Geology topics

Christopher Green

Publications and source records attributed to Christopher Green.

18 recordsLinked to original sources

USGS2026

Groundwater nutrient contributions to surface water in the upper Maumee River basin

Harmful algal blooms triggered by nutrient loading have negatively affected Lake Erie water quality for decades, but the role groundwater plays in transporting nutrients to tributary streams is unclear. Here, we apply a multi-scale approach to investigate groundwater nitrogen (N) and phosphorus (P) dynamics at the field scale, along a groundwater flow path, and at the Maumee River basin scale. Unsaturated zone monitoring indicates that macropore flow is the primary mechanism for N and P leaching with nitrate and orthophosphate (OP) concentrations from macropore samplers more than double those from soil-matrix samplers. Shallow (< 4.5 m) groundwater nitrate concentrations indicate the timing of saturated-zone nitrate fluxes is crop dependent with more nitrate mobilized during the maize growing season whereas concentrations below soybeans can remain elevated (> 3 mg/L) through winter months. A three-end-member hydrograph analysis for the upper Maumee River basin indicates that tile drains transport most subsurface N and P with greater than 15,000 kg per day of nitrate transported between January and May. Orthophosphate concentrations in tile drainage at an edge-of-field site correlate well with concentrations in a nearby tributary of the Maumee River. While runoff is the dominant source of OP loading, tile drainage and groundwater discharge contributed an estimated 36% of OP load during a 2018–2022 analysis period with most subsurface loading occurring between January and March. These findings highlight how best management practices (such as cover crops and tile drainage control structures) can be used to help limit leaching and tile effluent during the nongrowing season.

Indiana↗

USGS2026

PFAS from a discrete-event terrestrial source migrates with groundwater to intertidal seepages

Groundwater transport of per- and polyfluoroalkyl substances (PFAS) from terrestrial sources to marine coastal ecosystems is a topic of growing recognition, though with few published field studies. Furthermore, most research on PFAS fate and transport dynamics has focused on source areas with long histories like fire training and manufacturing facilities; the effect of single PFAS dispersal events to the coastal environment is not well documented. We have assessed PFAS mobility in groundwater to intertidal seepages from two discrete, adjacent tanker-truck rollover events that occurred in 1997 and 2000 where aqueous film forming foams (AFFF) were applied to suppress fuel vapors. Using a combination of groundwater-flow and particle-track modeling, geophysical field characterization, and chemical sampling, we found substantial concentrations of PFAS (up to 530.5 ng/L), mainly perfluorooctane sulfonate (PFOS), in groundwater discharging to a shoreline along an embayment used for swimming and recreational shellfish harvesting. The general groundwater trajectory to the embayment was predicted by numerical particle tracking. The highest concentrations of PFAS were found in discharges near the landward side of the intertidal zone. The discharges had variable salinity at the time of sampling. Our research generated the finding that even in areas of strong, preferential groundwater discharge at low tide, there was tidal “pumping” of ocean water into the aquifer sediments during higher tides. Therefore, there are likely to be continuously variable salinity conditions at similar seepage interfaces that have not been broadly considered in previous conceptual models of PFAS fate and transport.

Massachusetts↗

USGS2025

Modeling seawater intrusion along the Alabama coastline using physical and machine learning models to evaluate the effects of multiscale natural and anthropogenic stresses

