Stream synoptic sampling studies that include flow estimates derived from the stream tracer dilution method are now commonly performed to identify sources and processes controlling solute transport to streams. However, a limitation of this mass-loading approach is its inability to identify the side of the stream on which a source is located in the common case where loading is largely from groundwater discharge. Such bank-specific loading information can be particularly valuable at mining-affected sites where both mining-related and natural background metal sources are often closely intermingled. Here we combine the stream mass-loading approach with data from pairs of shallow hand-installed streambank wells located on opposite sides of a gaining metal-impacted headwater stream to estimate bank-specific metal loading rates. Study results successfully identify a right-bank zone in the upper half of the primary study reach as the dominant source of loading, where groundwater discharge is elevated in metals due to natural weathering of sulfide-rich bedrock. A left-bank zone in the lower half of the primary study reach was also identified as a secondary, yet still substantial, loading source, where groundwater is likely impacted by portal discharge and/or waste piles associated with an adjacent abandoned mine. Determining the dominance of the left-bank mining-related source compared to other potential right-bank natural sulfide weathering sources would not have been possible without the streambank wells. Streambank wells also enabled collection of dissolved gas samples for groundwater dating. Computed piston-flow 3H/3He groundwater ages show little to no correlation with either metal concentrations or loading rates, suggesting that groundwater residence time and flow path variations exert little influence on groundwater discharge chemistry compared to variations in bedrock and soil composition. This study thus provides proof of concept that the proposed method of combining streambank well data with stream tracer injection and synoptic sampling can provide useful information on bank-specific mass loading rates and sources of contaminants affecting stream water quality.
","language":"English","publisher":"Elsevier","doi":"10.1016/j.apgeochem.2022.105425","usgsCitation":"Manning, A.H., Runkel, R.L., Morrison, J.M., Wanty, R., and Walton-Day, K., 2022, Incorporating streambank wells in stream mass loading studies to more effectively identify sources of solutes in stream water: Applied Geochemistry, v. 145, 105425, 14 p., https://doi.org/10.1016/j.apgeochem.2022.105425.","productDescription":"105425, 14 p.","ipdsId":"IP-141156","costCenters":[{"id":191,"text":"Colorado Water Science Center","active":true,"usgs":true},{"id":211,"text":"Crustal Geophysics and Geochemistry Science Center","active":true,"usgs":true},{"id":35995,"text":"Geology, Geophysics, and Geochemistry Science Center","active":true,"usgs":true}],"links":[{"id":446668,"rank":1,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.1016/j.apgeochem.2022.105425","text":"Publisher Index Page"},{"id":435717,"rank":0,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/P9V0J8FS","text":"USGS data release","linkHelpText":"Geochemistry and Environmental Tracer Data for Groundwater, Stream Water, and Soil and Sediment from North Quartz Creek, Colorado"},{"id":408264,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","state":"Colorado","otherGeospatial":"North Quartz Creek","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -106.52,\n 38.60\n ],\n [\n -106.44344329833984,\n 38.60\n ],\n [\n -106.44344329833984,\n 38.65870536210694\n ],\n [\n -106.52,\n 38.65870536210694\n ],\n [\n -106.52,\n 38.60\n ]\n ]\n ]\n }\n }\n ]\n}","volume":"145","noUsgsAuthors":false,"publicationStatus":"PW","contributors":{"authors":[{"text":"Manning, Andrew H. 0000-0002-6404-1237 amanning@usgs.gov","orcid":"https://orcid.org/0000-0002-6404-1237","contributorId":1305,"corporation":false,"usgs":true,"family":"Manning","given":"Andrew","email":"amanning@usgs.gov","middleInitial":"H.","affiliations":[{"id":171,"text":"Central Mineral and Environmental Resources Science Center","active":true,"usgs":true}],"preferred":true,"id":854531,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Runkel, Robert L. 0000-0003-3220-481X runkel@usgs.gov","orcid":"https://orcid.org/0000-0003-3220-481X","contributorId":685,"corporation":false,"usgs":true,"family":"Runkel","given":"Robert","email":"runkel@usgs.gov","middleInitial":"L.","affiliations":[{"id":191,"text":"Colorado Water Science Center","active":true,"usgs":true}],"preferred":true,"id":854532,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Morrison, Jean M. 0000-0002-6614-8783 jmorrison@usgs.gov","orcid":"https://orcid.org/0000-0002-6614-8783","contributorId":994,"corporation":false,"usgs":true,"family":"Morrison","given":"Jean","email":"jmorrison@usgs.gov","middleInitial":"M.","affiliations":[{"id":211,"text":"Crustal Geophysics and Geochemistry Science Center","active":true,"usgs":true}],"preferred":true,"id":854533,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Wanty, Richard B. 0000-0002-2063-6423","orcid":"https://orcid.org/0000-0002-2063-6423","contributorId":209899,"corporation":false,"usgs":true,"family":"Wanty","given":"Richard","middleInitial":"B.","affiliations":[],"preferred":true,"id":854534,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Walton-Day, Katherine 0000-0002-9146-6193 kwaltond@usgs.gov","orcid":"https://orcid.org/0000-0002-9146-6193","contributorId":184043,"corporation":false,"usgs":true,"family":"Walton-Day","given":"Katherine","email":"kwaltond@usgs.gov","affiliations":[{"id":191,"text":"Colorado Water Science Center","active":true,"usgs":true}],"preferred":true,"id":854535,"contributorType":{"id":1,"text":"Authors"},"rank":5}]}} ,{"id":70245487,"text":"70245487 - 2022 - Stakeholder engagement to guide decision-relevant water data delivery","interactions":[],"lastModifiedDate":"2023-06-23T16:12:23.251924","indexId":"70245487","displayToPublicDate":"2022-08-24T11:01:37","publicationYear":"2022","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":7168,"text":"Journal of the American Water Resources Association (JAWRA)","active":true,"publicationSubtype":{"id":10}},"title":"Stakeholder engagement to guide decision-relevant water data delivery","docAbstract":"Water resources management and policy making require access to reliable scientific data. However, water managers may need to overcome various obstacles to accessing data. For example, insufficient technological infrastructures, low data literacy, and data format complexities often inhibit data user access. Thus, it is imperative to include stakeholders in the design of data delivery systems. The United States Geological Survey's Water Resources Mission Area is currently developing Integrated Water Availability Assessments (IWAAs) — multi-extent, stakeholder driven, near real-time water availability census and prediction for human and ecological uses. To provide appropriate user accessibility to data delivery systems developed for IWAAs, a user-centered design process including stakeholder focus groups was used to determine potential water data user needs and preferences. Focus groups identified five types of potential users: Public sector water resources managers, Public sector water resources manager data analysts, Industry and private companies, Tribal Nations, and Nonprofit organizations. Different water data user types depended on diverse spatial and temporal scale data. Public sector water resources managers benefitted most from data synthesized into user-friendly platforms and Public sector water resources data analysts preferred easy access to raw data. These findings can support the development of a water data delivery platform that meets a variety of user needs.
","language":"English","publisher":"Wiley","doi":"10.1111/1752-1688.13055","usgsCitation":"Restrepo-Osorio, D., Stoltz, A.D., and Herman-Mercer, N.M., 2022, Stakeholder engagement to guide decision-relevant water data delivery: Journal of the American Water Resources Association (JAWRA), v. 58, no. 6, p. 1531-1546, https://doi.org/10.1111/1752-1688.13055.","productDescription":"14 p.","startPage":"1531","endPage":"1546","ipdsId":"IP-130669","costCenters":[{"id":37316,"text":"WMA - Integrated Information Dissemination Division","active":true,"usgs":true}],"links":[{"id":435718,"rank":2,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/P9YI55UG","text":"USGS data release","linkHelpText":"Datasets from the focus group series of stakeholder engagement efforts to inform integrated water availability assessment data delivery"},{"id":418404,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"volume":"58","issue":"6","noUsgsAuthors":false,"publicationDate":"2022-08-24","publicationStatus":"PW","contributors":{"authors":[{"text":"Restrepo-Osorio, Diana 0000-0003-4230-0055 drestrepo-osorio@usgs.gov","orcid":"https://orcid.org/0000-0003-4230-0055","contributorId":189352,"corporation":false,"usgs":true,"family":"Restrepo-Osorio","given":"Diana","email":"drestrepo-osorio@usgs.gov","affiliations":[],"preferred":true,"id":876138,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Stoltz, Amanda D. 0000-0003-4656-6125","orcid":"https://orcid.org/0000-0003-4656-6125","contributorId":311692,"corporation":false,"usgs":true,"family":"Stoltz","given":"Amanda","email":"","middleInitial":"D.","affiliations":[{"id":291,"text":"Fort Collins Science Center","active":true,"usgs":true}],"preferred":true,"id":876139,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Herman-Mercer, Nicole M. 0000-0001-5933-4978 nhmercer@usgs.gov","orcid":"https://orcid.org/0000-0001-5933-4978","contributorId":3927,"corporation":false,"usgs":true,"family":"Herman-Mercer","given":"Nicole","email":"nhmercer@usgs.gov","middleInitial":"M.","affiliations":[{"id":438,"text":"National Research Program - Western Branch","active":true,"usgs":true},{"id":5044,"text":"National Research Program - Central Branch","active":true,"usgs":true}],"preferred":true,"id":876141,"contributorType":{"id":1,"text":"Authors"},"rank":3}]}} ,{"id":70236857,"text":"70236857 - 2022 - Variation in within-host replication kinetics among virus genotypes provides evidence of specialist and generalist infection strategies across three salmonid host species","interactions":[],"lastModifiedDate":"2022-09-20T12:20:01.426668","indexId":"70236857","displayToPublicDate":"2022-08-24T07:18:52","publicationYear":"2022","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":5051,"text":"Virus