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1,443 records · Page 54Linked to original sources
USGS2024

Substrate Enhancement Pilot Project—Monitoring summary and evaluation, Kootenai River, Idaho, 2012–22

To assess changes in substrate conditions and the efficacy of artificially placed substrates at select sites on the Kootenai River near Bonners Ferry, Idaho, the U.S. Geological Survey, in cooperation with the Kootenai Tribe of Idaho, completed repeat bathymetric, velocimetric, and underwater videography surveys. Collectively, three project sites throughout the Kootenai River make up the Substrate Enhancement Pilot Project (SEPP), an effort intended to improve spawning and egg incubation viability at locations identified to be aquatic habitat limited for the endangered Kootenai River white sturgeon (Acipenser transmontanus). Following the placement of coarse substrates at each site, bathymetric, velocimetric, and underwater videography data were collected from 2012 to 2022 to assess the role of deposition and erosion on maintaining suitable white sturgeon spawning and incubation substrates. Minimal erosion and deposition occurred at all Substrate Enhancement Pilot Project sites, according to interannual and intra-annual net volumetric changes between bathymetric surveys. Depending on the timing of bathymetric surveys relative to the annual peak streamflow conditions, isolated locations of deposition or erosion were observed at each site and the potential for deposition or erosion was supported by measured mean depth-averaged velocities. This study concluded that variability of deposition and scour were common at each site throughout the monitoring period and may be attributed to fluctuations in streamflow. Repeat bathymetric, underwater videography, and velocity mapping surveys were used to verify the interstitial spaces and surfaces of substrates at each SEPP site remained free of fine sediments for intervals longer than a year but were susceptible to deposition between high streamflow events.

Idaho↗
USGS2025

Rare earth elements on the Moon

Rare earth elements (REEs) are a scarce but vital resource for our modern economies and lifestyles. Since the late 1990s, China has supplied the vast majority of the world’s refined REEs. Increasing global demand has broadened the search for REE deposits to unconventional places, including the Moon. Although most lunar rocks have very low REE concentrations, Apollo samples showed that one type of lunar rock containing potassium (K), REEs, and phosphorus (P)—known by the acronym KREEP—has high concentrations of REEs. Data from orbiting satellites have identified locations where substantial deposits of KREEP are likely. The viability of mining these deposits depends on the evolution of REE economics, the development of the Earth-Moon infrastructure, and the findings from future lunar mineral exploration missions.

Fact Sheet↗
USGS2026

Revisiting the utility of regional-scale, high-quality geophysical data in mineral exploration - A case study featuring the Mammoth Magnetic Anomaly, Pinal County, Arizona

Regional aeromagnetic surveys passively measure the total magnetic intensity (TMI) and are a foundational tool used in mineral exploration (Airo, 2015). With the increased global demand and the number of critical mineral resources required for manufacturing high-tech devices, developing high-quality, regional-scale geophysical surveys could aid critical mineral exploration efforts and geologic mapping. In 2019, the U. S. Geological Survey launched the Earth Mapping Resources Initiative (Earth MRI) to modernize the geologic and geophysical mapping of regions that have the potential to contain critical mineral resources within the United States. In support of planning Earth MRI geophysical surveys, Drenth and Grauch (2019) defined five aeromagnetic data quality rankings (rank 1 through rank 5) applying them to the airborne geophysical survey inventory of the United States (Johnson et al., 2021). Rank 1 aeromagnetic surveys are of the highest quality, meeting modern standards and allowing best practices for qualitative and quantitative interpretation; whereas rank 5 aeromagnetic surveys are of the lowest quality, being useful only for qualitative interpretation of broad features. Through the Earth MRI effort, 48 high-quality, regional-scale rank 1 and 2 airborne magnetic and radiometric geophysical surveys have been planned, collected, or publicly release through May 2025 (U. S. Geological Survey, 2025). Here, a portion of a rank 1 Earth MRI aeromagnetic survey in southeast Arizona is presented and compared to a legacy rank 5 aeromagnetic survey over the Mammoth Magnetic Anomaly (MMA), demonstrating how modern, high-quality aeromagnetic data improves our view of crustal geology, aiding mineral exploration.

