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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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347 records · Page 2Linked to original sources
USGS2025

U.S. Geological Survey Groundwater Climate Response Network, 2024

As of October 2024, the U.S. Geological Survey (USGS) operated 588 sites across the United States and its territories as part of the Groundwater Climate Response Network (CRN). The CRN is comprised of wells selected to monitor the effects of climate variability, such as droughts, on groundwater levels nationwide. The CRN includes nearly 500 locations with real-time data and more than 100 sites with non-real-time data available to the public on the CRN web mapper and the USGS National Water Dashboard.

General Information Product↗
USGS2024

U.S. Geological Survey Groundwater Climate Response Network—2023

As of October 2023, the U.S. Geological Survey (USGS) operated more than 660 sites across the United States and its territories as part of the Groundwater Climate Response Network (CRN). The CRN is comprised of wells and springs selected to monitor the effects of climate variability, such as droughts, on groundwater levels and spring discharge nationwide. The CRN includes more than 550 locations with realtime data and more than 100 sites with non-real-time data available to the public on the CRN web mapper and the USGS National Water Dashboard.

General Information Product↗
USGS2025

Using probability difference to compare streamflow information of alternatives for efficient operation of monitoring networks

Efficient operation of streamflow monitoring networks requires investments in technology and labor that provide the greatest benefits from available resources. Economic analyses comparing the costs and benefits from different types of alternatives for monitoring have not been practical to implement. Streamflow information provides a generic measure of benefits that can be incorporated into operational decisions as an objective for monitoring networks. A methodology for comparing how accuracy, monitoring period, and monitoring instead of modeling affects streamflow information is developed from information-theoretic approaches for network design but contributes three novel features: (1) a probability-difference model for conditional probability of monotonically paired variables, (2) explicit discounting of unverified information that may exceed the accuracy of streamflow records, and (3) run analysis to account for non-stationarity in streamflow probabilities. Application of the methodology to the U.S. Geological Survey streamflow monitoring network indicates the value of monitoring period to reduce the uncertainty of streamflow probabilities and, thus, increase streamflow information. The methodology has important limitations, particularly for sites with non-perennial streamflow, but demonstrates that probability difference could be used to evaluate operational alternatives to increase the efficiency of monitoring networks.

PLOS Water↗
USGS2026

Bayesian belief network model to predict human-wildlife conflict in protected areas

Human-wildlife conflict (HWC) poses a pervasive global challenge, affecting livelihoods and threatening biodiversity. To better anticipate and mitigate HWC risk, we developed a large-scale predictive model using a Bayesian Belief Network (BBN). We surveyed 1,011 park rangers across 135 terrestrial protected areas in three Andean countries, documenting recent HWC incidents involving wildlife persecution or killing, livestock depredation, crop damage, or threats to human safety and property. We identified key drivers of HWC risk, including governance, wildlife acceptance, participation, and habitat quality. A sensitivity analysis revealed that enhancing governance and improving wildlife acceptance could reduce HWC risk by > 85%. The BBN model demonstrated scalability, effectively identifying strategies to reduce HWC risk at multiple scales, from individual protected areas to national networks. Our findings highlight the importance of strengthening governance, increasing wildlife acceptance, and enhancing community participation in conservation efforts. BBNs provide a flexible, cost-effective, and data-driven tool to guide protected areas and wildlife managers in monitoring, anticipating, and making informed decisions to mitigate conflict and promote coexistence.

