Soil temperature (Ts ) change is a key indicator of the dynamics of permafrost. On seasonal and inter-annual time scales, the variability of Ts determines the active layer depth, which regulates hydrological soil properties and biogeochemical processes. On the multi-decadal scale, increasing T 5 s not only drives permafrost thaw/retreat, but can also trigger and accelerate the decomposition of soil organic carbon. The magnitude of permafrost carbon feedbacks is thus closely linked to the rate of change of soil thermal regimes. In this study, we used nine process-based ecosystem models with permafrost processes, all forced by different observation-based climate forcing during the period 1960–2000, to characterize the warming rate of Ts 10 in permafrost regions. There is a large spread of Ts trends at 20 cm depth across the models, with trend values ranging from 0.010 ± 0.003 to 0.031 ± 0.005 ◦C yr−1 . Most models show smaller increase in Ts with increasing depth. Air temperature (Ta ) and longwave downward radiation (LWDR) are the main drivers of Ts trends, but their relative contributions differ 15 amongst the models. Different trends of LWDR used in the forcing of models can explain 61 % of their differences in Ts trends, while trends of Ta only explain 5 % of the differences in Ts trends. Uncertain climate forcing contributes a larger uncertainty in Ts trends (0.021 ± 0.008 ◦C yr−1 , mean ± SD) than the uncertainty of model structure (0.012 ± 0.001 ◦C yr−1 ), diagnosed from the range of response between different mod- 20 els, normalized to the same forcing. In addition, the loss rate of near-surface permafrost area, defined as total area where the maximum seasonal active layer thickness (ALT) is less than 3 m loss rate is found to be significantly correlated with the magnitude of the trends of Ts at 1 m depth across the models (R = −0.85, P = 0.003), but not with the initial total near-surface permafrost area (R = −0.30, P = 0.438). The sensitivity of the total boreal near-surface permafrost area to T 25 s at 1 m, is estimated to be of −2.80 ± 0.67 million km2 ◦C −1 . Finally, by using two long-term LWDR datasets and relationships between trends of LWDR and Ts across models, we infer an observationconstrained total boreal near-surface permafrost area decrease comprised between 39 ± 14 × 103 and 75 ± 14 × 103 km2 yr−1 from 1960 to 2000. This corresponds to 9– 18 % degradation of the current permafrost area.
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These airborne geophysical surveys provide high-resolution and spatially comprehensive datasets characterizing the resistivity structure of the shallow subsurface of each survey region, accompanied by magnetic-field information over matching areas. These data were collected to provide insight into the distribution of groundwater brine in the Paradox Valley, the extent of clay aquitards in the San Luis Valley, and to improve our understanding of the geologic framework for both regions. This report describes these contracted surveys and releases digital data supplied under contract to the USGS.
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J. S. 0000-0002-0761-3489 tien@usgs.gov","orcid":"https://orcid.org/0000-0002-0761-3489","contributorId":886,"corporation":false,"usgs":true,"family":"Grauch","given":"V.","email":"tien@usgs.gov","middleInitial":"J. S.","affiliations":[{"id":211,"text":"Crustal Geophysics and Geochemistry Science Center","active":true,"usgs":true}],"preferred":true,"id":543643,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Smith, Bruce D. 0000-0002-1643-2997 bsmith@usgs.gov","orcid":"https://orcid.org/0000-0002-1643-2997","contributorId":845,"corporation":false,"usgs":true,"family":"Smith","given":"Bruce","email":"bsmith@usgs.gov","middleInitial":"D.","affiliations":[{"id":211,"text":"Crustal Geophysics and Geochemistry Science Center","active":true,"usgs":true}],"preferred":true,"id":543644,"contributorType":{"id":1,"text":"Authors"},"rank":5}]}} ,{"id":70139236,"text":"ds903 - 2015 - Archive of sediment data from vibracores collected in 2010 offshore of the Mississippi barrier islands","interactions":[],"lastModifiedDate":"2015-03-30T13:48:16","indexId":"ds903","displayToPublicDate":"2015-03-30T14:45:00","publicationYear":"2015","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":"903","title":"Archive of sediment data from vibracores collected in 2010 offshore of the Mississippi barrier islands","docAbstract":"In 2010, scientists from the U.S. Geological Survey St. Petersburg Coastal and Marine Science Center collected sediment cores from coastal waters offshore of the Mississippi barrier islands. With funding support from the Northern Gulf of Mexico (NGOM) Ecosystem Change and Hazard Susceptibility project, 65 subaqueous sediment cores were collected over an area of 480 square kilometers (km2), from Ship Island to Petit Bois Island Pass, Mississippi, within the boundary of Gulf Islands National Seashore. This represents only a fraction of the total area encompassed by the NGOM project, which extends from Sabine Lake, Louisiana, to Perdido Bay, Alabama. The primary objectives of the NGOM project are to understand the evolution of coastal ecosystems on the northern gulf coast, the impact of human activities on these ecosystems, and the vulnerability of ecosystems and human communities to more frequent and intense hurricanes in the future.
\nSelection of the core site locations was based on geophysical surveys conducted around the islands from 2008 to 2010. The surveys, using acoustic systems to image and interpret the nearsurface stratigraphy, were conducted to investigate the geologic controls on island evolution. This data series serves as an archive of sediment data collected from August to September 2010, offshore of the Mississippi barrier islands. Data products, including descriptive core logs, core photographs, results of sediment grain-size analyses, sample location maps, and geographic information system (GIS) data files with accompanying formal Federal Geographic Data Committee (FDGC) metadata can be downloaded from the data products and downloads page.
","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/ds903","usgsCitation":"Kelso, K.W., and Flocks, J.G., 2015, Archive of sediment data from vibracores collected in 2010 offshore of the Mississippi barrier islands: U.S. Geological Survey Data Series 903, HTML Document, https://doi.org/10.3133/ds903.","productDescription":"HTML Document","onlineOnly":"Y","additionalOnlineFiles":"N","temporalStart":"2010-01-01","ipdsId":"IP-055663","costCenters":[{"id":574,"text":"St. Petersburg Coastal and Marine Science Center","active":true,"usgs":true}],"links":[{"id":299140,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/ds903.jpg"},{"id":299139,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/ds/0903/html/ds903_abstract.html","text":"Report","linkFileType":{"id":5,"text":"html"},"description":"Report"},{"id":299130,"type":{"id":15,"text":"Index Page"},"url":"https://pubs.usgs.gov/ds/0903/"}],"country":"United States","state":"Mississippi","otherGeospatial":"Mississippi barrier islands","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -89.20074462890625,\n 30.168875561169088\n ],\n [\n -89.20074462890625,\n 30.285159872426014\n ],\n [\n -88.36578369140625,\n 30.285159872426014\n ],\n [\n -88.36578369140625,\n 30.168875561169088\n ],\n [\n -89.20074462890625,\n 30.168875561169088\n ]\n ]\n ]\n }\n }\n ]\n}","publishingServiceCenter":{"id":8,"text":"Raleigh PSC"},"noUsgsAuthors":false,"publicationStatus":"PW","scienceBaseUri":"551a65a0e4b03238427833ef","contributors":{"authors":[{"text":"Kelso, Kyle W. 0000-0003-0615-242X kkelso@usgs.gov","orcid":"https://orcid.org/0000-0003-0615-242X","contributorId":4307,"corporation":false,"usgs":true,"family":"Kelso","given":"Kyle","email":"kkelso@usgs.gov","middleInitial":"W.","affiliations":[{"id":574,"text":"St. Petersburg Coastal and Marine Science Center","active":true,"usgs":true}],"preferred":true,"id":543625,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Flocks, James G. 0000-0002-6177-7433 jflocks@usgs.gov","orcid":"https://orcid.org/0000-0002-6177-7433","contributorId":816,"corporation":false,"usgs":true,"family":"Flocks","given":"James","email":"jflocks@usgs.gov","middleInitial":"G.","affiliations":[{"id":574,"text":"St. Petersburg Coastal and Marine Science Center","active":true,"usgs":true}],"preferred":true,"id":543626,"contributorType":{"id":1,"text":"Authors"},"rank":2}]}} ,{"id":70137943,"text":"ds910 - 2015 - Chemicals of emerging concern in water and bottom sediment in the Great Lakes Basin, 2012: collection methods, analytical methods, quality assurance, and study data","interactions":[],"lastModifiedDate":"2016-06-14T10:19:07","indexId":"ds910","displayToPublicDate":"2015-03-30T14:30:00","publicationYear":"2015","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":"910","title":"Chemicals of emerging concern in water and bottom sediment in the Great Lakes Basin, 2012: collection methods, analytical methods, quality assurance, and study data","docAbstract":"In synoptic surveys of surface-water quality across the United States, a large group of organic chemicals associated with agricultural, household, and industrial waste have been detected. These chemicals are referred to collectively as chemicals of emerging concern (CECs) and include prescription drugs and antibiotics, over-the-counter medications, reproductive hormones, personal-care products, detergent metabolites, and flame retardants.
\nThe U.S. Geological Survey (USGS) collaborated with the U.S. Fish and Wildlife Service and the U.S. Environmental Protection Agency on a study to identify the presence of CECs in water and bottom-sediment samples collected during 2012 at 66 sites throughout the Great Lakes Basin. The 2012 effort is part of a long-term study that was initiated in 2010.
\nThe purposes of this report are to document the collection and analytical methods, provide the quality-assurance data and analyses, and provide the water and bottom-sediment data for this study of CECs in the Great Lakes Basin for 2012. A previous report documents data collected during 2010 and 2011. The methods used for chemical analyses were identical between the 2010–11 and 2012 studies, with the exception that a method to determine nontarget chemicals was used during 2010–11. The data from this study are published as a USGS Data Series Report to ensure adequate documentation of the original methods and provide a citable source for study data. This report contains no interpretations of the study data. The chemical data are as reported by the laboratory and have not been censored or adjusted unless otherwise noted.
\nField measurements were recorded and samples were collected in April and May and in September 2012, by U.S. Geological Survey, U.S. Fish and Wildlife Service, and U.S. Environmental Protection Agency personnel. Study sites included tributaries to the Great Lakes located near Duluth, Minnesota; King, Wisconsin; Green Bay, Wis.; Detroit, Michigan; Monroe, Mich.; Toledo, Ohio, and Rochester, New York. Water and bottom-sediment samples were analyzed at the USGS National Water Quality Laboratory in Denver, Colorado, for a broad suite of CECs.
\nDuring this 2012 study, 140 environmental and 8 field duplicate samples of surface water and wastewater effluent, 1 field blank water sample, and 5 field spike water samples were collected or prepared. Water samples were analyzed at the USGS National Water Quality Laboratory using laboratory schedule 4433 for wastewater indicators, research method 8244 for pharmaceuticals, and laboratory schedule 4434 for steroid hormones, sterols, and bisphenol A. For wastewater indicators in unfiltered water, 61 of the 68 chemicals analyzed using laboratory schedule 4433 had detectable concentrations ranging from 0.002 to 64.4 micrograms per liter. Thirty-eight of the 48 chemicals analyzed using research method 8244 for pharmaceuticals in unfiltered water had detectable concentrations ranging from 0.002 to 3.32 micrograms per liter. Twelve of the 20 chemicals analyzed using laboratory schedule 4434 for steroid hormones, sterols, and bisphenol A in unfiltered water had detectable concentrations ranging from 0.43 to 120,000 nanograms per liter.
\nDuring this study, 53 environmental samples, 4 field duplicate samples, and 8 field spike samples of bottom sediment and laboratory matrix-spike samples were analyzed for a wide variety of CECs at the USGS National Water Quality Laboratory using laboratory schedule 5433 for wastewater indicators; research method 6434 for steroid hormones, sterols, and bisphenol A; and research method 9008 for human-use pharmaceuticals and antidepressants. Forty of the 57 chemicals analyzed using laboratory schedule 5433 had detectable concentrations ranging from 1 to 49,000 micrograms per kilogram. Fourteen of the 20 chemicals analyzed using research method 6434 had detectable concentrations ranging from 0.04 to 24,940 nanograms per gram. Ten of the 20 chemicals analyzed using research method 9008 had detectable concentrations ranging from 0.59 to 197.5 micrograms per kilogram. Five of the 11 chemicals analyzed using research method 9008 had detectable concentrations ranging from 1.16 to 25.0 micrograms per kilogram.
