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USGS2026

Estimating paleotemperature using stable isotopes of soil-formed phyllosilicates from paleosols: A review

Fossilized soils, or paleosols, contain soil-formed phyllosilicates whose stable isotopic compositions may be used to calculate paleotemperature and thus reconstruct ancient terrestrial environments. Though paleosols are common in the geologic record, the use of phyllosilicates as paleotemperature proxies is limited in the literature owing to difficulties with selecting optimal paleosols, isolation from non-clay minerals and organic materials, mixtures of phyllosilicates in natural samples, wide variations of chemical compositions for phyllosilicates, and limited to undefined equilibrium fractionation factors between phyllosilicates-water. Here, we address these challenges by examining and comparing methods used for sample selection, mineral isolation, pretreatments, mineral identification, conventional and developing methods for oxygen and hydrogen isotopic analyses, and determination of phyllosilicate-water equilibrium fractionation factors, concluding with recommendations for best approaches for paleotemperature estimation. Additionally, we discuss how to identify and avoid detrital phyllosilicates, the impacts of diagenesis, comparison of stable isotope and non-isotope paleosol paleotemperature proxies, and challenges and opportunities for broadly using paleosols as paleoclimate archives. With ongoing efforts to refine this multi-faceted paleotemperature approach, the stable isotope geochemistry of soil-formed phyllosilicates continues to be an invaluable proxy system, enhancing our understanding of terrestrial paleoenvironments and paleoclimate.

Kansas↗

USGS2024

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

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

New York, Virginia↗

USGS2025

USGS addresses needs for lithium calibration and quality control materials for pLIBS analysis

Lithium (Li) is a globally important commodity used for energy storage, national defense, human health, and advanced technologies. Lithium resource development requires identifying deposits with elevated concentrations and optimal mineralogy, typically associated with select clays and pegmatites. Lithium is a light, highly reactive alkali metal with low atomic mass that is difficult to detect and quantify using conventional portable geochemical techniques such as X-ray fluorescence (XRF). However, portable laser-induced breakdown spectroscopy (pLIBS) is a powerful analytical technique for lithium exploration due to its ability to analyze solids quickly with minimal preparation. The expanded utility of pLIBS is hampered by the lack of matrix-matched calibration and quality control (QC) materials. The United States Geological Survey (USGS) has developed in-house lithium calibration and QC materials for lithium in clay and pegmatite matrices to address this limitation. We present the workflow and implementation of a custom-built matrix specific calibration on a SciAps Z-300 pLIBS, using proprietary Profile Builder software. The implementation of the custom calibration and quality control standards enables us to collect semiquantitative results directly from the pLIBS while in the field. Ultimately, this calibration has improved confidence in sample selection and collection in the field, providing more efficient site characterization.

Conference Paper↗

USGS2025

A method to obtain remotely sensed grain size distributions from nonplanar granular deposits

Constraining the grain size distribution of granular deposits with complex surfaces is difficult with existing approaches. Field and laboratory techniques are time consuming and limited by the maximum grain size that laboratories can accommodate. In this study, we present a new method to identify the coarse fraction of the grain size distribution at a debris-flow fan deposit surveyed with terrestrial laser scanning (TLS) in Glenwood Canyon, Colorado, USA. This method is a novel grain segmentation algorithm developed for application to point cloud data of deposits with complex surfaces and angular grains ranging in size from centimeters to a meter. This approach combines an existing random forest machine learning method with a novel iterative clustering algorithm. We compared the grain size distribution from our algorithm with a Wolman pebble count conducted in the field, and found a root mean squared error of less than 2 cm from the 5th to 95th percentile of the grain size distribution of grains ranging from cobble to boulder sized (6.3–78 cm in our application). Finally, we compared our new algorithm with an existing open-source grain segregation algorithm, and our method outperformed the selected alternative when applied to the debris-flow deposit point cloud.

Colorado↗

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↗

USGS2025

Did they feel it? Legacy maroseismic data illuminates an engimatic 20th century earthquake

The challenges and the importance of preserving legacy instrumental records of earthquakes are now well-recognized (e.g., Richards & Hellweg, 2020, https://doi.org/10.1785/0220200053 ). Seismologists may not be aware of parallel challenges and opportunities with legacy macroseismic data for earthquakes in the United States. For much of the 20th century, macroseismic data were collected by a series of U.S. government agencies using a standard questionnaire distributed on postcards. Published summaries of postcards provide macroseismic data akin to modern Did You Feel It? questionnaire responses. In this paper we focus on the M 6.5 Fickle Hill, California earthquake, on 21 December 1954 (Hellweg et al., 2025) as a proof-of-concept, illustrating the potential of what we dub Did They Feel It? (DTFI) data to improve our understanding of significant 20th century U.S. earthquakes for which instrumental data are sparse. Legacy macroseismic data interpreted following modern conventions can potentially constrain traditional ShakeMaps at a level of detail and accuracy that in some respects rival maps for modern earthquakes. The updated ShakeMap for the 1954 Fickle Hill earthquake, also drawing from recently published media and first-person accounts, supports the location, depth, and stress drop value estimated from available instrumental data (Hellweg et al., 2025).

