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1,697 records · Page 43Linked to original sources
USGS2025

Hydrogeologic framework of the Mountain Home area, southern Idaho

In the arid western Snake River Plain around the City of Mountain Home, Idaho, declining groundwater levels concern agricultural, municipal, and other water users who rely on groundwater for sustenance because surface-water resources are limited. The U.S. Geological Survey developed this hydrogeologic framework to provide an updated characterization of groundwater resources in the western Snake River Plain around the City of Mountain Home. The hydrogeologic framework comprises: (1) a conceptual description of hydrogeologic units, (2) a three-dimensional hydrogeologic model and borehole database, (3) a map of groundwater levels and change, and (4) a discussion of groundwater occurrence and movement within the study area. Hydrogeologic units were defined based on existing literature and the borehole database compiled for this study; the five hydrogeologic units are granite, rhyolite, basalt, fine-grained sediments, and coarse-grained sediments. Each unit can bear water, but the main regional aquifer in the study area occurs in the basalt and fine-grained sediment units with depth to water ranging from 150 to 765 feet. A perched groundwater zone near the City of Mountain Home is primarily hosted in basalt and used domestically with most depths to water ranging from 30 to 100 feet. Interflow zones, scoria, and vertical fractures create heterogeneity within the basalt hydrogeologic unit that exerts strong control on groundwater movement, creating horizontal perching conditions and zones of enhanced vertical conductivity that facilitate downward groundwater percolation. In the fine- and coarse-grained sediments and rhyolite units, inferred faults both impede and enhance groundwater movement. The borehole database was constructed by digitizing 540 well-driller reports and was used to build a three-dimensional hydrogeologic framework model which reasonably represents the spatial distribution of hydrogeologic units in the study area. Generally, fine-grained sediments underlie much of the study area, with basalt concentrated in the central and western study area and rhyolite and granite in the uplands to the north. Groundwater levels were measured in 180 wells in March and November 2023; these data were used to develop water-table contour maps and describe groundwater-level change over an irrigation season. Groundwater generally flows south-southwest to the Snake River and groundwater levels declined across most of the study area (from 0.03 to 22.01 feet) between spring and autumn 2023, which is consistent with long-term declines in the Cinder Cone Butte Critical Groundwater Area and Mountain Home Groundwater Management Area. Groundwater levels rose (0.6 to 15.44 feet) over the irrigation season in most wells in the perched groundwater zone near the City of Mountain Home and near the Snake River, indicating the importance of surface-water recharge to groundwater in areas where surface water irrigation occurs. In aggregate, this hydrogeologic framework provides an updated characterization of and new insights into groundwater resources in the study area to help inform water resources management.

Idaho↗
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↗
USGS2026

Investigation of drivers and sources of potential harmful algal blooms in Lake Anna, Virginia, 2023-24

Algal blooms in freshwater reservoirs are an increasing concern because of the risk to aquatic ecosystem health, recreational use, and water quality. Among the taxa that comprise an algal bloom, cyanobacteria are of particular concern due to the potential to produce toxic compounds which can cause acute and chronic illness in humans if ingested through contaminated water or shellfish. When toxins are above a toxin-specific threshold in the water column, the Commonwealth of Virginia categorizes the bloom as a harmful algal bloom (HAB), distinguishing it from an algal bloom without toxins present. Toxins are of particular concern in bodies of water which serve as major recreational areas, such as Lake Anna in central Virginia. Lake Anna is a 27 km-long and 47 km2 reservoir in central Virginia. Since the Commonwealth of Virginia revised the HAB monitoring framework in 2018, algal bloom advisories for potentially toxigenic blooms have been issued in the western, riverine zone of the reservoir. There were detections of microcystin and anatoxin production but no detections of cylindrospermopsin and saxitoxin production in Lake Anna; however, toxin concentrations were low, at values less than 0.65 µg/L. In 2023, the U.S. Geological Survey, in cooperation with Virginia Department of Environmental Quality, initiated a 19-month intensive and extensive study of western Lake Anna and its tributaries (North Anna River and Pamunkey Creek) to (1) characterize the algal community, biomass, and toxin production; (2) identify potential drivers leading to algal bloom initiation, persistence, and decline; and (3) assess the primary sources of the drivers contributing to algal blooms. Continuous and discrete monitoring of water quality within the lake, watershed inputs, meteorological conditions, and algal communities were monitored. Multiple modeling scenarios identified that the formation and persistence of algal blooms in Lake Anna are driven by interactions among chemical, macro- and micronutrient, and physical factors. Key drivers include total nitrogen, total phosphorus, water temperature, suspended sediment concentration, wind speed, copper, iron, molybdenum, nickel, sodium, and alkalinity. During this study, winter stormflow events delivered the highest loading of macro- and micronutrients from the tributaries to the lake. The lake is a sink for these nutrients in the water column or in the lake-bed sediments. During high-occupancy and recreation periods on the lake in the summer months, boating activity can promote internal loading by the resuspension of the lake-bed sediments, thereby increasing the nutrient availability for algal uptake. Thermal stratification and hypoxic conditions were observed, which can contribute to the passive release of nutrients into the water column, further facilitating algal bloom proliferation. These processes may create a positive feedback loop that supports the formation, persistence, and annual recurrence of algal blooms in the western region of Lake Anna. Continued water-quality monitoring of early warning indicators may improve the detection and forecasting of algal growth in the lake and help managers proactively manage water quality and algal growth.

