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Geostatistical Approach (geostatistical + approach)
Selected AbstractsSimulating the spatial distribution of clay layer occurrence depth in alluvial soils with a Markov chain geostatistical approachENVIRONMETRICS, Issue 1 2010Weidong Li Abstract The spatial distribution information of clay layer occurrence depth (CLOD), particularly the spatial distribution maps of occurrence of clay layers at depths less than a certain threshold, in alluvial soils is crucial to designing appropriate plans and measures for precision agriculture and environmental management in alluvial plains. Markov chain geostatistics (MCG), which was proposed recently for simulating categorical spatial variables, can objectively decrease spatial uncertainty and consequently increase prediction accuracy in simulated results by using nonlinear estimators and incorporating various interclass relationships. In this paper, a MCG method was suggested to simulate the CLOD in a meso-scale alluvial soil area by encoding the continuous variable with several threshold values into binary variables (for single thresholds) or a multi-class variable (for all thresholds being considered together). Related optimal prediction maps, realization maps, and occurrence probability maps for all of these indicator-coded variables were generated. The simulated results displayed the spatial distribution characteristics of CLOD within different soil depths in the study area, which are not only helpful to understanding the spatial heterogeneity of clay layers in alluvial soils but also providing valuable quantitative information for precision agricultural management and environmental study. The study indicated that MCG could be a powerful method for simulating discretized continuous spatial variables. Copyright © 2009 John Wiley & Sons, Ltd. [source] Study of the space,time effects in the concentration of airborne pollutants in the Metropolitan Region of Rio de JaneiroENVIRONMETRICS, Issue 4 2003Marina Silva Paez Abstract In this article, we present an application of models with temporal and spatial components, from the Bayesian point of view, on data pollutants collected in 16 different monitoring sites located in the Metropolitan Area of Rio de Janeiro during 1999. All the models considered here assume conditionally independent observations, with a mean specified by the sum of random temporal and spatial components and a linear function of the maximum daily temperature and indicators of the day of the week. Our aim here is to analyze distinct specifications for the components, assuming different kinds of modeling that are not usually compared. The comparison is based on the posterior predictive loss function proposed by Gelfand and Ghosh (1998). The best specifications for the spatial component were the ones which considered a geostatistical approach to its correlation function. The best specification for the temporal component was the stationary autoregressive form. The pollutant concentrations were interpolated in a grid of points in the area of higher population density at a fixed period of time for the selected model. Copyright © 2003 John Wiley & Sons, Ltd. [source] Peat carbon stocks in the southern Mackenzie River Basin: uncertainties revealed in a high-resolution case studyGLOBAL CHANGE BIOLOGY, Issue 6 2008DAVID W. BEILMAN Abstract The organic carbon (C) stocks contained in peat were estimated for a wetland-rich boreal region of the Mackenzie River Basin, Canada, using high-resolution wetland map data, available peat C characteristic and peat depth datasets, and geostatistics. Peatlands cover 32% of the 25 119 km2 study area, and consist mainly of surface- and/or groundwater-fed treed peatlands. The thickness of peat deposits measured at 203 sites was 2.5 m on average but as deep as 6 m, and highly variable between sites. Peat depths showed little relationship with terrain data within 1 and 5 km, but were spatially autocorrelated, and were generalized using ordinary kriging. Polygon-scale calculations and Monte Carlo simulations yielded a total peat C stock of 982,1025 × 1012 g C that varied in C mass per unit area between 53 and 165 kg m,2. This geostatistical approach showed as much as 10% more peat C than calculations using mean depths. We compared this estimate with an overlapping 7868 km2 portion of an independent peat C stock estimate for western Canada, which revealed similar values for total peatland area, total C stock, and total peat C mass per unit area. However, agreement was poor within ,875 km2 grids owing to inconsistencies in peatland cover and little relationship in peat depth between estimates. The greatest disagreement in mean peat C mass per unit area occurred in grids with the largest peatland cover, owing to the spatial coincidence of large cover and deep peat in our high-resolution assessment. We conclude that total peat C stock estimates in the southern Mackenzie Basin and perhaps in boreal western Canada are likely of reasonable accuracy. However, owing to uncertainties particularly in peat depth, the quality of information regarding the location of these large stocks at scales as wide as several hundreds of square kilometers is presently much more limited. [source] Analyzing Bank Filtration by Deconvoluting Time Series of Electric ConductivityGROUND WATER, Issue 3 2007Olaf A. Cirpka Knowing the travel-time distributions from infiltrating rivers to pumping wells is important in the management of alluvial aquifers. Commonly, travel-time distributions are determined by releasing a tracer pulse into the river and measuring the breakthrough curve in the wells. As an alternative, one may measure signals of a time-varying natural tracer in the river and in adjacent wells and infer the travel-time distributions by deconvolution. Traditionally this is done by fitting a parametric function such as the solution of the one-dimensional advection-dispersion equation to the data. By choosing a certain parameterization, it is impossible to determine features of the travel-time distribution that do not follow the general shape of the parameterization, i.e., multiple peaks. We present a method to determine travel-time distributions by nonparametric deconvolution of electric-conductivity time series. Smoothness of the inferred transfer function is achieved by a geostatistical approach, in which the transfer function is assumed as a second-order intrinsic random time variable. Nonnegativity is enforced by the method of Lagrange multipliers. We present an approach to directly compute the best nonnegative estimate and to generate sets