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USGS2025

Disparate groundwater responses to wildfire

Post-wildfire investigations of groundwater response reveal a range of outcomes, varying from substantial increases to notable decreases in recharge and baseflow, with some studies indicating negligible or short-lived effects. This review assesses these varied responses within five critical categories: climate, vegetation, hydrogeology, fire characteristics, and the cryosphere, examining both short-term (within 2 years) and intermediate (2–10 years post-fire) effects. Despite considerable variability, some consistent patterns emerge. For instance, in hydroclimatic settings where water input and evaporative demand cycles are out of sync, post-wildfire groundwater responses tend to be positive (i.e., increased flux or storage), whereas under low fire severity conditions or in vegetation types that quickly recover, groundwater responses tend to be negative (i.e., decreased flux or storage). We synthesize relevant findings into a compendium of testable hypotheses aimed at explaining the spatiotemporal variability in observed post-wildfire groundwater responses. A recurring theme is the critical influence of the pre-wildfire groundwater regime on expected response and recovery. We identify opportunities for specific improvements in post-wildfire monitoring and modeling that would further advance capabilities to predict groundwater response. A key area for further research is understanding how wildfire effects on snow dynamics and other cryospheric processes translate to changes in groundwater.

WIREs Water↗

USGS2025

Quantitative evaluations of earthquake early warning performance using “Did You Feel It?” and post-alert surveys

We examine responses to the U.S. Geological Survey’s “Did You Feel It?” (DYFI) survey and its companion earthquake early warning (EEW) questionnaire to assess the performance of the U.S. ShakeAlert EEW system directly from the alert recipients’ perspectives. ShakeAlert rapidly detects earthquakes and develops alert information, but as official alert delivery partners issue these alerts, it is thus difficult to determine how many people were alerted and when. We investigate DYFI reports for six California earthquakes that had EEW alerts and substantial responses to the DYFI EEW questionnaire. Comparisons of ShakeAlert predictions to reported intensities demonstrate that magnitude estimation accuracy is not necessarily indicative of ground-motion prediction accuracy. Perceived warning time distributions indicate that estimating maximum-expected warning times using the S-wave arrival is a reasonable assumption when discussing public EEW performance. However, we also find many reports of shorter warning times, late alerts, and missed alerts than expected based on ShakeAlert publication times, suggesting alert delivery latencies are substantial and highly variable. The novelty of our analysis is that we demonstrate that the DYFI EEW survey provides useful EEW efficacy information—independent of the specific alerting pathway—that can be used to inform our choices for conveying EEW performance.

California↗

USGS2025

Microbial ecology of permafrost soils: Populations, processes, and perspectives

Permafrost microbial research has flourished in the past decades, due in part to improvements in sampling and molecular techniques, but also the increased focus on the permafrost greenhouse gas feedback to climate change and other ecological processes in high latitude and alpine permafrost soils. Permafrost microorganisms are adapted to these extreme environments and remain active at low temperatures and when resources are limited. They are also an important component of global elemental cycles as they regulate organic matter turnover and greenhouse gas production, particularly as permafrost thaws. Here we review the permafrost microbiology literature coupled with an exploration of its historical aspects, with a particular focus on a new understanding advanced by molecular biology techniques. We further identify knowledge gaps and ways forward to improve our understanding of microbial contributions to ecosystem biogeochemistry of permafrost-affected systems.

Permafrost and Periglacial Processes↗

USGS2026

Detecting snow avalanche activity using infrasound: Hooker Valley, New Zealand

Snow avalanches pose considerable hazards to people and infrastructure in alpine environments. Traditional avalanche monitoring relies on meteorological data and visual observations, which can be limited in scope and timeliness. Infrasound offers a promising complementary monitoring tool by detecting the low-frequency sound waves generated by avalanches. Here, we present infrasound and camera observations during a 50-day field campaign in the Hooker Valley of Aoraki/Mount Cook National Park, New Zealand. Our study detected seven avalanches with the cameras, whereas the infrasound system identified only one of these events, which was the largest and occurred under conditions that likely favoured infrasound propagation. The infrasound system recorded numerous other events not captured by the cameras, indicating the benefit of further investigation to determine their sources. These findings highlight the potential of infrasound technology for detecting avalanches and providing broad spatial coverage, capturing events in areas not monitored by cameras, while also showcasing limitations in infrasound capabilities. The limited detection of smaller avalanches underscores the opportunity for further research to enhance detection capabilities and understand environmental influences such as snow cover and wind noise. Overall, this study emphasises the utility of multidisciplinary monitoring techniques to improve avalanche detection in alpine environments.

