Estrogens and estrogen mimics are commonly found in surface waters and are associated with deleterious effects in fish populations. Impaired fertility and fecundity in fish following chronic exposures to estrogens and estrogen mimics during critical windows in development are well documented. However, information regarding differential reproductive effects of exposure within defined developmental stages remains sparse. In this study, reproductive capacity was assessed in Japanese medaka (Oryzias latipes) after exposure to two concentrations of 17β–estradiol (E2β; 2 ng/L and 50 ng/L) during four distinct stages of development: gonad development, gonad differentiation, development of secondary sex characteristics (SSC) and gametogenesis. Exposure to E2β did not adversely impact survival, hatch success, growth, or genotypic ratios. In contrast, exposure to 50 ng/L E2β during SSC development altered phenotypic ratios and SSC. Exposure to both E2β treatments reduced reproductive capacity (fertility, fecundity) by 7.3–57.4% in adult medaka breeding pairs, with hindrance of SSC development resulting in the largest disruption in breeding capacity (51.6–57.4% decrease) in the high concentration. This study documents differential effects among four critical stages of development and provides insight into factors (window of exposure, exposure concentration and duration of exposure period) contributing to reproductive disruption in fish.
","language":"English","publisher":"ACS Publications","doi":"10.1021/acs.est.7b01568","usgsCitation":"Lee Pow, C.S., Tilahun, K., Creech, K., Law, J.M., Cope, W., Kwak, T.J., Rice, J., Aday, D.D., and Kullman, S.W., 2017, Windows of susceptibility and consequences of early life exposures to 17β–estradiol on medaka (Oryzias latipes) reproductive success: Environmental Science & Technology, v. 51, no. 9, p. 5296-5305, https://doi.org/10.1021/acs.est.7b01568.","productDescription":"10 p.","startPage":"5296","endPage":"5305","ipdsId":"IP-086265","costCenters":[{"id":198,"text":"Coop Res Unit Atlanta","active":true,"usgs":true}],"links":[{"id":348537,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"volume":"51","issue":"9","publishingServiceCenter":{"id":9,"text":"Reston PSC"},"noUsgsAuthors":false,"publicationDate":"2017-04-20","publicationStatus":"PW","scienceBaseUri":"5a05771ce4b09af898c70865","contributors":{"authors":[{"text":"Lee Pow, Crystal S. D.","contributorId":176861,"corporation":false,"usgs":false,"family":"Lee Pow","given":"Crystal","email":"","middleInitial":"S. D.","affiliations":[],"preferred":false,"id":721443,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Tilahun, Kedamawit","contributorId":200213,"corporation":false,"usgs":false,"family":"Tilahun","given":"Kedamawit","email":"","affiliations":[],"preferred":false,"id":721444,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Creech, Kari","contributorId":200214,"corporation":false,"usgs":false,"family":"Creech","given":"Kari","email":"","affiliations":[],"preferred":false,"id":721445,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Law, J. Mac","contributorId":176862,"corporation":false,"usgs":false,"family":"Law","given":"J.","email":"","middleInitial":"Mac","affiliations":[],"preferred":false,"id":721446,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Cope, W. Gregory","contributorId":70353,"corporation":false,"usgs":true,"family":"Cope","given":"W. Gregory","affiliations":[],"preferred":false,"id":721447,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"Kwak, Thomas J. 0000-0002-0616-137X tkwak@usgs.gov","orcid":"https://orcid.org/0000-0002-0616-137X","contributorId":834,"corporation":false,"usgs":true,"family":"Kwak","given":"Thomas","email":"tkwak@usgs.gov","middleInitial":"J.","affiliations":[{"id":198,"text":"Coop Res Unit Atlanta","active":true,"usgs":true}],"preferred":true,"id":720597,"contributorType":{"id":1,"text":"Authors"},"rank":6},{"text":"Rice, James A.","contributorId":176863,"corporation":false,"usgs":false,"family":"Rice","given":"James A.","affiliations":[],"preferred":false,"id":721448,"contributorType":{"id":1,"text":"Authors"},"rank":7},{"text":"Aday, D. Derek","contributorId":176864,"corporation":false,"usgs":false,"family":"Aday","given":"D.","email":"","middleInitial":"Derek","affiliations":[],"preferred":false,"id":721449,"contributorType":{"id":1,"text":"Authors"},"rank":8},{"text":"Kullman, Seth W.","contributorId":62516,"corporation":false,"usgs":true,"family":"Kullman","given":"Seth","email":"","middleInitial":"W.","affiliations":[],"preferred":false,"id":721450,"contributorType":{"id":1,"text":"Authors"},"rank":9}]}} ,{"id":70190965,"text":"ofr20171118 - 2017 - Evaluation of the Eureka Manta2 Water-Quality Multiprobe Sonde ","interactions":[],"lastModifiedDate":"2017-11-10T09:59:07","indexId":"ofr20171118","displayToPublicDate":"2017-11-08T10:45:00","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":"2017-1118","title":"Evaluation of the Eureka Manta2 Water-Quality Multiprobe Sonde ","docAbstract":"Two Eureka Manta2 3.5 water-quality multiprobe sondes by Eureka Water Probes were tested at the U.S. Geological Survey (USGS) Hydrologic Instrumentation Facility (HIF) against known standards over the sonde operating temperatures to verify the manufacturer’s stated accuracy specifications for pH, specific conductance (SC) at 25 degrees Celsius (°C), dissolved oxygen (DO), and turbidity. The Manta2 sondes were evaluated for compliance with the USGS National Field Manual for the Collection of Water-Quality Data (NFM) criteria for continuous water-quality monitors, and for compliance with the manufacturer’s technical specifications. The Manta2 was also evaluated for its compliance to Serial Digital Interface at 1200 baud (SDI-12) version 1.3.
The Manta2 met the NFM recommendations and manufacturer’s accuracy specifications for DO and turbidity at all values tested. The Manta2 pH sensors met the NFM recommendations and manufacturer’s accuracy specification for nominal pH values of 10 and lower. One of the two sensors was out of compliance by 1.2 units for pH 11.16 at 15 °C and by 0.25 unit for pH 10.78 at 40 °C. The Manta2 sensors were within the NFM recommendations for SC, except at 100 microsiemens (μS/cm) at 40 °C, where the SC sensor exceeded the test standard value by as much as 25 percent. One of two sensors was within manufacturer’s accuracy specifications at 25 °C for all the tested SC values, while the other SC sensor was outside the manufacturer’s accuracy specifications at 100 μS/cm, exceeding the test standard value by 9 percent. One of two sensors was outside the manufacturer’s accuracy specifications at 10,000 μS/cm at 15°C, exceeding the test standard value by 3 percent. One Manta2 passed SDI-12 compliance testing with a NR Systems SDI-12 Verifier. One Manta2 was field tested for 6 weeks at USGS station 02492620, National Space Technology Laboratories (NSTL) Station, Mississippi, on the Pearl River and showed overall good agreement with a well-maintained Hydrolab Datasonde 5X site sonde for water temperature, pH, and DO. Differences in SC values between the Manta2 and the site sonde were most likely due to differences in the deployment depth of the sondes.
","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/ofr20171118","usgsCitation":"Tillman, E.F., 2017, Evaluation of the Eureka Manta2 Water-Quality Multiprobe Sonde: U.S. Geological Survey Open-File Report 2017–1118, 37 p., https://doi.org/10.3133/ofr20171118. ","productDescription":"vi, 37 p.","numberOfPages":"43","onlineOnly":"Y","additionalOnlineFiles":"N","ipdsId":"IP-076099","costCenters":[{"id":502,"text":"Office of Surface Water","active":true,"usgs":true}],"links":[{"id":347738,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/of/2017/1118/coverthb.jpg"},{"id":347739,"rank":2,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/of/2017/1118/ofr20171118.pdf","text":"Report","size":"1.31 MB","linkFileType":{"id":1,"text":"pdf"},"description":"OFR 2017-1118"}],"contact":"Chief, Hydrologic Instrumentation Facility
U.S. Geological Survey
Building 2101
Stennis Space Center, MS 39529
Understanding how both quality and quantity of prey affect the population dynamics of marine predators is a crucial step toward predicting the effects of environmental perturbations on population-level processes. The Junk Food Hypothesis, which posits that energetic content of prey species may influence reproductive capacity of marine top predators regardless of prey availability, has been proposed as a mechanism by which changes in prey populations could affect predator populations in high latitude systems; however, support for this hypothesis has been inconsistent across studies, and further data are needed to elucidate variation in the relative importance of prey quantity and quality, both among predator species and across ecological systems. We tested the relative importance of prey quantity and quality to nestling survival in the eastern brown pelican Pelecanus occidentalis carolinensis across 9 breeding colonies in the northern Gulf of Mexico that varied in underlying availability of a key prey resource, the Gulf menhaden Brevoortia patronus. Both feeding frequency and meal mass were significantly correlated to energy provisioning rates and nestling survival, while energy density of meals had little effect on either metric. Compared to previous results from cold-water systems, we found lower and less variable energy densities (4.4 kJ g-1, vs. 5.2 to 6.5 kJ g-1 in other studies) and lipid content (9% dry mass, vs. 16 to 23% in other studies) of common prey items. While Gulf menhaden was the most common prey species at all colonies, the proportion of menhaden fed to nestlings varied and was not strongly correlated to fledging success. We conclude that quantity rather than quality of prey, particularly small schooling fish, is the main driver of brown pelican reproductive success in this system, and that environmental perturbations affecting biomass, distribution, and abundance of forage fish could substantially affect brown pelican reproductive success.