Seawater intrusion threatens groundwater resources in coastal regions, including southern Baldwin County, Alabama, where the freshwater-saltwater interface dynamics remain poorly understood. To address this gap, this study uses combined physics-based and machine-learning models to quantify seawater intrusion caused by natural (storm surges) and anthropogenic (human activities) perturbations. The long short-term memory network and wavelet analysis were used to assess vertical aquifer vulnerabilities, revealing that the shallow part of the Coastal lowlands aquifer system (CL1) in the southern Baldwin County region is more susceptible to sea level rise and groundwater extraction than deeper aquifers. Based on these findings, a cross-sectional numerical model (physics approach) for the CL1 aquifer was developed to evaluate tidal and storm surge effects, using Tropical Storm Claudette (June 2021) as a case study. Results showed that tidal fluctuations had a minimal impact on the saltwater-freshwater interface location, whereas storm surges caused substantial inland movement, with effects lasting for nine months. The steady-state version of the three-dimensional (3D) physical model predicted seawater intrusion across the entire area, and convolutional neural network-based modeling further validated the model results. The 3D physical model was also applied to a smaller area to assess human impact on the saltwater interface due to two groundwater pumping scenarios (± 50% of the baseline pumping rate). Results revealed that a 50% increase in groundwater withdrawals caused seawater to advance ~ 320 m inland, whereas a 50% reduction led to a ~ 270-meter retreat. This study highlights the vulnerability of Alabama’s shallow coastal aquifers to seawater intrusion due to storm surges and human activities, and demonstrates that combining physics-based models with machine learning approaches can improve groundwater predictions, though its accuracy depends on the availability of site-specific data.

Alabama↗

USGS2025

Gaps in water quality modeling of hydrologic systems

This review assesses gaps in water quality modeling, emphasizing opportunities to improve next-generation models that are essential for managing water quality and are integral to meeting goals of scientific and management agencies. In particular, this paper identifies gaps in water quality modeling capabilities that, if addressed, could support assessments, projections, and evaluations of management alternatives to support ecosystem health and human beneficial use of water resources. It covers surface water and groundwater quality modeling, dealing with a broad suite of physical, biogeochemical, and anthropogenic drivers. Modeling capabilities for six constituents (or constituent categories) are explored: water temperature, salinity, nutrients, sediment, geogenic constituents, and contaminants of emerging concern. Each constituent was followed through the coupled atmospheric-hydrologic-human system, with prominent modeling gaps described for a diverse array of relevant inputs, processes, and human activities. Commonly identified modeling gaps primarily fall under three types: (1) model gaps, (2) data gaps, and (3) process understanding gaps. In addition to potential solutions for addressing specific individual modeling limitations, some broad approaches (e.g., enhanced data collection and compilation, machine learning, reduced-complexity modeling) are discussed as ways forward for tackling multiple gaps. This gap analysis establishes a framework of diverse approaches that may support improved process representation, scale, and accuracy of models for a wide range of water quality issues.

Water↗

USGS2025

Analyzing multi-year nitrate concentration evolution in Alabama aquatic systems using a machine learning model

Rising nitrate contamination in water systems poses significant risks to public health and ecosystem stability, necessitating advanced modeling to understand nitrate dynamics more accurately. This study applies the long short-term memory (LSTM) modeling to investigate the hydrologic and environmental factors influencing nitrate concentration dynamics in rivers and aquifers across the state of Alabama in the southeast of the United States. By integrating dynamic data such as streamflow and groundwater levels with static catchment attributes, the machine learning model identifies primary drivers of nitrate fluctuations, offering detailed insights into the complex interactions affecting multi-year nitrate concentrations in natural aquatic systems. In addition, a novel LSTM-based approach utilizes synthetic surface water nitrate data to predict groundwater nitrate levels, helping to address monitoring gaps in aquifers connected to these rivers. This method reveals potential correlations between surface water and groundwater nitrate dynamics, which is particularly meaningful given the lack of water quality observations in many aquifers. Field applications further show that, while the LSTM model effectively captures seasonal trends, limitations in representing extreme nitrate events suggest areas for further refinement. These findings contribute to data-driven water quality management, enhancing understanding of nitrate behavior in interconnected water systems.

