Saturated hydraulic conductivity (Ksat) is a key soil hydraulic parameter for representing infiltration and drainage in 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 the omission of soil structure limits the applicability of texture‐based predictions of Ksat in vegetated lands. To include effects of terrain, climate, and vegetation in the derivation of a new global Ksat map at 1 km resolution, we harness technological advances in machine learning and availability of remotely sensed surrogate information. For model training and testing, a global compilation of 6,814 geo‐referenced Ksat measurements from the literature was used. The accuracy assessment based on spatial cross‐validation shows a concordance correlation coefficient (CCC) of 0.16 and a root mean square error (RMSE) of 1.18 for log10 Ksat values in cm/day (CCC = 0.79 and RMSE = 0.72 for non‐spatial cross‐validation). The generated maps of Ksat represent spatial patterns of soil formation processes more distinctly than previous global maps of Ksat based on easy‐to‐measure soil properties. The validation of the model indicates that Ksat could be modeled without bias using Covariate‐based GeoTransfer Functions (CoGTFs) that harness spatially distributed surface and climate attributes, compared to soil information based PTFs. The relatively poor performance of all models in the validation (low CCC and high RMSE) highlights the need for the collection of additional Ksat values to train the model for regions with sparse data.

USLE-type models are widely used to estimate average annual soil loss at large scales, with the erodibility factor (K) being the sole component that accounts for soil's susceptibility to erosion. The factor includes the information on permeability in the equation, however, most definitions of the K factor consider the soil hydrological influence only very crudely and indirectly. Thus, the direct impact of surface runoff infiltration and drainage on soil erosion is largely neglected. The objective of this study is to incorporate soil hydraulic properties in the K factor map by merging available global-scale measured saturated hydraulic conductivity (Ksat) data with soil texture and organic carbon information into a modified K factor. To achieve this, the Wischmeier and Smith (1978) soil texture- and permeability-based equation (K_Wischmeier factor) was modified to include Ksat, called K_ksat factor. Using the Random Forest machine learning algorithm, the K_Wischmeier factor and the K_ksat factor were each correlated with soil and remote sensing covariates for spatial extrapolation of two independent K factor maps at 1 km spatial resolution. We noted a clear decrease in the mean value of the K_ksat factor (0.023 t ha h ha^-1 MJ^-1 mm^-1) compared to the mean value of the K_Wischmeier factor (0.027 t ha h ha^-1 MJ^-1 mm^-1). The reduction in K_ksat factor values was most pronounced in tropical regions reflecting the difference in soil properties (e.g., clay and iron), whereas other climate regions showed relatively minor changes in comparison to the K_Wischmeier factor as well as to the recent global modeling of Borrelli et al. (2017) (K_GloSEM factor maps). As many studies discussed an overall overestimation of (R)USLE based erosion rates compared to measurements, this reduction in the K factor might improve modeled erosion rates in the right direction. The K_ksat marks an important initial step in integrating hydraulic properties into the K factor of USLE-type models and can prove their significance in future studies.

The saturated soil hydraulic conductivity (K_sat) is a key parameter in many hydrological and climate models. K_sat values are primarily determined from basic soil properties and may vary over several orders of magnitude. Despite the availability of K_sat datasets in the literature, significant efforts are required to combine the data before they can be used for specific applications. In this work, a total of 13 258 K_sat measurements from 1908 sites were assembled from the published literature and other sources, standardized (i.e., units made identical), and quality checked in order to obtain a global database of soil saturated hydraulic conductivity (SoilKsatDB). The SoilKsatDB covers most regions across the globe, with the highest number of K_sat measurements from North America, followed by Europe, Asia, South America, Africa, and Australia. In addition to K_sat, other soil variables such as soil texture (11 584 measurements), bulk density (11 262 measurements), soil organic carbon (9787 measurements), moisture content at field capacity (7382), and wilting point (7411) are also included in the dataset. To show an application of SoilKsatDB, we derived K_sat pedotransfer functions (PTFs) for temperate regions and laboratory-based soil properties (sand and clay content, bulk density). Accurate models can be fitted using a random forest machine learning algorithm (best concordance correlation coefficient (CCC) equal to 0.74 and 0.72 for temperate area and laboratory measurements, respectively). However, when these K_sat PTFs are applied to soil samples obtained from tropical climates and field measurements, respectively, the model performance is significantly lower (CCC = 0.49 for tropical and CCC = 0.10 for field measurements). These results indicate that there are significant differences between K_sat data collected in temperate and tropical regions and K_sat measured in the laboratory or field. The SoilKsatDB dataset is available at https://doi.org/10.5281/zenodo.3752721 (Gupta et al., 2020) and the code used to extract the data from the literature and the applied random forest machine learning approach are publicly available under an open data license.

