{"pageNumber":"607","pageRowStart":"15150","pageSize":"25","recordCount":46883,"records":[{"id":70042464,"text":"fs20123033 - 2012 - Groundwater quality in the Antelope Valley, California","interactions":[],"lastModifiedDate":"2026-05-28T21:36:11.183427","indexId":"fs20123033","displayToPublicDate":"2013-01-09T00:00:00","publicationYear":"2012","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":"2012-3033","title":"Groundwater quality in the Antelope Valley, California","docAbstract":"Groundwater provides more than 40 percent of California’s drinking water. To protect this vital resource, the State of California created the Groundwater Ambient Monitoring and Assessment (GAMA) Program. The Priority Basin Project of the GAMA Program provides a comprehensive assessment of the State’s groundwater quality and increases public access to groundwater-quality information. Antelope Valley is one of the study areas being evaluated. The Antelope study area is approximately 1,600 square miles (4,144 square kilometers) and includes the Antelope Valley groundwater basin (California Department of Water Resources, 2003). Antelope Valley has an arid climate and is part of the Mojave Desert. Average annual rainfall is about 6 inches (15 centimeters). The study area has internal drainage, with runoff from the surrounding mountains draining towards dry lakebeds in the lower parts of the valley. Land use in the study area is approximately 68 percent (%) natural (mostly shrubland and grassland), 24% agricultural, and 8% urban. The primary crops are pasture and hay. The largest urban areas are the cities of Palmdale and Lancaster (2010 populations of 152,000 and 156,000, respectively). Groundwater in this basin is used for public and domestic water supply and for irrigation. The main water-bearing units are gravel, sand, silt, and clay derived from surrounding mountains. The primary aquifers in Antelope Valley are defined as those parts of the aquifers corresponding to the perforated intervals of wells listed in the California Department of Public Health database. Public-supply wells in Antelope Valley are completed to depths between 360 and 700 feet (110 to 213 meters), consist of solid casing from the land surface to a depth of 180 to 350 feet (55 to 107 meters), and are screened or perforated below the solid casing. Recharge to the groundwater system is primarily runoff from the surrounding mountains, and by direct infiltration of irrigation and sewer and septic systems. The primary sources of discharge are pumping wells and evapotranspiration near the dry lakebeds.","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/fs20123033","collaboration":"U.S. Geological Survey and the California State Water Resources Control Board. This report has related reports. Please see: SIR 2012-5040, FS 2012-3032, FS 2012-3034, FS 2012-3035, FS 2012-3036, FS 2012-3098.","usgsCitation":"Dawson, B.J., and Belitz, K., 2012, Groundwater quality in the Antelope Valley, California: U.S. Geological Survey Fact Sheet 2012-3033, Report: 4 p.; Related Reports: SIR 2012-5040, FS 2012-3032, FS 2012-3034, FS 2012-3035, FS 2012-3036, FS 2012-3098, https://doi.org/10.3133/fs20123033.","productDescription":"4 p.","additionalOnlineFiles":"Y","costCenters":[{"id":154,"text":"California Water Science Center","active":true,"usgs":true}],"links":[{"id":504845,"rank":10,"type":{"id":36,"text":"NGMDB Index Page"},"url":"https://ngmdb.usgs.gov/Prodesc/proddesc_98036.htm","linkFileType":{"id":5,"text":"html"}},{"id":265444,"rank":3,"type":{"id":22,"text":"Related Work"},"url":"https://pubs.usgs.gov/fs/2012/3098"},{"id":265443,"rank":4,"type":{"id":22,"text":"Related Work"},"url":"https://pubs.usgs.gov/fs/2012/3036"},{"id":265442,"rank":5,"type":{"id":22,"text":"Related Work"},"url":"https://pubs.usgs.gov/fs/2012/3035"},{"id":265441,"rank":6,"type":{"id":22,"text":"Related Work"},"url":"https://pubs.usgs.gov/fs/2012/3034"},{"id":265440,"rank":7,"type":{"id":22,"text":"Related Work"},"url":"https://pubs.usgs.gov/fs/2012/3032"},{"id":265439,"rank":8,"type":{"id":22,"text":"Related Work"},"url":"https://pubs.usgs.gov/sir/2012/5040/"},{"id":265438,"rank":1,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/fs/2012/3033/pdf/fs20123033.pdf"},{"id":265437,"rank":2,"type":{"id":15,"text":"Index Page"},"url":"https://pubs.usgs.gov/fs/2012/3033/"},{"id":265445,"rank":9,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/fs_2012_3033.jpg"}],"country":"United States","state":"California","otherGeospatial":"Antelope Valley","geographicExtents":"{ \"type\": \"FeatureCollection\", \"features\": [ { \"type\": \"Feature\", \"properties\": {}, \"geometry\": { \"type\": \"Polygon\", \"coordinates\": [ [ [ -118.75,34.25 ], [ -118.75,35.25 ], [ -117.5,35.25 ], [ -117.5,34.25 ], [ -118.75,34.25 ] ] ] } } ] }","noUsgsAuthors":false,"publicationStatus":"PW","scienceBaseUri":"50ee9170e4b0160a2d0ee32f","contributors":{"authors":[{"text":"Dawson, Barbara J. Milby 0000-0002-0209-8158","orcid":"https://orcid.org/0000-0002-0209-8158","contributorId":57334,"corporation":false,"usgs":true,"family":"Dawson","given":"Barbara","email":"","middleInitial":"J. Milby","affiliations":[],"preferred":false,"id":471594,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Belitz, Kenneth 0000-0003-4481-2345 kbelitz@usgs.gov","orcid":"https://orcid.org/0000-0003-4481-2345","contributorId":442,"corporation":false,"usgs":true,"family":"Belitz","given":"Kenneth","email":"kbelitz@usgs.gov","affiliations":[{"id":503,"text":"Office of Water Quality","active":true,"usgs":true},{"id":451,"text":"National Water Quality Assessment Program","active":true,"usgs":true},{"id":466,"text":"New England Water Science Center","active":true,"usgs":true},{"id":27111,"text":"National Water Quality Program","active":true,"usgs":true},{"id":376,"text":"Massachusetts Water Science Center","active":true,"usgs":true}],"preferred":true,"id":471593,"contributorType":{"id":1,"text":"Authors"},"rank":2}]}} ,{"id":70042357,"text":"sir20125269 - 2012 - Sediment transport to and from small impoundments in northeast Kansas, March 2009 through September 2011","interactions":[],"lastModifiedDate":"2013-01-17T11:22:10","indexId":"sir20125269","displayToPublicDate":"2013-01-08T00:00:00","publicationYear":"2012","noYear":false,"publicationType":{"id":18,"text":"Report"},"publicationSubtype":{"id":5,"text":"USGS Numbered Series"},"seriesTitle":{"id":334,"text":"Scientific Investigations Report","code":"SIR","onlineIssn":"2328-0328","printIssn":"2328-031X","active":true,"publicationSubtype":{"id":5}},"seriesNumber":"2012-5269","title":"Sediment transport to and from small impoundments in northeast Kansas, March 2009 through September 2011","docAbstract":"The U.S. Geological Survey, in cooperation with the Kansas Water Office, investigated sediment transport to and from three small impoundments (average surface area of 0.1 to 0.8 square miles) in northeast Kansas during March 2009 through September 2011. Streamgages and continuous turbidity sensors were operated upstream and downstream from Atchison County, Banner Creek, and Centralia Lakes to study the effect of varied watershed characteristics and agricultural practices on sediment transport in small watersheds in northeast Kansas. Atchison County Lake is located in a predominantly agricultural basin of row crops, with wide riparian buffers along streams, a substantial amount of tile drainage, and numerous small impoundments (less than 0.05 square miles; hereafter referred to as “ponds”). Banner Creek Lake is a predominantly grassland basin with numerous small ponds located in the watershed, and wide riparian buffers along streams. Centralia Lake is a predominantly agricultural basin of row crops with few ponds, few riparian buffers along streams, and minimal tile drainage. Upstream from Atchison County, Banner Creek, and Centralia Lakes 24, 38, and 32 percent, respectively, of the total load was transported during less than 0.1 percent (approximately 0.9 days) of the time. Despite less streamflow in 2011, larger sediment loads during that year indicate that not all storm events transport the same amount of sediment; larger, extreme storms during the spring may transport much larger sediment loads in small Kansas watersheds. Annual sediment yields were 360, 400, and 970 tons per square mile per year at Atchison County, Banner, and Centralia Lake watersheds, respectively, which were less than estimated yields for this area of Kansas (between 2,000 and 5,000 tons per square mile per year). Although Centralia and Atchison County Lakes had similar percentages of agricultural land use, mean annual sediment yields upstream from Centralia Lake were about 2.7 times those at Atchison County or Banner Creek Lakes. These data indicate larger yields of sediment from watersheds with row crops and those with fewer small ponds, and smaller yields in watersheds which are primarily grassland, or agricultural with substantial tile drainage and riparian buffers along streams. These results also indicated that a cultivated watershed can produce yields similar to those observed under the assumed reference (or natural) condition. Selected small ponds were studied in the Atchison County Lake watershed to characterize the role of small ponds in sediment trapping. Studied ponds trapped about 8 percent of the sediment upstream from the sediment-sampling site. When these results were extrapolated to the other ponds in the watershed, differences in the extent of these ponds was not the primary factor affecting differences in yields among the three watersheds. However, the selected small ponds were both 45 years old at the time of this study, and have reduced capacity because of being filled in with sediments. Additionally, trapping efficiency of these small ponds decreased over five observed storms, indicating that processes that suspended or resuspended sediments in these shallow ponds, such as wind and waves, affected their trapping efficiencies. While small ponds trapped sediments in small storms, they could be a source of sediment in larger or more closely spaced storm events. Channel slope was similar at all three watersheds, 0.40, 0.46, and 0.31 percent at Atchison County, Banner Creek, and Centralia Lake watersheds, respectively. Other factors, such as increased bank and stream erosion, differences in tile drainage, extent of grassland, or riparian buffers, could be the predominant factors affecting sediment yields from these basins. These results show that reference-like sediment yields may be observed in heavily agricultural watersheds through a combination of field-scale management activities and stream channel protection. When computing loads using published erosion rates obtained by single-point survey methodology, streambank contributions from the main stem of Banner Creek are three times more than the sediment load observed by this study at the sediment sampling site at Banner Creek, 2.6 times more than the sediment load observed by this study at the sediment sampling site at Clear Creek (upstream from Atchison County Lake), and are 22 percent of the load observed by this study at the sediment sampling site at Black Vermillion River above Centralia Lake. Comparisons of study sites to similarly sized urban and urbanizing watersheds in Johnson County, Kansas indicated that sediment yields from the Centralia Lake watershed were similar to those in construction-affected watersheds, while much smaller sediment yields in the Atchison County and Banner Creek watersheds were comparable to stable, heavily urbanized watersheds. Comparisons of study sites to larger watersheds upstream from Tuttle Creek Lake indicate the Black Vermillion River watershed continues to have high sediment yields despite 98 percent of sediment from the Centralia watershed (a headwater of the Black Vermillion River) being trapped in Centralia Lake. Estimated trapping efficiencies for the larger watershed lakes indicated that Banner Creek and Centralia Lakes trapped 98 percent of incoming sediment, whereas Atchison County Lake trapped 72 percent of incoming sediment during the 3-year study period.","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/sir20125269","collaboration":"Prepared in cooperation with the Kansas Water Office","usgsCitation":"Foster, G., Lee, C., and Ziegler, A., 2012, Sediment transport to and from small impoundments in northeast Kansas, March 2009 through September 2011: U.S. Geological Survey Scientific Investigations Report 2012-5269, vi, 38 p., https://doi.org/10.3133/sir20125269.","productDescription":"vi, 38 p.","numberOfPages":"48","onlineOnly":"Y","temporalStart":"2009-03-01","temporalEnd":"2011-09-30","ipdsId":"IP-035289","costCenters":[{"id":353,"text":"Kansas Water Science Center","active":false,"usgs":true}],"links":[{"id":265371,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/sir_2012_5269.gif"},{"id":265370,"type":{"id":15,"text":"Index Page"},"url":"https://pubs.usgs.gov/sir/2012/5269/"},{"id":265369,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/sir/2012/5269/sir12-5269.pdf"}],"country":"United States","state":"Kansas","county":"Atchison;Brown;Doniphan;Jackson;Jefferson;Marshall;Nemaha;Pottawatomie","geographicExtents":"{ \"type\": \"FeatureCollection\", \"features\": [ { \"type\": \"Feature\", \"properties\": {}, \"geometry\": { \"type\": \"Polygon\", \"coordinates\": [ [ [ -96.333333,39.366667 ], [ -96.333333,39.8 ], [ -95.25,39.8 ], [ -95.25,39.366667 ], [ -96.333333,39.366667 ] ] ] } } ] }","noUsgsAuthors":false,"publicationStatus":"PW","scienceBaseUri":"50ed3fe1e4b0438b00db0746","contributors":{"authors":[{"text":"Foster, Guy M. gfoster@usgs.gov","contributorId":3437,"corporation":false,"usgs":true,"family":"Foster","given":"Guy M.","email":"gfoster@usgs.gov","affiliations":[{"id":353,"text":"Kansas Water Science Center","active":false,"usgs":true}],"preferred":false,"id":471375,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Lee, Casey J. 0000-0002-5753-2038","orcid":"https://orcid.org/0000-0002-5753-2038","contributorId":31062,"corporation":false,"usgs":true,"family":"Lee","given":"Casey J.","affiliations":[],"preferred":false,"id":471376,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Ziegler, Andrew C. aziegler@usgs.gov","contributorId":433,"corporation":false,"usgs":true,"family":"Ziegler","given":"Andrew C.","email":"aziegler@usgs.gov","affiliations":[{"id":353,"text":"Kansas Water Science Center","active":false,"usgs":true}],"preferred":false,"id":471374,"contributorType":{"id":1,"text":"Authors"},"rank":3}]}} ,{"id":70042411,"text":"ds734 - 2012 - Quality of surface water in Missouri, water year 2011","interactions":[],"lastModifiedDate":"2026-05-12T17:35:21.38095","indexId":"ds734","displayToPublicDate":"2013-01-07T00:00:00","publicationYear":"2012","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":"734","title":"Quality of surface water in Missouri, water year 2011","docAbstract":"

The U.S. Geological Survey, in cooperation with the Missouri Department of Natural Resources, designed and operates a series of monitoring stations on streams throughout Missouri known as the Ambient Water-Quality Monitoring Network. During the 2011 water year (October 1, 2010, through September 30, 2011), data were collected at 75 stations—72 Ambient Water-Quality Monitoring Network stations, 2 U.S. Geological Survey National Stream Quality Accounting Network stations, and 1 spring sampled in cooperation with the U.S. Forest Service. Dissolved oxygen, specific conductance, water temperature, suspended solids, suspended sediment, fecal coliform bacteria, Escherichia coli bacteria, dissolved nitrate plus nitrite, total phosphorus, dissolved and total recoverable lead and zinc, and select pesticide compound summaries are presented for 72 of these stations. The stations primarily have been classified into groups corresponding to the physiography of the State, primary land use, or unique station types. In addition, a summary of hydrologic conditions in the State including peak discharges, monthly mean discharges, and 7-day low flow is presented.

