Soil thermal conductivity model by de Vries: Re‐examination and validation analysis

Abstract

The thermal conductivity (λ) of soils is an important property in a variety of science and engineering applications. One of the most widely used λ models in soil science was proposed by de Vries (deV‐0). This model is complicated and difficult to use as it is based on several controversial assumptions. The deV‐0 assumes that a soil system is composed of non‐contacting solid particles (rotated uniform ellipsoids) that are dispersed in a continuous homogeneous medium (air or water). Furthermore, deV‐0 assumes that soil solids consist of quartz and consolidated bulk minerals. These assumptions do not reflect the true nature of soils that are composed of several compacted minerals, diverse in shape and notably different in size. A critical analysis of this model concluded that its most controversial feature was inherited from an electrical conductivity model for a two‐phase dispersion system; specifically, from weighting shape factors of non‐contacting rotated oblate ellipsoids. Furthermore, deV‐0 has not yet been fully examined and verified with respect to a comprehensive and complete soil λ database. Also, there is a lack of comparable models to deV‐0 that would contain a complete set of clearly described and linked expressions. Consequently, two slightly adjusted versions of deV‐0 were developed; namely, deV‐1 with soil bulk mineralogy (quartz plus integrated residual minerals) and deV‐2 with complete soil mineralogy (i.e., including individual contributions from all soil minerals). Both models underwent successful calibration and verification against λ data of 39 Canadian field soils and three Standard sands. Markedly improved estimates (λest) were obtained when, instead of dry air thermal conductivity (λa), an apparent air thermal conductivity (λa‐app = λa + λv) was applied (λv represents thermal effects caused by migration of water vapour and evaporation/condensation processes). For deV‐1, the following reduction of standard deviation (SD) data was obtained: 53.5% for 17 coarse soils, 34% for 22 fine soils, and 44.5% for all 39 soils. Then, the same calibration factors of deV‐1 were applied to the deV‐2 model and a similar reduction of SD data was obtained (52.7, 24.1 and 40.1%, respectively). Generally, for 39 Canadian field soils, both models (deV‐1 and deV‐2), with quartz thermal conductivity (λqtz) of 7.6 W·m−1·K−1, produced very close λ estimates (SD ≈ 0.099 and 0.094 W·m−1·K−1, respectively). Taking into account the simplicity of mineral composition and fewer calibration coefficients, deV‐1 was a preferable choice. For that reason, soil bulk mineralogy appears to be a good equivalent to complete soil mineralogy. Also, for Standard sands (100% sand: C‐109, C‐190, NS‐04), improved λest were obtained by replacing λa with λa‐app. Finally, the deV‐1 model was successfully applied to 10 Chinese soils and the following average SD values were obtained: for four coarse soils 0.135 W·m−1·K−1, whereas for six fine soils 0.127 W·m−1·K−1. Highlights The original λ model by de Vries (deV‐0) underestimates exp...

Spatial variability of thermal properties in relation to the application of selected soil-improving cropping systems (SICS) on sandy soil

“…Heat transfer and the temperature distribution of the soil have important effects on seedling growth and its associated crop establishment (Abu-Hamdeh and Reeder, 2000;Mellander et al, 2004;Lipiec et al, 2011), microbial respiration rate, organic matter turnover (Andry et al, 2009;Xu et al, 2012), and greenhouse gas exchange (Peng et al, 2009;Xu et al, 2012). The measured soil thermal properties are required in coupled heat and water transfer models (Heitman et al, 2008;Tarnawski et al, 2021) in order to improve the prediction of the spatial and temporal dynamics of soil temperature and the effects of land management practices under climate change conditions (Zhao and Sia, 2019). The exploration of the spatial variability in soil thermal properties is important in predicting ecosystem function and precision agriculture Mitchell-Fostyk et al, 2021).…”

Estimating Soil Thermal Conductivity by Weighted Average Models with Soil Solids as a Continuous Medium

“…For this reason, estimating soil thermal conductivity from general soil properties has attracted increasing attention. Several thermal conductivity models have been developed in the past [5][6][7][8][9][10][11][12][13][14][15][16][17][18][19][20]. Of these, six are based on semi-physical principles [6,9,10,14,16,20], one is based on a statistical-physical principle [11] and the remaining eight are based on empirical principles [5,7,8,12,13,15,17,19].…”

“…Several thermal conductivity models have been developed in the past [5][6][7][8][9][10][11][12][13][14][15][16][17][18][19][20]. Of these, six are based on semi-physical principles [6,9,10,14,16,20], one is based on a statistical-physical principle [11] and the remaining eight are based on empirical principles [5,7,8,12,13,15,17,19]. For more details on the previous thermal conductivity models see [21].…”

“…Therefore, complete soil mineralogy data are essential for the successful development and verification of thermal conductivity models. Tarnawski et al [20] reviewed and analyzed de Vries' model [6] using a complete thermal conductivity database of 39 Canadian Field Soils and 3 Standard Sands; each soil thermal conductivity data set was measured using the transient thermal probe technique at room temperature (T). In general, the de Vries model (deV-1) provides good estimation (λ est ) for fine and coarse textured soils.…”

“…Both new models are subject to calibration and thorough verification by comparing their λ est with measured thermal conductivity values of 39 Canadian Field Soils and 3 Standard Sands. Then their predicted thermal conductivity is compared to the λ est obtained with the original deV-1 model [20].…”