@article{fu_horton_ren_heitman_2023, title={An Unsaturated Hydraulic Conductivity Model Based on the Capillary Bundle Model, the Brooks-Corey Model and Waxman-Smits Model}, volume={59}, ISSN={["1944-7973"]}, url={http://dx.doi.org/10.1029/2022wr034186}, DOI={10.1029/2022WR034186}, abstractNote={Soil unsaturated hydraulic conductivity (K), which depends on water content (θ) and matric potential (ψ), exhibits a high degree of variability at the field scale. Here we first develop a theoretical hydraulic‐electrical conductivity (σ) relationship under low and high salinity cases based on the capillary bundle model and Waxman and Smits model which can account for the non‐linear behavior of σ at low salinities. Then the K‐σ relationship is converted into a K(θ, ψ) model using the Brooks‐Corey model. The model includes two parameters c and γ. Parameter c accounts for the variation of the term (λ + 2)/(λ + 4) where λ is the pore size distribution parameter in the Brooks‐Corey model, and the term m‐n where m and n are Archie's saturation and cementation exponents, respectively. Parameter γ is the sum of the tortuosity factor accounting for the differences between hydraulic and electrical tortuosity and Archie's saturation exponent. Based on a calibration data set of 150 soils selected from the UNSODA database, the best fitting log(c) and γ values were determined as −2.53 and 1.92, −4.39 and −0.14, −5.01 and −1.34, and −5.79 and −2.27 for four textural groups. The estimated log10(K) values with the new K(θ, ψ) model compared well to the measured values from an independent data set of 49 soils selected from the UNSODA database, with mean error (ME), relative error (RE), root mean square error (RMSE) and coefficient of determination (R2) values of 0.02, 8.8%, 0.80 and 0.73, respectively. A second test of the new K(θ, ψ) model using a data set representing 23 soils reported in the literature also showed good agreement between estimated and measured log10(K) values with ME of −0.01, RE of 9.5%, RMSE of 0.77 and R2 of 0.85. The new K(θ, ψ) model outperformed the Mualem‐van Genuchten model and two recently published pedo‐transfer functions. The new K(θ, ψ) model can be applied for estimating K under field conditions and for hydrologic modeling without need for soil water retention curve data fitting to derive a K function.}, number={6}, journal={WATER RESOURCES RESEARCH}, publisher={American Geophysical Union (AGU)}, author={Fu, Yongwei and Horton, Robert and Ren, Tusheng and Heitman, Joshua}, year={2023}, month={Jun} }
 @article{fu_liu_lu_horton_ren_heitman_2023, title={Estimating soil water retention curves from thermal conductivity measurements: A percolation-based effective-medium approximation}, volume={624}, ISSN={["1879-2707"]}, url={http://dx.doi.org/10.1016/j.jhydrol.2023.129898}, DOI={10.1016/j.jhydrol.2023.129898}, abstractNote={A soil water retention curve (SWRC) describes the relationship between soil water content (θ) and suction (h, also the absolute value of pressure head). Earlier work indicated that correlations existed between the percolation-based effective medium approximation (P-EMA) thermal conductivity (λ) model parameters and soil hydraulic properties. In this study, the critical water content (θc) of the P-EMA model was related to the pore size distribution parameter (m) of the van Genuchten model, water content at the inflection point of a SWRC (θi) and hydraulic continuity water content (θhc). And a pedo-transfer function was established to estimate the van Genuchten model parameter α from soil properties and P-EMA parameters. Based on these relationships, three approaches were developed to estimate the van Genuchten models parameters from λ(θ) measurements, porosity, sand and clay contents. The three approaches were then validated on six independent soils, and results showed that all of the approaches estimated θ well at selected h values, with the average root mean square errors from 0.025 to 0.029 cm3 cm−3, the average mean relative absolute errors ranging from 0.111 to 0.157, and the average Akaike Information Criterion from −18.3 to −16.2. Two new approaches outperformed the original Fu et al approach but with fewer input parameters (no need for organic carbon content), thus also facilitating their broader application.}, journal={JOURNAL OF HYDROLOGY}, publisher={Elsevier BV}, author={Fu, Yongwei and Liu, Lin and Lu, Yili and Horton, Robert and Ren, Tusheng and Heitman, Joshua}, year={2023}, month={Sep} }
