Physics-based catchment models of mountain environments can suffer from equifinality when solely calibrated against streamfiow data. Inclusion of intra-catchment data such as soil moisture or groundwater levels in model calibration can reduce equifinality problems, though physical demands of installation and remote field sites can limit their availability. Non-invasive geophysical surveys such as electromagnetic (EM) induction have become practical alternative sources of information on the subsurface. As such, we are interested in addressing the applicability of EM data to directly calibrate hydraulic parameters in physics-based catchment models in hydrogeophysical inversions. This study explores the interrelationships between calibration data, hydraulic parameters, and calibrated model dynamics for a headwater catchment in the Reynolds Creek Experimental Watershed, Idaho, USA. Five calibration scenarios and a global sensitivity analysis are performed to quantify the ability of different combinations of hydrological (streamfiow, groundwater levels, soil moisture) and EM data (airborne and ground-based surveys) to predict both streamfiow and intra-catchment dynamics. Results indicate that calibrating against streamfiow data alone yields accurate streamfiow but inconsistent intra-catchment predictions (streamfiow, groundwater level, and soil moisture average Kling-Gupta efficiency values of KGE = 0.89,-0.53, and 0.44, respectively). Calibrating against all hydrological data yields reasonable predictions of hydrological dynamics (streamfiow, groundwater level, and soil moisture average KGE = 0.91, 0.23, and 0.62, respectively), though some calibrated parameter values do not match expectations from literature values. Reasonably accurate hydrological predictions were obtained when including EM data with either streamfiow data alone (streamfiow, groundwater level, and soil moisture average KGE = 0.83, 0.29, and 0.52, respectively) or all hydrological data (streamfiow, groundwater level, and soil moisture average KGE = 0.87, 0.39, and 0.51, respectively) during calibration. However, EM data alone yields hydraulic parameters that overpredict saturation throughout the catchment (streamfiow, groundwater level, and soil moisture average KGE = 0.09,-0.57, and 0.37, respectively). These results highlight potential advantages of collecting EM data in catchments with existing streamfiow data but poor coverage of intra-catchment hydrological data sets. Additional work regarding petrophysical model parameterizations, objective function definitions, and data set weighting schemes is needed to ensure that the contribution of EM data to hydraulic parameter identification is maximized.

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.

Both hydrological and geophysical data can be used to calibrate hillslope hydrologic models. However, these data often reflect hydrological dynamics occurring at disparate spatial scales. Their use as sole objectives in model calibrations may thus result in different optimum hydraulic parameters and hydrologic model behavior. This is especially true for mountain hillslopes where the subsurface is often heterogeneous and the representative elementary volume can be on the scale of several m 3 . This study explores differences in hydraulic parameters and hillslope‐scale storage and flux dynamics of models calibrated with different hydrological and geophysical data. Soil water content, groundwater level, and two time‐lapse electrical resistivity tomography (ERT) data sets (transfer resistance and inverted resistivity) from two mountain hillslopes in Wyoming, USA, are used to calibrate physics‐based surface–subsurface hydrologic models of the hillslopes. Calibrations are performed using each data set independently and all data together resulting in five calibrated parameter sets at each site. Model predicted hillslope runoff and internal hydrological dynamics vary significantly depending on the calibration data set. Results indicate that water content calibration data yield models that overestimate near‐surface water storage in mountain hillslopes. Groundwater level calibration data yield models that more reasonably represent hillslope‐scale storage and flux dynamics. Additionally, ERT calibration data yield models with reasonable hillslope runoff predictions but relatively poor predictions of internal hillslope dynamics. These observations highlight the importance of carefully selecting data for hydrologic model calibration in mountain environments. Poor selection of calibration data may yield models with limited predictive capability depending on modeling goals and model complexity.

