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Search indexed USGS publications on groundwater, aquifers, geologic maps, mineral resources and earthquakes. Explore source records by subject and place.

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1,662 records · Page 75Linked to original sources
USGS2000

Monthly variability and possible sources of nitrate in ground water beneath mixed agricultural land use, Suwannee and Lafayette Counties, Florida

In an area of mixed agricultural land use in Suwannee and Lafayette Counties of northern Florida, water samples were collected monthly from 14 wells tapping the Upper Floridan aquifer during July 1998 through June 1999 to assess hydrologic and land-use factors affecting the variability in nitrate concentrations in ground water. Unusually high amounts of rainfall in September and October 1998 (43.5 centimeters total for both months) resulted in an increase in water levels in all wells in October 1998. This was followed by unusually low amounts of rainfall during November 1998 through May 1999, when rainfall was 40.7 centimeters below 30-year mean monthly values. The presence of karst features (sinkholes, springs, solution conduits) and the highly permeable sands that overlie the Upper Floridan aquifer provide for rapid movement of water containing elevated nitrate concentrations to the aquifer. Nitrate was the dominant form of nitrogen in ground water collected at all sites and nitrate concentrations ranged from less than 0.02 to 22 milligrams per liter (mg/L), as nitrogen. Water samples from most wells showed substantial monthly or seasonal fluctuations in nitrate concentrations. Generally, water samples from wells with nitrate concentrations higher than 10 mg/L showed the greatest amount of monthly fluctuation. For example, water samples from six of eight wells had monthly nitrate concentrations that varied by at least 5 mg/L during the study period. Water from most wells with lower nitrate concentrations (less than 6 mg/L) also showed large monthly fluctuations. For instance, nitrate concentrations in water from four sites showed monthly variations of more than 50 percent. Large fluctuations in nitrate concentrations likely result from seasonal agricultural practices (fertilizer application and animal waste spreading) at a particular site. For example, an increase in nitrate concentrations observed in water samples from seven sites in February or March 1999 most likely results from application of synthetic fertilizers during the late winter months. Lower nitrate concentrations were detected in water samples from five of eight wells sampled during high-flow conditions for the Suwannee River in March 1998 compared to low-flow conditions in November 1998. Evidence for reduction of nitrate due to denitrification reactions was observed at one site (AC-1), as indicated by elevated concentrations of nitrogen gas and a corresponding increase in nitrogen isotope (d15N-NO3) values with a decrease in nitrate concentrations. Denitrification is unlikely at other sites based on the presence of dissolved oxygen concentrations greater than 2 mg/L in ground water and no observed trend between nitrate concentrations and values d15N-NO3 values. Nitrate was the dominant nitrogen species in most monthly rainfall samples; however, ammonium concentrations were similar or greater than nitrate during November and December 1998. During February through May 1999, both nitrate and ammonium concentrations were substantially higher in monthly rainfall samples collected at the study area compared to mean monthly concentrations at the Bradford Forest site located east of the study area, which is part of the National Atmospheric Deposition Program/National Trends Network. Also, higher nitrogen deposition rates in the study area compared to those at Bradford Forest could indicate that substantial amounts of ammonia are volatilized from fertilizers and animal wastes, released to the atmosphere, and incorporated as nitrate and ammonium in rainfall deposited in the middle Suwannee River Basin. Ground-water samples from most sites had d15N-NO3 values that indicated a mixture of inorganic and organic sources of nitrogen, which corresponded to multiple land uses where both synthetic fertilizers and manure are used on fields near these sites. Distinct d15N-NO3 signatures, however, were observed at some sites. For example, water samples from areas of row-crop farming as the dominant land use had d15N-NO3 values less than 4 per mil, indicating an inorganic nitrogen source such as synthetic fertilizer. In contrast, d15N-NO3 values greater than 9 per mil were found in water samples from three sites where manure from dairy and or poultry operations is spread on fields throughout the year.

Florida↗
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↗
USGS2026

Coastal and Marine Hazards and Resources Program decadal science strategy 2020–2030

Overview The decadal science strategy of the Coastal and Marine Hazards and Resources Program (CMHRP) of the U.S. Geological Survey for 2020 to 2030 describes the CMHRP's vision and mission and the strategic framework needed to support key program goals: Conduct research and develop science-based tools that lead to safer, more productive coastal communities and improved stewardship of natural resources.

