Landsat 9 (L9) was launched on 27 September 2021. This spacecraft contained two instruments, the Operational Land Imager-2 (OLI-2) and Thermal Infrared Sensor-2 (TIRS-2), that allow for a continuation of the Landsat program and the mission to acquire multi-spectral observations of the globe on a moderate scale. Following a period of commissioning, during which time the spacecraft and instruments were initialized and set up for operations, with the initial calibration performed, the mission moved to an operational mode This operational mode involved the same cadence and methods that were performed for the Landsat 8 (L8) spacecraft and the two instruments onboard, the Operational Land Imager-1 (OLI-1) and Thermal Infrared Sensor-1 (TIRS-1), with respect to calibration, characterization, and validation. This paper discusses the geometric operational aspects of the L9 instruments during the first year of the mission and post-commissioning, and compares these same geometric activities performed for L8 during the same time frame. During this time, optical axes of the two sensors, OLI-1 and OLI-2, were adjusted to stay aligned with the spacecraft’s Attitude Control System (ACS), and the TIRS-1 and TIRS-2 instruments were adjusted to stay aligned with the OLI-1 and OLI-2 instruments, respectively. In this paper, the L9 operational adjustments are compared to the same operational aspects of L8 during this same time frame. The comparisons shown in this paper will demonstrate that both instruments aboard L8 and L9 performed very similar geometric qualities while fully meeting the expected requirements. This paper describes the geometric differences between the L9 imagery that was made available to the public prior to the reprocessing campaign that was performed using the new calibration updates to the sensor and to ACS and TIRS-to-OLI alignment parameters. This reprocessing campaign of L9 products involved data acquired from the launch of the spacecraft up to early 2023.
","language":"English","publisher":"MDPI","doi":"10.3390/rs16010133","usgsCitation":"Choate, M., Rengarajan, R., Hasan, N., Denevan, A., and Ruslander, K., 2024, Operational aspects of Landsat 8 and 9 geometry: Remote Sensing, v. 16, no. 1, 133, 34 p., https://doi.org/10.3390/rs16010133.","productDescription":"133, 34 p.","ipdsId":"IP-157652","costCenters":[{"id":222,"text":"Earth Resources Observation and Science (EROS) Center","active":true,"usgs":true}],"links":[{"id":467043,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.3390/rs16010133","text":"Publisher Index Page"},{"id":462241,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"volume":"16","issue":"1","noUsgsAuthors":false,"publicationDate":"2023-12-28","publicationStatus":"PW","contributors":{"authors":[{"text":"Choate, Michael J. 0000-0002-8101-4994","orcid":"https://orcid.org/0000-0002-8101-4994","contributorId":251780,"corporation":false,"usgs":true,"family":"Choate","given":"Michael J.","affiliations":[{"id":222,"text":"Earth Resources Observation and Science (EROS) Center","active":true,"usgs":true}],"preferred":true,"id":913917,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Rengarajan, Rajagopalan 0000-0003-1860-7110","orcid":"https://orcid.org/0000-0003-1860-7110","contributorId":242014,"corporation":false,"usgs":false,"family":"Rengarajan","given":"Rajagopalan","affiliations":[{"id":48475,"text":"KBR, Contractor to USGS EROS","active":true,"usgs":false}],"preferred":false,"id":913918,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Hasan, Nahid 0000-0002-0463-601X","orcid":"https://orcid.org/0000-0002-0463-601X","contributorId":292342,"corporation":false,"usgs":false,"family":"Hasan","given":"Nahid","email":"","affiliations":[{"id":40546,"text":"KBR, Contractor to the USGS Earth Resources Observation and Science (EROS) Center","active":true,"usgs":false}],"preferred":false,"id":913919,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Denevan, Alex 0000-0002-1215-3261","orcid":"https://orcid.org/0000-0002-1215-3261","contributorId":270398,"corporation":false,"usgs":false,"family":"Denevan","given":"Alex","email":"","affiliations":[{"id":40546,"text":"KBR, Contractor to the USGS Earth Resources Observation and Science (EROS) Center","active":true,"usgs":false}],"preferred":false,"id":913920,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Ruslander, Kathryn 0000-0003-3036-1731","orcid":"https://orcid.org/0000-0003-3036-1731","contributorId":330181,"corporation":false,"usgs":false,"family":"Ruslander","given":"Kathryn","affiliations":[{"id":54490,"text":"KBR, Inc., under contract to USGS","active":true,"usgs":false}],"preferred":false,"id":913921,"contributorType":{"id":1,"text":"Authors"},"rank":5}]}} ,{"id":70250749,"text":"70250749 - 2024 - Modular compositional learning improves 1D hydrodynamic lake model performance by merging process-based modeling with deep learning","interactions":[],"lastModifiedDate":"2024-01-02T12:32:07.209065","indexId":"70250749","displayToPublicDate":"2023-12-28T06:30:40","publicationYear":"2024","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":5407,"text":"Journal of Advances in Modeling Earth Systems","active":true,"publicationSubtype":{"id":10}},"title":"Modular compositional learning improves 1D hydrodynamic lake model performance by merging process-based modeling with deep learning","docAbstract":"Hybrid Knowledge-Guided Machine Learning (KGML) models, which are deep learning models that utilize scientific theory and process-based model simulations, have shown improved performance over their process-based counterparts for the simulation of water temperature and hydrodynamics. We highlight the modular compositional learning (MCL) methodology as a novel design choice for the development of hybrid KGML models in which the model is decomposed into modular sub-components that can be process-based models and/or deep learning models. We develop a hybrid MCL model that integrates a deep learning model into a modularized, process-based model. To achieve this, we first train individual deep learning models with the output of the process-based models. In a second step, we fine-tune one deep learning model with observed field data. In this study, we replaced process-based calculations of vertical diffusive transport with deep learning. Finally, this fine-tuned deep learning model is integrated into the process-based model, creating the hybrid MCL model with improved overall projections for water temperature dynamics compared to the original process-based model. We further compare the performance of the hybrid MCL model with the process-based model and two alternative deep learning models and highlight how the hybrid MCL model has the best performance for projecting water temperature, Schmidt stability, buoyancy frequency, and depths of different isotherms. Modular compositional learning can be applied to existing modularized, process-based model structures to make the projections more robust and improve model performance by letting deep learning estimate uncertain process calculations.
Assessing the appropriateness of existing native plant materials can both determine which seed source to utilize for restoration projects, and identify locations for which new seed sources need to be developed. Here, we demonstrate an approach to meet these needs. This method identifies areas of high restoration need based on disturbance patterns, assesses the regional suitability of existing native plant materials based on climate similarity, and highlights geographic (and climatic) gaps where existing materials are likely unsuitable and where plant material development projects can be prioritized. We examined 12 high priority restoration species across the Colorado Plateau, a 38-million-ha region of the Intermountain West, United States to test our methodological pipeline. Fifty-four percent of the Colorado Plateau is disturbed by livestock grazing, wildfires that have burned in the past 20 years, or energy production from oil and gas wells, natural gas pipelines, and coal mines. Of the 28 commercially available plant materials for six of the focal species, only 3 have climate similarity that encompass more than 50% of the species modeled habitat on the Colorado Plateau. Across all commercial materials, most species (10 of 12) do not have any suitable plant material for 70% or more of their geographic range on the Colorado Plateau. Of those areas identified as not having any suitable plant materials, 47–56% are also disturbed. Our method provides usable, flexible protocols and spatially referenced data sources for optimizing the planning of new native plant materials in any region where restoration is needed and spatial data are available.
We present the 2023 U.S. Geological Survey time‐independent earthquake rupture forecast for the conterminous United States, which gives authoritative estimates of the magnitude, location, and time‐averaged frequency of potentially damaging earthquakes throughout the region. In addition to updating virtually all model components, a major focus has been to provide a better representation of epistemic uncertainties. For example, we have improved the representation of multifault ruptures, both in terms of allowing more and less fault connectivity than in the previous models, and in sweeping over a broader range of viable models. An unprecedented level of diagnostic information has been provided for assessing the model, and the development was overseen by a 19‐member participatory review panel. Although we believe the new model embodies significant improvements and represents the best available science, we also discuss potential model limitations, including the applicability of logic tree branch weights with respect different types of hazard and risk metrics. Future improvements are also discussed, with deformation model enhancements being particularly worthy of pursuit, as well as better representation of sampling errors in the gridded seismicity components. We also plan to add time‐dependent components, and assess implications with a wider range of hazard and risk metrics.
We present updated inversion‐based fault‐system solutions for the 2023 update to the National Seismic Hazard Model (NSHM23), standardizing earthquake rate model calculations on crustal faults across the western United States. We build upon the inversion methodology used in the Third Uniform California Earthquake Rupture Forecast (UCERF3) to solve for time‐independent rates of earthquakes in an interconnected fault system. The updated model explicitly maps out a wide range of fault recurrence and segmentation behavior (epistemic uncertainty), more completely exploring the solution space of viable models beyond those of UCERF3. We also improve the simulated annealing implementation, greatly increasing computational efficiency (and thus inversion convergence), and introduce an adaptive constraint weight calculation algorithm that helps to mediate between competing constraints. Hazard calculations show that ingredient changes (especially fault and deformation models) are the primary driver of hazard changes between NSHM23 and UCERF3. Updates to the inversion methodology are also consequential near faults in which the slip rate in UCERF3 was poorly fit or was satisfied primarily using large multifault ruptures that are now restricted by explicit b‐value and segmentation constraints.
