Applications of geostatistics in plant nematology.

PubMed

Wallace, M K; Hawkins, D M

1994-12-01

The application of geostatistics to plant nematology was made by evaluating soil and nematode data acquired from 200 soil samples collected from the A(p) horizon of a reed canary-grass field in northern Minnesota. Geostatistical concepts relevant to nematology include semi-variogram modelling, kriging, and change of support calculations. Soil and nematode data generally followed a spherical semi-variogram model, with little random variability associated with soil data and large inherent variability for nematode data. Block kriging of soil and nematode data provided useful contour maps of the data. Change of snpport calculations indicated that most of the random variation in nematode data was due to short-range spatial variability in the nematode population densities.

Application of geostatistics to risk assessment.

PubMed

Thayer, William C; Griffith, Daniel A; Goodrum, Philip E; Diamond, Gary L; Hassett, James M

2003-10-01

Geostatistics offers two fundamental contributions to environmental contaminant exposure assessment: (1) a group of methods to quantitatively describe the spatial distribution of a pollutant and (2) the ability to improve estimates of the exposure point concentration by exploiting the geospatial information present in the data. The second contribution is particularly valuable when exposure estimates must be derived from small data sets, which is often the case in environmental risk assessment. This article addresses two topics related to the use of geostatistics in human and ecological risk assessments performed at hazardous waste sites: (1) the importance of assessing model assumptions when using geostatistics and (2) the use of geostatistics to improve estimates of the exposure point concentration (EPC) in the limited data scenario. The latter topic is approached here by comparing design-based estimators that are familiar to environmental risk assessors (e.g., Land's method) with geostatistics, a model-based estimator. In this report, we summarize the basics of spatial weighting of sample data, kriging, and geostatistical simulation. We then explore the two topics identified above in a case study, using soil lead concentration data from a Superfund site (a skeet and trap range). We also describe several areas where research is needed to advance the use of geostatistics in environmental risk assessment.

An emission-weighted proximity model for air pollution exposure assessment.

PubMed

Zou, Bin; Wilson, J Gaines; Zhan, F Benjamin; Zeng, Yongnian

2009-08-15

Among the most common spatial models for estimating personal exposure are Traditional Proximity Models (TPMs). Though TPMs are straightforward to configure and interpret, they are prone to extensive errors in exposure estimates and do not provide prospective estimates. To resolve these inherent problems with TPMs, we introduce here a novel Emission Weighted Proximity Model (EWPM) to improve the TPM, which takes into consideration the emissions from all sources potentially influencing the receptors. EWPM performance was evaluated by comparing the normalized exposure risk values of sulfur dioxide (SO(2)) calculated by EWPM with those calculated by TPM and monitored observations over a one-year period in two large Texas counties. In order to investigate whether the limitations of TPM in potential exposure risk prediction without recorded incidence can be overcome, we also introduce a hybrid framework, a 'Geo-statistical EWPM'. Geo-statistical EWPM is a synthesis of Ordinary Kriging Geo-statistical interpolation and EWPM. The prediction results are presented as two potential exposure risk prediction maps. The performance of these two exposure maps in predicting individual SO(2) exposure risk was validated with 10 virtual cases in prospective exposure scenarios. Risk values for EWPM were clearly more agreeable with the observed concentrations than those from TPM. Over the entire study area, the mean SO(2) exposure risk from EWPM was higher relative to TPM (1.00 vs. 0.91). The mean bias of the exposure risk values of 10 virtual cases between EWPM and 'Geo-statistical EWPM' are much smaller than those between TPM and 'Geo-statistical TPM' (5.12 vs. 24.63). EWPM appears to more accurately portray individual exposure relative to TPM. The 'Geo-statistical EWPM' effectively augments the role of the standard proximity model and makes it possible to predict individual risk in future exposure scenarios resulting in adverse health effects from environmental pollution.

Qualitative and quantitative comparison of geostatistical techniques of porosity prediction from the seismic and logging data: a case study from the Blackfoot Field, Alberta, Canada

NASA Astrophysics Data System (ADS)

Maurya, S. P.; Singh, K. H.; Singh, N. P.

2018-05-01

In present study, three recently developed geostatistical methods, single attribute analysis, multi-attribute analysis and probabilistic neural network algorithm have been used to predict porosity in inter well region for Blackfoot field, Alberta, Canada, an offshore oil field. These techniques make use of seismic attributes, generated by model based inversion and colored inversion techniques. The principle objective of the study is to find the suitable combination of seismic inversion and geostatistical techniques to predict porosity and identification of prospective zones in 3D seismic volume. The porosity estimated from these geostatistical approaches is corroborated with the well log porosity. The results suggest that all the three implemented geostatistical methods are efficient and reliable to predict the porosity but the multi-attribute and probabilistic neural network analysis provide more accurate and high resolution porosity sections. A low impedance (6000-8000 m/s g/cc) and high porosity (> 15%) zone is interpreted from inverted impedance and porosity sections respectively between 1060 and 1075 ms time interval and is characterized as reservoir. The qualitative and quantitative results demonstrate that of all the employed geostatistical methods, the probabilistic neural network along with model based inversion is the most efficient method for predicting porosity in inter well region.

The Use of Geostatistics in the Study of Floral Phenology of Vulpia geniculata (L.) Link

PubMed Central

León Ruiz, Eduardo J.; García Mozo, Herminia; Domínguez Vilches, Eugenio; Galán, Carmen

2012-01-01

Traditionally phenology studies have been focused on changes through time, but there exist many instances in ecological research where it is necessary to interpolate among spatially stratified samples. The combined use of Geographical Information Systems (GIS) and Geostatistics can be an essential tool for spatial analysis in phenological studies. Geostatistics are a family of statistics that describe correlations through space/time and they can be used for both quantifying spatial correlation and interpolating unsampled points. In the present work, estimations based upon Geostatistics and GIS mapping have enabled the construction of spatial models that reflect phenological evolution of Vulpia geniculata (L.) Link throughout the study area during sampling season. Ten sampling points, scattered troughout the city and low mountains in the “Sierra de Córdoba” were chosen to carry out the weekly phenological monitoring during flowering season. The phenological data were interpolated by applying the traditional geostatitical method of Kriging, which was used to ellaborate weekly estimations of V. geniculata phenology in unsampled areas. Finally, the application of Geostatistics and GIS to create phenological maps could be an essential complement in pollen aerobiological studies, given the increased interest in obtaining automatic aerobiological forecasting maps. PMID:22629169

The use of geostatistics in the study of floral phenology of Vulpia geniculata (L.) link.

PubMed

León Ruiz, Eduardo J; García Mozo, Herminia; Domínguez Vilches, Eugenio; Galán, Carmen

2012-01-01

Traditionally phenology studies have been focused on changes through time, but there exist many instances in ecological research where it is necessary to interpolate among spatially stratified samples. The combined use of Geographical Information Systems (GIS) and Geostatistics can be an essential tool for spatial analysis in phenological studies. Geostatistics are a family of statistics that describe correlations through space/time and they can be used for both quantifying spatial correlation and interpolating unsampled points. In the present work, estimations based upon Geostatistics and GIS mapping have enabled the construction of spatial models that reflect phenological evolution of Vulpia geniculata (L.) Link throughout the study area during sampling season. Ten sampling points, scattered throughout the city and low mountains in the "Sierra de Córdoba" were chosen to carry out the weekly phenological monitoring during flowering season. The phenological data were interpolated by applying the traditional geostatitical method of Kriging, which was used to elaborate weekly estimations of V. geniculata phenology in unsampled areas. Finally, the application of Geostatistics and GIS to create phenological maps could be an essential complement in pollen aerobiological studies, given the increased interest in obtaining automatic aerobiological forecasting maps.

Multiobjective design of aquifer monitoring networks for optimal spatial prediction and geostatistical parameter estimation

NASA Astrophysics Data System (ADS)

Alzraiee, Ayman H.; Bau, Domenico A.; Garcia, Luis A.

2013-06-01

Effective sampling of hydrogeological systems is essential in guiding groundwater management practices. Optimal sampling of groundwater systems has previously been formulated based on the assumption that heterogeneous subsurface properties can be modeled using a geostatistical approach. Therefore, the monitoring schemes have been developed to concurrently minimize the uncertainty in the spatial distribution of systems' states and parameters, such as the hydraulic conductivity K and the hydraulic head H, and the uncertainty in the geostatistical model of system parameters using a single objective function that aggregates all objectives. However, it has been shown that the aggregation of possibly conflicting objective functions is sensitive to the adopted aggregation scheme and may lead to distorted results. In addition, the uncertainties in geostatistical parameters affect the uncertainty in the spatial prediction of K and H according to a complex nonlinear relationship, which has often been ineffectively evaluated using a first-order approximation. In this study, we propose a multiobjective optimization framework to assist the design of monitoring networks of K and H with the goal of optimizing their spatial predictions and estimating the geostatistical parameters of the K field. The framework stems from the combination of a data assimilation (DA) algorithm and a multiobjective evolutionary algorithm (MOEA). The DA algorithm is based on the ensemble Kalman filter, a Monte-Carlo-based Bayesian update scheme for nonlinear systems, which is employed to approximate the posterior uncertainty in K, H, and the geostatistical parameters of K obtained by collecting new measurements. Multiple MOEA experiments are used to investigate the trade-off among design objectives and identify the corresponding monitoring schemes. The methodology is applied to design a sampling network for a shallow unconfined groundwater system located in Rocky Ford, Colorado. Results indicate that the effect of uncertainties associated with the geostatistical parameters on the spatial prediction might be significantly alleviated (by up to 80% of the prior uncertainty in K and by 90% of the prior uncertainty in H) by sampling evenly distributed measurements with a spatial measurement density of more than 1 observation per 60 m × 60 m grid block. In addition, exploration of the interaction of objective functions indicates that the ability of head measurements to reduce the uncertainty associated with the correlation scale is comparable to the effect of hydraulic conductivity measurements.

Integration of GIS, Geostatistics, and 3-D Technology to Assess the Spatial Distribution of Soil Moisture

NASA Technical Reports Server (NTRS)

Betts, M.; Tsegaye, T.; Tadesse, W.; Coleman, T. L.; Fahsi, A.

1998-01-01

The spatial and temporal distribution of near surface soil moisture is of fundamental importance to many physical, biological, biogeochemical, and hydrological processes. However, knowledge of these space-time dynamics and the processes which control them remains unclear. The integration of geographic information systems (GIS) and geostatistics together promise a simple mechanism to evaluate and display the spatial and temporal distribution of this vital hydrologic and physical variable. Therefore, this research demonstrates the use of geostatistics and GIS to predict and display soil moisture distribution under vegetated and non-vegetated plots. The research was conducted at the Winfred Thomas Agricultural Experiment Station (WTAES), Hazel Green, Alabama. Soil moisture measurement were done on a 10 by 10 m grid from tall fescue grass (GR), alfalfa (AA), bare rough (BR), and bare smooth (BS) plots. Results indicated that variance associated with soil moisture was higher for vegetated plots than non-vegetated plots. The presence of vegetation in general contributed to the spatial variability of soil moisture. Integration of geostatistics and GIS can improve the productivity of farm lands and the precision of farming.

Reservoir studies with geostatistics to forecast performance

DOE Office of Scientific and Technical Information (OSTI.GOV)

Tang, R.W.; Behrens, R.A.; Emanuel, A.S.

1991-05-01

In this paper example geostatistics and streamtube applications are presented for waterflood and CO{sub 2} flood in two low-permeability sandstone reservoirs. Thy hybrid approach of combining fine vertical resolution in cross-sectional models with streamtubes resulted in models that showed water channeling and provided realistic performance estimates. Results indicate that the combination of detailed geostatistical cross sections and fine-grid streamtube models offers a systematic approach for realistic performance forecasts.

Stochastic hydrogeology: what professionals really need?

PubMed

Renard, Philippe

2007-01-01

Quantitative hydrogeology celebrated its 150th anniversary in 2006. Geostatistics is younger but has had a very large impact in hydrogeology. Today, geostatistics is used routinely to interpolate deterministically most of the parameters that are required to analyze a problem or make a quantitative analysis. In a small number of cases, geostatistics is combined with deterministic approaches to forecast uncertainty. At a more academic level, geostatistics is used extensively to study physical processes in heterogeneous aquifers. Yet, there is an important gap between the academic use and the routine applications of geostatistics. The reasons for this gap are diverse. These include aspects related to the hydrogeology consulting market, technical reasons such as the lack of widely available software, but also a number of misconceptions. A change in this situation requires acting at different levels. First, regulators must be convinced of the benefit of using geostatistics. Second, the economic potential of the approach must be emphasized to customers. Third, the relevance of the theories needs to be increased. Last, but not least, software, data sets, and computing infrastructure such as grid computing need to be widely available.

Random vectors and spatial analysis by geostatistics for geotechnical applications

DOE Office of Scientific and Technical Information (OSTI.GOV)

Young, D.S.

1987-08-01

Geostatistics is extended to the spatial analysis of vector variables by defining the estimation variance and vector variogram in terms of the magnitude of difference vectors. Many random variables in geotechnology are in vectorial terms rather than scalars, and its structural analysis requires those sample variable interpolations to construct and characterize structural models. A better local estimator will result in greater quality of input models; geostatistics can provide such estimators; kriging estimators. The efficiency of geostatistics for vector variables is demonstrated in a case study of rock joint orientations in geological formations. The positive cross-validation encourages application of geostatistics tomore » spatial analysis of random vectors in geoscience as well as various geotechnical fields including optimum site characterization, rock mechanics for mining and civil structures, cavability analysis of block cavings, petroleum engineering, and hydrologic and hydraulic modelings.« less

Spatial analysis of hazardous waste data using geostatistics

DOE Office of Scientific and Technical Information (OSTI.GOV)

Zirschky, J.H.

1984-01-01

The objective of this investigation was to determine if geostatistics could be a useful tool for evaluating hazardous waste sites. Three sites contaminated by dioxin (2,3,7,8-tetrachlorodibenzo-p-dioxin (TCDD)) were investigated. The first site evaluated was a creek into which TCDD-contaminated soil had eroded. The second site was a town in which TCDD-contaminated wastes had been sprayed onto the streets. Finally, the third site was a highway of which the shoulders were contaminated by dust deposition from a nearby hazardous waste site. The distribution of TCDD at the first and third sites were investigated using kriging, an optimal estimation technique. By usingmore » kriging, the areas of both sites requiring cleanup were successfully identified. At the second site, the town, satisfactory results were not obtained. The distribution of contamination in this town is believed to be very heterogeneous; thus, reasonable estimates could not be obtained. Additional sampling was therefore recommended at this site. Based upon this research, geostatistics appears to be a very useful tool for evaluating a hazardous waste site if the distribution of contaminants at the site is homogeneous, or can be divided into homogeneous areas.« less

Integration of geology, geostatistics, well logs and pressure data to model a heterogeneous supergiant field in Iran

DOE Office of Scientific and Technical Information (OSTI.GOV)

Samimi, B.; Bagherpour, H.; Nioc, A.

1995-08-01

The geological reservoir study of the supergiant Ahwaz field significantly improved the history matching process in many aspects, particularly the development of a geostatistical model which allowed a sound basis for changes and by delivering much needed accurate estimates of grid block vertical permeabilities. The geostatistical reservoir evaluation was facilitated by using the Heresim package and litho-stratigraphic zonations for the entire field. For each of the geological zones, 3-dimensional electrolithofacies and petrophysical property distributions (realizations) were treated which captured the heterogeneities which significantly affected fluid flow. However, as this level of heterogeneity was at a significantly smaller scale than themore » flow simulation grid blocks, a scaling up effort was needed to derive the effective flow properties of the blocks (porosity, horizontal and vertical permeability, and water saturation). The properties relating to the static reservoir description were accurately derived by using stream tube techniques developed in-house whereas, the relative permeabilities of the grid block were derived by dynamic pseudo relative permeability techniques. The prediction of vertical and lateral communication and water encroachment was facilitated by a close integration of pressure, saturation data, geostatistical modelling and sedimentological studies of the depositional environments and paleocurrents. The nature of reservoir barriers and baffles varied both vertically and laterally in this heterogeneous reservoir. Maps showing differences in pressure between zones after years of production served as a guide to integrating the static geological studies to the dynamic behaviour of each of the 16 reservoir zones. The use of deep wells being drilled to a deeper reservoir provided data to better understand the sweep efficiency and the continuity of barriers and baffles.« less

Comparison of geostatistical interpolation and remote sensing techniques for estimating long-term exposure to ambient PM2.5 concentrations across the continental United States.

PubMed

Lee, Seung-Jae; Serre, Marc L; van Donkelaar, Aaron; Martin, Randall V; Burnett, Richard T; Jerrett, Michael

2012-12-01

A better understanding of the adverse health effects of chronic exposure to fine particulate matter (PM2.5) requires accurate estimates of PM2.5 variation at fine spatial scales. Remote sensing has emerged as an important means of estimating PM2.5 exposures, but relatively few studies have compared remote-sensing estimates to those derived from monitor-based data. We evaluated and compared the predictive capabilities of remote sensing and geostatistical interpolation. We developed a space-time geostatistical kriging model to predict PM2.5 over the continental United States and compared resulting predictions to estimates derived from satellite retrievals. The kriging estimate was more accurate for locations that were about 100 km from a monitoring station, whereas the remote sensing estimate was more accurate for locations that were > 100 km from a monitoring station. Based on this finding, we developed a hybrid map that combines the kriging and satellite-based PM2.5 estimates. We found that for most of the populated areas of the continental United States, geostatistical interpolation produced more accurate estimates than remote sensing. The differences between the estimates resulting from the two methods, however, were relatively small. In areas with extensive monitoring networks, the interpolation may provide more accurate estimates, but in the many areas of the world without such monitoring, remote sensing can provide useful exposure estimates that perform nearly as well.

Estimation of geotechnical parameters on the basis of geophysical methods and geostatistics

NASA Astrophysics Data System (ADS)

Brom, Aleksander; Natonik, Adrianna

2017-12-01

The paper presents possible implementation of ordinary cokriging and geophysical investigation on humidity data acquired in geotechnical studies. The Author describes concept of geostatistics, terminology of geostatistical modelling, spatial correlation functions, principles of solving cokriging systems, advantages of (co-)kriging in comparison with other interpolation methods, obstacles in this type of attempt. Cross validation and discussion of results was performed with an indication of prospect of applying similar procedures in various researches..

Preferential sampling and Bayesian geostatistics: Statistical modeling and examples.

PubMed

Cecconi, Lorenzo; Grisotto, Laura; Catelan, Dolores; Lagazio, Corrado; Berrocal, Veronica; Biggeri, Annibale

2016-08-01

Preferential sampling refers to any situation in which the spatial process and the sampling locations are not stochastically independent. In this paper, we present two examples of geostatistical analysis in which the usual assumption of stochastic independence between the point process and the measurement process is violated. To account for preferential sampling, we specify a flexible and general Bayesian geostatistical model that includes a shared spatial random component. We apply the proposed model to two different case studies that allow us to highlight three different modeling and inferential aspects of geostatistical modeling under preferential sampling: (1) continuous or finite spatial sampling frame; (2) underlying causal model and relevant covariates; and (3) inferential goals related to mean prediction surface or prediction uncertainty. © The Author(s) 2016.

Geostatistics and Geographic Information System to Analyze the Spatial Distribution of the Diversity of Anastrepha Species (Diptera: Tephritidae): the Effect of Forest Fragments in an Urban Area.

PubMed

Garcia, A G; Araujo, M R; Uramoto, K; Walder, J M M; Zucchi, R A

2017-12-08

Fruit flies are among the most damaging insect pests of commercial fruit in Brazil. It is important to understand the landscape elements that may favor these flies. In the present study, spatial data from surveys of species of Anastrepha Schiner (Diptera: Tephritidae) in an urban area with forest fragments were analyzed, using geostatistics and Geographic Information System (GIS) to map the diversity of insects and evaluate how the forest fragments drive the spatial patterns. The results indicated a high diversity of species associated with large fragments, and a trend toward lower diversity in the more urbanized area, as the fragment sizes decreased. We concluded that the diversity of Anastrepha species is directly and positively related to large and continuous forest fragments in urbanized areas, and that combining geostatistics and GIS is a promising method for use in insect-pest management and sampling involving fruit flies. © The Authors 2017. Published by Oxford University Press on behalf of Entomological Society of America. All rights reserved. For permissions, please e-mail: journals.permissions@oup.com.

GEOSTATISTICS FOR WASTE MANAGEMENT: A USER'S MANUAL FOR THE GEOPACK (VERSION 1.0) GEOSTATISTICAL SOFTWARE SYSTEM

EPA Science Inventory

GEOPACK, a comprehensive user-friendly geostatistical software system, was developed to help in the analysis of spatially correlated data. The software system was developed to be used by scientists, engineers, regulators, etc., with little experience in geostatistical techniques...

GEOSTATISTICS FOR WASTE MANAGEMENT: A USER'S MANUEL FOR THE GEOPACK (VERSION 1.0) GEOSTATISTICAL SOFTWARE SYSTEM

EPA Science Inventory

A comprehensive, user-friendly geostatistical software system called GEOPACk has been developed. The purpose of this software is to make available the programs necessary to undertake a geostatistical analysis of spatially correlated data. The programs were written so that they ...

Nonpoint source solute transport normal to aquifer bedding in heterogeneous, Markov chain random fields

NASA Astrophysics Data System (ADS)

Zhang, Hua; Harter, Thomas; Sivakumar, Bellie

2006-06-01

Facies-based geostatistical models have become important tools for analyzing flow and mass transport processes in heterogeneous aquifers. Yet little is known about the relationship between these latter processes and the parameters of facies-based geostatistical models. In this study, we examine the transport of a nonpoint source solute normal (perpendicular) to the major bedding plane of an alluvial aquifer medium that contains multiple geologic facies, including interconnected, high-conductivity (coarse textured) facies. We also evaluate the dependence of the transport behavior on the parameters of the constitutive facies model. A facies-based Markov chain geostatistical model is used to quantify the spatial variability of the aquifer system's hydrostratigraphy. It is integrated with a groundwater flow model and a random walk particle transport model to estimate the solute traveltime probability density function (pdf) for solute flux from the water table to the bottom boundary (the production horizon) of the aquifer. The cases examined include two-, three-, and four-facies models, with mean length anisotropy ratios for horizontal to vertical facies, ek, from 25:1 to 300:1 and with a wide range of facies volume proportions (e.g., from 5 to 95% coarse-textured facies). Predictions of traveltime pdfs are found to be significantly affected by the number of hydrostratigraphic facies identified in the aquifer. Those predictions of traveltime pdfs also are affected by the proportions of coarse-textured sediments, the mean length of the facies (particularly the ratio of length to thickness of coarse materials), and, to a lesser degree, the juxtapositional preference among the hydrostratigraphic facies. In transport normal to the sedimentary bedding plane, traveltime is not lognormally distributed as is often assumed. Also, macrodispersive behavior (variance of the traveltime) is found not to be a unique function of the conductivity variance. For the parameter range examined, the third moment of the traveltime pdf varies from negatively skewed to strongly positively skewed. We also show that the Markov chain approach may give significantly different traveltime distributions when compared to the more commonly used Gaussian random field approach, even when the first- and second-order moments in the geostatistical distribution of the lnK field are identical. The choice of the appropriate geostatistical model is therefore critical in the assessment of nonpoint source transport, and uncertainty about that choice must be considered in evaluating the results.

Precipitation estimation in mountainous terrain using multivariate geostatistics. Part II: isohyetal maps

USGS Publications Warehouse

Hevesi, Joseph A.; Flint, Alan L.; Istok, Jonathan D.

1992-01-01

Values of average annual precipitation (AAP) may be important for hydrologic characterization of a potential high-level nuclear-waste repository site at Yucca Mountain, Nevada. Reliable measurements of AAP are sparse in the vicinity of Yucca Mountain, and estimates of AAP were needed for an isohyetal mapping over a 2600-square-mile watershed containing Yucca Mountain. Estimates were obtained with a multivariate geostatistical model developed using AAP and elevation data from a network of 42 precipitation stations in southern Nevada and southeastern California. An additional 1531 elevations were obtained to improve estimation accuracy. Isohyets representing estimates obtained using univariate geostatistics (kriging) defined a smooth and continuous surface. Isohyets representing estimates obtained using multivariate geostatistics (cokriging) defined an irregular surface that more accurately represented expected local orographic influences on AAP. Cokriging results included a maximum estimate within the study area of 335 mm at an elevation of 7400 ft, an average estimate of 157 mm for the study area, and an average estimate of 172 mm at eight locations in the vicinity of the potential repository site. Kriging estimates tended to be lower in comparison because the increased AAP expected for remote mountainous topography was not adequately represented by the available sample. Regression results between cokriging estimates and elevation were similar to regression results between measured AAP and elevation. The position of the cokriging 250-mm isohyet relative to the boundaries of pinyon pine and juniper woodlands provided indirect evidence of improved estimation accuracy because the cokriging result agreed well with investigations by others concerning the relationship between elevation, vegetation, and climate in the Great Basin. Calculated estimation variances were also mapped and compared to evaluate improvements in estimation accuracy. Cokriging estimation variances were reduced by an average of 54% relative to kriging variances within the study area. Cokriging reduced estimation variances at the potential repository site by 55% relative to kriging. The usefulness of an existing network of stations for measuring AAP within the study area was evaluated using cokriging variances, and twenty additional stations were located for the purpose of improving the accuracy of future isohyetal mappings. Using the expanded network of stations, the maximum cokriging estimation variance within the study area was reduced by 78% relative to the existing network, and the average estimation variance was reduced by 52%.

The geostatistic-based spatial distribution variations of soil salts under long-term wastewater irrigation.

PubMed

Wu, Wenyong; Yin, Shiyang; Liu, Honglu; Niu, Yong; Bao, Zhe

2014-10-01

The purpose of this study was to determine and evaluate the spatial changes in soil salinity by using geostatistical methods. The study focused on the suburb area of Beijing, where urban development led to water shortage and accelerated wastewater reuse to farm irrigation for more than 30 years. The data were then processed by GIS using three different interpolation techniques of ordinary kriging (OK), disjunctive kriging (DK), and universal kriging (UK). The normality test and overall trend analysis were applied for each interpolation technique to select the best fitted model for soil parameters. Results showed that OK was suitable for soil sodium adsorption ratio (SAR) and Na(+) interpolation; UK was suitable for soil Cl(-) and pH; DK was suitable for soil Ca(2+). The nugget-to-sill ratio was applied to evaluate the effects of structural and stochastic factors. The maps showed that the areas of non-saline soil and slight salinity soil accounted for 6.39 and 93.61%, respectively. The spatial distribution and accumulation of soil salt were significantly affected by the irrigation probabilities and drainage situation under long-term wastewater irrigation.

TiConverter: A training image converting tool for multiple-point geostatistics

NASA Astrophysics Data System (ADS)

Fadlelmula F., Mohamed M.; Killough, John; Fraim, Michael

2016-11-01

TiConverter is a tool developed to ease the application of multiple-point geostatistics whether by the open source Stanford Geostatistical Modeling Software (SGeMS) or other available commercial software. TiConverter has a user-friendly interface and it allows the conversion of 2D training images into numerical representations in four different file formats without the need for additional code writing. These are the ASCII (.txt), the geostatistical software library (GSLIB) (.txt), the Isatis (.dat), and the VTK formats. It performs the conversion based on the RGB color system. In addition, TiConverter offers several useful tools including image resizing, smoothing, and segmenting tools. The purpose of this study is to introduce the TiConverter, and to demonstrate its application and advantages with several examples from the literature.

Application of Bayesian geostatistics for evaluation of mass discharge uncertainty at contaminated sites

NASA Astrophysics Data System (ADS)

Troldborg, Mads; Nowak, Wolfgang; Lange, Ida V.; Santos, Marta C.; Binning, Philip J.; Bjerg, Poul L.

2012-09-01

Mass discharge estimates are increasingly being used when assessing risks of groundwater contamination and designing remedial systems at contaminated sites. Such estimates are, however, rather uncertain as they integrate uncertain spatial distributions of both concentration and groundwater flow. Here a geostatistical simulation method for quantifying the uncertainty of the mass discharge across a multilevel control plane is presented. The method accounts for (1) heterogeneity of both the flow field and the concentration distribution through Bayesian geostatistics, (2) measurement uncertainty, and (3) uncertain source zone and transport parameters. The method generates conditional realizations of the spatial flow and concentration distribution. An analytical macrodispersive transport solution is employed to simulate the mean concentration distribution, and a geostatistical model of the Box-Cox transformed concentration data is used to simulate observed deviations from this mean solution. By combining the flow and concentration realizations, a mass discharge probability distribution is obtained. The method has the advantage of avoiding the heavy computational burden of three-dimensional numerical flow and transport simulation coupled with geostatistical inversion. It may therefore be of practical relevance to practitioners compared to existing methods that are either too simple or computationally demanding. The method is demonstrated on a field site contaminated with chlorinated ethenes. For this site, we show that including a physically meaningful concentration trend and the cosimulation of hydraulic conductivity and hydraulic gradient across the transect helps constrain the mass discharge uncertainty. The number of sampling points required for accurate mass discharge estimation and the relative influence of different data types on mass discharge uncertainty is discussed.

Geostatistical applications in environmental remediation

DOE Office of Scientific and Technical Information (OSTI.GOV)

Stewart, R.N.; Purucker, S.T.; Lyon, B.F.

1995-02-01

Geostatistical analysis refers to a collection of statistical methods for addressing data that vary in space. By incorporating spatial information into the analysis, geostatistics has advantages over traditional statistical analysis for problems with a spatial context. Geostatistics has a history of success in earth science applications, and its popularity is increasing in other areas, including environmental remediation. Due to recent advances in computer technology, geostatistical algorithms can be executed at a speed comparable to many standard statistical software packages. When used responsibly, geostatistics is a systematic and defensible tool can be used in various decision frameworks, such as the Datamore » Quality Objectives (DQO) process. At every point in the site, geostatistics can estimate both the concentration level and the probability or risk of exceeding a given value. Using these probability maps can assist in identifying clean-up zones. Given any decision threshold and an acceptable level of risk, the probability maps identify those areas that are estimated to be above or below the acceptable risk. Those areas that are above the threshold are of the most concern with regard to remediation. In addition to estimating clean-up zones, geostatistics can assist in designing cost-effective secondary sampling schemes. Those areas of the probability map with high levels of estimated uncertainty are areas where more secondary sampling should occur. In addition, geostatistics has the ability to incorporate soft data directly into the analysis. These data include historical records, a highly correlated secondary contaminant, or expert judgment. The role of geostatistics in environmental remediation is a tool that in conjunction with other methods can provide a common forum for building consensus.« less

Comparative study of transient hydraulic tomography with varying parameterizations and zonations: Laboratory sandbox investigation

NASA Astrophysics Data System (ADS)

Luo, Ning; Zhao, Zhanfeng; Illman, Walter A.; Berg, Steven J.

2017-11-01

Transient hydraulic tomography (THT) is a robust method of aquifer characterization to estimate the spatial distributions (or tomograms) of both hydraulic conductivity (K) and specific storage (Ss). However, the highly-parameterized nature of the geostatistical inversion approach renders it computationally intensive for large-scale investigations. In addition, geostatistics-based THT may produce overly smooth tomograms when head data used to constrain the inversion is limited. Therefore, alternative model conceptualizations for THT need to be examined. To investigate this, we simultaneously calibrated different groundwater models with varying parameterizations and zonations using two cases of different pumping and monitoring data densities from a laboratory sandbox. Specifically, one effective parameter model, four geology-based zonation models with varying accuracy and resolution, and five geostatistical models with different prior information are calibrated. Model performance is quantitatively assessed by examining the calibration and validation results. Our study reveals that highly parameterized geostatistical models perform the best among the models compared, while the zonation model with excellent knowledge of stratigraphy also yields comparable results. When few pumping tests with sparse monitoring intervals are available, the incorporation of accurate or simplified geological information into geostatistical models reveals more details in heterogeneity and yields more robust validation results. However, results deteriorate when inaccurate geological information are incorporated. Finally, our study reveals that transient inversions are necessary to obtain reliable K and Ss estimates for making accurate predictions of transient drawdown events.

Use of geostatistics for remediation planning to transcend urban political boundaries.

PubMed

Milillo, Tammy M; Sinha, Gaurav; Gardella, Joseph A

2012-11-01

Soil remediation plans are often dictated by areas of jurisdiction or property lines instead of scientific information. This study exemplifies how geostatistically interpolated surfaces can substantially improve remediation planning. Ordinary kriging, ordinary co-kriging, and inverse distance weighting spatial interpolation methods were compared for analyzing surface and sub-surface soil sample data originally collected by the US EPA and researchers at the University at Buffalo in Hickory Woods, an industrial-residential neighborhood in Buffalo, NY, where both lead and arsenic contamination is present. Past clean-up efforts estimated contamination levels from point samples, but parcel and agency jurisdiction boundaries were used to define remediation sites, rather than geostatistical models estimating the spatial behavior of the contaminants in the soil. Residents were understandably dissatisfied with the arbitrariness of the remediation plan. In this study we show how geostatistical mapping and participatory assessment can make soil remediation scientifically defensible, socially acceptable, and economically feasible. Copyright © 2012 Elsevier Ltd. All rights reserved.

Spatial relationships among soil biota in a contaminated grassland ecosystem at Aberdeen Proving Ground, Maryland

DOE Office of Scientific and Technical Information (OSTI.GOV)

Kuperman, R.; Williams, G.; Parmelee, R.

1995-12-31

Spatial relationships among soil nematodes and soil microorganisms were investigated in a grassland ecosystem contaminated with heavy metals in the US Army`s Aberdeen Proving Ground. The study quantified fungal and bacterial biomass, the abundance of soil protozoa, and nematodes. Geostatistical techniques were used to determine spatial distributions of these parameters and to evaluate various cross-correlations. The cross-correlations among soil biota numbers were analyzed using two methods: a cross general relative semi-variogram and an interactive graphical data representation using geostatistically estimated data distributions. Both the visualization technique and the cross general relative semi-variogram and an interactive graphical data representation using geostatisticallymore » estimated data distributions. Both the visualization technique and the cross general relative semi-variogram showed a negative correlation between the abundance of fungivore nematodes and fungal biomass, the abundance of bacterivore nematodes and bacterial biomass, the abundance of omnivore/predator nematodes and numbers of protozoa, and between numbers of protozoa and both fungal and bacterial biomass. The negative cross-correlation between soil biota and metal concentrations showed that soil fungi were particularly sensitive to heavy metal concentrations and can be used for quantitative ecological risk assessment of metal-contaminated soils. This study found that geostatistics are a useful tool for describing and analyzing spatial relationships among components of food webs in the soil community.« less

Introduction to Geostatistics

NASA Astrophysics Data System (ADS)

Kitanidis, P. K.

1997-05-01

Introduction to Geostatistics presents practical techniques for engineers and earth scientists who routinely encounter interpolation and estimation problems when analyzing data from field observations. Requiring no background in statistics, and with a unique approach that synthesizes classic and geostatistical methods, this book offers linear estimation methods for practitioners and advanced students. Well illustrated with exercises and worked examples, Introduction to Geostatistics is designed for graduate-level courses in earth sciences and environmental engineering.

A JACKNIFE APPROACH TO EXAMINE UNCERTAINTY AND TEMPORAL CHANGES IN THE SPATIL CORRELATION OF A VOC PLUME

EPA Science Inventory

ABSTRACT: The application of geostatistics to spatial interpolation of time-invariant properties in ground-water studies (such as transmissivity or aquifer thickness) is well documented. The use of geostatistics on time-variant conditions such as ground-water quality is also be...

Stochastic modeling of a lava-flow aquifer system

USGS Publications Warehouse

Cronkite-Ratcliff, Collin; Phelps, Geoffrey A.

2014-01-01

This report describes preliminary three-dimensional geostatistical modeling of a lava-flow aquifer system using a multiple-point geostatistical model. The purpose of this study is to provide a proof-of-concept for this modeling approach. An example of the method is demonstrated using a subset of borehole geologic data and aquifer test data from a portion of the Calico Hills Formation, a lava-flow aquifer system that partially underlies Pahute Mesa, Nevada. Groundwater movement in this aquifer system is assumed to be controlled by the spatial distribution of two geologic units—rhyolite lava flows and zeolitized tuffs. The configuration of subsurface lava flows and tuffs is largely unknown because of limited data. The spatial configuration of the lava flows and tuffs is modeled by using a multiple-point geostatistical simulation algorithm that generates a large number of alternative realizations, each honoring the available geologic data and drawn from a geologic conceptual model of the lava-flow aquifer system as represented by a training image. In order to demonstrate how results from the geostatistical model could be analyzed in terms of available hydrologic data, a numerical simulation of part of an aquifer test was applied to the realizations of the geostatistical model.

Geostatistical Analysis of Mesoscale Spatial Variability and Error in SeaWiFS and MODIS/Aqua Global Ocean Color Data

NASA Astrophysics Data System (ADS)

Glover, David M.; Doney, Scott C.; Oestreich, William K.; Tullo, Alisdair W.

2018-01-01

Mesoscale (10-300 km, weeks to months) physical variability strongly modulates the structure and dynamics of planktonic marine ecosystems via both turbulent advection and environmental impacts upon biological rates. Using structure function analysis (geostatistics), we quantify the mesoscale biological signals within global 13 year SeaWiFS (1998-2010) and 8 year MODIS/Aqua (2003-2010) chlorophyll a ocean color data (Level-3, 9 km resolution). We present geographical distributions, seasonality, and interannual variability of key geostatistical parameters: unresolved variability or noise, resolved variability, and spatial range. Resolved variability is nearly identical for both instruments, indicating that geostatistical techniques isolate a robust measure of biophysical mesoscale variability largely independent of measurement platform. In contrast, unresolved variability in MODIS/Aqua is substantially lower than in SeaWiFS, especially in oligotrophic waters where previous analysis identified a problem for the SeaWiFS instrument likely due to sensor noise characteristics. Both records exhibit a statistically significant relationship between resolved mesoscale variability and the low-pass filtered chlorophyll field horizontal gradient magnitude, consistent with physical stirring acting on large-scale gradient as an important factor supporting observed mesoscale variability. Comparable horizontal length scales for variability are found from tracer-based scaling arguments and geostatistical decorrelation. Regional variations between these length scales may reflect scale dependence of biological mechanisms that also create variability directly at the mesoscale, for example, enhanced net phytoplankton growth in coastal and frontal upwelling and convective mixing regions. Global estimates of mesoscale biophysical variability provide an improved basis for evaluating higher resolution, coupled ecosystem-ocean general circulation models, and data assimilation.

Geostatistics applied to gas reservoirs

DOE Office of Scientific and Technical Information (OSTI.GOV)

Meunier, G.; Coulomb, C.; Laille, J.P.

1989-09-01

The spatial distribution of many of the physical parameters connected with a gas reservoir is of primary interest to both engineers and geologists throughout the study, development, and operation of a field. It is therefore desirable for the distribution to be capable of statistical interpretation, to have a simple graphical representation, and to allow data to be entered from either two- or three-dimensional grids. To satisfy these needs while dealing with the geographical variables, new methods have been developed under the name geostatistics. This paper describes briefly the theory of geostatistics and its most recent improvements for the specific problemmore » of subsurface description. The external-drift technique has been emphasized in particular, and in addition, four case studies related to gas reservoirs are presented.« less

Use of geostatistic techniques to describe a reservoir to be submitted into a secondary recovery process field case: {open_quotes}Eocene B-Inferior/VLG-3659, Ceuta, Venezuela{close_quotes}

DOE Office of Scientific and Technical Information (OSTI.GOV)

Hernandez, T.; Poquioma, W.

1997-08-01

This study presents the results of an integrated reservoir study of the Eocene B-Inferior/VLG-3659, Area 7, Ceuta filed. This field located in the Maracaibo Lake in the western side of Venezuela. The objective was to evaluating the feasibility to implement a secondary recovery project by means of water flooding. Core information was used for this study (194 ft), PVT analysis, RFI, build-up and statistic`s pressure analysis, modem logs and production history data. Using geostatistical techniques (Kriging) it was defined a low uncertainty geological model that was validated by means of a black oil simulator (Eclipse). The results showed a goodmore » comparison of historical pressure of the reservoir against those obtained from the model, without the need of {open_quotes}history matching{close_quotes}. It means without modifying neither the initial rock properties nor reservoir fluids. The results of this study recommended drilling in two new locations, also the reactivation of four producing wells and water flooding under peripherical array by means of four injection wells, with the recovery of an additional 30.2 MMSTB. The economical evaluation shows an internal return rate of 31.4%.« less

Geostatistics for environmental and geotechnical applications

DOE Office of Scientific and Technical Information (OSTI.GOV)

Rouhani, S.; Srivastava, R.M.; Desbarats, A.J.

1996-12-31

This conference was held January 26--27, 1995 in Phoenix, Arizona. The purpose of this conference was to provide a multidisciplinary forum for exchange of state-of-the-art information on the technology of geostatistics and its applicability for environmental studies, especially site characterization. Individual papers have been processed separately for inclusion in the appropriate data bases.

Usage of multivariate geostatistics in interpolation processes for meteorological precipitation maps

NASA Astrophysics Data System (ADS)

Gundogdu, Ismail Bulent

2017-01-01

Long-term meteorological data are very important both for the evaluation of meteorological events and for the analysis of their effects on the environment. Prediction maps which are constructed by different interpolation techniques often provide explanatory information. Conventional techniques, such as surface spline fitting, global and local polynomial models, and inverse distance weighting may not be adequate. Multivariate geostatistical methods can be more significant, especially when studying secondary variables, because secondary variables might directly affect the precision of prediction. In this study, the mean annual and mean monthly precipitations from 1984 to 2014 for 268 meteorological stations in Turkey have been used to construct country-wide maps. Besides linear regression, the inverse square distance and ordinary co-Kriging (OCK) have been used and compared to each other. Also elevation, slope, and aspect data for each station have been taken into account as secondary variables, whose use has reduced errors by up to a factor of three. OCK gave the smallest errors (1.002 cm) when aspect was included.

Combined SEM/AVS and attenuation of concentration models for the assessment of bioavailability and mobility of metals in sediments of Sepetiba Bay (SE Brazil).

PubMed

Ribeiro, Andreza Portella; Figueiredo, Ana Maria Graciano; dos Santos, José Osman; Dantas, Elizabeth; Cotrim, Marycel Elena Barboza; Figueira, Rubens Cesar Lopes; Silva Filho, Emmanoel V; Wasserman, Julio Cesar

2013-03-15

This study proposes a new methodology to study contamination, bioavailability and mobility of metals (Cd, Cu, Ni, Pb, and Zn) using chemical and geostatistics approaches in marine sediments of Sepetiba Bay (SE Brazil). The chemical model of SEM (simultaneously extracted metals)/AVS (acid volatile sulfides) ratio uses a technique of cold acid extraction of metals to evaluate their bioavailability, and the geostatistical model of attenuation of concentrations estimates the mobility of metals. By coupling the two it was observed that Sepetiba Port, the urban area of Sepetiba and the riverine discharges may constitute potential sources of metals to Sepetiba Bay. The metals are concentrated in the NE area of the bay, where they tend to have their lowest mobility, as shown by the attenuation model, and are not bioavailable, as they tend to associate with sulfide and organic matter originated in the mangrove forests of nearby Guaratiba area. Copyright © 2013 Elsevier Ltd. All rights reserved.

Geostatistical uncertainty of assessing air quality using high-spatial-resolution lichen data: A health study in the urban area of Sines, Portugal.

PubMed

Ribeiro, Manuel C; Pinho, P; Branquinho, C; Llop, Esteve; Pereira, Maria J

2016-08-15

In most studies correlating health outcomes with air pollution, personal exposure assignments are based on measurements collected at air-quality monitoring stations not coinciding with health data locations. In such cases, interpolators are needed to predict air quality in unsampled locations and to assign personal exposures. Moreover, a measure of the spatial uncertainty of exposures should be incorporated, especially in urban areas where concentrations vary at short distances due to changes in land use and pollution intensity. These studies are limited by the lack of literature comparing exposure uncertainty derived from distinct spatial interpolators. Here, we addressed these issues with two interpolation methods: regression Kriging (RK) and ordinary Kriging (OK). These methods were used to generate air-quality simulations with a geostatistical algorithm. For each method, the geostatistical uncertainty was drawn from generalized linear model (GLM) analysis. We analyzed the association between air quality and birth weight. Personal health data (n=227) and exposure data were collected in Sines (Portugal) during 2007-2010. Because air-quality monitoring stations in the city do not offer high-spatial-resolution measurements (n=1), we used lichen data as an ecological indicator of air quality (n=83). We found no significant difference in the fit of GLMs with any of the geostatistical methods. With RK, however, the models tended to fit better more often and worse less often. Moreover, the geostatistical uncertainty results showed a marginally higher mean and precision with RK. Combined with lichen data and land-use data of high spatial resolution, RK is a more effective geostatistical method for relating health outcomes with air quality in urban areas. This is particularly important in small cities, which generally do not have expensive air-quality monitoring stations with high spatial resolution. Further, alternative ways of linking human activities with their environment are needed to improve human well-being. Copyright © 2016 Elsevier B.V. All rights reserved.

EFFICIENT MODEL-FITTING AND MODEL-COMPARISON FOR HIGH-DIMENSIONAL BAYESIAN GEOSTATISTICAL MODELS. (R826887)

EPA Science Inventory

Geostatistical models are appropriate for spatially distributed data measured at irregularly spaced locations. We propose an efficient Markov chain Monte Carlo (MCMC) algorithm for fitting Bayesian geostatistical models with substantial numbers of unknown parameters to sizable...

Reservoir property grids improve with geostatistics

DOE Office of Scientific and Technical Information (OSTI.GOV)

Vogt, J.

1993-09-01

Visualization software, reservoir simulators and many other E and P software applications need reservoir property grids as input. Using geostatistics, as compared to other gridding methods, to produce these grids leads to the best output from the software programs. For the purpose stated herein, geostatistics is simply two types of gridding methods. Mathematically, these methods are based on minimizing or duplicating certain statistical properties of the input data. One geostatical method, called kriging, is used when the highest possible point-by-point accuracy is desired. The other method, called conditional simulation, is used when one wants statistics and texture of the resultingmore » grid to be the same as for the input data. In the following discussion, each method is explained, compared to other gridding methods, and illustrated through example applications. Proper use of geostatistical data in flow simulations, use of geostatistical data for history matching, and situations where geostatistics has no significant advantage over other methods, also will be covered.« less

Spatial interpolation of forest conditions using co-conditional geostatistical simulation

Treesearch

H. Todd Mowrer

2000-01-01

In recent work the author used the geostatistical Monte Carlo technique of sequential Gaussian simulation (s.G.s.) to investigate uncertainty in a GIS analysis of potential old-growth forest areas. The current study compares this earlier technique to that of co-conditional simulation, wherein the spatial cross-correlations between variables are included. As in the...

ON THE GEOSTATISTICAL APPROACH TO THE INVERSE PROBLEM. (R825689C037)

EPA Science Inventory

Abstract

The geostatistical approach to the inverse problem is discussed with emphasis on the importance of structural analysis. Although the geostatistical approach is occasionally misconstrued as mere cokriging, in fact it consists of two steps: estimation of statist...

Bayesian approach for three-dimensional aquifer characterization at the Hanford 300 Area

DOE Office of Scientific and Technical Information (OSTI.GOV)

Murakami, Haruko; Chen, X.; Hahn, Melanie S.

2010-10-21

This study presents a stochastic, three-dimensional characterization of a heterogeneous hydraulic conductivity field within DOE's Hanford 300 Area site, Washington, by assimilating large-scale, constant-rate injection test data with small-scale, three-dimensional electromagnetic borehole flowmeter (EBF) measurement data. We first inverted the injection test data to estimate the transmissivity field, using zeroth-order temporal moments of pressure buildup curves. We applied a newly developed Bayesian geostatistical inversion framework, the method of anchored distributions (MAD), to obtain a joint posterior distribution of geostatistical parameters and local log-transmissivities at multiple locations. The unique aspects of MAD that make it suitable for this purpose are itsmore » ability to integrate multi-scale, multi-type data within a Bayesian framework and to compute a nonparametric posterior distribution. After we combined the distribution of transmissivities with depth-discrete relative-conductivity profile from EBF data, we inferred the three-dimensional geostatistical parameters of the log-conductivity field, using the Bayesian model-based geostatistics. Such consistent use of the Bayesian approach throughout the procedure enabled us to systematically incorporate data uncertainty into the final posterior distribution. The method was tested in a synthetic study and validated using the actual data that was not part of the estimation. Results showed broader and skewed posterior distributions of geostatistical parameters except for the mean, which suggests the importance of inferring the entire distribution to quantify the parameter uncertainty.« less

Geostatistical mapping of effluent-affected sediment distribution on the Palos Verdes shelf

USGS Publications Warehouse

Murray, C.J.; Lee, H.J.; Hampton, M.A.

2002-01-01

Geostatistical techniques were used to study the spatial continuity of the thickness of effluent-affected sediment in the offshore Palos Verdes Margin area. The thickness data were measured directly from cores and indirectly from high-frequency subbottom profiles collected over the Palos Verdes Margin. Strong spatial continuity of the sediment thickness data was identified, with a maximum range of correlation in excess of 1.4 km. The spatial correlation showed a marked anisotropy, and was more than twice as continuous in the alongshore direction as in the cross-shelf direction. Sequential indicator simulation employing models fit to the thickness data variograms was used to map the distribution of the sediment, and to quantify the uncertainty in those estimates. A strong correlation between sediment thickness data and measurements of the mass of the contaminant p,p???-DDE per unit area was identified. A calibration based on the bivariate distribution of the thickness and p,p???-DDE data was applied using Markov-Bayes indicator simulation to extend the geostatistical study and map the contamination levels in the sediment. Integrating the map grids produced by the geostatistical study of the two variables indicated that 7.8 million m3 of effluent-affected sediment exist in the map area, containing approximately 61-72 Mg (metric tons) of p,p???-DDE. Most of the contaminated sediment (about 85% of the sediment and 89% of the p,p???-DDE) occurs in water depths < 100 m. The geostatistical study also indicated that the samples available for mapping are well distributed and the uncertainty of the estimates of the thickness and contamination level of the sediments is lowest in areas where the contaminated sediment is most prevalent. ?? 2002 Elsevier Science Ltd. All rights reserved.

Geo-statistical analysis of Culicoides spp. distribution and abundance in Sicily, Italy.

PubMed

Blanda, Valeria; Blanda, Marcellocalogero; La Russa, Francesco; Scimeca, Rossella; Scimeca, Salvatore; D'Agostino, Rosalia; Auteri, Michelangelo; Torina, Alessandra

2018-02-01

Biting midges belonging to Culicoides imicola, Culicoides obsoletus complex and Culicoides pulicaris complex (Diptera: Ceratopogonidae) are increasingly implicated as vectors of bluetongue virus in Palaearctic regions. Culicoides obsoletus complex includes C. obsoletus (sensu stricto), C. scoticus, C. dewulfi and C. chiopterus. Culicoides pulicaris and C. lupicaris belong to the Culicoides pulicaris complex. The aim of this study was a geo-statistical analysis of the abundance and spatial distribution of Culicoides spp. involved in bluetongue virus transmission. As part of the national bluetongue surveillance plan 7081 catches were collected in 897 Sicilian farms from 2000 to 2013. Onderstepoort-type blacklight traps were used for sample collection and each catch was analysed for the presence of Culicoides spp. and for the presence and abundance of Culicoides vector species (C. imicola, C. pulicaris / C. obsoletus complexes). A geo-statistical analysis was carried out monthly via the interpolation of measured values based on the Inverse Distance Weighted method, using a GIS tool. Raster maps were reclassified into seven classes according to the presence and abundance of Culicoides, in order to obtain suitable maps for Map Algebra operations. Sicilian provinces showing a very high abundance of Culicoides vector species were Messina (80% of the whole area), Palermo (20%) and Catania (12%). A total of 5654 farms fell within the very high risk area for bluetongue (21% of the 26,676 farms active in Sicily); of these, 3483 farms were in Messina, 1567 in Palermo and 604 in Catania. Culicoides imicola was prevalent in Palermo, C. pulicaris in Messina and C. obsoletus complex was very abundant over the whole island with the highest abundance value in Messina. Our study reports the results of a geo-statistical analysis concerning the abundance and spatial distribution of Culicoides spp. in Sicily throughout the fourteen year study. It provides useful decision support in the field of epidemiology, allowing the identification of areas to be monitored as bases for improved surveillance plans. Moreover, this knowledge can become a tool for the evaluation of virus transmission risks, especially if related to vector competence.

Delineating Hydrofacies Spatial Distribution by Integrating Ensemble Data Assimilation and Indicator Geostatistics

DOE Office of Scientific and Technical Information (OSTI.GOV)

Song, Xuehang; Chen, Xingyuan; Ye, Ming

2015-07-01

This study develops a new framework of facies-based data assimilation for characterizing spatial distribution of hydrofacies and estimating their associated hydraulic properties. This framework couples ensemble data assimilation with transition probability-based geostatistical model via a parameterization based on a level set function. The nature of ensemble data assimilation makes the framework efficient and flexible to be integrated with various types of observation data. The transition probability-based geostatistical model keeps the updated hydrofacies distributions under geological constrains. The framework is illustrated by using a two-dimensional synthetic study that estimates hydrofacies spatial distribution and permeability in each hydrofacies from transient head data.more » Our results show that the proposed framework can characterize hydrofacies distribution and associated permeability with adequate accuracy even with limited direct measurements of hydrofacies. Our study provides a promising starting point for hydrofacies delineation in complex real problems.« less

Spatial uncertainty of a geoid undulation model in Guayaquil, Ecuador

NASA Astrophysics Data System (ADS)

Chicaiza, E. G.; Leiva, C. A.; Arranz, J. J.; Buenańo, X. E.

2017-06-01

Geostatistics is a discipline that deals with the statistical analysis of regionalized variables. In this case study, geostatistics is used to estimate geoid undulation in the rural area of Guayaquil town in Ecuador. The geostatistical approach was chosen because the estimation error of prediction map is getting. Open source statistical software R and mainly geoR, gstat and RGeostats libraries were used. Exploratory data analysis (EDA), trend and structural analysis were carried out. An automatic model fitting by Iterative Least Squares and other fitting procedures were employed to fit the variogram. Finally, Kriging using gravity anomaly of Bouguer as external drift and Universal Kriging were used to get a detailed map of geoid undulation. The estimation uncertainty was reached in the interval [-0.5; +0.5] m for errors and a maximum estimation standard deviation of 2 mm in relation with the method of interpolation applied. The error distribution of the geoid undulation map obtained in this study provides a better result than Earth gravitational models publicly available for the study area according the comparison with independent validation points. The main goal of this paper is to confirm the feasibility to use geoid undulations from Global Navigation Satellite Systems and leveling field measurements and geostatistical techniques methods in order to use them in high-accuracy engineering projects.

Geostatistical radar-raingauge combination with nonparametric correlograms: methodological considerations and application in Switzerland

NASA Astrophysics Data System (ADS)

Schiemann, R.; Erdin, R.; Willi, M.; Frei, C.; Berenguer, M.; Sempere-Torres, D.

2011-05-01

Modelling spatial covariance is an essential part of all geostatistical methods. Traditionally, parametric semivariogram models are fit from available data. More recently, it has been suggested to use nonparametric correlograms obtained from spatially complete data fields. Here, both estimation techniques are compared. Nonparametric correlograms are shown to have a substantial negative bias. Nonetheless, when combined with the sample variance of the spatial field under consideration, they yield an estimate of the semivariogram that is unbiased for small lag distances. This justifies the use of this estimation technique in geostatistical applications. Various formulations of geostatistical combination (Kriging) methods are used here for the construction of hourly precipitation grids for Switzerland based on data from a sparse realtime network of raingauges and from a spatially complete radar composite. Two variants of Ordinary Kriging (OK) are used to interpolate the sparse gauge observations. In both OK variants, the radar data are only used to determine the semivariogram model. One variant relies on a traditional parametric semivariogram estimate, whereas the other variant uses the nonparametric correlogram. The variants are tested for three cases and the impact of the semivariogram model on the Kriging prediction is illustrated. For the three test cases, the method using nonparametric correlograms performs equally well or better than the traditional method, and at the same time offers great practical advantages. Furthermore, two variants of Kriging with external drift (KED) are tested, both of which use the radar data to estimate nonparametric correlograms, and as the external drift variable. The first KED variant has been used previously for geostatistical radar-raingauge merging in Catalonia (Spain). The second variant is newly proposed here and is an extension of the first. Both variants are evaluated for the three test cases as well as an extended evaluation period. It is found that both methods yield merged fields of better quality than the original radar field or fields obtained by OK of gauge data. The newly suggested KED formulation is shown to be beneficial, in particular in mountainous regions where the quality of the Swiss radar composite is comparatively low. An analysis of the Kriging variances shows that none of the methods tested here provides a satisfactory uncertainty estimate. A suitable variable transformation is expected to improve this.

Geostatistical radar-raingauge combination with nonparametric correlograms: methodological considerations and application in Switzerland

NASA Astrophysics Data System (ADS)

Schiemann, R.; Erdin, R.; Willi, M.; Frei, C.; Berenguer, M.; Sempere-Torres, D.

2010-09-01

Modelling spatial covariance is an essential part of all geostatistical methods. Traditionally, parametric semivariogram models are fit from available data. More recently, it has been suggested to use nonparametric correlograms obtained from spatially complete data fields. Here, both estimation techniques are compared. Nonparametric correlograms are shown to have a substantial negative bias. Nonetheless, when combined with the sample variance of the spatial field under consideration, they yield an estimate of the semivariogram that is unbiased for small lag distances. This justifies the use of this estimation technique in geostatistical applications. Various formulations of geostatistical combination (Kriging) methods are used here for the construction of hourly precipitation grids for Switzerland based on data from a sparse realtime network of raingauges and from a spatially complete radar composite. Two variants of Ordinary Kriging (OK) are used to interpolate the sparse gauge observations. In both OK variants, the radar data are only used to determine the semivariogram model. One variant relies on a traditional parametric semivariogram estimate, whereas the other variant uses the nonparametric correlogram. The variants are tested for three cases and the impact of the semivariogram model on the Kriging prediction is illustrated. For the three test cases, the method using nonparametric correlograms performs equally well or better than the traditional method, and at the same time offers great practical advantages. Furthermore, two variants of Kriging with external drift (KED) are tested, both of which use the radar data to estimate nonparametric correlograms, and as the external drift variable. The first KED variant has been used previously for geostatistical radar-raingauge merging in Catalonia (Spain). The second variant is newly proposed here and is an extension of the first. Both variants are evaluated for the three test cases as well as an extended evaluation period. It is found that both methods yield merged fields of better quality than the original radar field or fields obtained by OK of gauge data. The newly suggested KED formulation is shown to be beneficial, in particular in mountainous regions where the quality of the Swiss radar composite is comparatively low. An analysis of the Kriging variances shows that none of the methods tested here provides a satisfactory uncertainty estimate. A suitable variable transformation is expected to improve this.

Mine planning and emission control strategies using geostatistics

DOE Office of Scientific and Technical Information (OSTI.GOV)

Martino, F.; Kim, Y.C.

1983-03-01

This paper reviews the past four years' research efforts performed jointly by the University of Arizona and the Homer City Owners in which geostatistics were applied to solve various problems associated with coal characterization, mine planning, and development of emission control strategies. Because geostatistics is the only technique which can quantify the degree of confidence associated with a given estimate (or prediction), it played an important role throughout the research efforts. Through geostatistics, it was learned that there is an urgent need for closely spaced sample information, if short-term coal quality predictions are to be made for mine planning purposes.

Evaluation of spatial variability of soil arsenic adjacent to a disused cattle-dip site, using model-based geostatistics.

PubMed

Niazi, Nabeel K; Bishop, Thomas F A; Singh, Balwant

2011-12-15

This study investigated the spatial variability of total and phosphate-extractable arsenic (As) concentrations in soil adjacent to a cattle-dip site, employing a linear mixed model-based geostatistical approach. The soil samples in the study area (n = 102 in 8.1 m(2)) were taken at the nodes of a 0.30 × 0.35 m grid. The results showed that total As concentration (0-0.2 m depth) and phosphate-extractable As concentration (at depths of 0-0.2, 0.2-0.4, and 0.4-0.6 m) in soil adjacent to the dip varied greatly. Both total and phosphate-extractable soil As concentrations significantly (p = 0.004-0.048) increased toward the cattle-dip. Using the linear mixed model, we suggest that 5 samples are sufficient to assess a dip site for soil (As) contamination (95% confidence interval of ±475.9 mg kg(-1)), but 15 samples (95% confidence interval of ±212.3 mg kg(-1)) is desirable baseline when the ultimate goal is to evaluate the effects of phytoremediation. Such guidelines on sampling requirements are crucial for the assessment of As contamination levels at other cattle-dip sites, and to determine the effect of phytoremediation on soil As.

Spatial distribution of Munida intermedia and M. sarsi (crustacea: Anomura) on the Galician continental shelf (NW Spain): Application of geostatistical analysis

NASA Astrophysics Data System (ADS)

Freire, J.; González-Gurriarán, E.; Olaso, I.

1992-12-01

Geostatistical methodology was used to analyse spatial structure and distribution of the epibenthic crustaceans Munida intermedia and M. sarsi within sets of data which had been collected during three survey cruises carried out on the Galician continental shelf (1983 and 1984). This study investigates the feasibility of using geostatistics for data collected according to traditional methods and of enhancing such methodology. The experimental variograms were calculated (pooled variance minus spatial covariance between samples taken one pair at a time vs. distance) and fitted to a 'spherical' model. The spatial structure model was used to estimate the abundance and distribution of the populations studied using the technique of kriging. The species display spatial structures, which are well marked during high density periods and in some areas (especially northern shelf). Geostatistical analysis allows identification of the density gradients in space as well as the patch grain along the continental shelf of 16-25 km diameter for M. intermedia and 12-20 km for M. sarsi. Patches of both species have a consistent location throughout the different cruises. As in other geographical areas, M. intermedia and M. sarsi usually appear at depths ranging from 200 to 500 m, with the highest densities in the continental shelf area located between Fisterra and Estaca de Bares. Althouh sampling was not originally designed specifically for geostatistics, this assay provides a measurement of spatial covariance, and shows variograms with variable structure depending on population density and geographical area. These ideas are useful in improving the design of future sampling cruises.

Geostatistics: a new tool for describing spatially-varied surface conditions from timber harvested and burned hillslopes

Treesearch

Peter R. Robichaud

1997-01-01

Geostatistics provides a method to describe the spatial continuity of many natural phenomena. Spatial models are based upon the concept of scaling, kriging and conditional simulation. These techniques were used to describe the spatially-varied surface conditions on timber harvest and burned hillslopes. Geostatistical techniques provided estimates of the ground cover (...

Comparison of regression and geostatistical methods for mapping Leaf Area Index (LAI) with Landsat ETM+ data over a boreal forest.

Treesearch

Mercedes Berterretche; Andrew T. Hudak; Warren B. Cohen; Thomas K. Maiersperger; Stith T. Gower; Jennifer Dungan

2005-01-01

This study compared aspatial and spatial methods of using remote sensing and field data to predict maximum growing season leaf area index (LAI) maps in a boreal forest in Manitoba, Canada. The methods tested were orthogonal regression analysis (reduced major axis, RMA) and two geostatistical techniques: kriging with an external drift (KED) and sequential Gaussian...

Identification of hydraulic conductivity structure in sand and gravel aquifers: Cape Cod data set

USGS Publications Warehouse

Eggleston, J.R.; Rojstaczer, S.A.; Peirce, J.J.

1996-01-01

This study evaluates commonly used geostatistical methods to assess reproduction of hydraulic conductivity (K) structure and sensitivity under limiting amounts of data. Extensive conductivity measurements from the Cape Cod sand and gravel aquifer are used to evaluate two geostatistical estimation methods, conditional mean as an estimate and ordinary kriging, and two stochastic simulation methods, simulated annealing and sequential Gaussian simulation. Our results indicate that for relatively homogeneous sand and gravel aquifers such as the Cape Cod aquifer, neither estimation methods nor stochastic simulation methods give highly accurate point predictions of hydraulic conductivity despite the high density of collected data. Although the stochastic simulation methods yielded higher errors than the estimation methods, the stochastic simulation methods yielded better reproduction of the measured In (K) distribution and better reproduction of local contrasts in In (K). The inability of kriging to reproduce high In (K) values, as reaffirmed by this study, provides a strong instigation for choosing stochastic simulation methods to generate conductivity fields when performing fine-scale contaminant transport modeling. Results also indicate that estimation error is relatively insensitive to the number of hydraulic conductivity measurements so long as more than a threshold number of data are used to condition the realizations. This threshold occurs for the Cape Cod site when there are approximately three conductivity measurements per integral volume. The lack of improvement with additional data suggests that although fine-scale hydraulic conductivity structure is evident in the variogram, it is not accurately reproduced by geostatistical estimation methods. If the Cape Cod aquifer spatial conductivity characteristics are indicative of other sand and gravel deposits, then the results on predictive error versus data collection obtained here have significant practical consequences for site characterization. Heavily sampled sand and gravel aquifers, such as Cape Cod and Borden, may have large amounts of redundant data, while in more common real world settings, our results suggest that denser data collection will likely improve understanding of permeability structure.

Geostatistics - a tool applied to the distribution of Legionella pneumophila in a hospital water system.

PubMed

Laganà, Pasqualina; Moscato, Umberto; Poscia, Andrea; La Milia, Daniele Ignazio; Boccia, Stefania; Avventuroso, Emanuela; Delia, Santi

2015-01-01

Legionnaires' disease is normally acquired by inhalation of legionellae from a contaminated environmental source. Water systems of large buildings, such as hospitals, are often contaminated with legionellae and therefore represent a potential risk for the hospital population. The aim of this study was to evaluate the potential contamination of Legionella pneumophila (LP) in a large hospital in Italy through georeferential statistical analysis to assess the possible sources of dispersion and, consequently, the risk of exposure for both health care staff and patients. LP serogroups 1 and 2-14 distribution was considered in the wards housed on two consecutive floors of the hospital building. On the basis of information provided by 53 bacteriological analysis, a 'random' grid of points was chosen and spatial geostatistics or FAIk Kriging was applied and compared with the results of classical statistical analysis. Over 50% of the examined samples were positive for Legionella pneumophila. LP 1 was isolated in 69% of samples from the ground floor and in 60% of sample from the first floor; LP 2-14 in 36% of sample from the ground floor and 24% from the first. The iso-estimation maps show clearly the most contaminated pipe and the difference in the diffusion of the different L. pneumophila serogroups. Experimental work has demonstrated that geostatistical methods applied to the microbiological analysis of water matrices allows a better modeling of the phenomenon under study, a greater potential for risk management and a greater choice of methods of prevention and environmental recovery to be put in place with respect to the classical statistical analysis.

Soil moisture estimation by assimilating L-band microwave brightness temperature with geostatistics and observation localization.

PubMed

Han, Xujun; Li, Xin; Rigon, Riccardo; Jin, Rui; Endrizzi, Stefano

2015-01-01

The observation could be used to reduce the model uncertainties with data assimilation. If the observation cannot cover the whole model area due to spatial availability or instrument ability, how to do data assimilation at locations not covered by observation? Two commonly used strategies were firstly described: One is covariance localization (CL); the other is observation localization (OL). Compared with CL, OL is easy to parallelize and more efficient for large-scale analysis. This paper evaluated OL in soil moisture profile characterizations, in which the geostatistical semivariogram was used to fit the spatial correlated characteristics of synthetic L-Band microwave brightness temperature measurement. The fitted semivariogram model and the local ensemble transform Kalman filter algorithm are combined together to weight and assimilate the observations within a local region surrounding the grid cell of land surface model to be analyzed. Six scenarios were compared: 1_Obs with one nearest observation assimilated, 5_Obs with no more than five nearest local observations assimilated, and 9_Obs with no more than nine nearest local observations assimilated. The scenarios with no more than 16, 25, and 36 local observations were also compared. From the results we can conclude that more local observations involved in assimilation will improve estimations with an upper bound of 9 observations in this case. This study demonstrates the potentials of geostatistical correlation representation in OL to improve data assimilation of catchment scale soil moisture using synthetic L-band microwave brightness temperature, which cannot cover the study area fully in space due to vegetation effects.

Soil Moisture Estimation by Assimilating L-Band Microwave Brightness Temperature with Geostatistics and Observation Localization

PubMed Central

Han, Xujun; Li, Xin; Rigon, Riccardo; Jin, Rui; Endrizzi, Stefano

2015-01-01

The observation could be used to reduce the model uncertainties with data assimilation. If the observation cannot cover the whole model area due to spatial availability or instrument ability, how to do data assimilation at locations not covered by observation? Two commonly used strategies were firstly described: One is covariance localization (CL); the other is observation localization (OL). Compared with CL, OL is easy to parallelize and more efficient for large-scale analysis. This paper evaluated OL in soil moisture profile characterizations, in which the geostatistical semivariogram was used to fit the spatial correlated characteristics of synthetic L-Band microwave brightness temperature measurement. The fitted semivariogram model and the local ensemble transform Kalman filter algorithm are combined together to weight and assimilate the observations within a local region surrounding the grid cell of land surface model to be analyzed. Six scenarios were compared: 1_Obs with one nearest observation assimilated, 5_Obs with no more than five nearest local observations assimilated, and 9_Obs with no more than nine nearest local observations assimilated. The scenarios with no more than 16, 25, and 36 local observations were also compared. From the results we can conclude that more local observations involved in assimilation will improve estimations with an upper bound of 9 observations in this case. This study demonstrates the potentials of geostatistical correlation representation in OL to improve data assimilation of catchment scale soil moisture using synthetic L-band microwave brightness temperature, which cannot cover the study area fully in space due to vegetation effects. PMID:25635771

A Bayesian geostatistical approach for evaluating the uncertainty of contaminant mass discharges from point sources

NASA Astrophysics Data System (ADS)

Troldborg, M.; Nowak, W.; Binning, P. J.; Bjerg, P. L.

2012-12-01

Estimates of mass discharge (mass/time) are increasingly being used when assessing risks of groundwater contamination and designing remedial systems at contaminated sites. Mass discharge estimates are, however, prone to rather large uncertainties as they integrate uncertain spatial distributions of both concentration and groundwater flow velocities. For risk assessments or any other decisions that are being based on mass discharge estimates, it is essential to address these uncertainties. We present a novel Bayesian geostatistical approach for quantifying the uncertainty of the mass discharge across a multilevel control plane. The method decouples the flow and transport simulation and has the advantage of avoiding the heavy computational burden of three-dimensional numerical flow and transport simulation coupled with geostatistical inversion. It may therefore be of practical relevance to practitioners compared to existing methods that are either too simple or computationally demanding. The method is based on conditional geostatistical simulation and accounts for i) heterogeneity of both the flow field and the concentration distribution through Bayesian geostatistics (including the uncertainty in covariance functions), ii) measurement uncertainty, and iii) uncertain source zone geometry and transport parameters. The method generates multiple equally likely realizations of the spatial flow and concentration distribution, which all honour the measured data at the control plane. The flow realizations are generated by analytical co-simulation of the hydraulic conductivity and the hydraulic gradient across the control plane. These realizations are made consistent with measurements of both hydraulic conductivity and head at the site. An analytical macro-dispersive transport solution is employed to simulate the mean concentration distribution across the control plane, and a geostatistical model of the Box-Cox transformed concentration data is used to simulate observed deviations from this mean solution. By combining the flow and concentration realizations, a mass discharge probability distribution is obtained. Tests show that the decoupled approach is both efficient and able to provide accurate uncertainty estimates. The method is demonstrated on a Danish field site contaminated with chlorinated ethenes. For this site, we show that including a physically meaningful concentration trend and the co-simulation of hydraulic conductivity and hydraulic gradient across the transect helps constrain the mass discharge uncertainty. The number of sampling points required for accurate mass discharge estimation and the relative influence of different data types on mass discharge uncertainty is discussed.

Characterization and geostatistical mapping of water salinity: A case study of terminal complex in the Oued Righ Valley (southern Algeria)

NASA Astrophysics Data System (ADS)

Belkesier, Mohamed Saleh; Zeddouri, Aziez; Halassa, Younes; Kechiched, Rabah

2018-05-01

The region of Oued Righ contains large quantities of groundwater hosted by the three aquifers: the Terminal Complex (CT), the Continental Intercalary (CI) and the phreatic aquifer. The present study is focused on the water from CT aquifer in order to characterize their salinity using geostatistical tool for maping. Indeed, water in this aquifer show a high mineralization exceeding the OMS standards. The main hydro-chemical facies of this water is Chloride-Sodium and Sulfate-Sodium. The elementary statistics have been performed on the physico-chemical analysis from 97 wells whereas 766 wells were analyzed on salinity and are used for the geostatistical mapping. The obtained results show a spatial evolution of the salinity toward the direction South to the North. The salinity is locally strong in the central part of Oued Righ valley. The non-parametric geostatistic of indicator kriging was performed on the salinity data using a cut-off of 5230 mg/l which represents the average value in the studied area. The indicator Kriging allows the estimation of salinity probabilities I (5230 mg / l) in the water of the CT aquifer using bloc model (500 x 500 m). The automatic mapping is used to visualize the distribution of the kriged probabilities of salinity. These results can help to ensure a rational and a selective exploitation of groundwater according the salinity contents.

Topsoil moisture mapping using geostatistical techniques under different Mediterranean climatic conditions.

PubMed

Martínez-Murillo, J F; Hueso-González, P; Ruiz-Sinoga, J D

2017-10-01

Soil mapping has been considered as an important factor in the widening of Soil Science and giving response to many different environmental questions. Geostatistical techniques, through kriging and co-kriging techniques, have made possible to improve the understanding of eco-geomorphologic variables, e.g., soil moisture. This study is focused on mapping of topsoil moisture using geostatistical techniques under different Mediterranean climatic conditions (humid, dry and semiarid) in three small watersheds and considering topography and soil properties as key factors. A Digital Elevation Model (DEM) with a resolution of 1×1m was derived from a topographical survey as well as soils were sampled to analyzed soil properties controlling topsoil moisture, which was measured during 4-years. Afterwards, some topography attributes were derived from the DEM, the soil properties analyzed in laboratory, and the topsoil moisture was modeled for the entire watersheds applying three geostatistical techniques: i) ordinary kriging; ii) co-kriging considering as co-variate topography attributes; and iii) co-kriging ta considering as co-variates topography attributes and gravel content. The results indicated topsoil moisture was more accurately mapped in the dry and semiarid watersheds when co-kriging procedure was performed. The study is a contribution to improve the efficiency and accuracy of studies about the Mediterranean eco-geomorphologic system and soil hydrology in field conditions. Copyright © 2017 Elsevier B.V. All rights reserved.

Geostatistical risk estimation at waste disposal sites in the presence of hot spots.

PubMed

Komnitsas, Kostas; Modis, Kostas

2009-05-30

The present paper aims to estimate risk by using geostatistics at the wider coal mining/waste disposal site of Belkovskaya, Tula region, in Russia. In this area the presence of hot spots causes a spatial trend in the mean value of the random field and a non-Gaussian data distribution. Prior to application of geostatistics, subtraction of trend and appropriate smoothing and transformation of the data into a Gaussian form were carried out; risk maps were then generated for the wider study area in order to assess the probability of exceeding risk thresholds. Finally, the present paper discusses the need for homogenization of soil risk thresholds regarding hazardous elements that will enhance reliability of risk estimation and enable application of appropriate rehabilitation actions in contaminated areas.

Incorporating reservoir heterogeneity with geostatistics to investigate waterflood recoveries

DOE Office of Scientific and Technical Information (OSTI.GOV)

Wolcott, D.S.; Chopra, A.K.

1993-03-01

This paper presents an investigation of infill drilling performance and reservoir continuity with geostatistics and a reservoir simulator. The geostatistical technique provides many possible realizations and realistic descriptions of reservoir heterogeneity. Correlation between recovery efficiency and thickness of individual sand subunits is shown. Additional recovery from infill drilling results from thin, discontinuous subunits. The technique may be applied to variations in continuity for other sandstone reservoirs.

A practical primer on geostatistics

USGS Publications Warehouse

Olea, Ricardo A.

2009-01-01

The Challenge—Most geological phenomena are extraordinarily complex in their interrelationships and vast in their geographical extension. Ordinarily, engineers and geoscientists are faced with corporate or scientific requirements to properly prepare geological models with measurements involving a small fraction of the entire area or volume of interest. Exact description of a system such as an oil reservoir is neither feasible nor economically possible. The results are necessarily uncertain. Note that the uncertainty is not an intrinsic property of the systems; it is the result of incomplete knowledge by the observer.The Aim of Geostatistics—The main objective of geostatistics is the characterization of spatial systems that are incompletely known, systems that are common in geology. A key difference from classical statistics is that geostatistics uses the sampling location of every measurement. Unless the measurements show spatial correlation, the application of geostatistics is pointless. Ordinarily the need for additional knowledge goes beyond a few points, which explains the display of results graphically as fishnet plots, block diagrams, and maps.Geostatistical Methods—Geostatistics is a collection of numerical techniques for the characterization of spatial attributes using primarily two tools: probabilistic models, which are used for spatial data in a manner similar to the way in which time-series analysis characterizes temporal data, or pattern recognition techniques. The probabilistic models are used as a way to handle uncertainty in results away from sampling locations, making a radical departure from alternative approaches like inverse distance estimation methods.Differences with Time Series—On dealing with time-series analysis, users frequently concentrate their attention on extrapolations for making forecasts. Although users of geostatistics may be interested in extrapolation, the methods work at their best interpolating. This simple difference has significant methodological implications.Historical Remarks—As a discipline, geostatistics was firmly established in the 1960s by the French engineer Georges Matheron, who was interested in the appraisal of ore reserves in mining. Geostatistics did not develop overnight. Like other disciplines, it has built on previous results, many of which were formulated with different objectives in various fields.Pioneers—Seminal ideas conceptually related to what today we call geostatistics or spatial statistics are found in the work of several pioneers, including: 1940s: A.N. Kolmogorov in turbulent flow and N. Wiener in stochastic processing; 1950s: D. Krige in mining; 1960s: B. Mathern in forestry and L.S. Gandin in meteorologyCalculations—Serious applications of geostatistics require the use of digital computers. Although for most geostatistical techniques rudimentary implementation from scratch is fairly straightforward, coding programs from scratch is recommended only as part of a practice that may help users to gain a better grasp of the formulations.Software—For professional work, the reader should employ software packages that have been thoroughly tested to handle any sampling scheme, that run as efficiently as possible, and that offer graphic capabilities for the analysis and display of results. This primer employs primarily the package Stanford Geomodeling Software (SGeMS) - recently developed at the Energy Resources Engineering Department at Stanford University - as a way to show how to obtain results practically. This applied side of the primer should not be interpreted as the notes being a manual for the use of SGeMS. The main objective of the primer is to help the reader gain an understanding of the fundamental concepts and tools in geostatistics.Organization of the Primer—The chapters of greatest importance are those covering kriging and simulation. All other materials are peripheral and are included for better comprehension of these main geostatistical modeling tools. The choice of kriging versus simulation is often a big puzzle to the uninitiated, let alone the different variants of both of them. Chapters 14, 18, and 19 are intended to shed light on those subjects. The critical aspect of assessing and modeling spatial correlation is covered in chapter 7. Chapters 2 and 3 review relevant concepts in classical statistics.Course Objectives—This course offers stochastic solutions to common problems in the characterization of complex geological systems. At the end of the course, participants should have: an understanding of the theoretical foundations of geostatistics; a good grasp of its possibilities and limitations; and reasonable familiarity with the SGeMS software, thus opening the possibility of practically applying geostatistics.

Quantifying natural delta variability using a multiple-point geostatistics prior uncertainty model

NASA Astrophysics Data System (ADS)

Scheidt, Céline; Fernandes, Anjali M.; Paola, Chris; Caers, Jef

2016-10-01

We address the question of quantifying uncertainty associated with autogenic pattern variability in a channelized transport system by means of a modern geostatistical method. This question has considerable relevance for practical subsurface applications as well, particularly those related to uncertainty quantification relying on Bayesian approaches. Specifically, we show how the autogenic variability in a laboratory experiment can be represented and reproduced by a multiple-point geostatistical prior uncertainty model. The latter geostatistical method requires selection of a limited set of training images from which a possibly infinite set of geostatistical model realizations, mimicking the training image patterns, can be generated. To that end, we investigate two methods to determine how many training images and what training images should be provided to reproduce natural autogenic variability. The first method relies on distance-based clustering of overhead snapshots of the experiment; the second method relies on a rate of change quantification by means of a computer vision algorithm termed the demon algorithm. We show quantitatively that with either training image selection method, we can statistically reproduce the natural variability of the delta formed in the experiment. In addition, we study the nature of the patterns represented in the set of training images as a representation of the "eigenpatterns" of the natural system. The eigenpattern in the training image sets display patterns consistent with previous physical interpretations of the fundamental modes of this type of delta system: a highly channelized, incisional mode; a poorly channelized, depositional mode; and an intermediate mode between the two.

DOE Office of Scientific and Technical Information (OSTI.GOV)

Carle, S F

Compositional data are represented as vector variables with individual vector components ranging between zero and a positive maximum value representing a constant sum constraint, usually unity (or 100 percent). The earth sciences are flooded with spatial distributions of compositional data, such as concentrations of major ion constituents in natural waters (e.g. mole, mass, or volume fractions), mineral percentages, ore grades, or proportions of mutually exclusive categories (e.g. a water-oil-rock system). While geostatistical techniques have become popular in earth science applications since the 1970s, very little attention has been paid to the unique mathematical properties of geostatistical formulations involving compositional variables.more » The book 'Geostatistical Analysis of Compositional Data' by Vera Pawlowsky-Glahn and Ricardo Olea (Oxford University Press, 2004), unlike any previous book on geostatistics, directly confronts the mathematical difficulties inherent to applying geostatistics to compositional variables. The book righteously justifies itself with prodigious referencing to previous work addressing nonsensical ranges of estimated values and error, spurious correlation, and singular cross-covariance matrices.« less

Combining geostatistics with Moran's I analysis for mapping soil heavy metals in Beijing, China.

