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Abstract Soil heat flux plates (SHFPs) are widely used to measure soil heat flux (Gs). Gs is often underestimated by SHFPs (Gp). Although calibration methods are used, they are not always effective. The objective of this study is to evaluate the effectiveness of a field calibration method applied to various SHFPs installed in a full canopy maize field. A 5‐day measurement period with wet and dry soil conditions was used for calibration, while 80‐day and 60‐day measurement periods were used for evaluation. Uncorrected SHFP measured values (Gp) underestimated the actual reference Gs determined by the gradient method (Gs_grad) by 42%–64%. Gp values in the evaluation period were corrected (Gp_corr) by dividing them by the ratio of Gp/Gs_grad determined over the calibration period. After the correction, the Gp_corr agreed well with the Gs_grad, with Gp_corr/Gs_grad of four of six SHFPs being 0.90–1.01, improving to 74%–98%. The field calibration performed approximately the same with the wet and dry calibration periods, whether the calibration and evaluation periods were consecutive in time or had relatively long time intervals, indicating that this method accounted for almost all errors with SHFP. This is largely due to the slight variation in soil thermal conductivity and the linearity between soil temperature gradients from SHFP and the gradient method under relatively stable soil moisture conditions. This study deepens our understanding and improves the accuracy of soil heat flux measurements. Calibration of SHFPs under various land covers and weather conditions is warranted in future studies. 

Abstract Plastic liners are sometimes used with soil samplers in order to collect and store intact soil cores. Gaps at the soil–wall interface caused by the flexibility of plastic liners can result in wall flow, preventing accurate fluid flux density measurements. A subsampling method was developed to overcome problems with wall flow from soil samples collected with plastic liners in order to measure air permeability (k_a) and saturated hydraulic conductivity (K_sat) on the intact cores. Subsamples were obtained after first immobilizing the soil within plastic liners by injecting expanding foam into the gaps between the soil and the liners. Once the soil was fixed in place, the soil samples were cut to the desired length, and sharpened metal rings were inserted into the original soil sample with a vise. With the metal ring at the desired depth, the subsample was removed from the original soil sample by cutting the liner and removing excess soil from the ends of the rings. Initial attempts to measurek_aandK_saton samples within the original liners led to unrealistically high values because significant wall flow occurred. However, after implementing the improved subsampling approach, the measuredk_aandK_satof the subsamples were within the range of expected values based on the literature. The subsampling method effectively eliminated wall flow on soil originally collected in plastic liners and is relatively easy to implement without the need for specialized tools. 

Abstract The vadose zone—the variably saturated, near‐surface environment that is critical for ecosystem services such as food and water provisioning, climate regulation, and infrastructure support—faces increasing pressures from both anthropogenic and natural factors, including changing climatic conditions. A more comprehensive understanding of vadose zone processes and interactions is imperative to effectively address these challenges and safeguard water and soil resources. This review outlines selected key issues, knowledge gaps, and research opportunities across six thematic sections. Each section presents a problem statement, a summary of recent innovations, and a compilation of emerging challenges and study opportunities. The selected topics include scaling and modeling of vadose zone properties and processes, soil moisture monitoring initiatives, surface energy balance, interplay between preferential water flow paths and biogeochemical processes, interactions between fires and vadose zone dynamics, and emerging contaminants and their fate in the vadose zone. This overview is intended to serve as a compendium of vadose zone science that encompasses both insights gained from prior research and anticipated needs for the coming years. 

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 Soil compaction leads to an increase in bulk density () and results in a shift in pore‐size distribution toward smaller pores. These changes alter the soil hydraulic properties (SHPs), that is, the water retention curve and the hydraulic conductivity curve. Most existing models that address the impact of changes in on SHP have been confined to SHP models that consider only capillary water, neglecting water stored and transmitted within adsorbed films (noncapillary water). Recently, a new prediction model was developed that combines the Peters–Durner–Iden (PDI) SHP model system, which accounts for capillary and noncapillary water, with a prediction scheme for compaction effects. However, this new approach has yet to be calibrated and tested against data from soils with varying textures. The objective of this study was to calibrate and evaluate the new water retention model using a comprehensive dataset from the literature. Two different variants, which vary in the number of degrees of freedom have been tested. Remarkably, the variant with only one adjustable parameter, the one that shifts the pore‐size distribution by scaling the pressure head, was sufficient to accurately describe the data. All other parameters can either be fixed at the reference value or scaled based on straightforward physical reasoning. The model achieved low calibration errors (median root mean square error [RMSE]: 0.013; median mean error [ME]: 0.0014) and performed satisfactorily in validation (median RMSE: 0.025; median ME: −0.014). Based on our results, we hypothesize that the scaling approach is independent of the capillary saturation function and that this method might be applied to other models within the PDI system without new calibration. 

