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For the State of Hawaii, elevation data are critical for infrastructure and construction management, flood risk management, geologic resource assessment and hazard mitigation, natural resources conservation, coastal zone management, and other business uses. Today, high-density light detection and ranging (lidar) data are the primary sources for deriving elevation models and other datasets. Federal, State, Tribal, U.S. territorial, and local agencies work in partnership to (1) replace data that are older and of lower quality and (2) provide coverage where publicly accessible data do not exist. A joint goal of State and Federal partners is to acquire consistent, statewide coverage to support existing and emerging applications enabled by lidar data.</p><p>The National Enhanced Elevation Assessment evaluated multiple elevation data acquisition options to determine the optimal data quality and data replacement cycle relative to cost to meet the identified requirements of the user community. The evaluation demonstrated that lidar acquisition at quality level 2 for the conterminous United States, Hawaii, and selected U.S. territories, and quality level 5 interferometric synthetic aperture radar (IfSAR) data for Alaska, all with a 6- to 10-year acquisition cycle, provided the highest benefit/cost ratios. The 3D Elevation Program (3DEP) initiative selected an 8-year acquisition cycle for the respective quality levels. 3DEP, managed by the U.S. Geological Survey, the Office of Management and Budget Circular A–16 lead agency for terrestrial elevation data, responds to the growing need for high-quality topographic data and a wide range of other three-dimensional (3D) representations of the Nation’s natural and constructed features.</p>","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/fs20143079","usgsCitation":"Carswell, W., 2016, The 3D Elevation Program: summary for Hawaii (Version 1: Originally posted September 12, 2014; Version 1.1: February 2016): U.S. Geological Survey Fact Sheet 2014-3079, 2 p., https://doi.org/10.3133/fs20143079.","productDescription":"2 p.","numberOfPages":"2","onlineOnly":"Y","additionalOnlineFiles":"N","ipdsId":"IP-057032","costCenters":[{"id":423,"text":"National Geospatial 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 \"}}]}","edition":"Version 1: Originally posted September 12, 2014; Version 1.1: February 2016","contact":"<p>Director, National Geospatial Program<br /> U.S. Geological Survey<br /> 511 National Center<br /> Reston, VA 20192<br /> <a href=\"http://www.usgs.gov/ngpo/\">http://www.usgs.gov/ngpo/</a><br /> <a href=\"http://nationalmap.gov/3DEP/\">http://nationalmap.gov/3DEP/</a></p>","revisedDate":"2016-02-16","noUsgsAuthors":false,"publicationStatus":"PW","scienceBaseUri":"5413fd25e4b082fed288b8e3","contributors":{"authors":[{"text":"Carswell, William J. Jr. carswell@usgs.gov","contributorId":1787,"corporation":false,"usgs":true,"family":"Carswell","given":"William J.","suffix":"Jr.","email":"carswell@usgs.gov","affiliations":[{"id":423,"text":"National Geospatial Program","active":true,"usgs":true}],"preferred":false,"id":499167,"contributorType":{"id":1,"text":"Authors"},"rank":1}]}}
,{"id":70173910,"text":"70173910 - 2016 - A hierarchical community occurrence model for North Carolina stream fish","interactions":[],"lastModifiedDate":"2016-06-15T11:28:45","indexId":"70173910","displayToPublicDate":"2014-08-30T00:00:00","publicationYear":"2016","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":3624,"text":"Transactions of the American Fisheries Society","active":true,"publicationSubtype":{"id":10}},"title":"A hierarchical community occurrence model for North Carolina stream fish","docAbstract":"<p><span>The southeastern USA is home to one of the richest&mdash;and most imperiled and threatened&mdash;freshwater fish assemblages in North America. For many of these rare and threatened species, conservation efforts are often limited by a lack of data. Drawing on a unique and extensive data set spanning over 20&nbsp;years, we modeled occurrence probabilities of 126 stream fish species sampled throughout North Carolina, many of which occur more broadly in the southeastern USA. Specifically, we developed species-specific occurrence probabilities from hierarchical Bayesian multispecies models that were based on common land use and land cover covariates. We also used index of biotic integrity tolerance classifications as a second level in the model hierarchy; we identify this level as informative for our work, but it is flexible for future model applications. Based on the partial-pooling property of the models, we were able to generate occurrence probabilities for many imperiled and data-poor species in addition to highlighting a considerable amount of occurrence heterogeneity that supports species-specific investigations whenever possible. Our results provide critical species-level information on many threatened and imperiled species as well as information that may assist with re-evaluation of existing management strategies, such as the use of surrogate species. Finally, we highlight the use of a relatively simple hierarchical model that can easily be generalized for similar situations in which conventional models fail to provide reliable estimates for data-poor groups.</span></p>","language":"English","publisher":"American Fisheries Society","publisherLocation":"Bethesda, MD","doi":"10.1080/00028487.2014.931745","usgsCitation":"Midway, S., Wagner, T., and Tracy, B., 2016, A hierarchical community occurrence model for North Carolina stream fish: Transactions of the American Fisheries Society, v. 143, no. 5, p. 1348-1357, https://doi.org/10.1080/00028487.2014.931745.","productDescription":"10 p.","startPage":"1348","endPage":"1357","onlineOnly":"N","additionalOnlineFiles":"N","ipdsId":"IP-052105","costCenters":[{"id":199,"text":"Coop Res Unit Leetown","active":true,"usgs":true}],"links":[{"id":323672,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","state":"North 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,{"id":70048986,"text":"ofr20121266 - 2016 - Bathymetric terrain model of the Atlantic margin for marine geological investigations","interactions":[],"lastModifiedDate":"2025-04-10T15:47:38.104628","indexId":"ofr20121266","displayToPublicDate":"2013-12-03T14:03:00","publicationYear":"2016","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":"2012-1266","title":"Bathymetric terrain model of the Atlantic margin for marine geological investigations","docAbstract":"<p>A bathymetric terrain model of the Atlantic margin covering almost 725,000 square kilometers of seafloor from the New England Seamounts in the north to the Blake Basin in the south is compiled from existing multibeam bathymetric data for marine geological investigations. Although other terrain models of the same area are extant, they are produced from either satellite-derived bathymetry at coarse resolution (ETOPO1), or use older bathymetric data collected by using a combination of single beam and multibeam sonars (Coastal Relief Model). The new multibeam data used to produce this terrain model have been edited by using hydrographic data processing software to maximize the quality, usability, and cartographic presentation of the combined 100-meter resolution grid. The final grid provides the largest high-resolution, seamless terrain model of the Atlantic margin..</p>","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/ofr20121266","collaboration":"Prepared in cooperation with the U.S. Nuclear Regulatory Commission and the National Oceanic and Atmospheric Administration","usgsCitation":"Andrews, B., Chaytor, J., ten Brink, U., Brothers, D., Gardner, J.V., Lobecker, E.A., and Calder, B.R., 2016, Bathymetric terrain model of the Atlantic margin for marine geological investigations (Originally posted December 3, 2013; Version 2.0: May 25, 2016): U.S. Geological Survey Open-File Report 2012-1266, Report: vi, 12 p.; 1 Plate: 34 x 44 inches, https://doi.org/10.3133/ofr20121266.","productDescription":"Report: vi, 12 p.; 1 Plate: 34 x 44 inches","onlineOnly":"Y","additionalOnlineFiles":"Y","ipdsId":"IP-042694","costCenters":[{"id":678,"text":"Woods Hole Coastal and Marine Science Center","active":true,"usgs":true}],"links":[{"id":321436,"rank":5,"type":{"id":17,"text":"Plate"},"url":"https://pubs.usgs.gov/of/2012/1266/ofr20121266_plate1.pdf","size":"224 MB","linkFileType":{"id":1,"text":"pdf"},"description":"OFR 2012-1266"},{"id":280161,"rank":4,"type":{"id":2,"text":"Additional Report Piece"},"url":"https://pubs.usgs.gov/of/2012/1266/title_page.html","text":"Title Page","linkFileType":{"id":5,"text":"html"},"description":"OFR 2012-1266"},{"id":280162,"rank":3,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/ofr20121266.GIF"},{"id":280159,"rank":2,"type":{"id":15,"text":"Index Page"},"url":"https://pubs.usgs.gov/of/2012/1266/"},{"id":280160,"rank":1,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/of/2012/1266/pdf/ofr20121266_v2.pdf","text":"Report","size":"3.14 MB","linkFileType":{"id":1,"text":"pdf"},"description":"OFR 2012-1266"},{"id":321437,"rank":6,"type":{"id":25,"text":"Version History"},"url":"https://pubs.usgs.gov/of/2012/1266/versionHist.txt","size":"2 MB","linkFileType":{"id":2,"text":"txt"},"description":"OFR 2012-1266"}],"projection":"Mercator Projection","country":"United States","geographicExtents":"{\"crs\": {\"type\": \"name\", \"properties\": {\"name\": \"urn:ogc:def:crs:OGC:1.3:CRS84\"}}, \"geometry\": {\"type\": \"Polygon\", \"coordinates\": [[[-73.990880318804216, 33.01264651249938], [-76.177520201754476, 33.091051926629071], [-75.698376004295199, 34.511061093644571], [-74.875119155933476, 35.45555931929227], [-74.779290316441632, 37.146980850626349], [-74.522294792349953, 37.547719633955907], [-73.847137059566478, 38.309994493550107], [-73.193758608485723, 38.681660632687567], [-71.600531692308323, 40.026021055247213], [-69.508342808469592, 39.982462491841702], [-69.199077008291354, 40.056512049630946], [-69.107604025140176, 40.296084148360578], [-68.645883253042939, 40.161052601803874], [-68.144959773881169, 40.49209768368479], [-67.535139886205798, 40.422403982236055], [-66.942743423892523, 40.614061661219928], [-65.912845523979058, 41.517915052123776], [-65.276890498260514, 41.160734832199637], [-65.795237402784551, 40.616252789632284], [-65.69505270695214, 40.516068093799888], [-63.020011669307671, 40.487826667598029], [-62.954087379549208, 40.38069969674067], [-64.339510213776634, 39.77339458773816], [-64.213190379900993, 39.564313483392247], [-66.837158239441294, 38.363839475939926], [-67.22047359740867, 37.950033123588796], [-67.758421855465201, 37.826326803517404], [-67.706151579378741, 37.530128572360809], [-70.217302759699066, 37.434299732869007], [-72.491909742100063, 35.603103845332321], [-72.936207088835033, 34.334403118951634], [-73.053815210029597, 34.360538256994865], [-73.990880318804216, 33.01264651249938]]]}, \"properties\": {\"extentType\": \"Custom\", \"code\": \"\", \"name\": \"\", \"notes\": \"\", \"promotedForReuse\": false, \"abbreviation\": \"\", \"shortName\": \"\", \"description\": \"\"}, \"bbox\": [-76.177520201754476, 33.01264651249938, -62.954087379549208, 41.517915052123776], \"type\": \"Feature\", \"id\": \"3091982\"}","edition":"Originally posted December 3, 2013; Version 2.0: May 25, 2016","contact":"<p><a href=\"mailto:WHSC_science_director@usgs.gov\" data-mce-href=\"mailto:WHSC_science_director@usgs.gov\">Director</a>, Woods Hole Coastal and Marine Science Center<br> 384 Woods Hole Road<br> Quissett Campus<br> Woods Hole, MA 02543-1598<br> <a href=\"http://woodshole.er.usgs.gov/\" data-mce-href=\"http://woodshole.er.usgs.gov/\">http://woodshole.er.usgs.gov/</a></p>","tableOfContents":"<ul><li>Abstract</li><li>Introduction</li><li>Common Processing Methods</li><li>Data Catalog</li><li>Map Plate</li><li>References Cited</li><li>Appendix 1. Individual Surveys Used as Sources for the Bathymetric Terrain Model for the Atlantic Margin of the United States</li></ul>","revisedDate":"2016-05-25","noUsgsAuthors":false,"publicationStatus":"PW","scienceBaseUri":"529efd6ee4b01942f4ab8b80","contributors":{"authors":[{"text":"Andrews, Brian D.","contributorId":54180,"corporation":false,"usgs":true,"family":"Andrews","given":"Brian D.","affiliations":[],"preferred":false,"id":485934,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Chaytor, Jason D.","contributorId":88637,"corporation":false,"usgs":true,"family":"Chaytor","given":"Jason D.","affiliations":[],"preferred":false,"id":485937,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"ten Brink, Uri S. 0000-0001-6858-3001 utenbrink@usgs.gov","orcid":"https://orcid.org/0000-0001-6858-3001","contributorId":127560,"corporation":false,"usgs":true,"family":"ten Brink","given":"Uri S.","email":"utenbrink@usgs.gov","affiliations":[{"id":678,"text":"Woods Hole Coastal and Marine Science Center","active":true,"usgs":true},{"id":186,"text":"Coastal and Marine Geology Program","active":true,"usgs":true}],"preferred":false,"id":485936,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Brothers, Daniel S.","contributorId":72686,"corporation":false,"usgs":true,"family":"Brothers","given":"Daniel S.","affiliations":[],"preferred":false,"id":485935,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Gardner, James V.","contributorId":93035,"corporation":false,"usgs":true,"family":"Gardner","given":"James","email":"","middleInitial":"V.","affiliations":[],"preferred":false,"id":485938,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"Lobecker, Elizabeth A.","contributorId":98651,"corporation":false,"usgs":true,"family":"Lobecker","given":"Elizabeth","email":"","middleInitial":"A.","affiliations":[],"preferred":false,"id":629919,"contributorType":{"id":1,"text":"Authors"},"rank":6},{"text":"Calder, Brian R.","contributorId":79917,"corporation":false,"usgs":true,"family":"Calder","given":"Brian","email":"","middleInitial":"R.","affiliations":[],"preferred":false,"id":629920,"contributorType":{"id":1,"text":"Authors"},"rank":7}]}}
,{"id":70045254,"text":"ds760 - 2016 - Geophysical log database for the Floridan aquifer system and southeastern Coastal Plain aquifer system in Florida and parts of Georgia, Alabama,  and South Carolina","interactions":[],"lastModifiedDate":"2024-10-22T23:06:15.14091","indexId":"ds760","displayToPublicDate":"2013-04-04T00:00:00","publicationYear":"2016","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":"760","title":"Geophysical log database for the Floridan aquifer system and southeastern Coastal Plain aquifer system in Florida and parts of Georgia, Alabama,  and South Carolina","docAbstract":"<p>A database of borehole geophysical logs and other types of data files were compiled as part of ongoing studies of water availability and assessment of brackish- and saline-water resources. The database contains 4,883 logs from 1,248 wells in Florida, Georgia, Alabama, South Carolina, and from a limited number of offshore wells of the eastern Gulf of Mexico and the Atlantic Ocean. The logs can be accessed through a download directory organized by state and county for onshore wells and in a single directory for the offshore wells. A flat file database is provided that lists the wells, their coordinates, and the file listings.</p>","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/ds760","collaboration":"Groundwater Resources Program","usgsCitation":"Williams, L.J., Raines, J.E., and Lanning, A.E., 2016, Geophysical log database for the Floridan aquifer system and southeastern Coastal Plain aquifer system in Florida and parts of Georgia, Alabama, \nand South Carolina (ver. 1.1, December 2016): U.S. Geological Survey Data Series 760, 12 p., https://doi.org/10.3133/ds760.","productDescription":"Report: v, 12 p.; Geophysical Log Database; List of Wells in Database; List of Files in Database; Downloads Directory","numberOfPages":"22","onlineOnly":"Y","additionalOnlineFiles":"Y","costCenters":[{"id":13634,"text":"South Atlantic Water Science Center","active":true,"usgs":true}],"links":[{"id":270569,"rank":2,"type":{"id":15,"text":"Index Page"},"url":"https://pubs.usgs.gov/ds/760/"},{"id":270573,"rank":5,"type":{"id":7,"text":"Companion Files"},"url":"https://pubs.usgs.gov/ds/760/Downloads/FILES/LogDB_WellListRevision1.xls","text":"List of Wells (Excel)","size":"475 KB"},{"id":270570,"rank":3,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/ds/760/pdf/ds760.pdf","text":"Report","size":"1.45 MB","linkFileType":{"id":1,"text":"pdf"},"description":"DS 760"},{"id":270574,"rank":4,"type":{"id":7,"text":"Companion Files"},"url":"https://pubs.usgs.gov/ds/760/Downloads","text":"State Data Files"},{"id":270575,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/ds/760/images/coverthb3.jpg"},{"id":331292,"rank":9,"type":{"id":25,"text":"Version History"},"url":"https://pubs.usgs.gov/ds/760/versionHist.txt","size":"3.96 MB","linkFileType":{"id":2,"text":"txt"},"description":"Version History"},{"id":331324,"rank":6,"type":{"id":7,"text":"Companion Files"},"url":"https://pubs.usgs.gov/ds/760/Downloads/FILES/LogDB_WellListRevision1.csv","text":"List of Wells (CSV)","size":"189 KB"},{"id":331302,"rank":10,"type":{"id":20,"text":"Read Me"},"url":"https://pubs.usgs.gov/ds/760/Downloads/Readme.txt","linkFileType":{"id":2,"text":"txt"}},{"id":270572,"rank":7,"type":{"id":7,"text":"Companion Files"},"url":"https://pubs.usgs.gov/ds/760/Downloads/FILES/LogDB_FileListRevision1.xls","text":"List of Files (Excel)","size":"1.69 MB"},{"id":331323,"rank":8,"type":{"id":7,"text":"Companion Files"},"url":"https://pubs.usgs.gov/ds/760/Downloads/FILES/LogDB_FileListRevision1.csv","text":"List of Files (CSV)","size":"882 MB"}],"country":"United States","state":"Alabama, Florida, Georgia, South Carolina","geographicExtents":"{ \"type\": \"FeatureCollection\", \"features\": [ { \"type\": \"Feature\", \"properties\": {}, \"geometry\": { \"type\": \"Polygon\", \"coordinates\": [ [ [ -89.0,24.0 ], [ -89.0,34.0 ], [ -79.0,34.0 ], [ -79.0,24.0 ], [ -89.0,24.0 ] ] ] } } ] }","edition":"Version 1.0: Originally posted April 4, 2013; Version 1.1: December 30, 2016","contact":"<p>Director, South Atlantic Water Science Center<br> U.S. Geological Survey <br> 720 Gracern Road<br> Columbia, SC 29210 <br> <a href=\"http://www.usgs.gov/water/southatlantic//\" data-mce-href=\"http://www.usgs.gov/water/southatlantic//\">http://www.usgs.gov/water/southatlantic/</a></p>","tableOfContents":"<ul><li>Abstract</li><li>Introduction</li><li>Geophysical Log Database</li><li>References Cited</li></ul>","publishedDate":"2013-04-04","revisedDate":"2016-12-30","noUsgsAuthors":false,"publicationDate":"2013-04-04","publicationStatus":"PW","scienceBaseUri":"515e92f4e4b088aa2258091a","contributors":{"authors":[{"text":"Williams, Lester J. lesterw@usgs.gov","contributorId":2395,"corporation":false,"usgs":true,"family":"Williams","given":"Lester","email":"lesterw@usgs.gov","middleInitial":"J.","affiliations":[],"preferred":true,"id":477143,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Raines, Jessica E.","contributorId":17111,"corporation":false,"usgs":true,"family":"Raines","given":"Jessica","email":"","middleInitial":"E.","affiliations":[],"preferred":false,"id":477144,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Lanning, Amanda E.","contributorId":61310,"corporation":false,"usgs":true,"family":"Lanning","given":"Amanda","email":"","middleInitial":"E.","affiliations":[],"preferred":false,"id":477145,"contributorType":{"id":1,"text":"Authors"},"rank":3}]}}