","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/ds910","collaboration":"Prepared in cooperation with the U.S. Fish and Wildlife Service and the U.S. Environmental Protection Agency","usgsCitation":"Lee, K., Langer, S., Menheer, M.A., Hansen, D.S., Foreman, W., Furlong, E.T., Jorgenson, Z.G., Choy, S.J., Moore, J.N., Banda, J., and Gefell, D.J., 2015, Chemicals of emerging concern in water and bottom sediment in the Great Lakes Basin, 2012: collection methods, analytical methods, quality assurance, and study data: U.S. Geological Survey Data Series 910, Report:vi, 14 p.; 2 Appendices; 6 Tables, https://doi.org/10.3133/ds910.","productDescription":"Report:vi, 14 p.; 2 Appendices; 6 Tables","startPage":"14","numberOfPages":"26","onlineOnly":"Y","additionalOnlineFiles":"Y","temporalStart":"2012-04-01","temporalEnd":"2012-09-30","ipdsId":"IP-043757","costCenters":[{"id":392,"text":"Minnesota Water Science 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J.","contributorId":138668,"corporation":false,"usgs":false,"family":"Choy","given":"Steven","email":"","middleInitial":"J.","affiliations":[{"id":6678,"text":"U.S. Fish and Wildlife Service, Alaska Maritime National Wildlife Refuge","active":true,"usgs":false}],"preferred":false,"id":543621,"contributorType":{"id":1,"text":"Authors"},"rank":8},{"text":"Moore, Jeremy N.","contributorId":138669,"corporation":false,"usgs":false,"family":"Moore","given":"Jeremy","email":"","middleInitial":"N.","affiliations":[{"id":6678,"text":"U.S. Fish and Wildlife Service, Alaska Maritime National Wildlife Refuge","active":true,"usgs":false}],"preferred":false,"id":543622,"contributorType":{"id":1,"text":"Authors"},"rank":9},{"text":"Banda, JoAnn","contributorId":138670,"corporation":false,"usgs":false,"family":"Banda","given":"JoAnn","email":"","affiliations":[{"id":6678,"text":"U.S. Fish and Wildlife Service, Alaska Maritime National Wildlife Refuge","active":true,"usgs":false}],"preferred":false,"id":543623,"contributorType":{"id":1,"text":"Authors"},"rank":10},{"text":"Gefell, Daniel J.","contributorId":138671,"corporation":false,"usgs":false,"family":"Gefell","given":"Daniel","email":"","middleInitial":"J.","affiliations":[{"id":6678,"text":"U.S. Fish and Wildlife Service, Alaska Maritime National Wildlife Refuge","active":true,"usgs":false}],"preferred":false,"id":543624,"contributorType":{"id":1,"text":"Authors"},"rank":11}]}} ,{"id":70144383,"text":"70144383 - 2015 - Ecological drivers of variation in tool-use frequency across sea otter populations","interactions":[],"lastModifiedDate":"2015-03-30T10:54:05","indexId":"70144383","displayToPublicDate":"2015-03-30T10:45:00","publicationYear":"2015","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":981,"text":"Behavioral Ecology","active":true,"publicationSubtype":{"id":10}},"title":"Ecological drivers of variation in tool-use frequency across sea otter populations","docAbstract":"Sea otters are well-known tool users, employing objects such as rocks or shells to break open hard-shelled invertebrate prey. However, little is known about how the frequency of tool use varies among sea otter populations and the factors that drive these differences. We examined 17 years of observational data on prey capture and tool use from 8 sea otter populations ranging from southern California to the Aleutian Islands in Alaska. There were significant differences in the diets of these populations as well as variation in the frequency of tool use. Sea otters at Amchitka Island, Alaska, used tools on less than 1% of dives that resulted in the capture of prey compared with approximately 16% in Monterey, California. The percentage of individuals in the population that used tools ranged from 10% to 93%. In all populations, marine snails and thick-shelled bivalves were most likely to be associated with tool use, whereas soft-bodied prey items such as worms and sea stars were the least likely. The probability that a tool would be used on a given prey type varied across populations. The morphology of the prey item being handled and the prevalence of various types of prey in local diets were major ecological drivers of tool use: together they accounted for about 64% of the variation in tool-use frequency among populations. The remaining variation may be related to changes in the relative costs and benefits to an individual otter of learning to use tools effectively under differing ecological circumstances.
","language":"English","publisher":"Oxford University Press","doi":"10.1093/beheco/aru220","usgsCitation":"Fujii, J., Ralls, K., and Tinker, M.T., 2015, Ecological drivers of variation in tool-use frequency across sea otter populations: Behavioral Ecology, v. 26, no. 2, p. 519-526, https://doi.org/10.1093/beheco/aru220.","productDescription":"8 p.","startPage":"519","endPage":"526","numberOfPages":"8","onlineOnly":"N","additionalOnlineFiles":"N","ipdsId":"IP-061103","costCenters":[{"id":651,"text":"Western Ecological Research Center","active":true,"usgs":true}],"links":[{"id":299125,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","state":"Alaska, California","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -182.61474609375,\n 50.65294336725709\n ],\n [\n -182.61474609375,\n 52.281601868071434\n ],\n [\n -175.14404296874997,\n 52.281601868071434\n ],\n [\n -175.14404296874997,\n 50.65294336725709\n ],\n [\n -182.61474609375,\n 50.65294336725709\n ]\n ]\n ]\n }\n },\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -136.5655517578125,\n 58.09820267068277\n ],\n [\n -136.5655517578125,\n 58.819430209826066\n ],\n [\n -135.85693359375,\n 58.819430209826066\n ],\n [\n -135.85693359375,\n 58.09820267068277\n ],\n [\n -136.5655517578125,\n 58.09820267068277\n ]\n ]\n ]\n }\n },\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -121.89331054687499,\n 36.70365959719456\n ],\n [\n -121.89331054687499,\n 36.38591277287651\n ],\n [\n -120.640869140625,\n 35.110921809704756\n ],\n [\n -120.640869140625,\n 34.56990638085636\n ],\n [\n -120.36621093749999,\n 34.42503613021332\n ],\n [\n -119.17968749999999,\n 33.27543541298162\n ],\n [\n -119.520263671875,\n 32.95336814579932\n ],\n [\n -121.31103515625,\n 34.31621838080741\n ],\n [\n -122.73925781250001,\n 36.59788913307022\n ],\n [\n -121.89331054687499,\n 36.70365959719456\n ]\n ]\n ]\n }\n }\n ]\n}","volume":"26","issue":"2","publishingServiceCenter":{"id":1,"text":"Sacramento PSC"},"noUsgsAuthors":false,"publicationDate":"2014-12-22","publicationStatus":"PW","scienceBaseUri":"551a65a8e4b032384278340e","contributors":{"authors":[{"text":"Fujii, Jessica 0000-0003-4794-479X","orcid":"https://orcid.org/0000-0003-4794-479X","contributorId":139956,"corporation":false,"usgs":false,"family":"Fujii","given":"Jessica","affiliations":[{"id":6953,"text":"Monterey Bay Aquarium","active":true,"usgs":false}],"preferred":false,"id":543567,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Ralls, Katherine","contributorId":37900,"corporation":false,"usgs":false,"family":"Ralls","given":"Katherine","email":"","affiliations":[{"id":7035,"text":"Smithsonian Conservation Biology Institute, National Zoological Park","active":true,"usgs":false}],"preferred":false,"id":543568,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Tinker, M. 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To analyze photo data, studies focusing on carnivores with unique pelage patterns have utilized a mark-recapture framework and studies of carnivores without unique pelage patterns have used a mark-resight framework. We compared mark-resight and mark-recapture estimation methods to estimate bobcat (Lynx rufus) population sizes, which motivated the development of a new \"hybrid\" mark-resight model as an alternative to traditional methods. We deployed a sampling grid of 30 cameras throughout the urban southern California study area. Additionally, we physically captured and marked a subset of the bobcat population with GPS telemetry collars. Since we could identify individual bobcats with photos of unique pelage patterns and a subset of the population was physically marked, we were able to use traditional mark-recapture and mark-resight methods, as well as the new “hybrid” mark-resight model we developed to estimate bobcat abundance. We recorded 109 bobcat photos during 4,669 camera nights and physically marked 27 bobcats with GPS telemetry collars. Abundance estimates produced by the traditional mark-recapture, traditional mark-resight, and “hybrid” mark-resight methods were similar, however precision differed depending on the models used. Traditional mark-recapture and mark-resight estimates were relatively imprecise with percent confidence interval lengths exceeding 100% of point estimates. Hybrid mark-resight models produced better precision with percent confidence intervals not exceeding 57%. The increased precision of the hybrid mark-resight method stems from utilizing the complete encounter histories of physically marked individuals (including those never detected by a camera trap) and the encounter histories of naturally marked individuals detected at camera traps. This new estimator may be particularly useful for estimating abundance of uniquely identifiable species that are difficult to sample using camera traps alone.
","language":"English","publisher":"Public Library of Science","doi":"10.1371/journal.pone.0123032","usgsCitation":"Alanso, R.S., McClintock, B.T., Lyren, L.M., Boydston, E.E., and Crooks, K.R., 2015, Mark-recapture and mark-resight methods for estimating abundance with remote cameras: a carnivore case study: PLoS ONE, v. 10, no. 3, e0123032; 13 p., https://doi.org/10.1371/journal.pone.0123032.","productDescription":"e0123032; 13 p.","numberOfPages":"13","onlineOnly":"N","additionalOnlineFiles":"N","ipdsId":"IP-043232","costCenters":[{"id":651,"text":"Western Ecological Research Center","active":true,"usgs":true}],"links":[{"id":472397,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.1371/journal.pone.0123032","text":"Publisher Index Page"},{"id":299591,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","state":"California","county":"Orange County","otherGeospatial":"San Joaquin Hills","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -117.88604736328124,\n 33.48070852506531\n ],\n [\n -117.88604736328124,\n 33.63005717508159\n ],\n [\n -117.72193908691406,\n 33.63005717508159\n ],\n [\n -117.72193908691406,\n 33.48070852506531\n ],\n [\n -117.88604736328124,\n 33.48070852506531\n ]\n ]\n ]\n }\n }\n ]\n}","volume":"10","issue":"3","publishingServiceCenter":{"id":1,"text":"Sacramento PSC"},"noUsgsAuthors":false,"publicationDate":"2015-03-30","publicationStatus":"PW","scienceBaseUri":"5528f44ce4b026915857cb27","contributors":{"authors":[{"text":"Alanso, Robert S.","contributorId":140158,"corporation":false,"usgs":false,"family":"Alanso","given":"Robert","email":"","middleInitial":"S.","affiliations":[{"id":6737,"text":"Colorado State University, Department of Ecosystem Science and Sustainability, and Natural Resource Ecology Laboratory","active":true,"usgs":false}],"preferred":false,"id":544535,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"McClintock, Brett T. 0000-0001-6154-4376","orcid":"https://orcid.org/0000-0001-6154-4376","contributorId":83785,"corporation":false,"usgs":true,"family":"McClintock","given":"Brett","email":"","middleInitial":"T.","affiliations":[{"id":12448,"text":"U.S. National Oceanic and Atmospheric Administration","active":true,"usgs":false},{"id":531,"text":"Patuxent Wildlife Research Center","active":true,"usgs":true}],"preferred":true,"id":544536,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Lyren, Lisa M. llyren@usgs.gov","contributorId":2398,"corporation":false,"usgs":true,"family":"Lyren","given":"Lisa","email":"llyren@usgs.gov","middleInitial":"M.","affiliations":[{"id":651,"text":"Western Ecological Research Center","active":true,"usgs":true}],"preferred":true,"id":544534,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Boydston, Erin E. 0000-0002-8452-835X eboydston@usgs.gov","orcid":"https://orcid.org/0000-0002-8452-835X","contributorId":1705,"corporation":false,"usgs":true,"family":"Boydston","given":"Erin","email":"eboydston@usgs.gov","middleInitial":"E.","affiliations":[{"id":651,"text":"Western Ecological Research Center","active":true,"usgs":true}],"preferred":true,"id":544533,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Crooks, Kevin R.","contributorId":51137,"corporation":false,"usgs":false,"family":"Crooks","given":"Kevin","email":"","middleInitial":"R.","affiliations":[{"id":6621,"text":"Colorado State University","active":true,"usgs":false}],"preferred":false,"id":544537,"contributorType":{"id":1,"text":"Authors"},"rank":5}]}} ,{"id":70162208,"text":"70162208 - 2015 - Citizen science contributes to our knowledge of invasive plant species distributions","interactions":[],"lastModifiedDate":"2016-01-20T13:13:41","indexId":"70162208","displayToPublicDate":"2015-03-28T14:00:00","publicationYear":"2015","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":1018,"text":"Biological Invasions","active":true,"publicationSubtype":{"id":10}},"title":"Citizen science contributes to our knowledge of invasive plant species distributions","docAbstract":"Citizen science is commonly cited as an effective approach to expand the scale of invasive species data collection and monitoring. However, researchers often hesitate to use these data due to concerns over data quality. In light of recent research on the quality of data collected by volunteers, we aimed to demonstrate the extent to which citizen science data can increase sampling coverage, fill gaps in species distributions, and improve habitat suitability models compared to professionally generated data sets used in isolation. We combined data sets from professionals and volunteers for five invasive plant species (Alliaria petiolata, Berberis thunbergii, Cirsium palustre, Pastinaca sativa, Polygonum cuspidatum) in portions of Wisconsin. Volunteers sampled counties not sampled by professionals for three of the five species. Volunteers also added presence locations within counties not included in professional data sets, especially in southern portions of the state where professional monitoring activities had been minimal. Volunteers made a significant contribution to the known distribution, environmental gradients sampled, and the habitat suitability of P. cuspidatum. Models generated with professional data sets for the other four species performed reasonably well according to AUC values (>0.76). The addition of volunteer data did not greatly change model performance (AUC > 0.79) but did change the suitability surface generated by the models, making them more realistic. Our findings underscore the need to merge data from multiple sources to improve knowledge of current species distributions, and to predict their movement under present and future environmental conditions. The efficiency and success of these approaches require that monitoring efforts involve multiple stakeholders in continuous collaboration via established monitoring networks.
