Modelling of the land surface water-, energy-, and carbon balance provides insight into the behaviour of the Earth System, under current and future conditions. Currently, there exists a substantial variability between model outputs, for a range of model types, whereby differences between model input parameters could be an important reason. For large-scale land surface, hydrological, and crop models, soil hydraulic properties (SHP) are required as inputs, which are estimated from pedotransfer functions (PTFs). To analyse the functional sensitivity of widely used PTFs, the water fluxes for different scenarios using HYDRUS-1D was simulated and predictions compared. The results showed that using different PTFs causes substantial variability in predicted fluxes. In addition, an in-depth analysis of the soil SHPs and derived soil characteristics was performed to analyse why the SHPs estimated from the different PTFs cause the model to behave differently. The results obtained provide guidelines for the selection of PTFs in large scale models. The model performance in terms of numerical stability, time-integrated behaviour of cumulative fluxes, as well as instantaneous fluxes was evaluated, in order to compare the suitability of the PTFs. Based on this, the Rosetta, Wösten, and Tóth PTF seem to be the most robust PTFs for the Mualem van Genuchten SHPs and the PTF of Cosby et al. (1984) for the Brooks Corey functions. Based on our findings, we strongly recommend to harmonize the PTFs used in model inter-comparison studies to avoid artefacts originating from the choice of PTF rather from different model structures.

The saturated hydraulic conductivity (Ksat) is a key soil hydraulic parameter for representing infiltration and drainage in Earth system and land surface models. For large scale applications, Ksat is often estimated from pedotransfer functions (PTFs) based on easy-to-measure soil properties like soil texture and bulk density. The reliance of PTFs on data from uniform arable lands and omission of soil structure limits the applicability of texture-based predictions of Ksat in vegetated lands. A method to harness technological advances in machine learning and availability of remotely sensed surrogate information to derive a new global Ksat map at 1 km resolution using terrain, climate, vegetation, and soil covariates is proposed. For model training and testing, global compilation of 6,814 georeferenced Ksat measurements from the literature across the globe were used. The accuracy assessment results based on model cross-validations with re-fitting show a concordance correlation coefficient of 0.79 and root mean square error of 0.72 (in log10Ksat given in cm/day). The generated maps of Ksat represent spatial patterns of the vegetation-induced soil structure formation and clay mineralogy, more distinctly than previous global maps of Ksat such as computed with Rosetta 3 pedotransfer function. The validation of the model indicates that Ksat could be more accurately modeled using covariate-based Geo Transfer Functions (CoGTFs) that harness spatially distributed surface and climate attributes, compared to pedotransfer functions that rely only on soil information.