对多尺度下顾及不确定性的空间方向关系进行形式化描述,来适应尺度变化引起的空间关系不确定性的变化,以更好地描述空间方向关系。该模型使得方向关系的划分上有个平滑的过渡区,在方向概念的表达上更符合人的认知。     

沉积盆地定量动力学模拟

盆地的定量动力学模拟是以地质模型和数理模型为基础,对盆地形成演化的地球动力学过程进行拟合,用以探讨盆地形成演化的动力学机制,并通过各种动力学参数的提取,解决实际地质问题。本文简短回顾了盆地定量动力学模拟的历史和现状;从盆地动力学模似的基础和原理入手,重点介绍了拉伸盆地、前陆盆地和弧前盆地动力学模拟的最新进展;从模拟的流程探讨了不确定性的存在和来源;对我国的研究现状作了概括分析,认为盆地的定量动力学模拟可以在我国取得突破。     

集合卡尔曼滤波在流域水文模型流量预报中的应用

针对流域降雨入渗过程,引入集合卡尔曼滤波(EnKF)理论,视整个边坡流域为一个随机动态系统,将边坡流域流量观测值作为系统的输出,用集合卡尔曼滤波模型来描述系统的状态;结合流域流量计算方法,实现水文模型参数的随机动态估计,在有效获得待估参数的同时还给出估计值的不确定性.通过数值算例表明,集合卡尔曼滤波可以有效地对含噪声的量测数据进行处理,能够跟踪水文模型的动态变化.相对于常用最优化算法,集合卡尔曼滤波同时给出反演结果和先验知识的后验分布,显示出更好的实时性和可靠性.     

土地利用规划环境影响评价中不确定性分析

在分析土地利用规划环境影响评价中的不确定来源、特点及其对决策的影响基础上,提出了降低环境影响评价中不确定性的方法。通过研究认为:土地利用规划环境影响评价中的不确定主要由土地利用规划方案的不确定、环境信息的不确定和环境影响程度的不确定引起的,这些不确定性具有客观性、传递性、累积性,对决策会产生较大的干扰,但是可以通过使用基于情景分析的预测方法、多方协作的方式以及广泛开展公众参与等方法来降低。     

Uncertainty representation of ocean fronts based on fuzzy-rough set theory

Analysis of ocean fronts' uncertainties indicates that they result from indiseemibility of their spatial position and fuzzi-ness of their intensity. In view of this, a flow hierarchy for uncertainty representation of ocean fronts is proposed on the basis of fuzzy-rough set theory. Firstly, raster scanning and blurring are carried out on an ocean front, and the upper and lower approximate sets, the indiscernible relation in fuzzy-rough theories and related operators in fuzzy set theories are adopted to represent its uncer-tainties, then they are classified into three sets: with members one hundred pereent belonging to the ocean front, belonging to the ocean front's edge and definitely not belonging to the ocean front. Finally, the approximate precision and roughness degree are util-ized to evaluate the ocean front's degree of uncertainties and the precision of the representation. It has been proven that the method is not only capable of representing ocean fronts' uncertainties, but also provides a new theory and method for uncertainty representation of other oceanic phenomena.     

基于贝叶斯网络的地下水环境系统决策管理研究

应用贝叶斯网络解决地下水环境管理中具有不确定性的多目标决策问题,通过对决策变量氮肥施用量以及灌溉模式的调控,减少水中的硝酸盐含量,达到既能有效改善水环境又不至使农民经济利益受到损害的目标。通过分析具体的地下水环境管理系统中变量间的相互关系,构建描述变量间不确定性关系的贝叶斯网络模型,其中包括表示其依赖关系的有向无环图和表示其具体概率依赖程度的条件概率表。并在多个水环境管理目标均达到最优的前提下进行概率推理,得到决策变量氮肥施用量以及灌溉模式取不同值时目标变量的概率分布情况。最终确定出能使所有目标均达到最优的合理的水环境管理决策:(1)使用喷灌,将氮肥施用量控制在0.01~0.03 kg/m2;(2)使用漫灌,将氮肥施用量控制在0.01~0.02 kg/m2。     

