Field measurements of soil thermal conductivity

1986 ◽  
Vol 23 (1) ◽  
pp. 51-59 ◽  
Author(s):  
L. E. Goodrich
Keyword(s):  
Thermal Conductivity ◽  
Soil Layer ◽  
Field Measurements ◽  
Soil Conditions ◽  
Soil Composition ◽  
Soil Thermal Conductivity ◽  
Marine Clay ◽  
Engineering Structures ◽  
Seasonal Differences ◽  
Phase Relationships

Data representing the seasonal variation of thermal conductivity of the ground at depths within the seasonally active freezing/thawing zone are presented for a number of different soil conditions at four sites across Canada. An inexpensive probe apparatus suitable for routine field measurements is described.In all the cases examined, significant seasonal variations were confined to the first few decimetres. In addition to distinct seasonal differences associated with phase change, quite large changes occurred during the period when the soil was thawed in those cases where seasonal drying was possible. Below the seasonally active zone, thawed soil conductivities did not differ greatly among the three nonpermafrost sites in spite of soil composition ranging from marine clay to sandy silt. The data suggest that, even within a given soil layer, quite significant differences in thermal conductivity may be encountered in engineering structures such as embankments, presumably because of differences in drainage conditions. Key words: thermal conductivity, field measurements, phase relationships, drying, permafrost, clay, silt, peat.

A Study on the Calculation Model of Rock Soil Thermal Conductivity in the Design of the Ground Source Heat Pump System

2014 ◽  
Vol 889-890 ◽  
pp. 1347-1352
Author(s):  
Hong Wen Jin ◽  
Qing Shen Fang
Keyword(s):  
Heat Transfer ◽  
Thermal Conductivity ◽  
Heat Pump ◽  
Soil Layer ◽  
Heat Extraction ◽  
Ground Source Heat Pump ◽  
Soil Thermal Conductivity ◽  
Pump System ◽  
Heat Pump System ◽  
Ground Source

The rock soil thermal conductivity is the most important design parameter for the ground source heat pump system. Based on the equation applied for the heat transfer between the geothermal heat exchanger and its surrounding rock soil, a quasi-three dimensional heat conduction model showing the heat transfer inside the borehole of the U-tube was established to determine the thermal conductivity of the deep-layer rock soil. The results obtained show that the average thermal conductivity got through calculation and actual determination in a tube-embedding region of the ground source heat pump engineering were 1.895 and 1.955W/(m·°C), respectively. The soil layer, which has a great thermal conductivity and a strong integrated heat transfer capability, is suitable for the use of the ground source heat pump system with the tubes embedded underground. The soil layer, with a body temperature of 19 °C and a higher initial temperature, is suitable for the heat extraction from underground in winter. The deviation between the calculation and the determination of the average thermal conductivity in the abovementioned region was 0.06, which could meet the required precision, indicating that the results from the calculation could be used for design.

scholarly journals Evaluating and improving simulations of diurnal variation in land surface temperature with the Community Land Model for the Tibetan Plateau

PeerJ ◽  
2021 ◽  
Vol 9 ◽  
pp. e11040
Author(s):  
Xiaogang Ma ◽  
Jiming Jin ◽  
Lingjing Zhu ◽  
Jian Liu
Keyword(s):  
Thermal Conductivity ◽  
Tibetan Plateau ◽  
Surface Temperature ◽  
Land Surface Temperature ◽  
Land Surface ◽  
Soil Layer ◽  
Surface Model ◽  
The Tibetan Plateau ◽  
Soil Thermal Conductivity ◽  
Community Land Model

This study evaluated and improved the ability of the Community Land Model version 5.0 (CLM5.0) in simulating the diurnal land surface temperature (LST) cycle for the whole Tibetan Plateau (TP) by comparing it with Moderate Resolution Imaging Spectroradiometer satellite observations. During daytime, the model underestimated the LST on sparsely vegetated areas in summer, whereas cold biases occurred over the whole TP in winter. The lower simulated daytime LST resulted from weaker heat transfer resistances and greater soil thermal conductivity in the model, which generated a stronger heat flux transferred to the deep soil. During nighttime, CLM5.0 overestimated LST for the whole TP in both two seasons. These warm biases were mainly due to the greater soil thermal inertia, which is also related to greater soil thermal conductivity and wetter surface soil layer in the model. We employed the sensible heat roughness length scheme from Zeng, Wang & Wang (2012), the recommended soil thermal conductivity scheme from Dai et al. (2019), and the modified soil evaporation resistance parameterization, which was appropriate for the TP soil texture, to improve simulated daytime and nighttime LST, evapotranspiration, and surface (0–10 cm) soil moisture. In addition, the model produced lower daytime LST in winter because of overestimation of the snow cover fraction and an inaccurate atmospheric forcing dataset in the northwestern TP. In summary, this study reveals the reasons for biases when simulating LST variation, improves the simulations of turbulent fluxes and LST, and further shows that satellite-based observations can help enhance the land surface model parameterization and unobservable land surface processes on the TP.

