Evaluation and Development of Pedo-Transfer Functions for Predicting Soil Saturated Hydraulic Conductivity in the Alpine Frigid Hilly Region of Qinghai Province

• Main Authors: Yafan Zuo, Kangning He
• Format: Article
• Language: English
• Published: MDPI AG 2021-08-01
• Series: Agronomy
• Subjects: least-square method; Multiple Linear Regression; principal component analysis; Artificial Neural Networks
• Online Access: https://www.mdpi.com/2073-4395/11/8/1581

In recent years, Pedo-Transfer Functions (PTFs) have become a commonly used tool to predict the hydraulic properties of soil. As an important index to evaluate the function of forest water conservation, the prediction of saturated hydraulic conductivity (<i>K_S</i>) on the regional scale is of great significance to guide the vegetation construction of returning farmland to forest area. However, if the published PTFs are directly applied to areas where the soil conditions are different from those where the PTFs are established, their predictive performance will be greatly reduced. In this study, 10 basic soil properties were measured as input variables for PTFs to predict <i>K_S</i> in the three watersheds of Taergou, Anmentan, and Yangjiazhai in the alpine frigid hilly region of Qinghai Province, China. The parameters of the eight published PTFs were modified by the least-squares method and new PTFs were also constructed, and their prediction performance was evaluated. The results showed that the <i>K_S</i> of coniferous and broad-leaved mixed forests and broad-leaved pure forests in the study area were significantly higher than those of pure coniferous forests, and grassland and farmland were the lowest (<i>p</i> > 0.05). Soil Organic Matter plays an important role in predicting <i>K_S</i> and should be used as an input variable when establishing PTFs. The Analysis-Back Propagation Artificial Neural Network (BP ANN) PTF that was established, with input variables that were, Si·SOM, BD·Si, ln²Cl, SOM², and SOM·lnCl had a better predictive performance than published PTFs and MLR PTFs.