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USGS2025

“Leaky weirs” capture alluvial deposition and enhance seasonal mountain-front recharge in dryland streams

“Leaky weirs” are rock structures installed in dryland streams, which are anchored into exposed bedrock, loosely cemented, and designed to allow water to slowly pass through. They are being tested at a ranch in southeastern Arizona, USA, to restore and conserve the historic range and desert wetlands. Data are collected to assess how leaky weirs impact surface water, subsurface water, and groundwater recharge—including stream discharge, timing, and depth of infiltration, and groundwater elevations. Three adjacent watersheds, two with outlets just below leaky weirs and one with leaky weirs farther upstream, were instrumented with water-level loggers, wildlife cameras, and crest stage instruments with temperature sensors in the soil. As most groundwater recharge is assumed to be focused along the mountain fronts in this region, mountain-block recharge is also evaluated to differentiate between the two using isotope analyses. Finally, a single, late-season flood event is scrutinized to consider the leaky weir effect on all monitored components in the water budget. Results indicated groundwater flow is primarily from the mountains to the east via older, regional mountain-block recharge. However, the development of shallow alluvial aquifers is supported by the leaky weirs, that slow flows, capture permeable sediments, and allow infiltration, thus enhancing mountain-front recharge. In turn, these new pockets of water help support the restoration of historic wetlands. Sediment accumulates where leaky weirs are installed, reducing flashy peak flows, and resulting in a series of infiltration ponds along the channel that support vegetation during growing seasons and recharge the shallow aquifer during non-growing seasons.

Arizona↗

USGS2024

Volcanic gas monitoring

Introduction As magma rises through the crust, decreasing pressure conditions allow volatiles to exsolve from the magma. These volatiles then migrate upward through the crust, where they can be stored at shallower levels or escape to the atmosphere. Rising magma also heats rock masses beneath volcanic centers, causing water in shallow aquifers and hydrothermal systems to boil and release additional gases and steam (see chapter F , this volume; Ingebritsen and Hurwitz, 2024). The chemistry and quantity of gases that reach the surface during periods of quiescence or volcanic unrest can reveal that gas-rich magma is ascending, crystallizing, or alternatively stalling, with important implications for volcanic hazard (for example, Sutton and others, 1992; Aiuppa and others, 2007, 2021; Werner and others, 2009, 2011, 2012; Moretti and others, 2013; de Moor and others, 2016; Lewicki and others, 2019; Edmonds and others, 2022; Kern and others, 2022; Kunrat and others, 2022). Most volcanoes in Alaska and the western United States are characterized by weak degassing, with one or more low-temperature fumaroles (typically near the local boiling temperature of water) and connect to a deeper and sometimes extensive hydrothermal system (for example, McGee and others, 2001; Symonds and others, 2003a, b). Hydrothermal systems will affect the chemistry of rising gases exsolved from deeper magma (Symonds and others, 2001), including sulfur dioxide (SO 2 ), hydrogen chloride (HCl), and water vapor (for example, Doukas and Gerlach, 1995; Gerlach and others, 1998, 2008; Symonds and others, 2001; Werner and others, 2013). As an example, depending on factors such as temperature, pressure, and oxidation state, rising SO 2 will react with groundwater to form hydrogen sulfide (H 2 S) gas, dissolved sulfate (SO 4 2− ), or elemental sulfur (Christenson, 2000; Symonds and others, 2001; Werner and others, 2008). The reaction and dissolution of SO 2 into shallow groundwater is commonly referred to as scrubbing, and can reduce the likelihood that ascending, degassing magma can be detected. Carbon dioxide, however, in addition to exsolving from magma early in the ascent process, is not easily removed by hydrothermal fluids (Lowenstern, 2001). As scrubbing and other processes take place, the SO 2 /H 2 S, CO 2 /SO 2 , and CO 2 /H 2 S ratios may change. High rates of SO 2 emission indicate that magma has moved to relatively shallow levels in the volcano and that the system has heated up enough to establish dry pathways from depth to the surface. Monitoring multiple gas species and the total output of those species is thereby useful for volcano monitoring during both periods of quiescence, to establish background degassing conditions, and during unrest, when gas geochemistry and emission rates can provide information on changing conditions, such as magma ascent. To provide context for multidisciplinary volcano forecasts, we focus on the following two key required capabilities: (1) characterizing baseline geochemistry and gas discharge from volcanoes and volcanic regions and (2) monitoring changes in gas geochemistry and discharge to inform forecasts of volcanic eruptions and their effects. Sufficient baseline data must be collected to identify and interpret anomalous degassing associated with volcanic unrest (for example, Sorey and others, 1998; Rouwet and others, 2014). Differences in volcano type, baseline degassing rates, local hydrology, and geography (for example, high versus low latitude) will result in a different baseline for each volcano. Volcanoes of any threat level that exhibit one or more degassing phenomena would ideally be monitored by techniques needed to establish baseline degassing data, with the sampling frequency of baseline data dictated by the threat level (table E1). Additional monitoring techniques become necessary during periods of unrest. In general, three of the most important techniques for gas monitoring are (1) direct sampling of fumarole, spring, and soil gases for laboratory geochemical measurements, (2) measurements of the chemical composition of the volcanic plume and emission rates of major gas species (for example, H 2 O, CO 2 , SO 2 , and H 2 S) by satellite, airborne, or ground-based techniques, and (3) measurements of diffuse emissions of CO 2 and other gases through soils. Various methods and instruments may be useful both for baseline studies and during unrest.