","language":"English","publisher":"Kluwer Academic Publishers","publisherLocation":"Dordrecht","doi":"10.1007/s10530-015-0885-4","usgsCitation":"Crall, A.W., Jarnevich, C.S., Young, N.E., Panke, B., Renz, M., and Stohlgren, T., 2015, Citizen science contributes to our knowledge of invasive plant species distributions: Biological Invasions, v. 17, no. 8, p. 2415-2427, https://doi.org/10.1007/s10530-015-0885-4.","productDescription":"13 p.","startPage":"2415","endPage":"2427","numberOfPages":"13","onlineOnly":"N","additionalOnlineFiles":"N","ipdsId":"IP-064446","costCenters":[{"id":291,"text":"Fort Collins Science Center","active":true,"usgs":true}],"links":[{"id":314531,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","state":"Wisconsin","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n 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W.","contributorId":60123,"corporation":false,"usgs":true,"family":"Crall","given":"Alycia","email":"","middleInitial":"W.","affiliations":[],"preferred":false,"id":588850,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Jarnevich, Catherine S. 0000-0002-9699-2336 jarnevichc@usgs.gov","orcid":"https://orcid.org/0000-0002-9699-2336","contributorId":3424,"corporation":false,"usgs":true,"family":"Jarnevich","given":"Catherine","email":"jarnevichc@usgs.gov","middleInitial":"S.","affiliations":[{"id":291,"text":"Fort Collins Science Center","active":true,"usgs":true}],"preferred":true,"id":588849,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Young, Nicholas E.","contributorId":58572,"corporation":false,"usgs":true,"family":"Young","given":"Nicholas","email":"","middleInitial":"E.","affiliations":[],"preferred":false,"id":588851,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Panke, Brendon","contributorId":22244,"corporation":false,"usgs":true,"family":"Panke","given":"Brendon","email":"","affiliations":[],"preferred":false,"id":588852,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Renz, Mark","contributorId":89440,"corporation":false,"usgs":true,"family":"Renz","given":"Mark","affiliations":[],"preferred":false,"id":588853,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"Stohlgren, Thomas","contributorId":23091,"corporation":false,"usgs":true,"family":"Stohlgren","given":"Thomas","affiliations":[],"preferred":false,"id":588854,"contributorType":{"id":1,"text":"Authors"},"rank":6}]}} ,{"id":70134783,"text":"ofr20141250 - 2015 - Proceedings of the 9th U.S.-Japan natural resources panel for earthquake research","interactions":[],"lastModifiedDate":"2015-03-30T10:16:16","indexId":"ofr20141250","displayToPublicDate":"2015-03-27T11:00:00","publicationYear":"2015","noYear":false,"publicationType":{"id":18,"text":"Report"},"publicationSubtype":{"id":5,"text":"USGS Numbered Series"},"seriesTitle":{"id":330,"text":"Open-File Report","code":"OFR","onlineIssn":"2331-1258","printIssn":"0196-1497","active":true,"publicationSubtype":{"id":5}},"seriesNumber":"2014-1250","title":"Proceedings of the 9th U.S.-Japan natural resources panel for earthquake research","docAbstract":"The UJNR Panel on Earthquake Research promotes advanced study toward a more fundamental understanding of the earthquake process and hazard estimation. The Ninth Joint meeting was extremely beneficial in furthering cooperation and deepening understanding of problems common to both the U.S. and Japan. The meeting included productive exchanges of information on approaches to systematic observation and modeling of earthquake processes. Regarding the earthquake and tsunami of March 2011 off the Pacific coast of Tohoku, the Panel recognizes that further efforts are necessary to achieve our common goal of reducing earthquake risk through close collaboration and focused discussions at the 10th UJNR meeting. We look forward to continued cooperation on issues involving the densification of observation networks and the open exchange of data among scientific communities. We recognize the importance of making information publicly available in a timely manner. We also recognize the importance of information exchange on research policy and strategies, including the frameworks of research organizations.
\n–Specific areas of earthquake research where cooperative research between the U.S. and Japan may lead to significant advancement include, but are not limited to:
\n–Probabilistic earthquake and tsunami hazard estimation, including extraordinarily large earthquakes, both in our respective countries and worldwide, incorporating knowledge of current and past behavior, and physics based computational models;
\n–Real-time information from seismic, geodetic and strain measurements, including borehole strainmeters and seafloor observations using offshore cabled networks;
\n–Technologies for measuring crustal deformation;
\n–Early warning technologies for earthquakes and tsunamis; Studies of recurrence of large and extraordinary large earthquakes using paleoseismic, paleotsunami, geodetic and seismic methods;
\n–Laboratory, theoretical and in situ studies of fault-zone processes;
\n–Studies of episodic tremor and slow slip events using seismic, geodetic, and borehole strain measurements, and simulation techniques;
\n–Systematic studies of earthquake predictability through rigorously evaluated scientific prediction experiments and robust databases;
\n–Studies of near-source ground motions, geological effects and the response of engineered structures.
\nThe Panel strongly urges that the appropriate agencies in the U.S. and Japan that are represented on this panel work together with the academic sector to support and coordinate scientific work in these areas of cooperation. The Panel recognizes the importance of promoting the exchange of scientific personnel, exchange of data, and fundamental studies to advance progress in earthquake research. The U.S. and Japan should promote these exchanges throughout the world. The Panel endorses continuation of these activities.
","conferenceTitle":"9th U.S.-Japan Natural Resources Panel for Earthquake Research","conferenceDate":"October 9-12, 2012","conferenceLocation":"Denver, CO","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/ofr20141250","usgsCitation":"2015, Proceedings of the 9th U.S.-Japan natural resources panel for earthquake research: U.S. Geological Survey Open-File Report 2014-1250, iv, 89 p., https://doi.org/10.3133/ofr20141250.","productDescription":"iv, 89 p.","numberOfPages":"95","onlineOnly":"Y","additionalOnlineFiles":"N","ipdsId":"IP-044782","costCenters":[{"id":237,"text":"Earthquake Science Center","active":true,"usgs":true}],"links":[{"id":299044,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/ofr20141250.gif"},{"id":299042,"rank":2,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/of/2014/1250/downloads/of2014-1250.pdf","text":"Report","size":"9.8 MB","linkFileType":{"id":1,"text":"pdf"},"description":"Report"},{"id":299043,"rank":1,"type":{"id":15,"text":"Index Page"},"url":"https://pubs.usgs.gov/of/2014/1250/"}],"country":"Japan, United States","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -219.2431640625,\n 45.69083283645816\n ],\n [\n -217.37548828125,\n 45.460130637921004\n ],\n [\n -214.3212890625,\n 44.41808794374849\n ],\n [\n -214.73876953125,\n 43.78695837311561\n ],\n [\n -213.81591796875,\n 43.197167282501276\n ],\n [\n -217.1337890625,\n 32.2313896627376\n ],\n [\n -219.39697265624997,\n 30.06909396443887\n ],\n [\n -227.9443359375,\n 25.60190226111573\n ],\n [\n -234.40429687499997,\n 25.720735134412106\n ],\n [\n -230.97656250000003,\n 34.63320791137959\n ],\n [\n -223.30810546875,\n 38.13455657705411\n ],\n [\n -219.2431640625,\n 45.69083283645816\n ]\n ]\n ]\n }\n },\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -99.7119140625,\n 24.44714958973082\n ],\n [\n -111.62109375,\n 24.44714958973082\n ],\n [\n -124.98046874999999,\n 29.38217507514529\n ],\n [\n -128.4521484375,\n 47.98992166741417\n ],\n [\n -115.6640625,\n 50.62507306341437\n ],\n [\n -110.302734375,\n 50.65294336725709\n ],\n [\n -106.3037109375,\n 46.01222384063236\n ],\n [\n -107.6220703125,\n 42.032974332441405\n ],\n [\n -102.041015625,\n 38.47939467327645\n ],\n [\n -99.84374999999999,\n 29.726222319395504\n ],\n [\n -99.7119140625,\n 24.44714958973082\n ]\n ]\n ]\n }\n }\n ]\n}","noUsgsAuthors":false,"publicationStatus":"PW","scienceBaseUri":"5516711ae4b0323842781ad8","contributors":{"editors":[{"text":"Detweiler, Shane T. 0000-0001-5699-011X shane@usgs.gov","orcid":"https://orcid.org/0000-0001-5699-011X","contributorId":680,"corporation":false,"usgs":true,"family":"Detweiler","given":"Shane","email":"shane@usgs.gov","middleInitial":"T.","affiliations":[{"id":237,"text":"Earthquake Science Center","active":true,"usgs":true}],"preferred":true,"id":543523,"contributorType":{"id":2,"text":"Editors"},"rank":1},{"text":"Ellsworth, William L. ellsworth@usgs.gov","contributorId":787,"corporation":false,"usgs":true,"family":"Ellsworth","given":"William","email":"ellsworth@usgs.gov","middleInitial":"L.","affiliations":[{"id":237,"text":"Earthquake Science Center","active":true,"usgs":true}],"preferred":true,"id":543524,"contributorType":{"id":2,"text":"Editors"},"rank":2}]}} ,{"id":70143012,"text":"fs20153024 - 2015 - Return to normal streamflows and water levels: summary of hydrologic conditions in Georgia, 2013","interactions":[],"lastModifiedDate":"2016-12-07T11:48:45","indexId":"fs20153024","displayToPublicDate":"2015-03-27T09:30:00","publicationYear":"2015","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":"2015-3024","title":"Return to normal streamflows and water levels: summary of hydrologic conditions in Georgia, 2013","docAbstract":"The U.S. Geological Survey (USGS) South Atlantic Water Science Center (SAWSC) Georgia office, in cooperation with local, State, and other Federal agencies, maintains a long-term hydrologic monitoring network of more than 340 real-time continuous-record streamflow-gaging stations (streamgages), including 10 real-time lake-level monitoring stations, 67 real-time surface-water-quality monitors, and several water-quality sampling programs. Additionally, the SAWSC Georgia office operates more than 180 groundwater monitoring wells, 39 of which are real-time. The wide-ranging coverage of streamflow, reservoir, and groundwater monitoring sites allows for a comprehensive view of hydrologic conditions across the State. One of the many benefits of this monitoring network is that the analyses of the data provide a spatially distributed overview of the hydrologic conditions of creeks, rivers, reservoirs, and aquifers in Georgia.
\nStreamflow and groundwater data are verified throughout the year by USGS hydrographers. Hydrologic conditions are determined by comparing the results of statistical analyses of the data collected during the current water year (WY) to historical data collected over the period of record. Changing hydrologic conditions emphasize the need for accurate, timely data to help Federal, State, and local officials make informed decisions regarding the management and conservation of Georgia’s water resources for agricultural, recreational, ecological, and water-supply needs and for use in protecting life and property.
\nDrought conditions, persistent in the area since 2010, continued into the 2013 WY. In February 2013, Georgia was free of extreme (D3) drought conditions, as defined by the U.S. Drought Monitor, for the first time since August 2010 due to extended periods of heavy rainfall (U.S. Drought Monitor, 2013). According to the Office of the State Climatologist, the city of Savannah recorded 9.75 inches of rain in February 2013, the highest monthly total in February out of 143 years of record. Macon and Columbus also received record rainfalls in February 2013. Above-normal precipitation continued in June 2013, and the cities of Augusta and Savannah recorded the wettest June on record. In July, precipitation for the entire State of Georgia was 3.53 inches above normal (Dunkley, 2013). Above-normal rainfall from February to September 2013 increased streamflow and raised groundwater levels, and lakes and reservoirs were raised to full-pool elevations.
