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USGS2024

A global assessment of SAOCOM-1 L-band stripmap data for InSAR characterization of volcanic, tectonic, cryospheric, and anthropogenic deformation

SAOCOM-1 is an L-band (23.5 cm) synthetic aperture radar (SAR) constellation made up of two satellites launched in 2018 and 2020 by Comisión Nacional de Actividades Espaciales (CONAE, Argentina). In this contribution, we present a global summary of interferometric SAR (InSAR) observations of ground deformation with SAOCOM-1 stripmap data for tracking volcanic, tectonic, glacier, and anthropogenic deformation. These examples include: 1) episodes of unrest at volcanoes in the Aleutian Islands, Southern Andes, and Italy, with line-of-sight (LOS) deformation from 4 cm/yr in InSAR time series to ~70 cm in interferograms; 2) dike intrusions in Hawai’i; 3) earthquakes in the Andean fold and thrust belt and the East Anatolian fault; 4) ice flow of the Southern Patagonia icefield; and 5) subsidence due to lithium brine extraction in the Salar de Atacama basin (northern Chile). Comparisons between SAOCOM-1, ALOS-2 SM3, Sentinel-1, and TerraSAR-X/ TanDEM-X/PAZ (TSX/TDX/PAZ) mean velocities from InSAR time series show a 1:1 ± 3% correlation in the LOS velocity, which highlights the high accuracy of SAOCOM-1 data. The minimum deformation that we measured in individual interferograms is 4 ± 0.6 cm. One limitation of SAOCOM-1 is the lack of a global acquisition program, which reduces its global and broader applications. Considering the repeat periods, background observation program, and lack of a controlled orbital tube, the best suited targets for SAOCOM-1 InSAR are two. First, volcanoes that deform with secular rates located in vegetated regions in mid- and high-latitudes, and/or that undergo transient episodes of fast deformation in which C-band coherence is lost quickly. Second, glaciers where coherence can be sustained during the repeat period of eight days.

IEEE Transactions on Geoscience and Remote Sensing↗

USGS2026

Coastal barrier resilience and resistance: Analysis and metrics for characterizing coastal state

Barrier islands are shaped by a variety of short- and long-term environmental processes such as storms and relative sea-level rise. These islands, found along the estuarine-marine interface, provide ecosystem services including storm surge and wave attenuation, erosion protection to inland marshes, habitat for fish and wildlife, and recreation. Natural resource managers require actionable information on how barrier island resilience and resistance changes over time to understand how an island’s current state relates to past conditions and to inform restoration prioritization and implementation. The U.S. Geological Survey and The Water Institute collaborated on a study to develop indicators of resilience and resistance for barrier islands in Louisiana. Here, resilience captures island persistence on yearly to decadal time scales, and resistance captures persistence on event time scales of days to weeks. The indicators fall in two categories: Tier 1 Screening Metrics, that can be readily calculated from available data, are easily interpretable as an evaluation of barrier condition, and provide a high-level snapshot of overall barrier resilience and resistance; and Tier 2 Analysis Metrics, which are detailed metrics that required specialized analysis and interpretation and are more applicable to answering specific questions managers may have about barrier state. The research team derived Tier 1 resilience indicators from subaerial land and vegetation cover calculated from publicly available maps and products based on satellite imagery. By benchmarking the total land and vegetation extent against their respective historical maxima, this metric provides a snapshot of an island’s current state in the context of its long-term trajectory. The research team developed Tier 1 resistance indicators based on subaerial island configuration and water level recurrence as a proxy for evaluating island resistance to storms, which are the primary driver of short-term change. These Tier 1 metrics can be analyzed over time to provide a high-level assessment of how an island’s resistance decreases because of elevation loss or sea-level rise or increases due to restoration or natural recovery. The research team developed Tier 2 resilience and resistance indicators and associated analyses to provide detailed information for specific time periods or applications (e.g., wildlife management). These metrics include habitat coverage from high-resolution maps, which show composition changes over time to capture the evolving resilience of specific habitat types; high tide flooding analysis, which evaluate island area relative to specified flooding thresholds to characterize resistance in the short-term or, if analyzed over time, indicate changes in resilience; and hypsometric curve analysis, which allows managers to evaluate island area changes above their own elevation benchmarks of interest and similarly characterize resistance in the short-term or indicate changes in resilience if assessed over time. The research team calculated Tier 1 metrics of the barrier islands and headlands along the coast of Louisiana for the period of 1984 through 2021 and Tier 2 metrics for select times during that period depending on data available and quality. The results were captured in a report card for each barrier, which also includes an overview of the metrics and their interpretation; a restoration and storm history; and Tier 1 and Tier 2 metric analysis, including benchmarking against coastwide and regional values as well as to an island’s pre-restoration trajectory. These report cards provide a readily digestible synthesis of barrier condition and trajectory that coastal managers can use to support restoration prioritization and other decisions.

