Evaluation of Dual Domain Mass Transfer in Porous Media at the Pore Scale.

Dual-porosity models are often used to describe solute transport in heterogeneous media, but the parameters within these models (e.g., immobile porosity and mobile/immobile exchange rate coefficients) are difficult to identify experimentally or relate to measurable quantities. Here, we performed synthetic, pore-scale millifluidics simulations that coupled fluid flow, solute transport, and electrical resistivity (ER). A conductive-tracer test and the associated geoelectrical signatures were simulated for four flow rates in two distinct pore-scale model scenarios: one with intergranular porosity, and a second with an intragranular porosity also defined. With these models, we explore how the effective characteristic-length scale estimated from a best-fit dual-domain mass transfer (DDMT) model compares to geometric aspects of the flow field. In both model scenarios we find that: (1) mobile domains and immobile domains develop even in a system that is explicitly defined with one domain; (2) the ratio of immobile to mobile porosity is larger at faster flow rates as is the mass-transfer rate; and (3) a comparison of length scales associated with the mass-transfer rate (Lα ) and those associated with calculation of the Peclet number (LPe ) show LPe is commonly larger than Lα . These results suggest that estimated immobile porosities from a DDMT model are not only a function of physically mobile or immobile pore space, but also are a function of the average linear pore-water velocity and physical obstructions to flow, which can drive the development of immobile porosity even in single-porosity domains.

Field evaluation of semi-automated moisture estimation from geophysics using machine learning.

Geophysical methods can provide three-dimensional (3D), spatially continuous estimates of soil moisture. However, point-to-point comparisons of geophysical properties to measure soil moisture data are frequently unsatisfactory, resulting in geophysics being used for qualitative purposes only. This is because (1) geophysics requires models that relate geophysical signals to soil moisture, (2) geophysical methods have potential uncertainties resulting from smoothing and artifacts introduced from processing and inversion, and (3) results from multiple geophysical methods are not easily combined within a single soil moisture estimation framework. To investigate these potential limitations, an irrigation experiment was performed wherein soil moisture was monitored through time, and several surface geophysical datasets indirectly sensitive to soil moisture were collected before and after irrigation: ground penetrating radar, electrical resistivity tomography (ERT), and frequency domain electromagnetics (FDEM). Data were exported in both raw and processed form, and then snapped to a common 3D grid to facilitate moisture prediction by standard calibration techniques, multivariate regression, and machine learning. A combination of inverted ERT data, raw FDEM, and inverted FDEM data was most informative for predicting soil moisture using a random regression forest model (one-thousand 60/40 training/test cross-validation folds produced root mean squared errors ranging from 0.025-0.046 cm3/cm3). This cross-validated model was further supported by a separate evaluation using a test set from a physically separate portion of the study area. Machine learning was conducive to a semi-automated model-selection process that could be used for other sites and datasets to locally improve accuracy.

Benefits and Cautions in Data Assimilation Strategies: An Example of Modeling Groundwater Recharge.

Assimilating recent observations improves model outcomes for real-time assessments of groundwater processes. This is demonstrated in estimating time-varying recharge to a shallow fractured-rock aquifer in response to precipitation. Results from estimating the time-varying water-table altitude (h) and recharge, and their error covariances, are compared for forecasting, filtering, and fixed-lag smoothing (FLS), which are implemented using the Kalman Filter as applied to a data-driven, mechanistic model of recharge. Forecasting uses past observations to predict future states and is the current paradigm in most groundwater modeling investigations; filtering assimilates observations up to the current time to estimate current states; and FLS estimates states following a time lag over which additional observations are collected. Results for forecasting yield a large error covariance relative to the magnitude of the expected recharge. With assimilating recent observations of h, filtering and FLS produce estimates of recharge that better represent time-varying observations of h and reduce uncertainty in comparison to forecasting. Although model outcomes from applying data assimilation through filtering or FLS reduce model uncertainty, they are not necessarily mass conservative, whereas forecasting outcomes are mass conservative. Mass conservative outcomes from forecasting are not necessarily more accurate, because process errors are inherent in any model. Improvements in estimating real-time groundwater conditions that better represent observations need to be weighed for the model application against outcomes with inherent process deficiencies. Results from data assimilation strategies discussed in this investigation are anticipated to be relevant to other groundwater processes models where system states are sensitive to system inputs.

