Bear Lake in North Muskegon, Michigan, is listed as part of the Muskegon Lake area of concern as designated by the U.S. Environmental Protection Agency. This area of concern was designated as a result of eutrophication and beneficial use impairments. On the northeast end of Bear Lake, two man-made retention ponds (Willbrandt Pond East and Willbrandt Pond West), formerly used for celery farming, may contribute nutrients to Bear Lake. Willbrandt Ponds (East and West) were previously muck fields that were actively used for celery farming from the early 1900s until 2002. The restoration and reconnection of the Willbrandt Ponds into Bear Lake prompted concerns of groundwater nutrient loading into Bear Lake. Studies done by the State of Michigan and Grand Valley State University revised initial internal phosphorus load estimates and indicated an imbalance in the phosphorus budget in Bear Lake. From June through November 2015, the U.S. Geological Survey (USGS) did an investigative study to quantify the load of nutrients from shallow groundwater around the Willbrandt Ponds in an effort to update the phosphorus budget to Bear Lake. Seven sampling locations were established, including five shallow groundwater wells and two surface-water sites, in the Willbrandt pond study area and Bear Lake. A total of 12 nutrient samples and discrete water-level measurements were collected from each site from June through November 2015. Continuous water-level data were recorded for both surface-water monitoring locations for the entire sampling period.
Water-level data indicated that Willbrandt Pond West had the highest average water-level elevation of all sites monitored, which indicated the general direction of flux is from Willbrandt Pond West to Bear Lake. Nutrient and chloride loading from Willbrandt Pond West to Bear Lake was calculated using two distinct methods: Dupuit and direct seepage methods. Shallow groundwater loading calculations were determined by using groundwater levels to first determine a flux of shallow groundwater, then nutrient concentrations to determine a load. It was determined that Willbrandt Pond East and Willbrandt Pond West contributed between 2 to 4 percent of the total annual phosphorus load to Bear Lake by way of shallow groundwater flow. Annual loads calculated for other constituents include orthophosphate (40–100 pounds per year [lb P/yr]), total nitrogen (200–830 lb/yr), chloride (12,700–32,100 lb/yr), and ammonia (130–670 lb N/yr). Study results indicated that mean groundwater and surface-water nutrient concentrations calculated in this study were higher than reported Michigan statewide values. The data collected in this study allow understanding of groundwater nutrient loading into Bear Lake in an effort to help inform future restoration and management decisions.
","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/sir20175092","collaboration":"Prepared in cooperation with the U.S. Environmental Protection Agency","usgsCitation":"Totten, A.R., Maurer, J.A., and Duris, J.W., 2017, Groundwater flux and nutrient loading in the northeast section of Bear Lake, Muskegon County, Michigan, 2015: U.S. Geological Survey Scientific Investigations Report 2017–5092, 16 p., https://doi.org/10.3133/sir20175092.","productDescription":"v, 16 p.","numberOfPages":"26","onlineOnly":"Y","additionalOnlineFiles":"N","ipdsId":"IP-074168","costCenters":[{"id":382,"text":"Michigan Water Science Center","active":true,"usgs":true}],"links":[{"id":349260,"rank":3,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/F73J3BVJ","text":"USGS data release","description":"USGS data release","linkHelpText":"Groundwater Seepage Measurements in Northeast Section of Bear Lake, Muskegon County, Michigan, October 2015"},{"id":349259,"rank":2,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/sir/2017/5092/sir20175092.pdf","text":"Report","size":"10.5 MB","linkFileType":{"id":1,"text":"pdf"},"description":"SIR 2017-5092"},{"id":349258,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/sir/2017/5092/coverthb.jpg"}],"country":"United States","state":"Michigan","county":"Muskegon County","otherGeospatial":"Bear Lake","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -86.27194404602051,\n 43.25970598443754\n ],\n [\n -86.25323295593262,\n 43.25970598443754\n ],\n [\n -86.25323295593262,\n 43.27145609469072\n ],\n [\n -86.27194404602051,\n 43.27145609469072\n ],\n [\n -86.27194404602051,\n 43.25970598443754\n ]\n ]\n ]\n }\n }\n ]\n}","contact":"Director, Upper Midwest Water Science Center
U.S. Geological Survey
6520 Mercantile Way
Suite 5
Lansing, MI 48911
The exchange of groundwater and surface water (GW-SW), including dissolved constituents and energy, represents a critical yet challenging characterization problem for hydrogeologists and stream ecologists. Here, we describe the use of a suite of high spatial-resolution remote-sensing techniques, collected using a small unmanned aircraft system (sUAS), to provide novel and complementary data to analyze GW-SW exchange. sUAS provided centimeter-scale resolution topography and water surface elevations, which are often drivers of exchange along the river corridor. Additionally, sUAS-based vegetation imagery, vegetation-top elevation, and normalized difference vegetation index (NDVI) mapping indicated GW-SW exchange patterns that are difficult to characterize from the land surface and may not be resolved from coarser satellite-based imagery. We combined these data with estimates of sediment hydraulic conductivity to provide a direct estimate of GW “shortcutting” through meander necks, which was corroborated by temperature data at the riverbed interface.
","language":"English","publisher":"AGU","doi":"10.1002/2017GL075836","usgsCitation":"Pai, H., Malenda, H., Briggs, M.A., Singha, K., González-Pinzón, R., Gooseff, M., Tyler, S., and AirCTEMPS Team, 2017, Potential for Small Unmanned Aircraft Systems applications for identifying groundwater-surface water exchange in a meandering river reach: Geophysical Research Letters, v. 44, no. 23, p. 11868-11877, https://doi.org/10.1002/2017GL075836.","productDescription":"10 p.","startPage":"11868","endPage":"11877","ipdsId":"IP-092215","costCenters":[{"id":486,"text":"OGW Branch of Geophysics","active":true,"usgs":true}],"links":[{"id":469289,"rank":1,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.1002/2017gl075836","text":"Publisher Index Page"},{"id":438142,"rank":0,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/F7J9658M","text":"USGS data release","linkHelpText":"Fiber-optic distributed temperature data collected along the streambed of the East River, Crested Butte, CO, USA"},{"id":349585,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"volume":"44","issue":"23","publishingServiceCenter":{"id":9,"text":"Reston PSC"},"noUsgsAuthors":false,"publicationDate":"2017-12-11","publicationStatus":"PW","scienceBaseUri":"5a60fafbe4b06e28e9c22a78","contributors":{"authors":[{"text":"Pai, H.","contributorId":201023,"corporation":false,"usgs":false,"family":"Pai","given":"H.","email":"","affiliations":[],"preferred":false,"id":724125,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Malenda, H.","contributorId":201024,"corporation":false,"usgs":false,"family":"Malenda","given":"H.","affiliations":[],"preferred":false,"id":724126,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Briggs, Martin A. 0000-0003-3206-4132 mbriggs@usgs.gov","orcid":"https://orcid.org/0000-0003-3206-4132","contributorId":4114,"corporation":false,"usgs":true,"family":"Briggs","given":"Martin","email":"mbriggs@usgs.gov","middleInitial":"A.","affiliations":[{"id":37277,"text":"WMA - Earth System Processes Division","active":true,"usgs":true},{"id":493,"text":"Office of Ground Water","active":true,"usgs":true},{"id":610,"text":"Utah Water Science Center","active":true,"usgs":true},{"id":486,"text":"OGW Branch of Geophysics","active":true,"usgs":true}],"preferred":true,"id":724124,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Singha, K.","contributorId":201025,"corporation":false,"usgs":false,"family":"Singha","given":"K.","email":"","affiliations":[],"preferred":false,"id":724127,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"González-Pinzón, R.","contributorId":198635,"corporation":false,"usgs":false,"family":"González-Pinzón","given":"R.","affiliations":[],"preferred":false,"id":724128,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"Gooseff, M.","contributorId":201026,"corporation":false,"usgs":false,"family":"Gooseff","given":"M.","email":"","affiliations":[],"preferred":false,"id":724129,"contributorType":{"id":1,"text":"Authors"},"rank":6},{"text":"Tyler, S.W.","contributorId":85740,"corporation":false,"usgs":true,"family":"Tyler","given":"S.W.","email":"","affiliations":[],"preferred":false,"id":724130,"contributorType":{"id":1,"text":"Authors"},"rank":7},{"text":"AirCTEMPS Team","contributorId":201028,"corporation":true,"usgs":false,"organization":"AirCTEMPS Team","id":724134,"contributorType":{"id":1,"text":"Authors"},"rank":8}]}} ,{"id":70194655,"text":"70194655 - 2017 - Evidence for migratory spawning behavior by morphologically distinct Cisco (Coregonus artedi) from a small inland lake","interactions":[],"lastModifiedDate":"2017-12-11T10:38:03","indexId":"70194655","displayToPublicDate":"2017-11-30T00:00:00","publicationYear":"2017","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":5153,"text":"The American Midland Naturalist","active":true,"publicationSubtype":{"id":10}},"displayTitle":"Evidence for migratory spawning behavior by morphologically distinct Cisco (Coregonus artedi) from a small inland lake","title":"Evidence for migratory spawning behavior by morphologically distinct Cisco (Coregonus artedi) from a small inland lake","docAbstract":"Conservation and management of rare fishes relies on managers having the most informed understanding of the underlying ecology of the species under investigation. Cisco (Coregonus artedi), a species of conservation concern, is a cold-water pelagic fish that is notoriously variable in morphometry and life history. Published reports indicate, at spawning time, Cisco in great lakes may migrate into or through large rivers, whereas those in small lakes move inshore. Nonetheless, during a sampling trip to Follensby Pond, a 393 ha lake in the Adirondack Mountains, New York, we observed gravid Cisco swimming over an outlet sill from a narrow shallow stream and into the lake. We opportunistically dip-netted a small subsample of 11 individuals entering the lake from the stream (three female, eight male) and compared them to fish captured between 2013 and 2015 with gillnets in the lake. Stream-captured Cisco were considerably larger than lake-captured individuals at a given age, had significantly larger asymptotic length, and were present only as mature individuals between age of 3 and age 5. These results could suggest either Cisco are migrating from a nearby lake to spawn in Follensby Pond, or that a distinct morphotype of Cisco from Follensby Pond migrates out to the stream and then back in at spawning time. Our results appear to complement a handful of other cases in which Cisco spawning migrations have been documented and to provide the first evidence for such behavior in a small inland lake.","language":"English","publisher":"University of Notre Dame","doi":"10.1674/0003-0031-178.2.237","usgsCitation":"Ross, A.J., Weidel, B., Leneker, M., and Solomon, C.T., 2017, Evidence for migratory spawning behavior by morphologically distinct Cisco (Coregonus artedi) from a small inland lake: The American Midland Naturalist, v. 178, no. 2, p. 237-244, https://doi.org/10.1674/0003-0031-178.2.237.","productDescription":"8 p.","startPage":"237","endPage":"244","ipdsId":"IP-083647","costCenters":[{"id":324,"text":"Great Lakes Science Center","active":true,"usgs":true}],"links":[{"id":349901,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"volume":"178","issue":"2","publishingServiceCenter":{"id":6,"text":"Columbus PSC"},"noUsgsAuthors":false,"publicationStatus":"PW","scienceBaseUri":"5a60fafbe4b06e28e9c22a75","contributors":{"authors":[{"text":"Ross, Alexander J.","contributorId":201256,"corporation":false,"usgs":false,"family":"Ross","given":"Alexander","email":"","middleInitial":"J.","affiliations":[],"preferred":false,"id":724780,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Weidel, Brian 0000-0001-6095-2773 bweidel@usgs.gov","orcid":"https://orcid.org/0000-0001-6095-2773","contributorId":2485,"corporation":false,"usgs":true,"family":"Weidel","given":"Brian","email":"bweidel@usgs.gov","affiliations":[{"id":324,"text":"Great Lakes Science Center","active":true,"usgs":true}],"preferred":true,"id":724779,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Leneker, Mellisa","contributorId":201254,"corporation":false,"usgs":false,"family":"Leneker","given":"Mellisa","email":"","affiliations":[],"preferred":false,"id":724781,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Solomon, Christopher T.","contributorId":34014,"corporation":false,"usgs":false,"family":"Solomon","given":"Christopher","email":"","middleInitial":"T.","affiliations":[{"id":6646,"text":"McGill University","active":true,"usgs":false}],"preferred":false,"id":724782,"contributorType":{"id":1,"text":"Authors"},"rank":4}]}} ,{"id":70194441,"text":"70194441 - 2017 - Conceptual model for invasive bivalve control on wetland productivity","interactions":[],"lastModifiedDate":"2017-11-30T10:09:17","indexId":"70194441","displayToPublicDate":"2017-11-29T00:00:00","publicationYear":"2017","noYear":false,"publicationType":{"id":18,"text":"Report"},"publicationSubtype":{"id":4,"text":"Other Government Series"},"seriesTitle":{"id":5573,"text":"Interagency Ecological Program Technical Report","active":true,"publicationSubtype":{"id":4}},"seriesNumber":"91","title":"Conceptual model for invasive bivalve control on wetland productivity","docAbstract":"Tidal wetlands were the historically dominant features of many coastal regions around the world, including the San Francisco Estuary (Callaway et al. 2011; Whipple et al. 2012). These mosaics of varied interconnected habitats (Mitsch and Gosselink 1993) provide a host of ecosystem services, including biodiversity maintenance, fish and wildlife habitat, water quality improvement, flood abatement, and carbon sequestration (Rabenhorst 1995; Costanza et al. 1997; Bottom et al. 2005; Zedler and Kercher 2005; Barbier et al. 2010). They also support human activities and values such as recreation and aesthetic appreciation (Barbier et al. 2010; Milligan and Kraus-Polk 2016). Despite their critical functions, many wetland landscapes have been destroyed or irreparably altered, either incidentally or intentionally, by human activities (Holland et al. 2004; Zedler and Kercher 2005; Callaway et al. 2011; Cloern and Jassby 2012; Whipple et al. 2012; Schile et al. 2014).
