The U.S. Geological Survey (USGS), in cooperation with the U.S. Department of Defense Task Force for Business and Stability Operations, prepared databases for mineral-resource target areas in Afghanistan. The purpose of the databases is to (1) provide useful data to ground-survey crews for use in performing detailed assessments of the areas and (2) provide useful information to private investors who are considering investment in a particular area for development of its natural resources. The set of satellite-image mosaics provided in this Data Series (DS) is one such database. Although airborne digital color-infrared imagery was acquired for parts of Afghanistan in 2006, the image data have radiometric variations that preclude their use in creating a consistent image mosaic for geologic analysis. Consequently, image mosaics were created using ALOS (Advanced Land Observation Satellite; renamed Daichi) satellite images, whose radiometry has been well determined (Saunier, 2007a,b). This part of the DS consists of the locally enhanced ALOS image mosaics for the Farah mineral district, which has spectral reflectance anomalies indicative of copper, zinc, lead, silver, and gold deposits.
\n\nALOS was launched on January 24, 2006, and provides multispectral images from the AVNIR (Advanced Visible and Near-Infrared Radiometer) sensor in blue (420-500 nanometer, nm), green (520-600 nm), red (610-690 nm), and near-infrared (760-890 nm) wavelength bands with an 8-bit dynamic range and a 10-meter (m) ground resolution. The satellite also provides a panchromatic band image from the PRISM (Panchromatic Remote-sensing Instrument for Stereo Mapping) sensor (520-770 nm) with the same dynamic range but a 2.5-m ground resolution. The image products in this DS incorporate copyrighted data provided by the Japan Aerospace Exploration Agency ((c)JAXA, 2007, 2008, 2010), but the image processing has altered the original pixel structure and all image values of the JAXA ALOS data, such that original image values cannot be recreated from this DS. As such, the DS products match JAXA criteria for value added products, which are not copyrighted, according to the ALOS end-user license agreement.
\n\nThe selection criteria for the satellite imagery used in our mosaics were images having (1) the highest solar-elevation angles (near summer solstice) and (2) the least cloud, cloud-shadow, and snow cover. The multispectral and panchromatic data were orthorectified with ALOS satellite ephemeris data, a process which is not as accurate as orthorectification using digital elevation models (DEMs); however, the ALOS processing center did not have a precise DEM. As a result, the multispectral and panchromatic image pairs were generally not well registered to the surface and not coregistered well enough to perform resolution enhancement on the multispectral data. Therefore, it was necessary to (1) register the 10-m AVNIR multispectral imagery to a well-controlled Landsat image base, (2) mosaic the individual multispectral images into a single image of the entire area of interest, (3) register each panchromatic image to the registered multispectral image base, and (4) mosaic the individual panchromatic images into a single image of the entire area of interest. The two image-registration steps were facilitated using an automated control-point algorithm developed by the USGS that allows image coregistration to within one picture element. Before rectification, the multispectral and panchromatic images were converted to radiance values and then to relative-reflectance values using the methods described in Davis (2006). Mosaicking the multispectral or panchromatic images started with the image with the highest sun-elevation angle and the least atmospheric scattering, which was treated as the standard image. The band-reflectance values of all other multispectral or panchromatic images within the area were sequentially adjusted to that of the standard image by determining band-reflectance correspondence between overlapping images using linear least-squares analysis. The resolution of the multispectral image mosaic was then increased to that of the panchromatic image mosaic using the SPARKLE logic, which is described in Davis (2006). Each of the four-band images within the resolution-enhanced image mosaic was individually subjected to a local-area histogram stretch algorithm (described in Davis, 2007), which stretches each band's picture element based on the digital values of all picture elements within a 500-m radius. The final databases, which are provided in this DS, are three-band, color-composite images of the local-area-enhanced, natural-color data (the blue, green, and red wavelength bands) and color-infrared data (the green, red, and near-infrared wavelength bands).
\n\nAll image data were initially projected and maintained in Universal Transverse Mercator (UTM) map projection using the target area's local zone (41 for Farah) and the WGS84 datum. The final image mosaics were subdivided into four overlapping tiles or quadrants because of the large size of the target area. The four image tiles (or quadrants) for the Farah area are provided as embedded geotiff images, which can be read and used by most geographic information system (GIS) and image-processing software. The tiff world files (tfw) are provided, even though they are generally not needed for most software to read an embedded geotiff image. Within the Farah study area, five subareas were designated for detailed field investigations (that is, the FarahA through FarahE subareas); these subareas were extracted from the area’s image mosaic and are provided as separate embedded geotiff images.
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\n\nALOS was launched on January 24, 2006, and provides multispectral images from the AVNIR (Advanced Visible and Near-Infrared Radiometer) sensor in blue (420-500 nanometer, nm), green (520-600 nm), red (610-690 nm), and near-infrared (760-890 nm) wavelength bands with an 8-bit dynamic range and a 10-meter (m) ground resolution. The satellite also provides a panchromatic band image from the PRISM (Panchromatic Remote-sensing Instrument for Stereo Mapping) sensor (520-770 nm) with the same dynamic range but a 2.5-m ground resolution. The image products in this DS incorporate copyrighted data provided by the Japan Aerospace Exploration Agency ((c)JAXA, 2008, 2009), but the image processing has altered the original pixel structure and all image values of the JAXA ALOS data, such that original image values cannot be recreated from this DS. As such, the DS products match JAXA criteria for value added products, which are not copyrighted, according to the ALOS end-user license agreement.
\n\nThe selection criteria for the satellite imagery used in our mosaics were images having (1) the highest solar-elevation angles (near summer solstice) and (2) the least cloud, cloud-shadow, and snow cover. The multispectral and panchromatic data were orthorectified with ALOS satellite ephemeris data, a process which is not as accurate as orthorectification using digital elevation models (DEMs); however, the ALOS processing center did not have a precise DEM. As a result, the multispectral and panchromatic image pairs were generally not well registered to the surface and not coregistered well enough to perform resolution enhancement on the multispectral data. Therefore, it was necessary to (1) register the 10-m AVNIR multispectral imagery to a well-controlled Landsat image base, (2) mosaic the individual multispectral images into a single image of the entire area of interest, (3) register each panchromatic image to the registered multispectral image base, and (4) mosaic the individual panchromatic images into a single image of the entire area of interest. The two image-registration steps were facilitated using an automated control-point algorithm developed by the USGS that allows image coregistration to within one picture element. Before rectification, the multispectral and panchromatic images were converted to radiance values and then to relative-reflectance values using the methods described in Davis (2006). Mosaicking the multispectral or panchromatic images started with the image with the highest sun-elevation angle and the least atmospheric scattering, which was treated as the standard image. The band-reflectance values of all other multispectral or panchromatic images within the area were sequentially adjusted to that of the standard image by determining band-reflectance correspondence between overlapping images using linear least-squares analysis. The resolution of the multispectral image mosaic was then increased to that of the panchromatic image mosaic using the SPARKLE logic, which is described in Davis (2006). Each of the four-band images within the resolution-enhanced image mosaic was individually subjected to a local-area histogram stretch algorithm (described in Davis, 2007), which stretches each band's picture element based on the digital values of all picture elements within a 500-m radius. The final databases, which are provided in this DS, are three-band, color-composite images of the local-area-enhanced, natural-color data (the blue, green, and red wavelength bands) and color-infrared data (the green, red, and near-infrared wavelength bands).
\n\nAll image data were initially projected and maintained in Universal Transverse Mercator (UTM) map projection using the target area's local zone (42 for Ghazni2) and the WGS84 datum. The images for the Ghazni2 area are provided as embedded geotiff images, which can be read and used by most geographic information system (GIS) and image-processing software. The tiff world files (tfw) are provided, even though they are generally not needed for most software to read an embedded geotiff image.
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The purpose of the databases is to (1) provide useful data to ground-survey crews for use in performing detailed assessments of the areas and (2) provide useful information to private investors who are considering investment in a particular area for development of its natural resources. The set of satellite-image mosaics provided in this Data Series (DS) is one such database. Although airborne digital color-infrared imagery was acquired for parts of Afghanistan in 2006, the image data have radiometric variations that preclude their use in creating a consistent image mosaic for geologic analysis. Consequently, image mosaics were created using ALOS (Advanced Land Observation Satellite; renamed Daichi) satellite images, whose radiometry has been well determined (Saunier, 2007a,b). This part of the DS consists of the locally enhanced ALOS image mosaics for the Ghazni1 mineral district, which has spectral reflectance anomalies indicative of clay, aluminum, gold, silver, mercury, and sulfur deposits.
\nALOS was launched on January 24, 2006, and provides multispectral images from the AVNIR (Advanced Visible and Near-Infrared Radiometer) sensor in blue (420-500 nanometer, nm), green (520-600 nm), red (610-690 nm), and near-infrared (760-890 nm) wavelength bands with an 8-bit dynamic range and a 10-meter (m) ground resolution. The satellite also provides a panchromatic band image from the PRISM (Panchromatic Remote-sensing Instrument for Stereo Mapping) sensor (520-770 nm) with the same dynamic range but a 2.5-m ground resolution. The image products in this DS incorporate copyrighted data provided by the Japan Aerospace Exploration Agency ((c)JAXA, 2008, 2009), but the image processing has altered the original pixel structure and all image values of the JAXA ALOS data, such that original image values cannot be recreated from this DS. As such, the DS products match JAXA criteria for value added products, which are not copyrighted, according to the ALOS end-user license agreement.
\nThe selection criteria for the satellite imagery used in our mosaics were images having (1) the highest solar-elevation angles (near summer solstice) and (2) the least cloud, cloud-shadow, and snow cover. The multispectral and panchromatic data were orthorectified with ALOS satellite ephemeris data, a process which is not as accurate as orthorectification using digital elevation models (DEMs); however, the ALOS processing center did not have a precise DEM. As a result, the multispectral and panchromatic image pairs were generally not well registered to the surface and not coregistered well enough to perform resolution enhancement on the multispectral data. Therefore, it was necessary to (1) register the 10-m AVNIR multispectral imagery to a well-controlled Landsat image base, (2) mosaic the individual multispectral images into a single image of the entire area of interest, (3) register each panchromatic image to the registered multispectral image base, and (4) mosaic the individual panchromatic images into a single image of the entire area of interest. The two image-registration steps were facilitated using an automated control-point algorithm developed by the USGS that allows image coregistration to within one picture element. Before rectification, the multispectral and panchromatic images were converted to radiance values and then to relative-reflectance values using the methods described in Davis (2006). Mosaicking the multispectral or panchromatic images started with the image with the highest sun-elevation angle and the least atmospheric scattering, which was treated as the standard image. The band-reflectance values of all other multispectral or panchromatic images within the area were sequentially adjusted to that of the standard image by determining band-reflectance correspondence between overlapping images using linear least-squares analysis. The resolution of the multispectral image mosaic was then increased to that of the panchromatic image mosaic using the SPARKLE logic, which is described in Davis (2006). Each of the four-band images within the resolution-enhanced image mosaic was individually subjected to a local-area histogram stretch algorithm (described in Davis, 2007), which stretches each band's picture element based on the digital values of all picture elements within a 500-m radius. The final databases, which are provided in this DS, are three-band, color-composite images of the local-area-enhanced, natural-color data (the blue, green, and red wavelength bands) and color-infrared data (the green, red, and near-infrared wavelength bands).
\nAll image data were initially projected and maintained in Universal Transverse Mercator (UTM) map projection using the target area's local zone (42 for Ghazni1) and the WGS84 datum. The images for the Ghazni1 area are provided as embedded geotiff images, which can be read and used by most geographic information system (GIS) and image-processing software. The tiff world files (tfw) are provided, even though they are generally not needed for most software to read an embedded geotiff image.