Clay minerals dominate the soil colloidal fraction and its specific surface area. Differences among clay mineral types significantly influence their effects on soil hydrological and mechanical behavior. Presently, the soil clay content is used to parameterize soil hydraulic and mechanical properties (SHMP) for land surface models while disregarding the type of clay mineral. This undifferentiated use of clay leads to inconsistent parameterization, particularly between tropical and temperate soils, as shown herein. We capitalize on recent global maps of clay minerals that exhibit strong climatic and spatial segregation of active and inactive clays to consider spatially resolved clay mineral types in SHMP estimation. Clay mineral‐informed pedotransfer functions and machine learning algorithms trained with datasets including different clay types and soil structure formation processes improve SHMP representation regionally with broad implications for hydrological and geomechanical Earth surface processes.

Hydro-pedotransfer functions (PTFs) relate easy-to-measure and readily available soil information to soil hydraulic properties (SHPs) for applications in a wide range of process-based and empirical models, thereby enabling the assessment of soil hydraulic effects on hydrological, biogeochemical, and ecological processes. At least more than 4 decades of research have been invested to derive such relationships. However, while models, methods, data storage capacity, and computational efficiency have advanced, there are fundamental concerns related to the scope and adequacy of current PTFs, particularly when applied to parameterise models used at the field scale and beyond. Most of the PTF development process has focused on refining and advancing the regression methods, while fundamental aspects have remained largely unconsidered. Most soil systems are not represented in PTFs, which have been built mostly for agricultural soils in temperate climates. Thus, existing PTFs largely ignore how parent material, vegetation, land use, and climate affect processes that shape SHPs. The PTFs used to parameterise the Richards–Richardson equation are mostly limited to predicting parameters of the van Genuchten–Mualem soil hydraulic functions, despite sufficient evidence demonstrating their shortcomings. Another fundamental issue relates to the diverging scales of derivation and application, whereby PTFs are derived based on laboratory measurements while often being applied at the field to regional scales. Scaling, modulation, and constraining strategies exist to alleviate some of these shortcomings in the mismatch between scales. These aspects are addressed here in a joint effort by the members of the International Soil Modelling Consortium (ISMC) Pedotransfer Functions Working Group with the aim of systematising PTF research and providing a roadmap guiding both PTF development and use. We close with a 10-point catalogue for funders and researchers to guide review processes and research.

Hydrological and climatic modeling of near-surface water and energy fluxes is critically dependent on the availability of soil hydraulic parameters. Key among these parameters is the soil water characteristic curve (SWCC), a function relating soil water content ($\theta$) to matric potential ($\psi$). The direct measurement of SWCC is laborious, hence, reported values of SWCC are spatially sparse and usually have only a small number of data pairs ($\theta$, $\psi$) per sample. Pedotransfer function (PTF) models have been used to correlate SWCC with basic soil properties, but evidence suggests that SWCC is also shaped by vegetation-promoted soil structure and climate-modified clay minerals. To capture these effects in their spatial context, a machine learning framework (denoted as Covariate-based GeoTransfer Functions, CoGTFs) was trained using (a) a novel and comprehensive global dataset of SWCC parameters and (b) global maps of environmental covariates and soil properties at 1 km spatial resolution. Two CoGTF models were developed: one model (CoGTF-1) was based on predicted soil covariates because measured soil data are not generally available, and the other (CoGTF-2) used measured soil properties to model SWCC parameters. The spatial cross-validation of CoGTF-1 resulted, for the predicted van Genuchten SWCC parameters, in concordance correlation coefficients (CCC) of 0.321–0.565. To validate the resulting global maps of SWCC parameters and to compare the CoGTF framework to two pedotransfer functions from the literature, the predicted water contents at 0.1 m, 3.3 m, and 150 m matric potential were evaluated. The accuracy metrics for CoGTF were considerably better than PTF-based maps.