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822 records · Page 12Linked to original sources
USGS2025

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

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

Hawaii↗
USGS2024

Correlation analysis of groundwater and hydrologic data, Kaloko-Honokōhau National Historical Park, Hawai‘i

Designated in 1978, Kaloko-Honokōhau National Historical Park is located on the west coast of the Island of Hawaiʻi. The Kaloko-Honokōhau National Historical Park encompasses about 1,200 acres of coastal land and nearshore ecosystems, which include wetlands, anchialine pools (landlocked bodies of brackish water with hydrologic connections to the ocean), fishponds, a fishtrap, and coral reefs. These nearshore ecosystems are dependent on groundwater discharge with a freshwater component and provide habitat for threatened and endangered, endemic species, such as the orangeblack Hawaiian damselfly ( Megalagrion xanthomelas ) and the Hawaiian coot (ʻAlae keʻokeʻo, Fulica alai ). The populations of these native species, however, are threatened because of habitat loss related to urban development and environmental changes. Kaloko-Honokōhau National Historical Park is within the Keauhou aquifer system and the North Kona District, which experienced a 52 percent resident-population increase between 2000 and 2020 and a 41 percent visitor increase between 2008 and 2019. To support the current water demand associated with this growing population, groundwater is the primary source of freshwater used in the North Kona District, with about 15 million gallons of groundwater withdrawn from the Keauhou aquifer system per day since 2009. With anticipated development, future (2015–35) groundwater withdrawal from the Keauhou aquifer system is projected to be about 55 percent greater than recent (2012–14) withdrawal. Because Kaloko-Honokōhau National Historical Park is located within a coastal aquifer, natural and human-induced changes can affect the quality and quantity of groundwater, which can threaten groundwater-dependent ecosystems. To improve understanding of recent groundwater conditions, the U.S. Geological Survey, in cooperation with the National Park Service, undertook this study to document correlations between hydrologic time-series datasets from sites in and near Kaloko-Honokōhau National Historical Park using the nonparametric (distribution-free) Kendall’s tau statistical test. For the statistical analyses, dependent variables representing the groundwater system include groundwater level, the groundwater-level difference between pairs of sites, and specific conductance, and independent variables include datasets of sea level, rainfall, and groundwater withdrawal. About 34 percent of the 140 non-time-lagged Kendall’s tau statistical tests evaluated in this report are statistically significant ( p -value ≤ 0.050) with generally weak (0.1 ≤ tau ≤ 0.2) to moderate (0.2 ≤ tau ≤ 0.3) correlations. Groundwater levels measured at monitoring sites have the strongest correlation with the multivariate El Niño–Southern Oscillation index and withdrawal from production wells at the nearby Kohanaiki Private Club Community. Specific conductance is not consistently and significantly correlated with the independent hydrologic variables investigated in this report. Because the relations between hydrologic variables are commonly not instantaneous, a second set of correlations was evaluated after applying a range of time lags to the independent variable datasets. Relative to the non-time-lagged case (the set of correlations that did not use time-lagged independent variables), some of the time-lagged independent variables improved correlations with some of the dependent variables. For a particular independent variable, similar time lags were expected between the independent variable and dependent variable at all four monitoring sites. However, different time lags among the four sites sometimes produced the strongest correlations. This study identified several correlations that are statistically significant and hydrologically plausible, but the correlations could indicate that multiple concurrent factors are controlling the observed groundwater-system response, which might be better addressed using multivariate analyses. This study only investigates bivariate correlations, which may not explain all the variance in the data. The correlations analyzed in this report are limited by the quantity of available hydrologic data in the area near Kaloko-Honokōhau National Historical Park and are based on 14 years of time-series data, which were aggregated to a relatively coarse monthly temporal resolution that represents the minimum resolution common to all datasets.

Hawaii↗
USGS2024

New developments at the Center for Engineering Strong-Motion Data (CESMD)

The Center for Engineering Strong-Motion Data (CESMD), an internationally utilized joint center of the U.S. Geological Survey (USGS) and the California Geological Survey (CGS), provides a single access point for earthquake strong-motion records and station metadata from the CGS California Strong-Motion Instrumentation Program (CSMIP), the USGS National Strong-Motion Project (NSMP), the USGS Advanced National Seismic System, and other affiliates. The CESMD has been continuously improving its webtools to facilitate the access of strong-motion data and metadata for use in post-earthquake response and for scientific and engineering research applications. The Center provides raw and processed strong-motion data via the Engineering Data Center (EDC) and the Virtual Data Center (VDC) web portals. This paper focuses on the strong-motion products provided by the EDC where more than 48,000 records with peak ground accelerations greater than 0.1% g from over 2400 earthquakes are currently hosted. and on the ongoing efforts to develop data access tools and applications. The new developments and ongoing efforts in the EDC include: 1) enhancements to the CESMD webservices to facilitate access to station metadata, earthquake information, and strong motion records 2) new features to the interactive map interface, improving the visualization and access to earthquake, station, and record information, 3) efforts to develop a new web application tool for data format conversion from a number of data formats, 4) efforts to unify varying waveform data formats into a consistent format, 5) ongoing efforts to compile seismic station site geology, measured or inferred Vs30 values, shear-wave profiles, NEHRP site class, and available structural instrument deployment schematics, and 6) a special studies pages for research topic-specific ground motion datasets that offer uniform processing of records from a variety of sources.

