Mining Geostatistics to Quantify the Spatial Variability of Certain Soil Flow Properties

The functional dependence of the relative unsaturated hydraulic conductivity (UHC) Kr (ψ) exp(αψ) upon the matric potential ψ, [L], via the soil-dependent parameter α, [L−1], has been traditionally regarded as a deterministic process (i.e. α ∼ constant). However, in the practical applications one is concerned with flow domains of large extents where α undergoes to significant spatial variations as consequence of the disordered soil's structure. To account for such a variability (hereafter also termed as “heterogeneity”) we adopt the mining geostatistical approach, which regards α as a random space function (RSF). To quantify the heterogeneity of α, estimates of local-values were obtained from ∼ 100 locations along a trench where an internal drainage test was conducted. The analysis of the statistical moments of α demonstrates (in line with the current literature on the matter) that the log-transform ζ ln α can be regarded as a structureless, normally distributed, RSF. An novel implementation of the present study in the context of the “Internet of Things” (IoT) is outlined.