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USGS2026

Distinguishing natural from mining-related metal sources by including streambank groundwater data in a stream mass loading study

Distinguishing stream metal loading caused by mine features from that caused by natural background sources remains challenging, yet this distinction is essential for making effective remedial decisions at many legacy mine sites. We combine a stream tracer injection and synoptic sampling study with data from shallow near-stream groundwater wells to estimate left-bank versus right-bank metal loading contributions at the 100-m spatial scale. The study was performed in the East Mancos River, a mountain headwater stream in Colorado, USA. The dominant source of elevated stream metal concentrations could be either groundwater infiltration through right-bank Doyle Mine waste piles or natural acid rock drainage from hydrothermally altered bedrock located mainly on the left bank. For the five metals of concern (Cu, Al, Zn, Cd, and Mn), we find that 15 % of the load contributed by diffuse groundwater inputs in the section potentially influenced by Doyle mine waste originates from the right bank. This right-bank potential mine contribution equates to only 3 % of the total watershed instream load for these metals. Furthermore, apparent 3 H/ 3 He groundwater ages in segments contributing most of the right-bank metal loading are sufficiently old (9–12 yr) to suggest that infiltration through the waste piles, located only 140–180 m from the stream, is unlikely. Estimated potential Doyle mine loading contributions can therefore be considered maximum values. Study results thus indicate that Doyle mine waste piles are a minor source of metal loading under low-flow conditions, and streambank groundwater data can provide valuable additional information in stream mass loading studies.

Colorado↗
USGS2025

VIPER site analysis

We needed to evaluate available orbital data of NASA’s Volatiles Investigating Polar Exploration Rover (VIPER) mission area in order to derive a variety of maps to help the science team identify scientifically interesting places for the rover to visit and to provide scientific context for our mission. Some of these maps also fulfilled engineering and mission design needs to enable safe and efficient landing and roving. We incorporated data from the Lunar Reconnaissance Orbiter Camera, the Lunar Orbital Laser Altimeter, the Mini-RF instrument, the Chandrayaan-2 Orbital High Resolution Camera, the Korean Pathfinder Lunar Orbiter’s Shadowcam, the Kaguya Spectral Profiler and Multiband Imager, and the Chandrayaan-1 Moon Mineralogy Mapper. We used a variety of techniques to build these maps, including stereogrammetry, shape-from-shading, ice stability depth and surface temperature calculations, and the horizon method for solar illumination and direct-to-Earth communications maps. Altogether, these maps allowed us to survey for boulders, evaluate features in permanently shadowed regions that VIPER might explore, provide mineralogic context for what VIPER’s instruments may learn, estimate the ages and radar properties of craters in the VIPER mission area, and evaluate the potential for gravity traverses with the rover. These data and techniques provided a rich set of information from which both the VIPER science team and engineering teams were able to draw in order to plan a safe landing and to plan a VIPER surface mission that will be both scientifically valuable and robust from an operational perspective.

The Planetary Science Journal↗
USGS2025

A spatiotemporal deep learning approach for predicting daily air-water temperature signal coupling and identification of key watershed physical parameters in a montane watershed

Seasonal shifts from runoff to groundwater dominance influence daily headwater stream temperatures, especially where local groundwater input is strong. This input buffers temperature during hot periods, supporting cold-water habitats. Recent studies use air–water temperature signal metrics to identify zones of strong stream–groundwater connectivity. While Previous studies used air–water signal ratios as proxies for groundwater influence but were limited to specific sites and periods, without dynamic forecasting. This study is the first to forecast daily A r as a spatiotemporal signal using a Graph Convolutional Network–Long Short-Term Memory (GCN-LSTM) model. The model was trained using hydroclimate data (air temperature, precipitation, shortwave radiation, streamflow) and watershed physical features (e.g., sand content, slope). Results showed high predictive skill, achieving R 2 (NSE, RMSE) of 0.86 (0.73, 0.0004) for one-day-ahead to 0.52 (0.50, 0.0009) for seven-days ahead forecasts. Prior studies often have not explicitly incorporated spatial hydrogeologic drivers, but this model explicitly incorporates them to assess their impact on A r forecasting and stream-groundwater connectivity. Feature analysis identified mean sand, elevation, slope, clay, and TWI as key predictors of A r . Stronger groundwater signals appeared in hillslopes, elevations, and tributaries, highlighting watershed influence on streamflow. However, limitations include reliance on historical air–water temperature patterns for training and limited representation of extreme climate conditions. Despite these limitations, unlike previous studies relying on measured in-situ stream and air temperature, this study forecasts A r directly from climate and physiographic features after training, avoiding in-situ data requirements. Findings aiding predictions of stream ecosystem resilience.