Conference Paper↗
USGS2025

Dynamic baseflow storage estimates and the role of topography, geology and evapotranspiration on streamflow recession characteristics in the Neversink Reservoir Watershed, New York

Estimates of dynamic groundwater volumes supplying baseflow to streams are important for water availability projections during extended periods of drought. The primary goals of this study were to provide dynamic storage volume estimates, inferred from streamflow recession analysis, for baseflow regimes within seven gaged catchments within the Neversink Reservoir Watershed (NRW), a critical municipal water source for New York City. Additionally, geomorphological properties, surficial geology and hydro-meteorological processes were quantified and described in relation to time and spatially variable recession behaviour and storage estimates across the NRW. To explore these relationships, we (1) evaluated seasonal trends in streamflow recession behaviour in relation to modelled potential evapotranspiration (PET) and catchment runoff rates, (2) derived empirical streamflow models for cool-season runoff using both linear and nonlinear reservoir assumptions for baseflow and (3) calculated metrics related to the geology and geomorphology of each catchment and compared these metrics to area normalised baseflow dynamic storage estimates. Results show that baseflow recession behaves as a nonlinear reservoir, and applying linear groundwater reservoir assumptions may underestimate the total dynamic storage volumes compared to what would be predicted for a nonlinear reservoir. Increases in PET caused decreases in storage conditions that resulted in increased recession rates and nonlinearity in streamflow recession during the growing season. Additionally, we found that while no single physical catchment characteristic solely predicted catchment storage dynamics, sediment volume and stream gradients were stronger predictors of normalised storage volumes than catchment surface area or surface topography alone. Within the NRW, catchments with the highest sediment volume exhibited the lowest recession rates and higher dynamic storage volumes, while the smallest catchment, mostly devoid of sediment, had the fastest recession rate and lowest dynamic storage volume.

New York↗
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

Quality of groundwater used for domestic supply in the Gilroy-Hollister basin and surrounding areas, California, 2022

More than 2 million Californians rely on groundwater from domestic wells for drinking-water supply. This report summarizes a 2022 California Groundwater Ambient Monitoring and Assessment Priority Basin Project (GAMA-PBP) water-quality survey of 33 domestic and small-system drinking-water supply wells in the Gilroy-Hollister Valley groundwater basin and the surrounding areas, where more than 20,000 residents are estimated to utilize privately owned domestic wells. The study area includes the Llagas subbasin in the north, the North San Benito subbasin in the south, and the surrounding uplands. The study was focused on groundwater resources used for domestic drinking-water supply, which are mostly drawn from shallower parts of aquifer systems rather than those of groundwater resources used for public drinking-water supply in the same area. This assessment characterized the quality of ambient groundwater in the aquifer before filtration or treatment, rather than the quality of drinking water delivered to the tap. To provide context, the measured concentrations of constituents in groundwater were compared to Federal and California State regulatory and non-regulatory benchmarks for drinking-water quality. A grid-based method was used to estimate the areal proportions of groundwater resources used for domestic drinking wells that have water-quality constituents present at high concentrations (above the benchmark), moderate concentrations (between one-half of the benchmark and the benchmark for inorganic constituents, or between one-tenth of the benchmark and the benchmark for organic constituents), and low concentrations (less than one-half or one-tenth the benchmark for inorganic and organic constituents, respectively). This method provides statistically representative results at the study-area scale and permits comparisons to other GAMA-PBP study areas. In the study area, inorganic constituents in groundwater were greater than regulatory benchmarks (U.S. Environmental Protection Agency [EPA] or State of California maximum contaminant levels [MCLs]) for public drinking-water quality in 24 percent of domestic groundwater resources. The inorganic constituents present at concentrations greater than MCLs for drinking water were nitrate (as nitrogen), barium, chromium, and selenium. Total dissolved solids (TDS) or manganese were present at concentrations greater than the secondary maximum contaminant levels (SMCLs) that the State of California uses as aesthetic-based benchmarks in 48 percent of domestic groundwater resources. No volatile organic compounds or pesticide constituents were present at concentrations greater than regulatory benchmarks. Total coliform bacteria and enterococci were detected in 4 percent of domestic groundwater resources. Per- and polyfluoroalkyl substances (PFAS) were detected in 19 percent of domestic groundwater resources, and 10 percent had concentrations greater than recently enacted (April 2024) EPA MCLs. Physical and chemical factors from natural and anthropogenic sources that could affect the groundwater quality were evaluated using results from statistical testing of associations between constituent concentrations and potential explanatory variables. In this study, relevant physical factors include well construction characteristics, groundwater age, site proximity to groundwater recharge or discharge zones, and potential sources of contamination. Relevant chemical factors include the initial chemistry of the recharge water, the mineralogy of the aquifer sediments, and the subsequent shifts in chemistry as biologic and geologic reactions alter groundwater in the subsurface. Nitrate concentrations were correlated to agricultural land use, distance from the boundary of the Gilroy-Hollister Valley groundwater basin, and the proportion of modern (post-1950s) water captured by the well. Denitrification under anoxic redox conditions can mitigate some nitrate derived from fertilizer application. Total dissolved solids primarily were derived from water-rock interactions with soils and aquifer materials in the study area, but there were high concentrations where agricultural practices contributed additional TDS. Mineralogy of aquifer sediments and rocks also affect barium, selenium, boron, and chromium concentrations in the Gilroy-Hollister Valley groundwater basin. PFAS were positively correlated with urban land use and the proportion of modern water captured by the well.

