{"pageNumber":"460","pageRowStart":"11475","pageSize":"25","recordCount":46644,"records":[{"id":70156262,"text":"70156262 - 2015 - Source mechanism of small long-period events at Mount St. Helens in July 2005 using template matching, phase-weighted stacking, and full-waveform inversion","interactions":[],"lastModifiedDate":"2015-10-26T14:03:20","indexId":"70156262","displayToPublicDate":"2015-08-18T14:00:00","publicationYear":"2015","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":2312,"text":"Journal of Geophysical Research","active":true,"publicationSubtype":{"id":10}},"title":"Source mechanism of small long-period events at Mount St. Helens in July 2005 using template matching, phase-weighted stacking, and full-waveform inversion","docAbstract":"<p><span>Long-period (LP, 0.5-5&thinsp;Hz) seismicity, observed at volcanoes worldwide, is a recognized signature of unrest and eruption. Cyclic LP &ldquo;drumbeating&rdquo; was the characteristic seismicity accompanying the sustained dome-building phase of the 2004&ndash;2008 eruption of Mount St. Helens (MSH), WA. However, together with the LP drumbeating was a near-continuous, randomly occurring series of tiny LP seismic events (LP &ldquo;subevents&rdquo;), which may hold important additional information on the mechanism of seismogenesis at restless volcanoes. We employ template matching, phase-weighted stacking, and full-waveform inversion to image the source mechanism of one multiplet of these LP subevents at MSH in July 2005. The signal-to-noise ratios of the individual events are too low to produce reliable waveform-inversion results, but the events are repetitive and can be stacked. We apply network-based template matching to 8&thinsp;days of continuous velocity waveform data from 29 June to 7 July 2005 using a master event to detect 822 network triggers. We stack waveforms for 359 high-quality triggers at each station and component, using a combination of linear and phase-weighted stacking to produce clean stacks for use in waveform inversion. The derived source mechanism pointsto the volumetric oscillation (~10&thinsp;m</span><sup>3</sup><span>) of a subhorizontal crack located at shallow depth (~30&thinsp;m) in an area to the south of Crater Glacier in the southern portion of the breached MSH crater. A possible excitation mechanism is the sudden condensation of metastable steam from a shallow pressurized hydrothermal system as it encounters cool meteoric water in the outer parts of the edifice, perhaps supplied from snow melt.</span></p>","language":"English","publisher":"Wiley","doi":"10.1002/2015JB012279","usgsCitation":"Matoza, R.S., Chouet, B.A., Dawson, P.B., Shearer, P., Haney, M.M., Waite, G.P., Moran, S.C., and Mikesell, T.D., 2015, Source mechanism of small long-period events at Mount St. Helens in July 2005 using template matching, phase-weighted stacking, and full-waveform inversion: Journal of Geophysical Research, v. 120, no. 9, p. 6351-6364, https://doi.org/10.1002/2015JB012279.","productDescription":"14 p.","startPage":"6351","endPage":"6364","onlineOnly":"N","additionalOnlineFiles":"N","ipdsId":"IP-066288","costCenters":[{"id":617,"text":"Volcano Science Center","active":true,"usgs":true}],"links":[{"id":471869,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://escholarship.org/uc/item/7dv8w3bq","text":"Publisher Index Page"},{"id":306870,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","state":"Washington","otherGeospatial":"Mount St. Helens","geographicExtents":"{\n  \"type\": \"FeatureCollection\",\n  \"features\": [\n    {\n      \"type\": \"Feature\",\n      \"properties\": {},\n      \"geometry\": {\n        \"type\": \"Polygon\",\n        \"coordinates\": [\n          [\n            [\n              -122.2723388671875,\n              46.14939437647686\n            ],\n            [\n              -122.2723388671875,\n              46.283376780187254\n            ],\n            [\n              -122.09793090820311,\n              46.283376780187254\n            ],\n            [\n              -122.09793090820311,\n              46.14939437647686\n            ],\n            [\n              -122.2723388671875,\n              46.14939437647686\n            ]\n          ]\n        ]\n      }\n    }\n  ]\n}","volume":"120","issue":"9","publishingServiceCenter":{"id":14,"text":"Menlo Park PSC"},"noUsgsAuthors":false,"publicationDate":"2015-09-18","publicationStatus":"PW","scienceBaseUri":"55d44923e4b0518e3546947c","contributors":{"authors":[{"text":"Matoza, Robin S.","contributorId":54873,"corporation":false,"usgs":true,"family":"Matoza","given":"Robin","email":"","middleInitial":"S.","affiliations":[],"preferred":false,"id":568434,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Chouet, Bernard A. 0000-0001-5527-0532 chouet@usgs.gov","orcid":"https://orcid.org/0000-0001-5527-0532","contributorId":3304,"corporation":false,"usgs":true,"family":"Chouet","given":"Bernard","email":"chouet@usgs.gov","middleInitial":"A.","affiliations":[{"id":617,"text":"Volcano Science Center","active":true,"usgs":true}],"preferred":true,"id":568433,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Dawson, Phillip B. dawson@usgs.gov","contributorId":2751,"corporation":false,"usgs":true,"family":"Dawson","given":"Phillip","email":"dawson@usgs.gov","middleInitial":"B.","affiliations":[{"id":617,"text":"Volcano Science Center","active":true,"usgs":true}],"preferred":true,"id":568435,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Shearer, Peter M.","contributorId":78946,"corporation":false,"usgs":true,"family":"Shearer","given":"Peter M.","affiliations":[],"preferred":false,"id":568436,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Haney, Matthew M. mhaney@usgs.gov","contributorId":2943,"corporation":false,"usgs":true,"family":"Haney","given":"Matthew","email":"mhaney@usgs.gov","middleInitial":"M.","affiliations":[{"id":617,"text":"Volcano Science Center","active":true,"usgs":true}],"preferred":false,"id":568437,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"Waite, Gregory P.","contributorId":146613,"corporation":false,"usgs":false,"family":"Waite","given":"Gregory","email":"","middleInitial":"P.","affiliations":[{"id":16203,"text":"Michigan Technological university","active":true,"usgs":false}],"preferred":false,"id":568438,"contributorType":{"id":1,"text":"Authors"},"rank":6},{"text":"Moran, Seth C. 0000-0001-7308-9649 smoran@usgs.gov","orcid":"https://orcid.org/0000-0001-7308-9649","contributorId":548,"corporation":false,"usgs":true,"family":"Moran","given":"Seth","email":"smoran@usgs.gov","middleInitial":"C.","affiliations":[{"id":615,"text":"Volcano Hazards Program","active":true,"usgs":true},{"id":617,"text":"Volcano Science Center","active":true,"usgs":true}],"preferred":true,"id":568439,"contributorType":{"id":1,"text":"Authors"},"rank":7},{"text":"Mikesell, T. Dylan","contributorId":52856,"corporation":false,"usgs":true,"family":"Mikesell","given":"T.","email":"","middleInitial":"Dylan","affiliations":[],"preferred":false,"id":568440,"contributorType":{"id":1,"text":"Authors"},"rank":8}]}}
,{"id":70155011,"text":"70155011 - 2015 - The influence of logjams on largemouth bass (<i>Micropterus salmoides</i>) concentrations on the lower Roanoke River, a large sand-bed river","interactions":[],"lastModifiedDate":"2015-08-19T09:17:18","indexId":"70155011","displayToPublicDate":"2015-08-18T11:15:00","publicationYear":"2015","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":3301,"text":"River Research and Applications","active":true,"publicationSubtype":{"id":10}},"title":"The influence of logjams on largemouth bass (<i>Micropterus salmoides</i>) concentrations on the lower Roanoke River, a large sand-bed river","docAbstract":"<p>This study examines the relation between logjams and largemouth bass (Micropterus salmoides) on the alluvial sand-bed lower Roanoke River. Disparate data sets from previous bank erosion, fisheries, and large wood studies were used to compare the distribution of largemouth bass with logjam frequency. Logjams are related to the frequency of bank mass wasting increasing from near an upstream dam to the middle reach of the study segment and then decreasing as the river approaches sea level. The highest concentration of largemouth bass and logjams was in the middle reach (110 fish per hour and 21 jams per km). Another measure of largemouth bass distribution, fish biomass density (g h1 ), had a similar trend with logjams and was a better predictor of fish distribution versus logjams (R2= 0.6 and 0.8 and p = 0.08 and 0.02 for fish per hour and g h1 versus logjam, respectively). We theorize that the preference for adult bass to congregate near logjams indicates the use of the jams as feeding areas. The results of a principal component analysis indicate that fish biomass concentration is much more related to logjam frequency than channel geometry (width, depth, and bank height), bed grain size, bank erosion, or turbidity. The results of this research support recent studies on in-channel wood and fisheries: Logjams appear to be important for maintaining, or increasing, both largemouth bass numbers and total biomass of fish in large eastern North American rivers. Persistent logjams, important as habitat, exist where relatively undisturbed river reaches allow for bank erosion inputs of wood and available anchoring locations. Published 2014. This article is a U.S. Government work and is in the public domain in the USA.</p>","language":"English","publisher":"Wiley","doi":"10.1002/rra.2779","usgsCitation":"Schenk, E.R., McCargo, J.W., Moulin, B., Hupp, C.R., and Richter, J.M., 2015, The influence of logjams on largemouth bass (<i>Micropterus salmoides</i>) concentrations on the lower Roanoke River, a large sand-bed river: River Research and Applications, v. 31, no. 6, p. 704-711, https://doi.org/10.1002/rra.2779.","productDescription":"8 p.","startPage":"704","endPage":"711","onlineOnly":"N","additionalOnlineFiles":"N","ipdsId":"IP-056655","costCenters":[{"id":436,"text":"National Research Program - Eastern Branch","active":true,"usgs":true}],"links":[{"id":306846,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","state":"North Carolina","otherGeospatial":"Roanoke River","geographicExtents":"{\n  \"type\": \"FeatureCollection\",\n  \"features\": [\n    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,{"id":70157353,"text":"70157353 - 2015 - Dynamic models of an earthquake and tsunami offshore Ventura, California","interactions":[],"lastModifiedDate":"2022-11-03T14:49:10.300532","indexId":"70157353","displayToPublicDate":"2015-08-18T00:00:00","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":"Dynamic models of an earthquake and tsunami offshore Ventura, California","docAbstract":"<p><span>The Ventura basin in Southern California includes coastal dip-slip faults that can likely produce earthquakes of magnitude 7 or greater and significant local tsunamis. We construct a 3-D dynamic rupture model of an earthquake on the Pitas Point and Lower Red Mountain faults to model low-frequency ground motion and the resulting tsunami, with a goal of elucidating the seismic and tsunami hazard in this area. Our model results in an average stress drop of 6&thinsp;MPa, an average fault slip of 7.4&thinsp;m, and a moment magnitude of 7.7, consistent with regional paleoseismic data. Our corresponding tsunami model uses final seafloor displacement from the rupture model as initial conditions to compute local propagation and inundation, resulting in large peak tsunami amplitudes northward and eastward due to site and path effects. Modeled inundation in the Ventura area is significantly greater than that indicated by state of California's current reference inundation line.</span></p>","language":"English","publisher":"Wiley","doi":"10.1002/2015GL064507","usgsCitation":"Kenny J. Ryan, Geist, E.L., Barall, M., and David D. Oglesby, 2015, Dynamic models of an earthquake and tsunami offshore Ventura, California: Geophysical Research Letters, v. 42, no. 16, p. 6599-6606, https://doi.org/10.1002/2015GL064507.","productDescription":"8 p.","startPage":"6599","endPage":"6606","onlineOnly":"N","additionalOnlineFiles":"N","ipdsId":"IP-063730","costCenters":[{"id":520,"text":"Pacific Coastal and Marine Science Center","active":true,"usgs":true}],"links":[{"id":308337,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","state":"California","otherGeospatial":"Ventura","geographicExtents":"{\n  \"type\": \"FeatureCollection\",\n  \"features\": [\n    {\n      \"type\": \"Feature\",\n      \"properties\": {},\n      \"geometry\": {\n        \"coordinates\": [\n          [\n            [\n              -119.227446217791,\n              34.15691272027392\n            ],\n            [\n              -119.22195066976101,\n              34.15634427052608\n            ],\n            [\n              -119.22057678275345,\n              34.23588998927826\n            ],\n            [\n              -119.19790764713011,\n              34.23759371899423\n            ],\n            [\n              -119.13951744931241,\n              34.27847288706779\n            ],\n            [\n              -119.1443260538386,\n              34.37037834793102\n            ],\n            [\n              -119.30850555123234,\n              34.37037834793102\n            ],\n            [\n              -119.3806346191248,\n              34.3323802713181\n            ],\n            [\n              -119.37788684510998,\n              34.1546388983235\n            ],\n            [\n              -119.227446217791,\n              34.15691272027392\n            ]\n          ]\n        ],\n        \"type\": \"Polygon\"\n      }\n    }\n  ]\n}","volume":"42","issue":"16","publishingServiceCenter":{"id":14,"text":"Menlo Park PSC"},"noUsgsAuthors":false,"publicationDate":"2015-08-18","publicationStatus":"PW","scienceBaseUri":"56012a40e4b03bc34f5443f7","contributors":{"authors":[{"text":"Kenny J. 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Ryan","affiliations":[{"id":6984,"text":"UC Riverside","active":true,"usgs":false}],"preferred":false,"id":572819,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Geist, Eric L. 0000-0003-0611-1150 egeist@usgs.gov","orcid":"https://orcid.org/0000-0003-0611-1150","contributorId":1956,"corporation":false,"usgs":true,"family":"Geist","given":"Eric","email":"egeist@usgs.gov","middleInitial":"L.","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":572818,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Barall, Michael mbarall@usgs.gov","contributorId":147825,"corporation":false,"usgs":true,"family":"Barall","given":"Michael","email":"mbarall@usgs.gov","affiliations":[{"id":237,"text":"Earthquake Science Center","active":true,"usgs":true}],"preferred":true,"id":572820,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"David D. 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,{"id":70155908,"text":"ofr20151141 - 2015 - Collections management plan for the U.S. Geological Survey Woods Hole Coastal and Marine Science Center Data Library","interactions":[],"lastModifiedDate":"2015-08-17T09:35:06","indexId":"ofr20151141","displayToPublicDate":"2015-08-17T09:45:00","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":"2015-1141","title":"Collections management plan for the U.S. Geological Survey Woods Hole Coastal and Marine Science Center Data Library","docAbstract":"<p>The U.S. Geological Survey Woods Hole Coastal and Marine Science Center has created a Data Library to organize, preserve, and make available the field, laboratory, and modeling data collected and processed by Woods Hole Coastal and Marine Science Center staff. This Data Library supports current research efforts by providing unique, historic datasets with accompanying metadata. The Woods Hole Coastal and Marine Science Center&rsquo;s Data Library has custody of historic data and records that are still useful for research, and assists with preservation and distribution of marine science records and data in the course of scientific investigation and experimentation by researchers and staff at the science center.</p>\n<p>The data accession and retention policies employed by the Woods Hole Coastal and Marine Science Center Data Library are based on scientific need and the National Archives and Records Administration standards for Federal records retention. Criteria for inclusion of data and records into the Data Library, the scope of the Data Library holdings, and operating procedures for the management and running of the library are designed to support the research operations of the U.S. Geological Survey.</p>\n<p>This report explains the roles and detailed responsibilities of library and scientific staff, and provides step-by-step instructions for managing the collections of the Woods Hole Coastal and Marine Science Center Data Library.</p>\n<p>&nbsp;</p>","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/ofr20151141","usgsCitation":"List, K.M., Buczkowski, B.J., McCarthy, L.P., and Orton, A.M., 2015, Collections management plan for the U.S. Geological Survey Woods Hole Coastal and Marine Science Center Data Library: U.S. Geological Survey Open-File Report 2015–1141, 16 p., https://dx.doi.org/10.3133/ofr20151141.","productDescription":"vi, 16 p.","numberOfPages":"26","onlineOnly":"Y","additionalOnlineFiles":"N","ipdsId":"IP-060877","costCenters":[{"id":678,"text":"Woods Hole Coastal and Marine Science Center","active":true,"usgs":true}],"links":[{"id":306752,"rank":2,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/of/2015/1141/ofr20151141.pdf","text":"Report","size":"1.87 MB","description":"OFR 2015-1141"},{"id":306751,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/of/2015/1141/coverthb.jpg"}],"contact":"<p>Director, Woods Hole Coastal and Marine<br /> Science Center<br /> U.S. Geological Survey<br /> 384 Woods Hole Road<br /> Quissett Campus<br /> Woods Hole, MA 02543-1598<br /> <a href=\"mailto:WHSC_science_director@usgs.gov\">WHSC_science_director@usgs.gov</a><br /> 508-548-8700 or 508-457-2200</p>\n<p>Or visit our Web site at:<br /> <a href=\"http://woodshole.er.usgs.gov/\">http://woodshole.er.usgs.gov/</a></p>","tableOfContents":"<ul>\n<li>\n<p>Acknowledgments</p>\n</li>\n<li>\n<p>Abstract</p>\n</li>\n<li>\n<p>Introduction to the Woods Hole Coastal and Marine Science Center Data Library</p>\n</li>\n<li>\n<p>Scope of the Collections in the Data Library</p>\n</li>\n<li>\n<p>Geology Discipline Research Records Schedule</p>\n</li>\n<li>\n<p>Roles and Responsibilities</p>\n</li>\n<li>\n<p>Data Library Operations</p>\n</li>\n<li>\n<p>Summary</p>\n</li>\n<li>\n<p>Selected References</p>\n</li>\n<li>Glossary</li>\n</ul>","publishingServiceCenter":{"id":11,"text":"Pembroke PSC"},"publishedDate":"2015-08-17","noUsgsAuthors":false,"publicationDate":"2015-08-17","publicationStatus":"PW","scienceBaseUri":"57f7eec4e4b0bc0bec09ecab","contributors":{"authors":[{"text":"List, Kelleen M. klist@usgs.gov","contributorId":146246,"corporation":false,"usgs":true,"family":"List","given":"Kelleen","email":"klist@usgs.gov","middleInitial":"M.","affiliations":[{"id":678,"text":"Woods Hole Coastal and Marine Science Center","active":true,"usgs":true}],"preferred":false,"id":566733,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Buczkowski, Brian J. bbuczkowski@usgs.gov","contributorId":3524,"corporation":false,"usgs":true,"family":"Buczkowski","given":"Brian","email":"bbuczkowski@usgs.gov","middleInitial":"J.","affiliations":[{"id":678,"text":"Woods Hole Coastal and Marine Science Center","active":true,"usgs":true}],"preferred":false,"id":566734,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"McCarthy, Linda P. lpmccarthy@usgs.gov","contributorId":146247,"corporation":false,"usgs":true,"family":"McCarthy","given":"Linda","email":"lpmccarthy@usgs.gov","middleInitial":"P.","affiliations":[{"id":678,"text":"Woods Hole Coastal and Marine Science Center","active":true,"usgs":true}],"preferred":false,"id":566735,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Orton, Alice M.","contributorId":146248,"corporation":false,"usgs":false,"family":"Orton","given":"Alice","email":"","middleInitial":"M.","affiliations":[{"id":16643,"text":"Independent research","active":true,"usgs":false}],"preferred":false,"id":566736,"contributorType":{"id":1,"text":"Authors"},"rank":4}]}}
