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Predicting the hydrological response of watersheds to climate disturbances requires a detailed understanding of the processes connecting hillslopes and streams. Using a network of soil moisture and temperature sensors, electrical resistivity tomography monitoring, and a weather station we assess the above and below-ground processes driving the hydrological response of a hillslope during snowmelt and summer monsoon. The transect covers bedrock and vegetation gradients, with a steep upper part characterized by shallow bedrock, and gentle lower part underlain by colluvium. The main vegetation cover is conifers on the upper, and grass and veratrum on the lower part. Combined with a simplified hydrological model, we show that the thin soil layer of the steep slope acts as a preferential flow path, leading to mostly shallow lateral flow, interrupted by vertical flow, mostly at tree locations, and likely facilitated by flow along fractures and roots. Vertical flow and upstream-driven groundwater dynamics are prevailing at the colluvium, presenting a very different hydrological behavior compared to the upper part. These results show that subsurface structure and features have a strong control on the hydrological response of a hillslope and that those can create considerably varying hydrological dynamics across small spatial scales.

Assessing the lateral and vertical extent of permafrost is critical to understanding the fate of Arctic ecosystems under climate change. Yet, direct measurements of permafrost distribution and temperature are often limited to a small number of borehole locations. Here, we assess the use of co-located shallow temperature and electrical resistivity tomography (ERT) measurements to estimate at high-resolution the distribution of permafrost in three watersheds underlain by discontinuous permafrost. Synthetic modeling shows that co-located temperature and ERT measurements allow for supervised classification schemes that provide 60% higher accuracy compared to unsupervised methods. Linking resistivity and size of the identified permafrost bodies to surface observations, we show that tall vegetation (>0.5 m) and gentle slopes (

In complex terrain, non-parallel surfaces receive emitted radiation from adjacent surfaces. Qualitatively, where surface skin temperatures and lower tropospheric temperature and humidity are not uniform, the downwelling longwave radiation (DLR) will be determined not just by radiation from the atmosphere above a given location, but also by adjacent surface temperatures. We quantify this three-dimensional longwave radiative effect over the Upper Colorado River Basin in clear-sky conditions by calculating surface DLR with observed land-surface temperatures from ECOSTRESS. We find that this effect is due to terrain-subtended sky-view and represents ∼22% of the surface longwave flux, rising to ∼28% and ∼24% in the East and Southeast of the Basin, respectively, and can be >50% in extreme cases. The common omission of this effect in atmospheric radiation models leads to an underestimation of DLR in complex terrain, especially at higher elevations, which has significant implications for mountainous ecohydrology simulations.

Soil thickness plays a central role in the interactions between vegetation, soils, and topography, where it controls the retention and release of water, carbon, nitrogen, and metals. However, mapping soil thickness, here defined as the mobile regolith layer, at high spatial resolution remains challenging. Here, we develop a hybrid model that combines a process-based model and empirical relationships to estimate the spatial heterogeneity of soil thickness with fine spatial resolution (0.5ĝ€¯m). We apply this model to two aspects of hillslopes (southwest- and northeast-facing, respectively) in the East River watershed in Colorado. Two independent measurement methods - auger and cone penetrometer - are used to sample soil thickness at 78 locations to calibrate the local value of unconstrained parameters within the hybrid model. Sensitivity analysis using the hybrid model reveals that the diffusion coefficient used in hillslope diffusion modeling has the largest sensitivity among all input parameters. In addition, our results from both sampling and modeling show that, in general, the northeast-facing hillslope has a deeper soil layer than the southwest-facing hillslope. By comparing the soil thickness estimated between a machine-learning approach and this hybrid model, the hybrid model provides higher accuracy and requires less sampling data. Modeling results further reveal that the southwest-facing hillslope has a slightly faster surface soil erosion rate and soil production rate than the northeast-facing hillslope, which suggests that the relatively less dense vegetation cover and drier surface soils on the southwest-facing slopes influence soil properties. With seven parameters in total for calibration, this hybrid model can provide a realistic soil thickness map with a relatively small amount of sampling dataset comparing to machine-learning approach. Integrating process-based modeling and statistical analysis not only provides a thorough understanding of the fundamental mechanisms for soil thickness prediction but also integrates the strengths of both statistical approaches and process-based modeling approaches.

Working within a vineyard in the Pessac Léognan Appellation of Bordeaux, France, this study documents the potential of using simple statistical methods with spatially-resolved and increasingly available electromagnetic induction (EMI) geophysical and normalized difference vegetation index (NDVI) datasets to accurately estimate Bordeaux vineyard soil classes and to quantitatively explore the relationship between vineyard soil types and grapevine vigor. First, co-located electrical tomographic tomography (ERT) and EMI datasets were compared to gain confidence about how the EMI method averaged soil properties over the grapevine rooting depth. Then, EMI data were used with core soil texture and soil-pit based interpretations of Bordeaux soil types (Brunisol, Redoxisol, Colluviosol and Calcosol) to estimate the spatial distribution of geophysically-identified Bordeaux soil classes. A strong relationship (r = 0.75, p < 0.01) was revealed between the geophysically-identified Bordeaux soil classes and NDVI (both 2 m resolution), showing that the highest grapevine vigor was associated with the Bordeaux soil classes having the largest clay fraction. The results suggest that within-block variability of grapevine vigor was largely controlled by variability in soil classes, and that carefully collected EMI and NDVI datasets can be exceedingly helpful for providing quantitative estimates of vineyard soil and vigor variability, as well as their covariation. The method is expected to be transferable to other viticultural regions, providing an approach to use easy-to-acquire, high resolution datasets to guide viticultural practices, including routine management and replanting.

