Soil available water capacity (AWC) and bulk density (BD) are key properties for understanding water flows in soils, land-use planning and irrigation management. As measuring these properties is costly and time-consuming, pedotransfer functions (PTFs) are commonly used to predict BD and soil moisture at a specific matric potential and then estimate AWC. Currently, operational tools to estimate AWC at the soil profile scale from data easily available are lacking. In this study, new PTFs based on the regression-tree method Cubist were developed at a regional scale to predict soil water contents at -10 kPa (field capacity) and -1585 kPa (permanent wilting point), and BD. A first PTF (PC model) required commonly measured soil properties (sand, silt, clay, organic carbon contents), while the second (PM model) required four additional predictors: qualitative information deriving from description of the soil profile. The models were validated with an independent dataset. Both models outperformed existing PTFs. AWC was then estimated at the horizon scale, soil-profile scale and in the upper 30 cm of soil. The PM model performed better than the PC model with the training dataset at the profile scale (Nash-Sutcliffe efficiency = 0.87 and 0.60; RMSE = 0.186 and 0.326 mm cm(-1), respectively). Independent validation of AWC estimates at the profile scale by the PM and PC models yielded RMSE of 0.192-0.204 and 0.229-0.244 mm cm(-1), respectively. The sensitivity of AWC to estimates of soil depth and coarse-fragment content was tested. Results confirmed the importance of these variables resulting in soil observation to estimate AWC accurately.