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1,662 records · Page 54Linked to original sources
USGS2025

Temporal and spatial comparison of coal mine ventilation methane emissions and mitigation quantified using PRISMA satellite data and on-site measurements

Emission monitoring at the facility level (bottom-up, BU) is key for accurate reporting of coal mine methane (CMM) emissions. Recent advances in aerial and satellite observations (top-down, TD) indicate that these methods have the potential to support CMM emissions monitoring and reporting of, as well as track the effectiveness of, mitigation actions. That said, studies have shown discrepancies between BU and TD estimations. Performing TD monitoring with concurrent BU measurements at the same mines may help address the observed discrepancies and improve quantification methods to narrow the gap between BU and TD data. This paper presents a comparison of concurrent BU-TD methane emission fluxes monitored from two ventilation shafts at a southwestern Virginia, USA, longwall mine to complement the existing body of studies on satellite-based monitoring of coal mines by incorporating continuous ground-based monitoring with concurrent TD monitoring of methane emission fluxes to address the gap and provide valuable insights into temporal emission patterns. The shafts were monitored on multiple dates between 2020 and 2023. BU monitoring was performed at the exhaust fans, while TD quantifications used PRISMA hyperspectral satellite data and two different wind reanalysis datasets (i.e., GEOS-FP and ERA5). This mine also offered a unique opportunity for BU-TD comparisons before and after ventilation air methane (VAM) oxidizer operation, which was installed at one of the shafts in 2022. The results showed that TD-estimated mean fluxes were generally lower than BU data, which were attributed to quantification difficulties associated with the low albedo caused by heavy vegetation and the terrain of the area. However, despite the discrepancies in mean emissions, both the interquartile range and the data range of the distributions generally overlapped, and the estimates correctly showed the emission trends.

Virginia↗
USGS2021

Virginia and Landsat

From the shores of Jamestown and spreading north, south, and west, the lands that became the State of Virginia were some of the first in North America top experience rapid landscape change from European settlement. Imagery and data from the USGS Landsat series of satellites offer an unparalleled resource for the study, understanding, and preservation of Virginia’s land and water resources. From monitoring the health of water bodies to managing invasive species to planning for a range of climate change effects, the USGS National Land Imaging Program’s stewardship and public delivery of Landsat data have benefitted Virginians in myriad ways.

Virginia↗
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

Seismic response of rock towers at the Trona Pinnacles (U.S.A.) to the 2019 Ridgecrest earthquake sequence: Theory, observations, and models

We analyze the seismic response of a class of fragile geologic features (FGFs), referred to as rock towers (RTs) at the Trona Pinnacles, a group of RTs in southern California that suffered strong shaking during the 2019 Ridgecrest earthquake sequence. FGFs, including RTs, may provide maximum constraints on past earthquake shaking intensity, and thereby support probabilistic seismic hazard assessments (PSHAs). In a rare case study, we explore the hypothesis that RT structural integrity is time dependent, as damage accumulates progressively. We develop finite‐element method (FEM) models of the RTs using photogrammetric shape models. We validate the models by comparing numerical simulations of their response to broadband ground shaking with low‐intensity seismic recordings obtained at the Pinnacles. Results of our simulations are in good agreement with the seismic recordings of actual earthquake aftershocks. We next use the results of the FEM models to analyze the response and evolution of RTs. Our analyses elucidate the influence of geometry over their seismic response, providing a rationale that may explain the rarity of slender RTs at Trona: high‐aspect‐ratio structures that respond in bending develop detrimental tensile stresses that crack the rock, whereas low‐aspect‐ratio ones’ response also includes shearing, which does not compromise material integrity as much as tension. Field measurements with a rebound hammer support this finding, suggesting that the material around the base of slender rocks has been weakened relative to other parts of the RT. We also study how to define simplified mechanical models (“archetypes”) to predict the natural frequencies of RTs. Results from our work illuminate the fundamental mechanisms of seismic response and progressive failure of RTs, and open new avenues of research to potentially incorporate these geologic features as long‐return period constraints on PSHA, in ways analogous to those of the widely used precariously balanced rocks.

