Geology Reports⌕ Search

USGS · 70022240

Geochemical modeling of iron, sulfur, oxygen and carbon in a coastal plain aquifer

Abstract

Fe(III) reduction in the Magothy aquifer of Long Island, NY, results in high dissolved-iron concentrations that degrade water quality. Geochemical modeling was used to constrain iron-related geochemical processes and redox zonation along a flow path. The observed increase in dissolved inorganic carbon is consistent with the oxidation of sedimentary organic matter coupled to the reduction of O2 and SO4/2- in the aerobic zone, and to the reduction of SO4/2- in the anaerobic zone; estimated rates of CO2 production through reduction of Fe(III) were relatively minor by comparison. The rates of CO2 production calculated from dissolved inorganic carbon mass transfer (2.55 x 10-4 to 48.6 x 10-4 mmol 1-1 yr-1) generally were comparable to the calculated rates of CO2 production by the combined reduction of O2, Fe(III) and SO4/2- (1.31 x 10-4 to 15 x 10-4 mmol 1-1 yr-1). The overall increase in SO4/2- concentrations along the flow path, together with the results of mass-balance calculations, and variations in ??34S values along the flow path indicate that SO4/2- loss through microbial reduction is exceeded by SO4/2- gain through diffusion from sediments and through the oxidation of FeS2. Geochemichal and microbial data on cores indicate that Fe(III) oxyhydroxide coatings on sediment grains in local, organic carbon- and SO4/2- -rich zones have localized SO4/2- -reducing zones in which the formation of iron disulfides been depleted by microbial reduction and resulted in decreases dissolved iron concentrations. These localized zones of SO4/2- reduction, which are important for assessing zones of low dissolved iron for water-supply development, could be overlooked by aquifer studies that rely only on groundwater data from well-water samples for geochemical modeling. (C) 2000 Elsevier Science B.V.Fe(III) reduction in the Magothy aquifer of Long Island, NY, results in high dissolved-iron concentrations that degrade water quality. Geochemical modeling was used to constrain iron-related geochemical processes and redox zonation along a flow path. The observed increase in dissolved inorganic carbon is consistent with the oxidation of sedimentary organic matter coupled to the reduction of O2 and SO42- in the aerobic zone, and to the reduction of SO42- in the anaerobic zone; estimated rates of CO2 production through reduction of Fe(III) were relatively minor by comparison. The rates of CO2 production calculated from dissolved inorganic carbon mass transfer (2.55??10-4 to 48.6??10-4mmol l-1yr-1) generally were comparable to the calculated rates of CO2 production by the combined reduction of O2, Fe(III) and SO42- (1.31??10-4 to 15??10-4mmol l-1yr-1). The overall increase in SO42- concentrations along the flow path, together with the results of mass-balance calculations, and variations in ??34S values along the flow path indicate that SO42- loss through microbial reduction is exceeded by SO42- gain through diffusion from sediments and through the oxidation of FeS2. Geochemical and microbial data on cores indicate that Fe(III) oxyhydroxide coatings on sediment grains in local, organic carbon- and SO42--rich zones have been depleted by microbial reduction and resulted in localized SO42--reducing zones in which the formation of iron disulfides decreases dissolved iron concentrations. These localized zones of SO42- reduction, which are important for assessing zones of low dissolved iron for water-supply development, could be overlooked by aquifer studies that rely only on groundwater data from well-water samples for geochemical modeling.

Explore related subjects

Keep this discovery

Explore connections, maps & timelines

BibTeXRIS

C. J. Brown, M.A.A. Schoonen, J. L. Candela. 2000. Geochemical modeling of iron, sulfur, oxygen and carbon in a coastal plain aquifer. https://doi.org/10.1016/s0022-1694(00)00296-1

Cite the original work for its findings. Save a collection to share your selection of sources.