Evolution","onlineIssn":"2057-1577","active":true,"publicationSubtype":{"id":10}},"title":"Variation in within-host replication kinetics among virus genotypes provides evidence of specialist and generalist infection strategies across three salmonid host species","docAbstract":"Theory of the evolution of pathogen specialization suggests that a specialist pathogen gains high fitness in one host, but this comes with fitness loss in other hosts. By contrast, a generalist pathogen does not achieve high fitness in any host, but gains ecological fitness by exploiting different hosts, and has higher fitness than specialists in nonspecialized hosts. As a result, specialist pathogens are predicted to have greater variation in fitness across hosts, and generalists would have lower fitness variation across hosts. We test these hypotheses by measuring pathogen replicative fitness as within-host viral loads from the onset of infection to the beginning of virus clearance, using the rhabdovirus infectious hematopoietic necrosis virus (IHNV) in salmonid fish. Based on field prevalence and virulence studies, the IHNV subgroups UP, MD, and L are specialists, causing infection and mortality in sockeye salmon, steelhead, and Chinook salmon juveniles, respectively. The UC subgroup evolved naturally from a UP ancestor and is a generalist infecting all three host species but without causing severe disease. We show that the specialist subgroups had the highest peak and mean viral loads in the hosts in which they are specialized, and they had low viral loads in nonspecialized hosts, resulting in large variation in viral load across hosts. Viral kinetics show that the mechanisms of specialization involve the ability to both maximize early virus replication and avoid clearance at later times, with different mechanisms of specialization evident in different host–virus combinations. Additional nuances in the data included different fitness levels for nonspecialist interactions, reflecting different trade-offs for specialist viruses in other hosts. The generalist UC subgroup reached intermediate viral loads in all hosts and showed the smallest variation in fitness across hosts. The evolution of the UC generalist from an ancestral UP sockeye specialist was associated with fitness increases in steelhead and Chinook salmon, but only slight decreases in fitness in sockeye salmon, consistent with low- or no-cost generalism. Our results support major elements of the specialist–generalist theory, providing evidence of a specialist–generalist continuum in a vertebrate pathogen. These results also quantify within-host replicative fitness trade-offs resulting from the natural evolution of specialist and generalist virus lineages in multi-host ecosystems
","language":"English","publisher":"Oxford Academic","doi":"10.1093/ve/veac079","usgsCitation":"Paez, D.J., McKenney, D.G., Purcell, M.K., Naish, K.A., and Kurath, G., 2022, Variation in within-host replication kinetics among virus genotypes provides evidence of specialist and generalist infection strategies across three salmonid host species: Virus Evolution, v. 8, no. 2, veac079, 12 p., https://doi.org/10.1093/ve/veac079.","productDescription":"veac079, 12 p.","ipdsId":"IP-142038","costCenters":[{"id":654,"text":"Western Fisheries Research Center","active":true,"usgs":true}],"links":[{"id":446678,"rank":1,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.1093/ve/veac079","text":"Publisher Index Page"},{"id":435719,"rank":0,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/P98UQLLW","text":"USGS data release","linkHelpText":"Survival and viral load of chinook salmon, sockeye salmon, and steelhead trout exposed to 4 genogroups of infectious hematopoietic necrosis virus (IHNV)"},{"id":407051,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"volume":"8","issue":"2","noUsgsAuthors":false,"publicationDate":"2022-08-24","publicationStatus":"PW","contributors":{"authors":[{"text":"Páez, David James 0000-0001-9035-394X","orcid":"https://orcid.org/0000-0001-9035-394X","contributorId":296751,"corporation":false,"usgs":true,"family":"Páez","given":"David","middleInitial":"James","affiliations":[{"id":654,"text":"Western Fisheries Research Center","active":true,"usgs":true}],"preferred":true,"id":852373,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"McKenney, Douglas G.","contributorId":296750,"corporation":false,"usgs":false,"family":"McKenney","given":"Douglas","email":"","middleInitial":"G.","affiliations":[{"id":64163,"text":"Previously USGS, Western Fisheries Research Center","active":true,"usgs":false}],"preferred":false,"id":852374,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Purcell, Maureen K. 0000-0003-0154-8433 mpurcell@usgs.gov","orcid":"https://orcid.org/0000-0003-0154-8433","contributorId":168475,"corporation":false,"usgs":true,"family":"Purcell","given":"Maureen","email":"mpurcell@usgs.gov","middleInitial":"K.","affiliations":[{"id":654,"text":"Western Fisheries Research Center","active":true,"usgs":true}],"preferred":true,"id":852375,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Naish, Kerry A. 0000-0002-3275-8778","orcid":"https://orcid.org/0000-0002-3275-8778","contributorId":201136,"corporation":false,"usgs":false,"family":"Naish","given":"Kerry","email":"","middleInitial":"A.","affiliations":[],"preferred":false,"id":852376,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Kurath, Gael 0000-0003-3294-560X","orcid":"https://orcid.org/0000-0003-3294-560X","contributorId":220175,"corporation":false,"usgs":true,"family":"Kurath","given":"Gael","affiliations":[{"id":654,"text":"Western Fisheries Research Center","active":true,"usgs":true}],"preferred":true,"id":852377,"contributorType":{"id":1,"text":"Authors"},"rank":5}]}} ,{"id":70250035,"text":"70250035 - 2022 - Recent climate change has driven divergent hydrological shifts in high-latitude peatlands","interactions":[],"lastModifiedDate":"2023-11-15T13:10:39.101194","indexId":"70250035","displayToPublicDate":"2022-08-24T07:06:39","publicationYear":"2022","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":2842,"text":"Nature Communications","active":true,"publicationSubtype":{"id":10}},"title":"Recent climate change has driven divergent hydrological shifts in high-latitude peatlands","docAbstract":"High-latitude peatlands are changing rapidly in response to climate change, including permafrost thaw. Here, we reconstruct hydrological conditions since the seventeenth century using testate amoeba data from 103 high-latitude peat archives. We show that 54% of the peatlands have been drying and 32% have been wetting over this period, illustrating the complex ecohydrological dynamics of high latitude peatlands and their highly uncertain responses to a warming climate.
Macroecology research seeks to understand ecological phenomena with causes and consequences that accumulate, interact, and emerge across scales spanning several orders of magnitude. Broad-extent, fine-grain information (i.e., high spatial resolution data over large areas) is needed to adequately capture these cross-scale phenomena, but these data have historically been costly to acquire and process. Unoccupied aerial systems (UAS or drones carrying a sensor payload) and the National Ecological Observatory Network (NEON) make the broad-extent, fine-grain observational domain more accessible to researchers by lowering costs and reducing the need for highly specialized equipment. Integration of these tools can further democratize macroecological research, as their strengths and weaknesses are complementary. However, using these tools for macroecology can be challenging because mental models are lacking, thus requiring large up-front investments in time, energy, and creativity to become proficient. This challenge inspired a working group of UAS-using academic ecologists, NEON professionals, imaging scientists, remote sensing specialists, and aeronautical engineers at the 2019 NEON Science Summit in Boulder, Colorado, to synthesize current knowledge on how to use UAS with NEON in a mental model for an intended audience of ecologists new to these tools. Specifically, we provide (1) a collection of core principles for collecting high-quality UAS data for NEON integration and (2) a case study illustrating a sample workflow for processing UAS data into meaningful ecological information and integrating it with NEON data collected on the ground—with the Terrestrial Observation System—and remotely—from the Airborne Observation Platform. With this mental model, we advance the democratization of macroecology by making a key observational domain—the broad-extent, fine-grain domain—more accessible via NEON/UAS integration.
The Great Lakes basin was historically populated by multiple, coevolved coregonine species, but much of that diversity has been lost. In Lakes Erie and Ontario, both lake whitefish (Coregonus clupeaformis) and cisco (Coregonus artedi) occurred in high numbers before habitat degradation, overfishing, invasive species, and other factors caused significant declines. There is growing interest in restoring these populations, and suggested actions include restoration of critical habitats such as spawning habitat. Unfortunately, our current understanding of lake whitefish and cisco spawning habitat characteristics and locations in these lakes is limited. To highlight areas of potential importance for conservation and restoration, we used random forest models and data on historical spawning locations to predict lake whitefish and cisco spawning habitats based on hypothesized key factors including wind fetch, ice cover duration, distance from 1st and 6th order tributaries, and lake bottom substrate. Our model accurately predicted spawning habitat locations for 71% and 54% of cases for lake whitefish and cisco, respectively. Fetch was the most important variable in the lake whitefish model, with spawning habitats being most likely to occur in regions of low to moderate fetch. Cisco spawning habitats were most likely to occur in areas of relatively low fetch near a 1st order stream. We used these models to predict spawning habitat locations for both species across Lakes Erie, Ontario, and St. Clair. Our results improve our understanding of lake whitefish and cisco spawning habitat characteristics and will aid in the spatial prioritization of actions to restore these native fishes.
