Current geostatistical simulation methods allow generating multiple realizations that honor all available data, such as hard and secondary data under certain geological scenarios (e.g. 3D training image-based models, multi-Gaussian law, Boolean models). However, it is difficult to simulate large models that honor highly nonlinear response functions (e.g. remote sensing data, geophysical data or...

introduction the knowledge about spatial variability of precipitation is a key issue for regionalization in hydro-climatic studies. measurements of meteorological parameters by the traditional methods require a dense rain gauge network. but, due to the topography and cost problems, it is not possible to create such a network in practice. in these cases the spatial distribution pattern of precip...

changes in groundwater quality, is caused destruction of other resources, either directly or indirectly, due to bad management of groundwater extractions. considering the importance of groundwater resources, especially in arid and semiarid regions, this research was done to modeling the spatial distribution of some groundwater qualitative factors with emphasis on drinking using geostatistical m...

Conventional geostatistical methodology solves the problem of predicting the realized value of a linear functional of a Gaussian spatial stochastic process S…x) based on observations Yi ˆ S…xi † ‡ Zi at sampling locations xi , where the Zi are mutually independent, zero-mean Gaussian random variables. We describe two spatial applications for which Gaussian distributional assumptions are clearly...

Reference Evapotranspiration (ET0) is a basic parameter for determination of irrigation program. It is one of the most important factors in water resources management and considers as a requirement for every irrigation and drainage plans. Because of the spatial variations of ET0, it is necessary to use from intrapolation methods in order to estimation of this parameter in regional studie. Using...

Despite an ever-increasing number of spaceborne, airborne, and ground-based data acquisition platforms, remote sensing are still often spatially incomplete or temporally irregular. While deterministic interpolation techniques used, they tend to create unrealistic spatial patterns generally do not provide uncertainty quantification. Geostatistical simulation models effective in generating ensemb...

Abstract This article describes two R packages for probabilistic weather forecasting, ensembleBMA, which offers ensemble postprocessing via Bayesian model averaging (BMA), and ProbForecastGOP, which implements the geostatistical output perturbation (GOP) method. BMA forecasting models use mixture distributions, in which each component corresponds to an ensemble member, and the form of the compo...