Circular↗
USGS2026

Top-down targeted network analysis of critical mineral commodities applied to international geochemistry database

The global demand for critical mineral commodities is rapidly increasing, making domestic production an important factor in supporting the economy and national security. Large scale, publicly available geochemical databases allow for the application of data informatics methods to interrogate critical mineral commodities data for correlations in deposit formation and distribution, particularly for identifying enrichment of multiple critical mineral commodities at the same deposit. In this study, we applied network analysis to the Critical Minerals Mapping Initiative (CMMI) ore geochemistry (Critical Minerals in Ores, CMiO) database to identify the high concentration (defined as 10× bulk crustal abundance) co-occurrence of different critical mineral commodities across a mineral system hierarchy from deposit environments to individual deposits. Identifying patterns or unique outliers in enrichment in network communities will allow for the location of secondary critical mineral commodity resources from under-utilized deposits. We find trends in the enrichment of critical mineral commodities in network-communities between the elements praseodymium (Pr), neodymium (Nd), terbium (Tb), and dysprosium (Dy) across multiple CMiO database deposit environments and groups down to specific deposit types and sites. A separate trend in network community deposition is observed as well between iridium (Ir) and platinum (Pt) in deposit environments, groups, types, and sites. Network analysis focused on critical minerals in magmatic-hydrothermal deposits identified multiple deposit sites from different deposit types within the CMiO database with concentrations of Dy, Nd, Tb, Pr, Ir, and Pt that are at least ten times greater than the crustal average. This approach can be applied to any target element(s) or deposit(s) of interest, allowing broad investigation of co-enriched critical mineral commodities.

Journal of Geochemical Exploration↗
USGS2026

Uncertainty quantification of geologic energy storage in depleted gas reservoirs using material balance equations embedded in a hierarchical errors-in-variables model

The storage potential for gas in geologic settings, such as depleted hydrocarbon reservoirs and solution-mined salt caverns, is becoming salient to future energy infrastructure planning. Technologies such as carbon capture, utilization, and storage, carbon dioxide-enhanced oil recovery, and natural gas and hydrogen storage help meet growing energy demands, reduce carbon emissions to meet climate goals, and provide energy security amid geopolitical uncertainties. Therefore, estimates of underground gas storage capacity could be useful for efficiently navigating the energy transitions. Material balance is a fundamental method in reservoir engineering for estimating original gas in place and potential storage capacity at the scale necessary for national assessments of subsurface pore space resources. However, the deterministic method cannot accommodate multiple data sources or quantify uncertainty in predictions. In this study, a method that embeds material balance equations within a hierarchical errors-in-variables model is proposed which allows the estimation of the distributions of reservoir properties needed for assessments. Uncertainties associated with these reservoir properties have traditionally been expert-elicited, whereas the uncertainty estimates from the proposed models are data-driven. Capacity and uncertainty estimates can be used in a probabilistic resource assessment, supplementing information traditionally used by assessors or even replacing this expert elicitation step when data are unavailable. Various regression models are compared in a case study of the Michigan Basin, a large contributor to the United States’ current natural gas storage capacity. In particular, errors-in-variables models help ameliorate regression dilution and can quantify uncertainty in predictions of pressure in addition to storage capacity. Overfitting is addressed by quantifying generalization error and model averaging in simple and stratified cross-validation against reported working gas capacity, representing the varying quality and quantity of available data. Incorporating a statistical framework into existing numerical methods in reservoir engineering can improve the quality of estimation, and in particular, this method brings rigor to uncertainty quantification as part of a larger effort by the U.S. Geological Survey to assess domestic energy gas storage resources in depleted hydrocarbon reservoirs.