","largerWorkType":{"id":18,"text":"Report"},"largerWorkTitle":"Local-area-enhanced, high-resolution natural-color and color-infrared satellite-image mosaics of mineral districts in Afghanistan (Data Series 709)","largerWorkSubtype":{"id":5,"text":"USGS Numbered Series"},"language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/ds709DD","collaboration":"Prepared in cooperation with the U.S. Department of Defense Task Force for Business and Stability Operations and the Afghanistan Geological Survey","usgsCitation":"Davis, P.A., 2014, Local-area-enhanced, 2.5-meter resolution natural-color and color-infrared satellite-image mosaics of the Ghazni1 mineral district in Afghanistan: U.S. Geological Survey Data Series 709, Readme Text; Index Maps; Image Files; Metadata Files; Shapefiles, https://doi.org/10.3133/ds709DD.","productDescription":"Readme Text; Index Maps; Image Files; Metadata Files; Shapefiles","onlineOnly":"Y","ipdsId":"IP-054392","costCenters":[{"id":387,"text":"Mineral Resources Program","active":true,"usgs":true}],"links":[{"id":286957,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/ds709dd.jpg"},{"id":286960,"rank":11,"type":{"id":14,"text":"Image"},"url":"https://pubs.usgs.gov/ds/709/dd/image_files/image_files.html","text":"Image Files","linkFileType":{"id":5,"text":"html"}},{"id":286958,"rank":2,"type":{"id":20,"text":"Read Me"},"url":"https://pubs.usgs.gov/ds/709/dd/1_readme.doc"},{"id":286961,"rank":1,"type":{"id":16,"text":"Metadata"},"url":"https://pubs.usgs.gov/ds/709/dd/metadata/metadata.html","linkFileType":{"id":5,"text":"html"}},{"id":286963,"rank":1,"type":{"id":15,"text":"Index Page"},"url":"https://pubs.usgs.gov/ds/709/dd/"},{"id":286959,"type":{"id":17,"text":"Plate"},"url":"https://pubs.usgs.gov/ds/709/dd/index_maps/index_maps.html","text":"Index Maps","linkFileType":{"id":5,"text":"html"}},{"id":286962,"type":{"id":7,"text":"Companion Files"},"url":"https://pubs.usgs.gov/ds/709/dd/shapefiles/shapefiles.html"}],"scale":"7000000","country":"Afghanistan","otherGeospatial":"Ghazni1 Mineral District","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"coordinates\": [\n [\n [\n 68.87252342306118,\n 33.400794951645565\n ],\n [\n 68.5382513185329,\n 33.71681758841511\n ],\n [\n 68.30727405623549,\n 33.72224478024398\n ],\n [\n 68.08601269164748,\n 34.1414227946527\n ],\n [\n 67.2638552357352,\n 34.07256923954678\n ],\n [\n 67.23635530353977,\n 33.52157172943353\n ],\n [\n 66.91878464275106,\n 33.262493443111566\n ],\n [\n 67.42957551466375,\n 32.86586167317263\n ],\n [\n 67.7125922483599,\n 32.561357509486314\n ],\n [\n 68.87252342306118,\n 33.400794951645565\n ]\n ]\n ],\n \"type\": \"Polygon\"\n }\n }\n ]\n}","noUsgsAuthors":false,"publicationStatus":"PW","scienceBaseUri":"536b47d2e4b0a51a87c4b12a","contributors":{"authors":[{"text":"Davis, Philip A. pdavis@usgs.gov","contributorId":692,"corporation":false,"usgs":true,"family":"Davis","given":"Philip","email":"pdavis@usgs.gov","middleInitial":"A.","affiliations":[],"preferred":true,"id":492733,"contributorType":{"id":1,"text":"Authors"},"rank":1}]}} ,{"id":70099230,"text":"fs20143017 - 2014 - Water resources of Orleans Parish, Louisiana","interactions":[],"lastModifiedDate":"2014-05-07T11:55:36","indexId":"fs20143017","displayToPublicDate":"2014-05-07T11:49:00","publicationYear":"2014","noYear":false,"publicationType":{"id":18,"text":"Report"},"publicationSubtype":{"id":5,"text":"USGS Numbered Series"},"seriesTitle":{"id":313,"text":"Fact Sheet","code":"FS","onlineIssn":"2327-6932","printIssn":"2327-6916","active":true,"publicationSubtype":{"id":5}},"seriesNumber":"2014-3017","title":"Water resources of Orleans Parish, Louisiana","docAbstract":"Information concerning the availability, use, and quality of water in Orleans Parish, Louisiana, is critical for proper water-supply management. The purpose of this fact sheet is to present information that can be used by water managers, parish residents, and others for stewardship of this vital resource. Information on the availability, past and current use, use trends, and water quality from groundwater and surface-water sources in the parish is presented. Previously published reports and data stored in the U.S. Geological Survey’s National Water Information System (http://waterdata.usgs.gov/nwis) are the primary sources of the information presented here.","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/fs20143017","issn":"2327-6932","collaboration":"Prepared in cooperation with the Louisiana Department of Transportation and Development","usgsCitation":"Prakken, L., White, V.E., and Lovelace, J.K., 2014, Water resources of Orleans Parish, Louisiana: U.S. Geological Survey Fact Sheet 2014-3017, 6 p., https://doi.org/10.3133/fs20143017.","productDescription":"6 p.","numberOfPages":"6","onlineOnly":"N","ipdsId":"IP-052184","costCenters":[{"id":369,"text":"Louisiana Water Science Center","active":true,"usgs":true}],"links":[{"id":286956,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/fs20143017.jpg"},{"id":286954,"type":{"id":15,"text":"Index Page"},"url":"https://pubs.usgs.gov/fs/2014/3017/"},{"id":286955,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/fs/2014/3017/pdf/fs2014-3017.pdf"}],"projection":"Albers Equal-Area Conic projection","datum":"North American Datum of 1983","country":"United States","state":"Louisiana","otherGeospatial":"Orleans Parish","geographicExtents":"{ \"type\": \"FeatureCollection\", \"features\": [ { \"type\": \"Feature\", \"properties\": {}, \"geometry\": { \"type\": \"Polygon\", \"coordinates\": [ [ [ -90.166667,30.0 ], [ -90.166667,30.166667 ], [ -89.666667,30.166667 ], [ -89.666667,30.0 ], [ -90.166667,30.0 ] ] ] } } ] }","noUsgsAuthors":false,"publicationStatus":"PW","scienceBaseUri":"536b47d4e4b0a51a87c4b139","contributors":{"authors":[{"text":"Prakken, Lawrence B.","contributorId":73978,"corporation":false,"usgs":true,"family":"Prakken","given":"Lawrence B.","affiliations":[],"preferred":false,"id":491878,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"White, Vincent E. 0000-0002-1660-0102 vwhite@usgs.gov","orcid":"https://orcid.org/0000-0002-1660-0102","contributorId":5388,"corporation":false,"usgs":true,"family":"White","given":"Vincent","email":"vwhite@usgs.gov","middleInitial":"E.","affiliations":[{"id":369,"text":"Louisiana Water Science Center","active":true,"usgs":true},{"id":24708,"text":"Lower Mississippi-Gulf Water Science Center","active":true,"usgs":true}],"preferred":true,"id":491877,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Lovelace, John K. 0000-0002-8532-2599 jlovelac@usgs.gov","orcid":"https://orcid.org/0000-0002-8532-2599","contributorId":999,"corporation":false,"usgs":true,"family":"Lovelace","given":"John","email":"jlovelac@usgs.gov","middleInitial":"K.","affiliations":[{"id":369,"text":"Louisiana Water Science Center","active":true,"usgs":true},{"id":24708,"text":"Lower Mississippi-Gulf Water Science Center","active":true,"usgs":true}],"preferred":true,"id":491876,"contributorType":{"id":1,"text":"Authors"},"rank":3}]}} ,{"id":70099231,"text":"fs20143016 - 2014 - Water resources of Terrebonne Parish, Louisiana","interactions":[],"lastModifiedDate":"2014-05-07T11:54:19","indexId":"fs20143016","displayToPublicDate":"2014-05-07T11:48:07","publicationYear":"2014","noYear":false,"publicationType":{"id":18,"text":"Report"},"publicationSubtype":{"id":5,"text":"USGS Numbered Series"},"seriesTitle":{"id":313,"text":"Fact Sheet","code":"FS","onlineIssn":"2327-6932","printIssn":"2327-6916","active":true,"publicationSubtype":{"id":5}},"seriesNumber":"2014-3016","title":"Water resources of Terrebonne Parish, Louisiana","docAbstract":"Information concerning the availability, use, and quality of water in Terrebonne Parish, Louisiana, is critical for proper water-supply management. The purpose of this fact sheet is to present information that can be used by water managers, parish residents, and others for stewardship of this vital resource. Information on the availability, past and current use, use trends,and water quality from groundwater and surface-water sources in the parish is presented. Previously published reports and data stored in the U.S. Geological Survey’s National Water Information System http://waterdata.usgs.gov/nwis are the primary sources of the information presented here.","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/fs20143016","collaboration":"Prepared in cooperation with the Louisiana Department of Transportation and Development","usgsCitation":"Prakken, L., Lovelace, J.K., and White, V.E., 2014, Water resources of Terrebonne Parish, Louisiana: U.S. Geological Survey Fact Sheet 2014-3016, 6 p., https://doi.org/10.3133/fs20143016.","productDescription":"6 p.","ipdsId":"IP-052292","costCenters":[{"id":369,"text":"Louisiana Water Science Center","active":true,"usgs":true}],"links":[{"id":286953,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/fs20143016.jpg"},{"id":286951,"type":{"id":15,"text":"Index Page"},"url":"https://pubs.usgs.gov/fs/2014/3016/"},{"id":286952,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/fs/2014/3016/pdf/fs2014-3016.pdf"}],"country":"United States","state":"Louisiana","city":"Terrebonne Parish","geographicExtents":"{ \"type\": \"FeatureCollection\", \"features\": [ { \"type\": \"Feature\", \"properties\": {}, \"geometry\": { \"type\": \"Polygon\", \"coordinates\": [ [ [ -91.353,29.041 ], [ -91.353,29.778 ], [ -90.377,29.778 ], [ -90.377,29.041 ], [ -91.353,29.041 ] ] ] } } ] }","noUsgsAuthors":false,"publicationStatus":"PW","scienceBaseUri":"536b47d6e4b0a51a87c4b13e","contributors":{"authors":[{"text":"Prakken, Lawrence B.","contributorId":73978,"corporation":false,"usgs":true,"family":"Prakken","given":"Lawrence B.","affiliations":[],"preferred":false,"id":491881,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Lovelace, John K. 0000-0002-8532-2599 jlovelac@usgs.gov","orcid":"https://orcid.org/0000-0002-8532-2599","contributorId":999,"corporation":false,"usgs":true,"family":"Lovelace","given":"John","email":"jlovelac@usgs.gov","middleInitial":"K.","affiliations":[{"id":24708,"text":"Lower Mississippi-Gulf Water Science Center","active":true,"usgs":true},{"id":369,"text":"Louisiana Water Science Center","active":true,"usgs":true}],"preferred":true,"id":491879,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"White, Vincent E. 0000-0002-1660-0102 vwhite@usgs.gov","orcid":"https://orcid.org/0000-0002-1660-0102","contributorId":5388,"corporation":false,"usgs":true,"family":"White","given":"Vincent","email":"vwhite@usgs.gov","middleInitial":"E.","affiliations":[{"id":369,"text":"Louisiana Water Science Center","active":true,"usgs":true},{"id":24708,"text":"Lower Mississippi-Gulf Water Science Center","active":true,"usgs":true}],"preferred":true,"id":491880,"contributorType":{"id":1,"text":"Authors"},"rank":3}]}} ,{"id":70100415,"text":"ofr20141031 - 2014 - Nutrient budgets, marsh inundation under sea-level rise scenarios, and sediment chronologies for the Bass Harbor Marsh estuary at Acadia National Park","interactions":[],"lastModifiedDate":"2014-05-07T09:15:10","indexId":"ofr20141031","displayToPublicDate":"2014-05-07T09:04:00","publicationYear":"2014","noYear":false,"publicationType":{"id":18,"text":"Report"},"publicationSubtype":{"id":5,"text":"USGS Numbered Series"},"seriesTitle":{"id":330,"text":"Open-File Report","code":"OFR","onlineIssn":"2331-1258","printIssn":"0196-1497","active":true,"publicationSubtype":{"id":5}},"seriesNumber":"2014-1031","title":"Nutrient budgets, marsh inundation under sea-level rise scenarios, and sediment chronologies for the Bass Harbor Marsh estuary at Acadia National Park","docAbstract":"Eutrophication in the Bass Harbor Marsh estuary on Mount Desert Island, Maine, is an ongoing problem manifested by recurring annual blooms of green macroalgae species, principally Enteromorpha prolifera and Enteromorpha flexuosa, blooms that appear in the spring and summer. These blooms are unsightly and impair the otherwise natural beauty of this estuarine ecosystem. The macroalgae also threaten the integrity of the estuary and its inherent functions. The U.S. Geological Survey and Acadia National Park have collaborated for several years to better understand the factors related to this eutrophication problem with support from the U.S. Geological Survey and National Park Service Water Quality Assessment and Monitoring Program. The current study involved the collection of hydrologic and water-quality data necessary to investigate the relative contribution of nutrients from oceanic and terrestrial sources during summer 2011 and summer 2012. This report provides data on nutrient budgets for this estuary, sedimentation chronologies for the estuary and fringing marsh, and estuary bathymetry. The report also includes data, based on aerial photographs, on historical changes from 1944 to 2010 in estuary surface area and data, based on surface-elevation details, on changes in marsh area that may accompany sea-level rise.
\nThe LOADEST regression model was used to calculate nutrient loads into and out of the estuary during summer 2011 and summer 2012. During these summers, tidal inputs of ammonium to the estuary were more than seven times greater than the combined inputs in watershed runoff and precipitation. In 2011 tidal inputs of nitrate were about four times greater than watershed plus precipitation inputs, and in 2012 tidal inputs were only slightly larger than watershed plus precipitation inputs. In 2011, tidal inputs of total organic nitrogen were larger than watershed input by a factor of 1.6. By contrast, in 2012 inputs of total organic nitrogen in watershed runoff were much larger than tidal inputs, by a factor of 3.6. During the 2011 and 2012 summers, tidal inputs of total dissolved phosphorus to the estuary were more than seven times greater than inputs in watershed runoff. It is evident that during the summer tidal inputs of inorganic nitrogen and total dissolved phosphorus to the estuary exceed inputs from watershed runoff and precipitation.
\nProjected sea-level rise associated with ongoing climate warming will affect the area of land within the Bass Harbor Marsh estuary watershed that is inundated during conditions of mean higher high water and during mean lower low water and hence will affect the vegetation and marsh area. Given 100-centimeter sea-level rise, the inundated area would increase from 25.7 hectares at the current condition to 77.5 hectares at mean higher high water and from 21.6 hectares to 26.7 hectares at mean lower low water. Given 50-centimeter sea-level rise, flooding of the entire marsh surface, which currently occurs only under the highest spring tides, would occur on average every other day.
\nRadioisotope analysis of sediment cores from the estuary indicates that the sediment accumulation rate increased markedly from 1930 to 1980 and was relatively constant (0.4 to 0.5 centimeter per year) from 1980 to 2009. Similarly, from 1980 to 2009 there was a consistently high mass accumulation rate of 0.09 to 0.11 grams per square centimeter per year. The sediment accretion rates determined for the five cores collected from the marsh surface (east and west sides of the estuary) in 2011 show generally higher rates of 0.20 to 0.29 centimeter per year for the period between 1980 to 2011 than for the period before 1980, when sediment accretion rates were 0.06 to 0.25 centimeter per year.
\nThe data in this report provide resource managers at Acadia National Park with a baseline that can be used to evaluate future conditions within the estuary. Climate change, sea-level rise, and land-use change within the estuary’s watershed may influence nutrient dynamics, sedimentation, and eutrophication, and these potential effects can be studied in relation to the baseline data provided in this report. The Route 102 Bridge in Tremont, Maine is constructed over a sill that controls the amount of tidal flushing by restricting the duration of the flood tide, and structural changes to the bridge could alter tidal nutrient inputs and residence times for watershed and ocean-derived nutrients in the estuary. Ongoing sea-level rise is likely increasing ocean-derived nutrients and their residence time in the estuary on the one hand and decreasing the residence time of watershed-derived nutrients on the other.
","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/ofr20141031","collaboration":"Prepared in cooperation with the National Park Service","usgsCitation":"Huntington, T.G., Culbertson, C.W., Fuller, C.C., Glibert, P., and Sturtevant, L., 2014, Nutrient budgets, marsh inundation under sea-level rise scenarios, and sediment chronologies for the Bass Harbor Marsh estuary at Acadia National Park: U.S. Geological Survey Open-File Report 2014-1031, xii, 108 p., https://doi.org/10.3133/ofr20141031.","productDescription":"xii, 108 p.","numberOfPages":"20","onlineOnly":"Y","ipdsId":"IP-049630","costCenters":[{"id":466,"text":"New England Water Science Center","active":true,"usgs":true}],"links":[{"id":286945,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/ofr20141031.jpg"},{"id":285165,"type":{"id":15,"text":"Index Page"},"url":"https://pubs.usgs.gov/of/2014/1031"},{"id":286944,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/of/2014/1031/pdf/ofr2014-1031.pdf"}],"scale":"24000","country":"United States","state":"Maine","otherGeospatial":"Acadia National Park;Bass Harbor Marsh;Mount Desert Island","geographicExtents":"{ \"type\": \"FeatureCollection\", \"features\": [ { \"type\": \"Feature\", \"properties\": {}, \"geometry\": { \"type\": \"Polygon\", \"coordinates\": [ [ [ -68.375,44.25 ], [ -68.375,44.291667 ], [ -68.333333,44.291667 ], [ -68.333333,44.25 ], [ -68.375,44.25 ] ] ] } } ] }","noUsgsAuthors":false,"publicationStatus":"PW","scienceBaseUri":"536b47d3e4b0a51a87c4b134","contributors":{"authors":[{"text":"Huntington, Thomas G. 0000-0002-9427-3530 thunting@usgs.gov","orcid":"https://orcid.org/0000-0002-9427-3530","contributorId":1884,"corporation":false,"usgs":true,"family":"Huntington","given":"Thomas","email":"thunting@usgs.gov","middleInitial":"G.","affiliations":[{"id":371,"text":"Maine Water Science Center","active":true,"usgs":true},{"id":466,"text":"New England Water Science Center","active":true,"usgs":true}],"preferred":true,"id":492191,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Culbertson, Charles W. cculbert@usgs.gov","contributorId":1607,"corporation":false,"usgs":true,"family":"Culbertson","given":"Charles","email":"cculbert@usgs.gov","middleInitial":"W.","affiliations":[{"id":371,"text":"Maine Water Science Center","active":true,"usgs":true}],"preferred":true,"id":492189,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Fuller, Christopher C. 0000-0002-2354-8074 ccfuller@usgs.gov","orcid":"https://orcid.org/0000-0002-2354-8074","contributorId":1831,"corporation":false,"usgs":true,"family":"Fuller","given":"Christopher","email":"ccfuller@usgs.gov","middleInitial":"C.","affiliations":[{"id":374,"text":"Maryland Water Science Center","active":true,"usgs":true},{"id":36183,"text":"Hydro-Ecological Interactions Branch","active":true,"usgs":true},{"id":438,"text":"National Research Program - Western Branch","active":true,"usgs":true},{"id":37277,"text":"WMA - Earth System Processes Division","active":true,"usgs":true}],"preferred":true,"id":492190,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Glibert, Patricia","contributorId":94593,"corporation":false,"usgs":true,"family":"Glibert","given":"Patricia","email":"","affiliations":[],"preferred":false,"id":492192,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Sturtevant, Luke","contributorId":99893,"corporation":false,"usgs":true,"family":"Sturtevant","given":"Luke","affiliations":[],"preferred":false,"id":492193,"contributorType":{"id":1,"text":"Authors"},"rank":5}]}} ,{"id":70103927,"text":"70103927 - 2014 - Juvenile anadromous salmonid production in upper Columbia River side channels with different levels of hydrological connection","interactions":[],"lastModifiedDate":"2016-04-26T10:20:32","indexId":"70103927","displayToPublicDate":"2014-05-07T08:27:00","publicationYear":"2014","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":"Juvenile anadromous salmonid production in upper Columbia River side channels with different levels of hydrological connection","docAbstract":"We examined the contribution of three types of side channels based on their hydrologic connectivity (seasonally disconnected, partially connected, and connected) to production of juvenile anadromous salmonids. Juvenile steelhead Oncorhynchus mykiss and Chinook Salmon O. tshawytscha were found in all three of these side channel types and in each year of the study. Upon connection with the main stem at high flows, the seasonally disconnected side channels experienced an emptying out of the previous year's fish while filling with young-of-year fish during the 2- to 4-month period of hydrologic connection. There were no differences between the densities of juvenile steelhead and Chinook Salmon and the rate of smolts produced among the three types of side channels. Recently reintroduced Coho Salmon O. kisutch had sporadic presence and abundance in partially and connected side channels, but the smolt production rate was over two times that of steelhead and Chinook Salmon in seasonally disconnected side channels. Within seasonally disconnected side channels, young-of-year salmonids in deep pools (≥100 cm) had greater survival than those in shallow pools (<100 cm). Densities of juvenile steelhead in all side channel types were similar to those in tributaries and were higher than in main-stem lateral margins. Juvenile Chinook Salmon densities were higher in side channels than in both tributary and main-stem lateral margins. Our results suggest that improving quality of pool habitat within seasonally disconnected side channels can result in improved survival for juvenile anadromous salmonids during the period of disconnection. Habitat improvement in these seasonally disconnected side channels should be recognized as a worthy restoration strategy, especially when full connectivity of side channels may not be a feasible target (e.g., through lack of water availability) or when full connectivity may present too high a risk (e.g., flooding, stream capture, bank destabilization).