New York↗
USGS2026

Upper Triassic igneous rocks of the southern Kenai Peninsula, Alaska – Prelude to Early Jurassic subduction along the western Wrangellia composite terrane margin

New U–Pb zircon geochronology identifies a latest Triassic (ca 214–201 Ma) igneous suite of tuff, hypabyssal dikes, and a pluton on the southern Kenai Peninsula, Alaska. The igneous suite was emplaced within Upper Triassic sedimentary rocks along the southern margin of Western Wrangellia, the western-most fragment of the Wrangellia composite terrane. The igneous rocks range from mafic (50.6% SiO 2 ) to felsic (78.3% SiO 2 ), characteristically have less than 1.55% K 2 O, and generally have low trace element abundances. The tonalitic and trondhjemitic magmas were largely sourced in mafic-rich lower crust and incompletely assimilated quartz and other mineral xenocrysts are common. Fractionation involving plagioclase and amphibole is indicated for some magmas and composite intrusions and igneous xenoliths indicate magma mixing was possible. Paleozoic and Precambrian inherited zircons and initial 87 Sr/ 86 Sr (0.704103–0.705609) and 143 Nd/ 144 Nd (0.512396–0.512777) ratios indicate that the Western Wrangellia crustal sources are heterogeneous and contain sialic components. The latest Triassic magmatism reflects processes that preceded Early Jurassic subduction along the Wrangellia composite terrane and Pacific Ocean plate boundary. These processes involved heating and melting of mantle lithosphere and lower crust as mantle instabilities accompanied the breaking of the plate boundary linkages. The Late Triassic transition to subduction along the Wrangellia composite terrane margin coincided with the transition to subduction cessation in the Late Triassic arcs of the western Intermontane terranes of Canada. The shift to subduction along the outboard Wrangellia composite terrane margin marks the beginning of the Pacific Ocean–Cordillera plate interactions that came to dominate the tectonic evolution of the northern Cordillera from the Early Jurassic to today.

Alaska↗
USGS2026

Deep subsurface organic-rich shale supports abundant, diverse, and novel fungi

As Earth’s principal reservoir of organic carbon and microbial biomass, the deep subsurface hosts microorganisms capable of mobilizing this once-sequestered carbon. Contrary to standard assumptions of eukaryotic scarcity, this study documents abundant fungal communities, ranging from 4.2 × 10 3 to 6.8 × 10 3 fungal cells mL −1 , across a methane-producing organic-rich shale 247–556 meters below the surface. Although fungal:bacterial cell ratios ranged from 1:7028 to 1:713, application of biomass conversion factors developed for oceanic systems yielded a median fungal:bacterial biomass ratio of 1:4.7. 16S rRNA gene amplicons revealed bacterial and archaeal communities mirroring those found in well-characterized extremophilic, carbon-degrading environments, while sequencing of 18S rRNA gene and ITS rRNA spacer amplicons collectively identified a eukaryotic hotspot with 689 fungal OTUs across six phyla. The dominant fungal classes, Agaricomycetes and Dothideomycetes, are well-established degraders of recalcitrant carbon compounds at the surface, suggesting they may similarly contribute to organic matter degradation and ecosystem maintenance in the subsurface. Cultivation and isolation efforts yielded 205 fungal strains, including 13 candidate novel taxa, underscoring the deep subsurface as an underexplored eukaryotic habitat. Stable carbon isotopes indicate methane is predominantly generated via microbial conversion of the fossil carbon, while water isotopes suggest in situ geochemical conditions have been relatively stable since the Late Pleistocene, with subglacial recharge as a plausible mechanism for microbial introduction. Collectively, these findings suggest that fungi are underrecognized contributors to organic matter transformation and functional diversity in the deep biosphere, revealing a critical gap in our understanding of deep subsurface ecosystem processes.