Louisiana↗

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

Insights into Mountain Pass carbonatite formation from in-situ sulfur isotopes and geochemistry of sulfate and sulfide minerals

The Mountain Pass carbonatite stock hosts a world-class rare earth element deposit and may be classified as a carbonate-sulfate igneous rock, as it contains on average > 50 volume percent carbonate minerals and 20 to 30 volume percent sulfate minerals. The sulfates range in composition from barite to celestine and locally occur with sparse sulfide minerals. We investigate the origin of sulfur enrichment and the occurrence of sulfur-bearing minerals in the Mountain Pass carbonatite with in-situ sulfur isotope and mineral chemistry. Barite cores with δ 34 S of 1 to 3‰ do not coexist with sulfides, whereas celestine rims with δ 34 S of > 3‰ are associated with sulfides with δ 34 S < -10‰. We propose a model in which sulfur-bearing sediments were subducted during episodes of plate convergence in the Mojave Province that preceded Mountain Pass magmatism. Metasomatism of the overlying mantle by melts derived from the subducted sediments generated an unusually carbon- and sulfur-rich source to yield carbonatite magmas. Sulfur from primary carbonatite magmas and ~ 1 to 7% sulfur from subducted sediment melts yielded a slightly enriched δ 34 S composition (relative to depleted mantle δ 34 S of -1‰) for early crystallizing barite. Celestine rims on magmatic barite cores formed at low, hydrothermal temperatures (< 350 °C) based on S isotope thermometry for equilibrium celestine-galena and celestine-pyrite pairs. The sparse sulfides in the carbonatite stock are not in equilibrium with the primary barite cores and therefore do not permit S isotope thermometry estimates of magmatic temperatures. The S/Se ratios of sulfide minerals (> 3,400) typically exceed primitive mantle values (S/Se of 3,340), also consistent with their derivation from hydrothermal fluids. Trace occurrences of sulfide and sulfate minerals in alkaline silicate stocks related to the carbonatite stock have similar δ 34 S compositions and yield similarly low formation temperatures, suggesting regionally extensive and chemically similar sulfur-bearing hydrothermal fluids that imparted lithologically diverse rocks with a consistent sulfur isotope fingerprint.

California, Nevada↗

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↗

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↗

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↗

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↗

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↗

USGS1984

Techniques for estimating magnitude and frequency of floods on streams in Indiana

Equations are presented for estimating the magnitude and frequency of floods at ungaged sites on unregulated and nonurban streams in Indiana. The equations were developed by multiple-regression, analysis of basin characteristics and peak-flow statistical data from 242 gaged locations in Indiana, Ohio, and Illinois. The State of Indiana was divided into seven areas on the basis of the regression analysis. A set of equations for estimating peak discharges with recurrence intervals of 2, IO, 25, 50, and 100 years was developed for each area. Significant basin characteristics in the equations are drainage area, channel length, channel slope, mean annual precipitation, storage, precipitation intensity, and a runoff coefficient. Standard errors of estimate for the equations range from 24 to 45 percent. Methods are also presented for estimating flood magnitude and frequency at sites on gaged streams. Flood-frequency data based on observed peaks are given for 270 gaged locations. Twenty of these are on regulated streams, and six are on urban streams. Basin characteristics are also included car 245 of the gaged locations on unregulated and nonurban streams. No techniques are given for estimating flood magnitude and frequency at ungaged sites on regulated or urban streams. A rainfall-runoff model was tlsed to synthesize long-term peak data at 11 gaged locations on small streams. Flood-frequency curves developed from the long-term synthetic data were combined with curves based on short-term observed data to provide weighted estimates of flood magnitude and frequency at the rainfall-runoff stations.

Indiana↗