Arizona↗
USGS2026

Ground motions from finite faults with unknown geometry

Earthquake emergency response and loss modeling require accurate models of ground‐motion intensity metrics (IMs), such as those produced by ShakeMap. In the critical minutes and hours after an earthquake, however, knowledge of the earthquake source geometry limits the accuracy of these models, especially in areas where seismic instrumentation is insufficient to constrain the shaking intensity pattern. Previous attempts to overcome this limitation have centered on computing the mean distance metrics from a suite of assumed source geometries. That approach is inadequate with modern ground‐motion models (GMMs) because they typically include terms for the hanging wall effect, depth to top of rupture, and other nonlinear terms. Such approaches also propagate the distance uncertainty through the GMMs and may not properly allocate the additional uncertainty into the between‐ and within‐event components. Here we present Finite‐Fault SIMulation with Mixed Effects Regression (FFSIMMER), a method to address these limitations by computing IM distributions from a suite of rupture realizations that fully accounts for all geometry‐related GMM terms and allows for the separation of the between‐ and within‐event components of the total uncertainty. The accuracy of this method is limited by the available constraints on the source parameters. To address this, FFSIMMER can incorporate constraints based on knowledge of the source, such as the approximate strike and dip based on knowledge of the plate tectonics and focal mechanism of the earthquake. We demonstrate the improvement in accuracy that can be achieved with the 2025 M 7.7 Mandalay, Burma (Myanmar), earthquake by incorporating constraints on the range of plausible source parameters.

Bulletin of the Seismological Society of America↗
USGS2026

Lunar analog study using portable gamma-ray neutron detector: Radiochemical mapping of silicic-to-basaltic volcanic terrains in the San Francisco Volcanic Field, Arizona

Studying lunar silicic volcanism provides key insights into the Moon’s crustal evolution, magmatic processes, and volcanic history. The Lunar-VISE (Lunar Vulkan Imaging and Spectroscopy Explorer) mission will investigate the Gruithuisen domes, a unique lunar region hypothesized to have formed through silicic volcanism. Using instruments on a Firefly Aerospace lander and a Honeybee Robotics rover, Lunar-VISE will analyze mineralogy, geochemistry, and surface properties to determine the origin and evolution of the domes, with a gamma-ray and neutron spectrometer (LV-GRNS) among its payload instruments. In preparation for this mission, we conducted preliminary fieldwork using a handheld gamma-ray neutron detector with NaI(Tl) and Cs LiYCl :Ce (CLYC) scintillators. This study focuses on various rhyolitic and basaltic volcanic centers in the San Francisco Volcanic Field (SFVF) near Flagstaff, Arizona—specifically Sugarloaf Peak, Bonito Lava Flow, and Robinson Mountain, a region containing several well-characterized lunar analog sites. The SFVF was selected for this study due to its broad compositional diversity spanning basaltic to rhyolitic compositions, its well-preserved volcanic morphologies, and its extensive use in previous NASA field campaigns and astronaut training exercises, making it an ideal terrestrial laboratory for testing planetary exploration techniques. We measured natural radioactivity, specifically from potassium (K), thorium (Th), and uranium (U) and other elements in their decay chains, which serve as diagnostic tracers of magmatic differentiation and crustal evolution processes, to assess geochemical variability across compositionally diverse terrains. The detector’s sensitivity was assessed across varying concentration levels. Regionally averaged concentrations of radioisotopes were determined by gamma-ray spectroscopy at selected sites. Our measurements reveal significant compositional variations between sites, with Sugarloaf Peak (rhyolitic, silicic dome) exhibiting the highest average radioisotope concentrations (K: 3.29 ± 0.24 wt%, U: 14.35 ± 1.81 ppm, Th: 27.14 ± 2.43 ppm), while Bonito Lava Flow (K: 1.13 ± 0.08 wt%, U: 3.86 ± 0.52 ppm, Th: 7.52 ± 1.02 ppm), and Robinson Mountain (K: 1.46 ± 0.16 wt%, U: 5.59 ± 1.06 ppm, Th: 11.75 ± 1.98 ppm) show lower concentrations aligned with basaltic compositions. Gamma-ray fluxes were elevated by a factor of approximately three to four at Sugarloaf Peak relative to nearby basaltic terrains, consistent with expected geochemical differentiation patterns. Furthermore, at meter scales, proximity to geological features significantly affects measurements. Th concentrations adjacent to a cliff face at Sugarloaf’s base were 28% higher than values measured 3-5 meters away from the same feature, demonstrating localized compositional heterogeneity. Analysis of station-by-station measurements reveals that K, U, and Th concentrations show an elevation trend at Robinson Mountain and generally higher concentrations near Sugarloaf Peak’s summit, suggesting progressive magmatic differentiation and/or the presence of more evolved lithologies at higher elevations. By mapping these elements on Earth using GRNS, we aim to optimize measurement techniques for Lunar-VISE and similar rover-borne missions by demonstrating the applicability of portable GRNS instruments for planetary surface exploration in an analog environment.