Alabama↗

USGS2025

Ranking river basins for stream temperature research and monitoring in the contiguous United States

There is a need to prioritize research and data collection in river basins by integrating information from environmental, ecological, and socioeconomic datasets to maintain acceptable water quality for human uses and ecosystem health. Multiple anthropogenic and natural stressors are responsible for driving changes in stream temperatures that can alter ecosystems and degrade water quality. These stressors are variable spatially and temporally, which can be challenging for prioritizing monitoring and research. In this study, an evaluation of variables related to stream temperature was performed for 163 candidate basins of the contiguous United States to highlight potential focal areas. Thirty variables were combined in six components: anthropogenic forcing, natural forcing, climate change, ecological sensitivity, socioeconomic sensitivity, and data availability. The components were incorporated into three themes: vulnerability of streams to thermal change, vulnerability of ecosystems, and vulnerability of communities. By evaluating each theme separately, patterns of vulnerability and potential resiliency were identified as well as consistency in ranks within the themes. For example, results of the national scale ranking indicated the Southern California Coastal basin (within California-Nevada region) was the highest-ranking priority in vulnerability of streams to thermal change and vulnerability of ecosystems. The analysis also identified vulnerable basins with gaps in monitoring. For example, the Missouri-Oahe basin (within the Northern High Plains region) was the highest-ranking priority for vulnerable communities with limitations in data availability. The ranking approach provides insight into basins that are resilient and are ideal candidates for monitoring and research.

Journal of Hydrology↗

USGS2025

Projecting stream water quality using Weighted Regression on Time, Discharge, and Season (WRTDS): An example with drought conditions in the Delaware River Basin

Future water availability depends on understanding the responses of constituent concentrations to hydrologic change. Projecting future water quality remains a methodological challenge, particularly when using discrete observations with limited temporal resolution. This study introduces Weighted Regression on Time, Discharge, and Season for Projection (WRTDS-P), a novel, computationally efficient method that enables the projection of daily stream water quality under varying hydrologic conditions using commonly available discrete monitoring data. WRTDS-P model performance was validated using 39 sites in the Delaware River Basin (DRB) and four key constituents: specific conductance (SC), nitrate (NO 3 − ), magnesium (Mg 2+ ) and calcium (Ca 2+ ). Projections were tested against holdout data from the final 1 to 5 years of each time series, demonstrating robust predictive capability, with median Nash-Sutcliffe efficiencies of 0.67 for SC, 0.56 for NO 3 − , 0.65 for Ca 2+ , and 0.79 for Mg 2+ . Model uncertainty was correlated with indicators of hydrologic or geochemical mass-sinks, such as groundwater storage and adsorption in wetland soils. Drought scenario analyses for SC used ranges of reduced discharge including flows from the 1965 drought of record. Scenarios predicted widespread increases of SC, especially in southern DRB streams where baseline SC levels are already elevated. Fractional increases of SC were more uniformly distributed, indicating potential risk to sensitive ecosystems. Notably, drought-induced SC increases were positively correlated with interannual SC trends, indicating that hydrologic extremes could exacerbate ongoing salinization. This work provides a transferable and interpretable framework for projecting future water quality and assessing hydrologic risk to water resources and aquatic ecosystems.

Delaware, New Jersey, New York, Pennsylvania↗

USGS2024

Prioritizing river basins for nutrient studies

Increases in fluxes of nitrogen (N) and phosphorus (P) in the environment have led to negative impacts affecting drinking water, eutrophication, harmful algal blooms, climate change, and biodiversity loss. Because of the importance, scale, and complexity of these issues, it may be useful to consider methods for prioritizing nutrient research in representative drainage basins within a regional or national context. Two systematic, quantitative approaches were developed to (1) identify basins that geospatial data suggest are most impacted by nutrients and (2) identify basins that have the most variability in factors affecting nutrient sources and transport in order to prioritize basins for studies that seek to understand the key drivers of nutrient impacts. The “impact” approach relied on geospatial variables representing surface-water and groundwater nutrient concentrations, sources of N and P, and potential impacts on receptors (i.e., ecosystems and human health). The “variability” approach relied on geospatial variables representing surface-water nutrient concentrations, factors affecting sources and transport of nutrients, model accuracy, and potential receptor impacts. One hundred and sixty-three drainage basins throughout the contiguous United States were ranked nationally and within 18 hydrologic regions. Nationally, the top-ranked basins from the impact approach were concentrated in the Midwest, while those from the variability approach were dispersed across the nation. Regionally, the top-ranked basin selected by the two approaches differed in 15 of the 18 regions, with top-ranked basins selected by the variability approach having lower minimum concentrations and larger ranges in concentrations than top-ranked basins selected by the impact approach. The highest ranked basins identified using the variability approach may have advantages for exploring how landscape factors affect surface-water quality and how surface-water quality may affect ecosystems. In contrast, the impact approach prioritized basins in terms of human development and nutrient concentrations in both surface water and groundwater, thereby targeting areas where actions to reduce nutrient concentrations could have the largest effect on improving water availability and reducing ecosystem impacts.

