Earth System Science Data · 2021
SoilKsatDB: global database of soil saturated hydraulic conductivity measurements for geoscience applications
Gupta, S., Hengl, T., Lehmann, P., Bonetti, S., Or, D.
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SoilKsatDB: global database of soil saturated hydraulic cond…
Abstract
The saturated soil hydraulic conductivity ( K sat ) is a key parameter in many hydrological and climate models. K sat values are primarily determined from basic soil properties and may vary over several orders of magnitude. Despite the availability of K sat datasets in the literature, significant efforts are required to combine the data before they can be used for specific applications. In this work, a total of 13 258 K sat measurements from 1908 sites were assembled from the published literature and other sources, standardized (i.e., units made identical), and quality checked in order to obtain a global database of soil saturated hydraulic conductivity (SoilKsatDB). The SoilKsatDB covers most regions across the globe, with the highest number of K sat measurements from North America, followed by Europe, Asia, South America, Africa, and Australia. In addition to K sat , other soil variables such as soil texture (11 584 measurements), bulk density (11 262 measurements), soil organic carbon (9787 measurements), moisture content at field capacity (7382), and wilting point (7411) are also included in the dataset. To show an application of SoilKsatDB, we derived K sat pedotransfer functions (PTFs) for temperate regions and laboratory-based soil properties (sand and clay content, bulk density). Accurate models can be fitted using a random forest machine learning algorithm (best concordance correlation coefficient (CCC) equal to 0.74 and 0.72 for temperate area and laboratory measurements, respectively). However, when these K sat PTFs are applied to soil samples obtained from tropical climates and field measurements, respectively, the model performance is significantly lower (CCC = 0.49 for tropical and CCC = 0.10 for field measurements). These results indicate that there are significant differences between K sat data collected in temperate and tropical regions and K sat measured in the laboratory or field. The SoilKsatDB dataset is available at https://doi.org/10.5281/zenodo.3752721 (Gupta et al., 2020) and the code used to extract the data from the literature and the applied random forest machine learning approach are publicly available under an open data license.
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The saturated soil hydraulic conductivity ( K sat ) is a key parameter in many hydrological and climate models. K sat values are primarily determined from basic soil properties and may vary over several orders of magnitude. Despite the availability of K sat datasets in the literature, significant efforts are required to combine the data before they can be used for specific applications. In this work, a total of 13 258 K sat measurements from 1908 sites were assembled from the published literature and other sources, standardized (i.e., units made identical), and quality checked in order to obtain a global database of soil saturated hydraulic conductivity (SoilKsatDB). The SoilKsatDB covers most regions across the globe, with the highest number of K sat measurements from North America, followed by Europe, Asia, South America, Africa, and Australia. In addition to K sat , other soil variables such as soil texture (11 584 measurements), bulk density (11 262 measurements), soil organic carbon (9787 measurements), moisture content at field capacity (7382), and wilting point (7411) are also included in the dataset. To show an application of SoilKsatDB, we derived K sat pedotransfer functions (PTFs) for temperate regions and laboratory-based soil properties (sand and clay content, bulk density). Accurate models can be fitted using a random forest machine learning algorithm (best concordance correlation coefficient (CCC) equal to 0.74 and 0.72 for temperate area and laboratory measurements, respectively). However, when these K sat PTFs are applied to soil samples obtained from tropical climates and field measurements, respectively, the model performance is significantly lower (CCC = 0.49 for tropical and CCC = 0.10 for field measurements). These results indicate that there are significant differences between K sat data collected in temperate and tropical regions and K sat measured in the laboratory or field. The SoilKsatDB dataset is available at https://doi.org/10.5281/zenodo.3752721 (Gupta et al., 2020) and the code used to extract the data from the literature and the applied random forest machine learning approach are publicly available under an open data license.
The saturated soil hydraulic conductivity ( K sat ) is a key parameter in many hydrological and climate models. K sat values are primarily determined from basic soil properties and may vary over several orders of magnitude. Despite the availability of K sat datasets in the literature, significant efforts are required to combine the data before they can be used for specific applications. In this work, a total of 13 258 K sat measurements from 1908 sites were assembled from the published literature and other sources, standardized (i.e., units made identical), and quality checked in order to obtain a global database of soil saturated hydraulic conductivity (SoilKsatDB). The SoilKsatDB covers most regions across the globe, with the highest number of K sat measurements from North America, followed by Europe, Asia, South America, Africa, and Australia. In addition to K sat , other soil variables such as soil texture (11 584 measurements), bulk density (11 262 measurements), soil organic carbon (9787 measurements), moisture content at field capacity (7382), and wilting point (7411) are also included in the dataset. To show an application of SoilKsatDB, we derived K sat pedotransfer functions (PTFs) for temperate regions and laboratory-based soil properties (sand and clay content, bulk density). Accurate models can be fitted using a random forest machine learning algorithm (best concordance correlation coefficient (CCC) equal to 0.74 and 0.72 for temperate area and laboratory measurements, respectively). However, when these K sat PTFs are applied to soil samples obtained from tropical climates and field measurements, respectively, the model performance is significantly lower (CCC = 0.49 for tropical and CCC = 0.10 for field measurements). These results indicate that there are significant differences between K sat data collected in temperate and tropical regions and K sat measured in the laboratory or field. The SoilKsatDB dataset is available at https://doi.org/10.5281/zenodo.3752721 (Gupta et al., 2020) and the code used to extract the data from the literature and the applied random forest machine learning approach are publicly available under an open data license.