","language":"English","publisher":"Elsevier","doi":"10.1016/j.jglr.2022.08.014","usgsCitation":"Schaefer, H.M., Honsey, A.E., Bunnell, D., Weidel, B., DeBruyne, R., Diana, J.S., Gorsky, D., and Roseman, E., 2022, Predicting physical and geomorphic habitat associated with historical lake whitefish and cisco spawning locations in Lakes Erie and Ontario: Journal of Great Lakes Research, v. 48, no. 6, p. 1636-1646, https://doi.org/10.1016/j.jglr.2022.08.014.","productDescription":"11 p.","startPage":"1636","endPage":"1646","ipdsId":"IP-136743","costCenters":[{"id":324,"text":"Great Lakes Science Center","active":true,"usgs":true}],"links":[{"id":405591,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"Canada, United States","otherGeospatial":"Lake Erie, Lake Ontario","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -75.509033203125,\n 44.731125592643274\n ],\n [\n -80.343017578125,\n 44.22158376545796\n ],\n [\n -82.298583984375,\n 42.84375132629021\n ],\n [\n -83.265380859375,\n 42.80346172417078\n ],\n [\n -84.287109375,\n 41.705728515237524\n ],\n [\n -82.452392578125,\n 40.772221877329024\n ],\n [\n -79.376220703125,\n 41.30257109430557\n ],\n [\n -76.35498046875,\n 43.18915769654922\n ],\n [\n -75.498046875,\n 43.76315996157264\n ],\n [\n -75.509033203125,\n 44.731125592643274\n ]\n ]\n ]\n }\n }\n ]\n}","volume":"48","issue":"6","noUsgsAuthors":false,"publicationStatus":"PW","contributors":{"authors":[{"text":"Schaefer, Hannah M","contributorId":216810,"corporation":false,"usgs":false,"family":"Schaefer","given":"Hannah","email":"","middleInitial":"M","affiliations":[{"id":37387,"text":"University of Michigan","active":true,"usgs":false}],"preferred":false,"id":849641,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Honsey, Andrew Edgar 0000-0001-7535-1321","orcid":"https://orcid.org/0000-0001-7535-1321","contributorId":295468,"corporation":false,"usgs":true,"family":"Honsey","given":"Andrew","email":"","middleInitial":"Edgar","affiliations":[{"id":324,"text":"Great Lakes Science Center","active":true,"usgs":true}],"preferred":true,"id":849642,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Bunnell, David 0000-0003-3521-7747","orcid":"https://orcid.org/0000-0003-3521-7747","contributorId":217344,"corporation":false,"usgs":true,"family":"Bunnell","given":"David","affiliations":[{"id":324,"text":"Great Lakes Science Center","active":true,"usgs":true}],"preferred":true,"id":849643,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Weidel, Brian 0000-0001-6095-2773 bweidel@usgs.gov","orcid":"https://orcid.org/0000-0001-6095-2773","contributorId":2485,"corporation":false,"usgs":true,"family":"Weidel","given":"Brian","email":"bweidel@usgs.gov","affiliations":[{"id":324,"text":"Great Lakes Science Center","active":true,"usgs":true}],"preferred":true,"id":849644,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"DeBruyne, Robin 0000-0002-9232-7937","orcid":"https://orcid.org/0000-0002-9232-7937","contributorId":240598,"corporation":false,"usgs":false,"family":"DeBruyne","given":"Robin","affiliations":[{"id":48111,"text":"Univ. Toledo","active":true,"usgs":false}],"preferred":false,"id":849645,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"Diana, James S.","contributorId":216547,"corporation":false,"usgs":false,"family":"Diana","given":"James","email":"","middleInitial":"S.","affiliations":[{"id":37387,"text":"University of Michigan","active":true,"usgs":false}],"preferred":false,"id":849646,"contributorType":{"id":1,"text":"Authors"},"rank":6},{"text":"Gorsky, Dimitry 0000-0003-1708-539X","orcid":"https://orcid.org/0000-0003-1708-539X","contributorId":295528,"corporation":false,"usgs":false,"family":"Gorsky","given":"Dimitry","email":"","affiliations":[{"id":6661,"text":"US Fish and Wildlife Service","active":true,"usgs":false}],"preferred":false,"id":849647,"contributorType":{"id":1,"text":"Authors"},"rank":7},{"text":"Roseman, Edward F. 0000-0002-5315-9838","orcid":"https://orcid.org/0000-0002-5315-9838","contributorId":217909,"corporation":false,"usgs":true,"family":"Roseman","given":"Edward F.","affiliations":[{"id":324,"text":"Great Lakes Science Center","active":true,"usgs":true}],"preferred":true,"id":849648,"contributorType":{"id":1,"text":"Authors"},"rank":8}]}} ,{"id":70238032,"text":"70238032 - 2022 - NABat ML: Utilizing deep learning to enable crowdsourced development of automated, scalable solutions for documenting North American bat populations","interactions":[],"lastModifiedDate":"2022-11-04T12:26:51.26005","indexId":"70238032","displayToPublicDate":"2022-08-23T07:22:18","publicationYear":"2022","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":2163,"text":"Journal of Applied Ecology","active":true,"publicationSubtype":{"id":10}},"title":"NABat ML: Utilizing deep learning to enable crowdsourced development of automated, scalable solutions for documenting North American bat populations","docAbstract":"This report addresses system characterization of the Instituto Nacional de Pesquisas Espaciais Amazônia-1 satellite and is part of a series of system characterization reports produced and delivered by the U.S. Geological Survey Earth Resources Observation and Science Cal/Val Center of Excellence. These reports present and detail the methodology and procedures for characterization; present technical and operational information about the specific sensing system being evaluated; and provide a summary of test measurements, data retention practices, data analysis results, and conclusions.
Amazônia-1 is a four-band imager with a 64-meter (m) pixel ground sample distance. Amazônia-1 was launched in February 2021 into a Sun-synchronous orbit of 752 kilometers with an inclination of 98.4 degrees and a swath width of 850 kilometers. The satellite has an expected lifetime of about 4 years. More information on Amazônia-1 is available in the “Land Remote Sensing Satellites Online Compendium” (https://calval.cr.usgs.gov/apps/compendium).
The Earth Resources Observation and Science Cal/Val Center of Excellence system characterization team completed data analyses to characterize the geometric (interior and exterior), radiometric, and spatial performances. Results of these analyses indicate that the Amazônia-1 satellite has an interior geometric performance in the range of −3.584 m (−0.056 pixel) to 0.320 m (0.005 pixel) in easting and −1.984 m (−0.031 pixel) to 2.048 m (0.032 pixel) in northing in band-to-band registration, an exterior geometric performance of −37.256 m (−0.621 pixel) to 54.758 m (0.913 pixel) in easting and −12.684 m (−0.211 pixel) to 54.898 m (0.915 pixel) in northing offset in comparison to the Landsat 8 Operational Land Imager, a radiometric performance in the range of 0.030 to 0.143 in offset and 0.662 to 0.825 in slope, and a spatial performance in the range of 1.62 to 2.06 pixels for full width at half maximum, with a modulation transfer function at a Nyquist frequency in the range of 0.062 to 0.115.
","largerWorkType":{"id":18,"text":"Report"},"largerWorkTitle":"System characterization of Earth observation sensors","largerWorkSubtype":{"id":5,"text":"USGS Numbered Series"},"language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/ofr20211030N","usgsCitation":"Vrabel, J.C., Stensaas, G.L., Anderson, C., Christopherson, J., Kim, M., and Park, S., 2022, System characterization report on the Amazônia-1 multispectral sensor, chap. N of Ramaseri Chandra, S.N., comp., System characterization of Earth observation sensors: U.S. Geological Survey Open-File Report 2021–1030, 33 p., https://doi.org/10.3133/ofr20211030N.","productDescription":"v, 33 p.","numberOfPages":"44","onlineOnly":"Y","ipdsId":"IP-142103","costCenters":[{"id":222,"text":"Earth Resources Observation and Science (EROS) Center","active":true,"usgs":true}],"links":[{"id":405398,"rank":2,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/of/2021/1030/n/ofr20211030n.pdf","text":"Report","size":"2.29 MB","linkFileType":{"id":1,"text":"pdf"},"description":"OFR 2021–1030–N"},{"id":405397,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/of/2021/1030/n/coverthb.jpg"}],"contact":"Director, Earth Resources Observation and Science (EROS) Center
U.S. Geological Survey
47914 252nd Street
Sioux Falls, SD 57198
Surface-water-quality data in the Rapid Creek Basin in South Dakota were compiled to assess basic trends in the water quality of Rapid Creek. Spatial and temporal patterns in water quality were described for major ions, sediment, total suspended solids, nutrients, field measurements, bacteria, and select metals for the period of 1970–2020, and a water-quality trend analysis was completed for sites with enough data for selected constituents.
Major ions and total suspended solids had higher median concentrations in the lower basin (downstream from the city of Rapid City) relative to the upper and middle basins. Nutrient concentrations were generally low, and increased concentrations were only detected at the sites downstream from the City of Rapid City Water Reclamation Facility. Fecal indicator bacteria (Escherichia coli and fecal coliform) concentrations were highest downstream from the main urbanized area of Rapid City.
Water-quality trends were analyzed for total dissolved solids, specific conductance, calcium, magnesium, total suspended solids, total phosphorus, dissolved phosphorus, and total Kjeldahl nitrogen for the period of 1979–2019. Concentrations for major ions and total dissolved solids typically changed by less than 15 percent. Total dissolved solids concentrations upstream from Rapid City were generally decreasing, whereas concentrations downstream were generally increasing. The flow-averaged geometric mean concentration of total dissolved solids at three sites upstream from Rapid City decreased overall by 3–5 percent, and concentrations at two sites downstream from Rapid City increased by at least 7 percent between 1979 and 2019. Trends in specific conductance in the Rapid Creek Basin were mixed with alternating increasing and decreasing trends at many of the sites between 1979 and 2014. Total suspended solids concentrations were observed to be decreasing at two sites analyzed for trends. Concentrations in total phosphorus were observed to be decreasing at every site analyzed for trends between 1989 and 2014. Significant downward trends in total Kjeldahl nitrogen were observed at two sites in the lower Rapid Creek Basin for the trend period of 1999–2019. The decreases in total suspended solids and nutrient concentrations in the Rapid Creek Basin could be related to several processes such as the implementation of a stormwater management plan in Rapid City, improvements to the water reclamation facility downstream from Rapid City, and residual climatic effects.
","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/sir20225086","collaboration":"Prepared in cooperation with the City of Rapid City","usgsCitation":"Tatge, W.S., Hoogestraat, G.K., and Nustad, R.A., 2022, Water-quality data and trends in the Rapid Creek Basin, South Dakota, 1970–2020: U.S. Geological Survey Scientific Investigations Report 2022–5086, 67 p., https://doi.org/10.3133/sir20225086.","productDescription":"Report: viii, 67 p.; Data Release; Dataset","numberOfPages":"80","onlineOnly":"Y","ipdsId":"IP-133856","costCenters":[{"id":34685,"text":"Dakota Water Science Center","active":true,"usgs":true}],"links":[{"id":405392,"rank":7,"type":{"id":39,"text":"HTML Document"},"url":"https://pubs.usgs.gov/publication/sir20225086/full","text":"Report","linkFileType":{"id":5,"text":"html"}},{"id":405347,"rank":3,"type":{"id":31,"text":"Publication XML"},"url":"https://pubs.usgs.gov/sir/2022/5086/sir20225086.XML"},{"id":405350,"rank":6,"type":{"id":28,"text":"Dataset"},"url":"https://doi.org/10.5066/F7P55KJN","text":"USGS National Water Information System database","linkHelpText":"—USGS water data for the Nation"},{"id":405346,"rank":2,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/sir/2022/5086/sir20225086.pdf","text":"Report","size":"21.1 MB","linkFileType":{"id":1,"text":"pdf"},"description":"SIR 2022–5086"},{"id":405345,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/sir/2022/5086/coverthb.jpg"},{"id":405349,"rank":5,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/P9D8BSXR","text":"USGS data release","linkHelpText":"Model scripts and water-quality data for trends in the Rapid Creek Basin, South Dakota, 1970–2020"},{"id":405348,"rank":4,"type":{"id":34,"text":"Image Folder"},"url":"https://pubs.usgs.gov/sir/2022/5086/images"}],"country":"United States","state":"South Dakota","otherGeospatial":"Rapid Creek Basin","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -104.0185546875,\n 43.84443209873525\n ],\n [\n -102.64251708984374,\n 43.84443209873525\n ],\n [\n -102.64251708984374,\n 44.213709909702054\n ],\n [\n -104.0185546875,\n 44.213709909702054\n ],\n [\n -104.0185546875,\n 43.84443209873525\n ]\n ]\n ]\n }\n }\n ]\n}","contact":"Director, Dakota Water Science Center
U.S. Geological Survey
821 East Interstate Avenue, Bismarck, ND 58503
1608 Mountain View Road, Rapid City, SD 57702
In 2002 and 2003 a collaborative effort was undertaken between Lawrence Berkeley National Laboratory, Sandia National Laboratories, the U.S. Geological Survey (USGS) Menlo Park, the USGS Hawaiian Volcano Observatory, and Electromagnetic Instruments Inc. to study the Kīlauea volcano in Hawaii using the magnetotelluric (MT) technique. The work was motivated by a desire to improve understanding of the magma reservoirs and conduits within Kīlauea and the East and Southwest Rift zones, which has implications for understanding Kīlauea's plumbing system. An improved understanding of the rift zones has implications in understanding large-scale landslides that are generated in the Hilina Slump, which produce significant impacts on coastal communities. Up to eight stations operated simultaneously, with multiple remote reference sites, and data were processed using multi-station robust processing techniques. In total, data were acquired at 70 sites over the Southwest and East rift zones. Good to excellent quality data were obtained even in the harshest conditions, such as those encountered on the fresh lava flows of the East Rift Zone, where electrical contact resistances are on the order of 100 kΩ. A three-dimensional (3D) MT model study was done to guide interpretation of the observed MT measurements. Synthetic modeling demonstrates that conductive bodies in the upper 3 km can be spatially resolved where MT station sampling is good. Resistivity anomalies in the 3D inversions have a high degree of spatial correlation with previously published seismic velocity anomalies beneath Kīlauea. Melt fractions between 0.096 and 0.117 are calculated for the Kīlauea and Puʻuʻōʻō low resistivity anomalies, respectively.