,{"id":70176188,"text":"70176188 - 2016 - Unsteady flow in natural compound channel: Experiment and simulation","interactions":[],"lastModifiedDate":"2019-10-21T06:44:29","indexId":"70176188","displayToPublicDate":"2012-10-05T12:27:48","publicationYear":"2016","noYear":false,"publicationType":{"id":24,"text":"Conference Paper"},"publicationSubtype":{"id":19,"text":"Conference Paper"},"title":"Unsteady flow in natural compound channel: Experiment and simulation","docAbstract":"Phragmites Australis, or common reed, is an invasive plant species that has spread along channels of the Platte River (Nebraska, USA), adversely altering the biogeomorphology of the system. Of particular interest have been the impacts on riparian habitat, specifically the reduction of suitable areas for crane roosting and shorebird nesting. A program for managing and removing these and other invasive plant species has been in place to mitigate these effects. Spraying and mechanical removal of phragmites has been the established practice, but there is interest in evaluating other potential forms of maintenance, in particular the potential of uprooting seedlings using high velocity/high shear flows. For that purpose, in 2010 a high flow experiment was conducted on a return channel to the Platte River that feeds from the Thirty Mile Canal near Brady, Nebraska. As part of the experiment, a 250-meter long reach of the channel was monitored and data were collected for the set-up, testing, and evaluation of numerical models. This article presents a description of the experimental conditions, data-collection program, and application of an unsteady, depth-averaged numerical model to reproduce the event.","largerWorkType":{"id":4,"text":"Book"},"largerWorkTitle":"River flow 2012: Proceedings of the International Conference on Fluvial Hydraulics","largerWorkSubtype":{"id":12,"text":"Conference publication"},"conferenceTitle":"International Conference on Fluvial Hydraulics","conferenceDate":"September 5-7, 2012","conferenceLocation":"San José, Costa Rica","language":"English","publisher":"CRC Press","usgsCitation":"Simoes, F.J., and Kinzel, P.J., 2016, Unsteady flow in natural compound channel: Experiment and simulation, <i>in</i> River flow 2012: Proceedings of the International Conference on Fluvial Hydraulics, San José, Costa Rica, September 5-7, 2012, p. 271-278.","productDescription":"8 p.","startPage":"271","endPage":"278","ipdsId":"IP-038128","costCenters":[{"id":5044,"text":"National Research Program - 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 \"}}]}","publishingServiceCenter":{"id":2,"text":"Denver PSC"},"noUsgsAuthors":false,"publicationStatus":"PW","contributors":{"authors":[{"text":"Simoes, Francisco J. 0000-0002-0934-9730 frsimoes@usgs.gov","orcid":"https://orcid.org/0000-0002-0934-9730","contributorId":2019,"corporation":false,"usgs":true,"family":"Simoes","given":"Francisco","email":"frsimoes@usgs.gov","middleInitial":"J.","affiliations":[{"id":5044,"text":"National Research Program - Central Branch","active":true,"usgs":true}],"preferred":true,"id":647663,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Kinzel, Paul J. 0000-0002-6076-9730 pjkinzel@usgs.gov","orcid":"https://orcid.org/0000-0002-6076-9730","contributorId":743,"corporation":false,"usgs":true,"family":"Kinzel","given":"Paul","email":"pjkinzel@usgs.gov","middleInitial":"J.","affiliations":[{"id":438,"text":"National Research Program - Western Branch","active":true,"usgs":true},{"id":191,"text":"Colorado Water Science Center","active":true,"usgs":true},{"id":37778,"text":"WMA - Integrated Modeling and Prediction Division","active":true,"usgs":true},{"id":5044,"text":"National Research Program - Central Branch","active":true,"usgs":true}],"preferred":true,"id":647664,"contributorType":{"id":1,"text":"Authors"},"rank":2}]}}
,{"id":70038143,"text":"ds669 - 2016 - Tabulated Transmissivity and Storage Properties of the Floridan Aquifer System in Florida and Parts of Georgia, South Carolina, and Alabama","interactions":[],"lastModifiedDate":"2016-12-02T11:57:07","indexId":"ds669","displayToPublicDate":"2012-04-19T00:00:00","publicationYear":"2016","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":"669","title":"Tabulated Transmissivity and Storage Properties of the Floridan Aquifer System in Florida and Parts of Georgia, South Carolina, and Alabama","docAbstract":"<p>A goal of the U.S. Geological Survey Groundwater Resources Program is to assess the availability of fresh water within each of the principal aquifers in the United States with the greatest groundwater withdrawals. The Floridan aquifer system (FAS), which covers an area of approximately 100,000 square miles in Florida and parts of Georgia, Alabama, Mississippi, and South Carolina, is one such principal aquifer, having the fifth largest groundwater withdrawals in the Nation, totaling 3.64 billion gallons per day in 2000. Compilation of FAS hydraulic properties is critical to the development and calibration of groundwater flow models that can be used to develop water budgets spatially and temporally, as well as to evaluate resource changes over time. Wells with aquifer test data were identified as Upper Floridan aquifer (UFA), Lower Floridan aquifer (LFA), Floridan aquifer system (FAS, Upper Floridan with some middle and/or Lower Floridan), or middle Floridan confining unit (MCU), based on the identification from the original database or report description, or comparison of the open interval of the well with previously published maps.</p><p>This report consolidates aquifer hydraulic property data obtained from multiple databases and reports of the U.S. Geological Survey, various State agencies, and the Water Management Districts of Florida, that are compiled into tables to provide a single information source for transmissivity and storage properties of the FAS as of October 2011. Transmissivity calculated from aquifer pumping tests and specific-capacity data are included. Values for transmissivity and storage coefficients are intended for use in regional or sub regional groundwater flow models; thus, any tests (aquifer pumping tests and specific capacity data) that were conducted with packers or for open intervals less than 30 feet in length are excluded from the summary statistics and tables of this report, but are included in the database.</p><p>The transmissivity distribution from the aquifer pumping tests is highly variable. The transmissivity based on aquifer pumping tests (from 1,045 values for the UFA and FAS) ranges from 8 to about 9,300,000 square feet per day (ft<sup>2</sup>/d) and values of storage coefficient (646 reported) range from 3x10<sup>-9</sup> to 0.41. The 64 transmissivity values for the LFA range from about 130 to 4,500,000 ft<sup>2</sup>/d, and the 17 storage coefficient values range from 7x10<sup>-8</sup> to 0.03. The 14 transmissivity values for the MCU range from 1 to about 600,000 ft<sup>2</sup>/d and the 10 storage coefficient values range from 8x10<sup>-8</sup> to 0.03. Transmissivity estimates for the UFA and FAS for 442 specific capacity tests range from approximately 200 to 1,000,000 ft<sup>2</sup>/d.</p>","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/ds669","collaboration":"Product of the U.S. Geological Survey Groundwater Resources Program","usgsCitation":"Kuniansky, E.L., and Bellino, J.C., 2016, Tabulated transmissivity and storage properties of the Floridan aquifer system in Florida and parts of Georgia, South Carolina, and Alabama (ver. 1.1, May 2016): U.S. Geological Survey Data Series 669, 37 p., https://pubs.usgs.gov/ds/669.","productDescription":"v, 16 p.; Appendix; 2 Tables; Table 1: 17 inches x 11 inches, Table 2: 17 inches x 11 inches; DS699 FAS AQT DS zip file","onlineOnly":"Y","additionalOnlineFiles":"Y","costCenters":[{"id":13634,"text":"South Atlantic Water Science Center","active":true,"usgs":true}],"links":[{"id":321206,"rank":5,"type":{"id":27,"text":"Table"},"url":"https://pubs.usgs.gov/ds/669/tables/ds669_table1-v1.1.pdf","text":"Table 1 - Transmissivity and storage coefficients from aquifer pumping tests ","size":"321 KB","linkFileType":{"id":1,"text":"pdf"},"description":"DS 669"},{"id":321207,"rank":5,"type":{"id":27,"text":"Table"},"url":"https://pubs.usgs.gov/ds/669/tables/ds669_table1-v1.1.xls","text":"Table 1 - Excel ","size":"355 KB xls","description":"DS 669"},{"id":321208,"rank":6,"type":{"id":27,"text":"Table"},"url":"https://pubs.usgs.gov/ds/669/tables/ds669_table2-v1.1.pdf","text":"Table 2 -Transmissivity estimates from specific capacity data","size":"157 KB","linkFileType":{"id":1,"text":"pdf"},"description":"DS 669"},{"id":321209,"rank":7,"type":{"id":27,"text":"Table"},"url":"https://pubs.usgs.gov/ds/669/tables/ds669_table2-v1.1.xls","text":"Table 2 - Excel ","size":"132 KB xls","description":"DS 669"},{"id":321211,"rank":8,"type":{"id":9,"text":"Database"},"url":"https://pubs.usgs.gov/ds/669/DS669_v1.1FAS_AQT_DATA_04192016.zip","text":"Aquifer Data","size":"1 MB","linkFileType":{"id":6,"text":"zip"},"description":"DS 669"},{"id":321205,"rank":4,"type":{"id":25,"text":"Version History"},"url":"https://pubs.usgs.gov/ds/669/versionHist.txt","size":"3 KB","linkFileType":{"id":2,"text":"txt"},"description":"DS 669"},{"id":254562,"rank":2,"type":{"id":15,"text":"Index Page"},"url":"https://pubs.usgs.gov/ds/669/","linkFileType":{"id":5,"text":"html"},"description":"DS 669"},{"id":254564,"rank":0,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/ds/669/images/coverthb.jpg"}],"country":"United States","state":"Alabama, Florida, Georgia, South Carolina","otherGeospatial":"Floridan Aquifer System","geographicExtents":"{ \"type\": \"FeatureCollection\", \"features\": [ { \"type\": \"Feature\", \"properties\": {}, \"geometry\": { \"type\": \"Polygon\", \"coordinates\": [ [ [ -90,25 ], [ -90,34 ], [ -79.5,34 ], [ -79.5,25 ], [ -90,25 ] ] ] } } ] }","edition":"Version 1.1 May 13, 2016","contact":"<p>Director, Caribbean-Florida Water Science Center<br> U.S. Geological Surveyr<br> 4446 Pet Lane, Suite 108r<br> Lutz, FL 33559r<br> Telephone: (813) 498–5000r<br> <a href=\"http://fl.water.usgs.gov/\" data-mce-href=\"http://fl.water.usgs.gov/\">http://fl.water.usgs.gov/</a></p>","tableOfContents":"<ul><li>Abstract</li><li>Introduction</li><li>Summary Statistics</li><li>References Cited</li><li>Appendix 1</li></ul>","publishedDate":"2012-04-19","revisedDate":"2016-05-13","noUsgsAuthors":false,"publicationDate":"2012-04-19","publicationStatus":"PW","scienceBaseUri":"505ba3a5e4b08c986b31fdbc","contributors":{"authors":[{"text":"Kuniansky, Eve L. 0000-0002-5581-0225 elkunian@usgs.gov","orcid":"https://orcid.org/0000-0002-5581-0225","contributorId":932,"corporation":false,"usgs":true,"family":"Kuniansky","given":"Eve","email":"elkunian@usgs.gov","middleInitial":"L.","affiliations":[{"id":5064,"text":"Southeast Regional Director's Office","active":true,"usgs":true},{"id":509,"text":"Office of the Associate Director for Water","active":true,"usgs":true}],"preferred":true,"id":463509,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Bellino, Jason C. 0000-0001-9046-9344 jbellino@usgs.gov","orcid":"https://orcid.org/0000-0001-9046-9344","contributorId":3724,"corporation":false,"usgs":true,"family":"Bellino","given":"Jason","email":"jbellino@usgs.gov","middleInitial":"C.","affiliations":[{"id":270,"text":"FLWSC-Tampa","active":true,"usgs":true}],"preferred":true,"id":463510,"contributorType":{"id":1,"text":"Authors"},"rank":2}]}}
,{"id":70005430,"text":"sir20115136 - 2016 - Determination of dilution factors for discharge of aluminum-containing wastes by public water-supply treatment facilities into lakes and reservoirs in Massachusetts","interactions":[],"lastModifiedDate":"2017-03-03T15:28:13","indexId":"sir20115136","displayToPublicDate":"2011-09-16T00:00:00","publicationYear":"2016","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":"2011-5136","title":"Determination of dilution factors for discharge of aluminum-containing wastes by public water-supply treatment facilities into lakes and reservoirs in Massachusetts","docAbstract":"<p>Dilution of aluminum discharged to reservoirs in filter-backwash effluents at water-treatment facilities in Massachusetts was investigated by a field study and computer simulation. Determination of dilution is needed so that permits for discharge ensure compliance with water-quality standards for aquatic life. The U.S. Environmental Protection Agency chronic standard for aluminum, 87 micrograms per liter (μg/L), rather than the acute standard, 750 μg/L, was used in this investigation because the time scales of chronic exposure (days) more nearly match rates of change in reservoir concentrations than do the time scales of acute exposure (hours).</p><p>Whereas dilution factors are routinely computed for effluents discharged to streams solely on the basis of flow of the effluent and flow of the receiving stream, dilution determination for effluents discharged to reservoirs is more complex because (1), compared to streams, additional water is available for dilution in reservoirs during low flows as a result of reservoir flushing and storage during higher flows, and (2) aluminum removal in reservoirs occurs by aluminum sedimentation during the residence time of water in the reservoir. Possible resuspension of settled aluminum was not considered in this investigation. An additional concern for setting discharge standards is the substantial concentration of aluminum that can be naturally present in ambient surface waters, usually in association with dissolved organic carbon (DOC), which can bind aluminum and keep it in solution.</p><p>A method for dilution determination was developed using a mass-balance equation for aluminum and considering sources of aluminum from groundwater, surface water, and filter-backwash effluents and losses caused by sedimentation, water withdrawal, and spill discharge from the reservoir. The method was applied to 13 reservoirs. Data on aluminum and DOC concentrations in reservoirs and influent water were collected during the fall of 2009. Complete reservoir volume was determined to be available for mixing on the basis of vertical and horizontal aluminum-concentration profiling. Losses caused by settling of aluminum were assumed to be proportional to aluminum concentration and reservoir area. The constant of proportionality, as a function of DOC concentration, was established by simulations in each of five reservoirs that differed in DOC concentration.</p><p>In addition to computing dilution factors, the project determined dilution factors that would be protective with the same statistical basis (frequency of exceedance of the chronic standard) as dilutions computed for streams at the 7-day-average 10-year-recurrence annual low flow (the 7Q10). Low-flow dilutions are used for permitting so that receiving waters are protected even at the worst-case flow levels. The low-flow dilution factors that give the same statistical protection are the lowest annual 7-day-average dilution factors with a recurrence of 10 years, termed 7DF10s. Determination of 7DF10 values for reservoirs required that long periods of record be simulated so that dilution statistics could be determined. Dilution statistics were simulated for 13 reservoirs from 1960 to 2004 using U.S. Geological Survey Firm-Yield Estimator software to model reservoir inputs and outputs and present-day values of filter-effluent discharge and aluminum concentration.</p><p>Computed settling velocities ranged from 0 centimeters per day (cm/d) at DOC concentrations of 15.5 milligrams per liter (mg/L) to 21.5 cm/d at DOC concentrations of 2.7 mg/L. The 7DF10 values were a function of aluminum effluent discharged. At current (2009) effluent discharge rates, the 7DF10 values varied from 1.8 to 115 among the 13 reservoirs. In most cases, the present-day (2009) discharge resulted in receiving water concentrations that did not exceed the standard at the 7DF10. Exceptions were one reservoir with a very small area and three reservoirs with high concentrations of DOC. Maximum permissible discharges were determined for water-treatment plants by adjusting discharges upward in simulations until the 7DF10 resulted in reservoir concentrations that just met the standard. In terms of aluminum flux, these discharges ranged from 0 to 28 kilograms of aluminum per day.</p>","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/sir20115136","collaboration":"Prepared in cooperation with the Massachusetts Department of Environmental Protection","usgsCitation":"Colman, J.A., Massey, A.J., and Levin, S.B., 2016, Determination of dilution factors for discharge of aluminum-containing wastes by public water-supply treatment facilities into lakes and reservoirs in Massachusetts (ver. 1.1, December 2016): U.S. Geological Survey Scientific Investigations Report 2011–5136, 36 p., https://pubs.usgs.gov/sir/2011/5136.","productDescription":"vi, 36 p.","onlineOnly":"Y","additionalOnlineFiles":"Y","costCenters":[{"id":376,"text":"Massachusetts Water Science 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 \"}}]}","edition":"Version 1.0: Originally posted September 16, 2011; Version 1.1: December 30, 2016","contact":"<p><a href=\"mailto:dc_ma@usgs.gov\" data-mce-href=\"mailto:dc_ma@usgs.gov\">Director</a>, Massachusetts-Rhode Island Water Science Center<br> U.S. Geological Survey<br> 10 Bearfoot Road<br> Northborough, MA 01532<br> (508) 490-5000<br> <a href=\"http://ma.water.usgs.gov\" data-mce-href=\"http://ma.water.usgs.gov\">http://ma.water.usgs.gov</a></p>","publishedDate":"2011-09-16","revisedDate":"2016-12-30","noUsgsAuthors":false,"publicationDate":"2011-09-16","publicationStatus":"PW","scienceBaseUri":"4f4e4aa8e4b07f02db6677d3","contributors":{"authors":[{"text":"Colman, John A. 0000-0001-9327-0779 jacolman@usgs.gov","orcid":"https://orcid.org/0000-0001-9327-0779","contributorId":2098,"corporation":false,"usgs":true,"family":"Colman","given":"John","email":"jacolman@usgs.gov","middleInitial":"A.","affiliations":[{"id":376,"text":"Massachusetts Water Science Center","active":true,"usgs":true},{"id":466,"text":"New England Water Science Center","active":true,"usgs":true}],"preferred":true,"id":352501,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Massey, Andrew J. 0000-0003-3995-8657 ajmassey@usgs.gov","orcid":"https://orcid.org/0000-0003-3995-8657","contributorId":1862,"corporation":false,"usgs":true,"family":"Massey","given":"Andrew","email":"ajmassey@usgs.gov","middleInitial":"J.","affiliations":[{"id":466,"text":"New England Water Science Center","active":true,"usgs":true},{"id":376,"text":"Massachusetts Water Science Center","active":true,"usgs":true}],"preferred":true,"id":352500,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Brandt, Sara L.","contributorId":89240,"corporation":false,"usgs":true,"family":"Brandt","given":"Sara","email":"","middleInitial":"L.","affiliations":[],"preferred":false,"id":352502,"contributorType":{"id":1,"text":"Authors"},"rank":3}]}}
