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1,659 records · Page 80Linked to original sources
USGS2024

Integrating Sr isotopes, microchemistry, and genetics to reconstruct Salmonidae species and life history

Recent approaches to fisheries research emphasize the importance of the coproduction of knowledge in building resilient and culturally mindful fisheries management frameworks. Despite widespread recognition of the need for Indigenous knowledge and historical reference points as baseline data, archaeological data are rarely included in conservation biology research designs. Here we propose a novel multiproxy method to learn from former fisheries stewards by generating archaeological data on past salmonid population parameters. We used a newly developed, high throughput qPCR (HT-qPCR) chip, originally designed for environmental DNA (eDNA), for species identification of archaeological salmonid vertebrae. We combine this with the laser ablation split-stream (LASS) approach to identify ocean-migration versus freshwater residency. We test this multidisciplinary approach using both contemporary and archaeological salmonid samples and new radiocarbon dates from the Tronsdal Site on the Skagit River, Washington State, USA. This is a useful approach for extracting information about Salmonidae species and life history diversity from archaeological remains to reconstruct historic baselines for several population parameters in anadromous species with long periods of freshwater residency. The approach outlined in this paper may be particularly useful for research investigating past fisheries dynamics, offering hundreds to thousands of years of temporal depth for modern fisheries management, harvest policies, restoration ecology, and conservation biology.

Idaho, Oregon, Washington↗
USGS2026

Salinas Valley integrated hydrologic and reservoir operations models, Monterey and San Luis Obispo Counties, California

The area surrounding the Salinas Valley groundwater basin in Monterey and San Luis Obispo Counties of California is a highly productive agricultural area, contributes substantially to the local economy, and provides a substantial portion of vegetables and other agricultural commodities to the Nation. This region of California provides about half of the Nation’s lettuce, celery, broccoli, and spinach each year. Thus, this agricultural area provides substantial volumes of agricultural products not just for California but for the United States. Changes in population and increased agricultural development, which includes a shift toward more water-intensive crops, and climate variability, have put increasing demand on both surface-water and groundwater resources in the valley. This situation has resulted in water management challenges in the Salinas Valley that generally relate to the distribution of the water supply throughout the basin. Where and when the water is present in the surface and subsurface does not coincide with where and when the water is needed. Historically, to deal with the distribution issue, water has been used conjunctively in the valley. Conjunctive use is a water management strategy that coordinates surface-water and groundwater use to maximize water availability. Groundwater is used throughout the Salinas Valley to meet water demands when surface-water supplies are insufficient. The availability of surface water is constrained by climate. Precipitation and streamflow vary seasonally and year to year. Although there are two reservoirs in the Salinas Valley to capture and store water during wet periods, the only conveyance of reservoir water to coastal agricultural areas is the Salinas River. Increasing demand for groundwater and surface-water resources throughout the Salinas Valley has resulted in undesirable effects from unsustainable water use, such as surface-water depletion, groundwater-level declines, storage depletion in the principal aquifers, and seawater intrusion. To address these escalating issues, local communities, water management agencies, and groundwater sustainability agencies are evaluating how to sustainably manage both their surface-water and groundwater resources. To meet water demands and reduce the undesirable effects of unsustainable water use, continued conjunctive management of surface water and groundwater would ideally incorporate strategies to deal with increases in demand and climate variability. To evaluate the challenging water management issues in the Salinas Valley, the U.S. Geological Survey, Monterey County Water Resources Agency, and the Salinas Valley Basin Groundwater Sustainability Agency developed a comprehensive suite of models that represent the Salinas Valley hydrogeologic system called the Salinas Valley System Model. The geologic framework is known as the Salinas Valley Geologic Framework and was developed to characterize the subsurface using various topographic and geologic data sources, including information on hydrogeologic units, their surfaces and extents, geologic structures, lithology, and elevations from borehole data and cross sections, as well as details on faults and existing models. The surface-water model is called the Salinas Valley Watershed Model and simulates the Salinas River watershed. Monthly surface-water inflows into the integrated hydrologic model domain were simulated using the Salinas Valley Watershed Model. The historical model uses historical climate data, water and land use data, and reservoir releases to simulate agricultural operations, including landscape water demands, diversions, and reclaimed wastewater. The operational model adds an embedded reservoir operations framework to the simulation of the historical model that allows specified operational rules to simulate reservoir releases and changes in reservoir storage. The operational model assumes current reservoir operations and constant land use, which differs from historical conditions. Thus, the operational model is a hypothetical baseline model that can be used by local water managers to evaluate and quantify potential benefits of water supply projects. Together, the geologic framework, watershed, historical, and operational models form a tool that can be used to simulate irrigated agriculture and associated reservoir operations of the integrated hydrologic system of the Salinas Valley.

