水环境模拟中变异函数的稳健性分析

文章将稳健统计学（RS）引入地质统计学（GS）,对廊坊市地下水现场采样数据中Cl^-运用影响系数法和估计邻域法识别并处理特异值,取得了较好的稳健效果。在此基础上,用Cressie-Hawkins法、中位调节法两种稳健统计学方法系统的对进行特异值处理前后的数据作了处理和分析。结果表明,变量Cl^-离子经ICM法处理特异值后再进行稳健计算方法中以MA法有较大的优势,而且获得稳健变异函数γR（h）及其估计量对于改善变异函数的结构性和理论模型的主要参数有明显效果。因此,稳健地质统计学的方法可以移植到水环境科学中应用,这对加强水环境空间变异性以及稳健变异函数的基础研究有着重要的应用价值和学术价值。     

PREDICTION OF MEAN ANNUAL FLOWS IN NORTH AND CENTRAL FLORIDA1

ABSTRACT: A frequency analysis approach for the prediction of flow characteristics at ungaged locations is applied to a region of high annual precipitation and low topography in north and central Florida. Stationary time series of annual flows are fitted with the lognormal distribution and estimated parameters of the distribution are fitted by third order trend surfaces. These explain 65 and 74 percent of the observed variances in the mean and standard deviation, respectively. Predictions of parameters are then made for several locations previously unused in the study and they are used to estimate the return periods of various flows from the lognormal distribution. Application of the Kolmogorov-Smirnov goodness-of-fit test suggests that only one of the five test stations can be considered significantly different from the observed data, confirming the applicability of this technique.     

Accounting for large-scale factors in the study of understory vegetation using a conditional logistic model

Local-scale and large-scale factors can affect the presence of a species of understory vegetation in the forest. Local-scale factors may be the influence of surrounding trees, while climate and latitude are typically considered large-scale factors. A model for the presence of a species needs to take into account both scales. A conditional logistic model is proposed for those studies where only the local-scale factors are of interest and that avoids estimating the large-scale parameters. Conditioning is carried out by the number of quadrats in the plot where the vegetation is found. As the latter is a sufficient statistic for the large-scale factors, a model free from these parameters is obtained. Data gathered in the permanent sample plots of the 1985–1986 National Forest Inventory of Finland is used for illustration, where the local-scale factor of interest is the influence of the trees, quantified by an index based on the size and location of the trees. The model fitted to Vaccinium vitis-idaea showed a significant and positive influence of Scots pine on the presence of this species, while for Calamagrostis arundinacea, a decrease in the odds ratio was observed due to the influence of Norway spruce.     

Interpolation performance of a spatio-temporal model with spatially varying coefficients: application to PM<Subscript>10</Subscript> concentrations in Rio de Janeiro

In this work we present a Bayesian analysis in linear regression models with spatially varying coefficients for modeling and inference in spatio-temporal processes. This kind of model is particularly appealing in situations where the effect of one or more explanatory processes on the response present substantial spatial heterogeneity. We describe for this model how to make inference about the regression coefficients and response processes under two scenarios: when the explanatory processes are known throughout the study region, and when they are known only at the sampling locations. Using a simulation experiment we investigate how parameter inference and interpolation performance are affected by some features of the data and prior distribution that is used. The proposed methodology is used to model the dataset on PM₁₀ levels in the metropolitan region of Rio de Janeiro presented in Paez and Gamerman (2003).     

溶质运移的空间信息统计组合分析

空间信息统计分析理论与方法能够将空间结构性和随机性有机地结合起来,应用变异函数理论模型、方向综合变异指标以及各种空间最优估计方法,对地下水环境评价参数空间分布的不确定性评价方面,较经典统计方法更为合理。     

长江水系河流沉积物重金属元素含量的计算方法研究

采用长江水系约２６０个背景样点沉积物原样和小于６３μｍ细粒沉积物中１１种重金属元素（铜、铅、锌、镉、汞、钴、镍、砷、铬、锰和铁）含量的数据，研究了数据的频率分布特征和变异系数，比较了算术均值、几何均值和中位数与由Ｂｏｘ－Ｃｏｘ变换而得到的对称均值间的大小关系，结果表明，大多数重金属元素含量服从对数正态分布；采用算术均值表示均值会有较大的偏差，且这种偏差随变异系数的增大而增大；采用中位数偏差较小，但     