Land-atmosphere carbon exchange is known to be extremely heterogeneous in arctic ice-wedge polygonal tundra regions. In this study, a Kalman filter-based method was developed to estimate the spatio-temporal dynamics of daytime average net ecosystem exchange (NEEday) at 0.5-m resolution over a 550 m by 700 m study site. We integrated multi-scale, multi-type datasets, including normalized difference vegetation indices (NDVIs) obtained from a novel automated mobile sensor system (or tram system) and a greenness index map obtained from airborne imagery. We took advantage of the significant correlations between NDVI and NEEday identified based on flux chamber measurements. The weighted average of the estimated NEEday within the flux-tower footprint agreed with the flux tower data in term of its seasonal dynamics. We then evaluated the spatial variability of the growing season average NEEday, as a function of polygon geomorphic classes; i.e., the combination of polygon types—which are known to present different degradation stages associated with permafrost thaw—and microtopographic features (i.e., troughs, centers and rims). Our study suggests the importance of considering microtopographic features and their spatial coverage in computing spatially aggregated carbon exchange.

Climate change is causing rapid changes of Arctic ecosystems. Yet, data needed to unravel complex subsurface processes are very rare. Using geophysical and in situ sensing, this study closes an observational gap associated with thermohydrological dynamics in discontinuous permafrost systems. It highlights the impact of vegetation and snow thickness distribution on subsurface thermohydrological properties and processes. Large snow accumulation near tall shrubs insulates the ground and allows for rapid and downward heat flow. Thinner snow pack above graminoid results in surficial freezing and prevents water from infiltrating into the subsurface. Analyzing short-term disturbances, we found that lateral flow could be a driving factor in talik formation. Interannual measurements show that deep permafrost temperatures increased by about 0.2°C over 2 years. The results, which suggest that snow-vegetation-subsurface processes are tightly coupled, will be useful for improving predictions of Arctic feedback to climate change, including how subsurface thermohydrology influences CO2 and CH4 fluxes.

In recent years, the availability of airborne imaging spectroscopy (hyperspectral) data has expanded dramatically. The high spatial and spectral resolution of these data uniquely enable spatially explicit ecological studies including species mapping, assessment of drought mortality and foliar trait distributions. However, we have barely begun to unlock the potential of these data to use direct mapping of vegetation characteristics to infer subsurface properties of the critical zone. To assess their utility for Earth systems research, imaging spectroscopy data acquisitions require integration with large, coincident ground-based datasets collected by experts in ecology and environmental and Earth science. Without coordinated, well-planned field campaigns, potential knowledge leveraged from advanced airborne data collections could be lost. Despite the growing importance of this field, documented methods to couple such a wide variety of disciplines remain sparse. We coordinated the first National Ecological Observatory Network Airborne Observation Platform (AOP) survey performed outside of their core sites, which took place in the Upper East River watershed, Colorado. Extensive planning for sample tracking and organization allowed field and flight teams to update the ground-based sampling strategy daily. This enabled collection of an extensive set of physical samples to support a wide range of ecological, microbiological, biogeochemical and hydrological studies. We present a framework for integrating airborne and field campaigns to obtain high-quality data for foliar trait prediction and document an archive of coincident physical samples collected to support a systems approach to ecological research in the critical zone. This detailed methodological account provides an example of how a multi-disciplinary and multi-institutional team can coordinate to maximize knowledge gained from an airborne survey, an approach that could be extended to other studies. The coordination of imaging spectroscopy surveys with appropriately timed and extensive field surveys, along with high-quality processing of these data, presents a unique opportunity to reveal new insights into the structure and dynamics of the critical zone. To our knowledge, this level of co-aligned sampling has never been undertaken in tandem with AOP surveys and subsequent studies utilizing this archive will shed considerable light on the breadth of applications for which imaging spectroscopy data can be leveraged.

In the headwater catchments of the Rocky Mountains, plant productivity and its dynamics are largely dependent upon water availability, which is influenced by changing snowmelt dynamics associated with climate change. Understanding and quantifying the interactions between snow, plants and soil moisture is challenging, since these interactions are highly heterogeneous in mountainous terrain, particularly as they are influenced by microtopography within a hillslope. Recent advances in satellite remote sensing have created an opportunity for monitoring snow and plant dynamics at high spatiotemporal resolutions that can capture microtopographic effects. In this study, we investigate the relationships among topography, snowmelt, soil moisture and plant dynamics in the East River watershed, Crested Butte, Colorado, based on a time series of 3-meter resolution PlanetScope normalized difference vegetation index (NDVI) images. To make use of a large volume of high-resolution time-lapse images (17 images total), we use unsupervised machine learning methods to reduce the dimensionality of the time lapse images by identifying spatial zones that have characteristic NDVI time series. We hypothesize that each zone represents a set of similar snowmelt and plant dynamics that differ from other identified zones and that these zones are associated with key topographic features, plant species and soil moisture. We compare different distance measures (Ward and complete linkage) to understand the effects of their influence on the zonation map. Results show that the identified zones are associated with particular microtopographic features; highly productive zones are associated with low slopes and high topographic wetness index, in contrast with zones of low productivity, which are associated with high slopes and low topographic wetness index. The zones also correspond to particular plant species distributions; higher forb coverage is associated with zones characterized by higher peak productivity combined with rapid senescence in low moisture conditions, while higher sagebrush coverage is associated with low productivity and similar senescence patterns between high and low moisture conditions. In addition, soil moisture probe and sensor data confirm that each zone has a unique soil moisture distribution. This cluster-based analysis can tractably analyze high-resolution time-lapse images to examine plant-soil-snow interactions, guide sampling and sensor placements and identify areas likely vulnerable to ecological change in the future.