Mathematical Geosciences↗
USGS2024

Deformation by pressure solution and grain boundary sliding in a retrograde shear zone in southern New England, USA

Alleghanian phyllonites in a shear zone in southern New England were formed by the retrogression and hydration of a high-grade Acadian pelitic schist. The retrogression was locally incomplete, resulting in both heterogeneous mineralogy and mineral compositions, and in many arrested reaction textures. These features, documented by backscattered electron and element map imaging, and by 40 Ar/ 39 Ar dating, provide a record of the processes active as the schist was converted to a phyllonite. On the margins of the shear zone relic biotite and garnet grains survive but are partially replaced by chlorite. Relic high-grade muscovite flakes containing up to 35 % paragonite (pg) also persist but are entrained in swarms of pg-poor muscovite flakes producing folia with an anastomosing network of muscovite grains of variable composition. Single crystals of pg-rich muscovite are truncated by muscovite in successively younger cross-cutting folia that contain decreasing pg contents, falling to <5% pg in the youngest folia. In the core of the shear zone all high-grade minerals are destroyed, but recrystallization continues with earlier greenschist facies folia replaced by lower grade and pg-poorer muscovite in younger folia. Muscovite grains in truncated folia are commonly kinked and selective recrystallization of muscovite in kink bands to pg-poor-compositions demonstrates that strain energy helped drive recrystallization. The boundaries of these new pg-poor muscovite folia are sharp, and truncations indicate that grain boundary sliding (GBS) involved dissolution. GBS also enabled folding by flexural slip along muscovite grain boundaries. Fold amplitudes increased as chlorite dissolved from fold limbs precipitated in fold hinges producing crescent-shaped saddle-reef-like structures. Together these observations of truncation and replacement by dissolution and precipitation demonstrate that pressure solution and GBS facilitated both the formation of the phyllonitic shear zone and slip along it.

Connecticut↗
USGS2025

Rainfall thresholds for postfire debris-flow initiation vary with short-duration rainfall climatology

The size, frequency, and geographic scope of severe wildfires are expanding across the globe, including in the Western United States. Recently burned steeplands have an increased likelihood of debris flows, which pose hazards to downstream communities. The conditions for postfire debris-flow initiation are commonly expressed as rainfall intensity-duration thresholds, which can be estimated given sufficient observational history. However, the spread of wildfire across diverse climates poses a challenge for accurate threshold prediction in areas with limited observations. Studies of mass-movement processes in unburned areas indicate that thresholds vary with local climate, such that higher rainfall rates are required for initiation in climates characterized by frequent intense rainfall. Here, we use three independent methods to test whether initiation of postfire runoff-generated debris flows across the Western United States varies similarly with climate. Through the compilation of observed thresholds at various fires, analysis of the spatial density of observed debris flows, and quantification of feature importance at different spatial scales, we show that postfire debris-flow initiation thresholds vary systematically with short-duration rainfall-intensity climatology. The predictive power of climatological data sets that are readily available before a fire occurs offers a much-needed tool for hazard management in regions that are facing increased wildfire activity, have sparse observational history, and/or have limited resources for field-based hazard assessment. Furthermore, if the observed variation in thresholds reflects long-term adjustment of the landscape to local climate, rapid shifts in rainfall intensity related to climate change will likely induce spatially variable shifts in postfire debris-flow likelihood.

Arizona, California, Colorado, Nevada, New Mexico,↗
USGS2025

Structural controls on splay fault rupture dynamics during Cascadia megathrust earthquakes

Great subduction earthquakes ( M w ≥ 8.0) can generate devastating tsunamis by rapidly displacing the seafloor and overlying water column. These potentially tsunamigenic seafloor offsets result from coseismic fault slip and deformation beneath or within the accretionary wedge. The mechanics of these shallow rupture phenomena and their dependence on subduction zone properties remain unresolved, partly due to the sparsity of offshore observations of shallow megathrust earthquake deformation. Here, we analyze how offshore structure influences shallow rupture mechanics and slip partitioning using 3D dynamic earthquake simulations of the Cascadia subduction zone (CSZ) megathrust with and without variably dipping seaward- or landward-vergent splay faults in the wedge that sole into the megathrust. Resulting tradeoffs between splay and megathrust slip reveal structural controls on rupture partitioning, with greater splay slip leading to less shallow megathrust slip updip. Gently dipping and seaward-vergent splays host more slip than those with steeper, landward-vergent splays. To isolate the underlying mechanisms, we compare models with Andersonian and plunging principal stresses. Results suggest distinct static and dynamic processes control the dip- and vergence-dependence of splay rupture: static (mis)alignment relative to far-field tectonic loading favors slip on more optimally oriented, shallowly dipping splay faults. In contrast, dynamic stress interactions of an updip-propagating megathrust rupture front with the free surface and potential branch faults favor forward branching onto seaward-vergent splays and inhibit backward branching onto landward-vergent splays. Resulting seafloor displacements suggest splay fault structure may influence coseismic tsunami source processes, highlighting the importance of dynamically viable rupture scenarios in subduction hazard assessments.

