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USGS2025

Characterization and validation of tidally calibrated strains from the Alto Tiberina Near Fault Observatory Strainmeter Array (TABOO-NFO-STAR)

Six horizontal borehole tensor strainmeters (TSM1-6) installed from Fall 2021 to Spring 2022 comprise the Alto Tiberina Near Fault Observatory Strainmeter Array (STAR), providing an unprecedented opportunity to investigate seismic and aseismic deformation from hazardous high- and low-angle normal faults in Italy. Prior to use in tectonic applications, they require in-situ calibration and correction for non-tectonic signals. We tidally calibrate the instruments, characterize the calibration uncertainty, and test the results against environmental and earthquake signals originating from local to teleseismic distances. The STAR sites demonstrably deviate from assumptions common to the standard manufacturer's calibrations, including negative areal coupling at TSM3-6. While the tidally calibrated strains have ~3-56% uncertainty, the calibrated dynamic strains show interstation precision and accuracy to nanostrain levels, and static coseismic offsets in the array footprint are within uncertainty. TSM3 records a complex series of strains that may arise from dynamically triggered near-borehole fracture slip and fluid flow that does not appear to affect its sensitivity to lower strain rate deformation. Future calibration improvement may be afforded with longer stable timeseries, particularly for TSM4. Overall, our analyses demonstrate expanded geodetic capability for detecting deformation in the Alto Tiberina Near Fault Observatory.

Alto Tiberina Near Fault Observatory↗
USGS2025

Extremophile hotspots linked to containerized industrial waste dumping in a deep-sea basin

Decaying barrels on the seafloor linked to DDT contamination have raised concerns about the public health implications of decades old industrial waste dumped off the coast of Los Angeles. To explore their contents, we collected sediment cores perpendicular to five deep-sea barrels. The concentration of DDT and its breakdown products were highly elevated relative to control sites yet did not vary with distance from the barrels, suggesting that they were not associated with the contamination. Sediment cores collected through white halos surrounding three barrels were enriched in calcite and had elevated pH. The associated microbial communities were low diversity and dominated by alkalophilic bacteria with metagenome-assembled genomes adapted to high pH. A solid concretion sampled between a white halo and barrel was composed of brucite, a magnesium hydroxide mineral that forms at high pH. Based on these findings, we postulate that leakage of containerized alkaline waste triggered the formation of mineral concretions that are slowly dissolving and raising the pH of the surrounding sediment pore water. This selects for taxa adapted to extreme alkalinity and drives the precipitation of “anthropogenic” carbonates forming white halos, which serve as a visual identifier of barrels that contained alkaline waste. Remarkably, containerized alkaline waste discarded >50 years ago represents a persistent pollutant creating localized mineral formations and microbial communities that resemble those observed at some hydrothermal systems. These formations were observed at one-third of the visually identified barrels in the San Pedro Basin and have unforeseen, long-term consequences for benthic communities in the region.

California↗
USGS2025

Extracting data from maps: Lessons learned from the artificial intelligence for critical mineral assessment competition

The U.S. Geological Survey (USGS), Defense Advanced Projects Research Agency (DARPA), NASA Jet Propulsion Laboratory (JPL), and MITRE ran a 12-week machine learning competition aimed at accelerating development of AI tools for critical mineral assessments. The Artificial Intelligence for Critical Mineral Assessment Competition solicited innovative solutions for two challenges: 1) automated georeferencing of historical maps, and 2) automated feature extraction from historical maps. Competitors used a new dataset of historical map images to train, validate, and evaluate their models. Automated georeferencing pipelines attained a median root-mean square error of 1.1 km. Prompt-based extraction (i.e., with user input) of polygons, polylines, and points from geologic maps yielded median F1-scores of 0.77, 0.56, 0.35, respectively. Geologic maps pose numerous challenges for AI workflows because they vary significantly. However, despite its short duration, the competition yielded promising results that have since spurred further innovation in this area and led to the development of new AI tools to semi-automate key, time-consuming parts of the assessment workflow.

