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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 (

Estimates of soil properties such as Cation Exchange Capacity (CEC), water content, grain size characteristics, and permeability are important in geotechnical engineering, water resources, and agriculture. We develop a non-intrusive approach to estimate these properties in the field using spectral induced polarization (SIP) tomography. This geophysical method provides information about the frequency dependence of the complex electrical conductivity of porous media. Using 18 soil samples collected from a Bordeaux vineyard, we first conducted a laboratory study using SIP over the frequency range 10 mHz-45 kHz. The laboratory data were used to confirm the accuracy of a recently developed dynamic Stern layer petrophysical model. The results are consistent with published values from previous works using soils. A comparison was made by comparing the field complex conductivity spectra and the experimental data at two locations where core samples were obtained. The model was then used in concert with field data to image the spatial distribution of CEC, water content, permeability, and mean grain size along a vineyard transect. For clay and sandy textures found in the field, measured and estimated CEC agree rather well (from 6 to 40% discrepancy). Our approach provides an efficient way to estimate important soil properties in a non-invasive manner, in high resolution, and over field-relevant scales of the critical zone of the Earth.

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.

The U.S. Department of Energy's (DOE) Colorado East River Community Observatory (ER) in the Upper Colorado River Basin was established in 2015 as a representative mountainous, snow-dominated watershed to study hydrobiogeochemical responses to hydrological perturbations in headwater systems. The ER is characterized by steep elevation, geologic, hydrologic and vegetation gradients along floodplain, montane, subalpine, and alpine life zones, which makes it an ideal location for researchers to understand how different mountain subsystems contribute to overall watershed behaviour. The ER has both long-term and spatially-extensive observations and experimental campaigns carried out by the Watershed Function Scientific Focus Area (SFA), led by Lawrence Berkeley National Laboratory, and researchers from over 30 organizations who conduct cross-disciplinary process-based investigations and modelling of watershed behaviour. The heterogeneous data generated at the ER include hydrological, genomic, biogeochemical, climate, vegetation, geological, and remote sensing data, which combined with model inputs and outputs comprise a collection of datasets and value-added products within a mountainous watershed that span multiple spatiotemporal scales, compartments, and life zones. Within 5 years of collection, these datasets have revealed insights into numerous aspects of watershed function such as factors influencing snow accumulation and melt timing, water balance partitioning, and impacts of floodplain biogeochemistry and hillslope ecohydrology on riverine geochemical exports. Data generated by the SFA are managed and curated through its Data Management Framework. The SFA has an open data policy, and over 70 ER datasets are publicly available through relevant data repositories. A public interactive map of data collection sites run by the SFA is available to inform the broader community about SFA field activities. Here, we describe the ER and the SFA measurement network, present the public data collection generated by the SFA and partner institutions, and highlight the value of collecting multidisciplinary multiscale measurements in representative catchment observatories.

Atmospheric nitrous oxide contributes directly to global warming, yet models of the nitrogen cycle do not account for bedrock, the largest pool of terrestrial nitrogen, as a source of nitrous oxide. Although it is known that release rates of nitrogen from bedrock are large, there is an incomplete understanding of the connection between bedrock-hosted nitrogen and atmospheric nitrous oxide. Here, we quantify nitrogen fluxes and mass balances at a hillslope underlain by marine shale. We found that, at this site, bedrock weathering contributes 78% of the subsurface reactive nitrogen, while atmospheric sources (commonly regarded as the sole sources of reactive nitrogen in pristine environments) account for only the remaining 22%. About 56% of the total subsurface reactive nitrogen denitrifies, including 14% emitted as nitrous oxide. The remaining reactive nitrogen discharges in porewaters to a floodplain where additional denitrification probably occurs. We also found that the release of bedrock nitrogen occurs primarily within the zone of the seasonally fluctuating water table and suggest that the accumulation of nitrate in the vadose zone, often attributed to fertilization and soil leaching, may also include contributions from weathered nitrogen-rich bedrock. Our hillslope study suggests that, under oxygenated and moisture-rich conditions, weathering of deep, nitrogen-rich bedrock makes an important contribution to the nitrogen cycle.