Time‐lapse electrical resistivity tomography (ERT) data are increasingly used to inform the hydrologic dynamics of mountainous environments at the hillslope scale. Despite their popularity and recent advancements in hydrogeophysical inversion methods, few studies have shown how time‐lapse ERT data can be used to determine hydraulic parameters of subsurface water flow models. This study uses synthetic and field‐collected, hillslope‐scale, time‐lapse ERT data to determine subsurface hydraulic properties of a two‐layer, physics‐based, 2‐D vertical flow model with predefined layer and boundary locations. Uncoupled and coupled hydrogeophysical inversion methods are combined with a fine‐earth fraction optimization scheme to reduce the number of parameters needing calibration and interpret the influence of the hydraulic parameters on the hydrologic model predictions. Inversions of synthetic ERT data recover the prescribed fine‐earth fraction bulk density to within 0.1 g cm −3 . Field‐collected ERT data from a mountain hillslope result in hydrologic model dynamics that are consistent with previous studies and measured water content data but struggle to capture measured groundwater levels. The uncoupled hydrogeophysical inversion method is more sensitive to changes in hydraulic parameter values of the lower hydrologic model layer than the coupled hydrogeophysical inversion method. Time series of minimum objective function value simulations indicate that periodically collected ERT data may recover hydraulic parameters to a similar level of uncertainty as daily ERT data. Using simple hydrologic model domains within hydrogeophysical inversions shows promise for providing reasonable hydrologic predictions while maintaining relatively simple calibration schemes and should be explored further in future studies.

The belowground architecture of the critical zone (CZ) consists of soil and rock in various stages of weathering and wetness that acts as a medium for biological growth, mediates chemical reactions, and controls partitioning of hydrologic fluxes. Hydrogeophysical imaging provides unique insights into the geometries and properties of earth materials that are present in the CZ and beyond the reach of direct observation beside sparse wellbores. An improved understanding of CZ architecture can be achieved by leveraging the geophysical measurements of the subsurface. Creating categorical models of the CZ is valuable for driving hydrologic models and comparing belowground architectures between different sites to interpret weathering processes. The CZ architecture is revealed through a novel comparison of hillslopes by applying facies classification in the elastic-electric domain driven by surface-based hydrogeophysical measurements. Three pairs of hillslopes grouped according to common geologic substrates — granite, volcanic extrusive, and glacially altered — are classified by five different hydrofacies classes to reveal the relative wetness and weathering states. The hydrofacies classifications are robust to the choice of initial mean values used in the classification and noncontemporaneous timing of geophysical data acquisition. These results will lead to improved interdisciplinary models of CZ processes at various scales and to an increased ability to predict the hydrologic timing and partitioning. Beyond the hillslope scale, this enhanced capability to compare CZ architecture can also be exploited at the catchment scale with implications for improved understanding of the link between rock weathering, hydrochemical fluxes, and landscape morphology.

Soil water flow, heat transport, and solute transport in cold regions exhibit complex interactions that are difficult to quantify with measurements alone. For example, dissolved ions will lower the freezing point of soil water, while frozen soil will limit solute transport. In this study, a vertical one-dimensional numerical model for coupled water flow, heat transport, and solute transport was combined with 8 years of Hydra impedance sensor monitoring data to estimate solute transport parameters in a high elevation, cool, semi-arid mixed-grass rangeland near Laramie, WY. Model calibration focused on the linear adsorption coefficient and the dispersivity in the solute transport equation. The brute force method was used to examine the complete parameter space using bulk soil electrical conductivity (sigma (b)) alone, and sigma (b) divided by tortuosity (tau) in the objective function. The use of sigma (b) yielded positive Modeling Efficiency (ME) for three out of five sensor depths (ME <= 0.49), while use of sigma (b)tau (-1) yielded negative ME <= -0.01 for all depths. The low ME for sigma (b)tau (-1) was expected as this ratio serves as a proxy for solute storage, which is expected to fluctuate relatively little with time for most terrestrial ecosystems. Reliable adsorption and dispersion coefficients could be determined for the 0-10 cm depth, and to a lesser extent, the 10-30 cm depth. At deeper depths, solute transport was only sensitive to the adsorption coefficient, so that reliable values for dispersivity could not be determined.

