SoilKsatDB: a global compilation of soil saturated hydraulic conductivity measurements

Contains a total of 1,910 sites with 13,267 Ksat (soil saturated hydraulic conductivity) measurements were assembled from published literature and other sources, standardized, and quality-checked in order to provide a global database of soil saturated hydraulic conductivity. The SoilKsatDB covers most global regions, with the highest data density from the USA, followed by Europe, Asia, South America, Africa, and Australia. In addition to Ksat, other soil variables such as soil texture (11,667 measurements), bulk density (11,151 measurements), soil organic carbon (9,787 measurements), field capacity (7,389) and wilting point (7,418) are also included in the data set. To cite this dataset please use: Gupta, S., Hengl, T., Lehmann, P., Bonetti, S., and Or, D.: <strong>SoilKsatDB: global soil saturated hydraulic conductivity measurements for geoscience applications</strong>, Earth Syst. Sci. Data Discuss., https://doi.org/10.5194/essd-2020-149, in review, 2020. Examples of using the SoilKsatDB to generate global maps of Ksat can be found in: Gupta, S., Hengl, T., Lehmann, P., Bonetti, S., Papritz, A. and Or, D.: <strong>Global prediction of soil saturated hydraulic conductivity using random forest in a Covariate-based Geo Transfer Functions (CoGTF) framework</strong>. manuscript submitted to Journal of Advances in Modeling Earth Systems (JAMES). Import and bind steps are described in detail <strong>here</strong>. To report an issue or bug please use <strong>this link</strong>. SoilKsatDB is available in CSV, ARFF and RDS formats. ARFF was prepared using the farff package for R. ARFF' (Attribute-Relation File Format) files are like 'CSV' files, with a little bit of added meta information in a header and standardized NA values. Column codes are based on the National Cooperative Soil Survey (NCSS) Soil Characterization Database naming convention (see "README." for explanation of codes). The SoilKsatDB is a compilation of numerous existing datasets from which the most significant: SWIG data set (Rahmati et al., 2018), UNSODA (Leij et al., 1996), and HYBRAS (Ottoni et al., 2018). This study was supported by ETH Zurich (Grant ETH-18 18-1).