Hooker Valley↗

USGS2026

Microplastics undergo fragmentation during pressurized membrane filtration

Low-micrometer microplastics (<10 μm) have been detected in drinking water, driving growing interest in using pressure-driven membrane filtration to remove these particles and ensure drinking water safety. However, little attention is paid to the fate of microplastics concentrated in the filtration byproduct. In this study, using well-defined polystyrene (PS) and poly(methyl methacrylate) microspheres as model particles, we observed microplastic fragmentation during nanofiltration and the subsequent release of smaller fragments. After operating for 3 h at 20 bar, 67.9 ± 8.0% of the PS spheres in the concentrate, based on particles counted in selected fields of view, were transformed into fractured particles. The characteristic Raman band signals of microplastics were significantly weakened after fragmentation, leading to detection challenges. To address this, a data processing algorithm was developed to identify PS fragments as small as 1 μm. Preliminary statistical analysis reveals that within the tested pressure range, operation time has a stronger influence on fragmentation than pressure magnitude alone and that fragmentation is further governed by the intrinsic mechanical properties of the tested model polymers. This work provides direct evidence that pressure-driven membrane processes induce microplastic fragmentation and highlights the environmental risks associated with the discharge of fragmented microplastics into the concentrate.

Environmental Science & Technology Letters↗

USGS2026

Tracing mercury from land to river: Global sources, retention, and implications for sustainability

Mercury (Hg) pollution in river systems is a global sustainability challenge. Yet the transport, transformation, and retention of Hg within global rivers remain poorly quantified, particularly in regions with sparse observations such as Southeast Asia and Africa, hindering effective pollution mitigation and reinforcing geographic inequities in scientific knowledge and environmental governance. Here, we present the first global, high-resolution simulation of riverine Hg dynamics using a process-based model that traces Hg from land-based sources through river networks to the ocean. Under a realistic scenario, we estimate that ∼1900 megagrams per year (Mg/yr) of Hg enters global rivers, including 1500 Mg/yr from human-induced sources and 400 Mg/yr from soil erosion. Nearly half of this flux (∼1000 Mg/yr) is retained in reservoirs and dams, which act as major sinks. While such retention limits downstream delivery to the oceans, it also heightens in-reservoir Hg methylation risks. By bridging the gap between Hg releases and observed riverine exports, our framework offers a scalable tool for data-limited regions, promotes data access, and supports global freshwater and pollution-management strategies.

Environmental Science & Technology↗

USGS2024

On algorithmically determined versus traditional macroseismic intensity assignments

The utility of macroseismic data, defined as the effects of earthquakes on humans and the built environment, has been increasingly recognized following the advent of online systems that now produce unprecedented volumes of macroseismic intensity information. Contributed reports from the U.S. Geological Survey “Did You Feel It?” (DYFI) system ( Wald et al ., 1999 ) are used to generate intensity values with an algorithm based on seminal work by Dengler and Dewey (1998) . The algorithm was developed initially to reproduce intensity values assigned by expert opinion using questionnaire results collected by telephone survey. In this article, I discuss reasons why intensity values from (self‐selected) DYFI responses can differ from values that would be assigned by expert opinion given more complete data from randomly selected participants. For example, with the data used by Dengler and Dewey (1998) , intensities near 4 could be determined from the percentage of people who felt shaking in each town. With less spatially rich data from self‐selected participants, this percentage often cannot be determined reliably. Audible noises are key additional diagnostic criteria for modified Mercalli intensity (MMI) 4, but, although the DYFI system includes a question about noise, following Dengler and Dewey (1998) , the DYFI algorithm does not include a noise indicator. At the upper end of the scale, as defined the DYFI algorithm yields a maximum intensity value of 9.05, nominally corresponding to peak ground acceleration of 75% g . These and other factors can result in DYFI values that are low compared to traditional MMI values assigned using expert opinion, even absent factors that can bias traditional MMI assignments. Modern ground‐motion intensity conversion equations determined using DYFI intensities are expected to be appropriate for DYFI intensities, but the results of this study suggest that biases may be introduced if DYFI and traditional intensities are assumed to be interchangeable.

Seismological Research Letters↗

USGS2025

Nodal seismic deployment on Mauna Loa volcano, Hawaii: Dataset and preliminary insights

