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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 91Linked to original sources
USGS2024

Groundwater quality and groundwater levels in Dougherty County, Georgia, April 2020 through January 2023

The Upper Floridan aquifer is the uppermost reliable groundwater source in southwest Georgia. The aquifer lies on top of the Claiborne, Clayton, and Cretaceous aquifers, all of which exhibited water-level declines in the 1960s and 1970s. The U.S. Geological Survey has been working cooperatively with Albany Utilities to monitor groundwater quality and availability in these aquifers since 1977. Flow direction in the Upper Floridan aquifer is to the south and toward the Flint River. During the past 3 years, water levels varied above and below period-of-record median values. Water levels in the Upper Floridan aquifer were primarily above or at median levels during 2020 and 2021 and at or below median levels during 2022. Water levels in the Claiborne aquifer were above median levels, whereas water levels in the Clayton aquifer were at or below median levels, and in the Cretaceous aquifer system were close to median levels. During January 2021, eight wells were sampled for major ions, including nitrate plus nitrite as nitrogen (N). Nitrate plus nitrite as N concentrations ranged from 2.3 to 10.5 milligrams per liter (mg/L). During December 2021, seven wells were sampled for major ions, including nitrate plus nitrite as N. Nitrate plus nitrite as N concentrations ranged from 3.9 to 9.9 mg/L. During November 2022, eight wells were sampled for major ions, including nitrate plus nitrite as N. Nitrate plus nitrite as N concentrations ranged from 3.9 to 10.0 mg/L. Two wells were also sampled for per- and polyfluoroalkyl substances during November 2022.

Georgia↗
USGS2026

Surface variable‐based machine learning for scalable arsenic prediction in undersampled areas

In the United States, private wells are not federally regulated, and many households do not test for Arsenic (As). Chronic exposure is linked with multiple health outcomes, and risk can change sharply over short distances and with well depth. Coarse maps or sparse sampling often miss exceedances. Most existing models operate at ∼1 km resolution and use groundwater chemistry or detailed geologic logs, which limits their use in undersampled areas where improved guidance is most needed. We overcome these limitations by developing a machine learning model for Minnesota, USA, that predicts As exposure risk using only surficial variables from remote sensing and global data sets. Variables related to surface water hydrology and geomorphology are selected based on mechanistic links that control redox conditions and As mobilization. Local training was essential, and surficial geology variables that are more sensitive to local conditions were needed to maximize model accuracy. The resulting complete model was sufficiently sensitive to generate accurate and detailed risk maps and depth profiles of As concentrations above the 10 μg/L maximum contaminant level. Accuracy depended on local training data density. We identified a training data density of 0.07 wells/km 2 as a practical target for stable county-level performance. Maps of exceedance probabilities highlight priority areas for testing that are particularly important in rural communities that have received less sampling. These results support public health action by guiding where to install wells and where to test them, how much new sampling is needed, and where treatment outreach is most urgent.

Minnesota↗
USGS2026

A roadmap for identifying and interpreting physical processes and national water model prediction bias associated with baseflow index regimes across the contiguous United States

Understanding how groundwater–surface water interactions shape streamflow variability is critical for diagnosing low flow behavior and prediction bias in continental scale hydrologic models. We present a process informed framework that links observed baseflow (BF) dynamics, watershed attributes, and National Water Model (NWM) performance across the contiguous United States. Using daily observed streamflow from 797 reference quality streamgages, we developed monthly baseflow index (BFI) signatures using a streamgage specific, calibrated digital filter. Hierarchical clustering of these signatures identified seven distinct BFI regimes capturing regional and seasonal variability. We evaluated NWM v3.0 retrospective streamflow performance within each regime using multiple hydrograph and flow duration curve-based metrics. Model skill varied systematically across regimes: mixed flow systems were simulated most accurately, while predominantly BF dominated and quickflow dominated regimes exhibited substantially poorer performance. Across nearly all regimes, the NWM underestimated observed BFI magnitude and frequently failed to reproduce seasonal BF patterns, indicating systematic biases in simulated low flow contributions. To relate these regimes to potential process controls, we trained a Random Forest classifier using static watershed attributes and applied Shapley Additive Explanations to identify features most strongly associated with each regime. Results highlight regionally varying influences, including the dominant role of snow fraction and seasonal runoff timing in snow dominated basins and the importance of evapotranspiration and aridity in quickflow dominated systems. Collectively, these findings demonstrate how hydrologic signatures combined with interpretable machine learning can diagnose regime specific model biases and generate process-based hypotheses about limitations in large scale hydrologic prediction systems.

