Factorial kriging analysis leverages soil physical properties and exhaustive data to predict distinguished zones of hydraulic properties

Abstract

Measurement of unsaturated hydraulic properties of soil is a time-consuming endeavor which limits the scale of studies. A stratified sampling scheme could enable more efficient use of samples but some information about the strata is needed a priori. The aim of this study is to define zones of soil hydraulic properties using remotely sensed and soil physical inputs. Samples were collected from 50 locations at 4 depth intervals in a 20.8 ha field located along the margin of the Venice Lagoon, Italy, and characterized by paleo-channel structures and highly heterogeneous soils. Water retention curves (WRC) and unsaturated hydraulic conductivity curves (UHC) were determined via inversion of measurements taken in the lab using the Wind method. Factorial kriging analysis (FKA) was applied separately to hydraulic parameters and soil physical properties and the 4 depths treated as independent samples. The mapped principal components (PCs) were used in a fuzzy-c means algorithm to define zones of like properties. To examine the physical significance of these zones, curve parameters and hydraulic curves were investigated. Zonation was able to distinguish between θs (saturated water content), n (shape parameter) and α (inverse of air entry), while θr (residual water content) and Ks (saturated conductivity) were not statistically different between the groups. For curve comparisons, WRCs were found to be significantly different between zones at all tensions while effective saturation curves (Se) differ for the majority of tensions (except at 28 cm), and UHCs did not differ. The spatial relevance of the zones was examined by overlaying hydraulic zones with zones defined using the FKA and fuzzy-c means approach from soil physical properties such as texture and bulk density. The hydraulic zones overlaid the physical property zones with an areal accuracy ranging from 46.66% to 92.41%. The similarity between these zone sets suggests that there is a potential to predict hydraulic zones from zones defined from soil physical properties. This work illustrates the potential to incorporate geospatial statistics in a stratified sampling scheme for hydraulic properties.