Scientific Investigations Report↗

USGS2025

Evidence of mineral alteration in a salt marsh subterranean estuary: Implications for carbon and trace element cycling

Subterranean estuaries (STE) in salt marshes are biogeochemically active zones where interactions between terrestrial groundwater and seawater drive complex cycling of carbon and trace elements, influenced by mineral dissolution. These systems, characterized by fine-grained organic-rich peat overlying permeable coastal aquifers, play a crucial role as a blue carbon sink, yet their geochemical dynamics remain poorly understood. We investigated dissolved trace elements, carbon, silica, and radium isotopes in a salt marsh STE (Sage Lot Pond, Waquoit Bay, MA) over seasonal and annual cycles. Our results reveal that groundwater and estuarine water circulation through marsh peat and aquifer sediments leads to enrichments of dissolved organic and inorganic carbon (DOC and DIC), Si, Ba, and Mn, with variable source/sink behavior of Fe and net removal of U. Submarine groundwater discharge dominated Ba fluxes, whereas pore water drainage from marsh peat acted as the main sink for U and source of Si. Fe cycling was variable, with terrestrial Fe largely removed as groundwater passed through the STE, consistent with Fe-sulfide and amorphous phase formation. Radium isotope ratios identified two distinct subsurface flow pathways, influenced by metal-oxide cycling and organic matter breakdown. Si production was decoupled from DIC, suggesting Si originates from mineral alteration, whereas DIC results from both mineral weathering and microbial respiration. Silicate mineral alteration, coupled with marsh pore water drainage, accounts for up to 16% of annual DIC exports (66 g C m −2 y −1 ), highlighting the importance of STEs in coastal carbon and trace element cycling, especially as marshes face environmental change.

Massachusetts↗

USGS2026

A review and synthesis of post-wildfire shifts in hydrologic processes and streamflow generation mechanisms

Critical water supply watersheds in the western United States (WUS) are impacted by wildfires, with potential negative effects on water quality and quantity. Scientific understanding is currently insufficient to deliver estimates of wildfire consequences for water quantity that are regionally accurate. Regional variability in the directionality and magnitude of post-wildfire shifts in streamflow generation fuels uncertainty in estimates of wildfire effects on water supply. In this work we provide a narrative review of wildfire effects on hydrologic processes and the resulting changes in streamflow generation mechanisms with a focus on the WUS, incorporating other global regions when pertinent. A conceptual model summary of wildfire effects on streamflow generation emphasizes: (1) precipitation seasonality, (2) synchrony of precipitation and potential evapotranspiration, (3) net shifts in interception, evaporation, and transpiration relative to total annual precipitation, (4) vegetation changes, including compensatory uptake and type conversion, (5) degree of overlap in rainfall rates and infiltration, (6) fire extent and severity, (7) burn scar positioning (e.g. in headwaters or proximal to watershed outlet), (8) scale-dependent groundwater leakage, (9) near-surface water storage reduction, and (10) soil to groundwater connectivity. Ongoing gaps and challenges include separating the influences of precipitation variability, water withdrawals, and post-fire land management; compound and overlapping disturbances; and lack of pre-fire data. Notable future opportunities include: harnessing ever-improving gridded and remotely sensed precipitation and fire-effects data; linking geophysical, isotopic tracer, and geochemical signatures to diagnose hydrologic changes; leveraging physically based and data-driven model advancements; and analyzing streamflow generation recovery trajectories across diverse watersheds.