 @article{fu_jones_horton_heitman_2023, title={Excluding quartz content from the estimation of saturated soil thermal conductivity: Combined use of differential effective medium theory and geometric mean method}, volume={342}, ISSN={["1873-2240"]}, DOI={10.1016/j.agrformet.2023.109743}, abstractNote={Saturated soil thermal conductivity (λsat) is the maximum soil thermal conductivity value of a given soil. Although it can be determined accurately with a heat pulse sensor, there are challenges to prepare fully saturated soil samples. Numerous models have been developed to estimate λsat, and among these, the geometric mean method (GMM) generally performs well. The GMM requires soil mineral composition or quartz content information, which is unavailable for most soils. Earlier studies commonly used assumed that quartz content (fquartz) was equal to sand content (fsand) or to 0.5 × fsand, which led to significant λsat estimation errors especially on coarse-textured soils. We derived a novel method to estimate λsat from soil porosity (ϕ) based on a combination of the GMM and differential effective medium theory (DEM). The new DEM-GMM approach has a single parameter, cementation exponent (m). Using a calibration dataset of 43 soils, we determined best fit m values for soils in three groups: 1.66 for Group I (fsand < 0.4), 1.62 for Group II (0.4 <= fsand < 1) and m = -1.34ϕ+1.70 for Group III (fsand = 1). Using best fit m values for different groups, the new model can estimate λsat values from ϕ. Independent validation results on another 46 soils showed that the new model outperformed the GMM method with the assumption that fquartz = fsand or fquartz = 0.5 × fsand. The mean RMSE, Bias and R2 values of the DEM-GMM approach were 0.202 W m−1 K−1, 0.013 W m−1 K−1 and 0.89, respectively, and corresponding values of the GMM with the two assumptions were 0.295 and 0.476 W m−1 K−1, 0.056 and -0.28 W m−1 K−1, 0.80 and 0.82, respectively. The robust performance of the DEM-GMM approach suggests that it can be incorporated into thermal conductivity models to accurately estimate the thermal conductivity of unsaturated soils.}, journal={AGRICULTURAL AND FOREST METEOROLOGY}, author={Fu, Yongwei and Jones, Scott and Horton, Robert and Heitman, Joshua}, year={2023}, month={Nov} }
 @article{fu_ghanbarian_horton_heitman_2023, title={New insights into the correlation between soil thermal conductivity and water retention in unsaturated soils}, volume={12}, ISSN={["1539-1663"]}, DOI={10.1002/vzj2.20297}, abstractNote={The heat transfer and water retention in soils, governed by soil thermal conductivity (λ) and soil water retention curve (SWRC), are coupled. Soil water content (θ) significantly affects λ. Several models have been developed to describe λ(θ) relationships for unsaturated soils. Ghanbarian and Daigle presented a percolation‐based effective‐medium approximation (P‐EMA) for λ(θ) with two parameters: scaling exponent (ts) and critical water content (θc). In this study, we explored the new insights into the correlation between soil thermal conductivity and water retention using the P‐EMA and van Genuchten models. The θc was strongly correlated to selected soil hydraulic and physical properties, such as water contents at wilting point (θpwp), inflection point (θi), and hydraulic continuity (θhc) determined from measured SWRCs for a 23‐soil calibration dataset. The established relationships were then evaluated on a seven‐soil validation dataset to estimate θc. Results confirmed their robustness with root mean square error ranging from 0.011 to 0.015 cm3 cm−3, MAE ranging from 0.008 to 0.013 cm3 cm−3, and R2 of 0.98. Further discussion investigated the underlying mechanism for the correlation between θc with θhc which dominate both heat transfer and water flow. More importantly, this study revealed the possibility to further investigate the general relationship between λ(θ) and SWRC data in the future.}, journal={VADOSE ZONE JOURNAL}, author={Fu, Yongwei and Ghanbarian, Behzad and Horton, Robert and Heitman, Joshua}, year={2023}, month={Dec} }
 @article{fu_ghanbarian_horton_heitman_2023, title={Robust calibration and evaluation of a percolation-based effective-medium approximation model for thermal conductivity of unsaturated soils}, volume={438}, ISSN={["1872-6259"]}, DOI={10.1016/j.geoderma.2023.116631}, abstractNote={Thermal conductivity (λ) is a property characterizing heat transfer in porous media, such as soils and rocks, with broad applications to geothermal systems and aquifer characterizations. Numerous empirical and physically-based models have been developed for thermal conductivity in unsaturated soils. Recently, Ghanbarian and Daigle (G&D) proposed a theoretical model using the percolation-based effective-medium approximation. An explicit form of the G&D model relating λ to water content (θ) and equations to estimate the model parameters were also derived. In this study, we calibrated the G&D model and two widely applied empirical λ(θ) models using a robust calibration