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USGS2025

Estimated ultimate recovery (EUR) Prediction for Eagle Ford Shale using integrated datasets and artificial neural networks

The estimated ultimate recovery (EUR) is an important parameter for forecasting oil and gas production and informing decisions regarding field development strategies. In this study, we combined site-specific geologic, completion, and operational parameters with the predictive capabilities of machine learning (ML) models to predict EURs of the wells for the Eagle Ford Marl Continuous Oil Assessment Unit. We developed an extensive dataset of wells that have produced from the lower and upper Eagle Ford Shale intervals and reduced the model complexity using principal component analysis. We tested the ML models and estimated the sensitivities of ML-predicted EURs to changes in the values of different input variables. The results of applying the optimized ML model to the Eagle Ford suggest that the approach developed in this study could be promising. The ML estimates of the EURs fit the DCA-based values with an R 2 ~ 0.9 and a mean absolute error of ~36 × 10 3 bbl. In the lower Eagle Ford Shale, the EUR estimates were found to be most sensitive to changes in porosity, net thickness of the interval, clay volume, and the API gravity of the oil; and that in the upper Eagle Ford Shale they were most sensitive to changes in the total organic carbon and water saturation, which suggests that it could be important to consider these parameters in assessing these intervals or close analogs.

Louisiana, Mississippi, Texas↗
USGS2026

Hyperspectral narrowband imaging spectroscopy: A new paradigm for Earth observation

This editorial introduces the Special Issue entitled “Hyperspectral Narrowband Imaging Spectroscopy: A New Paradigm for Earth Observation” in the August 2026 issue of Photogrammetric Engineering & Remote Sensing (PE&RS), the flagship journal of the American Society for Photogrammetry and Remote Sensing (ASPRS). This volume represents the fourth dedicated hyperspectral special issue published in PE&RS, following earlier contributions by Thenkabail et al. (2025, 2024a, 2024b), and continues ASPRS’s commitment to advancing cutting‑edge imaging spectroscopy research and its applications across Earth system science. Remote sensing is undergoing one of the most profound transformations in its history. The emergence of hyperspectral narrowband (HNB) imaging spectroscopy data, capable of acquiring hundreds of contiguous, narrow spectral bands, has shifted the discipline from observing Earth in a handful of broad spectral windows to capturing continuous spectral signatures of the Planet. This transition marks a decisive break from the multispectral paradigm that has dominated satellite remote sensing for nearly five decades, driven by the advent of new orbital imaging spectrometers such as EnMAP, PRISMA, and NASA’s EMIT, and by the forthcoming Surface Biology and Geology (SBG) mission ((Pires Silva et al., 2026; Bourriz et al., 2025; Thenkabail et al., 2025; Chabrillat et al., 2024; Aneece et al., 2024; Dave et al., 2024; Thenkabail et al., 2024a; Thenkabail et al., 2024b; Thenkabail, 2024a; Thenkabail, 2024b; Thompson et al., 2022; Kokaly et al., 2022; Aneece & Thenkabail, 2022; Cawse Nicholson et al., 2021; Guanter et al., 2021; Vangi et al., 2021; Thenkabail et al., 2021). These missions (e.g., Table 1) deliver unprecedented spectral fidelity, improved signal to noise ratios, and global coverage capabilities, enabling a new era of quantitative, spectroscopy based Earth observation. Where multispectral broadbands (MBBs) provide only a few discrete measurements along the electromagnetic spectrum, HNB systems deliver rich, diagnostic information that enables scientists to characterize Earth’s surface with unprecedented biochemical, biophysical, and structural detail (Figure 1a, 1b). The implications for environmental monitoring, agriculture, water resources, and mineral exploration are profound. Several overarching themes emerge: • Spectral fidelity matters. The ability to preserve subtle absorption features is essential for mineral mapping, vegetation trait retrieval, and biochemical modeling. • AI and deep learning are indispensable. From destriping to classification, modern analytics must be scalable, label‑efficient, and capable of exploiting the full spectral–spatial richness of HNB data. • Physics‑based and data‑driven approaches must converge. Radiative transfer models such as PROSAIL, enhanced with localized soil parameterizations, remain foundational for biophysical retrievals and model‑based inference. • Dimensionality reduction and feature extraction are critical. Techniques such as L1‑ISOMAP demonstrate that intelligent manifold learning can unlock the structure of fused, high‑dimensional datasets. • Next‑generation architectures must be interactive and multimodal. ICTNet exemplifies the future of hyperspectral classification: hybrid, synergistic, and capable of modeling both local textures and global spectral dependencies.