","language":"English","publisher":"American Fisheries Society","doi":"10.1080/00028487.2014.880740","usgsCitation":"Martens, K.D., and Connolly, P., 2014, Juvenile anadromous salmonid production in upper Columbia River side channels with different levels of hydrological connection: Transactions of the American Fisheries Society, v. 3, no. 143, p. 757-767, https://doi.org/10.1080/00028487.2014.880740.","productDescription":"11 p.","startPage":"757","endPage":"767","onlineOnly":"N","additionalOnlineFiles":"N","ipdsId":"IP-051250","costCenters":[{"id":654,"text":"Western Fisheries Research Center","active":true,"usgs":true}],"links":[{"id":287015,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","state":"Washington","otherGeospatial":"Columbia River","geographicExtents":"{ \"type\": \"FeatureCollection\", \"features\": [ { \"type\": \"Feature\", \"properties\": {}, \"geometry\": { \"type\": \"Polygon\", \"coordinates\": [ [ [ -124.78,45.55 ], [ -124.78,49.00 ], [ -116.91,49.00 ], [ -116.91,45.55 ], [ -124.78,45.55 ] ] ] } } ] }","volume":"3","issue":"143","noUsgsAuthors":false,"publicationDate":"2014-04-30","publicationStatus":"PW","scienceBaseUri":"5377178fe4b02eab8669ed95","contributors":{"authors":[{"text":"Martens, Kyle D.","contributorId":12740,"corporation":false,"usgs":true,"family":"Martens","given":"Kyle","email":"","middleInitial":"D.","affiliations":[],"preferred":false,"id":493538,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Connolly, Patrick J. 0000-0001-7365-7618 pconnolly@usgs.gov","orcid":"https://orcid.org/0000-0001-7365-7618","contributorId":2920,"corporation":false,"usgs":true,"family":"Connolly","given":"Patrick J.","email":"pconnolly@usgs.gov","affiliations":[{"id":654,"text":"Western Fisheries Research Center","active":true,"usgs":true}],"preferred":true,"id":493537,"contributorType":{"id":1,"text":"Authors"},"rank":2}]}} ,{"id":70103569,"text":"70103569 - 2014 - Lack of sex-biased dispersal promotes fine-scale genetic structure in alpine ungulates","interactions":[],"lastModifiedDate":"2018-08-20T18:15:29","indexId":"70103569","displayToPublicDate":"2014-05-06T14:50:00","publicationYear":"2014","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":1324,"text":"Conservation Genetics","active":true,"publicationSubtype":{"id":10}},"title":"Lack of sex-biased dispersal promotes fine-scale genetic structure in alpine ungulates","docAbstract":"Identifying patterns of fine-scale genetic structure in natural populations can advance understanding of critical ecological processes such as dispersal and gene flow across heterogeneous landscapes. Alpine ungulates generally exhibit high levels of genetic structure due to female philopatry and patchy configuration of mountain habitats. We assessed the spatial scale of genetic structure and the amount of gene flow in 301 Dall’s sheep (Ovis dalli dalli) at the landscape level using 15 nuclear microsatellites and 473 base pairs of the mitochondrial (mtDNA) control region. Dall’s sheep exhibited significant genetic structure within contiguous mountain ranges, but mtDNA structure occurred at a broader geographic scale than nuclear DNA within the study area, and mtDNA structure for other North American mountain sheep populations. No evidence of male-mediated gene flow or greater philopatry of females was observed; there was little difference between markers with different modes of inheritance (pairwise nuclear DNA F ST = 0.004–0.325; mtDNA F ST = 0.009–0.544), and males were no more likely than females to be recent immigrants. Historical patterns based on mtDNA indicate separate northern and southern lineages and a pattern of expansion following regional glacial retreat. Boundaries of genetic clusters aligned geographically with prominent mountain ranges, icefields, and major river valleys based on Bayesian and hierarchical modeling of microsatellite and mtDNA data. Our results suggest that fine-scale genetic structure in Dall’s sheep is influenced by limited dispersal, and structure may be weaker in populations occurring near ancestral levels of density and distribution in continuous habitats compared to other alpine ungulates that have experienced declines and marked habitat fragmentation.","largerWorkType":{"id":2,"text":"Article"},"largerWorkTitle":"Conservation Genetics","largerWorkSubtype":{"id":10,"text":"Journal Article"},"language":"English","publisher":"Springer","doi":"10.1007/s10592-014-0583-2","usgsCitation":"Roffler, G.H., Talbot, S.L., Luikart, G., Sage, G.K., Pilgrim, K.L., Adams, L., and Schwartz, M.K., 2014, Lack of sex-biased dispersal promotes fine-scale genetic structure in alpine ungulates: Conservation Genetics, v. 15, no. 4, p. 837-851, https://doi.org/10.1007/s10592-014-0583-2.","productDescription":"15 p.","startPage":"837","endPage":"851","numberOfPages":"15","ipdsId":"IP-049059","costCenters":[{"id":114,"text":"Alaska Science Center","active":true,"usgs":true}],"links":[{"id":286931,"type":{"id":10,"text":"Digital Object Identifier"},"url":"https://dx.doi.org/10.1007/s10592-014-0583-2"},{"id":286936,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","state":"Alaska","geographicExtents":"{ \"type\": \"FeatureCollection\", \"features\": [ { \"type\": \"Feature\", \"properties\": {}, \"geometry\": { \"type\": \"Polygon\", \"coordinates\": [ [ [ -146.0,60.5 ], [ -146.0,63.0 ], [ -140.0,63.0 ], [ -140.0,60.5 ], [ -146.0,60.5 ] ] ] } } ] }","volume":"15","issue":"4","noUsgsAuthors":false,"publicationDate":"2014-02-17","publicationStatus":"PW","scienceBaseUri":"5369f650e4b063fb73c0a9d3","contributors":{"authors":[{"text":"Roffler, Gretchen H. groffler@usgs.gov","contributorId":1946,"corporation":false,"usgs":true,"family":"Roffler","given":"Gretchen","email":"groffler@usgs.gov","middleInitial":"H.","affiliations":[{"id":117,"text":"Alaska Science Center Biology WTEB","active":true,"usgs":true}],"preferred":true,"id":493394,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Talbot, Sandra L. 0000-0002-3312-7214 stalbot@usgs.gov","orcid":"https://orcid.org/0000-0002-3312-7214","contributorId":140512,"corporation":false,"usgs":true,"family":"Talbot","given":"Sandra","email":"stalbot@usgs.gov","middleInitial":"L.","affiliations":[{"id":117,"text":"Alaska Science Center Biology WTEB","active":true,"usgs":true},{"id":114,"text":"Alaska Science Center","active":true,"usgs":true}],"preferred":true,"id":493393,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Luikart, Gordon","contributorId":97409,"corporation":false,"usgs":false,"family":"Luikart","given":"Gordon","affiliations":[{"id":6580,"text":"University of Montana, Flathead Lake Biological Station, Polson, Montana 59860, USA","active":true,"usgs":false}],"preferred":false,"id":493398,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Sage, George K. 0000-0003-1431-2286 ksage@usgs.gov","orcid":"https://orcid.org/0000-0003-1431-2286","contributorId":87833,"corporation":false,"usgs":true,"family":"Sage","given":"George","email":"ksage@usgs.gov","middleInitial":"K.","affiliations":[{"id":117,"text":"Alaska Science Center Biology WTEB","active":true,"usgs":true}],"preferred":false,"id":493397,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Pilgrim, Kristy L.","contributorId":45222,"corporation":false,"usgs":true,"family":"Pilgrim","given":"Kristy","email":"","middleInitial":"L.","affiliations":[],"preferred":false,"id":493396,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"Adams, Layne G. 0000-0001-6212-2896 ladams@usgs.gov","orcid":"https://orcid.org/0000-0001-6212-2896","contributorId":2776,"corporation":false,"usgs":true,"family":"Adams","given":"Layne G.","email":"ladams@usgs.gov","affiliations":[{"id":117,"text":"Alaska Science Center Biology WTEB","active":true,"usgs":true}],"preferred":true,"id":493395,"contributorType":{"id":1,"text":"Authors"},"rank":6},{"text":"Schwartz, Michael K.","contributorId":102326,"corporation":false,"usgs":true,"family":"Schwartz","given":"Michael","email":"","middleInitial":"K.","affiliations":[],"preferred":false,"id":493399,"contributorType":{"id":1,"text":"Authors"},"rank":7}]}} ,{"id":70059074,"text":"ds811 - 2014 - Occurrence of pesticides in groundwater and sediments and mineralogy of sediments and grain coatings underlying the Rutgers Agricultural Research and Extension Center, Upper Deerfield, New Jersey, 2007","interactions":[],"lastModifiedDate":"2021-05-27T13:59:50.478121","indexId":"ds811","displayToPublicDate":"2014-05-06T14:15:00","publicationYear":"2014","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":"811","title":"Occurrence of pesticides in groundwater and sediments and mineralogy of sediments and grain coatings underlying the Rutgers Agricultural Research and Extension Center, Upper Deerfield, New Jersey, 2007","docAbstract":"Water and sediment samples were collected from June through October 2007 from seven plots at the Rutgers Agricultural Research and Extension Center in Upper Deerfield, New Jersey, and analyzed for a suite of pesticides (including fungicides) and other physical and chemical parameters (including sediment mineralogy) by the U.S. Geological Survey. Plots were selected for inclusion in this study on the basis of the crops grown and the pesticides used. Forty-one pesticides were detected in 14 water samples; these include 5 fungicides, 13 herbicides, 1 insecticide, and 22 pesticide degradates. The following pesticides and pesticide degradates were detected in 50 percent or more of the groundwater samples: 1-amide-4-hydroxy-chorothalonil, alachlor sulfonic acid, metolachlor oxanilic acid, metolachlor sulfonic acid, metalaxyl, and simazine. Dissolved-pesticide concentrations ranged from below their instrumental limit of detection to 36 micrograms per liter (for metolachlor sulfonic acid, a degradate of the herbicide metolachlor). The total number of pesticides found in groundwater samples ranged from 0 to 29. Fourteen pesticides were detected in sediment samples from continuous cores collected within each of the seven sampled plots; these include 4 fungicides, 2 herbicides, and 7 pesticide degradates. Pesticide concentrations in sediment samples ranged from below their instrumental limit of detection to 34.2 nanograms per gram (for azoxystrobin). The total number of pesticides found in sediment samples ranged from 0 to 8. Quantitative whole-rock and grain-coating mineralogy of sediment samples were determined by x-ray diffraction. Whole-rock analysis indicated that sediments were predominantly composed of quartz. The materials coating the quartz grains were removed to allow quantification of the trace mineral phases present.","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/ds811","usgsCitation":"Reilly, T.J., Smalling, K., Meyer, M.T., Sandstrom, M.W., Hladik, M., Boehlke, A., Fishman, N.S., Battaglin, W.A., and Kuivila, K., 2014, Occurrence of pesticides in groundwater and sediments and mineralogy of sediments and grain coatings underlying the Rutgers Agricultural Research and Extension Center, Upper Deerfield, New Jersey, 2007: U.S. Geological Survey Data Series 811, x, 53 p., https://doi.org/10.3133/ds811.","productDescription":"x, 53 p.","numberOfPages":"68","onlineOnly":"Y","ipdsId":"IP-043852","costCenters":[{"id":452,"text":"National Water Quality Laboratory","active":true,"usgs":true},{"id":470,"text":"New Jersey Water Science Center","active":true,"usgs":true}],"links":[{"id":286934,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/ds/0811/pdf/ds811.pdf"},{"id":286933,"type":{"id":15,"text":"Index Page"},"url":"https://pubs.usgs.gov/ds/0811/"},{"id":286939,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/ds811.jpg"}],"country":"United States","state":"New Jersey","city":"Upper Deerfield","geographicExtents":"{ \"type\": \"FeatureCollection\", \"features\": [ { \"type\": \"Feature\", \"properties\": {}, \"geometry\": { \"type\": \"Polygon\", \"coordinates\": [ [ [ -75.240104,39.499658 ], [ -75.240104,39.53987 ], [ -75.17473,39.53987 ], [ -75.17473,39.499658 ], [ -75.240104,39.499658 ] ] ] } } ] }","noUsgsAuthors":false,"publicationStatus":"PW","scienceBaseUri":"5369f652e4b063fb73c0a9f1","contributors":{"authors":[{"text":"Reilly, Timothy J. 0000-0002-2939-3050 tjreilly@usgs.gov","orcid":"https://orcid.org/0000-0002-2939-3050","contributorId":1858,"corporation":false,"usgs":true,"family":"Reilly","given":"Timothy","email":"tjreilly@usgs.gov","middleInitial":"J.","affiliations":[{"id":34983,"text":"Contaminant Biology Program","active":true,"usgs":true},{"id":470,"text":"New Jersey Water Science Center","active":true,"usgs":true}],"preferred":true,"id":487466,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Smalling, Kelly L.","contributorId":16105,"corporation":false,"usgs":true,"family":"Smalling","given":"Kelly L.","affiliations":[],"preferred":false,"id":487467,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Meyer, Michael T. 0000-0001-6006-7985 mmeyer@usgs.gov","orcid":"https://orcid.org/0000-0001-6006-7985","contributorId":866,"corporation":false,"usgs":true,"family":"Meyer","given":"Michael","email":"mmeyer@usgs.gov","middleInitial":"T.","affiliations":[{"id":353,"text":"Kansas Water Science Center","active":false,"usgs":true}],"preferred":true,"id":487463,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Sandstrom, Mark W. 0000-0003-0006-5675 sandstro@usgs.gov","orcid":"https://orcid.org/0000-0003-0006-5675","contributorId":706,"corporation":false,"usgs":true,"family":"Sandstrom","given":"Mark","email":"sandstro@usgs.gov","middleInitial":"W.","affiliations":[{"id":37464,"text":"WMA - Laboratory & Analytical Services Division","active":true,"usgs":true},{"id":452,"text":"National Water Quality Laboratory","active":true,"usgs":true},{"id":5046,"text":"Branch of Analytical Serv (NWQL)","active":true,"usgs":true},{"id":503,"text":"Office of Water Quality","active":true,"usgs":true}],"preferred":true,"id":487461,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Hladik, Michelle 0000-0002-0891-2712 mhladik@usgs.gov","orcid":"https://orcid.org/0000-0002-0891-2712","contributorId":784,"corporation":false,"usgs":true,"family":"Hladik","given":"Michelle","email":"mhladik@usgs.gov","affiliations":[{"id":154,"text":"California Water Science Center","active":true,"usgs":true}],"preferred":false,"id":487462,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"Boehlke, Adam R. 0000-0003-4980-431X","orcid":"https://orcid.org/0000-0003-4980-431X","contributorId":23835,"corporation":false,"usgs":true,"family":"Boehlke","given":"Adam R.","affiliations":[],"preferred":false,"id":487468,"contributorType":{"id":1,"text":"Authors"},"rank":6},{"text":"Fishman, Neil S.","contributorId":106464,"corporation":false,"usgs":true,"family":"Fishman","given":"Neil","email":"","middleInitial":"S.","affiliations":[],"preferred":false,"id":487469,"contributorType":{"id":1,"text":"Authors"},"rank":7},{"text":"Battaglin, William A. 0000-0001-7287-7096 wbattagl@usgs.gov","orcid":"https://orcid.org/0000-0001-7287-7096","contributorId":1527,"corporation":false,"usgs":true,"family":"Battaglin","given":"William","email":"wbattagl@usgs.gov","middleInitial":"A.","affiliations":[{"id":191,"text":"Colorado