Indiana, Michigan, Ohio↗
USGS2026

Assessing environmental drivers of denitrification in restored riverine floodplains

Restoration of impaired floodplains is an increasingly prevalent strategy for alleviating water quality concerns and reducing downstream flooding at watershed scales. Floodplains temporarily store water and slow flow velocity to promote sedimentation during overbank flooding and remove inorganic nitrogen from floodwater and groundwater via denitrification. Evaluating the impacts of different restoration strategies on denitrification can inform more strategic investments into floodplain modifications that improve water quality outcomes. Our research investigates how denitrification rates in floodplains respond to environmental factors that are actionable from an engineering perspective through design and water resources management. We seasonally measured soil denitrification enzyme activity and various environmental characteristics in 4 floodplains with different restoration design and management approaches at the confluence of the Wabash and Tippecanoe Rivers in Indiana, United States. Our results showed that denitrification rates in an agricultural floodplain were significantly lower than in restored floodplains with native vegetation. Certain soil conditions characteristic of floodplain wetlands were associated with higher denitrification, particularly elevated total nitrogen, moisture, silt, and organic matter contents. Vegetation species composition was correlated with denitrification rates. This link may reflect the direct effects of vegetation on soil conditions, such as supplying labile organic carbon, or indirect effects, such as vegetation acting as an indicator of hydrologic regime and land use. Denitrification seasonally varied, peaking in winter when nitrate supply from rivers draining agricultural watersheds in the region is also high. Substrate limitation of soil denitrification enzyme activity was most significant during the summer when overbank flooding, which replenishes soil nitrogen stocks, rarely occurs. Our findings indicate that denitrification capacity will likely be maximized in riverine floodplains that are restored as wetlands with diverse native vegetation and enhanced hydrologic connectivity. Such restoration activities promote higher denitrification rates via elevated moisture, fine sediment deposition, and soil organic matter.

Tippecanoe River, Wabash River↗
USGS2026

Northern bobwhites select for native grasses on working grazing land

Northern bobwhite ( Colinus virginianus ; bobwhite) populations have experienced an 85% decline across most of its range since the 1960s. The most drastic decreases have been in the southeastern United States where biologists attribute the decline to widespread habitat loss, including conversion of native grasslands to tall fescue ( Lolium arundinaceum ) and other exotic cool-season grass (CSG) pasture and hayfields. Including agricultural lands in conservation programs could improve habitat conditions on a regional scale. One working-lands conservation strategy involves the use of native warm-season grasses (NWSGs) rather than cool-season exotic grasses as economically viable cattle forage. To evaluate this management practice for creating bobwhite habitat, we conducted a field study on a bobwhite population in eastern Kentucky, USA, where grazed NWSG and burned NWSG fields were adjacent to exotic grazed and exotic hayed CSG fields. Between April 2019 and October 2022, we used radio telemetry to track bobwhites to evaluate resource selection at second- and third-order scales. Bobwhites used traditionally managed CSGs less than would be expected by chance at the second-order scale in both breeding and non-breeding seasons. Bobwhite use of grazed NWSG and burned NWSG was greater than would be expected by chance at the second-order scale in both breeding and non-breeding seasons. Similarly, at the third-order scale, bobwhites used CSGs less than would be expected by chance, whereas NWSGs and woody stems were used more than by chance alone. Our study suggests that bobwhites select NWSGs at multiple spatial scales despite broad-scale availability of managed exotic CSGs. Under a working-lands framework, the integration of NWSGs into working lands could create bobwhite habitat in the southeastern United States, especially if woody stems are present.

