Abstract The estimation of petrophysical properties, such as porosity and water saturation, from geophysical data through rock physics–based inversion is crucial for understanding groundwater and weathering processes in the critical zone (CZ). A unified rock physics model is developed to map porosity and water saturation to P- and S-wave velocities obtained from seismic data. The rock physics model is based on an exponential function whose parameters control the velocity values at the minimum and maximum porosity, as well as the rate at which velocity decreases with increasing porosity. The model incorporates Gassmann’s equation to account for partial saturation conditions. Unlike traditional rock physics models that apply only to specific geological settings, the new formulation accurately captures velocity changes in the near surface as materials transition from disaggregated sediments to fractured zones and ultimately to more coherent bedrock with depth, under fully saturated, partially saturated, and unsaturated conditions. By introducing Archie’s equation, the framework also models resistivity, allowing the integration of elastic and electrical rock physics relationships for an improved petrophysical characterization of the critical zone. Based on the proposed rock physics model, a Bayesian inversion workflow is developed to estimate porosity and water saturation from seismic velocity and electrical resistivity data. The inversion method provides probabilistic estimates of the petrophysical properties, accounting for measurement uncertainty and prior information. The rock physics model and the Bayesian inversion are applied to a field dataset from the Laramie Range, predicting porosity and water saturation from geophysical measurements. The method provides accurate spatial models of petrophysical data to inform hydrological analysis in the CZ and it enables data-driven characterization of weathered and fractured near-surface rocks.

Abstract Rock physics models link geophysical measurements with subsurface petrophysical properties, such as porosity, mineral composition, and fluid saturation. While originally developed for hydrocarbon exploration, these models are increasingly applied in the near surface for quantitative interpretation of geophysical data. This review focuses on their application to the subsurface component of the critical zone, which extends from soil to the base of weathered bedrock and controls key hydrological, geomorphological, and ecological processes. Its structure and heterogeneity remain difficult to characterize due to limited direct subsurface observations. As a result, critical zone studies of the subsurface have relied on indirect geophysical measurements, which are spatially extensive and are used to interpret structural variations, property heterogeneity, and physical processes. However, geophysical measurements alone do not yield petrophysical properties. Rock physics models combined with geophysical inversion provide a tool to translate geophysical data into subsurface petrophysical properties. In this review, we present a synthesis of rock physics models for the prediction of geophysical properties of unsaturated and saturated porous media, focusing on their formulations and assumptions in near‐surface applications involving seismic, electrical, electromagnetic, and nuclear magnetic resonance methods. We then discuss their integration in geophysical inversion studies for the petrophysical characterization of the critical zone. We assess the capabilities, strengths, and limitations of rock physics models and inversion methods in critical zone applications, illustrated with case studies from the Southern Sierra and Laramie Range, and show how the resulting quantitative estimates of petrophysical properties inform hydrogeological studies and reduce uncertainty in model predictions.

Quantification of fluid distribution and flow in the Earth’s near-surface benefits from precise estimation of electrical properties of fluid-saturated rocks, such as resistivity estimated from inversion of electrical resistivity tomography (ERT) data. The predicted resistivity values are often uncertain due to two main types of uncertainties: epistemic uncertainty in the inversion process (e.g., inaccuracy in the physical models) and aleatoric uncertainty in the data (e.g., measurement errors). This work focuses on the quantification of aleatoric variability in the ERT measurements and its effect on the inverted resistivity models. We first investigate how measurement uncertainty, in the form of reciprocal error, correlates with the measured electrical contact resistance of electrodes with ground. Next, we apply a statistical approach based on the stochastic perturbation and inversion of multiple realizations of resistance data to study the uncertainty in the predicted resistivity tomograms. We then study the effect of data uncertainty on the inverted resistivity model for individual data sets. We finally quantify the effect of variation in data quality over time on the inverted time-lapse resistivity results. The results from 20 campaigns and two time-lapse ERT data sets show that reciprocal error is positively correlated with both contact resistance and ground’s apparent resistivity, confirming the significance of practicing lowering electrode contact resistance during ERT field campaigns. Additionally, our results show that uncertainty in the estimated resistivity model depends on both the ground’s resistivity and measurement error of the input data. The time-lapse results provide additional insight that model uncertainty is the highest at the driest and coldest months of the year, corresponding to the highest measured contact resistance and reciprocal error.