Ice-wedge polygons are common Arctic landforms. The future of these landforms in a warming climate depends on the bidirectional feedback between the rate of ice-wedge degradation and changes in hydrological characteristics. This work aims to better understand the relative roles of vertical and horizontal water fluxes in the subsurface of polygonal landscapes, providing new insights and data to test and calibrate hydrological models. Field-scale investigations were conducted at an intensively instrumented location on the Barrow Environmental Observatory (BEO) near Utqiagvik, AK, USA. Using a conservative tracer, we examined controls of microtopography and the frost table on subsurface flow and transport within a low-centered and a high-centered polygon. Bromide tracer was applied at both polygons in July 2015 and transport was monitored through two thaw seasons. Sampler arrays placed in polygon centers, rims, and troughs were used to monitor tracer concentrations. In both polygons, the tracer first infiltrated vertically until encountering the frost table and was then transported horizontally. Horizontal flow occurred in more locations and at higher velocities in the low-centered polygon than in the high-centered polygon. Preferential flow, influenced by frost table topography, was significant between polygon centers and troughs. Estimates of horizontal hydraulic conductivity were within the range of previous estimates of vertical conductivity, highlighting the importance of horizontal flow in these systems. This work forms a basis for understanding complexity of flow in polygonal landscapes.

Studies indicate greenhouse gas emissions following permafrost thaw will amplify current rates of atmospheric warming, a process referred to as the permafrost carbon feedback. However, large uncertainties exist regarding the timing and magnitude of the permafrost carbon feedback, in part due to uncertainties associated with subsurface permafrost parameterization and structure. Development of robust parameter estimation methods for permafrost-rich soils is becoming urgent under accelerated warming of the Arctic. Improved parameterization of the subsurface properties in land system models would lead to improved predictions and a reduction of modeling uncertainty. In this work we set the groundwork for future parameter estimation (PE) studies by developing and evaluating a joint PE algorithm that estimates soil porosities and thermal conductivities from time series of soil temperature and moisture measurements and discrete in-time electrical resistivity measurements. The algorithm utilizes the Model-Independent Parameter Estimation and Uncertainty Analysis toolbox and coupled hydrological-thermal-geophysical modeling. We test the PE algorithm against synthetic data, providing a proof of concept for the approach. We use specified subsurface porosities and thermal conductivities and coupled models to set up a synthetic state, perturb the parameters, and then verify that our PE method is able to recover the parameters and synthetic state. To evaluate the accuracy and robustness of the approach we perform multiple tests for a perturbed set of initial starting parameter combinations. In addition, we varied types and quantities of data to better understand the optimal dataset needed to improve the PE method. The results of the PE tests suggest that using multiple types of data improve the overall robustness of the method. Our numerical experiments indicate that special care needs to be taken during the field experiment setup so that (1) the vertical distance between adjacent measurement sensors allows the signal variability in space to be resolved and (2) the longer time interval between resistivity snapshots allows signal variability in time to be resolved.

Soil temperature has been recognized as a property that strongly influences a myriad of hydro-biogeochemical processes and reflects how various properties modulate the soil thermal flux. In spite of its importance, our ability to acquire soil temperature data with high spatial and temporal resolution and coverage is limited because of the high cost of equipment, the difficulties of deployment, and the complexities of data management. Here we propose a new strategy that we call distributed temperature profiling (DTP) for improving the characterization and monitoring near-surface thermal properties through the use of an unprecedented number of laterally and vertically distributed temperature measurements. We developed a prototype DTP system, which consists of inexpensive, low-impact, low-power, and vertically resolved temperature probes that independently and autonomously record soil temperature. The DTP system concept was tested by moving sequentially the system across the landscape to identify near-surface permafrost distribution in a discontinuous permafrost environment near Nome, Alaska, during the summertime. Results show that the DTP system enabled successful acquisition of vertically resolved profiles of summer soil temperature over the top 0.8 m at numerous locations. DTP also enabled high-resolution identification and lateral delineation of near-surface permafrost locations from surrounding zones with no permafrost or deep permafrost table locations overlain by a perennially thawed layer. The DTP strategy overcomes some of the limitations associated with - and complements the strengths of - borehole-based soil temperature sensing as well as fiber-optic distributed temperature sensing (FO-DTS) approaches. Combining DTP data with co-located topographic and vegetation maps obtained using unmanned aerial vehicle (UAV) and electrical resistivity tomography (ERT) data allowed us to identify correspondences between surface and subsurface property distribution and in particular between topography, vegetation, shallow soil properties, and near-surface permafrost. Finally, the results highlight the considerable value of the newly developed DTP strategy for investigating the significant variability in and complexity of subsurface thermal and hydrological regimes in discontinuous permafrost regions.

