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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 issues associated with long‐screened wells (LSWs) (and open boreholes) at contaminated sites are well documented in the groundwater literature but are still not fully appreciated in practice. As established in seminal and review papers going back over three decades, the interpretation of sampling results from LSWs is challenging in the presence of vertical hydraulic gradients and borehole flow; furthermore, LSWs allow for vertical redistribution of contamination between aquifer layers. Acknowledgment of these issues has led to the development of new technologies and well designs to enable discrete‐zone monitoring (DZM), yet LSWs remain common for many reasons, for example, as multipurpose wells, for geophysical logging, and (or) as legacy installations. Despite the literature on LSWs and despite the adoption of DZM at many sites, the use of LSWs persists and the challenges of interpreting sampling results from LSWs remain. In this issue paper, we provide a conceptual overview of the problems posed by LSWs and review existing literature and past work to improve the interpretation of sampling in LSWs. We draw on experience from previous studies at the Hanford Site in eastern WA, USA, and use synthetic examples to illustrate key concepts and challenges for interpretation. A recently published analytical modeling framework is used to develop illustrative synthetic examples and demonstrate a workflow for building scientific intuition to understand issues around interpreting samples from LSWs, which is critical to effective characterization and groundwater remediation at sites with LSWs. [ABSTRACT FROM AUTHOR]

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. Article impact statement: Assimilating recent observations improves model outcomes and reduces uncertainty for real‐time assessments of groundwater processes. [ABSTRACT FROM AUTHOR]

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. [ABSTRACT FROM AUTHOR]

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. [ABSTRACT FROM AUTHOR]

The spreadsheet-based Geophysical Remediation Monitoring Method Selection Tool (GRM-MST) is intended to help practitioners identify relevant methods to monitor remediation operations and reduce the misapplication of geophysical methods. Relatively few environmental remediation professionals have backgrounds in geophysics, and relatively few geophysicists have backgrounds in remediation, yet some knowledge of both fields is required for appropriate method selection. The GRM-MST is a Microsoft Excel-based decision support tool designed similarly to the Fractured Rock Geophysical Toolbox Method Selection Tool (Day-Lewis et al. [1]) and the Groundwater/Surface-Water Method Selection Tool (Hammett et al. [2]). [Extracted from the article]

The USGS conducted a geophysical investigation in support of a US Naval Facilities Engineering Command, Southern Division Field-Scale biostimulation pilot project and the Anoka County Riverfront Part (ACP) in Minnesota. It was observed that the geophysical information provided a broader context for interpretation of water samples and evaluation of the biostimulation effort.

We present a geostatistics‐based stochastic salinity estimation framework for the Montebello Oil Field that capitalizes on available total dissolved solids (TDS) data from groundwater samples as well as electrical resistivity (ER) data from borehole logging. Data from TDS samples (n = 4924) was coded into an indicator framework based on falling below four selected thresholds (500, 1000, 3000, and 10,000 mg/L). Collocated TDS‐ER data from the surrounding groundwater basin were then employed to produce a kernel density estimator to establish conditional probabilities for ER data (n = 8 boreholes) falling below the selected TDS thresholds within the Montebello Oil Field area. Directional variograms were estimated from these indicator coded data, and 500 TDS realizations from conditional indicator simulation were generated for the subsurface region above the Montebello Oil Field reservoir. Simulations were summarized as 3D maps of median TDS, most likely salinity class, and probability for exceeding each of the specified TDS thresholds. Results suggested TDS was below 500 mg/L in most of the study area, with a trend toward higher values (500 to 1000 mg/L) to the southwest; consistent with the average regional groundwater flow direction. Discrete localized zones of TDS greater than 1000 mg/L were observed, with one of these zones in the greater than 10,000 mg/L range; however, these areas were not prevalent. The probabilistic approach used here is adaptable and is readily modified to include additional data and types and can be employed in time‐lapse salinity modeling through Bayesian updating. Article impact statement: A three‐dimensional adaptable geostatistical approach classifying groundwater salinity above the Montebello oil field is developed and applied. [ABSTRACT FROM AUTHOR]