","language":"English","publisher":"Inter-Research","doi":"10.3354/meps12301","usgsCitation":"Lamb, J.S., Jodice, P.G., and Satge, Y.G., 2017, Diet composition and provisioning rates of nestlings determine reproductive success in a subtropical seabird: Marine Ecology Progress Series, v. 581, p. 149-164, https://doi.org/10.3354/meps12301.","productDescription":"16 p.","startPage":"149","endPage":"164","ipdsId":"IP-083227","costCenters":[{"id":198,"text":"Coop Res Unit Atlanta","active":true,"usgs":true}],"links":[{"id":469338,"rank":1,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.3354/meps12301","text":"Publisher Index Page"},{"id":438155,"rank":0,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/F7R78D6J","text":"USGS data release","linkHelpText":"Composition of diet of juvenile Brown Pelican in the northern Gulf of Mexico (2013-2015)"},{"id":348429,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","otherGeospatial":"Gulf of Mexico","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -98.525390625,\n 25.403584973186703\n ],\n [\n -81.38671875,\n 25.403584973186703\n ],\n [\n -81.38671875,\n 30.826780904779774\n ],\n [\n -98.525390625,\n 30.826780904779774\n ],\n [\n -98.525390625,\n 25.403584973186703\n ]\n ]\n ]\n }\n }\n ]\n}","volume":"581","publishingServiceCenter":{"id":12,"text":"Tacoma PSC"},"noUsgsAuthors":false,"publicationStatus":"PW","scienceBaseUri":"5a0425aee4b0dc0b45b452f4","contributors":{"authors":[{"text":"Lamb, Juliet S. 0000-0003-0358-3240","orcid":"https://orcid.org/0000-0003-0358-3240","contributorId":198059,"corporation":false,"usgs":false,"family":"Lamb","given":"Juliet","email":"","middleInitial":"S.","affiliations":[],"preferred":false,"id":721086,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Jodice, Patrick G.R. 0000-0001-8716-120X pjodice@usgs.gov","orcid":"https://orcid.org/0000-0001-8716-120X","contributorId":200009,"corporation":false,"usgs":true,"family":"Jodice","given":"Patrick","email":"pjodice@usgs.gov","middleInitial":"G.R.","affiliations":[{"id":200,"text":"Coop Res Unit Seattle","active":true,"usgs":true},{"id":198,"text":"Coop Res Unit Atlanta","active":true,"usgs":true}],"preferred":true,"id":720623,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Satge, Yvan G.","contributorId":200132,"corporation":false,"usgs":false,"family":"Satge","given":"Yvan","email":"","middleInitial":"G.","affiliations":[],"preferred":false,"id":721087,"contributorType":{"id":1,"text":"Authors"},"rank":3}]}} ,{"id":70193726,"text":"ofr20171144 - 2017 - Acoustic tag detections of green sturgeon in the Columbia River and Coos Bay estuaries, Washington and Oregon, 2010–11","interactions":[],"lastModifiedDate":"2017-11-08T17:33:23","indexId":"ofr20171144","displayToPublicDate":"2017-11-08T00:00:00","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":"2017-1144","title":"Acoustic tag detections of green sturgeon in the Columbia River and Coos Bay estuaries, Washington and Oregon, 2010–11","docAbstract":"The Columbia River, in Washington and Oregon, and Coos Bay, in Oregon, are economically important shipping channels that are inhabited by several fishes protected under the Endangered Species Act (ESA). Maintenance of shipping channels involves dredge operations to maintain sufficient in-channel depths to allow large ships to navigate the waterways safely. Fishes entrained by dredge equipment often die or experience delayed mortality. Other potential negative effects of dredging include increased turbidity, reductions in prey resources, and the release of harmful contaminants from the dredged sediments. One species of concern is the ESA-listed green sturgeon (Acipenser medirostris; Southern Distinct Population Segment). In this study, we used acoustic telemetry to identify habitat use, arrival and departure timing, and the extent of upstream migration of green sturgeon in the Columbia River and Coos Bay to help inform dredge operations to minimize potential take of green sturgeon. Autonomous acoustic receivers were deployed in Coos Bay from the mouth to river kilometer (rkm) 21.6 from October 2009 through October 2010. In the Columbia River Estuary, receivers were deployed between the mouth and rkm 37.8 from April to November in 2010 and 2011. A total of 29 subadult and adult green sturgeon were tagged with temperature and pressure sensor tags and released during the study, primarily in Willapa Bay and Grays Harbor, Washington, and the Klamath River, Oregon. Green sturgeon detected during the study but released by other researchers also were included in the study.
The number of tagged green sturgeon detected in the two estuaries differed markedly. In Coos Bay, only one green sturgeon was detected for about 2 hours near the estuary mouth. In the Columbia River Estuary, 9 green sturgeon were detected in 2010 and 10 fish were detected in 2011. Green sturgeon entered the Columbia River from May through October during both years, with the greatest numbers of fish being present in August and September. One green sturgeon was detected at the uppermost receiver station (rkm 37.8), but overall, the number of fish detected upriver decreased rapidly with distance from the estuary mouth. Residence times of fish that were only detected in the lower 4.8 rkm generally were less than 24 hours, but fish detected farther upriver had a median residence time greater than 10 days. Green sturgeon were widely dispersed among channel and non-channel habitats in the lower estuary in 2010. In 2011, the fish were more concentrated near the estuary mouth. The intensity of use, measured as the total number of fish detections at each station, generally was greatest from Point Ellice (rkm 20.1) to Rice Island (rkm 33.0) in channel and shallow shoal areas, and lowest at the stations west of Point Ellice with the exception of the area near the entrance to the Ilwaco Channel.
Sensor tag data indicated that the deeper South and North Channel habitats (bottom depth ≥10 m) were used, as were the more shallow sandy shoal, shoreline, and bay habitats (bottom depth <10 m). Median fish depths among fish and receiver locations ranged from 2.5 to 28.2 m below water surface (bws) and water temperatures ranged from 9.1 to 22.0 °C during late May through mid-October. In the deeper channel habitat, near the Ilwaco Channel, fish inhabited water with median temperatures ranging from 11.4 to 16.7 °C, whereas east of Point Ellice, predominantly in shallow non-channel habitats, fish inhabited water with median temperatures ranging from about 17.0 to 21.0 °C.
","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/ofr20171144","collaboration":"Prepared in cooperation with the U.S. Army Corps of Engineers","usgsCitation":"Hansel, H.C., Romine, J.G., and Perry, R.W., 2017, Acoustic tag detections of green sturgeon in the Columbia River and Coos Bay estuaries, Washington and Oregon, 2010–11: U.S. Geological Survey Open-File Report 2017-1144, 30 p., https://doi.org/10.3133/ofr20171144.","productDescription":"vi, 30 p.","onlineOnly":"Y","ipdsId":"IP-088817","costCenters":[{"id":654,"text":"Western Fisheries Research Center","active":true,"usgs":true}],"links":[{"id":348413,"rank":2,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/of/2017/1144/ofr20171144.pdf","text":"Report","size":"1.3 MB","linkFileType":{"id":1,"text":"pdf"},"description":"OFR 2017-1144"},{"id":348412,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/of/2017/1144/coverthb.jpg"}],"country":"United States","state":"Oregon","city":"Astoria","otherGeospatial":"Coos Bay","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -124.36283111572264,\n 43.33067209551502\n ],\n [\n -124.12696838378908,\n 43.33067209551502\n ],\n [\n -124.12696838378908,\n 43.476591264232674\n ],\n [\n -124.36283111572264,\n 43.476591264232674\n ],\n [\n -124.36283111572264,\n 43.33067209551502\n ]\n ]\n ]\n }\n },\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -124.09263610839842,\n 46.14416148780093\n ],\n [\n -123.61129760742186,\n 46.14416148780093\n ],\n [\n -123.61129760742186,\n 46.32559414426375\n ],\n [\n -124.09263610839842,\n 46.32559414426375\n ],\n [\n -124.09263610839842,\n 46.14416148780093\n ]\n ]\n ]\n }\n }\n ]\n}","contact":"Director, Western Fisheries Research Center
U.S. Geological Survey
6505 NE 65th Street
Seattle, Washington 98115
Over recent decades, marine ecosystems of Prince William Sound (PWS), Alaska, have experienced concurrent effects of natural and anthropogenic perturbations, including variability in the climate system of the northeastern Pacific Ocean. We documented spatial and temporal patterns of variability in the summer marine bird community in relation to habitat and climate variability using boat-based surveys of marine birds conducted during the period 1989–2012. We hypothesized that a major factor structuring marine bird communities in PWS would be proximity to the shoreline, which is theorized to relate to aspects of food web structure. We also hypothesized that shifts in physical ecosystem drivers differentially affected nearshore-benthic and pelagic components of PWS food webs. We evaluated support for our hypotheses using an approach centered on community-level patterns of spatial and temporal variability. We found that an environmental gradient related to water depth and distance from shore was the dominant factor spatially structuring the marine bird community. Responses of marine birds to this onshore-offshore environmental gradient were related to dietary specialization, and separated marine bird taxa by prey type. The primary form of temporal variability over the study period was monotonic increases or decreases in abundance for 11 of 18 evaluated genera of marine birds; 8 genera had declined, whereas 3 had increased. The greatest declines occurred in genera associated with habitats that were deeper and farther from shore. Furthermore, most of the genera that declined primarily fed on pelagic prey resources, such as forage fish and mesozooplankton, and few were directly affected by the 1989 Exxon Valdez oil spill. Our observations of synchronous declines are indicative of a shift in pelagic components of PWS food webs. This pattern was correlated with climate variability at time-scales of several years to a decade.
","language":"English","publisher":"Elsevier","doi":"10.1016/j.dsr2.2017.07.012","usgsCitation":"Cushing, D., Roby, D.D., and Irons, D.B., 2017, Patterns of distribution, abundance, and change over time in a subarctic marine bird community: Deep Sea Research Part II: Topical Studies in Oceanography, v. 147, p. 148-163, https://doi.org/10.1016/j.dsr2.2017.07.012.","productDescription":"16 p.","startPage":"148","endPage":"163","ipdsId":"IP-077534","costCenters":[{"id":200,"text":"Coop Res Unit Seattle","active":true,"usgs":true}],"links":[{"id":469343,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.1016/j.dsr2.2017.07.012","text":"Publisher Index Page"},{"id":348386,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","state":"Alaska","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -148.6505126953125,\n 59.72317714492064\n ],\n [\n -146.2225341796875,\n 59.72317714492064\n ],\n [\n -146.2225341796875,\n 61.0689165862774\n ],\n [\n -148.6505126953125,\n 61.0689165862774\n ],\n [\n -148.6505126953125,\n 59.72317714492064\n ]\n ]\n ]\n }\n }\n ]\n}","volume":"147","publishingServiceCenter":{"id":12,"text":"Tacoma PSC"},"noUsgsAuthors":false,"publicationStatus":"PW","scienceBaseUri":"5a07e843e4b09af898c8cb20","contributors":{"authors":[{"text":"Cushing, Daniel","contributorId":199323,"corporation":false,"usgs":false,"family":"Cushing","given":"Daniel","affiliations":[],"preferred":false,"id":720954,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Roby, Daniel D. 0000-0001-9844-0992 droby@usgs.gov","orcid":"https://orcid.org/0000-0001-9844-0992","contributorId":3702,"corporation":false,"usgs":true,"family":"Roby","given":"Daniel","email":"droby@usgs.gov","middleInitial":"D.","affiliations":[{"id":200,"text":"Coop Res Unit Seattle","active":true,"usgs":true}],"preferred":true,"id":717361,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Irons, David B.","contributorId":63658,"corporation":false,"usgs":true,"family":"Irons","given":"David","email":"","middleInitial":"B.","affiliations":[],"preferred":false,"id":720955,"contributorType":{"id":1,"text":"Authors"},"rank":3}]}} ,{"id":70209685,"text":"70209685 - 2017 - Magnetotelluric imaging of lower crustal melt and lithospheric hydration in the Rocky Mountain Front transition zone, Colorado, USA","interactions":[],"lastModifiedDate":"2020-04-21T16:08:17.708545","indexId":"70209685","displayToPublicDate":"2017-11-06T11:01:32","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":"Magnetotelluric imaging of lower crustal melt and lithospheric hydration in the Rocky Mountain Front transition zone, Colorado, USA","docAbstract":"We present an electrical resistivity model of the crust and upper mantle from two‐dimensional (2‐D) anisotropic inversion of magnetotelluric data collected along a 450 km transect of the Rio Grande rift, southern Rocky Mountains, and High Plains in Colorado, USA. Our model provides a window into the modern‐day lithosphere beneath the Rocky Mountain Front to depths in excess of 150 km. Two key features of the 2‐D resistivity model are (1) a broad zone (~200 km wide) of enhanced electrical conductivity (<20 Ωm) in the midcrust to lower crust that is centered beneath the highest elevations of the southern Rocky Mountains and (2) hydrated lithospheric mantle beneath the Great Plains with water content in excess of 100 ppm. We interpret the high conductivity region of the lower crust as a zone of partially molten basalt and associated deep‐crustal fluids that is the result of recent (less than 10 Ma) tectonic activity in the region. The recent supply of volatiles and/or heat to the base of the crust in the late Cenozoic implies that modern‐day tectonic activity in the western United States extends to at least the western margin of the Great Plains. The transition from conductive to resistive upper mantle is caused by a gradient in lithospheric modification, likely including hydration of nominally anhydrous minerals, with maximum hydration occurring beneath the Rocky Mountain Front. This lithospheric “hydration front” has implications for the tectonic evolution of the continental interior and the mechanisms by which water infiltrates the lithosphere.