Because of the remote nature of permafrost, it is difficult to collect data over large geographic regions using ground surveys. Remote sensing enables us to study permafrost at high resolution and over large areas. The Arctic-Boreal Vulnerability Experiment's Permafrost Dynamics Observatory (PDO) contains data about permafrost subsidence, active layer thickness (ALT), soil water content, and water table depth, derived from airborne radar measurements at 66 image swaths in 2017. With nearly 58,000,000 pixels available for analysis, this data set enables new discoveries and can corroborate findings from previous studies across the Arctic-Boreal region. We analyze the distributions of these variables and use a space-for-time substitution to enable interpretation of the effects of climate trends. Higher soil volumetric water content (VWC) is associated with lower ALT and subsidence, suggesting that Arctic soil may become drier as the climate warms. Soil VWC is bimodal, with saturated soil occurring more commonly in burned areas, while unburned areas are more commonly unsaturated. All permafrost variables show statistically significant differences from one land cover type to another; in particular, cropland has thicker active layers and developed land has lower seasonal subsidence than most other land cover types, potentially related to disturbance and permafrost thaw. While vegetation browning is not strongly associated with any of the measured permafrost variables, more greening is associated with less subsidence and ALT and with higher bulk soil VWC.

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.

Quantification of fluid distribution and flow in the earth's near surface benefits from precise estimation of electrical properties of fluid-saturated rocks, such as resistivity estimated from inversion of electrical resistivity tomography (ERT) data. The predicted resistivity values are often uncertain due to two main types of uncertainties: epistemic uncertainty in the inversion process (e.g., inaccuracy in the physical models) and aleatoric uncertainty in the data (e.g., measurement errors). This work focuses on the quantification of aleatoric variability in the ERT measurements and its effect on the inverted resistivity models. We first investigate how measurement uncertainty, in the form of reciprocal error, correlates with the measured electrical contact resistance of electrodes with ground. Next, we apply a statistical approach based on the stochastic perturbation and inversion of multiple realizations of resistance data to study the uncertainty in the predicted resistivity tomograms. We then study the effect of data uncertainty on the inverted resistivity model for individual data sets. We finally quantify the effect of variation in data quality over time on the inverted time-lapse resistivity results. The results from 20 campaigns and two timelapse ERT data sets show that reciprocal error is positively correlated with contact resistance and ground's apparent resistivity, confirming the significance of practicing lowering electrode contact resistance during ERT field campaigns. In addition, our results show that uncertainty in the estimated resistivity model depends on the ground's resistivity and measurement error of the input data. The time-lapse results provide additional insight that model uncertainty is the highest in the driest and coldest months of the year, corresponding to the highest measured contact resistance and reciprocal error.

Massive ground ice in Arctic regions underlain using continuous permafrost influences hydrologic processes, leading to ground subsidence and the release of carbon dioxide and methane into the atmosphere. The relation of massive ground ice such as ice wedges to water tracks and seasonally saturated hydrologic pathways remains uncertain. Here, we examine the location of ice wedges along a water track on the North Slope of Alaska using Ground-Penetrating Radar (GPR) surveys, in situ measurements, soil cores, and forward modeling. Of nine unique GPR surveys collected in the summers of 2022 and 2023, seven exhibit distinctive "X"-shaped reflections above columnar reflectors that are spatially correlated with water track margins. Forward modeling of plausible geometries suggests that ice wedges produce reflection patterns most similar to the reflections observed in our GPR profiles. Additionally, a large magnitude (similar to 71 mm) rain event on 8 July 2023 led to a ground collapse that exposed four ice wedges on the margin of the studied water track, similar to 100 m downstream of our GPR surveys. Together, this suggests that GPR is a viable method for identifying the location of ice wedges as air temperatures in the Arctic continue to increase, we expect that ice wedges may thaw, destabilizing water tracks and causing ground collapse and expansion of thermo-erosional gullies. This ground collapse will increase greenhouse gas emissions and threaten the Arctic infrastructure. Future geophysical analysis of upland Arctic hillslopes should include additional water tracks to better characterize potential heterogeneity in permafrost vulnerability across the warming Arctic.

