SO2 camera systems are increasingly being used to image volcanic gas plumes. The ability to derive SO2 emission rates directly from the acquired imagery at high time resolution allows volcanic process studies that incorporate other high time-resolution datasets. Though the general principles behind the SO2 camera have remained the same for a number of years, recent advances in CCD technology and an improved understanding of the physics behind the measurements have driven a continuous evolution of the camera systems. Here we present an intercomparison of seven different SO2 cameras. In the first part of the experiment, the various technical designs are compared and the advantages and drawbacks of individual design options are considered. Though the ideal design was found to be dependent on the specific application, a number of general recommendations are made. Next, a time series of images recorded by all instruments at Stromboli Volcano (Italy) is compared. All instruments were easily able to capture SO2 clouds emitted from the summit vents. Quantitative comparison of the SO2 load in an individual cloud yielded an intra-instrument precision of about 12%. From the imagery, emission rates were then derived according to each group's standard retrieval process. A daily average SO2 emission rate of 61 ± 10 t/d was calculated. Due to differences in spatial integration methods and plume velocity determination, the time-dependent progression of SO2 emissions varied significantly among the individual systems. However, integration over distinct degassing events yielded comparable SO2 masses. Based on the intercomparison data, we find an approximate 1-sigma precision of 20% for the emission rates derived from the various SO2 cameras. Though it may still be improved in the future, this is currently within the typical accuracy of the measurement and is considered sufficient for most applications.
","language":"English","publisher":"Elsevier Science","publisherLocation":"Amsterdam, Netherlands","doi":"10.1016/j.jvolgeores.2014.08.026","usgsCitation":"Kern, C., Lübcke, P., Bobrowski, N., Campion, R., Mori, T., Smekens, J., Stebel, K., Tamburello, G., Burton, M., Platt, U., and Prata, F., 2015, Intercomparison of SO2 camera systems for imaging volcanic gas plumes: Journal of Volcanology and Geothermal Research, v. 300, p. 22-36, https://doi.org/10.1016/j.jvolgeores.2014.08.026.","productDescription":"15 p.","startPage":"22","endPage":"36","onlineOnly":"N","additionalOnlineFiles":"N","ipdsId":"IP-056592","costCenters":[{"id":617,"text":"Volcano Science Center","active":true,"usgs":true}],"links":[{"id":309083,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"volume":"300","publishingServiceCenter":{"id":14,"text":"Menlo Park PSC"},"noUsgsAuthors":false,"publicationStatus":"PW","scienceBaseUri":"560bb6bee4b058f706e53d09","contributors":{"authors":[{"text":"Kern, Christoph 0000-0002-8920-5701 ckern@usgs.gov","orcid":"https://orcid.org/0000-0002-8920-5701","contributorId":3387,"corporation":false,"usgs":true,"family":"Kern","given":"Christoph","email":"ckern@usgs.gov","affiliations":[{"id":617,"text":"Volcano Science Center","active":true,"usgs":true},{"id":114,"text":"Alaska Science Center","active":true,"usgs":true}],"preferred":true,"id":573759,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Lübcke, Peter","contributorId":82202,"corporation":false,"usgs":true,"family":"Lübcke","given":"Peter","affiliations":[],"preferred":false,"id":573760,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Bobrowski, Nicole","contributorId":45214,"corporation":false,"usgs":true,"family":"Bobrowski","given":"Nicole","email":"","affiliations":[],"preferred":false,"id":573761,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Campion, Robin","contributorId":148070,"corporation":false,"usgs":false,"family":"Campion","given":"Robin","email":"","affiliations":[{"id":16993,"text":"Instituto de Geofisica, Universidad Nacional Autónoma de México","active":true,"usgs":false}],"preferred":false,"id":573762,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Mori, Toshiya","contributorId":148071,"corporation":false,"usgs":false,"family":"Mori","given":"Toshiya","email":"","affiliations":[{"id":16994,"text":"School of Science, The University of Tokyo","active":true,"usgs":false}],"preferred":false,"id":573763,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"Smekens, Jean-Francois","contributorId":148072,"corporation":false,"usgs":false,"family":"Smekens","given":"Jean-Francois","email":"","affiliations":[{"id":16995,"text":"School of Earth and Space Exploration, Arizona State University","active":true,"usgs":false}],"preferred":false,"id":573764,"contributorType":{"id":1,"text":"Authors"},"rank":6},{"text":"Stebel, Kerstin","contributorId":148073,"corporation":false,"usgs":false,"family":"Stebel","given":"Kerstin","email":"","affiliations":[{"id":16996,"text":"Climate and Atmosphere Department, Norwegian Institute for Air Research","active":true,"usgs":false}],"preferred":false,"id":573765,"contributorType":{"id":1,"text":"Authors"},"rank":7},{"text":"Tamburello, Giancarlo","contributorId":148074,"corporation":false,"usgs":false,"family":"Tamburello","given":"Giancarlo","email":"","affiliations":[{"id":16997,"text":"Dipartimento di Scienze della Terra e del Mare, University of Palermo","active":true,"usgs":false}],"preferred":false,"id":573766,"contributorType":{"id":1,"text":"Authors"},"rank":8},{"text":"Burton, Michael","contributorId":148069,"corporation":false,"usgs":false,"family":"Burton","given":"Michael","email":"","affiliations":[{"id":37573,"text":"University of Manchester, UK","active":true,"usgs":false},{"id":16992,"text":"Istituto Nazionale di Geofisica e Vulcanologia Pisa","active":true,"usgs":false}],"preferred":false,"id":573767,"contributorType":{"id":1,"text":"Authors"},"rank":9},{"text":"Platt, Ulrich","contributorId":26609,"corporation":false,"usgs":true,"family":"Platt","given":"Ulrich","affiliations":[],"preferred":false,"id":573768,"contributorType":{"id":1,"text":"Authors"},"rank":10},{"text":"Prata, Fred","contributorId":148068,"corporation":false,"usgs":false,"family":"Prata","given":"Fred","email":"","affiliations":[{"id":16991,"text":"Norwegian Institute for Air Research","active":true,"usgs":false}],"preferred":false,"id":573769,"contributorType":{"id":1,"text":"Authors"},"rank":11}]}} ,{"id":70157599,"text":"70157599 - 2015 - An automated SO2 camera system for continuous, real-time monitoring of gas emissions from Kīlauea Volcano's summit Overlook Crater","interactions":[],"lastModifiedDate":"2020-10-01T19:40:32.197367","indexId":"70157599","displayToPublicDate":"2015-07-15T13:15:00","publicationYear":"2015","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":2499,"text":"Journal of Volcanology and Geothermal Research","active":true,"publicationSubtype":{"id":10}},"displayTitle":"An automated SO2 camera system for continuous, real-time monitoring of gas emissions from Kīlauea Volcano's summit Overlook Crater","title":"An automated SO2 camera system for continuous, real-time monitoring of gas emissions from Kīlauea Volcano's summit Overlook Crater","docAbstract":"SO2 camera systems allow rapid two-dimensional imaging of sulfur dioxide (SO2) emitted from volcanic vents. Here, we describe the development of an SO2 camera system specifically designed for semi-permanent field installation and continuous use. The integration of innovative but largely “off-the-shelf” components allowed us to assemble a robust and highly customizable instrument capable of continuous, long-term deployment at Kīlauea Volcano's summit Overlook Crater. Recorded imagery is telemetered to the USGS Hawaiian Volcano Observatory (HVO) where a novel automatic retrieval algorithm derives SO2 column densities and emission rates in real-time. Imagery and corresponding emission rates displayed in the HVO operations center and on the internal observatory website provide HVO staff with useful information for assessing the volcano's current activity. The ever-growing archive of continuous imagery and high-resolution emission rates in combination with continuous data from other monitoring techniques provides insight into shallow volcanic processes occurring at the Overlook Crater. An exemplary dataset from September 2013 is discussed in which a variation in the efficiency of shallow circulation and convection, the processes that transport volatile-rich magma to the surface of the summit lava lake, appears to have caused two distinctly different phases of lake activity and degassing. This first successful deployment of an SO2 camera for continuous, real-time volcano monitoring shows how this versatile technique might soon be adapted and applied to monitor SO2 degassing at other volcanoes around the world.
","language":"English","publisher":"Elsevier","doi":"10.1016/j.jvolgeores.2014.12.004","usgsCitation":"Kern, C., Sutton, J., Elias, T., Lee, R., Kamibayashi, K.P., Antolik, L., and Werner, C.A., 2015, An automated SO2 camera system for continuous, real-time monitoring of gas emissions from Kīlauea Volcano's summit Overlook Crater: Journal of Volcanology and Geothermal Research, v. 300, p. 81-94, https://doi.org/10.1016/j.jvolgeores.2014.12.004.","productDescription":"14 p.","startPage":"81","endPage":"94","onlineOnly":"N","additionalOnlineFiles":"N","ipdsId":"IP-056630","costCenters":[{"id":336,"text":"Hawaiian Volcano Observatory","active":false,"usgs":true},{"id":617,"text":"Volcano Science Center","active":true,"usgs":true}],"links":[{"id":309082,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","state":"Hawai'i","otherGeospatial":"Kīlauea Volcano","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n[\n [\n -155.4338836669922,\n 19.25605301966428\n ],\n [\n -155.4338836669922,\n 19.47500813674323\n ],\n [\n -155.11940002441406,\n 19.47500813674323\n ],\n [\n -155.11940002441406,\n 19.25605301966428\n ],\n [\n -155.4338836669922,\n 19.25605301966428\n ]\n ]\n ]\n }\n }\n ]\n}","volume":"300","publishingServiceCenter":{"id":14,"text":"Menlo Park PSC"},"noUsgsAuthors":false,"publicationStatus":"PW","scienceBaseUri":"560bb634e4b058f706e53b19","contributors":{"authors":[{"text":"Kern, Christoph 0000-0002-8920-5701 ckern@usgs.gov","orcid":"https://orcid.org/0000-0002-8920-5701","contributorId":3387,"corporation":false,"usgs":true,"family":"Kern","given":"Christoph","email":"ckern@usgs.gov","affiliations":[{"id":114,"text":"Alaska Science Center","active":true,"usgs":true},{"id":617,"text":"Volcano Science Center","active":true,"usgs":true}],"preferred":true,"id":573742,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Sutton, Jeff","contributorId":51287,"corporation":false,"usgs":true,"family":"Sutton","given":"Jeff","email":"","affiliations":[{"id":336,"text":"Hawaiian Volcano Observatory","active":false,"usgs":true}],"preferred":false,"id":573743,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Elias, Tamar 0000-0002-9592-4518 telias@usgs.gov","orcid":"https://orcid.org/0000-0002-9592-4518","contributorId":3916,"corporation":false,"usgs":true,"family":"Elias","given":"Tamar","email":"telias@usgs.gov","affiliations":[{"id":617,"text":"Volcano Science Center","active":true,"usgs":true}],"preferred":true,"id":573744,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Lee, Robert Lopaka rclee@usgs.gov","contributorId":1984,"corporation":false,"usgs":true,"family":"Lee","given":"Robert Lopaka","email":"rclee@usgs.gov","affiliations":[{"id":617,"text":"Volcano Science Center","active":true,"usgs":true}],"preferred":false,"id":573745,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Kamibayashi, Kevan P. kevank@usgs.gov","contributorId":5184,"corporation":false,"usgs":true,"family":"Kamibayashi","given":"Kevan","email":"kevank@usgs.gov","middleInitial":"P.","affiliations":[{"id":617,"text":"Volcano Science Center","active":true,"usgs":true}],"preferred":true,"id":573746,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"Antolik, Loren lantolik@usgs.gov","contributorId":4144,"corporation":false,"usgs":true,"family":"Antolik","given":"Loren","email":"lantolik@usgs.gov","affiliations":[{"id":617,"text":"Volcano Science Center","active":true,"usgs":true}],"preferred":true,"id":573747,"contributorType":{"id":1,"text":"Authors"},"rank":6},{"text":"Werner, Cynthia A. cwerner@usgs.gov","contributorId":2540,"corporation":false,"usgs":true,"family":"Werner","given":"Cynthia","email":"cwerner@usgs.gov","middleInitial":"A.","affiliations":[{"id":617,"text":"Volcano Science Center","active":true,"usgs":true},{"id":114,"text":"Alaska Science Center","active":true,"usgs":true}],"preferred":true,"id":573748,"contributorType":{"id":1,"text":"Authors"},"rank":7}]}} ,{"id":70148009,"text":"ofr20151095 - 2015 - Design and methods of the Southeast Stream Quality Assessment (SESQA), 2014","interactions":[],"lastModifiedDate":"2019-04-11T15:33:59","indexId":"ofr20151095","displayToPublicDate":"2015-07-15T10:45:00","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":"2015-1095","title":"Design and methods of the Southeast Stream Quality Assessment (SESQA), 2014","docAbstract":"During 2014, the U.S. Geological Survey (USGS) National Water-Quality Assessment Program (NAWQA) assessed stream quality across the Piedmont and southern Appalachian Mountain regions of the southeastern United States. This Southeast Stream Quality Assessment (SESQA) simultaneously characterized watershed and stream-reach water-quality stressors along with instream biological conditions, in order to better understand regional stressor-effects relations. The goal of SESQA is to provide communities and policymakers with information about those human and environmental factors that have the greatest impact on stream quality across the region. The SESQA design focused on hydrologic alteration and urbanization because of their importance as ecological stressors of particular concern to Southeast region resource managers.
