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Search indexed USGS publications on groundwater, aquifers, geologic maps, mineral resources and earthquakes. Explore source records by subject and place.

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USGS2026

Spatio-temporal modeling for assessing geoenergy resources: A workflow applied to gas in place variation in coal beds

The ability to estimate spatio-temporal changes in hydrocarbon reservoir properties and energy resources within pore volumes is essential for optimizing production, reservoir management, geologic energy storage, and safety in underground mining operations. In coal seams, predicting remaining methane gas-in-place (GIP) is critical for quantifying producible gas and improving mine safety and productivity through effective ventilation planning. Although such changes are commonly evaluated using physics-based numerical simulation models, these approaches often require extensive data, calibration effort, and time. This study presents a spatio-temporal geostatistical modeling approach that bridges the gap between purely spatial models and full numerical simulations. The method is applied to a case study of coal seam degasification in the Mary Lee coal group, Black Warrior Basin, Alabama, USA, to estimate GIP evolution over time within a selected mining district. The analysis uses published data from prior natural gas production history-matching of degasification using vertical wells. Empirical spatial and temporal statistics were calculated for reservoir pressure and water saturation, and spatio-temporal variogram models were fitted to experimental variograms. These models provided the structural basis for spatio-temporal kriging, integrated with spatial estimates of time-invariant parameters (porosity, density, and thickness) to estimate GIP. This approach enabled estimation of GIP changes over time, including periods without data. Boxplots of GIP estimates indicated systematic depletion and decreasing spatial variability, reflecting the impacts of degasification. Comparison with cumulative gas production from empirical well records showed approximately 85% agreement based on a relative similarity metric. Spatio-temporal GIP estimates were also used to estimate methane emissions to longwall ventilation systems and compared with reported emissions from the U.S. EPA Greenhouse Gas Reporting Program, showing similar distributions (≈80%) given data limitations. Overall, this integrated modeling approach provides time-dependent GIP estimates with broader implications for resource assessment applications.

Alabama↗
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

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↗
USGS2024

Sensitivity of Alabama freshwater gastropod species to nickel exposure

Snails are effective bioindicators due to their prolific distribution, high level of endemism, and capacity to accumulate contaminants. Freshwater snails have unique ecological niches which are imperiled by land‐use change and the introduction of hazardous chemicals. To assess how environmental alterations affect gastropods, lab‐based studies are needed to characterize the toxicity of specific stressors. This can help guide policy decisions and remediation efforts. The aim of this research was to characterize acute toxicity of nickel (Ni) on endemic snails ( Somatogyrus georgianus [Walker, 1904], Elimia cahawbensis [Lea, 1861], and Elimia spp.) and measure the accumulation of Ni and mineral elements including calcium (Ca), magnesium, potassium, and sodium (Na). Snails were exposed to six concentrations (25–800 µg/L) of Ni for 96 h. Among the studied snail species, E. cahawbensis was the most sensitive to Ni, with the lowest lethal concentration where 50% of the organisms died (LC50) at 88.88 µg/L Ni after 96 h. The LC50 at 96 h for S. georgianus was 167.78 µg/L Ni, and 393.13 μg/L Ni for Elimia spp. Except for Elimia spp., mortality of the other two snail species corresponded to the whole‐body uptake of Ni. Nickel exposure also influenced Ca and Na uptake for Elimia spp. All three endemic species are potential candidate species for evaluating localized effects of human activities, and the present study provides a first step in characterizing how snails would be affected by environmental alterations. More research could further characterize potential effects of other human stressors on these endemic snail species. Future research into subindividual responses and routes of exposure can further elucidate variations in species sensitivity.

Alabama↗
USGS2024

Pesticide occurrence in shallow groundwater in three regions of agricultural land use: Baldwin County, the Wiregrass region, and the Tennessee River valley region of Alabama, 2009–20

As part of a cooperative investigation between the U.S. Geological Survey and the Alabama Department of Agriculture and Industries, a network of 22 groundwater wells were sampled from 2014 through 2020 for about 230 pesticide and pesticide degradate compounds. Wells were located in three regions of intensive agricultural land use in Alabama: Baldwin County, the Wiregrass region, and the Tennessee River valley region. Metolachlor sulfonic acid, a degradate of the herbicide metolachlor, was the most frequently detected compound, occurring in about 70 percent of the samples. Three other compounds, metolachlor, atrazine, and 2-chloro-4-isopropylamino-6-amino-s-triazine, were also detected in more than half of the samples. Metolachlor and its degradates accounted for 33 of the 50 greatest compound concentrations study-wide, including the maximum pesticide concentration across all compounds (62,500 nanograms per liter). The frequency and magnitude of detections of many specific pesticide compounds varied among the three regions, but all detected pesticide concentrations were well below the U.S. Environmental Protection Agency maximum contaminant levels and applicable human health benchmarks. Sample results were combined with results of previous (2009–13) sampling to provide a continuous time-series of data for 2009–20. More than half of the 289 pesticide compounds analyzed during 2009–20 were not detected in any samples. Only four compounds were detected at great enough frequency throughout the 10 sampling years to evaluate patterns of change through time. Metolachlor and its degradate, metolachlor sulfonic acid, were frequently detected in all regions. Atrazine and its degradate, 2-chloro-4-isopropylamino-6-amino-s-triazine, were also detected in wells from all regions, but the variability and magnitude of concentrations were greatest in the Tennessee River valley region. No apparent temporal pattern in concentrations was found.

Alabama↗
USGS2024

Cross section N–N' through the Valley and Ridge province of the southern Appalachian basin, from Greene County, west-central Alabama, to Bibb County, central Alabama

Introduction Geologic cross section N–N′ is the sixth in a series of geologic cross sections constructed by the U.S. Geological Survey to document and improve understanding of the geologic framework and petroleum systems of the Appalachian basin. Cross section N–N′ provides a regional view of the structural and stratigraphic framework of the Appalachian basin in the Valley and Ridge province in western and central Alabama; it spans approximately 69 miles (mi) (111 kilometers [km]). This geologic cross section is a companion to geologic cross sections E–E′ , D–D′ , C–C′ , I–I′ , and A–A′ that are located approximately 350 to 550 mi (563 to 885 km) to the northeast. Cross section N–N' complements earlier geologic cross sections through the Alabama part of the Appalachian basin. Although some of the other cross sections show more structural and stratigraphic detail, they are of more limited extent geographically and stratigraphically. Cross section N–N′ contains information that is useful for evaluating energy resources in the Appalachian basin. Although the Appalachian basin petroleum systems are not shown on the cross section, many of their key elements (such as source rocks, reservoir rocks, seals, and traps) can be inferred from lithologic units, unconformities, and geologic structures shown on the cross section. Other aspects of petroleum systems (such as the timing of petroleum generation and petroleum migration pathways) may be evaluated by burial history, thermal history, and fluid flow models based on what is shown on the cross section. In addition, cross section N–N′ may be used as a reconnaissance tool to identify plausible geologic structures and strata for the subsurface storage of liquid waste or for the sequestration of carbon dioxide.

Alabama↗