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Abstract Many pedotransfer functions (PTFs) have been developed for predicting the soil water content at different matric potentials. The use of these functions has been encouraged because of the time and work typically required for measuring it, while the PTFs require commonly measured soil properties such as sand, silt, clay, organic matter content, or bulk density for predicting water retention. In addition, several environmental and ecosystem management simulation models such as DRAINMOD, HYDRUS, EPIC, SPAW, and WEPP use PTFs for computing soil hydraulic properties. Because of the increasing use of the PTFs and their effect in many soil water simulation and transport models, this study revised and tested 13 different PTFs for predicting soil water content at −33 and −1500 kPa, values usually known as field capacity and wilting point. Three of these PTFs were derived from tropical soils while the rest were developed with soil samples collected across the United States. These PTFs were evaluated in Chilean soils as an independent dataset and their improvement after calibration was assessed with this new data. The results demonstrate that the PTFs performance depends on the soils used for their development as the estimates showed a significant improvement after calibration. When predicting water content, Rawls et al. (2004) was the best function before calibration (RMSE = 0.08 for −33 and −1500 kPa), while Gupta and Larson (1979) was the best after calibration (RMSE of 0.06 and 0.05, and r2 values of 0.69 and 0.66 at −33 and −1500 kPa, respectively). Nonlinear PTFs performed better than linear PTFs when predicting water content at field capacity. Finally, bulk density proved to be the key variable and can be used as footprint for soils changes through time. Organic matter content was also a significant input but improved the estimates for some specific matric potentials and PTFs. Graphical abstract Unlabelled Image Highlights • The PTFs performance depends on the soils used for their development. • The best water content estimates were obtained with Gupta and Larson and Rawls PTFs. • Bulk density is the key variable in PTFs for predicting soil water contents. • Nonlinear PTFs showed provided the best water content estimates at FC. [ABSTRACT FROM AUTHOR]

Radon concentration has been monitored from 1995 to 1999 in the soil of the Sur-Frêtes ridge (French Alps), covered with snow from November to April. Measurements were performed at 70 cm depth, with a sampling time of 1 h, at two points: the summit of the ridge, at an altitude of 1792 m, and the bottom of the ridge, at an altitude of 1590 m. On the summit, radon concentration shows a moderate seasonal variation, with a high value from October to April (winter), and a low value from May to September (summer). At the bottom of the ridge, a large and opposite seasonal variation is observed, with a low value in winter and a high value in summer. Fluctuations of the radon concentration seem to be associated with temperature variations, an effect which is largely delusory. Indeed, these variations are actually due to water infiltration. A simplified mixing model is used to show that, at the summit of the ridge, two effects compete in the radon response: a slow infiltration response, rich in radon, with a typical time scale of days, and a fast infiltration of radon-poor rainwater. At the bottom of the ridge, similarly, two groundwater contributions compete: one slow infiltration response, similar to the response seen at the summit, and an additional slower response, with a typical time scale of about a month. This second slower response can be interpreted as the aquifer discharge in response to snow melt. This study shows that, while caution is necessary to properly interpret the various effects, the temporal variations of the radon concentration in soil can be understood reasonably well, and appear to be a sensitive tool to study the subtle interplay of near surface transfer processes of groundwater with different transit times. [Copyright &y& Elsevier]