Photogrammetric Engineering and Remote Sensing (PE↗
USGS2025

Hydrogeology of unconsolidated and bedrock aquifers along the Salmon River, including Malone, Franklin County, New York

The U.S. Geological Survey, in cooperation with the New York State Department of Environmental Conservation, investigated the hydrogeology of the unconsolidated and bedrock aquifers along the Salmon River corridor in northern Franklin County, New York. The study area covers roughly 147 square miles and includes the village of Malone and parts of the Towns of Malone, Bellmont, Burke, Constable, Westville, Bangor, Duane, and Franklin, New York. Groundwater is the primary source for water supply within the study area. Eighty-three percent of all residents use public water supplied from two production wells that draw water from a thick, highly productive sand-and-gravel aquifer likely receiving induced infiltration from the Salmon River. Twenty-four percent of the 187 verified domestic wells in the study area outside of the production well service area boundary are screened in typically discontinuous deposits of stratified sand and sand and gravel. Seventy-six percent of the wells are completed in bedrock aquifers including the Potsdam Sandstone, metamorphic rocks, Theresa Formation, and unknown bedrock. Characterizing and understanding potential groundwater resources is critical for protecting the quality of the groundwater. The information in this report may be used to guide delineation of groundwater contributing areas, assess potential threats to aquifers from both point and nonpoint sources, respond to contamination from spills or leaks from underground storage facilities or other sources, and to support assessments for future development of municipal water supplies.

New York↗
USGS2026

Multi-site evaluation of a postfire debris-flow runout forecast method

Postfire debris flows pose a hazard to human life, property, and infrastructure when they travel from steep source areas to urbanized alluvial fans or other developed areas. Existing methods for rapid (<1 week) postfire debris-flow hazard assessment document the increase in the likelihood and size of debris flows as the magnitude of high-intensity rain necessary to initiate debris flows increases but do not indicate the extent of downstream debris-flow runout. Although many models for the simulation of debris-flow motion are available, there is no established approach for using these models to delineate locations susceptible to postfire debris-flow runout that (a) is feasible to use at the spatial scale of an entire fire; (b) is appropriate for runout onto unconfined areas; (c) reproduces observed relations between runout and rainfall intensity; and (d) characterizes inherent uncertainty in runout, even without spatiotemporally variable rainfall. We propose and evaluate a method for generating postfire debris-flow runout hazard maps that has all the above qualities. Selection of case studies prioritized events triggered by a range of rainfall intensities, locations within and outside of southern California, and observed runout onto unconfined topography. Qualitative and quantitative assessment of performance for four events indicate that simulation results broadly match observations albeit with some discrepancies at a scale larger than structure or land parcel level (approximately 20-m by 20-m). The method may be used to identify potentially hazardous areas immediately following a fire and to provide approximate runout forecasts when a storm is imminent.

Arizona, California↗
USGS2025

Uncertainty reduction for subaerial landslide-tsunami hazards

Subaerial rock slopes may generate a tsunami by rapidly moving into the water. Large uncertainty in landslide characteristics propagates into large uncertainty in tsunami hazard, making hazard assessment more difficult for land and emergency managers. Once a potentially tsunamigenic landslide is identified, it may not be clear which landslide characteristics contribute most significantly to uncertainty in the tsunami hazard. Our aim is to document the relative worth of different landslide characteristics (e.g., size, material properties) for reducing uncertainty in landslide-tsunami hazard assessments. Isolating the relative importance of specific landslide characteristics may inform prioritization of data collection and improve efficiency in understanding hazard. To accomplish this, we generated a set of 288 landslide-tsunami simulations in which we systematically varied the size and material properties of possible failure extents at the Barry Arm landslide complex in northwestern Prince William Sound, Alaska, USA. We find that for landslides smaller than the receiving waterbody, the landslide volume has the strongest effect on resulting wave characteristics and thus the highest leverage on reducing uncertainty in tsunami hazard. In contrast, for landslides substantially larger than the waterbody, the duration of rapid movement of the landslide has the highest leverage. Based on our results, we propose a classification scheme for subaerial landslides based on the relative size of the landslide and waterbody. Additionally, our results support the generation of a tsunami height transfer function between existing tide gages and a nearby coastal city. These results have direct implications for the practice of operational early warning.