","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/ds734","collaboration":"Prepared in cooperation with the Missouri Department of Natural Resources","usgsCitation":"Barr, M.N., 2012, Quality of surface water in Missouri, water year 2011: U.S. Geological Survey Data Series 734, vi, 22 p., https://doi.org/10.3133/ds734.","productDescription":"vi, 22 p.","numberOfPages":"32","onlineOnly":"Y","additionalOnlineFiles":"N","temporalStart":"2010-10-01","temporalEnd":"2011-09-30","costCenters":[{"id":396,"text":"Missouri Water Science Center","active":true,"usgs":true}],"links":[{"id":265364,"rank":1,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/ds/734/ds734.pdf"},{"id":265363,"rank":2,"type":{"id":15,"text":"Index Page"},"url":"https://pubs.usgs.gov/ds/734/"},{"id":265365,"rank":3,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/ds_734.gif"},{"id":504283,"rank":4,"type":{"id":36,"text":"NGMDB Index Page"},"url":"https://ngmdb.usgs.gov/Prodesc/proddesc_98042.htm","linkFileType":{"id":5,"text":"html"}}],"projection":"Universal Transverse Mercator projection, Zone 15","datum":"North American Datum of 1983","country":"United States","state":"Missouri","geographicExtents":"{ \"type\": \"FeatureCollection\", \"features\": [ { \"type\": \"Feature\", \"properties\": {}, \"geometry\": { \"type\": \"Polygon\", \"coordinates\": [ [ [ -95.8,36.0 ], [ -95.8,40.6 ], [ -89.1,40.6 ], [ -89.1,36.0 ], [ -95.8,36.0 ] ] ] } } ] }","noUsgsAuthors":false,"publicationStatus":"PW","scienceBaseUri":"50ebee6ee4b07f1501afcfc0","contributors":{"authors":[{"text":"Barr, Miya N. 0000-0002-9961-9190 mnbarr@usgs.gov","orcid":"https://orcid.org/0000-0002-9961-9190","contributorId":3686,"corporation":false,"usgs":true,"family":"Barr","given":"Miya","email":"mnbarr@usgs.gov","middleInitial":"N.","affiliations":[{"id":396,"text":"Missouri Water Science Center","active":true,"usgs":true},{"id":36532,"text":"Central Midwest Water Science Center","active":true,"usgs":true}],"preferred":true,"id":471488,"contributorType":{"id":1,"text":"Authors"},"rank":1}]}} ,{"id":70042409,"text":"ds709J - 2012 - Local-area-enhanced, 2.5-meter resolution natural-color and color-infrared satellite-image mosaics of the Tourmaline mineral district in Afghanistan: Chapter J in Local-area-enhanced, high-resolution natural-color and color-infrared satellite-image mosaics of mineral districts in Afghanistan","interactions":[],"lastModifiedDate":"2013-02-01T11:10:57","indexId":"ds709J","displayToPublicDate":"2013-01-07T00:00:00","publicationYear":"2012","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":"709","chapter":"J","title":"Local-area-enhanced, 2.5-meter resolution natural-color and color-infrared satellite-image mosaics of the Tourmaline mineral district in Afghanistan: Chapter J in Local-area-enhanced, high-resolution natural-color and color-infrared satellite-image mosaics of mineral districts in Afghanistan","docAbstract":"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 Tourmaline mineral district, which has tin deposits. ALOS 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 (©JAXA,2008), 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. The 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. For this particular area, PRISM image orthorectification was performed by the Alaska Satellite Facility, applying its photogrammetric software to PRISM stereo images with vertical control points obtained from the digital elevation database produced by the Shuttle Radar Topography Mission (Farr and others, 2007) and horizontal adjustments based on a controlled Landsat image base (Davis, 2006). The 10-m AVNIR multispectral imagery was then coregistered to the orthorectified PRISM images and individual multispectral and panchromatic images were mosaicked into single images of the entire area of interest. The image coregistration was 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). All image data were initially projected and maintained in Universal Transverse Mercator (UTM) map projection using the target area's local zone (41 for Tourmaline) 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 Tourmaline 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 (DS 709)","largerWorkSubtype":{"id":5,"text":"USGS Numbered Series"},"language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/ds709J","collaboration":"Prepared in cooperation with the U.S. Department of Defense Task Force for Business and Stability Operations and the Afghanistan Geological Survey. This report is Chapter J in Local-area-enhanced, high-resolution natural-color and color-infrared satellite-image mosaics of mineral districts in Afghanistan. For more information, see: Data Series 709.","usgsCitation":"Davis, P.A., Cagney, L.E., Arko, S.A., and Harbin, M., 2012, Local-area-enhanced, 2.5-meter resolution natural-color and color-infrared satellite-image mosaics of the Tourmaline mineral district in Afghanistan: Chapter J in Local-area-enhanced, high-resolution natural-color and color-infrared satellite-image mosaics of mineral districts in Afghanistan: U.S. Geological Survey Data Series 709, Readme; 2 Maps: 11 x 8.5 inches and 26.58 x 24.67 inches; 8 Image Files; 8 Metadata Files; Shapefiles; DS 709, https://doi.org/10.3133/ds709J.","productDescription":"Readme; 2 Maps: 11 x 8.5 inches and 26.58 x 24.67 inches; 8 Image Files; 8 Metadata Files; Shapefiles; DS 709","onlineOnly":"Y","additionalOnlineFiles":"Y","temporalStart":"2006-01-24","costCenters":[{"id":387,"text":"Mineral Resources Program","active":true,"usgs":true}],"links":[{"id":265351,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/ds_709_j.jpg"},{"id":265344,"type":{"id":17,"text":"Plate"},"url":"https://pubs.usgs.gov/ds/709/j/index_maps/Tourmaline_Area-of-Interest_Index_Map.pdf"},{"id":265345,"type":{"id":17,"text":"Plate"},"url":"https://pubs.usgs.gov/ds/709/j/index_maps/Tourmaline_Image_Index_Map.pdf"},{"id":265346,"type":{"id":7,"text":"Companion Files"},"url":"https://pubs.usgs.gov/ds/709/j/index_maps/index_maps.html"},{"id":265347,"type":{"id":14,"text":"Image"},"url":"https://pubs.usgs.gov/ds/709/j/image_files/image_files.html"},{"id":265348,"type":{"id":16,"text":"Metadata"},"url":"https://pubs.usgs.gov/ds/709/j/metadata/metadata.html"},{"id":265349,"type":{"id":2,"text":"Additional Report Piece"},"url":"https://pubs.usgs.gov/ds/709/j/shapefiles/shapefiles.html"},{"id":265350,"type":{"id":22,"text":"Related Work"},"url":"https://pubs.usgs.gov/ds/709/index.html"},{"id":265342,"type":{"id":15,"text":"Index Page"},"url":"https://pubs.usgs.gov/ds/709/j/"},{"id":265343,"type":{"id":20,"text":"Read Me"},"url":"https://pubs.usgs.gov/ds/709/j/1_readme.txt"}],"country":"Afghanistan","state":"Farah;Herat","otherGeospatial":"Tourmaline Mineral District","geographicExtents":"{ \"type\": \"FeatureCollection\", \"features\": [ { \"type\": \"Feature\", \"properties\": {}, \"geometry\": { \"type\": \"Polygon\", \"coordinates\": [ [ [ 61.5,32.8 ], [ 61.5,33.25 ], [ 62.0,33.25 ], [ 62.0,32.8 ], [ 61.5,32.8 ] ] ] } } ] }","noUsgsAuthors":false,"publicationStatus":"PW","scienceBaseUri":"50ebee6de4b07f1501afcfbc","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":471481,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Cagney, Laura E. 0000-0003-3282-2458 lcagney@usgs.gov","orcid":"https://orcid.org/0000-0003-3282-2458","contributorId":4744,"corporation":false,"usgs":true,"family":"Cagney","given":"Laura","email":"lcagney@usgs.gov","middleInitial":"E.","affiliations":[{"id":568,"text":"Southwest Biological Science Center","active":true,"usgs":true}],"preferred":true,"id":471482,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Arko, Scott A.","contributorId":101929,"corporation":false,"usgs":true,"family":"Arko","given":"Scott","email":"","middleInitial":"A.","affiliations":[],"preferred":false,"id":471484,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Harbin, Michelle L.","contributorId":20590,"corporation":false,"usgs":true,"family":"Harbin","given":"Michelle L.","affiliations":[],"preferred":false,"id":471483,"contributorType":{"id":1,"text":"Authors"},"rank":4}]}} ,{"id":70042410,"text":"ds709K - 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ALOS 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 (©JAXA,2008), 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. 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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). All image data were initially projected and maintained in Universal Transverse Mercator (UTM) map projection using the target area’ local zone (41 for Dusar-Shaida) and the WGS84 datum. The final image mosaics were subdivided into eight overlapping tiles or quadrants because of the large size of the target area. The eight image tiles (or quadrants) for the Dusar-Shaida 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 Dusar-Shaida study area, three subareas were designated for detailed field investigations (that is, the Dahana-Misgaran, Kaftar VMS, and Shaida subareas); these subareas were extracted from the area’ image mosaic and are provided as separate embedded geotiff images.","largerWorkType":{"id":18,"text":"Report"},"largerWorkTitle":"Local-area-enhanced, high-resolution natural-color and color-infrared satellite-image mosaics of mineral districts in Afghanistan (DS 709)","largerWorkSubtype":{"id":5,"text":"USGS Numbered Series"},"language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/ds709I","collaboration":"Prepared in cooperation with the U.S. Department of Defense Task Force for Business and Stability Operations and the Afghanistan Geological Survey. This report is Chapter I in Local-area-enhanced, high-resolution natural-color and color-infrared satellite-image mosaics of mineral districts in Afghanistan. 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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 Kundalyan mineral district, which has porphyry copper and gold deposits. ALOS 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 (©JAXA,2008), 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. The 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. For this particular area, PRISM image orthorectification was performed by the Alaska Satellite Facility, applying its photogrammetric software to PRISM stereo images with vertical control points obtained from the digital elevation database produced by the Shuttle Radar Topography Mission (Farr and others, 2007) and horizontal adjustments based on a controlled Landsat image base (Davis, 2006). The 10-m AVNIR multispectral imagery was then coregistered to the orthorectified PRISM images and individual multispectral and panchromatic images were mosaicked into single images of the entire area of interest. The image coregistration was 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). All image data were initially projected and maintained in Universal Transverse Mercator (UTM) map projection using the target area’s local zone (42 for Kundalyan) and the WGS84 datum. The final image mosaics were subdivided into five overlapping tiles or quadrants because of the large size of the target area. The five image tiles (or quadrants) for the Kundalyan 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 Kundalyan study area, three subareas were designated for detailed field investigations (that is, the Baghawan-Garangh, Charsu-Ghumbad, and Kunag Skarn subareas); these subareas were extracted from the area’s image mosaic and are provided as separate embedded geotiff images.","largerWorkType":{"id":18,"text":"Report"},"largerWorkTitle":"Local-area-enhanced, high-resolution natural-color and color-infrared satellite-image mosaics of mineral districts in Afghanistan (DS 709)","largerWorkSubtype":{"id":5,"text":"USGS Numbered Series"},"language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/ds709H","collaboration":"Prepared in cooperation with the U.S. Department of Defense Task Force for Business and Stability Operations and the Afghanistan Geological Survey. This report is Chapter H in Local-area-enhanced, high-resolution natural-color and color-infrared satellite-image mosaics of mineral districts in Afghanistan. For more information, see: Data Series 709.","usgsCitation":"Davis, P.A., Cagney, L.E., Arko, S.A., and Harbin, M., 2012, Local-area-enhanced, 2.5-meter resolution natural-color and color-infrared satellite-image mosaics of the Kundalyan mineral district in Afghanistan: Chapter H in Local-area-enhanced, high-resolution natural-color and color-infrared satellite-image mosaics of mineral districts in Afghanistan: U.S. Geological Survey Data Series 709, Readme; 3 Maps: 11 x 8.5 inches and 41.22 x 49.43 inches; 16 Image Files; 16 Metadata Files; Shapefiles; DS 709, https://doi.org/10.3133/ds709H.","productDescription":"Readme; 3 Maps: 11 x 8.5 inches and 41.22 x 49.43 inches; 16 Image Files; 16 Metadata Files; Shapefiles; DS 709","onlineOnly":"Y","additionalOnlineFiles":"Y","temporalStart":"2006-01-24","costCenters":[{"id":387,"text":"Mineral Resources Program","active":true,"usgs":true}],"links":[{"id":265330,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/ds_709_h.jpg"},{"id":265320,"type":{"id":15,"text":"Index Page"},"url":"https://pubs.usgs.gov/ds/709/h/"},{"id":265321,"type":{"id":20,"text":"Read Me"},"url":"https://pubs.usgs.gov/ds/709/h/1_readme.txt"},{"id":265322,"type":{"id":17,"text":"Plate"},"url":"https://pubs.usgs.gov/ds/709/h/index_maps/Kundalyan_Area-of-Interest_Index_Map.pdf"},{"id":265323,"type":{"id":17,"text":"Plate"},"url":"https://pubs.usgs.gov/ds/709/h/index_maps/Kundalyan_Image_Index_Map.pdf"},{"id":265324,"type":{"id":17,"text":"Plate"},"url":"https://pubs.usgs.gov/ds/709/h/index_maps/Kundalyan_Subarea_Image_Index_Map.pdf"},{"id":265325,"type":{"id":7,"text":"Companion Files"},"url":"https://pubs.usgs.gov/ds/709/h/index_maps/index_maps.html"},{"id":265326,"type":{"id":14,"text":"Image"},"url":"https://pubs.usgs.gov/ds/709/h/image_files/image_files.html"},{"id":265327,"type":{"id":16,"text":"Metadata"},"url":"https://pubs.usgs.gov/ds/709/h/metadata/metadata.html"},{"id":265328,"type":{"id":2,"text":"Additional Report Piece"},"url":"https://pubs.usgs.gov/ds/709/h/shapefiles/shapefiles.html"},{"id":265329,"type":{"id":22,"text":"Related Work"},"url":"https://pubs.usgs.gov/ds/709/"}],"country":"Afghanistan","otherGeospatial":"Kundalyan Mineral District","geographicExtents":"{ \"type\": \"FeatureCollection\", \"features\": [ { \"type\": \"Feature\", \"properties\": {}, \"geometry\": { \"type\": \"Polygon\", \"coordinates\": [ [ [ 66.0,31.75 ], [ 66.0,33.0 ], [ 67.0,33.0 ], [ 67.0,31.75 ], [ 66.0,31.75 ] ] ] } } ] }","noUsgsAuthors":false,"publicationStatus":"PW","scienceBaseUri":"50ebee6de4b07f1501afcfb8","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":471466,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Cagney, Laura E. 0000-0003-3282-2458 lcagney@usgs.gov","orcid":"https://orcid.org/0000-0003-3282-2458","contributorId":4744,"corporation":false,"usgs":true,"family":"Cagney","given":"Laura","email":"lcagney@usgs.gov","middleInitial":"E.","affiliations":[{"id":568,"text":"Southwest Biological Science Center","active":true,"usgs":true}],"preferred":true,"id":471467,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Arko, Scott A.","contributorId":101929,"corporation":false,"usgs":true,"family":"Arko","given":"Scott","email":"","middleInitial":"A.","affiliations":[],"preferred":false,"id":471469,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Harbin, Michelle L.","contributorId":20590,"corporation":false,"usgs":true,"family":"Harbin","given":"Michelle L.","affiliations":[],"preferred":false,"id":471468,"contributorType":{"id":1,"text":"Authors"},"rank":4}]}} ,{"id":70042374,"text":"sir20125268 - 2012 - Hydrologic and sediment data collected from selected basins at the Fort Leonard Wood Military Reservation, Missouri--2010-11","interactions":[],"lastModifiedDate":"2013-01-06T13:53:14","indexId":"sir20125268","displayToPublicDate":"2013-01-06T00:00:00","publicationYear":"2012","noYear":false,"publicationType":{"id":18,"text":"Report"},"publicationSubtype":{"id":5,"text":"USGS Numbered Series"},"seriesTitle":{"id":334,"text":"Scientific Investigations Report","code":"SIR","onlineIssn":"2328-0328","printIssn":"2328-031X","active":true,"publicationSubtype":{"id":5}},"seriesNumber":"2012-5268","title":"Hydrologic and sediment data collected from selected basins at the Fort Leonard Wood Military Reservation, Missouri--2010-11","docAbstract":"Commercial and residential development within a basin often increases the amount of impervious area, which changes the natural hydrologic response to storm events by increasing runoff. Land development and disturbance combined with increased runoff from impervious areas potentially can increase sediment transport. At the Fort Leonard Wood Military Reservation in Missouri, there has been an increase in population and construction activities in the recent past, which has initiated an assessment of the hydrology in selected basins. From April 2010 to December 2011, the U.S. Geological Survey, in cooperation with the U.S. Army Maneuver Support Center at the Fort Leonard Wood Military Reservation, collected hydrologic