Environmental Monitoring and Assessment↗

USGS2024

Design and calibration of a nitrate decision support tool for groundwater wells in Wisconsin, USA

This paper describes development of a nitrate decision support tool for groundwater wells (GW-NDST) that combines nitrate leaching and groundwater lag-times to compute well concentrations. The GW-NDST uses output from support models that simulate leached nitrate, groundwater age distributions, and nitrate reduction rates. The support models are linked through convolution to simulate nitrate transport to wells. Spatially distributed parameters were adjusted through calibration to 34,255 nitrate sample targets. Prediction uncertainty is illustrated via Monte Carlo realizations informed during calibration. Over 78% of target concentrations were within the simulated range of results from 450 realizations. An example forecasting scenario illustrates that a range of feasible outcomes exist and should be considered when interpreting forecasts for decision making. Uncertainty in forecasting is unavoidable; the intent of characterizing uncertainty in the GW-NDST is to facilitate decision making by increasing insight into the response of nitrate contamination to physical and chemical processes.

Wisconsin↗

USGS2023

Long short-term memory models to quantify long-term evolution of streamflow discharge and groundwater depth in Alabama

Long short-term memory (LSTM) models have been shown to be efficient for rainfall-runoff modeling, and to a lesser extent, for groundwater depth forecasting. In this study, LSTMs were applied to quantify the spatiotemporal evolution of surface and subsurface hydrographs in Alabama in the Southeastern United States, where water sustainability has not been fully quantified across spatiotemporal scales. First, the surface water LSTM model with extensive dynamic (precipitation and other weather variables) and static (basin characteristics) inputs predicted the main characteristics of streamflow for six years at 19 gauged basins in Alabama. The model tended to underestimate extremely high streamflow but adding drainage density as an input feature slightly improved the predictions of extreme events. Second, to predict the groundwater depth evolution, a groundwater LSTM (GW-LSTM) model was proposed and applied using static inputs capturing the aquifers' hydrogeological properties and dynamic inputs of meteorological information. Three precipitation scenarios were also explored to evaluate the groundwater hydrograph evolution in the next two decades. The GW-LSTM model predicted the general trend of daily groundwater depth fluctuations (at 21 wells distributed across Alabama from 1990 to 2021) including most extremely high groundwater levels, and recovered groundwater depth for locations withheld from model training and validation. This study, therefore, extended the application of LSTMs in quantifying the spatiotemporal evolution of surface water and groundwater, two manifestations of a single integrated resource.

Alabama↗

USGS2021

Co-transport of biogenic nano-hydroxyapatite and Pb(II) in saturated sand columns: Controlling factors and stochastic modeling

Biogenic nano-hydroxyapatite (bio-nHAP) has recently gained great interest in many domains, especially in the remediation of heavy metal-contaminated soil, due to its high reactivity, low cost, and eco-friendly nature. The co-transport and reaction of bio-nHAP with Pb(II) in saturated porous media, however, are not well understood. This work investigated the effects of ionic strength (IS), ionic composition (IC), dissolved organic matter (DOM), and flow velocity on transport-reaction dynamics of Pb(II) and bio-nHAP by combining column breakthrough experiments and model simulations. Results showed that the mobility of Pb(II) was significantly enhanced with increasing IS/IC but less affected by flow velocity during the transport-reaction process of bio-nHAP and Pb(II) in the saturated sand column; while the transport of bio-nHAP was restricted by increasing IS/IC but facilitated by increasing velocity. IC, IS, and velocity only slightly affected the reaction kinetics between Pb(II) and bio-nHAP, likely due to the fast reaction rate between Pb(II) and bio-nHAP and precipitation of pyromorphite. The transport dynamics of bio-nHAP and Pb(II) were significantly changed by DOM, and this effect depended strongly on the type of DOM with different molecular weights. Breakthrough curves of Pb(II) and bio-nHAP exhibited apparent “anomalous”, sub-diffusive transport behaviors, which could be well quantified by a novel tempered fractional derivative bimolecular reaction equation (T-FBRE). Our findings highlighted the accurate simulation of the co-transport and reaction of bio-nHAP with Pb(II) using T-FBRE and had a great benefit for risk assessment and remediation strategy development for Pb(II) contaminated soil.

