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USGS2024

The new self-anchored suspension (SAS) San Francisco Bay Bridge- Its response to a small earthquake

This paper presents a summary of previously published work (Celebi 2023) related to the new Self-Anchored Suspension (SAS) bridge that went into service within the last decade as a replacement for the older truss bridge spanning between Yerba Buena Island and Oakland, California, within the San Francisco Bay Area. During the October 19, 1989 M6.9 Loma Prieta earthquake, which occurred ~100 km south of the Bay Bridge, a section of the upper deck of the truss bridge fell onto the lower deck – thus closing this important lifeline between San Francisco and Oakland. The SAS is unique, self-anchored, and suspended by a single tower that is pivotal in trafficking the cable and hanger system to support the decks. The SAS bridge is extensively instrumented by the California Geological Survey’s Strong Motion Instrumentation Program (CSMIP). There are approximately 85 channels of accelerometers in the seismic monitoring system that recorded the October 14, 2019 Mw4.6 Pleasant Hill earthquake. The data allow a complex but identifiable coupled response of the deck, tower, and cable system. Both acceleration and displacement time-history data are used to extract significant frequencies using system identification methods, including spectral analyses. Results are compared to those from finite-element-model (FEM) analyses carried out during the design and analysis process of the bridge in 2002 (Nader et al. 2002). There are differences between FEM analyses results and those from the low amplitude shaking caused by a seismic event. An apparent frequency (period) of the SAS bridge is assessed (approximately 5.2 seconds). In a plot of deck length versus period, there is an almost linear relationship with periods of other regular suspension bridges, such as the Golden Gate Bridge and the Carquinez Bridge, both in the San Francisco Bay.

California↗

USGS2025

Evaluating the potential of co-located non-petroleum energy resources in the Gulf Coast using play fairwayaAnalysis

Geological resources critical to the energy transition, such as sedimentary geothermal, carbon storage potential, and lithium in brines, commonly struggle for economic feasibility as stand-alone developments but can have greater viability when the potential for more than one of these resources exist within the same reservoir or stacked in different stratigraphic intervals. There may also be instances where development of one resource inhibits development of others and decisions need to be made on how to best prioritize the use of those resources in the subsurface. Subsurface data sets were analyzed and integrated to evaluate the distribution of non-petroleum energy and related resources in the onshore and nearshore U.S. Gulf Coast. Temperature, pressure, brine composition (lithium content), and reservoir quality data for thirty-four depositional units have been compiled and visualized to high-grade areas where multiple resource opportunities likely coexist. For sedimentary geothermal, possible resource areas are defined as low potential (<90°C), moderate potential (90–150°C), and high potential (>150°C). For CO 2 storage, high potential areas exhibit supercritical CO 2 conditions less than 80% of the fracture gradient. Areas with pore pressure between 80% and 100% of the fracture gradient are considered to exhibit moderate potential and areas where the fracture gradient is equal to or greater than 100% are assigned low potential. Lithium resources in brines were defined by lithium concentrations as low potential (<100ppm), moderate potential (100-200ppm), or high potential (>200ppm). Reservoir quality affects the viability of all three of these resources and is evaluated using depositional environment maps of each unit. The resulting play fairway maps can be used for regional scale screening evaluations of these resources and to identify areas of interest where more detailed, prospect-scale studies can be undertaken.

Gulf Coast↗

USGS2026

Neutron scattering reveals fractionation of natural gas mixtures in unconventional petroleum reservoir pores: Perspectives on energy resource recovery and storage

