Soil Thermal Diffusivity Research Articles

Random forest approach to estimate soil thermal diffusivity: Evaluation and comparison with traditional pedotransfer functions

Pedotransfer functions (PTFs) are widely used in science and engineering to calculate soil thermal diffusivity (κ) as a function of soil moisture content and other soil physical properties. However, available data on κ are rather scarce and already developed PTFs suffer from significant uncertainties in applicability and performance. Few studies attempted to systematically review and evaluate these PTFs with large dataset of κ. The objectives of this study were to (1) review and collate currently available PTFs for κ; (2) compile a κ dataset to evaluate performance of these PTFs, (3) build a machine learning (ML) (i.e., random forest-RF) model with three classes of soil physical properties to determine soil physical properties most appropriate to use as predictors for calculating κ, and (4) compare the RF modelling with the traditional PTFs. The correlation coefficient (r), centered root-mean-square (E'), and standard deviation (SD) were used to evaluate the performance of the above PTFs. A total of nine PTFs were synthesized and assessed with a compiled dataset consisting of 998 measurements from 106 soils with a wide range of textures. In general, the PTFs of Lukiashchenko and Arkhangelskaya (2018) (LA2018–1 and LA2018–2) perform the best. While the quadratic equation of Mady and Shein (2018) (MS2018–1 and MS2018–2) showed better performance for coarse and medium textured soil. However, the overall performance of the nine PTFs for predicting κ was not as accurate as RF. RF can satisfactorily calculate κ taking into account sand, silt, clay contents, and soil moisture content as predictors.

Local-scale heterogeneity of soil thermal dynamics and controlling factors in a discontinuous permafrost region

Abstract In permafrost regions, the strong spatial and temporal variability in soil temperature cannot be explained by the weather forcing only. Understanding the local heterogeneity of soil thermal dynamics and their controls is essential to understand how permafrost systems respond to climate change and to develop process-based models or remote sensing products for predicting soil temperature. In this study, we analyzed soil temperature dynamics and their controls in a discontinuous permafrost region on the Seward Peninsula, Alaska. We acquired one-year temperature time series at multiple depths (at 5 or 10 cm intervals up to 85 cm depth) at 45 discrete locations across a 2.3 km2 watershed. We observed a larger spatial variability in winter temperatures than that in summer temperatures at all depths, with the former controlling most of the spatial variability in mean annual temperatures. We also observed a strong correlation between mean annual ground temperature at a depth of 85 cm and mean annual or winter season ground surface temperature across the 45 locations. We demonstrate that soils classified as cold, intermediate, or warm using hierarchical clustering of full-year temperature data closely match their co-located vegetation (graminoid tundra, dwarf shrub tundra, and tall shrub tundra, respectively). We show that the spatial heterogeneity in soil temperature is primarily driven by spatial heterogeneity in snow cover, which induces variable winter insulation and soil thermal diffusivity. These effects further extend to the subsequent summer by causing variable latent heat exchanges. Finally, we discuss the challenges of predicting soil temperatures from snow depth and vegetation height alone by considering the complexity observed in the field data and reproduced in a model sensitivity analysis.

Effects of Diesel Concentration on the Thermal Conductivity, Specific Heat Capacity and Thermal Diffusivity of Diesel-Contaminated Soil

Effects of Diesel Concentration on the Thermal Conductivity, Specific Heat Capacity and Thermal Diffusivity of Diesel-Contaminated Soil

Variability of soil thermal diffusivity in three Iowa fields

AbstractSoil 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 (RN) slot planted. Thermocouples are used to measure soil temperature at two depths, that is, 1 and 10 cm, at 49 locations (49 grid locations; 1.5 m × 3 m) in each of three adjacent fields. After allowing the thermocouples to equilibrate with the soil environment for 2 weeks in each field, hourly 1‐ and 10‐cm depth soil temperature observations were recorded for a separate 24‐h 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 lowest 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 were similar for the three fields, ranging from 17% to 21%. The minimum number of samples needed to obtain a confidence interval of 7 cm2 h−1 for α was two, five, and seven, 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.

