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Two pedotransfer functions (PTFs) are available in the literature enabling the soil water retention function (WRF) to be estimated from knowledge of the soil particle-size distribution (PSD), oven-dry soil bulk density (b), and saturated soil water content (s): i) the Arya and Heitman model (PTF-AH) and ii) the Mohammadi and Vanclooster model (PTF-MV). These physico-empirical PTFs rely on the hypothesis of shape similarity between PSD and WRF, and do not require the calibration of the input parameters. In the first stage, twenty-seven PSD models were evaluated using 4,128 soil samples collected in Campania (southern Italy). These models were ranked according to the root mean square residuals (RMSR), corrected Akaike Information Criterion (AICc), and adjusted coefficient of determination (R2adj). In the second stage, three subsets of PSD and WRF data (DS-1, DS-2, and DS-3), comprising 282 soil samples, were used to evaluate the two PTFs using the best three PSD models selected in the first stage. The hypothesis of shape similarity was assumed as acceptable only when the RMSR value was lower than the field standard deviation of the WRFs (*), which is viewed as a tolerance threshold and computed from the physically-based scaling approach proposed by Kosugi and Hopmans (1998). In the first study area (DS-1), characterized by a fairly uniform, loamy textured volcanic soil, the PTF-AH outperformed the PTF-MV and both PTFs provided reasonable performance within the acceptance threshold (i.e., RMSR < *). In the other two heterogeneous field sites (DS-2 and DS-3, characterized by soil textural classes that span from clay and clay-loam to loam and even sandy-loam soils), the PTF-MV (with 3% to 6% RMSR surpassing *) outperformed the PTF-AH (with 8% to 30% RMSR surpassing *) and the majority of RMSR values were larger than those obtained in the original studies. The mean relative error (MRE) revealed that the PTF-MV systematically underestimates the measured WRFs, whereas the PTF-AH provided negative MRE values indicating an overall overestimation. The outcomes of our study provide a critical evaluation when using calibration-free PTFs to predict WRFs over large areas.

In the last decades, a growing interest in fostering advanced interdisciplinary studies is leading to the establishment of observatories in pilot catchments for long-term monitoring of hydrological variables and fluxes. Nevertheless prior to sensor network installation, this investment necessitates preliminary surveys on key-variables such as near-surface soil moisture in order to prevent risks of erronously distributing sensors by missing sufficient spatial information for understanding hydrological processes within the landatmosphere interactions. The availability of maps describing areas with similar morphological, topographical, soil, and vegetation characteristics enable preliminary surveys to be organized for capturing spatio-temporal variability of soil moisture as best as possible. The soil-landscape classification can be considered as an interesting approach for grouping mapping units with similar hydrological behavior. Therefore, we assume the soil-landscape units as hydrotopes or hydrological similar units. Six transects were established along two hillsides of the Upper Alento River catchment (southern Italy) which is a proper candidate to become a Critical Zone Observatory. In this paper we use a soil-landscape map to infer spatial and temporal dynamics of soil moisture measured along these transects, whereas quantitative analyses were obtained by using multivariate techniques. The effectiveness of available information on soil-landscape mapping units is evaluated with respect to different observed patterns of soil moisture: wetter- and drier-than average observation points belong to agricultural and forested hillslopes, respectively. Soil texture and topographical controlling factors, especially clay content and slope gradient, are found to explain approximately 70% of the observed spatial variations in soil moisture along the forested hillslopes. The spatial structure explained by the environmental controlling factors decreases to 45% in the cases of the agricultural hillslopes mainly due to perturbations induced by grazing and tillage practices.