Spatial Averaging of van Genuchten Hydraulic Parameters for Steady-State Flow in Heterogeneous Soils: A Numerical Study
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For meso- or regional-scale Soil-Vegetation-Atmosphere Transfer (SVAT) schemes in hydroclimatic models, pixel dimensions may range from several hundred square meters to several hundred square kilometers. Pixel-scale soil hydraulic parameters and their accuracy are critical for the success of hydroclimatic and soil hydrologic models. This study tries to answer a major question: What will be the effective and average hydraulic properties for the entire pixel (or footprint of a remote sensor) consisting of several textures if the soil hydraulic properties can be estimated for each individual texture? In this study, we examined the impact of areal heterogeneity in soil hydraulic parameters on soil ensemble behavior for steady-state evaporation and infiltration. Using the widely used van Genuchten model and hydraulic parameter statistics obtained from neural network-based pedotransfer functions (PTFs) for various soil textural classes, we address the impact of areal hydraulic property heterogeneity on ensemble behavior and uncertainty in steady-state vertical flow in large-scale heterogeneous fields. The various averaging schemes of van Genuchten parameters are compared with "effective parameters" calculated by conceptualizing the areally heterogeneous soil formation as an equivalent homogeneous medium that will discharge approximately the same amount of ensemble flux of the heterogeneous soil. The impact of boundary conditions and parameter correlation on the effective parameters, as well as the accuracy and uncertainty of the averaging schemes for the hydraulic parameters, are investigated and discussed. In light of our results, we suggest the following guidelines for van Genuchten hydraulic parameter averaging: arithmetic means for Ksand n, a value between arithmetic and geometric means for when Ksand are highly correlated, and a value between geometric and harmonic means for when Ksand are poorly correlated.
