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USGS2025

Reflections on a trio of North American earthquakes in 1925

In 1925, three moderately large damaging earthquakes occurred in North America over four months: the 28 February (local time; LT) M 6.2 Charlevoix, 27 June (LT) M 6.6 Montana, and 29 June M 6.5 Santa Barbara earthquakes. The centennial anniversaries of these events motivated this retrospective consideration focused on the ground motions generated by the three events, including a reconsideration of early intensity assignments for the Montana earthquake. At the time, these three earthquakes appeared to support the arguments of some geologists who downplayed the severity of seismic hazard in southern California relative to other parts of the country. Some of the arguments advanced at that time, for example that Los Angeles “has the least to fear from ‘Acts of God’ of any city under the American flag,” ( Hill, 1928 ) sound naïve if not laughable now, but a comparison of well‐constrained shaking distributions for the three earthquakes reveals the dramatic difference in wave propagation efficiency in western versus eastern North America (ENAM), which leads to moderate ENAM events being felt to much larger distances. At M 6.2, the 1925 Charlevoix earthquake was a notably large event in ENAM. This earthquake was the largest event in eastern Canada since 1870 and caused damage in the epicentral region in addition to towns as far away as 200 km, with felt shaking extending over 1000 km. In contrast, felt shaking from the Santa Barbara earthquake barely extended beyond ∼200 km. Compiling published intensity distributions for larger ENAM earthquakes, we show that perceptible earthquake shaking is not uncommon in ENAM over century time scales, but experience with weakly felt shaking may incline people to downplay potential earthquake risk.

California, Montana, Quebec↗

USGS2025

Estimated average annualized losses from potential building damage and fatalities due to earthquake-generated tsunamis in the United States

Earthquake-generated tsunamis represent substantial economic threats to states and territories in the United States (U.S.), but we are unaware of any effort to quantify potential impacts at the national level. This gap is partially due to the lack of nationally consistent data on tsunamigenic sources and associated return periods. This study addresses this issue and provides estimates of average annualized losses (AAL) for potential residential fatalities and capital stock losses associated with building damage (i.e., structural, non-structural, contents, and inventory damage) in the U.S. by curating tsunami-hazard information based on deterministic scenarios and probabilistic approaches, calculating potential losses, and estimating return periods where necessary. This assessment was done for the U.S. West Coast, Alaska, Hawaii, U.S. Pacific Territories, and U.S. Atlantic Territories. We estimate that earthquake-generated tsunamis that could affect these states and territories collectively represent $1 billion in potential AAL with 79 % of losses due to residential fatalities and 21 % of losses due to capital stock losses from building damage. We identify AAL variations based on county and county equivalents, states and territories, geographic regions, return periods, and departure-delay assumptions for evacuating residents. Results include high AAL values for potential fatalities in Puerto Rico and the U.S. Pacific Northwest region, high AAL values for potential building-related damage in Hawaii and California, and high building- and population-loss ratios for county equivalents in Alaska and U.S. territories.

International Journal of Disaster Risk Reduction↗

USGS2025

Manipulation of the Symbiodiniaceae microbiome confers multigenerational impacts on symbioses and reproductive ecology of its Exaiptasia diaphana host