California↗
USGS2024

Following the tug of the audience from complex to simplified hazards maps at Cascade Range volcanoes

Volcano-hazard maps are broadly recognized as important tools for forecasting and managing volcanic crises and for disseminating spatial information to authorities and people at risk. As scientists, we might presume that hazards maps can be developed at the time and with the methods of our discretion, yet the co-production of maps with stakeholder groups, who have programmatic needs of their own, can sway the timing, usability, and acceptance of map products. We examine two volcano hazard map-making efforts by staff at the U.S. Geological Survey. During the 1990s and early 2000s scientists developed a series of hazard assessments and maps with detailed zonations for volcanoes in Washington and Oregon. In 2009, the National Park Service expressed the need for simplified versions of the existing hazard maps for a high-profile visitor center exhibit. This request created an opportunity for scientists to rethink the objectives, scope, content, and map representations of hazards. The primary focus of this article is a discussion of processes used by scientists to distill the most critical information within the official parent maps into a series of simplified maps using criteria specified. We contextualize this project with information about development of the parent maps, public response to the simplified hazard maps, the value of user engagement in mapmaking, and with reference to the abundance of guidance available to the next generation of hazard-mapmakers. We argue that simplified versions of maps should be developed in tandem with any hazard maps that contain technical complexities, not as a replacement, but as a mechanism to broaden awareness of hazards. We found that when scientists endeavor to design vivid and easy-to-understand maps, people in many professions find uses for them within their organization’s information products, resulting in extensive distribution.

Washington↗
USGS2026

Salinas Valley integrated hydrologic and reservoir operations models, Monterey and San Luis Obispo Counties, California