Alaska↗
USGS2024

A benchmark dataset and workflow for landslide susceptibility zonation

Landslide susceptibility shows the spatial likelihood of landslide occurrence in a specific geographical area and is a relevant tool for mitigating the impact of landslides worldwide. As such, it is the subject of countless scientific studies. Many methods exist for generating a susceptibility map, mostly falling under the definition of statistical or machine learning. These models try to solve a classification problem: given a collection of spatial variables, and their combination associated with landslide presence or absence, a model should be trained, tested to reproduce the target outcome, and eventually applied to unseen data. Contrary to many fields of science that use machine learning for specific tasks, no reference data exist to assess the performance of a given method for landslide susceptibility. Here, we propose a benchmark dataset consisting of 7360 slope units encompassing an area of about 4,100 km 2 "> 4,100 km 2 in Central Italy. Using the dataset, we tried to answer two open questions in landslide research: (1) what effect does the human variability have in creating susceptibility models; (2) how can we develop a reproducible workflow for allowing meaningful model comparisons within the landslide susceptibility research community. With these questions in mind, we released a preliminary version of the dataset, along with a “call for collaboration,” aimed at collecting different calculations using the proposed data, and leaving the freedom of implementation to the respondents. Contributions were different in many respects, including classification methods, use of predictors, implementation of training/validation, and performance assessment. That feedback suggested refining the initial dataset, and constraining the implementation workflow. This resulted in a final benchmark dataset and landslide susceptibility maps obtained with many classification methods. Values of area under the receiver operating characteristic curve obtained with the final benchmark dataset were rather similar, as an effect of constraints on training, cross–validation, and use of data. Brier score results show larger variability, instead, ascribed to different model predictive abilities. Correlation plots show similarities between results of different methods applied by the same group, ascribed to a residual implementation dependence. We stress that the experiment did not intend to select the “best” method but only to establish a first benchmark dataset and workflow, that may be useful as a standard reference for calculations by other scholars. The experiment, to our knowledge, is the first of its kind for landslide susceptibility modeling. The data and workflow presented here comparatively assess the performance of independent methods for landslide susceptibility and we suggest the benchmark approach as a best practice for quantitative research in geosciences.

Earth-Science Reviews↗
USGS2025

Reconnaissance of potential alternate water supply sources for the City of Gary, West Virginia