California↗
USGS2026

Evaluating groundwater quality influences from oil field operations and other anthropogenic activities in an urban setting, Santa Fe Springs, California

Groundwater quality is often affected by anthropogenic activities in urban settings. This study examines groundwater quality in and around the Santa Fe Springs Oil Field in Los Angeles County, California, where oil and gas production commonly intersects with high density industrial, commercial and residential land uses. Utilizing a combination of new and historical data, we evaluated potential pathways that would allow for oil field formation fluids to migrate into groundwater and whether mixing may have occurred based on the distribution of groundwater and oil field formation fluid tracers in samples. Samples were analyzed for a wide array of constituents including volatile organic compounds, light hydrocarbons, major ions, and various isotopic compositions. Despite evidence of oil field infrastructure providing potential pathways of migration via uncemented annular spaces, casing breaches and historical disposal of oil field formation water in surface ponds, the distribution and occurrence of stable isotopes of water, chloride, boron, and total dissolved solids do not indicate mixing of oil field formation water and groundwater. However, methane isotopic signatures and the presence of heavier alkanes suggest gas from oil-bearing formations have migrated from depth via oil field well infrastructure. Volatile organic compound detections were mainly from manufactured compounds unrelated to oil and gas production, with a relatively limited number of petroleum hydrocarbons also detected. Volatile organic compounds were generally found in wells tapping shallow, modern aged groundwater, indicating anthropogenic activities occurring at or near land surface as the source. Study results suggest that while oil field infrastructure provides migration pathways for oil field formation fluids to be introduced into groundwater, urban land uses not related to oil and gas production are the primary drivers of groundwater quality degradation.

California↗
USGS2026

Detecting earthquakes in noisy real-time GNSS data with deep learning for improved PGD magnitude estimation

To disseminate accurate and useful warnings, earthquake early warning (EEW) systems must quickly determine the size and location of an earthquake to estimate expected shaking. Traditional seismic‐based algorithms tend to underestimate the true magnitudes of large earthquakes, a phenomenon known as magnitude saturation. This limitation motivated the recent inclusion of Global Navigation Satellite Systems (GNSS) data into the U.S. Geological Survey’s ShakeAlert EEW system with the Geodetic First Approximation of Size and Time (GFAST) algorithm because GNSS data do not saturate with large ground motions. However, the noise levels of GNSS data are very high compared with traditional seismic data, which obscures P ‐wave arrivals and can result in less accurate magnitude estimations if displacement amplitudes are low, such as for lower magnitude earthquakes or large source–station distances. In this study, we develop a deep‐learning model that detects earthquakes in GNSS data and use the Ridgecrest, California, earthquake sequence as a case study to demonstrate how the model could act as a filter to reduce the amount of low‐quality data that enters an algorithm like GFAST. To preserve our limited real earthquake data for model inference, we generated a training dataset composed of >700,000 synthetic displacement waveforms. We combined the synthetic waveforms with real‐time GNSS noise to produce realistically noisy training waveforms and then tested our model on additional synthetic data and performed inference using the real data that were held back. We discuss the performance of our trained model on both the unseen synthetic data and real inference data. Our model can be used to selectively filter only high‐quality data where an earthquake signal is observed for input into an algorithm like GFAST (outperforming a simple signal‐to‐noise ratio–based filter) to reduce the error in GFAST’s real‐time earthquake magnitude estimations.

California↗
USGS2024

Insights on gas hydrate formation and growth within an interbedded sand reservoir from well logging at the Qiongdongnan Basin, South China Sea

Although variable well log resolution and its control on saturation estimation has been studied, it has not been directly applied to a specific location to explore the nature of gas hydrate within a sand reservoir. We applied in-situ measurements of resistivities, neutron porosity, and gamma ray at two sites in the Qiongdongnan Basin, South China Sea (QDN-W05–2021 and QDN-W08–2021) to investigate the reservoir parameters of a hydrate-bearing sand reservoir. Our results show that gas hydrate is distributed in 5 zones with a total thickness of 10.7 m and an average saturation of 69% at the QDN-W05–2021 site, while they are distributed in 2 zones with a total thickness of 4.3 m and an average saturation of 49% at the QDN-W08–2021 site. We found that variances in saturations estimated from lateral-extra deep button (RX), phase shift (P40H-P40L), and attenuation (A40H-A40L) resistivities within the laterally mapped continuous sand body were affected by the nature of gas hydrate occurrences. Results indicate gas hydrate forms and accumulates at the center of the sand layer and tends to be less or not present toward the top and base. Integrated with seismic data, the in-situ measurements provide insights in the evolution of a mushroom-shaped, hydrate-gas reservoir system. In the system, free gas is likely horizontally transported from the top-center of the gas chimney to the surrounding areas in the early stage dominated by a warm-gas environment, whereas hydrate forms in the opposite pathway starting from the surrounding areas in the following stage with temperature reducing. Our study suggests that high-resolution in-situ measurements not only are a tool to identify the physical properties, but also can be used to help explain the physical process of hydrate growth and accumulation.