PubMed

Huo, Xiao-Ni; Li, Hong; Sun, Dan-Feng; Zhou, Lian-Di; Li, Bao-Guo

2012-03-01

Production of high quality interpolation maps of heavy metals is important for risk assessment of environmental pollution. In this paper, the spatial correlation characteristics information obtained from Moran's I analysis was used to supplement the traditional geostatistics. According to Moran's I analysis, four characteristics distances were obtained and used as the active lag distance to calculate the semivariance. Validation of the optimality of semivariance demonstrated that using the two distances where the Moran's I and the standardized Moran's I, Z(I) reached a maximum as the active lag distance can improve the fitting accuracy of semivariance. Then, spatial interpolation was produced based on the two distances and their nested model. The comparative analysis of estimation accuracy and the measured and predicted pollution status showed that the method combining geostatistics with Moran's I analysis was better than traditional geostatistics. Thus, Moran's I analysis is a useful complement for geostatistics to improve the spatial interpolation accuracy of heavy metals.

Imprecise (fuzzy) information in geostatistics

DOE Office of Scientific and Technical Information (OSTI.GOV)

Bardossy, A.; Bogardi, I.; Kelly, W.E.

1988-05-01

A methodology based on fuzzy set theory for the utilization of imprecise data in geostatistics is presented. A common problem preventing a broader use of geostatistics has been the insufficient amount of accurate measurement data. In certain cases, additional but uncertain (soft) information is available and can be encoded as subjective probabilities, and then the soft kriging method can be applied (Journal, 1986). In other cases, a fuzzy encoding of soft information may be more realistic and simplify the numerical calculations. Imprecise (fuzzy) spatial information on the possible variogram is integrated into a single variogram which is used in amore » fuzzy kriging procedure. The overall uncertainty of prediction is represented by the estimation variance and the calculated membership function for each kriged point. The methodology is applied to the permeability prediction of a soil liner for hazardous waste containment. The available number of hard measurement data (20) was not enough for a classical geostatistical analysis. An additional 20 soft data made it possible to prepare kriged contour maps using the fuzzy geostatistical procedure.« less

Combining Geostatistics with Moran’s I Analysis for Mapping Soil Heavy Metals in Beijing, China

PubMed Central

Huo, Xiao-Ni; Li, Hong; Sun, Dan-Feng; Zhou, Lian-Di; Li, Bao-Guo

2012-01-01

Production of high quality interpolation maps of heavy metals is important for risk assessment of environmental pollution. In this paper, the spatial correlation characteristics information obtained from Moran’s I analysis was used to supplement the traditional geostatistics. According to Moran’s I analysis, four characteristics distances were obtained and used as the active lag distance to calculate the semivariance. Validation of the optimality of semivariance demonstrated that using the two distances where the Moran’s I and the standardized Moran’s I, Z(I) reached a maximum as the active lag distance can improve the fitting accuracy of semivariance. Then, spatial interpolation was produced based on the two distances and their nested model. The comparative analysis of estimation accuracy and the measured and predicted pollution status showed that the method combining geostatistics with Moran’s I analysis was better than traditional geostatistics. Thus, Moran’s I analysis is a useful complement for geostatistics to improve the spatial interpolation accuracy of heavy metals. PMID:22690179

DOE Office of Scientific and Technical Information (OSTI.GOV)

Davis, J.M.

Three outcrop studies were conducted in deposits of different depositional environments. At each site, permeability measurements were obtained with an air-minipermeameter developed as part of this study. In addition, the geological units were mapped with either surveying, photographs, or both. Geostatistical analysis of the permeability data was performed to estimate the characteristics of the probability distribution function and the spatial correlation structure. The information obtained from the geological mapping was then compared with the results of the geostatistical analysis for any relationships that may exist. The main field site was located in the Albuquerque Basin of central New Mexico atmore » an outcrop of the Pliocene-Pleistocene Sierra Ladrones Formation. The second study was conducted on the walls of waste pits in alluvial fan deposits at the Nevada Test Site. The third study was conducted on an outcrop of an eolian deposit (miocene) south of Socorro, New Mexico. The results of the three studies were then used to construct a conceptual model relating depositional environment to geostatistical models of heterogeneity. The model presented is largely qualitative but provides a basis for further hypothesis formulation and testing.« less

Performance prediction using geostatistics and window reservoir simulation

DOE Office of Scientific and Technical Information (OSTI.GOV)

Fontanilla, J.P.; Al-Khalawi, A.A.; Johnson, S.G.

1995-11-01

This paper is the first window model study in the northern area of a large carbonate reservoir in Saudi Arabia. It describes window reservoir simulation with geostatistics to model uneven water encroachment in the southwest producing area of the northern portion of the reservoir. In addition, this paper describes performance predictions that investigate the sweep efficiency of the current peripheral waterflood. A 50 x 50 x 549 (240 m. x 260 m. x 0.15 m. average grid block size) geological model was constructed with geostatistics software. Conditional simulation was used to obtain spatial distributions of porosity and volume of dolomite.more » Core data transforms were used to obtain horizontal and vertical permeability distributions. Simple averaging techniques were used to convert the 549-layer geological model to a 50 x 50 x 10 (240 m. x 260 m. x 8 m. average grid block size) window reservoir simulation model. Flux injectors and flux producers were assigned to the outermost grid blocks. Historical boundary flux rates were obtained from a coarsely-ridded full-field model. Pressure distribution, water cuts, GORs, and recent flowmeter data were history matched. Permeability correction factors and numerous parameter adjustments were required to obtain the final history match. The permeability correction factors were based on pressure transient permeability-thickness analyses. The prediction phase of the study evaluated the effects of infill drilling, the use of artificial lifts, workovers, horizontal wells, producing rate constraints, and tight zone development to formulate depletion strategies for the development of this area. The window model will also be used to investigate day-to-day reservoir management problems in this area.« less

A Streamlined Approach by a Combination of Bioindication and Geostatistical Methods for Assessing Air Contaminants and Their Effects on Human Health in Industrialized Areas: A Case Study in Southern Brazil

PubMed Central

Ferreira, Angélica B.; Ribeiro, Andreza P.; Ferreira, Maurício L.; Kniess, Cláudia T.; Quaresma, Cristiano C.; Lafortezza, Raffaele; Santos, José O.; Saiki, Mitiko; Saldiva, Paulo H.

2017-01-01

Industrialization in developing countries associated with urban growth results in a number of economic benefits, especially in small or medium-sized cities, but leads to a number of environmental and public health consequences. This problem is further aggravated when adequate infrastructure is lacking to monitor the environmental impacts left by industries and refineries. In this study, a new protocol was designed combining biomonitoring and geostatistics to evaluate the possible effects of shale industry emissions on human health and wellbeing. Futhermore, the traditional and expensive air quality method based on PM2.5 measuring was also used to validate the low-cost geostatistical approach. Chemical analysis was performed using Energy Dispersive X-ray Fluorescence Spectrometer (EDXRF) to measure inorganic elements in tree bark and shale retorted samples in São Mateus do Sul city, Southern Brazil. Fe, S, and Si were considered potential pollutants in the study area. Distribution maps of element concentrations were generated from the dataset and used to estimate the spatial behavior of Fe, S, and Si and the range from their hot spot(s), highlighting the regions sorrounding the shale refinery. This evidence was also demonstrated in the measurements of PM2.5 concentrations, which are in agreement with the information obtained from the biomonitoring and geostatistical model. Factor and descriptive analyses performed on the concentrations of tree bark contaminants suggest that Fe, S, and Si might be used as indicators of industrial emissions. The number of cases of respiratory diseases obtained from local basic health unit were used to assess a possible correlation between shale refinery emissions and cases of repiratory disease. These data are public and may be accessed on the website of the the Brazilian Ministry of Health. Significant associations were found between the health data and refinery activities. The combination of the spatial characterization of air pollution and clinical health data revealed that adverse effects were significant for individuals over 38 years of age. These results also suggest that a protocol designed to monitor urban air quality may be an effective and low-cost strategy in environmentally contaminated cities, especially in low- and middle-income countries. PMID:28979271

Geostatistical noise filtering of geophysical images : application to unexploded ordnance (UXO) sites.

DOE Office of Scientific and Technical Information (OSTI.GOV)

Saito, Hirotaka; McKenna, Sean Andrew; Coburn, Timothy C.

2004-07-01

Geostatistical and non-geostatistical noise filtering methodologies, factorial kriging and a low-pass filter, and a region growing method are applied to analytic signal magnetometer images at two UXO contaminated sites to delineate UXO target areas. Overall delineation performance is improved by removing background noise. Factorial kriging slightly outperforms the low-pass filter but there is no distinct difference between them in terms of finding anomalies of interest.

[Multivariate geostatistics and GIS-based approach to study the spatial distribution and sources of heavy metals in agricultural soil in the Pearl River Delta, China].

PubMed

Cai, Li-mei; Ma, Jin; Zhou, Yong-zhang; Huang, Lan-chun; Dou, Lei; Zhang, Cheng-bo; Fu, Shan-ming

2008-12-01

One hundred and eighteen surface soil samples were collected from the Dongguan City, and analyzed for concentration of Cu, Zn, Ni, Cr, Pb, Cd, As, Hg, pH and OM. The spatial distribution and sources of soil heavy metals were studied using multivariate geostatistical methods and GIS technique. The results indicated concentrations of Cu, Zn, Ni, Pb, Cd and Hg were beyond the soil background content in Guangdong province, and especially concentrations of Pb, Cd and Hg were greatly beyond the content. The results of factor analysis group Cu, Zn, Ni, Cr and As in Factor 1, Pb and Hg in Factor 2 and Cd in Factor 3. The spatial maps based on geostatistical analysis show definite association of Factor 1 with the soil parent material, Factor 2 was mainly affected by industries. The spatial distribution of Factor 3 was attributed to anthropogenic influence.

G STL: the geostatistical template library in C++

NASA Astrophysics Data System (ADS)

Remy, Nicolas; Shtuka, Arben; Levy, Bruno; Caers, Jef

2002-10-01

The development of geostatistics has been mostly accomplished by application-oriented engineers in the past 20 years. The focus on concrete applications gave birth to many algorithms and computer programs designed to address different issues, such as estimating or simulating a variable while possibly accounting for secondary information such as seismic data, or integrating geological and geometrical data. At the core of any geostatistical data integration methodology is a well-designed algorithm. Yet, despite their obvious differences, all these algorithms share many commonalities on which to build a geostatistics programming library, lest the resulting library is poorly reusable and difficult to expand. Building on this observation, we design a comprehensive, yet flexible and easily reusable library of geostatistics algorithms in C++. The recent advent of the generic programming paradigm allows us elegantly to express the commonalities of the geostatistical algorithms into computer code. Generic programming, also referred to as "programming with concepts", provides a high level of abstraction without loss of efficiency. This last point is a major gain over object-oriented programming which often trades efficiency for abstraction. It is not enough for a numerical library to be reusable, it also has to be fast. Because generic programming is "programming with concepts", the essential step in the library design is the careful identification and thorough definition of these concepts shared by most geostatistical algorithms. Building on these definitions, a generic and expandable code can be developed. To show the advantages of such a generic library, we use G STL to build two sequential simulation programs working on two different types of grids—a surface with faults and an unstructured grid—without requiring any change to the G STL code.

Geostatistical analysis of the flood risk perception queries in the village of Navaluenga (Central Spain)

NASA Astrophysics Data System (ADS)

Guardiola-Albert, Carolina; Díez-Herrero, Andrés; Amérigo, María; García, Juan Antonio; María Bodoque, José; Fernández-Naranjo, Nuria

2017-04-01

Flash floods provoke a high average mortality as they are usually unexpected events which evolve rapidly and affect relatively small areas. The short time available for minimizing risks requires preparedness and response actions to be put into practice. Therefore, it is necessary the development of emergency response plans to evacuate and rescue people in the context of a flash-flood hazard. In this framework, risk management has to integrate the social dimension of flash-flooding and its spatial distribution by understanding the characteristics of local communities in order to enhance community resilience during a flash-flood. In this regard, the flash-flood social risk perception of the village of Navaluenga (Central Spain) has been recently assessed, as well as the level of awareness of civil protection and emergency management strategies (Bodoque et al., 2016). This has been done interviewing 254 adults, representing roughly 12% of the population census. The present study wants to go further in the analysis of the resulting questionnaires, incorporating in the analysis the location of home spatial coordinates in order to characterize the spatial distribution and possible geographical interpretation of flood risk perception. We apply geostatistical methods to analyze spatial relations of social risk perception and level of awareness with distance to the rivers (Alberche and Chorrerón) or to the flood-prone areas (50-year, 100-year and 500-year flood plains). We want to discover spatial patterns, if any, using correlation functions (variograms). Geostatistical analyses results can help to either confirm the logical pattern (i.e., less awareness further to the rivers or high return period of flooding) or reveal departures from expected. It can also be possible to identify hot spots, cold spots, and spatial outliers. The interpretation of these spatial patterns can give valuable information to define strategies to improve the awareness regarding preparedness and response actions, such as designing optimal evacuation routes during flood emergencies. Geostatistical tools also provide a set of interpolation techniques for the prediction of the variable value at unstudied similar locations, basing on the sample point values and other variables related with the measured variable. We attempt different geostatistical interpolation methods to obtain continuous surfaces of the risk perception and level of awareness in the study area. The use of these maps for future extensions and actualizations of the Civil Protection Plan is evaluated. References Bodoque, J. M., Amérigo, M., Díez-Herrero, A., García, J. A., Cortés, B., Ballesteros-Cánovas, J. A., & Olcina, J. (2016). Improvement of resilience of urban areas by integrating social perception in flash-flood risk management.Journal of Hydrology.

A training image evaluation and selection method based on minimum data event distance for multiple-point geostatistics

NASA Astrophysics Data System (ADS)

Feng, Wenjie; Wu, Shenghe; Yin, Yanshu; Zhang, Jiajia; Zhang, Ke

2017-07-01

A training image (TI) can be regarded as a database of spatial structures and their low to higher order statistics used in multiple-point geostatistics (MPS) simulation. Presently, there are a number of methods to construct a series of candidate TIs (CTIs) for MPS simulation based on a modeler's subjective criteria. The spatial structures of TIs are often various, meaning that the compatibilities of different CTIs with the conditioning data are different. Therefore, evaluation and optimal selection of CTIs before MPS simulation is essential. This paper proposes a CTI evaluation and optimal selection method based on minimum data event distance (MDevD). In the proposed method, a set of MDevD properties are established through calculation of the MDevD of conditioning data events in each CTI. Then, CTIs are evaluated and ranked according to the mean value and variance of the MDevD properties. The smaller the mean value and variance of an MDevD property are, the more compatible the corresponding CTI is with the conditioning data. In addition, data events with low compatibility in the conditioning data grid can be located to help modelers select a set of complementary CTIs for MPS simulation. The MDevD property can also help to narrow the range of the distance threshold for MPS simulation. The proposed method was evaluated using three examples: a 2D categorical example, a 2D continuous example, and an actual 3D oil reservoir case study. To illustrate the method, a C++ implementation of the method is attached to the paper.

SRS 2010 Vegetation Inventory GeoStatistical Mapping Results for Custom Reaction Intensity and Total Dead Fuels.

DOE Office of Scientific and Technical Information (OSTI.GOV)

Edwards, Lloyd A.; Paresol, Bernard

This report of the geostatistical analysis results of the fire fuels response variables, custom reaction intensity and total dead fuels is but a part of an SRS 2010 vegetation inventory project. For detailed description of project, theory and background including sample design, methods, and results please refer to USDA Forest Service Savannah River Site internal report “SRS 2010 Vegetation Inventory GeoStatistical Mapping Report”, (Edwards & Parresol 2013).

Analysis of dengue fever risk using geostatistics model in bone regency

NASA Astrophysics Data System (ADS)

Amran, Stang, Mallongi, Anwar

2017-03-01

This research aim is to analysis of dengue fever risk based on Geostatistics model in Bone Regency. Risk levels of dengue fever are denoted by parameter of Binomial distribution. Effect of temperature, rainfalls, elevation, and larvae abundance are investigated through Geostatistics model. Bayesian hierarchical method is used in estimation process. Using dengue fever data in eleven locations this research shows that temperature and rainfall have significant effect of dengue fever risk in Bone regency.

Geostatistical regularization operators for geophysical inverse problems on irregular meshes

NASA Astrophysics Data System (ADS)

Jordi, C.; Doetsch, J.; Günther, T.; Schmelzbach, C.; Robertsson, J. OA

2018-05-01

Irregular meshes allow to include complicated subsurface structures into geophysical modelling and inverse problems. The non-uniqueness of these inverse problems requires appropriate regularization that can incorporate a priori information. However, defining regularization operators for irregular discretizations is not trivial. Different schemes for calculating smoothness operators on irregular meshes have been proposed. In contrast to classical regularization constraints that are only defined using the nearest neighbours of a cell, geostatistical operators include a larger neighbourhood around a particular cell. A correlation model defines the extent of the neighbourhood and allows to incorporate information about geological structures. We propose an approach to calculate geostatistical operators for inverse problems on irregular meshes by eigendecomposition of a covariance matrix that contains the a priori geological information. Using our approach, the calculation of the operator matrix becomes tractable for 3-D inverse problems on irregular meshes. We tested the performance of the geostatistical regularization operators and compared them against the results of anisotropic smoothing in inversions of 2-D surface synthetic electrical resistivity tomography (ERT) data as well as in the inversion of a realistic 3-D cross-well synthetic ERT scenario. The inversions of 2-D ERT and seismic traveltime field data with geostatistical regularization provide results that are in good accordance with the expected geology and thus facilitate their interpretation. In particular, for layered structures the geostatistical regularization provides geologically more plausible results compared to the anisotropic smoothness constraints.

Spatial analysis of groundwater levels using Fuzzy Logic and geostatistical tools

NASA Astrophysics Data System (ADS)

Theodoridou, P. G.; Varouchakis, E. A.; Karatzas, G. P.

2017-12-01

The spatial variability evaluation of the water table of an aquifer provides useful information in water resources management plans. Geostatistical methods are often employed to map the free surface of an aquifer. In geostatistical analysis using Kriging techniques the selection of the optimal variogram is very important for the optimal method performance. This work compares three different criteria to assess the theoretical variogram that fits to the experimental one: the Least Squares Sum method, the Akaike Information Criterion and the Cressie's Indicator. Moreover, variable distance metrics such as the Euclidean, Minkowski, Manhattan, Canberra and Bray-Curtis are applied to calculate the distance between the observation and the prediction points, that affects both the variogram calculation and the Kriging estimator. A Fuzzy Logic System is then applied to define the appropriate neighbors for each estimation point used in the Kriging algorithm. The two criteria used during the Fuzzy Logic process are the distance between observation and estimation points and the groundwater level value at each observation point. The proposed techniques are applied to a data set of 250 hydraulic head measurements distributed over an alluvial aquifer. The analysis showed that the Power-law variogram model and Manhattan distance metric within ordinary kriging provide the best results when the comprehensive geostatistical analysis process is applied. On the other hand, the Fuzzy Logic approach leads to a Gaussian variogram model and significantly improves the estimation performance. The two different variogram models can be explained in terms of a fractional Brownian motion approach and of aquifer behavior at local scale. Finally, maps of hydraulic head spatial variability and of predictions uncertainty are constructed for the area with the two different approaches comparing their advantages and drawbacks.

Gstat: a program for geostatistical modelling, prediction and simulation

NASA Astrophysics Data System (ADS)

Pebesma, Edzer J.; Wesseling, Cees G.

1998-01-01

Gstat is a computer program for variogram modelling, and geostatistical prediction and simulation. It provides a generic implementation of the multivariable linear model with trends modelled as a linear function of coordinate polynomials or of user-defined base functions, and independent or dependent, geostatistically modelled, residuals. Simulation in gstat comprises conditional or unconditional (multi-) Gaussian sequential simulation of point values or block averages, or (multi-) indicator sequential simulation. Besides many of the popular options found in other geostatistical software packages, gstat offers the unique combination of (i) an interactive user interface for modelling variograms and generalized covariances (residual variograms), that uses the device-independent plotting program gnuplot for graphical display, (ii) support for several ascii and binary data and map file formats for input and output, (iii) a concise, intuitive and flexible command language, (iv) user customization of program defaults, (v) no built-in limits, and (vi) free, portable ANSI-C source code. This paper describes the class of problems gstat can solve, and addresses aspects of efficiency and implementation, managing geostatistical projects, and relevant technical details.

Geostatistical evaluation of integrated marsh management impact on mosquito vectors using before-after-control-impact (BACI) design

PubMed Central

Rochlin, Ilia; Iwanejko, Tom; Dempsey, Mary E; Ninivaggi, Dominick V

2009-01-01

Background In many parts of the world, salt marshes play a key ecological role as the interface between the marine and the terrestrial environments. Salt marshes are also exceedingly important for public health as larval habitat for mosquitoes that are vectors of disease and significant biting pests. Although grid ditching and pesticides have been effective in salt marsh mosquito control, marsh degradation and other environmental considerations compel a different approach. Targeted habitat modification and biological control methods known as Open Marsh Water Management (OMWM) had been proposed as a viable alternative to marsh-wide physical alterations and chemical control. However, traditional larval sampling techniques may not adequately assess the impacts of marsh management on mosquito larvae. To assess the effectiveness of integrated OMWM and marsh restoration techniques for mosquito control, we analyzed the results of a 5-year OMWM/marsh restoration project to determine changes in mosquito larval production using GIS and geostatistical methods. Methods The following parameters were evaluated using "Before-After-Control-Impact" (BACI) design: frequency and geographic extent of larval production, intensity of larval production, changes in larval habitat, and number of larvicide applications. The analyses were performed using Moran's I, Getis-Ord, and Spatial Scan statistics on aggregated before and after data as well as data collected over time. This allowed comparison of control and treatment areas to identify changes attributable to the OMWM/marsh restoration modifications. Results The frequency of finding mosquito larvae in the treatment areas was reduced by 70% resulting in a loss of spatial larval clusters compared to those found in the control areas. This effect was observed directly following OMWM treatment and remained significant throughout the study period. The greatly reduced frequency of finding larvae in the treatment areas led to a significant decrease (~44%) in the number of times when the larviciding threshold was reached. This reduction, in turn, resulted in a significant decrease (~74%) in the number of larvicide applications in the treatment areas post-project. The remaining larval habitat in the treatment areas had a different geographic distribution and was largely confined to the restored marsh surface (i.e. filled-in mosquito ditches); however only ~21% of the restored marsh surface supported mosquito production. Conclusion The geostatistical analysis showed that OMWM demonstrated considerable potential for effective mosquito control and compatibility with other natural resource management goals such as restoration, wildlife habitat enhancement, and invasive species abatement. GPS and GIS tools are invaluable for large scale project design, data collection, and data analysis, with geostatistical methods serving as an alternative or a supplement to the conventional inference statistics in evaluating the project outcome. PMID:19549297

A MS-lesion pattern discrimination plot based on geostatistics.

PubMed

Marschallinger, Robert; Schmidt, Paul; Hofmann, Peter; Zimmer, Claus; Atkinson, Peter M; Sellner, Johann; Trinka, Eugen; Mühlau, Mark

2016-03-01

A geostatistical approach to characterize MS-lesion patterns based on their geometrical properties is presented. A dataset of 259 binary MS-lesion masks in MNI space was subjected to directional variography. A model function was fit to express the observed spatial variability in x, y, z directions by the geostatistical parameters Range and Sill. Parameters Range and Sill correlate with MS-lesion pattern surface complexity and total lesion volume. A scatter plot of ln(Range) versus ln(Sill), classified by pattern anisotropy, enables a consistent and clearly arranged presentation of MS-lesion patterns based on geometry: the so-called MS-Lesion Pattern Discrimination Plot. The geostatistical approach and the graphical representation of results are considered efficient exploratory data analysis tools for cross-sectional, follow-up, and medication impact analysis.

Advantages and applicability of commonly used homogenisation methods for climate data

NASA Astrophysics Data System (ADS)

Ribeiro, Sara; Caineta, Júlio; Henriques, Roberto; Soares, Amílcar; Costa, Ana Cristina

2014-05-01

Homogenisation of climate data is a very relevant subject since these data are required as an input in a wide range of studies, such as atmospheric modelling, weather forecasting, climate change monitoring, or hydrological and environmental projects. Often, climate data series include non-natural irregularities which have to be detected and removed prior to their use, otherwise it would generate biased and erroneous results. Relocation of weather stations or changes in the measuring instruments are amongst the most relevant causes for these inhomogeneities. Depending on the climate variable, its temporal resolution and spatial continuity, homogenisation methods can be more or less effective. For example, due to its natural variability, precipitation is identified as a very challenging variable to be homogenised. During the last two decades, numerous methods have been proposed to homogenise climate data. In order to compare, evaluate and develop those methods, the European project COST Action ES0601, Advances in homogenisation methods of climate series: an integrated approach (HOME), was released in 2008. Existing homogenisation methods were improved based on the benchmark exercise issued by this project. A recent approach based on Direct Sequential Simulation (DSS), not yet evaluated by the benchmark exercise, is also presented as an innovative methodology for homogenising climate data series. DSS already proved to be a successful geostatistical method in environmental and hydrological studies, and it provides promising results for the homogenisation of climate data. Since DSS is a geostatistical stochastic approach, it accounts for the joint spatial and temporal dependence between observations, as well as the relative importance of stations both in terms of distance and correlation. This work presents a chronological review of the most commonly used homogenisation methods for climate data and available software packages. A short description and classification is provided for each method. Their advantages and applicability are discussed based on literature review and on the results of the HOME project. Acknowledgements: The authors gratefully acknowledge the financial support of "Fundação para a Ciência e Tecnologia" (FCT), Portugal, through the research project PTDC/GEO-MET/4026/2012 ("GSIMCLI - Geostatistical simulation with local distributions for the homogenization and interpolation of climate data").

Modelling Geomechanical Heterogeneity of Rock Masses Using Direct and Indirect Geostatistical Conditional Simulation Methods

NASA Astrophysics Data System (ADS)

Eivazy, Hesameddin; Esmaieli, Kamran; Jean, Raynald

2017-12-01

An accurate characterization and modelling of rock mass geomechanical heterogeneity can lead to more efficient mine planning and design. Using deterministic approaches and random field methods for modelling rock mass heterogeneity is known to be limited in simulating the spatial variation and spatial pattern of the geomechanical properties. Although the applications of geostatistical techniques have demonstrated improvements in modelling the heterogeneity of geomechanical properties, geostatistical estimation methods such as Kriging result in estimates of geomechanical variables that are not fully representative of field observations. This paper reports on the development of 3D models for spatial variability of rock mass geomechanical properties using geostatistical conditional simulation method based on sequential Gaussian simulation. A methodology to simulate the heterogeneity of rock mass quality based on the rock mass rating is proposed and applied to a large open-pit mine in Canada. Using geomechanical core logging data collected from the mine site, a direct and an indirect approach were used to model the spatial variability of rock mass quality. The results of the two modelling approaches were validated against collected field data. The study aims to quantify the risks of pit slope failure and provides a measure of uncertainties in spatial variability of rock mass properties in different areas of the pit.

Overview and technical and practical aspects for use of geostatistics in hazardous-, toxic-, and radioactive-waste-site investigations

DOE Office of Scientific and Technical Information (OSTI.GOV)

Bossong, C.R.; Karlinger, M.R.; Troutman, B.M.

1999-10-01

Technical and practical aspects of applying geostatistics are developed for individuals involved in investigation at hazardous-, toxic-, and radioactive-waste sites. Important geostatistical concepts, such as variograms and ordinary, universal, and indicator kriging, are described in general terms for introductory purposes and in more detail for practical applications. Variogram modeling using measured ground-water elevation data is described in detail to illustrate principles of stationarity, anisotropy, transformations, and cross validation. Several examples of kriging applications are described using ground-water-level elevations, bedrock elevations, and ground-water-quality data. A review of contemporary literature and selected public domain software associated with geostatistics also is provided, asmore » is a discussion of alternative methods for spatial modeling, including inverse distance weighting, triangulation, splines, trend-surface analysis, and simulation.« less

Geostatistics and spatial analysis in biological anthropology.

PubMed

Relethford, John H

2008-05-01

A variety of methods have been used to make evolutionary inferences based on the spatial distribution of biological data, including reconstructing population history and detection of the geographic pattern of natural selection. This article provides an examination of geostatistical analysis, a method used widely in geology but which has not often been applied in biological anthropology. Geostatistical analysis begins with the examination of a variogram, a plot showing the relationship between a biological distance measure and the geographic distance between data points and which provides information on the extent and pattern of spatial correlation. The results of variogram analysis are used for interpolating values of unknown data points in order to construct a contour map, a process known as kriging. The methods of geostatistical analysis and discussion of potential problems are applied to a large data set of anthropometric measures for 197 populations in Ireland. The geostatistical analysis reveals two major sources of spatial variation. One pattern, seen for overall body and craniofacial size, shows an east-west cline most likely reflecting the combined effects of past population dispersal and settlement. The second pattern is seen for craniofacial height and shows an isolation by distance pattern reflecting rapid spatial changes in the midlands region of Ireland, perhaps attributable to the genetic impact of the Vikings. The correspondence of these results with other analyses of these data and the additional insights generated from variogram analysis and kriging illustrate the potential utility of geostatistical analysis in biological anthropology. (c) 2008 Wiley-Liss, Inc.

Geospatial interpolation and mapping of tropospheric ozone pollution using geostatistics.

PubMed

Kethireddy, Swatantra R; Tchounwou, Paul B; Ahmad, Hafiz A; Yerramilli, Anjaneyulu; Young, John H

2014-01-10

Tropospheric ozone (O3) pollution is a major problem worldwide, including in the United States of America (USA), particularly during the summer months. Ozone oxidative capacity and its impact on human health have attracted the attention of the scientific community. In the USA, sparse spatial observations for O3 may not provide a reliable source of data over a geo-environmental region. Geostatistical Analyst in ArcGIS has the capability to interpolate values in unmonitored geo-spaces of interest. In this study of eastern Texas O3 pollution, hourly episodes for spring and summer 2012 were selectively identified. To visualize the O3 distribution, geostatistical techniques were employed in ArcMap. Using ordinary Kriging, geostatistical layers of O3 for all the studied hours were predicted and mapped at a spatial resolution of 1 kilometer. A decent level of prediction accuracy was achieved and was confirmed from cross-validation results. The mean prediction error was close to 0, the root mean-standardized-prediction error was close to 1, and the root mean square and average standard errors were small. O3 pollution map data can be further used in analysis and modeling studies. Kriging results and O3 decadal trends indicate that the populace in Houston-Sugar Land-Baytown, Dallas-Fort Worth-Arlington, Beaumont-Port Arthur, San Antonio, and Longview are repeatedly exposed to high levels of O3-related pollution, and are prone to the corresponding respiratory and cardiovascular health effects. Optimization of the monitoring network proves to be an added advantage for the accurate prediction of exposure levels.

Exploring prediction uncertainty of spatial data in geostatistical and machine learning Approaches

NASA Astrophysics Data System (ADS)

Klump, J. F.; Fouedjio, F.

2017-12-01

Geostatistical methods such as kriging with external drift as well as machine learning techniques such as quantile regression forest have been intensively used for modelling spatial data. In addition to providing predictions for target variables, both approaches are able to deliver a quantification of the uncertainty associated with the prediction at a target location. Geostatistical approaches are, by essence, adequate for providing such prediction uncertainties and their behaviour is well understood. However, they often require significant data pre-processing and rely on assumptions that are rarely met in practice. Machine learning algorithms such as random forest regression, on the other hand, require less data pre-processing and are non-parametric. This makes the application of machine learning algorithms to geostatistical problems an attractive proposition. The objective of this study is to compare kriging with external drift and quantile regression forest with respect to their ability to deliver reliable prediction uncertainties of spatial data. In our comparison we use both simulated and real world datasets. Apart from classical performance indicators, comparisons make use of accuracy plots, probability interval width plots, and the visual examinations of the uncertainty maps provided by the two approaches. By comparing random forest regression to kriging we found that both methods produced comparable maps of estimated values for our variables of interest. However, the measure of uncertainty provided by random forest seems to be quite different to the measure of uncertainty provided by kriging. In particular, the lack of spatial context can give misleading results in areas without ground truth data. These preliminary results raise questions about assessing the risks associated with decisions based on the predictions from geostatistical and machine learning algorithms in a spatial context, e.g. mineral exploration.

[Distribution of soil heavy metal and pollution evaluation on the different sampling scales in farmland on Yellow River irrigation area of Ningxia: a case study in Xingqing County of Yinchuan City].

PubMed

Wang, You-Qi; Bai, Yi-Ru; Wang, Jian-Yu

2014-07-01

Determining spatial distributions and analyses contamination condition of soil heavy metals play an important role in evaluation of the quality of agricultural ecological environment and the protection of food safety and human health. Topsoil samples (0-20 cm) from 223 sites in farmland were collected at two scales of sampling grid (1 m x 1 m, 10 m x 10 m) in the Yellow River irrigation area of Ningxia. The objectives of this study were to investigate the spatial variability of total copper (Cu), total zinc (Zn), total chrome (Cr), total cadmium (Cd) and total lead (Pb) on the two sampling scales by the classical and geostatistical analyses. The single pollution index (P(i)) and the Nemerow pollution index (P) were used to evaluate the soil heavy metal pollution. The classical statistical analyses showed that all soil heavy metals demonstrated moderate variability, the coefficient of variation (CV) changed in the following sequence: Cd > Pb > Cr > Zn > Cu. Geostatistical analyses showed that the nugget coefficient of Cd on the 10 m x 10 m scale and Pb on the 1 m x 1 m scale were 100% with pure nugget variograms, which showed weak variability affected by random factors. The nugget coefficient of the other indexes was less than 25%, which showed a strong variability affected by structural factors. The results combined with P(i) and P indicated that most soil heavy metals have slight pollution except total copper, and in general there were the trend of heavy metal accumulation in the study area.

Approaches in highly parameterized inversion: bgaPEST, a Bayesian geostatistical approach implementation with PEST: documentation and instructions

USGS Publications Warehouse

Fienen, Michael N.; D'Oria, Marco; Doherty, John E.; Hunt, Randall J.

2013-01-01

The application bgaPEST is a highly parameterized inversion software package implementing the Bayesian Geostatistical Approach in a framework compatible with the parameter estimation suite PEST. Highly parameterized inversion refers to cases in which parameters are distributed in space or time and are correlated with one another. The Bayesian aspect of bgaPEST is related to Bayesian probability theory in which prior information about parameters is formally revised on the basis of the calibration dataset used for the inversion. Conceptually, this approach formalizes the conditionality of estimated parameters on the specific data and model available. The geostatistical component of the method refers to the way in which prior information about the parameters is used. A geostatistical autocorrelation function is used to enforce structure on the parameters to avoid overfitting and unrealistic results. Bayesian Geostatistical Approach is designed to provide the smoothest solution that is consistent with the data. Optionally, users can specify a level of fit or estimate a balance between fit and model complexity informed by the data. Groundwater and surface-water applications are used as examples in this text, but the possible uses of bgaPEST extend to any distributed parameter applications.

Use of geostatistics to determine the spatial distribution and infestation rate of leaf-cutting ant nests (Hymenoptera: Formicidae) in eucalyptus plantations.

PubMed

Lasmar, O; Zanetti, R; dos Santos, A; Fernandes, B V

2012-08-01

One of the fundamental steps in pest sampling is the assessment of the population distribution in the field. Several studies have investigated the distribution and appropriate sampling methods for leaf-cutting ants; however, more reliable methods are still required, such as those that use geostatistics. The objective of this study was to determine the spatial distribution and infestation rate of leaf-cutting ant nests in eucalyptus plantations by using geostatistics. The study was carried out in 2008 in two eucalyptus stands in Paraopeba, Minas Gerais, Brazil. All of the nests in the studied area were located and used for the generation of GIS maps, and the spatial pattern of distribution was determined considering the number and size of nests. Each analysis and map was made using the R statistics program and the geoR package. The nest spatial distribution in a savanna area of Minas Gerais was clustered to a certain extent. The models generated allowed the production of kriging maps of areas infested with leaf-cutting ants, where chemical intervention would be necessary, reducing the control costs, impact on humans, and the environment.

Mapping aboveground woody biomass using forest inventory, remote sensing and geostatistical techniques.

PubMed

Yadav, Bechu K V; Nandy, S

2015-05-01

Mapping forest biomass is fundamental for estimating CO₂ emissions, and planning and monitoring of forests and ecosystem productivity. The present study attempted to map aboveground woody biomass (AGWB) integrating forest inventory, remote sensing and geostatistical techniques, viz., direct radiometric relationships (DRR), k-nearest neighbours (k-NN) and cokriging (CoK) and to evaluate their accuracy. A part of the Timli Forest Range of Kalsi Soil and Water Conservation Division, Uttarakhand, India was selected for the present study. Stratified random sampling was used to collect biophysical data from 36 sample plots of 0.1 ha (31.62 m × 31.62 m) size. Species-specific volumetric equations were used for calculating volume and multiplied by specific gravity to get biomass. Three forest-type density classes, viz. 10-40, 40-70 and >70% of Shorea robusta forest and four non-forest classes were delineated using on-screen visual interpretation of IRS P6 LISS-III data of December 2012. The volume in different strata of forest-type density ranged from 189.84 to 484.36 m(3) ha(-1). The total growing stock of the forest was found to be 2,024,652.88 m(3). The AGWB ranged from 143 to 421 Mgha(-1). Spectral bands and vegetation indices were used as independent variables and biomass as dependent variable for DRR, k-NN and CoK. After validation and comparison, k-NN method of Mahalanobis distance (root mean square error (RMSE) = 42.25 Mgha(-1)) was found to be the best method followed by fuzzy distance and Euclidean distance with RMSE of 44.23 and 45.13 Mgha(-1) respectively. DRR was found to be the least accurate method with RMSE of 67.17 Mgha(-1). The study highlighted the potential of integrating of forest inventory, remote sensing and geostatistical techniques for forest biomass mapping.

Applications of geostatistics and Markov models for logo recognition

NASA Astrophysics Data System (ADS)

Pham, Tuan

2003-01-01

Spatial covariances based on geostatistics are extracted as representative features of logo or trademark images. These spatial covariances are different from other statistical features for image analysis in that the structural information of an image is independent of the pixel locations and represented in terms of spatial series. We then design a classifier in the sense of hidden Markov models to make use of these geostatistical sequential data to recognize the logos. High recognition rates are obtained from testing the method against a public-domain logo database.

GEOSTATISTICAL SAMPLING DESIGNS FOR HAZARDOUS WASTE SITES

EPA Science Inventory

This chapter discusses field sampling design for environmental sites and hazardous waste sites with respect to random variable sampling theory, Gy's sampling theory, and geostatistical (kriging) sampling theory. The literature often presents these sampling methods as an adversari...

Geostatistics: a common link between medical geography, mathematical geology, and medical geology

PubMed Central

Goovaerts, P.

2015-01-01

Synopsis Since its development in the mining industry, geostatistics has emerged as the primary tool for spatial data analysis in various fields, ranging from earth and atmospheric sciences to agriculture, soil science, remote sensing, and more recently environmental exposure assessment. In the last few years, these tools have been tailored to the field of medical geography or spatial epidemiology, which is concerned with the study of spatial patterns of disease incidence and mortality and the identification of potential ‘causes’ of disease, such as environmental exposure, diet and unhealthy behaviours, economic or socio-demographic factors. On the other hand, medical geology is an emerging interdisciplinary scientific field studying the relationship between natural geological factors and their effects on human and animal health. This paper provides an introduction to the field of medical geology with an overview of geostatistical methods available for the analysis of geological and health data. Key concepts are illustrated using the mapping of groundwater arsenic concentration across eleven Michigan counties and the exploration of its relationship to the incidence of prostate cancer at the township level. PMID:25722963

Validating spatial structure in canopy water content using geostatistics

NASA Technical Reports Server (NTRS)

Sanderson, E. W.; Zhang, M. H.; Ustin, S. L.; Rejmankova, E.; Haxo, R. S.

1995-01-01

Heterogeneity in ecological phenomena are scale dependent and affect the hierarchical structure of image data. AVIRIS pixels average reflectance produced by complex absorption and scattering interactions between biogeochemical composition, canopy architecture, view and illumination angles, species distributions, and plant cover as well as other factors. These scales affect validation of pixel reflectance, typically performed by relating pixel spectra to ground measurements acquired at scales of 1m(exp 2) or less (e.g., field spectra, foilage and soil samples, etc.). As image analysis becomes more sophisticated, such as those for detection of canopy chemistry, better validation becomes a critical problem. This paper presents a methodology for bridging between point measurements and pixels using geostatistics. Geostatistics have been extensively used in geological or hydrogeolocial studies but have received little application in ecological studies. The key criteria for kriging estimation is that the phenomena varies in space and that an underlying controlling process produces spatial correlation between the measured data points. Ecological variation meets this requirement because communities vary along environmental gradients like soil moisture, nutrient availability, or topography.

Geostatistics: a common link between medical geography, mathematical geology, and medical geology.

PubMed

Goovaerts, P

2014-08-01

Since its development in the mining industry, geostatistics has emerged as the primary tool for spatial data analysis in various fields, ranging from earth and atmospheric sciences to agriculture, soil science, remote sensing, and more recently environmental exposure assessment. In the last few years, these tools have been tailored to the field of medical geography or spatial epidemiology, which is concerned with the study of spatial patterns of disease incidence and mortality and the identification of potential 'causes' of disease, such as environmental exposure, diet and unhealthy behaviours, economic or socio-demographic factors. On the other hand, medical geology is an emerging interdisciplinary scientific field studying the relationship between natural geological factors and their effects on human and animal health. This paper provides an introduction to the field of medical geology with an overview of geostatistical methods available for the analysis of geological and health data. Key concepts are illustrated using the mapping of groundwater arsenic concentration across eleven Michigan counties and the exploration of its relationship to the incidence of prostate cancer at the township level.

[Spatial distribution pattern of Chilo suppressalis analyzed by classical method and geostatistics].

PubMed

Yuan, Zheming; Fu, Wei; Li, Fangyi

2004-04-01

Two original samples of Chilo suppressalis and their grid, random and sequence samples were analyzed by classical method and geostatistics to characterize the spatial distribution pattern of C. suppressalis. The limitations of spatial distribution analysis with classical method, especially influenced by the original position of grid, were summarized rather completely. On the contrary, geostatistics characterized well the spatial distribution pattern, congregation intensity and spatial heterogeneity of C. suppressalis. According to geostatistics, the population was up to Poisson distribution in low density. As for higher density population, its distribution was up to aggregative, and the aggregation intensity and dependence range were 0.1056 and 193 cm, respectively. Spatial heterogeneity was also found in the higher density population. Its spatial correlativity in line direction was more closely than that in row direction, and the dependence ranges in line and row direction were 115 and 264 cm, respectively.

Geostatistical Evaluation of Natural Tree Regeneration of a Disturbed Forest

Treesearch

José Germán Flores Garnica; David Arturo Moreno Gonzalez; Juan de Dios Benavides Solorio

2006-01-01

The implementation of silvicultural strategies in a forest management has to guaranty forest sustainability, which is supported by an adequate regeneration. Therefore, quality and intensity of silvicultural practices is based on an accurate knowledge of the current spatial distribution of regeneration. At the same time, this regeneration is determined by the spatial...

Spatial variation of dental caries in late holocene samples of Southern South America: A geostatistical study.

PubMed

Menéndez, Lumila Paula

2016-11-01

The spatial variation of dental caries in late Holocene southern South American populations will be analyzed using geostatistical methods. The existence of a continuous geographical pattern of dental caries variation will be tested. The author recorded dental caries in 400 individuals, collated this information with published caries data from 666 additional individuals, and calculated a Caries Index. The caries spatial distribution was evaluated by means of 2D maps and scatterplots. Geostatistical analyses were performed by calculating Moran's I, correlograms and a Procrustes analysis. There is a relatively strong latitudinal continuous gradient of dental caries variation, especially in the extremes of the distribution. Moreover, the association between dental caries and geography was relatively high (m 12  = 0.6). Although northern and southern samples had the highest and lowest frequencies of dental caries, respectively, the central ones had the largest variation and had lower rates of caries than expected. The large variation in frequencies of dental caries in populations located in the center of the distribution could be explained by their subsistence strategies, characterized either by the consumption of wild cariogenic plants or cultigens (obtained locally or by exchange), a reliance on fishing, or the incorporation of plants rich in starch rather than carbohydrates. It is suggested that dental caries must be considered a multifactorial disease which results from the interaction of cultural practices and environmental factors. This can change how we understand subsistence strategies as well as how we interpret dental caries rates. Am. J. Hum. Biol., 2016. © 2016 Wiley Periodicals, Inc. Am. J. Hum. Biol. 28:825-836, 2016. © 2016Wiley Periodicals, Inc. © 2016 Wiley Periodicals, Inc.

A statistical evaluation of non-ergodic variogram estimators

USGS Publications Warehouse

Curriero, F.C.; Hohn, M.E.; Liebhold, A.M.; Lele, S.R.

2002-01-01

Geostatistics is a set of statistical techniques that is increasingly used to characterize spatial dependence in spatially referenced ecological data. A common feature of geostatistics is predicting values at unsampled locations from nearby samples using the kriging algorithm. Modeling spatial dependence in sampled data is necessary before kriging and is usually accomplished with the variogram and its traditional estimator. Other types of estimators, known as non-ergodic estimators, have been used in ecological applications. Non-ergodic estimators were originally suggested as a method of choice when sampled data are preferentially located and exhibit a skewed frequency distribution. Preferentially located samples can occur, for example, when areas with high values are sampled more intensely than other areas. In earlier studies the visual appearance of variograms from traditional and non-ergodic estimators were compared. Here we evaluate the estimators' relative performance in prediction. We also show algebraically that a non-ergodic version of the variogram is equivalent to the traditional variogram estimator. Simulations, designed to investigate the effects of data skewness and preferential sampling on variogram estimation and kriging, showed the traditional variogram estimator outperforms the non-ergodic estimators under these conditions. We also analyzed data on carabid beetle abundance, which exhibited large-scale spatial variability (trend) and a skewed frequency distribution. Detrending data followed by robust estimation of the residual variogram is demonstrated to be a successful alternative to the non-ergodic approach.

Breast Carcinoma, Intratumour Heterogeneity and Histological Grading, Using Geostatistics

PubMed Central

Sharifi‐Salamatian, Vénus; de Roquancourt, Anne; Rigaut, Jean Paul

2000-01-01

Tumour progression is currently believed to result from genetic instability. Chromosomal patterns specific of a type of cancer are frequent even though phenotypic spatial heterogeneity is omnipresent. The latter is the usual cause of histological grading imprecision, a well documented problem, without any fully satisfactory solution up to now. The present article addresses this problem in breast carcinoma. The assessment of a genetic marker for human tumours requires quantifiable measures of intratumoral heterogeneity. If any invariance paradigm representing a stochastic or geostatistic function could be discovered, this might help in solving the grading problem. A novel methodological approach using geostatistics to measure heterogeneity is used. Twenty tumours from the three usual (Scarff‐Bloom and Richardson) grades were obtained and paraffin sections stained by MIB‐1 (Ki‐67) and peroxidase staining. Whole two‐dimensional sections were sampled. Morphometric grids of variable sizes allowed a simple and fast recording of positions of epithelial nuclei, marked or not by MIB‐1. The geostatistical method is based here upon the asymptotic behaviour of dispersion variance. Measure of asymptotic exponent of dispersion variance shows an increase from grade 1 to grade 3. Preliminary results are encouraging: grades 1 and 3 on one hand and 2 and 3 on the other hand are totally separated. The final proof of an improved grading using this measure will of course require a confrontation with the results of survival studies. PMID:11153611

Breast carcinoma, intratumour heterogeneity and histological grading, using geostatistics.

PubMed

Sharifi-Salamatian, V; de Roquancourt, A; Rigaut, J P

2000-01-01

Tumour progression is currently believed to result from genetic instability. Chromosomal patterns specific of a type of cancer are frequent even though phenotypic spatial heterogeneity is omnipresent. The latter is the usual cause of histological grading imprecision, a well documented problem, without any fully satisfactory solution up to now. The present article addresses this problem in breast carcinoma. The assessment of a genetic marker for human tumours requires quantifiable measures of intratumoral heterogeneity. If any invariance paradigm representing a stochastic or geostatistic function could be discovered, this might help in solving the grading problem. A novel methodological approach using geostatistics to measure heterogeneity is used. Twenty tumours from the three usual (Scarff-Bloom and Richardson) grades were obtained and paraffin sections stained by MIB-1 (Ki-67) and peroxidase staining. Whole two-dimensional sections were sampled. Morphometric grids of variable sizes allowed a simple and fast recording of positions of epithelial nuclei, marked or not by MIB-1. The geostatistical method is based here upon the asymptotic behaviour of dispersion variance. Measure of asymptotic exponent of dispersion variance shows an increase from grade 1 to grade 3. Preliminary results are encouraging: grades 1 and 3 on one hand and 2 and 3 on the other hand are totally separated. The final proof of an improved grading using this measure will of course require a confrontation with the results of survival studies.

Hierarchical Nearest-Neighbor Gaussian Process Models for Large Geostatistical Datasets.

PubMed

Datta, Abhirup; Banerjee, Sudipto; Finley, Andrew O; Gelfand, Alan E

2016-01-01

Spatial process models for analyzing geostatistical data entail computations that become prohibitive as the number of spatial locations become large. This article develops a class of highly scalable nearest-neighbor Gaussian process (NNGP) models to provide fully model-based inference for large geostatistical datasets. We establish that the NNGP is a well-defined spatial process providing legitimate finite-dimensional Gaussian densities with sparse precision matrices. We embed the NNGP as a sparsity-inducing prior within a rich hierarchical modeling framework and outline how computationally efficient Markov chain Monte Carlo (MCMC) algorithms can be executed without storing or decomposing large matrices. The floating point operations (flops) per iteration of this algorithm is linear in the number of spatial locations, thereby rendering substantial scalability. We illustrate the computational and inferential benefits of the NNGP over competing methods using simulation studies and also analyze forest biomass from a massive U.S. Forest Inventory dataset at a scale that precludes alternative dimension-reducing methods. Supplementary materials for this article are available online.

Hierarchical Nearest-Neighbor Gaussian Process Models for Large Geostatistical Datasets

PubMed Central

Datta, Abhirup; Banerjee, Sudipto; Finley, Andrew O.; Gelfand, Alan E.

2018-01-01

Spatial process models for analyzing geostatistical data entail computations that become prohibitive as the number of spatial locations become large. This article develops a class of highly scalable nearest-neighbor Gaussian process (NNGP) models to provide fully model-based inference for large geostatistical datasets. We establish that the NNGP is a well-defined spatial process providing legitimate finite-dimensional Gaussian densities with sparse precision matrices. We embed the NNGP as a sparsity-inducing prior within a rich hierarchical modeling framework and outline how computationally efficient Markov chain Monte Carlo (MCMC) algorithms can be executed without storing or decomposing large matrices. The floating point operations (flops) per iteration of this algorithm is linear in the number of spatial locations, thereby rendering substantial scalability. We illustrate the computational and inferential benefits of the NNGP over competing methods using simulation studies and also analyze forest biomass from a massive U.S. Forest Inventory dataset at a scale that precludes alternative dimension-reducing methods. Supplementary materials for this article are available online. PMID:29720777

Hydrogeologic unit flow characterization using transition probability geostatistics.

PubMed

Jones, Norman L; Walker, Justin R; Carle, Steven F

2005-01-01

This paper describes a technique for applying the transition probability geostatistics method for stochastic simulation to a MODFLOW model. Transition probability geostatistics has some advantages over traditional indicator kriging methods including a simpler and more intuitive framework for interpreting geologic relationships and the ability to simulate juxtapositional tendencies such as fining upward sequences. The indicator arrays generated by the transition probability simulation are converted to layer elevation and thickness arrays for use with the new Hydrogeologic Unit Flow package in MODFLOW 2000. This makes it possible to preserve complex heterogeneity while using reasonably sized grids and/or grids with nonuniform cell thicknesses.

Coupling geostatistics to detailed reservoir description allows better visualization and more accurate characterization/simulation of turbidite reservoirs: Elk Hills oil field, California

DOE Office of Scientific and Technical Information (OSTI.GOV)

Allan, M.E.; Wilson, M.L.; Wightman, J.

1996-12-31

The Elk Hills giant oilfield, located in the southern San Joaquin Valley of California, has produced 1.1 billion barrels of oil from Miocene and shallow Pliocene reservoirs. 65% of the current 64,000 BOPD production is from the pressure-supported, deeper Miocene turbidite sands. In the turbidite sands of the 31 S structure, large porosity & permeability variations in the Main Body B and Western 31 S sands cause problems with the efficiency of the waterflooding. These variations have now been quantified and visualized using geostatistics. The end result is a more detailed reservoir characterization for simulation. Traditional reservoir descriptions based onmore » marker correlations, cross-sections and mapping do not provide enough detail to capture the short-scale stratigraphic heterogeneity needed for adequate reservoir simulation. These deterministic descriptions are inadequate to tie with production data as the thinly bedded sand/shale sequences blur into a falsely homogenous picture. By studying the variability of the geologic & petrophysical data vertically within each wellbore and spatially from well to well, a geostatistical reservoir description has been developed. It captures the natural variability of the sands and shales that was lacking from earlier work. These geostatistical studies allow the geologic and petrophysical characteristics to be considered in a probabilistic model. The end-product is a reservoir description that captures the variability of the reservoir sequences and can be used as a more realistic starting point for history matching and reservoir simulation.« less

Coupling geostatistics to detailed reservoir description allows better visualization and more accurate characterization/simulation of turbidite reservoirs: Elk Hills oil field, California

DOE Office of Scientific and Technical Information (OSTI.GOV)

Allan, M.E.; Wilson, M.L.; Wightman, J.

1996-01-01

The Elk Hills giant oilfield, located in the southern San Joaquin Valley of California, has produced 1.1 billion barrels of oil from Miocene and shallow Pliocene reservoirs. 65% of the current 64,000 BOPD production is from the pressure-supported, deeper Miocene turbidite sands. In the turbidite sands of the 31 S structure, large porosity permeability variations in the Main Body B and Western 31 S sands cause problems with the efficiency of the waterflooding. These variations have now been quantified and visualized using geostatistics. The end result is a more detailed reservoir characterization for simulation. Traditional reservoir descriptions based on markermore » correlations, cross-sections and mapping do not provide enough detail to capture the short-scale stratigraphic heterogeneity needed for adequate reservoir simulation. These deterministic descriptions are inadequate to tie with production data as the thinly bedded sand/shale sequences blur into a falsely homogenous picture. By studying the variability of the geologic petrophysical data vertically within each wellbore and spatially from well to well, a geostatistical reservoir description has been developed. It captures the natural variability of the sands and shales that was lacking from earlier work. These geostatistical studies allow the geologic and petrophysical characteristics to be considered in a probabilistic model. The end-product is a reservoir description that captures the variability of the reservoir sequences and can be used as a more realistic starting point for history matching and reservoir simulation.« less

An approach for land suitability evaluation using geostatistics, remote sensing, and geographic information system in arid and semiarid ecosystems.

PubMed

Emadi, Mostafa; Baghernejad, Majid; Pakparvar, Mojtaba; Kowsar, Sayyed Ahang

2010-05-01

This study was undertaken to incorporate geostatistics, remote sensing, and geographic information system (GIS) technologies to improve the qualitative land suitability assessment in arid and semiarid ecosystems of Arsanjan plain, southern Iran. The primary data were obtained from 85 soil samples collected from tree depths (0-30, 30-60, and 60-90 cm); the secondary information was acquired from the remotely sensed data from the linear imaging self-scanner (LISS-III) receiver of the IRS-P6 satellite. Ordinary kriging and simple kriging with varying local means (SKVLM) methods were used to identify the spatial dependency of soil important parameters. It was observed that using the data collected from the spectral values of band 1 of the LISS-III receiver as the secondary variable applying the SKVLM method resulted in the lowest mean square error for mapping the pH and electrical conductivity (ECe) in the 0-30-cm depth. On the other hand, the ordinary kriging method resulted in a reliable accuracy for the other soil properties with moderate to strong spatial dependency in the study area for interpolation in the unstamped points. The parametric land suitability evaluation method was applied on the density points (150 x 150 m(2)) instead of applying on the limited representative profiles conventionally, which were obtained by the kriging or SKVLM methods. Overlaying the information layers of the data was used with the GIS for preparing the final land suitability evaluation. Therefore, changes in land characteristics could be identified in the same soil uniform mapping units over a very short distance. In general, this new method can easily present the squares and limitation factors of the different land suitability classes with considerable accuracy in arbitrary land indices.

Spatial Analysis of Rice Blast in China at Three Different Scales.

PubMed

Guo, Fangfang; Chen, Xinglong; Lu, Minghong; Yang, Li; Wang, Shi Wei; Wu, Bo Ming

2018-05-22

In this study, spatial analyses were conducted at three different scales to better understand the epidemiology of rice blast, a major rice disease caused by Magnaporthe oryzae. At regional scale, across the major rice production regions in China, rice blast incidence was monitored on 101 dates at 193 stations from June 10 th to Sep. 10 th during 2009-2014, and surveyed in 143 fields in September, 2016; at county scale, 3 surveys were done covering 1-5 counties in 2015-2016; and at field scale, blast was evaluated in 6 fields in 2015-2016. Spatial cluster and hot spot analyses were conducted in GIS on the geographical pattern of the disease at regional scale, and geostatistical analysis performed at all the three scales. Cluster and hot spot analyses revealed that high-disease areas were clustered in mountainous areas in China. Geostatistical analyses detected spatial dependence of blast incidence with influence ranges of 399 to 1080 km at regional scale, and 5 to 10 m at field scale, but not at county scale. The spatial patterns at different scales might be determined by inherent properties of rice blast and environmental driving forces, and findings from this study provide helpful information to sampling and management of rice blast.

Outlier detection for groundwater data in France

NASA Astrophysics Data System (ADS)

Valmy, Larissa; de Fouquet, Chantal; Bourgine, Bernard

2014-05-01

Quality and quantity water in France are increasingly observed since the 70s. Moreover, in 2000, the EU Water Framework Directive established a framework for community action in the water policy field for the protection of inland surface waters (rivers and lakes), transitional waters (estuaries), coastal waters and groundwater. It will ensure that all aquatic ecosystems and, with regard to their water needs, terrestrial ecosystems and wetlands meet 'good status' by 2015. The Directive requires Member States to establish river basin districts and for each of these a river basin management plan. In France, monitoring programs for the water status were implemented in each basin since 2007. The data collected through these programs feed into an information system which contributes to check the compliance of water environmental legislation implementation, assess the status of water guide management actions (programs of measures) and evaluate their effectiveness, and inform the public. Our work consists in study quality and quantity groundwater data for some basins in France. We propose a specific mathematical approach in order to detect outliers and study trends in time series. In statistic, an outlier is an observation that lies outside the overall pattern of a distribution. Usually, the presence of an outlier indicates some sort of problem, thus, it is important to detect it in order to know the cause. In fact, techniques for temporal data analysis have been developed for several decades in parallel with geostatistical methods. However compared to standard statistical methods, geostatistical analysis allows incomplete or irregular time series analysis. Otherwise, tests carried out by the BRGM showed the potential contribution of geostatistical methods for characterization of environmental data time series. Our approach is to exploit this potential through the development of specific algorithms, tests and validation of methods. We will introduce and explain our method and approach by considering the Loire Bretagne basin case.

A multiple-point geostatistical approach to quantifying uncertainty for flow and transport simulation in geologically complex environments

NASA Astrophysics Data System (ADS)

Cronkite-Ratcliff, C.; Phelps, G. A.; Boucher, A.

2011-12-01

In many geologic settings, the pathways of groundwater flow are controlled by geologic heterogeneities which have complex geometries. Models of these geologic heterogeneities, and consequently, their effects on the simulated pathways of groundwater flow, are characterized by uncertainty. Multiple-point geostatistics, which uses a training image to represent complex geometric descriptions of geologic heterogeneity, provides a stochastic approach to the analysis of geologic uncertainty. Incorporating multiple-point geostatistics into numerical models provides a way to extend this analysis to the effects of geologic uncertainty on the results of flow simulations. We present two case studies to demonstrate the application of multiple-point geostatistics to numerical flow simulation in complex geologic settings with both static and dynamic conditioning data. Both cases involve the development of a training image from a complex geometric description of the geologic environment. Geologic heterogeneity is modeled stochastically by generating multiple equally-probable realizations, all consistent with the training image. Numerical flow simulation for each stochastic realization provides the basis for analyzing the effects of geologic uncertainty on simulated hydraulic response. The first case study is a hypothetical geologic scenario developed using data from the alluvial deposits in Yucca Flat, Nevada. The SNESIM algorithm is used to stochastically model geologic heterogeneity conditioned to the mapped surface geology as well as vertical drill-hole data. Numerical simulation of groundwater flow and contaminant transport through geologic models produces a distribution of hydraulic responses and contaminant concentration results. From this distribution of results, the probability of exceeding a given contaminant concentration threshold can be used as an indicator of uncertainty about the location of the contaminant plume boundary. The second case study considers a characteristic lava-flow aquifer system in Pahute Mesa, Nevada. A 3D training image is developed by using object-based simulation of parametric shapes to represent the key morphologic features of rhyolite lava flows embedded within ash-flow tuffs. In addition to vertical drill-hole data, transient pressure head data from aquifer tests can be used to constrain the stochastic model outcomes. The use of both static and dynamic conditioning data allows the identification of potential geologic structures that control hydraulic response. These case studies demonstrate the flexibility of the multiple-point geostatistics approach for considering multiple types of data and for developing sophisticated models of geologic heterogeneities that can be incorporated into numerical flow simulations.

Accounting for aquifer heterogeneity from geological data to management tools.

PubMed

Blouin, Martin; Martel, Richard; Gloaguen, Erwan

2013-01-01

A nested workflow of multiple-point geostatistics (MPG) and sequential Gaussian simulation (SGS) was tested on a study area of 6 km(2) located about 20 km northwest of Quebec City, Canada. In order to assess its geological and hydrogeological parameter heterogeneity and to provide tools to evaluate uncertainties in aquifer management, direct and indirect field measurements are used as inputs in the geostatistical simulations to reproduce large and small-scale heterogeneities. To do so, the lithological information is first associated to equivalent hydrogeological facies (hydrofacies) according to hydraulic properties measured at several wells. Then, heterogeneous hydrofacies (HF) realizations are generated using a prior geological model as training image (TI) with the MPG algorithm. The hydraulic conductivity (K) heterogeneity modeling within each HF is finally computed using SGS algorithm. Different K models are integrated in a finite-element hydrogeological model to calculate multiple transport simulations. Different scenarios exhibit variations in mass transport path and dispersion associated with the large- and small-scale heterogeneity respectively. Three-dimensional maps showing the probability of overpassing different thresholds are presented as examples of management tools. © 2012, The Author(s). Groundwater © 2012, National Ground Water Association.

Analysis and simulation of wireless signal propagation applying geostatistical interpolation techniques

NASA Astrophysics Data System (ADS)

Kolyaie, S.; Yaghooti, M.; Majidi, G.

2011-12-01

This paper is a part of an ongoing research to examine the capability of geostatistical analysis for mobile networks coverage prediction, simulation and tuning. Mobile network coverage predictions are used to find network coverage gaps and areas with poor serviceability. They are essential data for engineering and management in order to make better decision regarding rollout, planning and optimisation of mobile networks.The objective of this research is to evaluate different interpolation techniques in coverage prediction. In method presented here, raw data collected from drive testing a sample of roads in study area is analysed and various continuous surfaces are created using different interpolation methods. Two general interpolation methods are used in this paper with different variables; first, Inverse Distance Weighting (IDW) with various powers and number of neighbours and second, ordinary kriging with Gaussian, spherical, circular and exponential semivariogram models with different number of neighbours. For the result comparison, we have used check points coming from the same drive test data. Prediction values for check points are extracted from each surface and the differences with actual value are computed. The output of this research helps finding an optimised and accurate model for coverage prediction.

Soil texture and organic carbon fractions predicted from near-infrared spectroscopy and geostatistics

USDA-ARS?s Scientific Manuscript database

Field-specific management could help achieve agricultural sustainability by increasing production and decreasing environmental impacts. Near-infrared spectroscopy (NIRS) and geostatistics are relatively unexplored tools that could reduce time, labor, and costs of soil analysis. Our objective was to ...

Accounting for regional background and population size in the detection of spatial clusters and outliers using geostatistical filtering and spatial neutral models: the case of lung cancer in Long Island, New York

PubMed Central

Goovaerts, Pierre; Jacquez, Geoffrey M

2004-01-01

Background Complete Spatial Randomness (CSR) is the null hypothesis employed by many statistical tests for spatial pattern, such as local cluster or boundary analysis. CSR is however not a relevant null hypothesis for highly complex and organized systems such as those encountered in the environmental and health sciences in which underlying spatial pattern is present. This paper presents a geostatistical approach to filter the noise caused by spatially varying population size and to generate spatially correlated neutral models that account for regional background obtained by geostatistical smoothing of observed mortality rates. These neutral models were used in conjunction with the local Moran statistics to identify spatial clusters and outliers in the geographical distribution of male and female lung cancer in Nassau, Queens, and Suffolk counties, New York, USA. Results We developed a typology of neutral models that progressively relaxes the assumptions of null hypotheses, allowing for the presence of spatial autocorrelation, non-uniform risk, and incorporation of spatially heterogeneous population sizes. Incorporation of spatial autocorrelation led to fewer significant ZIP codes than found in previous studies, confirming earlier claims that CSR can lead to over-identification of the number of significant spatial clusters or outliers. Accounting for population size through geostatistical filtering increased the size of clusters while removing most of the spatial outliers. Integration of regional background into the neutral models yielded substantially different spatial clusters and outliers, leading to the identification of ZIP codes where SMR values significantly depart from their regional background. Conclusion The approach presented in this paper enables researchers to assess geographic relationships using appropriate null hypotheses that account for the background variation extant in real-world systems. In particular, this new methodology allows one to identify geographic pattern above and beyond background variation. The implementation of this approach in spatial statistical software will facilitate the detection of spatial disparities in mortality rates, establishing the rationale for targeted cancer control interventions, including consideration of health services needs, and resource allocation for screening and diagnostic testing. It will allow researchers to systematically evaluate how sensitive their results are to assumptions implicit under alternative null hypotheses. PMID:15272930

A geostatistical approach to predicting sulfur content in the Pittsburgh coal bed

USGS Publications Warehouse

Watson, W.D.; Ruppert, L.F.; Bragg, L.J.; Tewalt, S.J.

2001-01-01

The US Geological Survey (USGS) is completing a national assessment of coal resources in the five top coal-producing regions in the US. Point-located data provide measurements on coal thickness and sulfur content. The sample data and their geologic interpretation represent the most regionally complete and up-to-date assessment of what is known about top-producing US coal beds. The sample data are analyzed using a combination of geologic and Geographic Information System (GIS) models to estimate tonnages and qualities of the coal beds. Traditionally, GIS practitioners use contouring to represent geographical patterns of "similar" data values. The tonnage and grade of coal resources are then assessed by using the contour lines as references for interpolation. An assessment taken to this point is only indicative of resource quantity and quality. Data users may benefit from a statistical approach that would allow them to better understand the uncertainty and limitations of the sample data. To develop a quantitative approach, geostatistics were applied to the data on coal sulfur content from samples taken in the Pittsburgh coal bed (located in the eastern US, in the southwestern part of the state of Pennsylvania, and in adjoining areas in the states of Ohio and West Virginia). Geostatistical methods that account for regional and local trends were applied to blocks 2.7 mi (4.3 km) on a side. The data and geostatistics support conclusions concerning the average sulfur content and its degree of reliability at regional- and economic-block scale over the large, contiguous part of the Pittsburgh outcrop, but not to a mine scale. To validate the method, a comparison was made with the sulfur contents in sample data taken from 53 coal mines located in the study area. The comparison showed a high degree of similarity between the sulfur content in the mine samples and the sulfur content represented by the geostatistically derived contours. Published by Elsevier Science B.V.

Using direct current resistivity sounding and geostatistics to aid in hydrogeological studies in the Choshuichi alluvial fan, Taiwan.

PubMed

Yang, Chieh-Hou; Lee, Wei-Feng

2002-01-01

Ground water reservoirs in the Choshuichi alluvial fan, central western Taiwan, were investigated using direct-current (DC) resistivity soundings at 190 locations, combined with hydrogeological measurements from 37 wells. In addition, attempts were made to calculate aquifer transmissivity from both surface DC resistivity measurements and geostatistically derived predictions of aquifer properties. DC resistivity sounding data are highly correlated to the hydraulic parameters in the Choshuichi alluvial fan. By estimating the spatial distribution of hydraulic conductivity from the kriged well data and the cokriged thickness of the correlative aquifer from both resistivity sounding data and well information, the transmissivity of the aquifer at each location can be obtained from the product of kriged hydraulic conductivity and computed thickness of the geoelectric layer. Thus, the spatial variation of the transmissivities in the study area is obtained. Our work is more comparable to Ahmed et al. (1988) than to the work of Niwas and Singhal (1981). The first "constraint" from Niwas and Singhal's work is a result of their use of linear regression. The geostatistical approach taken here (and by Ahmed et al. [1988]) is a natural improvement on the linear regression approach.

Regional prediction of basin-scale brown trout habitat suitability

NASA Astrophysics Data System (ADS)

Ceola, S.; Pugliese, A.

2014-09-01

In this study we propose a novel method for the estimation of ecological indices describing the habitat suitability of brown trout (Salmo trutta). Traditional hydrological tools are coupled with an innovative regional geostatistical technique, aiming at the prediction of the brown trout habitat suitability index where partial or totally ungauged conditions occur. Several methods for the assessment of ecological indices are already proposed in the scientific literature, but the possibility of exploiting a geostatistical prediction model, such as Topological Kriging, has never been investigated before. In order to develop a regional habitat suitability model we use the habitat suitability curve, obtained from measured data of brown trout adult individuals collected in several river basins across the USA. The Top-kriging prediction model is then employed to assess the spatial correlation between upstream and downstream habitat suitability indices. The study area is the Metauro River basin, located in the central part of Italy (Marche region), for which both water depth and streamflow data were collected. The present analysis focuses on discharge values corresponding to the 0.1-, 0.5-, 0.9-empirical quantiles derived from flow-duration curves available for seven gauging stations located within the study area, for which three different suitability indices (i.e. ψ10, ψ50 and ψ90) are evaluated. The results of this preliminary analysis are encouraging showing Nash-Sutcliffe efficiencies equal to 0.52, 0.65, and 0.69, respectively.

Arsenic-Safe Aquifers in Coastal Bangladesh: AN Investigation with Ordinary Kriging Estimation

NASA Astrophysics Data System (ADS)

Hassan, M. M.; Ahamed, R.

2017-10-01

Spatial point pattern is one of the most suitable methods for analysing groundwater arsenic concentrations. Groundwater arsenic poisoning in Bangladesh has been one of the biggest environmental health disasters in recent times. About 85 million people are exposed to arsenic more than 50 μg/L in drinking water. The paper seeks to identify the existing suitable aquifers for arsenic-safe drinking water along with "spatial arsenic discontinuity" using GIS-based spatial geostatistical analysis in a small study site (12.69 km2) in the coastal belt of southwest Bangladesh (Dhopakhali union of Bagerhat district). The relevant spatial data were collected with Geographical Positioning Systems (GPS), arsenic data with field testing kits, tubewell attributes with observation and questionnaire survey. Geostatistics with kriging methods can design water quality monitoring in different aquifers with hydrochemical evaluation by spatial mapping. The paper presents the interpolation of the regional estimates of arsenic data for spatial discontinuity mapping with Ordinary Kriging (OK) method that overcomes the areal bias problem for administrative boundary. This paper also demonstrates the suitability of isopleth maps that is easier to read than choropleth maps. The OK method investigated that around 80 percent of the study site are contaminated following the Bangladesh Drinking Water Standards (BDWS) of 50 μg/L. The study identified a very few scattered "pockets" of arsenic-safe zone at the shallow aquifer.

Soil quality assessment using GIS-based chemometric approach and pollution indices: Nakhlak mining district, Central Iran.

PubMed

Moore, Farid; Sheykhi, Vahideh; Salari, Mohammad; Bagheri, Adel

2016-04-01

This paper is a comprehensive assessment of the quality of soil in the Nakhlak mining district in Central Iran with special reference to potentially toxic metals. In this regard, an integrated approach involving geostatistical, correlation matrix, pollution indices, and chemical fractionation measurement is used to evaluate selected potentially toxic metals in soil samples. The fractionation of metals indicated a relatively high variability. Some metals (Mo, Ag, and Pb) showed important enrichment in the bioavailable fractions (i.e., exchangeable and carbonate), whereas the residual fraction mostly comprised Sb and Cr. The Cd, Zn, Co, Ni, Mo, Cu, and As were retained in Fe-Mn oxide and oxidizable fractions, suggesting that they may be released to the environment by changes in physicochemical conditions. The spatial variability patterns of 11 soil heavy metals (Ag, As, Cd, Co, Cr, Cu, Mo, Ni, Pb, Sb, and Zn) were identified and mapped. The results demonstrated that Ag, As, Cd, Mo, Cu, Pb, Sb, and Zn pollution are associated with mineralized veins and mining operations in this area. Further environmental monitoring and remedial actions are required for management of soil heavy metals in the study area. The present study not only enhanced our knowledge regarding soil pollution in the study area but also introduced a better technique to analyze pollution indices by multivariate geostatistical methods.

Geostatistical Characteristic of Space -Time Variation in Underground Water Selected Quality Parameters in Klodzko Water Intake Area (SW Part of Poland)

NASA Astrophysics Data System (ADS)

Namysłowska-Wilczyńska, Barbara

2016-04-01

This paper presents selected results of research connected with the development of a (3D) geostatistical hydrogeochemical model of the Klodzko Drainage Basin, dedicated to the spatial and time variation in the selected quality parameters of underground water in the Klodzko water intake area (SW part of Poland). The research covers the period 2011÷2012. Spatial analyses of the variation in various quality parameters, i.e, contents of: ammonium ion [gNH4+/m3], NO3- (nitrate ion) [gNO3/m3], PO4-3 (phosphate ion) [gPO4-3/m3], total organic carbon C (TOC) [gC/m3], pH redox potential and temperature C [degrees], were carried out on the basis of the chemical determinations of the quality parameters of underground water samples taken from the wells in the water intake area. Spatial and time variation in the quality parameters was analyzed on the basis of archival data (period 1977÷1999) for 22 (pump and siphon) wells with a depth ranging from 9.5 to 38.0 m b.g.l., later data obtained (November 2011) from tests of water taken from 14 existing wells. The wells were built in the years 1954÷1998. The water abstraction depth (difference between the terrain elevation and the dynamic water table level) is ranged from 276÷286 m a.s.l., with an average of 282.05 m a.s.l. Dynamic water table level is contained between 6.22 m÷16.44 m b.g.l., with a mean value of 9.64 m b.g.l. The latest data (January 2012) acquired from 3 new piezometers, with a depth of 9÷10m, which were made in other locations in the relevant area. Thematic databases, containing original data on coordinates X, Y (latitude, longitude) and Z (terrain elevation and time - years) and on regionalized variables, i.e. the underground water quality parameters in the Klodzko water intake area determined for different analytical configurations (22 wells, 14 wells, 14 wells + 3 piezometers), were created. Both archival data (acquired in the years 1977÷1999) and the latest data (collected in 2011÷2012) were analyzed. These data were subjected to spatial analyses using statistical and geostatistical methods. The evaluation of basic statistics of the investigated quality parameters, including their histograms of distributions, scatter diagrams between these parameters and also correlation coefficients r were presented in this article. The directional semivariogram function and the ordinary (block) kriging procedure were used to build the 3D geostatistical model. The geostatistical parameters of the theoretical models of directional semivariograms of the studied water quality parameters, calculated along the time interval and along the wells depth (taking into account the terrain elevation), were used in the ordinary (block) kriging estimation. The obtained results of estimation, i.e. block diagrams allowed to determine the levels of increased values Z* of studied underground water quality parameters. Analysis of the variability in the selected quality parameters of underground water for an analyzed area in Klodzko water intake was enriched by referring to the results of geostatistical studies carried out for underground water quality parameters and also for a treated water and in Klodzko water supply system (iron Fe, manganese Mn, ammonium ion NH4+ contents), discussed in earlier works. Spatial and time variation in the latter-mentioned parameters was analysed on the basis of the data (2007÷2011, 2008÷2011). Generally, the behaviour of the underground water quality parameters has been found to vary in space and time. Thanks to the spatial analyses of the variation in the quality parameters in the Kłodzko underground water intake area some regularities (trends) in the variation in water quality have been identified.

Adapting geostatistics to analyze spatial and temporal trends in weed populations

USDA-ARS?s Scientific Manuscript database

Geostatistics were originally developed in mining to estimate the location, abundance and quality of ore over large areas from soil samples to optimize future mining efforts. Here, some of these methods were adapted to weeds to account for a limited distribution area (i.e., inside a field), variatio...

Estimating Solar PV Output Using Modern Space/Time Geostatistics (Presentation)

DOE Office of Scientific and Technical Information (OSTI.GOV)

Lee, S. J.; George, R.; Bush, B.

2009-04-29

This presentation describes a project that uses mapping techniques to predict solar output at subhourly resolution at any spatial point, develop a methodology that is applicable to natural resources in general, and demonstrate capability of geostatistical techniques to predict the output of a potential solar plant.

A Nonparametric Geostatistical Method For Estimating Species Importance

Treesearch

Andrew J. Lister; Rachel Riemann; Michael Hoppus

2001-01-01

Parametric statistical methods are not always appropriate for conducting spatial analyses of forest inventory data. Parametric geostatistical methods such as variography and kriging are essentially averaging procedures, and thus can be affected by extreme values. Furthermore, non normal distributions violate the assumptions of analyses in which test statistics are...

Three-dimensional imaging of aquifer and aquitard heterogeneity via transient hydraulic tomography at a highly heterogeneous field site

NASA Astrophysics Data System (ADS)

Zhao, Zhanfeng; Illman, Walter A.

2018-04-01

Previous studies have shown that geostatistics-based transient hydraulic tomography (THT) is robust for subsurface heterogeneity characterization through the joint inverse modeling of multiple pumping tests. However, the hydraulic conductivity (K) and specific storage (Ss) estimates can be smooth or even erroneous for areas where pumping/observation densities are low. This renders the imaging of interlayer and intralayer heterogeneity of highly contrasting materials including their unit boundaries difficult. In this study, we further test the performance of THT by utilizing existing and newly collected pumping test data of longer durations that showed drawdown responses in both aquifer and aquitard units at a field site underlain by a highly heterogeneous glaciofluvial deposit. The robust performance of the THT is highlighted through the comparison of different degrees of model parameterization including: (1) the effective parameter approach; (2) the geological zonation approach relying on borehole logs; and (3) the geostatistical inversion approach considering different prior information (with/without geological data). Results reveal that the simultaneous analysis of eight pumping tests with the geostatistical inverse model yields the best results in terms of model calibration and validation. We also find that the joint interpretation of long-term drawdown data from aquifer and aquitard units is necessary in mapping their full heterogeneous patterns including intralayer variabilities. Moreover, as geological data are included as prior information in the geostatistics-based THT analysis, the estimated K values increasingly reflect the vertical distribution patterns of permeameter-estimated K in both aquifer and aquitard units. Finally, the comparison of various THT approaches reveals that differences in the estimated K and Ss tomograms result in significantly different transient drawdown predictions at observation ports.

Geostatistical analysis of data on air temperature and plant phenology from Baden-Württemberg (Germany) as a basis for regional scaled models of climate change.

PubMed

Schröder, Winfried; Schmidt, Gunther; Hasenclever, Judith

2006-09-01

The rise of the air temperature is assured to be part of the global climatic change, but there is still a lack of knowledge about its effects at a regional scale. The article tackles the correlation of air temperature with the phenology of selected plants by the example of Baden-Württemberg to provide a spatial valid data base for regional climate change models. To this end, the data on air temperature and plant phenology, gathered from measurement sites without congruent coverage, were correlated after performing geostatistical analysis and estimation. In addition, geostatistics are used to analyze and cartographically depict the spatial structure of the phenology of plants in spring and in summer. The statistical analysis reveals a significant relationship between the rising air temperature and the earlier beginning of phenological phases like blooming or fruit maturation: From 1991 to 1999 spring time, as indicated by plant phenology, has begun up to 15 days earlier than from 1961 to 1990. As shown by geostatistics, this holds true for the whole territory of Baden-Württemberg. The effects of the rise of air temperature should be investigated not only by monitoring biological individuals, as for example plants, but on an ecosystem level as well. In Germany, the environmental monitoring should be supplemented by the study of the effects of the climatic change in ecosystems. Because air temperature and humidity have a great influence on the temporal and spatial distribution of pathogen carriers (vectors) and pathogens, mapping of the environmental determinants of vector and pathogen distribution in space and time should be performed in order to identify hot spots for risk assessment and further detailed epidemiological studies.

Geostatistical interpolation model selection based on ArcGIS and spatio-temporal variability analysis of groundwater level in piedmont plains, northwest China.