Arizona↗
USGS2026

Deep critical zone controls on shallow landslides

The deep critical zone (CZ) has long been recognized for its importance in influencing shallow landslides but was not considered feasible to include in slope stability models at the watershed scale. In this study, we demonstrate that simple approximations of the CZ in a fully coupled hydrologic and soil slope stability model can effectively capture the location, timing, and likely size of shallow landslides. To achieve this, we use coupled, process-based models that incorporate the effects of 1) deep CZ structures, 2) three-dimensional transient hydrology, and 3) multidimensional slope stability, calibrated with data from an intensively monitored field site. Our results show that the hydrologically active deep CZ guides groundwater flow, influencing where it drains from or exfiltrates to the soil mantle, producing distinct patterns of soil saturation and seepage forces at the soil-bedrock boundary. Deep conductive weathered critical zone drains the soil mantle, reducing the likelihood of destabilizing pore pressures, while the downslope thinning of the CZ forces groundwater to the surface. This creates localized instability and a tendency for similar-sized landslides across the landscape. In contrast, the absence of conductive weathered bedrock results in more widespread destabilizing pore pressures, leading to larger landslides and the likelihood of landslides earlier in a storm than in landscapes underlain by a deep CZ. Our findings suggest that first-order variations of deep CZ can provide physical explanations for variations observed in the susceptibility, magnitude, and timing of shallow landslides, and that CZ structure may be inferred from patterns and timing of landsliding.

Proceedings of the National Academy of Sciences↗
USGS2026

Continuous monitoring and temporal variability of fluorescence of dissolved organic matter in karst groundwater of the Edwards aquifer, south-central Texas, 2019–24

Widespread urbanization on the Edwards aquifer recharge zone in south-central Texas has prompted the development of new approaches and tools for evaluating the current and future status of water quality in the San Antonio and Barton Springs segments of the Edwards aquifer. The U.S. Geological Survey, in cooperation with the San Antonio Water System and the City of Austin, applied continuous monitoring of fluorescence of dissolved organic matter (fDOM) to characterize the sources and transport of dissolved organic matter and associated constituents in the Edwards aquifer. Continuous fDOM time-series data were adjusted using empirically derived temperature, turbidity, and inner-filter-effect correction factors prior to analysis and interpretation. Fully corrected fDOM time-series data from the study sites were evaluated for short- and long-term temporal variability in fDOM in the context of the hydrologic and climatic conditions that occurred during the 5-year study period to understand vulnerability of the aquifer to potential contaminants in recharge. Study results indicate that fDOM is an effective proxy for dissolved organic carbon that, in turn, is indicative of the influx of recent surface water and associated contaminants, and thus, aquifer vulnerability to those contaminants. Although other continuously monitored water-quality parameters provide insights into changes in water quality in response to varying hydrologic conditions, the quantitative relation between fDOM and dissolved organic carbon directly indicates the timing and magnitude of pulses of organic constituents derived from the land surface, whereas other water-quality parameters do not.

Texas↗
USGS2025

Footprints of past mining in Alaska (USA) derived from high-resolution satellite imagery

Mapping the land area used for mining in the past is essential for guiding the remediation of affected landscapes and assessing the resource potential of related waste products. Despite significant recent progress delineating footprints of active and inactive mining globally, the known inventory of such mine lands remains incomplete. Here, I describe a new map dataset of footprints of land surface disturbance and waste at sites of past mining in Alaska (USA) based on visual interpretation of satellite imagery. This dataset maps 6–14 times the area of previous regional and global mine footprint maps in Alaska and is the first in the region to explicitly delineate mine waste landforms (e.g., tailings piles). The data are publicly available from the U.S. Geological Survey under a “no rights reserved” Creative Commons (CC0) license agreement.