Applied Computing and Geosciences↗
USGS2025

Modeling interconnected minerals markets with multicommodity supply curves: Examining the copper-cobalt-nickel system

Demand for many of the metals used in the energy transition is expected to grow rapidly. Many of these are by-products, often considered critical because their production responds weakly to prices and is instead tied to the economics of the host mineral. We present a model of prices and production for jointly produced commodities that accounts for interconnectivity between host and by-product markets at the mine level. We demonstrate this method using the copper–cobalt–nickel system, in which approximately 99% of cobalt is a by-product of copper or nickel mining. Our results show that the model more accurately captures the economic benefits of diversified mine outputs than previous approaches. Furthermore, changes in demand drivers for any two commodities produce non-linear effects on production and price. We challenge the prior best-practice assumption that cobalt cannot impact the copper or nickel markets. Recognizing the importance of both copper and cobalt for future electrification, we emphasize that incentivizing the copper industry to reduce cobalt supply risks could inadvertently undermine copper supply.

Nature Communications↗
USGS2025

Toward a new framework to evaluate process-based model configurations and quantify data worth prior to calibration

Model criticism, discrimination, and selection methods often rely on calibrated model outputs. Because calibration can be computationally expensive, model criticism can first be undertaken by assessing model outputs obtained from limited prior parameter ensembles. However, such prior-based methods are often heuristic and do not formalize the notion of balancing model consistency with data and model complexity (i.e., model adequacy). We present a new framework to discriminate among candidate models prior to calibration that formalizes prior-to-calibration model adequacy into a metric to implicitly balance prior model output data coverage with model complexity represented by prior output (co)variance. The prior model adequacy metric “Mahalanobis distance deviation” quantifies the deviation of (a) the set of squared Mahalanobis distances of data from a prior model output distribution from (b) the set of squared Mahalanobis distances of data from their own distribution. A new data worth metric “discernment value” is also presented which quantifies the value of data for screening less-adequate models prior to calibration. Discernment value is calculated from the change in variance of a weighted average of prior model outputs from all candidate models due to less-adequate model outputs receiving lower weight. The framework is demonstrated using a one-dimensional groundwater flow model with eight possible configurations. A synthetic data network is used to test the framework. Results show the framework identifies the candidate models most similar to the true model used to create the synthetic data. Discernment values show variation in the value of different data types and locations for screening less-adequate models.

Water Resources Research↗
USGS2025

Prospectivity mapping for geologic hydrogen

Geologic, or naturally occurring, hydrogen has the potential to become a new, low-carbon, primary energy resource. Often referred to as “white” or “gold” hydrogen, this gas occurs naturally in the Earth’s subsurface, similar to petroleum resources. However, unlike petroleum, which releases carbon dioxide when burned, burning hydrogen only produces water as a byproduct. Exploration for geologic hydrogen remains in an early stage and discoveries of high concentrations of subsurface hydrogen are still relatively rare. To facilitate research and exploration for this potential resource, this report presents the first publicly available prospectivity map of geologic hydrogen accumulations in the conterminous United States. Prospective regions are those regions in which all major components necessary for a hydrogen accumulation likely are present—a source of sufficient hydrogen generation, porous reservoirs for storage, and seals to prevent leakage. The midcontinent region of the United States and the central California coast are revealed as having high prospectivity. This analysis also identifies previously unrecognized prospective regions that may be favorable due to long distance lateral migration of subsurface hydrogen, such as the offshore eastern seaboard of the United States, and can provide a linkage between surface observations of hydrogen degassing and far-field source regions. The methodology developed to create this map is expandable and flexible and may be adapted to incorporate new concepts in the hydrogen system and for application to other regions of the world.

conterminous United States↗
USGS2026

Estimating habitat availability for Chinook salmon (Oncorhynchus tshawytscha) and steelhead (O. mykiss) to inform reintroduction planning in the middle Snake River basin, USA