EarthArXiv↗
USGS2026

Water-withdrawal and groundwater-level fluctuations, physical and infiltration characteristics of Ohio River sediment, and isotope-based estimates of water sources relative to a riverbank filtration system in an alluvial aquifer, Louisville, Kentucky, 2000–20

The U.S. Geological Survey and the Louisville Water Company, from 2000 to 2020, cooperatively investigated physical and hydrologic conditions in the Ohio River alluvial aquifer and adjacent riverbed sediments in the northeast part of Louisville, Kentucky, to increase knowledge of processes affecting water sources to collector wells during operation of a riverbank filtration system (RBF). The RBF, in 2020, included an initial collector well (CW1) completed in 1999 and a tunnel and collector well system (TCWS) composed of four collector wells (CW2–CW5) completed in late 2010 that were connected by a tunnel that routed water to a pumping station and a treatment plant. Daily mean water-withdrawal rates typically ranged from about 10.4 to 20.7 million gallons per day (Mgal/d) from 2005 to 2007, then were cyclic from 2008 to 2010 and ranged from as low as about 9 to 13 Mgal/d in winter-spring months up to about 19 to 20 Mgal/d in summer-autumn months. From 2011 through 2017, peak daily mean water-withdrawal rates from CW2–CW5 fluctuated between winter-spring lows of about 20–30 Mgal/d to summer-autumn peak rates of about 41.6 Mgal/d in 2013 to about 48.2 Mgal/d in 2012. From 2017 to 2020, TCWS peak daily mean water-withdrawal rates decreased from about 45.7 Mgal/d in 2017 to about 37.5 Mgal/d in 2020 and peak daily mean rates from CW1 ranged from about 20.7 Mgal/d in 2020 to about 21.6 Mgal/d in 2018. Periods in 2019–20 with tunnel pumping station daily mean water-withdrawal rates smaller than about 30 Mgal/d had generally stable water levels in CW2 and indicated the sustainable yield of the TCWS collector wells under the prevailing hydrologic conditions. Drawdown during July–August of 2008 and 2009 was associated with CW1, the sole operating collector well. Groundwater-flow directions outside the CW1 cone of depression during July–August of 2008 and 2009 were from areas near the alluvial valley margin toward the Ohio River and in 2009 toward Harrods Creek. Collector wells CW2 through CW5 derived water during 2011–19 surveys from surface-water infiltration, as indicated by drawdown that extended beneath the Ohio River and Harrods Creek. Increases in drawdown toward the alluvial valley margins in July–August of 2013 and 2015, compared with July 2011, indicated less available recharge from those flow directions and decreased yield of groundwater from the aquifer after progressively longer TCWS water withdrawals. Drawdown at CW2–CW5 and at observation wells between them lessened in October 2019 and indicated that onshore groundwater levels can recover when collector well withdrawals decrease. Overlapping cones of depression in 2011–15 potentiometric surfaces centered around CW2, CW3, CW4, and CW5, and drawdown along divides between those wells indicated well interference between collector wells. Well-interference effects increased during 2013 and 2015 survey periods, despite decreased water-withdrawal rates compared with July 2011, and expanded to include CW1 when its use resumed. Groundwater-level fluctuations in observation wells near the Ohio River were consistent with gradual depletion of groundwater yield during tunnel pumping station operation at river elevations of about 420 feet above the National Geodetic Vertical Datum of 1929 (NGVD 29) and enhanced recharge of the aquifer when river elevations were above flood stage of about 430 feet above NGVD 29. Groundwater-level declines in observation wells near the Ohio River were most apparent in summer and some autumn months between 2012 and 2017 during periods when tunnel pumping station mean daily water-withdrawal rates were about 35 Mgal/d or more. Periods of relatively stable groundwater levels during 2019–20 in observation wells between CW2–CW5, when tunnel pumping station water-withdrawal rates were about 30–33 Mgal/d or less, also indicated that the prevailing sustainable yield was about 30 Mgal/d or less. Wells associated with CW3 and CW4 had the largest increases in slopes of best-fit lines relating water-level difference to water-withdrawal rate between 2012 and 2020, an indication of decreased efficiencies of those wells to supply water or of changes in groundwater yield around the well. Increased slopes of best-fit lines relating water-level difference to water-withdrawal rate from 2019 to 2020, when groundwater levels were otherwise relatively stable, also indicated that the specific capacity of one or more TCWS collector wells may have declined during that period. Riverbed surface elevation, roughness, and hardness changes between hydroacoustic surveys indicated active accretion and erosion of bed sediment offshore from the riverbank filtration system in 2002–4. Riverbed hydraulic conductivity computed from 2013 to 2015 in tests at 11 sites ranged from 0.2 to 230 feet per day. Ten temporary piezometer sites with adjacent operating collector wells had drawdowns that ranged from about 4 to 11.7 feet and hammer blows greater than or equal to 49 counts, which indicated a relatively great density of alluvial sediments and the potential for diminished riverbed infiltration rates. Contributions of Ohio River infiltration to collector wells estimated from deuterium and oxygen-18 isotopic ratios in representative Ohio River and groundwater samples were considerably larger in warmer months than cooler months and ranged from 80–90 percent in CW1–CW5 in May 2020 to 100 percent in May 2019 at CW2, CW4, and CW5. Groundwater contributions to water withdrawals by collector wells increased in cooler autumn months and ranged from 40 percent at CW3 and CW5 in November 2020 to 70 percent at CW2, CW4, and CW5 in November–December 2019. November 2019 and November 2020 isotopic compositions of water from the collector wells had enriched deuterium and oxygen-18 isotopic ratios consistent with mixtures of evaporation-modified water from the Ohio River and meteoric-origin groundwater.