地图综合中线要素综合的不确定性分析

地图综合是GIS领域研究的热点问题之一,也是GIS中最需要智能化的课题之一。为了保证地图综合的数据质量,地图综合的不确定性和地图综合的数据质量问题应该在地图综合领域中受到重视。通过研究对比分析4种线要素综合算法各自的优缺点,评价曲线综合算法和对数据不确定性进行分析。使用Autodesk公司的AutoCAD平台的Object-ARX二次开发包在VC＋＋语言平台下实现Douglas-Peucker综合算法和Li-Openshaw综合算法对地图线要素的综合;分析了目前主要的线要素综合方法的各自的特点与适用范围;通过对几种常用综合算法不确定性的分析,总结了线要素综合的不确定性的各种表现,包括原始数据保持的不确定性、阈值的不确定性与综合后数据评定指标和评定方法的不确定性等。     

暴雨山洪灾害预警方法研究进展

暴雨山洪灾害预警是中小流域山洪灾害防控体系的薄弱环节,也是决定山洪灾害防控成败的关键。论文围绕山洪灾害预警的核心问题,从中国山洪灾害区域差异特征、山洪灾害预警技术方法、山洪灾害概率预警现状3个方面进行了综述。中国山洪灾害分布存在明显的时空差异,因此有必要根据山洪灾害的区域差异发展有针对性的预警方法。以临界雨量为指标的雨量预警是目前中国中小流域暴雨山洪灾害预警的主要技术手段,但常规方法仅给出一个(组)确定的临界雨量阈值,导致预警结果存在突出的不确定性问题。概率预警可以定量评估诸多不确定性,给出山洪灾害概率预警结果,因此具备很好的理论优势与潜在应用价值。论文展望了山洪灾害概率预警未来的研究重点与方向：(1)充分挖掘暴雨洪水样本信息,开展山洪灾害概率预警基础方法与技术集成研究;(2)加强非平稳性条件下的临界雨量阈值估算与山洪灾害概率预警研究;(3)综合考虑预警阈值发生概率及其致灾概率,优化“多级预警、多级响应”技术方法,推进山洪灾害综合预警业务系统建设与应用。     

The Role of the Aerosol Indirect Effect in the Northern Indian Ocean Warming Simulated by CMIP5 Models

The northern Indian Ocean （NIO） experienced a decadal-scale persistent warming from 1950 to 2000, which has influenced both regional and global climate. Because the NIO is a region susceptible to aerosols emis- sion changes, and there are still large uncertainties in the representation of the aerosol indirect effect （ALE） in CMIP5 （Coupled Model Intercomparison Project Phase 5） models, it is necessary to investigate the role of the AIE in the NIO warming simulated by these models. In this study, the authors select seven CMIP5 models with both the aerosol direct and indirect effects to investigate their performance in simulating the basin-wide decadal-scale NIO warming. The results show that the decreasing trend of the downwelling shortwave flux （FSDS） at the surface has the major damping effect on the SST increasing trend, which counteracts the warming effect of greenhouse gases （GHGs）. The FSDS decreasing trend is mostly contrib- uted by the decreasing trend of cloudy-sky surface downwelling shortwave flux （FSDSCL）, a metric used to measure the strength of the AIE, and partly by the clear-sky surface downwelling shortwave flux （FSDSC）. Models with a relatively weaker AIE can simulate well the SST increasing trend, as compared to observation. In contrast, models with a relatively stronger AIE produce a much smaller magnitude of the increasing trend, indicat- ing that the strength of the AIE in these models may be overestimated in the NIO.     

The Combined Effect of Initial Error and Model Error on ENSO Prediction Uncertainty Generated by the Zebiak-Cane Model

Initial errors and model errors are the source of prediction errors. In this study, the authors compute the conditional nonlinear optimal perturbation （CNOP）-type initial errors and nonlinear forcing singular vector （NFSV）- type tendency errors of the Zebiak-Cane model with respect to El Nifio events and analyze their combined effect on the prediction errors for E1 Nino events. The CNOP- type initial error （NFSV-type tendency error） represents the initial errors （model errors） that have the largest effect on prediction uncertainties for E1 Nifio events under the perfect model （perfect initial conditions） scenario. How- ever, when the CNOP-type initial errors and the NFSV- type tendency errors are simultaneously considered in the model, the prediction errors caused by them are not am- plified as the authors expected. Specifically, the predic- tion errors caused by the combined mode of CNOP-type initial errors and NFSV-type tendency errors are a little larger than those caused by the NFSV-type tendency er- rors. This fact emphasizes a need to investigate the opti- mal combined mode of initial errors and tendency errors that cause the largest prediction error for E1 Nifio events.