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1,722 records · Page 89Linked to original sources
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↗
USGS2025

A northeast-dipping zone of low frequency earthquakes at the southern edge of Cascadia subduction

Tectonic tremor monitoring occasionally detects events in an anomalous zone in southern Cascadia, 50–100 km west of the main tremor band, near the expected southern edge of the subducting Gorda slab at the Mendocino triple junction. To investigate the geometry and temporal behavior of this tremor, we examine its constituent low-frequency earthquakes (LFEs) by developing 27 stacked LFE waveform templates that we use to detect events from 2018 to 2024. We then relocate LFE sources together with regional seismicity. We find that LFE hypocenters form a northeast-dipping alignment at 22–29 km depth, extending eastward from a zone of micro-earthquakes, ∼15 km south of the southern edge of Gorda slab seismicity. These LFE families exhibit small bursts of activity every few days. Considering the strong world-wide association of tremor and LFEs with high slip-rate, plate-bounding faults, we hypothesize these LFEs may demark the southern edge of Cascadia subduction.

California↗
USGS2024

Using stable oxygen isotope dual-inlet isotope-ratio mass spectrometry to elucidate uranium transport and mixed 230Th/U calcite formation ages at the seminal Devils Hole, Nevada, natural laboratory

Rationale Vein calcite in Devils Hole has been precipitating continuously in oxygen-isotope equilibrium at a constant temperature for over 500 000 years, providing an unmatched δ 18 O paleoclimate time series. A substantial issue is that coeval calcite (based on matching δ 18 O values) has uranium-series ages differing by 12 000 years. Methods An unparalleled high-accuracy δ 18 O chronology series from continuously submerged calcite was used to correct the published uranium-series ages of non-continuously formed calcite in two cores, cyclically exposed by water-table decline during glacial–interglacial transitions. This method relies on the premise that the δ 18 O values of coevally precipitated calcite are identical, allowing matching calcite δ 18 O values to establish formation ages. Results Exposed calcite can have apparent ages that are 12 000 years too young due to unrecognized uranium mobility and resulting mixed ages identified in over 50 mixed uranium-series ages from previous studies. Secondary uranium in fluids, sourced from the formation or dissolution of porous carbonate deposits (folia) with high uranium-238 ( 238 U) concentrations, has migrated up to 10 mm into vein calcite. Conclusions The continuously submerged Devils Hole δ 18 O chronology is not explained by orbital forcing. Rather, this chronology represents a regional climate record in the southern Great Basin of sea-surface-temperature (SST) variations off California, variations that preceded the last and penultimate deglaciations by 5000 to approximately 10 000 years. Temporal discrepancies between the continuously submerged Devils Hole chronology and other regional δ 18 O records (e.g., the Leviathan chronology) can be explained by unrecognized cryptic, pernicious uranium mobility, leading to model estimations that may be thousands of years younger than actual ages. Consequently, paleo-moisture availability, water-table, and groundwater recharge models based on these mixed uranium-series ages are too young by as much as 12 000 years. The potential for post-formation uranium addition in subaerial cores and speleothems underscores the need for caution in uranium-series dating, highlighting δ 18 O time-series comparisons as a method for identifying mixed ages.

Nevada↗
USGS1977

A multi-channel digital telemetry system for low frequency geophysical data

An inexpensive general purpose digital telemetry system for collection of low frequency geophysical data from U.S. Geological Survey instruments (eg. tilt, strain, gravity, creep, water level, radon, magnetic field, resistivity, telluric current, temperature, etc.) has been designed and built. This system provides data for a more general interactive data acquisition, retrieval and analysis system. The field stations are self-contained, battery operated and housed in weather proof containers. Each accepts up to 15 analog data inputs in the range of -5 to +5 volts. The dynamic range is 70db. The units transmit information as FSK (Frequency Shift Keyed) tones onto either a phone line or radio link with up to 150 transmitters sharing one line. The average power consumption is 0.06 nR watts where n is the 1 number of input channels transmitted and R is the sample rate in minutes -1 . The central receiver-recorder unit accepts and decodes the FSK tones and converts, formats and records the digital data together with time information and station identification on IBM combatible magnetic tape. The digital data are also converted and recorded in analog form for visual monitoring.