Seven potential sources of water, consisting of free-flowing discharge from abandoned coal mines at six locations and one abandoned flooded underground coal mine air shaft, were sampled for chemical analysis to assess the quality of the groundwater emanating from the seven mine sources. The six free-flowing mine discharge sources were also assessed for discharge by current-meter measurements on two separate occasions. The U.S. Geological Survey assessed these seven sources to provide information to the City of Gary, West Virginia (W. Va.), and the City of Gary’s consulting engineer with groundwater-quality and flow data to allow them to assess the seven sites as potential alternate sources of water for the City of Gary to augment its existing supply. For the six sites where discharge could be measured, discharge ranged from a minimum of 0.082 cubic feet per second (ft 3 /s) to a maximum of 3.685 ft 3 /s. Of the six sites measured, only two, Harmon Branch at Thorpe, W. Va. (USGS site 372201081303501) and the abandoned public-supply water wells near Havaco, W. Va. (USGS site 372358081344601), had discharge in excess of 1.00 ft 3 /s. Discharge from the abandoned public supply wells was 3.685 ft 3 /s on September 20, 2023, and 2.888 ft 3 /s on October 16, 2023, and discharge from Harmon Branch at Thorpe, W. Va., was 1.049 ft 3 /s on September 22, 2023, and 1.038 ft 3 /s on October 17, 2023. Discharge in the abandoned underground mine air shaft (USGS site 372224081340901) could not be assessed, but the air shaft drains an abandoned mine that likely contains water stored in approximately 1.7 square miles (mi 2 ) of abandoned underground coal mines in the Pocahontas No. 3 coal seam, and possibly an additional 0.9 mi 2 of leakage from the overlying Pocahontas No. 4 coal seam. Discharge for the six sites measured for the study was measured during a period between September 20 and October 18, 2023, and corresponded to the 12th to the 15th percentile of flow-duration statistics for the Tug Fork downstream of Elkhorn Creek at Welch, W. Va. streamgage (USGS site 03212750). Water-quality data for the seven sites sampled overall were acceptable with respect to drinking water standards. Of the 203 constituents analyzed, only a few failed to meet applicable U.S. Environmental Protection Agency (EPA) drinking water standards. Iron exceeded the 300 micrograms per liter (μg/L) secondary maximum contaminant level (SMCL) at only 1 of the 7 sites (14.3 percent) sampled. Iron concentrations ranged from a minimum of less than (<) 5.00 μg/L to a maximum of 724 μg/L with a median concentration of 7.62 μg/L. Manganese exceeded the 50.0 μg/L SMCL at 2 of the 7 sites (28.6 percent) sampled. Manganese concentrations ranged from a minimum of 1.93 μg/L to a maximum of 271 μg/L with a median concentration of 4.03 μg/L. No sites sampled exceeded the arsenic maximum contaminant level (MCL) of 10 μg/L. Arsenic concentrations ranged from a minimum of <0.100 μg/L to a maximum of 2.35 μg/L with a median arsenic concentration of 0.200 μg/L. None of the seven sites sampled for selenium for this study exceeded the EPA MCL of 50.0 μg/L. Selenium concentrations ranged from a minimum of <0.050 μg/L to a maximum of 5.26 μg/L with a median concentration of 3.21 μg/L. All seven sites were sampled for volatile organic compounds (VOCs), semivolatile organic compounds (SVOCs), and polychlorinated biphenyls (PCBs), but most had concentrations below the detection limit. Of the 10 PCB compounds analyzed for the seven sites sampled, none contained detectable concentrations of PCBs or Aroclor compounds. Of the 44 SVOCs analyzed at each of the seven sites sampled, only 1 SVOC, acenaphthene, was detected, at a concentration of 0.02 μg/L. Of the 96 VOCs analyzed, from each of the seven sites sampled, only two were found at detectable concentrations. Trichloromethane was detected only at 1 of the 7 (14.3 percent) sites sampled at a concentration of 0.027 μg/L, and benzene was detected at the same site and 3 additional sites (4 of the 7 sites or 57.1 percent of the sites sampled) at concentrations of 0.028, 0.029, 0.021, and 0.035 μg/L, but none exceeded the EPA MCL for benzene of 5.00 μg/L. Total coliform bacteria are ubiquitous in the environment, and their presence only suggests the potential for contamination by near-surface processes. Escherichia coli ( E. coli ) bacteria are derived from either human or animal fecal material and can be an indicator of potential contamination by pathogenic bacteria or viruses. Total coliform bacteria were detected at all 7 sites sampled at concentrations ranging from 17.5 to greater than (>) 2,420 most probable number per 100 mL (MPN/100 mL) of sample, with a median total coliform concentration of 1,553 MPN/100 mL. Escherichia coli bacteria were detected at 4 of the 7 sites sampled at concentrations ranging from <1 to 11.9 MPN/100 mL, with a median E. coli concentration of 5.1 MPN/100 mL.

West Virginia↗
USGS2025

Physicochemical properties and bioreactivity of sub-10 µm geogenic particles: Comparison of volcanic ash and desert dust

Exposure to ambient particulate matter (PM) with an aerodynamic diameter of <10 μm (PM 10 ) is a well-established health hazard. There is increasing evidence that geogenic (Earth-derived) particles can induce adverse biological effects upon inhalation, though there is high variability in particle bioreactivity that is associated with particle source and physicochemical properties. In this study, we investigated physicochemical properties and biological reactivity of volcanic ash from the April 2021 eruption of La Soufrière volcano, St. Vincent, and two desert dust samples: a standardized test dust from Arizona and an aeolian Gobi Desert dust sampled in China. We determined particle size, morphology, mineralogy, surface texture and chemistry in sub-10 μm material to investigate associations between particle physicochemical properties and observed bioreactivity. We assessed cellular responses (cytotoxic and pro-inflammatory effects) to acute particle exposures (24 hr) in monocultures at the air-liquid interface using two types of cells of the human airways: BEAS-2B bronchial epithelial cells and A549 alveolar type II epithelial cells. In acellular assays, we also assessed particle oxidative potential and the presence of microorganisms. The results showed that volcanic ash and desert dust exhibit intrinsically different particle morphology, surface textures and chemistry, and variable mineralogical content. We found that Gobi Desert dust is more bioreactive than freshly erupted volcanic ash and Arizona test dust, which is possibly linked to the presence of microorganisms (bacteria) and/or nanoscale elongated silicate minerals (potentially clay such as illite or vermiculite) on particle surfaces.