and suspended-sediment concentration data in six basins at Fort Leonard Wood. Storm-sediment concentration, load, and yield varied from basin to basin and from storm to storm. In general, storm-sediment yield, in pounds per square mile per minute, was greatest from Ballard Hollow tributary (06928410) and Dry Creek (06930250), and monthly storm-sediment yield, in tons per square mile, estimates were largest in Ballard Hollow tributary (06928410), East Gate Hollow tributary (06930058), and Dry Creek (06930250). Sediment samples, collected at nine sites, primarily were collected using automatic samplers and augmented with equal-width-increment cross-sectional samples and manually collected samples when necessary. Storm-sediment load and yield were computed from discharge and suspended-sediment concentration data. Monthly storm-sediment yields also were estimated from the total storm discharge and the mean suspended-sediment concentration at each given site.","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/sir20125268","isbn":"978-1-4113-3531-8","collaboration":"Prepared in cooperation with U.S. Army Maneuver Support Center at the Fort Leonard Wood Military Reservation","usgsCitation":"Richards, J.M., Rydlund, P.H., and Barr, M.N., 2012, Hydrologic and sediment data collected from selected basins at the Fort Leonard Wood Military Reservation, Missouri--2010-11: U.S. Geological Survey Scientific Investigations Report 2012-5268, vi, 23 p., https://doi.org/10.3133/sir20125268.","productDescription":"vi, 23 p.","numberOfPages":"36","additionalOnlineFiles":"N","temporalStart":"2010-04-01","temporalEnd":"2011-12-31","ipdsId":"IP-039458","costCenters":[{"id":396,"text":"Missouri Water Science Center","active":true,"usgs":true}],"links":[{"id":265315,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/sir_2012_5268.gif"},{"id":265313,"type":{"id":15,"text":"Index Page"},"url":"https://pubs.usgs.gov/sir/2012/5268/"},{"id":265314,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/sir/2012/5268/sir12-5268.pdf"}],"projection":"Universal Transverse Mercator projection, Zone 15","datum":"North American Datum of 1983","country":"United States","state":"Missouri","county":"Pulaski","otherGeospatial":"Fort Leonard Wood","geographicExtents":"{ \"type\": \"FeatureCollection\", \"features\": [ { \"type\": \"Feature\", \"properties\": {}, \"geometry\": { \"type\": \"Polygon\", \"coordinates\": [ [ [ -92.25,37.583333 ], [ -92.25,37.833333 ], [ -92.0,37.833333 ], [ -92.0,37.583333 ], [ -92.25,37.583333 ] ] ] } } ] }","noUsgsAuthors":false,"publicationStatus":"PW","scienceBaseUri":"50ea9ceee4b02dd6076fad8b","contributors":{"authors":[{"text":"Richards, Joseph M. 0000-0002-9822-2706 richards@usgs.gov","orcid":"https://orcid.org/0000-0002-9822-2706","contributorId":2370,"corporation":false,"usgs":true,"family":"Richards","given":"Joseph","email":"richards@usgs.gov","middleInitial":"M.","affiliations":[{"id":36532,"text":"Central Midwest Water Science Center","active":true,"usgs":true}],"preferred":true,"id":471403,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Rydlund, Paul H. Jr. 0000-0001-9461-9944 prydlund@usgs.gov","orcid":"https://orcid.org/0000-0001-9461-9944","contributorId":3840,"corporation":false,"usgs":true,"family":"Rydlund","given":"Paul","suffix":"Jr.","email":"prydlund@usgs.gov","middleInitial":"H.","affiliations":[{"id":502,"text":"Office of Surface Water","active":true,"usgs":true},{"id":36532,"text":"Central Midwest Water Science Center","active":true,"usgs":true},{"id":396,"text":"Missouri Water Science Center","active":true,"usgs":true}],"preferred":true,"id":471405,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Barr, Miya N. 0000-0002-9961-9190 mnbarr@usgs.gov","orcid":"https://orcid.org/0000-0002-9961-9190","contributorId":3686,"corporation":false,"usgs":true,"family":"Barr","given":"Miya","email":"mnbarr@usgs.gov","middleInitial":"N.","affiliations":[{"id":396,"text":"Missouri Water Science Center","active":true,"usgs":true},{"id":36532,"text":"Central Midwest Water Science Center","active":true,"usgs":true}],"preferred":true,"id":471404,"contributorType":{"id":1,"text":"Authors"},"rank":3}]}} ,{"id":70042074,"text":"70042074 - 2012 - Variance partitioning of stream diatom, fish, and invertebrate indicators of biological condition","interactions":[],"lastModifiedDate":"2014-05-14T12:41:22","indexId":"70042074","displayToPublicDate":"2013-01-05T11:15:00","publicationYear":"2012","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":1699,"text":"Freshwater Science","active":true,"publicationSubtype":{"id":10}},"title":"Variance partitioning of stream diatom, fish, and invertebrate indicators of biological condition","docAbstract":"Stream indicators used to make assessments of biological condition are influenced by many possible sources of variability. To examine this issue, we used multiple-year and multiple-reach diatom, fish, and invertebrate data collected from 20 least-disturbed and 46 developed stream segments between 1993 and 2004 as part of the US Geological Survey National Water Quality Assessment Program. We used a variance-component model to summarize the relative and absolute magnitude of 4 variance components (among-site, among-year, site × year interaction, and residual) in indicator values (observed/expected ratio [O/E] and regional multimetric indices [MMI]) among assemblages and between basin types (least-disturbed and developed). We used multiple-reach samples to evaluate discordance in site assessments of biological condition caused by sampling variability. Overall, patterns in variance partitioning were similar among assemblages and basin types with one exception. Among-site variance dominated the relative contribution to the total variance (64–80% of total variance), residual variance (sampling variance) accounted for more variability (8–26%) than interaction variance (5–12%), and among-year variance was always negligible (0–0.2%). The exception to this general pattern was for invertebrates at least-disturbed sites where variability in O/E indicators was partitioned between among-site and residual (sampling) variance (among-site  =  36%, residual  =  64%). This pattern was not observed for fish and diatom indicators (O/E and regional MMI). We suspect that unexplained sampling variability is what largely remained after the invertebrate indicators (O/E predictive models) had accounted for environmental differences among least-disturbed sites. The influence of sampling variability on discordance of within-site assessments was assemblage or basin-type specific. Discordance among assessments was nearly 2× greater in developed basins (29–31%) than in least-disturbed sites (15–16%) for invertebrates and diatoms, whereas discordance among assessments based on fish did not differ between basin types (least-disturbed  =  16%, developed  =  17%). Assessments made using invertebrate and diatom indicators from a single reach disagreed with other samples collected within the same stream segment nearly ⅓ of the time in developed basins, compared to ⅙ for all other cases.","largerWorkType":{"id":2,"text":"Article"},"largerWorkTitle":"Freshwater Science","largerWorkSubtype":{"id":10,"text":"Journal Article"},"language":"English","publisher":"The Society for Freshwater Science","doi":"10.1899/11-040.1","usgsCitation":"Zuellig, R.E., Carlisle, D.M., Meador, M., and Potapova, M., 2012, Variance partitioning of stream diatom, fish, and invertebrate indicators of biological condition: Freshwater Science, v. 31, no. 1, p. 182-190, https://doi.org/10.1899/11-040.1.","productDescription":"9 p.","startPage":"182","endPage":"190","ipdsId":"IP-011898","costCenters":[{"id":291,"text":"Fort Collins Science Center","active":true,"usgs":true}],"links":[{"id":474107,"rank":0,"type":{"id":41,"text":"Open Access External Repository Page"},"url":"http://www.bioone.org/doi/10.1899/11-040.1","text":"External Repository"},{"id":287130,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"},{"id":287129,"type":{"id":10,"text":"Digital Object Identifier"},"url":"https://dx.doi.org/10.1899/11-040.1"}],"country":"United States","volume":"31","issue":"1","noUsgsAuthors":false,"publicationStatus":"PW","scienceBaseUri":"5374907ae4b0870f4d23d007","contributors":{"authors":[{"text":"Zuellig, Robert E. 0000-0002-4784-2905 rzuellig@usgs.gov","orcid":"https://orcid.org/0000-0002-4784-2905","contributorId":1620,"corporation":false,"usgs":true,"family":"Zuellig","given":"Robert","email":"rzuellig@usgs.gov","middleInitial":"E.","affiliations":[{"id":191,"text":"Colorado Water Science Center","active":true,"usgs":true}],"preferred":true,"id":470743,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Carlisle, Daren M. 0000-0002-7367-348X dcarlisle@usgs.gov","orcid":"https://orcid.org/0000-0002-7367-348X","contributorId":513,"corporation":false,"usgs":true,"family":"Carlisle","given":"Daren","email":"dcarlisle@usgs.gov","middleInitial":"M.","affiliations":[{"id":37277,"text":"WMA - Earth System Processes Division","active":true,"usgs":true},{"id":503,"text":"Office of Water Quality","active":true,"usgs":true},{"id":27111,"text":"National Water Quality Program","active":true,"usgs":true},{"id":451,"text":"National Water Quality Assessment Program","active":true,"usgs":true},{"id":353,"text":"Kansas Water Science Center","active":false,"usgs":true}],"preferred":true,"id":470741,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Meador, Michael R. mrmeador@usgs.gov","contributorId":615,"corporation":false,"usgs":true,"family":"Meador","given":"Michael R.","email":"mrmeador@usgs.gov","affiliations":[{"id":451,"text":"National Water Quality Assessment Program","active":true,"usgs":true}],"preferred":true,"id":470742,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Potapova, Marina","contributorId":89274,"corporation":false,"usgs":true,"family":"Potapova","given":"Marina","email":"","affiliations":[],"preferred":false,"id":470744,"contributorType":{"id":1,"text":"Authors"},"rank":4}]}} ,{"id":70042372,"text":"ds709G - 2012 - Local-area-enhanced, 2.5-meter resolution natural-color and color-infrared satellite-image mosaics of the Zarkashan mineral district in Afghanistan: Chapter G in Local-area-enhanced, high-resolution natural-color and color-infrared satellite-image mosaics of mineral districts in Afghanistan","interactions":[],"lastModifiedDate":"2013-02-01T11:10:42","indexId":"ds709G","displayToPublicDate":"2013-01-04T00:00:00","publicationYear":"2012","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":"709","chapter":"G","title":"Local-area-enhanced, 2.5-meter resolution natural-color and color-infrared satellite-image mosaics of the Zarkashan mineral district in Afghanistan: Chapter G in Local-area-enhanced, high-resolution natural-color and color-infrared satellite-image mosaics of mineral districts in Afghanistan","docAbstract":"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 Zarkashan mineral district, which has copper and gold deposits. ALOS 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 (©JAXA,2006,2007, 2008), 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. The 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 315-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). All image data were initially projected and maintained in Universal Transverse Mercator (UTM) map projection using the target area’s local zone (42 for Zarkashan) and the WGS84 datum. The final image mosaics were subdivided into two overlapping tiles or quadrants because of the large size of the target area. The two image tiles (or quadrants) for the Zarkashan 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 Zarkashan study area, three subareas were designated for detailed field investigations (that is, the Mine Area, Bolo Gold Prospect, and Luman-Tamaki Gold Prospect subareas); these subareas were extracted from the area’s image mosaic and are provided as separate embedded geotiff images.","largerWorkType":{"id":18,"text":"Report"},"largerWorkTitle":"Local-area-enhanced, high-resolution natural-color and color-infrared satellite-image mosaics of mineral districts in Afghanistan (DS 709)","largerWorkSubtype":{"id":5,"text":"USGS Numbered Series"},"language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/ds709G","collaboration":"Prepared in cooperation with the U.S. Department of Defense Task Force for Business and Stability Operations and the Afghanistan Geological Survey. This report is Chapter G in Local-area-enhanced, high-resolution natural-color and color-infrared satellite-image mosaics of mineral districts in Afghanistan. For more information, see: Data Series 709.","usgsCitation":"Davis, P.A., and Cagney, L.E., 2012, Local-area-enhanced, 2.5-meter resolution natural-color and color-infrared satellite-image mosaics of the Zarkashan mineral district in Afghanistan: Chapter G in Local-area-enhanced, high-resolution natural-color and color-infrared satellite-image mosaics of mineral districts in Afghanistan: U.S. Geological Survey Data Series 709, Readme; 2 Maps: 11 x 8.5 inches and 37.63 x 40.38 inches; 10 Images; 10 Metadata Files; Shapefiles; DS 709, https://doi.org/10.3133/ds709G.","productDescription":"Readme; 2 Maps: 11 x 8.5 inches and 37.63 x 40.38 inches; 10 Images; 10 Metadata Files; Shapefiles; DS 709","onlineOnly":"Y","additionalOnlineFiles":"Y","costCenters":[{"id":387,"text":"Mineral Resources Program","active":true,"usgs":true}],"links":[{"id":265290,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/usgs_thumb.jpg"},{"id":265281,"type":{"id":15,"text":"Index Page"},"url":"https://pubs.usgs.gov/ds/709/g/"},{"id":265283,"type":{"id":17,"text":"Plate"},"url":"https://pubs.usgs.gov/ds/709/g/index_maps/Zarkashan_Area-of-Interest_Index_Map.pdf"},{"id":265282,"type":{"id":20,"text":"Read Me"},"url":"https://pubs.usgs.gov/ds/709/g/1_readme.txt"},{"id":265284,"type":{"id":17,"text":"Plate"},"url":"https://pubs.usgs.gov/ds/709/g/index_maps/Zarkashan_Image_Index_Map.pdf"},{"id":265285,"type":{"id":7,"text":"Companion Files"},"url":"https://pubs.usgs.gov/ds/709/g/index_maps/index_maps.html"},{"id":265286,"type":{"id":14,"text":"Image"},"url":"https://pubs.usgs.gov/ds/709/g/image_files/image_files.html"},{"id":265287,"type":{"id":16,"text":"Metadata"},"url":"https://pubs.usgs.gov/ds/709/g/metadata/metadata.html"},{"id":265288,"type":{"id":2,"text":"Additional Report Piece"},"url":"https://pubs.usgs.gov/ds/709/g/shapefiles/shapefiles.html"},{"id":265289,"type":{"id":22,"text":"Related Work"},"url":"https://pubs.usgs.gov/ds/709/index.html"}],"country":"Afghanistan","otherGeospatial":"Zarkashan Mineral District","geographicExtents":"{ \"type\": \"FeatureCollection\", \"features\": [ { \"type\": \"Feature\", \"properties\": {}, \"geometry\": { \"type\": \"Polygon\", \"coordinates\": [ [ [ 67.0,32.5 ], [ 67.0,33.5 ], [ 68.0,33.5 ], [ 68.0,32.5 ], [ 67.0,32.5 ] ] ] } } ] }","noUsgsAuthors":false,"publicationStatus":"PW","scienceBaseUri":"50e7f9ede4b033ce2d2433ed","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":471401,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Cagney, Laura E. 0000-0003-3282-2458 lcagney@usgs.gov","orcid":"https://orcid.org/0000-0003-3282-2458","contributorId":4744,"corporation":false,"usgs":true,"family":"Cagney","given":"Laura","email":"lcagney@usgs.gov","middleInitial":"E.","affiliations":[{"id":568,"text":"Southwest Biological Science Center","active":true,"usgs":true}],"preferred":true,"id":471402,"contributorType":{"id":1,"text":"Authors"},"rank":2}]}} ,{"id":70042337,"text":"ds736 - 2012 - Land Capability Potential Index (LCPI) and geodatabase for the Lower Missouri River Valley","interactions":[],"lastModifiedDate":"2026-05-18T16:18:54.111057","indexId":"ds736","displayToPublicDate":"2013-01-04T00:00:00","publicationYear":"2012","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":"736","title":"Land Capability Potential Index (LCPI) and geodatabase for the Lower Missouri River Valley","docAbstract":"The Land Capacity Potential Index (LCPI) is a coarse-scale index intended to delineate broad land-capability classes in the Lower Missouri River valley bottom from the Gavins Point Dam near Yankton, South Dakota to the mouth of the Missouri River near St. Louis, Missouri (river miles 811–0). The LCPI provides a systematic index of wetness potential and soil moisture-retention potential of the valley-bottom lands by combining the interactions among water-surface elevations, land-surface elevations, and the inherent moisture-retention capability of soils. A nine-class wetness index was generated by intersecting a digital elevation model for the valley bottom with sloping water-surface elevation planes derived from eight modeled discharges. The flow-recurrence index was then intersected with eight soil-drainage classes assigned to soils units in the digital Soil Survey Geographic (SSURGO) Database (Soil Survey Staff, 2010) to create a 72-class index of potential flow-recurrence and moisture-retention capability of Missouri River valley-bottom lands. The LCPI integrates the fundamental abiotic factors that determine long-term suitability of land for various uses, particularly those relating to vegetative communities and their