不同尺度下皎口水库水体主要指标空间变异研究

通过不同取样尺度下采集皎口水库水样,测定水体主要指标总氮、总磷、氨氮及高锰酸盐指数含量,研究了地统计分析法在不同取样尺度下对水库水体空间变异的适用性和空间插值的质量。研究结果表明,皎口水库100 m尺度与200 m取样尺度相比,4种主要水质指标在不同取样尺度下空间变异最佳变异模型、插值模拟及空间分布均存在一定差异。空间拟合模型方面,总氮、高锰酸盐指数最佳拟合模型为高斯模型,总磷、氨氮最佳拟合模型为球面模型;结合模型特征参数和空间插值综合评价,总氮和氨氮在200 m取样尺度下表现出较好的空间相关性,插值模拟质量较好,而总磷和高锰酸盐指数则在100 m尺度下表现出一定优势;空间分布上,水体氨氮、高锰酸盐的影响主要是人为因素,总磷受水库内源环境影响较大,而总氮则与库周农业种植等活动密切相关。以上结果表明,总氮和氨氮更适合在200 m尺度下取样,而总磷和高锰酸盐指数宜在100 m尺度下取样。     

喀斯特洼地表层土壤水分的空间异质性及其尺度效应

空间变异及其尺度问题在土壤学、地理学、水文学和生态学中均处于十分重要的地位,是当前研究热点之一[1~5]。变异结果使得不同景观斑块间的交换过程在从田块到流域的不同空间尺度上产生[6]。因此要实现景观生态过程中的尺度转换,关键是从实测到预测模型的复杂转变[7,8]。在土壤学研究中,观测尺度和模型尺度均包含尺度的三因素,即采样间隔、采样幅度和支撑效应,这三个因素是界定观测或模型空间尺度的必要条件[3]。而随采样尺度变化,土壤水分的表观变异与真实变异出现偏差,这种偏差是观测尺度的函数,可以采用地统计学中的半变异函数进行有效地尺度转化[9]。国内外学者对土壤水分的空间尺度问题作了大量研究。Bl sch     

采样尺度对土壤养分空间变异分析的影响

以高密度土壤养分采样数据为数据源,通过随机抽取生成不同采样尺度的样点数据,分析采样尺度对土壤养分空间变异特征分析的影响。研究结果表明:区域土壤养分预测均值随采样尺度减小呈下降趋势,而变异系数增加;养分空间分布的全局趋势随采样尺度增大而增强,但不影响半方差模型;当采样尺度较大,样点间自相关较弱时,相对较少的样点也能满足区域统计参数估测分析需要,但不能用于空间变异特征和插值分析;当样点数大于最佳采样数时,养分统计参数、空间变异特征和插值分析随着采样尺度减小而精度提高,当采样尺度达到0.2左右时,能够满足中等空间变异的土壤养分空间插值分析需要;样点空间布局对相关距和空间插值分析精度的影响比采样尺度本身更为显著。     

土壤空间变异研究进展

本文主要综述了土壤空间变异研究的进展,包括土壤水分特征及状态参数、物理性质、化学性质、重金属及其它元素的空间变异研究。讨论了尺度效应及套合结构模型在空间变异研究中的应用和研究进展,指出应根据不同研究目的采用不同的研究方法,并确定恰当的研究尺度,分析统计结果应结合区域生态系统进行分析。     

北京耕作土壤重金属多尺度空间结构

为了有效揭示土壤重金属的空间分布特征,为土壤环境质量评价提供科学依据,该研究以北京市耕作土壤中重金属为例,通过地统计学方法分析了重金属空间结构的尺度效应。研究结果表明：土壤重金属Cr、Ni、Zn和Hg的空间结构性对空间尺度有明显的依赖,理论半方差函数的最优性检验进一步表明多尺度套合模型能充分地挖掘和利用土壤重金属Cr、Ni、Zn和Hg的空间变异结构,并且采用多尺度套合模型法对重金属Cr、Ni、Zn和Hg进行克里格插值,其插值效果明显优于单一尺度下的普通克里格插值。因此,多尺度套合模型法能有效地揭示耕作土壤中重金属的空间分布特征。     