Interpreting Concentrations Sampled in Long-Screened Wells with Borehole Flow: An Inverse Modeling Approach.

New approaches are needed to assess contaminant mass based on samples from long-screened wells and open boreholes (LSW&OB). The interpretation of concentration samples collected in LSW&OB is complicated in the presence of vertical flow within the well. In the absence of pumping (i.e., ambient conditions), the well provides a conduit for flow to occur between aquifer layers or fractures as a result of head differences. Under pumping conditions, vertical borehole flow may vary with depth depending on far-field heads and hydraulic conductivity; furthermore, if pumping fails to overcome ambient gradients, outflow from the well to the aquifer may occur. Concentration samples thus represent flow-weighted averages of formation concentrations, but the averaging process is commonly unknown or difficult to identify. Recognition of the importance of borehole flow has motivated the use of multi-level wells, packers, and well liners; however, LSW&OB remain common for numerous reasons, including cost, multi-purpose design requirements (e.g., pump-and-treat, water supply), logging, and installation of instrumentation. Here, we present a simple analytical model for flow and transport within a well and interaction with the surrounding aquifer. We formulate an inverse problem to estimate formation concentration based on sampled concentrations and data from flowmeter logs. The approach is demonstrated using synthetic examples. Our results (1) underscore the importance of interpreting sampled concentrations within the context of hydraulic conditions and aquifer/well exchange; (2) demonstrate the value of flowmeter measurements for this purpose; and (3) point to the potential of the new inverse approach to better interpret results from samples collected in LSW&OB.

Application of Recursive Estimation to Heat Tracing for Groundwater/Surface-Water Exchange.

We present and demonstrate a recursive-estimation framework to infer groundwater/surface-water exchange based on temperature time series collected at different vertical depths below the sediment/water interface. We formulate the heat-transport problem as a state-space model (SSM), in which the spatial derivatives in the convection/conduction equation are approximated using finite differences. The SSM is calibrated to estimate time-varying specific discharge using the Extended Kalman Filter (EKF) and Extended Rauch-Tung-Striebel Smoother (ERTSS). Whereas the EKF is suited to real-time ("online") applications and uses only the past and current measurements for estimation (filtering), the ERTSS is intended for near-real time or batch-processing ("offline") applications and uses a window of data for batch estimation (smoothing). The two algorithms are demonstrated with synthetic and field-experimental data and are shown to be efficient and rapid for the estimation of time-varying flux over seasonal periods; further, the recursive approaches are effective in the presence of rapidly changing flux and (or) nonperiodic thermal boundary conditions, both of which are problematic for existing approaches to heat tracing of time-varying groundwater/surface-water exchange.

Incorporating Snowmelt into Daily Estimates of Recharge Using a State-Space Model of Infiltration.

A state-space model (SSM) of infiltration estimates daily groundwater recharge using time-series of groundwater-level altitude and meteorological inputs (liquid precipitation, snowmelt, and evapotranspiration). The model includes diffuse and preferential flow through the unsaturated zone, where preferential flow is a function of liquid precipitation and snowmelt rates and a threshold rate, above which there is direct recharge to the water table. Model parameters are estimated over seasonal periods and the SSM is coupled with the Kalman Filter (KF) to assimilate recent observations (hydraulic head) and meteorological inputs into recharge estimates. The approach can take advantage of real-time hydrologic and meteorological data to deliver real-time recharge estimates. The model is demonstrated on daily observations from two bedrock wells in carbonate aquifers of northwestern New York (USA) between 2013 and 2018. Meteorological inputs for liquid precipitation and snowmelt are compiled from SNODAS (2021). Results for recharge during winter and spring seasons show preferential flow events to the water table from liquid precipitation, snowmelt, or a combination of the two. Recharge estimates summed annually are consistent with previous estimates of recharge reported from groundwater flow and surface-process models. Results from the SSM and KF point to errors in meteorological inputs, such as the snowmelt rate, that are not compatible with hydraulic head observations. Whereas liquid and solid precipitation are measured at discrete stations and extrapolated to 1-km2 grid cells, snowmelt is a meteorological modeled outcome that may not represent conditions in the vicinity of monitoring well locations.

GW/SW-MST: A Groundwater/Surface-Water Method Selection Tool.

