The cratered highlands located northwest of Isidis Planitia have been recognized as one of the best preserved Noachian landscapes currently exposed on Mars; the area hosts a record of diverse surface processes, diagenesis, and aqueous alteration. This region has consistently been considered a high priority for landed-mission exploration and includes the anticipated landing site of the Mars 2020 Perseverance rover within Jezero crater. Past mapping, focused on Jezero crater and the surrounding area, Nili Planum, has varied in spatial extent, map scale, and purpose, though no previous maps have provided a continuous, high-resolution geologic map at uniform scale connecting the two locations. This map represents the first, large-scale, continuous geologic map spanning both Jezero crater and Nili Planum that is based on high-resolution images.
The map area contains the majority of both Jezero crater and Nili Planum at a publication map scale of 1:75,000, which was chosen to encompass the Jezero and southern Nili Planum landing sites under consideration for the Mars 2020 mission at the time of project initiation. This map covers an area that is exactly 1° by 1° (~60 by 60 km), spanning lat 76.8° N. to long 77.8° E. and lat 17.7° to long 18.7° N. The primary base map used for this geologic map is composed of Mars Reconnaissance Orbiter’s Context Camera (CTX) images, compiled into a 6 meter per pixel (m/pixel) mosaic. A nighttime Thermal Emission Imaging System 100 m/pixel image mosaic, digital terrain models constructed from CTX images, High-Resolution Stereo Camera (HRSC) topographic data, and High Resolution Imaging Science Experiment (HiRise) images also aided in unit identification and the assessment of stratigraphic relations. We defined map units on the basis of various characteristics visible in the CTX data at map scale, such as their texture, tone, morphology, marginal characteristics, geographic location, and stratigraphic relations to other units. Some units occur solely within Jezero crater, while Nili Planum contains a sequence of units that are present across the broader northwest Isidis Planitia region. Other units occur in both Jezero crater and Nili Planum, including bedrock, aeolian, and crater units. This map publication provides a regional geologic framework that connects the geologic units across Jezero crater and Nili Planum and the history they imply, facilitates future local-scale observations by landed missions of the Jezero crater and Nili Planum region, and enables the extrapolation of units that have been defined primarily by mineralogic composition to areas where there is no existing orbital spectroscopic data.
","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/sim3464","collaboration":"Prepared for the National Aeronautics and Space Administration","usgsCitation":"Sun, V.Z., and Stack, K.M., 2020, Geologic map of Jezero crater and the Nili Planum region, Mars: U.S. Geological Survey Scientific Investigations Map 3464, pamphlet 14 p., 1 sheet, scale 1:75,000, https://doi.org/10.3133/sim3464.","productDescription":"Pamphlet: iv, 14 p.; 1 Map: 56.60 x 45.62 inches; Metadata; Database; Read Me","numberOfPages":"14","onlineOnly":"N","additionalOnlineFiles":"Y","ipdsId":"IP-118085","costCenters":[{"id":131,"text":"Astrogeology Science Center","active":true,"usgs":true}],"links":[{"id":436704,"rank":9,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/P9CZYIO7","text":"USGS data release","linkHelpText":"Interactive Map: USGS SIM 3464 Geologic Map of Jezero Crater and the Nili Planum Region"},{"id":380236,"rank":3,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/sim/3464/sim3464_pamphlet.pdf","text":"Pamphlet","size":"728 KB","linkFileType":{"id":1,"text":"pdf"},"description":"SIM 3464 Pamphlet"},{"id":380235,"rank":2,"type":{"id":26,"text":"Sheet"},"url":"https://pubs.usgs.gov/sim/3464/sim3464.pdf","text":"Map","size":"36.4 MB","linkFileType":{"id":1,"text":"pdf"},"description":"SIM 3464"},{"id":380240,"rank":7,"type":{"id":9,"text":"Database"},"url":"https://pubs.usgs.gov/sim/3464/sim3464_database.zip","size":"349.3 MB","linkFileType":{"id":6,"text":"zip"},"description":"SIM 3464 Database"},{"id":380239,"rank":6,"type":{"id":20,"text":"Read Me"},"url":"https://pubs.usgs.gov/sim/3464/sim3464_readme.txt","size":"4 KB","linkFileType":{"id":2,"text":"txt"},"description":"SIM 3464 Readme txt"},{"id":380238,"rank":5,"type":{"id":16,"text":"Metadata"},"url":"https://pubs.usgs.gov/sim/3464/sim3464_metadata.xml","size":"21 KB","linkFileType":{"id":8,"text":"xml"},"description":"SIM 3464 Metadata xml"},{"id":380237,"rank":4,"type":{"id":16,"text":"Metadata"},"url":"https://pubs.usgs.gov/sim/3464/sim3464_metadata.txt","size":"21 KB","linkFileType":{"id":2,"text":"txt"},"description":"SIM 3464 Metadata txt"},{"id":380234,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/sim/3464/coverthb.jpg"},{"id":400813,"rank":8,"type":{"id":2,"text":"Additional Report Piece"},"url":"https://doi.org/10.5066/P9CZYIO7","text":"Interactive map","linkHelpText":"- Geologic Map of Jezero Crater and the Nili Planum Region, Mars, 1:75,000. Sun and Stack (2020)"}],"contact":"Contact Astrogeology Research Program staff
Astrogeology Science Center
U.S. Geological Survey
2255 N. Gemini Dr.
Flagstaff, AZ 86001
Water samples from 122 sites in the U.S. Geological Survey National Water Quality Network were collected in 2013–17 to document ambient water-quality conditions in surface water of the United States and to determine status and trends of loads and concentrations for nutrients, contaminants, and sediment to estuaries and streams. Quality-control (QC) samples collected in the field with environmental samples were combined with QC samples from laboratory processing to provide information and documentation about the quality of the environmental data.
Quality assurance for inorganic and organic compounds assessed in the National Water Quality Network includes collection of field blanks to determine contamination bias and field replicates to determine variability bias. No contamination bias was found for 6 of the 13 nutrient compounds analyzed, and some potential contamination bias for some years was found for the other 7 nutrient compounds. Contamination bias was not found for carbon compounds or ultraviolet-absorbance measurements and was not assessed for sediment. All major ions and trace elements except potassium and lithium showed moderate contamination bias for at least 1 water year; generally, this bias was not at environmentally relevant concentrations. All compounds in the nutrient, carbon, and sediment group and in the major ions and trace elements group had low variability both in detection frequency and in concentration. Exceptions to this low variability were total particulate inorganic carbon and sediment for 2015, both of which are particulate substances with intrinsically high sampling variability.
The risk of contamination bias for pesticides in National Water Quality Network samples was low, as indicated by very few detections in field blanks. Sixteen pesticide compounds showed potential contamination bias based on unexpected detections in third-party blind spikes (false-positive results for compounds that are not included in the spike mixture of a sample, where the identity as a QC sample is unknown to the analyst), and 47 different compounds (out of 225 pesticide compounds) showed potential contamination bias from laboratory blanks. However, when timing and relative magnitudes of detections in blank samples, environmental samples, and benchmark concentrations are considered, most of this potential contamination is not relevant to interpretation of published pesticide results. Overall variability in detection frequency for pesticides from field replicates was low or moderate. Also based on field replicates, 55 pesticides had overall high variability in concentrations for at least 1 water year, although these assessments likely overestimate high variability.
At least 1 QC issue was found for 87 pesticides; however, most of the QC issues had no or little effect on the interpretation of environmental results because the U.S. Geological Survey National Water Quality Laboratory addressed the QC issue before publishing the environmental results, environmental results were almost entirely nondetections, concentrations of environmental results were higher than potential contamination bias, or benchmark concentrations were orders of magnitude higher than all environmental results. Eight compounds affected by two QC issues had a benchmark less than 100 nanograms per liter and warranted careful consideration of timing and magnitude of QC results in relation to surface-water results before interpretive use.
","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/sir20205116","usgsCitation":"Medalie, L., and Bexfield, L.M., 2020, Quality of data from the U.S. Geological Survey National Water Quality Network for water years 2013–17: U.S. Geological Survey Scientific Investigations Report 2020–5116, 21 p., https://doi.org/10.3133/sir20205116.","productDescription":"Report: v, 21 p.; Data Releases; 9 Tables","numberOfPages":"21","onlineOnly":"Y","additionalOnlineFiles":"Y","ipdsId":"IP-115536","costCenters":[{"id":466,"text":"New England Water Science Center","active":true,"usgs":true}],"links":[{"id":436706,"rank":17,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/P94F31R8","text":"USGS data release","linkHelpText":"Nutrient and pesticide data collected from the USGS National Water Quality Network and previous networks, 1963-2018"},{"id":380873,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/sir/2020/5116/coverthb2.jpg"},{"id":380874,"rank":2,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/sir/2020/5116/sir20205116.pdf","text":"Report","size":"1.65 MB","linkFileType":{"id":1,"text":"pdf"},"description":"SIR 2020-5116"},{"id":380877,"rank":3,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/P90BFKA4","text":"USGS data release","linkHelpText":"Field, laboratory, and third-party data for assessment of the quality of pesticide results reported by the National Water Quality Laboratory for groundwater samples collected by the National Water-Quality Assessment Project, 2013–18"},{"id":380878,"rank":4,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/P96VY980","text":"USGS data release","linkHelpText":"Field blank and field replicate datasets for inorganic and organic compounds collected for the National Water Quality Network, water years 2013–17"},{"id":380879,"rank":5,"type":{"id":27,"text":"Table"},"url":"https://pubs.usgs.gov/sir/2020/5116/sir20205116_tables01-09.xlsx","text":"Tables 1-9","size":"221 KB","linkFileType":{"id":3,"text":"xlsx"},"linkHelpText":"- Tables 1-9 as a Microsoft Excel file"},{"id":380880,"rank":6,"type":{"id":27,"text":"Table"},"url":"https://pubs.usgs.gov/sir/2020/5116/sir20205116_tables01-09_csv.zip","text":"CSV Package","size":"44 KB","linkFileType":{"id":6,"text":"zip"},"linkHelpText":"- Tables 1-9 as nine CSV files and a Read Me file"},{"id":380881,"rank":7,"type":{"id":27,"text":"Table"},"url":"https://pubs.usgs.gov/sir/2020/5116/sir20205116_tables_readme.txt","text":"CSV Read Me","size":"19.6 KB","linkFileType":{"id":2,"text":"txt"},"linkHelpText":"- Read Me file to accompany the CSV files"},{"id":380887,"rank":13,"type":{"id":27,"text":"Table"},"url":"https://pubs.usgs.gov/sir/2020/5116/sir20205116_table06.csv","text":"Table 6","size":"14.7 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U.S. Geological Survey
10 Bearfoot Road
Northborough, MA 01532
No abstract available.