","language":"English","publisher":"American Geophysical Union","doi":"10.1002/2017JB014474","collaboration":"","usgsCitation":"Feucht, D., Sheehan, A.F., and Bedrosian, P.A., 2017, Magnetotelluric imaging of lower crustal melt and lithospheric hydration in the Rocky Mountain Front transition zone, Colorado, USA: Journal of Geophysical Research B: Solid Earth, v. 122, no. 12, p. 9489-9510, https://doi.org/10.1002/2017JB014474.","productDescription":"22 p.","startPage":"9489","endPage":"9510","ipdsId":"IP-091898","costCenters":[{"id":211,"text":"Crustal Geophysics and Geochemistry Science Center","active":true,"usgs":true}],"links":[{"id":469344,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.1002/2017jb014474","text":"Publisher Index Page"},{"id":374159,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","state":"Colorado","otherGeospatial":"Rocky Mountains ","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -109.061279296875,\n 37.01132594307015\n ],\n [\n -102.052001953125,\n 37.01132594307015\n ],\n [\n -102.052001953125,\n 40.98819156349393\n ],\n [\n -109.061279296875,\n 40.98819156349393\n ],\n [\n -109.061279296875,\n 37.01132594307015\n ]\n ]\n ]\n }\n }\n ]\n}","volume":"122","issue":"12","noUsgsAuthors":false,"publicationDate":"2017-12-04","publicationStatus":"PW","contributors":{"authors":[{"text":"Feucht, D. W. 0000-0002-3672-4719","orcid":"https://orcid.org/0000-0002-3672-4719","contributorId":224277,"corporation":false,"usgs":false,"family":"Feucht","given":"D. W.","affiliations":[],"preferred":false,"id":787515,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Sheehan, Anne F 0000-0002-9629-1687","orcid":"https://orcid.org/0000-0002-9629-1687","contributorId":224234,"corporation":false,"usgs":false,"family":"Sheehan","given":"Anne","email":"","middleInitial":"F","affiliations":[{"id":36621,"text":"University of Colorado","active":true,"usgs":false}],"preferred":false,"id":787516,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Bedrosian, Paul A. 0000-0002-6786-1038 pbedrosian@usgs.gov","orcid":"https://orcid.org/0000-0002-6786-1038","contributorId":839,"corporation":false,"usgs":true,"family":"Bedrosian","given":"Paul","email":"pbedrosian@usgs.gov","middleInitial":"A.","affiliations":[{"id":312,"text":"Geology, Minerals, Energy, and Geophysics Science Center","active":true,"usgs":true},{"id":211,"text":"Crustal Geophysics and Geochemistry Science Center","active":true,"usgs":true}],"preferred":true,"id":787517,"contributorType":{"id":1,"text":"Authors"},"rank":3}]}} ,{"id":70220386,"text":"70220386 - 2017 - Predicting landscape effects of Mississippi River diversions on soil organic carbon sequestration","interactions":[],"lastModifiedDate":"2021-05-10T14:36:35.266187","indexId":"70220386","displayToPublicDate":"2017-11-06T09:29:26","publicationYear":"2017","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":1475,"text":"Ecosphere","active":true,"publicationSubtype":{"id":10}},"title":"Predicting landscape effects of Mississippi River diversions on soil organic carbon sequestration","docAbstract":"Large Mississippi River (MR) diversions (peak water flow >1416 m3/s and sediment loads >165 kg/s) have been proposed as part of a suite of coastal restoration projects and are expected to rehabilitate and rebuild wetlands to alleviate the significant historic wetland loss in coastal Louisiana. These coastal wetlands are undergoing increasing eustatic sea‐level rise, land subsidence, climate change, and anthropogenic disturbances. However, the effect of MR diversions on wetland soil organic carbon (SOC) sequestration in receiving basins remains unknown. The rate of SOC sequestration or carbon burial in wetlands is one of the variables used to assess the role of wetland soils in carbon cycling and also to construct wetland carbon budgets. In this study, we examined the effects of MR water and sediment diversions on landscape‐scale SOC sequestration rates that were estimated from vertical accretion for the next 50 yr (2010–2060) under two environmental (moderate and less optimistic) scenarios. Our analyses were based on model simulations taken from the Wetland Morphology model developed for Louisiana's 2012 Coastal Master Plan. The master plan modeled a “future‐without‐action” scenario as well as eight individual MR diversion projects in two of the hydrologic basins (Barataria and Breton Sound). We examined the effects that discharge rates (peak flow) and locations of these individual diversion projects had on SOC sequestration rates. Modeling results indicate that large river diversions are capable of improving basin‐wide SOC sequestration capacity (162–222 g C·m−2·yr−1) by up to 14% (30 g C·m−2·yr−1) in Louisiana deltaic wetlands compared to the future‐without‐action scenario, especially under the less optimistic scenario. When large river diversions are placed in the upper receiving basin, SOC sequestration rates are 3.7–10.5% higher (6–24 g C·m−2·yr−1) than when these structures are placed in the lower receiving basin. Modeling results also indicate that both diversion discharge and location have large effects on SOC sequestration in low‐salinity (freshwater and intermediate marshes) as compared to high‐salinity marshes (brackish and saline marshes).
","language":"English","publisher":"Wiley","doi":"10.1002/ecs2.1984","usgsCitation":"Wang, H., Steyer, G.D., Couvillion, B., Beck, H.J., Rybczyk, J.M., Rivera-Monroy, V.H., Krauss, K.W., and Visser, J.M., 2017, Predicting landscape effects of Mississippi River diversions on soil organic carbon sequestration: Ecosphere, v. 8, no. 11, e01984, 15 p., https://doi.org/10.1002/ecs2.1984.","productDescription":"e01984, 15 p.","ipdsId":"IP-070521","costCenters":[{"id":5064,"text":"Southeast Regional Director's Office","active":true,"usgs":true},{"id":17705,"text":"Wetland and Aquatic Research Center","active":true,"usgs":true}],"links":[{"id":469345,"rank":1,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.1002/ecs2.1984","text":"Publisher Index Page"},{"id":438156,"rank":0,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/F72R3PWW","text":"USGS data release","linkHelpText":"Predicting landscape effects of Mississippi River diversions on soil organic carbon sequestration"},{"id":385545,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","state":"Louisiana","otherGeospatial":"Barataria Basin, Breton Sound Basin","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -91.3128662109375,\n 29.480252193344267\n ],\n [\n -89.00,\n 29.480252193344267\n ],\n [\n -89.00,\n 30.285159872426014\n ],\n [\n -91.3128662109375,\n 30.285159872426014\n ],\n [\n -91.3128662109375,\n 29.480252193344267\n ]\n ]\n ]\n }\n }\n ]\n}","volume":"8","issue":"11","noUsgsAuthors":false,"publicationDate":"2017-11-06","publicationStatus":"PW","contributors":{"authors":[{"text":"Wang, Hongqing 0000-0002-2977-7732 wangh@usgs.gov","orcid":"https://orcid.org/0000-0002-2977-7732","contributorId":140432,"corporation":false,"usgs":true,"family":"Wang","given":"Hongqing","email":"wangh@usgs.gov","affiliations":[{"id":17705,"text":"Wetland and Aquatic Research Center","active":true,"usgs":true},{"id":455,"text":"National Wetlands Research Center","active":true,"usgs":true}],"preferred":true,"id":815331,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Steyer, Gregory D. 0000-0001-7231-0110 steyerg@usgs.gov","orcid":"https://orcid.org/0000-0001-7231-0110","contributorId":2856,"corporation":false,"usgs":true,"family":"Steyer","given":"Gregory","email":"steyerg@usgs.gov","middleInitial":"D.","affiliations":[{"id":5064,"text":"Southeast Regional Director's Office","active":true,"usgs":true},{"id":455,"text":"National Wetlands Research Center","active":true,"usgs":true},{"id":5062,"text":"Office of the Chief Scientist for Ecosystems","active":true,"usgs":true},{"id":17705,"text":"Wetland and Aquatic Research Center","active":true,"usgs":true}],"preferred":true,"id":815332,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Couvillion, Brady 0000-0001-5323-1687 couvillionb@usgs.gov","orcid":"https://orcid.org/0000-0001-5323-1687","contributorId":146832,"corporation":false,"usgs":true,"family":"Couvillion","given":"Brady","email":"couvillionb@usgs.gov","affiliations":[{"id":455,"text":"National Wetlands Research Center","active":true,"usgs":true},{"id":17705,"text":"Wetland and Aquatic Research Center","active":true,"usgs":true}],"preferred":true,"id":815333,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Beck, Holly J. 0000-0002-0567-9329 hbeck@usgs.gov","orcid":"https://orcid.org/0000-0002-0567-9329","contributorId":257931,"corporation":false,"usgs":true,"family":"Beck","given":"Holly","email":"hbeck@usgs.gov","middleInitial":"J.","affiliations":[{"id":17705,"text":"Wetland and Aquatic Research Center","active":true,"usgs":true}],"preferred":true,"id":815334,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Rybczyk, John M","contributorId":257932,"corporation":false,"usgs":false,"family":"Rybczyk","given":"John","email":"","middleInitial":"M","affiliations":[{"id":12723,"text":"Western Washington University","active":true,"usgs":false}],"preferred":false,"id":815335,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"Rivera-Monroy, Victor H. 0000-0003-2804-4139","orcid":"https://orcid.org/0000-0003-2804-4139","contributorId":200322,"corporation":false,"usgs":false,"family":"Rivera-Monroy","given":"Victor","email":"","middleInitial":"H.","affiliations":[{"id":5115,"text":"Louisiana State University","active":true,"usgs":false}],"preferred":false,"id":815336,"contributorType":{"id":1,"text":"Authors"},"rank":6},{"text":"Krauss, Ken W. 0000-0003-2195-0729 kraussk@usgs.gov","orcid":"https://orcid.org/0000-0003-2195-0729","contributorId":2017,"corporation":false,"usgs":true,"family":"Krauss","given":"Ken","email":"kraussk@usgs.gov","middleInitial":"W.","affiliations":[{"id":455,"text":"National Wetlands Research Center","active":true,"usgs":true},{"id":17705,"text":"Wetland and Aquatic Research Center","active":true,"usgs":true}],"preferred":true,"id":815337,"contributorType":{"id":1,"text":"Authors"},"rank":7},{"text":"Visser, Jenneke M.","contributorId":178417,"corporation":false,"usgs":false,"family":"Visser","given":"Jenneke","email":"","middleInitial":"M.","affiliations":[],"preferred":false,"id":815338,"contributorType":{"id":1,"text":"Authors"},"rank":8}]}} ,{"id":70191563,"text":"ds1071 - 2017 - Groundwater data collection for the Quinault Indian Nation, Grays Harbor and Jefferson Counties, Washington","interactions":[],"lastModifiedDate":"2017-11-06T10:01:13","indexId":"ds1071","displayToPublicDate":"2017-11-03T00:00:00","publicationYear":"2017","noYear":false,"publicationType":{"id":18,"text":"Report"},"publicationSubtype":{"id":5,"text":"USGS Numbered Series"},"seriesTitle":{"id":310,"text":"Data Series","code":"DS","onlineIssn":"2327-638X","printIssn":"2327-0271","active":false,"publicationSubtype":{"id":5}},"seriesNumber":"1071","title":"Groundwater data collection for the Quinault Indian Nation, Grays Harbor and Jefferson Counties, Washington","docAbstract":"Groundwater data were collected on the Quinault Indian Reservation to provide the Quinualt Indian Nation (QIN) with basic knowledge of the existing wells and springs on the reservation, and to establish a water-level network to be monitored by QIN to begin building a long-term groundwater dataset. The 327 mi2 Quinault Indian Reservation is located within the heavily forested Queets-Quinault watershed along the west-central coast of Washington and includes the coastal communities of Taholah and Queets, and the inland community of Amanda Park. Groundwater data were collected or compiled for 87 sites—82 wells and 5 springs. In October 2016, a field inventory was done to locate the sites and acquire site data. Groundwater levels were measured in 15 of the field-inventoried wells and 3 of those wells were observed as flowing (artesian). A monthly groundwater‑level monitoring network of 13 wells was established by the U.S. Geological Survey in March 2017, and the network was transferred to QIN in June 2017 for continued measurements.