PubMed

Xiao, Yong; Gu, Xiaomin; Yin, Shiyang; Shao, Jingli; Cui, Yali; Zhang, Qiulan; Niu, Yong

2016-01-01

Based on the geo-statistical theory and ArcGIS geo-statistical module, datas of 30 groundwater level observation wells were used to estimate the decline of groundwater level in Beijing piedmont. Seven different interpolation methods (inverse distance weighted interpolation, global polynomial interpolation, local polynomial interpolation, tension spline interpolation, ordinary Kriging interpolation, simple Kriging interpolation and universal Kriging interpolation) were used for interpolating groundwater level between 2001 and 2013. Cross-validation, absolute error and coefficient of determination (R(2)) was applied to evaluate the accuracy of different methods. The result shows that simple Kriging method gave the best fit. The analysis of spatial and temporal variability suggest that the nugget effects from 2001 to 2013 were increasing, which means the spatial correlation weakened gradually under the influence of human activities. The spatial variability in the middle areas of the alluvial-proluvial fan is relatively higher than area in top and bottom. Since the changes of the land use, groundwater level also has a temporal variation, the average decline rate of groundwater level between 2007 and 2013 increases compared with 2001-2006. Urban development and population growth cause over-exploitation of residential and industrial areas. The decline rate of the groundwater level in residential, industrial and river areas is relatively high, while the decreasing of farmland area and development of water-saving irrigation reduce the quantity of water using by agriculture and decline rate of groundwater level in agricultural area is not significant.

Prediction of sedimentary facies of x-oilfield in northwest of China by geostatistical inversion

NASA Astrophysics Data System (ADS)

Lei, Zhao; Ling, Ke; Tingting, He

2017-03-01

In the early stage of oilfield development, there are only a few wells and well spacing can reach several kilometers. for the alluvial fans and other heterogeneous reservoirs, information from wells alone is not sufficient to derive detailed reservoir information. In this paper, the method of calculating sand thickness through geostatistics inversion is studied, and quantitative relationships between each sedimentary micro-facies are analyzed by combining with single well sedimentary facies. Further, the sedimentary facies plane distribution based on seismic inversion is obtained by combining with sedimentary model, providing the geological basis for the next exploration and deployment.

Combining area-based and individual-level data in the geostatistical mapping of late-stage cancer incidence.

PubMed

Goovaerts, Pierre

2009-01-01

This paper presents a geostatistical approach to incorporate individual-level data (e.g. patient residences) and area-based data (e.g. rates recorded at census tract level) into the mapping of late-stage cancer incidence, with an application to breast cancer in three Michigan counties. Spatial trends in cancer incidence are first estimated from census data using area-to-point binomial kriging. This prior model is then updated using indicator kriging and individual-level data. Simulation studies demonstrate the benefits of this two-step approach over methods (kernel density estimation and indicator kriging) that process only residence data.

Geostatistics as a tool to define various categories of resources

DOE Office of Scientific and Technical Information (OSTI.GOV)

Sabourin, R.

1983-02-01

Definition of 'measured' and 'indicated' resources tend to be vague. Yet, the calculation of such categories of resources in a mineral deposit calls for specific technical criteria. The author discusses how a geostatistical methodology provides the technical criteria required to classify reasonably assured resources by levels of assurance of their existence.

A geostatistical approach to the change-of-support problem and variable-support data fusion in spatial analysis

NASA Astrophysics Data System (ADS)

Wang, Jun; Wang, Yang; Zeng, Hui

2016-01-01

A key issue to address in synthesizing spatial data with variable-support in spatial analysis and modeling is the change-of-support problem. We present an approach for solving the change-of-support and variable-support data fusion problems. This approach is based on geostatistical inverse modeling that explicitly accounts for differences in spatial support. The inverse model is applied here to produce both the best predictions of a target support and prediction uncertainties, based on one or more measurements, while honoring measurements. Spatial data covering large geographic areas often exhibit spatial nonstationarity and can lead to computational challenge due to the large data size. We developed a local-window geostatistical inverse modeling approach to accommodate these issues of spatial nonstationarity and alleviate computational burden. We conducted experiments using synthetic and real-world raster data. Synthetic data were generated and aggregated to multiple supports and downscaled back to the original support to analyze the accuracy of spatial predictions and the correctness of prediction uncertainties. Similar experiments were conducted for real-world raster data. Real-world data with variable-support were statistically fused to produce single-support predictions and associated uncertainties. The modeling results demonstrate that geostatistical inverse modeling can produce accurate predictions and associated prediction uncertainties. It is shown that the local-window geostatistical inverse modeling approach suggested offers a practical way to solve the well-known change-of-support problem and variable-support data fusion problem in spatial analysis and modeling.

A New Approach to Image Fusion Based on Cokriging

NASA Technical Reports Server (NTRS)

Memarsadeghi, Nargess; LeMoigne, Jacqueline; Mount, David M.; Morisette, Jeffrey T.

2005-01-01

We consider the image fusion problem involving remotely sensed data. We introduce cokriging as a method to perform fusion. We investigate the advantages of fusing Hyperion with ALI. The evaluation is performed by comparing the classification of the fused data with that of input images and by calculating well-chosen quantitative fusion quality metrics. We consider the Invasive Species Forecasting System (ISFS) project as our fusion application. The fusion of ALI with Hyperion data is studies using PCA and wavelet-based fusion. We then propose utilizing a geostatistical based interpolation method called cokriging as a new approach for image fusion.

Stochastic reservoir simulation for the modeling of uncertainty in coal seam degasification

PubMed Central

Karacan, C. Özgen; Olea, Ricardo A.

2018-01-01

Coal seam degasification improves coal mine safety by reducing the gas content of coal seams and also by generating added value as an energy source. Coal seam reservoir simulation is one of the most effective ways to help with these two main objectives. As in all modeling and simulation studies, how the reservoir is defined and whether observed productions can be predicted are important considerations. Using geostatistical realizations as spatial maps of different coal reservoir properties is a more realistic approach than assuming uniform properties across the field. In fact, this approach can help with simultaneous history matching of multiple wellbores to enhance the confidence in spatial models of different coal properties that are pertinent to degasification. The problem that still remains is the uncertainty in geostatistical simulations originating from the partial sampling of the seam that does not properly reflect the stochastic nature of coal property realizations. Stochastic simulations and using individual realizations, rather than E-type, make evaluation of uncertainty possible. This work is an advancement over Karacan et al. (2014) in the sense of assessing uncertainty that stems from geostatistical maps. In this work, we batched 100 individual realizations of 10 coal properties that were randomly generated to create 100 bundles and used them in 100 separate coal seam reservoir simulations for simultaneous history matching. We then evaluated the history matching errors for each bundle and defined the single set of realizations that would minimize the error for all wells. We further compared the errors with those of E-type and the average realization of the best matches. Unlike in Karacan et al. (2014), which used E-type maps and average of quantile maps, using these 100 bundles created 100 different history match results from separate simulations, and distributions of results for in-place gas quantity, for example, from which uncertainty in coal property realizations could be evaluated. The study helped to determine the realization bundle that consisted of the spatial maps of coal properties, which resulted in minimum error. In addition, it was shown that both E-type and the average of realizations that gave the best match for invidual approximated the same properties resonably. Moreover, the determined realization bundle showed that the study field initially had 151.5 million m3 (cubic meter) of gas and 1.04 million m3 water in the coal, corresponding to Q90 of the entire range of probability for gas and close to Q75 for water. In 2013, in-place fluid amounts decreased to 138.9 million m3 and 0.997 million m3 for gas and water, respectively. PMID:29563647

Planning schistosomiasis control: investigation of alternative sampling strategies for Schistosoma mansoni to target mass drug administration of praziquantel in East Africa.

PubMed

Sturrock, Hugh J W; Gething, Pete W; Ashton, Ruth A; Kolaczinski, Jan H; Kabatereine, Narcis B; Brooker, Simon

2011-09-01

In schistosomiasis control, there is a need to geographically target treatment to populations at high risk of morbidity. This paper evaluates alternative sampling strategies for surveys of Schistosoma mansoni to target mass drug administration in Kenya and Ethiopia. Two main designs are considered: lot quality assurance sampling (LQAS) of children from all schools; and a geostatistical design that samples a subset of schools and uses semi-variogram analysis and spatial interpolation to predict prevalence in the remaining unsurveyed schools. Computerized simulations are used to investigate the performance of sampling strategies in correctly classifying schools according to treatment needs and their cost-effectiveness in identifying high prevalence schools. LQAS performs better than geostatistical sampling in correctly classifying schools, but at a cost with a higher cost per high prevalence school correctly classified. It is suggested that the optimal surveying strategy for S. mansoni needs to take into account the goals of the control programme and the financial and drug resources available.

Assessment of nitrate pollution in the Grand Morin aquifers (France): combined use of geostatistics and physically based modeling.

PubMed

Flipo, Nicolas; Jeannée, Nicolas; Poulin, Michel; Even, Stéphanie; Ledoux, Emmanuel

2007-03-01

The objective of this work is to combine several approaches to better understand nitrate fate in the Grand Morin aquifers (2700 km(2)), part of the Seine basin. cawaqs results from the coupling of the hydrogeological model newsam with the hydrodynamic and biogeochemical model of river ProSe. cawaqs is coupled with the agronomic model Stics in order to simulate nitrate migration in basins. First, kriging provides a satisfactory representation of aquifer nitrate contamination from local observations, to set initial conditions for the physically based model. Then associated confidence intervals, derived from data using geostatistics, are used to validate cawaqs results. Results and evaluation obtained from the combination of these approaches are given (period 1977-1988). Then cawaqs is used to simulate nitrate fate for a 20-year period (1977-1996). The mean nitrate concentrations increase in aquifers is 0.09 mgN L(-1)yr(-1), resulting from an average infiltration flux of 3500 kgN.km(-2)yr(-1).

Introduction to This Special Issue on Geostatistics and Geospatial Techniques in Remote Sensing

NASA Technical Reports Server (NTRS)

Atkinson, Peter; Quattrochi, Dale A.; Goodman, H. Michael (Technical Monitor)

2000-01-01

The germination of this special Computers & Geosciences (C&G) issue began at the Royal Geographical Society (with the Institute of British Geographers) (RGS-IBG) annual meeting in January 1997 held at the University of Exeter, UK. The snow and cold of the English winter were tempered greatly by warm and cordial discussion of how to stimulate and enhance cooperation on geostatistical and geospatial research in remote sensing 'across the big pond' between UK and US researchers. It was decided that one way forward would be to hold parallel sessions in 1998 on geostatistical and geospatial research in remote sensing at appropriate venues in both the UK and the US. Selected papers given at these sessions would be published as special issues of C&G on the UK side and Photogrammetric Engineering and Remote Sensing (PE&RS) on the US side. These issues would highlight the commonality in research on geostatistical and geospatial research in remote sensing on both sides of the Atlantic Ocean. As a consequence, a session on "Geostatistics and Geospatial Techniques for Remote Sensing of Land Surface Processes" was held at the RGS-IBG annual meeting in Guildford, Surrey, UK in January 1998, organized by the Modeling and Advanced Techniques Special Interest Group (MAT SIG) of the Remote Sensing Society (RSS). A similar session was held at the Association of American Geographers (AAG) annual meeting in Boston, Massachusetts in March 1998, sponsored by the AAG's Remote Sensing Specialty Group (RSSG). The 10 papers that make up this issue of C&G, comprise 7 papers from the UK and 3 papers from the LIS. We are both co-editors of each of the journal special issues, with the lead editor of each journal issue being from their respective side of the Atlantic. The special issue of PE&RS (vol. 65) that constitutes the other half of this co-edited journal series was published in early 1999, comprising 6 papers by US authors. We are indebted to the International Association for Mathematical Geology for allowing us to use C&G as a vehicle to convey how geostatistics and geospatial techniques can be used to analyze remote sensing and other types of spatial data. We see this special issue of C&G. and its complementary issue of PE&RS. as a testament to the vitality and interest in the application of geostatistical and geospatial techniques in remote sensing. We also see these special journal issues as the beginning of a fruitful. and hopefully long-term relationship, between American and British geographers and other researchers interested in geostatistical and geospatial techniques applied to remote sensing and other spatial data.

Assessment of spatial distribution of fallout radionuclides through geostatistics concept.

PubMed

Mabit, L; Bernard, C

2007-01-01

After introducing geostatistics concept and its utility in environmental science and especially in Fallout Radionuclide (FRN) spatialisation, a case study for cesium-137 ((137)Cs) redistribution at the field scale using geostatistics is presented. On a Canadian agricultural field, geostatistics coupled with a Geographic Information System (GIS) was used to test three different techniques of interpolation [Ordinary Kriging (OK), Inverse Distance Weighting power one (IDW1) and two (IDW2)] to create a (137)Cs map and to establish a radioisotope budget. Following the optimization of variographic parameters, an experimental semivariogram was developed to determine the spatial dependence of (137)Cs. It was adjusted to a spherical isotropic model with a range of 30 m and a very small nugget effect. This (137)Cs semivariogram showed a good autocorrelation (R(2)=0.91) and was well structured ('nugget-to-sill' ratio of 4%). It also revealed that the sampling strategy was adequate to reveal the spatial correlation of (137)Cs. The spatial redistribution of (137)Cs was estimated by Ordinary Kriging and IDW to produce contour maps. A radioisotope budget was established for the 2.16 ha agricultural field under investigation. It was estimated that around 2 x 10(7)Bq of (137)Cs were missing (around 30% of the total initial fallout) and were exported by physical processes (runoff and erosion processes) from the area under investigation. The cross-validation analysis showed that in the case of spatially structured data, OK is a better interpolation method than IDW1 or IDW2 for the assessment of potential radioactive contamination and/or pollution.

A geostatistics-informed hierarchical sensitivity analysis method for complex groundwater flow and transport modeling: GEOSTATISTICAL SENSITIVITY ANALYSIS

DOE Office of Scientific and Technical Information (OSTI.GOV)

Dai, Heng; Chen, Xingyuan; Ye, Ming

Sensitivity analysis is an important tool for quantifying uncertainty in the outputs of mathematical models, especially for complex systems with a high dimension of spatially correlated parameters. Variance-based global sensitivity analysis has gained popularity because it can quantify the relative contribution of uncertainty from different sources. However, its computational cost increases dramatically with the complexity of the considered model and the dimension of model parameters. In this study we developed a hierarchical sensitivity analysis method that (1) constructs an uncertainty hierarchy by analyzing the input uncertainty sources, and (2) accounts for the spatial correlation among parameters at each level ofmore » the hierarchy using geostatistical tools. The contribution of uncertainty source at each hierarchy level is measured by sensitivity indices calculated using the variance decomposition method. Using this methodology, we identified the most important uncertainty source for a dynamic groundwater flow and solute transport in model at the Department of Energy (DOE) Hanford site. The results indicate that boundary conditions and permeability field contribute the most uncertainty to the simulated head field and tracer plume, respectively. The relative contribution from each source varied spatially and temporally as driven by the dynamic interaction between groundwater and river water at the site. By using a geostatistical approach to reduce the number of realizations needed for the sensitivity analysis, the computational cost of implementing the developed method was reduced to a practically manageable level. The developed sensitivity analysis method is generally applicable to a wide range of hydrologic and environmental problems that deal with high-dimensional spatially-distributed parameters.« less

An application of geostatistics and fractal geometry for reservoir characterization

DOE Office of Scientific and Technical Information (OSTI.GOV)

Aasum, Y.; Kelkar, M.G.; Gupta, S.P.

1991-03-01

This paper presents an application of geostatistics and fractal geometry concepts for 2D characterization of rock properties (k and {phi}) in a dolomitic, layered-cake reservoir. The results indicate that lack of closely spaced data yield effectively random distributions of properties. Further, incorporation of geology reduces uncertainties in fractal interpolation of wellbore properties.

Regression and Geostatistical Techniques: Considerations and Observations from Experiences in NE-FIA

Treesearch

Rachel Riemann; Andrew Lister

2005-01-01

Maps of forest variables improve our understanding of the forest resource by allowing us to view and analyze it spatially. The USDA Forest Service's Northeastern Forest Inventory and Analysis unit (NE-FIA) has used geostatistical techniques, particularly stochastic simulation, to produce maps and spatial data sets of FIA variables. That work underscores the...

Hydrogeologic Unit Flow Characterization Using Transition Probability Geostatistics

DOE Office of Scientific and Technical Information (OSTI.GOV)

Jones, N L; Walker, J R; Carle, S F

2003-11-21

This paper describes a technique for applying the transition probability geostatistics method for stochastic simulation to a MODFLOW model. Transition probability geostatistics has several advantages over traditional indicator kriging methods including a simpler and more intuitive framework for interpreting geologic relationships and the ability to simulate juxtapositional tendencies such as fining upwards sequences. The indicator arrays generated by the transition probability simulation are converted to layer elevation and thickness arrays for use with the new Hydrogeologic Unit Flow (HUF) package in MODFLOW 2000. This makes it possible to preserve complex heterogeneity while using reasonably sized grids. An application of themore » technique involving probabilistic capture zone delineation for the Aberjona Aquifer in Woburn, Ma. is included.« less

Geostatistics and Geographic Information Systems to Study the Spatial Distribution of Grapholita molesta (Busck) (Lepidoptera: Tortricidae) in Peach Fields.

PubMed

Duarte, F; Calvo, M V; Borges, A; Scatoni, I B

2015-08-01

The oriental fruit moth, Grapholita molesta (Busck), is the most serious pest in peach, and several insecticide applications are required to reduce crop damage to acceptable levels. Geostatistics and Geographic Information Systems (GIS) are employed to measure the range of spatial correlation of G. molesta in order to define the optimum sampling distance for performing spatial analysis and to determine the current distribution of the pest in peach orchards of southern Uruguay. From 2007 to 2010, 135 pheromone traps per season were installed and georeferenced in peach orchards distributed over 50,000 ha. Male adult captures were recorded weekly from September to April. Structural analysis of the captures was performed, yielding 14 semivariograms for the accumulated captures analyzed by generation and growing season. Two sets of maps were constructed to describe the pest distribution. Nine significant models were obtained in the 14 evaluated periods. The range estimated for the correlation was from 908 to 6884 m. Three hot spots of high population level and some areas with comparatively low populations were constant over the 3-year period, while there is a greater variation in the size of the population in different generations and years in other areas.

Sequential Bayesian Geostatistical Inversion and Evaluation of Combined Data Worth for Aquifer Characterization at the Hanford 300 Area

NASA Astrophysics Data System (ADS)

Murakami, H.; Chen, X.; Hahn, M. S.; Over, M. W.; Rockhold, M. L.; Vermeul, V.; Hammond, G. E.; Zachara, J. M.; Rubin, Y.

2010-12-01

Subsurface characterization for predicting groundwater flow and contaminant transport requires us to integrate large and diverse datasets in a consistent manner, and quantify the associated uncertainty. In this study, we sequentially assimilated multiple types of datasets for characterizing a three-dimensional heterogeneous hydraulic conductivity field at the Hanford 300 Area. The datasets included constant-rate injection tests, electromagnetic borehole flowmeter tests, lithology profile and tracer tests. We used the method of anchored distributions (MAD), which is a modular-structured Bayesian geostatistical inversion method. MAD has two major advantages over the other inversion methods. First, it can directly infer a joint distribution of parameters, which can be used as an input in stochastic simulations for prediction. In MAD, in addition to typical geostatistical structural parameters, the parameter vector includes multiple point values of the heterogeneous field, called anchors, which capture local trends and reduce uncertainty in the prediction. Second, MAD allows us to integrate the datasets sequentially in a Bayesian framework such that it updates the posterior distribution, as a new dataset is included. The sequential assimilation can decrease computational burden significantly. We applied MAD to assimilate different combinations of the datasets, and then compared the inversion results. For the injection and tracer test assimilation, we calculated temporal moments of pressure build-up and breakthrough curves, respectively, to reduce the data dimension. A massive parallel flow and transport code PFLOTRAN is used for simulating the tracer test. For comparison, we used different metrics based on the breakthrough curves not used in the inversion, such as mean arrival time, peak concentration and early arrival time. This comparison intends to yield the combined data worth, i.e. which combination of the datasets is the most effective for a certain metric, which will be useful for guiding the further characterization effort at the site and also the future characterization projects at the other sites.

Automated connectionist-geostatistical classification as an approach to identify sea ice and land ice types, properties and provinces

NASA Astrophysics Data System (ADS)

Goetz-Weiss, L. R.; Herzfeld, U. C.; Trantow, T.; Hunke, E. C.; Maslanik, J. A.; Crocker, R. I.

2016-12-01

An important problem in model-data comparison is the identification of parameters that can be extracted from observational data as well as used in numerical models, which are typically based on idealized physical processes. Here, we present a suite of approaches to characterization and classification of sea ice and land ice types, properties and provinces based on several types of remote-sensing data. Applications will be given to not only illustrate the approach, but employ it in model evaluation and understanding of physical processes. (1) In a geostatistical characterization, spatial sea-ice properties in the Chukchi and Beaufort Sea and in Elsoon Lagoon are derived from analysis of RADARSAT and ERS-2 SAR data. (2) The analysis is taken further by utilizing multi-parameter feature vectors as inputs for unsupervised and supervised statistical classification, which facilitates classification of different sea-ice types. (3) Characteristic sea-ice parameters, as resultant from the classification, can then be applied in model evaluation, as demonstrated for the ridging scheme of the Los Alamos sea ice model, CICE, using high-resolution altimeter and image data collected from unmanned aircraft over Fram Strait during the Characterization of Arctic Sea Ice Experiment (CASIE). The characteristic parameters chosen in this application are directly related to deformation processes, which also underly the ridging scheme. (4) The method that is capable of the most complex classification tasks is the connectionist-geostatistical classification method. This approach has been developed to identify currently up to 18 different crevasse types in order to map progression of the surge through the complex Bering-Bagley Glacier System, Alaska, in 2011-2014. The analysis utilizes airborne altimeter data and video image data and satellite image data. Results of the crevasse classification are compare to fracture modeling and found to match.

Modeling dolomitized carbonate-ramp reservoirs: A case study of the Seminole San Andres unit. Part 2 -- Seismic modeling, reservoir geostatistics, and reservoir simulation

DOE Office of Scientific and Technical Information (OSTI.GOV)

Wang, F.P.; Dai, J.; Kerans, C.

1998-11-01

In part 1 of this paper, the authors discussed the rock-fabric/petrophysical classes for dolomitized carbonate-ramp rocks, the effects of rock fabric and pore type on petrophysical properties, petrophysical models for analyzing wireline logs, the critical scales for defining geologic framework, and 3-D geologic modeling. Part 2 focuses on geophysical and engineering characterizations, including seismic modeling, reservoir geostatistics, stochastic modeling, and reservoir simulation. Synthetic seismograms of 30 to 200 Hz were generated to study the level of seismic resolution required to capture the high-frequency geologic features in dolomitized carbonate-ramp reservoirs. Outcrop data were collected to investigate effects of sampling interval andmore » scale-up of block size on geostatistical parameters. Semivariogram analysis of outcrop data showed that the sill of log permeability decreases and the correlation length increases with an increase of horizontal block size. Permeability models were generated using conventional linear interpolation, stochastic realizations without stratigraphic constraints, and stochastic realizations with stratigraphic constraints. Simulations of a fine-scale Lawyer Canyon outcrop model were used to study the factors affecting waterflooding performance. Simulation results show that waterflooding performance depends strongly on the geometry and stacking pattern of the rock-fabric units and on the location of production and injection wells.« less

A simple prioritization tool to diagnose impairment of stream temperature for coldwater fishes in the Great Basin

USGS Publications Warehouse

Falke, Jeffrey A.; Dunham, Jason B.; Hockman-Wert, David; Pahl, Randy

2016-01-01

We provide a simple framework for diagnosing the impairment of stream water temperature for coldwater fishes across broad spatial extents based on a weight-of-evidence approach that integrates biological criteria, species distribution models, and geostatistical models of stream temperature. As a test case, we applied our approach to identify stream reaches most likely to be thermally impaired for Lahontan Cutthroat Trout Oncorhynchus clarkii henshawi in the upper Reese River, located in the northern Great Basin, Nevada. We first evaluated the capability of stream thermal regime descriptors to explain variation across 170 sites, and we found that the 7-d moving average of daily maximum stream temperatures (7DADM) provided minimal among-descriptor redundancy and, based on an upper threshold of 20°C, was also a good indicator of acute and chronic thermal stress. Next, we quantified the range of Lahontan Cutthroat Trout within our study area using a geographic distribution model. Finally, we used a geostatistical model to assess spatial variation in 7DADM and predict potential thermal impairment at the stream reach scale. We found that whereas 38% of reaches in our study area exceeded a 7DADM of 20°C and 35% were significantly warmer than predicted, only 17% both exceeded the biological criterion and were significantly warmer than predicted. This filtering allowed us to identify locations where physical and biological impairment were most likely within the network and that would represent the highest management priorities. Although our approach lacks the precision of more comprehensive approaches, it provides a broader context for diagnosing impairment and is a useful means of identifying priorities for more detailed evaluations across broad and heterogeneous stream networks.

Multivariate Geostatistical Analysis of Uncertainty for the Hydrodynamic Model of a Geological Trap for Carbon Dioxide Storage. Case study: Multilayered Geological Structure Vest Valcele, ROMANIA

NASA Astrophysics Data System (ADS)

Scradeanu, D.; Pagnejer, M.

2012-04-01

The purpose of the works is to evaluate the uncertainty of the hydrodynamic model for a multilayered geological structure, a potential trap for carbon dioxide storage. The hydrodynamic model is based on a conceptual model of the multilayered hydrostructure with three components: 1) spatial model; 2) parametric model and 3) energy model. The necessary data to achieve the three components of the conceptual model are obtained from: 240 boreholes explored by geophysical logging and seismic investigation, for the first two components, and an experimental water injection test for the last one. The hydrodinamic model is a finite difference numerical model based on a 3D stratigraphic model with nine stratigraphic units (Badenian and Oligocene) and a 3D multiparameter model (porosity, permeability, hydraulic conductivity, storage coefficient, leakage etc.). The uncertainty of the two 3D models was evaluated using multivariate geostatistical tools: a)cross-semivariogram for structural analysis, especially the study of anisotropy and b)cokriging to reduce estimation variances in a specific situation where is a cross-correlation between a variable and one or more variables that are undersampled. It has been identified important differences between univariate and bivariate anisotropy. The minimised uncertainty of the parametric model (by cokriging) was transferred to hydrodynamic model. The uncertainty distribution of the pressures generated by the water injection test has been additional filtered by the sensitivity of the numerical model. The obtained relative errors of the pressure distribution in the hydrodynamic model are 15-20%. The scientific research was performed in the frame of the European FP7 project "A multiple space and time scale approach for the quantification of deep saline formation for CO2 storage(MUSTANG)".

Geostatistical Methods For Determination of Roughness, Topography, And Changes of Antarctic Ice Streams From SAR And Radar Altimeter Data

NASA Technical Reports Server (NTRS)

Herzfeld, Ute C.

2002-01-01

The central objective of this project has been the development of geostatistical methods fro mapping elevation and ice surface characteristics from satellite radar altimeter (RA) and Syntheitc Aperture Radar (SAR) data. The main results are an Atlas of elevation maps of Antarctica, from GEOSAT RA data and an Atlas from ERS-1 RA data, including a total of about 200 maps with 3 km grid resolution. Maps and digital terrain models are applied to monitor and study changes in Antarctic ice streams and glaciers, including Lambert Glacier/Amery Ice Shelf, Mertz and Ninnis Glaciers, Jutulstraumen Glacier, Fimbul Ice Shelf, Slessor Glacier, Williamson Glacier and others.

The use of sequential indicator simulation to characterize geostatistical uncertainty; Yucca Mountain Site Characterization Project

DOE Office of Scientific and Technical Information (OSTI.GOV)

Hansen, K.M.

1992-10-01

Sequential indicator simulation (SIS) is a geostatistical technique designed to aid in the characterization of uncertainty about the structure or behavior of natural systems. This report discusses a simulation experiment designed to study the quality of uncertainty bounds generated using SIS. The results indicate that, while SIS may produce reasonable uncertainty bounds in many situations, factors like the number and location of available sample data, the quality of variogram models produced by the user, and the characteristics of the geologic region to be modeled, can all have substantial effects on the accuracy and precision of estimated confidence limits. It ismore » recommended that users of SIS conduct validation studies for the technique on their particular regions of interest before accepting the output uncertainty bounds.« less

A Comparison of Traditional, Step-Path, and Geostatistical Techniques in the Stability Analysis of a Large Open Pit

NASA Astrophysics Data System (ADS)

Mayer, J. M.; Stead, D.

2017-04-01

With the increased drive towards deeper and more complex mine designs, geotechnical engineers are often forced to reconsider traditional deterministic design techniques in favour of probabilistic methods. These alternative techniques allow for the direct quantification of uncertainties within a risk and/or decision analysis framework. However, conventional probabilistic practices typically discretize geological materials into discrete, homogeneous domains, with attributes defined by spatially constant random variables, despite the fact that geological media display inherent heterogeneous spatial characteristics. This research directly simulates this phenomenon using a geostatistical approach, known as sequential Gaussian simulation. The method utilizes the variogram which imposes a degree of controlled spatial heterogeneity on the system. Simulations are constrained using data from the Ok Tedi mine site in Papua New Guinea and designed to randomly vary the geological strength index and uniaxial compressive strength using Monte Carlo techniques. Results suggest that conventional probabilistic techniques have a fundamental limitation compared to geostatistical approaches, as they fail to account for the spatial dependencies inherent to geotechnical datasets. This can result in erroneous model predictions, which are overly conservative when compared to the geostatistical results.

Systematic evaluation of sequential geostatistical resampling within MCMC for posterior sampling of near-surface geophysical inverse problems

NASA Astrophysics Data System (ADS)

Ruggeri, Paolo; Irving, James; Holliger, Klaus

2015-08-01

We critically examine the performance of sequential geostatistical resampling (SGR) as a model proposal mechanism for Bayesian Markov-chain-Monte-Carlo (MCMC) solutions to near-surface geophysical inverse problems. Focusing on a series of simple yet realistic synthetic crosshole georadar tomographic examples characterized by different numbers of data, levels of data error and degrees of model parameter spatial correlation, we investigate the efficiency of three different resampling strategies with regard to their ability to generate statistically independent realizations from the Bayesian posterior distribution. Quite importantly, our results show that, no matter what resampling strategy is employed, many of the examined test cases require an unreasonably high number of forward model runs to produce independent posterior samples, meaning that the SGR approach as currently implemented will not be computationally feasible for a wide range of problems. Although use of a novel gradual-deformation-based proposal method can help to alleviate these issues, it does not offer a full solution. Further, we find that the nature of the SGR is found to strongly influence MCMC performance; however no clear rule exists as to what set of inversion parameters and/or overall proposal acceptance rate will allow for the most efficient implementation. We conclude that although the SGR methodology is highly attractive as it allows for the consideration of complex geostatistical priors as well as conditioning to hard and soft data, further developments are necessary in the context of novel or hybrid MCMC approaches for it to be considered generally suitable for near-surface geophysical inversions.

A GIS-based approach for the screening assessment of noise and vibration impacts from transit projects.

PubMed

Hamed, Maged; Effat, Waleed

2007-08-01

Urban transportation projects are essential in increasing the efficiency of moving people and goods within a city, and between cities. Environmental impacts from such projects must be evaluated and mitigated, as applicable. Spatial modeling is a valuable tool for quantifying the potential level of environmental consequences within the context of an environmental impact assessment (EIA) study. This paper presents a GIS-based tool for the assessment of airborne-noise and ground-borne vibration from public transit systems, and its application to an actual project. The tool is based on the US Federal Transit Administration's (FTA) approach, and incorporates spatial information, satellite imaging, geostatistical modeling, and software programming. The tool is applied on a case study of initial environmental evaluation of a light rail transit project in an urban city in the Middle East, to evaluate alternative layouts. The tool readily allowed the alternative evaluation and the results were used as input to a multi-criteria analytic framework.

Downscaling remotely sensed imagery using area-to-point cokriging and multiple-point geostatistical simulation

NASA Astrophysics Data System (ADS)

Tang, Yunwei; Atkinson, Peter M.; Zhang, Jingxiong

2015-03-01

A cross-scale data integration method was developed and tested based on the theory of geostatistics and multiple-point geostatistics (MPG). The goal was to downscale remotely sensed images while retaining spatial structure by integrating images at different spatial resolutions. During the process of downscaling, a rich spatial correlation model in the form of a training image was incorporated to facilitate reproduction of similar local patterns in the simulated images. Area-to-point cokriging (ATPCK) was used as locally varying mean (LVM) (i.e., soft data) to deal with the change of support problem (COSP) for cross-scale integration, which MPG cannot achieve alone. Several pairs of spectral bands of remotely sensed images were tested for integration within different cross-scale case studies. The experiment shows that MPG can restore the spatial structure of the image at a fine spatial resolution given the training image and conditioning data. The super-resolution image can be predicted using the proposed method, which cannot be realised using most data integration methods. The results show that ATPCK-MPG approach can achieve greater accuracy than methods which do not account for the change of support issue.

Robust spatialization of soil water content at the scale of an agricultural field using geophysical and geostatistical methods

NASA Astrophysics Data System (ADS)

Henine, Hocine; Tournebize, Julien; Laurent, Gourdol; Christophe, Hissler; Cournede, Paul-Henry; Clement, Remi

2017-04-01

Research on the Critical Zone (CZ) is a prerequisite for undertaking issues related to ecosystemic services that human societies rely on (nutrient cycles, water supply and quality). However, while the upper part of CZ (vegetation, soil, surface water) is readily accessible, knowledge of the subsurface remains limited, due to the point-scale character of conventional direct observations. While the potential for geophysical methods to overcome this limitation is recognized, the translation of the geophysical information into physical properties or states of interest remains a challenge (e.g. the translation of soil electrical resistivity into soil water content). In this study, we propose a geostatistical framework using the Bayesian Maximum Entropy (BME) approach to assimilate geophysical and point-scale data. We especially focus on the prediction of the spatial distribution of soil water content using (1) TDR point-scale measurements of soil water content, which are considered as accurate data, and (2) soil water content data derived from electrical resistivity measurements, which are uncertain data but spatially dense. We used a synthetic dataset obtained with a vertical 2D domain to evaluate the performance of this geostatistical approach. Spatio-temporal simulations of soil water content were carried out using Hydrus-software for different scenarios: homogeneous or heterogeneous hydraulic conductivity distribution, and continuous or punctual infiltration pattern. From the simulations of soil water content, conceptual soil resistivity models were built using a forward modeling approach and point sampling of water content values, vertically ranged, were done. These two datasets are similar to field measurements of soil electrical resistivity (using electrical resistivity tomography, ERT) and soil water content (using TDR probes) obtained at the Boissy-le-Chatel site, in Orgeval catchment (East of Paris, France). We then integrated them into a specialization framework to predict the soil water content distribution and the results were compared to initial simulations (Hydrus results). We obtained more reliable water content specialization models when using the BME method. The presented approach integrates ERT and TDR measurements, and results demonstrate that its use significantly improves the spatial distribution of water content estimations. The approach will be applied to the experimental dataset collected at the Boissy le Châtel site where ERT data were collected daily during one hydrological year, using Syscal pro 48 electrodes (with a financial support of Equipex-Critex) and 10 TDR probes were used to monitor water content variation. Hourly hydrological survey (tile drainage discharge, precipitation, evapotranspiration variables and water table depth) were conducted at the same site. Data analysis and the application of geostatistical framework on the experimental dataset of 2015-2016 show satisfactory results and are reliable with the hydrological behavior of the study site.

Evaluating shrub-associated spatial patterns of soil properties in a shrub-steppe ecosystem using multiple-variable geostatistics

DOE Office of Scientific and Technical Information (OSTI.GOV)

Halvorson, J.J.; Smith, J.L.; Bolton, H. Jr.

1995-09-01

Geostatistics are often calculated for a single variable at a time, even though many natural phenomena are functions of several variables. The objective of this work was to demonstrate a nonparametric approach for assessing the spatial characteristics of multiple-variable phenomena. Specifically, we analyzed the spatial characteristics of resource islands in the soil under big sagebrush (Artemisia tridentala Nutt.), a dominant shrub in the intermountain western USA. For our example, we defined resource islands as a function of six soil variables representing concentrations of soil resources, populations of microorganisms, and soil microbial physiological variables. By collectively evaluating the indicator transformations ofmore » these individual variables, we created a new data set, termed a multiple-variable indicator transform or MVIT. Alternate MVITs were obtained by varying the selection criteria. Each MVIT was analyzed with variography to characterize spatial continuity, and with indicator kriging to predict the combined probability of their occurrence at unsampled locations in the landscape. Simple graphical analysis and variography demonstrated spatial dependence for all individual soil variables. Maps derived from ordinary kriging of MVITs suggested that the combined probabilities for encountering zones of above-median resources were greatest near big sagebrush. 51 refs., 5 figs., 1 tab.« less

Monte Carlo Analysis of Reservoir Models Using Seismic Data and Geostatistical Models

NASA Astrophysics Data System (ADS)

Zunino, A.; Mosegaard, K.; Lange, K.; Melnikova, Y.; Hansen, T. M.

2013-12-01

We present a study on the analysis of petroleum reservoir models consistent with seismic data and geostatistical constraints performed on a synthetic reservoir model. Our aim is to invert directly for structure and rock bulk properties of the target reservoir zone. To infer the rock facies, porosity and oil saturation seismology alone is not sufficient but a rock physics model must be taken into account, which links the unknown properties to the elastic parameters. We then combine a rock physics model with a simple convolutional approach for seismic waves to invert the "measured" seismograms. To solve this inverse problem, we employ a Markov chain Monte Carlo (MCMC) method, because it offers the possibility to handle non-linearity, complex and multi-step forward models and provides realistic estimates of uncertainties. However, for large data sets the MCMC method may be impractical because of a very high computational demand. To face this challenge one strategy is to feed the algorithm with realistic models, hence relying on proper prior information. To address this problem, we utilize an algorithm drawn from geostatistics to generate geologically plausible models which represent samples of the prior distribution. The geostatistical algorithm learns the multiple-point statistics from prototype models (in the form of training images), then generates thousands of different models which are accepted or rejected by a Metropolis sampler. To further reduce the computation time we parallelize the software and run it on multi-core machines. The solution of the inverse problem is then represented by a collection of reservoir models in terms of facies, porosity and oil saturation, which constitute samples of the posterior distribution. We are finally able to produce probability maps of the properties we are interested in by performing statistical analysis on the collection of solutions.

A geostatistical approach to estimate mining efficiency indicators with flexible meshes

NASA Astrophysics Data System (ADS)

Freixas, Genis; Garriga, David; Fernàndez-Garcia, Daniel; Sanchez-Vila, Xavier

2014-05-01

Geostatistics is a branch of statistics developed originally to predict probability distributions of ore grades for mining operations by considering the attributes of a geological formation at unknown locations as a set of correlated random variables. Mining exploitations typically aim to maintain acceptable mineral laws to produce commercial products based upon demand. In this context, we present a new geostatistical methodology to estimate strategic efficiency maps that incorporate hydraulic test data, the evolution of concentrations with time obtained from chemical analysis (packer tests and production wells) as well as hydraulic head variations. The methodology is applied to a salt basin in South America. The exploitation is based on the extraction of brines through vertical and horizontal wells. Thereafter, brines are precipitated in evaporation ponds to obtain target potassium and magnesium salts of economic interest. Lithium carbonate is obtained as a byproduct of the production of potassium chloride. Aside from providing an assemble of traditional geostatistical methods, the strength of this study falls with the new methodology developed, which focus on finding the best sites to exploit the brines while maintaining efficiency criteria. Thus, some strategic indicator efficiency maps have been developed under the specific criteria imposed by exploitation standards to incorporate new extraction wells in new areas that would allow maintain or improve production. Results show that the uncertainty quantification of the efficiency plays a dominant role and that the use flexible meshes, which properly describe the curvilinear features associated with vertical stratification, provides a more consistent estimation of the geological processes. Moreover, we demonstrate that the vertical correlation structure at the given salt basin is essentially linked to variations in the formation thickness, which calls for flexible meshes and non-stationarity stochastic processes.

Improved Assimilation of Streamflow and Satellite Soil Moisture with the Evolutionary Particle Filter and Geostatistical Modeling

NASA Astrophysics Data System (ADS)

Yan, Hongxiang; Moradkhani, Hamid; Abbaszadeh, Peyman

2017-04-01

Assimilation of satellite soil moisture and streamflow data into hydrologic models using has received increasing attention over the past few years. Currently, these observations are increasingly used to improve the model streamflow and soil moisture predictions. However, the performance of this land data assimilation (DA) system still suffers from two limitations: 1) satellite data scarcity and quality; and 2) particle weight degeneration. In order to overcome these two limitations, we propose two possible solutions in this study. First, the general Gaussian geostatistical approach is proposed to overcome the limitation in the space/time resolution of satellite soil moisture products thus improving their accuracy at uncovered/biased grid cells. Secondly, an evolutionary PF approach based on Genetic Algorithm (GA) and Markov Chain Monte Carlo (MCMC), the so-called EPF-MCMC, is developed to further reduce weight degeneration and improve the robustness of the land DA system. This study provides a detailed analysis of the joint and separate assimilation of streamflow and satellite soil moisture into a distributed Sacramento Soil Moisture Accounting (SAC-SMA) model, with the use of recently developed EPF-MCMC and the general Gaussian geostatistical approach. Performance is assessed over several basins in the USA selected from Model Parameter Estimation Experiment (MOPEX) and located in different climate regions. The results indicate that: 1) the general Gaussian approach can predict the soil moisture at uncovered grid cells within the expected satellite data quality threshold; 2) assimilation of satellite soil moisture inferred from the general Gaussian model can significantly improve the soil moisture predictions; and 3) in terms of both deterministic and probabilistic measures, the EPF-MCMC can achieve better streamflow predictions. These results recommend that the geostatistical model is a helpful tool to aid the remote sensing technique and the EPF-MCMC is a reliable and effective DA approach in hydrologic applications.

Can Geostatistical Models Represent Nature's Variability? An Analysis Using Flume Experiments

NASA Astrophysics Data System (ADS)

Scheidt, C.; Fernandes, A. M.; Paola, C.; Caers, J.

2015-12-01

The lack of understanding in the Earth's geological and physical processes governing sediment deposition render subsurface modeling subject to large uncertainty. Geostatistics is often used to model uncertainty because of its capability to stochastically generate spatially varying realizations of the subsurface. These methods can generate a range of realizations of a given pattern - but how representative are these of the full natural variability? And how can we identify the minimum set of images that represent this natural variability? Here we use this minimum set to define the geostatistical prior model: a set of training images that represent the range of patterns generated by autogenic variability in the sedimentary environment under study. The proper definition of the prior model is essential in capturing the variability of the depositional patterns. This work starts with a set of overhead images from an experimental basin that showed ongoing autogenic variability. We use the images to analyze the essential characteristics of this suite of patterns. In particular, our goal is to define a prior model (a minimal set of selected training images) such that geostatistical algorithms, when applied to this set, can reproduce the full measured variability. A necessary prerequisite is to define a measure of variability. In this study, we measure variability using a dissimilarity distance between the images. The distance indicates whether two snapshots contain similar depositional patterns. To reproduce the variability in the images, we apply an MPS algorithm to the set of selected snapshots of the sedimentary basin that serve as training images. The training images are chosen from among the initial set by using the distance measure to ensure that only dissimilar images are chosen. Preliminary investigations show that MPS can reproduce fairly accurately the natural variability of the experimental depositional system. Furthermore, the selected training images provide process information. They fall into three basic patterns: a channelized end member, a sheet flow end member, and one intermediate case. These represent the continuum between autogenic bypass or erosion, and net deposition.

Transition probability-based stochastic geological modeling using airborne geophysical data and borehole data

NASA Astrophysics Data System (ADS)

He, Xin; Koch, Julian; Sonnenborg, Torben O.; Jørgensen, Flemming; Schamper, Cyril; Christian Refsgaard, Jens

2014-04-01

Geological heterogeneity is a very important factor to consider when developing geological models for hydrological purposes. Using statistically based stochastic geological simulations, the spatial heterogeneity in such models can be accounted for. However, various types of uncertainties are associated with both the geostatistical method and the observation data. In the present study, TProGS is used as the geostatistical modeling tool to simulate structural heterogeneity for glacial deposits in a head water catchment in Denmark. The focus is on how the observation data uncertainty can be incorporated in the stochastic simulation process. The study uses two types of observation data: borehole data and airborne geophysical data. It is commonly acknowledged that the density of the borehole data is usually too sparse to characterize the horizontal heterogeneity. The use of geophysical data gives an unprecedented opportunity to obtain high-resolution information and thus to identify geostatistical properties more accurately especially in the horizontal direction. However, since such data are not a direct measurement of the lithology, larger uncertainty of point estimates can be expected as compared to the use of borehole data. We have proposed a histogram probability matching method in order to link the information on resistivity to hydrofacies, while considering the data uncertainty at the same time. Transition probabilities and Markov Chain models are established using the transformed geophysical data. It is shown that such transformation is in fact practical; however, the cutoff value for dividing the resistivity data into facies is difficult to determine. The simulated geological realizations indicate significant differences of spatial structure depending on the type of conditioning data selected. It is to our knowledge the first time that grid-to-grid airborne geophysical data including the data uncertainty are used in conditional geostatistical simulations in TProGS. Therefore, it provides valuable insights regarding the advantages and challenges of using such comprehensive data.

Latin hypercube sampling and geostatistical modeling of spatial uncertainty in a spatially explicit forest landscape model simulation

Treesearch

Chonggang Xu; Hong S. He; Yuanman Hu; Yu Chang; Xiuzhen Li; Rencang Bu

2005-01-01

Geostatistical stochastic simulation is always combined with Monte Carlo method to quantify the uncertainty in spatial model simulations. However, due to the relatively long running time of spatially explicit forest models as a result of their complexity, it is always infeasible to generate hundreds or thousands of Monte Carlo simulations. Thus, it is of great...

Demonstration and Validation of the Geostatistical Temporal-Spatial Algorithm (GTS) for Optimization of Long-Term Monitoring (LTM) of Groundwater at Military and Government Sites

DTIC Science & Technology

2010-08-01

Long - Term Monitoring (LTM) of Groundwater at Military and...Geostatistical Temporal-Spatial Algorithm (GTS) for Optimization of Long - Term Monitoring (LTM) of Groundwater at Military and Government Sites 5a. CONTRACT NUMBER...Council LTM long - term monitoring LTMO long - term monitoring optimization LWQR locally weighted quadratic regression LZ Lower Zone MCL

Spatial correlation of shear-wave velocity in the San Francisco Bay Area sediments

USGS Publications Warehouse

Thompson, E.M.; Baise, L.G.; Kayen, R.E.

2007-01-01

Ground motions recorded within sedimentary basins are variable over short distances. One important cause of the variability is that local soil properties are variable at all scales. Regional hazard maps developed for predicting site effects are generally derived from maps of surficial geology; however, recent studies have shown that mapped geologic units do not correlate well with the average shear-wave velocity of the upper 30 m, Vs(30). We model the horizontal variability of near-surface soil shear-wave velocity in the San Francisco Bay Area to estimate values in unsampled locations in order to account for site effects in a continuous manner. Previous geostatistical studies of soil properties have shown horizontal correlations at the scale of meters to tens of meters while the vertical correlations are on the order of centimeters. In this paper we analyze shear-wave velocity data over regional distances and find that surface shear-wave velocity is correlated at horizontal distances up to 4 km based on data from seismic cone penetration tests and the spectral analysis of surface waves. We propose a method to map site effects by using geostatistical methods based on the shear-wave velocity correlation structure within a sedimentary basin. If used in conjunction with densely spaced shear-wave velocity profiles in regions of high seismic risk, geostatistical methods can produce reliable continuous maps of site effects. ?? 2006 Elsevier Ltd. All rights reserved.

Indoor radon variations in central Iran and its geostatistical map

NASA Astrophysics Data System (ADS)

Hadad, Kamal; Mokhtari, Javad

2015-02-01

We present the results of 2 year indoor radon survey in 10 cities of Yazd province in Central Iran (covering an area of 80,000 km2). We used passive diffusive samplers with LATEX polycarbonate films as Solid State Nuclear Track Detector (SSNTD). This study carried out in central Iran where there are major minerals and uranium mines. Our results indicate that despite few extraordinary high concentrations, average annual concentrations of indoor radon are within ICRP guidelines. When geostatistical spatial distribution of radon mapped onto geographical features of the province it was observed that risk of high radon concentration increases near the Saqand, Bafq, Harat and Abarkooh cities, this depended on the elevation and vicinity of the ores and mines.

Geostatistical Prediction of Microbial Water Quality Throughout a Stream Network Using Meteorology, Land Cover, and Spatiotemporal Autocorrelation.

PubMed

Holcomb, David A; Messier, Kyle P; Serre, Marc L; Rowny, Jakob G; Stewart, Jill R

2018-06-25

Predictive modeling is promising as an inexpensive tool to assess water quality. We developed geostatistical predictive models of microbial water quality that empirically modeled spatiotemporal autocorrelation in measured fecal coliform (FC) bacteria concentrations to improve prediction. We compared five geostatistical models featuring different autocorrelation structures, fit to 676 observations from 19 locations in North Carolina's Jordan Lake watershed using meteorological and land cover predictor variables. Though stream distance metrics (with and without flow-weighting) failed to improve prediction over the Euclidean distance metric, incorporating temporal autocorrelation substantially improved prediction over the space-only models. We predicted FC throughout the stream network daily for one year, designating locations "impaired", "unimpaired", or "unassessed" if the probability of exceeding the state standard was ≥90%, ≤10%, or >10% but <90%, respectively. We could assign impairment status to more of the stream network on days any FC were measured, suggesting frequent sample-based monitoring remains necessary, though implementing spatiotemporal predictive models may reduce the number of concurrent sampling locations required to adequately assess water quality. Together, these results suggest that prioritizing sampling at different times and conditions using geographically sparse monitoring networks is adequate to build robust and informative geostatistical models of water quality impairment.

Prediction of erodibility in Oxisols using iron oxides, soil color and diffuse reflectance spectroscopy

NASA Astrophysics Data System (ADS)

Arantes Camargo, Livia; Marques, José, Jr.

2015-04-01

The prediction of erodibility using indirect methods such as diffuse reflectance spectroscopy could facilitate the characterization of the spatial variability in large areas and optimize implementation of conservation practices. The aim of this study was to evaluate the prediction of interrill erodibility (Ki) and rill erodibility (Kr) by means of iron oxides content and soil color using multiple linear regression and diffuse reflectance spectroscopy (DRS) using regression analysis by least squares partial (PLSR). The soils were collected from three geomorphic surfaces and analyzed for chemical, physical and mineralogical properties, plus scanned in the spectral range from the visible and infrared. Maps of spatial distribution of Ki and Kr were built with the values calculated by the calibrated models that obtained the best accuracy using geostatistics. Interrill-rill erodibility presented negative correlation with iron extracted by dithionite-citrate-bicarbonate, hematite, and chroma, confirming the influence of iron oxides in soil structural stability. Hematite and hue were the attributes that most contributed in calibration models by multiple linear regression for the prediction of Ki (R2 = 0.55) and Kr (R2 = 0.53). The diffuse reflectance spectroscopy via PLSR allowed to predict Interrill-rill erodibility with high accuracy (R2adj = 0.76, 0.81 respectively and RPD> 2.0) in the range of the visible spectrum (380-800 nm) and the characterization of the spatial variability of these attributes by geostatistics.

On the value of incorporating spatial statistics in large-scale geophysical inversions: the SABRe case

NASA Astrophysics Data System (ADS)

Kokkinaki, A.; Sleep, B. E.; Chambers, J. E.; Cirpka, O. A.; Nowak, W.

2010-12-01

Electrical Resistance Tomography (ERT) is a popular method for investigating subsurface heterogeneity. The method relies on measuring electrical potential differences and obtaining, through inverse modeling, the underlying electrical conductivity field, which can be related to hydraulic conductivities. The quality of site characterization strongly depends on the utilized inversion technique. Standard ERT inversion methods, though highly computationally efficient, do not consider spatial correlation of soil properties; as a result, they often underestimate the spatial variability observed in earth materials, thereby producing unrealistic subsurface models. Also, these methods do not quantify the uncertainty of the estimated properties, thus limiting their use in subsequent investigations. Geostatistical inverse methods can be used to overcome both these limitations; however, they are computationally expensive, which has hindered their wide use in practice. In this work, we compare a standard Gauss-Newton smoothness constrained least squares inversion method against the quasi-linear geostatistical approach using the three-dimensional ERT dataset of the SABRe (Source Area Bioremediation) project. The two methods are evaluated for their ability to: a) produce physically realistic electrical conductivity fields that agree with the wide range of data available for the SABRe site while being computationally efficient, and b) provide information on the spatial statistics of other parameters of interest, such as hydraulic conductivity. To explore the trade-off between inversion quality and computational efficiency, we also employ a 2.5-D forward model with corrections for boundary conditions and source singularities. The 2.5-D model accelerates the 3-D geostatistical inversion method. New adjoint equations are developed for the 2.5-D forward model for the efficient calculation of sensitivities. Our work shows that spatial statistics can be incorporated in large-scale ERT inversions to improve the inversion results without making them computationally prohibitive.

Spatial analysis of the distribution of Spodoptera frugiperda (J.E. Smith) (Lepidoptera: Noctuidae) and losses in maize crop productivity using geostatistics.

PubMed

Farias, Paulo R S; Barbosa, José C; Busoli, Antonio C; Overal, William L; Miranda, Vicente S; Ribeiro, Susane M

2008-01-01

The fall armyworm, Spodoptera frugiperda (J.E. Smith), is one of the chief pests of maize in the Americas. The study of its spatial distribution is fundamental for designing correct control strategies, improving sampling methods, determining actual and potential crop losses, and adopting precise agricultural techniques. In São Paulo state, Brazil, a maize field was sampled at weekly intervals, from germination through harvest, for caterpillar densities, using quadrates. In each of 200 quadrates, 10 plants were sampled per week. Harvest weights were obtained in the field for each quadrate, and ear diameters and lengths were also sampled (15 ears per quadrate) and used to estimate potential productivity of the quadrate. Geostatistical analyses of caterpillar densities showed greatest ranges for small caterpillars when semivariograms were adjusted for a spherical model that showed greatest fit. As the caterpillars developed in the field, their spatial distribution became increasingly random, as shown by a model adjusted to a straight line, indicating a lack of spatial dependence among samples. Harvest weight and ear length followed the spherical model, indicating the existence of spatial variability of the production parameters in the maize field. Geostatistics shows promise for the application of precise methods in the integrated control of pests.

Using rank-order geostatistics for spatial interpolation of highly skewed data in a heavy-metal contaminated site.

PubMed

Juang, K W; Lee, D Y; Ellsworth, T R

2001-01-01

The spatial distribution of a pollutant in contaminated soils is usually highly skewed. As a result, the sample variogram often differs considerably from its regional counterpart and the geostatistical interpolation is hindered. In this study, rank-order geostatistics with standardized rank transformation was used for the spatial interpolation of pollutants with a highly skewed distribution in contaminated soils when commonly used nonlinear methods, such as logarithmic and normal-scored transformations, are not suitable. A real data set of soil Cd concentrations with great variation and high skewness in a contaminated site of Taiwan was used for illustration. The spatial dependence of ranks transformed from Cd concentrations was identified and kriging estimation was readily performed in the standardized-rank space. The estimated standardized rank was back-transformed into the concentration space using the middle point model within a standardized-rank interval of the empirical distribution function (EDF). The spatial distribution of Cd concentrations was then obtained. The probability of Cd concentration being higher than a given cutoff value also can be estimated by using the estimated distribution of standardized ranks. The contour maps of Cd concentrations and the probabilities of Cd concentrations being higher than the cutoff value can be simultaneously used for delineation of hazardous areas of contaminated soils.

Comparing the performance of geostatistical models with additional information from covariates for sewage plume characterization.

PubMed

Del Monego, Maurici; Ribeiro, Paulo Justiniano; Ramos, Patrícia

2015-04-01

In this work, kriging with covariates is used to model and map the spatial distribution of salinity measurements gathered by an autonomous underwater vehicle in a sea outfall monitoring campaign aiming to distinguish the effluent plume from the receiving waters and characterize its spatial variability in the vicinity of the discharge. Four different geostatistical linear models for salinity were assumed, where the distance to diffuser, the west-east positioning, and the south-north positioning were used as covariates. Sample variograms were fitted by the Matèrn models using weighted least squares and maximum likelihood estimation methods as a way to detect eventual discrepancies. Typically, the maximum likelihood method estimated very low ranges which have limited the kriging process. So, at least for these data sets, weighted least squares showed to be the most appropriate estimation method for variogram fitting. The kriged maps show clearly the spatial variation of salinity, and it is possible to identify the effluent plume in the area studied. The results obtained show some guidelines for sewage monitoring if a geostatistical analysis of the data is in mind. It is important to treat properly the existence of anomalous values and to adopt a sampling strategy that includes transects parallel and perpendicular to the effluent dispersion.

Nonpoint Source Solute Transport Normal to Aquifer Bedding in Heterogeneous, Markov Chain Random Fields

NASA Astrophysics Data System (ADS)

Zhang, H.; Harter, T.; Sivakumar, B.

2005-12-01

Facies-based geostatistical models have become important tools for the stochastic analysis of flow and transport processes in heterogeneous aquifers. However, little is known about the dependency of these processes on the parameters of facies- based geostatistical models. This study examines the nonpoint source solute transport normal to the major bedding plane in the presence of interconnected high conductivity (coarse- textured) facies in the aquifer medium and the dependence of the transport behavior upon the parameters of the constitutive facies model. A facies-based Markov chain geostatistical model is used to quantify the spatial variability of the aquifer system hydrostratigraphy. It is integrated with a groundwater flow model and a random walk particle transport model to estimate the solute travel time probability distribution functions (pdfs) for solute flux from the water table to the bottom boundary (production horizon) of the aquifer. The cases examined include, two-, three-, and four-facies models with horizontal to vertical facies mean length anisotropy ratios, ek, from 25:1 to 300:1, and with a wide range of facies volume proportions (e.g, from 5% to 95% coarse textured facies). Predictions of travel time pdfs are found to be significantly affected by the number of hydrostratigraphic facies identified in the aquifer, the proportions of coarse-textured sediments, the mean length of the facies (particularly the ratio of length to thickness of coarse materials), and - to a lesser degree - the juxtapositional preference among the hydrostratigraphic facies. In transport normal to the sedimentary bedding plane, travel time pdfs are not log- normally distributed as is often assumed. Also, macrodispersive behavior (variance of the travel time pdf) was found to not be a unique function of the conductivity variance. The skewness of the travel time pdf varied from negatively skewed to strongly positively skewed within the parameter range examined. We also show that the Markov chain approach may give significantly different travel time pdfs when compared to the more commonly used Gaussian random field approach even though the first and second order moments in the geostatistical distribution of the lnK field are identical. The choice of the appropriate geostatistical model is therefore critical in the assessment of nonpoint source transport.

Assessing the resolution-dependent utility of tomograms for geostatistics

USGS Publications Warehouse

Day-Lewis, F. D.; Lane, J.W.

2004-01-01

Geophysical tomograms are used increasingly as auxiliary data for geostatistical modeling of aquifer and reservoir properties. The correlation between tomographic estimates and hydrogeologic properties is commonly based on laboratory measurements, co-located measurements at boreholes, or petrophysical models. The inferred correlation is assumed uniform throughout the interwell region; however, tomographic resolution varies spatially due to acquisition geometry, regularization, data error, and the physics underlying the geophysical measurements. Blurring and inversion artifacts are expected in regions traversed by few or only low-angle raypaths. In the context of radar traveltime tomography, we derive analytical models for (1) the variance of tomographic estimates, (2) the spatially variable correlation with a hydrologic parameter of interest, and (3) the spatial covariance of tomographic estimates. Synthetic examples demonstrate that tomograms of qualitative value may have limited utility for geostatistics; moreover, the imprint of regularization may preclude inference of meaningful spatial statistics from tomograms.

Site characterization methodology for aquifers in support of bioreclamation activities. Volume 2: Borehole flowmeter technique, tracer tests, geostatistics and geology. Final report, August 1987-September 1989

DOE Office of Scientific and Technical Information (OSTI.GOV)

Young, S.C.

1993-08-01

This report discusses a field demonstration of a methodology for characterizing an aquifer's geohydrology in the detail required to design an optimum network of wells and/or infiltration galleries for bioreclamation systems. The project work was conducted on a 1-hectare test site at Columbus AFB, Mississippi. The technical report is divided into two volumes. Volume I describes the test site and the well network, the assumptions, and the application of equations that define groundwater flow to a well, the results of three large-scale aquifer tests, and the results of 160 single-pump tests. Volume II describes the bore hole flowmeter tests, themore » tracer tests, the geological investigations, the geostatistical analysis and the guidelines for using groundwater models to design bioreclamation systems. Site characterization, Hydraulic conductivity, Groundwater flow, Geostatistics, Geohydrology, Monitoring wells.« less

Geostatistical simulations for radon indoor with a nested model including the housing factor.

PubMed

Cafaro, C; Giovani, C; Garavaglia, M

2016-01-01

The radon prone areas definition is matter of many researches in radioecology, since radon is considered a leading cause of lung tumours, therefore the authorities ask for support to develop an appropriate sanitary prevention strategy. In this paper, we use geostatistical tools to elaborate a definition accounting for some of the available information about the dwellings. Co-kriging is the proper interpolator used in geostatistics to refine the predictions by using external covariates. In advance, co-kriging is not guaranteed to improve significantly the results obtained by applying the common lognormal kriging. Here, instead, such multivariate approach leads to reduce the cross-validation residual variance to an extent which is deemed as satisfying. Furthermore, with the application of Monte Carlo simulations, the paradigm provides a more conservative radon prone areas definition than the one previously made by lognormal kriging. Copyright © 2015 Elsevier Ltd. All rights reserved.

Geostatistical borehole image-based mapping of karst-carbonate aquifer pores

USGS Publications Warehouse

Michael Sukop,; Cunningham, Kevin J.

2016-01-01

Quantification of the character and spatial distribution of porosity in carbonate aquifers is important as input into computer models used in the calculation of intrinsic permeability and for next-generation, high-resolution groundwater flow simulations. Digital, optical, borehole-wall image data from three closely spaced boreholes in the karst-carbonate Biscayne aquifer in southeastern Florida are used in geostatistical experiments to assess the capabilities of various methods to create realistic two-dimensional models of vuggy megaporosity and matrix-porosity distribution in the limestone that composes the aquifer. When the borehole image data alone were used as the model training image, multiple-point geostatistics failed to detect the known spatial autocorrelation of vuggy megaporosity and matrix porosity among the three boreholes, which were only 10 m apart. Variogram analysis and subsequent Gaussian simulation produced results that showed a realistic conceptualization of horizontal continuity of strata dominated by vuggy megaporosity and matrix porosity among the three boreholes.

Three-dimensional geostatistical inversion of flowmeter and pumping test data.

PubMed

Li, Wei; Englert, Andreas; Cirpka, Olaf A; Vereecken, Harry

2008-01-01

We jointly invert field data of flowmeter and multiple pumping tests in fully screened wells to estimate hydraulic conductivity using a geostatistical method. We use the steady-state drawdowns of pumping tests and the discharge profiles of flowmeter tests as our data in the inference. The discharge profiles need not be converted to absolute hydraulic conductivities. Consequently, we do not need measurements of depth-averaged hydraulic conductivity at well locations. The flowmeter profiles contain information about relative vertical distributions of hydraulic conductivity, while drawdown measurements of pumping tests provide information about horizontal fluctuation of the depth-averaged hydraulic conductivity. We apply the method to data obtained at the Krauthausen test site of the Forschungszentrum Jülich, Germany. The resulting estimate of our joint three-dimensional (3D) geostatistical inversion shows an improved 3D structure in comparison to the inversion of pumping test data only.

Comparison of different wind data interpolation methods for a region with complex terrain in Central Asia

NASA Astrophysics Data System (ADS)

Reinhardt, Katja; Samimi, Cyrus

2018-01-01

While climatological data of high spatial resolution are largely available in most developed countries, the network of climatological stations in many other regions of the world still constitutes large gaps. Especially for those regions, interpolation methods are important tools to fill these gaps and to improve the data base indispensible for climatological research. Over the last years, new hybrid methods of machine learning and geostatistics have been developed which provide innovative prospects in spatial predictive modelling. This study will focus on evaluating the performance of 12 different interpolation methods for the wind components \\overrightarrow{u} and \\overrightarrow{v} in a mountainous region of Central Asia. Thereby, a special focus will be on applying new hybrid methods on spatial interpolation of wind data. This study is the first evaluating and comparing the performance of several of these hybrid methods. The overall aim of this study is to determine whether an optimal interpolation method exists, which can equally be applied for all pressure levels, or whether different interpolation methods have to be used for the different pressure levels. Deterministic (inverse distance weighting) and geostatistical interpolation methods (ordinary kriging) were explored, which take into account only the initial values of \\overrightarrow{u} and \\overrightarrow{v} . In addition, more complex methods (generalized additive model, support vector machine and neural networks as single methods and as hybrid methods as well as regression-kriging) that consider additional variables were applied. The analysis of the error indices revealed that regression-kriging provided the most accurate interpolation results for both wind components and all pressure heights. At 200 and 500 hPa, regression-kriging is followed by the different kinds of neural networks and support vector machines and for 850 hPa it is followed by the different types of support vector machine and ordinary kriging. Overall, explanatory variables improve the interpolation results.

Inhomogeneities detection in annual precipitation time series in Portugal using direct sequential simulation

NASA Astrophysics Data System (ADS)

Caineta, Júlio; Ribeiro, Sara; Costa, Ana Cristina; Henriques, Roberto; Soares, Amílcar

2014-05-01

Climate data homogenisation is of major importance in monitoring climate change, the validation of weather forecasting, general circulation and regional atmospheric models, modelling of erosion, drought monitoring, among other studies of hydrological and environmental impacts. This happens because non-climate factors can cause time series discontinuities which may hide the true climatic signal and patterns, thus potentially bias the conclusions of those studies. In the last two decades, many methods have been developed to identify and remove these inhomogeneities. One of those is based on geostatistical simulation (DSS - direct sequential simulation), where local probability density functions (pdf) are calculated at candidate monitoring stations, using spatial and temporal neighbouring observations, and then are used for detection of inhomogeneities. This approach has been previously applied to detect inhomogeneities in four precipitation series (wet day count) from a network with 66 monitoring stations located in the southern region of Portugal (1980-2001). This study revealed promising results and the potential advantages of geostatistical techniques for inhomogeneities detection in climate time series. This work extends the case study presented before and investigates the application of the geostatistical stochastic approach to ten precipitation series that were previously classified as inhomogeneous by one of six absolute homogeneity tests (Mann-Kendall test, Wald-Wolfowitz runs test, Von Neumann ratio test, Standard normal homogeneity test (SNHT) for a single break, Pettit test, and Buishand range test). Moreover, a sensibility analysis is implemented to investigate the number of simulated realisations that should be used to accurately infer the local pdfs. Accordingly, the number of simulations per iteration is increased from 50 to 500, which resulted in a more representative local pdf. A set of default and recommended settings is provided, which will help other users to implement this method. The need of user intervention is reduced to a minimum through the usage of a cross-platform script. Finally, as in the previous study, the results are compared with those from the SNHT, Pettit and Buishand range tests, which were applied to composite (ratio) reference series. Acknowledgements: The authors gratefully acknowledge the financial support of "Fundação para a Ciência e Tecnologia" (FCT), Portugal, through the research project PTDC/GEO-MET/4026/2012 ("GSIMCLI - Geostatistical simulation with local distributions for the homogenization and interpolation of climate data").

An assessment of gas emanation hazard using a geographic information system and geostatistics.

PubMed

Astorri, F; Beaubien, S E; Ciotoli, G; Lombardi, S

2002-03-01

This paper describes the use of geostatistical analysis and GIS techniques to assess gas emanation hazards. The Mt. Vulsini volcanic district was selected for this study because of the wide range of natural phenomena locally present that affect gas migration in the near surface. In addition, soil gas samples that were collected in this area should allow for a calibration between the generated risk/hazard models and the measured distribution of toxic gas species at surface. The approach used during this study consisted of three general stages. First data were digitally organized into thematic layers, then software functions in the GIS program "ArcView" were used to compare and correlate these various layers, and then finally the produced "potential-risk" map was compared with radon soil gas data in order to validate the model and/or to select zones for further, more-detailed soil gas investigations.

Soil risk assessment of As and Zn contamination in a coal mining region using geostatistics [corrected].

PubMed

Komnitsas, Kostas; Modis, Kostas

2006-12-01

The present paper aims to map As and Zn contamination and assess the risk for agricultural soils in a wider disposal site containing wastes derived from coal beneficiation. Geochemical data related to environmental studies show that the waste characteristics favor solubilisation and mobilization of inorganic contaminants and in some cases the generation of acidic leachates. 135 soil samples were collected from a 34 km(2) area and analysed by using geostatistics under the maximum entropy principle in order to produce risk assessment maps and estimate the probability of soil contamination. In addition, the present paper discusses the main issues related to risk assessment in wider mining and waste disposal sites in order to assist decision makers in selecting feasible rehabilitation schemes.

Detecting and modelling structures on the micro and the macro scales: Assessing their effects on solute transport behaviour

NASA Astrophysics Data System (ADS)

Haslauer, C. P.; Bárdossy, A.; Sudicky, E. A.

2017-09-01

This paper demonstrates quantitative reasoning to separate the dataset of spatially distributed variables into different entities and subsequently characterize their geostatistical properties, properly. The main contribution of the paper is a statistical based algorithm that matches the manual distinction results. This algorithm is based on measured data and is generally applicable. In this paper, it is successfully applied at two datasets of saturated hydraulic conductivity (K) measured at the Borden (Canada) and the Lauswiesen (Germany) aquifers. The boundary layer was successfully delineated at Borden despite its only mild heterogeneity and only small statistical differences between the divided units. The methods are verified with the more heterogeneous Lauswiesen aquifer K data-set, where a boundary layer has previously been delineated. The effects of the macro- and the microstructure on solute transport behaviour are evaluated using numerical solute tracer experiments. Within the microscale structure, both Gaussian and non-Gaussian models of spatial dependence of K are evaluated. The effects of heterogeneity both on the macro- and the microscale are analysed using numerical tracer experiments based on four scenarios: including or not including the macroscale structures and optimally fitting a Gaussian or a non-Gaussian model for the spatial dependence in the micro-structure. The paper shows that both micro- and macro-scale structures are important, as in each of the four possible geostatistical scenarios solute transport behaviour differs meaningfully.

Spatiotemporal Dynamics of Whitefly Bemisia tabaci (Hemiptera: Aleyrodidae) in Commercial Watermelon Crops.

PubMed

Lima, Carlos H O; Sarmento, Renato A; Galdino, Tarcísio V S; Pereira, Poliana S; Silva, Joedna; Souza, Danival J; Dos Santos, Gil R; Costa, Thiago L; Picanço, Marcelo C

2018-04-16

Spatiotemporal dynamics studies of crop pests enable the determination of the colonization pattern and dispersion of these insects in the landscape. Geostatistics is an efficient tool for these studies: to determine the spatial distribution pattern of the pest in the crops and to make maps that represent this situation. Analysis of these maps across the development of plants can be used as a tool in precision agriculture programs. Watermelon, Citrullus lanatus (Thunb.) Matsum. and Nakai (Cucurbitales: Cucurbitaceae), is the second most consumed fruit in the world, and the whitefly Bemisia tabaci (Gennadius) (Hemiptera: Aleyrodidae) is one of the most important pests of this crop. Thus, the objective of this work was to determine the spatiotemporal distribution of B. tabaci in commercial watermelon crops using geostatistics. For 2 yr, we monitored adult whitefly densities in eight watermelon crops in a tropical climate region. The location of the samples and other crops in the landscape was georeferenced. Experimental data were submitted to geostatistical analysis. The colonization of B. tabaci had two patterns. In the first, the colonization started at the outermost parts of the crop. In the second, the insects occupied the whole area of the crop since the beginning of cultivation. The maximum distance between sites of watermelon crops in which spatial dependence of B. tabaci densities was observed was 19.69 m. The adult B. tabaci densities in the eight watermelon fields were positively correlated with rainfall and relative humidity, whereas wind speed negatively affected whiteflies population.

Usability and potential of geostatistics for spatial discrimination of multiple sclerosis lesion patterns.

PubMed

Marschallinger, Robert; Golaszewski, Stefan M; Kunz, Alexander B; Kronbichler, Martin; Ladurner, Gunther; Hofmann, Peter; Trinka, Eugen; McCoy, Mark; Kraus, Jörg

2014-01-01

In multiple sclerosis (MS) the individual disease courses are very heterogeneous among patients and biomarkers for setting the diagnosis and the estimation of the prognosis for individual patients would be very helpful. For this purpose, we are developing a multidisciplinary method and workflow for the quantitative, spatial, and spatiotemporal analysis and characterization of MS lesion patterns from MRI with geostatistics. We worked on a small data set involving three synthetic and three real-world MS lesion patterns, covering a wide range of possible MS lesion configurations. After brain normalization, MS lesions were extracted and the resulting binary 3-dimensional models of MS lesion patterns were subject to geostatistical indicator variography in three orthogonal directions. By applying geostatistical indicator variography, we were able to describe the 3-dimensional spatial structure of MS lesion patterns in a standardized manner. Fitting a model function to the empirical variograms, spatial characteristics of the MS lesion patterns could be expressed and quantified by two parameters. An orthogonal plot of these parameters enabled a well-arranged comparison of the involved MS lesion patterns. This method in development is a promising candidate to complement standard image-based statistics by incorporating spatial quantification. The work flow is generic and not limited to analyzing MS lesion patterns. It can be completely automated for the screening of radiological archives. Copyright © 2013 by the American Society of Neuroimaging.

Spatial structure of monthly rainfall measurements average over 25 years and trends of the hourly variability of a current rainy day in Rwanda.

NASA Astrophysics Data System (ADS)

Nduwayezu, Emmanuel; Kanevski, Mikhail; Jaboyedoff, Michel

2013-04-01

Climate plays a vital role in a wide range of socio-economic activities of most nations particularly of developing countries. Climate (rainfall) plays a central role in agriculture which is the main stay of the Rwandan economy and community livelihood and activities. The majority of the Rwandan population (81,1% in 2010) relies on rain fed agriculture for their livelihoods, and the impacts of variability in climate patterns are already being felt. Climate-related events like heavy rainfall or too little rainfall are becoming more frequent and are impacting on human wellbeing.The torrential rainfall that occurs every year in Rwanda could disturb the circulation for many days, damages houses, infrastructures and causes heavy economic losses and deaths. Four rainfall seasons have been identified, corresponding to the four thermal Earth ones in the south hemisphere: the normal season (summer), the rainy season (autumn), the dry season (winter) and the normo-rainy season (spring). Globally, the spatial rainfall decreasing from West to East, especially in October (spring) and February (summer) suggests an «Atlantic monsoon influence» while the homogeneous spatial rainfall distribution suggests an «Inter-tropical front» mechanism. What is the hourly variability in this mountainous area? Is there any correlation with the identified zones of the monthly average series (from 1965 to 1990 established by the Rwandan meteorological services)? Where could we have hazards with several consecutive rainy days (using forecasted datas from the Norwegian Meteorological Institute)? Spatio-temporal analysis allows for identifying and explaining large-scale anomalies which are useful for understanding hydrological characteristics and subsequently predicting these hydrological events. The objective of our current research (Rainfall variability) is to proceed to an evaluation of the potential rainfall risk by applying advanced geospatial modelling tools in Rwanda: geostatistical predictions and simulations, machine learning algorithm (different types of neural networks) and GIS. Hybrid models - mixing geostatistics and machine learning, will be applied to study spatial non-stationarity of rainfall fields. The research will include rainfalls variability mapping and probabilistic analyses of extreme events. Key words: rainfall variability, Rwanda, extreme event, model, mapping, geostatistics.

Spatial heterogeneity and risk factors for stunting among children under age five in Ethiopia: A Bayesian geo-statistical model.

PubMed

Hagos, Seifu; Hailemariam, Damen; WoldeHanna, Tasew; Lindtjørn, Bernt

2017-01-01

Understanding the spatial distribution of stunting and underlying factors operating at meso-scale is of paramount importance for intervention designing and implementations. Yet, little is known about the spatial distribution of stunting and some discrepancies are documented on the relative importance of reported risk factors. Therefore, the present study aims at exploring the spatial distribution of stunting at meso- (district) scale, and evaluates the effect of spatial dependency on the identification of risk factors and their relative contribution to the occurrence of stunting and severe stunting in a rural area of Ethiopia. A community based cross sectional study was conducted to measure the occurrence of stunting and severe stunting among children aged 0-59 months. Additionally, we collected relevant information on anthropometric measures, dietary habits, parent and child-related demographic and socio-economic status. Latitude and longitude of surveyed households were also recorded. Local Anselin Moran's I was calculated to investigate the spatial variation of stunting prevalence and identify potential local pockets (hotspots) of high prevalence. Finally, we employed a Bayesian geo-statistical model, which accounted for spatial dependency structure in the data, to identify potential risk factors for stunting in the study area. Overall, the prevalence of stunting and severe stunting in the district was 43.7% [95%CI: 40.9, 46.4] and 21.3% [95%CI: 19.5, 23.3] respectively. We identified statistically significant clusters of high prevalence of stunting (hotspots) in the eastern part of the district and clusters of low prevalence (cold spots) in the western. We found out that the inclusion of spatial structure of the data into the Bayesian model has shown to improve the fit for stunting model. The Bayesian geo-statistical model indicated that the risk of stunting increased as the child's age increased (OR 4.74; 95% Bayesian credible interval [BCI]:3.35-6.58) and among boys (OR 1.28; 95%BCI; 1.12-1.45). However, maternal education and household food security were found to be protective against stunting and severe stunting. Stunting prevalence may vary across space at different scale. For this, it's important that nutrition studies and, more importantly, control interventions take into account this spatial heterogeneity in the distribution of nutritional deficits and their underlying associated factors. The findings of this study also indicated that interventions integrating household food insecurity in nutrition programs in the district might help to avert the burden of stunting.

Spatial variability of soil moisture retrieved by SMOS satellite

NASA Astrophysics Data System (ADS)

Lukowski, Mateusz; Marczewski, Wojciech; Usowicz, Boguslaw; Rojek, Edyta; Slominski, Jan; Lipiec, Jerzy

2015-04-01

Standard statistical methods assume that the analysed variables are independent. Since the majority of the processes observed in the nature are continuous in space and time, this assumption introduces a significant limitation for understanding the examined phenomena. In classical approach, valuable information about the locations of examined observations is completely lost. However, there is a branch of statistics, called geostatistics, which is the study of random variables, but taking into account the space where they occur. A common example of so-called "regionalized variable" is soil moisture. Using in situ methods it is difficult to estimate soil moisture distribution because it is often significantly diversified. Thanks to the geostatistical methods, by employing semivariance analysis, it is possible to get the information about the nature of spatial dependences and their lengths. Since the Soil Moisture and Ocean Salinity mission launch in 2009, the estimation of soil moisture spatial distribution for regional up to continental scale started to be much easier. In this study, the SMOS L2 data for Central and Eastern Europe were examined. The statistical and geostatistical features of moisture distributions of this area were studied for selected natural soil phenomena for 2010-2014 including: freezing, thawing, rainfalls (wetting), drying and drought. Those soil water "states" were recognized employing ground data from the agro-meteorological network of ground-based stations SWEX and SMUDP2 data from SMOS. After pixel regularization, without any upscaling, the geostatistical methods were applied directly on Discrete Global Grid (15-km resolution) in ISEA 4H9 projection, on which SMOS observations are reported. Analysis of spatial distribution of SMOS soil moisture, carried out for each data set, in most cases did not show significant trends. It was therefore assumed that each of the examined distributions of soil moisture in the adopted scale satisfies ergodicity and quasi-stationarity assumptions, required for geostatistical analysis. The semivariograms examinations revealed that spatial dependences occurring in the surface soil moisture distributions for the selected area were more or less 200 km. The exception was the driest of the studied days, when the spatial correlations of soil moisture were not disturbed for a long time by any rainfall. Spatial correlation length on that day was about 400 km. Because of zonal character of frost, the spatial dependences in the examined surface soil moisture distributions during freezing/thawing found to be disturbed. Probably, the amount of water remains the same, but it is not detected by SMOS, hence analysing dielectric constant instead of soil moisture would be more appropriate. Some spatial relations of soil moisture and freezing distribution with existing maps of soil granulometric fractions and soil specific surface area for Poland have also been found. The work was partially funded under the ELBARA_PD (Penetration Depth) project No. 4000107897/13/NL/KML. ELBARA_PD project is funded by the Government of Poland through an ESA (European Space Agency) Contract under the PECS (Plan for European Cooperating States).

A Novel Approach of Understanding and Incorporating Error of Chemical Transport Models into a Geostatistical Framework

NASA Astrophysics Data System (ADS)

Reyes, J.; Vizuete, W.; Serre, M. L.; Xu, Y.

2015-12-01

The EPA employs a vast monitoring network to measure ambient PM2.5 concentrations across the United States with one of its goals being to quantify exposure within the population. However, there are several areas of the country with sparse monitoring spatially and temporally. One means to fill in these monitoring gaps is to use PM2.5 modeled estimates from Chemical Transport Models (CTMs) specifically the Community Multi-scale Air Quality (CMAQ) model. CMAQ is able to provide complete spatial coverage but is subject to systematic and random error due to model uncertainty. Due to the deterministic nature of CMAQ, often these uncertainties are not quantified. Much effort is employed to quantify the efficacy of these models through different metrics of model performance. Currently evaluation is specific to only locations with observed data. Multiyear studies across the United States are challenging because the error and model performance of CMAQ are not uniform over such large space/time domains. Error changes regionally and temporally. Because of the complex mix of species that constitute PM2.5, CMAQ error is also a function of increasing PM2.5 concentration. To address this issue we introduce a model performance evaluation for PM2.5 CMAQ that is regionalized and non-linear. This model performance evaluation leads to error quantification for each CMAQ grid. Areas and time periods of error being better qualified. The regionalized error correction approach is non-linear and is therefore more flexible at characterizing model performance than approaches that rely on linearity assumptions and assume homoscedasticity of CMAQ predictions errors. Corrected CMAQ data are then incorporated into the modern geostatistical framework of Bayesian Maximum Entropy (BME). Through cross validation it is shown that incorporating error-corrected CMAQ data leads to more accurate estimates than just using observed data by themselves.

