Evaluation of pedotransfer functions for estimating soil hydraulic properties of prevalent soils in a catchment of the Bavarian Alps

In this study, two types of pedotransfer functions (PTFs) were evaluated for their accuracy and applicability to a broad range of Alpine soils in the Halbammer area in southern Bavaria (Germany). The first model is ROSETTA, which is based on neural network analyses. It implements five hierarchical PTFs using limited to more extend input data. The second model is SOILPROP that is based on physical methods and predicts the soil hydraulic properties from particle size distribution and bulk density. The PTF were evaluated by comparing predicted with measured water retention values. The accuracy was quantified by direct statistical evaluation with the correlation coefficient (R), the mean error (ME) and the root mean square difference (RMSD). Additionally, a process based functional validation was performed by simulating the water flow using the measured and predicted soil hydraulic data. The RMSD values from ROSETTA models ranged from 0.068 to 0.202 cm(3)/cm(3) for the water retention and from 0.450 to 0.579 log K-s (cm/day) concerning the hydraulic conductivity (K (s)). The ME indicated underestimated water contents at high suctions and for soils with high organic content. The functional evaluation was the better as the more input data were used in the hierarchical PTFs. The RMSD of SOILPROP was 0.073 cm(3)/cm(3) for water contents and 0.718 log K-s (cm/day) for the hydraulic conductivity. The water contents in the middle suction range were underestimated in sandy soils and overestimated in soils with low bulk density. The functional evaluation showed improved model accuracy when the predicted saturated conductivity was adjusted to more realistic values from literature showing its sensitiveness towards water flow modelling.