GeoHealth↗
USGS2023

Evaluation of models for estimating hydraulic conductivity in glacial aquifers from NMR logging

Nuclear magnetic resonance (NMR) logging is a promising method for estimating hydraulic conductivity ( K ). During the past ∼60 years, NMR logging has been used for petroleum applications, and different models have been developed for deriving estimates of permeability. These models involve calibration parameters whose values were determined through decades of research on sandstones and carbonates. We assessed the use of five models to derive estimates of K in glacial aquifers from NMR logging data acquired in two wells at each of two field sites in central Wisconsin, USA. Measurements of K , obtained with a direct push permeameter (DPP), K DPP , were used to obtain the calibration parameters in the Schlumberger-Doll Research, Seevers, Timur-Coates, Kozeny-Godefroy, and sum-of-echoes (SOE) models so as to predict K from the NMR data; and were also used to assess the ability of the models to predict K DPP . We obtained four well-scale calibration parameter values for each model using the NMR and DPP measurements in each well; and one study-scale parameter value for each model by using all data. The SOE model achieved an agreement with K DPP that matched or exceeded that of the other models. The Timur-Coates estimates of K were found to be substantially different from K DPP . Although the well-scale parameter values for the Schlumberger-Doll, Seevers, and SOE models were found to vary by less than a factor of 2, more research is needed to confirm their general applicability so that site-specific calibration is not required to obtain accurate estimates of K from NMR logging data.

Wisconsin↗
USGS1998

Stream habitat characteristics at selected sites in the Georgia-Florida coastal plain

Habitat characterization is part of a multidisciplinary approach to water-quality assessment implemented by the National Water-Quality Assessment Program. Habitat data were collected in the Georgia-Florida Coastal Plain study unit at 24 sites during 1993-95. Data were collected for habitat characteristics at three spatial scales: basin, segment, and reach. Basin data include physiography, land resource provinces, and land use, providing a description of the environmental setting at each site. Segment data include length, gradient, and sinuosity. A Kendall correlation analysis performed on segment characteristics and the log-of-basin area showed a correlation between segment gradient and the log-of-basin area and a correlation between sinuosity and segment length. Reach data consist of field-collected measurements of both instream and riparian habitats. Sand and detritus were the most common channel-bed substrates among the sampled sites. Measurements of channel width, water depth, and bank width and height were used to create cross-sectional profiles of each sampled area. Elevations of selected durations plotted on cross sections illustrated the percentage of time that the banks were inundated at each site. Sites were divided into two groups based on duration of bank inundation (less than or equal to 1 percent and greater than 1 percent). Bank woody vegetation was also sampled and a clustering algorithm known as Two-Way INdicator SPecies ANalysis (TWINSPAN) was used to analyze these data. TWINSPAN divided the sites into two groups based on their vegetation composition. A statistical comparison of the two types of site groups (duration of bank inundation and vegetation) was performed. The significant association between these groups was consistent with the hypothesis that inundation frequency affected riparian vegetation.

Florida, Georgia↗
USGS2026

Cajon Pass and the southern San Andreas Fault System: Earthquake cycle stress accumulation and present-day loading