,{"id":70156155,"text":"70156155 - 2015 - Trends in Rocky Mountain amphibians and the role of beaver as a keystone species","interactions":[],"lastModifiedDate":"2019-12-11T10:16:41","indexId":"70156155","displayToPublicDate":"2015-08-17T04:15:00","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":"Trends in Rocky Mountain amphibians and the role of beaver as a keystone species","docAbstract":"<p><span>Despite prevalent awareness of global amphibian declines, there is still little information on trends for many widespread species. To inform land managers of trends on protected landscapes and identify potential conservation strategies, we collected occurrence data for five wetland-breeding amphibian species in four national parks in the U.S. Rocky Mountains during 2002&ndash;2011. We used explicit dynamics models to estimate variation in annual occupancy, extinction, and colonization of wetlands according to summer drought and several biophysical characteristics (e.g., wetland size, elevation), including the influence of North American beaver (</span><i>Castor canadensis</i><span>). We found more declines in occupancy than increases, especially in Yellowstone and Grand Teton national parks (NP), where three of four species declined since 2002. However, most species in Rocky Mountain NP were too rare to include in our analysis, which likely reflects significant historical declines. Although beaver were uncommon, their creation or modification of wetlands was associated with higher colonization rates for 4 of 5 amphibian species, producing a 34% increase in occupancy in beaver-influenced wetlands compared to wetlands without beaver influence. Also, colonization rates and occupancy of boreal toads (</span><i>Anaxyrus boreas</i><span>) and Columbia spotted frogs (</span><i>Rana luteiventris</i><span>) were ⩾2 times higher in beaver-influenced wetlands. These strong relationships suggest management for beaver that fosters amphibian recovery could counter declines in some areas. Our data reinforce reports of widespread declines of formerly and currently common species, even in areas assumed to be protected from most forms of human disturbance, and demonstrate the close ecological association between beaver and wetland-dependent species.</span></p>","language":"English","publisher":"Elsevier","doi":"10.1016/j.biocon.2015.05.005","usgsCitation":"Hossack, B.R., Gould, W., Patla, D.A., Muths, E.L., Daley, R., Legg, K., and Corn, P.S., 2015, Trends in Rocky Mountain amphibians and the role of beaver as a keystone species: Biological Conservation, v. 187, p. 260-269, https://doi.org/10.1016/j.biocon.2015.05.005.","productDescription":"9 p.","startPage":"260","endPage":"269","numberOfPages":"9","onlineOnly":"N","additionalOnlineFiles":"N","ipdsId":"IP-061859","costCenters":[{"id":481,"text":"Northern Rocky Mountain Science Center","active":true,"usgs":true}],"links":[{"id":471874,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.1016/j.biocon.2015.05.005","text":"Publisher Index Page"},{"id":306813,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","geographicExtents":"{\n  \"type\": \"FeatureCollection\",\n  \"features\": [\n    {\n      \"type\": \"Feature\",\n      \"properties\": {},\n      \"geometry\": {\n        \"type\": \"Polygon\",\n        \"coordinates\": [\n          [\n            [\n              -112.939453125,\n              48.86471476180277\n            ],\n            [\n              -119.267578125,\n              48.980216985374994\n            ],\n            [\n              -117.158203125,\n              46.86019101567027\n            ],\n            [\n              -117.158203125,\n              43.32517767999296\n            ],\n            [\n              -113.203125,\n              40.713955826286046\n            ],\n            [\n              -112.587890625,\n              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Conservation","publicationDate":"7/2015","auditedOn":"7/24/2015"},"contributors":{"authors":[{"text":"Hossack, Blake R. 0000-0001-7456-9564 blake_hossack@usgs.gov","orcid":"https://orcid.org/0000-0001-7456-9564","contributorId":1177,"corporation":false,"usgs":true,"family":"Hossack","given":"Blake","email":"blake_hossack@usgs.gov","middleInitial":"R.","affiliations":[{"id":481,"text":"Northern Rocky Mountain Science Center","active":true,"usgs":true},{"id":291,"text":"Fort Collins Science Center","active":true,"usgs":true}],"preferred":true,"id":567924,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Gould, William R.","contributorId":63780,"corporation":false,"usgs":true,"family":"Gould","given":"William R.","affiliations":[],"preferred":false,"id":567925,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Patla, Debra A.","contributorId":40059,"corporation":false,"usgs":true,"family":"Patla","given":"Debra","email":"","middleInitial":"A.","affiliations":[],"preferred":false,"id":567926,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Muths, Erin L. 0000-0002-5498-3132 muthse@usgs.gov","orcid":"https://orcid.org/0000-0002-5498-3132","contributorId":1260,"corporation":false,"usgs":true,"family":"Muths","given":"Erin","email":"muthse@usgs.gov","middleInitial":"L.","affiliations":[{"id":291,"text":"Fort Collins Science Center","active":true,"usgs":true}],"preferred":true,"id":567927,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Daley, Rob","contributorId":146450,"corporation":false,"usgs":false,"family":"Daley","given":"Rob","affiliations":[{"id":16696,"text":"5National Park Service, Greater Yellowstone Network, 2327 University Way, Suite 2, Bozeman, MT 59715, USA","active":true,"usgs":false}],"preferred":false,"id":567928,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"Legg, Kristin","contributorId":146451,"corporation":false,"usgs":false,"family":"Legg","given":"Kristin","affiliations":[{"id":16697,"text":"National Park Service, Greater Yellowstone Network, 2327 University Way, Suite 2, Bozeman, MT 59715, USA","active":true,"usgs":false}],"preferred":false,"id":567929,"contributorType":{"id":1,"text":"Authors"},"rank":6},{"text":"Corn, P. Stephen 0000-0002-4106-6335 steve_corn@usgs.gov","orcid":"https://orcid.org/0000-0002-4106-6335","contributorId":3227,"corporation":false,"usgs":true,"family":"Corn","given":"P.","email":"steve_corn@usgs.gov","middleInitial":"Stephen","affiliations":[{"id":481,"text":"Northern Rocky Mountain Science Center","active":true,"usgs":true}],"preferred":true,"id":567930,"contributorType":{"id":1,"text":"Authors"},"rank":7}]}}
,{"id":70160533,"text":"70160533 - 2015 - The effects of body size and climate on post-weaning survival of elephant seals at Heard Island","interactions":[],"lastModifiedDate":"2019-12-12T09:51:14","indexId":"70160533","displayToPublicDate":"2015-08-15T01:15:00","publicationYear":"2015","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":2515,"text":"Journal of Zoology","active":true,"publicationSubtype":{"id":10}},"title":"The effects of body size and climate on post-weaning survival of elephant seals at Heard Island","docAbstract":"<p><span>The population size of southern elephant seals in the southern Indian and Pacific Oceans decreased precipitously between the 1950s and 1990s. To investigate the reasons behind this, we studied the population of southern elephant seals at Heard Island between 1949 and 1954, using data collected by the early Australian National Antarctic Research Expeditions. Seals were marked and measured (lengths) as weaned pups, and resighted at Heard and Marion islands and in the Vestfold Hills, Antarctica in subsequent years. Bayesian state-space mark-recapture models were used to determine post-weaning survival. Yearling survival was consistently lower (ϕy: 0.28&ndash;0.40) than sub-adult survival (ϕs: 0.79&ndash;0.83). We found evidence for constant sub-adult survival and time-dependent resight probabilities. Weaning length was an important determinate of yearling survival, with the probability of survival increasing with individual length. There was some suggestion that the Southern Annular Mode influenced yearling survival but this evidence was not strong. Nonetheless, our results provide further support showing that size at independence affects yearling survival. Given the known sensitivity of southern elephant seal populations to survival early in life, it is possible that the decline in population size at Heard Island between the 1950s and 1990s like that at Macquarie Island was due to low yearling survival mediated through maternal ability to produce large pups and the dominant environmental conditions mothers experience during pregnancy.</span></p>","language":"English","publisher":"Cambridge University Press","publisherLocation":"Cambridge, England","doi":"10.1111/jzo.12279","usgsCitation":"McMahon, C.R., New, L., Fairley, E., Hindell, M., and Burton, H., 2015, The effects of body size and climate on post-weaning survival of elephant seals at Heard Island: Journal of Zoology, v. 297, no. 4, p. 301-308, https://doi.org/10.1111/jzo.12279.","productDescription":"8 p.","startPage":"301","endPage":"308","onlineOnly":"N","additionalOnlineFiles":"N","ipdsId":"IP-065166","costCenters":[{"id":531,"text":"Patuxent Wildlife Research Center","active":true,"usgs":true}],"links":[{"id":312736,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"Australia","otherGeospatial":"Heard Island","geographicExtents":"{\n  \"type\": \"FeatureCollection\",\n  \"features\": [\n    {\n      \"type\": \"Feature\",\n      \"properties\": {},\n      \"geometry\": {\n        \"type\": \"Polygon\",\n        \"coordinates\": [\n          [\n            [\n              73.21563720703125,\n              -53.21672395086342\n            ],\n            [\n              73.89129638671875,\n              -53.21672395086342\n            ],\n            [\n              73.89129638671875,\n              -52.95360230002848\n            ],\n            [\n              73.21563720703125,\n              -52.95360230002848\n            ],\n            [\n              73.21563720703125,\n              -53.21672395086342\n            ]\n          ]\n        ]\n      }\n    }\n  ]\n}","volume":"297","issue":"4","publishingServiceCenter":{"id":10,"text":"Baltimore PSC"},"noUsgsAuthors":false,"publicationDate":"2015-08-19","publicationStatus":"PW","scienceBaseUri":"567a8246e4b0a04ef490fd1d","contributors":{"authors":[{"text":"McMahon, Clive R","contributorId":150800,"corporation":false,"usgs":false,"family":"McMahon","given":"Clive","email":"","middleInitial":"R","affiliations":[{"id":18107,"text":"Sydney Institute of Marine Science","active":true,"usgs":false}],"preferred":false,"id":583072,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"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":583071,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Fairley, E.J.","contributorId":150809,"corporation":false,"usgs":false,"family":"Fairley","given":"E.J.","email":"","affiliations":[],"preferred":false,"id":583093,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Hindell, M.A.","contributorId":150810,"corporation":false,"usgs":false,"family":"Hindell","given":"M.A.","email":"","affiliations":[],"preferred":false,"id":583094,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Burton, H.R.","contributorId":150811,"corporation":false,"usgs":false,"family":"Burton","given":"H.R.","email":"","affiliations":[],"preferred":false,"id":583095,"contributorType":{"id":1,"text":"Authors"},"rank":5}]}}
,{"id":70148358,"text":"70148358 - 2015 - Using 15-minute acoustic data to analyze suspended-sediment dynamics in the Rio Grande in the Big Bend Region","interactions":[],"lastModifiedDate":"2017-06-07T10:19:55","indexId":"70148358","displayToPublicDate":"2015-08-15T00:00:00","publicationYear":"2015","noYear":false,"publicationType":{"id":24,"text":"Conference Paper"},"publicationSubtype":{"id":19,"text":"Conference Paper"},"title":"Using 15-minute acoustic data to analyze suspended-sediment dynamics in the Rio Grande in the Big Bend Region","docAbstract":"<p>The Rio Grande in the Big Bend region is subject to rapid geomorphic change consisting of channel narrowing during years of low flow, and channel widening during rare, large, long duration floods. Since the 1940s, there have been large declines in mean and peak stream flow, and the channel has progressively narrowed. Large, channel widening floods are infrequent and have failed to widen the channel to widths measured prior to the onset of channel narrowing in the 1940s. Before the most recent channel-widening flood in September 2008, the Rio Grande in the Big Bend was more than 50 percent narrower than measured in the 1940s. </p><p>Channel narrowing results in increased flood frequency and flood magnitude due to the loss of channel capacity and flood conveyance (Dean and Schmidt, 2011). Channel narrowing also results in the loss of important aquatic habitats such as backwaters and side-channels, because these habitats accumulate sediment and are converted to floodplains. Environmental managers are attempting to construct an environmental flow program for the purposes of minimizing channel narrowing during low flow years such that channel capacity, flood conveyance, and important aquatic habitats are maintained. Effective mitigation of channel narrowing processes requires an in-depth understanding of the predominant sediment source areas, the quantity of sediment input from those source areas, the parts of the flow regime responsible for the greatest sediment deposition, and the effect of managed flows in ameliorating the sediment loading that occurs within the channel. </p><p>Here, we analyze data collected with acoustic instrumentation at high temporal resolution to quantify suspended-sediment transport during a variety of flood types. We also investigate the effect of long duration managed flows in promoting sediment export and minimizing channel narrowing. </p>","conferenceTitle":"3rd Joint Federal Interagency Conference","conferenceDate":"April 19-23, 2015","conferenceLocation":"Reno, NV","language":"English","publisher":"Joint Federal Interagency Conference","usgsCitation":"Dean, D.J., Topping, D.J., Griffiths, R.E., Sabol, T.A., Schmidt, J.C., and Bennett, J.B., 2015, Using 15-minute acoustic data to analyze suspended-sediment dynamics in the Rio Grande in the Big Bend Region, 3rd Joint Federal Interagency Conference, Reno, NV, April 19-23, 2015, p. 1234-1245.","productDescription":"12 p.","startPage":"1234","endPage":"1245","ipdsId":"IP-060850","costCenters":[{"id":568,"text":"Southwest Biological Science Center","active":true,"usgs":true}],"links":[{"id":342201,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"},{"id":300907,"type":{"id":15,"text":"Index Page"},"url":"https://acwi.gov/sos/pubs/3rdJFIC/index.html"}],"country":"Mexico, United States","otherGeospatial":"Rio Grande, Big Bend region","geographicExtents":"{\n  \"type\": \"FeatureCollection\",\n  \"features\": [\n    {\n      \"type\": \"Feature\",\n      \"properties\": {},\n      \"geometry\": {\n        \"type\": \"Polygon\",\n        \"coordinates\": [\n          [\n            [\n              -104.556884765625,\n              28.933650754875583\n            ],\n            [\n              -102.75238037109375,\n              28.933650754875583\n            ],\n            [\n              -102.75238037109375,\n              29.75\n            ],\n            [\n              -104.556884765625,\n              29.75\n            ],\n            [\n              -104.556884765625,\n              28.933650754875583\n            ]\n          ]\n        ]\n      }\n    }\n  ]\n}","publishingServiceCenter":{"id":14,"text":"Menlo Park PSC"},"noUsgsAuthors":false,"publicationStatus":"PW","scienceBaseUri":"593910b0e4b0764e6c5e8889","contributors":{"authors":[{"text":"Dean, David J. 0000-0003-0203-088X djdean@usgs.gov","orcid":"https://orcid.org/0000-0003-0203-088X","contributorId":131047,"corporation":false,"usgs":true,"family":"Dean","given":"David","email":"djdean@usgs.gov","middleInitial":"J.","affiliations":[{"id":568,"text":"Southwest Biological Science Center","active":true,"usgs":true}],"preferred":true,"id":547831,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Topping, David J. 0000-0002-2104-4577 dtopping@usgs.gov","orcid":"https://orcid.org/0000-0002-2104-4577","contributorId":140985,"corporation":false,"usgs":true,"family":"Topping","given":"David","email":"dtopping@usgs.gov","middleInitial":"J.","affiliations":[{"id":568,"text":"Southwest Biological Science Center","active":true,"usgs":true}],"preferred":true,"id":547832,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Griffiths, Ronald E. 0000-0003-3620-2926 rgriffiths@usgs.gov","orcid":"https://orcid.org/0000-0003-3620-2926","contributorId":162,"corporation":false,"usgs":true,"family":"Griffiths","given":"Ronald","email":"rgriffiths@usgs.gov","middleInitial":"E.","affiliations":[{"id":568,"text":"Southwest Biological Science Center","active":true,"usgs":true}],"preferred":true,"id":547833,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Sabol, Thomas A. 0000-0002-4299-2285 tsabol@usgs.gov","orcid":"https://orcid.org/0000-0002-4299-2285","contributorId":3403,"corporation":false,"usgs":true,"family":"Sabol","given":"Thomas","email":"tsabol@usgs.gov","middleInitial":"A.","affiliations":[{"id":568,"text":"Southwest Biological Science Center","active":true,"usgs":true}],"preferred":true,"id":547834,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Schmidt, John C. 0000-0002-2988-3869 jcschmidt@usgs.gov","orcid":"https://orcid.org/0000-0002-2988-3869","contributorId":1983,"corporation":false,"usgs":true,"family":"Schmidt","given":"John","email":"jcschmidt@usgs.gov","middleInitial":"C.","affiliations":[{"id":568,"text":"Southwest Biological Science Center","active":true,"usgs":true}],"preferred":true,"id":547835,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"Bennett, Jeffery B.","contributorId":82993,"corporation":false,"usgs":true,"family":"Bennett","given":"Jeffery","email":"","middleInitial":"B.","affiliations":[],"preferred":false,"id":547836,"contributorType":{"id":1,"text":"Authors"},"rank":6}]}}