Mauna Loa is the largest active volcano on Earth, comprising ∼51% of the Island of Hawai‘i’s landmass and posing significant risks to the island’s communities, infrastructure, and natural environment. Historical eruptions have produced lava flows that have reached the ocean in as little as 3 hr. The timing and location of such lava flows in the past 200 yr underscore how critical determination of the location and geometry of magma storage and structure is for volcanic hazard assessment and eruption forecasting. Now, after nearly 38 yr of volcanic quiescence, Mauna Loa has erupted again. On 27 November 2022, fissures initiated within the summit caldera and then migrated to the northeast rift zone, where they generated a large lava flow that threatened a major highway. To improve our understanding of the geometry of this magma system, we deployed a temporary nodal array on Mauna Loa in the summer of 2024. This increased our seismic coverage sufficiently to image this magmatic system. This nodal array consists of 33 seismometers distributed on and around the volcano and was deployed for over three months to record seismic signals. The primary objective of this project is to resolve the high‐resolution seismic velocity structure and characterize seismic features associated with magma storage and ascent pathways. In this article, we present an overview of the deployment, evaluate the quality of the data, and show example recordings to evaluate the suitability of the data set for future seismic investigations, including earthquake relocation, seismic tomography, and receiver function analysis. Comparisons with nearby permanent broadband and short‐period seismic stations demonstrate that the nodal array recorded high‐quality waveforms, making it a valuable resource for constraining the magmatic system beneath Mauna Loa at multiple scales.

Hawaii↗

USGS2024

Uncertainty and spatial correlation in station measurements for mb magnitude estimation

The body‐wave magnitude (⁠⁠) is a long‐standing network‐averaged, amplitude‐based magnitude used to estimate the magnitude of seismic sources from teleseismic observations. The U.S. Geological Survey National Earthquake Information Center (NEIC) relies on in its global real‐time earthquake monitoring mission. Although waveform modeling‐based moment magnitudes are the modern standard to characterize earthquake size, is important because (1) in many cases, waveform modeling is not possible (e.g., low signal‐to‐noise events), (2) is applicable over a broad range of magnitudes, ∼M 4–7, and (3) there is a many decades‐long history of estimating magnitudes. We use the NEIC Preliminary Determination of Epicenters earthquake catalog to investigate the uncertainty in NEIC station measurements. We show that measurements are spatially correlated, which can bias event ⁠, and we describe an empirical relation between this spatial correlation and station‐to‐station distance. We further describe an approach to mitigate bias from the spatial correlation. Accounting for the spatial covariance of observations can change the event from −0.15 to 0.07 units (10th to 90th percentile) for smaller events (⁠⁠). These smaller events have the largest standard deviations ranging from 0.05 to 0.15 units (10th to 90th percentile).

The Seismic Record↗

USGS2025

Dietary bioavailability of uranium to a model freshwater invertebrate

Uranium (U) mining increases environmental exposures. Understanding how U is taken up by organisms can aid in evaluating the potential for bioaccumulation and toxicity. Although the importance of aqueous geochemical speciation is well recognized for U bioavailability after dissolved exposures, far less is known about the processes controlling U bioavailability after dietary exposures. This study characterizes the biogeochemical drivers of dietary U uptake in the freshwater snail Lymnaea stagnalis in laboratory experiments. Solids tested included benthic diatoms pre-exposed to dissolved U(VI), soils from contaminated U mine sites, and colloidal hydrous ferric oxide (HFO) synthesized in the presence of dissolved U(VI) or with U complexed by natural organic matter (NOM). Results showed that U was bioavailable from all solids. Uranium assimilation efficiency (AE), a proxy for dietary U bioavailability, varied among solids. AE was lowest for the U-contaminated soils (25 ± 17%) and highest for the U-laden diatoms (71 ± 13%). AE varied slightly among HFO preparations, suggesting modest influences of NOM and iron on U bioavailability. Increases in dietary U exposures reduced feeding rates, and the extent of feeding inhibition appeared inversely related to U bioavailability. The high U assimilation and range of bioavailability have implications for toxicity risks inferred without considering dietary uptake.

Environmental Science and Technology↗

USGS2025

A soil velocity model for improved ground motion simulations in the U. S. Pacific Northwest

Near-surface seismic velocity structure may significantly impact the intensity, duration, and frequency content of ground shaking during an earthquake. In this study, we compile 649 shear wave velocity (Vs) profiles throughout the U.S. Pacific Northwest and southern British Columbia (PNW) and use these measured profiles to develop a representative soil velocity model for four major Holocene soil provinces: Puget Lowlands, Willamette Valley, fill and alluvium, and `other' soils. The resulting soil velocity model shows good agreement to measured data for a wide range of site conditions, with variability between different geologic domains reflecting fundamental differences in depositional environments. We then show that using this regional soil velocity model in simulations of the 2001 M6.8 Nisqually, Washington earthquake improves the fit to observed high-frequency (≥ 0.5 Hz) ground motions in the Puget Sound region compared to simulations that do not incorporate shallow (≤ 200 m) seismic velocity structure. Overall, this work shows that incorporating localized soil velocity profiles into seismic velocity models is important for accurately estimating high-frequency ground motion and regional seismic hazard in earthquake simulations. Future earthquake simulations and hazard studies in the PNW could incorporate these soil velocity profiles to capture the region's distinct site response characteristics.