Kentucky↗
USGS2025

Refinements to the attenuated propagation of local earthquake shaking (APPLES) ground-motion-based earthquake early warning algorithm

We refined the Attenuated ProPagation of Local Earthquake Shaking (APPLES) ground-motion-based earthquake early warning (EEW) approach, and directly compare APPLES performance with that of the source-characterization-based U.S. ShakeAlert EEW system for a suite of historical earthquakes in the U.S. West Coast and Japan. APPLES is an extension of the Propagation of Local Undamped Motion (PLUM) algorithm in which observed shaking intensity at seismic stations is used to forward-predict intensity distributions to surrounding areas using an attenuation model derived from an intensity prediction equation. We test new configuration options within APPLES, such as using the second highest estimated ground motion rather than the maximum, to better match median ground-motion observations and reduce alerts for small magnitude earthquakes, both of which are key alerting priorities within ShakeAlert. We evaluate these configurations alongside ShakeAlert by comparing the ground-motion estimation accuracy and available warning times relative to station observations and ShakeMap distributions. Our preferred APPLES configuration produces accurate ground-motion estimates and corresponds better with median observations compared to ShakeAlert’s estimates. This preferred configuration substantially reduces alert issuance for M < 5.0 earthquakes compared to the previous APPLES configuration, and alert-release criteria can further restrict alerts to primarily M ≥ 5.5 earthquakes without requiring magnitude estimation. Prioritizing matching median-observed ground motions may reduce APPLES warning times compared to configurations that were tuned to avoid missed alerts (such as those that use the maximum estimated ground motions), which can lead to shorter warning times compared to ShakeAlert for the same alert threshold. However, station-based warning time assessments demonstrate that APPLES can outperform ShakeAlert for high target thresholds. APPLES is a simple, independent EEW approach that may improve the robustness of EEW for the West Coast of the U.S.

California, Oregon, Washington↗
USGS2026

The petrogenesis of Þingmúli volcano, East Fjords, Iceland

In this work we revisit Þingmúli volcano (Þ = Th), a classic locality known as an example of a complete tholeiitic differentiation. Þingmúli is a ~ 9.5 Ma extinct central volcano located in the East Fjords of Iceland, in which the whole compositional spectrum from basalt to rhyolites have erupted. These volcanic products have been previously considered as petrogenetically related by an ideal fractionation trend, regardless any temporal relationship or volumetric considerations. Here we report new whole-rock geochemistry, mineral chemistry, isotope analyses, estimation of residence times of the different eruptive deposits, and an update of the original petrogenetic model. Our results highlight that an enriched source, likely spinel lherzolites, generated transitional-alkaline basaltic melts after 15–20% of partial melting at depths of 40–45 km. Many of these basaltic melts erupted at various stages of the volcano's history, while others remained longer in the volcanic plumbing system. These evolved by fractional crystallisation into basaltic andesite magmas with a residence time of ~5 years based on the crystal size distribution of the plagioclase population. Isotopic differences between the basalts/basaltic andesites ( 87 Sr/ 86 Sr ~ 0.7034; 143 Nd/ 144 Nd ~ 0.51315) and the erupted rhyolites ( 87 Sr/ 86 Sr ~ 0.7037; 143 Nd/ 144 Nd ~ 0.51304) indicate that the latter are not petrogenetically related to the former. Therefore, instead of a fractional crystallisation mechanism to generate the rhyolites, we propose the partial melting of ignimbrite layers located beneath the volcano. The broad range of trace element concentrations in andesites and dacites and their different isotopic values compared to the basalts strongly suggest that these magmas have been generated by magma mixing between basaltic and rhyolitic melts, similar to modern day Icelandic volcanoes such as Hekla. These results highlight the need to revisit previously studied Icelandic classic localities and reassess their traditionally proposed petrogenetic models.

Þingmúli volcano, East Fjords↗
USGS2025

Geologic models underpinning the 2024 U.S. Geological Survey assessment of undiscovered oil and gas resources in the Hosston and Travis Peak Formations of the onshore Gulf Coast region, U.S.A.

