Estimating Near‐Saturated Soil Hydraulic Conductivity Based on Its Scale‐Dependent Relationships with Soil Properties

Abstract

Decomposed into four intrinsic mode functions (IMFs) and a residue by NA‐MEMD, each K was found to significantly correlate with all six properties at one spatial scale at least. The variations in K were primarily regulated by soil structure, especially at the relatively small scales. Multiple linear regression (MLR) failed to regress either IMF1 or IMF2 of each K from the soil properties of the equivalent scales and only accounted for 13.7 to 43.6% of the total variance in calibration for the remaining half of the IMF1s and IMF2s. An artificial neural network was then adopted to estimate IMF1 and IMF2, and the corresponding results were added to the MLR estimates at other scales for which each K was estimated at the measurement scale. This prediction greatly outperformed the MLR modeling before NA‐MEMD and, on average, accounted for additional 74.4 and 73.4% of the total variance in calibration and validation, respectively. These findings suggest nonlinear correlations between K and the soil properties investigated at the small scales and hold important implications for future estimations of Kns and Ks as well as other hydraulic properties.