associated values. Therefore, the LCPI provides a mechanism allowing planners, land managers, landowners, and other stakeholders to assess land-use capability based on the physical properties of the land, in order to guide future land-management decisions. This report documents data compilation for the LCPI in a revised and expanded, 72-class version for the Lower Missouri River valley bottom, and inclusion of additional soil attributes to allow users flexibility in exploring land capabilities.","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/ds736","collaboration":"Prepared in cooperation with the U.S. Fish and Wildlife Service, Nebraska Game and Parks Commission, and the Nature Conservancy","usgsCitation":"Chojnacki, K.A., Struckhoff, M.A., and Jacobson, R.B., 2012, Land Capability Potential Index (LCPI) and geodatabase for the Lower Missouri River Valley: U.S. Geological Survey Data Series 736, Report: iv, 18 p.; Downloads Directory, https://doi.org/10.3133/ds736.","productDescription":"Report: iv, 18 p.; Downloads Directory","numberOfPages":"26","onlineOnly":"Y","additionalOnlineFiles":"N","ipdsId":"IP-037780","costCenters":[{"id":192,"text":"Columbia Environmental Research Center","active":true,"usgs":true}],"links":[{"id":504486,"rank":5,"type":{"id":36,"text":"NGMDB Index Page"},"url":"https://ngmdb.usgs.gov/Prodesc/proddesc_98023.htm","linkFileType":{"id":5,"text":"html"}},{"id":265275,"rank":2,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/ds/736/ds736.pdf"},{"id":265274,"rank":3,"type":{"id":15,"text":"Index Page"},"url":"https://pubs.usgs.gov/ds/736/"},{"id":265276,"rank":1,"type":{"id":7,"text":"Companion Files"},"url":"https://pubs.usgs.gov/ds/736/downloads/"},{"id":265277,"rank":4,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/ds_736.gif"}],"scale":"2000000","datum":"North American Datum 1983","country":"United States","state":"Iowa, Kansas, Missouri, Nebraska, South Dakota","otherGeospatial":"Missouri River Valley","geographicExtents":"{ \"type\": \"FeatureCollection\", \"features\": [ { \"type\": \"Feature\", \"properties\": {}, \"geometry\": { \"type\": \"Polygon\", \"coordinates\": [ [ [ -98.0,38.0 ], [ -98.0,43.5 ], [ -90.0,43.5 ], [ -90.0,38.0 ], [ -98.0,38.0 ] ] ] } } ] }","noUsgsAuthors":false,"publicationStatus":"PW","scienceBaseUri":"50e7f9ebe4b033ce2d2433e9","contributors":{"authors":[{"text":"Chojnacki, Kimberly A. kchojnacki@usgs.gov","contributorId":1978,"corporation":false,"usgs":true,"family":"Chojnacki","given":"Kimberly","email":"kchojnacki@usgs.gov","middleInitial":"A.","affiliations":[{"id":192,"text":"Columbia Environmental Research Center","active":true,"usgs":true}],"preferred":true,"id":471329,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Struckhoff, Matthew A. 0000-0002-4911-9956 mstruckhoff@usgs.gov","orcid":"https://orcid.org/0000-0002-4911-9956","contributorId":2095,"corporation":false,"usgs":true,"family":"Struckhoff","given":"Matthew","email":"mstruckhoff@usgs.gov","middleInitial":"A.","affiliations":[{"id":192,"text":"Columbia Environmental Research Center","active":true,"usgs":true}],"preferred":true,"id":471330,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Jacobson, Robert B. 0000-0002-8368-2064 rjacobson@usgs.gov","orcid":"https://orcid.org/0000-0002-8368-2064","contributorId":1289,"corporation":false,"usgs":true,"family":"Jacobson","given":"Robert","email":"rjacobson@usgs.gov","middleInitial":"B.","affiliations":[{"id":192,"text":"Columbia Environmental Research Center","active":true,"usgs":true}],"preferred":true,"id":471328,"contributorType":{"id":1,"text":"Authors"},"rank":3}]}} ,{"id":70042285,"text":"70042285 - 2012 - Program SPACECAP: software for estimating animal density using spatially explicit capture-recapture models","interactions":[],"lastModifiedDate":"2013-01-03T10:10:08","indexId":"70042285","displayToPublicDate":"2013-01-03T00:00:00","publicationYear":"2012","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":2717,"text":"Methods in Ecology and Evolution","active":true,"publicationSubtype":{"id":10}},"title":"Program SPACECAP: software for estimating animal density using spatially explicit capture-recapture models","docAbstract":"1. The advent of spatially explicit capture-recapture models is changing the way ecologists analyse capture-recapture data. However, the advantages offered by these new models are not fully exploited because they can be difficult to implement. 2. To address this need, we developed a user-friendly software package, created within the R programming environment, called SPACECAP. This package implements Bayesian spatially explicit hierarchical models to analyse spatial capture-recapture data. 3. Given that a large number of field biologists prefer software with graphical user interfaces for analysing their data, SPACECAP is particularly useful as a tool to increase the adoption of Bayesian spatially explicit capture-recapture methods in practice.","largerWorkType":{"id":2,"text":"Article"},"largerWorkTitle":"Methods in Ecology and Evolution","largerWorkSubtype":{"id":10,"text":"Journal Article"},"language":"English","publisher":"Wiley","publisherLocation":"Hoboken, NJ","doi":"10.1111/j.2041-210X.2012.00241.x","usgsCitation":"Gopalaswamy, A., Royle, J., Hines, J., Singh, P., Jathanna, D., Kumar, N.S., and Karanth, K.U., 2012, Program SPACECAP: software for estimating animal density using spatially explicit capture-recapture models: Methods in Ecology and Evolution, v. 3, no. 6, p. 1067-1072, https://doi.org/10.1111/j.2041-210X.2012.00241.x.","productDescription":"6 p.","startPage":"1067","endPage":"1072","ipdsId":"IP-039292","costCenters":[{"id":531,"text":"Patuxent Wildlife Research Center","active":true,"usgs":true}],"links":[{"id":474108,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.1111/j.2041-210x.2012.00241.x","text":"Publisher Index Page"},{"id":265030,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"},{"id":265029,"type":{"id":10,"text":"Digital Object Identifier"},"url":"https://dx.doi.org/10.1111/j.2041-210X.2012.00241.x"}],"volume":"3","issue":"6","noUsgsAuthors":false,"publicationDate":"2012-09-17","publicationStatus":"PW","scienceBaseUri":"50e5d023e4b0a4aa5bb0af86","contributors":{"authors":[{"text":"Gopalaswamy, Arjun M.","contributorId":12167,"corporation":false,"usgs":true,"family":"Gopalaswamy","given":"Arjun M.","affiliations":[],"preferred":false,"id":471203,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Royle, J. Andrew 0000-0003-3135-2167","orcid":"https://orcid.org/0000-0003-3135-2167","contributorId":80808,"corporation":false,"usgs":true,"family":"Royle","given":"J. Andrew","affiliations":[{"id":531,"text":"Patuxent Wildlife Research Center","active":true,"usgs":true}],"preferred":true,"id":471207,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Hines, James E. jhines@usgs.gov","contributorId":3506,"corporation":false,"usgs":true,"family":"Hines","given":"James E.","email":"jhines@usgs.gov","affiliations":[{"id":531,"text":"Patuxent Wildlife Research Center","active":true,"usgs":true}],"preferred":false,"id":471201,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Singh, Pallavi","contributorId":58919,"corporation":false,"usgs":true,"family":"Singh","given":"Pallavi","email":"","affiliations":[],"preferred":false,"id":471205,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Jathanna, Devcharan","contributorId":74270,"corporation":false,"usgs":true,"family":"Jathanna","given":"Devcharan","email":"","affiliations":[],"preferred":false,"id":471206,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"Kumar, N. Samba","contributorId":52701,"corporation":false,"usgs":true,"family":"Kumar","given":"N.","email":"","middleInitial":"Samba","affiliations":[],"preferred":false,"id":471204,"contributorType":{"id":1,"text":"Authors"},"rank":6},{"text":"Karanth, K. Ullas","contributorId":6984,"corporation":false,"usgs":true,"family":"Karanth","given":"K.","email":"","middleInitial":"Ullas","affiliations":[],"preferred":false,"id":471202,"contributorType":{"id":1,"text":"Authors"},"rank":7}]}} ,{"id":70042280,"text":"70042280 - 2012 - Interbasin water transfer, riverine connectivity, and spatial controls on fish biodiversity","interactions":[],"lastModifiedDate":"2013-01-18T21:15:21","indexId":"70042280","displayToPublicDate":"2013-01-02T00:00:00","publicationYear":"2012","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":2980,"text":"PLoS ONE","active":true,"publicationSubtype":{"id":10}},"title":"Interbasin water transfer, riverine connectivity, and spatial controls on fish biodiversity","docAbstract":"Background Large-scale inter-basin water transfer (IBWT) projects are commonly proposed as solutions to water distribution and supply problems. These problems are likely to intensify under future population growth and climate change scenarios. Scarce data on the distribution of freshwater fishes frequently limits the ability to assess the potential implications of an IBWT project on freshwater fish communities. Because connectivity in habitat networks is expected to be critical to species' biogeography, consideration of changes in the relative isolation of riverine networks may provide a strategy for controlling impacts of IBWTs on freshwater fish communities Methods/Principal Findings Using empirical data on the current patterns of freshwater fish biodiversity for rivers of peninsular India, we show here how the spatial changes alone under an archetypal IBWT project will (1) reduce freshwater fish biodiversity system-wide, (2) alter patterns of local species richness, (3) expand distributions of widespread species throughout peninsular rivers, and (4) decrease community richness by increasing inter-basin similarity (a mechanism for the observed decrease in biodiversity). Given the complexity of the IBWT, many paths to partial or full completion of the project are possible. We evaluate two strategies for step-wise implementation of the 11 canals, based on economic or ecological considerations. We find that for each step in the project, the impacts on freshwater fish communities are sensitive to which canal is added to the network. Conclusions/Significance Importantly, ecological impacts can be reduced by associating the sequence in which canals are added to characteristics of the links, except for the case when all 11 canals are implemented simultaneously (at which point the sequence of canal addition is inconsequential). By identifying the fundamental relationship between the geometry of riverine networks and freshwater fish biodiversity, our results will aid in assessing impacts of IBWT projects and balancing ecosystem and societal demands for freshwater, even in cases where biodiversity data are limited.","largerWorkType":{"id":2,"text":"Article"},"largerWorkTitle":"PLoS ONE","largerWorkSubtype":{"id":10,"text":"Journal Article"},"language":"English","publisher":"Public Library of Science","publisherLocation":"San Francisco, CA","doi":"10.1371/journal.pone.0034170","usgsCitation":"Grant, E., Lynch, H., Muneepeerakul, R., Muthukumarasamy, A., Rodríguez-Iturbe, I., and Fagan, W., 2012, Interbasin water transfer, riverine connectivity, and spatial controls on fish biodiversity: PLoS ONE, v. 7, no. 3, 7 p.; e34170, https://doi.org/10.1371/journal.pone.0034170.","productDescription":"7 p.; e34170","ipdsId":"IP-035454","costCenters":[{"id":531,"text":"Patuxent Wildlife Research Center","active":true,"usgs":true}],"links":[{"id":474110,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.1371/journal.pone.0034170","text":"Publisher Index Page"},{"id":265022,"type":{"id":10,"text":"Digital Object Identifier"},"url":"https://dx.doi.org/10.1371/journal.pone.0034170"},{"id":265023,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"volume":"7","issue":"3","noUsgsAuthors":false,"publicationDate":"2012-03-28","publicationStatus":"PW","scienceBaseUri":"50e5d009e4b0a4aa5bb0af33","contributors":{"authors":[{"text":"Grant, Evan H. Campbell","contributorId":14686,"corporation":false,"usgs":true,"family":"Grant","given":"Evan H. Campbell","affiliations":[],"preferred":false,"id":471178,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Lynch, Heather J.","contributorId":23824,"corporation":false,"usgs":true,"family":"Lynch","given":"Heather J.","affiliations":[],"preferred":false,"id":471179,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Muneepeerakul, Rachata","contributorId":66130,"corporation":false,"usgs":true,"family":"Muneepeerakul","given":"Rachata","email":"","affiliations":[],"preferred":false,"id":471180,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Muthukumarasamy, Arunachalam","contributorId":77016,"corporation":false,"usgs":true,"family":"Muthukumarasamy","given":"Arunachalam","affiliations":[],"preferred":false,"id":471181,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Rodríguez-Iturbe, Ignacio","contributorId":78626,"corporation":false,"usgs":true,"family":"Rodríguez-Iturbe","given":"Ignacio","affiliations":[],"preferred":false,"id":471182,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"Fagan, William F.","contributorId":108239,"corporation":false,"usgs":true,"family":"Fagan","given":"William F.","affiliations":[],"preferred":false,"id":471183,"contributorType":{"id":1,"text":"Authors"},"rank":6}]}} ,{"id":70042277,"text":"ofr20121259 - 2012 - Mars global digital dune database: MC-30","interactions":[],"lastModifiedDate":"2015-04-15T15:21:17","indexId":"ofr20121259","displayToPublicDate":"2013-01-02T00:00:00","publicationYear":"2012","noYear":false,"publicationType":{"id":18,"text":"Report"},"publicationSubtype":{"id":5,"text":"USGS Numbered Series"},"seriesTitle":{"id":330,"text":"Open-File Report","code":"OFR","onlineIssn":"2331-1258","printIssn":"0196-1497","active":true,"publicationSubtype":{"id":5}},"seriesNumber":"2012-1259","title":"Mars global digital dune database: MC-30","docAbstract":"

The Mars Global Digital Dune Database (MGD3) provides data and describes the methodology used in creating the global database of moderate- to large-size dune fields on Mars. The database is being released in a series of U.S. Geological Survey Open-File Reports. The first report (Hayward and others, 2007) included dune fields from lat 65° N. to 65° S. (http://pubs.usgs.gov/of/2007/1158/). The second report (Hayward and others, 2010) included dune fields from lat 60° N. to 90° N. (http://pubs.usgs.gov/of/2010/1170/). This report encompasses ~75,000 km2 of mapped dune fields from lat 60° to 90° S. The dune fields included in this global database were initially located using Mars Odyssey Thermal Emission Imaging System (THEMIS) Infrared (IR) images. In the previous two reports, some dune fields may have been unintentionally excluded for two reasons: (1) incomplete THEMIS IR (daytime) coverage may have caused us to exclude some moderate- to large-size dune fields or (2) resolution of THEMIS IR coverage (100 m/pixel) certainly caused us to exclude smaller dune fields. In this report, mapping is more complete. The Arizona State University THEMIS daytime IR mosaic provided complete IR coverage, and it is unlikely that we missed any large dune fields in the South Pole (SP) region. In addition, the increased availability of higher resolution images resulted in the inclusion of more small (~1 km2) sand dune fields and sand patches. To maintain consistency with the previous releases, we have identified the sand features that would not have been included in earlier releases. While the moderate to large dune fields in MGD3 are likely to constitute the largest compilation of sediment on the planet, we acknowledge that our database excludes numerous small dune fields and some moderate to large dune fields as well. Please note that the absence of mapped dune fields does not mean that dune fields do not exist and is not intended to imply a lack of saltating sand in other areas. Where availability and quality of THEMIS visible (VIS), Mars Orbiter Camera (MOC) narrow angle, Mars Express High Resolution Stereo Camera, or Mars Reconnaissance Orbiter Context Camera and High Resolution Imaging Science Experiment images allowed, we classified dunes and included some dune slipface measurements, which were derived from gross dune morphology and represent the approximate prevailing wind direction at the last time of significant dune modification. It was beyond the scope of this report to look at the detail needed to discern subtle dune modification. It was also beyond the scope of this report to measure all slipfaces. We attempted to include enough slipface measurements to represent the general circulation (as implied by gross dune morphology) and to give a sense of the complex nature of aeolian activity on Mars. The absence of slipface measurements in a given direction should not be taken as evidence that winds in that direction did not occur. When a dune field was located within a crater, the azimuth from crater centroid to dune field centroid was calculated, as another possible indicator of wind direction. Output from a general circulation model is also included. In addition to polygons locating dune fields, the database includes ~700 of the THEMIS VIS and MOC images that were used to build the database.