In unconventional petroleum reservoirs hydrocarbon fluids are hosted by both mineral and organic matter pores. These pores can have diameters that range from microns to less than a single nanometer and, for unconventional reservoirs, there is evidence that small pores ( <20 nm diameter) may constitute a large proportion of the available space. Understanding subsurface volumes and how fluids behave in them can be helpful for predicting hydrocarbon production and storage in the subsurface. One area with knowledge gaps regarding hydrocarbon behavior in small pores is the possibility for mixtures to fractionate (i.e., unmix) based on pore size or pore type. Mixture fractionation as a function of pore size could impact recovery of hydrocarbons, drive compositional shifts during production, and limit fluid storage within candidate reservoirs. To investigate natural gas fractionation in small geologic pores, we applied total neutron scattering to probe methane-ethane mixtures at reservoir pressures (up to ≈30 MPa) and temperature (60°C) within a sample from the Upper Cretaceous Niobrara Formation. Neutron scattering data reveal only minor fractionation occurs between methane and ethane in 20-nm diameter sample mesopores. Increased fractionation is observed for sample micropores, with up to 72% (±1% at 1-sigma) methane found in 2 nm diameter pores following injection of a 50%-50% methane-ethane mixture. These data provide rarely available direct experimental observations of hydrocarbon mixture behavior under nanoconfinement in a sample from an important unconventional petroleum reservoir. Our results are discussed in the context of evaluating hydrocarbon resources in unconventional reservoir meso- and micropores, reconciling observed gas composition changes during production, and more broadly, understanding subsurface pore volumes within an energy storage framework.

Fuel↗

USGS2025

Scoping decision-maker needs and science availability to support regional natural capital accounting in the U.S. Colorado River Basin

Natural capital accounting has the potential to yield important policy insights at multiple scales, but there remains a disconnect between regional-scale natural capital accounts and their use for informing policy. In this paper, we propose a roadmap that could lead to the creation of policy-relevant regional accounts, with steps split across an initial scoping phase and a subsequent development phase. We demonstrate the scoping steps in action with an application to the Colorado River Basin (“Basin”), a large watershed in the southwestern United States (U.S.) that has faced aridification and substantial high-profile tradeoffs around the use of its water and other natural resources. Drawing on prior U.S. Geological Survey science co-production efforts, we conducted a series of eight discussion sessions with 41 scientists and science representatives whose work is relevant to Basin water, riparian and riverine ecosystems, upland ecosystems and energy and minerals. We summarise participants' thoughts on key topics and economic linkages, their insights and questions of interest and their recommendations on existing scientific data sources and gaps. We evaluate the suitability of the available data for construction of System of Environmental-Economic Accounting (SEEA) Central Framework and SEEA Ecosystem Accounting accounts, including those for land, water, forests, energy and minerals and ecosystems (covering extent, condition and ecosystem services). We present a series of lessons learned during the scoping phase, as well as lessons that could be relevant for future practitioners engaging in the development phase. The information can help guide the development of timely and relevant regional-scale environmental-economic accounts in the U.S. and beyond.

Arizona, California, Colorado, Nevada, New Mexico,↗

USGS2025

A spatiotemporal deep learning approach for predicting daily air-water temperature signal coupling and identification of key watershed physical parameters in a montane watershed

Seasonal shifts from runoff to groundwater dominance influence daily headwater stream temperatures, especially where local groundwater input is strong. This input buffers temperature during hot periods, supporting cold-water habitats. Recent studies use air–water temperature signal metrics to identify zones of strong stream–groundwater connectivity. While Previous studies used air–water signal ratios as proxies for groundwater influence but were limited to specific sites and periods, without dynamic forecasting. This study is the first to forecast daily A r as a spatiotemporal signal using a Graph Convolutional Network–Long Short-Term Memory (GCN-LSTM) model. The model was trained using hydroclimate data (air temperature, precipitation, shortwave radiation, streamflow) and watershed physical features (e.g., sand content, slope). Results showed high predictive skill, achieving R 2 (NSE, RMSE) of 0.86 (0.73, 0.0004) for one-day-ahead to 0.52 (0.50, 0.0009) for seven-days ahead forecasts. Prior studies often have not explicitly incorporated spatial hydrogeologic drivers, but this model explicitly incorporates them to assess their impact on A r forecasting and stream-groundwater connectivity. Feature analysis identified mean sand, elevation, slope, clay, and TWI as key predictors of A r . Stronger groundwater signals appeared in hillslopes, elevations, and tributaries, highlighting watershed influence on streamflow. However, limitations include reliance on historical air–water temperature patterns for training and limited representation of extreme climate conditions. Despite these limitations, unlike previous studies relying on measured in-situ stream and air temperature, this study forecasts A r directly from climate and physiographic features after training, avoiding in-situ data requirements. Findings aiding predictions of stream ecosystem resilience.