","language":"English","publisher":"American Geophysical Union","doi":"10.1029/2022JB024418","usgsCitation":"Hoversten, G., Gasperikova, E., Mackie, R., Myer, D., Kauahikaua, J.P., Newman, G.A., and Cuevas, N., 2022, Magnetotelluric investigations of the Kīlauea Volcano, Hawaii: Journal of Geophysical Research: Solid Earth, v. 127, no. 8, e2022JB024418, 24 p., https://doi.org/10.1029/2022JB024418.","productDescription":"e2022JB024418, 24 p.","ipdsId":"IP-135899","costCenters":[{"id":617,"text":"Volcano Science Center","active":true,"usgs":true}],"links":[{"id":446701,"rank":0,"type":{"id":41,"text":"Open Access External Repository Page"},"url":"https://doi.org/10.1029/2022jb024418","text":"External Repository"},{"id":405386,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","state":"Hawai'i","otherGeospatial":"Kīlauea Volcano","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -155.29131889343262,\n 19.396901484778134\n ],\n [\n -155.28496742248535,\n 19.39892544698541\n ],\n [\n -155.2786159515381,\n 19.399087362874425\n ],\n [\n -155.2730369567871,\n 19.39827778181811\n ],\n [\n -155.2676296234131,\n 19.400949384016776\n ],\n [\n -155.26385307312012,\n 19.403944764615613\n ],\n [\n -155.25887489318848,\n 19.40629245679785\n ],\n [\n -155.2511501312256,\n 19.4109877394962\n ],\n [\n -155.24694442749023,\n 19.408882974361152\n ],\n [\n -155.24042129516602,\n 19.408478208711944\n ],\n [\n -155.23784637451172,\n 19.410906787494763\n ],\n [\n -155.23836135864258,\n 19.414306736852605\n ],\n [\n -155.24102210998535,\n 19.414306736852605\n ],\n [\n -155.2434253692627,\n 19.418758943963656\n ],\n [\n -155.24943351745605,\n 19.42353481237166\n ],\n [\n -155.25372505187985,\n 19.427662992293143\n ],\n [\n -155.25853157043457,\n 19.4298484568423\n ],\n [\n -155.26239395141602,\n 19.43081976498137\n ],\n [\n -155.2672004699707,\n 19.430576938491143\n ],\n [\n -155.2708911895752,\n 19.4313863587134\n ],\n [\n -155.27406692504883,\n 19.432033891986865\n ],\n [\n -155.2799892425537,\n 19.429605628899985\n ],\n [\n -155.28857231140134,\n 19.42013505603468\n ],\n [\n -155.29492378234863,\n 19.416492381036047\n ],\n [\n -155.2946662902832,\n 19.413416280797698\n ],\n [\n -155.29646873474118,\n 19.410663931248664\n ],\n [\n -155.29698371887207,\n 19.407021044033193\n ],\n [\n -155.29131889343262,\n 19.396901484778134\n ]\n ]\n ]\n }\n }\n ]\n}","volume":"127","issue":"8","noUsgsAuthors":false,"publicationDate":"2022-08-17","publicationStatus":"PW","contributors":{"authors":[{"text":"Hoversten, G.M.","contributorId":295409,"corporation":false,"usgs":false,"family":"Hoversten","given":"G.M.","email":"","affiliations":[{"id":38900,"text":"Lawrence Berkeley National Laboratory","active":true,"usgs":false}],"preferred":false,"id":849391,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Gasperikova, Erika","contributorId":193561,"corporation":false,"usgs":false,"family":"Gasperikova","given":"Erika","affiliations":[],"preferred":false,"id":849392,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Mackie, Randall","contributorId":295410,"corporation":false,"usgs":false,"family":"Mackie","given":"Randall","email":"","affiliations":[{"id":63861,"text":"CGG Multiphysics","active":true,"usgs":false}],"preferred":false,"id":849393,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Myer, David","contributorId":206497,"corporation":false,"usgs":false,"family":"Myer","given":"David","email":"","affiliations":[],"preferred":false,"id":849394,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Kauahikaua, James P. 0000-0003-3777-503X jimk@usgs.gov","orcid":"https://orcid.org/0000-0003-3777-503X","contributorId":2146,"corporation":false,"usgs":true,"family":"Kauahikaua","given":"James","email":"jimk@usgs.gov","middleInitial":"P.","affiliations":[{"id":617,"text":"Volcano Science Center","active":true,"usgs":true}],"preferred":true,"id":849395,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"Newman, Greg A.","contributorId":295412,"corporation":false,"usgs":false,"family":"Newman","given":"Greg","email":"","middleInitial":"A.","affiliations":[{"id":63862,"text":"Lawrence Berkeley National Laboratory, Sandia National Laboratory","active":true,"usgs":false}],"preferred":false,"id":849396,"contributorType":{"id":1,"text":"Authors"},"rank":6},{"text":"Cuevas, Nestor","contributorId":295414,"corporation":false,"usgs":false,"family":"Cuevas","given":"Nestor","email":"","affiliations":[{"id":63864,"text":"Electromagnetic Instruments","active":true,"usgs":false}],"preferred":false,"id":849397,"contributorType":{"id":1,"text":"Authors"},"rank":7}]}} ,{"id":70255669,"text":"70255669 - 2022 - GSPy: A new toolbox and data standard for Geophysical Datasets","interactions":[],"lastModifiedDate":"2026-03-10T13:23:35.700958","indexId":"70255669","displayToPublicDate":"2022-08-22T06:45:31","publicationYear":"2022","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":17985,"text":"Frontiers in Earth Science - Environmental Informatics and Remote Sensing","active":true,"publicationSubtype":{"id":10}},"title":"GSPy: A new toolbox and data standard for Geophysical Datasets","docAbstract":"The diversity of geophysical methods and datatypes, as well as the isolated nature of various specialties (e.g., electromagnetic, seismic, potential fields) leads to a profusion of separate data file formats and documentation conventions. This can hinder cooperation and reduce the impact of datasets researchers have invested in heavily to collect and prepare. An open, portable, and well-supported community data standard could greatly improve the interoperability, transferability, and long-term archival of geophysical data. Airborne geophysical methods particularly need an open and accessible data standard, and they exemplify the complexity that is common in geophysical datasets where critical auxiliary information on the survey and system parameters are required to fully utilize and understand the data. Here, we propose a new Geophysical Standard, termed the GS convention, that leverages the well-established and widely used NetCDF file format and builds on the Climate and Forecasts (CF) metadata convention. We also present an accompanying open-source Python package, GSPy, to provide methods and workflows for building the GS-standardized NetCDF files, importing and exporting between common data formats, preparing input files for geophysical inversion software, and visualizing data and inverted models. By using the NetCDF format, handled through the Xarray Python package, and following the CF conventions, we standardize how metadata is recorded and directly stored with the data, from general survey and system information down to specific variable attributes. Utilizing the hierarchical nature of NetCDF, GS-formatted files are organized with a root Survey group that contains global metadata about the geophysical survey. Data are then organized into subgroups beneath Survey and are categorized as Tabular or Raster depending on the geometry and point of origin for the data. Lastly, the standard ensures consistency in constructing and tracking coordinate reference systems, which is vital for accurate portability and analysis. Development and adoption of a NetCDF-based data standard for geophysical surveys can greatly improve how these complex datasets are shared and utilized, making the data more accessible to a broader science community. The architecture of GSPy can be easily transferred to additional geophysical datatypes and methods in future releases.
A paucity of detailed relative sea-level (RSL) reconstructions from low latitudes hinders efforts to understand the global, regional, and local processes that cause RSL change. We reconstruct RSL change during the past ~5 ka using cores of mangrove peat at two sites (Snipe Key and Swan Key) in the Florida Keys. Remote sensing and field surveys established the relationship between peat-forming mangroves and tidal elevation in South Florida. Core chronologies are developed from age-depth models applied to 72 radiocarbon dates (39 mangrove wood macrofossils and 33 fine-fraction bulk peat). RSL rose 3.7 m at Snipe Key and 5.0 m at Swan Key in the past 5 ka, with both sites recording the fastest century-scale rate of RSL rise since ~1900 CE (~2.1 mm/a). We demonstrate that it is feasible to produce near-continuous reconstructions of RSL from mangrove peat in regions with a microtidal regime and accommodation space created by millennial-scale RSL rise. Decomposition of RSL trends from a network of reconstructions across South Florida using a spatio-temporal model suggests that Snipe Key was representative of regional RSL trends, but Swan Key was influenced by an additional local-scale process acting over at least the past five millennia. Geotechnical analysis of modern and buried mangrove peat indicates that sediment compaction is not the local-scale process responsible for the exaggerated RSL rise at Swan Key. The substantial difference in RSL between two nearby sites highlights the critical need for within-region replication of RSL reconstructions to avoid misattribution of sea-level trends, which could also have implications for geophysical modeling studies using RSL data for model tuning and validation.