The area surrounding the Salinas Valley groundwater basin in Monterey and San Luis Obispo Counties of California is a highly productive agricultural area, contributes substantially to the local economy, and provides a substantial portion of vegetables and other agricultural commodities to the Nation. This region of California provides about half of the Nation’s lettuce, celery, broccoli, and spinach each year. Thus, this agricultural area provides substantial volumes of agricultural products not just for California but for the United States. Changes in population and increased agricultural development, which includes a shift toward more water-intensive crops, and climate variability, have put increasing demand on both surface-water and groundwater resources in the valley. This situation has resulted in water management challenges in the Salinas Valley that generally relate to the distribution of the water supply throughout the basin. Where and when the water is present in the surface and subsurface does not coincide with where and when the water is needed. Historically, to deal with the distribution issue, water has been used conjunctively in the valley. Conjunctive use is a water management strategy that coordinates surface-water and groundwater use to maximize water availability. Groundwater is used throughout the Salinas Valley to meet water demands when surface-water supplies are insufficient. The availability of surface water is constrained by climate. Precipitation and streamflow vary seasonally and year to year. Although there are two reservoirs in the Salinas Valley to capture and store water during wet periods, the only conveyance of reservoir water to coastal agricultural areas is the Salinas River. Increasing demand for groundwater and surface-water resources throughout the Salinas Valley has resulted in undesirable effects from unsustainable water use, such as surface-water depletion, groundwater-level declines, storage depletion in the principal aquifers, and seawater intrusion. To address these escalating issues, local communities, water management agencies, and groundwater sustainability agencies are evaluating how to sustainably manage both their surface-water and groundwater resources. To meet water demands and reduce the undesirable effects of unsustainable water use, continued conjunctive management of surface water and groundwater would ideally incorporate strategies to deal with increases in demand and climate variability. To evaluate the challenging water management issues in the Salinas Valley, the U.S. Geological Survey, Monterey County Water Resources Agency, and the Salinas Valley Basin Groundwater Sustainability Agency developed a comprehensive suite of models that represent the Salinas Valley hydrogeologic system called the Salinas Valley System Model. The geologic framework is known as the Salinas Valley Geologic Framework and was developed to characterize the subsurface using various topographic and geologic data sources, including information on hydrogeologic units, their surfaces and extents, geologic structures, lithology, and elevations from borehole data and cross sections, as well as details on faults and existing models. The surface-water model is called the Salinas Valley Watershed Model and simulates the Salinas River watershed. Monthly surface-water inflows into the integrated hydrologic model domain were simulated using the Salinas Valley Watershed Model. The historical model uses historical climate data, water and land use data, and reservoir releases to simulate agricultural operations, including landscape water demands, diversions, and reclaimed wastewater. The operational model adds an embedded reservoir operations framework to the simulation of the historical model that allows specified operational rules to simulate reservoir releases and changes in reservoir storage. The operational model assumes current reservoir operations and constant land use, which differs from historical conditions. Thus, the operational model is a hypothetical baseline model that can be used by local water managers to evaluate and quantify potential benefits of water supply projects. Together, the geologic framework, watershed, historical, and operational models form a tool that can be used to simulate irrigated agriculture and associated reservoir operations of the integrated hydrologic system of the Salinas Valley.

California↗
USGS2021

Geology, mineralogy, and cassiterite geochronology of the Ayawilca Zn-Pb-Ag-In-Sn-Cu deposit, Pasco, Peru

The Ayawilca deposit in Pasco, Peru, represents the most significant recent base-metal discovery in the central Andes and one of the largest undeveloped In resources globally. As of 2018, it hosts an 11.7 Mt indicated resource grading 6.9% Zn, 0.16% Pb, 15 g/t Ag, and 84 g/t In, an additional 45.0 Mt inferred resource grading 5.6% Zn, 0.23% Pb, 17 g/t Ag, and 67 g/t In, and a separate Sn-Cu-Ag inferred resource of 14.5 Mt grading 0.63% Sn, 0.21% Cu, and 18 g/t Ag. Newly obtained U–Pb dates for cassiterite by LA-ICP-MS (22.77 ± 0.41 and 23.05 ± 2.06 Ma) assign the Ayawilca deposit to the Miocene polymetallic belt of central Peru. The polymetallic mineralization occurs as up to 70-m-thick mantos hosted by carbonate rocks of the Late Triassic to Early Jurassic Pucará Group, and subordinately, as steeply dipping veins hosted by rocks of the Pucará Group and overlying Cretaceous sandstones-siltstones of the Goyllarisquizga Group. Relicts of a distal retrograde magnesian skarn and cassiterite (stage pre-A) were identified in the deepest mantos. The volumetrically most important mineralization at Ayawilca comprises a low-sulfidation assemblage (stage A) with quartz, pyrrhotite, arsenopyrite, chalcopyrite, Fe-rich sphalerite, and traces of stannite and herzenbergite. Stage A sphalerite records progressive Fe depletion, from 33 to 10 mol% FeS, which is compatible with the observed transition from low- to a subsequent intermediate-sulfidation stage (B) marked by the crystallization of abundant pyrite and marcasite. Finally, during a later intermediate-sulfidation stage (C) sphalerite (up to 11 mol% FeS), galena, native bismuth, Cu-Pb-Ag sulfosalts, siderite, Mn-Fe carbonates, kaolinite, dickite, and sericite were deposited. This paragenetic evolution shows striking similarities with that at the Cerro de Pasco Cordilleran-type polymetallic deposit, even if at Ayawilca stage C did not reach high-sulfidation conditions. The occurrence of an early retrograde skarn assemblage suggests that the manto bodies at Ayawilca formed at the transition between distal skarn and skarn-free (Cordilleran-type) carbonate-replacement mineralization. Mineral assemblages define a T- f S 2 evolutionary path close to the pyrrhotite-pyrite boundary. Buffering of hydrothermal fluids by underlying Devonian carbonaceous phyllites of the Excelsior Group imposed highly reduced conditions during stage A mineralization (log f O 2 < − 30 atm). The low f O 2 favored efficient Sn mobility during stages pre-A and A, in contrast to other known ore deposits in the polymetallic belt of central Peru, in which the occurrence of Sn minerals is minor. Subsequent cooling, progressive sealing of vein walls, and decreasing buffering potential of the host rocks promoted the shift from low- (stage A) to intermediate-sulfidation (stages B and C) states. LA-ICP-MS analyses reveal significant In contents in Fe-rich sphalerite (up to 1.7 wt%), stannite (up to 1908 ppm), and chalcopyrite (up to 1185 ppm). The highest In content was found in stage A sphalerite that precipitated along with chalcopyrite and stannite, thus pointing to the early, low-sulfidation assemblage as prospective for this high-tech metal in similar mineral systems. Indium was likely incorporated into the sphalerite crystal lattice via Cu + + In 3+ ↔ 2 Zn 2+ and (Sn, Ge) 4+ + (Ga, In) 3+ + (Cu + Ag) + ↔ 4 Zn 2+ coupled substitutions. Indium incorporation mechanisms into the stannite and chalcopyrite crystal lattices remain unclear.