Restricted spatial regression in practice: Geostatistical models, confounding, and robustness under model misspecification

USGS Publications Warehouse

Hanks, Ephraim M.; Schliep, Erin M.; Hooten, Mevin B.; Hoeting, Jennifer A.

2015-01-01

In spatial generalized linear mixed models (SGLMMs), covariates that are spatially smooth are often collinear with spatially smooth random effects. This phenomenon is known as spatial confounding and has been studied primarily in the case where the spatial support of the process being studied is discrete (e.g., areal spatial data). In this case, the most common approach suggested is restricted spatial regression (RSR) in which the spatial random effects are constrained to be orthogonal to the fixed effects. We consider spatial confounding and RSR in the geostatistical (continuous spatial support) setting. We show that RSR provides computational benefits relative to the confounded SGLMM, but that Bayesian credible intervals under RSR can be inappropriately narrow under model misspecification. We propose a posterior predictive approach to alleviating this potential problem and discuss the appropriateness of RSR in a variety of situations. We illustrate RSR and SGLMM approaches through simulation studies and an analysis of malaria frequencies in The Gambia, Africa.

DOE Office of Scientific and Technical Information (OSTI.GOV)

Aziz, Mohd Khairul Bazli Mohd, E-mail: mkbazli@yahoo.com; Yusof, Fadhilah, E-mail: fadhilahy@utm.my; Daud, Zalina Mohd, E-mail: zalina@ic.utm.my

Recently, many rainfall network design techniques have been developed, discussed and compared by many researchers. Present day hydrological studies require higher levels of accuracy from collected data. In numerous basins, the rain gauge stations are located without clear scientific understanding. In this study, an attempt is made to redesign rain gauge network for Johor, Malaysia in order to meet the required level of accuracy preset by rainfall data users. The existing network of 84 rain gauges in Johor is optimized and redesigned into a new locations by using rainfall, humidity, solar radiation, temperature and wind speed data collected during themore » monsoon season (November - February) of 1975 until 2008. This study used the combination of geostatistics method (variance-reduction method) and simulated annealing as the algorithm of optimization during the redesigned proses. The result shows that the new rain gauge location provides minimum value of estimated variance. This shows that the combination of geostatistics method (variance-reduction method) and simulated annealing is successful in the development of the new optimum rain gauge system.« less

Impact of radionuclide spatial variability on groundwater quality downstream from a shallow waste burial in the Chernobyl Exclusion Zone

NASA Astrophysics Data System (ADS)

Nguyen, H. L.; de Fouquet, C.; Courbet, C.; Simonucci, C. A.

2016-12-01

The effects of spatial variability of hydraulic parameters and initial groundwater plume localization on the possible extent of groundwater pollution plumes have already been broadly studied. However, only a few studies, such as Kjeldsen et al. (1995), take into account the effect of source term spatial variability. We explore this question with the 90Sr migration modeling from a shallow waste burial located in the Chernobyl Exclusion Zone to the underlying sand aquifer. Our work is based upon groundwater sampled once or twice a year since 1995 until 2015 from about 60 piezometers and more than 3,000 137Cs soil activity measurements. These measurements were taken in 1999 from one of the trenches dug after the explosion of the Chernobyl nuclear power plant, the so-called "T22 Trench", where radioactive waste was buried in 1987. The geostatistical analysis of 137Cs activity data in soils from Bugai et al. (2005) is first reconsidered to delimit the trench borders using georadar data as a covariable and to perform geostatistical simulations in order to evaluate the uncertainties of this inventory. 90Sr activity in soils is derived from 137Cs/154Eu and 90Sr/154Eu activity ratios in Chernobyl hot fuel particles (Bugai et al., 2003). Meanwhile, a coupled 1D non saturated/3D saturated transient transport model is constructed under the MELODIE software (IRSN, 2009). The previous 90Sr transport model developed by Bugai et al. (2012) did not take into account the effect of water table fluctuations highlighted by Van Meir et al. (2007) which may cause some discrepancies between model predictions and field observations. They are thus reproduced on a 1D vertical non saturated model. The equiprobable radionuclide localization maps produced by the geostatistical simulations are selected to illustrate different heterogeneities in the radionuclide inventory and are implemented in the 1D model. The obtained activity fluxes from all the 1D vertical models are then injected in a 3D saturated transient model to assess the extent of the radionuclide plume in the groundwater and its most likely evolution over time by taking into account uncertainties associated with the source term spatial variability.

Comments on ``Use of conditional simulation in nuclear waste site performance assessment`` by Carol Gotway

DOE Office of Scientific and Technical Information (OSTI.GOV)

Downing, D.J.

1993-10-01

This paper discusses Carol Gotway`s paper, ``The Use of Conditional Simulation in Nuclear Waste Site Performance Assessment.`` The paper centers on the use of conditional simulation and the use of geostatistical methods to simulate an entire field of values for subsequent use in a complex computer model. The issues of sampling designs for geostatistics, semivariogram estimation and anisotropy, turning bands method for random field generation, and estimation of the comulative distribution function are brought out.

Acoustic Determination of Near-Surface Soil Properties

DTIC Science & Technology

2008-12-01

requiring geostatistical analysis, while nearby others are spatially independent. In studies involving many different soil properties and chemistry ...Am 116(6), p. 3354-3369. Kravchenko, N., C.W. Boast, D.G. Bullock, 1991. Fractal analysis of soil spatial variability. Agronomy Journal 91

Periodicity in spatial data and geostatistical models: autocorrelation between patches

Treesearch

Volker C. Radeloff; Todd F. Miller; Hong S. He; David J. Mladenoff

2000-01-01

Several recent studies in landscape ecology have found periodicity in correlograms or semi-variograms calculated, for instance, from spatial data of soils, forests, or animal populations. Some of the studies interpreted this as an indication of regular or periodic landscape patterns. This interpretation is in disagreement with other studies that doubt whether such...

Using river distance and existing hydrography data can improve the geostatistical estimation of fish tissue mercury at unsampled locations.

PubMed

Money, Eric S; Sackett, Dana K; Aday, D Derek; Serre, Marc L

2011-09-15

Mercury in fish tissue is a major human health concern. Consumption of mercury-contaminated fish poses risks to the general population, including potentially serious developmental defects and neurological damage in young children. Therefore, it is important to accurately identify areas that have the potential for high levels of bioaccumulated mercury. However, due to time and resource constraints, it is difficult to adequately assess fish tissue mercury on a basin wide scale. We hypothesized that, given the nature of fish movement along streams, an analytical approach that takes into account distance traveled along these streams would improve the estimation accuracy for fish tissue mercury in unsampled streams. Therefore, we used a river-based Bayesian Maximum Entropy framework (river-BME) for modern space/time geostatistics to estimate fish tissue mercury at unsampled locations in the Cape Fear and Lumber Basins in eastern North Carolina. We also compared the space/time geostatistical estimation using river-BME to the more traditional Euclidean-based BME approach, with and without the inclusion of a secondary variable. Results showed that this river-based approach reduced the estimation error of fish tissue mercury by more than 13% and that the median estimate of fish tissue mercury exceeded the EPA action level of 0.3 ppm in more than 90% of river miles for the study domain.

Inversion using a new low-dimensional representation of complex binary geological media based on a deep neural network

NASA Astrophysics Data System (ADS)

Laloy, Eric; Hérault, Romain; Lee, John; Jacques, Diederik; Linde, Niklas

2017-12-01

Efficient and high-fidelity prior sampling and inversion for complex geological media is still a largely unsolved challenge. Here, we use a deep neural network of the variational autoencoder type to construct a parametric low-dimensional base model parameterization of complex binary geological media. For inversion purposes, it has the attractive feature that random draws from an uncorrelated standard normal distribution yield model realizations with spatial characteristics that are in agreement with the training set. In comparison with the most commonly used parametric representations in probabilistic inversion, we find that our dimensionality reduction (DR) approach outperforms principle component analysis (PCA), optimization-PCA (OPCA) and discrete cosine transform (DCT) DR techniques for unconditional geostatistical simulation of a channelized prior model. For the considered examples, important compression ratios (200-500) are achieved. Given that the construction of our parameterization requires a training set of several tens of thousands of prior model realizations, our DR approach is more suited for probabilistic (or deterministic) inversion than for unconditional (or point-conditioned) geostatistical simulation. Probabilistic inversions of 2D steady-state and 3D transient hydraulic tomography data are used to demonstrate the DR-based inversion. For the 2D case study, the performance is superior compared to current state-of-the-art multiple-point statistics inversion by sequential geostatistical resampling (SGR). Inversion results for the 3D application are also encouraging.

Geostatistical Borehole Image-Based Mapping of Karst-Carbonate Aquifer Pores.

PubMed

Sukop, Michael C; Cunningham, Kevin J

2016-03-01

Quantification of the character and spatial distribution of porosity in carbonate aquifers is important as input into computer models used in the calculation of intrinsic permeability and for next-generation, high-resolution groundwater flow simulations. Digital, optical, borehole-wall image data from three closely spaced boreholes in the karst-carbonate Biscayne aquifer in southeastern Florida are used in geostatistical experiments to assess the capabilities of various methods to create realistic two-dimensional models of vuggy megaporosity and matrix-porosity distribution in the limestone that composes the aquifer. When the borehole image data alone were used as the model training image, multiple-point geostatistics failed to detect the known spatial autocorrelation of vuggy megaporosity and matrix porosity among the three boreholes, which were only 10 m apart. Variogram analysis and subsequent Gaussian simulation produced results that showed a realistic conceptualization of horizontal continuity of strata dominated by vuggy megaporosity and matrix porosity among the three boreholes. © 2015, National Ground Water Association.

An interactive Bayesian geostatistical inverse protocol for hydraulic tomography

USGS Publications Warehouse

Fienen, Michael N.; Clemo, Tom; Kitanidis, Peter K.

2008-01-01

Hydraulic tomography is a powerful technique for characterizing heterogeneous hydrogeologic parameters. An explicit trade-off between characterization based on measurement misfit and subjective characterization using prior information is presented. We apply a Bayesian geostatistical inverse approach that is well suited to accommodate a flexible model with the level of complexity driven by the data and explicitly considering uncertainty. Prior information is incorporated through the selection of a parameter covariance model characterizing continuity and providing stability. Often, discontinuities in the parameter field, typically caused by geologic contacts between contrasting lithologic units, necessitate subdivision into zones across which there is no correlation among hydraulic parameters. We propose an interactive protocol in which zonation candidates are implied from the data and are evaluated using cross validation and expert knowledge. Uncertainty introduced by limited knowledge of dynamic regional conditions is mitigated by using drawdown rather than native head values. An adjoint state formulation of MODFLOW-2000 is used to calculate sensitivities which are used both for the solution to the inverse problem and to guide protocol decisions. The protocol is tested using synthetic two-dimensional steady state examples in which the wells are located at the edge of the region of interest.

Synthesis of instrumentally and historically recorded earthquakes and studying their spatial statistical relationship (A case study: Dasht-e-Biaz, Eastern Iran)

NASA Astrophysics Data System (ADS)

Jalali, Mohammad; Ramazi, Hamidreza

2018-06-01

Earthquake catalogues are the main source of statistical seismology for the long term studies of earthquake occurrence. Therefore, studying the spatiotemporal problems is important to reduce the related uncertainties in statistical seismology studies. A statistical tool, time normalization method, has been determined to revise time-frequency relationship in one of the most active regions of Asia, Eastern Iran and West of Afghanistan, (a and b were calculated around 8.84 and 1.99 in the exponential scale, not logarithmic scale). Geostatistical simulation method has been further utilized to reduce the uncertainties in the spatial domain. A geostatistical simulation produces a representative, synthetic catalogue with 5361 events to reduce spatial uncertainties. The synthetic database is classified using a Geographical Information System, GIS, based on simulated magnitudes to reveal the underlying seismicity patterns. Although some regions with highly seismicity correspond to known faults, significantly, as far as seismic patterns are concerned, the new method highlights possible locations of interest that have not been previously identified. It also reveals some previously unrecognized lineation and clusters in likely future strain release.

Goal-Oriented Intelligence in Optimization of Distributed Parameter Systems

DTIC Science & Technology

2004-08-01

Yarus, and R.L. Chambers, editors, AAPG Computer Applications in geology, No. 3, The American Association of Petroleum Geologists, Tulsa, OK, USA...Stochastic Modeling and Geostatistics – Principles, Methods, and Case Studies, AAPG Computer Applications in geology, No. 3, The American

Sequential Gaussian co-simulation of rate decline parameters of longwall gob gas ventholes.

PubMed

Karacan, C Özgen; Olea, Ricardo A

2013-04-01

Gob gas ventholes (GGVs) are used to control methane inflows into a longwall mining operation by capturing the gas within the overlying fractured strata before it enters the work environment. Using geostatistical co-simulation techniques, this paper maps the parameters of their rate decline behaviors across the study area, a longwall mine in the Northern Appalachian basin. Geostatistical gas-in-place (GIP) simulations were performed, using data from 64 exploration boreholes, and GIP data were mapped within the fractured zone of the study area. In addition, methane flowrates monitored from 10 GGVs were analyzed using decline curve analyses (DCA) techniques to determine parameters of decline rates. Surface elevation showed the most influence on methane production from GGVs and thus was used to investigate its relation with DCA parameters using correlation techniques on normal-scored data. Geostatistical analysis was pursued using sequential Gaussian co-simulation with surface elevation as the secondary variable and with DCA parameters as the primary variables. The primary DCA variables were effective percentage decline rate, rate at production start, rate at the beginning of forecast period, and production end duration. Co-simulation results were presented to visualize decline parameters at an area-wide scale. Wells located at lower elevations, i.e., at the bottom of valleys, tend to perform better in terms of their rate declines compared to those at higher elevations. These results were used to calculate drainage radii of GGVs using GIP realizations. The calculated drainage radii are close to ones predicted by pressure transient tests.

Analysis of the spatio-temporal distribution of Eurygaster integriceps (Hemiptera: Scutelleridae) by using spatial analysis by distance indices and geostatistics.

PubMed

Karimzadeh, R; Hejazi, M J; Helali, H; Iranipour, S; Mohammadi, S A

2011-10-01

Eurygaster integriceps Puton (Hemiptera: Scutelleridae) is the most serious insect pest of wheat (Triticum aestivum L.) and barley (Hordeum vulgare L.) in Iran. In this study, spatio-temporal distribution of this pest was determined in wheat by using spatial analysis by distance indices (SADIE) and geostatistics. Global positioning and geographic information systems were used for spatial sampling and mapping the distribution of this insect. The study was conducted for three growing seasons in Gharamalek, an agricultural region to the west of Tabriz, Iran. Weekly sampling began when E. integriceps adults migrated to wheat fields from overwintering sites and ended when the new generation adults appeared at the end of season. The adults were sampled using 1- by 1-m quadrat and distance-walk methods. A sweep net was used for sampling the nymphs, and five 180° sweeps were considered as the sampling unit. The results of spatial analyses by using geostatistics and SADIE indicated that E. integriceps adults were clumped after migration to fields and had significant spatial dependency. The second- and third-instar nymphs showed aggregated spatial structure in the middle of growing season. At the end of the season, population distribution changed toward random or regular patterns; and fourth and fifth instars had weaker spatial structure compared with younger nymphs. In Iran, management measures for E. integriceps in wheat fields are mainly applied against overwintering adults, as well as second and third instars. Because of the aggregated distribution of these life stages, site-specific spraying of chemicals is feasible in managing E. integriceps.

Geostatistical and GIS analysis of the spatial variability of alluvial gold content in Ngoura-Colomines area, Eastern Cameroon: Implications for the exploration of primary gold deposit

NASA Astrophysics Data System (ADS)

Takodjou Wambo, Jonas Didero; Ganno, Sylvestre; Djonthu Lahe, Yannick Sthopira; Kouankap Nono, Gus Djibril; Fossi, Donald Hermann; Tchouatcha, Milan Stafford; Nzenti, Jean Paul

2018-06-01

Linear and nonlinear geostatistic is commonly used in ore grade estimation and seldom used in Geographical Information System (GIS) technology. In this study, we suggest an approach based on geostatistic linear ordinary kriging (OK) and Geographical Information System (GIS) techniques to investigate the spatial distribution of alluvial gold content, mineralized and gangue layers thicknesses from 73 pits at the Ngoura-Colomines area with the aim to determine controlling factors for the spatial distribution of mineralization and delineate the most prospective area for primary gold mineralization. Gold content varies between 0.1 and 4.6 g/m3 and has been broadly grouped into three statistical classes. These classes have been spatially subdivided into nine zones using ordinary kriging model based on physical and topographical characteristics. Both mineralized and barren layer thicknesses show randomly spatial distribution, and there is no correlation between these parameters and the gold content. This approach has shown that the Ngoura-Colomines area is located in a large shear zone compatible with the Riedel fault system composed of P and P‧ fractures oriented NE-SW and NNE-SSW respectively; E-W trending R fractures and R‧ fractures with NW-SE trends that could have contributed significantly to the establishment of this gold mineralization. The combined OK model and GIS analysis have led to the delineation of Colomines, Tissongo, Madubal and Boutou villages as the most prospective areas for the exploration of primary gold deposit in the study area.

Sequential Gaussian co-simulation of rate decline parameters of longwall gob gas ventholes

USGS Publications Warehouse

Karacan, C. Özgen; Olea, Ricardo A.

2013-01-01

Gob gas ventholes (GGVs) are used to control methane inflows into a longwall mining operation by capturing the gas within the overlying fractured strata before it enters the work environment. Using geostatistical co-simulation techniques, this paper maps the parameters of their rate decline behaviors across the study area, a longwall mine in the Northern Appalachian basin. Geostatistical gas-in-place (GIP) simulations were performed, using data from 64 exploration boreholes, and GIP data were mapped within the fractured zone of the study area. In addition, methane flowrates monitored from 10 GGVs were analyzed using decline curve analyses (DCA) techniques to determine parameters of decline rates. Surface elevation showed the most influence on methane production from GGVs and thus was used to investigate its relation with DCA parameters using correlation techniques on normal-scored data. Geostatistical analysis was pursued using sequential Gaussian co-simulation with surface elevation as the secondary variable and with DCA parameters as the primary variables. The primary DCA variables were effective percentage decline rate, rate at production start, rate at the beginning of forecast period, and production end duration. Co-simulation results were presented to visualize decline parameters at an area-wide scale. Wells located at lower elevations, i.e., at the bottom of valleys, tend to perform better in terms of their rate declines compared to those at higher elevations. These results were used to calculate drainage radii of GGVs using GIP realizations. The calculated drainage radii are close to ones predicted by pressure transient tests.

Sequential Gaussian co-simulation of rate decline parameters of longwall gob gas ventholes

PubMed Central

Karacan, C.Özgen; Olea, Ricardo A.

2015-01-01

Gob gas ventholes (GGVs) are used to control methane inflows into a longwall mining operation by capturing the gas within the overlying fractured strata before it enters the work environment. Using geostatistical co-simulation techniques, this paper maps the parameters of their rate decline behaviors across the study area, a longwall mine in the Northern Appalachian basin. Geostatistical gas-in-place (GIP) simulations were performed, using data from 64 exploration boreholes, and GIP data were mapped within the fractured zone of the study area. In addition, methane flowrates monitored from 10 GGVs were analyzed using decline curve analyses (DCA) techniques to determine parameters of decline rates. Surface elevation showed the most influence on methane production from GGVs and thus was used to investigate its relation with DCA parameters using correlation techniques on normal-scored data. Geostatistical analysis was pursued using sequential Gaussian co-simulation with surface elevation as the secondary variable and with DCA parameters as the primary variables. The primary DCA variables were effective percentage decline rate, rate at production start, rate at the beginning of forecast period, and production end duration. Co-simulation results were presented to visualize decline parameters at an area-wide scale. Wells located at lower elevations, i.e., at the bottom of valleys, tend to perform better in terms of their rate declines compared to those at higher elevations. These results were used to calculate drainage radii of GGVs using GIP realizations. The calculated drainage radii are close to ones predicted by pressure transient tests. PMID:26190930

The use of a genetic algorithm-based search strategy in geostatistics: application to a set of anisotropic piezometric head data

NASA Astrophysics Data System (ADS)

Abedini, M. J.; Nasseri, M.; Burn, D. H.

2012-04-01

In any geostatistical study, an important consideration is the choice of an appropriate, repeatable, and objective search strategy that controls the nearby samples to be included in the location-specific estimation procedure. Almost all geostatistical software available in the market puts the onus on the user to supply search strategy parameters in a heuristic manner. These parameters are solely controlled by geographical coordinates that are defined for the entire area under study, and the user has no guidance as to how to choose these parameters. The main thesis of the current study is that the selection of search strategy parameters has to be driven by data—both the spatial coordinates and the sample values—and cannot be chosen beforehand. For this purpose, a genetic-algorithm-based ordinary kriging with moving neighborhood technique is proposed. The search capability of a genetic algorithm is exploited to search the feature space for appropriate, either local or global, search strategy parameters. Radius of circle/sphere and/or radii of standard or rotated ellipse/ellipsoid are considered as the decision variables to be optimized by GA. The superiority of GA-based ordinary kriging is demonstrated through application to the Wolfcamp Aquifer piezometric head data. Assessment of numerical results showed that definition of search strategy parameters based on both geographical coordinates and sample values improves cross-validation statistics when compared with that based on geographical coordinates alone. In the case of a variable search neighborhood for each estimation point, optimization of local search strategy parameters for an elliptical support domain—the orientation of which is dictated by anisotropic axes—via GA was able to capture the dynamics of piezometric head in west Texas/New Mexico in an efficient way.

PID temperature controller in pig nursery: spatial characterization of thermal environment

NASA Astrophysics Data System (ADS)

de Souza Granja Barros, Juliana; Rossi, Luiz Antonio; Menezes de Souza, Zigomar

2018-05-01

The use of enhanced technologies of temperature control can improve the thermal conditions in environments of livestock facilities. The objective of this study was to evaluate the spatial distribution of the thermal environment variables in a pig nursery with a heating system with two temperature control technologies based on the geostatistical analysis. The following systems were evaluated: overhead electrical resistance with Proportional, Integral, and Derivative (PID) controller and overhead electrical resistance with a thermostat. We evaluated the climatic variables: dry bulb temperature (Tbs), air relative humidity (RH), temperature and humidity index (THI), and enthalpy in the winter, at 7:00, 12:00, and 18:00 h. The spatial distribution of these variables was mapped by kriging. The results showed that the resistance heating system with PID controllers improved the thermal comfort conditions in the pig nursery in the coldest hours, maintaining the spatial distribution of the air temperature more homogeneous in the pen. During the hottest weather, neither system provided comfort.

PID temperature controller in pig nursery: spatial characterization of thermal environment

NASA Astrophysics Data System (ADS)

de Souza Granja Barros, Juliana; Rossi, Luiz Antonio; Menezes de Souza, Zigomar

2017-11-01

The use of enhanced technologies of temperature control can improve the thermal conditions in environments of livestock facilities. The objective of this study was to evaluate the spatial distribution of the thermal environment variables in a pig nursery with a heating system with two temperature control technologies based on the geostatistical analysis. The following systems were evaluated: overhead electrical resistance with Proportional, Integral, and Derivative (PID) controller and overhead electrical resistance with a thermostat. We evaluated the climatic variables: dry bulb temperature (Tbs), air relative humidity (RH), temperature and humidity index (THI), and enthalpy in the winter, at 7:00, 12:00, and 18:00 h. The spatial distribution of these variables was mapped by kriging. The results showed that the resistance heating system with PID controllers improved the thermal comfort conditions in the pig nursery in the coldest hours, maintaining the spatial distribution of the air temperature more homogeneous in the pen. During the hottest weather, neither system provided comfort.

High Performance Geostatistical Modeling of Biospheric Resources

NASA Astrophysics Data System (ADS)

Pedelty, J. A.; Morisette, J. T.; Smith, J. A.; Schnase, J. L.; Crosier, C. S.; Stohlgren, T. J.

2004-12-01

We are using parallel geostatistical codes to study spatial relationships among biospheric resources in several study areas. For example, spatial statistical models based on large- and small-scale variability have been used to predict species richness of both native and exotic plants (hot spots of diversity) and patterns of exotic plant invasion. However, broader use of geostastics in natural resource modeling, especially at regional and national scales, has been limited due to the large computing requirements of these applications. To address this problem, we implemented parallel versions of the kriging spatial interpolation algorithm. The first uses the Message Passing Interface (MPI) in a master/slave paradigm on an open source Linux Beowulf cluster, while the second is implemented with the new proprietary Xgrid distributed processing system on an Xserve G5 cluster from Apple Computer, Inc. These techniques are proving effective and provide the basis for a national decision support capability for invasive species management that is being jointly developed by NASA and the US Geological Survey.

Integrating geophysical data for mapping the contamination of industrial sites by polycyclic aromatic hydrocarbons: A geostatistical approach

DOE Office of Scientific and Technical Information (OSTI.GOV)

Colin, P.; Nicoletis, S.; Froidevaux, R.

1996-12-31

A case study is presented of building a map showing the probability that the concentration in polycyclic aromatic hydrocarbon (PAH) exceeds a critical threshold. This assessment is based on existing PAH sample data (direct information) and on an electrical resistivity survey (indirect information). Simulated annealing is used to build a model of the range of possible values for PAH concentrations and of the bivariate relationship between PAH concentrations and electrical resistivity. The geostatistical technique of simple indicator kriging is then used, together with the probabilistic model, to infer, at each node of a grid, the range of possible values whichmore » the PAH concentration can take. The risk map is then extracted for this characterization of the local uncertainty. The difference between this risk map and a traditional iso-concentration map is then discussed in terms of decision-making.« less

Quantifying Groundwater Fluctuations in the Southern High Plains with GIS and Geostatistics

NASA Astrophysics Data System (ADS)

Whitehead, B.

2008-12-01

Groundwater as a dwindling non-renewable natural resource has been an important research theme in agricultural studies coupled with human-environment interaction. This research incorporated contemporary Geographic Information System (GIS) methodologies and a universal kriging interpolator (geostatistics) to develop depth to groundwater surfaces for the southern portion of the High Plains, or Ogallala, aquifer. The variations in the interpolated surfaces were used to calculate the volume of water mined from the aquifer from 1980 to 2005. The findings suggest a nearly inverse relationship to the water withdrawal scenarios derived by the United States Geological Survey (USGS) during the Regional Aquifer System Analysis (RASA) performed in the early 1980's. These results advocate further research into regional climate change, groundwater-surface water interaction, and recharge mechanisms in the region, and provide a substantial contribution to the continuing and contentious issue concerning the environmental sustainability of the High Plains.

Constraining geostatistical models with hydrological data to improve prediction realism

NASA Astrophysics Data System (ADS)

Demyanov, V.; Rojas, T.; Christie, M.; Arnold, D.

2012-04-01

Geostatistical models reproduce spatial correlation based on the available on site data and more general concepts about the modelled patters, e.g. training images. One of the problem of modelling natural systems with geostatistics is in maintaining realism spatial features and so they agree with the physical processes in nature. Tuning the model parameters to the data may lead to geostatistical realisations with unrealistic spatial patterns, which would still honour the data. Such model would result in poor predictions, even though although fit the available data well. Conditioning the model to a wider range of relevant data provide a remedy that avoid producing unrealistic features in spatial models. For instance, there are vast amounts of information about the geometries of river channels that can be used in describing fluvial environment. Relations between the geometrical channel characteristics (width, depth, wave length, amplitude, etc.) are complex and non-parametric and are exhibit a great deal of uncertainty, which is important to propagate rigorously into the predictive model. These relations can be described within a Bayesian approach as multi-dimensional prior probability distributions. We propose a way to constrain multi-point statistics models with intelligent priors obtained from analysing a vast collection of contemporary river patterns based on previously published works. We applied machine learning techniques, namely neural networks and support vector machines, to extract multivariate non-parametric relations between geometrical characteristics of fluvial channels from the available data. An example demonstrates how ensuring geological realism helps to deliver more reliable prediction of a subsurface oil reservoir in a fluvial depositional environment.

Conditioning geostatistical simulations of a heterogeneous paleo-fluvial bedrock aquifer using lithologs and pumping tests

NASA Astrophysics Data System (ADS)

Niazi, A.; Bentley, L. R.; Hayashi, M.

2016-12-01

Geostatistical simulations are used to construct heterogeneous aquifer models. Optimally, such simulations should be conditioned with both lithologic and hydraulic data. We introduce an approach to condition lithologic geostatistical simulations of a paleo-fluvial bedrock aquifer consisting of relatively high permeable sandstone channels embedded in relatively low permeable mudstone using hydraulic data. The hydraulic data consist of two-hour single well pumping tests extracted from the public water well database for a 250-km2 watershed in Alberta, Canada. First, lithologic models of the entire watershed are simulated and conditioned with hard lithological data using transition probability - Markov chain geostatistics (TPROGS). Then, a segment of the simulation around a pumping well is used to populate a flow model (FEFLOW) with either sand or mudstone. The values of the hydraulic conductivity and specific storage of sand and mudstone are then adjusted to minimize the difference between simulated and actual pumping test data using the parameter estimation program PEST. If the simulated pumping test data do not adequately match the measured data, the lithologic model is updated by locally deforming the lithology distribution using the probability perturbation method and the model parameters are again updated with PEST. This procedure is repeated until the simulated and measured data agree within a pre-determined tolerance. The procedure is repeated for each well that has pumping test data. The method creates a local groundwater model that honors both the lithologic model and pumping test data and provides estimates of hydraulic conductivity and specific storage. Eventually, the simulations will be integrated into a watershed-scale groundwater model.

3-D transient hydraulic tomography in unconfined aquifers with fast drainage response

NASA Astrophysics Data System (ADS)

Cardiff, M.; Barrash, W.

2011-12-01

We investigate, through numerical experiments, the viability of three-dimensional transient hydraulic tomography (3DTHT) for identifying the spatial distribution of groundwater flow parameters (primarily, hydraulic conductivity K) in permeable, unconfined aquifers. To invert the large amount of transient data collected from 3DTHT surveys, we utilize an iterative geostatistical inversion strategy in which outer iterations progressively increase the number of data points fitted and inner iterations solve the quasi-linear geostatistical formulas of Kitanidis. In order to base our numerical experiments around realistic scenarios, we utilize pumping rates, geometries, and test lengths similar to those attainable during 3DTHT field campaigns performed at the Boise Hydrogeophysical Research Site (BHRS). We also utilize hydrologic parameters that are similar to those observed at the BHRS and in other unconsolidated, unconfined fluvial aquifers. In addition to estimating K, we test the ability of 3DTHT to estimate both average storage values (specific storage Ss and specific yield Sy) as well as spatial variability in storage coefficients. The effects of model conceptualization errors during unconfined 3DTHT are investigated including: (1) assuming constant storage coefficients during inversion and (2) assuming stationary geostatistical parameter variability. Overall, our findings indicate that estimation of K is slightly degraded if storage parameters must be jointly estimated, but that this effect is quite small compared with the degradation of estimates due to violation of "structural" geostatistical assumptions. Practically, we find for our scenarios that assuming constant storage values during inversion does not appear to have a significant effect on K estimates or uncertainty bounds.

Assessing the spatial distribution of Tuta absoluta (Lepidoptera: Gelechiidae) eggs in open-field tomato cultivation through geostatistical analysis.

PubMed

Martins, Júlio C; Picanço, Marcelo C; Silva, Ricardo S; Gonring, Alfredo Hr; Galdino, Tarcísio Vs; Guedes, Raul Nc

2018-01-01

The spatial distribution of insects is due to the interaction between individuals and the environment. Knowledge about the within-field pattern of spatial distribution of a pest is critical to planning control tactics, developing efficient sampling plans, and predicting pest damage. The leaf miner Tuta absoluta (Meyrick) (Lepidoptera: Gelechiidae) is the main pest of tomato crops in several regions of the world. Despite the importance of this pest, the pattern of spatial distribution of T. absoluta on open-field tomato cultivation remains unknown. Therefore, this study aimed to characterize the spatial distribution of T. absoluta in 22 commercial open-field tomato cultivations with plants at the three phenological development stages by using geostatistical analysis. Geostatistical analysis revealed that there was strong evidence for spatially dependent (aggregated) T. absoluta eggs in 19 of the 22 sample tomato cultivations. The maps that were obtained demonstrated the aggregated structure of egg densities at the edges of the crops. Further, T. absoluta was found to accomplish egg dispersal along the rows more frequently than it does between rows. Our results indicate that the greatest egg densities of T. absoluta occur at the edges of tomato crops. These results are discussed in relation to the behavior of T. absoluta distribution within fields and in terms of their implications for improved sampling guidelines and precision targeting control methods that are essential for effective pest monitoring and management. © 2017 Society of Chemical Industry. © 2017 Society of Chemical Industry.

On the importance of geological data for hydraulic tomography analysis: Laboratory sandbox study

NASA Astrophysics Data System (ADS)

Zhao, Zhanfeng; Illman, Walter A.; Berg, Steven J.

2016-11-01

This paper investigates the importance of geological data in Hydraulic Tomography (HT) through sandbox experiments. In particular, four groundwater models with homogeneous geological units constructed with borehole data of varying accuracy are jointly calibrated with multiple pumping test data of two different pumping and observation densities. The results are compared to those from a geostatistical inverse model. Model calibration and validation performances are quantitatively assessed using drawdown scatterplots. We find that accurate and inaccurate geological models can be well calibrated, despite the estimated K values for the poor geological models being quite different from the actual values. Model validation results reveal that inaccurate geological models yield poor drawdown predictions, but using more calibration data improves its predictive capability. Moreover, model comparisons among a highly parameterized geostatistical and layer-based geological models show that, (1) as the number of pumping tests and monitoring locations are reduced, the performance gap between the approaches decreases, and (2) a simplified geological model with a fewer number of layers is more reliable than the one based on the wrong description of stratigraphy. Finally, using a geological model as prior information in geostatistical inverse models results in the preservation of geological features, especially in areas where drawdown data are not available. Overall, our sandbox results emphasize the importance of incorporating geological data in HT surveys when data from pumping tests is sparse. These findings have important implications for field applications of HT where well distances are large.

Using Moran's I and GIS to study the spatial pattern of forest litter carbon density in a subtropical region of southeastern China

NASA Astrophysics Data System (ADS)

Fu, W. J.; Jiang, P. K.; Zhou, G. M.; Zhao, K. L.

2014-04-01

Spatial pattern information of carbon density in forest ecosystem including forest litter carbon (FLC) plays an important role in evaluating carbon sequestration potentials. The spatial variation of FLC density in the typical subtropical forests in southeastern China was investigated using Moran's I, geostatistics and a geographical information system (GIS). A total of 839 forest litter samples were collected based on a 12 km (south-north) × 6 km (east-west) grid system in Zhejiang province. Forest litter carbon density values were very variable, ranging from 10.2 kg ha-1 to 8841.3 kg ha-1, with an average of 1786.7 kg ha-1. The aboveground biomass had the strongest positive correlation with FLC density, followed by forest age and elevation. Global Moran's I revealed that FLC density had significant positive spatial autocorrelation. Clear spatial patterns were observed using local Moran's I. A spherical model was chosen to fit the experimental semivariogram. The moderate "nugget-to-sill" (0.536) value revealed that both natural and anthropogenic factors played a key role in spatial heterogeneity of FLC density. High FLC density values were mainly distributed in northwestern and western part of Zhejiang province, which were related to adopting long-term policy of forest conservation in these areas, while Hang-Jia-Hu (HJH) Plain, Jin-Qu (JQ) Basin and coastal areas had low FLC density due to low forest coverage and intensive management of economic forests. These spatial patterns were in line with the spatial-cluster map described by local Moran's I. Therefore, Moran's I, combined with geostatistics and GIS, could be used to study spatial patterns of environmental variables related to forest ecosystem.

Selective remediation of contaminated sites using a two-level multiphase strategy and geostatistics.

PubMed

Saito, Hirotaka; Goovaerts, Pierre

2003-05-01

Selective soil remediation aims to reduce costs by cleaning only the fraction of an exposure unit (EU) necessary to lower the average concentration below the regulatory threshold. This approach requires a prior stratification of each EU into smaller remediation units (RU) which are then selected according to various criteria. This paper presents a geostatistical framework to account for uncertainties attached to both RU and EU average concentrations in selective remediation. The selection of RUs is based on their impact on the postremediation probability for the EU average concentration to exceed the regulatory threshold, which is assessed using geostatistical stochastic simulation. Application of the technique to a set of 600 dioxin concentrations collected at Piazza Road EPA Superfund site in Missouri shows a substantial decrease in the number of RU remediated compared with single phase remediation. The lower remediation costs achieved by the new strategy are obtained to the detriment of a higher risk of false negatives, yet for this data set this risk remains below the 5% rate set by EPA region 7.

Granulometric analysis at Lampulo Fishing Port (LFP) substrate, Banda Aceh, Indonesia

NASA Astrophysics Data System (ADS)

Purnawan, S.; Setiawan, I.; Haridhi, H. A.; Irham, M.

2018-01-01

The study of sediment granulometry was completed at Lampulo fishing port (LFP). The LFP is a main fishing port in Aceh Province, Indonesia, located at 5°34’35” N; 95°19’23” E. The purpose of the research is to study and construct the environment condition of the bottom substrate. The data was taken by incorporating coring method at 10 stations using purposive random sampling. The wet sieve method was used to analyze the grain size for geostatistical analysis. The geostatistical parameters analysis in this study is classified as mean, sorting, skewness and kurtosis. The result informs that the types of sediments are sand, sandy clay and clayey sand for all stations. Station 1, however, is found as the coarsest compares to the other stations. All of the sediment collected at each station displays moderately sorted to poor sorted, while kurtosis values may be categorized as very leptokurtic. The results of the sediment parameters indicate that the environment of harbor pool was in a stable state, related to a sheltered condition.

Application of geostatistical simulation to compile seismotectonic provinces based on earthquake databases (case study: Iran)

NASA Astrophysics Data System (ADS)

Jalali, Mohammad; Ramazi, Hamidreza

2018-04-01

This article is devoted to application of a simulation algorithm based on geostatistical methods to compile and update seismotectonic provinces in which Iran has been chosen as a case study. Traditionally, tectonic maps together with seismological data and information (e.g., earthquake catalogues, earthquake mechanism, and microseismic data) have been used to update seismotectonic provinces. In many cases, incomplete earthquake catalogues are one of the important challenges in this procedure. To overcome this problem, a geostatistical simulation algorithm, turning band simulation, TBSIM, was applied to make a synthetic data to improve incomplete earthquake catalogues. Then, the synthetic data was added to the traditional information to study the seismicity homogeneity and classify the areas according to tectonic and seismic properties to update seismotectonic provinces. In this paper, (i) different magnitude types in the studied catalogues have been homogenized to moment magnitude (Mw), and earthquake declustering was then carried out to remove aftershocks and foreshocks; (ii) time normalization method was introduced to decrease the uncertainty in a temporal domain prior to start the simulation procedure; (iii) variography has been carried out in each subregion to study spatial regressions (e.g., west-southwestern area showed a spatial regression from 0.4 to 1.4 decimal degrees; the maximum range identified in the azimuth of 135 ± 10); (iv) TBSIM algorithm was then applied to make simulated events which gave rise to make 68,800 synthetic events according to the spatial regression found in several directions; (v) simulated events (i.e., magnitudes) were classified based on their intensity in ArcGIS packages and homogenous seismic zones have been determined. Finally, according to the synthetic data, tectonic features, and actual earthquake catalogues, 17 seismotectonic provinces were introduced in four major classes introduced as very high, high, moderate, and low seismic potential provinces. Seismotectonic properties of very high seismic potential provinces have been also presented.

Geostatistical enhancement of european hydrological predictions

NASA Astrophysics Data System (ADS)

Pugliese, Alessio; Castellarin, Attilio; Parajka, Juraj; Arheimer, Berit; Bagli, Stefano; Mazzoli, Paolo; Montanari, Alberto; Blöschl, Günter

2016-04-01

Geostatistical Enhancement of European Hydrological Prediction (GEEHP) is a research experiment developed within the EU funded SWITCH-ON project, which proposes to conduct comparative experiments in a virtual laboratory in order to share water-related information and tackle changes in the hydrosphere for operational needs (http://www.water-switch-on.eu). The main objective of GEEHP deals with the prediction of streamflow indices and signatures in ungauged basins at different spatial scales. In particular, among several possible hydrological signatures we focus in our experiment on the prediction of flow-duration curves (FDCs) along the stream-network, which has attracted an increasing scientific attention in the last decades due to the large number of practical and technical applications of the curves (e.g. hydropower potential estimation, riverine habitat suitability and ecological assessments, etc.). We apply a geostatistical procedure based on Top-kriging, which has been recently shown to be particularly reliable and easy-to-use regionalization approach, employing two different type of streamflow data: pan-European E-HYPE simulations (http://hypeweb.smhi.se/europehype) and observed daily streamflow series collected in two pilot study regions, i.e. Tyrol (merging data from Austrian and Italian stream gauging networks) and Sweden. The merger of the two study regions results in a rather large area (~450000 km2) and might be considered as a proxy for a pan-European application of the approach. In a first phase, we implement a bidirectional validation, i.e. E-HYPE catchments are set as training sites to predict FDCs at the same sites where observed data are available, and vice-versa. Such a validation procedure reveals (1) the usability of the proposed approach for predicting the FDCs over the entire river network of interest using alternatively observed data and E-HYPE simulations and (2) the accuracy of E-HYPE-based predictions of FDCs in ungauged sites. In a second phase, we develop a module, to be added to the flow-duration curve prediction framework, capable of enhancing E-HYPE-based predictions of FDCs by modelling the residuals obtained from the first phase. Among all possible methods, we apply geostatistical modelling of residuals and, alternatively, regional regression, so that residuals between empirical and E-HYPE-base predicted FDCs are described in terms of geomorphological and climatic catchment descriptors.

Testing geostatistical methods to combine radar and rain gauges for precipitation mapping in a mountainous region

NASA Astrophysics Data System (ADS)

Erdin, R.; Frei, C.; Sideris, I.; Kuensch, H.-R.

2010-09-01

There is an increasing demand for accurate mapping of precipitation at a spatial resolution of kilometers. Radar and rain gauges - the two main precipitation measurement systems - exhibit complementary strengths and weaknesses. Radar offers high spatial and temporal resolution but lacks accuracy of absolute values, whereas rain gauges provide accurate values at their specific point location but suffer from poor spatial representativeness. Methods of geostatistical mapping have been proposed to combine radar and rain gauge data for quantitative precipitation estimation (QPE). The aim is to combine the respective strengths and compensate for the respective weaknesses of the two observation platforms. Several studies have demonstrated the potential of these methods over topography of moderate complexity, but their performance remains unclear for high-mountain regions where rainfall patterns are complex, the representativeness of rain gauge measurements is limited and radar observations are obstructed. In this study we examine the potential and limitations of two frequently used geostatistical mapping methods for the territory of Switzerland, where the mountain chain of the Alps poses particular challenges to QPE. The two geostatistical methods explored are kriging with external drift (KED) using radar as drift variable and ordinary kriging of radar errors (OKRE). The radar data is a composite from three C-band radars using a constant Z-R relationship, advanced correction processings for visibility, ground clutter and beam shielding and a climatological bias adjustment. The rain gauge data originates from an automatic network with a typical inter-station distance of 25 km. Both combination methods are applied to a set of case examples representing typical rainfall situations in the Alps with their inherent challenges at daily and hourly time resolution. The quality of precipitation estimates is assessed by several skill scores calculated from cross validation errors at gauge locations. These scores assess different characteristics such as bias, distinction between dry and wet areas (HK, SLEEPS), accuracy of values at wet locations (SCATTER) and overall performance (RMSE, MAD). Special attention is paid to the subject of appropriate case-dependent transformation of variables in order to fulfill model assumptions. Our analyses show that geostatistical merging techniques can provide significant added value compared to pure radar and pure rain gauge data - also in mountainous terrain. Yet, the high a-priori quality of the radar product may have been essential for the good performance of methods. The comparison between the two combination methods shows better results in general for KED, the more flexible of the two methods. However, there are features, such as the differentiation between wet and dry areas (HK), and situations, such as small isolated convective cells, where OKRE outperforms KED. Our discussion conveys interesting insights into the potential and limitations of the two analyzed methods and leads to suggestions for further improvements of combination techniques.

Hydraulic Tomography and the Curse of Storativity

NASA Astrophysics Data System (ADS)

Cirpka, O. A.; Li, W.; Englert, A.

2006-12-01

Pumping tests are among the most common techniques for hydrogeological site investigation. Their traditional analysis is based on fitting analytical expressions to measured time series of drawdown. These expressions were derived for homogeneous conditions, whereas all natural aquifers are heterogeneous. The mentioned conceptual inconsistency complicates the hydrogeological interpretation of the obtained coefficients. In particularly, it has been shown that the heterogeneity of transmissivity is aliased to variability in the estimated storativity. In hydraulic tomography, multiple pumping tests are jointly analyzed. The hydraulic parameters to be estimated are allowed to fluctuate in space. For regularization, a geostatistical smoothness criterion may be introduced. Thus, the inversion results in the most likely spatial distribution of parameters that is consistent with the drawdown measurements and follows a predefined geostatistical model. Applying the restricted maximum likelihood approach, the parameters of the prior covariance function (i.e., the prior variance and correlation length) can be inferred from the data as well. We have applied the quasi-linear geostatistical approach of inverse modeling to drawdown measurements of multiple, overlapping pumping tests performed at the test site Krauthausen near Jülich, Germany. To reduce the computational costs, we have characterized the drawdown curves by their temporal moments. In the estimation of the geostatistical parameters, the measurement error of heads turned out to be of vital importance. The less we trust the data, the larger is the estimated correlation length, resulting in a more uniform distribution of transmissivity. Similar to conventional pumping test analysis, the data analysis point to a high variability of storativity although the properties making up storativity are known to be only mildly heterogeneous. We conjecture that the unresolved small-scale spatial variability of conductivity is mapped to variability of storativity. This is rather unfortunate since reliable field data on the variability of storativity are missing. The study underscores that structural information is difficult to extract from hydraulic data alone. Information on length scales and major deterministic features may be gained by geophysical surveying, even if rock-laws directly relating geophysical to hydraulic properties are considered unreliable.

Precipitation estimation in mountainous terrain using multivariate geostatistics. Part I: structural analysis

USGS Publications Warehouse

Hevesi, Joseph A.; Istok, Jonathan D.; Flint, Alan L.

1992-01-01

Values of average annual precipitation (AAP) are desired for hydrologic studies within a watershed containing Yucca Mountain, Nevada, a potential site for a high-level nuclear-waste repository. Reliable values of AAP are not yet available for most areas within this watershed because of a sparsity of precipitation measurements and the need to obtain measurements over a sufficient length of time. To estimate AAP over the entire watershed, historical precipitation data and station elevations were obtained from a network of 62 stations in southern Nevada and southeastern California. Multivariate geostatistics (cokriging) was selected as an estimation method because of a significant (p = 0.05) correlation of r = .75 between the natural log of AAP and station elevation. A sample direct variogram for the transformed variable, TAAP = ln [(AAP) 1000], was fitted with an isotropic, spherical model defined by a small nugget value of 5000, a range of 190 000 ft, and a sill value equal to the sample variance of 163 151. Elevations for 1531 additional locations were obtained from topographic maps to improve the accuracy of cokriged estimates. A sample direct variogram for elevation was fitted with an isotropic model consisting of a nugget value of 5500 and three nested transition structures: a Gaussian structure with a range of 61 000 ft, a spherical structure with a range of 70 000 ft, and a quasi-stationary, linear structure. The use of an isotropic, stationary model for elevation was considered valid within a sliding-neighborhood radius of 120 000 ft. The problem of fitting a positive-definite, nonlinear model of coregionalization to an inconsistent sample cross variogram for TAAP and elevation was solved by a modified use of the Cauchy-Schwarz inequality. A selected cross-variogram model consisted of two nested structures: a Gaussian structure with a range of 61 000 ft and a spherical structure with a range of 190 000 ft. Cross validation was used for model selection and for comparing the geostatistical model with six alternate estimation methods. Multivariate geostatistics provided the best cross-validation results.

Analysis of variograms with various sample sizes from a multispectral image

USDA-ARS?s Scientific Manuscript database

Variogram plays a crucial role in remote sensing application and geostatistics. It is very important to estimate variogram reliably from sufficient data. In this study, the analysis of variograms with various sample sizes of remotely sensed data was conducted. A 100x100-pixel subset was chosen from ...

Analysis of variograms with various sample sizes from a multispectral image

USDA-ARS?s Scientific Manuscript database

Variograms play a crucial role in remote sensing application and geostatistics. In this study, the analysis of variograms with various sample sizes of remotely sensed data was conducted. A 100 X 100 pixel subset was chosen from an aerial multispectral image which contained three wavebands, green, ...

A multiple-point geostatistical method for characterizing uncertainty of subsurface alluvial units and its effects on flow and transport

USGS Publications Warehouse

Cronkite-Ratcliff, C.; Phelps, G.A.; Boucher, A.

2012-01-01

This report provides a proof-of-concept to demonstrate the potential application of multiple-point geostatistics for characterizing geologic heterogeneity and its effect on flow and transport simulation. The study presented in this report is the result of collaboration between the U.S. Geological Survey (USGS) and Stanford University. This collaboration focused on improving the characterization of alluvial deposits by incorporating prior knowledge of geologic structure and estimating the uncertainty of the modeled geologic units. In this study, geologic heterogeneity of alluvial units is characterized as a set of stochastic realizations, and uncertainty is indicated by variability in the results of flow and transport simulations for this set of realizations. This approach is tested on a hypothetical geologic scenario developed using data from the alluvial deposits in Yucca Flat, Nevada. Yucca Flat was chosen as a data source for this test case because it includes both complex geologic and hydrologic characteristics and also contains a substantial amount of both surface and subsurface geologic data. Multiple-point geostatistics is used to model geologic heterogeneity in the subsurface. A three-dimensional (3D) model of spatial variability is developed by integrating alluvial units mapped at the surface with vertical drill-hole data. The SNESIM (Single Normal Equation Simulation) algorithm is used to represent geologic heterogeneity stochastically by generating 20 realizations, each of which represents an equally probable geologic scenario. A 3D numerical model is used to simulate groundwater flow and contaminant transport for each realization, producing a distribution of flow and transport responses to the geologic heterogeneity. From this distribution of flow and transport responses, the frequency of exceeding a given contaminant concentration threshold can be used as an indicator of uncertainty about the location of the contaminant plume boundary.

Multilayer perceptron with local constraint as an emerging method in spatial data analysis

NASA Astrophysics Data System (ADS)

de Bollivier, M.; Dubois, G.; Maignan, M.; Kanevsky, M.

1997-02-01

The use of Geographic Information Systems has revolutionalized the handling and the visualization of geo-referenced data and has underlined the critic role of spatial analysis. The usual tools for such a purpose are geostatistics which are widely used in Earth science. Geostatistics are based upon several hypothesis which are not always verified in practice. On the other hand, Artificial Neural Network (ANN) a priori can be used without special assumptions and are known to be flexible. This paper proposes to discuss the application of ANN in the case of the interpolation of a geo-referenced variable.

Hybrid Optimal Design of the Eco-Hydrological Wireless Sensor Network in the Middle Reach of the Heihe River Basin, China

PubMed Central

Kang, Jian; Li, Xin; Jin, Rui; Ge, Yong; Wang, Jinfeng; Wang, Jianghao

2014-01-01

The eco-hydrological wireless sensor network (EHWSN) in the middle reaches of the Heihe River Basin in China is designed to capture the spatial and temporal variability and to estimate the ground truth for validating the remote sensing productions. However, there is no available prior information about a target variable. To meet both requirements, a hybrid model-based sampling method without any spatial autocorrelation assumptions is developed to optimize the distribution of EHWSN nodes based on geostatistics. This hybrid model incorporates two sub-criteria: one for the variogram modeling to represent the variability, another for improving the spatial prediction to evaluate remote sensing productions. The reasonability of the optimized EHWSN is validated from representativeness, the variogram modeling and the spatial accuracy through using 15 types of simulation fields generated with the unconditional geostatistical stochastic simulation. The sampling design shows good representativeness; variograms estimated by samples have less than 3% mean error relative to true variograms. Then, fields at multiple scales are predicted. As the scale increases, estimated fields have higher similarities to simulation fields at block sizes exceeding 240 m. The validations prove that this hybrid sampling method is effective for both objectives when we do not know the characteristics of an optimized variables. PMID:25317762

A geostatistical approach to assess the spatial association between indoor radon concentration, geological features and building characteristics: the case of Lombardy, Northern Italy.

PubMed

Borgoni, Riccardo; Tritto, Valeria; Bigliotto, Carlo; de Bartolo, Daniela

2011-05-01

Radon is a natural gas known to be the main contributor to natural background radiation exposure and second to smoking, a major leading cause of lung cancer. The main source of radon is the soil, but the gas can enter buildings in many different ways and reach high indoor concentrations. Monitoring surveys have been promoted in many countries in order to assess the exposure of people to radon. In this paper, two complementary aspects are investigated. Firstly, we mapped indoor radon concentration in a large and inhomogeneous region using a geostatistical approach which borrows strength from the geologic nature of the soil. Secondly, knowing that geologic and anthropogenic factors, such as building characteristics, can foster the gas to flow into a building or protect against this, we evaluated these effects through a multiple regression model which takes into account the spatial correlation of the data. This allows us to rank different building typologies, identified by architectonic and geological characteristics, according to their proneness to radon. Our results suggest the opportunity to differentiate construction requirements in a large and inhomogeneous area, as the one considered in this paper, according to different places and provide a method to identify those dwellings which should be monitored more carefully.

APPLICATION OF BAYESIAN AND GEOSTATISTICAL MODELING TO THE ENVIRONMENTAL MONITORING OF CS-137 AT THE IDAHO NATIONAL LABORATORY

DOE Office of Scientific and Technical Information (OSTI.GOV)

Kara G. Eby

2010-08-01

At the Idaho National Laboratory (INL) Cs-137 concentrations above the U.S. Environmental Protection Agency risk-based threshold of 0.23 pCi/g may increase the risk of human mortality due to cancer. As a leader in nuclear research, the INL has been conducting nuclear activities for decades. Elevated anthropogenic radionuclide levels including Cs-137 are a result of atmospheric weapons testing, the Chernobyl accident, and nuclear activities occurring at the INL site. Therefore environmental monitoring and long-term surveillance of Cs-137 is required to evaluate risk. However, due to the large land area involved, frequent and comprehensive monitoring is limited. Developing a spatial model thatmore » predicts Cs-137 concentrations at unsampled locations will enhance the spatial characterization of Cs-137 in surface soils, provide guidance for an efficient monitoring program, and pinpoint areas requiring mitigation strategies. The predictive model presented herein is based on applied geostatistics using a Bayesian analysis of environmental characteristics across the INL site, which provides kriging spatial maps of both Cs-137 estimates and prediction errors. Comparisons are presented of two different kriging methods, showing that the use of secondary information (i.e., environmental characteristics) can provide improved prediction performance in some areas of the INL site.« less

Delineation of estuarine management areas using multivariate geostatistics: the case of Sado Estuary.

PubMed

Caeiro, Sandra; Goovaerts, Pierre; Painho, Marco; Costa, M Helena

2003-09-15

The Sado Estuary is a coastal zone located in the south of Portugal where conflicts between conservation and development exist because of its location near industrialized urban zones and its designation as a natural reserve. The aim of this paper is to evaluate a set of multivariate geostatistical approaches to delineate spatially contiguous regions of sediment structure for Sado Estuary. These areas will be the supporting infrastructure of an environmental management system for this estuary. The boundaries of each homogeneous area were derived from three sediment characterization attributes through three different approaches: (1) cluster analysis of dissimilarity matrix function of geographical separation followed by indicator kriging of the cluster data, (2) discriminant analysis of kriged values of the three sediment attributes, and (3) a combination of methods 1 and 2. Final maximum likelihood classification was integrated into a geographical information system. All methods generated fairly spatially contiguous management areas that reproduce well the environment of the estuary. Map comparison techniques based on kappa statistics showed thatthe resultant three maps are similar, supporting the choice of any of the methods as appropriate for management of the Sado Estuary. However, the results of method 1 seem to be in better agreement with estuary behavior, assessment of contamination sources, and previous work conducted at this site.

Arsenic in groundwater in Bangladesh: A geostatistical and epidemiological framework for evaluating health effects and potential remedies

NASA Astrophysics Data System (ADS)

Yu, Winston H.; Harvey, Charles M.; Harvey, Charles F.

2003-06-01

This paper examines the health crisis in Bangladesh due to dissolved arsenic in groundwater. First, we use geostatistical methods to construct a map of arsenic concentrations that divides Bangladesh into regions and estimate vertical concentration trends in these regions. Then, we use census data to estimate exposure distributions in the regions; we use epidemiological data from West Bengal and Taiwan to estimate dose response functions for arsenicosis and arsenic-induced cancers; and we combine the regional exposure distributions and the dose response models to estimate the health effects of groundwater arsenic in Bangladesh. We predict that long-term exposure to present arsenic concentrations will result in approximately 1,200,000 cases of hyperpigmentation, 600,000 cases of keratosis, 125,000 cases of skin cancer, and 3000 fatalities per year from internal cancers. Although these estimates are very uncertain, the method provides a framework for incorporating better data as it becomes available. Moreover, we examine the remedy of drilling deeper wells in selected regions of Bangladesh. By replacing 31% of the wells in the country with deeper wells the health effects of drinking groundwater arsenic could be reduced by approximately 70% provided that arsenic concentrations in deep wells remain relatively low.

Delineating Facies Spatial Distribution by Integrating Ensemble Data Assimilation and Indicator Geostatistics with Level Set Transformation.

DOE Office of Scientific and Technical Information (OSTI.GOV)

Hammond, Glenn Edward; Song, Xuehang; Ye, Ming

A new approach is developed to delineate the spatial distribution of discrete facies (geological units that have unique distributions of hydraulic, physical, and/or chemical properties) conditioned not only on direct data (measurements directly related to facies properties, e.g., grain size distribution obtained from borehole samples) but also on indirect data (observations indirectly related to facies distribution, e.g., hydraulic head and tracer concentration). Our method integrates for the first time ensemble data assimilation with traditional transition probability-based geostatistics. The concept of level set is introduced to build shape parameterization that allows transformation between discrete facies indicators and continuous random variables. Themore » spatial structure of different facies is simulated by indicator models using conditioning points selected adaptively during the iterative process of data assimilation. To evaluate the new method, a two-dimensional semi-synthetic example is designed to estimate the spatial distribution and permeability of two distinct facies from transient head data induced by pumping tests. The example demonstrates that our new method adequately captures the spatial pattern of facies distribution by imposing spatial continuity through conditioning points. The new method also reproduces the overall response in hydraulic head field with better accuracy compared to data assimilation with no constraints on spatial continuity on facies.« less

A Geostatistical Approach to Assess the Spatial Association between Indoor Radon Concentration, Geological Features and Building Characteristics: The Case of Lombardy, Northern Italy

PubMed Central

Borgoni, Riccardo; Tritto, Valeria; Bigliotto, Carlo; de Bartolo, Daniela

2011-01-01

Radon is a natural gas known to be the main contributor to natural background radiation exposure and second to smoking, a major leading cause of lung cancer. The main source of radon is the soil, but the gas can enter buildings in many different ways and reach high indoor concentrations. Monitoring surveys have been promoted in many countries in order to assess the exposure of people to radon. In this paper, two complementary aspects are investigated. Firstly, we mapped indoor radon concentration in a large and inhomogeneous region using a geostatistical approach which borrows strength from the geologic nature of the soil. Secondly, knowing that geologic and anthropogenic factors, such as building characteristics, can foster the gas to flow into a building or protect against this, we evaluated these effects through a multiple regression model which takes into account the spatial correlation of the data. This allows us to rank different building typologies, identified by architectonic and geological characteristics, according to their proneness to radon. Our results suggest the opportunity to differentiate construction requirements in a large and inhomogeneous area, as the one considered in this paper, according to different places and provide a method to identify those dwellings which should be monitored more carefully. PMID:21655128

Geostatistical analysis of effective vertical hydraulic conductivity and presence of confining layers in the Shallow Glacial Drift Aquifer, Oakland County, Michigan

USGS Publications Warehouse

Bissell, E.G.; Aichele, Stephen S.

2004-01-01

About 400,000 residents of Oakland County, Mich., rely on ground water for their primary drinking-water supply. More than 90 percent of these residents draw ground water from the shallow glacial drift aquifer. Understanding the vertical hydraulic conductivity of the shallow glacial drift aquifer is important both in identifying areas of ground-water recharge and in evaluating susceptibility to contamination. The geologic environment throughout much of the county, however, is poorly understood and heterogeneous, making conventional aquifer mapping techniques difficult. Geostatistical procedures are therefore used to describe the effective vertical hydraulic conductivity of the top 50 ft of the glacial deposits and to predict the probability of finding a potentially protective confining layer at a given location. The results presented synthesize the available well-log data; however, only about 40 percent of the explainable variation in the dataset is accounted for, making the results more qualitative than quantitative. Most of the variation in the effective vertical hydraulic conductivity cannot be explained with the well-log data currently available (as of 2004). Although the geologic environment is heterogeneous, the quality-assurance process indicated that more than half of the wells in the county’s Wellkey database (statewide database for monitoring drinking-water wells) had inconsistent identifications of lithology.

Hybrid optimal design of the eco-hydrological wireless sensor network in the middle reach of the Heihe River Basin, China.

PubMed

Kang, Jian; Li, Xin; Jin, Rui; Ge, Yong; Wang, Jinfeng; Wang, Jianghao

2014-10-14

The eco-hydrological wireless sensor network (EHWSN) in the middle reaches of the Heihe River Basin in China is designed to capture the spatial and temporal variability and to estimate the ground truth for validating the remote sensing productions. However, there is no available prior information about a target variable. To meet both requirements, a hybrid model-based sampling method without any spatial autocorrelation assumptions is developed to optimize the distribution of EHWSN nodes based on geostatistics. This hybrid model incorporates two sub-criteria: one for the variogram modeling to represent the variability, another for improving the spatial prediction to evaluate remote sensing productions. The reasonability of the optimized EHWSN is validated from representativeness, the variogram modeling and the spatial accuracy through using 15 types of simulation fields generated with the unconditional geostatistical stochastic simulation. The sampling design shows good representativeness; variograms estimated by samples have less than 3% mean error relative to true variograms. Then, fields at multiple scales are predicted. As the scale increases, estimated fields have higher similarities to simulation fields at block sizes exceeding 240 m. The validations prove that this hybrid sampling method is effective for both objectives when we do not know the characteristics of an optimized variables.

Land degradation assessment by geo-spatially modeling different soil erodibility equations in a semi-arid catchment.

PubMed

Saygın, Selen Deviren; Basaran, Mustafa; Ozcan, Ali Ugur; Dolarslan, Melda; Timur, Ozgur Burhan; Yilman, F Ebru; Erpul, Gunay

2011-09-01

Land degradation by soil erosion is one of the most serious problems and environmental issues in many ecosystems of arid and semi-arid regions. Especially, the disturbed areas have greater soil detachability and transportability capacity. Evaluation of land degradation in terms of soil erodibility, by using geostatistical modeling, is vital to protect and reclaim susceptible areas. Soil erodibility, described as the ability of soils to resist erosion, can be measured either directly under natural or simulated rainfall conditions, or indirectly estimated by empirical regression models. This study compares three empirical equations used to determine the soil erodibility factor of revised universal soil loss equation prediction technology based on their geospatial performances in the semi-arid catchment of the Saraykoy II Irrigation Dam located in Cankiri, Turkey. A total of 311 geo-referenced soil samples were collected with irregular intervals from the top soil layer (0-10 cm). Geostatistical analysis was performed with the point values of each equation to determine its spatial pattern. Results showed that equations that used soil organic matter in combination with the soil particle size better agreed with the variations in land use and topography of the catchment than the one using only the particle size distribution. It is recommended that the equations which dynamically integrate soil intrinsic properties with land use, topography, and its influences on the local microclimates, could be successfully used to geospatially determine sites highly susceptible to water erosion, and therefore, to select the agricultural and bio-engineering control measures needed.

Integrating indicator-based geostatistical estimation and aquifer vulnerability of nitrate-N for establishing groundwater protection zones

NASA Astrophysics Data System (ADS)

Jang, Cheng-Shin; Chen, Shih-Kai

2015-04-01

Groundwater nitrate-N contamination occurs frequently in agricultural regions, primarily resulting from surface agricultural activities. The focus of this study is to establish groundwater protection zones based on indicator-based geostatistical estimation and aquifer vulnerability of nitrate-N in the Choushui River alluvial fan in Taiwan. The groundwater protection zones are determined by univariate indicator kriging (IK) estimation, aquifer vulnerability assessment using logistic regression (LR), and integration of the IK estimation and aquifer vulnerability using simple IK with local prior means (sIKlpm). First, according to the statistical significance of source, transport, and attenuation factors dominating the occurrence of nitrate-N pollution, a LR model was adopted to evaluate aquifer vulnerability and to characterize occurrence probability of nitrate-N exceeding 0.5 mg/L. Moreover, the probabilities estimated using LR were regarded as local prior means. IK was then used to estimate the actual extent of nitrate-N pollution. The integration of the IK estimation and aquifer vulnerability was obtained using sIKlpm. Finally, groundwater protection zones were probabilistically determined using the three aforementioned methods, and the estimated accuracy of the delineated groundwater protection zones was gauged using a cross-validation procedure based on observed nitrate-N data. The results reveal that the integration of the IK estimation and aquifer vulnerability using sIKlpm is more robust than univariate IK estimation and aquifer vulnerability assessment using LR for establishing groundwater protection zones. Rigorous management practices for fertilizer use should be implemented in orchards situated in the determined groundwater protection zones.

Spatial distribution, sampling precision and survey design optimisation with non-normal variables: The case of anchovy (Engraulis encrasicolus) recruitment in Spanish Mediterranean waters

NASA Astrophysics Data System (ADS)

Tugores, M. Pilar; Iglesias, Magdalena; Oñate, Dolores; Miquel, Joan

2016-02-01

In the Mediterranean Sea, the European anchovy (Engraulis encrasicolus) displays a key role in ecological and economical terms. Ensuring stock sustainability requires the provision of crucial information, such as species spatial distribution or unbiased abundance and precision estimates, so that management strategies can be defined (e.g. fishing quotas, temporal closure areas or marine protected areas MPA). Furthermore, the estimation of the precision of global abundance at different sampling intensities can be used for survey design optimisation. Geostatistics provide a priori unbiased estimations of the spatial structure, global abundance and precision for autocorrelated data. However, their application to non-Gaussian data introduces difficulties in the analysis in conjunction with low robustness or unbiasedness. The present study applied intrinsic geostatistics in two dimensions in order to (i) analyse the spatial distribution of anchovy in Spanish Western Mediterranean waters during the species' recruitment season, (ii) produce distribution maps, (iii) estimate global abundance and its precision, (iv) analyse the effect of changing the sampling intensity on the precision of global abundance estimates and, (v) evaluate the effects of several methodological options on the robustness of all the analysed parameters. The results suggested that while the spatial structure was usually non-robust to the tested methodological options when working with the original dataset, it became more robust for the transformed datasets (especially for the log-backtransformed dataset). The global abundance was always highly robust and the global precision was highly or moderately robust to most of the methodological options, except for data transformation.

3D Hydraulic tomography from joint inversion of the hydraulic heads and self-potential data. (Invited)

NASA Astrophysics Data System (ADS)

Jardani, A.; Soueid Ahmed, A.; Revil, A.; Dupont, J.

2013-12-01

Pumping tests are usually employed to predict the hydraulic conductivity filed from the inversion of the head measurements. Nevertheless, the inverse problem is strongly underdetermined and a reliable imaging requires a considerable number of wells. We propose to add more information to the inversion of the heads by adding (non-intrusive) streaming potentials (SP) data. The SP corresponds to perturbations in the local electrical field caused directly by the fow of the ground water. These SP are obtained with a set of the non-polarising electrodes installed at the ground surface. We developed a geostatistical method for the estimation of the hydraulic conductivity field from measurements of hydraulic heads and SP during pumping and injection experiments. We use the adjoint state method and a recent petrophysical formulation of the streaming potential problem in which the streaming coupling coefficient is derived from the hydraulic conductivity allowed reducing of the unknown parameters. The geostatistical inverse framework is applied to three synthetic case studies with different number of the wells and electrodes used to measure the hydraulic heads and the streaming potentials. To evaluate the benefits of the incorporating of the streaming potential to the hydraulic data, we compared the cases in which the data are coupled or not to map the hydraulic conductivity. The results of the inversion revealed that a dense distribution of electrodes can be used to infer the heterogeneities in the hydraulic conductivity field. Incorporating the streaming potential information to the hydraulic head data improves the estimate of hydraulic conductivity field especially when the number of piezometers is limited.

Multivariate-Statistical Assessment of Heavy Metals for Agricultural Soils in Northern China

PubMed Central

Yang, Pingguo; Yang, Miao; Mao, Renzhao; Shao, Hongbo

2014-01-01

The study evaluated eight heavy metals content and soil pollution from agricultural soils in northern China. Multivariate and geostatistical analysis approaches were used to determine the anthropogenic and natural contribution of soil heavy metal concentrations. Single pollution index and integrated pollution index could be used to evaluate soil heavy metal risk. The results show that the first factor explains 27.3% of the eight soil heavy metals with strong positive loadings on Cu, Zn, and Cd, which indicates that Cu, Zn, and Cd are associated with and controlled by anthropic activities. The average value of heavy metal is lower than the second grade standard values of soil environmental quality standards in China. Single pollution index is lower than 1, and the Nemerow integrated pollution index is 0.305, which means that study area has not been polluted. The semivariograms of soil heavy metal single pollution index fitted spherical and exponential models. The variable ratio of single pollution index showed moderately spatial dependence. Heavy metal contents showed relative safety in the study area. PMID:24892058

Hydrostratigraphic Framework of the Raton, Vermejo, and Trinidad Aquifers in the Raton Basin, Las Animas County, Colorado

USGS Publications Warehouse

Watts, Kenneth R.

2006-01-01

Exploration for and production of coalbed methane has increased substantially in the Rocky Mountain region of the United States since the 1990s. During 1999-2004, annual production of natural gas (coalbed methane) from the Raton Basin in Las Animas County, Colorado, increased from 28,129,515 to 80,224,130 thousand cubic feet, and the annual volume of ground water coproduced by coalbed methane wells increased from about 949 million gallons to about 2,879 million gallons. Better definition of the hydrostratigraphic framework of the Raton, Vermejo, and Trinidad aquifers in the Raton Basin of southern Colorado is needed to evaluate the long-term effects of coalbed methane development on the availability and sustainability of ground-water resources. In 2001, the U.S. Geological Survey, in cooperation with the Colorado Water Conservation Board, began a study to evaluate the hydrogeology of the Raton Basin in Huerfano and Las Animas Counties, Colorado. Geostatistical methods were used to map the altitude of and depths to the bottoms and tops (structure) and the apparent thicknesses of the Trinidad Sandstone, the Vermejo Formation, and the Raton Formation in Las Animas County, based on completion reports and drillers' logs from about 1,400 coalbed methane wells in the Raton Basin. There was not enough subsurface control to map the structural surfaces and apparent thicknesses of the aquifers in Huerfano County. Geostatistical methods also were used to map the regional water table in the northern part of Las Animas County, based on reported depth to water from completion reports of water-supply wells. Although these maps were developed to better define the hydrostratigraphic framework, they also can be used to determine the contributing aquifer(s) of existing water wells and to estimate drilling depths of proposed water wells. These maps of the hydrostratigraphic framework could be improved with the addition of measured sections and mapping of geologic contacts at outcrops along the eastern and western margins of the Raton Basin.

An assessment of air pollutant exposure methods in Mexico City, Mexico.

PubMed

Rivera-González, Luis O; Zhang, Zhenzhen; Sánchez, Brisa N; Zhang, Kai; Brown, Daniel G; Rojas-Bracho, Leonora; Osornio-Vargas, Alvaro; Vadillo-Ortega, Felipe; O'Neill, Marie S

2015-05-01

Geostatistical interpolation methods to estimate individual exposure to outdoor air pollutants can be used in pregnancy cohorts where personal exposure data are not collected. Our objectives were to a) develop four assessment methods (citywide average (CWA); nearest monitor (NM); inverse distance weighting (IDW); and ordinary Kriging (OK)), and b) compare daily metrics and cross-validations of interpolation models. We obtained 2008 hourly data from Mexico City's outdoor air monitoring network for PM10, PM2.5, O3, CO, NO2, and SO2 and constructed daily exposure metrics for 1,000 simulated individual locations across five populated geographic zones. Descriptive statistics from all methods were calculated for dry and wet seasons, and by zone. We also evaluated IDW and OK methods' ability to predict measured concentrations at monitors using cross validation and a coefficient of variation (COV). All methods were performed using SAS 9.3, except ordinary Kriging which was modeled using R's gstat package. Overall, mean concentrations and standard deviations were similar among the different methods for each pollutant. Correlations between methods were generally high (r=0.77 to 0.99). However, ranges of estimated concentrations determined by NM, IDW, and OK were wider than the ranges for CWA. Root mean square errors for OK were consistently equal to or lower than for the IDW method. OK standard errors varied considerably between pollutants and the computed COVs ranged from 0.46 (least error) for SO2 and PM10 to 3.91 (most error) for PM2.5. OK predicted concentrations measured at the monitors better than IDW and NM. Given the similarity in results for the exposure methods, OK is preferred because this method alone provides predicted standard errors which can be incorporated in statistical models. The daily estimated exposures calculated using these different exposure methods provide flexibility to evaluate multiple windows of exposure during pregnancy, not just trimester or pregnancy-long exposures. Many studies evaluating associations between outdoor air pollution and adverse pregnancy outcomes rely on outdoor air pollution monitoring data linked to information gathered from large birth registries, and often lack residence location information needed to estimate individual exposure. This study simulated 1,000 residential locations to evaluate four air pollution exposure assessment methods, and describes possible exposure misclassification from using spatial averaging versus geostatistical interpolation models. An implication of this work is that policies to reduce air pollution and exposure among pregnant women based on epidemiologic literature should take into account possible error in estimates of effect when spatial averages alone are evaluated.

Spatial distribution of soil organic carbon and total nitrogen based on GIS and geostatistics in a small watershed in a hilly area of northern China.

PubMed

Peng, Gao; Bing, Wang; Guangpo, Geng; Guangcan, Zhang

2013-01-01

The spatial variability of soil organic carbon (SOC) and total nitrogen (STN) levels is important in both global carbon-nitrogen cycle and climate change research. There has been little research on the spatial distribution of SOC and STN at the watershed scale based on geographic information systems (GIS) and geostatistics. Ninety-seven soil samples taken at depths of 0-20 cm were collected during October 2010 and 2011 from the Matiyu small watershed (4.2 km(2)) of a hilly area in Shandong Province, northern China. The impacts of different land use types, elevation, vegetation coverage and other factors on SOC and STN spatial distributions were examined using GIS and a geostatistical method, regression-kriging. The results show that the concentration variations of SOC and STN in the Matiyu small watershed were moderate variation based on the mean, median, minimum and maximum, and the coefficients of variation (CV). Residual values of SOC and STN had moderate spatial autocorrelations, and the Nugget/Sill were 0.2% and 0.1%, respectively. Distribution maps of regression-kriging revealed that both SOC and STN concentrations in the Matiyu watershed decreased from southeast to northwest. This result was similar to the watershed DEM trend and significantly correlated with land use type, elevation and aspect. SOC and STN predictions with the regression-kriging method were more accurate than those obtained using ordinary kriging. This research indicates that geostatistical characteristics of SOC and STN concentrations in the watershed were closely related to both land-use type and spatial topographic structure and that regression-kriging is suitable for investigating the spatial distributions of SOC and STN in the complex topography of the watershed.

Spatial Distribution of Soil Organic Carbon and Total Nitrogen Based on GIS and Geostatistics in a Small Watershed in a Hilly Area of Northern China

PubMed Central

Peng, Gao; Bing, Wang; Guangpo, Geng; Guangcan, Zhang

2013-01-01

The spatial variability of soil organic carbon (SOC) and total nitrogen (STN) levels is important in both global carbon-nitrogen cycle and climate change research. There has been little research on the spatial distribution of SOC and STN at the watershed scale based on geographic information systems (GIS) and geostatistics. Ninety-seven soil samples taken at depths of 0–20 cm were collected during October 2010 and 2011 from the Matiyu small watershed (4.2 km2) of a hilly area in Shandong Province, northern China. The impacts of different land use types, elevation, vegetation coverage and other factors on SOC and STN spatial distributions were examined using GIS and a geostatistical method, regression-kriging. The results show that the concentration variations of SOC and STN in the Matiyu small watershed were moderate variation based on the mean, median, minimum and maximum, and the coefficients of variation (CV). Residual values of SOC and STN had moderate spatial autocorrelations, and the Nugget/Sill were 0.2% and 0.1%, respectively. Distribution maps of regression-kriging revealed that both SOC and STN concentrations in the Matiyu watershed decreased from southeast to northwest. This result was similar to the watershed DEM trend and significantly correlated with land use type, elevation and aspect. SOC and STN predictions with the regression-kriging method were more accurate than those obtained using ordinary kriging. This research indicates that geostatistical characteristics of SOC and STN concentrations in the watershed were closely related to both land-use type and spatial topographic structure and that regression-kriging is suitable for investigating the spatial distributions of SOC and STN in the complex topography of the watershed. PMID:24391791

A flexible Bayesian assessment for the expected impact of data on prediction confidence for optimal sampling designs

NASA Astrophysics Data System (ADS)

Leube, Philipp; Geiges, Andreas; Nowak, Wolfgang

2010-05-01

Incorporating hydrogeological data, such as head and tracer data, into stochastic models of subsurface flow and transport helps to reduce prediction uncertainty. Considering limited financial resources available for the data acquisition campaign, information needs towards the prediction goal should be satisfied in a efficient and task-specific manner. For finding the best one among a set of design candidates, an objective function is commonly evaluated, which measures the expected impact of data on prediction confidence, prior to their collection. An appropriate approach to this task should be stochastically rigorous, master non-linear dependencies between data, parameters and model predictions, and allow for a wide variety of different data types. Existing methods fail to fulfill all these requirements simultaneously. For this reason, we introduce a new method, denoted as CLUE (Cross-bred Likelihood Uncertainty Estimator), that derives the essential distributions and measures of data utility within a generalized, flexible and accurate framework. The method makes use of Bayesian GLUE (Generalized Likelihood Uncertainty Estimator) and extends it to an optimal design method by marginalizing over the yet unknown data values. Operating in a purely Bayesian Monte-Carlo framework, CLUE is a strictly formal information processing scheme free of linearizations. It provides full flexibility associated with the type of measurements (linear, non-linear, direct, indirect) and accounts for almost arbitrary sources of uncertainty (e.g. heterogeneity, geostatistical assumptions, boundary conditions, model concepts) via stochastic simulation and Bayesian model averaging. This helps to minimize the strength and impact of possible subjective prior assumptions, that would be hard to defend prior to data collection. Our study focuses on evaluating two different uncertainty measures: (i) expected conditional variance and (ii) expected relative entropy of a given prediction goal. The applicability and advantages are shown in a synthetic example. Therefor, we consider a contaminant source, posing a threat on a drinking water well in an aquifer. Furthermore, we assume uncertainty in geostatistical parameters, boundary conditions and hydraulic gradient. The two mentioned measures evaluate the sensitivity of (1) general prediction confidence and (2) exceedance probability of a legal regulatory threshold value on sampling locations.

A multi-analysis approach for space-time and economic evaluation of risks related with livestock diseases: the example of FMD in Peru.

PubMed

Martínez-López, B; Ivorra, B; Fernández-Carrión, E; Perez, A M; Medel-Herrero, A; Sánchez-Vizcaíno, F; Gortázar, C; Ramos, A M; Sánchez-Vizcaíno, J M

2014-04-01

This study presents a multi-disciplinary decision-support tool, which integrates geo-statistics, social network analysis (SNA), spatial-stochastic spread model, economic analysis and mapping/visualization capabilities for the evaluation of the sanitary and socio-economic impact of livestock diseases under diverse epidemiologic scenarios. We illustrate the applicability of this tool using foot-and-mouth disease (FMD) in Peru as an example. The approach consisted on a flexible, multistep process that may be easily adapted based on data availability. The first module (mI) uses a geo-statistical approach for the estimation (if needed) of the distribution and abundance of susceptible population (in the example here, cattle, swine, sheep, goats, and camelids) at farm-level in the region or country of interest (Peru). The second module (mII) applies SNA for evaluating the farm-to-farm contact patterns and for exploring the structure and frequency of between-farm animal movements as a proxy for potential disease introduction or spread. The third module (mIII) integrates mI-II outputs into a spatial-stochastic model that simulates within- and between-farm FMD-transmission. The economic module (mIV) connects outputs from mI-III to provide an estimate of associated direct and indirect costs. A visualization module (mV) is also implemented to graph and map the outputs of module I-IV. After 1000 simulated epidemics, the mean (95% probability interval) number of outbreaks, infected animals, epidemic duration, and direct costs were 37 (1, 1164), 2152 (1, 13, 250), 63 days (0, 442), and US$ 1.2 million (1072, 9.5 million), respectively. Spread of disease was primarily local (<4.5km), but geolocation and type of index farm strongly influenced the extent and spatial patterns of an epidemic. The approach is intended to support decisions in the last phase of the FMD eradication program in Peru, in particular to inform and support the implementation of risk-based surveillance and livestock insurance systems that may help to prevent and control potential FMD virus incursions into Peru. Copyright © 2014 Elsevier B.V. All rights reserved.