Groundwater/surface-water (GW/SW) exchange and hyporheic processes are topics receiving increasing attention from the hydrologic community. Hydraulic, chemical, temperature, geophysical, and remote sensing methods are used to achieve various goals (e.g., inference of GW/SW exchange, mapping of bed materials, etc.), but the application of these methods is constrained by site conditions such as water depth, specific conductance, bed material, and other factors. Researchers and environmental professionals working on GW/SW problems come from diverse fields and rarely have expertise in all available field methods; hence there is a need for guidance to design field campaigns and select methods that both contribute to study goals and are likely to work under site-specific conditions. Here, we present the spreadsheet-based GW/SW-Method Selection Tool (GW/SW-MST) to help practitioners identify methods for use in GW/SW and hyporheic studies. The GW/SW-MST is a Microsoft Excel-based decision support tool in which the user selects answers to questions about GW/SW-related study goals and site parameters and characteristics. Based on user input, the tool indicates which methods from a toolbox of 32 methods could potentially contribute to achieving the specified goals at the site described.

Post-remediation geophysical assessment: Investigating long-term electrical geophysical signatures resulting from bioremediation at a chlorinated solvent contaminated site.

There is a growing need to assess long-term impacts of active remediation strategies on treated aquifers. A variety of biogeochemical alterations can result from interactions of the amendment with the aquifer, conceivably leading to a geophysical signal associated with the long-term alteration of an aquifer. This concept of post-remediation geophysical assessment was investigated in a shallow, chlorinated solvent-contaminated aquifer six to eight years after amendment delivery. Surface resistivity imaging and cross-borehole resistivity and induced polarization (IP) imaging were performed on a transect that spanned treated and untreated zones of the aquifer. Established relationships between IP parameters and surface electrical conductivity were used to predict vertical profiles of electrolytic conductivity and surface conductivity from the inverted cross-borehole images. Aqueous geochemistry data, along with natural gamma and magnetic susceptibility logs, were used to constrain the interpretation. The electrical conductivity structure determined from surface and borehole imaging was foremost controlled by the electrolytic conductivity of the interconnected pore space, being linearly related to fluid specific conductance. The electrolytic conductivity (and thus the conductivity images alone) did not discriminate between treated and untreated zones of the aquifer. In contrast, inverted phase angles and surface conductivities did discriminate between treated and untreated zones of the aquifer, with the treated zone being up to an order of magnitude more polarizable in places. Supporting aqueous chemistry and borehole logging datasets indicate that this geophysical signal from the long-term impact of the remediation on the aquifer is most likely associated with the formation of polarizable, dispersed iron sulfide minerals.

A New R Program for Flow-Log Analysis of Single Holes (FLASH-R).

A new version of the computer program FLASH (Flow-Log Analysis of Single Holes) is presented for the analysis of borehole vertical flow logs to estimate fracture (or layer) transmissivities and far-field hydraulic heads. The program is written in R, an open-source environment. All previous features have been retained and new features incorporated including more rigorous parameter estimation, uncertainty analysis, and improved data import. The program has a dynamic user interface compatible with most operating systems.

DTSGUI: A Python Program to Process and Visualize Fiber-Optic Distributed Temperature Sensing Data.

Fiber-optic distributed temperature sensing (FO-DTS) has proven to be a transformative technology for the hydrologic sciences, with application to diverse problems including hyporheic exchange, groundwater/surface-water interaction, fractured-rock characterization, and cold regions hydrology. FO-DTS produces large, complex, and information-rich datasets. Despite the potential of FO-DTS, adoption of the technology has been impeded by lack of tools for data processing, analysis, and visualization. New tools are needed to efficiently and fully capitalize on the information content of FO-DTS datasets. To this end, we present DTSGUI, a public-domain Python-based software package for editing, parsing, processing, statistical analysis, georeferencing, and visualization of FO-DTS data.

Return flows from beaver ponds enhance floodplain-to-river metals exchange in alluvial mountain catchments.