The prediction of snowmelt in mountainous forests strongly depends on the accurate description of sensible and latent heat turbulent fluxes. Uncertainty about the within‐canopy wind conditions especially poses a challenge, with relatively few studies examining both above‐ and below‐canopy turbulent fluxes. In this study, turbulent flux predictions from a state‐of‐the‐art watershed model GEOtop were verified against eddy covariance data from one above‐canopy tower and two below‐canopy towers in a snow‐dominated coniferous forest in south‐eastern Wyoming. The model was applied in one‐dimensional vertical mode using field‐observed vegetation parameters and laboratory‐measured soil water retention data. The model was calibrated by identifying optimum values for the canopy fraction and the within‐canopy eddy decay coefficient using the brute‐force method. Above‐canopy sensible heat flux at the Glacier Lakes Ecosystem Experiments Site was predicted reasonably well (r2 = .851). The prediction of above‐canopy latent heat flux was weaker (r2 = .426). For latent heat flux, errors in 30‐min values offset each other when fluxes were aggregated over time, resulting in realistic mean diurnal trends. Below‐canopy turbulent flux at two sites in the Libby Creek Experimental Watershed were predicted with variable success with r2 = .031–.146 for sensible heat flux and r2 = .445–.581 for latent heat flux. Modelled below‐canopy sensible heat flux was too low due to the underestimation of daytime ground surface temperature, because of not enough solar radiation reaching the soil surface. This study suggests that future work on GEOtop and related models should include better parameterizations of the ground surface energy balance to more reliably predict snowmelt and streamflow from mountainous forests.

Integrated watershed models can be used to calculate streamflow generation in snow‐dominated mountainous catchments. Parameterization of water flow is often complicated by the lack of information on subsurface hydraulic properties. In this study, bulk density optimization was used to determine hydraulic parameters for the upper and lower regolith in the GEOtop model. The methodology was tested in two small catchments in the Dry Creek Watershed in Idaho and the Libby Creek Watershed in Wyoming. Modelling efficiencies for profile‐average soil–water content for the two catchments were between 0.52 and 0.64. Modelling efficiencies for stream discharge (cumulative stream discharge) were 0.45 (0.91) and 0.54 (0.94) for the Idaho and Wyoming catchments, respectively. The calculated hydraulic properties suggest that lateral flow across the upper–lower regolith interface is an important driver of streamflow in both the Idaho and Wyoming watersheds. The overall calibration procedure is computationally efficient because only two bulk density values are optimized. The two‐parameter calibration procedure was complicated by uncertainty in hydraulic conductivity anisotropy. Different upper regolith hydraulic conductivity anisotropy factors had to be tested in order to describe streamflow in both catchments.

In subalpine watersheds of the intermountain western United States, snowpack melt is the dominant water input to the hydrologic system. The primary focus of this work is to understand the partitioning of water from the snowpack during the snowmelt period and through the remainder of the growing season. We conducted a time-lapse electrical resistivity tomography (ERT) study in conjunction with a water budget analysis to track water from the snow-on through snow-off season (May-August 2015). Seismic velocities provided an estimate of regolith thickness while transpiration measurements from sap flow in conifer trees provided insight into root water uptake. We observed four hydrologic process-periods and found that deep flow and tree water fluxes are the primary pathways through which water moves off of the hillslope. Overland flow and interflow were negligible. We observed temporal changes in vadose zone water content more than 3.0 m below the surface. Our results show that vertical flow through the thin soil mantle overlaying coarse colluvial regolith was the primary pathway to a local unconfined aquifer.

Core Ideas The GEOtop model was used to calculate water flow in mountain soils. Dry soil bulk density was optimized to determine soil hydraulic properties. Soil water contents for two watersheds were used to test the optimization procedure. A new method for determining profile‐average and depth‐wise hydraulic properties in heterogeneous mountain soils is presented using the GEOtop watershed model in 1‐D vertical mode. Dry soil bulk density–converted volumetric soil water retention data are used to determine van Genuchten soil water retention parameters, and the Kozeny–Carman equation is used to determine saturated soil hydraulic conductivity. Optimum dry soil bulk densities are identified by minimizing the sum of squared error between measured and calculated soil water content time series. The new method was tested using soil moisture data from soil profiles at the Dry Creek Experimental Watershed, Boise, ID, and the Libby Creek Experimental Watershed, Laramie, WY. Results of different scenarios showed that the optimization of a single profile‐average dry soil bulk density is a good option for describing soil water flow in the heterogeneous mountain soils. Soil water content modeling efficiency (ME) values of 0.084 ≤ ME ≤ 0.745 and –2.443 ≤ ME ≤ 0.373 were found for the Dry Creek and Libby Creek sites, respectively. Relatively low ME values for the deepest sensor depths for some scenarios were attributed to the overestimation of soil water freezing and uncertainty in the soil water retention function near saturation. The resulting calibration procedure is computationally efficient because only one parameter (dry soil bulk density) is optimized.