California↗
USGS2024

Trimming the UCERF3-TD logic tree: Model order reduction for an earthquake rupture forecast considering loss exceedance

The Uniform California Earthquake Rupture Forecast version 3-Time Dependent depicts California’s seismic faults and their activity. Its logic tree has 5760 leaves. Considering 30 more model combinations related to ground motion produces 172,800 distinct models representing so-called epistemic uncertainties. To calculate risk to a portfolio of buildings, one also considers millions of earthquakes and spatially correlated ground-motion variability. We offer a tree-trimming technique that retains the probability distribution of portfolio loss and identifies the leading sources of uncertainty for further study. We applied it to a California statewide building portfolio and various levels of nonexceedance probability between one in 100 and one in 2500. We trimmed the logic tree from 172,800 leaves to as few as 15. The result: a supercomputer that would otherwise run 24 h to estimate the distribution of one-in-250-year loss can calculate it in moments with the reduced-order model. Others can use the reduced-order model to calculate risk to different California portfolios, and scientists can prioritize study to reduce the remaining epistemic uncertainty.

Earthquake Spectra↗
USGS2025

Waning greenhouse gas emissions from U.S. Federal lease coal production by the mid-21st century

This study presents estimates of future years (2024–2051) United States Federal lease coal production and the resulting greenhouse gas (GHG) emissions from the combustion, transport, and mining of that fuel. Results from the coal production estimate indicate a decline in production from Federal leases; with known production of 240 million short tons (mtn) in 2023 and a projected decline to 34.0 mtn by 2051, which represents a reduction to 14.2% of the 2023 value. In parallel with this projection, total GHG emissions are estimated to decrease from 402.2 million metric tons of carbon dioxide equivalent (MMT CO 2 eq.) in 2024 to 55.0 MMT CO 2 eq. in 2051, a decline to 13.7% of 2024 emissions estimates. The reductions in coal production and emissions are mainly the result of planned coal combustion power plant closures, with major projected closures in 2037 and 2048. However, GHG emissions estimates for future years can be uncertain as they rely heavily on coal production estimates from operators' public business plans and other publicly available resources. Forward looking plans of this type are subject to significant changes if economic and political factors deviate from current information. Results suggest that average GHG emissions over the time series breakout to 95% end point combustion, 3.7% transportation combustion emissions, and 1.3% fugitive emissions, although there is uncertainty associated with these figures. Uncertainty stemming from production projections, sector distributions, and emissions factors on the future emissions estimates increases with time, ranging from −28% to +48% within the 2024–2051 timeframe.