","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/ofr20121259","usgsCitation":"Hayward, R., Fenton, L., Titus, T., Colaprete, A., and Christensen, P.R., 2012, Mars global digital dune database: MC-30: U.S. Geological Survey Open-File Report 2012-1259, Pamphlet: 8 p.; Map: 1 p.; ReadMe; Metadata; Data; GIS, https://doi.org/10.3133/ofr20121259.","productDescription":"Pamphlet: 8 p.; Map: 1 p.; ReadMe; Metadata; Data; GIS","numberOfPages":"8","onlineOnly":"Y","additionalOnlineFiles":"Y","ipdsId":"IP-039013","costCenters":[{"id":131,"text":"Astrogeology Science Center","active":true,"usgs":true}],"links":[{"id":265025,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/ofr_2012_1259.gif"},{"id":265040,"type":{"id":15,"text":"Index Page"},"url":"https://pubs.usgs.gov/of/2012/1259/"}],"otherGeospatial":"Mars","noUsgsAuthors":false,"publicationStatus":"PW","scienceBaseUri":"50e5d015e4b0a4aa5bb0af5a","contributors":{"authors":[{"text":"Hayward, R.K.","contributorId":31885,"corporation":false,"usgs":true,"family":"Hayward","given":"R.K.","email":"","affiliations":[],"preferred":false,"id":471172,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Fenton, L.K.","contributorId":102189,"corporation":false,"usgs":true,"family":"Fenton","given":"L.K.","affiliations":[],"preferred":false,"id":471173,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Titus, T.N.","contributorId":102615,"corporation":false,"usgs":true,"family":"Titus","given":"T.N.","email":"","affiliations":[],"preferred":false,"id":471174,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Colaprete, A.","contributorId":26047,"corporation":false,"usgs":true,"family":"Colaprete","given":"A.","affiliations":[],"preferred":false,"id":471171,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Christensen, P. R.","contributorId":7819,"corporation":false,"usgs":false,"family":"Christensen","given":"P.","email":"","middleInitial":"R.","affiliations":[],"preferred":false,"id":471170,"contributorType":{"id":1,"text":"Authors"},"rank":5}]}} ,{"id":70042275,"text":"ofr20121241 - 2012 - Advanced earthquake monitoring system for U.S. Department of Veterans Affairs medical buildings--instrumentation","interactions":[],"lastModifiedDate":"2013-01-04T14:50:35","indexId":"ofr20121241","displayToPublicDate":"2013-01-02T00:00:00","publicationYear":"2012","noYear":false,"publicationType":{"id":18,"text":"Report"},"publicationSubtype":{"id":5,"text":"USGS Numbered Series"},"seriesTitle":{"id":330,"text":"Open-File Report","code":"OFR","onlineIssn":"2331-1258","printIssn":"0196-1497","active":true,"publicationSubtype":{"id":5}},"seriesNumber":"2012-1241","title":"Advanced earthquake monitoring system for U.S. Department of Veterans Affairs medical buildings--instrumentation","docAbstract":"In collaboration with the U.S. Department of Veterans Affairs (VA), the National Strong Motion Project (NSMP; http://nsmp.wr.usgs.gov/) of the U.S. Geological Survey has been installing sophisticated seismic systems that will monitor the structural integrity of 28 VA hospital buildings located in seismically active regions of the conterminous United States, Alaska, and Puerto Rico during earthquake shaking. These advanced monitoring systems, which combine the use of sensitive accelerometers and real-time computer calculations, are designed to determine the structural health of each hospital building rapidly after an event, helping the VA to ensure the safety of patients and staff. This report presents the instrumentation component of this project by providing details of each hospital building, including a summary of its structural, geotechnical, and seismic hazard information, as well as instrumentation objectives and design. The structural-health monitoring component of the project, including data retrieval and processing, damage detection and localization, automated alerting system, and finally data dissemination, will be presented in a separate report.","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/ofr20121241","collaboration":"In cooperation with the Department of Veterans Affairs","usgsCitation":"Kalkan, E., Banga, K., Ulusoy, H.S., Fletcher, J.P., Leith, W.S., Reza, S., and Cheng, T., 2012, Advanced earthquake monitoring system for U.S. Department of Veterans Affairs medical buildings--instrumentation: U.S. Geological Survey Open-File Report 2012-1241, xi, 143 p.; col. ill.; map (col.), https://doi.org/10.3133/ofr20121241.","productDescription":"xi, 143 p.; col. ill.; map (col.)","startPage":"i","endPage":"143","numberOfPages":"154","onlineOnly":"Y","additionalOnlineFiles":"N","ipdsId":"IP-041791","costCenters":[{"id":237,"text":"Earthquake Science Center","active":true,"usgs":true}],"links":[{"id":265007,"type":{"id":15,"text":"Index Page"},"url":"https://pubs.usgs.gov/of/2012/1241/"},{"id":265008,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/of/2012/1241/of2012-1241.pdf"},{"id":265009,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/ofr_2012_1241.gif"}],"country":"United States","geographicExtents":"{ \"type\": \"FeatureCollection\", \"features\": [ { \"type\": \"Feature\", \"properties\": {}, \"geometry\": { \"type\": \"Polygon\", \"coordinates\": [ [ [ 172.5,18.9 ], [ 172.5,71.4 ], [ -66.9,71.4 ], [ -66.9,18.9 ], [ 172.5,18.9 ] ] ] } } ] }","noUsgsAuthors":false,"publicationStatus":"PW","scienceBaseUri":"50e5cfdde4b0a4aa5bb0ae64","contributors":{"authors":[{"text":"Kalkan, Erol 0000-0002-9138-9407 ekalkan@usgs.gov","orcid":"https://orcid.org/0000-0002-9138-9407","contributorId":1218,"corporation":false,"usgs":true,"family":"Kalkan","given":"Erol","email":"ekalkan@usgs.gov","affiliations":[{"id":237,"text":"Earthquake Science Center","active":true,"usgs":true}],"preferred":true,"id":471164,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Banga, Krishna","contributorId":33152,"corporation":false,"usgs":true,"family":"Banga","given":"Krishna","email":"","affiliations":[],"preferred":false,"id":471168,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Ulusoy, Hasan S. hulusoy@usgs.gov","contributorId":5360,"corporation":false,"usgs":true,"family":"Ulusoy","given":"Hasan","email":"hulusoy@usgs.gov","middleInitial":"S.","affiliations":[],"preferred":true,"id":471166,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Fletcher, Jon Peter B. 0000-0001-8885-6177 jfletcher@usgs.gov","orcid":"https://orcid.org/0000-0001-8885-6177","contributorId":1216,"corporation":false,"usgs":true,"family":"Fletcher","given":"Jon","email":"jfletcher@usgs.gov","middleInitial":"Peter B.","affiliations":[{"id":237,"text":"Earthquake Science Center","active":true,"usgs":true}],"preferred":true,"id":471163,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Leith, William S. 0000-0002-3463-3119 wleith@usgs.gov","orcid":"https://orcid.org/0000-0002-3463-3119","contributorId":2248,"corporation":false,"usgs":true,"family":"Leith","given":"William","email":"wleith@usgs.gov","middleInitial":"S.","affiliations":[{"id":300,"text":"Geologic Hazards Science Center","active":true,"usgs":true},{"id":234,"text":"Earthquake Hazards Program","active":true,"usgs":true}],"preferred":true,"id":471165,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"Reza, Shahneam sreza@usgs.gov","contributorId":5361,"corporation":false,"usgs":true,"family":"Reza","given":"Shahneam","email":"sreza@usgs.gov","affiliations":[],"preferred":true,"id":471167,"contributorType":{"id":1,"text":"Authors"},"rank":6},{"text":"Cheng, Timothy","contributorId":54869,"corporation":false,"usgs":true,"family":"Cheng","given":"Timothy","email":"","affiliations":[],"preferred":false,"id":471169,"contributorType":{"id":1,"text":"Authors"},"rank":7}]}} ,{"id":70042274,"text":"ofr20121252 - 2012 - Future scenarios of land-use and land-cover change in the United States--the Marine West Coast Forests Ecoregion","interactions":[],"lastModifiedDate":"2018-03-08T12:52:33","indexId":"ofr20121252","displayToPublicDate":"2013-01-02T00:00:00","publicationYear":"2012","noYear":false,"publicationType":{"id":18,"text":"Report"},"publicationSubtype":{"id":5,"text":"USGS Numbered Series"},"seriesTitle":{"id":330,"text":"Open-File Report","code":"OFR","onlineIssn":"2331-1258","printIssn":"0196-1497","active":true,"publicationSubtype":{"id":5}},"seriesNumber":"2012-1252","title":"Future scenarios of land-use and land-cover change in the United States--the Marine West Coast Forests Ecoregion","docAbstract":"Detecting, quantifying, and projecting historical and future changes in land use and land cover (LULC) has emerged as a core research area for the U.S. Geological Survey (USGS). Changes in LULC are important drivers of changes to biogeochemical cycles, the exchange of energy between the Earth’s surface and atmosphere, biodiversity, water quality, and climate change. To quantify the rates of recent historical LULC change, the USGS Land Cover Trends project recently completed a unique ecoregion-based assessment of late 20th century LULC change for the western United States. To characterize present LULC, the USGS and partners have created the National Land Cover Database (NLCD) for the years 1992, 2001, and 2006. Both Land Cover Trends and NLCD projects continue to evolve in an effort to better characterize historical and present LULC conditions and are the foundation of the data presented in this report.\n\nProjecting future changes in LULC requires an understanding of the rates and patterns of change, the major driving forces, and the socioeconomic and biophysical determinants and capacities of regions. The data presented in this report is the result of an effort by USGS scientists to downscale the Intergovernmental Panel on Climate Change (IPCC) Special Report on Emission Scenarios (SRES) to ecoregions of the conterminous United States as part of the USGS Biological Carbon Sequestration Assessment. The USGS biological carbon assessment was mandated by Section 712 of the Energy Independence and Security Act of 2007. As part of the legislative mandate, the USGS is required to publish a methodology describing, in detail, the approach to be used for the assessment. The development of future LULC scenarios is described in chapter 3.2 and appendix A. Spatial modeling is described in chapter 3.3.2 and appendix B and in Sohl and others (2011). In this report, we briefly summarize the major components and methods used to downscale IPCC-SRES scenarios to ecoregions of the conterminous United States, followed by a description of the Marine West Coast Forests Ecoregion, and lastly a description of the data being published as part of this report.","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/ofr20121252","usgsCitation":"Wilson, T.S., Sleeter, B.M., Sohl, T.L., Griffith, G., Acevedo, W., Bennett, S., Bouchard, M., Reker, R.R., Ryan, C., Sayler, K., Sleeter, R., and Soulard, C.E., 2012, Future scenarios of land-use and land-cover change in the United States--the Marine West Coast Forests Ecoregion: U.S. Geological Survey Open-File Report 2012-1252, Report: iii, 14 p.; Appendices: A-C, https://doi.org/10.3133/ofr20121252.","productDescription":"Report: iii, 14 p.; Appendices: 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forward in studies of animal populations is to efficiently make use of all the information available, either as raw data or as published sources, on critical parameters of interest. In this study, we demonstrate two approaches to the use of multiple sources of information on a parameter of fundamental interest to ecologists: animal density. The first approach produces estimates simultaneously from two different sources of data. The second approach was developed for situations in which initial data collection and analysis are followed up by subsequent data collection and prior knowledge is updated with new data using a stepwise process. Both approaches are used to estimate density of a rare and elusive predator, the tiger, by combining photographic and fecal DNA spatial capture–recapture data. The model, which combined information, provided the most precise estimate of density (8.5 ± 1.95 tigers/100 km2 [posterior mean ± SD]) relative to a model that utilized only one data source (photographic, 12.02 ± 3.02 tigers/100 km2 and fecal DNA, 6.65 ± 2.37 tigers/100 km2). Our study demonstrates that, by accounting for multiple sources of available information, estimates of animal density can be significantly improved.","largerWorkType":{"id":2,"text":"Article"},"largerWorkTitle":"Ecology","largerWorkSubtype":{"id":10,"text":"Journal Article"},"language":"English","publisher":"ESA","publisherLocation":"Ithaca, NY","doi":"10.1890/11-2110.1","usgsCitation":"Gopalaswamy, A., Royle, J., Delampady, M., Nichols, J., Karanth, K.U., and Macdonald, D.W., 2012, Density estimation in tiger populations: combining information for strong inference: Ecology, v. 93, no. 7, p. 1741-1751, https://doi.org/10.1890/11-2110.1.","productDescription":"11 p.","startPage":"1741","endPage":"1751","ipdsId":"IP-039030","costCenters":[{"id":531,"text":"Patuxent Wildlife Research Center","active":true,"usgs":true}],"links":[{"id":265020,"type":{"id":10,"text":"Digital Object Identifier"},"url":"https://dx.doi.org/10.1890/11-2110.1"},{"id":265021,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"volume":"93","issue":"7","noUsgsAuthors":false,"publicationStatus":"PW","scienceBaseUri":"50e5cfefe4b0a4aa5bb0aebb","contributors":{"authors":[{"text":"Gopalaswamy, Arjun M.","contributorId":12167,"corporation":false,"usgs":true,"family":"Gopalaswamy","given":"Arjun M.","affiliations":[],"preferred":false,"id":471186,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Royle, J. 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However, when variables influencing abundance are of interest, if individuals detected via different methods are influenced by the landscape differently, separate analysis of multiple detection methods may be more appropriate. We evaluated the effects of combining two detection methods on the identification of variables important to local abundance using detections of grizzly bears with hair traps (systematic) and bear rubs (opportunistic). We used hierarchical abundance models (N-mixture models) with separate model components for each detection method. If both methods sample the same population, the use of either data set alone should (1) lead to the selection of the same variables as important and (2) provide similar estimates of relative local abundance. We hypothesized that the inclusion of 2 detection methods versus either method alone should (3) yield more support for variables identified in single method analyses (i.e. fewer variables and models with greater weight), and (4) improve precision of covariate estimates for variables selected in both separate and combined analyses because sample size is larger. As expected, joint analysis of both methods increased precision as well as certainty in variable and model selection. However, the single-method analyses identified different variables and the resulting predicted abundances had different spatial distributions. We recommend comparing single-method and jointly modeled results to identify the presence of individual heterogeneity between detection methods in N-mixture models, along with consideration of detection probabilities, correlations among variables, and tolerance to risk of failing to identify variables important to a subset of the population. The benefits of increased precision should be weighed against those risks. The analysis framework presented here will be useful for other species exhibiting heterogeneity by detection method.","largerWorkType":{"id":2,"text":"Article"},"largerWorkTitle":"PLoS ONE","largerWorkSubtype":{"id":10,"text":"Journal Article"},"language":"English","publisher":"Public Library of Science","publisherLocation":"San Francisco, CA","doi":"10.1371/journal.pone.0049410","usgsCitation":"Graves, T.A., Royle, J., Kendall, K.C., Beier, P., Stetz, J.B., and Macleod, A., 2012, Balancing precision and risk: should multiple detection methods be analyzed separately in N-mixture models?: PLoS ONE, v. 7, no. 12, 9 p.; e49410, https://doi.org/10.1371/journal.pone.0049410.","productDescription":"9 p.; e49410","ipdsId":"IP-042009","costCenters":[{"id":531,"text":"Patuxent Wildlife Research Center","active":true,"usgs":true}],"links":[{"id":474109,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.1371/journal.pone.0049410","text":"Publisher Index Page"},{"id":265018,"type":{"id":10,"text":"Digital Object Identifier"},"url":"https://dx.doi.org/10.1371/journal.pone.0049410"},{"id":265019,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"volume":"7","issue":"12","noUsgsAuthors":false,"publicationDate":"2012-12-12","publicationStatus":"PW","scienceBaseUri":"50e5cfe4e4b0a4aa5bb0ae8c","contributors":{"authors":[{"text":"Graves, Tabitha A. 0000-0001-5145-2400 tgraves@usgs.gov","orcid":"https://orcid.org/0000-0001-5145-2400","contributorId":5898,"corporation":false,"usgs":true,"family":"Graves","given":"Tabitha","email":"tgraves@usgs.gov","middleInitial":"A.","affiliations":[{"id":481,"text":"Northern Rocky Mountain Science Center","active":true,"usgs":true}],"preferred":true,"id":471191,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Royle, J. 