We paired mercury (Hg) concentrations in dragonfly larvae with water chemistry in 29 U.S. national parks to highlight how ecological and biogeochemical context (habitat, dissolved organic carbon [DOC]) influence drivers of Hg bioaccumulation. Although prior studies have defined influences of biogeochemical variables on Hg production and bioaccumulation, it has been challenging to determine their influence across diverse habitats, regions, or biogeochemical conditions within a single study. We compared global (i.e., all sites), habitat-specific, and DOC-class models to illuminate how these controls on biotic Hg vary. Although the suite of important biogeochemical factors across all sites (e.g., aqueous Hg, DOC, sulfate [SO42−], and pH) was consistent with general findings in the literature, contrasting the restricted models revealed more nuanced controls on biosentinel Hg. Comparing habitats, aqueous (filtered) total mercury (THg) and SO42− were important in lentic systems whereas aqueous (filtered) methylmercury (MeHg), DOC, pH, and SO42− were important in lotic and wetland systems. The ability to identify important variables varied among habitats, with less certainty in lentic (model weight (W) = 0.05) than lotic (W = 0.11) or wetland habitats (W = 0.23), suggesting that biogeochemical drivers of bioaccumulation are more variable, or obscured by other aspects of Hg cycling, in these habitats. Results revealed a contrast in the importance of aqueous MeHg versus aqueous THg between DOC-classes: in low-DOC sites (<8.5 mg/L), availability of upstream inputs of MeHg appeared more important for bioaccumulation; in high-DOC sites (>8.5 mg/L) THg was more important, suggesting a link to in-situ controls on bioavailability of Hg for MeHg production. Mercury bioaccumulation (indicated by bioaccumulation factor) was more efficient in low DOC-class sites, likely due to reduced partitioning of aqueous MeHg to DOC. Together, findings highlight substantial variation in the drivers of Hg bioaccumulation and suggest consideration of these factors in natural resource management and decision-making.
","language":"English","publisher":"Elsevier","doi":"10.1016/j.scitotenv.2023.169396","usgsCitation":"Nelson, S.J., Willacker, J., Eagles-Smith, C., Flanagan Pritz, C.M., Chen, C.Y., Klemmer, A.J., and Krabbenhoft, D.P., 2024, Habitat and dissolved organic carbon modulate variation in the biogeochemical drivers of mercury bioaccumulation in dragonfly larvae at the national scale: Science of the Total Environment, v. 912, 169396, https://doi.org/10.1016/j.scitotenv.2023.169396.","productDescription":"169396","ipdsId":"IP-159769","costCenters":[{"id":290,"text":"Forest and Rangeland Ecosystem Science Center","active":false,"usgs":true}],"links":[{"id":425714,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"volume":"912","noUsgsAuthors":false,"publicationStatus":"PW","contributors":{"authors":[{"text":"Nelson, Sarah J.","contributorId":167269,"corporation":false,"usgs":false,"family":"Nelson","given":"Sarah","email":"","middleInitial":"J.","affiliations":[{"id":7063,"text":"University of Maine","active":true,"usgs":false}],"preferred":false,"id":894958,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Willacker, James 0000-0002-6286-5224","orcid":"https://orcid.org/0000-0002-6286-5224","contributorId":207883,"corporation":false,"usgs":true,"family":"Willacker","given":"James","email":"","affiliations":[{"id":290,"text":"Forest and Rangeland Ecosystem Science Center","active":false,"usgs":true}],"preferred":true,"id":894959,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Eagles-Smith, Collin A. 0000-0003-1329-5285","orcid":"https://orcid.org/0000-0003-1329-5285","contributorId":221745,"corporation":false,"usgs":true,"family":"Eagles-Smith","given":"Collin A.","affiliations":[{"id":290,"text":"Forest and Rangeland Ecosystem Science Center","active":false,"usgs":true}],"preferred":true,"id":894960,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Flanagan Pritz, Colleen M 0000-0002-0466-2103","orcid":"https://orcid.org/0000-0002-0466-2103","contributorId":299600,"corporation":false,"usgs":false,"family":"Flanagan Pritz","given":"Colleen","email":"","middleInitial":"M","affiliations":[{"id":36189,"text":"National Park Service","active":true,"usgs":false}],"preferred":false,"id":894961,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Chen, Celia Y.","contributorId":145630,"corporation":false,"usgs":false,"family":"Chen","given":"Celia","email":"","middleInitial":"Y.","affiliations":[{"id":16179,"text":"Dartmouth College, Hanover NH","active":true,"usgs":false}],"preferred":false,"id":894962,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"Klemmer, Amanda J","contributorId":219891,"corporation":false,"usgs":false,"family":"Klemmer","given":"Amanda","email":"","middleInitial":"J","affiliations":[],"preferred":false,"id":894963,"contributorType":{"id":1,"text":"Authors"},"rank":6},{"text":"Krabbenhoft, David P. 0000-0003-1964-5020 dpkrabbe@usgs.gov","orcid":"https://orcid.org/0000-0003-1964-5020","contributorId":1658,"corporation":false,"usgs":true,"family":"Krabbenhoft","given":"David","email":"dpkrabbe@usgs.gov","middleInitial":"P.","affiliations":[{"id":37947,"text":"Upper Midwest Water Science Center","active":true,"usgs":true},{"id":677,"text":"Wisconsin Water Science Center","active":true,"usgs":true},{"id":37464,"text":"WMA - Laboratory & Analytical Services Division","active":true,"usgs":true},{"id":5044,"text":"National Research Program - Central Branch","active":true,"usgs":true}],"preferred":true,"id":894964,"contributorType":{"id":1,"text":"Authors"},"rank":7}]}} ,{"id":70250846,"text":"70250846 - 2024 - Using an open-source tool to develop a three-dimensional hydrogeologic framework of the Kobo Valley, Ethiopia","interactions":[],"lastModifiedDate":"2024-01-09T16:47:27.725312","indexId":"70250846","displayToPublicDate":"2023-12-20T10:36:40","publicationYear":"2024","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":1816,"text":"Geosciences","active":true,"publicationSubtype":{"id":10}},"title":"Using an open-source tool to develop a three-dimensional hydrogeologic framework of the Kobo Valley, Ethiopia","docAbstract":"Groundwater resource management requires understanding the groundwater basin’s hydrogeology and would be improved with the development of a three-dimensional hydrogeologic framework model (HFM). A wide range of methods and software exist to quantify the extent, structure, and properties of geologic systems. However, most geologic software is proprietary and cost-prohibitive for use in developing countries. GemPy is a Python-based, open-source (no-cost) tool for generating three-dimensional geological models. This study uses available data and GemPy to develop the Kobo Valley Hydrogeologic Framework Model (KV-HFM), a three-dimensional HFM for Kobo Valley in northern Ethiopia, which is part of the East African Rift System. The KV-HFM is a conceptual model that comprises the hydrostratigraphy, structural features, and hydraulic properties of the Kobo Valley groundwater system. The limited data described the extent and altitude of the hydrostratigraphic units using the GemPy implicit potential–field interpolation. The KV-HFM showed the existence of an east-to-west, structural-based groundwater divide composed of volcanic rock and clay. This divide splits the catchment into two groundwater systems with limited interconnected flow. This study illustrates the use of open-source software for developing an HFM using sparse, existing geologic data.
","language":"English","publisher":"MDPI","doi":"10.3390/geosciences14010003","usgsCitation":"Mekonen, S.S., Boyce, S.E., Mohammed, A.K., and Disse, M., 2024, Using an open-source tool to develop a three-dimensional hydrogeologic framework of the Kobo Valley, Ethiopia: Geosciences, v. 14, no. 1, 3, 27 p., https://doi.org/10.3390/geosciences14010003.","productDescription":"3, 27 p.","ipdsId":"IP-133573","costCenters":[{"id":154,"text":"California Water Science Center","active":true,"usgs":true}],"links":[{"id":440882,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.3390/geosciences14010003","text":"Publisher Index Page"},{"id":424222,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"Ethiopia","otherGeospatial":"Afar Depression, Kobo Valley catchment","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"coordinates\": [\n [\n [\n 39.3,\n 12.4\n ],\n [\n 39.3,\n 11.9\n ],\n [\n 39.8333,\n 11.9\n ],\n [\n 39.8333,\n 12.4\n ],\n [\n 39.3,\n 12.4\n ]\n ]\n ],\n \"type\": \"Polygon\"\n }\n }\n ]\n}","volume":"14","issue":"1","noUsgsAuthors":false,"publicationDate":"2023-12-20","publicationStatus":"PW","contributors":{"authors":[{"text":"Mekonen, Sisay Simachew","contributorId":333048,"corporation":false,"usgs":false,"family":"Mekonen","given":"Sisay","email":"","middleInitial":"Simachew","affiliations":[{"id":79717,"text":"Hydrology and River Basin Management Department, Technical University of Munich","active":true,"usgs":false}],"preferred":false,"id":891768,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Boyce, Scott 0000-0003-0626-9492 seboyce@usgs.gov","orcid":"https://orcid.org/0000-0003-0626-9492","contributorId":4766,"corporation":false,"usgs":true,"family":"Boyce","given":"Scott","email":"seboyce@usgs.gov","affiliations":[{"id":154,"text":"California Water Science Center","active":true,"usgs":true}],"preferred":true,"id":891769,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Mohammed, Abdella K.","contributorId":333049,"corporation":false,"usgs":false,"family":"Mohammed","given":"Abdella","email":"","middleInitial":"K.","affiliations":[{"id":79718,"text":"Hydraulic and Water Resources Engineering, Arba Minch University","active":true,"usgs":false}],"preferred":false,"id":891770,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Disse, Markus","contributorId":333050,"corporation":false,"usgs":false,"family":"Disse","given":"Markus","email":"","affiliations":[{"id":79717,"text":"Hydrology and River Basin Management Department, Technical University of Munich","active":true,"usgs":false}],"preferred":false,"id":891771,"contributorType":{"id":1,"text":"Authors"},"rank":4}]}} ,{"id":70250627,"text":"70250627 - 2024 - Legacy sediment as a potential source of orthophosphate: Preliminary conceptual and geochemical models for the Susquehanna River, Chesapeake Bay watershed, USA","interactions":[],"lastModifiedDate":"2023-12-21T12:59:23.83208","indexId":"70250627","displayToPublicDate":"2023-12-20T06:56:07","publicationYear":"2024","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":3352,"text":"Science of the Total Environment","active":true,"publicationSubtype":{"id":10}},"title":"Legacy sediment as a potential source of orthophosphate: Preliminary conceptual and geochemical models for the Susquehanna River, Chesapeake Bay watershed, USA","docAbstract":"Nutrient pollution from agriculture and urban areas plus acid mine drainage (AMD) from legacy coal mines are primary causes of water-quality impairment in the Susquehanna River, which is the predominant source of freshwater and nutrients entering the Chesapeake Bay. Recent increases in the delivery of dissolved orthophosphate (PO4) from the river to the bay may be linked to long-term increases in pH, decreased acidity of precipitation, and decreased acidity, iron, and aluminum loading from widespread AMD. Since the 1950s, baseline pH increased from ~6.5 to ~8 in the West Branch and “North Branch” of the Susquehanna River, which drain bituminous and anthracite coalfields of Pennsylvania. A current baseline pH of ~8 and daily maxima exceeding 9 have been documented along the lower Susquehanna River. In response to improved river quality, bioavailable PO4 now may be released into solution from legacy sediment that has filled major impoundments in lower reaches of the river. At typical pH (5–8) of natural water, aqueous PO4 species tend to be adsorbed by hydrous iron, aluminum, and manganese oxides that coat soil and sediment particles; however, PO4 may be substantially desorbed at pH >8. We created a geochemical model that simulates equilibrium aqueous/solid distributions of PO4 as pH and other solution characteristics change. Considering current conditions in the lower Susquehanna River, the model demonstrates potential for extensive release of adsorbed PO4 at pH >8. Empirical data from laboratory experiments corroborate model results. The transfer of PO4 into the water column may increase algae growth, which removes CO2 and drives pH to higher values, facilitating additional PO4 release and exacerbating the potential for harmful algal blooms. Thus, legacy sediment is a currently unquantified source of PO4 that warrants consideration by resource managers and programs collaborating to reduce phosphorus loads to the bay and similar settings worldwide.