Alabama, Colorado, Montana, North Dakota, Utah, Wy↗
USGS2025

A generalized deep learning model to detect and classify volcano seismicity

Volcano seismicity is often detected and classified based on its spectral properties. However, the wide variety of volcano seismic signals and increasing amounts of data make accurate, consistent, and efficient detection and classification challenging. Machine learning (ML) has proven very effective at detecting and classifying tectonic seismicity, particularly using Convolutional Neural Networks (CNNs) and leveraging labeled datasets from regional seismic networks. Progress has been made applying ML to volcano seismicity, but efforts have typically been focused on a single volcano and are often hampered by the limited availability of training data. We build on the method of Tan et al. [2024] ( 10.1029/2024JB029194 ) to generalize a spectrogram-based CNN termed the VOlcano Infrasound and Seismic Spectrogram Neural Network ( VOISS-Net ) to detect and classify volcano seismicity at any volcano. We use a diverse training dataset of over 270,000 spectrograms from multiple volcanoes: Pavlof, Semisopochnoi, Tanaga, Takawangha, and Redoubt volcanoes\replaced (Alaska, USA); Mt. Etna (Italy); and Kīlauea, Hawai`i (USA). These volcanoes present a wide range of volcano seismic signals, source-receiver distances, and eruption styles. Our generalized VOISS-Net model achieves an accuracy of 87 % on the test set. We apply this model to continuous data from several volcanoes and eruptions included within and outside our training set, and find that multiple types of tremor, explosions, earthquakes, long-period events, and noise are successfully detected and classified. The model occasionally confuses transient signals such as earthquakes and explosions and misclassifies seismicity not included in the training dataset (e.g. teleseismic earthquakes). We envision the generalized VOISS-Net model to be applicable in both research and operational volcano monitoring settings.

Volcanica↗
USGS2024

A methodology to estimate CO2 and energy gas storage resources in depleted conventional gas reservoirs

Depleted hydrocarbon reservoirs are subsurface geological structures capable of sequestering vast quantities of carbon dioxide (CO 2 ) as well as storing other energy gases for later usage, such as natural gas, and potentially hydrogen (H 2 ). Here we outline a methodology to quantify multi-gas storage resources in depleted conventional gas reservoirs for usage in assessments by the United States Geological Survey (USGS) at the scale of sedimentary basins. The methodology consists first of quantifying accessible pore volume in a depleted reservoir for natural gas storage using up to three equations. Input data are derived from commonly reported or estimated reservoir parameters and natural gas production volumes, and equations may be combined in linear models to improve pore volume estimates. Storage estimates from these equations are tested and validated for 31 reservoirs in the Michigan Basin Province, USA that were previously converted to underground gas storage facilities and have known (federally reported) natural gas storage capacities. Secondly, natural gas storage capacities can be transformed via fluid substitution calculations to estimate the storage resources for non-native fluids, applied here for, CO 2 , H 2 , and methane-H 2 blends, accounting for molecule-specific deviations from ideal gas behavior at reservoir pressures and temperatures as well as differing storage efficiencies. Importantly, the storage of non-native fluids may not be appropriate in all depleted gas reservoir settings due to potential risks like leakage, in particular in the case of H 2 storage, requiring additional knowledge of caprock sealing capacity. Given this caveat, we demonstrate the fluid substitution method for natural gas reservoirs of the Northern Niagaran Reef and Southern Niagaran Reef USGS plays in the Michigan Basin Province, as these trends of Silurian pinnacle reefs are capped with tight-sealing evaporite facies. The deterministic equations outlined from this methodology can be incorporated into future probabilistic USGS gas storage assessments for CO 2 , H 2 , and natural gas resources in the United States.

Michigan↗
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

Detecting hidden sedimentary geothermal systems in the Upper Colorado River Basin

Geothermal resources exist in sedimentary rock where circulation of water for efficient extraction or storage of heat is possible. Except in rare instances where hot water is expressed at the land surface, sedimentary geothermal resources are hidden, so the identification of these systems is optimally accomplished using predictive subsurface modeling. An integrated approach using detailed paleogeographic interpretations, subsurface geologic mapping, and numerical modeling has produced regional geologic and temperature models for the Upper Colorado River Basin, a large watershed in central North America that contains many sedimentary basins. These models identify areas of hidden sedimentary geothermal resource potential in low temperature (<90°C), moderate temperature (90–150°C), and high temperature (>150°C) fairways across the study area. These models incorporate maps of key horizons in outcrop and the subsurface to create a robust structural framework that can be used to target favorable geology for natural or engineered permeability. This framework is populated with lithologies derived from detailed palaeogeographical maps and over 40,000 bottom hole temperature (BHT) values were used to create a calibrated three-dimensional (3D) temperature model across the region. The resulting maps serve as a regional sedimentary geothermal play fairway screening tool for evaluating different grades of sedimentary geothermal resources and for identifying areas of interest where more detailed, prospect-scale studies can be undertaken.