This study aims to investigate the microtopographic controls that dictate the heterogeneity of plant communities in a mountainous floodplain-hillslope system, using remote sensing and surface geophysical techniques. Working within a lower montane floodplain-hillslope study site (750 m × 750 m) in the Upper Colorado River Basin, we developed a new data fusion framework, based on machine learning and feature engineering, that exploits remote sensing optical and light detection and ranging (LiDAR) data to estimate the distribution of key plant meadow communities at submeter resolution. We collected surface electrical resistivity tomography data to explore the variability in soil properties along a floodplain-hillslope transect at 0.50-m resolution and extracted LiDAR-derived metrics to model the rapid change in microtopography. We then investigated the covariability among the estimated plant community distributions, soil information, and topographic metrics. Results show that our framework estimated the distribution of nine plant communities with higher accuracy (87% versus 80% overall; 85% versus 60% for shrubs) compared to conventional classification approaches. Analysis of the covariabilities reveals a strong correlation between plant community distribution, soil electric conductivity, and slope, indicating that soil moisture is a primary control on heterogeneous spatial distribution. At the same time, microtopography plays an important role in creating particular ecosystem niches for some of the communities. Such relationships could be exploited to provide information about the spatial variability of soil properties. This highly transferable framework can be employed within long-term monitoring to capture community-specific physiological responses to perturbations, offering the possibility of bridging local plot-scale observations with large landscape monitoring.

Evapotranspiration (ET) is a key component of the water balance, which influences hydrometeorology, water resources, carbon and other biogeochemical cycles, and ecosystem diversity. This study aims to investigate the spatio-temporal variations of ET at the East River watershed in Colorado and analyze the factors that control these variations. ET was acquired using the community land model (CLM) simulations and was compared with the values estimated using Fu's equation and a watershed-scale water balance equation. The simulation results showed that 55% of annual precipitation in the East River is lost to ET, in which 75% of the ET comes from the summer months (May to September). We also found that the contribution of transpiration to the total ET was ~50%, which is much larger than that of soil evaporation (32%) and canopy evaporation (18%). Spatial analysis indicated that the ET is greater at elevations of 2950-3200 m and lower along the river valley ( < 2750 m) and at the high elevations ( > 3900 m). A correlation analysis of factors affecting ET showed that the land elevation, air temperature, and vegetation are closely correlated and together they govern the ET spatial variability. The results also suggested that ET in areas with more finely textured soil is slightly larger than regions with coarse-texture soil. This study presents a promising approach to the assessment of ET with a high spatiotemporal resolution over watershed scales and investigates factors controlling ET spatiotemporal variations.

The U.S. Department of Energy's Watershed Function Scientific Focus Area (SFA), centered in the East River, Colorado, generates diverse datasets including hydrological, geological, geochemical, geophysical, ecological, microbiological and remote sensing data. The project has deployed extensive field infrastructure involving hundreds of sensors that measure highly diverse phenomena (e.g. stream and groundwater hydrology, water quality, soil moisture, weather) across the watershed. Data from the sensor network are telemetered and automatically ingested into a queryable database. The data are subsequently quality checked, integrated with the United States Geological Survey's stream monitoring network using a custom data integration broker, and published to a portal with interactive visualizations. The resulting data products are used in a variety of scientific modeling and analytical efforts. This paper describes the SFA's end-to-end infrastructure and services that support the generation of integrated datasets from a watershed sensor network. The development and maintenance of this infrastructure, presents a suite of challenges from practical field logistics to complex data processing, which are addressed through various solutions. In particular, the SFA adopts a holistic view for data collection, assessment and integration, which dramatically improves the products generated, and enables a co-design approach wherein data collection is informed by model results and vice-versa.

To understand how redox processes influence carbon, nitrogen, and iron cycling within the intrameander hyporheic zone, we developed a biotic and abiotic reaction network and incorporated it into the reactive transport simulator PFLOTRAN. Two-dimensional reactive flow and transport simulations were performed (1) to evaluate how transient hydrological conditions control the lateral redox zonation within an intrameander region of the East River in Colorado and (2) to quantify the impact of a single meander on subsurface exports of carbon and other geochemical species to the river. The meander's overall contribution to the river was quantified by integrating geochemical outfluxes along the outside of the meander bend. The model was able to capture the field-observed trends of dissolved oxygen, nitrate, iron, pH, and total inorganic carbon along a 2-D transect. Consistent with field observations, simulated dissolved oxygen and nitrate decreased along the intrameander flow paths while iron (Fe2+) concentration increased. The simulation results further demonstrated that the reductive potential of the lateral redox zonation was controlled by groundwater velocities resulting from river stage fluctuations, with low-water conditions promoting reducing conditions. The sensitivity analysis results showed that permeability had a more significant impact on biogeochemical zonation compared to the reaction pathways under transient hydrologic conditions. The simulation results further indicated that the meander acted as a sink for organic and inorganic carbon as well as iron during the extended baseflow and high-water conditions; however, these geochemical species were released into the river during the falling limb of the hydrograph.

Quantitative understanding of controls on thaw layer thickness (TLT) dynamics in the Arctic peninsula is essential for predictive understanding of permafrost degradation feedbacks to global warming and hydrobiochemical processes. This study jointly interprets electrical resistivity tomography (ERT) measurements and hydro-thermal numerical simulation results to assess spatiotemporal variations of TLT and to determine its controlling factors in Barrow, Alaska. Time-lapse ERT measurements along a 35-m transect were autonomously collected from 2013 to 2015 and inverted to obtain soil electrical resistivity. Based on several probe-based TLT measurements and co-located soil electrical resistivity, we estimated the electrical resistivity thresholds associated with the boundary between the thaw layer and permafrost using a grid search optimization algorithm. Then, we used the obtained thresholds to derive the TLT from all soil electrical resistivity images. The spatiotemporal analysis of the ERT-derived TLT shows that the TLT at high-centered polygons (HCPs) is smaller than that at low-centered polygons (LCPs), and that both thawing and freezing occur earlier at the HCPs compared to the LCPs. In order to provide a physical explanation for dynamics in the thaw layer, we performed 1-D hydro-thermal simulations using the community land model (CLM). Simulation results showed that air temperature and precipitation jointly govern the temporal variations of TLT, while the topsoil organic content (SOC) and polygon morphology are responsible for its spatial variations. When the topsoil SOC and its thickness increase, TLT decreases. Meanwhile, at LCPs, a thicker snow layer and saturated soil contribute to a thicker TLT and extend the time needed for TLT to freeze and thaw. This research highlights the importance of combination of measurements and numerical modeling to improve our understanding spatiotemporal variations and key controls of TLT in cold regions.