Chemosphere↗

USGS2021

Machine learning predictions of mean ages of shallow well samples in the Great Lakes Basin, USA

The travel time or “age” of groundwater affects catchment responses to hydrologic changes , geochemical reactions, and time lags between management actions and responses at down-gradient streams and wells. Use of atmospheric tracers has facilitated the characterization of groundwater ages, but most wells lack such measurements. This study applied machine learning to predict ages in wells across a large region around the Great Lakes Basin using well, chemistry, and landscape characteristics. For a dataset of age tracers in 961 samples, the travel time from the land surface to the sample location was estimated for each sample using parametric functions. The mean travel times were then modeled using a gradient boosting machine (GBM) algorithm with cross validation tuning of model metaparameters. The GBM approach was able to closely match estimated ages for the training data (RMSE = 0.26 natural-log scale years) and provided a reasonable match to testing data (RMSE = 0.84). Of the variables tested, well characteristics (e.g. depth), land use, hydrologic indicators (e.g. topographic wetness index), and water chemistry (e.g. nitrate, fluoride, and pH), substantially affected the predictions of age. GBM prediction was applied to 14,335 groundwater samples with median sample depth of 5.4 m, indicating for the Great Lakes Basin a broad distribution of ages among wells with a median of 32.9 years. Lag times of decades are likely for these wells to respond to changing solute fluxes near land surface. While depth variables most strongly affected predicted mean ages, chemical constituents exhibited smooth trends with age, consistent with prevailing conceptual models of evolving sources and geochemistry flowpaths. The results provide proof of concept for use of readily available variables of well, landscape, and chemical characteristics to improve groundwater age estimates across large regions.

Great Lakes basin↗

USGS2021

Complexity of groundwater age mixing near a seawater intrusion zone based on multiple tracers and Bayesian inference

Aquifer flow systems near seawater interfaces can be complicated by density-driven flows and the formation of stagnation zones, which inevitably introduces uncertainty into groundwater age-dating. While age-dating has proved effective to understand the seawater intrusion and aquifer salinization process in coastal aquifers, further efforts are needed to propagate model and data uncertainty to the uncertainty associated with the inferred age distributions. This study was performed in a coastal aquifer located close to the Yellow Sea, South Korea, where there is a decreasing trend of groundwater levels due to recent heavy exploitation, raising a warning of induced seawater intrusion. We inferred the groundwater age distributions in wells around the intrusion zone and estimated the uncertainty associated with the inference based on multiple age tracers including 3 H, tritiogenic 3 He, radiogenic 4 He, CFC-11, CFC-12 and CFC-113 using Bayesian inference. We examined various models representing the age distributions including traditional parametric Lumped Parameter Models (LPMs) and two non-parametric “shape-free” models. The results showed that the mean ages at the study site ranged from 10.9 to 522.5 y. Complex, multimodal distributions of ages occurred near a seawater intrusion area and upland recharge zones, implying converging paths of a wide range of different ages in those regions. In particular, the age distributions estimated near the seawater intrusion interface were characterized by heavy-tailed mixing structures with elevated concentrations of 4 He. This likely indicates density-driven upward flow at the seawater intrusion interface, forcing old groundwater rich in 4 He into the shallow aquifer. The Bayesian inference estimated large uncertainties particularly for the old age distributions, which was attributed partly to the gradual accumulation of 4 He in groundwater. The Bayesian inference improved understanding of flow dynamics at a complex seawater interface and identified opportunities to further reduce uncertainty of old water age estimates that characterize upwelling groundwater near the interface.