Anadromous fishes have been blocked from the middle Snake River basin since the construction of flood control, irrigation, and hydroelectric projects during the 19 th and 20 th centuries, culminating with the construction of Hells Canyon Dam (river kilometer [rkm] 398) in 1967. Seven large watersheds in the blocked area of the basin are under consideration for Pacific salmon reintroduction. The primary objective of this study was to identify and characterize potential reintroduction sites with high-quality rearing and spawning habitat for Chinook salmon ( Oncorhynchus tshawytscha ) and steelhead ( O. mykiss ) upstream of the Hells Canyon Complex in Idaho, Oregon, and Nevada, USA. We created and tested habitat models to predict rearing presence/absence, rearing abundance, and spawning presence/absence using channel morphology, hydrology, and stream temperature variables obtained from regional peer-reviewed datasets. Habitat models were trained with salmonid presence and abundance records collected in the lower Snake River basin (downstream of Hells Canyon Dam), where anadromous fish can currently access, from 1993 to 2011. Model performance was tested with set-aside data comprised of randomly selected reaches and independent environmental DNA data. An index model was created in the middle Snake River basin for each species by combining results from the habitat models. Modeling covariates differed by species and life stage and included different combinations of August stream temperature, summer flow, channel slope, and quadratic terms for temperature and slope. The habitat models predicted high versus low Chinook salmon and steelhead probability and abundance with 66% to 85% accuracy depending on species and life stage. The index models predicted a total of 2,887 km of Chinook salmon habitat and 2,434 km of steelhead habitat in the blocked area. The three basins in the blocked area with the greatest amount of predicted habitat were the Powder River, South Fork Payette River, and North Fork Payette River for Chinook salmon, and the Powder River, South Fork Boise River, and South Fork Payette River for steelhead. The modeling approach presented here is complementary to other planning efforts for Chinook salmon and steelhead reintroduction in the blocked area of the Snake River basin, and similar approaches may be useful for reintroduction planning in other systems.

Idaho, Nevada, Oregon, Utah, Washington, Wyoming↗
USGS2025

Constraining landslide frequency across the United States to inform county-level risk reduction

Informative landslide hazard estimates are needed to support landslide mitigation strategies to reduce landslide risk across the United States. Whereas existing national-scale landslide susceptibility products assess where landslides are likely to occur, they do not address how often , which is a critical element of landslide hazard and risk assessments. In particular, the U.S. Federal Emergency Management Agency's National Risk Index (NRI) requires landslide frequency estimates to inform expected annual loss estimates. We present county-level landslide frequency (landslides per area per year) estimates for the 50 US states. We applied Bayesian negative binomial regression to estimate both the expected (average) reported landslide frequency and full distribution of annual landslide counts for each county. We compared a suite of models that used combinations of landslide-susceptible area, probability of potentially triggering earthquakes, frequency of potentially triggering precipitation, and ecological region as predictors. We trained our models with landslide inventory data from counties with the most comprehensive records available nationwide and used zero-inflated negative binomial distributions as an incompleteness model to correct for temporal reporting gaps. We selected a preferred frequency model to inform the NRI based on information criteria and physically plausible parameter estimates. The model showed that average annual reported landslide frequencies vary by 5 orders of magnitude across US counties, ranging from 0.002 (0.00015–0.05) landslides 1000 km −2 yr −1 in Kusilvak Census Area, Alaska, to 29 (19–46) landslides 1000 km −2 yr −1 in Lake County, California, reflecting the country's strong variations in landslide susceptibility, earthquake probability, and other factors for which ecological region serves as a proxy. Counties with estimated frequencies in the top 20 % of all counties are predominately along the West Coast of the continental United States, in mountainous regions of the Pacific Northwest and Intermountain West, in locally steep or earthquake-prone regions of the Midwest and Southeast, along the Appalachians, in southern and southeastern Alaska, and on some Hawaiian islands. By examining the number of landslides predicted in 99th percentile years for each county, we identified that 26 % of US counties likely have potential for widespread landsliding with more than 10 landslides 1000 km −2 yr −1 , even when such large events have not been reported in the training data for that county. Overall, our results better represent the range of possible landslide frequencies and spatial variations than previous national-scale estimates reported in the NRI, and our approach can inform other risk-reduction and loss-mitigation efforts across the United States and globally.