Symbiodiniaceae-associated microbiota strongly influence cnidarian symbioses. We systematically reduced the bacterial and fungal communities associated with Symbiodiniaceae to study potential effects on the cnidarian holobiont Exaiptasia diaphana (Aiptasia). Clonal anemones were inoculated with xenic Breviolum minutum (SSB01) and microbiome-manipulated cultures after antibacterial or antifungal treatment. The asexual reproduction of pedal laceration allowed for three generations of clonal aposymbiotic Aiptasia to be utilised in this study, from the initial adult generation (G0), to the first (G1), and second (G2) generation. We inoculated small and large G1 Aiptasia with SSB01 algae and monitored onset of symbiosis, rate of algal proliferation, and holobiont characteristics. Sequencing the 16S and 18S rRNA gene regions identified significant differences in the bacterial and fungal communities of the G0 and G1 generations, alongside differences between the size classes of small and large G1 anemones. The microbiome of larger G1 individuals was distinct to the smaller G1 anemones, suggesting a microbiome maturation process. Control Breviolum minutum cultures exhibited a significantly greater proliferation rate in large G1 anemones when compared to antibacterial or antifungal treated cultures, whereas the opposite trend was documented in the small G1 anemones. Although no differences were observed between algal photochemical parameters, or the growth and polyp activity of G1 juveniles, we observed a significant influence in the production of G2 clones between treatments. Overall, we provide strong ecological implications of manipulating Symbiodiniaceae microbiome, not for the algae themselves, but for the maturation of the host Aiptasia, as well as for the cnidarian holobiont over multiple generations.

The ISME Journal↗

USGS2025

Estimated average annualized tsunami losses for the United States

Tsunami hazards are substantial threats to coastal communities across the United States (U.S.) and its territories. U.S. states and territories collaborate through the National Tsunami Hazard Mitigation Program (NTHMP) to develop their own tsunami-hazard information for outreach and evacuation planning. An effort to curate this tsunami-hazard information to support comprehensive risk analysis at the national level has not yet been completed. In support of this effort, the Federal Emergency Management Agency (FEMA) collaborated with the NTHMP, the National Oceanic and Atmospheric Administration (NOAA) and the U.S. Geological Survey (USGS) starting in 2023. This collaboration included the collection and analysis of existing tsunami hazard data and methods in the U.S. Tsunami subject matter experts identified and selected scientifically defensible methods for estimating the risks to buildings and populations in coastal communities. These efforts may support decision making regarding resilience policies, priorities, strategies and funding levels. Tsunamis can be triggered by earthquakes, subaerial or submarine landslides, volcanic eruptions, glacial calving, near-earth objects, weather or other events. These events can cause severe destruction, injuries, and loss of life due to powerful currents and flooding. Tsunamis pose a substantial threat to the western United States and all U.S. territories, as described below. ■ Hawaii is threatened by distant tsunamis due to its central location in the Pacific Ocean basin and has a history of local events. ■ Alaska, particularly the Aleutian Islands, faces local tsunami threats due to proximity to the Alaska-Aleutian Subduction Zone, as well as distant tsunamis from around the Pacific Ocean basin. ■ The western coast of the U.S. is threatened by distant tsunamis from around the Pacific Ocean basin and local source tsunamis from earthquakes generated within the Cascadia Subduction Zone in the Pacific Northwest. ■ American Samoa faces local tsunami threats from earthquakes generated in the nearby Tonga Trench, as well as distant tsunami threats. ■ Guam and the Commonwealth of the Northern Mariana Islands are threatened by local tsunamis from the nearby Mariana Subduction Zone, as well as distant sources from around the Pacific Ocean Basin. ■ Puerto Rico and the United States Virgin Islands are threatened by multiple local and distant tsunami sources, such as the Puerto Rico Trench (PRT), given their location in the complex seismic region of the Caribbean Sea. Several historical events stand out because of their catastrophic impacts. ■ In the Pacific Northwest, the 1700 Cascadia earthquake caused a tsunami that affected coastal Native American communities, though the extent of the damage is not fully documented (Ludwin, et al., 2005). ■ In Puerto Rico, the 1918 earthquake triggered a tsunami that caused $77 million in damage in 2022 dollars and 116 fatalities, primarily along the western coast (Coffman et al., 1982). ■ The 1946 Aleutian Islands earthquake triggered a massive tsunami that devastated Hilo, Hawaii, killing 158 people and resulting in approximately $375 million in damage (adjusted to 2022 dollars) (Fisher et al., 2023). ■ The 1964 Alaska earthquake (M 9.2) generated tsunamis that caused severe destruction in some communities across Alaska, Oregon, and California. This disaster led to a total of 124 fatalities and approximately $2.9 billion in property damage (adjusted to 2022 dollars) (Brocher et al., 2014) (Alaska Science Center, 2024). ■ In American Samoa, a tsunami generated by the 2009 Samoa earthquake (Mw 8.1) caused widespread devastation, resulting in 34 confirmed fatalities (Apatu et al., 2013) and economic losses exceeding $160 million (adjusted to 2022 dollars) (DHS, 2011). More recent events, including the 2010 Chile earthquake, the 2011 Japan earthquake, and the 2022 Tonga volcanic eruption, resulted in millions of dollars in damage to numerous ports and harbors in the U.S. South Pacific territories, Hawaii, and along the west coast of the U.S. (Lynett, et al., 2022) (Wilson, et al., 2013). Since these events, the expansion of the built environment in lowlying areas along the coast has increased the exposure of buildings and people, thereby further escalating community risk from tsunamis. This report provides a comprehensive national assessment of earthquake-generated tsunami risk. It does not include impacts from tsunamis generated by landslides, volcanic eruptions, glacial calving, near-earth objects, weather, or other events. This study is based on the best available hazard data from the U.S. Pacific Coast (California, Oregon and Washington), Alaska, Hawaii, U.S. Pacific Territories (American Samoa, Guam and Commonwealth of the Northern Mariana Islands) and Caribbean Territories (Puerto Rico and United States Virgin Islands). Tsunami risks associated with states along the East Coast, Gulf Coast, and Great Lakes are not included in this study because Hazus 6.1 software (FEMA 2024a) does not currently include the ability to analyze tsunami risk in those states. Once modeling capabilities and tsunami hazard data become available for additional states, FEMA may incorporate these data into future editions of this study.