,{"id":70155952,"text":"ofr20151152 - 2015 - A conceptual  model for site-level  ecology of the giant gartersnake (<i>Thamnophis gigas</i>) in the Sacramento Valley, California","interactions":[],"lastModifiedDate":"2015-08-17T09:41:12","indexId":"ofr20151152","displayToPublicDate":"2015-08-14T18:00:00","publicationYear":"2015","noYear":false,"publicationType":{"id":18,"text":"Report"},"publicationSubtype":{"id":5,"text":"USGS Numbered Series"},"seriesTitle":{"id":330,"text":"Open-File Report","code":"OFR","onlineIssn":"2331-1258","printIssn":"0196-1497","active":true,"publicationSubtype":{"id":5}},"seriesNumber":"2015-1152","title":"A conceptual  model for site-level  ecology of the giant gartersnake (<i>Thamnophis gigas</i>) in the Sacramento Valley, California","docAbstract":"<p>Giant gartersnakes (Thamnophis gigas) comprise a species of semi-aquatic snakes precinctive to marshes in the Central Valley of California (Hansen and Brode, 1980; Rossman and others, 1996). Because more than 90 percent of their historical wetland habitat has been converted to other uses (Frayer and others, 1989; Garone, 2007), giant gartersnakes have been listed as threatened by the State of California (California Department of Fish and Game Commission , 1971) and the United States (U.S. Fish and Wildlife Service, 1993). Giant gartersnakes currently occur in a highly modified landscape, with most extant populations occurring in the rice - growing regions of the Sacramento Valley, especially near areas that historically were tule marsh habitat (Halstead and others, 2010, 2014).</p>\n<p>In ricelands and managed marshes, many operational decisions likely affect the health and viability of giant gartersnake populations. Land-use decisions, including the management of water, aquatic vegetation, terrestrial vegetation, and co-occurring species, have the potential to affect giant gartersnakes. Little is known, however, about the effects of these types of decisions on the viability of giant gartersnake populations. Bayesian network models are a useful tool to help guide decisions with uncertain outcomes. These models require the articulation of what experts think they know about a system, and facilitate learning about the hypothesized relations (Marcot and others, 2001; Uusitalo , 2007).</p>\n<p>Bayesian networks further provide a clear visual display of the model that facilitates understanding among various stakeholders (Marcot and others, 2001; Uusitalo , 2007). Empirical data and expert judgment can be combined, as continuous or categorical variables, to update knowledge about the system (Marcot and others, 2001; Uusitalo , 2007). Importantly, Bayesian network models allow inference from causes to consequences, but also from consequences to&nbsp;causes, so that data can inform the states of nodes (values of different random variables) in either direction (Marcot and others, 2001; Uusitalo , 2007). Because they can incorporate both decision nodes that represent management actions and utility nodes that quantify the costs and benefits of outcomes, Bayesian networks are ideally suited to risk analysis and adaptive management (Nyberg and others, 2006; Howes and others, 2010). Thus, Bayesian network models are useful in situations where empirical data are not available, such as questions concerning the responses of giant gartersnakes to management.</p>","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/ofr20151152","collaboration":"Prepared in cooperation with the California Department of Water Resources","usgsCitation":"Halstead, B.J., Wylie, G.D., Casazza, M.L., Hansen, E.C., Scherer, R.D., and Patterson, L.C., 2015, A conceptual model for site-level ecology of the giant gartersnake (<em>Thamnophis gigas</em>) in the Sacramento Valley, California: U.S. Geological Survey Open-File Report 2015-1152, 152 p., https://dx.doi.org/10.3133/ofr20151152.","productDescription":"iv, 152 p.","numberOfPages":"160","onlineOnly":"Y","additionalOnlineFiles":"N","ipdsId":"IP-061941","costCenters":[{"id":651,"text":"Western Ecological Research Center","active":true,"usgs":true}],"links":[{"id":306765,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/of/2015/1152/coverthb.jpg"},{"id":306766,"rank":2,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/of/2015/1152/ofr20151152.pdf","text":"Report","size":"4.2 MB","linkFileType":{"id":1,"text":"pdf"},"description":"OFR 2015-1152"}],"country":"United States","state":"California","otherGeospatial":"Sacramento Valley","geographicExtents":"{\n  \"type\": \"FeatureCollection\",\n  \"features\": [\n    {\n      \"type\": \"Feature\",\n      \"properties\": {},\n      \"geometry\": {\n        \"type\": \"Polygon\",\n        \"coordinates\": [\n          [\n            [\n              -122.26684570312499,\n              38.47079371120379\n            ],\n            [\n              -122.26684570312499,\n              39.51675478434244\n            ],\n            [\n              -121.42639160156249,\n              39.51675478434244\n            ],\n            [\n              -121.42639160156249,\n              38.47079371120379\n            ],\n            [\n              -122.26684570312499,\n              38.47079371120379\n            ]\n          ]\n        ]\n      }\n    }\n  ]\n}","contact":"<p class=\"p1\">Director, Western Ecological Research Center <br />U.S. Geological Survey<br />3020 State University Drive East <br />Sacramento, California 95819<br /><a href=\"http://werc.usgs.gov/\">http://werc.usgs.gov/</a></p>","tableOfContents":"<ul>\n<li>Background</li>\n<li>Study Objective&nbsp;</li>\n<li>Methods&nbsp;</li>\n<li>Results and Interpretation</li>\n<li>Acknowledgments&nbsp;</li>\n<li>References Cited&nbsp;</li>\n<li>Glossary&nbsp;</li>\n<li>Appendix A. Narrative Description of Nodes, and Logic and Assumptions Underlying Conditional Probability Table Values</li>\n<li>Appendix B. Conditional Probability Tables</li>\n</ul>","publishingServiceCenter":{"id":1,"text":"Sacramento PSC"},"publishedDate":"2015-08-14","noUsgsAuthors":false,"publicationDate":"2015-08-14","publicationStatus":"PW","scienceBaseUri":"57f7eec4e4b0bc0bec09ecad","contributors":{"authors":[{"text":"Halstead, Brian J. 0000-0002-5535-6528 bhalstead@usgs.gov","orcid":"https://orcid.org/0000-0002-5535-6528","contributorId":3051,"corporation":false,"usgs":true,"family":"Halstead","given":"Brian J.","email":"bhalstead@usgs.gov","affiliations":[{"id":651,"text":"Western Ecological Research Center","active":true,"usgs":true},{"id":200,"text":"Coop Res Unit Seattle","active":true,"usgs":true}],"preferred":true,"id":567336,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Wylie, Glenn D. 0000-0002-7061-6658 glenn_wylie@usgs.gov","orcid":"https://orcid.org/0000-0002-7061-6658","contributorId":3052,"corporation":false,"usgs":true,"family":"Wylie","given":"Glenn","email":"glenn_wylie@usgs.gov","middleInitial":"D.","affiliations":[{"id":651,"text":"Western Ecological Research Center","active":true,"usgs":true}],"preferred":true,"id":567337,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Casazza, Michael L. 0000-0002-5636-735X mike_casazza@usgs.gov","orcid":"https://orcid.org/0000-0002-5636-735X","contributorId":2091,"corporation":false,"usgs":true,"family":"Casazza","given":"Michael","email":"mike_casazza@usgs.gov","middleInitial":"L.","affiliations":[{"id":651,"text":"Western Ecological Research Center","active":true,"usgs":true}],"preferred":true,"id":567338,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Hansen, Eric C.","contributorId":146299,"corporation":false,"usgs":false,"family":"Hansen","given":"Eric","email":"","middleInitial":"C.","affiliations":[{"id":16663,"text":"Eric C. Hansen Consulting","active":true,"usgs":false}],"preferred":false,"id":567339,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Scherer, Rick D.","contributorId":97368,"corporation":false,"usgs":false,"family":"Scherer","given":"Rick","email":"","middleInitial":"D.","affiliations":[{"id":6674,"text":"Department of Integrative Biology, University of Colorado Denver","active":true,"usgs":false}],"preferred":false,"id":567340,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"Patterson, Laura C.","contributorId":146300,"corporation":false,"usgs":false,"family":"Patterson","given":"Laura","email":"","middleInitial":"C.","affiliations":[{"id":6952,"text":"California Department of Fish and Wildlife","active":true,"usgs":false}],"preferred":false,"id":567341,"contributorType":{"id":1,"text":"Authors"},"rank":6}]}}
,{"id":70155191,"text":"ofr20151125 - 2015 - The Boulder magnetic observatory","interactions":[],"lastModifiedDate":"2015-08-17T08:45:46","indexId":"ofr20151125","displayToPublicDate":"2015-08-14T13:00:00","publicationYear":"2015","noYear":false,"publicationType":{"id":18,"text":"Report"},"publicationSubtype":{"id":5,"text":"USGS Numbered Series"},"seriesTitle":{"id":330,"text":"Open-File Report","code":"OFR","onlineIssn":"2331-1258","printIssn":"0196-1497","active":true,"publicationSubtype":{"id":5}},"seriesNumber":"2015-1125","title":"The Boulder magnetic observatory","docAbstract":"<p>The Boulder magnetic observatory has, since 1963, been operated by the Geomagnetism Program of the U.S. Geological Survey in accordance with Bureau and national priorities. Data from the observatory are used for a wide variety of scientific purposes, both pure and applied. The observatory also supports developmental projects within the Geomagnetism Program and collaborative projects with allied geophysical agencies.</p>","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/ofr20151125","usgsCitation":"Love, J.J., Finn, C.A., Pedrie, K.L., and Blum, C.C., 2015, The Boulder magnetic observatory: U.S. Geological Survey\nOpen-File Report 2015–1125, 8 p., https://dx.doi.org/10.3133/ofr20151125.","productDescription":"iii, 13 p.","numberOfPages":"15","onlineOnly":"Y","additionalOnlineFiles":"N","ipdsId":"IP-066290","costCenters":[{"id":300,"text":"Geologic Hazards Science Center","active":true,"usgs":true}],"links":[{"id":306618,"rank":2,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/of/2015/1125/ofr20151125.pdf","text":"Report","size":"4.89","linkFileType":{"id":1,"text":"pdf"},"description":"OFR 2015-1125"},{"id":306617,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/of/2015/1125/coverthb.jpg"}],"country":"United States","state":"Colorado","city":"Boulder","otherGeospatial":"Boulder Magnetic Observatory","geographicExtents":"{\n  \"type\": \"FeatureCollection\",\n  \"features\": [\n    {\n      \"type\": \"Feature\",\n      \"properties\": {},\n      \"geometry\": {\n        \"type\": \"Polygon\",\n        \"coordinates\": [\n          [\n            [\n              -105.25279998779297,\n              40.118515137824204\n            ],\n            [\n              -105.25279998779297,\n              40.148438503139076\n            ],\n            [\n              -105.22430419921875,\n              40.148438503139076\n            ],\n            [\n              -105.22430419921875,\n              40.118515137824204\n            ],\n            [\n              -105.25279998779297,\n              40.118515137824204\n            ]\n          ]\n        ]\n      }\n    }\n  ]\n}","contact":"<p>Geomagnetism Program<br /> U.S. Geological Survey<br /> Box 25046, MS&ndash;966<br /> Denver, CO 80225&ndash;0046<br /><a href=\"http://geomag.usgs.gov/\">http://geomag.usgs.gov/</a></p>","tableOfContents":"<ul>\n<li>Abstract</li>\n<li>Introduction</li>\n<li>Location</li>\n<li>Operational History</li>\n<li>Facilities</li>\n<li>Data Types</li>\n<li>Partners</li>\n<li>Natural Signals</li>\n<li>Derived Products</li>\n<li>Workshop</li>\n<li>Induction Hazards: Future Developments</li>\n<li>Acknowledgments</li>\n<li>References Cited</li>\n</ul>","publishingServiceCenter":{"id":2,"text":"Denver PSC"},"publishedDate":"2015-08-14","noUsgsAuthors":false,"publicationDate":"2015-08-14","publicationStatus":"PW","scienceBaseUri":"57f7eed2e4b0bc0bec09ed01","contributors":{"authors":[{"text":"Love, Jeffrey J. 0000-0002-3324-0348 jlove@usgs.gov","orcid":"https://orcid.org/0000-0002-3324-0348","contributorId":760,"corporation":false,"usgs":true,"family":"Love","given":"Jeffrey","email":"jlove@usgs.gov","middleInitial":"J.","affiliations":[{"id":300,"text":"Geologic Hazards Science Center","active":true,"usgs":true}],"preferred":true,"id":565033,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Finn, Carol A. 0000-0003-3144-1645 cafinn@usgs.gov","orcid":"https://orcid.org/0000-0003-3144-1645","contributorId":2144,"corporation":false,"usgs":true,"family":"Finn","given":"Carol","email":"cafinn@usgs.gov","middleInitial":"A.","affiliations":[{"id":300,"text":"Geologic Hazards Science Center","active":true,"usgs":true}],"preferred":false,"id":565034,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Pedrie, Kolby L. kpedrie@usgs.gov","contributorId":145707,"corporation":false,"usgs":true,"family":"Pedrie","given":"Kolby L.","email":"kpedrie@usgs.gov","affiliations":[{"id":300,"text":"Geologic Hazards Science Center","active":true,"usgs":true}],"preferred":false,"id":565035,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Blum, Cletus C. cblum@usgs.gov","contributorId":145708,"corporation":false,"usgs":true,"family":"Blum","given":"Cletus","email":"cblum@usgs.gov","middleInitial":"C.","affiliations":[{"id":300,"text":"Geologic Hazards Science Center","active":true,"usgs":true}],"preferred":false,"id":565036,"contributorType":{"id":1,"text":"Authors"},"rank":4}]}}
,{"id":70156372,"text":"70156372 - 2015 - A new temperature profiling probe for investigating groundwater-surface water interaction","interactions":[],"lastModifiedDate":"2015-10-26T14:05:33","indexId":"70156372","displayToPublicDate":"2015-08-14T11:00:00","publicationYear":"2015","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":3722,"text":"Water Resources Research","onlineIssn":"1944-7973","printIssn":"0043-1397","active":true,"publicationSubtype":{"id":10}},"title":"A new temperature profiling probe for investigating groundwater-surface water interaction","docAbstract":"<p><span>Measuring vertically nested temperatures at the streambed interface poses practical challenges that are addressed here with a new discrete subsurface temperature profiling probe. We describe a new temperature probe and its application for heat as a tracer investigations to demonstrate the probe's utility. Accuracy and response time of temperature measurements made at 6 discrete depths in the probe were analyzed in the laboratory using temperature bath experiments. We find the temperature probe to be an accurate and robust instrument that allows for easily installation and long-term monitoring in highly variable environments. Because the probe is inexpensive and versatile, it is useful for many environmental applications that require temperature data collection for periods of several months in environments that are difficult to access or require minimal disturbance.</span></p>","language":"English","publisher":"American Geophysical Union","publisherLocation":"Washington, D.C.","doi":"10.1002/2015WR017574","usgsCitation":"Naranjo, R.C., and Turcotte, R., 2015, A new temperature profiling probe for investigating groundwater-surface water interaction: Water Resources Research, v. 51, no. 9, p. 7790-7797, https://doi.org/10.1002/2015WR017574.","productDescription":"8 p.","startPage":"7790","endPage":"7797","onlineOnly":"N","additionalOnlineFiles":"N","ipdsId":"IP-058645","costCenters":[{"id":465,"text":"Nevada Water Science Center","active":true,"usgs":true}],"links":[{"id":307101,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"volume":"51","issue":"9","publishingServiceCenter":{"id":1,"text":"Sacramento PSC"},"noUsgsAuthors":false,"publicationDate":"2015-09-13","publicationStatus":"PW","scienceBaseUri":"55d84bade4b0518e3546efc5","contributors":{"authors":[{"text":"Naranjo, Ramon C. 0000-0003-4469-6831 rnaranjo@usgs.gov","orcid":"https://orcid.org/0000-0003-4469-6831","contributorId":3391,"corporation":false,"usgs":true,"family":"Naranjo","given":"Ramon","email":"rnaranjo@usgs.gov","middleInitial":"C.","affiliations":[{"id":465,"text":"Nevada Water Science Center","active":true,"usgs":true}],"preferred":true,"id":568927,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Turcotte, Robert","contributorId":146772,"corporation":false,"usgs":false,"family":"Turcotte","given":"Robert","email":"","affiliations":[{"id":16740,"text":"Alpha Mach, INC","active":true,"usgs":false}],"preferred":false,"id":568928,"contributorType":{"id":1,"text":"Authors"},"rank":2}]}}
,{"id":70148360,"text":"70148360 - 2015 - Hydroacoustic signatures of Colorado Riverbed sediments in Marble and Grand Canyons using multibeam sonar","interactions":[],"lastModifiedDate":"2018-04-23T13:09:31","indexId":"70148360","displayToPublicDate":"2015-08-14T00:00:00","publicationYear":"2015","noYear":false,"publicationType":{"id":24,"text":"Conference Paper"},"publicationSubtype":{"id":19,"text":"Conference Paper"},"title":"Hydroacoustic signatures of Colorado Riverbed sediments in Marble and Grand Canyons using multibeam sonar","docAbstract":"<p>Characterizing the large-scale sedimentary make-up of heterogeneous riverbeds (Nelson et al., 2014), which consist of a patchwork of sediment types over small scales (less than one to several tens of meters) (Dietrich and Smith, 1984) requires high resolution measurements of sediment grain size. Capturing such variability with conventional physical (e.g. grabs, cores, and dredges) or underwater photographic sampling (Rubin et al., 2007; Buscombe et al., 2014a) would be prohibitively costly and time-consuming. However, characterizing bed sediments using high-frequency (several hundred kilohertz) acoustic backscatter from swath-mapping systems has the potential to provide near complete coverage of the bed (Brown and Blondel, 2009; Brown et al., 2011; Snellen et al., 2013), at resolutions down to a few centimeters, which photographic sampling could not practically achieve within the same time and with the same positional accuracy. </p><p>In shallow water, the physics of high frequency scattering of sound are relatively poorly understood, therefore acoustic sediment classification are almost always statistical (Snellen et al., 2013). Many such methods proposed to date are designed for characterizing large areas of seabed (Brown and Blondel, 2009; Brown et al., 2011) at relatively poor resolution (tens of meters to several hundred meters) and therefore rely on aggregation of data over scales much larger than the typical scales of sediment patchiness on heterogeneous riverbeds. In response to this need, Buscombe et al. (2014b, 2014c) developed a new statistical method for acoustic sediment classification based on spectral analysis of backscatter. This method is both continuous in coverage and of sufficient resolution (order meter or less) to characterize sediment variability on patchy riverbeds. Here, we apply these methods to multibeam echosounder (MBES) data collected from the bed of the Colorado River in Marble and Grand Canyons. </p><p>Sediment dynamics on the Colorado River in Grand Canyon National Park have been studied for several decades (e.g. Howard and Dolan, 1981; Rubin et al., 2002). Particular focus has been given to sandbars in large eddies downstream of tributary debris fans (Schmidt, 1990) because they are considered valuable resources by stakeholders and managers. Due to the severe limitations in sand supply imposed by Glen Canyon Dam (Howard and Dolan, 1981; Topping et al., 2000; Hazel et al., 2006), understanding the effectiveness of sandbar management practices, such as controlled floods (Rubin et al. 2002; Topping et al., 2006; Hazel et al., 2010), and the long-term fate of sand in Grand Canyon over decadal timescales, requires construction of accurate sand budgets, which involves detailed monitoring of influx, efflux and changes in sand storage (Topping et al., 2000; Topping et al., 2010; Grams et al., 2013) and assessments of uncertainties in sand-budget calculations (Grams et al., 2013). </p><p>In order to estimate the sand budget, it is necessary to estimate what component of observed morphological changes is sand and what component is coarser. Grams et al. (2013) classified sand and coarse substrates using topographic roughness derived from digital elevation models, but the classification skill was estimated to be only 60-70%. In addition, sand bedforms had to be delineated manually, and validation was based on grain-size observations with positional uncertainties up to tens of meters. Because the morphology of the Colorado riverbed in Grand Canyon is mapped - to a large extent - using MBES (Kaplinski et al., 2009), the primary motivation for the present study is to examine how uncertainties in sand budgets can be constrained by producing maps of surface sediment types using the completely automated methods of Buscombe et al (2014b, 2014c) based on statistical analysis of MBES acoustic backscatter.</p>","conferenceTitle":"3rd Joint Federal Interagency Conference","conferenceDate":"April 19-23, 2015","conferenceLocation":"Reno, NV","language":"English","publisher":"Joint Federal Interagency Conference","usgsCitation":"Buscombe, D.D., Grams, P.E., Kaplinski, M., Tusso, R.B., and Rubin, D.M., 2015, Hydroacoustic signatures of Colorado Riverbed sediments in Marble and Grand Canyons using multibeam sonar, 3rd Joint Federal Interagency Conference, Reno, NV, April 19-23, 2015, 12 p.","productDescription":"12 p.","ipdsId":"IP-060883","costCenters":[{"id":568,"text":"Southwest Biological Science Center","active":true,"usgs":true}],"links":[{"id":342198,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"},{"id":353659,"rank":2,"type":{"id":15,"text":"Index Page"},"url":"https://www.sedhyd.org/2015/openconf/modules/request.php?module=oc_program&action=summary.php&id=76"}],"country":"United States","state":"Arizona","otherGeospatial":"Grand Canyon, Marble Canyon","geographicExtents":"{\n  \"type\": \"FeatureCollection\",\n  \"features\": [\n    {\n      \"type\": \"Feature\",\n      \"properties\": {},\n      \"geometry\": {\n        \"type\": \"Polygon\",\n        \"coordinates\": [\n          [\n            [\n              -112.5,\n              36\n            ],\n            [\n              -111.25,\n              36\n            ],\n            [\n              -111.25,\n              37\n            ],\n            [\n              -112.5,\n              37\n            ],\n            [\n              -112.5,\n              36\n            ]\n          ]\n        ]\n      }\n    }\n  ]\n}","publishingServiceCenter":{"id":14,"text":"Menlo Park PSC"},"noUsgsAuthors":false,"publicationStatus":"PW","scienceBaseUri":"593910b0e4b0764e6c5e888c","contributors":{"authors":[{"text":"Buscombe, Daniel D. 0000-0001-6217-5584 dbuscombe@usgs.gov","orcid":"https://orcid.org/0000-0001-6217-5584","contributorId":5020,"corporation":false,"usgs":false,"family":"Buscombe","given":"Daniel","email":"dbuscombe@usgs.gov","middleInitial":"D.","affiliations":[{"id":568,"text":"Southwest Biological Science Center","active":true,"usgs":true}],"preferred":true,"id":547841,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Grams, Paul E. 0000-0002-0873-0708 pgrams@usgs.gov","orcid":"https://orcid.org/0000-0002-0873-0708","contributorId":1830,"corporation":false,"usgs":true,"family":"Grams","given":"Paul","email":"pgrams@usgs.gov","middleInitial":"E.","affiliations":[{"id":568,"text":"Southwest Biological Science Center","active":true,"usgs":true}],"preferred":true,"id":547842,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Kaplinski, Matthew","contributorId":14917,"corporation":false,"usgs":true,"family":"Kaplinski","given":"Matthew","affiliations":[],"preferred":false,"id":547843,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Tusso, Robert B. 0000-0001-7541-3713 rtusso@usgs.gov","orcid":"https://orcid.org/0000-0001-7541-3713","contributorId":4079,"corporation":false,"usgs":true,"family":"Tusso","given":"Robert","email":"rtusso@usgs.gov","middleInitial":"B.","affiliations":[{"id":568,"text":"Southwest Biological Science Center","active":true,"usgs":true}],"preferred":true,"id":547844,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Rubin, David M. 0000-0003-1169-1452 drubin@usgs.gov","orcid":"https://orcid.org/0000-0003-1169-1452","contributorId":3159,"corporation":false,"usgs":true,"family":"Rubin","given":"David","email":"drubin@usgs.gov","middleInitial":"M.","affiliations":[{"id":520,"text":"Pacific Coastal and Marine Science Center","active":true,"usgs":true}],"preferred":true,"id":547845,"contributorType":{"id":1,"text":"Authors"},"rank":5}]}}