Water Science Center","active":true,"usgs":true}],"preferred":true,"id":487465,"contributorType":{"id":1,"text":"Authors"},"rank":8},{"text":"Kuivila, Kathryn 0000-0001-7940-489X kkuivila@usgs.gov","orcid":"https://orcid.org/0000-0001-7940-489X","contributorId":1367,"corporation":false,"usgs":true,"family":"Kuivila","given":"Kathryn ","email":"kkuivila@usgs.gov","affiliations":[{"id":518,"text":"Oregon Water Science Center","active":true,"usgs":true}],"preferred":false,"id":487464,"contributorType":{"id":1,"text":"Authors"},"rank":9}]}} ,{"id":70102111,"text":"70102111 - 2014 - Modeling nitrate at domestic and public-supply well depths in the Central Valley, California","interactions":[],"lastModifiedDate":"2018-09-26T09:54:48","indexId":"70102111","displayToPublicDate":"2014-05-06T11:59:00","publicationYear":"2014","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":1565,"text":"Environmental Science & Technology","onlineIssn":"1520-5851","printIssn":"0013-936X","active":true,"publicationSubtype":{"id":10}},"title":"Modeling nitrate at domestic and public-supply well depths in the Central Valley, California","docAbstract":"Aquifer vulnerability models were developed to map groundwater nitrate concentration at domestic and public-supply well depths in the Central Valley, California. We compared three modeling methods for ability to predict nitrate concentration >4 mg/L: logistic regression (LR), random forest classification (RFC), and random forest regression (RFR). All three models indicated processes of nitrogen fertilizer input at the land surface, transmission through coarse-textured, well-drained soils, and transport in the aquifer to the well screen. The total percent correct predictions were similar among the three models (69–82%), but RFR had greater sensitivity (84% for shallow wells and 51% for deep wells). The results suggest that RFR can better identify areas with high nitrate concentration but that LR and RFC may better describe bulk conditions in the aquifer. A unique aspect of the modeling approach was inclusion of outputs from previous, physically based hydrologic and textural models as predictor variables, which were important to the models. Vertical water fluxes in the aquifer and percent coarse material above the well screen were ranked moderately high-to-high in the RFR models, and the average vertical water flux during the irrigation season was highly significant (p < 0.0001) in logistic regression.","language":"English","publisher":"American Chemical Society","doi":"10.1021/es405452q","usgsCitation":"Nolan, B.T., Gronberg, J.M., Faunt, C., Eberts, S., and Belitz, K., 2014, Modeling nitrate at domestic and public-supply well depths in the Central Valley, California: Environmental Science & Technology, v. 48, no. 10, p. 5643-5651, https://doi.org/10.1021/es405452q.","productDescription":"9 p.","startPage":"5643","endPage":"5651","ipdsId":"IP-053144","costCenters":[{"id":154,"text":"California Water Science Center","active":true,"usgs":true},{"id":451,"text":"National Water Quality Assessment Program","active":true,"usgs":true}],"links":[{"id":286937,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"},{"id":286929,"type":{"id":10,"text":"Digital Object Identifier"},"url":"https://dx.doi.org/10.1021/es405452q"}],"country":"United States","state":"California","otherGeospatial":"Central Valley","geographicExtents":"{ \"type\": \"FeatureCollection\", \"features\": [ { \"type\": \"Feature\", \"properties\": {}, \"geometry\": { \"type\": \"Polygon\", \"coordinates\": [ [ [ -124.41,32.53 ], [ -124.41,42.01 ], [ -114.13,42.01 ], [ -114.13,32.53 ], [ -124.41,32.53 ] ] ] } } ] }","volume":"48","issue":"10","noUsgsAuthors":false,"publicationDate":"2014-04-29","publicationStatus":"PW","scienceBaseUri":"5369f651e4b063fb73c0a9e2","contributors":{"authors":[{"text":"Nolan, Bernard T. 0000-0002-6945-9659 btnolan@usgs.gov","orcid":"https://orcid.org/0000-0002-6945-9659","contributorId":2190,"corporation":false,"usgs":true,"family":"Nolan","given":"Bernard","email":"btnolan@usgs.gov","middleInitial":"T.","affiliations":[{"id":451,"text":"National Water Quality Assessment Program","active":true,"usgs":true},{"id":27111,"text":"National Water Quality Program","active":true,"usgs":true}],"preferred":true,"id":492828,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Gronberg, JoAnn M. 0000-0003-4822-7434 jmgronbe@usgs.gov","orcid":"https://orcid.org/0000-0003-4822-7434","contributorId":3548,"corporation":false,"usgs":true,"family":"Gronberg","given":"JoAnn","email":"jmgronbe@usgs.gov","middleInitial":"M.","affiliations":[{"id":154,"text":"California Water Science Center","active":true,"usgs":true}],"preferred":true,"id":492830,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Faunt, Claudia C. 0000-0001-5659-7529 ccfaunt@usgs.gov","orcid":"https://orcid.org/0000-0001-5659-7529","contributorId":1491,"corporation":false,"usgs":true,"family":"Faunt","given":"Claudia C.","email":"ccfaunt@usgs.gov","affiliations":[{"id":595,"text":"U.S. Geological Survey","active":false,"usgs":true}],"preferred":false,"id":492827,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Eberts, Sandra M. smeberts@usgs.gov","contributorId":2264,"corporation":false,"usgs":true,"family":"Eberts","given":"Sandra M.","email":"smeberts@usgs.gov","affiliations":[{"id":513,"text":"Ohio Water Science Center","active":true,"usgs":true}],"preferred":false,"id":492829,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Belitz, Ken 0000-0003-4481-2345","orcid":"https://orcid.org/0000-0003-4481-2345","contributorId":108032,"corporation":false,"usgs":true,"family":"Belitz","given":"Ken","affiliations":[],"preferred":false,"id":492831,"contributorType":{"id":1,"text":"Authors"},"rank":5}]}} ,{"id":70103631,"text":"70103631 - 2014 - Metabolism of a nitrogen-enriched coastal marine lagoon during the summertime","interactions":[],"lastModifiedDate":"2014-05-07T11:16:50","indexId":"70103631","displayToPublicDate":"2014-05-06T11:03:23","publicationYear":"2014","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":1007,"text":"Biogeochemistry","active":true,"publicationSubtype":{"id":10}},"title":"Metabolism of a nitrogen-enriched coastal marine lagoon during the summertime","docAbstract":"We measured metabolism rates in a shallow, nitrogen-enriched coastal marine ecosystem on Cape Cod (MA, USA) during seven summers using an open-water diel oxygen method. We compared two basins, one directly receiving most of the nitrogen (N) load (“Snug Harbor”) and another further removed from the N load and better flushed (“Outer Harbor”). Both dissolved oxygen and pH varied greatly over the day, increasing in daylight and decreasing at night. The more N-enriched basin frequently went hypoxic during the night, and the pH in both basins was low (compared to standard seawater) when the oxygen levels were low, due to elevated carbon dioxide. Day-to-day variation in gross primary production (GPP) was high and linked in part to variation in light. Whole-ecosystem respiration tended to track this short-term variation in GPP, suggesting that respiration by the primary producers often dominated whole-system respiration. GPP was higher in the more N-loaded Snug Harbor. Seagrasses covered over 60 % of the area of the better-flushed, Outer Harbor throughout our study and were the major contributors to GPP there. Seagrasses covered 20 % of the area in Snug Harbor for the first 5 years of our study, and their contribution to GPP was relatively small. The seagrasses in Snug Harbor died off completely in the 6th year, but GPP remained high then and in the subsequent year. Overall, rates of phytoplankton GPP were relatively low, suggesting that benthic micro- and macro-algae may be the dominant primary producers in Snug Harbor in most years. Net ecosystem production in both Snug Harbor and the Outer Harbor was variable from year to year, showing net heterotrophy in some years and net autotrophy in others, with a trend towards increasing autotrophy over the 7 years reported here.","largerWorkType":{"id":2,"text":"Article"},"largerWorkTitle":"Biogeochemistry","largerWorkSubtype":{"id":10,"text":"Journal Article"},"language":"English","publisher":"Springer","doi":"10.1007/s10533-013-9901-x","usgsCitation":"Howarth, R.W., Hayn, M., Marino, R.M., Ganju, N., Foreman, K.H., McGlathery, K., Giblin, A.E., Berg, P., and Walker, J.D., 2014, Metabolism of a nitrogen-enriched coastal marine lagoon during the summertime: Biogeochemistry, v. 118, no. 1-3, p. 1-20, https://doi.org/10.1007/s10533-013-9901-x.","productDescription":"20 p.","startPage":"1","endPage":"20","ipdsId":"IP-049130","costCenters":[{"id":678,"text":"Woods Hole Coastal and Marine Science Center","active":true,"usgs":true}],"links":[{"id":286949,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"},{"id":286948,"type":{"id":10,"text":"Digital Object Identifier"},"url":"https://dx.doi.org/10.1007/s10533-013-9901-x"}],"country":"United States","state":"Massachusetts","otherGeospatial":"West Falmouth Harbor","geographicExtents":"{ \"type\": \"FeatureCollection\", \"features\": [ { \"type\": \"Feature\", \"properties\": {}, \"geometry\": { \"type\": \"Polygon\", \"coordinates\": [ [ [ -70.665,41.597 ], [ -70.665,41.614 ], [ -70.663,41.614 ], [ -70.663,41.597 ], [ -70.665,41.597 ] ] ] } } ] }","volume":"118","issue":"1-3","noUsgsAuthors":false,"publicationDate":"2013-09-01","publicationStatus":"PW","scienceBaseUri":"536b55f6e4b0a51a87c4b179","contributors":{"authors":[{"text":"Howarth, Robert W.","contributorId":32066,"corporation":false,"usgs":false,"family":"Howarth","given":"Robert","email":"","middleInitial":"W.","affiliations":[{"id":13003,"text":"Department of Ecology and Evolutionary Biology, Cornell University, Ithaca, New York","active":true,"usgs":false}],"preferred":false,"id":493412,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Hayn, Melanie","contributorId":57754,"corporation":false,"usgs":false,"family":"Hayn","given":"Melanie","email":"","affiliations":[{"id":13003,"text":"Department of Ecology and Evolutionary Biology, Cornell University, Ithaca, New York","active":true,"usgs":false}],"preferred":false,"id":493417,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Marino, Roxanne M.","contributorId":62523,"corporation":false,"usgs":true,"family":"Marino","given":"Roxanne","email":"","middleInitial":"M.","affiliations":[],"preferred":false,"id":493418,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Ganju, Neil 0000-0002-1096-0465","orcid":"https://orcid.org/0000-0002-1096-0465","contributorId":40902,"corporation":false,"usgs":true,"family":"Ganju","given":"Neil","affiliations":[],"preferred":false,"id":493415,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Foreman, Kenneth H.","contributorId":45631,"corporation":false,"usgs":true,"family":"Foreman","given":"Kenneth","email":"","middleInitial":"H.","affiliations":[],"preferred":false,"id":493416,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"McGlathery, Karen","contributorId":36057,"corporation":false,"usgs":true,"family":"McGlathery","given":"Karen","affiliations":[],"preferred":false,"id":493414,"contributorType":{"id":1,"text":"Authors"},"rank":6},{"text":"Giblin, Anne E.","contributorId":103966,"corporation":false,"usgs":true,"family":"Giblin","given":"Anne","email":"","middleInitial":"E.","affiliations":[],"preferred":false,"id":493419,"contributorType":{"id":1,"text":"Authors"},"rank":7},{"text":"Berg, Peter","contributorId":32828,"corporation":false,"usgs":true,"family":"Berg","given":"Peter","email":"","affiliations":[],"preferred":false,"id":493413,"contributorType":{"id":1,"text":"Authors"},"rank":8},{"text":"Walker, Jeffrey D.","contributorId":15526,"corporation":false,"usgs":true,"family":"Walker","given":"Jeffrey","email":"","middleInitial":"D.","affiliations":[],"preferred":false,"id":493411,"contributorType":{"id":1,"text":"Authors"},"rank":9}]}} ,{"id":70103632,"text":"70103632 - 2014 - Water level response in back-barrier bays unchanged following Hurricane Sandy","interactions":[],"lastModifiedDate":"2014-06-06T10:50:29","indexId":"70103632","displayToPublicDate":"2014-05-06T10:49:00","publicationYear":"2014","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":1807,"text":"Geophysical Research Letters","active":true,"publicationSubtype":{"id":10}},"title":"Water level response in back-barrier bays unchanged following Hurricane Sandy","docAbstract":"On 28–30 October 2012, Hurricane Sandy caused severe flooding along portions of the northeast coast of the United States and cut new inlets across barrier islands in New Jersey and New York. About 30% of the 20 highest daily maximum water levels observed between 2007 and 2013 in Barnegat and Great South Bay occurred in 5 months following Hurricane Sandy. Hurricane Sandy provided a rare opportunity to determine whether extreme events alter systems protected by barrier islands, leaving the mainland more vulnerable to flooding. Comparisons between water levels before and after Hurricane Sandy at bay stations and an offshore station show no significant differences in the transfer of sea level fluctuations from offshore to either bay following Sandy. The post-Hurricane Sandy bay high water levels reflected offshore sea levels caused by winter storms, not by barrier island breaching or geomorphic changes within the bays.","largerWorkType":{"id":2,"text":"Article"},"largerWorkTitle":"Geophysical Research Letters","largerWorkSubtype":{"id":10,"text":"Journal Article"},"language":"English","publisher":"Wiley Online Library","doi":"10.1002/2014GL059957","usgsCitation":"Aretxabaleta, A., Butman, B., and Ganju, N., 2014, Water level response in back-barrier bays unchanged following Hurricane Sandy: Geophysical Research Letters, v. 41, no. 9, p. 3163-3171, https://doi.org/10.1002/2014GL059957.","productDescription":"9 p.","startPage":"3163","endPage":"3171","numberOfPages":"9","ipdsId":"IP-055885","costCenters":[{"id":678,"text":"Woods Hole Coastal and Marine Science Center","active":true,"usgs":true}],"links":[{"id":473001,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.1002/2014gl059957","text":"Publisher Index Page"},{"id":286947,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"},{"id":286946,"type":{"id":10,"text":"Digital Object Identifier"},"url":"https://dx.doi.org/10.1002/2014GL059957"}],"country":"United States","state":"New Jersey;New York","geographicExtents":"{ \"type\": \"FeatureCollection\", \"features\": [ { \"type\": \"Feature\", \"properties\": {}, \"geometry\": { \"type\": \"Polygon\", \"coordinates\": [ [ [ -76.0,38.0 ], [ -76.0,42.0 ], [ -70.0,42.0 ], [ -70.0,38.0 ], [ -76.0,38.0 ] ] ] } } ] }","volume":"41","issue":"9","noUsgsAuthors":false,"publicationDate":"2014-05-02","publicationStatus":"PW","scienceBaseUri":"536b55fae4b0a51a87c4b192","contributors":{"authors":[{"text":"Aretxabaleta, Alfredo