Alaska, California, Hawaii Oregon, Washington↗

USGS2026

Incorporating location uncertainty improves inference with stop-level North American Breeding Bird Survey data

Ecological models should account for uncertainty to be most effective and useful. Yet, uncertainty from model covariates—unlike that from other sources, such as sampling error or process variability—is seldom explicitly incorporated. This can cause underestimates of uncertainty to cascade through model parameter estimates, predictions, and downstream uses. Burner et al. proposed a method for quantifying uncertainty in covariates and incorporating it into models using informative Bayesian priors. This method was applied to stop-level Breeding Bird Survey (BBS) analyses, where land cover uncertainty at each stop arises from substantial stop location uncertainty. A limited validation of model-estimated land cover, using stops with known locations, indicated the method’s potential effectiveness, but it was not rigorously evaluated. We conduct a robust simulation-based test, generating stop locations, extracting land cover, and simulating bird communities across 210 BBS routes in the upper Midwest. We compare 3 models: a “known” model with true land cover, a “naive” model assuming consistent 800-m stop spacing, and a “full” model using informative priors to estimate land cover. Species parameter estimates and predicted prevalence patterns across gradients in land cover from the full model approached those of the known model and were substantially closer to the true values used in simulations relative to those from the naive model. Naive model parameters were more biased relative to the other models, and credible intervals of predicted species prevalence rarely included the true simulated values. The full model also produced land cover covariate estimates closer to true simulation values relative to the mean informative priors. Our results show that, for the BBS, informative priors enable more accurate stop-level analyses despite location uncertainty. In contrast, naive models that ignore this uncertainty yield poor inferences. More broadly, we demonstrate empirically the utility of informative priors to account for covariate uncertainty in ecological models.

