Variations, controls and predictions of soil saturated hydraulic conductivity under different land use types in the alpine region of Tibet, China

Abstract

Understanding the variations and controls of soil saturated hydraulic conductivity (Ks) under different land use types in the alpine region of Tibet is fundamental for predicting Ks in high-altitude regions where sample points are difficult to obtain and is important for establishing the regional soil hydrological model. In this study, soils samples under farmland, forestland, and grassland were collected from an east-west transect in southeastern Tibet, China. Basic soil properties and Ks were measured to analyze the influencing factors of Ks under different land use types and to establish suitable pedotransfer functions (PTFs) for predicting Ks in the alpine region. The average value of Ks followed the decreasing order of grassland (2.33 cm h−1) > farmland (2.12 cm h−1) > forestland (1.59 cm h−1). Ks of grassland (0.25–9.19 cm h−1) decreased gradually with increasing soil depth. Ks of farmland (0.11–8.82 cm h−1) increased initially and then decreased with increasing soil depth, whereas Ks of forestland (0.12–4.46 cm h−1) exhibited the opposite trend. There were significant differences in the main controlling factors of Ks under different land use types. In farmland, soil bulk density (BD), non-capillary porosity (Pn), clay and gravel content (GC) were the main drivers of Ks (P < 0.05). The dominant influencing factors of Ks in forestland were BD and total porosity (Pt) (P < 0.05), while Pn and GC were the main factors controlling Ks in grassland (P < 0.05). Compared with the multiple linear stepwise regression models and published PTFs, the BP neural network transfer model had better prediction accuracy for Ks in the alpine region. Our results not only provide hydraulic parameters for regional hydrological modeling and but also lay a foundation to construct Ks prediction models for other alpine regions in the world.