,{"id":70154775,"text":"70154775 - 2015 - Landscapes for energy and wildlife: conservation prioritization for golden eagles across large spatial scales","interactions":[],"lastModifiedDate":"2019-06-03T13:24:23","indexId":"70154775","displayToPublicDate":"2015-08-13T11:30:00","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":"Landscapes for energy and wildlife: conservation prioritization for golden eagles across large spatial scales","docAbstract":"<p><span>Proactive conservation planning for species requires the identification of important spatial attributes across ecologically relevant scales in a model-based framework. However, it is often difficult to develop predictive models, as the explanatory data required for model development across regional management scales is rarely available. Golden eagles are a large-ranging predator of conservation concern in the United States that may be negatively affected by wind energy development. Thus, identifying landscapes least likely to pose conflict between eagles and wind development via shared space prior to development will be critical for conserving populations in the face of imposing development. We used publicly&nbsp;available data on golden eagle nests to generate predictive models of golden eagle nesting sites in Wyoming, USA, using a suite of environmental and anthropogenic variables. By overlaying predictive models of golden eagle nesting habitat with wind energy resource maps, we highlight areas of potential conflict among eagle nesting habitat and wind development. However, our results suggest that wind potential and the relative probability of golden eagle nesting are not necessarily spatially correlated. Indeed, the majority of our sample frame includes areas with disparate predictions between suitable nesting habitat and potential for developing wind energy resources. Map predictions cannot replace on-the-ground monitoring for potential risk of wind turbines on wildlife populations, though they provide industry and managers a useful framework to first assess potential development.</span></p>","language":"English","publisher":"PLOS","doi":"10.1371/journal.pone.0134781","usgsCitation":"Tack, J., and Fedy, B., 2015, Landscapes for energy and wildlife: conservation prioritization for golden eagles across large spatial scales: PLoS ONE, v. 10, no. 8, e0134781: 18 p., https://doi.org/10.1371/journal.pone.0134781.","productDescription":"e0134781: 18 p.","onlineOnly":"N","additionalOnlineFiles":"N","ipdsId":"IP-066450","costCenters":[{"id":291,"text":"Fort Collins Science Center","active":true,"usgs":true}],"links":[{"id":471879,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.1371/journal.pone.0134781","text":"Publisher Index Page"},{"id":306635,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","state":"Wyoming","geographicExtents":"{\"type\":\"FeatureCollection\",\"features\":[{\"type\":\"Feature\",\"geometry\":{\"type\":\"Polygon\",\"coordinates\":[[[-110.048476,40.997555],[-110.121639,40.997101],[-110.125709,40.99655],[-110.237848,40.995427],[-110.250709,40.996089],[-110.375714,40.994947],[-110.500718,40.994746],[-110.539819,40.996346],[-110.715026,40.996347],[-110.750727,40.996847],[-111.046723,40.997959],[-111.046551,41.251716],[-111.0466,41.360692],[-111.046264,41.377731],[-111.045789,41.565571],[-111.045818,41.579845],[-111.046689,42.001567],[-111.047109,42.142497],[-111.047107,42.148971],[-111.047058,42.182672],[-111.047097,42.194773],[-111.047074,42.280787],[-111.04708,42.34942],[-111.046801,42.504946],[-111.046719,42.513118],[-111.046017,42.582723],[-111.043564,42.722624],[-111.044135,42.874924],[-111.043959,42.96445],[-111.043957,42.969482],[-111.043924,42.975063],[-111.044129,43.018702],[-111.044156,43.020052],[-111.044206,43.022614],[-111.044034,43.024581],[-111.044034,43.024844],[-111.044033,43.026411],[-111.044094,43.02927],[-111.043997,43.041415],[-111.044058,43.04464],[-111.044063,43.046302],[-111.044086,43.054819],[-111.044117,43.060309],[-111.04415,43.066172],[-111.044162,43.068222],[-111.044143,43.072364],[-111.044235,43.177121],[-111.044266,43.177236],[-111.044232,43.18444],[-111.044168,43.189244],[-111.044229,43.195579],[-111.044617,43.31572],[-111.045205,43.501136],[-111.045706,43.659112],[-111.04588,43.681033],[-111.046118,43.684902],[-111.046051,43.685812],[-111.04611,43.687848],[-111.046421,43.722059],[-111.046435,43.726545],[-111.04634,43.726957],[-111.046715,43.815832],[-111.046515,43.908376],[-111.046917,43.974978],[-111.047064,43.983467],[-111.047349,43.999921],[-111.049077,44.020072],[-111.048751,44.060403],[-111.048751,44.060838],[-111.048633,44.062903],[-111.048452,44.114831],[-111.049119,44.124923],[-111.049695,44.353626],[-111.049148,44.374925],[-111.049216,44.435811],[-111.049194,44.438058],[-111.048974,44.474072],[-111.055208,44.624927],[-111.055333,44.666263],[-111.055511,44.725343],[-111.056416,44.749928],[-111.056888,44.866658],[-111.055629,44.933578],[-111.056207,44.935901],[-111.055199,45.001321],[-111.044275,45.001345],[-110.785008,45.002952],[-110.761554,44.999934],[-110.750767,44.997948],[-110.705272,44.992324],[-110.552433,44.992237],[-110.547165,44.992459],[-110.48807,44.992361],[-110.402927,44.99381],[-110.362698,45.000593],[-110.342131,44.999053],[-110.324441,44.999156],[-110.28677,44.99685],[-110.199503,44.996188],[-110.110103,45.003905],[-110.026347,45.003665],[-110.025544,45.003602],[-109.99505,45.003174],[-109.875735,45.003275],[-109.798687,45.002188],[-109.75073,45.001605],[-109.663673,45.002536],[-109.574321,45.002631],[-109.386432,45.004887],[-109.375713,45.00461],[-109.269294,45.005283],[-109.263431,45.005345],[-109.103445,45.005904],[-109.08301,44.99961],[-109.062262,44.999623],[-108.621313,45.000408],[-108.578484,45.000484],[-108.565921,45.000578],[-108.500679,44.999691],[-108.271201,45.000251],[-108.249345,44.999458],[-108.238139,45.000206],[-108.218479,45.000541],[-108.14939,45.001062],[-108.000663,45.001223],[-107.997353,45.001565],[-107.911743,45.001292],[-107.750654,45.000778],[-107.608854,45.00086],[-107.607824,45.000929],[-107.49205,45.00148],[-107.351441,45.001407],[-107.13418,45.000109],[-107.125633,44.999388],[-107.105685,44.998734],[-107.084939,44.996599],[-107.074996,44.997004],[-107.050801,44.996424],[-106.892875,44.995947],[-106.888773,44.995885],[-106.263586,44.993788],[-106.024814,44.993688],[-105.928184,44.993647],[-105.914258,44.999986],[-105.913382,45.000941],[-105.848065,45.000396],[-105.076607,45.000347],[-105.038405,45.000345],[-105.025266,45.00029],[-105.019284,45.000329],[-105.01824,45.000437],[-104.765063,44.999183],[-104.759855,44.999066],[-104.72637,44.999518],[-104.665171,44.998618],[-104.663882,44.998869],[-104.470422,44.998453],[-104.470117,44.998453],[-104.250145,44.99822],[-104.057698,44.997431],[-104.055914,44.874986],[-104.056496,44.867034],[-104.055963,44.768236],[-104.055963,44.767962],[-104.055934,44.72372],[-104.05587,44.723422],[-104.055777,44.700466],[-104.055938,44.693881],[-104.05581,44.691343],[-104.055877,44.571016],[-104.055892,44.543341],[-104.055927,44.51773],[-104.055389,44.249983],[-104.054487,44.180381],[-104.054562,44.141081],[-104.05495,43.93809],[-104.055077,43.936535],[-104.055488,43.853477],[-104.055488,43.853476],[-104.055138,43.750421],[-104.055133,43.747105],[-104.054902,43.583852],[-104.054885,43.583512],[-104.05484,43.579368],[-104.055032,43.558603],[-104.054787,43.503328],[-104.054786,43.503072],[-104.054779,43.477815],[-104.054766,43.428914],[-104.054614,43.390949],[-104.054403,43.325914],[-104.054218,43.30437],[-104.053884,43.297047],[-104.053876,43.289801],[-104.053127,43.000585],[-104.052863,42.754569],[-104.052809,42.749966],[-104.052583,42.650062],[-104.052741,42.633982],[-104.052586,42.630917],[-104.052773,42.611766],[-104.052775,42.61159],[-104.052775,42.610813],[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,{"id":70155814,"text":"70155814 - 2015 - Natural recharge estimation and uncertainty analysis of an adjudicated groundwater basin using a regional-scale flow and subsidence model (Antelope Valley, California, USA)","interactions":[],"lastModifiedDate":"2015-08-13T10:33:01","indexId":"70155814","displayToPublicDate":"2015-08-13T11:30:00","publicationYear":"2015","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":1923,"text":"Hydrogeology Journal","active":true,"publicationSubtype":{"id":10}},"title":"Natural recharge estimation and uncertainty analysis of an adjudicated groundwater basin using a regional-scale flow and subsidence model (Antelope Valley, California, USA)","docAbstract":"<p>Groundwater has provided 50&ndash;90 % of the total water supply in Antelope Valley, California (USA). The associated groundwater-level declines have led the Los Angeles County Superior Court of California to recently rule that the Antelope Valley groundwater basin is in overdraft, i.e., annual pumpage exceeds annual recharge. Natural recharge consists primarily of mountain-front recharge and is an important component of the total groundwater budget in Antelope Valley. Therefore, natural recharge plays a major role in the Court&rsquo;s decision. The exact quantity and distribution of natural recharge is uncertain, with total estimates from previous studies ranging from 37 to 200 gigaliters per year (GL/year). In order to better understand the uncertainty associated with natural recharge and to provide a tool for groundwater management, a numerical model of groundwater flow and land subsidence was developed. The transient model was calibrated using PEST with water-level and subsidence data; prior information was incorporated through the use of Tikhonov regularization. The calibrated estimate of natural recharge was 36 GL/year, which is appreciably less than the value used by the court (74 GL/year). The effect of parameter uncertainty on the estimation of natural recharge was addressed using the Null-Space Monte Carlo method. A Pareto trade-off method was also used to portray the reasonableness of larger natural recharge rates. The reasonableness of the 74 GL/year value and the effect of uncertain pumpage rates were also evaluated. The uncertainty analyses indicate that the total natural recharge likely ranges between 34.5 and 54.3 GL/year.</p>","language":"English","publisher":"Springer","publisherLocation":"Heidelberg, Germany","doi":"10.1007/s10040-015-1281-y","usgsCitation":"Siade, A.J., Nishikawa, T., and Martin, P., 2015, Natural recharge estimation and uncertainty analysis of an adjudicated groundwater basin using a regional-scale flow and subsidence model (Antelope Valley, California, USA): Hydrogeology Journal, v. 23, no. 6, p. 1267-1291, https://doi.org/10.1007/s10040-015-1281-y.","productDescription":"25 p.","startPage":"1267","endPage":"1291","numberOfPages":"25","onlineOnly":"N","additionalOnlineFiles":"N","ipdsId":"IP-037195","costCenters":[{"id":154,"text":"California Water Science Center","active":true,"usgs":true}],"links":[{"id":471880,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.1007/s10040-015-1281-y","text":"Publisher Index Page"},{"id":306633,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","state":"California","otherGeospatial":"Antelope Valley","geographicExtents":"{\n  \"type\": \"FeatureCollection\",\n  \"features\": [\n    {\n      \"type\": \"Feature\",\n      \"properties\": {},\n      \"geometry\": {\n        \"type\": \"Polygon\",\n        \"coordinates\": [\n          [\n            [\n              -118.19503784179688,\n              35.16819542676796\n            ],\n            [\n              -118.90228271484374,\n              34.84536693184099\n            ],\n            [\n              -118.91189575195312,\n              34.78222760653013\n            ],\n            [\n              -117.45620727539062,\n              34.30260622622907\n            ],\n            [\n              -117.54959106445312,\n              35.163704834815874\n            ],\n            [\n              -118.19503784179688,\n              35.16819542676796\n            ]\n          ]\n        ]\n      }\n    }\n  ]\n}","volume":"23","issue":"6","publishingServiceCenter":{"id":1,"text":"Sacramento PSC"},"noUsgsAuthors":false,"publicationDate":"2015-07-24","publicationStatus":"PW","scienceBaseUri":"55cdb1ade4b08400b1fe13b1","contributors":{"authors":[{"text":"Siade, Adam J. asiade@usgs.gov","contributorId":1533,"corporation":false,"usgs":true,"family":"Siade","given":"Adam","email":"asiade@usgs.gov","middleInitial":"J.","affiliations":[{"id":154,"text":"California Water Science Center","active":true,"usgs":true}],"preferred":true,"id":566453,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Nishikawa, Tracy 0000-0002-7348-3838 tnish@usgs.gov","orcid":"https://orcid.org/0000-0002-7348-3838","contributorId":1515,"corporation":false,"usgs":true,"family":"Nishikawa","given":"Tracy","email":"tnish@usgs.gov","affiliations":[{"id":154,"text":"California Water Science Center","active":true,"usgs":true}],"preferred":true,"id":566455,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Martin, Peter pmmartin@usgs.gov","contributorId":799,"corporation":false,"usgs":true,"family":"Martin","given":"Peter","email":"pmmartin@usgs.gov","affiliations":[{"id":154,"text":"California Water Science Center","active":true,"usgs":true}],"preferred":true,"id":566454,"contributorType":{"id":1,"text":"Authors"},"rank":3}]}}
,{"id":70155827,"text":"70155827 - 2015 - Multiscale analysis of river networks using the R package linbin","interactions":[],"lastModifiedDate":"2017-11-22T17:41:33","indexId":"70155827","displayToPublicDate":"2015-08-13T11:00:00","publicationYear":"2015","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":2886,"text":"North American Journal of Fisheries Management","active":true,"publicationSubtype":{"id":10}},"title":"Multiscale analysis of river networks using the R package linbin","docAbstract":"<p>Analytical tools are needed in riverine science and management to bridge the gap between GIS and statistical packages that were not designed for the directional and dendritic structure of streams. We introduce linbin, an R package developed for the analysis of riverscapes at multiple scales. With this software, riverine data on aquatic habitat and species distribution can be scaled and plotted automatically with respect to their position in the stream network or&mdash;in the case of temporal data&mdash;their position in time. The linbin package aggregates data into bins of different sizes as specified by the user. We provide case studies illustrating the use of the software for (1) exploring patterns at different scales by aggregating variables at a range of bin sizes, (2) comparing repeat observations by aggregating surveys into bins of common coverage, and (3) tailoring analysis to data with custom bin designs. Furthermore, we demonstrate the utility of linbin for summarizing patterns throughout an entire stream network, and we analyze the diel and seasonal movements of tagged fish past a stationary receiver to illustrate how linbin can be used with temporal data. In short, linbin enables more rapid analysis of complex data sets by fisheries managers and stream ecologists and can reveal underlying spatial and temporal patterns of fish distribution and habitat throughout a riverscape.</p>","language":"English","publisher":"American Fisheries Society","doi":"10.1080/02755947.2015.1044764","usgsCitation":"Welty, E.Z., Torgersen, C.E., Brenkman, S.J., Duda, J., and Armstrong, J., 2015, Multiscale analysis of river networks using the R package linbin: North American Journal of Fisheries Management, v. 4, no. 35, p. 802-809, https://doi.org/10.1080/02755947.2015.1044764.","productDescription":"8 p.","startPage":"802","endPage":"809","numberOfPages":"9","onlineOnly":"Y","additionalOnlineFiles":"N","ipdsId":"IP-061892","costCenters":[{"id":290,"text":"Forest and Rangeland Ecosystem Science Center","active":false,"usgs":true},{"id":29789,"text":"John Wesley Powell Center for Analysis and Synthesis","active":true,"usgs":true}],"links":[{"id":306629,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"volume":"4","issue":"35","publishingServiceCenter":{"id":12,"text":"Tacoma PSC"},"noUsgsAuthors":false,"publicationDate":"2015-07-17","publicationStatus":"PW","scienceBaseUri":"55cdb1ade4b08400b1fe13af","contributors":{"authors":[{"text":"Welty, Ethan Z.","contributorId":146461,"corporation":false,"usgs":true,"family":"Welty","given":"Ethan","email":"","middleInitial":"Z.","affiliations":[{"id":289,"text":"Forest and Rangeland Ecosys Science Center","active":true,"usgs":true}],"preferred":false,"id":567958,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Torgersen, Christian E. 0000-0001-8325-2737 ctorgersen@usgs.gov","orcid":"https://orcid.org/0000-0001-8325-2737","contributorId":3578,"corporation":false,"usgs":true,"family":"Torgersen","given":"Christian","email":"ctorgersen@usgs.gov","middleInitial":"E.","affiliations":[{"id":289,"text":"Forest and Rangeland Ecosys Science Center","active":true,"usgs":true}],"preferred":false,"id":566509,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Brenkman, Samuel J.","contributorId":138941,"corporation":false,"usgs":false,"family":"Brenkman","given":"Samuel","email":"","middleInitial":"J.","affiliations":[{"id":12587,"text":"Olympic National Park, Port Angeles, WA","active":true,"usgs":false}],"preferred":false,"id":567959,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Duda, Jeffrey J. 0000-0001-7431-8634 jduda@usgs.gov","orcid":"https://orcid.org/0000-0001-7431-8634","contributorId":3323,"corporation":false,"usgs":true,"family":"Duda","given":"Jeffrey J.","email":"jduda@usgs.gov","affiliations":[{"id":654,"text":"Western Fisheries Research Center","active":true,"usgs":true}],"preferred":false,"id":567960,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Armstrong, Jonathan B.","contributorId":98567,"corporation":false,"usgs":true,"family":"Armstrong","given":"Jonathan B.","affiliations":[],"preferred":false,"id":567961,"contributorType":{"id":1,"text":"Authors"},"rank":5}]}}