western United States↗

USGS2025

A generalized deep learning model to detect and classify volcano seismicity

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

Volcanica↗

USGS2026

Cascadia Subduction Zone science: Call for the next generation community seismic velocity model

The Cascadia subduction zone (CSZ) hosts major seismic and tsunami hazards, yet key questions persist about the relationship between margin structure, fluid distribution, episodic tremor and slip, shallow megathrust behavior, shaking and tsunamigenesis, and the resulting hazard estimates. Addressing these problems requires an empirically grounded, three‐dimensional seismic velocity model to illuminate subsurface structure and properties and to provide a basis for geophysical studies such as earthquake simulations and ground‐motion estimation. In May 2024, the National Science Foundation‐funded Cascadia Region Earthquake Science Center (CRESCENT) community velocity model (CVM) working group, with U.S. Geological Survey and regional partners, convened a workshop to identify priorities for such a model. Participants emphasized the features necessary for addressing key science questions, including implementing findability, accessibility, interoperability, and reusability (FAIR) access, capturing along‐strike and along‐dip structural heterogeneity, resolving shallow offshore–onshore structure, constraining elastic properties and quantifying their uncertainties for numerical wave propagation simulations, their validation benchmarks, and supporting associated accurate earthquake ground‐motion simulations and hazard assessments. This article describes the priorities defined in the workshop, and a description of how, guided by these needs, CRESCENT plans to develop multiple generations of a CVM to advance CSZ science and improve seismic and tsunami hazard modeling across the Pacific Northwest. The CVM will span the CSZ from the surface to ∼100 km depth, offshore and east of the Cascades into Idaho (∼132°–110° W) and the southern and northern tectonic regime transitions (∼36°–52° N) to capture the entire tectonic system as well as its surroundings.

Cascadia Subduction Zone↗

USGS2026

Timescales of cumulate mobilization and mixing for the 1868 A.D. eruption of Mauna Loa, Island of Hawai‘i

The deadly 1868 A.D. eruption of Mauna Loa’s lower Southwest Rift Zone (Island of Hawai‘i) included a M7.9 earthquake and associated tsunami and landslides, demonstrating the severe hazards posed by Earth’s largest active subaerial volcano. To better understand the relationship between intense seismic activity, dike emplacement, magma storage, transport histories, and mobilization of olivine cumulates at Mauna Loa, we examine compositional zoning of olivine in the 1868 lava flows. Samples range from basalt (< 10% olivine) to picrite (30–40% olivine). The olivine cargo is heterogeneous (Fo 78.2–89.2 ; forsterite = [Mg/(Mg + Fe) × 100]) but dominated by ~ Fo 89 cores that lie above the Fe-Mg equilibrium field of host glasses. Crystal rims < Fo 80 are due to post-eruptive modification in slow cooling lava flows. Minor element compositions fall within the range of other Mauna Loa olivine erupted in the past 200 years. Olivine crystals exhibit both normal and complex Fo zoning patterns that yield timescales of diffusive re-equilibration that range from 3 to 258 days, with 72% of crystals recording 71 days or less. These timescales correspond to magmatic priming of the summit reservoir system ~ 2 months prior to the eruption and the M7.9 earthquake likely facilitated the transport of the crystal-rich summit-derived magmas downrift shortly prior to eruption. If the recently proposed faster Fe-Mg diffusion coefficient is used, timescales instead range from < 1 day to 25 days, with most recording 1 week or less. In this scenario, most of the olivine zoning would have to have been generated after the M7.9 earthquake perturbed the system.

