Effective hydraulic properties of 3D virtual stony soils identified by inverse modeling

Abstract

Abstract. Stony soils that have a considerable amount of rock fragments are widespread around the world. However, experiments to determine effective hydraulic properties of stony soils (SHP), i.e. the water retention curve (WRC) and hydraulic conductivity curve (HCC), are challenging. Installation of measurement devices and sensors in these soils is difficult and the data are less reliable because of high local heterogeneity. Therefore, effective properties of stony soils especially in unsaturated hydraulic conditions are still not well understood. An alternative approach to evaluate the SHP of these systems with internal structural heterogeneity is numerical simulation. We used the Hydrus 2D/3D software to create virtual stony soils in 3D and simulate water flow for different volumetric rock fragment contents, f. Soils with volumetric stone contents from 11 to 37 % were created by placing impermeable spheres in the form of rock fragments in a sandy loam soil. Time series of local pressure heads in various depths, mean water contents and fluxes across the upper boundary were generated in a virtual evaporation experiment. Additionally, a multi-step unit gradient simulation was applied to determine effective values of hydraulic conductivity near saturation up to pF = 2. The generated data were evaluated by inverse modeling, assuming a homogeneous system, and the effective hydraulic properties were identified. The effective properties were compared with predictions from available scaling models of SHP for different volumes of rock fragments. Our results showed that scaling the WRC of the background soil based on only the value of f gives acceptable results in the case of impermeable rock fragments. However, the reduction of conductivity could not be simply scaled by the value of f. Predictions were highly improved by applying the Novák, Maxwell, and GEM models to scale the HCC. The Maxwell model matched the numerically identified HCC best.