,{"id":75513,"text":"sir20055289 - 2016 - Development and evaluation of clear-water pier and contraction scour envelope curves in the Coastal Plain and Piedmont Provinces of South Carolina","interactions":[],"lastModifiedDate":"2017-06-01T08:57:09","indexId":"sir20055289","displayToPublicDate":"2006-03-13T00:00:00","publicationYear":"2016","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":"2005-5289","title":"Development and evaluation of clear-water pier and contraction scour envelope curves in the Coastal Plain and Piedmont Provinces of South Carolina","docAbstract":"<p>The U.S. Geological Survey in cooperation with the South Carolina Department of Transportation collected clear-water pier- and contraction-scour data at 116 bridges in the Coastal Plain and Piedmont Physiographic Provinces of South Carolina. Pier-scour depths collected in both provinces ranged from 0 to 8.0 feet. Contraction-scour depths collected in the Coastal Plain ranged from 0 to 3.9 feet. Using hydraulic data estimated with a one-dimensional flow model, predicted clear-water scour depths were computed with scour equations from the Federal Highway Administration Hydraulic Engineering Circular 18 and compared with measured scour. This comparison indicated that predicted clear-water scour depths, in general, exceeded measured scour depths and at times were excessive. Predicted clear-water contraction scour, however, was underpredicted approximately 30 percent of the time by as much as 7.1 feet. </p><p>The investigation focused on clear-water pier scour, comparing trends in the laboratory and field data. This comparison indicated that the range of dimensionless variables (relative depth, flow intensity, relative grain size) used in laboratory investigations of pier scour, were similar to the range for field data in South Carolina, further indicating that laboratory relations may have some applicability to field conditions in South Carolina. Variables determined to be important in developing pier scour in laboratory studies were investigated to understand their influence on the South Carolina field data, and many of these variables appeared to be insignificant under field conditions in South Carolina. The strongest explanatory variables were pier width and approach velocity. Envelope curves developed from the field data are useful tools for evaluating reasonable ranges of clear-water pier and contraction scour in South Carolina. A modified version of the Hydraulic Engineering Circular 18 pier-scour equation also was developed as a tool for evaluating clearwater pier scour. The envelope curves and modified equation offer an improvement over the current methods for predicting clear-water scour in South Carolina. </p><p>Data from this study were compiled into a database that includes photographs, measured scour depths, predicted scour depths, limited basin characteristics, limited soil data, and modeled hydraulic data. The South Carolina database can be used to compare studied sites with unstudied sites to evaluate the potential for scour at the unstudied sites. In addition, the database can be used to evaluate the performance of various methods for predicting clear-water pier and contraction scour.</p>","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/sir20055289","collaboration":"Prepared in cooperation with the South Carolina Department of Transportaton","usgsCitation":"Benedict, S., and Caldwell, A.W., 2016, Development and evaluation of clear-water pier and contraction scour envelope curves in the Coastal Plain and Piedmont Provinces of South Carolina (Version 1.0: Originally posted 2006; Version 1.1: August 31, 2016): U.S. Geological Survey Scientific Investigations Report 2005-5289, Report: x, 98 p.; Database, https://doi.org/10.3133/sir20055289.","productDescription":"Report: x, 98 p.; Database","numberOfPages":"112","onlineOnly":"Y","additionalOnlineFiles":"Y","costCenters":[{"id":13634,"text":"South Atlantic Water Science Center","active":true,"usgs":true}],"links":[{"id":7316,"rank":100,"type":{"id":25,"text":"Version History"},"url":"https://pubs.usgs.gov/sir/2005/5289/versionHist.txt","linkFileType":{"id":2,"text":"txt"}},{"id":192878,"rank":0,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/sir/2005/5289/images/coverthb.jpg"},{"id":328624,"rank":4,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/sir/2005/5289/pdf/sir20055289.pdf","text":"Report","size":"5.12 MB","linkFileType":{"id":1,"text":"pdf"},"description":"SIR 2005-5289"},{"id":328596,"rank":3,"type":{"id":9,"text":"Database"},"url":"https://pubs.usgs.gov/sir/2005/5289/SCPCSD.mdb","size":"800 MB","description":"SIR 2005-5289","linkHelpText":"- Measured and  Predicted Bridge-Scour Database"}],"country":"United States","state":"South 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Carolina\",\"nation\":\"USA  \"}}]}","edition":"Version 1.0: Originally posted 2006; Version 1.1: August 31, 2016","contact":"<p>South Carolina Water Science Center<br /> U.S. Geological Survey<br />720 Gracern Road<br /> Stephenson Center, Suite 129<br /> Columbia, SC 29210<br /> <a href=\"http://sc.water.usgs.gov/\">http://sc.water.usgs.gov/</a></p>","tableOfContents":"<ul>\n<li>Abstract</li>\n<li>Introduction</li>\n<li>Approach</li>\n<li>Data Collection</li>\n<li>Development of the Predicted Bridge-Scour Database</li>\n<li>Development of the South Carolina Pier-Scour Envelope Curve</li>\n<li>Evaluation of Selected Methods for Predicting Clear-Water Pier Scour in South Carolina</li>\n<li>Guidance for Evaluating Pier-Scour Depth in South Carolina</li>\n<li>Development of the South Carolina Clear-Water Contraction-Scour Envelope Curves</li>\n<li>The South Carolina Clear-Water Pier- and Contraction-Scour Database</li>\n<li>Summary</li>\n<li>Selected References</li>\n<li>Appendix 1</li>\n<li>Appendix 2</li>\n</ul>","revisedDate":"2016-08-31","noUsgsAuthors":false,"publicationStatus":"PW","scienceBaseUri":"4f4e4aa7e4b07f02db66728f","contributors":{"authors":[{"text":"Benedict, Stephen T. benedict@usgs.gov","contributorId":3198,"corporation":false,"usgs":true,"family":"Benedict","given":"Stephen T.","email":"benedict@usgs.gov","affiliations":[{"id":559,"text":"South Carolina Water Science Center","active":true,"usgs":true}],"preferred":false,"id":286891,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Caldwell, Andral W. 0000-0003-1269-5463 acaldwel@usgs.gov","orcid":"https://orcid.org/0000-0003-1269-5463","contributorId":3228,"corporation":false,"usgs":true,"family":"Caldwell","given":"Andral","email":"acaldwel@usgs.gov","middleInitial":"W.","affiliations":[{"id":559,"text":"South Carolina Water Science Center","active":true,"usgs":true}],"preferred":true,"id":286892,"contributorType":{"id":1,"text":"Authors"},"rank":2}]}}
,{"id":70175163,"text":"ds383 - 2016 - National Land Cover Database 2001 (NLCD01)","interactions":[],"lastModifiedDate":"2016-08-17T12:12:14","indexId":"ds383","displayToPublicDate":"2005-11-06T10:30:00","publicationYear":"2016","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":"383","title":"National Land Cover Database 2001 (NLCD01)","docAbstract":"<p>This 30-meter data set represents land use and land cover for the conterminous United States for the 2001 time period. The data have been arranged into four tiles to facilitate timely display and manipulation within a Geographic Information System (see http://water.usgs.gov/GIS/browse/nlcd01-partition.jpg). The National Land Cover Data Set for 2001 was produced through a cooperative project conducted by the Multi-Resolution Land Characteristics (MRLC) Consortium. The MRLC Consortium is a partnership of Federal agencies (http://www.mrlc.gov), consisting of the U.S. Geological Survey (USGS), the National Oceanic and Atmospheric Administration (NOAA), the U.S. Environmental Protection Agency (USEPA), the U.S. Department of Agriculture (USDA), the U.S. Forest Service (USFS), the National Park Service (NPS), the U.S. Fish and Wildlife Service (USFWS), the Bureau of Land Management (BLM), and the USDA Natural Resources Conservation Service (NRCS). One of the primary goals of the project is to generate a current, consistent, seamless, and accurate National Land Cover Database (NLCD) circa 2001 for the United States at medium spatial resolution. For a detailed definition and discussion on MRLC and the NLCD 2001 products, refer to Homer and others (2004), (see: http://www.mrlc.gov/mrlc2k.asp). The NLCD 2001 was created by partitioning the United States into mapping zones. A total of 68 mapping zones (see http://water.usgs.gov/GIS/browse/nlcd01-mappingzones.jpg), were delineated within the conterminous United States based on ecoregion and geographical characteristics, edge-matching features, and the size requirement of Landsat mosaics. Mapping zones encompass the whole or parts of several states. Questions about the NLCD mapping zones can be directed to the NLCD 2001 Land Cover Mapping Team at the USGS/EROS, Sioux Falls, SD (605) 594-6151 or mrlc@usgs.gov.</p>","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston VA","doi":"10.3133/ds383","usgsCitation":"LaMotte, A.E., 2016, National Land Cover Database 2001 (NLCD01): U.S. Geological Survey Data Series 383, https://doi.org/10.3133/ds383.","onlineOnly":"N","additionalOnlineFiles":"N","ipdsId":"IP-009439","costCenters":[{"id":374,"text":"Maryland Water Science Center","active":true,"usgs":true}],"links":[{"id":326673,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/usgs_thumb.jpg"},{"id":325941,"type":{"id":15,"text":"Index Page"},"url":"https://water.usgs.gov/GIS/metadata/usgswrd/XML/nlcd01_1.xml"}],"country":"United States","geographicExtents":"{\n  \"type\": \"FeatureCollection\",\n  \"features\": [\n    {\n      \"type\": \"Feature\",\n      \"properties\": {},\n     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,{"id":50958,"text":"wri034064 - 2016 - Clear-water abutment and contraction scour in the Coastal Plain and Piedmont Provinces of South Carolina, 1996-99","interactions":[],"lastModifiedDate":"2017-01-20T09:25:23","indexId":"wri034064","displayToPublicDate":"2003-07-01T00:00:00","publicationYear":"2016","noYear":false,"publicationType":{"id":18,"text":"Report"},"publicationSubtype":{"id":5,"text":"USGS Numbered Series"},"seriesTitle":{"id":342,"text":"Water-Resources Investigations Report","code":"WRI","active":false,"publicationSubtype":{"id":5}},"seriesNumber":"2003-4064","title":"Clear-water abutment and contraction scour in the Coastal Plain and Piedmont Provinces of South Carolina, 1996-99","docAbstract":"<p>The U.S. Geological Survey, in cooperation with the South Carolina Department of Transportation, collected observations of clear-water aburment and contraction scour at 146 bridges in the Coastal Plain and Piedmont of South Carolina. Scour depths ranged from 0 to 23.6 feet. Theoretical scour depths were computed at each bridge and compared with observed scour. This comparison showed that theoretical scour depths, in general, exceeded the observed scour depths and often were excessive. A comparison of field data with dimensionless relations for laboratory data showed that the range of dimensionless variables used in laboratory investigations was outside of the range for field data in South Carolina, suggesting laboratory relations may not be applicable to field conditions in South Carolina. Variables determined to be important in developing scour within laboratory studies were investigated to understand their influence within the South Carolina field data, and many of these variables appeared to be insignificant under field conditions found in South Carolina. The strongest explanatory variables were embankment length, geometric-contraction ratio, approach velocity, and soil cohesion. Envelope curves developed with the field data are useful tools for assessing reasonable ranges of scour depth in South Carolina. These tools are simple to apply and are an improvement over the current methods for predicting theoretical scour.</p>\n<p>Data from this study have been compiled into a database that includes photographs, figures, observed scour depths, theoretical scour depths, limited basin characteristics, limited soil data, and theoretical hydraulic data. The database can be used to compare studied sites with unstudied sites to assess the potential for scour at the unstudied sites. In addition, the database can be used to assess the performance of various theoretical methods for predicting clear-water abutment and contraction scour.</p>","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/wri034064","collaboration":"Prepared in cooperation with the South Carolina Department of Transportation","usgsCitation":"Benedict, S., 2016, Clear-water abutment and contraction scour in the Coastal Plain and Piedmont Provinces of South Carolina, 1996-99 (Version 1.0: Originally posted in 2003; Version 1.1: August 31, 2016): U.S. Geological Survey Water-Resources Investigations Report 2003-4064, Report: x, 137 p.; Appendix A; Database, https://doi.org/10.3133/wri034064.","productDescription":"Report: x, 137 p.; Appendix A; Database","onlineOnly":"N","additionalOnlineFiles":"Y","costCenters":[{"id":13634,"text":"South Atlantic Water Science 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Carolina\",\"nation\":\"USA  \"}}]}","edition":"Version 1.0: Originally posted in 2003; Version 1.1: August 31, 2016","contact":"<p>Director, South Carolina Water Science Center<br /> U.S. Geological Survey<br /> 720 Gracern Road<br /> Stephenson Center, Suite 129<br /> Columbia, SC 29210<br /> <a href=\"http://sc.water.usgs.gov/\">http://sc.water.usgs.gov/</a></p>","tableOfContents":"<ul>\n<li>Abstract</li>\n<li>Introduction</li>\n<li>Data Assumptions</li>\n<li>Site Selection</li>\n<li>Techniques for the Collection and Interpretation of Field Data</li>\n<li>Estimating Hydraulic Data.</li>\n<li>Development of Theoretical Bridge-Scour Database</li>\n<li>Variables Influencing Abutment Scour</li>\n<li>Abutment Scour in the Piedmont</li>\n<li>Abutment Scour in the Coastal Plain</li>\n<li>Effect of Upstream Channel Alignment on Abutment Scour</li>\n<li>Guidance for Assessing Abutment-Scour Depth Using the Envelope Curves</li>\n<li>Guidance for Assessing Abutment-Scour Hole Location, Width, and Shape</li>\n<li>Clear-Water Contraction Scour in the Piedmont</li>\n<li>The South Carolina Bridge-Scour Database</li>\n<li>Summary</li>\n<li>Selected References</li>\n<li>Appendix A: Explanation of Variables in the South Carolina Bridge-Scour Database</li>\n<li>Appendix B: South Carolina Bridge-Scour Study Sites and Reference Numbers for Figures 1, 5, and 6.</li>\n</ul>","revisedDate":"2016-08-31","noUsgsAuthors":false,"publicationStatus":"PW","scienceBaseUri":"4f4e4a62e4b07f02db636385","contributors":{"authors":[{"text":"Benedict, Stephen T. benedict@usgs.gov","contributorId":3198,"corporation":false,"usgs":true,"family":"Benedict","given":"Stephen T.","email":"benedict@usgs.gov","affiliations":[{"id":559,"text":"South Carolina Water Science Center","active":true,"usgs":true}],"preferred":false,"id":242672,"contributorType":{"id":1,"text":"Authors"},"rank":1}]}}
,{"id":70141846,"text":"ofr20151021 - 2015 - GIS-Based Identification of Areas with Mineral Resource Potential for Six Selected Deposit Groups, Bureau of Land Management Central Yukon Planning Area, Alaska","interactions":[],"lastModifiedDate":"2020-01-15T07:25:06","indexId":"ofr20151021","displayToPublicDate":"2020-01-15T08:30:00","publicationYear":"2015","noYear":false,"publicationType":{"id":18,"text":"Report"},"publicationSubtype":{"id":5,"text":"USGS Numbered Series"},"seriesTitle":{"id":330,"text":"Open-File Report","code":"OFR","onlineIssn":"2331-1258","printIssn":"0196-1497","active":true,"publicationSubtype":{"id":5}},"seriesNumber":"2015-1021","displayTitle":"GIS-based identification of areas with mineral resource potential for six selected deposit groups, Bureau of Land Management Central Yukon Planning Area, Alaska","title":"GIS-Based Identification of Areas with Mineral Resource Potential for Six Selected Deposit Groups, Bureau of Land Management Central Yukon Planning Area, Alaska","docAbstract":"<p>This study, covering the Bureau of Land Management (BLM) Central Yukon Planning Area (CYPA), Alaska, was prepared to aid BLM mineral resource management planning. Estimated mineral resource potential and certainty are mapped for six selected mineral deposit groups: (1) rare earth element (REE) deposits associated with peralkaline to carbonatitic intrusive igneous rocks, (2) placer and paleoplacer gold, (3) platinum group element (PGE) deposits associated with mafic and ultramafic intrusive igneous rocks, (4) carbonate-hosted copper deposits, (5) sandstone uranium deposits, and (6) tin-tungsten-molybdenum-fluorspar deposits associated with specialized granites. These six deposit groups include most of the strategic and critical elements of greatest interest in current exploration.</p>\n<p>This study has used a data-driven, geographic information system (GIS)-based method for evaluating the mineral resource potential across the large region of the CYPA. This method systematically and simultaneously analyzes geoscience data from multiple geospatially referenced datasets and uses individual subwatersheds (12-digit hydrologic unit codes or HUCs) as the spatial unit of classification. The final map output indicates an estimated potential (high, medium, low) for a given mineral deposit group and indicates the certainty (high, medium, low) of that estimate for any given subwatershed (HUC). Accompanying tables describe the data layers used in each analysis, the values assigned for specific analysis parameters, and the relative weighting of each data layer that contributes to the estimated potential and certainty determinations. Core datasets used include the U.S. Geological Survey (USGS) Alaska Geochemical Database (AGDB2), the Alaska Division of Geologic and Geophysical Surveys Web-based geochemical database, data from an anticipated USGS geologic map of Alaska, and the USGS Alaska Resource Data File. Map plates accompanying this report illustrate the mineral prospectivity for the six deposit groups across the CYPA and estimates of mineral resource potential. There are numerous areas, some of them large, rated with high potential for one or more of the selected deposit groups within the CYPA.</p>","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/ofr20151021","collaboration":"Prepared in cooperation with the Alaska Division of Geological and Geophysical Surveys","usgsCitation":"Jones, J.V., III, Karl, S.M., Labay, K.A., Shew, N.B., Granitto, M., Hayes, T.S., Mauk, J.L., Schmidt, J.M., Todd, E., Wang, B., Werdon, M.B., and Yager, D.B., 2015, GIS-based identification of areas with mineral resource potential for six selected deposit groups, Bureau of Land Management Central Yukon Planning Area, Alaska: U.S. Geological Survey Open-File Report 2015–1021, 78 p., 5 appendixes, 12 pls., https://dx.doi.org/10.3133/ofr20151021.","productDescription":"Report: vii, 78 p.; 12 Plates: 11 inches x 16.3 inches; Metadata; 5 Appendices","numberOfPages":"86","onlineOnly":"Y","additionalOnlineFiles":"Y","ipdsId":"IP-056688","costCenters":[{"id":119,"text":"Alaska Science Center Geology Minerals","active":true,"usgs":true}],"links":[{"id":371212,"rank":7,"type":{"id":3,"text":"Appendix"},"url":"https://pubs.usgs.gov/of/2015/1021/ofr20151021_appxD.xlsx","text":"Appendix D","size":"17.2 KB","linkFileType":{"id":3,"text":"xlsx"},"linkHelpText":" - Lithology keyword search terms for an anticipated U.S. Geological Survey geologic map of Alaska."},{"id":371213,"rank":8,"type":{"id":3,"text":"Appendix"},"url":"https://pubs.usgs.gov/of/2015/1021/ofr20151021_appxE.zip","text":"Appendix E","size":"411 MB","linkFileType":{"id":6,"text":"zip"},"linkHelpText":" - Scoring results for HUC analysis of selected deposit groups (folder containing Excel spreadsheet and geospatial data files)"},{"id":371214,"rank":9,"type":{"id":7,"text":"Companion Files"},"url":"https://pubs.usgs.gov/of/2015/1021/ofr20151021.zip","text":"Complete data package","size":"451 MB","linkFileType":{"id":6,"text":"zip"},"linkHelpText":" - A single ZIP file that contains the report, appendixes, metadata and plates."},{"id":371215,"rank":10,"type":{"id":16,"text":"Metadata"},"url":"https://pubs.usgs.gov/of/2015/1021/ofr20151021_meta.txt","size":"83.3 KB","linkFileType":{"id":2,"text":"txt"}},{"id":371211,"rank":6,"type":{"id":3,"text":"Appendix"},"url":"https://pubs.usgs.gov/of/2015/1021/ofr20151021_appxC.xlsx","text":"Appendix C","size":"38.6 KB","linkFileType":{"id":3,"text":"xlsx"},"linkHelpText":" - Alaska Resource Data File (ARDF) mineral deposit keyword and scoring templates."