SoilKsatDB: Global Soil Hydraulic Conductivity Measurements
by Gupta Surya / ETH Zurich
Available on 1 platform
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Description
A global compilation of soil saturated hydraulic conductivity (Ksat) measurements from 1,910 sites, totaling 13,267 standardized and quality-checked observations. The database also includes related soil variables such as texture, bulk density, organic carbon, field capacity, and wilting point, with the highest data density in the USA, followed by Europe, Asia, South America, Africa, and Australia. It is assembled from published literature and other sources, including significant contributions from the SWIG, UNSODA, and HYBRAS datasets.
Use Cases
Generating global maps of soil saturated hydraulic conductivity for hydrological modeling.
Training machine learning models, such as random forests, for predicting Ksat using covariate-based geo-transfer functions.
Analyzing relationships between soil hydraulic conductivity and other soil properties like texture and organic carbon.
Calibrating and validating soil water retention and flow models using the provided water retention curve data.
Strengths
Contains a substantial number of standardized measurements, with 13,267 Ksat values from 1,910 global sites.
Includes a wide range of related soil variables, such as 11,667 soil texture measurements and 9,787 soil organic carbon measurements.
Data is quality-checked and compiled from multiple authoritative sources, including SWIG, UNSODA, and HYBRAS.
Limitations
Specific column names and the total number of rows for the complete dataset are not provided in the available metadata.
The last update date is unknown, which may affect assessments of data freshness.
Data density is uneven globally, with the highest concentration in the USA and lower representation in other continents.
Provenance
Source
Compiled from published literature and existing datasets, including SWIG, UNSODA, and HYBRAS.
Collection Method
Measurements were assembled, standardized, and quality-checked.
Geography
Global, with sites across most regions, including the USA, Europe, Asia, South America, Africa, and Australia.
The dataset uses column codes based on the National Cooperative Soil Survey (NCSS) Soil Characterization Database naming convention, which may require reference documentation for interpretation.