Open-File Report↗
USGS2026

New constraints on northeast Seattle basin structure from converted seismic waves

The Seattle basin is a deep sedimentary basin in the Seattle–Bellevue, Washington metropolitan area within the Puget Lowland of Washington State. We determine the structure of a portion of the basin and the underlying basement using analysis of P waves converted from direct S incident from below. A deep local crustal event beneath Monroe, about 35 km northeast of Seattle, was recorded by a 100‐station nodal array deployed in 2019. The event produced a variety of coherent seismic phases, including converted waves from the sediment—basement boundary, internal structure within the basin, and additional crustal discontinuities. Using observed Sp converted waves, we apply an adjoint‐based full waveform inversion (FWI) method to determine the amplitude and extent of seismic discontinuities at depth. We find the strongest source of converted waves for this event lies ∼6 to 7 km depth below northern Lake Washington, interpreted to be the local depth to basement rock. The newly imaged shallow basement structure may be part of a deformation zone associated with the Siletzia eastern boundary. Our results highlight the utility of converted seismic waves recorded by a dense array, combined with an FWI method, to illuminate crustal structure.

Washington↗
USGS2026

Increasing artificial light at night enhances salmonid predator foraging efficiency in an urbanized lake

Artificial light at night (ALAN) poses a threat to ecosystems globally. It includes both direct and indirect light, or skyglow, which occurs when ALAN scatters in the atmosphere, extending beyond its original source. We analyzed ALAN trends in Lake Washington, WA, from 2014 to 2023 using Visible Infrared Imaging Radiometer Suite (VIIRS) nighttime light measurements, evaluated the relationship between in situ and satellite measurements, and modeled predator search volumes for a juvenile salmon predator, cutthroat trout ( Oncorhynchus clarki ), as a function of ambient light and turbidity conditions. Open water regions experienced significant increases in ALAN, while nearshore areas primarily showed no or negative trends, revealing the role of skyglow in shaping open water light environments. Using a visual foraging model for cutthroat trout, we found that juvenile salmon at the shallow southern pelagic site experienced light 28 times brighter, resulting in a 168% greater predation vulnerability than those at the northern site. In the Ship Canal, a narrow corridor for outmigrating salmon, predator search volumes were 249% higher than at the southern site. These contrasts in predation vulnerability demonstrate how local conditions influence predator–prey dynamics and provide critical insight for targeting mitigation of both nearshore and distant light sources.

Washington↗
USGS2026

The damability function: A probabilistic approach to regional landslide dam susceptibility analysis applied to the Oregon Coast Range, USA

Landslides can dam rivers and require rapid response to mitigate catastrophic outburst floods. Here, we present a workflow to map landslide dam formation susceptibility at a regional scale. We define a probabilistic function that combines river valley width and landslide volume to efficiently determine the likelihood of a landslide dam or “damability”. We combine damability values with landslide susceptibility to estimate landslide dam susceptibility. The valley width measurements are automated using a new elevation threshold-based algorithm. Landslide volume is represented as a statistical distribution from mapped landslides. We validate and apply our approach to the Oregon Coast Range, USA and find that 36 % of river stretches exceed a dam potential threshold; these are in river headwaters and steeper terrain, which in this case correlate with more resistant lithologies. We also estimate volumes of the potential dammed lakes and find that most rivers with high dam susceptibility are less likely to impound large lakes because they have low drainage areas. However, widespread susceptibility, and the potential impacts from exceptionally large landslides, suggest that this hazard should be considered in the Pacific Northwest. The damability function workflow can ingest new data and be applied more broadly to assess future landslide dam hazards.

Oregon↗
USGS2024

Flood-inundation maps for the Cuyahoga River at Jaite, Ohio, 2024

Digital flood-inundation maps for a nearly 6-mile reach of the Cuyahoga River at Jaite, Ohio, were created by the U.S. Geological Survey (USGS) in cooperation with the Northeast Ohio Regional Sewer District Board of Trustees. The maps depict estimates of the extent and depth of flooding corresponding to selected water levels (stages) at USGS streamgage 04206425 on the Cuyahoga River at Jaite, Ohio. Water-surface profiles were computed for the stream reach by using a one-dimensional steady-state step-backwater model. The hydraulic model was calibrated to the current USGS streamgage data and then used to compute 15 water-surface profiles for flood stages at 1-foot intervals referenced to the streamgage datum and ranging from 6 to 20 feet, which correspond to below “action stage” to “major flood stage” as reported by the National Weather Service. The simulated water-surface profiles were then used with a geographic information system digital elevation model derived from light detection and ranging data to delineate the areas flooded at each stage. These maps, along with current stage data from the USGS streamgage and forecasted high-flow stages from the National Weather Service, can provide emergency management personnel and residents with information that is critical for flood response activities such as evacuations and road closures, as well as for postflood recovery efforts.