Scientific Reports↗
USGS2024

Earthquake relocations delineate discrete a fault network and deformation corridor throughout Southeast Alaska and Southwest Yukon

Deformation in southeastern Alaska and southwest Yukon is governed by the subduction and translation of the Pacific-Yakutat plates relative to the North American plate in the St. Elias region. Despite notable historical seismicity and major regional faults, studies of the region between the Fairweather and Denali faults are complicated by glacial coverage and the remote setting. In the last decade, significant improvements have been made to the density of regional broadband seismometer networks. We relocate more than 5,000 earthquakes between 2010 and 2021 in the region of southeastern Alaska and southwestern Yukon utilizing these improved seismic networks. With reductions in catalog uncertainty, particularly in depth, we quantify the thickness of the seismogenic layer in the crust throughout the region and locate seismicity on a shallow network of upper-crustal faults. Relocated earthquakes, combined with an updated focal-mechanism catalog, permit estimating and classifying motion of active faults. This includes mapping the Totschunda-Fairweather “Connector” fault, which plays an important role in explaining regional deformation, and identifying new faults like the Kathleen Lake fault. We draw similarities between our seismic observations and simplified conceptual models of regional tectonics, which describe a dominant transpressional regime and localized slip partitioning. Our results support a hypothesis where current deformation is taking place on a well-defined and evolved network of shallow faults in the corridor between the Totschunda-Fairweather “Connector” and Denali faults.

Alaska, Yukon↗
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↗
USGS2025

PFAS sampling activities in the U.S. Geological Survey national networks

Per- and polyfluoroalkyl substances (PFAS), frequently called “forever chemicals,” are used for a wide variety of industrial purposes and are often found in common household and industrial items such as firefighting foams, non-stick cookware, and water-resistant materials. The contamination of water, air, and soil by PFAS is a national and global issue due to their widespread occurrence in multiple applications and resistance to biodegradation and other traditional treatment processes. Research indicates that many PFAS can be emitted to the atmosphere and transported and deposited long distances from the source. The U.S. Geological Survey (USGS) Water Resources Mission Area received funding to implement a national-scale sampling effort to assess PFAS occurrence. To follow agency directives, the National Water Quality Network (NWQN) added PFAS sample monitoring for both surface water and groundwater, and also added PFAS monitoring to selected sites in the National Atmospheric Deposition Program (NADP).

General Information Product↗
USGS2024

Methods to evaluate and improve the modeling of rupture directivity in assessment of seismic hazard

In recent years, there have been several advancements related to the modelling of near-source effects of earthquake rupture on strong ground shaking, leading to an improved characterization of ground motions and resulting seismic hazard. Some of these modifications have stemmed from physics-based numerical modelling of the earthquake rupture process, using physics-based dynamic rupture simulations. These contributions have led to a better understanding of how fault rupture characteristics, geometry, and the style of faulting can interact with the hypocenter-dependence on the path from source to site that may ultimately guide the development of seismic directivity models. Moving forward, the application of modern techniques can be used to incorporate these source characteristics and near-fault ground motion behavior that contribute to the azimuthally varying effects that result in rupture directivity. One example is the application of machine learning methods to support more automated integration of new predictor variables in model development and open more evaluation opportunities to access residuals. Here, we utilize several techniques to take advantage of the plethora of synthetic data and its ability to supplement preexisting trends observed in data. We showcase two examples of how models can be either developed, expanded upon, or constrained using artificial neural network model (ANNs). We evaluate the performance of the ANN with existing methods, comparing misfit, potential limitations, and ability to continue to improve upon these methods in the future. One approach uses a set of simulations with corresponding synthetic ground motions from the Southern California Earthquake Center (SCEC) CyberShake study to develop a ground motion model adapted to incorporate seismic directivity information using an ANN. This large database (TBs) enables us to train the model to capture magnitude, period, and distance variations and how these parameters relate to amplification from hypocenters located along finite-faults. In some cases, there is reduced misfit from better representing source features that aren’t included in base ground motion models that neglect hypocenter location (e.g. azimuthal variation, source-to-site terms). Another ANN method uses a shallow-layered neural network model to better fit a hypocenter-independent model. This method adjusts the median and aleatory variability to account for the averaged impact of various hypocenter distributions to fit the underlying directivity adjustment model. This method serves as a template to apply to other directivity models, improving computational efficiency and more readily enabling integration in hazard codes.

