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.

Core Ideas: Stem water content of trees was successfully monitored using GS3 capacitance sensors. Temperature calibration was essential for correct interpretation of monitoring data. The monitoring clarified water use characteristics of the trees in arid environments. Dielectric soil moisture sensors have the potential for nondestructive and real‐time monitoring of the stem water content (θ _st ) of living trees. This study was conducted to investigate the water use characteristics of trees in drylands through monitoring of θ _st using newly developed capacitance sensors (GS3). The plants used for data collection were Prosopis juliflora (Sw.) DC. (mesquite, invasive) in Sudan and Tamarix ramosissima Ledeb. (tamarisk, invasive) and Prosopis pubescens Benth.(screwbean mesquite, native) in the United States. The GS3 probes were installed into the trunks of two trees for each species. Stem‐specific calibration equations and temperature calibration equations were derived through laboratory experiments and analysis of field observation data. The temperature calibration equations reduced inappropriate variations of θ _st caused by daily fluctuations in stem temperature, suggesting that these are essential for correct interpretation of monitoring data of θ _st in arid environments. The θ _st of the mesquite trees in Sudan clearly increased after heavy rainfall events and started decreasing when the soil water content became close to the wilting point. These findings indicate that mesquite trees use soil water in rainy seasons, even though they are generally considered to use groundwater through deep tap roots. The θ _st of neither species in the United States responded clearly to rainfall events, indicating that they depend on shallow saline groundwater. The θ _st of the tamarisk decreased monotonically throughout the monitoring period, apparently in response to feeding damage caused by the tamarisk leaf beetle ( Diohabda sp.), which had been released for biological control of tamarisk.

Prediction of soil characteristics over large areas is desirable for environmental modeling. In arid environments, soil characteristics often show strong ecological connectivity with natural vegetation, specifically biomass and/or canopy cover, suggesting that the soil characteristics may be predicted from vegetation data. The objective of this study was to predict soil infiltration characteristics and horizon (soil layer) thickness using vegetation data for a large-scale water balance model in an arid region. Double-ring infiltrometer tests (at 23 sites), horizon thickness measurements (58 sites) and vegetation surveys (35 sites) were conducted in a 30 km × 50 km area in Western Australia during 1999 to 2003. The relationships between soil parameters and vegetation data were evaluated quantitatively by simple linear regression. The parameters for initial-term infiltration had strong and positive correlations with biomass and canopy coverage (R² = 0.64 − 0.81). The horizon thickness also had strong positive correlations with vegetation properties (R² = 0.53 − 0.67). These results suggest that the soil infiltration parameters and horizon thickness can be spatially predicted by properties of vegetation using their linear regression based equations and vegetation maps. The background and reasons of the strong ecological connectivity between soil and vegetation in this region were also considered.