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Increases likely are related to road salt and other anthropogenic sources, such as septic systems, wastewater, and contamination from landfills and salt-storage areas. According to water-quality data reported to the New Hampshire Department of Environmental Services (NHDES), about 100 public water systems (5 percent) in 2010 had at least one groundwater sample with chloride concentrations that were equal to or exceeded the U.S. Environmental Protection Agency (USEPA) secondary maximum contaminant level (SMCL) of 250 mg/L before the water was treated for public consumption. The SMCL for chloride is a measurement of potential cosmetic or aesthetic effects of chloride in water. High concentrations of chloride and sodium in drinking-water sources can be costly to remove.</p>\n<p>A new cooperative study between the U.S. Geological Survey (USGS) and the NHDES (Medalie, 2012) assessed chloride and sodium levels in groundwater in New Hampshire from the 1960s through 2011. The purpose of the study was to integrate all data on concentrations of chloride and sodium from groundwater in New Hampshire available from various Federal and State sources, including from the NHDES, the New Hamsphire Department of Health and Human Services, the USGS, and the U.S. Environmental Protection SurveyAgency (USEPA), for public and private (domestic) wells and to organize the data into a database. Medalie (2012) explained the many assumptions and limitations of disparate data that were collected to meet wide-ranging objectives. This fact sheet summarizes the most important findings of the data.</p>","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/fs20133011","collaboration":"Prepared in cooperation with the New Hampshire Department of Environmental Services","usgsCitation":"Medalie, L., 2013, Concentrations of chloride and sodium in groundwater in New Hampshire from 1960 through 2011: U.S. Geological Survey Fact Sheet 2013-3011, 2 p., https://doi.org/10.3133/fs20133011.","productDescription":"2 p.","numberOfPages":"2","onlineOnly":"Y","additionalOnlineFiles":"N","temporalStart":"1960-01-01","temporalEnd":"2011-12-31","costCenters":[{"id":466,"text":"New England Water Science Center","active":true,"usgs":true}],"links":[{"id":268539,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/fs_2013_3011.gif"},{"id":268537,"type":{"id":15,"text":"Index Page"},"url":"https://pubs.usgs.gov/fs/2013/3011/"},{"id":268538,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/fs/2013/3011/pdf/fs2013-3011_report_508.pdf","text":"Report","size":"6.01 MB","linkFileType":{"id":1,"text":"pdf"},"description":"Report"}],"country":"United States","state":"New 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,{"id":70044134,"text":"70044134 - 2013 - Overview of intercalibration of satellite instruments","interactions":[],"lastModifiedDate":"2013-02-27T17:45:28","indexId":"70044134","displayToPublicDate":"2013-02-27T00:00:00","publicationYear":"2013","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":1944,"text":"IEEE Transactions on Geoscience and Remote Sensing","active":true,"publicationSubtype":{"id":10}},"title":"Overview of intercalibration of satellite instruments","docAbstract":"Inter-calibration of satellite instruments is critical for detection and quantification of changes in the Earth’s environment, weather forecasting, understanding climate processes, and monitoring climate and land cover change. These applications use data from many satellites; for the data to be inter-operable, the instruments must be cross-calibrated. To meet the stringent needs of such applications requires that instruments provide reliable, accurate, and consistent measurements over time. Robust techniques are required to ensure that observations from different instruments can be normalized to a common scale that the community agrees on. The long-term reliability of this process needs to be sustained in accordance with established reference standards and best practices. Furthermore, establishing physical meaning to the information through robust Système International d'unités (SI) traceable Calibration and Validation (Cal/Val) is essential to fully understand the parameters under observation. The processes of calibration, correction, stability monitoring, and quality assurance need to be underpinned and evidenced by comparison with “peer instruments” and, ideally, highly calibrated in-orbit reference instruments. Inter-calibration between instruments is a central pillar of the Cal/Val strategies of many national and international satellite remote sensing organizations. Inter-calibration techniques as outlined in this paper not only provide a practical means of identifying and correcting relative biases in radiometric calibration between instruments but also enable potential data gaps between measurement records in a critical time series to be bridged. Use of a robust set of internationally agreed upon and coordinated inter-calibration techniques will lead to significant improvement in the consistency between satellite instruments and facilitate accurate monitoring of the Earth’s climate at uncertainty levels needed to detect and attribute the mechanisms of change. This paper summarizes the state-of-the-art of post-launch radiometric calibration of remote sensing satellite instruments, through inter-calibration.","largerWorkType":{"id":2,"text":"Article"},"largerWorkTitle":"IEEE Transactions on Geoscience and Remote Sensing","largerWorkSubtype":{"id":10,"text":"Journal Article"},"language":"English","publisher":"IEEE","publisherLocation":"Washington, D.C.","doi":"10.1109/TGRS.2012.2228654","usgsCitation":"Chander, G., Hewison, T., Fox, N., Wu, X., Xiong, X., and Blackwell, W., 2013, Overview of intercalibration of satellite instruments: IEEE Transactions on Geoscience and Remote Sensing, v. 51, no. 3, p. 1056-1080, https://doi.org/10.1109/TGRS.2012.2228654.","productDescription":"25 p.","startPage":"1056","endPage":"1080","ipdsId":"IP-038923","costCenters":[{"id":222,"text":"Earth Resources Observation and Science (EROS) Center","active":true,"usgs":true}],"links":[{"id":268515,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"},{"id":268514,"type":{"id":10,"text":"Digital Object Identifier"},"url":"https://dx.doi.org/10.1109/TGRS.2012.2228654"}],"volume":"51","issue":"3","noUsgsAuthors":false,"publicationStatus":"PW","scienceBaseUri":"512f2afee4b0cad81a732d87","contributors":{"authors":[{"text":"Chander, G.","contributorId":51449,"corporation":false,"usgs":true,"family":"Chander","given":"G.","affiliations":[],"preferred":false,"id":474856,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Hewison, T.J.","contributorId":75403,"corporation":false,"usgs":true,"family":"Hewison","given":"T.J.","email":"","affiliations":[],"preferred":false,"id":474857,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Fox, N.","contributorId":90905,"corporation":false,"usgs":true,"family":"Fox","given":"N.","email":"","affiliations":[],"preferred":false,"id":474858,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Wu, X.","contributorId":31925,"corporation":false,"usgs":true,"family":"Wu","given":"X.","email":"","affiliations":[],"preferred":false,"id":474854,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Xiong, X.","contributorId":37885,"corporation":false,"usgs":true,"family":"Xiong","given":"X.","affiliations":[],"preferred":false,"id":474855,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"Blackwell, W.J.","contributorId":23405,"corporation":false,"usgs":true,"family":"Blackwell","given":"W.J.","email":"","affiliations":[],"preferred":false,"id":474853,"contributorType":{"id":1,"text":"Authors"},"rank":6}]}}
,{"id":70044136,"text":"70044136 - 2013 - Applications of spectral band adjustment factors (SBAF) for cross-calibration","interactions":[],"lastModifiedDate":"2013-02-27T17:44:54","indexId":"70044136","displayToPublicDate":"2013-02-27T00:00:00","publicationYear":"2013","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":1944,"text":"IEEE Transactions on Geoscience and Remote Sensing","active":true,"publicationSubtype":{"id":10}},"title":"Applications of spectral band adjustment factors (SBAF) for cross-calibration","docAbstract":"To monitor land surface processes over a wide range of temporal and spatial scales, it is critical to have coordinated observations of the Earth's surface acquired from multiple spaceborne imaging sensors. However, an integrated global observation framework requires an understanding of how land surface processes are seen differently by various sensors. This is particularly true for sensors acquiring data in spectral bands whose relative spectral responses (RSRs) are not similar and thus may produce different results while observing the same target. The intrinsic offsets between two sensors caused by RSR mismatches can be compensated by using a spectral band adjustment factor (SBAF), which takes into account the spectral profile of the target and the RSR of the two sensors. The motivation of this work comes from the need to compensate the spectral response differences of multispectral sensors in order to provide a more accurate cross-calibration between the sensors. In this paper, radiometric cross-calibration of the Landsat 7 Enhanced Thematic Mapper Plus (ETM+) and the Terra Moderate Resolution Imaging Spectroradiometer (MODIS) sensors was performed using near-simultaneous observations over the Libya 4 pseudoinvariant calibration site in the visible and near-infrared spectral range. The RSR differences of the analogous ETM+ and MODIS spectral bands provide the opportunity to explore, understand, quantify, and compensate for the measurement differences between these two sensors. The cross-calibration was initially performed by comparing the top-of-atmosphere (TOA) reflectances between the two sensors over their lifetimes. The average percent differences in the long-term trends ranged from $-$5% to $+$6%. The RSR compensated ETM+ TOA reflectance (ETM+$^{ast}$) measurements were then found to agree with MODIS TOA reflectance to within 5% for all bands when Earth Observing-1 Hy- erion hyperspectral data were used to produce the SBAFs. These differences were later reduced to within 1% for all bands (except band 2) by using Environmental Satellite Scanning Imaging Absorption Spectrometer for Atmospheric Cartography hyperspectral data to produce the SBAFs.","largerWorkType":{"id":2,"text":"Article"},"largerWorkTitle":"IEEE Transactions on Geoscience and Remote Sensing","largerWorkSubtype":{"id":10,"text":"Journal Article"},"language":"English","publisher":"IEEE","publisherLocation":"Washington, D.C.","doi":"10.1109/TGRS.2012.2228007","usgsCitation":"Chander, G., 2013, Applications of spectral band adjustment factors (SBAF) for cross-calibration: IEEE Transactions on Geoscience and Remote Sensing, v. 51, no. 3, p. 1267-1281, https://doi.org/10.1109/TGRS.2012.2228007.","productDescription":"15 p.","startPage":"1267","endPage":"1281","ipdsId":"IP-037261","costCenters":[{"id":222,"text":"Earth Resources Observation and Science (EROS) Center","active":true,"usgs":true}],"links":[{"id":268517,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"},{"id":268516,"type":{"id":10,"text":"Digital Object Identifier"},"url":"https://dx.doi.org/10.1109/TGRS.2012.2228007"}],"volume":"51","issue":"3","noUsgsAuthors":false,"publicationStatus":"PW","scienceBaseUri":"512f2adfe4b0cad81a732d73","contributors":{"authors":[{"text":"Chander, Gyanesh gchander@usgs.gov","contributorId":3013,"corporation":false,"usgs":true,"family":"Chander","given":"Gyanesh","email":"gchander@usgs.gov","affiliations":[],"preferred":true,"id":474863,"contributorType":{"id":1,"text":"Authors"},"rank":1}]}}
,{"id":70044294,"text":"sir20135016 - 2013 - Macrophyte and pH buffering updates to the Klamath River water-quality model upstream of Keno Dam, Oregon","interactions":[],"lastModifiedDate":"2013-03-01T14:08:40","indexId":"sir20135016","displayToPublicDate":"2013-02-27T00:00:00","publicationYear":"2013","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":"2013-5016","title":"Macrophyte and pH buffering updates to the Klamath River water-quality model upstream of Keno Dam, Oregon","docAbstract":"A hydrodynamic, water temperature, and water-quality model of the Link River to Keno Dam reach of the upper Klamath River was updated to account for macrophytes and enhanced pH buffering from dissolved organic matter, ammonia, and orthophosphorus. Macrophytes had been observed in this reach by field personnel, so macrophyte field data were collected in summer and fall (June-October) 2011 to provide a dataset to guide the inclusion of macrophytes in the model. Three types of macrophytes were most common: pondweed (Potamogeton species), coontail (Ceratophyllum demersum), and common waterweed (Elodea canadensis). Pondweed was found throughout the Link River to Keno Dam reach in early summer with densities declining by mid-summer and fall. Coontail and common waterweed were more common in the lower reach near Keno Dam and were at highest density in summer. All species were most dense in shallow water (less than 2 meters deep) near shore. The highest estimated dry weight biomass for any sample during the study was 202 grams per square meter for coontail in August. Guided by field results, three macrophyte groups were incorporated into the CE-QUAL-W2 model for calendar years 2006-09. The CE-QUAL-W2 model code was adjusted to allow the user to initialize macrophyte populations spatially across the model grid. The default CE-QUAL-W2 model includes pH buffering by carbonates, but does not include pH buffering by organic matter, ammonia, or orthophosphorus. These three constituents, especially dissolved organic matter, are present in the upper Klamath River at concentrations that provide substantial pH buffering capacity. In this study, CE-QUAL-W2 was updated to include this enhanced buffering capacity in the simulation of pH. Acid dissociation constants for ammonium and phosphoric acid were taken from the literature. For dissolved organic matter, the number of organic acid groups and each group's acid dissociation constant (K<sub>a</sub>) and site density (moles of sites per mole of carbon) were derived by fitting a theoretical buffering response to measured upper Klamath River alkalinity titration curves. The organic matter buffering in the Klamath River was modeled with two monoprotic organic acids: carboxylic acids with a mean pK<sub>a</sub> of 5.584 and site density of 0.1925, and phenolic organic acids with a mean pK<sub>a</sub> of 9.594 and site density of 0.6466. Total inorganic carbon concentrations in the model boundary inputs were recalculated based on the new buffering equations. CE-QUAL-W2 was also adjusted to allow the simulation of nonconservative alkalinity caused by nitrification, denitrification, photosynthesis, and respiration. The Klamath River model was recalibrated after the macrophyte and pH buffering updates producing improved predictions for pH, dissolved oxygen, and particulate carbon.","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/sir20135016","collaboration":"Prepared in cooperation with the Bureau of Reclamation","usgsCitation":"Sullivan, A.B., Rounds, S.A., Asbill-Case, J.R., and Deas, M., 2013, Macrophyte and pH buffering updates to the Klamath River water-quality model upstream of Keno Dam, Oregon: U.S. Geological Survey Scientific Investigations Report 2013-5016, viii, 54 p., https://doi.org/10.3133/sir20135016.","productDescription":"viii, 54 p.","numberOfPages":"64","onlineOnly":"N","costCenters":[{"id":518,"text":"Oregon Water Science Center","active":true,"usgs":true}],"links":[{"id":268629,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/sir/2013/5016/pdf/sir20135016.pdf"},{"id":268628,"type":{"id":15,"text":"Index Page"},"url":"https://pubs.usgs.gov/sir/2013/5016/index.html"},{"id":268630,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/sir_2013_5016.jpg"}],"country":"United States","state":"Oregon","geographicExtents":"{ \"type\": \"FeatureCollection\", \"features\": [ { \"type\": \"Feature\", \"properties\": {}, \"geometry\": { \"type\": \"Polygon\", \"coordinates\": [ [ [ 122,42.03 ], [ 122,42.33 ], [ 121.75,42.33 ], [ 121.75,42.03 ], [ 122,42.03 ] ] ] } } ] }","noUsgsAuthors":false,"publicationStatus":"PW","scienceBaseUri":"5131dc02e4b0140546f53bf9","contributors":{"authors":[{"text":"Sullivan, Annett B. 0000-0001-7783-3906 annett@usgs.gov","orcid":"https://orcid.org/0000-0001-7783-3906","contributorId":56317,"corporation":false,"usgs":true,"family":"Sullivan","given":"Annett","email":"annett@usgs.gov","middleInitial":"B.","affiliations":[],"preferred":false,"id":475250,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Rounds, Stewart A. 0000-0002-8540-2206 sarounds@usgs.gov","orcid":"https://orcid.org/0000-0002-8540-2206","contributorId":905,"corporation":false,"usgs":true,"family":"Rounds","given":"Stewart","email":"sarounds@usgs.gov","middleInitial":"A.","affiliations":[{"id":518,"text":"Oregon Water Science Center","active":true,"usgs":true}],"preferred":true,"id":475248,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Asbill-Case, Jessica R.","contributorId":32058,"corporation":false,"usgs":true,"family":"Asbill-Case","given":"Jessica","email":"","middleInitial":"R.","affiliations":[],"preferred":false,"id":475249,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Deas, Michael L.","contributorId":98830,"corporation":false,"usgs":true,"family":"Deas","given":"Michael L.","affiliations":[],"preferred":false,"id":475251,"contributorType":{"id":1,"text":"Authors"},"rank":4}]}}