River to floodplain hydrologic connectivity is strongly enhanced by beaver- (Castor canadensis) engineered channel water diversions. The hydroecological impacts are wide ranging and generally positive, however, the hydrogeochemical characteristics of beaver-induced flowpaths have not been thoroughly examined. Using a suite of complementary ground- and drone-based heat tracing and remote sensing methodology we characterized the physical template of beaver-induced floodplain exchange for two alluvial mountain streams near Crested Butte, Colorado, USA. A flowpath-oriented perspective to water quality sampling allowed characterization of the chemical evolution of channel water diverted through floodplain beaver ponds and ultimately back to the channel in 'beaver pond return flows'. Subsurface return flow seepages were universally suboxic, while ponds and surface return flows showed a range of oxygen concentration due to in-situ photosynthesis and atmospheric mixing. Median concentrations of reduced metals: manganese (Mn), iron (Fe), aluminum (Al), and arsenic (As) were substantially higher along beaver-induced flowpaths than in geologically controlled seepages and upstream main channel locations. The areal footprint of reduced return seepage flowpaths were imaged with surface electromagnetic methods, indicating extensive zones of high-conductivity shallow groundwater flowing back toward the main channels and emerging at relatively warm bank seepage zones observed with infrared. Multiple-depth redox dynamics within one focused seepage zone showed coupled variation over time, likely driven by observed changes in seepage rate that may be controlled by pond stage. High-resolution times series of dissolved Mn and Fe collected downstream of the beaver-impacted reaches demonstrated seasonal dynamics in mixed river metal concentrations. Al time series concentrations showed proportional change to Fe at the smaller stream location, indicating chemically reduced flowpaths were sourcing Al to the channel. Overall our results indicated beaver-induced floodplain exchanges create important, and perhaps dominant, transport pathways for floodplain metals by expanding chemically-reduced zones paired with strong advective exchange.

The Dual-Domain Porosity Apparatus: Characterizing Dual Porosity at the Sediment/Water Interface.

The characterization of pore-space connectivity in porous media at the sediment/water interface is critical in understanding contaminant transport and reactive biogeochemical processes in zones of groundwater and surface-water exchange. Previous in situ studies of dual-domain (i.e., mobile/less-mobile porosity) systems have been limited to solute tracer injections at scales of meters to hundreds of meters and subsequent numerical model parameterization using fluid concentration histories. Pairing fine-scale (e.g., sub-meter) geoelectrical measurements with fluid tracer data over time alleviates dependence on flowpath-scale experiments, enabling spatially targeted characterization of shallow sediment/water interface media where biogeochemical reactivity is often high. The Dual-Domain Porosity Apparatus is a field-tested device capable of variable rate-controlled downward flow experiments. The Dual-Domain Porosity Apparatus facilitates inference of dual-domain parameters, i.e., mobile/less-mobile exchange rate coefficient and the ratio of less mobile to mobile porosity. The Dual-Domain Porosity Apparatus experimental procedure uses water electrical conductivity as a conservative tracer of differential loading and flushing of pore spaces within the region of measurement. Variable injection rates permit the direct quantification of the flow-dependence of dual-domain parameters, which has been theorized for decades but remains challenging to assess using existing experimental methodologies.

Evaluating long-term patterns of decreasing groundwater discharge through a lake-bottom permeable reactive barrier.

Identifying and quantifying groundwater exchange is critical when considering contaminant fate and transport at the groundwater/surface-water interface. In this paper, areally distributed temperature and point seepage measurements are used to efficiently assess spatial and temporal groundwater discharge patterns through a glacial-kettle lakebed area containing a zero-valent iron permeable reactive barrier (PRB). Concern was that the PRB was becoming less permeable with time owing to biogeochemical processes within the PRB. Patterns of groundwater discharge over an 8-year period were examined using fiber-optic distributed temperature sensing (FO-DTS) and snapshot-in-time point measurements of temperature. The resulting thermal maps show complex and uneven distributions of temperatures across the lakebed and highlight zones of rapid seepage near the shoreline and along the outer boundaries of the PRB. Repeated thermal mapping indicates an increase in lakebed temperatures over time at periods of similar stage and surface-water temperature. Flux rates in six seepage meters permanently installed on the lakebed in the PRB area decreased on average by 0.021 md-1 (or about 4.5 percent) annually between 2004 and 2015. Modeling of diurnal temperature signals from shallow vertical profiles yielded mean flux values ranging from 0.39 to 1.15 md-1, with stronger fluxes generally related to colder lakebed temperatures. The combination of an increase in lakebed temperatures, declines in direct seepage, and observations of increased cementation of the lakebed surface provide in situ evidence that the permeability of the PRB is declining. The presence of temporally persistent rapid seepage zones is also discussed.

