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.

Este estudo avalia a distribuição espacial do potencial natural e atual do solo à erosão hídrica na Bacia do Rio Sapucaí, sul de Minas Gerais, utilizando a equação universal de perda de solos revisada, através de modelagem cartográfica, para a obtenção da erosão potencial (EP) e da erosão atual (EA). Os resultados indicaram que a EP para a Bacia do Rio Sapucaí variou de "Muito forte", na sua região de cabeceira (Serra da Mantiqueira), a "Fraca", nas áreas com topografia mais suavizada e menor erosividade da chuva. Em relação à EA, mais de 55,17 % da Bacia do Rio Sapucaí apresenta perdas de solo abaixo de 10 t.ha^-1.ano^-1, significando baixo potencial atual de erosão. A identificação de áreas de risco associadas à erosão acelerada, realizadas neste estudo, fornecem subsídios fundamentais para medidas associadas ao manejo, conservação e planejamento do uso do solo.

Sediment delivery rate (SDR) is traditionally defined as a fraction of upland gross erosion that is transported through the outlet of a given basin, thus representing an index of the sediment transport efficiency. The objective of this study was to evaluate a model-based approach for SDR determination in the Paraopeba River Basin (PRB), located at the central region of Minas Gerais State, Southeastern Brazil. The empirical model known as RUSLE, which stands for Revised Universal Loss Equation, was used embedded in a geographical information system (GIS) to estimate the soil erosion potential. Based on a monitoring network, considering the period between 1997 and 2008, the sediment transport was determined in five sub-basins of the PRB. The average water erosion for PRB was 2.7 ton ha⁻¹ yr⁻¹, while the sediment transport varied from 0.25 to 1.63 ton ha⁻¹ yr⁻¹. Considering the area and the slope as the independent variables, three empirical models were applied for estimating SDR. The results showed adequate fit for the proposed model, resulting in improvements for SDR estimates in PRB. On evaluating only sub-basins, the observed average SDR was 0.38, while the corresponding estimated value was 0.37. It was found that the three traditional equations did not generate acceptable results and this can be explained by the overestimation of SDR values for PRB.