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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. Core Ideas: Various geophysical methods (e.g., frequency domain electromagnetics [FDEM], ground penetrating radar, electrical resistivity tomography [ERT]) are sensitive to soil moisture (volumetric water content [VWC]).Machine learning provides methods for data fusion and less need for assumptions/advanced data processing.Raw and processed geophysical data were evaluated in traditional and machine learning models to predict VWC.Random regression forest models using FDEM and ERT information gave the highest overall VWC accuracy.Models yielded good results even when trained on only half of a physically separated portion of the dataset. [ABSTRACT FROM AUTHOR]

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. Article impact statement: A Microsoft Excel spreadsheet‐based decision support tool is presented to identify methods for use in studies of groundwater/surface‐water exchange. [ABSTRACT FROM AUTHOR]

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. Plain Language Summary: We present and demonstrate a new approach to infer groundwater/surface‐water exchange based on temperature time series collected at different depths below the sediment/water interface. Algorithms for real‐time ("online") and "offline" applications are presented. The new algorithms are effective in the presence of rapidly changing flow across the sediment/water interface, which has posed challenges to existing approaches. Key Points: Recursive filtering applied to heat tracing enables real‐time estimation of groundwater/surface‐water exchangeRecursive filtering and smoothing applied to heat tracing improve estimation of groundwater/surface‐water exchangeRecursive filtering applied to heat tracing allows quantification of uncertainty in groundwater/surface water exchange estimates [ABSTRACT FROM AUTHOR]

The investigation of the interdependence between geoelectrical signatures at underground petroleum plumes and the structures of subsurface microbial communities revealed that through their growth and metabolic activities, microbial populations adapt to the use of petroleum as a carbon source and could strongly influence their geophysical surroundings.

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. Article impact statement: DTSGUI is a software package with a graphical user interface for processing and visualization of distributed temperature sensing data. [ABSTRACT FROM AUTHOR]

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. Article impact statement: Introducing a novel instrument designed to study the exchange of solute between mobile and less‐mobile porosity (i.e., dual‐domain behavior) near the sediment/water interface. [ABSTRACT FROM AUTHOR]

Ground-penetrating radar (GPR) has a wide range of applications, from geologic mapping to concrete inspection. A recently emerging GPR application is deployment in biological investigations as a non-invasive technique. Geophysical mapping of features such as tree roots and turtle burrows has proved valuable for the understanding of these subsurface systems for ecological, environmental, or engineering purposes. Four case histories of GPR investigations pertaining to animal burrows are described: cutter ants in Brazil, groundhogs in Michigan, and groundhogs, and burrowing bees in Pennsylvania. Cutter ants (Atta spp.) in Amazonian Brazil are known to construct burrows of nearly the same dimensions as groundhogs as they excavate galleries up to 7 m deep for leaf storage. Cutter ant burrows are hazardous to heavy equipment and may also cause loss of mud circulation during rotary drilling. Groundhogs (Marmota monax), found throughout the United States, cause unseen hazards, particularly for equestrian facilities where a sudden collapse can cause severe injuries to both horse and rider. Burrowing bees (Colletes inaequalis) are common in the northeastern United States. The size of the bee burrows is significantly smaller than that of the cutter ants and the groundhogs. The data for these surveys were collected over a twenty-year span, crossing several generations of survey equipment and processing techniques. Together, these four case histories highlight the historic and current capabilities of GPR systems applied to mapping subsurface burrow systems. These examples demonstrate the important impact near surface heterogeneities have in altering ecological, environmental, or engineering systems and the utility of GPR for mapping such heterogeneities. [ABSTRACT FROM AUTHOR]