,{"id":70148339,"text":"70148339 - 2015 - Terrain parameters of glide snow avalanches and a simple spatial glide snow avalanche model","interactions":[],"lastModifiedDate":"2020-10-29T20:20:51.220065","indexId":"70148339","displayToPublicDate":"2015-08-13T10:45:00","publicationYear":"2015","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":1264,"text":"Cold Regions Science and Technology","active":true,"publicationSubtype":{"id":10}},"title":"Terrain parameters of glide snow avalanches and a simple spatial glide snow avalanche model","docAbstract":"<p id=\"sp0005\">Glide snow avalanches are dangerous and difficult to predict. Despite substantial recent research there is still inadequate understanding regarding the controls of glide snow avalanche release. Glide snow avalanches often occur in similar terrain or the same locations annually, and repeat observations and prior work suggest that specific topography may be critical. Thus, to gain a better understanding of the terrain component of these types of avalanches we examined terrain parameters associated with the specific area of glide snow avalanche release in comparison to avalanche starting zones where no glide snow avalanches were observed (i.e. non-glide snow avalanche terrain).</p><p id=\"sp0010\">Glide snow avalanche occurrences visible from the Going-to-the-Sun Road corridor in Glacier National Park, Montana from 2003 to 2013 are investigated using a database of all avalanche occurrences derived of daily observations each year from 1 April to 1 June. This yielded 192 glide snow avalanches in 53 distinct avalanche paths. Each avalanche was digitized in a GIS using satellite, oblique, and aerial imagery as reference. A set of 117 non-glide snow avalanche starting zones were also selected in this manner. These were start zones with avalanche activity potential, but without glide avalanches observed. Topographical parameters such as area, slope, aspect, curvature, potential incoming solar radiation, distance from ridge, and elevation were then derived for the entire dataset utilizing tools with a GIS and a 10&nbsp;m DEM. Ground class and a glide factor were calculated using a four level classification index with in-situ observations and a land surface type layer in a GIS.</p><p id=\"sp0015\">A total of 21 terrain variables were examined using a univariate analysis between areas where glide snow avalanches occurred and areas where glide snow avalanches were never observed, despite crack formation. Only two variables were not significantly different. The significantly different variables were then used to train a classification tree to distinguish between glide and non-glide snow avalanche terrain. A 10-fold cross validated tree resulted in four decision nodes to classify the data. The nodes split on glide factor, maximum slope angle, seasonal sum of incoming solar radiation, and maximum curvature to distinguish between glide snow avalanche and non-glide snow avalanche terrain with an unweighted average accuracy (RPC) of 0.95 and probability of detection of events (POD) of 0.99.</p><p id=\"sp0025\">Finally, the results of the cross-validated tree were used in a GIS to examine other areas, not used in the training dataset of the classification tree, of potential glide snow avalanche release within Glacier National Park. Using this understanding of the role of topographic parameters on glide snow avalanche activity, a spatial terrain based model was developed to identify other areas with high glide snow avalanche potential outside of the immediate observation area. This simple spatial model correctly classified 78&nbsp;percent of actual glide snow avalanche terrain (pixel count) of a small test area of four independent observed glide snow avalanches.</p>","language":"English","publisher":"Elsevier","doi":"10.1016/j.coldregions.2015.08.002","usgsCitation":"Peitzsch, E.H., Hendrikx, J., and Fagre, D.B., 2015, Terrain parameters of glide snow avalanches and a simple spatial glide snow avalanche model: Cold Regions Science and Technology, v. 120, p. 237-250, https://doi.org/10.1016/j.coldregions.2015.08.002.","productDescription":"14 p.","startPage":"237","endPage":"250","numberOfPages":"8","onlineOnly":"N","additionalOnlineFiles":"N","ipdsId":"IP-061113","costCenters":[{"id":481,"text":"Northern Rocky Mountain Science Center","active":true,"usgs":true}],"links":[{"id":301089,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","state":"Montana","otherGeospatial":"Glacier National Park","geographicExtents":"{\n  \"type\": \"FeatureCollection\",\n  \"features\": [\n    {\n      \"type\": \"Feature\",\n      \"properties\": {},\n      \"geometry\": {\n        \"type\": \"Polygon\",\n        \"coordinates\": [\n          [\n            [\n              -114.345703125,\n              48.23016176791893\n            ],\n            [\n              -113.15093994140625,\n              48.23016176791893\n            ],\n            [\n              -113.15093994140625,\n              48.980216985374994\n            ],\n            [\n              -114.345703125,\n              48.980216985374994\n            ],\n            [\n              -114.345703125,\n              48.23016176791893\n            ]\n          ]\n        ]\n      }\n    }\n  ]\n}","volume":"120","publishingServiceCenter":{"id":2,"text":"Denver PSC"},"noUsgsAuthors":false,"publicationStatus":"PW","scienceBaseUri":"55780e29e4b032353cbeb6f1","contributors":{"authors":[{"text":"Peitzsch, Erich H. 0000-0001-7624-0455 epeitzsch@usgs.gov","orcid":"https://orcid.org/0000-0001-7624-0455","contributorId":3786,"corporation":false,"usgs":true,"family":"Peitzsch","given":"Erich","email":"epeitzsch@usgs.gov","middleInitial":"H.","affiliations":[{"id":481,"text":"Northern Rocky Mountain Science Center","active":true,"usgs":true}],"preferred":true,"id":547717,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Hendrikx, Jordy 0000-0001-6194-3596","orcid":"https://orcid.org/0000-0001-6194-3596","contributorId":140954,"corporation":false,"usgs":false,"family":"Hendrikx","given":"Jordy","email":"","affiliations":[{"id":13628,"text":"Department of Earth Sciences, P.O. Box 173480, Montana State University, Bozeman, MT, USA. 59717.","active":true,"usgs":false}],"preferred":false,"id":547718,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Fagre, Daniel B. 0000-0001-8552-9461 dan_fagre@usgs.gov","orcid":"https://orcid.org/0000-0001-8552-9461","contributorId":2036,"corporation":false,"usgs":true,"family":"Fagre","given":"Daniel","email":"dan_fagre@usgs.gov","middleInitial":"B.","affiliations":[{"id":481,"text":"Northern Rocky Mountain Science Center","active":true,"usgs":true}],"preferred":true,"id":547719,"contributorType":{"id":1,"text":"Authors"},"rank":3}]}}
,{"id":70154747,"text":"70154747 - 2015 - Mortality patterns and detection bias from carcass data: An example from wolf recovery in Wisconsin","interactions":[],"lastModifiedDate":"2016-04-13T12:14:03","indexId":"70154747","displayToPublicDate":"2015-08-13T10:30:00","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":"Mortality patterns and detection bias from carcass data: An example from wolf recovery in Wisconsin","docAbstract":"<p><span>We developed models and provide computer code to make carcass recovery data more useful to wildlife managers. With these tools, wildlife managers can understand the spatial, temporal (e.g., across time periods, seasons), and demographic patterns in mortality causes from carcass recovery datasets. From datasets of radio-collared and non-collared carcasses, managers can calculate the detection bias by mortality cause in a non-collared carcass dataset compared to a collared carcass dataset. As a first step, we provide a standard procedure to assign mortality causes to carcasses. We provide an example of these methods for radio-collared wolves (</span><i>n&thinsp;</i><span>=&thinsp;208) and non-collared wolves (</span><i>n&thinsp;</i><span>=&thinsp;668) found dead in Wisconsin (1979&ndash;2012). We analyzed differences in mortality cause relative to season, age and sex classes, wolf harvest zones, and recovery phase (1979&ndash;1995: initial recovery, 1996&ndash;2002: early growth, 2003&ndash;2012: late growth). Seasonally, illegal kills and natural deaths were proportionally higher in winter (Oct&ndash;Mar) than summer (Apr&ndash;Sep) for collared wolves, whereas vehicle strikes and legal kills were higher in summer than winter. Spatially, more illegally killed collared wolves occurred in eastern wolf harvest zones where wolves reestablished more slowly and in the central forest region where optimal habitat is isolated by agriculture. Natural mortalities of collared wolves (e.g., disease, intraspecific strife, or starvation) were highest in western wolf harvest zones where wolves established earlier and existed at higher densities. Calculating detection bias in the non-collared dataset revealed that more than half of the non-collared carcasses on the landscape are not found. The lowest detection probabilities for non-collared carcasses (0.113&ndash;0.176) occurred in winter for natural, illegal, and unknown mortality causes.</span></p>","language":"English","publisher":"Wildlife Society","doi":"10.1002/jwmg.922","usgsCitation":"Stenglein, J.L., Van Deelen, T.R., Wydeven, A.P., Mladenoff, D.J., Wiedenhoft, J.E., Businga, N.K., Langenberg, J.A., Thomas, N.J., and Heisey, D.M., 2015, Mortality patterns and detection bias from carcass data: An example from wolf recovery in Wisconsin: Journal of Wildlife Management, v. 79, no. 7, p. 1173-1184, https://doi.org/10.1002/jwmg.922.","productDescription":"12 p.","startPage":"1173","endPage":"1184","onlineOnly":"N","additionalOnlineFiles":"N","ipdsId":"IP-056314","costCenters":[{"id":456,"text":"National Wildlife Health Center","active":true,"usgs":true}],"links":[{"id":306626,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United 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,{"id":70155980,"text":"70155980 - 2015 - Assessing juvenile salmon rearing habitat and associated predation risk in a lower Snake River reservoir","interactions":[],"lastModifiedDate":"2016-12-19T11:31:31","indexId":"70155980","displayToPublicDate":"2015-08-13T03:00:00","publicationYear":"2015","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":3301,"text":"River Research and Applications","active":true,"publicationSubtype":{"id":10}},"title":"Assessing juvenile salmon rearing habitat and associated predation risk in a lower Snake River reservoir","docAbstract":"<p>Subyearling fall Chinook salmon (Oncorhynchus&thinsp;tshawytscha) in the Columbia River basin exhibit a transient rearing strategy and depend on connected shoreline habitats during freshwater rearing. Impoundment has greatly reduced the amount of shallow-water rearing habitat that is exacerbated by the steep topography of reservoirs. Periodic dredging creates opportunities to strategically place spoils to increase the amount of shallow-water habitat for subyearlings while at the same time reducing the amount of unsuitable area that is often preferred by predators. We assessed the amount and spatial arrangement of subyearling rearing habitat in Lower Granite Reservoir on the Snake River to guide future habitat improvement efforts. A spatially explicit habitat assessment was conducted using physical habitat data, two-dimensional hydrodynamic modelling and a statistical habitat model in a geographic information system framework. We used field collections of subyearlings and a common predator [smallmouth bass (Micropterus&thinsp;dolomieu)] to draw inferences about predation risk within specific habitat types. Most of the high-probability rearing habitat was located in the upper half of the reservoir where gently sloping landforms created low lateral bed slopes and shallow-water habitats. Only 29% of shorelines were predicted to be suitable (probability &gt;0.5) for subyearlings, and the occurrence of these shorelines decreased in a downstream direction. The remaining, less suitable areas were composed of low-probability habitats in unmodified (25%) and riprapped shorelines (46%). As expected, most subyearlings were found in high-probability habitat, while most smallmouth bass were found in low-probability locations. However, some subyearlings were found in low-probability habitats, such as riprap, where predation risk could be high. Given their transient rearing strategy and dependence on shoreline habitats, subyearlings could benefit from habitat creation efforts in the lower reservoir where high-probability habitat is generally lacking. Published 2015. This article is a U.S. Government work and is in the public domain in the USA.</p>","language":"English","publisher":"Wiley","doi":"10.1002/rra.2934","usgsCitation":"Tiffan, K.F., Hatten, J.R., and Trachtenbarg, D.A., 2015, Assessing juvenile salmon rearing habitat and associated predation risk in a lower Snake River reservoir: River Research and Applications, v. 32, no. 5, p. 1030-1038, https://doi.org/10.1002/rra.2934.","productDescription":"9 p.","startPage":"1030","endPage":"1038","onlineOnly":"N","additionalOnlineFiles":"N","ipdsId":"IP-064916","costCenters":[{"id":654,"text":"Western Fisheries Research Center","active":true,"usgs":true}],"links":[{"id":306672,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","otherGeospatial":"Lower Snake river reservior","geographicExtents":"{\n  \"type\": \"FeatureCollection\",\n  \"features\": [\n    {\n      \"type\": \"Feature\",\n      \"properties\": {},\n      \"geometry\": {\n        \"type\": \"Polygon\",\n        \"coordinates\": [\n          [\n            [\n              -117.69653320312499,\n              46.67582559793001\n            ],\n            [\n              -117.53173828125,\n              46.66074749832068\n            ],\n            [\n              -117.40264892578124,\n              46.6268063953552\n            ],\n            [\n              -117.29553222656249,\n              46.53052428878426\n            ],\n            [\n              -117.21313476562499,\n              46.411351502899215\n            ],\n            [\n              -117.07305908203124,\n              46.39998810407942\n            ],\n            [\n              -117.07855224609376,\n              46.34313560260196\n            ],\n            [\n              -117.0098876953125,\n              46.28622391806706\n            ],\n            [\n              -116.98242187499999,\n              46.214050815339526\n            ],\n            [\n              -116.97418212890625,\n              46.11513371326539\n            ],\n            [\n              -116.91375732421875,\n              46.128459837044915\n            ],\n            [\n              -116.93847656250001,\n              46.26534147068603\n            ],\n            [\n              -116.99615478515624,\n              46.36588370484979\n            ],\n            [\n              -116.98242187499999,\n              46.40188216826328\n            ],\n            [\n              -116.82861328125001,\n              46.430285240839964\n            ],\n            [\n              -116.84234619140624,\n              46.464349400461124\n            ],\n            [\n              -117.103271484375,\n              46.45110475854117\n            ],\n            [\n              -117.26257324218749,\n              46.56452573114373\n            ],\n            [\n              -117.3065185546875,\n              46.62492015414768\n            ],\n            [\n              -117.3944091796875,\n              46.685247274319565\n            ],\n            [\n              -117.46307373046874,\n              46.717268685073954\n            ],\n            [\n              -117.69653320312499,\n              46.72291755083757\n            ],\n            [\n              -117.69653320312499,\n              46.67582559793001\n            ]\n          ]\n        ]\n      }\n    }\n  ]\n}","volume":"32","issue":"5","publishingServiceCenter":{"id":12,"text":"Tacoma PSC"},"noUsgsAuthors":false,"publicationDate":"2015-08-03","publicationStatus":"PW","scienceBaseUri":"55cdb1a7e4b08400b1fe139d","contributors":{"authors":[{"text":"Tiffan, Kenneth F. 0000-0002-5831-2846 ktiffan@usgs.gov","orcid":"https://orcid.org/0000-0002-5831-2846","contributorId":3200,"corporation":false,"usgs":true,"family":"Tiffan","given":"Kenneth","email":"ktiffan@usgs.gov","middleInitial":"F.","affiliations":[{"id":654,"text":"Western Fisheries Research Center","active":true,"usgs":true}],"preferred":true,"id":567529,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Hatten, James R. 0000-0003-4676-8093 jhatten@usgs.gov","orcid":"https://orcid.org/0000-0003-4676-8093","contributorId":3431,"corporation":false,"usgs":true,"family":"Hatten","given":"James","email":"jhatten@usgs.gov","middleInitial":"R.","affiliations":[{"id":654,"text":"Western Fisheries Research Center","active":true,"usgs":true}],"preferred":true,"id":567530,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Trachtenbarg, David A","contributorId":146351,"corporation":false,"usgs":false,"family":"Trachtenbarg","given":"David","email":"","middleInitial":"A","affiliations":[{"id":16680,"text":"U.S. Army Corps of Engineers, Walla Walla District, Walla Walla, WA 99362","active":true,"usgs":false}],"preferred":false,"id":567531,"contributorType":{"id":1,"text":"Authors"},"rank":3}]}}
