Global 250m Soil Hydraulic Properties

This dataset provides a comprehensive assessment of soil hydraulic properties, leveraging the high-resolution SoilGrids250m 2.0 data on the Google Earth Engine (GEE) platform. It focuses on the derivation of critical parameters such as Saturated Hydraulic Conductivity (Ks), Saturated Water Content (θ_s), Field Capacity (θ_fld), and Permanent Wilting Point (θ_wlt), across six distinct soil layers (0-5 cm, 5-15 cm, 15-30 cm, 30-60 cm, 60-100 cm, and 100-200 cm). These parameters are essential for modeling water flow and retention in soils, making the dataset invaluable for agricultural, hydrological, and environmental research. Soil properties data, including percentages of sand, clay, and silt content, are meticulously represented, facilitating a detailed analysis of soil hydraulic characteristics across varying depths. Additionally, the classification of Hydrologic Soil Groups (HSGs) is derived from USDA-based soil texture classes and depth to bedrock, further enriching the dataset. Catering to a broad spectrum of applications, from agricultural planning to drought assessment, this dataset is a pivotal resource for enhancing soil-water interaction understanding and managing water resources efficiently in diverse ecosystems.\nLineage: Utilizing the SoilGrids250m 2.0 dataset, this study provides a robust estimation of soil hydraulic properties across six defined soil layers (ranging from 0 to 200 cm depth) with a fine spatial resolution of 250 meters. The dataset delivers comprehensive soil composition metrics, highlighting the relative contents of sand, clay, and silt within these layers. Key soil water characteristics, including Saturated Water Content (θ_s), Field Capacity (θ_fld), and Permanent Wilting Point (θ_wlt), are meticulously computed for each depth interval employing methodologies originally formulated by Campbell (1974) and further refined by Cosby et al. (1984). Additionally, Saturated Hydraulic Conductivity (Ks) values are derived through the application of equations provided by Saxton and Rawls (2006), enhancing the dataset's utility in hydrological modeling and agricultural planning. Further enriching this dataset, the classification of Hydrologic Soil Groups (HSGs) integrates USDA-based soil texture classes with the depth to bedrock criteria, as per the innovative approach developed by Ross et al. (2018).