","conferenceTitle":"5th Colloquium of the International Geoscience Programme (IGCP-638)","conferenceDate":"December 4-5, 2022","conferenceLocation":"Accra, Ghana","language":"English","publisher":"UNESCO (ICGP)","usgsCitation":"Taylor, C.D., Bradley, D., Finn, C.A., Zerrouki, A., Ayad, B., Belanteur, N.F., Bouchilaoune, N., Johnson, M., Meziane, G., Mihalasky, M.J., Mouchene, H., Oughou, S., Smith, S.M., Solano, F., and Zerrouk, S., 2020, Metallogenic implications of a new geodynamic model for the Eglab, Algeria, 5th Colloquium of the International Geoscience Programme (IGCP-638), Accra, Ghana, December 4-5, 2022, 3 p.","productDescription":"3 p.","ipdsId":"IP-111457","costCenters":[{"id":171,"text":"Central Mineral and Environmental Resources Science Center","active":true,"usgs":true},{"id":211,"text":"Crustal Geophysics and Geochemistry Science Center","active":true,"usgs":true},{"id":245,"text":"Eastern Mineral and Environmental Resources Science 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smsmith@usgs.gov","orcid":"https://orcid.org/0000-0003-3591-5377","contributorId":1460,"corporation":false,"usgs":true,"family":"Smith","given":"Steven","email":"smsmith@usgs.gov","middleInitial":"M.","affiliations":[{"id":171,"text":"Central Mineral and Environmental Resources Science Center","active":true,"usgs":true},{"id":387,"text":"Mineral Resources Program","active":true,"usgs":true}],"preferred":true,"id":819874,"contributorType":{"id":1,"text":"Authors"},"rank":13},{"text":"Solano, Federico 0000-0002-0308-5850 fsolanoc@usgs.gov","orcid":"https://orcid.org/0000-0002-0308-5850","contributorId":4302,"corporation":false,"usgs":true,"family":"Solano","given":"Federico","email":"fsolanoc@usgs.gov","affiliations":[{"id":245,"text":"Eastern Mineral and Environmental Resources Science Center","active":true,"usgs":true}],"preferred":true,"id":819875,"contributorType":{"id":1,"text":"Authors"},"rank":14},{"text":"Zerrouk, Siham","contributorId":261350,"corporation":false,"usgs":false,"family":"Zerrouk","given":"Siham","affiliations":[{"id":40298,"text":"Algerian Geological Survey Agency","active":true,"usgs":false}],"preferred":false,"id":819876,"contributorType":{"id":1,"text":"Authors"},"rank":15}]}} ,{"id":70228275,"text":"70228275 - 2020 - Ecological risk assessment of environmental stress and bioactive chemicals to riverine fish populations: An individual-based model of smallmouth bass Micropterus dolomieu","interactions":[],"lastModifiedDate":"2022-02-08T16:10:19.163337","indexId":"70228275","displayToPublicDate":"2020-12-01T09:47:52","publicationYear":"2020","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":1458,"text":"Ecological Modelling","active":true,"publicationSubtype":{"id":10}},"displayTitle":"Ecological risk assessment of environmental stress and bioactive chemicals to riverine fish populations: An individual-based model of smallmouth bass Micropterus dolomieu","title":"Ecological risk assessment of environmental stress and bioactive chemicals to riverine fish populations: An individual-based model of smallmouth bass Micropterus dolomieu","docAbstract":"Ecological risk assessments play an important role in environmental management and decision-making. Although empirical measurements of the effects of habitat changes and chemical exposure are often made at molecular and individual levels, environmental decision-making often requires the quantification of management-relevant, population-level outcomes. In this study, we generalized a modeling framework to evaluate population-level ecological risk of environmental stress and bioactive chemicals. The modeling framework includes (1) a biological model module that incorporates complex and interacting biological and ecological processes, and environmental stochasticity, (2) an effect module that links the impacts of environmental changes and chemical exposure to individual characteristics, and (3) a population module that makes decisions on the choice of population-level properties to best capture the effects and thus to track in the model based on the target species and the research and management interest. This framework is a 3-module procedure that provides an alternative way for researchers to organize, present and communicate the risk assessment modeling studies. To demonstrate this framework, we used a socioeconomically important riverine fish species, smallmouth bass Micropterus dolomieu, as the model species. We developed an individual-based model as the biological model module. We evaluated the impacts of changing water temperature and flow regimes, and the impacts of exposure to estrogenic endocrine disrupting compounds (EEDC) on smallmouth bass populations in the Chesapeake Bay Watershed, USA. Warm summer water temperatures and year-round high flows had the most severe impacts on the smallmouth bass population. An increase in exposure level to EEDC, both year-round and in summer months, substantially reduced population size, spawner and recruit abundance, and the proportion of quality-length individuals. Acute exposure to EEDC was more detrimental to the population than chronic exposure. Acute exposure during spawning season had the most severe impacts. This modeling framework can be extended to other species, environmental factors and chemicals, and can be used to inform management and conservation decisions.
Telemetry of acoustically tagged bigheaded carp (i.e., bighead carp Hypophthalmichthys nobilis and silver carp H. molitrix) and surrogate fish species has become an invaluable tool in management for these species in the upper Illinois Waterway Systems (i.e., upper Illinois River, lower Des Plaines River, and Chicago Area Waterway System). For example, movement probabilities between adjacent navigation pools need to be estimated to parameterize the Spatially Explicit Asian Carp Population Model (SEAcarP). SEAcarP is a population model used in scenario planning by the Monitoring and Response Workgroup (MRWG) to evaluate alternative management actions. These movement probabilities are estimated from the telemetry data obtained from a longitudinal network of strategically placed receivers that detect bigheaded carp that have been implanted with acoustic transmitters. In addition, fish removal by contracted fishers has become the primary method of controlling bigheaded carp in the upper Illinois and lower Des Plaines Rivers. Variable patterns in bigheaded carp distribution, habitat, and movement, influenced by seasonal and environmental conditions, make targeting bigheaded carp for removal and containment challenging and costly. Understanding these movement patterns for bigheaded carp through modeling and real-time telemetry applications informs removal efforts and facilitates monitoring and contingency actions based on fish movements.
To develop a better understanding of fish movement dynamics to meet management objectives, an existing network of real-time and data-logging acoustic receivers in the upper Illinois Waterway Systems is collaboratively managed by a multi-agency team (see Participating Agencies section above). A Telemetry Workgroup has been established by the MRWG to ensure that the multi-agency telemetry efforts are coordinated to efficiently and effectively meet the MRWG goals. This workgroup plans and executes the placement of receivers, tagging of bigheaded carp with acoustic tags, and management of the telemetry data. Three primary objectives to meet MRWG goals identified by the Telemetry Workgroup included (1) development of a common standardized telemetry database with visualization and analysis tools, (2) transitioning from Program MARK (http://www.phidot.org/software/mark/) to a custom Bayesian multi-state model for estimating movement probabilities needed for SEAcarP and (3) deploying, maintaining, and serving data from real-time acoustic receivers to inform contingency planning and fish removal.
A telemetry database and visualization tools (FishTracks) will facilitate standardization, archiving, sharing, quality assurance, visualization and analysis of the telemetry data needed for management. Modifications and additions to FishTracks will facilitate more problem-free use of the database and associated applications, as well as useful extraction of information to meet management goals. The transition to a custom Bayesian multi-state model to estimate movement probabilities will support more efficient, effective, and robust population modeling with SEAcarP by overcoming short comings of Program MARK for this purpose. These shortcomings include lack of customizability and extensibility, problems of singularities and poor-convergence, software crashes, parameter exclusion from models, an inability to consistently generate estimates of movement probability, and a lack of uncertainty estimates for movement probabilities. A real-time receiver network that is maintained and tested annually will ensure reliability and accuracy of the real-time alerts to bigheaded carp movements that can be used by management to plan contingency actions.