San Francisco Estuary (SFE) (see Figure 1) tidal wetlands were largely converted to other land uses in the late 1800s and early 1900s, with the extent of loss and new use varying by region. Wetland losses in the North, Central, and South San Francisco bays and Suisun Bay ranged from 70 percent to 93 percent to accommodate agricultural uses, salt production, managed waterfowl habitat, and urban development (Callaway et al. 2011). Landscape transformation within the most inland portion of the SFE, the Sacramento-San Joaquin Delta (Delta), was even more dramatic. Overall, today’s Delta contains 97 percent less freshwater tidal wetland than its historical state and nearly double the open water area (Whipple et al. 2012). The majority of the modern Delta consists of agricultural tracts protected from tidal waters by human-made dikes or levees, which are commonly armored with riprap. The de-watered, rich peat soils of these created islands have supported abundant agricultural production, but have oxidized, compacted, and blown away in the process, causing significant subsidence (Deverel and Leighton 2010). Occasional levee failures turn islands into lakes; a few large shallow lakes remain after accidental levee breaches were not repaired (Whipple et al. 2012).
","largerWorkType":{"id":18,"text":"Report"},"largerWorkTitle":"Effects of tidal wetland restoration on fish: A suite of conceptual models","largerWorkSubtype":{"id":4,"text":"Other Government Series"},"language":"English","publisher":"Interagency Ecological Program","usgsCitation":"Hartman, R., Brown, L.R., Thompson, J.K., and Parchaso, F., 2017, Conceptual model for invasive bivalve control on wetland productivity: Interagency Ecological Program Technical Report 91, 34 p.","productDescription":"34 p.","startPage":"225","endPage":"258","ipdsId":"IP-084615","costCenters":[{"id":438,"text":"National Research Program - Western Branch","active":true,"usgs":true}],"links":[{"id":349521,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"},{"id":349520,"rank":1,"type":{"id":11,"text":"Document"},"url":"https://www.water.ca.gov/iep/docs/tech_rpts/TR91.Wetland_CM_2Nov2017.pdf"}],"country":"United States","state":"California","city":"San Francisco","otherGeospatial":"San Francisco Bay-Delta Estuary","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -123.2720947265625,\n 37.02886944696474\n ],\n [\n -121.124267578125,\n 37.02886944696474\n ],\n [\n -121.124267578125,\n 38.65119833229951\n ],\n [\n -123.2720947265625,\n 38.65119833229951\n ],\n [\n -123.2720947265625,\n 37.02886944696474\n ]\n ]\n ]\n }\n }\n ]\n}","publishingServiceCenter":{"id":14,"text":"Menlo Park PSC"},"noUsgsAuthors":false,"publicationStatus":"PW","scienceBaseUri":"5a60fafde4b06e28e9c22a9b","contributors":{"authors":[{"text":"Hartman, Rosemary","contributorId":200388,"corporation":false,"usgs":false,"family":"Hartman","given":"Rosemary","email":"","affiliations":[],"preferred":false,"id":723822,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Brown, Larry R. 0000-0001-6702-4531 lrbrown@usgs.gov","orcid":"https://orcid.org/0000-0001-6702-4531","contributorId":1717,"corporation":false,"usgs":true,"family":"Brown","given":"Larry","email":"lrbrown@usgs.gov","middleInitial":"R.","affiliations":[{"id":154,"text":"California Water Science Center","active":true,"usgs":true}],"preferred":true,"id":723824,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Thompson, Janet K. 0000-0002-1528-8452 jthompso@usgs.gov","orcid":"https://orcid.org/0000-0002-1528-8452","contributorId":1009,"corporation":false,"usgs":true,"family":"Thompson","given":"Janet","email":"jthompso@usgs.gov","middleInitial":"K.","affiliations":[{"id":438,"text":"National Research Program - Western Branch","active":true,"usgs":true},{"id":36183,"text":"Hydro-Ecological Interactions Branch","active":true,"usgs":true}],"preferred":true,"id":723821,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Parchaso, Francis 0000-0002-9471-7787 parchaso@usgs.gov","orcid":"https://orcid.org/0000-0002-9471-7787","contributorId":150620,"corporation":false,"usgs":true,"family":"Parchaso","given":"Francis","email":"parchaso@usgs.gov","affiliations":[{"id":36183,"text":"Hydro-Ecological Interactions Branch","active":true,"usgs":true},{"id":37464,"text":"WMA - Laboratory & Analytical Services Division","active":true,"usgs":true},{"id":438,"text":"National Research Program - Western Branch","active":true,"usgs":true}],"preferred":true,"id":723823,"contributorType":{"id":1,"text":"Authors"},"rank":4}]}} ,{"id":70194483,"text":"70194483 - 2017 - Combining remote sensing and water-balance evapotranspiration estimates for the conterminous United States","interactions":[],"lastModifiedDate":"2022-04-22T16:02:15.153901","indexId":"70194483","displayToPublicDate":"2017-11-29T00:00:00","publicationYear":"2017","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":3250,"text":"Remote Sensing","active":true,"publicationSubtype":{"id":10}},"title":"Combining remote sensing and water-balance evapotranspiration estimates for the conterminous United States","docAbstract":"Evapotranspiration (ET) is a key component of the hydrologic cycle, accounting for ~70% of precipitation in the conterminous U.S. (CONUS), but it has been a challenge to predict accurately across different spatio-temporal scales. The increasing availability of remotely sensed data has led to significant advances in the frequency and spatial resolution of ET estimates, derived from energy balance principles with variables such as temperature used to estimate surface latent heat flux. Although remote sensing methods excel at depicting spatial and temporal variability, estimation of ET independently of other water budget components can lead to inconsistency with other budget terms. Methods that rely on ground-based data better constrain long-term ET, but are unable to provide the same temporal resolution. Here we combine long-term ET estimates from a water-balance approach with the SSEBop (operational Simplified Surface Energy Balance) remote sensing-based ET product for 2000–2015. We test the new combined method, the original SSEBop product, and another remote sensing ET product (MOD16) against monthly measurements from 119 flux towers. The new product showed advantages especially in non-irrigated areas where the new method showed a coefficient of determination R2 of 0.44, compared to 0.41 for SSEBop or 0.35 for MOD16. The resulting monthly data set will be a useful, unique contribution to ET estimation, due to its combination of remote sensing-based variability and ground-based long-term water balance constraints.
","language":"English","publisher":"MDPI","doi":"10.3390/rs9121181","usgsCitation":"Reitz, M., Senay, G., and Sanford, W.E., 2017, Combining remote sensing and water-balance evapotranspiration estimates for the conterminous United States: Remote Sensing, v. 9, no. 12, 1181, 17 p.; Data release, https://doi.org/10.3390/rs9121181.","productDescription":"1181, 17 p.; Data release","ipdsId":"IP-090961","costCenters":[{"id":222,"text":"Earth Resources Observation and Science (EROS) Center","active":true,"usgs":true},{"id":436,"text":"National Research Program - Eastern Branch","active":true,"usgs":true}],"links":[{"id":469292,"rank":3,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.3390/rs9121181","text":"Publisher Index Page"},{"id":349568,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"},{"id":397955,"rank":2,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/F7QC02FK","text":"USGS data 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\"}}]}","volume":"9","issue":"12","publishingServiceCenter":{"id":9,"text":"Reston PSC"},"noUsgsAuthors":false,"publicationDate":"2017-11-29","publicationStatus":"PW","scienceBaseUri":"5a60fafbe4b06e28e9c22a82","contributors":{"authors":[{"text":"Reitz, Meredith 0000-0001-9519-6103 mreitz@usgs.gov","orcid":"https://orcid.org/0000-0001-9519-6103","contributorId":196694,"corporation":false,"usgs":true,"family":"Reitz","given":"Meredith","email":"mreitz@usgs.gov","affiliations":[{"id":37786,"text":"WMA - Observing Systems Division","active":true,"usgs":true},{"id":436,"text":"National Research Program - Eastern Branch","active":true,"usgs":true}],"preferred":true,"id":724058,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Senay, Gabriel B. 0000-0002-8810-8539 senay@usgs.gov","orcid":"https://orcid.org/0000-0002-8810-8539","contributorId":166812,"corporation":false,"usgs":true,"family":"Senay","given":"Gabriel","email":"senay@usgs.gov","middleInitial":"B.","affiliations":[{"id":222,"text":"Earth Resources Observation and Science (EROS) Center","active":true,"usgs":true},{"id":223,"text":"Earth Resources Observation and Science (EROS) Center (Geography)","active":false,"usgs":true}],"preferred":true,"id":724059,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Sanford, Ward E. 0000-0002-6624-0280 wsanford@usgs.gov","orcid":"https://orcid.org/0000-0002-6624-0280","contributorId":2268,"corporation":false,"usgs":true,"family":"Sanford","given":"Ward","email":"wsanford@usgs.gov","middleInitial":"E.","affiliations":[{"id":37778,"text":"WMA - Integrated Modeling and Prediction Division","active":true,"usgs":true},{"id":436,"text":"National Research Program - Eastern Branch","active":true,"usgs":true}],"preferred":true,"id":724060,"contributorType":{"id":1,"text":"Authors"},"rank":3}]}} ,{"id":70194472,"text":"70194472 - 2017 - Progress and lessons learned from water-quality monitoring networks","interactions":[],"lastModifiedDate":"2017-11-30T10:00:52","indexId":"70194472","displayToPublicDate":"2017-11-29T00:00:00","publicationYear":"2017","noYear":false,"publicationType":{"id":5,"text":"Book chapter"},"publicationSubtype":{"id":24,"text":"Book Chapter"},"seriesTitle":{"id":5570,"text":"Chemistry and Water","active":true,"publicationSubtype":{"id":24}},"title":"Progress and lessons learned from water-quality monitoring networks","docAbstract":"Stream-quality monitoring networks in the United States were initiated and expanded after passage of successive federal water-pollution control laws from 1948 to 1972. The first networks addressed information gaps on the extent and severity of stream pollution and served as early warning systems for spills. From 1965 to 1972, monitoring networks expanded to evaluate compliance with stream standards, track emerging issues, and assess water-quality status and trends. After 1972, concerns arose regarding the ability of monitoring networks to determine if water quality was getting better or worse and why. As a result, monitoring networks adopted a hydrologic systems approach targeted to key water-quality issues, accounted for human and natural factors affecting water quality, innovated new statistical methods, and introduced geographic information systems and models that predict water quality at unmeasured locations. Despite improvements, national-scale monitoring networks have declined over time. Only about 1%, or 217, of more than 36,000 US Geological Survey monitoring sites sampled from 1975 to 2014 have been operated throughout the four decades since passage of the 1972 Clean Water Act. Efforts to sustain monitoring networks are important because these networks have collected information crucial to the description of water-quality trends over time and are providing information against which to evaluate future trends.
","largerWorkType":{"id":4,"text":"Book"},"largerWorkTitle":"The science behind sustaining the world's most crucial resource","language":"English","publisher":"Elsevier","doi":"10.1016/B978-0-12-809330-6.00002-7","usgsCitation":"Myers, D.N., and Ludtke, A.S., 2017, Progress and lessons learned from water-quality monitoring networks, chap. of The science behind sustaining the world's most crucial resource: Chemistry and Water, p. 23-120, https://doi.org/10.1016/B978-0-12-809330-6.00002-7.","productDescription":"98 p.","startPage":"23","endPage":"120","ipdsId":"IP-079349","costCenters":[{"id":503,"text":"Office of Water Quality","active":true,"usgs":true}],"links":[{"id":349508,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","publishingServiceCenter":{"id":9,"text":"Reston PSC"},"noUsgsAuthors":false,"publicationStatus":"PW","scienceBaseUri":"5a60fafce4b06e28e9c22a92","contributors":{"authors":[{"text":"Myers, Donna N. 0000-0001-6359-2865 dnmyers@usgs.gov","orcid":"https://orcid.org/0000-0001-6359-2865","contributorId":512,"corporation":false,"usgs":true,"family":"Myers","given":"Donna","email":"dnmyers@usgs.gov","middleInitial":"N.","affiliations":[{"id":503,"text":"Office of Water Quality","active":true,"usgs":true}],"preferred":true,"id":723988,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Ludtke, Amy S. asludtke@usgs.gov","contributorId":4735,"corporation":false,"usgs":true,"family":"Ludtke","given":"Amy","email":"asludtke@usgs.gov","middleInitial":"S.","affiliations":[{"id":503,"text":"Office of Water Quality","active":true,"usgs":true}],"preferred":true,"id":723989,"contributorType":{"id":1,"text":"Authors"},"rank":2}]}} ,{"id":70194473,"text":"70194473 - 2017 - Constraining the magmatic system at Mount St. Helens (2004–2008) using Bayesian inversion with physics-based models including gas escape and crystallization","interactions":[],"lastModifiedDate":"2017-11-29T10:34:41","indexId":"70194473","displayToPublicDate":"2017-11-29T00:00:00","publicationYear":"2017","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":2314,"text":"Journal of Geophysical Research B: Solid Earth","active":true,"publicationSubtype":{"id":10}},"title":"Constraining the magmatic system at Mount St. Helens (2004–2008) using Bayesian inversion with physics-based models including gas escape and crystallization","docAbstract":"Physics-based models of volcanic eruptions track conduit processes as functions of depth and time. When used in inversions, these models permit integration of diverse geological and geophysical data sets to constrain important parameters of magmatic systems. We develop a 1-D steady state conduit model for effusive eruptions including equilibrium crystallization and gas transport through the conduit and compare with the quasi-steady dome growth phase of Mount St. Helens in 2005. Viscosity increase resulting from pressure-dependent crystallization leads to a natural transition from viscous flow to frictional sliding on the conduit margin. Erupted mass flux depends strongly on wall rock and magma permeabilities due to their impact on magma density. Including both lateral and vertical gas transport reveals competing effects that produce nonmonotonic behavior in the mass flux when increasing magma permeability. Using this physics-based model in a Bayesian inversion, we link data sets from Mount St. Helens such as extrusion flux and earthquake depths with petrological data to estimate unknown model parameters, including magma chamber pressure and water content, magma permeability constants, conduit radius, and friction along the conduit walls. Even with this relatively simple model and limited data, we obtain improved constraints on important model parameters. We find that the magma chamber had low (<5wt%) total volatiles and that the magma permeability scale is well constrained at ~10-11.4 m2 to reproduce observed dome rock porosities. Compared with previous results, higher magma overpressure and lower wall friction are required to compensate for increased viscous resistance while keeping extrusion rate at the observed value.