scholarly journals Root Response to Soil Water Status via Interaction of Crop Genotype and Environment

Agronomy ◽  
2021 ◽  
Vol 11 (4) ◽  
pp. 708
Author(s):  
Phanthasin Khanthavong ◽  
Shin Yabuta ◽  
Hidetoshi Asai ◽  
Md. Amzad Hossain ◽  
Isao Akagi ◽  
...  
Keyword(s):  
Soil Moisture ◽  
Soil Water ◽  
Root Distribution ◽  
Water Status ◽  
Soil Layer ◽  
Shoot Biomass ◽  
Soil Conditions ◽  
Crop Adaptation ◽  
Soil Moisture Status ◽  
Root Distributions

Flooding and drought are major causes of reductions in crop productivity. Root distribution indicates crop adaptation to water stress. Therefore, we aimed to identify crop roots response based on root distribution under various soil conditions. The root distribution of four crops—maize, millet, sorghum, and rice—was evaluated under continuous soil waterlogging (CSW), moderate soil moisture (MSM), and gradual soil drying (GSD) conditions. Roots extended largely to the shallow soil layer in CSW and grew longer to the deeper soil layer in GSD in maize and sorghum. GSD tended to promote the root and shoot biomass across soil moisture status regardless of the crop species. The change of specific root density in rice and millet was small compared with maize and sorghum between different soil moisture statuses. Crop response in shoot and root biomass to various soil moisture status was highest in maize and lowest in rice among the tested crops as per the regression coefficient. Thus, we describe different root distributions associated with crop plasticity, which signify root spread changes, depending on soil water conditions in different crop genotypes as well as root distributions that vary depending on crop adaptation from anaerobic to aerobic conditions.

scholarly journals Soil Erosion Estimates in Arid Region: A Case Study of the Koutine Catchment, Southeastern Tunisia

2021 ◽  
Vol 11 (15) ◽  
pp. 6763
Author(s):  
Mongi Ben Zaied ◽  
Seifeddine Jomaa ◽  
Mohamed Ouessar
Keyword(s):  
Soil Erosion ◽  
Soil Loss ◽  
Scientific Evidence ◽  
Field Measurements ◽  
Spatiotemporal Variability ◽  
Soil Conditions ◽  
Storm Events ◽  
Erosion Processes ◽  
Study Results ◽  
Soil Losses

Soil erosion remains one of the principal environmental problems in arid regions. This study aims to assess and quantify the variability of soil erosion in the Koutine catchment using the RUSLE (Revised Universal Soil Loss Equation) model. The Koutine catchment is located in an arid area in southeastern Tunisia and is characterized by an annual mean precipitation of less than 200 mm. The model was used to examine the influence of topography, extreme rainstorm intensity and soil texture on soil loss. The data used for model validation were obtained from field measurements by monitoring deposited sediment in settlement basins of 25 cisterns (a traditional water harvesting and storage technique) over 4 years, from 2015 to 2018. Results showed that slope is the most controlling factor of soil loss. The average annual soil loss in monitoring sites varies between 0.01 and 12.5 t/ha/y. The storm events inducing the largest soil losses occurred in the upstream part of the Koutine catchment with a maximum value of 7.3 t/ha per event. Soil erosion is highly affected by initial and preceding soil conditions. The RUSLE model reasonably reproduced (R2 = 0.81) the spatiotemporal variability of measured soil losses in the study catchment during the observation period. This study revealed the importance of using the cisterns in the data-scarce dry areas as a substitute for the classic soil erosion monitoring fields. Besides, combining modeling of outputs and field measurements could improve our physical understanding of soil erosion processes and their controlling factors in an arid catchment. The study results are beneficial for decision-makers to evaluate the existing soil conservation and water management plans, which can be further adjusted using appropriate soil erosion mitigation options based on scientific evidence.

scholarly journals Assessment for Thermal Conductivity of Frozen Soil Based on Nonlinear Regression and Support Vector Regression Methods

