Increased periods of sparse sea ice over the continental shelf of the Chukchi Sea in late summer have reduced offshore haulout habitat for Pacific walruses (Odobenus rosmarus divergens) and increased opportunities for human activities in the region. Knowing how many walruses could be affected by human activities would be useful to conservation decisions. Currently, there are no adequate estimates of walrus abundance in the northeastern Chukchi Sea during summer–early autumn. Estimating abundance in autumn might be possible from coastal surveys of hauled out walruses during periods when offshore sea ice is unavailable to walruses. We evaluated methods to estimate the size of the walrus population that was using a haulout on the coast of northwestern Alaska in autumn by using aerial photography to count the number of hauled out walruses (herd size) and data from 37 tagged walruses to estimate availability (proportion of population hauled out). We used two methods to estimate availability, direct proportions of hauled out tagged walruses and smoothed proportions using local polynomial regression. Point estimates of herd size (4200–38,000 walruses) and total population size (76,000–287,000 walruses) ranged widely among days and between the two methods of estimating availability. Estimates of population size were influenced most by variation in estimates of availability. Coastal surveys might be improved most by counting walruses when the greatest numbers are hauled out, thereby reducing the influence of availability on population size estimates. The chance of collecting data during peak haulout periods would be improved by conducting multiple surveys.
","language":"English","publisher":"Springer","doi":"10.1007/s00300-016-2060-5","usgsCitation":"Battaile, B., Jay, C.V., Udevitz, M.S., and Fischbach, A.S., 2017, Evaluation of a method using survey counts and tag data to estimate the number of Pacific walruses (Odobenus rosmarus divergens) using a coastal haulout in northwestern Alaska: Polar Biology, v. 40, no. 7, p. 1359-1369, https://doi.org/10.1007/s00300-016-2060-5.","productDescription":"11 p.","startPage":"1359","endPage":"1369","ipdsId":"IP-070731","costCenters":[{"id":116,"text":"Alaska Science Center Biology MFEB","active":true,"usgs":true}],"links":[{"id":438417,"rank":0,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/F7B27SB2","text":"USGS data release","linkHelpText":"Walrus Haulout Photographs Near Pt. Lay Alaska, September 2014"},{"id":337549,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"volume":"40","issue":"7","publishingServiceCenter":{"id":12,"text":"Tacoma PSC"},"noUsgsAuthors":false,"publicationDate":"2017-01-04","publicationStatus":"PW","scienceBaseUri":"58c90123e4b0849ce97abcb2","contributors":{"authors":[{"text":"Battaile, Brian bbattaile@usgs.gov","contributorId":189069,"corporation":false,"usgs":true,"family":"Battaile","given":"Brian","email":"bbattaile@usgs.gov","affiliations":[],"preferred":true,"id":683780,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Jay, Chadwick V. 0000-0002-9559-2189 cjay@usgs.gov","orcid":"https://orcid.org/0000-0002-9559-2189","contributorId":192736,"corporation":false,"usgs":true,"family":"Jay","given":"Chadwick","email":"cjay@usgs.gov","middleInitial":"V.","affiliations":[{"id":116,"text":"Alaska Science Center Biology MFEB","active":true,"usgs":true}],"preferred":true,"id":684324,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Udevitz, Mark S. 0000-0003-4659-138X mudevitz@usgs.gov","orcid":"https://orcid.org/0000-0003-4659-138X","contributorId":3189,"corporation":false,"usgs":true,"family":"Udevitz","given":"Mark","email":"mudevitz@usgs.gov","middleInitial":"S.","affiliations":[{"id":116,"text":"Alaska Science Center Biology MFEB","active":true,"usgs":true}],"preferred":true,"id":684325,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Fischbach, Anthony S. 0000-0002-6555-865X afischbach@usgs.gov","orcid":"https://orcid.org/0000-0002-6555-865X","contributorId":2865,"corporation":false,"usgs":true,"family":"Fischbach","given":"Anthony","email":"afischbach@usgs.gov","middleInitial":"S.","affiliations":[{"id":116,"text":"Alaska Science Center Biology MFEB","active":true,"usgs":true}],"preferred":true,"id":684326,"contributorType":{"id":1,"text":"Authors"},"rank":4}]}} ,{"id":70185664,"text":"70185664 - 2017 - Molecular analyses reveal high species diversity of trematodes in a sub-Arctic lake","interactions":[],"lastModifiedDate":"2017-11-27T12:43:19","indexId":"70185664","displayToPublicDate":"2017-03-14T00:00:00","publicationYear":"2017","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":2024,"text":"International Journal for Parasitology","active":true,"publicationSubtype":{"id":10}},"title":"Molecular analyses reveal high species diversity of trematodes in a sub-Arctic lake","docAbstract":"To identify trematode diversity and life-cycles in the sub-Arctic Lake Takvatn, Norway, we characterised 120 trematode isolates from mollusc first intermediate hosts, metacercariae from second intermediate host fishes and invertebrates, and adults from fish and invertebrate definitive hosts, using molecular techniques. Phylogenies based on nuclear and/or mtDNA revealed high species richness (24 species or species-level genetic lineages), and uncovered trematode diversity (16 putative new species) from five families typical in lake ecosystems (Allocreadiidae, Diplostomidae, Plagiorchiidae, Schistosomatidae and Strigeidae). Sampling potential invertebrate hosts allowed matching of sequence data for different stages, thus achieving molecular elucidation of trematode life-cycles and exploration of host-parasite interactions. Phylogenetic analyses also helped identify three major mollusc intermediate hosts (Radix balthica, Pisidium casertanum and Sphaerium sp.) in the lake. Our findings increase the known trematode diversity at the sub-Arctic Lake Takvatn, showing that digenean diversity is high in this otherwise depauperate sub-Arctic freshwater ecosystem, and indicating that sub-Arctic and Arctic ecosystems may be characterised by unique trematode assemblages.","language":"English","publisher":"Elsevier","doi":"10.1016/j.ijpara.2016.12.008","usgsCitation":"Soldanova, M., Georgieva, S., Rohacovaa, J., Knudsen, R., Kuhn, J.A., Henriksen, E.H., Siwertsson, A., Shaw, J.C., Kuris, A.M., Amundsen, P., Scholz, T., Lafferty, K.D., and Kostadinova, A., 2017, Molecular analyses reveal high species diversity of trematodes in a sub-Arctic lake: International Journal for Parasitology, v. 47, no. 6, p. 327-345, https://doi.org/10.1016/j.ijpara.2016.12.008.","productDescription":"19 p. ","startPage":"327","endPage":"345","ipdsId":"IP-076672","costCenters":[{"id":651,"text":"Western Ecological Research Center","active":true,"usgs":true}],"links":[{"id":470012,"rank":0,"type":{"id":41,"text":"Open Access External Repository Page"},"url":"https://hdl.handle.net/10037/12450","text":"External Repository"},{"id":338384,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"Norway","otherGeospatial":"Lake Takvatn","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n 20.4345703125,\n 70.24460360904779\n ],\n [\n 20.093994140624996,\n 70.35570565618842\n ],\n [\n 19.368896484374996,\n 70.32613725493573\n ],\n [\n 18.69873046875,\n 70.21115551555049\n ],\n [\n 17.633056640625,\n 69.86611024667063\n ],\n [\n 16.907958984375,\n 69.46911584774982\n ],\n [\n 16.6552734375,\n 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]\n}","volume":"47","issue":"6","publishingServiceCenter":{"id":1,"text":"Sacramento PSC"},"noUsgsAuthors":false,"publicationStatus":"PW","scienceBaseUri":"58da2519e4b0543bf7fda7f4","contributors":{"authors":[{"text":"Soldanova, Miroslava","contributorId":189852,"corporation":false,"usgs":false,"family":"Soldanova","given":"Miroslava","email":"","affiliations":[],"preferred":false,"id":686279,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Georgieva, Simona","contributorId":189853,"corporation":false,"usgs":false,"family":"Georgieva","given":"Simona","email":"","affiliations":[],"preferred":false,"id":686280,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Rohacovaa, Jana","contributorId":189854,"corporation":false,"usgs":false,"family":"Rohacovaa","given":"Jana","email":"","affiliations":[],"preferred":false,"id":686281,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Knudsen, Rune","contributorId":189855,"corporation":false,"usgs":false,"family":"Knudsen","given":"Rune","email":"","affiliations":[],"preferred":false,"id":686282,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Kuhn, Jesper A.","contributorId":189856,"corporation":false,"usgs":false,"family":"Kuhn","given":"Jesper","email":"","middleInitial":"A.","affiliations":[],"preferred":false,"id":686283,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"Henriksen, Eirik H.","contributorId":189857,"corporation":false,"usgs":false,"family":"Henriksen","given":"Eirik","email":"","middleInitial":"H.","affiliations":[],"preferred":false,"id":686284,"contributorType":{"id":1,"text":"Authors"},"rank":6},{"text":"Siwertsson, Anna","contributorId":150856,"corporation":false,"usgs":false,"family":"Siwertsson","given":"Anna","email":"","affiliations":[{"id":18120,"text":"UiT The Arctic University of Norway","active":true,"usgs":false}],"preferred":false,"id":686285,"contributorType":{"id":1,"text":"Authors"},"rank":7},{"text":"Shaw, Jenny C.","contributorId":189858,"corporation":false,"usgs":false,"family":"Shaw","given":"Jenny","email":"","middleInitial":"C.","affiliations":[],"preferred":false,"id":686286,"contributorType":{"id":1,"text":"Authors"},"rank":8},{"text":"Kuris, Armand M.","contributorId":189859,"corporation":false,"usgs":false,"family":"Kuris","given":"Armand","email":"","middleInitial":"M.","affiliations":[],"preferred":false,"id":686287,"contributorType":{"id":1,"text":"Authors"},"rank":9},{"text":"Amundsen, Per-Arne","contributorId":83448,"corporation":false,"usgs":true,"family":"Amundsen","given":"Per-Arne","affiliations":[],"preferred":false,"id":686288,"contributorType":{"id":1,"text":"Authors"},"rank":10},{"text":"Scholz, Tomas","contributorId":189861,"corporation":false,"usgs":false,"family":"Scholz","given":"Tomas","email":"","affiliations":[],"preferred":false,"id":686289,"contributorType":{"id":1,"text":"Authors"},"rank":11},{"text":"Lafferty, Kevin D. 0000-0001-7583-4593 klafferty@usgs.gov","orcid":"https://orcid.org/0000-0001-7583-4593","contributorId":1415,"corporation":false,"usgs":true,"family":"Lafferty","given":"Kevin","email":"klafferty@usgs.gov","middleInitial":"D.","affiliations":[{"id":651,"text":"Western Ecological Research Center","active":true,"usgs":true}],"preferred":true,"id":686278,"contributorType":{"id":1,"text":"Authors"},"rank":12},{"text":"Kostadinova, Aneta","contributorId":189862,"corporation":false,"usgs":false,"family":"Kostadinova","given":"Aneta","email":"","affiliations":[],"preferred":false,"id":686290,"contributorType":{"id":1,"text":"Authors"},"rank":13}]}} ,{"id":70185003,"text":"70185003 - 2017 - Territory occupancy and breeding success of Peregrine Falcons Falco peregrinus at various stages of population recovery","interactions":[],"lastModifiedDate":"2017-03-13T13:43:38","indexId":"70185003","displayToPublicDate":"2017-03-13T00:00:00","publicationYear":"2017","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":1961,"text":"Ibis","active":true,"publicationSubtype":{"id":10}},"title":"Territory occupancy and breeding success of Peregrine Falcons Falco peregrinus at various stages of population recovery","docAbstract":"Organochlorine pesticides disrupted reproduction and killed many raptorial birds, and contributed to population declines during the 1940s to 1970s. We sought to discern whether and to what extent territory occupancy and breeding success changed from the pesticide era to recent years in a resident population of Peregrine Falcons Falco peregrinus in southern Scotland using long-term (1964–2015) field data and multi-state, multi-season occupancy models. Peregrine territories that were occupied with successful reproduction in one year were much more likely to be occupied and experience reproductive success in the following year, compared with those that were unoccupied or occupied by unsuccessful breeders in the previous year. Probability of territory occupancy differed between territories in the eastern and western parts of the study area, and varied over time. The probability of occupancy of territories that were unoccupied and those that were occupied with successful reproduction during the previous breeding season generally increased over time, whereas the probability of occupancy of territories that were occupied after failed reproduction decreased. The probability of reproductive success (conditional on occupancy) in territories that were occupied during the previous breeding season increased over time. Specifically, for territories that had been successful in the previous year, the probability of occupancy as well as reproductive success increased steadily over time; these probabilities were substantially higher in recent years than earlier, when the population was still exposed to direct or residual effects of organochlorine pesticides. These results are consistent with the hypothesis that progressive reduction, followed by a complete ban, in the use of organochlorine pesticides improved reproductive success of Peregrines in southern Scotland. Differences in the temporal pattern of probability of reproductive success between south-eastern and south-western Scotland suggest that the effect of organochlorine pesticides on Peregrine reproductive success and/or the recovery from pesticide effects varied geographically and was possibly affected by other factors such as persecution.