With over a century since the last major rupture affecting the wider Los Angeles region, tectonic stress has steadily built along the southern San Andreas and San Jacinto fault systems, raising concerns of an imminent large earthquake. Cajon Pass, located at the junction of these faults, represents a critical site for potential through-going ruptures in Southern California. We constructed new 4D earthquake cycle simulations using a 1000-year paleoseismic rupture history of the San Andreas Fault System (SAFS) to assess spatial and temporal variations in stress. A semi-analytic Fourier transform model was used to compute stress from 3D dislocations in an elastic plate overlying a Maxwell viscoelastic half-space, assuming a complete coseismic reset of resolved shear stress on ruptured elements. Results show highest stress accumulation north of Cajon Pass (∼1.8 MPa/100 years) due to greater slip rates, and lower rates south of Cajon Pass (∼1.0–1.5 MPa/100 years). By 2025, Coulomb stress is estimated at 2.8 MPa on the Mojave South (MOS) segment, 1.8 MPa on the North San Bernardino (NSB1) segment and 3.6 MPa on the San Jacinto Bernardino (SJB) segment. Segments accumulate stresses with characteristic ranges of pre-event stress interpreted as failure thresholds: 1.2–2.7 MPa for MOS, 0.4–1.6 MPa for NSB1, and 1.2–2.9 MPa for SJB. When the stress disparity between segments SJB and MOS narrows, the faults appear to rupture jointly, suggesting that stress levels may control how Cajon Pass acts as an earthquake gate. These results may inform seismic hazard assessments by linking stress evolution to fault interactions.

California↗
USGS2026

Numerical modeling of Late Pleistocene to Holocene earthquake-induced progressive rock slope damage in the paraglacial Serpentine valley, Prince William Sound, Alaska

Landslides in deglaciating fjords pose a potential tsunami threat to nearby communities; however, processes contributing to long-term progressive rock damage and landslide conditioning remain poorly constrained in paraglacial settings. Here, we analyse the role of earthquake-induced rock mass damage over late Pleistocene to Holocene time scales, as a conditioning factor for modern landslides, using distinct element numerical modelling to assess spatial and temporal patterns of fracture propagation influenced by varying glacier thickness. Conceptualised numerical models were parameterised by in situ rock mass, glacial and topographic conditions in Serpentine valley, located in Prince William Sound, Alaska, where several large landslides are actively developing along the western valley wall. Results show that although rigid glacier buttressing reduces co-seismic rock mass damage, it does not suppress it completely, and damage occurs both above and below the glacier surface elevation. We demonstrate that topography, and especially steep slopes with topographic convexity, as well as preexisting damage of joint networks and faults inherited from tectonic and exhumation induced stresses, exert primary control on the location of new co-seismic damage. Simulations representing a simplified deglaciation sequence over the past 25 ka, with a series of 10 evenly spaced earthquakes, generated rock mass damage patterns that qualitatively match in situ landslide structural and kinematic observations at one instability in Serpentine valley. Our conceptual study helps clarify the role of repeated seismicity over glacial timescales as a long-term conditioning process for paraglacial rock slope failure, highlighting spatial and temporal patterns of progressive damage accumulation, with outcomes relevant for modern landslide hazard assessment.

Alaska↗
USGS1976

Distribution functions for statistics derived from bivariate normal and bivariate two-parameter log-normal populations

The distribution functions for statistics that may be used to assess the significance of differences between sample means, standard deviations, coefficients of skewness, and coefficients of variation are obtained by Monte Carlo experiments. The distributions are expressed as functions of sample size, cross correlation, and skewness. In general, the distributions are more sensitive to cross correlation than to skewness. As sample size increases, however, the distributions tend to become more sensitive to skewness.

Open-File Report↗
USGS2025

Land-based nutrient flux to a fringing reef: Insights from Ofu Island, American Samoa

Submarine groundwater discharge (SGD) is a critical driver of nutrient transport in coral reef ecosystems, shaping water quality, primary productivity, and overall reef health. This study quantifies SGD fluxes and associated nutrient dynamics in two reef flat pools within the Ofu Unit of the National Park of American Samoa: Papaloloa and Fatuana. A multi-method approach integrating unoccupied aerial system-based thermal infrared (UAS-TIR) surveys, radon-based SGD measurements, multichannel electrical resistivity tomography (ERT), and discrete water sampling was used to assess SGD rates and nutrient contributions. UAS-TIR imagery revealed cooler sea surface temperatures in both pools, indicative of SGD, with the higher fluxes observed in Papaloloa. Radon measurements revealed a strong inverse correlation between SGD rates and tidal stage, with a more immediate SGD response at Papaloloa due to its highly permeable calcareous sand and gravel substrate. In contrast, a 2–3-hour lag in SGD response at Fatuana suggests discharge from a more inland aquifer that has lower diffusivity. Nutrient concentrations correlated with temperature and salinity, confirming SGD as the dominant nutrient transport mechanism, whereas isotopic analyses indicated inputs from both groundwater and potential anthropogenic sources. Despite lower SGD flux at Fatuana, higher algal cover suggests additional factors influencing algal proliferation, including substrate availability and hydrodynamic conditions. Excess nutrient inputs from SGD may contribute to algal overgrowth, which threatens Ofu’s thermally tolerant corals by increasing competition for space and light. These findings underscore the complexity of SGD-mediated nutrient dynamics in reef environments and emphasize the need for integrated hydrological and ecological assessments to support effective reef conservation and management strategies.