Application of machine learning algorithms to model soil thermal diffusivity

Soil thermal diffusivity (k) is an important thermal property that significantly affects ground energy storage and heat transfer. Direct measurements of soil thermal diffusivity are challenging due to sensor limitations and variable soil physical properties, such as water content, bulk density, mineralogy and texture. Therefore, indirect estimations of k are commonly used. In this study, the abilities of six machine learning (ML) models, including k-nearest neighbours (KNN), multilayer perceptron (MLP), support vector regression (SVR), decision tree (DT), random forest (RF) and gradient boosting decision tree (GBDT), to estimate k values are evaluated based on a compiled database consisting of 999 samples. The ML model performances are evaluated by three model performance indicators (i.e., coefficient of determination-R2, mean absolute error-MAE and root mean square error-RMSE). The GDBT model best estimates soil thermal diffusivity, showing the highest fitness (R2 = 0.99). Model estimation accuracies are determined for varying numbers of available model inputs, and recommended minimum numbers of model inputs needed to accurately estimate thermal diffusivity are tabulated. This study demonstrates the ability of ML models to estimate values of soil thermal diffusivity, and it provides reference information for future thermo-related soil science and soil engineering applications.

Is the Thermal Diffusivity of Unsaturated Soils a Mono-Maximum Function of their Moisture Content?

Is the Thermal Diffusivity of Unsaturated Soils a Mono-Maximum Function of their Moisture Content?

Estimation of soil thermal properties using conduction and conduction–convection heat transfer equations in the Brazilian Pampa biome

Soil temperature, composition, and structure directly affect the heat transfer mechanisms in soil. Understanding the thermal behavior of soil is fundamental for describing the processes of mass and energy exchange in the soil-plant-atmosphere system. In addition, estimating thermal property values is necessary to determine the amount of heat transferred, stored, or transmitted by conduction and convection between the soil surface and deeper layers. This study used two soil heat transfer models (the conduction and conduction-convection methods) to estimate soil thermal properties. Soil data were collected in a natural pasture area in the Pampa biome in southern Brazil over 1.5 years. The results showed that soil thermal diffusivity (k) has an inverse seasonal variation with soil temperature (Ts). The k and soil thermal conductivity (λ) values are higher as the soil depth and soil moisture (θ) increase, confirming the vertical variation of thermal properties. The liquid water flux density (W) represents the vertical movement of water in the soil and decreases in periods of higher Ts. Increasing values of W led to an increase in the variability of k with depth. The conduction–convection method provides the best estimates for Ts with R2 = 0.99 for deeper layers compared to the conduction method. However, we found that both models gave similar results for near-surface layers since, in this case, the conduction process was responsible for most of the heat transfer from the surface to the ground. The results obtained here allowed us to evaluate heat transfer at different depths and estimate thermal properties as a function of soil moisture in a natural pasture area in the Brazilian Pampa biome. Furthermore, the results can be integrated into global land surface models intended to represent the behavior of the energy exchange between soil, surface, and atmosphere.

Effects of Corn Straw Biochar, Soil Bulk Density and Soil Water Content on Thermal Properties of a Light Sierozem Soil

This research aimed to quantify the effects of biochar derived from corn straw on soil thermal conductivity, capacity, and diffusivity. Firstly, the amount of biochar application (w/w) added to light sierozem soil was 0% to 5%, and the mixtures were packed into soil columns at a consistent bulk density (1.20 g.cm-3). Secondly, soil columns with a consistent biochar addition rate (5%) were packed to different bulk densities of 1.30, 1.25, 1.20, 1.15, and 1.10 g.cm-3. Soil thermal characteristics were measured under the control of soil moisture content from 0% to 40%. Under consistent bulk-density conditions, biochar could significantly reduce soil thermal conductivity and diffusivity. Still, there wasn’t a significant influence on soil heat capacity in most soil moisture content levels. With the decrease of soil bulk density, soil thermal conductivity, capacity, and diffusion coefficient reduced significantly. As soil water content increased, all the indexes of thermal properties largely improved, and the effects were much more significant than those of biochar amendment and bulk density change on soil thermal performances. This research could supply an implication to evaluate the influence of biochar amendment on soil thermal performances.

Effects of biochar particle size, biochar application rate, and moisture content on thermal properties of an unsaturated sandy loam soil