California↗
USGS2026

SlideDetect: Spatio-temporal landslide detection using a three-dimensional convolutional neural network

Landslides pose a serious and ongoing threat to both human lives and infrastructure worldwide; therefore, it is of interest to predict where and when landslides are likely to occur. Advances in machine learning techniques have spurred numerous studies aimed at estimating relative landslide propensity, but are limited to spatial (as opposed to temporal) prediction due to the sparsity of landslide timing data. We address this data gap by training SlideDetect, a 3-dimensional convolutional neural network (3D CNN), to identify landslides based on their spatial and temporal occurrence within multitemporal image stacks. We use an inventory of landsides triggered by the 2018 Hokkaido earthquake and two years of monthly composite optical imagery spanning this event. The model can identify not only landslide location but also landslide date with an area under the precision-recall curve (PR-AUC) of 0.84. We further present a new standard for presenting PR curve results that explicitly compares model performance at different confidence thresholds, allowing for clearer model evaluation and comparison. Our new approach to constraining landslide timing paired with this more consistent and objective method for evaluating model performance shows considerable promise, and with further application and testing, SlideDetect could enhance the data availability and tools needed to advance landslide hazard and risk assessments.

JGR Machine Learning and Computation↗
USGS2024

Representation of surface-water flows using Gradient-Related Discharge in an Everglades Network

The Everglades Depth Estimation Network interpolates water-level gage data to produce daily water-level elevations for the Everglades in south Florida. These elevations were used to estimate flow vectors (gradients and directions) and volumetric flow rates using the Gradient-Related Discharge in an Everglades Network (GARDEN) application developed by the U.S. Geological Survey in cooperation with the U.S. Army Corps of Engineers. Flow rates in both the east-west and north-south directions were computed on a 400-meter square grid using modified parameters in the Manning’s equation. The frictional resistance parameter in the Manning’s equation was calibrated to measured flow rates at coastal creeks fed by Everglades Depth Estimation Network boundary flows. Levees and other features that act as barriers to flow were defined as “no-flow” grid cells where vectors were set to zero. The flow volume magnitudes were calibrated with 2020 daily values of coastal river flows, and verification was performed using 2021 data. Within a given day, the measured coastal river flows fluctuate more than the GARDEN boundary flows because of tidal and wind forcings. Because the GARDEN boundary flows were the upstream water source for the coastal rivers, calibration focused on matching average daily flow volumes rather than daily fluctuations. The Pearson’s correlation coefficient is 0.766 for the 2020 calibration period and 0.566 for the 2021 verification period. Applying GARDEN to periods with hydraulic-control-structure releases allows the propagation of structure flows to be seen in the daily flow-vector maps along with the multiday response of flows farther downgradient. Flow vectors may be overestimated near control structures because of difficulties in resolving the water gradient downstream from the structure. Flow vectors farther from the structure are more accurate than those near the structure.

Florida↗
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↗
USGS1997

An optimized network for phosphorus load monitoring for Lake Okeechobee, Florida

Phosphorus load data were evaluated for Lake Okeechobee, Florida, for water years 1982 through 1991. Standard errors for load estimates were computed from available phosphorus concentration and daily discharge data. Components of error were associated with uncertainty in concentration and discharge data and were calculated for existing conditions and for 6 alternative load-monitoring scenarios for each of 48 distinct inflows. Benefit-cost ratios were computed for each alternative monitoring scenario at each site by dividing estimated reductions in load uncertainty by the 5-year average costs of each scenario in 1992 dollars. Absolute and marginal benefit-cost ratios were compared in an iterative optimization scheme to determine the most cost-effective combination of discharge and concentration monitoring scenarios for the lake. If the current (1992) discharge-monitoring network around the lake is maintained, the water-quality sampling at each inflow site twice each year is continued, and the nature of loading remains the same, the standard error of computed mean-annual load is estimated at about 98 metric tons per year compared to an absolute loading rate (inflows and outflows) of 530 metric tons per year. This produces a relative uncertainty of nearly 20 percent. The standard error in load can be reduced to about 20 metric tons per year (4 percent) by adopting an optimized set of monitoring alternatives at a cost of an additional $200,000 per year. The final optimized network prescribes changes to improve both concentration and discharge monitoring. These changes include the addition of intensive sampling with automatic samplers at 11 sites, the initiation of event-based sampling by observers at another 5 sites, the continuation of periodic sampling 12 times per year at 1 site, the installation of acoustic velocity meters to improve discharge gaging at 9 sites, and the improvement of a discharge rating at 1 site.

