干旱区生态环境知识资源中心

A precise description of saturated hydraulic conductivity (K-s) and its spatial variability is required for modeling soil and water transport in the vadose zone. Nevertheless, the direct measurement of K-s is expensive and laborious especially for large domains crossing hundreds of kilometers. The objective was to estimate K-s 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 K-s 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 K s 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 K-s over the entire transect. The adjusted coefficients of determination (Radj(2)) for the state-space models were all greater than 0.9, whereas the corresponding Radj(2) 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 Radj(2) of similar to 1 but rather poorly in validation with Radj(2) typically >0.4. The ANN method exhibited the best K-s estimations at all depths. Both wavelet coherency and state-space modeling quantified the spatial correlations of K-s with the eight factors investigated and manifested consistent results, that is, bulk density, clay content, and topography were the primary properties controlling K-s 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.