Washington↗

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↗

USGS2026

Identifying overlap between native fish movements and a possible seasonal Grass Carp deterrent in the Sandusky River, Ohio, USA

Riverine fishes are vulnerable to habitat fragmentation caused by interrupted access to vital habitats. Fragmentation using artificial structures can be used to limit the spread of invasive fishes by inhibiting movement between critical habitats. Thus, tradeoffs exist between maintaining connectivity for native species and restricting movement of invasive fishes. A nonphysical deterrent has been proposed to prevent invasive Grass Carp ( Ctenopharyngodon idella ) from reaching spawning habitats in the Sandusky River, a tributary to Lake Erie, during late spring–summer. However, this timing overlaps with spawning periods of a suite of native fishes. We used acoustic telemetry and ichthyoplankton surveys to examine movement and spawning activity of multiple native species and to assess potential impacts of deterrent operation on reproduction. All species used the proposed deterrent location during the Grass Carp spawning period, and their movements corresponded with larval fish collections, indicating upstream spawning. These findings suggest the deterrent could unintentionally restrict native species’ access to essential habitats. Balancing habitat connectivity and invasive species control will likely depend on species-specific responses and careful timing of deterrent operation.

Ohio↗

USGS2024

Subducting plate structure and megathrust morphology from deep seismic imaging linked to earthquake rupture segmentation at Cascadia

The origin of rupture segmentation along subduction zone megathrusts and linkages to the structural evolution of the subduction zone are poorly understood. Here, regional-scale seismic imaging of the Cascadia margin is used to characterize the megathrust spanning ~900 km from Vancouver Island to the California border, across the seismogenic zone to a few tens of kilometers from the coast. Discrete domains in lower plate geometry and sediment underthrusting are identified, not evident in prior regional plate models, which align with changes in lithology and structure of the upper plate and interpreted paleo-rupture patches. Strike-slip faults in the lower plate associated with oblique subduction mark boundaries between regions of distinct lower plate geometry. Their formation may be linked to changes in upper plate structure across long-lived upper plate faults. The Juan de Fuca plate is fragmenting within the seismogenic zone at Cascadia as the young plate bends beneath the heterogeneous upper plate resulting in structural domains that coincide with paleo-rupture segmentation.

Oregon, Washington↗

USGS2024

Turbidite correlation for paleoseismology

Marine turbidite paleoseismology relies on the assumption of synchronous triggering of turbidity currents by earthquake shaking to infer rupture extent and recurrence. Such inference commonly depends on age dating and correlation of the physical stratigraphy of deposits carried by turbidity currents (i.e., turbidites) across great distances. Along the Cascadia subduction zone, which lies offshore the Pacific Northwest, USA, turbidite facies in core photographs, X-ray computed tomography images, and magnetic susceptibility (MS) data exhibit differences in character over relatively short distances, which implies that not all deposits can be correlated with confidence. Thus, subjective correlation based on expected similarity over great distances and weak age constraints does not independently support paleoseismic models. We present a new method for correlating turbidites along the Cascadia margin that can yield a more objective and repeatable stratigraphic framework to underpin earthquake recurrence. We use dynamic time warping to correlate MS logs and measure correlation coefficients of core pairs to evaluate correlation strength. We then compare these measures to a distribution of correlation coefficients of randomly generated turbidite sequences and find that only a small number of core pairs can be correlated more confidently than randomly stacked turbidites. This methodology promises a more robust correlation strategy for future stratigraphic studies.

Oregon, Washington↗

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↗

USGS2026

Remote sensing enables basin-scale inventories of coal mine methane

Underground coal mines are important global sources of methane, but emission estimates are uncertain. We show that emission estimates for individual mines from aircraft remote-sensing surveys in the United States agree within 40% with direct measurements used for national emission reporting (IPCC Tier 3 estimate). Such direct measurements are unavailable in most countries, which rely on estimated emission factors (EFs) applied to coal-production rates. We find that EFs from IPCC Tier 1 and the Model for Calculating Coal Mine Methane (MC2M) methods overestimate U.S. emissions 3-fold due to incorrect dependence on mine depth. An IPCC Tier 2 method using measured basin-specific mine gas content agrees with direct emission measurements but does not account for gob well emissions and requires gas content data that are generally unavailable. We show that aircraft remote sensing for a small sample of mines can successfully estimate basin-specific EFs for ventilation shafts and gob wells, enabling estimates of basin- and national-scale emissions. We discuss how the method can be applied with satellite remote sensing to quantify coal emissions worldwide.

Alabama, Colorado, Kentucky, New Mexico, Ohio, Pen↗

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↗