The McKay’s Bunting (Plectrophenax hyperboreus) is endemic to Alaska, breeds solely on the remote and uninhabited St. Matthew and Hall islands (332 km2) in the central Bering Sea, and is designated as a species of high conservation concern due to its small population size and restricted range. A previous hypothesized population estimate (~2,800—6,000 individuals) was greatly increased (~31,200 individuals) after systematic surveys of the species’ entire breeding range in 2003, establishing McKay’s Bunting as one of the rarest passerines in North America. In 2018, we replicated the 2003 surveys and used density surface models to estimate breeding season densities, distributions, and population change over the intervening time period. Our results indicate that the McKay's Bunting population declined by 38% (95% CI: 27—48%) from ~31,560 to 19,481 individuals since 2003. Spatial model predictions showed no areas with an increase of birds on either St. Matthew or Hall islands but revealed declines across 13% (42 km2) of St. Matthew Island. Declines disproportionately occurred both in marginal habitats with reduced rocky nesting substrate and in high-density hotspots along the coast of St. Matthew Island. The total area occupied by breeding adults decreased by 8%, and high-density hotspots shifted inland from the coast of St. Matthew Island to higher elevations on both islands, the latter potentially responses to exceptionally warm weather and reduced spring snow cover in 2018. Additionally, we observed low numbers of predators and interspecific competitors in 2018 suggesting these did not cause the decline. Our findings indicate that McKay’s Bunting meets international standards for elevating its conservation status from Least Concern to Endangered based on the International Union for Conservation of Nature Red List of Threatened Species ranking criteria. Additional population monitoring and studies to identify the causal mechanisms of the recent population decline of this rare species could assist future population assessments.
","language":"English","publisher":"Oxford Academic","doi":"10.1093/ornithapp/duad064","usgsCitation":"Richardson, R.M., Amundson, C.L., Johnson, J.A., Romano, M.D., Taylor, A.R., Fleming, M., and Matsuoka, S.M., 2024, Rapid population decline in McKay's Bunting, an Alaskan endemic, highlights the species’ current status relative to international standards for vulnerable species: Ornithological Applications, v. 126, no. 2, duad064, 12 p., https://doi.org/10.1093/ornithapp/duad064.","productDescription":"duad064, 12 p.","ipdsId":"IP-156671","costCenters":[{"id":65299,"text":"Alaska Science Center Ecosystems","active":true,"usgs":true}],"links":[{"id":440896,"rank":3,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.1093/ornithapp/duad064","text":"Publisher Index Page"},{"id":435072,"rank":2,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/P94JY2KH","text":"USGS data release","linkHelpText":"Data for Estimating McKay's Bunting (Plectrophenax hyperboreus) Population Change on St. Matthew and Hall Islands, Alaska"},{"id":423862,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","state":"Alaska","otherGeospatial":"Hall Island, St. Matthew Island","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"coordinates\": [\n [\n [\n -172.261171193919,\n 60.283813219091456\n ],\n [\n -172.19823889848468,\n 60.313577467466075\n ],\n [\n -172.38560550534595,\n 60.400588373260575\n ],\n [\n -172.5472270822568,\n 60.39634932448172\n ],\n [\n -172.88334275105373,\n 60.53524320077517\n ],\n [\n -172.8876335893788,\n 60.61534716459596\n ],\n [\n -173.08072048870488,\n 60.7127489098923\n ],\n [\n -173.13364082804736,\n 60.65742694272734\n ],\n [\n -173.05497545875448,\n 60.546497343244624\n ],\n [\n -173.0735690914964,\n 60.49370736988794\n ],\n [\n -172.95628617727795,\n 60.46833761065767\n ],\n [\n -172.7925190827116,\n 60.3796448928919\n ],\n [\n -172.6030069581948,\n 60.31074342132811\n ],\n [\n -172.261171193919,\n 60.283813219091456\n ]\n ]\n ],\n \"type\": \"Polygon\"\n }\n }\n ]\n}","volume":"126","issue":"2","noUsgsAuthors":false,"publicationDate":"2023-12-19","publicationStatus":"PW","contributors":{"authors":[{"text":"Richardson, Rachel M. 0000-0001-8501-250X rrichardson@usgs.gov","orcid":"https://orcid.org/0000-0001-8501-250X","contributorId":205918,"corporation":false,"usgs":true,"family":"Richardson","given":"Rachel","email":"rrichardson@usgs.gov","middleInitial":"M.","affiliations":[{"id":117,"text":"Alaska Science Center Biology WTEB","active":true,"usgs":true}],"preferred":true,"id":890693,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Amundson, Courtney L.","contributorId":332619,"corporation":false,"usgs":false,"family":"Amundson","given":"Courtney","email":"","middleInitial":"L.","affiliations":[{"id":79516,"text":"Former USGS employee, now with USDA","active":true,"usgs":false}],"preferred":false,"id":890694,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Johnson, James A. 0000-0002-2312-0633","orcid":"https://orcid.org/0000-0002-2312-0633","contributorId":299054,"corporation":false,"usgs":false,"family":"Johnson","given":"James","email":"","middleInitial":"A.","affiliations":[{"id":36188,"text":"U.S. Fish and Wildlife Service","active":true,"usgs":false}],"preferred":false,"id":890695,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Romano, Marc D.","contributorId":224656,"corporation":false,"usgs":false,"family":"Romano","given":"Marc","email":"","middleInitial":"D.","affiliations":[{"id":6654,"text":"USFWS","active":true,"usgs":false}],"preferred":false,"id":890696,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Taylor, Audrey R.","contributorId":10396,"corporation":false,"usgs":false,"family":"Taylor","given":"Audrey","email":"","middleInitial":"R.","affiliations":[],"preferred":false,"id":890697,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"Fleming, Michael D.","contributorId":332620,"corporation":false,"usgs":false,"family":"Fleming","given":"Michael D.","affiliations":[{"id":79518,"text":"Images Unlimited","active":true,"usgs":false}],"preferred":false,"id":890698,"contributorType":{"id":1,"text":"Authors"},"rank":6},{"text":"Matsuoka, Steven M. 0000-0001-6415-1885 smatsuoka@usgs.gov","orcid":"https://orcid.org/0000-0001-6415-1885","contributorId":184173,"corporation":false,"usgs":true,"family":"Matsuoka","given":"Steven","email":"smatsuoka@usgs.gov","middleInitial":"M.","affiliations":[{"id":117,"text":"Alaska Science Center Biology WTEB","active":true,"usgs":true}],"preferred":true,"id":890699,"contributorType":{"id":1,"text":"Authors"},"rank":7}]}} ,{"id":70250612,"text":"70250612 - 2024 - The 2022 Chaos Canyon landslide in Colorado: Insights revealed by seismic analysis, field investigations, and remote sensing","interactions":[],"lastModifiedDate":"2024-01-25T14:51:22.947677","indexId":"70250612","displayToPublicDate":"2023-12-19T06:59:31","publicationYear":"2024","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":2604,"text":"Landslides","active":true,"publicationSubtype":{"id":10}},"title":"The 2022 Chaos Canyon landslide in Colorado: Insights revealed by seismic analysis, field investigations, and remote sensing","docAbstract":"An unusual, high-alpine, rapid debris slide originating in ice-rich debris occurred on June 28, 2022, at 16:33:16 MDT at the head of Chaos Canyon, a formerly glacier-covered valley in Rocky Mountain National Park, CO, USA. In this study, we integrate eyewitness videos and seismic records of the event with meteorological data, field observations, pre- and post-event satellite imagery, and uncrewed aircraft vehicle imagery to characterize the event and future hazards it may pose. Deformation of the eventual slide mass preceded rapid failure by decades, starting in the early to mid-2000s, accelerating in 2018 (the warmest year on record), and reaching ~ 20 m/year in 2021. The main event, which was preceded by smaller sliding episodes earlier that day, had a volume of ~ 2.1 million m3, reached peak velocities of about 5 m/s, slid on a surface up to 80 m deep, and moved up to ~ 245 m downslope in < 2 min. We observed blocks of frozen debris (permafrost) in the landslide deposits. Within ~ 2 weeks, these blocks had melted and became dry, conical debris mounds (molards). We hypothesize that the rapid slide was induced by gradually increasing long-term air temperatures that thawed ice and increased pore pressures. The presence and suspected influence of permafrost on the occurrence of this landslide indicate other slopes in the park, and other moderate-to-low latitude alpine regions may experience similar slope stability issues as temperatures continue to warm.
