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Geology topics

Frederick Day-Lewis

Publications and source records attributed to Frederick Day-Lewis.

15 recordsLinked to original sources
USGS2023

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.

Groundwater↗
USGS2022

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.

Water Resources Research↗
USGS2022

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-km 2 grid cells, snowmelt is a meteorological modeled outcome that may not represent conditions in the vicinity of monitoring well locations.

Groundwater↗
USGS2022

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.

Groundwater↗
USGS2022

Electrical imaging for hydrogeology

Geophysical methods offer hydrogeologists unprecedented access to understanding subsurface parameters and processes. In this book, we outline the theory and application of electrical imaging methods, which inject current into the ground and measure the resultant potentials. These data are sensitive to rock type, grain size, porosity, pore fluid electrical conductivity, saturation, and temperature. Here, we describe the physical basis for electrical imaging, parallels between electrical flow equations and the groundwater flow equation, practical considerations for field investigations, data processing and inverse modeling of field data, and how to QA/QC data. We additionally cover two case studies, including a 2-D waterborne survey and a 4-D dataset from a biostimulation experiment.

Book↗
USGS2021

Geostatistical mapping of salinity conditioned on borehole logs, Montebello Oil Field, California

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.

California↗
USGS2021

Estimating and forecasting time-varying groundwater recharge in fractured rock: A state-space formulation with preferential and diffuse flow to the water table

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 versus 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.

Water Resources Research↗
USGS2021

Exploring environmental factors that drive diel variations in tree water storage using wavelet analysis

Internal water storage within trees can be a critical reservoir that helps trees overcome both short- and long-duration environmental stresses. We monitored changes in internal tree water storage in a ponderosa pine on daily and seasonal scales using moisture probes, a dendrometer, and time-lapse electrical resistivity imaging (ERI). These data were used to investigate how patterns of in-tree water storage are affected by changes in sapflow rates, soil moisture, and meteorologic factors such as vapor pressure deficit. Measurements of xylem fluid electrical conductivity were constant in the early growing season while inverted sapwood electrical conductivity steadily increased, suggesting that increases in sapwood electrical conductivity did not result from an increase in xylem fluid electrical conductivity. Seasonal increases in stem electrical conductivity corresponded with seasonal increases in trunk diameter, suggesting that increased electrical conductivity may result from new growth. On the daily scale, changes in inverted sapwood electrical conductivity correspond to changes in sapwood moisture. Wavelet analyses indicated that lag times between inverted electrical conductivity and sapflow increased after storm events, suggesting that as soils wetted, reliance on internal water storage decreased, as did the time required to refill daily deficits in internal water storage. We found short time lags between sapflow and inverted electrical conductivity with dry conditions, when ponderosa pine are known to reduce stomatal conductance to avoid xylem cavitation. A decrease in diel amplitudes of inverted sapwood electrical conductivity during dry periods suggest that the ponderosa pine relied on internal water storage to supplement transpiration demands, but as drought conditions progressed, tree water storage contributions to transpiration decreased. Time-lapse ERI- and wavelet-analysis results highlight the important role internal tree water storage plays in supporting transpiration throughout a day and during periods of declining subsurface moisture.

Colorado↗
USGS2021

Evaluation of riverbed magnetic susceptibility for mapping biogeochemical hot spots in groundwater-impacted rivers

Redox hot spots occurring as metal-rich anoxic groundwater discharges through oxic wetland and river sediments commonly result in the formation of iron (Fe) oxide precipitates. These redox-sensitive precipitates influence the release of nutrients and metals to surface water and can act as ‘contaminant sponges’ by absorbing toxic compounds. We explore the feasibility of a non-invasive, high-resolution magnetic susceptibility (MS) technique to efficiently map the spatial variations of magnetic Fe oxide precipitates in the shallow bed of three rivers impacted by anoxic groundwater discharge. Laboratory analyses on Mashpee River (MA, USA) sediments demonstrate the sensitivity of MS to sediment Fe concentrations. Field surveys in the Mashpee and Quashnet rivers (MA, USA) reveal several discrete high MS zones, which are associated with likely anoxic groundwater discharge as evaluated by riverbed temperature, vertical head gradient, and groundwater chemistry measurements. In the East River (CO, USA), widespread cobbles/rocks exhibit high background MS from geological ferrimagnetic minerals, thereby obscuring the relatively small enhancement of MS from groundwater induced Fe oxide precipitates. Our study suggests that, in settings with low geological sources of magnetic minerals such as lowland rivers and wetlands, MS may serve as a complementary tool to temperature methods for efficiently mapping Fe oxide accumulation zones due to anoxic groundwater discharges that may function as biogeochemical hot spots and water quality control points in gaining systems.