,{"id":70044171,"text":"ds709S - 2013 - Local-area-enhanced, 2.5-meter resolution natural-color and color-infrared satellite-image mosaics of the Kunduz mineral district in Afghanistan: Chapter S in <i>Local-area-enhanced, high-resolution natural-color and color-infrared satellite-image mosaics of mineral districts in Afghanistan</i>","interactions":[],"lastModifiedDate":"2013-02-27T16:24:33","indexId":"ds709S","displayToPublicDate":"2013-02-27T00:00:00","publicationYear":"2013","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":"S","title":"Local-area-enhanced, 2.5-meter resolution natural-color and color-infrared satellite-image mosaics of the Kunduz mineral district in Afghanistan: Chapter S in <i>Local-area-enhanced, high-resolution natural-color and color-infrared satellite-image mosaics of mineral districts in Afghanistan</i>","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 Kunduz mineral district, which has celestite 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,2007,2008,2009), but the image processing has altered the original pixel structure and all image values of the JAXA ALOS data, such that original image values cannot be recreated from this DS. As such, the DS products match JAXA criteria for value added products, which are not copyrighted, according to the ALOS end-user license agreement. 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 Kunduz) 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 Kunduz 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-S)","largerWorkSubtype":{"id":5,"text":"USGS Numbered Series"},"language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/ds709S","collaboration":"Prepared in cooperation with the U.S. Department of Defense Task Force for Business and Stability Operations and the Afghanistan Geological Survey; Chapter S in <i>Local-area-enhanced, high-resolution natural-color and color-infrared satellite-image mosaics of mineral districts in Afghanistan</i>","usgsCitation":"Davis, P.A., Arko, S.A., and Harbin, M., 2013, Local-area-enhanced, 2.5-meter resolution natural-color and color-infrared satellite-image mosaics of the Kunduz mineral district in Afghanistan: Chapter S in <i>Local-area-enhanced, high-resolution natural-color and color-infrared satellite-image mosaics of mineral districts in Afghanistan</i>: U.S. Geological Survey Data Series 709, HTML Document; Readme; 4 Index Maps; 16 Image Files; 16 Metadata Files; 1 Shapefile, https://doi.org/10.3133/ds709S.","productDescription":"HTML Document; Readme; 4 Index Maps; 16 Image Files; 16 Metadata Files; 1 Shapefile","onlineOnly":"Y","additionalOnlineFiles":"Y","costCenters":[{"id":387,"text":"Mineral Resources Program","active":true,"usgs":true}],"links":[{"id":268501,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/ds_709_S.png"},{"id":268498,"type":{"id":14,"text":"Image"},"url":"https://pubs.usgs.gov/ds/709/s/image_files/image_files.html"},{"id":268499,"type":{"id":16,"text":"Metadata"},"url":"https://pubs.usgs.gov/ds/709/s/metadata/metadata.html"},{"id":268500,"type":{"id":7,"text":"Companion Files"},"url":"https://pubs.usgs.gov/ds/709/s/shapefiles/shapefiles.html"},{"id":268495,"type":{"id":15,"text":"Index Page"},"url":"https://pubs.usgs.gov/ds/709/s/"},{"id":268496,"type":{"id":20,"text":"Read Me"},"url":"https://pubs.usgs.gov/ds/709/s/1_readme.txt"},{"id":268497,"type":{"id":17,"text":"Plate"},"url":"https://pubs.usgs.gov/ds/709/s/index_maps/index_maps.html"}],"country":"Afghanistan","otherGeospatial":"Kunduz Mineral District","geographicExtents":"{ \"type\": \"FeatureCollection\", \"features\": [ { \"type\": \"Feature\", \"properties\": {}, \"geometry\": { \"type\": \"Polygon\", \"coordinates\": [ [ [ 58.0,29.0 ], [ 58.0,40.0 ], [ 77.0,40.0 ], [ 77.0,29.0 ], [ 58.0,29.0 ] ] ] } } ] }","noUsgsAuthors":false,"publicationStatus":"PW","scienceBaseUri":"512f2afde4b0cad81a732d7f","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":474973,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Arko, Scott A.","contributorId":101929,"corporation":false,"usgs":true,"family":"Arko","given":"Scott","email":"","middleInitial":"A.","affiliations":[],"preferred":false,"id":474975,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Harbin, Michelle L.","contributorId":20590,"corporation":false,"usgs":true,"family":"Harbin","given":"Michelle L.","affiliations":[],"preferred":false,"id":474974,"contributorType":{"id":1,"text":"Authors"},"rank":3}]}}
,{"id":70044146,"text":"70044146 - 2013 - Assessment of spectral, misregistration, and spatial uncertainties inherent in the cross-calibration study","interactions":[],"lastModifiedDate":"2017-05-10T15:48:47","indexId":"70044146","displayToPublicDate":"2013-02-27T00:00:00","publicationYear":"2013","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":1944,"text":"IEEE Transactions on Geoscience and Remote Sensing","active":true,"publicationSubtype":{"id":10}},"title":"Assessment of spectral, misregistration, and spatial uncertainties inherent in the cross-calibration study","docAbstract":"Cross-calibration of satellite sensors permits the quantitative comparison of measurements obtained from different Earth Observing (EO) systems. Cross-calibration studies usually use simultaneous or near-simultaneous observations from several spaceborne sensors to develop band-by-band relationships through regression analysis. The investigation described in this paper focuses on evaluation of the uncertainties inherent in the cross-calibration process, including contributions due to different spectral responses, spectral resolution, spectral filter shift, geometric misregistrations, and spatial resolutions. The hyperspectral data from the Environmental Satellite SCanning Imaging Absorption SpectroMeter for Atmospheric CartograpHY and the EO-1 Hyperion, along with the relative spectral responses (RSRs) from the Landsat 7 Enhanced Thematic Mapper (TM) Plus and the Terra Moderate Resolution Imaging Spectroradiometer sensors, were used for the spectral uncertainty study. The data from Landsat 5 TM over five representative land cover types (desert, rangeland, grassland, deciduous forest, and coniferous forest) were used for the geometric misregistrations and spatial-resolution study. The spectral resolution uncertainty was found to be within 0.25%, spectral filter shift within 2.5%, geometric misregistrations within 0.35%, and spatial-resolution effects within 0.1% for the Libya 4 site. The one-sigma uncertainties presented in this paper are uncorrelated, and therefore, the uncertainties can be summed orthogonally. Furthermore, an overall total uncertainty was developed. In general, the results suggested that the spectral uncertainty is more dominant compared to other uncertainties presented in this paper. Therefore, the effect of the sensor RSR differences needs to be quantified and compensated to avoid large uncertainties in cross-calibration results.","largerWorkType":{"id":2,"text":"Article"},"largerWorkTitle":"IEEE Transactions on Geoscience and Remote Sensing","largerWorkSubtype":{"id":10,"text":"Journal Article"},"language":"English","publisher":"IEEE","publisherLocation":"Washington, D.C.","doi":"10.1109/TGRS.2012.2228008","usgsCitation":"Chander, G., Helder, D., Aaron, D., Mishra, N., and Shrestha, A., 2013, Assessment of spectral, misregistration, and spatial uncertainties inherent in the cross-calibration study: IEEE Transactions on Geoscience and Remote Sensing, v. 51, no. 3, p. 1282-1296, https://doi.org/10.1109/TGRS.2012.2228008.","productDescription":"15 p.","startPage":"1282","endPage":"1296","ipdsId":"IP-039167","costCenters":[{"id":222,"text":"Earth Resources Observation and Science (EROS) Center","active":true,"usgs":true}],"links":[{"id":268519,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"volume":"51","issue":"3","noUsgsAuthors":false,"publicationStatus":"PW","scienceBaseUri":"512f2af9e4b0cad81a732d77","contributors":{"authors":[{"text":"Chander, G.","contributorId":51449,"corporation":false,"usgs":true,"family":"Chander","given":"G.","affiliations":[],"preferred":false,"id":474896,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Helder, D. L. 0000-0002-7379-4679","orcid":"https://orcid.org/0000-0002-7379-4679","contributorId":51496,"corporation":false,"usgs":true,"family":"Helder","given":"D. L.","affiliations":[],"preferred":false,"id":474897,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Aaron, David","contributorId":83809,"corporation":false,"usgs":false,"family":"Aaron","given":"David","email":"","affiliations":[{"id":5089,"text":"South Dakota State University","active":true,"usgs":false}],"preferred":false,"id":474899,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Mishra, N.","contributorId":67379,"corporation":false,"usgs":true,"family":"Mishra","given":"N.","email":"","affiliations":[],"preferred":false,"id":474898,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Shrestha, A.K.","contributorId":104783,"corporation":false,"usgs":true,"family":"Shrestha","given":"A.K.","email":"","affiliations":[],"preferred":false,"id":474900,"contributorType":{"id":1,"text":"Authors"},"rank":5}]}}
,{"id":70044170,"text":"ds709R - 2013 - Local-area-enhanced, 2.5-meter resolution natural-color and color-infrared satellite-image mosaics of the Dudkash mineral district in Afghanistan: Chapter R in <i>Local-area-enhanced, high-resolution natural-color and color-infrared satellite-image mosaics of mineral districts in Afghanistan</i>","interactions":[],"lastModifiedDate":"2013-02-27T16:10:20","indexId":"ds709R","displayToPublicDate":"2013-02-27T00:00:00","publicationYear":"2013","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":"R","title":"Local-area-enhanced, 2.5-meter resolution natural-color and color-infrared satellite-image mosaics of the Dudkash mineral district in Afghanistan: Chapter R in <i>Local-area-enhanced, high-resolution natural-color and color-infrared satellite-image mosaics of mineral districts in Afghanistan</i>","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 Dudkash mineral district, which has industrial mineral 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,2006,2007,2008,2009), but the image processing has altered the original pixel structure and all image values of the JAXA ALOS data, such that original image values cannot be recreated from this DS. As such, the DS products match JAXA criteria for value added products, which are not copyrighted, according to the ALOS end-user license agreement. 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 Dudkash) 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 Dudkash 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/ds709R","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 R in <i>Local-area-enhanced, high-resolution natural-color and color-infrared satellite-image mosaics of mineral districts in Afghanistan</i> (DS 709-R)","usgsCitation":"Davis, P.A., Arko, S.A., and Harbin, M., 2013, Local-area-enhanced, 2.5-meter resolution natural-color and color-infrared satellite-image mosaics of the Dudkash mineral district in Afghanistan: Chapter R in <i>Local-area-enhanced, high-resolution natural-color and color-infrared satellite-image mosaics of mineral districts in Afghanistan</i>: U.S. Geological Survey Data Series 709, HTML Document; Readme; 4 Index Maps; 16 Image Files; 16 Metadata Files; 1 Shapefile, https://doi.org/10.3133/ds709R.","productDescription":"HTML Document; Readme; 4 Index Maps; 16 Image Files; 16 Metadata Files; 1 Shapefile","onlineOnly":"Y","additionalOnlineFiles":"Y","costCenters":[{"id":387,"text":"Mineral Resources Program","active":true,"usgs":true}],"links":[{"id":268489,"type":{"id":20,"text":"Read Me"},"url":"https://pubs.usgs.gov/ds/709/r/1_readme.txt"},{"id":268490,"type":{"id":17,"text":"Plate"},"url":"https://pubs.usgs.gov/ds/709/r/index_maps/index_maps.html"},{"id":268491,"type":{"id":14,"text":"Image"},"url":"https://pubs.usgs.gov/ds/709/r/image_files/image_files.html"},{"id":268492,"type":{"id":16,"text":"Metadata"},"url":"https://pubs.usgs.gov/ds/709/r/metadata/metadata.html"},{"id":268493,"type":{"id":7,"text":"Companion Files"},"url":"https://pubs.usgs.gov/ds/709/r/shapefiles/shapefiles.html"},{"id":268488,"type":{"id":15,"text":"Index Page"},"url":"https://pubs.usgs.gov/ds/709/r/"},{"id":268494,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/ds_709_R.png"}],"country":"Afghanistan","otherGeospatial":"Dudkash Mineral District","geographicExtents":"{ \"type\": \"FeatureCollection\", \"features\": [ { \"type\": \"Feature\", \"properties\": {}, \"geometry\": { \"type\": \"Polygon\", \"coordinates\": [ [ [ 58.0,29.0 ], [ 58.0,40.0 ], [ 77.0,40.0 ], [ 77.0,29.0 ], [ 58.0,29.0 ] ] ] } } ] }","noUsgsAuthors":false,"publicationStatus":"PW","scienceBaseUri":"512f2afbe4b0cad81a732d7b","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":474970,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Arko, Scott A.","contributorId":101929,"corporation":false,"usgs":true,"family":"Arko","given":"Scott","email":"","middleInitial":"A.","affiliations":[],"preferred":false,"id":474972,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Harbin, Michelle L.","contributorId":20590,"corporation":false,"usgs":true,"family":"Harbin","given":"Michelle L.","affiliations":[],"preferred":false,"id":474971,"contributorType":{"id":1,"text":"Authors"},"rank":3}]}}