Pasco↗
USGS2026

A ground-motion model derived using a generalized mean rupture distance for large slab interface earthquakes

Source–station distance is a central input to ground‐motion models (GMMs) for predicting seismic shaking. GMM development uses distance metrics including the Joyner–Boore distance, which is the shortest distance from an observation point to the surface projection of the earthquake rupture, and R rup the shortest distance to the rupture in three dimensions. Thompson and Baltay (2018) proposed the generalized mean rupture distance R p to address observed near‐fault ground‐motion saturation. R p accounts for the contribution to the shaking of all parts of the rupture and provides a simple method for incorporating spatially variable slip. They used R p to develop a GMM for shallow crustal earthquakes, assuming uniform slip. Here, we investigate the improvement offered by an R p ‐based GMM for large subduction interface earthquakes by recalibrating the path term for a published GMM ( Parker et al. , 2022 ) using R p derived from distributed slip models (DSMs). Inspection of within‐event and total residuals indicates that the recalibrated model fits the data at least as well as an alternative in which the path term was recalibrated using R rup and the same dataset. Incorporating slip information in its entirety or trimming the DSM geometry is preferable to assuming uniform moment release on a prescribed model fault that extends beyond the actual ruptured area. R p tuned to minimize model uncertainty is closer to the maximum distance to rupture for peak ground velocity (PGV) than acceleration, possibly reflecting the contribution to high‐frequency shaking of slip on local asperities. However, when the moment is concentrated far from stations, PGV is fit adequately with R p closer to R rup ⁠ . Our results suggest incorporating slip‐derived moment release through R p could improve GMMs for slab interface events, especially if the implementation of R p ‐based models is refined using a larger dataset of earthquakes with greater geographic diversity to account for regional ground‐motion variations.

Seismological Research Letters↗
USGS2025

Time-varying rates of organic and inorganic mass accumulation in southeast Louisiana marshes: Relationships to sea-level anomalies and tropical storms

Louisiana's coastal wetlands are complex systems that require a continuous input of organic and inorganic material to keep pace with relative sea-level rise. Coastal restoration projects such as sediment diversions are being implemented to mitigate land loss and increase availability of inorganic sediment to coastal wetlands, and marshes specifically rely on organic material to build soil volume and maintain surface elevation. Interannual-to-decadal sea-level anomalies such as hurricanes can affect marsh accretion, mineral deposition, and plant productivity. In this light, complex ecogeomorphic feedback controls whether a marsh surface is sustainable or eroded/drowns. This study performs some of the first differential vertical accretion rates (VARs) and organic and inorganic mass accumulation rates (MARs) over time in SE Louisiana marshes determined from the 210 Pb Constant Rate of Supply model, coupled with standard 137 Cs VARs. These accumulation rates over the past ∼100 years were measured from a total of six brackish and salt marsh locations in Barataria Basin near the proposed Mid-Barataria Sediment Diversion. They were then related to interannual sea-surface elevations at Grand Isle, Louisiana, over the last ∼60 years and recorded hurricane activity in the delta. Results show VARs range from 0.63 cm/y to 1.69 cm/y and total MARs range from 0.11 to 0.43 g/cm 2 /y. Temporally, VARs and MARs (total, inorganic, and organic) are characterized by gradual increases in rates with decreasing age along with episodic peaks in VARs and MARs. The findings of this study indicate that no relationship occurs between sea-level anomalies and VARs or organic and inorganic MARs; however, a strong relationship appears to occur between major hurricanes to VAR and MAR contributions. Furthermore, high water content (81 ± 8%) and organic-rich soils in the sediment cores highlight the significance of belowground biomass and associated pore volume in maintaining marsh elevation in the study area.