Coastal mangrove forests provide a suite of environmental services, including sequestration of anthropogenic contamination. Yet, research lags on the environmental fate and potential human health risks of mangrove-sequestered contaminants in the context of mangrove removal for development and range shifts due to climate change. To address this, we conducted a study on Moloka'i, Hawai'i, comparing microplastic and pesticide contamination in coastal compartments both at areas modified by non-native red mangroves (Rhizophora mangle) and unmodified, open coastline. Sediment, porewater, and mangrove plant tissues were collected to quantify microplastic and pesticide concentrations across ecosystem type. Average microplastics were similar between mangrove (8.89 items/kg) and non-mangrove areas (9.01 items/kg) in sediment and porewater, but mangrove roots were a substantial reservoir of microplastics (2004 items/kg). Additionally, there was a strong relationship between proximity to urban development and microplastics detected. Six pesticides were detected, most commonly the insecticide bifenthrin, found in most sediment samples (11.3 ng/g), all root samples (243.3 ng/g), and one propagule sample (8.60 ng/g). Other pesticides detected with appreciable concentrations include the neonicotinoid insecticide imidacloprid and the legacy insecticide transformation product, p,p’-DDE. The other detections, all at concentrations < 1 ng/g, were p,p’-DDT, trifluralin, and permethrin. The high concentrations of bifenthrin in roots compared to lower concentrations detected in sediment suggest that mangrove roots strongly accumulate some pesticides, indicating mangrove roots as a sink for organic contaminants. Study methods could be applied to other Hawaiian Islands and other locations where mangroves have been introduced to further examine the observed trends. Additional information is needed to investigate the fate and cycling of pesticides and microplastics adhered to mangrove roots, to better inform non-native mangrove removal efforts on Moloka'i and elsewhere.
Rare earth elements and yttrium (REEs) have a wide range of applications in high- and low-carbon technologies. The strategic significance of REEs has grown due to their expanding applications in manufacturing industries and the constrained availability of these essential resources. This research explores the applicability of machine learning models and their uncertainty for assessing the REE potential in coal beds using various coal parameters as inputs. The work focuses on developing a predictive model based on geological variables, excluding considerations related to potential shifts in the commodities market. The Indiana Coal Quality Database was used as the data source. The promising and unpromising indicators derived from the outlook coefficient of samples from the database were used as the REE potential indicator for machine learning classification models. The filter-based approach with bootstrap was used to evaluate the importance of the coal parameters and their prediction uncertainties. Four machine learning methods (linear discriminant analysis (LDA), random forest (RF), support vector machine (SVM), and artificial neural networks (ANN), a data balancing and augmentation approach (Synthetic Minority Over-sampling Technique), and bootstrap resampling techniques were used for building the models and evaluating their prediction capabilities under uncertainty. It was determined that the SVM bootstrap model with ten-times balanced and augmented data provided superior results compared with other models. Finally, stochastic spatial maps of the REE potential within the coal basin were generated using sequential indicator simulation. The spatial maps of the REE potential showed that a 29% area of the Indiana section of the Illinois coal basin has economic potential of REEs, with 90% confidence.
Water level is an important guide for water resource management and wetland ecosystems, defining one of the most basic processes in hydrology. This research seeks to investigate the possibility of complementing numerical modeling with a Machine Learning (ML) model to forecast daily water levels in the southern Everglades in Florida, USA. An exact analytical solution to water level may not be possible, but using the computational methods afforded by ML, the traditional numerical techniques may be enhanced to generate more robust, scalable predictions. Five locations were chosen for application of the Time-Delayed Neural Network (TDNN) and Long-Short Term Memory Recurrent Neural Network (LSTM-RNN) ML models, which were built to estimate water level with 1, 2, 3, 7 and 10 day forecasts using a simulation step of 1 day. The results showed that rainfall forecasts from weather models could improve water-level forecasts if the accuracy and performance of the weather models can be improved. The ML models presented here improve water-level predictions from a historical hydrologic model for a 24 hour forecast horizon.
","largerWorkType":{"id":4,"text":"Book"},"largerWorkTitle":"Advanced hydroinformatics: Machine learning and optimization for water resources","largerWorkSubtype":{"id":15,"text":"Monograph"},"language":"English","publisher":"American Geophysical Union","doi":"10.1002/9781119639268.ch7","usgsCitation":"Forde, C.S., Bhattacharya, B., Solomatine, D., Swain, E., and Aumen, N., 2024, Forecasting water levels using machine (deep) learning to complement numerical modelling in the southern Everglades, USA, chap. 7 of Advanced hydroinformatics: Machine learning and optimization for water resources, p. 177-211, https://doi.org/10.1002/9781119639268.ch7.","productDescription":"35 p.","startPage":"177","endPage":"211","ipdsId":"IP-140554","costCenters":[{"id":27821,"text":"Caribbean-Florida Water Science Center","active":true,"usgs":true}],"links":[{"id":440912,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"http://dx.doi.org/10.1002/9781119639268.ch7","text":"Publisher Index Page"},{"id":423841,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","state":"Florida","otherGeospatial":"Everglades","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"coordinates\": [\n [\n [\n -80.1037093889116,\n 26.92620905192487\n ],\n [\n -81.32930131865726,\n 26.92620905192487\n ],\n [\n -81.32930131865726,\n 25.096697437852114\n ],\n [\n -80.1037093889116,\n 25.096697437852114\n ],\n [\n -80.1037093889116,\n 26.92620905192487\n ]\n ]\n ],\n \"type\": \"Polygon\"\n }\n }\n ]\n}","noUsgsAuthors":false,"publicationDate":"2023-12-15","publicationStatus":"PW","contributors":{"editors":[{"text":"Corzo Perez, Gerald A.","contributorId":332614,"corporation":false,"usgs":false,"family":"Corzo Perez","given":"Gerald","email":"","middleInitial":"A.","affiliations":[],"preferred":false,"id":890674,"contributorType":{"id":2,"text":"Editors"},"rank":1},{"text":"Solomatine, Dimitri 0000-0003-2031-9871","orcid":"https://orcid.org/0000-0003-2031-9871","contributorId":298962,"corporation":false,"usgs":false,"family":"Solomatine","given":"Dimitri","email":"","affiliations":[{"id":49677,"text":"IHE Delft Institute for Water Education","active":true,"usgs":false}],"preferred":false,"id":890675,"contributorType":{"id":2,"text":"Editors"},"rank":2}],"authors":[{"text":"Forde, Courtney S 0000-0003-2084-6698","orcid":"https://orcid.org/0000-0003-2084-6698","contributorId":298960,"corporation":false,"usgs":false,"family":"Forde","given":"Courtney","email":"","middleInitial":"S","affiliations":[{"id":64740,"text":"Caribbean Institute for Meteorology and Hydrology","active":true,"usgs":false}],"preferred":false,"id":856773,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Bhattacharya, Biswa 0000-0002-8046-589X","orcid":"https://orcid.org/0000-0002-8046-589X","contributorId":298961,"corporation":false,"usgs":false,"family":"Bhattacharya","given":"Biswa","email":"","affiliations":[{"id":49677,"text":"IHE Delft Institute for Water Education","active":true,"usgs":false}],"preferred":false,"id":856774,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Solomatine, Dimitri 0000-0003-2031-9871","orcid":"https://orcid.org/0000-0003-2031-9871","contributorId":298962,"corporation":false,"usgs":false,"family":"Solomatine","given":"Dimitri","email":"","affiliations":[{"id":49677,"text":"IHE Delft Institute for Water Education","active":true,"usgs":false}],"preferred":false,"id":856775,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Swain, Eric 0000-0001-7168-708X","orcid":"https://orcid.org/0000-0001-7168-708X","contributorId":223705,"corporation":false,"usgs":true,"family":"Swain","given":"Eric","affiliations":[{"id":269,"text":"FLWSC-Ft. Lauderdale","active":true,"usgs":true}],"preferred":true,"id":856776,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Aumen, Nicholas 0000-0002-5277-2630","orcid":"https://orcid.org/0000-0002-5277-2630","contributorId":223550,"corporation":false,"usgs":true,"family":"Aumen","given":"Nicholas","affiliations":[{"id":269,"text":"FLWSC-Ft. Lauderdale","active":true,"usgs":true}],"preferred":true,"id":856777,"contributorType":{"id":1,"text":"Authors"},"rank":5}]}} ,{"id":70250538,"text":"70250538 - 2024 - A new method for bioassessment of ecosystems with complex communities and environmental gradients","interactions":[],"lastModifiedDate":"2023-12-15T13:15:44.445057","indexId":"70250538","displayToPublicDate":"2023-12-14T07:13:26","publicationYear":"2024","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":1456,"text":"Ecological Indicators","active":true,"publicationSubtype":{"id":10}},"title":"A new method for bioassessment of ecosystems with complex communities and environmental gradients","docAbstract":"Bioassessment of complex and heterogeneous ecosystems is a challenge when there are multiple, strong, natural environmental gradients; unknown, or spatially varying, mixtures of stressors; and large numbers of taxa with unknown responses to both the environmental gradients and the stressors. Current methods of bioassessment are not designed for use under this set of constraints. To address this gap, we have developed an assessment method appropriate for well-sampled, heterogeneous systems with many taxa. In the bioassessment described below, we model taxa occurrence as a function of natural environmental gradients, then use residual covariance patterns between all pairs of taxa to estimate the impact of human disturbance across sites as a latent construct. The derivation of the method from an underlying causal model allows the metric value at each site and the associated taxa responses to be partitioned into contributions from a set of putative stressors. We apply this method as a case study to the subtidal benthic invertebrate community of Puget Sound, WA (USA) and demonstrate a partial decomposition of the metric values to a set of stressors including sediment organic carbon, nitrogen, metals, and organic pollutants. While this method provides new opportunities to estimate, communicate, and understand the ecological condition of complex, heterogeneous ecosystems, due to the requirement for broad, detailed data to inform its estimates, it will likely be most appropriate for monitoring programs.