,{"id":70155509,"text":"sir20155106 - 2015 - Hydrologic budget and conditions of Permian, Pennsylvanian, and Mississippian aquifers in the Appalachian Plateaus physiographic province","interactions":[],"lastModifiedDate":"2015-10-26T14:28:11","indexId":"sir20155106","displayToPublicDate":"2015-08-12T15:45:00","publicationYear":"2015","noYear":false,"publicationType":{"id":18,"text":"Report"},"publicationSubtype":{"id":5,"text":"USGS Numbered Series"},"seriesTitle":{"id":334,"text":"Scientific Investigations Report","code":"SIR","onlineIssn":"2328-0328","printIssn":"2328-031X","active":true,"publicationSubtype":{"id":5}},"seriesNumber":"2015-5106","title":"Hydrologic budget and conditions of Permian, Pennsylvanian, and Mississippian aquifers in the Appalachian Plateaus physiographic province","docAbstract":"<p>In response to challenges to groundwater availability posed by historic land-use practices, expanding development of hydrocarbon resources, and drought, the U.S. Geological Survey Groundwater Resources Program began a regional assessment of the Appalachian Plateaus aquifers in 2013 that incorporated a hydrologic landscape approach to estimate all components of the hydrologic system: surface runoff, base flow from groundwater, and interaction with atmospheric water (precipitation and evapotranspiration). This assessment was intended to complement other Federal and State investigations and provide foundational groundwater-related datasets in the Appalachian Plateaus.</p>\n<p>A regional Soil-Water-Balance model was constructed for a 160,000-square-mile study area that extended to the topographic divide of all streams originating outside but flowing into areas underlain by Appalachian Plateaus aquifers. The model incorporated soil, landscape, and climate variables to estimate an annual water budget for the 32-year period from 1980 to 2011 and was calibrated using base-flow data estimated by hydrograph separation techniques from 20 streamflow gaging stations across the study area. Over this period, an average of 47 inches per year (in/yr) of precipitation fell on Appalachian Plateaus aquifers. Simulations from the regional Soil-Water-Balance model indicate that only 19 percent of the precipitation or an average 9 in/yr recharged aquifers, and 19 percent resulted in surface runoff to streams. The remaining 62 percent, an average of 27 in/yr of water, was returned to the atmosphere via evapotranspiration. Because withdrawals from aquifers due to pumping equated to less than 1 percent of the water budget, differences in predevelopment and postdevelopment regional water budgets of the Appalachian Plateaus were minimal. Storage changes caused by filling of abandoned coal-mine aquifers and long-term differences in aquifer storage resulting from climate fluctuations constitute a small portion of the overall water budget.</p>\n<p>The percentage of precipitation that results in recharge, runoff, or evapotranspiration from the landscape varies annually by up to a factor of two depending on temporal changes in prevailing climate conditions and spatial changes in basin characteristics, precipitation patterns, and sources of atmospheric moisture over a large study area. A comparison of water-budget estimates from the regional Soil-Water-Balance model for a dry year (1988) and wet year (2004) showed that evapotranspiration accounts for most of the annual differences in precipitation. As a portion of annual precipitation, evapotranspiration ranged from 69 percent (dry year) to 52 percent (wet year), a range four times greater than the 15 percent (dry year) to 18 percent (wet year) range estimated for recharge. Evapotranspiration as a percentage of precipitation peaks during dry periods, whereas base flow and runoff tend to reach minimum values. During wet periods, this relationship is reversed and base flow and runoff as a percentage of precipitation generally peak while evapotranspiration percentages reach minimum values. Annual recharge in the Appalachian Plateaus reaches a maximum at near 20 percent of annual precipitation, regardless of the severity of wet conditions.</p>\n<p>Hydrograph separation data from 849 streamflow gaging stations in the study area were used to assess trends in streamflow, base flow, surface runoff, and base-flow index, or ratio of base flow to streamflow, in the Appalachian Plateaus for the period from 1930 to 2011. Annual data anomalies for each of the four variables were individually defined as the annual standard deviation from the mean at all 849 streamflow gaging stations. Annual data anomalies confirm the close relation of annual precipitation to both base flow and runoff components of streamflow, and both components increased during the period of analysis. Around 1970, conditions shifted streamflow from values generally below to above long-term means. At a regional scale, increases in base flow account for most of these observed increases in mean annual streamflow. The independence of the base-flow index to annual climate trends indicate that changes in the components of streamflow of the Appalachian Plateaus are probably in response to shifts in seasonal precipitation or widespread land-use practices.</p>\n<p>A subset of 77 index streamgages, defined as having 60 or more years of complete record between the years 1930 and 2011 with no more than 20 percent missing data, was selected to show spatial patterns of change in the water budget. Data from the index streamgages showed that the overall trends in base flow are dependent upon the period of evaluation. Long-term (1930&ndash;2011) increases in base flow were observed throughout the study area. For two shorter periods (1930&ndash;1969 and 1970&ndash;2011) trends in base flow were largely negative. In general, spatial patterns of change in streamflow, base flow, and runoff were mixed but generally consistent with prevailing climate patterns and land-use changes.</p>","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/sir20155106","collaboration":"Groundwater Resources Program","usgsCitation":"McCoy, K.J., Yager, R.M., Nelms, D.L., Ladd, D.E., Monti, Jack, Jr., and Kozar, M.D., 2015, Hydrologic budget and conditions of Permian, Pennsylvanian, and Mississippian aquifers in the Appalachian Plateaus Physiographic Province (ver. 1.1, October 2015): U.S. Geological Survey Scientific Investigations Report 2015–5106, 77 p.,  https://dx.doi.org/10.3133/sir20155106.","productDescription":"vii, 77 p.","numberOfPages":"90","onlineOnly":"N","additionalOnlineFiles":"N","ipdsId":"IP-060623","costCenters":[{"id":614,"text":"Virginia Water Science Center","active":true,"usgs":true}],"links":[{"id":306582,"rank":2,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/sir/2015/5106/sir20155106.pdf","text":"Report","size":"36.6 MB","linkFileType":{"id":1,"text":"pdf"},"description":"SIR 2015-5106"},{"id":306581,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/sir/2015/5106/images/coverthb.jpg"},{"id":309929,"rank":3,"type":{"id":25,"text":"Version History"},"url":"https://pubs.usgs.gov/sir/2015/5106/versionHist.txt","text":"October 26, 2015","size":"1.06 KB","linkFileType":{"id":2,"text":"txt"},"description":"SIR 2015-5106"}],"country":"United States","state":"Alabama, Kentucky, Maryland, Ohio, Pennslyvania, Virginia, Tennessee, West Virginia","otherGeospatial":"Mississippian aquifer, Pennsylvanian aquifer, Permian aquifer","geographicExtents":"{\n  \"type\": \"FeatureCollection\",\n  \"features\": [\n    {\n      \"type\": \"Feature\",\n      \"properties\": {},\n      \"geometry\": {\n        \"type\": \"Polygon\",\n        \"coordinates\": [\n          [\n            [\n              -76.31103515625,\n              41.705728515237524\n            ],\n            [\n              -81.2109375,\n              41.83682786072714\n            ],\n            [\n              -82.79296874999999,\n              41.36031866306708\n            ],\n            [\n              -83.8037109375,\n              38.66835610151509\n            ],\n            [\n              -86.98974609375,\n              34.97600151317591\n            ],\n            [\n              -88.22021484375,\n              34.79576153473033\n            ],\n            [\n              -88.39599609375,\n              32.62087018318113\n            ],\n            [\n              -85.4736328125,\n              34.95799531086792\n            ],\n            [\n              -83.3203125,\n              36.5978891330702\n            ],\n            [\n              -80.22216796875,\n              37.474858084971046\n            ],\n            [\n              -78.5302734375,\n              39.707186656826565\n            ],\n            [\n              -76.31103515625,\n              41.705728515237524\n            ]\n          ]\n        ]\n      }\n    }\n  ]\n}","edition":"Version 1.0: Originally posted August 13, 2015; Version 1.1: October 26, 2015","contact":"<p>Director, Virginia Water Science Center<br /> U.S. Geological Survey<br /> 1730 East Parham Road<br /> Richmond, VA 23228<br /> <a href=\"http://va.water.usgs.gov\"> http://va.water.usgs.gov</a></p>","tableOfContents":"<ul>\n<li>Abstract</li>\n<li>Introduction</li>\n<li>Hydrologic Budget</li>\n<li>Hydrologic Conditions</li>\n<li>Summary and Conclusions</li>\n<li>References Cited</li>\n</ul>","publishingServiceCenter":{"id":8,"text":"Raleigh PSC"},"publishedDate":"2015-08-13","revisedDate":"2015-10-26","noUsgsAuthors":false,"publicationDate":"2015-08-13","publicationStatus":"PW","scienceBaseUri":"562f4eb5e4b093cee780a293","contributors":{"authors":[{"text":"McCoy, Kurt J. 0000-0002-9756-8238 kjmccoy@usgs.gov","orcid":"https://orcid.org/0000-0002-9756-8238","contributorId":1391,"corporation":false,"usgs":true,"family":"McCoy","given":"Kurt","email":"kjmccoy@usgs.gov","middleInitial":"J.","affiliations":[{"id":37280,"text":"Virginia and West Virginia Water Science Center ","active":true,"usgs":true}],"preferred":true,"id":565613,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Yager, Richard M. 0000-0001-7725-1148 ryager@usgs.gov","orcid":"https://orcid.org/0000-0001-7725-1148","contributorId":950,"corporation":false,"usgs":true,"family":"Yager","given":"Richard","email":"ryager@usgs.gov","middleInitial":"M.","affiliations":[{"id":614,"text":"Virginia Water Science Center","active":true,"usgs":true},{"id":474,"text":"New York Water Science Center","active":true,"usgs":true}],"preferred":true,"id":565614,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Nelms, David L. 0000-0001-5747-642X dlnelms@usgs.gov","orcid":"https://orcid.org/0000-0001-5747-642X","contributorId":1892,"corporation":false,"usgs":true,"family":"Nelms","given":"David","email":"dlnelms@usgs.gov","middleInitial":"L.","affiliations":[{"id":37759,"text":"VA/WV Water Science Center","active":true,"usgs":true},{"id":614,"text":"Virginia Water Science Center","active":true,"usgs":true}],"preferred":true,"id":565615,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Ladd, David E. 0000-0002-9247-7839 deladd@usgs.gov","orcid":"https://orcid.org/0000-0002-9247-7839","contributorId":1646,"corporation":false,"usgs":true,"family":"Ladd","given":"David","email":"deladd@usgs.gov","middleInitial":"E.","affiliations":[{"id":581,"text":"Tennessee Water Science Center","active":true,"usgs":true}],"preferred":true,"id":565616,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Monti,, Jack Jr. jmonti@usgs.gov","contributorId":145900,"corporation":false,"usgs":true,"family":"Monti,","given":"Jack","suffix":"Jr.","email":"jmonti@usgs.gov","affiliations":[{"id":474,"text":"New York Water Science Center","active":true,"usgs":true}],"preferred":false,"id":565617,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"Kozar, Mark D. 0000-0001-7755-7657 mdkozar@usgs.gov","orcid":"https://orcid.org/0000-0001-7755-7657","contributorId":1963,"corporation":false,"usgs":true,"family":"Kozar","given":"Mark","email":"mdkozar@usgs.gov","middleInitial":"D.","affiliations":[{"id":37280,"text":"Virginia and West Virginia Water Science Center ","active":true,"usgs":true}],"preferred":true,"id":565618,"contributorType":{"id":1,"text":"Authors"},"rank":6}]}}
,{"id":70155229,"text":"tm5A11 - 2015 - U.S. Geological Survey Noble Gas Laboratory’s standard operating procedures for the measurement of dissolved gas in water samples","interactions":[],"lastModifiedDate":"2015-08-12T16:00:22","indexId":"tm5A11","displayToPublicDate":"2015-08-12T13:00:00","publicationYear":"2015","noYear":false,"publicationType":{"id":18,"text":"Report"},"publicationSubtype":{"id":5,"text":"USGS Numbered Series"},"seriesTitle":{"id":335,"text":"Techniques and Methods","code":"TM","onlineIssn":"2328-7055","printIssn":"2328-7047","active":true,"publicationSubtype":{"id":5}},"seriesNumber":"5-A11","title":"U.S. Geological Survey Noble Gas Laboratory’s standard operating procedures for the measurement of dissolved gas in water samples","docAbstract":"<p>This report addresses the standard operating procedures used by the U.S. Geological Survey&rsquo;s Noble Gas Laboratory in Denver, Colorado, U.S.A., for the measurement of dissolved gases (methane, nitrogen, oxygen, and carbon dioxide) and noble gas isotopes (helium-3, helium-4, neon-20, neon-21, neon-22, argon-36, argon-38, argon-40, kryton-84, krypton-86, xenon-103, and xenon-132) dissolved in water. A synopsis of the instrumentation used, procedures followed, calibration practices, standards used, and a quality assurance and quality control program is presented. The report outlines the day-to-day operation of the Residual Gas Analyzer Model 200, Mass Analyzer Products Model 215&ndash;50, and ultralow vacuum extraction line along with the sample handling procedures, noble gas extraction and purification, instrument measurement procedures, instrumental data acquisition, and calculations for the conversion of raw data from the mass spectrometer into noble gas concentrations per unit mass of water analyzed. Techniques for the preparation of artificial dissolved gas standards are detailed and coupled to a quality assurance and quality control program to present the accuracy of the procedures used in the laboratory.</p>","largerWorkType":{"id":18,"text":"Report"},"largerWorkTitle":"Section A: Water analysis in Book 5 <i>Laboratory Analysis</i>","largerWorkSubtype":{"id":5,"text":"USGS Numbered Series"},"language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/tm5A11","usgsCitation":"Hunt, A.G., 2015, Noble Gas Laboratory’s standard operating procedures for the measurement of dissolved gas in water samples: U.S. Geological Survey Techniques and Methods, book 5, chap. A11, 22 p., https://dx.doi.org/10.3133/tm5A11.","productDescription":"vi, 21 p.","numberOfPages":"31","onlineOnly":"Y","additionalOnlineFiles":"N","ipdsId":"IP-065997","costCenters":[{"id":211,"text":"Crustal Geophysics and Geochemistry Science Center","active":true,"usgs":true}],"links":[{"id":306599,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/tm/05/a11/coverthb.jpg"},{"id":306600,"rank":2,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/tm/05/a11/tm5a11.pdf","text":"Report","size":"2.07 MB","linkFileType":{"id":1,"text":"pdf"},"description":"T&M 5-A11"}],"publicComments":"This report is Chapter 11 of Section A: Water analysis in Book 5 <i>Laboratory Analysis</i>.","contact":"<p>Director, Crustal Geophysics and Geochemistry Science Center<br /> U.S. Geological Survey<br /> Box 25046, MS 964<br /> Denver, CO 80225<br /><a href=\"http://crustal.usgs.gov/\">http://crustal.usgs.gov/</a></p>","tableOfContents":"<ul>\n<li>Abstract</li>\n<li>Introduction</li>\n<li>Laboratory Physical Description (Instrumentation)</li>\n<li>Procedures for the Separation and Measurement of Dissolved Gases from Water</li>\n<li>Data Processing, Recording, and Calibration</li>\n<li>Quality Assurance and Quality Control</li>\n<li>Summary</li>\n<li>References Cited</li>\n</ul>","publishingServiceCenter":{"id":2,"text":"Denver PSC"},"publishedDate":"2015-08-12","noUsgsAuthors":false,"publicationDate":"2015-08-12","publicationStatus":"PW","scienceBaseUri":"57f7eed3e4b0bc0bec09ed03","contributors":{"authors":[{"text":"Hunt, Andrew G. 0000-0002-3810-8610 ahunt@usgs.gov","orcid":"https://orcid.org/0000-0002-3810-8610","contributorId":1582,"corporation":false,"usgs":true,"family":"Hunt","given":"Andrew","email":"ahunt@usgs.gov","middleInitial":"G.","affiliations":[{"id":211,"text":"Crustal Geophysics and Geochemistry Science Center","active":true,"usgs":true}],"preferred":true,"id":565502,"contributorType":{"id":1,"text":"Authors"},"rank":1}]}}
,{"id":70155815,"text":"sir20155093 - 2015 - Simulation of groundwater flow and analysis of the effects of water-management options in the North Platte Natural Resources District, Nebraska","interactions":[],"lastModifiedDate":"2015-08-12T15:22:47","indexId":"sir20155093","displayToPublicDate":"2015-08-12T11:30:00","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":"2015-5093","title":"Simulation of groundwater flow and analysis of the effects of water-management options in the North Platte Natural Resources District, Nebraska","docAbstract":"<p>The North Platte Natural Resources District (NPNRD) has been actively collecting data and studying groundwater resources because of concerns about the future availability of the highly inter-connected surface-water and groundwater resources. This report, prepared by the U.S. Geological Survey in cooperation with the North Platte Natural Resources District, describes a groundwater-flow model of the North Platte River valley from Bridgeport, Nebraska, extending west to 6 miles into Wyoming. The model was built to improve the understanding of the interaction of surface-water and groundwater resources, and as an optimization tool, the model is able to analyze the effects of water-management options on the simulated stream base flow of the North Platte River. The groundwater system and related sources and sinks of water were simulated using a newton formulation of the U.S. Geological Survey modular three-dimensional groundwater model, referred to as MODFLOW&ndash;NWT, which provided an improved ability to solve nonlinear unconfined aquifer simulations with wetting and drying of cells. Using previously published aquifer-base-altitude contours in conjunction with newer test-hole and geophysical data, a new base-of-aquifer altitude map was generated because of the strong effect of the aquifer-base topography on groundwater-flow direction and magnitude. The largest inflow to groundwater is recharge originating from water leaking from canals, which is much larger than recharge originating from infiltration of precipitation. The largest component of groundwater discharge from the study area is to the North Platte River and its tributaries, with smaller amounts of discharge to evapotranspiration and groundwater withdrawals for irrigation. Recharge from infiltration of precipitation was estimated with a daily soil-water-balance model. Annual recharge from canal seepage was estimated using available records from the Bureau of Reclamation and then modified with canal-seepage potentials estimated using geophysical data. Groundwater withdrawals were estimated using land-cover data, precipitation data, and published crop water-use data. For fields irrigated with surface water and groundwater, surface-water deliveries were subtracted from the estimated net irrigation requirement, and groundwater withdrawal was assumed to be equal to any demand unmet by surface water.</p>\n<p>The groundwater-flow model was calibrated to measured groundwater levels and stream base flows estimated using the base-flow index method. The model was calibrated through automated adjustments using statistical techniques through parameter estimation using the parameter estimation suite of software (PEST). PEST was used to adjust 273 parameters, grouped as hydraulic conductivity of the aquifer, spatial multipliers to recharge, temporal multipliers to recharge, and two specific recharge parameters. Base flow of the North Platte River at Bridgeport, Nebraska, streamgage near the eastern, downstream end of the model was one of the primary calibration targets. Simulated base flow reasonably matched estimated base flow for this streamgage during 1950&ndash;2008, with an average difference of 15 percent. Overall, 1950&ndash;2008 simulated base flow followed the trend of the estimated base flow reasonably well, in cases with generally increasing or decreasing base flow from the start of the simulation to the end. Simulated base flow also matched estimated base flow reasonably well for most of the North Platte River tributaries with estimated base flow. Average simulated groundwater budgets during 1989&ndash;2008 were nearly three times larger for irrigation seasons than for non-irrigation seasons.</p>\n<p>The calibrated groundwater-flow model was used with the Groundwater-Management Process for the 2005 version of the U.S. Geological Survey modular three-dimensional groundwater model, MODFLOW&ndash;2005, to provide a tool for the NPNRD to better understand how water-management decisions could affect stream base flows of the North Platte River at Bridgeport, Nebr., streamgage in a future period from 2008 to 2019 under varying climatic conditions. The simulation-optimization model was constructed to analyze the maximum increase in simulated stream base flow that could be obtained with the minimum amount of reductions in groundwater withdrawals for irrigation. A second analysis extended the first to analyze the simulated base-flow benefit of groundwater withdrawals along with application of intentional recharge, that is, water from canals being released into rangeland areas with sandy soils. With optimized groundwater withdrawals and intentional recharge, the maximum simulated stream base flow was 15&ndash;23 cubic feet per second (ft<sup>3</sup>/s) greater than with no management at all, or 10&ndash;15 ft<sup>3</sup>/s larger than with managed groundwater withdrawals only. These results indicate not only the amount that simulated stream base flow can be increased by these management options, but also the locations where the management options provide the most or least benefit to the simulated stream base flow. For the analyses in this report, simulated base flow was best optimized by reductions in groundwater withdrawals north of the North Platte River and in the western half of the area. Intentional recharge sites selected by the optimization had a complex distribution but were more likely to be closer to the North Platte River or its tributaries. Future users of the simulation-optimization model will be able to modify the input files as to type, location, and timing of constraints, decision variables of groundwater withdrawals by zone, and other variables to explore other feasible management scenarios that may yield different increases in simulated future base flow of the North Platte River.