contiguous United States↗
USGS2026

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

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

Cascadia Subduction Zone↗
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↗
USGS2026

Satellite time series analysis to quantify changing climax ciénegas using a state and transition model approach

Ciénegas are rare wetlands in arid landscapes of the North American Southwest, historically providing critical ecological and hydrological functions but increasingly threatened by changing climate and land use pressures. This study quantifies changes in ciénega condition and floodplain dynamics using a state-and-transition model (STM) informed by expert knowledge and remote sensing. Key factors include woody plant encroachment, water availability, and soil aggradation. We mapped 31 ciénegas with high-resolution imagery and analyzed Landsat data (1985–2023) to assess vegetation health and moisture using the Normalized Difference Vegetation Index (NDVI) and Normalized Difference Infrared Index (NDII). Results show substantial interannual variability in phenology, water stress, and soil moisture, with regional drying and elevation strongly influencing ciénega resilience. We classified ciénegas into three functional states—healthy, desiccated, and dormant—and mapped their 2023 condition. Trend analyses indicate most ciénegas exhibit greening despite drought, though localized variability underscores the need for site-specific management. None are in a stable climax (reference) state; rather, they transition among states in response to external drivers. Increasing woody plant cover and surface drying, likely linked to declining regional water tables, favor deep-rooted species over wetland grasses—a pattern mirrored in adjacent control plots. Spatially explicit analysis revealed intra-ciénega variability often masked by aggregated data, highlighting the importance of high-resolution monitoring. Seasonal and long-term trends provide context for understanding ciénega dynamics, including degradation and restoration pathways. This study emphasizes the importance of groundwater conservation and demonstrates how remote sensing supports long-term monitoring. The STM framework offers a practical tool for adaptive management to sustain freshwater resources in arid environments.

Arizona, New Mexico↗
USGS2026

Predictable seismic cycles result from structural rupture barriers on oceanic transform faults

Earthquakes of magnitude ( M ) >5.5 on oceanic transform faults (OTFs) repeatedly rupture the same locked patches, sometimes quasiperiodically. These patches are separated by “barriers” that halt earthquake propagation and slip mostly aseismically. However, the physical processes governing this systematic behavior remain unclear. We analyzed two barriers along the Gofar transform fault that have arrested ~15 M 6 earthquakes over the past three decades. Ocean bottom seismometer data indicate that the barriers hosted intense microseismicity before the mainshocks and comprise multistrand faults and transtensional stepovers with 100- to 400-m lateral offset. These characteristics contradict earthquake rupture termination models invoking velocity-strengthening friction or large geometric steps and instead point to damage-enhanced porosity and dilatancy-strengthening mechanisms. By isolating rupture segments, the barriers regulate the quasiperiodic recurrence of OTF earthquakes.

Science↗
USGS2026

Water table rise sustains carbon release from soils in wetland-dominated landscapes: An intact soil core study

Wetland-dominated landscapes influence carbon cycling through their potential to act as both carbon sinks and sources. Wetlands in low-relief landscapes have dynamic terrestrial-aquatic interfaces that change seasonally with variable surface water and groundwater levels. However, few studies have directly quantified dissolved organic matter (DOM) release and greenhouse gas (CO 2 , CH 4 ) fluxes from wetland soils along terrestrial-aquatic interfaces as they are seasonally re-saturated by groundwater. To estimate groundwater-mediated soil DOM and gas fluxes, we performed laboratory simulations of vertical groundwater rise on intact soil cores collected from four Delmarva bay wetlands located in the Mid-Atlantic United States. At each wetland, one core was collected from within the wetland basin and the other from the transitional zone at the basin edge. Cores were re-saturated with groundwater over 15 days and then kept fully saturated for an additional 25 days. Source groundwater, soil porewater, and exfiltrated surface water samples were collected and analyzed for pH, ORP, DOM concentration, and DOM optical indices. In both the wetland and transition zone cores, porewater DOM concentrations increased over the wet up and were sustained during prolonged saturation. Optical indices shifted from recently produced, microbial-like signatures towards aromatic, terrestrial-like signatures. Fluxes of CO 2 decreased as the duration of soil saturation increased and soil cores switched from CH 4 sinks to sources upon full soil core saturation. Results indicate that groundwater rise sustains carbon mobilization from soils in wetland-dominated landscapes, emphasizing the need to understand how climate-driven changes to groundwater dynamics may affect carbon fluxes along terrestrial-aquatic interfaces.