","largerWorkType":{"id":18,"text":"Report"},"largerWorkTitle":"Interim summary report 2020","largerWorkSubtype":{"id":3,"text":"Organization Series"},"language":"English","publisher":"Asian Carp Regional Coordinating Committee","usgsCitation":"Knights, B.C., Brey, M.K., Stanton, J.C., Harrison, T.J., Appel, D., Hlavacek, E., and Duncker, J.J., 2020, USGS Telemetry Project: Interim Summary Report, 6 p.","productDescription":"6 p.","startPage":"41","endPage":"46","ipdsId":"IP-128212","costCenters":[{"id":606,"text":"Upper Midwest Environmental Sciences Center","active":true,"usgs":true}],"links":[{"id":391250,"rank":1,"type":{"id":15,"text":"Index Page"},"url":"https://invasivecarp.us/PlansReports.html"},{"id":426889,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","state":"Illinois","otherGeospatial":"upper Illinois Waterway","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"coordinates\": [\n [\n [\n -87.34655836479192,\n 42.36183831544582\n ],\n [\n -89.74494117447833,\n 42.36183831544582\n ],\n [\n -89.74494117447833,\n 40.51767088873834\n ],\n [\n -87.34655836479192,\n 40.51767088873834\n ],\n [\n -87.34655836479192,\n 42.36183831544582\n ]\n ]\n ],\n \"type\": \"Polygon\"\n }\n }\n ]\n}","noUsgsAuthors":false,"publicationStatus":"PW","contributors":{"authors":[{"text":"Knights, Brent C. 0000-0001-8526-8468 bknights@usgs.gov","orcid":"https://orcid.org/0000-0001-8526-8468","contributorId":2906,"corporation":false,"usgs":true,"family":"Knights","given":"Brent","email":"bknights@usgs.gov","middleInitial":"C.","affiliations":[{"id":606,"text":"Upper Midwest Environmental Sciences Center","active":true,"usgs":true}],"preferred":true,"id":826139,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Brey, Marybeth K. 0000-0003-4403-9655 mbrey@usgs.gov","orcid":"https://orcid.org/0000-0003-4403-9655","contributorId":187651,"corporation":false,"usgs":true,"family":"Brey","given":"Marybeth","email":"mbrey@usgs.gov","middleInitial":"K.","affiliations":[{"id":606,"text":"Upper Midwest Environmental Sciences Center","active":true,"usgs":true}],"preferred":true,"id":826140,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Stanton, Jessica C. 0000-0002-6225-3703 jcstanton@usgs.gov","orcid":"https://orcid.org/0000-0002-6225-3703","contributorId":5634,"corporation":false,"usgs":true,"family":"Stanton","given":"Jessica","email":"jcstanton@usgs.gov","middleInitial":"C.","affiliations":[{"id":606,"text":"Upper Midwest Environmental Sciences Center","active":true,"usgs":true}],"preferred":true,"id":826141,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Harrison, Travis J. 0000-0002-9195-738X","orcid":"https://orcid.org/0000-0002-9195-738X","contributorId":213966,"corporation":false,"usgs":true,"family":"Harrison","given":"Travis","email":"","middleInitial":"J.","affiliations":[{"id":606,"text":"Upper Midwest Environmental Sciences Center","active":true,"usgs":true}],"preferred":true,"id":826142,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Appel, Douglas 0000-0001-8775-1058","orcid":"https://orcid.org/0000-0001-8775-1058","contributorId":268159,"corporation":false,"usgs":true,"family":"Appel","given":"Douglas","email":"","affiliations":[{"id":606,"text":"Upper Midwest Environmental Sciences Center","active":true,"usgs":true}],"preferred":true,"id":826143,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"Hlavacek, Enrika 0000-0002-9872-2305 ehlavacek@usgs.gov","orcid":"https://orcid.org/0000-0002-9872-2305","contributorId":149114,"corporation":false,"usgs":true,"family":"Hlavacek","given":"Enrika","email":"ehlavacek@usgs.gov","affiliations":[{"id":606,"text":"Upper Midwest Environmental Sciences Center","active":true,"usgs":true}],"preferred":true,"id":826144,"contributorType":{"id":1,"text":"Authors"},"rank":6},{"text":"Duncker, James J. 0000-0001-5464-7991 jduncker@usgs.gov","orcid":"https://orcid.org/0000-0001-5464-7991","contributorId":4316,"corporation":false,"usgs":true,"family":"Duncker","given":"James","email":"jduncker@usgs.gov","middleInitial":"J.","affiliations":[{"id":344,"text":"Illinois Water Science Center","active":true,"usgs":true},{"id":35680,"text":"Illinois-Iowa-Missouri Water Science Center","active":true,"usgs":true},{"id":36532,"text":"Central Midwest Water Science Center","active":true,"usgs":true}],"preferred":true,"id":826145,"contributorType":{"id":1,"text":"Authors"},"rank":7}]}} ,{"id":70228975,"text":"70228975 - 2020 - A multispecies approach to manage effects of land cover and weather on upland game birds","interactions":[],"lastModifiedDate":"2022-02-25T15:21:38.85868","indexId":"70228975","displayToPublicDate":"2020-12-01T09:11:55","publicationYear":"2020","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":1467,"text":"Ecology and Evolution","active":true,"publicationSubtype":{"id":10}},"title":"A multispecies approach to manage effects of land cover and weather on upland game birds","docAbstract":"Loss and degradation of grasslands in the Great Plains region have resulted in major declines in abundance of grassland bird species. To ensure future viability of grassland bird populations, it is crucial to evaluate specific effects of environmental factors among species to determine drivers of population decline and develop effective conservation strategies. We used threshold models to quantify the effects of land cover and weather changes in \"lesser prairie-chicken\" and \"greater prairie-chicken\" (Tympanuchus pallidicinctus and T. cupido, respectively), northern bobwhites (Colinus virginianus), and ring-necked pheasants (Phasianus colchicus). We demonstrated a novel approach for estimating landscape conditions needed to optimize abundance across multiple species at a variety of spatial scales. Abundance of all four species was highest following wet summers and dry winters. Prairie chicken and ring-necked pheasant abundance was highest following cool winters, while northern bobwhite abundance was highest following warm winters. Greater prairie chicken and northern bobwhite abundance was also highest following cooler summers. Optimal abundance of each species occurred in landscapes that represented a grassland and cropland mosaic, though prairie chicken abundance was optimized in landscapes with more grassland and less edge habitat than northern bobwhites and ring-necked pheasants. Because these effects differed among species, managing for an optimal landscape for multiple species may not be the optimal scenario for any one species.
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dhaukos@usgs.gov","orcid":"https://orcid.org/0000-0001-5372-9960","contributorId":3664,"corporation":false,"usgs":true,"family":"Haukos","given":"David","email":"dhaukos@usgs.gov","middleInitial":"A.","affiliations":[{"id":200,"text":"Coop Res Unit Seattle","active":true,"usgs":true},{"id":198,"text":"Coop Res Unit Atlanta","active":true,"usgs":true}],"preferred":true,"id":836061,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Hagen, C.A.","contributorId":276129,"corporation":false,"usgs":false,"family":"Hagen","given":"C.A.","email":"","affiliations":[{"id":6680,"text":"Oregon State University","active":true,"usgs":false}],"preferred":false,"id":836062,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Ross, Beth 0000-0001-5634-4951 bross@usgs.gov","orcid":"https://orcid.org/0000-0001-5634-4951","contributorId":199242,"corporation":false,"usgs":true,"family":"Ross","given":"Beth","email":"bross@usgs.gov","affiliations":[{"id":198,"text":"Coop Res Unit Atlanta","active":true,"usgs":true}],"preferred":true,"id":836063,"contributorType":{"id":1,"text":"Authors"},"rank":4}]}} ,{"id":70216843,"text":"70216843 - 2020 - Development and application of an empirical dune growth model for evaluating barrier island recovery from storms","interactions":[],"lastModifiedDate":"2020-12-09T14:30:57.032964","indexId":"70216843","displayToPublicDate":"2020-12-01T08:21:00","publicationYear":"2020","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":2380,"text":"Journal of Marine Science and Engineering","active":true,"publicationSubtype":{"id":10}},"title":"Development and application of an empirical dune growth model for evaluating barrier island recovery from storms","docAbstract":"Coastal zone managers require models that predict barrier island change on decadal time scales to estimate coastal vulnerability, and plan habitat restoration and coastal protection projects. To meet these needs, methods must be available for predicting dune recovery as well as dune erosion. In the present study, an empirical dune growth model (EDGR) was developed to predict the evolution of the primary foredune of a barrier island. Within EDGR, an island is represented as a sum of Gaussian shape functions representing dunes, berms, and the underlying island form. The model evolves the foredune based on estimated terminal dune height and location inputs. EDGR was assessed against observed dune evolution along the western end of Dauphin Island, Alabama over the 10 years following Hurricane Katrina (2005). The root mean square error with EDGR (ranging from 0.18 to 0.74 m over the model domain) was reduced compared to an alternate no-change model (0.69–0.96 m). Hindcasting with EDGR also supports the study of dune evolution processes. At Dauphin Island, results suggest that a low-lying portion of the site was dominated by overwash for ~5 years after Katrina, before approaching their terminal height and becoming growth-limited after 2010. EDGR’s computational efficiency allows dune evolution to be rapidly predicted and enables ensemble predictions to constrain the uncertainty that may result if terminal dune characteristics are unknown. In addition, EDGR can be coupled with an external model for estimating dune erosion and/or the long-term evolution of other subaerial features to allow decadal-scale prediction of barrier island evolution.
","language":"English","publisher":"MDPI","doi":"10.3390/jmse8120977","usgsCitation":"Dalyander, P., Mickey, R.C., Passeri, D., and Plant, N.G., 2020, Development and application of an empirical dune growth model for evaluating barrier island recovery from storms: Journal of Marine Science and Engineering, v. 8, no. 12, 9, 21 p., https://doi.org/10.3390/jmse8120977.","productDescription":"9, 21 p.","ipdsId":"IP-104554","costCenters":[{"id":574,"text":"St. Petersburg Coastal and Marine Science Center","active":true,"usgs":true}],"links":[{"id":454720,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.3390/jmse8120977","text":"Publisher Index Page"},{"id":381165,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","state":"Alabama","otherGeospatial":"Dauphin Island","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -88.34930419921875,\n 30.216948502671475\n ],\n [\n -88.06640625,\n 30.216948502671475\n ],\n [\n -88.06640625,\n 30.271521387805628\n ],\n [\n -88.34930419921875,\n 30.271521387805628\n ],\n [\n -88.34930419921875,\n 30.216948502671475\n ]\n ]\n ]\n }\n }\n ]\n}","volume":"8","issue":"12","noUsgsAuthors":false,"publicationDate":"2020-12-01","publicationStatus":"PW","contributors":{"authors":[{"text":"Dalyander, Patricia (Soupy) 0000-0001-9583-0872 sdalyander@usgs.gov","orcid":"https://orcid.org/0000-0001-9583-0872","contributorId":191931,"corporation":false,"usgs":true,"family":"Dalyander","given":"Patricia (Soupy)","email":"sdalyander@usgs.gov","affiliations":[{"id":574,"text":"St. Petersburg Coastal and Marine Science Center","active":true,"usgs":true}],"preferred":true,"id":806595,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Mickey, Rangley C. 0000-0001-5989-1432 rmickey@usgs.gov","orcid":"https://orcid.org/0000-0001-5989-1432","contributorId":141016,"corporation":false,"usgs":true,"family":"Mickey","given":"Rangley","email":"rmickey@usgs.gov","middleInitial":"C.","affiliations":[{"id":574,"text":"St. Petersburg Coastal and Marine Science Center","active":true,"usgs":true}],"preferred":true,"id":806596,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Passeri, Davina 0000-0002-9760-3195 dpasseri@usgs.gov","orcid":"https://orcid.org/0000-0002-9760-3195","contributorId":166889,"corporation":false,"usgs":true,"family":"Passeri","given":"Davina","email":"dpasseri@usgs.gov","affiliations":[{"id":574,"text":"St. Petersburg Coastal and Marine Science Center","active":true,"usgs":true}],"preferred":true,"id":806597,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Plant, Nathaniel G. 0000-0002-5703-5672 nplant@usgs.gov","orcid":"https://orcid.org/0000-0002-5703-5672","contributorId":3503,"corporation":false,"usgs":true,"family":"Plant","given":"Nathaniel","email":"nplant@usgs.gov","middleInitial":"G.","affiliations":[{"id":508,"text":"Office of the AD Hazards","active":true,"usgs":true},{"id":574,"text":"St. Petersburg Coastal and Marine Science Center","active":true,"usgs":true}],"preferred":true,"id":806598,"contributorType":{"id":1,"text":"Authors"},"rank":4}]}} ,{"id":70213224,"text":"70213224 - 2020 - Quantitative textural measures of the aeromagnetic field: Two examples at regional scale","interactions":[],"lastModifiedDate":"2021-01-26T12:43:53.716355","indexId":"70213224","displayToPublicDate":"2020-11-30T11:34:52","publicationYear":"2020","noYear":false,"publicationType":{"id":5,"text":"Book chapter"},"publicationSubtype":{"id":24,"text":"Book Chapter"},"chapter":"6","title":"Quantitative textural measures of the aeromagnetic field: Two examples at regional scale","docAbstract":"No abstract available.