American Samoa, Ofu Island, Olosega Island↗
USGS2021

Critical review of mercury methylation and methylmercury demethylation rate constants in aquatic sediments for biogeochemical modeling

Mercury is a toxin that causes neurological impairments in adults, is particularly harmful for fetuses and children, and is deadly in severe cases, making it a worldwide health concern. Methylmercury (MeHg) is the environmentally relevant form of mercury (Hg) because it biomagnifies along the food chain. Methylmercury is mainly produced in aquatic sediments via methylation of inorganic Hg (Hg(II)) and transformed back via demethylation. Because transformation rates determine MeHg concentrations, quantification of methylation and demethylation rates is needed to inform management of MeHg. Published rate constants for Hg(II) methylation ( 𝑘 𝑚 ) and MeHg demethylation ( 𝑘 𝑑 ) vary greatly, stemming partly from differences in experimental methods. We conducted a comprehensive review of rate laws, evaluated published rate constants, and performed biogeochemical simulations to assess variability in reported 𝑘 𝑚 and 𝑘 𝑑 . Based on selected studies employing the same pseudo-first-order rate law and similar experimental methods, we found that 𝑘 𝑚 = 0.04 ± 0.03 d −1 is a reasonable range for wetland sediments. Over a number of environments, maximum 𝑘 𝑑 was smaller at sites without Hg source ( 𝑘 𝑑 = 0.5 d −1 ) than at sites with identified Hg source ( 𝑘 𝑑 = 1.8 d −1 ). Larger variability and higher uncertainty in 𝑘 𝑑 compared to 𝑘 𝑚 highlight the need for more research on MeHg demethylation rates. This critical review: (a) aids the design of future experimental studies of 𝑘 𝑚 and 𝑘 𝑑 ; (b) provides guidance for comparing rate constants from different studies; (c) presents a biogeochemical reaction model to assess rate constants; and (d) informs selection of 𝑘 𝑚 and 𝑘 𝑑 values from the literature for use in model simulations.

Critical Reviews in Environmental Science and Tech↗
USGS2025

Emerging investigator series: Post-wildfire sediment geochemical characterization reveals manganese reactivity and a potential link to water quality impairment in the Gallinas Creek watershed, New Mexico

Water quality post-wildfire is often impaired by increased turbidity and elevated concentrations of elements such as manganese (Mn) and iron (Fe). Precipitation events exacerbate these issues, due in part to increased erosion and transport of sediment from hillslopes to surface water. Both Mn and Fe are major redox-active elements in sediments that drive a variety of biogeochemical cycles, precipitate adsorptive phases, and can themselves be drinking water contaminants. By investigating Mn and Fe sediment geochemistry in post-wildfire sediment deposits, related water quality hazards can be assessed. To establish and strengthen this connection, we analyzed the geochemistry of sediment deposits and surface water in the Gallinas Creek watershed, New Mexico over 1.5 years post-wildfire. Analyses included particle size analysis, water extractions, sequential extractions and aqua regia extractions to determine metal partitioning in sediment deposits. Data demonstrate Mn concentrations were distributed across labile and reactive fractions, such as the exchangeable and oxyhydroxide fractions, while Fe concentrations were mainly associated with the residual fraction. Manganese concentrations in aqua regia extractions and several fractions of sequential extractions were also strongly and significantly correlated with fine-grained sediment while the same pools of Fe concentrations were not. Dissolved Mn concentrations in surface water were elevated (>50 μg L −1 ) multiple times over the 1.5 years post-wildfire, highlighting a relationship between sediment geochemistry and water quality. This work shows Mn in sediments mobilized post-wildfire has an influence on water quality and highlights how further investigation into Mn sediment redox processes and mineralogy post-wildfire can inform risk assessments and resource management.