","language":"English","publisher":"Ibis Society","publisherLocation":"London","doi":"10.1111/ibi.12443","usgsCitation":"McGrady, M.J., Hines, J.E., Rollie, C., Smith, G.D., Morton, E.R., Moore, J.F., Mearns, R.M., Newton, I., Murillo-Garcia, O.E., and Oli, M.K., 2017, Territory occupancy and breeding success of Peregrine Falcons Falco peregrinus at various stages of population recovery: Ibis, v. 159, no. 2, p. 285-296, https://doi.org/10.1111/ibi.12443.","productDescription":"12 p.","startPage":"285","endPage":"296","ipdsId":"IP-077722","costCenters":[{"id":531,"text":"Patuxent Wildlife Research Center","active":true,"usgs":true}],"links":[{"id":470018,"rank":0,"type":{"id":41,"text":"Open Access External Repository Page"},"url":"https://doi.org/10.1111/ibi.12443","text":"External Repository"},{"id":337439,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"volume":"159","issue":"2","publishingServiceCenter":{"id":10,"text":"Baltimore PSC"},"noUsgsAuthors":false,"publicationDate":"2017-01-02","publicationStatus":"PW","scienceBaseUri":"58c7af95e4b0849ce9795e68","contributors":{"authors":[{"text":"McGrady, Michael J.","contributorId":189117,"corporation":false,"usgs":false,"family":"McGrady","given":"Michael","email":"","middleInitial":"J.","affiliations":[],"preferred":false,"id":683934,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Hines, James E. 0000-0001-5478-7230 jhines@usgs.gov","orcid":"https://orcid.org/0000-0001-5478-7230","contributorId":146530,"corporation":false,"usgs":true,"family":"Hines","given":"James","email":"jhines@usgs.gov","middleInitial":"E.","affiliations":[{"id":531,"text":"Patuxent Wildlife Research Center","active":true,"usgs":true}],"preferred":true,"id":683895,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Rollie, Chris","contributorId":189118,"corporation":false,"usgs":false,"family":"Rollie","given":"Chris","email":"","affiliations":[],"preferred":false,"id":683935,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Smith, George D.","contributorId":189119,"corporation":false,"usgs":false,"family":"Smith","given":"George","email":"","middleInitial":"D.","affiliations":[],"preferred":false,"id":683936,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Morton, Elise R.","contributorId":189121,"corporation":false,"usgs":false,"family":"Morton","given":"Elise","email":"","middleInitial":"R.","affiliations":[],"preferred":false,"id":683937,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"Moore, Jennifer F.","contributorId":189122,"corporation":false,"usgs":false,"family":"Moore","given":"Jennifer","email":"","middleInitial":"F.","affiliations":[],"preferred":false,"id":683938,"contributorType":{"id":1,"text":"Authors"},"rank":6},{"text":"Mearns, Richard M.","contributorId":189123,"corporation":false,"usgs":false,"family":"Mearns","given":"Richard","email":"","middleInitial":"M.","affiliations":[],"preferred":false,"id":683939,"contributorType":{"id":1,"text":"Authors"},"rank":7},{"text":"Newton, Ian","contributorId":111901,"corporation":false,"usgs":true,"family":"Newton","given":"Ian","email":"","affiliations":[],"preferred":false,"id":683903,"contributorType":{"id":1,"text":"Authors"},"rank":8},{"text":"Murillo-Garcia, Oscar E.","contributorId":189120,"corporation":false,"usgs":false,"family":"Murillo-Garcia","given":"Oscar","email":"","middleInitial":"E.","affiliations":[],"preferred":false,"id":683940,"contributorType":{"id":1,"text":"Authors"},"rank":9},{"text":"Oli, Madan K.","contributorId":86089,"corporation":false,"usgs":true,"family":"Oli","given":"Madan","email":"","middleInitial":"K.","affiliations":[],"preferred":false,"id":683904,"contributorType":{"id":1,"text":"Authors"},"rank":10}]}} ,{"id":70184375,"text":"ds1041 - 2017 - Coastal single-beam bathymetry data collected in 2015 from Raccoon Point to Point Au Fer Island, Louisiana","interactions":[],"lastModifiedDate":"2017-03-10T10:36:47","indexId":"ds1041","displayToPublicDate":"2017-03-10T09:15:00","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":"1041","title":"Coastal single-beam bathymetry data collected in 2015 from Raccoon Point to Point Au Fer Island, Louisiana","docAbstract":"As part of the Barrier Island Comprehensive Monitoring Program (BICM), scientists from the U.S. Geological Survey (USGS) St. Petersburg Coastal and Marine Science Center conducted a nearshore single-beam bathymetry survey along the south-central coast of Louisiana, from Raccoon Point to Point Au Fer Island, in July 2015. The goal of the BICM program is to provide long-term data on Louisiana’s coastline and use this data to plan, design, evaluate, and maintain current and future barrier island restoration projects. The data described in this report will provide baseline bathymetric information for future research investigating island evolution, sediment transport, and recent and long-term geomorphic change, and will support modeling of future changes in response to restoration and storm impacts. The survey area encompasses more than 300 square kilometers of nearshore environment from Raccoon Point to Point Au Fer Island. This data series serves as an archive of processed single-beam bathymetry data, collected from July 22–29, 2015, under USGS Field Activity Number 2015-320-FA. Geographic information system data products include a 200-meter-cell-size interpolated bathymetry grid, trackline maps, and point data files. Additional files include error analysis maps, Field Activity Collection System logs, and formal Federal Geographic Data Committee metadata.
","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/ds1041","usgsCitation":"Stalk, C.A., DeWitt, N.T., Kindinger, J.L., Flocks, J.G., Reynolds, B.J., Kelso, K.W., Fredericks, J.J., and Tuten, T.M., 2017, Coastal single-beam bathymetry data collected in 2015 from Raccoon Point to Point Au Fer Island, Louisiana: U.S. Geological Survey Data Series 1041, https://doi.org/10.3133/ds1041.","productDescription":"HTML Document","onlineOnly":"Y","additionalOnlineFiles":"Y","ipdsId":"IP-076353","costCenters":[{"id":574,"text":"St. Petersburg Coastal and Marine Science Center","active":true,"usgs":true}],"links":[{"id":337140,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/ds/1041/coverthb.jpg"},{"id":337141,"rank":2,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/ds/1041/","text":"Report HTML","linkFileType":{"id":5,"text":"html"},"description":"DS 1041"}],"country":"United States ","state":"Louisiana","otherGeospatial":"Point Au Fer Island, Raccoon Point","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -90.0384521484375,\n 30.90693797089463\n ],\n [\n -92.0819091796875,\n 30.888083515609047\n ],\n [\n -92.1258544921875,\n 28.85429649869795\n ],\n [\n -90.0604248046875,\n 28.849485201023\n ],\n [\n -90.0384521484375,\n 30.90693797089463\n ]\n ]\n ]\n }\n }\n ]\n}","contact":"St. Petersburg Coastal and Marine Science Center
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Between 1997 and 2011, Mongolia established three specially protected areas in the north-central part of the country to protect various high-value resources. These areas are jointly referred to as the Ulaan Taiga Specially Protected Areas. In accordance with the goals of the draft general management plan, this report identifies options for initiating an inventory and monitoring program for the three protected areas. Together, the three areas comprise over 1.5 million hectares of mountainous terrain west of Lake Hovsgol and bordering the Darkhad Valley. The area supports numerous rare ungulates, endangered fish, and over 40 species of threatened plants. Illegal mining, illegal logging, and poaching pose the most immediate threats to resources. As a first step, a review of published literature would inform natural resource management at the Ulaan Taiga Specially Protected Areas because it would inform other inventories.
Vegetation classification and mapping also would inform other inventory efforts because the process incorporates geographic analysis to identify environmental gradients, fine-scale sampling that captures species composition and structure, and landscape-scale results that represent the variety and extent of habitats for various organisms. Mapping using satellite imagery reduces the cost per hectare.
Following a determination of existing knowledge, field surveys of vertebrates and vascular plants would serve to build species lists and fill in gaps in existing knowledge. For abiotic resources, a focus on monitoring air quality, evaluating and monitoring water quality, and assembling and storing weather data would provide information for correlating resource response status with changing environmental conditions.
Finally, we identify datasets that, if incorporated into a geographic information system, would inform resource management. They include political boundaries, infrastructure, topography, surficial geology, hydrology, fire history, and soils.
In terms of tracking high-value resources, vegetation monitoring at the plot scale would provide a basis for detecting change in such characteristics as plant species composition, vegetation structure, and productivity that are associated with landscape-scale factors such as climate change or biotic interactions. Continued population monitoring of rare ungulates, particularly argali or wild sheep (Ovis ammon), would provide information on how populations are responding to natural and anthropogenic stressors. Siberian taimen (Hucho taimen) also is an important monitoring target given ongoing threats of poaching and climate change.
","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/ofr20171025","usgsCitation":"Moore, P.E., Meyer, J.B., and Chow, L.S., 2017, Natural resource inventory and monitoring for Ulaan Taiga Specially Protected Areas—An assessment of needs and opportunities in northern Mongolia: U.S. Geological Survey Open-File Report 2017–1025, 35 p., https://doi.org/10.3133/ofr20171025.","productDescription":"viii, 35 p.","numberOfPages":"48","onlineOnly":"Y","ipdsId":"IP-082861","costCenters":[{"id":651,"text":"Western Ecological Research Center","active":true,"usgs":true}],"links":[{"id":337345,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/of/2017/1025/coverthb.jpg"},{"id":337346,"rank":2,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/of/2017/1025/ofr20171025.pdf","text":"Report","size":"3.5 MB","linkFileType":{"id":1,"text":"pdf"},"description":"OFR 2017-1025"}],"country":"Mongolia","otherGeospatial":"Ulaan Taiga Specially Protected Areas","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n 97.55859375,\n 49.89463439573421\n ],\n [\n 102.48046875,\n 49.89463439573421\n ],\n [\n 102.48046875,\n 52.24125614966341\n ],\n [\n 97.55859375,\n 52.24125614966341\n ],\n [\n 97.55859375,\n 49.89463439573421\n ]\n ]\n ]\n }\n }\n ]\n}","contact":"Director, Western Ecological Research Center
U.S. Geological Survey
3020 State University Drive East
Sacramento, California 95819
http://www.werc.usgs.gov/
The Priority Basin Project (PBP) of the Groundwater Ambient Monitoring and Assessment (GAMA) program was developed in response to the Groundwater Quality Monitoring Act of 2001 and is being conducted by the U.S. Geological Survey in cooperation with the California State Water Resources Control Board. From 2004 through 2012, the GAMA-PBP collected samples and assessed the quality of groundwater resources that supply public drinking water in 35 study units across the State. Selected sites in each study unit were sampled again approximately 3 years after initial sampling as part of an assessment of temporal trends in water quality by the GAMA-PBP. Twelve of the study units, initially sampled during 2006–11 (initial sampling period) and sampled a second time during 2008–13 (trend sampling period) to assess temporal trends, are the subject of this report.
The initial sampling was designed to provide a spatially unbiased assessment of the quality of untreated groundwater used for public water supplies in the 12 study units. In these study units, 550 sampling sites were selected by using a spatially distributed, randomized, grid-based method to provide spatially unbiased representation of the areas assessed (grid sites, also called “status sites”). After the initial sampling period, 76 of the previously sampled status sites (approximately 10 percent in each study unit) were randomly selected for trend sampling (“trend sites”). The 12 study units sampled both during the initial sampling and during the trend sampling period were distributed among 6 hydrogeologic provinces: Coastal (Northern and Southern), Transverse Ranges and Selected Peninsular Ranges, Klamath, Modoc Plateau and Cascades, and Sierra Nevada Hydrogeologic Provinces. For the purposes of this trend report, the six hydrogeologic provinces were grouped into two hydrogeologic regions based on location: Coastal and Mountain.
The groundwater samples were analyzed for a number of synthetic organic constituents (volatile organic compounds, pesticides, and pesticide degradates), constituents of special interest (perchlorate and 1,2,3-trichloropropane), and natural inorganic constituents (nutrients, major and minor ions, and trace elements). Isotopic tracers (tritium, carbon-14, and stable isotopes of hydrogen and oxygen in water) also were measured to help identify processes affecting groundwater quality and the sources and ages of the sampled groundwater. More than 200 constituents and water-quality indicators were measured during the trend sampling period.
Quality-control samples (blanks, replicates, matrix-spikes, and surrogate compounds) were collected at about one-third of the trend sites, and the results for these samples were used to evaluate the quality of the data for the groundwater samples. On the basis of detections in laboratory and field blank samples collected by GAMA-PBP study units, including the 12 study units presented here, reporting levels for some groundwater results were adjusted in this report. Differences between replicate samples were mostly within acceptable ranges, indicating low variability in analytical results. Matrix-spike recoveries were largely within the acceptable range (70 to 130 percent).
This study did not attempt to evaluate the quality of water delivered to consumers. After withdrawal, groundwater used for drinking water typically is treated, disinfected, and blended with other waters to achieve acceptable water quality. The comparison benchmarks used in this report apply to treated water that is served to the consumer, not to untreated groundwater. To provide some context for the results, however, concentrations of constituents measured in these groundwater samples were compared with benchmarks established by the U.S. Environmental Protection Agency and the State of California. Comparisons between data collected for this study and benchmarks for drinking water are for illustrative purposes only and are not indicative of compliance or non-compliance with those benchmarks.