Michigan, Minnesota, Wisconson↗

USGS2026

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

Groundwater spatial variability within an atoll island: Assessing shallow aquifer heterogeneity with geophysical and physicochemical measurements

This study examines the spatial variability of shallow groundwater on Dhigelaabadhoo Island using electromagnetic induction surveys, groundwater monitoring, and sediment analyses. The research reveals how variations in island morphology—such as differences in elevation, reef flat width, and sediment composition—affect the spatial distribution of groundwater lenses and the overall aquifer dynamics. Saltwater intrusion is especially pronounced in low elevated areas, with narrow reef flat plate and areas where higher hydraulic conductivity—driven by the presence of coarser sediments—is observed, whereas regions characterized by finer sediments, higher elevation, and wider reef flat plates tend to support more symmetric and less saline groundwater lenses. The geophysical investigations reveal that tidal oscillations alter groundwater movement by markedly changing water levels and conductivity, thereby underscoring the critical need to account for temporal dynamics in atoll coastal aquifer systems and the importance of integrating tidal dynamics into the aquifer zone. The findings highlight the significant role of intrinsic morphological and external hydrodynamic factors in shaping groundwater distribution on atoll islands, offering critical insights for sustainable freshwater resource management.

Dhigelaabadhoo Island↗

USGS2025

Forecasting water levels using the ConvLSTM algorithm in the Everglades, USA

Forecasting water levels in complex ecosystems like wetlands can support effective water resource management, ecological conservation, and understanding surface and groundwater hydrology. Predictive models can be used to simulate the complex interactions among natural processes, hydrometeorological factors, and human activities. The Greater Everglades in the USA is a well-known example of an ecosystem where complexity has motivated adoption of machine learning algorithms in water level prediction studies. This paper aims to contribute to extending existing machine learning algorithms by integrating spatiotemporal data with deep-learning algorithms in the forecasting process. In this study, a deep-learning model is developed to predict water levels on a regional scale, covering a large area of approximately 9,138 square kilometers in the Everglades ecosystem. This model has the architecture of Convolutional Long Short-Term Memory which can deal with spatiotemporal data by capturing both spatial and temporal dependencies in the training data. The forecasting capabilities of this model (referred to as the global model) are assessed by comparing the global model to two Artificial Neural Networks developed at two different gaging stations, referred to here as local models. One local model is developed at a gaging station directly influenced by nearby water control structures, whereas the other is developed at a gaging station located farther away from these structures. By leveraging data from the Everglades Depth Estimation Network spanning from January 2002 to May 2023, the global and local models were trained to forecast water levels with a two-day lead time. Our findings suggest that both the global and local models perform with approximately the same level of accuracy, with Mean Absolute Relative Error values ranging from 0.38% to 1.4% at the selected stations. The developed global model has demonstrated strong potential as a standalone forecasting tool for the entire study area in the Everglades and could eliminate the need for developing multiple local models. This finding also highlights how machine learning can capture complex spatial and temporal relationships to generate accurate water level predictions on a regional scale.

Florida↗

USGS2024

U.S.-Mexico Borderland & vegetation community map

People on both sides of the United States-Mexico border need a high-resolution, binational vegetation community map that spans the entire United States-Mexico borderlands. Traditionally, mapping efforts in this region were impeded by complex logistics related to the international border, differing national needs and plans, and resource allocations and priorities. To address this need, scientists from the U.S. Geological Survey (USGS) Southwest Biological Science Center partnered with the Sonoran Joint Venture, the U.S. Fish and Wildlife Service (FWS) Migratory Bird Program, data engineers from the Department of Biosystems Engineering at the University of Arizona, and collaborators from the Wildlands Network, the Borderlands Program to produce the first prototype land cover map within the overlapping Mojave Desert, Sonoran Desert, and the North American Bird Conservation Initiative’s Bird Conservation Region 33 (BCR33) using Landsat satellite data . BCR33 is an area of high biodiversity, providing habitat for bird species of concern and other wildlife. The land cover map supports FWS recovery plan efforts related to conservation planning activities for many species, including Yellow-billed Cuckoo ( Coccyzus americanus ), Cactus Ferruginous Pygmy-Owl ( Glaucidium brasilianum cactorum ), Southwestern Willow Flycatcher ( Empidonax traillii extimus ), Yuma Ridgway’s Rail ( Rallus obsoletus yumanensis ), Bendire’s thrasher ( Toxostoma bendirei ), LeConte’s thrasher ( Toxostoma lecontei ), Masked Bobwhite ( Colinus virginianus ridgwayi ), jaguar ( Panthera onca ), and endangered plants such as Bartram’s stonecrop ( Graptopetalum bartramii ) and the Pima pineapple cactus ( Coryphantha robustispina ssp. robustispina ). In 2024, a Phase-II map for the full BCR33 region was completed, increasing the understanding of the binational nature of natural communities. The published map and associated paper can be found here .