Arizona, Colorado, New Mexico, Utah, Wyoming↗
USGS2025

U.S. Geological Survey Groundwater Climate Response Network, 2024

As of October 2024, the U.S. Geological Survey (USGS) operated 588 sites across the United States and its territories as part of the Groundwater Climate Response Network (CRN). The CRN is comprised of wells selected to monitor the effects of climate variability, such as droughts, on groundwater levels nationwide. The CRN includes nearly 500 locations with real-time data and more than 100 sites with non-real-time data available to the public on the CRN web mapper and the USGS National Water Dashboard.

General Information Product↗
USGS2026

Decoding the oxidative digestion mechanism for polystyrene nanoplastic detection in the Great Lakes using a customizable Raman spectral processing algorithm

Despite the concerns surging around nanoplastics (NPs) regarding their prevalence and bioavailability in freshwater systems, robust detection of NPs in complex environmental matrices is hindered by the lack of standardized sample pretreatment and a mechanistic understanding of oxidative digestion. Here, we systematically investigate the interaction between hydrogen peroxide (H 2 O 2 ) and polystyrene (PS) NPs during digestion in deionized (DI) water and four environmental matrices from in and around the Great Lakes Basin. To facilitate high-throughput analysis, we develop Pre_peak, a customizable Raman spectral processing algorithm that achieves >99% accuracy for both NP identification and interference rejection, allowing reliable NP quantification via pixel counting and systematic decoding of the oxidative digestion mechanisms. In DI water, varying H 2 O 2 doses from 0 to 30% has negligible effects on the recovery and Raman signal intensity of PS NPs over 24 hours of digestion. However, morphological changes and aggregation of PS NPs are observed when the H 2 O 2 dose exceeds 20%. Prolonged digestion further leads to progressive NP loss. In natural waters, the optimal dosage and digestion duration depend on matrix characteristics, including dissolved organic matter (DOM) and ion composition. This study provides mechanistic insights into NP–oxidant interactions and underscores the need for matrix-tailored digestion protocols to advance standardized NP detection in freshwater environments.

Great Lakes↗
USGS2025

Arctic speleothems reveal nearly permafrost-free Northern Hemisphere in the Late Miocene

Arctic warming is happening at nearly four times the global average rate. Long-term trends of permafrost dynamics cannot be estimated directly from monitoring of present-day thaw processes, requiring paleoclimate-proxy information. Here we use cave carbonates (speleothems) from a northern Siberian cave to determine when the Northern Hemisphere was mostly permafrost-free. At present, thick continuous permafrost in this region prevents speleothem growth. In a series of partially eroded caves, speleothems grew during the late Tortonian stage (8.68 ± 0.09 Ma), a time when the geographic position of this site was already similar to today. Paleotemperatures reconstructed from speleothems show that mean annual air temperatures (MAAT) in the region were + 6.6°C to + 11.1°C, when contemporary global MAAT were ~ 4.5 °C higher than modern. Our findings provide direct evidence that warming to Tortonian-like temperatures would leave most of the Northern Hemisphere permafrost-free. This may release up to ~ 130 petagrams of carbon, enhancing further warming.