Alaska↗
USGS2024

Stream nitrate dynamics driven primarily by discharge and watershed physical and soil characteristics at intensively monitored sites: Insights from deep learning

We developed a suite of models using deep learning to make hindcast predictions of the 7‐day average backward‐looking nitrate concentration at 46 predominantly agricultural sites across the midwestern and eastern United States. The models used daily observations of discharge and meteorological variables and watershed attributes describing anthropogenic modification to hydrology, nitrogen application, climate, groundwater, land use, watershed physiographic attributes, and soils. Across all sites, discharge and watershed soil and physiographic attributes showed a strong influence on model performance. Analysis of drivers across sites revealed considerable regional differences related to controlling processes such as groundwater contributions. We tested several ways to pool data across sites to develop accurate models and make the most effective use of available data. Single‐site models, in which models are trained and tested at a single location, showed generally strong predictive performance (median Kling‐Gupta Efficiency = 0.66), and accuracy at poorly performing sites could be improved by grouping sites with similar characteristics. Developing a single model for all sites reduced performance at several locations with distinct characteristics, suggesting that there is a threshold of dissimilarity beyond which more data does not improve the model. While many deep learning studies have shown that national or even global models can outperform local models, it is not clear that this is true for water quality constituents. This study demonstrates how data can be combined effectively, using deep learning to develop accurate and interpretable models of instream nitrate at sites where varying processes are responsible for changes in nitrate concentration.

Water Resources Research↗
USGS2026

Status and understanding of groundwater quality in the San Joaquin Valley Kern County subbasin domestic-supply aquifer study unit, 2022—California GAMA Priority Basin Project

The quality of water accessed by domestic wells (here referred to as domestic groundwater resources) in the San Joaquin Valley Kern County subbasin (basin number 5-022.14) was assessed as part of the California Groundwater Ambient Monitoring and Assessment (GAMA) Program Priority Basin Project (GAMA-PBP), in cooperation with the California State Water Resources Control Board. Kern County is at the southern end of the San Joaquin Valley in California, and about 30,000 residents are estimated to use privately owned domestic wells for drinking water. Domestic wells typically draw from shallower parts of the aquifer system than public-supply wells and can be more vulnerable to effects from surface activities. Kern County is host to a highly productive agricultural industry, with Bakersfield as the main urban center. The Kern River runs through Bakersfield from the southern Sierra Nevada and intersects the Kern Water Bank, one of the largest groundwater banking operations in California, at the Kern River Intertie. The section of the Kern River running through the Kern Water Bank is dry most years. Kern County also encompasses some of the most productive oil and gas basins in California, with extensive underground and surface disposal of oil-field wastewater. This study was based on data collected from 33 sites sampled by the U.S. Geological Survey for the GAMA-PBP in 2022. To provide context for the water quality assessment, measured concentrations were compared to regulatory and non-regulatory health-based and aesthetic benchmarks. A grid-based method was used to estimate the proportions of the groundwater resources used for domestic-supply wells that have water-quality constituents below (low relative concentration), approaching (moderate relative concentration), or above (high relative concentration) benchmark concentrations. At least one measured constituent with a regulatory benchmark was categorized as having a high relative concentration in 72 percent of the aquifer area used for domestic groundwater resources. Inorganic constituents were detected at high concentrations in 45 percent of the domestic groundwater resources, and the constituents detected above regulatory benchmarks were arsenic, nitrate, and uranium. At least one organic constituent was detected at high concentrations in 41 percent of the domestic groundwater resources, and the constituents exceeding regulatory benchmarks were the fumigants 1,2,3-trichloropropane (1,2,3-TCP), 1,2-dibromo-3-chloropropane (dibromochloropropane [DBCP]), 1,2-dibromoethane (EDB), and the per-and polyfluoroalkyl substance (PFAS) perfluorooctanesulfonate. The disinfection by-product chloroform, the fumigant 1,2-dichloropropane, the herbicides atrazine and hexazinone, and the herbicide degradates 2-chloro-6-ethylamino-4-amino-s-triazine, 2-chloro-4,6-diamino-s-triazine, 4-hydroxychlorothalonil, and metolachlor sulfonic acid were detected in more than 10 percent of domestic groundwater resources, but concentrations did not exceed regulatory benchmarks. Land use, groundwater age (fraction of modern water and mean age), and geochemical environment (oxic or anoxic conditions, pH, alkalinity) were associated with the distribution of high relative concentrations of inorganic and organic constituents. Young, oxygenated water is recharged along the Kern River and adjacent recharge ponds, or as irrigation water in the agricultural areas. High concentrations of nitrate and volatile organic compounds occurred in the oxic water in urban and agricultural areas. The fumigants 1,2,3-TCP, DBCP, and EDB were reported throughout the agricultural areas, whereas chloroform, tetrachloroethene, and PFAS were associated with urban land use. High uranium concentrations were associated with young, modern groundwater in agricultural areas with low pH and high bicarbonate. Total dissolved solids increased with distance from the Kern River, as the contributions of fresh, oxic water decreased. High concentrations of arsenic were present in older anoxic or alkaline groundwater away from areas of recharge. Overall, groundwater age, redox conditions, and the source of recharge as a result of different land uses contribute to large aquifer-scale portions of domestic groundwater resources that exceed health-based benchmarks for nitrate, uranium, and fumigant concentrations.