\nStreamflow and land-use data were used to identify and select sites representing gradients in urbanization and streamflow alteration across the region. One hundred fifteen sites were selected and sampled for as many as 10 weeks during April, May, and June 2014 for contaminants, nutrients, and sediment. This water-quality “index” period culminated with an ecological survey of habitat, periphyton, benthic macroinvertebrates, and fish at all sites. Sediment was collected during the ecological survey for analysis of sediment chemistry and toxicity testing. Of the 115 sites, 59 were on streams in watersheds with varying degrees of urban land use, 5 were on streams with multiple confined animal feeding operations, and 13 were reference sites with little or no development in their watersheds. The remaining 38 “hydro” sites were on streams in watersheds with relatively little agricultural or urban development but with hydrologic alteration, such as a dam or reservoir.
\nThis report provides a detailed description of the SESQA study components, including surveys of ecological conditions, routine water sampling, deployment of passive polar organic compound integrative samplers for pesticides and contaminants of emerging concern, and synoptic sediment sampling and toxicity testing at all urban, confined animal feeding operation, and reference sites. Continuous water-quality monitoring and daily pesticide sampling efforts conducted at a subset of urban sites are also described.
","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/ofr20151095","collaboration":"Prepared in cooperation with the National Water-Quality Assessment Program","usgsCitation":"Journey, C.A., Van Metre, P.C., Bell, A.H., Garrett, J.D., Button, D.T., Nakagaki, N., Qi, S.L., and Bradley, P.M., 2015, Design and methods of the Southeast Stream Quality Assessment (SESQA), 2014: U.S. Geological Survey Open-File Report 2015–1095, 46 p., https://dx.doi.org/10.3133/ofr20151095.","productDescription":"Report: vii, 46 p.; 3 Appendices","numberOfPages":"58","onlineOnly":"Y","additionalOnlineFiles":"Y","ipdsId":"IP-063365","costCenters":[{"id":13634,"text":"South Atlantic Water Science Center","active":true,"usgs":true}],"links":[{"id":305724,"rank":4,"type":{"id":3,"text":"Appendix"},"url":"https://pubs.usgs.gov/of/2015/1095/ofr20151095_appendix2.xlsx","text":"Appendix 2","size":"89 KB","linkFileType":{"id":3,"text":"xlsx"},"description":"Description of the U.S. Geological Survey National Water Quality Laboratory Schedules Used for Water, Sediment, and Periphyton"},{"id":305722,"rank":2,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/of/2015/1095/ofr20151095.pdf","text":"Report","size":"3.8 MB","linkFileType":{"id":1,"text":"pdf"},"description":"OFR 2015-1095"},{"id":305723,"rank":3,"type":{"id":3,"text":"Appendix"},"url":"https://pubs.usgs.gov/of/2015/1095/ofr20151095_appendix1.xlsx","text":"Appendix 1","size":"560 KB","linkFileType":{"id":3,"text":"xlsx"},"description":"Description of the Sampling Timelines, Matrix, Collection, and Processing for Water, Sediment, and Ecological Samples"},{"id":305725,"rank":5,"type":{"id":3,"text":"Appendix"},"url":"https://pubs.usgs.gov/of/2015/1095/ofr20151095_appendix3.xlsx","text":"Appendix 3","size":"50 KB","linkFileType":{"id":3,"text":"xlsx"},"description":"Counts of Environmental (Environ), Field Blank, Replicate (Rep), and Spike Samples of Streamwater by Site and Laboratory Analysis From the 115 Stream Sites Sampled in the U.S. Geological Survey (USGS) Southeastern Stream Quality Assessment (SESQA) Study in 2014"},{"id":305721,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/of/2015/1095/coverthb.jpg"}],"country":"United States","state":"Alabama, Georgia, North Carolina, South Carolina, Tennessee, Virginia","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -77.51953125,\n 39.13006024213511\n ],\n [\n -77.89306640625,\n 38.788345355085625\n ],\n [\n -77.1240234375,\n 38.976492485539424\n ],\n [\n -76.79443359375,\n 38.788345355085625\n ],\n [\n -77.431640625,\n 38.22091976683121\n ],\n [\n -77.27783203125,\n 37.85750715625203\n ],\n [\n -77.05810546875,\n 37.23032838760387\n ],\n [\n -77.34374999999999,\n 36.54494944148322\n ],\n [\n -77.58544921874999,\n 36.049098959065645\n ],\n [\n -78.64013671875,\n 35.35321610123821\n ],\n [\n -79.40917968749999,\n 35.22767235493586\n ],\n [\n -79.89257812499999,\n 34.77771580360469\n ],\n [\n -80.2001953125,\n 34.43409789359469\n ],\n [\n -80.7275390625,\n 34.08906131584996\n ],\n [\n -81.27685546875,\n 33.50475906922606\n ],\n [\n -81.80419921875,\n 33.211116472416855\n ],\n [\n -83.1884765625,\n 32.80574473290688\n ],\n [\n -84.66064453125,\n 32.379961464357315\n ],\n [\n -85.078125,\n 32.287132632616355\n ],\n [\n -86.572265625,\n 32.24997445586331\n ],\n [\n -87.3193359375,\n 32.602361666817515\n ],\n [\n -86.90185546874999,\n 33.358061612778876\n ],\n [\n -86.2646484375,\n 34.14363482031264\n ],\n [\n -85.58349609375,\n 34.92197103616377\n ],\n [\n -83.82568359375,\n 36.58024660149866\n ],\n [\n -81.4306640625,\n 37.35269280367274\n ],\n [\n -80.39794921875,\n 37.77071473849609\n ],\n [\n -79.7607421875,\n 38.65119833229951\n ],\n [\n -79.38720703125,\n 38.685509760012\n ],\n [\n -78.7060546875,\n 39.21523130910493\n ],\n [\n -78.3544921875,\n 39.53793974517628\n ],\n [\n -77.51953125,\n 39.13006024213511\n ]\n ]\n ]\n }\n }\n ]\n}","contact":"Director, South Atlantic Water Science Center
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720 Gracern Road, Suite 129
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http://www.usgs.gov/water/southatlantic/
","tableOfContents":"
We evaluated predation on Lost River suckers (Deltistes luxatus) and shortnose suckers (Chasmistes brevirostris), both listed under the Endangered Species Act (ESA), from American white pelicans (Pelecanus erythrorhynchos) and double-crested cormorants (Phalacrocorax auritus) nesting at mixed species colonies on Clear Lake Reservoir, CA and Upper Klamath Lake, OR during 2009-2014. Predation was evaluated by recovering passive integrated transponder (PIT) tags that were implanted in suckers, subsequently consumed by pelicans or cormorants, and deposited on the birds’ nesting colonies. Data from PIT tag recoveries were used to estimate predation rates (proportion of available tagged suckers consumed) by birds to evaluate the relative susceptibility of suckers to avian predation in Upper Klamath Basin. Data on the size of pelican and cormorant colonies (number of breeding adults) at Clear Lake and Upper Klamath Lake were also collected and reported in the context of predation on suckers.
\nResults indicate that predation rates varied by sucker species (Lost River, shortnose), sucker age-class (adult, juvenile), bird colony location (Upper Klamath Lake, Clear Lake), and year (2009-2014), demonstrating that predator-prey interactions in the system were dynamic during the study period. Tagged suckers ranging from 72 mm to 730 mm were susceptible to cormorant or pelican predation; all but the largest of the tagged Lost River suckers were susceptible to avian predation. Estimates of minimum, annual predation rates ranged from <0.1% to 4.6% of the available Lost River suckers and from <0.1% to 4.2% of the available shortnose suckers during the study period. Of the two colony locations evaluated, predation rates on suckers in Clear Lake were generally higher by birds nesting at mixed-species colonies on Clear Lake. Birds nesting on Clear Lake also commuted over 75 kilometers to forage on suckers in Upper Klamath Lake. Conversely, there was no evidence that birds nesting in Upper Klamath Lake foraged on tagged suckers in Clear Lake. Although sample sizes of tagged juvenile suckers were small and limited to fish tagged in Upper Klamath Lake, there was evidence that bird predation on juvenile suckers was higher than on adult suckers, with annual predate rate estimates on juvenile suckers ranging from 5.7% to 8.4% of available fish.
\nThe minimum annual predation rates presented here suggests that avian predation may be a factor limiting recovery of populations of Lost River and shortnose suckers, particularly juvenile suckers in Upper Klamath Lake and adult suckers in Clear Lake. Additional research is needed, however, to better assess the impacts of avian predation on sucker populations by (1) recovering PIT tags in a manner so that the species of avian predator is known (i.e., pelican vs. cormorant), (2) measuring predator-specific PIT tag deposition probabilities at each colony, (3) increasing the sample of juvenile suckers in the population that are PIT-tagged, and (4) recovering sufficient sample sizes of PIT tags on bird colonies to describe how various biotic and abiotic factors (e.g., fish size and condition, water levels and quality, and other factors) contribute to sucker susceptibility to avian predation in the Upper Klamath Basin.
\n","language":"English","publisher":"Oregon Cooperative Fish and Wildlife Research Unit","collaboration":"Real Time Research, Inc.","usgsCitation":"Evans, A., Payton, Q., Cramer, B., Collis, K., Hewitt, D.A., and Roby, D.D., 2015, Colonial waterbird predation on Lost River and shortnose suckers based on recoveries of passive integrated transponder tags, 22 p.","productDescription":"22 p.","onlineOnly":"N","additionalOnlineFiles":"N","ipdsId":"IP-067047","costCenters":[{"id":654,"text":"Western Fisheries Research Center","active":true,"usgs":true}],"links":[{"id":320548,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","state":"California, Oregon","otherGeospatial":"Clear Lake Resevoir, Upper Klamath Lake","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -122.03613281249999,\n 42.54498667313236\n ],\n [\n -121.9757080078125,\n 42.52272381854158\n ],\n [\n -121.8988037109375,\n 42.48830197960227\n ],\n [\n -121.84661865234374,\n 42.45791402988027\n ],\n [\n -121.7889404296875,\n 42.409262623071186\n ],\n [\n -121.78619384765624,\n 42.334184385939416\n ],\n [\n -121.80267333984376,\n 42.29559582449642\n ],\n [\n -121.85760498046875,\n 42.27730877423709\n ],\n [\n -121.90979003906249,\n 42.279340930853145\n ],\n [\n -121.9647216796875,\n 42.31590854308647\n ],\n [\n -121.9976806640625,\n 42.35448465106744\n ],\n [\n -122.05810546875,\n 42.39506551565123\n ],\n [\n -122.1185302734375,\n 42.437647200108685\n ],\n [\n -122.14599609375001,\n 42.494377798972465\n ],\n [\n -122.14874267578125,\n 42.53486817758702\n ],\n [\n -122.10205078125,\n 42.553080288955826\n ],\n [\n -122.03613281249999,\n 42.54498667313236\n ]\n ]\n ]\n }\n },\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -121.10504150390625,\n 41.90329915269292\n ],\n [\n -121.06933593749999,\n 41.88898809959183\n ],\n [\n -121.058349609375,\n 41.852173524388824\n ],\n [\n -121.08856201171875,\n 41.820455096140314\n ],\n [\n -121.14486694335936,\n 41.81738473661009\n ],\n [\n -121.16546630859375,\n 41.832735062152615\n ],\n [\n -121.19018554687499,\n 41.85728792769137\n ],\n [\n -121.17507934570311,\n 41.887965758804484\n ],\n [\n -121.14486694335936,\n 41.902277040963696\n ],\n [\n -121.10504150390625,\n 41.90329915269292\n ]\n ]\n ]\n }\n }\n ]\n}","publishingServiceCenter":{"id":12,"text":"Tacoma PSC"},"noUsgsAuthors":false,"publicationStatus":"PW","scienceBaseUri":"5720912fe4b071321fe655f6","contributors":{"authors":[{"text":"Evans, Allen","contributorId":149989,"corporation":false,"usgs":false,"family":"Evans","given":"Allen","affiliations":[{"id":17879,"text":"Real Time Research, Inc., 231 SW Scalehouse Loop, Suite 101, Bend, OR 97702","active":true,"usgs":false}],"preferred":false,"id":580260,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Payton, Quinn","contributorId":149990,"corporation":false,"usgs":false,"family":"Payton","given":"Quinn","email":"","affiliations":[{"id":17879,"text":"Real Time Research, Inc., 231 SW Scalehouse Loop, Suite 101, Bend, OR 97702","active":true,"usgs":false}],"preferred":false,"id":580261,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Cramer, Bradley D.","contributorId":51562,"corporation":false,"usgs":true,"family":"Cramer","given":"Bradley D.","affiliations":[],"preferred":false,"id":580262,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Collis, Ken","contributorId":149991,"corporation":false,"usgs":false,"family":"Collis","given":"Ken","email":"","affiliations":[{"id":17879,"text":"Real Time Research, Inc., 231 SW Scalehouse Loop, Suite 101, Bend, OR 97702","active":true,"usgs":false}],"preferred":false,"id":580263,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Hewitt, David A. 0000-0002-5387-0275 dhewitt@usgs.gov","orcid":"https://orcid.org/0000-0002-5387-0275","contributorId":3767,"corporation":false,"usgs":false,"family":"Hewitt","given":"David","email":"dhewitt@usgs.gov","middleInitial":"A.","affiliations":[{"id":654,"text":"Western Fisheries Research Center","active":true,"usgs":true}],"preferred":true,"id":580259,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"Roby, Daniel D. 0000-0001-9844-0992 droby@usgs.gov","orcid":"https://orcid.org/0000-0001-9844-0992","contributorId":3702,"corporation":false,"usgs":true,"family":"Roby","given":"Daniel","email":"droby@usgs.gov","middleInitial":"D.","affiliations":[{"id":200,"text":"Coop Res Unit Seattle","active":true,"usgs":true}],"preferred":true,"id":580264,"contributorType":{"id":1,"text":"Authors"},"rank":6}]}} ,{"id":70155001,"text":"ofr20151127 - 2015 - Hydrologic conditions in Rhode Island during water year 2014","interactions":[],"lastModifiedDate":"2015-07-15T15:18:33","indexId":"ofr20151127","displayToPublicDate":"2015-07-15T01:30:00","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":"2015-1127","title":"Hydrologic conditions in Rhode Island during water year 2014","docAbstract":"
Hydrologic data and conditions throughout Rhode Island during water year 2014 are presented in this report. Stream discharge and groundwater level conditions varied geographically across the State. Ten streamgages reached record-low minimum monthly mean discharges during the year, and a record-high maximum groundwater level was observed at one groundwater well.
