红松阔叶混交林林隙极端地面温度的地统计学分析

以小兴安岭原始红松阔叶混交林林隙为研究对象,采用网格法布点,通过对生长季内林隙各样点极端地面温度的连续观测,利用经典统计学和地统计学的方法分析并揭示了林隙极端地面温度的时空分布格局。研究结果表明:林隙极端地面温度不同空间样点之间存在异质性,而且异质性的强度、 尺度和空间结构组成随时间而改变,各月平均极端地面温度斑块形状复杂,最高地面温度大小顺序均为6月>7月>8月>9月,最低地面温度大小顺序为7月>8月>6月>9月,月平均地温的最大值和最小值分布位置不固定,同一月份地面最高温度的大小顺序是空旷地>林隙>郁闭林分,地面最低温度大小顺序是郁闭林分>林隙>空旷地。研究旨在为红松阔叶混交林的可持续经营提供基础数据和理论参考。     

Assessing the risk of soil contamination in the Swiss Jura using indicator geostatistics

The probability that the concentrations of toxic substances in soil or other medium exceed tolerablemaxima at any unsampled place can be estimated by indicator geostatistics. The method is developed and used to estimate and map the risk of contamination by cadmium, copper and lead in the topsoil of a 14.5 km 2 region in the Swiss Jura. It combines both direct measurements of metal concentrations and thecalibration of a geological map, and it shows that the risk of toxicity is least on Argovian rocks. Two approaches are proposed to divide a region into safe' and 'hazardous' zones on the basis of probability maps. The first declares as contaminated all places where the risk of contamination exceeds a given threshold. The second approach first evaluates the financial costs that might result from a wrongdeclaration, after which the site is allocated to a class so as to minimize that cost. The risk of exposure for humans and animals is generally greater for contaminated agricultural land than for forest soil, and so land use is taken into account in both procedures.     

基于多环境介质氮素和同位素的滦平盆地地下水硝酸盐来源示踪

选择密云水库上游承德市滦平盆地为研究区,通过不同土地利用类型地下水"三氮"含量、土壤全氮含量和包气带可溶硝态氮含量,结合水体硝酸盐氮氧双同位素、硫酸盐硫氧双同位素多种环境同位素特征和地下水放射性碳同位素测年示踪硝酸盐来源.结果表明,滦平盆地水体氮形态以硝态氮为主,地下水NO₃^-质量浓度与居民用地、旱地土地利用类型显著相关,硝酸盐污染主要集中于居民建设用地和农用地区域浅层地下水中.13.79%地下水样品NO₃^-质量浓度超过国标（GB/T 14848-2017）地下水硝酸盐限值Ⅲ类标准,超标范围为1.04~3.86倍;37.93%地下水样品NO₃^-质量浓度超WHO饮用水硝酸盐浓度限值,超标范围为1.08~6.83倍.地下水NO₃^-质量浓度、土壤全氮和浅层土壤可溶硝态氮空间变异受结构性因素和人为因素共同作用影响.地下水硝酸盐来源主要为家畜粪尿和生活污水混合污染,其次为化学肥料淋滤;盆地山前地下水径流区包气带-地下水氮循环主导过程为硝化作用.以盆地系统作为独立单元研究水环境硝酸盐污染来源和归趋规律,对流域整体地下水污染防治和修复具有重要意义.     

CO-KRIGING TO INCORPORATE SCREENING DATA: HUDSON RIVER SEDIMENT PCBs1

ABSTRACT: Kriging methods of geostatistical analysis provide valuable techniques for analysis of sediment contamination problems, including interpolation of concentration maps from point data and estimation of global mean concentrations. Sample collection efforts frequently include preliminary screening data of considerably more extensive coverage than the laboratory analyses on which estimation is usually based. How should these be incorporated in kriging? Screening and laboratory analysis constitute two separate estimates of the same spatial field but of very different characteristics. A modified version of co-kriging is developed to include the imprecise screening information in the analysis of contaminant distribution. Use of the method is demonstrated on a data set of sediment PCB samples from the Upper Hudson River, for which preliminary categorical mass spectrometry screening was used to select a smaller set of samples for gas chromatograph analysis. The method is widely applicable to many situations of contaminant and natural resource estimation.     

STATISTICAL AND GEOSTATISTICAL MODELLING OF PRELIMINARILY ADJUSTED DEFOLIATION ON AN EUROPEAN SCALE

Since 1987 tree crown condition is surveyed annually in large parts of Europe mainly in terms of defoliation. The plot-wise means of defoliation from 1994 to 2000 were evaluated by General Linear Models (GLM) in order to describe country-specific levels of defoliation and age-trends. Additional runs with estimates for influences of insects and fungi were performed. The amount of variance of defoliation explained by country, age and its interaction was between 35% and 59% for the main tree species, except for Quercus ilex. Additionally, up to 10% could be explained by the inclusion of estimates for infestations by insects and fungi. Residuals of the GLMs were taken as a measure of forest condition not biased by country or age effects and interpreted as preliminarily adjusted defoliation (PAD). PAD values were analysed using geostatistical methods. The modelled spatial autocorrelations were used for kriging. The resulting maps give an overview on regions with elevated defoliation, which may pinpoint regional causes of defoliation. The elimination of methodologically caused variance is a precondition of any cause–effect oriented analyses. The combination of explorative modelling and geostatistics will promote the choice of further promising predictors.     