Seasonal snowpack is an important predictor of the water resources available in the following spring and early-summer melt season. Total basin snow water equivalent (SWE) estimation usually requires a form of statistical analysis that is implicitly built upon the Gaussian framework. However, it is important to characterize the non-Gaussian properties of snow distribution for accurate large-scale SWE estimation based on remotely sensed or sparse ground-based observations. This study quantified non-Gaussianity using sample negentropy; the Kullback-Leibler divergence from the Gaussian distribution for field-observed snow depth data from the North Slope, Alaska; and three representative SWE distributions in the western USA from the Airborne Snow Observatory (ASO). Snowdrifts around lakeshore cliffs and deep gullies can bring moderate non-Gaussianity in the open, lowland tundra of North Slope, Alaska, while the ASO dataset suggests that subalpine forests may effectively suppress the non-Gaussianity of snow distribution. Thus, non-Gaussianity is found in areas with partial snow cover and wind-induced snowdrifts around topographic breaks on slopes and on other steep terrain features. The snowpacks may be considered weakly Gaussian in coastal regions with open tundra in Alaska and alpine and subalpine terrains in the western USA if the land is completely covered by snow. The wind-induced snowdrift effect can potentially be partitioned from the observed snow spatial distribution guided by its Gaussianity.

Permafrost-affected ecosystems of the Arctic–boreal zone in northwestern North America are undergoing profound transformation due to rapid climate change. NASA's Arctic Boreal Vulnerability Experiment (ABoVE) is investigating characteristics that make these ecosystems vulnerable or resilient to this change. ABoVE employs airborne synthetic aperture radar (SAR) as a powerful tool to characterize tundra, taiga, peatlands, and fens. Here, we present an annotated guide to the L-band and P-band airborne SAR data acquired during the 2017, 2018, 2019, and 2022 ABoVE airborne campaigns. We summarize the ∼80 SAR flight lines and how they fit into the ABoVE experimental design (Miller et al., 2023; https://doi.org/10.3334/ORNLDAAC/2150). The Supplement provides hyperlinks to extensive maps, tables, and every flight plan as well as individual flight lines. We illustrate the interdisciplinary nature of airborne SAR data with examples of preliminary results from ABoVE studies including boreal forest canopy structure from TomoSAR data over Delta Junction, AK, and the Boreal Ecosystem Research and Monitoring Sites (BERMS) area in northern Saskatchewan and active layer thickness and soil moisture data product validation. This paper is presented as a guide to enable interested readers to fully explore the ABoVE L- and P-band airborne SAR data (https://uavsar.jpl.nasa.gov/cgi-bin/data.pl).

In July and August of 2023, we visited Costa Rica to examine some of the country’s peatlands. The purpose of our trip was to collect peat samples from a variety of wetland habitats from the coast to the highlands for future analysis. We summarize our observations in this short essay.

Understanding the near-surface structure of the Earth requires accurate prediction of physical properties of the subsurface, such as velocity estimated from tomographic inversion of seismic refraction data. The predicted velocity values are often uncertain due to epistemic uncertainty in the inversion process (i.e., imperfectly known underlying physics) and aleatoric variability in the data (i.e., inherent noise in observations). Although seismic refraction is widely used in near-surface applications, the associated uncertainty is rarely quantified and presented alongside the inverted velocity tomograms. In this study, the effect of epistemic uncertainty due to local variability in the initial model and aleatoric variability due to first-arrival picking error on the velocity prediction uncertainty are investigated. A stochastic framework is implemented based on a statistical approach where multiple realizations of stochastically perturbed initial models and travel time picks are generated and the uncertainty in the predicted velocity models is quantified. The two sources of uncertainty are first studied independently and then the combined effect is investigated. The results show that both sources affect the posterior uncertainty, but the uncertainty in the initial model has a greater effect than picking error on the uncertainty of the posterior velocity model. In addition, joint analysis of both sources of uncertainty shows that the uncertainty in the inverted model depends on predicted velocity values, depths, velocity gradients and ray coverages.