},{"id":371191,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/of/2015/1021/coverthb3.jpg"},{"id":371192,"rank":2,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/of/2015/1021/ofr20151021.pdf","text":"Report","size":"2.17 MB","linkFileType":{"id":1,"text":"pdf"},"description":"OFR 2015-1021"},{"id":371210,"rank":5,"type":{"id":3,"text":"Appendix"},"url":"https://pubs.usgs.gov/of/2015/1021/ofr20151021_appxB.pdf","text":"Appendix B","size":"138 KB","linkFileType":{"id":1,"text":"pdf"},"linkHelpText":" - Igneous rock geochemistry peer-reviewed literature sources."},{"id":371208,"rank":11,"type":{"id":16,"text":"Metadata"},"url":"https://pubs.usgs.gov/of/2015/1021/ofr20151021_metadatafaq.pdf","text":"Metadata FAQ","size":"243 KB","linkFileType":{"id":1,"text":"pdf"}},{"id":371209,"rank":4,"type":{"id":3,"text":"Appendix"},"url":"https://pubs.usgs.gov/of/2015/1021/ofr20151021_appxE.zip","text":"Appendix A","size":"28.4 KB","linkFileType":{"id":3,"text":"xlsx"},"linkHelpText":" - Stream-sediment geochemistry summary statistics and percentile cutoffs."},{"id":371207,"rank":3,"type":{"id":17,"text":"Plate"},"url":"https://pubs.usgs.gov/of/2015/1021/ofr20151021_plates.pdf","text":"Plates 1-12","size":"39.0 MB","linkFileType":{"id":1,"text":"pdf"},"linkHelpText":" - A single PDF file that contains the 12 plates not included in the report."}],"country":"United States","state":"Alaska","otherGeospatial":"Central Yukon","geographicExtents":"{\n  \"type\": \"FeatureCollection\",\n  \"features\": [\n    {\n      \"type\": \"Feature\",\n      \"properties\": {},\n      \"geometry\": {\n        \"type\": \"Polygon\",\n        \"coordinates\": [\n          [\n            [\n              -165.05859375,\n              62.431074232920906\n            ],\n            [\n              -165.05859375,\n              71.1877539181316\n            ],\n            [\n              -141.328125,\n              71.1877539181316\n            ],\n            [\n              -141.328125,\n              62.431074232920906\n            ],\n            [\n              -165.05859375,\n              62.431074232920906\n            ]\n          ]\n        ]\n      }\n    }\n  ]\n}","contact":"<p><a href=\"https://www.usgs.gov/centers/asc/connect\" data-mce-href=\"https://www.usgs.gov/centers/asc/connect\">Alaska Science Center staff</a><br>U.S. Geological Survey<br>4210 University Dr.<br>Anchorage, AK 99508<br><br><a href=\"https://www.usgs.gov/centers/asc\" data-mce-href=\"https://www.usgs.gov/centers/asc\">Alaska Science Center</a></p>","tableOfContents":"<ul><li>Abstract</li><li>Introduction</li><li>Deposit Group Characteristics</li><li>Datasets</li><li>GIS-Based Methodology and Results by Deposit Group</li><li>Summary</li><li>Data Resources</li><li>References Cited</li></ul>","publishingServiceCenter":{"id":14,"text":"Menlo Park PSC"},"publishedDate":"2015-02-23","noUsgsAuthors":false,"publicationDate":"2015-02-23","publicationStatus":"PW","scienceBaseUri":"54f1953be4b02419550ceac4","contributors":{"authors":[{"text":"Jones, James V. III 0000-0002-6602-5935 jvjones@usgs.gov","orcid":"https://orcid.org/0000-0002-6602-5935","contributorId":201245,"corporation":false,"usgs":true,"family":"Jones","given":"James","suffix":"III","email":"jvjones@usgs.gov","middleInitial":"V.","affiliations":[{"id":119,"text":"Alaska Science Center Geology Minerals","active":true,"usgs":true}],"preferred":true,"id":541153,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Karl, Susan M. 0000-0003-1559-7826 skarl@usgs.gov","orcid":"https://orcid.org/0000-0003-1559-7826","contributorId":502,"corporation":false,"usgs":true,"family":"Karl","given":"Susan","email":"skarl@usgs.gov","middleInitial":"M.","affiliations":[{"id":119,"text":"Alaska Science Center Geology Minerals","active":true,"usgs":true}],"preferred":true,"id":541154,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Labay, Keith A. 0000-0002-6763-3190 klabay@usgs.gov","orcid":"https://orcid.org/0000-0002-6763-3190","contributorId":2097,"corporation":false,"usgs":true,"family":"Labay","given":"Keith A.","email":"klabay@usgs.gov","affiliations":[{"id":119,"text":"Alaska Science Center Geology Minerals","active":true,"usgs":true}],"preferred":false,"id":541155,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Shew, Nora B. 0000-0003-0025-7220 nshew@usgs.gov","orcid":"https://orcid.org/0000-0003-0025-7220","contributorId":3382,"corporation":false,"usgs":true,"family":"Shew","given":"Nora","email":"nshew@usgs.gov","middleInitial":"B.","affiliations":[{"id":119,"text":"Alaska Science Center Geology Minerals","active":true,"usgs":true}],"preferred":true,"id":541156,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Granitto, Matthew 0000-0003-3445-4863 granitto@usgs.gov","orcid":"https://orcid.org/0000-0003-3445-4863","contributorId":1224,"corporation":false,"usgs":true,"family":"Granitto","given":"Matthew","email":"granitto@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":541157,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"Hayes, Timothy S. thayes@usgs.gov","contributorId":1547,"corporation":false,"usgs":true,"family":"Hayes","given":"Timothy","email":"thayes@usgs.gov","middleInitial":"S.","affiliations":[{"id":662,"text":"Western Mineral and Environmental Resources Science Center","active":true,"usgs":true},{"id":312,"text":"Geology, Minerals, Energy, and Geophysics Science Center","active":true,"usgs":true}],"preferred":true,"id":541158,"contributorType":{"id":1,"text":"Authors"},"rank":6},{"text":"Mauk, Jeffrey L. 0000-0002-6244-2774 jmauk@usgs.gov","orcid":"https://orcid.org/0000-0002-6244-2774","contributorId":4101,"corporation":false,"usgs":true,"family":"Mauk","given":"Jeffrey","email":"jmauk@usgs.gov","middleInitial":"L.","affiliations":[{"id":171,"text":"Central Mineral and Environmental Resources Science Center","active":true,"usgs":true}],"preferred":true,"id":541159,"contributorType":{"id":1,"text":"Authors"},"rank":7},{"text":"Schmidt, Jeanine M. jschmidt@usgs.gov","contributorId":3138,"corporation":false,"usgs":true,"family":"Schmidt","given":"Jeanine","email":"jschmidt@usgs.gov","middleInitial":"M.","affiliations":[{"id":119,"text":"Alaska Science Center Geology Minerals","active":true,"usgs":true}],"preferred":true,"id":541160,"contributorType":{"id":1,"text":"Authors"},"rank":8},{"text":"Todd, Erin 0000-0002-4871-9730 etodd@usgs.gov","orcid":"https://orcid.org/0000-0002-4871-9730","contributorId":202811,"corporation":false,"usgs":true,"family":"Todd","given":"Erin","email":"etodd@usgs.gov","affiliations":[{"id":119,"text":"Alaska Science Center Geology Minerals","active":true,"usgs":true}],"preferred":true,"id":541161,"contributorType":{"id":1,"text":"Authors"},"rank":9},{"text":"Wang, Bronwen 0000-0003-1044-2227 bwang@usgs.gov","orcid":"https://orcid.org/0000-0003-1044-2227","contributorId":2351,"corporation":false,"usgs":true,"family":"Wang","given":"Bronwen","email":"bwang@usgs.gov","affiliations":[{"id":119,"text":"Alaska Science Center Geology Minerals","active":true,"usgs":true}],"preferred":true,"id":541162,"contributorType":{"id":1,"text":"Authors"},"rank":10},{"text":"Werdon, Melanie B.","contributorId":53345,"corporation":false,"usgs":true,"family":"Werdon","given":"Melanie B.","affiliations":[],"preferred":false,"id":541163,"contributorType":{"id":1,"text":"Authors"},"rank":11},{"text":"Yager, Douglas B. 0000-0001-5074-4022 dyager@usgs.gov","orcid":"https://orcid.org/0000-0001-5074-4022","contributorId":798,"corporation":false,"usgs":true,"family":"Yager","given":"Douglas","email":"dyager@usgs.gov","middleInitial":"B.","affiliations":[{"id":171,"text":"Central Mineral and Environmental Resources Science Center","active":true,"usgs":true}],"preferred":true,"id":541164,"contributorType":{"id":1,"text":"Authors"},"rank":12}]}}
,{"id":70148551,"text":"sir20155080 - 2015 - Surface-water quality in agricultural watersheds of the North Carolina Coastal Plain associated with concentrated animal feeding operations","interactions":[],"lastModifiedDate":"2019-08-13T08:57:12","indexId":"sir20155080","displayToPublicDate":"2019-08-13T10:00:00","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":"2015-5080","title":"Surface-water quality in agricultural watersheds of the North Carolina Coastal Plain associated with concentrated animal feeding operations","docAbstract":"<p>The effects of concentrated animal feeding operations (CAFOs) on water quality were investigated at 54 agricultural stream sites throughout the North Carolina Coastal Plain during 2012 and 2013. Three general watershed land-use types were examined during the study, including 18 background watersheds with no active CAFOs (BK sites), 18 watersheds with one or more active swine CAFOs but no poultry CAFOs (SW sites), and 18 watersheds with at least one active swine CAFO and one active dry-litter poultry CAFO (SP sites). The watershed drainage areas for these 54 stream sites ranged from 1.2 to 17.5 square miles. Conventional fertilizers used for crop production are the primary source of nutrients at the BK sites. Animal-waste manures represent an additional source of nutrients at the SW and SP study sites.</p>\n<p>Land cover, soil drainage, and CAFO attributes were compiled for each watershed. Water-quality field measurements were made and samples were collected at the 54 primary sites during 6 bimonthly sampling periods from June 2012 to April 2013. An additional 23 secondary sites were sampled once during April 2013 to provide supplemental data at stream locations directly adjacent or in close proximity to swine CAFOs and (or) background agricultural areas within 9 of the primary watersheds. The watershed drainage areas for the 23 secondary sites ranged from 0.2 to 8.9 square miles. Water temperature, specific conductance, dissolved-oxygen concentration, and pH were measured directly in the streams. Water samples were analyzed for major ions, nutrients, and stable isotopes, including delta hydrogen-2 (&delta;<sup>2</sup>H) and delta oxygen-18 (&delta;<sup>18</sup>O) of water and delta nitrogen-15 (&delta;<sup>15</sup>N) and &delta;<sup>18</sup>O of dissolved nitrate plus nitrite.</p>\n<p>Most of the water-quality properties and constituents varied significantly among the six sampling periods, changing both seasonally and in response to hydrologic conditions. The differences noted among the sampling periods indicate that the interactions between seasonal climatic differences, streamflow conditions, and instream biotic and abiotic processes are complex and their integrated effects can have varying degrees of influence on individual nutrients.</p>\n<p>Water-quality differences were noted for the SW and SP land-use groups relative to the BK group. Median values of specific conductance, several major ions (magnesium, sodium, potassium, and chloride), and nitrogen fractions (ammonia plus organic nitrogen, ammonia, nitrate plus nitrite, total nitrogen, and &delta;<sup>15</sup>N of nitrate plus nitrite) were higher for the SW and SP groups compared to the BK group. No significant differences in water temperature, dissolved oxygen, calcium, total organic nitrogen, orthophosphate, total phosphorus, or &delta;<sup>18</sup>O of nitrate plus nitrite were noted among the land-use groups. When compared on the basis of land-use type, there was an overall measurable effect of CAFO waste manures on stream water quality for the SW and SP watershed groups.</p>\n<p>Some individual sites within the SW and SP groups showed no measurable CAFO effects on water quality despite having CAFOs present upstream. An evaluation of sodium plus potassium concentrations coupled with &delta;<sup>15</sup>N values of nitrate plus nitrite proved valuable for distinguishing which SW and SP sites had a water-quality signature indicative of CAFO waste manures. Sites with CAFO manure effects were characterized by higher sodium plus potassium concentrations (commonly between 11 and 33 milligrams per liter) and &delta;<sup>15</sup>N values of nitrate plus nitrite (commonly between 11 and 26 parts per thousand) relative to sites reflecting background agricultural conditions, which commonly had sodium plus potassium concentrations between 6 and 14 milligrams per liter and &delta;<sup>15</sup>N values of nitrate plus nitrite between 6 and 15 parts per thousand. On the basis of the results of this study, land applications of waste manure at swine CAFOs influenced ion and nutrient chemistry in many of the North Carolina Coastal Plain streams that were studied.</p>\n<p>A classification tree model was developed to examine relations of watershed environmental attributes among the study sites with and without CAFO manure effects. Model results indicated that variations in swine barn density, percentage of wetlands, and total acres available for applying swine-waste manures had an important influence on those watersheds where CAFO effects on water quality were either evident or mitigated. Measurable effects of CAFO waste manures on stream water quality were most evident in those SW and SP watersheds having lower percentages of wetlands combined with higher swine barn densities and (or) higher total acres available for applying waste manure at the swine CAFOs. Stream water quality was similar to background agricultural conditions in SW and SP watersheds with lower swine barn densities coupled with higher percentages of wetlands or lower acres available for swine manure applications. The model provides a useful tool for exploring and identifying similar, unmonitored watersheds in the North Carolina Coastal Plain with potential CAFO manure influences on water quality that might warrant further examination.</p>","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/sir20155080","collaboration":"Prepared in cooperation with the North Carolina Department of Environment and Natural Resources, Division of Water Resources","usgsCitation":"Harden, S.L., 2015, Surface-water quality in agricultural watersheds of the North Carolina Coastal Plain associated with concentrated animal feeding operations: U.S. Geological Survey Scientific Investigations Report 2015-5080, Report: ix, 55 p.; 7 Appendices, https://doi.org/10.3133/sir20155080.","productDescription":"Report: ix, 55 p.; 7 Appendices","numberOfPages":"70","onlineOnly":"N","additionalOnlineFiles":"N","temporalStart":"2012-01-02","temporalEnd":"2013-12-31","ipdsId":"IP-060201","costCenters":[{"id":13634,"text":"South Atlantic Water Science Center","active":true,"usgs":true}],"links":[{"id":301512,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/sir20155080.jpg"},{"id":366493,"rank":5,"type":{"id":3,"text":"Appendix"},"url":"https://pubs.usgs.gov/sir/2015/5080/downloads/sir2015-5080_appendixA2/sir20155080_appendixA2_2.xlsx","text":"Appendix A2-2","size":"28 KB","linkFileType":{"id":3,"text":"xlsx"},"description":"Compiled hydrologic soil group data for each study site"},{"id":366494,"rank":7,"type":{"id":3,"text":"Appendix"},"url":"https://pubs.usgs.gov/sir/2015/5080/downloads/sir2015-5080_appendixA3/sir20155080_appendixA3_2.xlsx","text":"Appendix A3-2","size":"13 KB","linkFileType":{"id":3,"text":"xlsx"},"description":"Inactive swine CAFO permits in sites"},{"id":366498,"rank":11,"type":{"id":3,"text":"Appendix"},"url":"https://pubs.usgs.gov/sir/2015/5080/downloads/sir2015-5080_appendixA3/sir20155080_appendixA3_6.xlsx","text":"Appendix A3-6","size":"14 KB","linkFileType":{"id":3,"text":"xlsx"},"description":"Poultry CAFO attribute data by facility"},{"id":366495,"rank":8,"type":{"id":3,"text":"Appendix"},"url":"https://pubs.usgs.gov/sir/2015/5080/downloads/sir2015-5080_appendixA3/sir20155080_appendixA3_3.xlsx","text":"Appendix A3-3","size":"37 KB","linkFileType":{"id":3,"text":"xlsx"},"description":"Active swine CAFO permits in sites"},{"id":366496,"rank":9,"type":{"id":3,"text":"Appendix"},"url":"https://pubs.usgs.gov/sir/2015/5080/downloads/sir2015-5080_appendixA3/sir20155080_appendixA3_4.xlsx","text":"Appendix A3-4","size":"24 KB","linkFileType":{"id":3,"text":"xlsx"},"description":"Swine CAFO attribute data by permit"},{"id":366499,"rank":12,"type":{"id":3,"text":"Appendix"},"url":"https://pubs.usgs.gov/sir/2015/5080/downloads/sir2015-5080_appendixA3/sir20155080_appendixA3_7.xlsx","text":"Appendix A3-7","size":"12 KB","linkFileType":{"id":3,"text":"xlsx"},"description":"Poultry CAFO attribute data by study site"},{"id":366500,"rank":14,"type":{"id":3,"text":"Appendix"},"url":"https://pubs.usgs.gov/sir/2015/5080/downloads/sir2015-5080_appendixA4/sir20155080_appendixA4_2.xlsx","text":"Appendix A4-2","size":"13 KB","linkFileType":{"id":3,"text":"xlsx"},"description":"Compiled water-quality data for precipitation samples"},{"id":366506,"rank":10,"type":{"id":3,"text":"Appendix"},"url":"https://pubs.usgs.gov/sir/2015/5080/downloads/sir2015-5080_appendixA3/sir20155080_appendixA3_5.xlsx","text":"Appendix A3-5","size":"20 KB","linkFileType":{"id":3,"text":"xlsx"},"description":"Summary of swine CAFO attribute data by watershed study site"},{"id":366501,"rank":15,"type":{"id":3,"text":"Appendix"},"url":"https://pubs.usgs.gov/sir/2015/5080/downloads/sir2015-5080_appendixA4/sir20155080_appendixA4_3.xlsx","text":"Appendix A4-3","size":"14 KB","linkFileType":{"id":3,"text":"xlsx"},"description":"Results of field blanks"},{"id":366502,"rank":16,"type":{"id":3,"text":"Appendix"},"url":"https://pubs.usgs.gov/sir/2015/5080/downloads/sir2015-5080_appendixA4/sir20155080_appendixA4_4.xlsx","text":"Appendix A4-4","size":"20 KB","linkFileType":{"id":3,"text":"xlsx"},"description":"Results of replicate samples"},{"id":301478,"rank":6,"type":{"id":3,"text":"Appendix"},"url":"https://pubs.usgs.gov/sir/2015/5080/downloads/sir2015-5080_appendixA3/sir20155080_appendixA3_1.xlsx","text":"Appendix A3-1","size":"13 KB","linkFileType":{"id":3,"text":"xlsx"},"description":"Permitted non-discharge facilities in sites"},{"id":301479,"rank":13,"type":{"id":3,"text":"Appendix"},"url":"https://pubs.usgs.gov/sir/2015/5080/downloads/sir2015-5080_appendixA4/sir20155080_appendixA4_1.xlsx","text":"Appendix A4-1","size":"94 KB","linkFileType":{"id":3,"text":"xlsx"},"description":"Compiled water-quality data for surface-water samples"},{"id":301481,"rank":19,"type":{"id":3,"text":"Appendix"},"url":"https://pubs.usgs.gov/sir/2015/5080/downloads/sir2015-5080_appendixA6/sir20155080_appendixA6.xlsx","text":"Appendix A6","size":"26 KB","linkFileType":{"id":3,"text":"xlsx"},"description":"Data used for distinguishing sites with manure influences"},{"id":301480,"rank":17,"type":{"id":3,"text":"Appendix"},"url":"https://pubs.usgs.gov/sir/2015/5080/downloads/sir2015-5080_appendixA5/sir2015-5080_appendixA5.pdf","text":"Appendix A5","size":"11.56 MB","linkFileType":{"id":1,"text":"pdf"},"description":"Detailed Evaluations of the April 2013 Water-Quality Dataset"},{"id":301477,"rank":4,"type":{"id":3,"text":"Appendix"},"url":"https://pubs.usgs.gov/sir/2015/5080/downloads/sir2015-5080_appendixA2/sir20155080_appendixA2_1.xlsx","text":"Appendix A2-1","size":"28 KB","linkFileType":{"id":3,"text":"xlsx"},"description":"Compiled land-cover data for each study site"},{"id":301482,"rank":20,"type":{"id":3,"text":"Appendix"},"url":"https://pubs.usgs.gov/sir/2015/5080/downloads/sir2015-5080_appendixA7/sir20155080_appendixA7.xlsx","text":"Appendix A7","size":"24 KB","linkFileType":{"id":3,"text":"xlsx"},"description":"Compiled study site data used in classification tree analyses"},{"id":301476,"rank":3,"type":{"id":3,"text":"Appendix"},"url":"https://pubs.usgs.gov/sir/2015/5080/downloads/sir2015-5080_appendixA1/sir20155080_appendixA1.pdf","text":"Appendix A1","size":"31.59 