Borderland↗

USGS2007

Divisions of geologic time—Major chronostratigraphic and geochronologic units

Effective communication in the geosciences requires consistent uses of stratigraphic nomenclature, especially divisions of geologic time. A geologic time scale is composed of standard stratigraphic divisions based on rock sequences and calibrated in years. Over the years, the development of new dating methods and refinement of previous ones have stimulated revisions to geologic time scales. Since the mid-1990s, geologists from the U.S. Geological Survey (USGS), State geological surveys, academia, and other organizations have sought a consistent time scale to be used in communicating ages of geologic units in the United States. Many international debates have occurred over names and boundaries of units, and various time scales have been used by the geoscience community. For consistency purposes, the USGS Geologic Names Committee and the Association of American State Geologists developed Divisions of Geologic Time .

Fact Sheet↗

USGS2025

Identifying precursors and tracking pulses of magma ascent in multidisciplinary data during the 2018–2023 phreatomagmatic eruption at Semisopochnoi Island, Alaska

The 2018–2023 phreatomagmatic eruptions at Semisopochnoi Island, Alaska produced abundant long-period (LP) seismicity, harmonic and broadband tremor, and explosion signals over several well-monitored periods of eruption and quiescence. The corresponding dataset provides an excellent opportunity to investigate precursory and syn -eruptive geophysical signals of long-lived phreatomagmatic eruptions using multiparameter observations. We generated explosion and LP event catalogs through novel implementations of the REDPy ( Hotovec-Ellis, 2024 ) repeating event detector in mid-2021 following a network upgrade and the onset of a new phase of the eruption. The hundreds of detected explosions show a high degree of infrasound waveform similarity over more than a year, indicating a repeating source mechanism likely associated with explosive magma-water interaction. The seismic LP catalog shows that events began over a month prior to renewed explosive activity at the beginning of August 2021, and that lower frequency index (FI) LPs were generated in the week prior to the onset of explosions. We applied a recently developed machine learning tool (VOISS-Net, Tan et al., 2024 ) to catalog abundant broadband and harmonic seismic tremor recorded before and during the renewed explosive activity, along with LPs and explosions. The tremor catalogs complement the LP and explosion catalogs by filling out the seismic sequence with the dominant signal types. Together, these catalogs reveal a seismic sequence of renewed unrest that started with several weeks of LP events, followed by LPs with lower FI values and harmonic tremor in the days prior to explosive activity, and finally the onset of discrete explosions and broadband eruption tremor. We interpret this sequence as the ascent of a new pulse of magma that first interacted with the hydrothermal/groundwater system to produce LPs, followed by harmonic tremor, and that ultimately drove explosive magma-water interactions and periods of continuous ash emissions. The 2021 seismic sequence, in combination with long-term records of satellite SO 2 emissions, deformation from interferometric synthetic aperture radar (InSAR) analysis, ash sample analysis, infrasound, and volcano tectonic seismicity, allows us to interpret the entire 9-year period of unrest and eruption that began with an intrusion and earthquake swarm in 2014.