The Early Cretaceous (Berriasian–Hauterivian) Hosston Formation in Louisiana and eastward is time correlative to the Travis Peak Formation of Texas and southern Arkansas. The formation is a first-order clastic sequence with a regional carbonate transgressive surface as an upper contact. The Hosston and Travis Peak formations contain conventional natural gas and oil accumulations that have been produced for nearly a century. These mature reservoirs contain terrigenous fluvial-deltaic, shore-zone, and paralic deposits across the productive trend; organic-lean mudstone and siltstone lithologies are found outboard of the Lower Cretaceous shelf margin. Producing reservoirs exhibit normal pressure gradients from 0.43 to 0.55 psi/ft (9.7 to 12.4 kpa/m), depths from 4000 to over 20,000 ft (1220 to 6100 m), and temperatures from 150 to 385°F (65 to 196°C). Wells are primarily vertical completions. The number of new field wildcats has been declining since the late 1990s. This paper presents comprehensive geologic models, which include lithofacies maps, structure and isopach maps, burial history models, regional seismic interpretations, and events charts that underpin the recently completed U.S. Geological Survey assessment of undiscovered, technically recoverable hydrocarbons within the Hosston and Travis Peak formations. This study also provides geographic and stratigraphic distributions of Hosston–Travis Peak reservoir properties, including geopressure, reservoir temperature, porosity, permeability, API gravity, and gas-oil ratios. Results indicate estimated undiscovered, technically recoverable mean resources of 28 million barrels of oil and 35.8 trillion cubic ft of gas in conventional and continuous accumulations within the Lower Cretaceous Hosston and Travis Peak formations of the onshore U.S. Gulf Coast region. Quantitative assessment results are detailed in U.S. Geological Survey Fact Sheet 2025–3021 and associated Data Release.

GCAGS Journal↗
USGS2021

Remote sensing-based actual evapotranspiration assessment in a data-scarce area of Brazil: A case study of the Urucuia Aquifer System

The large groundwater reserves of the Urucuia Aquifer System (UAS) enabled agricultural development and economic growth in the western Bahia State, in northeastern Brazil. Over the last several years, concern has grown around the aquifer’s diminishing water levels, and water balance (WB) studies are in demand. Considering the lack of measured actual evapotranspiration (ET a ), a major component of the water cycle, this work uses the Operational Simplified Surface Energy Balance (SSEBop) model to estimate ET a , and compares it to basin-scale estimates from the Soil Moisture Accounting Procedure (SMAP) monthly model and from an annual WB closure method, based on gridded meteorological data and the Gravity Recovery and Climate Experiment (GRACE) product. Additionally, a comparative assessment of different versions of the SSEBop parameterization was performed. Moderate Resolution Imaging Spectroradiometer (MODIS) imagery was used to implement eight different versions of the SSEBop algorithm over the UAS between 2000 and 2013. SSEBop and SMAP ET a yielded similar seasonal patterns, with correlation coefficient (r) up to 0.65, mean difference (MD) of 0.8 mm/month and mean absolute difference (MAD) of 18.5 mm/month. Comparison of SSEBop annual ET a estimates to annual SMAP and WB closure estimates yielded low MD (12.1 and −7.3 mm/year, respectively) and MAD (82.5 and 82.8 mm/year, respectively), but also low r values (0.00 and 0.37, respectively). The comparison of the different SSEBop versions indicated the need to incorporate a calibration step of the aerodynamic heat resistance (r ah ) parameter. SSEBop results were also used for land cover and drought monitoring. Analysis indicates that agriculture, associated with an increasing trend of atmospheric evaporative demand, is responsible for the decrease in groundwater levels and streamflow in the studied time period.

Urucuia Aquifer System↗
USGS2026

Nonstationary demographic state-space models using unreplicated counts for species undergoing environmental stressors