Several data needs were identified that would provide a more complete understanding of the groundwater system of the Quinault Indian Reservation. The collection of monthly water-level data for multiple years is an important first step in understanding seasonal and long term changes in water levels. Additionally, the collection of baseline groundwater chemistry and quality data across the reservation would help with future efforts to monitor existing and potentially changing groundwater quality conditions. Development of a water budget of the Queets-Quinault Watershed and the reservation within that area would provide water users with a better understanding of this important resource and provide needed information about the competing demands on local water sources.
","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/ds1071","collaboration":"Prepared in cooperation with the Quinault Indian Nation","usgsCitation":"Kahle, S.C., Fasser, E.T., and Olsen, T.D., 2017, Groundwater data collection for the Quinault Indian Nation, Grays Harbor and Jefferson Counties, Washington: U.S. Geological Survey Data Series 1071, 13 p., https://doi.org/10.3133/ds1071.","productDescription":"iv, 13 p.","numberOfPages":"22","onlineOnly":"Y","ipdsId":"IP-088886","costCenters":[{"id":622,"text":"Washington Water Science Center","active":true,"usgs":true}],"links":[{"id":348178,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/ds/1071/coverthb.jpg"},{"id":348179,"rank":2,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/ds/1071/ds1071.pdf","text":"Report","size":"5.1 MB","linkFileType":{"id":1,"text":"pdf"},"description":"DS 1071"}],"country":"United States","state":"Washington","county":" Grays Harbor County, Jefferson County","otherGeospatial":" Quinault Indian Reservation","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -124.36386108398438,\n 47.245678021018755\n ],\n [\n -123.87359619140624,\n 47.245678021018755\n ],\n [\n -123.87359619140624,\n 47.56726060598141\n ],\n [\n -124.36386108398438,\n 47.56726060598141\n ],\n [\n -124.36386108398438,\n 47.245678021018755\n ]\n ]\n ]\n }\n }\n ]\n}","contact":"Director, Washington Water Science Center
U.S. Geological Survey
934 Broadway, Suite 300
Tacoma, Washington 98402
Floodplain lakes are important wetlands on many lowland floodplains of the world but depressional floodplain lakes are rare in the Mississippi River Alluvial Valley. One of the largest is Catahoula Lake, which has existed with seasonally fluctuating water levels for several thousand years but is now in an increasingly hydrologically altered floodplain. Woody vegetation has been encroaching into the lake bed and the rate of this expansion has increased since major human hydrologic modifications, such as channelization, levee construction, and dredging for improvement of navigation, but it remains unknown what role those modifications may have played in altering lake sedimentation processes. Profiles of thirteen 137Cs sediment cores indicate sedimentation has been about 0.26 cm y− 1 over the past 60 years and has been near this rate since land use changes began about 200 years ago (210Pb, and 14C in Tedford, 2009). Carbon sequestration was low (10.4 g m− 2 y− 1), likely because annual drying promotes mineralization and export. Elemental composition (high Zr and Ti and low Ca and K) and low pH of recent (<~60 y) or surface sediments suggest Gulf Coastal Plain origin, but below the recent sediment deposits, 51% of sediment profiles showed influence of Mississippi River alluvium, rich in base cations such as K+, Ca2 +, and Mg2 +. The recent shift to dominance of Coastal Plain sediments on the lake-bed surface suggests hydrologic modification has disconnected the lake from sediment-bearing flows from the Mississippi River. Compared to its condition prior to hydrologic alterations that intensified in the 1930s, Catahoula Lake is about 15 cm shallower and surficial sediments are more acidic. Although these results are not sufficient to attribute ecological changes directly to sedimentological changes, it is likely the altered sedimentary and hydrologic environment is contributing to the increased dominance of woody vegetation.
","language":"English","publisher":"Elsevier","doi":"10.1016/j.catena.2017.04.020","usgsCitation":"Latuso, K.D., Keim, R.F., King, S.L., Weindorf, D.C., and DeLaune, R.D., 2017, Sediment deposition and sources into a Mississippi River floodplain lake; Catahoula Lake, Louisiana: Catena, v. 156, p. 290-297, https://doi.org/10.1016/j.catena.2017.04.020.","productDescription":"8 p.","startPage":"290","endPage":"297","ipdsId":"IP-061448","costCenters":[{"id":198,"text":"Coop Res Unit Atlanta","active":true,"usgs":true}],"links":[{"id":348169,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","state":"Louisiana","otherGeospatial":"Catahoula Lake","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -92.1697998046875,\n 31.42749129448044\n ],\n [\n -92.04277038574219,\n 31.502458420817206\n ],\n [\n -91.99607849121094,\n 31.552793227677334\n ],\n [\n -91.99745178222656,\n 31.613626970322684\n ],\n [\n -92.06748962402344,\n 31.610703179979982\n ],\n [\n -92.12448120117188,\n 31.577950455417472\n ],\n [\n -92.17666625976562,\n 31.52411741833466\n ],\n [\n -92.22198486328125,\n 31.48313670206181\n ],\n [\n -92.23915100097656,\n 31.454439514853256\n ],\n [\n -92.22335815429688,\n 31.433350262414404\n ],\n [\n -92.19863891601562,\n 31.42749129448044\n ],\n [\n -92.1697998046875,\n 31.42749129448044\n ]\n ]\n ]\n }\n }\n ]\n}","volume":"156","publishingServiceCenter":{"id":8,"text":"Raleigh PSC"},"noUsgsAuthors":false,"publicationStatus":"PW","scienceBaseUri":"59fd8027e4b0531197b5013b","contributors":{"authors":[{"text":"Latuso, Karen D.","contributorId":113984,"corporation":false,"usgs":false,"family":"Latuso","given":"Karen","email":"","middleInitial":"D.","affiliations":[{"id":5115,"text":"Louisiana State University","active":true,"usgs":false}],"preferred":false,"id":720022,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Keim, Richard F.","contributorId":117125,"corporation":false,"usgs":false,"family":"Keim","given":"Richard","email":"","middleInitial":"F.","affiliations":[{"id":5115,"text":"Louisiana State University","active":true,"usgs":false}],"preferred":false,"id":720023,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"King, Sammy L. 0000-0002-5364-6361 sking@usgs.gov","orcid":"https://orcid.org/0000-0002-5364-6361","contributorId":557,"corporation":false,"usgs":true,"family":"King","given":"Sammy","email":"sking@usgs.gov","middleInitial":"L.","affiliations":[{"id":198,"text":"Coop Res Unit Atlanta","active":true,"usgs":true}],"preferred":true,"id":720024,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Weindorf, David C.","contributorId":140924,"corporation":false,"usgs":false,"family":"Weindorf","given":"David","email":"","middleInitial":"C.","affiliations":[{"id":27688,"text":"Texas Tech University, Lubbock, TX 79409","active":true,"usgs":false}],"preferred":false,"id":720025,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"DeLaune, Ronald D.","contributorId":61581,"corporation":false,"usgs":false,"family":"DeLaune","given":"Ronald","email":"","middleInitial":"D.","affiliations":[{"id":5115,"text":"Louisiana State University","active":true,"usgs":false}],"preferred":false,"id":720026,"contributorType":{"id":1,"text":"Authors"},"rank":5}]}} ,{"id":70193728,"text":"70193728 - 2017 - Oiling accelerates loss of salt marshes, southeastern Louisiana","interactions":[],"lastModifiedDate":"2017-11-03T18:59:09","indexId":"70193728","displayToPublicDate":"2017-11-03T00:00:00","publicationYear":"2017","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":2980,"text":"PLoS ONE","active":true,"publicationSubtype":{"id":10}},"title":"Oiling accelerates loss of salt marshes, southeastern Louisiana","docAbstract":"The 2010 BP Deepwater Horizon (DWH) oil spill damaged thousands of km2 of intertidal marsh along shorelines that had been experiencing elevated rates of erosion for decades. Yet, the contribution of marsh oiling to landscape-scale degradation and subsequent land loss has been difficult to quantify. Here, we applied advanced remote sensing techniques to map changes in marsh land cover and open water before and after oiling. We segmented the marsh shorelines into non-oiled and oiled reaches and calculated the land loss rates for each 10% increase in oil cover (e.g. 0% to >70%), to determine if land loss rates for each reach oiling category were significantly different before and after oiling. Finally, we calculated background land-loss rates to separate natural and oil-related erosion and land loss. Oiling caused significant increases in land losses, particularly along reaches of heavy oiling (>20% oil cover). For reaches with ≥20% oiling, land loss rates increased abruptly during the 2010–2013 period, and the loss rates during this period are significantly different from both the pre-oiling (p < 0.0001) and 2013–2016 post-oiling periods (p < 0.0001). The pre-oiling and 2013–2016 post-oiling periods exhibit no significant differences in land loss rates across oiled and non-oiled reaches (p = 0.557). We conclude that oiling increased land loss by more than 50%, but that land loss rates returned to background levels within 3–6 years after oiling, suggesting that oiling results in a large but temporary increase in land loss rates along the shoreline.
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Locations of transportation assets (bridges, roadways, and railroad lines) and selected utility assets (high-capacity overhead power-transmission lines, underground pipelines, water treatment facilities, and in-channel dams) were determined using aerial imagery hosted by the Google Earth platform. Identified assets were aggregated by stream reach, county, and class. Accompanying the report is a polyline shapefile of the stream reaches documented by Robinson. The shapefile, derived from line work in the National Hydrography Dataset and attributed with channel migration rates, is released with complete Federal Geographic Data Committee metadata. The data presented in this report are intended to help stakeholders and others identify high-risk areas where transportation and utility assets may be threatened by fluvial erosion hazards thus warranting consideration for mitigation strategies.
","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/ds1068","collaboration":"Prepared in cooperation with the Indiana Office of Community and Rural Affairs","usgsCitation":"Sperl, B.J., 2017, Vulnerable transportation and utility assets near actively migrating streams in Indiana: U.S. Geological Survey Data Series 1068, 11 p., https://doi.org/10.3133/ds1068.","productDescription":"Report: iv, 11 p.; Data Release","numberOfPages":"20","onlineOnly":"Y","additionalOnlineFiles":"N","ipdsId":"IP-086741","costCenters":[{"id":27231,"text":"Indiana-Kentucky Water Science Center","active":true,"usgs":true}],"links":[{"id":347784,"rank":2,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/ds/1068/ds1068.pdf","text":"Report","size":"4.47 MB","linkFileType":{"id":1,"text":"pdf"},"description":"DS 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\"}}]}","contact":"Director, Ohio-Kentucky-Indiana Water Science Center
U.S. Geological Survey
5957 Lakeside Boulevard
Indianapolis, IN 46278
Hurricane Matthew moved adjacent to the coasts of Florida, Georgia, South Carolina, and North Carolina. The hurricane made landfall once near McClellanville, South Carolina, on October 8, 2016, as a Category 1 hurricane on the Saffir-Simpson Hurricane Wind Scale. The U.S. Geological Survey (USGS) deployed a temporary monitoring network of storm-tide sensors at 284 sites along the Atlantic coast from Florida to North Carolina to record the timing, areal extent, and magnitude of hurricane storm tide and coastal flooding generated by Hurricane Matthew. Storm tide, as defined by the National Oceanic and Atmospheric Administration, is the water-level rise generated by a combination of storm surge and astronomical tide during a coastal storm.