KEEP EXPLORING

Related USGS reports

Inverse modeling for post-wildfire hydrologic process understanding over snow-dominated watersheds

Wildfire-induced changes to vegetation and soil properties can alter snowpack dynamics and hydrologic processes in ways that complicate predictions of post-wildfire water availability. To better understand these effects, we coupled the Parameter ESTimation in C++ (PEST++) software with the Weather Research & Forecasting Hydrologic model (WRF-Hydro) across three burned, snow-dominated watersheds in the western United States. We incorporated three burn-severity-based land cover categories into WRF-Hydro and used an iterative Ensemble Smoother (iES) within PEST++ to estimate model parameters controlling snow accumulation and melt, infiltration, and streamflow generation. This framework supported three complementary objectives: (1) assessing parameter sensitivities under pre- and post-wildfire conditions, (2) comparing pre- and post-wildfire parameter ensembles to identify wildfire-driven shifts in hydrologic processes, and (3) using counterfactual simulations to isolate the hydrologic response to wildfire from interannual climate variability. Our findings indicate that model parameter sensitivity varied by domain and shifted following wildfire. Maximum snow albedo and snow albedo decay were most influential in the higher-elevation East Troublesome domain, while the rain-snow partitioning temperature threshold dominated in the lower-elevation Chippy Creek and Las Conchas domains. Following wildfire, streamflow sensitivity shifted away from snowpack parameters toward soil hydraulic parameters, consistent with wildfire-driven reductions in evapotranspiration and increases in soil moisture. Incorporating wildfire effects led to complex changes in simulated snowpack accumulation and streamflow timing, generally leading to earlier snowmelt freshet initiation and cessation and reduced late season flows. Simulations were more sensitive to interannual climate variations than wildfire land cover changes. These results highlight both the potential and challenges of ensemble-based inverse modeling for understanding post-wildfire hydrologic change in snow-dominated watersheds.

Colorado, Montana, New Mexico↗

Reservoir releases and land cover interact to drive event-scale nitrate export in a large agricultural basin

Understanding the drivers of nitrate export in rivers is critical for developing effective nutrient management strategies. However, few studies have explored event-scale drivers of export in large river basins with human modifications like reservoirs. Here, we analyzed nitrate concentration-discharge (C-Q) relationships from 215 events at the outlet of the Kansas River Basin, USA (155,690 km 2 ) from 2014 to 2022 to (1) characterize event-scale nitrate export behaviors in a large agricultural basin, and (2) determine how different event characteristics are linked to these behaviors. We found that C-Q behaviors varied greatly, with 60% of events producing nitrate enrichment (n = 130) and 40% of events producing nitrate dilution (n = 85). These behaviors were correlated with complex spatial interlinkages between climate and land cover: across most of the basin, nitrate enrichment was correlated with drier antecedent conditions, but in wetter areas with higher proportions of urban and forested land cover, enrichment was more strongly correlated with precipitation magnitude/intensity. This difference in hydroclimatic controls on nitrate export might be related to differential distributions of nitrate sources within these land covers. Upstream of major reservoirs, however, neither variable was strongly correlated with C-Q behavior, suggesting that nitrate attenuation within reservoirs decouples event-scale concentration signals in upstream waters from those downstream. Reservoir outflows had variable impacts on C-Q behavior, reflecting reservoir-specific variations in nitrate attenuation efficiency. Together, these results identify specific complex interactions between hydroclimate, land cover, reservoir positioning, and individual reservoir properties that control event-scale nitrate export from large basins.

Colorado, Kansas, Nebraska↗

Deep learning error post-processing improves stochastic watershed modeling

Hydrologic extremes, including floods and droughts, pose substantial societal risks that are expected to intensify with climate change. Deterministic watershed models (DWMs) remain a mainstay for modeling these extremes, but lack explicit representation of uncertainty, limiting their utility for risk-informed planning. Stochastic watershed models (SWMs) address this limitation by generating ensembles of streamflow via models of observed DWM residuals. However, most SWMs struggle with the complex dependence between DWM residuals and the underlying hydrologic state, which can complicate stochastic simulations under nonstationary climates. Deep learning (DL) models, whether used as standalone models or post-processors for process-based DWMs, offer a pathway to address this challenge by reducing conditional dependence. In this study, we evaluate SWMs applied to seven models: three process-based models (PRMS, Hymod, and HBV), their hybrid process-DL counterparts, and a pure DL DWM, focusing on daily simulations and extremes under both historical conditions and synthetic climate change scenarios. Results for a case study watershed in Massachusetts show that SWMs applied to hybrid or pure DL DWMs consistently outperform those applied to process-based DWMs. However, an SWM applied to the pure DL model exhibits weaknesses at low flows for this study basin, underscoring the value of hybrid approaches. Extending the analysis across 73 additional basins demonstrates that these improvements are robust and generalizable statewide. This work highlights the potential of a DL-enhanced stochastic watershed modeling framework to advance hydrologic risk prediction under changing climate conditions, offering a scalable methodology for integrating uncertainty into watershed modeling for long-term planning.

Journal of Hydrology↗