Indiana, Kentucky↗
USGS2026

Conventional hydrothermal power-producing systems of the Great Basin, USA

As part of the update to the electric-grade conventional hydrothermal assessment of the Great Basin, USA, Monte Carlo analyses of identified resources within explored regions will be performed to make estimates of discovered resources and associated uncertainty. Analyses use conditional statistics where estimates are conditioned upon a hydrothermal favorability map, allowing for the likelihood that more resources exist in regions of higher hydrothermal favorability. For these analyses, a dataset of identified hydrothermal systems is compiled, and the new compilation is described herein. Recognizing that a single hydrothermal system may be developed with multiple power plants, and that the hydrothermal upflow zone may be several kilometers across with many measurements characterizing a single hydrothermal system, a procedure was developed and employed to create clusters of points (power plants, measurements, etc.) that are associated with a single system, and a new central point was defined as the best estimator of the center of the hydrothermal system. Hydrothermal systems were uniquely identified by grouping electric-grade hydrothermal measurements and operating power plants within a distance of 10 km. Groups that are >10 km apart are assumed to be different electric-grade hydrothermal systems. While 10 km was used as the threshold, most systems were significantly further apart, and most points within groups were typically within 5 km of each other. A well measurement was considered an electric-grade measurement of a hydrothermal system if it had two properties: a measured temperature of >85 °C and evidence of hydrothermal convection. Other points that were added to the dataset are locations of operating powerplants or locations that have been classified as an electric-grade hydrothermal resource by either the U.S Geological Survey (USGS) or the Great Basin Center for Geothermal Energy. After all points are assigned to systems, new points were computed with the goal of identifying the center of the throat of the hydrothermal upflow zone. If operating powerplants exist for a system, then the arithmetic average of all power plant locations is used. Otherwise, if USGS made an estimate, that location is used. In the absence of both powerplants or USGS estimates, the arithmetic average of all electric-grade measurement locations is used. An example is shown of how these newly compiled locations might be ranked for uncertainty analyses, where higher confidence is assumed if measured temperature is higher and there are many supporting measurements indicating an electric-grade resource. In summary, 28 systems have operating power plants, an additional 78 systems are known identified electric-grade hydrothermal resources, and 99 new systems were identified as probable electric-grade systems with varying levels of confidence. These 205 locations are shown as a function of a recent hydrothermal favorability map, conceptually illustrating the conditional statistics that can be used to make estimates of the undiscovered resources of the Great Basin. An accompanying data release provides summaries of developed capacity by system and USGS estimates of likely total capacity and associated uncertainty.