Hawaii↗

USGS2025

Predictive regressive models of recent marsh sediment thickness improve the quantification of coastal marsh sediment budgets

Coastal marsh wetlands experience variations in vertical gains and losses through time, which have allowed them to infill relict topography and record variations in drivers. The stratigraphic unit associated with the development of the marsh also reflects the long-term importance of key ecosystem services supplied by the marsh environment, including carbon storage and storm mitigation. Mapping these coastal wetland sediments and the marsh unit thickness is challenging as traditional coastal geophysical tools are not easily deployable (acoustic methods) or are unreliable in saline-soil environments (e.g., ground-penetrating radar), leaving core-based methods the most viable mapping method. In the present study, we utilized prior information on the geologic architecture of the region to select spatial and physical metrics that likely persisted throughout evolution of the marsh during the late Holocene. We then assessed the individual and collective power of these metrics to predict marsh thickness observed from cores. Employing regressive predictive models powered by these data, we improve the quantification of marsh thickness for a coastal fringing marsh within the Grand Bay estuary in Mississippi and Alabama (USA). The information gained from this approach yields improved estimates of the carbon stocks in this environment. Additionally, the stored sediment masses reflect the past, and potential future, persistence of the Grand Bay marsh under historical and present marsh-estuarine sediment exchange fluxes. Such improvements to both the sediment budget of recent marsh stratigraphic units and the spatial extent provide new resources for comparison with large-scale landscape models, the latter of which may be used, when validated, to predict future change and ecosystem transformations.

Alabama. Mississippi↗

USGS2024

Time varying crustal anisotropy at Whakaari/White Island volcano

Whakaari/White Island has been the most active New Zealand volcano in the 21st century, producing small phreatic and phreatomagmatic eruptions, which are hard to predict. The most recent eruption occurred in 2019, tragically claiming the lives of 22 individuals and causing numerous injuries. We employed shear-wave splitting analyses to investigate variations in anisotropy during quiescence, unrest, and an eruption between 2018 and 2020. We examined spatial and temporal variations in 7972 shear-wave splitting measurements and derived 1402 V p /IV s ratio measurements. Comparing shear-wave splitting parameters from similar earthquake paths across different times indicates that the observed temporal changes are unlikely to result from variations in earthquake paths through media with spatial variability. Instead, these changes may stem from variations in anisotropy over time, likely caused by changes in crack alignment due to stress or varying fluid content.

Whakaari/White Island volcano↗

USGS2026

Patterns and drivers of cliff erosion in Big Sur, California, USA using repeat photogrammetry, 2017–2023

Seacliff erosion in steep terrain poses major risks to transportation and critical infrastructure. In Big Sur, California, USA, seacliff erosion threatens the sustainability of the central coast stretch of California State Route 1, a transportation corridor that is critical to the region's economy. Published cliff retreat rates for the region range from 1 to 40 cm yr −1 , highlighting that high-resolution, process-based studies could enhance understanding of the causes of spatial and temporal variability. We quantified cliff erosion and investigated its drivers along ∼13 km of the Big Sur coastline at week–month timescales during the late fall to early spring wet seasons between January 2017 and June 2023 by analyzing 3D point clouds developed from aerial imagery using four-dimensional structure-from-motion (4D SfM) photogrammetry techniques. We calculated cliff face retreat rates of 2.23 ± 3.06 cm yr −1 (mean ±1 σ ), an order of magnitude lower than long-term estimated rates for the region (which included large deep-seated landslides), but in line with short-term rates reported across California. Change detection imagery comparison, cliff profiles through time, and statistical analysis reveal a cyclical cliff evolution process in which erosion by wave action at the cliff base destabilizes the cliff and primes it for subsequent failure during precipitation events. Although more erosion by volume could be attributed to precipitation-induced increases in soil moisture (784 m 3 km −1 yr −1 ) compared with erosion attributed to wave power (282 m 3 km −1 yr −1 ), our observations underscore the coupled nature of these processes in driving cliff evolution, consistent with established theory and observations.