,{"id":70044100,"text":"70044100 - 2013 - Underestimating the effects of spatial heterogeneity due to individual movement and spatial scale: infectious disease as an example","interactions":[],"lastModifiedDate":"2013-02-26T17:59:42","indexId":"70044100","displayToPublicDate":"2013-02-26T00:00:00","publicationYear":"2013","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":2602,"text":"Landscape Ecology","active":true,"publicationSubtype":{"id":10}},"title":"Underestimating the effects of spatial heterogeneity due to individual movement and spatial scale: infectious disease as an example","docAbstract":"Many ecological and epidemiological studies occur in systems with mobile individuals and heterogeneous landscapes. Using a simulation model, we show that the accuracy of inferring an underlying biological process from observational data depends on movement and spatial scale of the analysis. As an example, we focused on estimating the relationship between host density and pathogen transmission. Observational data can result in highly biased inference about the underlying process when individuals move among sampling areas. Even without sampling error, the effect of host density on disease transmission is underestimated by approximately 50 % when one in ten hosts move among sampling areas per lifetime. Aggregating data across larger regions causes minimal bias when host movement is low, and results in less biased inference when movement rates are high. However, increasing data aggregation reduces the observed spatial variation, which would lead to the misperception that a spatially targeted control effort may not be very effective. In addition, averaging over the local heterogeneity will result in underestimating the importance of spatial covariates. Minimizing the bias due to movement is not just about choosing the best spatial scale for analysis, but also about reducing the error associated with using the sampling location as a proxy for an individual’s spatial history. This error associated with the exposure covariate can be reduced by choosing sampling regions with less movement, including longitudinal information of individuals’ movements, or reducing the window of exposure by using repeated sampling or younger individuals.","largerWorkType":{"id":2,"text":"Article"},"largerWorkTitle":"Landscape Ecology","largerWorkSubtype":{"id":10,"text":"Journal Article"},"language":"English","publisher":"Springer","publisherLocation":"Amsterdam, Netherlands","doi":"10.1007/s10980-012-9830-4","usgsCitation":"Cross, P.C., Caillaud, D., and Heisey, D.M., 2013, Underestimating the effects of spatial heterogeneity due to individual movement and spatial scale: infectious disease as an example: Landscape Ecology, v. 28, no. 2, p. 247-257, https://doi.org/10.1007/s10980-012-9830-4.","productDescription":"11 p.","startPage":"247","endPage":"257","ipdsId":"IP-034645","costCenters":[{"id":481,"text":"Northern Rocky Mountain Science Center","active":true,"usgs":true}],"links":[{"id":268415,"type":{"id":10,"text":"Digital Object Identifier"},"url":"https://dx.doi.org/10.1007/s10980-012-9830-4"},{"id":268416,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"volume":"28","issue":"2","noUsgsAuthors":false,"publicationDate":"2012-11-30","publicationStatus":"PW","scienceBaseUri":"53cd7a2ae4b0b2908510d4ed","contributors":{"authors":[{"text":"Cross, Paul C. 0000-0001-8045-5213 pcross@usgs.gov","orcid":"https://orcid.org/0000-0001-8045-5213","contributorId":2709,"corporation":false,"usgs":true,"family":"Cross","given":"Paul","email":"pcross@usgs.gov","middleInitial":"C.","affiliations":[{"id":481,"text":"Northern Rocky Mountain Science Center","active":true,"usgs":true}],"preferred":true,"id":474810,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Caillaud, Damien","contributorId":31650,"corporation":false,"usgs":true,"family":"Caillaud","given":"Damien","email":"","affiliations":[],"preferred":false,"id":474811,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Heisey, Dennis M. dheisey@usgs.gov","contributorId":2455,"corporation":false,"usgs":true,"family":"Heisey","given":"Dennis","email":"dheisey@usgs.gov","middleInitial":"M.","affiliations":[{"id":456,"text":"National Wildlife Health Center","active":true,"usgs":true}],"preferred":true,"id":474809,"contributorType":{"id":1,"text":"Authors"},"rank":3}]}}
,{"id":70044062,"text":"70044062 - 2013 - Estimation of evapotranspiration across the conterminous United States using a regression with climate and land-cover data","interactions":[],"lastModifiedDate":"2016-03-28T09:01:08","indexId":"70044062","displayToPublicDate":"2013-02-26T00:00:00","publicationYear":"2013","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":2529,"text":"Journal of the American Water Resources Association","active":true,"publicationSubtype":{"id":10}},"title":"Estimation of evapotranspiration across the conterminous United States using a regression with climate and land-cover data","docAbstract":"<p>Evapotranspiration (ET) is an important quantity for water resource managers to know because it often represents the largest sink for precipitation (<i>P</i>) arriving at the land surface. In order to estimate actual ET across the conterminous United States (U.S.) in this study, a water-balance method was combined with a climate and land-cover regression equation. Precipitation and streamflow records were compiled for 838 watersheds for 1971-2000 across the U.S. to obtain long-term estimates of actual ET. A regression equation was developed that related the ratio ET/<i>P</i> to climate and land-cover variables within those watersheds. Precipitation and temperatures were used from the PRISM climate dataset, and land-cover data were used from the USGS National Land Cover Dataset. Results indicate that ET can be predicted relatively well at a watershed or county scale with readily available climate variables alone, and that land-cover data can also improve those predictions. Using the climate and land-cover data at an 800-m scale and then averaging to the county scale, maps were produced showing estimates of ET and ET/<i>P</i> for the entire conterminous U.S. Using the regression equation, such maps could also be made for more detailed state coverages, or for other areas of the world where climate and land-cover data are plentiful.</p>","largerWorkType":{"id":2,"text":"Article"},"largerWorkTitle":"Journal of the American Water Resources Association","largerWorkSubtype":{"id":10,"text":"Journal Article"},"language":"English","publisher":"Wiley","publisherLocation":"Hoboken, NJ","doi":"10.1111/jawr.12010","usgsCitation":"Sanford, W.E., and Selnick, D.L., 2013, Estimation of evapotranspiration across the conterminous United States using a regression with climate and land-cover data: Journal of the American Water Resources Association, v. 49, no. 1, p. 217-230, https://doi.org/10.1111/jawr.12010.","productDescription":"14 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,{"id":70044075,"text":"ds713 - 2013 - Drill hole data for coal beds in the Powder River Basin, Montana and Wyoming","interactions":[],"lastModifiedDate":"2013-02-26T13:04:16","indexId":"ds713","displayToPublicDate":"2013-02-26T00:00:00","publicationYear":"2013","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":"713","title":"Drill hole data for coal beds in the Powder River Basin, Montana and Wyoming","docAbstract":"This report by the U.S. Geological Survey (USGS) of the Powder River Basin (PRB) of Montana and Wyoming is part of the U.S. Coal Resources and Reserves Assessment Project. Essential to that project was the creation of a comprehensive drill hole database that was used for coal bed correlation and for coal resource and reserve assessments in the PRB. This drill hole database was assembled using data from the USGS National Coal Resources Data System, several other Federal and State agencies, and selected mining companies. Additionally, USGS personnel manually entered lithologic picks into the database from geophysical logs of coalbed methane, oil, and gas wells. Of the 29,928 drill holes processed, records of 21,393 are in the public domain and are included in this report. The database contains location information, lithology, and coal bed names for each drill hole.","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/ds713","usgsCitation":"Haacke, J.E., and Scott, D.C., 2013, Drill hole data for coal beds in the Powder River Basin, Montana and Wyoming: U.S. Geological Survey Data Series 713, Report: iv, 15 p.; Downloads Directory; Readme, https://doi.org/10.3133/ds713.","productDescription":"Report: iv, 15 p.; Downloads Directory; Readme","onlineOnly":"Y","additionalOnlineFiles":"Y","ipdsId":"IP-037728","costCenters":[{"id":164,"text":"Central Energy Resources Science Center","active":true,"usgs":true}],"links":[{"id":268387,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/ds_713.gif"},{"id":268384,"type":{"id":7,"text":"Companion Files"},"url":"https://pubs.usgs.gov/ds/713/downloads/"},{"id":268385,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/ds/713/DS713.pdf"},{"id":268383,"type":{"id":15,"text":"Index Page"},"url":"https://pubs.usgs.gov/ds/713/"},{"id":268386,"type":{"id":20,"text":"Read Me"},"url":"https://pubs.usgs.gov/ds/713/00Readme.txt"}],"country":"United States","state":"Montana;Wyoming","geographicExtents":"{ \"type\": \"FeatureCollection\", \"features\": [ { \"type\": \"Feature\", \"properties\": {}, \"geometry\": { \"type\": \"Polygon\", \"coordinates\": [ [ [ -104.0076,42.6259 ], [ -104.0076,46.7850 ], [ -108.1714,46.7850 ], [ -108.1714,42.6259 ], [ -104.0076,42.6259 ] ] ] } } ] }","noUsgsAuthors":false,"publicationStatus":"PW","scienceBaseUri":"53cd55d2e4b0b290850f68c5","contributors":{"authors":[{"text":"Haacke, Jon E. 0000-0002-6910-2852 jhaacke@usgs.gov","orcid":"https://orcid.org/0000-0002-6910-2852","contributorId":630,"corporation":false,"usgs":true,"family":"Haacke","given":"Jon","email":"jhaacke@usgs.gov","middleInitial":"E.","affiliations":[{"id":164,"text":"Central Energy Resources Science Center","active":true,"usgs":true}],"preferred":false,"id":474784,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Scott, David C. 0000-0002-7925-7452 dscott@usgs.gov","orcid":"https://orcid.org/0000-0002-7925-7452","contributorId":629,"corporation":false,"usgs":true,"family":"Scott","given":"David","email":"dscott@usgs.gov","middleInitial":"C.","affiliations":[{"id":164,"text":"Central Energy Resources Science Center","active":true,"usgs":true}],"preferred":false,"id":474783,"contributorType":{"id":1,"text":"Authors"},"rank":2}]}}
,{"id":70043595,"text":"70043595 - 2013 - A data-based conservation planning tool for Florida panthers","interactions":[],"lastModifiedDate":"2013-03-04T21:06:08","indexId":"70043595","displayToPublicDate":"2013-02-26T00:00:00","publicationYear":"2013","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":1550,"text":"Environmental Modeling & Assessment","onlineIssn":" 1573-296","printIssn":"1420-2026","active":true,"publicationSubtype":{"id":10}},"title":"A data-based conservation planning tool for Florida panthers","docAbstract":"Habitat loss and fragmentation are the greatest threats to the endangered Florida panther (<i>Puma concolor coryi</i>). We developed a data-based habitat model and user-friendly interface so that land managers can objectively evaluate Florida panther habitat. We used a geographic information system (GIS) and the Mahalanobis distance statistic (<i>D</i><sup>2</sup>) to develop a model based on broad-scale landscape characteristics associated with panther home ranges. Variables in our model were Euclidean distance to natural land cover, road density, distance to major roads, human density, amount of natural land cover, amount of semi-natural land cover, amount of permanent or semi-permanent flooded area–open water, and a cost–distance variable. We then developed a Florida Panther Habitat Estimator tool, which automates and replicates the GIS processes used to apply the statistical habitat model. The estimator can be used by persons with moderate GIS skills to quantify effects of land-use changes on panther habitat at local and landscape scales. Example applications of the tool are presented.","largerWorkType":{"id":2,"text":"Article"},"largerWorkTitle":"Environmental Modeling and Assessment","largerWorkSubtype":{"id":10,"text":"Journal Article"},"language":"English","publisher":"Springer","publisherLocation":"Amsterdam, Netherlands","doi":"10.1007/s10666-012-9336-0","usgsCitation":"Murrow, J.L., Thatcher, C., van Manen, F., and Clark, J.D., 2013, A data-based conservation planning tool for Florida panthers: Environmental Modeling & Assessment, v. 18, no. 2, p. 159-170, https://doi.org/10.1007/s10666-012-9336-0.","productDescription":"12 p.","startPage":"159","endPage":"170","ipdsId":"IP-040629","costCenters":[{"id":365,"text":"Leetown Science Center","active":true,"usgs":true}],"links":[{"id":268388,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"},{"id":268382,"type":{"id":10,"text":"Digital Object Identifier"},"url":"https://dx.doi.org/10.1007/s10666-012-9336-0"}],"country":"United States","state":"Florida","geographicExtents":"{ \"type\": \"FeatureCollection\", \"features\": [ { \"type\": \"Feature\", \"properties\": {}, \"geometry\": { \"type\": \"Polygon\", \"coordinates\": [ [ [ -87.63,24.52 ], [ -87.63,31.0 ], [ -80.0,31.0 ], [ -80.0,24.52 ], [ -87.63,24.52 ] ] ] } } ] }","volume":"18","issue":"2","noUsgsAuthors":false,"publicationDate":"2012-09-09","publicationStatus":"PW","scienceBaseUri":"5135d072e4b03b8ec4025b38","contributors":{"authors":[{"text":"Murrow, Jennifer L.","contributorId":92945,"corporation":false,"usgs":true,"family":"Murrow","given":"Jennifer","email":"","middleInitial":"L.","affiliations":[],"preferred":false,"id":473934,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Thatcher, Cindy A.","contributorId":79604,"corporation":false,"usgs":true,"family":"Thatcher","given":"Cindy A.","affiliations":[],"preferred":false,"id":473933,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"van Manen, Frank T.","contributorId":51172,"corporation":false,"usgs":true,"family":"van Manen","given":"Frank T.","affiliations":[],"preferred":false,"id":473932,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Clark, Joseph D. 0000-0002-8547-8112 jclark1@usgs.gov","orcid":"https://orcid.org/0000-0002-8547-8112","contributorId":2265,"corporation":false,"usgs":true,"family":"Clark","given":"Joseph","email":"jclark1@usgs.gov","middleInitial":"D.","affiliations":[{"id":365,"text":"Leetown Science Center","active":true,"usgs":true},{"id":481,"text":"Northern Rocky Mountain Science Center","active":true,"usgs":true}],"preferred":true,"id":473931,"contributorType":{"id":1,"text":"Authors"},"rank":4}]}}