California↗

USGS2025

Probing lower-crustal fault properties with frequency-dependent tidal tremor triggering

The way seismicity responds to periodic stress perturbations offers crucial insights into the processes that can trigger an earthquake. Laboratory and theoretical analyses have shown that the period of imposed forcing and source properties affect the sensitivity to triggering, but frequency-dependent triggering of tectonic faults is poorly understood. The rate of low-frequency earthquakes (LFEs) near Parkfield, California has been found to be strongly correlated with solid earth tides. Tidal forcing acts over multiple frequencies, and the sensitivity to tidal triggering of LFEs therefore provides a unique opportunity to probe the physics of earthquake triggering and underlying fault properties. Here, we find that the response of LFEs to solid earth tides at diurnal and semi-diurnal frequencies is highly variable but spatially coherent along the San Andreas Fault. Using rate-state friction modeling, we find that the variation of the amplitude of tidal modulation is mainly affected by the spatial variation of the background effective normal stress, whereas the spatially varied tidal modulation at diurnal and semi-diurnal frequencies is mainly affected by the fault frictional property. The spatial variations of the LFE response reveal a heterogeneous lower-crustal geologic structure and complex physical faulting processes below the rupture zone of eventual great earthquakes.

California↗

USGS2024

A great tsunami earthquake component of the 1957 Aleutian Islands earthquake

The great 1957 Aleutian Islands earthquake ruptured ∼1200 km of the plate boundary along the Aleutian subduction zone and produced a destructive tsunami across Hawaiʻi. Early seismic and tsunami analyses indicated that large megathrust fault slip was concentrated in the western Aleutian Islands, but tsunami waves generated by slip in the west cannot explain the large observed runup in Hawaiʻi far to the southeast. Recently mapped 1957 geologic deposits on eastern Aleutian Islands suggest occurrence of very large nearby slip. Jointly modeling tsunami runup along the eastern Aleutian and Hawaiian Islands together with tide gauge recordings across the Pacific resolves 12-26 m shallow slip along 600 km of the eastern Aleutian Islands in addition to modest, deeper western slip inferred from seismic records. The eastern near-trench slip results in an M W 8.3-8.6 tsunami earthquake component of the M W 8.6-8.8 rupture, comparable in size to the adjacent 1946 Aleutian tsunami earthquake to the east. The reexamination of the 1957 rupture confirms the tsunami hazards posed by the eastern Aleutian subduction zone to Hawaiʻi and lays the groundwork for investigation of large prehistoric earthquakes through modeling tsunami runup inferred from stratigraphic observations to constrain their rupture processes.

Alaska↗

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↗

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

Classification of lakebed geologic substrate in autonomously collected benthic imagery using machine learning

Mapping benthic habitats with bathymetric, acoustic, and spectral data requires georeferenced ground-truth information about habitat types and characteristics. New technologies like autonomous underwater vehicles (AUVs) collect tens of thousands of images per mission making image-based ground truthing particularly attractive. Two types of machine learning (ML) models, random forest (RF) and deep neural network (DNN), were tested to determine whether ML models could serve as an accurate substitute for manual classification of AUV images for substrate type interpretation. RF models were trained to predict substrate class as a function of texture, edge, and intensity metrics (i.e., features) calculated for each image. Models were tested using a manually classified image dataset with 9-, 6-, and 2-class schemes based on the Coastal and Marine Ecological Classification Standard (CMECS). Results suggest that both RF and DNN models achieve comparable accuracies, with the 9-class models being least accurate (~73–78%) and the 2-class models being the most accurate (~95–96%). However, the DNN models were more efficient to train and apply because they did not require feature estimation before training or classification. Integrating ML models into benthic habitat mapping process can improve our ability to efficiently and accurately ground-truth large areas of benthic habitat using AUV or similar images.