Large-area, high-resolution digital elevation models (DEMs) created from light detection and ranging (LIDAR) and/or multibeam echosounder data sets are commonly used in many scientific disciplines. These DEMs can span thousands of square kilometers, typically with a spatial resolution of 1 m or finer, and can be difficult to process and analyze without specialized computers and software. Such DEMs often can be subsampled to expedite analysis with negligible impact on results for large-scale geospatial analyses. Subsampling can be achieved by creating a grid of points that specify the locations from which to extract elevation values from the DEM. This paper presents a method that can be used to accurately perform subsampling of large-scale, high-resolution DEMs using GIS software. This subsampling method was applied to two LIDAR-derived DEMs encompassing 242 km2 of the northern Florida Reef Tract as an example application and to test subsampling accuracy. Results indicate that subsampling 1-m-resolution DEMs using a 2-m-spaced grid results in no significant difference in mean elevation or other basic statistics for analyses performed over multiple spatial scales ranging from 1 km2 to 242 km2.
An uncrewed aerial vehicle (UAV) multirotor aeromagnetic system using a 5-m sling load for a magnetic sensor system is described and characterized. Four magnetic surveys with identical flight lines were completed, at two nominal altitudes of 25 and 40 m. The surveys were used to assess the repeatability of data collected with the described UAV aeromagnetic system, and comparison with a ground survey was used to assess the precision. The 5-m sling is designed to reduce magnetic interference from the UAV. A magnetic compensation model was developed for this particular UAV aeromagnetic system. This custom compensation model reduces the noise in the collected data by a factor of five over the uncompensated data, and the 5-m sling further reduces the noise by an estimated factor of four over a similar system with a 3-m sling. The precision of the UAV aeromagnetic system was then estimated to be sub-nT, with 50% of the noise component <0.3 nT, and 90% <0.6 nT.
","language":"English","publisher":"Elsevier","doi":"10.1016/j.jappgeo.2022.104779","usgsCitation":"Phelps, G., Bracken, R.E., Spritzer, J., and White, D.S., 2022, Achieving sub-nanoTesla precision in multirotor UAV aeromagnetic surveys: Journal of Applied Geophysics, v. 206, 104779, 16 p., https://doi.org/10.1016/j.jappgeo.2022.104779.","productDescription":"104779, 16 p.","ipdsId":"IP-130875","costCenters":[{"id":312,"text":"Geology, Minerals, Energy, and Geophysics Science Center","active":true,"usgs":true}],"links":[{"id":446713,"rank":1,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.1016/j.jappgeo.2022.104779","text":"Publisher Index Page"},{"id":435723,"rank":0,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/P9HCY1NQ","text":"USGS data release","linkHelpText":"UASmagpy: Python code for compensating rotary-wing sling-load UAS aeromagnetic data"},{"id":435722,"rank":0,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/P92MXMM5","text":"USGS data release","linkHelpText":"Uncrewed aerial system aeromagnetic test survey at the Boulder Magnetic Observatory"},{"id":411343,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"volume":"206","noUsgsAuthors":false,"publicationStatus":"PW","contributors":{"authors":[{"text":"Phelps, Geoffrey 0000-0003-1958-2736 gphelps@usgs.gov","orcid":"https://orcid.org/0000-0003-1958-2736","contributorId":127489,"corporation":false,"usgs":true,"family":"Phelps","given":"Geoffrey","email":"gphelps@usgs.gov","affiliations":[{"id":312,"text":"Geology, Minerals, Energy, and Geophysics Science Center","active":true,"usgs":true}],"preferred":true,"id":860859,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Bracken, Robert E. 0000-0001-7759-2743 rbracken@usgs.gov","orcid":"https://orcid.org/0000-0001-7759-2743","contributorId":2640,"corporation":false,"usgs":true,"family":"Bracken","given":"Robert","email":"rbracken@usgs.gov","middleInitial":"E.","affiliations":[{"id":35995,"text":"Geology, Geophysics, and Geochemistry Science Center","active":true,"usgs":true}],"preferred":true,"id":860860,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Spritzer, John 0000-0002-2147-530X jspritzer@usgs.gov","orcid":"https://orcid.org/0000-0002-2147-530X","contributorId":244361,"corporation":false,"usgs":true,"family":"Spritzer","given":"John","email":"jspritzer@usgs.gov","affiliations":[{"id":300,"text":"Geologic Hazards Science Center","active":true,"usgs":true}],"preferred":true,"id":860861,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"White, David S.","contributorId":173069,"corporation":false,"usgs":false,"family":"White","given":"David","email":"","middleInitial":"S.","affiliations":[],"preferred":false,"id":860862,"contributorType":{"id":1,"text":"Authors"},"rank":4}]}} ,{"id":70242886,"text":"70242886 - 2022 - Revised earthquake recurrence intervals in California, USA: New paleoseismic sites and application of event likelihoods","interactions":[],"lastModifiedDate":"2023-04-21T12:02:06.957613","indexId":"70242886","displayToPublicDate":"2022-08-21T07:00:25","publicationYear":"2022","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":3372,"text":"Seismological Research Letters","onlineIssn":"1938-2057","printIssn":"0895-0695","active":true,"publicationSubtype":{"id":10}},"title":"Revised earthquake recurrence intervals in California, USA: New paleoseismic sites and application of event likelihoods","docAbstract":"Recurrence intervals for ground rupturing earthquakes are critical data for assessing seismic hazard. Recurrence intervals are presented here for 38 paleoseismic sites in California. Eleven of these include new or updated data; the remainder use data previously included in the Unified California Earthquake Rupture Forecast Version 3 (UCERF3). The methods and results are consistent with UCERF3. In addition, revised recurrence intervals are presented at every site. The revised recurrence intervals incorporate uncertainty in the interpretation of paleoseismic evidence, which is expressed as event likelihood. Event likelihood is the probability that the evidence has been correctly interpreted as a unique earthquake. Event likelihoods are estimated here for 85 inferred past earthquakes at eight paleoseismic sites in California, using a single, consistent methodology. The average event likelihood is 0.85. The revised recurrence intervals are 16% longer, on average, than conventional estimates, and their confidence intervals are disproportionately wider. These recurrence intervals are suitable for inclusion in a “grand inversion” rupture forecast, and they may be important for addressing a systematic misfit in the UCERF3 grand inversion. The revised recurrence intervals may also be important for assessing the unusually long earthquake hiatus in California. Other applications may not need to consider event likelihoods because the effects are small relative to typical uncertainties.
Synthesizing large, complex data sets to inform resource managers towards effective environmental stewardship is a universal challenge. In Chesapeake Bay, a well-studied and intensively monitored estuary in North America, the challenge of synthesizing data on water quality and land use as factors related to a key habitat, submerged aquatic vegetation, was tackled by a team of scientists and resource managers operating at multiple levels of governance (state, federal). The synthesis effort took place over a two-year period (2016–2018), and the results were communicated widely to a) scientists via peer review publications and conference presentations; b) resource managers via web materials and workshop presentations; and c) the public through newspaper articles, radio interviews, and podcasts. The synthesis effort was initiated by resource managers at the United States Environmental Protection Agencys’ Chesapeake Bay Program and 16 scientist participants were recruited from a diversity of organizations. Multiple short, immersive workshops were conducted regularly to conceptualize the problem, followed by data analysis and interpretation that supported the preparation of the synthetic products that were communicated widely. Reflections on the process indicate that there are a variety of structural and functional requirements, as well as enabling conditions, that need to be considered to achieve successful outcomes from synthesis efforts.
The U.S. Geological Survey maintains a network of hydrologic monitoring stations across Kansas in cooperation with Federal, State, Tribal, and local agencies. During water year 2021, this network included 230 real-time surface water data collection sites, referred to as “streamgages.” A water year is the 12-month period from October 1 through September 30 and is designated by the calendar year in which it ends. These real-time data are routinely collected and calibrated by U.S. Geological Survey personnel via regular measurements of streamflow and water levels. Analyses of these data provide an understanding of hydrologic conditions in the State critical to the management of water resources, industrial and agricultural uses, protection of life and property during flooding, operation of reservoirs, recreation, and other activities.
","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/fs20223071","usgsCitation":"Puls, K.A., 2022, Hydrologic conditions in Kansas, water year 2021: U.S. Geological Survey Fact Sheet 2022–3071, 6 p., https://doi.org/10.3133/fs20223071.","productDescription":"6 p.","numberOfPages":"6","onlineOnly":"N","ipdsId":"IP-141048","costCenters":[{"id":353,"text":"Kansas Water Science Center","active":false,"usgs":true}],"links":[{"id":405358,"rank":4,"type":{"id":34,"text":"Image Folder"},"url":"https://pubs.usgs.gov/fs/2022/3071/images"},{"id":405356,"rank":2,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/fs/2022/3071/fs20223071.pdf","text":"Report","size":"4.09 MB","linkFileType":{"id":1,"text":"pdf"},"description":"FS 2022–3071"},{"id":405355,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/fs/2022/3071/coverthb.jpg"},{"id":501508,"rank":6,"type":{"id":36,"text":"NGMDB Index 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U.S. Geological Survey
1217 Biltmore Drive
Lawrence, KS 66049
The glacial geology and hydrogeology of valley-fill aquifers and their surrounding uplands are described within a 112-square-mile area in southern Otsego and northwestern Delaware Counties, New York, centered around the City of Oneonta. The major valleys include those of the Susquehanna River, Otego Creek, Charlotte Creek, and Schenevus Creek. A variety of data were analyzed to provide a broad picture of the glacial deposits, hydrogeologic framework, aquifer occurrence, and water-resource potential in the area. Both valley-fill and bedrock aquifers are used for water supply within the study area. The valley-fill aquifers consist of coarse-grained stratified drift, are mostly limited to the larger valleys, and have well yields that typically are much greater than those obtained from the bedrock aquifers. The bedrock aquifers generally have lower well yields, are the sole source of groundwater in upland areas, and are tapped in valley areas where sediments are very silty or are absent.