A fundamental task in ecological statistics is to estimate abundance and growth rate distributions from wildlife monitoring data to inform conservation management. Modeling time series of wildlife populations presents a number of challenges from both statistical and ecological perspectives, including discreteness; lack of replication; nonstationarity; and observation, demographic, and other phenomenological processes. Nonstationary dynamics are often exhibited by populations undergoing environmental stressors. Models must account for these characteristics to produce reliable estimates of abundance and trends, yet estimation can be challenging with unreplicated data. We propose nonstationary demographic state-space models using unreplicated counts for populations undergoing environmental stressors. A reduced growth rate model matches the complexity of the unreplicated count data, and a fecundity bound on growth rate distributions allows the separation of processes affecting growth rates like environmental stressors from those affecting abundance external to growth rates like migration. NDSSMs allow for the embedding of nonstationary model components, and we explore the use of changepoints, volatility clustering, and migration processes. We apply the proposed nonstationary models in case studies of herons affected by predator/competitor reestablishment and three bat species affected by a fungal pathogen causing white-nose syndrome. Nonstationary models outperform stationary models and generalized linear mixed effects models according to model scoring and visual inspection of predictions, and provide estimates more consistent with published values. Incorporating migration improves model fit universally, even with approximate one-way immigration, most likely because populations are extirpated, recolonized, and increase multiple-fold over the upper bound set by species fecundity. In addition, estimates of the timing and severity of the environmental stressor differed for models with migration. Including nonstationary and demographic components in a fecundity-bounded growth rate model improves inference and benefits interpretability of hyperparameters. In turn, this adjusts uncertainties in predictions of abundance and growth rates over time, providing the ingredients needed for informed conservation analysis and for directing future monitoring of at-risk species.

Journal of Agricultural, Biological and Environmen↗
USGS2026

Groundwater drought in the United States: Spatial and temporal variability

Many communities and ecosystems in the United States that are dependent on groundwater are potentially adversely affected by groundwater drought. We computed yearly groundwater-drought metrics and mean groundwater levels at well locations across the conterminous United States (CONUS), using data from wells and remotely sensed and modeled Gravity Recovery and Climate Experiment Drought Monitor Data Assimilation (GRACE-DADM). We also modeled the probability of low or high human impact at each well location. The spatial distribution of groundwater-drought duration and severity from 2001 to 2020 for 1,510 wells shows longer maximum duration and higher maximum severity events in drier regions like the Southwest than in wetter regions like the Northeast. Based on 613 wells in CONUS from 1981 to 2020, there are many significant decreases in drought duration and severity in the Northeast and many significant increases in annual-mean groundwater levels. In contrast, there are many significant increases in drought metrics and decreases in mean water levels in parts of the Southeast. There are major differences in trends from 2001 to 2020 between well-based and GRACE-DADM-based groundwater metrics in some CONUS regions and a very low correlation between trends at individual locations across CONUS. A potential reason for this disparity is the low GRACE-DADM resolution (∼12 km) and the potential for a large amount of groundwater variation at the local scale. Also, GRACE-DADM represents shallow, unconfined aquifers which may not match the screened interval of the monitoring wells we evaluated. Large spatial gaps in long-term, high frequency, and quality-assured groundwater-well monitoring data present a challenge for understanding groundwater-drought variability across CONUS. Remote sensing tools such as GRACE can help but cannot fully replace well monitoring, as highlighted by our study results. Substantially more long-term monitoring wells would more accurately represent groundwater-drought trends and spatial variability across CONUS, particularly in western regions.

conterminous United States↗
USGS2026

Groundwater quality near an oil field in a stream-dominated recharge setting, California, USA

Alluvial valley aquifers are important sources of water supply in many areas but effects of co-located oil and gas development on these resources have not been widely reported, especially in settings where recharge is dominated by stream infiltration. Interpreting the presence of geochemical indicators in the context of hydrology, geology, and other factors provides a more complete understanding of the relations between groundwater and sources of oil-field fluids and aids in identifying risks associated with oil and gas development. Groundwater and Salinas River water samples were collected in an alluvial valley near the San Ardo Oil Field in Monterey County, California and analyzed for a wide range of dissolved chemical, gas, and isotopic constituents to determine if oil-field fluids (water and gas from oil-producing and non-producing zones) have mixed with fresh groundwater used for supply. Hydraulic gradients, age-dating tracers, and other geochemical indicators show that recharge from the Salinas River has the potential to dilute oil-field fluids that might migrate or seep into the aquifer. Groundwater and Salinas River water collected downgradient of the San Ardo Oil Field showed little or no evidence of mixing with oil-field fluids. Some samples within the oil field contained trace amounts of hydrocarbons or elevated temperatures, indicating that any potential effects from oil-field activities are minor or have been diluted by recharge from the Salinas River. The two samples with the most geochemical evidence of potential mixing with oil-field fluids (SP-18 and GW-17) were collected west of or along the Los Lobos fault, where naturally occurring hydrocarbons are near the land surface. Those samples are also near active or inactive oil-field wells, and so anthropogenic activities and pathways cannot be ruled out as a cause of trace detections of hydrocarbons and elevated temperatures in the aquifer.