","largerWorkType":{"id":4,"text":"Book"},"largerWorkTitle":"An Introduction to Approaches and Modern Applications with Ensemble Learning","largerWorkSubtype":{"id":15,"text":"Monograph"},"language":"English","publisher":"Nova Science Publishers, Inc.","usgsCitation":"Gettings, M.E., 2020, Quantitative textural measures of the aeromagnetic field: Two examples at regional scale, chap. 6 of An Introduction to Approaches and Modern Applications with Ensemble Learning.","ipdsId":"IP-093118","costCenters":[{"id":312,"text":"Geology, Minerals, Energy, and Geophysics Science Center","active":true,"usgs":true}],"links":[{"id":382561,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"noUsgsAuthors":false,"publicationStatus":"PW","contributors":{"authors":[{"text":"Gettings, Mark E. 0000-0002-2910-2321 mgetting@usgs.gov","orcid":"https://orcid.org/0000-0002-2910-2321","contributorId":602,"corporation":false,"usgs":true,"family":"Gettings","given":"Mark","email":"mgetting@usgs.gov","middleInitial":"E.","affiliations":[{"id":312,"text":"Geology, Minerals, Energy, and Geophysics Science Center","active":true,"usgs":true}],"preferred":true,"id":798643,"contributorType":{"id":1,"text":"Authors"},"rank":1}]}} ,{"id":70219169,"text":"70219169 - 2020 - Decreases in aluminum toxicity and mortality of caged brook trout in Adirondack Mountain Streams","interactions":[],"lastModifiedDate":"2021-03-29T14:38:42.342371","indexId":"70219169","displayToPublicDate":"2020-11-30T09:33:20","publicationYear":"2020","noYear":false,"publicationType":{"id":18,"text":"Report"},"publicationSubtype":{"id":2,"text":"State or Local Government Series"},"seriesTitle":{"id":5792,"text":"Summary Report","active":true,"publicationSubtype":{"id":2}},"seriesNumber":"20-35","title":"Decreases in aluminum toxicity and mortality of caged brook trout in Adirondack Mountain Streams","docAbstract":"Mortality of juvenile brook trout and water chemistry were characterized in six western Adirondack streams in northern New York State during spring 2015, 2016, and 2017 and compared with results from comparable tests done between 1980 and 2003 in many of the same streams to assess temporal changes in inorganic monomeric aluminum (Ali) concentrations, Ali-toxicity, and the role of Ali-exposure duration on mortality. Ali concentrations of 2 and 4 micromoles per liter (µmol L-1) corresponded to chronic- and acute-mortality thresholds for brook trout, but prolonged exposure to ≥ 1 µmol Ali L-1 also produced low-to-moderate mortality levels. The variability, mean, and highest Ali concentrations in Buck Creek (BUC) year-round, and in several other streams during spring, decreased significantly over the past 30 years. Predictive models indicate that Ali surpassed highly toxic concentrations at BUC for three to four months annually during 2001–2003 but for only two to three weeks annually during 2015–2017. The current lack of extremely high Ali concentrations indicate toxicity has declined markedly between the 1989–1990, 2001–2003, and 2015–2017 test periods, yet acid- Ali episodes can still cause moderate-to-high levels of brook trout mortality during high springtime flows. Assembled models show how mortality of brook trout in several Adirondack streams likely declined in response to the 1990 Clean Air Act Amendments and offer a means to predict how changes in United States regulations that limit the atmospheric emissions of nitrogen (N) and sulfur (S) oxides, and the deposition of N and S, could affect brook trout survival and impaired stream ecosystems in the western Adirondack region.","language":"English","publisher":"New York Energy Research and Development Authority","usgsCitation":"Baldigo, B.P., and George, S.D., 2020, Decreases in aluminum toxicity and mortality of caged brook trout in Adirondack Mountain Streams: Summary Report 20-35, vi, 31 p.","productDescription":"vi, 31 p.","ipdsId":"IP-107974","costCenters":[{"id":474,"text":"New York Water Science Center","active":true,"usgs":true}],"links":[{"id":384719,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"},{"id":384707,"type":{"id":15,"text":"Index Page"},"url":"https://www.nyserda.ny.gov/-/media/Files/Publications/Research/Transportation/20-35-Decreases-in-Aluminium-toxicity-and-mortality-of-caged-brook-trout.pdf"}],"country":"United States","state":"New York","otherGeospatial":"Adirondack Mountains","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -75.0146484375,\n 43.56447158721811\n ],\n [\n -74.44335937499999,\n 43.56447158721811\n ],\n [\n -74.44335937499999,\n 43.830564195198264\n ],\n [\n -75.0146484375,\n 43.830564195198264\n ],\n [\n -75.0146484375,\n 43.56447158721811\n ]\n ]\n ]\n }\n }\n ]\n}","noUsgsAuthors":false,"publicationStatus":"PW","contributors":{"authors":[{"text":"Baldigo, Barry P. 0000-0002-9862-9119 bbaldigo@usgs.gov","orcid":"https://orcid.org/0000-0002-9862-9119","contributorId":1234,"corporation":false,"usgs":true,"family":"Baldigo","given":"Barry","email":"bbaldigo@usgs.gov","middleInitial":"P.","affiliations":[{"id":474,"text":"New York Water Science Center","active":true,"usgs":true}],"preferred":true,"id":813103,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"George, Scott D. 0000-0002-8197-1866 sgeorge@usgs.gov","orcid":"https://orcid.org/0000-0002-8197-1866","contributorId":3014,"corporation":false,"usgs":true,"family":"George","given":"Scott","email":"sgeorge@usgs.gov","middleInitial":"D.","affiliations":[{"id":474,"text":"New York Water Science Center","active":true,"usgs":true}],"preferred":true,"id":813104,"contributorType":{"id":1,"text":"Authors"},"rank":2}]}} ,{"id":70216789,"text":"70216789 - 2020 - Comparing methods to estimate the proportion of turbine-induced bird and bat mortality in the search area under a road and pad search protocol","interactions":[],"lastModifiedDate":"2020-12-07T14:56:49.886538","indexId":"70216789","displayToPublicDate":"2020-11-30T08:53:33","publicationYear":"2020","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":1573,"text":"Environmental and Ecological Statistics","active":true,"publicationSubtype":{"id":10}},"title":"Comparing methods to estimate the proportion of turbine-induced bird and bat mortality in the search area under a road and pad search protocol","docAbstract":"Estimating bird and bat mortality at wind facilities typically involves searching for carcasses on the ground near turbines. Some fraction of carcasses inevitably lie outside the search plots, and accurate mortality estimation requires accounting for those carcasses using models to extrapolate from searched to unsearched areas. Such models should account for variation in carcass density with distance, and ideally also for variation with direction (anisotropy). We compare five methods of accounting for carcasses that land outside the searched area (ratio, weighted distribution, non-parametric, and two generalized linear models (glm)) by simulating spatial arrival patterns and the detection process to mimic observations which result from surveying only, or primarily, roads and pads (R&P) and applying the five methods. Simulations vary R&P configurations, spatial carcass distributions (isotropic and anisotropic), and per turbine fatality rates. Our results suggest that the ratio method is less accurate with higher variation relative to the other four methods which all perform similarly under isotropy. All methods were biased under anisotropy; however, including direction covariates in the glm method substantially reduced bias. In addition to comparing methods of accounting for unsearched areas, we suggest a semiparametric bootstrap to produce confidence-based bounds for the proportion of carcasses that land in the searched area.
| The R package colorspace provides a flexible toolbox for selecting individual colors or color palettes, manipulating these colors, and employing them in statistical graphics and data visualizations. In particular, the package provides a broad range of color palettes based on the HCL (hue-chroma-luminance) color space. The three HCL dimensions have been shown to match those of the human visual system very well, thus facilitating intuitive selection of color palettes through trajectories in this space. Using the HCL color model, general strategies for three types of palettes are implemented: (1) Qualitative for coding categorical information, i.e., where no particular ordering of categories is available. (2) Sequential for coding ordered/numeric information, i.e., going from high to low (or vice versa). (3) Diverging for coding ordered/numeric information around a central neutral value, i.e., where colors diverge from neutral to two extremes. To aid selection and application of these palettes, the package also contains scales for use with ggplot2, shiny and tcltk apps for interactive exploration, visualizations of palette properties, accompanying manipulation utilities (like desaturation and lighten/darken), and emulation of color vision deficiencies. The shiny apps are also hosted online at http://hclwizard.org/. |
The Landsat Collection-2 distribution introduces a new global Digital Elevation Model (DEM) for scene orthorectification. The new global DEM is a composite of the latest and most accurate freely available DEM sources and will include reprocessed Shuttle Radar Topographic Mission (SRTM) data (called NASADEM), high-resolution stereo optical data (ArcticDEM), a new National Elevation Dataset (NED) and various publicly available national datasets including the Canadian Digital Elevation Model (CDEM) and DEMs for Sweden, Norway and Finland (SNF). The new DEM will be available world-wide with few exceptions. It is anticipated that the transition from the Collection-1 DEM at 3 arcsecond to the new DEM will be seamless because processing methods to maintain a seamless transition were employed, void filling techniques were used, where persistent gaps were found, and the pixel spacing is the same between the two collections. Improvements to the vertical accuracy were realized by differencing accuracies of other elevation datasets to the new DEM. The greatest improvement occurred where ArcticDEM data were used, where an improvement of 35 m was measured. By using theses improved vertical values in a line of sight algorithm, horizontal improvements were noted in some of the most mountainous regions over multiple 30-m Landsat pixels. This new DEM will be used to process all of the scenes from Landsat 1-8 in Collection-2 processing and will be made available to the public by the end of 2020.