Permafrost physicochemical parameters play a key role in controlling the response of permafrost carbon to climate change. We studied the physicochemical parameters of permafrost in an Arctic tundra region to evaluate (1) how soil parameters vary with depth and whether and how they are interrelated, (2) whether and how permafrost soil differs from its overlaying active layer, and (3) whether soil property-depth relationships are different across geomorphic features (e.g., low, flat, and high centered polygons). We also explored the possible biogeochemical processes that led to these soil characteristics and how they may affect biogeochemical reactions upon permafrost thaw. We observed (1) consistent relationships between soil property and depth and between major parameters, (2) large contrasts of key soil parameters between active layer and permafrost, indicative of potentially different response of the permafrost carbon to warming when compared to the active layer, and (3) a correlation between soil hydraulic conductivity and topographic features that impacts soil hydrologic processes. Our analysis suggests that the permafrost has a marine-derived chemical signature that differs from the active layer and shapes the physicochemical fingerprints of the different geomorphic features. Specifically, we revealed the unique signatures of the high center polygons, indicative of possible microbial activity at depth (>1 m). Our study suggested consistent key soil parameter-depth correlations while demonstrating complex lateral and vertical variabilities. These results are valuable for identifying approaches to upscale point-based measurements and for improving model parameterization to predict permafrost carbon behavior and feedback under future climate.

Extreme weather, fires, and land use and climate change are significantly reshaping interactions within watersheds throughout the world. Although hydrological–biogeochemical interactions within watersheds can impact many services valued by society, uncertainty associated with predicting hydrologydriven biogeochemical watershed dynamics remains high. With an aim to reduce this uncertainty, an approximately 300-km2 mountainous headwater observatory has been developed at the East River, CO, watershed of the Upper Colorado River Basin. The site is being used as a testbed for the Department of Energy supported Watershed Function Project and collaborative efforts. Building on insights gained from research at the “sister” Rifle, CO, site, coordinated studies are underway at the East River site to gain a predictive understanding of how the mountainous watershed retains and releases water, nutrients, carbon, and metals. In particular, the project is exploring how early snowmelt, drought, and other disturbances influence hydrological–biogeochemical watershed dynamics at seasonal to decadal timescales. A system-of-systems perspective and a scale-adaptive simulation approach, involving the combined use of archetypal watershed subsystem “intensive sites” are being tested at the site to inform aggregated watershed predictions of downgradient exports. Complementing intensive site hydrological, geochemical, geophysical, microbiological, geological, and vegetation datasets are long-term, distributed measurement stations and specialized experimental and observational campaigns. Several recent research advances provide insights about the intensive sites as well as aggregated watershed behavior. The East River “community testbed” is currently hosting scientists from more than 30 institutions to advance mountainous watershed methods and understanding.

Microtopographic features, such as polygonal ground, are characteristic sources of landscape heterogeneity in the Alaskan Arctic coastal plain. Here, we analyze the effects of snow redistribution (SR) and lateral subsurface processes on hydrologic and thermal states at a polygonal tundra site near Barrow, Alaska. We extended the land model integrated in the E3SM to redistribute incoming snow by accounting for microtopography and incorporated subsurface lateral transport of water and energy (ELM-3D v1.0). Multiple 10-year-long simulations were performed for a transect across a polygonal tundra landscape at the Barrow Environmental Observatory in Alaska to isolate the impact of SR and subsurface process representation. When SR was included, model predictions better agreed (higher R2, lower bias and RMSE) with observed differences in snow depth between polygonal rims and centers. The model was also able to accurately reproduce observed soil temperature vertical profiles in the polygon rims and centers (overall bias, RMSE, and R2 of 0.59°C, 1.82°C, and 0.99, respectively). The spatial heterogeneity of snow depth during the winter due to SR generated surface soil temperature heterogeneity that propagated in depth and time and led to ∼10 cm shallower and ∼5 cm deeper maximum annual thaw depths under the polygon rims and centers, respectively. Additionally, SR led to spatial heterogeneity in surface energy fluxes and soil moisture during the summer. Excluding lateral subsurface hydrologic and thermal processes led to small effects on mean states but an overestimation of spatial variability in soil moisture and soil temperature as subsurface liquid pressure and thermal gradients were artificially prevented from spatially dissipating over time. The effect of lateral subsurface processes on maximum thaw depths was modest, with mean absolute differences of ∼3 cm. Our integration of three-dimensional subsurface hydrologic and thermal subsurface dynamics in the E3SM land model will facilitate a wide range of analyses heretofore impossible in an ESM context.