Geostatistical estimation of forest biomass in interior Alaska combining Landsat-derived tree cover, sampled airborne lidar and field observations

NASA Astrophysics Data System (ADS)

Babcock, Chad; Finley, Andrew O.; Andersen, Hans-Erik; Pattison, Robert; Cook, Bruce D.; Morton, Douglas C.; Alonzo, Michael; Nelson, Ross; Gregoire, Timothy; Ene, Liviu; Gobakken, Terje; Næsset, Erik

2018-06-01

The goal of this research was to develop and examine the performance of a geostatistical coregionalization modeling approach for combining field inventory measurements, strip samples of airborne lidar and Landsat-based remote sensing data products to predict aboveground biomass (AGB) in interior Alaska's Tanana Valley. The proposed modeling strategy facilitates pixel-level mapping of AGB density predictions across the entire spatial domain. Additionally, the coregionalization framework allows for statistically sound estimation of total AGB for arbitrary areal units within the study area---a key advance to support diverse management objectives in interior Alaska. This research focuses on appropriate characterization of prediction uncertainty in the form of posterior predictive coverage intervals and standard deviations. Using the framework detailed here, it is possible to quantify estimation uncertainty for any spatial extent, ranging from pixel-level predictions of AGB density to estimates of AGB stocks for the full domain. The lidar-informed coregionalization models consistently outperformed their counterpart lidar-free models in terms of point-level predictive performance and total AGB precision. Additionally, the inclusion of Landsat-derived forest cover as a covariate further improved estimation precision in regions with lower lidar sampling intensity. Our findings also demonstrate that model-based approaches that do not explicitly account for residual spatial dependence can grossly underestimate uncertainty, resulting in falsely precise estimates of AGB. On the other hand, in a geostatistical setting, residual spatial structure can be modeled within a Bayesian hierarchical framework to obtain statistically defensible assessments of uncertainty for AGB estimates.

Accuracy and uncertainty analysis of soil Bbf spatial distribution estimation at a coking plant-contaminated site based on normalization geostatistical technologies.

PubMed

Liu, Geng; Niu, Junjie; Zhang, Chao; Guo, Guanlin

2015-12-01

Data distribution is usually skewed severely by the presence of hot spots in contaminated sites. This causes difficulties for accurate geostatistical data transformation. Three types of typical normal distribution transformation methods termed the normal score, Johnson, and Box-Cox transformations were applied to compare the effects of spatial interpolation with normal distribution transformation data of benzo(b)fluoranthene in a large-scale coking plant-contaminated site in north China. Three normal transformation methods decreased the skewness and kurtosis of the benzo(b)fluoranthene, and all the transformed data passed the Kolmogorov-Smirnov test threshold. Cross validation showed that Johnson ordinary kriging has a minimum root-mean-square error of 1.17 and a mean error of 0.19, which was more accurate than the other two models. The area with fewer sampling points and that with high levels of contamination showed the largest prediction standard errors based on the Johnson ordinary kriging prediction map. We introduce an ideal normal transformation method prior to geostatistical estimation for severely skewed data, which enhances the reliability of risk estimation and improves the accuracy for determination of remediation boundaries.

PCTO-SIM: Multiple-point geostatistical modeling using parallel conditional texture optimization

NASA Astrophysics Data System (ADS)

Pourfard, Mohammadreza; Abdollahifard, Mohammad J.; Faez, Karim; Motamedi, Sayed Ahmad; Hosseinian, Tahmineh

2017-05-01

Multiple-point Geostatistics is a well-known general statistical framework by which complex geological phenomena have been modeled efficiently. Pixel-based and patch-based are two major categories of these methods. In this paper, the optimization-based category is used which has a dual concept in texture synthesis as texture optimization. Our extended version of texture optimization uses the energy concept to model geological phenomena. While honoring the hard point, the minimization of our proposed cost function forces simulation grid pixels to be as similar as possible to training images. Our algorithm has a self-enrichment capability and creates a richer training database from a sparser one through mixing the information of all surrounding patches of the simulation nodes. Therefore, it preserves pattern continuity in both continuous and categorical variables very well. It also shows a fuzzy result in its every realization similar to the expected result of multi realizations of other statistical models. While the main core of most previous Multiple-point Geostatistics methods is sequential, the parallel main core of our algorithm enabled it to use GPU efficiently to reduce the CPU time. One new validation method for MPS has also been proposed in this paper.

Geostatistical regularization of inverse models for the retrieval of vegetation biophysical variables

NASA Astrophysics Data System (ADS)

Atzberger, C.; Richter, K.

2009-09-01

The robust and accurate retrieval of vegetation biophysical variables using radiative transfer models (RTM) is seriously hampered by the ill-posedness of the inverse problem. With this research we further develop our previously published (object-based) inversion approach [Atzberger (2004)]. The object-based RTM inversion takes advantage of the geostatistical fact that the biophysical characteristics of nearby pixel are generally more similar than those at a larger distance. A two-step inversion based on PROSPECT+SAIL generated look-up-tables is presented that can be easily implemented and adapted to other radiative transfer models. The approach takes into account the spectral signatures of neighboring pixel and optimizes a common value of the average leaf angle (ALA) for all pixel of a given image object, such as an agricultural field. Using a large set of leaf area index (LAI) measurements (n = 58) acquired over six different crops of the Barrax test site, Spain), we demonstrate that the proposed geostatistical regularization yields in most cases more accurate and spatially consistent results compared to the traditional (pixel-based) inversion. Pros and cons of the approach are discussed and possible future extensions presented.

Time-REferenced data Kriging (TREK): mapping hydrological statistics given their time of reference

NASA Astrophysics Data System (ADS)

Porcheron, Delphine; Leblois, Etienne; Sauquet, Eric

2016-04-01

A major issue in water sciences is to predict runoff parameters at ungauged sites. Estimates can be obtained by various methods. Among them, geostatistical approaches provide interpolation methods that consequently use explicit assumptions on the variable of interest. Geostatistical techniques have been applied to precipitation and temperature fields and later extended to estimate runoff features considered as basin-support variates along the river network (e.g. Gottschalk, 1993; Sauquet et al., 2000; Skoien et al., 2006; Gottschalk et al., 2011). To obtain robust estimations, the first step is to collect a relevant dataset. Sauquet et al. (2000) and Sauquet (2006) suggest including a large number of catchments with long and common observation periods to ensure both reliability and temporal consistency in runoff estimates. However most observation networks evolve with time. Several choices are thus possible to define an optimal reference period maximizing either spatial or temporal overlap. However, the constraints usually lead to discard a significant number of stations. Time-REferenced data Kriging method (TREK) has been developed to overcome this issue. Here is proposed a method of geostatistical estimation considering the temporal support over which a hydrological statistic has been estimated. This allows attenuating the loss of data previously caused by the application of a strict reference period. The time reference remains for the targeted map itself. The weights depend on the observation period of the data included in the dataset and how near this is to the target period. In this presentation, the concepts of TREK will be introduced and thereafter illustrated to map mean annual runoff in France. References Gottschalk, L., 1993, Correlation and covariance of runoff. Stochastic Hydrology and Hydraulics 7(2), 85-101. Sauquet, E., Gottschalk, L. and Leblois, E., 2000, Mapping average annual runoff: a hierarchical approach applying a stochastic interpolation scheme. Hydrological Sciences Journal 45(6), 799-815. Skoien, J.O., Merz, R. and Bloschl, G., 2006, Top-kriging - geostatistics on stream networks. Hydrology and Earth System Sciences 10(2), 277-287. Gottschalk, L., Leblois, E. and Skoien, J.O., 2011, Correlation and covariance of runoff revisited. Journal of Hydrology 398(1-2), 76-90. Sauquet, E., 2006, Mapping mean annual river discharges: Geostatistical developments for incorporating river network dependencies. Journal of Hydrology 331(1-2), 300-314.

[Spatial structure analysis and distribution simulation of Therioaphis trifolii population based on geostatistics and GIS].

PubMed

Zhang, Rong; Leng, Yun-fa; Zhu, Meng-meng; Wang, Fang

2007-11-01

Based on geographic information system and geostatistics, the spatial structure of Therioaphis trifolii population of different periods in Yuanzhou district of Guyuan City, the southern Ningxia Province, was analyzed. The spatial distribution of Therioaphis trifolii population was also simulated by ordinary Kriging interpretation. The results showed that Therioaphis trifolii population of different periods was correlated spatially in the study area. The semivariograms of Therioaphis trifolii could be described by exponential model, indicating an aggregated spatial arrangement. The spatial variance varied from 34.13%-48.77%, and the range varied from 8.751-12.049 km. The degree and direction of aggregation showed that the trend was increased gradually from southwest to northeast. The dynamic change of Therioaphis trifolii population in different periods could be analyzed intuitively on the simulated maps of the spatial distribution from the two aspects of time and space, The occurrence position and degree of Therioaphis trifolii to a state of certain time could be determined easily.

Geostatistical analysis of ground-penetrating radar data: A means of describing spatial variation in the subsurface

NASA Astrophysics Data System (ADS)

Rea, Jane; Knight, Rosemary

1998-03-01

We have investigated the use of ground-penetrating radar (GFR) as a means of characterizing the heterogeneity of the subsurface. Radar data were collected at several sites in southwestern British Columbia underlain by glaciodeltaic sediments. A cliff face study was conducted in which geostatistical analysis of a digitized photograph of the face and the radar image of the face showed excellent agreement in the maximum correlation direction and the correlation length determined from these two data sets. Other two-dimensional (2-D) sections of radar data were divided into sedimentary architectural elements on the basis of the distinct radar appearance of these sedimentary units. Examples of four sedimentary units were used to obtain semivariograms from the radar data and resulted in maximum correlation lengths between 0.5 and 4.8 m. A 3-D radar survey, collected over a package of gravel and sand foresets, was analyzed to determine the paleoflow direction; a correlation length of 4 m was found in that direction.

The geostatistical approach for structural and stratigraphic framework analysis of offshore NW Bonaparte Basin, Australia

DOE Office of Scientific and Technical Information (OSTI.GOV)

Wahid, Ali, E-mail: ali.wahid@live.com; Salim, Ahmed Mohamed Ahmed, E-mail: mohamed.salim@petronas.com.my; Yusoff, Wan Ismail Wan, E-mail: wanismail-wanyusoff@petronas.com.my

2016-02-01

Geostatistics or statistical approach is based on the studies of temporal and spatial trend, which depend upon spatial relationships to model known information of variable(s) at unsampled locations. The statistical technique known as kriging was used for petrophycial and facies analysis, which help to assume spatial relationship to model the geological continuity between the known data and the unknown to produce a single best guess of the unknown. Kriging is also known as optimal interpolation technique, which facilitate to generate best linear unbiased estimation of each horizon. The idea is to construct a numerical model of the lithofacies and rockmore » properties that honor available data and further integrate with interpreting seismic sections, techtonostratigraphy chart with sea level curve (short term) and regional tectonics of the study area to find the structural and stratigraphic growth history of the NW Bonaparte Basin. By using kriging technique the models were built which help to estimate different parameters like horizons, facies, and porosities in the study area. The variograms were used to determine for identification of spatial relationship between data which help to find the depositional history of the North West (NW) Bonaparte Basin.« less

Rockslide susceptibility and hazard assessment for mitigation works design along vertical rocky cliffs: workflow proposal based on a real case-study conducted in Sacco (Campania), Italy

NASA Astrophysics Data System (ADS)

Pignalosa, Antonio; Di Crescenzo, Giuseppe; Marino, Ermanno; Terracciano, Rosario; Santo, Antonio

2015-04-01

The work here presented concerns a case study in which a complete multidisciplinary workflow has been applied for an extensive assessment of the rockslide susceptibility and hazard in a common scenario such as a vertical and fractured rocky cliffs. The studied area is located in a high-relief zone in Southern Italy (Sacco, Salerno, Campania), characterized by wide vertical rocky cliffs formed by tectonized thick successions of shallow-water limestones. The study concerned the following phases: a) topographic surveying integrating of 3d laser scanning, photogrammetry and GNSS; b) gelogical surveying, characterization of single instabilities and geomecanichal surveying, conducted by geologists rock climbers; c) processing of 3d data and reconstruction of high resolution geometrical models; d) structural and geomechanical analyses; e) data filing in a GIS-based spatial database; f) geo-statistical and spatial analyses and mapping of the whole set of data; g) 3D rockfall analysis; The main goals of the study have been a) to set-up an investigation method to achieve a complete and thorough characterization of the slope stability conditions and b) to provide a detailed base for an accurate definition of the reinforcement and mitigation systems. For this purposes the most up-to-date methods of field surveying, remote sensing, 3d modelling and geospatial data analysis have been integrated in a systematic workflow, accounting of the economic sustainability of the whole project. A novel integrated approach have been applied both fusing deterministic and statistical surveying methods. This approach enabled to deal with the wide extension of the studied area (near to 200.000 m2), without compromising an high accuracy of the results. The deterministic phase, based on a field characterization of single instabilities and their further analyses on 3d models, has been applied for delineating the peculiarity of each single feature. The statistical approach, based on geostructural field mapping and on punctual geomechanical data from scan-line surveying, allowed the rock mass partitioning in homogeneous geomechanical sectors and data interpolation through bounded geostatistical analyses on 3d models. All data, resulting from both approaches, have been referenced and filed in a single spatial database and considered in global geo-statistical analyses for deriving a fully modelled and comprehensive evaluation of the rockslide susceptibility. The described workflow yielded the following innovative results: a) a detailed census of single potential instabilities, through a spatial database recording the geometrical, geological and mechanical features, along with the expected failure modes; b) an high resolution characterization of the whole slope rockslide susceptibility, based on the partitioning of the area according to the stability and mechanical conditions which can be directly related to specific hazard mitigation systems; c) the exact extension of the area exposed to the rockslide hazard, along with the dynamic parameters of expected phenomena; d) an intervention design for hazard mitigation.

Integration of vertical and in-seam horizontal well production analyses with stochastic geostatistical algorithms to estimate pre-mining methane drainage efficiency from coal seams: Blue Creek seam, Alabama

PubMed Central

Karacan, C. Özgen

2015-01-01

Coal seam degasification and its efficiency are directly related to the safety of coal mining. Degasification activities in the Black Warrior basin started in the early 1980s by using vertical boreholes. Although the Blue Creek seam, which is part of the Mary Lee coal group, has been the main seam of interest for coal mining, vertical wellbores have also been completed in the Pratt, Mary Lee, and Black Creek coal groups of the Upper Pottsville formation to degasify multiple seams. Currently, the Blue Creek seam is further degasified 2–3 years in advance of mining using in-seam horizontal boreholes to ensure safe mining. The studied location in this work is located between Tuscaloosa and Jefferson counties in Alabama and was degasified using 81 vertical boreholes, some of which are still active. When the current long mine expanded its operation into this area in 2009, horizontal boreholes were also drilled in advance of mining for further degasification of only the Blue Creek seam to ensure a safe and a productive operation. This paper presents an integrated study and a methodology to combine history matching results from vertical boreholes with production modeling of horizontal boreholes using geostatistical simulation to evaluate spatial effectiveness of in-seam boreholes in reducing gas-in-place (GIP). Results in this study showed that in-seam wells' boreholes had an estimated effective drainage area of 2050 acres with cumulative production of 604 MMscf methane during ~2 years of operation. With horizontal borehole production, GIP in the Blue Creek seam decreased from an average of 1.52 MMscf to 1.23 MMscf per acre. It was also shown that effective gas flow capacity, which was independently modeled using vertical borehole data, affected horizontal borehole production. GIP and effective gas flow capacity of coal seam gas were also used to predict remaining gas potential for the Blue Creek seam. PMID:26435557

Integration of vertical and in-seam horizontal well production analyses with stochastic geostatistical algorithms to estimate pre-mining methane drainage efficiency from coal seams: Blue Creek seam, Alabama.

PubMed

Karacan, C Özgen

2013-07-30

Coal seam degasification and its efficiency are directly related to the safety of coal mining. Degasification activities in the Black Warrior basin started in the early 1980s by using vertical boreholes. Although the Blue Creek seam, which is part of the Mary Lee coal group, has been the main seam of interest for coal mining, vertical wellbores have also been completed in the Pratt, Mary Lee, and Black Creek coal groups of the Upper Pottsville formation to degasify multiple seams. Currently, the Blue Creek seam is further degasified 2-3 years in advance of mining using in-seam horizontal boreholes to ensure safe mining. The studied location in this work is located between Tuscaloosa and Jefferson counties in Alabama and was degasified using 81 vertical boreholes, some of which are still active. When the current long mine expanded its operation into this area in 2009, horizontal boreholes were also drilled in advance of mining for further degasification of only the Blue Creek seam to ensure a safe and a productive operation. This paper presents an integrated study and a methodology to combine history matching results from vertical boreholes with production modeling of horizontal boreholes using geostatistical simulation to evaluate spatial effectiveness of in-seam boreholes in reducing gas-in-place (GIP). Results in this study showed that in-seam wells' boreholes had an estimated effective drainage area of 2050 acres with cumulative production of 604 MMscf methane during ~2 years of operation. With horizontal borehole production, GIP in the Blue Creek seam decreased from an average of 1.52 MMscf to 1.23 MMscf per acre. It was also shown that effective gas flow capacity, which was independently modeled using vertical borehole data, affected horizontal borehole production. GIP and effective gas flow capacity of coal seam gas were also used to predict remaining gas potential for the Blue Creek seam.

Geostatistics as a tool to study mite dispersion in physic nut plantations.

PubMed

Rosado, J F; Picanço, M C; Sarmento, R A; Pereira, R M; Pedro-Neto, M; Galdino, T V S; de Sousa Saraiva, A; Erasmo, E A L

2015-08-01

Spatial distribution studies in pest management identify the locations where pest attacks on crops are most severe, enabling us to understand and predict the movement of such pests. Studies on the spatial distribution of two mite species, however, are rather scarce. The mites Polyphagotarsonemus latus and Tetranychus bastosi are the major pests affecting physic nut plantations (Jatropha curcas). Therefore, the objective of this study was to measure the spatial distributions of P. latus and T. bastosi in the physic nut plantations. Mite densities were monitored over 2 years in two different plantations. Sample locations were georeferenced. The experimental data were analyzed using geostatistical analyses. The total mite density was found to be higher when only one species was present (T. bastosi). When both the mite species were found in the same plantation, their peak densities occurred at different times. These mites, however, exhibited uniform spatial distribution when found at extreme densities (low or high). However, the mites showed an aggregated distribution in intermediate densities. Mite spatial distribution models were isotropic. Mite colonization commenced at the periphery of the areas under study, whereas the high-density patches extended until they reached 30 m in diameter. This has not been reported for J. curcas plants before.

Spatial heterogeneity distribution of soil total nitrogen and total phosphorus in the Yaoxiang watershed in a hilly area of northern China based on geographic information system and geostatistics.

PubMed

Liu, Yu; Gao, Peng; Zhang, Liyong; Niu, Xiang; Wang, Bing

2016-10-01

Soil total nitrogen (STN) and total phosphorus (STP) are important indicators of soil nutrients and the important indexes of soil fertility and soil quality evaluation. Using geographic information system (GIS) and geostatistics, the spatial heterogeneity distribution of STN and STP in the Yaoxiang watershed in a hilly area of northern China was studied. The results showed that: (1) The STN and STP contents showed a declining trend with the increase in soil depth; the variation coefficients ( C v ) of STN and STP in the 0- to 10-cm soil layer (42.25% and 14.77%, respectively) were higher than in the 10- to 30-cm soil layer (28.77% and 11.60%, respectively). Moreover, the C v of STN was higher than that of STP. (2) The maximum C 0 /( C 0  +  C 1 ) of STN and STP in the soil layers was less than 25%, this indicated that a strong spatial distribution autocorrelation existed for STN and STP; and the STP showed higher intensity and more stable variation than the STN. (3) From the correlation analysis, we concluded that the topographic indexes such as elevation and slope direction all influenced the spatial distribution of STN and STP (correlation coefficients were 0.688 and 0.518, respectively). (4) The overall distribution of STN and STP in the Yaoxiang watershed decreased from the northwest to the southeast. This variation trend was similar to the watershed DEM trend and was significantly influenced by vegetation and topographic factors. These results revealed the spatial heterogeneity distribution of STN and STP, and addressed the influences of forest vegetation coverage, elevation, and other topographic factors on the spatial distribution of STN and STP at the watershed scale.

Training-Image Based Geostatistical Inversion Using a Spatial Generative Adversarial Neural Network

NASA Astrophysics Data System (ADS)

Laloy, Eric; Hérault, Romain; Jacques, Diederik; Linde, Niklas

2018-01-01

Probabilistic inversion within a multiple-point statistics framework is often computationally prohibitive for high-dimensional problems. To partly address this, we introduce and evaluate a new training-image based inversion approach for complex geologic media. Our approach relies on a deep neural network of the generative adversarial network (GAN) type. After training using a training image (TI), our proposed spatial GAN (SGAN) can quickly generate 2-D and 3-D unconditional realizations. A key characteristic of our SGAN is that it defines a (very) low-dimensional parameterization, thereby allowing for efficient probabilistic inversion using state-of-the-art Markov chain Monte Carlo (MCMC) methods. In addition, available direct conditioning data can be incorporated within the inversion. Several 2-D and 3-D categorical TIs are first used to analyze the performance of our SGAN for unconditional geostatistical simulation. Training our deep network can take several hours. After training, realizations containing a few millions of pixels/voxels can be produced in a matter of seconds. This makes it especially useful for simulating many thousands of realizations (e.g., for MCMC inversion) as the relative cost of the training per realization diminishes with the considered number of realizations. Synthetic inversion case studies involving 2-D steady state flow and 3-D transient hydraulic tomography with and without direct conditioning data are used to illustrate the effectiveness of our proposed SGAN-based inversion. For the 2-D case, the inversion rapidly explores the posterior model distribution. For the 3-D case, the inversion recovers model realizations that fit the data close to the target level and visually resemble the true model well.

Interpolation of Regional Groundwater Quality Parameters With Categorical and Real-Valued Secondary Information in the State of Baden-Württemberg, Germany

NASA Astrophysics Data System (ADS)

Haslauer, C. P.; Allmendinger, M.; Gnann, S.; Heisserer, T.; Bárdossy, A.

2017-12-01

The basic problem of geostatistics is to estimate the primary variable (e.g. groundwater quality, nitrate) at an un-sampled location based on point measurements at locations in the vicinity. Typically, models are being used that describe the spatial dependence based on the geometry of the observation network. This presentation demonstrates methods that take the following properties additionally into account: the statistical distribution of the measurements, a different degree of dependence in different quantiles, censored measurements, the composition of categorical additional information in the neighbourhood (exhaustive secondary information), and the spatial dependence of a dependent secondary variable, possibly measured with a different observation network (non-exhaustive secondary data). Two modelling approaches are demonstrated individually and combined: The non-stationarity in the marginal distribution is accounted for by locally mixed distribution functions that depend on the composition of the categorical variable in the neighbourhood of each interpolation location. This methodology is currently being implemented for operational use at the environmental state agency of Baden-Württemberg. An alternative to co-Kriging in copula space with an arbitrary number of secondary parameters is presented: The method performs better than traditional techniques if the primary variable is undersampled and does not produce erroneous negative estimates. Even more, the quality of the uncertainty estimates is much improved. The worth of the secondary information is thoroughly evaluated. The improved geostatistical hydrogeological models are being analyzed using measurements of a large observation network ( 2500 measurement locations) in the state of Baden-Württemberg ( 36.000 km2). Typical groundwater quality parameters such as nitrate, chloride, barium, antrazine, and desethylatrazine are being assessed, cross-validated, and compared with traditional geostatistical methods. The secondary information of land use is available on a 30m x 30m raster. We show that the presented methods are not only better estimators (e.g. in the sense of an average quadratic error), but exhibit a much more realistic structure of the uncertainty and hence are improvements compared to existing methods.

A Reduced-Order Successive Linear Estimator for Geostatistical Inversion and its Application in Hydraulic Tomography

NASA Astrophysics Data System (ADS)

Zha, Yuanyuan; Yeh, Tian-Chyi J.; Illman, Walter A.; Zeng, Wenzhi; Zhang, Yonggen; Sun, Fangqiang; Shi, Liangsheng

2018-03-01

Hydraulic tomography (HT) is a recently developed technology for characterizing high-resolution, site-specific heterogeneity using hydraulic data (nd) from a series of cross-hole pumping tests. To properly account for the subsurface heterogeneity and to flexibly incorporate additional information, geostatistical inverse models, which permit a large number of spatially correlated unknowns (ny), are frequently used to interpret the collected data. However, the memory storage requirements for the covariance of the unknowns (ny × ny) in these models are prodigious for large-scale 3-D problems. Moreover, the sensitivity evaluation is often computationally intensive using traditional difference method (ny forward runs). Although employment of the adjoint method can reduce the cost to nd forward runs, the adjoint model requires intrusive coding effort. In order to resolve these issues, this paper presents a Reduced-Order Successive Linear Estimator (ROSLE) for analyzing HT data. This new estimator approximates the covariance of the unknowns using Karhunen-Loeve Expansion (KLE) truncated to nkl order, and it calculates the directional sensitivities (in the directions of nkl eigenvectors) to form the covariance and cross-covariance used in the Successive Linear Estimator (SLE). In addition, the covariance of unknowns is updated every iteration by updating the eigenvalues and eigenfunctions. The computational advantages of the proposed algorithm are demonstrated through numerical experiments and a 3-D transient HT analysis of data from a highly heterogeneous field site.

Use of geostatistics in planning optimum drilling program

DOE Office of Scientific and Technical Information (OSTI.GOV)

Ghose S.

1989-08-01

Application of geostatistics in the natural resources industry is well established. In a typical process of estimation, the statistically dependent geological data are used to predict the characteristics of a deposit. The estimator used is the best linear unbiased estimator (or BLUE), and a numerical factor of confidence is also provided. The natural inhomogeneity and anisotropy of a deposit are also quantified with preciseness. Drilling is the most reliable way of obtaining data for mining and related industries. However, it is often difficult to decide what is the optimum number of drill holes necessary for evaluation. In this paper, sequentialmore » measures of percent variation at 95% confidence level of a geological variable have been used to decipher economically optimum drilling density. A coal reserve model has been used to illustrate the method and findings. Fictitious drilling data were added (within the domain of population characteristics) in stages, to obtain a point of stability, beyond which the gain was significant (diminishing marginal benefit). The final relations are established by graphically projecting and comparing two variables - cost and precision. By mapping the percent variation at each stage, the localized areas of discrepancies can be identified. These are the locations where additional drilling is needed. The system can be controlled if performed at progressive stages and the preciseness toward stability is monitored.« less

Dixie Valley Engineered Geothermal System Exploration Methodology Project, Baseline Conceptual Model Report

DOE Data Explorer

Joe Iovenitti

2013-05-15

The Engineered Geothermal System (EGS) Exploration Methodology Project is developing an exploration approach for EGS through the integration of geoscientific data. The Project chose the Dixie Valley Geothermal System in Nevada as a field laboratory site for methodlogy calibration purposes because, in the public domain, it is a highly characterized geothermal systems in the Basin and Range with a considerable amount of geoscience and most importantly, well data. This Baseline Conceptual Model report summarizes the results of the first three project tasks (1) collect and assess the existing public domain geoscience data, (2) design and populate a GIS database, and (3) develop a baseline (existing data) geothermal conceptual model, evaluate geostatistical relationships, and generate baseline, coupled EGS favorability/trust maps from +1km above sea level (asl) to -4km asl for the Calibration Area (Dixie Valley Geothermal Wellfield) to identify EGS drilling targets at a scale of 5km x 5km. It presents (1) an assessment of the readily available public domain data and some proprietary data provided by Terra-Gen Power, LLC, (2) a re-interpretation of these data as required, (3) an exploratory geostatistical data analysis, (4) the baseline geothermal conceptual model, and (5) the EGS favorability/trust mapping. The conceptual model presented applies to both the hydrothermal system and EGS in the Dixie Valley region.

Geostatistics, remote sensing and precision farming.

PubMed

Mulla, D J

1997-01-01

Precision farming is possible today because of advances in farming technology, procedures for mapping and interpolating spatial patterns, and geographic information systems for overlaying and interpreting several soil, landscape and crop attributes. The key component of precision farming is the map showing spatial patterns in field characteristics. Obtaining information for this map is often achieved by soil sampling. This approach, however, can be cost-prohibitive for grain crops. Soil sampling strategies can be simplified by use of auxiliary data provided by satellite or aerial photo imagery. This paper describes geostatistical methods for estimating spatial patterns in soil organic matter, soil test phosphorus and wheat grain yield from a combination of Thematic Mapper imaging and soil sampling.

Rainfall Observed Over Bangladesh 2000-2008: A Comparison of Spatial Interpolation Methods

NASA Astrophysics Data System (ADS)

Pervez, M.; Henebry, G. M.

2010-12-01

In preparation for a hydrometeorological study of freshwater resources in the greater Ganges-Brahmaputra region, we compared the results of four methods of spatial interpolation applied to point measurements of daily rainfall over Bangladesh during a seven year period (2000-2008). Two univariate (inverse distance weighted and spline-regularized and tension) and two multivariate geostatistical (ordinary kriging and kriging with external drift) methods were used to interpolate daily observations from a network of 221 rain gauges across Bangladesh spanning an area of 143,000 sq km. Elevation and topographic index were used as the covariates in the geostatistical methods. The validity of the interpolated maps was analyzed through cross-validation. The quality of the methods was assessed through the Pearson and Spearman correlations and root mean square error measurements of accuracy in cross-validation. Preliminary results indicated that the univariate methods performed better than the geostatistical methods at daily scales, likely due to the relatively dense sampled point measurements and a weak correlation between the rainfall and covariates at daily scales in this region. Inverse distance weighted produced the better results than the spline. For the days with extreme or high rainfall—spatially and quantitatively—the correlation between observed and interpolated estimates appeared to be high (r2 ~ 0.6 RMSE ~ 10mm), although for low rainfall days the correlations were poor (r2 ~ 0.1 RMSE ~ 3mm). The performance quality of these methods was influenced by the density of the sample point measurements, the quantity of the observed rainfall along with spatial extent, and an appropriate search radius defining the neighboring points. Results indicated that interpolated rainfall estimates at daily scales may introduce uncertainties in the successive hydrometeorological analysis. Interpolations at 5-day, 10-day, 15-day, and monthly time scales are currently under investigation.

Regional flow duration curves: Geostatistical techniques versus multivariate regression

USGS Publications Warehouse

Pugliese, Alessio; Farmer, William H.; Castellarin, Attilio; Archfield, Stacey A.; Vogel, Richard M.

2016-01-01

A period-of-record flow duration curve (FDC) represents the relationship between the magnitude and frequency of daily streamflows. Prediction of FDCs is of great importance for locations characterized by sparse or missing streamflow observations. We present a detailed comparison of two methods which are capable of predicting an FDC at ungauged basins: (1) an adaptation of the geostatistical method, Top-kriging, employing a linear weighted average of dimensionless empirical FDCs, standardised with a reference streamflow value; and (2) regional multiple linear regression of streamflow quantiles, perhaps the most common method for the prediction of FDCs at ungauged sites. In particular, Top-kriging relies on a metric for expressing the similarity between catchments computed as the negative deviation of the FDC from a reference streamflow value, which we termed total negative deviation (TND). Comparisons of these two methods are made in 182 largely unregulated river catchments in the southeastern U.S. using a three-fold cross-validation algorithm. Our results reveal that the two methods perform similarly throughout flow-regimes, with average Nash-Sutcliffe Efficiencies 0.566 and 0.662, (0.883 and 0.829 on log-transformed quantiles) for the geostatistical and the linear regression models, respectively. The differences between the reproduction of FDC's occurred mostly for low flows with exceedance probability (i.e. duration) above 0.98.

[Spatial variability of soil nutrients based on geostatistics combined with GIS--a case study in Zunghua City of Hebei Province].

PubMed

Guo, X; Fu, B; Ma, K; Chen, L

2000-08-01

Geostatistics combined with GIS was applied to analyze the spatial variability of soil nutrients in topsoil (0-20 cm) in Zunghua City of Hebei Province. GIS can integrate attribute data with geographical data of system variables, which makes the application of geostatistics technique for large spatial scale more convenient. Soil nutrient data in this study included available N (alkaline hydrolyzing nitrogen), total N, available K, available P and organic matter. The results showed that the semivariograms of soil nutrients were best described by spherical model, except for that of available K, which was best fitted by complex structure of exponential model and linear with sill model. The spatial variability of available K was mainly produced by structural factor, while that of available N, total N, available P and organic matter was primarily caused by random factor. However, their spatial heterogeneity degree was different: the degree of total N and organic matter was higher, and that of available P and available N was lower. The results also indicated that the spatial correlation of the five tested soil nutrients at this large scale was moderately dependent. The ranges of available N and available P were almost same, which were 5 km and 5.5 km, respectively. The range of total N was up to 18 km, and that of organic matter was 8.5 km. For available K, the spatial variability scale primarily expressed exponential model between 0-3.5 km, but linear with sill model between 3.5-25.5 km. In addition, five soil nutrients exhibited different isotropic ranges. Available N and available P were isotropic through the whole research range (0-28 km). The isotropic range of available K was 0-8 km, and that of total N and organic matter was 0-10 km.

Estimation of river pollution index in a tidal stream using kriging analysis.

PubMed

Chen, Yen-Chang; Yeh, Hui-Chung; Wei, Chiang

2012-08-29

Tidal streams are complex watercourses that represent a transitional zone between riverine and marine systems; they occur where fresh and marine waters converge. Because tidal circulation processes cause substantial turbulence in these highly dynamic zones, tidal streams are the most productive of water bodies. Their rich biological diversity, combined with the convenience of land and water transports, provide sites for concentrated populations that evolve into large cities. Domestic wastewater is generally discharged directly into tidal streams in Taiwan, necessitating regular evaluation of the water quality of these streams. Given the complex flow dynamics of tidal streams, only a few models can effectively evaluate and identify pollution levels. This study evaluates the river pollution index (RPI) in tidal streams by using kriging analysis. This is a geostatistical method for interpolating random spatial variation to estimate linear grid points in two or three dimensions. A kriging-based method is developed to evaluate RPI in tidal streams, which is typically considered as 1D in hydraulic engineering. The proposed method efficiently evaluates RPI in tidal streams with the minimum amount of water quality data. Data of the Tanshui River downstream reach available from an estuarine area validate the accuracy and reliability of the proposed method. Results of this study demonstrate that this simple yet reliable method can effectively estimate RPI in tidal streams.

Spatial earthquake hazard assessment of Evansville, Indiana

USGS Publications Warehouse

Rockaway, T.D.; Frost, J.D.; Eggert, D.L.; Luna, R.

1997-01-01

The earthquake hazard has been evaluated for a 150-square-kilometer area around Evansville, Indiana. GIS-QUAKE, a system that combines liquefaction and ground motion analysis routines with site-specific geological, geotechnical, and seismological information, was used for the analysis. The hazard potential was determined by using 586 SPT borings, 27 CPT sounding, 39 shear-wave velocity profiles and synthesized acceleration records for body-wave magnitude 6.5 and 7.3 mid-continental earthquakes, occurring at distances of 50 km and 250 km, respectively. The results of the GIS-QUAKE hazard analyses for Evansville identify areas with a high hazard potential that had not previously been identified in earthquake zonation studies. The Pigeon Creek area specifically is identified as having significant potential for liquefaction-induced damage. Damage as a result of ground motion amplification is determined to be a moderate concern throughout the area. Differences in the findings of this zonation study and previous work are attributed to the size and range of the database, the hazard evaluation methodologies, and the geostatistical interpolation techniques used to estimate the hazard potential. Further, assumptions regarding the groundwater elevations made in previous studies are also considered to have had a significant effect on the results.

Within-stand spatial distribution of tree mortality caused by the Douglas-Fir beetle (Coleoptera: Scolytidae)

Treesearch

Jose F. Negron; John A. Anhold; A. Steve Munson

2001-01-01

The Douglas-fir beetle, Dendroctonus pseudotsugae Hopkins, causes considerable mortality in Douglas-fir, Pseudotsuga menziesii (Mirb.) Franco, forests. Within-stand distribution of mortality was examined in affected stands using geostatistical techniques. A 10 x 10 m grid was established in two 4-ha study sites. Live and beetle-killed host basal area was measured at...

Multiple-point statistical simulation for hydrogeological models: 3-D training image development and conditioning strategies

NASA Astrophysics Data System (ADS)

Høyer, Anne-Sophie; Vignoli, Giulio; Mejer Hansen, Thomas; Thanh Vu, Le; Keefer, Donald A.; Jørgensen, Flemming

2017-12-01

Most studies on the application of geostatistical simulations based on multiple-point statistics (MPS) to hydrogeological modelling focus on relatively fine-scale models and concentrate on the estimation of facies-level structural uncertainty. Much less attention is paid to the use of input data and optimal construction of training images. For instance, even though the training image should capture a set of spatial geological characteristics to guide the simulations, the majority of the research still relies on 2-D or quasi-3-D training images. In the present study, we demonstrate a novel strategy for 3-D MPS modelling characterized by (i) realistic 3-D training images and (ii) an effective workflow for incorporating a diverse group of geological and geophysical data sets. The study covers an area of 2810 km2 in the southern part of Denmark. MPS simulations are performed on a subset of the geological succession (the lower to middle Miocene sediments) which is characterized by relatively uniform structures and dominated by sand and clay. The simulated domain is large and each of the geostatistical realizations contains approximately 45 million voxels with size 100 m × 100 m × 5 m. Data used for the modelling include water well logs, high-resolution seismic data, and a previously published 3-D geological model. We apply a series of different strategies for the simulations based on data quality, and develop a novel method to effectively create observed spatial trends. The training image is constructed as a relatively small 3-D voxel model covering an area of 90 km2. We use an iterative training image development strategy and find that even slight modifications in the training image create significant changes in simulations. Thus, this study shows how to include both the geological environment and the type and quality of input information in order to achieve optimal results from MPS modelling. We present a practical workflow to build the training image and effectively handle different types of input information to perform large-scale geostatistical modelling.

Geostatistical assessment of Pb in soil around Paris, France.

PubMed

Saby, N; Arrouays, D; Boulonne, L; Jolivet, C; Pochot, A

2006-08-15

This paper presents a survey on soil Pb contamination around Paris (France) using the French soil monitoring network. The first aim of this study is to estimate the total amount of anthropogenic Pb inputs in soils and to distinguish Pb due to diffuse pollution from geochemical background Pb. Secondly, this study tries to find the main controlling factors of the spatial distribution of anthropogenic Pb. We used the technique of relative topsoil enhancement to evaluate the anthropogenic stock of Pb and we performed lognormal kriging to map Pb regional distribution. The results show a strong gradient of anthropogenic stock of Pb around the urban Paris area. We estimate a total amount of anthropogenic stock of Pb close to 143,000 metric tons, which corresponds to an average accumulation of 5.9 t km(-2). Our study suggests that a grid-based survey can help to quantify diffuse Pb contamination by using robust techniques of calculation and that it might also be used to validate predictions of deposition models.

Identification of the Hydrogeochemical Processes in Groundwater Using Classic Integrated Geochemical Methods and Geostatistical Techniques, in Amol-Babol Plain, Iran

PubMed Central

Sheikhy Narany, Tahoora; Ramli, Mohammad Firuz; Aris, Ahmad Zaharin; Sulaiman, Wan Nor Azmin; Juahir, Hafizan; Fakharian, Kazem

2014-01-01

Hydrogeochemical investigations had been carried out at the Amol-Babol Plain in the north of Iran. Geochemical processes and factors controlling the groundwater chemistry are identified based on the combination of classic geochemical methods with geographic information system (GIS) and geostatistical techniques. The results of the ionic ratios and Gibbs plots show that water rock interaction mechanisms, followed by cation exchange, and dissolution of carbonate and silicate minerals have influenced the groundwater chemistry in the study area. The hydrogeochemical characteristics of groundwater show a shift from low mineralized Ca-HCO3, Ca-Na-HCO3, and Ca-Cl water types to high mineralized Na-Cl water type. Three classes, namely, C1, C2, and C3, have been classified using cluster analysis. The spatial distribution maps of Na+/Cl−, Mg2+/Ca2+, and Cl−/HCO3 − ratios and electrical conductivity values indicate that the carbonate and weathering of silicate minerals played a significant role in the groundwater chemistry on the southern and western sides of the plain. However, salinization process had increased due to the influence of the evaporation-precipitation process towards the north-eastern side of the study area. PMID:24523640

Geostatistical analysis and isoscape of ice core derived water stable isotope records in an Antarctic macro region

NASA Astrophysics Data System (ADS)

Hatvani, István Gábor; Leuenberger, Markus; Kohán, Balázs; Kern, Zoltán

2017-09-01

Water stable isotopes preserved in ice cores provide essential information about polar precipitation. In the present study, multivariate regression and variogram analyses were conducted on 22 δ2H and 53 δ18O records from 60 ice cores covering the second half of the 20th century. Taking the multicollinearity of the explanatory variables into account, as also the model's adjusted R2 and its mean absolute error, longitude, elevation and distance from the coast were found to be the main independent geographical driving factors governing the spatial δ18O variability of firn/ice in the chosen Antarctic macro region. After diminishing the effects of these factors, using variography, the weights for interpolation with kriging were obtained and the spatial autocorrelation structure of the dataset was revealed. This indicates an average area of influence with a radius of 350 km. This allows the determination of the areas which are as yet not covered by the spatial variability of the existing network of ice cores. Finally, the regional isoscape was obtained for the study area, and this may be considered the first step towards a geostatistically improved isoscape for Antarctica.

Detection of terrain indices related to soil salinity and mapping salt-affected soils using remote sensing and geostatistical techniques.

PubMed

Triki Fourati, Hela; Bouaziz, Moncef; Benzina, Mourad; Bouaziz, Samir

2017-04-01

Traditional surveying methods of soil properties over landscapes are dramatically cost and time-consuming. Thus, remote sensing is a proper choice for monitoring environmental problem. This research aims to study the effect of environmental factors on soil salinity and to map the spatial distribution of this salinity over the southern east part of Tunisia by means of remote sensing and geostatistical techniques. For this purpose, we used Advanced Spaceborne Thermal Emission and Reflection Radiometer data to depict geomorphological parameters: elevation, slope, plan curvature (PLC), profile curvature (PRC), and aspect. Pearson correlation between these parameters and soil electrical conductivity (EC soil ) showed that mainly slope and elevation affect the concentration of salt in soil. Moreover, spectral analysis illustrated the high potential of short-wave infrared (SWIR) bands to identify saline soils. To map soil salinity in southern Tunisia, ordinary kriging (OK), minimum distance (MD) classification, and simple regression (SR) were used. The findings showed that ordinary kriging technique provides the most reliable performances to identify and classify saline soils over the study area with a root mean square error of 1.83 and mean error of 0.018.

Mercury emissions from coal combustion in Silesia, analysis using geostatistics

NASA Astrophysics Data System (ADS)

Zasina, Damian; Zawadzki, Jaroslaw

2015-04-01

Data provided by the UNEP's report on mercury [1] shows that solid fuel combustion in significant source of mercury emission to air. Silesia, located in southwestern Poland, is notably affected by mercury emission due to being one of the most industrialized Polish regions: the place of coal mining, production of metals, stone mining, mineral quarrying and chemical industry. Moreover, Silesia is the region with high population density. People are exposed to severe risk of mercury emitted from both: industrial and domestic sources (i.e. small household furnaces). Small sources have significant contribution to total emission of mercury. Official and statistical analysis, including prepared for international purposes [2] did not provide data about spatial distribution of the mercury emitted to air, however number of analysis on Polish public power and energy sector had been prepared so far [3; 4]. The distribution of locations exposed for mercury emission from small domestic sources is interesting matter merging information from various sources: statistical, economical and environmental. This paper presents geostatistical approach to distibution of mercury emission from coal combustion. Analysed data organized in 2 independent levels: individual, bottom-up approach derived from national emission reporting system [5; 6] and top down - regional data calculated basing on official statistics [7]. Analysis, that will be presented, will include comparison of spatial distributions of mercury emission using data derived from sources mentioned above. Investigation will include three voivodeships of Poland: Lower Silesian, Opole (voivodeship) and Silesian using selected geostatistical methodologies including ordinary kriging [8]. References [1] UNEP. Global Mercury Assessment 2013: Sources, Emissions, Releases and Environmental Transport. UNEP Chemicals Branch, Geneva, Switzerland, 2013. [2] NCEM. Poland's Informative Inventory Report 2014. NCEM at the IEP-NRI, 2014. http://www.ceip.at/. [3] Zyśk J., Wyrwa A. and Pluta M. Emissions of mercury from the power sector in Poland. Atmospheric Environment, 45:605-610, 2011. http://dx.doi.org/10.1016/j.atmosenv.2010.10.041/. [4] Głodek A., Pacyna J. Mercury emission from coal-fired power plants in Poland. Atmospheric Environment, 43:5668-5673, 2009. http://dx.doi.org/10.1016/j.atmosenv.2009.07.041. [5] NCEM. National emission database, 2014. NCEM Management at the IEP-NRI. [6] Zasina D. and Zawadzki J. Disaggregation problems using data derived from polish air pollutant emission management system, Systems Supporting Production Engineering. Review of Problems and Solutions, ISBN 978-83-937845-9-2, pp. 128-137, 2014. [7] EUROSTAT. EUROSTAT Energy Database, 2014. [8] Wackernagel H. Basics in Geostatistics 3 Geostatistical Monte-Carlo methods: Conditional simulation, 2013.

Mathematical Geology.

ERIC Educational Resources Information Center

Jones, Thomas A.

1983-01-01

Mathematical techniques used to solve geological problems are briefly discussed (including comments on use of geostatistics). Highlights of conferences/meetings and conference papers in mathematical geology are also provided. (JN)

Supervised restoration of degraded medical images using multiple-point geostatistics.

PubMed

Pham, Tuan D

2012-06-01

Reducing noise in medical images has been an important issue of research and development for medical diagnosis, patient treatment, and validation of biomedical hypotheses. Noise inherently exists in medical and biological images due to the acquisition and transmission in any imaging devices. Being different from image enhancement, the purpose of image restoration is the process of removing noise from a degraded image in order to recover as much as possible its original version. This paper presents a statistically supervised approach for medical image restoration using the concept of multiple-point geostatistics. Experimental results have shown the effectiveness of the proposed technique which has potential as a new methodology for medical and biological image processing. Copyright © 2010 Elsevier Ireland Ltd. All rights reserved.

A geostatistical extreme-value framework for fast simulation of natural hazard events

PubMed Central

Stephenson, David B.

2016-01-01

We develop a statistical framework for simulating natural hazard events that combines extreme value theory and geostatistics. Robust generalized additive model forms represent generalized Pareto marginal distribution parameters while a Student’s t-process captures spatial dependence and gives a continuous-space framework for natural hazard event simulations. Efficiency of the simulation method allows many years of data (typically over 10 000) to be obtained at relatively little computational cost. This makes the model viable for forming the hazard module of a catastrophe model. We illustrate the framework by simulating maximum wind gusts for European windstorms, which are found to have realistic marginal and spatial properties, and validate well against wind gust measurements. PMID:27279768

Geostatistical methods in the assessment of the spatial variability of the quality of river water

NASA Astrophysics Data System (ADS)

Krasowska, Małgorzata; Banaszuk, Piotr

2017-11-01

The research was conducted in the agricultural catchment in north-eastern Poland. The aim of this study was to check how geostatistical analysis can be useful for the detection zones and forms of supply stream by water from different sources. The work was included the implementation of hydrochemical profiles. These profiles were made by measuring the electrical conductivity (EC) values and temperature along the river. On the basis of these results, the authors calculated the coefficient of Moran I and performed semivariogram and found that the EC values are correlated on a stretch of about 140 m. This means that the spatial correlation between samples of water in the stream is readable over a distance of about 140 meters. Therefore it is believed that the degree of water mineralization on this section is shaped by water entering the river channel migration in different ways: through tributaries, leachate drainage and surface runoff. In the case of the analyzed catchment, the potential sources of pollution were drainage systems. Therefore, the spatial analysis allowed the identification pollution sources in a catchment, especially in drained agricultural catchments.

Analysis of alluvial hydrostratigraphy using indicator geostatistics, with examples from Santa Clara Valley, California

DOE Office of Scientific and Technical Information (OSTI.GOV)

NONE

1995-03-01

Current trends in hydrogeology seek to enlist sedimentary concepts in the interpretation of permeability structures. However, existing conceptual models of alluvial deposition tend to inadequately account for the heterogeneity caused by complex sedimentological and external factors. This dissertation presents three analyses of alluvial hydrostratigraphy using indicator geostatistics. This approach empirically acknowledges both the random and structured qualities of alluvial structures at scales relevant to site investigations. The first analysis introduces the indicator approach, whereby binary values are assigned to borehole-log intervals on the basis of inferred relative permeability; it presents a case study of indicator variography at a well-documented ground-watermore » contamination site, and uses indicator kriging to interpolate an aquifer-aquitard sequence in three dimensions. The second analysis develops an alluvial-architecture context for interpreting semivariograms, and performs comparative variography for a suite of alluvial sites in Santa Clara Valley, California. The third analysis investigates the use of a water well perforation indicator for assessing large-scale hydrostratigraphic structures within relatively deep production zones.« less

High variation subarctic topsoil pollutant concentration prediction using neural network residual kriging

NASA Astrophysics Data System (ADS)

Sergeev, A. P.; Tarasov, D. A.; Buevich, A. G.; Subbotina, I. E.; Shichkin, A. V.; Sergeeva, M. V.; Lvova, O. A.

2017-06-01

The work deals with the application of neural networks residual kriging (NNRK) to the spatial prediction of the abnormally distributed soil pollutant (Cr). It is known that combination of geostatistical interpolation approaches (kriging) and neural networks leads to significantly better prediction accuracy and productivity. Generalized regression neural networks and multilayer perceptrons are classes of neural networks widely used for the continuous function mapping. Each network has its own pros and cons; however both demonstrated fast training and good mapping possibilities. In the work, we examined and compared two combined techniques: generalized regression neural network residual kriging (GRNNRK) and multilayer perceptron residual kriging (MLPRK). The case study is based on the real data sets on surface contamination by chromium at a particular location of the subarctic Novy Urengoy, Russia, obtained during the previously conducted screening. The proposed models have been built, implemented and validated using ArcGIS and MATLAB environments. The networks structures have been chosen during a computer simulation based on the minimization of the RMSE. MLRPK showed the best predictive accuracy comparing to the geostatistical approach (kriging) and even to GRNNRK.

A spatial model for a stream networks of Citarik River with the environmental variables: potential of hydrogen (PH) and temperature

NASA Astrophysics Data System (ADS)

Bachrudin, A.; Mohamed, N. B.; Supian, S.; Sukono; Hidayat, Y.

2018-03-01

Application of existing geostatistical theory of stream networks provides a number of interesting and challenging problems. Most of statistical tools in the traditional geostatistics have been based on a Euclidean distance such as autocovariance functions, but for stream data is not permissible since it deals with a stream distance. To overcome this autocovariance developed a model based on the distance the flow with using convolution kernel approach (moving average construction). Spatial model for a stream networks is widely used to monitor environmental on a river networks. In a case study of a river in province of West Java, the objective of this paper is to analyze a capability of a predictive on two environmental variables, potential of hydrogen (PH) and temperature using ordinary kriging. Several the empirical results show: (1) The best fit of autocovariance functions for temperature and potential hydrogen (ph) of Citarik River is linear which also yields the smallest root mean squared prediction error (RMSPE), (2) the spatial correlation values between the locations on upstream and on downstream of Citarik river exhibit decreasingly

A geostatistical methodology for the optimal design of space-time hydraulic head monitoring networks and its application to the Valle de Querétaro aquifer.

PubMed

Júnez-Ferreira, H E; Herrera, G S

2013-04-01

This paper presents a new methodology for the optimal design of space-time hydraulic head monitoring networks and its application to the Valle de Querétaro aquifer in Mexico. The selection of the space-time monitoring points is done using a static Kalman filter combined with a sequential optimization method. The Kalman filter requires as input a space-time covariance matrix, which is derived from a geostatistical analysis. A sequential optimization method that selects the space-time point that minimizes a function of the variance, in each step, is used. We demonstrate the methodology applying it to the redesign of the hydraulic head monitoring network of the Valle de Querétaro aquifer with the objective of selecting from a set of monitoring positions and times, those that minimize the spatiotemporal redundancy. The database for the geostatistical space-time analysis corresponds to information of 273 wells located within the aquifer for the period 1970-2007. A total of 1,435 hydraulic head data were used to construct the experimental space-time variogram. The results show that from the existing monitoring program that consists of 418 space-time monitoring points, only 178 are not redundant. The implied reduction of monitoring costs was possible because the proposed method is successful in propagating information in space and time.

Statistical Estimation of Heterogeneities: A New Frontier in Well Testing

NASA Astrophysics Data System (ADS)

Neuman, S. P.; Guadagnini, A.; Illman, W. A.; Riva, M.; Vesselinov, V. V.

2001-12-01

Well-testing methods have traditionally relied on analytical solutions of groundwater flow equations in relatively simple domains, consisting of one or at most a few units having uniform hydraulic properties. Recently, attention has been shifting toward methods and solutions that would allow one to characterize subsurface heterogeneities in greater detail. On one hand, geostatistical inverse methods are being used to assess the spatial variability of parameters, such as permeability and porosity, on the basis of multiple cross-hole pressure interference tests. On the other hand, analytical solutions are being developed to describe the mean and variance (first and second statistical moments) of flow to a well in a randomly heterogeneous medium. Geostatistical inverse interpretation of cross-hole tests yields a smoothed but detailed "tomographic" image of how parameters actually vary in three-dimensional space, together with corresponding measures of estimation uncertainty. Moment solutions may soon allow one to interpret well tests in terms of statistical parameters such as the mean and variance of log permeability, its spatial autocorrelation and statistical anisotropy. The idea of geostatistical cross-hole tomography is illustrated through pneumatic injection tests conducted in unsaturated fractured tuff at the Apache Leap Research Site near Superior, Arizona. The idea of using moment equations to interpret well-tests statistically is illustrated through a recently developed three-dimensional solution for steady state flow to a well in a bounded, randomly heterogeneous, statistically anisotropic aquifer.

Geostatistical analysis of allele presence patterns among American black bears in eastern North Carolina

USGS Publications Warehouse

Thompson, L.M.; Van Manen, F.T.; King, T.L.

2005-01-01

Highways are one of the leading causes of wildlife habitat fragmentation and may particularly affect wide-ranging species, such as American black bears (Ursus americanus). We initiated a research project in 2000 to determine potential effects of a 4-lane highway on black bear ecology in Washington County, North Carolina. The research design included a treatment area (highway construction) and a control area and a pre- and post-construction phase. We used data from the pre-construction phase to determine whether we could detect scale dependency or directionality among allele occurrence patterns using geostatistics. Detection of such patterns could provide a powerful tool to measure the effects of landscape fragmentation on gene flow. We sampled DNA from roots of black bear hair at 70 hair-sampling sites on each study area for 7 weeks during fall of 2000. We used microsatellite analysis based on 10 loci to determine unique multi-locus genotypes. We examined all alleles sampled at ???25 sites on each study area and mapped their presence or absence at each hair-sample site. We calculated semivariograms, which measure the strength of statistical correlation as a function of distance, and adjusted them for anisotropy to determine the maximum direction of spatial continuity. We then calculated the mean direction of spatial continuity for all examined alleles. The mean direction of allele frequency variation was 118.3?? (SE = 8.5) on the treatment area and 172.3?? (SE = 6.0) on the control area. Rayleigh's tests showed that these directions differed from random distributions (P = 0.028 and P < 0.001, respectively), indicating consistent directional patterns for the alleles we examined in each area. Despite the small spatial scale of our study (approximately 11,000 ha for each study area), we observed distinct and consistent patterns of allele occurrence, suggesting different directions of gene flow between the study areas. These directions seemed to coincide with the primary orientation of the best habitat areas. Furthermore, the patterns we observed suggest directions of potential source populations beyond the 2 study areas. Indeed, nearby areas classified as core black bear habitat exist in the directions indicated by our analysis. Geostatistical analysis of allele occurrence patterns may provide a useful technique to identify potential barriers to gene flow among bear populations.

The role of geostatistics in medical geology

NASA Astrophysics Data System (ADS)

Goovaerts, Pierre

2014-05-01

Since its development in the mining industry, geostatistics has emerged as the primary tool for spatial data analysis in various fields, ranging from earth and atmospheric sciences, to agriculture, soil science, remote sensing, and more recently environmental exposure assessment. In the last few years, these tools have been tailored to the field of medical geography or spatial epidemiology, which is concerned with the study of spatial patterns of disease incidence and mortality and the identification of potential 'causes' of disease, such as environmental exposure, diet and unhealthy behaviors, economic or socio-demographic factors. On the other hand, medical geology is an emerging interdisciplinary scientific field studying the relationship between natural geological factors and their effects on human and animal health. This paper provides an introduction to the field of medical geology with an overview of geostatistical methods available for the analysis of geological and health data. Key concepts are illustrated using the mapping of groundwater arsenic concentrations across eleven Michigan counties and the exploration of its relationship to the incidence of prostate cancer at the township level. Arsenic in drinking-water is a major problem and has received much attention because of the large human population exposed and the extremely high concentrations (e.g. 600 to 700 μg/L) recorded in many instances. Few studies have however assessed the risks associated with exposure to low levels of arsenic (say < 50 μg/L) most commonly found in drinking water in the United States. In the Michigan thumb region, arsenopyrite (up to 7% As by weight) has been identified in the bedrock of the Marshall Sandstone aquifer, one of the region's most productive aquifers. Epidemiologic studies have suggested a possible associationbetween exposure to inorganic arsenic and prostate cancer mortality, including a study of populations residing in Utah. The information available for the present ecological study (i.e. analysis of aggregated health outcomes) consist of: 1) 9,188 arsenic concentrations measured at 8,212 different private wells that were sampled between 1993 and 2002, 2) prostate cancer incidence recorded at the township level over the period 1985-2002, and 3) block-group population density that served as proxy for urbanization and use of regulated public water supply versus use of potentially contaminated private wells in rural areas.

Pattern-Based Inverse Modeling for Characterization of Subsurface Flow Models with Complex Geologic Heterogeneity

NASA Astrophysics Data System (ADS)

Golmohammadi, A.; Jafarpour, B.; M Khaninezhad, M. R.

2017-12-01

Calibration of heterogeneous subsurface flow models leads to ill-posed nonlinear inverse problems, where too many unknown parameters are estimated from limited response measurements. When the underlying parameters form complex (non-Gaussian) structured spatial connectivity patterns, classical variogram-based geostatistical techniques cannot describe the underlying connectivity patterns. Modern pattern-based geostatistical methods that incorporate higher-order spatial statistics are more suitable for describing such complex spatial patterns. Moreover, when the underlying unknown parameters are discrete (geologic facies distribution), conventional model calibration techniques that are designed for continuous parameters cannot be applied directly. In this paper, we introduce a novel pattern-based model calibration method to reconstruct discrete and spatially complex facies distributions from dynamic flow response data. To reproduce complex connectivity patterns during model calibration, we impose a feasibility constraint to ensure that the solution follows the expected higher-order spatial statistics. For model calibration, we adopt a regularized least-squares formulation, involving data mismatch, pattern connectivity, and feasibility constraint terms. Using an alternating directions optimization algorithm, the regularized objective function is divided into a continuous model calibration problem, followed by mapping the solution onto the feasible set. The feasibility constraint to honor the expected spatial statistics is implemented using a supervised machine learning algorithm. The two steps of the model calibration formulation are repeated until the convergence criterion is met. Several numerical examples are used to evaluate the performance of the developed method.

Bayesian geostatistics in health cartography: the perspective of malaria.

PubMed

Patil, Anand P; Gething, Peter W; Piel, Frédéric B; Hay, Simon I

2011-06-01

Maps of parasite prevalences and other aspects of infectious diseases that vary in space are widely used in parasitology. However, spatial parasitological datasets rarely, if ever, have sufficient coverage to allow exact determination of such maps. Bayesian geostatistics (BG) is a method for finding a large sample of maps that can explain a dataset, in which maps that do a better job of explaining the data are more likely to be represented. This sample represents the knowledge that the analyst has gained from the data about the unknown true map. BG provides a conceptually simple way to convert these samples to predictions of features of the unknown map, for example regional averages. These predictions account for each map in the sample, yielding an appropriate level of predictive precision.

Bayesian geostatistics in health cartography: the perspective of malaria

PubMed Central

Patil, Anand P.; Gething, Peter W.; Piel, Frédéric B.; Hay, Simon I.

2011-01-01

Maps of parasite prevalences and other aspects of infectious diseases that vary in space are widely used in parasitology. However, spatial parasitological datasets rarely, if ever, have sufficient coverage to allow exact determination of such maps. Bayesian geostatistics (BG) is a method for finding a large sample of maps that can explain a dataset, in which maps that do a better job of explaining the data are more likely to be represented. This sample represents the knowledge that the analyst has gained from the data about the unknown true map. BG provides a conceptually simple way to convert these samples to predictions of features of the unknown map, for example regional averages. These predictions account for each map in the sample, yielding an appropriate level of predictive precision. PMID:21420361

Surge of Bering Glacier and Bagley Ice Field: Parameterization of surge characteristics based on automated analysis of crevasse image data and laser altimeter data

NASA Astrophysics Data System (ADS)

Stachura, M.; Herzfeld, U. C.; McDonald, B.; Weltman, A.; Hale, G.; Trantow, T.

2012-12-01

The dynamical processes that occur during the surge of a large, complex glacier system are far from being understood. The aim of this paper is to derive a parameterization of surge characteristics that captures the principle processes and can serve as the basis for a dynamic surge model. Innovative mathematical methods are introduced that facilitate derivation of such a parameterization from remote-sensing observations. Methods include automated geostatistical characterization and connectionist-geostatistical classification of dynamic provinces and deformation states, using the vehicle of crevasse patterns. These methods are applied to analyze satellite and airborne image and laser altimeter data collected during the current surge of Bering Glacier and Bagley Ice Field, Alaska.

Modeling and scaleup of steamflood in a heterogeneous reservoir

DOE Office of Scientific and Technical Information (OSTI.GOV)

Dehghani, K.; Basham, W.M.; Durlofsky, L.J.

1995-11-01

A series of simulation runs was conducted for different geostatistically derived cross-sectional models to study the degree of heterogeneity required for proper modeling of steamfloods in a thick, heavy-oil reservoir with thin diatomite barriers Different methods for coarsening the most detailed models were applied, and performance predictions for the coarsened and detailed models compared. Use of a general scaleup method provided the most accurate coarse grid models.

Epidemiological study of hazelnut bacterial blight in central Italy by using laboratory analysis and geostatistics.

PubMed

Lamichhane, Jay Ram; Fabi, Alfredo; Ridolfi, Roberto; Varvaro, Leonardo

2013-01-01

Incidence of Xanthomonas arboricola pv. corylina, the causal agent of hazelnut bacterial blight, was analyzed spatially in relation to the pedoclimatic factors. Hazelnut grown in twelve municipalities situated in the province of Viterbo, central Italy was studied. A consistent number of bacterial isolates were obtained from the infected tissues of hazelnut collected in three years (2010-2012). The isolates, characterized by phenotypic tests, did not show any difference among them. Spatial patterns of pedoclimatic data, analyzed by geostatistics showed a strong positive correlation of disease incidence with higher values of rainfall, thermal shock and soil nitrogen; a weak positive correlation with soil aluminium content and a strong negative correlation with the values of Mg/K ratio. No correlation of the disease incidence was found with soil pH. Disease incidence ranged from very low (<1%) to very high (almost 75%) across the orchards. Young plants (4-year old) were the most affected by the disease confirming a weak negative correlation of the disease incidence with plant age. Plant cultivars did not show any difference in susceptibility to the pathogen. Possible role of climate change on the epidemiology of the disease is discussed. Improved management practices are recommended for effective control of the disease.

Epidemiological Study of Hazelnut Bacterial Blight in Central Italy by Using Laboratory Analysis and Geostatistics

PubMed Central

Lamichhane, Jay Ram; Fabi, Alfredo; Ridolfi, Roberto; Varvaro, Leonardo

2013-01-01

Incidence of Xanthomonas arboricola pv. corylina, the causal agent of hazelnut bacterial blight, was analyzed spatially in relation to the pedoclimatic factors. Hazelnut grown in twelve municipalities situated in the province of Viterbo, central Italy was studied. A consistent number of bacterial isolates were obtained from the infected tissues of hazelnut collected in three years (2010–2012). The isolates, characterized by phenotypic tests, did not show any difference among them. Spatial patterns of pedoclimatic data, analyzed by geostatistics showed a strong positive correlation of disease incidence with higher values of rainfall, thermal shock and soil nitrogen; a weak positive correlation with soil aluminium content and a strong negative correlation with the values of Mg/K ratio. No correlation of the disease incidence was found with soil pH. Disease incidence ranged from very low (<1%) to very high (almost 75%) across the orchards. Young plants (4-year old) were the most affected by the disease confirming a weak negative correlation of the disease incidence with plant age. Plant cultivars did not show any difference in susceptibility to the pathogen. Possible role of climate change on the epidemiology of the disease is discussed. Improved management practices are recommended for effective control of the disease. PMID:23424654

Estimation of Fine Particulate Matter in Taipei Using Landuse Regression and Bayesian Maximum Entropy Methods

PubMed Central

Yu, Hwa-Lung; Wang, Chih-Hsih; Liu, Ming-Che; Kuo, Yi-Ming

2011-01-01

Fine airborne particulate matter (PM2.5) has adverse effects on human health. Assessing the long-term effects of PM2.5 exposure on human health and ecology is often limited by a lack of reliable PM2.5 measurements. In Taipei, PM2.5 levels were not systematically measured until August, 2005. Due to the popularity of geographic information systems (GIS), the landuse regression method has been widely used in the spatial estimation of PM concentrations. This method accounts for the potential contributing factors of the local environment, such as traffic volume. Geostatistical methods, on other hand, account for the spatiotemporal dependence among the observations of ambient pollutants. This study assesses the performance of the landuse regression model for the spatiotemporal estimation of PM2.5 in the Taipei area. Specifically, this study integrates the landuse regression model with the geostatistical approach within the framework of the Bayesian maximum entropy (BME) method. The resulting epistemic framework can assimilate knowledge bases including: (a) empirical-based spatial trends of PM concentration based on landuse regression, (b) the spatio-temporal dependence among PM observation information, and (c) site-specific PM observations. The proposed approach performs the spatiotemporal estimation of PM2.5 levels in the Taipei area (Taiwan) from 2005–2007. PMID:21776223

Estimation of fine particulate matter in Taipei using landuse regression and bayesian maximum entropy methods.

PubMed

Yu, Hwa-Lung; Wang, Chih-Hsih; Liu, Ming-Che; Kuo, Yi-Ming

2011-06-01

Fine airborne particulate matter (PM2.5) has adverse effects on human health. Assessing the long-term effects of PM2.5 exposure on human health and ecology is often limited by a lack of reliable PM2.5 measurements. In Taipei, PM2.5 levels were not systematically measured until August, 2005. Due to the popularity of geographic information systems (GIS), the landuse regression method has been widely used in the spatial estimation of PM concentrations. This method accounts for the potential contributing factors of the local environment, such as traffic volume. Geostatistical methods, on other hand, account for the spatiotemporal dependence among the observations of ambient pollutants. This study assesses the performance of the landuse regression model for the spatiotemporal estimation of PM2.5 in the Taipei area. Specifically, this study integrates the landuse regression model with the geostatistical approach within the framework of the Bayesian maximum entropy (BME) method. The resulting epistemic framework can assimilate knowledge bases including: (a) empirical-based spatial trends of PM concentration based on landuse regression, (b) the spatio-temporal dependence among PM observation information, and (c) site-specific PM observations. The proposed approach performs the spatiotemporal estimation of PM2.5 levels in the Taipei area (Taiwan) from 2005-2007.

Linking in situ LAI and fine resolution remote sensing data to map reference LAI over cropland and grassland using geostatistical regression method

NASA Astrophysics Data System (ADS)

He, Yaqian; Bo, Yanchen; Chai, Leilei; Liu, Xiaolong; Li, Aihua

2016-08-01

Leaf Area Index (LAI) is an important parameter of vegetation structure. A number of moderate resolution LAI products have been produced in urgent need of large scale vegetation monitoring. High resolution LAI reference maps are necessary to validate these LAI products. This study used a geostatistical regression (GR) method to estimate LAI reference maps by linking in situ LAI and Landsat TM/ETM+ and SPOT-HRV data over two cropland and two grassland sites. To explore the discrepancies of employing different vegetation indices (VIs) on estimating LAI reference maps, this study established the GR models for different VIs, including difference vegetation index (DVI), normalized difference vegetation index (NDVI), and ratio vegetation index (RVI). To further assess the performance of the GR model, the results from the GR and Reduced Major Axis (RMA) models were compared. The results show that the performance of the GR model varies between the cropland and grassland sites. At the cropland sites, the GR model based on DVI provides the best estimation, while at the grassland sites, the GR model based on DVI performs poorly. Compared to the RMA model, the GR model improves the accuracy of reference LAI maps in terms of root mean square errors (RMSE) and bias.

Characterizing the spatial structure of endangered species habitat using geostatistical analysis of IKONOS imagery

USGS Publications Warehouse

Wallace, C.S.A.; Marsh, S.E.

2005-01-01

Our study used geostatistics to extract measures that characterize the spatial structure of vegetated landscapes from satellite imagery for mapping endangered Sonoran pronghorn habitat. Fine spatial resolution IKONOS data provided information at the scale of individual trees or shrubs that permitted analysis of vegetation structure and pattern. We derived images of landscape structure by calculating local estimates of the nugget, sill, and range variogram parameters within 25 ?? 25-m image windows. These variogram parameters, which describe the spatial autocorrelation of the 1-m image pixels, are shown in previous studies to discriminate between different species-specific vegetation associations. We constructed two independent models of pronghorn landscape preference by coupling the derived measures with Sonoran pronghorn sighting data: a distribution-based model and a cluster-based model. The distribution-based model used the descriptive statistics for variogram measures at pronghorn sightings, whereas the cluster-based model used the distribution of pronghorn sightings within clusters of an unsupervised classification of derived images. Both models define similar landscapes, and validation results confirm they effectively predict the locations of an independent set of pronghorn sightings. Such information, although not a substitute for field-based knowledge of the landscape and associated ecological processes, can provide valuable reconnaissance information to guide natural resource management efforts. ?? 2005 Taylor & Francis Group Ltd.

Spatial distribution of antibodies to Salmonella enterica serovar Typhimurium O antigens in bulk milk from Texas dairy herds.

PubMed

Graham, S L; Barling, K S; Waghela, S; Scott, H M; Thompson, J A

2005-06-10

Environmental factors that enhance either the survivability or dispersion of Salmonella enterica serovar Typhimurium (S. Typhimurium) could result in a spatial pattern of disease risk. The objectives of this study were to: (1) describe herd status based on antibody response to Salmonella Typhimurium as estimated from bulk tank milk samples and (2) to describe the resulting geographical patterns found among Texas dairy herds. Eight hundred and fifty-two bulk milk samples were collected from georeferenced dairy farms and assayed by an indirect enzyme-linked immunosorbent assay (ELISA) using S. Typhimurium lipopolysaccharide (LPS). ELISA signal-to-noise ratios for each bulk tank milk sample were calculated and used for geostatistical analyses. Best-fit parameters for the exponential theoretical variogram included a range of 438.8 km, partial sill of 0.060 and nugget of 0.106. The partial sill is the classical geostatistical term for the variance that can be explained by the herd's location and the nugget is the spatially random component of the variance. We have identified a spatial process in bulk milk tank titers for S. Typhimurium in Texas dairy herds and present a map of the expected smoothed surface. Areas with higher expected titers should be targeted in further studies on controlling Salmonella infection with environmental modifications.

Relationship between RADARSAT-2 Derived Snow Thickness on Winter First Year Sea-Ice and Aerial Melt-Pond Distribution using Geostatistics and GLCM Texture

NASA Astrophysics Data System (ADS)

Ramjan, S.; Geldsetzer, T.; Yackel, J.

2016-12-01

A contemporary shift from primarily thicker, older multi-year sea ice (MYI) to thinner, smoother first-year sea ice (FYI) has been attributed to increased atmospheric and oceanic warming in the Arctic, with a steady diminishing of Arctic sea ice thickness due to a reduction of thick MYI compared to FYI. With an increase in FYI fraction, increased melting takes place during the summer months, exposing the sea ice to additional incoming solar radiation. With this change, an increase in melt pond fraction has been observed during the summer melt season. Prior research advocated that thin/thick snow leads to dominant surface flooding/snow patches during summer because of an enhanced ice-albedo feedback. For instance, thin snow cover areas form melt ponds first. Therefore, aerial measurements of melt pond fraction provide a proxy for relative snow thickness. RADARSAT-2 polarimetric SAR data can provide enhanced information about both surface scattering and volume scattering mechanisms, as well as recording the phase difference between polarizations. These polarimetric parameters can be computed that have a useful physical interpretation. The principle research focus is to establish a methodology to determine the relationship between selected geostatistics and image texture measures of pre-melt RADARSAT-2 parameters and aerially-measured melt pond fraction. Overall, the notion of this study is to develop an algorithm to estimate relative snow thickness variability in winter through an integrated approach utilizing SAR polarimetric parameters, geostatistical analysis and texture measures. Results are validated with test sets of melt pond fractions, and in situ snow thickness measurements. Preliminary findings show significant correlations with pond fraction for the standard deviation of HH and HV parameters at small incidence angles, and for the mean of the co-pol phase difference parameter at large incidence angles.

Spatial mapping and prediction of Plasmodium falciparum infection risk among school-aged children in Côte d'Ivoire.

PubMed

Houngbedji, Clarisse A; Chammartin, Frédérique; Yapi, Richard B; Hürlimann, Eveline; N'Dri, Prisca B; Silué, Kigbafori D; Soro, Gotianwa; Koudou, Benjamin G; Assi, Serge-Brice; N'Goran, Eliézer K; Fantodji, Agathe; Utzinger, Jürg; Vounatsou, Penelope; Raso, Giovanna

2016-09-07

In Côte d'Ivoire, malaria remains a major public health issue, and thus a priority to be tackled. The aim of this study was to identify spatially explicit indicators of Plasmodium falciparum infection among school-aged children and to undertake a model-based spatial prediction of P. falciparum infection risk using environmental predictors. A cross-sectional survey was conducted, including parasitological examinations and interviews with more than 5,000 children from 93 schools across Côte d'Ivoire. A finger-prick blood sample was obtained from each child to determine Plasmodium species-specific infection and parasitaemia using Giemsa-stained thick and thin blood films. Household socioeconomic status was assessed through asset ownership and household characteristics. Children were interviewed for preventive measures against malaria. Environmental data were gathered from satellite images and digitized maps. A Bayesian geostatistical stochastic search variable selection procedure was employed to identify factors related to P. falciparum infection risk. Bayesian geostatistical logistic regression models were used to map the spatial distribution of P. falciparum infection and to predict the infection prevalence at non-sampled locations via Bayesian kriging. Complete data sets were available from 5,322 children aged 5-16 years across Côte d'Ivoire. P. falciparum was the predominant species (94.5 %). The Bayesian geostatistical variable selection procedure identified land cover and socioeconomic status as important predictors for infection risk with P. falciparum. Model-based prediction identified high P. falciparum infection risk in the north, central-east, south-east, west and south-west of Côte d'Ivoire. Low-risk areas were found in the south-eastern area close to Abidjan and the south-central and west-central part of the country. The P. falciparum infection risk and related uncertainty estimates for school-aged children in Côte d'Ivoire represent the most up-to-date malaria risk maps. These tools can be used for spatial targeting of malaria control interventions.

Modelling seasonal variations of natural radioactivity in soils: A case study in southern Italy

NASA Astrophysics Data System (ADS)

Guagliardi, Ilaria; Rovella, Natalia; Apollaro, Carmine; Bloise, Andrea; Rosa, Rosanna De; Scarciglia, Fabio; Buttafuoco, Gabriele

2016-12-01

The activity of natural radionuclides in soil has become an environmental concern for local public and national authorities because of the harmful effects of radiation exposure on human health. In this context, modelling and mapping the activity of natural radionuclides in soil is an important research topic. The study was aimed to model, in a spatial sense, the soil radioactivity in an urban and peri-urban soils area in southern Italy to analyse the seasonal influence on soil radioactivity. Measures of gamma radiation naturally emitted through the decay of radioactive isotopes (potassium, uranium and thorium) were analysed using a geostatistical approach to map the spatial distribution of soil radioactivity. The activity of three radionuclides was measured at 181 locations using a high-resolution ?-ray spectrometry. To take into account the influence of season, the measurements were carried out in summer and in winter. Activity data were analysed by using a geostatistical approach and zones of relatively high or low radioactivity were delineated. Among the main processes which influence natural radioactivity such as geology, geochemical, pedological, and ecological processes, results of this study showed a prominent control of radio-emission measurements by seasonal changes. Low natural radioactivity levels were measured in December associated with winter weather and moist soil conditions (due to high rainfall and low temperature), and higher activity values in July, when the soil was dry and no precipitations occurred.

Comparative soil CO2 flux measurements and geostatistical estimation methods on Masaya volcano, Nicaragua

USGS Publications Warehouse

Lewicki, Jennifer L.; Bergfeld, Deborah; Cardellini, Carlo; Chiodini, Giovanni; Granieri, Domenico; Varley, Nick; Werner, Cynthia A.

2005-01-01

We present a comparative study of soil CO2 flux (FCO2">FCO2) measured by five groups (Groups 1–5) at the IAVCEI-CCVG Eighth Workshop on Volcanic Gases on Masaya volcano, Nicaragua. Groups 1–5 measured FCO2 using the accumulation chamber method at 5-m spacing within a 900 m2 grid during a morning (AM) period. These measurements were repeated by Groups 1–3 during an afternoon (PM) period. Measured FCO2 ranged from 218 to 14,719 g m−2 day−1. The variability of the five measurements made at each grid point ranged from ±5 to 167%. However, the arithmetic means of fluxes measured over the entire grid and associated total CO2 emission rate estimates varied between groups by only ±22%. All three groups that made PM measurements reported an 8–19% increase in total emissions over the AM results. Based on a comparison of measurements made during AM and PM times, we argue that this change is due in large part to natural temporal variability of gas flow, rather than to measurement error. In order to estimate the mean and associated CO2 emission rate of one data set and to map the spatial FCO2 distribution, we compared six geostatistical methods: arithmetic and minimum variance unbiased estimator means of uninterpolated data, and arithmetic means of data interpolated by the multiquadric radial basis function, ordinary kriging, multi-Gaussian kriging, and sequential Gaussian simulation methods. While the total CO2 emission rates estimated using the different techniques only varied by ±4.4%, the FCO2 maps showed important differences. We suggest that the sequential Gaussian simulation method yields the most realistic representation of the spatial distribution of FCO2, but a variety of geostatistical methods are appropriate to estimate the total CO2 emission rate from a study area, which is a primary goal in volcano monitoring research.

STATISTICAL SAMPLING AND DATA ANALYSIS

EPA Science Inventory

Research is being conducted to develop approaches to improve soil and sediment sampling techniques, measurement design and geostatistics, and data analysis via chemometric, environmetric, and robust statistical methods. Improvements in sampling contaminated soil and other hetero...

Improving imperfect data from health management information systems in Africa using space-time geostatistics.

PubMed

Gething, Peter W; Noor, Abdisalan M; Gikandi, Priscilla W; Ogara, Esther A A; Hay, Simon I; Nixon, Mark S; Snow, Robert W; Atkinson, Peter M

2006-06-01

Reliable and timely information on disease-specific treatment burdens within a health system is critical for the planning and monitoring of service provision. Health management information systems (HMIS) exist to address this need at national scales across Africa but are failing to deliver adequate data because of widespread underreporting by health facilities. Faced with this inadequacy, vital public health decisions often rely on crudely adjusted regional and national estimates of treatment burdens. This study has taken the example of presumed malaria in outpatients within the largely incomplete Kenyan HMIS database and has defined a geostatistical modelling framework that can predict values for all data that are missing through space and time. The resulting complete set can then be used to define treatment burdens for presumed malaria at any level of spatial and temporal aggregation. Validation of the model has shown that these burdens are quantified to an acceptable level of accuracy at the district, provincial, and national scale. The modelling framework presented here provides, to our knowledge for the first time, reliable information from imperfect HMIS data to support evidence-based decision-making at national and sub-national levels.

Integrating Address Geocoding, Land Use Regression, and Spatiotemporal Geostatistical Estimation for Groundwater Tetrachloroethylene

PubMed Central

Messier, Kyle P.; Akita, Yasuyuki; Serre, Marc L.

2012-01-01

Geographic Information Systems (GIS) based techniques are cost-effective and efficient methods used by state agencies and epidemiology researchers for estimating concentration and exposure. However, budget limitations have made statewide assessments of contamination difficult, especially in groundwater media. Many studies have implemented address geocoding, land use regression, and geostatistics independently, but this is the first to examine the benefits of integrating these GIS techniques to address the need of statewide exposure assessments. A novel framework for concentration exposure is introduced that integrates address geocoding, land use regression (LUR), below detect data modeling, and Bayesian Maximum Entropy (BME). A LUR model was developed for Tetrachloroethylene that accounts for point sources and flow direction. We then integrate the LUR model into the BME method as a mean trend while also modeling below detects data as a truncated Gaussian probability distribution function. We increase available PCE data 4.7 times from previously available databases through multistage geocoding. The LUR model shows significant influence of dry cleaners at short ranges. The integration of the LUR model as mean trend in BME results in a 7.5% decrease in cross validation mean square error compared to BME with a constant mean trend. PMID:22264162

[Geostatistics analyzing to cause of formation of circle distribution of plant communities in Horqin Sandy Land].