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Island\",\"nation\":\"USA \"}}]}","contact":"Director, New England Water Science Center
U.S. Geological Survey
10 Bearfoot Road
Northborough, MA 01532
http://ma.water.usgs.gov
http://ri.water.usgs.gov
Wave heights, periods, and directions were forecast for 2081–2100 using output from four coupled atmosphere–ocean global climate models for representative concentration pathway scenarios RCP4.5 and RCP8.5. Global climate model wind fields were used to drive the global WAVEWATCH-III wave model to generate hourly time-series of bulk wave parameters for 25 islands in the mid to western tropical Pacific. December–February 95th percentile extreme significant wave heights under both climate scenarios decreased by 2100 compared to 1976–2010 historical values. Trends under both scenarios were similar, with the higher-emission RCP8.5 scenario displaying a greater decrease in extreme significant wave heights than where emissions are reduced in the RCP4.5 scenario. Central equatorial Pacific Islands displayed the greatest departure from historical values; significant wave heights decreased there by as much as 0.32 m during December–February and associated wave directions rotated approximately 30° clockwise during June–August compared to hindcast data.
","conferenceTitle":"Coastal Sediments","conferenceDate":"May 11-15, 2015","conferenceLocation":"San Diego, CA","language":"English","usgsCitation":"Shope, J., Storlazzi, C.D., Erikson, L., and Hegermiller, C., 2015, Modeled changes in extreme wave climates of the tropical Pacific over the 21st century: Implications for U.S. and U.S.-Affiliated atoll islands, Coastal Sediments, San Diego, CA, May 11-15, 2015, 13 p.","productDescription":"13 p.","ipdsId":"IP-063074","costCenters":[{"id":520,"text":"Pacific Coastal and Marine Science Center","active":true,"usgs":true}],"links":[{"id":341825,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","otherGeospatial":"Pacific atoll islands","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n 114.60937499999999,\n 2.1088986592431382\n ],\n [\n 154.68749999999997,\n 2.1088986592431382\n ],\n [\n 154.68749999999997,\n 40.713955826286046\n ],\n [\n 114.60937499999999,\n 40.713955826286046\n ],\n [\n 114.60937499999999,\n 2.1088986592431382\n ]\n ]\n ]\n }\n }\n ]\n}","publishingServiceCenter":{"id":14,"text":"Menlo Park PSC"},"noUsgsAuthors":false,"publicationStatus":"PW","scienceBaseUri":"592e84bbe4b092b266f10d45","contributors":{"authors":[{"text":"Shope, J.B.","contributorId":145942,"corporation":false,"usgs":false,"family":"Shope","given":"J.B.","email":"","affiliations":[{"id":10653,"text":"University of California at Santa Cruz, Earth and Planetary Science Department","active":true,"usgs":false}],"preferred":false,"id":565716,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Storlazzi, Curt D. 0000-0001-8057-4490 cstorlazzi@usgs.gov","orcid":"https://orcid.org/0000-0001-8057-4490","contributorId":140584,"corporation":false,"usgs":true,"family":"Storlazzi","given":"Curt","email":"cstorlazzi@usgs.gov","middleInitial":"D.","affiliations":[{"id":520,"text":"Pacific Coastal and Marine Science Center","active":true,"usgs":true},{"id":186,"text":"Coastal and Marine Geology Program","active":true,"usgs":true}],"preferred":true,"id":565715,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Erikson, Li H. lerikson@usgs.gov","contributorId":145944,"corporation":false,"usgs":true,"family":"Erikson","given":"Li H.","email":"lerikson@usgs.gov","affiliations":[{"id":520,"text":"Pacific Coastal and Marine Science Center","active":true,"usgs":true}],"preferred":false,"id":565718,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Hegermiller, C.A.","contributorId":145943,"corporation":false,"usgs":false,"family":"Hegermiller","given":"C.A.","email":"","affiliations":[{"id":10653,"text":"University of California at Santa Cruz, Earth and Planetary Science Department","active":true,"usgs":false}],"preferred":false,"id":565717,"contributorType":{"id":1,"text":"Authors"},"rank":4}]}} ,{"id":70155017,"text":"ofr20151131 - 2015 - Archiving California’s historical duck nesting data","interactions":[],"lastModifiedDate":"2017-07-01T17:16:02","indexId":"ofr20151131","displayToPublicDate":"2015-07-14T19:30:00","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":"2015-1131","title":"Archiving California’s historical duck nesting data","docAbstract":"The U.S. Geological Survey (USGS), in partnership with the California Waterfowl Association (CWA) and other organizations, have compiled large datasets on the nesting ecology and management of dabbling ducks and associated upland nesting birds (Northern Harriers [Circus cyaneus], Short-eared Owls [Asio flammeus], Ring-necked Pheasants [Phasianus colchicus], and American Bitterns [Botaurus lentiginosus]) throughout California on Federal Refuges, State Wildlife Areas, and private lands, some participating in State and Federal habitat programs. These datasets encompass several long-term monitoring programs at multiple sites throughout California, and include data from more than 26,000 nests and span nearly 30 years.
\nThese historical datasets represent some of the longest term datasets on nesting ducks in North America, if not the world. They are extremely valuable for ongoing waterfowl management and habitat conservation efforts in California, as well as throughout the world. However, without organization and electronic access, these data are an untapped resource and are not being used to the full extent possible. Prior to this project, these datasets were scattered among various agencies and organizations, and original paper nest cards were being stored in cardboard boxes in attics and storage containers that were not suitable for long-term archival storage. In addition, most of these data had not been entered into a computerized database and thus were at high risk for permanent data loss.
\nTo protect this irreplaceable dataset, we submitted a series of proposals to obtain funds to complete this data archival project over the past 5 years. The Central Valley Joint Venture, USGS Data Rescue Program, and USGS Ecosystems Mission Area funded this data archival project. In addition, we leveraged other USGS projects on nesting shorebirds, songbirds, and seabirds to use further resources to more fully develop the nest database structure for use on nesting waterfowl. Specifically, this large dataset on ducks was archived by USGS, but the dataset is owned and managed by a consortium of organizations. Therefore, any access and use of this data must occur through the principal investigators, who contributed data and resources to this archival project, as detailed in section, “Data Availability.”
\nWith the conclusion of this project, most duck nest data have been entered, but all nest-captured hen data and other breeding waterfowl data that were outside the scope of this project have still not been entered and electronically archived. Maintaining an up-to-date archive will require additional resources to archive and enter the new duck nest data each year in an iterative process. Further, data proofing should be conducted whenever possible, and also should be considered an iterative process as there was sometimes missing data that could not be filled in without more direct knowledge of specific projects. Despite these disclaimers, this duck data archive represents a massive and useful dataset to inform future research and management questions.
","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/ofr20151131","collaboration":"Prepared in cooperation with the California Waterfowl Association, University of California-Davis, and U.S. Fish and Wildlife Service","usgsCitation":"Ackerman, J.T., Herzog, M.P., Brady, C., Eadie, J.M., and Yarris, G.S., 2015, Archiving California’s historical duck nesting data: U.S. Geological Survey Open-File Report 2015-1131, 26 p., https://dx.doi.org/10.3133/ofr20151131.","productDescription":"vi, 26 p.; Appendix","numberOfPages":"36","onlineOnly":"Y","additionalOnlineFiles":"Y","ipdsId":"IP-066560","costCenters":[{"id":651,"text":"Western Ecological Research Center","active":true,"usgs":true}],"links":[{"id":305718,"rank":1,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/of/2015/1131/ofr20151131.pdf","text":"Report","size":"1.5 MB","linkFileType":{"id":1,"text":"pdf"},"description":"OFR 2015-1131 Report"},{"id":305719,"rank":2,"type":{"id":3,"text":"Appendix"},"url":"https://pubs.usgs.gov/of/2015/1131/ofr20151131_appendixa.xlsx","text":"Appendix A","size":"220 KB","linkFileType":{"id":3,"text":"xlsx"},"description":"OFR 2015-1131 Appendix A"},{"id":305720,"rank":3,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/of/2015/1131/coverthb.jpg"}],"country":"United 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\"}}]}","contact":"Director, Western Ecological Research Center
U.S. Geological Survey
3020 State University Drive East
Sacramento, California 95819
http://werc.usgs.gov/
As part of the U.S. Geological Survey’s Groundwater Resources Program study of the Appalachian Plateaus aquifers, annual and average estimates of water-budget components based on hydrograph separation and precipitation data from parameter-elevation regressions on independent slopes model (PRISM) were determined at 849 continuous-record streamflow-gaging stations from Mississippi to New York and covered the period of 1900 to 2011. Only complete calendar years (January to December) of streamflow record at each gage were used to determine estimates of base flow, which is that part of streamflow attributed to groundwater discharge; such estimates can serve as a proxy for annual recharge. For each year, estimates of annual base flow, runoff, and base-flow index were determined using computer programs—PART, HYSEP, and BFI—that have automated the separation procedures. These streamflow-hydrograph analysis methods are provided with version 1.0 of the U.S. Geological Survey Groundwater Toolbox, which is a new program that provides graphing, mapping, and analysis capabilities in a Windows environment. Annual values of precipitation were estimated by calculating the average of cell values intercepted by basin boundaries where previously defined in the GAGES–II dataset. Estimates of annual evapotranspiration were then calculated from the difference between precipitation and streamflow.