Abstract Warming across the western United States continues to reduce snowpack, lengthen growing seasons, and increase atmospheric demand, leading to uncertainty about moisture availability in montane forests. As many upland forests have thin soils and extensive rooting into weathered bedrock, deep vadose‐zone water may be a critical late‐season water source for vegetation and mitigate forest water stress. A key impediment to understanding the role of the deep vadose zone as a reservoir is quantifying the plant‐available water held there. We quantify the spatiotemporal dynamics of rock moisture held in the deep vadose zone in a montane catchment of the Rocky Mountains. Direct measurements of rock moisture were accompanied by monitoring of precipitation, transpiration, soil moisture, leaf‐water potentials, and groundwater. Using repeat nuclear magnetic resonance and neutron‐probe measurements, we found depletion of rock moisture among all our monitored plots. The magnitude of growing season depletion in rock moisture mirrored above‐ground vegetation density and transpiration, and depleted rock moisture was from ∼0.3 to 5 m below ground surface. Estimates of storage indicated weathered rock stored at least 4%–12% of mean annual precipitation. Persistent transpiration and discrepancies between estimated soil matric potentials and leaf‐water potentials suggest rock moisture may mitigate drought stress. These findings provide some of the first measurements of rock moisture use in the Rocky Mountains and indicated rock moisture use is not just confined to periods of drought or Mediterranean climates.

Abstract Seasonal subsidence induced by ground ice melt can be measured by interferometric synthetic aperture radar (InSAR) techniques to infer active layer thickness (ALT) in permafrost regions. The magnitude of subsidence depends on both how deep the soil thawed and how much ice/water content existed in the active layer soil. To provide the later, P‐band polarimetric synthetic aperture radar (PolSAR) backscatter is used due to its sensitivity to subsurface soil moisture and freeze/thaw conditions. In this study, which is the second in a two‐part series of Permafrost Dynamics Observatory (PDO), we exploit L‐band InSAR subsidence and P‐band PolSAR backscatter in a joint retrieval scheme to simultaneously estimate ALT and soil moisture profile of permafrost active layer. Both subsidence and backscatter are explicitly characterized by physics‐based models and share a common set of soil parameters including porosity and water saturation profiles. The PDO joint retrieval has been applied to the L‐ and P‐band SAR data acquired by National Aeronautics and Space Administration/Jet Propulsion Laboratory's Uninhabited Aerial Vehicle Synthetic Aperture Radar over Alaska and western Canada during the 2017 Arctic‐Boreal Vulnerability Experiment (ABoVE) airborne campaign. This high‐resolution (30 m) regional estimates of ALT and soil moisture profile spanning over the ABoVE study domain can help link the ground‐based field surveys with satellite observations to further understand the permafrost and active layer soil process dynamics to disturbances and climate change occurring across the northern circumpolar region.

An accepted paradigm of hydrothermal systems is the process of phase separation, or boiling, of a deep, homogeneous hydrothermal fluid as it ascends through the subsurface resulting in gas rich and gas poor fluids. While phase separation helps to explain first-order patterns in the chemistry and biology of a hot spring’s surficial expression, we know little about the subsurface architecture beneath “phase-separated” pools and the timescales over which phase separation processes occur. Essentially, we have a two-dimensional understanding of a four-dimensional process. By combining geophysical, geochemical, isotopic, and microbiological measurements of two adjacent phase-separated hot springs in Norris Geyser Basin, Yellowstone National Park, we provide a four-dimensional assessment of phase separation processes and their biological manifestation. We uniquely show that Yellowstone’s hydrothermal waters originate from a deep sedimentary aquifer and that both meteoric recharge and shallow reactive transport processes are required to establish the geobiological feedbacks that drive bimodal distributions in the geochemical and microbial composition of hot springs. Specifically, over periods of tens of years, gas-enriched fluids containing volcanic sulfide mix with meteoric waters resulting in microbially-mediated production of sulfuric acid by thermoacidophilic Archaea in the near subsurface. In contrast, over periods of hundreds of years, anoxic residual liquid rises to the surface where it is infused with atmospheric gas fostering Archaea and Bacteria that are largely dependent on oxygen. As such, our results provide formative insight into the causative links between subsurface geological processes, the development of geochemical fluids, and the assembly and diversification of thermophilic microbial communities in hydrothermal systems.

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.