","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/fs20153024","usgsCitation":"Knaak, A.E., Caslow, K., and Peck, M., 2015, Return to normal streamflows and water levels: summary of hydrologic conditions in Georgia, 2013: U.S. Geological Survey Fact Sheet 2015-3024, 8 p., https://doi.org/10.3133/fs20153024.","productDescription":"8 p.","numberOfPages":"5","onlineOnly":"Y","additionalOnlineFiles":"N","temporalStart":"2013-01-01","temporalEnd":"2013-12-31","ipdsId":"IP-061982","costCenters":[{"id":13634,"text":"South Atlantic Water Science Center","active":true,"usgs":true}],"links":[{"id":299018,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/fs20153024.jpg"},{"id":299016,"rank":1,"type":{"id":15,"text":"Index Page"},"url":"https://pubs.usgs.gov/fs/2015/3024/"},{"id":299017,"rank":2,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/fs/2015/3024/pdf/fs2015-3024.pdf","size":"5.65 MB","linkFileType":{"id":1,"text":"pdf"}}],"country":"United States","state":"Georgia","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -85.60546875,\n 35.0120020431607\n ],\n [\n -83.07861328125,\n 35.0120020431607\n ],\n [\n -83.3203125,\n 34.70549341022544\n ],\n [\n -82.85888671875,\n 34.50655662164561\n ],\n [\n -82.1337890625,\n 33.669496972795535\n ],\n [\n -81.4306640625,\n 32.99023555965106\n ],\n [\n -80.9912109375,\n 32.008075959291055\n ],\n [\n -81.474609375,\n 30.619004797647808\n ],\n [\n -81.9580078125,\n 30.78903675126116\n ],\n [\n -82.0458984375,\n 30.315987718557867\n ],\n [\n -82.2216796875,\n 30.315987718557867\n ],\n [\n -82.30957031249999,\n 30.543338954230222\n ],\n [\n -84.90234375,\n 30.694611546632302\n ],\n [\n -85.14404296875,\n 31.203404950917395\n ],\n [\n -85.14404296875,\n 31.522361470421437\n ],\n [\n -85.23193359375,\n 31.82156451492074\n ],\n [\n -85.05615234375,\n 32.342841356393045\n ],\n [\n -85.60546875,\n 35.0120020431607\n ]\n ]\n ]\n }\n }\n ]\n}","publishingServiceCenter":{"id":8,"text":"Raleigh PSC"},"noUsgsAuthors":false,"publicationStatus":"PW","scienceBaseUri":"5516711ce4b0323842781ade","contributors":{"authors":[{"text":"Knaak, Andrew E. 0000-0003-1813-8959 aknaak@usgs.gov","orcid":"https://orcid.org/0000-0003-1813-8959","contributorId":3123,"corporation":false,"usgs":true,"family":"Knaak","given":"Andrew","email":"aknaak@usgs.gov","middleInitial":"E.","affiliations":[{"id":13634,"text":"South Atlantic Water Science Center","active":true,"usgs":true}],"preferred":true,"id":542431,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Caslow, Kerry","contributorId":139935,"corporation":false,"usgs":true,"family":"Caslow","given":"Kerry","affiliations":[{"id":316,"text":"Georgia Water Science Center","active":true,"usgs":true}],"preferred":false,"id":543498,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Peck, Michael F. mfpeck@usgs.gov","contributorId":1467,"corporation":false,"usgs":true,"family":"Peck","given":"Michael F.","email":"mfpeck@usgs.gov","affiliations":[],"preferred":false,"id":543499,"contributorType":{"id":1,"text":"Authors"},"rank":3}]}} ,{"id":70144282,"text":"70144282 - 2015 - Long-term controls of soil organic carbon with depth and time: a case study from the Cowlitz River Chronosequence, WA USA","interactions":[],"lastModifiedDate":"2015-03-27T08:53:51","indexId":"70144282","displayToPublicDate":"2015-03-27T08:30:00","publicationYear":"2015","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":1760,"text":"Geoderma","active":true,"publicationSubtype":{"id":10}},"title":"Long-term controls of soil organic carbon with depth and time: a case study from the Cowlitz River Chronosequence, WA USA","docAbstract":"Over timescales of soil development (millennia), the capacity of soils to stabilize soil organic carbon (SOC) is linked to soil development through changes in soil mineralogy and other soil properties. In this study, an extensive dataset of soil profile chemistry and mineralogy is compiled from the Cowlitz River Chronosequence (CRC), WA USA. The CRC soils range in age from 0.25 to 1200 kyr, spanning a developmental gradient encompassing clear changes in soil mineralogy, chemistry, and surface area. Comparison of these and other metrics of soil development with SOC properties reveal several relationships that may be diagnostic of the long-term coupling of soil development and C cycling. Specifically, SOC content was significantly correlated with sodium pyrophosphate extractable metals emphasizing the relevance of organo-metal complexes in volcanic soils. The depth distributions of organo-metals and other secondary weathering products, including the kaolin and short-range order (SRO) minerals, support the so-called “binary composition” of volcanic soils. The formation of organo-metal complexes limits the accumulation of secondary minerals in shallow soils, whereas in deep soils with lower SOC content, secondary minerals accumulate. In the CRC soils, secondary minerals formed in deep soils (below 50 cm) including smectite, allophane, Fe-oxides and dominated by the kaolin mineral halloysite. The abundance of halloysite was significantly correlated with bulk soil surface area and 14C content (a proxy for the mean age of SOC), implying enhanced stability of C in deep soils. Allophane, an SRO mineral commonly associated with SOC storage, was not correlated with SOC content or 14C values in CRC soils. We propose conceptual framework to describe these observations based on a general understanding of pedogenesis in volcanic soils, where SOC cycling is coupled with soil development through the formation of and fate of organo-metal or other mobile weathering products. This framework highlights interactions between SOC and soil development, which may be applicable to other soils where organic inputs interact with the products of chemical weathering.
","language":"English","publisher":"Elsevier","doi":"10.1016/j.geoderma.2015.02.005","usgsCitation":"Lawrence, C., Harden, J.W., Xu, X., Schulz, M., and Trumbore, S., 2015, Long-term controls of soil organic carbon with depth and time: a case study from the Cowlitz River Chronosequence, WA USA: Geoderma, v. 247-248, p. 73-87, https://doi.org/10.1016/j.geoderma.2015.02.005.","productDescription":"15 p.","startPage":"73","endPage":"87","numberOfPages":"15","onlineOnly":"N","additionalOnlineFiles":"Y","ipdsId":"IP-057605","costCenters":[{"id":438,"text":"National Research Program - Western Branch","active":true,"usgs":true}],"links":[{"id":472188,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.1016/j.geoderma.2015.02.005","text":"Publisher Index Page"},{"id":299019,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","state":"Washington","otherGeospatial":"Cowlitz River","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -122.947998046875,\n 46.372990544913755\n ],\n [\n -122.947998046875,\n 46.60699758458517\n ],\n [\n -122.46116638183594,\n 46.60699758458517\n ],\n [\n -122.46116638183594,\n 46.372990544913755\n ],\n [\n -122.947998046875,\n 46.372990544913755\n ]\n ]\n ]\n }\n }\n ]\n}","volume":"247-248","publishingServiceCenter":{"id":14,"text":"Menlo Park PSC"},"noUsgsAuthors":false,"publicationStatus":"PW","scienceBaseUri":"5516711ae4b0323842781ad6","contributors":{"authors":[{"text":"Lawrence, Corey R. clawrence@usgs.gov","contributorId":139914,"corporation":false,"usgs":true,"family":"Lawrence","given":"Corey R.","email":"clawrence@usgs.gov","affiliations":[{"id":438,"text":"National Research Program - Western Branch","active":true,"usgs":true}],"preferred":false,"id":543433,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Harden, Jennifer W. 0000-0002-6570-8259 jharden@usgs.gov","orcid":"https://orcid.org/0000-0002-6570-8259","contributorId":1971,"corporation":false,"usgs":true,"family":"Harden","given":"Jennifer","email":"jharden@usgs.gov","middleInitial":"W.","affiliations":[{"id":615,"text":"Volcano Hazards Program","active":true,"usgs":true},{"id":312,"text":"Geology, Minerals, Energy, and Geophysics Science Center","active":true,"usgs":true},{"id":617,"text":"Volcano Science Center","active":true,"usgs":true}],"preferred":true,"id":543434,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Xu, Xiaomei","contributorId":139915,"corporation":false,"usgs":false,"family":"Xu","given":"Xiaomei","email":"","affiliations":[{"id":13312,"text":"University of California-Irvine","active":true,"usgs":false}],"preferred":false,"id":543435,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Schulz, Marjorie S. 0000-0001-5597-6447 mschulz@usgs.gov","orcid":"https://orcid.org/0000-0001-5597-6447","contributorId":3720,"corporation":false,"usgs":true,"family":"Schulz","given":"Marjorie S.","email":"mschulz@usgs.gov","affiliations":[{"id":438,"text":"National Research Program - Western Branch","active":true,"usgs":true},{"id":312,"text":"Geology, Minerals, Energy, and Geophysics Science Center","active":true,"usgs":true}],"preferred":true,"id":543436,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Trumbore, Susan E. 0000-0003-3885-6202","orcid":"https://orcid.org/0000-0003-3885-6202","contributorId":139916,"corporation":false,"usgs":false,"family":"Trumbore","given":"Susan E.","affiliations":[{"id":13313,"text":"Max Planck Institute of Biogeochemistry","active":true,"usgs":false}],"preferred":false,"id":543437,"contributorType":{"id":1,"text":"Authors"},"rank":5}]}} ,{"id":70173415,"text":"70173415 - 2015 - Upstream dispersal of an invasive crayfish aided by a fish passage facility","interactions":[],"lastModifiedDate":"2016-08-17T12:12:57","indexId":"70173415","displayToPublicDate":"2015-03-27T07:45:00","publicationYear":"2015","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":2655,"text":"Management of Biological Invasions","active":true,"publicationSubtype":{"id":10}},"title":"Upstream dispersal of an invasive crayfish aided by a fish passage facility","docAbstract":"Fish passage facilities for reservoir dams have been used to restore habitat connectivity within riverine networks by allowing upstream passage for native species. These facilities may also support the spread of invasive species, an unintended consequence and potential downside of upstream passage structures. We documented dam passage of the invasive virile crayfish, Orconectes virilis (Hagen, 1870), at fish ladders designed for upstream passage of American eels, Anguilla rostrata (Lesueur, 1817), in the Shenandoah River drainage, USA. Ladder use and upstream passage of 11 virile crayfish occurred from 2007–2014 during periods of low river discharge (<30 m3s–1) and within a wide range of water temperatures from 9.0–28.6 °C. Virile crayfish that used the eel ladders were large adults with a mean carapace length and width of 48.0 mm and 24.1 mm, respectively. Our data demonstrated the use of species-specific fish ladders by a non-target non-native species, which has conservation and management implications for the spread of aquatic invasive species and upstream passage facilities. 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\nAcoustic surveys were conducted in late summer/early fall during the years 1992-1996 and 2001-2014 to estimate pelagic prey fish biomass in Lake Michigan. Midwater trawling during the surveys as well as target strength provided a measure of species and size composition of the fish community for use in scaling acoustic data and providing species-specific abundance estimates. The 2014 survey consisted of 27 acoustic transects (603 km total) and 31 midwater trawl tows. Four additional transects were sampled in Green Bay but were not included in lakewide estimates. Mean prey fish biomass was 6.5 kg/ha [31.7 kilotonnes (kt = 1,000 metric tons)], equivalent to 69.9 million pounds, which was similar to the estimate in 2013 (29.6 kt) and 25% of the long-term (19 years) mean. The numeric density of the 2014 alewife year-class was 3% of the time series average and was the lowest observed in the 19 years of sampling. This year-class contributed <1% of total alewife biomass (4.6 kg/ha). Alewife ≥age-1 comprised 99.5% of alewife biomass. Numeric density of alewife in Green Bay was more than three times that of the main lake. In 2014, alewife comprised 71% of total prey fish biomass, while rainbow smelt and bloater were 1% and 28% of total biomass, respectively. Rainbow smelt biomass in 2014 (0.08 kg/ha) was 66% lower than in 2013, 2% of the long-term mean, and lower than in any previous year. Bloater biomass in 2014 was 1.8 kg/ha, nearly three times more than the 2013 biomass, and 20% of the long-term mean. Mean density of small bloater in 2014 (122 fish/ha) was lower than peak values observed in 2007-2009 but was similar to the time series mean (124 fish/ha). In 2014, pelagic prey fish biomass in Lake Michigan was 71% of that in Lake Huron (all basins), where the community is dominated by bloater.