Natural Hazards and Earth System Sciences↗
USGS2024

A partially nonergodic ground-motion model for Fourier amplitude spectra for the San Francisco Bay area, California, USA

We develop a partially nonergodic ground-motion model (GMM) for Fourier amplitude spectra for the San Francisco Bay Area, California, USA, using the Bayless and Abrahamson (2019) GMM as a reference ergodic GMM and developing location-dependent adjustments to the predicted median and variance. We compile regional ground-motion data from moment magnitude (𝑀 w ) >3 earthquakes occurring during 2000–2022 for which magnitude information is available in the U.S. Geological Survey Comprehensive Catalog (Guy et al., 2015). The data set predominantly consists of records from 𝑀 w 3.5–4.5 earthquakes but includes three well-recorded 𝑀 w > 5 events. Ground-motion residuals are evaluated using the time-averaged shear-wave velocity in the top 30 m (𝑉 S30 ) from the California-specific map of Thompson et al. (2018) and basin-depth site parameters from the seismic velocity model of Aagaard and Hirakawa (2021). The 𝑉 S30 dependence and basin-depth scaling of the reference ergodic GMM of Bayless and Abrahamson (2019) are evaluated and modified with the updated data set. We compute maps of site adjustments using a varying-coefficient model that considers the spatial correlation structure and uncertainties at each observation location. The spatial covariance model is developed using ground-motion residuals that are standardized by the uncertainty model, which allows for consideration of the aleatory variability in developing the site adjustments. The covariance model is fit considering the means and standard deviations of the site terms at all locations. The use of partially nonergodic median adjustments results in modified variance components of the within-event variability. Due to the low number of large-magnitude earthquakes that control seismic hazard in the data set, we do not modify between-event variance; however, we present adjustments to site-to-site variability for use in partially nonergodic hazard assessments.

California↗
USGS2026

Groundwater budget for the Mountain Home area, southern Idaho, 2022–23

The U.S. Geological Survey, with funding from the Idaho Department of Water Resources, developed a groundwater budget for the Mountain Home area in southern Idaho for irrigation year 2023 (November 1, 2022–October 31, 2023). This study focused on the water balance across the Cinder Cone Butte Critical Groundwater Area (CGWA), Mountain Home Groundwater Management Area (GWMA), and the rest of the study area (RoSA), compiling data from various sources, including precipitation records, groundwater level measurements, metered groundwater pumpage data, surface water diversions and evapotranspiration (ET) estimates derived from remote sensing satellite imagery, and ground-based reference data. Key inflow components included recharge from applied surface water irrigation (which incorporates incidental recharge from irrigation practices and conveyance losses), estimated tributary streamflow, and estimated mountain block recharge. The key outflow components were groundwater pumpage for irrigation, municipal, industrial, and domestic uses, and ET. Recharge from applied irrigation and mountain block recharge were the largest inflows, and groundwater pumpage for irrigation was the largest outflow. The CGWA had a positive groundwater budget residual of 2,170 acre-feet (acre-ft), which contrasts with observed long-term groundwater level declines and historical trends of storage depletion. This positive residual is likely associated with unquantified outflows, including lateral groundwater flow out of the subregion, or other complexities, such as overestimated tributary contributions relative to the actual recharge for the 2023 water budget. The GWMA exhibited a positive residual of 56,563 acre-ft, primarily owing to recharge from applied surface water irrigation and areal recharge during a wetter-than-average year, which allowed irrigation entities to deliver more water from in-basin and out-of-basin reservoirs. The RoSA showed a large positive residual of 124,933 acre-ft. The interpretation of these positive residuals must account for significant uncertainties, including estimations of areal recharge, tributary streamflow (particularly losses and diversions), ET, the volume of surface water loss to the Snake River, lateral groundwater flows between subregions and across study area boundaries, and the unquantified groundwater discharge to the Snake River. These uncertainties, in combination with the complex hydrogeologic controls on water movement and limitations of remotely sensed data, directly affect the accuracy of water availability assessments. Future data collection efforts would help reduce these uncertainties and support water resource management decisions in the Mountain Home area. Key efforts could include installing additional streamflow gaging stations (particularly to quantify tributary losses and gains and surface water losses to the Snake River), improving groundwater pumpage metering, and validating remotely sensed ET data with ground-based measurements. Furthermore, to better quantify unrepresented or highly uncertain fluxes, focused investigations on groundwater discharge to the Snake River, lateral groundwater flows between subregions and across study area boundaries, and a more robust determination of the actual influence and volume of mountain block recharge would help refine future water availability assessments for the Mountain Home area.