The deployment for Hurricane Matthew was the largest deployment of storm-tide sensors in USGS history and was completed as part of a coordinated Federal emergency response as outlined by the Stafford Act (Public Law 92–288, 42 U.S.C. 5121–5207) under a directed mission assignment by the Federal Emergency Management Agency. In total, 543 high-water marks (HWMs) also were collected after Hurricane Matthew, and this was the second largest HWM recovery effort in USGS history after Hurricane Sandy in 2012.
During the hurricane, real-time water-level data collected at temporary rapid deployment gages (RDGs) and long-term USGS streamgage stations were relayed immediately for display on the USGS Flood Event Viewer (https://stn.wim.usgs.gov/FEV/#MatthewOctober2016). These data provided emergency managers and responders with critical information for tracking flood-effected areas and directing assistance to effected communities. Data collected from this hurricane can be used to calibrate and evaluate the performance of storm-tide models for maximum and incremental water level and flood extent, and the site-specific effects of storm tide on natural and anthropogenic features of the environment.
","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/ofr20171122","collaboration":"Prepared in cooperation with the Federal Emergency Management Agency","usgsCitation":"Frantz, E.R., Byrne, M.J., Sr., Caldwell, A.W., and Harden, S.L., 2017, Monitoring storm tide and flooding from Hurricane Matthew along the Atlantic coast of the United States, October 2016: U.S. Geological Survey Open-File Report 2017–1122, 37 p., https://doi.org/10.3133/ofr20171122.","productDescription":"vi, 37 p.","numberOfPages":"48","onlineOnly":"Y","additionalOnlineFiles":"N","ipdsId":"IP-081187","costCenters":[{"id":13634,"text":"South Atlantic Water Science Center","active":true,"usgs":true}],"links":[{"id":347956,"rank":2,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/of/2017/1122/ofr20171122.pdf","text":"Report","size":"5.25 MB","linkFileType":{"id":1,"text":"pdf"},"description":"OFR 2017-1122"},{"id":347955,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/of/2017/1122/coverthb.jpg"}],"country":"United States","state":"Florida, Georgia, North Carolina, South Carolina","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -78.57421875,\n 36.56260003738545\n ],\n [\n -78.50830078125,\n 35.585851593232356\n ],\n [\n -79.69482421875,\n 34.903952965590065\n ],\n [\n -81.9140625,\n 33.358061612778876\n ],\n [\n -82.44140625,\n 31.70947636001935\n ],\n [\n -82.7490234375,\n 30.713503990354965\n ],\n [\n -82.0458984375,\n 28.998531814051795\n ],\n [\n -81.2548828125,\n 27.00040800352175\n ],\n [\n -81.650390625,\n 25.224820176765036\n ],\n [\n -80.595703125,\n 24.666986385216273\n ],\n [\n -79.3212890625,\n 25.20494115356912\n ],\n [\n -79.4970703125,\n 26.96124577052697\n ],\n [\n -79.82666015625,\n 27.994401411046148\n ],\n [\n -80.26611328125,\n 29.592565403314087\n ],\n [\n -80.35400390625,\n 31.259769987394286\n ],\n [\n -78.57421875,\n 32.80574473290688\n ],\n [\n -76.3330078125,\n 33.46810795527896\n ],\n [\n -74.619140625,\n 34.397844946449865\n ],\n [\n -73.32275390625,\n 36.56260003738545\n ],\n [\n -78.57421875,\n 36.56260003738545\n ]\n ]\n ]\n }\n }\n ]\n}","contact":"Director, South Atlantic Water Science Center
U.S. Geological Survey
720 Gracern Road
Columbia, SC 29210
The Regional Ocean Modeling System (ROMS) has been coupled with the Water Quality Analysis Simulation Program (WASP) to be used in a comprehensive analysis of water quality in Barnegat Bay, New Jersey. The coupler can spatially aggregate hydrodynamic information in ROMS cells into larger WASP segments. It can also be used to resample ROMS output at a finer temporal scale to meet WASP time-stepping requirements. The coupler aggregates flow components, temperature, and salinity in ROMS output for input to WASP via a hydrodynamic linkage file. The coupler was tested initially with idealized cases designed to verify the water mass balance and conservation of constituent mass using one-to-one and one-to-many connectivity options between segments. A realistic example from the Toms River embayment, a subdomain of Barnegat Bay, was used to demonstrate the functionality of the coupling. A WASP eutrophication model accounting for dissolved oxygen (DO), nitrogen, and constant phytoplankton concentrations was applied to explore the distribution and trends in DO and nitrogen in the embayment for the period of July–August 2012. Results of DO modeling indicate satisfactory agreement with measurements collected at in-bay stations and also indicate that this coupled approach, despite substantial differences in spatiotemporal discretization between the models, provides adequate predictive capabilities.
Digital flood-inundation maps for a 7.5-mile reach of the White River at Noblesville, Indiana, were created by the U.S. Geological Survey (USGS) in cooperation with the Indiana Department of Transportation. The flood-inundation maps, which can be accessed through the USGS Flood Inundation Mapping Science website at https://water.usgs.gov/osw/flood_inundation/, depict estimates of the areal extent and depth of flooding corresponding to selected water levels (stages) at the White River at Noblesville, Ind., streamgage (USGS station number 03349000). Real-time stages at this streamgage may be obtained from the USGS National Water Information System at https://waterdata.usgs.gov/nwis or the National Weather Service (NWS) Advanced Hydrologic Prediction Service at http:/water.weather.gov/ahps/, which also forecasts flood hydrographs at the same site as the USGS streamgage (NWS site NBLI3).
Flood profiles were computed for the stream reach by means of a one-dimensional, step-backwater hydraulic modeling software developed by the U.S. Army Corps of Engineers. The hydraulic model was calibrated using the current (2016) stage-discharge rating at the USGS streamgage 03349000, White River at Noblesville, Ind., and documented high-water marks from the floods of September 4, 2003, and May 6, 2017. The hydraulic model was then used to compute 15 water-surface profiles for flood stages at 1-foot (ft) intervals referenced to the streamgage datum ranging from 10.0 ft (the NWS “action stage”) to 24.0 ft, which is the highest stage interval of the current (2016) USGS stage-discharge rating curve and 2 ft higher than the NWS “major flood stage.” The simulated water-surface profiles were then combined with a geographic information system digital elevation model (derived from light detection and ranging data having a 0.98-ft vertical accuracy and 4.9-ft horizontal resolution) to delineate the area flooded at each stage.
The availability of these maps, along with internet information regarding current stage from the USGS streamgage and forecasted high-flow stages from the NWS, will provide emergency management personnel and residents with information that is critical for flood response activities, such as evacuations and road closures, as well as for postflood recovery efforts.
","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/sir20175123","collaboration":"Prepared in cooperation with the Indiana Department of Transportation","usgsCitation":"Martin, Z.W., 2017, Flood-inundation maps for the White River at Noblesville, Indiana: U.S. Geological Survey Scientific Investigations Report 2017–5123, 11 p., https://doi.org/10.3133/sir20175123.","productDescription":"Report: vi, 11 p.; Data Release","numberOfPages":"22","onlineOnly":"Y","additionalOnlineFiles":"Y","ipdsId":"IP-086871","costCenters":[{"id":27231,"text":"Indiana-Kentucky Water Science Center","active":true,"usgs":true}],"links":[{"id":347858,"rank":2,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/sir/2017/5123/sir20175123.pdf","text":"Report","size":"1.94 MB","linkFileType":{"id":1,"text":"pdf"},"description":"SIR 2017–5123"},{"id":347859,"rank":3,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/F7MG7N0J","text":"USGS data release","description":"USGS Data Release","linkHelpText":"White River at Noblesville, Indiana, flood-inundation model and GIS data"},{"id":347857,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/sir/2017/5123/coverthb.jpg"}],"country":"United States","state":"Indiana","city":"Noblesville","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -86.03616714477539,\n 40.033792168980135\n ],\n [\n -85.95720291137695,\n 40.033792168980135\n ],\n [\n -85.95720291137695,\n 40.10919420673381\n ],\n [\n -86.03616714477539,\n 40.10919420673381\n ],\n [\n -86.03616714477539,\n 40.033792168980135\n ]\n ]\n ]\n }\n }\n ]\n}","contact":"Director, Ohio-Kentucky-Indiana Water Science Center
U.S. Geological Survey
5957 Lakeside Boulevard
Indianapolis, IN 46278–1996
The Upper Mississippian Heath Formation, which accumulated in the Big Snowy Trough of central Montana, has been known for three decades to contain mudrocks highly enriched in Zn, V, Mo, Ni and other metals, and source rocks for oil. The unit has more recently been recognized as a prospective tight oil play. Here we present petrographic, paleontologic, geochemical, and carbon and sulfur isotope data on seven continuous drill cores spanning ≤146 m of immature to marginally mature strata in order to improve understanding of the depositional setting of the Heath.
The unit consists of five third-order transgressive-regressive cycles (C1–C5 from bottom to top) that were deposited during a fluctuating climatic regime. Cycles comprise thinly interbedded gray to black mudrock and carbonate strata capped by either coal, implying a humid climate (C1, C3 and C4), or gypsum, implying more arid conditions (C2); the upper part of C5 is not preserved in our study area. Mfs (maximum flooding surfaces) in C1, C2, C4, and C5 lie within black mudrock beds ~0.5–3-m thick with >10% TOC (total organic carbon), type I and type II kerogen (determined by programmed pyrolysis), high contents of Zn, V, Mo, and other metals, relatively low values of δ13CTOC and δ34Spyrite, and a limited-diversity fauna of locally abundant, thin-shelled pelecypods (Dunbarella? sp.).
The mfs in C2 is within the Cox Ranch oil shale bed, which is known from previous studies to be metalliferous; new analyses reported here show ≤28 wt % TOC, 5140 ppm Zn, 1910 ppm V, 1590 ppm Mo, and 509 ppm Ni. Strata that contain the mfs of C1, C4, and C5 are shown here for the first time to also have high metal contents, with maximum values of 1030–7340 ppm Zn, 446–1980 ppm V, 72–859 ppm Mo, and 221–452 ppm Ni. Cycle C3, which contains more gray mudrock and carbonate beds than the other cycles, has lower TOC (≤4.2 wt %), lower metals, and mainly type III kerogen. Carbonate beds include normal-marine crinoidal mudstone to packstone and lesser (dolo)mudstone with fenestral fabric, peloids, intraclasts, and a euryhaline fauna. Mid-Chesterian (early Serpukhovian) foraminifers in C3, combined with previously published fossil data, suggest that third-order cycles in the Heath Formation were ~1–2 myr in duration. They formed during a time of active block faulting in the Big Snowy Trough and global cooling linked to Gondwanan glaciation.
Tectonic, climatic, and paleogeographic factors shaped the cycles of the Heath Formation. Faunal and geochemical evidence indicate that conditions were most favorable for marine life during C3. Molybdenum concentrations >100 ppm and organic geochemical data suggest euxinic conditions during deposition of the black mudrock in C2, C4, and C5, but the presence of shell beds (1 mm–6-cm thick) within this mudrock requires bottom water with sufficient oxygen to support life, at least periodically. The apparent conflict between the geochemical and paleontologic observations likely reflects the different time scales of these two environmental proxies: 1000s of yrs vs <1–10s of yrs, respectively.