Delaware, Maryland↗
USGS2026

Asynchronous landslide seasonality across the United States

Mid-range landslide outlooks can facilitate weather-related landslide preparedness and disaster response planning, but seasonal landslide activity remains poorly quantified at continental scales. Leveraging >55,000 reported landslides from across the United States (U.S.), we used circular statistics to quantify landslide seasonality in 67 National Weather Service County Warning Areas (CWAs). We found regional differences in landslide season timing and duration, with transitions between domains variably corresponding to climate class or river basin. We assessed differences in seasonality by movement type for slides, flows, and falls, detecting apparent, but uncertain, differences between slide and fall seasonalities in 27 of 35 (77%) of CWAs with both types reported. In the Pacific Northwest, where long records exist, we found a credible shift toward a later mean landslide season in western Washington from 1990 to 2020, but no trend in western Oregon. Our results can provide emergency planners a resource to assess seasonal landslide probability nationwide.

Geophysical Research Letters↗
USGS2026

Critical minerals memory match game

An educational information packet about the 2025 List of Critical Minerals, which includes a memory match game about select critical minerals and how they are used.

General Information Product↗
USGS2025

Observing northern high-latitude river systems to understand changes in a warming Arctic

Purpose of Review Streams and rivers are undergoing rapid change as the Arctic warms and thaws. We review recent observations in Arctic stream systems to identify ubiquitous changes and the most useful tools for observing change and exploring the underlying processes. Recent Findings Recent literature indicates increasingly significant trends in river hydrology and chemistry due to persistent warming in the Arctic and longer observational records for analysis. However, regional differences in the magnitude and direction of these trends persist. We also observe thresholds in ground thaw and surface–groundwater interactions that can impact river hydrology and chemistry. Summary Warming and thaw are occurring rapidly at high latitudes, resulting in increasing, yet variable responses in stream systems across regions and scales. These differences highlight the need for long-term records and an interdisciplinary approach to explain trends and predict future states. Stream systems respond to multiple landscape changes related to hydrology (changing precipitation and subsurface flow), geology (ground thaw dynamics), and ecology (vegetation change).

Current Climate Change Reports↗
USGS2026

Temperate wetlands lose climate-cooling capacity under warming

Temperate inland wetlands have long been viewed as climate regulators, with sustained CO₂ sequestration offsetting continuous CH₄ emissions over centennial–millennial timescales. However, the net climatic effect of these opposing fluxes remains uncertain under anthropogenic warming as they differ in both magnitude and atmospheric lifetime. We combine 19,000 CH₄ chamber measurements from 202 temperate inland wetlands with carbon burial rates from 16 dated sediment cores in North America’s Prairie Pothole Region to estimate greenhouse gas dynamics over two centuries. Here we show that CH₄ emissions rise by ~1.39 kg CH₄ ha⁻¹ yr⁻¹ over the historical period and are projected to approach 950 kg CH₄ ha⁻¹ yr⁻¹ under high-forcing scenarios by 2100, while CO₂ sequestration is expected to decline by ~0.3 kg CO₂ ha⁻¹ yr⁻¹, driving a shift toward persistent net CO₂-equivalent source behaviour and a weakening of the long-term cooling effect that wetlands have provided under natural conditions.

Prairie Pothole Region↗
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↗
USGS2026

A 481 m-high landslide-tsunami in a cruise ship-frequented Alaska fjord

Early in the morning of 10 August 2025, a >64 × 10 6 –cubic meter landslide struck Tracy Arm fjord in Alaska. The landslide was preconditioned by glacial retreat caused by climate change. The resulting 481-meter runup megatsunami followed an initial 100-meter-high breaking wave traveling at >70 meters per second. The landslide was preceded by several days of microseismicity, which increased in rate and magnitude until ~1 hour before failure. The landslide produced globally observed long-period seismic waves equivalent in size to a moment magnitude 5.4 earthquake. A long-period (~66 second) global seismic signal, produced by a landslide-induced seiche trapped within the fjord, persisted for up to 36 hours, the second time a days-long seiche had thus been observed. With fjord regions increasingly visited by cruise ships, and climate change making similar events more likely, this unanticipated, near-miss event highlights the growing risk from landslides and tsunamis in coastal environments.