Cokriging optimization of monitoring network configuration based on fuzzy and non-fuzzy variogram evaluation

A number of optimization approaches regarding monitoring networkdesign and sampling optimization procedures have been reported inthe literature. Cokriging Estimation Variance (CEV) is a usefuloptimization tool to determine the influence of the spatial configuration of monitoring networks on parameter estimations. Itwas used in order to derive a reduced configuration of a nitrateconcentration monitoring well network. The reliability of the reduced monitoring configuration suffers from the uncertainties caused by the variographer's choices and several inherent assumptions. These uncertainties can be described considering thevariogram parameters as fuzzy numbers and the uncertainties by means of membership functions.Fuzzy and non-fuzzy approaches were used to evaluate differencesamong well network configurations. Both approaches permitted estimates of acceptable levels of information loss for nitrate concentrations in the monitoring network of the aquifer of the Plain of Modena, Northern Italy. The fuzzy approach was found torequire considerably more computational time and numbers of wellsat comparable level of information loss.     

THREE‐DIMENSIONAL KRIGING ANALYSIS OF SEDIMENT MERCURY DISTRIBUTION: A CASE STUDY1

ABSTRACT: This study presents an application of a three‐dimensional kriging technique to estimate spatial distribution of total mercury (Hg) in the Cedar‐Ortega Rivers watershed located in the lower St. Johns River basin, Florida. The kriging procedures, including preliminary data analysis, structural data analysis and the log kriging estimation, are presented. Results show watershed wide Hg contamination of river sediment to a depth of 1.0 m. A three‐dimensional plot of Hg against the Florida Sediment Assessment Guidelines (i.e., the probable effect level or PEL) demonstrates that the Cedar River is more contaminated with Hg than the rest of the watershed. The maximum sediment depth with Hg concentrations above PEL value (0.696 mg/kg) is 1.5 m. Hg concentrations at or above this level could pose a significant hazard to aquatic organisms. Analysis of the spatial distribution of Hg in the watershed finds multiple input sources. This study suggests that there is a need to identify the major sources of Hg in the watershed, and to determine the pathways that allow Hg to enter the river.     

GIS‐Based Spatial Precipitation Estimation: A Comparison of Geostatistical Approaches1

Abstract: As one of the primary inputs that drive watershed dynamics, the estimation of spatial variability of precipitation has been shown to be crucial for accurate distributed hydrologic modeling. In this study, a Geographic Information System program, which incorporates Nearest Neighborhood (NN), Inverse Distance Weighted (IDW), Simple Kriging (SK), Ordinary Kriging (OK), Simple Kriging with Local Means (SKlm), and Kriging with External Drift (KED), was developed to facilitate automatic spatial precipitation estimation. Elevation and spatial coordinate information were used as auxiliary variables in SKlm and KED methods. The above spatial interpolation methods were applied in the Luohe watershed with an area of 5,239 km², which is located downstream of the Yellow River basin, for estimating 10 years’ (1991‐2000) daily spatial precipitation using 41 rain gauges. The results obtained in this study show that the spatial precipitation maps estimated by different interpolation methods have similar areal mean precipitation depth, but significantly different values of maximum precipitation, minimum precipitation, and coefficient of variation. The accuracy of the spatial precipitation estimated by different interpolation methods was evaluated using a correlation coefficient, Nash‐Sutcliffe efficiency, and relative mean absolute error. Compared with NN and IDW methods that are widely used in distributed hydrologic modeling systems, the geostatistical methods incorporated in this GIS program can provide more accurate spatial precipitation estimation. Overall, the SKlm_EL_X and KED_EL_X, which incorporate both elevation and spatial coordinate as auxiliary into SKlm and KED, respectively, obtained higher correlation coefficient and Nash‐Sutcliffe efficiency, and lower relative mean absolute error than other methods tested. The GIS program developed in this study can serve as an effective and efficient tool to implement advanced geostatistics methods that incorporate auxiliary information to improve spatial precipitation estimation for hydrologic models.     

Remote sensing: Using the spatial domain

Objects in the terrestrial environment interact differentially with electromagnetic radiation according to their essential physical, chemical and biological properties. This differential interaction is manifest as variability in scattered radiation according to wavelength, location, time, geometries of illumination and observation and polarization. If the population of scattered radiation could be measured, then estimation of these essential properties would be straightforward. The only problem would be linking such estimates to environmental variables of interest. This review paper is divided into three parts. Part 1 is an overview of the attempts that have been made to sample the five domains of scattered radiation (spectral, spatial, temporal, geometrical, polarization) and then to use the results of this sampling to estimate environmental variables of interest. Part one highlights three issues: first, that relationships between remotely sensed data and environmental variables of interest are indirect; second, our ability to estimate these environmental variables is dependent upon our ability to capture a sound representation of variability in scattered radiation and third, a considerable portion of the useful information in remotely sensed images resides in the spatial domain (within the relations between the pixels in the image). This final point is developed in Part 2 that explores ways in which the spatial domain is utilized to describe spatial variation in remotely sensed and ground data; to design optimum sampling schemes for image data and ground data and to increase the accuracy with which remotely sensed data can be used to estimate both discontinuous and continuous variables. Part 3 outlines two specific uses of information in the spatial domain; first, to select an optimum spatial resolution and second, to inform an image classification.