Snowdrifts formed by wind transported snow deposition represent a vital component of the earth surface processes on Arctic tundra. Snow accumulation on steep slopes particularly at the margins of rivers, coasts, lakes, and drained lake basins (DLBs) comprise a significant water storage component for the ecosystem during spring and summer snowmelt. The tundra landscape is in constant change as lakes drain, substantially altering the surface morphology that partially controls how snow drifts and accumulates throughout the cold seasons. Here, we combine field measurements, remote sensing observations, and snow modeling to investigate how lake drainage affects snow redistribution at Inigok on the Arctic Coastal Plain of Alaska, where the snow movement is controlled by wind. Field observations included measurements of snow depth using ground penetrating radar and probe. We mapped mid‐July snow cover and modeled snow redistribution before and after drainage simulation for 33 lakes (∼30 km2) in our study area (∼140 km2). Our results show the advantage of using a wide range of snow depth measurements on frozen lakes, DLBs, and upland to validate the snow modeling in order to capture the variability inherent in the landscape. The lake drainage simulation suggests an increase in snow storage of up to ∼24% at DLBs compared to extant lakes, ∼35% considering only snowdrifts (assumed as ≥1 m depth), and ∼4% considering the whole study area. This increase in snow accumulation could significantly impact the landscape when it melts, including wildlife, vegetation, biogeochemical processes, and potential natural hazards like snow‐dam outburst floods.

Abstract Permafrost warming and degradation is well documented across the Arctic. However, observation‐ and model‐based studies typically consider thaw to occur at 0°C, neglecting the widespread occurrence of saline permafrost in coastal plain regions. In this study, we document rapid saline permafrost thaw below a shallow arctic lake. Over the 15‐year period, the lakebed subsided by 0.6 m as ice‐rich, saline permafrost thawed. Repeat transient electromagnetic measurements show that near‐surface bulk sediment electrical conductivity increased by 198% between 2016 and 2022. Analysis of wintertime Synthetic Aperture Radar satellite imagery indicates a transition from a bedfast to a floating ice lake with brackish water due to saline permafrost thaw. The regime shift likely contributed to the 65% increase in thermokarst lake lateral expansion rates. Our results indicate that thawing saline permafrost may be contributing to an increase in landscape change rates in the Arctic faster than anticipated.

Integrated modeling of headwater watersheds in mountain environments is often limited by the lack of hydrological characterization and monitoring data. For the No-Name watershed in the Medicine Bow Mountains in Wyoming (USA), this research integrates regional surface and subsurface hydrological and geophysical measurements to create three-dimensional integrated hydrological models with which interactions between surface water, soil water, and groundwater are elucidated. Data used to build and calibrate the integrated model include a digital elevation model (DEM), stream discharge at the outlet of the watershed, soil-moisture data, weather data, and geophysical surveys including seismic refraction, airborne resistivity, and nuclear magnetic resonance (NMR). Based on interpretation of geophysical measurements, subsurface hydrostratigraphy consists of a top unconsolidated layer, a middle layer of fractured granite and metamorphic bedrock, and a lower protolith. Given that both measurements and interpretations have uncertainty, a sensitivity analysis was carried out to evaluate conceptual model uncertainty, which suggests the following: (1) for predicting stream discharge at the No-Name outlet, the most influential parameters are the Manning coefficient, DEM, hydrostratigraphy and hydraulic conductivity, and land cover. Compared to a lower-resolution DEM, a LiDAR DEM can lead to more accurate predictions of the stream discharge and stream elevation profile. (2) For predicting soil moisture, the most influential parameters are hydrostratigraphy and the associated hydraulic conductivities and porosities. (3) Based on a calibration exercise, the likely values for subsurface hydraulic conductivity at No-Name are ~10 –5 m/s (the unconsolidated layer), ~10 –6 m/s (fractured bedrock), and ~10 –6 m/s or lower (protolith).

High fidelity observations of the amount and state of water within permafrost help constrain the seasonal behavior of soil moisture and the effects of soil moisture on the surface energy balance. This work emphasizes the necessity for temperature-specific calibrations of low-frequency borehole NMR measurements. Constraining the effects of temperature on NMR signatures will allow for more reliable NMR inspection of hydrogeochemical parameters in permafrost ecosystems. We find that calibration at typical laboratory temperatures (20 degrees C) and subsequent measurement at typical permafrost active layer temperatures (similar to 0 degrees C) can result in an 18% bias in reported NMR water content values, and therefore temperature compensation is required under most scenarios. This is particularly important for active layer conditions that may include steep vertical temperature gradients. Similarly, seasonal time-lapse measurements of permafrost active layer may encounter substantial soil temperature variations which would also require temperature compensation on the observed NMR water content estimate.