MB","linkFileType":{"id":1,"text":"pdf"},"description":"Maps for the primary and secondary study sites"},{"id":301474,"rank":0,"type":{"id":15,"text":"Index Page"},"url":"https://pubs.usgs.gov/sir/2015/5080/"},{"id":301475,"rank":2,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/sir/2015/5080/pdf/sir2015-5080.pdf","text":"Report","size":"2.27 MB","linkFileType":{"id":1,"text":"pdf"},"description":"SIR 2015-5080"},{"id":301483,"rank":21,"type":{"id":3,"text":"Appendix"},"url":"https://pubs.usgs.gov/sir/2015/5080/downloads/sir2015-5080_appendixes.zip","text":"Zipped appendixes","size":"42.35 MB","linkFileType":{"id":6,"text":"zip"},"description":"All appendixes zipped"}],"country":"United States","state":"North Carolina","geographicExtents":"{\n  \"type\": \"FeatureCollection\",\n  \"features\": [\n    {\n      \"type\": \"Feature\",\n      \"properties\": {},\n      \"geometry\": {\n        \"type\": \"Polygon\",\n        \"coordinates\": [\n          [\n            [\n              -75.87158203125,\n              36.55377524336086\n            ],\n            [\n              -75.41015624999999,\n              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,{"id":70147329,"text":"sir20155066 - 2015 - Preliminary methodology to assess the national and regional impact of U.S. wind energy development on birds and bats","interactions":[{"subject":{"id":70147329,"text":"sir20155066 - 2015 - Preliminary methodology to assess the national and regional impact of U.S. wind energy development on birds and bats","indexId":"sir20155066","publicationYear":"2015","noYear":false,"title":"Preliminary methodology to assess the national and regional impact of U.S. wind energy development on birds and bats"},"predicate":"SUPERSEDED_BY","object":{"id":70200937,"text":"sir20185157 - 2019 - A Methodology to Assess the National and Regional Impacts of U.S. Wind Energy Development on Birds and Bats","indexId":"sir20185157","publicationYear":"2019","noYear":false,"title":"A Methodology to Assess the National and Regional Impacts of U.S. Wind Energy Development on Birds and Bats"},"id":1}],"supersededBy":{"id":70200937,"text":"sir20185157 - 2019 - A Methodology to Assess the National and Regional Impacts of U.S. Wind Energy Development on Birds and Bats","indexId":"sir20185157","publicationYear":"2019","noYear":false,"title":"A Methodology to Assess the National and Regional Impacts of U.S. Wind Energy Development on Birds and Bats"},"lastModifiedDate":"2019-07-23T13:11:00","indexId":"sir20155066","displayToPublicDate":"2019-07-23T14:11:00","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":"2015-5066","title":"Preliminary methodology to assess the national and regional impact of U.S. wind energy development on birds and bats","docAbstract":"<p>The U.S. Geological Survey has developed a methodology to assess the impacts of wind energy development on wildlife; it is a probabilistic, quantitative assessment methodology that can communicate to decision makers and the public the magnitude of these effects on species populations. The methodology is currently applicable to birds and bats, focuses primarily on the effects of collisions, and can be applied to any species that breeds in, migrates through, or otherwise uses any part of the United States. The methodology is intended to assess species at the national scale and is fundamentally different from existing methods focusing on impacts at individual facilities.</p>\n<p>Publicly available fatality information, population estimates, species range maps, turbine location data, biological characteristics, and generic population models are used to generate both a ranked list of species based on relative risk as well as quantitative measures of the magnitude of the effect on species' population trend and size. Three metrics are combined to determine direct and indirect relative risk to populations. A generic population model is used to estimate the expected change in population trend and includes additive mortality from collisions with wind turbines. Lastly, the methodology uses observed fatalities and an estimate of potential biological removal to assess the risk of a decline in population size. Data for six bird species have been processed through the entire methodology as a test case, and the results are presented in this report.</p>\n<p>Components of the methodology are based on simplifying assumptions and require information that, for many species, may be sparse or unreliable. These assumptions are presented in the report and should be carefully considered when using output from the methodology. In addition, this methodology can be used to recommend species for more intensive demographic modeling or highlight those species that may not require any additional protection because effects of wind energy development on their populations are projected to be small.</p>","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/sir20155066","usgsCitation":"Diffendorfer, J., Beston, J.A., Merrill, M., Stanton, J.C., Corum, M., Loss, S., Thogmartin, W.E., Johnson, D.H., Erickson, R.A., and Heist, K.W., 2015, Preliminary methodology to assess the national and regional impact of U.S. wind energy development on birds and bats: U.S. Geological Survey Scientific Investigations Report 2015-5066, 51 p., https://doi.org/10.3133/sir20155066.","productDescription":"51 p.","onlineOnly":"Y","additionalOnlineFiles":"N","ipdsId":"IP-063536","costCenters":[{"id":241,"text":"Eastern Energy Resources Science 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douglas_h_johnson@usgs.gov","orcid":"https://orcid.org/0000-0002-7778-6641","contributorId":1387,"corporation":false,"usgs":true,"family":"Johnson","given":"Douglas","email":"douglas_h_johnson@usgs.gov","middleInitial":"H.","affiliations":[{"id":480,"text":"Northern Prairie Wildlife Research Center","active":true,"usgs":true}],"preferred":true,"id":545803,"contributorType":{"id":1,"text":"Authors"},"rank":8},{"text":"Erickson, Richard A. 0000-0003-4649-482X rerickson@usgs.gov","orcid":"https://orcid.org/0000-0003-4649-482X","contributorId":5455,"corporation":false,"usgs":true,"family":"Erickson","given":"Richard","email":"rerickson@usgs.gov","middleInitial":"A.","affiliations":[{"id":606,"text":"Upper Midwest Environmental Sciences Center","active":true,"usgs":true}],"preferred":true,"id":545804,"contributorType":{"id":1,"text":"Authors"},"rank":9},{"text":"Heist, Kevin 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,{"id":70195581,"text":"70195581 - 2015 - Activity-specific ecological niche models for planning reintroductions of California condors (Gymnogyps californianus)","interactions":[],"lastModifiedDate":"2018-02-23T10:50:34","indexId":"70195581","displayToPublicDate":"2018-02-23T00:00:00","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}},"displayTitle":"Activity-specific ecological niche models for planning reintroductions of California condors (<i>Gymnogyps californianus</i>)","title":"Activity-specific ecological niche models for planning reintroductions of California condors (Gymnogyps californianus)","docAbstract":"<p><span>Ecological niche models can be a useful tool to identify candidate reintroduction sites for endangered species but have been infrequently used for this purpose. In this paper, we (1) develop activity-specific ecological niche models (nesting, roosting, and feeding) for the critically endangered California condor (</span><i>Gymnogyps californianus</i><span>) to aid in reintroduction planning in California, Oregon, and Washington, USA, (2) test the accuracy of these models using empirical data withheld from model development, and (3) integrate model results with information on condor movement ecology and biology to produce predictive maps of reintroduction site suitability. Our approach, which disentangles niche models into activity-specific components, has applications for other species where it is routinely assumed (often incorrectly) that individuals fulfill all requirements for life within a single environmental space. Ecological niche models conformed to our understanding of California condor ecology, had good predictive performance when tested with data withheld from model development, and aided in the identification of several candidate reintroduction areas outside of the current distribution of the species. Our results suggest there are large unoccupied regions of the California condor’s historical range that have retained ecological features similar to currently occupied habitats, and thus could be considered for future reintroduction efforts. Combining our activity-specific ENMs with ground reconnaissance and information on other threat factors that could not be directly incorporated into empirical ENMs will ultimately improve our ability to select successful reintroduction sites for the California condor.</span></p>","language":"English","publisher":"Elsevier","doi":"10.1016/j.biocon.2015.01.002","usgsCitation":"D'Elia, J., Haig, S.M., Johnson, M.J., Marcot, B.G., and Young, R., 2015, Activity-specific ecological niche models for planning reintroductions of California condors (Gymnogyps californianus): Biological Conservation, v. 184, p. 90-99, https://doi.org/10.1016/j.biocon.2015.01.002.","productDescription":"10 p.","startPage":"90","endPage":"99","ipdsId":"IP-061907","costCenters":[{"id":290,"text":"Forest and Rangeland Ecosystem Science Center","active":false,"usgs":true}],"links":[{"id":351901,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","state":"California, Oregon, Washington","geographicExtents":"{\n  \"type\": \"FeatureCollection\",\n  \"features\": [\n    {\n      \"type\": \"Feature\",\n      \"properties\": {},\n      \"geometry\": {\n        \"type\": \"Polygon\",\n        \"coordinates\": [\n          [\n            [\n              -126.38671874999999,\n              32.54681317351514\n            ],\n            [\n              -116.89453125,\n              32.54681317351514\n            ],\n            [\n              -116.89453125,\n              49.26780455063753\n            ],\n            [\n              -126.38671874999999,\n              49.26780455063753\n            ],\n            [\n              -126.38671874999999,\n              32.54681317351514\n            ]\n          ]\n        ]\n      }\n    }\n  ]\n}","volume":"184","publishingServiceCenter":{"id":12,"text":"Tacoma PSC"},"noUsgsAuthors":false,"publicationStatus":"PW","scienceBaseUri":"5afeeb03e4b0da30c1bfc63e","contributors":{"authors":[{"text":"D'Elia, Jesse","contributorId":63152,"corporation":false,"usgs":true,"family":"D'Elia","given":"Jesse","affiliations":[],"preferred":false,"id":729364,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Haig, Susan M. 0000-0002-6616-7589 susan_haig@usgs.gov","orcid":"https://orcid.org/0000-0002-6616-7589","contributorId":719,"corporation":false,"usgs":true,"family":"Haig","given":"Susan","email":"susan_haig@usgs.gov","middleInitial":"M.","affiliations":[{"id":289,"text":"Forest and Rangeland Ecosys Science Center","active":true,"usgs":true},{"id":290,"text":"Forest and Rangeland Ecosystem Science Center","active":false,"usgs":true}],"preferred":true,"id":729365,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Johnson, Matthew J. mjjohnson@usgs.gov","contributorId":3604,"corporation":false,"usgs":true,"family":"Johnson","given":"Matthew","email":"mjjohnson@usgs.gov","middleInitial":"J.","affiliations":[{"id":27989,"text":"Colorado Plateau Research Station, Northern Arizona University, Flagstaff, AZ","active":true,"usgs":false}],"preferred":false,"id":729366,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Marcot, Bruce G.","contributorId":152612,"corporation":false,"usgs":false,"family":"Marcot","given":"Bruce","email":"","middleInitial":"G.","affiliations":[{"id":18944,"text":"Pacific Northwest Research Station, USDA Forest Service","active":true,"usgs":false}],"preferred":false,"id":729367,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Young, Richard","contributorId":202719,"corporation":false,"usgs":false,"family":"Young","given":"Richard","affiliations":[],"preferred":false,"id":729368,"contributorType":{"id":1,"text":"Authors"},"rank":5}]}}
,{"id":70231261,"text":"70231261 - 2015 - Projected changes in diverse ecosystems from climate warming and biophysical drivers in northwest Alaska","interactions":[],"lastModifiedDate":"2022-05-04T14:07:39.294065","indexId":"70231261","displayToPublicDate":"2018-01-15T08:58:11","publicationYear":"2015","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":1252,"text":"Climatic Change","active":true,"publicationSubtype":{"id":10}},"title":"Projected changes in diverse ecosystems from climate warming and biophysical drivers in northwest Alaska","docAbstract":"<p>Climate warming affects arctic and boreal ecosystems by interacting with numerous biophysical factors across heterogeneous landscapes. To assess potential effects of warming on diverse local-scale ecosystems (ecotypes) across northwest Alaska, we compiled data on historical areal changes over the last 25–50&nbsp;years. Based on historical rates of change relative to time and temperature, we developed three state-transition models to project future changes in area for 60 ecotypes involving 243 potential transitions during three 30-year periods (ending 2040, 2070, 2100). The time model, assuming changes over the past 30&nbsp;years continue at the same rate, projected a net change, or directional shift, of 6&nbsp;% by 2100. The temperature model, using past rates of change relative to the past increase in regional mean annual air temperatures (1&nbsp;°C/30&nbsp;year), projected a net change of 17&nbsp;% in response to expected warming of 2, 4, and 6&nbsp;°C at the end of the three periods. A rate-adjusted temperature model, which adjusted transition rates (±50&nbsp;%) based on assigned feedbacks associated with 23 biophysical drivers, estimated a net change of 13&nbsp;%, with 33 ecotypes gaining and 23 ecotypes losing area. Major drivers included shrub and tree expansion, fire, succession, and thermokarst. Overall, projected changes will be modest over the next century even though climate warming increased transition rates up to 9 fold. The strength of this state-transition modeling is that it used a large dataset of past changes to provide a comprehensive assessment of likely future changes associated with numerous drivers affecting the full diversity of ecosystems across a broad region.</p>","language":"English","publisher":"Springer","doi":"10.1007/s10584-014-1302-1","usgsCitation":"Jorgenson, M.T., Marcot, B.G., Swanson, D.K., Jorgenson, J.C., and DeGange, A.R., 2015, Projected changes in diverse ecosystems from climate warming and biophysical drivers in northwest Alaska: Climatic Change, v. 130, p. 131-144, https://doi.org/10.1007/s10584-014-1302-1.","productDescription":"14 p.","startPage":"131","endPage":"144","ipdsId":"IP-051465","costCenters":[{"id":114,"text":"Alaska Science Center","active":true,"usgs":true}],"links":[{"id":471483,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.1007/s10584-014-1302-1","text":"Publisher Index Page"},{"id":400129,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","state":"Alaska","geographicExtents":"{\n  \"type\": \"FeatureCollection\",\n  \"features\": [\n    {\n      \"type\": \"Feature\",\n      \"properties\": {},\n      \"geometry\": {\n        \"type\": \"Polygon\",\n        \"coordinates\": [\n          [\n            [\n              -168.4423828125,\n              64.32087157990324\n            ],\n            [\n              -149.326171875,\n              64.32087157990324\n            ],\n            [\n              -149.326171875,\n              71.63599288330609\n            ],\n            [\n              -168.4423828125,\n              71.63599288330609\n            ],\n            [\n              -168.4423828125,\n              64.32087157990324\n            ]\n          ]\n        ]\n      }\n    }\n  ]\n}","volume":"130","noUsgsAuthors":false,"publicationDate":"2015-01-18","publicationStatus":"PW","contributors":{"authors":[{"text":"Jorgenson, Mark Torre 0000-0002-9834-8851","orcid":"https://orcid.org/0000-0002-9834-8851","contributorId":169365,"corporation":false,"usgs":false,"family":"Jorgenson","given":"Mark","email":"","middleInitial":"Torre","affiliations":[{"id":13506,"text":"Alaska Ecoscience","active":true,"usgs":false}],"preferred":false,"id":842149,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Marcot, Bruce G.","contributorId":140456,"corporation":false,"usgs":false,"family":"Marcot","given":"Bruce","email":"","middleInitial":"G.","affiliations":[{"id":12647,"text":"U.S. Forest Service, Pacific Northwest Research Station","active":true,"usgs":false}],"preferred":false,"id":842150,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Swanson, David K.","contributorId":178902,"corporation":false,"usgs":false,"family":"Swanson","given":"David","email":"","middleInitial":"K.","affiliations":[],"preferred":false,"id":842151,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Jorgenson, Janet C.","contributorId":191903,"corporation":false,"usgs":false,"family":"Jorgenson","given":"Janet","email":"","middleInitial":"C.","affiliations":[],"preferred":false,"id":842152,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"DeGange, Anthony R. tdegange@usgs.gov","contributorId":139765,"corporation":false,"usgs":true,"family":"DeGange","given":"Anthony","email":"tdegange@usgs.gov","middleInitial":"R.","affiliations":[{"id":114,"text":"Alaska Science Center","active":true,"usgs":true}],"preferred":false,"id":842153,"contributorType":{"id":1,"text":"Authors"},"rank":5}]}}
