Abstract:Field water retention capacities (θ-30 kPa) and wilting coefficients (θ-1 500 kPa) of ninety soil samples in the Dagu River Basin were predicted separately with four PTFS, i.e. point regression method, linear regression method, nonlinear regression method and artificial neural network method, and their spatial variabilities were analyzed with the aid of traditional statistic and geostatistic methods. The traditional statistics revealed that the nonlinear regression method was the best with the variation coefficients of θ-30 kPa of all the soil samples, being always less than θ-1 500 kPa, however, no matter measured or predicted values, both belonged to the category of moderate in spatial variability. The geostatistics also showed that both measured and predicted θ-30 kPa and θ-1 500 kPa demonstrated varied nugget effects, moreover, θ-30 kPa always had stronger spatial dependence than θ-1 500 kPa did. Analysis of the parameters of semivariance model for θ-30 kPa and θ-1 500 kPa ultimately revealed that the artificial neural network model could most truthfully characterize spatial variability of the soil water retention capability in the experimental zone.