We use 3-D high-resolution reactive transport modeling to investigate whether the spatial distribution of organic-carbon-rich and chemically reduced sediments located in the riparian zone and temporal variability in groundwater flow direction impact the formation and distribution of nitrogen hot spots (regions that exhibit higher reaction rates when compared to other locations nearby) and hot moments (times that exhibit high reaction rates as compared to longer intervening time periods) within the Rifle floodplain in Colorado. Groundwater flows primarily toward the Colorado River from the floodplain but changes direction at times of high river stage. The result is that oxic river water infiltrates the Rifle floodplain during these relatively short-term events. Simulation results indicate that episodic rainfall in the summer season leads to the formation of nitrogen hot moments associated with Colorado River rise and resulting river infiltration into the floodplain. The results further demonstrate that the naturally reduced zones (NRZs) present in sediments of the Rifle floodplain have a higher potential for nitrate removal, approximately 70% greater than non-NRZs for typical hydrological conditions. During river water infiltration, nitrate reduction capacity remains the same within the NRZs, however, these conditions impact non-NRZs to a greater extent (approximately 95% less nitrate removal). Model simulations indicate chemolithoautotrophs are primarily responsible for the removal of nitrate in the Rifle floodplain. These nitrogen hot spots and hot moments are sustained by microbial respiration and the chemolithoautotrophic oxidation of reduced minerals in the riparian zone.

© 2017 We conducted laboratory studies on the geophysical signals from Arctic saline permafrost soils to help understand the physical and mechanical processes during freeze-thaw cycles. Our results revealed low electrical resistivity (< 20 Ωm) and elastic moduli (7.7 GPa for Young's modulus and 2.9 GPa for shear modulus) at temperatures down to ~− 10 °C, indicating the presence of a significant amount of unfrozen saline water under the current field conditions. The spectral induced polarization signal showed a systematic shift during the freezing process, affected by concurrent changes of temperature, salinity, and ice formation. An anomalous induced polarization response was first observed during the transient period of supercooling and the onset of ice nucleation. Seismic measurements showed a characteristic maximal attenuation at the temperatures immediately below the freezing point, followed by a decrease with decreasing temperature. The calculated elastic moduli showed a non-hysteric response during the freeze – thaw cycle, which was different from the concurrently measured electrical resistivity response where a differential resistivity signal is observed depending on whether the soil is experiencing freezing or thawing. The differential electrical resistivity signal presents challenges for unfrozen water content estimation based on Archie's law. Using an improved formulation of Archie's law with a variable cementation exponent, the unfrozen water content estimation showed a large variation depending on the choice of the resistivity data during either a freezing or thawing cycle. Combining the electrical and seismic results, we suggest that, rather than a large hysteresis in the actual unfrozen water content, the shift of the resistivity response may reflect the changes of the distribution pattern of the unfrozen water (or ice) in the soil matrix during repeated freeze and thaw processes. Collectively, our results provide an improved petrophysical understanding of the physical and mechanical properties of saline permafrost during freeze – thaw transitions, and suggest that large uncertainty may exist when estimating the unfrozen water content using electrical resistivity data.

Improving understanding of Arctic ecosystem climate feedback and parameterization of models that simulate freeze-Thaw dynamics require advances in quantifying soil and snow properties. Due to the significant spatiotemporal variability of soil properties and the limited information provided by point-scale measurements (e.g., cores), geophysical methods hold potential for improving soil and permafrost characterization. In this study, we evaluate the use of a ground-penetrating radar (GPR) to estimate thaw layer thickness, snow depth, and ice-wedge characteristics in an ice-wedge-dominated tundra region near Barrow, AK, USA. To this end, we analyze GPR and point-scale measurements collected along several parallel transects at the end of the growing season and the end of frozen season. In addition, we compare the structural information extracted from the GPR data with electrical resistivity tomography (ERT) information about ice-wedge characteristics. Our study generally highlights the value of GPR data collected in the frozen season, when conditions lead to the improved GPR signal-To-noise ratio, facilitate data acquisition, and reduce acquisition-related ecosystem disturbance relative to growing season. We document for the first time that GPR data collected during the frozen season can provide reliable estimates of active layer thickness and geometry of ice wedges. We find that the ice-wedge geometry extracted from GPR data collected during the frozen season is consistent with ERT data, and that GPR data can be used to constrain the ERT inversion. Consistent with recent studies, we also find that GPR data collected during the frozen season can provide good estimates of snow thickness, and that GPR data collected during the growing season can provide reliable estimate thaw depth. Our quantification of the value of the GPR and ERT data collected during growing and frozen seasons paves the way for coupled inversion of the datasets to improve understanding of permafrost variability.

