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Abstract A petrophysical model that accurately relates bulk electrical conductivity (σ) to pore fluid conductivity (σ_w) is critical to the interpretation of geophysical measurements. Classical models are either only applicable over a limited salinity regime or incorrectly explain the nonlinear‐to‐linear behavior of the σ(σ_w) relationship. In this study, asymptotic limits at zero and infinite salinity are first established in which, σ is expressed as a linear function of σ_wwith four parameters: cementation exponent (m), the equivalent value of volumetric surface electrical conductivity (σ_s), the volume fraction of overlapped diffuse layer (ϕ_od) and parameter χ representing the ratio of the volume fraction of the water phase to that of the solid phases in the surface conduction pathway. Subsequently, we bridge the gap between the two extremes by employing the Padé approximant (PA). Given that parameter χ exhibits a marginal influence on the σ(σ_w) curve, based on measurements for 15 samples, we identify its optimal value to be 0.4. After setting the optimal value ofχ, we proceed to evaluate the performance of the PA model by comparing its estimates and estimates made by two existing models to measured values from 27 rock samples and eight sediment samples. The comparison confirms that the PA model estimates are more accurate than estimates made by existing models, particularly at low salinity and for samples with higher cation exchange capacity. The PA model is advantageous in scenarios involving the interpretation of electrical data in freshwater environments. 

Abstract A thermo‐time domain reflectometry (thermo‐TDR) sensor combines a heat‐pulse sensor with a TDR waveguide to simultaneously measure coupled processes of water, heat, and solute transfer. The sensor can provide repeated in situ measurements of several soil state properties (temperature, soil water content, and ice content), thermal properties (thermal diffusivity, thermal conductivity, heat capacity), and electromagnetic properties (dielectric constant and bulk electrical conductivity) with minimal soil disturbance. Combined with physical or empirical models, structural indicators, such as bulk density and air‐filled porosity, can be derived from measured soil thermal and electrical properties. Successful applications are available to determine fine‐scale heat, water, and vapor fluxes with thermo‐TDR sensors. Applications of thermo‐TDR sensors in complicated scenarios, such as heterogeneous root zones and saline environments, are also possible. Therefore, the multi‐functional uses of thermo‐TDR sensors are invaluable for in situ observations of several soil physical properties and processes in critical zone soils. 

Abstract 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 (t_s) and critical water content (θ_c). 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 θ_cwas strongly correlated to selected soil hydraulic and physical properties, such as water contents at wilting point (θ_pwp), inflection point (θ_i), and hydraulic continuity (θ_hc) determined from measured SWRCs for a 23‐soil calibration dataset. The established relationships were then evaluated on a seven‐soil validation dataset to estimate θ_c. Results confirmed their robustness with root mean square error ranging from 0.011 to 0.015 cm³cm⁻³, MAE ranging from 0.008 to 0.013 cm³cm⁻³, andR²of 0.98. Further discussion investigated the underlying mechanism for the correlation between θ_cwith θ_hcwhich 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.