Through and non-through valleys and their orientations relative to ice flow have resulted in a variety of deglacial environments and deposits, some of which depart from glacial stratigraphy typically observed elsewhere in central New York. In comparison to through valleys with low in-valley divides, the regional thinning of ice over the high bedrock divides of the non-through valleys resulted in the earlier and more widespread stagnation of glacial ice, development of dead-ice sinks, and earlier diversion of meltwater from ice north of the divides. As the main through valley in the study area, the Susquehanna River valley is characterized by multiple inferred ice-margin positions with associated outwash deposition or ice-contact deposits. Throughout the study area, valleys orientated parallel or subparallel to the ice flow facilitated the development of long ice tongues; valleys oriented perpendicular to the ice flow led to little ice-tongue development, but they did facilitate the deposition of the extensive kame moraines that now occupy several-mile-long valley reaches. Lacustrine sediments were deposited in proglacial lakes. These sediments underlie most valleys that were oriented parallel and subparallel to ice flow, but they are largely absent in the Charlotte Creek valley, which was oriented perpendicular to the ice flow and now contains an extensive kame moraine. Beneath these lacustrine deposits, sand and gravel were deposited as subaqueous fans, eskers, and the distal parts of delta (kame) terraces, each with variable silt content.
The presence of coarse-grained stratified deposits, their saturated thicknesses, and their recharge potential are the primary controls on aquifer locations in the study area. The most widespread aquifers in the study area consist of sand and gravel and are confined mostly beneath lacustrine deposits. Confined aquifer yields are enhanced by hydraulic connections with unconfined ice-contact deposits along the valley walls, especially where tributary streams cross these deposits and provide additional recharge through streambed infiltration. The Susquehanna River and other large valley creeks provide a potentially large source of recharge to aquifers where groundwater withdrawals from nearby production wells induce infiltration of river water into aquifers. Unconfined aquifers are present where ice-contact deposits extend below the valley floor and are sufficiently saturated. Most surficial outwash deposits in the study area are thinly saturated; thus their water-resource potential is likely to be limited.
The upland areas contain very little stratified drift; therefore, characterization was limited to delineating areas of thick till and thin, or absent, till. Recharge of bedrock aquifers is greatest in areas overlain by thin till or where bedrock is exposed at land surface.
","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/sir20225069","collaboration":"Prepared in cooperation with the New York State Department of Environmental Conservation","usgsCitation":"Heisig, P.M., and Fleisher, P.J., 2022, Glacial geology and hydrogeology of valley-fill aquifers in the Oneonta area, Otsego and Delaware Counties, New York: U.S. Geological Survey Scientific Investigations Report 2022–5069, 35 p., 1 pl., https://doi.org/10.3133/sir20225069.","productDescription":"Report: vii, 35 p.; 1 Plate: 36.00 × 40.00 inches; 1 Figure: 25.00 × 17.00 inches ; Data Releases","numberOfPages":"35","onlineOnly":"Y","additionalOnlineFiles":"Y","ipdsId":"IP-118408","costCenters":[{"id":474,"text":"New York Water Science Center","active":true,"usgs":true}],"links":[{"id":405214,"rank":6,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/P9RCQS14","text":"USGS data release","linkHelpText":"Geospatial datasets of the glacial geology and hydrogeology of valley-fill aquifers in the Oneonta area, Otsego and Delaware Counties, New York"},{"id":405211,"rank":3,"type":{"id":39,"text":"HTML Document"},"url":"https://pubs.usgs.gov/publication/sir20225069/full","text":"Report","linkFileType":{"id":5,"text":"html"},"description":"SIR 2022-5069"},{"id":405199,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/sir/2022/5069/coverthb.jpg"},{"id":405219,"rank":10,"type":{"id":17,"text":"Plate"},"url":"https://pubs.usgs.gov/sir/2022/5069/sir20225069_plate01.pdf","text":"Plate 1","size":"177 MB","linkFileType":{"id":1,"text":"pdf"},"linkHelpText":"- Map of glacial geology and hydrogeology of valley-fill aquifers in the Oneonta area, Otsego and Delaware Counties, New York [layered pdf; to toggle layers, download the file (right-click and select \"Save link as...\") and open it with Adobe Acrobat Reader]"},{"id":405210,"rank":2,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/sir/2022/5069/sir20225069.pdf","text":"Report","size":"12.6 MB","linkFileType":{"id":1,"text":"pdf"},"description":"SIR 2022-5069"},{"id":405212,"rank":4,"type":{"id":31,"text":"Publication XML"},"url":"https://pubs.usgs.gov/sir/2022/5069/sir20225069.XML"},{"id":405213,"rank":5,"type":{"id":34,"text":"Image Folder"},"url":"https://pubs.usgs.gov/sir/2022/5069/images/"},{"id":405218,"rank":9,"type":{"id":29,"text":"Figure"},"url":"https://pubs.usgs.gov/sir/2022/5069/sir20225069_fig04a.pdf","text":"Figure 4A","size":"423 KB","linkFileType":{"id":1,"text":"pdf"},"linkHelpText":"- Primary longitudinal hydrogeologic section A.1–A.1′ and secondary longitudinal hydrogeologic section A.2–A.2′ along the Susquehanna River valley, Otsego County, New York"},{"id":405216,"rank":8,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/P9HGQUJL","text":"USGS data release","linkHelpText":"Horizontal-to-vertical spectral ratio (HVSR) soundings in Broome, Chenango, Franklin, Orange, Rensselaer, and Saratoga Counties, New York, and Susquehanna County, Pennsylvania 2010–2019"},{"id":405215,"rank":7,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/P92NSO7T","text":"USGS data release","linkHelpText":"Horizontal-to-vertical spectral ratio soundings and depth-to-bedrock data for valley-fill aquifers in the Oneonta area, Otsego and Delaware Counties, New York, 2016–2018"}],"country":"United States","state":"New York","county":"Delaware County, Otsego County","otherGeospatial":"Oneonta area","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -75.1667,\n 42.4167\n ],\n [\n -74.9167,\n 42.4167\n ],\n [\n -74.9167,\n 42.5833\n ],\n [\n -75.1667,\n 42.5833\n ],\n [\n -75.1667,\n 42.4167\n ]\n ]\n ]\n }\n }\n ]\n}","contact":"Director, New York Water Science Center
U.S. Geological Survey
425 Jordan Road
Troy, NY 12180–8349
The use of simple models for response prediction of building structures is preferred in earthquake engineering for risk evaluations at regional scales, as they make computational studies more feasible. The primary impediment in their gainful use presently is the lack of viable methods for quantifying (and reducing upon) the modeling errors/uncertainties they bear. This study presents a Bayesian calibration method wherein the modeling error is embedded into the parameters of the model. The method is specifically described for coupled shear-flexural beam models here, but it can be applied to any parametric surrogate model. The major benefit the method offers is the ability to consider the modeling uncertainty in the forward prediction of any degree-of-freedom or composite response regardless of the data used in calibration. The method is extensively verified using two synthetic examples. In the first example, the beam model is calibrated to represent a similar beam model but with enforced modeling errors. In the second example, the beam model is used to represent the detailed finite element model of a 52-story building. Both examples show the capability of the proposed solution to provide realistic uncertainty estimation around the mean prediction.
","language":"English","publisher":"Wiley","doi":"10.1002/stc.3078","usgsCitation":"Ghahari, S., Sargsyan, K., Celebi, M., and Taciroglu, E., 2022, Quantifying modeling uncertainty in simplified beam models for building response prediction: Structural Control and Health Monitoring, v. 29, no. 11, e3078, https://doi.org/10.1002/stc.3078.","productDescription":"e3078","ipdsId":"IP-139979","costCenters":[{"id":237,"text":"Earthquake Science Center","active":true,"usgs":true}],"links":[{"id":446722,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://www.osti.gov/biblio/1882634","text":"Publisher Index Page"},{"id":407408,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"volume":"29","issue":"11","noUsgsAuthors":false,"publicationDate":"2022-08-19","publicationStatus":"PW","contributors":{"authors":[{"text":"Ghahari, S. Farid","contributorId":296977,"corporation":false,"usgs":false,"family":"Ghahari","given":"S. Farid","affiliations":[{"id":13399,"text":"UCLA","active":true,"usgs":false}],"preferred":false,"id":853025,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Sargsyan, Khachik","contributorId":296978,"corporation":false,"usgs":false,"family":"Sargsyan","given":"Khachik","email":"","affiliations":[{"id":64263,"text":"Sandia Laboratories","active":true,"usgs":false}],"preferred":false,"id":853026,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Celebi, Mehmet 0000-0002-4769-7357 celebi@usgs.gov","orcid":"https://orcid.org/0000-0002-4769-7357","contributorId":200969,"corporation":false,"usgs":true,"family":"Celebi","given":"Mehmet","email":"celebi@usgs.gov","affiliations":[],"preferred":true,"id":853027,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Taciroglu, Ertugrul","contributorId":296979,"corporation":false,"usgs":false,"family":"Taciroglu","given":"Ertugrul","affiliations":[{"id":13399,"text":"UCLA","active":true,"usgs":false}],"preferred":false,"id":853028,"contributorType":{"id":1,"text":"Authors"},"rank":4}]}} ,{"id":70259623,"text":"70259623 - 2022 - High-resolution marine seismic imaging of the Seattle fault zone: Near surface insights into fault zone geometry, Quaternary deformation, and long-term evolution","interactions":[],"lastModifiedDate":"2024-10-17T12:00:47.133274","indexId":"70259623","displayToPublicDate":"2022-08-19T06:59:31","publicationYear":"2022","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":1135,"text":"Bulletin of the Seismological Society of America","onlineIssn":"1943-3573","printIssn":"0037-1106","active":true,"publicationSubtype":{"id":10}},"title":"High-resolution marine seismic imaging of the Seattle fault zone: Near surface insights into fault zone geometry, Quaternary deformation, and long-term evolution","docAbstract":"The Seattle fault zone (SFZ) is a north‐directed thrust fault system that underlies the greater Seattle metropolitan area. Evidence of past land level changes, landslides, liquefaction, and a local tsunami indicate that this 70‐km‐long fault system can host up to M 7–7.5 earthquakes. Both the geometry and earthquake recurrence of the SFZ are debated and surveys of the shallow subsurface have not yet been incorporated into deeper crustal‐scale structural interpretations, especially where the SFZ cuts across marine portions of the Puget Lowland. Here we use a new high‐resolution marine seismic reflection dataset to image fault‐related deformation in Quaternary sediments and Tertiary bedrock throughout Puget Sound and Lake Washington. We use this perspective of shallow geology as a link between existing crustal‐scale geophysical insights into fault geometry at depth and paleoseismological observations of faulting at the surface and propose a refined structural model for the SFZ. We interpret that our new seismic reflection data in the Rich Passage area of Puget Sound images evidence of an inactive, south‐dipping strand of the SFZ, which is overprinted by Quaternary folding and slip along north‐dipping backthrusts within the hanging wall of a blind, south‐dipping fault located 6 km farther north. To explain these results, we propose that the SFZ is a normal sequence fault propagation fold that has stepped northward through time, and we show the plausibility of this model through trishear forward modeling. Growth strata and faulting imaged in Quaternary sediments in Lake Washington and Rich Passage are consistent with the spatial distribution of folding and backthrusting that occurred during an M 7–7.5 earthquake in A.D. 900–930, corroborating existing evidence that the SFZ has been active throughout the Quaternary.