Cascadia subduction zone↗
USGS2024

A comparative analysis of OpenET for evaluating evapotranspiration in California almond orchards

The almond industry in California faces water management challenges that are being exacerbated by droughts, climate change, and groundwater sustainability legislation. The Tree-crop Remote sensing of Evapotranspiration eXperiment (T-REX) aims to explore opportunities to improve precision irrigation management for woody perennial cropping systems. Almond orchards in the California Central Valley were equipped with eddy covariance flux measurements to evaluate satellite remote sensing-based evapotranspiration (RSET) models. OpenET provides high-resolution (30-m spatial and daily temporal) RSET data, synthesizing decades of research for practical water management. This study provides an evaluation of OpenET performance at six almond sites covering a large range in soils, age, and variety. It also compares OpenET ensemble evapotranspiration (ET) data with applied irrigation and precipitation records over an additional 148 almond orchards located in the Central Valley of California. Results show OpenET models, including the ensemble ET value, produced reasonable and actionable ET values, with overall coefficient of determination (R 2 ) and mean absolute error values of 0.73- and 0.95-mm d −1 at the daily time step, respectively. However, given the temporal sampling of Landsat (8-day revisit) and the interpolation methods used, the assessed ET models had difficulty in capturing short-term variability in almond ET; for example, the rapid decline in measured ET observed as a response to lack of irrigation preceding and during almond harvest. The study also drew attention to the spatial complexity in scenarios where irrigated orchards are surrounded by hot/dry areas, causing discrepancies between measured and modeled ET values. In comparison with irrigation records, OpenET ensemble ET was capable of quantifying water input (applied irrigation + precipitation) in almond orchards to within 13 % when evaluating monthly data. Initial results presented here reinforce the idea that RSET models, such as in OpenET, are powerful tools, yet their application requires nuanced understanding and careful consideration of local conditions.

California↗
USGS2025

Analyzing multi-year nitrate concentration evolution in Alabama aquatic systems using a machine learning model

Rising nitrate contamination in water systems poses significant risks to public health and ecosystem stability, necessitating advanced modeling to understand nitrate dynamics more accurately. This study applies the long short-term memory (LSTM) modeling to investigate the hydrologic and environmental factors influencing nitrate concentration dynamics in rivers and aquifers across the state of Alabama in the southeast of the United States. By integrating dynamic data such as streamflow and groundwater levels with static catchment attributes, the machine learning model identifies primary drivers of nitrate fluctuations, offering detailed insights into the complex interactions affecting multi-year nitrate concentrations in natural aquatic systems. In addition, a novel LSTM-based approach utilizes synthetic surface water nitrate data to predict groundwater nitrate levels, helping to address monitoring gaps in aquifers connected to these rivers. This method reveals potential correlations between surface water and groundwater nitrate dynamics, which is particularly meaningful given the lack of water quality observations in many aquifers. Field applications further show that, while the LSTM model effectively captures seasonal trends, limitations in representing extreme nitrate events suggest areas for further refinement. These findings contribute to data-driven water quality management, enhancing understanding of nitrate behavior in interconnected water systems.