Jaancheon basin↗

USGS2021

Time-fractional flow equations (t-FFEs) to upscale transient groundwater flow characterized by temporally non-darcian flow due to medium heterogeneity

Upscaling groundwater flow is a fundamental challenge in hydrogeology. This study proposed time-fractional flow equations (t-FFEs) for upscaling long-term, transient groundwater flow and propagation of pressure heads in heterogeneous media. Monte Carlo simulations showed that, with increasing variance and correlation of the hydraulic conductivity ( K ), flow dynamics gradually deviated from Darcian flow and exhibit sub-diffusive, time-dependent evolution which can be separated into three major stages. At the early stage, the interconnected high- K zones dominated flow, while at intermediate times, the transverse flow due to mixed high- and low- K zones caused delayed rise of the piezometric head. At late times when flow in the relatively high- K domains reached stability, cells with very low- K continued to block the entry of water and generate “islands” with low piezometric head, significantly extending the temporal evolution of the piezometric head. The elongated water breakthrough curve cannot be quantified by the flow equation with an effective K , the space-fractional flow equation, or the multi-rate mass transfer (MRMT) flow model with a few rates, motivating the development of t-FFEs assuming temporally non-Darcian flow. Model applications showed that both the early and intermediate stages of flow dynamics can be captured by a single-index t-FFE (whose index is the exponent of the power-law probability density function of the random operational time for water parcels), but the overall evolution of flow dynamics, especially the enhanced retention of flow at later times, required a distributed-order t-FFE with variable indexes for different flow phases that can dominate flow dynamics at different stages. Therefore, transient groundwater flow in aquifers with spatially stationary heterogeneity can be temporally non-Darcian and non-stationary, due to the time-sensitive, combined effects of interconnected high- K channels and isolated low- K deposits on flow dynamics (which is the hydrogeological mechanism for the temporally non-Darcian flow and sub-diffusive pressure propagation), whose long-term behavior can be quantified by multi-index stochastic models.

Water Resources Research↗

USGS2019

Stratification of reactivity determines nitrate removal in groundwater

Biogeochemical reactions occur unevenly in space and time, but this heterogeneity is often simplified as a linear average due to sparse data, especially in subsurface environments where access is limited. For example, little is known about the spatial variability of groundwater denitrification, an important process in removing nitrate originating from agriculture and land use conversion. Information about the rate, arrangement, and extent of denitrification is needed to determine sustainable limits of human activity and to predict recovery time frames. Here, we developed and validated a method for inferring the spatial organization of sequential biogeochemical reactions in an aquifer in France. We applied it to five other aquifers in different geological settings located in the United States and compared results among 44 locations across the six aquifers to assess the generality of reactivity trends. Of the sampling locations, 79% showed pronounced increases of reactivity with depth. This suggests that previous estimates of denitrification have underestimated the capacity of deep aquifers to remove nitrate, while overestimating nitrate removal in shallow flow paths. Oxygen and nitrate reduction likely increases with depth because there is relatively little organic carbon in agricultural soils and because excess nitrate input has depleted solid phase electron donors near the surface. Our findings explain the long-standing conundrum of why apparent reaction rates of oxygen in aquifers are typically smaller than those of nitrate, which is energetically less favorable. This stratified reactivity framework is promising for mapping vertical reactivity trends in aquifers, generating new understanding of subsurface ecosystems and their capacity to remove contaminants.