Arizona, California, Idaho, Nevada, Oregon, Utah↗
USGS2025

Review and synthesis of the applications of machine learning to coalbed methane recovery

Over the last 30 years, a substantial literature has evolved on the use of machine learning (ML) to assess, predict, and improve the efficiency of coalbed methane (CBM) recovery. In the United States, the production of CBM declined as shale gas production matured, but CBM continues to be an important energy resource in other parts of the world. ML applications that have the potential to improve CBM reservoir management and production forecasts, and to increase exploration and operational efficiency, are still of significant interest. The integration of geostatistical techniques into the CBM ML applications has been largely absent but represents an opportunity for improvement. The literature demonstrates the widespread interest in, and applicability of, ML algorithms applied to CBM problems, and that they continue to result in improvements in predictive performance. However, (1) much of the research is more academic than operational, (2) many results are based on simulations, or small or proprietary datasets, (3) ML performance information can be inconsistent and sometimes entirely omitted, (4) most methodologies are unique to the specific CBM situation and likely not generalizable, (5) no standard data repositories are available to directly compare the performance of competing algorithms, and (6) the spatial component is often omitted. Finally, relatively new ML protocols involving causality analysis and reinforced learning, as well as hybrid workflows combining both supervised and unsupervised learning, are anticipated to dominate the future investigations. Integration of geostatistical and geospatial analysis with ML should enhance performance.

Book chapter↗
USGS1976

Neutron activation analysis in hydrology

Neutron activation analysis in hydrogeochemical and related investigations is increasingly practical because of developments in radiation detectors and computerized data processing. While neutron activation is the most widely used kind of activation analysis other kinds (for example, charged particle activation) are available for special applications. Neutron activation analysis is promising because approximately 70 percent of the elements in the periodic table are detected with satisfactory sensitivity, contamination from reagents is eliminated, simultaneous determination of more than 20 elements is often possible, technique is versatile, is applicable to small samples and is non-destructive in the instrumental form. Developments in theU.S. Geological Survey include a special sampling technique for water; a comprehensive technique using carriers precipitated with sulfide to concentrate desired trace elements; a determination of mercury, silver, and gold; determination of aluminum, vanadium and nine other elements; determinations of two groups of rare earth elements; and adaptations of the analytical methods to sediment and biota.

Open-File Report↗
USGS2026

Groundwater-level elevations in the bedrock aquifers of the Denver Basin aquifer system, Elbert County, Colorado, 2015–23

Water users in Elbert County, Colorado, rely on groundwater from bedrock aquifers in the Denver Basin aquifer system (upper Dawson, lower Dawson, Denver, Arapahoe, and Laramie-Fox Hills aquifers) for approximately half of their water uses. Withdrawals from the bedrocks aquifers have increased to meet the water use needs of expanding regional population growth and development. The U.S. Geological Survey, in cooperation with the Elbert County Board of County Commissioners, began a study in 2015 to monitor groundwater levels within Elbert County. The primary purpose of this report is to present a summary of groundwater levels measured during the study period (2015–23) and present results from statistical analyses of changes in groundwater-level elevations through time. Discrete groundwater levels were measured at 36 wells within Elbert County. Seven of those wells contained equipment to make and record continuous groundwater-level measurements at hourly intervals. All aquifers, except the lower Dawson aquifer, had only declining groundwater-level elevations in discrete measurements for wells with statistically significant trends. Of the eight statistically significant trends in the lower Dawson aquifer, two wells indicated increasing groundwater-level elevation from discrete measurements. The groundwater-level elevation trend medians in the upper Dawson, lower Dawson, Denver, Arapahoe, and Laramie-Fox Hills aquifers were −0.23, −0.66, −0.64, −0.39, and −0.63 feet per year, respectively, for discrete groundwater-level elevation measurements. Trends in continuous groundwater-level elevations were in agreement with statistically significant trends in discrete groundwater-level elevations for all wells. The groundwater-level elevation trend medians in this study, compared to the overall trends in a 2015−2018 study, both indicated declining groundwater-level elevations except in the upper Dawson aquifer, where the trend direction was opposite, a positive trend from 2015 to 2018 and a negative trend (declining groundwater elevations) from 2015 to 2023. The change in trends within the upper Dawson aquifer may be affected by differences in the study period and the trend analysis applied. Trends during the 2015–23 study period were compared to departures from the median 2015 groundwater-level elevation for each site in each aquifer. In general, the departures from the 2015 median supported trends observed at each site and correlated spatially with greater departures near the western border of Elbert County. Additionally, 30-year precipitation data showing wet and dry periods were overlaid with the departure from the 2015 median to assess groundwater-level patterns in wells in the five aquifers. Departures from the 2015 median groundwater-level elevations appeared greatest during the dry period between 2020 and 2023. Potentiometric-surface maps of the upper and lower Dawson aquifers created from static April 2023 groundwater elevations indicated groundwater-flow direction is generally from the south to the north. Results of this study could be used to guide additional groundwater monitoring in Elbert County and could aid in long-term planning of water resources.