Stream Temperature Modeling Using Functional Regression Models

Stream temperature is one of the most important environmental variables in lotic habitats as it has important and direct impacts on the ecosystem. Given the continuous nature of this variable, the aim of this paper was to introduce functional regression for the air‐stream temperature relation, being capable to model an entire seasonal or annual curve of temperatures as one entity, rather than multiple daily or weekly values in classical models. Three types of functional models were explored in the study and compared to two classical models (Generalized Additive Model and Logistic Model) for six rivers from the United States The results show the functional models have the best performance for all the considered rivers. When comparing functional models between them, one variant of the historical functional model performs better than the two other models and is the most parsimonious. Functional regression leads to encouraging results to model the complete annual stream temperature curve as one entity compared to other classical approaches.     

A Parallel Computation Tool to Enable Dynamic Sensitivity and Model Performance Analysis of APEX: Evapotranspiration Modeling

Global sensitivity analysis can be used for assessing the relative importance of model parameters on model outputs. The sensitivity of parameters usually indicates a temporal variation due to variation in the environmental conditions (e.g., variation in weather or plant growth). In addition, the size of averaging window by which the outputs of a model are aggregated or averaged may impact parameter sensitivities. In this study, temporal variation of parameters sensitives, model performance, as well as the impact of the size of time‐averaging window on evapotranspiration (ET) prediction using the Agricultural Policy/Environmental eXtender (APEX) model are investigated. To achieve these goals, an open‐source package named PARAPEX was developed in R and used to perform dynamic sensitivity and model performance analysis of APEX using parallel computation. PARAPEX reduced the computation time from 5,939 to 379 s (using 20 and 1 computation nodes, respectively). The sensitivity analysis results indicated the parameters accounting for the reducing effect of plant cover on evaporation from the soil surface, the effect of soil on the plant root growth, and the effect of cycling and transformation dynamics of organic matter at the top soil layer as the top sensitive parameters based on the mean daily simulated ET and the Nash–Sutcliffe model performance measure. The dynamic performance analysis indicated poor ET predictions by APEX during the growing seasons. Editor's note : This paper is part of the featured series on Optimizing Ogallala Aquifer Water Use to Sustain Food Systems. See the February 2019 issue for the introduction and background to the series.     

适应城市消防规划需求的火灾风险评估研究

研究城市火灾风险评估,可以为城市消防规划提供有效参考,对推进城市防灾减灾工程具有重要意义。面向所要研究区域的城市消防规划需求,首先分析区域火灾历史统计数据,提炼区域的火灾特点,从火灾危险性、承灾体易损性、城市抗灾能力3方面分析指标影响因子,建立了城市火灾风险评估指标体系;其次,提出了以建设用地地块为评估单元,基于现场调研和卫星遥感影像、城市用地分类相结合的指标因子快速分析方法,并通过加权平均评估城市火灾风险;最后,通过城市火灾风险评估案例研究,阐释了评估的技术流程和结果形式。研究结果表明:适应城市消防规划需求的火灾风险评估方法可以快速定量评估区域火灾风险的空间分布特征,还能识别出消防安全布局的重点地区,进一步支撑城市消防规划编制。     

2014年我国煤矿事故统计与规律分析

为了更深入地研究我国煤矿事故的一般性规律,基于2014年我国煤矿事故数据,采用数理统计的方法对煤矿百万t死亡率、事故区域、类型、月份、时间段、诱因6个维度进行了研究.结果表明:湖南省、重庆市煤矿事故发生频率较高,贵州省瓦斯事故发生频率较高;瓦斯事故、顶板事故是导致人员伤亡的主要事故类型,瓦斯爆炸是煤矿瓦斯事故的主要类型;8月煤矿事故情况较为严重;10:00-11:59是煤矿事故频发时间;违章作业是导致煤矿事故发生的主要诱因.同时,针对煤矿事故发生规律提出了相应的对策措施.