A Series-Parallel Model for Estimating the Thermal Conductivity of Unsaturated Soils

Tarnawski, V. R.; Leong, Wey H.

doi:10.1007/s10765-012-1282-1

Cited by 72 publications

(28 citation statements)

References 29 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…In the Johansen () scheme and its derivatives, soil components were assumed to be parallel or in a way between parallel and serial to heat flow, and thus, the bulk thermal conductivity could be calculated as the arithmetic or geometric mean of the thermal conductivities of all soil components (Zhang & Wang, ). As a more complex scheme, Tarnawski and Leong () proposed a mixed serial‐parallel arrangement for soil components. They assumed that heat was conducted through three pathways, namely, a solid uniform passage ( Θ sb ), a series‐parallel passage composed of solids connected to a parallel path of a portion of soil water ( n w ) and a portion of soil air ( n a ), and a path of water ( Θ w ) and air ( Θ a ) in a parallel arrangement.…”

Section: Materials and Methodologymentioning

confidence: 99%

Evaluation of Soil Thermal Conductivity Schemes for Use in Land Surface Modeling

Dai

Wei

Yuan

et al. 2019

J Adv Model Earth Syst

View full text Add to dashboard Cite

Soil thermal conductivity (STC) is an important physical parameter in modeling land surface processes. Previous studies on evaluations of STC schemes are mostly based on direct measurements of local conditions, and their recommendations cannot be used as a reference in selecting STC schemes for land modeling use. In this work, seven typical STC schemes are incorporated into the Common Land Model to evaluate their applications in land surface modeling. Statistical analyses show that the Johansen (1975) scheme and its three derivatives, Côté and Konrad (2005, https://doi.org/10.1139/t04-106), Balland and Arp (2005, https://doi.org/10.1139/s05-007), and Lu et al. (2007, https://doi.org/10.2136/sssaj2006.0041), are significantly superior to the other schemes with respect to both STC estimations and their applications in modeling soil temperature and the partitioning of surface available energy in the Common Land Model. The Balland and Arp (2005, https://doi.org/10.1139/s05-007) scheme ranks at the top among the selected schemes. Uncertainty analyses based on single-point and global simulations both show that the differences in STC estimations can induce significant differences in simulated soil temperature in arid/semiarid and seasonally frozen regions, especially at deep layers. The hydrology-related variables are slightly affected by STC variations, but slight changes in these variables can induce notable changes in soil temperature by altering soil thermal properties. These results emphasize the important role of STC in modeling soil thermodynamics and suggest the necessity of further developing STC schemes based on land modeling applications. According to the evaluation analyses, we recommend the Balland and Arp (2005, https://doi. org/10.1139/s05-007) scheme as a more suitable selection for use in land surface models.Plain Language Summary Soil thermal conductivity (STC) is essential for simulating soil temperature and heat flux in land surface models. Previous studies have proposed quite a few parameterization schemes to estimate STC. Although many works have tried to determine which scheme performed best, most of them were based on direct measurements from specific experimental conditions or local soil samples, and few studies have considered the performances of STC schemes in land surface modeling. Here, we present an evaluation of seven typical STC schemes based on their land modeling applications. The results show that four of the schemes, the Johansen (1975) scheme, Côté and Konrad (2005, https://doi.org/10.1139/t04-106) scheme, Balland and Arp (2005, https://doi.org/10.1139/s05-007) scheme, and Lu et al. (2007, https://doi.org/10.2136/sssaj2006.0041) scheme, perform comparatively better than the other schemes and that the Balland and Arp (2005, https://doi.org/10.1139/s05-007) scheme ranks at the top.Uncertainty analyses demonstrate that soil thermodynamics are strongly sensitive to STC estimates, especially in dry and partly frozen regions. This relationship highlights the important role of STC in modeli...

show abstract

Section: Materials and Methodologymentioning

confidence: 99%

Evaluation of Soil Thermal Conductivity Schemes for Use in Land Surface Modeling

Dai

Wei

Yuan

et al. 2019

J Adv Model Earth Syst

View full text Add to dashboard Cite

show abstract

“…Although much effort has been invested in the development of l-q models (e.g., De Vries, 1963;Johansen, 1975;McCumber and Pielke, 1981;Campbell, 1985;Campbell et al, 1994;Lu et al, 2007;Chen, 2008;Konrad, 2005, 2009;Tarnawski and Leong, 2012;Haigh, 2012;Lu and Dong, 2015), none of the models specifically address the applicability to soils that are mixed, including binary mixtures. Additionally, few l-q models are physically based and rely on the use of fitting parameters.…”

mentioning

confidence: 99%

Thermal Conductivity of Binary Sand Mixtures Evaluated through Full Water Content Range