","language":"English","publisher":"USGS, Great Lake Science Center","collaboration":"Michigan Department of Natural Resources, US Fish and Wildlife Service, Great Lakes Fishery Commission","usgsCitation":"Warner, D.M., Farha, S.A., Claramunt, R., Hanson, D., and O’Brien, T.P., 2015, Status of Pelagic Prey Fishes in Lake Michigan, 2014, 11 p.","productDescription":"11 p.","startPage":"49","endPage":"59","onlineOnly":"N","additionalOnlineFiles":"N","ipdsId":"IP-063602","costCenters":[{"id":324,"text":"Great Lakes Science Center","active":true,"usgs":true}],"links":[{"id":312023,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","state":"Michigan, Wisconsin, Indiana, and Illinois","otherGeospatial":"Lake Michigan","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -84.78149414062499,\n 45.95878764035642\n ],\n [\n -85.40771484375,\n 46.14939437647686\n ],\n [\n -86.19873046875,\n 45.99696161820381\n ],\n [\n -86.693115234375,\n 45.90529985724796\n ],\n [\n -87.07763671875,\n 45.96642454131025\n ],\n [\n -88.22021484375,\n 44.653024159812\n ],\n [\n -87.91259765625,\n 44.38669150215206\n ],\n [\n -87.593994140625,\n 44.41808794374849\n ],\n [\n -87.945556640625,\n 43.492782808225\n ],\n [\n -87.923583984375,\n 42.52879629320373\n ],\n [\n -87.71484375,\n 41.85319643776675\n ],\n [\n -87.3193359375,\n 41.48389104267175\n ],\n [\n -86.36352539062499,\n 41.91045347666418\n ],\n [\n -86.033935546875,\n 42.88401467044253\n ],\n [\n -86.275634765625,\n 43.58834891179792\n ],\n [\n -86.2646484375,\n 44.071800467511565\n ],\n [\n -85.869140625,\n 44.653024159812\n ],\n [\n -85.36376953125,\n 44.74673324024678\n ],\n [\n -85.352783203125,\n 45.166547157856016\n ],\n [\n -85.166015625,\n 45.31352900692261\n ],\n [\n -84.847412109375,\n 45.359865333959746\n ],\n [\n -84.74853515625,\n 45.48324350868221\n ],\n [\n -84.72656249999999,\n 45.91294412737392\n ],\n [\n -84.78149414062499,\n 45.95878764035642\n ]\n ]\n ]\n }\n }\n ]\n}","publishingServiceCenter":{"id":6,"text":"Columbus PSC"},"noUsgsAuthors":false,"publicationStatus":"PW","scienceBaseUri":"5666bbf3e4b06a3ea36c8b4e","contributors":{"authors":[{"text":"Warner, David M. 0000-0003-4939-5368 dmwarner@usgs.gov","orcid":"https://orcid.org/0000-0003-4939-5368","contributorId":2986,"corporation":false,"usgs":true,"family":"Warner","given":"David","email":"dmwarner@usgs.gov","middleInitial":"M.","affiliations":[{"id":324,"text":"Great Lakes Science Center","active":true,"usgs":true}],"preferred":true,"id":543283,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Farha, Steven A.","contributorId":79026,"corporation":false,"usgs":true,"family":"Farha","given":"Steven","email":"","middleInitial":"A.","affiliations":[],"preferred":false,"id":581493,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Claramunt, Randall M.","contributorId":19047,"corporation":false,"usgs":true,"family":"Claramunt","given":"Randall M.","affiliations":[],"preferred":false,"id":581494,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Hanson, Dale","contributorId":43676,"corporation":false,"usgs":true,"family":"Hanson","given":"Dale","affiliations":[],"preferred":false,"id":581495,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"O’Brien, Timothy P. 0000-0003-4502-5204 tiobrien@usgs.gov","orcid":"https://orcid.org/0000-0003-4502-5204","contributorId":2662,"corporation":false,"usgs":true,"family":"O’Brien","given":"Timothy","email":"tiobrien@usgs.gov","middleInitial":"P.","affiliations":[{"id":324,"text":"Great Lakes Science Center","active":true,"usgs":true}],"preferred":true,"id":581496,"contributorType":{"id":1,"text":"Authors"},"rank":5}]}} ,{"id":70141336,"text":"sir20155022 - 2015 - Field-based description of rhyolite lava flows of the Calico Hills Formation, Nevada National Security Site, Nevada","interactions":[],"lastModifiedDate":"2015-03-26T11:53:07","indexId":"sir20155022","displayToPublicDate":"2015-03-26T12:45:00","publicationYear":"2015","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":"2015-5022","title":"Field-based description of rhyolite lava flows of the Calico Hills Formation, Nevada National Security Site, Nevada","docAbstract":"Contaminants introduced into the subsurface of Pahute Mesa, Nevada National Security Site, by underground nuclear testing are of concern to the U.S. Department of Energy and regulators responsible for protecting human health and safety. The potential for contaminant movement away from the underground test areas at Pahute Mesa and into the accessible environment is greatest by groundwater transport through fractured volcanic rocks. The 12.9 Ma (mega-annums, million years) Calico Hills Formation, which consists of a mixture of rhyolite lava flows and intercalated nonwelded and bedded tuff and pyroclastic flow deposits, occurs in two areas of the Nevada National Security Site. One area is north of the Rainier Mesa caldera, buried beneath Pahute Mesa, and serves as a heterogeneous volcanic-rock aquifer but is only available to study through drilling and is not described in this report. A second accumulation of the formation is south of the Rainier Mesa caldera and is exposed in outcrop along the western boundary of the Nevada National Security Site at the Calico Hills near Yucca Mountain. These outcrops expose in three dimensions an interlayered sequence of tuff and lava flows similar to those intercepted in the subsurface beneath Pahute Mesa. Field description and geologic mapping of these exposures described lithostratigraphic variations within lava flows and assisted in, or at least corroborated, conceptualization of the rhyolite lava-bearing parts of the formation.
\nIn the area south of the Rainier Mesa caldera, surface exposures and nearby subsurface equivalents were studied through compilation of geologic maps, new field mapping, subsurface information from boreholes, and data extracted from three-dimensional geologic framework models. Rhyolite lava flows within the Calico Hills Formation are described in terms of lithostratigraphic variations established for rhyolite lava flows in other volcanic fields. In general, the flows consist of a core of crystallized, flow-banded rhyolite lava, surrounded by a carapace of obsidian, commonly mantled by blocky, pumiceous rhyolite lava and flow breccia. Rhyolite lava flows were correlated and mapped on the basis of distinctive appearance in outcrop, stratigraphic sequence, and the presence of stratigraphic markers. Pyroclastic deposits that are spatially, temporally, and genetically related to the rhyolite lava flows consist of a series of intercalated pyroclastic flows, bedded ash-fall, and reworked tuff that have varying amounts of pumice and volcanic rock clasts.
\nIn the area south of the Rainier Mesa caldera, surface and subsurface geologic data are combined to interpret the overall thickness of the Calico Hills Formation and the proportion of lava flow lithology across the study area. The formation is at least 500 meters (m) thick and contains the greatest proportion of rhyolite lava flow to the northeast of Yucca Mountain in the lower part of Fortymile Canyon. The formation thins to the south and southwest where it is between 50 and 200 m thick beneath Yucca Mountain and contains no rhyolite lavas. Geologic mapping and field-based correlation of individual lava flows allow for the interpretation of the thickness and extent of specific flows and the location of their source areas. The most extensive flows have widths from 2 to 3 kilometers (km) and lengths of at least 5–6 km. Lava flow thickness varies from 150 to 250 m above interpreted source vents to between 30 and 80 m in more distal locations. Rhyolite lavas have length-to-height ratios of 10:1 or greater and, in one instance, a length-to-width ratio of 2:1 or greater, implying a tongue-shaped geometry instead of circular domes or tabular bodies. Although geologic mapping did not identify any physical feature that could be positively identified as a vent, lava flow thickness and the size of clasts in subjacent pyroclastic deposits suggest that primary vent areas for at least some of the flows in the study area are on the east side of Fortymile Canyon, to the northeast of Yucca Mountain.
","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/sir20155022","collaboration":"Prepared in cooperation with the U.S. Department of Energy, National Nuclear Security Administration Nevada Site Office, Office of Environmental Management under Interagency Agreement, DE-NA0001654/004","usgsCitation":"Sweetkind, D., and Bova, S.C., 2015, Field-based description of rhyolite lava flows of the Calico Hills Formation, Nevada National Security Site, Nevada: U.S. Geological Survey Scientific Investigations Report 2015-5022, Report: v, 36 p.; Appendix, https://doi.org/10.3133/sir20155022.","productDescription":"Report: v, 36 p.; Appendix","numberOfPages":"46","onlineOnly":"Y","additionalOnlineFiles":"Y","ipdsId":"IP-057359","costCenters":[{"id":318,"text":"Geosciences and Environmental Change Science Center","active":true,"usgs":true}],"links":[{"id":299004,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/sir20155022.jpg"},{"id":299002,"rank":2,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/sir/2015/5022/pdf/sir2015-5022.pdf","text":"Report","size":"11.4 MB","linkFileType":{"id":1,"text":"pdf"},"description":"Report"},{"id":299003,"rank":3,"type":{"id":3,"text":"Appendix"},"url":"https://pubs.usgs.gov/sir/2015/5022/downloads/sir2015-5022_appendix1.pdf","text":"Appendix 1","size":"268 KB","linkFileType":{"id":1,"text":"pdf"},"description":"Appendix 1","linkHelpText":"Lithologic Description of Calico Hills Formation From Selected Boreholes in the Study Area"},{"id":299001,"rank":1,"type":{"id":15,"text":"Index Page"},"url":"https://pubs.usgs.gov/sir/2015/5022/"}],"country":"United States","state":"Nevada","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -116.69677734375,\n 36.54494944148322\n ],\n [\n -116.69677734375,\n 37.317751851636906\n ],\n [\n -115.68603515624999,\n 37.317751851636906\n ],\n [\n -115.68603515624999,\n 36.54494944148322\n ],\n [\n -116.69677734375,\n 36.54494944148322\n ]\n ]\n ]\n }\n }\n ]\n}","publishingServiceCenter":{"id":2,"text":"Denver PSC"},"noUsgsAuthors":false,"publicationStatus":"PW","scienceBaseUri":"55151f98e4b03238427816b4","contributors":{"authors":[{"text":"Sweetkind, Donald S. dsweetkind@usgs.gov","contributorId":735,"corporation":false,"usgs":true,"family":"Sweetkind","given":"Donald S.","email":"dsweetkind@usgs.gov","affiliations":[{"id":271,"text":"Federal Center","active":false,"usgs":true}],"preferred":false,"id":540676,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Bova, Shiera C.","contributorId":45607,"corporation":false,"usgs":true,"family":"Bova","given":"Shiera","email":"","middleInitial":"C.","affiliations":[],"preferred":false,"id":540677,"contributorType":{"id":1,"text":"Authors"},"rank":2}]}} ,{"id":70143992,"text":"70143992 - 2015 - Re-estimating temperature-dependent consumption parameters in bioenergetics models for juvenile Chinook salmon","interactions":[],"lastModifiedDate":"2015-03-26T11:09:07","indexId":"70143992","displayToPublicDate":"2015-03-26T12:00:00","publicationYear":"2015","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":3624,"text":"Transactions of the American Fisheries Society","active":true,"publicationSubtype":{"id":10}},"title":"Re-estimating temperature-dependent consumption parameters in bioenergetics models for juvenile Chinook salmon","docAbstract":"Researchers have cautioned against the borrowing of consumption and growth parameters from other species and life stages in bioenergetics growth models. In particular, the function that dictates temperature dependence in maximum consumption (Cmax) within the Wisconsin bioenergetics model for Chinook Salmon Oncorhynchus tshawytscha produces estimates that are lower than those measured in published laboratory feeding trials. We used published and unpublished data from laboratory feeding trials with subyearling Chinook Salmon from three stocks (Snake, Nechako, and Big Qualicum rivers) to estimate and adjust the model parameters for temperature dependence in Cmax. The data included growth measures in fish ranging from 1.5 to 7.2 g that were held at temperatures from 14°C to 26°C. Parameters for temperature dependence in Cmax were estimated based on relative differences in food consumption, and bootstrapping techniques were then used to estimate the error about the parameters. We found that at temperatures between 17°C and 25°C, the current parameter values did not match the observed data, indicating that Cmax should be shifted by about 4°C relative to the current implementation under the bioenergetics model. We conclude that the adjusted parameters for Cmax should produce more accurate predictions from the bioenergetics model for subyearling Chinook Salmon.
","language":"English","publisher":"Taylor & Francis","doi":"10.1080/00028487.2014.986336","usgsCitation":"Plumb, J.M., and Moffitt, C.M., 2015, Re-estimating temperature-dependent consumption parameters in bioenergetics models for juvenile Chinook salmon: Transactions of the American Fisheries Society, v. 144, no. 2, p. 323-330, https://doi.org/10.1080/00028487.2014.986336.","productDescription":"8 p.","startPage":"323","endPage":"330","onlineOnly":"N","additionalOnlineFiles":"N","ipdsId":"IP-056812","costCenters":[{"id":654,"text":"Western Fisheries Research Center","active":true,"usgs":true}],"links":[{"id":299000,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"volume":"144","issue":"2","publishingServiceCenter":{"id":12,"text":"Tacoma PSC"},"noUsgsAuthors":false,"publicationDate":"2015-02-27","publicationStatus":"PW","scienceBaseUri":"55151f98e4b03238427816ba","contributors":{"authors":[{"text":"Plumb, John M. 0000-0003-4255-1612 jplumb@usgs.gov","orcid":"https://orcid.org/0000-0003-4255-1612","contributorId":3569,"corporation":false,"usgs":true,"family":"Plumb","given":"John","email":"jplumb@usgs.gov","middleInitial":"M.","affiliations":[{"id":654,"text":"Western Fisheries Research Center","active":true,"usgs":true}],"preferred":true,"id":543243,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Moffitt, Christine M. 0000-0001-6020-9728 cmoffitt@usgs.gov","orcid":"https://orcid.org/0000-0001-6020-9728","contributorId":2583,"corporation":false,"usgs":true,"family":"Moffitt","given":"Christine","email":"cmoffitt@usgs.gov","middleInitial":"M.","affiliations":[{"id":200,"text":"Coop Res Unit Seattle","active":true,"usgs":true}],"preferred":true,"id":543244,"contributorType":{"id":1,"text":"Authors"},"rank":2}]}} ,{"id":70143528,"text":"fs20153026 - 2015 - Streamflow of 2014: water year summary","interactions":[],"lastModifiedDate":"2015-03-26T09:23:08","indexId":"fs20153026","displayToPublicDate":"2015-03-26T10:15:00","publicationYear":"2015","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":"2015-3026","title":"Streamflow of 2014: water year summary","docAbstract":"The maps and graphs in this summary describe streamflow conditions for water year 2014 (October 1, 2013, to September 30, 2014) in the context of the 85-year period from 1930 through 2014, unless otherwise noted. The illustrations are based on observed data from the U.S. Geological Survey’s (USGS) National Streamflow Information Program (NSIP) (http://water.usgs.gov/nsip/). The period 1930–2014 was used because, prior to 1930, the number of streamgages was too small to provide representative data for computing statistics for most regions of the country.