Quantitative characterization of soil organic carbon (OC) content is essential due to its significant impacts on surface-subsurface hydrological-Thermal processes and microbial decomposition of OC, which both in turn are important for predicting carbon-climate feedbacks. While such quantification is particularly important in the vulnerable organic-rich Arctic region, it is challenging to achieve due to the general limitations of conventional core sampling and analysis methods, and to the extremely dynamic nature of hydrological-Thermal processes associated with annual freeze-Thaw events. In this study, we develop and test an inversion scheme that can flexibly use single or multiple datasets-including soil liquid water content, temperature and electrical resistivity tomography (ERT) data-to estimate the vertical distribution of OC content. Our approach relies on the fact that OC content strongly influences soil hydrological-Thermal parameters and, therefore, indirectly controls the spatiotemporal dynamics of soil liquid water content, temperature and their correlated electrical resistivity. We employ the Community Land Model to simulate nonisothermal surface-subsurface hydrological dynamics from the bedrock to the top of canopy, with consideration of land surface processes (e.g., solar radiation balance, evapotranspiration, snow accumulation and melting) and ice-liquid water phase transitions. For inversion, we combine a deterministic and an adaptive Markov chain Monte Carlo (MCMC) optimization algorithm to estimate a posteriori distributions of desired model parameters. For hydrological-Thermal-To-geophysical variable transformation, the simulated subsurface temperature, liquid water content and ice content are explicitly linked to soil electrical resistivity via petrophysical and geophysical models. We validate the developed scheme using different numerical experiments and evaluate the influence of measurement errors and benefit of joint inversion on the estimation of OC and other parameters. We also quantify the propagation of uncertainty from the estimated parameters to prediction of hydrological-Thermal responses. We find that, compared to inversion of single dataset (temperature, liquid water content or apparent resistivity), joint inversion of these datasets significantly reduces parameter uncertainty. We find that the joint inversion approach is able to estimate OC and sand content within the shallow active layer (top 0.3ĝ€m of soil) with high reliability. Due to the small variations of temperature and moisture within the shallow permafrost (here at about 0.6ĝ€m depth), the approach is unable to estimate OC with confidence. However, if the soil porosity is functionally related to the OC and mineral content, which is often observed in organic-rich Arctic soil, the uncertainty of OC estimate at this depth remarkably decreases. Our study documents the value of the new surface-subsurface, deterministic-stochastic inversion approach, as well as the benefit of including multiple types of data to estimate OC and associated hydrological-Thermal dynamics.

Coincident monitoring of the spatiotemporal distribution of and interactions between land, soil, and permafrost properties is important for advancing our understanding of ecosystem dynamics. In this study, a novel monitoring strategy was developed to quantify complex Arctic ecosystem responses to the seasonal freeze-thaw-growing season conditions. The strategy exploited autonomous measurements obtained through electrical resistivity tomography to monitor soil properties, pole-mounted optical cameras to monitor vegetation dynamics, point probes to measure soil temperature, and periodic manual measurements of thaw layer thickness, snow thickness, and soil dielectric permittivity. The spatially and temporally dense monitoring data sets revealed several insights about tundra system behavior at a site located near Barrow, AK. In the active layer, the soil electrical conductivity (a proxy for soil water content) indicated an increasing positive correlation with the green chromatic coordinate (a proxy for vegetation vigor) over the growing season, with the strongest correlation (R = 0.89) near the typical peak of the growing season. Soil conductivity and green chromatic coordinate also showed significant positive correlations with thaw depth, which is influenced by soil and surface properties. In the permafrost, soil electrical conductivity revealed annual variations in solute concentration and unfrozen water content, even at temperatures well below 0°C in saline permafrost. These conditions may contribute to an acceleration of long-term thaw in Coastal permafrost regions. Demonstration of this first aboveground and belowground geophysical monitoring approach within an Arctic ecosystem illustrates its significant potential to remotely “visualize” permafrost, soil, and vegetation ecosystem codynamics in high resolution over field relevant scales.

This paper compares and integrates different strategies to characterize the variability of end-of-winter snow depth and its relationship to topography in ice-wedge polygon tundra of Arctic Alaska. Snow depth was measured using in situ snow depth probes and estimated using groundpenetrating radar (GPR) surveys and the photogrammetric detection and ranging (phodar) technique with an unmanned aerial system (UAS).We found that GPR data provided highprecision estimates of snow depth (RMSE D2.9 cm), with a spatial sampling of 10 cm along transects. Phodar-based approaches provided snow depth estimates in a less laborious manner compared to GPR and probing, while yielding a high precision (RMSED6.0 cm) and a fine spatial sampling (4cm-4cm). We then investigated the spatial variability of snow depth and its correlation to micro-and macrotopography using the snow-free lidar digital elevation map (DEM) and the wavelet approach. We found that the end-of-winter snow depth was highly variable over short (several meter) distances, and the variability was correlated with microtopography. Microtopographic lows (i.e., troughs and centers of low-centered polygons) were filled in with snow, which resulted in a smooth and even snow surface following macrotopography. We developed and implemented a Bayesian approach to integrate the snow-free lidar DEM and multiscale measurements (probe and GPR) as well as the topographic correlation for estimating snow depth over the landscape. Our approach led to high-precision estimates of snow depth (RMSED6.0 cm), at 0.5m resolution and over the lidar domain (750m × 700m).

Improving our ability to estimate the parameters that control water and heat fluxes in the shallow subsurface is particularly important due to their strong control on recharge, evaporation and biogeochemical processes. The objectives of this study are to develop and test a new inversion scheme to simultaneously estimate subsurface hydrological, thermal and petrophysical parameters using hydrological, thermal and electrical resistivity tomography (ERT) data. The inversion scheme-which is based on a nonisothermal, multiphase hydrological model-provides the desired subsurface property estimates in high spatiotemporal resolution. A particularly novel aspect of the inversion scheme is the explicit incorporation of the dependence of the subsurface electrical resistivity on both moisture and temperature. The scheme was applied to synthetic case studies, as well as to real datasets that were autonomously collected at a biogeochemical field study site in Rifle, Colorado. At the Rifle site, the coupled hydrological-thermal-geophysical inversion approach well predicted the matric potential, temperature and apparent resistivity with the Nash-Sutcliffe efficiency criterion greater than 0.92. Synthetic studies found that neglecting the subsurface temperature variability, and its effect on the electrical resistivity in the hydrogeophysical inversion, may lead to an incorrect estimation of the hydrological parameters. The approach is expected to be especially useful for the increasing number of studies that are taking advantage of autonomously collected ERT and soil measurements to explore complex terrestrial system dynamics.