2020 ◽  
Vol 2020 ◽  
pp. 1-12
Author(s):  
Fu-Qing Cui ◽  
Wei Zhang ◽  
Zhi-Yun Liu ◽  
Wei Wang ◽  
Jian-bing Chen ◽  
...  
Keyword(s):  
Thermal Conductivity ◽  
Support Vector Regression ◽  
Nonlinear Regression ◽  
Prediction Accuracy ◽  
Prediction Models ◽  
Frozen Soil ◽  
Support Vector ◽  
Soil Thermal Conductivity ◽  
Fine Grained ◽  
Fine Grained Soil

The comprehensive understanding of the variation law of soil thermal conductivity is the prerequisite of design and construction of engineering applications in permafrost regions. Compared with the unfrozen soil, the specimen preparation and experimental procedures of frozen soil thermal conductivity testing are more complex and challengeable. In this work, considering for essentially multiphase and porous structural characteristic information reflection of unfrozen soil thermal conductivity, prediction models of frozen soil thermal conductivity using nonlinear regression and Support Vector Regression (SVR) methods have been developed. Thermal conductivity of multiple types of soil samples which are sampled from the Qinghai-Tibet Engineering Corridor (QTEC) are tested by the transient plane source (TPS) method. Correlations of thermal conductivity between unfrozen and frozen soil has been analyzed and recognized. Based on the measurement data of unfrozen soil thermal conductivity, the prediction models of frozen soil thermal conductivity for 7 typical soils in the QTEC are proposed. To further facilitate engineering applications, the prediction models of two soil categories (coarse and fine-grained soil) have also been proposed. The results demonstrate that, compared with nonideal prediction accuracy of using water content and dry density as the fitting parameter, the ternary fitting model has a higher thermal conductivity prediction accuracy for 7 types of frozen soils (more than 98% of the soil specimens’ relative error are within 20%). The SVR model can further improve the frozen soil thermal conductivity prediction accuracy and more than 98% of the soil specimens’ relative error are within 15%. For coarse and fine-grained soil categories, the above two models still have reliable prediction accuracy and determine coefficient (R2) ranges from 0.8 to 0.91, which validates the applicability for small sample soils. This study provides feasible prediction models for frozen soil thermal conductivity and guidelines of the thermal design and freeze-thaw damage prevention for engineering structures in cold regions.

PREDICTING THE EFFECT OF TEMPERATURE ON SOIL THERMAL CONDUCTIVITY

Soil Science ◽  
1994 ◽  
Vol 158 (5) ◽  
pp. 307-313 ◽  
Author(s):  
G. S. CAMPBELL ◽  
J. D. JUNGBAUER ◽  
W. R. BIDLAKE ◽  
R. D. HUNGERFORD
Keyword(s):  
Thermal Conductivity ◽  
Effect Of Temperature ◽  
Soil Thermal Conductivity

Soil thermal conductivity prediction for district heating pre-insulated pipeline in operation

Energy ◽  
2012 ◽  
Vol 44 (1) ◽  
pp. 197-210 ◽  
Author(s):  
Matjaz Perpar ◽  
Zlatko Rek ◽  
Suvad Bajric ◽  
Iztok Zun
Keyword(s):  
Thermal Conductivity ◽  
District Heating ◽  
Soil Thermal Conductivity

The Primary and Secondary Analysis of Uncertain Factors in Soil Thermal Properties for GSHP

2012 ◽  
Vol 614-615 ◽  
pp. 688-694 ◽  
Author(s):  
Yi Wang ◽  
Guo Min Shen
Keyword(s):  
Thermal Conductivity ◽  
Thermal Properties ◽  
Water Table ◽  
Unsaturated Soil ◽  
Solid Phase ◽  
Saturated Soil ◽  
Soil Thermal Conductivity ◽  
Saturation Degree ◽  
Soil Thermal Properties ◽  
Soil Solid Phase

In this paper, at first, an effective soil thermal conductivity model was established. Single factor regression analysis for 6 uncertain factors contained in the model was then conducted respectively. Finally, the primary and secondary characters of these uncertain factors were analyzed by using the orthogonal test. The analysis results show that the effective soil thermal conductivity has linear relationships with the saturation degree of unsaturated soil and the depth of water table and has power function relationships with other 4 uncertain factors; the porosity of unsaturated soil has the greatest effect on the effective soil thermal properties, followed by saturation degree of unsaturated soil, porosity of saturated soil, solid phase thermal conductivity of unsaturated soil, solid phase thermal conductivity of saturated soil and the depth of water table.

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