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Abstract

Soil saturated hydraulic conductivity (K
s) is a key soil hydraulic property that determines the hydrological cycle of check dam–dominated catchment areas. However, K
s data are lacking due to the difficulty of directly measuring this variable in deep soil layers. In this study, 45 soil profiles (0–200 cm) in 15 check dams in three typical watersheds (Xinshui River, Zhujiachuan, and Kuye River) in a hilly gully region on the Chinese Loess Plateau were selected, and a total of 586 soil samples were collected along the soil profiles. Backpropagation neural network (BPNN) and support vector regression (SVR) models based on the genetic algorithm (GA) were tested, and pedotransfer functions for K
s estimation were established for check dams on the Loess Plateau. Basic soil characteristics, such as soil depth, sand, silt, clay, soil organic matter, and bulk density, were adopted as the model inputs to estimate K
s. Combinations of these parameters could be used to suitably estimate K
s, and the models were found to require relatively few soil characteristics to achieve similar accuracy. In comparison to GA-BPNN, the GA-SVR model attained good practicability and was more stable in K
s prediction (the geometric mean error ratio was between 0.942 and 1.101; RMSE was between 0.069 and 0.073). Our research can make some contributions to the solution of land restoration and watershed governance on the Chinese Loess Plateau.

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Wiley: Vadose Zone Journal: Table of Contents

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