\nIn the summary, reference is made to the term “runoff,” which is the depth to which a river basin or other geographic area, such as a State, would be covered with water if all the streamflow within the area during a specified time period was uniformly distributed over the area. Runoff can also be used to quantify the magnitude of water flowing through rivers and streams in measurement units that can be compared from one area of the Nation to another.
","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/fs20153026","usgsCitation":"Jian, X., Wolock, D.M., Jenter, H.L., and Brady, S., 2015, Streamflow of 2014: water year summary: U.S. Geological Survey Fact Sheet 2015-3026, 8 p., https://doi.org/10.3133/fs20153026.","productDescription":"8 p.","numberOfPages":"8","onlineOnly":"Y","additionalOnlineFiles":"N","temporalStart":"2013-10-01","temporalEnd":"2014-09-30","ipdsId":"IP-062607","costCenters":[{"id":502,"text":"Office of Surface Water","active":true,"usgs":true}],"links":[{"id":298990,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/fs20153026.jpg"},{"id":298988,"rank":1,"type":{"id":15,"text":"Index Page"},"url":"https://pubs.usgs.gov/fs/2015/3026/"},{"id":298989,"rank":2,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/fs/2015/3026/pdf/fs2015-3026.pdf","text":"Report","size":"1.11 MB","linkFileType":{"id":1,"text":"pdf"},"description":"Report"}],"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 -66.884765625,\n 44.59046718130883\n ],\n [\n -81.2109375,\n 30.826780904779774\n ],\n [\n -79.013671875,\n 26.115985925333536\n ],\n [\n -81.9140625,\n 24.686952411999155\n ],\n [\n -84.462890625,\n 29.458731185355344\n ],\n [\n -94.658203125,\n 28.92163128242129\n ],\n [\n -97.55859375,\n 25.720735134412106\n ],\n [\n -107.75390625,\n 31.50362930577303\n ],\n [\n -117.59765625,\n 32.39851580247402\n ],\n [\n -124.1015625,\n 38.685509760012\n ],\n [\n -124.1015625,\n 49.03786794532644\n ],\n [\n -95.2734375,\n 49.15296965617039\n ],\n [\n -82.265625,\n 45.460130637921004\n ],\n [\n -81.9140625,\n 42.293564192170095\n ],\n [\n -68.203125,\n 48.3416461723746\n ],\n [\n -66.884765625,\n 44.59046718130883\n ]\n ]\n ]\n }\n },\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -67.8955078125,\n 17.43451055152291\n ],\n [\n -67.8955078125,\n 19.020577110966798\n ],\n [\n -65.0830078125,\n 19.020577110966798\n ],\n [\n -65.0830078125,\n 17.43451055152291\n ],\n [\n -67.8955078125,\n 17.43451055152291\n ]\n ]\n ]\n }\n }\n ]\n}","publishingServiceCenter":{"id":9,"text":"Reston PSC"},"noUsgsAuthors":false,"publicationStatus":"PW","scienceBaseUri":"55151f99e4b03238427816be","contributors":{"authors":[{"text":"Jian, Xiaodong 0000-0002-9173-3482 xjian@usgs.gov","orcid":"https://orcid.org/0000-0002-9173-3482","contributorId":1282,"corporation":false,"usgs":true,"family":"Jian","given":"Xiaodong","email":"xjian@usgs.gov","affiliations":[{"id":353,"text":"Kansas Water Science Center","active":false,"usgs":true},{"id":502,"text":"Office of Surface Water","active":true,"usgs":true}],"preferred":true,"id":542784,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Wolock, David M. 0000-0002-6209-938X dwolock@usgs.gov","orcid":"https://orcid.org/0000-0002-6209-938X","contributorId":540,"corporation":false,"usgs":true,"family":"Wolock","given":"David","email":"dwolock@usgs.gov","middleInitial":"M.","affiliations":[{"id":503,"text":"Office of Water Quality","active":true,"usgs":true},{"id":37778,"text":"WMA - 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2015 - The 3D Elevation Program: summary for Nevada","interactions":[],"lastModifiedDate":"2016-08-17T15:02:00","indexId":"fs20153028","displayToPublicDate":"2015-03-26T10:00:00","publicationYear":"2015","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":"2015-3028","title":"The 3D Elevation Program: summary for Nevada","docAbstract":"Elevation data are essential to a broad range of applications, including forest resources management, wildlife and habitat management, national security, recreation, and many others. For the State of Nevada, elevation data are critical for infrastructure and construction management, natural resources conservation, flood risk management, geologic resource assessment and hazard mitigation, agriculture and precision farming, and other business uses. Today, high-density light detection and ranging (lidar) data are the primary sources for deriving elevation models and other datasets. Federal, State, Tribal, and local agencies work in partnership to (1) replace data that are older and of lower quality and (2) provide coverage where publicly accessible data do not exist. A joint goal of State and Federal partners is to acquire consistent, statewide coverage to support existing and emerging applications enabled by lidar data.
\nThe National Enhanced Elevation Assessment evaluated multiple elevation data acquisition options to determine the optimal data quality and data replacement cycle relative to cost to meet the identified requirements of the user community. The evaluation demonstrated that lidar acquisition at quality level 2 for the conterminous United States and quality level 5 interferometric synthetic aperture radar (ifsar) data for Alaska with a 6- to 10-year acquisition cycle provided the highest benefit/cost ratios. The 3D Elevation Program (3DEP) initiative selected an 8-year acquisition cycle for the respective quality levels. 3DEP, managed by the U.S. Geological Survey, the Office of Management and Budget Circular A–16 lead agency for terrestrial elevation data, responds to the growing need for high-quality topographic data and a wide range of other 3D representations of the Nation’s natural and constructed features.
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Jr. carswell@usgs.gov","contributorId":127609,"corporation":false,"usgs":true,"family":"Carswell","given":"William J.","suffix":"Jr.","email":"carswell@usgs.gov","affiliations":[{"id":423,"text":"National Geospatial Program","active":true,"usgs":true}],"preferred":false,"id":543013,"contributorType":{"id":1,"text":"Authors"},"rank":1}]}} ,{"id":70160093,"text":"70160093 - 2015 - Status and trends of prey fish populations in Lake Michigan, 2014","interactions":[],"lastModifiedDate":"2016-09-08T14:23:28","indexId":"70160093","displayToPublicDate":"2015-03-26T00:00:00","publicationYear":"2015","noYear":false,"publicationType":{"id":18,"text":"Report"},"publicationSubtype":{"id":4,"text":"Other Government Series"},"title":"Status and trends of prey fish populations in Lake Michigan, 2014","docAbstract":"The U.S. Geological Survey Great Lakes Science Center has conducted lake-wide surveys of the fish community in Lake Michigan each fall since 1973 using standard 12-m bottom trawls towed along contour at depths of 9 to 110 m at each of seven index transects. The resulting data on relative abundance, size and age structure, and condition of individual fishes are used to estimate various population parameters that are in turn used by state and tribal agencies in managing Lake Michigan fish stocks. All seven established index transects of the survey were completed in 2014. The survey provides relative abundance and biomass estimates between the 5-m and 114-m depth contours of the lake (herein, lake-wide) for prey fish populations, as well as burbot, yellow perch, and the introduced dreissenid mussels. Lake-wide biomass of alewives in 2014 was estimated at 1.6 kilotonnes (kt, 1 kt = 1000 metric tonnes), which was a record low and only 16% of the average biomass estimated since 2005. Moreover, the age distribution of alewives remained truncated with no alewife exceeding an age of 5. Record low biomass was also observed for nearly every other prey fish species: bloater (0.3 kt), rainbow smelt (0.02 kt), slimy sculpin (0.09 kt), deepwater sculpin (1.0 kt) and ninespine stickleback (0.004 kt). Round goby was the only prey fish species to avoid a record-low biomass estimate (2.04 kt); the 2014 estimate was 58% of the average lakewide biomass observed since 2006 when round gobies became relatively abundant in our catches. Burbot lake-wide biomass (0.5 kt in 2014) has remained below 3 kt since 2001. No age-0 yellow perch (i.e., < 100 mm) were captured during the survey, which is indicative of a poor year-class. Lake-wide biomass estimate of dreissenid mussels in 2014 was 23.9 kt, not different from 2013 (23.2 kt). Overall, the total lake-wide prey fish biomass estimate (sum of alewife, bloater, rainbow smelt, deepwater sculpin, slimy sculpin, round goby, and ninespine stickleback) in 2014 was only 5.1 kt, compared to the previous record-low prey fish biomass of 15.2 kt in 2012. In 2014, alewives and round gobies constituted 71% of this total, following a trend of dominance by these species since 2012.","language":"English","publisher":"Great Lakes Fishery Commission","usgsCitation":"Bunnell, D., Madenjian, C.P., Desorcie, T.J., Kostich, M.J., Woelmer, W., and Adams, J.V., 2015, Status and trends of prey fish populations in Lake Michigan, 2014, 16 p. .","productDescription":"16 p. 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California","interactions":[],"lastModifiedDate":"2015-03-25T14:33:42","indexId":"70144118","displayToPublicDate":"2015-03-25T14:15:00","publicationYear":"2015","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":2342,"text":"Journal of Hydrology","active":true,"publicationSubtype":{"id":10}},"title":"Storage and mobilization of natural and septic nitrate in thick unsaturated zones, California","docAbstract":"Mobilization of natural and septic nitrate from the unsaturated zone as a result of managed aquifer recharge has degraded water quality from public-supply wells near Yucca Valley in the western Mojave Desert, California. The effect of nitrate storage and potential for denitrification in the unsaturated zone to mitigate increasing nitrate concentrations were investigated. Storage of water extractable nitrate in unsaturated alluvium up to 160 meters (m) thick, ranged from 420 to 6600 kilograms per hectare (kg/ha) as nitrogen (N) beneath undeveloped sites, from 6100 to 9200 kg/ha as N beneath unsewered sites. Nitrate reducing and denitrifying bacteria were less abundant under undeveloped sites and more abundant under unsewered sites; however, δ15N–NO3, and δ18O–NO3 data show only about 5–10% denitrification of septic nitrate in most samples—although as much as 40% denitrification occurred in some parts the unsaturated zone and near the top of the water table. Storage of nitrate in thick unsaturated zones and dilution with low-nitrate groundwater are the primary attenuation mechanisms for nitrate from septic discharges in the study area. Numerical simulations of unsaturated flow, using the computer program TOUGH2, showed septic effluent movement through the unsaturated zone increased as the number and density of the septic tanks increased, and decreased with increased layering, and increased slope of layers, within the unsaturated zone. Managing housing density can delay arrival of septic discharges at the water table, especially in layered unsaturated alluvium, allowing time for development of strategies to address future water-quality issues.
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superstorm Sandy on the lower shoreface and inner shelf offshore the barrier island system of Fire Island, NY using before-and-after surveys involving swath bathymetry, backscatter and CHIRP acoustic reflection data. As sea level rises over the long term, the shoreface and inner shelf are eroded as barrier islands migrate landward; large storms like Sandy are thought to be a primary driver of this largely evolutionary process. The “before” data were collected in 2011 by the U.S. Geological Survey as part of a long-term investigation of the Fire Island barrier system. The “after” data were collected in January, 2013, ~two months after the storm. Surprisingly, no widespread erosional event was observed. Rather, the primary impact of Sandy on the shoreface and inner shelf was to force migration of major bedforms (sand ridges and sorted bedforms) 10’s of meters WSW alongshore, decreasing in migration distance with increasing water depth. Although greater in rate, this migratory behavior is no different than observations made over the 15-year span prior to the 2011 survey. Stratigraphic observations of buried, offshore-thinning fluvial channels indicate that long-term erosion of older sediments is focused in water depths ranging from the base of the shoreface (~13–16 m) to ~21 m on the inner shelf, which is coincident with the range of depth over which sand ridges and sorted bedforms migrated in response to Sandy. We hypothesize that bedform migration regulates erosion over these water depths and controls the formation of a widely observed transgressive ravinement; focusing erosion of older material occurs at the base of the stoss (upcurrent) flank of the bedforms. Secondary storm impacts include the formation of ephemeral hummocky bedforms and the deposition of a mud event layer.