Proceedings of the National Academy of Sciences↗

USGS2019

Spatial fingerprinting of biogenic and anthropogenic volatile organic compounds in an arid unsaturated zone

Subsurface volatile organic compounds (VOCs) can pose risks to human and environmental health and mediate biological processes. VOCs have both anthropogenic and biogenic origins, but the relative importance of these sources has not been explored in subsurface environments. This study synthesizes 17 years of VOC data from the Amargosa Desert Research Site (ADRS) with the goal of improving understanding of spatial and temporal variations that distinguish sources of VOCs from a landfill and surrounding ambient sources including biogenic VOCs (bVOCs). Gas samples were collected from 1999 to 2016 from an array of shallow sample points (0.5 m and 1.5 m depth) and from vertical profiles at three deep boreholes, two (109 m deep) near the border of a waste facility (33 and 100 m distant), and one (29 m deep) in a remote area 3 km to the south. Samples were analyzed for target VOCs and a subset was analyzed for non-target VOCs to enumerate a greater variety of potential bVOCs. Principal components analysis of the target and non-target VOCs provided an assessment of spatial variability of VOCs originating from the landfill site and from ambient sources. Ambient VOCs occurred at all sample sites over a range of depths and most were consistent with biogenic origins, indicating, for the first time, presence of bVOCs in the deep unsaturated zone. Because some VOCs have both anthropogenic and biogenic sources, discrimination of sources can be important for estimating the extent and migration of anthropogenic plumes in arid unsaturated zones.

Nevada↗

USGS2018

Unsaturated zone CO2, CH4, and δ13C-CO2 at an arid region low-level radioactive waste disposal site

Elevated tritium, radiocarbon, Hg, and volatile organic compounds associated with low-level radioactive waste (LLRW) at the USGS Amargosa Desert Research Site (ADRS) have stimulated research on factors and processes that affect contaminant gas distribution and transport. Consequently, we examined the sources, mixing, and biogeochemistry of CO 2 and CH 4 , two additional important species in the unsaturated zone at ADRS. In spring 2015 and 2016, shallow unsaturated zone gas samples were collected from the 1.5-m depth both inside and outside the LLRW disposal area. Samples also were collected from two 110-m-deep multilevel gas-sampling boreholes and a distant background site. These samples were analyzed for CO 2 mole fraction ( x CO 2 ) and C isotopic composition (δ 13 C-CO 2 ) and CH 4 mole fraction ( x CH 4 ). Graphical analysis of the results indicates mixing of CO 2 characteristic of the root zone (δ 13 C −18 to −19‰), deep soil gas of the capillary fringe (−13 to −15‰), and CO 2 produced by microbial respiration of organic matter disposed in the LLRW trenches (−22 to −25‰). Distribution of CH 4 overall reflects atmospheric sources and production in anaerobic microzones in the LLRW area and methanotrophy in the undisturbed shallow subsurface outside the LLRW area. Although x CH 4 reflecting lateral transport from the LLRW area is decreasing with time in the deep profiles, deep unsaturated zone x CO 2 has changed little in recent decades. The results imply that CH 4 and δ 13 C-CO 2 may serve as good tracers of anthropogenic effects in the unsaturated zone even when CO 2 primarily reflects natural processes.

Nevada↗

USGS2018

Comparison of groundwater age models for assessing nitrate loading, transport pathways, and management options in a complex aquifer system

In an aquifer system with complex hydrogeology, mixing of groundwater with different ages could occur associated with various flow pathways. In this study, we applied different groundwater age estimation techniques (lumped parameter model, and numerical model) to characterize groundwater age distributions and the major pathways of nitrate contamination in the Gosan agricultural field, Jeju Island. According to the lumped parameter model, groundwater age in the study area could be explained by the binary mixing of the young groundwater (4-33 years) and the old water component (>60 years). The complex hydrogeologic regimes and local heterogeneity observed in the study area (multi-layered aquifer, well leakage hydraulics) were particularly well reflected in the numerical model. The numerical model predicted that the regional aquifer of Gosan responded to the fertilizer applications more rapidly (mean age: 9.7-22.3 years) than as estimated by other models. Our study results demonstrated that application and comparison of multiple age estimation methods can be useful to understand better the flow regimes and the mixing characteristics of groundwater with different ages (pathways), hence, to reduce the risk of improper groundwater management plan arising from the aquifer heterogeneity.

Jeju Island↗