Idaho↗
USGS1990

Recharge rates and aquifer hydraulic characteristics for selected drainage basins in middle and east Tennessee

Quantitative information concerning aquifer hydrologic and hydraulic characteristics is needed to manage the development of ground-water resources. These characteristics are poorly defined for the bedrock aquifers in Middle and East Tennessee where demand for water is increasing. This report presents estimates of recharge rate, storage coefficient, diffusivity, and transmissivity for representative drainage basins in Middle and East Tennessee, as determined from analyses of stream-aquifer interactions. The drainage basins have been grouped according to the underlying major aquifer, then statistical descriptions applied to each group, in order to define area1 distribution of these characteristics. Aquifer recharge rates are estimated for representative low, average, and high flow years for 63 drainage basins using hydrograph analysis techniques. Net annual recharge during average flow years for all basins ranges from 4.1 to 16.8 in/yr (inches per year), with a mean value of 7.3 in. In general, recharge rates are highest for basins underlain by the Blue Ridge aquifer (mean value11.7 in/yr) and lowest for basins underlain by the Central Basin aquifer (mean value 5.6 in/yr). Mean recharge values for the Cumberland Plateau, Highland Rim, and Valley and Ridge aquifers are 6.5, 7.4, and 6.6 in/yr, respectively. Gravity drainage characterizes ground-water flow in most surficial bedrock aquifer in Tennessee. Accordingly, a gravity yield analysis, which compares concurrent water-level and streamflow hydrographs, was used to estimate aquifer storage coefficient for nine study basins. The basin estimates range from 0.002 to 0.140; however, most estimates are within a narrow range of values, from 0.01 to 0.025. Accordingly, storage coefficient is estimated to be 0.01 for all aquifers in Middle and East Tennessee, with the exception of the aquifer in the inner part of the Central Basin, for which storage coefficient is estimated to be 0.002. Estimates of aquifer hydraulic diffusivity are derived from estimates of the streamflow recession index and drainage density for 75 drainage basins; values range from 3,300 to 130,000 ft^2/d (feet squared per day). Basin-specific and site-specific estimates of transmissivity are computed from estimates of hydraulic diffusivity and specific-capacity test data, respectively. Basin-specific, or areal, estimates of transmissivity range from 22 to 1,300 ft^2/d, with a mean of 240 ft^2/d In general, areal transmissivity is highest for basins underlain by the Cumberland Plateau aquifer (mean value 480 ft^2/d) and lowest for basins underlain by the Central Basin aquifer (mean value 79 ft^2/d). Mean transmissivity values for the Highland Rim, Valley and Ridge, and Blue Ridge aquifer are 320,140, and 120 ft^2/d respectively. Site-specific estimates of transmissivity, computed from specific-capacity data from 118 test wells in Middle and East Tennessee range from 2 to 93,000 ft^2/d with a mean of 2,600 ft^2/d Mean transmissivity values for the Cumberland Plateau, Highland Rim, Central Basin, Valley and Ridge, and Blue Ridge aquifers are 2,800,1,200, 7,800, 390, and 65Oft Id, respectively.

Tennessee↗
USGS1976

Heat-flow data from southeastern Oregon

With the exception of values from two holes drilled within 2 km of Mickey Hot Springs, 17 new heat-flow values in southeastern Oregon are within or somewhat below the range one would normally expect in non-anomalous parts of the North American Cordillera. This is not surprising for a region in which most igneous rocks on the surface are 5 m.y. old or more. There is a suggestion of a thermal anomaly associated with the very young (late Pleistocene or Holocene) Diamond Craters lava field, and the thermal regime on both sides of Steens Mountain seems to be controlled, to some degree, by lateral and vertical movement of water.