Urban wet-weather discharges from combined sewer overflows (CSO) and stormwater outlets (SWO) are a potential pathway for micropollutants (trace contaminants) to surface waters, posing a threat to the environment and possible water reuse applications. Despite large efforts to monitor micropollutants in the last decade, the gained information is still limited and scattered. In a metastudy we performed a data-driven analysis of measurements collected at 77 sites (683 events, 297 detected micropollutants) over the last decade to investigate which micropollutants are most relevant in terms of 1) occurrence and 2) potential risk for the aquatic environment, 3) estimate the minimum number of data to be collected in monitoring studies to reliably obtain concentration estimates, and 4) provide recommendations for future monitoring campaigns. We highlight micropollutants to be prioritized due to their high occurrence and critical concentration levels compared to environmental quality standards. These top-listed micropollutants include contaminants from all chemical classes (pesticides, heavy metals, polycyclic aromatic hydrocarbons, personal care products, pharmaceuticals, and industrial and household chemicals). Analysis of over 30,000 event mean concentrations shows a large fraction of measurements (> 50%) were below the limit of quantification, stressing the need for reliable, standard monitoring procedures. High variability was observed among events and sites, with differences between micropollutant classes. The number of events required for a reliable estimate of site mean concentrations (error bandwidth of 1 around the “true\" value) depends on the individual micropollutant. The median minimum number of events is 7 for CSO (2 to 31, 80%-interquantile) and 6 for SWO (1 to 25 events, 80%-interquantile). Our analysis indicates the minimum number of sites needed to assess global pollution levels and our data collection and analysis can be used to estimate the required number of sites for an urban catchment. Our data-driven analysis demonstrates how future wet-weather monitoring programs will be more effective if the consequences of high variability inherent in urban wet-weather discharges are considered.
","language":"English","publisher":"Elsevier","doi":"10.1016/j.watres.2022.118968","usgsCitation":"Mutzner, L., Furrer, V., Castebrunet, H., Dittmer, U., Fuchs, S., Gernjak, W., Gromaire, M., Matzinger, A., Mikkelsen, P.S., Selbig, W.R., and Vezzaro, L., 2022, A decade of monitoring micropollutants in urban wet-weather flows: What did we learn?: Water Research, v. 223, 118968, 14 p., https://doi.org/10.1016/j.watres.2022.118968.","productDescription":"118968, 14 p.","ipdsId":"IP-140246","costCenters":[{"id":37947,"text":"Upper Midwest Water Science Center","active":true,"usgs":true}],"links":[{"id":446726,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.1016/j.watres.2022.118968","text":"Publisher Index Page"},{"id":407377,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"volume":"223","noUsgsAuthors":false,"publicationStatus":"PW","contributors":{"authors":[{"text":"Mutzner, Lena","contributorId":296932,"corporation":false,"usgs":false,"family":"Mutzner","given":"Lena","email":"","affiliations":[{"id":50046,"text":"Technical University of Denmark","active":true,"usgs":false}],"preferred":false,"id":852914,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Furrer, Viviane","contributorId":296933,"corporation":false,"usgs":false,"family":"Furrer","given":"Viviane","email":"","affiliations":[{"id":64243,"text":"Swiss Federal Institute of Aquatic Science and Technology","active":true,"usgs":false}],"preferred":false,"id":852915,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Castebrunet, Helene","contributorId":296934,"corporation":false,"usgs":false,"family":"Castebrunet","given":"Helene","email":"","affiliations":[{"id":13426,"text":"University of Lyon","active":true,"usgs":false}],"preferred":false,"id":852916,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Dittmer, Ulrich","contributorId":296935,"corporation":false,"usgs":false,"family":"Dittmer","given":"Ulrich","email":"","affiliations":[{"id":64244,"text":"Technical University Kaiserslautern","active":true,"usgs":false}],"preferred":false,"id":852917,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Fuchs, Stephan","contributorId":296936,"corporation":false,"usgs":false,"family":"Fuchs","given":"Stephan","email":"","affiliations":[{"id":64245,"text":"Karlsruhe Institute of Technology (KIT)","active":true,"usgs":false}],"preferred":false,"id":852918,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"Gernjak, Wolfgang","contributorId":296937,"corporation":false,"usgs":false,"family":"Gernjak","given":"Wolfgang","email":"","affiliations":[{"id":64246,"text":"ICRA, Catalan Institute for Water Research","active":true,"usgs":false}],"preferred":false,"id":852919,"contributorType":{"id":1,"text":"Authors"},"rank":6},{"text":"Gromaire, Marie-Christine","contributorId":296938,"corporation":false,"usgs":false,"family":"Gromaire","given":"Marie-Christine","email":"","affiliations":[{"id":64247,"text":"Leesu, École des Ponts ParisTech","active":true,"usgs":false}],"preferred":false,"id":852920,"contributorType":{"id":1,"text":"Authors"},"rank":7},{"text":"Matzinger, Andreas","contributorId":296939,"corporation":false,"usgs":false,"family":"Matzinger","given":"Andreas","email":"","affiliations":[{"id":64248,"text":"Kompetenzzentrum Wasser Berlin (KWB)","active":true,"usgs":false}],"preferred":false,"id":852921,"contributorType":{"id":1,"text":"Authors"},"rank":8},{"text":"Mikkelsen, Peter Steen","contributorId":296940,"corporation":false,"usgs":false,"family":"Mikkelsen","given":"Peter","email":"","middleInitial":"Steen","affiliations":[{"id":50046,"text":"Technical University of Denmark","active":true,"usgs":false}],"preferred":false,"id":852922,"contributorType":{"id":1,"text":"Authors"},"rank":9},{"text":"Selbig, William R. 0000-0003-1403-8280 wrselbig@usgs.gov","orcid":"https://orcid.org/0000-0003-1403-8280","contributorId":877,"corporation":false,"usgs":true,"family":"Selbig","given":"William","email":"wrselbig@usgs.gov","middleInitial":"R.","affiliations":[{"id":37947,"text":"Upper Midwest Water Science Center","active":true,"usgs":true}],"preferred":true,"id":852923,"contributorType":{"id":1,"text":"Authors"},"rank":10},{"text":"Vezzaro, Luca","contributorId":296941,"corporation":false,"usgs":false,"family":"Vezzaro","given":"Luca","email":"","affiliations":[{"id":50046,"text":"Technical University of Denmark","active":true,"usgs":false}],"preferred":false,"id":852924,"contributorType":{"id":1,"text":"Authors"},"rank":11}]}} ,{"id":70235898,"text":"70235898 - 2022 - Collateral damage: Anticoagulant rodenticides pose threats to California condors","interactions":[],"lastModifiedDate":"2022-08-25T15:53:53.204495","indexId":"70235898","displayToPublicDate":"2022-08-18T10:41:25","publicationYear":"2022","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":1555,"text":"Environmental Pollution","active":true,"publicationSubtype":{"id":10}},"title":"Collateral damage: Anticoagulant rodenticides pose threats to California condors","docAbstract":"Anticoagulant rodenticides (ARs) are widespread environmental contaminants that pose risks to scavenging birds because they routinely occur within their prey and can cause secondary poisoning. However, little is known about AR exposure in one of the rarest avian scavengers in the world, the California condor (Gymnogyps californianus). We assessed AR exposure in California condors and surrogate turkey vultures (Cathartes aura) to gauge potential hazard to a proposed future condor flock by determining how application rate and environmental factors influence exposure. Additionally, we examined whether ARs might be correlated with prolonged blood clotting time and potential mortality in condors. Only second-generation ARs (SGARs) were detected, and exposure was detected in all condor flocks. Liver AR residues were detected in 42% of the condors (27 of 65) and 93% of the turkey vultures (66 of 71). Although concentrations were generally low (<10 ng/g ww), 48% of the California condors and 64% of the turkey vultures exposed to ARs exceeded the 5% probability of exhibiting signs of toxicosis (>20 ng/g ww), and 10% and 13% exceeded the 20% probability of exhibiting signs toxicosis (>80 ng/g ww). There was evidence of prolonged blood clotting time in 16% of the free-flying condors. For condors, there was a relationship between the interaction of AR exposure index (legal use across regions where condors existed) and precipitation, and the probability of detecting ARs in liver. Exposure to ARs may complicate recovery efforts of condor populations within their current range and in the soon to be established northern California experimental population. Continued monitoring of AR exposure using plasma blood clotting assays and residue analysis would allow for an improved understanding of their hazard to condors, particularly if paired with recent movement data that could elucidate exposure sources on the landscape occupied by this endangered species.
