Dry soil thermal conductivity (λ_dry) is a critical parameter required to estimate soil thermal conductivity with models based on the normalized concept. A great variety of models to estimate λ_dry are available in literature, but there hasn’t been any systematic evaluation of their performance. This may be attributed to the fact that they are only based on a limited number of measurements and were validated with data from a limited number of soil types. The objectives of this study were to (1) conduct an extensive review of the current available λ_dry models; (2) collate and compile a large dataset containing λ_dry measurements of various soil types; and (3) evaluate the performance of available models and to establish new models with wider applications. A total of 48 models from the literature were assessed using a large dataset consisting of more than 350 soils (659 measurements) from 34 published articles. All 48 models performed unsatisfactory with root mean square error (RMSE)>0.09 W m^{−1 o}C^-1, Nash-Sutcliff Efficiency (NSE) < 0.49, and Akaike’s information criterion (AIC)>∼-500. We developed eight new empirical thermal conductivity models with a non-linear regression method. These new models outperformed the 48 published models, with RMSE < 0.09 W m^{−1 o}C^-1, NSE > 0.49 and AIC < ∼-3210. They can be chosen depending on the availability of soil information (e.g., texture, quartz content, bulk density and porosity).

干土热导率 ( λ _dry ) 是使用基于归一化概念的模型估算土壤热导率所需的关键参数。文献中提供了多种估计λ _dry的模型，但尚未对其性能进行任何系统评估。这可能归因于这样一个事实，即它们仅基于有限数量的测量，并已使用来自有限数量土壤类型的数据进行验证。本研究的目标是 (1) 对当前可用的λ_干模型进行广泛审查；（2）比较和编译大型数据集含有λ_干各种土壤类型的测量；(3) 评估可用模型的性能并建立具有更广泛应用的新模型。使用由来自 34 篇已发表文章的 350 多个土壤（659 个测量值）组成的大型数据集对文献中的 48 个模型进行了评估。所有 48 个模型的表现都不令人满意，均方根误差 (RMSE)>0.09 W m ^{-1 o} C ^-1，Nash-Sutcliff Efficiency (NSE) < 0.49，以及 Akaike 的信息准则 (AIC)>∼-500。我们使用非线性回归方法开发了八种新的经验热导率模型。这些新模型的表现优于 48 个已发布的模型，RMSE < 0.09 W m ^{-1 o} C ^-1, NSE > 0.49 和 AIC < ∼-3210。可以根据土壤信息的可用性（例如质地、石英含量、体积密度和孔隙率）来选择它们。