Marine Geology↗
USGS2026

Cambrian and Ordovician stratigraphy, conodont biostratigraphy, and microfacies analysis to support 1:24,000-scale geologic mapping of the southern Lake Champlain valley, New York and Vermont

Introduction Geologic mapping in the southern Lake Champlain valley of New York and Vermont (fig. 1) has required evaluation of stratigraphic nomenclature used since the early 1900s. The paleogeography of the Cambrian and Ordovician adjacent to the Adirondack Highlands, the high-relief mountains of Proterozoic igneous and metamorphic rocks of northern New York, has impacted the lithostratigraphic succession, facies changes, and several hiatuses that occur within the package of clastic and carbonate rocks that demonstrates overall deepening consistent from the southern to northern Appalachians. The stratigraphic nomenclature used for the geologic mapping of the Cambrian and Ordovician strata of the study area is derived from evaluation of names put forth by previous workers from areas of northern New York and western Vermont. Descriptions of type localities or type sections and other criteria set forth by the North American Stratigraphic Code (North American Commission on Stratigraphic Nomenclature [NACSN], 2021) were consulted. The North American Stratigraphic Code states that lithostratigraphic units, such as the fundamental unit, the formation, be defined by lithic character and be mappable (NACSN, 2021, Article 24). However, some early workers (Walcott, 1912; Kay, 1937; Fisher and Hanson, 1951; Oxley and Kay, 1959) have defined stratigraphic units based on fossil content, which may or may not be facies dependent, and hence may not be mappable as lithostratigraphic units. Although preservation of older names takes priority in stratigraphic nomenclature (NACSN, 2021, Article 7), some do not have adequately defined stratotypes or are located a distance away with different lithologies than that in the study area. Biostratigraphy can be a helpful tool for understanding correlations and facies changes. Herein, conodont biostratigraphy is utilized to date and correlate separate lithologic units. Further, microfacies analyses of units through inspection of petrographic thin sections provide paleogeographic information that helps in understanding the genesis of geologic units.

New York, Vermont↗
USGS2025

Multi-scale geophysical mapping of the brine and bedrock surfaces along the Dolores River, Paradox Valley, Colorado, December 2023

Total dissolved solids derived from salt dome–sourced brine in the underlying alluvial aquifer substantially increase with distance in the reach of the Dolores River that passes through Paradox Valley in southwestern Colorado. The area has been the site of salinity control operations since the 1990s to reduce salt loading to the downstream Colorado River. Previous airborne and ground/water-based electromagnetic (EM) geophysical data have successfully mapped the top of the brine surface, albeit with relatively coarse near-surface resolution and limited spatial coverage. This present December 2023 study used ground-based high-resolution EM and passive seismic (horizontal-to-vertical spectral ratio, HVSR) tools to map in detail the depth and thickness of the brine zone in the alluvial aquifer (top of the brine down to bedrock contact) in areas immediately surrounding the Dolores River where previous airborne EM (AEM) results indicated brine within 10 m of land surface. Results indicate the deepest bedrock is generally associated with the shallowest brine and local depressions in the collapse breccia (caprock to the Paradox Formation salt) may facilitate vertical migration of brine into the alluvial aquifer. Additionally, the ground-based EM mapping corroborated general patterns in depth to brine that were observed in previous AEM results while also revealing additional detail, including suspected focused brine discharge zones to the Dolores River. A river-based survey wherein EM data, channel depth and river water–specific conductance information were collected augmented these findings and indicated specific areas on both the western and eastern sides of the river where focused brine discharge may occur. This study comprises a large-scale, ground- and water-based geophysical mapping effort, including hundreds of HVSR soundings and 100s of kilometres of EM data, which were successfully translated into digital brine and bedrock surfaces that could be incorporated into groundwater modelling efforts, future well siting or other decision-making.