Metal- and organic-rich strata in the Heath Formation formed by slow, condensed sedimentation from periodically anoxic or euxinic bottom waters in a marine basin. Fossil data indicate that anoxia was episodic, perhaps seasonal and/or linked to longer-duration climate shifts. On a millennial time scale, metal enrichments in the Heath reflect a balance between primary productivity that was high enough for oxygen to be consumed by sinking organic matter and oxic seawater inflow that was strong enough to maintain a supply of metals without compromising anoxia. Organic-rich mudrock in the Heath shares intriguing lithologic and geochemical similarities with mudrock in other Middle to Upper Paleozoic units such as the Devonian-Mississippian Bakken Formation and Pennsylvanian cyclothems (e.g., Excello Shale).
","language":"English","publisher":"Micropress","doi":"10.29041/strat.14.1-4.97-122","usgsCitation":"Dumoulin, J.A., Johnson, C.A., Kelley, K.D., Botterell, P.J., Hackley, P.C., Scott, C., and Slack, J.F., 2017, Transgressive-regressive cycles in the metalliferous, oil-shale-bearing Heath Formation (Upper Mississippian), central Montana: Stratigraphy, v. 14, no. 1-4, p. 97-122, https://doi.org/10.29041/strat.14.1-4.97-122.","productDescription":"26 p.","startPage":"97","endPage":"122","ipdsId":"IP-077939","costCenters":[{"id":119,"text":"Alaska Science Center Geology Minerals","active":true,"usgs":true},{"id":171,"text":"Central Mineral and Environmental Resources Science Center","active":true,"usgs":true},{"id":241,"text":"Eastern Energy Resources Science Center","active":true,"usgs":true},{"id":245,"text":"Eastern Mineral and Environmental Resources Science Center","active":true,"usgs":true}],"links":[{"id":438158,"rank":0,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/F7668BPP","text":"USGS data release","linkHelpText":"Appendices for Transgressive-regressive cycles in the metalliferous, oil shale-bearing Heath Formation (Upper Mississippian), central Montana"},{"id":358777,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","state":"Montana","otherGeospatial":"Heath Formation","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -109.75,\n 46.75\n ],\n [\n -108.8,\n 46.75\n ],\n [\n -108.8,\n 47\n ],\n [\n -109.75,\n 47\n ],\n [\n -109.75,\n 46.75\n ]\n ]\n ]\n }\n }\n ]\n}","volume":"14","issue":"1-4","publishingServiceCenter":{"id":12,"text":"Tacoma PSC"},"noUsgsAuthors":false,"publicationDate":"2017-11-28","publicationStatus":"PW","scienceBaseUri":"5c10aacce4b034bf6a7e5cfd","contributors":{"authors":[{"text":"Dumoulin, Julie A. 0000-0003-1754-1287 dumoulin@usgs.gov","orcid":"https://orcid.org/0000-0003-1754-1287","contributorId":203209,"corporation":false,"usgs":true,"family":"Dumoulin","given":"Julie","email":"dumoulin@usgs.gov","middleInitial":"A.","affiliations":[{"id":114,"text":"Alaska Science Center","active":true,"usgs":true},{"id":119,"text":"Alaska Science Center Geology Minerals","active":true,"usgs":true}],"preferred":true,"id":749684,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Johnson, Craig A. 0000-0002-1334-2996 cjohnso@usgs.gov","orcid":"https://orcid.org/0000-0002-1334-2996","contributorId":909,"corporation":false,"usgs":true,"family":"Johnson","given":"Craig","email":"cjohnso@usgs.gov","middleInitial":"A.","affiliations":[{"id":211,"text":"Crustal Geophysics and Geochemistry Science Center","active":true,"usgs":true},{"id":171,"text":"Central Mineral and Environmental Resources Science Center","active":true,"usgs":true},{"id":35995,"text":"Geology, Geophysics, and Geochemistry Science Center","active":true,"usgs":true}],"preferred":true,"id":749685,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Kelley, Karen D. 0000-0002-3232-5809 kdkelley@usgs.gov","orcid":"https://orcid.org/0000-0002-3232-5809","contributorId":179012,"corporation":false,"usgs":true,"family":"Kelley","given":"Karen","email":"kdkelley@usgs.gov","middleInitial":"D.","affiliations":[{"id":35995,"text":"Geology, Geophysics, and Geochemistry Science Center","active":true,"usgs":true}],"preferred":true,"id":749686,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Botterell, Palma J. 0000-0001-7140-0915 pjarboe@usgs.gov","orcid":"https://orcid.org/0000-0001-7140-0915","contributorId":5805,"corporation":false,"usgs":true,"family":"Botterell","given":"Palma","email":"pjarboe@usgs.gov","middleInitial":"J.","affiliations":[{"id":241,"text":"Eastern Energy Resources Science Center","active":true,"usgs":true}],"preferred":true,"id":749687,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Hackley, Paul C. 0000-0002-5957-2551 phackley@usgs.gov","orcid":"https://orcid.org/0000-0002-5957-2551","contributorId":592,"corporation":false,"usgs":true,"family":"Hackley","given":"Paul","email":"phackley@usgs.gov","middleInitial":"C.","affiliations":[{"id":255,"text":"Energy Resources Program","active":true,"usgs":true},{"id":241,"text":"Eastern Energy Resources Science Center","active":true,"usgs":true}],"preferred":true,"id":749688,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"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":749689,"contributorType":{"id":1,"text":"Authors"},"rank":6},{"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":243,"text":"Eastern Geology and Paleoclimate Science Center","active":true,"usgs":true},{"id":245,"text":"Eastern Mineral and Environmental Resources Science Center","active":true,"usgs":true},{"id":387,"text":"Mineral Resources Program","active":true,"usgs":true}],"preferred":true,"id":749690,"contributorType":{"id":1,"text":"Authors"},"rank":7}]}} ,{"id":70206545,"text":"70206545 - 2017 - Observationally constrained surface mass balance of Larsen C ice shelf, Antarctica","interactions":[],"lastModifiedDate":"2019-11-08T09:41:49","indexId":"70206545","displayToPublicDate":"2017-11-01T09:20:57","publicationYear":"2017","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":3554,"text":"The Cryosphere","active":true,"publicationSubtype":{"id":10}},"title":"Observationally constrained surface mass balance of Larsen C ice shelf, Antarctica","docAbstract":"The surface mass balance (SMB) of the Larsen C ice shelf (LCIS), Antarctica, is poorly constrained due to a dearth of in situ observations. Combining several geophysical techniques, we reconstruct spatial and temporal patterns of SMB over the LCIS. Continuous time series of snow height (2.5–6 years) at five locations allow for multi-year estimates of seasonal and annual SMB over the LCIS. There is high interannual variability in SMB as well as spatial variability: in the north, SMB is 0.40 ± 0.06 to 0.41 ± 0.04 m w.e. year−1, while farther south, SMB is up to 0.50 ± 0.05 m w.e. year−1. This difference between north and south is corroborated by winter snow accumulation derived from an airborne radar survey from 2009, which showed an average snow thickness of 0.34 m w.e. north of 66° S, and 0.40 m w.e. south of 68° S. Analysis of ground-penetrating radar from several field campaigns allows for a longer-term perspective of spatial variations in SMB: a particularly strong and coherent reflection horizon below 25–44 m of water-equivalent ice and firn is observed in radargrams collected across the shelf. We propose that this horizon was formed synchronously across the ice shelf. Combining snow height observations, ground and airborne radar, and SMB output from a regional climate model yields a gridded estimate of SMB over the LCIS. It confirms that SMB increases from north to south, overprinted by a gradient of increasing SMB to the west, modulated in the west by föhn-induced sublimation. Previous observations show a strong decrease in firn air content toward the west, which we attribute to spatial patterns of melt, refreezing, and densification rather than SMB.
","language":"English","publisher":"Copernicus Publications","doi":"10.5194/tc-11-2411-2017","usgsCitation":"Kuipers Munneke, P., Mcgrath, D., Medley, B., Luckman, A., Bevan, S., Kulessa, B., Jansen, D., Booth, A., Smeets, P., Hubbard, B., Ashmore, D., Van den Broeke, M., Sevestre, H., Steffen, K., Shepard, A., and Gourmelen, N., 2017, Observationally constrained surface mass balance of Larsen C ice shelf, Antarctica: The Cryosphere, v. 11, p. 2411-2426, https://doi.org/10.5194/tc-11-2411-2017.","productDescription":"16 p.","startPage":"2411","endPage":"2426","ipdsId":"IP-086024","costCenters":[{"id":114,"text":"Alaska Science Center","active":true,"usgs":true},{"id":120,"text":"Alaska Science Center Water","active":true,"usgs":true}],"links":[{"id":469354,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.5194/tc-11-2411-2017","text":"Publisher Index Page"},{"id":369082,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"Antarctica","otherGeospatial":"Larsen C Ice Shelf","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -67.7197265625,\n -74.91370815675299\n ],\n [\n -59.19433593750001,\n -74.91370815675299\n ],\n [\n -59.19433593750001,\n -69.17818443567214\n ],\n [\n -67.7197265625,\n -69.17818443567214\n ],\n [\n -67.7197265625,\n -74.91370815675299\n ]\n ]\n ]\n }\n }\n ]\n}","volume":"11","publishingServiceCenter":{"id":12,"text":"Tacoma PSC"},"noUsgsAuthors":false,"publicationDate":"2017-11-01","publicationStatus":"PW","contributors":{"authors":[{"text":"Kuipers Munneke, Peter","contributorId":220418,"corporation":false,"usgs":false,"family":"Kuipers Munneke","given":"Peter","email":"","affiliations":[{"id":40168,"text":"IMAU, Utrecht University","active":true,"usgs":false}],"preferred":false,"id":774929,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Mcgrath, Daniel 0000-0002-9462-6842","orcid":"https://orcid.org/0000-0002-9462-6842","contributorId":220417,"corporation":false,"usgs":true,"family":"Mcgrath","given":"Daniel","affiliations":[{"id":114,"text":"Alaska Science Center","active":true,"usgs":true}],"preferred":true,"id":774928,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Medley, Brooke","contributorId":220419,"corporation":false,"usgs":false,"family":"Medley","given":"Brooke","email":"","affiliations":[{"id":7049,"text":"NASA Goddard Space Flight Center","active":true,"usgs":false}],"preferred":false,"id":774930,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Luckman, Adrian","contributorId":220420,"corporation":false,"usgs":false,"family":"Luckman","given":"Adrian","email":"","affiliations":[{"id":16759,"text":"Swansea University","active":true,"usgs":false}],"preferred":false,"id":774931,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Bevan, Suzanne 0000-0003-2649-2982","orcid":"https://orcid.org/0000-0003-2649-2982","contributorId":220451,"corporation":false,"usgs":false,"family":"Bevan","given":"Suzanne","email":"","affiliations":[],"preferred":false,"id":774958,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"Kulessa, Bernd","contributorId":220452,"corporation":false,"usgs":false,"family":"Kulessa","given":"Bernd","email":"","affiliations":[],"preferred":false,"id":774959,"contributorType":{"id":1,"text":"Authors"},"rank":6},{"text":"Jansen, 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strong, negative impacts on ecosystems, vulnerable species, and valuable natural resources. Arctic regions have experienced a relatively low number of biological introductions to date. Their geographical remoteness, cold waters, and presence of sea ice present challenging conditions for both non-native organisms and the vessels that transport them, presumably leading to low rates of introduction and establishment. However, observed increases in water temperatures reductions in sea ice, and projected increases in shipping traffic are expected to render arctic marine regions more susceptible to the arrival and colonization of marine invasives. Risk assessments for these Arctic regions are important to inform management and monitoring priorities by determining which species pose the greatest risk. To this end, we developed a ranking system for non-native marine species and used this system to assess the risk of non-native species to the Bering Sea. Using species’ published physiological tolerances, we mapped habitat suitability under current and future climate scenarios to identify geographic areas of current and future concern. In addition, we described shipping traffic from commercial and fishing vessels to identify ports of entry for non-native species. Collectively, these analyses identify which marine species have the greatest risk for invasion, where in the Bering Sea invasion risk and species establishment is greatest, and which ports are most likely to serve as an entry point for marine invasives into Alaska’s Bering Sea. The ranking system we developed for non-native marine species consists of 33 questions grouped into five categories. The first four categories evaluate a species’ ability to arrive and establish in the Bering Sea, its reliance on humans for introductions, its biology, and its impacts on ecological and human systems. The fifth category is not included in the total ranking score, but provides information on management considerations. The ranking system has methods to account for data deficiencies and calculates these deficiencies to allow readers to weigh the lack of knowledge with the ranking score. We prioritized non-native species for ranking based on their geographic proximity to the Bering Sea. We evaluated 46 species and ranking scores ranged from 29.1 to 74.3 (out of a possible 100), with highest scores indicating greatest risk. Taxonomy at the level of phylum did not explain variation in ranking values, likely due to the substantial biological variation relative to our ranking criteria among members of the same phylum. To investigate where non-native species may survive and persist in the Bering Sea, we compared species’ temperature and salinity thresholds to environmental conditions of the Bering Sea. Environmental conditions were obtained from three Regional Ocean Modeling Systems (ROMS) and investigated under two time periods: current (2003-2012) and mid-century (2030-2039). We identified potential habitat for survival for 42 species, and potential habitat for reproduction for 29 species. Under current conditions, all species had temperature and salinity thresholds that would allow survival in the Bering Sea for at least part of the year, and most species (79% to 83%) had thresholds that would allow for survival year-round. For species with temperature and salinity thresholds unsuitable for survival in the Bering Sea, winter temperatures appear to be the limiting factor. Most species had six to nine weeks of suitable conditions for reproduction. Future increases in water temperatures are expected to open more habitat for marine invasives. Two of the three ROMs project an increase in the number of non-native species that would be able to survive year-round by mid-century. Moreover, models project between 37% and 60% of the Bering Sea shelf habitat to become more suitable under mid-century climate condition.
