An Empirical Model for Estimating Soil Thermal Diffusivity from Texture, Bulk Density, and Degree of Saturation

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

doi:10.1175/jhm-d-17-0131.1

Cited by 23 publications

(20 citation statements)

References 40 publications

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“…The heat pulse κ values vs. the Xie et al (2018) modeled estimates were generally within the ±10% error lines (Fig. 3b).…”

Section: Resultssupporting

confidence: 67%

“…Figure 3 compares the C , κ, and λ values derived from the new thermo‐TDR sensor with the values estimated with the de Vries (1963) C model, the Xie et al (2018) κ model, and the Lu et al (2014) λ model, respectively. Each data point represents the mean of measurements from two outer probes (i.e., mean of six values).…”

Section: Resultsmentioning

confidence: 99%

“… Comparison of thermo‐time domain reflectometry measured thermal properties and model values on disturbed and intact soil samples: (a) measured heat capacity ( C ) vs. C estimated with the de Vries (1963) model, (b) measured thermal diffusivity (κ) vs. κ estimated with the Xie et al (2018) model, and (c) measured thermal conductivity (λ) vs. λ estimated with the Lu et al (2014) model. The solid line is the 1:1 line and the dashed lines are ±10% error lines.…”

Section: Resultsmentioning

confidence: 99%

“…The performance of the new thermo‐TDR sensor was evaluated by comparing the measured thermal property values with model estimates. The de Vries (1963) model, Xie et al (2018) model, and Lu et al (2014) model were used to estimate C , κ, and λ, respectively. Model inputs included ρ b , θ, f sa , and f cl .…”

Section: Methodsmentioning

confidence: 99%

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An Improved Thermo‐TDR Technique for Monitoring Soil Thermal Properties, Water Content, Bulk Density, and Porosity

Peng

Xie

et al. 2019

Vadose Zone Journal

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Core Ideas A new thermo‐TDR sensor can determine soil thermal properties, water content, bulk density, porosity, and air‐filled porosity. The new theories are used to analyze the heat‐pulse data and TDR waveforms. The new sensor provides greater sensing volume and more accurate results than previous designs. The thermo‐time domain reflectometry (thermo‐TDR) technique is valuable for monitoring in situ soil water content (θ), thermal properties, bulk density (ρb), porosity (n), and air‐filled porosity (na) in the vadose zone. However, the previous thermo‐TDR sensor has several weaknesses, including limited precision of TDR waveforms due to the short probe length, small measurement volume, and thermal property estimation errors resulting from finite probe properties not accounted for by the heat pulse method. We have developed a new thermo‐TDR sensor design for monitoring θ, thermal properties, ρb, n, and na. The new sensor has a robust heater probe (outer diameter of 2.38 mm and length of 70 mm) and a 10‐mm spacing between the heater and sensing probes, which provides a sensing volume three times larger than that of the previous sensor. The identical cylindrical perfect conductors and the tangent line–second‐order bounded mean oscillation theories were applied to analyze the raw data. Laboratory tests showed that θ values determined with the new sensor had a RMSE of 0.014 m3 m−3 compared with 0.016 to 0.026 m3 m−3 with the previous sensor. Soil thermal property estimates with the new sensor agreed well with modeled values. Soil ρb, n, and na derived from θ and thermal properties were consistent with those derived from gravimetric measurements. Thus, the new thermo‐TDR sensor provides more accurate θ, thermal properties, ρb, n, and na values than the previous sensor.

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“…The heat pulse κ values vs. the Xie et al (2018) modeled estimates were generally within the ±10% error lines (Fig. 3b).…”

Section: Resultssupporting

confidence: 67%

Section: Resultsmentioning

confidence: 99%

Section: Resultsmentioning

confidence: 99%

Section: Methodsmentioning

confidence: 99%

See 2 more Smart Citations

An Improved Thermo‐TDR Technique for Monitoring Soil Thermal Properties, Water Content, Bulk Density, and Porosity

Peng

Xie

et al. 2019

Vadose Zone Journal

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“…The magnitudes of soil thermal properties depend largely on soil texture, mineral composition, water content ( θ ) and bulk density ( ρ b ) (Campbell, ; de Vries, ). Several models have been developed to estimate soil thermal properties from water content (Chung & Horton, ; Tong, Gao, Horton, Li, & Wang, ) or from water content, bulk density and texture (Campbell, ; Côté & Konrad, ; de Vries, ; Johansen, ; Lu, Lu, Horton, & Ren, ; Lu, Ren, Gong, & Horton, ; Lukiashchenko & Arkhangelskaya, ; Sadeghi, Ghanbarian, & Horton, ; Xie, Lu, Ren, & Horton, ).…”