California↗
USGS2026

Hydrogeology, groundwater salinity distributions, and assessment of the effect of oil-production activities on groundwater in the Midway Valley area, western Kern County, San Joaquin Valley, California

This study seeks to determine the effects of oil field produced water disposal operations and well mechanical integrity issues on groundwater quality in oil fields in the southwest San Joaquin Valley, California. Whereas previous studies used groundwater wells to study shallow aquifers outside the oil fields, this study demonstrates that future approaches may use oil well geophysical logs to map groundwater head gradients, create salinity profiles and document changes in salinity over time in oil field areas with sparse groundwater well data and at depths greater than 330 m. We also incorporate an analysis of well histories to determine potential effects of compromised wellbore seals on changes in aquifer quality that cannot be explained by water disposal practices. Water quality in the aquifers is naturally brackish across most of the area, with better quality groundwater occurring in the eastern part. Geophysical logs are used to determine salinity variations within aquifers including the depth at which TDS exceeds 10,000 mg/L. This depth ranges from 366 m in the northwest to approximately 1,500 m in the southeast. Oil well porosity logs are used to determine water table elevations. These logs indicate the water table slopes south-southeast, showing the predominant groundwater flow direction is from oil field disposal areas toward better quality groundwater east of the oil fields. Geophysical logs show formation resistivity near some disposal facilities has decreased over time, indicating the salinity of the aquifer has increased due to disposal of saline produced water in injection wells and ponds. Oil well history analysis suggests that increased salinity over time in water-saturated sand intervals >1.5 km from disposal facilities may be caused by mechanical failures and/or incomplete borehole seals in poorly constructed or abandoned wellbores prevalent throughout the study area—particularly wells drilled prior to 1930.

California↗
USGS2026

Shallow differentiation of primitive arc magmas at the Jurassic Emigrant Gap mafic complex, Sierra Nevada, California

The Emigrant Gap composite pluton exposes ultramafic to silicic intrusive rocks that preserve the chemical evolution of primitive mafic arc magmas and their open-system interactions in the upper crust during mid-Jurassic growth of the Sierra Nevada batholith (California). We present field and petrographic observations and mineral and whole-rock chemistry of the ~35-km 2 ultramafic to dioritic Emigrant Gap mafic complex and an adjacent penecontemporaneous ~90-km 2 granodiorite that together make up the composite pluton. In the Emigrant Gap mafic complex, four roughly central masses of dunite, wehrlite, and olivine clinopyroxenite are surrounded by weakly layered gabbronorite and non-layered diorite. The ultramafic rocks are cumulates formed from near-liquidus minerals of primitive arc magmas that accumulated in steep feeder zones with substantial modification by melt–mush reaction as primitive liquids repeatedly transited the mush-filled conduits. The dominant gabbronoritic rocks are the variably accumulative products of more advanced crystallization–differentiation of arc tholeiitic basalts and basaltic andesites. The adjacent granodiorite intrusion originated separately and preserves field and geochemical evidence for assimilation of metasedimentary rocks. Open-system hybridization between the gabbronoritic mushes and the granodioritic magma produced an intervening body of two-pyroxene diorite. We infer that the ultramafic rocks and gabbronorite of the Emigrant Gap mafic complex crystallized from near-primitive arc basaltic to basaltic andesitic magmas at ~0.15–0.3 GPa, with estimated f O 2 of ≥FMQ +1 and dissolved H 2 O concentrations of only ~0.5–2 wt %. Notably, the Emigrant Gap composite pluton is distinct from other Mesozoic plutons in the Sierra Nevada batholith because of (1) its abundance of mafic and ultramafic rocks that crystallized from relatively primitive mafic melts and (2) the low inferred H 2 O concentrations of its parental magmas, indicated by a near absence of igneous amphibole and by the intermediate rather than calcic compositions of plagioclase. A Jurassic regional extension event probably accounts for the formation of relatively dry primitive arc magmas, as well as for their ascending to the upper crust.