Alaska↗
USGS2026

Unprecedented burning in tropical peatlands during the 20th century compared to the previous two millennia

Tropical peatland wildfire incidence has risen in recent decades, driven by drainage for land use and intensified by severe droughts with global climate change. These disturbances have altered vegetation structure, disrupted ecosystem functioning, and increased carbon emissions, particularly in Southeast Asia. However, the long-term history and characteristics of wildfires in tropical peatlands remain largely unknown. Here, we compiled fifty-eight macro-charcoal records from peatlands across the tropics, ranging from lowland forested to montane peatlands, to assess millennia-scale changes and controlling factors of tropical peatland burning. We divided the datasets into four main sub-regions: Neotropical, Afrotropical, Indomalayan and Australasian ecoregions to explore regional variability. Tropical peatlands had high burning levels between 0 and 850 ce , followed by a relatively low and stable period until a marked increase during the 20th century. The general trend in tropical peatland burning follows changes in global temperature, and climate variables that control the length and severity of drought events have a notable influence on peat burning before 1900 ce . During the 20th century, regional differences were observed, with declining fire trends in the Neotropical and Afrotropical regions and increasing fire trends in the Indomalayan and Australasian regions. This difference is likely attributable to human activities, and such intervention is also evident in palm swamps and hardwood swamps under similar wet, weakly seasonal climates. With the increase in anthropogenic pressures on peatlands and greater climate variability, future wildfires in peatlands are likely to become more frequent and widespread across all tropical ecoregions. Conservation and sustainable land-use practices could be used to mitigate and control peatland burning and protect these carbon-rich sinks.

Global Change Biology↗
USGS2026

Rapid earthquake magnitude classification via P-wave strains from borehole strainmeters and Distributed Acoustic Sensing

Distributed Acoustic Sensing (DAS) offers a promising approach for earthquake early warning (EEW) in settings where seismic networks are costly to maintain. By repurposing fiber-optic cables as dense strainmeter arrays, DAS enables real-time earthquake detection wherever those fibers are accessible. However, poor azimuthal coverage and challenges in estimating magnitude from strain measurements remain key hurdles in applying for earthquake monitoring. Here, we develop a machine learning method to distinguish large (M≥5.4) earthquakes from smaller ones within the first 4 seconds of a strain waveform after a P-wave arrival without determining location. Using ensemble decision tree models trained on borehole strainmeter data (3.5≤M≤7.1) and tested on onshore DAS waveforms (including the 2024 M7 Offshore Cape Mendocino earthquake), we find that low-frequency (0.2–0.5 Hz) continuous wavelet transform coefficients are the strongest predictors of magnitude, in addition to strain amplitude. Both DAS and borehole strainmeters effectively capture long-period strain signals, making these findings valuable for EEW systems. Our method shows high precision compared to the real-time EEW system, ShakeAlert®, supporting the position that DAS is a viable technology for earthquake monitoring and magnitude classification.

California↗
USGS2024

Rock sample photogrammetry

This step-by-step protocol describes the photogrammetry process used by the U.S. Geological Survey Spokane Imaging Lab (SPIMG) lab to create 3D models of geologic samples. Steps related to photographing small objects are applicable to photogrammetry in general, however, SPIMG-specific steps involving lab hardware and software may not be.

Protocols.io↗
USGS2025

Subduction zone earthquake catalog separation tool: Implementation in the USGS 2025 Puerto Rico and U.S. Virgin Islands National Seismic Hazard Model

The U.S. Geological Survey (USGS) periodically releases updates to National Seismic Hazard Model (NSHM) for the United States and its territories leveraging current scientific knowledge and methodologies to guide public policy, building codes, and risk assessments regarding potential ground shaking due to earthquakes that may result in infrastructure damage. In subduction zones, there is a need to separate the earthquake catalog into tectonic regimes to create specific seismicity models for which the most appropriate ground‐motion models are then applied. Here, we describe newly developed methods and software, called CatSep, that classifies subduction zone events into three primary tectonic regimes: crustal, interface, and intraslab. This method incorporates information about the location of the earthquake relative to the subducting slab, the depth of the Mohorovičić discontinuity, and the earthquake’s moment tensor. Applying this method is a first step in the NSHM workflow for regions covering U.S. subduction zones. Results using this subduction zone earthquake catalog separation tool for the 2025 Puerto Rico and U.S. Virgin Islands NSHM earthquake catalog are presented and analyzed.

Puerto Rico, U.S. Virgin Islands↗