Colorado↗
USGS2026

Development of liquefaction-targeted design basis in U.S. seismic provisions

Seismic design criteria for new buildings in the United States have historically focused on life safety performance objectives through minimizing the potential for structural collapse. Development of design criteria to meet this performance objective has evolved over time, leading to the current, risk-targeted maximum considered earthquake (MCE R ) design basis. Corresponding MCE R seismic design loads incorporate the full range of probabilistic ground motion hazard information available from U.S. Geological Survey National Seismic Hazard Models, as well as a representation of the uncertainty in structural collapse capacity. Despite these considerable advances in structural design criteria, guidelines for geotechnical hazards such as liquefaction-induced damage, which continue to be based on uniform ground-shaking hazard (i.e., MCE ground motions), are not well-connected with risk-based, collapse prevention performance objectives. This study, undertaken as part of Building Seismic Safety Council efforts to update recommended seismic provisions through the National Earthquake Hazards Reduction Program, presents probabilistic, liquefaction-targeted design objectives and corresponding ground motion parameters, with a focus on improving consistency in first-level screening criteria for liquefaction hazard assessment. This study identifies a potential maximum acceptable annualized probability of liquefaction triggering ( P L,max ) of 2.5% in 50 years, above which liquefaction consequences and potential mitigation measures would be considered. The P L,max -based design basis is calibrated to effective probabilistic liquefaction hazard levels obtained in practice using current MCE peak ground accelerations, and could be implemented via a new liquefaction-targeted maximum considered earthquake (MCE L ) peak ground acceleration ( PGA L ). The proposed PGA L is derived from probabilistic liquefaction hazard curves and includes uncertainties inherent to liquefaction hazard modeling. The potential design effects of the proposed PGA L are demonstrated via comparison with current MCE PGA levels, effects on first-level liquefaction screening analyses, and improvements in the consistency of P L,max levels across the United States, and for a wide range of site conditions.

conterminous United States↗
USGS2026

Evaluating machine learning approaches to identify and predict oil and gas produced water lithium concentrations

Recently, the demand for battery-grade lithium has substantially increased, largely due to electrification of the transportation sector. The search for new lithium sources has turned to produced waters (frequently brines), a large-volume wastewater by-product of oil and gas extraction. Geochemical analysis indicates the presence of varying concentrations of lithium from produced water samples collected across the United States and represented in the U.S. Geological Survey’s National Produced Water Geochemical Database, as well as mixtures of Marcellus Shale produced water included in the Pennsylvania Department of Environmental Protection’s Oil and Gas Well Waste Reports. We first examined whether the geochemical signature of the lithium-bearing produced waters is sufficiently distinct so that machine learning (ML) can be used to correctly classify samples to the formation of origin. The produced water sample data used to assess classification accuracy were from the Marcellus Shale, Utica Shale and Point Pleasant Formation (Utica), and Smackover Formation oil and gas wells. Further, we evaluated the potential for ML to accurately classify Marcellus Shale produced water spatially (i.e., northeast versus southwest Pennsylvania). We then investigated whether ML algorithms applied to a suite of geochemical concentration data (i.e. Ba, Br, Cl, K, Mg, Sr) may be used to predict the lithium concentration of an unknown sample. Finally, we applied an estimated economic lithium grade cutoff of 150 milligrams per liter (mg/l) and assessed the utility of ML to predict whether a produced water sample would fall above or below the grade cutoff based on the suite of geochemical parameters. Four machine learning algorithms—Random Forest (RF), Gradient Boosting Trees (GBT), Extreme Boosting (XGBoost), and Deep Neural Networks (DNN) were assessed. This study successfully demonstrates that all four machine learning methods can precisely and accurately estimate lithium concentrations and geologic formation classification. The products of this study contribute to the growing body of knowledge aimed at expanding the lithium resource base within the United States.