Brook trout (Salvelinus fontinalis) spawn in fall and overwintering egg development can benefit from stable, relatively warm temperatures in groundwater-seepage zones. However, eggs are also sensitive to dissolved oxygen concentration, which may be reduced in discharging groundwater (i.e., seepage). We investigated a 2 km reach of the coastal Quashnet River in Cape Cod, Massachusetts, USA, to relate preferred fish spawning habitats to geology, geomorphology, and discharging groundwater geochemistry. Thermal reconnaissance methods were used to locate zones of rapid groundwater discharge, which were predominantly found along the central channel of a wider stream valley section. Pore-water chemistry and temporal vertical groundwater flux were measured at a subset of these zones during field campaigns over several seasons. Seepage zones in open-valley sub-reaches generally showed suboxic conditions and higher dissolved solutes compared to the underlying glacial outwash aquifer. These discharge zones were cross-referenced with preferred brook trout redds and evaluated during 10 years of observation, all of which were associated with discrete alcove features in steep cutbanks, where stream meander bends intersect the glacial valley walls. Seepage in these repeat spawning zones was generally stronger and more variable than in open-valley sites, with higher dissolved oxygen and reduced solute concentrations. The combined evidence indicates that regional groundwater discharge along the broader valley bottom is predominantly suboxic due to the influence of near-stream organic deposits; trout show no obvious preference for these zones when spawning. However, the meander bends that cut into sandy deposits near the valley walls generate strong oxic seepage zones that are utilized routinely for redd construction and the overwintering of trout eggs. Stable water isotopic data support the conclusion that repeat spawning zones are located directly on preferential discharges of more localized groundwater. In similar coastal systems with extensive valley peat deposits, the specific use of groundwater-discharge points by brook trout may be limited to morphologies such as cutbanks, where groundwater flow paths do not encounter substantial buried organic material and remain oxygen-rich. [ABSTRACT FROM AUTHOR]

Abstract: 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. [ABSTRACT FROM AUTHOR]

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. [ABSTRACT FROM AUTHOR]

Groundwater flow advects heat, and thus, the deviation of subsurface temperatures from an expected conduction-dominated regime can be analysed to estimate vertical water fluxes. A number of analytical approaches have been proposed for using heat as a groundwater tracer, and these have typically assumed a homogeneous medium. However, heterogeneous thermal properties are ubiquitous in subsurface environments, both at the scale of geologic strata and at finer scales in streambeds. Herein, we apply the analytical solution of Shan and Bodvarsson (2004), developed for estimating vertical water fluxes in layered systems, in 2 new environments distinct from previous vadose zone applications. The utility of the solution for studying groundwater-surface water exchange is demonstrated using temperature data collected from an upwelling streambed with sediment layers, and a simple sensitivity analysis using these data indicates the solution is relatively robust. Also, a deeper temperature profile recorded in a borehole in South Australia is analysed to estimate deeper water fluxes. The analytical solution is able to match observed thermal gradients, including the change in slope at sediment interfaces. Results indicate that not accounting for layering can yield errors in the magnitude and even direction of the inferred Darcy fluxes. A simple automated spreadsheet tool (Flux-LM) is presented to allow users to input temperature and layer data and solve the inverse problem to estimate groundwater flux rates from shallow (e.g., <1 m) or deep (e.g., up to 100 m) profiles. The solution is not transient, and thus, it should be cautiously applied where diel signals propagate or in deeper zones where multi-decadal surface signals have disturbed subsurface thermal regimes. [ABSTRACT FROM AUTHOR]