","language":"English","publisher":"North Pacific Research Board","collaboration":"University of Alaska Anchorage, NOAA, Aleutian Bering Sea Islands Landscape Conservation Cooperative","usgsCitation":"Reimer, J., Droghini, A., Fischbach, A., Watson, J., Bernard, B., and Poe, A., 2017, Assessing the risk of non-native marine species in the Bering Sea, 46 p.","productDescription":"46 p.","ipdsId":"IP-094656","costCenters":[{"id":116,"text":"Alaska Science Center Biology MFEB","active":true,"usgs":true}],"links":[{"id":372180,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"},{"id":372163,"type":{"id":15,"text":"Index Page"},"url":"https://accs.uaa.alaska.edu/wp-content/uploads/Reimeretal2017_FinalReport.pdf"}],"country":"United States","state":"Alaska","otherGeospatial":"Bering Sea","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -172.79296875,\n 51.890053935216926\n ],\n [\n -161.89453125,\n 51.890053935216926\n ],\n [\n -161.89453125,\n 65.94647177615738\n ],\n [\n -172.79296875,\n 65.94647177615738\n ],\n [\n -172.79296875,\n 51.890053935216926\n ]\n ]\n ]\n }\n }\n ]\n}","publishingServiceCenter":{"id":12,"text":"Tacoma PSC"},"noUsgsAuthors":false,"publicationStatus":"PW","contributors":{"authors":[{"text":"Reimer, Jesika","contributorId":222311,"corporation":false,"usgs":false,"family":"Reimer","given":"Jesika","affiliations":[{"id":40516,"text":"Alaska Center for Conservation Science University of Alaska Anchorage","active":true,"usgs":false}],"preferred":false,"id":781853,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Droghini, Amanda 0000-0001-6692-2348","orcid":"https://orcid.org/0000-0001-6692-2348","contributorId":222312,"corporation":false,"usgs":false,"family":"Droghini","given":"Amanda","email":"","affiliations":[{"id":40516,"text":"Alaska Center for Conservation Science University of Alaska Anchorage","active":true,"usgs":false}],"preferred":false,"id":781854,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Fischbach, Anthony S. 0000-0002-6555-865X afischbach@usgs.gov","orcid":"https://orcid.org/0000-0002-6555-865X","contributorId":200780,"corporation":false,"usgs":true,"family":"Fischbach","given":"Anthony S.","email":"afischbach@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":781852,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Watson, Jordan 0000-0002-1686-0377","orcid":"https://orcid.org/0000-0002-1686-0377","contributorId":222313,"corporation":false,"usgs":false,"family":"Watson","given":"Jordan","email":"","affiliations":[{"id":40517,"text":"NOAA Alaska Fisheries Science Center Auke Bay Laboratories","active":true,"usgs":false}],"preferred":false,"id":781855,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Bernard, Bonnie","contributorId":222314,"corporation":false,"usgs":false,"family":"Bernard","given":"Bonnie","email":"","affiliations":[{"id":40516,"text":"Alaska Center for Conservation Science University of Alaska Anchorage","active":true,"usgs":false}],"preferred":false,"id":781856,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"Poe, Aaron","contributorId":222315,"corporation":false,"usgs":false,"family":"Poe","given":"Aaron","email":"","affiliations":[{"id":40518,"text":"Aleutian and Bering Sea Islands LCC U.S. Fish and Wildlife Service","active":true,"usgs":false}],"preferred":false,"id":781857,"contributorType":{"id":1,"text":"Authors"},"rank":6}]}} ,{"id":70196923,"text":"70196923 - 2017 - Marine geology and tectonics--What is under all that water?","interactions":[],"lastModifiedDate":"2018-06-12T13:48:20","indexId":"70196923","displayToPublicDate":"2017-11-01T00:00:00","publicationYear":"2017","noYear":false,"publicationType":{"id":5,"text":"Book chapter"},"publicationSubtype":{"id":24,"text":"Book Chapter"},"title":"Marine geology and tectonics--What is under all that water?","docAbstract":"This chapter is divided into two main sections. The first section is on Marine Geology Seascapes (what earth scientists call bathymetry). 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Variables representing geologic sources, geochemical, hydrologic, and physical features were among the significant predictors of high arsenic. For U.S. Census blocks, the mean probability of arsenic >10 μg/L was multiplied by the population using domestic wells to estimate the potential high-arsenic domestic-well population. Approximately 44.1 M people in the U.S. use water from domestic wells. The population in the conterminous U.S. using water from domestic wells with predicted arsenic concentration >10 μg/L is 2.1 M people (95% CI is 1.5 to 2.9 M). Although areas of the U.S. were underrepresented with arsenic data, predictive variables available in national data sets were used to estimate high arsenic in unsampled areas. Additionally, by predicting to all of the conterminous U.S., we identify areas of high and low potential exposure in areas of limited arsenic data. These areas may be viewed as potential areas to investigate further or to compare to more detailed local information. Linking predictive modeling to private well use information nationally, despite the uncertainty, is beneficial for broad screening of the population at risk from elevated arsenic in drinking water from private wells.
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\"}}]}\n","volume":"51","issue":"21","publishingServiceCenter":{"id":11,"text":"Pembroke PSC"},"noUsgsAuthors":false,"publicationDate":"2017-10-18","publicationStatus":"PW","scienceBaseUri":"5a60fb22e4b06e28e9c22d29","contributors":{"authors":[{"text":"Ayotte, Joseph D. 0000-0002-1892-2738 jayotte@usgs.gov","orcid":"https://orcid.org/0000-0002-1892-2738","contributorId":149619,"corporation":false,"usgs":true,"family":"Ayotte","given":"Joseph","email":"jayotte@usgs.gov","middleInitial":"D.","affiliations":[{"id":466,"text":"New England Water Science Center","active":true,"usgs":true},{"id":405,"text":"NH/VT office of New England Water Science Center","active":true,"usgs":true}],"preferred":true,"id":716074,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Medalie, Laura 0000-0002-2440-2149 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C.","contributorId":198459,"corporation":false,"usgs":false,"family":"Backer","given":"Lorraine","email":"","middleInitial":"C.","affiliations":[{"id":16974,"text":"US Centers for Disease Control and Prevention (CDC)","active":true,"usgs":false}],"preferred":true,"id":716077,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Nolan, Bernard T. 0000-0002-6945-9659 btnolan@usgs.gov","orcid":"https://orcid.org/0000-0002-6945-9659","contributorId":2190,"corporation":false,"usgs":true,"family":"Nolan","given":"Bernard","email":"btnolan@usgs.gov","middleInitial":"T.","affiliations":[{"id":27111,"text":"National Water Quality Program","active":true,"usgs":true},{"id":451,"text":"National Water Quality Assessment Program","active":true,"usgs":true}],"preferred":true,"id":716078,"contributorType":{"id":1,"text":"Authors"},"rank":5}]}} ,{"id":70194442,"text":"70194442 - 2017 - Tree sampling as a method to assess vapor intrusion potential at a site characterized by VOC-contaminated groundwater and soil","interactions":[],"lastModifiedDate":"2017-11-29T13:19:10","indexId":"70194442","displayToPublicDate":"2017-11-01T00:00:00","publicationYear":"2017","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":1565,"text":"Environmental Science & Technology","onlineIssn":"1520-5851","printIssn":"0013-936X","active":true,"publicationSubtype":{"id":10}},"title":"Tree sampling as a method to assess vapor intrusion potential at a site characterized by VOC-contaminated groundwater and soil","docAbstract":"Vapor intrusion (VI) by volatile organic compounds (VOCs) in the built environment presents a threat to human health. Traditional VI assessments are often time-, cost-, and labor-intensive; whereas traditional subsurface methods sample a relatively small volume in the subsurface and are difficult to collect within and near structures. Trees could provide a similar subsurface sample where roots act as the “sampler’ and are already onsite. Regression models were developed to assess the relation between PCE concentrations in over 500 tree-core samples with PCE concentrations in over 50 groundwater and 1000 soil samples collected from a tetrachloroethylene- (PCE-) contaminated Superfund site and analyzed using gas chromatography. Results indicate that in planta concentrations are significantly and positively related to PCE concentrations in groundwater samples collected at depths less than 20 m (adjusted R2 values greater than 0.80) and in soil samples (adjusted R2 values greater than 0.90). Results indicate that a 30 cm diameter tree characterizes soil concentrations at depths less than 6 m over an area of 700–1600 m2, the volume of a typical basement. These findings indicate that tree sampling may be an appropriate method to detect contamination at shallow depths at sites with VI.
