水环境模拟中多孔介质非均质性空间变异最优估计模型

在生产实践中,经常要求对含水介质渗透性参数的空间分布数值作出可靠的估计.如何利用已知数据对指定点处的渗透性参数作出令人满意的估计,历来是水文地质工作者和应用数学工作者都关心的问题.文章应用能够刻画区域化变量随机性与结构性特征的地质统计学的理论和方法对能够反映多孔介质非均质特性的参数进行了分析,通过分析结果得出了参数空间变异性局部最优估计模型--普通克里格法.     

Effects of productivity on biodiversity in forest ecosystems across the United States and China

In the global campaign against biodiversity loss in forest ecosystems, land managers need to know the status of forest biodiversity, but practical guidelines for conserving biodiversity in forest management are lacking. A major obstacle is the incomplete understanding of the relationship between site primary productivity and plant diversity, due to insufficient ecosystem‐wide data, especially for taxonomically and structurally diverse forest ecosystems. We investigated the effects of site productivity (the site's inherent capacity to grow timber) on tree species richness across 19 types of forest ecosystems in North America and China through 3 ground‐sourced forest inventory data sets (U.S. Forest Inventory and Analysis, Cooperative Alaska Forest Inventory, and Chinese Forest Management Planning Inventory). All forest types conformed to a consistent and highly significant (P < 0.001) hump‐shaped unimodal relationship, of which the generalized coefficients of determination averaged 20.5% over all the forest types. That is, tree species richness first increased as productivity increased at a progressively slower rate, and, after reaching a maximum, richness started to decline. Our consistent findings suggest that forests of high productivity would sustain few species because they consist mostly of flat homogeneous areas lacking an environmental gradient along which a diversity of species with different habitats can coexist. The consistency of the productivity–biodiversity relationship among the 3 data sets we examined makes it possible to quantify the expected tree species richness that a forest stand is capable of sustaining, and a comparison between the actual species richness and the sustainable values can be useful in prioritizing conservation efforts.     

上海城市样带土壤重金属空间变异特征及污染评价

为揭示城市化、工业化等人为活动对土壤环境质量的影响,选择能反映上海城郊乡梯度差异的城市样带,采用地统计学方法对表层土壤样品Cu、Zn、Pb、Cr、Mn共5种重金属的空间变异结构和分布格局进行了分析,并利用单因子指数法和内梅罗综合指数法评价了土壤重金属的污染程度.结果表明:土壤样品Cu、Pb、Zn、Cr、Mn这5种重金属平均含量分别为27.80、28.86、99.36、87.72、556.97 mg.kg-1.表层土壤Cu、Cr、Mn、Pb、Zn均属中等变异,Mn、Cr呈正态分布,Cu、Pb、Zn呈对数正态分布;半方差函数模型拟合结果显示Cu、Pb、Zn、Cr符合线状模型,Mn符合指数模型.通过泛克里格插值得到城市样带表层土壤重金属含量空间分布图,发现Cu呈条带状,Cr、Mn呈岛状,Pb、Zn呈条带和岛状分布相结合的特点.土壤污染评价结果说明土壤Cr、Zn、Pb污染相对严重.土壤Cr、Zn、Pb、Mn和Cu之间呈显著相关,土壤重金属之间表现为复合污染.土壤重金属污染城郊乡梯度差异明显,工业化、城市化与城市土壤重金属空间分布密切相关.     

龙口市污水灌溉区农田重金属来源、空间分布及污染评价

采集龙口市污水灌溉区农田土壤,分析测定土壤pH和重金属含量,以采集的70个土壤样点为研究对象,根据多元统计中的相关分析和主成分分析探究研究区重金属的来源;采用地统计理论与GIS空间插值相结合的方法研究重金属元素的空间结构和分布特征;最后利用内梅罗指数法和改进的模糊综合评判法做重金属污染评价.结果表明,研究区内9种重金属元素在土壤中均有一定程度的富集,其中重金属Cd的均值是当地背景值的3.06倍,富集情况最为明显;指数法进行的污染评价显示Cu、Cd和Pb的综合污染指数分别为7.06、6.10和5.54,三者均属重度污染;相关分析和主成分分析结果显示,Cu、Zn和Pb、Cd主要受人为因素影响,污水灌溉是它们的共同污染因素,前两者为农业生产中化肥农药的过量使用与长时间的累积作用,而北部煤矿开采和煤矸石的堆积产生的污染以及电镀、机械制造等工业污染是后两者的污染来源;Co、Cr、Mn、Ni和As主要是受成土母质等自然因素的影响;据模糊综合评判的结果,研究区70个样点,有13个是中污染程度,23个属于轻污染,28个是警戒程度,6个样点处在安全范围内;从空间分布上看,重金属含量的高值区主要集中在黄水河下游的诸由观镇和徐福镇,说明污水灌溉给当地土壤造成了一定程度的重金属污染.     

Predicting Water Quality Impaired Stream Segments using Landscape-Scale Data and a Regional Geostatistical Model: A Case Study in Maryland