Wallen

Smits

Sakaki

et al. 2016

Soil Science Soc of Amer J

View full text Add to dashboard Cite

A soil's grain-size distribution affects its physical and hydraulic properties; however, little is known about its effect on soil thermal properties. To better understand how grain-size distribution affects soil thermal properties, specifically the effective thermal conductivity, a set of laboratory experiments was performed using binary mixtures of two uniform sands tightly packed with seven different mixing fractions over the full range of saturation. For each binary mixture, the effective thermal conductivity, l, capillary pressure, h c , and volumetric water content, q, were measured. Results demonstrated that the l-q relationship exhibited distinct characteristics based on the percentage of fine-and coarse-grained sands. We further compared measured l-q properties with independent estimates from two semi-empirical models (Campbell Model and Lu and Dong Model) to evaluate the models' applicability in relation to physically based parameters associated with changes in soil mixing (e.g., porosity and grain size). Both models were able to fit experimental data but to varying degrees based on the number of physically based parameters used. In general, model improvements are needed to capture the l-q relationship solely on physically based parameters.Abbreviations: SWRC, soil water retention curve. U nderstanding soil thermal properties is important to properly determine the impact of heat transfer on the distribution, circulation, and evaporation of water in soil. Heat transfer within soil influences micrometeorological phenomena (Hanks et al., 1967), engineering efforts such as cooling electronic devices with heat pipes and insulating buildings (Bussing and Bart, 1997;Sakaguchi et al., 2009;Moradi et al., 2015), agricultural production (Al Nakshabandi and Kohnke, 1965;Usowicz et al., 1996;Lipiec et al., 2007), and landmine detection efforts that rely on thermal contrast (e.g., Garcia-Padron et al., 2002;Martínez et al., 2004). Previous studies provide insight into the effect of soil moisture, temperature, and soil physical characteristics on soil thermal properties, specifically the effective thermal conductivity, l (W m -1 K -1 ; e.g., De Vries, 1963;Johansen, 1975;Hopmans and Dane, 1986;Ochsner et al., 2001;Tarnawski and Gori, 2002). However, to our knowledge, none of the studies to date has investigated the effect of mixing differently sized particles on controlling the l behavior under varying soil water contents (q, cm 3 cm -3 ). Knowledge of the l-q relationship for differently sized soil fractions is particularly important in many engineering applications such as soil borehole thermal energy storage (SBTES), landfill covers, and engineered material stabilization.Previous research provides strong evidence that l is affected by a wide range of parameters including q (e.g., Philip and de Vries, 1957;De Vries, 1963;Yadav and Saxena, 1977; Core Ideas• Thermal conductivity and water content relationship based upon mixtures are distinct.• Systematic change in particle fine fraction directly affects thermal cond...

show abstract

“…Many attempts have been made to examine and model the relationships among λ, soil texture, and ρ b (Gangadhara Rao and Singh, 1999; Singh and Devid, 2000; Côté and Konrad, 2005; Tarnawski and Leong, 2012); however, as shown by Lu et al (2014), these models have limited success beyond a few soils. There is also limited information on the relationships between soil physical properties and TN cum (θ) or TN max (θ) (Sayde et al, 2010; Ciocca et al, 2012).…”

Section: Resultsmentioning

confidence: 99%

Single‐Probe Heat Pulse Method for Soil Water Content Determination: Comparison of Methods

Cheng

et al. 2016

Vadose Zone Journal

View full text Add to dashboard Cite

Core Ideas Six methods to obtain soil water content by single probe are presented and compared. Errors of estimated soil water content by six methods increase with water content. Heating duration affects soil water content estimation errors. Four of the six methods are probe dependent but can be easily calibrated. Each method works for some soils, and combining different methods is a solution. The estimation of soil thermal conductivity (λ) using the single‐probe heat pulse (SPHP) method is well known, but estimation of soil water content (θ) using the SPHP is poorly understood. In this study, we examined six methods—λ, normalized cumulative temperature increase (TNcum), normalized maximum temperature increase (TNmax), and the reciprocals of each—for θ estimation using the SPHP. The temperature response curves of four soils at different θ were measured following 600‐s heat pulses with heating strengths of about 6 W m−1, from which λ, TNcum, and TNmax values were determined. The maximum measurement errors of these three methods were 0.11 m3 m−3 for the coarse sand and 0.01 m3 m−3 for the fine sand, sandy loam, and silty clay, except for 0.05 m3 m−3 for the fine sand by the λ(θ) method. The predicted θ from all of the λ, TNcum, and TNmax methods agreed well with that from the oven‐dry method for all soils with the exception of the TNcum(θ) and TNmax(θ) methods for the coarse sand for θ > 0.20 m3 m−3. The measurement errors and θ predictions of the 1/λ(θ) method were similar to that of the TNcum(θ) and TNmax(θ) methods, and that of the 1/TNcum(θ) and 1/TNmax(θ) methods were similar to that of the λ(θ) method. Because each of the six methods worked well for only some soils, improved estimations were obtained when the λ(θ) method was combined with the 1/TNcum(θ) [or 1/TNmax(θ)] method for coarse‐textured soils and the 1/λ(θ) method was combined with the TNcum(θ) [or TNmax(θ)] method for fine‐textured soils.

show abstract

A Series-Parallel Model for Estimating the Thermal Conductivity of Unsaturated Soils

Cited by 72 publications

References 29 publications

Evaluation of Soil Thermal Conductivity Schemes for Use in Land Surface Modeling

Evaluation of Soil Thermal Conductivity Schemes for Use in Land Surface Modeling

Thermal Conductivity of Binary Sand Mixtures Evaluated through Full Water Content Range

Single‐Probe Heat Pulse Method for Soil Water Content Determination: Comparison of Methods

Contact Info

Product

Resources

About