","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/ds944","collaboration":"Groundwater Resources Program","usgsCitation":"Nelms, D.L., Messinger, Terence, and McCoy, K.J., 2015, Annual and average estimates of water-budget components based on hydrograph separation and PRISM precipitation for gaged basins in the Appalachian Plateaus Region, 1900–2011: U.S. Geological Survey Data Series 944, 10 p., https://dx.doi.org/10.3133/ds944.","productDescription":"Report: iv, 10 p.; 3 Appendices; Database; Metadata","numberOfPages":"18","onlineOnly":"Y","additionalOnlineFiles":"Y","ipdsId":"IP-060622","costCenters":[{"id":614,"text":"Virginia Water Science 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The North Pacific Pelagic Seabird Database (NPPSD) was created in 2005 to consolidate data on the oceanic distribution of marine bird species in the North Pacific. Most of these data were collected on surveys by counting species within defined areas and at known locations (that is, on strip transects). The NPPSD also contains observations of other bird species and marine mammals. The original NPPSD combined data from 465 surveys conducted between 1973 and 2002, primarily in waters adjacent to Alaska. These surveys included 61,195 sample transects with location, environment, and metadata information, and the data were organized in a flat-file format. In developing NPPSD 2.0, our goals were to add new datasets, to make significant improvements to database functionality and to provide the database online. NPPSD 2.0 includes data from a broader geographic range within the North Pacific, including new observations made offshore of the Russian Federation, Japan, Korea, British Columbia (Canada), Oregon, and California. These data were imported into a relational database, proofed, and structured in a common format. NPPSD 2.0 contains 351,674 samples (transects) collected between 1973 and 2012, representing a total sampled area of 270,259 square kilometers, and extends the time series of samples in some areas—notably the Bering Sea—to four decades. It contains observations of 16,988,138 birds and 235,545 marine mammals and is available on the NPPSD Web site. Supplementary materials include an updated set of standardized taxonomic codes, reference maps that show the spatial and temporal distribution of the survey efforts and a downloadable query tool.
","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/ofr20151123","usgsCitation":"Drew, G.S., Piatt, J.F., and Renner, M., 2015, User’s guide to the North Pacific Pelagic Seabird Database 2.0:\nU.S. Geological Survey Open-File Report 2015-1123, 52 p., https://dx.doi.org/10.3133/ofr20151123.","productDescription":"iv, 52 p.","numberOfPages":"60","onlineOnly":"Y","additionalOnlineFiles":"Y","ipdsId":"IP-057923","costCenters":[{"id":114,"text":"Alaska Science Center","active":true,"usgs":true},{"id":116,"text":"Alaska Science Center Biology MFEB","active":true,"usgs":true}],"links":[{"id":438690,"rank":4,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/F7WQ01T3","text":"USGS data release","linkHelpText":"North Pacific Pelagic Seabird Database (NPPSD)"},{"id":305444,"rank":2,"type":{"id":9,"text":"Database"},"url":"https://dx.doi.org/10.5066/F7WQ01T3","text":"North Pacific Pelagic Seabird Database (NPPSD)","size":"67.4 MB","linkFileType":{"id":6,"text":"zip"},"description":"Database"},{"id":305552,"rank":3,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/of/2015/1123/cover.jpg"},{"id":305443,"rank":1,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/of/2015/1123/ofr20151123.pdf","text":"Report","size":"16.5 MB","linkFileType":{"id":1,"text":"pdf"},"description":"OFR 2015-1123"}],"contact":"Director, Alaska Science Center
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LANDFIRE, also known as the Landscape Fire and Resource Management Planning Tools Program, is a vegetation, fire, and fuel characteristic mapping program managed by the U.S. Department of Agriculture Forest Service and the U.S. Department of the Interior. LANDFIRE represents the first, and only, complete, nationally consistent collection of spatial resource datasets with an ecological foundation that can be used across multiple disciplines.
\nLANDFIRE data products are primarily designed and developed to be used at the landscape level to facilitate national and regional strategic planning and reporting of wild land fire and other natural resource management activities. However, LANDFIRE’s spatially comprehensive dataset can also be adapted to support a variety of local management applications that need current and comprehensive geospatial data.
","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/fs20153047","usgsCitation":"Zahn, S.G., 2015, LANDFIRE: U.S. Geological Survey Fact Sheet 2015-3047, 2 p., https://dx.doi.org/10.3133/fs20153047.","productDescription":"2 p.","numberOfPages":"2","onlineOnly":"N","additionalOnlineFiles":"N","ipdsId":"IP-064616","costCenters":[{"id":222,"text":"Earth Resources Observation and Science (EROS) Center","active":true,"usgs":true}],"links":[{"id":305605,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/fs/2015/3047/coverthb.jpg"},{"id":305606,"rank":2,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/fs/2015/3047/fs20153047.pdf","text":"Report","size":"3.28","linkFileType":{"id":1,"text":"pdf"},"description":"FS 2015-3047"}],"contact":"Earth Resources Observation and Science (EROS) Center
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The relative merits of model complexity and types of observations employed in model calibration are compared. An existing groundwater flow model coupled with an advective transport simulation of the Salt Lake Valley, Utah (USA), is adapted for advective transport, and effective porosity is adjusted until simulated tritium concentrations match concentrations in samples from wells. Two calibration approaches are used: a “complex” highly parameterized porosity field and a “simple” parsimonious model of porosity distribution. The use of an atmospheric tracer (tritium in this case) and apparent ages (from tritium/helium) in model calibration also are discussed. Of the models tested, the complex model (with tritium concentrations and tritium/helium apparent ages) performs best. Although tritium breakthrough curves simulated by complex and simple models are very generally similar, and there is value in the simple model, the complex model is supported by a more realistic porosity distribution and a greater number of estimable parameters. Culling the best quality data did not lead to better calibration, possibly because of processes and aquifer characteristics that are not simulated. Despite many factors that contribute to shortcomings of both the models and the data, useful information is obtained from all the models evaluated. Although any particular prediction of tritium breakthrough may have large errors, overall, the models mimic observed trends.
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\nQuality-control data are those generated from the collection and analysis of quality-control samples, and are used to estimate the magnitude of errors in the process of obtaining environmental data. “Bias” and “variability” are the terms used in this report for the two types of errors in environmental data that are quantified by the data from quality-control samples. Bias is the systematic error inherent in a method or measurement system. Variability is the random error that occurs in independent measurements. The types of field quality-control samples discussed in this report include blanks, spikes, and replicates. Blanks are samples prepared with water that is intended to be free of measurable constituents that will be analyzed by the laboratory; blanks are used to estimate bias caused by contamination. Spiked samples are modified by addition of specific analytes; spikes are used to determine the performance of analytical methods and to estimate the potential bias due to matrix interference or analyte degradation. Replicate samples are two or more samples that are considered to be essentially identical in composition. Replicates are used to evaluate variability in analytical results. Various sub-types of these quality-control samples are defined and discussed in this report, and guidance is provided for incorporating the proper samples into the design for a project. The concept of inference space is introduced to help determine where and when quality-control samples should be collected as well as which environmental samples are related to a set of quality-control samples. The recommended basic quality-control design incorporates project-specific considerations, such as the objectives and scale of the study, and hydrologic and chemical conditions within the study area.
\nThe report provides extensive information about statistical methods used to analyze quality-control data in order to estimate potential bias and variability in environmental data. These methods include construction of confidence intervals on various statistical measures, such as the mean, percentiles and percentages, and standard deviation. The methods are used to compare quality-control results with the larger set of environmental data in order to determine whether the effects of bias and variability might interfere with interpretation of these data. Examples from published reports are presented to illustrate how the methods are applied, how bias and variability are reported, and how the interpretation of environmental data can be qualified based on the quality-control analysis.
","largerWorkType":{"id":18,"text":"Report"},"largerWorkTitle":"Section C in Book 4 Hydrologic analysis and interpretation","largerWorkSubtype":{"id":5,"text":"USGS Numbered Series"},"language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston VA","doi":"10.3133/tm4C4","usgsCitation":"Mueller, D.K., Schertz, T.L., Martin, J.D., and Sandstrom, M.W., 2015, Design, analysis, and interpretation of field quality-control data for water-sampling projects: U.S. Geological Survey Techniques and Methods 4-C4, viii, 54 p., https://doi.org/10.3133/tm4C4.","productDescription":"viii, 54 p.","numberOfPages":"65","onlineOnly":"Y","additionalOnlineFiles":"N","ipdsId":"IP-056948","costCenters":[{"id":452,"text":"National Water Quality Laboratory","active":true,"usgs":true},{"id":503,"text":"Office of Water Quality","active":true,"usgs":true}],"links":[{"id":305661,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/tm4C4.jpg"},{"id":305660,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/tm/04/c04/pdf/tm4c4.pdf","text":"Report","size":"1.72 MB","linkFileType":{"id":1,"text":"pdf"},"description":"Report"},{"id":305622,"type":{"id":15,"text":"Index Page"},"url":"https://pubs.usgs.gov/tm/04/c04/"}],"publicComments":"This report is Chapter 4 of Section C in Book 4 Hydrologic analysis and interpretation","publishingServiceCenter":{"id":9,"text":"Reston PSC"},"noUsgsAuthors":false,"publicationStatus":"PW","scienceBaseUri":"57f7eee2e4b0bc0bec09edae","contributors":{"authors":[{"text":"Mueller, David K. mueller@usgs.gov","contributorId":1585,"corporation":false,"usgs":true,"family":"Mueller","given":"David","email":"mueller@usgs.gov","middleInitial":"K.","affiliations":[{"id":503,"text":"Office of Water Quality","active":true,"usgs":true}],"preferred":true,"id":564508,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Schertz, Terry L. tschertz@usgs.gov","contributorId":188,"corporation":false,"usgs":true,"family":"Schertz","given":"Terry","email":"tschertz@usgs.gov","middleInitial":"L.","affiliations":[{"id":583,"text":"Texas Water Science Center","active":true,"usgs":true}],"preferred":true,"id":564509,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Martin, Jeffrey D. 0000-0003-1994-5285 jdmartin@usgs.gov","orcid":"https://orcid.org/0000-0003-1994-5285","contributorId":1066,"corporation":false,"usgs":true,"family":"Martin","given":"Jeffrey","email":"jdmartin@usgs.gov","middleInitial":"D.","affiliations":[{"id":451,"text":"National Water Quality Assessment Program","active":true,"usgs":true},{"id":346,"text":"Indiana Water Science Center","active":true,"usgs":true},{"id":27231,"text":"Indiana-Kentucky Water Science Center","active":true,"usgs":true}],"preferred":true,"id":564510,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Sandstrom, Mark W. 0000-0003-0006-5675 sandstro@usgs.gov","orcid":"https://orcid.org/0000-0003-0006-5675","contributorId":706,"corporation":false,"usgs":true,"family":"Sandstrom","given":"Mark","email":"sandstro@usgs.gov","middleInitial":"W.","affiliations":[{"id":503,"text":"Office of Water Quality","active":true,"usgs":true},{"id":5046,"text":"Branch of Analytical Serv (NWQL)","active":true,"usgs":true},{"id":37464,"text":"WMA - Laboratory & Analytical Services Division","active":true,"usgs":true},{"id":452,"text":"National Water Quality Laboratory","active":true,"usgs":true}],"preferred":true,"id":564511,"contributorType":{"id":1,"text":"Authors"},"rank":4}]}} ,{"id":70159195,"text":"70159195 - 2015 - When do we need more data? A primer on calculating the value of information for applied ecologists","interactions":[],"lastModifiedDate":"2015-10-19T09:10:47","indexId":"70159195","displayToPublicDate":"2015-07-10T15:45:00","publicationYear":"2015","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":2717,"text":"Methods in Ecology and Evolution","active":true,"publicationSubtype":{"id":10}},"title":"When do we need more data? A primer on calculating the value of information for applied ecologists","docAbstract":"When emergencies occur, first responders and disaster response teams often need rapid access to aerial photography and satellite imagery that is acquired before and after the event. The U.S. Geological Survey (USGS) Hazards Data Distribution System (HDDS) provides quick and easy access to pre- and post-event imagery and geospatial datasets that support emergency response and recovery operations. The HDDS provides a single, consolidated point-of-entry and distribution system for USGS-hosted remotely sensed imagery and other geospatial datasets related to an event response. The data delivery services are provided through an interactive map-based interface that allows emergency response personnel to rapidly select and download pre-event (\"baseline\") and post-event emergency response imagery.
","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/fs20153048","usgsCitation":"Jones, B.K., and Lamb, R.M., 2015, Hazards data distribution system (HDDS): U.S. Geological Survey Fact Sheet 2015–3048, 2 p., https://dx.doi.org/10.3133/fs20153048.","productDescription":"2 p.","numberOfPages":"2","onlineOnly":"Y","additionalOnlineFiles":"N","ipdsId":"IP-065098","costCenters":[{"id":222,"text":"Earth Resources Observation and Science (EROS) Center","active":true,"usgs":true}],"links":[{"id":305592,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/fs/2015/3048/coverthb.jpg"},{"id":305593,"rank":2,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/fs/2015/3048/fs20153048.pdf","text":"Report","size":"4.2 MB","linkFileType":{"id":1,"text":"pdf"},"description":"FS 2015-3048"}],"contact":"Earth Resources Observation
and Science (EROS) Center
U.S. Geological Survey
47914 252nd Street
Sioux Falls, South Dakota 57198
http://eros.usgs.gov/
Coal is a chemically complex commodity that often contains most of the natural elements in the periodic table. Coal constituents are conventionally grouped into four components (proximate analysis): fixed carbon, ash, inherent moisture, and volatile matter. These four parts, customarily measured as weight losses and expressed as percentages, share all properties and statistical challenges of compositional data. Consequently, adequate modeling should be done in terms of a logratio transformation, a requirement that is commonly overlooked by modelers. The transformation of choice is the isometric logratio transformation because of its geometrical and statistical advantages. The modeling is done through a series of realizations prepared by applying sequential simulation for the purpose of displaying the parts in maps incorporating uncertainty. The approach makes realistic assumptions and the results honor the data and basic considerations, such as percentages between 0 and 100, all four parts adding to 100% at any location in the study area, and a style of spatial fluctuation in the realizations equal to that of the data. The realizations are used to prepare different results, including probability distributions across a deposit, E-type maps displaying average properties, and probability maps summarizing joint fluctuations of several parts. Application of these maps to a lignite bed clearly delineates the deposit boundary, reveals a channel cutting across, and shows that the most favorable coal quality is to the north and deteriorates toward the southeast.