New York↗

USGS2026

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

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

Arizona, New Mexico↗

USGS2024

Mapping the resistivity structure of Walker Ridge 313 in the Gulf of Mexico using the marine CSEM method

A marine controlled source electromagnetic (CSEM) campaign was carried out in the Gulf of Mexico to further develop marine electromagnetic techniques in order to aid the detection and mapping of gas hydrate deposits. Marine CSEM methods are used to obtain an electrical resistivity structure of the subsurface which can indicate the type of substance filling the pore space, such as gas hydrates which are more resistive. Results from the Walker Ridge 313 study (WR 313) are presented in this paper and compared with the Gulf of Mexico Gas Hydrate Joint Industry Project II (JIP2) logging while drilling (LWD) results and available seismic data. The hydrate, known to exist within sheeted sand deposits, is mapped as a resistive region in the two dimensional (2D) CSEM inversion models. This is consistent with the JIP2 LWD resistivity results. CSEM inversions that use seismic horizons provide more realistic results compared to the unconstrained inversions by providing sharp boundaries and architectural control on the location of the resistive and conductive regions in the CSEM model. The seismic horizons include: 1) the base of the gas hydrate stability zone (BGHSZ), 2) the top of salt, and 3) the top and bottom of a fine grained marine mud interval with near vertical hydrate filled fractures, to constrain the CSEM inversion model. The top of salt provides improved location for brines, water saturated salt, and resistive salt. Inversions of the CSEM data map the occurrence of a ‘halo’ of conductive brines above salt. The use of the BGHSZ as a constraint on the inversion helps distinguish between free gas and gas hydrate as well as gas hydrate and water saturated sediments.

Louisiana↗

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↗

USGS2025

Ultramafic float rocks at Jezero crater (Mars): Excavation of lower crustal rocks or mantle peridotites by impact cratering?

Based on observation and data from meteorites and in situ scientific missions, experiments as well as models, the Martian mantle is assumed to share some compositional and mineralogical affinity with the terrestrial mantle. However, there might be subtle differences like the Martian mantle being more ferroan. Yet, we do not have any direct analysis of a Martian mantle rock to confirm this assumption. NASA’s Perseverance rover found olivine-rich boulder-sized float rocks on the upper Jezero fan (Mars). These boulders have an ultramafic composition and their mineralogy is dominantly composed of Fo 73±3 olivine with high-Mg orthopyroxene, Cr-rich Ti-Fe oxides and minor plagioclase and high-Ca pyroxene. Microtextural and petrological analysis reveals that these minerals crystallized at equilibrium. In addition, these boulders are different from all the bedrocks analyzed by Perseverance along its traverse which are crustal igneous rocks and sediments. Comparing our data to Martian meteorites and available Mars bulk silicate models (BSM), we discuss that these boulders could represent primitive melts and/or lower crustal material, and we specifically hypothesize that they could be mantle peridotites. We propose that these putative mantle rocks could have been excavated by the succession of impacts from the shallow mantle or lower crust in the Isidis region where Jezero crater is located. These olivine-rich boulders could thereby constitute the first direct analysis of a Martian mantle rock.