Florida↗
USGS2026

Decadal trends and occurrence of geogenic constituents and mixtures in groundwater across the continental United States

Worldwide, about 50% of the population is served by groundwater-sourced drinking water. Numerous groundwater quality assessments have found that geogenic constituents are among the most common contaminants in drinking-water aquifers. Documenting changing groundwater quality is a crucial aspect of water availability assessments. We assess trends and occurrence of geogenic constituent concentrations in groundwater across the continental United States using 3 decades of data from the U.S. Geological Survey’s National Water Quality Network. Thousands of groundwater wells were grouped into agricultural, urban, or domestic supply network types. Although most networks and constituents had no statistically significant change in concentration, many had increasing concentration trends, elevated concentrations, or both. Lithium, sodium, radium, sulfate, and uranium had increasing trends in more than 10% of the study networks. Urban and domestic well networks had increasing lithium and sodium trends more often than agricultural networks. Manganese most commonly increased in domestic well networks; uranium more commonly increased in agricultural and urban networks. Elevated concentration mixtures were widespread, and mixture complexities appeared to increase over time. Our results indicate that more than 2.3 million domestic-well users may be affected by elevated concentrations of one or more geogenic constituents.

continental United States↗
USGS2025

Estimated ultimate recovery (EUR) Prediction for Eagle Ford Shale using integrated datasets and artificial neural networks

The estimated ultimate recovery (EUR) is an important parameter for forecasting oil and gas production and informing decisions regarding field development strategies. In this study, we combined site-specific geologic, completion, and operational parameters with the predictive capabilities of machine learning (ML) models to predict EURs of the wells for the Eagle Ford Marl Continuous Oil Assessment Unit. We developed an extensive dataset of wells that have produced from the lower and upper Eagle Ford Shale intervals and reduced the model complexity using principal component analysis. We tested the ML models and estimated the sensitivities of ML-predicted EURs to changes in the values of different input variables. The results of applying the optimized ML model to the Eagle Ford suggest that the approach developed in this study could be promising. The ML estimates of the EURs fit the DCA-based values with an R 2 ~ 0.9 and a mean absolute error of ~36 × 10 3 bbl. In the lower Eagle Ford Shale, the EUR estimates were found to be most sensitive to changes in porosity, net thickness of the interval, clay volume, and the API gravity of the oil; and that in the upper Eagle Ford Shale they were most sensitive to changes in the total organic carbon and water saturation, which suggests that it could be important to consider these parameters in assessing these intervals or close analogs.

Louisiana, Mississippi, Texas↗
USGS2024

Diel temperature signals track seasonal shifts in localized groundwater contributions to headwater streamflow generation at network scale