Siberia↗
USGS2026

Riverscape heterogeneity shapes population diversity for a migratory fish

Habitat patch dynamics can scale up to influence population demography and diversity with implications for resilience to environmental stochasticity. But how the spatial arrangement and size of habitat patches interact with other components of habitat heterogeneity to shape population diversity at larger spatial scales is not well understood. For riverine fishes, there is increasing evidence that tributary streams provide critical demographic support to main stem rivers. However, the extent to which main stem rivers rely on demographic contributions from tributaries, and the factors underlying this dependence, have not been assessed. Here, we used genetic stock identification to evaluate the effect of tributaries on population diversity of Yellowstone cutthroat trout ( Oncorhynchus virginalis bouvieri ) occupying the main stem Snake River, Wyoming, USA. We found that the main stem relied almost entirely on tributaries for demographic support, but main stem composition varied spatially among river sections. Distance between habitat patches, catchment area, and groundwater availability acted in concert to determine the contribution of specific tributaries to the main stem, but contributions were ultimately modulated by habitat connectivity. We also found evidence for multi-scale spatial structure in tributary contributions, providing insight into untested drivers of main stem river population diversity. Our results demonstrate how spatially discrete and distributed riverscape attributes influence population diversity at broader spatial scales, illustrating how ecosystem resilience emerges from the dynamic, two-way exchange of individuals and energy across habitat networks. Management plans for large rivers that address the ecological contributions of tributaries may be needed to achieve optimal outcomes. Similarly, conservation strategies that exclusively focus on headwater streams may fail to capture the broader habitat requirements necessary to maintain robust cold-water fish populations and associated recreational fisheries, particularly under global environmental change.

Wyoming↗
USGS2024

Geologic input databases for the 2025 Puerto Rico – U.S. Virgin Islands National Seismic Hazard Model update: Crustal faults component

The last National Seismic Hazard Model (NSHM) for Puerto Rico and the U.S. Virgin Islands (PRVI) was published in 2003. In advance of the 2025 PRVI NSHM update, we created three geologic input databases to summarize new onshore and offshore fault source information in the northern Caribbean region between 62°–70° W and 16°–21° N. These databases, of fault sections, fault‐zone polygons, and geologic estimates of fault activity (fault‐slip rate and earthquake recurrence intervals) at specific sites, document updates to fault parameters used in prior seismic hazard models in PRVI. Fault sources were reviewed from published studies since 2003, which document substantial changes to the understanding of fault location, geometry, or activity. New fault section sources were added for features that meet the criteria of (1) length ≥7 km, (2) unequivocal evidence of recurrent tectonic Quaternary activity, and (3) documentation that is publicly available in a peer‐reviewed source. In addition, we revised several broad areal sources, such as the Mona and Anegada extensional zones. The 2003 model included three fault sections and two fault‐zone polygons (areal sources). These databases include 35 fault sections, 6 fault‐zone polygons, and 51 earthquake geology sites. To characterize fault activity rates, slip‐rate bins were assigned based on landscape expression and paleoseismic trench observations for faults without published slip‐rate sites. Additional fault sources were evaluated but not included in these databases due to a lack of published information about fault location, geometry, or recurrent Quaternary activity. The PRVI NSHM 2025 geologic input databases describe crustal faulting; the geometries and coupling of Puerto Rico subduction zone and Muertos Trough models are considered in a separate database. Updates to the fault sections, fault‐zone polygons, and earthquake geology databases can help inform the location and recurrence rate of damaging earthquakes in the PRVI NSHM implementation.

Puerto Rico, U.S. Virgin Islands↗
USGS2024

U.S. Geological Survey Groundwater Climate Response Network—2023

As of October 2023, the U.S. Geological Survey (USGS) operated more than 660 sites across the United States and its territories as part of the Groundwater Climate Response Network (CRN). The CRN is comprised of wells and springs selected to monitor the effects of climate variability, such as droughts, on groundwater levels and spring discharge nationwide. The CRN includes more than 550 locations with realtime data and more than 100 sites with non-real-time data available to the public on the CRN web mapper and the USGS National Water Dashboard.

General Information Product↗
USGS2024

The 3D Elevation Program—Supporting Ohio's economy

Introduction High-quality elevation data are proving to be a resource of great economic value in dealing with many important issues in Ohio. Current and accurate high-resolution elevation data support flood risk management, water quantity and quality assessment, precision farming, conservation planning, impervious-surface modeling, forest and other natural resources management, abandoned mine and geologic hazard assessment, karst mapping, and siting of wellhead pads for horizontal drilling. These data also support coastal zone management, traffic safety and preliminary engineering site-selection studies for transportation infrastructure, solar potential and other renewable energy planning, aviation safety, and identification of features of interest or concern such as archaeological sites and orphan oil and gas wells. Critical applications that meet the State’s management needs depend on light detection and ranging (lidar) data that provide a highly detailed three-dimensional (3D) model of the Earth’s surface and aboveground features.