Colorado↗
USGS2026

Chronic exposure to waterborne nickel significantly reduced growth of juvenile crayfish (Faxonius virilis)

Crayfish are critical functional components of aquatic ecosystems. Previous research has documented adverse effects of mineral extraction on crayfish. Here, we characterize potential risks of mining-derived waterborne nickel (Ni) to crayfish by documenting the effects of dissolved Ni on growth and food consumption of juvenile virile crayfish ( Faxonius virilis) in a 28-day chronic laboratory exposure. Nominal Ni concentrations ranged from 31.25 to 500 micrograms per liter (µg/L; pH = 7.96 ± 0.20, hardness = 150 ± 1 milligrams per liter as calcium carbonate). Crayfish survival, carapace length, and wet weight were measured. After 28 days of exposure, a 24-h feeding trial was performed to determine differences in food consumption. During the growth trial, 99% of crayfish survived. Change in wet weight and final wet weight were the most sensitive endpoints, with 20% effect concentrations of 24.8 and 22.6 µg/L Ni, respectively. Crayfish exposed to an average of 438 µg/L Ni consumed 41% less, and weighed 65.1% less, than control crayfish. These results suggest chronic, sublethal exposure to waterborne Ni may have negative effects on crayfish growth. Reduced growth and consumption rates in crayfish could have wide-ranging consequences throughout aquatic ecosystems since crayfish are consumers, prey, keystone trophic regulators, and ecosystem engineers. Finally, these results could inform bioenergetics and may be coupled with population models to predict potential changes in population sizes of native and invasive crayfishes.

Ecotoxicology↗
USGS2026

Core microbiomes as a potential fingerprinting method of Western USA dust sources

Introduction: Changing frequency and intensity of dust emissions impacts ecosystems and human health. Dust carries microbes, nutrients, heavy metals, and other materials that may change environmental biogeochemistry at deposition sites. Identifying dust sources provides key information on where and when mitigation strategies should be employed. However, commonly used geochemical or isotopic tracers are often not capable of distinguishing between geographic regions. Methods: We explored whether soil bacterial communities may provide distinct fingerprints of dust sources in the western United States. We identified bacterial core communities of dust from ten locations monitored by the National Wind Erosion Research Network (NWERN) with varied land use (cropland, rangeland, and playa), and compared communities to location, soil, and regional characteristics. Samples were collected monthly from Modified Wilson and Cooke (MWAC) samplers, composited by season (spring, summer, and fall), and analyzed using 16S rRNA sequencing. Results: We found distinct bacterial core communities that reflected dust source characteristics. In order of importance, precipitation levels ( p = 0.0001), location ( p = 0.0001), soil texture ( p = 0.0001), seasonality ( p = 0.0001), and elevation (p = 0.0002) were correlated with bacterial community composition. Discussion: Distinct bacterial core communities were associated with site characteristics such as biocrusts, playas, and military base proximity. Our results suggest that the use of core microbiomes may offer a fingerprinting method to identify dust source regions.

Colorado, Nevada, New Mexico, North Dakota, Oklaho↗
USGS2025

Understanding market sensitivity: Estimation of supply and demand elasticities for non-fuel minerals

In today’s rapidly changing economic landscape, understanding market responsiveness to price changes and the factors influencing commodity prices has become increasingly relevant. Price elasticities serve as indicators of how variations in market conditions affect supply and demand, providing insights into the sensitivity of commodity markets to price fluctuations. This paper presents a comprehensive analysis of price elasticities of supply and demand for 74 non-fuel mineral commodities including precious metals, base metals, minor metals, and industrial minerals that are utilized across various industries. We employ various econometric techniques, including fixed effects models for panel data and two-stage dynamic ordinary least squares (2S-DOLS) alongside autoregressive distributed lag (ARDL) models for time series analysis, to derive robust estimates of price elasticities. Our findings reveal variability in elasticities among different commodities and indicate that all studied mineral commodities exhibit price inelastic supply and demand in the short run, which we define as one year for the purposes of our analysis, given that the data is all annual. This research provides original estimates of price responsiveness for a wide range of commodities that have not been previously addressed in the literature, thereby enhancing the understanding of market dynamics in the mineral sector. Given that price elasticities can be influenced by factors such as market structure, technological advancements, mining costs, and industry-specific demand drivers, we use variables that serve as proxies for these factors.