","language":"English","publisher":"Elsevier","doi":"10.1016/j.coal.2015.10.003","usgsCitation":"Olea, R.A., and Luppens, J.A., 2015, Mapping of coal quality using stochastic simulation and isometric logratio transformation with an application to a Texas lignite: International Journal of Coal Geology, v. 152, no. Part B, p. 80-93, https://doi.org/10.1016/j.coal.2015.10.003.","productDescription":"14 p.","startPage":"80","endPage":"93","ipdsId":"IP-069055","costCenters":[{"id":241,"text":"Eastern Energy Resources Science Center","active":true,"usgs":true}],"links":[{"id":342888,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"volume":"152","issue":"Part B","publishingServiceCenter":{"id":9,"text":"Reston PSC"},"noUsgsAuthors":false,"publicationStatus":"PW","scienceBaseUri":"59521d22e4b062508e3c3691","contributors":{"authors":[{"text":"Olea, Ricardo A. 0000-0003-4308-0808 rolea@usgs.gov","orcid":"https://orcid.org/0000-0003-4308-0808","contributorId":139555,"corporation":false,"usgs":true,"family":"Olea","given":"Ricardo","email":"rolea@usgs.gov","middleInitial":"A.","affiliations":[{"id":241,"text":"Eastern Energy Resources Science Center","active":true,"usgs":true}],"preferred":false,"id":700520,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Luppens, James A. 0000-0001-7607-8750 jluppens@usgs.gov","orcid":"https://orcid.org/0000-0001-7607-8750","contributorId":550,"corporation":false,"usgs":true,"family":"Luppens","given":"James","email":"jluppens@usgs.gov","middleInitial":"A.","affiliations":[{"id":164,"text":"Central Energy Resources Science Center","active":true,"usgs":true}],"preferred":true,"id":700521,"contributorType":{"id":1,"text":"Authors"},"rank":2}]}} ,{"id":70154802,"text":"70154802 - 2015 - Truth, models, model sets, AIC, and multimodel inference: a Bayesian perspective","interactions":[],"lastModifiedDate":"2016-04-13T12:35:45","indexId":"70154802","displayToPublicDate":"2015-07-08T14:45:00","publicationYear":"2015","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":2508,"text":"Journal of Wildlife Management","active":true,"publicationSubtype":{"id":10}},"title":"Truth, models, model sets, AIC, and multimodel inference: a Bayesian perspective","docAbstract":"Statistical inference begins with viewing data as realizations of stochastic processes. Mathematical models provide partial descriptions of these processes; inference is the process of using the data to obtain a more complete description of the stochastic processes. Wildlife and ecological scientists have become increasingly concerned with the conditional nature of model-based inference: what if the model is wrong? Over the last 2 decades, Akaike's Information Criterion (AIC) has been widely and increasingly used in wildlife statistics for 2 related purposes, first for model choice and second to quantify model uncertainty. We argue that for the second of these purposes, the Bayesian paradigm provides the natural framework for describing uncertainty associated with model choice and provides the most easily communicated basis for model weighting. Moreover, Bayesian arguments provide the sole justification for interpreting model weights (including AIC weights) as coherent (mathematically self consistent) model probabilities. This interpretation requires treating the model as an exact description of the data-generating mechanism. We discuss the implications of this assumption, and conclude that more emphasis is needed on model checking to provide confidence in the quality of inference.
","language":"English","publisher":"Wildlife Society","doi":"10.1002/jwmg.890","usgsCitation":"Barker, R., and Link, W., 2015, Truth, models, model sets, AIC, and multimodel inference: a Bayesian perspective: Journal of Wildlife Management, v. 79, no. 5, p. 730-738, https://doi.org/10.1002/jwmg.890.","productDescription":"9 p.","startPage":"730","endPage":"738","onlineOnly":"N","additionalOnlineFiles":"N","ipdsId":"IP-063229","costCenters":[{"id":531,"text":"Patuxent Wildlife Research Center","active":true,"usgs":true}],"links":[{"id":471945,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.1002/jwmg.890","text":"Publisher Index Page"},{"id":305619,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"volume":"79","issue":"5","publishingServiceCenter":{"id":10,"text":"Baltimore PSC"},"noUsgsAuthors":false,"publicationDate":"2015-05-07","publicationStatus":"PW","scienceBaseUri":"559e3ba6e4b0b94a64018f54","contributors":{"authors":[{"text":"Barker, Richard J.","contributorId":6987,"corporation":false,"usgs":true,"family":"Barker","given":"Richard J.","affiliations":[],"preferred":false,"id":564201,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Link, William A. wlink@usgs.gov","contributorId":145491,"corporation":false,"usgs":true,"family":"Link","given":"William A.","email":"wlink@usgs.gov","affiliations":[{"id":531,"text":"Patuxent Wildlife Research Center","active":true,"usgs":true}],"preferred":false,"id":564200,"contributorType":{"id":1,"text":"Authors"},"rank":2}]}} ,{"id":70154817,"text":"70154817 - 2015 - Comparing spatial capture–recapture modeling and nest count methods to estimate orangutan densities in the Wehea Forest, East Kalimantan, Indonesia","interactions":[],"lastModifiedDate":"2015-07-08T13:35:48","indexId":"70154817","displayToPublicDate":"2015-07-08T14:30:00","publicationYear":"2015","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":1015,"text":"Biological Conservation","active":true,"publicationSubtype":{"id":10}},"title":"Comparing spatial capture–recapture modeling and nest count methods to estimate orangutan densities in the Wehea Forest, East Kalimantan, Indonesia","docAbstract":"Accurate information on the density and abundance of animal populations is essential for understanding species' ecology and for conservation planning, but is difficult to obtain. The endangered orangutan (Pongo spp.) is an example; due to its elusive behavior and low densities, researchers have relied on methods that convert nest counts to orangutan densities and require substantial effort for reliable results. Camera trapping and spatial capture–recapture (SCR) models could provide an alternative but have not been used for primates. We compared density estimates calculated using the two methods for orangutans in the Wehea Forest, East Kalimantan, Indonesia. Camera trapping/SCR modeling produced a density estimate of 0.16 ± 0.09–0.29 indiv/km2, and nest counts produced a density estimate of 1.05 ± 0.18–6.01 indiv/km2. The large confidence interval of the nest count estimate is probably due to high variance in nest encounter rates, indicating the need for larger sample size and the substantial effort required to produce reliable results using this method. The SCR estimate produced a very low density estimate and had a narrower but still fairly wide confidence interval. This was likely due to unmodeled heterogeneity and small sample size, specifically a low number of individual captures and recaptures. We propose methodological fixes that could address these issues and improve precision. A comparison of the overall costs and benefits of the two methods suggests that camera trapping/SCR modeling can potentially be a useful tool for assessing the densities of orangutans and other elusive primates, and warrant further investigation to determine broad applicability and methodological adjustments needed.
\n\n
Delineating the climate processes governing precipitation variability in drought-prone Texas is critical for predicting and mitigating climate change effects, and requires the reconstruction of past climate beyond the instrumental record. We synthesize existing paleoclimate proxy data and climate simulations to provide an overview of climate variability in Texas during the Holocene. Conditions became progressively warmer and drier transitioning from the early to mid Holocene, culminating between 7 and 3 ka (thousand years ago), and were more variable during the late Holocene. The timing and relative magnitude of Holocene climate variability, however, is poorly constrained owing to considerable variability among the different records. To help address this, we present a new speleothem (NBJ) reconstruction from a central Texas cave that comprises the highest resolution proxy record to date, spanning the mid to late Holocene. NBJ trace-element concentrations indicate variable moisture conditions with no clear temporal trend. There is a decoupling between NBJ growth rate, trace-element concentrations, and δ18O values, which indicate that (i) the often direct relation between speleothem growth rate and moisture availability is likely complicated by changes in the overlying ecosystem that affect subsurface CO2 production, and (ii) speleothem δ18O variations likely reflect changes in moisture source (i.e., proportion of Pacific-vs. Gulf of Mexico-derived moisture) that appear not to be linked to moisture amount.
","language":"English","publisher":"Pergamon Press","publisherLocation":"New York, NY","doi":"10.1016/j.quascirev.2015.06.023","usgsCitation":"Wong, C., Banner, J., and Musgrove, M., 2015, Holocene climate variability in Texas, USA: An integration of existing paleoclimate data and modeling with a new, high-resolution speleothem record: Quaternary Science Reviews, v. 127, p. 155-173, https://doi.org/10.1016/j.quascirev.2015.06.023.","productDescription":"19 p.","startPage":"155","endPage":"173","onlineOnly":"N","additionalOnlineFiles":"N","ipdsId":"IP-062965","costCenters":[{"id":583,"text":"Texas Water Science Center","active":true,"usgs":true}],"links":[{"id":306533,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","state":"Texas","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -103.0517578125,\n 36.491973470593685\n ],\n [\n -99.97558593749999,\n 36.50963615733049\n ],\n [\n -99.99755859375,\n 34.66935854524543\n ],\n [\n -99.7119140625,\n 34.415973384481866\n ],\n [\n -99.404296875,\n 34.52466147177172\n ],\n [\n -99.16259765625,\n 34.27083595165\n ],\n [\n -98.525390625,\n 34.10725639663118\n ],\n [\n -98.23974609375,\n 34.21634468843465\n ],\n [\n -96.3720703125,\n 33.96158628979907\n ],\n [\n -95.55908203125,\n 34.052659421375964\n ],\n [\n -94.8779296875,\n 34.03445260967645\n ],\n [\n -94.39453125,\n 33.669496972795535\n ],\n [\n -93.9990234375,\n 33.65120829920497\n ],\n [\n -93.9990234375,\n 31.970803930433096\n ],\n [\n -93.4716796875,\n 31.2221970321032\n ],\n [\n -93.40576171875,\n 30.90222470517144\n ],\n [\n -93.7353515625,\n 30.543338954230222\n ],\n [\n -93.8232421875,\n 29.954934549656144\n ],\n [\n -93.88916015625,\n 29.66896252599253\n ],\n [\n -95.51513671875,\n 28.806173508854776\n ],\n [\n -96.13037109375,\n 28.497660832963472\n ],\n [\n -96.83349609375,\n 28.14950321154457\n ],\n [\n -97.09716796875,\n 27.488781168937997\n ],\n [\n -97.49267578125,\n 27.059125784374068\n ],\n [\n -97.42675781249999,\n 26.509904531413927\n ],\n [\n -97.119140625,\n 25.997549919572112\n ],\n [\n -97.44873046875,\n 25.799891182088334\n ],\n [\n -98.59130859375,\n 26.15543796871355\n ],\n [\n -99.228515625,\n 26.47057302237511\n ],\n [\n -99.84374999999999,\n 27.527758206861886\n ],\n [\n -100.92041015625,\n 29.0945770775118\n ],\n [\n -101.49169921875,\n 29.630771207229\n ],\n [\n -102.3046875,\n 29.783449456820605\n ],\n [\n -102.76611328125,\n 29.516110386062277\n ],\n [\n -103.1396484375,\n 28.844673680771795\n ],\n [\n -104.8974609375,\n 29.726222319395504\n ],\n [\n -104.78759765625,\n 30.088107753367257\n ],\n [\n -105.1171875,\n 30.58117925738696\n ],\n [\n -105.77636718749999,\n 30.996445897426373\n ],\n [\n -106.787109375,\n 31.87755764334002\n ],\n [\n -106.67724609375,\n 32.045332838858506\n ],\n [\n -103.07373046875,\n 31.989441837922904\n ],\n [\n -103.0517578125,\n 36.491973470593685\n ]\n ]\n ]\n }\n }\n ]\n}","volume":"127","publishingServiceCenter":{"id":5,"text":"Lafayette PSC"},"noUsgsAuthors":false,"publicationStatus":"PW","scienceBaseUri":"55c9cb34e4b08400b1fdb70e","contributors":{"authors":[{"text":"Wong, Corinne I.","contributorId":36018,"corporation":false,"usgs":true,"family":"Wong","given":"Corinne I.","affiliations":[],"preferred":false,"id":565675,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Banner, Jay L.","contributorId":58200,"corporation":false,"usgs":true,"family":"Banner","given":"Jay L.","affiliations":[],"preferred":false,"id":565676,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Musgrove, MaryLynn 0000-0003-1607-3864 mmusgrov@usgs.gov","orcid":"https://orcid.org/0000-0003-1607-3864","contributorId":1316,"corporation":false,"usgs":true,"family":"Musgrove","given":"MaryLynn","email":"mmusgrov@usgs.gov","affiliations":[{"id":583,"text":"Texas Water Science Center","active":true,"usgs":true},{"id":451,"text":"National Water Quality Assessment Program","active":true,"usgs":true}],"preferred":false,"id":565674,"contributorType":{"id":1,"text":"Authors"},"rank":3}]}} ,{"id":70158682,"text":"70158682 - 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We then used the selected distribution and estimates of regional prevalence to calculate and map statistical power to detect hotspots and coldspots, and estimate the p-value from Monte Carlo significance tests that specific lease blocks are in fact hotspots or coldspots relative to regional average abundance. The power to detect species-specific hotspots was higher than that of coldspots for most species because species-specific prevalence was relatively low (mean: 0.111; SD: 0.110). The number of surveys required for adequate power (> 0.6) was large for most species (tens to hundreds) using this hotspot definition. Regulators may need to accept higher proportional effect sizes, combine species into groups, and/or broaden the spatial scale by combining lease blocks in order to determine optimal placement of wind farms. 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aoconnell@usgs.gov","orcid":"https://orcid.org/0000-0001-7032-7023","contributorId":471,"corporation":false,"usgs":true,"family":"O’Connell","given":"Allan","email":"aoconnell@usgs.gov","middleInitial":"F.","affiliations":[{"id":531,"text":"Patuxent Wildlife Research Center","active":true,"usgs":true}],"preferred":true,"id":576486,"contributorType":{"id":1,"text":"Authors"},"rank":6}]}} ,{"id":70160102,"text":"70160102 - 2015 - Renewed inflation of Long Valley Caldera, California (2011 to 2014)","interactions":[],"lastModifiedDate":"2015-12-14T11:17:50","indexId":"70160102","displayToPublicDate":"2015-07-07T00:00:00","publicationYear":"2015","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":1807,"text":"Geophysical Research Letters","active":true,"publicationSubtype":{"id":10}},"title":"Renewed inflation of Long Valley Caldera, California (2011 to 2014)","docAbstract":"Slow inflation began at Long Valley Caldera in late 2011, coinciding with renewed swarm seismicity. Ongoing deformation is concentrated within the caldera. We analyze this deformation using a combination of GPS and InSAR (TerraSAR-X) data processed with a persistent scatterer technique. The extension rate of the dome-crossing baseline during this episode (CA99 to KRAC) is 1 cm/yr, similar to past inflation episodes (1990–1995 and 2002–2003), and about a tenth of the peak rate observed during the 1997 unrest. The current deformation is well modeled by the inflation of a prolate spheroidal magma reservoir ∼7 km beneath the resurgent dome, with a volume change of ∼6 × 106 m3/yr from 2011.7 through the end of 2014. The current data cannot resolve a second source, which was required to model the 1997 episode. This source appears to be in the same region as previous inflation episodes, suggesting a persistent reservoir.