","language":"English","publisher":"Elsevier","doi":"10.1016/j.csr.2015.03.001","usgsCitation":"Goff, J.A., Flood, R.D., Austin, J.A., Schwab, W.C., Christensen, B.A., Browne, C.M., Denny, J.F., and Baldwin, W.E., 2015, The impact of Hurricane Sandy on the shoreface and inner shelf of Fire Island, New York: large bedform migration but limited erosion: Continental Shelf Research, v. 98, p. 13-25, https://doi.org/10.1016/j.csr.2015.03.001.","productDescription":"13 p.","startPage":"13","endPage":"25","numberOfPages":"13","onlineOnly":"N","additionalOnlineFiles":"N","ipdsId":"IP-063373","costCenters":[{"id":678,"text":"Woods Hole Coastal and Marine Science Center","active":true,"usgs":true}],"links":[{"id":472198,"rank":0,"type":{"id":41,"text":"Open Access External Repository Page"},"url":"https://hdl.handle.net/1912/7284","text":"External Repository"},{"id":298973,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","state":"New York","otherGeospatial":"Fire Island","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -73.29048156738281,\n 40.62177060472069\n ],\n [\n -73.05427551269531,\n 40.66813955408042\n ],\n [\n -72.89291381835938,\n 40.724364221722716\n ],\n [\n -72.79815673828124,\n 40.724884598773755\n ],\n [\n -72.8009033203125,\n 40.66188943992171\n ],\n [\n -73.24790954589844,\n 40.54198241319326\n ],\n [\n -73.29048156738281,\n 40.62177060472069\n ]\n ]\n ]\n }\n }\n ]\n}","volume":"98","publishingServiceCenter":{"id":11,"text":"Pembroke PSC"},"noUsgsAuthors":false,"publicationStatus":"PW","scienceBaseUri":"5513ce1ae4b032384276c99b","contributors":{"authors":[{"text":"Goff, John A.","contributorId":96087,"corporation":false,"usgs":false,"family":"Goff","given":"John","email":"","middleInitial":"A.","affiliations":[{"id":12811,"text":"Institute for Geophysics, Jackson School of Geosciences, University of Texas, Austin","active":true,"usgs":false}],"preferred":false,"id":543349,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Flood, Roger D.","contributorId":139894,"corporation":false,"usgs":false,"family":"Flood","given":"Roger","email":"","middleInitial":"D.","affiliations":[{"id":13306,"text":"School of Marine and Atmospheric Sciences, Stony Brook University, Stony Brook, NY","active":true,"usgs":false}],"preferred":false,"id":543350,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Austin, James A. 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Geologic, groundwater, and surface-water data were collected during 2006–9 to estimate hydrologic properties in the watershed. Streamflow and groundwater levels in the shallow glacial deposits in the Stoney Brook watershed were analyzed to estimate groundwater-flow directions, groundwater recharge, and evapotranspiration within the watershed and to assess the effect of ditches on surrounding groundwater resources. Groundwater, streamflow, and precipitation data collected during the study (2006–9) can be used to update the U.S. Department of Agriculture’s Natural Resource Conservation Service and Fond du Lac Resource Management Division surface-water models, which are used to evaluate the effect of proposed adjustments to the ditching system on streamflow on wild rice production and aquatic habitats.
\nSpecific yields calculated from the well water levels ranged from 0.11 to 0.40, and hydraulic conductivities determined from water levels measured during well slug tests ranged from 1 to 7 feet per day. The values for specific yields were similar to values obtained in other studies done in glacial materials of similar composition in Minnesota. The higher hydraulic conductivity estimate (7 feet per day) was similar to lower hydraulic conductivities estimated in another hydrologic study conducted in Carlton County, Minnesota.
\nThe installation of drainage ditches in the Stoney Brook watershed has reduced water levels in lakes connected to the ditch system, and has locally reduced groundwater levels in shallow groundwater adjacent to the ditches and lakes. Differences in near-ditch groundwater hydrographs relative to far-ditch groundwater hydrographs indicate that the effect of the ditches on groundwater is only localized to near-ditch areas. These hydrograph differences resulted in large differences between recharge estimated at wells near and far from ditches. In this study, recharge estimated at wells within 50 feet of a ditch was influenced by ditch-water levels. Annual groundwater recharge estimates from water levels and streamflows during 2006–9 ranged from 0.36 to 34.8 inches, and varied with climate, geology, and well location relative to ditches. The higher recharge estimates were determined from analysis of groundwater levels in wells near the ditches because the shallow groundwater in these wells received both infiltration from ditches and areal groundwater recharge from precipitation. The water-table fluctuation method using a manual groundwater recession approach for wells far from ditches provided the best estimates of areal groundwater recharge to the shallow glacial aquifer because water levels in these wells were not affected by water infiltrating from ditches (bank storage). For wells more than 400 feet from ditches, mean annual areal groundwater recharge estimates using the manual groundwater recession approach for wells screened mostly in outwash sands during 2007, 2008, and 2009 ranged from 4.47 to 18.6 inches (wells 5, 7, 13, 14 and 15), and ranged from 0.43 to 2.85 inches for wells screened mostly in clayey sand or sandy clay (wells 9 and 16). Recharge estimates at wells far from ditches were similar to basinwide recharge estimates from streamflow.
\nDaily fluctuations in water levels in two wells indicated that the evapotranspiration extinction depth in the Stoney Brook watershed is approximately 4.6 to 6 feet below the land surface. A polynomial regression fit of the daily evapotranspiration rates during 2006–9 for well 1 produced a total evapotranspiration estimate of 16.1 inches from June 26 to October 6 for every year. Evapotranspiration estimated from daily water-level fluctuations in wells near ditches is relatively high. The ditch-water surface allowed for relatively high evaporation compared to the land surface, which, with a good hydraulic connection to surrounding groundwater, resulted in relatively high fluctuations in daily groundwater levels near ditches, resulting in high evapotranspiration estimates.
","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/sir20155007","collaboration":"Prepared in cooperation with the Fond du Lac Band of Lake Superior Chippewa","usgsCitation":"Jones, P.M., and Tomasek, A.A., 2015, Assessment of aquifer properties, evapotranspiration, and the effects of ditching in the Stoney Brook watershed, Fond du Lac Reservation, Minnesota, 2006-9: U.S. Geological Survey Scientific Investigations Report 2015-5007, vi, 33 p., https://doi.org/10.3133/sir20155007.","productDescription":"vi, 33 p.","numberOfPages":"44","onlineOnly":"Y","additionalOnlineFiles":"N","temporalStart":"2006-01-01","temporalEnd":"2009-12-31","ipdsId":"IP-048896","costCenters":[{"id":392,"text":"Minnesota Water Science Center","active":true,"usgs":true}],"links":[{"id":298967,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/sir20155007.jpg"},{"id":298965,"rank":1,"type":{"id":15,"text":"Index Page"},"url":"https://pubs.usgs.gov/sir/2015/5007/"},{"id":298966,"rank":2,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/sir/2015/5007/pdf/sir2015-5007.pdf","text":"Report","size":"2.86 MB","linkFileType":{"id":1,"text":"pdf"},"description":"Report"}],"projection":"Universal Transverse Mercator projection, Zone 15","datum":"North American Datum of 1983","country":"United States","state":"Minnesota","otherGeospatial":"Fond du Lac Reservation, Stoney Brook watershed","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -92.64461517333984,\n 46.79488875091874\n ],\n [\n -92.67894744873045,\n 46.79935438115391\n ],\n [\n -92.7187728881836,\n 46.83553581454299\n ],\n [\n -92.7304458618164,\n 46.836944988044465\n ],\n [\n -92.82159805297852,\n 46.7988843322654\n ],\n [\n -92.82142639160156,\n 46.78830714664984\n ],\n [\n -92.80477523803711,\n 46.7660882900233\n ],\n [\n -92.80082702636719,\n 46.71915170604123\n ],\n [\n -92.76477813720702,\n 46.68100772325949\n ],\n [\n -92.70709991455078,\n 46.641422536237094\n ],\n [\n -92.63671875,\n 46.641422536237094\n ],\n [\n -92.63980865478514,\n 46.713267047330255\n ],\n [\n -92.62504577636719,\n 46.722682193238484\n ],\n [\n -92.625732421875,\n 46.75773915478246\n ],\n [\n -92.60307312011719,\n 46.76926297371475\n ],\n [\n -92.60307312011719,\n 46.784780956138846\n ],\n [\n -92.64461517333984,\n 46.79488875091874\n ]\n ]\n ]\n }\n }\n ]\n}","publishingServiceCenter":{"id":4,"text":"Rolla PSC"},"noUsgsAuthors":false,"publicationStatus":"PW","scienceBaseUri":"5513ce17e4b032384276c98d","contributors":{"authors":[{"text":"Jones, Perry M. 0000-0002-6569-5144 pmjones@usgs.gov","orcid":"https://orcid.org/0000-0002-6569-5144","contributorId":2231,"corporation":false,"usgs":true,"family":"Jones","given":"Perry","email":"pmjones@usgs.gov","middleInitial":"M.","affiliations":[{"id":392,"text":"Minnesota Water Science Center","active":true,"usgs":true}],"preferred":true,"id":543297,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Tomasek, Abigail A.","contributorId":138614,"corporation":false,"usgs":false,"family":"Tomasek","given":"Abigail","email":"","middleInitial":"A.","affiliations":[{"id":6672,"text":"former: USGS Southwest Biological Science Center, Colorado Plateau Research Station, Flagstaff, AZ. Current address: TN-SCORE, Univ of Tennessee, Knoxville, TN, e-mail: jennen@gmail.com","active":true,"usgs":false}],"preferred":false,"id":543298,"contributorType":{"id":1,"text":"Authors"},"rank":2}]}} ,{"id":70148094,"text":"70148094 - 2015 - Downscaling 250-m MODIS growing season NDVI based on multiple-date landsat images and data mining approaches","interactions":[],"lastModifiedDate":"2017-01-18T10:04:27","indexId":"70148094","displayToPublicDate":"2015-03-24T14:00:00","publicationYear":"2015","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":3250,"text":"Remote Sensing","active":true,"publicationSubtype":{"id":10}},"title":"Downscaling 250-m MODIS growing season NDVI based on multiple-date landsat images and data mining approaches","docAbstract":"The satellite-derived growing season time-integrated Normalized Difference Vegetation Index (GSN) has been used as a proxy for vegetation biomass productivity. The 250-m GSN data estimated from the Moderate Resolution Imaging Spectroradiometer (MODIS) sensors have been used for terrestrial ecosystem modeling and monitoring. High temporal resolution with a wide range of wavelengths make the MODIS land surface products robust and reliable. The long-term 30-m Landsat data provide spatial detailed information for characterizing human-scale processes and have been used for land cover and land change studies. The main goal of this study is to combine 250-m MODIS GSN and 30-m Landsat observations to generate a quality-improved high spatial resolution (30-m) GSN database. A rule-based piecewise regression GSN model based on MODIS and Landsat data was developed. Results show a strong correlation between predicted GSN and actual GSN (r = 0.97, average error = 0.026). The most important Landsat variables in the GSN model are Normalized Difference Vegetation Indices (NDVIs) in May and August. The derived MODIS-Landsat-based 30-m GSN map provides biophysical information for moderate-scale ecological features. This multiple sensor study retains the detailed seasonal dynamic information captured by MODIS and leverages the high-resolution information from Landsat, which will be useful for regional ecosystem studies.