Most organic constituents that were detected in groundwater samples from the trend sites were found at concentrations less than health-based benchmarks. One volatile organic compound—perchloroethene—was detected at a concentration greater than the health-based benchmark in samples from one trend site during the initial and trend sampling periods. Chloroform was detected in at least 10 percent of the samples at trend sites in both sampling periods. Methyl tert-butyl ether was detected in samples from more than 10 percent of the trend sites during the initial sampling period. No pesticide or pesticide degradate was detected in greater than 10 percent of the samples from trend sites or at concentrations greater than their health-based benchmarks during either sampling period. Nutrients were not detected at concentrations greater than their health-based benchmarks during either sampling period.
Most detections of major ions and trace elements in samples from trend sites were less than health-based benchmarks during both sampling periods. Arsenic and boron each were detected at concentrations greater than the health-based benchmark in samples from four trend sites during the initial and trend sampling periods. Molybdenum was detected in samples from four trend sites at concentrations greater than the health-based benchmark during both sampling periods. Samples from two of these trend sites had similar molybdenum concentrations, and two had substantially different concentrations during the initial and trend sampling periods. Uranium was detected at a concentration greater than the health-based benchmark only at two trend sites.
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\"}}]}","contact":"Director, California Water Science Center
U.S. Geological Survey
6000 J Street, Placer Hall
Sacramento, California 95819
http://ca.water.usgs.gov
Breton Island, located at the southern end of the Chandeleur Islands, supports one of Louisiana’s largest historical brown pelican (Pelecanus occidentalis) nesting colonies. Although the brown pelican was delisted as an endangered species in 2009, nesting areas are threatened by continued land loss and are extremely vulnerable to storm impacts. The U.S. Fish and Wildlife Service proposed to restore Breton Island to pre-Hurricane Katrina conditions through rebuilding the shoreface, dune, and back-barrier marsh environments. Prior to restoration, scientists from the U.S. Geological Survey’s (USGS) St. Petersburg Coastal and Marine Science Center Geologic and Morphologic Evolution of Coastal Margins project collected high-resolution geophysical (topography, bathymetry, and sub-bottom profiles) and sedimentologic data from around Breton Island to characterize the geologic framework of the island platform, nearshore, and shelf environments. These data will be used to characterize the geologic framework around Breton Island, identify potential borrow areas for restoration efforts, quantify seafloor change, and provide information for sediment transport and morphologic change models to assess island response to restoration and natural processes.
This report, along with the accompanying USGS data release, serves as an archive of sediment data from vibracores, push cores, and submerged grab samples collected from around Breton and Gosier Islands, Louisiana, during two surveys conducted in July 2014 and January 2015 (USGS Field Activity Numbers 2014–314–FA and 2014–336–FA, respectively). Sedimentologic and stratigraphic metrics (for example, sediment texture or unit thicknesses) derived from these data can be used to ground-truth the geophysical data and characterize potential sand resources or can be incorporated into sediment transport or morphologic change models. Data products, including sample location tables, descriptive core logs, core photographs and x-radiographs, results of sediment grain-size analyses, and geographic information system data files with accompanying formal Federal Geographic Data Committee metadata can be downloaded from the accompanying data release.
","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/ds1037","usgsCitation":"Bernier, J.C., Kelso, K.W., Tuten, T.M., Stalk, C.A., and Flocks, J.G., 2017, Sediment data collected in 2014 and 2015 from around Breton and Gosier Islands, Breton National Wildlife Refuge, Louisiana: U.S. Geological Survey Data Series 1037, https://doi.org/10.3133/ds1037.","productDescription":"HTML Document","onlineOnly":"Y","additionalOnlineFiles":"Y","ipdsId":"IP-081095","costCenters":[{"id":574,"text":"St. Petersburg Coastal and Marine Science Center","active":true,"usgs":true}],"links":[{"id":336951,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/ds/1037/coverthb.jpg"},{"id":336945,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/ds/1037","text":"Report HTML","linkFileType":{"id":5,"text":"html"},"description":"DS 1037"},{"id":336959,"rank":3,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/F79C6VKF","text":"USGS data release ","linkHelpText":"Archive of Sediment Data Collected in 2014 and 2015 From Around Breton and Gosier Islands, Breton National Wildlife Refuge, Louisiana"}],"country":"United States","state":"Louisiana","otherGeospatial":"Breton Islands, Breton National Wildlife Refuge, Gosier Island","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -89.241943359375,\n 29.450360671054415\n ],\n [\n -88.97140502929688,\n 29.450360671054415\n ],\n [\n -88.97140502929688,\n 29.60987920220905\n ],\n [\n -89.241943359375,\n 29.60987920220905\n ],\n [\n -89.241943359375,\n 29.450360671054415\n ]\n ]\n ]\n }\n }\n ]\n}","contact":"Director, St. Petersburg Coastal and Marine Science Center
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This scientific investigations map is a product of the U.S. Geological Survey (USGS) National Water-Quality Assessment (NAWQA) project modeling and mapping team. The prediction grids depicted in this map are of continuous pH and are intended to provide an understanding of groundwater-quality conditions at the domestic and public supply drinking water zones in the groundwater of the Central Valley of California. The chemical quality of groundwater and the fate of many contaminants is often influenced by pH in all aquifers. These grids are of interest to water-resource managers, water-quality researchers, and groundwater modelers concerned with the occurrence of natural and anthropogenic contaminants related to pH. In this work, the median well depth categorized as domestic supply was 30 meters below land surface, and the median well depth categorized as public supply is 100 meters below land surface. Prediction grids were created using prediction modeling methods, specifically boosted regression trees (BRT) with a Gaussian error distribution within a statistical learning framework within the computing framework of R (http://www.r-project.org/). The statistical learning framework seeks to maximize the predictive performance of machine learning methods through model tuning by cross validation. The response variable was measured pH from 1,337 wells and was compiled from two sources: USGS National Water Information System (NWIS) database (all data are publicly available from the USGS: http://waterdata.usgs.gov/ca/nwis/nwis) and the California State Water Resources Control Board Division of Drinking Water (SWRCB-DDW) database (water quality data are publicly available from the SWRCB: http://www.waterboards.ca.gov/gama/geotracker_gama.shtml). Only wells with measured pH and well depth data were selected, and for wells with multiple records, only the most recent sample in the period 1993–2014 was used. A total of 1,003 wells (training dataset) were used to train the BRT model, and 334 wells (hold-out dataset) were used to validate the prediction model. The training r-squared was 0.70, and the root-mean-square error (RMSE) in standard pH units was 0.26. The hold-out r-squared was 0.43, and RMSE in standard pH units was 0.37. Predictor variables consisting of more than 60 variables from 7 sources were assembled to develop a model that incorporates regional-scale soil properties, soil chemistry, land use, aquifer textures, and aquifer hydrology. Previously developed Central Valley model outputs of textures (Central Valley Textural Model, CVTM; Faunt and others, 2010) and MODFLOW-simulated vertical water fluxes and predicted depth to water table (Central Valley Hydrologic Model, CVHM; Faunt, 2009) were used to represent aquifer textures and groundwater hydraulics, respectively. In this work, wells were attributed to predictor variable values in ArcGIS using a 500-meter buffer.
Faunt, C.C., ed., 2009, Groundwater availability in the Central Valley aquifer, California: U.S. Geological Survey Professional Paper 1776, 225 p., accessed at https://pubs.usgs.gov/pp/1766/.
Faunt, C.C., Belitz, K., and Hanson, R.T., 2010, Development of a three-dimensional model of sedimentary texture in valley-fill deposits of Central Valley, California, USA: Hydrogeology Journal, v. 18, no. 3, p. 625–649, https://doi.org/10.1007/s10040-009-0539-7.
","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/sim3377","usgsCitation":"Rosecrans, C.Z., Nolan, B.T., Gronberg, J.M., 2017, Predicted pH at the domestic and public supply drinking water depths, Central Valley, California: U.S. Geological Survey Scientific Investigations Map 3377, 1 sheet, scale 1:2,400,000, https://doi.org/10.3133/sim3377.","productDescription":"Sheet: 19.00 x 21.00 inches; Data Release","onlineOnly":"Y","ipdsId":"IP-079912","costCenters":[{"id":154,"text":"California Water Science Center","active":true,"usgs":true},{"id":451,"text":"National Water Quality Assessment Program","active":true,"usgs":true}],"links":[{"id":336887,"rank":3,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/F7FX77K4","text":"USGS data release","description":"USGS data release","linkHelpText":"Ascii grids of predicted pH in depth zones used by domestic and public drinking water supply depths, Central Valley, California."},{"id":336878,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/sim/3377/coverthb.jpg"},{"id":336879,"rank":2,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/sim/3377/sim3377.pdf","text":"Report","size":"1.7 MB","linkFileType":{"id":1,"text":"pdf"},"description":"SIM 3377"}],"country":"United States","state":"California","otherGeospatial":"Central Valley","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -122.22290039062499,\n 40.75557964275589\n ],\n [\n -122.958984375,\n 40.38839687388361\n ],\n [\n -122.574462890625,\n 39.32579941789298\n ],\n [\n -122.08007812499999,\n 38.07404145941957\n ],\n [\n -120.7177734375,\n 36.77409249464195\n ],\n [\n -119.83886718750001,\n 35.33529320309328\n ],\n [\n -119.267578125,\n 34.912962495216966\n ],\n [\n -118.740234375,\n 35.110921809704756\n ],\n [\n -118.740234375,\n 35.8356283888737\n ],\n [\n -118.91601562499999,\n 36.359374956015856\n ],\n [\n -119.84985351562499,\n 37.32648861334206\n ],\n [\n -120.82763671875,\n 38.24680876017446\n ],\n [\n -121.39892578125,\n 39.2492708462234\n ],\n [\n -122.1240234375,\n 40.53050177574321\n ],\n [\n -122.22290039062499,\n 40.75557964275589\n ]\n ]\n ]\n }\n }\n ]\n}","contact":"Director, California Water Science Center
6000 J Street, Placer Hall
Sacramento, CA 95819
Telephone number: (916) 278-3000
http://ca.water.usgs.gov/
The U.S. Geological Survey, in cooperation with the Puerto Rico Department of Natural and Environmental Resources, completed a study to determine whether a relation exists between the extent of forest cover and the magnitude of base flow at two sets of paired drainage basins in the highlands of the municipalities of Adjuntas and Utuado within the mountainous interior of Puerto Rico. One set of paired basins includes the Río Guaónica and Río Tanamá, both tributaries of the Río Grande de Arecibo. The other set includes two smaller basins in the drainage basin of the Río Coabey, which is a tributary of the Río Tanamá. The paired basins in each set have similar rainfall patterns, geologic substrate, and aspect; the principal difference identified in the study is the extent of forest cover and related land uses such as the cultivation of shade and sun coffee. Data describing the hydrology, hydrogeology, and streamflow were used in the analysis. The principal objective of the study was to compare base flow per unit area among basins having different areal extents of forest cover and land uses such as shade coffee and sun coffee cultivation.
Within the mountainous interior of Puerto Rico, a substantial amount of the annual rainfall (45 to 39 percent in the Rio Guaónica and Rio Tanamá, respectively) can migrate to the subsurface and later emerge as base flow in streams. The magnitude of base flow within the two sets of paired basins varies seasonally. Minimum base flows occur during the annual dry season (generally from January to March), and maximum base flows occur during the wet season (generally from August to October). During the dry season or periods of below-normal rainfall, base flow is either the primary or the sole component of streamflow. Daily mean base flow ranged from 3.2 to 20.5 cubic feet per second (ft3 /s) at the Rio Guaónica Basin, and from 4.2 to 23.0 ft3 /s at the Rio Tanamá Basin. The daily mean base flows during 2010 ranged from 0.28 to 0.98 ft3 /s at Tributary 1 and from 0.22 to 0.58 ft3 /s at Tributary 2 of the Rio Coabey. The normalized daily base flow at the Río Guaónica and Río Tanamá Basin during 2010 ranged from 1.3 to 8.1 cubic feet per second per square mile (ft3 /s)/mi2 and from 1.1 to 6.1 (ft3 /s)/mi2 , respectively. The normalized daily base flow for the basins of Tributary 1 and Tributary 2 of Río Coabey during 2010 ranged from 1.0 to 3.6 (ft3 /s)/mi2 and from 1.5 to 3.9 (ft3 /s)/mi2 , respectively.
The normalized mean annual base flow is similar within the larger paired basins of Río Tanamá (2.74 [ft3 /s]/mi2 ) and Río Guaónica (3.15 [ft3 /s]/mi2 ). The mean annual base flow per unit area for both of these basins is about 79 percent of the mean annual streamflow. In the large paired basins, the proportion of Type I land use (forest patches, shade and mixed shade/sun coffee with associated cash crops) is substantially higher in Rio Guaónica Basin (81 percent) than in the Rio Tanamá Basin (59 percent), and the base flow per unit area is also higher. In the small paired basins of Rio Coabey, the proportion of Type I land use is much higher at Tributary 1 (52 percent) than at Tributary 2 (15 percent), but, in contrast to the large basins, the mean annual base flow per unit area is lower (2.22 and 2.62 [ft3 /s]/mi2 , respectively). There is no consistent relation between land use and normalized base flow between the two sets of paired basins in the study.