","language":"English","publisher":"American Geophysical Union","publisherLocation":"Washington, DC","doi":"10.1002/2015GL064338","usgsCitation":"Montgomery-Brown, E., Wicks, C.W., Cervelli, P.F., Langbein, J.O., Svarc, J.L., Shelly, D.R., Hill, D.P., and Lisowski, M., 2015, Renewed inflation of Long Valley Caldera, California (2011 to 2014): Geophysical Research Letters, v. 42, no. 13, p. 5250-5257, https://doi.org/10.1002/2015GL064338.","productDescription":"8 p.","startPage":"5250","endPage":"5257","onlineOnly":"N","additionalOnlineFiles":"N","ipdsId":"IP-064951","costCenters":[{"id":617,"text":"Volcano Science Center","active":true,"usgs":true}],"links":[{"id":471949,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.1002/2015gl064338","text":"Publisher Index Page"},{"id":312244,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","state":"California","otherGeospatial":"Long Valley Caldera","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -119.09454345703125,\n 38.348118547988065\n ],\n [\n -118.9105224609375,\n 38.21660403859855\n ],\n [\n -118.65509033203125,\n 38.0545795282119\n ],\n [\n -118.4381103515625,\n 37.846663684549156\n ],\n [\n -118.3172607421875,\n 37.65338320128765\n ],\n [\n -118.27880859375001,\n 37.35487607348372\n ],\n [\n -118.24859619140626,\n 37.07271048132946\n ],\n [\n -119.05059814453125,\n 37.64903402157866\n ],\n [\n -119.22637939453124,\n 37.844494798834596\n ],\n [\n -119.39117431640625,\n 38.1669547678699\n ],\n [\n -119.36920166015624,\n 38.34165619279593\n ],\n [\n -119.21539306640626,\n 38.429925130409366\n ],\n [\n -119.09454345703125,\n 38.348118547988065\n ]\n ]\n ]\n }\n }\n ]\n}","volume":"42","issue":"13","publishingServiceCenter":{"id":14,"text":"Menlo Park PSC"},"noUsgsAuthors":false,"publicationDate":"2015-07-07","publicationStatus":"PW","scienceBaseUri":"566ff656e4b09cfe53ca79c2","chorus":{"doi":"10.1002/2015gl064338","url":"http://dx.doi.org/10.1002/2015gl064338","publisher":"Wiley-Blackwell","authors":"Montgomery-Brown E. 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Jr. 0000-0002-0809-1328 cwicks@usgs.gov","orcid":"https://orcid.org/0000-0002-0809-1328","contributorId":127701,"corporation":false,"usgs":true,"family":"Wicks","given":"Charles","suffix":"Jr.","email":"cwicks@usgs.gov","middleInitial":"W.","affiliations":[{"id":237,"text":"Earthquake Science Center","active":true,"usgs":true}],"preferred":true,"id":581997,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Cervelli, Peter F. 0000-0001-6765-1009 pcervelli@usgs.gov","orcid":"https://orcid.org/0000-0001-6765-1009","contributorId":1936,"corporation":false,"usgs":true,"family":"Cervelli","given":"Peter","email":"pcervelli@usgs.gov","middleInitial":"F.","affiliations":[{"id":617,"text":"Volcano Science Center","active":true,"usgs":true}],"preferred":true,"id":581998,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Langbein, John O. 0000-0002-7821-8101 langbein@usgs.gov","orcid":"https://orcid.org/0000-0002-7821-8101","contributorId":3293,"corporation":false,"usgs":true,"family":"Langbein","given":"John","email":"langbein@usgs.gov","middleInitial":"O.","affiliations":[{"id":237,"text":"Earthquake Science Center","active":true,"usgs":true}],"preferred":true,"id":581999,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Svarc, Jerry L. 0000-0002-2802-4528 jsvarc@usgs.gov","orcid":"https://orcid.org/0000-0002-2802-4528","contributorId":2413,"corporation":false,"usgs":true,"family":"Svarc","given":"Jerry","email":"jsvarc@usgs.gov","middleInitial":"L.","affiliations":[{"id":237,"text":"Earthquake Science Center","active":true,"usgs":true}],"preferred":true,"id":582000,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"Shelly, David R. dshelly@usgs.gov","contributorId":2978,"corporation":false,"usgs":true,"family":"Shelly","given":"David","email":"dshelly@usgs.gov","middleInitial":"R.","affiliations":[{"id":617,"text":"Volcano Science Center","active":true,"usgs":true},{"id":114,"text":"Alaska Science Center","active":true,"usgs":true}],"preferred":true,"id":582001,"contributorType":{"id":1,"text":"Authors"},"rank":6},{"text":"Hill, David P. hill@usgs.gov","contributorId":2600,"corporation":false,"usgs":true,"family":"Hill","given":"David","email":"hill@usgs.gov","middleInitial":"P.","affiliations":[{"id":114,"text":"Alaska Science Center","active":true,"usgs":true},{"id":617,"text":"Volcano Science Center","active":true,"usgs":true},{"id":615,"text":"Volcano Hazards Program","active":true,"usgs":true}],"preferred":false,"id":582002,"contributorType":{"id":1,"text":"Authors"},"rank":7},{"text":"Lisowski, Michael 0000-0003-4818-2504 mlisowski@usgs.gov","orcid":"https://orcid.org/0000-0003-4818-2504","contributorId":637,"corporation":false,"usgs":true,"family":"Lisowski","given":"Michael","email":"mlisowski@usgs.gov","affiliations":[{"id":615,"text":"Volcano Hazards Program","active":true,"usgs":true},{"id":617,"text":"Volcano Science Center","active":true,"usgs":true}],"preferred":true,"id":582003,"contributorType":{"id":1,"text":"Authors"},"rank":8}]}} ,{"id":70146246,"text":"ds923 - 2015 - Installation of a groundwater monitoring-well network on the east side of the Uncompahgre River in the Lower Gunnison River Basin, Colorado, 2012","interactions":[],"lastModifiedDate":"2015-10-07T12:03:32","indexId":"ds923","displayToPublicDate":"2015-07-06T10:15:00","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":"923","title":"Installation of a groundwater monitoring-well network on the east side of the Uncompahgre River in the Lower Gunnison River Basin, Colorado, 2012","docAbstract":"The east side of the Uncompahgre River Basin has been a known contributor of dissolved selenium to recipient streams. Discharge of groundwater containing dissolved selenium contributes to surface-water selenium concentrations and loads; however, the groundwater system on the east side of the Uncompahgre River Basin is not well characterized. The U.S. Geological Survey, in cooperation with the Colorado Water Conservation Board and the Bureau of Reclamation, has established a groundwater-monitoring network on the east side of the Uncompahgre River Basin. Ten monitoring wells were installed during October and November 2012. This report presents location data, lithologic logs, well-construction diagrams, and well-development information. Understanding the groundwater system will provide managers with an additional metric for evaluating the effectiveness of salinity and selenium control projects.
","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/ds923","collaboration":"Prepared in cooperation with Colorado Water Conservation Board and the Bureau of Reclamation","usgsCitation":"Thomas, J.C., and Arnold, L.R., 2015, Installation of a groundwater monitoring-well network on the east side of the Uncompahgre River in the Lower Gunnison River Basin, Colorado, 2012: U.S. Geological Survey Data Series 923, 29 p., https://dx.doi.org/10.3133/ds923.","productDescription":"iv, 29 p.","numberOfPages":"36","onlineOnly":"Y","additionalOnlineFiles":"N","ipdsId":"IP-059394","costCenters":[{"id":191,"text":"Colorado Water Science Center","active":true,"usgs":true}],"links":[{"id":309728,"rank":4,"type":{"id":22,"text":"Related Work"},"url":"https://dx.doi.org/10.3133/ds955","text":"DS 955"},{"id":305498,"rank":2,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/ds/0923/pdf/ds923.pdf","text":"Report","size":"17.0 MB","linkFileType":{"id":1,"text":"pdf"},"description":"DS 923"},{"id":305497,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/ds/0923/coverthb.jpg"},{"id":305608,"rank":3,"type":{"id":15,"text":"Index Page"},"url":"https://pubs.usgs.gov/publication/ds923"}],"country":"United States","state":"Colorado","otherGeospatial":"Uncompahgre River","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -107.6154613494873,\n 37.91705002583544\n ],\n [\n -107.6154613494873,\n 37.928153945306555\n ],\n [\n -107.59949684143065,\n 37.928153945306555\n ],\n [\n -107.59949684143065,\n 37.91705002583544\n ],\n [\n -107.6154613494873,\n 37.91705002583544\n ]\n ]\n ]\n }\n }\n ]\n}","contact":"Director, Colorado Water Science Center
U.S. Geological Survey
Box 25046, Mail Stop 415
Denver, CO 80225
http://co.water.usgs.gov
Contours and derivative raster files of depth-to-basement, sediment-thickness, and bathymetry data for the area offshore of Washington, Oregon, and California are provided here as GIS-ready shapefiles and GeoTIFF files. The data were used to generate paper maps in 1992 and 1993 from 1984 surveys of the U.S. Exclusive Economic Zone by the U.S. Geological Survey for depth to basement and sediment thickness, and from older data for the bathymetry.