New Mexico↗
USGS2025

Exploring the science and data foundation for Federal public lands decisions

Public lands provide diverse resources, values, and services worldwide. Laws and policies typically require consideration of science in public lands decisions, and resource managers are committed to science-informed decision-making. However, it can be challenging for managers to use, and document the use of, science and data in their decisions. To better understand science and data use in Federal public lands decisions in the United States, we assessed the number, type, and age of documents cited in 70 Environmental Assessments (EAs) completed by the Bureau of Land Management (BLM) in Colorado from 2015–2019. We focused on the BLM, as they manage the largest area of public lands in the United States. We selected Colorado as our study area, as actions proposed on BLM lands in Colorado are representative of those across the nation. Fifty percent of citations were categorized as science and 23% as data. EAs contained an average of 17 citations (range 0–111), with documents analyzing effects of oil and gas development and recreation actions including the highest and lowest mean number of citations (41 and 6, respectively). Of individual resource analysis sections within EAs, 24% contained ≥1 science citation and 21% contained ≥1 data citation. Journal articles were the most cited type of document (26% of citations) followed by non-BLM inventories (13%). Forty-seven percent of citations were relatively recent (2010 or later); the oldest citation was from 1927. Commonly analyzed resources with the highest mean number of citations were socioeconomics, mineral resources, and noise. Fourteen of 33 commonly analyzed resources included <1 citation on average. Actions and resources with no or few citations represent opportunities for strengthening the transparent use of science and data in public lands decision-making.

Colorado↗
USGS1999

Environmental setting and water-quality issues in the lower Tennessee River basin

The goals of the National Water-Quality Assessment Program are to describe current water-quality conditions for a large part of the Nation's water resources, identify water-quality changes over time, and identify the primary natural and human factors that affect water quality. The lower Tennessee River Basin is one of 59 river basins selected for study. The water-quality assessment of the lower Tennessee River Basin study unit began in 1997. The lower Tennessee River Basin study unit encompasses an area of about 19,500 square miles and extends from Chattanooga, Tennessee, to Paducah, Kentucky. The study unit had a population of about 1.5 million people in 1995. The study unit was subdivided into subunits with relatively homogeneous geology and physiography. Subdivision of the study unit creates a framework to assess the effects of natural and cultural settings on water quality. Nine subunits were delineated in the study unit; their boundaries generally coincide with level III and level IV ecoregion boundaries. The nine subunits are the Coastal Plain, Transition, Western Highland Rim, Outer Nashville Basin, Inner Nashville Basin, Eastern Highland Rim, Plateau Escarpment and Valleys, Cumberland Plateau, and Valley and Ridge.The lower Tennessee River Basin consists of predominantly forest (51 percent) and agricultural land (40 percent). Activities related to agricultural land use, therefore, are the primary cultural factors likely to have a widespread effect on surface- and ground-water quality in the study unit. Inputs of total nitrogen and phosphorus from agricultural activities in 1992 were about 161,000 and 37,900 tons, respectively. About 3.7 million pounds (active ingredient) of pesticides was applied to crops in the lower Tennessee River Basin in 1992. State water-quality agencies identified nutrient enrichment and pathogens as water-quality issues affecting both surface and ground water in the lower Tennessee River Basin. Water-quality data collected by State and Federal agencies between 1980 and 1996 were summarized to characterize surface- and ground-water quality of the subunits with respect to these issues. Median concentrations of nitrogen species generally were less than 1 milligram per liter in surface and ground water in all subunits, and were highest throughout the subunits that had the largest percentages of agricultural land use. Median phosphorus concentrations also were less than 1 milligram per liter in all subunits. Phosphatic limestones present in two subunits had a larger effect on phosphorus concentrations in surface and ground water than did the amount of agricultural land use in these subunits. Median counts of fecal coliform were higher in surface water than in ground water in all subunits. The highest median counts in surface water were in the Valley and Ridge (7,500 colonies per 100 milliliters) and the Outer Nashville Basin subunits (5,000 colonies per 100 milliliters). Highest median counts in ground water were in the Inner and Outer Nashville Basin subunit. Natural setting likely has an important effect with respect to fecal contamination of surface and ground water in the lower Tennessee River Basin.

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