</p>","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/sir20155093","collaboration":"Prepared in cooperation with the North Platte Natural Resources District","usgsCitation":"Peterson, S.M, Flynn, A.T., Vrabel, Joseph, and Ryter, D.W., 2015, Simulation of groundwater flow and analysis of the effects of water-management options in the North Platte Natural Resources District, Nebraska: U.S. Geological Survey Scientific Investigations Report 2015–5093, 67 p., https://dx.doi.org/10.3133/sir20155093.","productDescription":"ix, 67 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-103.8262939453125,\n              41.85421933478601\n            ],\n            [\n              -103.875732421875,\n              41.85421933478601\n            ],\n            [\n              -103.92654418945312,\n              41.843989628462204\n            ],\n            [\n              -103.96636962890625,\n              41.80714914168836\n            ],\n            [\n              -103.98284912109374,\n              41.82454867985508\n            ],\n            [\n              -103.99246215820312,\n              41.80817277478235\n            ],\n            [\n              -104.04464721679688,\n              41.801006999656636\n            ],\n            [\n              -104.08172607421875,\n              41.81738473661009\n            ],\n            [\n              -104.12155151367188,\n              41.812778921301515\n            ]\n          ]\n        ]\n      }\n    }\n  ]\n}","contact":"<p>Director, Nebraska Water Science Center<br /> U.S. Geological Survey<br /> 5231 South 19th Street<br /> Lincoln, Nebraska 68512<br /><a href=\"http://ne.water.usgs.gov/\">http://ne.water.usgs.gov/</a></p>","tableOfContents":"<ul>\n<li>Acknowledgments</li>\n<li>Abstract</li>\n<li>Introduction</li>\n<li>Simulation of Groundwater Flow</li>\n<li>Effects of Water-Management Options</li>\n<li>Summary</li>\n<li>References Cited</li>\n</ul>","publishingServiceCenter":{"id":4,"text":"Rolla PSC"},"publishedDate":"2015-08-12","noUsgsAuthors":false,"publicationDate":"2015-08-12","publicationStatus":"PW","scienceBaseUri":"57f7eed3e4b0bc0bec09ed05","contributors":{"authors":[{"text":"Peterson, Steven M. 0000-0002-9130-1284 speterson@usgs.gov","orcid":"https://orcid.org/0000-0002-9130-1284","contributorId":847,"corporation":false,"usgs":true,"family":"Peterson","given":"Steven","email":"speterson@usgs.gov","middleInitial":"M.","affiliations":[{"id":464,"text":"Nebraska Water Science Center","active":true,"usgs":true}],"preferred":true,"id":566456,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Flynn, Amanda T. aflynn@usgs.gov","contributorId":4411,"corporation":false,"usgs":true,"family":"Flynn","given":"Amanda","email":"aflynn@usgs.gov","middleInitial":"T.","affiliations":[{"id":464,"text":"Nebraska Water Science Center","active":true,"usgs":true}],"preferred":false,"id":566457,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Vrabel, Joseph 0000-0002-8773-0764 jvrabel@usgs.gov","orcid":"https://orcid.org/0000-0002-8773-0764","contributorId":1577,"corporation":false,"usgs":true,"family":"Vrabel","given":"Joseph","email":"jvrabel@usgs.gov","affiliations":[{"id":583,"text":"Texas Water Science Center","active":true,"usgs":true}],"preferred":true,"id":566458,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Ryter, Derek W. 0000-0002-2488-626X dryter@usgs.gov","orcid":"https://orcid.org/0000-0002-2488-626X","contributorId":3395,"corporation":false,"usgs":true,"family":"Ryter","given":"Derek","email":"dryter@usgs.gov","middleInitial":"W.","affiliations":[{"id":154,"text":"California Water Science Center","active":true,"usgs":true},{"id":516,"text":"Oklahoma Water Science Center","active":true,"usgs":true}],"preferred":true,"id":567877,"contributorType":{"id":1,"text":"Authors"},"rank":4}]}}
,{"id":70155846,"text":"70155846 - 2015 - Normalization of stable isotope data for carbonate minerals: implementation of IUPAC guideline","interactions":[],"lastModifiedDate":"2015-08-12T08:49:16","indexId":"70155846","displayToPublicDate":"2015-08-12T08:00:00","publicationYear":"2015","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":1759,"text":"Geochimica et Cosmochimica Acta","active":true,"publicationSubtype":{"id":10}},"title":"Normalization of stable isotope data for carbonate minerals: implementation of IUPAC guideline","docAbstract":"<p id=\"sp0005\">Carbonate minerals provide a rich source of geochemical information because their <i>&delta;</i><sup>13</sup>C and <i>&delta;</i><sup>18</sup>O values provide information about surface and subsurface Earth processes. However, a significant problem is that the same <i>&delta;</i><sup>18</sup>O value is not reported for the identical carbonate sample when analyzed in different isotope laboratories in spite of the fact that the International Union of Pure and Applied Chemistry (IUPAC) has provided reporting guidelines for two decades. This issue arises because (1) the <i>&delta;</i><sup>18</sup>O measurements are performed on CO<sub>2</sub> evolved by reaction of carbonates with phosphoric acid, (2) the acid-liberated CO<sub>2</sub> is isotopically fractionated (enriched in <sup>18</sup>O) because it contains only two-thirds of the oxygen from the solid carbonate, (3) this oxygen isotopic fractionation factor is a function of mineralogy, temperature, concentration of the phosphoric acid, and <i>&delta;</i><sup>18</sup>O value of water in the phosphoric acid, (4) researchers may use any one of an assortment of oxygen isotopic fractionation factors that have been published for various minerals at various reaction temperatures, and (5) it sometimes is not clear how one should calculate <i>&delta;</i><sup>18</sup>O<sub>VPDB</sub> values on a scale normalized such that the <i>&delta;</i><sup>18</sup>O value of SLAP reference water is &minus;55.5&nbsp;&permil; relative to VSMOW reference water.</p>\n<p id=\"sp0010\">To enable researchers worldwide to publish the same <i>&delta;</i><sup>18</sup>O value (within experimental uncertainty) for the same carbonate sample, we have re-evaluated reported acid fractionation factors for calcite at 25, 50, and 75&nbsp;&deg;C and propose a revised relation for the temperature dependence of oxygen isotopic acid fractionation factor, <span id=\"mmlsi1\" class=\"mathmlsrc\"><span class=\"formulatext stixSupport mathImg\" title=\"Click to view the MathML source\" data-mathurl=\"/science?_ob=MathURL&amp;_method=retrieve&amp;_eid=1-s2.0-S0016703715000940&amp;_mathId=si1.gif&amp;_user=111111111&amp;_pii=S0016703715000940&amp;_rdoc=1&amp;_issn=00167037&amp;md5=28b351c4a3364b91eef66f01686e710c\"><sub>&alpha;</sub>CO<sub>2</sub><sub>(</sub>ACID<sub>)</sub>-calcite</span><span class=\"mathContainer hidden\"><span class=\"mathCode\">&alpha;CO2(ACID)-calcite</span></span></span>, of</p>\n<div id=\"e0005\" class=\"formula\">\n<div class=\"mathml\"><span class=\"mathmlsrc\"><span class=\"mathmlsrc\"><span class=\"formulatext stixSupport mathImg\" title=\"Click to view the MathML source\" data-mathurl=\"/science?_ob=MathURL&amp;_method=retrieve&amp;_eid=1-s2.0-S0016703715000940&amp;_mathId=si2.gif&amp;_user=111111111&amp;_pii=S0016703715000940&amp;_rdoc=1&amp;_issn=00167037&amp;md5=41e78095788c282f1d19094fba748d4b\">1000ln<sub>&alpha;</sub>CO<sub>2</sub><sub>(</sub>ACID<sub>)</sub>-calcite=3.48(10<sup>3</sup>/T)-1.47</span><span class=\"mathContainer hidden\"><span class=\"mathCode\">1000ln&alpha;CO2(ACID)-calcite=3.48(103/T)-1.47</span></span></span></span>\n<div class=\"btContainer\">\n<div class=\"mathjax\"><a class=\"mathjax firstFormula\" title=\"Turn MathJax on\">Turn<span class=\"mathjax \">&nbsp;</span><span class=\"offscreen\">MathJax </span>on</a></div>\n</div>\n<img class=\"temp\" src=\"http://www.sciencedirect.com/sd/blank.gif\" alt=\"\" /></div>\n</div>\n<p id=\"sp0015\">where <i>T</i> is temperature in kelvin. At 25&nbsp;&deg;C, <span id=\"mmlsi3\" class=\"mathmlsrc\"><span class=\"formulatext stixSupport mathImg\" title=\"Click to view the MathML source\" data-mathurl=\"/science?_ob=MathURL&amp;_method=retrieve&amp;_eid=1-s2.0-S0016703715000940&amp;_mathId=si3.gif&amp;_user=111111111&amp;_pii=S0016703715000940&amp;_rdoc=1&amp;_issn=00167037&amp;md5=4ed80d0dbcfcc5bd9e919d5d2bbd40c1\"><sub>&alpha;</sub>CO<sub>2</sub><sub>(</sub>ACID<sub>)</sub>-calcite=1.01025</span><span class=\"mathContainer hidden\"><span class=\"mathCode\">&alpha;CO2(ACID)-calcite=1.01025</span></span></span>, the most commonly accepted value for this quantity. We propose a normalization protocol in which (1) the internationally distributed carbonate isotopic reference materials NBS 18 and NBS 19 are interspersed among carbonate samples analyzed by treatment with phosphoric acid, (2) the <i>&delta;</i><sup>18</sup>O values of the calcite reference materials and the carbonate samples are calculated, respectively, by using the <span id=\"mmlsi4\" class=\"mathmlsrc\"><span class=\"formulatext stixSupport mathImg\" title=\"Click to view the MathML source\" data-mathurl=\"/science?_ob=MathURL&amp;_method=retrieve&amp;_eid=1-s2.0-S0016703715000940&amp;_mathId=si4.gif&amp;_user=111111111&amp;_pii=S0016703715000940&amp;_rdoc=1&amp;_issn=00167037&amp;md5=9f65c84ff2f44959b66e08525b62895a\"><sub>&alpha;</sub>CO<sub>2</sub><sub>(</sub>ACID<sub>)</sub>-calcite</span><span class=\"mathContainer hidden\"><span class=\"mathCode\">&alpha;CO2(ACID)-calcite</span></span></span> relation above and oxygen-isotope acid fractionation factors appropriate for the sample mineralogy and reaction temperature, (3) the <i>&delta;</i><sup>18</sup>O values of solid carbonate samples are determined on the VPDB scale (<i>&delta;</i><sup>18</sup>O<sub>VPDB</sub>) with IUPAC-recommended scale expansion such that the <i>&delta;</i><sup>18</sup>O of SLAP reference water is &minus;55.5&nbsp;&permil; relative to VSMOW reference water by normalizing <i>&delta;</i><sup>18</sup>O values of carbonate samples with 2014-IUPAC-recommended <i>&delta;</i><sup>18</sup>O values of NBS 18 and NBS 19, and (4) <i>&delta;</i><sup>18</sup>O values on the VPDB scale are converted to <i>&delta;</i><sup>18</sup>O values on the VSMOW-SLAP scale by using IUPAC recommendations.</p>\n<p id=\"sp0020\">To ease calculations in the protocol, a software application titled &ldquo;Carbon and Oxygen Isotopic Normalization Tool for Carbonates&rdquo; is available that relies upon IUPAC-recommended <i>&delta;</i><sup>13</sup>C and <i>&delta;</i><sup>18</sup>O values of carbonate isotopic reference materials</p>\n<p>(<span id=\"ir0045\" class=\"interref\" data-locatortype=\"url\" data-locatorkey=\"http://isotopes.usgs.gov/research/topics/carbonatesnormalizationtool.html\"><a class=\"cExLink\" href=\"http://isotopes.usgs.gov/research/topics/carbonatesnormalizationtool.html\" target=\"externObjLink\" data-itrprs=\"Y\" data-url=\"/science/RedirectURL?_method=externObjLink&amp;_locator=url&amp;_cdi=271865&amp;_issn=00167037&amp;_origin=article&amp;_zone=art_page&amp;_targetURL=http%253A%252F%252Fisotopes.usgs.gov%252Fresearch%252Ftopics%252Fcarbonatesnormalizationtool.html\">http://isotopes.usgs.gov/research/topics/carbonatesnormalizationtool.html</a></span>).</p>","language":"English","publisher":"Elsevier","publisherLocation":"New York, NY","doi":"10.1016/j.gca.2015.02.011","usgsCitation":"Kim, S., Coplen, T.B., and Horita, J., 2015, Normalization of stable isotope data for carbonate minerals: implementation of IUPAC guideline: Geochimica et Cosmochimica Acta, v. 158, p. 276-289, https://doi.org/10.1016/j.gca.2015.02.011.","productDescription":"14 p.","startPage":"276","endPage":"289","numberOfPages":"14","onlineOnly":"N","additionalOnlineFiles":"N","ipdsId":"IP-062909","costCenters":[{"id":436,"text":"National Research Program - Eastern Branch","active":true,"usgs":true}],"links":[{"id":306604,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"volume":"158","publishingServiceCenter":{"id":9,"text":"Reston PSC"},"noUsgsAuthors":false,"publicationStatus":"PW","scienceBaseUri":"55cc6022e4b08400b1fe0fb7","contributors":{"authors":[{"text":"Kim, Sang-Tae","contributorId":146204,"corporation":false,"usgs":false,"family":"Kim","given":"Sang-Tae","email":"","affiliations":[{"id":16624,"text":"School of Geography and Earth Sciences, McMaster University, ON, Canada","active":true,"usgs":false}],"preferred":false,"id":566587,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Coplen, Tyler B. 0000-0003-4884-6008 tbcoplen@usgs.gov","orcid":"https://orcid.org/0000-0003-4884-6008","contributorId":508,"corporation":false,"usgs":true,"family":"Coplen","given":"Tyler","email":"tbcoplen@usgs.gov","middleInitial":"B.","affiliations":[{"id":27111,"text":"National Water Quality Program","active":true,"usgs":true},{"id":37277,"text":"WMA - Earth System Processes Division","active":true,"usgs":true},{"id":436,"text":"National Research Program - Eastern Branch","active":true,"usgs":true},{"id":37464,"text":"WMA - Laboratory & Analytical Services Division","active":true,"usgs":true}],"preferred":true,"id":566586,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Horita, Juske","contributorId":146205,"corporation":false,"usgs":false,"family":"Horita","given":"Juske","email":"","affiliations":[{"id":16625,"text":"Department of Geosciences, Texas Tech University, Lubbock, Texas","active":true,"usgs":false}],"preferred":false,"id":566588,"contributorType":{"id":1,"text":"Authors"},"rank":3}]}}
,{"id":70148090,"text":"sir20155075 - 2015 - Median nitrate concentrations in groundwater in the New Jersey Highlands Region estimated using regression models and land-surface characteristics","interactions":[],"lastModifiedDate":"2015-09-03T13:16:16","indexId":"sir20155075","displayToPublicDate":"2015-08-12T00:00:00","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":"2015-5075","title":"Median nitrate concentrations in groundwater in the New Jersey Highlands Region estimated using regression models and land-surface characteristics","docAbstract":"<p>Nitrate-concentration data are used in conjunction with land-use and land-cover data to estimate median nitrate concentrations in groundwater underlying the New Jersey (NJ) Highlands Region. Sources of data on nitrate in 19,670 groundwater samples are from the U.S. Geological Survey (USGS) National Water Information System (NWIS) and the NJ Private Well Testing Act (PWTA).</p>\n<p>In a study conducted by the USGS, in cooperation with the New Jersey Department of Environmental Protection, logistic regression was used to relate measured nitrate concentrations to five explanatory variables (percent urban and agricultural land use, septic-system density, total length of streams, and number of known contaminated sites) quantified in 610-meter-square grid cells. A method for calculating the median concentrations of nitrate from a series of logistic regression models was developed. Two calibration and two validation procedures showed that the logistic-regression-based method can estimate groundwater-nitrate concentrations in the Highlands Region accurately to within 0.1 milligram per liter as nitrogen (mg/L as N). Limitations of the logistic-regression-based method include the inability to select a logistic model with exactly 0.5 probability of exceeding the threshold value and lack of an algorithm to directly calculate the median value. Quantile regression was evaluated as a suitable alternative and was slightly less accurate than the logistic-regression method in estimating median groundwater nitrate concentrations in the Highlands Region.</p>\n<p>Multiple-linear regression with log-transformed nitrate-concentration data and the same five explanatory values was less accurate than either logistic or quantile regression in estimating median nitrate concentrations. On the basis of 4,516 2000 x 2000 foot grid cells that contain wells with data stored in NWIS and the PWTA database, the estimated median nitrate concentration for the entire Highlands Region is about 1.25 mg/L as N, and estimated median concentrations range from about 1.05 to 1.78 mg/L as N among 11 smaller administratively defined areas within the Highlands Region that vary in percentages of urban land use, agricultural land use, and septic-system density.</p>\n<p>The Kaplan-Meier method of estimating summary statistics from left-censored data was applied in order to include nondetects (left-censored data) in median nitrate-concentration calculations. Median concentrations also were determined using three alternative methods of handling nondetects. Treatment of the 23 percent of samples that were nondetects had little effect on estimated median nitrate concentrations because method detection limits were mostly less than median values.</p>","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/sir20155075","collaboration":"Prepared in cooperation with the New Jersey Department of Environmental Protection","usgsCitation":"Baker, R.J., Chepiga, M., and Cauller, S.J., 2015, Median nitrate concentrations in groundwater in the New Jersey Highlands Region estimated using regression models and land-surface characteristics: U.S. Geological Survey Scientific Investigations Report 2015-5075, Report: vii, 26 p.; 2 Appendices, https://doi.org/10.3133/sir20155075.","productDescription":"Report: vii, 26 p.; 2 Appendices","numberOfPages":"39","onlineOnly":"Y","additionalOnlineFiles":"Y","ipdsId":"IP-060925","costCenters":[{"id":470,"text":"New Jersey Water Science Center","active":true,"usgs":true}],"links":[{"id":305639,"rank":3,"type":{"id":3,"text":"Appendix"},"url":"https://pubs.usgs.gov/sir/2015/5075/support/sir20155075_appendix01.xlsx","text":"Appendix 1","size":"17.2 MB","linkFileType":{"id":3,"text":"xlsx"},"description":"SIR 2015-5075 Appendix 1","linkHelpText":"Example spreadsheet for calculating median nitrate concentrations with logistic-regression models"},{"id":305637,"rank":1,"type":{"id":15,"text":"Index Page"},"url":"https://pubs.usgs.gov/sir/2015/5075/"},{"id":305638,"rank":2,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/sir/2015/5075/support/sir20155075.pdf","text":"Report","size":"16.2 MB","linkFileType":{"id":1,"text":"pdf"},"description":"SIR 2015-5075 Report"},{"id":305640,"rank":4,"type":{"id":3,"text":"Appendix"},"url":"https://pubs.usgs.gov/sir/2015/5075/support/sir20155075_appendix02.pdf","text":"Appendix 2","size":"180 KB","linkFileType":{"id":1,"text":"pdf"},"description":"SIR 2015-5075 Appendix 2","linkHelpText":"Geographic and environmental characteristics evaluated as possible explanatory variables in models of median nitrate concentrations in groundwater in the NJ Highlands Region"},{"id":305641,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/sir20155075.jpg"}],"projection":"Universal Transverse Mercator projection","country":"United States","state":"New Jersey","otherGeospatial":"New Jersey Highlands","geographicExtents":"{\n  \"type\": \"FeatureCollection\",\n  \"features\": [\n    {\n      \"type\": \"Feature\",\n      \"properties\": {},\n      \"geometry\": {\n        \"type\": \"Polygon\",\n        \"coordinates\": [\n          [\n            [\n              -75.0531005859375,\n              40.86783384138491\n            ],\n            [\n              -74.9212646484375,\n              41.03793062246529\n            ],\n            [\n              -74.805908203125,\n              41.03793062246529\n            ],\n            [\n       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PSC"},"noUsgsAuthors":false,"publicationStatus":"PW","scienceBaseUri":"560bb6cbe4b058f706e53d66","contributors":{"authors":[{"text":"Baker, Ronald J. rbaker@usgs.gov","contributorId":1436,"corporation":false,"usgs":true,"family":"Baker","given":"Ronald","email":"rbaker@usgs.gov","middleInitial":"J.","affiliations":[{"id":470,"text":"New Jersey Water Science Center","active":true,"usgs":true}],"preferred":true,"id":547539,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Chepiga, Mary M. mchepiga@usgs.gov","contributorId":888,"corporation":false,"usgs":true,"family":"Chepiga","given":"Mary M.","email":"mchepiga@usgs.gov","affiliations":[{"id":470,"text":"New Jersey Water Science Center","active":true,"usgs":true}],"preferred":false,"id":547540,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Cauller, Stephen J. 0000-0002-1823-8813 sjcaulle@usgs.gov","orcid":"https://orcid.org/0000-0002-1823-8813","contributorId":3641,"corporation":false,"usgs":true,"family":"Cauller","given":"Stephen","email":"sjcaulle@usgs.gov","middleInitial":"J.","affiliations":[{"id":470,"text":"New Jersey Water Science Center","active":true,"usgs":true}],"preferred":false,"id":564557,"contributorType":{"id":1,"text":"Authors"},"rank":3}]}}