Soil disturbance during arid region energy development often redistributes subsurface salts, including sodium (Na), to the surface. Elevated Na degrades soil structure and inhibits germination and establishment of native plants important for ecosystem functions. Strategies for rapid reclamation of thin surface horizons are needed. Study objectives were to evaluate chemical amendments and compost for remediation of saline–sodic soils on a well pad that had electrical conductivity (EC) of 8.7 dS m −1 and exchangeable sodium percentage of 38.6% following reclamation practices in south-central Wyoming. Eight treatments applied in October 2012 included gypsum, langbeinite, and elemental sulfur (S) with and without compost, plus compost alone, and an untreated control. Plots were sampled four times over 1 year at depths of 0–3, 3–8, and 8–15 cm. Samples were analyzed for exchangeable plus solution concentrations of Na, calcium (Ca), magnesium (Mg), and potassium (K). Results indicate that Na was leached from surface soils in all the treatments and the untreated control. Langbeinite most effectively enhanced movement of Na and caused initial increases in salinity that abated within 1 year. Gypsum enhanced movement of Na to lesser degree, and movement under S and compost was equivalent to untreated controls. Added compost did not affect activity of langbeinite, gypsum, or S. Langbeinite effectively reduced sodicity, but high cost and initial increases in soil salinity warrant additional investigation of appropriate application rates and alternative management practices, such as delaying planting until high-salinity abates.

12 Snow water equivalence (SWE) is typically computed from snow weight by the SNOTEL system in the 13 US. However, a snow pillow, the main snow weight sensor used by SNOTEL, requires a large, open, flat 14 area (at least 9 square meters) and substantial maintenance costs. This article presents the snow water 15 equivalence estimation (SWEE) algorithm that estimates the SWE evolution merely from continuous 16 snow depth and temperature measurements using common sensors. The key component is a depth17 averaged snow density model that is available in the literature, but is underutilized. Here, we demonstrate 18 that the snow density model can estimate mass exchanges (SWE changes due to snowfall, erosion, 19 deposition, and snowmelt) as well as the SWE. The SWEE algorithm can potentially increase the number 20 of snow monitoring locations because snow depth and temperature sensors are considerably more 21 accessible and economical than snow weighing sensor. 22

Water transport may be driven across dense thin-films when contacted with soil to create a non-pressure driven desalination process; however, the roles played by the different soil-water potential components (matric, osmotic, and vapor pressure) in driving water flux are not completely understood. Bench-scale tests were done using a reverse osmosis membrane and three different soils: sand, sandy-loam, and kaolin clay. The diverse physical and chemical characteristics of these soils were used to quantify membrane performance (water flux) and relate it to the magnitudes of the three soil-water potentials. Water flux was highest at initial contact between the soil and membrane, and then declined to reach a steady-state value, irrespective of soil type. The highest steady-state water flux, 1.8 l/m(2)/h, was measured for the pure clay system. Matric and osmotic potential gradients were the primary drivers for water flux, while vapor pressure played a minor role. Membrane orientation (active or support side facing the soil) and internal concentration polarization were also identified as determinants of water flux.

Soils in arid climates affected by drastic disturbance do not recover without reclamation efforts, and saline-sodic conditions caused by development activities are especially problematic. To improve reclamation success, we created seeded depressions that held three types of sand capillary barriers in October 2013. Sand was placed above (mulch), below (capillary barrier), and encompassing (dual barrier) seeded native soil at ridge and depression sites near Wamsutter, WY, representative of natural gas extraction areas. We compared grass growth, salinity, and moisture among the treatments and under depressions without sand amendments (pit) and plots seeded by standard procedures (control). At the ridge site, the mulch treatment supported 249 stemsm(-2) in the seeded patches surviving to August 2014, compared with 110 and 89 stemsm(-2) in the pit and lower barrier treatments, respectively, less than 50 stemsm(-2) in the dual barrier treatments, and none in the control. The dual barrier and mulch treatments performed best at the depression site, with 40-50 stemsm(-2) in August compared with none in the other three treatments. Changes in soil moisture and salinity were variable, but indicate positive effects of capillary barriers. Capillary barriers led to reductions or smaller increases in salinity than in treatments without a capillary barrier. While mulch treatments effectively increased grass growth at both sites, the dual barriers only showed positive impact at the depression site, possibly due to adequate moisture. Sand is readily available in many regions, and scaling up from test plots may be achieved with existing equipment.