Development and evaluation of models for tectonic evolution in the Cascadia forearc require understanding of along-strike heterogeneity of strain distribution, uplift, and upper-plate characteristics. Here, we investigated the Neogene geologic record of the Klamath Mountains province in southernmost Cascadia and obtained apatite (U-Th)/He (AHe) thermochronology of Mesozoic plutons, Neogene graben sediment thickness, detrital zircon records from Neogene grabens, gravity and magnetic data, and kinematic analysis of faults. We documented three aspects of Neogene tectonics: early Miocene and younger rock exhumation, development of topographic relief sufficient to isolate Neogene graben-filling sediments from sources outside of the Klamath Mountains, and initiation of mid-Miocene or younger right-lateral and reverse faulting. Key findings are: (1) 10 new apatite AHe mean cooling ages from the Canyon Creek and Granite Peak plutons in the Trinity Alps range from 24.7 ± 2.1 Ma to 15.7 ± 2.1 Ma. Inverse thermal modeling of these data and published apatite fission-track ages indicate the most rapid rock cooling between ca. 25 and 15 Ma. One new AHe mean cooling age (26.7 ± 3.2 Ma) from the Ironside Mountain batholith 40 km west of the Trinity Alps, combined with previously published AHe ages, suggests geographically widespread latest Oligocene to Miocene cooling in the southern Klamath Mountains province. (2) AHe ages of 39.4 ± 5.1 Ma on the downthrown side and 22.7 ± 3.0 Ma on the upthrown side of the Browns Meadow fault suggest early Miocene to younger fault activity. (3) U-Pb detrital zircon ages (n = 862) and Lu-Hf isotope geochemistry from Miocene Weaverville Formation sediments in the Weaverville, Lowden Ranch, Hayfork, and Hyampom grabens south and southwest of the Trinity Alps can be traced to entirely Klamath Mountains sources; they suggest the south-central Klamath Mountains had, by the middle Miocene, sufficient relief to isolate these grabens from more distal sediment sources. (4) Two Miocene detrital zircon U-Pb ages of 10.6 ± 0.4 Ma and 16.7 ± 0.2 Ma from the Lowden Ranch graben show that the maximum depositional age of the upper Weaverville Formation here is younger than previously recognized. (5) A prominent steep-sided negative gravity anomaly associated with the Hayfork graben shows that both the north and south margins are fault-controlled, and inversion of gravity data suggests basin fill is between 1 km and 1.9 km thick. Abrupt elevation changes of basin fill-to-bedrock contacts reported in well logs record E-side-up and right-lateral faulting at the eastern end of the Hayfork graben. A NE-striking gravity gradient separates the main graben on the west from a narrower, thinner basin to the east, supporting this interpretation. (6) Of fset of both the base of the Weaverville Formation and the cataclasite-capped La Grange fault surface by a fault on the southwest margin of the Weaverville basin documents 200 m of reverse and 1500 m of right-lateral strike-slip motion on this structure, here named the Democrat Gulch fault; folded and steeply dipping strata adjacent to the fault confirm that faulting postdated deposition of the Weaverville Formation.
","language":"English","publisher":"Geological Society of America","doi":"10.1130/GES02612.1","usgsCitation":"Michalak, M.J., Cashman, S.M., Langenheim, V., Team, T.C., and Christensen, D.J., 2024, Neogene faulting, basin development, and relief generation in the southern Klamath Mountains (USA): Geosphere, v. 20, no. 1, p. 237-266, https://doi.org/10.1130/GES02612.1.","productDescription":"30 p.","startPage":"237","endPage":"266","ipdsId":"IP-144540","costCenters":[{"id":312,"text":"Geology, Minerals, Energy, and Geophysics Science Center","active":true,"usgs":true}],"links":[{"id":440927,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.1130/ges02612.1","text":"Publisher Index Page"},{"id":425101,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","state":"California, Oregon","otherGeospatial":"Southern Klamath Mountains","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"coordinates\": [\n [\n [\n -125.98917316134096,\n 44.02774490532599\n ],\n [\n -125.98917316134096,\n 38.79194801722443\n ],\n [\n -120.47403644259101,\n 38.79194801722443\n ],\n [\n -120.47403644259101,\n 44.02774490532599\n ],\n [\n -125.98917316134096,\n 44.02774490532599\n ]\n ]\n ],\n \"type\": \"Polygon\"\n }\n }\n ]\n}","volume":"20","issue":"1","noUsgsAuthors":false,"publicationDate":"2023-12-13","publicationStatus":"PW","contributors":{"authors":[{"text":"Michalak, Melanie J.","contributorId":317978,"corporation":false,"usgs":false,"family":"Michalak","given":"Melanie","email":"","middleInitial":"J.","affiliations":[{"id":7067,"text":"Humboldt State University","active":true,"usgs":false}],"preferred":false,"id":893555,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Cashman, Susan M.","contributorId":333685,"corporation":false,"usgs":false,"family":"Cashman","given":"Susan","email":"","middleInitial":"M.","affiliations":[{"id":63943,"text":"Cal Poly Humboldt","active":true,"usgs":false}],"preferred":false,"id":893556,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Langenheim, Victoria 0000-0003-2170-5213","orcid":"https://orcid.org/0000-0003-2170-5213","contributorId":217113,"corporation":false,"usgs":true,"family":"Langenheim","given":"Victoria","affiliations":[{"id":312,"text":"Geology, Minerals, Energy, and Geophysics Science Center","active":true,"usgs":true}],"preferred":true,"id":893557,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Team, Taylor C.","contributorId":333686,"corporation":false,"usgs":false,"family":"Team","given":"Taylor","email":"","middleInitial":"C.","affiliations":[{"id":63943,"text":"Cal Poly Humboldt","active":true,"usgs":false}],"preferred":false,"id":893558,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Christensen, Dana J.","contributorId":333687,"corporation":false,"usgs":false,"family":"Christensen","given":"Dana","email":"","middleInitial":"J.","affiliations":[{"id":63943,"text":"Cal Poly Humboldt","active":true,"usgs":false}],"preferred":false,"id":893559,"contributorType":{"id":1,"text":"Authors"},"rank":5}]}} ,{"id":70250797,"text":"70250797 - 2024 - Pliocene Model Intercomparison Project Phase 3 (PlioMIP3) – Science plan and experimental design","interactions":[],"lastModifiedDate":"2024-01-05T13:06:07.483136","indexId":"70250797","displayToPublicDate":"2023-12-12T07:02:09","publicationYear":"2024","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":17115,"text":"Global and Planeatary Change","active":true,"publicationSubtype":{"id":10}},"title":"Pliocene Model Intercomparison Project Phase 3 (PlioMIP3) – Science plan and experimental design","docAbstract":"The Pliocene Model Intercomparison Project (PlioMIP) was initiated in 2008. Over two phases PlioMIP has helped co-ordinate the experimental design and publication strategy of the community, which has included an increasing number of climate models and modelling groups from around the world. It has engaged with palaeoenvironmental scientists to foster new data synthesis supporting the construction of new model boundary conditions, as well as to facilitate new data-model comparisons. The work has advanced our understanding of Pliocene climates and environments, enhanced our knowledge regarding the ability of complex climate and Earth System models to accurately simulate climate change, and helped to refine our estimates of how sensitive the climate system is to forcing conditions.
In this community protocol paper, we outline the scientific plan for PlioMIP Phase 3 (PlioMIP3). This plan provides the required guidance to participating modelling groups from around the world to successfully set up and perform PlioMIP3 climate model experiments. The project is open to new participants from the scientific community (both from the climate modelling and geosciences communities).
In PlioMIP3, we retain the PlioMIP2 Core experiments (Eoi400, E280) and extend the Core requirements to include either an experiment focussed on the Early Pliocene or an alternative Late Pliocene simulation (or both). These additions (a) allow a comparison of Early and Late Pliocene warm intervals and help build research connections and synergy with the MioMIP (Miocene Model Intercomparison Project - also known as DeepMIP-Miocene) and PlioMioVAR projects (Pliocene-Miocene Variability Working Group), and (b) create an alternative time slice simulation for 3.205 Ma (MIS KM5c) through removal of some of the largest palaeogeographic differences introduced between PlioMIP1 and 2 resulting in minimal land-sea mask variations from the modern. In addition, we present ten optional experiments designed to enhance our assessment of climate sensitivity and to explore the uncertainty in greenhouse gas-related forcing. For the first time, we introduce orbital sensitivity experiments into the science plan, as well as simulations incorporating dynamic vegetation-climate feedbacks and an experiment designed to examine the potential significance of East Antarctic Ice Sheet boundary condition uncertainty. These changes enhance palaeo-to-future scientific connections and enable an exploration of the significance of palaeogeographic uncertainties on climate simulations.