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Resilience is a measure of a system's ability to remain organized around a particular set of mutually reinforcing processes and structures, known as a regime. We define scale as the geographic extent over which a process operates and the frequency with which a process occurs. Species can be categorized into functional groups that are a link between ecosystem processes and structures and ecological resilience. We applied the cross-scale resilience model to avian species in a grassland ecosystem. A species’ morphology is shaped in part by its interaction with ecological structure and pattern, so animal body mass reflects the spatial and temporal distribution of resources. We used the log-transformed rank-ordered body masses of breeding birds associated with grasslands to identify aggregations and discontinuities in the distribution of those body masses. We assessed cross-scale resilience on the basis of 3 metrics: overall number of functional groups, number of functional groups within an aggregation, and the redundancy of functional groups across aggregations. We assessed how the loss of threatened species would affect cross-scale resilience by removing threatened species from the data set and recalculating values of the 3 metrics. We also determined whether more function was retained than expected after the loss of threatened species by comparing observed loss with simulated random loss in a Monte Carlo process. The observed distribution of function compared with the random simulated loss of function indicated that more functionality in the observed data set was retained than expected. On the basis of our results, we believe an ecosystem with a full complement of species can sustain considerable species losses without affecting the distribution of functions within and across aggregations, although ecological resilience is reduced. We propose that the mechanisms responsible for shaping discontinuous distributions of body mass and the nonrandom distribution of functions may also shape species losses such that local extinctions will be nonrandom with respect to the retention and distribution of functions and that the distribution of function within and across aggregations will be conserved despite extinctions.","largerWorkType":{"id":2,"text":"Article"},"largerWorkTitle":"Conservation Biology","largerWorkSubtype":{"id":10,"text":"Journal Article"},"language":"English","publisher":"Wiley","doi":"10.1111/j.1523-1739.2011.01822.x","usgsCitation":"Sundstrom, S.M., Allen, C.R., and Barichievy, C., 2012, Species, functional groups, and thresholds in ecological resilience: Conservation Biology, v. 26, no. 2, p. 305-314, https://doi.org/10.1111/j.1523-1739.2011.01822.x.","productDescription":"10 p.","startPage":"305","endPage":"314","ipdsId":"IP-026575","costCenters":[{"id":463,"text":"Nebraska Cooperative Fish and Wildlife Research Unit","active":false,"usgs":true}],"links":[{"id":275418,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"},{"id":275417,"type":{"id":10,"text":"Digital Object Identifier"},"url":"https://dx.doi.org/10.1111/j.1523-1739.2011.01822.x"}],"country":"United States","volume":"26","issue":"2","noUsgsAuthors":false,"publicationDate":"2012-03-23","publicationStatus":"PW","scienceBaseUri":"51f25423e4b0279fe2e1c032","contributors":{"authors":[{"text":"Sundstrom, Shana M.","contributorId":7159,"corporation":false,"usgs":true,"family":"Sundstrom","given":"Shana","email":"","middleInitial":"M.","affiliations":[],"preferred":false,"id":348432,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Allen, Craig R. 0000-0001-8655-8272 allencr@usgs.gov","orcid":"https://orcid.org/0000-0001-8655-8272","contributorId":1979,"corporation":false,"usgs":true,"family":"Allen","given":"Craig","email":"allencr@usgs.gov","middleInitial":"R.","affiliations":[{"id":198,"text":"Coop Res Unit Atlanta","active":true,"usgs":true},{"id":200,"text":"Coop Res Unit Seattle","active":true,"usgs":true}],"preferred":true,"id":348431,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Barichievy, Chris","contributorId":17119,"corporation":false,"usgs":true,"family":"Barichievy","given":"Chris","email":"","affiliations":[],"preferred":false,"id":348433,"contributorType":{"id":1,"text":"Authors"},"rank":3}]}} ,{"id":70047610,"text":"70047610 - 2012 - Do Daphnia use metalimnetic organic matter in a north temperate lake? An analysis of vertical migration","interactions":[],"lastModifiedDate":"2013-08-14T14:53:05","indexId":"70047610","displayToPublicDate":"2013-01-01T14:44:00","publicationYear":"2012","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":1999,"text":"Inland Waters","active":true,"publicationSubtype":{"id":10}},"title":"Do Daphnia use metalimnetic organic matter in a north temperate lake? An analysis of vertical migration","docAbstract":"Diel vertical migration of zooplankton is influenced by a variety of factors including predation, food, and temperature. Research has recently shifted from a focus on factors influencing migration to how migration affects nutrient cycling and habitat coupling. Here we evaluate the potential for Daphnia migrations to incorporate metalimnetic productivity in a well-studied northern Wisconsin lake. We use prior studies conducted between 1985 and 1990 and current diel migration data (2008) to compare day and night Daphnia vertical distributions with the depth of the metalimnion (between the thermocline and 1% light depth). Daphnia migrate from a daytime mean residence depth of between about 1.7 and 2.5 m to a nighttime mean residence depth of between 0 and 2.0 m. These migrations are consistent between the prior period and current measurements. Daytime residence depths of Daphnia are rarely deep enough to reach the metalimnion; hence, metalimnetic primary production is unlikely to be an important resource for Daphnia in this system.","largerWorkType":{"id":2,"text":"Article"},"largerWorkTitle":"Inland Waters","largerWorkSubtype":{"id":10,"text":"Journal Article"},"language":"English","publisher":"International Society of Limnology","usgsCitation":"Brosseau, C.J., Cline, T.J., Cole, J.J., Hodgson, J.R., Pace, M., and Weidel, B., 2012, Do Daphnia use metalimnetic organic matter in a north temperate lake? An analysis of vertical migration: Inland Waters, v. 2, no. 4, p. 193-198.","productDescription":"6 p.","startPage":"193","endPage":"198","ipdsId":"IP-041611","costCenters":[{"id":324,"text":"Great Lakes Science Center","active":true,"usgs":true}],"links":[{"id":276611,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"volume":"2","issue":"4","noUsgsAuthors":false,"publicationStatus":"PW","scienceBaseUri":"520ca6e4e4b081fa6136d3db","contributors":{"authors":[{"text":"Brosseau, Chase Julian","contributorId":45213,"corporation":false,"usgs":true,"family":"Brosseau","given":"Chase","email":"","middleInitial":"Julian","affiliations":[],"preferred":false,"id":482524,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Cline, Timothy J.","contributorId":28889,"corporation":false,"usgs":true,"family":"Cline","given":"Timothy","email":"","middleInitial":"J.","affiliations":[],"preferred":false,"id":482523,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Cole, Jonathan J.","contributorId":16738,"corporation":false,"usgs":true,"family":"Cole","given":"Jonathan","email":"","middleInitial":"J.","affiliations":[],"preferred":false,"id":482522,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Hodgson, James R.","contributorId":74281,"corporation":false,"usgs":true,"family":"Hodgson","given":"James","email":"","middleInitial":"R.","affiliations":[],"preferred":false,"id":482526,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Pace, Michael L.","contributorId":54498,"corporation":false,"usgs":true,"family":"Pace","given":"Michael L.","affiliations":[],"preferred":false,"id":482525,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"Weidel, Brian 0000-0001-6095-2773 bweidel@usgs.gov","orcid":"https://orcid.org/0000-0001-6095-2773","contributorId":2485,"corporation":false,"usgs":true,"family":"Weidel","given":"Brian","email":"bweidel@usgs.gov","affiliations":[{"id":324,"text":"Great Lakes Science Center","active":true,"usgs":true}],"preferred":true,"id":482521,"contributorType":{"id":1,"text":"Authors"},"rank":6}]}} ,{"id":70039016,"text":"70039016 - 2012 - Spring onset variations and trends in the continental United States: past and regional assessment using temperature-based indices","interactions":[],"lastModifiedDate":"2014-02-25T15:49:17","indexId":"70039016","displayToPublicDate":"2013-01-01T14:04:00","publicationYear":"2012","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":2032,"text":"International Journal of Climatology","active":true,"publicationSubtype":{"id":10}},"title":"Spring onset variations and trends in the continental United States: past and regional assessment using temperature-based indices","docAbstract":"Phenological data are simple yet sensitive indicators of climate change impacts on ecosystems, but observations have not been made routinely or extensively enough to evaluate spatial and temporal patterns across most continents, including North America. As an alternative, many studies use weather-based algorithms to simulate specific phenological responses. Spring Indices (SI) are a set of complex phenological models that have been successfully applied to evaluate variations and trends in the onset of spring across the Northern Hemisphere’s temperate regions. To date, SI models have been limited by only producing output in locations where both the plants’ chilling and warmth requirements are met. Here, we develop an extended form of the SI (abbreviated SI-x) that expands their application into the subtropics by ignoring chilling requirements while still retaining the utility and accuracy of the original SI (now abbreviated SI-o). The validity of the new indices is tested, and regional SI anomalies are explored across the data-rich continental United States. SI-x variations from 1900 to 2010 show an abrupt and sustained delay in spring onset of about 4–8 d (around 1958) in parts of the Southeast and southern Great Plains, and a comparable advance of 4–8 d (around 1984) in parts of the northern Great Plains and the West. Atmospheric circulation anomalies, linked to large-scale modes of variability, exert modest but significant roles in the timing of spring onset across the United States on interannual and longer timescales. The SI-x are promising metrics for tracking spring onset variations and trends in mid-latitudes, relating them to relevant ecological, hydrological, and socioeconomic phenomena, and exploring connections between atmospheric drivers and seasonal timing.","largerWorkType":{"id":2,"text":"Article"},"largerWorkTitle":"International Journal of Climatology","largerWorkSubtype":{"id":10,"text":"Journal Article"},"language":"English","publisher":"Royal Meteorological Society","doi":"10.1002/joc.3625","usgsCitation":"Schwartz, M., Ault, T., and Betancourt, J.L., 2012, Spring onset variations and trends in the continental United States: past and regional assessment using temperature-based indices: International Journal of Climatology, 6 p., https://doi.org/10.1002/joc.3625.","productDescription":"6 p.","ipdsId":"IP-039075","costCenters":[{"id":147,"text":"Branch of Regional Research-Water Resources","active":false,"usgs":true}],"links":[{"id":282783,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"},{"id":282778,"type":{"id":10,"text":"Digital Object Identifier"},"url":"https://dx.doi.org/10.1002/joc.3625"}],"country":"United States","geographicExtents":"{ \"type\": \"FeatureCollection\", \"features\": [ { \"type\": \"Feature\", \"properties\": {}, \"geometry\": { \"type\": \"Polygon\", \"coordinates\": [ [ [ -124.8,24.5 ], [ -124.8,49.38333 ], [ -66.95,49.38333 ], [ -66.95,24.5 ], [ -124.8,24.5 ] ] ] } } ] }","noUsgsAuthors":false,"publicationDate":"2012-11-16","publicationStatus":"PW","scienceBaseUri":"53cd73d8e4b0b290851092da","contributors":{"authors":[{"text":"Schwartz, Mark D.","contributorId":11092,"corporation":false,"usgs":true,"family":"Schwartz","given":"Mark D.","affiliations":[],"preferred":false,"id":465435,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Ault, Toby R.","contributorId":48852,"corporation":false,"usgs":true,"family":"Ault","given":"Toby R.","affiliations":[],"preferred":false,"id":465436,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Betancourt, Julio L. 0000-0002-7165-0743 jlbetanc@usgs.gov","orcid":"https://orcid.org/0000-0002-7165-0743","contributorId":3376,"corporation":false,"usgs":true,"family":"Betancourt","given":"Julio","email":"jlbetanc@usgs.gov","middleInitial":"L.","affiliations":[{"id":554,"text":"Science and Decisions Center","active":true,"usgs":true},{"id":436,"text":"National Research Program - Eastern Branch","active":true,"usgs":true},{"id":438,"text":"National Research Program - Western Branch","active":true,"usgs":true}],"preferred":true,"id":465434,"contributorType":{"id":1,"text":"Authors"},"rank":3}]}} ,{"id":70045100,"text":"70045100 - 2012 - Spectral damping scaling factors for shallow crustal earthquakes in active tectonic regions","interactions":[],"lastModifiedDate":"2013-07-30T12:54:41","indexId":"70045100","displayToPublicDate":"2013-01-01T12:49:00","publicationYear":"2012","noYear":false,"publicationType":{"id":18,"text":"Report"},"publicationSubtype":{"id":3,"text":"Organization Series"},"seriesTitle":{"id":204,"text":"PEER Report","active":false,"publicationSubtype":{"id":3}},"seriesNumber":"2012/01","title":"Spectral damping scaling factors for shallow crustal earthquakes in active tectonic regions","docAbstract":"Ground motion prediction equations (GMPEs) for elastic response spectra, including the Next Generation Attenuation (NGA) models, are typically developed at a 5% viscous damping ratio. In reality, however, structural and non-structural systems can have damping ratios other than 5%, depending on various factors such as structural types, construction materials, level of ground motion excitations, among others. This report provides the findings of a comprehensive study to develop a new model for a Damping Scaling Factor (DSF) that can be used to adjust the 5% damped spectral ordinates predicted by a GMPE to spectral ordinates with damping ratios between 0.5 to 30%. Using the updated, 2011 version of the NGA database of ground motions recorded in worldwide shallow crustal earthquakes in active tectonic regions (i.e., the NGA-West2 database), dependencies of the DSF on variables