Patterns of watershed nitrogen (N) retention and loss are shaped by how watershed biogeochemical processes retain, biogeochemically transform, and lose incoming atmospheric deposition of N. Loss patterns represented by concentration, discharge, and their associated stream exports are important indicators of integrated watershed N retention behaviors. We examined continental United States (CONUS) scale N deposition (e.g., wet and dry atmospheric deposition), vegetation trends, and stream trends as potential indicators of watershed N-saturation and retention conditions, and how watershed N retention and losses vary over space and time. By synthesizing changes and modalities in watershed nitrogen loss patterns based on stream data from 2200 U.S. watersheds over a 50 years record, our work revealed two patterns of watershed N-retention and loss. One was a hysteresis pattern that reflects the integrated influence of hydrology, atmospheric inputs, land-use, stream temperature, elevation, and vegetation. The other pattern was a one-way transition to a new state. We found that regions with increasing atmospheric deposition and increasing vegetation health/biomass patterns have the highest N-retention capacity, become increasingly N-saturated over time, and are associated with the strongest declines in stream N exports—a pattern, that is, consistent across all land cover categories. We provide a conceptual model, validated at an unprecedented scale across the CONUS that links instream nitrogen signals to upstream mechanistic landscape processes. Our work can aid in the future interpretation of in-stream concentrations of DOC and DIN as indicators of watershed N-retention status and integrators of watershed hydrobiogeochemical processes.

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.

Long-term plot-scale studies have found water limitation to be a key factor driving ecosystem productivity in the Rocky Mountains. Specifically, the intensity of early summer (the 'foresummer' period from May to June) drought conditions appears to impose critical controls on peak ecosystem productivity. This study aims to (1) assess the importance of early snowmelt and foresummer drought in controlling peak plant productivity, based on the historical Landsat normalized-difference vegetation index (NDVI) and climate data; (2) map the spatial heterogeneity of foresummer drought sensitivity; and (3) identify the environmental controls (e.g. geomorphology, elevation, geology, plant types) on drought sensitivity. Our domain (15 15 km) includes four drainages within the East Water watershed near Gothic, Colorado, USA. We define foresummer drought sensitivity based on the regression slopes of the annual peak NDVI against the June Palmer Drought Severity Index between 1992 and 2010. Results show that foresummer drought sensitivity is spatially heterogeneous, and primarily dependent on the plant type and elevation. In support of the plot-based studies, we find that years with earlier snowmelt and drier foresummer conditions lead to lower peak NDVI; particularly in the low-elevation regions. Using random forest analysis, we identify additional key controls related to surface energy exchanges (i.e. potential net radiation), hydrological processes (i.e. microtopography and slope), and underlying geology. This remote-sensing-based approach for quantifying foresummer drought sensitivity can be used to identify the regions that are vulnerable or resilient to climate perturbations, as well as to inform future sampling, characterization, and modeling studies.

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.

Major components of hydrologic and elemental cycles reside underground, where their complex dynamics and linkages to surface waters are obscure. We delineated seasonal subsurface flow and transport dynamics along a hillslope in the Rocky Mountains (USA), where precipitation occurs primarily as winter snow and drainage discharges into the East River, a tributary of the Gunnison River. Hydraulic and geochemical measurements down to 10 m below ground surface supported application of transmissivity feedback of snowmelt to describe subsurface flow and transport through three zones: soil, weathering shale, and saturated fractured shale. Groundwater flow is predicted to depths of at least 176 m, although a shallower limit exists if hillslope-scale hydraulic conductivities are higher than our local measurements. Snowmelt during the high snowpack water year 2017 sustained flow along the weathering zone and downslope within the soil, while negligible downslope flow occurred along the soil during the low snowpack water year 2018. We introduce subsurface concentration-discharge (C-Q) relations for explaining hillslope contributions to C-Q observed in rivers and demonstrate their calculations based on transmissivity fluxes and measured pore water specific conductance and dissolved organic carbon. The specific conductance data show that major ions in the hillslope pore waters, primarily from the weathering and fractured shale, are about six times more concentrated than in the river, indicating hillslope solute loads are disproportionately high, while flow from this site and similar regions are relatively smaller. This methodology is applicable in different representative environments within snow-dominated watersheds for linking their subsurface exports to surface waters.

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.

Water is essential to human health and economic development due to its utilization in sanitation, agriculture, and energy. Supplying water to an expanding world population requires simultaneous consideration of multiple societal sectors competing for limited resources. Water conservation, supply augmentation, distribution, and treatment of contaminants must work in concert to ensure water sustainability. Water is linked to other sectors, and the quantity and quality of water resources are changing. The efficient use of water in agriculture, the largest user of water worldwide, via drip irrigation is described as is the use of energy-intensive reverse osmosis to supplement freshwater supplies. Efforts to manage watersheds and model their responses to severe weather events are discussed along with efforts to improve the predictability of their function. The regional competition for water resources impacts both energy and water supply reliability, which requires that nations balance both for sustainable economic development. The use of water and energy in the US is described which provides a lens through which to both rethink the interrelationship of water and energy as well as evaluate technological developments. Advances in nanotechnology are highlighted as one emerging technology. These results underscore the multifaceted nature of water sustainability, its interrelationship to energy and economic development, and the need to develop, manage and regulate water systems in a concerted manner.