","language":"English","publisher":"American Geophysical Union","doi":"10.1002/2017JB014343","usgsCitation":"Wong, Y., Segall, P., Bradley, A., and Anderson, K.R., 2017, Constraining the magmatic system at Mount St. Helens (2004–2008) using Bayesian inversion with physics-based models including gas escape and crystallization: Journal of Geophysical Research B: Solid Earth, v. 122, no. 10, p. 7789-7812, https://doi.org/10.1002/2017JB014343.","productDescription":"34 p.","startPage":"7789","endPage":"7812","ipdsId":"IP-086340","costCenters":[{"id":617,"text":"Volcano Science Center","active":true,"usgs":true}],"links":[{"id":469293,"rank":0,"type":{"id":41,"text":"Open Access External Repository Page"},"url":"https://www.osti.gov/biblio/1411224","text":"External Repository"},{"id":349506,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","state":"Washington","otherGeospatial":"Mount St. Helens","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -122.63214111328125,\n 45.94160076422081\n ],\n [\n -121.77246093750001,\n 45.94160076422081\n ],\n [\n -121.77246093750001,\n 46.494610770689384\n ],\n [\n -122.63214111328125,\n 46.494610770689384\n ],\n [\n -122.63214111328125,\n 45.94160076422081\n ]\n ]\n ]\n }\n }\n ]\n}","volume":"122","issue":"10","publishingServiceCenter":{"id":14,"text":"Menlo Park PSC"},"noUsgsAuthors":false,"publicationDate":"2017-10-30","publicationStatus":"PW","scienceBaseUri":"5a60fafce4b06e28e9c22a90","contributors":{"authors":[{"text":"Wong, Ying-Qi","contributorId":200978,"corporation":false,"usgs":false,"family":"Wong","given":"Ying-Qi","email":"","affiliations":[],"preferred":false,"id":723991,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Segall, Paul","contributorId":75942,"corporation":false,"usgs":true,"family":"Segall","given":"Paul","affiliations":[],"preferred":false,"id":723992,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Bradley, Andrew","contributorId":200980,"corporation":false,"usgs":false,"family":"Bradley","given":"Andrew","affiliations":[],"preferred":false,"id":723993,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Anderson, Kyle R. 0000-0001-8041-3996 kranderson@usgs.gov","orcid":"https://orcid.org/0000-0001-8041-3996","contributorId":3522,"corporation":false,"usgs":true,"family":"Anderson","given":"Kyle","email":"kranderson@usgs.gov","middleInitial":"R.","affiliations":[{"id":617,"text":"Volcano Science Center","active":true,"usgs":true}],"preferred":true,"id":723990,"contributorType":{"id":1,"text":"Authors"},"rank":4}]}} ,{"id":70194477,"text":"70194477 - 2017 - The hyper-enrichment of V and Zn in black shales of the Late Devonian-Early Mississippian Bakken Formation (USA)","interactions":[],"lastModifiedDate":"2018-11-19T11:34:54","indexId":"70194477","displayToPublicDate":"2017-11-29T00:00:00","publicationYear":"2017","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":1213,"text":"Chemical Geology","active":true,"publicationSubtype":{"id":10}},"title":"The hyper-enrichment of V and Zn in black shales of the Late Devonian-Early Mississippian Bakken Formation (USA)","docAbstract":"Black shales of the Late Devonian to Early Mississippian Bakken Formation are characterized by high concentrations of organic carbon and the hyper-enrichment (> 500 to 1000s of mg/kg) of V and Zn. Deposition of black shales resulted from shallow seafloor depths that promoted rapid development of euxinic conditions. Vanadium hyper-enrichments, which are unknown in modern environments, are likely the result of very high levels of dissolved H2S (~ 10 mM) in bottom waters or sediments. Because modern hyper-enrichments of Zn are documented only in Framvaren Fjord (Norway), it is likely that the biogeochemical trigger responsible for Zn hyper-enrichment in Framvaren Fjord was also present in the Bakken basin. With Framvaren Fjord as an analogue, we propose a causal link between the activity of phototrophic sulfide oxidizing bacteria, related to the development of photic-zone euxinia, and the hyper-enrichment of Zn in black shales of the Bakken Formation.
","language":"English","publisher":"Elsevier","doi":"10.1016/j.chemgeo.2017.01.026","usgsCitation":"Scott, C., Slack, J.F., and Kelley, K.D., 2017, The hyper-enrichment of V and Zn in black shales of the Late Devonian-Early Mississippian Bakken Formation (USA): Chemical Geology, v. 452, p. 24-33, https://doi.org/10.1016/j.chemgeo.2017.01.026.","productDescription":"10 p.","startPage":"24","endPage":"33","ipdsId":"IP-078833","costCenters":[{"id":241,"text":"Eastern Energy Resources Science Center","active":true,"usgs":true},{"id":245,"text":"Eastern Mineral and Environmental Resources Science Center","active":true,"usgs":true}],"links":[{"id":461343,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.1016/j.chemgeo.2017.01.026","text":"Publisher Index Page"},{"id":349501,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"Canada, United States","state":"Manitoba, Montana, North Dakota, Saskatchewan, South Dakota, Wyoming","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -108,\n 43\n ],\n [\n -96,\n 43\n ],\n [\n -96,\n 50\n ],\n [\n -108,\n 50\n ],\n [\n -108,\n 43\n ]\n ]\n ]\n }\n }\n ]\n}","volume":"452","publishingServiceCenter":{"id":9,"text":"Reston PSC"},"noUsgsAuthors":false,"publicationStatus":"PW","scienceBaseUri":"5a60fafce4b06e28e9c22a87","contributors":{"authors":[{"text":"Scott, Clint 0000-0003-2778-2711 clintonscott@usgs.gov","orcid":"https://orcid.org/0000-0003-2778-2711","contributorId":5332,"corporation":false,"usgs":true,"family":"Scott","given":"Clint","email":"clintonscott@usgs.gov","affiliations":[{"id":241,"text":"Eastern Energy Resources Science Center","active":true,"usgs":true}],"preferred":true,"id":724012,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Slack, John F. 0000-0001-6600-3130 jfslack@usgs.gov","orcid":"https://orcid.org/0000-0001-6600-3130","contributorId":1032,"corporation":false,"usgs":true,"family":"Slack","given":"John","email":"jfslack@usgs.gov","middleInitial":"F.","affiliations":[{"id":245,"text":"Eastern Mineral and Environmental Resources Science Center","active":true,"usgs":true},{"id":387,"text":"Mineral Resources Program","active":true,"usgs":true},{"id":243,"text":"Eastern Geology and Paleoclimate Science Center","active":true,"usgs":true}],"preferred":true,"id":724013,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Kelley, Karen Duttweiler 0000-0002-3232-5809 kdkelley@usgs.gov","orcid":"https://orcid.org/0000-0002-3232-5809","contributorId":192758,"corporation":false,"usgs":true,"family":"Kelley","given":"Karen","email":"kdkelley@usgs.gov","middleInitial":"Duttweiler","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":724014,"contributorType":{"id":1,"text":"Authors"},"rank":3}]}} ,{"id":70199758,"text":"70199758 - 2017 - Flood runoff in relation to water vapor transport by atmospheric rivers over the western United States, 1949–2015","interactions":[],"lastModifiedDate":"2018-09-27T13:56:26","indexId":"70199758","displayToPublicDate":"2017-11-28T13:56:08","publicationYear":"2017","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":1807,"text":"Geophysical Research Letters","active":true,"publicationSubtype":{"id":10}},"title":"Flood runoff in relation to water vapor transport by atmospheric rivers over the western United States, 1949–2015","docAbstract":"Atmospheric rivers (ARs) have a significant role in generating floods across the western United States. We analyze daily streamflow for water years 1949 to 2015 from 5,477 gages in relation to water vapor transport by ARs using a 6 h chronology resolved to 2.5° latitude and longitude. The probability that an AR will generate 50 mm/d of runoff in a river on the Pacific Coast increases from 12% when daily mean water vapor transport, DVT, is greater than 300 kg m−1 s−1 to 54% when DVT > 600 kg m−1 s−1. Extreme runoff, represented by the 99th quantile of daily values, doubles from 80 mm/d at DVT = 300 kg m−1 s−1 to 160 mm/d at DVT = 500 kg m−1 s−1. Forecasts and predictions of water vapor transport by atmospheric rivers can support flood risk assessment and estimates of future flood frequencies and magnitude in the western United States.
","language":"English","publisher":"AGU","doi":"10.1002/2017GL075399","usgsCitation":"Konrad, C.P., and Dettinger, M.D., 2017, Flood runoff in relation to water vapor transport by atmospheric rivers over the western United States, 1949–2015: Geophysical Research Letters, v. 44, no. 22, p. 11456-11462, https://doi.org/10.1002/2017GL075399.","productDescription":"7 p.","startPage":"11456","endPage":"11462","ipdsId":"IP-089312","costCenters":[{"id":622,"text":"Washington Water Science Center","active":true,"usgs":true}],"links":[{"id":469294,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.1002/2017gl075399","text":"Publisher Index Page"},{"id":357838,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","volume":"44","issue":"22","publishingServiceCenter":{"id":12,"text":"Tacoma PSC"},"noUsgsAuthors":false,"publicationDate":"2017-11-29","publicationStatus":"PW","scienceBaseUri":"5bc030a6e4b0fc368eb53a08","contributors":{"authors":[{"text":"Konrad, Christopher P. 0000-0002-7354-547X cpkonrad@usgs.gov","orcid":"https://orcid.org/0000-0002-7354-547X","contributorId":1716,"corporation":false,"usgs":true,"family":"Konrad","given":"Christopher","email":"cpkonrad@usgs.gov","middleInitial":"P.","affiliations":[{"id":622,"text":"Washington Water Science Center","active":true,"usgs":true}],"preferred":true,"id":746507,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Dettinger, Michael D. 0000-0002-7509-7332 mddettin@usgs.gov","orcid":"https://orcid.org/0000-0002-7509-7332","contributorId":149896,"corporation":false,"usgs":true,"family":"Dettinger","given":"Michael","email":"mddettin@usgs.gov","middleInitial":"D.","affiliations":[{"id":438,"text":"National Research Program - Western Branch","active":true,"usgs":true},{"id":37277,"text":"WMA - Earth System Processes Division","active":true,"usgs":true},{"id":436,"text":"National Research Program - Eastern Branch","active":true,"usgs":true}],"preferred":true,"id":746508,"contributorType":{"id":1,"text":"Authors"},"rank":2}]}} ,{"id":70194335,"text":"70194335 - 2017 - A swath across the great divide: Kelp forests across the Samalga Pass biogeographic break","interactions":[],"lastModifiedDate":"2017-11-29T09:56:13","indexId":"70194335","displayToPublicDate":"2017-11-28T00:00:00","publicationYear":"2017","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":1333,"text":"Continental Shelf Research","active":true,"publicationSubtype":{"id":10}},"title":"A swath across the great divide: Kelp forests across the Samalga Pass biogeographic break","docAbstract":"Biogeographic breaks are often described as locations where a large number of species reach their geographic range limits. Samalga Pass, in the eastern Aleutian Archipelago, is a known biogeographic break for the spatial distribution of several species of offshore-pelagic communities, including numerous species of cold-water corals, zooplankton, fish, marine mammals, and seabirds. However, it remains unclear whether Samalga Pass also serves as a biogeographic break for nearshore benthic communities. The occurrence of biogeographic breaks across multiple habitats has not often been described. In this study, we examined if the biogeographic break for offshore-pelagic communities applies to nearshore kelp forests. To examine whether Samalga Pass serves as a biogeographic break for kelp forest communities, this study compared abundance, biomass and percent bottom cover of species associated with kelp forests on either side of the pass. We observed marked differences in kelp forest community structure, with some species reaching their geographic range limits on the opposing sides of the pass. In particular, the habitat-forming kelp Nereocystis luetkeana, and the predatory sea stars Pycnopodia helianthoides and Orthasterias koehleri all occurred on the eastern side of Samalga Pass but were not observed west of the pass. In contrast, the sea star Leptasterias camtschatica dispar was observed only on the western side of the pass. We also observed differences in overall abundance and biomass of numerous associated fish, invertebrate and macroalgal species on opposing sides of the pass. We conclude that Samalga Pass is important biogeographic break for kelp forest communities in the Aleutian Archipelago and may demark the geographic range limits of several ecologically important species.","language":"English","publisher":"Elsevier","doi":"10.1016/j.csr.2017.06.007","usgsCitation":"Konar, B.H., Edwards, M.S., Bland, A., Metzger, J., Ravelo, A., Traiger, S., and Weitzman, B., 2017, A swath across the great divide: Kelp forests across the Samalga Pass biogeographic break: Continental Shelf Research, v. 143, p. 78-88, https://doi.org/10.1016/j.csr.2017.06.007.","productDescription":"11 p.","startPage":"78","endPage":"88","ipdsId":"IP-082946","costCenters":[{"id":116,"text":"Alaska Science Center Biology MFEB","active":true,"usgs":true}],"links":[{"id":469296,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.1016/j.csr.2017.06.007","text":"Publisher Index Page"},{"id":349445,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","state":"Alaska","otherGeospatial":"Aleutian Archipelago, Samalga Pass","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -177.5390625,\n 49.26780455063753\n ],\n [\n -159.521484375,\n 49.26780455063753\n ],\n [\n -159.521484375,\n 56.48676175249086\n ],\n [\n -177.5390625,\n 56.48676175249086\n ],\n [\n -177.5390625,\n 49.26780455063753\n ]\n ]\n ]\n }\n }\n ]\n}","volume":"143","publishingServiceCenter":{"id":12,"text":"Tacoma PSC"},"noUsgsAuthors":false,"publicationStatus":"PW","scienceBaseUri":"5a60fb00e4b06e28e9c22ae1","contributors":{"authors":[{"text":"Konar, Brenda H. 0000-0002-8998-1612","orcid":"https://orcid.org/0000-0002-8998-1612","contributorId":200787,"corporation":false,"usgs":false,"family":"Konar","given":"Brenda","email":"","middleInitial":"H.","affiliations":[],"preferred":false,"id":723339,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Edwards, Matthew