Michigan, Wisconsin↗

USGS2026

Restoring dryland water cycles for precipitation feedback and climate stability; a review

Drylands across the globe are experiencing intensifying water scarcity, land degradation, and hydroclimatic extremes. This review integrates evidence from multidecadal field studies, hydrologic monitoring, geomorphic and ecological assessments, remote sensing, and land–atmosphere science to evaluate how restoration influences key components of the terrestrial water cycle. Low-tech natural infrastructure in dryland streams (NIDS)—including check dams, leaky weirs, one-rock dams, and gabions—has emerged as a promising but under-synthesized nature-based solution for restoring hydrologic function in these environments. We describe the mechanisms through which these interventions modify runoff detention, infiltration, sediment and alluvial storage, shallow-groundwater recharge, vegetation recovery, and surface-energy partitioning, and we summarize outcomes across diverse dryland settings. Findings consistently show increased water residence time, enhanced soil-moisture storage, expanded riparian vegetation, extended flow duration, and shifts toward greater latent-heat flux—producing localized cooling and strengthened ecohydrological feedbacks. Building on these localized effects, we articulate a hypothesis that links the spatial extent of restoration, the density of NIDS per unit drainage area, and the magnitude of the latent-to-sensible-heat contrast generated by wetter post-rainfall conditions. Specifically, we hypothesize that when NIDS are implemented at densities permitted by topography and across areas large enough to maintain elevated soil moisture after storm events, the resulting increases in latent heat flux, surface cooling, and boundary-layer moistening may enhance moisture convergence and boundary-layer development, potentially increasing the likelihood or stability of convective precipitation, analogous to how reductions in these processes have contributed to regional drought intensification. These land–atmosphere feedbacks remain untested at scale but represent an important research Frontier. By integrating hydrologic, geomorphic, ecological, and atmospheric perspectives, this review provides a comprehensive framework for considering how low-tech, landscape-scale interventions can strengthen watershed resilience and contribute to climate-relevant nature-based solutions.

Arizona↗

USGS2026

Continental-scale prediction of hydrologic signatures and processes

Understanding how dominant hydrologic processes and their drivers vary across diverse continental-scale landscapes is critical for hydrologic modeling and water management applications. Our research addresses this question by synthesizing large-sample watershed datasets, Caravan and GAGES-II, and developing random forest models to identify patterns in hydrologic function. We assessed dominant processes by examining hydrologic signatures – summary indicators of watershed function derived from hydroclimatic time series and random forest models across 14 146 gauged United States watersheds. The results reveal clear continental-scale gradients in hydrologic processes, including baseflow, overland flow, storage, and water balance losses. Our map of dominant processes highlights, for example, the transition from baseflow to fast responses and back to baseflow along the elevation gradient from the Appalachian spine, through the Piedmont, to the Eastern Coastal Plain; a distinct outer ring around the Great Lakes region; and sharp contrasts between coastal and inland processes in the West. Variable importance analysis from random forest models show that processes in the western U.S. are primarily controlled by climate, whereas in the eastern U.S., soil, geology, and topography play larger roles, with distinct human influences apparent in urban areas. Our approach of estimating dominant processes and their drivers facilitates extending process knowledge from research watersheds to the continental scale, assessing current hydrological understanding, and evaluating hydrological model structures.

conterminous United States↗

USGS2025

Cave records reveal recent origin of North America’s deepest canyon

We explore how and when Hells Canyon, North America’s deepest river gorge (~2,400 m deep), formed, addressing these fundamental questions first posed by W. Lindgren [ The Gold Belt of the Blue Mountains of Oregon (1901)]. Existing hypotheses about the canyon’s formation and timing of incision remain speculative due to a lack of direct constraints and geomorphic analysis in the canyon. Herein, we combine cosmogenic nuclide dating of cave-bound river deposits, river profile analysis, and numerical modeling to provide the first direct age constraints and systematic analysis of incision processes at work in Hells Canyon. Our study reveals a significant drainage capture triggered rapid incision at ~2.1 ± 1.0 Ma, establishing the Snake River’s modern route into the Columbia River system. The increased drainage area and subsequent increase in stream power resulted in the rapid incision of Hells Canyon and the formation of tributary knickpoints (KPs) that decrease in elevation away from the capture location. Cosmogenic dating of cave deposits indicates incision rates increased from ~0.01 to ~0.16 mm y −1 . Numerical modeling of the stream capture supports these observations, demonstrating how abrupt drainage area increase drives rapid river incision. Our findings from Hells Canyon provide a well-constrained example of how drainage capture can dramatically shape the evolution of a major river gorge.