dataset of 41 soils. All three λ(θ) model performances were evaluated using a validation dataset of 58 soils. After calibration, the root mean square error (RMSE), mean absolute error (MAE) and coefficient of determination (R2) of the G&D model were 0.092 W−1 m−1 K−1, 0.067 W−1 m−1 K−1 and 0.97, respectively. For the two empirical models, RMSEs ranged from 0.086 to 0.096 W−1 m−1 K−1, MAEs from 0.063 to 0.071 W−1 m−1 K−1, and R2 values were about 0.97. All three metrics indicated that calibration improved the performance of the G&D model, and it had an accuracy similar to that of the two empirical λ(θ) models. Such a robust performance confirmed that the theoretically-based G&D model can be applied to study soil heat transfer and potentially other related fields.}, journal={GEODERMA}, author={Fu, Yongwei and Ghanbarian, Behzad and Horton, Robert and Heitman, Joshua}, year={2023}, month={Oct} }
 @article{liu_lu_fu_horton_ren_2022, title={Estimating soil water suction from texture, bulk density and electrical resistivity}, volume={409}, ISSN={["1872-6259"]}, url={http://www.scopus.com/inward/record.url?eid=2-s2.0-85120309948&partnerID=MN8TOARS}, DOI={10.1016/j.geoderma.2021.115630}, abstractNote={Information on soil water suction (h) is essential to study water flow and solute transport in soils, and to understand engineering behaviors of unsaturated soils. Due to limited availability of field methods that can accurately measure h, numerous studies have been performed to estimate h from more readily available soil properties. In this study, a new relationship between h and soil electrical resistivity (ρ), developed from the Gardner water retention model and Archie’s second law, was used to estimate h values lower than the air entry value and a formation factor (expressed on log-scale) less than 1. The two model parameters (A and B), which are functions of soil texture and bulk density, were obtained by fitting the model to ρ and h data measured on soil columns of eight textures and various bulk densities. Laboratory and field evaluations with independent h and ρ data showed that the model estimated h values agreed well with the measured values, with root mean square errors less than 0.85 kPa. The model provides a new opportunity to evaluate in situ h dynamics and study coupled transport of water and solutes in the field.}, journal={GEODERMA}, author={Liu, Lin and Lu, Yili and Fu, Yongwei and Horton, Robert and Ren, Tusheng}, year={2022}, month={Mar} }
 @article{fu_horton_ren_heitman_2021, title={A general form of Archie's model for estimating bulk soil electrical conductivity}, volume={597}, ISSN={["1879-2707"]}, url={http://www.scopus.com/inward/record.url?eid=2-s2.0-85102588939&partnerID=MN8TOARS}, DOI={10.1016/j.jhydrol.2021.126160}, abstractNote={Electrical conductivity can be used as a surrogate to study the spatial and temporal variabilities of a number of soil properties, e.g., porosity, salinity, clay content and soil moisture. In this study, we develop a general form of Archie’s model that describes the relationship between soil electrical conductivity (σ) and volumetric water content (θ). The input parameters include θ, σ values at dry and saturated conditions (σdry and σsat), soil porosity (ϕ) and sand, silt and clay contents. A value of 2 was given to the water phase exponent (w) based on model calibration with σ and θ datasets obtained from 15 soils. The general form of Archie’s model was evaluated by comparing soil σ estimates to measured σ values from an additional 6 soils. The new model performed well by providing estimates with root mean square errors in the range of 0.008–0.399 dS m−1 and relative errors ranging from 0.7% to 29.8%. The new model is simple, easy to use and ready for further evaluation on a wide range of soil conditions.}, journal={JOURNAL OF HYDROLOGY}, author={Fu, Yongwei and Horton, Robert and Ren, Tusheng and Heitman, J. L.}, year={2021}, month={Jun} }
 @article{fu_lu_ren_horton_heitman_2021, title={Estimating soil water retention curves from soil thermal conductivity measurements}, volume={603}, ISSN={["1879-2707"]}, url={http://www.scopus.com/inward/record.url?eid=2-s2.0-85119952884&partnerID=MN8TOARS}, DOI={10.1016/j.jhydrol.2021.127171}, abstractNote={The soil water retention curve represents the relationship between soil water content (θ) and matric potential (ψ). The van Genuchten (vG) model is commonly used to characterize the shape of a θ(ψ) curve. Based on the similarities between θ(ψ) curves and soil thermal conductivity (λ) versus θ curves, Lu and Dong proposed a unified conceptual λ(θ) model (LD model) for estimating λ(θ) curves from θ(ψ) curves. Their work makes it possible to relate the shapes of λ(θ) curves to θ(ψ) curves. In this study, we present