","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/sir20165142","collaboration":"Prepared in cooperation with the Puerto Rico Department of Natural and Environmental Resources","usgsCitation":"Rodríguez-Martínez, Jesús, and Santiago, Marilyn, 2017, The effects of forest cover on base flow of streams in the mountainous interior of Puerto Rico, 2010: U.S. Geological Survey Scientific Investigations Report 2016–5142, 19 p., https://doi.org/10.3133/sir20165142.","productDescription":"Report: vii, 19 p.; Data Release","numberOfPages":"32","onlineOnly":"Y","additionalOnlineFiles":"Y","ipdsId":"IP-061550","costCenters":[{"id":156,"text":"Caribbean Water Science Center","active":true,"usgs":true}],"links":[{"id":438424,"rank":4,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/F7N58JG5","text":"USGS data release","linkHelpText":"Hydrologic data for the effects of forest cover on base flow of streams in the mountainous interior of Puerto Rico"},{"id":336264,"rank":3,"type":{"id":30,"text":"Data Release"},"url":"https://dx.doi.org/10.5066/F7N58JG5","text":"USGS data release ","description":"USGS data release","linkHelpText":"Hydrologic data for the effects of forest cover on base flow of streams in the mountainous interior of Puerto Rico"},{"id":336240,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/sir/2016/5142/coverthb.jpg"},{"id":336241,"rank":2,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/sir/2016/5142/sir20165142.pdf","text":"Report","size":"15.1 MB","linkFileType":{"id":1,"text":"pdf"},"description":"SIR 2016-5142"}],"otherGeospatial":"Puerto Rico","contact":"Director, Caribbean-Florida Water Science Center
4446 Pet Lane
Suite 108
Lutz, FL 33559
https://pr.water.usgs.gov
Datum conversions from the National Geodetic Vertical Datum of 1929 to the North American Vertical Datum of 1988 among inland and coastal gages throughout the hydrologic regions of New England, the Mid-Atlantic, and the South Atlantic-Gulf have implications among river and storm surge forecasting, general commerce, and water-control operations. The process of data conversions may involve the application of a recovered National Geodetic Vertical Datum of 1929–North American Vertical Datum of 1988 offset, a simplistic datum transformation using VDatum or VERTCON software, or a survey, depending on a gaging network datum evaluation, anticipated uncertainties for data use among the cooperative water community, and methods used to derive the conversion. Datum transformations from National Geodetic Vertical Datum of 1929 to North American Vertical Datum of 1988 using VERTCON purport errors of ± 0.13 foot at the 95 percent confidence level among modeled points, claiming more consistency along the east coast. Survey methods involving differential and trigonometric leveling, along with observations using Global Navigation Satellite System technology, afford a variety of approaches to establish or perpetuate a datum during a survey. Uncertainties among leveling approaches are generally < 0.1 foot, and and Global Navigation Satellite System approaches may be categorized with uncertainties of ≤0.1 foot for a Level I quality category and ≥0.1 foot for Level II or III quality categories (defined by the U.S. Geological Survey) by observation and review of experienced practice. The conversion process is initiated with an evaluation of the inland and coastal gage network datum, beginning with altitude datum components and the history of those components queried through the U.S. Geological Survey Groundwater Site Inventory database. Subsequent edits to the Groundwater Site Inventory database may be required and a consensus reached among the U.S. Geological Survey Water Science Centers to identify the outstanding workload categorized as in-office datum transformations or offset applications versus out-of-office survey efforts. Datum conversions or datum establishment for the inland or coastal gaging network should meet datum uncertainty requirements among other Federal agencies. Datum uncertainty requirements are ±0.25 foot for U.S. Army Corps of Engineers water-control or construction projects and ±0.16 foot for Federal Emergency Management Agency field surveys and checkpoint surveys used for mapping. River level forecasts generally are defined as ± 0.10 foot among the National Oceanic and Atmospheric Administration–National Weather Service. Collaboration and communication among the cooperative water community is necessary during a datum conversion or datum change. Datum notification time-change requirements set by the National Oceanic and Atmospheric Administration–National Weather Service vary from 30 to 120 days, depending on datum conversion or datum-change case scenarios. Notification times associated with these case scenarios may be useful to the National Oceanic and Atmospheric Administration–National Weather Service and U.S. Army Corps of Engineers, because their daily operations are time sensitive, unlike the notification time change requirements of other entities that make up the cooperative water community. At the time of this writing, a future geopotential datum resulting from Gravity for the Redefinition of the American Vertical Datum is anticipated in 2022. A future version of VDatum and VERTCON is anticipated to provide a transformation among North American Vertical Datum of 1988 elevations to the new geopotential datum.
","largerWorkType":{"id":18,"text":"Report"},"largerWorkTitle":"Section B: U.S. Geological Survey Standards in Book 11: Collection and delineation of spatial data","largerWorkSubtype":{"id":5,"text":"USGS Numbered Series"},"language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/tm11B8","usgsCitation":"Rydlund, P.H., Jr., and Noll, M.L., 2017, Vertical datum conversion process for the inland and coastal gage network located in the New England, Mid-Atlantic, and South Atlantic-Gulf hydrologic regions (ver. 1.1, July 2017) U.S. Geological Survey Techniques and Methods, book 11, chap. B8, 29 p., https://doi.org/10.3133/tm11B8.","productDescription":"ix, 29 p.","numberOfPages":"44","onlineOnly":"N","additionalOnlineFiles":"Y","ipdsId":"IP-078726","costCenters":[{"id":474,"text":"New York Water Science Center","active":true,"usgs":true},{"id":502,"text":"Office of Surface Water","active":true,"usgs":true}],"links":[{"id":399692,"rank":4,"type":{"id":36,"text":"NGMDB Index Page"},"url":"https://ngmdb.usgs.gov/Prodesc/proddesc_105491.htm"},{"id":344212,"rank":3,"type":{"id":25,"text":"Version History"},"url":"https://pubs.usgs.gov/tm/11/b08/versionHist.txt"},{"id":336266,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/tm/11/b08/coverthb2.jpg"},{"id":336267,"rank":2,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/tm/11/b08/tm11B8.pdf","text":"Report","size":"11.5 MB","linkFileType":{"id":1,"text":"pdf"},"description":"TM 11-8B"}],"country":"United States","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -89.6484375,\n 30.183121842195515\n ],\n [\n -89.23095703125,\n 30.278044377800153\n ],\n [\n -88.39599609375,\n 30.221101852485987\n ],\n [\n -87.07763671875,\n 30.35391637229704\n ],\n [\n -86.06689453125,\n 30.164126343161097\n ],\n [\n -85.23193359375,\n 29.49698759653577\n ],\n [\n -84.08935546875,\n 30.012030680358613\n ],\n [\n -83.60595703125,\n 29.630771207229\n ],\n [\n -82.77099609375,\n 28.671310915880834\n ],\n [\n -82.99072265625,\n 27.858503954841247\n ],\n [\n -81.93603515625,\n 26.017297563851745\n ],\n [\n -81.32080078125,\n 25.105497373014686\n ],\n [\n -80.15625,\n 25.045792240303445\n ],\n [\n -79.8486328125,\n 26.70635985763354\n ],\n [\n -80.39794921875,\n 28.013801376380712\n ],\n [\n -81.1669921875,\n 30.939924331023445\n ],\n [\n -80.22216796875,\n 32.194208672875384\n ],\n [\n -77.93701171875,\n 33.687781758439364\n ],\n [\n -75.322265625,\n 35.08395557927643\n ],\n [\n -75.1025390625,\n 35.51434313431818\n ],\n [\n -75.7177734375,\n 36.87962060502676\n ],\n [\n -73.6962890625,\n 40.36328834091583\n ],\n [\n -69.85107421874999,\n 41.178653972331674\n ],\n [\n -69.49951171875,\n 41.83682786072714\n ],\n [\n -70.51025390625,\n 42.24478535602799\n ],\n [\n -70.42236328125,\n 43.197167282501276\n ],\n [\n -69.6533203125,\n 43.644025847699496\n ],\n [\n -66.81884765625,\n 44.74673324024678\n ],\n [\n -67.7197265625,\n 45.72152152227954\n ],\n [\n -67.7197265625,\n 47.100044694025215\n ],\n [\n -68.26904296875,\n 47.35371061951363\n ],\n [\n -69.10400390625,\n 47.517200697839414\n ],\n [\n -69.9609375,\n 46.89023157359399\n ],\n [\n -70.51025390625,\n 45.767522962149876\n ],\n [\n -71.25732421875,\n 45.336701909968134\n ],\n [\n -71.71875,\n 45.02695045318546\n ],\n [\n -73.41064453125,\n 45.01141864227728\n ],\n [\n -74.5751953125,\n 44.63739123445585\n ],\n [\n -76.97021484375,\n 43.13306116240612\n ],\n [\n -78.3544921875,\n 42.114523952464246\n ],\n [\n -79.189453125,\n 39.791654835253425\n ],\n [\n -80.26611328125,\n 37.82280243352756\n ],\n [\n -80.88134765625,\n 36.65079252503471\n ],\n [\n -82.41943359375,\n 35.28150065789119\n ],\n [\n -89.49462890625,\n 35.02999636902566\n ],\n [\n -90.41748046874999,\n 32.47269502206151\n ],\n [\n -90.10986328125,\n 31.034108344903512\n ],\n [\n -89.6484375,\n 30.183121842195515\n ]\n ]\n ]\n }\n }\n ]\n}","edition":"Version 1.0: Originally posted March 2017; Version 1.1: July 2017","publicComments":"This report is Chapter 8 in Section B: U.S. Geological Survey Standards in Book 11: Collection and delineation of spatial data.","contact":"Director, Missouri Water Science Center
U.S. Geological Survey
1400 Independence Road, MS 100
Rolla, MO 65401
https://mo.water.usgs.gov/
The quartz monzodiorite of Mount Edith and the concentrically zoned intrusive suite of Boulder Baldy constitute the principal Late Cretaceous igneous intrusions hosted by Mesoproterozoic sedimentary rocks of the Newland Formation in the Big Belt Mountains, Montana. These calc-alkaline plutonic masses are manifestations of subduction-related magmatism that prevailed along the western edge of North America during the Cretaceous. Radiogenic isotope data for neodymium, strontium, and lead indicate that the petrogenesis of the associated magmas involved a combination of (1) sources that were compositionally heterogeneous at the scale of the geographically restricted intrusive rocks in the Big Belt Mountains and (2) variable contamination by crustal assimilants also having diverse isotopic compositions. Altered and mineralized rocks temporally, spatially, and genetically related to these intrusions manifest at least two isotopically distinct mineralizing events, both of which involve major inputs from spatially associated Late Cretaceous igneous rocks. Alteration and mineralization of rock associated with the intrusive suite of Boulder Baldy requires a component characterized by significantly more radiogenic strontium than that characteristic of the associated igneous rocks. However, the source of such a component was not identified in the Big Belt Mountains. Similarly, altered and mineralized rocks associated with the quartz monzodiorite of Mount Edith include a component characterized by significantly more radiogenic strontium and lead, particularly as defined by 207Pb/204Pb values. The source of this component appears to be fluids that equilibrated with proximal Newland Formation rocks. Oxygen isotope data for rocks of the intrusive suite of Boulder Baldy are similar to those of subduction-related magmatism that include mantle-derived components; oxygen isotope data for altered and mineralized equivalents are slightly lighter.
","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/ds1040","usgsCitation":"du Bray, E.A., Unruh, D.M., and Hofstra, A.H., 2017, Isotopic data for Late Cretaceous intrusions and associated altered and mineralized rocks in the Big Belt Mountains, Montana: U.S. Geological Survey Data Series 1040, 12 p., https://doi.org/10.3133/ds1040.","productDescription":"iii, 12 p.","numberOfPages":"20","onlineOnly":"Y","ipdsId":"IP-079476","costCenters":[{"id":171,"text":"Central Mineral and Environmental Resources Science Center","active":true,"usgs":true}],"links":[{"id":336901,"rank":2,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/ds/1040/ds1040.pdf","text":"Report","size":"5.99 MB","linkFileType":{"id":1,"text":"pdf"},"description":"DS 1040"},{"id":336899,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/ds/1040/coverthb.jpg"}],"country":"United States","state":"Montana","otherGeospatial":"Big Belt Mountains","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -111.5,\n 46.25\n ],\n [\n -111,\n 46.25\n ],\n [\n -111,\n 46.74\n ],\n [\n -111.5,\n 46.74\n ],\n [\n -111.5,\n 46.25\n ]\n ]\n ]\n }\n }\n ]\n}","contact":"Center Director, USGS Central Mineral and Environmental Resources Science Center
Box 25046, Mail Stop 973
Denver, CO 80225
Regional-scale, three-dimensional continuous probability models, were constructed for aspects of redox conditions in the groundwater system of the Central Valley, California. These models yield grids depicting the probability that groundwater in a particular location will have dissolved oxygen (DO) concentrations less than selected threshold values representing anoxic groundwater conditions, or will have dissolved manganese (Mn) concentrations greater than selected threshold values representing secondary drinking water-quality contaminant levels (SMCL) and health-based screening levels (HBSL). The probability models were constrained by the alluvial boundary of the Central Valley to a depth of approximately 300 m. Probability distribution grids can be extracted from the 3-D models at any desired depth, and are of interest to water-resource managers, water-quality researchers, and groundwater modelers concerned with the occurrence of natural and anthropogenic contaminants related to anoxic conditions.
