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.
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FZAB, GIS, IJS, KILJ, NLZOH, NUK, OILJ, SBCE, SBMB, UL, UM, UPUK
Recent observations reveal a paradox of anaerobic respiration occurring in seemingly oxic‐saturated sediments. Here we demonstrate a residence time‐based explanation for this paradox. Specifically, ...we show how microzones favorable to anaerobic respiration processes (e.g., denitrification, metal reduction, and methanogenesis) can develop in the embedded less mobile porosity of bulk‐oxic hyporheic zones. Anoxic microzones develop when transport time from the streambed to the pore center exceeds a characteristic uptake time of oxygen. A two‐dimensional pore‐network model was used to quantify how anoxic microzones develop across a range of hyporheic flow and oxygen uptake conditions. Two types of microzones develop: flow invariant and flow dependent. The former is stable across variable hydrologic conditions, whereas the formation and extent of the latter are sensitive to flow rate and orientation. Therefore, pore‐scale residence time heterogeneity, which can now be evaluated in situ, offers a simple explanation for anaerobic signals occurring in oxic pore waters.
Key Points
Denitrification occurs in anoxic microzones of bulk oxic hyporheic sediments
Microzones develop in less mobile porosity due to increased local residence time
Geophysical methods have potential to evaluate hyporheic less mobile porosity
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FZAB, GIS, IJS, KILJ, NLZOH, NUK, OILJ, SBCE, SBMB, UL, UM, UPUK
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.
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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.
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FZAB, GIS, IJS, KILJ, NLZOH, NUK, OILJ, SBCE, SBMB, UL, UM, UPUK
This software spotlight presents the Geophysical Remediation Monitoring Method Selection Tool (GRM-MST). The GRM-MST is a spreadsheet-based tool designed to help practitioners identify geophysical ...methods to monitor remediation operations that are well suited to (1) the planned remedy or remedies, and (2) site-specific conditions that might limit the effectiveness of some geophysical methods.
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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 exchange
Recursive filtering and smoothing applied to heat tracing improve estimation of groundwater/surface‐water exchange
Recursive filtering applied to heat tracing allows quantification of uncertainty in groundwater/surface water exchange estimates
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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.
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Rapid infiltration following precipitation may result in groundwater contamination from surface contaminants or pathogens. In fractured rock, contaminants can migrate rapidly to points of groundwater ...withdrawals. In contrast to the temporal availability of groundwater quality chemical indicators, meteorological and groundwater level observations are available in real‐time to estimate time‐varying recharge, which can act as a surrogate to identify periods of rapid infiltration that may indicate contamination susceptibility. Estimating recharge using methods, such as base‐flow recession, unsaturated infiltration models, or water‐table fluctuations (WTF), cannot capitalize on currently available technologies and telecommunication infrastructure to conduct real‐time recharge estimation at scales relevant to characterizing rapid infiltration. We present a linear, physics‐based state‐space (SS) model of one‐dimensional infiltration to estimate recharge, which includes preferential and diffuse‐flow to the water table. The model can take advantage of real‐time data for water‐table altitude, precipitation, and evapotranspiration. Model parameters are calibrated over an observation period, and the Kalman Filter (KF) is subsequently applied to continuously update the observed (water‐table altitude) and unobserved (groundwater recharge) system states and predict future states as new data become available. The SS/KF algorithm is demonstrated at the Masser Groundwater Recharge Site in Pennsylvania, USA and comparisons are made with recharge estimates from WTF methods. Model results indicate that the frequency of observations (daily vs. sub‐daily) dictates the allocation between preferential and diffuse flow. Additionally, because infiltration processes encompass many nonlinearities, model parameters estimated from observation periods need to be updated at least seasonally to account for changing recharge conditions.
Key Points
A state‐space model interprets time series for water table altitude and meteorological inputs to estimate and forecast recharge
Preferential and diffuse flow through the unsaturated zone are conceptualized using a precipitation rate threshold
The frequency of observations dictates the recharge allocation between preferential and diffuse flow through the unsaturated zone
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