PubMed

He, Xingdong; Gao, Yubao; Zhao, Wenzhi; Cong, Zili

2004-09-01

Investigation results in the present study showed that plant communities took typical concentric circles distribution patterns along habitat gradient from top, slope to interdune on a few large fixed dunes in middle part of Korqin Sandy Land. In order to explain this phenomenon, analysis of water content and its spatial heterogeneity in sand layers on different locations of dunes was conducted. In these dunes, water contents in sand layers of the tops were lower than those of the slopes; both of them were lower than those of the interdunes. According to the results of geostatistics analysis, whether shifting dune or fixed dune, spatial heterogeneity of water contents in sand layers took on regular changes, such as ratios between nugget and sill and ranges reduced gradually, fractal dimension increased gradually, the regular changes of these parameters indicated that random spatial heterogeneity reduced gradually, and autocorrelation spatial heterogeneity increased gradually from the top, the slope to the interdune. The regular changes of water contents in sand layers and their spatial heterogeneity of different locations of the dunes, thus, might be an important cause resulted in the formation of the concentric circles patterns of the plant communities on these fixed dunes.

Integrating address geocoding, land use regression, and spatiotemporal geostatistical estimation for groundwater tetrachloroethylene.

PubMed

Messier, Kyle P; Akita, Yasuyuki; Serre, Marc L

2012-03-06

Geographic information systems (GIS) based techniques are cost-effective and efficient methods used by state agencies and epidemiology researchers for estimating concentration and exposure. However, budget limitations have made statewide assessments of contamination difficult, especially in groundwater media. Many studies have implemented address geocoding, land use regression, and geostatistics independently, but this is the first to examine the benefits of integrating these GIS techniques to address the need of statewide exposure assessments. A novel framework for concentration exposure is introduced that integrates address geocoding, land use regression (LUR), below detect data modeling, and Bayesian Maximum Entropy (BME). A LUR model was developed for tetrachloroethylene that accounts for point sources and flow direction. We then integrate the LUR model into the BME method as a mean trend while also modeling below detects data as a truncated Gaussian probability distribution function. We increase available PCE data 4.7 times from previously available databases through multistage geocoding. The LUR model shows significant influence of dry cleaners at short ranges. The integration of the LUR model as mean trend in BME results in a 7.5% decrease in cross validation mean square error compared to BME with a constant mean trend.

Definition of radon prone areas in Friuli Venezia Giulia region, Italy, using geostatistical tools.

PubMed

Cafaro, C; Bossew, P; Giovani, C; Garavaglia, M

2014-12-01

Studying the geographical distribution of indoor radon concentration, using geostatistical interpolation methods, has become common for predicting and estimating the risk to the population. Here we analyse the case of Friuli Venezia Giulia (FVG), the north easternmost region of Italy. Mean value and standard deviation are, respectively, 153 Bq/m(3) and 183 Bq/m(3). The geometric mean value is 100 Bq/m(3). Spatial datasets of indoor radon concentrations are usually affected by clustering and apparent non-stationarity issues, which can eventually yield arguable results. The clustering of the present dataset seems to be non preferential. Therefore the areal estimations are not expected to be affected. Conversely, nothing can be said on the non stationarity issues and its effects. After discussing the correlation of geology with indoor radon concentration It appears they are created by the same geologic features influencing the mean and median values, and can't be eliminated via a map-based approach. To tackle these problems, in this work we deal with multiple definitions of RPA, but only in quaternary areas of FVG, using extensive simulation techniques. Copyright © 2014 Elsevier Ltd. All rights reserved.

Mapping spatial patterns of denitrifiers at large scales (Invited)

NASA Astrophysics Data System (ADS)

Philippot, L.; Ramette, A.; Saby, N.; Bru, D.; Dequiedt, S.; Ranjard, L.; Jolivet, C.; Arrouays, D.

2010-12-01

Little information is available regarding the landscape-scale distribution of microbial communities and its environmental determinants. Here we combined molecular approaches and geostatistical modeling to explore spatial patterns of the denitrifying community at large scales. The distribution of denitrifrying community was investigated over 107 sites in Burgundy, a 31 500 km2 region of France, using a 16 X 16 km sampling grid. At each sampling site, the abundances of denitrifiers and 42 soil physico-chemical properties were measured. The relative contributions of land use, spatial distance, climatic conditions, time and soil physico-chemical properties to the denitrifier spatial distribution were analyzed by canonical variation partitioning. Our results indicate that 43% to 85% of the spatial variation in community abundances could be explained by the measured environmental parameters, with soil chemical properties (mostly pH) being the main driver. We found spatial autocorrelation up to 739 km and used geostatistical modelling to generate predictive maps of the distribution of denitrifiers at the landscape scale. Studying the distribution of the denitrifiers at large scale can help closing the artificial gap between the investigation of microbial processes and microbial community ecology, therefore facilitating our understanding of the relationships between the ecology of denitrifiers and N-fluxes by denitrification.

A geostatistical approach to data harmonization - Application to radioactivity exposure data

NASA Astrophysics Data System (ADS)

Baume, O.; Skøien, J. O.; Heuvelink, G. B. M.; Pebesma, E. J.; Melles, S. J.

2011-06-01

Environmental issues such as air, groundwater pollution and climate change are frequently studied at spatial scales that cross boundaries between political and administrative regions. It is common for different administrations to employ different data collection methods. If these differences are not taken into account in spatial interpolation procedures then biases may appear and cause unrealistic results. The resulting maps may show misleading patterns and lead to wrong interpretations. Also, errors will propagate when these maps are used as input to environmental process models. In this paper we present and apply a geostatistical model that generalizes the universal kriging model such that it can handle heterogeneous data sources. The associated best linear unbiased estimation and prediction (BLUE and BLUP) equations are presented and it is shown that these lead to harmonized maps from which estimated biases are removed. The methodology is illustrated with an example of country bias removal in a radioactivity exposure assessment for four European countries. The application also addresses multicollinearity problems in data harmonization, which arise when both artificial bias factors and natural drifts are present and cannot easily be distinguished. Solutions for handling multicollinearity are suggested and directions for further investigations proposed.

Improving Imperfect Data from Health Management Information Systems in Africa Using Space–Time Geostatistics

PubMed Central

Gething, Peter W; Noor, Abdisalan M; Gikandi, Priscilla W; Ogara, Esther A. A; Hay, Simon I; Nixon, Mark S; Snow, Robert W; Atkinson, Peter M

2006-01-01

Background Reliable and timely information on disease-specific treatment burdens within a health system is critical for the planning and monitoring of service provision. Health management information systems (HMIS) exist to address this need at national scales across Africa but are failing to deliver adequate data because of widespread underreporting by health facilities. Faced with this inadequacy, vital public health decisions often rely on crudely adjusted regional and national estimates of treatment burdens. Methods and Findings This study has taken the example of presumed malaria in outpatients within the largely incomplete Kenyan HMIS database and has defined a geostatistical modelling framework that can predict values for all data that are missing through space and time. The resulting complete set can then be used to define treatment burdens for presumed malaria at any level of spatial and temporal aggregation. Validation of the model has shown that these burdens are quantified to an acceptable level of accuracy at the district, provincial, and national scale. Conclusions The modelling framework presented here provides, to our knowledge for the first time, reliable information from imperfect HMIS data to support evidence-based decision-making at national and sub-national levels. PMID:16719557

Clustering of Multivariate Geostatistical Data

NASA Astrophysics Data System (ADS)

Fouedjio, Francky

2017-04-01

Multivariate data indexed by geographical coordinates have become omnipresent in the geosciences and pose substantial analysis challenges. One of them is the grouping of data locations into spatially contiguous clusters so that data locations belonging to the same cluster have a certain degree of homogeneity while data locations in the different clusters have to be as different as possible. However, groups of data locations created through classical clustering techniques turn out to show poor spatial contiguity, a feature obviously inconvenient for many geoscience applications. In this work, we develop a clustering method that overcomes this problem by accounting the spatial dependence structure of data; thus reinforcing the spatial contiguity of resulting cluster. The capability of the proposed clustering method to provide spatially contiguous and meaningful clusters of data locations is assessed using both synthetic and real datasets. Keywords: clustering, geostatistics, spatial contiguity, spatial dependence.

Geological, geomechanical and geostatistical assessment of rockfall hazard in San Quirico Village (Abruzzo, Italy)

NASA Astrophysics Data System (ADS)

Chiessi, Vittorio; D'Orefice, Maurizio; Scarascia Mugnozza, Gabriele; Vitale, Valerio; Cannese, Christian

2010-07-01

This paper describes the results of a rockfall hazard assessment for the village of San Quirico (Abruzzo region, Italy) based on an engineering-geological model. After the collection of geological, geomechanical, and geomorphological data, the rockfall hazard assessment was performed based on two separate approaches: i) simulation of detachment of rock blocks and their downhill movement using a GIS; and ii) application of geostatistical techniques to the analysis of georeferenced observations of previously fallen blocks, in order to assess the probability of arrival of blocks due to potential future collapses. The results show that the trajectographic analysis is significantly influenced by the input parameters, with particular reference to the coefficients of restitution values. In order to solve this problem, the model was calibrated based on repeated field observations. The geostatistical approach is useful because it gives the best estimation of point-source phenomena such as rockfalls; however, the sensitivity of results to basic assumptions, e.g. assessment of variograms and choice of a threshold value, may be problematic. Consequently, interpolations derived from different variograms have been used and compared among them; hence, those showing the lowest errors were adopted. The data sets which were statistically analysed are relevant to both kinetic energy and surveyed rock blocks in the accumulation area. The obtained maps highlight areas susceptible to rock block arrivals, and show that the area accommodating the new settlement of S. Quirico Village has the highest level of hazard according to both probabilistic and deterministic methods.

Bayesian Geostatistical Analysis and Ecoclimatic Determinants of Corynebacterium pseudotuberculosis Infection among Horses

PubMed Central

Boysen, Courtney; Davis, Elizabeth G.; Beard, Laurie A.; Lubbers, Brian V.; Raghavan, Ram K.

2015-01-01

Kansas witnessed an unprecedented outbreak in Corynebacterium pseudotuberculosis infection among horses, a disease commonly referred to as pigeon fever during fall 2012. Bayesian geostatistical models were developed to identify key environmental and climatic risk factors associated with C. pseudotuberculosis infection in horses. Positive infection status among horses (cases) was determined by positive test results for characteristic abscess formation, positive bacterial culture on purulent material obtained from a lanced abscess (n = 82), or positive serologic evidence of exposure to organism (≥1:512)(n = 11). Horses negative for these tests (n = 172)(controls) were considered free of infection. Information pertaining to horse demographics and stabled location were obtained through review of medical records and/or contact with horse owners via telephone. Covariate information for environmental and climatic determinants were obtained from USDA (soil attributes), USGS (land use/land cover), and NASA MODIS and NASA Prediction of Worldwide Renewable Resources (climate). Candidate covariates were screened using univariate regression models followed by Bayesian geostatistical models with and without covariates. The best performing model indicated a protective effect for higher soil moisture content (OR = 0.53, 95% CrI = 0.25, 0.71), and detrimental effects for higher land surface temperature (≥35°C) (OR = 2.81, 95% CrI = 2.21, 3.85) and habitat fragmentation (OR = 1.31, 95% CrI = 1.27, 2.22) for C. pseudotuberculosis infection status in horses, while age, gender and breed had no effect. Preventative and ecoclimatic significance of these findings are discussed. PMID:26473728

Spatial Analysis of Phytophthora infestans Genotypes and Late Blight Severity on Tomato and Potato in the Del Fuerte Valley Using Geostatistics and Geographic Information Systems.

PubMed

Jaime-Garcia, R; Orum, T V; Felix-Gastelum, R; Trinidad-Correa, R; Vanetten, H D; Nelson, M R

2001-12-01

ABSTRACT Genetic structure of Phytophthora infestans, the causal agent of potato and tomato late blight, was analyzed spatially in a mixed potato and tomato production area in the Del Fuerte Valley, Sinaloa, Mexico. Isolates of P. infestans were characterized by mating type, allozyme analysis at the glucose-6-phosphate isomerase and peptidase loci, restriction fragment length polymorphism with probe RG57, metalaxyl sensitivity, and aggressiveness to tomato and potato. Spatial patterns of P. infestans genotypes were analyzed by geographical information systems and geo-statistics during the seasons of 1994-95, 1995-96, and 1996-97. Spatial analysis of the genetic structure of P. infestans indicates that geographic substructuring of this pathogen occurs in this area. Maps displaying the probabilities of occurrence of mating types and genotypes of P. infestans, and of disease severity at a regional scale, were presented. Some genotypes that exhibited differences in epidemiologically important features such as metalaxyl sensitivity and aggressiveness to tomato and potato had a restricted spread and were localized in isolated areas. Analysis of late blight severity showed recurring patterns, such as the earliest onset of the disease in the area where both potato and tomato were growing, strengthening the hypothesis that infected potato tubers are the main source of primary inoculum. The information that geostatistical analysis provides might help improve management programs for late blight in the Del Fuerte Valley.

Bridges between multiple-point geostatistics and texture synthesis: Review and guidelines for future research

NASA Astrophysics Data System (ADS)

Mariethoz, Gregoire; Lefebvre, Sylvain

2014-05-01

Multiple-Point Simulations (MPS) is a family of geostatistical tools that has received a lot of attention in recent years for the characterization of spatial phenomena in geosciences. It relies on the definition of training images to represent a given type of spatial variability, or texture. We show that the algorithmic tools used are similar in many ways to techniques developed in computer graphics, where there is a need to generate large amounts of realistic textures for applications such as video games and animated movies. Similarly to MPS, these texture synthesis methods use training images, or exemplars, to generate realistic-looking graphical textures. Both domains of multiple-point geostatistics and example-based texture synthesis present similarities in their historic development and share similar concepts. These disciplines have however remained separated, and as a result significant algorithmic innovations in each discipline have not been universally adopted. Texture synthesis algorithms present drastically increased computational efficiency, patterns reproduction and user control. At the same time, MPS developed ways to condition models to spatial data and to produce 3D stochastic realizations, which have not been thoroughly investigated in the field of texture synthesis. In this paper we review the possible links between these disciplines and show the potential and limitations of using concepts and approaches from texture synthesis in MPS. We also provide guidelines on how recent developments could benefit both fields of research, and what challenges remain open.

Efficient geostatistical inversion of transient groundwater flow using preconditioned nonlinear conjugate gradients

NASA Astrophysics Data System (ADS)

Klein, Ole; Cirpka, Olaf A.; Bastian, Peter; Ippisch, Olaf

2017-04-01

In the geostatistical inverse problem of subsurface hydrology, continuous hydraulic parameter fields, in most cases hydraulic conductivity, are estimated from measurements of dependent variables, such as hydraulic heads, under the assumption that the parameter fields are autocorrelated random space functions. Upon discretization, the continuous fields become large parameter vectors with O (104 -107) elements. While cokriging-like inversion methods have been shown to be efficient for highly resolved parameter fields when the number of measurements is small, they require the calculation of the sensitivity of each measurement with respect to all parameters, which may become prohibitive with large sets of measured data such as those arising from transient groundwater flow. We present a Preconditioned Conjugate Gradient method for the geostatistical inverse problem, in which a single adjoint equation needs to be solved to obtain the gradient of the objective function. Using the autocovariance matrix of the parameters as preconditioning matrix, expensive multiplications with its inverse can be avoided, and the number of iterations is significantly reduced. We use a randomized spectral decomposition of the posterior covariance matrix of the parameters to perform a linearized uncertainty quantification of the parameter estimate. The feasibility of the method is tested by virtual examples of head observations in steady-state and transient groundwater flow. These synthetic tests demonstrate that transient data can reduce both parameter uncertainty and time spent conducting experiments, while the presented methods are able to handle the resulting large number of measurements.

Bayesian Geostatistical Analysis and Ecoclimatic Determinants of Corynebacterium pseudotuberculosis Infection among Horses.

PubMed

Boysen, Courtney; Davis, Elizabeth G; Beard, Laurie A; Lubbers, Brian V; Raghavan, Ram K

2015-01-01

Kansas witnessed an unprecedented outbreak in Corynebacterium pseudotuberculosis infection among horses, a disease commonly referred to as pigeon fever during fall 2012. Bayesian geostatistical models were developed to identify key environmental and climatic risk factors associated with C. pseudotuberculosis infection in horses. Positive infection status among horses (cases) was determined by positive test results for characteristic abscess formation, positive bacterial culture on purulent material obtained from a lanced abscess (n = 82), or positive serologic evidence of exposure to organism (≥ 1:512)(n = 11). Horses negative for these tests (n = 172)(controls) were considered free of infection. Information pertaining to horse demographics and stabled location were obtained through review of medical records and/or contact with horse owners via telephone. Covariate information for environmental and climatic determinants were obtained from USDA (soil attributes), USGS (land use/land cover), and NASA MODIS and NASA Prediction of Worldwide Renewable Resources (climate). Candidate covariates were screened using univariate regression models followed by Bayesian geostatistical models with and without covariates. The best performing model indicated a protective effect for higher soil moisture content (OR = 0.53, 95% CrI = 0.25, 0.71), and detrimental effects for higher land surface temperature (≥ 35°C) (OR = 2.81, 95% CrI = 2.21, 3.85) and habitat fragmentation (OR = 1.31, 95% CrI = 1.27, 2.22) for C. pseudotuberculosis infection status in horses, while age, gender and breed had no effect. Preventative and ecoclimatic significance of these findings are discussed.

Large-scale inverse model analyses employing fast randomized data reduction

NASA Astrophysics Data System (ADS)

Lin, Youzuo; Le, Ellen B.; O'Malley, Daniel; Vesselinov, Velimir V.; Bui-Thanh, Tan

2017-08-01

When the number of observations is large, it is computationally challenging to apply classical inverse modeling techniques. We have developed a new computationally efficient technique for solving inverse problems with a large number of observations (e.g., on the order of 107 or greater). Our method, which we call the randomized geostatistical approach (RGA), is built upon the principal component geostatistical approach (PCGA). We employ a data reduction technique combined with the PCGA to improve the computational efficiency and reduce the memory usage. Specifically, we employ a randomized numerical linear algebra technique based on a so-called "sketching" matrix to effectively reduce the dimension of the observations without losing the information content needed for the inverse analysis. In this way, the computational and memory costs for RGA scale with the information content rather than the size of the calibration data. Our algorithm is coded in Julia and implemented in the MADS open-source high-performance computational framework (http://mads.lanl.gov). We apply our new inverse modeling method to invert for a synthetic transmissivity field. Compared to a standard geostatistical approach (GA), our method is more efficient when the number of observations is large. Most importantly, our method is capable of solving larger inverse problems than the standard GA and PCGA approaches. Therefore, our new model inversion method is a powerful tool for solving large-scale inverse problems. The method can be applied in any field and is not limited to hydrogeological applications such as the characterization of aquifer heterogeneity.

Pedodiversity and Its Significance in the Context of Modern Soil Geography

NASA Astrophysics Data System (ADS)

Krasilnikov, P. V.; Gerasimova, M. I.; Golovanov, D. L.; Konyushkova, M. V.; Sidorova, V. A.; Sorokin, A. S.

2018-01-01

Methodological basics of the study and quantitative assessment of pedodiversity are discussed. It is shown that the application of various indices and models of pedodiversity can be feasible for solving three major issues in pedology: a comparative geographical analysis of different territories, a comparative historical analysis of soil development in the course of landscape evolution, and the analysis of relationships between biodiversity and pedodiversity. Analogous geographic concepts of geodiversity and landscape diversity are also discussed. Certain limitations in the use of quantitative estimates of pedodiversity related to their linkage to the particular soil classification systems and with the initial soil maps are considered. Problems of the interpretation of the results of pedodiversity assessments are emphasized. It is shown that scientific explanations of biodiversity cannot be adequately applied in soil studies. Promising directions of further studies of pedodiversity are outlined. They include the assessment of the functional diversity of soils on the basis of data on their properties, integration with geostatistical methods of evaluation of soil variability, and assessment of pedodiversity on different scales.

Quantifying Groundwater Availability in Fractured Rock Aquifers of Northern Ugandan Refugee Settlements

NASA Astrophysics Data System (ADS)

Frederiks, R.; Lowry, C.; Mutiibwa, R.; Moisy, S.; Thapa, L.; Oriba, J.

2017-12-01

In the past two years, Uganda has witnessed an influx of nearly one million refugees who have settled in the sparsely populated northwestern region of the country. This rapid population growth has created high demand for clean water resources. Water supply has been unable to keep pace with demand because the fractured rock aquifers underlying the region often produce low yielding wells. To facilitate management of groundwater resources, it is necessary to quantify the spatial distribution of groundwater. In fractured rock aquifers, there is significant spatial variability in water storage because fractures must be both connected and abundant for water to be extracted in usable quantities. Two conceptual models were evaluated to determine the groundwater storage mechanism in the fractured crystalline bedrock aquifers of northwestern Uganda where by permeability is controlled by faulting, which opens up fractures in the bedrock, or weathering, which occurs when water dissolves components of rock. In order to test these two conceptual models, geologic well logs and available hydrologic data were collected and evaluated using geostatistical and numerical groundwater models. The geostatistical analysis focused on identifying spatially distributed patterns of high and low water yield. The conceptual models were evaluated numerically using four inverse groundwater MODFLOW models based on head and estimated flux targets. The models were based on: (1) the mapped bedrock units using an equivalent porous media approach (2) bedrock units with the addition of known fault zones (3) bedrock units with predicted units of deep weathering based on surface slopes, and (4) bedrock units with discrete faults and simulated weathered zones. Predicting permeable zones is vital for water well drilling in much of East Africa and South America where there is an abundance of both fractured rock and tectonic activity. Given that the population of these developing regions is growing, the demand for sufficient clean water is likely to increase significantly in the next few decades. Thus, it is necessary to improve our ability to predict locations of permeable zones in fractured rock aquifers.

Geostatistics as a tool to improve the natural background level definition: An application in groundwater.

PubMed

Dalla Libera, Nico; Fabbri, Paolo; Mason, Leonardo; Piccinini, Leonardo; Pola, Marco

2017-11-15

The Natural Background Level (NBL), suggested by UE BRIDGE project, is suited for spatially distributed datasets providing a regional value that could be higher than the Threshold Value (TV) set by every country. In hydro-geochemically dis-homogeneous areas, the use of a unique regional NBL, higher than TV, could arise problems to distinguish between natural occurrences and anthropogenic contaminant sources. Hence, the goal of this study is to improve the NBL definition employing a geostatistical approach, which reconstructs the contaminant spatial structure accounting geochemical and hydrogeological relationships. This integrated mapping is fundamental to evaluate the contaminant's distribution impact on the NBL, giving indications to improve it. We decided to test this method on the Drainage Basin of Venice Lagoon (DBVL, NE Italy), where the existing NBL is seven times higher than the TV. This area is notoriously affected by naturally occurring arsenic contamination. An available geochemical dataset collected by 50 piezometers was used to reconstruct the spatial distribution of arsenic in the densely populated area of the DBVL. A cokriging approach was applied exploiting the geochemical relationships among As, Fe and NH4+. The obtained spatial predictions of arsenic concentrations were divided into three different zones: i) areas with an As concentration lower than the TV, ii) areas with an As concentration between the TV and the median of the values higher than the TV, and iii) areas with an As concentration higher than the median. Following the BRIDGE suggestions, where enough samples were available, the 90th percentile for each zone was calculated to obtain a local NBL (LNBL). Differently from the original NBL, this local value gives more detailed water quality information accounting the hydrogeological and geochemical setting, and contaminant spatial variation. Hence, the LNBL could give more indications about the distinction between natural occurrence and anthropogenic contamination. Copyright © 2017 Elsevier B.V. All rights reserved.

Fine-grained sediment spatial distribution on the basis of a geostatistical analysis: Example of the eastern Bay of the Seine (France)

NASA Astrophysics Data System (ADS)

Méar, Y.; Poizot, E.; Murat, A.; Lesueur, P.; Thomas, M.

2006-12-01

The eastern Bay of the Seine (English Channel) was the subject in 1991 of a sampling survey of superficial sediments. Geostatistic tools were used to examine the complexity of the spatial distribution of the fine-grained fraction (<50 μm). A central depocentre of fine sediments (i.e. content up to 50%) oriented in a NW-SE direction in a muddy coastal strip, in a very high energy hydrodynamical situation due to storm swells and its megatidal setting, is for the first time recognised and discussed. Within this sedimentary unit, the distribution of the fine fraction is very heterogeneous, with mud patches of less than 4000 m diameter; the boundary between these mud patches and their substratum is very sharp. The distribution of this fine fraction appears to be controlled by an anticyclonic eddy located off the Pays de Caux. Under the influence of this, the suspended material expelled from the Seine estuary moves along the coast and swings off Antifer harbour, towards the NW. It is trapped within this eddy because of the settling of suspended particulate matter. Both at a general scale and a local scale the morphology (whether inherited or due to modern processes) has a strong influence on the spatial distribution of the fine fraction. At the general scale, the basin-like shape of the area facilitates the silting, and the presence of the submarine dunes, called "Ridins d'Antifer", clearly determines the northern limit of the muddy zone. At a local scale, the same influence is obvious: paleovalleys trap the fine sediments, whereas isolated sand dunes and ripples limit the silting. This duality of role of the morphology is therefore one of the reasons why the muddy surface is extremely heterogeneous spatially. The presence of an important population of suspension feeding echinoderm, the brittle-star Ophiothrix fragilis Abildgaard, has led to a local increase in the silting, and to the modification of the physicochemical and sedimentological parameters. A complex relationship is shown to occur between the amount of fine fraction and the number of brittle-stars (ind. m -2). Classical statistical methods are not appropriate to study the spatial distribution of the mud fraction, because the spatial component of the percentage of the distribution is not integrated in the analysis. On the other hand, this is the main property of the geostatistic concepts. The use of geostatistic tools within a strict and clearly identified procedure enables the proposal of an accurate cartography. Further application of the proposed protocol (based on a semivariographic study and a conditional simulation interpolation) for surficial sediments mapping will help explain spatial and temporal variations of fine-grained fraction. Then assessments of sedimentation and erosion stages allow highlighting signature of environmental processes.

Comparison of ArcGIS and SAS Geostatistical Analyst to Estimate Population-Weighted Monthly Temperature for US Counties.

PubMed

Xiaopeng, Q I; Liang, Wei; Barker, Laurie; Lekiachvili, Akaki; Xingyou, Zhang

Temperature changes are known to have significant impacts on human health. Accurate estimates of population-weighted average monthly air temperature for US counties are needed to evaluate temperature's association with health behaviours and disease, which are sampled or reported at the county level and measured on a monthly-or 30-day-basis. Most reported temperature estimates were calculated using ArcGIS, relatively few used SAS. We compared the performance of geostatistical models to estimate population-weighted average temperature in each month for counties in 48 states using ArcGIS v9.3 and SAS v 9.2 on a CITGO platform. Monthly average temperature for Jan-Dec 2007 and elevation from 5435 weather stations were used to estimate the temperature at county population centroids. County estimates were produced with elevation as a covariate. Performance of models was assessed by comparing adjusted R 2 , mean squared error, root mean squared error, and processing time. Prediction accuracy for split validation was above 90% for 11 months in ArcGIS and all 12 months in SAS. Cokriging in SAS achieved higher prediction accuracy and lower estimation bias as compared to cokriging in ArcGIS. County-level estimates produced by both packages were positively correlated (adjusted R 2 range=0.95 to 0.99); accuracy and precision improved with elevation as a covariate. Both methods from ArcGIS and SAS are reliable for U.S. county-level temperature estimates; However, ArcGIS's merits in spatial data pre-processing and processing time may be important considerations for software selection, especially for multi-year or multi-state projects.

A stochastic-geometric model of soil variation in Pleistocene patterned ground

NASA Astrophysics Data System (ADS)

Lark, Murray; Meerschman, Eef; Van Meirvenne, Marc

2013-04-01

In this paper we examine the spatial variability of soil in parent material with complex spatial structure which arises from complex non-linear geomorphic processes. We show that this variability can be better-modelled by a stochastic-geometric model than by a standard Gaussian random field. The benefits of the new model are seen in the reproduction of features of the target variable which influence processes like water movement and pollutant dispersal. Complex non-linear processes in the soil give rise to properties with non-Gaussian distributions. Even under a transformation to approximate marginal normality, such variables may have a more complex spatial structure than the Gaussian random field model of geostatistics can accommodate. In particular the extent to which extreme values of the variable are connected in spatially coherent regions may be misrepresented. As a result, for example, geostatistical simulation generally fails to reproduce the pathways for preferential flow in an environment where coarse infill of former fluvial channels or coarse alluvium of braided streams creates pathways for rapid movement of water. Multiple point geostatistics has been developed to deal with this problem. Multiple point methods proceed by sampling from a set of training images which can be assumed to reproduce the non-Gaussian behaviour of the target variable. The challenge is to identify appropriate sources of such images. In this paper we consider a mode of soil variation in which the soil varies continuously, exhibiting short-range lateral trends induced by local effects of the factors of soil formation which vary across the region of interest in an unpredictable way. The trends in soil variation are therefore only apparent locally, and the soil variation at regional scale appears random. We propose a stochastic-geometric model for this mode of soil variation called the Continuous Local Trend (CLT) model. We consider a case study of soil formed in relict patterned ground with pronounced lateral textural variations arising from the presence of infilled ice-wedges of Pleistocene origin. We show how knowledge of the pedogenetic processes in this environment, along with some simple descriptive statistics, can be used to select and fit a CLT model for the apparent electrical conductivity (ECa) of the soil. We use the model to simulate realizations of the CLT process, and compare these with realizations of a fitted Gaussian random field. We show how statistics that summarize the spatial coherence of regions with small values of ECa, which are expected to have coarse texture and so larger saturated hydraulic conductivity, are better reproduced by the CLT model than by the Gaussian random field. This suggests that the CLT model could be used to generate an unlimited supply of training images to allow multiple point geostatistical simulation or prediction of this or similar variables.

Indoor terrestrial gamma dose rate mapping in France: a case study using two different geostatistical models.

PubMed

Warnery, E; Ielsch, G; Lajaunie, C; Cale, E; Wackernagel, H; Debayle, C; Guillevic, J

2015-01-01

Terrestrial gamma dose rates show important spatial variations in France. Previous studies resulted in maps of arithmetic means of indoor terrestrial gamma dose rates by "departement" (French district). However, numerous areas could not be characterized due to the lack of data. The aim of our work was to obtain more precise estimates of the spatial variability of indoor terrestrial gamma dose rates in France by using a more recent and complete data base and geostatistics. The study was based on the exploitation of 97,595 measurements results distributed in 17,404 locations covering all of France. Measurements were done by the Institute for Radioprotection and Nuclear Safety (IRSN) using RPL (Radio Photo Luminescent) dosimeters, exposed during several months between years 2011 and 2012 in French dentist surgeries and veterinary clinics. The data used came from dosimeters which were not exposed to anthropic sources. After removing the cosmic rays contribution in order to study only the telluric gamma radiation, it was decided to work with the arithmetic means of the time-series measurements, weighted by the time-exposure of the dosimeters, for each location. The values varied between 13 and 349 nSv/h, with an arithmetic mean of 76 nSv/h. The observed statistical distribution of the gamma dose rates was skewed to the right. Firstly, ordinary kriging was performed in order to predict the gamma dose rate on cells of 1*1 km(2), all over the domain. The second step of the study was to use an auxiliary variable in estimates. The IRSN achieved in 2010 a classification of the French geological formations, characterizing their uranium potential on the bases of geology and local measurement results of rocks uranium content. This information is georeferenced in a map at the scale 1:1,000,000. The geological uranium potential (GUP) was classified in 5 qualitative categories. As telluric gamma rays mostly come from the progenies of the (238)Uranium series present in rocks, this information, which is exhaustive throughout France, could help in estimating the telluric gamma dose rates. Such an approach is possible using multivariate geostatistics and cokriging. Multi-collocated cokriging has been performed on 1*1 km(2) cells over the domain. This model used gamma dose rate measurement results and GUP classes. Our results provide useful information on the variability of the natural terrestrial gamma radiation in France ('natural background') and exposure data for epidemiological studies and risk assessment from low dose chronic exposures. Copyright © 2014 Elsevier Ltd. All rights reserved.

Use of geostatistics to predict virus decay rates for determination of septic tank setback distances.

PubMed Central

Yates, M V; Yates, S R; Warrick, A W; Gerba, C P

1986-01-01

Water samples were collected from 71 public drinking-water supply wells in the Tucson, Ariz., basin. Virus decay rates in the water samples were determined with MS-2 coliphage as a model virus. The correlations between the virus decay rates and the sample locations were shown by fitting a spherical model to the experimental semivariogram. Kriging, a geostatistical technique, was used to calculate virus decay rates at unsampled locations by using the known values at nearby wells. Based on the regional characteristics of groundwater flow and the kriged estimates of virus decay rates, a contour map of the area was constructed. The map shows the variation in separation distances that would have to be maintained between wells and sources of contamination to afford similar degrees of protection from viral contamination of the drinking water in wells throughout the basin. PMID:3532954

Optimal health and disease management using spatial uncertainty: a geographic characterization of emergent artemisinin-resistant Plasmodium falciparum distributions in Southeast Asia.

PubMed

Grist, Eric P M; Flegg, Jennifer A; Humphreys, Georgina; Mas, Ignacio Suay; Anderson, Tim J C; Ashley, Elizabeth A; Day, Nicholas P J; Dhorda, Mehul; Dondorp, Arjen M; Faiz, M Abul; Gething, Peter W; Hien, Tran T; Hlaing, Tin M; Imwong, Mallika; Kindermans, Jean-Marie; Maude, Richard J; Mayxay, Mayfong; McDew-White, Marina; Menard, Didier; Nair, Shalini; Nosten, Francois; Newton, Paul N; Price, Ric N; Pukrittayakamee, Sasithon; Takala-Harrison, Shannon; Smithuis, Frank; Nguyen, Nhien T; Tun, Kyaw M; White, Nicholas J; Witkowski, Benoit; Woodrow, Charles J; Fairhurst, Rick M; Sibley, Carol Hopkins; Guerin, Philippe J

2016-10-24

Artemisinin-resistant Plasmodium falciparum malaria parasites are now present across much of mainland Southeast Asia, where ongoing surveys are measuring and mapping their spatial distribution. These efforts require substantial resources. Here we propose a generic 'smart surveillance' methodology to identify optimal candidate sites for future sampling and thus map the distribution of artemisinin resistance most efficiently. The approach uses the 'uncertainty' map generated iteratively by a geostatistical model to determine optimal locations for subsequent sampling. The methodology is illustrated using recent data on the prevalence of the K13-propeller polymorphism (a genetic marker of artemisinin resistance) in the Greater Mekong Subregion. This methodology, which has broader application to geostatistical mapping in general, could improve the quality and efficiency of drug resistance mapping and thereby guide practical operations to eliminate malaria in affected areas.

Spatial variability of isoproturon mineralizing activity within an agricultural field: geostatistical analysis of simple physicochemical and microbiological soil parameters.

PubMed

El Sebai, T; Lagacherie, B; Soulas, G; Martin-Laurent, F

2007-02-01

We assessed the spatial variability of isoproturon mineralization in relation to that of physicochemical and biological parameters in fifty soil samples regularly collected along a sampling grid delimited across a 0.36 ha field plot (40 x 90 m). Only faint relationships were observed between isoproturon mineralization and the soil pH, microbial C biomass, and organic nitrogen. Considerable spatial variability was observed for six of the nine parameters tested (isoproturon mineralization rates, organic nitrogen, genetic structure of the microbial communities, soil pH, microbial biomass and equivalent humidity). The map of isoproturon mineralization rates distribution was similar to that of soil pH, microbial biomass, and organic nitrogen but different from those of structure of the microbial communities and equivalent humidity. Geostatistics revealed that the spatial heterogeneity in the rate of degradation of isoproturon corresponded to that of soil pH and microbial biomass.

Estimation of water table level and nitrate pollution based on geostatistical and multiple mass transport models

NASA Astrophysics Data System (ADS)

Matiatos, Ioannis; Varouhakis, Emmanouil A.; Papadopoulou, Maria P.

2015-04-01

As the sustainable use of groundwater resources is a great challenge for many countries in the world, groundwater modeling has become a very useful and well established tool for studying groundwater management problems. Based on various methods used to numerically solve algebraic equations representing groundwater flow and contaminant mass transport, numerical models are mainly divided into Finite Difference-based and Finite Element-based models. The present study aims at evaluating the performance of a finite difference-based (MODFLOW-MT3DMS), a finite element-based (FEFLOW) and a hybrid finite element and finite difference (Princeton Transport Code-PTC) groundwater numerical models simulating groundwater flow and nitrate mass transport in the alluvial aquifer of Trizina region in NE Peloponnese, Greece. The calibration of groundwater flow in all models was performed using groundwater hydraulic head data from seven stress periods and the validation was based on a series of hydraulic head data for two stress periods in sufficient numbers of observation locations. The same periods were used for the calibration of nitrate mass transport. The calibration and validation of the three models revealed that the simulated values of hydraulic heads and nitrate mass concentrations coincide well with the observed ones. The models' performance was assessed by performing a statistical analysis of these different types of numerical algorithms. A number of metrics, such as Mean Absolute Error (MAE), Root Mean Square Error (RMSE), Bias, Nash Sutcliffe Model Efficiency (NSE) and Reliability Index (RI) were used allowing the direct comparison of models' performance. Spatiotemporal Kriging (STRK) was also applied using separable and non-separable spatiotemporal variograms to predict water table level and nitrate concentration at each sampling station for two selected hydrological stress periods. The predictions were validated using the respective measured values. Maps of water table level and nitrate concentrations were produced and compared with those obtained from groundwater and mass transport numerical models. Preliminary results showed similar efficiency of the spatiotemporal geostatistical method with the numerical models. However data requirements of the former model were significantly less. Advantages and disadvantages of the methods performance were analysed and discussed indicating the characteristics of the different approaches.

Spatial variability of soil available phosphorous and potassium at three different soils located in Pannonian Croatia

NASA Astrophysics Data System (ADS)

Bogunović, Igor; Pereira, Paulo; Đurđević, Boris

2017-04-01

Information on spatial distribution of soil nutrients in agroecosystems is critical for improving productivity and reducing environmental pressures in intensive farmed soils. In this context, spatial prediction of soil properties should be accurate. In this study we analyse 704 data of soil available phosphorus (AP) and potassium (AK); the data derive from soil samples collected across three arable fields in Baranja region (Croatia) in correspondence of different soil types: Cambisols (169 samples), Chernozems (131 samples) and Gleysoils (404 samples). The samples are collected in a regular sampling grid (distance 225 x 225 m). Several geostatistical techniques (Inverse Distance to a Weight (IDW) with the power of 1, 2 and 3; Radial Basis Functions (RBF) - Inverse Multiquadratic (IMT), Multiquadratic (MTQ), Completely Regularized Spline (CRS), Spline with Tension (SPT) and Thin Plate Spline (TPS); and Local Polynomial (LP) with the power of 1 and 2; two geostatistical techniques -Ordinary Kriging - OK and Simple Kriging - SK) were tested in order to evaluate the most accurate spatial variability maps using criteria of lowest RMSE during cross validation technique. Soil parameters varied considerably throughout the studied fields and their coefficient of variations ranged from 31.4% to 37.7% and from 19.3% to 27.1% for soil AP and AK, respectively. The experimental variograms indicate a moderate spatial dependence for AP and strong spatial dependence for all three locations. The best spatial predictor for AP at Chernozem field was Simple kriging (RMSE=61.711), and for AK inverse multiquadratic (RMSE=44.689). The least accurate technique was Thin plate spline (AP) and Inverse distance to a weight with a power of 1 (AK). Radial basis function models (Spline with Tension for AP at Gleysoil and Cambisol and Completely Regularized Spline for AK at Gleysol) were the best predictors, while Thin Plate Spline models were the least accurate in all three cases. The best interpolator for AK at Cambisol was the local polynomial with the power of 2 (RMSE=33.943), while the least accurate was Thin Plate Spline (RMSE=39.572).

Use of portable X-ray fluorescence spectroscopy and geostatistics for health risk assessment.

PubMed

Yang, Meng; Wang, Cheng; Yang, Zhao-Ping; Yan, Nan; Li, Feng-Ying; Diao, Yi-Wei; Chen, Min-Dong; Li, Hui-Ming; Wang, Jin-Hua; Qian, Xin

2018-05-30

Laboratory analysis of trace metals using inductively coupled plasma (ICP) spectroscopy is not cost effective, and the complex spatial distribution of soil trace metals makes their spatial analysis and prediction problematic. Thus, for the health risk assessment of exposure to trace metals in soils, portable X-ray fluorescence (PXRF) spectroscopy was used to replace ICP spectroscopy for metal analysis, and robust geostatistical methods were used to identify spatial outliers in trace metal concentrations and to map trace metal distributions. A case study was carried out around an industrial area in Nanjing, China. The results showed that PXRF spectroscopy provided results for trace metal (Cu, Ni, Pb and Zn) levels comparable to ICP spectroscopy. The results of the health risk assessment showed that Ni posed a higher non-carcinogenic risk than Cu, Pb and Zn, indicating a higher priority of concern than the other elements. Sampling locations associated with adverse health effects were identified as 'hotspots', and high-risk areas were delineated from risk maps. These 'hotspots' and high-risk areas were in close proximity to and downwind from petrochemical plants, indicating the dominant role of industrial activities as the major sources of trace metals in soils. The approach used in this study could be adopted as a cost-effective methodology for screening 'hotspots' and priority areas of concern for cost-efficient health risk management. Copyright © 2018 Elsevier Inc. All rights reserved.

Spatial Prediction of Coxiella burnetii Outbreak Exposure via Notified Case Counts in a Dose-Response Model.

PubMed

Brooke, Russell J; Kretzschmar, Mirjam E E; Hackert, Volker; Hoebe, Christian J P A; Teunis, Peter F M; Waller, Lance A

2017-01-01

We develop a novel approach to study an outbreak of Q fever in 2009 in the Netherlands by combining a human dose-response model with geostatistics prediction to relate probability of infection and associated probability of illness to an effective dose of Coxiella burnetii. The spatial distribution of the 220 notified cases in the at-risk population are translated into a smooth spatial field of dose. Based on these symptomatic cases, the dose-response model predicts a median of 611 asymptomatic infections (95% range: 410, 1,084) for the 220 reported symptomatic cases in the at-risk population; 2.78 (95% range: 1.86, 4.93) asymptomatic infections for each reported case. The low attack rates observed during the outbreak range from (Equation is included in full-text article.)to (Equation is included in full-text article.). The estimated peak levels of exposure extend to the north-east from the point source with an increasing proportion of asymptomatic infections further from the source. Our work combines established methodology from model-based geostatistics and dose-response modeling allowing for a novel approach to study outbreaks. Unobserved infections and the spatially varying effective dose can be predicted using the flexible framework without assuming any underlying spatial structure of the outbreak process. Such predictions are important for targeting interventions during an outbreak, estimating future disease burden, and determining acceptable risk levels.

Modeling of surface dust concentration in snow cover at industrial area using neural networks and kriging

NASA Astrophysics Data System (ADS)

Sergeev, A. P.; Tarasov, D. A.; Buevich, A. G.; Shichkin, A. V.; Tyagunov, A. G.; Medvedev, A. N.

2017-06-01

Modeling of spatial distribution of pollutants in the urbanized territories is difficult, especially if there are multiple emission sources. When monitoring such territories, it is often impossible to arrange the necessary detailed sampling. Because of this, the usual methods of analysis and forecasting based on geostatistics are often less effective. Approaches based on artificial neural networks (ANNs) demonstrate the best results under these circumstances. This study compares two models based on ANNs, which are multilayer perceptron (MLP) and generalized regression neural networks (GRNNs) with the base geostatistical method - kriging. Models of the spatial dust distribution in the snow cover around the existing copper quarry and in the area of emissions of a nickel factory were created. To assess the effectiveness of the models three indices were used: the mean absolute error (MAE), the root-mean-square error (RMSE), and the relative root-mean-square error (RRMSE). Taking into account all indices the model of GRNN proved to be the most accurate which included coordinates of the sampling points and the distance to the likely emission source as input parameters for the modeling. Maps of spatial dust distribution in the snow cover were created in the study area. It has been shown that the models based on ANNs were more accurate than the kriging, particularly in the context of a limited data set.

Modern space/time geostatistics using river distances: data integration of turbidity and E. coli measurements to assess fecal contamination along the Raritan River in New Jersey.

PubMed

Money, Eric S; Carter, Gail P; Serre, Marc L

2009-05-15

Escherichia coli (E. coli) is a widely used indicator of fecal contamination in water bodies. External contact and subsequent ingestion of bacteria coming from fecal contamination can lead to harmful health effects. Since E. coli data are sometimes limited, the objective of this study is to use secondary information in the form of turbidity to improve the assessment of E. coli at unmonitored locations. We obtained all E. coli and turbidity monitoring data available from existing monitoring networks for the 2000-2006 time period for the Raritan River Basin, New Jersey. Using collocated measurements, we developed a predictive model of E. coli from turbidity data. Using this model, soft data are constructed for E. coli given turbidity measurements at 739 space/time locations where only turbidity was measured. Finally, the Bayesian Maximum Entropy (BME) method of modern space/time geostatistics was used for the data integration of monitored and predicted E. coli data to produce maps showing E. coli concentration estimated daily across the river basin. The addition of soft data in conjunction with the use of river distances reduced estimation error by about 30%. Furthermore, based on these maps, up to 35% of river miles in the Raritan Basin had a probability of E coli impairment greater than 90% on the most polluted day of the study period.

A Simulated Annealing based Optimization Algorithm for Automatic Variogram Model Fitting

NASA Astrophysics Data System (ADS)

Soltani-Mohammadi, Saeed; Safa, Mohammad

2016-09-01

Fitting a theoretical model to an experimental variogram is an important issue in geostatistical studies because if the variogram model parameters are tainted with uncertainty, the latter will spread in the results of estimations and simulations. Although the most popular fitting method is fitting by eye, in some cases use is made of the automatic fitting method on the basis of putting together the geostatistical principles and optimization techniques to: 1) provide a basic model to improve fitting by eye, 2) fit a model to a large number of experimental variograms in a short time, and 3) incorporate the variogram related uncertainty in the model fitting. Effort has been made in this paper to improve the quality of the fitted model by improving the popular objective function (weighted least squares) in the automatic fitting. Also, since the variogram model function (£) and number of structures (m) too affect the model quality, a program has been provided in the MATLAB software that can present optimum nested variogram models using the simulated annealing method. Finally, to select the most desirable model from among the single/multi-structured fitted models, use has been made of the cross-validation method, and the best model has been introduced to the user as the output. In order to check the capability of the proposed objective function and the procedure, 3 case studies have been presented.

Evolution of Western Mediterranean Sea Surface Temperature between 1985 and 2005: a complementary study in situ, satellite and modelling approaches

NASA Astrophysics Data System (ADS)

Troupin, C.; Lenartz, F.; Sirjacobs, D.; Alvera-Azcárate, A.; Barth, A.; Ouberdous, M.; Beckers, J.-M.

2009-04-01

In order to evaluate the variability of the sea surface temperature (SST) in the Western Mediterranean Sea between 1985 and 2005, an integrated approach combining geostatistical tools and modelling techniques has been set up. The objectives are: underline the capability of each tool to capture characteristic phenomena, compare and assess the quality of their outputs, infer an interannual trend from the results. Diva (Data Interpolating Variationnal Analysis, Brasseur et al. (1996) Deep-Sea Res.) was applied on a collection of in situ data gathered from various sources (World Ocean Database 2005, Hydrobase2, Coriolis and MedAtlas2), from which duplicates and suspect values were removed. This provided monthly gridded fields in the region of interest. Heterogeneous time data coverage was taken into account by computing and removing the annual trend, provided by Diva detrending tool. Heterogeneous correlation length was applied through an advection constraint. Statistical technique DINEOF (Data Interpolation with Empirical Orthogonal Functions, Alvera-Azc

A geostatistical approach for quantification of contaminant mass discharge uncertainty using multilevel sampler measurements

NASA Astrophysics Data System (ADS)

Li, K. Betty; Goovaerts, Pierre; Abriola, Linda M.

2007-06-01

Contaminant mass discharge across a control plane downstream of a dense nonaqueous phase liquid (DNAPL) source zone has great potential to serve as a metric for the assessment of the effectiveness of source zone treatment technologies and for the development of risk-based source-plume remediation strategies. However, too often the uncertainty of mass discharge estimated in the field is not accounted for in the analysis. In this paper, a geostatistical approach is proposed to estimate mass discharge and to quantify its associated uncertainty using multilevel transect measurements of contaminant concentration (C) and hydraulic conductivity (K). The approach adapts the p-field simulation algorithm to propagate and upscale the uncertainty of mass discharge from the local uncertainty models of C and K. Application of this methodology to numerically simulated transects shows that, with a regular sampling pattern, geostatistics can provide an accurate model of uncertainty for the transects that are associated with low levels of source mass removal (i.e., transects that have a large percentage of contaminated area). For high levels of mass removal (i.e., transects with a few hot spots and large areas of near-zero concentration), a total sampling area equivalent to 6˜7% of the transect is required to achieve accurate uncertainty modeling. A comparison of the results for different measurement supports indicates that samples taken with longer screen lengths may lead to less accurate models of mass discharge uncertainty. The quantification of mass discharge uncertainty, in the form of a probability distribution, will facilitate risk assessment associated with various remediation strategies.

Principal Component Geostatistical Approach for large-dimensional inverse problems

PubMed Central

Kitanidis, P K; Lee, J

2014-01-01

The quasi-linear geostatistical approach is for weakly nonlinear underdetermined inverse problems, such as Hydraulic Tomography and Electrical Resistivity Tomography. It provides best estimates as well as measures for uncertainty quantification. However, for its textbook implementation, the approach involves iterations, to reach an optimum, and requires the determination of the Jacobian matrix, i.e., the derivative of the observation function with respect to the unknown. Although there are elegant methods for the determination of the Jacobian, the cost is high when the number of unknowns, m, and the number of observations, n, is high. It is also wasteful to compute the Jacobian for points away from the optimum. Irrespective of the issue of computing derivatives, the computational cost of implementing the method is generally of the order of m2n, though there are methods to reduce the computational cost. In this work, we present an implementation that utilizes a matrix free in terms of the Jacobian matrix Gauss-Newton method and improves the scalability of the geostatistical inverse problem. For each iteration, it is required to perform K runs of the forward problem, where K is not just much smaller than m but can be smaller that n. The computational and storage cost of implementation of the inverse procedure scales roughly linearly with m instead of m2 as in the textbook approach. For problems of very large m, this implementation constitutes a dramatic reduction in computational cost compared to the textbook approach. Results illustrate the validity of the approach and provide insight in the conditions under which this method perform best. PMID:25558113

Medical Geography: a Promising Field of Application for Geostatistics.

PubMed

Goovaerts, P

2009-01-01

The analysis of health data and putative covariates, such as environmental, socio-economic, behavioral or demographic factors, is a promising application for geostatistics. It presents, however, several methodological challenges that arise from the fact that data are typically aggregated over irregular spatial supports and consist of a numerator and a denominator (i.e. population size). This paper presents an overview of recent developments in the field of health geostatistics, with an emphasis on three main steps in the analysis of areal health data: estimation of the underlying disease risk, detection of areas with significantly higher risk, and analysis of relationships with putative risk factors. The analysis is illustrated using age-adjusted cervix cancer mortality rates recorded over the 1970-1994 period for 118 counties of four states in the Western USA. Poisson kriging allows the filtering of noisy mortality rates computed from small population sizes, enhancing the correlation with two putative explanatory variables: percentage of habitants living below the federally defined poverty line, and percentage of Hispanic females. Area-to-point kriging formulation creates continuous maps of mortality risk, reducing the visual bias associated with the interpretation of choropleth maps. Stochastic simulation is used to generate realizations of cancer mortality maps, which allows one to quantify numerically how the uncertainty about the spatial distribution of health outcomes translates into uncertainty about the location of clusters of high values or the correlation with covariates. Last, geographically-weighted regression highlights the non-stationarity in the explanatory power of covariates: the higher mortality values along the coast are better explained by the two covariates than the lower risk recorded in Utah.

MoisturEC: an R application for geostatistical estimation of moisture content from electrical conductivity data

NASA Astrophysics Data System (ADS)

Terry, N.; Day-Lewis, F. D.; Werkema, D. D.; Lane, J. W., Jr.

2017-12-01

Soil moisture is a critical parameter for agriculture, water supply, and management of landfills. Whereas direct data (as from TDR or soil moisture probes) provide localized point scale information, it is often more desirable to produce 2D and/or 3D estimates of soil moisture from noninvasive measurements. To this end, geophysical methods for indirectly assessing soil moisture have great potential, yet are limited in terms of quantitative interpretation due to uncertainty in petrophysical transformations and inherent limitations in resolution. Simple tools to produce soil moisture estimates from geophysical data are lacking. We present a new standalone program, MoisturEC, for estimating moisture content distributions from electrical conductivity data. The program uses an indicator kriging method within a geostatistical framework to incorporate hard data (as from moisture probes) and soft data (as from electrical resistivity imaging or electromagnetic induction) to produce estimates of moisture content and uncertainty. The program features data visualization and output options as well as a module for calibrating electrical conductivity with moisture content to improve estimates. The user-friendly program is written in R - a widely used, cross-platform, open source programming language that lends itself to further development and customization. We demonstrate use of the program with a numerical experiment as well as a controlled field irrigation experiment. Results produced from the combined geostatistical framework of MoisturEC show improved estimates of moisture content compared to those generated from individual datasets. This application provides a convenient and efficient means for integrating various data types and has broad utility to soil moisture monitoring in landfills, agriculture, and other problems.

Principal Component Geostatistical Approach for large-dimensional inverse problems.

PubMed

Kitanidis, P K; Lee, J

2014-07-01

The quasi-linear geostatistical approach is for weakly nonlinear underdetermined inverse problems, such as Hydraulic Tomography and Electrical Resistivity Tomography. It provides best estimates as well as measures for uncertainty quantification. However, for its textbook implementation, the approach involves iterations, to reach an optimum, and requires the determination of the Jacobian matrix, i.e., the derivative of the observation function with respect to the unknown. Although there are elegant methods for the determination of the Jacobian, the cost is high when the number of unknowns, m , and the number of observations, n , is high. It is also wasteful to compute the Jacobian for points away from the optimum. Irrespective of the issue of computing derivatives, the computational cost of implementing the method is generally of the order of m 2 n , though there are methods to reduce the computational cost. In this work, we present an implementation that utilizes a matrix free in terms of the Jacobian matrix Gauss-Newton method and improves the scalability of the geostatistical inverse problem. For each iteration, it is required to perform K runs of the forward problem, where K is not just much smaller than m but can be smaller that n . The computational and storage cost of implementation of the inverse procedure scales roughly linearly with m instead of m 2 as in the textbook approach. For problems of very large m , this implementation constitutes a dramatic reduction in computational cost compared to the textbook approach. Results illustrate the validity of the approach and provide insight in the conditions under which this method perform best.

Assessing spatial uncertainty in reservoir characterization for carbon sequestration planning using public well-log data: A case study

USGS Publications Warehouse

Venteris, E.R.; Carter, K.M.

2009-01-01

Mapping and characterization of potential geologic reservoirs are key components in planning carbon dioxide (CO2) injection projects. The geometry of target and confining layers is vital to ensure that the injected CO2 remains in a supercritical state and is confined to the target layer. Also, maps of injection volume (porosity) are necessary to estimate sequestration capacity at undrilled locations. Our study uses publicly filed geophysical logs and geostatistical modeling methods to investigate the reliability of spatial prediction for oil and gas plays in the Medina Group (sandstone and shale facies) in northwestern Pennsylvania. Specifically, the modeling focused on two targets: the Grimsby Formation and Whirlpool Sandstone. For each layer, thousands of data points were available to model structure and thickness but only hundreds were available to support volumetric modeling because of the rarity of density-porosity logs in the public records. Geostatistical analysis based on this data resulted in accurate structure models, less accurate isopach models, and inconsistent models of pore volume. Of the two layers studied, only the Whirlpool Sandstone data provided for a useful spatial model of pore volume. Where reliable models for spatial prediction are absent, the best predictor available for unsampled locations is the mean value of the data, and potential sequestration sites should be planned as close as possible to existing wells with volumetric data. ?? 2009. The American Association of Petroleum Geologists/Division of Environmental Geosciences. All rights reserved.

Introduction to this Special Issue on Geostatistics and Scaling of Remote Sensing

NASA Technical Reports Server (NTRS)

Quattrochi, Dale A.

1999-01-01

The germination of this special PE&RS issue began at the Royal Geographical Society (with the Institute of British Geographers)(RCS-IBC) annual meeting in January, 1997 held at the University of Exeter in Exeter, England. The cold and snow of an England winter were greatly tempered by the friendly and cordial discussions that ensued at the meeting on possible ways to foster both dialog and research across "the Big Pond" between geographers in the US and the UK on the use of geostatistics and geospatial techniques for remote sensing of land surface processes. It was decided that one way to stimulate and enhance cooperation on the application of geostatistics and geospatial methods in remote sensing was to hold parallel sessions on these topics at appropriate meeting venues in 1998 in both the US and the UK Selected papers given at these sessions would be published as a special issue of PE&RS on the US side, and as a special issue of Computers and Geosciences (C&G) on the UK side, to highlight the commonality in research on geostatistics and geospatial methods in remote sensing and spatial data analysis on both sides of the Atlantic Ocean. As a consequence, a session on "Ceostatistics and Geospatial Techniques for Remote Sensing of Land Surface Processes" was held at the Association of American Geographers (AAG) annual meeting in Boston, Massachusetts in March, 1998, sponsored by the AAG's Remote Sensing Specialty Group (RSSG). A similar session was held at the RGS-IBG annual meeting in Guildford, Surrey, England in January 1998, organized by the Modeling and Advanced Techniques Special Interest Group (MAT SIG) of the Remote Sensing Society (RSS). The six papers that in part, comprise this issue of PE&RS, are the US complement to such a dual journal publication effort. Both of us are co-editors of each of the journal special issues, with the lead editor of each journal being from their respective side of the Atlantic where the journals are published. The special issue of C&G that constitutes the other half of this co-edited journal series will be published in early 1999, with 3 papers by US authors being published along with 6 papers authored by individuals from the UK and other places in Europe.

Spatial distribution of nematodes in soil cultivated with sugarcane under different uses

NASA Astrophysics Data System (ADS)

Cardoso, M. O.; Pedrosa, E. M. R.; Vicente, T. F. S.; Siqueira, G. M.; Montenegro, A. A. A.

2012-04-01

Sugarcane is a crop of major importance within the Brazilian economy, being an activity that generates energy and with high capacity to develop various economic sectors. Currently the greatest challenge is to maximize productivity and minimize environmental impacts. The plant-parasites nematodes have great expression, because influence directly the productive potential of sugarcane crops. Accordingly, little research has been devoted to the study of spatial variability of nematodes. Thus, the purpose of this work is to analyze the spatial distribution of nematodes in a soil cultivated with sugarcane in areas with and without irrigation, with distinct spacing of sampling to determine the differences between the sampling scales. The study area is located in the municipality of Goiana (Pernambuco State, Brazil). The experiment was conducted in two areas with 40 hectares each, being collected 90 samples at different spacing: 18 samples with spacing of 200.00 x 200.00 m, 36 samples with spacing of 20.00 m x 20.00 m and 36 samples with spacing of 2.00 m x 2.00 m. Soil samples were collected at deep of 0.00-0.20 m and nematodes were extracted per 300 cm3 of soil through centrifugal flotation in sucrose being quantified, classified according trophic habit (plant-parasites, fungivores, bacterivores, omnivores and predators) and identified in level of genus or family. In irrigated area the amount of water applied was determined considering the evapotranspiration of culture. The data were analyzed using classical statistics and geostatistics. The results demonstrated that the data showed high values of coefficient of variation in both study areas. All attributes studied showed log normal frequency distribution. The area B (irrigated) has a population of nematodes more stable than the area A (non-irrigated), a fact confirmed by its mean value of the total population of nematodes (282.45 individuals). The use of geostatistics not allowed to assess the spatial distribution of populations of nematodes even with the data being collected at different scales, describing the spatial variability of groups of nematodes present in the areas evaluated is smaller than the smallest spacing used. Even with the data showing pure nugget effect was possible to verify the semivariogram for the groups of nematodes in the area A, where pairs of semivariance showed great dispersion.

Framework for the mapping of the monthly average daily solar radiation using an advanced case-based reasoning and a geostatistical technique.

PubMed

Lee, Minhyun; Koo, Choongwan; Hong, Taehoon; Park, Hyo Seon

2014-04-15

For the effective photovoltaic (PV) system, it is necessary to accurately determine the monthly average daily solar radiation (MADSR) and to develop an accurate MADSR map, which can simplify the decision-making process for selecting the suitable location of the PV system installation. Therefore, this study aimed to develop a framework for the mapping of the MADSR using an advanced case-based reasoning (CBR) and a geostatistical technique. The proposed framework consists of the following procedures: (i) the geographic scope for the mapping of the MADSR is set, and the measured MADSR and meteorological data in the geographic scope are collected; (ii) using the collected data, the advanced CBR model is developed; (iii) using the advanced CBR model, the MADSR at unmeasured locations is estimated; and (iv) by applying the measured and estimated MADSR data to the geographic information system, the MADSR map is developed. A practical validation was conducted by applying the proposed framework to South Korea. It was determined that the MADSR map developed through the proposed framework has been improved in terms of accuracy. The developed MADSR map can be used for estimating the MADSR at unmeasured locations and for determining the optimal location for the PV system installation.

Field-scale soil moisture space-time geostatistical modeling for complex Palouse landscapes in the inland Pacific Northwest

NASA Astrophysics Data System (ADS)

Chahal, M. K.; Brown, D. J.; Brooks, E. S.; Campbell, C.; Cobos, D. R.; Vierling, L. A.

2012-12-01

Estimating soil moisture content continuously over space and time using geo-statistical techniques supports the refinement of process-based watershed hydrology models and the application of soil process models (e.g. biogeochemical models predicting greenhouse gas fluxes) to complex landscapes. In this study, we model soil profile volumetric moisture content for five agricultural fields with loess soils in the Palouse region of Eastern Washington and Northern Idaho. Using a combination of stratification and space-filling techniques, we selected 42 representative and distributed measurement locations in the Cook Agronomy Farm (Pullman, WA) and 12 locations each in four additional grower fields that span the precipitation gradient across the Palouse. At each measurement location, soil moisture was measured on an hourly basis at five different depths (30, 60, 90, 120, and 150 cm) using Decagon 5-TE/5-TM soil moisture sensors (Decagon Devices, Pullman, WA, USA). This data was collected over three years for the Cook Agronomy Farm and one year for each of the grower fields. In addition to ordinary kriging, we explored the correlation of volumetric water content with external, spatially exhaustive indices derived from terrain models, optical remote sensing imagery, and proximal soil sensing data (electromagnetic induction and VisNIR penetrometer)

Sediment distribution pattern mapped from the combination of objective analysis and geostatistics in the large shallow Taihu Lake, China.

PubMed

Luo, Lian-Cong; Qin, Bo-Qiang; Zhu, Guang-Wei

2004-01-01

Investigation was made into sediment depth at 723 irregularly scattered measurement points which cover all the regions in Taihu Lake, China. The combination of successive correction scheme and geostatistical method was used to get all the values of recent sediment thickness at the 69 x 69 grids in the whole lake. The results showed that there is the significant difference in sediment depth between the eastern area and the western region, and most of the sediments are located in the western shore-line and northern regimes but just a little in the center and eastern parts. The notable exception is the patch between the center and Xishan Island where the maximum sediment depth is more than 4.0 m. This sediment distribution pattern is more than likely related to the current circulation pattern induced by the prevailing wind-forcing in Taihu Lake. The numerical simulation of hydrodynamics can strong support the conclusion. Sediment effects on water quality was also studied and the results showed that the concentrations of TP, TN and SS in the western part are obviously larger than those in the eastern regime, which suggested that more nutrients can be released from thicker sediment areas.

Predicting polycyclic aromatic hydrocarbons using a mass fraction approach in a geostatistical framework across North Carolina.

PubMed

Reyes, Jeanette M; Hubbard, Heidi F; Stiegel, Matthew A; Pleil, Joachim D; Serre, Marc L

2018-01-09

Currently in the United States there are no regulatory standards for ambient concentrations of polycyclic aromatic hydrocarbons (PAHs), a class of organic compounds with known carcinogenic species. As such, monitoring data are not routinely collected resulting in limited exposure mapping and epidemiologic studies. This work develops the log-mass fraction (LMF) Bayesian maximum entropy (BME) geostatistical prediction method used to predict the concentration of nine particle-bound PAHs across the US state of North Carolina. The LMF method develops a relationship between a relatively small number of collocated PAH and fine Particulate Matter (PM2.5) samples collected in 2005 and applies that relationship to a larger number of locations where PM2.5 is routinely monitored to more broadly estimate PAH concentrations across the state. Cross validation and mapping results indicate that by incorporating both PAH and PM2.5 data, the LMF BME method reduces mean squared error by 28.4% and produces more realistic spatial gradients compared to the traditional kriging approach based solely on observed PAH data. The LMF BME method efficiently creates PAH predictions in a PAH data sparse and PM2.5 data rich setting, opening the door for more expansive epidemiologic exposure assessments of ambient PAH.

An LUR/BME framework to estimate PM2.5 explained by on road mobile and stationary sources.

PubMed

Reyes, Jeanette M; Serre, Marc L

2014-01-01

Knowledge of particulate matter concentrations <2.5 μm in diameter (PM2.5) across the United States is limited due to sparse monitoring across space and time. Epidemiological studies need accurate exposure estimates in order to properly investigate potential morbidity and mortality. Previous works have used geostatistics and land use regression (LUR) separately to quantify exposure. This work combines both methods by incorporating a large area variability LUR model that accounts for on road mobile emissions and stationary source emissions along with data that take into account incompleteness of PM2.5 monitors into the modern geostatistical Bayesian Maximum Entropy (BME) framework to estimate PM2.5 across the United States from 1999 to 2009. A cross-validation was done to determine the improvement of the estimate due to the LUR incorporation into BME. These results were applied to known diseases to determine predicted mortality coming from total PM2.5 as well as PM2.5 explained by major contributing sources. This method showed a mean squared error reduction of over 21.89% oversimple kriging. PM2.5 explained by on road mobile emissions and stationary emissions contributed to nearly 568,090 and 306,316 deaths, respectively, across the United States from 1999 to 2007.

[Spatial pattern of land surface dead combustible fuel load in Huzhong forest area in Great Xing'an Mountains].

PubMed

Liu, Zhi-Hua; Chang, Yu; Chen, Hong-Wei; Zhou, Rui; Jing, Guo-Zhi; Zhang, Hong-Xin; Zhang, Chang-Meng

2008-03-01

By using geo-statistics and based on time-lag classification standard, a comparative study was made on the land surface dead combustible fuels in Huzhong forest area in Great Xing'an Mountains. The results indicated that the first level land surface dead combustible fuel, i. e., 1 h time-lag dead fuel, presented stronger spatial auto-correlation, with an average of 762.35 g x m(-2) and contributing to 55.54% of the total load. Its determining factors were species composition and stand age. The second and third levels land surface dead combustible fuel, i. e., 10 h and 100 h time-lag dead fuels, had a sum of 610.26 g x m(-2), and presented weaker spatial auto-correlation than 1 h time-lag dead fuel. Their determining factor was the disturbance history of forest stand. The complexity and heterogeneity of the factors determining the quality and quantity of forest land surface dead combustible fuels were the main reasons for the relatively inaccurate interpolation. However, the utilization of field survey data coupled with geo-statistics could easily and accurately interpolate the spatial pattern of forest land surface dead combustible fuel loads, and indirectly provide a practical basis for forest management.

Mapping the distribution of the denitrifier community at large scales (Invited)

NASA Astrophysics Data System (ADS)

Philippot, L.; Bru, D.; Ramette, A.; Dequiedt, S.; Ranjard, L.; Jolivet, C.; Arrouays, D.

2010-12-01

Little information is available regarding the landscape-scale distribution of microbial communities and its environmental determinants. Here we combined molecular approaches and geostatistical modeling to explore spatial patterns of the denitrifying community at large scales. The distribution of denitrifrying community was investigated over 107 sites in Burgundy, a 31 500 km2 region of France, using a 16 X 16 km sampling grid. At each sampling site, the abundances of denitrifiers and 42 soil physico-chemical properties were measured. The relative contributions of land use, spatial distance, climatic conditions, time and soil physico-chemical properties to the denitrifier spatial distribution were analyzed by canonical variation partitioning. Our results indicate that 43% to 85% of the spatial variation in community abundances could be explained by the measured environmental parameters, with soil chemical properties (mostly pH) being the main driver. We found spatial autocorrelation up to 740 km and used geostatistical modelling to generate predictive maps of the distribution of denitrifiers at the landscape scale. Studying the distribution of the denitrifiers at large scale can help closing the artificial gap between the investigation of microbial processes and microbial community ecology, therefore facilitating our understanding of the relationships between the ecology of denitrifiers and N-fluxes by denitrification.

Targeting trachoma control through risk mapping: the example of Southern Sudan.

PubMed

Clements, Archie C A; Kur, Lucia W; Gatpan, Gideon; Ngondi, Jeremiah M; Emerson, Paul M; Lado, Mounir; Sabasio, Anthony; Kolaczinski, Jan H

2010-08-17

Trachoma is a major cause of blindness in Southern Sudan. Its distribution has only been partially established and many communities in need of intervention have therefore not been identified or targeted. The present study aimed to develop a tool to improve targeting of survey and control activities. A national trachoma risk map was developed using Bayesian geostatistics models, incorporating trachoma prevalence data from 112 geo-referenced communities surveyed between 2001 and 2009. Logistic regression models were developed using active trachoma (trachomatous inflammation follicular and/or trachomatous inflammation intense) in 6345 children aged 1-9 years as the outcome, and incorporating fixed effects for age, long-term average rainfall (interpolated from weather station data) and land cover (i.e. vegetation type, derived from satellite remote sensing), as well as geostatistical random effects describing spatial clustering of trachoma. The model predicted the west of the country to be at no or low trachoma risk. Trachoma clusters in the central, northern and eastern areas had a radius of 8 km after accounting for the fixed effects. In Southern Sudan, large-scale spatial variation in the risk of active trachoma infection is associated with aridity. Spatial prediction has identified likely high-risk areas to be prioritized for more data collection, potentially to be followed by intervention.

Spatial epidemiology of bovine tuberculosis in Mexico.

PubMed

Martínez, Horacio Zendejas; Suazo, Feliciano Milián; Cuador Gil, José Quintín; Bello, Gustavo Cruz; Anaya Escalera, Ana María; Márquez, Gabriel Huitrón; Casanova, Leticia García

2007-01-01

The purpose of this study was to use geographic information systems (GIS) and geo-statistical methods of ordinary kriging to predict the prevalence and distribution of bovine tuberculosis (TB) in Jalisco, Mexico. A random sample of 2 287 herds selected from a set of 48 766 was used for the analysis. Spatial location of herds was obtained by either a personal global positioning system (GPS), a database from the Instituto Nacional de Estadìstica Geografìa e Informàtica (INEGI) or Google Earth. Information on TB prevalence was provided by the Jalisco Commission for the Control and Eradication of Tuberculosis (COEETB). Prediction of TB was obtained using ordinary kriging in the geostatistical analyst module in ArcView8. A predicted high prevalence area of TB matching the distribution of dairy cattle was observed. This prediction was in agreement with the prevalence calculated on the total 48 766 herds. Validation was performed taking estimated values of TB prevalence at each municipality, extracted from the kriging surface and then compared with the real prevalence values using a correlation test, giving a value of 0.78, indicating that GIS and kriging are reliable tools for the estimation of TB distribution based on a random sample. This resulted in a significant savings of resources.

Targeting Trachoma Control through Risk Mapping: The Example of Southern Sudan

PubMed Central

Clements, Archie C. A.; Kur, Lucia W.; Gatpan, Gideon; Ngondi, Jeremiah M.; Emerson, Paul M.; Lado, Mounir; Sabasio, Anthony; Kolaczinski, Jan H.

2010-01-01

Background Trachoma is a major cause of blindness in Southern Sudan. Its distribution has only been partially established and many communities in need of intervention have therefore not been identified or targeted. The present study aimed to develop a tool to improve targeting of survey and control activities. Methods/Principal Findings A national trachoma risk map was developed using Bayesian geostatistics models, incorporating trachoma prevalence data from 112 geo-referenced communities surveyed between 2001 and 2009. Logistic regression models were developed using active trachoma (trachomatous inflammation follicular and/or trachomatous inflammation intense) in 6345 children aged 1–9 years as the outcome, and incorporating fixed effects for age, long-term average rainfall (interpolated from weather station data) and land cover (i.e. vegetation type, derived from satellite remote sensing), as well as geostatistical random effects describing spatial clustering of trachoma. The model predicted the west of the country to be at no or low trachoma risk. Trachoma clusters in the central, northern and eastern areas had a radius of 8 km after accounting for the fixed effects. Conclusion In Southern Sudan, large-scale spatial variation in the risk of active trachoma infection is associated with aridity. Spatial prediction has identified likely high-risk areas to be prioritized for more data collection, potentially to be followed by intervention. PMID:20808910

Increased oil production and reserves from improved completion techniques in the Bluebell field, Uinta Basin, Utah. Tenth quarterly technical progress report, January 1, 1996--March 31, 1996. Revised

DOE Office of Scientific and Technical Information (OSTI.GOV)

Allison, M.L.

1996-05-13

The objective of this project is to increase oil production and reserves in the Uinta Basin by demonstrating improved completion techniques. Low productivity of Uinta Basin will is caused by gross production intervals of several thousand feet that contain perforated thief zones, water-bearing zones, and unperforated oil- bearing intervals. Geologic and engineering characterization and computer simulation of the Green River and Wasatch Formations in the Bluefell field will determine reservoir heterogeneities related to fractures and depositional trends. This will be followed by techniques based on the reservoir characterization. Transfer of the project results will be an ongoing component of themore » project. Data (net pay thickness, porosity, and water saturation) of more than 100 individuals beds in he lower Green River and Wasatch Formations were used to generate geostatistical realization (numerical- representation) of the reservoir properties. The data set was derived from the Michelle Ute and Malnar Pike demonstration wells and 22 other wells in a 20 (52 km{sup 2}) square-mile area. Beds were studied independently of each other. Principles of sequential Gaussian simulations were used to generate geostatistical realizations of the beds.« less

Spatial variability of soil properties and soil erodibility in the Alqueva reservoir watershed

NASA Astrophysics Data System (ADS)

Ferreira, V.; Panagopoulos, T.; Andrade, R.; Guerrero, C.; Loures, L.

2015-04-01

The aim of this work is to investigate how the spatial variability of soil properties and soil erodibility (K factor) were affected by the changes in land use allowed by irrigation with water from a reservoir in a semiarid area. To this end, three areas representative of different land uses (agroforestry grassland, lucerne crop and olive orchard) were studied within a 900 ha farm. The interrelationships between variables were analyzed by multivariate techniques and extrapolated using geostatistics. The results confirmed differences between land uses for all properties analyzed, which was explained mainly by the existence of diverse management practices (tillage, fertilization and irrigation), vegetation cover and local soil characteristics. Soil organic matter, clay and nitrogen content decreased significantly, while the K factor increased with intensive cultivation. The HJ-Biplot methodology was used to represent the variation of soil erodibility properties grouped in land uses. Native grassland was the least correlated with the other land uses. The K factor demonstrated high correlation mainly with very fine sand and silt. The maps produced with geostatistics were crucial to understand the current spatial variability in the Alqueva region. Facing the intensification of land-use conversion, a sustainable management is needed to introduce protective measures to control soil erosion.

Spatial variability of soil properties and soil erodibility in the Alqueva dam watershed, Portugal

NASA Astrophysics Data System (ADS)

Ferreira, V.; Panagopoulos, T.; Andrade, R.; Guerrero, C.; Loures, L.

2015-01-01

The aim of this work is to investigate how the spatial variability of soil properties and soil erodibility (K factor) were affected by the changes in land use allowed by irrigation with water from a reservoir in a semiarid area. To this, three areas representative of different land uses (agroforestry grassland, Lucerne crop and olive orchard) were studied within a 900 ha farm. The interrelationships between variables were analyzed by multivariate techniques and extrapolated using geostatistics. The results confirmed differences between land uses for all properties analyzed, which was explained mainly by the existence of diverse management practices (tillage, fertilization and irrigation), vegetation cover and local soil characteristics. Soil organic matter, clay and nitrogen content decreased significantly, while K factor increased with intensive cultivation. The HJ-biplot methodology was used to represent the variation of soil erodibility properties grouped in land uses. Native grassland was the least correlated with the other land uses. K factor demonstrated high correlation mainly with very fine sand and silt. The maps produced with geostatistics were crucial to understand the current spatial variability in the Alqueva region. Facing the intensification of land-use conversion, a sustainable management is needed to introduce protective measures to control soil erosion.

COST-EFFECTIVE SAMPLING FOR SPATIALLY DISTRIBUTED PHENOMENA

EPA Science Inventory

Various measures of sampling plan cost and loss are developed and analyzed as they relate to a variety of multidisciplinary sampling techniques. The sampling choices examined include methods from design-based sampling, model-based sampling, and geostatistics. Graphs and tables ar...

Kriging: Understanding allays intimidation

USGS Publications Warehouse

Olea, R.A.

1996-01-01

In 1938 Daniel Gerhardus "Danie" Krige obtained an undergraduate degree in mining engineering and started a brilliant career centered on analyzing the gold and uranium mines in the Witwatersrand conglomerates of South Africa. He became interested in the disharmony between the poor reliability of reserve estimation reports and the magnitude of the economic decisions that were based on these studies. Back at the University of Witwatersrand, he wrote a master's thesis that began a revolution in mining evaluation methods. Krige was not alone in his research. Another mining engineer, Georges Matheron, a Frenchman, thought space data analysis belonged in a separate discipline, just as geophysics is a separate branch from physics. He named the new field geostatistics. Kriging is the name given in geostatistics to a collection of generalized linear regression techniques for the estimation of spatial phenomena. Pierre Carlier, another Frenchman, coined the term krigeage in the late 1950s to honor Krige's seminal work. Matheron anglicized the term to kriging when he published a paper for English-speaking readers. France dominated the development and application of geostatistics for several years. However, geostatistics in general, and kriging in particular, are employed by few and are regarded with apprehension by many. One of the possible applications of kriging is in computer mapping. Computer contouring methods can be grouped into two families: triangulation and gridding. The former is a direct procedure in which the contour lines are computed straight from the data by partitioning the sampling area into triangles with one observation per vertex. Kriging belongs in the gridding family. A grid is a regular arrangement of locations or nodes. In the gridding method the isolines are determined from interpolated values at the nodes. The difference between kriging and other weighting methods is in the calculation of the weights. Even for the simplest form of kriging, the calculations are more demanding. The kriging system of equations differs from classical regression in that the observations are allowed to be correlated and that neither the estimate nor the observations are necessarily points - they may have a volume, shape, and orientation. The mean square error is the average of the squares of the differences between the true and the estimated values. Simple kriging, the most basic form of kriging in that the system of equations has the fewest terms, requires the phenomena to have a constant and known mean. The next step up, ordinary kriging, does not require knowledge of the population mean. The external drift method, universal kriging, and intrinsic kriging go even further by allowing fluctuations in the mean. In practice, estimation by kriging is not as difficult to handle as it may look at first glance. In these days of high technology, all the details in the procedure are coded into computer programs. When properly used, kriging has several appealing attributes, the most important being that it does the work more accurately. By design, kriging provides the weights that result in the minimum mean square error. And yes, there have been people who have tested its superiority with real data. Practice has consistently confirmed theory. Kriging is also robust. Within reasonable limits, kriging tends to persist in yielding correct estimates even when the user selects the wrong model, misspecifies parameters, or both. This property should be an incentive for the novice to try the method. Gross misuse of kriging, though, can lead to poor results, worse even than those produced by alternative methods. Kriging has evolved and continues to expand to accommodate the estimation of increasingly demanding realities. Conclusions Theory and practice show that computer contour maps generated using kriging have the least mean square estimation error. In addition, the method provides information to assess the reliability of the maps.

Application of geostatistics with Indicator Kriging for analyzing spatial variability of groundwater arsenic concentrations in Southwest Bangladesh.

PubMed

Hassan, M Manzurul; Atkins, Peter J

2011-01-01

This article seeks to explore the spatial variability of groundwater arsenic (As) concentrations in Southwestern Bangladesh. Facts about spatial pattern of As are important to understand the complex processes of As concentrations and its spatial predictions in the unsampled areas of the study site. The relevant As data for this study were collected from Southwest Bangladesh and were analyzed with Flow Injection Hydride Generation Atomic Absorption Spectrometry (FI-HG-AAS). A geostatistical analysis with Indicator Kriging (IK) was employed to investigate the regionalized variation of As concentration. The IK prediction map shows a highly uneven spatial pattern of arsenic concentrations. The safe zones are mainly concentrated in the north, central and south part of the study area in a scattered manner, while the contamination zones are found to be concentrated in the west and northeast parts of the study area. The southwest part of the study area is contaminated with a highly irregular pattern. A Generalized Linear Model (GLM) was also used to investigate the relationship between As concentrations and aquifer depths. A negligible negative correlation between aquifer depth and arsenic concentrations was found in the study area. The fitted value with 95 % confidence interval shows a decreasing tendency of arsenic concentrations with the increase of aquifer depth. The adjusted mean smoothed lowess curve with a bandwidth of 0.8 shows an increasing trend of arsenic concentration up to a depth of 75 m, with some erratic fluctuations and regional variations at the depth between 30 m and 60 m. The borehole lithology was considered to analyze and map the pattern of As variability with aquifer depths. The study has performed an investigation of spatial pattern and variation of As concentrations.