Oregon↗
USGS2024

Groundwater and surface-water interactions in the Lower Duwamish Waterway, Seattle, Washington

The U.S. Geological Survey (USGS), in cooperation with the Washington State Department of Ecology (Ecology), conducted a study to describe the current understanding of the regional groundwater system of the lower Duwamish River valley and groundwater and surface-water interactions in the lower Duwamish Waterway. The lower Duwamish Waterway is the final 5-mile (mi) reach of the Duwamish River before it empties into Elliott Bay in Puget Sound near Seattle, Washington. A nearshore site (hereinafter referred to as “Nearshore Site” to distinguish the particular site from general discussions of nearshore areas) along the western shoreline of the Duwamish River, about 1.5 mi upstream from the river mouth, was selected for focused groundwater data collection by USGS. Data loggers were deployed in seven groundwater wells and one stilling well in the Duwamish River to measure specific conductance, temperature, and depth at 15-minute intervals for a period of about 2 years. At the Nearshore Site during 2020–22, water levels in the shallow wells were 3–8 feet (ft) higher than water levels in the deep wells, providing evidence for a low-permeability layer between the shallow and deep aquifers in this area. The shallow wells had a pronounced seasonal variability, with high water levels in winter and low water levels in summer. Data from the deep wells showed far less seasonal variability, with slight increases in winter and a near-constant water level from spring to autumn. The deep wells had a strong hydraulic connection to the Duwamish River, as evidenced by the synchronous water-level variability during the tidal cycle, whereas the shallow wells had minimal to no tidal response. The potentiometric maps developed for the Nearshore Site and surrounding areas indicate large differences in groundwater-flow directions for the shallow and deep aquifers at low and high tides. For the shallow aquifer, flow is toward the lower Duwamish Waterway near the Nearshore Site, regardless of the tidal condition. For the deep aquifer, a potentiometric trough forms parallel to the shoreline during high tide, indicating that groundwater flow converges from the uplands to the west and the Duwamish River to the east. The geometry of the potentiometric surfaces between the nearshore-most well and the shoreline is complex and is further confounded by intermittent shoreline armoring and other buried infrastructure, which could serve as either a barrier or a conduit to flow. Groundwater and surface-water interactions in the lower Duwamish Waterway are inherently complex as a result of three overarching factors. First, water levels in the lower reaches of the Duwamish River vary daily by 11–16 ft because of tides from Puget Sound, which create large swings in the hydraulic gradient in the nearshore groundwater system. Second, the density and chemical composition of water in the Duwamish River change daily with the tides and seasonally, which constrains how river water entering the nearshore sediments interacts with discharging groundwater. Third, the nearshore subsurface and shoreline conditions are heterogenous because of extensive shoreline armoring over the past century, which governs the flow of groundwater and infiltrating river water. These unique features of groundwater and surface-water interactions in the lower Duwamish Waterway thus govern the transport of terrestrial contaminants to the lower Duwamish Waterway. Furthermore, the heterogenous aquifer properties in the lower Duwamish Waterway contribute to spatially and temporally dynamic contaminant-transport processes.

Washington↗
USGS2024

Parameter ESTimation with the Gauss–Levenberg–Marquardt algorithm: An intuitive guide

In this paper, we review the derivation of the Gauss–Levenberg–Marquardt (GLM) algorithm and its extension to ensemble parameter estimation. We explore the use of graphical methods to provide insights into how the algorithm works in practice and discuss the implications of both algorithm tuning parameters and objective function construction in performance. Some insights include understanding the control of both parameter trajectory and step size for GLM as a function of tuning parameters. Furthermore, for the iterative Ensemble Smoother (iES), we discuss the importance of noise on observations and show how iES can cope with non-unique outcomes based on objective function construction. These insights are valuable for modelers using PEST, PEST++, or similar parameter estimation tools.