","language":"English","publisher":"Elsevier","doi":"10.1016/j.gloplacha.2023.104316","usgsCitation":"Haywood, A.M., Tindall, J.C., Burton, L., Chandler, M., Dolan, A.M., Dowsett, H., Feng, R., Fletcher, T., Foley, K.M., Hill, D., Hunter, S., Otto-Bliesner, B., Lunt, D., Robinson, M., and Salzmann, U., 2024, Pliocene Model Intercomparison Project Phase 3 (PlioMIP3) – Science plan and experimental design: Global and Planeatary Change, v. 232, 104316, 11 p., https://doi.org/10.1016/j.gloplacha.2023.104316.","productDescription":"104316, 11 p.","ipdsId":"IP-152048","costCenters":[{"id":243,"text":"Eastern Geology and Paleoclimate Science Center","active":true,"usgs":true},{"id":40020,"text":"Florence Bascom Geoscience Center","active":true,"usgs":true}],"links":[{"id":440939,"rank":1,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.1016/j.gloplacha.2023.104316","text":"Publisher Index Page"},{"id":435078,"rank":0,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/P14PKB9A","text":"USGS data release","linkHelpText":"Community-sourced lower Zanclean [early Pliocene] sea surface temperature (SST) data"},{"id":424131,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"volume":"232","noUsgsAuthors":false,"publicationStatus":"PW","contributors":{"authors":[{"text":"Haywood, Alan M","contributorId":206288,"corporation":false,"usgs":false,"family":"Haywood","given":"Alan","email":"","middleInitial":"M","affiliations":[{"id":13344,"text":"University of Leeds","active":true,"usgs":false}],"preferred":false,"id":891523,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Tindall, Julia C.","contributorId":147376,"corporation":false,"usgs":false,"family":"Tindall","given":"Julia","email":"","middleInitial":"C.","affiliations":[{"id":13344,"text":"University of Leeds","active":true,"usgs":false}],"preferred":false,"id":891532,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Burton, Lauren","contributorId":332960,"corporation":false,"usgs":false,"family":"Burton","given":"Lauren","email":"","affiliations":[{"id":13344,"text":"University of Leeds","active":true,"usgs":false}],"preferred":false,"id":891524,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Chandler, M.A.","contributorId":26874,"corporation":false,"usgs":true,"family":"Chandler","given":"M.A.","email":"","affiliations":[],"preferred":false,"id":891543,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Dolan, Aisling M","contributorId":206287,"corporation":false,"usgs":false,"family":"Dolan","given":"Aisling","email":"","middleInitial":"M","affiliations":[{"id":13344,"text":"University of Leeds","active":true,"usgs":false}],"preferred":false,"id":891525,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"Dowsett, Harry J. 0000-0003-1983-7524","orcid":"https://orcid.org/0000-0003-1983-7524","contributorId":316789,"corporation":false,"usgs":true,"family":"Dowsett","given":"Harry J.","affiliations":[{"id":40020,"text":"Florence Bascom Geoscience Center","active":true,"usgs":true}],"preferred":true,"id":891526,"contributorType":{"id":1,"text":"Authors"},"rank":6},{"text":"Feng, R.","contributorId":291865,"corporation":false,"usgs":false,"family":"Feng","given":"R.","email":"","affiliations":[{"id":36710,"text":"University of Connecticut","active":true,"usgs":false}],"preferred":false,"id":891544,"contributorType":{"id":1,"text":"Authors"},"rank":7},{"text":"Fletcher, Tamara","contributorId":332961,"corporation":false,"usgs":false,"family":"Fletcher","given":"Tamara","email":"","affiliations":[{"id":13344,"text":"University of Leeds","active":true,"usgs":false}],"preferred":false,"id":891527,"contributorType":{"id":1,"text":"Authors"},"rank":8},{"text":"Foley, Kevin M. 0000-0003-1013-462X kfoley@usgs.gov","orcid":"https://orcid.org/0000-0003-1013-462X","contributorId":2543,"corporation":false,"usgs":true,"family":"Foley","given":"Kevin","email":"kfoley@usgs.gov","middleInitial":"M.","affiliations":[{"id":243,"text":"Eastern Geology and Paleoclimate Science Center","active":true,"usgs":true},{"id":40020,"text":"Florence Bascom Geoscience Center","active":true,"usgs":true}],"preferred":true,"id":891528,"contributorType":{"id":1,"text":"Authors"},"rank":9},{"text":"Hill, Daniel","contributorId":206286,"corporation":false,"usgs":false,"family":"Hill","given":"Daniel","affiliations":[{"id":13344,"text":"University of Leeds","active":true,"usgs":false}],"preferred":false,"id":891529,"contributorType":{"id":1,"text":"Authors"},"rank":10},{"text":"Hunter, Stephen","contributorId":332962,"corporation":false,"usgs":false,"family":"Hunter","given":"Stephen","affiliations":[{"id":13344,"text":"University of Leeds","active":true,"usgs":false}],"preferred":false,"id":891530,"contributorType":{"id":1,"text":"Authors"},"rank":11},{"text":"Otto-Bliesner, B.","contributorId":291867,"corporation":false,"usgs":false,"family":"Otto-Bliesner","given":"B.","affiliations":[{"id":24610,"text":"NCAR","active":true,"usgs":false}],"preferred":false,"id":891545,"contributorType":{"id":1,"text":"Authors"},"rank":12},{"text":"Lunt, D.J.","contributorId":105127,"corporation":false,"usgs":true,"family":"Lunt","given":"D.J.","email":"","affiliations":[],"preferred":false,"id":891546,"contributorType":{"id":1,"text":"Authors"},"rank":13},{"text":"Robinson, Marci M. 0000-0002-9200-4097","orcid":"https://orcid.org/0000-0002-9200-4097","contributorId":261664,"corporation":false,"usgs":true,"family":"Robinson","given":"Marci M.","affiliations":[{"id":40020,"text":"Florence Bascom Geoscience Center","active":true,"usgs":true}],"preferred":true,"id":891531,"contributorType":{"id":1,"text":"Authors"},"rank":14},{"text":"Salzmann, U.","contributorId":95711,"corporation":false,"usgs":true,"family":"Salzmann","given":"U.","email":"","affiliations":[],"preferred":false,"id":891547,"contributorType":{"id":1,"text":"Authors"},"rank":15}]}} ,{"id":70251247,"text":"70251247 - 2024 - Co-production of models to evaluate conservation alternatives for a threatened fish in a rapidly changing landscape","interactions":[],"lastModifiedDate":"2024-01-31T12:59:01.02621","indexId":"70251247","displayToPublicDate":"2023-12-12T06:56:13","publicationYear":"2024","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":873,"text":"Aquatic Sciences","active":true,"publicationSubtype":{"id":10}},"title":"Co-production of models to evaluate conservation alternatives for a threatened fish in a rapidly changing landscape","docAbstract":"Reintroductions are one means of managing species distributions, but the feasibility of such efforts is uncertain. Here we consider reintroduction for threatened bull trout (Salvelinus confluentus) that currently occupy a small fraction of historically occupied habitats in the upper Klamath River basin owing to climate warming and human modifications of ecosystems. We engaged stakeholders across multiple organizations to co-produce a decision support model that estimated the potential of reintroduction to establish new populations and persistence of donor populations. Stakeholders identified recipient and donor populations, strategy (e.g., artificial propagation, translocation), number of individuals, and life stage of bull trout. The most optimal decision for reintroduction was artificial propagation of 10,000 fry into Annie Creek. This strategy may have negative consequences on donor populations, with the exception of Sun Creek, which was resilient to simulated removal of bull trout. Donor populations and recipient streams identified as most feasible were generally consistent across all of these scenarios. During model development, however, an unexpected and intense wildfire affected half of the streams considered and may have dramatically impacted donor populations. With models in hand from the initial feasibility assessment, we adapted them to further evaluate the potential of supplementation following this massive disturbance. Overall, results of this study indicate the value of developing co-produced tools that can be rapidly adapted to evaluate the consequences of whole-system transformations in near-real-time assessments.
Ecological restoration is an essential strategy for mitigating the current biodiversity crisis, yet restoration actions are costly. We used systematic conservation planning principles to design an approach that prioritizes restoration sites for birds and tested it in a riparian forest restoration program in the Colorado River Delta. Restoration goals were to maximize the abundance and diversity of 15 priority birds with a variety of habitat preferences. We built abundance models for priority birds based on the current landscape, and predicted bird distributions and relative abundances under a scenario of complete riparian forest restoration throughout our study area. Then, we used Zonation conservation planning software to rank this restored landscape based on core areas for all priority birds. The locations with the highest ranks represented the highest priorities for restoration and were located throughout the river reach. We optimized how much of the available landscape to restore by simulating restoration of the top 10–90% of ranked sites in 10% intervals. We found that total diversity was maximized when 40% of the landscape was restored, and mean relative abundance was maximized when 80% of the landscape was restored. The results suggest that complete restoration is not optimal for this community of priority birds and restoration of approximately 60% of the landscape would provide a balance between maximum relative abundance and diversity. Subsequent planning efforts will combine our results with an assessment of restoration costs to provide further decision support for the restoration-siting process. Our approach can be applied to any landscape-scale restoration program to improve the return on investment of limited economic resources for restoration.
Fisheries and aquaculture provide food and economic security, especially in the developing world, but both face challenges from infectious disease. Here, we consider management of disease issues from a structured decision-making perspective to examine how infectious disease can threaten seafood production and influence management decisions. For both wild fisheries and aquaculture, disease-management objectives generally aim to mitigate the severity and economic burden of outbreaks. General management strategies include manipulating host densities, reducing system connectivity, conserving or improving habitat, and implementing direct treatments or some other biological interventions. To inform decisions, mathematical models can be used to explore disease dynamics and to forecast the potential effectiveness of alternative management actions. Developing and implementing disease-management strategies also involve considering uncertainties and balancing competing stakeholder interests and risk tolerances. We conclude by outlining several steps for applying structured decision making that are broadly useful to decision makers facing issues related to disease.