Section: Introductionmentioning

confidence: 99%

Thermal property values of a central Iowa soil as functions of soil water content and bulk density or of soil air content

Bing

Kool

Heitman

et al. 2019

European J Soil Science

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Soil thermal properties play important roles in dynamic heat and mass transfer processes, and they vary with soil water content (θ) and bulk density (ρ b). Both θ and ρ b change with time, particularly in recently tilled soil. However, few studies have addressed the full extent of soil thermal property changes with θ and ρ b. The objective of this study is to examine how changes in ρ b with time after tillage impact soil thermal properties (volumetric heat capacity, C v, thermal diffusivity, k, and thermal conductivity, λ). The study provides thermal property values as functions of θ and ρ b and of air content (n air) on undisturbed soil cores obtained at selected times following tillage. Heat pulse probe measurements of thermal properties were obtained on each soil core at saturated, partially saturated (θ at pressure head of −50 kPa) and oven‐dry conditions. Generally, k and λ increased with increasing ρ b at the three water conditions. The C v increased as ρ b increased in the oven‐dry and unsaturated conditions and decreased as ρ b increased in the saturated condition. For a given θ, a larger ρ b was associated with larger thermal property values, especially for λ. The figures of C v, k and λ versus θ and ρ b, as well as C v, k and λ versus n air, represented the range of soil conditions following tillage. Trends in the relationships of thermal property values with θ and ρ b were described by 3‐D surfaces, whereas each thermal property had a linear relationship with n air. Clearly, recently tilled soil thermal property values were quite dynamic temporally due to varying θ and ρ b. The dynamic soil thermal property values should be considered in soil heat and mass transfer models either as 3‐D functions of θ and ρ b or as linear functions of n air. Highlights Thermal property values for a range of θ and ρ b were measured on undisturbed soil cores. Freshly tilled soil thermal property values were quite dynamic temporally. The thermal property values of a tilled soil were described as 3‐D surfaces with θ and ρ b. The thermal property values of a tilled soil varied linearly with n air.

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Variability of soil thermal diffusivity in three Iowa fields

Nassar,

Abdallah,

Horton

2023

Soil Science Soc of Amer J

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Soil thermal properties influence the rates of chemical and biological reactions in the soil, seed germination, crop establishment, and productivity. Soil temperature varies with time and depth, and thermal diffusivity (α) is the main property associated with soil temperature variations. Variations in α within row‐cropped fields under long‐term tillage practices are not well documented in Iowa fields. The objective of this study is to determine α variations within three central Iowa fields under long‐term tillage practices and to estimate the required number of measurement sites within each field to determine α at a specified precision. Each field receives different tillage operations; one field is fall moldboard plowed followed by spring disking (MP), another field is fall chisel plowed followed by spring disking (CP), and the third field is ridge no‐tilled slot planted (RN). Thermocouples are used to measure soil temperature at two depths, i.e., 1‐ and 10 cm, at 49 locations (49 grid locations; 1.5×3 m) in each the of three adjacent fields. After allowing the thermocouples to equilibrate with the soil environment for two weeks in each field, hourly 1‐ and 10 cm depth soil temperature observations were recorded for a separate 24‐hour period in each field following rainfall events. Soil temperature measurements with the amplitude and phase equations were analyzed, and 49 α values were determined in each field. The larger the bulk density, the larger the thermal diffusivity. MP had the largest α values, while CP had the smallest values. The mean and standard deviation values for α in the MP, CP, and RN fields were 23±4.8, 13±2.2, and 21±4.0 cm2 h−1, respectively. The coefficients of variation, CV, were similar for the three fields, ranging from 17–21%. The minimum number of samples needed to obtain a confidence interval of 7 cm2 h−1 for α were 2, 5, and 7, respectively, for the CP, RN, and MP fields. The MP field had the largest mean α value and required the largest number of samples to determine α at a specified precision.This article is protected by copyright. All rights reserved

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An Empirical Model for Estimating Soil Thermal Diffusivity from Texture, Bulk Density, and Degree of Saturation

Cited by 23 publications

References 40 publications

An Improved Thermo‐TDR Technique for Monitoring Soil Thermal Properties, Water Content, Bulk Density, and Porosity

An Improved Thermo‐TDR Technique for Monitoring Soil Thermal Properties, Water Content, Bulk Density, and Porosity

Thermal property values of a central Iowa soil as functions of soil water content and bulk density or of soil air content

Variability of soil thermal diffusivity in three Iowa fields

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