L.","contributorId":41311,"corporation":false,"usgs":true,"family":"Aretxabaleta","given":"Alfredo L.","affiliations":[],"preferred":false,"id":493421,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Butman, Bradford 0000-0002-4174-2073 bbutman@usgs.gov","orcid":"https://orcid.org/0000-0002-4174-2073","contributorId":943,"corporation":false,"usgs":true,"family":"Butman","given":"Bradford","email":"bbutman@usgs.gov","affiliations":[{"id":678,"text":"Woods Hole Coastal and Marine Science Center","active":true,"usgs":true}],"preferred":true,"id":493420,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Ganju, Neil K. 0000-0002-1096-0465","orcid":"https://orcid.org/0000-0002-1096-0465","contributorId":93543,"corporation":false,"usgs":true,"family":"Ganju","given":"Neil K.","affiliations":[],"preferred":false,"id":493422,"contributorType":{"id":1,"text":"Authors"},"rank":3}]}} ,{"id":70060527,"text":"70060527 - 2014 - Mechanisms of drift-feeding behavior in juvenile Chinook salmon and the role of inedible debris in a clear water Alaskan stream","interactions":[],"lastModifiedDate":"2014-05-06T11:43:59","indexId":"70060527","displayToPublicDate":"2014-05-06T10:23:00","publicationYear":"2014","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":1528,"text":"Environmental Biology of Fishes","active":true,"publicationSubtype":{"id":10}},"title":"Mechanisms of drift-feeding behavior in juvenile Chinook salmon and the role of inedible debris in a clear water Alaskan stream","docAbstract":"Drift-feeding fish are challenged to discriminate between prey and similar-sized particles of debris, which are ubiquitous even in clear-water streams. Spending time and energy pursuing debris mistaken as prey could affect fish growth and the fitness potential of different foraging strategies. Our goal was to determine the extent to which debris influences drift-feeding fish in clear water under low-flow conditions when the distracting effect of debris should be at a minimum. We used high-definition video to measure the reactions of drift-feeding juvenile Chinook salmon (Oncorhynchus tshawytscha) to natural debris and prey in situ in the Chena River, Alaska. Among all potential food items fish pursued, 52 % were captured and quickly expelled from the mouth, 39 % were visually inspected but not captured, and only 9 % were ingested. Foraging attempt rate was only moderately correlated with ingestion rate (Kendall’s τ = 0.55), raising concerns about the common use of foraging attempts as a presumed index of foraging success. The total time fish spent handling debris increased linearly with foraging attempt rate and ranged between 4 and 25 % of total foraging time among observed groups. Our results help motivate a revised theoretical view of drift feeding that emphasizes prey detection and discrimination, incorporating ideas from signal detection theory and the study of visual attention in cognitive ecology. We discuss how these ideas could lead to better explanations and predictions of the spatial behavior, prey selection, and energy intake of drift-feeding fish.","largerWorkType":{"id":2,"text":"Article"},"largerWorkTitle":"Environmental Biology of Fishes","largerWorkSubtype":{"id":10,"text":"Journal Article"},"language":"English","publisher":"Springer","doi":"10.1007/s10641-014-0227-x","usgsCitation":"Neuswanger, J.R., Wipfli, M.S., Rosenberger, A.E., and Hughes, N.F., 2014, Mechanisms of drift-feeding behavior in juvenile Chinook salmon and the role of inedible debris in a clear water Alaskan stream: Environmental Biology of Fishes, v. 97, no. 5, p. 489-503, https://doi.org/10.1007/s10641-014-0227-x.","productDescription":"15 p.","startPage":"489","endPage":"503","numberOfPages":"15","ipdsId":"IP-045976","costCenters":[{"id":200,"text":"Coop Res Unit Seattle","active":true,"usgs":true}],"links":[{"id":286926,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"},{"id":286925,"type":{"id":10,"text":"Digital Object Identifier"},"url":"https://dx.doi.org/10.1007/s10641-014-0227-x"}],"country":"United States","state":"Alaska","otherGeospatial":"Chena River","geographicExtents":"{ \"type\": \"FeatureCollection\", \"features\": [ { \"type\": \"Feature\", \"properties\": {}, \"geometry\": { \"type\": \"Polygon\", \"coordinates\": [ [ [ -147.915305,64.784024 ], [ -147.915305,64.857769 ], [ -146.995475,64.857769 ], [ -146.995475,64.784024 ], [ -147.915305,64.784024 ] ] ] } } ] }","volume":"97","issue":"5","noUsgsAuthors":false,"publicationDate":"2014-01-29","publicationStatus":"PW","scienceBaseUri":"5369f650e4b063fb73c0a9d8","contributors":{"authors":[{"text":"Neuswanger, Jason R.","contributorId":15530,"corporation":false,"usgs":true,"family":"Neuswanger","given":"Jason","email":"","middleInitial":"R.","affiliations":[],"preferred":false,"id":487896,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Wipfli, Mark S. 0000-0002-4856-6068 mwipfli@usgs.gov","orcid":"https://orcid.org/0000-0002-4856-6068","contributorId":1425,"corporation":false,"usgs":true,"family":"Wipfli","given":"Mark","email":"mwipfli@usgs.gov","middleInitial":"S.","affiliations":[{"id":200,"text":"Coop Res Unit Seattle","active":true,"usgs":true}],"preferred":true,"id":487894,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Rosenberger, Amanda E. 0000-0002-5520-8349 arosenberger@usgs.gov","orcid":"https://orcid.org/0000-0002-5520-8349","contributorId":5581,"corporation":false,"usgs":true,"family":"Rosenberger","given":"Amanda","email":"arosenberger@usgs.gov","middleInitial":"E.","affiliations":[{"id":396,"text":"Missouri Water Science Center","active":true,"usgs":true},{"id":198,"text":"Coop Res Unit Atlanta","active":true,"usgs":true}],"preferred":true,"id":487895,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Hughes, Nicholas F.","contributorId":40497,"corporation":false,"usgs":true,"family":"Hughes","given":"Nicholas","email":"","middleInitial":"F.","affiliations":[],"preferred":false,"id":487897,"contributorType":{"id":1,"text":"Authors"},"rank":4}]}} ,{"id":70101481,"text":"ofr20141074 - 2014 - Sediment-hosted gold deposits of the world: Database and grade and tonnage models","interactions":[],"lastModifiedDate":"2023-05-26T15:29:00.030413","indexId":"ofr20141074","displayToPublicDate":"2014-05-06T10:06:00","publicationYear":"2014","noYear":false,"publicationType":{"id":18,"text":"Report"},"publicationSubtype":{"id":5,"text":"USGS Numbered Series"},"seriesTitle":{"id":330,"text":"Open-File Report","code":"OFR","onlineIssn":"2331-1258","printIssn":"0196-1497","active":true,"publicationSubtype":{"id":5}},"seriesNumber":"2014-1074","title":"Sediment-hosted gold deposits of the world: Database and grade and tonnage models","docAbstract":"All sediment-hosted gold deposits (as a single population) share one characteristic—they all have disseminated micron-sized invisible gold in sedimentary rocks. Sediment-hosted gold deposits are recognized in the Great Basin province of the western United States and in China along with a few recognized deposits in Indonesia, Iran, and Malaysia. Three new grade and tonnage models for sediment-hosted gold deposits are presented in this paper: (1) a general sediment-hosted gold type model, (2) a Carlin subtype model, and (3) a Chinese subtype model. These models are based on grade and tonnage data from a database compilation of 118 sediment-hosted gold deposits including a total of 123 global deposits. The new general grade and tonnage model for sediment-hosted gold deposits (n=118) has a median tonnage of 5.7 million metric tonnes (Mt) and a gold grade of 2.9 grams per tonne (g/t). This new grade and tonnage model is remarkable in that the estimated parameters of the resulting grade and tonnage distributions are comparable to the previous model of Mosier and others (1992). A notable change is in the reporting of silver in more than 10 percent of deposits; moreover, the previous model had not considered deposits in China. From this general grade and tonnage model, two significantly different subtypes of sediment-hosted gold deposits are differentiated: Carlin and Chinese. The Carlin subtype includes 88 deposits in the western United States, Indonesia, Iran, and Malaysia, with median tonnage and grade of 7.1 Mt and 2.0 g/t Au, respectively. The silver grade is 0.78 g/t Ag for the 10th percentile of deposits. The Chinese subtype represents 30 deposits in China, with a median tonnage of 3.9 Mt and medium grade of 4.6 g/t Au. Important differences are recognized in the mineralogy and alteration of the two sediment-hosted gold subtypes such as: increased sulfide minerals in the Chinese subtype and decalcification alteration dominant in the Carlin type. We therefore recommend using the appropriate grade and tonnage model presented in this study for mineral resource assessments depending on the geologic and mineralogical data available for a region. Tonnage and contained gold within the general sediment-hosted gold model are analyzed based on major geologic features such as tectonic setting and magmatic (dikes, sills, and stocks) or amagmatic environment. The results show a significant difference in tonnage and contained gold, with higher median values in deposits spatially associated with igneous rocks, regardless of structural style of the deposit. These results suggest that magmatic environments control mineralization intensity—an important consideration in the regional assessment of prospective areas for sediment-hosted gold deposits.","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/ofr20141074","usgsCitation":"Berger, V.I., Mosier, D.L., Bliss, J.D., and Moring, B.C., 2014, Sediment-hosted gold deposits of the world: Database and grade and tonnage models (Originally posted May 5, 2014; Version 1.1 June 19, 2014): U.S. Geological Survey Open-File Report 2014-1074, Report: v, 46 p.; Appendixes 1-6, https://doi.org/10.3133/ofr20141074.","productDescription":"Report: v, 46 p.; Appendixes 1-6","numberOfPages":"51","onlineOnly":"Y","additionalOnlineFiles":"Y","ipdsId":"IP-046320","costCenters":[{"id":312,"text":"Geology, Minerals, Energy, and Geophysics Science Center","active":true,"usgs":true}],"links":[{"id":417504,"rank":5,"type":{"id":36,"text":"NGMDB Index Page"},"url":"https://ngmdb.usgs.gov/Prodesc/proddesc_100014.htm","linkFileType":{"id":5,"text":"html"}},{"id":286923,"rank":1,"type":{"id":3,"text":"Appendix"},"url":"https://pubs.usgs.gov/of/2014/1074/downloads/ofr2014-1074_appendixes.zip"},{"id":286228,"rank":3,"type":{"id":15,"text":"Index Page"},"url":"https://pubs.usgs.gov/of/2014/1074/"},{"id":286924,"rank":4,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/ofr20141074.GIF"},{"id":286922,"rank":2,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/of/2014/1074/pdf/ofr2014-1074.pdf"}],"edition":"Originally posted May 5, 2014; Version 1.1 June 19, 2014","noUsgsAuthors":false,"publicationStatus":"PW","scienceBaseUri":"5369f652e4b063fb73c0a9f6","contributors":{"authors":[{"text":"Berger, Vladimir I.","contributorId":15246,"corporation":false,"usgs":true,"family":"Berger","given":"Vladimir","email":"","middleInitial":"I.","affiliations":[],"preferred":false,"id":492718,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Mosier, Dan L.","contributorId":42593,"corporation":false,"usgs":true,"family":"Mosier","given":"Dan","email":"","middleInitial":"L.","affiliations":[],"preferred":false,"id":492719,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Bliss, James D. jbliss@usgs.gov","contributorId":2790,"corporation":false,"usgs":true,"family":"Bliss","given":"James","email":"jbliss@usgs.gov","middleInitial":"D.","affiliations":[{"id":312,"text":"Geology, Minerals, Energy, and Geophysics Science Center","active":true,"usgs":true}],"preferred":true,"id":492716,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Moring, Barry C. 0000-0001-6797-9258 moring@usgs.gov","orcid":"https://orcid.org/0000-0001-6797-9258","contributorId":2794,"corporation":false,"usgs":true,"family":"Moring","given":"Barry","email":"moring@usgs.gov","middleInitial":"C.","affiliations":[{"id":312,"text":"Geology, Minerals, Energy, and Geophysics Science Center","active":true,"usgs":true}],"preferred":true,"id":492717,"contributorType":{"id":1,"text":"Authors"},"rank":4}]}} ,{"id":70103513,"text":"70103513 - 2014 - Mercury concentrations in breast feathers of three upper trophic level marine predators from the western Aleutian Islands, Alaska","interactions":[],"lastModifiedDate":"2018-10-11T16:38:03","indexId":"70103513","displayToPublicDate":"2014-05-06T09:33:00","publicationYear":"2014","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":2676,"text":"Marine Pollution Bulletin","active":true,"publicationSubtype":{"id":10}},"title":"Mercury concentrations in breast feathers of three upper trophic level marine predators from the western Aleutian Islands, Alaska","docAbstract":"Mercury (Hg) is a toxic element distributed globally through atmospheric transport. Agattu Island, located in the western Aleutian Islands, Alaska, has no history of point-sources of Hg contamination. We provide baseline levels of total mercury (THg) concentrations in breast feathers of three birds that breed on the island. Geometric mean THg concentrations in feathers of fork-tailed storm-petrels (Oceanodroma furcata; 6703 ± 1635, ng/g fresh weight [fw]) were higher than all other species, including snowy owl (Bubo scandiacus; 2105 ± 1631, ng/g fw), a raptor with a diet composed largely of storm-petrels at Agattu Island. There were no significant differences in mean THg concentrations of breast feathers among adult Kittlitz’s murrelet (Brachyramphus brevirostris; 1658 ± 1276, ng/g fw) and chicks (1475 ± 671, ng/g fw) and snowy owls. The observed THg concentrations in fork-tailed storm-petrel feathers emphasizes the need for further study of Hg pollution in the western Aleutian Islands.","largerWorkType":{"id":2,"text":"Article"},"largerWorkTitle":"Marine Pollution Bulletin","largerWorkSubtype":{"id":10,"text":"Journal Article"},"language":"English","publisher":"Elsevier","doi":"10.1016/j.marpolbul.2014.02.034","usgsCitation":"Kaler, R., Kenney, L., Bond, A., and Eagles-Smith, C.A., 2014, Mercury concentrations in breast feathers of three upper trophic level marine predators from the western Aleutian Islands, Alaska: Marine Pollution Bulletin, v. 82, no. 1-2, p. 189-193, https://doi.org/10.1016/j.marpolbul.2014.02.034.","productDescription":"5 p.","startPage":"189","endPage":"193","numberOfPages":"5","ipdsId":"IP-052145","costCenters":[{"id":290,"text":"Forest and Rangeland Ecosystem Science Center","active":false,"usgs":true},{"id":34983,"text":"Contaminant Biology Program","active":true,"usgs":true}],"links":[{"id":286917,"type":{"id":10,"text":"Digital Object Identifier"},"url":"https://dx.doi.org/10.1016/j.marpolbul.2014.02.034"},{"id":286921,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","state":"Alaska","otherGeospatial":"Aleutian Islands","geographicExtents":"{ \"type\": \"FeatureCollection\", \"features\": [ { \"type\": \"Feature\", \"properties\": {}, \"geometry\": { \"type\": \"Polygon\", \"coordinates\": [ [ [ 171.6958,50.8194 ], [ 171.6958,53.2347 ], [ 179.9486,53.2347 ], [ 179.9486,50.8194 ], [ 171.6958,50.8194 ] ] ] } } ] }","volume":"82","issue":"1-2","noUsgsAuthors":false,"publicationStatus":"PW","scienceBaseUri":"5369f650e4b063fb73c0a9dd","contributors":{"authors":[{"text":"Kaler, Robb S.A.","contributorId":69066,"corporation":false,"usgs":true,"family":"Kaler","given":"Robb S.A.","affiliations":[],"preferred":false,"id":493379,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Kenney, Leah A.","contributorId":67011,"corporation":false,"usgs":true,"family":"Kenney","given":"Leah A.","affiliations":[],"preferred":false,"id":493377,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Bond, Alexander L.","contributorId":69004,"corporation":false,"usgs":true,"family":"Bond","given":"Alexander L.","affiliations":[],"preferred":false,"id":493378,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Eagles-Smith, Collin A. 0000-0003-1329-5285 ceagles-smith@usgs.gov","orcid":"https://orcid.org/0000-0003-1329-5285","contributorId":505,"corporation":false,"usgs":true,"family":"Eagles-Smith","given":"Collin","email":"ceagles-smith@usgs.gov","middleInitial":"A.","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},{"id":518,"text":"Oregon Water Science Center","active":true,"usgs":true}],"preferred":true,"id":493376,"contributorType":{"id":1,"text":"Authors"},"rank":4}]}} ,{"id":70103497,"text":"70103497 - 2014 - A comment on \"bats killed in large numbers at United States wind energy facilities\"","interactions":[],"lastModifiedDate":"2018-09-18T16:29:35","indexId":"70103497","displayToPublicDate":"2014-05-06T09:11:00","publicationYear":"2014","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":997,"text":"BioScience","active":true,"publicationSubtype":{"id":10}},"title":"A comment on \"bats killed in large numbers at United States wind energy facilities\"","docAbstract":"Widespread reports of bat fatalities caused by wind turbines have raised concerns about the impacts of wind power development. Reliable \nestimates of the total number killed and the potential effects on populations are needed, but it is crucial that they be based on sound data. In a \nrecent BioScience article, Hayes (2013) estimated that over 600,000 bats were killed at wind turbines in the United States in 2012. The scientific \nerrors in the analysis are numerous, with the two most serious being that the included sites constituted a convenience sample, not a representative \nsample, and that the individual site estimates are derived from such different methodologies that they are inherently not comparable. This \nestimate is almost certainly inaccurate, but whether the actual number is much smaller, much larger, or about the same is uncertain. An accurate \nestimate of total bat fatality is not currently possible, given the shortcomings of the available data.","language":"English","publisher":"Oxford University Press","doi":"10.1093/biosci/biu056","usgsCitation":"Huso, M., and Dalthorp, D., 2014, A comment on \"bats killed in large numbers at United States wind energy facilities\": BioScience, v. 64, no. 6, p. 546-547, https://doi.org/10.1093/biosci/biu056.","productDescription":"2 p.","startPage":"546","endPage":"547","numberOfPages":"2","ipdsId":"IP-054230","costCenters":[{"id":290,"text":"Forest and Rangeland Ecosystem Science Center","active":false,"usgs":true},{"id":34983,"text":"Contaminant Biology Program","active":true,"usgs":true}],"links":[{"id":473002,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.1093/biosci/biu056","text":"Publisher Index Page"},{"id":286918,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"},{"id":286904,"type":{"id":10,"text":"Digital Object Identifier"},"url":"https://dx.doi.org/10.1093/biosci/biu056"}],"volume":"64","issue":"6","noUsgsAuthors":false,"publicationDate":"2014-04-24","publicationStatus":"PW","scienceBaseUri":"5369f64fe4b063fb73c0a9ce","contributors":{"authors":[{"text":"Huso, Manuela M.P.","contributorId":80566,"corporation":false,"usgs":true,"family":"Huso","given":"Manuela M.P.","affiliations":[],"preferred":false,"id":493375,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Dalthorp, Dan","contributorId":51197,"corporation":false,"usgs":true,"family":"Dalthorp","given":"Dan","email":"","affiliations":[],"preferred":false,"id":493374,"contributorType":{"id":1,"text":"Authors"},"rank":2}]}} ,{"id":70173908,"text":"70173908 - 2014 - Estimating habitat carrying capacity for migrating and wintering waterfowl: Considerations, pitfalls and improvements","interactions":[],"lastModifiedDate":"2016-06-22T13:35:54","indexId":"70173908","displayToPublicDate":"2014-05-06T05:30:00","publicationYear":"2014","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":3764,"text":"Wildfowl","onlineIssn":"2052-6458","printIssn":"0954-6324","active":true,"publicationSubtype":{"id":10}},"title":"Estimating habitat carrying capacity for migrating and wintering waterfowl: Considerations, pitfalls and improvements","docAbstract":"Population-based habitat conservation planning for migrating and wintering waterfowl in North America is carried out by habitat Joint Venture (JV) initiatives and is based on the premise that food can limit demography (i.e. food limitation hypothesis). Consequently, planners use bioenergetic models to estimate food (energy) availability and population-level energy demands at appropriate spatial and temporal scales, and translate these values into regional habitat objectives. While simple in principle, there are both empirical and theoretical challenges associated with calculating energy supply and demand including: 1) estimating food availability, 2) estimating the energy content of specific foods, 3) extrapolating site-specific estimates of food availability to landscapes for focal species, 4) applicability of estimates from a single species to other species, 5) estimating resting metabolic rate, 6) estimating cost of daily behaviours, and 7) estimating costs of thermoregulation or tissue synthesis. Most models being used are daily ration models (DRMs) whose set of simplifying assumptions are well established and whose use is widely accepted and feasible given the empirical data available to populate such models. However, DRMs do not link habitat objectives to metrics of ultimate ecological importance such as individual body condition or survival, and largely only consider food-producing habitats. Agent-based models (ABMs) provide a possible alternative for creating more biologically realistic models under some conditions; however, ABMs require different types of empirical inputs, many of which have yet to be estimated for key North American waterfowl. Decisions about how JVs can best proceed with habitat conservation would benefit from the use of sensitivity analyses that could identify the empirical and theoretical uncertainties that have the greatest influence on efforts to estimate habitat carrying capacity. Development of ABMs at restricted, yet biologically relevant spatial scales, followed by comparisons of their outputs to those generated from more simplistic, deterministic models can provide a means of assessing degrees of dissimilarity in how alternative models describe desired landscape conditions for migrating and wintering waterfowl.
","language":"English","publisher":"InterMedia Outdoors","usgsCitation":"Williams, C., Dugger, B., Brasher, M., Coluccy, J.M., Cramer, D.M., Eadie, J.M., Gray, M., Hagy, H.M., Livolsi, M., McWilliams, S.R., Petrie, M., Soulliere, G.J., Tirpak, J.M., and Webb, E.B., 2014, Estimating habitat carrying capacity for migrating and wintering waterfowl: Considerations, pitfalls and improvements: Wildfowl, no. 4, p. 407-435.","productDescription":"29 p.","startPage":"407","endPage":"435","onlineOnly":"N","additionalOnlineFiles":"N","ipdsId":"IP-055427","costCenters":[{"id":198,"text":"Coop Res Unit Atlanta","active":true,"usgs":true}],"links":[{"id":324226,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"},{"id":324227,"type":{"id":15,"text":"Index Page"},"url":"https://wildfowl.wwt.org.uk/index.php/wildfowl/article/view/2614"}],"issue":"4","publishingServiceCenter":{"id":8,"text":"Raleigh PSC"},"noUsgsAuthors":false,"publicationStatus":"PW","scienceBaseUri":"576bb6b3e4b07657d1a2289f","contributors":{"authors":[{"text":"Williams, Christopher","contributorId":36592,"corporation":false,"usgs":true,"family":"Williams","given":"Christopher","affiliations":[],"preferred":false,"id":640344,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Dugger, Bruce D.","contributorId":81236,"corporation":false,"usgs":true,"family":"Dugger","given":"Bruce D.","affiliations":[],"preferred":false,"id":640345,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Brasher, Michael G.","contributorId":17139,"corporation":false,"usgs":true,"family":"Brasher","given":"Michael G.","affiliations":[],"preferred":false,"id":640346,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Coluccy, John M.","contributorId":111382,"corporation":false,"usgs":true,"family":"Coluccy","given":"John","email":"","middleInitial":"M.","affiliations":[],"preferred":false,"id":640347,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Cramer, Dane M.","contributorId":172325,"corporation":false,"usgs":false,"family":"Cramer","given":"Dane","email":"","middleInitial":"M.","affiliations":[{"id":13073,"text":"Ducks Unlimited, Inc.","active":true,"usgs":false}],"preferred":false,"id":640348,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"Eadie, John M.","contributorId":65219,"corporation":false,"usgs":false,"family":"Eadie","given":"John","email":"","middleInitial":"M.","affiliations":[{"id":7082,"text":"University of California - Davis","active":true,"usgs":false}],"preferred":false,"id":640349,"contributorType":{"id":1,"text":"Authors"},"rank":6},{"text":"Gray, Matthew J.","contributorId":101343,"corporation":false,"usgs":true,"family":"Gray","given":"Matthew J.","affiliations":[],"preferred":false,"id":640350,"contributorType":{"id":1,"text":"Authors"},"rank":7},{"text":"Hagy, Heath M.","contributorId":172326,"corporation":false,"usgs":false,"family":"Hagy","given":"Heath","email":"","middleInitial":"M.","affiliations":[],"preferred":false,"id":640351,"contributorType":{"id":1,"text":"Authors"},"rank":8},{"text":"Livolsi, Mark","contributorId":172327,"corporation":false,"usgs":false,"family":"Livolsi","given":"Mark","email":"","affiliations":[{"id":13359,"text":"University of Delaware","active":true,"usgs":false}],"preferred":false,"id":640352,"contributorType":{"id":1,"text":"Authors"},"rank":9},{"text":"McWilliams, Scott R.","contributorId":172328,"corporation":false,"usgs":false,"family":"McWilliams","given":"Scott","email":"","middleInitial":"R.","affiliations":[{"id":6922,"text":"University of Rhode Island","active":true,"usgs":false}],"preferred":false,"id":640353,"contributorType":{"id":1,"text":"Authors"},"rank":10},{"text":"Petrie, Matthew mpetrie@usgs.gov","contributorId":167013,"corporation":false,"usgs":true,"family":"Petrie","given":"Matthew","email":"mpetrie@usgs.gov","affiliations":[{"id":568,"text":"Southwest Biological Science Center","active":true,"usgs":true}],"preferred":true,"id":640354,"contributorType":{"id":1,"text":"Authors"},"rank":11},{"text":"Soulliere, Gregory J.","contributorId":172329,"corporation":false,"usgs":false,"family":"Soulliere","given":"Gregory","email":"","middleInitial":"J.","affiliations":[{"id":6987,"text":"U.S. Fish and Wildlife Sevice","active":true,"usgs":false}],"preferred":false,"id":640355,"contributorType":{"id":1,"text":"Authors"},"rank":12},{"text":"Tirpak, John M.","contributorId":85704,"corporation":false,"usgs":true,"family":"Tirpak","given":"John","email":"","middleInitial":"M.","affiliations":[],"preferred":false,"id":640356,"contributorType":{"id":1,"text":"Authors"},"rank":13},{"text":"Webb, Elisabeth B. 0000-0003-3851-6056 ewebb@usgs.gov","orcid":"https://orcid.org/0000-0003-3851-6056","contributorId":3981,"corporation":false,"usgs":true,"family":"Webb","given":"Elisabeth","email":"ewebb@usgs.gov","middleInitial":"B.","affiliations":[{"id":199,"text":"Coop Res Unit Leetown","active":true,"usgs":true}],"preferred":true,"id":638956,"contributorType":{"id":1,"text":"Authors"},"rank":14}]}} ,{"id":70156135,"text":"70156135 - 2014 - Using nuclear magnetic resonance and transient electromagnetics to characterise water distribution beneath an ice covered volcanic crater: The case of Sherman Crater Mt. Baker Washington.","interactions":[],"lastModifiedDate":"2019-03-11T14:03:42","indexId":"70156135","displayToPublicDate":"2014-05-06T01:00:00","publicationYear":"2014","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":2850,"text":"Near Surface Geophysics","active":true,"publicationSubtype":{"id":10}},"title":"Using nuclear magnetic resonance and transient electromagnetics to characterise water distribution beneath an ice covered volcanic crater: The case of Sherman Crater Mt. Baker Washington.","docAbstract":"Surface and laboratory Nuclear Magnetic Resonance (NMR) measurements combined with transient electromagnetic (TEM) data are powerful tools for subsurface water detection. Surface NMR (sNMR) and TEM soundings, laboratory NMR, complex resistivity, and X-Ray Diffraction (XRD) analysis were all conducted to characterise the distribution of water within Sherman Crater on Mt. Baker, WA. Clay rich rocks, particularly if water saturated, can weaken volcanoes, thereby increasing the potential for catastrophic sector collapses that can lead to far-travelled, destructive debris flows. Detecting the presence and volume of shallow groundwater is critical for evaluating these landslide hazards. The TEM data identified a low resistivity layer (<10 ohm-m), under 60 m of glacial ice related to water saturated clays. The TEM struggles to resolve the presence or absence of a plausible thin layer of bulk liquid water on top of the clay. The sNMR measurements did not produce any observable signal, indicating the lack of substantial accumulated bulk water below the ice. Laboratory analysis on a sample from the crater wall that likely represented the clays beneath the ice confirmed that the controlling factor for the lack of sNMR signal was the fine-grained nature of the media. The laboratory measurements further indicated that small pores in clays detected by the XRD contain as much as 50% water, establishing an upper bound on the water content in the clay layer. Forward modelling of geologic scenarios revealed that bulk water layers as thin as ½ m between the ice and clay layer would have been detectable using sNMR. The instrumentation conditions which would allow for sNMR detection of the clay layer are investigated. Using current instrumentation the combined analysis of the TEM and sNMR data allow for valuable characterisation of the groundwater system in the crater. The sNMR is able to reduce the uncertainty of the TEM in regards to the presence of a bulk water layer, a valuable piece of information in hazard assessment.
