Estimating Saturated Hydraulic Conductivity along a South‐North Transect in the Loess Plateau of China

Abstract

Core Ideas Ks was estimated using MLR‐type PTFs, ANN‐type PTFs and state‐space analysis. State‐space modeling was scale‐sensitive in estimating Ks in Loess Plateau. Spatial correlations revealed in state‐space analyses were consistent with wavelet coherency. Bulk density, clay content and topography dominated Ks spatial distribution. A precise description of saturated hydraulic conductivity (Ks) and its spatial variability is required for modeling soil and water transport in the vadose zone. Nevertheless, the direct measurement of Ks is expensive and laborious especially for large domains crossing hundreds of kilometers. The objective was to estimate Ks from easily accessible soil properties and environmental factors using pedotransfer functions (PTFs) and state‐space analysis. Along an 860‐km south–north transect in the Loess Plateau of China, soil cores for Ks measurements were collected at depths of 0 to 10, 10 to 20, and 20 to 40 cm at 10‐km intervals from 15 Apr. to 15 May 2013. Multiple linear regression (MLR) and artificial neural network (ANN) were used to derive PTFs for Ks estimation. Based on the eight factors of bulk density, soil organic carbon, sand content, clay content, mean annual precipitation and temperature, slope gradient and elevation, the state‐space analysis appeared to outperform the PTFs in calibrating Ks over the entire transect. The adjusted coefficients of determination (R2adj) for the state‐space models were all greater than 0.9, whereas the corresponding R2adj were much lower for the MLR‐ and ANN‐type PTFs (ranging from 0.398 to 0.880). However, the state‐space approach is quite scale‐sensitive, and overfitting occurred when it was cross‐validated with a leave‐one‐out procedure. It performed almost perfectly in calibration as implied in the R2adj of ∼1 but rather poorly in validation with R2adj typically >0.4. The ANN method exhibited the best Ks estimations at all depths. Both wavelet coherency and state‐space modeling quantified the spatial correlations of Ks with the eight factors investigated and manifested consistent results, that is, bulk density, clay content, and topography were the primary properties controlling Ks distribution. These findings are critical for hydrological modeling and irrigation management in the Loess Plateau of China and possibly other arid and semi‐arid regions.