Thermal conductivity of mineral soils relates linearly to air‐filled porosity

Xie, Xianjun; Lu, Yili; Ren, Tusheng; Horton, Robert

doi:10.1002/saj2.20016

Cited by 12 publications

(5 citation statements)

References 20 publications

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“…Although the effects of θ tot , v s , ρ b , salt concentration, and organic matter content on λ of unfrozen and partially frozen soils have been investigated (Abu‐Hamdeh & Reeder, 2000; Mustamo, Ronkanen, Berglund, Berglund, & Kløve, 2019; Zhao & Si, 2019), no universal relationship has been established between λ and these variables. Alternately, in unfrozen soils, λ has been observed to exhibit a general linear correlation with n a across a wide range of textures (Ochsner et al., 2001; Tong et al., 2019; Xie et al., 2019). Figure 2 illustrates λ as a function of θ tot , v s , or n a for 28 partially frozen soils.…”

Section: Resultsmentioning

confidence: 99%

“…Ochsner, Horton and Ren (2001), however, observed that the air‐filled porosity ( n a ), rather than θ w and ρ b , had a dominant effect on λ of unfrozen soils. Xie, Lu, Ren, and Horton (2019) showed that, for unfrozen soils, the λ and n a relationship could be approximated using a general linear function. For partially frozen soils, the n a is approximated by the difference between soil porosity (η) and total water content (θ tot ) because liquid water and ice have fairly similar densities (about 1 and 0.92 Mg m −3 , respectively), which differ substantially from that of the soil solid phase (about 2.65 Mg m −3 for many mineral soils).…”

Section: Introductionmentioning

confidence: 99%

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Estimating thermal conductivity of frozen soils from air‐filled porosity

Tian

Ren

Heitman

et al. 2020

Soil Science Soc of Amer J

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Soil thermal conductivity (λ) is an important thermal property for environmental, agricultural, and engineering heat transfer applications. Existing λ models for frozen soils are complicated to use because they require estimates of both liquid water content and ice content. This study introduces a new approach to estimate λ of partially frozen soils from air-filled porosity (n a), which can be determined by using an oven-drying method. A λ and n a relationship was established based on measurements for 28 partially frozen soils. A strong exponential relationship between λ and n a was found (with R 2 of 0.82). Independent tests on 10 partially frozen soils showed that the exponential λ-n a model produced reliable λ estimates with a RMSE of 0.319 W m −1 K −1 , which was smaller than those of two widely used λ models for partially frozen soils. The λ-n a model is easier to use than existing models, because it requires fewer parameters. Note that the λ-n a model ignores the effect of temperature on λ of frozen soils and is most applicable to soil at temperatures of at least −4 • C. 1 INTRODUCTION Soil thermal conductivity (λ) is a key parameter in modeling heat transfer in the vadose zone, which is impor-Abbreviations: RMSE, root mean square error; SE, standard error of the regression; TDR, time domain reflectometry.

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Section: Resultsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Estimating thermal conductivity of frozen soils from air‐filled porosity

Tian

Ren

Heitman

et al. 2020

Soil Science Soc of Amer J

Self Cite

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“…First, SUTRA calculates the subsurface bulk thermal conductivity with a weighted arithmetic mean from the thermal conductivities of constituents of the porous matrix (i.e., soil particles and water), but not the air phase. However, the arithmetic mean is not considered to be the correct estimation of soil bulk thermal conductivity [ 39 ] and ignoring the air phase may amplify these inaccuracies [ 40 ]. The SUTRA bulk thermal conductivity equation ( K ) was modified to integrate the air phase with a weighted harmonic mean [ 39 ]:

where ε is porosity, S L is water saturation, and K L , K A , and K S are the thermal conductivities of water, air phase, and soil particles, respectively.…”

Section: Methodsmentioning

confidence: 99%

Uncertainties in Measuring Soil Moisture Content with Actively Heated Fiber-Optic Distributed Temperature Sensing

Lamontagne‐Hallé

McKenzie

2021

Sensors

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Actively heated fiber-optic distributed temperature sensing (aFO-DTS) measures soil moisture content at sub-meter intervals across kilometres of fiber-optic cable. The technology has great potential for environmental monitoring but calibration at field scales with variable soil conditions is challenging. To better understand and quantify the errors associated with aFO-DTS soil moisture measurements, we use a parametric numerical modeling approach to evaluate different error factors for uniform soil. A thermo-hydrogeologic, unsaturated numerical model is used to simulate a 0.01 m by 0.01 m two-dimensional domain, including soil and a fiber-optic cable. Results from the model are compared to soil moisture values calculated using the commonly used Tcum calibration method for aFO-DTS. The model is found to have high accuracy between measured and observed saturations for static hydrologic conditions but shows discrepancies for more realistic settings with active recharge. We evaluate the performance of aFO-DTS soil moisture calculations for various scenarios, including varying recharge duration and heterogeneous soils. The aFO-DTS accuracy decreases as the variability in soil properties and intensity of recharge events increases. Further, we show that the burial of the fiber-optic cable within soil may adversely affect calculated results. The results demonstrate the need for careful selection of calibration data for this emerging method of measuring soil moisture content.

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“…The authors concluded that the soil thermal property values and the soil AP exhibited simple linear relationships. The λ-AP linear relationship was verified by Xie et al (2020) who pointed out that it could be applied potentially in land surface models for estimating λ from θ and ρ b .…”

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confidence: 85%

Thermal properties of Cambisols in mountain regions under different vegetation covers

Doneva¹,

Kercheva²,

Dimitrov³

et al. 2022

Soil & Water Res.

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Soil thermal properties regulate the thermal and water balance and influence the soil temperature distribution. The aim of the current study is to present data on the changes in the thermal properties of Cambisols at different ratios between the water content and the air in the pore space under different vegetation covers in mountain regions. The undisturbed soil samples were taken from the surface soil layers under grassland, deciduous and coniferous forests in three experimental stations of the Forest Research Institute – Gabra in Lozen Mountain, Govedartsi in Rila Mountain and Igralishte in Maleshevska Mountain. The soil thermal conductivity (λ), the thermal diffusivity (α) and the volumetric heat capacity (C<sub>v</sub>) were measured with the SH-1 sensor of a KD2Pro device at different matric potentials in laboratory conditions. The thermal conductivity of the investigated soils was also measured with the TR-1 sensor of a KD2Pro device at the transitory soil moisture in field conditions. An increase in the thermal properties with the soil water content was best pronounced for λ and depended inversely on the total porosity. As the total porosity increased with the soil organic carbon content and decreased with the skeleton content, the lowest value of λ was established in the surface horizons of Dystric Cambisols (Humic) in the experimental station in Govedartsi. The soil thermal conductivity increased with the depth under the deciduous forest (Gabra and Igralishte) due to the lower soil organic carbon content (SOC) and the total porosity. There were no such changes in the subsurface horizon under the grassed associations. The increase in the heat capacity with the water content depended on the SOC to less extent. In the horizons with a SOC of less than 1.5%, the changes in the thermal diffusivity over the whole range of wetness were 1.7 times higher than those with a higher SOC.

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Thermal conductivity of mineral soils relates linearly to air‐filled porosity

Cited by 12 publications

References 20 publications

Estimating thermal conductivity of frozen soils from air‐filled porosity

Estimating thermal conductivity of frozen soils from air‐filled porosity

Uncertainties in Measuring Soil Moisture Content with Actively Heated Fiber-Optic Distributed Temperature Sensing

Thermal properties of Cambisols in mountain regions under different vegetation covers

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