Pedotransfer functions for predicting available water capacity in French soils, their applicability domain and associated uncertainty

Soil available water capacity is an important soil property for land use planning, drought risk assessment, and modelling crop production or carbon cycling. Measurements of soil moisture at several soil water potentials are expensive and time-consuming, hence it is common to estimate available water capacity with pedotransfer functions (PTFs). Available PTFs for France rarely provide uncertainty estimates for the model coefficients and predictions, which are often required for error propagation analysis and modelling ecological processes. The objectives of this study were: 1) to develop class-PTFs and 2) continuous-PTFs with associated uncertainties, and 3) to assess the domain of applicability of the PTFs across metropolitan France. We used the SOLHYDRO database for calibrating continuous and class PTFs. For continuous PTFs we used linear regression models using sand, clay, organic carbon (SOC), and bulk density (BD) as predictor variables, whereas class-PTFs were defined by texture class, bulk density, and horizon type (all horizons, topsoils, subsoils). The models were validated with an independent validation dataset. The domain of applicability of the PTFs was evaluated calculating the Mahalanobis distance between the calibration dataset and horizon data from the French soil monitoring network (RMQS). At independent validation, texture class-PTFs had a RMSE between 0.047 cm(3) cm(-3) and 0.058 cm(3) cm(-3) and continuous-PTFs had a RMSE between 0.040 cm(3) cm(-3) and 0.053 cm(3) cm(-3). Texture class-PTFs had similar or better predictive performance than texture-structural class-PTFs. The prediction performance of continuous-PTFs improved slightly when including SOC and BD in the models. The variance of the predictions of continuous-PTFs associated to error in the model coefficients was rather small, and increased as the values of the predictor variables were more distant to the centroid of the calibration data. We provide the PTF users with tools for classifying new samples as within or outside the applicability domain of the PTFs. When applied to the RMQS dataset, > 50% of the horizons outside the domain of applicability were located in forests and natural areas or managed pastures. The spatial distribution of RMQS horizons outside the domain of applicability of the PTF can inform future sampling locations for increasing the diversity of the soil properties and site conditions represented by the SOLHYDRO dataset.