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Soil shrinkage characteristic curves are used to describe the shrinkage behavior and hydraulic properties of unsaturated soils. To construct soil shrinkage characteristic curves, a high‐data‐density measurement method is needed that relates water content to soil volume changes. We present a fully automated soil shrinkage measurement setup, based on the simplified evaporation method, to characterize the shrinkage behavior of undisturbed natural expansive clay soils. The high data density creates the opportunity to produce soil shrinkage characteristic curves without the need for a mathematical model. The technique allows for resaturation of the samples after drying, enabling differentiation between reversible and irreversible shrinkage. The setup consists of the commercialized HYPROP2 apparatus combined with optical distance sensors to measure the horizontal and vertical dimensions of the samples, yielding data on the sample volume, weight, and soil water suction. The measurement frequency is once per 10 min, and the measurement period is up to 4 weeks, providing a detailed time series of the drying and shrinkage characteristics. The setup can capture the different shrinkage phases and offers the opportunity to relate the soil shrinkage characteristic curve to soil water suction. The measurement data acquisition rate and accuracy enable detailed interpretation of soil water retention curves for nonrigid soils and are shown to be essential for understanding and quantifying the shrinkage potential of several types of deposits. [ABSTRACT FROM AUTHOR]

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 depth‐specific 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 real‐time 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. Core Ideas: This study developed a comprehensive soil physical property database for the Kansas Mesonet.The database comprises 14 site‐ and depth‐specific soil hydraulic properties for 40 stations.The soil database is dominated by fine‐textured soils with 92% of the soils having >20% clay content.Laboratory sensor calibration equations reduced the uncertainty of the water content measurements by at least 29%. [ABSTRACT FROM AUTHOR]

There is a need for improved knowledge of the limits to the available water range for root growth in the subsoil. The objective of this study was to recalculate the upper and lower limits of the least limiting water range (LLWR) concept by using respectively the air-filled porosity (εa) at which 0.005 of the relative gas diffusivity (Ds/Do) is reached and readily available water (RAW). The refined upper limit estimates the variation in εa related to pore connectivity and the refined lower limit expresses the boundary at which plants suffer physiological water stress. This study was based on soil sampled in compaction trials on two sandy loam soils. Soil samples were taken from plots with no compaction (Control), and compaction with 78 kN (M8) and 58 kN (M6) wheel loads with multiple wheel passes. The soil cores were analyzed for εa, Ds/Do, bulk density (rb) and penetration resistance (PR). Heavy farm machinery impact of M8 and M6 led to subsoil compaction up to depth of 0.5 to 0.7 m for the soils under study. The subsoil structure was affected by compaction across depths with the decrease in εa (~33-46%) and Ds/Do (~37-61%) and increase in rb (~4-8%) and PR (~40-50%, at -100 hPa at 30-cm depth). The refined LLWR showed a wider water range compared to the original approach. We anticipate that the refined LLWR well reflects the limiting soil physical conditions for root growth for the studied soils, but validation by combined soil physical and plant growth measurements is needed.

The increased use of soil moisture retrieval from satellites has heightened the need for improved accuracy of point measurements that are used to validate remotely sensed soil moisture products. A wide range of devices can be installed for operational monitoring of soil moisture; however, many of these devices have not been tested in situ in soils with a very high reactive clay content. The objective of this study was to evaluate the accuracy in field performance of five soil moisture sensors: the enviroSCAN probe, the Diviner 2000 (both from Sentek Technologies), the Hydra Probe soil sensor (Stevens Water Monitoring Systems), the Thetaprobe ML2x (Delta-T Devices), and the ECH2O EC-5 (decagon devices) in soils that had about 71% clay content. The instruments' default calibrations were tested against observed soil moisture from core samples using the thermogravimetric method. New calibration equations were developed for each device, which were evaluated using an independent data set. The ThetaProbe had the lowest root mean square error (RMSE) of 0.025 m³ m-3 and mean bias error (MBE) of 0.002 m³ m-3 in the precalibration analysis. Although the Hydra Probe showed the highest precalibration errors, the instrument made the greatest improvement in post-calibration analysis, with an RMSE of 0.129 m³ m-3 using the default equation reduced to 0.014 m³ m-3 using in situ calibration, and the 0.110 m³ m-3 MBE was reduced to 0 after applying in situ calibration. [ABSTRACT FROM AUTHOR]