,{"id":70043719,"text":"70043719 - 2013 - A comprehensive change detection method for updating the National Land Cover Database to circa 2011","interactions":[],"lastModifiedDate":"2013-02-26T12:57:26","indexId":"70043719","displayToPublicDate":"2013-02-26T00:00:00","publicationYear":"2013","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":3254,"text":"Remote Sensing of Environment","printIssn":"0034-4257","active":true,"publicationSubtype":{"id":10}},"title":"A comprehensive change detection method for updating the National Land Cover Database to circa 2011","docAbstract":"The importance of characterizing, quantifying, and monitoring land cover, land use, and their changes has been widely recognized by global and environmental change studies. Since the early 1990s, three U.S. National Land Cover Database (NLCD) products (circa 1992, 2001, and 2006) have been released as free downloads for users. The NLCD 2006 also provides land cover change products between 2001 and 2006. To continue providing updated national land cover and change datasets, a new initiative in developing NLCD 2011 is currently underway. We present a new Comprehensive Change Detection Method (CCDM) designed as a key component for the development of NLCD 2011 and the research results from two exemplar studies. The CCDM integrates spectral-based change detection algorithms including a Multi-Index Integrated Change Analysis (MIICA) model and a novel change model called Zone, which extracts change information from two Landsat image pairs. The MIICA model is the core module of the change detection strategy and uses four spectral indices (CV, RCVMAX, dNBR, and dNDVI) to obtain the changes that occurred between two image dates. The CCDM also includes a knowledge-based system, which uses critical information on historical and current land cover conditions and trends and the likelihood of land cover change, to combine the changes from MIICA and Zone. For NLCD 2011, the improved and enhanced change products obtained from the CCDM provide critical information on location, magnitude, and direction of potential change areas and serve as a basis for further characterizing land cover changes for the nation. An accuracy assessment from the two study areas show 100% agreement between CCDM mapped no-change class with reference dataset, and 18% and 82% disagreement for the change class for WRS path/row p22r39 and p33r33, respectively. The strength of the CCDM is that the method is simple, easy to operate, widely applicable, and capable of capturing a variety of natural and anthropogenic disturbances potentially associated with land cover changes on different landscapes.","largerWorkType":{"id":2,"text":"Article"},"largerWorkTitle":"Remote Sensing of Environment","largerWorkSubtype":{"id":10,"text":"Journal Article"},"language":"English","publisher":"Elsevier","publisherLocation":"Amsterdam, Netherlands","doi":"10.1016/j.rse.2013.01.012","usgsCitation":"Jin, S., Yang, L., Danielson, P., Homer, C.G., Fry, J., and Xian, G., 2013, A comprehensive change detection method for updating the National Land Cover Database to circa 2011: Remote Sensing of Environment, v. 132, p. 159-175, https://doi.org/10.1016/j.rse.2013.01.012.","productDescription":"17 p.","startPage":"159","endPage":"175","ipdsId":"IP-041925","costCenters":[{"id":222,"text":"Earth Resources Observation and Science (EROS) Center","active":true,"usgs":true}],"links":[{"id":268381,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"},{"id":268380,"type":{"id":10,"text":"Digital Object Identifier"},"url":"https://dx.doi.org/10.1016/j.rse.2013.01.012"}],"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 ] ] ] } } ] }","volume":"132","noUsgsAuthors":false,"publicationStatus":"PW","scienceBaseUri":"53cd49a9e4b0b290850ef516","chorus":{"doi":"10.1016/j.rse.2013.01.012","url":"http://dx.doi.org/10.1016/j.rse.2013.01.012","publisher":"Elsevier BV","authors":"Jin Suming, Yang Limin, Danielson Patrick, Homer Collin, Fry Joyce, Xian George","journalName":"Remote Sensing of Environment","publicationDate":"5/2013","auditedOn":"4/22/2016"},"contributors":{"authors":[{"text":"Jin, Suming 0000-0001-9919-8077 sjin@usgs.gov","orcid":"https://orcid.org/0000-0001-9919-8077","contributorId":4397,"corporation":false,"usgs":true,"family":"Jin","given":"Suming","email":"sjin@usgs.gov","affiliations":[{"id":222,"text":"Earth Resources Observation and Science (EROS) Center","active":true,"usgs":true},{"id":223,"text":"Earth Resources Observation and Science (EROS) Center (Geography)","active":false,"usgs":true}],"preferred":true,"id":474161,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Yang, Limin 0000-0002-2843-6944 lyang@usgs.gov","orcid":"https://orcid.org/0000-0002-2843-6944","contributorId":4305,"corporation":false,"usgs":true,"family":"Yang","given":"Limin","email":"lyang@usgs.gov","affiliations":[{"id":223,"text":"Earth Resources Observation and Science (EROS) Center (Geography)","active":false,"usgs":true}],"preferred":true,"id":474160,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Danielson, Patrick 0000-0002-2990-2783 pdanielson@usgs.gov","orcid":"https://orcid.org/0000-0002-2990-2783","contributorId":3551,"corporation":false,"usgs":true,"family":"Danielson","given":"Patrick","email":"pdanielson@usgs.gov","affiliations":[{"id":222,"text":"Earth Resources Observation and Science (EROS) Center","active":true,"usgs":true},{"id":223,"text":"Earth Resources Observation and Science (EROS) Center (Geography)","active":false,"usgs":true}],"preferred":true,"id":474159,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Homer, Collin G. 0000-0003-4755-8135 homer@usgs.gov","orcid":"https://orcid.org/0000-0003-4755-8135","contributorId":2262,"corporation":false,"usgs":true,"family":"Homer","given":"Collin","email":"homer@usgs.gov","middleInitial":"G.","affiliations":[{"id":222,"text":"Earth Resources Observation and Science (EROS) Center","active":true,"usgs":true},{"id":223,"text":"Earth Resources Observation and Science (EROS) Center (Geography)","active":false,"usgs":true}],"preferred":true,"id":474157,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Fry, Joyce 0000-0002-8466-9582 jfry@usgs.gov","orcid":"https://orcid.org/0000-0002-8466-9582","contributorId":3147,"corporation":false,"usgs":true,"family":"Fry","given":"Joyce","email":"jfry@usgs.gov","affiliations":[],"preferred":true,"id":474158,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"Xian, George 0000-0001-5674-2204","orcid":"https://orcid.org/0000-0001-5674-2204","contributorId":76589,"corporation":false,"usgs":true,"family":"Xian","given":"George","affiliations":[],"preferred":false,"id":474162,"contributorType":{"id":1,"text":"Authors"},"rank":6}]}}
,{"id":70042646,"text":"70042646 - 2013 - Salmon-mediated nutrient flux in selected streams of the Columbia River basin, USA","interactions":[],"lastModifiedDate":"2013-04-20T19:45:03","indexId":"70042646","displayToPublicDate":"2013-02-26T00:00:00","publicationYear":"2013","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":1169,"text":"Canadian Journal of Fisheries and Aquatic Sciences","active":true,"publicationSubtype":{"id":10}},"title":"Salmon-mediated nutrient flux in selected streams of the Columbia River basin, USA","docAbstract":"Salmon provide an important resource subsidy and linkage between marine and land-based ecosystems. This flow of energy and nutrients is not uni-directional (i.e., upstream only); in addition to passive nutrient export via stream flow, juvenile emigrants actively export nutrients from freshwater environments. In some cases, nutrient export can exceed import. We evaluated nutrient fluxes in streams across central Idaho, USA using Chinook salmon (Oncorhynchus tshawytscha) adult escapement and juvenile production data from 1998 to 2008. We found in the majority of stream-years evaluated, adults imported more nutrients than progeny exported; however, in 3% of the years, juveniles exported more nutrients than their parents imported. On average, juvenile emigrants exported 22 ± 3% of the nitrogen and 30 ± 4% of the phosphorus their parents imported. This relationship was density dependent and nonlinear; during periods of low adult abundance juveniles were larger and exported up to 194% and 268% of parental nitrogen and phosphorus inputs, respectively. We highlight minimum escapement thresholds that appear to 1) maintain consistently positive net nutrient flux and 2) reduce the average proportional rate of export across study streams. Our results suggest a state-shift occurs when adult spawner abundance falls below a threshold to a point where the probability of juvenile nutrient exports exceeding adult imports becomes increasingly likely.","largerWorkType":{"id":2,"text":"Article"},"largerWorkTitle":"Canadian Journal of Fisheries and Aquatic Sciences","largerWorkSubtype":{"id":10,"text":"Journal Article"},"language":"English","publisher":"Canadian Science Publishing","publisherLocation":"Ottawa, Ontario","doi":"10.1139/cjfas-2012-0347","usgsCitation":"Kohler, A.E., Kusnierz, P.C., Copeland, T., Venditti, D.A., Denny, L., Gable, J., Lewis, B., Kinzer, R., Barnett, B., and Wipfli, M.S., 2013, Salmon-mediated nutrient flux in selected streams of the Columbia River basin, USA: Canadian Journal of Fisheries and Aquatic Sciences, v. 70, no. 3, p. 502-512, https://doi.org/10.1139/cjfas-2012-0347.","productDescription":"11 p.","startPage":"502","endPage":"512","ipdsId":"IP-039469","costCenters":[{"id":108,"text":"Alaska Cooperative Fish and Wildlife Research Unit","active":false,"usgs":true}],"links":[{"id":268392,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"},{"id":268391,"type":{"id":10,"text":"Digital Object Identifier"},"url":"https://dx.doi.org/10.1139/cjfas-2012-0347"}],"country":"United States","state":"Idaho","otherGeospatial":"Columbia River Basin","geographicExtents":"{ \"type\": \"FeatureCollection\", \"features\": [ { \"type\": \"Feature\", \"properties\": {}, \"geometry\": { \"type\": \"Polygon\", \"coordinates\": [ [ [ -121.46,42.15 ], [ -121.46,48.13 ], [ -111.19,48.13 ], [ -111.19,42.15 ], [ -121.46,42.15 ] ] ] } } ] }","volume":"70","issue":"3","noUsgsAuthors":false,"publicationStatus":"PW","scienceBaseUri":"515ea0f6e4b088aa2258098c","contributors":{"authors":[{"text":"Kohler, Andre E.","contributorId":62491,"corporation":false,"usgs":true,"family":"Kohler","given":"Andre","email":"","middleInitial":"E.","affiliations":[],"preferred":false,"id":471974,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Kusnierz, Paul C.","contributorId":13881,"corporation":false,"usgs":true,"family":"Kusnierz","given":"Paul","email":"","middleInitial":"C.","affiliations":[],"preferred":false,"id":471970,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Copeland, Timothy","contributorId":27760,"corporation":false,"usgs":true,"family":"Copeland","given":"Timothy","email":"","affiliations":[],"preferred":false,"id":471971,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Venditti, David A.","contributorId":38036,"corporation":false,"usgs":true,"family":"Venditti","given":"David","email":"","middleInitial":"A.","affiliations":[],"preferred":false,"id":471972,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Denny, Lytle","contributorId":96172,"corporation":false,"usgs":true,"family":"Denny","given":"Lytle","email":"","affiliations":[],"preferred":false,"id":471977,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"Gable, Josh","contributorId":7156,"corporation":false,"usgs":true,"family":"Gable","given":"Josh","email":"","affiliations":[],"preferred":false,"id":471969,"contributorType":{"id":1,"text":"Authors"},"rank":6},{"text":"Lewis, Bert","contributorId":92138,"corporation":false,"usgs":true,"family":"Lewis","given":"Bert","email":"","affiliations":[],"preferred":false,"id":471976,"contributorType":{"id":1,"text":"Authors"},"rank":7},{"text":"Kinzer, Ryan","contributorId":45201,"corporation":false,"usgs":true,"family":"Kinzer","given":"Ryan","email":"","affiliations":[],"preferred":false,"id":471973,"contributorType":{"id":1,"text":"Authors"},"rank":8},{"text":"Barnett, Bruce","contributorId":82565,"corporation":false,"usgs":true,"family":"Barnett","given":"Bruce","email":"","affiliations":[],"preferred":false,"id":471975,"contributorType":{"id":1,"text":"Authors"},"rank":9},{"text":"Wipfli, Mark S. 0000-0002-4856-6068 mwipfli@usgs.gov","orcid":"https://orcid.org/0000-0002-4856-6068","contributorId":1425,"corporation":false,"usgs":true,"family":"Wipfli","given":"Mark","email":"mwipfli@usgs.gov","middleInitial":"S.","affiliations":[{"id":200,"text":"Coop Res Unit Seattle","active":true,"usgs":true}],"preferred":true,"id":471968,"contributorType":{"id":1,"text":"Authors"},"rank":10}]}}
,{"id":70188865,"text":"70188865 - 2013 -  Correlation of geothermal springs with sub-surface fault terminations revealed by high-resolution, UAV-acquired magnetic data","interactions":[],"lastModifiedDate":"2017-06-27T14:49:17","indexId":"70188865","displayToPublicDate":"2013-02-26T00:00:00","publicationYear":"2013","noYear":false,"publicationType":{"id":24,"text":"Conference Paper"},"publicationSubtype":{"id":19,"text":"Conference Paper"},"title":" Correlation of geothermal springs with sub-surface fault terminations revealed by high-resolution, UAV-acquired magnetic data","docAbstract":"<p><span>There is widespread agreement that geothermal springs in extensional geothermal systems are concentrated at fault tips and in fault interaction zones where porosity and permeability are dynamically maintained (Curewitz and Karson, 1997; Faulds et al., 2010). Making these spatial correlations typically involves geological and geophysical studies in order to map structures and their relationship to springs at the surface. Geophysical studies include gravity and magnetic surveys, which are useful for identifying buried, intra-basin structures, especially in areas where highly magnetic, dense mafic volcanic rocks are interbedded with, and faulted against less magnetic, less dense sedimentary rock. High-resolution magnetic data can also be collected from the air in order to provide continuous coverage. Unmanned aerial systems (UAS) are well-suited for conducting these surveys as they can provide uniform, low-altitude, high-resolution coverage of an area without endangering crew. In addition, they are more easily adaptable to changes in flight plans as data are collected, and improve efficiency. We have developed and tested a new system to collect magnetic data using small-platform UAS. We deployed this new system in Surprise Valley, CA, in September, 2012, on NASA's SIERRA UAS to perform a reconnaissance survey of the entire valley as well as detailed surveys in key transition zones. This survey has enabled us to trace magnetic anomalies seen in ground-based profiles along their length. Most prominent of these is an intra-basin magnetic high that we interpret as a buried, faulted mafic dike that runs a significant length of the valley. Though this feature lacks surface expression, it appears to control the location of geothermal springs. All of the major hot springs on the east side of the valley lie along the edge of the high, and more specifically, at structural transitions where the high undergoes steps, bends, or breaks. The close relationship between the springs and structure terminations revealed by this study is unprecedented. Collecting magnetic data via UAS represents a new capability in geothermal exploration of remote and dangerous areas that significantly enhances our ability to map the subsurface.