an empirical approach to estimate the vG model parameter m from the LD model shape parameter p based on a model calibration with θ(ψ) and λ(θ) datasets obtained from 10 soils. The saturated water content θs and the vG model parameter α are estimated from selected soil properties (i.e., bulk density, particle density, particle size distribution and organic carbon content), and the residual water content θr is estimated from the LD model parameter θf. For model evaluation, the θ(ψ) curves of six soils were estimated from measured λ(θ) values and selected soil properties, and were compared to direct θ(ψ) measurements. The proposed method performed well with root mean square errors of estimated θ values ranging from 0.015 to 0.052 cm3 cm−3 and bias ranging from −0.009 to 0.040 cm3 cm−3. We conclude that the proposed method accurately estimates θ(ψ) curves from λ(θ) curves and selected soil properties.}, journal={JOURNAL OF HYDROLOGY}, author={Fu, Yongwei and Lu, Sen and Ren, Tusheng and Horton, Robert and Heitman, J. L.}, year={2021}, month={Dec} }
 @article{fu_horton_heitman_2021, title={Estimation of soil water retention curves from soil bulk electrical conductivity and water content measurements}, volume={209}, ISSN={["1879-3444"]}, url={http://www.scopus.com/inward/record.url?eid=2-s2.0-85100632832&partnerID=MN8TOARS}, DOI={10.1016/j.still.2021.104948}, abstractNote={Measurement of soil water retention curves (SWRCs) is time consuming, and there is no single laboratory device available to measure a SWRC over an entire range of relevant pressures. The van Genuchten (vG) model is commonly used to characterize the shape of the SWRC. Bulk soil electrical conductivity as a function of water content, σ(θ), has been used to estimate hydraulic properties of unsaturated soils, thus making it possible to relate σ(θ) and SWRC. The saturated and residual water content values, θs and θr, can be estimated from soil bulk density and particle size distribution. In this study, we present an approach to estimate vG parameters m and α from σ measured at saturated and residual soil water contents, as well as σ(θ) values measured at intermediate water contents. A thermo-time domain reflectometry (thermo-TDR) sensor is used to measure σ and θ of the same soil sample volume. SWRCs for three soils (Glassil 530 sand, Tennessee silt loam and Illinois clay loam with bulk densities ranging from 1.52 to 1.67 g cm−3, 1.05 to 1.25 g cm−3 and 1.05 to 1.2 g cm−3, respectively) are estimated from σ(θ) measurements and compared with direct SWRC measurements obtained with a tension table and pressure plate extractors. Additional comparisons are made using data obtained from the literature. The proposed method to estimate SWRCs performs well when compared to direct SWRC measurements (with an average RMSE and an average bias of 0.041 cm3 cm−3 and 0.008 cm3 cm−3, respectively). Results indicate that the new σ(θ) based method accurately estimates SWRCs.}, journal={SOIL & TILLAGE RESEARCH}, author={Fu, Yongwei and Horton, Robert and Heitman, Josh}, year={2021}, month={May} }
 @article{fu_lu_heitman_ren_2021, title={Root influences on soil bulk density measurements with thermo-time domain reflectometry}, volume={403}, ISSN={["1872-6259"]}, url={http://www.scopus.com/inward/record.url?eid=2-s2.0-85105587572&partnerID=MN8TOARS}, DOI={10.1016/j.geoderma.2021.115195}, abstractNote={• Root influences on soil bulk density measurements with thermo-TDR technique are quantified. • The extended de Vires heat capacity model is used to account for the root effects. • A critical root density that affects thermo-TDR measurements is determined. The thermo-TDR (time domain reflectometry) technique has been applied for measuring soil bulk density (ρ b ) in-situ. However, the accuracy of thermo-TDR measured ρ b data, as influenced by plant roots, has not been studied. In this study, we applied the extended de Vries heat capacity model to examine the influences of roots on thermo-TDR sensor performance for measuring ρ b dynamics in the root zone. Soil samples were collected at multiple depths and horizontal positions over time during a maize growth period, and ρ b values were determined gravimetrically and indirectly from thermo-TDR measurements. Results showed that by using the extended de Vries model, the thermo-TDR measured ρ b agreed well with the gravimetric values. Ignoring root contribution to bulk soil heat capacity introduced 6.7%, 13.8% and 13.9% errors in thermo-TDR measured ρ b data on the loamy sand, sandy loam, and clay loam soils, respectively. A critical root density of 0.037 g cm −3 was determined beyond which roots may induce ρ b errors greater than 0.1 g cm −3 with the thermo-TDR technique.}, journal={GEODERMA}, author={Fu, Yongwei and Lu, Yili and Heitman, Joshua and Ren, Tusheng}, year={2021}, month={Dec} }