Wildlife-related recreationists play an important role in conservation. Understanding constraints to wildlife-related activities is critical for maintaining or increasing participation in activities like birdwatching and hunting. A mail-out survey was administered to a generalized sample representative of U.S. residents (i.e., not specific to birdwatching or hunting) in early 2017 to determine what would limit them from participating in birdwatching and hunting (n = 1030). We employed a concurrent nested mixed-methods design: open-ended responses were thematically coded qualitatively in two distinct cycles (i.e., inductive, and then mixed inductive-deductive coding), and then the probability of expressing the second cycle codes was quantitatively modeled using multinomial logit models for the respective activities. Doing so empirically determined various groups’ constraints that are important to recruitment, retention, and reactivation (R3) efforts for birdwatching and hunting. We found that the likelihood of experiencing unique constraints varied based on sociodemographic characteristics, and these relationships differed between birdwatching and hunting. Gender had a limited effect on constraints to birdwatching but was a strong indicator of intrapersonal constraints and limitations to involvement in hunting. The likelihood of expressing structural constraints decreased with age for both activities. Possessing strong social ties to the activities tended to reduce the likelihood of expressing constraints overall but this was especially true for hunting. Our findings inform R3 efforts for wildlife-related recreation and provide direct results that organizations can apply in seeking to help Americans negotiate constraints and increase and diversify participation in wildlife-related recreation and conservation behavior.
","language":"English","publisher":"Elsevier","doi":"10.1016/j.jort.2023.100712","usgsCitation":"Cole, N.W., Wilkins, E.J., Clements, K., Schuster, R., Dayer, A., Harshaw, H.W., Fulton, D.C., Duberstein, J., and Raedeke, A., 2024, Perceived constraints to participating in wildlife-related recreation: Journal of Outdoor Recreation and Tourism, v. 45, 100712, 10 p., https://doi.org/10.1016/j.jort.2023.100712.","productDescription":"100712, 10 p.","ipdsId":"IP-112562","costCenters":[{"id":291,"text":"Fort Collins Science Center","active":true,"usgs":true}],"links":[{"id":440952,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.1016/j.jort.2023.100712","text":"Publisher Index Page"},{"id":424223,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"volume":"45","noUsgsAuthors":false,"publicationStatus":"PW","contributors":{"authors":[{"text":"Cole, Nicholas W. 0000-0003-1204-971X","orcid":"https://orcid.org/0000-0003-1204-971X","contributorId":278636,"corporation":false,"usgs":true,"family":"Cole","given":"Nicholas","email":"","middleInitial":"W.","affiliations":[{"id":291,"text":"Fort Collins Science Center","active":true,"usgs":true}],"preferred":true,"id":891746,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Wilkins, Emily J. 0000-0003-3055-4808","orcid":"https://orcid.org/0000-0003-3055-4808","contributorId":328409,"corporation":false,"usgs":true,"family":"Wilkins","given":"Emily","email":"","middleInitial":"J.","affiliations":[{"id":291,"text":"Fort Collins Science Center","active":true,"usgs":true}],"preferred":true,"id":891747,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Clements, Kaylin 0000-0002-0014-4376","orcid":"https://orcid.org/0000-0002-0014-4376","contributorId":333041,"corporation":false,"usgs":false,"family":"Clements","given":"Kaylin","affiliations":[{"id":27102,"text":"USGS student contractor","active":true,"usgs":false}],"preferred":false,"id":891748,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Schuster, Rudy 0000-0003-2353-8500 schusterr@usgs.gov","orcid":"https://orcid.org/0000-0003-2353-8500","contributorId":3119,"corporation":false,"usgs":true,"family":"Schuster","given":"Rudy","email":"schusterr@usgs.gov","affiliations":[],"preferred":true,"id":891749,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Dayer, Ashley A.","contributorId":278637,"corporation":false,"usgs":false,"family":"Dayer","given":"Ashley A.","affiliations":[{"id":36967,"text":"Virginia Tech University","active":true,"usgs":false}],"preferred":false,"id":891750,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"Harshaw, H. W. 0000-0001-9568-772X","orcid":"https://orcid.org/0000-0001-9568-772X","contributorId":333042,"corporation":false,"usgs":false,"family":"Harshaw","given":"H.","email":"","middleInitial":"W.","affiliations":[{"id":36696,"text":"University of Alberta","active":true,"usgs":false}],"preferred":false,"id":891751,"contributorType":{"id":1,"text":"Authors"},"rank":6},{"text":"Fulton, David C. 0000-0001-5763-7887","orcid":"https://orcid.org/0000-0001-5763-7887","contributorId":333043,"corporation":false,"usgs":true,"family":"Fulton","given":"David","email":"","middleInitial":"C.","affiliations":[{"id":79716,"text":"Minnesota Cooperative Unit","active":true,"usgs":false}],"preferred":true,"id":891752,"contributorType":{"id":1,"text":"Authors"},"rank":7},{"text":"Duberstein, Jennifer N.","contributorId":278642,"corporation":false,"usgs":false,"family":"Duberstein","given":"Jennifer N.","affiliations":[{"id":6654,"text":"USFWS","active":true,"usgs":false}],"preferred":false,"id":891753,"contributorType":{"id":1,"text":"Authors"},"rank":8},{"text":"Raedeke, Andrew H.","contributorId":278640,"corporation":false,"usgs":false,"family":"Raedeke","given":"Andrew H.","affiliations":[{"id":16971,"text":"Missouri Department of Conservation","active":true,"usgs":false}],"preferred":false,"id":891754,"contributorType":{"id":1,"text":"Authors"},"rank":9}]}} ,{"id":70250575,"text":"70250575 - 2024 - Bobcat occupancy, tree islands, and invasive Burmese pythons in an Everglades conservation area","interactions":[],"lastModifiedDate":"2024-01-25T14:47:10.411708","indexId":"70250575","displayToPublicDate":"2023-12-11T06:50:41","publicationYear":"2024","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":16872,"text":"The Journal of Wildlife Management","active":true,"publicationSubtype":{"id":10}},"title":"Bobcat occupancy, tree islands, and invasive Burmese pythons in an Everglades conservation area","docAbstract":"Bobcats (Lynx rufus) are terrestrial mammals that also inhabit tree islands (i.e., topographically elevated patches of forested land) embedded in the subtropical Everglades wetlands, which serve as a dry refuge habitat during the wet season in this region of Florida, USA. The Comprehensive Everglades Restoration Plan seeks to restore Everglades water flow to pre-drainage conditions, but little is known about how water levels or other landscape-level factors may influence mammalian occurrence, such as bobcats, on the tree islands in this ecosystem. We used game camera records and occupancy modeling to test for effects of static habitat variables and dynamic hydrologic variables. We hypothesized that deep water levels would limit the accessibility of tree islands to bobcats; therefore, we predicted that bobcat occupancy would decline with higher water levels. We also tested for the effect of an expanding invasive snake (i.e., Burmese python [Python molarus bivittatus]) using output from a model constructed to predict density and spread of Burmese pythons across southern Florida. We hypothesized that increases in Burmese pythons on the landscape would influence the food resources of bobcats, resulting in reduced bobcat occupancy at higher predicted densities of pythons. We built detection histories using 1,855 bobcat images from game cameras set on 87 tree islands in an Everglades conservation area from 2005–2019. Bobcat occupancy was significantly diminished when predicted Burmese python densities exceeded approximately 3 Burmese pythons/km2. Bobcat occupancy probability also increased with tree-island density around the focal tree island. Although water depth and hydroperiod surrounding tree islands appeared in our top 3 candidate models, the hydrologic variables had weak effects on bobcat occupancy. Our results suggest that while hydrologic dynamics may play a role, the invasive Burmese python has stronger influences on bobcat occupancy of tree islands in this Everglades conservation area.
Wildfires are increasing in size and severity across much of the western United States, exposing vulnerable wildland-urban interfaces to post-fire hazards. The Mediterranean chaparral region of Northern California contains many high sloping watersheds prone to hazardous post-fire flood events and identifying watersheds at high risk of soil loss and debris flows is a priority for post-fire response and management. Uncrewed Aerial Systems (UAS; aka drones) offer post-fire management teams the ability to quickly mobilize and survey burned areas with very high-resolution imagery (∼1 cm), facilitating emergency management and post-fire hazard assessment. However, adoption of this technology by hazard response teams may be hindered by complicated workflows for UAS data acquisition, image processing and analysis. We present an open-source workflow using mature Geographic Information Systems (GIS) software and Python packages in a Jupyter Notebook environment that guides users through classification of true-color UAS imagery to generate high resolution burn severity maps which can then be scaled across larger watersheds using Sentinel-2 normalized burn ratio (NBR) images. Soil burn severity classifications using a weighted brightness (WB) image and Char Index (CI) generated from UAS imagery were validated with in-situ data and random stratified points, resulting in the CI having the highest overall accuracy of 87.5%. CI also displayed a marginally stronger relationship over the WB with the post-fire Sentinel-2 NBR, R2 = 0.79 and R2 = 0.78 respectively. Our methods offer the unique opportunity to standardize GIS workflows, promoting replication through transparency, while improving the user's understanding of scientific GIS functionality.
