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Abstract. 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.

A B S T R A C TCheck dams can be used as a source of information for studies on sediment characteristics and soil particle erodibility. In this study, sediment yield and grain size distribution (GSD) were measured in twenty small catchments draining into a rock check dam in NW Iran for different runoffs during 2010-2011. Significant correlations were found between sediment yield and slope steepness, vegetation cover and soil erodibility factor (K) of the catchments. The erodibility of soil particles was determined using the comparison of GSD between sediment and original soil. Clay was the most erodible soil particle which showed 2.05 times more percentage in sediment than the original soil. The erodibility of soil particles were strongly affected by the rainfall erosivity (EI 30 ). Check dams showed more effectiveness in trapping coarse particles (sand and gravel). The effectiveness of check dams in trapping coarse particles enhanced with increase in the remaining capacity of check dams.

Land abandonment is widespread in the Mediterranean mountains. The impact of agricultural abandonment results in a shift in ecosystem evolution due to changes in soil erosion, but little is known about long-term soil and water losses. This paper uses 11 years of measurements in two paired plots (abandoned vs control) with four subplots to determine how soil and water losses evolved after abandonment within an agricultural parcel. For two years (2004–2005) both plots were under tillage, and after 2006 one plot was abandoned. The monitored plots measured runoff and sediment concentration after each rainfall event. The results show that during the two years after abandonment there was an increase in sediment yield followed by a decrease. Once the field was abandoned, a sudden increase in runoff (× 2.1 times) and sediment concentration (× 1.2 times) was found due to the lack of vegetation and tillage. After one year, the sediment concentration and, after two years, the runoff rates were lower in the abandoned than in the tilled plots. This short transition period ended in contrasting responses between the control and abandoned plot as the impact of abandonment resulted in 21 times less sediment yield after nine years of abandonment. This occurred despite the fact that the year after the abandonment the abandoned plot had 2.9 times more erosion due to low vegetation recovery and the development of a soil crust. Agriculture land abandonment resulted in lower erosion rates over the long term, but showed an increase in soil and water losses over the short term (two years). Therefore, in the first two years after abandonment there is a particular need to apply nature-based soil and water conservation strategies to prevent soil erosion.

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Modeling sediment yield is a complex task because of the nonlinearity of natural processes intervening at slope and basin scale. In this study slope steepness, vegetation cover, and soil properties along with sediment yield were studied in 20 pasture micro‐catchments in a semi‐arid region, NW Iran in order to understand and predict sediment yield. The micro‐catchments included only one first‐order gully and drain toward a rock check dam in the outlet. The sediment yield of each micro‐catchment was calculated using the measurement of sediment mass in the check dams for a 16‐year period (1994–2010). Relationships between sediment yield and drainage characteristics were analyzed using correlation matrix and multiple linear regression method. Based on the results, sediment yield in the micro‐catchments varied from 0.29 Mg ha−1 y−1 to 14.81 Mg ha−1 y−1, with an average of 5.04 Mg ha−1 y−1. It was significantly related to slope steepness, vegetation cover, and soil organic matter using a linear regression equation (R2 = 0.87, p < 0.001). The slope, vegetation, and soil organic matter explained about 44%, 23%, and 20% of total variance in sediment yield, respectively. The spatial validation of the model using data from eight different micro‐catchments located nearby showed that the model efficiency is 0.94. Therefore, the model can be used for predicting sediment yield in this and similar study area, with a high degree of accuracy. Copyright © 2016 John Wiley & Sons, Ltd.