","language":"English","publisher":"MDPI","doi":"10.3390/rs12233909","usgsCitation":"Franks, S., Storey, J., and Rengarajan, R., 2020, The new Landsat Collection-2 Digital Elevation Model: Remote Sensing, v. 12, no. 23, 3909, 24 p., https://doi.org/10.3390/rs12233909.","productDescription":"3909, 24 p.","ipdsId":"IP-123106","costCenters":[{"id":222,"text":"Earth Resources Observation and Science (EROS) Center","active":true,"usgs":true}],"links":[{"id":454735,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.3390/rs12233909","text":"Publisher Index Page"},{"id":381037,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"volume":"12","issue":"23","noUsgsAuthors":false,"publicationDate":"2020-11-28","publicationStatus":"PW","contributors":{"authors":[{"text":"Franks, Shannon 0000-0003-1335-5401","orcid":"https://orcid.org/0000-0003-1335-5401","contributorId":245457,"corporation":false,"usgs":false,"family":"Franks","given":"Shannon","email":"","affiliations":[{"id":49197,"text":"KBR, Contractor to NASA Goddard Space Flight Center","active":true,"usgs":false}],"preferred":false,"id":806216,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Storey, James C. 0000-0002-6664-7232","orcid":"https://orcid.org/0000-0002-6664-7232","contributorId":242015,"corporation":false,"usgs":false,"family":"Storey","given":"James C.","affiliations":[{"id":48475,"text":"KBR, Contractor to USGS EROS","active":true,"usgs":false}],"preferred":false,"id":806217,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Rengarajan, Rajagopalan 0000-0003-1860-7110","orcid":"https://orcid.org/0000-0003-1860-7110","contributorId":242014,"corporation":false,"usgs":false,"family":"Rengarajan","given":"Rajagopalan","affiliations":[{"id":48475,"text":"KBR, Contractor to USGS EROS","active":true,"usgs":false}],"preferred":false,"id":806218,"contributorType":{"id":1,"text":"Authors"},"rank":3}]}} ,{"id":70240902,"text":"70240902 - 2020 - Evidence of spawning by lake trout Salvelinus namaycush on substrates at the base of large boulders in northern Lake Huron","interactions":[],"lastModifiedDate":"2023-03-01T12:39:03.288748","indexId":"70240902","displayToPublicDate":"2020-11-27T06:36:06","publicationYear":"2020","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":2330,"text":"Journal of Great Lakes Research","active":true,"publicationSubtype":{"id":10}},"title":"Evidence of spawning by lake trout Salvelinus namaycush on substrates at the base of large boulders in northern Lake Huron","docAbstract":"Identification of lake trout spawning sites has focused on cobble substrates associated with bathymetric relief (e.g., ‘contour’ or ‘slope’ along reefs), but this ‘model’ may be narrow in scope. Previous telemetry work conducted near Drummond Island, USA, Lake Huron, identified egg presence in substrates at the base of large boulders (>1 m diameter); however, the extent of this phenomenon was unknown. Telemetry data paired with multi-beam bathymetry identified a 0.63 km2 area used by lake trout characterized by low bathymetric relief and numerous (~269) large boulders (>1 m diameter) with small-diameter substrates at their bases. Diver surveys revealed egg presence at all 40 boulders surveyed, exclusively associated with clean gravel-cobble (0.6–42 cm) substrates in undercut areas beneath overhanging edges of boulders and in narrow spaces between adjacent boulders. Egg presence was not associated with boulder or substrate physical characteristics which highlighted the possible importance of interstitial currents. Successful incubation in these habitats was inferred by capture of free embryos and post-embryos the following spring using traps and an electrofishing ROV although at lower densities than at popular spawning habitats nearby (1–3 km away). Free embryos and post-embryos were also caught where eggs were not observed the previous fall including unexpectedly on top of boulders which suggested that post-hatch stages may move more than previously thought. Extensive use of boulder-associated habitats for spawning, egg incubation, and early growth suggested this undescribed habitat type may provide an unanticipated contribution to total available lake trout spawning habitat and recruitment in the Great Lakes.
During explosive volcanic eruptions, large quantities of tephra can be dispersed and deposited over wide areas. Following deposition, subsequent aeolian remobilisation of ash can potentially exacerbate primary impacts on timescales of months to millennia. Recent ash remobilisation events (e.g., following eruptions of Cordón Caulle 2011; Chile, and Eyjafjallajökull 2010, Iceland) have highlighted this to be a recurring phenomenon with consequences for human health, economic sectors, and critical infrastructure. Consequently, scientists from observatories and Volcanic Ash Advisory Centers (VAACs), as well as researchers from fields including volcanology, aeolian processes and soil sciences, convened at the San Carlos de Bariloche headquarters of the Argentinian National Institute of Agricultural Technology to discuss the “state of the art” for field studies of remobilised deposits as well as monitoring, modeling and understanding ash remobilisation. In this article, we identify practices for field characterisation of deposits and active processes, including mapping, particle characterisation and sediment traps. Furthermore, since forecast models currently rely on poorly-constrained dust emission schemes, we call for laboratory and field measurements to better parameterise the flux of volcanic ash as a function of friction velocity. While source area location and extent are currently the primary inputs for dispersion models, once emission schemes become more sophisticated and better constrained, other parameters will also become important (e.g., source material volume and properties, effective precipitation, type and distribution of vegetation cover, friction velocity). Thus, aeolian ash remobilisation hazard and associated impact assessment require systematic monitoring, including the development of a regularly-updated spatial database of resuspension source areas.
","language":"English","publisher":"Frontiers Media","doi":"10.3389/feart.2020.575184","usgsCitation":"Jarvis, P.A., Bonadonna, C., Dominguez, L., Forte, P., Frischknecht, C., Bran, D., Aguilar, R., Beckett, F., Elissondo, M., Gillies, J., Kueppers, U., Merrison, J., Varley, N., and Wallace, K.L., 2020, Aeolian remobilisation of volcanic ash: Outcomes of a workshop in the Argentinian Patagonia: Frontiers in Earth Science, v. 8, 575184, 9 p., https://doi.org/10.3389/feart.2020.575184.","productDescription":"575184, 9 p.","ipdsId":"IP-120112","costCenters":[{"id":617,"text":"Volcano Science Center","active":true,"usgs":true}],"links":[{"id":467270,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.3389/feart.2020.575184","text":"Publisher Index Page"},{"id":463343,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"volume":"8","noUsgsAuthors":false,"publicationDate":"2020-11-27","publicationStatus":"PW","contributors":{"authors":[{"text":"Jarvis, Paul A.","contributorId":345634,"corporation":false,"usgs":false,"family":"Jarvis","given":"Paul","email":"","middleInitial":"A.","affiliations":[{"id":82666,"text":"Department of Earth Sciences, University of Geneva, Geneva, Switzerland","active":true,"usgs":false}],"preferred":false,"id":917147,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Bonadonna, Costanza","contributorId":345635,"corporation":false,"usgs":false,"family":"Bonadonna","given":"Costanza","affiliations":[{"id":82666,"text":"Department of Earth Sciences, University of Geneva, Geneva, Switzerland","active":true,"usgs":false}],"preferred":false,"id":917148,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Dominguez, Lucia","contributorId":345636,"corporation":false,"usgs":false,"family":"Dominguez","given":"Lucia","email":"","affiliations":[{"id":82666,"text":"Department of Earth Sciences, University of Geneva, Geneva, Switzerland","active":true,"usgs":false}],"preferred":false,"id":917149,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Forte, Pablo","contributorId":345637,"corporation":false,"usgs":false,"family":"Forte","given":"Pablo","affiliations":[{"id":82667,"text":"IDEAN (UBA-CONICET), Buenos Aires, Argentina","active":true,"usgs":false}],"preferred":false,"id":917150,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Frischknecht, Corine","contributorId":345638,"corporation":false,"usgs":false,"family":"Frischknecht","given":"Corine","email":"","affiliations":[{"id":82666,"text":"Department of Earth Sciences, University of Geneva, Geneva, Switzerland","active":true,"usgs":false}],"preferred":false,"id":917151,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"Bran, Donaldo","contributorId":345639,"corporation":false,"usgs":false,"family":"Bran","given":"Donaldo","affiliations":[{"id":82668,"text":"Institute of National Agricultural Technology, San Carlos de Bariloche, Argentina","active":true,"usgs":false}],"preferred":false,"id":917152,"contributorType":{"id":1,"text":"Authors"},"rank":6},{"text":"Aguilar, Rigoberto","contributorId":345640,"corporation":false,"usgs":false,"family":"Aguilar","given":"Rigoberto","email":"","affiliations":[{"id":82669,"text":"INGEMMET, Observatorio Vulcanológico del INGEMMET, Arequipa, Peru","active":true,"usgs":false}],"preferred":false,"id":917153,"contributorType":{"id":1,"text":"Authors"},"rank":7},{"text":"Beckett, Frances","contributorId":345641,"corporation":false,"usgs":false,"family":"Beckett","given":"Frances","affiliations":[{"id":36557,"text":"Met Office, Exeter, UK","active":true,"usgs":false}],"preferred":false,"id":917154,"contributorType":{"id":1,"text":"Authors"},"rank":8},{"text":"Elissondo, Manuela","contributorId":345642,"corporation":false,"usgs":false,"family":"Elissondo","given":"Manuela","email":"","affiliations":[{"id":82670,"text":"Servicio Geológico Minero Argentino (SEGEMAR), Buenos Aires, Argentina","active":true,"usgs":false}],"preferred":false,"id":917155,"contributorType":{"id":1,"text":"Authors"},"rank":9},{"text":"Gillies, John","contributorId":345643,"corporation":false,"usgs":false,"family":"Gillies","given":"John","email":"","affiliations":[{"id":82671,"text":"Desert Research Institute, Reno, NV, USA","active":true,"usgs":false}],"preferred":false,"id":917156,"contributorType":{"id":1,"text":"Authors"},"rank":10},{"text":"Kueppers, Ulrich","contributorId":345644,"corporation":false,"usgs":false,"family":"Kueppers","given":"Ulrich","affiliations":[{"id":82672,"text":"Earth and Environmental Sciences, Ludwig-Maximillans-Universität München, Munich, Germany","active":true,"usgs":false}],"preferred":false,"id":917157,"contributorType":{"id":1,"text":"Authors"},"rank":11},{"text":"Merrison, Jonathan","contributorId":345645,"corporation":false,"usgs":false,"family":"Merrison","given":"Jonathan","email":"","affiliations":[{"id":82673,"text":"Department of Physics and Astronomy, Aarhus University, Aarhus, Denmark","active":true,"usgs":false}],"preferred":false,"id":917158,"contributorType":{"id":1,"text":"Authors"},"rank":12},{"text":"Varley, Nick","contributorId":345646,"corporation":false,"usgs":false,"family":"Varley","given":"Nick","affiliations":[{"id":82674,"text":"Facultad de Ciencias, Universidad de Colima, Colima, Mexico","active":true,"usgs":false}],"preferred":false,"id":917159,"contributorType":{"id":1,"text":"Authors"},"rank":13},{"text":"Wallace, Kristi L. 0000-0002-0962-048X kwallace@usgs.gov","orcid":"https://orcid.org/0000-0002-0962-048X","contributorId":3454,"corporation":false,"usgs":true,"family":"Wallace","given":"Kristi","email":"kwallace@usgs.gov","middleInitial":"L.","affiliations":[{"id":617,"text":"Volcano Science Center","active":true,"usgs":true}],"preferred":true,"id":917160,"contributorType":{"id":1,"text":"Authors"},"rank":14}]}} ,{"id":70216842,"text":"70216842 - 2020 - Evaluating the impacts of foreshore sand and birds on microbiological contamination at a freshwater beach","interactions":[],"lastModifiedDate":"2020-12-10T12:47:42.117676","indexId":"70216842","displayToPublicDate":"2020-11-26T08:00:43","publicationYear":"2020","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":3716,"text":"Water Research","onlineIssn":"1879-2448","printIssn":"0043-1354","active":true,"publicationSubtype":{"id":10}},"title":"Evaluating the impacts of foreshore sand and birds on microbiological contamination at a freshwater beach","docAbstract":"Beaches along the Great Lakes shorelines are important recreational and economic resources. However, contamination at the beaches can threaten their usage during the swimming season, potentially resulting in beach closures and/or advisories. Thus, understanding the dynamics that control nearshore water quality is integral to effective beach management. There have been significant improvements in this effort, including incorporating modeling (empirical, mechanistic) in recent years. Mechanistic modeling frameworks can contribute to this understanding of dynamics by determining sources and interactions that substantially impact fecal indicator bacteria concentrations, an index routinely used in water quality monitoring programs. To simulate E. coli concentrations at Jeorse Park beaches in southwest Lake Michigan, a coupled hydrodynamic and wave–current interaction model was developed that progressively added contaminant sources from river inputs, avian presence, bacteria–sediment interactions, and bacteria–sand–sediment interactions. Results indicated that riverine inputs affected E. coli concentrations at Jeorse Park beaches only marginally, while avian, shoreline sand, and sediment sources were much more substantial drivers of E. coli contamination at the beach. By including avian and riverine inputs, as well as bacteria–sand–sediment interactions at the beach, models can reasonably capture the variability in observed E. coli concentrations in nearshore water and bed sediments at Jeorse Park beaches. Consequently, it will be crucial to consider avian contamination sources and water-sand-sediment interactions in effective management of the beach for public health and as a recreational resource and to extend these findings to similar beaches affected by shoreline embayment.