S.","contributorId":200788,"corporation":false,"usgs":false,"family":"Edwards","given":"Matthew","email":"","middleInitial":"S.","affiliations":[],"preferred":false,"id":723340,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Bland, Aaron","contributorId":200789,"corporation":false,"usgs":false,"family":"Bland","given":"Aaron","email":"","affiliations":[],"preferred":false,"id":723341,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Metzger, Jacob","contributorId":200790,"corporation":false,"usgs":false,"family":"Metzger","given":"Jacob","email":"","affiliations":[],"preferred":false,"id":723342,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Ravelo, Alexandra","contributorId":200791,"corporation":false,"usgs":false,"family":"Ravelo","given":"Alexandra","email":"","affiliations":[],"preferred":false,"id":723343,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"Traiger, Sarah","contributorId":200792,"corporation":false,"usgs":false,"family":"Traiger","given":"Sarah","affiliations":[],"preferred":false,"id":723344,"contributorType":{"id":1,"text":"Authors"},"rank":6},{"text":"Weitzman, Ben P. 0000-0001-7559-3654 bweitzman@usgs.gov","orcid":"https://orcid.org/0000-0001-7559-3654","contributorId":5123,"corporation":false,"usgs":true,"family":"Weitzman","given":"Ben P.","email":"bweitzman@usgs.gov","affiliations":[{"id":114,"text":"Alaska Science Center","active":true,"usgs":true},{"id":116,"text":"Alaska Science Center Biology MFEB","active":true,"usgs":true}],"preferred":true,"id":723338,"contributorType":{"id":1,"text":"Authors"},"rank":7}]}} ,{"id":70194343,"text":"70194343 - 2017 - Exploration of diffuse and discrete sources of acid mine drainage to a headwater mountain stream in Colorado, USA","interactions":[],"lastModifiedDate":"2017-11-28T11:00:53","indexId":"70194343","displayToPublicDate":"2017-11-28T00:00:00","publicationYear":"2017","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":2745,"text":"Mine Water and the Environment","active":true,"publicationSubtype":{"id":10}},"title":"Exploration of diffuse and discrete sources of acid mine drainage to a headwater mountain stream in Colorado, USA","docAbstract":"We investigated the impact of acid mine drainage (AMD) contamination from the Minnesota Mine, an inactive gold and silver mine, on Lion Creek, a headwater mountain stream near Empire, Colorado. The objective was to map the sources of AMD contamination, including discrete sources visible at the surface and diffuse inputs that were not readily apparent. This was achieved using geochemical sampling, in-stream and in-seep fluid electrical conductivity (EC) logging, and electrical resistivity imaging (ERI) of the subsurface. The low pH of the AMD-impacted water correlated to high fluid EC values that served as a target for the ERI. From ERI, we identified two likely sources of diffuse contamination entering the stream: (1) the subsurface extent of two seepage faces visible on the surface, and (2) rainfall runoff washing salts deposited on the streambank and in a tailings pile on the east bank of Lion Creek. Additionally, rainfall leaching through the tailings pile is a potential diffuse source of contamination if the subsurface beneath the tailings pile is hydraulically connected with the stream. In-stream fluid EC was lowest when stream discharge was highest in early summer and then increased throughout the summer as stream discharge decreased, indicating that the concentration of dissolved solids in the stream is largely controlled by mixing of groundwater and snowmelt. Total dissolved solids (TDS) load is greatest in early summer and displays a large diel signal. Identification of diffuse sources and variability in TDS load through time should allow for more targeted remediation options.","language":"English","publisher":"Springer Berlin Heidelberg","doi":"10.1007/s10230-017-0452-6","usgsCitation":"Johnston, A., Runkel, R.L., Navarre-Sitchler, A., and Singha, K., 2017, Exploration of diffuse and discrete sources of acid mine drainage to a headwater mountain stream in Colorado, USA: Mine Water and the Environment, v. 36, no. 4, p. 463-478, https://doi.org/10.1007/s10230-017-0452-6.","productDescription":"16 p.","startPage":"463","endPage":"478","ipdsId":"IP-077543","costCenters":[{"id":191,"text":"Colorado Water Science Center","active":true,"usgs":true}],"links":[{"id":349427,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","state":"Colorado","city":"Empire","otherGeospatial":"Lion Creek","volume":"36","issue":"4","publishingServiceCenter":{"id":2,"text":"Denver PSC"},"noUsgsAuthors":false,"publicationDate":"2017-04-29","publicationStatus":"PW","scienceBaseUri":"5a60fb00e4b06e28e9c22ada","contributors":{"authors":[{"text":"Johnston, Allison","contributorId":200808,"corporation":false,"usgs":false,"family":"Johnston","given":"Allison","email":"","affiliations":[],"preferred":false,"id":723380,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Runkel, Robert L. 0000-0003-3220-481X runkel@usgs.gov","orcid":"https://orcid.org/0000-0003-3220-481X","contributorId":685,"corporation":false,"usgs":true,"family":"Runkel","given":"Robert","email":"runkel@usgs.gov","middleInitial":"L.","affiliations":[{"id":191,"text":"Colorado Water Science Center","active":true,"usgs":true}],"preferred":true,"id":723379,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Navarre-Sitchler, Alexis","contributorId":190441,"corporation":false,"usgs":false,"family":"Navarre-Sitchler","given":"Alexis","email":"","affiliations":[],"preferred":false,"id":723381,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Singha, Kamini","contributorId":76733,"corporation":false,"usgs":true,"family":"Singha","given":"Kamini","affiliations":[],"preferred":false,"id":723382,"contributorType":{"id":1,"text":"Authors"},"rank":4}]}} ,{"id":70194439,"text":"70194439 - 2017 - Estimating virus occurrence using Bayesian modeling in multiple drinking water systems of the United States","interactions":[],"lastModifiedDate":"2017-11-28T11:46:05","indexId":"70194439","displayToPublicDate":"2017-11-28T00:00:00","publicationYear":"2017","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":3352,"text":"Science of the Total Environment","active":true,"publicationSubtype":{"id":10}},"title":"Estimating virus occurrence using Bayesian modeling in multiple drinking water systems of the United States","docAbstract":"Drinking water treatment plants rely on purification of contaminated source waters to provide communities with potable water. One group of possible contaminants are enteric viruses. Measurement of viral quantities in environmental water systems are often performed using polymerase chain reaction (PCR) or quantitative PCR (qPCR). However, true values may be underestimated due to challenges involved in a multi-step viral concentration process and due to PCR inhibition. In this study, water samples were concentrated from 25 drinking water treatment plants (DWTPs) across the US to study the occurrence of enteric viruses in source water and removal after treatment. The five different types of viruses studied were adenovirus, norovirus GI, norovirus GII, enterovirus, and polyomavirus. Quantitative PCR was performed on all samples to determine presence or absence of these viruses in each sample. Ten DWTPs showed presence of one or more viruses in source water, with four DWTPs having treated drinking water testing positive. Furthermore, PCR inhibition was assessed for each sample using an exogenous amplification control, which indicated that all of the DWTP samples, including source and treated water samples, had some level of inhibition, confirming that inhibition plays an important role in PCR based assessments of environmental samples. PCR inhibition measurements, viral recovery, and other assessments were\nincorporated into a Bayesian model to more accurately determine viral load in both source and treated water. Results of the Bayesian model indicated that viruses are present in source water and treated water. By using a Bayesian framework that incorporates inhibition, as well as many other parameters that affect viral detection, this study offers an approach for more accurately estimating the occurrence of viral pathogens in environmental waters.","language":"English","publisher":"Elsevier","doi":"10.1016/j.scitotenv.2017.10.267","usgsCitation":"Varughese, E.A., Brinkman, N., Anneken, E.M., Cashdollar, J.S., Fout, G., Furlong, E.T., Kolpin, D.W., Glassmeyer, S.T., and Keely, S.P., 2017, Estimating virus occurrence using Bayesian modeling in multiple drinking water systems of the United States: Science of the Total Environment, v. 619-620, p. 1330-1339, https://doi.org/10.1016/j.scitotenv.2017.10.267.","productDescription":"10 p.","startPage":"1330","endPage":"1339","ipdsId":"IP-089619","costCenters":[{"id":351,"text":"Iowa Water Science Center","active":true,"usgs":true}],"links":[{"id":469298,"rank":0,"type":{"id":41,"text":"Open Access External Repository 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States\"}}]}","volume":"619-620","publishingServiceCenter":{"id":6,"text":"Columbus PSC"},"noUsgsAuthors":false,"publicationStatus":"PW","scienceBaseUri":"5a60fafee4b06e28e9c22abd","contributors":{"authors":[{"text":"Varughese, Eunice A.","contributorId":194446,"corporation":false,"usgs":false,"family":"Varughese","given":"Eunice","email":"","middleInitial":"A.","affiliations":[],"preferred":false,"id":723796,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Brinkman, Nichole E","contributorId":200915,"corporation":false,"usgs":false,"family":"Brinkman","given":"Nichole E","affiliations":[],"preferred":false,"id":723797,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Anneken, Emily M","contributorId":200916,"corporation":false,"usgs":false,"family":"Anneken","given":"Emily","email":"","middleInitial":"M","affiliations":[],"preferred":false,"id":723798,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Cashdollar, Jennifer S","contributorId":200917,"corporation":false,"usgs":false,"family":"Cashdollar","given":"Jennifer","email":"","middleInitial":"S","affiliations":[],"preferred":false,"id":723799,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Fout, G. Shay","contributorId":200918,"corporation":false,"usgs":false,"family":"Fout","given":"G. 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There is particular interest in commercial offshore wind development within Federal waters (i.e., > 3 nautical miles from shore) of the mid-Atlantic. In order to understand the potential for adverse effects on marine birds in this area, information on distribution and behavior (e.g., flight pathways, timing, etc.) is required for a broad suite of species. In areas where offshore wind development is likely to occur, such information can be used to identify high use areas during critical life stages, which can inform the siting of offshore facilities. 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In this management-guided study, we use a very large suite of synthetic climate scenarios in a statistical modeling framework to simultaneously evaluate how (1) average temperature and precipitation changes, (2) initial basin conditions, and (3) temporal characteristics of the input climate data influence water-year flow in the Upper Colorado River. The results here suggest that existing studies may underestimate the degree of uncertainty in future streamflow, particularly under moderate temperature and precipitation changes. However, we also find that the relative severity of future flow projections within a given climate scenario can be estimated with simple metrics that characterize the input climate data and basin conditions. These results suggest that simple testing, like the analyses presented in this paper, may be helpful in understanding differences between existing studies or in identifying specific conditions for physically based mechanistic modeling. Both options could reduce overall cost and improve the efficiency of conducting climate change impacts studies.","language":"English","publisher":"Elsevier","doi":"10.1016/j.cliser.2017.10.003","usgsCitation":"McAfee, S., Pederson, G.T., Woodhouse, C.A., and McCabe, G.J., 2017, Application of synthetic scenarios to address water resource concerns: A management-guided case study from the Upper Colorado River Basin: Climate Services, v. 8, p. 26-35, https://doi.org/10.1016/j.cliser.2017.10.003.","productDescription":"10 p.","startPage":"26","endPage":"35","ipdsId":"IP-086422","costCenters":[{"id":481,"text":"Northern Rocky Mountain Science Center","active":true,"usgs":true}],"links":[{"id":469299,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.1016/j.cliser.2017.10.003","text":"Publisher Index Page"},{"id":349418,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","state":"Arizona, Colorado, New Mexico, Utah, Wyoming","otherGeospatial":"Upper Colorado River Basin","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -110.74218749999999,\n 41.27780646738183\n ],\n [\n -110.91796875,\n 39.842286020743394\n ],\n [\n -110.91796875,\n 38.89103282648846\n ],\n [\n -111.181640625,\n 37.64903402157866\n ],\n [\n -111.4453125,\n 37.3002752813443\n ],\n [\n -111.09374999999999,\n 36.24427318493909\n ],\n [\n -109.86328125,\n 35.67514743608467\n ],\n [\n -109.072265625,\n 35.38904996691167\n ],\n [\n -108.017578125,\n 34.95799531086792\n ],\n [\n -106.171875,\n 35.38904996691167\n ],\n [\n -105.8203125,\n 36.03133177633187\n ],\n [\n -105.46875,\n 37.92686760148135\n ],\n [\n -105.908203125,\n 38.41055825094609\n ],\n [\n -106.083984375,\n 39.50404070558415\n ],\n [\n -105.27099609375,\n 39.690280594818034\n ],\n [\n -105.29296874999999,\n 40.1452892956766\n ],\n [\n -105.1171875,\n 40.329795743702064\n ],\n [\n -105.13916015625,\n 40.94671366508002\n ],\n [\n -105.57861328125,\n 41.36031866306708\n ],\n [\n -106.14990234375,\n 41.73852846935917\n ],\n [\n -106.58935546875,\n 41.77131167976407\n ],\n [\n -108.30322265624999,\n 41.73852846935917\n ],\n [\n -108.43505859374999,\n 42.19596877629178\n ],\n [\n -108.61083984375,\n 42.97250158602597\n ],\n [\n -109.2041015625,\n 43.32517767999296\n ],\n [\n -109.8193359375,\n 43.70759350405294\n ],\n [\n -110.9619140625,\n 43.91372326852401\n ],\n [\n -111.005859375,\n 43.389081939117496\n ],\n [\n -110.89599609375,\n 42.87596410238256\n ],\n [\n -110.85205078124999,\n 41.78769700539063\n ],\n [\n -110.74218749999999,\n 41.27780646738183\n ]\n ]\n ]\n }\n }\n ]\n}","volume":"8","publishingServiceCenter":{"id":2,"text":"Denver PSC"},"noUsgsAuthors":false,"publicationStatus":"PW","scienceBaseUri":"5a60faffe4b06e28e9c22acb","contributors":{"authors":[{"text":"McAfee, Stephanie A.","contributorId":167115,"corporation":false,"usgs":false,"family":"McAfee","given":"Stephanie A.","affiliations":[{"id":24618,"text":"Department of Geography, University of Nevada, Reno, Reno, NV","active":true,"usgs":false}],"preferred":false,"id":723594,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Pederson, Gregory T. 0000-0002-6014-1425 gpederson@usgs.gov","orcid":"https://orcid.org/0000-0002-6014-1425","contributorId":3106,"corporation":false,"usgs":true,"family":"Pederson","given":"Gregory","email":"gpederson@usgs.gov","middleInitial":"T.","affiliations":[{"id":481,"text":"Northern