Soil thermal conductivity (λ) models are needed frequently in studying coupled heat and water transfer in soils. Several models are available, but some are complicated and some produce relatively large errors. In this study, we developed a simple model for estimating λ from soil texture, bulk density (ρb), and water content (θ). Three parameters, α, β, and λdry, are included in the model, where λdry is determined by ρb and α and β are shape factors estimated from soil texture and ρb. Empirical relations were developed for α and β by fitting the model to heat‐pulse (HP) measurements of λ(θ) on seven soils of various textures. The model performance was evaluated with independent λ(θ) data from independent HP measurements and literature values. The results show that the model is able to express the λ(θ) curves from oven dry to saturation at fixed ρb values. When ρb is varied, the estimated λ data agree well with measured values. The root mean square errors are <0.15 W m−1 K−1, and the bias is within 0.10 W m−1 K−1. The new model has the potential for use in studying heat movement in soils of varying texture, bulk density, and water content and can be incorporated into numerical algorithms for describing coupled heat and mass transfer processes.

Soil thermal conductivity (λ) is an important property in soil physics, environmental science, thermal science and engineering disciplines. In situ measurement of λ is complicated and there is a need for a model to predict λ accurately in both unfrozen and frozen soil. In this research, we developed a simplified de Vries‐based model to estimate λ of both unfrozen and frozen soil. The simplified model follows the basic assumptions of the de Vries model, but simplifies or improves the estimates of λair, λminerals and ga(minerals), ga(air) and ga(ice) (ga are the shape factors for various soil components): λminerals and ga(minerals) are related to soil mineral composition, and ga(air) and ga(ice) are related linearly to the volumetric fractions of air and ice, respectively. The λ and ga values of sand, silt and clay are required for calculating λminerals and ga(minerals). The values of λsilt and ga(silt), however, are not available in the literature. In this research, we estimated λsilt and ga(silt) by fitting the simplified model to measured λ values of 17 soil samples with various textures, water contents and bulk densities. The simplified model was validated with measured λ data from an additional 10 unfrozen and 18 frozen soil samples, obtained either from the literature or from our own measurements. The performance of the simplified model was evaluated by comparing estimates of λ with those from other available models. The results showed that the simplified de Vries‐based model provided accurate and consistent data for λ, and it performed better than other de Vries‐based models. Highlights We developed a simplified de Vries‐based model to estimate thermal conductivity, λ, of unfrozen and frozen soil. We improved methods to calculate thermal conductivities of soil air and minerals and shape factors of minerals, air and ice. Thermal conductivity and shape factor of silt were estimated with inverse modelling. Our new, simplified model gave more accurate predictions of λ than did other de Vries‐based models.

Soil‐probe contact resistance and finite radius and heat capacity of the heat pulse (HP) probe produce significant errors in thermal property estimates. In this study, we demonstrated that estimating soil thermal properties from late‐time data of the temperature change‐by‐time (ΔT(t)) curve reduces these errors effectively. The weighted nonlinear curve fitting method was applied to estimate soil thermal properties following the pulsed infinite line source (PILS) theory using ΔT(t) data from the complete (PILS‐Complete), peak‐time (PILS‐Peak), and late‐time (PILS‐Late) ranges. Three experiments on specific heat of soil solids (cs), soil thermal properties, and soil water content (θHP) were conducted to examine the performance of these approaches. The results showed that the PILS‐Complete and PILS‐Peak methods overestimated cs by 16.6% and 13.0% respectively, and the error from the PILS‐Late method reduced to 3.2%. Soil thermal conductivity measurements from the PILS‐Late method agreed well with those from the identical‐cylindrical‐perfect‐conductors theory and with the estimates from the heat flux plate data. The PILS‐Late method also effectively reduced the overestimation of soil heat capacity and underestimation of soil thermal diffusivity. In comparing to the PILS‐Complete method, the PILS‐Late method reduced the root mean square error (RMSE) of θHP from 0.039 to 0.021 m3 m−3 on a sand soil, and from 0.032 to 0.018 m3 m−3 on a clay loam soil. Thus, using late‐time data improved the accuracy of HP method for measuring soil thermal properties.

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