Comparison of ArcGIS and SAS Geostatistical Analyst to Estimate Population-Weighted Monthly Temperature for US Counties

PubMed Central

Xiaopeng, QI; Liang, WEI; BARKER, Laurie; LEKIACHVILI, Akaki; Xingyou, ZHANG

2015-01-01

Temperature changes are known to have significant impacts on human health. Accurate estimates of population-weighted average monthly air temperature for US counties are needed to evaluate temperature’s association with health behaviours and disease, which are sampled or reported at the county level and measured on a monthly—or 30-day—basis. Most reported temperature estimates were calculated using ArcGIS, relatively few used SAS. We compared the performance of geostatistical models to estimate population-weighted average temperature in each month for counties in 48 states using ArcGIS v9.3 and SAS v 9.2 on a CITGO platform. Monthly average temperature for Jan-Dec 2007 and elevation from 5435 weather stations were used to estimate the temperature at county population centroids. County estimates were produced with elevation as a covariate. Performance of models was assessed by comparing adjusted R2, mean squared error, root mean squared error, and processing time. Prediction accuracy for split validation was above 90% for 11 months in ArcGIS and all 12 months in SAS. Cokriging in SAS achieved higher prediction accuracy and lower estimation bias as compared to cokriging in ArcGIS. County-level estimates produced by both packages were positively correlated (adjusted R2 range=0.95 to 0.99); accuracy and precision improved with elevation as a covariate. Both methods from ArcGIS and SAS are reliable for U.S. county-level temperature estimates; However, ArcGIS’s merits in spatial data pre-processing and processing time may be important considerations for software selection, especially for multi-year or multi-state projects. PMID:26167169

Assessment and visualization of uncertainty for countrywide soil organic matter map of Hungary using local entropy

NASA Astrophysics Data System (ADS)

Szatmári, Gábor; Pásztor, László

2016-04-01

Uncertainty is a general term expressing our imperfect knowledge in describing an environmental process and we are aware of it (Bárdossy and Fodor, 2004). Sampling, laboratory measurements, models and so on are subject to uncertainty. Effective quantification and visualization of uncertainty would be indispensable to stakeholders (e.g. policy makers, society). Soil related features and their spatial models should be stressfully targeted to uncertainty assessment because their inferences are further used in modelling and decision making process. The aim of our present study was to assess and effectively visualize the local uncertainty of the countrywide soil organic matter (SOM) spatial distribution model of Hungary using geostatistical tools and concepts. The Hungarian Soil Information and Monitoring System's SOM data (approximately 1,200 observations) and environmental related, spatially exhaustive secondary information (i.e. digital elevation model, climatic maps, MODIS satellite images and geological map) were used to model the countrywide SOM spatial distribution by regression kriging. It would be common to use the calculated estimation (or kriging) variance as a measure of uncertainty, however the normality and homoscedasticity hypotheses have to be refused according to our preliminary analysis on the data. Therefore, a normal score transformation and a sequential stochastic simulation approach was introduced to be able to model and assess the local uncertainty. Five hundred equally probable realizations (i.e. stochastic images) were generated. The number of the stochastic images is fairly enough to provide a model of uncertainty at each location, which is a complete description of uncertainty in geostatistics (Deutsch and Journel, 1998). Furthermore, these models can be applied e.g. to contour the probability of any events, which can be regarded as goal oriented digital soil maps and are of interest for agricultural management and decision making as well. A standardized measure of the local entropy was used to visualize uncertainty, where entropy values close to 1 correspond to high uncertainty, whilst values close to 0 correspond low uncertainty. The advantage of the usage of local entropy in this context is that it combines probabilities from multiple members into a single number for each location of the model. In conclusion, it is straightforward to use a sequential stochastic simulation approach to the assessment of uncertainty, when normality and homoscedasticity are violated. The visualization of uncertainty using the local entropy is effective and communicative to stakeholders because it represents the uncertainty through a single number within a [0, 1] scale. References: Bárdossy, Gy. & Fodor, J., 2004. Evaluation of Uncertainties and Risks in Geology. Springer-Verlag, Berlin Heidelberg. Deutsch, C.V. & Journel, A.G., 1998. GSLIB: geostatistical software library and user's guide. Oxford University Press, New York. Acknowledgement: Our work was supported by the Hungarian National Scientific Research Foundation (OTKA, Grant No. K105167).

Robust geostatistical analysis of spatial data

NASA Astrophysics Data System (ADS)

Papritz, Andreas; Künsch, Hans Rudolf; Schwierz, Cornelia; Stahel, Werner A.

2013-04-01

Most of the geostatistical software tools rely on non-robust algorithms. This is unfortunate, because outlying observations are rather the rule than the exception, in particular in environmental data sets. Outliers affect the modelling of the large-scale spatial trend, the estimation of the spatial dependence of the residual variation and the predictions by kriging. Identifying outliers manually is cumbersome and requires expertise because one needs parameter estimates to decide which observation is a potential outlier. Moreover, inference after the rejection of some observations is problematic. A better approach is to use robust algorithms that prevent automatically that outlying observations have undue influence. Former studies on robust geostatistics focused on robust estimation of the sample variogram and ordinary kriging without external drift. Furthermore, Richardson and Welsh (1995) proposed a robustified version of (restricted) maximum likelihood ([RE]ML) estimation for the variance components of a linear mixed model, which was later used by Marchant and Lark (2007) for robust REML estimation of the variogram. We propose here a novel method for robust REML estimation of the variogram of a Gaussian random field that is possibly contaminated by independent errors from a long-tailed distribution. It is based on robustification of estimating equations for the Gaussian REML estimation (Welsh and Richardson, 1997). Besides robust estimates of the parameters of the external drift and of the variogram, the method also provides standard errors for the estimated parameters, robustified kriging predictions at both sampled and non-sampled locations and kriging variances. Apart from presenting our modelling framework, we shall present selected simulation results by which we explored the properties of the new method. This will be complemented by an analysis a data set on heavy metal contamination of the soil in the vicinity of a metal smelter. Marchant, B.P. and Lark, R.M. 2007. Robust estimation of the variogram by residual maximum likelihood. Geoderma 140: 62-72. Richardson, A.M. and Welsh, A.H. 1995. Robust restricted maximum likelihood in mixed linear models. Biometrics 51: 1429-1439. Welsh, A.H. and Richardson, A.M. 1997. Approaches to the robust estimation of mixed models. In: Handbook of Statistics Vol. 15, Elsevier, pp. 343-384.

Robust geostatistical analysis of spatial data

NASA Astrophysics Data System (ADS)

Papritz, A.; Künsch, H. R.; Schwierz, C.; Stahel, W. A.

2012-04-01

Most of the geostatistical software tools rely on non-robust algorithms. This is unfortunate, because outlying observations are rather the rule than the exception, in particular in environmental data sets. Outlying observations may results from errors (e.g. in data transcription) or from local perturbations in the processes that are responsible for a given pattern of spatial variation. As an example, the spatial distribution of some trace metal in the soils of a region may be distorted by emissions of local anthropogenic sources. Outliers affect the modelling of the large-scale spatial variation, the so-called external drift or trend, the estimation of the spatial dependence of the residual variation and the predictions by kriging. Identifying outliers manually is cumbersome and requires expertise because one needs parameter estimates to decide which observation is a potential outlier. Moreover, inference after the rejection of some observations is problematic. A better approach is to use robust algorithms that prevent automatically that outlying observations have undue influence. Former studies on robust geostatistics focused on robust estimation of the sample variogram and ordinary kriging without external drift. Furthermore, Richardson and Welsh (1995) [2] proposed a robustified version of (restricted) maximum likelihood ([RE]ML) estimation for the variance components of a linear mixed model, which was later used by Marchant and Lark (2007) [1] for robust REML estimation of the variogram. We propose here a novel method for robust REML estimation of the variogram of a Gaussian random field that is possibly contaminated by independent errors from a long-tailed distribution. It is based on robustification of estimating equations for the Gaussian REML estimation. Besides robust estimates of the parameters of the external drift and of the variogram, the method also provides standard errors for the estimated parameters, robustified kriging predictions at both sampled and unsampled locations and kriging variances. The method has been implemented in an R package. Apart from presenting our modelling framework, we shall present selected simulation results by which we explored the properties of the new method. This will be complemented by an analysis of the Tarrawarra soil moisture data set [3].

Spatial assessment of soil organic carbon and physicochemical properties in a horticultural orchard at arid zone of India using geostatistical approaches.

PubMed

Singh, Akath; Santra, Priyabrata; Kumar, Mahesh; Panwar, Navraten; Meghwal, P R

2016-09-01

Soil organic carbon (SOC) is a major indicator of long-term sustenance of agricultural production system. Apart from sustaining productivity, SOC plays a crucial role in context of climate change. Keeping in mind these potentials, spatial variation of SOC contents of a fruit orchard comprising several arid fruit plantations located at arid region of India is assessed in this study through geostatistical approaches. For this purpose, surface and subsurface soil samples from 175 locations from a fruit orchard spreading over 14.33 ha area were collected along with geographical coordinates. SOC content and soil physicochemical properties of collected soil samples were determined followed by geostatistical analysis for mapping purposes. Average SOC stock density of the orchard was 14.48 Mg ha(-1) for 0- to 30-cm soil layer ranging from 9.01 Mg ha(-1) in Carissa carandas to 19.52 Mg ha(-1) in Prosopis cineraria block. Range of spatial variation of SOC content was found about 100 m, while two other soil physicochemical properties, e.g., pH and electrical conductivity (EC) also showed similar spatial trend. This indicated that minimum sampling distance for future SOC mapping programme may be kept lower than 100 m for better accuracy. Ordinary kriging technique satisfactorily predicted SOC contents (in percent) at unsampled locations with root-mean-squared residual (RMSR) of 0.35-0.37. Co-kriging approach was found slightly superior (RMSR = 0.26-0.28) than ordinary kriging for spatial prediction of SOC contents because of significant correlations of SOC contents with pH and EC. Uncertainty of SOC estimation was also presented in terms of 90 % confidence interval. Spatial estimates of SOC stock through ordinary kriging or co-kriging approach were also found with low uncertainty of estimation than non-spatial estimates, e.g., arithmetic averaging approach. Among different fruit block plantations of the orchard, the block with Prosopis cineraria ('khejri') has higher SOC stock density than others.

Increasing Confidence In Treatment Performance Assessment Using Geostatistical Methods

EPA Science Inventory

It is well established that the presence of dense non-aqueous phase liquids (DNAPLs) such as trichloroethylene (TCE) in aquifer systems represents a very long-term source of groundwater contamination. Significant effort in recent years has been focussed on developing effective me...

Rtop - an R package for interpolation along the stream network

NASA Astrophysics Data System (ADS)

Skøien, J. O.

2009-04-01

Rtop - an R package for interpolation along the stream network Geostatistical methods have been used to a limited extent for estimation along stream networks, with a few exceptions(Gottschalk, 1993; Gottschalk, et al., 2006; Sauquet, et al., 2000; Skøien, et al., 2006). Interpolation of runoff characteristics are more complicated than the traditional random variables estimated by geostatistical methods, as the measurements have a more complicated support, and many catchments are nested. Skøien et al. (2006) presented the model Top-kriging which takes these effects into account for interpolation of stream flow characteristics (exemplified by the 100 year flood). The method has here been implemented as a package in the statistical environment R (R Development Core Team, 2004). Taking advantage of the existing methods in R for working with spatial objects, and the extensive possibilities for visualizing the result, this makes it considerably easier to apply the method on new data sets, in comparison to earlier implementation of the method. Gottschalk, L. 1993. Interpolation of runoff applying objective methods. Stochastic Hydrology and Hydraulics, 7, 269-281. Gottschalk, L., I. Krasovskaia, E. Leblois, and E. Sauquet. 2006. Mapping mean and variance of runoff in a river basin. Hydrology and Earth System Sciences, 10, 469-484. R Development Core Team. 2004. R: A language and environment for statistical computing. Vienna, Austria: R Foundation for Statistical Computing. Sauquet, E., L. Gottschalk, and E. Leblois. 2000. Mapping average annual runoff: a hierarchical approach applying a stochastic interpolation scheme. Hydrological Sciences Journal, 45 (6), 799-815. Skøien, J. O., R. Merz, and G. Blöschl. 2006. Top-kriging - geostatistics on stream networks. Hydrology and Earth System Sciences, 10, 277-287.

Identifying and closing gaps in environmental monitoring by means of metadata, ecological regionalization and geostatistics using the UNESCO biosphere reserve Rhoen (Germany) as an example.

PubMed

Schröder, Winfried; Pesch, Roland; Schmidt, Gunther

2006-03-01

In Germany, environmental monitoring is intended to provide a holistic view of the environmental condition. To this end the monitoring operated by the federal states must use harmonized, resp., standardized methods. In addition, the monitoring sites should cover the ecoregions without any geographical gaps, the monitoring design should have no gaps in terms of ecologically relevant measurement parameters, and the sample data should be spatially without any gaps. This article outlines the extent to which the Rhoen Biosphere Reserve, occupying a part of the German federal states of Bavaria, Hesse and Thuringia, fulfills the listed requirements. The investigation considered collection, data banking and analysis of monitoring data and metadata, ecological regionalization and geostatistics. Metadata on the monitoring networks were collected by questionnaires and provided a complete inventory and description of the monitoring activities in the reserve and its surroundings. The analysis of these metadata reveals that most of the monitoring methods are harmonized across the boundaries of the three federal states the Rhoen is part of. The monitoring networks that measure precipitation, surface water levels, and groundwater quality are particularly overrepresented in the central ecoregions of the biosphere reserve. Soil monitoring sites are more equally distributed within the ecoregions of the Rhoen. The number of sites for the monitoring of air pollutants is not sufficient to draw spatially valid conclusions. To fill these spatial gaps, additional data on the annual average values of the concentrations of air pollutants from monitoring sites outside of the biosphere reserve had therefore been subject to geostatistical analysis and estimation. This yields valid information on the spatial patterns and temporal trends of air quality. The approach illustrated is applicable to similar cases, as, for example, the harmonization of international monitoring networks.

Medical Geography: a Promising Field of Application for Geostatistics

PubMed Central

Goovaerts, P.

2008-01-01

The analysis of health data and putative covariates, such as environmental, socio-economic, behavioral or demographic factors, is a promising application for geostatistics. It presents, however, several methodological challenges that arise from the fact that data are typically aggregated over irregular spatial supports and consist of a numerator and a denominator (i.e. population size). This paper presents an overview of recent developments in the field of health geostatistics, with an emphasis on three main steps in the analysis of areal health data: estimation of the underlying disease risk, detection of areas with significantly higher risk, and analysis of relationships with putative risk factors. The analysis is illustrated using age-adjusted cervix cancer mortality rates recorded over the 1970–1994 period for 118 counties of four states in the Western USA. Poisson kriging allows the filtering of noisy mortality rates computed from small population sizes, enhancing the correlation with two putative explanatory variables: percentage of habitants living below the federally defined poverty line, and percentage of Hispanic females. Area-to-point kriging formulation creates continuous maps of mortality risk, reducing the visual bias associated with the interpretation of choropleth maps. Stochastic simulation is used to generate realizations of cancer mortality maps, which allows one to quantify numerically how the uncertainty about the spatial distribution of health outcomes translates into uncertainty about the location of clusters of high values or the correlation with covariates. Last, geographically-weighted regression highlights the non-stationarity in the explanatory power of covariates: the higher mortality values along the coast are better explained by the two covariates than the lower risk recorded in Utah. PMID:19412347

Long-term Monitoring Program Optimization for Chlorinated Volatile Organic Compound Plume, Naval Air Station Brunswick, Maine

NASA Astrophysics Data System (ADS)

Calderone, G. M.

2006-12-01

A long-term monitoring program was initiated in 1995 at 6 sites at NAS Brunswick, including 3 National Priorities List (Superfund) sites. Primary contaminants of concern include chlorinated volatile organic compounds, including tetrachloroethane, trichloroethene, and vinyl chloride, in addition to metals. More than 80 submersible pumping systems were installed to facilitate sample collection utilizing the low-flow sampling technique. Long-term monitoring of the groundwater is conducted to assess the effectiveness of remedial measures, and monitor changes in contaminant concentrations in the Eastern Plume Operable Unit. Long-term monitoring program activities include quarterly groundwater sampling and analysis at more than 90 wells across 6 sites; surface water, sediment, seep, and leachate sampling and analysis at 3 sites; landfill gas monitoring; well maintenance; engineering inspections of landfill covers and other sites or evidence of stressed vegetation; water level gauging; and treatment plant sampling and analysis. Significant cost savings were achieved by optimizing the sampling network and reducing sampling frequency from quarterly to semi- annual or annual sampling. As part of an ongoing optimization effort, a geostatistical assessment of the Eastern Plume was conducted at the Naval Air Station, Brunswick, Maine. The geostatistical assessment used 40 monitoring points and analytical data collected over 3 years. For this geostatistical assessment, EA developed and utilized a database of analytical results generated during 3 years of long-term monitoring which was linked to a Geographic Information System to enhance data visualization capacity. The Geographic Information System included themes for groundwater volatile organic compound concentration, groundwater flow directions, shallow and deep wells, and immediate access to point-specific analytical results. This statistical analysis has been used by the site decision-maker and its conclusions supported a significant reduction in the Long-Term Monitoring Program.

Learning Kriging by an instructive program.

NASA Astrophysics Data System (ADS)

Cuador, José

2016-04-01

There are three types of problem classification: the deterministic, the approximated and the stochastic problems. First, in the deterministic problems the law of the phenomenon and the data are known in the entire domain and for each instant of time. In the approximated problems, the law of the phenomenon behavior is unknown but the data can be known in the entire domain and for each instant of time. In the stochastic problems much of the law and the data are unknown in the domain, so in this case the spatial behavior of the data can only be explained with probabilistic laws. This is the most important reason why the students of geo-sciences careers and others related careers need to take courses in advance estimation methods. A good example of this situation is the estimation grades in ore mineral deposit for which the Geostatistics was formalized by G. Matheron in 1962 [6]. Geostatistics is defined as the application of the theory of Random Function to the recognition and estimation of natural phenomenon [4]. Nowadays, Geostatistics is widely used in several fields of earth sciences, for example: Mining, Oil exploration, Environment, Agricultural, Forest and others [3]. It provides a wide variety of tools for spatial data analysis and allows analysing models which are subjected to degrees of uncertainty with the rigor of mathematics and formal statistical analysis [9]. Adequate models for the Kriging interpolator has been developed according to the data behavior; however there are two key steps in applying this interpolator properly: the semivariogram determination and the Kriging neighborhood selection. The main objective of this paper is to present these two elements using an instructive program.

Geostatistical three-dimensional modeling of oolite shoals, St. Louis Limestone, southwest Kansas

USGS Publications Warehouse

Qi, L.; Carr, T.R.; Goldstein, R.H.

2007-01-01

In the Hugoton embayment of southwestern Kansas, reservoirs composed of relatively thin (<4 m; <13.1 ft) oolitic deposits within the St. Louis Limestone have produced more than 300 million bbl of oil. The geometry and distribution of oolitic deposits control the heterogeneity of the reservoirs, resulting in exploration challenges and relatively low recovery. Geostatistical three-dimensional (3-D) models were constructed to quantify the geometry and spatial distribution of oolitic reservoirs, and the continuity of flow units within Big Bow and Sand Arroyo Creek fields. Lithofacies in uncored wells were predicted from digital logs using a neural network. The tilting effect from the Laramide orogeny was removed to construct restored structural surfaces at the time of deposition. Well data and structural maps were integrated to build 3-D models of oolitic reservoirs using stochastic simulations with geometry data. Three-dimensional models provide insights into the distribution, the external and internal geometry of oolitic deposits, and the sedimentologic processes that generated reservoir intervals. The structural highs and general structural trend had a significant impact on the distribution and orientation of the oolitic complexes. The depositional pattern and connectivity analysis suggest an overall aggradation of shallow-marine deposits during pulses of relative sea level rise followed by deepening near the top of the St. Louis Limestone. Cemented oolitic deposits were modeled as barriers and baffles and tend to concentrate at the edge of oolitic complexes. Spatial distribution of porous oolitic deposits controls the internal geometry of rock properties. Integrated geostatistical modeling methods can be applicable to other complex carbonate or siliciclastic reservoirs in shallow-marine settings. Copyright ?? 2007. The American Association of Petroleum Geologists. All rights reserved.

Geostatistical analysis of fault and joint measurements in Austin Chalk, Superconducting Super Collider Site, Texas

DOE Office of Scientific and Technical Information (OSTI.GOV)

Mace, R.E.; Nance, H.S.; Laubach, S.E.

1995-06-01

Faults and joints are conduits for ground-water flow and targets for horizontal drilling in the petroleum industry. Spacing and size distribution are rarely predicted accurately by current structural models or documented adequately by conventional borehole or outcrop samples. Tunnel excavations present opportunities to measure fracture attributes in continuous subsurface exposures. These fracture measurements ran be used to improve structural models, guide interpretation of conventional borehole and outcrop data, and geostatistically quantify spatial and spacing characteristics for comparison to outcrop data or for generating distributions of fracture for numerical flow and transport modeling. Structure maps of over 9 mi of nearlymore » continuous tunnel excavations in Austin Chalk at the Superconducting Super Collider (SSC) site in Ellis County, Texas, provide a unique database of fault and joint populations for geostatistical analysis. Observationally, small faults (<10 ft. throw) occur in clusters or swarms that have as many as 24 faults, fault swarms are as much as 2,000 ft. wide and appear to be on average 1,000 ft. apart, and joints are in swarms spaced 500 to more than 2l,000 ft. apart. Semi-variograms show varying degrees of spatial correlation. These variograms have structured sills that correlate directly to highs and lows in fracture frequency observed in the tunnel. Semi-variograms generated with respect to fracture spacing and number also have structured sills, but tend to not show any near-field correlation. The distribution of fault spacing can be described with a negative exponential, which suggests a random distribution. However, there is clearly some structure and clustering in the spacing data as shown by running average and variograms, which implies that a number of different methods should be utilized to characterize fracture spacing.« less

Soil aggregate stability and wind erodible fraction in a semi-arid environment of White Nile State, Sudan

NASA Astrophysics Data System (ADS)

Elhaja, Mohamed Eltom; Ibrahim, Ibrahim Saeed; Adam, Hassan Elnour; Csaplovics, Elmar

2014-11-01

One of the most important recent issues facing White Nile State, Sudan, as well as Sub Saharan Africa, is the threat of continued land degradation and desertification as a result of climatic factors and human activities. Remote sensing and satellites imageries with multi-temporal and spectral and GIS capability, plays a major role in developing a global and local operational capability for monitoring land degradation and desertification in dry lands, as well as in White Nile State. The process of desertification in form of sand encroachment in White Nile State has increased rapidly, and much effort has been devoted to define and study its causes and impacts. This study depicts the capability afforded by remote sensing and GIS to analyze and map the aggregate stability as indicator for the ability of soil to wind erosion process in White Nile State by using Geo-statistical techniques. Cloud-free subset Landsat; Enhance Thematic Mapper plus (ETM +) scenes covering the study area dated 2008 was selected in order to identify the different features covering the study area as well as to make the soil sampling map. Wet-sieving method was applied to determine the aggregate stability. The geo-statistical methods in EARDAS 9.1 software was used for mapping the aggregate stability. The results showed that the percentage of aggregate stability ranged from (0 to 61%) in the study area, which emphasized the phenomena of sand encroachment from the western part (North Kordofan) to the eastern part (White Nile State), following the wind direction. The study comes out with some valuable recommendations and comments, which could contribute positively in reducing sand encroachments

Contaminant source and release history identification in groundwater: A multi-step approach

NASA Astrophysics Data System (ADS)

Gzyl, G.; Zanini, A.; Frączek, R.; Kura, K.

2014-02-01

The paper presents a new multi-step approach aiming at source identification and release history estimation. The new approach consists of three steps: performing integral pumping tests, identifying sources, and recovering the release history by means of a geostatistical approach. The present paper shows the results obtained from the application of the approach within a complex case study in Poland in which several areal sources were identified. The investigated site is situated in the vicinity of a former chemical plant in southern Poland in the city of Jaworzno in the valley of the Wąwolnica River; the plant has been in operation since the First World War producing various chemicals. From an environmental point of view the most relevant activity was the production of pesticides, especially lindane. The application of the multi-step approach enabled a significant increase in the knowledge of contamination at the site. Some suspected contamination sources have been proven to have minor effect on the overall contamination. Other suspected sources have been proven to have key significance. Some areas not taken into consideration previously have now been identified as key sources. The method also enabled estimation of the magnitude of the sources and, a list of the priority reclamation actions will be drawn as a result. The multi-step approach has proven to be effective and may be applied to other complicated contamination cases. Moreover, the paper shows the capability of the geostatistical approach to manage a complex real case study.

Modern Space/Time Geostatistics using River Distances: Data Integration of Turbidity and E.coli Measurements to Assess Fecal Contamination Along the Raritan River in New Jersey

PubMed Central

Money, Eric S.; Carter, Gail P.; Serre, Marc L.

2009-01-01

Escherichia coli (E.coli) is a widely used indicator of fecal contamination in water bodies. External contact and subsequent ingestion of bacteria coming from fecal contamination can lead to harmful health effects. Since E.coli data are sometimes limited, the objective of this study is to use secondary information in the form of turbidity to improve the assessment of E.coli at un-monitored locations. We obtained all E.coli and turbidity monitoring data available from existing monitoring networks for the 2000 – 2006 time period for the Raritan River Basin, New Jersey. Using collocated measurements we developed a predictive model of E.coli from turbidity data. Using this model, soft data are constructed for E.coli given turbidity measurements at 739 space/time locations where only turbidity was measured. Finally, the Bayesian Maximum Entropy (BME) method of modern space/time geostatistics was used for the data integration of monitored and predicted E.coli data to produce maps showing E.coli concentration estimated daily across the river basin. The addition of soft data in conjunction with the use of river distances reduced estimation error by about 30%. Furthermore, based on these maps, up to 35% of river miles in the Raritan Basin had a probability of E.coli impairment greater than 90% on the most polluted day of the study period. PMID:19544881

[Explore the spatial and temporal patterns of water pollution in the Yincungang canal of the Lake Taihu basin, China].

PubMed

Yang, Xiao-Ying; Luo, Xing-Zhang; Zheng, Zheng; Fang, Shu-Bo

2012-09-01

Two high-density snap-shot samplings were conducted along the Yincungang canal, one important tributary of the Lake Tai, in April (low flow period) and June (high flow period) of 2010. Geostatistical analysis based on the river network distance was used to analyze the spatial and temporal patterns of the pollutant concentrations along the canal with an emphasis on chemical oxygen demand (COD) and total nitrogen (TN). Study results have indicated: (1) COD and TN concentrations display distinctly different spatial and temporal patterns between the low and high flow periods. COD concentration in June is lower than that in April, while TN concentration has the contrary trend. (2) COD load is relatively constant during the period between the two monitoring periods. The spatial correlation structure of COD is exponential for both April and June, and the change of COD concentration is mainly influenced by hydrological conditions. (3) Nitrogen load from agriculture increased significantly during the period between the two monitoring periods. Large amount of chaotic fertilizing by individual farmers has led to the loss of the spatial correlation among the observed TN concentrations. Hence, changes of TN concentration in June are under the dual influence of agricultural fertilizing and hydrological conditions. In the view of the complex hydrological conditions and serious water pollution in the Lake Taihu region, geostatistical analysis is potentially a useful tool for studying the characteristics of pollutant distribution and making predictions in the region.

ORTHONORMAL RESIDUALS IN GEOSTATISTICS: MODEL CRITICISM AND PARAMETER ESTIMATION. (R825689C037)

EPA Science Inventory

The perspectives, information and conclusions conveyed in research project abstracts, progress reports, final reports, journal abstracts and journal publications convey the viewpoints of the principal investigator and may not represent the views and policies of ORD and EPA. Concl...

SPATIAL COVARIANCE IN PLANT COMMUNITIES: INTEGRATING ORDINATION, GEOSTATISTICS, AND VARIANCE TESTING. (R826764)

EPA Science Inventory

The perspectives, information and conclusions conveyed in research project abstracts, progress reports, final reports, journal abstracts and journal publications convey the viewpoints of the principal investigator and may not represent the views and policies of ORD and EPA. Concl...

GEOSTATISTICAL INTERPOLATION OF CHEMICAL CONCENTRATION. (R825689C037)

EPA Science Inventory

Abstract

Measurements of contaminant concentration at a hazardous waste site typically vary over many orders of magnitude and have highly skewed distributions. This work presents a practical methodology for the estimation of solute concentration contour maps and volume...

Effectiveness of Hydraulic Parameterization Strategies for Simulating Moisture Dynamics in a Deep Semi-Arid Vadose Zone

NASA Astrophysics Data System (ADS)

Zhang, Y.; Schaap, M. G.

2012-12-01

Over the past fifteen years, the University of Arizona has carried out four controlled infiltration experiments in a 3600 m2, 15 meter deep vadose zone (Maricopa, Arizona) in which the evolution of moisture content (9 wells, 25 cm resolution), and matric potential (27 locations) was monitored and the subsurface stratigraphy, texture (1042 samples), and bulk density (251 samples) was characterized. In order to simulate the subsurface moisture dynamics it is necessary to define the 3D structure of the subsurface hydraulic characteristics (i.e. moisture retention and hydraulic functions). Several simple to complex strategies are possible ranging from stratigraphy based layering using hydraulic parameters derived from core samples to sophisticated numerical inversions based on 3D geostatistics and site-specific pedotransfer functions. A range of approaches will be evaluated on objective metrics that quantify how well the observed moisture dynamics are matched by simulations. We will evaluate the worth of auxiliary data such as observed matric potentials and quantity the number of texture samples needed to arrive at effective descriptions of subsurface structure. In addition, we will discuss more subjective metrics that evaluate the relative effort involved and estimate monetary cost of each method. While some of the results will only be valid for the studied site, some general conclusions will be possible about the effectiveness of particular methods for other semi-arid sites.

Detection of masses in mammogram images using CNN, geostatistic functions and SVM.

PubMed

Sampaio, Wener Borges; Diniz, Edgar Moraes; Silva, Aristófanes Corrêa; de Paiva, Anselmo Cardoso; Gattass, Marcelo

2011-08-01

Breast cancer occurs with high frequency among the world's population and its effects impact the patients' perception of their own sexuality and their very personal image. This work presents a computational methodology that helps specialists detect breast masses in mammogram images. The first stage of the methodology aims to improve the mammogram image. This stage consists in removing objects outside the breast, reducing noise and highlighting the internal structures of the breast. Next, cellular neural networks are used to segment the regions that might contain masses. These regions have their shapes analyzed through shape descriptors (eccentricity, circularity, density, circular disproportion and circular density) and their textures analyzed through geostatistic functions (Ripley's K function and Moran's and Geary's indexes). Support vector machines are used to classify the candidate regions as masses or non-masses, with sensitivity of 80%, rates of 0.84 false positives per image and 0.2 false negatives per image, and an area under the ROC curve of 0.87. Copyright © 2011 Elsevier Ltd. All rights reserved.

Geostatistics as a validation tool for setting ozone standards for durum wheat.

PubMed

De Marco, Alessandra; Screpanti, Augusto; Paoletti, Elena

2010-02-01

Which is the best standard for protecting plants from ozone? To answer this question, we must validate the standards by testing biological responses vs. ambient data in the field. A validation is missing for European and USA standards, because the networks for ozone, meteorology and plant responses are spatially independent. We proposed geostatistics as validation tool, and used durum wheat in central Italy as a test. The standards summarized ozone impact on yield better than hourly averages. Although USA criteria explained ozone-induced yield losses better than European criteria, USA legal level (75 ppb) protected only 39% of sites. European exposure-based standards protected > or =90%. Reducing the USA level to the Canadian 65 ppb or using W126 protected 91% and 97%, respectively. For a no-threshold accumulated stomatal flux, 22 mmol m(-2) was suggested to protect 97% of sites. In a multiple regression, precipitation explained 22% and ozone explained <0.9% of yield variability. Copyright (c) 2009 Elsevier Ltd. All rights reserved.

Implementation of the Iterative Proportion Fitting Algorithm for Geostatistical Facies Modeling

DOE Office of Scientific and Technical Information (OSTI.GOV)

Li Yupeng, E-mail: yupeng@ualberta.ca; Deutsch, Clayton V.

2012-06-15

In geostatistics, most stochastic algorithm for simulation of categorical variables such as facies or rock types require a conditional probability distribution. The multivariate probability distribution of all the grouped locations including the unsampled location permits calculation of the conditional probability directly based on its definition. In this article, the iterative proportion fitting (IPF) algorithm is implemented to infer this multivariate probability. Using the IPF algorithm, the multivariate probability is obtained by iterative modification to an initial estimated multivariate probability using lower order bivariate probabilities as constraints. The imposed bivariate marginal probabilities are inferred from profiles along drill holes or wells.more » In the IPF process, a sparse matrix is used to calculate the marginal probabilities from the multivariate probability, which makes the iterative fitting more tractable and practical. This algorithm can be extended to higher order marginal probability constraints as used in multiple point statistics. The theoretical framework is developed and illustrated with estimation and simulation example.« less

The Applications of Model-Based Geostatistics in Helminth Epidemiology and Control

PubMed Central

Magalhães, Ricardo J. Soares; Clements, Archie C.A.; Patil, Anand P.; Gething, Peter W.; Brooker, Simon

2011-01-01

Funding agencies are dedicating substantial resources to tackle helminth infections. Reliable maps of the distribution of helminth infection can assist these efforts by targeting control resources to areas of greatest need. The ability to define the distribution of infection at regional, national and subnational levels has been enhanced greatly by the increased availability of good quality survey data and the use of model-based geostatistics (MBG), enabling spatial prediction in unsampled locations. A major advantage of MBG risk mapping approaches is that they provide a flexible statistical platform for handling and representing different sources of uncertainty, providing plausible and robust information on the spatial distribution of infections to inform the design and implementation of control programmes. Focussing on schistosomiasis and soil-transmitted helminthiasis, with additional examples for lymphatic filariasis and onchocerciasis, we review the progress made to date with the application of MBG tools in large-scale, real-world control programmes and propose a general framework for their application to inform integrative spatial planning of helminth disease control programmes. PMID:21295680

Using geostatistical methods to estimate snow water equivalence distribution in a mountain watershed

USGS Publications Warehouse

Balk, B.; Elder, K.; Baron, Jill S.

1998-01-01

Knowledge of the spatial distribution of snow water equivalence (SWE) is necessary to adequately forecast the volume and timing of snowmelt runoff.  In April 1997, peak accumulation snow depth and density measurements were independently taken in the Loch Vale watershed (6.6 km2), Rocky Mountain National Park, Colorado.  Geostatistics and classical statistics were used to estimate SWE distribution across the watershed.  Snow depths were spatially distributed across the watershed through kriging interpolation methods which provide unbiased estimates that have minimum variances.  Snow densities were spatially modeled through regression analysis.  Combining the modeled depth and density with snow-covered area (SCA produced an estimate of the spatial distribution of SWE.  The kriged estimates of snow depth explained 37-68% of the observed variance in the measured depths.  Steep slopes, variably strong winds, and complex energy balance in the watershed contribute to a large degree of heterogeneity in snow depth.

LSHSIM: A Locality Sensitive Hashing based method for multiple-point geostatistics

NASA Astrophysics Data System (ADS)

Moura, Pedro; Laber, Eduardo; Lopes, Hélio; Mesejo, Daniel; Pavanelli, Lucas; Jardim, João; Thiesen, Francisco; Pujol, Gabriel

2017-10-01

Reservoir modeling is a very important task that permits the representation of a geological region of interest, so as to generate a considerable number of possible scenarios. Since its inception, many methodologies have been proposed and, in the last two decades, multiple-point geostatistics (MPS) has been the dominant one. This methodology is strongly based on the concept of training image (TI) and the use of its characteristics, which are called patterns. In this paper, we propose a new MPS method that combines the application of a technique called Locality Sensitive Hashing (LSH), which permits to accelerate the search for patterns similar to a target one, with a Run-Length Encoding (RLE) compression technique that speeds up the calculation of the Hamming similarity. Experiments with both categorical and continuous images show that LSHSIM is computationally efficient and produce good quality realizations. In particular, for categorical data, the results suggest that LSHSIM is faster than MS-CCSIM, one of the state-of-the-art methods.

Uncovering hidden heterogeneity: Geo-statistical models illuminate the fine scale effects of boating infrastructure on sediment characteristics and contaminants.

PubMed

Hedge, L H; Dafforn, K A; Simpson, S L; Johnston, E L

2017-06-30

Infrastructure associated with coastal communities is likely to not only directly displace natural systems, but also leave environmental footprints' that stretch over multiple scales. Some coastal infrastructure will, there- fore, generate a hidden layer of habitat heterogeneity in sediment systems that is not immediately observable in classical impact assessment frameworks. We examine the hidden heterogeneity associated with one of the most ubiquitous coastal modifications; dense swing moorings fields. Using a model based geo-statistical framework we highlight the variation in sedimentology throughout mooring fields and reference locations. Moorings were correlated with patches of sediment with larger particle sizes, and associated metal(loid) concentrations in these patches were depressed. Our work highlights two important ideas i) mooring fields create a mosaic of habitat in which contamination decreases and grain sizes increase close to moorings, and ii) model- based frameworks provide an information rich, easy-to-interpret way to communicate complex analyses to stakeholders. Crown Copyright © 2017. Published by Elsevier Ltd. All rights reserved.

Spatial autocorrelation of West Nile virus vector mosquito abundance in a seasonally wet suburban environment

NASA Astrophysics Data System (ADS)

Trawinski, P. R.; Mackay, D. S.

2009-03-01

The objective of this study is to quantify and model spatial dependence in mosquito vector populations and develop predictions for unsampled locations using geostatistics. Mosquito control program trap sites are often located too far apart to detect spatial dependence but the results show that integration of spatial data over time for Cx. pipiens-restuans and according to meteorological conditions for Ae. vexans enables spatial analysis of sparse sample data. This study shows that mosquito abundance is spatially correlated and that spatial dependence differs between Cx. pipiens-restuans and Ae. vexans mosquitoes.

Spatial distribution of soil moisture obtained from gravimetric and TDR methods for SMOS validation, at the Polesie test site SVRT 3275, in Poland

NASA Astrophysics Data System (ADS)

Usowicz, B.; Marczewski, W.; Lipiec, J.; Usowicz, J. B.; Sokolowska, Z.; Dabkowska-Naskret, H.; Hajnos, M.; Lukowski, M. I.

2009-04-01

The purpose is obtaining trustful ground based measurement data of SM (Soil Moisture) for validating SMOS, respectively to spatial and temporal distribution and variations. A use of Time Domain Reflectometric (TDR) method is fast, simple and less destructive, to the soil matter, than a usual standard gravimetric method. TDR tools operate efficiently, enable nearly instant measurements, and allow on collecting many measurements from numerous sites, even when operated manually in short time intervals. The method enables also very frequent sampling of SM at few selected fixed sites, when long terms of temporal variations are needed. In effect one obtains reasonably large data base for determining spatial and temporal distributions of SM. The study is devoted to determining a plan on collecting TDR data, in the scales of small and large field areas, and checking their relevance to those available from gravimetric methods. Finally, the ground based SM distributions are needed for validating other SM distributions, available remotely in larger scales, from the satellite data of ENVISAT-ASAR, and from SMOS (Soil Moisture and Ocean Salinity Mission) when it becomes operational. The ground based evaluations are served mainly by geo-statistical analysis. The space borne estimations are retrieved by image processing and physical models, proper to relevant Remote Sensing (RS) instruments on the orbit. Finally, validation must engage again the geo-statistical evaluations, to assess the agreement between direct and remote sensing means, and provide a measure of trust for extending the limited scales of the ground based data, on concluding the agreement in scales proper to the satellite data. The study is focused mainly on trustful evaluating data from the ground, provided independently on satellite data sources. SM ground based data are collected permanently at 2 selected tests sites, and temporary in areas around the tests sites, in one day sessions, repeated several times per vegetation season. Permanent measurements are provided in profiles, down to 50 cm below surface. Temporary SM measurements are collected by hand held TDR (FOM/mts type, Easy Test Ltd., Lublin, Poland) from the top surface layer (1-6 cm), in a grid covering small and large areas, containing few hundred sites. The same places are served by collecting soil samples for the gravimetric analysis of SM, bulk density, other physical and textural characteristics. Sessions on measurement in large areas on the scale of community are repeated for separate days. The two methods used were compared with correlation coefficient, regression equation and differences of values. The spatial variability of soil moisture from gravimetric and TDR measurements were analyzed using geostatistical methods. The semivariogram parameters were determined and mathematical functions were fitted to empirically derived semivariograms. These functions were used for estimation of spatial distribution of soil moisture in cultivated fields by the kriging method. The results showed that spatial distribution patterns of topsoil soil moisture in the investigated areas obtained from TDR and gravimetric methods were in general similar to each other. The TDR soil moisture contents were dependent on bulk density and texture of soil. In areas with fine-textured soils of lower soil bulk densities (approximately below 1.35 Mg m^-3) we observed that TDR soil moisture and spatial differentiation were greater compared to those with gravimetric method. However at higher bulk densities the inverse was true. The spatial patterns were further modified in areas with domination of coarse-textured soils. Decrease of measurement points results in smoothing soil moisture pattern and at the same time in a greater estimation error. The TDR method can be useful tool for ground moisture measurements and validation of satellite data. The use of specific calibration or correction for soil bulk density and texture with respect to the reflectometric method is recommended. The study is a contribution to the project SWEX (AO-3275) and funded by the Polish Ministry of Science and Higher Education (in part by Grant No. N305 046 31/1707 and in part by Grant No. N305 107 32/3865).

Channel characterization using multiple-point geostatistics, neural network, and modern analogy: A case study from a carbonate reservoir, southwest Iran

NASA Astrophysics Data System (ADS)

Hashemi, Seyyedhossein; Javaherian, Abdolrahim; Ataee-pour, Majid; Tahmasebi, Pejman; Khoshdel, Hossein

2014-12-01

In facies modeling, the ideal objective is to integrate different sources of data to generate a model that has the highest consistency to reality with respect to geological shapes and their facies architectures. Multiple-point (geo)statistics (MPS) is a tool that gives the opportunity of reaching this goal via defining a training image (TI). A facies modeling workflow was conducted on a carbonate reservoir located southwest Iran. Through a sequence stratigraphic correlation among the wells, it was revealed that the interval under a modeling process was deposited in a tidal flat environment. Bahamas tidal flat environment which is one of the most well studied modern carbonate tidal flats was considered to be the source of required information for modeling a TI. In parallel, a neural network probability cube was generated based on a set of attributes derived from 3D seismic cube to be applied into the MPS algorithm as a soft conditioning data. Moreover, extracted channel bodies and drilled well log facies came to the modeling as hard data. Combination of these constraints resulted to a facies model which was greatly consistent to the geological scenarios. This study showed how analogy of modern occurrences can be set as the foundation for generating a training image. Channel morphology and facies types currently being deposited, which are crucial for modeling a training image, was inferred from modern occurrences. However, there were some practical considerations concerning the MPS algorithm used for facies simulation. The main limitation was the huge amount of RAM and CPU-time needed to perform simulations.

Applications of stochastic models and geostatistical analyses to study sources and spatial patterns of soil heavy metals in a metalliferous industrial district of China.

PubMed

Zhong, Buqing; Liang, Tao; Wang, Lingqing; Li, Kexin

2014-08-15

An extensive soil survey was conducted to study pollution sources and delineate contamination of heavy metals in one of the metalliferous industrial bases, in the karst areas of southwest China. A total of 597 topsoil samples were collected and the concentrations of five heavy metals, namely Cd, As (metalloid), Pb, Hg and Cr were analyzed. Stochastic models including a conditional inference tree (CIT) and a finite mixture distribution model (FMDM) were applied to identify the sources and partition the contribution from natural and anthropogenic sources for heavy metal in topsoils of the study area. Regression trees for Cd, As, Pb and Hg were proved to depend mostly on indicators of anthropogenic activities such as industrial type and distance from urban area, while the regression tree for Cr was found to be mainly influenced by the geogenic characteristics. The FMDM analysis showed that the geometric means of modeled background values for Cd, As, Pb, Hg and Cr were close to their background values previously reported in the study area, while the contamination of Cd and Hg were widespread in the study area, imposing potentially detrimental effects on organisms through the food chain. Finally, the probabilities of single and multiple heavy metals exceeding the threshold values derived from the FMDM were estimated using indicator kriging (IK) and multivariate indicator kriging (MVIK). The high probabilities exceeding the thresholds of heavy metals were associated with metalliferous production and atmospheric deposition of heavy metals transported from the urban and industrial areas. Geostatistics coupled with stochastic models provide an effective way to delineate multiple heavy metal pollution to facilitate improved environmental management. Copyright © 2014 Elsevier B.V. All rights reserved.

Land management decisions in a carbonatic geo-environment

NASA Astrophysics Data System (ADS)

Siska, P.; Hung, I.-K.

2017-10-01

Land is the uppermost territorial unit of the earth’s surface that is quasi-homogeneous in its physical, natural, and also anthropogenic properties. The fundamental component of land is lithosphere. The focus of this work is on a carbonatic geo-environment that is dominantly characterized by Mesozoic rock complexes, significant chemical weathering, and a set of landforms that are unique to this type of a geological structures. In general, optimal land management is a composite of land sharing and land sparing practices; however, in order to answer the question: ‘What is a parcel of land best suited for?’ often requires well-organized spatial data. In this work, we have focused on developing a model that would evaluate the suitability of a carbonatic geo-environment for land management practices. Due to the potential hazards of some sinkhole infested areas, the risk of natural hazards must be first evaluated. In addition, the level of hazards depends on population pressure and the intensity of human impact on this particular environment. In this research, we have applied the principles of geostatistics to evaluate the probabilities for sinkhole hazards as well as fuzzy logic to evaluate the suitability of land sharing and land sparing management.

Mass Fraction Spatiotemporal Geostatistics and its Application to Map Atmospheric Polycyclic Aromatic Hydrocarbons after 9/11

EPA Science Inventory

Extensive research has been conducted on effects resulting from exposure to ambient particulate matter. Particulate matter has been linked to cardiovascular diseases, respiratory problems, and reproductive effects. A large body of work on particulate matter focuses on atmospher...

ANALYTICAL EXPRESSIONS OF CONDITIONAL MENA, COVARIANCE, AND SAMPLE FUNCTIONS IN GEOSTATISTICS. (R825689C037)

EPA Science Inventory

The perspectives, information and conclusions conveyed in research project abstracts, progress reports, final reports, journal abstracts and journal publications convey the viewpoints of the principal investigator and may not represent the views and policies of ORD and EPA. Concl...

Geostatistical Model-Based Estimates of Schistosomiasis Prevalence among Individuals Aged ≤20 Years in West Africa

PubMed Central

Schur, Nadine; Hürlimann, Eveline; Garba, Amadou; Traoré, Mamadou S.; Ndir, Omar; Ratard, Raoult C.; Tchuem Tchuenté, Louis-Albert; Kristensen, Thomas K.; Utzinger, Jürg; Vounatsou, Penelope

2011-01-01

Background Schistosomiasis is a water-based disease that is believed to affect over 200 million people with an estimated 97% of the infections concentrated in Africa. However, these statistics are largely based on population re-adjusted data originally published by Utroska and colleagues more than 20 years ago. Hence, these estimates are outdated due to large-scale preventive chemotherapy programs, improved sanitation, water resources development and management, among other reasons. For planning, coordination, and evaluation of control activities, it is essential to possess reliable schistosomiasis prevalence maps. Methodology We analyzed survey data compiled on a newly established open-access global neglected tropical diseases database (i) to create smooth empirical prevalence maps for Schistosoma mansoni and S. haematobium for individuals aged ≤20 years in West Africa, including Cameroon, and (ii) to derive country-specific prevalence estimates. We used Bayesian geostatistical models based on environmental predictors to take into account potential clustering due to common spatially structured exposures. Prediction at unobserved locations was facilitated by joint kriging. Principal Findings Our models revealed that 50.8 million individuals aged ≤20 years in West Africa are infected with either S. mansoni, or S. haematobium, or both species concurrently. The country prevalence estimates ranged between 0.5% (The Gambia) and 37.1% (Liberia) for S. mansoni, and between 17.6% (The Gambia) and 51.6% (Sierra Leone) for S. haematobium. We observed that the combined prevalence for both schistosome species is two-fold lower in Gambia than previously reported, while we found an almost two-fold higher estimate for Liberia (58.3%) than reported before (30.0%). Our predictions are likely to overestimate overall country prevalence, since modeling was based on children and adolescents up to the age of 20 years who are at highest risk of infection. Conclusion/Significance We present the first empirical estimates for S. mansoni and S. haematobium prevalence at high spatial resolution throughout West Africa. Our prediction maps allow prioritizing of interventions in a spatially explicit manner, and will be useful for monitoring and evaluation of schistosomiasis control programs. PMID:21695107

The concurrent multiplicative-additive approach for gauge-radar/satellite multisensor precipitation estimates

NASA Astrophysics Data System (ADS)

Garcia-Pintado, J.; Barberá, G. G.; Erena Arrabal, M.; Castillo, V. M.

2010-12-01

Objective analysis schemes (OAS), also called ``succesive correction methods'' or ``observation nudging'', have been proposed for multisensor precipitation estimation combining remote sensing data (meteorological radar or satellite) with data from ground-based raingauge networks. However, opposite to the more complex geostatistical approaches, the OAS techniques for this use are not optimized. On the other hand, geostatistical techniques ideally require, at the least, modelling the covariance from the rain gauge data at every time step evaluated, which commonly cannot be soundly done. Here, we propose a new procedure (concurrent multiplicative-additive objective analysis scheme [CMA-OAS]) for operational rainfall estimation using rain gauges and meteorological radar, which does not require explicit modelling of spatial covariances. On the basis of a concurrent multiplicative-additive (CMA) decomposition of the spatially nonuniform radar bias, within-storm variability of rainfall and fractional coverage of rainfall are taken into account. Thus both spatially nonuniform radar bias, given that rainfall is detected, and bias in radar detection of rainfall are handled. The interpolation procedure of CMA-OAS is built on the OAS, whose purpose is to estimate a filtered spatial field of the variable of interest through a successive correction of residuals resulting from a Gaussian kernel smoother applied on spatial samples. The CMA-OAS, first, poses an optimization problem at each gauge-radar support point to obtain both a local multiplicative-additive radar bias decomposition and a regionalization parameter. Second, local biases and regionalization parameters are integrated into an OAS to estimate the multisensor rainfall at the ground level. The approach considers radar estimates as background a priori information (first guess), so that nudging to observations (gauges) may be relaxed smoothly to the first guess, and the relaxation shape is obtained from the sequential optimization. The procedure is suited to relatively sparse rain gauge networks. To show the procedure, six storms are analyzed at hourly steps over 10,663 km2. Results generally indicated an improved quality with respect to other methods evaluated: a standard mean-field bias adjustment, an OAS spatially variable adjustment with multiplicative factors, ordinary cokriging, and kriging with external drift. In theory, it could be equally applicable to gauge-satellite estimates and other hydrometeorological variables.

Rtop - an R package for interpolation along the stream network

NASA Astrophysics Data System (ADS)

Skøien, J. O.; Laaha, G.; Koffler, D.; Blöschl, G.; Pebesma, E.; Parajka, J.; Viglione, A.

2012-04-01

Geostatistical methods have a long tradition within analysis of data that can be conceptualized as simple point data, such as soil properties, or for regular blocks, such as mining data. However, these methods have been used to a limited extent for estimation along stream networks. A few exceptions are given by (Gottschalk 1993, Sauquet et al. 2000, Gottschalk et al. 2006, Skøien et al. 2006), and an overview by Laaha and Blöschl (2011). Interpolation of runoff characteristics are more complicated than the traditional random variables estimated by geostatistical methods, as the measurements have a more complicated support, and many catchments are nested. Skøien et al. (2006) presented the model Top-kriging which takes these effects into account for interpolation of stream flow characteristics (exemplified by the 100 year flood). The method has here been implemented as a package in the open source statistical environment R (R Development Core Team 2011). Taking advantage of the existing methods in R for working with spatial objects, and the extensive possibilities for visualizing the result, this makes it considerably easier to apply the method on new data sets, in comparison to earlier implementation of the method. In addition to user feedback, the package has also been tested by colleagues whose only responsibility has been to search for bugs, inconsistencies and shortcomings of the documentation. The last part is often the part that gets the least attention in small open source projects, and we have solved this by acknowledging their effects as co-authors. The model will soon be uploaded to CRAN, but is in the meantime also available from R-forge and can be installed by: > install.packages("rtop", repos="http://R-Forge.R-project.org") Gottschalk, L., 1993. Interpolation of runoff applying objective methods. Stochastic Hydrology and Hydraulics, 7, 269-281. Gottschalk, L., Krasovskaia, I., Leblois, E. & Sauquet, E., 2006. Mapping mean and variance of runoff in a river basin. Hydrology and Earth System Sciences, 10, 469-484. Laaha, G. & Blöschl, G. 2011. Geostatistics on river networks - a reviewed. EGU General Assembly, Vienna, Austria. R Development Core Team, 2011. R: A language and environment for statistical computing. Vienna, Austria, ISBN 3-900051-07-0. Sauquet, E., Gottschalk, L. & Leblois, E., 2000. Mapping average annual runoff: A hierarchical approach applying a stochastic interpolation scheme. Hydrological Sciences Journal, 45 (6), 799-815. Skøien, J.O., Merz, R. & Blöschl, G., 2006. Top-kriging - geostatistics on stream networks. Hydrology and Earth System Sciences, 10, 277-287.

RockFall analyst: A GIS extension for three-dimensional and spatially distributed rockfall hazard modeling

NASA Astrophysics Data System (ADS)

Lan, Hengxing; Derek Martin, C.; Lim, C. H.

2007-02-01

Geographic information system (GIS) modeling is used in combination with three-dimensional (3D) rockfall process modeling to assess rockfall hazards. A GIS extension, RockFall Analyst (RA), which is capable of effectively handling large amounts of geospatial information relative to rockfall behaviors, has been developed in ArcGIS using ArcObjects and C#. The 3D rockfall model considers dynamic processes on a cell plane basis. It uses inputs of distributed parameters in terms of raster and polygon features created in GIS. Two major components are included in RA: particle-based rockfall process modeling and geostatistics-based rockfall raster modeling. Rockfall process simulation results, 3D rockfall trajectories and their velocity features either for point seeders or polyline seeders are stored in 3D shape files. Distributed raster modeling, based on 3D rockfall trajectories and a spatial geostatistical technique, represents the distribution of spatial frequency, the flying and/or bouncing height, and the kinetic energy of falling rocks. A distribution of rockfall hazard can be created by taking these rockfall characteristics into account. A barrier analysis tool is also provided in RA to aid barrier design. An application of these modeling techniques to a case study is provided. The RA has been tested in ArcGIS 8.2, 8.3, 9.0 and 9.1.

Assessment of groundwater and soil quality degradation using multivariate and geostatistical analyses, Dakhla Oasis, Egypt

NASA Astrophysics Data System (ADS)

Masoud, Alaa A.; El-Horiny, Mohamed M.; Atwia, Mohamed G.; Gemail, Khaled S.; Koike, Katsuaki

2018-06-01

Salinization of groundwater and soil resources has long been a serious environmental hazard in arid regions. This study was conducted to investigate and document the factors controlling such salinization and their inter-relationships in the Dakhla Oasis (Egypt). To accomplish this, 60 groundwater samples and 31 soil samples were collected in February 2014. Factor analysis (FA) and hierarchical cluster analysis (HCA) were integrated with geostatistical analyses to characterize the chemical properties of groundwater and soil and their spatial patterns, identify the factors controlling the pattern variability, and clarify the salinization mechanism. Groundwater quality standards revealed emergence of salinization (av. 885.8 mg/L) and extreme occurrences of Fe2+ (av. 17.22 mg/L) and Mn2+ (av. 2.38 mg/L). Soils were highly salt-affected (av. 15.2 dS m-1) and slightly alkaline (av. pH = 7.7). Evaporation and ion-exchange processes governed the evolution of two main water types: Na-Cl (52%) and Ca-Mg-Cl (47%), respectively. Salinization leads the chemical variability of both resources. Distinctive patterns of slight salinization marked the northern part and intense salinization marked the middle and southern parts. Congruence in the resources clusters confirmed common geology, soil types, and urban and agricultural practices. Minimizing the environmental and socioeconomic impacts of the resources salinization urges the need for better understanding of the hydrochemical characteristics and prediction of quality changes.

Application of Geostatistical Methods and Machine Learning for spatio-temporal Earthquake Cluster Analysis

NASA Astrophysics Data System (ADS)

Schaefer, A. M.; Daniell, J. E.; Wenzel, F.

2014-12-01

Earthquake clustering tends to be an increasingly important part of general earthquake research especially in terms of seismic hazard assessment and earthquake forecasting and prediction approaches. The distinct identification and definition of foreshocks, aftershocks, mainshocks and secondary mainshocks is taken into account using a point based spatio-temporal clustering algorithm originating from the field of classic machine learning. This can be further applied for declustering purposes to separate background seismicity from triggered seismicity. The results are interpreted and processed to assemble 3D-(x,y,t) earthquake clustering maps which are based on smoothed seismicity records in space and time. In addition, multi-dimensional Gaussian functions are used to capture clustering parameters for spatial distribution and dominant orientations. Clusters are further processed using methodologies originating from geostatistics, which have been mostly applied and developed in mining projects during the last decades. A 2.5D variogram analysis is applied to identify spatio-temporal homogeneity in terms of earthquake density and energy output. The results are mitigated using Kriging to provide an accurate mapping solution for clustering features. As a case study, seismic data of New Zealand and the United States is used, covering events since the 1950s, from which an earthquake cluster catalogue is assembled for most of the major events, including a detailed analysis of the Landers and Christchurch sequences.

Geostatistical conditional simulation for the assessment of contaminated land by abandoned heavy metal mining.

PubMed

Ersoy, Adem; Yunsel, Tayfun Yusuf; Atici, Umit

2008-02-01

Abandoned mine workings can undoubtedly cause varying degrees of contamination of soil with heavy metals such as lead and zinc has occurred on a global scale. Exposure to these elements may cause to harm human health and environment. In the study, a total of 269 soil samples were collected at 1, 5, and 10 m regular grid intervals of 100 x 100 m area of Carsington Pasture in the UK. Cell declustering technique was applied to the data set due to no statistical representativity. Directional experimental semivariograms of the elements for the transformed data showed that both geometric and zonal anisotropy exists in the data. The most evident spatial dependence structure of the continuity for the directional experimental semivariogram, characterized by spherical and exponential models of Pb and Zn were obtained. This study reports the spatial distribution and uncertainty of Pb and Zn concentrations in soil at the study site using a probabilistic approach. The approach was based on geostatistical sequential Gaussian simulation (SGS), which is used to yield a series of conditional images characterized by equally probable spatial distributions of the heavy elements concentrations across the area. Postprocessing of many simulations allowed the mapping of contaminated and uncontaminated areas, and provided a model for the uncertainty in the spatial distribution of element concentrations. Maps of the simulated Pb and Zn concentrations revealed the extent and severity of contamination. SGS was validated by statistics, histogram, variogram reproduction, and simulation errors. The maps of the elements might be used in the remediation studies, help decision-makers and others involved in the abandoned heavy metal mining site in the world.

A spatially constrained ecological classification: rationale, methodology and implementation

Treesearch

Franz Mora; Louis Iverson; Louis Iverson

2002-01-01

The theory, methodology and implementation for an ecological and spatially constrained classification are presented. Ecological and spatial relationships among several landscape variables are analyzed in order to define a new approach for a landscape classification. Using ecological and geostatistical analyses, several ecological and spatial weights are derived to...

Quantitative geomorphology with geographical information systems (GIS) for evolving societies and science

NASA Astrophysics Data System (ADS)

Gomez, C.; Oguchi, T.; Evans, I. S.

2016-05-01

Based on the two sessions on spatial analysis, GIS and geostatistics convened by T. Oguchi, I. Evans and C. Gomez at the 2013 International Association of Geomorphology in Paris, the conveners have edited two special issues on the topic: volume 242 and the present one.

A geostatistical approach to identify and mitigate agricultural nitrous oxide emission hotspots

USDA-ARS?s Scientific Manuscript database

Anthropogenic emissions of nitrous oxide (N2O), a trace gas with severe environmental costs, are greatest from agricultural soils amended with nitrogen (N) fertilizer. However, accurate N2O emission estimates at fine spatial scales are made difficult by their high variability, which represents a cr...

GY SAMPLING THEORY AND GEOSTATISTICS: ALTERNATE MODELS OF VARIABILITY IN CONTINUOUS MEDIA

EPA Science Inventory

In the sampling theory developed by Pierre Gy, sample variability is modeled as the sum of a set of seven discrete error components. The variogram used in geostatisties provides an alternate model in which several of Gy's error components are combined in a continuous mode...

Uses of GIS for Homeland Security and Emergency Management for Higher Education Institutions

ERIC Educational Resources Information Center

Murchison, Stuart B.

2010-01-01

Geographic information systems (GIS) are a major component of the geospatial sciences, which are also composed of geostatistical analysis, remote sensing, and global positional satellite systems. These systems can be integrated into GIS for georeferencing, pattern analysis, visualization, and understanding spatial concepts that transcend…

Mapping extreme rainfall in the Northwest Portugal region: statistical analysis and spatial modelling

NASA Astrophysics Data System (ADS)

Santos, Monica; Fragoso, Marcelo

2010-05-01

Extreme precipitation events are one of the causes of natural hazards, such as floods and landslides, making its investigation so important, and this research aims to contribute to the study of the extreme rainfall patterns in a Portuguese mountainous area. The study area is centred on the Arcos de Valdevez county, located in the northwest region of Portugal, the rainiest of the country, with more than 3000 mm of annual rainfall at the Peneda-Gerês mountain system. This work focus on two main subjects related with the precipitation variability on the study area. First, a statistical analysis of several precipitation parameters is carried out, using daily data from 17 rain-gauges with a complete record for the 1960-1995 period. This approach aims to evaluate the main spatial contrasts regarding different aspects of the rainfall regime, described by ten parameters and indices of precipitation extremes (e.g. mean annual precipitation, the annual frequency of precipitation days, wet spells durations, maximum daily precipitation, maximum of precipitation in 30 days, number of days with rainfall exceeding 100 mm and estimated maximum daily rainfall for a return period of 100 years). The results show that the highest precipitation amounts (from annual to daily scales) and the higher frequency of very abundant rainfall events occur in the Serra da Peneda and Gerês mountains, opposing to the valleys of the Lima, Minho and Vez rivers, with lower precipitation amounts and less frequent heavy storms. The second purpose of this work is to find a method of mapping extreme rainfall in this mountainous region, investigating the complex influence of the relief (e.g. elevation, topography) on the precipitation patterns, as well others geographical variables (e.g. distance from coast, latitude), applying tested geo-statistical techniques (Goovaerts, 2000; Diodato, 2005). Models of linear regression were applied to evaluate the influence of different geographical variables (altitude, latitude, distance from sea and distance to the highest orographic barrier) on the rainfall behaviours described by the studied variables. The techniques of spatial interpolation evaluated include univariate and multivariate methods: cokriging, kriging, IDW (inverse distance weighted) and multiple linear regression. Validation procedures were used, assessing the estimated errors in the analysis of descriptive statistics of the models. Multiple linear regression models produced satisfactory results in relation to 70% of the rainfall parameters, suggested by lower average percentage of error. However, the results also demonstrates that there is no an unique and ideal model, depending on the rainfall parameter in consideration. Probably, the unsatisfactory results obtained in relation to some rainfall parameters was motivated by constraints as the spatial complexity of the precipitation patterns, as well as to the deficient spatial coverage of the territory by the rain-gauges network. References Diodato, N. (2005). The influence of topographic co-variables on the spatial variability of precipitation over small regions of complex terrain. Internacional Journal of Climatology, 25(3), 351-363. Goovaerts, P. (2000). Geostatistical approaches for incorporating elevation into the spatial interpolation of rainfall. Journal of Hydrology, 228, 113 - 129.

Geostatistical Investigations of Displacements on the Basis of Data from the Geodetic Monitoring of a Hydrotechnical Object

NASA Astrophysics Data System (ADS)

Namysłowska-Wilczyńska, Barbara; Wynalek, Janusz

2017-12-01

Geostatistical methods make the analysis of measurement data possible. This article presents the problems directed towards the use of geostatistics in spatial analysis of displacements based on geodetic monitoring. Using methods of applied (spatial) statistics, the research deals with interesting and current issues connected to space-time analysis, modeling displacements and deformations, as applied to any large-area objects on which geodetic monitoring is conducted (e.g., water dams, urban areas in the vicinity of deep excavations, areas at a macro-regional scale subject to anthropogenic influences caused by mining, etc.). These problems are very crucial, especially for safety assessment of important hydrotechnical constructions, as well as for modeling and estimating mining damage. Based on the geodetic monitoring data, a substantial basic empirical material was created, comprising many years of research results concerning displacements of controlled points situated on the crown and foreland of an exemplary earth dam, and used to assess the behaviour and safety of the object during its whole operating period. A research method at a macro-regional scale was applied to investigate some phenomena connected with the operation of the analysed big hydrotechnical construction. Applying a semivariogram function enabled the spatial variability analysis of displacements. Isotropic empirical semivariograms were calculated and then, theoretical parameters of analytical functions were determined, which approximated the courses of the mentioned empirical variability measure. Using ordinary (block) kriging at the grid nodes of an elementary spatial grid covering the analysed object, the values of the Z* estimated means of displacements were calculated together with the accompanying assessment of uncertainty estimation - a standard deviation of estimation σk. Raster maps of the distribution of estimated averages Z* and raster maps of deviations of estimation σk (in perspective) were obtained for selected years (1995 and 2007), taking the ground height 136 m a.s.l. into calculation. To calculate raster maps of Z* interpolated values, methods of quick interpolation were also used, such as the technique of the inverse distance squares, a linear model of kriging, a spline kriging, which made the recognition of the general background of displacements possible, without the accuracy assessment of Z* value estimation, i.e., the value of σk. These maps are also related to 1995 and 2007 and the elevation. As a result of applying these techniques, clear boundaries of subsiding areas, upthrusting and also horizontal displacements on the examined hydrotechnical object were marked out, which can be interpreted as areas of local deformations of the object, important for the safety of the construction. The effect of geostatistical research conducted, including the structural analysis, semivariograms modeling, estimating the displacements of the hydrotechnical object, are rich cartographic characteristic (semivariograms, raster maps, block diagrams), which present the spatial visualization of the conducted various analyses of the monitored displacements. The prepared geostatistical model (3D) of displacement variability (analysed within the area of the dam, during its operating period and including its height) will be useful not only in the correct assessment of displacements and deformations, but it will also make it possible to forecast these phenomena, which is crucial when the operating safety of such constructions is taken into account.

HT2DINV: A 2D forward and inverse code for steady-state and transient hydraulic tomography problems

NASA Astrophysics Data System (ADS)

Soueid Ahmed, A.; Jardani, A.; Revil, A.; Dupont, J. P.

2015-12-01

Hydraulic tomography is a technique used to characterize the spatial heterogeneities of storativity and transmissivity fields. The responses of an aquifer to a source of hydraulic stimulations are used to recover the features of the estimated fields using inverse techniques. We developed a 2D free source Matlab package for performing hydraulic tomography analysis in steady state and transient regimes. The package uses the finite elements method to solve the ground water flow equation for simple or complex geometries accounting for the anisotropy of the material properties. The inverse problem is based on implementing the geostatistical quasi-linear approach of Kitanidis combined with the adjoint-state method to compute the required sensitivity matrices. For undetermined inverse problems, the adjoint-state method provides a faster and more accurate approach for the evaluation of sensitivity matrices compared with the finite differences method. Our methodology is organized in a way that permits the end-user to activate parallel computing in order to reduce the computational burden. Three case studies are investigated demonstrating the robustness and efficiency of our approach for inverting hydraulic parameters.

Spatial and temporal variability in the R-5 infiltration data set: Déjà vu and rainfall-runoff simulations

NASA Astrophysics Data System (ADS)

Loague, Keith; Kyriakidis, Phaedon C.

1997-12-01

This paper is a continuation of the event-based rainfall-runoff model evaluation study reported by Loague and Freeze [1985[. Here we reevaluate the performance of a quasi-physically based rainfall-runoff model for three large events from the well-known R-5 catchment. Five different statistical criteria are used to quantitatively judge model performance. Temporal variability in the large R-5 infiltration data set [Loague and Gander, 1990] is filtered by working in terms of permeability. The transformed data set is reanalyzed via geostatistical methods to model the spatial distribution of permeability across the R-5 catchment. We present new estimates of the spatial distribution of infiltration that are in turn used in our rainfall-runoff simulations with the Horton rainfall-runoff model. The new rainfall-runoff simulations, complicated by reinfiltration impacts at the smaller scales of characterization, indicate that the near-surface hydrologic response of the R-5 catchment is most probably dominated by a combination of the Horton and Dunne overland flow mechanisms.

Injectivity Evaluation for Offshore CO 2 Sequestration in Marine Sediments

DOE PAGES

Dai, Zhenxue; Zhang, Ye; Stauffer, Philip; ...

2017-08-18

Global and regional climate change caused by greenhouse gases emissions has stimulated interest in developing various technologies (such as carbon dioxide (CO 2) geologic sequestration in brine reservoirs) to reduce the concentrations of CO 2 in the atmosphere. Our study develops a statistical framework to identify gravitational CO 2 trapping processes and to quantitatively evaluate both CO 2 injectivity (or storage capacity) and leakage potential from marine sediments which exhibit heterogeneous permeability and variable thicknesses. Here, we focus on sets of geostatistically-based heterogeneous models populated with fluid flow parameters from several reservoir sites in the U.S. Gulf of Mexico (GOM).more » A computationally efficient uncertainty quantification study was conducted with results suggesting that permeability heterogeneity and anisotropy, seawater depth, and sediment thickness can all significantly impact CO 2 flow and trapping. Large permeability/porosity heterogeneity can enhance gravitational, capillary, and dissolution trapping, which acts to deter CO 2 upward migration and subsequent leakage onto the seafloor. When log permeability variance is 5, self-sealing with heterogeneity-enhanced gravitation trapping can be achieved even when water depth is 1.2 km. This extends the previously identified self-sealing condition that water depth be greater than 2.7 km. Our results have yielded valuable insight into the conditions under which safe storage of CO 2 can be achieved in offshore environments. The developed statistical framework is general and can be adapted to study other offshore sites worldwide.« less

Injectivity Evaluation for Offshore CO 2 Sequestration in Marine Sediments

DOE Office of Scientific and Technical Information (OSTI.GOV)

Dai, Zhenxue; Zhang, Ye; Stauffer, Philip

Global and regional climate change caused by greenhouse gases emissions has stimulated interest in developing various technologies (such as carbon dioxide (CO 2) geologic sequestration in brine reservoirs) to reduce the concentrations of CO 2 in the atmosphere. Our study develops a statistical framework to identify gravitational CO 2 trapping processes and to quantitatively evaluate both CO 2 injectivity (or storage capacity) and leakage potential from marine sediments which exhibit heterogeneous permeability and variable thicknesses. Here, we focus on sets of geostatistically-based heterogeneous models populated with fluid flow parameters from several reservoir sites in the U.S. Gulf of Mexico (GOM).more » A computationally efficient uncertainty quantification study was conducted with results suggesting that permeability heterogeneity and anisotropy, seawater depth, and sediment thickness can all significantly impact CO 2 flow and trapping. Large permeability/porosity heterogeneity can enhance gravitational, capillary, and dissolution trapping, which acts to deter CO 2 upward migration and subsequent leakage onto the seafloor. When log permeability variance is 5, self-sealing with heterogeneity-enhanced gravitation trapping can be achieved even when water depth is 1.2 km. This extends the previously identified self-sealing condition that water depth be greater than 2.7 km. Our results have yielded valuable insight into the conditions under which safe storage of CO 2 can be achieved in offshore environments. The developed statistical framework is general and can be adapted to study other offshore sites worldwide.« less

Space-Time Urban Air Pollution Forecasts

NASA Astrophysics Data System (ADS)

Russo, A.; Trigo, R. M.; Soares, A.

2012-04-01

Air pollution, like other natural phenomena, may be considered a space-time process. However, the simultaneous integration of time and space is not an easy task to perform, due to the existence of different uncertainties levels and data characteristics. In this work we propose a hybrid method that combines geostatistical and neural models to analyze PM10 time series recorded in the urban area of Lisbon (Portugal) for the 2002-2006 period and to produce forecasts. Geostatistical models have been widely used to characterize air pollution in urban areas, where the pollutant sources are considered diffuse, and also to industrial areas with localized emission sources. It should be stressed however that most geostatistical models correspond basically to an interpolation methodology (estimation, simulation) of a set of variables in a spatial or space-time domain. The temporal prediction of a pollutant usually requires knowledge of the main trends and complex patterns of physical dispersion phenomenon. To deal with low resolution problems and to enhance reliability of predictions, an approach based on neural network short term predictions in the monitoring stations which behave as a local conditioner to a fine grid stochastic simulation model is presented here. After the pollutant concentration is predicted for a given time period at the monitoring stations, we can use the local conditional distributions of observed values, given the predicted value for that period, to perform the spatial simulations for the entire area and consequently evaluate the spatial uncertainty of pollutant concentration. To attain this objective, we propose the use of direct sequential simulations with local distributions. With this approach one succeed to predict the space-time distribution of pollutant concentration that accounts for the time prediction uncertainty (reflecting the neural networks efficiency at each local monitoring station) and the spatial uncertainty as revealed by the spatial variograms. The dataset used consists of PM10 concentrations recorded hourly by 12 monitoring stations within the Lisbon's area, for the period 2002-2006. In addition, meteorological data recorded at 3 monitoring stations and boundary layer height (BLH) daily values from the ECMWF (European Centre for Medium Weather Forecast), ERA Interim, were also used. Based on the large-scale standard pressure fields from the ERA40/ECMWF, prevailing circulation patterns at regional scale where determined and used on the construction of the models. After the daily forecasts were produced, the difference between the average maps based on real observations and predicted values were determined and the model's performance was assessed. Based on the analysis of the results, we conclude that the proposed approach shows to be a very promising alternative for urban air quality characterization because of its good results and simplicity of application.

Transfer of uncertainty of space-borne high resolution rainfall products at ungauged regions

NASA Astrophysics Data System (ADS)

Tang, Ling

Hydrologically relevant characteristics of high resolution (˜ 0.25 degree, 3 hourly) satellite rainfall uncertainty were derived as a function of season and location using a six year (2002-2007) archive of National Aeronautics and Space Administration (NASA)'s Tropical Rainfall Measuring Mission (TRMM) Multi-satellite Precipitation Analysis (TMPA) precipitation data. The Next Generation Radar (NEXRAD) Stage IV rainfall data over the continental United States was used as ground validation (GV) data. A geostatistical mapping scheme was developed and tested for transfer (i.e., spatial interpolation) of uncertainty information from GV regions to the vast non-GV regions by leveraging the error characterization work carried out in the earlier step. The open question explored here was, "If 'error' is defined on the basis of independent ground validation (GV) data, how are error metrics estimated for a satellite rainfall data product without the need for much extensive GV data?" After a quantitative analysis of the spatial and temporal structure of the satellite rainfall uncertainty, a proof-of-concept geostatistical mapping scheme (based on the kriging method) was evaluated. The idea was to understand how realistic the idea of 'transfer' is for the GPM era. It was found that it was indeed technically possible to transfer error metrics from a gauged to an ungauged location for certain error metrics and that a regionalized error metric scheme for GPM may be possible. The uncertainty transfer scheme based on a commonly used kriging method (ordinary kriging) was then assessed further at various timescales (climatologic, seasonal, monthly and weekly), and as a function of the density of GV coverage. The results indicated that if a transfer scheme for estimating uncertainty metrics was finer than seasonal scale (ranging from 3-6 hourly to weekly-monthly), the effectiveness for uncertainty transfer worsened significantly. Next, a comprehensive assessment of different kriging methods for spatial transfer (interpolation) of error metrics was performed. Three kriging methods for spatial interpolation are compared, which are: ordinary kriging (OK), indicator kriging (IK) and disjunctive kriging (DK). Additional comparison with the simple inverse distance weighting (IDW) method was also performed to quantify the added benefit (if any) of using geostatistical methods. The overall performance ranking of the kriging methods was found to be as follows: OK=DK > IDW > IK. Lastly, various metrics of satellite rainfall uncertainty were identified for two large continental landmasses that share many similar Koppen climate zones, United States and Australia. The dependence of uncertainty as a function of gauge density was then investigated. The investigation revealed that only the first and second ordered moments of error are most amenable to a Koppen-type climate type classification in different continental landmasses.

Assessing the cost of groundwater pollution: the case of diffuse agricultural pollution in the Upper Rhine valley aquifer.

PubMed

Rinaudo, J-D; Arnal, C; Blanchin, R; Elsass, P; Meilhac, A; Loubier, S

2005-01-01

This paper presents an assessment of the costs of diffuse groundwater pollution by nitrates and pesticides for the industrial and the drinking water sectors in the Upper Rhine valley, France. Pollution costs which occurred between 1988 and 2002 are described and assessed using the avoidance cost method. Geo-statistical methods (kriging) are then used to construct three scenarios of nitrate concentration evolution. The economic consequences of each scenario are then assessed. The estimates obtained are compared with the results of a contingent valuation study carried out in the same study area ten years earlier.

Spatio-temporal patterns of Barmah Forest virus disease in Queensland, Australia.

PubMed

Naish, Suchithra; Hu, Wenbiao; Mengersen, Kerrie; Tong, Shilu

2011-01-01

Barmah Forest virus (BFV) disease is a common and wide-spread mosquito-borne disease in Australia. This study investigated the spatio-temporal patterns of BFV disease in Queensland, Australia using geographical information system (GIS) tools and geostatistical analysis. We calculated the incidence rates and standardised incidence rates of BFV disease. Moran's I statistic was used to assess the spatial autocorrelation of BFV incidences. Spatial dynamics of BFV disease was examined using semi-variogram analysis. Interpolation techniques were applied to visualise and display the spatial distribution of BFV disease in statistical local areas (SLAs) throughout Queensland. Mapping of BFV disease by SLAs reveals the presence of substantial spatio-temporal variation over time. Statistically significant differences in BFV incidence rates were identified among age groups (χ(2) = 7587, df = 7327,p<0.01). There was a significant positive spatial autocorrelation of BFV incidence for all four periods, with the Moran's I statistic ranging from 0.1506 to 0.2901 (p<0.01). Semi-variogram analysis and smoothed maps created from interpolation techniques indicate that the pattern of spatial autocorrelation was not homogeneous across the state. This is the first study to examine spatial and temporal variation in the incidence rates of BFV disease across Queensland using GIS and geostatistics. The BFV transmission varied with age and gender, which may be due to exposure rates or behavioural risk factors. There are differences in the spatio-temporal patterns of BFV disease which may be related to local socio-ecological and environmental factors. These research findings may have implications in the BFV disease control and prevention programs in Queensland.

Mapping the Risk of Snakebite in Sri Lanka - A National Survey with Geospatial Analysis.

PubMed

Ediriweera, Dileepa Senajith; Kasturiratne, Anuradhani; Pathmeswaran, Arunasalam; Gunawardena, Nipul Kithsiri; Wijayawickrama, Buddhika Asiri; Jayamanne, Shaluka Francis; Isbister, Geoffrey Kennedy; Dawson, Andrew; Giorgi, Emanuele; Diggle, Peter John; Lalloo, David Griffith; de Silva, Hithanadura Janaka

2016-07-01

There is a paucity of robust epidemiological data on snakebite, and data available from hospitals and localized or time-limited surveys have major limitations. No study has investigated the incidence of snakebite across a whole country. We undertook a community-based national survey and model based geostatistics to determine incidence, envenoming, mortality and geographical pattern of snakebite in Sri Lanka. The survey was designed to sample a population distributed equally among the nine provinces of the country. The number of data collection clusters was divided among districts in proportion to their population. Within districts clusters were randomly selected. Population based incidence of snakebite and significant envenoming were estimated. Model-based geostatistics was used to develop snakebite risk maps for Sri Lanka. 1118 of the total of 14022 GN divisions with a population of 165665 (0.8%of the country's population) were surveyed. The crude overall community incidence of snakebite, envenoming and mortality were 398 (95% CI: 356-441), 151 (130-173) and 2.3 (0.2-4.4) per 100000 population, respectively. Risk maps showed wide variation in incidence within the country, and snakebite hotspots and cold spots were determined by considering the probability of exceeding the national incidence. This study provides community based incidence rates of snakebite and envenoming for Sri Lanka. The within-country spatial variation of bites can inform healthcare decision making and highlights the limitations associated with estimates of incidence from hospital data or localized surveys. Our methods are replicable, and these models can be adapted to other geographic regions after re-estimating spatial covariance parameters for the particular region.

Characterizing groundwater quality ranks for drinking purposes in Sylhet district, Bangladesh, using entropy method, spatial autocorrelation index, and geostatistics.