Mineral Economics↗
USGS2025

A new groundwater energy transport model for the MODFLOW hydrologic simulator

Heat transport in the subsurface is an important aspect of research related to the effects of a warming climate on ecological services (i.e., cold-water refugia); the development of geothermal resources for energy banking schemes (i.e., aquifer thermal energy storage [ATES]); and the effects of temperature on other aspects of groundwater quality, such as nutrient cycling. Historically, simulation of heat transport using the MODFLOW groundwater simulator and related codes was performed by scaling the input parameters of a solute-transport model to emulate heat transport. However, that approach required additional pre- and post-processing of input and output and could not account for the variation in effective thermal storage and transport properties during transient, unsaturated flow, for example. True heat-transport capabilities in the context of MODFLOW were first introduced in a variant called USG-Transport. More recently, a new groundwater energy-transport (GWE) model type has been added to MODFLOW 6, the core version of the MODFLOW hydrologic simulator. GWE supports the simulation of heat transport on structured or unstructured grids as well as within and between features of advanced packages that represent streams, lakes, multi-aquifer wells, and the unsaturated zone. GWE is integrated within MODFLOW 6 and is accessible through the FloPy Python package and the MODFLOW 6 application programming interface (API). An example simulation demonstrates conduction between grid cells through both the water and the solid aquifer material, including thermal bleeding from saturated overburden cells into a groundwater flow field.

Groundwater↗
USGS2026

A Lake Charr pangenome reveals highly conserved Ohnologs as drivers of phenotypic diversity

Whole-genome duplication (WGD) is hypothesized to spur evolutionary diversification by producing genome-wide duplicate gene sets (Ohnologs) that are initially functionally redundant but can diverge markedly as the effects of relaxed selection accumulate over time. However, the underlying mechanisms remain unclear, in part because genomic studies often reconstruct Ohnolog evolution over millions of years, during which subsequent mutations can obscure deep-time signals. Investigating the relationship between Ohnolog evolution and diversification on a contemporary timescale offers clearer insights. We explore this relationship in Lake Charr ( Salvelinus namaycush ), where ∼10% of genes are retained highly conserved polyploid duplicates following the Salmonid-Specific Fourth Round WGD. Using 31 chromosome-level assemblies of Lake Charr from morphologically and ecologically diverse populations, joined into a pangenome graph, we characterized 189,555 structural variants (SVs) that were significantly less likely to affect genes retained as sequence-conserved Ohnolog pairs, nuancing the hypothesis that gene redundancy, relaxed selection, and functional diversification are intertwined. However, we found that SVs affecting such conserved Ohnologs may be potent drivers of adaptive evolution. Notably, we identified a putative 938-Kb interchromosomal translocation containing 25 genes with highly conserved Ohnologs in a paralogous (but untranslocated) genomic block. This putative translocation appears to have facilitated Ohnolog divergence in ankrd11 and hp , genes putatively linked to craniofacial and lipid metabolic diversity in sympatric Lake Superior morphs. This research reveals that conserved Ohnologs previously presumed to be redundant remain a reservoir for adaptive change.

BioRxiv↗
USGS2024

Simulated mean monthly groundwater-transported nitrogen loads in watersheds on the north shore of Long Island Sound, 1993–2022