","language":"English","publisher":"Molecular Diversity Preservation International","publisherLocation":"Basel, Switzerland","doi":"10.3390/rs70403489","usgsCitation":"Gu, Y., and Wylie, B.K., 2015, Downscaling 250-m MODIS growing season NDVI based on multiple-date landsat images and data mining approaches: Remote Sensing, v. 7, no. 4, p. 3489-3506, https://doi.org/10.3390/rs70403489.","productDescription":"18 p.","startPage":"3489","endPage":"3506","numberOfPages":"18","onlineOnly":"N","additionalOnlineFiles":"N","ipdsId":"IP-064005","costCenters":[{"id":222,"text":"Earth Resources Observation and Science (EROS) Center","active":true,"usgs":true}],"links":[{"id":472202,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.3390/rs70403489","text":"Publisher Index Page"},{"id":300605,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"volume":"7","issue":"4","publishingServiceCenter":{"id":4,"text":"Rolla PSC"},"noUsgsAuthors":false,"publicationDate":"2015-03-24","publicationStatus":"PW","scienceBaseUri":"555db03ee4b0a92fa7eb82fc","contributors":{"authors":[{"text":"Gu, Yingxin 0000-0002-3544-1856 ygu@usgs.gov","orcid":"https://orcid.org/0000-0002-3544-1856","contributorId":139586,"corporation":false,"usgs":true,"family":"Gu","given":"Yingxin","email":"ygu@usgs.gov","affiliations":[{"id":222,"text":"Earth Resources Observation and Science (EROS) Center","active":true,"usgs":true},{"id":223,"text":"Earth Resources Observation and Science (EROS) Center (Geography)","active":false,"usgs":true}],"preferred":true,"id":547324,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Wylie, Bruce K. 0000-0002-7374-1083 wylie@usgs.gov","orcid":"https://orcid.org/0000-0002-7374-1083","contributorId":750,"corporation":false,"usgs":true,"family":"Wylie","given":"Bruce","email":"wylie@usgs.gov","middleInitial":"K.","affiliations":[{"id":222,"text":"Earth Resources Observation and Science (EROS) Center","active":true,"usgs":true},{"id":223,"text":"Earth Resources Observation and Science (EROS) Center (Geography)","active":false,"usgs":true}],"preferred":true,"id":547325,"contributorType":{"id":1,"text":"Authors"},"rank":2}]}} ,{"id":70138819,"text":"sir20105090X - 2015 - Porphyry copper assessment of the Central Asian Orogenic Belt and eastern Tethysides: China, Mongolia, Russia, Pakistan, Kazakhstan, Tajikistan, and India: Chapter X in Global mineral resource assessment","interactions":[{"subject":{"id":70138819,"text":"sir20105090X - 2015 - Porphyry copper assessment of the Central Asian Orogenic Belt and eastern Tethysides: China, Mongolia, Russia, Pakistan, Kazakhstan, Tajikistan, and India: Chapter X in Global mineral resource assessment","indexId":"sir20105090X","publicationYear":"2015","noYear":false,"chapter":"X","title":"Porphyry copper assessment of the Central Asian Orogenic Belt and eastern Tethysides: China, Mongolia, Russia, Pakistan, Kazakhstan, Tajikistan, and India: Chapter X in Global mineral resource assessment"},"predicate":"IS_PART_OF","object":{"id":70040436,"text":"sir20105090 - 2010 - Global mineral resource assessment","indexId":"sir20105090","publicationYear":"2010","noYear":false,"title":"Global mineral resource assessment"},"id":1}],"isPartOf":{"id":70040436,"text":"sir20105090 - 2010 - Global mineral resource assessment","indexId":"sir20105090","publicationYear":"2010","noYear":false,"title":"Global mineral resource assessment"},"lastModifiedDate":"2018-10-29T11:10:30","indexId":"sir20105090X","displayToPublicDate":"2015-03-24T12:15:00","publicationYear":"2015","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":"2010-5090","chapter":"X","title":"Porphyry copper assessment of the Central Asian Orogenic Belt and eastern Tethysides: China, Mongolia, Russia, Pakistan, Kazakhstan, Tajikistan, and India: Chapter X in Global mineral resource assessment","docAbstract":"The U.S. Geological Survey collaborated with international colleagues to assess undiscovered resources in porphyry copper deposits in the Central Asian Orogenic Belt and eastern Tethysides. These areas host 20 known porphyry copper deposits, including the world class Oyu Tolgoi deposit in Mongolia that was discovered in the late 1990s. The study area covers major parts of the world’s largest orogenic systems. The Central Asian Orogenic Belt is a collage of amalgamated Precambrian through Mesozoic terranes that extends from the Ural Mountains in the west nearly to the Pacific Coast of Asia in the east and records the evolution and final closure of the Paleo-Asian Ocean in Permian time. The eastern Tethysides, the orogenic belt to the south of the Central Asian Orogenic Belt, records the evolution of another ancient ocean system, the Tethys Ocean. The evolution of these orogenic belts involved magmatism associated with a variety of geologic settings appropriate for formation of porphyry copper deposits, including subduction-related island arcs, continental arcs, and collisional and postconvergent settings. The original settings are difficult to trace because the arcs have been complexly deformed and dismembered by younger tectonic events. Twelve mineral resource assessment tracts were delineated to be permissive for the occurrence of porphyry copper deposits based on mapped and inferred subsurface distributions of igneous rocks of specific age ranges and compositions. These include (1) nine Paleozoic tracts in the Central Asian Orogenic Belt, which range in area from about 60,000 to 800,000 square kilometers (km2); (2) a complex area of about 400,000 km2 on the northern margin of the Tethysides, the Qinling-Dabie tract, which spans central China and areas to the west, encompassing Paleozoic through Triassic igneous rocks that formed in diverse settings; and (3) assemblages of late Paleozoic and Mesozoic rocks that define two other tracts in the Tethysides, the 100,000 km2 Jinsajiang tract and the 300,000 km2 Tethyan-Gangdese tract. Assessment participants evaluated applicable grade and tonnage models and estimated numbers of undiscovered deposits at different confidence levels for each permissive tract. The estimates were then combined with the selected grade and tonnage models using Monte Carlo simulations to generate probabilistic estimates of undiscovered resources. Additional resources in extensions of deposits with identified resources were not specifically evaluated. Assessment results, presented in tables and graphs, show amounts of metal and rock in undiscovered deposits at selected quantile levels of probability (0.95, 0.9, 0.5, 0.1, and 0.05 confidence levels), as well as the arithmetic mean and associated standard deviations and variances for each tract. This assessment estimated a total of 97 undiscovered porphyry copper deposits within the assessed permissive tracts. This represents nearly five times the 20 known deposits. Predicted mean resources that could be associated with these undiscovered deposits are about 370,000,000 metric tons (t) of copper, 10,000 t of gold, 7,700,000 t of molybdenum, and 120,000 t of silver. The assessment area is estimated to contain about five times as much copper in undiscovered deposits as has been identified to date. This report includes a summary of the data used in the assessment, a brief overview of the geologic framework of the area, descriptions of permissive tracts and known deposits, maps, and tables. A geographic information system database that accompanies this report includes the tract boundaries and known porphyry copper deposits, significant prospects, and prospects. Assessments of overlapping younger rocks and adjacent areas are included in separate reports available on-line at http://minerals.usgs.gov/global/.
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India: Chapter X in Global mineral resource assessment: U.S. Geological Survey Scientific Investigations Report 2010-5090, Report: xi, 106 p.; 2 Plates: 11.00 x 17.00 inches; Appendix B; GIS package, https://doi.org/10.3133/sir20105090X.","productDescription":"Report: xi, 106 p.; 2 Plates: 11.00 x 17.00 inches; Appendix B; GIS package","numberOfPages":"122","onlineOnly":"Y","additionalOnlineFiles":"Y","ipdsId":"IP-053011","costCenters":[{"id":245,"text":"Eastern Mineral and Environmental Resources Science Center","active":true,"usgs":true},{"id":312,"text":"Geology, Minerals, Energy, and Geophysics Science Center","active":true,"usgs":true}],"links":[{"id":298910,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/sir20105090X.gif"},{"id":298904,"rank":3,"type":{"id":17,"text":"Plate"},"url":"https://pubs.usgs.gov/sir/2010/5090/x/pdf/sir2010-5090-X_Fig_1.pdf","text":"Tabloid Figure 1","size":"15.7 MB","linkFileType":{"id":1,"text":"pdf"},"description":"Map of the study area in central and eastern Asia, showing geographic features mentioned in this report","linkHelpText":"Map of the study area in central and eastern Asia, showing geographic features mentioned in this report"},{"id":298902,"rank":1,"type":{"id":15,"text":"Index Page"},"url":"https://pubs.usgs.gov/sir/2010/5090/x/"},{"id":298908,"rank":6,"type":{"id":23,"text":"Spatial Data"},"url":"https://pubs.usgs.gov/sir/2010/5090/x/SIR2010-5090-X_database.zip","text":"GIS package","size":"13.6 MB","description":"SIR 2010-5090-X Database in ZIP format","linkHelpText":"Contains: geospatial database"},{"id":298905,"rank":4,"type":{"id":17,"text":"Plate"},"url":"https://pubs.usgs.gov/sir/2010/5090/x/pdf/sir2010-5090-X_Fig_E1_p104_105.pdf","text":"Tabloid Figure E1","size":"826 KB","linkFileType":{"id":1,"text":"pdf"},"description":"Correlations among geologic time division duration and symbols as used in Russia (Katalog Mineralov, 2005), China (Ma and others, 2002), and Mongolia (Mineral Resources Authority of Mongolia and others, 1998)","linkHelpText":"[pages 1, 2 of 3] Correlations among geologic time division duration and symbols as used in Russia (Katalog Mineralov, 2005), China (Ma and others, 2002), and Mongolia (Mineral Resources Authority of Mongolia and others, 1998)"},{"id":298903,"rank":2,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/sir/2010/5090/x/pdf/sir2010-5090-X.pdf","text":"Report","size":"48.8 MB","linkFileType":{"id":1,"text":"pdf"},"description":"SIR 2010-5090-X Report"},{"id":298906,"rank":5,"type":{"id":3,"text":"Appendix"},"url":"https://pubs.usgs.gov/sir/2010/5090/x/SIR2010-5090-X_Appendix_B.xlsx","text":"Appendix B","size":"153 KB","linkFileType":{"id":3,"text":"xlsx"},"description":"SIR 2010-5090-X Appendix B","linkHelpText":"Excel Workbook for Deposits, Significant Prospects, and Prospects for the Porphyry Copper Assessment of the Central Asian Orogenic Belt and Eastern Tethysides"}],"projection":"Asia North Albers Equal 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of aeromagnetic data from the Patagonia Mountains area, southeastern Arizona","interactions":[],"lastModifiedDate":"2015-03-24T09:15:44","indexId":"sir20155029","displayToPublicDate":"2015-03-24T10:15:00","publicationYear":"2015","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":"2015-5029","title":"Detailed interpretation of aeromagnetic data from the Patagonia Mountains area, southeastern Arizona","docAbstract":"The induced magnetic field and the remanent magnetic field of rock masses are important to geologic modeling based on Earth’s magnetic field data. The orientation of the induced magnetic field is approximately parallel to the orientation of Earth’s geomagnetic field and its intensity can be derived from measured magnetic susceptibilities of rocks in a study area. The orientation and intensity of the natural remanent magnetic field is much harder to determine; therefore, few investigators have included magnetic remanence as a contributing factor to studies of continental magnetic anomalies. All rocks have remanent magnetism and, in intrusive or volcanic rocks, this component of the total magnetic intensity of the Earth’s magnetic field can be as large as or larger than the induced component.
\nThe Patagonia Mountains in southeastern Arizona were selected to produce a subsurface geologic model from aeromagnetic data by incorporating physical properties of rock including measured magnetic susceptibilities, estimated remanent magnetic field orientations and intensities, a known association of intrusive events, and information from existing geologic mapping. The result is a model of geology at depth that may better represent reality than previous poorly substantiated cross sectional models. This new model includes concealed intrusive rocks and defines areas where concealed mineral deposits may be found. It also shows that volcanic rocks might occupy basins at relatively shallow depths in basins with low aeromagnetic anomalies.
\nEuler deconvolution depth estimates derived from aeromagnetic data with a structural index of 0 show that mapped faults on the northern margin of the Patagonia Mountains generally agree with the depth estimates in the new geologic model. The deconvolution depth estimates also show that the concealed Patagonia Fault southwest of the Patagonia Mountains is more complex than recent geologic mapping represents. Additionally, Euler deconvolution depth estimates with a structural index of 2 locate many potential intrusive bodies that might be associated with known and unknown mineralization.
","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/sir20155029","usgsCitation":"Bultman, M.W., 2015, Detailed interpretation of aeromagnetic data from the Patagonia Mountains area, southeastern Arizona: U.S. Geological Survey Scientific Investigations Report 2015-5029, iv, 25 p., https://doi.org/10.3133/sir20155029.","productDescription":"iv, 25 p.","numberOfPages":"34","onlineOnly":"Y","additionalOnlineFiles":"N","ipdsId":"IP-046013","costCenters":[{"id":312,"text":"Geology, Minerals, Energy, and Geophysics Science Center","active":true,"usgs":true}],"links":[{"id":298881,"rank":1,"type":{"id":15,"text":"Index Page"},"url":"https://pubs.usgs.gov/sir/2015/5029/"},{"id":298886,"rank":2,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/sir/2015/5029/pdf/sir2015-5029.pdf","size":"2.7 MB","linkFileType":{"id":1,"text":"pdf"}},{"id":298888,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/sir20155029.jpg"}],"projection":"United States Continuous Albers Equal Area Conic project USGS version","country":"United States","state":"Arizona","otherGeospatial":"Patagonia Mountains","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -110.82321166992188,\n 31.33252503230784\n ],\n [\n -110.82321166992188,\n 31.577365480690492\n ],\n [\n -110.6378173828125,\n 31.577365480690492\n ],\n [\n -110.6378173828125,\n 31.33252503230784\n ],\n [\n -110.82321166992188,\n 31.33252503230784\n ]\n ]\n ]\n }\n }\n ]\n}","publishingServiceCenter":{"id":14,"text":"Menlo Park PSC"},"noUsgsAuthors":false,"publicationStatus":"PW","scienceBaseUri":"55127ca5e4b02e76d75bd5e3","contributors":{"authors":[{"text":"Bultman, Mark W. 0000-0001-8352-101X mbultman@usgs.gov","orcid":"https://orcid.org/0000-0001-8352-101X","contributorId":3348,"corporation":false,"usgs":true,"family":"Bultman","given":"Mark","email":"mbultman@usgs.gov","middleInitial":"W.","affiliations":[{"id":312,"text":"Geology, Minerals, Energy, and Geophysics Science Center","active":true,"usgs":true}],"preferred":false,"id":543101,"contributorType":{"id":1,"text":"Authors"},"rank":1}]}} ]}