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: We propose a Hydrophysical Database for Brazilian Soils (HYBRAS). Data in HYBRAS comprise 445 soil profiles, with 1075 samples of Brazilian soils. We compared the accuracy of water retention estimates based on climate‐based PTFs. In general, the Brazilian PTFs performed better than the temperate models for HYBRAS. Model performance differed due to clay content and mineralogical properties. Soil water retention data are fundamental in soil modeling studies. Temperate pedotransfer functions (PTFs) have been commonly used to estimate water retention of Brazilian soils, mainly because of the lack of soil data for Brazil. However, these PTFs may not be suitable for tropical or subtropical conditions such as those found in Brazil. The objective of this study was to establish a dedicated Hydrophysical Database for Brazilian Soils (HYBRAS) suitable for PTF development. Data present in HYBRAS comprise 445 soil profiles with 1075 samples and are representative of a wide range of Brazilian soils. The data are organized in a relational structure of tables that cover general site descriptions, land cover, and hydrophysical and chemical measurement methods. Raw data (e.g., water retention points covering the 0–15,000‐cm suction range) and derived data are included in the tables. Another objective of this study was to use the database to compare the accuracy of water retention estimates based on PTFs developed for Brazilian and temperate regions. In general, the Brazilian PTFs performed better than the temperate models, especially for weathered (Ferralsols, Acrisols, and Nitisols) fine‐textured (clay, sandy clay, clay loam, silty clay loam, and silty clay) soils. Silt content was not a successful criterion for distinguishing performance of Brazilian and temperate PTFs for Brazilian weathered soils. The water retention of weathered soils was shown to differ from that of temperate soils due to differences in pore structure resulting from their clay content and mineralogical nature, thus confirming results reported in the literature.

Extreme rainfall can be a catastrophic trigger for natural disaster events at urban scales. However, there remains large uncertainties as to how satellite precipitation can identify these triggers at a city scale. The objective of this study is to evaluate the potential of satellite-based rainfall estimates to monitor natural disaster triggers in urban areas. Rainfall estimates from the Global Precipitation Measurement (GPM) mission are evaluated over the city of Rio de Janeiro, Brazil, where urban floods and landslides occur periodically as a result of extreme rainfall events. Two rainfall products derived from the Integrated Multi-satellite Retrievals for GPM (IMERG), the IMERG Early and IMERG Final products, are integrated into the Noah Multi-Parameterization (Noah-MP) land surface model in order to simulate the spatial and temporal dynamics of two key hydrometeorological disaster triggers across the city over the wet seasons during 2001–2019. Here, total runoff (TR) and rootzone soil moisture (RZSM) are considered as flood and landslide triggers, respectively. Ground-based observations at 33 pluviometric stations are interpolated, and the resulting rainfall fields are used in an in-situ precipitation-based simulation, considered as the reference for evaluating the IMERG-driven simulations. The evaluation is performed during the wet seasons (November-April), when average rainfall over the city is 4.4 mm/day. Results show that IMERG products show low spatial variability at the city scale, generally overestimate rainfall rates by 12–35%, and impacts on TR and RZSM vary spatially mostly as a function of land cover and soil types. Results based on statistical and categorical metrics show that IMERG skill in detecting extreme events is moderate, with IMERG Final performing slightly better for most metrics. By analyzing two recent storms, we observe that IMERG detects mostly hourly extreme events, but underestimates rainfall rates, resulting in underestimated TR and RZSM. An evaluation of normalized time series using percentiles shows that both satellite products have significantly improved skill in detecting extreme events when compared to the evaluation using absolute values, indicating that IMERG precipitation could be potentially used as a predictor for natural disasters in urban areas.

Taking into account the nature of the hydrological processes involved in in situ measurement of Field Capacity (FC), this study proposes a variation of the definition of FC aiming not only at minimizing the inadequacies of its determination, but also at maintaining its original, practical meaning. Analysis of FC data for 22 Brazilian soils and additional FC data from the literature, all measured according to the proposed definition, which is based on a 48-h drainage time after infiltration by shallow ponding, indicates a weak dependency on the amount of infiltrated water, antecedent moisture level, soil morphology, and the level of the groundwater table, but a strong dependency on basic soil properties. The dependence on basic soil properties allowed determination of FC of the 22 soil profiles by pedotransfer functions (PTFs) using the input variables usually adopted in prediction of soil water retention. Among the input variables, soil moisture content θ (6 kPa) had the greatest impact. Indeed, a linear PTF based only on it resulted in an FC with a root mean squared residue less than 0.04 m³ m-3 for most soils individually. Such a PTF proved to be a better FC predictor than the traditional method of using moisture content at an arbitrary suction. Our FC data were compatible with an equivalent and broader USA database found in the literature, mainly for medium-texture soil samples. One reason for differences between FCs of the two data sets of fine-textured soils is due to their different drainage times. Thus, a standardized procedure for in situ determination of FC is recommended.

Bulk density (BD) is a soil physical property used as a soil quality indicator and variations in this measurement influence soil water content and carbon stock estimates. This study aims to compile a database of samples of bulk density, textural fractions, and organic carbon values, as well as evaluate the accuracy of published pedotransfer functions (PTF) that predict bulk density, and propose a hierarchical PTF to predict the bulk density of Brazilian Soils. The performance of eleven PTFs and the newly proposed PTFs were evaluated and compared using the root mean square error (RMSE) and coefficient of determination (R²) based on a testing soil database collected from the literature. We noticed a slight improvement in accuracy when organic carbon and coarse and fine sand fractions were included as predictors alongside silt and clay. The best results with existing PTFs were obtained by PTF-A in Tomasella and Hodnett (1998) (RMSE = 0.20 g cm^–3) and PTF-F in Benites et al. (2007) (RMSE = 0.17 g cm^–3). Our proposed PTFs use textural fractions and organic carbon as predictors in a hierarchical form. The proposed PTF-4, which uses fine sand, coarse sand, clay, and organic carbon, presented the lowest value for RMSE (0.14 g cm^–3) for BD prediction.