[1] As the incidence of groundwater contamination continues to grow, a number of inverse modeling methods have been developed to address forensic groundwater problems. In this work the geostatistical approach to inverse modeling is extended to allow for the recovery of the antecedent distribution of a contaminant at a given point back in time, which is critical to the assessment of historical e...

Spatial and temporal soil moisture dynamics are critically needed to improve the parameterization for hydrological and meteorological modeling processes. This study evaluates the statistical spatial structure of large-scale observed and simulated estimates of soil moisture under pre- and post-precipitation event conditions. This large scale variability is a crucial in calibration and validation...

accurate knowledge of spatial distribution of soil physical and chemical properties is needed for suitable management and proper use of rangelands in masileh plain, qom, iran. in present study, for the spatial modeling of chemical and physical parameters such as sodium (na), calcium (ca), soluble potassium (k), magnesium (mg), electrical conductivity (ec), saturation percentage (sp%), silt, cla...

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. Geostatis...

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...