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.

The investigation of plant roots is inherently difficult and often neglected. Being out of sight, roots are often out of mind. Nevertheless, roots play a key role in the exchange of mass and energy between soil and the atmosphere, in addition to the many practical applications in agriculture. In this paper, we propose a method for roots imaging based on the joint use of two electrical noninvasive methods: electrical resistivity tomography (ERT) and mise-à-la-masse (MALM). The approach is based on the key assumption that the plant root system acts as an electrically conductive body, so that injecting electrical current into the plant stem will ultimately result in the injection of current into the subsoil through the root system, and particularly through the root terminations via hair roots. Evidence from field data, showing that voltage distribution is very different whether current is injected into the tree stem or in the ground, strongly supports this hypothesis. The proposed procedure involves a stepwise inversion of both ERT and MALM data that ultimately leads to the identification of electrical resistivity (ER) distribution and of the current injection root distribution in the three-dimensional soil space. This, in turn, is a proxy to the active (hair) root density in the ground. We tested the proposed procedure on synthetic data and, more importantly, on field data collected in a vineyard, where the estimated depth of the root zone proved to be in agreement with literature on similar crops. The proposed noninvasive approach is a step forward towards a better quantification of root structure and functioning.

Understanding terrestrial carbon cycling has relied primarily on studies of topsoils that are typically characterized to depths shallower than 0.5 m. At a semiarid site instrumented down to 7 m, we measured seasonal- and depth-resolved carbon inventories and fluxes and groundwater and unsaturated zone flow rates. Measurements showed that ~30% of the CO2 efflux to the atmosphere (60% in winter) originates from below 1 m, contrary to predictions of less than 1% by Earth System Model land modules. Respiration from deeper roots and deeper microbial communities is supported by favorable subsurface temperatures, moisture, and oxygen availability. Below 1 m, dissolved organic carbon fluxes from the overlying soil and C from deep roots and exudates are expected to be important in sustaining microbial respiration. Because these conditions are characteristic of semiarid climate regions, we contend that Earth System Model land modules should incorporate such deeper soil processes to improve CO2 flux predictions.

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.

Adsorption at the solid-pore fluid interface is a key mechanism controlling the mobility of nutrients and contaminants in subsurface soils and sediments. The spectral induced polarization (SIP) method has been shown to be sensitive to the quantity and type of adsorbed ions. Extending previous results, we investigated the relevance of pH, solution conductivity, and ion type on the SIP response of saturated silica sand. We also performed adsorption experiments to evaluate whether adsorption plays a relevant role on the effect of saturating solution conductivity and pH. Given their environmental relevance and different electrochemical characteristics, we focused on exploring the influence of Cu2+ and Na+ adsorption on the SIP signature. The adsorption results confirm the expected and modeled pH influence on the adsorption of both Cu2+ and Na+. The measured quadrature conductivity spectra indicate that pH and solution conductivity control the electrical double layer electrochemical state and its capacitive behavior. On the contrary, no appreciable SIP signal changes are associated with ion substitution. The adsorption experiments highlight low values of site occupancy for Na and Cu, which suggests that the effects of pH and fluid conductivity are unrelated to their control on the ion adsorption. We interpret the solution conductivity as a proxy for ionic strength. The relative importance of pH and solution conductivity over ion type helps to further constrain the interpretation of SIP results in field geochemical and biogeochemical characterization and monitoring.

Rivers in climatic zones characterized by dry and wet seasons often experience periodic transitions between losing and gaining conditions across the river-aquifer continuum. Infiltration shifts can stimulate hyporheic microbial biomass growth and cycling of riverine carbon and nitrogen leading to major exports of biogenic CO2 and N2 to rivers. In this study, we develop and test a numerical model that simulates biological-physical feedback in the hyporheic zone. We used the model to explore different initial conditions in terms of dissolved organic carbon availability, sediment characteristics, and stochastic variability in aerobic and anaerobic conditions from water table fluctuations. Our results show that while highly losing rivers have greater hyporheic CO2 and N2 production, gaining rivers allowed the greatest fraction of CO2 and N2 production to return to the river. Hyporheic aerobic respiration and denitrification contributed 0.1–2 g/m2/d of CO2 and 0.01–0.2 g/m2/d of N2; however, the suite of potential microbial behaviors varied greatly among sediment characteristics. We found that losing rivers that consistently lacked an exit pathway can store up to 100% of the entering C/N as subsurface biomass and dissolved gas. Our results demonstrate the importance of subsurface feedbacks whereby microbes and hydrology jointly control fate of C and N and are strongly linked to wet-season control of initial sediment conditions and hydrologic control of seepage direction. These results provide a new understanding of hydrobiological and sediment-based controls on hyporheic zone respiration, including a new explanation for the occurrence of anoxic microzones and large denitrification rates in gravelly riverbeds.