Earth and Planetary Science Letters↗

USGS2026

A framework for integrating spatiotemporal deep learning methods with landsat for annual land cover and impervious surface mapping

Land cover information is essential for understanding Earth’s surface dynamics and how vegetation, water, soil, climate, and terrain interact. The National Land Cover Database (NLCD) has been the authoritative source for consistent U.S. land cover mapping. To extend NLCD’s temporal resolution and reduce production latency, we developed the Land Cover Artificial Mapping System (LCAMS)—a prototype spatiotemporal deep learning framework piloted as the foundation for the new Annual NLCD. LCAMS builds on concepts from legacy NLCD and the U.S. Geological Survey Land Change Monitoring, Assessment, and Projection (LCMAP) initiatives. It employs a loosely coupled two-stage architecture consisting of independent but functionally interdependent spatial and temporal models. Spatial models extract per-year information from Landsat data, while the temporal models refine the spatial outputs to enforce inter-annual consistency—critical for reliable land change monitoring. LCAMS produces annual 30 m resolution land cover and impervious surface outputs, with region-specific fine-tuning to generalize across diverse landscapes and temporal dynamics. Validation was conducted using an independent dataset of 1925 randomly sampled plots from five U.S. Landsat Analysis Ready Data (ARD) tiles spanning 1985-2021, selected for spatial and temporal variability. This dataset was used consistently to evaluate LCAMS, Legacy NLCD, and LCMAP. Using the NLCD legend, LCAMS achieved 72.1 ± 1.60% overall agreement, compared to 71.1 ± 1.7% agreement for Legacy NLCD. Using the LCMAP legend, LCAMS achieved 83.4 ± 1.22% agreement, compared to 84.6 ± 1.11% agreement for LCMAP. Overall, LCAMS delivers comparable accuracy while offering higher thematic resolution, longer temporal coverage, and automated production of annual 30 m CONUS land cover.

Remote Sensing of Environment↗

USGS2025

The systematics of stable hydrogen (δ2H) and oxygen (δ18O) isotopes and tritium (3H) in the hydrothermal system of the Yellowstone Plateau volcanic field, USA

To improve our understanding of hydrothermal activity on the Yellowstone Plateau volcanic field, we collected and analyzed a large data set of δ 2 H, δ 18 O, and the 3 H concentrations of circum-neutral and alkaline waters. We find that (a) hot springs are fed by recharge throughout the volcanic plateau, likely focused through fractured, permeable tuff units. Previous work had stressed the need for light δ 2 H water recharge restricted to the northern part of the plateau or recharge during past cold periods. However, new data from the Y-7 drill hole suggests that recharge is not restricted to a certain area or a cold period. (b) δ 18 O values of thermal waters in the geyser basins are shifted from the global meteoric water line by temperature-dependent water-rock reactions with higher subsurface temperatures resulting in a greater shift. (c) Large temporal variations in the isotopic composition of meteoric water recharge and small temporal variability in the isotopic composition of hot spring discharge implies that the volume of groundwater in, and around the Yellowstone caldera is substantially larger than the volume of annual water recharge. (d) Hot springs discharged through different rhyolitic units correlate with identifiable differences in δ 2 H and δ 18 O compositions, 3 H concentrations, and water chemistry that imply equilibration at different temperatures and travel along different flow paths. (e) Based on measured 3 H concentrations, we calculate that hot spring waters in the central part of the geyser basins mostly contain <2% post-1950 meteoric water, whereas waters discharged at the basin margins contain larger fractions of post-1950s meteoric water.

Wyoming↗

USGS2026

Ground-motion aleatory-variability models for Puerto Rico and the U.S. Virgin Islands