Louisiana↗
USGS2023

Don’t Let Negatives Hold You Back: Accounting for Underlying Physics and Natural Distributions of Hydrothermal Systems When Selecting Negative Training Sites Leads to Better Machine Learning Predictions

Selecting negative training sites is an important challenge to resolve when utilizing machine learning (ML) for predicting hydrothermal resource favorability because ideal models would discriminate between hydrothermal systems (positives) and all types of locations without hydrothermal systems (negatives). The Nevada Machine Learning project (NVML) fit an artificial neural network to identify areas favorable for hydrothermal systems by selecting 62 negative sites where the research team had confidence that no hydrothermal resource exists. Herein, we compare the implications of the expert selection of negatives (i.e., the NVML strategy) with a random sample strategy, where it is assumed that areas outside the favorable structural ellipses defined by NVML are negative. Because hydrothermal systems are sparse, it is highly probable that, in the absence of a favorable geological structure, hydrothermal favorability is low. We compare three training strategies: 1) the positive and negative labeled examples from NVML; 2) the positive examples from NVML with randomly selected negatives in equal frequency as NVML; and 3) the positive examples from NVML with randomly selected negatives reflecting the expected natural distribution of hydrothermal systems relative to the total area. We apply these training strategies to the NVML feature data (input data) using two ML algorithms (XGBoost and logistic regression) to create six favorability maps for hydrothermal resources. When accounting for the expected natural distribution of hydrothermal systems, we find that XGBoost performs better than the NVML neural network and its negatives. Model validation was less reliable using F1 scores, a common performance metric, than comparing probability estimates at known positives, likely because of the extreme natural class imbalance and the lack of negatively labeled sites. This work demonstrates that expert selection of negatives for training in NVML likely imparted modeling bias. Accounting for the sparsity of hydrothermal systems and all the types of locations without hydrothermal systems allows us to create better models for predicting hydrothermal resource favorability.

Geothermal Resources Council Transactions↗
USGS2026

Efficient physics‐informed ground‐motion simulations with reduced‐order models: CyberShake implications and high‐resolution site terms for southern San Andreas fault earthquakes

Recent advances in Probabilistic Seismic Hazard Analysis (PSHA) leverage physics‐based ground‐motion simulations to estimate seismic hazard, such as the CyberShake project. However, computational costs quickly escalate when performing PSHA for numerous faults or sites and can become prohibitively expensive. To reduce computational demands, CyberShake uses reciprocity and interpolates physics‐informed corrections from simulations conducted at fewer locations, but the accuracy of these interpolations remains poorly quantified. To quantify the interpolation accuracy, we derive high‐resolution, frequency‐dependent site terms for southern California and compare them with interpolated site terms using the CyberShake approach. We accomplish this by performing a set of earthquake point‐source simulations distributed along the nonplanar fault geometry for the southern San Andreas fault (SSAF) extending from Bombay Beach to Lake Hughes. Using SeisSol, we simulate three minutes of viscoelastic seismic wave propagation for these sources and store the horizontal‐component Green’s functions for 480,000 sites. We then use a scientific machine learning approach based on interpolated proper orthogonal decomposition to construct an accurate reduced‐order model of the Green’s functions to efficiently predict effective amplitude spectra (EAS) for finite‐source rupture models of SSAF earthquakes. Using minimum curvature interpolation with tension, as used in CyberShake, we compare the interpolated site terms against our high‐resolution site terms. We identify local discrepancies with EAS differing by up to a factor of approximately three. Furthermore, we identify locations where unexpectedly high or low ground motions are missed when using the interpolated dataset for these earthquakes. We estimate that our approach may be used within CyberShake to reduce the time‐to‐solution by a factor of 336 for the entire earthquake rupture forecast. Our analysis of physics‐based site terms provides more insight into the seismic hazard due to SSAF ruptures and guides future developments by combining high‐performance computing and reduced‐order modeling techniques for PSHA.

California↗
USGS2024

Separating signals in elevation data improves supervised machine learning predictions for hydrothermal favorability