Elevated nitrogen loads are pervasive in the Long Island Sound, an estuary that receives freshwater and nutrients from both surface-water and groundwater discharge. Surface-water nitrogen loads to the Long Island Sound are relatively well characterized, but less is known about groundwater-transported nitrogen loads. Prior work on the northern shore of Long Island Sound (Connecticut and areas of New York and Rhode Island) suggested that groundwater travel times are relatively short (median less than 2 years) and that decade-long nutrient legacies are not widespread. Because the travel times are short, groundwater flow and nutrient loads likely vary substantially between months. In the current study, the U.S. Geological Survey, in cooperation with the U.S. Environmental Protection Agency’s Long Island Sound Study and the Connecticut Department of Energy and Environmental Protection, developed a set of models to better characterize spatial and temporal patterns of groundwater-transported nitrogen loading from atmospheric deposition, septic systems, and fertilizers within the study area. The models provide an estimate, with uncertainty, of groundwater-transported nitrogen loads in the study area, filling a key gap in the nitrogen budget for Long Island Sound. The models also highlight the spatial and temporal variation in nitrogen loading throughout the study area. The modeling workflow involved four models. (1) A soil-water-balance model was developed by using the Soil-Water-Balance software to simulate groundwater recharge across the study area for water years 2005 through 2022. The simulated mean monthly recharge from the soil-water-balance model was used as input into a groundwater-flow model. (2) The groundwater-flow model was developed by using the MODFLOW 6 software and data for water years 1993 through 2022 and simulates average monthly hydrologic conditions. The groundwater-flow model was calibrated by using the Iterative Ensemble Smoother method within the PEST++ software. The Iterative Ensemble Smoother method generates an ensemble of sets of parameter values, with each set producing reasonable simulated hydrologic parameter values. (3) An ensemble of MODPATH particle-tracking simulations were run to generate particle flow paths and travel times, with each simulation using a different set of the flow model parameters. (4) A nitrogen load model uses the MODPATH simulation outputs to track nitrogen from the land surface through multiple attenuation zones until it discharges into fresh or saline surface water. As with the groundwater-flow model, the nitrogen model simulated average monthly groundwater-transported nitrogen loads for water years 1993 through 2022. One novel aspect of the nitrogen load model is that the nitrogen attenuation parameters were calibrated to observed nitrogen loads. Across the ensemble of simulated nitrogen loads, the median study-area-wide monthly simulated nitrogen loads from the aquifer to Long Island Sound throughout the year ranged from 900 to 18,600 kilograms of nitrogen per day, with a median load of 5,100 kilograms of nitrogen per day. The simulated loads were based on average monthly conditions for water years 1993 through 2022. Loads were highest during the winter and early spring and lowest during the late summer. However, simulated travel times for groundwater and nitrogen loads discharged to Long Island Sound during summer were longer than travel times for groundwater and loads discharged during the winter, indicating that, on average, groundwater discharged during summer traveled along different, and longer, flow paths, than groundwater discharged during winter. This indicates that summer loads would respond more slowly to changes in nitrogen inputs at the water table than winter loads. Over the entire study area, approximately 15 percent of the simulated load is from atmospheric deposition sources, 30 to 40 percent is from fertilizer, and 50 to 60 percent is from septic systems. The final analysis of the study involved simulating the change in groundwater-transported nitrogen load in response to upgrading septic systems or reducing fertilizing inputs to areas of turf grass. Both management interventions reduced the groundwater-transported nitrogen load, and reductions were greater in areas with greater loads from septic systems or turf-grass fertilizers. The delay between management actions and substantial reductions in groundwater-transported nitrogen loads varied seasonally; loads during the late summer months remained elevated longer than the winter loads.

Connecticut, Rhode Island↗
USGS2025

Machine learning provides reconnaissance-type estimates of carbon dioxide storage resources in oil and gas reservoirs

Oil and gas reservoirs represent suitable containers to sequester carbon dioxide (CO 2 ) in a supercritical state because they are accessible, reservoir properties are known, and they previously contained stored buoyant fluids. However, planners must quantify the relative magnitude of the CO 2 storage resource in these reservoirs to formulate a comprehensive strategy for CO 2 mitigation. Even reconnaissance-type estimates of CO 2 storage resources of known oil and gas reservoirs may require complicated calculations involving 1) estimates of recoverable oil and gas, 2) reservoir properties (depth, temperature, pressure, etc.), and 3) the physical qualities of the retained fluids. We demonstrate the application of machine learning (ML) algorithms to bypass these computations to yield more rapid estimates of CO 2 storage resources in reservoirs capable of hosting CO 2 in a supercritical state. ML algorithms are computationally efficient because they do not impose the strong assumptions on the data-generating process that standard statistical or engineering procedures require. Further, ML algorithms can capture highly complex, particularly nonlinear, relationships among predictor variables. We demonstrate the application of four different ML algorithms using data from onshore and offshore oil and gas reservoirs in Europe, and show they perform well when predictions are compared to engineering estimates. The proposed methods and models provide an effective and novel way to more rapidly and directly determine the subsurface CO 2 storage capacity of oil and gas reservoirs around the world, information that operators, researchers, and policymakers alike require to meet energy transition and decarbonization goals.