Alaska↗

USGS2026

Variability and consistency in wildfire susceptibility: Insights from a national compilation

Background Wildfire risk in the United States is rising and remains a land management priority. The quantitative wildfire risk assessment (QWRA) framework integrates fuels, topography, weather and values at risk to estimate the potential change in value from wildfire. Within this, response functions (RFs) represent how values respond to fire intensity. These are often based on expert judgment, but variation across assessments is unclear. Aims This study uses data from the US Geological Survey (USGS) Wildfire Hazard and Risk Assessment Clearinghouse to characterize consistency and variation across categories and contexts. Methods We applied descriptive statistics to summarize RFs, using tables, box-and-whisker plots and heat maps stratified by highly valued resource or asset (HVRA) category and spatial scale. Key results RFs and value definitions vary, especially for ecosystem-related resources. Some functions, such as for buildings in the wildland–urban interface (WUI), translate well across contexts, while others require more input. Conclusions Some functions are broadly transferable, while others need customization. This analysis provides references and starting points for improvement to RFs in QWRAs. Interpretations Expanding the clearinghouse and dataset and building more transparency in expert elicitation can build trust among communities, agencies and end-users, and can support efficient use of limited resources to mitigate wildfire risk.

International Journal of Wildland Fire↗

USGS2026

Magmatic source of the opening phase of Kīlauea’s 2018 Lower East Rift Zone eruption

The 2018 eruption of Kīlauea volcano in its Lower East Rift Zone began with the discharge of evolved high-Ti basalt as weak lava fountains and short, slow-moving lava flows. The lavas were quickly geochemically recognized as being derived from magmas stored within the rift zone and remobilized by a new intrusion, a sequence that is common at Kīlauea. This initial phase of the 2018 eruption, referred to as phase 1a, lasted for 6 days and was followed by extrusion of mixed magma after a 3-day pause. Even though remobilization of older rift zone magmas is common within Kīlauea’s rift zones, it is difficult to determine which past intrusion(s) may have initially emplaced those stored magmas. This difficulty stems from the tendency for Kīlauea magmas to follow very similar differentiation paths without significant variations in major, minor, or even trace element chemistry. We investigate possible magma sources for the lavas erupted during phase 1a of the 2018 eruption using whole-rock, mineral, and glass major and trace element compositions from historical East Rift Zone eruptions with adjacent fissures. We consider two primary hypotheses for the phase 1a source: magmas associated with the 1955 Lower East Rift Zone eruption or the nine eruptions in the Middle and Upper East Rift Zone during the 1960s. Our results suggest that magma associated with the earliest phases of Kīlauea’s 1955 eruption was the most likely source of the 2018 phase 1a remobilized magma. We determine volatile saturation pressures from melt inclusion chemistry and find similar storage depths for the 2018 phase 1a and early 1955 magmas. The phase 1a and early 1955 lavas are nearly indistinguishable in all of the compositional criteria considered, implying that the leftover 1955 magma body barely cooled and differentiated in the 63 years between eruptions (cooling rates of ~0.1 °C/year). This study sheds light on the potential for protracted storage of eruptible magmas in rift zones at Kīlauea, and highlights some of the challenges and solutions to identifying genetic relationships between magmas at Kīlauea.

Hawaii↗

USGS2000

Storm impact scale for barrier islands

A new scale is proposed that categorizes impacts to natural barrier islands resulting from tropical and extra-tropical storms. The proposed scale is fundamentally different than existing storm-related scales in that the coupling between forcing processes and the geometry of the coast is explicitly included. Four regimes, representing different levels of impact, are defined. Within each regime, patterns and relative magnitudes of net erosion and accretion are argued to be unique. The borders between regimes represent thresholds defining where processes and magnitudes of impacts change dramatically. Impact level 1 is the 'swash' regime describing a storm where runup is confined to the foreshore. The foreshore typically erodes during the storm and recovers following the storm; hence, there is no net change. Impact level 2 is the 'collision' regime describing a storm where the wave runup exceeds the threshold of the base of the foredune ridge. Swash impacts the dune forcing net erosion. Impact level 3 is the 'overwash' regime describing a storm where wave runup overtops the berm or, if present, the foredune ridge. The associated net landward sand transport contributes to net migration of the barrier landward. Impact level 4 is the 'inundation' regime describing a storm where the storm surge is sufficient to completely and continuously submerge the barrier island. Sand undergoes net landward transport over the barrier island; limited evidence suggests the quantities and distance of transport are much greater than what occurs during the 'overwash' regime.