Rocky Mountain Science Center","active":true,"usgs":true}],"preferred":true,"id":723593,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Woodhouse, Connie A.","contributorId":187601,"corporation":false,"usgs":false,"family":"Woodhouse","given":"Connie","email":"","middleInitial":"A.","affiliations":[{"id":32413,"text":"University of Arizona, Tucson, AZ, USA, 85721","active":true,"usgs":false}],"preferred":false,"id":723595,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"McCabe, Gregory J. 0000-0002-9258-2997 gmccabe@usgs.gov","orcid":"https://orcid.org/0000-0002-9258-2997","contributorId":200854,"corporation":false,"usgs":true,"family":"McCabe","given":"Gregory","email":"gmccabe@usgs.gov","middleInitial":"J.","affiliations":[{"id":37778,"text":"WMA - Integrated Modeling and Prediction Division","active":true,"usgs":true},{"id":438,"text":"National Research Program - Western Branch","active":true,"usgs":true},{"id":37277,"text":"WMA - Earth System Processes Division","active":true,"usgs":true},{"id":5044,"text":"National Research Program - Central Branch","active":true,"usgs":true}],"preferred":true,"id":723596,"contributorType":{"id":1,"text":"Authors"},"rank":4}]}} ,{"id":70194354,"text":"70194354 - 2017 - Solid-phase arsenic speciation in aquifer sediments: A micro-X-ray absorption spectroscopy approach for quantifying trace-level speciation","interactions":[],"lastModifiedDate":"2018-11-26T09:39:13","indexId":"70194354","displayToPublicDate":"2017-11-28T00:00:00","publicationYear":"2017","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":1759,"text":"Geochimica et Cosmochimica Acta","active":true,"publicationSubtype":{"id":10}},"title":"Solid-phase arsenic speciation in aquifer sediments: A micro-X-ray absorption spectroscopy approach for quantifying trace-level speciation","docAbstract":"e of this research is to identify the solid-phase sources and geochemical mechanisms of release of As in aquifers of the Des Moines Lobe glacial advance. The overarching concept is that conditions present at the aquifer-aquitard interfaces promote a suite of geochemical reactions leading to mineral alteration and release of As to groundwater. A microprobe X-ray absorption spectroscopy (lXAS) approach is developed and applied to rotosonic drill core samples to identify the solid-phase speciation of As in aquifer, aquitard, and aquifer-aquitard interface sediments. This approach addresses the low solid-phase As concentrations, as well as the fine-scale physical and chemical heterogeneity of the sediments. The spectroscopy data are analyzed using novel cosine-distance and correlation-distance hierarchical clustering for Fe 1s and As 1s lXAS datasets. The solid-phase Fe and As speciation is then interpreted using sediment and well-water chemical data to propose solid-phase As reservoirs and release mechanisms. The results confirm that in two of the three locations studied, the glacial sediment forming the aquitard is the source of As to the aquifer sediments. The results are consistent with three different As release mechanisms: (1) desorption from Fe (oxyhydr)oxides, (2) reductive dissolution of Fe (oxyhydr)oxides, and (3) oxidative dissolution of Fe sulfides. The findings confirm that glacial sediments at the interface between aquifer and aquitard are geochemically active zones for As. The diversity of As release mechanisms is consistent with the geographic heterogeneity observed in the distribution of elevated-As wells.","language":"English","publisher":"Elsevier","doi":"10.1016/j.gca.2017.05.018","usgsCitation":"Nicholas, S.L., Erickson, M., Woodruff, L.G., Knaeble, A.R., Marcus, M.A., Lynch, J.K., and Toner, B.M., 2017, Solid-phase arsenic speciation in aquifer sediments: A micro-X-ray absorption spectroscopy approach for quantifying trace-level speciation: Geochimica et Cosmochimica Acta, v. 211, p. 228-255, https://doi.org/10.1016/j.gca.2017.05.018.","productDescription":"28 p.","startPage":"228","endPage":"255","ipdsId":"IP-081306","costCenters":[{"id":245,"text":"Eastern Mineral and Environmental Resources Science Center","active":true,"usgs":true},{"id":392,"text":"Minnesota Water Science Center","active":true,"usgs":true}],"links":[{"id":469295,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.1016/j.gca.2017.05.018","text":"Publisher Index Page"},{"id":349424,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -98.87695312499999,\n 41.57436130598913\n ],\n [\n -89.384765625,\n 41.57436130598913\n ],\n [\n -89.384765625,\n 50.51342652633956\n ],\n [\n -98.87695312499999,\n 50.51342652633956\n ],\n [\n -98.87695312499999,\n 41.57436130598913\n ]\n ]\n ]\n }\n }\n ]\n}","volume":"211","publishingServiceCenter":{"id":4,"text":"Rolla PSC"},"noUsgsAuthors":false,"publicationStatus":"PW","scienceBaseUri":"5a60faffe4b06e28e9c22ad1","contributors":{"authors":[{"text":"Nicholas, Sarah L.","contributorId":200812,"corporation":false,"usgs":false,"family":"Nicholas","given":"Sarah","email":"","middleInitial":"L.","affiliations":[],"preferred":false,"id":723436,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Erickson, Melinda L. 0000-0002-1117-2866 merickso@usgs.gov","orcid":"https://orcid.org/0000-0002-1117-2866","contributorId":3671,"corporation":false,"usgs":true,"family":"Erickson","given":"Melinda L.","email":"merickso@usgs.gov","affiliations":[{"id":392,"text":"Minnesota Water Science Center","active":true,"usgs":true},{"id":37947,"text":"Upper Midwest Water Science Center","active":true,"usgs":true}],"preferred":true,"id":723434,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Woodruff, Laurel G. 0000-0002-2514-9923 woodruff@usgs.gov","orcid":"https://orcid.org/0000-0002-2514-9923","contributorId":2224,"corporation":false,"usgs":true,"family":"Woodruff","given":"Laurel","email":"woodruff@usgs.gov","middleInitial":"G.","affiliations":[{"id":245,"text":"Eastern Mineral and Environmental Resources Science Center","active":true,"usgs":true}],"preferred":true,"id":723435,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Knaeble, Alan R.","contributorId":200813,"corporation":false,"usgs":false,"family":"Knaeble","given":"Alan","email":"","middleInitial":"R.","affiliations":[],"preferred":false,"id":723437,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Marcus, Matthew A.","contributorId":200814,"corporation":false,"usgs":false,"family":"Marcus","given":"Matthew","email":"","middleInitial":"A.","affiliations":[],"preferred":false,"id":723438,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"Lynch, Joshua K.","contributorId":200815,"corporation":false,"usgs":false,"family":"Lynch","given":"Joshua","email":"","middleInitial":"K.","affiliations":[],"preferred":false,"id":723439,"contributorType":{"id":1,"text":"Authors"},"rank":6},{"text":"Toner, Brandy M.","contributorId":200816,"corporation":false,"usgs":false,"family":"Toner","given":"Brandy","email":"","middleInitial":"M.","affiliations":[],"preferred":false,"id":723440,"contributorType":{"id":1,"text":"Authors"},"rank":7}]}} ,{"id":70236753,"text":"70236753 - 2017 - Discriminating between natural vs induced seismicity from long-term deformation history of intraplate faults","interactions":[],"lastModifiedDate":"2022-09-19T15:19:31.845284","indexId":"70236753","displayToPublicDate":"2017-11-24T10:08:28","publicationYear":"2017","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":5010,"text":"Science Advances","active":true,"publicationSubtype":{"id":10}},"title":"Discriminating between natural vs induced seismicity from long-term deformation history of intraplate faults","docAbstract":"To assess whether recent seismicity is induced by human activity or is of natural origin, we analyze fault displacements on high-resolution seismic reflection profiles for two regions in the central United States (CUS): the Fort Worth Basin (FWB) of Texas, and the northern Mississippi embayment (NME). Since 2009 earthquake activity in the CUS has increased dramatically, and numerous publications suggest that this increase is primarily due to induced earthquakes caused by deep-well injection of wastewater, both flowback water from hydrofracturing operations and produced water accompanying hydrocarbon production. Alternatively, some argue that these earthquakes are natural, and that the seismicity increase is a normal variation that occurs over millions of years. Our analysis shows that within the NME, faults deform both Quaternary alluvium and underlying sediments dating from Paleozoic through Tertiary, with displacement increasing with geologic unit age, documenting a long history of natural activity. In the FWB, a region of ongoing wastewater injection, basement faults show deformation of the Proterozoic and Paleozoic units, but little or no deformation of younger strata. Specifically, vertical displacements in the post-Pennsylvanian formations, if any, are below the resolution (~15 m) of the seismic data, far less than expected had these faults accumulated deformation over millions of years. Our results support the assertion that recent FWB earthquakes are of induced origin; this conclusion is entirely independent of analyses correlating seismicity and wastewater injection practices. To our knowledge this is the first study to discriminate natural and induced seismicity using classical structural geology analysis techniques.","language":"English","publisher":"American Association for the Advancement of Science","doi":"10.1126/sciadv.1701593","usgsCitation":"Magnani, M.B., Blanpied, M.L., DeShon, H.R., and Hornbach, M., 2017, Discriminating between natural vs induced seismicity from long-term deformation history of intraplate faults: Science Advances, v. 3, no. 11, e1701593, 12 p., https://doi.org/10.1126/sciadv.1701593.","productDescription":"e1701593, 12 p.","ipdsId":"IP-090248","costCenters":[{"id":234,"text":"Earthquake Hazards Program","active":true,"usgs":true}],"links":[{"id":469300,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.1126/sciadv.1701593","text":"Publisher Index Page"},{"id":406969,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","state":"Arkansas, Illinois, Mississippi, Missouri, Tennessee, Texas","city":"Dallas, Irving, Venus","otherGeospatial":"Fort Worth Basin, Mississippi Embayment, Mississippi River","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -97.75634765625,\n 30.259067203213018\n ],\n [\n -96.558837890625,\n 30.826780904779774\n ],\n [\n -95.8447265625,\n 31.728167146023935\n ],\n [\n -95.899658203125,\n 33.054716488042736\n ],\n [\n -96.30615234375,\n 33.8247936182649\n ],\n [\n -96.92138671875,\n 33.99802726234877\n ],\n [\n -97.14111328125,\n 33.90689555128866\n ],\n [\n -98.1298828125,\n 34.17090836352573\n ],\n [\n -99.195556640625,\n 34.243594729697406\n ],\n [\n -100.30517578125,\n 33.54139466898275\n ],\n [\n -100.12939453125,\n 32.79651010951669\n ],\n [\n -99.82177734375,\n 32.52828936482526\n ],\n [\n -100.04150390625,\n 32.01739159980399\n ],\n [\n -99.942626953125,\n 31.50362930577303\n ],\n [\n -98.756103515625,\n 31.23159167205059\n ],\n [\n -98.45947265625,\n 30.56226095049944\n ],\n [\n -97.75634765625,\n 30.259067203213018\n ]\n ]\n ]\n }\n },\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -91.395263671875,\n 33.02708758002874\n ],\n [\n -90.758056640625,\n 33.02708758002874\n ],\n [\n -90.703125,\n 33.779147331286474\n ],\n [\n -89.044189453125,\n 36.474306755095235\n ],\n [\n -88.912353515625,\n 36.94111143010769\n ],\n [\n -89.49462890625,\n 37.89219554724437\n ],\n [\n -89.923095703125,\n 37.80544394934271\n ],\n [\n -89.571533203125,\n 36.84446074079564\n ],\n [\n -91.483154296875,\n 34.08906131584994\n ],\n [\n -91.395263671875,\n 33.02708758002874\n ]\n ]\n ]\n }\n }\n ]\n}","volume":"3","issue":"11","noUsgsAuthors":false,"publicationStatus":"PW","contributors":{"authors":[{"text":"Magnani, Maria Beatrice","contributorId":296647,"corporation":false,"usgs":false,"family":"Magnani","given":"Maria","email":"","middleInitial":"Beatrice","affiliations":[{"id":20300,"text":"Southern Methodist University","active":true,"usgs":false}],"preferred":false,"id":852093,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Blanpied, Michael L. 0000-0002-3294-4458 mblanpied@usgs.gov","orcid":"https://orcid.org/0000-0002-3294-4458","contributorId":203801,"corporation":false,"usgs":true,"family":"Blanpied","given":"Michael","email":"mblanpied@usgs.gov","middleInitial":"L.","affiliations":[{"id":234,"text":"Earthquake Hazards Program","active":true,"usgs":true}],"preferred":true,"id":852094,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"DeShon, Heather R.","contributorId":244313,"corporation":false,"usgs":false,"family":"DeShon","given":"Heather","email":"","middleInitial":"R.","affiliations":[{"id":20301,"text":"SMU","active":true,"usgs":false}],"preferred":false,"id":852095,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Hornbach, Matthew","contributorId":296649,"corporation":false,"usgs":false,"family":"Hornbach","given":"Matthew","affiliations":[{"id":20300,"text":"Southern Methodist University","active":true,"usgs":false}],"preferred":false,"id":852096,"contributorType":{"id":1,"text":"Authors"},"rank":4}]}} ,{"id":70188289,"text":"ofr20171052 - 2017 - Integrated wetland management for waterfowl and shorebirds at Mattamuskeet National Wildlife Refuge, North Carolina","interactions":[],"lastModifiedDate":"2024-03-04T18:57:59.926401","indexId":"ofr20171052","displayToPublicDate":"2017-11-22T07:15:00","publicationYear":"2017","noYear":false,"publicationType":{"id":18,"text":"Report"},"publicationSubtype":{"id":5,"text":"USGS Numbered Series"},"seriesTitle":{"id":330,"text":"Open-File Report","code":"OFR","onlineIssn":"2331-1258","printIssn":"0196-1497","active":true,"publicationSubtype":{"id":5}},"seriesNumber":"2017-1052","title":"Integrated wetland management for waterfowl and shorebirds at Mattamuskeet National Wildlife Refuge, North Carolina","docAbstract":"Mattamuskeet National Wildlife Refuge (MNWR) offers a mix of open water, marsh, forest, and cropland habitats on 20,307 hectares in coastal North Carolina. In 1934, Federal legislation (Executive Order 6924) established MNWR to benefit wintering waterfowl and other migratory bird species. On an annual basis, the refuge staff decide how to manage 14 impoundments to benefit not only waterfowl during the nonbreeding season, but also shorebirds during fall and spring migration. In making these decisions, the challenge is to select a portfolio, or collection, of management actions for the impoundments that optimizes use by the three groups of birds while respecting budget constraints. In this study, a decision support tool was developed for these annual management decisions.