","language":"English","publisher":"Elsevier","doi":"10.1016/j.envpol.2022.119925","usgsCitation":"Herring, G., Eagles-Smith, C., Wolstenholme, R., Welch, A., West, C., and Rattner, B.A., 2022, Collateral damage: Anticoagulant rodenticides pose threats to California condors: Environmental Pollution, v. 311, 119925, 9 p., https://doi.org/10.1016/j.envpol.2022.119925.","productDescription":"119925, 9 p.","ipdsId":"IP-139709","costCenters":[{"id":290,"text":"Forest and Rangeland Ecosystem Science Center","active":false,"usgs":true}],"links":[{"id":446732,"rank":1,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.1016/j.envpol.2022.119925","text":"Publisher Index Page"},{"id":435724,"rank":0,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/P9NHPLHX","text":"USGS data release","linkHelpText":"Anticoagulant rodenticide concentrations in blood and tissue of California condors and turkey vultures (ver. 2.0, May 2023)"},{"id":405589,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","state":"California","otherGeospatial":"Pinnacles National Park","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -121.2506103515625,\n 36.39696752441776\n ],\n [\n -121.10229492187501,\n 36.39696752441776\n ],\n [\n -121.10229492187501,\n 36.56370306576917\n ],\n [\n -121.2506103515625,\n 36.56370306576917\n ],\n [\n -121.2506103515625,\n 36.39696752441776\n ]\n ]\n ]\n }\n }\n ]\n}","volume":"311","noUsgsAuthors":false,"publicationStatus":"PW","contributors":{"authors":[{"text":"Herring, Garth 0000-0003-1106-4731 gherring@usgs.gov","orcid":"https://orcid.org/0000-0003-1106-4731","contributorId":4403,"corporation":false,"usgs":true,"family":"Herring","given":"Garth","email":"gherring@usgs.gov","affiliations":[{"id":289,"text":"Forest and Rangeland Ecosys Science Center","active":true,"usgs":true},{"id":290,"text":"Forest and Rangeland Ecosystem Science Center","active":false,"usgs":true}],"preferred":true,"id":849634,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"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":849635,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Wolstenholme, Rachel","contributorId":295522,"corporation":false,"usgs":false,"family":"Wolstenholme","given":"Rachel","affiliations":[{"id":36189,"text":"National Park Service","active":true,"usgs":false}],"preferred":false,"id":849636,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Welch, Alacia","contributorId":206083,"corporation":false,"usgs":false,"family":"Welch","given":"Alacia","email":"","affiliations":[{"id":37236,"text":"Pinnacles National Park","active":true,"usgs":false}],"preferred":false,"id":849637,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"West, Chris","contributorId":295524,"corporation":false,"usgs":false,"family":"West","given":"Chris","email":"","affiliations":[{"id":38097,"text":"Yurok Tribe","active":true,"usgs":false}],"preferred":false,"id":849638,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"Rattner, Barnett A. 0000-0003-3676-2843 brattner@usgs.gov","orcid":"https://orcid.org/0000-0003-3676-2843","contributorId":4142,"corporation":false,"usgs":true,"family":"Rattner","given":"Barnett","email":"brattner@usgs.gov","middleInitial":"A.","affiliations":[{"id":531,"text":"Patuxent Wildlife Research Center","active":true,"usgs":true}],"preferred":true,"id":849639,"contributorType":{"id":1,"text":"Authors"},"rank":6}]}} ,{"id":70235777,"text":"70235777 - 2022 - How does precipitation variability control bedload response across a mountainous channel network in a maritime climate?","interactions":[],"lastModifiedDate":"2022-08-18T15:04:08.1626","indexId":"70235777","displayToPublicDate":"2022-08-18T09:51:52","publicationYear":"2022","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":3722,"text":"Water Resources Research","onlineIssn":"1944-7973","printIssn":"0043-1397","active":true,"publicationSubtype":{"id":10}},"title":"How does precipitation variability control bedload response across a mountainous channel network in a maritime climate?","docAbstract":"Modeled stream discharge is often used to drive sediment transport models across channel networks. Because sediment transport varies non-linearly with flow rates, discharge modeled from daily total precipitation distributed evenly over 24-hrs may significantly underestimate actual bedload transport capacity. In this study, we assume bedload transport capacity determined from a hydrograph resulting from the use of hourly (1-h) precipitation is a close approximation of actual transport capacity and quantify the error introduced into a network-scale bedload transport model driven by daily precipitation at channel network locations varying from lowland pool-riffle channels to upland colluvial channels in a watershed where snow accumulation and melt can affect runoff processes. Transport capacity is determined using effective stresses and the Wilcock and Crowe (2003) equations and expressed in terms of transport capacity normalized by the bankfull value. We find that, depending on channel network location, cumulative error can range from 10 - 20% to more than two orders of magnitude. Surprisingly, variation in flow rates due to differences in hillslope and channel runoff do not seem to dictate the network locations where the largest errors in predicted bedload transport capacity occur. Rather, spatial variability of the magnitude of the effective-bankfull-excess shear stress and changes in runoff due to snow accumulation and melt exert the greatest influence. These findings have implications for flood-hazard and aquatic habitat models that rely on modeled sediment transport driven by coarse-temporal-resolution climate data.","language":"English","publisher":"Wiley","doi":"10.1029/2021WR030358","usgsCitation":"Keck, J., Istanbulluoglu, E., Lundquist, J., Bandaragoda, C., Jaeger, K.L., Mauger, G.S., and Horner-Devine, A., 2022, How does precipitation variability control bedload response across a mountainous channel network in a maritime climate?: Water Resources Research, v. 58, no. 8, e2021WR030358, 28 p., https://doi.org/10.1029/2021WR030358.","productDescription":"e2021WR030358, 28 p.","ipdsId":"IP-142534","costCenters":[{"id":622,"text":"Washington Water Science Center","active":true,"usgs":true}],"links":[{"id":405308,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","state":"Washington","otherGeospatial":"Sauk River basin","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -121.66671752929688,\n 47.89148526708789\n ],\n [\n -120.69030761718749,\n 47.89148526708789\n ],\n [\n -120.69030761718749,\n 48.47565256743914\n ],\n [\n -121.66671752929688,\n 48.47565256743914\n ],\n [\n -121.66671752929688,\n 47.89148526708789\n ]\n ]\n ]\n }\n }\n ]\n}","volume":"58","issue":"8","noUsgsAuthors":false,"publicationDate":"2022-08-08","publicationStatus":"PW","contributors":{"authors":[{"text":"Keck, Jeffrey 0000-0002-0646-8574","orcid":"https://orcid.org/0000-0002-0646-8574","contributorId":295347,"corporation":false,"usgs":false,"family":"Keck","given":"Jeffrey","email":"","affiliations":[{"id":63850,"text":"University of Washington; Washington State Dept of Natrual Resources","active":true,"usgs":false}],"preferred":false,"id":849240,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Istanbulluoglu, Erkan 0000-0001-9453-4676","orcid":"https://orcid.org/0000-0001-9453-4676","contributorId":295348,"corporation":false,"usgs":false,"family":"Istanbulluoglu","given":"Erkan","email":"","affiliations":[{"id":6934,"text":"University of Washington","active":true,"usgs":false}],"preferred":false,"id":849241,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Lundquist, Jessica 0000-0003-2193-5633","orcid":"https://orcid.org/0000-0003-2193-5633","contributorId":295349,"corporation":false,"usgs":false,"family":"Lundquist","given":"Jessica","email":"","affiliations":[{"id":6934,"text":"University of Washington","active":true,"usgs":false}],"preferred":false,"id":849242,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Bandaragoda, Christina 0000-0003-1617-1288","orcid":"https://orcid.org/0000-0003-1617-1288","contributorId":295350,"corporation":false,"usgs":false,"family":"Bandaragoda","given":"Christina","email":"","affiliations":[{"id":6934,"text":"University of Washington","active":true,"usgs":false}],"preferred":false,"id":849243,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Jaeger, Kristin L. 0000-0002-1209-8506","orcid":"https://orcid.org/0000-0002-1209-8506","contributorId":206935,"corporation":false,"usgs":true,"family":"Jaeger","given":"Kristin","middleInitial":"L.","affiliations":[{"id":622,"text":"Washington Water Science Center","active":true,"usgs":true}],"preferred":true,"id":849244,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"Mauger, Guillaume S.","contributorId":138608,"corporation":false,"usgs":false,"family":"Mauger","given":"Guillaume","email":"","middleInitial":"S.","affiliations":[{"id":12463,"text":"Climate Impacts Group, College of the Environment, University of Washington","active":true,"usgs":false}],"preferred":false,"id":849245,"contributorType":{"id":1,"text":"Authors"},"rank":6},{"text":"Horner-Devine, Alex 0000-0003-2323-7150","orcid":"https://orcid.org/0000-0003-2323-7150","contributorId":295351,"corporation":false,"usgs":false,"family":"Horner-Devine","given":"Alex","email":"","affiliations":[{"id":6934,"text":"University of Washington","active":true,"usgs":false}],"preferred":false,"id":849246,"contributorType":{"id":1,"text":"Authors"},"rank":7}]}} ,{"id":70235758,"text":"70235758 - 2022 - Using machine learning to improve predictions and provide insight into fluvial sediment transport","interactions":[],"lastModifiedDate":"2022-08-18T14:43:16.365462","indexId":"70235758","displayToPublicDate":"2022-08-18T09:36:27","publicationYear":"2022","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":1924,"text":"Hydrological Processes","active":true,"publicationSubtype":{"id":10}},"title":"Using machine learning to improve predictions and provide insight into fluvial sediment transport","docAbstract":"A thorough understanding of fluvial sediment transport is critical to addressing many environmental concerns such as exacerbated flooding, degradation of aquatic habitat, excess nutrients, and the economic challenges of restoring aquatic systems. Fluvial sediment samples are integral for addressing these environmental concerns but cannot be collected at every river and time of interest. Therefore, to gain a better understanding for rivers where direct measurements have not been made, extreme gradient boosting machine learning (ML) models were developed and trained to predict suspended sediment and bedload from sampling data collected in Minnesota, United States (U.S.), by the U.S. Geological Survey. Approximately 400 watershed (full upstream area), catchment (nearby landscape), near-channel, channel, and streamflow features were retrieved or developed from multiple sources, reduced to approximately 30 uncorrelated features, and used in the final ML models. The results indicate suspended sediment and bedload ML models explain approximately 70% of the variance in the datasets. Important features used in the models were interpreted with Shapley additive explanation (SHAP) plots, which provided insight into sediment transport processes. The most important features in the models were developed to normalize streamflow by the 2-year recurrence interval and quantify the rate of change in streamflow (slope), which helped account for sediment hysteresis. Generally, this study also showed a combination of mostly watershed and catchment geospatial features were important in ML models that predict sediment transport from physical samples. This study is a promising step forward in making fluvial sediment transport predictions using machine learning models trained by physically collected samples. The approach developed here can be used wherever similar datasets exists and will be useful for landscape and water management.","language":"English","publisher":"Wiley","doi":"10.1002/hyp.14648","usgsCitation":"Lund, J.W., Groten, J.T., Karwan, D.L., and Babcock, C., 2022, Using machine learning to improve predictions and provide insight into fluvial sediment transport: Hydrological Processes, v. 36, no. 8, e14648, 21 p., https://doi.org/10.1002/hyp.14648.","productDescription":"e14648, 21 p.","ipdsId":"IP-133936","costCenters":[{"id":37947,"text":"Upper Midwest Water Science Center","active":true,"usgs":true}],"links":[{"id":446739,"rank":1,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.1002/hyp.14648","text":"Publisher Index Page"},{"id":435725,"rank":0,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/P9VOPSEJ","text":"USGS data release","linkHelpText":"Extreme gradient boosting machine learning models, suspended sediment, 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