In this study, we advance the understanding of three-dimensional (3-D) electrical resistivity tomography (ERT) for monitoring long-term CO2 storage by analyzing two previously published field time-lapse data sets. We address two important aspects of ERT inversion-the issue of resolution decay, a general impediment to the ERT method, and the issue of potentially misleading imaging artifacts due to 2-D model assumptions. The first study analyzes data from a shallow dissolved-CO2 injection experiment near Escatawpa (Mississippi), where ERT data were collected in a 3-D crosswell configuration. We apply a focusing approach designed for crosswell configurations to counteract resolution loss in the inter-wellbore area, with synthetic studies demonstrating its effectiveness. The 3-D field data analysis reveals an initially southwards-trending flow path development and a dispersing plume development in the downgradient inter-well region. The second data set was collected during a deep (over 3 km) injection of supercritical CO2 near Cranfield (Mississippi). Comparative 2-D and 3-D inversions reveal the projection of off-planar anomalies onto the cross-section, a typical artifact introduced by 2-D model assumptions. Conforming 3-D images from two different algorithms support earlier hydrological investigations, indicating a conduit system where flow velocity variations lead to a circumvention of a close observation well and an onset of increased CO2 saturation downgradient from this well. We relate lateral permeability variations indicated by an independently obtained hydrological analysis to this consistently observed pattern in the CO2 spatial plume evolution.

TOUGH2 and iTOUGH2 are powerful models that simulate the heat and fluid flows in porous and fracture media, and perform parameter estimation, sensitivity analysis and uncertainty propagation analysis. However, setting up the input files is not only tedious, but error prone, and processing output files is time consuming. In this study, we present an open source Matlab-based tool (iMatTOUGH) that supports the generation of all necessary inputs for both TOUGH2 and iTOUGH2 and visualize their outputs. The tool links the inputs of TOUGH2 and iTOUGH2, making sure the two input files are consistent. It supports the generation of rectangular computational mesh, i.e., it automatically generates the elements and connections as well as their properties as required by TOUGH2. The tool also allows the specification of initial and time-dependent boundary conditions for better subsurface heat and water flow simulations. The effectiveness of the tool is illustrated by an example that uses TOUGH2 and iTOUGH2 to estimate soil hydrological and thermal properties from soil temperature data and simulate the heat and water flows at the Rifle site in Colorado.

In floodplain environments, a naturally reduced zone (NRZ) is considered to be a common biogeochemical hot spot, having distinct microbial and geochemical characteristics. Although important for understanding their role in mediating floodplain biogeochemical processes, mapping the subsurface distribution of NRZs over the dimensions of a floodplain is challenging, as conventional wellbore data are typically spatially limited and the distribution of NRZs is heterogeneous. In this study, we present an innovative methodology for the probabilistic mapping of NRZs within a three-dimensional (3-D) subsurface domain using induced polarization imaging, which is a noninvasive geophysical technique. Measurements consist of surface geophysical surveys and drilling-recovered sediments at the U.S. Department of Energy field site near Rifle, CO (USA). Inversion of surface time domain-induced polarization (TDIP) data yielded 3-D images of the complex electrical resistivity, in terms of magnitude and phase, which are associated with mineral precipitation and other lithological properties. By extracting the TDIP data values colocated with wellbore lithological logs, we found that the NRZs have a different distribution of resistivity and polarization from the other aquifer sediments. To estimate the spatial distribution of NRZs, we developed a Bayesian hierarchical model to integrate the geophysical and wellbore data. In addition, the resistivity images were used to estimate hydrostratigraphic interfaces under the floodplain. Validation results showed that the integration of electrical imaging and wellbore data using a Bayesian hierarchical model was capable of mapping spatially heterogeneous interfaces and NRZ distributions thereby providing a minimally invasive means to parameterize a hydrobiogeochemical model of the floodplain.

Shallow permafrost distribution and characteristics are important for predicting ecosystem feedbacks to a changing climate over decadal to century timescales because they can drive active layer deepening and land surface deformation, which in turn can significantly affect hydrologic and biogeochemical responses, including greenhouse gas dynamics. As part of the U.S. Department of Energy Next-Generation Ecosystem Experiments- Arctic, we have investigated shallow Arctic permafrost characteristics at a site in Barrow, Alaska, with the objective of improving our understanding of the spatial distribution of shallow permafrost, its associated properties, and its links with landscape microtopography. To meet this objective, we have acquired and integrated a variety of information, including electric resistance tomography data, frequency-domain electromagnetic induction data, laboratory core analysis, petrophysical studies, high-resolution digital surface models, and color mosaics inferred from kite-based landscape imaging. The results of our study provide a comprehensive and high-resolution examination of the distribution and nature of shallow permafrost in the Arctic tundra, including the estimation of ice content, porosity, and salinity. Among other results, porosity in the top 2 m varied between 85% (besides ice wedges) and 40%, and was negatively correlated with fluid salinity. Salinity directly influenced ice and unfrozen water content and indirectly influenced the soil organic matter content. A relatively continuous but depth-variable increase in salinity led to a partially unfrozen saline layer (cryopeg) located below the top of the permafrost. The cryopeg environment could lead to year-round microbial production of greenhouse gases. Results also indicated a covariability between topography and permafrost characteristics including icewedge and salinity distribution. In addition to providing insight about the Arctic ecosystem, through integration of lab-based petrophysical results with field data, this study also quantified the key controls on electric resistivity at this Arctic permafrost site, including salinity, porosity, water content, ice content, soil organic matter content, and lithologic properties.