[1] The influence of aquifer property correlation on multiphase fluid migration and entrapment was explored through the use of correlated and uncorrelated porosity, permeability, and capillary pressure-saturation (Pc-Sat) parameter fields in a crosssectional numerical multiphase flow model. Data collected from core samples in a nonuniform sandy aquifer were used to generate three-dimensional aq...

This paper describes a geostatistical approach for environmental data modeling based on value descriptive analysis, variographic analysis, estimation method and validation. It shows an application related to atmospheric pollutant dispersion (ozone) detected by active biomonitoring in the province of Livorno (Italy). This work proposes a two-stage approach based on: the computation of an index o...

Seismic site effects are influenced mainly by geospatial uncertainties corresponding to geological or geotechnical spatial variance. Therefore, the development of a geospatial database is essential to characterize site-specific geotechnical information in multiscale areas and to optimize geospatial zonation methods with potentially high degrees of spatial variability based on trial-and-error ge...

Meteorological data are used in many studies, especially in planning, disaster management, water resources management, hydrology, agriculture and environment. Analyzing changes in meteorological variables is very important to understand a climate system and minimize the adverse effects of the climate changes. One of the main issues in meteorological analysis is the interpolation of spatial data...

In this report the main focuses are geostatistical Gaussian Markov random field (GMRF) models and parallel exact sampling of GMRFs. There are also brief overviews of parallel computing and Markov chain Monte Carlo (MCMC) methods, and a literature review of parallel MCMC. The geostatistical GMRF models are constructed by discretising the domain region using a lattice. Instead of giving this latt...

Reliable prediction of subsurface flow and contaminant transport depends on the accuracy with which the values and spatial distribution of process-relevant model parameters can be identified. Successful characterization methods for complex soil systems are based on (1) an adequate parameterization of the subsurface, capable of capturing both random and structured aspects of the heterogeneous sy...

Sciarretta A., Trematerra P. (2014): Geostatistical tools for the study of insect spatial distribution: practical implications in the integrated management of orchard and vineyard pests. Plant Protect. Sci., 50: 97–110. Spatial heterogeneity in agricultural systems is recognised as an important source of variability to be investigated. In the evolution of Integrated Pest Management (IPM), patte...

Sporisorium reilianum has caused significant economical damages in Mexico, in temperate and relatively dry areas, where maize is cultivated. The knowledge about the spatial distribution of this pathogen is basic to elaborate integrated management programs, and precise and eff icient the development of sampling methods and control techniques. Unfortunately, in Mexico there are no studies on spat...

BACKGROUND The success of genome-wide selection (GS) approaches will depend crucially on the availability of efficient and easy-to-use computational tools. Therefore, approaches that can be implemented using mixed models hold particular promise and deserve detailed study. A particular class of mixed models suitable for GS is given by geostatistical mixed models, when genetic distance is treated...