Ohio↗
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

Making many out of one: Synthetic geologic deformation model distributions for use in USGS NSHM25‐PRVI Puerto Rico-U.S. Virgin Island update

A key use‐case of geologic slip rates is within deformation models used in probabilistic seismic hazard analyses. Field‐derived geologic slip rates have formed the cornerstone of deformation models in such applications for decades. Recent advancements in seismic hazard analyses have expanded the use of faults for which geologic slip rates are not well constrained using categorical slip rate estimates. Because of these advancements, application of a geologic deformation model for use in 2025 U.S. Geological Survey National Seismic Hazard Model Puerto Rico‐U.S. Virgin Islands (NSHM25‐PRVI) proved challenging due to: (1) a lack of field‐based geologic slip rates, and (2) a lack of epistemic uncertainty distributions within a broad range of estimated slip rates. Preliminary versions of the NSHM25‐PRVI model sampled these slip rate bins in a coincident manner along preferred and extreme value branches, which yielded untenable correlations in mean hazard results. To minimize the influence of correlated uncertainties amid these challenges, we develop a synthetic epistemic uncertainty distribution for deformation rate on each crustal fault. Each fault has a weighting schema across four possible distribution shapes: uniform, normal, triangular favoring local minima, and triangular favoring local maxima. The synthetic distributions are then sampled several times for each logic tree branch. The results provide a more realistic distribution of rates across the study region as compared with using correlated extrema sampling. This exploration of our method in a small region like PRVI can pave the way for larger‐scale, more complicated applications (e.g., western United States).

Puerto Rico, US Virgin Islands↗
USGS2025

No evidence for an active margin-spanning megasplay fault at the Cascadia Subduction Zone

It has been previously proposed that a megasplay fault within the Cascadia accretionary wedge, spanning from offshore Vancouver Island to Oregon, has the potential to slip during a future Cascadia subduction zone earthquake. This hypothetical fault has major implications for tsunami size and arrival times and is included in disaster-planning scenarios currently in use in the region. This hypothesis is evaluated in this study using CASIE21 deep-penetrating and U.S. Geological Survey high-resolution seismic reflection profiles. We map changes in wedge structural style and seismic character to identify the inner-outer wedge transition zone where a megasplay fault has been previously hypothesized to exist and evaluate evidence for active faulting within this zone. Our results indicate that there is not an active, through-going megasplay fault in Cascadia, but instead, the structure and activity of faulting at the inner-outer wedge transition zone is highly variable and segmented along strike, consistent with the segmentation of other physical and mechanical properties in Cascadia. Wedge sedimentation, plate dip, and subducting topography are proposed to play a major role in controlling megasplay fault development and evolution. Incorporating updated megasplay fault location, geometry, and activity into modeling of Cascadia earthquakes and tsunamis could help better constrain associated hazards.

British Columbia, Oregon, Washington↗
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↗
USGS2025

Lithium from magma to mine in an early Yellowstone hotspot caldera

Renewable energy technologies rely on the extraction of metals not historically in high demand, such as lithium (Li), for which ore deposit models are incompletely understood. One of the world’s largest Li deposits is hosted in lake sediments of the 16.4 Ma McDermitt caldera, which formed during the early stages of Yellowstone hotspot volcanism in the western United States. Eruptive and posteruptive mobility of Li are major challenges in elucidating deposit formation. Melt inclusions preserved in quartz crystals provide a means to assess pre-eruptive magmatic Li contents. Concentrations of Li determined by ion microprobe for melt inclusions in a McDermitt rhyolite lava are 400−1350 ppm, compared to 20−70 ppm Li in matrix rhyolite glasses. Synthesis with melt inclusion data for eight additional calderas demonstrates a recurrence of Li-rich rhyolitic magmas (200−2000 ppm Li) in the western part of the Yellowstone hotspot track. However, unlike the multicyclic caldera complexes with overlapping fault networks that may have compromised Li retention, the McDermitt caldera remained a closed hydrologic system throughout its evolution. Modeling indicates 100 km 3 of resurgent magma could yield 25−150 Mt Li in a magmatic fluid and supports accumulation of Li-rich magmatic fluid in a closed intracaldera lake, followed by evaporative concentration and sequestration of Li within clay minerals to generate the McDermitt deposit.