I develop independent logic trees for aleatory variability for crustal and subduction-zone (interface and intraslab) earthquakes for seismic hazards analyses in Puerto Rico and the U.S. Virgin Islands (PRVI) from existing suites of ground-motion models (GMMs) and from ground-motion datasets, including a regional PRVI dataset. The aleatory variability models are parameterized using a partially nonergodic partitioning of standard deviation that consists of independently developed between-event ( ), site-to-site ( ), and event-corrected single-station ( ) standard deviation components. The effects of nonlinear site response on aleatory variability are incorporated through additional terms that modify the standard deviation components. Because one goal of this work is to develop independent logic trees for aleatory variability that synthesize the aleatory variability models from GMMs, I make use of the functional forms of the input GMMs. The PRVI dataset contains a limited number of stations with high-quality site metadata and does not contain records from earthquakes with magnitudes greater than 6.1, so I choose not to develop the aleatory variability models from the regional dataset alone. Instead, the standard deviation components from regional ground-motion data are evaluated against the components derived from GMMs and from available global datasets, and regionalized standard deviation components are incorporated where there is evidence that regional effects exhibit substantial differences. The resulting logic trees for aleatory variability consist of models of and that are consistent with semiempirical GMMs for active crustal and subduction-zone regimes, and two alternative models of , including one model that exhibits site-to-site variability informed by PRVI data, with values that exceed global models. The aleatory variability models may be considered in future hazards assessments in PRVI to simplify the hazard calculations, to incorporate regional ground-motion variability effects, and to enable direct logic-tree weighs of aleatory variability.

Puerto Rico, U.S. Virgin Islands↗

USGS2025

Using gridded seismicity to forecast the long-term spatial distribution of earthquakes for the 2025 Puerto Rico and U.S. Virgin Islands National Seismic Hazard Model

Gridded (or background) seismicity models are a critical component of probabilistic seismic hazard assessments, accounting for off‐fault and smaller‐magnitude earthquakes. They are typically developed by declustering and spatially smoothing an earthquake catalog to estimate a long‐term seismicity rate that can be used to forecast future earthquakes. Here, we present new gridded seismicity models for use in the 2025 National Seismic Hazard Model (NSHM) for Puerto Rico and the U.S. Virgin Islands (PRVI). The previous PRVI NSHM was released in 2003, and our new models incorporate updates to both data and methodology. We utilize an updated earthquake catalog based on improved Puerto Rico Seismic Network data with newly characterized completeness epochs. The catalog is divided into crustal, subduction interface, and intraslab seismicity using new methods and Slab2 subduction zone geometries. To forecast the long‐term spatial distribution of earthquakes, we use an updated methodology developed for the 2023 U.S. 50‐state NSHM, considering three declustering methods and two spatial smoothing methods based on 2D Gaussian kernels. To adapt it for the complex seismotectonics of the region, we also adopt probabilistic methods to account for events with unknown depths and uncertainties in tectonic classification, and develop a new method for spatial scaling to counteract the effects of spatial variability in network coverage while maintaining the use of smaller events. Finally, we test the performance of these spatial models in forecasting the location of M w ≥ 5earthquakes in the region. Our updated methodology improves the representation of epistemic uncertainty relative to the 2003 model, and our results demonstrate the effectiveness of the new measures we have introduced to address heterogeneities in network detection and systematically evaluate forecast performance.

Puerto Rico, U.S. Virgin Islands↗

USGS2024

Characterization of change in tree cover state and condition over the conterminous United States

Variability in the effects of disturbances and extreme climate events can lead to changes in tree cover over time, including partial or complete loss, with diverse ecological consequences. It is therefore critical to identify in space and time the change processes that lead to tree cover change. Studies of change are often hampered by the lack of data capable of consistently detecting different types of change. Using the Landsat satellite record to create a long time-series of land cover and land cover change, the U.S. Geological Survey Land Change Monitoring Assessment and Projection (LCMAP) project has made an annual time series of land cover across the conterminous United States for the period 1985 to 2018. Multiple LCMAP products analyzed together with map validation reference plots provide a robust basis for understanding tree cover change. In LCMAP (Collection 1.2), annual change detection is based on harmonic model breaks calculated at each Landsat pixel from the Continuous Change Detection and Classification (CCDC) algorithm. The results showed that the majority of CCDC harmonic model breaks (signifying change) indicated partial tree cover loss (associated with management practices such as tree cover thinning) as compared to complete tree cover loss (associated with practices like clearcut harvest or fire disturbance). Substantially fewer occurrences of complete tree cover loss were associated with change in land cover state. The area of annual tree cover change increased after the late 1990s and stayed high for the rest of the study period. The reference data showed that tree harvest dominated across the conterminous United States. The majority of tree cover change occurred in evergreen forests. Large estimates of disturbance-related tree cover change indicated that tree cover loss may have previously been underreported due to omission of partial tree cover loss in prior studies. This has considerable implications for forest carbon accounting along with tracking ecosystem goods and services.

