Estimating saturated hydraulic conductivity and air permeability from soil physical properties using state-space analysis

Estimates of soil hydraulic conductivity (K) and air permeability (k_a) at given soil-water potentials are often used as reference points in constitutive models for K and k_a as functions of moisture content and are, therefore, a prerequisite for predicting migration of water, air, and dissolved and gaseous chemicals in the vadose zone. In this study, three modeling approaches were used to identify the dependence of saturated hydraulic conductivity (K_S) and air permeability at -100 cm H₂O soil-water potential (k_a100) on soil physical properties in undisturbed soil: (i) Multiple regression, (ii) ARIMA (autoregressive integrated moving average) modeling, and (iii) State-space modeling. In addition to actual soil property values, ARIMA and state-space models account for effects of spatial correlation in soil properties. Measured data along two 70-m-long transects at a 20-year old constructed field were used. Multiple regression and ARIMA models yielded similar prediction accuracy, whereas state-space models generally gave significantly higher accuracy. State-space modeling suggested K_S at a given location could be predicted using nearby values of K_S, k_a100 and air-filled porosity at -100 cm H₂O soil-water potential (ε₁₀₀. Similarly, k_a100 could be predicted from nearby values of k_a100 and ε₁₀₀. Including soil total porosity in the state-space modeling did not improve prediction accuracy. Thus, macro-porosity ε₁₀₀) was the key porosity parameter for predicting both K_S and k_a100 in undisturbed soil.