Frozen soil thermal conductivity (FSTC, λ_eff) is a critical thermophysical property that is required for a variety of science and engineering applications. Measurements of λ_eff in frozen soils are prone to errors because the currently available thermal methods result in phase change (i.e., thawing and refreezing of ice) that affects the estimated λ_eff, especially near the freezing point of soil water (e.g., −4 to 0 °C) where rapid phase change occurs. In addition, measured λ_eff data are few compared to other physical properties. Therefore, many FSTC algorithms have been developed and a few of them have been incorporated in numerical simulation programs for calculating λ_eff. However, large discrepancies between simulated and observed soil thermal regimes have been reported. Previous studies either evaluated the performance of a few FSTC algorithms with limited λ_eff or simply compared the performance of the algorithms in numerical simulation programs. No study has been performed to systematically assess the performance of the FSTC algorithms included in numerical simulation programs with both observations and model simulations. In this study, 14 FSTC algorithms incorporated in various numerical simulation programs were evaluated with a compiled dataset consisting of 331 λ_eff measurements on 27 soils from seven studies made at temperatures on or below −4 °C. The Becker 1992 algorithm provided the best estimates of the λ_eff measurements, but the accuracy of the estimates was not good (i.e., RMSE = 0.46 W m⁻¹ °C⁻¹, Bias = -0.04 W m⁻¹ °C⁻¹ and NSE = 0.51). These FSTC algorithms were also incorporated in the Simultaneous Heat and Water (SHAW) model to compare their effects on the simulating soil temperature and water content at two field sites with contrasting soil textures in USA. The simulation results showed that the average bias of simulated and observed soil temperature for all depths ranged from −2.2 to 2.8 °C and the average differences of liquid water content ranged from −0.08 to 0.1 cm³ cm⁻³. Generally no FSTC algorithm combined with the SHAW model satisfactorily estimated the dynamic soil thermal regime. Perspectives on future studies are discussed.

冻土的热导率（FSTC，λ _EFF）是需要为各种科学和工程应用的关键热物性。测量λ _EFF在冻土很容易出错，因为目前可用的热处理方法造成了影响估计λ相变（即融化冰的再冻结）_EFF，尤其是靠近土壤水分的冰点（例如，-4至0°C）发生快速相变的地方。此外，测量λ _EFF相比其它物理性质数据也很少。因此，已经开发了许多FSTC算法，其中一些已被纳入用于计算λ的数值模拟程序中_eff。然而，已经报道了模拟的和观察到的土壤热状况之间的巨大差异。以前的研究评估任一具有限于几FSTC算法的性能λ _EFF或简单地进行比较的数值模拟程序算法的性能。尚无研究通过观测和模型模拟来系统地评估数值模拟程序中包含的FSTC算法的性能。在这项研究中，在各种数值模拟程序并入14种FSTC算法被用由331编译的数据集进行评估λ _EFF在−4°C或以下的温度下进行的七项研究对27种土壤进行了测量。贝克尔1992算法提供的的最佳估计λ _EFF测量，但估计的准确性并不好（即，RMSE = 0.46脉冲W M ^-1 ℃^下-1，偏置= -0.04脉冲W M ^-1 ℃， ^-1和NSE = 0.51）。这些FSTC算法也被并入了“同时加热和水（SHAW）”模型中，以比较它们在两个田间站点模拟土壤温度和含水量时的效果，并在美国对比了土壤质地。模拟结果表明，所有深度的模拟和观测土壤温度的平均偏差范围为-2.2至2.8°C，液态水含量的平均差异范围为-0.08至0.1 cm ³ cm ^-3。通常，没有FSTC算法与SHAW模型相结合可以令人满意地估算土壤的动态热态。讨论了对未来研究的看法。