Alabama, Arkansas, Florida, Georgia, Louisiana, Mi↗
USGS2024

Groundwater quality near the Placerita Oil Field, California, 2018

Groundwater-quality data and potential fluid-migration pathways near the Placerita Oil Field in Los Angeles County, California, were examined by the U.S. Geological Survey to determine if oil-field fluids (water and gas from oil-producing and non-producing zones) have mixed with groundwater resources. Six of the 13 new groundwater samples collected for this study contained petroleum hydrocarbons, thermogenic gas, inorganic chemical signatures, and (or) isotopic values consistent with potential mixing with fluids from hydrocarbon-bearing formations. For historical groundwater samples, benzene was the most detected petroleum hydrocarbon. The historical groundwater samples with a benzene concentration greater than 0.5 micrograms per liter were from environmental monitoring wells at industrial or commercial facilities unrelated to oil and gas development that, in many cases, have identified soil or groundwater contamination and were not typically analyzed for other constituents that could provide additional lines of evidence for potential mixing with oil-field fluids. Methane was not detected in any of the 12 historical samples with a reported measurement. Reviewing historical data revealed factors that could potentially adversely affect groundwater quality in the study area. These factors include modified hydraulic gradients caused by large volumes of water extracted from the main production area and reinjected downgradient into nonproducing zones, well-barrier failures in wells constructed in the northern part of the oil field before the 1970s, well-barrier failures in produced-water disposal wells downgradient from the main production area, and naturally occurring hydrocarbons at shallow intervals. The groundwater samples most geochemically similar to samples from hydrocarbon-bearing formations were in areas where hydrocarbons are naturally occurring at shallow intervals and where oil development is at shallow depths. Additional data for hydraulic heads, water quality, and formation temperatures at multiple depths in areas with large injection volumes and well-integrity issues are needed to evaluate whether those factors have contributed to mixing between fluids from oil-producing or injection formations and groundwater resources.

California↗
USGS2025

Mapping potential sensitivity to hydrogeomorphic change in the UMRS riverscape

In 2020 the U.S. Geological Survey (USGS), as part of the Upper Mississippi River Restoration (UMRR) Program, began a new project to characterize potential hydrogeomorphic change associated with hydrogeomorphic units (HGUs) and their catenae (units linked by their association with sediment sources and flow origins). The goal of the project was to develop a geographic information system (GIS) database of HGUs for the Upper Mississippi River System (UMRS) available to both scientists and river managers working on UMRR studies and HREP planning and design studies. The characterization was based on a hydrogeomorphic change hierarchical classification developed previously for the UMRS. The products were generated with automated techniques in a GIS using systemic datasets. Landforms were mapped from the 2015 UMRS topobathymetric dataset with geomorphon (shape-based) tools tailored for the large riverscape, valley bottom environments in the UMRS. A clustering analysis was applied to the resulting landforms to identify HGUs associated with zones of perennial low flows, bankfull flows, and overbank floodplains. Catenae were assembled based on the proximity of the units to the main channel, tributary mouths, and side channels from previously published aquatic areas (USACE, 2018) coupled with least-cost flowpath linkages between potential sediment origins and planform change units developed by Rogala, Fitzpatrick, and Henderson (2020). These GIS-based analyses were successful at identifying a range of HGUs using an automated technique with available data across the entire riverscape, with emphasis on those that have the potential for hydrogeomorphic change. Most of the resulting features are depositional, as expected in a large river system. However, this is the first attempt of linking tributary inputs, side channel erosion and levee breaches with their depositional counterparts. The approach was successfully piloted in Pools 8 and 10 in the Upper Impounded Reach and Pool 14 in the Lower Impounded Reach, with next steps for application in reaches of the unimpounded section and Illinois River. This report emphasizes results from Pool 10, which was the focus of most of our attention during the pilot phase.

Illinois, Indiana, Iowa, Minnesota, Missouri, Sout↗
USGS2025

Land cover change within wetland complexes at Dixie Meadows, Churchill County, Nevada: 2015 – 2023