Models were constructed using a Boosted Regression Trees (BRT) machine learning technique that produces many trees as part of an additive model and has the ability to handle many variables, automatically incorporate interactions, and is resistant to collinearity. Machine learning methods for statistical prediction are becoming increasing popular in that they do not require assumptions associated with traditional hypothesis testing. Models were constructed using measured dissolved oxygen and manganese concentrations sampled from 2767 wells within the alluvial boundary of the Central Valley, and over 60 explanatory variables representing regional-scale soil properties, soil chemistry, land use, aquifer textures, and aquifer hydrologic properties. Models were trained on a USGS dataset of 932 wells, and evaluated on an independent hold-out dataset of 1835 wells from the California Division of Drinking Water. We used cross-validation to assess the predictive performance of models of varying complexity, as a basis for selecting final models. Trained models were applied to cross-validation testing data and a separate hold-out dataset to evaluate model predictive performance by emphasizing three model metrics of fit: Kappa; accuracy; and the area under the receiver operator characteristic curve (ROC). The final trained models were used for mapping predictions at discrete depths to a depth of 304.8 m. Trained DO and Mn models had accuracies of 86–100%, Kappa values of 0.69–0.99, and ROC values of 0.92–1.0. Model accuracies for cross-validation testing datasets were 82–95% and ROC values were 0.87–0.91, indicating good predictive performance. Kappas for the cross-validation testing dataset were 0.30–0.69, indicating fair to substantial agreement between testing observations and model predictions. Hold-out data were available for the manganese model only and indicated accuracies of 89–97%, ROC values of 0.73–0.75, and Kappa values of 0.06–0.30. The predictive performance of both the DO and Mn models was reasonable, considering all three of these fit metrics and the low percentages of low-DO and high-Mn events in the data.
","language":"English","publisher":"Elsevier","doi":"10.1016/j.jhydrol.2017.01.014","usgsCitation":"Rosecrans, C.Z., Nolan, B.T., and Gronberg, J.M., 2017, Prediction and visualization of redox conditions in the groundwater of Central Valley, California: Journal of Hydrology, v. 546, p. 341-356, https://doi.org/10.1016/j.jhydrol.2017.01.014.","productDescription":"16 p.","startPage":"341","endPage":"356","ipdsId":"IP-075668","costCenters":[{"id":154,"text":"California Water Science Center","active":true,"usgs":true},{"id":451,"text":"National Water Quality Assessment Program","active":true,"usgs":true}],"links":[{"id":336939,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","state":"California","otherGeospatial":"Central Valley","volume":"546","publishingServiceCenter":{"id":1,"text":"Sacramento PSC"},"noUsgsAuthors":false,"publicationStatus":"PW","scienceBaseUri":"58bfd4f0e4b014cc3a3ba483","contributors":{"authors":[{"text":"Rosecrans, Celia Z. 0000-0003-1456-4360 crosecrans@usgs.gov","orcid":"https://orcid.org/0000-0003-1456-4360","contributorId":187542,"corporation":false,"usgs":true,"family":"Rosecrans","given":"Celia","email":"crosecrans@usgs.gov","middleInitial":"Z.","affiliations":[{"id":154,"text":"California Water Science Center","active":true,"usgs":true}],"preferred":false,"id":680860,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"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":451,"text":"National Water Quality Assessment Program","active":true,"usgs":true},{"id":27111,"text":"National Water Quality Program","active":true,"usgs":true}],"preferred":true,"id":680862,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Gronberg, JoAnn M. 0000-0003-4822-7434 jmgronbe@usgs.gov","orcid":"https://orcid.org/0000-0003-4822-7434","contributorId":3548,"corporation":false,"usgs":true,"family":"Gronberg","given":"JoAnn","email":"jmgronbe@usgs.gov","middleInitial":"M.","affiliations":[{"id":154,"text":"California Water Science Center","active":true,"usgs":true}],"preferred":true,"id":680861,"contributorType":{"id":1,"text":"Authors"},"rank":3}]}} ,{"id":70182091,"text":"sir20175009 - 2017 - Enhanced and updated spatially referenced statistical assessment of dissolved-solids load sources and transport in streams of the Upper Colorado River Basin","interactions":[],"lastModifiedDate":"2017-03-08T09:08:36","indexId":"sir20175009","displayToPublicDate":"2017-03-07T00:00:00","publicationYear":"2017","noYear":false,"publicationType":{"id":18,"text":"Report"},"publicationSubtype":{"id":5,"text":"USGS Numbered Series"},"seriesTitle":{"id":334,"text":"Scientific Investigations Report","code":"SIR","onlineIssn":"2328-0328","printIssn":"2328-031X","active":true,"publicationSubtype":{"id":5}},"seriesNumber":"2017-5009","title":"Enhanced and updated spatially referenced statistical assessment of dissolved-solids load sources and transport in streams of the Upper Colorado River Basin","docAbstract":"Approximately 6.4 million tons of dissolved solids are discharged from the Upper Colorado River Basin (UCRB) to the Lower Colorado River Basin each year. This results in substantial economic damages, and tens of millions of dollars are spent annually on salinity control projects designed to reduce salinity loads in surface waters of the UCRB. Dissolved solids in surface water and groundwater have been studied extensively over the past century, and these studies have contributed to a conceptual understanding of sources and transport of dissolved solids. This conceptual understanding was incorporated into a Spatially Referenced Regressions on Watershed Attributes (SPARROW) model to examine sources and transport of dissolved solids in the UCRB. The results of this model were published in 2009. The present report documents the methods and data used to develop an updated dissolved-solids SPARROW model for the UCRB, and incorporates data defining current basin attributes not available in the previous model, including delineation of irrigated lands by irrigation type (sprinkler or flood irrigation), and calibration data from additional monitoring sites.
Dissolved-solids loads estimated for 312 monitoring sites were used to calibrate the SPARROW model, which predicted loads for each of 10,789 stream reaches in the UCRB. The calibrated model provided a good fit to the calibration data as evidenced by R2 and yield R2 values of 0.96 and 0.73, respectively, and a root-mean-square error of 0.47. The model included seven geologic sources that have estimated dissolved-solids yields ranging from approximately 1 to 45 tons per square mile (tons/mi2). Yields generated from irrigated agricultural lands are substantially greater than those from geologic sources, with sprinkler irrigated lands generating an average of approximately 150 tons/mi2 and flood irrigated lands generating between 770 and 2,300 tons/mi2 depending on underlying lithology. The coefficients estimated for six landscape transport characteristics that influence the delivery of dissolved solids from sources to streams, are consistent with the process understanding of dissolved-solids loading to streams in the UCRB.
Dissolved-solids loads and the proportion of those loads among sources in the entire UCRB as well as in major tributaries in the basin are reported, as are loads generated from irrigated lands, rangelands, Bureau of Land Management (BLM) lands, and grazing allotments on BLM lands. Model-predicted loads also are compared with load estimates from 1957 and 1991 at selected locations in three divisions of the UCRB. At the basin scale, the model estimates that 32 percent of the dissolved-solids loads are from irrigated agricultural land sources that compose less than 2 percent of the land area in the UCRB. This estimate is less than previously reported estimates of 40 to 45 percent of basin-scale dissolved-solids loads from irrigated agricultural land sources. This discrepancy could be a result of the implementation of salinity control projects in the basin. Notably, results indicate that the conversion of flood irrigated agricultural lands to sprinkler irrigated agricultural lands is a likely process contributing to the temporal decrease in dissolved-solids loads from irrigated lands.
","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/sir20175009","collaboration":"Prepared in cooperation with the Colorado River Basin Salinity Control Forum","usgsCitation":"Miller, M.P., Buto, S.G., Lambert, P.M., and Rumsey, C.A., 2017, Enhanced and updated spatially referenced statistical assessment of dissolved-solids load sources and transport in streams of the Upper Colorado River Basin: U.S. Geological Survey Scientific Investigations Report 2017–5009, 23 p., https://doi.org/10.3133/sir20175009.","productDescription":"vi, 23 p.","numberOfPages":"34","onlineOnly":"Y","ipdsId":"IP-076357","costCenters":[{"id":610,"text":"Utah Water Science Center","active":true,"usgs":true}],"links":[{"id":438425,"rank":4,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/P9LO3JV2","text":"USGS data release","linkHelpText":"SPARROW model input datasets and predictions of total dissolved loads in streams of the Upper Colorado River Basin watershed"},{"id":336947,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/sir/2017/5009/coverthb.jpg"},{"id":336948,"rank":2,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/sir/2017/5009/sir20175009.pdf","text":"Report","size":"5.6 MB","linkFileType":{"id":1,"text":"pdf"},"description":"SIR 2017-5009"},{"id":336949,"rank":3,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/F76T0JT4","text":"USGS data release","description":"USGS data release","linkHelpText":"Catchment-flowline network and selected model inputs for an enhanced and updated spatially referenced statistical assessment of dissolved-solids load sources and transport in streams of the Upper Colorado River Basin"}],"country":"United States","otherGeospatial":"Colorado River Basin","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -111.500244140625,\n 35.53222622770337\n ],\n [\n -106.14990234375,\n 35.53222622770337\n ],\n [\n -106.14990234375,\n 43.27720532212024\n ],\n [\n -111.500244140625,\n 43.27720532212024\n ],\n [\n -111.500244140625,\n 35.53222622770337\n ]\n ]\n ]\n }\n }\n ]\n}","contact":"The distribution of volcanogenic massive sulfide (VMS), porphyry-epithermal, Alaska-type ultramafic-mafic complexes, intrusion-related Au, and granitoid Sn-W ore deposits in southwest Alaska supports current metallogenic models linking the formation of these deposit types to the emplacement of different suites of igneous rocks during the evolution of this convergent plate margin. Regional-scale aeromagnetic data provide a continuous set of observations over the deposits and show contrasting patterns over the igneous rock suites hosting the various deposit types. Combined with surface geologic data and regional metallogenic constraints, aeromagnetic data—filtered to enhance the anomalous magnetic field and map magnetic domains—were used to produce a mineral potential map across this accreted island-arc setting. The reduced-to-pole, upward continuation, and total horizontal gradient transform maps show anomalies that could represent porphyry-epithermal deposits within the intraoceanic- and continental-arc terranes. The tilt derivative transform highlights lineaments within the back arc that may represent zones with potential for VMS deposits. The truncations of tilt derivative lineaments outline a major magnetic domain boundary between the back-arc and craton margin, which is prospective for granitoid Sn-W deposits. Annular tilt derivative highs outline granitoids that could be associated with intrusion-related Au deposits within the craton margin. Shallow, magnetite-rich Alaska-type ultramafic-mafic complexes are mapped by their short-wavelength, high-amplitude anomalies. Successful mineral potential mapping across southwestern Alaska as performed in the present study suggests that filtered aeromagnetic data can be effectively used in mineral exploration in convergent continental margin settings.