California↗
USGS2024

Are field observations of surface rupture useful? An example from the 2023 Mw 7.8 Pazarcık, Turkey (Türkiye), earthquake

Field investigations have long been an important component of the scientific response to surface‐faulting earthquakes. However, in light of advances in remote data and models, the question arises whether field‐based observations of surface rupture remain useful for understanding rupture processes and seismic hazards. We approach this question using a field‐based study of the central 2023 M w 7.8 Pazarcık, Turkey (Türkiye), earthquake rupture, at the intersection of the east Anatolian fault (EAF) and Narlı fault. Our field observations include the surface rupture expression and extent of the central EAF and northernmost Narlı fault in generally forested and steep terrain and 68 measurements of left‐lateral surface displacement. These data improve our understanding of the Pazarcık rupture complexity, resolve the surface geometry of the Narlı fault–EAF connection, and exhibit a clear (>2 m) change in surface displacement across this intersection zone that confirms remote‐based coseismic slip models. Our study shows that focusing field efforts in areas of obscured or low‐resolution remote data can yield essential data for refining rupture extent, documenting perishable on‐fault displacement, and improving postearthquake situational awareness. A comparison of similarly large‐magnitude continental surface‐rupturing earthquakes indicates that displacement uncertainties relate to a complex set of factors, including measurement methods, rupture complexity, and displacement magnitude. Our study validates the need for postearthquake field observations, which, when driven by clear motivating questions and knowledge of methodological strengths and limitations, provide high‐resolution rupture data that complement remote‐based models.

Pazarcık↗
USGS2026

Widespread terrestrial ecosystem disruption at the onset of the Paleocene–Eocene Thermal Maximum

The Paleocene–Eocene Thermal Maximum (PETM, ~56 Mya) interval was marked by massive 13 C-depleted carbon emissions into the ocean/atmosphere system, manifested as a negative carbon isotope excursion (CIE) in sedimentary components, and ~5 °C global average warming. Episodes of hydrological perturbations and soil-erosion have been widely documented for the PETM but their link with vegetation- and carbon cycle changes remain poorly constrained. Here, we present organic microfossil evidence showing a strong increase in fern-dominated pioneer vegetation that replaced coniferous forests on the margin of the Norwegian Sea during the first millennia of the CIE. With the present stratigraphic constraints, the “fern spike” occurred simultaneously in terrestrial settings along the North Sea, Arctic Ocean, the US east coast and in southern Australia, indicating that pioneer vegetation persisted for several millennia following a partial collapse of previously stable terrestrial ecosystems. Both the ferns and influx of microcharcoal imply recurrent physical disturbance, including soil destabilization and erosion, potentially linked to droughts, wildfires, and strong hydrological forcing resulting from extreme climate change. Together with evidence for reworked clay minerals and ancient organic matter (kerogen), these findings show that highly disturbed terrestrial ecosystems were widespread across mid- and high-latitude regions globally. Carbon cycle model simulations suggest that a substantial loss of standing and buried biomass, along with oxidation of soil organic matter, acted as important positive feedbacks during the onset of the CIE. Additionally, enhanced kerogen weathering likely contributed as another major positive feedback throughout both the onset and main phase of the CIE.