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.

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).

Geochemical hot moments are defined here as short periods of time that are associated with disproportionally high levels of concentrations (biogeochemically-driven or transport-related) relative to longer intervening time periods. We used entropy and wavelet techniques to identify temporal variability in geochemical constituents and their controls along three transects within a contaminated floodplain system near Rifle CO. Results indicated that transport-dominated hot moments drove overall geochemical processing in the contaminated groundwater and seep zones. These hot moments were associated with seasonal hydrologic variability (∼4 months) in the contaminated aquifer and with annual hydrologic cycle and residence times in the seep zone. Hot moments associated with a naturally reduced zone within the aquifer were found to be biogeochemically-driven, with a different dominant frequency (∼3 months) and no correlation to hydrologic or weather variations, in contrast to what is observed in other regions of the floodplain.

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.

Although CO2 fluxes from soils are often assumed to originate within shallow soil horizons (

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.

Bioclogging in rivers can detrimentally impact aquifer recharge. This is particularly so in dry regions, where losing rivers are common, and where disconnection between surface water and groundwater (leading to the development of an unsaturated zone) can occur. Reduction in riverbed permeability due to biomass growth is a time-variable parameter that is often neglected, yet permeability reduction from bioclogging can introduce order of magnitude changes in seepage fluxes from rivers over short (i.e.; monthly) timescales. To address the combined effects of bioclogging and disconnection on infiltration, we developed numerical representations of bioclogging processes within a one-dimensional, variably saturated flow model representing losing-connected and losing-disconnected rivers. We tested these formulations using a synthetic case study informed with biological data obtained from the Russian River, California, USA. Our findings show that modeled biomass growth reduced seepage for losing-connected and losing-disconnected rivers. However, for rivers undergoing disconnection, infiltration declines occurred only after the system was fully disconnected. Before full disconnection, biologically induced permeability declines were not significant enough to offset the infiltration gains introduced by disconnection. The two effects combine to lead to a characteristic infiltration curve where peak infiltration magnitude and timing is controlled by permeability declines relative to hydraulic gradient gains. Biomass growth was found to hasten the onset of full disconnection; a condition we term 'effective disconnection'. Our results show that river infiltration can respond dynamically to bioclogging and subsequent permeability declines that are highly dependent on river connection status.

We show how to estimate the fluid permeability changes due to accumulated biopolymer within the pore space of a granular material using laboratory measurements of overall permeability, together with various well-known quantitative measures (e.g., porosity, specific surface area, and formation factor) of the granular medium microstructure. The main focus of the paper is on mutual validation of existing theory and a synthesis of new experimental results. We find that the theory and data are in good agreement within normal experimental uncertainties. We also establish quantitative empirical relationships between seismic and/or acoustic attenuation and overall permeability for these same systems.

Long-term prediction of groundwater dynamics is important for assessing water resources and their impacts on biogeochemical cycling. However, estimating future groundwater dynamics is challenging due to the wide range of spatiotemporal scales in hydrological processes and uncertainty in future climate conditions. In this study, we develop a Bayesian model to combine small-scale historical hydrological data with large-scale climate information to estimate groundwater dynamics at a floodplain site in Rifle, Colorado. Although we have only a few years of groundwater elevation measurements, we have 47 years of streamflow data from a gaging station approximately 43 km upstream and long-term climate prediction on the Upper Colorado River Basin. To estimate future daily groundwater dynamics, we first develop a time series model to downscale the monthly streamflow derived from climate information to daily streamflow, and then transform the daily streamflow to groundwater dynamics at the downstream floodplain site. We use Monte Carlo methods to estimate future groundwater dynamics at the site through sampling from the joint posterior probability distribution. The results suggest that although future groundwater levels are expected to be similar to the current levels, the timing of the high groundwater levels is predicted to occur about 1 month earlier. The developed framework is extendable to other sites to estimate future groundwater dynamics given disparate data sets and climate projections. Additionally, the obtained estimates are being used as input to a site-specific watershed reactive transport models to predict how climate-induced changes will influence future biogeochemical cycling relevant to a variety of ecosystem services.

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.