of plausible solutions. We show how the smoothness of the transfer function can be estimated from the data. The approach is applied to electric-conductivity measurements taken at River Thur, Switzerland, and five wells in the adjacent aquifer, but the method can also be applied to other time-varying natural tracers such as temperature. At our field site, electric-conductivity fluctuations appear to be an excellent natural tracer. [source] Rain-gauge network evaluation and augmentation using geostatisticsHYDROLOGICAL PROCESSES, Issue 14 2008Ke-Sheng Cheng Abstract Rain-gauge networks are often used to provide estimates of area average rainfall or point rainfalls at ungauged locations. The level of accuracy a network can achieve depends on the total number and locations of gauges in the network. A geostatistical approach for evaluation and augmentation of an existing rain-gauge network is proposed in this study. Through variogram analysis, hourly rainfalls are shown to have higher spatial variability than annual rainfalls, with hourly Mei-Yu rainfalls having the highest spatial variability. A criterion using ordinary kriging variance is proposed to assess the accuracy of rainfall estimation using the acceptance probability defined as the probability that estimation error falls within a desired range. Based on the criterion, the percentage of the total area with acceptable accuracy Ap under certain network configuration can be calculated. A sequential algorithm is also proposed to prioritize rain-gauges of the existing network, identify the base network, and relocate non-base gauges. Percentage of the total area with acceptable accuracy is mostly contributed by the base network. In contrast, non-base gauges provide little contribution to Ap and are subject to removal or relocation. Using a case study in northern Taiwan, the proposed approach demonstrates that the identified base network which comprises of approximately two-thirds of the total rain-gauges can achieve almost the same level of performance (expressed in terms of percentage of the total area with acceptable accuracy) as the complete network for hourly Mei-Yu rainfall estimation. The percentage of area with acceptable accuracy can be raised from 56% to 88% using an augmented network. A threshold value for the percentage of area with acceptable accuracy is also recommended to help determine the number of non-base gauges which need to be relocated. Copyright © 2007 John Wiley & Sons, Ltd. [source] Characterization and reconstruction of a rock fracture surface by geostatisticsINTERNATIONAL JOURNAL FOR NUMERICAL AND ANALYTICAL METHODS IN GEOMECHANICS, Issue 9 2002A. Marache Abstract It is well understood that, in studying the mechanical and hydromechanical behaviour of rock joints, their morphology must be taken into account. A geostatistical approach has been developed for characterizing the morphology of fracture surfaces at a decimetre scale. This allows the analysis of the spatial variability of elevations, and their first and second derivatives, with the intention of producing a model that gives a numerical three-dimensional (3D) representation of the lower and upper surfaces of the fracture. Two samples (I and II) located close together were cored across a natural fracture. The experimental data are the elevations recorded along profiles (using recording steps of 0.5 and 0.02 mm, respectively, for the samples I and II). The goal of this study is to model the surface topography of sample I, so getting estimates for elevations at each node of a square grid whose mesh size will be, for mechanical purposes, no larger than the recording step. Since the fracture surface within the sample core is not strictly horizontal, geostatistical methods are applied to residuals of elevations of sample I. Further, since structural information is necessary at very low scale, theoretical models of variograms of elevations, first and second derivatives are fitted using data of both that sample I and sample II. The geostatistical reconstructions are computed using kriging and conditional simulation methods. In order to validate these reconstructions, variograms and distributions of experimental data are compared with variograms and distributions of the fitted data. Copyright © 2002 John Wiley & Sons, Ltd. [source] Diminishing Spatial Heterogeneity in Soil Organic Matter across a Prairie Restoration ChronosequenceRESTORATION ECOLOGY, Issue 2 2005Diana R. Lane Abstract Habitat restoration resulting in changes in plant community composition or species dominance can affect the spatial pattern and variability of soil nutrients. Questions about how these changes in soil spatial heterogeneity develop over time at restoration sites, however, remain unaddressed. In this study, a geostatistical approach was used to quantify changes over time in the spatial heterogeneity of soil organic matter (SOM) across a 26-year chronosequence of tallgrass prairie restoration sites at FermiLab, outside of Chicago, Illinois. We used total soil N and C as an index of the quantity of SOM. We also examined changes in C:N ratio, which can influence the turnover of SOM. Specifically, the spatial structure of total N, total C, and C:N ratio in the top 10 cm of soil was quantified at a macroscale (minimum spacing of 1.5 m) and a microscale (minimum spacing of 0.2 m). The magnitude of spatial heterogeneity (MSH) was characterized as the proportion of total sample variation explained by spatially structured variation. At the macroscale, the MSH for total N decreased with time since restoration (r2= 0.99, p < 0.001). The decrease in spatial heterogeneity over time corresponded with a significant increase in the dominance of the C4 grasses. At the microscale, there was significant spatial structure for total N at the 4-year-old, 16-year-old, and 26-year-old sites, and significant spatial structure for total C at the 16-year-old and 26-year-old sites. These results suggest that an increase in dominance of C4 grasses across the chronosequence is homogenizing organic matter variability at the field scale while creating fine-scale patterns associated with the spacing of vegetation. Areas of higher soil moisture were associated with higher soil N and C at the two oldest restoration sites and at the native prairie site, potentially suggesting patches of increased belowground productivity in areas of higher soil moisture. This study is one of the first to report significant changes over time in the spatial structure of organic matter in response to successional changes initiated by restoration. 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