Within the decision framework, there are three different management objectives: shorebird-use days during fall and spring migrations, and waterfowl-use days during the nonbreeding season. Sixteen potential management actions were identified for impoundments; each action represents a combination of hydroperiod and vegetation manipulation. Example hydroperiods include semi-permanent and seasonal drawdowns, and vegetation manipulations include mechanical-chemical treatment, burning, disking, and no action. Expert elicitation was used to build a Bayesian Belief Network (BBN) model that predicts shorebird- and waterfowl-use days for each potential management action. The BBN was parameterized for a representative impoundment, MI-9, and predictions were re-scaled for this impoundment to predict outcomes at other impoundments on the basis of size. Parameter estimates in the BBN model can be updated using observations from ongoing monitoring that is part of the Integrated Waterbird Management and Monitoring (IWMM) program.
The optimal portfolio of management actions depends on the importance, that is, weights, assigned to the three objectives, as well as the budget. Five scenarios with a variety of objective weights and budgets were developed. Given the large number of possible portfolios (1614), a heuristic genetic algorithm was used to identify a management action portfolio that maximized use-day objectives while respecting budget constraints. The genetic algorithm identified a portfolio of management actions for each of the five scenarios, enabling refuge staff to explore the sensitivity of their management decisions to objective weights and budget constraints.
The decision framework developed here provides a transparent, defensible, and testable foundation for decision making at MNWR. The BBN model explicitly structures and parameterizes a mental model previously used by an expert to assign management actions to the impoundments. With ongoing IWMM monitoring, predictions from the model can be tested, and model parameters updated, to reflect empirical observations. This framework is intended to be a living document that can be updated to reflect changes in the decision context (for example, new objectives or constraints, or new models to compete with the current BBN model). Rather than a mandate to refuge staff, this framework is intended to be a decision support tool; tool outputs can become part of the deliberations of refuge staff when making difficult management decisions for multiple objectives.
","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/ofr20171052","usgsCitation":"Tavernia, B.G., Stanton, J.D., and Lyons, J.E., 2017, Integrated wetland management for waterfowl and shorebirds at Mattamuskeet National Wildlife Refuge, North Carolina: U.S. Geological Survey Open-File Report 2017–1052, 43 p., https://doi.org/10.3133/ofr20171052.","productDescription":"vii, 43 p.","numberOfPages":"55","onlineOnly":"Y","additionalOnlineFiles":"N","ipdsId":"IP-074603","costCenters":[{"id":531,"text":"Patuxent Wildlife Research Center","active":true,"usgs":true},{"id":50464,"text":"Eastern Ecological Science Center","active":true,"usgs":true}],"links":[{"id":348384,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/of/2017/1052/coverthb.jpg"},{"id":348385,"rank":2,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/of/2017/1052/ofr20171052.pdf","text":"Report","size":"9.75 MB","linkFileType":{"id":1,"text":"pdf"},"description":"OFR 2017-1052"}],"country":"United States","state":"North Carolina","otherGeospatial":"Mattamuskeet National Wildlife Refuge","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -76.36459350585938,\n 35.42262976362149\n ],\n [\n -76.03363037109374,\n 35.42262976362149\n ],\n [\n -76.03363037109374,\n 35.59031875398378\n ],\n [\n -76.36459350585938,\n 35.59031875398378\n ],\n [\n -76.36459350585938,\n 35.42262976362149\n ]\n ]\n ]\n }\n }\n ]\n}","contact":"Director, Eastern Ecological Science Center
U.S. Geological Survey
12100 Beech Forest Road, Ste 4039
Laurel, MD 20708
Burial in sediments removes organic carbon (OC) from the short-term biosphere-atmosphere carbon (C) cycle, and therefore prevents greenhouse gas production in natural systems. Although OC burial in lakes and reservoirs is faster than in the ocean, the magnitude of inland water OC burial is not well constrained. Here we generate the first global-scale and regionally resolved estimate of modern OC burial in lakes and reservoirs, deriving from a comprehensive compilation of literature data. We coupled statistical models to inland water area inventories to estimate a yearly OC burial of 0.15 (range, 0.06–0.25) Pg C, of which ~40% is stored in reservoirs. Relatively higher OC burial rates are predicted for warm and dry regions. While we report lower burial than previously estimated, lake and reservoir OC burial corresponded to ~20% of their C emissions, making them an important C sink that is likely to increase with eutrophication and river damming.
","language":"English","publisher":"Nature","doi":"10.1038/s41467-017-01789-6","usgsCitation":"Mendonca, R., Muller, R.A., Clow, D.W., Verpoorter, C., Raymond, P., Tranvik, L., and Sobek, S., 2017, Organic carbon burial in global lakes and reservoirs: Nature Communications, v. 8, Article 1694; 7 p., https://doi.org/10.1038/s41467-017-01789-6.","productDescription":"Article 1694; 7 p.","ipdsId":"IP-088515","costCenters":[{"id":191,"text":"Colorado Water Science Center","active":true,"usgs":true}],"links":[{"id":469301,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.1038/s41467-017-01789-6","text":"Publisher Index Page"},{"id":349264,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"volume":"8","publishingServiceCenter":{"id":2,"text":"Denver PSC"},"noUsgsAuthors":false,"publicationDate":"2017-11-22","publicationStatus":"PW","scienceBaseUri":"5a60fb01e4b06e28e9c22af4","contributors":{"authors":[{"text":"Mendonca, Raquel","contributorId":200761,"corporation":false,"usgs":false,"family":"Mendonca","given":"Raquel","email":"","affiliations":[],"preferred":false,"id":723275,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Muller, Roger A.","contributorId":200762,"corporation":false,"usgs":false,"family":"Muller","given":"Roger","email":"","middleInitial":"A.","affiliations":[],"preferred":false,"id":723276,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Clow, David W. 0000-0001-6183-4824 dwclow@usgs.gov","orcid":"https://orcid.org/0000-0001-6183-4824","contributorId":1671,"corporation":false,"usgs":true,"family":"Clow","given":"David","email":"dwclow@usgs.gov","middleInitial":"W.","affiliations":[{"id":191,"text":"Colorado Water Science Center","active":true,"usgs":true}],"preferred":true,"id":723274,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Verpoorter, Charles","contributorId":200763,"corporation":false,"usgs":false,"family":"Verpoorter","given":"Charles","email":"","affiliations":[],"preferred":false,"id":723277,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Raymond, Peter","contributorId":200764,"corporation":false,"usgs":false,"family":"Raymond","given":"Peter","affiliations":[],"preferred":false,"id":723278,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"Tranvik, Lars","contributorId":200765,"corporation":false,"usgs":false,"family":"Tranvik","given":"Lars","email":"","affiliations":[],"preferred":false,"id":723279,"contributorType":{"id":1,"text":"Authors"},"rank":6},{"text":"Sobek, Sebastian","contributorId":169974,"corporation":false,"usgs":false,"family":"Sobek","given":"Sebastian","email":"","affiliations":[],"preferred":false,"id":723280,"contributorType":{"id":1,"text":"Authors"},"rank":7}]}} ,{"id":70192099,"text":"sir20175126 - 2017 - Macroinvertebrate communities evaluated prior to and following a channel restoration project in Silver Creek, Blaine County, Idaho, 2001-16","interactions":[],"lastModifiedDate":"2017-11-28T12:27:48","indexId":"sir20175126","displayToPublicDate":"2017-11-22T00:00:00","publicationYear":"2017","noYear":false,"publicationType":{"id":18,"text":"Report"},"publicationSubtype":{"id":5,"text":"USGS Numbered Series"},"seriesTitle":{"id":334,"text":"Scientific Investigations Report","code":"SIR","onlineIssn":"2328-0328","printIssn":"2328-031X","active":true,"publicationSubtype":{"id":5}},"seriesNumber":"2017-5126","title":"Macroinvertebrate communities evaluated prior to and following a channel restoration project in Silver Creek, Blaine County, Idaho, 2001-16","docAbstract":"The U.S. Geological Survey, in cooperation with Blaine County and The Nature Conservancy, evaluated the status of macroinvertebrate communities prior to and following a channel restoration project in Silver Creek, Blaine County, Idaho. The objective of the evaluation was to determine whether 2014 remediation efforts to restore natural channel conditions in an impounded area of Silver Creek caused declines in local macroinvertebrate communities. Starting in 2001 and ending in 2016, macroinvertebrates were sampled every 3 years at two long-term trend sites and sampled seasonally (spring, summer, and autumn) in 2013, 2015, and 2016 at seven synoptic sites. Trend-site communities were collected from natural stream-bottom substrates to represent locally established macroinvertebrate assemblages. Synoptic site communities were sampled using artificial (multi-plate) substrates to represent recently colonized (4–6 weeks) assemblages. Statistical summaries of spatial and temporal patterns in macroinvertebrate taxonomic composition at both trend and synoptic sites were completed.
The potential effect of the restoration project on resident macroinvertebrate populations was determined by comparing the following community assemblage metrics:
A significant decrease in one or more metric values in the period following stream channel restoration was the basis for determining impairment to the macroinvertebrate communities in Silver Creek.
Comparison of pre-restoration (2001–13) and post‑restoration (2016) macroinvertebrate community composition at trend sites determined that no significant decreases occurred in any metric parameter for communities sampled in 2016. Taxa and EPT richness of colonized assemblages at synoptic sites increased significantly from pre-restoration in 2013 to post-restoration in 2015 and 2016. Similarly, total and EPT abundances at synoptic sites showed non-significant increases from 2013 to 2015 and 2016. Significant seasonal differences in macroinvertebrate assemblages were apparent at synoptic site locations and likely reflected typical life-history patterns of increased insect emergence and development in the late spring and early summer months. Taxa and EPT richness were each significantly higher in spring and summer than in autumn, and total abundances were significantly higher in spring than in summer and autumn. No significant differences in community diversity or evenness of colonized communities were noted at synoptic site locations between pre- and post-restoration years or among seasons. Select community-metric results from the trend- and synoptic‑site sampling indicated that the Silver Creek restoration effort in 2014 did not result in a significant decline in resident macroinvertebrate communities.
","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/sir20175126","collaboration":"Prepared in cooperation with Blaine County and The Nature Conservancy","usgsCitation":"MacCoy, D.E., and Short, T.M., Macroinvertebrate communities evaluated prior to and following a channel restoration project in Silver Creek, Blaine County, Idaho, 2001-16: U.S. Geological Survey Scientific Investigations Report 2017-5126, 25 p., https://doi.org/10.3133/sir20175126.","productDescription":"Report: vi, 25 p.; Appendixes A-B","numberOfPages":"36","onlineOnly":"Y","additionalOnlineFiles":"Y","ipdsId":"IP-046209","costCenters":[{"id":343,"text":"Idaho Water Science 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Idaho Water Science Center
U.S. Geological Survey
230 Collins Road Boise, Idaho 83702
The myriad hydrologic and biogeochemical processes taking place in watersheds occurring across space and time are integrated and reflected in the quantity and quality of water in streams and rivers. Collection of high‐frequency water quality data with sensors in surface waters provides new opportunities to disentangle these processes and quantify sources and transport of water and solutes in the coupled groundwater‐surface water system. A new approach for separating the streamflow hydrograph into three components was developed and coupled with high‐frequency nitrate data to estimate time‐variable nitrate loads from chemically dilute quick flow, chemically concentrated quick flow, and slowflow groundwater end‐member pathways for periods of up to 2 years in a groundwater‐dominated and a quick‐flow‐dominated stream in central Wisconsin, using only streamflow and in‐stream water quality data. The dilute and concentrated quick flow end‐members were distinguished using high‐frequency specific conductance data. Results indicate that dilute quick flow contributed less than 5% of the nitrate load at both sites, whereas 89 ± 8% of the nitrate load at the groundwater‐dominated stream was from slowflow groundwater, and 84 ± 25% of the nitrate load at the quick‐flow‐dominated stream was from concentrated quick flow. Concentrated quick flow nitrate concentrations varied seasonally at both sites, with peak concentrations in the winter that were 2–3 times greater than minimum concentrations during the growing season. Application of this approach provides an opportunity to assess stream vulnerability to nonpoint source nitrate loading and expected stream responses to current or changing conditions and practices in watersheds.
