We reviewed the use of the van Genuchten–Mualem (VGM) model to parameterize soil hydraulic properties and for developing pedotransfer functions (PTFs). Analysis of literature data showed that the moisture retention characteristic (MRC) parameterization by setting shape parameters m = 1 − 1/n produced the largest deviations between fitted and measured water contents for pressure head values between 330 (log10 pressure head [pF] 2.5) and 2500 cm (pF 3.4). The Schaap–van Genuchten model performed best in describing the unsaturated hydraulic conductivity, K The classical VGM model using fixed parameters produced increasingly higher root mean squared residual, RMSR, values when the soil became drier. The most accurate PTFs for estimating the MRC were obtained when using textural properties, bulk density, soil organic matter, and soil moisture content. The RMSR values for these PTFs approached those of the direct fit, thus suggesting a need to improve both PTFs and the MRC parameterization. Inclusion of the soil water content in the PTFs for K only marginally improved their prediction compared with the PTFs that used only textural properties and bulk density. Including soil organic matter to predict K had more effect on the prediction than including soil moisture. To advance the development of PTFs, we advocate the establishment of databases of soil hydraulic properties that (i) are derived from standardized and harmonized measurement procedures, (ii) contain new predictors such as soil structural properties, and (iii) allow the development of time‐dependent PTFs. Successful use of structural properties in PTFs will require parameterizations that account for the effect of structural properties on the soil hydraulic functions.

A range of continental-scale soil datasets exists in Europe with different spatial representation and based on different principles. We developed comprehensive pedotransfer functions (PTFs) for applications principally on spatial datasets with continental coverage. The PTF development included the prediction of soil water retention at various matric potentials and prediction of parameters to characterize soil moisture retention and the hydraulic conductivity curve (MRC and HCC) of European soils. We developed PTFs with a hierarchical approach, determined by the input requirements. The PTFs were derived by using three statistical methods: (i) linear regression where there were quantitative input variables, (ii) a regression tree for qualitative, quantitative and mixed types of information and (iii) mean statistics of developer-defined soil groups (class PTF) when only qualitative input parameters were available. Data of the recently established European Hydropedological Data Inventory (EU-HYDI), which holds the most comprehensive geographical and thematic coverage of hydro-pedological data in Europe, were used to train and test the PTFs. The applied modelling techniques and the EU-HYDI allowed the development of hydraulic PTFs that are more reliable and applicable for a greater variety of input parameters than those previously available for Europe. Therefore the new set of PTFs offers tailored advanced tools for a wide range of applications in the continent.

We revisited the Vereecken database, which has been used to derive pedotransfer functions (PTFs) to estimate the soil hydraulic parameters of Belgian soils. We developed new PTFs based on the Mualem–van Genuchten model, constraining m = 1 − 1/n and using fewer parameters. The goodness‐of‐fit was similar to the one originally obtained by Vereecken. We used a one‐step procedure that allows direct quantification of the correlation matrix and the uncertainties of the estimated parameter values. The coefficients of the new PTFs were estimated using a global search algorithm and they were validated against independent data. The PTFs have a wider range of applicability since: (i) they allow the use of the closed‐form solution of the unsaturated hydraulic conductivity in the Mualem–van Genuchten model; and (ii) they consider the effect of macroporosity. We determined that the hydraulic conductivity measured close to saturation could not be estimated based on the available estimators; however, the hydraulic conductivity in the matrix domain was predicted with high accuracy.

Soil hydraulic properties are required in various modelling schemes. We propose a consistent spatial soil hydraulic database at 7 soil depths up to 2 m calculated for Europe based on SoilGrids250m and 1 km datasets and pedotransfer functions trained on the European Hydropedological Data Inventory. Saturated water content, water content at field capacity and wilting point, saturated hydraulic conductivity and Mualem‐van Genuchten parameters for the description of the moisture retention, and unsaturated hydraulic conductivity curves have been predicted. The derived 3D soil hydraulic layers (EU‐SoilHydroGrids ver1.0) can be used for environmental modelling purposes at catchment or continental scale in Europe. Currently, only EU‐SoilHydroGrids provides information on the most frequently required soil hydraulic properties with full European coverage up to 2 m depth at 250 m resolution.