Ohio↗
USGS2026

An improved empirical model for predicting postfire debris-flow volume in the western United States

Reliable estimates of debris-flow volume can be used to help predict the magnitude of debris-flow hazards following wildfire in the western United States. In this study, we compiled and used a database of 227 postfire debris-flow volumes that were collected across the western United States to develop a multiple linear regression model for predicting postfire debris-flow volume. We explored 36 predictor variables related to rainfall, terrain, and fire characteristics, and selected the model with the combination of variables that yielded the most accurate predictions of debris-flow volume. We evaluated model performance against the entire volume database, as well as against four subsets of volume data from southern California, the Intermountain West, the Southwest, and regions with limited volume data, such as northern California and Washington. We also compared model performance against 3 existing postfire debris-flow volume models that were developed for use in southern California, the Intermountain West, and the Southwest. We demonstrate that the new volume model performs as well as the regional models in the regions for which they were developed and outperforms existing models when applied to volumes from data-limited regions in the western United States. These results indicate that the debris-flow volume model introduced in this study can be used to improve postfire hazard assessments across the western United States, especially outside of southern California.

Arizona, California, Colorado, New Mexico, Utah, W↗
USGS2025

Critical Minerals in Ores (CMiO) database

Critical minerals are commodities essential to modern industrial and strategic technologies and are highly vulnerable to supply chain disruption. The Critical Minerals Mapping Initiative (CMMI) is a collaboration among the U.S. Geological Survey (USGS), the Geological Survey of Canada, and Geoscience Australia that aims to deepen global understanding of where critical minerals are located. A key output of this initiative is the Critical Minerals in Ores (CMiO) database that is advancing our collective understanding of critical minerals distributions. For instance, publicly available data on the concentrations of many critical minerals are sparse because these commodities can only be produced in small, yet essential, quantities compared to the primary commodities like copper and zinc. The CMiO database helps bridge this gap by offering high-quality, multielement geochemical data from a wide variety of critical mineral-bearing deposits around the world. Importantly, it uses a novel consensus deposit environment, group, and type classification scheme developed by the agencies that allows comparisons among ore deposits from different regions. The CMiO database contains geochemical data for more than 20,000 samples from more than 100 deposit types comprising 10 deposit environments.

Fact Sheet↗
USGS2026

Indirect mineral import reliance and provenance

Mineral commodity supply chain analyses rely on international trade data reported by individual countries as quantities of a mineral commodity form imported from (or exported to) a partner. However, export quantities frequently exceed a country’s domestic production, or occur when no production data are reported, suggesting that the trade partner is merely an intermediary in a transshipment. These discrepancies can result in misleading conclusions regarding supply chain vulnerabilities and dependencies. We present a two-stage methodology to reconcile gaps between reported material sources and actual producers. First, we construct trade networks for specific mineral forms, treating production as a type of import to distinguish producing nations from entrepôts. By tracing flows through these networks, we attribute a target country’s imports to original producers via both direct (in a single trade link) and indirect (transferring through intermediaries) pathways. Second, these production-attributed flows are incorporated into multi-stage supply chains to determine the upstream provenance of feedstock for domestic refining and processing. This approach provides a more representative picture of trade reliance. For example, while the United States (U.S.) Geological Survey reports no imports of unwrought antimony metal from Russia in 2022 (U.S. Geological Survey (2025). Mineral Commodity Summaries 2025. 10.3133/mcs2025), our analysis reveals that over 16% of U.S. imports can be traced back to Russian mining through intermediate processing in countries such as China, India, and Vietnam. Additionally, our analysis of the aluminum supply chain shows that while the U.S. is reported as 52% net import reliant on aluminum materials in 2022, it is 100% reliant on foreign bauxite, 7% of which arrived indirectly. This unreported reliance, which is predominantly tied to bauxite mined in Brazil (43%) and Jamaica (28%), highlights our methods ability to capture the supply chain’s dependence on foreign feedstock that may be missing in single-stage trade data.

Mineral Economics↗