","language":"English ","publisher":"American Geophysical Union","doi":"10.1002/2017WR021654","usgsCitation":"Miller, M.P., Tesoriero, A.J., Hood, K., Terziotti, S., and Wolock, D.M., 2017, Estimating discharge and nonpoint source nitrate loading to streams from three end‐member pathways using high‐frequency water quality data: Water Resources Research, v. 53, no. 12, p. 10201-10216, https://doi.org/10.1002/2017WR021654.","productDescription":"16 p.","startPage":"10201","endPage":"10216","ipdsId":"IP-091844","costCenters":[{"id":353,"text":"Kansas Water Science Center","active":false,"usgs":true},{"id":518,"text":"Oregon Water Science Center","active":true,"usgs":true},{"id":610,"text":"Utah Water Science Center","active":true,"usgs":true},{"id":13634,"text":"South Atlantic Water Science Center","active":true,"usgs":true},{"id":27111,"text":"National Water Quality Program","active":true,"usgs":true}],"links":[{"id":469303,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.1002/2017wr021654","text":"Publisher Index Page"},{"id":357845,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"volume":"53","issue":"12","noUsgsAuthors":false,"publicationDate":"2017-12-07","publicationStatus":"PW","scienceBaseUri":"5bc030a6e4b0fc368eb53a0a","contributors":{"authors":[{"text":"Miller, Matthew P. 0000-0002-2537-1823 mamiller@usgs.gov","orcid":"https://orcid.org/0000-0002-2537-1823","contributorId":3919,"corporation":false,"usgs":true,"family":"Miller","given":"Matthew","email":"mamiller@usgs.gov","middleInitial":"P.","affiliations":[{"id":610,"text":"Utah Water Science Center","active":true,"usgs":true}],"preferred":true,"id":746513,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Tesoriero, Anthony J. 0000-0003-4674-7364 tesorier@usgs.gov","orcid":"https://orcid.org/0000-0003-4674-7364","contributorId":2693,"corporation":false,"usgs":true,"family":"Tesoriero","given":"Anthony","email":"tesorier@usgs.gov","middleInitial":"J.","affiliations":[{"id":518,"text":"Oregon Water Science Center","active":true,"usgs":true}],"preferred":true,"id":746514,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Hood, Krista","contributorId":208243,"corporation":false,"usgs":false,"family":"Hood","given":"Krista","email":"","affiliations":[],"preferred":false,"id":746521,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Terziotti, Silvia 0000-0003-3559-5844 seterzio@usgs.gov","orcid":"https://orcid.org/0000-0003-3559-5844","contributorId":1613,"corporation":false,"usgs":true,"family":"Terziotti","given":"Silvia","email":"seterzio@usgs.gov","affiliations":[{"id":476,"text":"North Carolina Water Science Center","active":true,"usgs":true},{"id":13634,"text":"South Atlantic Water Science Center","active":true,"usgs":true}],"preferred":true,"id":746522,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Wolock, David M. 0000-0002-6209-938X dwolock@usgs.gov","orcid":"https://orcid.org/0000-0002-6209-938X","contributorId":540,"corporation":false,"usgs":true,"family":"Wolock","given":"David","email":"dwolock@usgs.gov","middleInitial":"M.","affiliations":[{"id":27111,"text":"National Water Quality Program","active":true,"usgs":true},{"id":353,"text":"Kansas Water Science Center","active":false,"usgs":true},{"id":503,"text":"Office of Water Quality","active":true,"usgs":true},{"id":37778,"text":"WMA - Integrated Modeling and Prediction Division","active":true,"usgs":true},{"id":451,"text":"National Water Quality Assessment Program","active":true,"usgs":true}],"preferred":true,"id":746523,"contributorType":{"id":1,"text":"Authors"},"rank":5}]}} ,{"id":70193922,"text":"ofr20171146 - 2017 - Timing of warm water refuge use in Crystal River National Wildlife Refuge by manatees—Results and insights from Global Positioning System telemetry data","interactions":[],"lastModifiedDate":"2017-11-21T15:53:56","indexId":"ofr20171146","displayToPublicDate":"2017-11-21T00:00:00","publicationYear":"2017","noYear":false,"publicationType":{"id":18,"text":"Report"},"publicationSubtype":{"id":5,"text":"USGS Numbered Series"},"seriesTitle":{"id":330,"text":"Open-File Report","code":"OFR","onlineIssn":"2331-1258","printIssn":"0196-1497","active":true,"publicationSubtype":{"id":5}},"seriesNumber":"2017-1146","title":"Timing of warm water refuge use in Crystal River National Wildlife Refuge by manatees—Results and insights from Global Positioning System telemetry data","docAbstract":"Managers at the U.S. Fish and Wildlife Service Crystal River National Wildlife Refuge (CRNWR) desire to update their management plan regarding the operation of select springs including Three Sisters Springs. They wish to refine existing parameters used to predict the presence of federally threatened Trichechus manatus latirostris (Florida manatee) in the springs and thereby improve their manatee management options. The U.S. Geological Survey Sirenia Project has been tracking manatees in the CRNWR area since 2006 with floating Global Positioning System (GPS) satellite-monitored telemetry tags. Analyzing movements of these tagged manatees will provide valuable insight into their habitat use patterns.
A total of 136 GPS telemetry bouts were available for this project, representing 730,009 locations generated from 40 manatees tagged in the Gulf of Mexico north of Tampa, Florida. Dates from October through March were included to correspond to the times that cold ambient temperatures were expected, thus requiring a need for manatee thermoregulation and a physiologic need for warm water. Water level (tide) and water temperatures were obtained for the study from Salt River, Crystal River mouth, Bagley Cove, Kings Bay mouth, and Magnolia Spring. Polygons were drawn to subdivide the manatee locations into areas around the most-used springs (Three Sisters/Idiots Delight, House/Hunter/Jurassic, Magnolia and King), Kings Bay, Crystal/Salt Rivers and the Gulf of Mexico.
Manatees were found in the Crystal or Salt Rivers or in the Gulf of Mexico when ambient temperatures were warmer (>20 °C), while they were found in or near the springs (especially Three Sisters Springs) at colder ambient water temperatures. There was a trend of manatees entering springs early in the morning and leaving in the afternoon. There was a strong association of manatee movements in and out of the Three Sisters/Idiots Delight polygon with tide cycles: manatees were more likely to enter the Three Sisters/Idiots Delight polygon on an incoming tide, and leave the polygon on an outgoing tide. Both movement directions were associated with midtide. Future analysis will incorporate human activity and a finer spatial scale, including movements between Three Sisters Springs and Idiots Delight and nearby canals.
","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/ofr20171146","collaboration":"Prepared in cooperation with the U.S. Fish and Wildlife Service","usgsCitation":"Slone, D.H., Butler, S.M., Reid, J.P., and Haase, C.G., 2017, Timing of warm water refuge use in Crystal River National Wildlife Refuge by manatees—Results and insights from Global Positioning System telemetry data: U.S. Geological Survey Open-File Report 2017–1146, 17 p., https://doi.org/10.3133/ofr20171146.","productDescription":"Report: v, 17 p.; Data Release","numberOfPages":"22","onlineOnly":"Y","additionalOnlineFiles":"Y","ipdsId":"IP-091745","costCenters":[{"id":17705,"text":"Wetland and Aquatic Research Center","active":true,"usgs":true}],"links":[{"id":349180,"rank":2,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/of/2017/1146/ofr20171146.pdf","text":"Report","size":"1.58 MB","linkFileType":{"id":1,"text":"pdf"},"description":"OFR 2017–1146"},{"id":349181,"rank":3,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/F78P5ZGR","text":"USGS data release","description":"USGS Data Release","linkHelpText":"Water temperature in Three Sisters Springs, and water temperature and level in Magnolia Spring: Winter 2014–15"},{"id":349179,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/of/2017/1146/coverthb.jpg"}],"country":"United States","state":"Florida","otherGeospatial":"Crystal River","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -82.850,\n 28.830\n ],\n [\n -82.570,\n 28.830\n ],\n [\n -82.570,\n 28.955\n ],\n [\n -82.850,\n 28.955\n ],\n [\n -82.850,\n 28.830\n ]\n ]\n ]\n }\n }\n ]\n}","contact":"Director, Wetland and Aquatic Research Center
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7920 NW 71 Street
Gainesville, FL 32653
Annual peak streamflow (peak flow) at a streamgage is defined as the maximum instantaneous flow in a water year. A water year begins on October 1 and continues through September 30 of the following year; for example, water year 2015 extends from October 1, 2014, through September 30, 2015. The accuracy, characterization, and completeness of the peak streamflow data are critical in determining flood-frequency estimates that are used daily to design water and transportation infrastructure, delineate flood-plain boundaries, and regulate development and utilization of lands throughout the United States and are essential to understanding the implications of climate and land-use change on flooding and high-flow conditions.
As of November 14, 2016, peak-flow data existed for 27,240 unique streamgages in the United States and its territories. The data, collectively referred to as the “peak-flow file,” are available as part of the U.S. Geological Survey (USGS) public web interface, the National Water Information System, at https://nwis.waterdata.usgs.gov/usa/nwis/peak. Although the data have been routinely subjected to periodic review by the USGS Office of Surface Water and screening at the USGS Water Science Center level, these data were not reviewed in a national, systematic manner until 2008 when automated scripts were developed and applied to detect potential errors in peak-flow values and their associated dates, gage heights, and peak-flow qualification codes, as well as qualification codes associated with the gage heights. USGS scientists and hydrographers studied the resulting output, accessed basic records and field notes, and corrected observed errors or, more commonly, confirmed existing data as correct.
This report summarizes the changes in peak-flow file data at a national level, illustrates their nature and causation, and identifies the streamgages affected by these changes. Specifically, the peak-flow data were compared for streamgages with peak flow measured as of November 19, 2008 (before the automated scripts were widely applied) and on November 14, 2016 (after several rounds of corrections). There were 659,332 peak-flow values in the 2008 dataset and 731,965 peak-flow values in the 2016 dataset. When compared to the 2016 dataset, 5,179 (0.79 percent) peak-flow values had changed; 36,506 (5.54 percent) of the peak-flow qualification codes had changed; 1,938 (0.29 percent) peak-flow dates had changed; 18,599 (2.82 percent) of the peak-flow gage heights had changed; and 20,683 (3.14 percent) of the gage-height qualification codes had changed—most as a direct result of the peak-flow file data verification effort led by USGS personnel. The various types of changes are summarized and mapped in this report. In addition to this report, a corresponding USGS data release is provided to identify changes in peak flows at individual streamgages. The data release and the procedures to access the data release are described in this report.
","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/sir20175119","usgsCitation":"Ryberg, K.R., Goree, B.B., Williams-Sether, Tara, and Mason, R.R., Jr., 2017, The U.S. Geological Survey Peak-Flow File Data Verification Project, 2008–16: U.S. Geological Survey Scientific Investigations Report 2017–5119, 61 p., https://doi.org/10.31333/sir20175119.","productDescription":"Report: vii, 63 p.; Appendixes 1-2; Data Release","numberOfPages":"76","onlineOnly":"Y","additionalOnlineFiles":"Y","ipdsId":"IP-068669","costCenters":[{"id":502,"text":"Office of Surface Water","active":true,"usgs":true}],"links":[{"id":347884,"rank":5,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/F7GH9G3P","text":"USGS data release","description":"USGS Data Release","linkHelpText":"Data documenting the U.S. Geological Survey peak-flow file data verification project, 2008-16"},{"id":347883,"rank":4,"type":{"id":3,"text":"Appendix"},"url":"https://pubs.usgs.gov/sir/2017/5119/sir20175119_appendix2.R","text":"Appendix 2","size":"22 kB","description":"SIR 2017–5119 Appendix 2"},{"id":347882,"rank":3,"type":{"id":3,"text":"Appendix"},"url":"https://pubs.usgs.gov/sir/2017/5119/sir20175119_appendix1.pdf","text":"Appendix 1","size":"220 kB","linkFileType":{"id":1,"text":"pdf"},"description":"SIR 2017–5119 Appendix 1"},{"id":347880,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/sir/2017/5119/coverthb.jpg"},{"id":347881,"rank":2,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/sir/2017/5119/sir20175119.pdf","size":"5.87 MB","linkFileType":{"id":1,"text":"pdf"},"description":"SIR 2017–5119"}],"country":"United 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States\"}}]}","contact":"Director, Office of Surface Water
U.S. Geological Survey
415 National Center
12201 Sunrise Valley Drive
Reston, VA 20192
Accurate pathogen detection is essential for developing management strategies to address emerging infectious diseases, an increasingly prominent threat to wildlife. Sampling for free-living pathogens outside of their hosts has benefits for inference and study efficiency, but is still uncommon. We used a laboratory experiment to evaluate the influences of pathogen concentration, water type, and qPCR inhibitors on the detection and quantification of Batrachochytrium dendrobatidis (Bd) using water filtration. We compared results pre- and post-inhibitor removal, and assessed inferential differences when single versus multiple samples were collected across space or time. We found that qPCR inhibition influenced both Bd detection and quantification in natural water samples, resulting in biased inferences about Bd occurrence and abundance. Biases in occurrence could be mitigated by collecting multiple samples in space or time, but biases in Bd quantification were persistent. Differences in Bd concentration resulted in variation in detection probability, indicating that occupancy modeling could be used to explore factors influencing heterogeneity in Bd abundance among samples, sites, or over time. Our work will influence the design of studies involving amphibian disease dynamics and studies utilizing environmental DNA (eDNA) to understand species distributions.
The Edwards and Trinity aquifers are major sources of water in south-central Texas and are both classified as major aquifers by the State of Texas. The population in Hays and Comal Counties is rapidly growing, increasing demands on the area’s water resources. To help effectively manage the water resources in the area, refined maps and descriptions of the geologic structures and hydrostratigraphic units of the aquifers are needed. This report presents the detailed 1:24,000-scale bedrock hydrostratigraphic map as well as names and descriptions of the geologic and hydrostratigraphic units of the Driftwood and Wimberley 7.5-minute quadrangles in Hays and Comal Counties, Tex.