</span></p>","largerWorkTitle":"Proceedings Thirty-eighth Workshop on Geothermal Reservoir Engineering","conferenceTitle":"Thirty-Eighth Workshop on Geothermal Reservoir Engineering","conferenceDate":"February 11-13, 2013","conferenceLocation":"Stanford University, Stanford, California","language":"English","usgsCitation":"Glen, J.M., A.E. Egger, C. Ippolito, and , N., 2013,  Correlation of geothermal springs with sub-surface fault terminations revealed by high-resolution, UAV-acquired magnetic data, <i>in</i> Proceedings Thirty-eighth Workshop on Geothermal Reservoir Engineering, Stanford University, Stanford, California, February 11-13, 2013, 8 p. .","productDescription":"8 p. ","ipdsId":"IP-044179","costCenters":[{"id":312,"text":"Geology, Minerals, Energy, and Geophysics Science Center","active":true,"usgs":true}],"links":[{"id":343009,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"},{"id":343008,"rank":1,"type":{"id":15,"text":"Index Page"},"url":"https://digitalcommons.cwu.edu/geological_sciences/2/"}],"country":"United States","state":"California","county":"Modoc County ","otherGeospatial":"Surprise Valley ","geographicExtents":"{\n  \"type\": \"FeatureCollection\",\n  \"features\": [\n    {\n      \"type\": \"Feature\",\n      \"properties\": {},\n      \"geometry\": {\n        \"type\": \"Polygon\",\n        \"coordinates\": [\n          [\n            [\n              -120.10528564453125,\n              41.92271616673922\n            ],\n            [\n              -120.14648437499999,\n              41.864447405239375\n            ],\n            [\n              -120.18356323242188,\n              41.78769700539063\n            ],\n            [\n              -120.20278930664062,\n              41.70982942509964\n            ],\n            [\n              -120.21514892578125,\n              41.66162721430806\n            ],\n            [\n              -120.19454956054686,\n              41.59182393372352\n            ],\n            [\n              -120.17807006835936,\n              41.549700145132725\n            ],\n            [\n              -120.18905639648438,\n              41.49932105451145\n            ],\n            [\n              -120.1519775390625,\n              41.43860847395721\n            ],\n            [\n              -120.12451171875,\n              41.35104125623227\n            ],\n            [\n              -120.10253906249999,\n              41.29122180718259\n            ],\n            [\n              -120.03799438476561,\n              41.1724519493126\n            ],\n            [\n              -120.00503540039061,\n              41.176586696571015\n            ],\n            [\n              -120.0146484375,\n              41.27058168052551\n            ],\n            [\n              -120.01190185546875,\n              41.307729208348015\n            ],\n            [\n              -120.01327514648438,\n              41.38608229923676\n            ],\n            [\n              -120.00778198242186,\n              41.54764462357737\n            ],\n            [\n              -120.02975463867188,\n              41.790768787851285\n            ],\n            [\n              -120.00228881835938,\n              41.92782492551717\n            ],\n            [\n              -120.02014160156249,\n              41.96051129429777\n            ],\n            [\n              -120.10528564453125,\n              41.92271616673922\n            ]\n          ]\n        ]\n      }\n    }\n  ]\n}","publishingServiceCenter":{"id":14,"text":"Menlo Park PSC"},"noUsgsAuthors":false,"publicationStatus":"PW","scienceBaseUri":"59536eaee4b062508e3c7ab5","contributors":{"authors":[{"text":"Glen, Jonathan M.G. 0000-0002-3502-3355 jglen@usgs.gov","orcid":"https://orcid.org/0000-0002-3502-3355","contributorId":176530,"corporation":false,"usgs":true,"family":"Glen","given":"Jonathan","email":"jglen@usgs.gov","middleInitial":"M.G.","affiliations":[{"id":312,"text":"Geology, Minerals, Energy, and Geophysics Science Center","active":true,"usgs":true},{"id":309,"text":"Geology and Geophysics Science Center","active":true,"usgs":true}],"preferred":true,"id":700741,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"A.E. Egger","contributorId":193534,"corporation":false,"usgs":false,"family":"A.E. Egger","affiliations":[],"preferred":false,"id":700742,"contributorType":{"id":1,"text":"Authors"},"rank":11},{"text":"C. Ippolito","contributorId":193535,"corporation":false,"usgs":false,"family":"C. Ippolito","affiliations":[],"preferred":false,"id":700743,"contributorType":{"id":1,"text":"Authors"},"rank":12},{"text":" N.Athens","contributorId":193536,"corporation":false,"usgs":false,"given":"N.Athens","email":"","affiliations":[],"preferred":false,"id":700744,"contributorType":{"id":1,"text":"Authors"},"rank":13}]}}
,{"id":70057429,"text":"ofr20131202B - 2013 - Hyperspectral surface materials map of quadrangle 3562, Khawja-Jir (403) and Murghab (404) quadrangles, Afghanistan, showing iron-bearing minerals and other materials","interactions":[],"lastModifiedDate":"2014-03-10T10:09:31","indexId":"ofr20131202B","displayToPublicDate":"2013-02-25T12:00:00","publicationYear":"2013","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":"2013-1202","chapter":"B","title":"Hyperspectral surface materials map of quadrangle 3562, Khawja-Jir (403) and Murghab (404) quadrangles, Afghanistan, showing iron-bearing minerals and other materials","docAbstract":"<p>This map shows the spatial distribution of selected iron-bearing minerals and other materials derived from analysis of airborne HyMap™ imaging spectrometer (hyperspectral) data of Afghanistan collected in late 2007. This map is one in a series of U.S. Geological Survey/Afghanistan Geological Survey quadrangle maps covering Afghanistan.</p>\n<br/>\n<p>Flown at an altitude of 50,000 feet (15,240 meters (m)), the HyMap™ imaging spectrometer measured reflected sunlight in 128 channels, covering wavelengths between 0.4 and 2.5 μm. The data were georeferenced, atmospherically corrected and converted to apparent surface reflectance, empirically adjusted using ground-based reflectance measurements, and combined into a mosaic with 23-m pixel spacing. Variations in water vapor and dust content of the atmosphere, in solar angle, and in surface elevation complicated correction; therefore, some classification differences may be present between adjacent flight lines.</p>\n<br/>\n<p>The reflectance spectrum of each pixel of HyMap™ imaging spectrometer data was compared to the reference materials in a spectral library of minerals, vegetation, water, and other materials. Minerals occurring abundantly at the surface and those having unique spectral features were easily detected and discriminated, while minerals having slightly different compositions but similar spectral features were less easily discriminated; thus, some map classes consist of several minerals having similar spectra, such as “Goethite and jarosite.” A designation of “Not classified” was assigned to the pixel when there was no match with reference spectra.</p>","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/ofr20131202B","collaboration":"Prepared in cooperation with the U.S. Geological Survey under the auspices of the U.S. Department of Defense Task Force for Business and Stability Operations","usgsCitation":"King, T., Hoefen, T.M., Kokaly, R., Livo, K.E., Johnson, M., and Giles, S.A., 2013, Hyperspectral surface materials map of quadrangle 3562, Khawja-Jir (403) and Murghab (404) quadrangles, Afghanistan, showing iron-bearing minerals and other materials: U.S. Geological Survey Open-File Report 2013-1202, 37 x 23 inches, https://doi.org/10.3133/ofr20131202B.","productDescription":"37 x 23 inches","onlineOnly":"Y","ipdsId":"IP-050472","costCenters":[{"id":211,"text":"Crustal Geophysics and Geochemistry Science Center","active":true,"usgs":true}],"links":[{"id":282356,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/ofr20131202b.jpg"},{"id":283578,"type":{"id":15,"text":"Index Page"},"url":"https://pubs.usgs.gov/of/2013/1202/B/"},{"id":283579,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/of/2013/1202/B/pdf/ofr2013-1202b.pdf"}],"scale":"250000","projection":"Universal Transverse Mercator","datum":"WGS 1984","country":"Afghanistan","geographicExtents":"{ \"type\": \"FeatureCollection\", \"features\": [ { \"type\": \"Feature\", \"properties\": {}, \"geometry\": { \"type\": \"Polygon\", \"coordinates\": [ [ [ 62.0,35.0 ], [ 62.0,36.0 ], [ 64.0,36.0 ], [ 64.0,35.0 ], [ 62.0,35.0 ] ] ] } } ] }","noUsgsAuthors":false,"publicationStatus":"PW","scienceBaseUri":"53cd61dae4b0b290850fdc9e","contributors":{"authors":[{"text":"King, Trude","contributorId":29831,"corporation":false,"usgs":true,"family":"King","given":"Trude","email":"","affiliations":[],"preferred":false,"id":486684,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Hoefen, Todd M. 0000-0002-3083-5987 thoefen@usgs.gov","orcid":"https://orcid.org/0000-0002-3083-5987","contributorId":403,"corporation":false,"usgs":true,"family":"Hoefen","given":"Todd","email":"thoefen@usgs.gov","middleInitial":"M.","affiliations":[{"id":171,"text":"Central Mineral and Environmental Resources Science Center","active":true,"usgs":true},{"id":211,"text":"Crustal Geophysics and Geochemistry Science Center","active":true,"usgs":true}],"preferred":true,"id":486680,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Kokaly, Raymond F. 0000-0003-0276-7101","orcid":"https://orcid.org/0000-0003-0276-7101","contributorId":81442,"corporation":false,"usgs":true,"family":"Kokaly","given":"Raymond F.","affiliations":[],"preferred":false,"id":486685,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Livo, Keith E. 0000-0001-7331-8130 elivo@usgs.gov","orcid":"https://orcid.org/0000-0001-7331-8130","contributorId":1750,"corporation":false,"usgs":true,"family":"Livo","given":"Keith","email":"elivo@usgs.gov","middleInitial":"E.","affiliations":[{"id":211,"text":"Crustal Geophysics and Geochemistry Science Center","active":true,"usgs":true}],"preferred":true,"id":486683,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Johnson, Michaela R. 0000-0001-6133-0247 mrjohns@usgs.gov","orcid":"https://orcid.org/0000-0001-6133-0247","contributorId":1013,"corporation":false,"usgs":true,"family":"Johnson","given":"Michaela R.","email":"mrjohns@usgs.gov","affiliations":[{"id":171,"text":"Central Mineral and Environmental Resources Science Center","active":true,"usgs":true},{"id":211,"text":"Crustal Geophysics and Geochemistry Science Center","active":true,"usgs":true},{"id":583,"text":"Texas Water Science Center","active":true,"usgs":true}],"preferred":true,"id":486681,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"Giles, Stuart A. 0000-0002-8696-5078 sgiles@usgs.gov","orcid":"https://orcid.org/0000-0002-8696-5078","contributorId":1233,"corporation":false,"usgs":true,"family":"Giles","given":"Stuart","email":"sgiles@usgs.gov","middleInitial":"A.","affiliations":[{"id":171,"text":"Central Mineral and Environmental Resources Science Center","active":true,"usgs":true},{"id":387,"text":"Mineral Resources Program","active":true,"usgs":true}],"preferred":true,"id":486682,"contributorType":{"id":1,"text":"Authors"},"rank":6}]}}
,{"id":70146651,"text":"70146651 - 2013 - Fens as whole-ecosystem gauges of groundwater recharge under climate change","interactions":[],"lastModifiedDate":"2015-04-20T09:17:35","indexId":"70146651","displayToPublicDate":"2013-02-25T10:15:00","publicationYear":"2013","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":2342,"text":"Journal of Hydrology","active":true,"publicationSubtype":{"id":10}},"title":"Fens as whole-ecosystem gauges of groundwater recharge under climate change","docAbstract":"<p>Currently, little is known about the impact of climate change on groundwater recharge in the Sierra Nevada and southern Cascade Range of California or other mountainous regions of the world. The purpose of this study was to determine whether small alpine peat lands called fens can be used as whole-ecosystem gauges of groundwater recharge through time. Fens are sustained by groundwater discharge and are highly sensitive to changes in groundwater flow due to hydrologic disturbance including climate change. Seven fens in the Sierra Nevada and southern Cascade Range were studied over a 50-80 year period using historic aerial photography. In each aerial photograph, fen areas were identified as open lawn and partially treed areas that exhibited (1) dark brownish-green coloring or various shades of gray and black in black and white imagery and (2) mottling of colors and clustering of vegetation, which signified a distinct moss canopy with overlying clumped sedge vegetation. In addition to the aerial photography study, a climate analysis for the study sites was carried out using both measured data (U.S. Department of Agriculture Natural Resources Conservation Service SNOwpack TELemetry system) and modeled data (a downscaled version of the Parameter-elevation Regressions on Independent Slopes Model) for the period from 1951 to 2010. Over the study period, the five fens in the Sierra Nevada were found to be decreasing between 10% and 16% in delineated area. The climate analysis revealed significant increases through time in annual mean minimum temperature (T<sub>min</sub>) between 1951-1980 and 1981-2010. In addition, April 1 snow water equivalent and snowpack longevity also decreased between 1951-1980 and 1981-2010. For the fens in the Cascade Range, there were no discernible changes in delineated area. At these sites, increases in T<sub>min</sub> occurred only within the past 20-25 years and decreases in snowpack longevity were more subtle. A conceptual model is presented, which illustrates that basic differences in hydrogeology of the Sierra Nevada vs. the Cascade Range may control the threshold at which changes in delineated fen areas are discernible. Overall, the results from this study show that fens in the Sierra Nevada have strong potential as whole ecosystem gauges for determining long-term changes in groundwater recharge under climate change. Due to either more moderate climate change and/or hydrogeological differences, fens in the southern Cascade Range currently do not appear to have the same utility. A greater sample size of fens in the Sierra Nevada is needed to confirm the general applicability of this method. In addition, future work needs to focus on integrating fen monitoring with geochemical and/or isotopic process-level studies in order to quantify changes in groundwater recharge identified using this new approach.</p>","language":"English","publisher":"European Geophysical Society","publisherLocation":"New York, NY","doi":"10.1016/j.jhydrol.2012.11.056","usgsCitation":"Drexler, J., Knifong, D.L., Tuil, J., Flint, L.E., and Flint, A.L., 2013, Fens as whole-ecosystem gauges of groundwater recharge under climate change: Journal of Hydrology, v. 481, p. 22-34, https://doi.org/10.1016/j.jhydrol.2012.11.056.","productDescription":"13 p.","startPage":"22","endPage":"34","numberOfPages":"13","onlineOnly":"N","additionalOnlineFiles":"N","ipdsId":"IP-040704","costCenters":[{"id":154,"text":"California Water Science Center","active":true,"usgs":true}],"links":[{"id":299768,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"volume":"481","publishingServiceCenter":{"id":1,"text":"Sacramento PSC"},"noUsgsAuthors":false,"publicationStatus":"PW","scienceBaseUri":"5536233ae4b0b22a15807a94","contributors":{"authors":[{"text":"Drexler, Judith Z. 0000-0002-0127-3866 jdrexler@usgs.gov","orcid":"https://orcid.org/0000-0002-0127-3866","contributorId":1659,"corporation":false,"usgs":true,"family":"Drexler","given":"Judith Z.","email":"jdrexler@usgs.gov","affiliations":[{"id":154,"text":"California Water Science Center","active":true,"usgs":true}],"preferred":false,"id":545228,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Knifong, Donna L. dknifong@usgs.gov","contributorId":1517,"corporation":false,"usgs":true,"family":"Knifong","given":"Donna","email":"dknifong@usgs.gov","middleInitial":"L.","affiliations":[],"preferred":true,"id":545227,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Tuil, JayLee","contributorId":140341,"corporation":false,"usgs":false,"family":"Tuil","given":"JayLee","email":"","affiliations":[{"id":13461,"text":"U.C. Davis","active":true,"usgs":false}],"preferred":false,"id":545230,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Flint, Lorraine E. 0000-0002-7868-441X lflint@usgs.gov","orcid":"https://orcid.org/0000-0002-7868-441X","contributorId":1184,"corporation":false,"usgs":true,"family":"Flint","given":"Lorraine","email":"lflint@usgs.gov","middleInitial":"E.","affiliations":[{"id":154,"text":"California Water Science Center","active":true,"usgs":true}],"preferred":true,"id":545229,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Flint, Alan L. 0000-0002-5118-751X aflint@usgs.gov","orcid":"https://orcid.org/0000-0002-5118-751X","contributorId":1492,"corporation":false,"usgs":true,"family":"Flint","given":"Alan","email":"aflint@usgs.gov","middleInitial":"L.","affiliations":[{"id":154,"text":"California Water Science Center","active":true,"usgs":true},{"id":657,"text":"Western Geographic Science Center","active":true,"usgs":true}],"preferred":true,"id":545226,"contributorType":{"id":1,"text":"Authors"},"rank":5}]}}