","language":"English","publisher":"Society of Economic Geologists","doi":"10.2113/econgeo.112.2.375","usgsCitation":"Anderson, E., Monecke, T., Hitzman, M.W., Zhou, W., and Bedrosian, P.A., 2017, Mineral potential mapping in an accreted island-arc setting using aeromagnetic data: An example from southwest Alaska: Economic Geology, v. 112, no. 2, p. 375-396, https://doi.org/10.2113/econgeo.112.2.375.","productDescription":"22 p.","startPage":"375","endPage":"396","ipdsId":"IP-069250","costCenters":[{"id":35995,"text":"Geology, Geophysics, and Geochemistry Science Center","active":true,"usgs":true}],"links":[{"id":327805,"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 -157.23632812499997,\n 56.992882804633986\n ],\n [\n -132.275390625,\n 56.992882804633986\n ],\n [\n -132.275390625,\n 62.55285695857292\n ],\n [\n -157.23632812499997,\n 62.55285695857292\n ],\n [\n -157.23632812499997,\n 56.992882804633986\n ]\n ]\n ]\n }\n }\n ]\n}","volume":"112","issue":"2","publishingServiceCenter":{"id":2,"text":"Denver PSC"},"noUsgsAuthors":false,"publicationDate":"2017-02-07","publicationStatus":"PW","scienceBaseUri":"57c6b175e4b0f2f0cebe6f32","contributors":{"authors":[{"text":"Anderson, Eric D. 0000-0002-0138-6166 ericanderson@usgs.gov","orcid":"https://orcid.org/0000-0002-0138-6166","contributorId":172766,"corporation":false,"usgs":true,"family":"Anderson","given":"Eric","email":"ericanderson@usgs.gov","middleInitial":"D.","affiliations":[{"id":211,"text":"Crustal Geophysics and Geochemistry Science Center","active":true,"usgs":true}],"preferred":true,"id":645153,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Monecke, Thomas","contributorId":50423,"corporation":false,"usgs":true,"family":"Monecke","given":"Thomas","affiliations":[],"preferred":false,"id":645155,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Hitzman, Murray W. 0000-0002-3876-0537 mhitzman@usgs.gov","orcid":"https://orcid.org/0000-0002-3876-0537","contributorId":200913,"corporation":false,"usgs":true,"family":"Hitzman","given":"Murray","email":"mhitzman@usgs.gov","middleInitial":"W.","affiliations":[],"preferred":false,"id":645156,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Zhou, Wendy","contributorId":205989,"corporation":false,"usgs":false,"family":"Zhou","given":"Wendy","email":"","affiliations":[{"id":6606,"text":"Colorado School of Mines","active":true,"usgs":false}],"preferred":false,"id":814483,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"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":211,"text":"Crustal Geophysics and Geochemistry Science Center","active":true,"usgs":true},{"id":312,"text":"Geology, Minerals, Energy, and Geophysics Science Center","active":true,"usgs":true}],"preferred":true,"id":814484,"contributorType":{"id":1,"text":"Authors"},"rank":5}]}} ,{"id":70184252,"text":"sim3375 - 2017 - Bathymetry of Clear Creek Reservoir, Chaffee County, Colorado, 2016","interactions":[],"lastModifiedDate":"2017-03-07T11:02:19","indexId":"sim3375","displayToPublicDate":"2017-03-06T16:15:00","publicationYear":"2017","noYear":false,"publicationType":{"id":18,"text":"Report"},"publicationSubtype":{"id":5,"text":"USGS Numbered Series"},"seriesTitle":{"id":333,"text":"Scientific Investigations Map","code":"SIM","onlineIssn":"2329-132X","printIssn":"2329-1311","active":true,"publicationSubtype":{"id":5}},"seriesNumber":"3375","title":"Bathymetry of Clear Creek Reservoir, Chaffee County, Colorado, 2016","docAbstract":"To better characterize the water supply capacity of Clear Creek Reservoir, Chaffee County, Colorado, the U.S. Geological Survey, in cooperation with the Pueblo Board of Water Works and Colorado Mountain College, carried out a bathymetry survey of Clear Creek Reservoir. A bathymetry map of the reservoir is presented here with the elevation-surface area and the elevation-volume relations. The bathymetry survey was carried out June 6–9, 2016, using a man-operated boat-mounted, multibeam echo sounder integrated with a Global Positioning System and a terrestrial survey using real-time kinematic Global Navigation Satellite Systems. The two collected datasets were merged and imported into geographic information system software. The equipment and methods used in this study allowed water-resource managers to maintain typical reservoir operations, eliminating the need to empty the reservoir to carry out the survey.
","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/sim3375","collaboration":"Prepared in cooperation with the Pueblo Board of Water Works and Colorado Mountain College","usgsCitation":"Kohn, M.S., Kinzel, P.J., and Mohrmann, J.S., 2017, Bathymetry of Clear Creek Reservoir, Chaffee County, Colorado, 2016: U.S. Geological Survey Scientific Investigations Map 3375, 1 sheet, https://doi.org/10.3133/sim3375","productDescription":"Map: 36.01 x 28.00 inches; Data Release, Read Me","onlineOnly":"Y","costCenters":[{"id":191,"text":"Colorado Water Science Center","active":true,"usgs":true}],"links":[{"id":336842,"rank":2,"type":{"id":26,"text":"Sheet"},"url":"https://pubs.usgs.gov/sim/3375/sim3375.pdf","text":"Map","size":"9.37 MB","linkFileType":{"id":1,"text":"pdf"},"description":"SIM 3375"},{"id":336843,"rank":3,"type":{"id":20,"text":"Read Me"},"url":"https://pubs.usgs.gov/sim/3375/sim3375_readme.txt","size":"8.00 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Denver Federal Center
Box 25046, Mail Stop 415
Denver, CO 80225
Groundwater is a major source of drinking water in Lycoming County and adjacent counties in north-central and northeastern Pennsylvania, which are largely forested and rural and are currently undergoing development for hydrocarbon gases. Water-quality data are needed for assessing the natural characteristics of the groundwater resource and the potential effects from energy and mineral extraction, timber harvesting, agriculture, sewage and septic systems, and other human influences.
This report, prepared in cooperation with Lycoming County, presents analytical data for groundwater samples from 75 domestic wells sampled throughout Lycoming County in June, July, and August 2014. The samples were collected using existing pumps and plumbing prior to any treatment and analyzed for physical and chemical characteristics, including nutrients, major ions, metals and trace elements, volatile organic compounds, gross-alpha particle and gross beta-particle activity, uranium, and dissolved gases, including methane and radon-222.
Results indicate groundwater quality generally met most drinking-water standards, but that some samples exceeded primary or secondary maximum contaminant levels (MCLs) for arsenic, iron, manganese, total dissolved solids (TDS), chloride, pH, bacteria, or radon-222. Arsenic concentrations were higher than the MCL of 10 micrograms per liter (µg/L) in 9 of the 75 (12 percent) well-water samples, with concentrations as high as 23.6 μg/L; arsenic concentrations were higher than the health advisory level (HAL) of 2 μg/L in 23 samples (31 percent). Total iron concentrations exceeded the secondary maximum contaminant level (SMCL) of 300 μg/L in 20 of the 75 samples. Total manganese concentrations exceeded the SMCL of 50 μg/L in 20 samples and the HAL of 300 μg/L in 2 of those samples. Three samples had chloride concentrations that exceeded the SMCL of 250 milligrams per liter (mg/L); two of those samples exceeded the SMCL of 500 mg/L for TDS. The pH ranged from 5.3 to 9.15 and did not meet the SMCL range of 6.5 to 8.5 in 22 samples, with 17 samples having a pH less than 6.5 and 8 samples having pH greater than 8.5. Generally, the samples that had elevated TDS, chloride, or arsenic concentrations had high pH.
Total coliform bacteria were detected in 39 of 75 samples (52 percent), with Escherichia coli detected in 10 of those 39 samples. Radon-222 activities ranged from non-detect to 7,420 picocuries per liter (pCi/L), with a median of 863 pCi/L, and exceeded the proposed drinking-water standard of 300 pCi/L in 50 (67 percent) of the 75 samples; radon-222 activities were higher than the alternative proposed standard of 4,000 pCi/L in 3 samples.
Water from 15 of 75 (20 percent) wells had concentrations of methane greater than the reporting level of 0.01 mg/L; detectable methane concentrations ranged from 0.04 to 16.8 mg/L. Two samples had methane concentrations (13.1 and 16.8 mg/L) exceeding the action level of 7 mg/L. Low levels of ethane (up to 0.12 mg/L) were present in the five samples with the highest methane concentrations (near or above 1 mg/L) that were analyzed for hydrocarbon compounds and isotopic composition. The isotopic composition of methane in four of these groundwater samples, from the Catskill and Lock Haven Formations and the Hamilton Group, have sample carbon isotopic ratio delta values (carbon-13/carbon-12) ranging from –42.36 to –36.08 parts per thousand (‰) and hydrogen isotopic ratio delta values (deuterium/protium) ranging from –212.0 to –188.4 ‰, which are consistent with the isotopic compositions reported for mud-gas logging samples from these geologic units and a thermogenic source of the methane. However, the isotopic composition and ratios of methane to ethane in a fifth sample indicate the methane in that sample may be of microbial origin that subsequently underwent oxidation. The fifth sample had the highest concentration of methane, 16.8 mg/L, with an carbon isotopic ratio delta values of -50.59 ‰ and a hydrogen isotopic ratio delta values of -209.7 ‰.
The six well-water samples with the highest methane concentrations also had among the highest pH values (8.25 to 9.15) and elevated concentrations of sodium, lithium, boron, fluoride, arsenic, and bromide. Relatively elevated concentrations of some other constituents, such as barium, strontium, and chloride, commonly were present in, but not limited to, those well-water samples with elevated methane.
Three of the six groundwater samples with the highest methane concentrations had chloride/bromide ratios that indicate mixing with a small amount of brine (0.02 percent or less) similar in composition to those reported at undetermined depth below the freshwater aquifer and for gas and oil well brines in Pennsylvania. The sample with the highest methane concentration and most other samples with low methane concentrations (less than about 1 mg/L) have chloride/bromide ratios that indicate predominantly anthropogenic sources of chloride, such as road-deicing salt, septic systems, and (or) animal waste. Brines that are naturally present may originate from deeper parts of the aquifer system, while anthropogenic sources are more likely to affect shallow groundwater because they occur on or near the land-surface.
The spatial distribution of groundwater compositions generally indicate that (1) uplands along the western border of Lycoming County usually have dilute, slightly acidic oxygenated, calcium-bicarbonate type waters; (2) intermediate altitudes or areas of carbonate bedrock usually have water of near neutral pH, with highest amounts of hardness (calcium and magnesium); (3) stream valleys, low elevations where groundwater may be discharging, and deep wells in uplands usually have water with pH values greater than 8 and highest arsenic, sodium, lithium, bromide concentrations. Geochemical modeling indicated that for samples with elevated pH, sodium, lithium, bromide, and alkalinity, the water chemistry could have resulted by dissolution of calcite (calcium carbonate) combined with cation-exchange and mixing with a small amount of brine. Through cation-exchange reactions between water and bedrock, which are equivalent to processes in a water softener, calcium ions released by calcite dissolution are exchanged for sodium ions on clay minerals. Thus, the assessment of groundwater quality in Lycoming County indicates groundwater is generally of good quality, but various parts of Lycoming County can have groundwater with low to moderate concentrations of methane and other constituents that appear in naturally present brine and produced waters from gas and oil wells at high concentrations.\"
","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/sir20165143","collaboration":"Prepared in cooperation with the County of Lycoming, Pennsylvania","usgsCitation":"Gross, E.L., and Cravotta, C.A., III, 2017, Groundwater quality for 75 domestic wells in Lycoming County, Pennsylvania, 2014: U.S. Geological Survey Scientific Investigations Report 2016–5143, 74 p., https://doi.org/10.3133/sir20165143.","productDescription":"Report: xi, 74 p.; Appendixes 1-2","onlineOnly":"Y","additionalOnlineFiles":"Y","ipdsId":"IP-076071","costCenters":[{"id":532,"text":"Pennsylvania Water Science Center","active":true,"usgs":true}],"links":[{"id":336804,"rank":4,"type":{"id":3,"text":"Appendix"},"url":"https://pubs.usgs.gov/sir/2016/5143/sir20165143_appendix2.xlsx","text":"Appendix 2 - Table 2-1 - ","size":"27.3 KB","linkFileType":{"id":3,"text":"xlsx"},"description":"SIR 2016-5143","linkHelpText":"Spearman rank correlation coefficient (r) matrix for groundwater chemical data Lycoming County, 2014"},{"id":336802,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/sir/2016/5143/coverthb.jpg"},{"id":336803,"rank":3,"type":{"id":3,"text":"Appendix"},"url":"https://pubs.usgs.gov/sir/2016/5143/sir20165143_appendix1.xlsx","text":"Appendix 1 - Table 1-1 - ","size":"15.3 KB","linkFileType":{"id":3,"text":"xlsx"},"description":"SIR 2016-5143","linkHelpText":" Compilation of data not available in the National Water Information System"},{"id":336722,"rank":2,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/sir/2016/5143/sir20165143.pdf","text":"Report","size":"8.63 MB","linkFileType":{"id":1,"text":"pdf"},"description":"SIR 2016-5143"}],"country":"United States","state":"Pennsylvania","county":"Lycoming 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Pennsylvania Water Science Center
U.S. Geological Survey
215 Limekiln Road
New Cumberland, PA 17070
http://pa.water.usgs.gov/
Ontogenetic niche shifts (ONS) are important drivers of population and community dynamics, but they can be difficult to identify for species with prolonged larval or juvenile stages, or for species that inhabit continuous habitats. Most studies of ONS focus on single transitions among discrete habitat patches at local scales. However, for species with long larval or juvenile periods, affinity for particular locations within connected habitat networks may differ among cohorts. The resulting spatial patterns of distribution can result from a combination of landscape-scale habitat structure, position of a habitat patch within a network, and local habitat characteristics—all of which may interact and change as individuals grow. We estimated such spatial ONS for spring salamanders (Gyrinophilus porphyriticus), which have a larval period that can last 4 years or more. Using mixture models to identify larval cohorts from size frequency data, we fit occupancy models for each age class using two measures of the branching structure of stream networks and three measures of stream network position. Larval salamander cohorts showed different preferences for the position of a site within the stream network, and the strength of these responses depended on the basin-wide spatial structure of the stream network. The isolation of a site had a stronger effect on occupancy in watersheds with more isolated headwater streams, while the catchment area, which is associated with gradients in stream habitat, had a stronger effect on occupancy in watersheds with more paired headwater streams. Our results show that considering the spatial structure of habitat networks can provide new insights on ONS in long-lived species.
