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.

In his seminal paper on solution of the infiltration equation, Philip (1957) proposed a gravity time, tgrav, to estimate practical convergence time of his infinite time series expansion, TSE. The parameter tgrav refers to a point in time where infiltration is dominated equally by capillarity and gravity derived from the first two (dominant) terms of the TSE expansion. Evidence that higher order TSE terms describe the infiltration process better for longer times. Since the conceptual definition of tgrav is valid regardless of the infiltration model used, we opted to reformulate tgrav using the analytic approximation proposed by Parlange et al. (1982) valid for all times. In addition to the roles of soil sorptivity (S) and saturated (Ks) and initial (Ki) hydraulic conductivities, we explored effects of a soil specific shape parameter β on the behavior of tgrav. We show that the reformulated tgrav (notably tgrav= F(β) S^2/(Ks - Ki)^2 where F(β) is a β-dependent function) is about 3 times larger than the classical tgrav given by tgrav, Philip= S^2/(Ks - Ki)^2. The differences between original tgrav, Philip and the revised tgrav increase for fine textured soils. Results show that the proposed tgrav is a better indicator for convergence time than tgrav, Philip. For attainment of the steady-state infiltration, both time parameters are suitable for coarse-textured soils, but not for fine-textured soils for which tgrav is too conservative and tgrav, Philip too short. Using tgrav will improve predictions of the soil hydraulic parameters (particularly Ks) from infiltration data as compared to tgrav, Philip.

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.

Soil bacteria live in patchy and dynamic environments where cells in adjacent microhabitats may realize vastly different generation times ranging from hours to years within small soil volumes. This study links bacterial population demographics with soil conditions to better estimate mean bacterial cell ages by tracking individual lineages over space and time using a mechanistic model of bacterial life in soil. Results show heavy-tailed distributions of generation times that follow a power law across all hydration conditions, implying no simple definition of mean soil bacterial age where soil volumes may harbor cells with very broad range of ages living side by side. The study highlights ubiquitous conditions that support a “genetic reservoir” of physiological traits for each bacterial species that may be preserved in soil cold spots and reintroduced during episodic reunification events (soil wetting).