We investigate the utility of audio-magnetotelluric (AMT) data for detecting high electrical conductivity zones indicative of fresh and saline groundwater lenses at depths of < ∼1000 m at seven survey sites in the southwestern corner of Bradford County, PA. Dimensionality analysis indicates the AMT data can be interpreted using two-dimensional (2-D) models. 2-D electrical resistivity models obtained from inverting the AMT data reveal conductive zones at depths of <30 m at all sites, between depths of 50–150 m at six sites, and between depths of 400–600 m at one site. The models show slightly lower resistivities (∼15–30 Ω m) in the deeper zones compared to the shallower zones (>30 Ω m). Consistent with well log data from two boreholes in the study area and salinity estimates obtained from the imaged resistivities, we interpret the <30 m deep conductive zone as the freshwater aquifer, and the two deeper conductive zones as salty/briny groundwater lenses. Our salinity estimates for the deeper zones vary from 1 to 380 (unitless Practical Salinity Scale), which agrees well with previous estimates of Appalachian brine salinity of 10–343. We attribute the high-salinity groundwater lenses to Appalachian Basin brine migrating upwards from deeper in the basin via bedrock fracture and fault systems. Our findings indicate AMT surveying can be useful for imaging fresh and saline aquifers at shallow depths (< ∼1000 m), at least within the northeastern section of the Appalachian Basin in Pennsylvania.
","language":"English","publisher":"Elsevier","doi":"10.1016/j.jappgeo.2023.105255","usgsCitation":"Rathnayaka, S., Gustafson, C., Yoxtheimer, D., and Nyblade, A., 2024, Imaging freshwater and saline aquifers beneath Bradford County, Pennsylvania, USA, using Audio-Magnetotelluric (AMT) data: Journal of Applied Geophysics, v. 220, 105255, 12 p., https://doi.org/10.1016/j.jappgeo.2023.105255.","productDescription":"105255, 12 p.","ipdsId":"IP-156937","costCenters":[{"id":35995,"text":"Geology, Geophysics, and Geochemistry Science Center","active":true,"usgs":true}],"links":[{"id":503298,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","state":"Pennsylvania","county":"Bradford County","geographicExtents":"{\"type\":\"FeatureCollection\",\"features\":[{\"type\":\"Feature\",\"geometry\":{\"type\":\"Polygon\",\"coordinates\":[[[-76.9291,42.0024],[-76.9095,42.0025],[-76.8966,42.0026],[-76.6476,42.0019],[-76.6334,42.0017],[-76.5964,42.0013],[-76.5618,42.0009],[-76.5531,42.0008],[-76.5229,42.0005],[-76.466,41.9999],[-76.3826,41.9989],[-76.1467,41.9991],[-76.1382,41.898],[-76.1336,41.8467],[-76.1285,41.7935],[-76.1258,41.773],[-76.1219,41.7217],[-76.1171,41.6531],[-76.1959,41.648],[-76.1996,41.6467],[-76.2015,41.6435],[-76.2015,41.6426],[-76.2015,41.6408],[-76.2016,41.6353],[-76.2016,41.6344],[-76.2023,41.6335],[-76.2029,41.6322],[-76.2063,41.6145],[-76.209,41.6004],[-76.2091,41.5982],[-76.2184,41.5579],[-76.2217,41.5447],[-76.2383,41.5458],[-76.2432,41.5463],[-76.2487,41.5468],[-76.3277,41.5526],[-76.4454,41.5608],[-76.5,41.5649],[-76.5975,41.5715],[-76.6367,41.5745],[-76.6478,41.5755],[-76.6619,41.5765],[-76.679,41.578],[-76.6938,41.579],[-76.6993,41.5795],[-76.7496,41.5834],[-76.7569,41.5839],[-76.787,41.5872],[-76.7949,41.5882],[-76.8005,41.5887],[-76.8103,41.5896],[-76.8133,41.5901],[-76.8219,41.5911],[-76.8379,41.593],[-76.8747,41.5968],[-76.8747,41.599],[-76.8805,41.6363],[-76.8833,41.6681],[-76.8838,41.6717],[-76.885,41.6781],[-76.8873,41.6999],[-76.8907,41.7267],[-76.8936,41.7503],[-76.8976,41.783],[-76.8987,41.8007],[-76.8993,41.808],[-76.9022,41.8248],[-76.9022,41.8257],[-76.9051,41.8466],[-76.9162,41.918],[-76.9209,41.9507],[-76.9238,41.9711],[-76.9291,42.0024]]]},\"properties\":{\"name\":\"Bradford\",\"state\":\"PA\"}}]}","volume":"220","noUsgsAuthors":false,"publicationStatus":"PW","contributors":{"authors":[{"text":"Rathnayaka, Sampath","contributorId":370185,"corporation":false,"usgs":false,"family":"Rathnayaka","given":"Sampath","affiliations":[{"id":7260,"text":"Pennsylvania State University","active":true,"usgs":false}],"preferred":false,"id":959928,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Gustafson, Chloe Danielle 0000-0001-8323-2568","orcid":"https://orcid.org/0000-0001-8323-2568","contributorId":346924,"corporation":false,"usgs":true,"family":"Gustafson","given":"Chloe Danielle","affiliations":[{"id":35995,"text":"Geology, Geophysics, and Geochemistry Science Center","active":true,"usgs":true}],"preferred":true,"id":959929,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Yoxtheimer, David","contributorId":370186,"corporation":false,"usgs":false,"family":"Yoxtheimer","given":"David","affiliations":[{"id":7260,"text":"Pennsylvania State University","active":true,"usgs":false}],"preferred":false,"id":959930,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Nyblade, Andrew","contributorId":370187,"corporation":false,"usgs":false,"family":"Nyblade","given":"Andrew","affiliations":[{"id":7260,"text":"Pennsylvania State University","active":true,"usgs":false}],"preferred":false,"id":959931,"contributorType":{"id":1,"text":"Authors"},"rank":4}]}} ,{"id":70250440,"text":"70250440 - 2024 - Hyperspectral (VNIR-SWIR) analysis of roll front uranium host rocks and industrial minerals from Karnes and Live Oak Counties, Texas Coastal Plain","interactions":[],"lastModifiedDate":"2023-12-09T14:44:29.728919","indexId":"70250440","displayToPublicDate":"2023-12-06T08:38:08","publicationYear":"2024","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":2302,"text":"Journal of Geochemical Exploration","active":true,"publicationSubtype":{"id":10}},"title":"Hyperspectral (VNIR-SWIR) analysis of roll front uranium host rocks and industrial minerals from Karnes and Live Oak Counties, Texas Coastal Plain","docAbstract":"VNIR-SWIR (400–2500 nm) reflectance measurements were made on the surfaces of various cores, cuttings and sample splits of sedimentary rocks from the Tertiary Jackson Group, and Catahoula, Oakville and Goliad Formations. These rocks vary in composition and texture from mudstone and claystone to sandstone and are known host rocks for roll front uranium occurrences in Karnes and Live Oak Counties, Texas. Spectral reflectance profiles, 569 in total, were reduced to 125 representative spectral signatures, which were analyzed using the U.S. Geological Survey's (USGS) Material Identification and Characterization Algorithm (MICA). MICA uses an automated continuum-removal procedure together with a least-squares linear regression to determine the fit of observed sample spectral absorption features to those of reference mineral standards in a spectral library. The reference minerals include various clay, mica, carbonate, ferric and ferrous iron minerals and their mixtures. In addition, absorption feature band-depth analysis was done to identify rock surfaces exhibiting absorption features related to uranium and zeolite minerals, which were not included in the command files used to execute MICA.
Rocks from each of the four geologic units produced broadly similar spectral signatures as a result of comparable mineral compositions, but there were some notable differences. For example, Ca- and Na-montmorillonite was matched most frequently to the spectral absorption features in 2-μm (∼2000–2500 nm) wavelengths, while goethite occurred often at 1-μm (∼400–1000 nm) wavelengths. The latter is related to limonitic iron-staining in and around oxidized zones of the uranium roll front as described in previous papers. Rocks of the Jackson Group differed from those of the Catahoula, Oakville and Goliad units in that the former exhibited spectral features we interpret as being due to the presence of lignite-bearing mudstone layers. Goliad rocks exhibit spectral features related to dolomite, gypsum, anhydrite, and an unidentified green clay mineral that is possibly glauconite. Jackson Group rocks also exhibit weak but well-resolved absorption features at 964 and 1157 nm related to either or both zeolite minerals clinoptilolite and heulandite. These zeolite minerals and a few spectra exhibiting hydrous silica absorption features are indicative of alteration of volcanic glass in tuffaceous mudstone and claystone layers. A few sample spectra exhibited strong absorption features at around 1135 nm related to the uranium mineral coffinite. Both the 1135 nm coffinite and 1157 nm zeolite absorption features overlap somewhat, potentially making them difficult to distinguish without additional hyperspectral field, laboratory or remote sensing data.
The results of this study were compared to mixtures of minerals described for ore, gangue and alteration minerals in deposit models for sandstone-hosted uranium, sedimentary bentonite and sedimentary zeolite. Use of these spectra can help facilitate mapping of both waste materials from the legacy mining of the above commodities, as well as future exploration and resource assessment activities.
","language":"English","publisher":"Elsever","doi":"10.1016/j.gexplo.2023.107370","usgsCitation":"Hubbard, B.E., Gallegos, T., Stengel, V.G., Hoefen, T.M., Kokaly, R.F., and Elliott, B., 2024, Hyperspectral (VNIR-SWIR) analysis of roll front uranium host rocks and industrial minerals from Karnes and Live Oak Counties, Texas Coastal Plain: Journal of Geochemical Exploration, v. 257, 107370, 20 p., https://doi.org/10.1016/j.gexplo.2023.107370.","productDescription":"107370, 20 p.","ipdsId":"IP-136836","costCenters":[{"id":171,"text":"Central Mineral and Environmental Resources Science Center","active":true,"usgs":true},{"id":241,"text":"Eastern Energy Resources Science Center","active":true,"usgs":true},{"id":245,"text":"Eastern Mineral and Environmental Resources Science Center","active":true,"usgs":true},{"id":583,"text":"Texas Water Science Center","active":true,"usgs":true},{"id":49175,"text":"Geology, Energy & Minerals Science Center","active":true,"usgs":true}],"links":[{"id":440969,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.1016/j.gexplo.2023.107370","text":"Publisher Index Page"},{"id":423384,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","state":"Texas","county":"Karnes County, Live Oak 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