A recent study identified topography (land surface elevation above sea level) as an important input dataset (feature) for predicting the location of hydrothermal systems in the Great Basin in Nevada. Yet, topography is generally a result of more than one geological process and may consequently contain multiple distinct signals. For example, the geologic evolution of the Great Basin has produced both crustal thickening (i.e., regional-scale trends in elevation) and thinning via Basin and Range extensional faulting (i.e., valley-scale topographic relief). We postulate that these geologic processes may affect the occurrence of hydrothermal systems differently. Therefore, we separate the regional trend from the valley-scale signal in the Great Basin, and then use them separately to evaluate the importance of each as predictors for hydrothermal favorability. Our prior work applying supervised machine learning (ML) using the data from the Nevada Machine Learning Project demonstrated that employing a training strategy that randomly selects negative training sites produces better performing models for predicting hydrothermal favorability than a training strategy that uses expert-selected negatives. The models created using both training strategies exhibited a west-east geographic trend in the predictions for the favorability of hydrothermal resources. These models generally predicted higher favorability in western Nevada and lower favorability in eastern Nevada. This west-east trend in predicted favorability correlates with elevation across the Great Basin, which trends higher from west to east. By separating the original elevation feature into distinct features for elevation trend (i.e., regional-scale topography) and detrended elevation (i.e., valley-scale or local relative topography), we find that models using the separated topographic signals consistently outperform competing models that use the original elevation feature. Although western Nevada still exhibits higher favorability than eastern Nevada, using separated signals for regional elevation and local structure reduces the west-east prediction trend in the region and emphasizes structures associated with hydrothermal upflow. This work emphasizes how carefully engineering features to represent geological conditions relevant to hydrothermal systems allows ML algorithms to detect important patterns for predicting hydrothermal resource favorability and leads to better model performance.

Conference Paper↗
USGS2026

Ground-motion characterization for the 2025 U.S. National Seismic Hazard Model for Puerto Rico and the U.S. Virgin Islands

We develop the ground-motion characterization (GMC) for the 2025 U.S. National Seismic Hazard Model for Puerto Rico and the U.S. Virgin Islands (NSHM-PRVI) for earthquakes in active crustal, subduction interface, and subduction intraslab regimes. Using ground-motion models (GMMs) from the Next-Generation Attenuation (NGA)-West2 and NGA-Subduction projects, the GMC is parameterized by scaled-backbone models for median ground motions and by independent logic trees of aleatory variability. We introduce several novel GMC features into the U.S. NSHM: (1) use of regional ground-motion data for modeling median ground motions; (2) development of scaled-backbone models for median ground motions; and (3) development of independent logic trees of aleatory variability from variance components of GMMs and computed from multiple ground-motion datasets, and incorporating regional ground-motion data effects on variability. We compute probabilistic seismic hazard curves and maps to evaluate the contributions from the GMC components and for comparison with the 2003 GMC. Contributions of the GMC to epistemic uncertainty in seismic hazard are evaluated through spatial variations in epistemic uncertainty in hazard maps, comparisons between mean hazard curves and fractiles, as well as investigations of the contributions of logic-tree branches to hazard maps and curves. Comparisons between seismic hazard from the 2025 and 2003 GMCs allow examination of the changes in hazard curves and mapped values with 2% and 10% probabilities of exceedance in 50 years. The 2025 GMC exhibits modest changes in median predictions, relative to the 2003 GMC; overall, values of aleatory variability are higher, except at long periods ( ), resulting in changes to probabilistic ground motions at low probabilities of exceedance (<10% probability of exceedance in 50 years). Changes in hazard at 2% and 10% probabilities of exceedance in 50 years are also relatively modest (within 20%) at most sites, with the impacts from the 2025 GMC exhibiting minor reductions and increases at 0.2 and 1.0 s periods, respectively, relative to the 2003 GMC.

Puerto Rico, U.S. Virgin Islands↗
USGS2024

Geophysical modeling of a possible blind geothermal system near Battle Mountain, NV

The northeastern portion of the Reese River basin in north-central Nevada is the focus of detailed geophysical and geological studies as part of the INGENIOUS project, which aims to identify new, commercially viable hidden geothermal systems in the Great Basin region of the western U.S. This location, herein referred to as Argenta Rise, occupies a broad (~15km wide) left-step between major range-front fault systems along the northwestern edge of the Shoshone Range and Argenta Rim, with numerous ENE-striking intra-basin faults presumably accommodating sinistral-normal oblique slip across the step-over. Four discrete regions have been identified within the study area that have favorable structural settings for hosting a blind hydrothermal system. However, with no definitive or extensive surface manifestations of an active hydrothermal system (e.g., geysers, steam vents, sinter, etc.), detailed geophysical studies are necessary to resolve subsurface geology and structure, and identify zones of enhanced structural complexity that may promote hydrothermal fluid flow. Hence, we collected high-resolution gravity, MT, and rock property data (density, magnetic susceptibility), and analyzed the recently acquired GeoDAWN aeromagnetic data to characterize potential geothermal resources in this region. Using the new geophysical datasets, we jointly modeled gravity and magnetic data along a series of intersecting 2D profiles that integrated information from recent, local-scale fault mapping. Rock property measurements performed on outcrops and hand samples throughout the study area constrained the models. The MT data were used to construct a 3D resistivity model that highlights the location of inferred alteration and fluids in the subsurface. Combined MT and potential field results reveal which structures may be most important for controlling hydrothermal fluid migration, as well as which geologic units may host hydrothermal fluids. Our gravity derived depth to basement surface coincides well with the base of shallow conductive anomalies, suggesting hydrothermal fluids may be confined to basin fill sediments and volcanics. This work supports our development of 3D geophysical and geologic models that are focused along the western flank of the northern Shoshone Range and aids the process of selecting sites for temperature gradient drilling.