Zones of strong groundwater upwelling to streams enhance thermal stability and moderate thermal extremes, which is particularly important to aquatic ecosystems in a warming climate. Passive thermal tracer methods used to quantify vertical upwelling rates rely on downward conduction of surface temperature signals. However, moderate to high groundwater flux rates (>−1.5 m d−1) restrict downward propagation of diurnal temperature signals, and therefore the applicability of several passive thermal methods. Active streambed heating from within high-resolution fiber-optic temperature sensors (A-HRTS) has the potential to define multidimensional fluid-flux patterns below the extinction depth of surface thermal signals, allowing better quantification and separation of local and regional groundwater discharge. To demonstrate this concept, nine A-HRTS were emplaced vertically into the streambed in a grid with ∼0.40 m lateral spacing at a stream with strong upward vertical flux in Mashpee, Massachusetts, USA. Long-term (8-9 h) heating events were performed to confirm the dominance of vertical flow to the 0.6 m depth, well below the extinction of ambient diurnal signals. To quantify vertical flux, short-term heating events (28 min) were performed at each A-HRTS, and heat-pulse decay over vertical profiles was numerically modeled in radial two dimension (2-D) using SUTRA. Modeled flux values are similar to those obtained with seepage meters, Darcy methods, and analytical modeling of shallow diurnal signals. We also observed repeatable differential heating patterns along the length of vertically oriented sensors that may indicate sediment layering and hyporheic exchange superimposed on regional groundwater discharge. [ABSTRACT FROM AUTHOR]

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. [ABSTRACT FROM AUTHOR]

Conceptual models for the geophysical responses associated with hydrocarbon degradation suggest that the long-term evolution of an oil plume will result in a more conductive anomaly than the initial contamination. In response to the Deepwater Horizon (DH) oil spill into the Gulf of Mexico in 2010, an autonomous resistivity monitoring system was deployed on Grand Terre, Louisiana, in an attempt to monitor natural degradation processes in hydrocarbon-impacted beach sediments of this island. A 48-electrode surface array with a 0.5-m spacing was installed to obtain twice-daily images of the resistivity structure of the shallow subsurface impacted by oil. Over the course of approximately 18 months, we observed a progressive decrease in the resistivity of the DH spill-impacted region. Detailed analysis of pixel/point resistivity variation within the imaged area showed that long-term decreases in resistivity were largely associated with the DH-impacted sediments. A microbial diversity survey revealed the presence of hydrocarbon-degrading organisms throughout the test site. However, hydrocarbon degradation activity was much higher in the DH-impacted locations compared to nonimpacted locations, suggesting the presence of active hydrocarbon degraders, supporting biodegradation processes. The results of this long-term monitoring experiment suggested that resistivity might be used to noninvasively monitor the long-term degradation of crude oil spills. [ABSTRACT FROM AUTHOR]

A new computer program, 1DTempPro, is presented for the analysis of vertical one-dimensional ( 1D) temperature profiles under saturated flow conditions. 1DTempPro is a graphical user interface to the U.S. Geological Survey code Variably Saturated 2-Dimensional Heat Transport ( VS2DH), which numerically solves the flow and heat-transport equations. Pre- and postprocessor features allow the user to calibrate VS2DH models to estimate vertical groundwater/surface-water exchange and also hydraulic conductivity for cases where hydraulic head is known. [ABSTRACT FROM AUTHOR]

The demand for more efficient and economic oil recovery techniques has driven research into novel extraction approaches, including microbial enhanced methods. Microbial enhanced oil recovery (MEOR) is an underutilized technology that could significantly enhance tertiary oil recovery. Previous research has shown the spectral induced polarization (SIP) method to be sensitive to microbial degradation of hydrocarbons, so the method should therefore be sensitive to MEOR treatments. To test this hypothesis, heavy-oil-containing sands were monitored for a period of approximately six months while undergoing MEOR treatment. SIP monitoring showed significant sensitivity to biodegradation induced changes. Increases in phase and imaginary conductivity, with a polarization peak centered on approximately 3-8 Hz, were observed for the two MEOR active columns. Similarly, the normalized chargeability, an integrated parameter of a Debye decomposition analysis of the spectra, showed a linear increase in time. Chromatographic methods confirmed oil biodegradation in the active columns. The SIP responses are likely the result of microbial processes and the changes they promote to oil properties, such as altering wettability, or possibly the effect of organic acid production. The results of this experiment indicate that SIP may be a viable method of monitoring MEOR processes. [ABSTRACT FROM AUTHOR]