农牧交错带本氏针茅坡地土壤-植物系统磷素分布特征

明确农牧交错带草坡地土壤-植物系统磷素分布特征对于草坡地生态系统磷素管理、空间分布和磷素流失预测具有重要科学意义。本研究针对黄土高原农牧交错带本氏针茅坡地,分析了草坡地土壤-植物系统磷素沿坡面的分布特征,并且从不同空间尺度研究了土壤-植物系统磷素的空间结构特征,以揭示农牧交错带草坡地土壤-植物系统磷素分布规律。结果表明,随坡位的降低,草坡地植物生物量、植物磷素含量和储量均逐渐增加,土壤全磷和速效磷含量在坡上部和坡中部相近,且均低于坡底部;这些变量的变异系数均以坡底部最小。植物磷素含量和土壤全磷的变异系数由坡顶向下依次为34.25%、 25.98%、 10.02% 和 24.05%、 15.27%、 10.85%,表明坡底部土壤-植物系统磷素分布相对均匀。各变量的最佳拟合模型在3个分析尺度上均保持一致,植物生物量和土壤速效磷的最佳拟合模型为指数模型,土壤全磷以球状模型拟合效果最佳,而植物磷素以高斯模型拟合效果最好。随分析尺度的增加,块金常数、块金效应逐渐增大。植物磷素含量在5、 10、 15 m 3个尺度上的块金常数和块金效应依次为0.010、 0.010、 0.011和7.9、 8.5、 8.9,即样点间的空间依赖性逐渐减弱,空间相关性减弱。这表明农牧交错带草坡地土壤-植物系统磷素在不同尺度上空间结构比较稳定,属中等或强空间相关性,其空间变异主要来源于结构性因素。     

新疆安集海灌区膜下滴灌棉田根系层土壤盐分多尺度空间分布特征

明晰土壤盐渍化空间分布特征是盐碱地改良的基础.与单一尺度相比,嵌套多尺度研究可更好地分析土壤盐分的空间变异结构特征,更精确地表达盐渍化程度的自相关随尺度的变化情况.采用传统统计学和地统计学相结合的方法,通过野外实地分层采集土样,分析了4 km、500 m和100 m三个嵌套尺度条件下,新疆安集海灌区膜下滴灌棉田根系层土...     

广州市农业表层土壤镉和铅多尺度空间结构

基于广州市2005年641个表层农业土壤样点数据,使用地统计学和多尺度嵌套模型,探讨了土壤Cd和Pb在多粒度尺度上的空间结构并进行插值估计。研究发现,随着空间粒度由粗到细,土壤Cd和Pb空间相关性依次减弱,半方差函数对重金属空间变异的代表性依次增强。多尺度嵌套模型插值效果略优于单尺度的普通克里格法,前者插值结果显示:广州农业土壤Cd和Pb含量普遍未超过国家土壤环境质量二级标准,Cd超标区主要位于中心城区的菜地,少量位于从化区、南沙区以及花都区与白云区交界。     

黄土区不同土层饱和导水率空间变异与影响因素

为掌握黄土区区域尺度不同土层饱和导水率(K_s)空间分布特征,在典型黄土高原地区网格布设243个样点,获取0~10 cm、10~20 cm和20~40 cm土壤K_s及相关环境因子。采用经典统计学与地统计学方法,分析了K_s的空间分布规律、变异特征及影响因素。结果表明:(1)区域尺度不同土层K_s均呈中等程度变异,K_s随土层深度增加而逐渐降低,且不同土层K_s平均值表现为林地草地农地;(2)0~10 cm、10~20 cm和20~40 cm土层K_s半方差函数最佳拟合模型分别为球状模型、高斯模型和球状模型,且均具有强烈的空间自相关性;(3)气候、土壤、地形和土地利用方式是影响黄土区区域尺度K_s空间分布的主要因素,可作为模拟区域K_s空间分布状况的预测变量。     

黄灌沙区农田耕层土壤性状空间异质性分析

用地统计学的方法,研究了黄灌沙区农田耕作层(030 cm)土壤的有机质、全氮、碱解氮、速效磷、速效钾、pH和电导率的空间变异规律。结果表明,农田耕作层土壤养分的空间异质性主要由随机性因子如土壤耕作措施,包括施肥、灌溉和作物轮作等造成,这与该区农田耕作层土壤是引黄灌溉改良的结果有关。土壤养分含量的变异函数均可很好地拟合成线性模型,块金值和基台值之比(C0/C0 C)均为1,说明随机性因子是导致该区土壤性状空间变异的主要因素,在整个尺度上具有恒定的变异,造成了土壤性状的相对均一。pH的变异函数为指数模型,其空间异质性组成中随机性和结构性因子各占50%。电导率的变异函数为球状模型,空间变异主要由结构性因子导致,结构性因子引起的空间异质性占总空间异质性的67%。分维数也表明耕层土壤性状空间依赖性较小,随机因子如农业活动是引起黄灌沙区土壤性状空间异质性的关键原因。Moran s I系数表现为除了pH和电导率受结构性和随机性因子共同作用外,其余土壤性状均呈较弱的相关关系。因此,干旱沙区通过人为措施改良的土壤性状空间上的变异主要受人为活动如灌溉、施肥和耕作等影响,在农业活动中更应该注重施肥和灌溉方式、灌溉水源等问题,以避免因土壤性状的变异导致的肥料利用效率降低。     