PubMed

Islam, Abu Reza Md Towfiqul; Ahmed, Nasir; Bodrud-Doza, Md; Chu, Ronghao

2017-12-01

Drinking water is susceptible to the poor quality of contaminated water affecting the health of humans. Thus, it is an essential study to investigate factors affecting groundwater quality and its suitability for drinking uses. In this paper, the entropy theory, multivariate statistics, spatial autocorrelation index, and geostatistics are applied to characterize groundwater quality and its spatial variability in the Sylhet district of Bangladesh. A total of 91samples have been collected from wells (e.g., shallow, intermediate, and deep tube wells at 15-300-m depth) from the study area. The results show that NO 3 - , then SO 4 2- , and As are the most contributed parameters influencing the groundwater quality according to the entropy theory. The principal component analysis (PCA) and correlation coefficient also confirm the results of the entropy theory. However, Na + has the highest spatial autocorrelation and the most entropy, thus affecting the groundwater quality. Based on the entropy-weighted water quality index (EWQI) and groundwater quality index (GWQI) classifications, it is observed that 60.45 and 53.86% of water samples are classified as having an excellent to good qualities, while the remaining samples vary from medium to extremely poor quality domains for drinking purposes. Furthermore, the EWQI classification provides the more reasonable results than GWQIs due to its simplicity, accuracy, and ignoring of artificial weight. A Gaussian semivariogram model has been chosen to the best fit model, and groundwater quality indices have a weak spatial dependence, suggesting that both geogenic and anthropogenic factors play a pivotal role in spatial heterogeneity of groundwater quality oscillations.

Quantifying the exposure of humans and the environment to oil pollution in the Niger Delta using advanced geostatistical techniques.

PubMed

Obida, Christopher B; Alan Blackburn, G; Duncan Whyatt, J; Semple, Kirk T

2018-02-01

The Niger Delta is one of the largest oil producing regions of the world. Large numbers and volumes of oil spills have been reported in this region. What has not been quantified is the putative exposure of humans and/or the environment to this hydrocarbon pollution. In this novel study, advanced geostatistical techniques were applied to an extensive database of oil spill incidents from 2007 to 2015. The aims were to (i) identify and analyse spill hotspots along the oil pipeline network and (ii) estimate the exposure of the hydrocarbon pollution to the human population and the environment within the Niger Delta. Over the study period almost 90millionlitres of oil were released. Approximately 29% of the human population living in proximity to the pipeline network has been potentially exposed to oil contamination, of which 565,000 people live within high or very high spill intensity sectors. Over 1000km 2 of land has been contaminated by oil pollution, with broadleaved forest, mangroves and agricultural land the most heavily impacted land cover types. Proximity to the coast, roads and cities are the strongest spatial factors contributing to spill occurrence, which largely determine the accessibility of sites for pipeline sabotage and oil theft. Overall, the findings demonstrate the high levels of environmental and human exposure to hydrocarbon pollutants in the Niger Delta. These results provide evidence with which to spatially target interventions to reduce future spill incidents and mitigate the impacts of previous spills on human communities and ecosystem health. Copyright © 2017 Elsevier Ltd. All rights reserved.

Application of multiple-point geostatistics to simulate the effect of small-scale aquifer heterogeneity on the efficiency of aquifer thermal energy storage

NASA Astrophysics Data System (ADS)

Possemiers, Mathias; Huysmans, Marijke; Batelaan, Okke

2015-08-01

Adequate aquifer characterization and simulation using heat transport models are indispensible for determining the optimal design for aquifer thermal energy storage (ATES) systems and wells. Recent model studies indicate that meter-scale heterogeneities in the hydraulic conductivity field introduce a considerable uncertainty in the distribution of thermal energy around an ATES system and can lead to a reduction in the thermal recoverability. In a study site in Bierbeek, Belgium, the influence of centimeter-scale clay drapes on the efficiency of a doublet ATES system and the distribution of the thermal energy around the ATES wells are quantified. Multiple-point geostatistical simulation of edge properties is used to incorporate the clay drapes in the models. The results show that clay drapes have an influence both on the distribution of thermal energy in the subsurface and on the efficiency of the ATES system. The distribution of the thermal energy is determined by the strike of the clay drapes, with the major axis of anisotropy parallel to the clay drape strike. The clay drapes have a negative impact (3.3-3.6 %) on the energy output in the models without a hydraulic gradient. In the models with a hydraulic gradient, however, the presence of clay drapes has a positive influence (1.6-10.2 %) on the energy output of the ATES system. It is concluded that it is important to incorporate small-scale heterogeneities in heat transport models to get a better estimate on ATES efficiency and distribution of thermal energy.

Geostatistics-based groundwater-level monitoring network design and its application to the Upper Floridan aquifer, USA.

PubMed

Bhat, Shirish; Motz, Louis H; Pathak, Chandra; Kuebler, Laura

2015-01-01

A geostatistical method was applied to optimize an existing groundwater-level monitoring network in the Upper Floridan aquifer for the South Florida Water Management District in the southeastern United States. Analyses were performed to determine suitable numbers and locations of monitoring wells that will provide equivalent or better quality groundwater-level data compared to an existing monitoring network. Ambient, unadjusted groundwater heads were expressed as salinity-adjusted heads based on the density of freshwater, well screen elevations, and temperature-dependent saline groundwater density. The optimization of the numbers and locations of monitoring wells is based on a pre-defined groundwater-level prediction error. The newly developed network combines an existing network with the addition of new wells that will result in a spatial distribution of groundwater monitoring wells that better defines the regional potentiometric surface of the Upper Floridan aquifer in the study area. The network yields groundwater-level predictions that differ significantly from those produced using the existing network. The newly designed network will reduce the mean prediction standard error by 43% compared to the existing network. The adoption of a hexagonal grid network for the South Florida Water Management District is recommended to achieve both a uniform level of information about groundwater levels and the minimum required accuracy. It is customary to install more monitoring wells for observing groundwater levels and groundwater quality as groundwater development progresses. However, budget constraints often force water managers to implement cost-effective monitoring networks. In this regard, this study provides guidelines to water managers concerned with groundwater planning and monitoring.

Using Moran's I and GIS to study spatial pattern of forest litter carbon density in typical subtropical region, China

NASA Astrophysics Data System (ADS)

Fu, W. J.; Jiang, P. K.; Zhou, G. M.; Zhao, K. L.

2013-12-01

The spatial variation of forest litter carbon (FLC) density in the typical subtropical forests in southeast China was investigated using Moran's I, geostatistics and a geographical information system (GIS). A total of 839 forest litter samples were collected based on a 12 km (South-North) × 6 km (East-West) grid system in Zhejiang Province. Forest litter carbon density values were very variable, ranging from 10.2 kg ha-1 to 8841.3 kg ha-1, with an average of 1786.7 kg ha-1. The aboveground biomass had the strongest positive correlation with FLC density, followed by forest age and elevation. Global Moran's I revealed that FLC density had significant positive spatial autocorrelation. Clear spatial patterns were observed using Local Moran's I. A spherical model was chosen to fit the experimental semivariogram. The moderate "nugget-to-sill" (0.536) value revealed that both natural and anthropogenic factors played a key role in spatial heterogeneity of FLC density. High FLC density values were mainly distributed in northwestern and western part of Zhejiang province, which were related to adopting long-term policy of forest conservation in these areas. While Hang-Jia-Hu (HJH) Plain, Jin-Qu (JQ) basin and coastal areas had low FLC density due to low forest coverage and intensive management of economic forests. These spatial patterns in distribution map were in line with the spatial-cluster map described by local Moran's I. Therefore, Moran's I, combined with geostatistics and GIS could be used to study spatial patterns of environmental variables related to forest ecosystem.

A new algorithm combining geostatistics with the surrogate data approach to increase the accuracy of comparisons of point radiation measurements with cloud measurements

NASA Astrophysics Data System (ADS)

Venema, V. K. C.; Lindau, R.; Varnai, T.; Simmer, C.

2009-04-01

Two main groups of statistical methods used in the Earth sciences are geostatistics and stochastic modelling. Geostatistical methods, such as various kriging algorithms, aim at estimating the mean value for every point as well as possible. In case of sparse measurements, such fields have less variability at small scales and a narrower distribution as the true field. This can lead to biases if a nonlinear process is simulated on such a kriged field. Stochastic modelling aims at reproducing the structure of the data. One of the stochastic modelling methods, the so-called surrogate data approach, replicates the value distribution and power spectrum of a certain data set. However, while stochastic methods reproduce the statistical properties of the data, the location of the measurement is not considered. Because radiative transfer through clouds is a highly nonlinear process it is essential to model the distribution (e.g. of optical depth, extinction, liquid water content or liquid water path) accurately as well as the correlations in the cloud field because of horizontal photon transport. This explains the success of surrogate cloud fields for use in 3D radiative transfer studies. However, up to now we could only achieve good results for the radiative properties averaged over the field, but not for a radiation measurement located at a certain position. Therefore we have developed a new algorithm that combines the accuracy of stochastic (surrogate) modelling with the positioning capabilities of kriging. In this way, we can automatically profit from the large geostatistical literature and software. The algorithm is tested on cloud fields from large eddy simulations (LES). On these clouds a measurement is simulated. From the pseudo-measurement we estimated the distribution and power spectrum. Furthermore, the pseudo-measurement is kriged to a field the size of the final surrogate cloud. The distribution, spectrum and the kriged field are the inputs to the algorithm. This algorithm is similar to the standard iterative amplitude adjusted Fourier transform (IAAFT) algorithm, but has an additional iterative step in which the surrogate field is nudged towards the kriged field. The nudging strength is gradually reduced to zero. We work with four types of pseudo-measurements: one zenith pointing measurement (which together with the wind produces a line measurement), five zenith pointing measurements, a slow and a fast azimuth scan (which together with the wind produce spirals). Because we work with LES clouds and the truth is known, we can validate the algorithm by performing 3D radiative transfer calculations on the original LES clouds and on the new surrogate clouds. For comparison also the radiative properties of the kriged fields and standard surrogate fields are computed. Preliminary results already show that these new surrogate clouds reproduce the structure of the original clouds very well and the minima and maxima are located where the pseudo-measurements sees them. The main limitation seems to be the amount of data, which is especially very limited in case of just one zenith pointing measurement.

Geostatistical modeling of the gas emission zone and its in-place gas content for Pittsburgh-seam mines using sequential Gaussian simulation

USGS Publications Warehouse

Karacan, C.O.; Olea, R.A.; Goodman, G.

2012-01-01

Determination of the size of the gas emission zone, the locations of gas sources within, and especially the amount of gas retained in those zones is one of the most important steps for designing a successful methane control strategy and an efficient ventilation system in longwall coal mining. The formation of the gas emission zone and the potential amount of gas-in-place (GIP) that might be available for migration into a mine are factors of local geology and rock properties that usually show spatial variability in continuity and may also show geometric anisotropy. Geostatistical methods are used here for modeling and prediction of gas amounts and for assessing their associated uncertainty in gas emission zones of longwall mines for methane control.This study used core data obtained from 276 vertical exploration boreholes drilled from the surface to the bottom of the Pittsburgh coal seam in a mining district in the Northern Appalachian basin. After identifying important coal and non-coal layers for the gas emission zone, univariate statistical and semivariogram analyses were conducted for data from different formations to define the distribution and continuity of various attributes. Sequential simulations performed stochastic assessment of these attributes, such as gas content, strata thickness, and strata displacement. These analyses were followed by calculations of gas-in-place and their uncertainties in the Pittsburgh seam caved zone and fractured zone of longwall mines in this mining district. Grid blanking was used to isolate the volume over the actual panels from the entire modeled district and to calculate gas amounts that were directly related to the emissions in longwall mines.Results indicated that gas-in-place in the Pittsburgh seam, in the caved zone and in the fractured zone, as well as displacements in major rock units, showed spatial correlations that could be modeled and estimated using geostatistical methods. This study showed that GIP volumes may change up to 3. MMscf per acre and, in a multi-panel district, may total 9. Bcf of methane within the gas emission zone. Therefore, ventilation and gas capture systems should be designed accordingly. In addition, rock displacements within the gas emission zone are spatially distributed. From an engineering and practical point of view, spatial distributions of GIP and distributions of rock displacements should be correlated with in-mine emissions and gob gas venthole productions. ?? 2011.

Geostatistical modeling of the gas emission zone and its in-place gas content for Pittsburgh-seam mines using sequential Gaussian simulation

PubMed Central

Karacan, C. Özgen; Olea, Ricardo A.; Goodman, Gerrit

2015-01-01

Determination of the size of the gas emission zone, the locations of gas sources within, and especially the amount of gas retained in those zones is one of the most important steps for designing a successful methane control strategy and an efficient ventilation system in longwall coal mining. The formation of the gas emission zone and the potential amount of gas-in-place (GIP) that might be available for migration into a mine are factors of local geology and rock properties that usually show spatial variability in continuity and may also show geometric anisotropy. Geostatistical methods are used here for modeling and prediction of gas amounts and for assessing their associated uncertainty in gas emission zones of longwall mines for methane control. This study used core data obtained from 276 vertical exploration boreholes drilled from the surface to the bottom of the Pittsburgh coal seam in a mining district in the Northern Appalachian basin. After identifying important coal and non-coal layers for the gas emission zone, univariate statistical and semivariogram analyses were conducted for data from different formations to define the distribution and continuity of various attributes. Sequential simulations performed stochastic assessment of these attributes, such as gas content, strata thickness, and strata displacement. These analyses were followed by calculations of gas-in-place and their uncertainties in the Pittsburgh seam caved zone and fractured zone of longwall mines in this mining district. Grid blanking was used to isolate the volume over the actual panels from the entire modeled district and to calculate gas amounts that were directly related to the emissions in longwall mines. Results indicated that gas-in-place in the Pittsburgh seam, in the caved zone and in the fractured zone, as well as displacements in major rock units, showed spatial correlations that could be modeled and estimated using geostatistical methods. This study showed that GIP volumes may change up to 3 MMscf per acre and, in a multi-panel district, may total 9 Bcf of methane within the gas emission zone. Therefore, ventilation and gas capture systems should be designed accordingly. In addition, rock displacements within the gas emission zone are spatially distributed. From an engineering and practical point of view, spatial distributions of GIP and distributions of rock displacements should be correlated with in-mine emissions and gob gas venthole productions. PMID:26435558

Spatial assessment of soil contamination by heavy metals from informal electronic waste recycling in Agbogbloshie, Ghana

PubMed Central

Greve, Klaus; Atiemo, Sampson M.

2016-01-01

Objectives This study examined the spatial distribution and the extent of soil contamination by heavy metals resulting from primitive, unconventional informal electronic waste recycling in the Agbogbloshie e-waste processing site (AEPS) in Ghana. Methods A total of 132 samples were collected at 100 m intervals, with a handheld global position system used in taking the location data of the soil sample points. Observing all procedural and quality assurance measures, the samples were analyzed for barium (Ba), cadmium (Cd), cobalt (Co), chromium (Cr), copper (Cu), mercury (Hg), nickel (Ni), lead (Pb), and zinc (Zn), using X-ray fluorescence. Using environmental risk indices of contamination factor and degree of contamination (Cdeg), we analyzed the individual contribution of each heavy metal contamination and the overall Cdeg. We further used geostatistical techniques of spatial autocorrelation and variability to examine spatial distribution and extent of heavy metal contamination. Results Results from soil analysis showed that heavy metal concentrations were significantly higher than the Canadian Environmental Protection Agency and Dutch environmental standards. In an increasing order, Pb>Cd>Hg>Cu>Zn>Cr>Co>Ba>Ni contributed significantly to the overall Cdeg. Contamination was highest in the main working areas of burning and dismantling sites, indicating the influence of recycling activities. Geostatistical analysis also revealed that heavy metal contamination spreads beyond the main working areas to residential, recreational, farming, and commercial areas. Conclusions Our results show that the studied heavy metals are ubiquitous within AEPS and the significantly high concentration of these metals reflect the contamination factor and Cdeg, indicating soil contamination in AEPS with the nine heavy metals studied. PMID:26987962

Measuring and predicting reservoir heterogeneity in complex deposystems: The fluvial-deltaic Big Injun sandstone in West Virginia

DOE Office of Scientific and Technical Information (OSTI.GOV)

Patchen, D.G.; Hohn, M.E.; Aminian, K.

1993-04-01

The purpose of this research is to develop techniques to measure and predict heterogeneities in oil reservoirs that are the products of complex deposystems. The unit chosen for study is the Lower Mississippian Big Injun sandstone, a prolific oil producer (nearly 60 fields) in West Virginia. This research effort has been designed and is being implemented as an integrated effort involving stratigraphy, structural geology, petrology, seismic study, petroleum engineering, modeling and geostatistics. Sandstone bodies are being mapped within their regional depositional systems, and then sandstone bodies are being classified in a scheme of relative heterogeneity to determine heterogeneity across depositionalmore » systems. Facies changes are being mapped within given reservoirs, and the environments of deposition responsible for each facies are being interpreted to predict the inherent relative heterogeneity of each facies. Structural variations will be correlated both with production, where the availability of production data will permit, and with variations in geologic and engineering parameters that affect production. A reliable seismic model of the Big Injun reservoirs in Granny Creek field is being developed to help interpret physical heterogeneity in that field. Pore types are being described and related to permeability, fluid flow and diagenesis, and petrographic data are being integrated with facies and depositional environments to develop a technique to use diagenesis as a predictive tool in future reservoir development. Another objective in the Big Injun study is to determine the effect of heterogeneity on fluid flow and efficient hydrocarbon recovery in order to improve reservoir management. Graphical methods will be applied to Big Injun production data and new geostatistical methods will be developed to detect regional trends in heterogeneity.« less

Measuring and predicting reservoir heterogeneity in complex deposystems: The fluvial-deltaic Big Injun sandstone in West Virginia. Annual report, September 20, 1991--September 20, 1992

DOE Office of Scientific and Technical Information (OSTI.GOV)

Patchen, D.G.; Hohn, M.E.; Aminian, K.

1993-04-01

The purpose of this research is to develop techniques to measure and predict heterogeneities in oil reservoirs that are the products of complex deposystems. The unit chosen for study is the Lower Mississippian Big Injun sandstone, a prolific oil producer (nearly 60 fields) in West Virginia. This research effort has been designed and is being implemented as an integrated effort involving stratigraphy, structural geology, petrology, seismic study, petroleum engineering, modeling and geostatistics. Sandstone bodies are being mapped within their regional depositional systems, and then sandstone bodies are being classified in a scheme of relative heterogeneity to determine heterogeneity across depositionalmore » systems. Facies changes are being mapped within given reservoirs, and the environments of deposition responsible for each facies are being interpreted to predict the inherent relative heterogeneity of each facies. Structural variations will be correlated both with production, where the availability of production data will permit, and with variations in geologic and engineering parameters that affect production. A reliable seismic model of the Big Injun reservoirs in Granny Creek field is being developed to help interpret physical heterogeneity in that field. Pore types are being described and related to permeability, fluid flow and diagenesis, and petrographic data are being integrated with facies and depositional environments to develop a technique to use diagenesis as a predictive tool in future reservoir development. Another objective in the Big Injun study is to determine the effect of heterogeneity on fluid flow and efficient hydrocarbon recovery in order to improve reservoir management. Graphical methods will be applied to Big Injun production data and new geostatistical methods will be developed to detect regional trends in heterogeneity.« less

Spatial assessment of soil contamination by heavy metals from informal electronic waste recycling in Agbogbloshie, Ghana.

PubMed

Kyere, Vincent Nartey; Greve, Klaus; Atiemo, Sampson M

2016-01-01

This study examined the spatial distribution and the extent of soil contamination by heavy metals resulting from primitive, unconventional informal electronic waste recycling in the Agbogbloshie e-waste processing site (AEPS) in Ghana. A total of 132 samples were collected at 100 m intervals, with a handheld global position system used in taking the location data of the soil sample points. Observing all procedural and quality assurance measures, the samples were analyzed for barium (Ba), cadmium (Cd), cobalt (Co), chromium (Cr), copper (Cu), mercury (Hg), nickel (Ni), lead (Pb), and zinc (Zn), using X-ray fluorescence. Using environmental risk indices of contamination factor and degree of contamination (C deg ), we analyzed the individual contribution of each heavy metal contamination and the overall C deg . We further used geostatistical techniques of spatial autocorrelation and variability to examine spatial distribution and extent of heavy metal contamination. Results from soil analysis showed that heavy metal concentrations were significantly higher than the Canadian Environmental Protection Agency and Dutch environmental standards. In an increasing order, Pb>Cd>Hg>Cu>Zn>Cr>Co>Ba>Ni contributed significantly to the overall C deg . Contamination was highest in the main working areas of burning and dismantling sites, indicating the influence of recycling activities. Geostatistical analysis also revealed that heavy metal contamination spreads beyond the main working areas to residential, recreational, farming, and commercial areas. Our results show that the studied heavy metals are ubiquitous within AEPS and the significantly high concentration of these metals reflect the contamination factor and C deg , indicating soil contamination in AEPS with the nine heavy metals studied.

Sampling design optimisation for rainfall prediction using a non-stationary geostatistical model

NASA Astrophysics Data System (ADS)

Wadoux, Alexandre M. J.-C.; Brus, Dick J.; Rico-Ramirez, Miguel A.; Heuvelink, Gerard B. M.

2017-09-01

The accuracy of spatial predictions of rainfall by merging rain-gauge and radar data is partly determined by the sampling design of the rain-gauge network. Optimising the locations of the rain-gauges may increase the accuracy of the predictions. Existing spatial sampling design optimisation methods are based on minimisation of the spatially averaged prediction error variance under the assumption of intrinsic stationarity. Over the past years, substantial progress has been made to deal with non-stationary spatial processes in kriging. Various well-documented geostatistical models relax the assumption of stationarity in the mean, while recent studies show the importance of considering non-stationarity in the variance for environmental processes occurring in complex landscapes. We optimised the sampling locations of rain-gauges using an extension of the Kriging with External Drift (KED) model for prediction of rainfall fields. The model incorporates both non-stationarity in the mean and in the variance, which are modelled as functions of external covariates such as radar imagery, distance to radar station and radar beam blockage. Spatial predictions are made repeatedly over time, each time recalibrating the model. The space-time averaged KED variance was minimised by Spatial Simulated Annealing (SSA). The methodology was tested using a case study predicting daily rainfall in the north of England for a one-year period. Results show that (i) the proposed non-stationary variance model outperforms the stationary variance model, and (ii) a small but significant decrease of the rainfall prediction error variance is obtained with the optimised rain-gauge network. In particular, it pays off to place rain-gauges at locations where the radar imagery is inaccurate, while keeping the distribution over the study area sufficiently uniform.

Regional-specific Stochastic Simulation of Spatially-distributed Ground-motion Time Histories using Wavelet Packet Analysis

NASA Astrophysics Data System (ADS)

Huang, D.; Wang, G.

2014-12-01

Stochastic simulation of spatially distributed ground-motion time histories is important for performance-based earthquake design of geographically distributed systems. In this study, we develop a novel technique to stochastically simulate regionalized ground-motion time histories using wavelet packet analysis. First, a transient acceleration time history is characterized by wavelet-packet parameters proposed by Yamamoto and Baker (2013). The wavelet-packet parameters fully characterize ground-motion time histories in terms of energy content, time- frequency-domain characteristics and time-frequency nonstationarity. This study further investigates the spatial cross-correlations of wavelet-packet parameters based on geostatistical analysis of 1500 regionalized ground motion data from eight well-recorded earthquakes in California, Mexico, Japan and Taiwan. The linear model of coregionalization (LMC) is used to develop a permissible spatial cross-correlation model for each parameter group. The geostatistical analysis of ground-motion data from different regions reveals significant dependence of the LMC structure on regional site conditions, which can be characterized by the correlation range of Vs30 in each region. In general, the spatial correlation and cross-correlation of wavelet-packet parameters are stronger if the site condition is more homogeneous. Using the regional-specific spatial cross-correlation model and cokriging technique, wavelet packet parameters at unmeasured locations can be best estimated, and regionalized ground-motion time histories can be synthesized. Case studies and blind tests demonstrated that the simulated ground motions generally agree well with the actual recorded data, if the influence of regional-site conditions is considered. The developed method has great potential to be used in computational-based seismic analysis and loss estimation in a regional scale.

Effective sampling range of a synthetic protein-based attractant for Ceratitis capitata (Diptera: Tephritidae).

PubMed

Epsky, Nancy D; Espinoza, Hernán R; Kendra, Paul E; Abernathy, Robert; Midgarden, David; Heath, Robert R

2010-10-01

Studies were conducted in Honduras to determine effective sampling range of a female-targeted protein-based synthetic attractant for the Mediterranean fruit fly, Ceratitis capitata (Wiedemann) (Diptera: Tephritidae). Multilure traps were baited with ammonium acetate, putrescine, and trimethylamine lures (three-component attractant) and sampled over eight consecutive weeks. Field design consisted of 38 traps (over 0.5 ha) placed in a combination of standard and high-density grids to facilitate geostatistical analysis, and tests were conducted in coffee (Coffea arabica L.),mango (Mangifera indica L.),and orthanique (Citrus sinensis X Citrus reticulata). Effective sampling range, as determined from the range parameter obtained from experimental variograms that fit a spherical model, was approximately 30 m for flies captured in tests in coffee or mango and approximately 40 m for flies captured in orthanique. For comparison, a release-recapture study was conducted in mango using wild (field-collected) mixed sex C. capitata and an array of 20 baited traps spaced 10-50 m from the release point. Contour analysis was used to document spatial distribution of fly recaptures and to estimate effective sampling range, defined by the area that encompassed 90% of the recaptures. With this approach, effective range of the three-component attractant was estimated to be approximately 28 m, similar to results obtained from variogram analysis. Contour maps indicated that wind direction had a strong influence on sampling range, which was approximately 15 m greater upwind compared with downwind from the release point. Geostatistical analysis of field-captured insects in appropriately designed trapping grids may provide a supplement or alternative to release-recapture studies to estimate sampling ranges for semiochemical-based trapping systems.

Comparative Geostatistical Analysis of Flowmeter and Direct-Push Hydraulic Conductivity Profiles at the MADE Site

NASA Astrophysics Data System (ADS)

Bohling, G.; Liu, G.; Knobbe, S. J.; Reboulet, E. C.; Hyndman, D. W.; Dietrich, P.; Butler, J. J.

2010-12-01

Spatial variations in hydraulic conductivity (K) are a critical control on subsurface solute transport. Characterization of such variations at the resolution (cm to dm) required for transport investigations, however, has proven to be a formidable challenge. A new generation of direct-push (DP) tools has now been developed for the characterization of vertical K variations at this resolution. These tools, which can be run in high- (0.015-m) and low- (0.4 m) resolution modes, were recently applied to the extensively studied and highly heterogeneous MADE site. Results from a geostatistical analysis of 64 DP K profiles compare favorably with the flowmeter K data that have served as the primary basis for previous MADE studies. The global statistics of the low-resolution DP and flowmeter K data are in excellent agreement. The correlation structures for the high-resolution DP data show excellent agreement with those computed from the flowmeter data. However, the geometric mean DP K value for high-resolution profiling is roughly one order of magnitude lower than the geometric mean flowmeter K value, possibly as a result of the biases inherent in each approach compounded with differences in the areal distribution of flowmeter and DP profile locations. A DP profile through the MADE aquifer to a depth of 12 m can be completed as rapidly as 1.5-2 hours, a small fraction of the time required to obtain a single flowmeter profile when well drilling, installation, and development are considered. The results of this study demonstrate that DP profiling is a practically feasible approach for characterization of spatial variations in K at the resolution required for transport investigations in highly heterogeneous systems.

Network analysis reveals multiscale controls on streamwater chemistry

Treesearch

Kevin J. McGuire; Christian E. Torgersen; Gene E. Likens; Donald C. Buso; Winsor H. Lowe; Scott W. Bailey

2014-01-01

By coupling synoptic data from a basin-wide assessment of streamwater chemistry with network-based geostatistical analysis, we show that spatial processes differentially affect biogeochemical condition and pattern across a headwater stream network. We analyzed a high-resolution dataset consisting of 664 water samples collected every 100 m throughout 32 tributaries in...

Geostatistical Modeling of the Spatial Distribution of Sediment Oxygen Demand Within a Coastal Plain Blackwater Watershed

USDA-ARS?s Scientific Manuscript database

Blackwater streams of the Georgia Coastal Plain are often listed as impaired due to chronically low DO levels. Previous research has shown that high sediment oxygen demand (SOD) values, a hypothesized cause of lowered DO within these waters, are significantly positively correlated with TOC within th...

Rtop - an R package for interpolation of data with a variable spatial support - examples from river networks

NASA Astrophysics Data System (ADS)

Olav Skøien, Jon; Laaha, Gregor; Koffler, Daniel; Blöschl, Günter; Pebesma, Edzer; Parajka, Juraj; Viglione, Alberto

2013-04-01

Geostatistical methods have been applied only to a limited extent for spatial interpolation in applications where the observations have an irregular support, such as runoff characteristics or population health data. Several studies have shown the potential of such methods (Gottschalk 1993, Sauquet et al. 2000, Gottschalk et al. 2006, Skøien et al. 2006, Goovaerts 2008), but these developments have so far not led to easily accessible, versatile, easy to apply and open source software. Based on the top-kriging approach suggested by Skøien et al. (2006), we will here present the package rtop, which has been implemented in the statistical environment R (R Core Team 2012). Taking advantage of the existing methods in R for analysis of spatial objects (Bivand et al. 2008), and the extensive possibilities for visualizing the results, rtop makes it easy to apply geostatistical interpolation methods when observations have a non-point spatial support. Although the package is flexible regarding data input, the main application so far has been for interpolation along river networks. We will present some examples showing how the package can easily be used for such interpolation. The model will soon be uploaded to CRAN, but is in the meantime also available from R-forge and can be installed by: > install.packages("rtop", repos="http://R-Forge.R-project.org") Bivand, R.S., Pebesma, E.J. & Gómez-Rubio, V., 2008. Applied spatial data analysis with r: Springer. Goovaerts, P., 2008. Kriging and semivariogram deconvolution in the presence of irregular geographical units. Mathematical Geosciences, 40 (1), 101-128. Gottschalk, L., 1993. Interpolation of runoff applying objective methods. Stochastic Hydrology and Hydraulics, 7, 269-281. Gottschalk, L., Krasovskaia, I., Leblois, E. & Sauquet, E., 2006. Mapping mean and variance of runoff in a river basin. Hydrology and Earth System Sciences, 10, 469-484. R Core Team, 2012. R: A language and environment for statistical computing. Vienna, Austria, ISBN 3-900051-07-0. Sauquet, E., Gottschalk, L. & Leblois, E., 2000. Mapping average annual runoff: A hierarchical approach applying a stochastic interpolation scheme. Hydrological Sciences Journal, 45 (6), 799-815. Skøien, J.O., Merz, R. & Blöschl, G., 2006. Top-kriging - geostatistics on stream networks. Hydrology and Earth System Sciences, 10, 277-287.

Assessing efficiency of spatial sampling using combined coverage analysis in geographical and feature spaces

NASA Astrophysics Data System (ADS)

Hengl, Tomislav

2015-04-01

Efficiency of spatial sampling largely determines success of model building. This is especially important for geostatistical mapping where an initial sampling plan should provide a good representation or coverage of both geographical (defined by the study area mask map) and feature space (defined by the multi-dimensional covariates). Otherwise the model will need to extrapolate and, hence, the overall uncertainty of the predictions will be high. In many cases, geostatisticians use point data sets which are produced using unknown or inconsistent sampling algorithms. Many point data sets in environmental sciences suffer from spatial clustering and systematic omission of feature space. But how to quantify these 'representation' problems and how to incorporate this knowledge into model building? The author has developed a generic function called 'spsample.prob' (Global Soil Information Facilities package for R) and which simultaneously determines (effective) inclusion probabilities as an average between the kernel density estimation (geographical spreading of points; analysed using the spatstat package in R) and MaxEnt analysis (feature space spreading of points; analysed using the MaxEnt software used primarily for species distribution modelling). The output 'iprob' map indicates whether the sampling plan has systematically missed some important locations and/or features, and can also be used as an input for geostatistical modelling e.g. as a weight map for geostatistical model fitting. The spsample.prob function can also be used in combination with the accessibility analysis (cost of field survey are usually function of distance from the road network, slope and land cover) to allow for simultaneous maximization of average inclusion probabilities and minimization of total survey costs. The author postulates that, by estimating effective inclusion probabilities using combined geographical and feature space analysis, and by comparing survey costs to representation efficiency, an optimal initial sampling plan can be produced which satisfies both criteria: (a) good representation (i.e. within a tolerance threshold), and (b) minimized survey costs. This sampling analysis framework could become especially interesting for generating sampling plans in new areas e.g. for which no previous spatial prediction model exists. The presentation includes data processing demos with standard soil sampling data sets Ebergotzen (Germany) and Edgeroi (Australia), also available via the GSIF package.

Application of spatial technology in malaria research & control: some new insights.

PubMed

Saxena, Rekha; Nagpal, B N; Srivastava, Aruna; Gupta, S K; Dash, A P

2009-08-01

Geographical information System (GIS) has emerged as the core of the spatial technology which integrates wide range of dataset available from different sources including Remote Sensing (RS) and Global Positioning System (GPS). Literature published during the decade (1998-2007) has been compiled and grouped into six categories according to the usage of the technology in malaria epidemiology. Different GIS modules like spatial data sources, mapping and geo-processing tools, distance calculation, digital elevation model (DEM), buffer zone and geo-statistical analysis have been investigated in detail, illustrated with examples as per the derived results. These GIS tools have contributed immensely in understanding the epidemiological processes of malaria and examples drawn have shown that GIS is now widely used for research and decision making in malaria control. Statistical data analysis currently is the most consistent and established set of tools to analyze spatial datasets. The desired future development of GIS is in line with the utilization of geo-statistical tools which combined with high quality data has capability to provide new insight into malaria epidemiology and the complexity of its transmission potential in endemic areas.

Area-to-point regression kriging for pan-sharpening

NASA Astrophysics Data System (ADS)

Wang, Qunming; Shi, Wenzhong; Atkinson, Peter M.

2016-04-01

Pan-sharpening is a technique to combine the fine spatial resolution panchromatic (PAN) band with the coarse spatial resolution multispectral bands of the same satellite to create a fine spatial resolution multispectral image. In this paper, area-to-point regression kriging (ATPRK) is proposed for pan-sharpening. ATPRK considers the PAN band as the covariate. Moreover, ATPRK is extended with a local approach, called adaptive ATPRK (AATPRK), which fits a regression model using a local, non-stationary scheme such that the regression coefficients change across the image. The two geostatistical approaches, ATPRK and AATPRK, were compared to the 13 state-of-the-art pan-sharpening approaches summarized in Vivone et al. (2015) in experiments on three separate datasets. ATPRK and AATPRK produced more accurate pan-sharpened images than the 13 benchmark algorithms in all three experiments. Unlike the benchmark algorithms, the two geostatistical solutions precisely preserved the spectral properties of the original coarse data. Furthermore, ATPRK can be enhanced by a local scheme in AATRPK, in cases where the residuals from a global regression model are such that their spatial character varies locally.

Fast Geostatistical Inversion using Randomized Matrix Decompositions and Sketchings for Heterogeneous Aquifer Characterization

NASA Astrophysics Data System (ADS)

O'Malley, D.; Le, E. B.; Vesselinov, V. V.

2015-12-01

We present a fast, scalable, and highly-implementable stochastic inverse method for characterization of aquifer heterogeneity. The method utilizes recent advances in randomized matrix algebra and exploits the structure of the Quasi-Linear Geostatistical Approach (QLGA), without requiring a structured grid like Fast-Fourier Transform (FFT) methods. The QLGA framework is a more stable version of Gauss-Newton iterates for a large number of unknown model parameters, but provides unbiased estimates. The methods are matrix-free and do not require derivatives or adjoints, and are thus ideal for complex models and black-box implementation. We also incorporate randomized least-square solvers and data-reduction methods, which speed up computation and simulate missing data points. The new inverse methodology is coded in Julia and implemented in the MADS computational framework (http://mads.lanl.gov). Julia is an advanced high-level scientific programing language that allows for efficient memory management and utilization of high-performance computational resources. Inversion results based on series of synthetic problems with steady-state and transient calibration data are presented.

The applications of model-based geostatistics in helminth epidemiology and control.

PubMed

Magalhães, Ricardo J Soares; Clements, Archie C A; Patil, Anand P; Gething, Peter W; Brooker, Simon

2011-01-01

Funding agencies are dedicating substantial resources to tackle helminth infections. Reliable maps of the distribution of helminth infection can assist these efforts by targeting control resources to areas of greatest need. The ability to define the distribution of infection at regional, national and subnational levels has been enhanced greatly by the increased availability of good quality survey data and the use of model-based geostatistics (MBG), enabling spatial prediction in unsampled locations. A major advantage of MBG risk mapping approaches is that they provide a flexible statistical platform for handling and representing different sources of uncertainty, providing plausible and robust information on the spatial distribution of infections to inform the design and implementation of control programmes. Focussing on schistosomiasis and soil-transmitted helminthiasis, with additional examples for lymphatic filariasis and onchocerciasis, we review the progress made to date with the application of MBG tools in large-scale, real-world control programmes and propose a general framework for their application to inform integrative spatial planning of helminth disease control programmes. Copyright © 2011 Elsevier Ltd. All rights reserved.

Estimating the volume and age of water stored in global lakes using a geo-statistical approach

PubMed Central

Messager, Mathis Loïc; Lehner, Bernhard; Grill, Günther; Nedeva, Irena; Schmitt, Oliver

2016-01-01

Lakes are key components of biogeochemical and ecological processes, thus knowledge about their distribution, volume and residence time is crucial in understanding their properties and interactions within the Earth system. However, global information is scarce and inconsistent across spatial scales and regions. Here we develop a geo-statistical model to estimate the volume of global lakes with a surface area of at least 10 ha based on the surrounding terrain information. Our spatially resolved database shows 1.42 million individual polygons of natural lakes with a total surface area of 2.67 × 106 km2 (1.8% of global land area), a total shoreline length of 7.2 × 106 km (about four times longer than the world's ocean coastline) and a total volume of 181.9 × 103 km3 (0.8% of total global non-frozen terrestrial water stocks). We also compute mean and median hydraulic residence times for all lakes to be 1,834 days and 456 days, respectively. PMID:27976671

Kriging in the Shadows: Geostatistical Interpolation for Remote Sensing

NASA Technical Reports Server (NTRS)

Rossi, Richard E.; Dungan, Jennifer L.; Beck, Louisa R.

1994-01-01

It is often useful to estimate obscured or missing remotely sensed data. Traditional interpolation methods, such as nearest-neighbor or bilinear resampling, do not take full advantage of the spatial information in the image. An alternative method, a geostatistical technique known as indicator kriging, is described and demonstrated using a Landsat Thematic Mapper image in southern Chiapas, Mexico. The image was first classified into pasture and nonpasture land cover. For each pixel that was obscured by cloud or cloud shadow, the probability that it was pasture was assigned by the algorithm. An exponential omnidirectional variogram model was used to characterize the spatial continuity of the image for use in the kriging algorithm. Assuming a cutoff probability level of 50%, the error was shown to be 17% with no obvious spatial bias but with some tendency to categorize nonpasture as pasture (overestimation). While this is a promising result, the method's practical application in other missing data problems for remotely sensed images will depend on the amount and spatial pattern of the unobscured pixels and missing pixels and the success of the spatial continuity model used.

Evaluating susceptibility of karst dolines (sinkholes) for collapse in Sango, Tennessee, USA.

PubMed

Siska, Peter P; Goovaerts, Pierre; Hung, I-K

2016-08-01

Dolines or sinkholes are earth depressions that develop in soluble rocks complexes such as limestone, dolomite, gypsum, anhydrite, and halite; dolines appear in a variety of shapes from nearly circular to complex structures with highly curved perimeters. The occurrence of dolines in the studied karst area is not random; they are the results of geomorphic, hydrologic, and chemical processes that have caused partial subsidence, even the total collapse of the land surface when voids and caves are present in the bedrock and the regolith arch overbridging these voids is unstable. In the study area, the majority of collapses occur in the regolith (bedrock cover) that bridges voids in the bedrock. Because these collapsing dolines may result in property damage and even cause the loss of lives, there is a need to develop methods for evaluating karst hazards. These methods can then be used by planners and practitioners for urban and economic development, especially in regions with a growing population. The purpose of this project is threefold: 1) to develop a karst feature database, 2) to investigate critical indicators associated with doline collapse, and 3) to develop a doline susceptibility model for potential doline collapse based on external morphometric data. The study has revealed the presence of short range spatial dependence in the distribution of the dolines' morphometric parameters such as circularity, the geographic orientation of the main doline axes, and the length-to-width doline ratios; therefore, geostatistics can be used to spatially evaluate the susceptibility of the karst area for doline collapse. The partial susceptibility estimates were combined into a final probability map enabling the identification of areas where, until now, undetected dolines may cause significant hazards.

Evaluating susceptibility of karst dolines (sinkholes) for collapse in Sango, Tennessee, USA

PubMed Central

Siska, Peter P.; Goovaerts, Pierre; Hung, I-K

2016-01-01

Dolines or sinkholes are earth depressions that develop in soluble rocks complexes such as limestone, dolomite, gypsum, anhydrite, and halite; dolines appear in a variety of shapes from nearly circular to complex structures with highly curved perimeters. The occurrence of dolines in the studied karst area is not random; they are the results of geomorphic, hydrologic, and chemical processes that have caused partial subsidence, even the total collapse of the land surface when voids and caves are present in the bedrock and the regolith arch overbridging these voids is unstable. In the study area, the majority of collapses occur in the regolith (bedrock cover) that bridges voids in the bedrock. Because these collapsing dolines may result in property damage and even cause the loss of lives, there is a need to develop methods for evaluating karst hazards. These methods can then be used by planners and practitioners for urban and economic development, especially in regions with a growing population. The purpose of this project is threefold: 1) to develop a karst feature database, 2) to investigate critical indicators associated with doline collapse, and 3) to develop a doline susceptibility model for potential doline collapse based on external morphometric data. The study has revealed the presence of short range spatial dependence in the distribution of the dolines’ morphometric parameters such as circularity, the geographic orientation of the main doline axes, and the length-to-width doline ratios; therefore, geostatistics can be used to spatially evaluate the susceptibility of the karst area for doline collapse. The partial susceptibility estimates were combined into a final probability map enabling the identification of areas where, until now, undetected dolines may cause significant hazards. PMID:27616807

Evaluation of GIS Technology in Assessing and Modeling Land Management Practices

NASA Technical Reports Server (NTRS)

Archer, F.; Coleman, T. L.; Manu, A.; Tadesse, W.; Liu, G.

1997-01-01

There is an increasing concern of land owners to protect and maintain healthy and sustainable agroecosystems through the implementation of best management practices (BMP). The objectives of this study were: (1) To develop and evaluate the use of a Geographic Information System (GIS) technology for enhancing field-scale management practices; (2) evaluate the use of 2-dimensional displays of the landscape and (3) define spatial classes of variables from interpretation of geostatistical parameters. Soil samples were collected to a depth of 2 m at 15 cm increments. Existing data from topographic, land use, and soil survey maps of the Winfred Thomas Agricultural Research Station were converted to digital format. Additional soils data which included texture, pH, and organic matter were also generated. The digitized parameters were used to create a multilayered field-scale GIS. Two dimensional (2-D) displays of the parameters were generated using the ARC/INFO software. The spatial distribution of the parameters evaluated in both fields were similar which could be attributed to the similarity in vegetation and surface elevation. The ratio of the nugget to total semivariance, expressed as a percentage, was used to assess the degree of spatial variability. The results indicated that most of the parameters were moderate spatially dependent Biophysical constraint maps were generated from the database layers, and used in multiple combination to visualize results of the BMP. Understanding the spatial relationships of physical and chemical parameters that exists within a field should enable land managers to more effectively implement BMP to ensure a safe and sustainable environment.

Preliminary Groundwater Simulations To Compare Different Reconstruction Methods of 3-d Alluvial Heterogeneity

NASA Astrophysics Data System (ADS)

Teles, V.; de Marsily, G.; Delay, F.; Perrier, E.

Alluvial floodplains are extremely heterogeneous aquifers, whose three-dimensional structures are quite difficult to model. In general, when representing such structures, the medium heterogeneity is modeled with classical geostatistical or Boolean meth- ods. Another approach, still in its infancy, is called the genetic method because it simulates the generation of the medium by reproducing sedimentary processes. We developed a new genetic model to obtain a realistic three-dimensional image of allu- vial media. It does not simulate the hydrodynamics of sedimentation but uses semi- empirical and statistical rules to roughly reproduce fluvial deposition and erosion. The main processes, either at the stream scale or at the plain scale, are modeled by simple rules applied to "sediment" entities or to conceptual "erosion" entities. The model was applied to a several kilometer long portion of the Aube River floodplain (France) and reproduced the deposition and erosion cycles that occurred during the inferred climate periods (15 000 BP to present). A three-dimensional image of the aquifer was gener- ated, by extrapolating the two-dimensional information collected on a cross-section of the floodplain. Unlike geostatistical methods, this extrapolation does not use a statis- tical spatial analysis of the data, but a genetic analysis, which leads to a more realistic structure. Groundwater flow and transport simulations in the alluvium were carried out with a three-dimensional flow code or simulator (MODFLOW), using different rep- resentations of the alluvial reservoir of the Aube River floodplain: first an equivalent homogeneous medium, and then different heterogeneous media built either with the traditional geostatistical approach simulating the permeability distribution, or with the new genetic model presented here simulating sediment facies. In the latter case, each deposited entity of a given lithology was assigned a constant hydraulic conductivity value. Results of these models have been compared to assess the value of the genetic approach and will be presented.

Model-Based Geostatistical Mapping of the Prevalence of Onchocerca volvulus in West Africa

PubMed Central

O’Hanlon, Simon J.; Slater, Hannah C.; Cheke, Robert A.; Boatin, Boakye A.; Coffeng, Luc E.; Pion, Sébastien D. S.; Boussinesq, Michel; Zouré, Honorat G. M.; Stolk, Wilma A.; Basáñez, María-Gloria

2016-01-01

Background The initial endemicity (pre-control prevalence) of onchocerciasis has been shown to be an important determinant of the feasibility of elimination by mass ivermectin distribution. We present the first geostatistical map of microfilarial prevalence in the former Onchocerciasis Control Programme in West Africa (OCP) before commencement of antivectorial and antiparasitic interventions. Methods and Findings Pre-control microfilarial prevalence data from 737 villages across the 11 constituent countries in the OCP epidemiological database were used as ground-truth data. These 737 data points, plus a set of statistically selected environmental covariates, were used in a Bayesian model-based geostatistical (B-MBG) approach to generate a continuous surface (at pixel resolution of 5 km x 5km) of microfilarial prevalence in West Africa prior to the commencement of the OCP. Uncertainty in model predictions was measured using a suite of validation statistics, performed on bootstrap samples of held-out validation data. The mean Pearson’s correlation between observed and estimated prevalence at validation locations was 0.693; the mean prediction error (average difference between observed and estimated values) was 0.77%, and the mean absolute prediction error (average magnitude of difference between observed and estimated values) was 12.2%. Within OCP boundaries, 17.8 million people were deemed to have been at risk, 7.55 million to have been infected, and mean microfilarial prevalence to have been 45% (range: 2–90%) in 1975. Conclusions and Significance This is the first map of initial onchocerciasis prevalence in West Africa using B-MBG. Important environmental predictors of infection prevalence were identified and used in a model out-performing those without spatial random effects or environmental covariates. Results may be compared with recent epidemiological mapping efforts to find areas of persisting transmission. These methods may be extended to areas where data are sparse, and may be used to help inform the feasibility of elimination with current and novel tools. PMID:26771545

Enhancing multiple-point geostatistical modeling: 1. Graph theory and pattern adjustment

NASA Astrophysics Data System (ADS)

Tahmasebi, Pejman; Sahimi, Muhammad

2016-03-01

In recent years, higher-order geostatistical methods have been used for modeling of a wide variety of large-scale porous media, such as groundwater aquifers and oil reservoirs. Their popularity stems from their ability to account for qualitative data and the great flexibility that they offer for conditioning the models to hard (quantitative) data, which endow them with the capability for generating realistic realizations of porous formations with very complex channels, as well as features that are mainly a barrier to fluid flow. One group of such models consists of pattern-based methods that use a set of data points for generating stochastic realizations by which the large-scale structure and highly-connected features are reproduced accurately. The cross correlation-based simulation (CCSIM) algorithm, proposed previously by the authors, is a member of this group that has been shown to be capable of simulating multimillion cell models in a matter of a few CPU seconds. The method is, however, sensitive to pattern's specifications, such as boundaries and the number of replicates. In this paper the original CCSIM algorithm is reconsidered and two significant improvements are proposed for accurately reproducing large-scale patterns of heterogeneities in porous media. First, an effective boundary-correction method based on the graph theory is presented by which one identifies the optimal cutting path/surface for removing the patchiness and discontinuities in the realization of a porous medium. Next, a new pattern adjustment method is proposed that automatically transfers the features in a pattern to one that seamlessly matches the surrounding patterns. The original CCSIM algorithm is then combined with the two methods and is tested using various complex two- and three-dimensional examples. It should, however, be emphasized that the methods that we propose in this paper are applicable to other pattern-based geostatistical simulation methods.

Spatial Probability Distribution of Strata's Lithofacies and its Impacts on Land Subsidence in Huairou Emergency Water Resources Region of Beijing

NASA Astrophysics Data System (ADS)

Li, Y.; Gong, H.; Zhu, L.; Guo, L.; Gao, M.; Zhou, C.

2016-12-01

Continuous over-exploitation of groundwater causes dramatic drawdown, and leads to regional land subsidence in the Huairou Emergency Water Resources region, which is located in the up-middle part of the Chaobai river basin of Beijing. Owing to the spatial heterogeneity of strata's lithofacies of the alluvial fan, ground deformation has no significant positive correlation with groundwater drawdown, and one of the challenges ahead is to quantify the spatial distribution of strata's lithofacies. The transition probability geostatistics approach provides potential for characterizing the distribution of heterogeneous lithofacies in the subsurface. Combined the thickness of clay layer extracted from the simulation, with deformation field acquired from PS-InSAR technology, the influence of strata's lithofacies on land subsidence can be analyzed quantitatively. The strata's lithofacies derived from borehole data were generalized into four categories and their probability distribution in the observe space was mined by using the transition probability geostatistics, of which clay was the predominant compressible material. Geologically plausible realizations of lithofacies distribution were produced, accounting for complex heterogeneity in alluvial plain. At a particular probability level of more than 40 percent, the volume of clay defined was 55 percent of the total volume of strata's lithofacies. This level, equaling nearly the volume of compressible clay derived from the geostatistics, was thus chosen to represent the boundary between compressible and uncompressible material. The method incorporates statistical geological information, such as distribution proportions, average lengths and juxtaposition tendencies of geological types, mainly derived from borehole data and expert knowledge, into the Markov chain model of transition probability. Some similarities of patterns were indicated between the spatial distribution of deformation field and clay layer. In the area with roughly similar water table decline, locations in the subsurface having a higher probability for the existence of compressible material occur more than that in the location with a lower probability. Such estimate of spatial probability distribution is useful to analyze the uncertainty of land subsidence.

Geostatistical Interpolation of Particle-Size Curves in Heterogeneous Aquifers

NASA Astrophysics Data System (ADS)

Guadagnini, A.; Menafoglio, A.; Secchi, P.

2013-12-01

We address the problem of predicting the spatial field of particle-size curves (PSCs) from measurements associated with soil samples collected at a discrete set of locations within an aquifer system. Proper estimates of the full PSC are relevant to applications related to groundwater hydrology, soil science and geochemistry and aimed at modeling physical and chemical processes occurring in heterogeneous earth systems. Hence, we focus on providing kriging estimates of the entire PSC at unsampled locations. To this end, we treat particle-size curves as cumulative distribution functions, model their densities as functional compositional data and analyze them by embedding these into the Hilbert space of compositional functions endowed with the Aitchison geometry. On this basis, we develop a new geostatistical methodology for the analysis of spatially dependent functional compositional data. Our functional compositional kriging (FCK) approach allows providing predictions at unsampled location of the entire particle-size curve, together with a quantification of the associated uncertainty, by fully exploiting both the functional form of the data and their compositional nature. This is a key advantage of our approach with respect to traditional methodologies, which treat only a set of selected features (e.g., quantiles) of PSCs. Embedding the full PSC into a geostatistical analysis enables one to provide a complete characterization of the spatial distribution of lithotypes in a reservoir, eventually leading to improved predictions of soil hydraulic attributes through pedotransfer functions as well as of soil geochemical parameters which are relevant in sorption/desorption and cation exchange processes. We test our new method on PSCs sampled along a borehole located within an alluvial aquifer near the city of Tuebingen, Germany. The quality of FCK predictions is assessed through leave-one-out cross-validation. A comparison between hydraulic conductivity estimates obtained via FCK approach and those predicted by classical kriging of effective particle diameters (i.e., quantiles of the PSCs) is finally performed.

DOE Office of Scientific and Technical Information (OSTI.GOV)

Hallenbeck, L.D.; Harpole, K.J.; Gerard, M.G.

The work reported here covers Budget Phase I of the project. The principal tasks in Budget Phase I are the Reservoir Analysis and Characterization Task and the Advanced Technology Definition Task. Completion of these tasks have enabled an optimum carbon dioxide (CO{sub 2}) flood project to be designed and evaluated from an economic and risk analysis standpoint. Field implementation of the project has been recommended to the working interest owner of the South Cowden Unit (SCU) and approval has been obtained. The current project has focused on reducing initial investment cost by utilizing horizontal injection wells and concentrating the projectmore » in the best productivity area of the field. An innovative CO{sub 2} purchase agreement (no take or pay requirements, CO{sub 2} purchase price tied to West Texas Intermediate crude oil price) and gas recycle agreements (expensing cost as opposed to large capital investments for compression) were negotiated to further improve project economics. A detailed reservoir characterization study was completed by an integrated team of geoscientists and engineers. The study consisted of detailed core description, integration of log response to core descriptions, mapping of the major flow units, evaluation of porosity and permeability relationships, geostatistical analysis of permeability trends, and direct integration of reservoir performance with the geological interpretation. The study methodology fostered iterative bidirectional feedback between the reservoir characterization team and the reservoir engineering/simulation team to allow simultaneous refinement and convergence of the geological interpretation with the reservoir model. The fundamental conclusion from the study is that South Cowden exhibits favorable enhanced oil recovery characteristics, particularly reservoir quality and continuity.« less

GIS, geostatistics, metadata banking, and tree-based models for data analysis and mapping in environmental monitoring and epidemiology.

PubMed

Schröder, Winfried

2006-05-01

By the example of environmental monitoring, some applications of geographic information systems (GIS), geostatistics, metadata banking, and Classification and Regression Trees (CART) are presented. These tools are recommended for mapping statistically estimated hot spots of vectors and pathogens. GIS were introduced as tools for spatially modelling the real world. The modelling can be done by mapping objects according to the spatial information content of data. Additionally, this can be supported by geostatistical and multivariate statistical modelling. This is demonstrated by the example of modelling marine habitats of benthic communities and of terrestrial ecoregions. Such ecoregionalisations may be used to predict phenomena based on the statistical relation between measurements of an interesting phenomenon such as, e.g., the incidence of medically relevant species and correlated characteristics of the ecoregions. The combination of meteorological data and data on plant phenology can enhance the spatial resolution of the information on climate change. To this end, meteorological and phenological data have to be correlated. To enable this, both data sets which are from disparate monitoring networks have to be spatially connected by means of geostatistical estimation. This is demonstrated by the example of transformation of site-specific data on plant phenology into surface data. The analysis allows for spatial comparison of the phenology during the two periods 1961-1990 and 1991-2002 covering whole Germany. The changes in both plant phenology and air temperature were proved to be statistically significant. Thus, they can be combined by GIS overlay technique to enhance the spatial resolution of the information on the climate change and use them for the prediction of vector incidences at the regional scale. The localisation of such risk hot spots can be done by geometrically merging surface data on promoting factors. This is demonstrated by the example of the transfer of heavy metals through soils. The predicted hot spots of heavy metal transfer can be validated empirically by measurement data which can be inquired by a metadata base linked with a geographic information system. A corresponding strategy for the detection of vector hot spots in medical epidemiology is recommended. Data on incidences and habitats of the Anophelinae in the marsh regions of Lower Saxony (Germany) were used to calculate a habitat model by CART, which together with climate data and data on ecoregions can be further used for the prediction of habitats of medically relevant vector species. In the future, this approach should be supported by an internet-based information system consisting of three components: metadata questionnaire, metadata base, and GIS to link metadata, surface data, and measurement data on incidences and habitats of medically relevant species and related data on climate, phenology, and ecoregional characteristic conditions.

Local Geostatistical Models and Big Data in Hydrological and Ecological Applications

NASA Astrophysics Data System (ADS)

Hristopulos, Dionissios

2015-04-01

The advent of the big data era creates new opportunities for environmental and ecological modelling but also presents significant challenges. The availability of remote sensing images and low-cost wireless sensor networks implies that spatiotemporal environmental data to cover larger spatial domains at higher spatial and temporal resolution for longer time windows. Handling such voluminous data presents several technical and scientific challenges. In particular, the geostatistical methods used to process spatiotemporal data need to overcome the dimensionality curse associated with the need to store and invert large covariance matrices. There are various mathematical approaches for addressing the dimensionality problem, including change of basis, dimensionality reduction, hierarchical schemes, and local approximations. We present a Stochastic Local Interaction (SLI) model that can be used to model local correlations in spatial data. SLI is a random field model suitable for data on discrete supports (i.e., regular lattices or irregular sampling grids). The degree of localization is determined by means of kernel functions and appropriate bandwidths. The strength of the correlations is determined by means of coefficients. In the "plain vanilla" version the parameter set involves scale and rigidity coefficients as well as a characteristic length. The latter determines in connection with the rigidity coefficient the correlation length of the random field. The SLI model is based on statistical field theory and extends previous research on Spartan spatial random fields [2,3] from continuum spaces to explicitly discrete supports. The SLI kernel functions employ adaptive bandwidths learned from the sampling spatial distribution [1]. The SLI precision matrix is expressed explicitly in terms of the model parameter and the kernel function. Hence, covariance matrix inversion is not necessary for parameter inference that is based on leave-one-out cross validation. This property helps to overcome a significant computational bottleneck of geostatistical models due to the poor scaling of the matrix inversion [4,5]. We present applications to real and simulated data sets, including the Walker lake data, and we investigate the SLI performance using various statistical cross validation measures. References [1] T. Hofmann, B. Schlkopf, A.J. Smola, Annals of Statistics, 36, 1171-1220 (2008). [2] D. T. Hristopulos, SIAM Journal on Scientific Computing, 24(6): 2125-2162 (2003). [3] D. T. Hristopulos and S. N. Elogne, IEEE Transactions on Signal Processing, 57(9): 3475-3487 (2009) [4] G. Jona Lasinio, G. Mastrantonio, and A. Pollice, Statistical Methods and Applications, 22(1):97-112 (2013) [5] Sun, Y., B. Li, and M. G. Genton (2012). Geostatistics for large datasets. In: Advances and Challenges in Space-time Modelling of Natural Events, Lecture Notes in Statistics, pp. 55-77. Springer, Berlin-Heidelberg.

Decorrelation distance of snow in the Colorado River Basin

NASA Technical Reports Server (NTRS)

Chang, A. T. C.; Chiu, L. S.

1989-01-01

The problem of estimating areal averages from point measurement has been extensively studied by mining engineers and hydrologists. Its application to satellite measurements has recently been introduced. The semivariaogram has been used in many geostatistical applications to estimate spatial structures of observed properties, such as mineral distributions. An examination is made of snow variations in Colorado from daily snow data collected in 11 SNOTEL stations. The associated semivariogram is estimated. The objective is to estimate the spatial structure of the snow field so that the point data can be used for comparison with, and validation for, satellite measurements.

Quantifying aggregated uncertainty in Plasmodium falciparum malaria prevalence and populations at risk via efficient space-time geostatistical joint simulation.

PubMed

Gething, Peter W; Patil, Anand P; Hay, Simon I

2010-04-01

Risk maps estimating the spatial distribution of infectious diseases are required to guide public health policy from local to global scales. The advent of model-based geostatistics (MBG) has allowed these maps to be generated in a formal statistical framework, providing robust metrics of map uncertainty that enhances their utility for decision-makers. In many settings, decision-makers require spatially aggregated measures over large regions such as the mean prevalence within a country or administrative region, or national populations living under different levels of risk. Existing MBG mapping approaches provide suitable metrics of local uncertainty--the fidelity of predictions at each mapped pixel--but have not been adapted for measuring uncertainty over large areas, due largely to a series of fundamental computational constraints. Here the authors present a new efficient approximating algorithm that can generate for the first time the necessary joint simulation of prevalence values across the very large prediction spaces needed for global scale mapping. This new approach is implemented in conjunction with an established model for P. falciparum allowing robust estimates of mean prevalence at any specified level of spatial aggregation. The model is used to provide estimates of national populations at risk under three policy-relevant prevalence thresholds, along with accompanying model-based measures of uncertainty. By overcoming previously unchallenged computational barriers, this study illustrates how MBG approaches, already at the forefront of infectious disease mapping, can be extended to provide large-scale aggregate measures appropriate for decision-makers.

A geostatistics-informed hierarchical sensitivity analysis method for complex groundwater flow and transport modeling

NASA Astrophysics Data System (ADS)

Dai, Heng; Chen, Xingyuan; Ye, Ming; Song, Xuehang; Zachara, John M.

2017-05-01

Sensitivity analysis is an important tool for development and improvement of mathematical models, especially for complex systems with a high dimension of spatially correlated parameters. Variance-based global sensitivity analysis has gained popularity because it can quantify the relative contribution of uncertainty from different sources. However, its computational cost increases dramatically with the complexity of the considered model and the dimension of model parameters. In this study, we developed a new sensitivity analysis method that integrates the concept of variance-based method with a hierarchical uncertainty quantification framework. Different uncertain inputs are grouped and organized into a multilayer framework based on their characteristics and dependency relationships to reduce the dimensionality of the sensitivity analysis. A set of new sensitivity indices are defined for the grouped inputs using the variance decomposition method. Using this methodology, we identified the most important uncertainty source for a dynamic groundwater flow and solute transport model at the Department of Energy (DOE) Hanford site. The results indicate that boundary conditions and permeability field contribute the most uncertainty to the simulated head field and tracer plume, respectively. The relative contribution from each source varied spatially and temporally. By using a geostatistical approach to reduce the number of realizations needed for the sensitivity analysis, the computational cost of implementing the developed method was reduced to a practically manageable level. The developed sensitivity analysis method is generally applicable to a wide range of hydrologic and environmental problems that deal with high-dimensional spatially distributed input variables.

Merging parallel tempering with sequential geostatistical resampling for improved posterior exploration of high-dimensional subsurface categorical fields

NASA Astrophysics Data System (ADS)

Laloy, Eric; Linde, Niklas; Jacques, Diederik; Mariethoz, Grégoire

2016-04-01

The sequential geostatistical resampling (SGR) algorithm is a Markov chain Monte Carlo (MCMC) scheme for sampling from possibly non-Gaussian, complex spatially-distributed prior models such as geologic facies or categorical fields. In this work, we highlight the limits of standard SGR for posterior inference of high-dimensional categorical fields with realistically complex likelihood landscapes and benchmark a parallel tempering implementation (PT-SGR). Our proposed PT-SGR approach is demonstrated using synthetic (error corrupted) data from steady-state flow and transport experiments in categorical 7575- and 10,000-dimensional 2D conductivity fields. In both case studies, every SGR trial gets trapped in a local optima while PT-SGR maintains an higher diversity in the sampled model states. The advantage of PT-SGR is most apparent in an inverse transport problem where the posterior distribution is made bimodal by construction. PT-SGR then converges towards the appropriate data misfit much faster than SGR and partly recovers the two modes. In contrast, for the same computational resources SGR does not fit the data to the appropriate error level and hardly produces a locally optimal solution that looks visually similar to one of the two reference modes. Although PT-SGR clearly surpasses SGR in performance, our results also indicate that using a small number (16-24) of temperatures (and thus parallel cores) may not permit complete sampling of the posterior distribution by PT-SGR within a reasonable computational time (less than 1-2 weeks).

A Geostatistics-Informed Hierarchical Sensitivity Analysis Method for Complex Groundwater Flow and Transport Modeling

NASA Astrophysics Data System (ADS)

Dai, H.; Chen, X.; Ye, M.; Song, X.; Zachara, J. M.

2017-12-01

Sensitivity analysis is an important tool for development and improvement of mathematical models, especially for complex systems with a high dimension of spatially correlated parameters. Variance-based global sensitivity analysis has gained popularity because it can quantify the relative contribution of uncertainty from different sources. However, its computational cost increases dramatically with the complexity of the considered model and the dimension of model parameters. In this study we developed a new sensitivity analysis method that integrates the concept of variance-based method with a hierarchical uncertainty quantification framework. Different uncertain inputs are grouped and organized into a multi-layer framework based on their characteristics and dependency relationships to reduce the dimensionality of the sensitivity analysis. A set of new sensitivity indices are defined for the grouped inputs using the variance decomposition method. Using this methodology, we identified the most important uncertainty source for a dynamic groundwater flow and solute transport model at the Department of Energy (DOE) Hanford site. The results indicate that boundary conditions and permeability field contribute the most uncertainty to the simulated head field and tracer plume, respectively. The relative contribution from each source varied spatially and temporally. By using a geostatistical approach to reduce the number of realizations needed for the sensitivity analysis, the computational cost of implementing the developed method was reduced to a practically manageable level. The developed sensitivity analysis method is generally applicable to a wide range of hydrologic and environmental problems that deal with high-dimensional spatially-distributed input variables.

Characterizing regional-scale temporal evolution of air dose rates after the Fukushima Daiichi Nuclear Power Plant accident.

PubMed

Wainwright, Haruko M; Seki, Akiyuki; Mikami, Satoshi; Saito, Kimiaki

2018-09-01

In this study, we quantify the temporal changes of air dose rates in the regional scale around the Fukushima Dai-ichi Nuclear Power Plant in Japan, and predict the spatial distribution of air dose rates in the future. We first apply the Bayesian geostatistical method developed by Wainwright et al. (2017) to integrate multiscale datasets including ground-based walk and car surveys, and airborne surveys, all of which have different scales, resolutions, spatial coverage, and accuracy. This method is based on geostatistics to represent spatial heterogeneous structures, and also on Bayesian hierarchical models to integrate multiscale, multi-type datasets in a consistent manner. We apply this method to the datasets from three years: 2014 to 2016. The temporal changes among the three integrated maps enables us to characterize the spatiotemporal dynamics of radiation air dose rates. The data-driven ecological decay model is then coupled with the integrated map to predict future dose rates. Results show that the air dose rates are decreasing consistently across the region. While slower in the forested region, the decrease is particularly significant in the town area. The decontamination has contributed to significant reduction of air dose rates. By 2026, the air dose rates will continue to decrease, and the area above 3.8 μSv/h will be almost fully contained within the non-residential forested zone. Copyright © 2018 Elsevier Ltd. All rights reserved.

CO 2 Sequestration and Enhanced Oil Recovery at Depleted Oil/Gas Reservoirs

DOE PAGES

Dai, Zhenxue; Viswanathan, Hari; Xiao, Ting; ...

2017-08-18

This study presents a quantitative evaluation of the operational and technical risks of an active CO 2-EOR project. A set of risk factor metrics is defined to post-process the Monte Carlo (MC) simulations for statistical analysis. The risk factors are expressed as measurable quantities that can be used to gain insight into project risk (e.g. environmental and economic risks) without the need to generate a rigorous consequence structure, which include (a) CO 2 injection rate, (b) net CO 2 injection rate, (c) cumulative CO 2 storage, (d) cumulative water injection, (e) oil production rate, (f) cumulative oil production, (g) cumulativemore » CH 4 production, and (h) CO 2 breakthrough time. The Morrow reservoir at the Farnsworth Unit (FWU) site, Texas, is used as an example for studying the multi-scale statistical approach for CO 2 accounting and risk analysis. A set of geostatistical-based MC simulations of CO 2-oil/gas-water flow and transport in the Morrow formation are conducted for evaluating the risk metrics. A response-surface-based economic model has been derived to calculate the CO 2-EOR profitability for the FWU site with a current oil price, which suggests that approximately 31% of the 1000 realizations can be profitable. If government carbon-tax credits are available, or the oil price goes up or CO 2 capture and operating expenses reduce, more realizations would be profitable.« less

Local deposition of mercury in topsoils around coal-fired power plants: is it always true?

PubMed

Rodriguez Martin, José Antonio; Nanos, Nikos; Grigoratos, Theodoros; Carbonell, Gregoria; Samara, Constantini

2014-09-01

Mercury (Hg) is a toxic element that is emitted to the atmosphere through human activities, mainly fossil fuel combustion. Hg accumulations in soil are associated with atmospheric deposition, while coal-burning power plants remain the most important source of anthropogenic mercury emissions. In this study, we analyzed the Hg concentration in the topsoil of the Kozani-Ptolemais basin where four coal-fired power plants (4,065 MW) run to provide 50 % of electricity in Greece. The study aimed to investigate the extent of soil contamination by Hg using geostatistical techniques to evaluate the presumed Hg enrichment around the four power plants. Hg variability in agricultural soils was evaluated using 276 soil samples from 92 locations covering an area of 1,000 km(2). We were surprised to find a low Hg content in soil (range 1-59 μg kg(-1)) and 50 % of samples with a concentration lower than 6 μg kg(-1). The influence of mercury emissions from the four coal-fired power plants on soil was poor or virtually nil. We associate this effect with low Hg contents in the coal (1.5-24.5 μg kg(-1)) used in the combustion of these power plants (one of the most Hg-poor in the world). Despite anthropic activity in the area, we conclude that Hg content in the agricultural soils of the Kozani-Ptolemais basin is present in low concentrations.

CO 2 Sequestration and Enhanced Oil Recovery at Depleted Oil/Gas Reservoirs

DOE Office of Scientific and Technical Information (OSTI.GOV)

Dai, Zhenxue; Viswanathan, Hari; Xiao, Ting

This study presents a quantitative evaluation of the operational and technical risks of an active CO 2-EOR project. A set of risk factor metrics is defined to post-process the Monte Carlo (MC) simulations for statistical analysis. The risk factors are expressed as measurable quantities that can be used to gain insight into project risk (e.g. environmental and economic risks) without the need to generate a rigorous consequence structure, which include (a) CO 2 injection rate, (b) net CO 2 injection rate, (c) cumulative CO 2 storage, (d) cumulative water injection, (e) oil production rate, (f) cumulative oil production, (g) cumulativemore » CH 4 production, and (h) CO 2 breakthrough time. The Morrow reservoir at the Farnsworth Unit (FWU) site, Texas, is used as an example for studying the multi-scale statistical approach for CO 2 accounting and risk analysis. A set of geostatistical-based MC simulations of CO 2-oil/gas-water flow and transport in the Morrow formation are conducted for evaluating the risk metrics. A response-surface-based economic model has been derived to calculate the CO 2-EOR profitability for the FWU site with a current oil price, which suggests that approximately 31% of the 1000 realizations can be profitable. If government carbon-tax credits are available, or the oil price goes up or CO 2 capture and operating expenses reduce, more realizations would be profitable.« less

Speleogenesis, geometry, and topology of caves: A quantitative study of 3D karst conduits

NASA Astrophysics Data System (ADS)

Jouves, Johan; Viseur, Sophie; Arfib, Bruno; Baudement, Cécile; Camus, Hubert; Collon, Pauline; Guglielmi, Yves

2017-12-01

Karst systems are hierarchically spatially organized three-dimensional (3D) networks of conduits behaving as drains for groundwater flow. Recently, geostatistical approaches proposed to generate karst networks from data and parameters stemming from analogous observed karst features. Other studies have qualitatively highlighted relationships between speleogenetic processes and cave patterns. However, few studies have been performed to quantitatively define these relationships. This paper reports a quantitative study of cave geometries and topologies that takes the underlying speleogenetic processes into account. In order to study the spatial organization of caves, a 3D numerical database was built from 26 caves, corresponding to 621 km of cumulative cave passages representative of the variety of karst network patterns. The database includes 3D speleological surveys for which the speleogenetic context is known, allowing the polygenic karst networks to be divided into 48 monogenic cave samples and classified into four cave patterns: vadose branchwork (VB), water-table cave (WTC), looping cave (LC), and angular maze (AM). Eight morphometric cave descriptors were calculated, four geometrical parameters (width-height ratio, tortuosity, curvature, and vertical index) and four topological ones (degree of node connectivity, α and γ graph indices, and ramification index) respectively. The results were validated by statistical analyses (Kruskal-Wallis test and PCA). The VB patterns are clearly distinct from AM ones and from a third group including WTC and LC. A quantitative database of cave morphology characteristics is provided, depending on their speleogenetic processes. These characteristics can be used to constrain and/or validate 3D geostatistical simulations. This study shows how important it is to relate the geometry and connectivity of cave networks to recharge and flow processes. Conversely, the approach developed here provides proxies to estimate the evolution of the vadose zone to epiphreatic and phreatic zones in limestones from the quantitative analysis of existing cave patterns.

Megacity pumping and preferential flow threaten groundwater quality

PubMed Central

Khan, Mahfuzur R.; Koneshloo, Mohammad; Knappett, Peter S. K.; Ahmed, Kazi M.; Bostick, Benjamin C.; Mailloux, Brian J.; Mozumder, Rajib H.; Zahid, Anwar; Harvey, Charles F.; van Geen, Alexander; Michael, Holly A.

2016-01-01

Many of the world's megacities depend on groundwater from geologically complex aquifers that are over-exploited and threatened by contamination. Here, using the example of Dhaka, Bangladesh, we illustrate how interactions between aquifer heterogeneity and groundwater exploitation jeopardize groundwater resources regionally. Groundwater pumping in Dhaka has caused large-scale drawdown that extends into outlying areas where arsenic-contaminated shallow groundwater is pervasive and has potential to migrate downward. We evaluate the vulnerability of deep, low-arsenic groundwater with groundwater models that incorporate geostatistical simulations of aquifer heterogeneity. Simulations show that preferential flow through stratigraphy typical of fluvio-deltaic aquifers could contaminate deep (>150 m) groundwater within a decade, nearly a century faster than predicted through homogeneous models calibrated to the same data. The most critical fast flowpaths cannot be predicted by simplified models or identified by standard measurements. Such complex vulnerability beyond city limits could become a limiting factor for megacity groundwater supplies in aquifers worldwide. PMID:27673729

Destructive testings: dry drilling operations with TruPro system to collect samples in a powder form, from two hulls containing immobilized wastes in a hydraulic binder

DOE Office of Scientific and Technical Information (OSTI.GOV)

Pombet, Denis; Desnoyers, Yvon; Charters, Grant

2013-07-01

The TruPro{sup R} process enables to collect a significant number of samples to characterize radiological materials. This innovative and alternative technique is experimented for the ANDRA quality-control inspection of cemented packages. It proves to be quicker and more prolific than the current methodology. Using classical statistics and geo-statistics approaches, the physical and radiological characteristics of two hulls containing immobilized wastes (sludges or concentrates) in a hydraulic binder are assessed in this paper. The waste homogeneity is also evaluated in comparison to ANDRA criterion. Sensibility to sample size (support effect), presence of extreme values, acceptable deviation rate and minimum number ofmore » data are discussed. The final objectives are to check the homogeneity of the two characterized radwaste packages and also to validate and reinforce this alternative characterization methodology. (authors)« less

Quantitative analysis of spatial variability of geotechnical parameters

NASA Astrophysics Data System (ADS)

Fang, Xing

2018-04-01

Geotechnical parameters are the basic parameters of geotechnical engineering design, while the geotechnical parameters have strong regional characteristics. At the same time, the spatial variability of geotechnical parameters has been recognized. It is gradually introduced into the reliability analysis of geotechnical engineering. Based on the statistical theory of geostatistical spatial information, the spatial variability of geotechnical parameters is quantitatively analyzed. At the same time, the evaluation of geotechnical parameters and the correlation coefficient between geotechnical parameters are calculated. A residential district of Tianjin Survey Institute was selected as the research object. There are 68 boreholes in this area and 9 layers of mechanical stratification. The parameters are water content, natural gravity, void ratio, liquid limit, plasticity index, liquidity index, compressibility coefficient, compressive modulus, internal friction angle, cohesion and SP index. According to the principle of statistical correlation, the correlation coefficient of geotechnical parameters is calculated. According to the correlation coefficient, the law of geotechnical parameters is obtained.

A controlled experiment in ground water flow model calibration

USGS Publications Warehouse

Hill, M.C.; Cooley, R.L.; Pollock, D.W.

1998-01-01

Nonlinear regression was introduced to ground water modeling in the 1970s, but has been used very little to calibrate numerical models of complicated ground water systems. Apparently, nonlinear regression is thought by many to be incapable of addressing such complex problems. With what we believe to be the most complicated synthetic test case used for such a study, this work investigates using nonlinear regression in ground water model calibration. Results of the study fall into two categories. First, the study demonstrates how systematic use of a well designed nonlinear regression method can indicate the importance of different types of data and can lead to successive improvement of models and their parameterizations. Our method differs from previous methods presented in the ground water literature in that (1) weighting is more closely related to expected data errors than is usually the case; (2) defined diagnostic statistics allow for more effective evaluation of the available data, the model, and their interaction; and (3) prior information is used more cautiously. Second, our results challenge some commonly held beliefs about model calibration. For the test case considered, we show that (1) field measured values of hydraulic conductivity are not as directly applicable to models as their use in some geostatistical methods imply; (2) a unique model does not necessarily need to be identified to obtain accurate predictions; and (3) in the absence of obvious model bias, model error was normally distributed. The complexity of the test case involved implies that the methods used and conclusions drawn are likely to be powerful in practice.Nonlinear regression was introduced to ground water modeling in the 1970s, but has been used very little to calibrate numerical models of complicated ground water systems. Apparently, nonlinear regression is thought by many to be incapable of addressing such complex problems. With what we believe to be the most complicated synthetic test case used for such a study, this work investigates using nonlinear regression in ground water model calibration. Results of the study fall into two categories. First, the study demonstrates how systematic use of a well designed nonlinear regression method can indicate the importance of different types of data and can lead to successive improvement of models and their parameterizations. Our method differs from previous methods presented in the ground water literature in that (1) weighting is more closely related to expected data errors than is usually the case; (2) defined diagnostic statistics allow for more effective evaluation of the available data, the model, and their interaction; and (3) prior information is used more cautiously. Second, our results challenge some commonly held beliefs about model calibration. For the test case considered, we show that (1) field measured values of hydraulic conductivity are not as directly applicable to models as their use in some geostatistical methods imply; (2) a unique model does not necessarily need to be identified to obtain accurate predictions; and (3) in the absence of obvious model bias, model error was normally distributed. The complexity of the test case involved implies that the methods used and conclusions drawn are likely to be powerful in practice.

Landscape scale mapping of forest inventory data by nearest neighbor classification

Treesearch

Andrew Lister

2009-01-01

One of the goals of the Forest Service, U.S. Department of Agriculture's Forest Inventory and Analysis (FIA) program is large-area mapping. FIA scientists have tried many methods in the past, including geostatistical methods, linear modeling, nonlinear modeling, and simple choropleth and dot maps. Mapping methods that require individual model-based maps to be...

Determination of differences in crop injury from aerial application of glyphosate using vegetation indices and geostatistics

USDA-ARS?s Scientific Manuscript database

Injury to crops caused by off-target drift of glyphosate can seriously reduce growth and yield, and is of great concern to farmers and aerial applicators. Determining an indirect method for assessing the levels and extent of crop injury could support management decisions. The objectives of this stud...

Sediment transport modelling based on grain size trend analysis in Augusta Harbour (Sicily)

NASA Astrophysics Data System (ADS)

Barbera, Giuseppe; Feo, Roberto; Freni, Gabriele

2015-12-01

To support marine civil engineer in pollutant studies, sediment management or dredging operations, is useful to know how the sediments move in accumulation basin. This paper investigates the dynamic of the sediment path using a two-dimensional numeric model: the Grain Size Trend Analysis (GSTA). The GSTA was applied using GiSedTrend plugin, under GIS software. The case study is the Augusta Harbour, which is one of the most polluted Italian harbours. It is the marine part of the Site of National Interest (SNI) of Priolo Gargallo (Siracusa, Italy) and it can be hydrodynamically considered as a lagoon. Two scenarios were obtained by using different geostatistical criteria.

Spatio-Temporal Patterns of Barmah Forest Virus Disease in Queensland, Australia

PubMed Central

Naish, Suchithra; Hu, Wenbiao; Mengersen, Kerrie; Tong, Shilu

2011-01-01

Background Barmah Forest virus (BFV) disease is a common and wide-spread mosquito-borne disease in Australia. This study investigated the spatio-temporal patterns of BFV disease in Queensland, Australia using geographical information system (GIS) tools and geostatistical analysis. Methods/Principal Findings We calculated the incidence rates and standardised incidence rates of BFV disease. Moran's I statistic was used to assess the spatial autocorrelation of BFV incidences. Spatial dynamics of BFV disease was examined using semi-variogram analysis. Interpolation techniques were applied to visualise and display the spatial distribution of BFV disease in statistical local areas (SLAs) throughout Queensland. Mapping of BFV disease by SLAs reveals the presence of substantial spatio-temporal variation over time. Statistically significant differences in BFV incidence rates were identified among age groups (χ2 = 7587, df = 7327,p<0.01). There was a significant positive spatial autocorrelation of BFV incidence for all four periods, with the Moran's I statistic ranging from 0.1506 to 0.2901 (p<0.01). Semi-variogram analysis and smoothed maps created from interpolation techniques indicate that the pattern of spatial autocorrelation was not homogeneous across the state. Conclusions/Significance This is the first study to examine spatial and temporal variation in the incidence rates of BFV disease across Queensland using GIS and geostatistics. The BFV transmission varied with age and gender, which may be due to exposure rates or behavioural risk factors. There are differences in the spatio-temporal patterns of BFV disease which may be related to local socio-ecological and environmental factors. These research findings may have implications in the BFV disease control and prevention programs in Queensland. PMID:22022430