 @article{fu_lu_heitman_ren_2020, title={Root-induced changes in soil thermal and dielectric properties should not be ignored}, volume={370}, url={http://www.scopus.com/inward/record.url?eid=2-s2.0-85082710178&partnerID=MN8TOARS}, DOI={10.1016/j.geoderma.2020.114352}, abstractNote={There is a lack of quantitative understandings on root-mediated changes of soil physical properties. Here we investigated the influences of maize roots on soil heat capacity (C), thermal conductivity (λ) and dielectric constant (ε) of the root zone. Root-zone soil cores were collected at different depths and horizontal positions from the eighth leaf to milk stage of maize, and C, λ and ε of the samples were measured with the thermo-TDR technique. Improved C, λ and ε models that included the influences of roots were developed and validated. The patterns and magnitudes of root effects on C, λ and ε generally followed the configurations of root distribution. On average, the presence of roots caused increases in soil C, λ and ε by 8.8%, 7.1% and 13.6%, respectively. For the root-zone soils, root influences on thermal properties and dielectric constant were significant and could be described quantitatively by using the proposed models.}, journal={Geoderma}, author={Fu, Y. and Lu, Y. and Heitman, J. and Ren, T.}, year={2020} }
 @article{fu_tian_amoozegar_heitman_2019, title={Measuring dynamic changes of soil porosity during compaction}, volume={193}, ISSN={0167-1987}, url={http://dx.doi.org/10.1016/j.still.2019.05.016}, DOI={10.1016/j.still.2019.05.016}, abstractNote={Soil porosity and pore-size distribution changes in response to compaction are important for heat, water, and air flow in soils. In this study, we used the thermo-time domain reflectometry (thermo-TDR) technique to investigate dynamics of in-situ soil porosity and pore-size distribution as affected by number of traffic passes, water content and soil depth. The study was conducted at a field site located near Clayton, NC, USA. A roller was dragged across the length of a 3- by 12-m plot three to five times to repeatedly compact the soil after tillage. Nine thermo-TDR probes, installed at 2.5-, 7.5-, and 12.5-cm depths (representing 0–5, 5–10, and 10–15 cm depth intervals, respectively) at three locations within the plot, were used to determine dynamic changes in soil porosity after each compaction event. Pore-size distribution changes within the top soil layer were determined for a subset of conditions by measuring in-situ infiltration at low tension using a mini disk infiltrometer. Nine core samples were also collected (considered to be a destructive method) near each thermo-TDR probe for measuring total porosity and water content after each compaction. Results showed that the thermo-TDR technique can accurately monitor the change of soil porosity during soil compaction compared to the destructive core method. Variability of replicated soil porosity measurements by the thermo-TDR technique (with a root mean square error (RMSE) of 0.011 m3 m−3 and mean standard error (MSE) of 0.010 m3 m−3) was lower than that of the core method (RMSE = 0.017 m3 m−3, MSE = 0.019 m3 m−3). As expected, total soil porosity decreased with the number of passes; a major portion of compaction (59–89% of the total porosity decrease) occurred during the first pass. The trend of topsoil (0–5 cm) compaction differed from that of subsoil layers (5–10 and 10–15 cm). Changes in porosity were highly sensitive to soil water content. For the sandy-textured soil in this study, soil porosity decreased as water content increased (during compaction period), and the maximum compaction (associated with the lowest porosity) was reached at an initial water content range between 0.08 and 0.10 g g-1. Above this range, the compaction level decreased with increasing water content. In addition, there was a shift in pore-size distribution for the surface layer. More importantly, pore-size distribution continued to change with additional traffic passes even after soil total porosity became stable.}, journal={Soil and Tillage Research}, publisher={Elsevier BV}, author={Fu, Yongwei and Tian, Zhengchao and Amoozegar, Aziz and Heitman, Josh}, year={2019}, month={Oct}, pages={114–121} }
 @article{fu_lu_ren_2014, title={Influences of finite probe property on soil thermal property estimated by heat pulse technique}, volume={30}, url={http://www.scopus.com/inward/record.url?eid=2-s2.0-84910048844&partnerID=MN8TOARS}, DOI={10.3969/j.issn.1002-6819.2014.19.009}, number={19}, journal={Nongye Gongcheng Xuebao/Transactions of the Chinese Society of Agricultural Engineering}, author={Fu, Y. and Lu, Y. and Ren, T.}, year={2014}, pages={71–77} }