","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/ofr20151118","usgsCitation":"Wong, F.L., and Grim, M.S., 2015, Depth-to-basement, sediment-thickness, and bathymetry data for the deep-sea basins offshore of Washington, Oregon, and California: U.S. Geological Survey Open-File Report 2015-1118, Report: iv, 13 p.; Data Catalog, https://doi.org/10.3133/ofr20151118.","productDescription":"Report: iv, 13 p.; Data Catalog","numberOfPages":"17","onlineOnly":"Y","additionalOnlineFiles":"Y","ipdsId":"IP-057812","costCenters":[{"id":520,"text":"Pacific Coastal and Marine Science Center","active":true,"usgs":true}],"links":[{"id":305574,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/ofr20151118.gif"},{"id":305572,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/of/2015/1118/ofr20151118.pdf","text":"Report","size":"5.9 MB","linkFileType":{"id":1,"text":"pdf"},"description":"Report"},{"id":305573,"type":{"id":9,"text":"Database"},"url":"https://pubs.usgs.gov/of/2015/1118/data_catalog.html","text":"Data Catalog","linkFileType":{"id":5,"text":"html"},"description":"Data Catalog"},{"id":305561,"type":{"id":15,"text":"Index Page"},"url":"https://pubs.usgs.gov/of/2015/1118/"}],"country":"United States","state":"California, Oregon, Washington","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -125.8154296875,\n 47.96050238891509\n ],\n [\n -125.068359375,\n 46.437856895024225\n ],\n [\n -124.892578125,\n 44.96479793033104\n ],\n [\n -125.068359375,\n 43.45291889355465\n ],\n [\n -125.20019531249999,\n 41.80407814427237\n ],\n [\n -125.0244140625,\n 40.51379915504413\n ],\n [\n -124.71679687499999,\n 39.36827914916014\n ],\n [\n -124.3212890625,\n 38.20365531807149\n ],\n [\n -123.79394531249999,\n 36.949891786813296\n ],\n [\n -122.607421875,\n 35.38904996691167\n ],\n [\n -120.80566406250001,\n 33.7243396617476\n ],\n [\n -119.83886718750001,\n 32.694865977875075\n ],\n [\n -120.9814453125,\n 32.02670629333614\n ],\n [\n -122.34374999999999,\n 31.653381399664\n ],\n [\n -123.92578125,\n 32.287132632616355\n ],\n [\n -126.0791015625,\n 34.88593094075317\n ],\n [\n -127.529296875,\n 37.055177106660814\n ],\n [\n -128.32031249999997,\n 39.30029918615029\n ],\n [\n -128.7158203125,\n 41.64007838467894\n ],\n [\n -128.32031249999997,\n 43.67581809328344\n ],\n [\n -127.96875,\n 45.521743896993634\n ],\n [\n -129.6826171875,\n 45.67548217560647\n ],\n [\n -130.078125,\n 47.27922900257082\n ],\n [\n -126.65039062499999,\n 47.60616304386874\n ],\n [\n -125.8154296875,\n 47.96050238891509\n ]\n ]\n ]\n }\n }\n ]\n}","publishingServiceCenter":{"id":14,"text":"Menlo Park PSC"},"noUsgsAuthors":false,"publicationStatus":"PW","scienceBaseUri":"57f7eef3e4b0bc0bec09ee10","contributors":{"authors":[{"text":"Wong, Florence L. 0000-0002-3918-5896 fwong@usgs.gov","orcid":"https://orcid.org/0000-0002-3918-5896","contributorId":1990,"corporation":false,"usgs":true,"family":"Wong","given":"Florence","email":"fwong@usgs.gov","middleInitial":"L.","affiliations":[{"id":520,"text":"Pacific Coastal and Marine Science Center","active":true,"usgs":true}],"preferred":true,"id":564143,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Grim, Muriel S.","contributorId":85591,"corporation":false,"usgs":true,"family":"Grim","given":"Muriel","email":"","middleInitial":"S.","affiliations":[],"preferred":false,"id":564144,"contributorType":{"id":1,"text":"Authors"},"rank":2}]}} ,{"id":70142044,"text":"sir20155035 - 2015 - Alteration, slope-classified alteration, and potential lahar inundation maps of volcanoes for the Advanced Spaceborne Thermal Emission and Reflection Radiometer (ASTER) Volcano Archive","interactions":[],"lastModifiedDate":"2015-07-06T11:56:29","indexId":"sir20155035","displayToPublicDate":"2015-07-03T10:15:00","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":"2015-5035","title":"Alteration, slope-classified alteration, and potential lahar inundation maps of volcanoes for the Advanced Spaceborne Thermal Emission and Reflection Radiometer (ASTER) Volcano Archive","docAbstract":"This study identifies areas prone to lahars from hydrothermally altered volcanic edifices on a global scale, using visible and near infrared (VNIR) and short wavelength infrared (SWIR) reflectance data from the Advanced Spaceborne Thermal Emission and Reflection Radiometer (ASTER) and digital elevation data from the ASTER Global Digital Elevation Model (GDEM) dataset. This is the first study to create a global database of hydrothermally altered volcanoes showing quantitatively compiled alteration maps and potentially affected drainages, as well as drainage-specific maps illustrating modeled lahars and their potential inundation zones. We (1) identified and prioritized 720 volcanoes based on population density surrounding the volcanoes using the Smithsonian Institution Global Volcanism Program database (GVP) and LandScan™ digital population dataset; (2) validated ASTER hydrothermal alteration mapping techniques using Airborne Visible and Infrared Imaging Spectrometer (AVIRIS) and ASTER data for Mount Shasta, California, and Pico de Orizaba (Citlaltépetl), Mexico; (3) mapped and slope-classified hydrothermal alteration using ASTER VNIR-SWIR reflectance data on 100 of the most densely populated volcanoes; (4) delineated drainages using ASTER GDEM data that show potential flow paths of possible lahars for the 100 mapped volcanoes; (5) produced potential alteration-related lahar inundation maps using the LAHARZ GIS code for Iztaccíhuatl, Mexico, and Mount Hood and Mount Shasta in the United States that illustrate areas likely to be affected based on DEM-derived volume estimates of hydrothermally altered rocks and the ~2x uncertainty factor inherent within a statistically-based lahar model; and (6) saved all image and vector data for 3D and 2D display in Google Earth™, ArcGIS® and other graphics display programs. In addition, these data are available from the ASTER Volcano Archive (AVA) for distribution (available at http://ava.jpl.nasa.gov/recent_alteration_zones.php).
\nUsing the GVP and the LandScan™ digital population dataset, 350 of the most densely populated stratovolcanoes were assessed for study. Of the 350 volcanoes, 250 volcanoes were not mapped due to excessive snow, ice, and (or) vegetation. Results from mapping the remaining 100 stratovolcanoes show that 87 contain slopes with hydrothermal alteration, and 49 have hydrothermally altered rocks on steep slopes situated above areas with populations >100 people per km2. Of these, 17 stratovolcanoes exhibit laterally extensive hydrothermal alteration on slopes >35° and cover an area >0.25 km2, which may pose a significant possibility of generating debris flows.
\nThis study was undertaken during 2012–2013 in cooperation with the National Aeronautics and Space Administration (NASA). Since completion of this study, a new lahar modeling program (LAHAR_pz) has been released, which may produce slightly different modeling results from the LAHARZ model used in this study. The maps and data from this study should not be used in place of existing volcano hazard maps published by local authorities. For volcanoes without hazard maps and (or) published lahar-related hazard studies, this work will provide a starting point from which more accurate hazard maps can be produced. This is the first dataset to provide digital maps of altered volcanoes and adjacent watersheds that can be used for assessing volcanic hazards, hydrothermal alteration, and other volcanic processes in future studies.
","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/sir20155035","usgsCitation":"Mars, J., Hubbard, B.E., Pieri, D., and Linick, J., 2015, Alteration, slope-classified alteration, and potential lahar inundation maps of volcanoes for the Advanced Spaceborne Thermal Emission and Reflection Radiometer (ASTER) Volcano Archive: U.S. Geological Survey Scientific Investigations Report 2015-5035, https://doi.org/10.3133/sir20155035.","onlineOnly":"Y","additionalOnlineFiles":"N","ipdsId":"IP-054579","costCenters":[{"id":245,"text":"Eastern Mineral and Environmental Resources Science Center","active":true,"usgs":true}],"links":[{"id":305571,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/sir20155035.gif"},{"id":305570,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/sir/2015/5035/pdf/sir2015-5035.pdf","text":"Report","size":"13 MB","linkFileType":{"id":1,"text":"pdf"},"description":"Report"},{"id":305557,"type":{"id":15,"text":"Index Page"},"url":"https://pubs.usgs.gov/sir/2015/5035/"}],"publishingServiceCenter":{"id":9,"text":"Reston PSC"},"noUsgsAuthors":false,"publicationStatus":"PW","scienceBaseUri":"57f7eef3e4b0bc0bec09ee12","contributors":{"authors":[{"text":"Mars, John C. jmars@usgs.gov","contributorId":127493,"corporation":false,"usgs":true,"family":"Mars","given":"John C.","email":"jmars@usgs.gov","affiliations":[{"id":245,"text":"Eastern Mineral and Environmental Resources Science Center","active":true,"usgs":true}],"preferred":false,"id":564125,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Hubbard, Bernard E. 0000-0002-9315-2032 bhubbard@usgs.gov","orcid":"https://orcid.org/0000-0002-9315-2032","contributorId":2342,"corporation":false,"usgs":true,"family":"Hubbard","given":"Bernard","email":"bhubbard@usgs.gov","middleInitial":"E.","affiliations":[{"id":245,"text":"Eastern Mineral and Environmental Resources Science Center","active":true,"usgs":true}],"preferred":true,"id":564126,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Pieri, David","contributorId":139492,"corporation":false,"usgs":false,"family":"Pieri","given":"David","affiliations":[{"id":7023,"text":"Jet Propulsion Laboratory, California Institute of Technology","active":true,"usgs":false}],"preferred":false,"id":564127,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Linick, Justin","contributorId":139493,"corporation":false,"usgs":false,"family":"Linick","given":"Justin","email":"","affiliations":[{"id":7023,"text":"Jet Propulsion Laboratory, California Institute of Technology","active":true,"usgs":false}],"preferred":false,"id":564128,"contributorType":{"id":1,"text":"Authors"},"rank":4}]}} ,{"id":70143962,"text":"sir20155032 - 2015 - Groundwater quality in Geauga County, Ohio: status, including detection frequency of methane in water wells, 2009, and changes during 1978-2009","interactions":[],"lastModifiedDate":"2015-07-03T11:05:27","indexId":"sir20155032","displayToPublicDate":"2015-07-03T10:00:00","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":"2015-5032","title":"Groundwater quality in Geauga County, Ohio: status, including detection frequency of methane in water wells, 2009, and changes during 1978-2009","docAbstract":"Domestic wells that are not safeguarded by regular water-quality testing provide drinking water for 79 percent of the residents of Geauga County, in northeastern Ohio. Since 1978, the U.S. Geological Survey (USGS) has worked cooperatively with the Board of Commissioners and Geauga County Planning Commission to monitor the quality of groundwater in four commonly used aquifers in county—the glacial deposits, the Pottsville Formation, the Cuyahoga Group, and the Berea Sandstone. A 33-percent growth in population from 1980 to 2009 increased the potential for humans to influence groundwater resources by withdrawing more groundwater, disposing of more human waste near the land surface, treating an expanded network of township roads with deicing salt, and likely using more solvents, pesticides, and other chemicals on the land surface than were used in preceding decades.
\nTo describe the status of groundwater quality in 2009 and its suitability for drinking, USGS personnel collected samples of water prior to treatment from 16 wells (mostly domestic) during June 9–19. The samples were analyzed for 92 properties and constituents, 41 of which had human-health benchmarks to which analytical results could be compared to evaluate suitability for drinking. Four of these benchmarks were exceeded at the following frequencies: arsenic (2 of 16 wells, 12.5 percent), total coliform bacteria (2 of 16 wells, 12.5 percent), fecal coliform bacteria (1 of 14 wells, 7 percent), and sodium (6 of 16 wells, 38 percent). No domestic wells sampled in 2009 exceeded the health-based benchmark of 300 micrograms per liter (µg/L) for manganese, although 5 of 65 wells (8 percent) sampled since 1978 have. Analyses from domestic wells were augmented with water-quality data from seven public-supply well fields that were obtained from the Ohio Environmental Protection Agency. These public-supply data were typically collected between 2000 and 2010 and represent water samples that were collected prior to treatment or that were treated by a method that does not effectively remove the constituents of interest. Similar to the domestic-well data, these data indicated that some samples from public-supply wells have also exceeded health-based benchmarks for arsenic and sodium, along with occasional exceedances of health-based benchmarks for cadmium and lead. Concentrations of nitrate, pesticides, and volatile organic compounds in ground-water samples from domestic and (or) public-supply wells were either considerably less than the human-health benchmarks for these constituents or were not detected.