including damping ratio, spectral period, moment magnitude, source-to-site distance, duration, and local site conditions are examined. The strong influence of duration is captured by inclusion of both magnitude and distance in the DSF model. Site conditions are found to have less significant influence on DSF and are not included in the model. The proposed model for DSF provides functional forms for the median value and the logarithmic standard deviation of DSF. This model is heteroscedastic, where the variance is a function of the damping ratio. Damping Scaling Factor models are developed for the “average” horizontal ground motion components, i.e., RotD50 and GMRotI50, as well as the vertical component of ground motion.","language":"English","publisher":"Pacific Earthquake Engineering Research Center","publisherLocation":"Berkeley, CA","usgsCitation":"Rezaeian, S., Bozorgnia, Y., Idriss, I., Campbell, K., Abrahamson, N., and Silva, W., 2012, Spectral damping scaling factors for shallow crustal earthquakes in active tectonic regions: PEER Report 2012/01, xiv, 142 p.","productDescription":"xiv, 142 p.","numberOfPages":"168","ipdsId":"IP-035624","costCenters":[{"id":300,"text":"Geologic Hazards Science Center","active":true,"usgs":true}],"links":[{"id":275582,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/70045100.PNG"},{"id":275580,"type":{"id":11,"text":"Document"},"url":"https://peer.berkeley.edu/publications/peer_reports/reports_2012/webPEER-2012-01-REZAEIAN.pdf"}],"noUsgsAuthors":false,"publicationStatus":"PW","scienceBaseUri":"51f8e065e4b0cecbe8fa98b6","contributors":{"authors":[{"text":"Rezaeian, Sanaz 0000-0001-7589-7893 srezaeian@usgs.gov","orcid":"https://orcid.org/0000-0001-7589-7893","contributorId":4395,"corporation":false,"usgs":true,"family":"Rezaeian","given":"Sanaz","email":"srezaeian@usgs.gov","affiliations":[{"id":300,"text":"Geologic Hazards Science Center","active":true,"usgs":true}],"preferred":true,"id":476789,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Bozorgnia, Yousef","contributorId":40101,"corporation":false,"usgs":false,"family":"Bozorgnia","given":"Yousef","affiliations":[{"id":6643,"text":"University of California - Berkeley","active":true,"usgs":false}],"preferred":false,"id":476790,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Idriss, I.M.","contributorId":105412,"corporation":false,"usgs":true,"family":"Idriss","given":"I.M.","email":"","affiliations":[],"preferred":false,"id":476794,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Campbell, Kenneth","contributorId":86246,"corporation":false,"usgs":true,"family":"Campbell","given":"Kenneth","affiliations":[],"preferred":false,"id":476793,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Abrahamson, Norman","contributorId":66990,"corporation":false,"usgs":true,"family":"Abrahamson","given":"Norman","affiliations":[],"preferred":false,"id":476792,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"Silva, Walter","contributorId":50429,"corporation":false,"usgs":true,"family":"Silva","given":"Walter","affiliations":[],"preferred":false,"id":476791,"contributorType":{"id":1,"text":"Authors"},"rank":6}]}} ,{"id":70093199,"text":"ofr20121024 - 2012 - Geologic framework for the national assessment of carbon dioxide storage resources","interactions":[{"subject":{"id":70037931,"text":"ofr20121024A - 2012 - Geologic framework for the national assessment of carbon dioxide storage resources: Bighorn Basin, Wyoming and Montana: Chapter A in Geologic framework for the national assessment of carbon dioxide storage resources","indexId":"ofr20121024A","publicationYear":"2012","noYear":false,"chapter":"A","title":"Geologic framework for the national assessment of carbon dioxide storage resources: Bighorn Basin, Wyoming and Montana: Chapter A in Geologic framework for the national assessment of carbon dioxide storage resources"},"predicate":"IS_PART_OF","object":{"id":70093199,"text":"ofr20121024 - 2012 - Geologic framework for the national assessment of carbon dioxide storage resources","indexId":"ofr20121024","publicationYear":"2012","noYear":false,"title":"Geologic framework for the national assessment of carbon dioxide storage resources"},"id":1},{"subject":{"id":70040574,"text":"ofr20121024B - 2012 - Geologic framework for the national assessment of carbon dioxide storage resources: Powder River Basin, Wyoming, Montana, South Dakota, and Nebraska: Chapter B in Geologic framework for the national assessment of carbon dioxide storage resources","indexId":"ofr20121024B","publicationYear":"2012","noYear":false,"chapter":"B","title":"Geologic framework for the national assessment of carbon dioxide storage resources: Powder River Basin, Wyoming, Montana, South Dakota, and Nebraska: Chapter B in Geologic framework for the national assessment of carbon dioxide storage resources"},"predicate":"IS_PART_OF","object":{"id":70093199,"text":"ofr20121024 - 2012 - Geologic framework for the national assessment of carbon dioxide storage resources","indexId":"ofr20121024","publicationYear":"2012","noYear":false,"title":"Geologic framework for the national assessment of carbon dioxide storage resources"},"id":2},{"subject":{"id":70040597,"text":"ofr20121024C - 2012 - Geologic framework for the national assessment of carbon dioxide storage resources: Hanna, Laramie, and Shirley Basins, Wyoming: Chapter C in Geologic framework for the national assessment of carbon dioxide storage resources","indexId":"ofr20121024C","publicationYear":"2012","noYear":false,"chapter":"C","title":"Geologic framework for the national assessment of carbon dioxide storage resources: Hanna, Laramie, and Shirley Basins, Wyoming: Chapter C in Geologic framework for the national assessment of carbon dioxide storage resources"},"predicate":"IS_PART_OF","object":{"id":70093199,"text":"ofr20121024 - 2012 - Geologic framework for the national assessment of carbon dioxide storage resources","indexId":"ofr20121024","publicationYear":"2012","noYear":false,"title":"Geologic framework for the national assessment of carbon dioxide storage resources"},"id":3},{"subject":{"id":70049006,"text":"ofr20121024E - 2014 - Geologic framework for the national assessment of carbon dioxide storage resources: Greater Green River Basin, Wyoming, Colorado, and Utah, and Wyoming-Idaho-Utah Thrust Belt","indexId":"ofr20121024E","publicationYear":"2014","noYear":false,"chapter":"E","title":"Geologic framework for the national assessment of carbon dioxide storage resources: Greater Green River Basin, Wyoming, Colorado, and Utah, and Wyoming-Idaho-Utah Thrust Belt"},"predicate":"IS_PART_OF","object":{"id":70093199,"text":"ofr20121024 - 2012 - Geologic framework for the national assessment of carbon dioxide storage resources","indexId":"ofr20121024","publicationYear":"2012","noYear":false,"title":"Geologic framework for the national assessment of carbon dioxide storage resources"},"id":4},{"subject":{"id":70059317,"text":"ofr20121024D - 2013 - Geologic framework for the national assessment of carbon dioxide storage resources: Columbia Basin of Oregon, Washington, and Idaho, and the Western Oregon-Washington basins","indexId":"ofr20121024D","publicationYear":"2013","noYear":false,"chapter":"D","title":"Geologic framework for the national assessment of carbon dioxide storage resources: Columbia Basin of Oregon, Washington, and Idaho, and the Western Oregon-Washington basins"},"predicate":"IS_PART_OF","object":{"id":70093199,"text":"ofr20121024 - 2012 - Geologic framework for the national assessment of carbon dioxide storage resources","indexId":"ofr20121024","publicationYear":"2012","noYear":false,"title":"Geologic framework for the national assessment of carbon dioxide storage resources"},"id":5},{"subject":{"id":70059593,"text":"ofr20121024F - 2013 - Geologic framework for the national assessment of carbon dioxide storage resources: Arkoma Basin, Kansas Basins, and Midcontinent Rift Basin study areas","indexId":"ofr20121024F","publicationYear":"2013","noYear":false,"chapter":"F","title":"Geologic framework for the national assessment of carbon dioxide storage resources: Arkoma Basin, Kansas Basins, and Midcontinent Rift Basin study areas"},"predicate":"IS_PART_OF","object":{"id":70093199,"text":"ofr20121024 - 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2014 - Geologic framework for the national assessment of carbon dioxide storage resources: Denver Basin, Colorado, Wyoming, and Nebraska","indexId":"ofr20121024G","publicationYear":"2014","noYear":false,"chapter":"G","title":"Geologic framework for the national assessment of carbon dioxide storage resources: Denver Basin, Colorado, Wyoming, and Nebraska"},"predicate":"IS_PART_OF","object":{"id":70093199,"text":"ofr20121024 - 2012 - Geologic framework for the national assessment of carbon dioxide storage resources","indexId":"ofr20121024","publicationYear":"2012","noYear":false,"title":"Geologic framework for the national assessment of carbon dioxide storage resources"},"id":8},{"subject":{"id":70101009,"text":"ofr20121024I - 2014 - Geologic framework for the national assessment of carbon dioxide storage resources: Alaska North Slope and Kandik Basin, Alaska","indexId":"ofr20121024I","publicationYear":"2014","noYear":false,"chapter":"I","title":"Geologic framework for the national assessment of carbon dioxide storage resources: Alaska North Slope and Kandik Basin, Alaska"},"predicate":"IS_PART_OF","object":{"id":70093199,"text":"ofr20121024 - 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2015 - Geologic framework for the national assessment of carbon dioxide storage resources: Permian and Palo Duro Basins and Bend Arch-Fort Worth Basin: Chapter K in Geologic framework for the national assessment of carbon dioxide storage resources","indexId":"ofr20121024K","publicationYear":"2015","noYear":false,"chapter":"K","title":"Geologic framework for the national assessment of carbon dioxide storage resources: Permian and Palo Duro Basins and Bend Arch-Fort Worth Basin: Chapter K in Geologic framework for the national assessment of carbon dioxide storage resources"},"predicate":"IS_PART_OF","object":{"id":70093199,"text":"ofr20121024 - 2012 - Geologic framework for the national assessment of carbon dioxide storage resources","indexId":"ofr20121024","publicationYear":"2012","noYear":false,"title":"Geologic framework for the national assessment of carbon dioxide storage resources"},"id":11},{"subject":{"id":70154998,"text":"ofr20121024L - 2015 - Geologic framework for the national assessment of carbon dioxide storage resources─South Florida Basin: Chapter L in Geologic framework for the national assessment of carbon dioxide storage resources","indexId":"ofr20121024L","publicationYear":"2015","noYear":false,"chapter":"L","title":"Geologic framework for the national assessment of carbon dioxide storage resources─South Florida Basin: Chapter L in Geologic framework for the national assessment of carbon dioxide storage resources"},"predicate":"IS_PART_OF","object":{"id":70093199,"text":"ofr20121024 - 2012 - Geologic framework for the national assessment of carbon dioxide storage resources","indexId":"ofr20121024","publicationYear":"2012","noYear":false,"title":"Geologic framework for the national assessment of carbon dioxide storage resources"},"id":12},{"subject":{"id":70170801,"text":"ofr20121024M - 2016 - Geologic framework for the national assessment of carbon dioxide storage resources—Southern Rocky Mountain Basins: Chapter M in Geologic framework for the national assessment of carbon dioxide storage resources","indexId":"ofr20121024M","publicationYear":"2016","noYear":false,"chapter":"M","title":"Geologic framework for the national assessment of carbon dioxide storage resources—Southern Rocky Mountain Basins: Chapter M in Geologic framework for the national assessment of carbon dioxide storage resources"},"predicate":"IS_PART_OF","object":{"id":70093199,"text":"ofr20121024 - 2012 - Geologic framework for the national assessment of carbon dioxide storage resources","indexId":"ofr20121024","publicationYear":"2012","noYear":false,"title":"Geologic framework for the national assessment of carbon dioxide storage resources"},"id":13},{"subject":{"id":70197875,"text":"ofr20121024N - 2018 - Geologic framework for the national assessment of carbon dioxide storage resources—Atlantic Coastal Plain and Eastern Mesozoic Rift Basins","indexId":"ofr20121024N","publicationYear":"2018","noYear":false,"chapter":"N","title":"Geologic framework for the national assessment of carbon dioxide storage resources—Atlantic Coastal Plain and Eastern Mesozoic Rift Basins"},"predicate":"IS_PART_OF","object":{"id":70093199,"text":"ofr20121024 - 2012 - Geologic framework for the national assessment of carbon dioxide storage resources","indexId":"ofr20121024","publicationYear":"2012","noYear":false,"title":"Geologic framework for the national assessment of carbon dioxide storage resources"},"id":14}],"lastModifiedDate":"2025-03-18T14:52:17.544948","indexId":"ofr20121024","displayToPublicDate":"2013-01-01T12:03:00","publicationYear":"2012","noYear":false,"publicationType":{"id":18,"text":"Report"},"publicationSubtype":{"id":5,"text":"USGS Numbered Series"},"seriesTitle":{"id":330,"text":"Open-File Report","code":"OFR","onlineIssn":"2331-1258","printIssn":"0196-1497","active":true,"publicationSubtype":{"id":5}},"seriesNumber":"2012-1024","title":"Geologic framework for the national assessment of carbon dioxide storage resources","docAbstract":"

The 2007 Energy Independence and Security Act (Public Law 110–140) directs the U.S. Geological Survey (USGS) to conduct a national assessment of potential geologic storage resources for carbon dioxide (CO2) and to consult with other Federal and State agencies to locate the pertinent geological data needed for the assessment. The geologic sequestration of CO2 is one possible way to mitigate its effects on climate change. The methodology used for the national CO2 assessment (Open-File Report 2010-1127; http://pubs.usgs.gov/of/2010/1127/) is based on previous USGS probabilistic oil and gas assessment methodologies. The methodology is non-economic and intended to be used at regional to subbasinal scales. The operational unit of the assessment is a storage assessment unit (SAU), composed of a porous storage formation with fluid flow and an overlying sealing unit with low permeability. Assessments are conducted at the SAU level and are aggregated to basinal and regional results. This report identifies and contains geologic descriptions of SAUs in separate packages of sedimentary rocks within the assessed basin and focuses on the particular characteristics, specified in the methodology, that influence the potential CO2 storage resource in those SAUs. Specific descriptions of the SAU boundaries as well as their sealing and reservoir units are included. Properties for each SAU such as depth to top, gross thickness, net porous thickness, porosity, permeability, groundwater quality, and structural reservoir traps are provided to illustrate geologic factors critical to the assessment. Although assessment results are not contained in this report, the geologic information included here will be employed, as specified in the methodology, to calculate a statistical Monte Carlo-based distribution of potential storage space in the various SAUs. Figures in this report show SAU boundaries and cell maps of well penetrations through the sealing unit into the top of the storage formation. Wells sharing the same well borehole are treated as a single penetration. Cell maps show the number of penetrating wells within one square mile and are derived from interpretations of incompletely attributed well data, a digital compilation that is known not to include all drilling. The USGS does not expect to know the location of all wells and cannot guarantee the amount of drilling through specific formations in any given cell shown on cell maps.