MoisturEC: A New R Program for Moisture Content Estimation from Electrical Conductivity Data.

Noninvasive geophysical estimation of soil moisture has potential to improve understanding of flow in the unsaturated zone for problems involving agricultural management, aquifer recharge, and optimization of landfill design and operations. In principle, several geophysical techniques (e.g., electrical resistivity, electromagnetic induction, and nuclear magnetic resonance) offer insight into soil moisture, but data-analysis tools are needed to "translate" geophysical results into estimates of soil moisture, consistent with (1) the uncertainty of this translation and (2) direct measurements of moisture. Although geostatistical frameworks exist for this purpose, straightforward and user-friendly tools are required to fully capitalize on the potential of geophysical information for soil-moisture estimation. Here, we present MoisturEC, a simple R program with a graphical user interface to convert measurements or images of electrical conductivity (EC) to soil moisture. Input includes EC values, point moisture estimates, and definition of either Archie parameters (based on experimental or literature values) or empirical data of moisture vs. EC. The program produces two- and three-dimensional images of moisture based on available EC and direct measurements of moisture, interpolating between measurement locations using a Tikhonov regularization approach.

Scenario Evaluator for Electrical Resistivity Survey Pre-modeling Tool.

Geophysical tools have much to offer users in environmental, water resource, and geotechnical fields; however, techniques such as electrical resistivity imaging (ERI) are often oversold and/or overinterpreted due to a lack of understanding of the limitations of the techniques, such as the appropriate depth intervals or resolution of the methods. The relationship between ERI data and resistivity is nonlinear; therefore, these limitations depend on site conditions and survey design and are best assessed through forward and inverse modeling exercises prior to field investigations. In this approach, proposed field surveys are first numerically simulated given the expected electrical properties of the site, and the resulting hypothetical data are then analyzed using inverse models. Performing ERI forward/inverse modeling, however, requires substantial expertise and can take many hours to implement. We present a new spreadsheet-based tool, the Scenario Evaluator for Electrical Resistivity (SEER), which features a graphical user interface that allows users to manipulate a resistivity model and instantly view how that model would likely be interpreted by an ERI survey. The SEER tool is intended for use by those who wish to determine the value of including ERI to achieve project goals, and is designed to have broad utility in industry, teaching, and research.

An overview of geophysical technologies appropriate for characterization and monitoring at fractured-rock sites.

Geophysical methods are used increasingly for characterization and monitoring at remediation sites in fractured-rock aquifers. The complex heterogeneity of fractured rock poses enormous challenges to groundwater remediation professionals, and new methods are needed to cost-effectively infer fracture and fracture-zone locations, orientations and properties, and to develop conceptual site models for flow and transport. Despite the potential of geophysical methods to "see" between boreholes, two issues have impeded the adoption of geophysical methods by remediation professionals. First, geophysical results are commonly only indirectly related to the properties of interest (e.g., permeability) to remediation professionals, and qualitative or quantitative interpretation is required to convert geophysical results to hydrogeologic information. Additional demonstration/evaluation projects are needed in the site remediation literature to fully transfer geophysical methods from research to practice. Second, geophysical methods are commonly viewed as inherently risky by remediation professionals. Although it is widely understood that a given method may or may not work at a particular site, the reasons are not always clear to end users of geophysical products. Synthetic modeling tools are used in research to assess the potential of a particular method to successfully image a target, but these tools are not widely used in industry. Here, we seek to advance the application of geophysical methods to solve problems facing remediation professionals with respect to fractured-rock aquifers. To this end, we (1) provide an overview of geophysical methods applied to characterization and monitoring of fractured-rock aquifers; (2) review case studies showcasing different geophysical methods; and (3) discuss best practices for method selection and rejection based on synthetic modeling and decision support tools.

1DTempPro V2: New Features for Inferring Groundwater/Surface-Water Exchange.

A new version of the computer program 1DTempPro extends the original code to include new capabilities for (1) automated parameter estimation, (2) layer heterogeneity, and (3) time-varying specific discharge. The code serves as an interface to the U.S. Geological Survey model VS2DH and supports analysis of vertical one-dimensional temperature profiles under saturated flow conditions to assess groundwater/surface-water exchange and estimate hydraulic conductivity for cases where hydraulic head is known.