California↗

USGS2025

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

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

California↗

USGS2026

High-resolution magnetic survey using an unoccupied aerial vehicle to constrain buried lava flow geometry, volume, and eruptive history of Little Cones, Crater Flat, Nevada

Magnetic surveys are an important tool used to augment geologic mapping in distributed volcanic fields. Using magnetic anomalies, it is possible to model the geometry of shallowly buried volcanic features, such as conduits, sills, and lava flows. This subsurface mapping is important for understanding eruption dynamics and emplacement of lava flows, and it sometimes reveals buried volcanoes no longer visible at the surface. These data are critical to better interpret the numbers, styles, and magnitudes of eruptions in distributed volcanic fields and their associated volcanic hazards. New advances in unoccupied aerial vehicles (UAVs) offer an attractive middle range of resolution and aerial coverage between ground-based magnetic surveys and aeromagnetic surveys. Here, we present the results of a UAV fluxgate magnetic survey of the Little Cones, Nevada, scoria cones, which have been the target of previous ground and aeromagnetic surveys. The magnetic anomalies at Little Cones are of interest because the surrounding alluvium conceals lava flows that erupted from Little Cones, making it very difficult to understand the volume and morphology of lava flows from geologic mapping alone. Nonlinear inversion of UAV-collected magnetic data were used to model the thickness and morphology of buried Little Cones’ lava flows with higher precision than achieved previously. The sequence of events and calculated flow characteristics are then interpreted. The total volume of Little Cones, including concealed lava flows, is approximately 0.016 cubic kilometer, and the initial sheet flow erupted in less than 24 hours. The findings presented herein demonstrate that UAV-based magnetic surveys are a reliable method of data collection and an efficient alternative to other survey methods, facilitating development of a three-dimensional perspective of distributed volcanic fields.

Nevada↗

USGS2025

Projecting stream water quality using Weighted Regression on Time, Discharge, and Season (WRTDS): An example with drought conditions in the Delaware River Basin

Future water availability depends on understanding the responses of constituent concentrations to hydrologic change. Projecting future water quality remains a methodological challenge, particularly when using discrete observations with limited temporal resolution. This study introduces Weighted Regression on Time, Discharge, and Season for Projection (WRTDS-P), a novel, computationally efficient method that enables the projection of daily stream water quality under varying hydrologic conditions using commonly available discrete monitoring data. WRTDS-P model performance was validated using 39 sites in the Delaware River Basin (DRB) and four key constituents: specific conductance (SC), nitrate (NO 3 − ), magnesium (Mg 2+ ) and calcium (Ca 2+ ). Projections were tested against holdout data from the final 1 to 5 years of each time series, demonstrating robust predictive capability, with median Nash-Sutcliffe efficiencies of 0.67 for SC, 0.56 for NO 3 − , 0.65 for Ca 2+ , and 0.79 for Mg 2+ . Model uncertainty was correlated with indicators of hydrologic or geochemical mass-sinks, such as groundwater storage and adsorption in wetland soils. Drought scenario analyses for SC used ranges of reduced discharge including flows from the 1965 drought of record. Scenarios predicted widespread increases of SC, especially in southern DRB streams where baseline SC levels are already elevated. Fractional increases of SC were more uniformly distributed, indicating potential risk to sensitive ecosystems. Notably, drought-induced SC increases were positively correlated with interannual SC trends, indicating that hydrologic extremes could exacerbate ongoing salinization. This work provides a transferable and interpretable framework for projecting future water quality and assessing hydrologic risk to water resources and aquatic ecosystems.

Delaware, New Jersey, New York, Pennsylvania↗

USGS2023

Prediction of the probability of elevated nitrate concentrations at groundwater depths used for drinking-water supply in the Puget Sound basin, Washington, 2004–19

