Key Points: Efficient two‐stage MCMC simulation for CPU‐demanding sampling Real‐world application to the inference of a multi‐layered 3D aquifer model A 2‐5 times observed speed up in sampling efficiency

Unsaturated hydraulic properties of field soils are needed for water flow and solute transport calculations in variably saturated soils. The purpose of this study was to quantify the spatial variability and spatial crosscorrelation of estimated parameter values of a flexible retention model that was fitted to measured retention data. Moisture retention characteristic (MRC) curves were measured on 100-cm³ undisturbed soil cores collected at 180 locations along a 31-m-long transect in a three-layered soil profile at depths of 0.1, 0.5, and 0.9 m. Sampling intervals in the horizontal direction were, alternately, 0.1 and 0.9 m. Saturated hydraulic conductivity (K_s) was determined on the same soil cores using a constant head permeameter. The drying part of the MRC curves was described by the four-parameter retention model of van Genuchten with fitting parameters, namely θ_r, θ_s, α, and n. Spatial variability of the five parameters, θ_r, θ_s, α, n, and K_s, was investigated for the three horizons using conventional statistics and geostatistical techniques. Maximum coefficient of variation (CV) was found for K_s, i.e., 599%, 322%, and 897% for the 0.1-, 0.5-, and 0.9-m soil depths, respectively. Macropores and small sampling volume contributed to this large variability of K_s. When all three soil depths are considered, residual water content (θ_r) and shape factor α showed moderate heterogeneity with a maximum CV of 156 and 53%, respectively. Small spatial heterogeneity was observed for shape factor n and saturated water content θ_s, with a maximum CV of 22 (for 0.1-m depth) and 8% (for 0.9-m depth), respectively. Most hydraulic parameters at different layers exhibited convex experimental semivariograms that could be described by means of spherical models, with a spatial range between 4 and 7 m. The correlation scales for cross-semivariograms for pairs of cross-correlated parameters were found to be of similar magnitude to those pertaining to the direct semivariograms of correlated variables.

In this paper, we present and analyze a novel global database of soil infiltration measurements, the Soil Water Infiltration Global (SWIG) database. In total, 5023 infiltration curves were collected across all continents in the SWIG database. These data were either provided and quality checked by the scientists who performed the experiments or they were digitized from published articles. Data from 54 different countries were included in the database with major contributions from Iran, China, and the USA. In addition to its extensive geographical coverage, the collected infiltration curves cover research from 1976 to late 2017. Basic information on measurement location and method, soil properties, and land use was gathered along with the infiltration data, making the database valuable for the development of pedotransfer functions (PTFs) for estimating soil hydraulic properties, for the evaluation of infiltration measurement methods, and for developing and validating infiltration models. Soil textural information (clay, silt, and sand content) is available for 3842 out of 5023 infiltration measurements (∼ 76%) covering nearly all soil USDA textural classes except for the sandy clay and silt classes. Information on land use is available for 76 % of the experimental sites with agricultural land use as the dominant type (∼ 40%). We are convinced that the SWIG database will allow for a better parameterization of the infiltration process in land surface models and for testing infiltration models. All collected data and related soil characteristics are provided online in *.xlsx and *.csv formats for reference, and we add a disclaimer that the database is for public domain use only and can be copied freely by referencing it. Supplementary data are available at https://doi.org/10.1594/PANGAEA.885492 (Rahmati et al., 2018). Data quality assessment is strongly advised prior to any use of this database. Finally, we would like to encourage scientists to extend and update the SWIG database by uploading new data to it.