Abstract. Snow water equivalence (SWE) is typically computed from snow weight by the SNOTEL system in the US. However, a snow pillow, the main snow weight sensor used by SNOTEL, requires a large, open, flat area (at least 9 square meters) and substantial maintenance costs. This article presents the snow water equivalence estimation (SWEE) algorithm that estimates the SWE evolution merely from continuous snow depth and temperature measurements using common sensors. The key component is a depth-averaged snow density model that is available in the literature, but is underutilized. Here, we demonstrate that the snow density model can estimate mass exchanges (SWE changes due to snowfall, erosion, deposition, and snowmelt) as well as the SWE. The SWEE algorithm can potentially increase the number of snow monitoring locations because snow depth and temperature sensors are considerably more accessible and economical than snow weighing sensor.

Snowpack dynamics through October 2014–June 2017 were described for a forested, sub‐alpine field site in southeastern Wyoming. Point measurements of wetness and density were combined with numerical modeling and continuous time series of snow depth, snow temperature, and snowpack outflow to identify 5 major classes of distinct snowpack conditions. Class (i) is characterized by no snowpack outflow and variable average snowpack temperature and density. Class (ii) is characterized by short durations of liquid water in the upper snowpack, snowpack outflow values of 0.0008–0.005 cm hr−1, an increase in snowpack temperature, and average snow density between 0.25–0.35 g cm−3. Class (iii) is characterized by a partially saturated wetness profile, snowpack outflow values of 0.005–0.25 cm hr−1, snowpack temperature near 0 °C, and average snow density between 0.25–0.40 g cm−3. Class (iv) is characterized by strong diurnal snowpack outflow pattern with values as high as 0.75 cm hr−1, stable snowpack temperature near 0 °C, and stable average snow density between 0.35–0.45 g cm−3. Class (v) occurs intermittently between Classes (ii)–(iv) and displays low snowpack outflow values between 0.0008–0.04 cm hr−1, a slight decrease in temperature relative to the preceding class, and similar densities to the preceding class. Numerical modeling of snowpack properties with SNOWPACK using both the Storage Threshold scheme and Richards' equation was used to quantify the effect of snowpack capillarity on predictions of snowpack outflow and other snowpack properties. Results indicate that both simulations are able to predict snow depth, snow temperature, and snow density reasonably well with little difference between the 2 water transport schemes. Richards' equation more accurately simulates the timing of snowpack outflow over the Storage Threshold scheme, especially early in the melt season and at diurnal timescales.

A one-dimensional vertical numerical model for coupled water flow and heat transport in soil and snow was modified to include all three phases of water: vapor, liquid, and ice. The top boundary condition in the model is driven by incoming precipitation and the surface energy balance. The model was applied to three different terrestrial systems: a warm desert bare lysimeter soil in Boulder City, NV; a cool mixed-grass rangeland soil near Laramie, WY; and a snow-dominated mountainous forest soil about 50 km west of Laramie, WY. Comparison of measured and calculated soil water contents with depth yielded modeling efficiency (ME) values (maximum range: -infinity < ME <= 1) of 0.32 <= ME <= 0.75 for the bare soil, 0.05 <= ME <= 0.30 for the rangeland soil, and 0.06 <= ME <= 0.37 for the forest soil. Results for soil temperature with depth were 0.87 <= ME <= 0.91 for the bare soil, 0.92 <= ME <= 0.94 for the rangeland soil, and 0.85 <= ME <= 0.88 for the forest soil. The model described the mass change in the bare soil lysimeter due to outgoing evaporation with moderate accuracy (ME = 0.41, based on 4 yr of data and using weekly evaporation rates). Snow height for the rangeland soil and the forest soil was captured reasonably well (ME = 0.57 for both sites based on 5 yr of data for each site). The model is physics based, with few empirical parameters, making it applicable to a wide range of terrestrial ecosystems.