,{"id":70043969,"text":"70043969 - 2013 - Adjusting survival estimates for premature transmitter failure: A case study from the Sacramento-San Joaquin Delta","interactions":[],"lastModifiedDate":"2016-05-04T15:45:36","indexId":"70043969","displayToPublicDate":"2013-02-25T00:00:00","publicationYear":"2013","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":1528,"text":"Environmental Biology of Fishes","active":true,"publicationSubtype":{"id":10}},"title":"Adjusting survival estimates for premature transmitter failure: A case study from the Sacramento-San Joaquin Delta","docAbstract":"<p>In telemetry studies, premature tag failure causes negative bias in fish survival estimates because tag failure is interpreted as fish mortality. We used mark-recapture modeling to adjust estimates of fish survival for a previous study where premature tag failure was documented. High rates of tag failure occurred during the Vernalis Adaptive Management Plan&rsquo;s (VAMP) 2008 study to estimate survival of fall-run Chinook salmon (<i class=\"EmphasisTypeItalic \">Oncorhynchus tshawytscha</i>) during migration through the San Joaquin River and Sacramento-San Joaquin Delta, California. Due to a high rate of tag failure, the observed travel time distribution was likely negatively biased, resulting in an underestimate of tag survival probability in this study. Consequently, the bias-adjustment method resulted in only a small increase in estimated fish survival when the observed travel time distribution was used to estimate the probability of tag survival. Since the bias-adjustment failed to remove bias, we used historical travel time data and conducted a sensitivity analysis to examine how fish survival might have varied across a range of tag survival probabilities. Our analysis suggested that fish survival estimates were low (95% confidence bounds range from 0.052 to 0.227) over a wide range of plausible tag survival probabilities (0.48&ndash;1.00), and this finding is consistent with other studies in this system. When tags fail at a high rate, available methods to adjust for the bias may perform poorly. Our example highlights the importance of evaluating the tag life assumption during survival studies, and presents a simple framework for evaluating adjusted survival estimates when auxiliary travel time data are available.</p>","language":"English","publisher":"Kluwer Academic Publishers","doi":"10.1007/s10641-012-0016-3","usgsCitation":"Holbrook, C., Perry, R.W., Brandes, P., and Adams, N.S., 2013, Adjusting survival estimates for premature transmitter failure: A case study from the Sacramento-San Joaquin Delta: Environmental Biology of Fishes, v. 96, no. 2, p. 165-173, https://doi.org/10.1007/s10641-012-0016-3.","productDescription":"9 p.","startPage":"165","endPage":"173","numberOfPages":"9","onlineOnly":"N","additionalOnlineFiles":"N","ipdsId":"IP-027244","costCenters":[{"id":654,"text":"Western Fisheries Research Center","active":true,"usgs":true}],"links":[{"id":268202,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","state":"California","otherGeospatial":"Sacramento-San Joaquin Delta, San Joaquin River","geographicExtents":"{ \"type\": \"FeatureCollection\", \"features\": [ { \"type\": \"Feature\", \"properties\": {}, \"geometry\": { \"type\": \"Polygon\", \"coordinates\": [ [ [ -121.929093,37.735197 ], [ -121.929093,38.126074 ], [ -121.300766,38.126074 ], [ -121.300766,37.735197 ], [ -121.929093,37.735197 ] ] ] } } ] }","volume":"96","issue":"2","noUsgsAuthors":false,"publicationDate":"2012-04-26","publicationStatus":"PW","scienceBaseUri":"512c87dfe4b0855fde669728","contributors":{"authors":[{"text":"Holbrook, Christopher M. 0000-0001-8203-6856 cholbrook@usgs.gov","orcid":"https://orcid.org/0000-0001-8203-6856","contributorId":4198,"corporation":false,"usgs":true,"family":"Holbrook","given":"Christopher M.","email":"cholbrook@usgs.gov","affiliations":[{"id":324,"text":"Great Lakes Science Center","active":true,"usgs":true}],"preferred":false,"id":474561,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Perry, Russell W. 0000-0003-4110-8619 rperry@usgs.gov","orcid":"https://orcid.org/0000-0003-4110-8619","contributorId":2820,"corporation":false,"usgs":true,"family":"Perry","given":"Russell","email":"rperry@usgs.gov","middleInitial":"W.","affiliations":[{"id":654,"text":"Western Fisheries Research Center","active":true,"usgs":true}],"preferred":true,"id":474559,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Brandes, Patricia L.","contributorId":25834,"corporation":false,"usgs":true,"family":"Brandes","given":"Patricia L.","affiliations":[],"preferred":false,"id":474562,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Adams, Noah S. 0000-0002-8354-0293 nadams@usgs.gov","orcid":"https://orcid.org/0000-0002-8354-0293","contributorId":3521,"corporation":false,"usgs":true,"family":"Adams","given":"Noah","email":"nadams@usgs.gov","middleInitial":"S.","affiliations":[{"id":654,"text":"Western Fisheries Research Center","active":true,"usgs":true}],"preferred":true,"id":474560,"contributorType":{"id":1,"text":"Authors"},"rank":4}]}}
,{"id":70044020,"text":"ofr20131034 - 2013 - Water quality in the Anacostia River, Maryland and Rock Creek, Washington, D.C.: Continuous and discrete monitoring with simulations to estimate concentrations and yields of nutrients, suspended sediment, and bacteria","interactions":[],"lastModifiedDate":"2023-03-09T20:14:16.958533","indexId":"ofr20131034","displayToPublicDate":"2013-02-25T00:00:00","publicationYear":"2013","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":"2013-1034","title":"Water quality in the Anacostia River, Maryland and Rock Creek, Washington, D.C.: Continuous and discrete monitoring with simulations to estimate concentrations and yields of nutrients, suspended sediment, and bacteria","docAbstract":"Concentrations and loading estimates for nutrients, suspended sediment, and <i>E. coli</i> bacteria were summarized for three water-quality monitoring stations on the Anacostia River in Maryland and one station on Rock Creek in Washington, D.C. Both streams are tributaries to the Potomac River in the Washington, D.C. metropolitan area and contribute to the Chesapeake Bay estuary. Two stations on the Anacostia River, Northeast Branch at Riverdale, Maryland and Northwest Branch near Hyattsville, Maryland, have been monitored for water quality during the study period from 2003 to 2011 and are located near the shift from nontidal to tidal conditions near Bladensburg, Maryland. A station on Paint Branch is nested above the station on the Northeast Branch Anacostia River, and has slightly less developed land cover than the Northeast and Northwest Branch stations. The Rock Creek station is located in Rock Creek Park, but the land cover in the watershed surrounding the park is urbanized. Stepwise log-linear regression models were developed to estimate the concentrations of suspended sediment, total nitrogen, total phosphorus, and <i>E. coli</i> bacteria from continuous field monitors. Turbidity was the strongest predictor variable for all water-quality parameters. For bacteria, water temperature improved the models enough to be included as a second predictor variable due to the strong dependence of stream metabolism on temperature. Coefficients of determination (R<sup>2</sup>) for the models were highest for log concentrations of suspended sediment (0.9) and total phosphorus (0.8 to 0.9), followed by <i>E. coli</i> bacteria (0.75 to 0.8), and total nitrogen (0.6). Water-quality data provided baselines for conditions prior to accelerated implementation of multiple stormwater controls in the watersheds. Counties are currently in the process of enhancing stormwater controls in both watersheds. Annual yields were estimated for suspended sediment, total nitrogen, total phosphorus, and <i>E. coli</i> bacteria using the U.S. Geological Survey model LOADEST with hourly time steps of turbidity, flow, and time. Yields of all four parameters were within ranges found in other urbanized watersheds in Chesapeake Bay. Annual yields for all four watersheds over the period of study were estimated for suspended sediment (65,500 – 166,000 kilograms per year per square kilometer; kg/yr/km<sup>2</sup>), total nitrogen (465 - 911 kg/yr/km<sup>2</sup>), total phosphorus (36 - 113 kg/yr/km<sup>2</sup>), and <i>E. coli</i> bacteria (6.0 – 38 x 10<sup>12</sup> colony forming units/yr/km<sup>2</sup>). The length of record was not sufficient to determine trends for any of the water-quality parameters; within confidence intervals of the models, results were similar to loads determined by previous studies for the Northeast and Northwest Branch stations of the Anacostia River.","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/ofr20131034","collaboration":"Prepared in cooperation with Montgomery County, Maryland","usgsCitation":"Miller, C.V., Chanat, J.G., and Bell, J.M., 2013, Water quality in the Anacostia River, Maryland and Rock Creek, Washington, D.C.: Continuous and discrete monitoring with simulations to estimate concentrations and yields of nutrients, suspended sediment, and bacteria: U.S. Geological Survey Open-File Report 2013-1034, vi, 37 p., https://doi.org/10.3133/ofr20131034.","productDescription":"vi, 37 p.","startPage":"i","endPage":"37","numberOfPages":"48","onlineOnly":"Y","additionalOnlineFiles":"N","costCenters":[{"id":41514,"text":"Maryland-Delaware-District of Columbia  Water Science Center","active":true,"usgs":true}],"links":[{"id":268259,"rank":3,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/ofr_2013_1034.gif"},{"id":268257,"rank":2,"type":{"id":15,"text":"Index Page"},"url":"https://pubs.usgs.gov/of/2013/1034/"},{"id":268258,"rank":1,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/of/2013/1034/pdf/ofr2013-1034.pdf"}],"country":"United States","state":"Maryl","city":"Washington;D.C.","geographicExtents":"{ \"type\": \"FeatureCollection\", \"features\": [ { \"type\": \"Feature\", \"properties\": {}, \"geometry\": { \"type\": \"Polygon\", \"coordinates\": [ [ [ -79.49,37.89 ], [ -79.49,39.72 ], [ -75.05,39.72 ], [ -75.05,37.89 ], [ -79.49,37.89 ] ] ] } } ] }","noUsgsAuthors":false,"publicationStatus":"PW","scienceBaseUri":"512c87eae4b0855fde669734","contributors":{"authors":[{"text":"Miller, Cherie V. 0000-0001-7765-5919 cvmiller@usgs.gov","orcid":"https://orcid.org/0000-0001-7765-5919","contributorId":863,"corporation":false,"usgs":true,"family":"Miller","given":"Cherie","email":"cvmiller@usgs.gov","middleInitial":"V.","affiliations":[{"id":503,"text":"Office of Water Quality","active":true,"usgs":true}],"preferred":true,"id":474638,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Chanat, Jeffrey G. 0000-0002-3629-7307 jchanat@usgs.gov","orcid":"https://orcid.org/0000-0002-3629-7307","contributorId":5062,"corporation":false,"usgs":true,"family":"Chanat","given":"Jeffrey","email":"jchanat@usgs.gov","middleInitial":"G.","affiliations":[{"id":614,"text":"Virginia Water Science Center","active":true,"usgs":true}],"preferred":true,"id":474639,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Bell, Joseph M. 0000-0002-2536-2070 jmbell@usgs.gov","orcid":"https://orcid.org/0000-0002-2536-2070","contributorId":5063,"corporation":false,"usgs":true,"family":"Bell","given":"Joseph","email":"jmbell@usgs.gov","middleInitial":"M.","affiliations":[{"id":374,"text":"Maryland Water Science Center","active":true,"usgs":true}],"preferred":true,"id":474640,"contributorType":{"id":1,"text":"Authors"},"rank":3}]}}
,{"id":70044000,"text":"70044000 - 2013 - Nitrate in watersheds: straight from soils to streams?","interactions":[],"lastModifiedDate":"2013-04-20T19:35:59","indexId":"70044000","displayToPublicDate":"2013-02-25T00:00:00","publicationYear":"2013","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":2319,"text":"Journal of Geophysical Research G: Biogeosciences","active":true,"publicationSubtype":{"id":10}},"title":"Nitrate in watersheds: straight from soils to streams?","docAbstract":"Human activities are rapidly increasing the global supply of reactive N and substantially altering the structure and hydrologic connectivity of managed ecosystems. There is long-standing recognition that N must be removed along hydrologic flowpaths from uplands to streams, yet it has proven difficult to assess the generality of this removal across ecosystem types, and whether these patterns are influenced by land-use change. To assess how well upland nitrate (NO<sub>3</sub><sup>-</sup>) loss is reflected in stream export, we gathered information from >50 watershed biogeochemical studies that reported nitrate concentrations ([NO<sub>3</sub><sup>-</sup>]) for stream water and for either upslope soil solution or groundwater NO<sub>3</sub><sup>-</sup> to examine whether stream export of NO3- accurately reflects upland NO<sub>3</sub><sup>-</sup> losses. In this dataset, soil solution and streamwater [NO<sub>3</sub><sup>-</sup>] were correlated across 40 undisturbed forest watersheds, with streamwater [NO<sub>3</sub><sup>-</sup>] typically half (median = 50%) soil solution [NO<sub>3</sub><sup>-</sup>]. A similar relationship was seen in 10 disturbed forest watersheds. However, for 12 watersheds with significant agricultural or urban development, the intercept and slope were both significantly higher than the relationship seen in forest watersheds. Differences in concentration between soil solution or groundwater and stream water may be attributed to biological uptake, microbial processes including denitrification, and/or preferential flow routing. The results of this synthesis are consistent with the hypotheses that undisturbed watersheds have a significant capacity to remove nitrate after it passes below the rooting zone and that land use changes tend to alter the efficiency or the length of watershed flowpaths, leading to reductions in nitrate removal and increased stream nitrate concentrations.","largerWorkType":{"id":2,"text":"Article"},"largerWorkTitle":"Journal of Geophysical Research G: Biogeosciences","largerWorkSubtype":{"id":10,"text":"Journal Article"},"language":"English","publisher":"AGU","publisherLocation":"Washington, D.C.","doi":"10.1002/jgrg.20030","usgsCitation":"Sudduth, E.B., Perakis, S., and Bernhardt, E., 2013, Nitrate in watersheds: straight from soils to streams?: Journal of Geophysical Research G: Biogeosciences, v. 118, no. G1, p. 291-302, https://doi.org/10.1002/jgrg.20030.","productDescription":"45 p.","startPage":"291","endPage":"302","ipdsId":"IP-018046","costCenters":[{"id":290,"text":"Forest and Rangeland Ecosystem Science Center","active":false,"usgs":true}],"links":[{"id":268260,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"},{"id":268256,"type":{"id":10,"text":"Digital Object Identifier"},"url":"https://dx.doi.org/10.1002/jgrg.20030"}],"volume":"118","issue":"G1","noUsgsAuthors":false,"publicationDate":"2013-03-21","publicationStatus":"PW","scienceBaseUri":"512c87e9e4b0855fde669730","contributors":{"authors":[{"text":"Sudduth, Elizabeth B.","contributorId":8747,"corporation":false,"usgs":true,"family":"Sudduth","given":"Elizabeth","email":"","middleInitial":"B.","affiliations":[],"preferred":false,"id":474588,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Perakis, Steven S. 0000-0003-0703-9314","orcid":"https://orcid.org/0000-0003-0703-9314","contributorId":16797,"corporation":false,"usgs":true,"family":"Perakis","given":"Steven S.","affiliations":[],"preferred":false,"id":474589,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Bernhardt, Emily S.","contributorId":92143,"corporation":false,"usgs":false,"family":"Bernhardt","given":"Emily S.","affiliations":[{"id":27331,"text":"Duke University, Durham, NC","active":true,"usgs":false}],"preferred":false,"id":474590,"contributorType":{"id":1,"text":"Authors"},"rank":3}]}}