An experimental field study was performed to investigate riverbed clogging processes and associated monitoring approaches near a dam-controlled riverbank filtration facility in Northern California. Motivated by previous studies at the site that indicated riverbed clogging plays an important role in the performance of the riverbank filtration system, we investigated the spatiotemporal variability and nature of the clogging. In particular, we investigated whether the clogging was due to abiotic or biotic mechanisms. A secondary aspect of the study was the testing of different methods to monitor riverbed clogging and related processes, such as seepage. Monitoring was conducted using both point-based approaches and spatially extensive geophysical approaches, including: grain-size analysis, temperature sensing, electrical resistivity tomography, seepage meters, microbial analysis, and cryocoring, along two transects. The point monitoring measurements suggested a substantial increase in riverbed biomass (2 orders of magnitude) after the dam was raised compared to the small increase (~2%) in fine-grained sediment. These changes were concomitant with decreased seepage. The decreased seepage eventually led to the development of an unsaturated zone beneath the riverbed, which further decreased infiltration capacity. Comparison of our time-lapse grain-size and biomass datasets suggested that biotic processes played a greater role in clogging than did abiotic processes. Cryocoring and autonomous temperature loggers were most useful for locally monitoring clogging agents, while electrical resistivity data were useful for interpreting the spatial extent of a pumping-induced unsaturated zone that developed beneath the riverbed after riverbed clogging was initiated. The improved understanding of spatiotemporally variable riverbed clogging and monitoring approaches is expected to be useful for optimizing the riverbank filtration system operations.

The disparity in scale, complexity, and control level between laboratory experiments and field observational studies has shaped both the methodologies employed and the nature of the research questions pursued in ecology and hydrology. While lysimeters and fabricated ecosystems suitably fit in this gap, their use as mesoscale experimental facilities has not been fully explored because of the limited manipulating capabilities and integration with imaging and monitoring methods, particularly for soil functioning. The proposed fabricated ecosystem (4.7 L × 1.2 W × 1.2 H m) focuses on the spatiotemporal integration of point sensors and imaging methods along the soil-plant-atmosphere continuum. Because energy and water fluxes are key environmental drivers, the designed setup was first applied to a multi-approach evapotranspiration investigation. Below the ground, electrical resistivity tomography (ERT) was combined with soil water sensors and a distributed temperature profiling system. Together, they provided the 3D monitoring of water and temperature changes, and thus an estimation of the evapotranspiration, as well as the interpretation of its below-ground controlling processes. Above-ground sensors supported a classical energy balance investigation that was compared with the lysimeter load changes and the ERT-based ET estimation. Our results provide first experimental evidence of water and temperature spatiotemporal variability at the lysimeter scale, and thus explain the discrepancies among the three estimated evapotranspiration time series and their seasonality. Beyond evapotranspiration, the multi-approach investigation of water and energy fluxes emphasizes how mesoscale setups can further support the development and upscaling of methods and models, as well as their integration and application under expected climate disturbances., Competing Interests: Declaration of competing interest The authors have no competing interests to declare., (Copyright © 2024 The Authors. Published by Elsevier B.V. All rights reserved.)

Understanding the interactions among agricultural processes, soil, and plants is necessary for optimizing crop yield and productivity. This study focuses on developing effective monitoring and analysis methodologies that estimate key soil and plant properties. These methodologies include data acquisition and processing approaches that use unmanned aerial vehicles (UAVs) and surface geophysical techniques. In particular, we applied these approaches to a soybean farm in Arkansas to characterize the soil-plant coupled spatial and temporal heterogeneity, as well as to identify key environmental factors that influence plant growth and yield. UAV-based multitemporal acquisition of high-resolution RGB (red-green-blue) imagery and direct measurements were used to monitor plant height and photosynthetic activity. We present an algorithm that efficiently exploits the high-resolution UAV images to estimate plant spatial abundance and plant vigor throughout the growing season. Such plant characterization is extremely important for the identification of anomalous areas, providing easily interpretable information that can be used to guide near-real-time farming decisions. Additionally, high-resolution multitemporal surface geophysical measurements of apparent soil electrical conductivity were used to estimate the spatial heterogeneity of soil texture. By integrating the multiscale multitype soil and plant datasets, we identified the spatiotemporal co-variance between soil properties and plant development and yield. Our novel approach for early season monitoring of plant spatial abundance identified areas of low productivity controlled by soil clay content, while temporal analysis of geophysical data showed the impact of soil moisture and irrigation practice (controlled by topography) on plant dynamics. Our study demonstrates the effective coupling of UAV data products with geophysical data to extract critical information for farm management.