","language":"English","publisher":"ACS Publications","doi":"10.1021/acs.est.7b02667","usgsCitation":"Wilson, J.L., Limmer, M.A., Samaranayake, V., Schumacher, J., and Burken, J.G., 2017, Tree sampling as a method to assess vapor intrusion potential at a site characterized by VOC-contaminated groundwater and soil: Environmental Science & Technology, v. 51, no. 18, p. 10369-10378, https://doi.org/10.1021/acs.est.7b02667.","productDescription":"10 p.","startPage":"10369","endPage":"10378","ipdsId":"IP-083556","costCenters":[{"id":396,"text":"Missouri Water Science Center","active":true,"usgs":true}],"links":[{"id":438169,"rank":0,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/F71835D8","text":"USGS data release","linkHelpText":"Tetrachloroethylene, trichloroethylene, and 1,1,2-Trichloro-1,2,2-trifluoroethane concentrations in tree-core, groundwater, and soil samples at the Vienna Wells Site: Maries County, Missouri, 2011-2016"},{"id":349537,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","state":"Missouri","city":"Vienna","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -91.9442,\n 38.1883\n ],\n [\n -91.9417,\n 38.1883\n ],\n [\n -91.9417,\n 38.19\n ],\n [\n -91.9442,\n 38.19\n ],\n [\n -91.9442,\n 38.1883\n ]\n ]\n ]\n }\n }\n ]\n}","volume":"51","issue":"18","publishingServiceCenter":{"id":4,"text":"Rolla PSC"},"noUsgsAuthors":false,"publicationDate":"2017-08-31","publicationStatus":"PW","scienceBaseUri":"5a60fb21e4b06e28e9c22d02","contributors":{"authors":[{"text":"Wilson, Jordan L. 0000-0003-0490-9062 jlwilson@usgs.gov","orcid":"https://orcid.org/0000-0003-0490-9062","contributorId":5416,"corporation":false,"usgs":true,"family":"Wilson","given":"Jordan","email":"jlwilson@usgs.gov","middleInitial":"L.","affiliations":[{"id":36532,"text":"Central Midwest Water Science Center","active":true,"usgs":true},{"id":396,"text":"Missouri Water Science Center","active":true,"usgs":true}],"preferred":true,"id":723833,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Limmer, Matthew A.","contributorId":200927,"corporation":false,"usgs":false,"family":"Limmer","given":"Matthew","email":"","middleInitial":"A.","affiliations":[],"preferred":false,"id":723834,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Samaranayake, V.A.","contributorId":200928,"corporation":false,"usgs":false,"family":"Samaranayake","given":"V.A.","affiliations":[],"preferred":false,"id":723835,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Schumacher, John G. jschu@usgs.gov","contributorId":2055,"corporation":false,"usgs":true,"family":"Schumacher","given":"John G.","email":"jschu@usgs.gov","affiliations":[{"id":396,"text":"Missouri Water Science Center","active":true,"usgs":true}],"preferred":true,"id":723836,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Burken, Joel G.","contributorId":21218,"corporation":false,"usgs":true,"family":"Burken","given":"Joel","email":"","middleInitial":"G.","affiliations":[],"preferred":false,"id":723837,"contributorType":{"id":1,"text":"Authors"},"rank":5}]}} ,{"id":70194478,"text":"70194478 - 2017 - Climate and land-use change in wetlands: A dedication","interactions":[],"lastModifiedDate":"2017-11-29T12:49:10","indexId":"70194478","displayToPublicDate":"2017-11-01T00:00:00","publicationYear":"2017","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":5075,"text":"Ecosystem Health and Sustainability","active":true,"publicationSubtype":{"id":10}},"title":"Climate and land-use change in wetlands: A dedication","docAbstract":"Future climate and land-use change may wreak havoc on wetlands, with the potential to erode their values as harbors for biota and providers of human services. Wetlands are important to protect, particularly because these provide a variety of ecosystem services including wildlife habitat, water purification, flood storage, and storm protection (Mitsch, Bernal, and Hernandez 2015). Without healthy wetlands, future generations may become increasingly less in harmony with the sustainability of the Earth.
To this end, the thematic feature on climate and land-use change in wetlands explores the critical role of wetlands in the overall health and well-being of humans and our planet. Our special feature contributes to the understanding of the idea that the health of natural ecosystems and humans are linked and potentially stressed by climate change and land-use change (Horton and Lo 2015; McDonald 2015). In particular, this special issue considers the important role of wetlands in the environment, and how land-use and environmental change might affect them in the future.
","language":"English","publisher":"Taylor & Francis","doi":"10.1080/20964129.2017.1392831","usgsCitation":"Middleton, B.A., 2017, Climate and land-use change in wetlands: A dedication: Ecosystem Health and Sustainability, v. 3, no. 9, p. 1-2, https://doi.org/10.1080/20964129.2017.1392831.","productDescription":"Article 1392831; 2 p.","startPage":"1","endPage":"2","ipdsId":"IP-088273","costCenters":[{"id":17705,"text":"Wetland and Aquatic Research Center","active":true,"usgs":true}],"links":[{"id":469356,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.1080/20964129.2017.1392831","text":"Publisher Index Page"},{"id":349529,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"volume":"3","issue":"9","publishingServiceCenter":{"id":5,"text":"Lafayette PSC"},"noUsgsAuthors":false,"publicationDate":"2017-11-25","publicationStatus":"PW","scienceBaseUri":"5a60fb21e4b06e28e9c22cfd","contributors":{"authors":[{"text":"Middleton, Beth A. 0000-0002-1220-2326 middletonb@usgs.gov","orcid":"https://orcid.org/0000-0002-1220-2326","contributorId":2029,"corporation":false,"usgs":true,"family":"Middleton","given":"Beth","email":"middletonb@usgs.gov","middleInitial":"A.","affiliations":[{"id":17705,"text":"Wetland and Aquatic Research Center","active":true,"usgs":true}],"preferred":true,"id":724020,"contributorType":{"id":1,"text":"Authors"},"rank":1}]}} ,{"id":70193003,"text":"70193003 - 2017 - Methodological considerations regarding online extraction of water from soils for stable isotope determination","interactions":[],"lastModifiedDate":"2017-11-22T16:45:46","indexId":"70193003","displayToPublicDate":"2017-11-01T00:00:00","publicationYear":"2017","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":3233,"text":"Rapid Communications in Mass Spectrometry","active":true,"publicationSubtype":{"id":10}},"title":"Methodological considerations regarding online extraction of water from soils for stable isotope determination","docAbstract":"No abstract available.
","language":"English","publisher":"Wiley","doi":"10.1002/rcm.7948","usgsCitation":"Lazarus, B.E., and Germino, M., 2017, Methodological considerations regarding online extraction of water from soils for stable isotope determination: Rapid Communications in Mass Spectrometry, v. 31, no. 19, p. 1677-1680, https://doi.org/10.1002/rcm.7948.","productDescription":"4 p.","startPage":"1677","endPage":"1680","ipdsId":"IP-084312","costCenters":[{"id":290,"text":"Forest and Rangeland Ecosystem Science Center","active":false,"usgs":true}],"links":[{"id":348046,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"volume":"31","issue":"19","publishingServiceCenter":{"id":12,"text":"Tacoma PSC"},"noUsgsAuthors":false,"publicationDate":"2017-09-07","publicationStatus":"PW","scienceBaseUri":"59fadd1ee4b0531197b13c69","contributors":{"authors":[{"text":"Lazarus, Brynne E. 0000-0002-6352-486X blazarus@usgs.gov","orcid":"https://orcid.org/0000-0002-6352-486X","contributorId":4901,"corporation":false,"usgs":true,"family":"Lazarus","given":"Brynne","email":"blazarus@usgs.gov","middleInitial":"E.","affiliations":[{"id":290,"text":"Forest and Rangeland Ecosystem Science Center","active":false,"usgs":true},{"id":289,"text":"Forest and Rangeland Ecosys Science Center","active":true,"usgs":true}],"preferred":true,"id":717587,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Germino, Matthew J. 0000-0001-6326-7579 mgermino@usgs.gov","orcid":"https://orcid.org/0000-0001-6326-7579","contributorId":152582,"corporation":false,"usgs":true,"family":"Germino","given":"Matthew J.","email":"mgermino@usgs.gov","affiliations":[{"id":289,"text":"Forest and Rangeland Ecosys Science Center","active":true,"usgs":true},{"id":290,"text":"Forest and Rangeland Ecosystem Science Center","active":false,"usgs":true}],"preferred":true,"id":717586,"contributorType":{"id":1,"text":"Authors"},"rank":2}]}} ,{"id":70196515,"text":"70196515 - 2017 - Organic chemical characterization and mass balance of a hydraulically fractured well: From fracturing fluid to produced water over 405 days","interactions":[],"lastModifiedDate":"2018-04-12T16:04:25","indexId":"70196515","displayToPublicDate":"2017-11-01T00:00:00","publicationYear":"2017","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":1565,"text":"Environmental Science & Technology","onlineIssn":"1520-5851","printIssn":"0013-936X","active":true,"publicationSubtype":{"id":10}},"title":"Organic chemical characterization and mass balance of a hydraulically fractured well: From fracturing fluid to produced water over 405 days","docAbstract":"A long-term field study (405 days) of a hydraulically fractured well from the Niobrara Formation in the Denver-Julesburg Basin was completed. Characterization of organic chemicals used in hydraulic fracturing and their changes through time, from the preinjected fracturing fluid to the produced water, was conducted. The characterization consisted of a mass balance by dissolved organic carbon (DOC), volatile organic analysis by gas chromatography/mass spectrometry, and nonvolatile organic analysis by liquid chromatography/mass spectrometry. DOC decreased from 1500 mg/L in initial flowback to 200 mg/L in the final produced water. Only ∼11% of the injected DOC returned by the end of the study, with this 11% representing a maximum fraction returned since the formation itself contributes DOC. Furthermore, the majority of returning DOC was of the hydrophilic fraction (60–85%). Volatile organic compound analysis revealed substantial concentrations of individual BTEX compounds (0.1–11 mg/L) over the 405-day study. Nonvolatile organic compounds identified were polyethylene glycols (PEGs), polypropylene glycols (PPG), linear alkyl-ethoxylates, and triisopropanolamine (TIPA). The distribution of PEGs, PPGs, and TIPA and their ubiquitous presence in our samples and the literature illustrate their potential as organic tracers for treatment operations or in the event of an environmental spill.
","language":"English","publisher":"ACS","doi":"10.1021/acs.est.7b03362","usgsCitation":"Rosenblum, J., Thurman, E.M., Ferrer, I., Aiken, G.R., and Linden, K.G., 2017, Organic chemical characterization and mass balance of a hydraulically fractured well: From fracturing fluid to produced water over 405 days: Environmental Science & Technology, v. 51, no. 23, p. 14006-14015, https://doi.org/10.1021/acs.est.7b03362.","productDescription":"10 p.","startPage":"14006","endPage":"14015","ipdsId":"IP-090640","costCenters":[{"id":5044,"text":"National Research Program - Central Branch","active":true,"usgs":true}],"links":[{"id":353385,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"volume":"51","issue":"23","publishingServiceCenter":{"id":2,"text":"Denver PSC"},"noUsgsAuthors":false,"publicationDate":"2017-11-22","publicationStatus":"PW","scienceBaseUri":"5afee7c6e4b0da30c1bfc36e","contributors":{"authors":[{"text":"Rosenblum, James","contributorId":204203,"corporation":false,"usgs":false,"family":"Rosenblum","given":"James","email":"","affiliations":[{"id":30224,"text":"Univeristy of Colorado Boulder","active":true,"usgs":false}],"preferred":false,"id":733354,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Thurman, E. Michael","contributorId":9636,"corporation":false,"usgs":true,"family":"Thurman","given":"E.","email":"","middleInitial":"Michael","affiliations":[],"preferred":false,"id":733355,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Ferrer, Imma","contributorId":169362,"corporation":false,"usgs":false,"family":"Ferrer","given":"Imma","email":"","affiliations":[{"id":25480,"text":"Univ of Colorado, Boulder","active":true,"usgs":false}],"preferred":false,"id":733356,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Aiken, George R. 0000-0001-8454-0984 graiken@usgs.gov","orcid":"https://orcid.org/0000-0001-8454-0984","contributorId":1322,"corporation":false,"usgs":true,"family":"Aiken","given":"George","email":"graiken@usgs.gov","middleInitial":"R.","affiliations":[{"id":5044,"text":"National Research Program - Central Branch","active":true,"usgs":true},{"id":191,"text":"Colorado Water Science Center","active":true,"usgs":true}],"preferred":true,"id":733353,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Linden, Karl G.","contributorId":194690,"corporation":false,"usgs":false,"family":"Linden","given":"Karl","email":"","middleInitial":"G.","affiliations":[],"preferred":false,"id":733357,"contributorType":{"id":1,"text":"Authors"},"rank":5}]}} ]}