Groundwater↗
USGS2025

Deterministic physics-based earthquake sequence simulators match empirical ground-motion models and enable extrapolation to data poor regimes: Application to multifault multimechanism ruptures

We use the deterministic earthquake simulator RSQSim to generate complex sequences of ruptures on fault systems used for hazard assessment. We show that the source motions combined with a wave propagation code create surface ground motions that fall within the range of epistemic uncertainties for the Next Generation Attenuation‐West2 set of empirical models. We show the model is well calibrated where there are good data constraints, and has good correspondence in regions with fewer data constraints. We show magnitude, distance, and mechanism dependence all arising naturally from the same underlying friction. The deterministic physics‐based approach provides an opportunity for better understanding the physical origins of ground motions. For example, we find that reduced stress drops in shallow layers relative to constant stress drop with depth lead to peak ground velocities in the near field that better match empirical models. The simulators may also provide better extrapolations into regimes that are poorly empirically constrained by data because physics, rather than surface shaking data parameterizations, is underlying the extrapolations. Having shown the model is credible, we apply it to a problem where observations are lacking. We examine the case of crustal faults above a shallow subduction interface seen to break coseismically in simulations of the New Zealand fault system. These types of events were left out of consideration in the most recent New Zealand national seismic hazard model due to the modeling complexity and lack of observational data to constrain ground‐motion models (GMMs). Here, we show that in the model, by breaking up the coseismic crustal and interface rupturing fault motions into two separate subevents, and then recombining the resulting ground‐motion measures in a square‐root‐of‐sum‐of‐squares incoherent manner, we reproduce well the ground‐motion measures from the full event rupture. This provides a new method for extrapolating GMMs to more complex multifault ruptures.

Seismological Research Letters↗
USGS2024

Federal lands greenhouse gas emissions and sequestration in the United States: Estimates for 2005–22

In 2016, the Secretary of the U.S. Department of the Interior requested that the U.S. Geological Survey (USGS) produce a publicly available and annually updated database of estimated greenhouse gas emissions associated with the extraction and use of fossil fuels from Federal lands. The first report in this series included emissions estimates from 2005 to 2014 and were reported for 29 States and two offshore areas. Native American and Tribal lands were not included in that analysis. This report recalculates those previous years (2005–14) with updated data and methods and extends the estimates to 2022. Nationwide emissions from fossil fuels produced on Federal lands in 2022 were 1,081.2 million metric tons of carbon dioxide equivalent (MMT CO 2 Eq.) for CO 2 , 33.4 MMT CO 2 Eq. for methane (CH 4 ), and 4.3 MMT CO 2 Eq. for nitrous oxide (N 2 O). Compared to 2005, the 2022 totals represent decreases in emissions for all three greenhouse gases (by 17 percent for CO 2 , 37 percent for CH 4 , and 30 percent for N 2 O). Emissions from fossil fuels produced on Federal lands represent, on average, 21.8 percent of U.S. emissions for CO 2 , 6.1 percent for CH 4 , and 1.3 percent for N 2 O over the 18 years included in this estimate. The trends and relative magnitudes of the greenhouse gas emissions estimated are roughly parallel to the Federal lands fossil fuel production volumes. In 2021, Federal lands of the conterminous United States stored 70,424 MMT CO 2 Eq. in terrestrial ecosystems. Soils stored most of the terrestrial ecosystem carbon (66 percent), followed by live vegetation (25 percent), deadwood (5 percent), and litter (4 percent). Carbon sequestration on Federal lands was highly variable over time, owing primarily to interannual variability in climate and weather, and variability in land use and land cover (LULC) change and disturbances, among these are wildfires and logging. Between 2005 and 2021, Federal lands sequestered an average of 83 MMT CO 2 Eq./yr. By subtracting the cumulative effects of LULC and disturbance-related CO 2 losses to the atmosphere from the total, we estimate that ecosystems at the national level sequestered CO 2 at an annual mean rate of 17 MMT CO 2 Eq./yr in a term called the net ecosystem exchange (NEE). This annual NEE sequestration value represents about 1.4 percent of average fossil fuel emissions over the same period. The USGS estimates presented in this report represent an accounting for the emissions resulting from fossil fuel extraction on Federal lands and the end-use combustion of those fuels, as well as for the sequestration of carbon in terrestrial ecosystems on Federal lands. A combined net CO 2 emissions estimate, which is the difference between the emitted and sequestered CO 2 from both the fossil fuel and ecosystems estimates, provides context for evaluating the greenhouse gas contributions of activities on these lands. The estimates included in this report can provide context for future energy decisions, as well as a basis to track change in the future.