Understanding spatial and temporal patterns in land susceptibility to wind erosion is essential to design effective management strategies to control land degradation. The knowledge about the land surface susceptible to wind erosion in European contexts shows significant gaps. The lack of researches, particularly at the landscape to regional scales, prevents national and European institutions from taking actions aimed at an effective mitigating of land degradation. This study provides a preliminary pan-European assessment that delineates the spatial patterns of land susceptibility to wind erosion and lays the groundwork for future modelling activities. An Index of Land Susceptibility to Wind Erosion (ILSWE) was created by combining spatiotemporal variations of the most influential wind erosion factors (i.e. climatic erosivity, soil erodibility, vegetation cover and landscape roughness). The sensitivity of each input factor was ranked according to fuzzy logic techniques. State-of-the-art findings within the literature on soil erodibility and land susceptibility were used to evaluate the outcomes of the proposed modelling activity. Results show that the approach is suitable for integrating wind erosion information and environmental factors. Within the 34 European countries under investigation, moderate and high levels of land susceptibility to wind erosion were predicted, ranging from 25·8 to 13·0 M ha, respectively (corresponding to 5·3 and 2·9% of total area). New insights into the geography of wind erosion susceptibility in Europe were obtained and provide a solid basis for further investigations into the spatial variability and susceptibility of land to wind erosion across Europe.

The objective of this study was to develop pedotransfer functions (PTFs) for converting soil particle‐size distribution (PSD) data from the laser diffraction method (LDM) to the classical sieve–pipette method (SPM) for use on a wide range of temperate soil types. Four hundred soil samples, representative of European soil types and climate zones, were selected from the LUCAS (Land Use/Land Cover Area Frame Survey) topsoil database and their PSDs were determined with LDM and SPM. The LDM measurements were made on samples with (i) their organic matter (OM) removed and (ii) their OM content present. The ranges of PSD obtained with the two pretreatment methods enabled clay–silt and silt–sand boundaries from LDM (6.6 and 60.3 µm for soil with OM, respectively, and 5.8 and 69.2 µm for soil without OM, respectively) to be optimized. Optimization of the boundaries of the fractions considerably improved the prediction performance of SPM PSD from LDM PSD. Specific PTFs with different input requirements were developed for continental scale applications in Europe to convert data from LDM to SPM. The predictions of SPM clay, silt and sand contents were the most accurate with PTFs that used PSD from LDM and soil chemical properties (R2 0.83, 0.81, 0.94; RMSE 6.14, 7.91 and 6.58%, respectively). For the most accurate results no pretreatment for OM removal was required, but data on chemical properties were necessary. If no soil chemical data are available, the most accurate PTFs need input data of LDM PSD that originate from samples on which the OM content was removed prior to the PSD analysis. Highlights PTFs are developed to harmonize PSD data between laser diffraction (LDM) and sieve–pipette (SPM) methods. PTFs are derived from a representative dataset from Europe for application at the continental scale. Clay–silt and silt–sand boundaries for LDM without removing OM are at 6.6 and 60.3 µm, respectively. Clay–silt and silt–sand boundaries for LDM with OM removed are at 5.8 and 69.2 µm, respectively.

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Abstract. Soil hydraulic properties are often derived indirectly, i.e. computed from easily available soil properties with pedotransfer functions (PTFs), when those are needed for catchment, regional or continental scale applications. When predicted soil hydraulic parameters are used for the modelling of the state and flux of water in soils, uncertainty of the computed values can provide more detailed information when drawing conclusions. The aim of this study was to update the previously published European PTFs (Tóth et al., 2015, euptf v1.4.0) by providing prediction uncertainty calculation built into the transfer functions. The new set of algorithms was derived for point predictions of soil water content at saturation (0 cm matric potential head), field capacity (both −100 and −330 cm matric potential head), wilting point (−15.000 cm matric potential head), plant available water, and saturated hydraulic conductivity, as well as the Mualem-van Genuchten model parameters of the moisture retention and hydraulic conductivity curve. The minimum set of input properties for the prediction is soil depth and sand, silt and clay content. The effect of including additional information like soil organic carbon content, bulk density, calcium carbonate content, pH and cation exchange capacity were extensively analysed. The PTFs were derived adopting the random forest method. The advantage of the new PTFs is that they i) provide information about prediction uncertainty, ii) are significantly more accurate than the euptfv1, iii) can be applied for more predictor variable combinations than the euptfv1, 32 instead of 5, and iv) are now also derived for the prediction of water content at −100 cm matric potential head and plant available water content.