,{"id":70155520,"text":"70155520 - 2015 - Earthquake shaking hazard estimates and exposure changes in the conterminous United States","interactions":[],"lastModifiedDate":"2016-06-29T13:20:28","indexId":"70155520","displayToPublicDate":"2015-08-11T13:00:00","publicationYear":"2015","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":1436,"text":"Earthquake Spectra","active":true,"publicationSubtype":{"id":10}},"title":"Earthquake shaking hazard estimates and exposure changes in the conterminous United States","docAbstract":"<p><span>A large portion of the population of the United States lives in areas vulnerable to earthquake hazards. This investigation aims to quantify population and infrastructure exposure within the conterminous U.S. that are subjected to varying levels of earthquake ground motions by systematically analyzing the last four cycles of the U.S. Geological Survey's (USGS) National Seismic Hazard Models (published in 1996, 2002, 2008 and 2014). Using the 2013 LandScan data, we estimate the numbers of people who are exposed to potentially damaging ground motions (peak ground accelerations at or above 0.1g). At least 28 million (~9% of the total population) may experience 0.1g level of shaking at relatively frequent intervals (annual rate of 1 in 72 years or 50% probability of exceedance (PE) in 50 years), 57 million (~18% of the total population) may experience this level of shaking at moderately frequent intervals (annual rate of 1 in 475 years or 10% PE in 50 years), and 143 million (~46% of the total population) may experience such shaking at relatively infrequent intervals (annual rate of 1 in 2,475 years or 2% PE in 50 years). We also show that there is a significant number of critical infrastructure facilities located in high earthquake-hazard areas (Modified Mercalli Intensity &ge; VII with moderately frequent recurrence interval).</span></p>","language":"English","publisher":"The Earthquake Engineering Research Institute","publisherLocation":"Berkeley, CA","doi":"10.1193/111814EQS195M","usgsCitation":"Jaiswal, K., Petersen, M.D., Rukstales, K.S., and Leith, W.S., 2015, Earthquake shaking hazard estimates and exposure changes in the conterminous United States: Earthquake Spectra, v. 31, no. 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,{"id":70155823,"text":"70155823 - 2015 - Understory vegetation as an indicator for floodplain forest restoration in the Mississippi River Alluvial Valley, U.S.A.","interactions":[],"lastModifiedDate":"2017-01-11T15:38:09","indexId":"70155823","displayToPublicDate":"2015-08-11T13:00:00","publicationYear":"2015","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":3271,"text":"Restoration Ecology","active":true,"publicationSubtype":{"id":10}},"title":"Understory vegetation as an indicator for floodplain forest restoration in the Mississippi River Alluvial Valley, U.S.A.","docAbstract":"<p>In the Mississippi River Alluvial Valley (MAV), complete alteration of river-floodplain hydrology allowed for widespread<br />conversion of forested bottomlands to intensive agriculture, resulting in nearly 80% forest loss. Governmental programs have<br />attempted to restore forest habitat and functions within this altered landscape by the methods of tree planting (afforestation)<br />and local hydrologic enhancement on reclaimed croplands. Early assessments identified factors that influenced whether<br />planting plus tree colonization could establish an overstory community similar to natural bottomland forests. The extent<br />to which afforested sites develop typical understory vegetation has not been evaluated, yet understory composition may be<br />indicative of restored site conditions. As part of a broad study quantifying the ecosystem services gained from restoration<br />efforts, understory vegetation was compared between 37 afforested sites and 26 mature forest sites. Differences in vegetation<br />attributes for species growth forms, wetland indicator classes, and native status were tested with univariate analyses;<br />floristic composition data were analyzed by multivariate techniques. Understory vegetation of restoration sites was generally<br />hydrophytic, but species composition differed from that of mature bottomland forest because of young successional age and<br />differing responses of plant growth forms. Attribute and floristic variation among restoration sites was related to variation<br />in canopy development and local wetness conditions, which in turn reflected both intrinsic site features and outcomes of<br />restoration practices. Thus, understory vegetation is a useful indicator of functional progress in floodplain forest restoration.</p>","language":"English","publisher":"Society for Ecological Restoration","publisherLocation":"Cambridge, MA","doi":"10.1111/rec.12210","usgsCitation":"De Steven, D., Faulkner, S., Keeland, B.D., Baldwin, M., McCoy, J.W., and Hughes, S., 2015, Understory vegetation as an indicator for floodplain forest restoration in the Mississippi River Alluvial Valley, U.S.A.: Restoration Ecology, v. 23, no. 4, p. 402-412, https://doi.org/10.1111/rec.12210.","productDescription":"11 p.","startPage":"402","endPage":"412","numberOfPages":"11","onlineOnly":"N","additionalOnlineFiles":"N","ipdsId":"IP-059857","costCenters":[{"id":365,"text":"Leetown Science Center","active":true,"usgs":true}],"links":[{"id":306578,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","state":"Arkansas, Louisiana, Mississippi, Tennessee","otherGeospatial":"Mississippi River Alluvial Valley","geographicExtents":"{\n  \"type\": \"FeatureCollection\",\n  \"features\": [\n    {\n      \"type\": \"Feature\",\n      \"properties\": {},\n      \"geometry\": {\n        \"type\": \"Polygon\",\n        \"coordinates\": [\n          [\n            [\n              -89.384765625,\n              35.782170703266075\n            ],\n            [\n              -89.97802734375,\n              35.90684930677121\n            ],\n            [\n              -91.23046875,\n              35.90684930677121\n            ],\n            [\n              -91.95556640625,\n              35.585851593232356\n            ],\n            [\n              -92.30712890625,\n              34.84987503195418\n            ],\n            [\n              -92.4169921875,\n              33.46810795527896\n            ],\n            [\n              -92.48291015625,\n              32.509761735919426\n            ],\n            [\n             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PSC"},"noUsgsAuthors":false,"publicationDate":"2015-04-09","publicationStatus":"PW","scienceBaseUri":"55cb0ea4e4b08400b1fdd45f","chorus":{"doi":"10.1111/rec.12210","url":"http://dx.doi.org/10.1111/rec.12210","publisher":"Wiley-Blackwell","authors":"De Steven Diane, Faulkner Stephen P., Keeland Bobby D., Baldwin Michael J., McCoy John W., Hughes Steven C.","journalName":"Restoration Ecology","publicationDate":"4/9/2015","auditedOn":"11/17/2015"},"contributors":{"authors":[{"text":"De Steven, Diane","contributorId":146153,"corporation":false,"usgs":false,"family":"De Steven","given":"Diane","affiliations":[{"id":7134,"text":"USFS","active":true,"usgs":false}],"preferred":false,"id":566488,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Faulkner, Stephen 0000-0001-5295-1383 faulkners@usgs.gov","orcid":"https://orcid.org/0000-0001-5295-1383","contributorId":146152,"corporation":false,"usgs":true,"family":"Faulkner","given":"Stephen","email":"faulkners@usgs.gov","affiliations":[{"id":365,"text":"Leetown Science Center","active":true,"usgs":true}],"preferred":true,"id":566487,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Keeland, Bobby D.","contributorId":103506,"corporation":false,"usgs":true,"family":"Keeland","given":"Bobby","email":"","middleInitial":"D.","affiliations":[{"id":455,"text":"National Wetlands Research Center","active":true,"usgs":true},{"id":17705,"text":"Wetland and Aquatic Research Center","active":true,"usgs":true}],"preferred":true,"id":566489,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Baldwin, M.J. 0000-0002-7865-6590 baldwinm@usgs.gov","orcid":"https://orcid.org/0000-0002-7865-6590","contributorId":146154,"corporation":false,"usgs":true,"family":"Baldwin","given":"M.J.","email":"baldwinm@usgs.gov","affiliations":[{"id":455,"text":"National Wetlands Research Center","active":true,"usgs":true},{"id":17705,"text":"Wetland and Aquatic Research Center","active":true,"usgs":true}],"preferred":true,"id":566490,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"McCoy, John W. 0000-0003-3013-730X mccoyj@usgs.gov","orcid":"https://orcid.org/0000-0003-3013-730X","contributorId":3082,"corporation":false,"usgs":true,"family":"McCoy","given":"John","email":"mccoyj@usgs.gov","middleInitial":"W.","affiliations":[{"id":455,"text":"National Wetlands Research Center","active":true,"usgs":true},{"id":17705,"text":"Wetland and Aquatic Research Center","active":true,"usgs":true}],"preferred":true,"id":566491,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"Hughes, Steven 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,{"id":70148270,"text":"ds942 - 2015 - Geochemical, modal, and geochronologic data for 1.4 Ga A-type granitoid intrusions of the conterminous United States","interactions":[],"lastModifiedDate":"2016-06-29T13:23:03","indexId":"ds942","displayToPublicDate":"2015-08-10T11:45:00","publicationYear":"2015","noYear":false,"publicationType":{"id":18,"text":"Report"},"publicationSubtype":{"id":5,"text":"USGS Numbered Series"},"seriesTitle":{"id":310,"text":"Data Series","code":"DS","onlineIssn":"2327-638X","printIssn":"2327-0271","active":false,"publicationSubtype":{"id":5}},"seriesNumber":"942","title":"Geochemical, modal, and geochronologic data for 1.4 Ga A-type granitoid intrusions of the conterminous United States","docAbstract":"<h1>Introduction</h1>\n<p>The purpose of this report is to present available geochemical, modal, and geochronologic data for approximately 1.4 billion year (Ga) A-type granitoid intrusions of the United States and to make those data available to ongoing petrogenetic investigations of these rocks. A-type granites, as originally defined by Loiselle and Wones (1979), are iron-enriched granitoids (synonymous with the ferroan granitoids of Frost and Frost, 2011) that occur in an anorogenic, within-continent setting. Relative to other granitic rocks, A-type granites have high FeO*/(FeO*+MgO), high K<sub>2</sub>O and K<sub>2</sub>O/Na<sub>2</sub>O, are metaluminous to weakly peraluminous, and are enriched in incompatible trace elements. Loiselle and Wones (1979) further suggested that A-type granites are relatively anhydrous. Anderson (1983) provides an early compilation of data for the products of 1.4 Ga magmatism in North America and notes the spatial and temporal association of a trio of rock types, which includes gabbro to anorthosite, intermediate composition mangerite, and granitic rapakivi rocks. In North America, the majority of known A-type intrusions were emplaced between 1.5 and 1.3 Ga and are predominantly of the granitic variety (Anderson, 1983).</p>\n<p>This report addresses the broadly Mesoproterozoic-age granitic rocks of the conterminous United States. Constituents of this group of intrusive rocks were defined using a variety of spatial, compositional, and geochronologic metrics. Thomas and others (2012) provided an updated synthesis, largely based on new isotopic and geochronologic data (for example, Fisher and others, 2010), for the large-scale geologic and tectonic evolution of the eastern United States. Their findings suggest that the basement rocks of the central and southern Appalachian region are allochthonous relative to the remainder of Laurentia and were accreted along the Grenville front between 1.25 and 1.0 Ga. Accordingly, Mesoproterozoic rocks east of the Grenville front and south of the approximate latitude of New York City do not represent North American magmatism. Consequently, geochemical, modal, and geochronologic data for these rocks are not included in the compilation described herein. Further, the structural styles and compositions of granitoid rocks east of the Grenville front, mostly highly deformed gneissic rocks, are dissimilar to those characteristic of the A-type granitoid rocks described herein.</p>\n<p>A variety of compositional and age information further characterizes the 1.4 Ga A-type granitoid rocks in the conterminous United States. Most samples included in this compilation have felsic compositions, although some extend to intermediate compositions. SiO2 contents range from 56 to almost 78 weight percent, and median and mean SiO2 contents are 72.0 and 71.1 weight percent, respectively. The majority of these rocks for which modal data are available are composed of monzogranite (Streckeisen, 1976), although the dataset also contains many samples composed of granodiorite and syenogranite. A smaller group of the granitoid rocks in this dataset are composed of quartz monzodiorite and quartz monzonite, and a very small subset of samples is composed of alkali-feldspar granite, tonalite, alkali-feldspar quartz syenite, and quartz syenite (fig. 1). Many of the 1.4 Ga granitoid rocks are further characterized by medium- to coarse-grain size and are also conspicuously porphyritic; alkali feldspar phenocrysts or megacrysts (2&ndash;10 cm), often with rapakivi overgrowths, are a common feature of many of these rocks (Anderson, 1983; Anderson and Bender, 1989; Anderson and Cullers, 1978; Condie and Budding, 1979). The age of A-type magmatism in North America ranges from about 1.8 to 1.0 Ga, although Anderson (1983) suggests that more than 70 percent (by volume) of A-type magmatism in this region occurred between 1.49 and 1.41 Ga. In the conterminous United States, ages of A-type granitoid rocks are restricted to the period between about 1.49 and 1.33 Ga (Anderson, 1983; Bauer and Pollock, 1993; Bickford and Mose, 1975; Bickford, Harrower, and others, 1981; Bickford and others, 1989; Dewane and Van Schmus, 2007; Hoppe and others, 1983; Peterman and Hedge, 1968; Van Schmus and Bickford, 1981; Van Schmus and others, 1975). Using these recognition criteria, we identified A-type granitoid intrusions of the conterminous United States; for those intrusions, we compiled available geochemical, modal, isotopic (Sr and Nd) and geochronologic data for inclusion in the databases described herein.</p>\n<p>The significance of 1.4 Ga granitoid rocks relative to the geologic evolution of the conterminous United States remains unclear, despite Anderson&rsquo;s (1983) compilation and synthesis of compositional data pertinent to these rocks. The large-volume magmatic events indicated by these rocks, as well as their broad geographic distribution, tectonic significance, and association with mineral deposits, underscore their importance. The broad distribution of these rocks, from the northern mid-continent to the southwestern United States (in New Mexico, Arizona, California, and southernmost Nevada), throughout the Rocky Mountains in New Mexico and Colorado (and sporadically in southern Wyoming and central Idaho), and beneath much of the Plains region (as indicated by drilling), has led to the large-scale tectonic and magmatic processes responsible for genesis of the associated magmas being actively studied.</p>\n<p>In addition, Kisvarsanyi (1972) suggests that iron-copper deposits in the St. Francois Mountains of southeastern Missouri are petrogenetically associated with 1.4 Ga A-type granitoids that occur in that region. Similarly, Dall&rsquo;Agnol and others (2012) summarize important global associations between A-type granitoid rocks and a variety of important ore deposit types, particularly tin, high-field-strength elements (Zr, Hf, Nb, Ta), rare-earth elements, and iron oxide-copper-gold deposits. Consequently, the need to better understand relations between A-type granitoid rocks, tectonic setting, and magma petrogenesis, as well as their genetic associations with important types of ore deposits, suggests that developing a definitive geochemical, modal, and geochronologic database for these rocks in the conterminous United States is of considerable value.</p>","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/ds942","usgsCitation":"du Bray, E.A., Holm-Denoma, C.S., San Juan, C.A., Lund, Karen, Premo, W.R., and DeWitt, Ed, 2015, Geochemical, modal, and geochronologic data for 1.4 Ga A-type granitoid intrusions of the conterminous United States: U.S.\nGeological Survey Data Series 942, 19 p., https://dx.doi.org/10.3133/ds942.","productDescription":"Report: iii, 19 p.; Plate: 24 x 17 inches; 2 Appendices; Database; Metadata; 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Cited</li>\n</ul>","publishingServiceCenter":{"id":2,"text":"Denver PSC"},"publishedDate":"2015-08-10","noUsgsAuthors":false,"publicationDate":"2015-08-10","publicationStatus":"PW","scienceBaseUri":"5774f24ae4b07dd077c6a1e7","contributors":{"authors":[{"text":"du Bray, Edward A. 0000-0002-4383-8394 edubray@usgs.gov","orcid":"https://orcid.org/0000-0002-4383-8394","contributorId":755,"corporation":false,"usgs":true,"family":"du Bray","given":"Edward","email":"edubray@usgs.gov","middleInitial":"A.","affiliations":[{"id":171,"text":"Central Mineral and Environmental Resources Science Center","active":true,"usgs":true},{"id":164,"text":"Central Energy Resources Science Center","active":true,"usgs":true}],"preferred":true,"id":566848,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Holm-Denoma, Christopher S. 0000-0003-3229-5440 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Karen 0000-0002-4249-3582 klund@usgs.gov","orcid":"https://orcid.org/0000-0002-4249-3582","contributorId":1235,"corporation":false,"usgs":true,"family":"Lund","given":"Karen","email":"klund@usgs.gov","affiliations":[{"id":171,"text":"Central Mineral and Environmental Resources Science Center","active":true,"usgs":true},{"id":387,"text":"Mineral Resources Program","active":true,"usgs":true}],"preferred":true,"id":566851,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Premo, Wayne R. 0000-0001-9904-4801 wpremo@usgs.gov","orcid":"https://orcid.org/0000-0001-9904-4801","contributorId":1697,"corporation":false,"usgs":true,"family":"Premo","given":"Wayne","email":"wpremo@usgs.gov","middleInitial":"R.","affiliations":[],"preferred":true,"id":566852,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"DeWitt, Ed edewitt@usgs.gov","contributorId":725,"corporation":false,"usgs":true,"family":"DeWitt","given":"Ed","email":"edewitt@usgs.gov","affiliations":[{"id":171,"text":"Central Mineral and Environmental Resources Science Center","active":true,"usgs":true}],"preferred":false,"id":566924,"contributorType":{"id":1,"text":"Authors"},"rank":6}]}}
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