Groundwater contributions to streamflow sustain aquatic ecosystem resilience; streams without significant groundwater inputs often have well-coupled air and water temperatures that degrade cold-water habitat during warm low flow periods. Widespread uncertainty in stream-groundwater connectivity across space and time has created disparate predictions of energy and nutrient fluxes across headwater networks, hindering predictions of cold-water habitat resilience under climate change scenarios. Recently, annual paired air and water temperature signals have been harnessed to indicate stream water thermal sensitivity and the dominance of deep versus shallow groundwater influence, although the utility of diel air–water temperature signal metrics for hydrologic inference has remained unexplored. Here we analyzed two consecutive years of locally paired, air–water temperature data from 47 headwater stream sites in the Catskill Mountains, New York, USA, and discovered characteristic seasonal patterns in diel temperature signal sinusoid metrics (amplitude ratio, phase lag, and mean ratio) driven by shifts in streamflow generation mechanisms and stream network position. Hydrologic interpretations of observed patterns were supported by stream heat budget model scenarios and additional analysis of paired air–water temperature data from two streams in Shenandoah National Park, Virginia, USA, with well characterized stream-groundwater connectivity. We found that within smaller tributaries, streamflow generation transitions from runoff to groundwater dominance were driven by hillslope drying during seasonal periods of lower precipitation. This was evidenced by significant correlations (p < 0.01) between daily water:air temperature signal amplitudes (non-linear decreases of ∼ 50 %) and derived base-flow index at 22 of the 28 sites, indicating enhanced local groundwater influence on streamflow promotes decoupling of diel air–water temperature signals. Additionally, ratios between daily water:air temperature signal means were lower in tributaries (∼0.68) when compared to main-stem (∼0.8) sites, increasing linearly throughout the observational period. In conceptual stream heat budget models, groundwater inflow had minimal effects on daily phase lags (∼0.2 hr), but increases in fractional groundwater discharge (0–50 %) depressed daily amplitude (∼20 % to 50 %) and mean ratios (∼15 %), supporting the sensitivity of daily metrics to interpreted changes in seasonal groundwater contributions to streamflow. During observational periods (i.e., April through October 2021 and 2022), significant differences (p < 0.01) between tributary and main-stem air–water metrics occurred when base-flow contributions were highest (∼0.93 vs. ∼ 0.68), as sites lower in the network had daily temperature metrics dominated by stream channel thermal inertia, rather than local groundwater connectivity, showing enhanced air–water diel signal coupling during warmer, drier periods. Divergent air temperature coupling across the network was interpreted as being driven by distance from local groundwater source zones, additional lateral groundwater inflows do not contribute a meaningful fraction to channel discharge lower in the network. Given the growing footprint of stream temperature observations, diel air–water temperature signals can provide distributed metrics sensitive to upstream groundwater discharge. Consequently, these metrics can support ongoing efforts by resource managers and researchers seeking to forecast the resilience of cold-water habitat to climate warming and changing precipitation regimes in mountain headwater streams.

New York, Virginia↗
USGS2026

U.S. Geological Survey Monitoring Milestones—Oe-151 at Woodgate, NY (433112075091501)

On July 9, 1926, monitoring well Oe-151 at Woodgate, New York (USGS ID 433112075091501) recorded its first groundwater data. Since then, the well has provided water data nearly continuously and has now reached a 100-year milestone for data collection. The well is part of the U.S. Geological Survey (USGS) Climate Response Network (CRN), which is a national network of wells selected to monitor natural groundwater conditions. Well Oe-151 is the first well in the network to reach a 100-year Centennial milestone.

New York↗
USGS2026

Open-source gravity reduction workflows for geothermal resource assessment

Potential-field geophysical data such as gravity can enhance understanding of geothermal resources at all stages of the resource life cycle, including assessment, exploration, development, and monitoring, and at multiple scales, from the reservoir scale to regional scale. However, to make gravity data useful for geothermal resource characterization, several processing steps are required to isolate the effects of density variations in the Earth’s crust to enable the identification of structural features associated with geothermal resources. Although this process is well-established, standard computational implementations for processing gravity data that are FAIR (Findable, Accessible, Interoperable, and Reproduceable) are still lacking. This paper details ongoing efforts at the U.S. Geological Survey (USGS) to develop a standard set of open-source Python tools for gravity data reduction that align with the FAIR principles. This workflow makes use of existing open-source tools for geophysical data processing with the goal of maximizing opportunities for rapid improvements, interoperability, and adaptability to other types of geophysical data.

Conference Paper↗