Hydrostratigraphically, the rocks exposed in the study area represent a section of the upper confining unit to the Edwards aquifer, the Edwards aquifer, the upper zone of the Trinity aquifer, and the middle zone of the Trinity aquifer. In the study area, the Edwards aquifer is composed of the Georgetown Formation and the rocks forming the Edwards Group. The Trinity aquifer is composed of the rocks forming the Trinity Group. The Edwards and Trinity aquifers are karstic with high secondary porosity along bedding and fractures. The Del Rio Clay is a confining unit above the Edwards aquifer and does not supply appreciable amounts of water to wells in the study area.
The hydrologic connection between the Edwards and Trinity aquifers and the various hydrostratigraphic units is complex because the aquifer system is a combination of the original Cretaceous depositional environment, bioturbation, primary and secondary porosity, diagenesis, and fracturing of the area from Miocene faulting. All of these factors have resulted in development of modified porosity, permeability, and transmissivity within and between the aquifers. Faulting produced highly fractured areas which allowed for rapid infiltration of water and subsequently formed solutionally enhanced fractures, bedding planes, channels, and caves that are highly permeable and transmissive. Because of faulting the juxtaposition of the aquifers and hydrostratigraphic units has resulted in areas of interconnectedness between the Edwards and Trinity aquifers and the various hydrostratigraphic units that form the aquifers.
","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/sim3386","usgsCitation":"Clark, A.K., and Morris, R.R., 2017, Bedrock geology and hydrostratigraphy of the Edwards and Trinity aquifers within the Driftwood and Wimberley 7.5-minute quadrangles, Hays and Comal Counties, Texas: U.S. Geological Survey Scientific Investigations Map 3386, 12 p., 1 sheet, scale 1:24,000, https://doi.org/10.3133/sim3386.","productDescription":"Report: iv, 12 p.; Plates: 34.78 x 53.96 inches; Table; Data Release; Read Me","onlineOnly":"Y","ipdsId":"IP-078819","costCenters":[{"id":318,"text":"Geosciences and Environmental Change Science Center","active":true,"usgs":true}],"links":[{"id":348918,"rank":5,"type":{"id":20,"text":"Read Me"},"url":"https://pubs.usgs.gov/sim/3386/sim3386_ReadMe.txt","text":"Read Me","size":"8.00 kB","linkFileType":{"id":2,"text":"txt"},"description":"SIM 3386 Read Me"},{"id":348916,"rank":3,"type":{"id":17,"text":"Plate"},"url":"https://pubs.usgs.gov/sim/3386/sim3386_map.pdf","text":"Map","size":"63.2 MB","linkFileType":{"id":1,"text":"pdf"},"description":"SIM 3386 Map"},{"id":348917,"rank":4,"type":{"id":17,"text":"Plate"},"url":"https://pubs.usgs.gov/sim/3386/sim3386_geomap.pdf","text":"Georeferenced map","size":"70.2 MB","linkFileType":{"id":1,"text":"pdf"},"description":"SIM 3386 Georeferenced Map"},{"id":348913,"rank":2,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/sim/3386/sim3386_pamphlet.pdf","text":"Report","size":"46.0 MB","linkFileType":{"id":1,"text":"pdf"},"description":"SIM 3386 Report"},{"id":348933,"rank":8,"type":{"id":27,"text":"Table"},"url":"https://pubs.usgs.gov/sim/3386/sim3386_Table_1.pdf","text":"Table 1—","size":"264 kB","linkFileType":{"id":1,"text":"pdf"},"description":"SIM 3386 Table 1","linkHelpText":"Summary of bedrock geology and hydrostratigraphy of the Edwards and Trinity aquifers within the Driftwood and Wimberley 7.5-minute quadrangles, Hays and Comal Counties, Texas"},{"id":348912,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/sim/3386/coverthb.jpg"},{"id":348921,"rank":7,"type":{"id":22,"text":"Related Work"},"url":"https://doi.org/10.3133/sim3363","text":"Scientific Investigations Map 3363—","linkHelpText":"Geologic framework, hydrostratigraphy, and ichnology of the Blanco, Payton, and Rough Hollow 7.5-minute quadrangles, Blanco, Comal, Hays, and Kendall Counties, Texas"},{"id":348919,"rank":6,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/F76D5RXQ","text":"USGS Data Release","description":"USGS Data Release","linkHelpText":"Data release for bedrock geology and hydrostratigraphy of the Edwards and Trinity Aquifers within the Driftwood and Wimberley 7.5-Minute Quadrangles, Hays and Comal Counties, Texas at 1:24,000 scale"}],"country":"United States","state":"Texas","county":"Comal County, Hays County","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -98.125,\n 30.125\n ],\n [\n -98,\n 30.125\n ],\n [\n -98,\n 29.875\n ],\n [\n -98.125,\n 29.875\n ],\n [\n -98.125,\n 30.125\n ]\n ]\n ]\n }\n }\n ]\n}","contact":"Director, Texas Water Science Center
U.S. Geological Survey
1505 Ferguson Lane
Austin, Texas 78754-4501
Sediment management is a challenge faced by reservoir managers who have several potential options, including dredging, for mitigation of storage capacity lost to sedimentation. As sediment is removed from reservoir storage, potential use of the sediment for socioeconomic or ecological benefit could potentially defray some costs of its removal. Rivers that transport a sandy sediment load will deposit the sand load along a reservoir-headwaters reach where the current of the river slackens progressively as its bed approaches and then descends below the reservoir water level. Given a rare combination of factors, a reservoir deposit of alluvial sand has potential to be suitable for use as proppant for hydraulic fracturing in unconventional oil and gas development. In 2015, the U.S. Geological Survey began a program of researching potential sources of proppant sand from reservoirs, with an initial focus on the Missouri River subbasins that receive sand loads from the Nebraska Sand Hills. This report documents the methods and results of assessments of the suitability of river delta sediment as proppant for a pilot study area in the delta headwaters of Lewis and Clark Lake, Nebraska and South Dakota. Results from surface-geophysical surveys of electrical resistivity guided borings to collect 3.7-meter long cores at 25 sites on delta sandbars using the direct-push method to recover duplicate, 3.8-centimeter-diameter cores in April 2015. In addition, the U.S. Geological Survey collected samples of upstream sand sources in the lower Niobrara River valley.
At the laboratory, samples were dried, weighed, washed, dried, and weighed again. Exploratory analysis of natural sand for determining its suitability as a proppant involved application of a modified subset of the standard protocols known as American Petroleum Institute (API) Recommended Practice (RP) 19C. The RP19C methods were not intended for exploration-stage evaluation of raw materials. Results for the washed samples are not directly applicable to evaluations of suitability for use as fracture sand because, except for particle-size distribution, the API-recommended practices for assessing proppant properties (sphericity, roundness, bulk density, and crush resistance) require testing of specific proppant size classes. An optical imaging particle-size analyzer was used to make measurements of particle-size distribution and particle shape. Measured samples were sieved to separate the dominant-size fraction, and the separated subsample was further tested for roundness, sphericity, bulk density, and crush resistance.
For the bulk washed samples collected from the Missouri River delta, the geometric mean size averaged 0.27 millimeters (mm), 80 percent of the samples were predominantly sand in the API 40/70 size class, and 17 percent were predominantly sand in the API 70/140 size class. Distributions of geometric mean size among the four sandbar complexes were similar, but samples collected from sandbar complex B were slightly coarser sand than those from the other three complexes. The average geometric mean sizes among the four sandbar complexes ranged only from 0.26 to 0.30 mm. For 22 main-stem sampling locations along the lower Niobrara River, geometric mean size averaged 0.26 mm, an average of 61 percent was sand in the API 40/70 size class, and 28 percent was sand in the API 70/140 size class. Average composition for lower Niobrara River samples was 48 percent medium sand, 37 percent fine sand, and about 7 percent each very fine sand and coarse sand fractions. On average, samples were moderately well sorted.
Particle shape and strength were assessed for the dominant-size class of each sample. For proppant strength, crush resistance was tested at a predetermined level of stress (34.5 megapascals [MPa], or 5,000 pounds-force per square inch). To meet the API minimum requirement for proppant, after the crush test not more than 10 percent of the tested sample should be finer than the precrush dominant-size class. For particle shape, all samples surpassed the recommended minimum criteria for sphericity and roundness, with most samples being well-rounded.
For proppant strength, of 57 crush-resistance tested Missouri River delta samples of 40/70-sized sand, 23 (40 percent) were interpreted as meeting the minimum criterion at 34.5 MPa, or 5,000 pounds-force per square inch. Of 12 tested samples of 70/140-sized sand, 9 (75 percent) of the Missouri River delta samples had less than 10 percent fines by volume following crush testing, achieving the minimum criterion at 34.5 MPa. Crush resistance for delta samples was strongest at sandbar complex A, where 67 percent of tested samples met the 10-percent fines criterion at the 34.5-MPa threshold. This frequency was higher than was indicated by samples from sandbar complexes B, C, and D that had rates of 50, 46, and 42 percent, respectively. The group of sandbar complex A samples also contained the largest percentages of samples dominated by the API 70/140 size class, which overall had a higher percentage of samples meeting the minimum criterion compared to samples dominated by coarser size classes; however, samples from sandbar complex A that had the API 40/70 size class tested also had a higher rate for meeting the minimum criterion (57 percent) than did samples from sandbar complexes B, C, and D (50, 43, and 40 percent, respectively).
For samples collected along the lower Niobrara River, of the 25 tested samples of 40/70-sized sand, 9 samples passed the API minimum criterion at 34.5 MPa, but only 3 samples passed the more-stringent criterion of 8 percent postcrush fines. All four tested samples of 70/140 sand passed the minimum criterion at 34.5 MPa, with postcrush fines percentage of at most 4.1 percent.
For two reaches of the lower Niobrara River, where hydraulic sorting was energized artificially by the hydraulic head drop at and immediately downstream from Spencer Dam, suitability of channel deposits for potential use as fracture sand was confirmed by test results. All reach A washed samples were well-rounded and had sphericity scores above 0.65, and samples for 80 percent of sampled locations met the crush-resistance criterion at the 34.5-MPa stress level. A conservative lower-bound estimate of sand volume in the reach A deposits was about 86,000 cubic meters. All reach B samples were well-rounded but sphericity averaged 0.63, a little less than the average for upstream reaches A and SP. All four samples tested passed the crush-resistance test at 34.5 MPa. Of three reach B sandbars, two had no more than 3 percent fines after the crush test, surpassing more stringent criteria for crush resistance that accept a maximum of 6 percent fines following the crush test for the API 70/140 size class.
Relative to the crush-resistance test results for the API 40/70 size fraction of two samples of mine output from Loup River settling-basin dredge spoils near Genoa, Nebr., four of five reach A sample locations compared favorably. The four samples had increases in fines composition of 1.6–5.9 percentage points, whereas fines in the two mine-output samples increased by an average 6.8 percentage points.
","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/sir20175105","collaboration":"Prepared in cooperation with Midwest Region Initiative on Natural Sources of Fracture Sand","usgsCitation":"Zelt, R.B., Hobza, C.M., Burton, B.L., Schaepe, N.J., and Piatak, Nadine, 2017, Suitability of river delta sediment as proppant, Missouri and Niobrara Rivers, Nebraska and South Dakota, 2015: U.S. Geological Survey Scientific Investigations Report 2017–5105, 51 p., https://doi.org/10.3133/sir20175105.","productDescription":"Report: viii, 51 p.; Tables: 4; Data Release","numberOfPages":"64","onlineOnly":"Y","additionalOnlineFiles":"Y","ipdsId":"IP-077776","costCenters":[{"id":245,"text":"Eastern Mineral and Environmental Resources Science Center","active":true,"usgs":true},{"id":464,"text":"Nebraska Water Science Center","active":true,"usgs":true}],"links":[{"id":348988,"rank":2,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/sir/2017/5105/sir20175105.pdf","text":"Report","size":"5.51 MB","linkFileType":{"id":1,"text":"pdf"},"description":"SIR 2017–5105"},{"id":348987,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/sir/2017/5105/coverthb2.jpg"},{"id":348989,"rank":3,"type":{"id":27,"text":"Table"},"url":"https://pubs.usgs.gov/sir/2017/5105/sir20175105_table4.xlsx","text":"Table 4","size":"38.0 kB","linkFileType":{"id":3,"text":"xlsx"},"description":"SIR 2017–5105 Table 4"},{"id":348990,"rank":4,"type":{"id":27,"text":"Table"},"url":"https://pubs.usgs.gov/sir/2017/5105/sir20175105_table5.xlsx","text":"Table 5","size":"26.6 kB","linkFileType":{"id":3,"text":"xlsx"},"description":"SIR 2017–5105 Table 5"},{"id":348991,"rank":5,"type":{"id":27,"text":"Table"},"url":"https://pubs.usgs.gov/sir/2017/5105/sir20175105_table6.xlsx","text":"Table 6","size":"70.8 kB","linkFileType":{"id":3,"text":"xlsx"},"description":"SIR 2017–5105 Table 6"},{"id":348992,"rank":6,"type":{"id":27,"text":"Table"},"url":"https://pubs.usgs.gov/sir/2017/5105/sir20175105_table9.xlsx","text":"Table 9","size":"75.0","linkFileType":{"id":3,"text":"xlsx"},"description":"SIR 2017–5105 Table 9"},{"id":348993,"rank":7,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/F79W0CQB","text":"USGS data release","description":"USGS Data Release","linkHelpText":"Streambed sediment data for Missouri and Niobrara Rivers, Nebraska and South Dakota, 2015"}],"country":"United States","state":"Nebraska, South Dakota","otherGeospatial":"Missouri River, Niobrara River","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -98.75,\n 42.5\n ],\n [\n -97.45,\n 42.5\n ],\n [\n -97.45,\n 43\n ],\n [\n -98.75,\n 43\n ],\n [\n -98.75,\n 42.5\n ]\n ]\n ]\n }\n }\n ]\n}","contact":"Director, Nebraska Water Science Center
U.S. Geological Survey
5231 South 19th Street
Lincoln, NE 68512