","language":"English","publisher":"European Association of Geoscientists & Engineers","doi":"10.3997/1873-0604.2014009","usgsCitation":"Irons, T.P., Martin, K., Finn, C.A., Bloss, B.R., and Horton, R., 2014, Using nuclear magnetic resonance and transient electromagnetics to characterise water distribution beneath an ice covered volcanic crater: The case of Sherman Crater Mt. Baker Washington.: Near Surface Geophysics, v. 12, no. 2, p. 285-296, https://doi.org/10.3997/1873-0604.2014009.","productDescription":"12 p.","startPage":"285","endPage":"296","onlineOnly":"N","additionalOnlineFiles":"N","ipdsId":"IP-053051","costCenters":[{"id":211,"text":"Crustal Geophysics and Geochemistry Science Center","active":true,"usgs":true},{"id":615,"text":"Volcano Hazards Program","active":true,"usgs":true}],"links":[{"id":306810,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","state":"Washington","otherGeospatial":"Mount Baker, Sherman Crater","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -122.05947875976562,\n 48.62110864256238\n ],\n [\n -122.05947875976562,\n 48.89722676235673\n ],\n [\n -121.55548095703125,\n 48.89722676235673\n ],\n [\n -121.55548095703125,\n 48.62110864256238\n ],\n [\n -122.05947875976562,\n 48.62110864256238\n ]\n ]\n ]\n }\n }\n ]\n}","volume":"12","issue":"2","publishingServiceCenter":{"id":2,"text":"Denver PSC"},"noUsgsAuthors":false,"publicationStatus":"PW","scienceBaseUri":"55d305bce4b0518e35468d35","contributors":{"authors":[{"text":"Irons, Trevor P. tirons@usgs.gov","contributorId":4851,"corporation":false,"usgs":true,"family":"Irons","given":"Trevor","email":"tirons@usgs.gov","middleInitial":"P.","affiliations":[],"preferred":true,"id":567909,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Martin, Kathryn","contributorId":146449,"corporation":false,"usgs":false,"family":"Martin","given":"Kathryn","email":"","affiliations":[{"id":16695,"text":"Army Corps of Engineers","active":true,"usgs":false}],"preferred":false,"id":567910,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Finn, Carol A. 0000-0002-6178-0405 cfinn@usgs.gov","orcid":"https://orcid.org/0000-0002-6178-0405","contributorId":1326,"corporation":false,"usgs":true,"family":"Finn","given":"Carol","email":"cfinn@usgs.gov","middleInitial":"A.","affiliations":[{"id":211,"text":"Crustal Geophysics and Geochemistry Science Center","active":true,"usgs":true}],"preferred":true,"id":567908,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Bloss, Benjamin R. 0000-0002-1678-8571 bbloss@usgs.gov","orcid":"https://orcid.org/0000-0002-1678-8571","contributorId":139981,"corporation":false,"usgs":true,"family":"Bloss","given":"Benjamin","email":"bbloss@usgs.gov","middleInitial":"R.","affiliations":[{"id":211,"text":"Crustal Geophysics and Geochemistry Science Center","active":true,"usgs":true}],"preferred":true,"id":567911,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Horton, Robert 0000-0001-5578-3733 rhorton@usgs.gov","orcid":"https://orcid.org/0000-0001-5578-3733","contributorId":612,"corporation":false,"usgs":true,"family":"Horton","given":"Robert","email":"rhorton@usgs.gov","affiliations":[{"id":211,"text":"Crustal Geophysics and Geochemistry Science Center","active":true,"usgs":true}],"preferred":true,"id":567912,"contributorType":{"id":1,"text":"Authors"},"rank":5}]}} ,{"id":70056060,"text":"70056060 - 2014 - Lipid and moisture content modeling of amphidromous Dolly Varden using bioelectrical impedance analysis","interactions":[],"lastModifiedDate":"2014-05-06T09:49:15","indexId":"70056060","displayToPublicDate":"2014-05-05T16:06:00","publicationYear":"2014","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":2886,"text":"North American Journal of Fisheries Management","active":true,"publicationSubtype":{"id":10}},"title":"Lipid and moisture content modeling of amphidromous Dolly Varden using bioelectrical impedance analysis","docAbstract":"The physiological well-being or condition of fish is most commonly estimated from aspects of individual morphology. However, these metrics may be only weakly correlated with nutritional reserves stored as lipid, the primary form of accumulated energy in fish. We constructed and evaluated bioelectrical impedance analysis (BIA) models as an alternative method of assessing condition in amphidromous Dolly Varden Salvelinus malma collected from nearshore estuarine and lotic habitats of the Alaskan Arctic. Data on electrical resistance and reactance were collected from the lateral and ventral surfaces of 192 fish, and whole-body percent lipid and moisture content were determined using standard laboratory methods. Significant inverse relationships between temperature and resistance and reactance prompted the standardization of these data to a constant temperature using corrective equations developed herein. No significant differences in resistance or reactance were detected among spawning and nonspawning females after accounting for covariates, suggesting that electrical pathways do not intersect the gonads. Best-fit BIA models incorporating electrical variables calculated from the lateral and ventral surfaces produced the strongest associations between observed and model-predicted estimates of proximate content. These models explained between 6% and 20% more of the variability in laboratory-derived estimates of proximate content than models developed from single-surface BIA data and 32% more than models containing only length and weight data. While additional research is required to address the potential effects of methodological variation, bioelectrical impedance analysis shows promise as a way to provide high-quality, minimally invasive estimates of Dolly Varden lipid or moisture content in the field with only small increases in handling time.","largerWorkType":{"id":2,"text":"Article"},"largerWorkTitle":"North American Journal of Fisheries Management","largerWorkSubtype":{"id":10,"text":"Journal Article"},"language":"English","publisher":"Taylor & Francis","doi":"10.1080/02755947.2014.880764","usgsCitation":"Stolarski, J., Margraf, F., Carlson, J., and Sutton, T., 2014, Lipid and moisture content modeling of amphidromous Dolly Varden using bioelectrical impedance analysis: North American Journal of Fisheries Management, v. 34, no. 3, p. 471-481, https://doi.org/10.1080/02755947.2014.880764.","productDescription":"11 p.","startPage":"471","endPage":"481","numberOfPages":"11","ipdsId":"IP-044095","costCenters":[{"id":199,"text":"Coop Res Unit Leetown","active":true,"usgs":true}],"links":[{"id":286920,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"},{"id":286919,"type":{"id":10,"text":"Digital Object Identifier"},"url":"https://dx.doi.org/10.1080/02755947.2014.880764"}],"country":"United States","state":"Alaska","geographicExtents":"{ \"type\": \"FeatureCollection\", \"features\": [ { \"type\": \"Feature\", \"properties\": {}, \"geometry\": { \"type\": \"Polygon\", \"coordinates\": [ [ [ -145.1541,69.6619 ], [ -145.1541,70.1599 ], [ -141.6989,70.1599 ], [ -141.6989,69.6619 ], [ -145.1541,69.6619 ] ] ] } } ] }","volume":"34","issue":"3","noUsgsAuthors":false,"publicationDate":"2014-04-15","publicationStatus":"PW","scienceBaseUri":"536a0463e4b063fb73c0aa10","contributors":{"authors":[{"text":"Stolarski, J.T.","contributorId":96487,"corporation":false,"usgs":true,"family":"Stolarski","given":"J.T.","affiliations":[],"preferred":false,"id":486315,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Margraf, F.J.","contributorId":47738,"corporation":false,"usgs":true,"family":"Margraf","given":"F.J.","email":"","affiliations":[],"preferred":false,"id":486312,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Carlson, J.G.","contributorId":74681,"corporation":false,"usgs":true,"family":"Carlson","given":"J.G.","email":"","affiliations":[],"preferred":false,"id":486314,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Sutton, T.M.","contributorId":72193,"corporation":false,"usgs":true,"family":"Sutton","given":"T.M.","email":"","affiliations":[],"preferred":false,"id":486313,"contributorType":{"id":1,"text":"Authors"},"rank":4}]}} ,{"id":70103370,"text":"ofr20141087 - 2014 - Characterization of potential transport pathways and implications for groundwater management near an anticline in the Central Basin area, Los Angeles County, California","interactions":[],"lastModifiedDate":"2014-05-05T15:36:05","indexId":"ofr20141087","displayToPublicDate":"2014-05-05T15:11:14","publicationYear":"2014","noYear":false,"publicationType":{"id":18,"text":"Report"},"publicationSubtype":{"id":5,"text":"USGS Numbered Series"},"seriesTitle":{"id":330,"text":"Open-File Report","code":"OFR","onlineIssn":"2331-1258","printIssn":"0196-1497","active":true,"publicationSubtype":{"id":5}},"seriesNumber":"2014-1087","title":"Characterization of potential transport pathways and implications for groundwater management near an anticline in the Central Basin area, Los Angeles County, California","docAbstract":"The Central Groundwater Basin (Central Basin) of southern Los Angeles County includes ~280 mi2 of the Los Angeles Coastal Plain and serves as the primary source of water for more than two million residents. In the Santa Fe Springs–Whittier–Norwalk area, located in the northeastern part of the basin, several sources of volatile organic compounds have been identified. The volatile organic compunds are thought to have contributed to a large, commingled contaminant plume in groundwater that extends south-southwest downgradient from the Omega Chemical Corporation Superfund Site across folded geologic strata, known as the Santa Fe Springs Anticline. A multifaceted study—that incorporated a three-dimensional sequence-stratigraphic geologic model, two-dimensional groundwater particle-tracking simulations, and new groundwater chemistry data—was conducted to gain insight into the geologic and hydrologic controls on contaminant migration in the study area and to assess the potential for this shallow groundwater contamination to migrate into producing aquifer zones. Conceptual flow models were developed along a flow-parallel cross section based on the modeled stratigraphic architecture, observed geochemistry, and numerical model simulations that generally agree with observed water levels and contaminant distributions. These models predict that contaminants introduced into groundwater at shallow depths near the Omega Chemical Corporation Superfund Site and along the study cross section will likely migrate downgradient to depths intercepted by public supply wells. These conclusions, however, are subject to limitations and simplifications inherent in the modeling approaches used, as well as a significant scarcity of available geologic and hydrogeochemical information at depth and in the downgradient parts of the study area.","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/ofr20141087","collaboration":"Prepared in cooperation with the Water Replenishment District of Southern California","usgsCitation":"Ponti, D.J., Wagner, B.J., Land, M., and Landon, M.K., 2014, Characterization of potential transport pathways and implications for groundwater management near an anticline in the Central Basin area, Los Angeles County, California: U.S. Geological Survey Open-File Report 2014-1087, Report: vii, 75 p.; Appendix A: 49 p.; 1 Plate: 28.00 x 19.50 inches; Tables 1,4,7; High resolution figures, https://doi.org/10.3133/ofr20141087.","productDescription":"Report: vii, 75 p.; Appendix A: 49 p.; 1 Plate: 28.00 x 19.50 inches; Tables 1,4,7; High resolution figures","numberOfPages":"84","onlineOnly":"Y","ipdsId":"IP-037058","costCenters":[{"id":154,"text":"California Water Science Center","active":true,"usgs":true}],"links":[{"id":286913,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/ofr20141087.jpg"},{"id":286906,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/of/2014/1087/pdf/ofr2014-1087.pdf"},{"id":286907,"type":{"id":3,"text":"Appendix"},"url":"https://pubs.usgs.gov/of/2014/1087/pdf/ofr2014-1087_appendixA.pdf"},{"id":286905,"type":{"id":15,"text":"Index Page"},"url":"https://pubs.usgs.gov/of/2014/1087/"},{"id":286909,"type":{"id":7,"text":"Companion Files"},"url":"https://pubs.usgs.gov/of/2014/1087/downloads/ofr2014-1087_table4.xlsx"},{"id":286908,"type":{"id":7,"text":"Companion Files"},"url":"https://pubs.usgs.gov/of/2014/1087/downloads/ofr2014-1087_table1.xlsx"},{"id":286910,"type":{"id":7,"text":"Companion Files"},"url":"https://pubs.usgs.gov/of/2014/1087/downloads/ofr2014-1087_table7.xlsx"},{"id":286911,"type":{"id":7,"text":"Companion Files"},"url":"https://pubs.usgs.gov/of/2014/1087/downloads/figures/"},{"id":286912,"type":{"id":17,"text":"Plate"},"url":"https://pubs.usgs.gov/of/2014/1087/pdf/ofr2014-1087_plate1.pdf"}],"country":"United States","state":"California","county":"Los Angeles County","geographicExtents":"{ \"type\": \"FeatureCollection\", \"features\": [ { \"type\": \"Feature\", \"properties\": {}, \"geometry\": { \"type\": \"Polygon\", \"coordinates\": [ [ [ -118.5,33.583 ], [ -118.5,34.25 ], [ -117.66,34.25 ], [ -117.66,33.583 ], [ -118.5,33.583 ] ] ] } } ] }","noUsgsAuthors":false,"publicationStatus":"PW","scienceBaseUri":"5368a4d0e4b059f7e82882f5","contributors":{"authors":[{"text":"Ponti, Daniel J. 0000-0002-2437-5144 dponti@usgs.gov","orcid":"https://orcid.org/0000-0002-2437-5144","contributorId":1020,"corporation":false,"usgs":true,"family":"Ponti","given":"Daniel","email":"dponti@usgs.gov","middleInitial":"J.","affiliations":[{"id":237,"text":"Earthquake Science Center","active":true,"usgs":true}],"preferred":true,"id":493274,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Wagner, Brian J. bjwagner@usgs.gov","contributorId":427,"corporation":false,"usgs":true,"family":"Wagner","given":"Brian","email":"bjwagner@usgs.gov","middleInitial":"J.","affiliations":[],"preferred":true,"id":493273,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Land, Michael 0000-0001-5141-0307","orcid":"https://orcid.org/0000-0001-5141-0307","contributorId":56613,"corporation":false,"usgs":true,"family":"Land","given":"Michael","affiliations":[],"preferred":false,"id":493275,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Landon, Matthew K. 0000-0002-5766-0494 landon@usgs.gov","orcid":"https://orcid.org/0000-0002-5766-0494","contributorId":392,"corporation":false,"usgs":true,"family":"Landon","given":"Matthew","email":"landon@usgs.gov","middleInitial":"K.","affiliations":[{"id":154,"text":"California Water Science Center","active":true,"usgs":true}],"preferred":true,"id":493272,"contributorType":{"id":1,"text":"Authors"},"rank":4}]}} ,{"id":70103489,"text":"70103489 - 2014 - Seasonal thaw settlement at drained thermokarst lake basins, Arctic Alaska","interactions":[],"lastModifiedDate":"2018-06-16T18:00:26","indexId":"70103489","displayToPublicDate":"2014-05-05T14:08:00","publicationYear":"2014","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":3554,"text":"The Cryosphere","active":true,"publicationSubtype":{"id":10}},"title":"Seasonal thaw settlement at drained thermokarst lake basins, Arctic Alaska","docAbstract":"Drained thermokarst lake basins (DTLBs) are ubiquitous landforms on Arctic tundra lowland. Their dynamic states are seldom investigated, despite their importance for landscape stability, hydrology, nutrient fluxes, and carbon cycling. Here we report results based on high-resolution Interferometric Synthetic Aperture Radar (InSAR) measurements using space-borne data for a study area located on the North Slope of Alaska near Prudhoe Bay, where we focus on the seasonal thaw settlement within DTLBs, averaged between 2006 and 2010. The majority (14) of the 18 DTLBs in the study area exhibited seasonal thaw settlement of 3–4 cm. However, four of the DTLBs examined exceeded 4 cm of thaw settlement, with one basin experiencing up to 12 cm. Combining the InSAR observations with the in situ active layer thickness measured using ground penetrating radar and mechanical probing, we calculated thaw strain, an index of thaw settlement strength along a transect across the basin that underwent large thaw settlement. We found thaw strains of 10–35% at the basin center, suggesting the seasonal melting of ground ice as a possible mechanism for the large settlement. These findings emphasize the dynamic nature of permafrost landforms, demonstrate the capability of the InSAR technique to remotely monitor surface deformation of individual DTLBs, and illustrate the combination of ground-based and remote sensing observations to estimate thaw strain. Our study highlights the need for better description of the spatial heterogeneity of landscape-scale processes for regional assessment of surface dynamics on Arctic coastal lowlands.","largerWorkType":{"id":2,"text":"Article"},"largerWorkTitle":"The Cryosphere","largerWorkSubtype":{"id":10,"text":"Journal Article"},"language":"English","publisher":"European Geosciences Union","doi":"10.5194/tc-8-815-2014","usgsCitation":"Liu, L., Schaefer, K., Gusmeroli, A., Grosse, G., Jones, B.M., Zhang, T., Parsekian, A., and Zebker, H., 2014, Seasonal thaw settlement at drained thermokarst lake basins, Arctic Alaska: The Cryosphere, v. 8, p. 815-826, https://doi.org/10.5194/tc-8-815-2014.","productDescription":"12 p.","startPage":"815","endPage":"826","ipdsId":"IP-051116","costCenters":[{"id":118,"text":"Alaska Science Center Geography","active":true,"usgs":true}],"links":[{"id":473003,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.5194/tc-8-815-2014","text":"Publisher Index Page"},{"id":286890,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"},{"id":286886,"type":{"id":10,"text":"Digital Object Identifier"},"url":"https://dx.doi.org/10.5194/tc-8-815-2014"}],"country":"United States","state":"Alaska","geographicExtents":"{ \"type\": \"FeatureCollection\", \"features\": [ { \"type\": \"Feature\", \"properties\": {}, \"geometry\": { \"type\": \"Polygon\", \"coordinates\": [ [ [ -149.9132,70.0825 ], [ -149.9132,70.5707 ], [ -147.7664,70.5707 ], [ -147.7664,70.0825 ], [ -149.9132,70.0825 ] ] ] } } ] }","volume":"8","noUsgsAuthors":false,"publicationDate":"2014-05-05","publicationStatus":"PW","scienceBaseUri":"5368a4d3e4b059f7e828830e","contributors":{"authors":[{"text":"Liu, Lin","contributorId":92950,"corporation":false,"usgs":false,"family":"Liu","given":"Lin","email":"","affiliations":[{"id":36342,"text":"Earth System Science Programme, Faculty of Science, Chinese University of Hong Kong, Hong Kong, China","active":true,"usgs":false}],"preferred":false,"id":493369,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Schaefer, Kevin","contributorId":63323,"corporation":false,"usgs":true,"family":"Schaefer","given":"Kevin","affiliations":[],"preferred":false,"id":493367,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Gusmeroli, Alessio","contributorId":106003,"corporation":false,"usgs":true,"family":"Gusmeroli","given":"Alessio","affiliations":[],"preferred":false,"id":493371,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Grosse, Guido","contributorId":101475,"corporation":false,"usgs":true,"family":"Grosse","given":"Guido","affiliations":[{"id":34291,"text":"University of Potsdam, Germany","active":true,"usgs":false}],"preferred":false,"id":493370,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Jones, Benjamin M. 0000-0002-1517-4711 bjones@usgs.gov","orcid":"https://orcid.org/0000-0002-1517-4711","contributorId":2286,"corporation":false,"usgs":true,"family":"Jones","given":"Benjamin","email":"bjones@usgs.gov","middleInitial":"M.","affiliations":[{"id":118,"text":"Alaska Science Center Geography","active":true,"usgs":true},{"id":114,"text":"Alaska Science Center","active":true,"usgs":true}],"preferred":true,"id":493366,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"Zhang, Tinjun","contributorId":14742,"corporation":false,"usgs":true,"family":"Zhang","given":"Tinjun","email":"","affiliations":[],"preferred":false,"id":493364,"contributorType":{"id":1,"text":"Authors"},"rank":6},{"text":"Parsekian, Andrew","contributorId":21466,"corporation":false,"usgs":true,"family":"Parsekian","given":"Andrew","affiliations":[],"preferred":false,"id":493365,"contributorType":{"id":1,"text":"Authors"},"rank":7},{"text":"Zebker, Howard","contributorId":88072,"corporation":false,"usgs":true,"family":"Zebker","given":"Howard","affiliations":[],"preferred":false,"id":493368,"contributorType":{"id":1,"text":"Authors"},"rank":8}]}} ,{"id":70100414,"text":"70100414 - 2014 - Mineral commodity summaries 2014","interactions":[],"lastModifiedDate":"2014-05-05T14:08:57","indexId":"70100414","displayToPublicDate":"2014-05-05T12:19:00","publicationYear":"2014","noYear":false,"publicationType":{"id":18,"text":"Report"},"publicationSubtype":{"id":6,"text":"USGS Unnumbered Series"},"seriesTitle":{"id":368,"text":"Mineral Commodity Summaries","active":false,"publicationSubtype":{"id":6}},"title":"Mineral commodity summaries 2014","docAbstract":"Each chapter of the 2014 edition of the U.S. Geological Survey (USGS) Mineral Commodity Summaries (MCS) includes information on events, trends, and issues for each mineral commodity as well as discussions and tabular presentations on domestic industry structure, Government programs, tariffs, 5-year salient statistics, and world production and resources. The MCS is the earliest comprehensive source of 2013 mineral production data for the world. More than 90 individual minerals and materials are covered by two-page synopses.