","language":"English","publisher":"Elsevier","doi":"10.1016/j.watres.2020.116671","usgsCitation":"Saffaie, A., Weiskerger, C.J., Nevers, M., Byappanahalli, M., and Phanikumar, M.S., 2020, Evaluating the impacts of foreshore sand and birds on microbiological contamination at a freshwater beach: Water Research, v. 190, 116671, 13 p., https://doi.org/10.1016/j.watres.2020.116671.","productDescription":"116671, 13 p.","ipdsId":"IP-120391","costCenters":[{"id":324,"text":"Great Lakes Science Center","active":true,"usgs":true}],"links":[{"id":381163,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","state":"Illlinois","city":"Chicago","otherGeospatial":"Jeorse Park Beach","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -87.440185546875,\n 41.64803176818231\n ],\n [\n -87.43228912353514,\n 41.64803176818231\n ],\n [\n -87.43228912353514,\n 41.656625449889276\n ],\n [\n -87.440185546875,\n 41.656625449889276\n ],\n [\n -87.440185546875,\n 41.64803176818231\n ]\n ]\n ]\n }\n }\n ]\n}","volume":"190","noUsgsAuthors":false,"publicationStatus":"PW","contributors":{"authors":[{"text":"Saffaie, Ammar","contributorId":245601,"corporation":false,"usgs":false,"family":"Saffaie","given":"Ammar","email":"","affiliations":[{"id":6601,"text":"Michigan State University","active":true,"usgs":false}],"preferred":false,"id":806590,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Weiskerger, Chelsea J.","contributorId":150865,"corporation":false,"usgs":false,"family":"Weiskerger","given":"Chelsea","email":"","middleInitial":"J.","affiliations":[{"id":18126,"text":"National Park Service, Indiana Dunes National Lakeshore","active":true,"usgs":false}],"preferred":false,"id":806591,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Nevers, Meredith B. 0000-0001-6963-6734","orcid":"https://orcid.org/0000-0001-6963-6734","contributorId":201531,"corporation":false,"usgs":true,"family":"Nevers","given":"Meredith B.","affiliations":[{"id":324,"text":"Great Lakes Science Center","active":true,"usgs":true}],"preferred":true,"id":806592,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Byappanahalli, Muruleedhara 0000-0001-5376-597X byappan@usgs.gov","orcid":"https://orcid.org/0000-0001-5376-597X","contributorId":147923,"corporation":false,"usgs":true,"family":"Byappanahalli","given":"Muruleedhara","email":"byappan@usgs.gov","affiliations":[{"id":324,"text":"Great Lakes Science Center","active":true,"usgs":true}],"preferred":true,"id":806593,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Phanikumar, Mantha S.","contributorId":208872,"corporation":false,"usgs":false,"family":"Phanikumar","given":"Mantha","email":"","middleInitial":"S.","affiliations":[{"id":6601,"text":"Michigan State University","active":true,"usgs":false}],"preferred":false,"id":806594,"contributorType":{"id":1,"text":"Authors"},"rank":5}]}} ,{"id":70224312,"text":"70224312 - 2020 - Post-fire vegetation response in a repeatedly burned low-elevation sagebrush steppe protected area provides insights about resilience and invasion resistance","interactions":[],"lastModifiedDate":"2021-09-21T12:37:06.443373","indexId":"70224312","displayToPublicDate":"2020-11-26T07:33:24","publicationYear":"2020","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":3910,"text":"Frontiers in Ecology and Evolution","onlineIssn":"2296-701X","active":true,"publicationSubtype":{"id":10}},"title":"Post-fire vegetation response in a repeatedly burned low-elevation sagebrush steppe protected area provides insights about resilience and invasion resistance","docAbstract":"Sagebrush steppe ecosystems are threatened by human land-use legacies, biological invasions, and altered fire and climate dynamics. Steppe protected areas are therefore of heightened conservation importance but are few and vulnerable to the same impacts broadly affecting sagebrush steppe. To address this problem, sagebrush steppe conservation science is increasingly emphasizing a focus on resilience to fire and resistance to non-native annual grass invasion as a decision framework. It is well-established that the positive feedback loop between fire and annual grass invasion is the driving process of most contemporary steppe degradation. We use a newly developed ordinal zero-augmented beta regression model fit to large-sample vegetation monitoring data from John Day Fossil Beds National Monument, USA, spanning 7 years to evaluate fire responses of two native perennial foundation bunchgrasses and two non-native invasive annual grasses in a repeatedly burned, historically grazed, and inherently low-resilient protected area. We structured our model hierarchically to support inferences about variation among ecological site types and over time after also accounting for growing-season water deficit, fine-scale topographic variation, and burn severity. We use a state-and-transition conceptual diagram and abundances of plants listed in ecological site reference conditions to formalize our hypothesis of fire-accelerated transition to ecologically novel annual grassland. Notably, big sagebrush (Artemisia tridentata) and other woody species were entirely removed by fire. The two perennial grasses, bluebunch wheatgrass (Pseudoroegneria spicata) and Thurber's needlegrass (Achnatherum thurberianum) exhibited fire resiliency, with no apparent trend after fire. The two annual grasses, cheatgrass (Bromus tectorum) and medusahead (Taeniatherum caput-medusae), increased in response to burn severity, most notably medusahead. Surprisingly, we found no variation in grass cover among ecological sites, suggesting fire-driven homogenization as shrubs were removed and annual grasses became dominant. We found contrasting responses among all four grass species along gradients of topography and water deficit, informative to protected-area conservation strategies. The fine-grained influence of topography was particularly important to variation in cover among species and provides a foothold for conservation in low-resilient, aridic steppe. Broadly, our study demonstrates how to operationalize resilience and resistance concepts for protected areas by integrating empirical data with conceptual and statistical models.
>In the current era of rapid climate change, accurate characterization of climate-relevant gas dynamics – namely production, consumption, and net emissions – is required for all biomes, especially those ecosystems most susceptible to the impact of change. Marine environments include regions that act as net sources or sinks for numerous climate-active trace gases including methane (CH4) and nitrous oxide (N2O). The temporal and spatial distributions of CH4 and N2O are controlled by the interaction of complex biogeochemical and physical processes. To evaluate and quantify how these mechanisms affect marine CH4 and N2O cycling requires a combination of traditional scientific disciplines including oceanography, microbiology, and numerical modeling. Fundamental to these efforts is ensuring that the datasets produced by independent scientists are comparable and interoperable. Equally critical is transparent communication within the research community about the technical improvements required to increase our collective understanding of marine CH4 and N2O. A workshop sponsored by Ocean Carbon and Biogeochemistry (OCB) was organized to enhance dialogue and collaborations pertaining to marine CH4 and N2O. Here, we summarize the outcomes from the workshop to describe the challenges and opportunities for near-future CH4 and N2O research in the marine environment.