,{"id":70043889,"text":"ofr20131023 - 2013 - A conceptual prototype for the next-generation national elevation dataset","interactions":[],"lastModifiedDate":"2017-05-16T16:13:06","indexId":"ofr20131023","displayToPublicDate":"2013-02-22T00:00:00","publicationYear":"2013","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":"2013-1023","title":"A conceptual prototype for the next-generation national elevation dataset","docAbstract":"In 2012 the U.S. Geological Survey's (USGS) National Geospatial Program (NGP) funded a study to develop a conceptual prototype for a new National Elevation Dataset (NED) design with expanded capabilities to generate and deliver a suite of bare earth and above ground feature information over the United States. This report details the research on identifying operational requirements based on prior research, evaluation of what is needed for the USGS to meet these requirements, and development of a possible conceptual framework that could potentially deliver the kinds of information that are needed to support NGP's partners and constituents. This report provides an initial proof-of-concept demonstration using an existing dataset, and recommendations for the future, to inform NGP's ongoing and future elevation program planning and management decisions. The demonstration shows that this type of functional process can robustly create derivatives from lidar point cloud data; however, more research needs to be done to see how well it extends to multiple datasets.","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/ofr20131023","usgsCitation":"Stoker, J.M., Heidemann, H.K., Evans, G.A., and Greenlee, S.K., 2013, A conceptual prototype for the next-generation national elevation dataset: U.S. Geological Survey Open-File Report 2013-1023, 52 p., https://doi.org/10.3133/ofr20131023.","productDescription":"52 p.","numberOfPages":"52","onlineOnly":"Y","additionalOnlineFiles":"N","ipdsId":"IP-042370","costCenters":[{"id":222,"text":"Earth Resources Observation and Science (EROS) Center","active":true,"usgs":true}],"links":[{"id":267933,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/ofr_2013_1023.gif"},{"id":267931,"type":{"id":15,"text":"Index Page"},"url":"https://pubs.usgs.gov/of/2013/1023/"},{"id":267932,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/of/2013/1023/ofr13-1023.pdf"}],"country":"United States","geographicExtents":"{ \"type\": \"FeatureCollection\", \"features\": [ { \"type\": \"Feature\", \"properties\": {}, \"geometry\": { \"type\": \"Polygon\", \"coordinates\": [ [ [ 173.0,16.916 ], [ 173.0,71.833 ], [ -66.95,71.833 ], [ -66.95,16.916 ], [ 173.0,16.916 ] ] ] } } ] }","noUsgsAuthors":false,"publicationStatus":"PW","scienceBaseUri":"5128935fe4b01b9ee8b50c4b","contributors":{"authors":[{"text":"Stoker, Jason M. 0000-0003-2455-0931 jstoker@usgs.gov","orcid":"https://orcid.org/0000-0003-2455-0931","contributorId":3021,"corporation":false,"usgs":true,"family":"Stoker","given":"Jason","email":"jstoker@usgs.gov","middleInitial":"M.","affiliations":[{"id":223,"text":"Earth Resources Observation and Science (EROS) Center (Geography)","active":false,"usgs":true},{"id":423,"text":"National Geospatial Program","active":true,"usgs":true}],"preferred":true,"id":474398,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Heidemann, Hans Karl 0000-0003-4306-359X","orcid":"https://orcid.org/0000-0003-4306-359X","contributorId":30085,"corporation":false,"usgs":true,"family":"Heidemann","given":"Hans","email":"","middleInitial":"Karl","affiliations":[],"preferred":false,"id":474397,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Evans, Gayla A. 0000-0001-5072-4232 gevans@usgs.gov","orcid":"https://orcid.org/0000-0001-5072-4232","contributorId":3125,"corporation":false,"usgs":true,"family":"Evans","given":"Gayla","email":"gevans@usgs.gov","middleInitial":"A.","affiliations":[{"id":223,"text":"Earth Resources Observation and Science (EROS) Center (Geography)","active":false,"usgs":true},{"id":222,"text":"Earth Resources Observation and Science (EROS) Center","active":true,"usgs":true}],"preferred":true,"id":474395,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Greenlee, Susan K. sgreenlee@usgs.gov","contributorId":3326,"corporation":false,"usgs":true,"family":"Greenlee","given":"Susan","email":"sgreenlee@usgs.gov","middleInitial":"K.","affiliations":[],"preferred":true,"id":474396,"contributorType":{"id":1,"text":"Authors"},"rank":4}]}}
,{"id":70043868,"text":"sir20125222 - 2013 - Community exposure to tsunami hazards in California","interactions":[],"lastModifiedDate":"2013-02-21T16:02:43","indexId":"sir20125222","displayToPublicDate":"2013-02-21T00:00:00","publicationYear":"2013","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":"2012-5222","title":"Community exposure to tsunami hazards in California","docAbstract":"Evidence of past events and modeling of potential events suggest that tsunamis are significant threats to low-lying communities on the California coast. To reduce potential impacts of future tsunamis, officials need to understand how communities are vulnerable to tsunamis and where targeted outreach, preparedness, and mitigation efforts may be warranted. Although a maximum tsunami-inundation zone based on multiple sources has been developed for the California coast, the populations and businesses in this zone have not been documented in a comprehensive way. To support tsunami preparedness and risk-reduction planning in California, this study documents the variations among coastal communities in the amounts, types, and percentages of developed land, human populations, and businesses in the maximum tsunami-inundation zone. The tsunami-inundation zone includes land in 94 incorporated cities, 83 unincorporated communities, and 20 counties on the California coast. According to 2010 U.S. Census Bureau data, this tsunami-inundation zone contains 267,347 residents (1 percent of the 20-county resident population), of which 13 percent identify themselves as Hispanic or Latino, 14 percent identify themselves as Asian, 16 percent are more than 65 years in age, 12 percent live in unincorporated areas, and 51 percent of the households are renter occupied. Demographic attributes related to age, race, ethnicity, and household status of residents in tsunami-prone areas demonstrate substantial range among communities that exceed these regional averages. The tsunami-inundation zone in several communities also has high numbers of residents in institutionalized and noninstitutionalized group quarters (for example, correctional facilities and military housing, respectively). Communities with relatively high values in the various demographic categories are identified throughout the report. The tsunami-inundation zone contains significant nonresidential populations based on 2011 economic data from Infogroup (2011), including 168,565 employees (2 percent of the 20-county labor force) at 15,335 businesses that generate approximately $30 billion in annual sales. Although the regional percentage of at-risk employees is low, certain communities, such as Belvedere, Alameda, and Crescent City, have high percentages of their local workforce in the tsunami-inundation zone. Employees in the tsunami-inundation zone are primarily in businesses associated with tourism (for example, accommodations, food services, and retail trade) and shipping (for example, transportation and warehousing, manufacturing, and wholesale trade), although the dominance of these sectors varies substantially among the 94 cities. Although the number of occupants is not known for each site, the tsunami-inundation zone contains numerous dependent-population facilities, such as schools and child daycare centers, which may have individuals with limited mobility. The tsunami-inundation zone includes a substantial number of facilities that provide community services, such as banks, religious organizations, and grocery stores, where local residents may be unaware of evacuation procedures if previous awareness efforts focused on home preparedness. There are also numerous recreational areas in the tsunami-inundation zone, such as amusement parks, marinas, city and county beaches, and State and national parks, which attract visitors who may not be aware of tsunami hazards or evacuation procedures. During peak summer months, estimated daily attendance at city and county beaches can be approximately six times larger than the total number of residents in the tsunami-inundation zone. Community exposure to tsunamis in California varies considerably—some communities may experience great losses that reflect only a small part of their community and others may experience relatively small losses that devastate them. Among 94 incorporated communities and the remaining unincorporated areas of the 20 coastal counties, the communities of Alameda, Oakland, Long Beach, Los Angeles, Huntington Beach, and San Diego have the highest number of people and businesses in the tsunami-inundation zone. The communities of Belvedere, Alameda, Crescent City, Emeryville, Seal Beach, and Sausalito have the highest percentages of people and businesses in this zone. On the basis of a composite index, the cities of Alameda, Belvedere, Crescent City, Emeryville, Oakland, and Long Beach have the highest combinations of the number and percentage of people and businesses in tsunami-prone areas.","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/sir20125222","collaboration":"Prepared in cooperation with the California Emergency Management Agency and the California Geological Survey","usgsCitation":"Wood, N.J., Ratliff, J., and Peters, J., 2013, Community exposure to tsunami hazards in California: U.S. Geological Survey Scientific Investigations Report 2012-5222, iv, 49 p., https://doi.org/10.3133/sir20125222.","productDescription":"iv, 49 p.","startPage":"i","endPage":"49","numberOfPages":"58","onlineOnly":"Y","additionalOnlineFiles":"N","costCenters":[{"id":657,"text":"Western Geographic Science Center","active":true,"usgs":true}],"links":[{"id":267900,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/sir_2012_5222.gif"},{"id":267898,"type":{"id":15,"text":"Index Page"},"url":"https://pubs.usgs.gov/sir/2012/5222/"},{"id":267899,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/sir/2012/5222/sir2012-5222.pdf"}],"country":"United States","state":"California","geographicExtents":"{ \"type\": \"FeatureCollection\", \"features\": [ { \"type\": \"Feature\", \"properties\": {}, \"geometry\": { \"type\": \"Polygon\", \"coordinates\": [ [ [ -124.41,32.53 ], [ -124.41,42.0 ], [ -114.13,42.0 ], [ -114.13,32.53 ], [ -124.41,32.53 ] ] ] } } ] }","noUsgsAuthors":false,"publicationStatus":"PW","scienceBaseUri":"512741fde4b07fa41a6044ce","contributors":{"authors":[{"text":"Wood, Nathan J. 0000-0002-6060-9729 nwood@usgs.gov","orcid":"https://orcid.org/0000-0002-6060-9729","contributorId":3347,"corporation":false,"usgs":true,"family":"Wood","given":"Nathan","email":"nwood@usgs.gov","middleInitial":"J.","affiliations":[{"id":657,"text":"Western Geographic Science Center","active":true,"usgs":true}],"preferred":true,"id":474345,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Ratliff, Jamie","contributorId":102915,"corporation":false,"usgs":true,"family":"Ratliff","given":"Jamie","email":"","affiliations":[],"preferred":false,"id":474347,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Peters, Jeff 0000-0003-4312-0590 jpeters@usgs.gov","orcid":"https://orcid.org/0000-0003-4312-0590","contributorId":4711,"corporation":false,"usgs":true,"family":"Peters","given":"Jeff","email":"jpeters@usgs.gov","affiliations":[{"id":617,"text":"Volcano Science Center","active":true,"usgs":true},{"id":657,"text":"Western Geographic Science Center","active":true,"usgs":true}],"preferred":true,"id":474346,"contributorType":{"id":1,"text":"Authors"},"rank":3}]}}