Colorado, Massachusetts↗
USGS2021

Beware of spatial autocorrelation when applying machine learning algorithms to borehole geophysical logs

Although many of the algorithms now considered to be machine learning algorithms (MLAs) have existed for nearly a century (e.g., Rosenblatt 1958 ), interest in MLAs has recently increased exponentially for solving data-driven problems across a variety of fields due to the expanded availability of large, complex datasets that may be difficult to interrogate using other methods, increases in computing power, and a growing library of easily implemented machine learning tools. While MLAs are often similar to statistical methods, there are key differences in the approach to problem solving. Namely, statistical methods are more concerned with generating informative models from “long” data (i.e., many more observations than explanatory variables), whereas MLAs are typically concerned with generating accurate predictions from “wide” data (i.e., a large number of variables with relatively fewer observations, Bzdok et al. 2018 ). In hydrogeologic studies, such wide datasets may be available from boreholes, where various types of geophysical, geochemical, and lithological information may exist. Borehole datasets are therefore a tempting target for MLAs to reveal hidden relations among gathered data and parameters of interest (e.g., contaminant concentration), and as a method of parameter reduction (e.g., reduce costs by collecting fewer datasets).

Groundwater↗
USGS2021

Characterizing physical properties of streambed interface sediments using in situ complex electrical conductivity measurements

Streambed sediment physical properties such as surface area, are difficult to quantify in situ but exert a high‐level control on a wide range of biogeochemical processes and sorption of contaminants. We introduce the use of complex electrical conductivity (CC) methods (also known as spectral induced polarization (SIP)) that measure both real and imaginary conductivity to non‐invasively and efficiently characterize shallow streambed sediments. We explore the method through synthetic modeling, laboratory, and field measurements to demonstrate the sensitivity of imaginary conductivity to sediment surface area, controlled in part by fine‐grained iron oxides produced by anoxic groundwater discharge. Laboratory measurements verify expected relationships between CC parameters and sediment properties. Synthetic modeling using a 1D analytical model illustrates the influence of water layer depth and conductivity on the field CC measurements made at the streambed‐stream water interface. Specifically, the inverted sediment imaginary conductivity is less impacted by uncertainty in the water layer depth and conductivity relative to the real conductivity and phase shift. Field CC measurements along a landfill‐impacted river reveal discrete streambed zones with enhanced bulk surface area generally corresponding to anoxic groundwater discharges zones with high concentrations of fine‐grained iron oxide precipitates.

Water Resources Research↗
USGS2020

Experimental shifts of hydrologic residence time in a sandy urban stream sediment-water interface alter nitrate removal and nitrous oxide fluxes

The sediment–water interfaces (SWI) of streams serve as important biogeochemical hotspots in watersheds and contribute to whole-catchment reactive nitrogen budgets and water-quality conditions. Recently, the SWI has been identified as an important source of nitrous oxide (N 2 O) produced in streams, with SWI residence time among the principal controls on its production. Here, we conducted a series of controlled manipulations of SWI exchange in an urban stream that has high dissolved N 2 O concentrations and where we concurrently evaluated less-mobile porosity dynamics. Our experiments took place within isolated portions of two sediment types: a coarse sandy stream bed resulting from excess road-sand application in the watershed, and a coarse sand mixed with clay and organic particles. In these manipulation experiments we systematically varied SWI vertical-flux rates and residence times to evaluate their effect on the fate of nitrate and production rates of N 2 O. Our experiments demonstrate that the fate and transport of nitrate and N 2 O production are influenced by hydrologic flux rates through SWI sediments and associated residence times. Specifically, we show that manipulations of hydrologic flux systematically shifted the depth of the bulk oxic–anoxic interface in the sediments, and that nitrate removal increased with residence time. Our results also support the emerging hypothesis of a ‘Goldilocks’ timescale for the production of nitrous oxide, when transport and reaction timescales favor incomplete denitrification. Areal N 2 O production rates were up to threefold higher during an intermediate residence-time experiment, compared to shorter or longer residence times. In our companion study we documented that the studied sediments were dominated by a long-residence-time less-mobile porosity domain, which could explain why we observed N 2 O production even in bulk-oxic sediments. Overall, we have experimentally demonstrated that changes to SWI hydrologic residence times and SWI substrate associated with urbanization can change the biogeochemical function of the river corridor.

Massachusetts↗
USGS2019

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.

Colorado↗
USGS2017

Pore network modeling of the electrical signature of solute transport in dual-domain media

Dual-domain models are used to explain anomalous solute transport behavior observed in diverse hydrologic settings and applications, from groundwater remediation to hyporheic exchange. To constrain such models, new methods are needed with sensitivity to both immobile and mobile domains. Recent experiments indicate that dual-domain transport of ionic tracers has an observable geoelectrical signature, appearing as a nonlinear, hysteretic relation between paired bulk and fluid electrical conductivity. Here we present a mechanistic explanation for this geoelectrical signature and evaluate assumptions underlying a previously published petrophysical model for bulk conductivity in dual-domain media. Pore network modeling of fluid flow, solute transport, and electrical conduction (1) verifies the geoelectrical signature of dual-domain transport, (2) reveals limitations of the previously used petrophysical model, and (3) demonstrates that a new petrophysical model, based on differential effective media theory, closely approximates the simulated bulk/fluid conductivity relation. These findings underscore the potential of geophysically based calibration of dual-domain models.

Geophysical Research Letters↗