Estimating the saturated hydraulic conductivity in a spatially variable soil with different permeameters: a stochastic Kozeny–Carman relation

The spatial variability of the saturated hydraulic conductivity (Ks) of a greenhouse banana plantation volcanic soil was investigated with three different permeameters: (a) the Philip-Dunne field permeameter, an easy to implement and low cost device; (b) the Guelph field permeameter; (c) the constant head laboratory permeameter. Ks was measured on a 14×5 array of 2.5 m×5 m rectangles at 0.15 m depth using the above three methods. Ks differences obtained with the different permeameters are explained in terms of flow dimensionality and elementary volume explored by the three methods. A sinusoidal spatial variation of Ks was coincident with the underlying alignment of banana plants on the field. This was explained in terms of soil disturbances, such as soil compaction, originated by management practices and tillage. Soil salinity showed some coincidence in space with the hydraulic conductivity, because of the irrigation system distribution, but a causal relationship between the two is however difficult to support. To discard the possibility of an artefact, the original 70 point mesh was doubled by intercalation of a second 14× 5 grid, such that the laboratory Ks was finally determined on a 140 points 2.5 m× 2.5 m square grid. Far from diluting such anisotropy this was further strengthened after inclusion of the new 70 points. The porosity (φ) determined on the same laboratory cores shows a similar sigmoid trend, thus pointing towards a plausible explanation for such variability. A power-law relationship was found between saturated hydraulic conductivity and porosity, Ksαφ (r2 = 0.38), as stated by the Kozeny–Carman relation. A statistical reformulation of the Kozeny–Carman relation is proposed that both improved its predictability potential and allows comparisons between different representative volumes, or Ks data sets with different origin. Although the two-field methods: Guelph and Philip-Dunne, also follow a similar alignment trend, this is not so evident, suggesting that additional factors affect Ks measured in the field. Finally, geostatistical techniques such as cross correlograms estimation are used to further investigate this spatial dependence. © 2004 Elsevier B.V. All rights reserved.