The Puget Sound basin encompasses the 13,700-square-mile area that drains to the Puget Sound and the adjacent marine waters of Washington State. Well more than 4 million people live within the basin, with numbers continuing to increase, who rely on the basin’s natural resources including groundwater. The Puget Sound Partnership was created by a Washington State statute to implement a science-based recovery of the Puget Sound to help address impacts to these resources. As part of the recovery, the partnership developed the Puget Sound Vital Signs as measures of ecosystem health that guide the assessment of progress toward Puget Sound recovery goals. The Puget Sound Partnership Leadership Council adopted a Drinking Water Vital Sign associated with human health and quality of life, recognizing certain indicators as integral to the sustainability of Puget Sound recovery efforts. One such Vital Sign indicator was the vulnerability of groundwater throughout the aquifers of the Puget Sound basin to elevated nitrate concentrations as defined by the probability of exceeding 2 milligrams/liter (mg/L) at a specific location and well depth. The U.S. Geological Survey (USGS) led the effort to characterize groundwater vulnerability. For this study, groundwater vulnerability refers to a probability with which a contaminant applied at or near the land surface can migrate to the aquifer of interest for a given set of land-use practices. Nitrate concentration data were selected for evaluation because elevated nitrate concentrations are typically caused by anthropogenic activities and have been associated with deleterious impacts on human health. To identify groundwater vulnerability to elevated nitrate concentrations, logistic regression was used to relate anthropogenic (human associated) and natural variables to the occurrence of elevated nitrate concentrations in untreated groundwater from large public water supply system wells found within the Washington State Department of Health Sentry database. Variables that were analyzed included well depth, soil hydraulic conductivity, precipitation, population density, fertilizer application amounts, and land-use types. Statistically significant models that predicted the probabilities of groundwater nitrate concentrations greater than 2 mg/L based on the predictor variables were created for the time periods 2000–04, 2005–09, 2010–14, and 2015–19. For all time periods, well depth and a measure of the abundance of urban and agricultural land over or near the well consistently helped explain the vulnerability of the well to elevated nitrate concentrations defined as a probability of exceeding 2 mg/L of nitrate. Precipitation and (or) soil hydraulic conductivity were also important predictor variables in the models. The models for each time period were used to create maps of groundwater vulnerability at 150- and 300-foot depths throughout the Puget Sound basin. As expected, the most vulnerable locations were associated with shallower well depths and increased agriculture and urban land cover. Across all four time periods, groundwater vulnerability throughout the Puget Sound was low, with probabilities of exceeding 2 mg/L concentrations of nitrate at depths at 150 and 300 feet typically less than 50 percent. Results also found a slight decrease in probabilities of elevated nitrate concentrations throughout the basin over time. More specifically, additional statistical tests found that groundwater with probabilities of less than about 60 percent declined from 2000 to 2019 and represented more than 75 percent of the modeled Puget Sound basin aquifer. Wells with greater than 60 percent probability increased over the same time period but represented only about 25 percent of the aquifer. The maps and statistical analysis presented in the study provide valuable and informative evaluation of the vulnerability of groundwater in the Puget Sound basin to elevated nitrate concentrations. The probability maps do not represent measured nitrate concentrations in groundwater, but rather they present the probability that nitrate concentrations exceed 2 mg/L. The models and predictions from this study are a viable indicator for the Puget Sound Partnership’s Healthy Human Population—Drinking Water Vital Sign. The logistic regression modeling approach presented here benefits water managers by allowing them to assess temporal trends in a range of probabilities, explore vulnerability changes as new regional land cover and anthropogenic data are generated, and distinguish vulnerabilities at different depths within the aquifer.

Washington↗

USGS2026

The mineral chemistry networks of tin and tungsten reflect metallogenic eras of the Mesozoic

Continental remobilization is a crucial driver of metallogenesis and the formation of ore deposits. Some of the world’s largest mineral deposits of the economically valuable elements tin (Sn), tungsten (W), gold (Au), copper (Cu), lead (Pb), and zinc (Zn) formed during the Mesozoic Era. Additionally, the chemistry and distribution of the elements Sn and W have been investigated in previous studies to understand planetary formation and differentiation processes. These two elements are largely co-located during certain South China Mesozoic metallogenic events but are not co-located during other time periods in the same regions. Here, we investigated the mineral chemistry network similarities and dissimilarities of Sn and W to understand their mineral formation and distribution during the Mesozoic Era and throughout Earth history. Mineral chemistry network community detection analysis and electronegativity associations among mineral constituent elements of Sn minerals and W minerals indicate that the elements have similar chemistry among their oxide minerals. However, Sn forms a much wider range of minerals that also contain S compared to W, which occurs in a limited number of S-containing minerals. The divergent constituent element interactions among S-containing Sn minerals and W minerals reflect the redox sensitivity and importance of oxygen (O) fugacity in Sn mineral formation. Conversely, extensive W mineral deposits are known to form at both high and low O fugacities. The similarities and differences between the mineral chemistry networks of Sn and W reflect the mineral distribution of the two elements in the Sn-W mineralization event from 160 to 139 Ma vs. the Sn–uranium (U) mineralization event from 125 to 98 million years ago (Ma). The mineral chemistry and distribution of Mesozoic Sn and W deposits illustrate the contrasting importance of redox and O fugacity on the mineral formation of different elements, and the dynamic crustal evolution that took place during this period of Earth history.