Dixie Meadows, Nevada, is a system of geothermal springs and seeps that feed a complex of marshes and wetland meadows that are located within lands managed by the Bureau of Land Management (BLM) and the Department of Defense (DOD). A previous U.S. Geological Survey report documented variability in satellite imagery-based land cover classifications for seven wetland complexes at near monthly time intervals between October 2015 and January 2022. This report presents additional data, extending analysis to November 2023. Land cover classifications between October 2015 and November 2023 demonstrated an association between vegetation cover characteristics and surface moisture, with Class 1 having dry, bare soil or sparse upland vegetation, Class 2 having moist, bare soil or sparse to small vegetation, Class 3 having dense green vegetation with potentially saturated soil conditions, Class 4 having a mix of shallow surface water, saturated soil, and dense green vegetation, and Class 5 having open surface water. Most of the wetland complexes occur close to spring outflows primarily within land managed by the DOD, though portions are also within BLM lands. The intervening and surrounding landscape outside of the wetland complexes assessed in this study are managed by the BLM. As a result, Class 1 land covers had the largest areal coverage for BLM managed lands. Classes 2 and 3 land covers were primarily mapped inside the wetland complexes and thus had the largest area coverage within DOD managed lands. Class 4 was almost exclusively mapped within the wetland complexes and thus was largely contained within DOD managed lands. Class 5 (open water) was exclusively mapped in and adjacent to a single wetland complex with catchment ponds on land managed by the BLM. The distribution of these land cover classes over the study period was seasonally and annually variable. Land cover areas of Classes 1 and 2 were larger during the spring months. Conversely, land cover areas of Classes 3 and 4 tended to be greatest during the summer or fall. These patterns might be influenced by differences in seasonal water sources and phenology.

Nevada↗
USGS1998

Ground-water flow in the surficial aquifer system and potential movement of contaminants from selected waste-disposal sites at Cecil Field Naval Air Station, Jacksonville, Florida

As part of the Installation Restoration Program, Cecil Field Naval Air Station, Jacksonville, Florida, is considering remedialaction alternatives to control the possible movement of contaminants from sites that may discharge to the surface. This requires a quantifiable understanding of ground-water flow through the surficial aquifer system and how the system will respond to any future stresses. The geologic units of interest in the study area consist of sediments of Holocene to Miocene age that extend from land surface to the base of the Hawthorn Group. The hydrogeology within the study area was determined from gamma-ray and geologists' logs. Ground-water flow through the surficial aquifer system was simulated with a seven-layer, finite-difference model that extended vertically from the water table to the top of the Upper Floridan aquifer. Results from the calibrated model were based on a long-term recharge rate of 6 inches per year, which fell in the range of 4 to 10 inches per year, estimated using stream hydrograph separation methods. More than 80 percent of ground-water flow circulates within the surficial-sand aquifer, which indicates that most contaminant movement also can be expected to move through the surficial-sand aquifer alone. The surficial-sand aquifer is the uppermost unit of the surficial aquifer system. Particle-tracking results showed that the distances of most flow paths were 1,500 feet or less from a given site to its discharge point. For an assumed effective porosity of 20 percent, typical traveltimes are 40 years or less. At all of the sites investigated, particles released 10 feet below the water table had shorter traveltimes than those released 40 feet below the water table. Traveltimes from contaminated sites to their point of discharge ranged from 2 to 300 years. The contributing areas of the domestic supply wells are not very extensive. The shortest traveltimes for particles to reach the domestic supply wells from their respective contributing areas ranged from 70 to 200 years.

Florida↗
USGS2026

Uncertainty quantification of geologic energy storage in depleted gas reservoirs using material balance equations embedded in a hierarchical errors-in-variables model

The storage potential for gas in geologic settings, such as depleted hydrocarbon reservoirs and solution-mined salt caverns, is becoming salient to future energy infrastructure planning. Technologies such as carbon capture, utilization, and storage, carbon dioxide-enhanced oil recovery, and natural gas and hydrogen storage help meet growing energy demands, reduce carbon emissions to meet climate goals, and provide energy security amid geopolitical uncertainties. Therefore, estimates of underground gas storage capacity could be useful for efficiently navigating the energy transitions. Material balance is a fundamental method in reservoir engineering for estimating original gas in place and potential storage capacity at the scale necessary for national assessments of subsurface pore space resources. However, the deterministic method cannot accommodate multiple data sources or quantify uncertainty in predictions. In this study, a method that embeds material balance equations within a hierarchical errors-in-variables model is proposed which allows the estimation of the distributions of reservoir properties needed for assessments. Uncertainties associated with these reservoir properties have traditionally been expert-elicited, whereas the uncertainty estimates from the proposed models are data-driven. Capacity and uncertainty estimates can be used in a probabilistic resource assessment, supplementing information traditionally used by assessors or even replacing this expert elicitation step when data are unavailable. Various regression models are compared in a case study of the Michigan Basin, a large contributor to the United States’ current natural gas storage capacity. In particular, errors-in-variables models help ameliorate regression dilution and can quantify uncertainty in predictions of pressure in addition to storage capacity. Overfitting is addressed by quantifying generalization error and model averaging in simple and stratified cross-validation against reported working gas capacity, representing the varying quality and quantity of available data. Incorporating a statistical framework into existing numerical methods in reservoir engineering can improve the quality of estimation, and in particular, this method brings rigor to uncertainty quantification as part of a larger effort by the U.S. Geological Survey to assess domestic energy gas storage resources in depleted hydrocarbon reservoirs.