Alabama↗
USGS2025

Status of water-quality conditions in the United States, 2010–20

Degradation of water quality can make water harmful or unusable for humans and ecosystems. Although many studies have assessed the effect of individual constituents or narrow suites of constituents on freshwater systems, no consistent, comprehensive assessment exists over the wide range of water-quality effects on water availability. Using published studies, data, and models completed at regional or national scales in the United States during 2010–20, this chapter moves towards a comprehensive assessment by summarizing how selected anthropogenic and geogenic water-quality constituents affect national-scale water availability for human and ecosystem needs. Several types of human health, agricultural, ecological, and beneficial-use standards or thresholds were used to provide context for categorizing surface-water and groundwater quality. Water availability for human and ecological use is limited by elevated concentrations of geogenic and anthropogenic constituents in surface and groundwater. Elevated concentrations of five geogenic constituents (arsenic, manganese, strontium, radium, and adjusted gross alpha) are common in groundwater and collectively affect the drinking water supply to over 30 million people. Surface water sourced drinking water supplies are impaired in about a third of assessed stream miles, most commonly because of non-mercury metals and salinity. Health-based violations at community water systems may disproportionately affect socially vulnerable communities. Ecological water uses are predominantly limited by nutrients, sediment, temperature, pathogens, salinity, and pesticides. Water availability for human and ecological use is adversely affected by human activities including human contaminant sources (for example, wastewater, agriculture), processes (for example, dredging, groundwater pumping), or permanent landscape modifications (for example, dams, urbanization). Primary contaminant sources vary spatially and include fertilizer and manure, atmospheric deposition, wastewater treatment plants, urban land, and a range of natural sources. Contaminants of emerging concern, contaminants without regulatory thresholds, and mixtures of geogenic and anthropogenic water contaminants also contribute to ecological degradation and human exposure.

conterminous United States↗
USGS2025

Forecast, monitor, adapt: A multi-agency strategy to protect people from postfire debris flows

In 2020, a wildfire burned across Glenwood Canyon in Colorado, USA. A history of postfire debris flows in the region and a hazard assessment for the burn area indicated that potentially life-threatening debris flows could be triggered by rainfall within months of a wildfire. As a result, four government agencies evaluated strategies to help mitigate hazards, including the loss of human life, that may be associated with debris-flow events. After the fire, 26 large debris flows occurred in the summer of 2021 and three sediment-laden flows occurred in the summer of 2023, but there were no major injuries or fatalities reported. We found that integrating hazard assessment/ forecasting, monitoring, and adaptation scenarios was a successful strategy for reducing postfire debris-flow risks to human life (including injuries and fatalities). Weather forecasts and estimates of debris-flow triggering rainfall thresholds, likelihood, and volume were used to anticipate the timing, location, and magnitude of debris-flow events. Rainfall monitoring and detailed recordkeeping of storms that triggered debris flows were used to validate and update debris-flow warning thresholds that varied with time following the wildfire. Although the governmental agencies working in this burn area had distinct and differing agency mandates, they were able to integrate information to reduce the risk of debris-flow events to human life.

Colorado↗
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↗
USGS2024

Wave propagation in layered soil deposits

Recent advances in the general theory of viscoelastic waves and rays in layered media provide a rigorous mathematical framework for site-specific, soil-response models used for earthquake resistant design. The advances provide general closed-form anelastic solutions for the classic problems of the response of a stack of soil layers to S and P waves, ray theory for reflected and refracted waves, Rayleigh- and Love-Type surface waves, and head waves. These general solutions valid for anelastic media regardless of the amount of material damping yield new insights regarding the characteristics of seismic waves and their ray paths that are not provided by conventional models. They provide corresponding numerical ground-response models and ray-tracing computation algorithms that account for changes in velocity and attenuation of anelastic waves associated with changes in inhomogeneity of the waves induced by anelastic soil and soil-rock boundaries. These anelastic effects manifest themselves as variations in amplitude response, amplitude attenuation, raypath location, and travel time as observed at the Earth’s surface. Implications of these anelastic effects for soil-response models used for earthquake resistant design are provided herein.

Conference Paper↗
USGS2025

USGS addresses needs for lithium calibration and quality control materials for pLIBS analysis

Lithium (Li) is a globally important commodity used for energy storage, national defense, human health, and advanced technologies. Lithium resource development requires identifying deposits with elevated concentrations and optimal mineralogy, typically associated with select clays and pegmatites. Lithium is a light, highly reactive alkali metal with low atomic mass that is difficult to detect and quantify using conventional portable geochemical techniques such as X-ray fluorescence (XRF). However, portable laser-induced breakdown spectroscopy (pLIBS) is a powerful analytical technique for lithium exploration due to its ability to analyze solids quickly with minimal preparation. The expanded utility of pLIBS is hampered by the lack of matrix-matched calibration and quality control (QC) materials. The United States Geological Survey (USGS) has developed in-house lithium calibration and QC materials for lithium in clay and pegmatite matrices to address this limitation. We present the workflow and implementation of a custom-built matrix specific calibration on a SciAps Z-300 pLIBS, using proprietary Profile Builder software. The implementation of the custom calibration and quality control standards enables us to collect semiquantitative results directly from the pLIBS while in the field. Ultimately, this calibration has improved confidence in sample selection and collection in the field, providing more efficient site characterization.