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NASA develops remote-sensing instruments and spacecraft, launches satellites, and validates their performance in orbit. The USGS owns and operates Landsat satellites in space and manages their data transmissions, including ground reception, archiving, product generation, and public distribution. In 2008, with support from the U.S. Department of the Interior, the USGS made its Landsat data free to anyone in the world.
The current satellites in the Landsat program, Landsat 7 (launched in 1999) and Landsat 8 (launched in 2013), provide complete coverage of the Earth every eight days. A Landsat 9 satellite is scheduled for launch in late 2020.
","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/fs20173018","usgsCitation":"Campbell, Jon, 2017, Landsat eyes help guard the world's forests: U.S. Geological Survey Fact Sheet 2017–3018, 2 p., https://doi.org/10.3133/fs20173018.","productDescription":"2 p.","onlineOnly":"Y","additionalOnlineFiles":"N","ipdsId":"IP-084384","costCenters":[{"id":5072,"text":"Office of Communication and Publishing","active":true,"usgs":true}],"links":[{"id":336823,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/fs/2017/3018/coverthb.jpg"},{"id":336824,"rank":2,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/fs/2017/3018/fs20173018.pdf","text":"Report","size":"2.87 MB","linkFileType":{"id":1,"text":"pdf"},"description":"FS 2017-3018"}],"otherGeospatial":"Earth","contact":"Associate Director
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Climate and Land Use Change Mission Area
12201 Sunrise Valley Drive
Reston, VA 20192
Web site: Climate and Land Use Change
Due to its slow turnover rates in soil, pyrogenic carbon (PyC) is considered an important C pool and relevant to climate change processes. Therefore, the amounts of soil PyC were compared to environmental covariates over an area of 327,757 km2 in the northeastern United States in order to understand the controls on PyC distribution over large areas. Topsoil (defined as the soil A horizon, after removal of any organic horizons) samples were collected at 165 field sites in a generalised random tessellation stratified design that corresponded to approximately 1 site per 1600 km2 and PyC was estimated from diffuse reflectance mid-infrared spectroscopy measurements using a partial least-squares regression analysis in conjunction with a large database of PyC measurements based on a solid-state 13C nuclear magnetic resonance spectroscopy technique. Three spatial models were applied to the data in order to relate critical environmental covariates to the changes in spatial density of PyC over the landscape. Regional mean density estimates of PyC were 11.0 g kg− 1 (0.84 Gg km− 2) for Ordinary Kriging, 25.8 g kg− 1(12.2 Gg km− 2) for Multivariate Linear Regression, and 26.1 g kg− 1 (12.4 Gg km− 2) for Bayesian Regression Kriging. Akaike Information Criterion (AIC) indicated that the Multivariate Linear Regression model performed best (AIC = 842.6; n = 165) compared to Ordinary Kriging (AIC = 982.4) and Bayesian Regression Kriging (AIC = 979.2). Soil PyC concentrations correlated well with total soil sulphur (P < 0.001; n = 165), plant tissue lignin (P = 0.003), and drainage class (P = 0.008). This suggests the opportunity of including related environmental parameters in the spatial assessment of PyC in soils. Better estimates of the contribution of PyC to the global carbon cycle will thus also require more accurate assessments of these covariates.
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\"}}]}","volume":"296","publishingServiceCenter":{"id":2,"text":"Denver PSC"},"noUsgsAuthors":false,"publicationStatus":"PW","scienceBaseUri":"58ba8ebbe4b0bcef64f0b92f","contributors":{"authors":[{"text":"Jauss, Verena","contributorId":187475,"corporation":false,"usgs":false,"family":"Jauss","given":"Verena","email":"","affiliations":[],"preferred":false,"id":680578,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Sullivan, Patrick J.","contributorId":187476,"corporation":false,"usgs":false,"family":"Sullivan","given":"Patrick","email":"","middleInitial":"J.","affiliations":[],"preferred":false,"id":680579,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Sanderman, Jonathan","contributorId":187477,"corporation":false,"usgs":false,"family":"Sanderman","given":"Jonathan","email":"","affiliations":[],"preferred":false,"id":680580,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Smith, David B. 0000-0001-8396-9105 dsmith@usgs.gov","orcid":"https://orcid.org/0000-0001-8396-9105","contributorId":138565,"corporation":false,"usgs":true,"family":"Smith","given":"David","email":"dsmith@usgs.gov","middleInitial":"B.","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}],"preferred":true,"id":680577,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Lehmann, Johannes","contributorId":187478,"corporation":false,"usgs":false,"family":"Lehmann","given":"Johannes","email":"","affiliations":[],"preferred":false,"id":680581,"contributorType":{"id":1,"text":"Authors"},"rank":5}]}} ,{"id":70184199,"text":"70184199 - 2017 - A physics-based earthquake simulator and its application to seismic hazard assessment in Calabria (Southern Italy) region","interactions":[],"lastModifiedDate":"2017-06-07T14:16:50","indexId":"70184199","displayToPublicDate":"2017-03-03T00:00:00","publicationYear":"2017","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":5309,"text":"Acta Geophysica","active":true,"publicationSubtype":{"id":10}},"title":"A physics-based earthquake simulator and its application to seismic hazard assessment in Calabria (Southern Italy) region","docAbstract":"The use of a newly developed earthquake simulator has allowed the production of catalogs lasting 100 kyr and containing more than 100,000 events of magnitudes ≥4.5. The model of the fault system upon which we applied the simulator code was obtained from the DISS 3.2.0 database, selecting all the faults that are recognized on the Calabria region, for a total of 22 fault segments. The application of our simulation algorithm provides typical features in time, space and magnitude behavior of the seismicity, which can be compared with those of the real observations. The results of the physics-based simulator algorithm were compared with those obtained by an alternative method using a slip-rate balanced technique. Finally, as an example of a possible use of synthetic catalogs, an attenuation law has been applied to all the events reported in the synthetic catalog for the production of maps showing the exceedance probability of given values of PGA on the territory under investigation.
","language":"English","publisher":"Springer-Verlag","publisherLocation":"Berlin","doi":"10.1007/s11600-017-0020-2","usgsCitation":"Console, R., Nardi, A., Carluccio, R., Murru, M., Falcone, G., and Parsons, T.E., 2017, A physics-based earthquake simulator and its application to seismic hazard assessment in Calabria (Southern Italy) region: Acta Geophysica, v. 65, no. 1, p. 243-257, https://doi.org/10.1007/s11600-017-0020-2.","productDescription":"15 p.","startPage":"243","endPage":"257","ipdsId":"IP-083545","costCenters":[{"id":520,"text":"Pacific Coastal and Marine Science Center","active":true,"usgs":true}],"links":[{"id":336810,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"Italy","otherGeospatial":"Calabria region","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n 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,{"id":70184189,"text":"fs20173012 - 2017 - Geospatial analysis identifies critical mineral-resource potential in Alaska","interactions":[],"lastModifiedDate":"2018-06-19T19:29:48","indexId":"fs20173012","displayToPublicDate":"2017-03-03T00:00:00","publicationYear":"2017","noYear":false,"publicationType":{"id":18,"text":"Report"},"publicationSubtype":{"id":5,"text":"USGS Numbered Series"},"seriesTitle":{"id":313,"text":"Fact Sheet","code":"FS","onlineIssn":"2327-6932","printIssn":"2327-6916","active":true,"publicationSubtype":{"id":5}},"seriesNumber":"2017-3012","title":"Geospatial analysis identifies critical mineral-resource potential in Alaska","docAbstract":"Alaska consists of more than 663,000 square miles (1,717,000 square kilometers) of land—more than a sixth of the total area of the United States—and large tracts of it have not been systematically studied or sampled for mineral-resource potential. Many regions of the State are known to have significant mineral-resource potential, and there are currently six operating mines in the State along with numerous active mineral exploration projects. The U.S. Geological Survey and the Alaska Division of Geological & Geophysical Surveys have developed a new geospatial tool that integrates and analyzes publicly available databases of geologic information and estimates the mineral-resource potential for critical minerals, which was recently used to evaluate Alaska. The results of the analyses highlight areas that have known mineral deposits and also reveal areas that were not previously considered to be prospective for these deposit types. These results will inform land management decisions by Federal, State, and private landholders, and will also help guide future exploration activities and scientific investigations in Alaska.
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\":{\"name\":\"Alaska\",\"nation\":\"USA \"}}]}","contact":"Alaska Science Center staff
U.S. Geological Survey
4210 University Dr.
Anchorage, AK 99508
Alaska Mineral Resources
Alaska Science Center
Information concerning the availability, use, and quality of groundwater in the 10 parishes overlying the Southern Hills regional aquifer system of Louisiana (fig. 1) is critical for water-supply management. The purpose of this fact sheet is to present information that can be used by water managers, residents, and others for stewardship of this vital resource. Information on the availability, past and current use, use trends, and water quality from groundwater sources in these parishes is presented. Previously published reports (see References Cited section) and data stored in the U.S. Geological Survey’s National Water Information System (U.S. Geological Survey, 2017) are the primary sources of the information presented here.
","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/fs20173010","collaboration":"Prepared in cooperation with the Louisiana Department of Transportation and Development","usgsCitation":"White, V.E., 2017, Water resources of the Southern Hills regional aquifer system, southeastern Louisiana: U.S. Geological Survey Fact Sheet 2017–3010,Director, Lower Mississippi-Gulf Water Science Center
U.S. Geological Survey
3535 S. Sherwood Forest Blvd.
Suite 120
Baton Rouge, LA 70816
https://la.water.usgs.gov
Stratiform Zn-Pb deposits hosted in unmetamorphosed carbonaceous and siliceous mudstones of the Ordovician to Silurian Duo Lake Formation define the Howards Pass district in Yukon Territory and Northwest Territories, western Canada. Collectively, the deposits are amongst the largest in the world, containing drill-indicated and inferred resources of 423 Mt at 4.84 % Zn and 1.59 % Pb. Sulphide textures include (a) fine-scale laminations of sphalerite, galena, and pyrite from <0.05 mm to 1 cm thick, interbedded with carbonaceous sedimentary rock; (b) layers of coarse sulphide that are structurally controlled by microfolds; and (c) veins that cut bedding and sulphide laminations. The finely interlaminated nature of sulphides with mudstone has been used as evidence for syngenetic mineralizing processes, whereas paleomagnetic data determined on coarse layered sulphides suggest a Middle Jurassic age of mineralization. Here, we present new rhenium-osmium (Re-Os) isotopic data for 12 pyrite separates obtained from 4 laminated sulphide-rich samples from the XY Central (XYC) and Don (DON) deposits and for 1 unmineralized organic-rich mudstone ∼20 m stratigraphically below the sulphide-bearing zone. Pyrite separates that lack mudstone inclusions (“pure”) from the XYC deposit contain 2.2 to 4.0 ppb Re and 93.4 to 123.4 ppt Os; pure pyrite from the DON deposit is significantly more enriched in Re and Os (34–37 ppb Re; 636.8–694.9 ppt Os). The 187Re/188Os values of pure pyrite separates from the XYC and DON deposits range from 137.6 to 197 and 182.1 to 201.4, respectively. Regression of all pure pyrite Re-Os data from both deposits yields an isochron age of 442 ± 14 Ma (MSWD = 7.4) and an initial 187Os/188Os (Osi) value of 0.71 ± 0.07. The Re-Os age indicates that the early phase of pyrite precipitation (and by inference, sphalerite and galena) occurred during the early Silurian, consistent with biostratigraphic ages of the host rocks. The Osi value of ∼0.8 for earliest Silurian seawater recorded from organic-rich shale in the basal Silurian Global Stratotype Section and Point (GSSP) at Dobs Linn, Scotland is very similar to that provided by the Howards Pass pyrite regression and hence suggests a hydrogenous (seawater) source of Os for the pyrite. Therefore, two possible sources of Os are (1) the Zn- and Pb-bearing hydrothermal fluid that leached Os from footwall sedimentary rocks, which were deposited in seawater, or (2) directly from seawater during precipitation of the pyrite, which suggests that the Os content of the hydrothermal fluid was minor relative to that of seawater.