Mathematical Geosciences↗
USGS2024

A comparative analysis of OpenET for evaluating evapotranspiration in California almond orchards

The almond industry in California faces water management challenges that are being exacerbated by droughts, climate change, and groundwater sustainability legislation. The Tree-crop Remote sensing of Evapotranspiration eXperiment (T-REX) aims to explore opportunities to improve precision irrigation management for woody perennial cropping systems. Almond orchards in the California Central Valley were equipped with eddy covariance flux measurements to evaluate satellite remote sensing-based evapotranspiration (RSET) models. OpenET provides high-resolution (30-m spatial and daily temporal) RSET data, synthesizing decades of research for practical water management. This study provides an evaluation of OpenET performance at six almond sites covering a large range in soils, age, and variety. It also compares OpenET ensemble evapotranspiration (ET) data with applied irrigation and precipitation records over an additional 148 almond orchards located in the Central Valley of California. Results show OpenET models, including the ensemble ET value, produced reasonable and actionable ET values, with overall coefficient of determination (R 2 ) and mean absolute error values of 0.73- and 0.95-mm d −1 at the daily time step, respectively. However, given the temporal sampling of Landsat (8-day revisit) and the interpolation methods used, the assessed ET models had difficulty in capturing short-term variability in almond ET; for example, the rapid decline in measured ET observed as a response to lack of irrigation preceding and during almond harvest. The study also drew attention to the spatial complexity in scenarios where irrigated orchards are surrounded by hot/dry areas, causing discrepancies between measured and modeled ET values. In comparison with irrigation records, OpenET ensemble ET was capable of quantifying water input (applied irrigation + precipitation) in almond orchards to within 13 % when evaluating monthly data. Initial results presented here reinforce the idea that RSET models, such as in OpenET, are powerful tools, yet their application requires nuanced understanding and careful consideration of local conditions.

California↗
USGS2024

Distribution of ancient carbon in groundwater and soil gas from degradation of petroleum near the Red Hill Bulk Fuel Storage Facility, O‘ahu, Hawai‘i

The groundwater below the Red Hill Bulk Fuel Storage Facility (the facility) in Oʻahu, Hawaiʻi, contains fuel compounds from past spills. This study used carbon-14 analyses to distinguish fuel-derived carbon from background carbon, along with other biodegradation indicators, to address two goals: (1) determine the extent and migration direction of groundwater affected by residual fuel below the facility and (2) determine if residual fuel locations in the subsurface could be identified by analyzing soil gas at the surface above the facility. Groundwater from 19 wells was sampled between September 2022 and April 2023. Nonvolatile dissolved organic carbon (NVDOC) from a well presumed to be unaffected by past spills contained 38 percent ancient carbon indicating a natural source of ancient carbon in the subsurface. The NVDOC concentrations and ancient carbon percentages indicate fuel biodegradation products are likely present on the north and south of Red Hill with the greatest effects at well RHMW02 near the 2014 spill site. The NVDOC concentrations are almost three times higher than diesel range organic (DRO) concentrations in groundwater from the same sites. Major ion data indicate that iron reduction is an important biodegradation process. Soil probe samples and soil carbon traps were used to determine the carbon-14 content of soil carbon dioxide. Ancient carbon from fuel biodegradation was not detected at any soil probe or carbon trap site in contrast to a 2017 study which reported ancient carbon detections. A reanalysis of the 2017 results using a range of local values for background carbon-14 indicates that ancient carbon from fuel biodegradation was probably only detected in lower tunnel exhaust system samples and not in any soil carbon trap samples. Measurements of carbon dioxide efflux with a dynamic closed chamber were highly variable. The soil gas results indicate that soil gas measurements at land surface were not useful for detecting residual fuel at the facility.

Hawaii↗