Frontiers in Enviornmental Science↗
USGS2026

Incorporating location uncertainty improves inference with stop-level North American Breeding Bird Survey data

Ecological models should account for uncertainty to be most effective and useful. Yet, uncertainty from model covariates—unlike that from other sources, such as sampling error or process variability—is seldom explicitly incorporated. This can cause underestimates of uncertainty to cascade through model parameter estimates, predictions, and downstream uses. Burner et al. proposed a method for quantifying uncertainty in covariates and incorporating it into models using informative Bayesian priors. This method was applied to stop-level Breeding Bird Survey (BBS) analyses, where land cover uncertainty at each stop arises from substantial stop location uncertainty. A limited validation of model-estimated land cover, using stops with known locations, indicated the method’s potential effectiveness, but it was not rigorously evaluated. We conduct a robust simulation-based test, generating stop locations, extracting land cover, and simulating bird communities across 210 BBS routes in the upper Midwest. We compare 3 models: a “known” model with true land cover, a “naive” model assuming consistent 800-m stop spacing, and a “full” model using informative priors to estimate land cover. Species parameter estimates and predicted prevalence patterns across gradients in land cover from the full model approached those of the known model and were substantially closer to the true values used in simulations relative to those from the naive model. Naive model parameters were more biased relative to the other models, and credible intervals of predicted species prevalence rarely included the true simulated values. The full model also produced land cover covariate estimates closer to true simulation values relative to the mean informative priors. Our results show that, for the BBS, informative priors enable more accurate stop-level analyses despite location uncertainty. In contrast, naive models that ignore this uncertainty yield poor inferences. More broadly, we demonstrate empirically the utility of informative priors to account for covariate uncertainty in ecological models.

Michigan, Minnesota, Wisconson↗
USGS2024

The dominance and growth of shallow groundwater resources in continuous permafrost environments

Water is a limited resource in Arctic watersheds with continuous permafrost because freezing conditions in winter and the impermeability of permafrost limit storage and connectivity between surface water and deep groundwater. However, groundwater can still be an important source of surface water in such settings, feeding springs and large aufeis fields that are abundant in cold regions and generating runoff when precipitation is rare. Whether groundwater is sourced from suprapermafrost taliks or deeper regional aquifers will impact water availability as the Arctic continues to warm and thaw. Previous research is ambiguous about the role of deep groundwater, leading to uncertainty regarding Arctic water availability and changing water resources. We analyzed chemistry and residence times of spring, stream, and river waters in the continuous permafrost zone of Alaska, spanning the mountains to the coastal plain. Water chemistry and age tracers show that surface waters are predominately sourced from recent precipitation and have short (<50 y) subsurface residence times. Remote sensing indicates trends in the areal extent of aufeis over the last 37 y, and correlations between aufeis extent and previous year summer temperature. Together, these data indicate that surface waters in continuous permafrost regions may be impacted by short flow paths and shallow suprapermafrost aquifers that are highly sensitive to climatic and hydrologic change over annual timescales. Despite the lack of connection to regional aquifers, continued warming and permafrost thaw may promote deepening of the shallow subsurface aquifers and creation of shallow taliks, providing some resilience to Arctic freshwater ecosystems.

Proceedings of the National Academy of Sciences↗
USGS2025

Beyond the mangroves: A global synthesis of tidal forested wetland types, drivers and future information opportunities

There is increasing awareness of the global diversity of tidal forested wetlands (TFWs) and their significance in the provision of ecosystem services. These ecosystems, including mangrove forests, tidal freshwater forested wetlands, supratidal forests and transitional forests together span multiple climatic zones, geomorphic settings, and inundation and salinity regimes. We utilise case studies across five continents to demonstrate the state of knowledge among TFWs. Intertidal mangroves are the best-defined of the TFWs thanks to decades of research on their geomorphology, hydrology and ecology across their broad distribution. Non-mangrove forest settings, however, demonstrate more diverse hydrological, biochemical and vegetation conditions. In many cases, non-mangrove forests are situated at upper intertidal or supratidal elevations, where surface waters and groundwater are subject to interactions between tides freshwater inputs. Salinity datasets show variations ranging from tidal freshwater forested wetlands and ‘low-salinity mangroves’ to mesohaline or marine salinities, often with high temporal variability. While the floristic composition of non-mangrove forests vary among biogeographic regions, locally dominant TFW species are commonly distributed beyond the tidal niche into non-tidal wetland and upland forests. This presents challenges for traditional remote sensing approaches to ecosystem mapping, which are mostly lacking for non-mangrove forests. Geomorphic approaches and developments in machine learning offer opportunities to address this.

Preprint↗