Geosciences↗

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↗

USGS1999

Water-quality assessment of the New England coastal basins in Maine, Massachusetts, New Hampshire, and Rhode Island: Environmental settings and implications for water quality and aquatic biota

The New England Coastal Basins in Maine, Massachusetts, New Hampshire, and Rhode Island constitute one of 59 study units selected for water-quality assessment as part of the U.S. Geological Survey's National Water-Quality Assessment (NAWQA) program. England Coastal Basins study unit encompasses the fresh surface waters and ground waters in a 23,000 square-mile area that drains to the Atlantic Ocean. Major basins include those of the Kennebec, Androscoggin, Saco, Merrimack, Charles, Blackstone, Taunton, and Pawcatuck Rivers. Defining the environmental setting of the study unit is the first step in designing and conducting a multi-disciplinary regional water-quality assessment. The report describes the natural and human factors that affect water quality in the basins and includes descriptions of the physiography, climate, geology, soils, surface- and ground-water hydrology, land use, and the aquatic ecosystem. Although surface-water quality has greatly improved over the past 30 years as a result of improved wastewater treatment at municipal and industrial wastewater facilities, a number of water-quality problems remain. Industrial and municipal wastewater discharges, combined sewer overflows, hydrologic modifications from dams and water diversions, and runoff from urban land use are the major causes of water-quality degradation in 1998. The most frequently detected contaminants in ground water in the study area are volatile organic compounds, petroleum-related products, nitrates, and chloride and sodium. Sources of these contaminants include leaking storage tanks, accidental spills, landfills, road salting, and septic systems and lagoons. Elevated concentrations of mercury are found in fish tissue from streams and lakes throughout the study area.

Maine, Massachusetts, New Hampshire, Rhode Island↗

USGS2025

Modeling seawater intrusion along the Alabama coastline using physical and machine learning models to evaluate the effects of multiscale natural and anthropogenic stresses

Seawater intrusion threatens groundwater resources in coastal regions, including southern Baldwin County, Alabama, where the freshwater-saltwater interface dynamics remain poorly understood. To address this gap, this study uses combined physics-based and machine-learning models to quantify seawater intrusion caused by natural (storm surges) and anthropogenic (human activities) perturbations. The long short-term memory network and wavelet analysis were used to assess vertical aquifer vulnerabilities, revealing that the shallow part of the Coastal lowlands aquifer system (CL1) in the southern Baldwin County region is more susceptible to sea level rise and groundwater extraction than deeper aquifers. Based on these findings, a cross-sectional numerical model (physics approach) for the CL1 aquifer was developed to evaluate tidal and storm surge effects, using Tropical Storm Claudette (June 2021) as a case study. Results showed that tidal fluctuations had a minimal impact on the saltwater-freshwater interface location, whereas storm surges caused substantial inland movement, with effects lasting for nine months. The steady-state version of the three-dimensional (3D) physical model predicted seawater intrusion across the entire area, and convolutional neural network-based modeling further validated the model results. The 3D physical model was also applied to a smaller area to assess human impact on the saltwater interface due to two groundwater pumping scenarios (± 50% of the baseline pumping rate). Results revealed that a 50% increase in groundwater withdrawals caused seawater to advance ~ 320 m inland, whereas a 50% reduction led to a ~ 270-meter retreat. This study highlights the vulnerability of Alabama’s shallow coastal aquifers to seawater intrusion due to storm surges and human activities, and demonstrates that combining physics-based models with machine learning approaches can improve groundwater predictions, though its accuracy depends on the availability of site-specific data.

Alabama↗