,{"id":70189615,"text":"70189615 - 2015 - The 2014 Mw6.1 South Napa Earthquake: A unilateral rupture with shallow asperity and rapid afterslip","interactions":[],"lastModifiedDate":"2017-07-19T09:33:52","indexId":"70189615","displayToPublicDate":"2017-07-19T00:00:00","publicationYear":"2015","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":3372,"text":"Seismological Research Letters","onlineIssn":"1938-2057","printIssn":"0895-0695","active":true,"publicationSubtype":{"id":10}},"title":"The 2014 Mw6.1 South Napa Earthquake: A unilateral rupture with shallow asperity and rapid afterslip","docAbstract":"<p>The M<sub>w</sub>6.1 South Napa earthquake occurred near Napa, California on August 24, 2014 (UTC), and was the largest inland earthquake in Northern California since the 1989 M<sub>w</sub>6.9 Loma Prieta earthquake. The first report of the earthquake from the Northern California Earthquake Data Center (NCEDC) indicates a hypocentral depth of 11.0km with longitude and latitude of (122.3105°W, 38.217°N). Surface rupture was documented by field observations and Lidar imaging (Brooks et al. 2014; Hudnut et al. 2014; Brocher et al., 2015), with about 12 km of continuous rupture starting near the epicenter and extending to the northwest. The southern part of the rupture is relatively straight, but the strike changes by about 15° at the northern end over a 6-km segment. The peak dextral offset was observed near the Buhman residence with right-.‐lateral motion of 46 cm, near the location where the strike of fault begins to rotate clock-.‐wise (Hudnut et al., 2014). The earthquake was well recorded by the strong motion network operated by the NCEDC, the California Geological Survey and the U.S. Geological Survey (USGS). There are about 12 sites within an epicentral distance of 15km, with relatively good azimuthal coverage (Fig.1). The largest peak-ground-velocity (PGV) of nearly 100 cm/s was observed on station 1765, which is the closest station to the rupture and lies about 3 km east of the northern segment (Fig. 1). The ground deformation associated with the earthquake was also well recorded by the high-resolution COSMO-SkyMed satellite and Sentinel-1A satellite, providing independent static observations.</p>","language":"English","publisher":"Seismological Society of America","doi":"10.1785/0220140249","usgsCitation":"Wei, S., Barbot, S., Graves, R., Lienkaemper, J.J., Wang, T., Hudnut, K.W., Fu, Y., and Helmberger, D., 2015, The 2014 Mw6.1 South Napa Earthquake: A unilateral rupture with shallow asperity and rapid afterslip: Seismological Research Letters, v. 86, no. 2A, p. 344-354, https://doi.org/10.1785/0220140249.","productDescription":"11 p.","startPage":"344","endPage":"354","ipdsId":"IP-061960","costCenters":[{"id":237,"text":"Earthquake Science Center","active":true,"usgs":true}],"links":[{"id":471484,"rank":0,"type":{"id":41,"text":"Open Access External Repository Page"},"url":"https://doi.org/10.1785/0220140249","text":"External Repository"},{"id":344019,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","state":"California","geographicExtents":"{\n  \"type\": \"FeatureCollection\",\n  \"features\": [\n    {\n      \"type\": \"Feature\",\n      \"properties\": {},\n      \"geometry\": {\n        \"type\": \"Polygon\",\n        \"coordinates\": [\n          [\n            [\n              -122.0833,\n              38.01667\n            ],\n            [\n              -122.0167,\n               38.01667\n            ],\n            [\n              -122.0167,\n              38.08333\n            ],\n            [\n              -122.0833,\n              38.08333\n            ],\n            [\n              -122.0833,\n              38.01667\n            ]\n          ]\n        ]\n      }\n    }\n  ]\n}","volume":"86","issue":"2A","publishingServiceCenter":{"id":14,"text":"Menlo Park PSC"},"noUsgsAuthors":false,"publicationDate":"2015-03-04","publicationStatus":"PW","scienceBaseUri":"59706fb8e4b0d1f9f065a8a9","contributors":{"authors":[{"text":"Wei, Shengji","contributorId":192953,"corporation":false,"usgs":false,"family":"Wei","given":"Shengji","email":"","affiliations":[],"preferred":false,"id":705447,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Barbot, Sylvain","contributorId":194835,"corporation":false,"usgs":false,"family":"Barbot","given":"Sylvain","email":"","affiliations":[],"preferred":false,"id":705448,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Graves, Robert 0000-0001-9758-453X rwgraves@usgs.gov","orcid":"https://orcid.org/0000-0001-9758-453X","contributorId":140738,"corporation":false,"usgs":true,"family":"Graves","given":"Robert","email":"rwgraves@usgs.gov","affiliations":[{"id":237,"text":"Earthquake Science Center","active":true,"usgs":true}],"preferred":true,"id":705446,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Lienkaemper, James J. 0000-0002-7578-7042 jlienk@usgs.gov","orcid":"https://orcid.org/0000-0002-7578-7042","contributorId":1941,"corporation":false,"usgs":true,"family":"Lienkaemper","given":"James","email":"jlienk@usgs.gov","middleInitial":"J.","affiliations":[{"id":237,"text":"Earthquake Science Center","active":true,"usgs":true}],"preferred":true,"id":705449,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Wang, Teng","contributorId":156235,"corporation":false,"usgs":false,"family":"Wang","given":"Teng","email":"","affiliations":[{"id":20300,"text":"Southern Methodist University","active":true,"usgs":false}],"preferred":false,"id":705450,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"Hudnut, Kenneth W. 0000-0002-3168-4797 hudnut@usgs.gov","orcid":"https://orcid.org/0000-0002-3168-4797","contributorId":2550,"corporation":false,"usgs":true,"family":"Hudnut","given":"Kenneth","email":"hudnut@usgs.gov","middleInitial":"W.","affiliations":[{"id":508,"text":"Office of the AD Hazards","active":true,"usgs":true},{"id":237,"text":"Earthquake Science Center","active":true,"usgs":true}],"preferred":true,"id":705451,"contributorType":{"id":1,"text":"Authors"},"rank":6},{"text":"Fu, Yuning","contributorId":194836,"corporation":false,"usgs":false,"family":"Fu","given":"Yuning","email":"","affiliations":[],"preferred":false,"id":705452,"contributorType":{"id":1,"text":"Authors"},"rank":7},{"text":"Helmberger, Don","contributorId":192954,"corporation":false,"usgs":false,"family":"Helmberger","given":"Don","email":"","affiliations":[],"preferred":false,"id":705453,"contributorType":{"id":1,"text":"Authors"},"rank":8}]}}
,{"id":70154783,"text":"70154783 - 2015 - Stratigraphic reconnaissance of the Middle Jurassic Red Glacier Formation, Tuxedni Group, at Red Glacier, Cook Inlet, Alaska","interactions":[],"lastModifiedDate":"2017-06-07T14:33:28","indexId":"70154783","displayToPublicDate":"2017-06-07T00:00:00","publicationYear":"2015","noYear":false,"publicationType":{"id":18,"text":"Report"},"publicationSubtype":{"id":2,"text":"State or Local Government Series"},"title":"Stratigraphic reconnaissance of the Middle Jurassic Red Glacier Formation, Tuxedni Group, at Red Glacier, Cook Inlet, Alaska","docAbstract":"<p>The Alaska Division of Geological &amp; Geophysical Surveys (DGGS) and U.S. Geological Survey (USGS) are implementing ongoing programs to characterize the petroleum potential of Cook Inlet basin. Since 2009 this program has included work on the Mesozoic stratigraphy of lower Cook Inlet, including the Middle Jurassic Tuxedni Group between Tuxedni and Iniskin bays (LePain and others, 2013; Stanley and others, 2013; fig. 5-1). The basal unit in the group, the Red Glacier Formation (fig. 5-2), is thought to be the principal source rock for oil produced in upper Cook Inlet, and available geochemical data support this contention (Magoon and Anders, 1992; Magoon, 1994). Despite its economic significance very little has been published on the formation since Detterman and Hartsock’s (1966) seminal contribution on the geology of the Iniskin–Tuxedni area nearly 50 years ago. Consequently its stratigraphy, contact relations with bounding formations, and source rock characteristics are poorly known. During the 2014 field season, a nearly continuous stratigraphic section through the Red Glacier Formation in its type area at Red Glacier was located and measured to characterize sedimentary facies and to collect a suite of samples for analyses of biostratigraphy, Rock-Eval pyrolysis, vitrinite reflectance, and sandstone composition (fig. 5-3).</p><p>The poorly known nature of the Red Glacier Formation is likely due to its remote location, steep terrain, and the fact that the type section is split into two segments that are more than 3 km apart. The lower 375 m segment of the formation is on the ridge between Red Glacier and Lateral Glacier and the upper 1,009 m segment is on the ridge between Red Glacier and Boulder Creek (fig. 5-3). Structural complications in the area add to the difficulty in understanding how these two segments fit together.</p>","language":"English","publisher":"Alaska Division of Geological and Geophysical Surveys","doi":"10.14509/29460","usgsCitation":"LePain, D., and Stanley, R.G., 2015, Stratigraphic reconnaissance of the Middle Jurassic Red Glacier Formation, Tuxedni Group, at Red Glacier, Cook Inlet, Alaska, 6 p. , https://doi.org/10.14509/29460.","productDescription":"6 p. ","startPage":"23","endPage":"28","ipdsId":"IP-062973","costCenters":[{"id":312,"text":"Geology, Minerals, Energy, and Geophysics Science Center","active":true,"usgs":true}],"links":[{"id":471486,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.14509/29460","text":"Publisher Index Page"},{"id":342235,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"},{"id":305560,"type":{"id":11,"text":"Document"},"url":"https://dggs.alaska.gov/webpubs/dggs/pir/text/pir2015_005_05.pdf"}],"country":"United States ","state":"Alaska","otherGeospatial":"Cook Inlet ","geographicExtents":"{\n  \"type\": \"FeatureCollection\",\n  \"features\": [\n    {\n      \"type\": \"Feature\",\n      \"properties\": {},\n      \"geometry\": {\n        \"type\": \"Polygon\",\n        \"coordinates\": [\n          [\n            [\n              -152.0947265625,\n              59.977005492196\n            ],\n            [\n              -148.38134765625,\n              59.977005492196\n            ],\n            [\n              -148.38134765625,\n              61.6794500443896\n            ],\n            [\n              -152.0947265625,\n              61.6794500443896\n            ],\n            [\n              -152.0947265625,\n              59.977005492196\n            ]\n          ]\n        ]\n      }\n    },\n    {\n      \"type\": \"Feature\",\n      \"properties\": {},\n      \"geometry\": {\n        \"type\": \"Polygon\",\n        \"coordinates\": [\n          [\n            [\n              -155.43457031249997,\n              58.790978406215565\n            ],\n            [\n              -151.34765625,\n              58.790978406215565\n            ],\n            [\n              -151.34765625,\n              60.74842446462361\n            ],\n            [\n              -155.43457031249997,\n              60.74842446462361\n            ],\n            [\n              -155.43457031249997,\n              58.790978406215565\n            ]\n          ]\n        ]\n      }\n    }\n  ]\n}","publishingServiceCenter":{"id":14,"text":"Menlo Park PSC"},"noUsgsAuthors":false,"publicationStatus":"PW","scienceBaseUri":"593910afe4b0764e6c5e8871","contributors":{"authors":[{"text":"LePain, David L.","contributorId":105209,"corporation":false,"usgs":true,"family":"LePain","given":"David L.","affiliations":[],"preferred":false,"id":564137,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Stanley, Richard G. 0000-0001-6192-8783 rstanley@usgs.gov","orcid":"https://orcid.org/0000-0001-6192-8783","contributorId":1832,"corporation":false,"usgs":true,"family":"Stanley","given":"Richard","email":"rstanley@usgs.gov","middleInitial":"G.","affiliations":[{"id":312,"text":"Geology, Minerals, Energy, and Geophysics Science Center","active":true,"usgs":true}],"preferred":true,"id":564136,"contributorType":{"id":1,"text":"Authors"},"rank":2}]}}
,{"id":70187116,"text":"70187116 - 2015 - Evaluation of perchlorate sources in the Rialto-Colton and Chino California subbasins using chlorine and oxygen isotope ratio analysis","interactions":[],"lastModifiedDate":"2017-04-25T09:23:28","indexId":"70187116","displayToPublicDate":"2017-04-25T00:00:00","publicationYear":"2015","noYear":false,"publicationType":{"id":18,"text":"Report"},"publicationSubtype":{"id":4,"text":"Other Government Series"},"seriesNumber":"ER-200942","title":"Evaluation of perchlorate sources in the Rialto-Colton and Chino California subbasins using chlorine and oxygen isotope ratio analysis","docAbstract":"Perchlorate (ClO4-) in groundwater can be from synthetic or natural sources, the latter of which include both historical application of imported nitrate fertilizers from the Atacama Desert of Chile and naturally deposited ClO4- that forms atmospherically and accumulates in arid regions such as the southwestern US. The objective of this study was to evaluate the use of isotopic data to distinguish sources of ClO4- in groundwater in a specific region of the Rialto-Colton and Chino, CA groundwater subbasins (Study Area). This region includes two groundwater ClO4- plumes emanating from known military/industrial source areas, and a larger area outside of these plumes having measurable ClO4-. Perchlorate extracted from wells in this region was analyzed for chlorine and oxygen stable isotope ratios (δ37Cl, δ18O, δ17O) and radioactive chlorine-36 (36Cl) isotopic abundance, along with other geochemical, isotopic, and hydrogeologic data. Isotope data indicate synthetic, Atacama, and indigenous natural ClO4- were present in the Study Area. Stable isotope data from nearly all sampled wells within the contours of the two characterized plumes, including those located in a perched zone and within the regional groundwater aquifer, were consistent with a dominant synthetic ClO4- source. In wells downgradient from the synthetic plumes and in the Chino subbasin to the southwest, isotopic data indicate the dominant source of ClO4- largely was Atacama, presumably from historical application of nitrate fertilizer in this region. Past agricultural land use and historical records are consistent with this source being present in groundwater. The 36Cl and δ18O data indicate that wells having predominantly synthetic or Atacama ClO4- also commonly contained small fractions of indigenous natural ClO4-. The indigenous ClO4- was most evident isotopically in wells having the lowest overall ClO4- concentrations (< 1 μg/L), consistent with its occurrence as a low-level background constituent in the region. A small subset of wells outside the contours of the two synthetic plumes, including an upgradient well, had characteristics indicating small amounts of synthetic ClO4- mixed with one or both of the natural source types. Hydrogeologic data indicate synthetic ClO4- in the upgradient well may be from a source other than the identified plume sources, but it is not known whether this source might also be present in other wells at low concentrations. The stable isotope and 36Cl data provided relatively unambiguous discrimination of synthetic and Atacama ClO4- sources in most wells having relatively high concentrations, providing regional perspective on anthropogenic ClO4- contamination in the Rialto-Colton and Chino subbasins. Where indigenous natural ClO4- was indicated as a substantial component, total ClO4- concentrations were low and concentrations of anthropogenic components were near background levels.","language":"English","publisher":"U.S. Department of Defense","usgsCitation":"Hatzinger, P.B., Bohlke, J., Izbicki, J.A., Teague, N.F., and Sturchio, N.C., 2015, Evaluation of perchlorate sources in the Rialto-Colton and Chino California subbasins using chlorine and oxygen isotope ratio analysis, x, 79 p.","productDescription":"x, 79 p.","ipdsId":"IP-062818","costCenters":[{"id":436,"text":"National Research Program - Eastern Branch","active":true,"usgs":true}],"links":[{"id":340220,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"},{"id":340161,"type":{"id":15,"text":"Index Page"},"url":"https://serdp-estcp.org/Program-Areas/Environmental-Restoration/Contaminated-Groundwater/Emerging-Issues/ER-200942"}],"country":"United States","state":"California","county":"San Bernadino","geographicExtents":"{\n  \"type\": \"FeatureCollection\",\n  \"features\": [\n    {\n      \"type\": \"Feature\",\n      \"properties\": {},\n      \"geometry\": {\n        \"type\": \"Polygon\",\n        \"coordinates\": [\n          [\n            [\n              -118.17993164062499,\n              33.815666308702774\n            ],\n            [\n              -114.993896484375,\n              33.815666308702774\n            ],\n            [\n              -115.037841796875,\n              34.95799531086792\n            ],\n            [\n              -118.16894531249999,\n              34.97600151317588\n            ],\n            [\n              -118.17993164062499,\n              33.815666308702774\n            ]\n          ]\n        ]\n      }\n    }\n  ]\n}","publishingServiceCenter":{"id":9,"text":"Reston PSC"},"noUsgsAuthors":false,"publicationStatus":"PW","scienceBaseUri":"59006063e4b0e85db3a5dddd","contributors":{"authors":[{"text":"Hatzinger, Paul B.","contributorId":149376,"corporation":false,"usgs":false,"family":"Hatzinger","given":"Paul","email":"","middleInitial":"B.","affiliations":[{"id":17721,"text":"Shaw Environmental, Princeton, NJ","active":true,"usgs":false}],"preferred":false,"id":692534,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Bohlke, J.K. 0000-0001-5693-6455 jkbohlke@usgs.gov","orcid":"https://orcid.org/0000-0001-5693-6455","contributorId":191103,"corporation":false,"usgs":true,"family":"Bohlke","given":"J.K.","email":"jkbohlke@usgs.gov","affiliations":[{"id":436,"text":"National Research Program - Eastern Branch","active":true,"usgs":true},{"id":37277,"text":"WMA - Earth System Processes Division","active":true,"usgs":true},{"id":36183,"text":"Hydro-Ecological Interactions Branch","active":true,"usgs":true}],"preferred":true,"id":692533,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Izbicki, John A. 0000-0003-0816-4408 jaizbick@usgs.gov","orcid":"https://orcid.org/0000-0003-0816-4408","contributorId":152474,"corporation":false,"usgs":true,"family":"Izbicki","given":"John","email":"jaizbick@usgs.gov","middleInitial":"A.","affiliations":[{"id":154,"text":"California Water Science Center","active":true,"usgs":true},{"id":493,"text":"Office of Ground Water","active":true,"usgs":true}],"preferred":true,"id":692536,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Teague, Nicholas F. 0000-0001-5289-1210 nteague@usgs.gov","orcid":"https://orcid.org/0000-0001-5289-1210","contributorId":2145,"corporation":false,"usgs":true,"family":"Teague","given":"Nicholas","email":"nteague@usgs.gov","middleInitial":"F.","affiliations":[{"id":154,"text":"California Water Science Center","active":true,"usgs":true},{"id":493,"text":"Office of Ground Water","active":true,"usgs":true}],"preferred":true,"id":692537,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Sturchio, Neil C.","contributorId":149375,"corporation":false,"usgs":false,"family":"Sturchio","given":"Neil","email":"","middleInitial":"C.","affiliations":[{"id":15289,"text":"University of Illinois, Ven Te Chow Hydrosystems Laboratory","active":true,"usgs":false}],"preferred":false,"id":692535,"contributorType":{"id":1,"text":"Authors"},"rank":5}]}}