","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/ofr20121024","usgsCitation":"2012, Geologic framework for the national assessment of carbon dioxide storage resources: U.S. Geological Survey Open-File Report 2012-1024, 14 Chapters; 4 Data Releases; Spatial Data, https://doi.org/10.3133/ofr20121024.","productDescription":"14 Chapters; 4 Data Releases; Spatial Data","onlineOnly":"N","additionalOnlineFiles":"Y","costCenters":[{"id":164,"text":"Central Energy Resources Science Center","active":true,"usgs":true},{"id":241,"text":"Eastern Energy Resources Science Center","active":true,"usgs":true},{"id":255,"text":"Energy Resources Program","active":true,"usgs":true}],"links":[{"id":483457,"rank":7,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/P14E9HDA","text":"USGS data release","linkHelpText":"Carbon Dioxide Storage Resources - Anadarko and Southern Oklahoma Basins: Chapter R. Spatial Data"},{"id":483456,"rank":6,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/P1KEV3C2","text":"USGS data release","linkHelpText":"Carbon Dioxide Storage Resources - California Basins: Chapter Q, Spatial Data"},{"id":438796,"rank":5,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/P13D7IQS","text":"USGS data release","linkHelpText":"Carbon Dioxide Storage Resources - Appalachian Basin, Black Warrior Basin, Illinois Basin, and Michigan Basin: Chapter P, Spatial Data"},{"id":483455,"rank":4,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/P1YASJBA","text":"USGS data release","linkHelpText":"Carbon Dioxide Storage Resources - Wind River Basin: Chapter O, Spatial Data"},{"id":282039,"rank":2,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/of/2012/1024/","text":"Index Page","linkFileType":{"id":5,"text":"html"}},{"id":282040,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.er.usgs.gov/thumbnails/ofr20121024.png"},{"id":374895,"rank":3,"type":{"id":23,"text":"Spatial Data"},"url":"https://pubs.usgs.gov/of/2012/1024/ofr20121024_shapefiles.pdf","text":"Shapefiles","size":"89.2 KB","linkFileType":{"id":1,"text":"pdf"},"description":"Shapefiles"}],"noUsgsAuthors":false,"publicationStatus":"PW","scienceBaseUri":"53cd5bb9e4b0b290850fa140","contributors":{"editors":[{"text":"Warwick, Peter D. 0000-0002-3152-7783 pwarwick@usgs.gov","orcid":"https://orcid.org/0000-0002-3152-7783","contributorId":762,"corporation":false,"usgs":true,"family":"Warwick","given":"Peter","email":"pwarwick@usgs.gov","middleInitial":"D.","affiliations":[{"id":241,"text":"Eastern Energy Resources Science Center","active":true,"usgs":true}],"preferred":false,"id":544645,"contributorType":{"id":2,"text":"Editors"},"rank":1},{"text":"Corum, M.D. 0000-0002-9038-3935 mcorum@usgs.gov","orcid":"https://orcid.org/0000-0002-9038-3935","contributorId":2249,"corporation":false,"usgs":true,"family":"Corum","given":"M.D.","email":"mcorum@usgs.gov","affiliations":[{"id":241,"text":"Eastern Energy Resources Science Center","active":true,"usgs":true},{"id":255,"text":"Energy Resources Program","active":true,"usgs":true}],"preferred":true,"id":544646,"contributorType":{"id":2,"text":"Editors"},"rank":2}]}} ,{"id":70042683,"text":"70042683 - 2012 - On the use of wave parameterizations and a storm impact scaling model in National Weather Service Coastal Flood and decision support operations","interactions":[],"lastModifiedDate":"2013-07-09T10:45:54","indexId":"70042683","displayToPublicDate":"2013-01-01T10:38:46","publicationYear":"2012","noYear":false,"publicationType":{"id":24,"text":"Conference Paper"},"publicationSubtype":{"id":19,"text":"Conference Paper"},"title":"On the use of wave parameterizations and a storm impact scaling model in National Weather Service Coastal Flood and decision support operations","docAbstract":"National Weather Service (NWS) Weather Forecast Offices (WFO) are responsible for issuing coastal flood watches, warnings, advisories, and local statements to alert decision makers and the general public when rising water levels may lead to coastal impacts such as inundation, erosion, and wave battery. Both extratropical and tropical cyclones can generate the prerequisite rise in water level to set the stage for a coastal impact event. Forecasters use a variety of tools including computer model guidance and local studies to help predict the potential severity of coastal flooding. However, a key missing component has been the incorporation of the effects of waves in the prediction of total water level and the associated coastal impacts.\n\nSeveral recent studies have demonstrated the importance of incorporating wave action into the NWS coastal flood program. To follow up on these studies, this paper looks at the potential of applying recently developed empirical parameterizations of wave setup, swash, and runup to the NWS forecast process. Additionally, the wave parameterizations are incorporated into a storm impact scaling model that compares extreme water levels to beach elevation data to determine the mode of coastal change at predetermined “hotspots” of interest. Specifically, the storm impact model compares the approximate storm-induced still water level, which includes contributions from tides, storm surge, and wave setup, to dune crest elevation to determine inundation potential. The model also compares the combined effects of tides, storm surge, and the 2 % exceedance level for vertical wave runup (including both wave setup and swash) to dune toe and crest elevations to determine if erosion and/or ocean overwash may occur. The wave parameterizations and storm impact model are applied to two cases in 2009 that led to significant coastal impacts and unique forecast challenges in North Carolina: the extratropical “Nor'Ida” event during 11-14 November and the large swell event from distant Hurricane Bill on 22 August. The coastal impacts associated with Nor'Ida were due to the combined effects of surge, tide, and wave processes and led to an estimated 5.8 million dollars in damage. While the impacts from Hurricane Bill were not as severe as Nor'Ida, they were mainly associated with wave processes. Thus, this event exemplifies the importance of incorporating waves into the total water level and coastal impact prediction process. These examples set the stage for potential future applications including adaption to the more complex topography along the New England coast.","largerWorkType":{"id":4,"text":"Book"},"largerWorkTitle":"92nd American Meteorological Society Annual Meeting, January 22-26, 2012","largerWorkSubtype":{"id":12,"text":"Conference publication"},"language":"English","publisher":"American Meteorological Society","usgsCitation":"Mignone, A., Stockdon, H., Willis, M., Cannon, J., and Thompson, R., 2012, On the use of wave parameterizations and a storm impact scaling model in National Weather Service Coastal Flood and decision support operations, in 92nd American Meteorological Society Annual Meeting, January 22-26, 2012, 9 p.","productDescription":"9 p.","ipdsId":"IP-034554","costCenters":[{"id":574,"text":"St. Petersburg Coastal and Marine Science Center","active":true,"usgs":true}],"links":[{"id":274744,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"},{"id":274743,"type":{"id":15,"text":"Index Page"},"url":"https://ams.confex.com/ams/92Annual/webprogram/Paper196615.html"}],"noUsgsAuthors":false,"publicationStatus":"PW","scienceBaseUri":"51dd30eee4b0f72b44719cb2","contributors":{"authors":[{"text":"Mignone, Anthony","contributorId":77825,"corporation":false,"usgs":true,"family":"Mignone","given":"Anthony","email":"","affiliations":[],"preferred":false,"id":472050,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Stockdon, H.","contributorId":71351,"corporation":false,"usgs":true,"family":"Stockdon","given":"H.","affiliations":[],"preferred":false,"id":472049,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Willis, M.","contributorId":82910,"corporation":false,"usgs":true,"family":"Willis","given":"M.","email":"","affiliations":[],"preferred":false,"id":472051,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Cannon, J.W.","contributorId":39676,"corporation":false,"usgs":true,"family":"Cannon","given":"J.W.","email":"","affiliations":[],"preferred":false,"id":472048,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Thompson, R.","contributorId":103444,"corporation":false,"usgs":true,"family":"Thompson","given":"R.","affiliations":[],"preferred":false,"id":472052,"contributorType":{"id":1,"text":"Authors"},"rank":5}]}} ,{"id":70044105,"text":"70044105 - 2012 - Primary mapping and stratigraphic data and field methods for the Snowmastodon Project","interactions":[],"lastModifiedDate":"2013-07-25T10:39:59","indexId":"70044105","displayToPublicDate":"2013-01-01T10:16:12","publicationYear":"2012","noYear":false,"publicationType":{"id":18,"text":"Report"},"publicationSubtype":{"id":3,"text":"Organization Series"},"seriesTitle":{"id":222,"text":"Technical Report","active":false,"publicationSubtype":{"id":3}},"seriesNumber":"2012-04","title":"Primary mapping and stratigraphic data and field methods for the Snowmastodon Project","docAbstract":"During the Snowmastodon Project, many different people collected data for a wide array of purposes under a variety of conditions. Early in the process and in an attempt to provide project-wide consistency, Kirk Johnson appointed Carol Lucking as the project’s data manager both in the field and the lab. She was responsible for using GIS to create maps on an ongoing basis throughout the project. Jeff Pigati agreed to measure stratigraphic sections and coordinate the collection of various nonvertebrate samples to make sure that all resulting data could be plotted on common diagrams. Kirk Johnson was onsite for the entire project and measured the basin margin stratigraphy on a daily basis as it was destroyed by the digging teams. In the fall of 2010, we treated the upper part of the site (which included discrete excavations for the mammoth, deer, and bison skeletons) as an archaeological excavation and the lower part of the site (which contained isolated mastodon, ground sloth, and bison bones) as a construction salvage site.","language":"English","publisher":"Denver Museum of Nature & Science","publisherLocation":"Denver, CO","usgsCitation":"Lucking, C., Johnson, K.R., Pigati, J., and Miller, I., 2012, Primary mapping and stratigraphic data and field methods for the Snowmastodon Project: Technical Report 2012-04, 101 p.","productDescription":"101 p.","numberOfPages":"102","ipdsId":"IP-038917","costCenters":[{"id":308,"text":"Geology and Environmental Change Science Center","active":false,"usgs":true}],"links":[{"id":275384,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"},{"id":275380,"type":{"id":2,"text":"Additional Report Piece"},"url":"https://www.dmns.org/media/1280453/pages_61-72_from_technical_report.pdf"},{"id":275378,"type":{"id":2,"text":"Additional Report Piece"},"url":"https://www.dmns.org/media/1280447/pages_16-48_from_technical_report.pdf"},{"id":275379,"type":{"id":2,"text":"Additional Report Piece"},"url":"https://www.dmns.org/media/1280450/pages_49-60_from_technical_report.pdf"},{"id":275375,"type":{"id":11,"text":"Document"},"url":"https://www.dmns.org/media/1280444/pages_1-15_from_technical_reportred.pdf"},{"id":275381,"type":{"id":2,"text":"Additional Report Piece"},"url":"https://www.dmns.org/media/1280456/pages_74-79_from_technical_report.pdf"},{"id":275382,"type":{"id":2,"text":"Additional Report Piece"},"url":"https://www.dmns.org/media/1280459/pages_80-85_from_technical_report.pdf"},{"id":275383,"type":{"id":2,"text":"Additional Report Piece"},"url":"https://www.dmns.org/media/1280462/pages_87-102_from_technical_report.pdf"}],"country":"United States","state":"Colorado","city":"Snowmass Village","geographicExtents":"{ \"type\": \"FeatureCollection\", \"features\": [ { \"type\": \"Feature\", \"properties\": {}, \"geometry\": { \"type\": \"Polygon\", \"coordinates\": [ [ [ -106.989321,39.155859 ], [ -106.989321,39.291971 ], [ -106.897133,39.291971 ], [ -106.897133,39.155859 ], [ -106.989321,39.155859 ] ] ] } } ] }","noUsgsAuthors":false,"publicationStatus":"PW","scienceBaseUri":"51f25422e4b0279fe2e1c01e","contributors":{"authors":[{"text":"Lucking, Carol","contributorId":36035,"corporation":false,"usgs":true,"family":"Lucking","given":"Carol","email":"","affiliations":[],"preferred":false,"id":474820,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Johnson, Kirk R.","contributorId":16877,"corporation":false,"usgs":true,"family":"Johnson","given":"Kirk","email":"","middleInitial":"R.","affiliations":[],"preferred":false,"id":474819,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Pigati, Jeffery S. jpigati@usgs.gov","contributorId":1270,"corporation":false,"usgs":true,"family":"Pigati","given":"Jeffery S.","email":"jpigati@usgs.gov","affiliations":[{"id":318,"text":"Geosciences and Environmental Change Science Center","active":true,"usgs":true}],"preferred":false,"id":474818,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Miller, Ian","contributorId":66573,"corporation":false,"usgs":true,"family":"Miller","given":"Ian","affiliations":[],"preferred":false,"id":474821,"contributorType":{"id":1,"text":"Authors"},"rank":4}]}} ]}