Although biochar is considered as a potential soil amendment, the effect of its rates and particle sizes on soil properties such as soil thermal properties, especially in a light-textured soil has not been widely discussed. Due to the importance of soil thermal properties in many engineering, climatological and agricultural applications, this study aims to investigate and model the effects of biochar particle size, biochar rate, and soil moisture content on the thermal properties of a sandy loam soil through column experiments. The experiments were conducted using three biochar application rates (0 (as control), 1%, and 5% w/w), three biochar particle diameters (<0.15, 0.15–1, and 1–2 mm), and three water saturation (Sw) levels (0.07, 0.2, and 0.78) in three replicates. Given the experimental data, soil thermal properties were modeled using the Campbell, Zhao et al., and de Vries models. According to the results, a higher biochar application rate (i.e. 5%) significantly (P < 0.05) altered soil physical and chemical properties, i.e. bulk density, porosity, EC and pH, and soil thermal properties. Biochar particle sizes had a remarkable effect on soil thermal properties. Adding 5% biochar with particle sizes of < 0.15, 0.15–1 and 1–2 mm decreased soil thermal conductivity to 21%, 26%, and 32% at Sw= 0.07, respectively indicating a lower reduction of thermal conductivity when biochar with a smaller particle size (<0.15 mm) is applied. Applying 5% biochar of the smallest size decreased soil thermal conductivity and diffusivity, and heat capacity by 21%, 17%, and 9%, respectively, and increased thermal resistivity by 27% at Sw= 0.07. The effect of soil moisture on thermal properties (especially thermal conductivity and resistivity) was more robust than biochar amendment. Experimental modeling showed a linear relationship between soil moisture and thermal conductivity, diffusivity and heat capacity, and a power law relationship between soil moisture and thermal resistivity. Mathematical modeling showed the Campbell, de Vries, and Campbell/de Vries models perform the best in estimating thermal conductivity, heat capacity, and thermal diffusivity, respectively. The findings of this research could be used for controlling and predicting soil temperature, improving irrigating scheduling, and optimizing the quantity of biochar amendment required in agricultural fields.

An Analytical Solution for the Heat Conduction–Convection Equation in Non-homogeneous Soil

Heat transfer through the soil is important in shallow geothermal applications, in plant growth as well as in the surface energy balance. In this work, an analytical solution for a conduction–convection equation that models heat transfer is presented for non-homogeneous soil. The main assumption underlying the solution is that the thermal diffusivity of soil is piecewise-constant. A method to determine the depth-dependent thermal diffusivity and the water flux density through the porous medium based on temperature measurements is also developed and applied to field data from a site in Ioannina, Greece. The thermal diffusivity was found to increase in the layer below the surface and decrease for larger depths, while convection through the porous medium was found to be present in wet conditions and to account for about 10% of the heat flux in terms of the annual variability of temperature. Finally, the capability of the novel method in capturing the spatio-temporal variability of soil temperature is compared to three commonly used algorithms: the amplitude, the phase, and the conduction–convection algorithm. The novel method is able to reduce the root-mean-square error for the predicted variability of temperature at all depths by an order of magnitude compared to the other three algorithms.

On a question of non-constant thermal diffusivity of soils

The general heat conductivity equation includes time- and depth-dependent soil properties (soil heat capacity and thermal conductivity). The simplified form of the heat conductivity equation contains only the soil thermal diffusivity parameter. Numerical solutions of the general and simplified equations were compared to quantify the possibility of equation reduction. Two test runs for soils with different compositions were done. The thermal regime for both peat soil and dark chestnut soil does not change significantly after using a simplified heat equation according to model estimations. The maximal soil temperature discrepancy was about 0.5 °C for peat soil and 2.2-3.3 °C for dark chestnut soil, which results in 4-6% error in methane efflux estimations.

Analytical and Numerical Studies of Transient Heat Transfer in Soil for Geothermal Systems

The study of geothermal systems requires a good knowledge of heat transfers in the depth of the soil. The aim of this work is to study the distribution of temperature in the ground under the climatic conditions of Togo. The analytical and numerical solutions of unidirectional heat transfer equation assuming the soil as a semi-infinite medium are found. The analytical solution is validated by comparing the results of the present work to those found in literature. A good qualitative agreement between these results was noted. The results show that the attenuation depth decreases when the attenuation accuracy of the thermal wave increases. The analysis of the effect of moisture content indicates that the increasing in soil conductivity with moisture result in the decreasing of the attenuation depth. Soil temperature increases when increasing soil thermal diffusivity. It is found also that, soil temperatures decrease with depth and stabilize around an average value of 30°C for depths greater than about 5.8 m for all type of soil studied in the month of March which is the hottest month of the year in Togo. A gradual decrease in temperature can be seen from March to August (hot period) followed by stabilization at around 28°C with a depth of 5.8 m. The phenomenon is reversed for the months of September to February (cold period). The soil warms up slowly during the day and cools down slowly at night because of the thermal inertia of the soil. A decrease in amplitude of the thermal wave near ground surface, is observed when the Leaf Area Index (LAI) increases. However, ils influence on the stabilization depth is not very significant. Stabilization of soil temperatures in March month is observed at a depth of about 40 cm for all LAI with a value of 37°C.