Journal of Coastal Research↗

USGS2018

Divisions of geologic time—Major chronostratigraphic and geochronologic units

Introduction Effective communication in the geosciences requires a consistent nomenclature for stratigraphic units and, especially, for divisions of geologic time. A geologic time scale is composed of standard stratigraphic divisions based on rock sequences and is calibrated in years. Geologists from the U.S. Geological Survey (USGS), State geological surveys, academia, and other organizations require a consistent time scale to be used in communicating ages of geologic units in the United States. Many international debates have occurred over names and boundaries of units, and various time scales have been used by the geoscience community. For consistent usage of time terms, the USGS Geologic Names Committee and the Association of American State Geologists developed the Divisions of Geologic Time ; the 2018 update in this fact sheet contains the unit names and boundary age estimates ratified by the International Commission on Stratigraphy in 2018. Scientists may use other published time scales, provided that these are specified and referenced.

Fact Sheet↗

USGS2026

Hyperspectral narrowband imaging spectroscopy: A new paradigm for Earth observation

This editorial introduces the Special Issue entitled “Hyperspectral Narrowband Imaging Spectroscopy: A New Paradigm for Earth Observation” in the August 2026 issue of Photogrammetric Engineering & Remote Sensing (PE&RS), the flagship journal of the American Society for Photogrammetry and Remote Sensing (ASPRS). This volume represents the fourth dedicated hyperspectral special issue published in PE&RS, following earlier contributions by Thenkabail et al. (2025, 2024a, 2024b), and continues ASPRS’s commitment to advancing cutting‑edge imaging spectroscopy research and its applications across Earth system science. Remote sensing is undergoing one of the most profound transformations in its history. The emergence of hyperspectral narrowband (HNB) imaging spectroscopy data, capable of acquiring hundreds of contiguous, narrow spectral bands, has shifted the discipline from observing Earth in a handful of broad spectral windows to capturing continuous spectral signatures of the Planet. This transition marks a decisive break from the multispectral paradigm that has dominated satellite remote sensing for nearly five decades, driven by the advent of new orbital imaging spectrometers such as EnMAP, PRISMA, and NASA’s EMIT, and by the forthcoming Surface Biology and Geology (SBG) mission ((Pires Silva et al., 2026; Bourriz et al., 2025; Thenkabail et al., 2025; Chabrillat et al., 2024; Aneece et al., 2024; Dave et al., 2024; Thenkabail et al., 2024a; Thenkabail et al., 2024b; Thenkabail, 2024a; Thenkabail, 2024b; Thompson et al., 2022; Kokaly et al., 2022; Aneece & Thenkabail, 2022; Cawse Nicholson et al., 2021; Guanter et al., 2021; Vangi et al., 2021; Thenkabail et al., 2021). These missions (e.g., Table 1) deliver unprecedented spectral fidelity, improved signal to noise ratios, and global coverage capabilities, enabling a new era of quantitative, spectroscopy based Earth observation. Where multispectral broadbands (MBBs) provide only a few discrete measurements along the electromagnetic spectrum, HNB systems deliver rich, diagnostic information that enables scientists to characterize Earth’s surface with unprecedented biochemical, biophysical, and structural detail (Figure 1a, 1b). The implications for environmental monitoring, agriculture, water resources, and mineral exploration are profound. Several overarching themes emerge: • Spectral fidelity matters. The ability to preserve subtle absorption features is essential for mineral mapping, vegetation trait retrieval, and biochemical modeling. • AI and deep learning are indispensable. From destriping to classification, modern analytics must be scalable, label‑efficient, and capable of exploiting the full spectral–spatial richness of HNB data. • Physics‑based and data‑driven approaches must converge. Radiative transfer models such as PROSAIL, enhanced with localized soil parameterizations, remain foundational for biophysical retrievals and model‑based inference. • Dimensionality reduction and feature extraction are critical. Techniques such as L1‑ISOMAP demonstrate that intelligent manifold learning can unlock the structure of fused, high‑dimensional datasets. • Next‑generation architectures must be interactive and multimodal. ICTNet exemplifies the future of hyperspectral classification: hybrid, synergistic, and capable of modeling both local textures and global spectral dependencies.

