资源环境科技发展态势分析平台

We start from the well‐documented scale dependence displayed by the probability distribution and associated statistical moments of a variety of hydrogeological and soil science variables and their spatial or temporal increments. These features can be captured by a Generalized Sub‐Gaussian (GSG) model, according to which a given variable, Y, is subordinated to a (typically spatially correlated) Gaussian random field, G , through a subordinator, U . This study extends the theoretical framework originally proposed by Riva et al. (2015a) to include the possibility of selecting a general form of the subordinator, thus enhancing the flexibility of the GSG framework for data interpretation and modeling. Analytical expressions for the GSG process associated with lognormal, Pareto, and Gamma subordinator distributions are then derived. We demonstrate the ability of the GSG modeling framework to capture the way key features of the statistics associated with two datasets transition across scales. The latter correspond to variables which are typical of a geochemical and a hydrogeological setting, i.e., (i ) data characterizing the micrometer‐scale surface roughness of a crystal of calcite, collected within a laboratory‐scale setting, resulting from induced mineral dissolution; and (ii ) a vertical distribution of decimeter‐scale porosity data, collected along a deep km‐scale borehole within a sandstone formation and typically used in hydrogeological and geophysical characterization of aquifer systems. The theoretical developments and the successful applications of the approach we propose provide a unique framework within which one can interpret a broad range of scaling behaviors displayed by a variety of Earth and environmental variables in various scenarios.