Joint Modeling of Spatial Variability and Within-Row Interplot Competition to Increase the Efficiency of Plant Improvement

Trials in the early stages of selection are often subject to variation arising from spatial variability and interplot competition, which can seriously bias the assessment of varietal performance and reduce genetic progress. An approach to jointly model both sources of bias is presented. It models genotypic and residual competition and also global and extraneous spatial variation. Variety effects were considered random and residual maximum likelihood was used for parameter estimation. Competition at the residual level was examined using two special simultaneous autoregressive models. An equal-roots second-order autoregressive (EAR(2)) model is proposed for trials where competition is dominant. An equal-roots third-order autoregressive (EAR(3)) model allows for competition and spatial variability. These models are applied to two yield data sets from an Australian sugarcane selection program. One data set is in the paper and the other is in supplementary material available online. To determine the effect of simultaneously adjusting for spatial variability and interplot competition on selection, the percentages of superior varieties in common in the top 15% for the joint model and classical approaches were compared. Agreement between the two approaches was 45 and 84%. Hence, for some trials there are large differences in varieties advanced to the next stage of selection.     

PRECIS和RegCM3对中国区域气候的长期模拟比较

以欧洲中期天气预报中心的再分析资料(ERA40)为侧边界及初始条件,在相同模拟区域和分辨率设置下,完成了PRECIS和RegCM3两个区域气候模式对中国区域近44a(1958年12月-2001年11月)的气候模拟,并分析比较两个模式对中国区域温度和降水的气候态和年际变率的模拟能力。结果表明,PRECIS和RegCM3均能较好地模拟出中国区域多年平均气温的空间分布特征,但PRECIS比观测气温平均偏暖1.5℃左右,而RegCM3则以冷偏差为主,平均偏低0.8℃左右,PRECIS整体上比RegCM3偏高2～3℃;两个模式均可基本再现气温年际变率的分布特征,但变率中心的位置和强度与观测值存在一定偏差;对于中国区域降水量的平均态及其年际变率而言,PRECIS和RegCM3均具有较好的模拟能力,但PRECIS模拟的降水高值中心、变率较大区域的位置和强度比RegCM3更接近实际。     

Power analysis based on spatial effects mixed models: A tool for comparing design and analysis strategies in the presence of spatial variability

Spatial variability among experimental units is known to exist in many designed experiments. Agronomic field trials are a particularly well-known example, but there are others. Historically, spatial variability has been dealt with in one of two ways: either though design, by blocking to account for spatial effects, or though analysis, by nearest neighbor adjustment. More recently, mixed models with spatial covariance structures such as those used in geostatistics have been proposed. These mixed model procedures have tempted some to conclude—to the dismay of many consulting statisticians—that design principles may be bypassed, since spatial covariance models can recover any lost information. Although design principles clearly should not be ignored, spatial procedures do raise questions. Are traditional notions of appropriate design affected? If so, how? How do spatial effects mixed models compare to conventional analysis of variance used in conjunction with blocked designs? This article presents mixed model methods to assess power and precision of proposed designs in the presence of spatial variability and to compare competing design and analysis strategies. The main conclusion is that, if anything, spatial models reinforce the need for sound design principles, particularly the use of incomplete block designs.     

Hierarchical modeling of cluster size in wildlife surveys

Clusters or groups of individuals are the fundamental unit of observation in many wildlife sampling problems, including aerial surveys of waterfowl, marine mammals, and ungulates. Explicit accounting of cluster size in models for estimating abundance is necessary because detection of individuals within clusters is not independent and detectability of clusters is likely to increase with cluster size. This induces a cluster size bias in which the average cluster size in the sample is larger than in the population at large. Thus, failure to account for the relationship between detectability and cluster size will tend to yield a positive bias in estimates of abundance or density. I describe a hierarchical modeling framework for accounting for cluster-size bias in animal sampling. The hierarchical model consists of models for the observation process conditional on the cluster size distribution and the cluster size distribution conditional on the total number of clusters. Optionally, a spatial model can be specified that describes variation in the total number of clusters per sample unit. Parameter estimation, model selection, and criticism may be carried out using conventional likelihood-based methods. An extension of the model is described for the situation where measurable covariates at the level of the sample unit are available. Several candidate models within the proposed class are evaluated for aerial survey data on mallard ducks (Anas platyrhynchos).     