California↗
USGS2025

Relating systematic molecular and textural properties of graptolite pyrolyzed via gold tube hydrous pyrolysis: Implications for thermal proxies in lower Paleozoic marine shales

A series of gold tube pyrolysis experiments (72 h, 300–550 °C, 50 MPa) conducted on a graptolite-rich lower Paleozoic marine shale generated pyrolysis residues for a comprehensive evaluation of the molecular and structural variability of three types of graptolite periderm. Organic petrology, Raman spectroscopy, and field emission scanning electron microscopy (FE-SEM) with energy dispersive spectroscopy (EDS) were combined to evaluate the thermal evolution process. The three types of graptolite periderm, namely granular, non-granular, and nodular graptolite, were analyzed by Raman spectroscopy wherein point measurements were obtained after the maceral was identified and the location verified by organic petrology. Distinct thermal evolution pathways among non-granular, granular, and nodular graptolite periderms were recorded. The evolution patterns of the Raman parameters, particularly D1 and G bands, highlight the differences in geochemical composition of the graptolite periderm types and the alteration of molecular structure with increasing thermal maturity. Raman parameters D1 (position of the D1 peak), G-FWHM (full width at half maximum of the G peak), and ratios D1-FWHM/G-FWHM (full width at half maximum of the D1 peak ratioed to G-FWHM) and A D1 /A G (ratio of D1 and G peak intensities) showed effectiveness in assessing thermal maturity. Bireflectance with increasing gold tube pyrolysis temperature followed a hierarchy: non-granular > granular > nodular, reflecting different molecular alignment intensities. Qualitative FE-SEM evaluation showed that fine-grained mineral inclusions (primarily Fe-sulfide as determined via EDS) were associated with the graptolite populations, with granular graptolite containing greater amounts of coarser-grained (e.g., ∼300–1400 nm) mineral inclusions relative to non-granular and nodular graptolite, which contain finer-grained (e.g., ∼100–200 nm) inclusions difficult to resolve with optical microscopy. These findings are investigated to highlight the mechanisms that drive organic matter evolution within graptolite during thermal maturation, as well as to explore some of the limitations of using spectroscopic parameters as thermal maturity proxies.

International Journal of Coal Geology↗
USGS2025

Forecasting water levels using the ConvLSTM algorithm in the Everglades, USA

Forecasting water levels in complex ecosystems like wetlands can support effective water resource management, ecological conservation, and understanding surface and groundwater hydrology. Predictive models can be used to simulate the complex interactions among natural processes, hydrometeorological factors, and human activities. The Greater Everglades in the USA is a well-known example of an ecosystem where complexity has motivated adoption of machine learning algorithms in water level prediction studies. This paper aims to contribute to extending existing machine learning algorithms by integrating spatiotemporal data with deep-learning algorithms in the forecasting process. In this study, a deep-learning model is developed to predict water levels on a regional scale, covering a large area of approximately 9,138 square kilometers in the Everglades ecosystem. This model has the architecture of Convolutional Long Short-Term Memory which can deal with spatiotemporal data by capturing both spatial and temporal dependencies in the training data. The forecasting capabilities of this model (referred to as the global model) are assessed by comparing the global model to two Artificial Neural Networks developed at two different gaging stations, referred to here as local models. One local model is developed at a gaging station directly influenced by nearby water control structures, whereas the other is developed at a gaging station located farther away from these structures. By leveraging data from the Everglades Depth Estimation Network spanning from January 2002 to May 2023, the global and local models were trained to forecast water levels with a two-day lead time. Our findings suggest that both the global and local models perform with approximately the same level of accuracy, with Mean Absolute Relative Error values ranging from 0.38% to 1.4% at the selected stations. The developed global model has demonstrated strong potential as a standalone forecasting tool for the entire study area in the Everglades and could eliminate the need for developing multiple local models. This finding also highlights how machine learning can capture complex spatial and temporal relationships to generate accurate water level predictions on a regional scale.

Florida↗