","language":"English","publisher":"Springer","doi":"10.1007/s00126-016-0663-y","usgsCitation":"Kelley, K.D., Selby, D., Falck, H., and Slack, J.F., 2017, Re-Os systematics and age of pyrite associated with stratiform Zn-Pb mineralization in the Howards Pass district, Yukon and Northwest Territories, Canada: Mineralium Deposita, v. 52, no. 3, p. 317-335, https://doi.org/10.1007/s00126-016-0663-y.","productDescription":"19 p.","startPage":"317","endPage":"335","ipdsId":"IP-068931","costCenters":[{"id":171,"text":"Central Mineral and Environmental Resources Science Center","active":true,"usgs":true},{"id":245,"text":"Eastern Mineral and Environmental Resources Science Center","active":true,"usgs":true}],"links":[{"id":470028,"rank":0,"type":{"id":41,"text":"Open Access External Repository Page"},"url":"https://durham-repository.worktribe.com/output/1380298","text":"External Repository"},{"id":356287,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"Canada","state":"Northwest Territories, Yukon","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -130.968017578125,\n 62.32410603355886\n ],\n [\n -127.9248046875,\n 62.32410603355886\n ],\n [\n -127.9248046875,\n 63.027565805785244\n ],\n [\n -130.968017578125,\n 63.027565805785244\n ],\n [\n -130.968017578125,\n 62.32410603355886\n ]\n ]\n ]\n }\n }\n ]\n}","volume":"52","issue":"3","publishingServiceCenter":{"id":2,"text":"Denver PSC"},"noUsgsAuthors":false,"publicationDate":"2016-05-30","publicationStatus":"PW","scienceBaseUri":"5b6fc720e4b0f5d57878ebc3","contributors":{"authors":[{"text":"Kelley, Karen D. kdkelley@usgs.gov","contributorId":431,"corporation":false,"usgs":true,"family":"Kelley","given":"Karen","email":"kdkelley@usgs.gov","middleInitial":"D.","affiliations":[{"id":171,"text":"Central Mineral and Environmental Resources Science Center","active":true,"usgs":true}],"preferred":false,"id":623200,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Selby, David","contributorId":58167,"corporation":false,"usgs":true,"family":"Selby","given":"David","affiliations":[],"preferred":false,"id":623201,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Falck, Hendrik","contributorId":167705,"corporation":false,"usgs":false,"family":"Falck","given":"Hendrik","email":"","affiliations":[{"id":24811,"text":"NWT Geoscience Office, Yellowknife, Canada","active":true,"usgs":false}],"preferred":false,"id":623202,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"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":387,"text":"Mineral Resources Program","active":true,"usgs":true},{"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}],"preferred":true,"id":623203,"contributorType":{"id":1,"text":"Authors"},"rank":4}]}} ,{"id":70198630,"text":"70198630 - 2017 - Walrus haul-out and in water activity levels relative to sea ice availability in the Chukchi Sea","interactions":[],"lastModifiedDate":"2019-06-11T11:43:29","indexId":"70198630","displayToPublicDate":"2017-03-01T13:27:06","publicationYear":"2017","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":2373,"text":"Journal of Mammalogy","onlineIssn":"1545-1542","printIssn":"0022-2372","active":true,"publicationSubtype":{"id":10}},"title":"Walrus haul-out and in water activity levels relative to sea ice availability in the Chukchi Sea","docAbstract":"An animal’s energetic costs are dependent on the amount of time it allocates to various behavioral activities. For Arctic pinnipeds, the time allocated to active and resting behaviors could change with future reductions in sea ice cover and longer periods of open water. The Pacific walrus (Odobenus rosmarus divergens) is a large Arctic pinniped that rests on sea ice or land between foraging trips to feed on the seafloor. We used behavioral data collected from radiotagged walruses in the Chukchi Sea (2008–2014) in a Bayesian generalized linear mixed effects model to estimate the probability a walrus was in water foraging, in water not foraging, or hauled out, as a function of environmental covariates. The probability of a walrus being in water increased with wind speed and decreased with air temperature, and the probability a walrus was foraging, given it was in water, increased with available benthic macrofaunal biomass. The probability of each behavior was also related to the nature and availability of haul-out substrates. The amount of time walruses spent in water foraging and hauled out was greatest when only sea ice was available, which typically occurs when walruses occupy feeding areas during summer and early autumn. This situation may be most energy efficient for walruses because it allows the highest proportion of in water energy expenditure to be allocated to foraging. Conversely, the amount of time walruses spent in water foraging and hauled out was lowest when only land was available, which typically occurs in late autumn, in years when walruses were constrained to land haul-outs because sea ice was absent over the continental shelf.
","language":"English","publisher":"Oxford","doi":"10.1093/jmammal/gyw195","usgsCitation":"Jay, C.V., Taylor, R.L., Fischbach, A., Udevitz, M.S., and Beatty, W.S., 2017, Walrus haul-out and in water activity levels relative to sea ice availability in the Chukchi Sea: Journal of Mammalogy, v. 98, no. 2, p. 386-396, https://doi.org/10.1093/jmammal/gyw195.","productDescription":"11 p.","startPage":"386","endPage":"396","ipdsId":"IP-076658","costCenters":[{"id":116,"text":"Alaska Science Center Biology MFEB","active":true,"usgs":true}],"links":[{"id":470029,"rank":1,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.1093/jmammal/gyw195","text":"Publisher Index Page"},{"id":438428,"rank":0,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/F7XD0ZTG","text":"USGS data release","linkHelpText":"Walrus Haulout and In-water Activity Levels Relative to Sea Ice Availability in the Chukchi Sea: 2008-2014"},{"id":356409,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","otherGeospatial":"Chukchi Sea","volume":"98","issue":"2","noUsgsAuthors":false,"publicationDate":"2017-02-08","publicationStatus":"PW","scienceBaseUri":"5b98a47ee4b0702d0e843086","contributors":{"authors":[{"text":"Jay, Chadwick V. 0000-0002-9559-2189 cjay@usgs.gov","orcid":"https://orcid.org/0000-0002-9559-2189","contributorId":192736,"corporation":false,"usgs":true,"family":"Jay","given":"Chadwick","email":"cjay@usgs.gov","middleInitial":"V.","affiliations":[{"id":116,"text":"Alaska Science Center Biology MFEB","active":true,"usgs":true}],"preferred":true,"id":742291,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Taylor, Rebecca L. 0000-0001-8459-7614 rebeccataylor@usgs.gov","orcid":"https://orcid.org/0000-0001-8459-7614","contributorId":5112,"corporation":false,"usgs":true,"family":"Taylor","given":"Rebecca","email":"rebeccataylor@usgs.gov","middleInitial":"L.","affiliations":[{"id":116,"text":"Alaska Science Center Biology MFEB","active":true,"usgs":true}],"preferred":true,"id":742292,"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":116,"text":"Alaska Science Center Biology MFEB","active":true,"usgs":true},{"id":114,"text":"Alaska Science Center","active":true,"usgs":true}],"preferred":true,"id":742293,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Udevitz, Mark S. 0000-0003-4659-138X mudevitz@usgs.gov","orcid":"https://orcid.org/0000-0003-4659-138X","contributorId":3189,"corporation":false,"usgs":true,"family":"Udevitz","given":"Mark","email":"mudevitz@usgs.gov","middleInitial":"S.","affiliations":[{"id":116,"text":"Alaska Science Center Biology MFEB","active":true,"usgs":true}],"preferred":true,"id":742294,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Beatty, William S. 0000-0003-0013-3113 wbeatty@usgs.gov","orcid":"https://orcid.org/0000-0003-0013-3113","contributorId":173946,"corporation":false,"usgs":true,"family":"Beatty","given":"William","email":"wbeatty@usgs.gov","middleInitial":"S.","affiliations":[{"id":606,"text":"Upper Midwest Environmental Sciences Center","active":true,"usgs":true}],"preferred":true,"id":742295,"contributorType":{"id":1,"text":"Authors"},"rank":5}]}} ,{"id":70186330,"text":"70186330 - 2017 - Surface geophysical methods for characterising frozen ground in transitional permafrost landscapes","interactions":[],"lastModifiedDate":"2018-01-13T15:10:14","indexId":"70186330","displayToPublicDate":"2017-03-01T00:00:00","publicationYear":"2017","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":3032,"text":"Permafrost and Periglacial Processes","active":true,"publicationSubtype":{"id":10}},"title":"Surface geophysical methods for characterising frozen ground in transitional permafrost landscapes","docAbstract":"The distribution of shallow frozen ground is paramount to research in cold regions, and is subject to temporal and spatial changes influenced by climate, landscape disturbance and ecosystem succession. Remote sensing from airborne and satellite platforms is increasing our understanding of landscape-scale permafrost distribution, but typically lacks the resolution to characterise finer-scale processes and phenomena, which are better captured by integrated surface geophysical methods. Here, we demonstrate the use of electrical resistivity imaging (ERI), electromagnetic induction (EMI), ground penetrating radar (GPR) and infrared imaging over multiple summer field seasons around the highly dynamic Twelvemile Lake, Yukon Flats, central Alaska, USA. Twelvemile Lake has generally receded in the past 30 yr, allowing permafrost aggradation in the receded margins, resulting in a mosaic of transient frozen ground adjacent to thick, older permafrost outside the original lakebed. ERI and EMI best evaluated the thickness of shallow, thin permafrost aggradation, which was not clear from frost probing or GPR surveys. GPR most precisely estimated the depth of the active layer, which forward electrical resistivity modelling indicated to be a difficult target for electrical methods, but could be more tractable in time-lapse mode. Infrared imaging of freshly dug soil pit walls captured active-layer thermal gradients at unprecedented resolution, which may be useful in calibrating emerging numerical models. GPR and EMI were able to cover landscape scales (several kilometres) efficiently, and new analysis software showcased here yields calibrated EMI data that reveal the complicated distribution of shallow permafrost in a transitional landscape.
","language":"English","publisher":"Wiley","doi":"10.1002/ppp.1893","usgsCitation":"Briggs, M.A., Campbell, S., Nolan, J., Walvoord, M.A., Ntarlagiannis, D., Day-Lewis, F.D., and Lane, J.W., 2017, Surface geophysical methods for characterising frozen ground in transitional permafrost landscapes: Permafrost and Periglacial Processes, v. 28, no. 1, p. 52-65, https://doi.org/10.1002/ppp.1893.","productDescription":"14 p.","startPage":"52","endPage":"65","ipdsId":"IP-069599","costCenters":[{"id":486,"text":"OGW Branch of Geophysics","active":true,"usgs":true}],"links":[{"id":438431,"rank":0,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/P9UST855","text":"USGS data release","linkHelpText":"Surface geophysical data for characterizing shallow, discontinuous frozen ground near Fort Yukon, Alaska"},{"id":339120,"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 -145.5,\n 66.45\n ],\n [\n -145.25,\n 66.45\n ],\n [\n -145.25,\n 66.6\n ],\n [\n -145.5,\n 66.6\n ],\n [\n -145.5,\n 66.45\n ]\n ]\n ]\n }\n }\n ]\n}","volume":"28","issue":"1","publishingServiceCenter":{"id":9,"text":"Reston PSC"},"noUsgsAuthors":false,"publicationDate":"2016-05-24","publicationStatus":"PW","scienceBaseUri":"58e4b0b1e4b09da67999777a","contributors":{"authors":[{"text":"Briggs, Martin A. 0000-0003-3206-4132 mbriggs@usgs.gov","orcid":"https://orcid.org/0000-0003-3206-4132","contributorId":4114,"corporation":false,"usgs":true,"family":"Briggs","given":"Martin","email":"mbriggs@usgs.gov","middleInitial":"A.","affiliations":[{"id":610,"text":"Utah Water Science Center","active":true,"usgs":true},{"id":37277,"text":"WMA - Earth System Processes Division","active":true,"usgs":true},{"id":493,"text":"Office of Ground Water","active":true,"usgs":true},{"id":486,"text":"OGW Branch of Geophysics","active":true,"usgs":true}],"preferred":true,"id":688344,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Campbell, Seth","contributorId":190402,"corporation":false,"usgs":false,"family":"Campbell","given":"Seth","affiliations":[],"preferred":false,"id":688345,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Nolan, Jay","contributorId":190403,"corporation":false,"usgs":false,"family":"Nolan","given":"Jay","email":"","affiliations":[],"preferred":false,"id":688346,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Walvoord, Michelle Ann 0000-0003-4269-8366 walvoord@usgs.gov","orcid":"https://orcid.org/0000-0003-4269-8366","contributorId":147211,"corporation":false,"usgs":true,"family":"Walvoord","given":"Michelle","email":"walvoord@usgs.gov","middleInitial":"Ann","affiliations":[{"id":5044,"text":"National Research Program - Central Branch","active":true,"usgs":true}],"preferred":true,"id":688347,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Ntarlagiannis, Dimitrios","contributorId":150729,"corporation":false,"usgs":false,"family":"Ntarlagiannis","given":"Dimitrios","affiliations":[{"id":12727,"text":"Rutgers University","active":true,"usgs":false}],"preferred":false,"id":688348,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"Day-Lewis, Frederick D. 0000-0003-3526-886X daylewis@usgs.gov","orcid":"https://orcid.org/0000-0003-3526-886X","contributorId":1672,"corporation":false,"usgs":true,"family":"Day-Lewis","given":"Frederick","email":"daylewis@usgs.gov","middleInitial":"D.","affiliations":[{"id":486,"text":"OGW Branch of Geophysics","active":true,"usgs":true},{"id":37277,"text":"WMA - Earth System Processes Division","active":true,"usgs":true},{"id":493,"text":"Office of Ground Water","active":true,"usgs":true}],"preferred":true,"id":688349,"contributorType":{"id":1,"text":"Authors"},"rank":6},{"text":"Lane, John W. Jr. 0000-0002-3558-243X jwlane@usgs.gov","orcid":"https://orcid.org/0000-0002-3558-243X","contributorId":189168,"corporation":false,"usgs":true,"family":"Lane","given":"John","suffix":"Jr.","email":"jwlane@usgs.gov","middleInitial":"W.","affiliations":[{"id":486,"text":"OGW Branch of Geophysics","active":true,"usgs":true},{"id":493,"text":"Office of Ground Water","active":true,"usgs":true}],"preferred":false,"id":688350,"contributorType":{"id":1,"text":"Authors"},"rank":7}]}} ]}