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 Soil surface cover is one of the most critical factors affecting soil water vapor transport, especially in drylands where water is limited, and the water movement occurs predominantly in the form of vapor instead of liquid. Biocrusts are an important living ground cover of dryland soils and play a vital role in modifying near‐surface soil properties and maintaining soil structure. The role of biocrusts in mediating soil water vapor transport during daytime water evaporation and nighttime condensation remains unclear. We investigated the differences in vapor diffusion properties, vapor adsorption capacity, and water evaporation between bare soil and three types of biocrusts (cyanobacterial, cyanobacterial‐moss mixed, and moss crusts) in the Chinese Loess Plateau. Our results showed that the three types of biocrusts had 5%–39% higher vapor diffusivity than bare soil. At the same level of ambient relative humidity and temperature, the initial vapor adsorption rates and cumulative adsorption amounts of the biocrusts were 10%–70% and 11%–85% higher than those of bare soil, respectively. Additionally, the late‐stage evaporation rate of cyanobacterial‐, cyanobacterial‐moss mixed‐, and moss‐biocrusts were 31%–217%, 79%–492%, and 146%–775% higher than that of bare soil, respectively. The effect of biocrusts on increasing vapor transport properties was attributed to the higher soil porosity, clay content, and specific surface area induced by the biocrust layer. All of these modifications caused by biocrusts on surface soil vapor transport properties suggest that biocrusts play a vital role in reshaping surface soil water and energy balance in drylands. 

Abstract Biocrusts are a critical surface cover in global drylands, but knowledge about their influences on surface soil thermal properties are still lacking because it is quite challenging to make accurate thermal property measurements for biocrust layers, which are only millimetres thick. In this study, we repacked biocrust layers (moss‐ and cyanobacteria‐dominated, respectively) that had the same material as the original intact biocrusts but was more homogeneous and thicker. The thermal conductivity (λ), heat capacity (C) and thermal diffusivity (k) of the repacked and intact biocrusts were measured by the heat pulse (HP) technique at different mass water contents (θ_m) and mass ratios (W_t), and the differences between repacked and intact biocrusts were analysed. Our results show that biocrusts substantially alter the thermal properties of the soil surface. The averageλof moss (0.37 W m⁻¹ K⁻¹) and cyanobacteria biocrusts (0.90 W m⁻¹ K⁻¹) were reduced by 63.0% and 10.3% compared with bare soil (1.00 W m⁻¹ K⁻¹), respectively. Edge effects including heat loss and water evaporation caused theλandkof the biocrusts to be underestimated, but theCto be overestimated. The differences in thermal properties were significant (p <0.001), except for the differences in thermal conductivity between repacked and intact cyanobacteria biocrusts, which were not significant (p = 0.379). Specifically, in the volumetric water content (θ_v) range of 0 to 20%, theλandkof the repacked moss biocrusts were underestimated by 59.1% and 61.8%, respectively, and theCwas overestimated by 23.9% compared with the intact moss biocrusts. Theλandkof the repacked cyanobacteria biocrusts were underestimated by 15.8% and 79.2%, respectively, and theCwas overestimated by 34.8% compared with the intact cyanobacteria biocrusts at theθ_vrange of 0 to 30%. Typically, this difference increased as theθ_vrises between repacked and intact biocrusts. Our new measurements provide evidence that the thermal properties of biocrusts were previously misjudged due to the measurement limitations imposed by their limited thickness when measured in situ. Biocrusts are likely more significant in regulating soil heat and temperature in drylands than was previously assumed. 

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. 

Abstract Interpreting time domain reflectometry (TDR) waveforms obtained in soils with non‐uniform water content is an open question. We design a new TDR waveform interpretation model based on convolutional neural networks (CNNs) that can reveal the spatial variations of soil relative permittivity and water content along a TDR sensor. The proposed model, namely TDR‐CNN, is constructed with three modules. First, the geometrical features of the TDR waveforms are extracted with a simplified version of VGG16 network. Second, the reflection positions in a TDR waveform are traced using a 1D version of the region proposal network. Finally, the soil relative permittivity values are estimated via a CNN regression network. The three modules are developed in Python using Google TensorFlow and Keras API, and then stacked together to formulate the TDR‐CNN architecture. Each module is trained separately, and data transfer among the modules can be facilitated automatically. TDR‐CNN is evaluated using simulated TDR waveforms with varying relative permittivity but under a relatively stable soil electrical conductivity, and the accuracy and stability of the TDR‐CNN are shown. TDR measurements from a water infiltration study provide an application for TDR‐CNN and a comparison between TDR‐CNN and an inverse model. The proposed TDR‐CNN model is simple to implement, and modules in TDR‐CNN can be updated or fine‐tuned individually with new data sets. In conclusion, TDR‐CNN presents a model architecture that can be used to interpret TDR waveforms obtained in soil with a heterogeneous water content distribution.