Proceedings of the National Academy of Sciences↗
USGS2026

Magnitude conversion relations create substantial differences in seismic hazard models

Earthquake catalogs are essential data inputs for seismic hazard modeling. Because earthquake magnitudes are reported in a variety of types (e.g., local magnitudes and moment magnitudes), magnitude conversion relationships must be used to convert the different magnitude types present in a catalog to a uniform magnitude type to avoid biases in the hazard computation. However, these conversion relationships are often uncertain and have been shown to sometimes perform poorly. Here, we investigate the sensitivity of the gridded seismicity component of the National Seismic Hazard Model (NSHM) to the catalog conversion equations in the Eastern United States. In the 2023 NSHM, magnitudes of various types were converted to moment magnitudes using equations developed by the Central and Eastern United States Seismic Source Characterization for Nuclear Facilities (CEUS‐SSCn), based on least‐squares (LS) regressions made using data from a catalog containing events up through 2008. We recompute these equations using events in the Advanced National Seismic System Comprehensive Earthquake Catalog with multiple magnitudes from 2000 to 2023. Although we prefer the use of orthogonal regressions for our datasets, LS regressions produce broadly similar results, with both approaches exhibiting large deviations from the CEUS‐SSCn conversions, especially at smaller magnitudes. We compare the spatial distribution of annual rates using three different models: (1) the 2023 NSHM conversions, (2) our updated conversions, and (3) no conversions. We find that the choice of conversions leads to substantial differences in the rate forecasts, which can greatly impact the seismic hazard model, particularly in regions with low‐seismicity rates such as the Eastern United States, where the hazard is dominated by gridded seismicity rather than a fault model.

Seismological Research Letters↗
USGS2024

Status of water-level altitudes and long-term and short-term water-level changes in the Chicot and Evangeline (undifferentiated) and Jasper aquifers, greater Houston area, Texas, 2023

Since the early 1900s, groundwater withdrawn from the primary aquifers that compose the Gulf Coast aquifer system—the Chicot, Evangeline, and Jasper aquifers—has been an important source of water in the greater Houston area, Texas. This report, prepared by the U.S. Geological Survey in cooperation with the Harris-Galveston Subsidence District, City of Houston, Fort Bend Subsidence District, Lone Star Groundwater Conservation District, and Brazoria County Groundwater Conservation District, is one in an annual series of reports depicting the status of water-level altitudes and water-level changes in these aquifers in the greater Houston area. In this report, the Chicot and Evangeline aquifers are treated as a single aquifer for the purposes of providing annual assessments of regional-scale water-level altitudes and water-level changes over time. In 2023, shaded depictions of water-level altitudes for the Chicot and Evangeline aquifers (undifferentiated) ranged from about 286 feet (ft) below the North American Vertical Datum of 1988 (NAVD 88) to about 169 ft above NAVD 88. The largest decline in water-level altitudes indicated by the 1977–2023 long-term water-level-change map was in south-central Montgomery County southeast of The Woodlands. In comparison, the 1990–2023 long-term water-level-change map depicts the largest declines in water-level altitudes in localized areas at or near certain wells in parts of northwestern Harris County and south-central Montgomery County. The largest rise in water-level altitudes for 1977–2023 is depicted in a relatively large area in southeastern Harris County, whereas the largest rise in water-level altitudes for 1990–2023 is depicted in a relatively large area in central Harris County. The 5-year short-term water-level-change map depicts the largest declines at three wells in northern Fort Bend County, one well in western Harris County, and three wells in south-central Montgomery County and the largest rise at one well in central Harris County. The 1-year short-term water-level-change map depicts the largest declines at one well in northern Fort Bend County and two wells in southwestern Harris County and the largest rises at one well in northern Brazoria County and one well in south-central Montgomery County. In 2023, shaded depictions of water-level altitudes for the Jasper aquifer ranged from about 242 ft below NAVD 88 to about 218 ft above NAVD 88. The 2000–23 long-term water-level-change map depicts water-level declines throughout the study area where water-level-measurement data from the aquifer were collected, with the largest declines in north-central Harris County and south-central Montgomery County south of The Woodlands. The 5-year short-term water-level-change map depicts the largest declines at two wells in central Montgomery County near Conroe and two wells in south-central Montgomery County southeast of The Woodlands and the largest rise at one well in western Montgomery County. The 1-year short-term water-level-change map depicts the largest declines at four wells in south-central Montgomery County southeast of The Woodlands and one well in central Montgomery County near Conroe and the largest rises at two wells in western Montgomery County.