\nWater-quality data collected in 2009 were also compared to aesthetically based benchmarks developed by the U.S. Environmental Protection Agency, called Secondary Maximum Contaminant Levels (SMCLs). Iron and manganese most frequently exceeded SMCLs (in samples from 10 of 16 domestic wells and in untreated water from 3 of 4 public-supply well fields).
\nTo evaluate the frequency of methane detection in water wells in the county, the USGS sampled 16 wells across the county and screened the samples for combustible gas within the headspace (the air above the water in a closed container). Water from three (19 percent) of the wells contained detectable combustible gas (0.10 to 0.40 percent by volume). All three detections were from wells tapping the Cuyahoga Group or the Berea Sandstone, and all detections were less than the lower explosive limit of 5 percent by volume—the concentration at which methane in air can be flammable if an ignition source is present. Analyses of dissolved gas composition in water from these three wells showed methane concentrations ranging from 0.007 to 1.8 milligrams per liter (mg/L).
\nThe primary effect of human activities on groundwater quality found during this study is the input of salinity, or chloride, near land surface. On the basis of ratios of chloride to bromide, the main sources of chloride are road salt and septic leachate rather than oil-field brines (either spilled at land surface or sprayed on roads for dust control). The correlation of chloride concentration to distance of well from road for 31 wells in the county sampled by the U.S. Geological Survey in 1999 suggests that road salt is the dominant source of chloride.
\nThe majority of constituents exceeding health-based and aesthetically based benchmarks in groundwater were those that are naturally present in aquifer rocks and sediments rather than constituents introduced by human activities. Concentrations of such natural contaminants are controlled by geochemical processes in the subsurface, particularly by oxidation-reduction (redox) reactions. The categorization of redox conditions based on the water quality of 116 samples collected from 65 wells in Geauga County during 1978 through 2009 indicates that most groundwater samples were strongly reducing (60 percent) or oxic (18 percent). Oxic waters were found only in the Pottsville Formation and Berea Sandstone and were generally associated with nitrate at concentrations of 0.38 to 6.0 mg/L. Strongly reducing waters occurred in all four commonly used aquifers and were associated with the following naturally occurring contaminants: (1) arsenic and manganese at concentrations exceeding the health-based benchmarks (10 µg/L and 300 µg/L, respectively) in some samples, (2) iron and manganese at concentrations exceeding the aesthetically based standards (300 µg/L and 50 µg/L, respectively) in most samples, and (3) total sulfides (consisting of hydrogen sulfide gas with its characteristic rotten-egg odor and [or] iron sulfide minerals that appear as finely disseminated particulates in water).
\nBecause of the association of redox conditions with specific contaminants, attempts were made to further document spatially where oxic and strongly reducing conditions occur so that contaminant occurrence can be better anticipated by planners and well owners. Within the Pottsville Formation, wells tapping strongly reducing groundwater tended to have a greater thickness of overlying low-permeability (recharge-inhibiting) material such as clay and shale than other wells tapping oxic or nitrate-reducing groundwater. In the Berea Sandstone, oxic conditions were found at well locations where either depth to groundwater was shallow (less than 45 feet [ft] below land surface) or the measured water level was within the open interval (uncased portion) of the well, whereas strongly reducing groundwater was found at well locations where depths to water were greater than 60 ft below land surface and measured water levels were 15 ft or more above the open interval of the well.
\nTo evaluate whether constituent concentrations consistently increased or decreased over time, the strength of the association between sampling year (time) and constituent concentration was statistically evaluated for 116 water-quality samples collected by the USGS in 1978, 1980, 1986, 1999, and 2009 from a total of 65 wells across the county (generally domestic wells or wells serving small businesses or churches). Results indicate that many of the constituents that have been analyzed for decades exhibited no consistent temporal trends at a statistically significant level (p-value less than 0.05); fluctuations in concentrations of these constituents represent natural variation in groundwater quality. Dissolved oxygen, calcium, and sulfate concentrations and chloride:bromide ratios increased over time in one or more aquifers, while pH and concentrations of bromide and dissolved organic carbon decreased over time. Detections of total coliform bacteria and nitrate did not become more frequent from 1986 to 2009, even though potential sources of these constituents, such as number of septic systems (linked to population) and percent developed land in the county, increased during this period.
","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/sir20155032","collaboration":"Geauga County Planning Commission; Geauga County Board of County Commissioners","usgsCitation":"Jagucki, M.L., Kula, S.P., and Mailot, B.E., 2015, Groundwater quality in Geauga County, Ohio: status, including detection frequency of methane in water wells, 2009, and changes during 1978-2009: U.S. Geological Survey Scientific Investigations Report 2015-5032, Report: x, 116 p.; Appendix, https://doi.org/10.3133/sir20155032.","productDescription":"Report: x, 116 p.; Appendix","numberOfPages":"130","onlineOnly":"N","additionalOnlineFiles":"Y","temporalStart":"1978-01-01","temporalEnd":"2009-12-31","ipdsId":"IP-048863","costCenters":[{"id":513,"text":"Ohio Water Science Center","active":true,"usgs":true}],"links":[{"id":305569,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/sir20155032.jpg"},{"id":305553,"type":{"id":15,"text":"Index Page"},"url":"https://pubs.usgs.gov/sir/2015/5032/"},{"id":305567,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/sir/2015/5032/pdf/sir20155032.pdf","text":"Report","size":"6 MB","linkFileType":{"id":1,"text":"pdf"},"description":"Report"},{"id":305568,"type":{"id":3,"text":"Appendix"},"url":"https://pubs.usgs.gov/sir/2015/5032/table/sir20155032_table4-1.xls","text":"Appendix Table 4-1","size":"411 KB","linkFileType":{"id":3,"text":"xlsx"},"description":"Appendix Table 4-1","linkHelpText":"Selected chemical characteristics of water samples collected by the U.S. Geological Survey in Geauga County, Ohio, 1978–2009."}],"country":"United States","county":"Geauga County","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -81.41693115234375,\n 41.34588656996289\n ],\n [\n -81.41693115234375,\n 41.71085461169185\n ],\n [\n -80.9967041015625,\n 41.71085461169185\n ],\n [\n -80.9967041015625,\n 41.34588656996289\n ],\n [\n -81.41693115234375,\n 41.34588656996289\n ]\n ]\n ]\n }\n }\n ]\n}","publishingServiceCenter":{"id":6,"text":"Columbus PSC"},"noUsgsAuthors":false,"publicationStatus":"PW","scienceBaseUri":"57f7eef3e4b0bc0bec09ee14","contributors":{"authors":[{"text":"Jagucki, Martha L. 0000-0003-3798-8393 mjagucki@usgs.gov","orcid":"https://orcid.org/0000-0003-3798-8393","contributorId":1794,"corporation":false,"usgs":true,"family":"Jagucki","given":"Martha","email":"mjagucki@usgs.gov","middleInitial":"L.","affiliations":[{"id":513,"text":"Ohio Water Science Center","active":true,"usgs":true}],"preferred":true,"id":564106,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Kula, Stephanie P. spkula@usgs.gov","contributorId":4666,"corporation":false,"usgs":true,"family":"Kula","given":"Stephanie","email":"spkula@usgs.gov","middleInitial":"P.","affiliations":[{"id":513,"text":"Ohio Water Science Center","active":true,"usgs":true}],"preferred":true,"id":564107,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Mailot, Brian E. bemailot@usgs.gov","contributorId":2569,"corporation":false,"usgs":true,"family":"Mailot","given":"Brian","email":"bemailot@usgs.gov","middleInitial":"E.","affiliations":[{"id":513,"text":"Ohio Water Science Center","active":true,"usgs":true}],"preferred":true,"id":564108,"contributorType":{"id":1,"text":"Authors"},"rank":3}]}} ,{"id":70154794,"text":"70154794 - 2015 - A collision risk model to predict avian fatalities at wind facilities: an example using golden eagles, Aquila chrysaetos","interactions":[],"lastModifiedDate":"2015-07-06T11:41:25","indexId":"70154794","displayToPublicDate":"2015-07-02T12:45:00","publicationYear":"2015","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":2980,"text":"PLoS ONE","active":true,"publicationSubtype":{"id":10}},"title":"A collision risk model to predict avian fatalities at wind facilities: an example using golden eagles, Aquila chrysaetos","docAbstract":"Wind power is a major candidate in the search for clean, renewable energy. Beyond the technical and economic challenges of wind energy development are environmental issues that may restrict its growth. Avian fatalities due to collisions with rotating turbine blades are a leading concern and there is considerable uncertainty surrounding avian collision risk at wind facilities. This uncertainty is not reflected in many models currently used to predict the avian fatalities that would result from proposed wind developments. We introduce a method to predict fatalities at wind facilities, based on pre-construction monitoring. Our method can directly incorporate uncertainty into the estimates of avian fatalities and can be updated if information on the true number of fatalities becomes available from post-construction carcass monitoring. Our model considers only three parameters: hazardous footprint, bird exposure to turbines and collision probability. By using a Bayesian analytical framework we account for uncertainties in these values, which are then reflected in our predictions and can be reduced through subsequent data collection. The simplicity of our approach makes it accessible to ecologists concerned with the impact of wind development, as well as to managers, policy makers and industry interested in its implementation in real-world decision contexts. We demonstrate the utility of our method by predicting golden eagle (Aquila chrysaetos) fatalities at a wind installation in the United States. Using pre-construction data, we predicted 7.48 eagle fatalities year-1 (95% CI: (1.1, 19.81)). The U.S. Fish and Wildlife Service uses the 80th quantile (11.0 eagle fatalities year-1) in their permitting process to ensure there is only a 20% chance a wind facility exceeds the authorized fatalities. Once data were available from two-years of post-construction monitoring, we updated the fatality estimate to 4.8 eagle fatalities year-1 (95% CI: (1.76, 9.4); 80th quantile, 6.3). In this case, the increased precision in the fatality prediction lowered the level of authorized take, and thus lowered the required amount of compensatory mitigation.
","language":"English","publisher":"Public Library of Science","publisherLocation":"San Francisco, CA","doi":"10.1371/journal.pone.0130978","usgsCitation":"New, L., Bjerre, E., Millsap, B.A., Otto, M.C., and Runge, M.C., 2015, A collision risk model to predict avian fatalities at wind facilities: an example using golden eagles, Aquila chrysaetos: PLoS ONE, v. 10, no. 7, p. 1-12, https://doi.org/10.1371/journal.pone.0130978.","productDescription":"12 p.","startPage":"1","endPage":"12","numberOfPages":"12","onlineOnly":"N","additionalOnlineFiles":"N","ipdsId":"IP-049300","costCenters":[{"id":531,"text":"Patuxent Wildlife Research Center","active":true,"usgs":true}],"links":[{"id":471954,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.1371/journal.pone.0130978","text":"Publisher Index Page"},{"id":305581,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"volume":"10","issue":"7","publishingServiceCenter":{"id":10,"text":"Baltimore PSC"},"noUsgsAuthors":false,"publicationDate":"2015-07-02","publicationStatus":"PW","scienceBaseUri":"559ba6a8e4b0b94a640170c5","contributors":{"authors":[{"text":"New, Leslie lnew@usgs.gov","contributorId":145484,"corporation":false,"usgs":true,"family":"New","given":"Leslie","email":"lnew@usgs.gov","affiliations":[{"id":531,"text":"Patuxent Wildlife Research Center","active":true,"usgs":true}],"preferred":true,"id":564175,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Bjerre, Emily","contributorId":44451,"corporation":false,"usgs":true,"family":"Bjerre","given":"Emily","affiliations":[],"preferred":false,"id":564176,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Millsap, Brian A.","contributorId":75841,"corporation":false,"usgs":true,"family":"Millsap","given":"Brian","email":"","middleInitial":"A.","affiliations":[],"preferred":false,"id":564177,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Otto, Mark C.","contributorId":6307,"corporation":false,"usgs":true,"family":"Otto","given":"Mark","email":"","middleInitial":"C.","affiliations":[],"preferred":false,"id":564178,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Runge, Michael C. 0000-0002-8081-536X mrunge@usgs.gov","orcid":"https://orcid.org/0000-0002-8081-536X","contributorId":3358,"corporation":false,"usgs":true,"family":"Runge","given":"Michael","email":"mrunge@usgs.gov","middleInitial":"C.","affiliations":[{"id":531,"text":"Patuxent Wildlife Research Center","active":true,"usgs":true}],"preferred":true,"id":564174,"contributorType":{"id":1,"text":"Authors"},"rank":5}]}} ]}