Forests↗

USGS2024

Are the horizontal-to-vertical spectral ratios of earthquakes and microtremors the same?

We consider the similarities and differences between earthquake and microtremor horizontal‐to‐vertical spectral ratios (eHVSR and mHVSR, respectively) using a dataset of 161 sites in southern California. Quantitative comparisons are made in terms of the eHVSR and mHVSR lognormal median curves, as well as the frequencies and amplitudes associated with the fundamental‐ and higher‐mode resonances where present. The results show only 58% of the eHVSR–mHVSR pairs agree in terms of their median curve and only 25% of the eHVSR–mHVSR pairs agree in terms of shared resonances, which increases to 68% if flat HVSRs are considered equivalent. Furthermore, while the shared resonances match very well in terms of frequency (root mean square error, RMSE, <0.11 Hz), the amplitudes of those resonances do not agree (RMSE >1.6). These findings demonstrate that while eHVSR and mHVSR agree at some sites, they are not equivalent at all sites. To investigate if the agreement between eHVSR and mHVSR could be related to features of the microtremor data, earthquake recordings, and/or the site conditions, three machine learning (ML) models at varying levels of interpretability are presented. The ML models—which include multivariate logistic regression, gradient‐boosted trees, and support vector machines—show only partial success at using site‐specific data to predict whether eHVSR and mHVSR will likely agree in terms of their median curve (accuracy of 78%) and number of resonances (accuracy of 84%). Therefore, we conclude that while eHVSR and mHVSR can be quite similar in terms of resonant frequencies at some sites, they are not identical at all sites. Furthermore, preliminary evidence shows that the agreement of eHVSR and mHVSR can be predicted a priori given features of the microtremor measurements, earthquake recordings, and site conditions, although a larger dataset will be necessary for developing a robust predictive model.

California↗

USGS2026

Effects of human activity on Khumbu Glacier: Towards a sustainable Everest Base Camp

The Everest Base Camp (EBC), a critical staging area for mountaineers attempting to summit Mount Everest, has seen a significant increase in human activity over the past few decades. The increasing number of climbers, trekkers, and support staff, particularly at the campsite area, has intensified environmental impacts on the Khumbu Glacier. This study investigates the effects of human activity at EBC, focusing on three contributors to glacier melt: global climate change, local fossil fuel consumption, and human urinary discharge. Through field data collected during the spring 2023 climbing season, this study examines the energy released through the use of liquefied petroleum gas (LPG), kerosene, and petrol for cooking, heating, and electricity, as well as the heat generated by human urinary discharge. The total heat energy released from these activities at EBC in the spring of 2023 was 849,174 ± 179,774 MJ, which would be sufficient to melt 2492 ± 528 tons of glacier ice and snow. Trends in land surface temperature at EBC and adjacent environments during the 1991–2023 period were calculated from Landsat 5, 7, and 9 satellite data. The 32-year Landsat record reveals that EBC surface temperature increased by 0.28 °C year −1 , which was roughly twice the rate of warming at the surface of Khumbu Glacier adjacent to EBC and about 15% higher than the temperature increase of the debris-covered terrain immediately to the north of EBC. The findings suggest that addressing these anthropogenic influences could help to preserve the Khumbu Glacier and support the sustainability of mountaineering in the region. This paper also takes a transformative approach and explores stable and safer locations in case there is a necessity to relocate the current EBC. Two sites have been identified southwest of the existing EBC, which, unlike the current supraglacial site, are situated on stable ground.