In the United States, probability-based water quality surveys are typically used to meet the requirements of Section 305(b) of the Clean Water Act. The survey design allows an inference to be generated concerning regional stream condition, but it cannot be used to identify water quality impaired stream segments. Therefore, a rapid and cost-efficient method is needed to locate potentially impaired stream segments throughout large areas. We fit a set of geostatistical models to 312 samples of dissolved organic carbon (DOC) collected in 1996 for the Maryland Biological Stream Survey using coarse-scale watershed characteristics. The models were developed using two distance measures, straight-line distance (SLD) and weighted asymmetric hydrologic distance (WAHD). We used the Corrected Spatial Akaike Information Criterion and the mean square prediction error to compare models. The SLD models predicted more variability in DOC than models based on WAHD for every autocovariance model except the spherical model. The SLD model based on the Mariah autocovariance model showed the best fit (r² = 0.72). DOC demonstrated a positive relationship with the watershed attributes percent water, percent wetlands, and mean minimum temperature, but was negatively correlated to percent felsic rock type. We used universal kriging to generate predictions and prediction variances for 3083 stream segments throughout Maryland. The model predicted that 90.2% of stream kilometers had DOC values less than 5 mg/l, 6.7% were between 5 and 8 mg/l, and 3.1% of streams produced values greater than 8 mg/l. The geostatistical model generated more accurate DOC predictions than previous models, but did not fit the data equally well throughout the state. Consequently, it may be necessary to develop more than one geostatistical model to predict stream DOC throughout Maryland. Our methodology is an improvement over previous methods because additional field sampling is not necessary, inferences about regional stream condition can be made, and it can be used to locate potentially impaired stream segments. Further, the model results can be displayed visually, which allows results to be presented to a wide variety of audiences easily.     

上海城市样带土壤有机碳空间变异性研究

为揭示城市化、工业化等人为活动对土壤有机碳的影响,选择能反映上海城郊乡梯度差异的城市样带,采用地统计学方法对表层土壤样品土壤有机碳的空间变异结构和分布格局进行了分析。结果表明：城市表层土壤有机碳含量均属中等变异,徐汇区土壤有机碳含量呈正态分布,奉贤区、闵行区和所研究样带土壤有机碳含量呈对数正态分布。半方差函数模型拟合结果显示徐汇和闵行区土壤有机碳符合指数模型,奉贤和所研究样带土壤有机碳符合球状模型。通过泛克里格插值得到城市表层土壤有机碳含量空间分布图,发现徐汇、闵行区土壤有机碳呈岛状,奉贤区呈条带状,而所研究样带呈条带和岛状分布相结合的特点。土壤有机碳含量城郊乡梯度差异明显,工业化、城市化、肥料投入与管理等人为因素对城市土壤有机碳空间分布密切相关     

苏南某市河流水质参数时空变异性研究

以苏南某市区320km2内的河流为研究对象,基于对高锰酸盐指数、NH3-N、TP3个主要水质参数的监测,应用地质统计学的变差函数球状模型和Kriging插值法,对河流有机污染指标、富营养化指标进行了空间插值,用以揭示其时空分布特征及变化趋势,并绘制了时空分布等值线图。结果表明,受不同区域污染物来源的差异、不同河道自身条件的差异和不同水期水生植物、入流水量、河水流动性的差异等因素的影响,研究区河流水质参数呈现出不同的时空变异特征;各水质参数污染均相当严重,尤以富营养化指标氮磷最为显著。     

Comparison of Point Estimation Techniques in the Spatial Analysis of Radium-226, Radium-228 and Potassium-40 in Soil

Many environmental surveys require the implementation of estimation techniques to determine the spatial distribution of the variable being investigated. Traditional methods of interpolation and estimation, for example, inverse distance squared and triangulation often ignore features of the data set such as anisotropy which may have a significant impact on the quality of the estimates produced. Geostatistical techniques may offer an improved method of estimation by modelling the spatial continuity of the variable using semi-variogram analysis. The theoretical model fitted to the semi-variogram is then used in the assignation of weighting factors to the samples surrounding the location to be estimated. This paper outlines the results of a comparison between three common estimation methods, polygonal, triangulation and inverse distance squared and a geostatistical method, in the estimation of soil radionuclide activities. The geostatistical estimation method known as kriging performed best over a range of parameters used to test the performance of the methods. Kriging exhibited the best correlation between actual and estimated values, the narrowest error distribution and the lowest overall estimation error. Polygonal estimation was best at reproducing the data set distribution. Conditional bias was evident in all the methods, low values being over-estimated and high values being under-estimated.     

USE OF GEOSTATISTICS TO EVALUATE WATER QUALITY CHANGES DUE TO COAL MINING1

ABSTRACT: Large variability of water quality data often makes it difficult to judge whether or not the conditions in an aquifier or a stream are deteriorating or not the conditions in an aquifer or a stream are deteriorating or improving. A technique for separation of variograms into positive and negative difference components is proposed. This technique was applied to water quality data from a requality improved with time on the stripmined and undisturbed areas, declined in a seep and an adjoining stream and remained about the same in the deep mined zone.     

Mapping Three‐Dimensional Water‐Quality Data in the Chesapeake Bay Using Geostatistics1

Abstract:  Data interpretation and visualization software tools with geostatistical capabilities were adapted, customized, and tested to assist the Chesapeake Bay Program in improving its water‐quality modeling protocols. Tools were required to interpolate, map, and visualize three‐dimensional (3D) water‐quality data, with the capability to determine estimation errors. Components of the software, originally developed for ground‐water modeling, were customized for application in estuaries. Additional software components were developed for retrieval, and for pre‐ and post‐ processing of data. The Chesapeake Bay Program uses the 3D mapped data for input to the Bay water‐quality model that projects the future health of the Bay and its tidal tributary system. In determining water‐quality attainment criteria, 3D kriging estimation errors are needed as a statistical measure of uncertainty. Furthermore, given the high cost of installing and operating new monitoring stations, geostatistical techniques can assist the Chesapeake Bay Program in the identification of suitable data collection locations. Following the evaluation, selection, and development of the software components phase, 3D ordinary kriging techniques with directional semi‐variograms to account for anisotropy were successfully demonstrated for mapping 3D fixed station water‐quality data, such as dissolved oxygen and salinity. Additionally, an improved delineation tool was implemented to simulate the upper and lower pycnocline boundary surfaces allowing the segregation of the interpolated 3D data into three separate zones for a better characterization of the pycnocline layer.