","language":"English","publisher":"Copernicus","doi":"10.5194/bg-17-5809-2020","usgsCitation":"Wilson, S., Al-Haj, A., Bourbonnais, A., Frey, C., Fulweiler, R., Kessler, J.D., Marchant, H., Milucka, J., Ray, N., Suntharalingham, P., Thornton, B., Upstill-Goddard, R., Weber, T., Arévalo-Martínez, D., Bange, H., Benway, H., Bianchi, D., Borges, A., Chang, B., Crill, P., del Valle, D., Farias, L., Joye, S., Kock, A., Labidi, J., Manning, C., Pohlman, J., Rehder, G., Sparrow, K., Tortell, P., Truede, T., Valentine, D., Ward, B., Yang, S., and Yurganov, L., 2020, Ideas and perspectives: A strategic assessment of methane and nitrous oxide measurements in the marine environment: Biogeosciences, v. 17, no. 22, p. 5809-5828, https://doi.org/10.5194/bg-17-5809-2020.","productDescription":"20 p.","startPage":"5809","endPage":"5828","ipdsId":"IP-123280","costCenters":[{"id":678,"text":"Woods Hole Coastal and Marine Science Center","active":true,"usgs":true}],"links":[{"id":454748,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.5194/bg-17-5809-2020","text":"Publisher Index Page"},{"id":382916,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"volume":"17","issue":"22","noUsgsAuthors":false,"publicationDate":"2020-11-26","publicationStatus":"PW","contributors":{"authors":[{"text":"Wilson, S.T.","contributorId":248724,"corporation":false,"usgs":false,"family":"Wilson","given":"S.T.","email":"","affiliations":[{"id":49988,"text":"University of Hawai’i at Manoa, Daniel K. Inouye Center for Microbial Oceanography","active":true,"usgs":false}],"preferred":false,"id":809756,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Al-Haj, A.N.","contributorId":248725,"corporation":false,"usgs":false,"family":"Al-Haj","given":"A.N.","email":"","affiliations":[{"id":49990,"text":"Boston University, Department of Earth and Environment, Massachusetts","active":true,"usgs":false}],"preferred":false,"id":809757,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Bourbonnais, A.","contributorId":248726,"corporation":false,"usgs":false,"family":"Bourbonnais","given":"A.","email":"","affiliations":[{"id":49991,"text":"University of South Carolina, School of the Earth, Ocean and Environment, South Carolina","active":true,"usgs":false}],"preferred":false,"id":809758,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Frey, 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vulnerability","interactions":[],"lastModifiedDate":"2021-01-18T13:54:14.320789","indexId":"70217298","displayToPublicDate":"2020-11-25T07:49:19","publicationYear":"2020","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":2900,"text":"Northwest Science","onlineIssn":"2161-9859","printIssn":"0029-344X","active":true,"publicationSubtype":{"id":10}},"title":"Whitebark pine in the national parks of the Pacific states: An assessment of population vulnerability","docAbstract":"Whitebark pine (Pinus albicaulis) is a long-lived tree found in high-elevation forests of western North America that is declining due to the non-native white pine blister rust (Cronartium ribicola) and climate-driven outbreaks of mountain pine beetle (Dendroctonus ponderosae; MPB). The National Park Service established a monitoring program for whitebark pine in seven parks, including Sequoia & Kings Canyon, Yosemite, Lassen Volcanic, Crater Lake, Mount Rainier, Olympic, and North Cascades National Parks. Using these data, we summarized stand structure, presence of blister rust, and MPB prevalence to provide a baseline for future monitoring. Next, we used a stochastic, size-structured population model to speculate on future trends in the seven national park populations under conditions of increased MPB activity and ongoing blister rust infection observed in Crater Lake. We found that blister rust infected 29 to 54% of whitebark pine in all the parks except the two southernmost, Sequoia & Kings Canyon and Yosemite, where infections rates were 0.3% and 0.2%, respectively. The proportion of dead trees in Sequoia & Kings Canyon and Yosemite was low (0 to 1%), while they ranged from 10 to 43% in the other parks. Model projections suggested an average population decline of 25% in the parks over the next century using Crater Lake conditions, declines which are possible if blister rust continues to spread and climate change results in a significant increase in the frequency or severity of MPB outbreaks. Overall, our study describes conditions at seven western parks and illustrates potential rates of whitebark pine decline if pest outbreaks and/or blister rust infections worsen.
Thirty water wells were sampled in 2018 to understand the geochemistry and age of groundwater in the Williston Basin and assess potential effects of shale-oil production from the Three Forks-Bakken petroleum system (TBPS) on groundwater quality. Two geochemical groups are identified using hierarchical cluster analysis. Group 1 represents the younger (median 4He = 21.49 × 10−8 cm3 STP/g), less chemically evolved water. Group 2 represents the older (median 4He = 1389 × 10−8 cm3 STP/g), more chemically evolved water. At least two samples from each group contain elevated Cl concentrations (>70 mg/L). Br/Cl, B/Cl, and Li/Cl ratios indicate multiple sources account for the elevated Cl concentrations: septic-system leachate/road deicing salt, lignite beds in the aquifers, Pierre Shale beneath the aquifers, and water associated with the TBPS (one sample). 3H and 14C data indicate that 10.8, 21.6, and 67.6% of the samples are modern (post-1952), mixed age, and premodern (pre-1953), respectively. Lumped-parameter modeling of 3H, SF6, 3He, and 14C concentrations indicates mean ages of the modern and premodern fractions range from ~1 to 30 years and 1300 to >30,000 years, respectively. Group 2 contains the highest CH4 concentrations (0.0018–32 mg/L). δ13C–CH4 and C1/C2+C3 data in groundwater (−91.7 to −70.0‰ and 1280 to 13,600) indicate groundwater CH4 is biogenic in origin and not from thermogenic shale gas. Four volatile organic compounds (VOCs) were detected in two samples. One mixed-age sample contains chloroform (0.25 μg/L) and dichloromethane (0.05 μg/L), which are probably associated with septic leachate. One premodern sample contains butane (0.082 μg/L) and n-pentane (0.032 μg/L), which are probably associated with thermogenic gas from a nearby oil well. The data indicate hydrocarbon production activities do not currently (2018) widely affect Cl, CH4, and VOC concentrations in groundwater. The predominance of premodern recharge in the aquifers indicates the groundwater moves relatively slowly, which could inhibit widespread chemical movement in groundwater overlying the TBPS. Comparison of groundwater-age data from five major unconventional hydrocarbon-production areas indicates aquifer zones used for water supply in the TBPS area have a lower risk of widespread chemical movement in groundwater than similar aquifer zones in the Fayetteville (Arkansas) and Marcellus (Pennsylvania) Shale production areas, but have a higher risk than similar aquifer zones in the Eagle Ford (Texas) and Haynesville (Texas, Louisiana) Shale production areas.
Bathymetric and velocimetric data were collected by the U.S. Geological Survey, in cooperation with the Missouri Department of Transportation, near 7 bridges at 6 highway crossings of the Missouri and Mississippi Rivers on the periphery of the State of Missouri from July 16 to August 13, 2018. A multibeam echosounder mapping system was used to obtain channel-bed elevations for river reaches about 1,640 feet longitudinally and generally extending laterally across the active channel from bank to bank during moderate flood-flow conditions. These surveys indicate the channel conditions at the time of the surveys and provide characteristics of scour holes that may be useful in the development of predictive guidelines or equations for scour holes. These data also may be useful to the Missouri Department of Transportation as a low to moderate flood-flow comparison to help assess the bridges for stability and integrity issues with respect to bridge scour during floods.
Bathymetric data were collected around every pier that was in water, except those at the edge of water, and scour holes were present at most piers for which bathymetry could be obtained, except those on banks, on bedrock, or surrounded by riprap. Occasionally, the scour hole near a pier was difficult to discern from nearby bed features. The observed scour holes at the surveyed bridges were generally examined with respect to shape and depth.
Although partial exposure of substructural support elements was observed at several piers, at most sites the exposure likely can be considered minimal compared to the overall substructure that remains buried in bed material at these piers. The notable exceptions are piers 12 and 13 at structure L0135 on State Highway 51 at Chester, Illinois, at which the bedrock material was fully exposed around the piers.
The presence of riprap blankets, pier size and nose shape, and alignment to flow had a substantial effect on the size of the scour hole observed for a given pier. Piers that were surrounded by riprap blankets had scour holes that were substantially smaller (to nonexistent) compared to piers at which no rock or riprap were present. Narrow piers having round or sharp noses that were aligned with flow often had scour holes that were difficult to discern from nearby bed features, whereas piers having wide or blunt noses resulted in larger, deeper scour holes. Several of the structures had piers that were skewed to primary approach flow, and scour holes near these piers generally displayed deposition on the leeward side of the pier and greater depth on the side of the pier with impinging flow.
","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/sir20205088","collaboration":"Prepared in cooperation with the Missouri Department of Transportation","usgsCitation":"Huizinga, R.J., 2020, Bathymetric and velocimetric surveys at highway bridges crossing the Missouri and Mississippi Rivers on the periphery of Missouri, July–August 2018: U.S. Geological Survey Scientific Investigations Report 2020–5088, 100 p., https://doi.org/10.3133/sir20205088.","productDescription":"Report: vii, 100 p.; Data Release","numberOfPages":"112","onlineOnly":"Y","ipdsId":"IP-115831","costCenters":[{"id":36532,"text":"Central Midwest Water Science Center","active":true,"usgs":true}],"links":[{"id":380760,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/sir/2020/5088/coverthb.jpg"},{"id":380761,"rank":2,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/sir/2020/5088/sir20205088.pdf","text":"Report","size":"21.9 MB","linkFileType":{"id":1,"text":"pdf"},"description":"SIR 2020–5088"},{"id":380762,"rank":3,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/P9WDI9YF","text":"USGS data release","description":"USGS Data Release","linkHelpText":"Bathymetry and velocity data from surveys at highway bridges crossing the Missouri and Mississippi Rivers on the periphery of Missouri, December 2008 through August 2018"}],"country":"United States","state":"Missouri","otherGeospatial":"Mississippi River, Missouri River","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -94.68017578125,\n 39.257778150283364\n ],\n [\n -94.72412109375,\n 39.07890809706475\n ],\n [\n -94.603271484375,\n 38.993572058209466\n ],\n [\n -94.28466796874999,\n 39.00211029922515\n ],\n [\n -93.88916015625,\n 39.00211029922515\n ],\n [\n -93.526611328125,\n 39.06184913429154\n ],\n [\n -93.09814453125,\n 39.155622393423215\n ],\n [\n -93.05419921875,\n 39.00211029922515\n ],\n [\n -92.92236328125,\n 38.87392853923629\n ],\n [\n -92.65869140625,\n 38.805470223177466\n ],\n [\n -92.59277343749999,\n 38.659777730712534\n ],\n [\n -92.30712890625,\n 38.53097889440024\n ],\n [\n -92.054443359375,\n 38.46219172306828\n ],\n [\n -91.42822265625,\n 38.51378825951165\n ],\n [\n -91.01074218749999,\n 38.436379603\n ],\n [\n -90.802001953125,\n 38.41916639395372\n ],\n [\n -90.3515625,\n 38.66835610151506\n ],\n [\n -90.164794921875,\n 38.77978137804918\n ],\n [\n -90.054931640625,\n 38.950865400919994\n ],\n [\n -90.19775390625,\n 38.95940879245423\n ],\n [\n -90.54931640625,\n 38.84826438869913\n ],\n [\n -90.90087890624999,\n 38.69408504756833\n ],\n [\n -91.16455078125,\n 38.831149809348744\n ],\n [\n -91.593017578125,\n 38.85682013474361\n ],\n [\n -92.10937499999999,\n 38.7283759182398\n ],\n [\n -92.373046875,\n 38.96795115401593\n ],\n [\n -92.7685546875,\n 39.27478966170308\n ],\n [\n -93.09814453125,\n 39.470125122358176\n ],\n [\n -93.955078125,\n 39.317300373271024\n ],\n [\n -94.22973632812499,\n 39.33429742980725\n ],\n [\n -94.68017578125,\n 39.257778150283364\n ]\n ]\n ]\n }\n }\n ]\n}","contact":"Director, Central Midwest Water Science Center
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1400 Independence Road
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Regional aquifers in the Mississippi embayment are the principal sources of water used for public and domestic supply, irrigation, and industrial uses throughout the region. An understanding of how water quality varies spatially, temporally, and with depth are critical aspects to ensuring long-term sustainable use of these resources. A boosted regression tree (BRT) model was used by the U.S. Geological Survey (USGS) to map water quality in the three regional aquifers with the largest groundwater withdrawals in the embayment: the Mississippi River Valley alluvial (MRVA) aquifer, middle Claiborne aquifer (MCAQ), and lower Claiborne aquifer (LCAQ).
