Kaihua Liao;Xu Su;Jichun Wu;Shuhua Ji;Lin Qin

Department of Environmental Science, Qingdao University, Qingdao 266071 (China);Department of Hydrosciences, Nanjing University, Nanjing 210093 (China);Department of Environmental Science, Qingdao University, Qingdao 266071 (China

ptf;ptfs;used to;kpa;matric potential;variabilit

A total of 107 soil samples were taken from the city of Qingdao, Shandong Province, China. Soil water retention data at 2.5, 6, 10, 33, 100, 300, and 1 500 kPa matric potentials were measured using a pressure membrane apparatus. Multiple linear regression (MLR) was used to develop pedotransfer functions (PTFs) for single point estimation and van Genuchten parameter estimation based on readily measurable soil properties, i.e., MLR-based point (MLRP) PTF and MLR-based parametric (MLRV) PTF. The double cross-validation method was used to evaluate the accuracy of PTF estimates and the stability of the PTFs developed in this study. The performance of MLR,P and MLRV PTFs in estimating water contents at matric potentials of -10, -33, and -1500 kPa was compared with that of two existing PTFs, the Rawls PTF and the Vereecken PTF. In addition, geostatistical analyses were conducted to assess the capabilities of these PTFs in describing the spatial variability of soil water retention characteristics. Results showed that among all PTFs only the Vereecken PTF failed to accurately estimate water retention characteristics. Although the MLRP PTF can be used to predict retention characteristics through traditional statistical analyses, it failed to describe the spatial variability of soil water retention characteristics. Although the MLRV and Rawls PTFs failed to describe the spatial variability of water contents at a matric potential of -10 kPa, they can be used to quantify the spatial variability of water contents at matric potentials of -33 and -1500 kPa.