Idaho, Oregon, Washington↗

USGS2026

Integrating habitat suitability and climate constraints to predict nonnative fish invasion risk for U.S. streams and inland lakes

Introduction: Nonnative fishes in streams and inland lakes can substantially alter aquatic ecosystems by degrading habitat conditions, competing with native species, and restructuring food webs. Identifying invasion hotspots can inform proactive management and prevention of nonnative species establishment. Methods: In this study, we identified streams and lakes across the conterminous United States that are vulnerable to invasion by 18 nonnative stream and 28 nonnative lake fish species. Habitat suitability models were developed separately for stream and lake species across nine ecoregions using natural and anthropogenic predictors, and species occurrences were predicted by integrating model results with climate match scores based on comparisons between environmental conditions in species’ native and potential nonnative ranges. We classified areas with both high habitat suitability and strong climate similarity as having high invasion potential and generated hotspot maps by stacking species-level predictions. Results: Florida exhibits the highest invasion risk for both habitat types. Stream hotspots were concentrated near major metropolitan areas, whereas lake hotspots were more widely distributed across the Great Lakes basin and the northeastern U.S. Twelve species were predicted to invade both habitat types, with goldfish ( Carassius auratus ) and pirapitinga ( Piaractus brachypomus ) showing particularly high invasion potential. Discussion: These findings provide a comprehensive assessment of nonnative freshwater fish invasion risk and support targeted monitoring and management strategies.

conterminous United States↗

USGS2026

Magnetic storms and geoelectric hazards

Magnetic storms induce geoelectric fields at Earth's surface that can interfere with grounded long-line systems. The September 1859 storm disrupted global telegraph operations, the March 1989 storm caused a blackout in Canada and interfered with electric-power-transmission systems in the United States, and other storms have had related impacts. The geographic and temporal dependence of geoelectric fields are functions of both geomagnetic variation and local surface impedance, which differ considerably across different geological regions. These dependencies can be mapped across the contiguous United States by combining magnetotelluric impedance tensors with ground magnetometer time series. This review illustrates such mapping for the 1989 storm and shows that power-system interference was experienced where surface impedance is high, and when and where geoelectric fields were intense. Statistical analyses indicate that storms comparable to that of March 1989 occur roughly once every four solar cycles. Ongoing developments in numerical modeling and real-time monitoring are anticipated to enable prediction of geoelectric hazards. ▪ Magnetic storms can induced electric fields in the solid Earth that interfere with electric-power-transmission systems. ▪ Geoelectric hazards depend on the storm-time geomagnetic disturbance and the electrical conductivity structure of Earth. ▪ Historically, impacts on telecommunication and power-transmission systems in the United States have been concentrated in the East and Midwest. ▪ The future occurrence of a magnetic superstorm could cause widespread disruption of electric-power-transmission systems.

Annual Review of Earth and Planetary Sciences↗

USGS2025

Geochemical processes related to mined, milled, or natural metal deposits in a rapidly changing global environment

The demand for metals and raw materials, such as nickel and copper, has been projected to expand in the coming decades, driven by the global energy transition, the need for green technologies, and expanding infrastructure. Consequently, the increasing extraction and production of mining waste can have adverse impacts on surrounding environments and human health. The aim of this thematic collection is to fill critical knowledge gaps in the present-day cycles of metal(loid)s from source to larger sinks, and the effect of environmental management, anthropogenic development, and climate change. Altogether, the studies have been conducted in different natural settings around the world and comprise investigations in laterites, a soil-medicinal plant system, watersheds, and banded iron formations, among others. The geochemical applications in tracing mineralization, its secondary products, and/or potential impact on the immediate environment are highly diverse with applied tools ranging from isotope tracers to major and trace element systematics. Particularly the use of rare earth elements, their patterns and anomalies are methods employed by several studies in this collection. We summarize the findings to offer a potential future direction for the use of geochemical tracing techniques in resource exploration in the context of climate change and environmental challenges.

Geochemistry: Exploration, Environment, Analysis↗

USGS2026

Uranium—Deposits, production and resources, market dynamics, and supply chain risks

Introduction Interest in nuclear power for the generation of electricity has risen with the increase in the need for more diverse baseload power, enhanced energy security, and the development of new technologies, such as small modular reactors (SMRs), which could provide power for remote areas, industrial applications, and artificial intelligence (AI) data centers. In 2024, the U.S. Department of Energy received $2.7 billion in congressional funding to bolster the domestic uranium production and nuclear fuel supply chain and address reliance on imports from foreign suppliers. In 2025, the U.S. Government issued several Executive and Secretary’s orders aimed at revitalizing the U.S. nuclear sector. If SMRs are to be as widely deployed in the United States and worldwide as envisioned, demand for uranium (nuclear reactor fuel) will likely increase. After the Fukushima nuclear accident in 2011, the market spot price of uranium began a decline, followed by a decrease in U.S. and global uranium exploration and mine development expenditures that led to a uranium supply deficit until 2020, when prices started to recover, prompting a resurgence in uranium exploration and development. In January of 2024, the uranium spot price rose to a 17-year high $106 (U.S. dollars) per pound of U3O8 (triuranium oxide, commonly known as “yellowcake”), which is expected to increase uranium exploration, mine development, and uranium production domestically and worldwide.

Fact Sheet↗