Effects of cover crops on soil thermal properties of a corn cropping system

AbstractSoil thermal properties play many major roles in a soil as they influence the soil temperature and define the soil microclimate, which regulates many soil health parameters as well as life cycles of soil biota. Although cover crops (CC) can affect soil thermal properties, they are one of the least studied areas. This study compared the effects of CC on soil thermal properties in a corn (Zea mays L.) cropping system at the Elsberry Plant Material Center–USDA in Missouri (USA). The study design consisted of (a) no‐till CC (NC), (b) conventional till no CC (CN), (c) no‐till no CC (NN), and (d) grass (G) treatments. The first CC establishment in the NC treatment was done in October 2019. Soil cores were collected in April 2021 from 0‐to‐40‐cm depth at 10‐cm increments (96 samples) and analyzed for volumetric heat capacity (Cv), soil thermal conductivity (λ), soil thermal diffusivity (α), soil organic carbon (SOC), bulk density (BD), and volumetric water content (θ). As per the results, NC did not significantly improve SOC, BD, or soil thermal properties, because the biomass accumulation by CC was not enough due to the lack of CC establishment duration. The G treatment showed significantly higher Cv and lower λ in several soil depths. The study outcomes suggested that long‐term maintenance of CC with cropping systems are needed in quantifying the effects of CC on soil thermal properties.

Probabilistic estimation of depth-resolved profiles of soil thermal diffusivity from temperature time series

Abstract. Improving the quantification of soil thermal and physical properties is key to achieving a better understanding and prediction of soil hydro-biogeochemical processes and their responses to changes in atmospheric forcing. Obtaining such information at numerous locations and/or over time with conventional soil sampling is challenging. The increasing availability of low-cost, vertically resolved temperature sensor arrays offers promise for improving the estimation of soil thermal properties from temperature time series, and the possible indirect estimation of physical properties. Still, the reliability and limitations of such an approach need to be assessed. In the present study, we develop a parameter estimation approach based on a combination of thermal modeling, sliding time windows, Bayesian inference, and Markov chain Monte Carlo simulation to estimate thermal diffusivity and its uncertainty over time, at numerous locations and at an unprecedented vertical spatial resolution (i.e., down to 5 to 10 cm vertical resolution) from soil temperature time series. We provide the necessary framework to assess under which environmental conditions (soil temperature gradient, fluctuations, and trend), temperature sensor characteristics (bias and level of noise), and deployment geometries (sensor number and position) soil thermal diffusivity can be reliably inferred. We validate the method with synthetic experiments and field studies. The synthetic experiments show that in the presence of median diurnal fluctuations ≥ 1.5 ∘C at 5 cm below the ground surface, temperature gradients &gt; 2 ∘C m−1, and a sliding time window of at least 4 d the proposed method provides reliable depth-resolved thermal diffusivity estimates with percentage errors ≤ 10 % and posterior relative standard deviations ≤ 5 % up to 1 m depth. Reliable thermal diffusivity under such environmental conditions also requires temperature sensors to be spaced precisely (with accuracy to a few millimeters), with a level of noise ≤ 0.02 ∘C, and with a bias defined by a standard deviation ≤ 0.01 ∘C. Finally, the application of the developed approach to field data indicates significant repeatability in results and similarity with independent measurements, as well as promise in using a sliding time window to estimate temporal changes in soil thermal diffusivity, as needed to potentially capture changes in bulk density or water content.

Dynamics of Temperature Variation in Soil under Fallow Tillage at Different Depths

One of the important processes taking place during soil surface tillage is the variation of the soil temperature at different depths. The paper presents the results of research into the dynamics of soil temperature variation in terms of such parameters of the soil as its thermal diffusivity and temperature oscillation frequency. It has been established that the temperature oscillation amplitude decreases when the depth increases, following the logarithmic law. In this variation, the temperature difference between the 0–5 cm layer and the adjacent 5–10 cm layer has the greatest value. This circumstance is a favorable factor for the fallow tillage technology, where the soil is regularly cultivated to a depth of no more than 5 cm, in case of a moisture deficit. A decrease in the frequency of the soil temperature oscillations can result in the reduced level of its heating in different layers. In terms of qualitative changes, the reduction in soil thermal diffusivity results in the amplitude of the soil temperature oscillations being lowered and at the same time the maximum of these oscillations being shifted towards a delay.

Apparent thermal diffusivity of soil in ice-free areas of Keller peninsula in maritime Antarctica.