Photogrammetric Engineering and Remote Sensing (PE↗

USGS2024

RegionGrow3D: A deterministic analysis for characterizing discrete three-dimensional landslide source areas on a regional scale

Regional-scale characterization of shallow landslide hazards is important for reducing their destructive impact on society. These hazards are commonly characterized by (a) their location and likelihood using susceptibility maps, (b) landslide size and frequency using geomorphic scaling laws, and (c) the magnitude of disturbance required to cause landslides using initiation thresholds. Typically, this is accomplished through the use of inventories documenting the locations and triggering conditions of previous landslides. In the absence of comprehensive landslide inventories, physics-based slope stability models can be used to estimate landslide initiation potential and provide plausible distributions of landslide characteristics for a range of environmental and forcing conditions. However, these models are sometimes limited in their ability to capture key mechanisms tied to discrete three-dimensional (3D) landslide mechanics while possessing the computational efficiency required for broad-scale application. In this study, the RegionGrow3D (RG3D) model is developed to broadly simulate the area, volume, and location of landslides on a regional scale (≥1,000 km 2 ) using 3D, limit-equilibrium (LE)-based slope stability modeling. Furthermore, RG3D is incorporated into a susceptibility framework that quantifies landsliding uncertainty using a distribution of soil shear strengths and their associated probabilities, back-calculated from inventoried landslides using 3D LE-based landslide forensics. This framework is used to evaluate the influence of uncertainty tied to shear strength, rainfall scenarios, and antecedent soil moisture on potential landsliding and rainfall thresholds over a large region of the Oregon Coast Range, USA.

Journal of Geophysical Research: Earth Surface↗

USGS2025

Applying U-Th disequilbrium for dating siliceous sinters

Continental hydrothermal systems are critical avenues for the crustal transport of heat and mass captured for geothermal energy and mineral exploration. Thus, understanding their temporal evolution and longevity is important for resource characterization. Deposits of microlaminated siliceous sinter, common surface expressions of high temperature reservoirs (> 170 °C), have the potential to trace hydrothermal histories. Geothermal reservoirs are often located within uranium-bearing silicic volcanic rock where subsurface fluid-rock interactions extract U into hydrothermal fluids. U incorporated in the surface sinter deposit has the potential to provide a U— Th disequilibrium dating option. We focus on samples from El Tatio geyser field in the Altiplano of northern Chile, the largest geothermal system in the Andes. Our resulting 230 Th/U ages, along with the water and deposit elemental compositions, suggest concentrations of U and Th vary predictably along the sinter apron. While distal facies containing the highest U concentrations (> 50 μg/g) are least affected by detrital Th corrections, they can display suspected open-system behavior. In contrast, more medial facies, where bacterial mats and other porous textures are co mmonly concentrated, have only trace amounts of U (< 0.1 μg/g), which leads to unreliable or geologically improbable dates. Proximal facies tend to date most consistently. By comparing existing 14 C ages with 230 Th/U results, 230 Th/U ages tend to be younger than the 14 C ages, supporting the presence of a 14 C-dead carbon influence. New data confirm that the onset of geothermal activity at El Tatio goes back to the late Pleistocene.

Altiplano, El Tatio geyser field↗