Nevada↗
USGS2024

Isotopic evaluation of the National Water Model reveals missing agricultural irrigation contributions to streamflow across the western United States

The National Water Model (NWM) provides critical analyses and projections of streamflow that support water management decisions. However, the NWM performs poorly in lower-elevation rivers of the western United States (US). The accuracy of the NWM depends on the fidelity of the model inputs and the representation and calibration of model processes and water sources. To evaluate the NWM performance in the western US, we compared observations of river water isotope ratios ( 18 O / "> 16 O and 2 H / "> 1 H expressed in δ notation) to NWM-flux-estimated (model) river reach isotope ratios. The modeled estimates were calculated from long-term (2000–2019) mean summer (June, July, and August) NWM hydrologic fluxes and gridded isotope ratios using a mass balance approach. The observational dataset comprised 4503 in-stream water isotope observations in 877 reaches across 5 basins. A simple regression between observed and modeled isotope ratios explained 57.9 % ( δ 18 O) and 67.1 % ( δ 2 H) of variance, although observations were 0.5 ‰ ( δ 18 O) and 4.8 ‰ ( δ 2 H) higher, on average, than mass balance estimates. The unexplained variance suggest that the NWM does not include all relevant water fluxes to rivers. To infer possible missing water fluxes, we evaluated patterns in observation–model differences using δ 18 O diff ( δ 18 O obs − δ 18 O mod ) and d diff ( &#x3B4; 2 H diff - 8 &#x22C5; &#x3B4; 18 O diff "> ). We detected evidence of evaporation in observations but not model estimates (negative d diff and positive δ 18 O diff ) at lower-elevation, higher-stream-order, arid sites. The catchment actual-evaporation-to-precipitation ratio, the fraction of streamflow estimated to be derived from agricultural irrigation, and whether a site was reservoir-affected were all significant predictors of d diff in a linear mixed-effects model, with up to 15.2 % of variance explained by fixed effects. This finding is supported by seasonal patterns, groundwater levels, and isotope ratios, and it suggests the importance of including irrigation return flows to rivers, especially in lower-elevation, higher-stream-order, arid rivers of the western US.

Hydrology and Earth Systems Science↗
USGS2023

Connecting flood-related fluvial erosion and deposition with vulnerable downstream road-stream crossings

Fluvial erosion is increasingly responsible for infrastructure and building damages associated with floods as the intensity of extreme rainfalls hit rural and urban rivers in a variety of climate settings across the United States. Extreme floods in 2016 and 2018 caused widespread culvert blockages and road failures, including extensive damage along steep tributaries and ravines in the Marengo River, Wisconsin, watershed during 2016 and 2018. A study conducted by the U.S. Geological Survey (USGS), Wisconsin Wetlands Association (WWA), Ashland County, and the Northwest Wisconsin Regional Planning Commission (NWRPC) investigated the special concern of fluvial erosion hazards (FEHs) associated with gullying, streamside landslides, and the loss of wetland storage in headwaters. In 2019, a pilot study was begun to map and classify ephemeral and perennial streams and wetlands in terms of their sensitivity to FEHs. This study combined data from field-based rapid geomorphic assessments (RGAs) coupled with a stream network-wide geographic information system (GIS) approach for mapping stream segments, referred to as fluvial process zones (FPZ), sensitive to erosion, deposition, and channel change. The GIS approach used nationally available 10-meter (m) resolution topology and an extended stream network to map FPZs based on Strahler stream order, stream power, channel slope, presence of adjacent steep valley sides and headwater flats, and adjacent landform setting. Bankfull channel widths derived from RGA-based hydraulic geometry curves combined with drainage areas, an estimate of bankfull flow, and channel slope were used to calculate specific stream power for the FPZs. Lastly, the FPZs were characterized by their location within three major landform settings that affect erosion potential. The resulting vulnerability maps provided a screening framework to identify FPZs that are sensitive to incision, gullying and mass wasting along steep headwater ephemeral channels, as well as downstream perennial channels that have the potential for valley-side landslides, coarse sediment deposition, and channel change. Lastly, each FPZ was characterized in terms of hydrologic alteration associated with ditching. The vulnerability mapping products and rankings of sensitivity of FPZs will ultimately be used by Ashland County and their collaborators to prioritize natural flood management projects that mitigate FEHs, restore hydrology, and reconnect channels with adjacent wetlands and floodplains.

Wisconsin↗