Heat transfer process in the soil active layer is important for the knowledge of its thermal properties linked with climate issues. The objective of this work was to analyze the energy flux in different soil profiles by estimating the apparent soil thermal diffusivity ($ATD$). The study was carried out in Keller Peninsula, located at King George Island in four different sites differing by soil characteristics, as well as vegetation coverage and landscape setting. The $ATD$ was estimated in function of the long-term hourly temperature records at different soil depths. In addition, we estimated the seasonal mean of the $ATD$ and the freezing $N$-factor. Results showed that $ATD$ values were smaller at shallow depths and increased with depth. The diffusivity values presented lower variability in colder conditions, especially at deeper soil layers. Water content was the main factor affecting soil thermal diffusivity at sites $1$ and $3$ (more than $70$ and $63\%$ of probability). At sites $3$ and $4$ lower $N$-factors were observed, suggesting higher snow pack and permafrost closer to the soil surface. Hence, positive $ATD$ appears in the summer due to thawing increases soil moisture, while negative $ATD$ appears during the freeze of the snow pack and precipitation.

Estimation of soil and grout thermal properties for energy piles: A new parameter estimation method

It is critical to determine the thermal parameters of the soil and pile for designing energy piles. Based on the infinite different-thermal properties cylindrical heat source model, this paper proposes a method that can use short-term thermal response test (TRT) data to estimate the soil thermal conductivity, thermal diffusivity and thermal resistance in the pile. A three-dimensional numerical model was established and verified by a large sandbox test. The numerical model was used to generate virtual TRT data, and the pattern search algorithm was adopted to estimate the parameters. Then the accuracy of the parameter identification results was discussed. It is found that, compared with the previous methods, the proposed method can greatly shorten the time required for TRT. At the same time, this method can estimate the thermal parameters more accurately, especially the soil thermal conductivity that has the greatest impact on heat transfer performance, with an estimated error of 1.23%.

Estimation of apparent thermal diffusivity of soil at lesser-Himalayan experimental catchment, Uttarakhand, India, for analytical subsoil temperature modelling

Estimation of apparent thermal diffusivity of soil at lesser-Himalayan experimental catchment, Uttarakhand, India, for analytical subsoil temperature modelling

Prediction of the temperature profiles for shallow ground in cold region and cold winter hot summer region of China

The lacking of ground temperature measurement data has been preventing the development of underground engineering in China. Therefore, this paper proposed and validated a numerical prediction model that comprehensively considered the effects of short-wave solar radiation, long-wave radiation, latent evaporation energy, heat convection, heat conduction, soil freezing and thawing latent heat on ground temperature. Three frequently-used typical meteorological year databases were used to provide the weather parameters for the numerical model. Comparison of three city cases showed that the model using the “Chinese typical year weather” performed best in accuracy. Moreover, due to the much calculation time required for the numerical model, the two-harmonic analytical correlation (THAC) with higher accuracy than Kusada correlation and Baggs correlation was introduced to obtain ground temperature rapidly. Considering the variety of soil types, the universal correction formulas about the relationship between the THAC coefficients and the soil thermal diffusivity were developed through a series of typical city cases. Simultaneously, THACs of main cities in two climate regions corresponding to soil thermal diffusivity of 8.13*10-2 m2/day were provided as the standard THACs. The THACs of other types of soil were proved to be derived according to the universal correction formulas and the standard THACs.

Variations in soil heat transfer under different land use types in Abia State, South eastern Nigeria

An in-depth knowledge on variations in soil heat transfer under different land uses is essential for proper understanding of the variations in thermal energy transfer under different human activities and modifications on land. This paper presents an investigation on the variations in soil heat transfer under different land use types in Abia State. This study evaluates three land use types: forest land (FL), continuously cultivated land (CC) and excavation site (EX). The parameters investigated in this study include; particle size distribution, bulk density, volumetric moisture content, atmospheric temperature, soil temperature, soil thermal conductivity, soil heat flux, soil volumetric heat capacity and soil thermal diffusivity. The results show that the different land use types studied influenced the soil heat energy transfer and had a significant effect on soil thermal properties. The results revealed that excavation site recorded the highest soil bulk density (1.70 Mg m-3) and soil temperature (42.6oC) while forest land recorded the lowest bulk density (1.36 Mg m-3) and soil temperature (30.3oC). The transmission of heat through a unit length of soil per unit cross-sectional area (2.476 W mk-1) was higher in forest land than the other land use types studied. Soil under continuously cultivated land recorded the highest volumetric heat capacity (1.407 J (m3K)-1). This study will help farmers and land owners in terms of choice and management of different land use types for agricultural and industrial purposes.