Scientific Investigations Report↗
USGS2024

Predicting hydrothermal reservoir depth from chemical geothermometers using a three-dimensional temperature model in the Great Basin, USA

Recent work in the Great Basin region of the western United States has made it possible to predict the depth of hydrothermal reservoirs (i.e., the depth at which heat is accumulated prior to ascent via hydrothermal upflow) identified through geochemistry and to contextualize the spatial patterns of these reservoir depths. Chemical geothermometers use the chemical and mineral constituents of hydrothermal fluids to predict the temperature at which fluids equilibrated with the host rocks at depth. Assuming that most of the Great Basin is dominated by conductive conditions until a vertically connected hydrothermal flow path is created (e.g., by faulting), geothermometers reflect the chemical and thermal conditions at the depth interval that the fluid has conductively equilibrated over a long period before a vertical conduit allows convective upflow. By pairing geothermometer temperature estimates with our recent three-dimensional temperature model of conductive heat flow in the Great Basin, we estimate the corresponding reservoir depths and construct a map of circulation depths. The predicted depths from geothermometers have spatial patterns across the Great Basin that relate to patterns seen in other geologic and geophysical data. Deeper springs generally occur disproportionately in areas with higher strain rates and in basins. We posit that current elevated strain rates reflect patterns of historic deformation where ongoing tectonic activity maintains permeable pathways to deeper reservoirs, some of which are estimated to exceed 6 km depth. Basins, as expected, contain a disproportionate number of these deep systems, because the underlying aquifers are closer to the surface in basins, thus requiring less water pressure to reach the surface than in mountain ranges. Most springs estimated to have their source in a deep reservoir occur at places known to host a hydrothermal system; these refined depth estimates of the source reservoir can help to better constrain the source depth for many known hydrothermal systems across the Great Basin.

Arizona, California, Idaho, Nevada, Oregon, Utah↗
USGS2026

A targeted approach for mapping groundwater discharge to surface water and fish thermal refuge in four Lake Ontario tributaries

The duration, magnitude, and frequency of heatwaves are predicted to increase in the coming decades, a combination that can reduce the survival of many fish species. Across the world, there is broad interest in identifying thermal refuge for heat-intolerant fish species and exploring opportunities to enhance or protect these areas. Because deeper groundwater maintains a relatively constant temperature, groundwater-influenced areas along streams can provide cool-water refuge for fish during periods of extreme heat. A targeted approach was developed for identifying existing cold-water zones and areas of substantial groundwater discharge in four high priority Lake Ontario tributaries. Our approach included: (1) predicting where groundwater discharge is most likely with a simple geospatial model and (2) using model predictions to select field sites for intensive high-resolution study, including ground-based mapping of groundwater features (springs, seeps, tributaries) as well as drone-based optical and thermal infrared surveys. Results from field sites were used to both verify model performance and map different types and aerial extents of thermal anomalies. Geospatial modelling successfully predicted regions of widespread groundwater upwelling, later verified and mapped by field and drone surveys. Comparison of model and field survey results further highlighted specific geospatial layers, such as soil/bedrock types and topographic wetness index, as being particularly useful for predicting groundwater influence on streams in the study area. In addition, a comparison of geospatial model results with a model of fish abundances along the studied streams showed significant positive correlations for many heat-intolerant fish species over a wide geographic area. The approach developed in this study can be applied to other watersheds to highlight areas of probable groundwater discharge and could be used by fishery and water resource managers to support cold-water fish habitat management decision-making and resource conservation.

New York↗