Texas↗
USGS2024

Spatial differences in soil nutrients along a hydrographic gradient on floodplains in Dongting Lake

The spatial heterogeneity of soil nutrients is crucial for the water bird and whole floodplain wetland ecosystem in large lakes, and it is influenced by the dramatic water level changes and sedimentation progress in West Dongting Lake (WDL). Soil samples were collected at various soil depths along the Yuan River and Li River that feed into WDL. The concentrations of soil total organic carbon (TOC), total nitrogen (TN), total phosphorus (TP), and soil grain size were tested. The stoichiometric ratios of C, N, P, and the mean value of soil grain size ( Mz ) were calculated. The differences of soil TOC, TN, TP and the stoichiometric ratio at different sites and soil depths were compared. Linear regression was used to explore the relationships of Mz and nutrient concentrations, and relationships between TOC, TN, and TP. Redundancy analysis was used to explore the relationship between soil nutrients, heavy metal concentrations, and plant community diversity. The results showed that the distributions of soil TOC, TN, and TP concentrations differed across regions in west Dongting Lake along the Yuan and Li Rivers. Total organic carbon concentration differed at different sedimentation depths. Soil grain size showed negative effect with soil TOC, TN, and TP concentrations in this region. Plant community diversity correlated positively with soil TOC and negatively with Hg. West Dongting Lake was N limited despite the high wet deposition of N. It could potentially be attributed to the insufficient presence of aerobic environments for microbes during intermittent flooding of the floodplain, coupled with feeble mineralization. This study can provide valuable insights for the conservation of water bird habitats and wetland ecosystems.

West Dongting Lake↗
USGS2025

Analysis of the potential effects of Uzbekistan’s mineral endowment on the critical mineral supply of tungsten

Tungsten appears on the 2018 and 2022 U.S. Geological Survey critical mineral lists in part because of a very high global production concentration in China, which produces almost 83 percent of the world’s mined tungsten. Using known parameters and values from other tungsten mining operations, we created hypothetical scenarios in which three tungsten deposits in Uzbekistan are considered for development. Our results show that all three deposits are likely to be economically viable to develop under 2024 market conditions. If the three studied tungsten deposits were put into production, Uzbekistan could become the third-leading tungsten-producing country in the world and increase world output of tungsten by 2.7 percent. Putting these tungsten deposits in Uzbekistan into production could slightly reduce the tungsten global market concentration, therefore reducing the supply disruption potential for tungsten.

Open-File Report↗
USGS2025

Mapping bedrock outcrops in the Sierra Nevada Mountains (California, USA) using machine learning

Accurate, high-resolution maps of bedrock outcrops can be valuable for applications such as models of land–atmosphere interactions, mineral assessments, ecosystem mapping, and hazard mapping. The increasing availability of high-resolution imagery can be coupled with machine learning techniques to improve regional bedrock outcrop maps. In the United States, the existing 30 m U.S. Geological Survey (USGS) National Land Cover Database (NLCD) tends to misestimate extents of barren land, which includes bedrock outcrops. This impacts many calculations beyond bedrock mapping, including soil carbon storage, hydrologic modeling, and erosion susceptibility. Here, we tested if a machine learning (ML) model could more accurately map exposed bedrock than NLCD across the entire Sierra Nevada Mountains (California, USA). The ML model was trained to identify pixels that are likely bedrock from 0.6 m imagery from the National Agriculture Imagery Program (NAIP). First, we labeled exposed bedrock at twenty sites covering more than 83 km 2 (0.13%) of the Sierra Nevada region. These labels were then used to train and test the model, which gave 83% precision and 78% recall, with a 90% overall accuracy of correctly predicting bedrock. We used the trained model to map bedrock outcrops across the entire Sierra Nevada region and compared the ML map with the NLCD map. At the twenty labeled sites, we found the NLCD barren land class, even though it includes more than just bedrock outcrops, accounted for only 41% and 40% of mapped bedrock from our labels and ML predictions, respectively. This substantial difference illustrates that ML bedrock models can have a role in improving land-cover maps, like NLCD, for a range of science applications.

California↗
USGS2025

Bayesian mapping of regionally grouped, sparse, univariate earth science data

Some earth science data are naturally grouped by region, and it is often desirable to map these data by region. However, if there are only a few samples within each region, then the map should be smoothed in an appropriate way to mitigate the problems that arise from having only a few samples. A smoothing algorithm based on a Bayesian hierarchical model is developed and presented in this report. This algorithm has several features that make it especially suitable for mapping earth science data: it can account for measurements that are censored, it can process multiple datasets with different measurement errors and different censoring thresholds, and it can calculate the uncertainty in any statistic that is mapped. The algorithm is demonstrated by mapping gold concentrations that are measured in streambed sediments in the Taylor Mountains quadrangle in southwestern Alaska.

Alaska↗