A database of soil physical properties for the Kansas Mesonet

Abstract

This study developed a comprehensive database of soil physical properties to expand the potential applications of soil moisture observations across the Kansas Mesonet environmental monitoring network. The database comprises 14 site-and depthspecific soil hydraulic properties for 40 stations monitoring soil water content. The measured soil hydraulic properties include sand, clay, and silt contents; bulk density; particle density; total porosity; effective saturation; saturated hydraulic conductivity; and water retention at six matric potentials. The soil database spans eight soil textural classes, with silty clay loam, silt loam, and silty clay soils dominating the fine-textured soils, and sandy loam and sandy clay loam soils comprising the only coarse-textured soils. All the measured soil hydraulic properties showed low coefficient of variation (CV ≤10%) within soil textural classes, except for the saturated hydraulic conductivity, which varied from a median of 0.520 ± 308 cm d -1 in the clay soils to 47.1 ± 119 cm d -1 in the sandy loam soils. The uncertainty of the Kansas Mesonet soil water content observations was reduced by at least 29% using laboratory sensor calibration equations instead of the factory default sensor equation. Our soil physical property database offers new prospects to use historical and near realtime in situ soil water content observations across the Kansas Mesonet for diverse agricultural and hydrological applications, including drought monitoring, wildfire preparedness, and estimating potential drainage and groundwater recharge rates. INTRODUCTIONThe Kansas Mesonet is a mesoscale environmental monitoring network that was established in 1986 by Kansas State University Research and Extension, and currently consists of 75 automated stations distributed across the state of Kansas over an area of 213,000 km 2 . Each station is instrumented with research-grade sensors that record atmospheric (i.e., precipitation, air temperature, relative humidity, wind speed, solar radiation) and soil variables (i.e., soil moisture, soil Abbreviations: MBE, mean bias error; PAWC, plant available water capacity; RMSD, root mean square difference.

Validation of the percolation‐based effective‐medium approximation model to estimate soil thermal conductivity

“…Observed and modeled thermal conductivity with the percolation‐based effective‐medium approximation (P‐EMA, a) and Johansen (b) models for 38 undisturbed samples from the Kansas Mesonet soil physical property database (Parker et al., 2022). The dashed line represents the 1:1 line.…”

“…The selected soil samples correspond to the Rossville 2SE, Elmdale 1SE, Parsons, Sedan, Jewell, and Hays stations of the Kansas Mesonet. Detailed description of the methods for determining the soil physical properties can be found in Parker et al (2022). But briefly, the first step consisted of measuring effective saturation by placing the soil samples in a solution of 5 mmol L −1 CaCl 2 to prevent clay dispersion and letting the samples saturate from the bottom.…”

“…We used data for 38 undisturbed soil samples (Table 1) from a larger soil physical property database (Parker et al., 2022) developed for the Kansas Mesonet environmental monitoring network (Patrignani et al., 2020). We specifically selected soil samples for which observations of thermal conductivity, volumetric soil water content, particle size analysis, saturated hydraulic conductivity, and effective saturation were available.…”

Assessment and Characterization of Agricultural Salt-Affected Soils around Abaya and Chamo Lakes, South Ethiopia Rift Valley

“…Soil color was measured under uniform conditions using the Munsell Soil Color Chart [27]. Te pycnometer method was used to determine the particle density (PD) [28]. Particle size was determined by a hydrometer [29], and bulk density (BD) was determined by the core method [30].…”