\n\nFor mineral commodities for which there is a Government stockpile, detailed information concerning the stockpile status is included in the two-page synopsis.
\n\nAbbreviations and units of measure, and definitions of selected terms used in the report, are in Appendix A and Appendix B, respectively. “Appendix C—Reserves and Resources” includes “Part A—Resource/Reserve Classification for Minerals” and “Part B—Sources of Reserves Data.” A directory of USGS minerals information country specialists and their responsibilities is Appendix D.
\n\nThe USGS continually strives to improve the value of its publications to users. Constructive comments and suggestions by readers of the MCS 2014 are welcomed.
","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/70100414","collaboration":"This summary is available online, in print and CD-ROM format. Please see the verso of the title page in this summary for ordering information.","usgsCitation":"Mineral commodity summaries 2014; 2014; USGS Unnumbered Series; MINERAL; U.S. Geological Survey","productDescription":"Report: 196 p.; Appendixes A-D","numberOfPages":"199","onlineOnly":"Y","ipdsId":"IP-055293","costCenters":[{"id":432,"text":"National Minerals Information Center","active":true,"usgs":true}],"links":[{"id":286884,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/70100414.GIF"},{"id":286875,"type":{"id":15,"text":"Index Page"},"url":"https://minerals.usgs.gov/minerals/pubs/mcs/"},{"id":286876,"type":{"id":11,"text":"Document"},"url":"https://minerals.usgs.gov/minerals/pubs/mcs/2014/mcs2014.pdf"},{"id":286877,"type":{"id":3,"text":"Appendix"},"url":"https://minerals.usgs.gov/minerals/pubs/mcs/2014/mcsapp2014.pdf"}],"otherGeospatial":"Earth","geographicExtents":"{ \"type\": \"FeatureCollection\", \"features\": [ { \"type\": \"Feature\", \"properties\": {}, \"geometry\": { \"type\": \"Polygon\", \"coordinates\": [ [ [ 180.0,-90.0 ], [ 180.0,90.0 ], [ -180.0,90.0 ], [ -180.0,-90.0 ], [ 180.0,-90.0 ] ] ] } } ] }","noUsgsAuthors":false,"publicationStatus":"PW","scienceBaseUri":"5368a4d2e4b059f7e8288309","contributors":{"authors":[{"text":"Water Resources Division, U.S. Geological Survey","contributorId":128075,"corporation":true,"usgs":false,"organization":"Water Resources Division, U.S. Geological Survey","id":535647,"contributorType":{"id":1,"text":"Authors"},"rank":1}]}} ,{"id":70104357,"text":"70104357 - 2014 - Reservoir controls on the occurrence and production of gas hydrates in nature","interactions":[],"lastModifiedDate":"2014-07-31T10:02:01","indexId":"70104357","displayToPublicDate":"2014-05-05T11:38:00","publicationYear":"2014","noYear":false,"publicationType":{"id":4,"text":"Book"},"publicationSubtype":{"id":12,"text":"Conference publication"},"title":"Reservoir controls on the occurrence and production of gas hydrates in nature","docAbstract":"Gas hydrates in both arctic permafrost regions and deep marine settings can occur at high concentrations in sand-dominated reservoirs, which have been the focus of gas hydrate exploration and production studies in\nnorthern Alaska and Canada, and offshore in the Gulf of Mexico, off the southeastern coast of Japan, in the Ulleung Basin off the east coast of the Korean Peninsula, and along the eastern margin of India. Production testing and\nmodeling has shown that concentrated gas hydrate occurrences in sand reservoirs are conducive to existing well-based production technologies. The resource potential of gas hydrate accumulations in sand-dominated reservoirs have been assessed for several polar terrestrial basins. In 1995, the U.S. Geological Survey (USGS) assigned an in-place resource of 16.7 trillion cubic meters of gas for hydrates in sand-dominated reservoirs on the Alaska North Slope. In a more recent assessment, the USGS indicated that there are about 2.42 trillion cubic meters of technically recoverable gas resources within concentrated, sand-dominated, gas hydrate accumulations in northern Alaska. Estimates of the amount of in-place gas in the sand dominated gas hydrate accumulations of the Mackenzie Delta Beaufort Sea region of the Canadian arctic range from 1.0 to 10 trillion cubic meters of gas. Another prospective gas hydrate resources are those of moderate-to-high concentrations within sandstone reservoirs in marine environments. In 2008, the Bureau of Ocean Energy Management estimated that the Gulf of Mexico contains about 190 trillion cubic meters of gas in highly concentrated hydrate accumulations within sand reservoirs. In 2008, the Japan Oil, Gas and Metals National Corporation reported on a resource assessment of gas hydrates in which they estimated that the volume of gas within the hydrates of the eastern Nankai Trough at about 1.1 trillion cubic meters, with about half concentrated in sand reservoirs. Because conventional production technologies favor sand-dominated gas hydrate reservoirs, sand reservoirs are considered to be the most viable economic target for gas hydrate production and will be the prime focus of most future gas hydrate exploration and development projects.
","conferenceTitle":"Offshore Technology Conference","conferenceDate":"2014-05-04T00:00:00","conferenceLocation":"Houston, TX","language":"English","publisher":"Offshore Technology Conference","publisherLocation":"Houston, TX","doi":"10.4043/25242-MS","usgsCitation":"Collett, T.S., 2014, Reservoir controls on the occurrence and production of gas hydrates in nature, 12 p., https://doi.org/10.4043/25242-MS.","productDescription":"12 p.","numberOfPages":"12","ipdsId":"IP-053730","costCenters":[{"id":164,"text":"Central Energy Resources Science Center","active":true,"usgs":true}],"links":[{"id":289374,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"},{"id":289373,"type":{"id":10,"text":"Digital Object Identifier"},"url":"https://dx.doi.org/10.4043/25242-MS"}],"noUsgsAuthors":false,"publicationDate":"2014-05-05","publicationStatus":"PW","scienceBaseUri":"53b7b205e4b0388651d918b3","contributors":{"authors":[{"text":"Collett, Timothy Scott 0000-0002-7598-4708","orcid":"https://orcid.org/0000-0002-7598-4708","contributorId":90640,"corporation":false,"usgs":true,"family":"Collett","given":"Timothy","email":"","middleInitial":"Scott","affiliations":[],"preferred":false,"id":493719,"contributorType":{"id":1,"text":"Authors"},"rank":1}]}} ,{"id":70103395,"text":"70103395 - 2014 - Habitat used by juvenile lake sturgeon (Acipenser fulvescens) in the North Channel of the St. Clair River (Michigan, USA)","interactions":[],"lastModifiedDate":"2014-06-19T09:29:15","indexId":"70103395","displayToPublicDate":"2014-05-05T11:21:00","publicationYear":"2014","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":2330,"text":"Journal of Great Lakes Research","active":true,"publicationSubtype":{"id":10}},"title":"Habitat used by juvenile lake sturgeon (Acipenser fulvescens) in the North Channel of the St. Clair River (Michigan, USA)","docAbstract":"Lake sturgeon (Acipenser fulvescens) occupy the St. Clair River, part of a channel connecting lakes Huron and Erie in the Laurentian Great Lakes. In the North Channel of the St. Clair River, juvenile lake sturgeon (3–7 years old and 582–793 mm in length) were studied to determine movement patterns and habitat usage. Fourteen juveniles were implanted with ultrasonic transmitters and tracked June–August of 2004, 2005 and 2006. Telemetry data, Geographic Information System software, side-scan sonar, video images of the river bottom, scuba diving, and benthic substrate samples were used to determine the extent and composition of habitats they occupied. Juvenile lake sturgeon habitat selection was strongly related to water depth. No fish were found in <6 m of water and over 97% of the relocations were found at depths greater than 9 m. Available water depths exceeding 18 m only represented 3.5% of the available habitat, however 34.9% of the relocations were found at depths exceeding 18 m. Juvenile lake sturgeon did not use most areas in proportion to their availability. Sturgeon avoided clay ledges and shallow areas with silt or soft clay, which comprised approximately 39% of the benthic habitat in the North Channel. A total of 300 out of 351 documented locations were on sand and gravel habitat types mixed with clay. Lake sturgeon > 700 mm in length selected sand and gravel areas mixed with zebra mussels and areas dominated by zebra mussels, while fish < 700 mm used these habitat types in proportion to their availability.","largerWorkType":{"id":2,"text":"Article"},"largerWorkTitle":"Journal of Great Lakes Research","largerWorkSubtype":{"id":10,"text":"Journal Article"},"language":"English","publisher":"Elsevier","doi":"10.1016/j.jglr.2013.11.002","usgsCitation":"Boase, J., Manny, B.A., Donald, K.A., Kennedy, G.W., Diana, J., Thomas, M.V., and Chiotti, J., 2014, Habitat used by juvenile lake sturgeon (Acipenser fulvescens) in the North Channel of the St. Clair River (Michigan, USA): Journal of Great Lakes Research, v. 40, p. 81-88, https://doi.org/10.1016/j.jglr.2013.11.002.","productDescription":"8 p.","startPage":"81","endPage":"88","numberOfPages":"8","ipdsId":"IP-055169","costCenters":[{"id":324,"text":"Great Lakes Science Center","active":true,"usgs":true}],"links":[{"id":286873,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"},{"id":286872,"type":{"id":10,"text":"Digital Object Identifier"},"url":"https://dx.doi.org/10.1016/j.jglr.2013.11.002"}],"country":"United States","state":"Michigan","otherGeospatial":"St. Clair River","geographicExtents":"{ \"type\": \"FeatureCollection\", \"features\": [ { \"type\": \"Feature\", \"properties\": {}, \"geometry\": { \"type\": \"Polygon\", \"coordinates\": [ [ [ -83.4285,41.9625 ], [ -83.4285,43.0146 ], [ -82.2876,43.0146 ], [ -82.2876,41.9625 ], [ -83.4285,41.9625 ] ] ] } } ] }","volume":"40","noUsgsAuthors":false,"publicationStatus":"PW","scienceBaseUri":"5368a4d1e4b059f7e82882fa","contributors":{"authors":[{"text":"Boase, James C.","contributorId":72713,"corporation":false,"usgs":true,"family":"Boase","given":"James C.","affiliations":[],"preferred":false,"id":493311,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Manny, Bruce A. 0000-0002-4074-9329 bmanny@usgs.gov","orcid":"https://orcid.org/0000-0002-4074-9329","contributorId":3699,"corporation":false,"usgs":true,"family":"Manny","given":"Bruce","email":"bmanny@usgs.gov","middleInitial":"A.","affiliations":[{"id":324,"text":"Great Lakes Science Center","active":true,"usgs":true}],"preferred":true,"id":493305,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Donald, Katherine A.L.","contributorId":56978,"corporation":false,"usgs":true,"family":"Donald","given":"Katherine","email":"","middleInitial":"A.L.","affiliations":[],"preferred":false,"id":493310,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Kennedy, Gregory W. 0000-0003-1686-6960 gkennedy@usgs.gov","orcid":"https://orcid.org/0000-0003-1686-6960","contributorId":3700,"corporation":false,"usgs":true,"family":"Kennedy","given":"Gregory","email":"gkennedy@usgs.gov","middleInitial":"W.","affiliations":[{"id":324,"text":"Great Lakes Science Center","active":true,"usgs":true}],"preferred":true,"id":493306,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Diana, James S.","contributorId":52137,"corporation":false,"usgs":true,"family":"Diana","given":"James S.","affiliations":[],"preferred":false,"id":493309,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"Thomas, Michael V.","contributorId":47629,"corporation":false,"usgs":true,"family":"Thomas","given":"Michael","email":"","middleInitial":"V.","affiliations":[],"preferred":false,"id":493308,"contributorType":{"id":1,"text":"Authors"},"rank":6},{"text":"Chiotti, Justin A.","contributorId":26629,"corporation":false,"usgs":false,"family":"Chiotti","given":"Justin A.","affiliations":[{"id":12428,"text":"U. 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