,{"id":70184484,"text":"70184484 - 2015 - Demographic rates and population viability of black bears in Louisiana","interactions":[],"lastModifiedDate":"2017-03-10T11:39:39","indexId":"70184484","displayToPublicDate":"2017-03-10T00:00:00","publicationYear":"2015","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":3773,"text":"Wildlife Monographs","active":true,"publicationSubtype":{"id":10}},"title":"Demographic rates and population viability of black bears in Louisiana","docAbstract":"<p><span>The Louisiana black bear (</span><i>Ursus americanus luteolus</i><span>) was reduced to a few small, fragmented, and isolated subpopulations in the Lower Mississippi Alluvial Valley by the mid-twentieth century resulting from loss and fragmentation of habitat. In 1992, the United States Fish and Wildlife Service (USFWS) granted the Louisiana black bear threatened status under the United States Endangered Species Act of 1973. Since that time, a recovery plan was developed, a reintroduced population was established, and habitat recovery has occurred. The Recovery Plan states that a minimum of 2 populations must be viable (i.e., persistence probabilities over 100 years &gt;0.95), 1 in the Tensas River Basin and 1 in the Atchafalaya River Basin. Consequently, our objectives were to 1) estimate demographic rates of Louisiana black bear subpopulations, 2) develop data-driven stochastic population projection models, and 3) determine how different projection model assumptions affect population trajectories and predictions about long-term persistence. Our overall goal was to assess long-term persistence of the bear subpopulations in Louisiana, individually and as a whole. We collected data using varying combinations of non-invasive DNA sampling, live capture, winter den visits, and radio monitoring from 2002 to 2012 in the 4 areas currently supporting breeding subpopulations in Louisiana: Tensas River Basin (TRB), Upper Atchafalaya River Basin (UARB), Lower Atchafalaya River Basin (LARB), and a recently reintroduced population at the Three Rivers Complex (TRC). From 2002 to 2012, we radio monitored fates of 86 adult females within the TRB and 43 in the TRC. Mean estimates of annual adult survival for the TRB and TRC were 0.997 and 0.990, respectively, when unknown fates were assumed alive and 0.970 and 0.926 when unknown fates were assumed dead. From 2003 to 2013, we observed 130 cub litters from 74 females in the TRB, and 74 cub litters from 45 females in the TRC. During the same period, we observed 43 yearling litters for 33 females in the TRB and 21 yearling litters for 19 females in the TRC. The estimated number of cubs and number of yearlings produced per breeding adult female was 0.47 and 0.20, respectively, in the TRB and 0.32 and 0.18 in the TRC. On the basis of matrix projection models, asymptotic growth rates ranged from 1.053 to 1.078 for the TRB and from 1.005 to 1.062 for the TRC, depending on how we treated unresolved fates of adult females. Persistence probabilities estimated from stochastic population models based on telemetry data ranged from 0.997 to 0.998 for the TRC subpopulation depending on model assumptions and were &gt;0.999 for the TRB regardless of model assumptions. We extracted DNA from hair collected at baited, barbed-wire enclosures in the TRB, UARB, and LARB to determine individual identities for capture-mark-recapture (CMR) analysis. We used those detection histories to estimate apparent survival (</span><i>φ</i><span>), per-capita recruitment (</span><i>f</i><span>), abundance (</span><i>N</i><span>), realized growth rate (</span><i>λ</i><span>), and long-term viability, based on Bayesian hierarchical modeling methods that allowed estimation of temporal process variance and parameter uncertainty. Based on 23,312 hair samples, annual </span><i>N</i><span> for females in the TRB ranged from 133 to 164 during 2006–2012, depending on year and how detection heterogeneity was modeled. Geometric mean of </span><i>λ</i><span> ranged from 0.996 to 1.002. In the UARB, we collected 11,643 hair samples from 2007 to 2012, from which estimates of </span><i>N</i><span> for females ranged from 23 to 43 during the study period, depending on detection heterogeneity model. The geometric mean of </span><i>λ</i><span> ranged from 1.038 to 1.059. Estimated </span><i>N</i><span> for females in LARB ranged from 69 to 96, and annual </span><i>λ</i><span> ranged from 0.80 to 1.11 based on 3,698 hair samples collected during 2010–2012, also depending on year and heterogeneity model. Probabilities of persistence over 100 years for the TRC and TRB based on stochastic matrix projection models that used vital rate estimates from telemetry data were &gt;0.95 for all scenarios. Probability of persistence at the TRB and the UARB based on projection models that used vital rate estimates from CMR analyses ranged from 0.928 to 0.954 and from 0.906 to 0.959, respectively, depending on model assumptions. Data from the LARB were insufficient for a viability assessment. Thus, individual persistence probabilities for TRB and UARB did not meet the strict definition of viability (i.e., &gt;0.95) under some model assumptions. However, the joint probability of bears persisting either in the TRB or UARB was &gt;0.993 assuming individual population dynamics were independent and was &gt;0.958 assuming dynamics were perfectly correlated. Furthermore, including the TRC increased the joint probability of bears persisting somewhere in the TRB, UARB, or TRC to &gt;0.999 based on the most pessimistic individual persistence estimates from those subpopulations. Therefore, if the intent of specifying that 2 subpopulations should be viable was to ensure the persistence of Louisiana black bears somewhere within its historical range, then the viability threshold was met. © 2016 The Wildlife Society.</span></p>","language":"English","publisher":"The Wildlife Society","doi":"10.1002/wmon.1018","collaboration":"U.S. Fish and Wildlife Service; Louisiana Department of Wildlife and Fisheries","usgsCitation":"Laufenberg, J.S., Clark, J.D., Hooker, M.J., Lowe, C.L., O’Connell-Goode, K.C., Troxler, J.C., Davidson, M., Chamberlain, M.J., and Chandler, R.B., 2015, Demographic rates and population viability of black bears in Louisiana: Wildlife Monographs, v. 194, no. 1, p. 1-37, https://doi.org/10.1002/wmon.1018.","productDescription":"37 p.","startPage":"1","endPage":"37","ipdsId":"IP-069827","costCenters":[{"id":481,"text":"Northern Rocky Mountain Science 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J.","contributorId":187784,"corporation":false,"usgs":false,"family":"Hooker","given":"Michael","email":"","middleInitial":"J.","affiliations":[],"preferred":false,"id":681671,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Lowe, Carrie L.","contributorId":187785,"corporation":false,"usgs":false,"family":"Lowe","given":"Carrie","email":"","middleInitial":"L.","affiliations":[],"preferred":false,"id":681672,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"O’Connell-Goode, Kaitlin C.","contributorId":187786,"corporation":false,"usgs":false,"family":"O’Connell-Goode","given":"Kaitlin","email":"","middleInitial":"C.","affiliations":[],"preferred":false,"id":681673,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"Troxler, Jesse C.","contributorId":187787,"corporation":false,"usgs":false,"family":"Troxler","given":"Jesse","email":"","middleInitial":"C.","affiliations":[],"preferred":false,"id":681674,"contributorType":{"id":1,"text":"Authors"},"rank":6},{"text":"Davidson, Maria M.","contributorId":187788,"corporation":false,"usgs":false,"family":"Davidson","given":"Maria M.","affiliations":[],"preferred":false,"id":681675,"contributorType":{"id":1,"text":"Authors"},"rank":7},{"text":"Chamberlain, Michael J.","contributorId":179350,"corporation":false,"usgs":false,"family":"Chamberlain","given":"Michael","email":"","middleInitial":"J.","affiliations":[],"preferred":false,"id":681676,"contributorType":{"id":1,"text":"Authors"},"rank":8},{"text":"Chandler, Richard B. 0000-0003-4930-2790 rchandler@usgs.gov","orcid":"https://orcid.org/0000-0003-4930-2790","contributorId":187789,"corporation":false,"usgs":false,"family":"Chandler","given":"Richard","email":"rchandler@usgs.gov","middleInitial":"B.","affiliations":[{"id":13267,"text":"Warnell School of Forestry and Natural Resources, University of Georgia","active":true,"usgs":false}],"preferred":false,"id":681677,"contributorType":{"id":1,"text":"Authors"},"rank":9}]}}
,{"id":70182748,"text":"70182748 - 2015 - Dating base flow in streams using dissolved gases and diurnal temperature changes","interactions":[],"lastModifiedDate":"2017-02-28T09:17:56","indexId":"70182748","displayToPublicDate":"2017-02-28T00:00:00","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":"Dating base flow in streams using dissolved gases and diurnal temperature changes","docAbstract":"<p><span>A method is presented for using dissolved CFCs or SF</span><sub>6</sub><span> to estimate the apparent age of stream base flow by indirectly estimating the mean concentration of the tracer in the inflowing groundwater. The mean value is estimated simultaneously with the mean residence times of the gas and water in the stream by sampling the stream for one or both age tracers, along with dissolved nitrogen and argon at a single location over a period of approximately 12–14 h. The data are fitted to an equation representing the temporal in-stream gas exchange as it responds to the diurnal temperature fluctuation. The efficacy of the method is demonstrated by collecting and analyzing samples at six different stream locations across parts of northern Virginia, USA. The studied streams drain watersheds with areas of between 2 and 122 km</span><sup>2</sup><span> during periods when the diurnal stream temperature ranged between 2 and 5°C. The method has the advantage of estimating the mean groundwater residence time of discharge from the watershed to the stream without the need for the collection of groundwater infiltrating to streambeds or local groundwater sampled from shallow observation wells near the stream.</span></p>","language":"English","publisher":"Wiley","doi":"10.1002/2014WR016796","usgsCitation":"Sanford, W.E., Casile, G.C., and Haase, K.B., 2015, Dating base flow in streams using dissolved gases and diurnal temperature changes: Water Resources Research, v. 51, no. 12, p. 9790-9803, https://doi.org/10.1002/2014WR016796.","productDescription":"14 p.","startPage":"9790","endPage":"9803","ipdsId":"IP-067414","costCenters":[{"id":436,"text":"National Research Program - Eastern Branch","active":true,"usgs":true}],"links":[{"id":471487,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.1002/2014wr016796","text":"Publisher Index Page"},{"id":336311,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","state":"Virginia","volume":"51","issue":"12","publishingServiceCenter":{"id":9,"text":"Reston PSC"},"noUsgsAuthors":false,"publicationDate":"2015-12-19","publicationStatus":"PW","scienceBaseUri":"58b69a42e4b01ccd54ff3fa8","contributors":{"authors":[{"text":"Sanford, Ward E. 0000-0002-6624-0280 wsanford@usgs.gov","orcid":"https://orcid.org/0000-0002-6624-0280","contributorId":2268,"corporation":false,"usgs":true,"family":"Sanford","given":"Ward","email":"wsanford@usgs.gov","middleInitial":"E.","affiliations":[{"id":436,"text":"National Research Program - Eastern Branch","active":true,"usgs":true},{"id":37778,"text":"WMA - Integrated Modeling and Prediction Division","active":true,"usgs":true}],"preferred":true,"id":673562,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Casile, Gerolamo C. jcasile@usgs.gov","contributorId":4007,"corporation":false,"usgs":true,"family":"Casile","given":"Gerolamo","email":"jcasile@usgs.gov","middleInitial":"C.","affiliations":[{"id":436,"text":"National Research Program - Eastern Branch","active":true,"usgs":true}],"preferred":true,"id":673563,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Haase, Karl B. 0000-0002-6897-6494 khaase@usgs.gov","orcid":"https://orcid.org/0000-0002-6897-6494","contributorId":3405,"corporation":false,"usgs":true,"family":"Haase","given":"Karl","email":"khaase@usgs.gov","middleInitial":"B.","affiliations":[{"id":37277,"text":"WMA - Earth System Processes Division","active":true,"usgs":true},{"id":436,"text":"National Research Program - Eastern Branch","active":true,"usgs":true}],"preferred":true,"id":673564,"contributorType":{"id":1,"text":"Authors"},"rank":3}]}}
,{"id":70178118,"text":"70178118 - 2015 - Application-ready expedited MODIS data for operational land surface monitoring of vegetation condition","interactions":[],"lastModifiedDate":"2018-02-21T14:08:18","indexId":"70178118","displayToPublicDate":"2016-11-03T00:00:00","publicationYear":"2015","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":3250,"text":"Remote Sensing","active":true,"publicationSubtype":{"id":10}},"title":"Application-ready expedited MODIS data for operational land surface monitoring of vegetation condition","docAbstract":"<p><span>Monitoring systems benefit from high temporal frequency image data collected from the Moderate Resolution Imaging Spectroradiometer (MODIS) system. Because of near-daily global coverage, MODIS data are beneficial to applications that require timely information about vegetation condition related to drought, flooding, or fire danger. Rapid satellite data streams in operational applications have clear benefits for monitoring vegetation, especially when information can be delivered as fast as changing surface conditions. An “expedited” processing system called “eMODIS” operated by the U.S. Geological Survey provides rapid MODIS surface reflectance data to operational applications in less than 24 h offering tailored, consistently-processed information products that complement standard MODIS products. We assessed eMODIS quality and consistency by comparing to standard MODIS data. Only land data with known high quality were analyzed in a central U.S. study area. When compared to standard MODIS (MOD/MYD09Q1), the eMODIS Normalized Difference Vegetation Index (NDVI) maintained a strong, significant relationship to standard MODIS NDVI, whether from morning (Terra) or afternoon (Aqua) orbits. The Aqua eMODIS data were more prone to noise than the Terra data, likely due to differences in the internal cloud mask used in MOD/MYD09Q1 or compositing rules. Post-processing temporal smoothing decreased noise in eMODIS data.</span></p>","language":"English","publisher":"Molecular Diversity Preservation International","publisherLocation":"Basel, Switzerland","doi":"10.3390/rs71215825","usgsCitation":"Brown, J.F., Howard, D., Wylie, B.K., Friesz, A.M., Ji, L., and Gacke, C., 2015, Application-ready expedited MODIS data for operational land surface monitoring of vegetation condition: Remote Sensing, v. 7, no. 12, p. 16226-16240, https://doi.org/10.3390/rs71215825.","productDescription":"15 p.","startPage":"16226","endPage":"16240","ipdsId":"IP-066456","costCenters":[{"id":222,"text":"Earth Resources Observation and Science (EROS) Center","active":true,"usgs":true}],"links":[{"id":471489,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.3390/rs71215825","text":"Publisher Index Page"},{"id":330712,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"volume":"7","issue":"12","publishingServiceCenter":{"id":4,"text":"Rolla PSC"},"noUsgsAuthors":false,"publicationDate":"2015-12-03","publicationStatus":"PW","scienceBaseUri":"581c4cc4e4b09688d6e90fd9","contributors":{"authors":[{"text":"Brown, Jesslyn F. 0000-0002-9976-1998 jfbrown@usgs.gov","orcid":"https://orcid.org/0000-0002-9976-1998","contributorId":176609,"corporation":false,"usgs":true,"family":"Brown","given":"Jesslyn","email":"jfbrown@usgs.gov","middleInitial":"F.","affiliations":[{"id":222,"text":"Earth Resources Observation and Science (EROS) Center","active":true,"usgs":true},{"id":223,"text":"Earth Resources Observation and Science (EROS) Center (Geography)","active":false,"usgs":true}],"preferred":true,"id":652851,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Howard, Daniel M. 0000-0002-7563-7538 dhoward@usgs.gov","orcid":"https://orcid.org/0000-0002-7563-7538","contributorId":139585,"corporation":false,"usgs":true,"family":"Howard","given":"Daniel M.","email":"dhoward@usgs.gov","affiliations":[{"id":223,"text":"Earth Resources Observation and Science (EROS) Center (Geography)","active":false,"usgs":true}],"preferred":false,"id":652852,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Wylie, Bruce K. 0000-0002-7374-1083 wylie@usgs.gov","orcid":"https://orcid.org/0000-0002-7374-1083","contributorId":750,"corporation":false,"usgs":true,"family":"Wylie","given":"Bruce","email":"wylie@usgs.gov","middleInitial":"K.","affiliations":[{"id":223,"text":"Earth Resources Observation and Science (EROS) Center (Geography)","active":false,"usgs":true},{"id":222,"text":"Earth Resources Observation and Science (EROS) Center","active":true,"usgs":true}],"preferred":true,"id":652853,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Friesz, Aaron M. 0000-0003-4096-3824 afriesz@usgs.gov","orcid":"https://orcid.org/0000-0003-4096-3824","contributorId":5943,"corporation":false,"usgs":true,"family":"Friesz","given":"Aaron","email":"afriesz@usgs.gov","middleInitial":"M.","affiliations":[{"id":223,"text":"Earth Resources Observation and Science (EROS) Center (Geography)","active":false,"usgs":true}],"preferred":true,"id":652995,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Ji, Lei 0000-0002-6133-1036 lji@usgs.gov","orcid":"https://orcid.org/0000-0002-6133-1036","contributorId":139587,"corporation":false,"usgs":true,"family":"Ji","given":"Lei","email":"lji@usgs.gov","affiliations":[{"id":222,"text":"Earth Resources Observation and Science (EROS) Center","active":true,"usgs":true},{"id":223,"text":"Earth Resources Observation and Science (EROS) Center (Geography)","active":false,"usgs":true}],"preferred":true,"id":652996,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"Gacke, Carolyn 0000-0001-6922-2814 cgacke@usgs.gov","orcid":"https://orcid.org/0000-0001-6922-2814","contributorId":152416,"corporation":false,"usgs":true,"family":"Gacke","given":"Carolyn","email":"cgacke@usgs.gov","affiliations":[{"id":223,"text":"Earth Resources Observation and Science (EROS) Center (Geography)","active":false,"usgs":true}],"preferred":true,"id":652997,"contributorType":{"id":1,"text":"Authors"},"rank":6}]}}
,{"id":70178044,"text":"70178044 - 2015 - Assessing shoreline exposure and oyster habitat suitability maximizes potential success for sustainable shoreline protection using restored oyster reefs","interactions":[],"lastModifiedDate":"2016-11-01T12:50:03","indexId":"70178044","displayToPublicDate":"2016-11-01T00:00:00","publicationYear":"2015","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":3840,"text":"PeerJ","active":true,"publicationSubtype":{"id":10}},"title":"Assessing shoreline exposure and oyster habitat suitability maximizes potential success for sustainable shoreline protection using restored oyster reefs","docAbstract":"<p><span>Oyster reefs provide valuable ecosystem services that contribute to coastal resilience. Unfortunately, many reefs have been degraded or removed completely, and there are increased efforts to restore oysters in many coastal areas. In particular, much attention has recently been given to the restoration of shellfish reefs along eroding shorelines to reduce erosion. Such fringing reef approaches, however, often lack empirical data to identify locations where reefs are most effective in reducing marsh erosion, or fully take into account habitat suitability. Using monitoring data from 5 separate fringing reef projects across coastal Louisiana, we quantify shoreline exposure (fetch + wind direction + wind speed) and reef impacts on shoreline retreat. Our results indicate that fringing oyster reefs have a higher impact on shoreline retreat at higher exposure shorelines. At higher exposures, fringing reefs reduced marsh edge erosion an average of 1.0 m y</span><sup>−1</sup><span>. Using these data, we identify ranges of shoreline exposure values where oyster reefs are most effective at reducing marsh edge erosion and apply this knowledge to a case study within one Louisiana estuary. In Breton Sound estuary, we calculate shoreline exposure at 500 random points and then overlay a habitat suitability index for oysters. This method and the resulting visualization show areas most likely to support sustainable oyster populations as well as significantly reduce shoreline erosion. Our results demonstrate how site selection criteria, which include shoreline exposure and habitat suitability, are critical to ensuring greater positive impacts and longevity of oyster reef restoration projects.</span></p>","language":"English","publisher":"PeerJ","doi":"10.7717/peerj.1317","usgsCitation":"LaPeyre, M.K., Serra, K., Joyner, T.A., and Humphries, A.T., 2015, Assessing shoreline exposure and oyster habitat suitability maximizes potential success for sustainable shoreline protection using restored oyster reefs: PeerJ, v. 3, e1317; 17 p., https://doi.org/10.7717/peerj.1317.","productDescription":"e1317; 17 p.","ipdsId":"IP-067597","costCenters":[{"id":198,"text":"Coop Res Unit Atlanta","active":true,"usgs":true}],"links":[{"id":471490,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.7717/peerj.1317","text":"Publisher Index Page"},{"id":330604,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","state":"Louisiana","geographicExtents":"{\n  \"type\": \"FeatureCollection\",\n  \"features\": [\n    {\n      \"type\": \"Feature\",\n      \"properties\": {},\n      \"geometry\": {\n        \"type\": \"Polygon\",\n        \"coordinates\": [\n          [\n            [\n              -92.3291015625,\n              28.8975881579445\n            ],\n            [\n              -92.3291015625,\n              30.424992973925598\n            ],\n            [\n              -88.714599609375,\n              30.424992973925598\n            ],\n            [\n              -88.714599609375,\n              28.8975881579445\n            ],\n            [\n              -92.3291015625,\n              28.8975881579445\n            ]\n          ]\n        ]\n      }\n    }\n  ]\n}","volume":"3","publishingServiceCenter":{"id":8,"text":"Raleigh PSC"},"noUsgsAuthors":false,"publicationDate":"2015-10-06","publicationStatus":"PW","scienceBaseUri":"5819a9c4e4b0bb36a4c9102f","contributors":{"authors":[{"text":"LaPeyre, Megan K. 0000-0001-9936-2252 mlapeyre@usgs.gov","orcid":"https://orcid.org/0000-0001-9936-2252","contributorId":585,"corporation":false,"usgs":true,"family":"LaPeyre","given":"Megan","email":"mlapeyre@usgs.gov","middleInitial":"K.","affiliations":[{"id":198,"text":"Coop Res Unit Atlanta","active":true,"usgs":true},{"id":17705,"text":"Wetland and Aquatic Research Center","active":true,"usgs":true}],"preferred":true,"id":652589,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Serra, Kayla","contributorId":176509,"corporation":false,"usgs":false,"family":"Serra","given":"Kayla","email":"","affiliations":[],"preferred":false,"id":652618,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Joyner, T. Andrew","contributorId":176510,"corporation":false,"usgs":false,"family":"Joyner","given":"T.","email":"","middleInitial":"Andrew","affiliations":[],"preferred":false,"id":652619,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Humphries, Austin T.","contributorId":15943,"corporation":false,"usgs":true,"family":"Humphries","given":"Austin","email":"","middleInitial":"T.","affiliations":[],"preferred":false,"id":652620,"contributorType":{"id":1,"text":"Authors"},"rank":4}]}}