Idaho, Nevada, Oregon, Wyoming↗
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↗
USGS2024

Mapping Arundo donax (Arundo cane) with multispectral imagery before, during, and after herbicide treatment along the Rio Grande in Webb County, Texas, 2020–21

Arundo donax , commonly called Arundo cane, giant reed, or Carrizo cane, is an invasive bamboo-like perennial grass common in riparian areas throughout the southwestern United States. In Texas, not only does it negatively affect riparian ecosystems, but it has also become a problem for border security because it reduces visibility along the Rio Grande. To address these problems, in 2015 the Texas State Soil and Water Conservation Board was authorized by the Texas State Legislature to develop a program to eradicate Arundo cane along the Rio Grande. In 2020, the Texas State Soil and Water Conservation Board applied imazapyr and glyphosate herbicides along a 19.3-kilometer reach of the Rio Grande, northwest of Laredo, Texas. The U.S. Geological Survey, in cooperation with the Texas State Soil and Water Conservation Board and the Webb Soil and Water Conservation District, used WorldView-3 Standard high-resolution satellite imagery to map Arundo cane extent along the reach before , during , and after the herbicide-treatment period on June 30, 2020, September 26, 2020, and May 7, 2021, respectively. A maximum likelihood supervised classification analysis was computed on the images to map the spatial extent and estimate the area covered by Arundo cane. The estimated area covered by Arundo cane in the before classification was 1,282,000 square meters, in the during classification was 1,064,000 square meters, and in the after classification was 1,108,000 square meters. The qualitative comparison of the three images shows that there was an overall decrease in vegetation classified as Arundo cane throughout the study area.

Texas↗
USGS2025

Updating regional‐scale geospatial liquefaction models with locally available geotechnical data

We present a method to update the geospatial liquefaction model used by the U.S. Geological Survey’s near‐real‐time ground failure product with subsurface geotechnical data. The geospatial model estimates liquefaction probability from peak ground velocity (via ShakeMap) and geospatial susceptibility proxies. In many regions, additional information relevant to constraining liquefaction likelihood is also available, including surface geology maps and subsurface geotechnical measurements. There is currently no mechanism to use these data in the ground failure product liquefaction model, even though these data could provide more precise constraints on spatial variations in the lithologic character of the soil (surface geology) and direct measurements of the subsurface mechanical properties that affect liquefaction occurrence and severity (geotechnical measurements). In this study, we develop a method to integrate these data with the geospatial model and assess how these data can improve regional‐scale predictions. We develop a Bayesian updating framework and apply it to the 1989 magnitude 6.9 Loma Prieta, California, earthquake, for which mapped observations are available to evaluate performance. We constrain the Bayesian framework with 373 Northern California cone penetration tests and liquefaction susceptibility classes based on the mapped surface geology. This Bayesian model incorporates geotechnical information into the geospatial model and more accurately predicts liquefaction occurrences than the geospatial model, while sacrificing less accuracy in terms of predicting the absence of liquefaction than the geotechnical model. In future applications, this approach could be adapted to update other geospatial models using locally available subsurface data.

California↗
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↗
USGS1990

Preconditioned Conjugate-Gradient 2 (PCG2), a computer program for solving ground-water flow equations

This report documents PCG2: a numerical code to be used with the U.S. Geological Survey modular three-dimensional, finite-difference, ground-water flow model. PCG2 uses the preconditioned conjugate-gradient method to solve the equations produced by the model for hydraulic head. Linear or nonlinear flow conditions may be simulated. PCG2 includes two reconditioning options: modified incomplete Cholesky preconditioning, which is efficient on scalar computers; and polynomial preconditioning, which requires less computer storage and, with modifications that depend on the computer used, is most efficient on vector computers. Convergence of the solver is determined using both head-change and residual criteria. Nonlinear problems are solved using Picard iterations. This documentation provides a description of the preconditioned conjugate gradient method and the two preconditioners, detailed instructions for linking PCG2 to the modular model, sample data inputs, a brief description of PCG2, and a FORTRAN listing.

Water-Resources Investigations Report↗