Conference Paper↗
USGS2024

Examining 22 years of ambient seismic wavefield at Mount St. Helens

An increase in seismic activity precedes most volcanic eruptions. Whereas event-based forecasting approaches have been successful, some eruptions remain unanticipated, resulting in casualties and damage. Our study leverages the recent advancements in ambient field seismology. We explore features extracted from continuous ambient fields using traditional methods, for example, peak ground velocity, peak ground acceleration, root mean square, root median square, real-time seismic amplitude measurement, and novel methods (displacement seismic amplitude ratio and spectral width). In addition, we explore unsupervised learning of higher order wavelet features using scattering networks. We find that combining all the methods was necessary to disentangle the effects of seismic sources from structural changes at Mount St. Helens. Although the ambient wavefield-based approach does not yield additional or more significant precursory signals than event-based methods at Mount St. Helens, our study demonstrates that the ambient wavefield provides supplementary information, mainly about structural changes and complements traditional methods. The ambient seismic wavefield offers additional insights into long-lasting processes. We find enhanced wave attenuation correlating with geochemical measurements. We interpret this as ongoing structural changes, such as dome growth or the evolution of the volcanic conduit system. On annual and decadal timescales, we interpret seasonal seismic attenuation in the shallow subsurface as groundwater fluctuations, corroborated by observations at the nearby Spirit Lake level. This multimethod approach at Mount St. Helens sheds light on a volcanic system’s underlying dynamics and structure.

Washington↗
USGS2026

Localization of spatiotemporally heterogeneous subsurface flows using autoencoder-based deep learning framework for time-lapse self-potential tomography

Self-potential (SP) monitoring has emerged as a valuable method for characterizing subsurface hydrogeological features and processes due to its sensitivity to fluid-induced electrokinetic effects. Despite advancements in SP inversion, challenges remain in imaging groundwater dynamics from SP activities due to complex hydrological settings and transient noise. In this study, a deep learning autoencoder (AE)-based framework is proposed for the spatiotemporal localization of subsurface fluid movement from time-lapse SP tomography. Temporal segments of time-lapse numerical inversions were first derived from long-term SP monitoring conducted from a floodplain site in Oak Ridge, Tennessee, known for active hyporheic exchange. Subsequently, AE models based on vision transformer (ViT), convolutional long short-term memory (ConvLSTM), convolutional neural network, and temporal convolutional network were individually trained and compared on the SP tomography segments for reconstruction performance. Finally, the reconstruction error over time serves as an anomaly score to identify moments of active SP variation, whereas spatial distributions of errors within these moments are analyzed to image and localize regions associated with anomalous subsurface fluid movement. The results demonstrate that ConvLSTM- and ViT-AE are most capable for the localization task with contrasting error distributions and consistent delineation of anomalies. Applying the method to both SP arrays parallel and perpendicular to the stream produced consistent anomaly zones near a fault or karst feature, validating the robustness and generalization of the approach. These results demonstrate the potential of the proposed framework as a scalable and interpretable tool for spatiotemporal analysis of subsurface flow dynamics in complex hydrogeological systems.

Tennessee↗
USGS1976

Results of phytoplankton sampling at National Stream Quality Accounting Network stations in Montana, 1975 water year

Twelve National Stream Quality Accounting Network stations were operated in Montana during the 1975 water year. The network was established for the purpose of acquiring a base of hydrologic data for use by agencies engaged in water-resources planning on a national or regional scale. Among the characteristics analyzed were phytoplankton identification and cell counts. Samples consisted of composites of equal aliquots, collected at the center of each quartile of flow, using modified suspended-sediment samplers and sediment collection techniques. Identification and counting were done using the Sedgwick-Rafter cell method. Cell counts ranged from 21 cells per millilitre at Flathead River at Flathead, British Columbia to 27,000 cells per millilitre at Yellowstone River near Sidney. The class Bacillariophyceae was most abundant in both number and variety at all sampling sites. Anabaena and Aphanizomenon of the phyllum Cyanophyta were found at six stations and two stations respectively. These two genera of blue-green algae often become abundant in enriched waters resulting in nuisance conditions.

Montana↗