退化红壤区不同模式重建森林土壤水分空间变异性

采用地统计学的方法,研究了江西泰和退化红壤区不同模式重建森林在高温干旱季节土壤水分空间变异规律及其分布格局。结果表明,在该研究尺度下,湿地松纯林、枫香纯林、湿地松与枫香混交林0~20 cm,20~40cm土层含水量分布都符合正态分布,都可较好地拟合成球状模型,表现出明显的空间相关性和空间分布格局。3种重建模式样地0~20 cm,20~40 cm层土壤含水量变异函数的基台值、变程都较无林荒地明显增大,而且基台值和变程在0~20 cm,20~40 cm两土层间的差异明显大于无林荒地,说明植被恢复以后,土壤水分的空间分布特征明显改变,空间变异性增强,空间变异的程度和尺度都明显增大,土壤水分的空间变异也具有较为明显的层次性。与纯林相比,湿地松×枫香混交林土壤水分的空间变异程度较低,水分分布趋于均匀化。     

不同尺度下田间土壤水分和混合电导率空间变异性与套合结构模型

应用地统计学原理和方法 ,分析了夏玉米田间 90m90m(L区 ) ,内套一个 30m 30m(M区) ,再内套一个 10m 10m(S区 ) 3个尺度下土壤含水量和电导率的空间变异性。结果表明 ,各尺度间土壤含水量的标准差和变异系数比较接近而电导率的差别较大。在L和M尺度下土壤含水量在 3个置信度下呈正态或对数正态分布 ,在S尺度下只在 0.01置信度下呈对数正态分布 ;在L尺度下土壤电导率仅在信度为 0.05和0.01时呈对数正态分布 ,在M尺度下仅在信度为 0.01时正态或对数正态分布 ,较高信度时呈对数正态分布 ,在S尺度下不呈正态或对数正态分布。各尺度下它们的空间结构可用球状模型描述 ,其变程分别约为 2.2～19.8m(土壤含水量 )和 2.5～17.2m(电导率 ) ,不同尺度间土壤含水量和电导率的空间变异结构函数差别较大 ,用套合结构模型能更好地描述不同尺度下土壤含水量和电导率的空间变异性。     

Identification of model components for a class of continuous spatiotemporal models

Environmental data routinely are collected at irregularly spaced monitoring stations and at intermittent times, times which may differ by location. This article introduces a class of continuous-time, continuous-space statistical models that can accommodate many of these more complex environmental processes. This class of models in corporates temporal and spatial variability in a cohesive manner and is broad enough to include temporal processes that are assumed to be generated by stochastic differential equations with possibly temporally and spatially correlated errors. A wide range of ARIMA temporal models and geostatistical spatial models are included in the class of models investigated. Techniques for identifying the structure of the temporal and spatial components of this class of models are detailed. Point estimates of model parameters, asymptotic distributions, and Kalman-filter prediction methods are discussed.     

空间变异在土壤性质长期定位观测及取样中的应用

通过对中国科学院沈阳生态实验站30m×42m样地进行网格法分层取样,采用经典统计学与地统计学相结合的方法,以土壤pH和有机碳(TOC)为例,探讨了空间变异在土壤性质定位试验及取样中的应用。结果表明,49对样本的pH和TOC变异系数均小于10%,变量在0~10cm和10~20cm两个层次的基本统计数值基本接近,最佳理论模型的参数显示各变量空间变异主要受结构性因素的影响,且以135°方向的变异为主;相对误差为10%时采集6个和10个土样可分别达到95%和99%的置信水平,结合空间分布格局可以确定更为精确的取样方案;综合分析表明待选样地可以作为土壤性质长期定位观测试验用地。     

Influence of Yearly Variability of Agricultural Products on Calibration Process: A Triticale Example

Many elements can influence the calibration process for near-infrared spectroscopy: variability within the population of interest, environmental effects, hardware instability, and others. This study evaluated two techniques to develop prediction models for triticale grain moisture and protein. The spectral addition strategy added new samples to the calibration set year after year. The spectral adaptation strategy selected only the spectral variability needed to successfully apply the model to new material. Triticale was a good study material because it was undergoing genetic change through the study period and is very responsive to climate variability. The two calibration techniques were significantly different from each other in terms of precision and accuracy. Spectral adaptation was the best technique with a relative predictive determinant (RPD) of 4.33 and a bias of 0.17% versus RPD of 3.50 and a bias of –0.52% for spectral addition. These results are contradictory to common practices that tend to add to the calibration set a maximum variability over as much time as possible. For highly variable matrixes, a constant adaptation rather than expansion of the calibration pool may be more appropriate.     

不同水质灌溉土壤磁化率空间变异性研究

采用地统计学和GIS相结合的方法,对太行山山前平原城郊区不同水源灌溉条件下土壤磁化率的空间变异性进行研究,并探讨了土壤磁畴状态。结果表明：不同水源灌溉条件下,土壤磁化率均服从正态分布;在一定范围内均存在空间相关性,半方差函数均符合球状模型,具有中等变异程度;变程变化在5.14～10.08 km。污灌区和清灌区的土壤磁化率差异明显,东西(EW)和西北—东南(NW-SE)方向的变异最大,土壤磁性物质主要为细的超顺磁颗粒。