The BRT model was used to predict pH to 1-kilometer raster grid cells for seven aquifer layers (one MRVA, four MCAQ, two LCAQ) following the hydrogeologic framework of the Mississippi embayment aquifer system regional MODFLOW model. The methods and approach used for pH predictions are the same as those used recently by the USGS to predict specific conductance and chloride in the aquifers. Explanatory variables for the BRT models included variables describing well location and construction, surficial variables such as soil properties and land use, and variables extracted from the groundwater flow model, such as groundwater levels and ages. The primary source of pH data was the USGS National Water Information System database. Additional data from State ambient groundwater monitoring programs and the Safe Drinking Water Information System also were used. For wells sampled multiple times, the most recent sample was used. Because groundwater residence times are long (greater than 100 years) throughout much of the study area, the possible effects of changes in water quality over time were considered small compared to the improvement in overall model accuracy by using available historical data. Values of pH from 3,362 wells for samples collected between 1960 and 2018 were used as training data for the BRT model. An additional 839 samples were used as holdout data to evaluate model performance. The predictive performance of the pH model is lower than for the training dataset, as indicated by an r-squared value of 0.89 for the training data and an r-squared of 0.71 for the holdout data. The root mean squared errors for the training and holdout data are 0.32 and 0.50 standard pH units, respectively. Data generated during this study and the model output are available from the companion data release.
","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/sim3465","usgsCitation":"Kingsbury, J.A., Knierim, K.J., and Haugh, C.J., 2020, Predicted pH of groundwater in the Mississippi River Valley alluvial and Claiborne aquifers, South-Central United States: U.S. Geological Survey Scientific Investigations Map 3465, 1 sheet, https://doi.org/10.3133/sim3465.","productDescription":"1 Sheet: 34.60 x 28.70 inches; Data Release","onlineOnly":"Y","ipdsId":"IP-111848","costCenters":[{"id":24708,"text":"Lower Mississippi-Gulf Water Science Center","active":true,"usgs":true}],"links":[{"id":380668,"rank":3,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/P9CXX7LN","text":"USGS data release","linkHelpText":"Prediction grids of pH for the Mississippi River Valley alluvial and Claiborne aquifers"},{"id":380666,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/sim/3465/coverthb2.jpg"},{"id":380667,"rank":2,"type":{"id":26,"text":"Sheet"},"url":"https://pubs.usgs.gov/sim/3465/sim3465.pdf","text":"Report","size":"3.18 MB","linkFileType":{"id":1,"text":"pdf"},"description":"SIM 3465"}],"country":"United States","state":"Alabama, Arkansas, Louisiana, Mississippi, Missouri","otherGeospatial":"Mississippi River Valley alluvial, Claiborne aquifers","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -89.296875,\n 37.020098201368114\n ],\n [\n -90.1318359375,\n 36.66841891894786\n ],\n [\n -91.93359375,\n 35.28150065789119\n ],\n [\n -93.33984375,\n 33.65120829920497\n ],\n [\n -94.04296874999999,\n 33.100745405144245\n ],\n [\n -93.91113281249999,\n 31.952162238024975\n ],\n [\n -93.1640625,\n 31.090574094954192\n ],\n [\n -91.7578125,\n 30.939924331023445\n ],\n [\n -91.0986328125,\n 31.952162238024975\n ],\n [\n -90.703125,\n 32.24997445586331\n ],\n [\n -89.3408203125,\n 32.175612478499325\n ],\n [\n -88.0224609375,\n 31.57853542647338\n ],\n [\n -87.4951171875,\n 31.80289258670676\n ],\n [\n -86.748046875,\n 32.99023555965106\n ],\n [\n -87.4072265625,\n 33.211116472416855\n ],\n [\n -88.9892578125,\n 33.94335994657882\n ],\n [\n -89.7802734375,\n 34.74161249883172\n ],\n [\n -90,\n 35.24561909420681\n ],\n [\n -89.56054687499999,\n 36.13787471840729\n ],\n [\n -89.3408203125,\n 36.421282443649496\n ],\n [\n -89.2529296875,\n 36.84446074079564\n ],\n [\n -89.296875,\n 37.020098201368114\n ]\n ]\n ]\n }\n }\n ]\n}","contact":"Director, Lower Mississippi Gulf Water Science Center
U.S. Geological Survey
640 Grassmere Park, Suite 100
Nashville, TN 37211
Exceptionally low river flows are predicted to become more frequent and more severe across many global regions as a consequence of climate change. Investigations of trace metal transport dynamics across streamflows reveal stark changes in water chemistry, metal transformation processes, and remediation effectiveness under exceptionally low-flow conditions. High spatial resolution hydrological and water quality datasets indicate that metal-rich groundwater will exert a greater control on stream water chemistry and metal concentrations because of climate change. This is because the proportion of stream water sourced from mined areas and mineralized strata will increase under predicted future low-flow scenarios (from 25% under Q45 flow to 66% under Q99 flow in this study). However, mineral speciation modelling indicates that changes in stream pH and hydraulic conditions at low flow will decrease aqueous metal transport and increase sediment metal concentrations by enhancing metal sorption directly to streambed sediments. Solute transport modelling further demonstrates how increases in the importance of metal-rich diffuse groundwater sources at low flow could minimize the benefits of point source metal contamination treatment. Understanding metal transport dynamics under exceptionally low flows, as well as under high flows, is crucial to evaluate ecosystem service provision and remediation effectiveness in watersheds under future climate change scenarios.
This report presents abundance and occurrence data for three species of ambystomad salamanders (Ambystoma maculatum, A. jeffersonianum, and A. opacum) collected over a 3-year period (2000, 2001, and 2002) at 200 potentional breeding sies within the Delaware Water Gap National Recreation Area (DEWA). In addition, numerous measures of inpond, near-pond, and landscape attributes were measured and used to inform statistical models to determine species-habitat relationships in the DEWA.
The results of a 3-year study of ambystomatid salamander breeding habits and habitats in the (DEWA) that was conducted by the U.S. Geological Survey, in cooperation with the National Park Service, are described in the report. The objectives of the study were to document the population status and critical breeding habitats of the three species of ambystomatid salamanders known to be present in the DEWA—Ambystoma maculatum (spotted salamander), A. opacum (marbled salamander), and A. jeffersonianum (Jefferson salamander). DEWA managers are interested in ecological information on these species for several reasons. First, at the time the study began, there was little known regarding the status of pond-breeding amphibians and their habitats in the DEWA. Second, because they require undegraded habitats in both terrestrial and aquatic habitats to successfully complete their life cycles, the status of ambystomatid salamanders is widely viewed as indicative of overall ecosystem health. Third, because ambystomatid salamanders and other pond-breeding amphibians have been observed in numerous artificial impoundments with the DEWA, park managers would like to assess whether dismantling or discontinuing maintenance of artificial impoundments could affect pond-breeding amphibians and possibly other species that use pond or wetland habitats in the Park.
In 2001, 2002, and 2003, the size and location of 200 wetlands, ponds, and artificial impoundments, and related landscape positions (Ridge versus Valley; Pennsylvania side versus New Jersey side of the Delaware river) were mapped, and site habitat data relating to salamander occurrence and abundance patterns were collected. The data collected during this study provide important new baseline information on ambystomatid salamanders and wetland habitats in the DEWA that will enhance long-term inventory and monitoring efforts. In addition, breeding habitat assessments indicate that ambystomatid salamanders may be sensitive to a wide variety of stresses important in the DEWA and in the region. In particular, recent trends in development (for example, roads) in and near the DEWA, regional increases in the acidity of precipitation, and predicted long-term warming trends for the region could be detrimental to pond-breeding salamander populations because of their effects on breeding site quality and quantity, and on the integrity of migration corridors. In contrast, the results of the study indicate management plans to eliminate small impoundments are not likely to adversely affect salamanders in DEWA, at least in the short-term. However, it is possible that these small impoundments may offer stable habitats that provide a rescure effect during long-term droughts.
","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/sir20205081","collaboration":"Prepared in cooperation with the National Park Service","usgsCitation":"Snyder, C.D., Young, J.A., Julian, J.T., King, T.L., and Julian, S.E., 2020, Assessment of Ambystomatid salamander populations and their breeding habitats in the Delaware Water Gap National Recreation Area: U.S. Geological Survey Scientific Investigations Report 2020–5081, 41 p., https://doi.org/10.3133/sir20205081.","productDescription":"Report: viii, 41 p.; Data Release","numberOfPages":"41","onlineOnly":"Y","additionalOnlineFiles":"Y","ipdsId":"IP-113175","costCenters":[{"id":365,"text":"Leetown Science Center","active":true,"usgs":true},{"id":50464,"text":"Eastern Ecological Science Center","active":true,"usgs":true}],"links":[{"id":380510,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/sir/2020/5081/coverthb.jpg"},{"id":380511,"rank":2,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/sir/2020/5081/sir20205081.pdf","text":"Report","size":"3.33 MB","linkFileType":{"id":1,"text":"pdf"},"description":"SIR 2020-5081"},{"id":380512,"rank":3,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/P9XCVHY3","text":"USGS data release","linkHelpText":"Ambystomatid salamander population and breeding pond habitat data for the Delaware Water Gap National Recreation Area (2001–2003)"}],"country":"United States","state":"New Jersey, Pennsylvania","otherGeospatial":"Delaware Water Gap National Recreation Area","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -74.7564697265625,\n 41.380930388318\n ],\n [\n -74.8992919921875,\n 41.29844430929419\n ],\n [\n -74.9761962890625,\n 41.18278832811288\n ],\n [\n -75.1080322265625,\n 41.06692773019345\n ],\n [\n -75.179443359375,\n 40.992337919312305\n ],\n [\n -75.1629638671875,\n 40.93011520598305\n ],\n [\n -75.0970458984375,\n 40.93841495689795\n ],\n [\n -74.893798828125,\n 41.075210270566636\n ],\n [\n -74.6630859375,\n 41.253032440653186\n ],\n [\n -74.7564697265625,\n 41.380930388318\n ]\n ]\n ]\n }\n }\n ]\n}","contact":"Director, Eastern Ecological Science Center
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