Everest Base Camp, Khumbu Glacier↗

USGS2026

As above, so below? A framework for integrating long-term water quantity trends reveals divergent patterns in groundwater and low streamflow across the United States

Climate, land-use, and disturbance drive long-term global trends in groundwater levels and streamflow. At large scales, these trends are typically considered separately, despite the well-established concept that groundwater and surface water comprise a single resource. Joint trend assessment at national scales is challenging because it requires pairing and aggregating data from spatially disparate streamflow and groundwater monitoring sites for which no established framework exists. Here, we evaluate alternative approaches for integrating groundwater and streamflow data to enable joint trend analysis—a critical step toward understanding how water-budget components respond concurrently and interactively to environmental drivers. Mann–Kendall trends were computed for individual groundwater (annual mean depth) and streamflow (annual low of 7 d averages) sites across the U.S over 21- (2000–2020), 31- (1990–2020), and 41-year (1980–2020) periods. Regional Kendall trends were calculated using five spatially contiguous and noncontiguous regional classifications for aggregation based on subsurface (e.g. aquifer, geology) and surface (e.g. watershed, landscape) characteristics. Site-level results revealed contrasting trends, with tendencies toward increasing low flows (wetting) and increasing groundwater depths (drying). Agreement between streamflow and groundwater trends increased with regional aggregation and longer timeframes, though persistent skew toward streamflow wetting and groundwater drying remained. Results varied by region and trend period, with notable consistencies: unified drying in the West/Southwest and wetting in the Upper Midwest. Directional mismatches in long-term trends were prominent in the High Plains and Mississippi Alluvial Plain, whereas near-term mismatches were most evident in the Northwest. Aggregation by hydrologic landscape regions (HLR) yielded the greatest agreement between groundwater and streamflow trends. These findings indicate that coupled responses may represent combined influences of climate, relief, and geology, as captured by HLR, more strongly than geography or geology alone. Integrated water availability assessments may benefit from a multi-characteristic classification framework to treat groundwater and surface water as a unified resource.

Environmental Research: Water↗

USGS2025

3D Converted wave reverse time migration imaging

We describe a newly developed method for recovering high-resolution images of seismic discontinuities, such as subducting slabs, in 3D. Our method makes use of converted S → P or P → S waves observed by dense arrays of seismometers to infer the locations and relative strengths of seismic discontinuities at depth in a target region. Observed direct and converted waves are backpropagated to their times of origin. The time-reversed wavefield is then separated into its constituent P and S components via the Helmholtz decomposition, and those separated wavefields are used to compute imaging functions that characterize the locations and relative strengths of seismic discontinuities. Imaging functions may be designed to use either S → P or P → S waves, so that users can target those arrivals expected to be most dominant in a given dataset. We have previously demonstrated the efficacy of our method in two dimensions, and we now present a 3D implementation of our technique which addresses the significant computational challenges posed by the size of volumetric wavefield data in three dimensions. Through a series of synthetic examples, we demonstrate that our method is capable of recovering the fine scale structure of a subducting slab given realistic station coverage and earthquake sources. We investigate optimal seismic station geometries for our technique and explore image interpretability in regions with poor data coverage. We find that linear station geometries yield more optimal, interpretable imaging functions than collections of small arrays can. We also show that our method can successfully recover bothS → P or P → S images when realistic shear earthquake sources are used, and we explore the additional computational challenges presented by the high frequency content of S waves. Our results demonstrate the potential for our technique to recover high-resolution information about subducting slabs in real-world regions, given that relatively sparse seismic arrays with only approximately 100 stations are capable of recovering interpretable imaging functions from just a few realistic earthquake sources for multiple discontinuities at significant depth in an area of approximately 400~sq~km.

Seismica↗