,{"id":70176619,"text":"70176619 - 2015 - Estimation of historic flows and sediment loads to San Francisco Bay,1849–2011","interactions":[],"lastModifiedDate":"2017-10-30T11:26:17","indexId":"70176619","displayToPublicDate":"2016-09-01T00:00:00","publicationYear":"2015","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":2342,"text":"Journal of Hydrology","active":true,"publicationSubtype":{"id":10}},"title":"Estimation of historic flows and sediment loads to San Francisco Bay,1849–2011","docAbstract":"River flow and sediment transport in estuaries influence morphological development over decadal and century time scales, but hydrological and sedimentological records are typically too short to adequately characterize long-term trends. In this study, we recover archival records and apply a rating curve approach to develop the first instrumental estimates of daily delta inflow and sediment loads to San Francisco Bay (1849–1929). The total sediment load is constrained using sedimentation/erosion estimated from bathymetric survey data to produce continuous daily sediment transport estimates from 1849 to 1955, the time period prior to sediment load measurements. We estimate that ∼55% (45–75%) of the ∼1500 ± 400 million tons (Mt) of sediment delivered to the estuary between 1849 and 2011 was the result of anthropogenic alteration in the watershed that increased sediment supply. Also, the seasonal timing of sediment flux events has shifted because significant spring-melt floods have decreased, causing estimated springtime transport (April 1st to June 30th) to decrease from ∼25% to ∼15% of the annual total. By contrast, wintertime sediment loads (December 1st to March 31st) have increased from ∼70% to ∼80%. A ∼35% reduction of annual flow since the 19th century along with decreased sediment supply has resulted in a ∼50% reduction in annual sediment delivery. The methods developed in this study can be applied to other systems for which unanalyzed historic data exist.","language":"English","publisher":"Elsevier","publisherLocation":"Amsterdam, Netherlands","doi":"10.1016/j.jhydrol.2015.08.043","usgsCitation":"Moftakhari, H., Jay, D., Talke, S., and Schoellhamer, D., 2015, Estimation of historic flows and sediment loads to San Francisco Bay,1849–2011: Journal of Hydrology, v. 529, no. 3, p. 1247-1261, https://doi.org/10.1016/j.jhydrol.2015.08.043.","productDescription":"5 p.","startPage":"1247","endPage":"1261","numberOfPages":"5","ipdsId":"IP-061977","costCenters":[{"id":154,"text":"California Water Science Center","active":true,"usgs":true},{"id":552,"text":"San Francisco Bay-Delta","active":false,"usgs":true}],"links":[{"id":471491,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.1016/j.jhydrol.2015.08.043","text":"Publisher Index Page"},{"id":347672,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","state":"California","city":"San Francisco","otherGeospatial":"San Francisco Bay area","geographicExtents":"{\n  \"type\": \"FeatureCollection\",\n  \"features\": [\n    {\n      \"type\": \"Feature\",\n      \"properties\": {},\n      \"geometry\": {\n        \"type\": \"Polygon\",\n        \"coordinates\": [\n          [\n            [\n              -122.70629882812499,\n              38.06539235133249\n            ],\n            [\n              -122.56072998046875,\n              38.25543637637947\n            ],\n            [\n              -122.40142822265625,\n              38.28346905497185\n            ],\n            [\n              -121.78344726562499,\n              38.35027253825765\n            ],\n            [\n              -121.37420654296875,\n              38.31795595794451\n            ],\n            [\n              -121.22589111328126,\n              37.93553306183642\n            ],\n            [\n              -121.25335693359374,\n              37.572882155556194\n            ],\n            [\n              -121.44012451171874,\n              37.40289194122376\n            ],\n            [\n              -121.761474609375,\n              37.21720611325497\n            ],\n            [\n              -122.20642089843749,\n              37.17126017626408\n            ],\n            [\n              -122.47283935546874,\n              37.199706196161735\n            ],\n            [\n              -122.70629882812499,\n              38.06539235133249\n            ]\n          ]\n        ]\n      }\n    }\n  ]\n}","volume":"529","issue":"3","publishingServiceCenter":{"id":1,"text":"Sacramento PSC"},"noUsgsAuthors":false,"publicationStatus":"PW","scienceBaseUri":"57f7c657e4b0bc0bec09c90f","contributors":{"authors":[{"text":"Moftakhari, H.R.","contributorId":174830,"corporation":false,"usgs":false,"family":"Moftakhari","given":"H.R.","email":"","affiliations":[{"id":24698,"text":"PSU","active":true,"usgs":false}],"preferred":false,"id":649393,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Jay, D.A.","contributorId":174832,"corporation":false,"usgs":false,"family":"Jay","given":"D.A.","email":"","affiliations":[{"id":24698,"text":"PSU","active":true,"usgs":false}],"preferred":false,"id":649395,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Talke, S.A.","contributorId":174831,"corporation":false,"usgs":false,"family":"Talke","given":"S.A.","email":"","affiliations":[{"id":24698,"text":"PSU","active":true,"usgs":false}],"preferred":false,"id":649394,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Schoellhamer, David H. 0000-0001-9488-7340 dschoell@usgs.gov","orcid":"https://orcid.org/0000-0001-9488-7340","contributorId":631,"corporation":false,"usgs":true,"family":"Schoellhamer","given":"David H.","email":"dschoell@usgs.gov","affiliations":[{"id":154,"text":"California Water Science Center","active":true,"usgs":true}],"preferred":true,"id":649392,"contributorType":{"id":1,"text":"Authors"},"rank":4}]}}
,{"id":70176171,"text":"70176171 - 2015 - Resolving bathymetry from airborne gravity along Greenland fjords","interactions":[],"lastModifiedDate":"2016-08-31T10:08:15","indexId":"70176171","displayToPublicDate":"2016-08-31T11:00:00","publicationYear":"2015","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":2314,"text":"Journal of Geophysical Research B: Solid Earth","active":true,"publicationSubtype":{"id":10}},"title":"Resolving bathymetry from airborne gravity along Greenland fjords","docAbstract":"<p><span>Recent glacier mass loss in Greenland has been attributed to encroaching warming waters, but knowledge of fjord bathymetry is required to investigate this mechanism. The bathymetry in many Greenland fjords is unmapped and difficult to measure. From 2010 to 2012, National Aeronautics and Space Administration's Operation IceBridge collected a unique set of airborne gravity, magnetic, radar, and lidar data along the major outlet glaciers and fjords in Greenland. We applied a consistent technique using the IceBridge gravity data to create 90 bathymetric profiles along 54 Greenland fjords. We also used this technique to recover subice topography where warm or crevassed ice prevents the radar system from imaging the bed. Here we discuss our methodology, basic assumptions and error analysis. We present the new bathymetry data and discuss observations in six major regions of Greenland covered by IceBridge. The gravity models provide a total of 1950 line kilometers of bathymetry, 875 line kilometers of subice topography, and 12 new grounding line depths.</span></p>","language":"English","publisher":"AGU Publications","doi":"10.1002/2015JB012129","usgsCitation":"Boghosian, A., Tinto, K., Cochran, J.R., Porter, D., Elieff, S., Burton, B.L., and Bell, R.E., 2015, Resolving bathymetry from airborne gravity along Greenland fjords: Journal of Geophysical Research B: Solid Earth, v. 120, no. 12, p. 8516-8533, https://doi.org/10.1002/2015JB012129.","productDescription":"18 p.","startPage":"8516","endPage":"8533","ipdsId":"IP-065813","costCenters":[{"id":211,"text":"Crustal Geophysics and Geochemistry Science Center","active":true,"usgs":true}],"links":[{"id":471492,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.1002/2015jb012129","text":"Publisher Index Page"},{"id":328092,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"volume":"120","issue":"12","publishingServiceCenter":{"id":2,"text":"Denver PSC"},"noUsgsAuthors":false,"publicationDate":"2015-12-02","publicationStatus":"PW","scienceBaseUri":"57c7f1ade4b0f2f0cebf11b1","contributors":{"authors":[{"text":"Boghosian, Alexandra","contributorId":174154,"corporation":false,"usgs":false,"family":"Boghosian","given":"Alexandra","email":"","affiliations":[{"id":27369,"text":"Lamont-Doherty Earth Observatory at Columbia University","active":true,"usgs":false}],"preferred":false,"id":647558,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Tinto, Kirsty","contributorId":174155,"corporation":false,"usgs":false,"family":"Tinto","given":"Kirsty","email":"","affiliations":[{"id":27369,"text":"Lamont-Doherty Earth Observatory at Columbia University","active":true,"usgs":false}],"preferred":false,"id":647559,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Cochran, James R.","contributorId":127762,"corporation":false,"usgs":false,"family":"Cochran","given":"James","email":"","middleInitial":"R.","affiliations":[{"id":7135,"text":"Lamont Doherty Earth Observatory, Columbia University, Palisades, NY","active":true,"usgs":false}],"preferred":false,"id":647560,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Porter, David","contributorId":174156,"corporation":false,"usgs":false,"family":"Porter","given":"David","email":"","affiliations":[{"id":27369,"text":"Lamont-Doherty Earth Observatory at Columbia University","active":true,"usgs":false}],"preferred":false,"id":647561,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Elieff, Stefan","contributorId":174157,"corporation":false,"usgs":false,"family":"Elieff","given":"Stefan","email":"","affiliations":[{"id":27370,"text":"Sander Geophysics Ltd.","active":true,"usgs":false}],"preferred":false,"id":647562,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"Burton, Bethany L. 0000-0001-5011-7862 blburton@usgs.gov","orcid":"https://orcid.org/0000-0001-5011-7862","contributorId":138925,"corporation":false,"usgs":true,"family":"Burton","given":"Bethany","email":"blburton@usgs.gov","middleInitial":"L.","affiliations":[{"id":211,"text":"Crustal Geophysics and Geochemistry Science Center","active":true,"usgs":true}],"preferred":true,"id":647557,"contributorType":{"id":1,"text":"Authors"},"rank":6},{"text":"Bell, Robin E.","contributorId":26902,"corporation":false,"usgs":true,"family":"Bell","given":"Robin","email":"","middleInitial":"E.","affiliations":[],"preferred":false,"id":647563,"contributorType":{"id":1,"text":"Authors"},"rank":7}]}}
,{"id":70156343,"text":"70156343 - 2015 - A geochemical and geophysical assessment of coastal groundwater discharge at select sites in Maui and O’ahu, Hawai’i","interactions":[],"lastModifiedDate":"2025-05-14T13:51:14.144202","indexId":"70156343","displayToPublicDate":"2016-08-29T00:00:00","publicationYear":"2015","noYear":false,"publicationType":{"id":5,"text":"Book chapter"},"publicationSubtype":{"id":24,"text":"Book Chapter"},"chapter":"3","title":"A geochemical and geophysical assessment of coastal groundwater discharge at select sites in Maui and O’ahu, Hawai’i","docAbstract":"This chapter summarizes fieldwork conducted to derive new estimates of coastal groundwater discharge and associated nutrient loadings at select coastal sites in Hawai’i, USA. Locations for this work were typically identified based on pronounced, recent ecosystem degradation that may at least partially be attributable to sustained coastal groundwater discharge. Our suite of tools used to evaluate groundwater discharge included select U/Th series radionuclides, a broad spectrum of geochemical analytes, multi-channel electrical resistivity, and in situ oceanographic observations.\nBased on the submarine groundwater discharge tracer 222Rn, coastal groundwater discharge rates ranged from about 22–50 cm per day at Kahekili, a site in the Ka’anapali region north of Lahaina in west Maui, while at Black Point in Maunalua Bay along southern O’ahu, coastal groundwater discharge rates ranged up to 700 cm per day, although the mean discharge rate at this site was 60 cm per day. The water chemistry of the discharging groundwater can be dramatically different than ambient seawater at both coastal sites. For example, at Kahekili the average concentrations of dissolved inorganic nitrogen (DIN), dissolved silicate (DSi) and total dissolved phosphorus (TDP) were roughly 188-, 36-, and 106-times higher in the discharging groundwater relative to ambient seawater, respectively. Such data extend our basic understanding of the physical controls on coastal groundwater discharge and provide an estimate of the magnitude and physical forcings of submarine groundwater discharge and associated trace metal and nutrient loads conveyed by this submarine route.","largerWorkType":{"id":4,"text":"Book"},"largerWorkTitle":"Coastal research library: Groundwater in the coastal zones of Asia-Pacific","largerWorkSubtype":{"id":15,"text":"Monograph"},"language":"English","publisher":"Springer Netherlands","publisherLocation":"Amsterdam, Netherlands","doi":"10.1007/978-94-007-5648-9_3","usgsCitation":"Swarzenski, P.W., Dulaiova, H., Dalier, M., Glenn, C., C.G. Smith, and Storlazzi, C.D., 2015, A geochemical and geophysical assessment of coastal groundwater discharge at select sites in Maui and O’ahu, Hawai’i, chap. 3 <i>of</i> Coastal research library: Groundwater in the coastal zones of Asia-Pacific, v. 7, p. 27-46, https://doi.org/10.1007/978-94-007-5648-9_3.","productDescription":"20 p.","startPage":"27","endPage":"46","numberOfPages":"20","ipdsId":"IP-042463","costCenters":[{"id":520,"text":"Pacific Coastal and Marine Science Center","active":true,"usgs":true}],"links":[{"id":328825,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","state":"Hawaii","city":"Maui, O'ahu","geographicExtents":"{\n  \"type\": \"FeatureCollection\",\n  \"features\": [\n    {\n      \"type\": \"Feature\",\n      \"properties\": {},\n      \"geometry\": {\n        \"coordinates\": [\n          [\n            [\n              -157.6600949791486,\n              21.269361506834343\n            ],\n            [\n              -157.6949675456284,\n              21.46700583601971\n            ],\n            [\n              -157.95789125401657,\n              21.74385192825359\n            ],\n            [\n              -158.3221708427871,\n              21.58782147768129\n            ],\n            [\n              -158.13211569548483,\n              21.222121254169394\n            ],\n            [\n              -157.7646573391795,\n              21.204403402071037\n            ],\n            [\n              -157.6600949791486,\n              21.269361506834343\n            ]\n          ]\n        ],\n        \"type\": \"Polygon\"\n      }\n    },\n    {\n      \"type\": \"Feature\",\n      \"properties\": {},\n      \"geometry\": {\n        \"coordinates\": [\n          [\n            [\n              -155.95195776228937,\n              20.794932328731093\n            ],\n            [\n              -156.25865776036534,\n              20.982229050344785\n            ],\n            [\n              -156.46430525468506,\n              20.940601340556853\n            ],\n            [\n              -156.61162773826533,\n              21.068594890662055\n            ],\n            [\n              -156.71957486742892,\n              20.974229683794633\n            ],\n            [\n              -156.67673870506238,\n              20.806144286691563\n            ],\n            [\n              -156.52600355322568,\n              20.748526500353066\n            ],\n            [\n              -156.4883531420766,\n              20.777357824937326\n            ],\n            [\n              -156.44542517216485,\n              20.56730442959163\n            ],\n            [\n              -156.2912476914077,\n              20.552893949175314\n            ],\n            [\n              -156.01192146513426,\n              20.631457216375253\n            ],\n            [\n              -155.95195776228937,\n              20.794932328731093\n            ]\n          ]\n        ],\n        \"type\": \"Polygon\"\n      }\n    }\n  ]\n}","volume":"7","publishingServiceCenter":{"id":14,"text":"Menlo Park PSC"},"noUsgsAuthors":false,"publicationDate":"2013-04-20","publicationStatus":"PW","scienceBaseUri":"57f7ee23e4b0bc0bec09e8a3","contributors":{"authors":[{"text":"Swarzenski, Peter W. 0000-0003-0116-0578 pswarzen@usgs.gov","orcid":"https://orcid.org/0000-0003-0116-0578","contributorId":1070,"corporation":false,"usgs":true,"family":"Swarzenski","given":"Peter","email":"pswarzen@usgs.gov","middleInitial":"W.","affiliations":[{"id":520,"text":"Pacific Coastal and Marine Science Center","active":true,"usgs":true}],"preferred":true,"id":568786,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Dulaiova, H.","contributorId":35507,"corporation":false,"usgs":true,"family":"Dulaiova","given":"H.","affiliations":[],"preferred":false,"id":936994,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Dalier, M.L.","contributorId":146723,"corporation":false,"usgs":false,"family":"Dalier","given":"M.L.","email":"","affiliations":[{"id":590,"text":"U.S. Army Corps of Engineers","active":false,"usgs":false}],"preferred":false,"id":568789,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Glenn, C.R.","contributorId":146722,"corporation":false,"usgs":false,"family":"Glenn","given":"C.R.","email":"","affiliations":[{"id":590,"text":"U.S. Army Corps of Engineers","active":false,"usgs":false}],"preferred":false,"id":568788,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"C.G. 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