,{"id":70043802,"text":"70043802 - 2013 - Using hand proportions to test taxonomic boundaries within the <i>Tupaia glis</i> species complex (Scandentia, Tupaiidae)","interactions":[],"lastModifiedDate":"2013-02-21T14:06:21","indexId":"70043802","displayToPublicDate":"2013-02-21T00:00:00","publicationYear":"2013","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":2373,"text":"Journal of Mammalogy","onlineIssn":"1545-1542","printIssn":"0022-2372","active":true,"publicationSubtype":{"id":10}},"title":"Using hand proportions to test taxonomic boundaries within the <i>Tupaia glis</i> species complex (Scandentia, Tupaiidae)","docAbstract":"Treeshrews (order Scandentia) comprise 2 families of squirrel-sized terrestrial, arboreal, and scansorial mammals distributed throughout much of tropical South and Southeast Asia. The last comprehensive taxonomic revision of treeshrews was published in 1913, and a well-supported phylogeny clarifying relationships among all currently recognized extant species within the order has only recently been published. Within the family Tupaiidae, 2 widely distributed species, the northern treeshrew, <i>Tupaia belangeri</i> (Wagner, 1841), and the common treeshrew, <i>T. glis</i> (Diard, 1820), represent a particularly vexing taxonomic complex. These 2 species are currently distinguished primarily based on their respective distributions north and south of the Isthmus of Kra on the Malay Peninsula and on their different mammae counts. This problematic species complex includes 54 published synonyms, many of which represent putative island endemics. The widespread <i>T. glis</i> and <i>T. belangeri</i> collectively comprise a monophyletic assemblage representing the sister lineage to a clade composed of the golden-bellied treeshrew, <i>T. chrysogaster</i> Miller, 1903 (Mentawai Islands), and the long-footed treeshrew, <i>T. longipes</i> (Thomas, 1893) (Borneo). As part of a morphological investigation of the <i>T. glis</i>–<i>T. belangeri</i> complex, we studied the proportions of hand bones, which have previously been shown to be useful in discriminating species of soricids (true shrews). We measured 38 variables from digital X-ray images of 148 museum study skins representing several subspecies of <i>T. glis</i>, <i>T. belangeri</i>, <i>T. chrysogaster</i>, and <i>T. longipes</i> and analyzed these data using principal components and cluster analyses. Manus proportions among these 4 species readily distinguish them, particularly in the cases of <i>T. chrysogaster</i> and <i>T. longipes</i>. We then tested the distinctiveness of several of the populations comprising <i>T. glis</i> and <i>T. longipes</i>. <i>T. longipes longipes</i> and <i>T. l. salatana</i> Lyon, 1913, are distinguishable from each other, and populations of <i>T. \"glis\"</i> from Bangka Island and Sumatra are distinct from those on the Malay Peninsula, supporting the recognition of <i>T. salatana</i>, <i>T. discolor</i> Lyon, 1906, and <i>T. ferruginea</i> Raffles, 1821 as distinct species in Indonesia. These relatively small, potentially vulnerable treeshrew populations occur in the Sundaland biodiversity hotspot and will require additional study to determine their appropriate conservation status.","largerWorkType":{"id":2,"text":"Article"},"largerWorkTitle":"Journal of Mammalogy","largerWorkSubtype":{"id":10,"text":"Journal Article"},"language":"English","publisher":"American Society of Mammalogists","publisherLocation":"Lawrence, KS","doi":"10.1644/11-MAMM-A-343.1","usgsCitation":"Sargos, E.J., Woodman, N., Reese, A.T., and Olson, L., 2013, Using hand proportions to test taxonomic boundaries within the <i>Tupaia glis</i> species complex (Scandentia, Tupaiidae): Journal of Mammalogy, v. 94, no. 1, p. 183-201, https://doi.org/10.1644/11-MAMM-A-343.1.","productDescription":"19 p.","startPage":"183","endPage":"201","ipdsId":"IP-041158","costCenters":[{"id":531,"text":"Patuxent Wildlife Research Center","active":true,"usgs":true}],"links":[{"id":473948,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.1644/11-mamm-a-343.1","text":"Publisher Index Page"},{"id":267895,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"},{"id":267894,"type":{"id":10,"text":"Digital Object Identifier"},"url":"https://dx.doi.org/10.1644/11-MAMM-A-343.1"}],"country":"United States","volume":"94","issue":"1","noUsgsAuthors":false,"publicationStatus":"PW","scienceBaseUri":"51274203e4b07fa41a6044e2","contributors":{"authors":[{"text":"Sargos, Eric J.","contributorId":11091,"corporation":false,"usgs":true,"family":"Sargos","given":"Eric","email":"","middleInitial":"J.","affiliations":[],"preferred":false,"id":474246,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Woodman, Neal 0000-0003-2689-7373 nwoodman@usgs.gov","orcid":"https://orcid.org/0000-0003-2689-7373","contributorId":3547,"corporation":false,"usgs":true,"family":"Woodman","given":"Neal","email":"nwoodman@usgs.gov","affiliations":[{"id":531,"text":"Patuxent Wildlife Research Center","active":true,"usgs":true}],"preferred":true,"id":474245,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Reese, Aspen T.","contributorId":23826,"corporation":false,"usgs":true,"family":"Reese","given":"Aspen","email":"","middleInitial":"T.","affiliations":[],"preferred":false,"id":474247,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Olson, Link E.","contributorId":60927,"corporation":false,"usgs":true,"family":"Olson","given":"Link E.","affiliations":[],"preferred":false,"id":474248,"contributorType":{"id":1,"text":"Authors"},"rank":4}]}}
,{"id":70043832,"text":"ofr20131032 - 2013 - Development of flood profiles and flood-inundation maps for the Village of Killbuck, Ohio","interactions":[],"lastModifiedDate":"2013-02-20T16:25:51","indexId":"ofr20131032","displayToPublicDate":"2013-02-20T00:00:00","publicationYear":"2013","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":"2013-1032","title":"Development of flood profiles and flood-inundation maps for the Village of Killbuck, Ohio","docAbstract":"Digital flood-inundation maps for a reach of Killbuck Creek near the Village of Killbuck, Ohio, were created by the U.S. Geological Survey (USGS), in cooperation with Holmes County, Ohio. The inundation maps depict estimates of the areal extent of flooding corresponding to water levels (stages) at the USGS streamgage Killbuck Creek near Killbuck (03139000) and were completed as part of an update to Federal Emergency Management Agency Flood-Insurance Study. The maps were provided to the National Weather Service (NWS) for incorporation into a Web-based flood-warning system that can be used in conjunction with NWS flood-forecast data to show areas of predicted flood inundation associated with forecasted flood-peak stages. The digital maps also have been submitted for inclusion in the data libraries of the USGS interactive Flood Inundation Mapper. Data from the streamgage can be used by emergency-management personnel, in conjunction with the flood-inundation maps, to help determine a course of action when flooding is imminent. Flood profiles for selected reaches were prepared by calibrating a steady-state step-backwater model to an established streamgage rating curve. The step-backwater model then was used to determine water-surface-elevation profiles for 10 flood stages at the streamgage with corresponding streamflows ranging from approximately the 50- to 0.2-percent annual exceedance probabilities. The computed flood profiles were used in combination with digital elevation data to delineate flood-inundation areas.","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/ofr20131032","collaboration":"Prepared in cooperation with Holmes County, Ohio","usgsCitation":"Ostheimer, C.J., 2013, Development of flood profiles and flood-inundation maps for the Village of Killbuck, Ohio: U.S. Geological Survey Open-File Report 2013-1032, iv, 8 p.; Downloads Directory, https://doi.org/10.3133/ofr20131032.","productDescription":"iv, 8 p.; Downloads Directory","startPage":"i","endPage":"8","numberOfPages":"15","onlineOnly":"Y","additionalOnlineFiles":"Y","costCenters":[{"id":513,"text":"Ohio Water Science Center","active":true,"usgs":true}],"links":[{"id":267855,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/ofr_2013_1032.gif"},{"id":267854,"type":{"id":7,"text":"Companion Files"},"url":"https://pubs.usgs.gov/of/2013/1032/GIS_data_downloads"},{"id":267852,"type":{"id":15,"text":"Index Page"},"url":"https://pubs.usgs.gov/of/2013/1032/"},{"id":267853,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/of/2013/1032/pdf/OFR2013-1032.pdf"}],"country":"United States","state":"Ohio","city":"Killbuck","geographicExtents":"{ \"type\": \"FeatureCollection\", \"features\": [ { \"type\": \"Feature\", \"properties\": {}, \"geometry\": { \"type\": \"Polygon\", \"coordinates\": [ [ [ -81.987175,40.487845 ], [ -81.987175,40.506004 ], [ -81.975585,40.506004 ], [ -81.975585,40.487845 ], [ -81.987175,40.487845 ] ] ] } } ] }","noUsgsAuthors":false,"publicationStatus":"PW","scienceBaseUri":"5125f084e4b09d00759cd050","contributors":{"authors":[{"text":"Ostheimer, Chad J. ostheime@usgs.gov","contributorId":2160,"corporation":false,"usgs":true,"family":"Ostheimer","given":"Chad","email":"ostheime@usgs.gov","middleInitial":"J.","affiliations":[{"id":513,"text":"Ohio Water Science Center","active":true,"usgs":true}],"preferred":false,"id":474286,"contributorType":{"id":1,"text":"Authors"},"rank":1}]}}
,{"id":70043831,"text":"sir20125275 - 2013 - Hydrogeologic framework and estimates of groundwater storage for the Hualapai Valley, Detrital Valley, and Sacramento Valley basins, Mohave County, Arizona","interactions":[],"lastModifiedDate":"2013-02-20T16:12:11","indexId":"sir20125275","displayToPublicDate":"2013-02-20T00:00:00","publicationYear":"2013","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":"2012-5275","title":"Hydrogeologic framework and estimates of groundwater storage for the Hualapai Valley, Detrital Valley, and Sacramento Valley basins, Mohave County, Arizona","docAbstract":"We have investigated the hydrogeology of the Hualapai Valley, Detrital Valley, and Sacramento Valley basins of Mohave County in northwestern Arizona to develop a better understanding of groundwater storage within the basin fill aquifers. In our investigation we used geologic maps, well-log data, and geophysical surveys to delineate the sedimentary textures and lithology of the basin fill. We used gravity data to construct a basin geometry model that defines smaller subbasins within the larger basins, and airborne transient-electromagnetic modeled results along with well-log lithology data to infer the subsurface distribution of basin fill within the subbasins. Hydrogeologic units (HGUs) are delineated within the subbasins on the basis of the inferred lithology of saturated basin fill. We used the extent and size of HGUs to estimate groundwater storage to depths of 400 meters (m) below land surface (bls). The basin geometry model for the Hualapai Valley basin consists of three subbasins: the Kingman, Hualapai, and southern Gregg subbasins. In the Kingman subbasin, which is estimated to be 1,200 m deep, saturated basin fill consists of a mixture of fine- to coarse-grained sedimentary deposits. The Hualapai subbasin, which is the largest of the subbasins, contains a thick halite body from about 400 m to about 4,300 m bls. Saturated basin fill overlying the salt body consists predominately of fine-grained older playa deposits. In the southern Gregg subbasin, which is estimated to be 1,400 m deep, saturated basin fill is interpreted to consist primarily of fine- to coarse-grained sedimentary deposits. Groundwater storage to 400 m bls in the Hualapai Valley basin is estimated to be 14.1 cubic kilometers (km<sup>3</sup>). The basin geometry model for the Detrital Valley basin consists of three subbasins: northern Detrital, central Detrital, and southern Detrital subbasins. The northern and central Detrital subbasins are characterized by a predominance of playa evaporite and fine-grained clastic deposits; evaporite deposits in the northern Detrital subbasin include halite. The northern Detrital subbasin is estimated to be 600 m deep and the middle Detrital subbasin is estimated to be 700 m deep. The southern Detrital subbasin, which is estimated to be 1,500 m deep, is characterized by a mixture of fine- to coarse-grained basin fill deposits. Groundwater storage to 400 m bls in the Detrital Valley basin is estimated to be 9.8 km<sup>3</sup>. The basin geometry model for the Sacramento Valley basin consists of three subbasins: the Chloride, Golden Valley, and Dutch Flat subbasins. The Chloride subbasin, which is estimated to be 900 m deep, is characterized by fine- to coarse-grained basin fill deposits. In the Golden Valley subbasin, which is elongated north-south, and is estimated to be 1,300 m deep, basin fill includes fine-grained sedimentary deposits overlain by coarse-grained sedimentary deposits in much of the subbasin. The Dutch Flat subbasin is estimated to be 2,600 m deep, and well-log lithologic data suggest that the basin fill consists of interlayers of gravel, sand, and clay. Groundwater storage to 400 m bls in the Sacramento Valley basin is estimated to be 35.1 km<sup>3</sup>.","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/sir20125275","collaboration":"Prepared in cooperation with the Arizona Department of Water Resources and Mohave County, Arizona","usgsCitation":"Truini, M., Beard, L.S., Kennedy, J., and Anning, D., 2013, Hydrogeologic framework and estimates of groundwater storage for the Hualapai Valley, Detrital Valley, and Sacramento Valley basins, Mohave County, Arizona: U.S. Geological Survey Scientific Investigations Report 2012-5275, vi, 47 p., https://doi.org/10.3133/sir20125275.","productDescription":"vi, 47 p.","startPage":"i","endPage":"47","numberOfPages":"56","onlineOnly":"Y","additionalOnlineFiles":"N","costCenters":[{"id":128,"text":"Arizona Water Science Center","active":true,"usgs":true}],"links":[{"id":267851,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/sir_2012_5275.gif"},{"id":267850,"type":{"id":15,"text":"Index Page"},"url":"https://pubs.usgs.gov/sir/2012/5275/"},{"id":267849,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/sir/2012/5275/sir2012-5275.pdf"}],"country":"United States","state":"Arizona","county":"Mohave County","otherGeospatial":"Hualapai Valley;Detrital Valley;Sacramento Valley","geographicExtents":"{ \"type\": \"FeatureCollection\", \"features\": [ { \"type\": \"Feature\", \"properties\": {}, \"geometry\": { \"type\": \"Polygon\", \"coordinates\": [ [ [ -114.82,31.33 ], [ -114.82,37.0 ], [ -109.0,37.0 ], [ -109.0,31.33 ], [ -114.82,31.33 ] ] ] } } ] }","noUsgsAuthors":false,"publicationStatus":"PW","scienceBaseUri":"5125f086e4b09d00759cd054","contributors":{"authors":[{"text":"Truini, Margot mtruini@usgs.gov","contributorId":599,"corporation":false,"usgs":true,"family":"Truini","given":"Margot","email":"mtruini@usgs.gov","affiliations":[{"id":128,"text":"Arizona Water Science Center","active":true,"usgs":true}],"preferred":true,"id":474282,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Beard, L. Sue","contributorId":87607,"corporation":false,"usgs":true,"family":"Beard","given":"L.","email":"","middleInitial":"Sue","affiliations":[],"preferred":false,"id":474284,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Kennedy, Jeffrey 0000-0002-3365-6589","orcid":"https://orcid.org/0000-0002-3365-6589","contributorId":101124,"corporation":false,"usgs":true,"family":"Kennedy","given":"Jeffrey","affiliations":[],"preferred":false,"id":474285,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Anning, Dave W.","contributorId":36025,"corporation":false,"usgs":true,"family":"Anning","given":"Dave W.","affiliations":[],"preferred":false,"id":474283,"contributorType":{"id":1,"text":"Authors"},"rank":4}]}}
,{"id":70043604,"text":"sim3212 - 2013 - Paleoseismology of a newly discovered scarp in the Yakima fold-and-thrust belt, Kittitas County, Washington","interactions":[],"lastModifiedDate":"2013-02-15T08:53:02","indexId":"sim3212","displayToPublicDate":"2013-02-15T00:00:00","publicationYear":"2013","noYear":false,"publicationType":{"id":18,"text":"Report"},"publicationSubtype":{"id":5,"text":"USGS Numbered Series"},"seriesTitle":{"id":333,"text":"Scientific Investigations Map","code":"SIM","onlineIssn":"2329-132X","printIssn":"2329-1311","active":true,"publicationSubtype":{"id":5}},"seriesNumber":"3212","title":"Paleoseismology of a newly discovered scarp in the Yakima fold-and-thrust belt, Kittitas County, Washington","docAbstract":"The Boylston Mountains anticlinal ridge is one of several that are cored by rocks of the Columbia River Basalt Group and, with the interceding synclinal valleys, constitute the Yakima fold-and-thrust belt of central Washington. Lidar data acquired from the U.S. Army's Yakima Training Center reveal a prominent, northwest-side-up, 65°- to 70°-trending, 3- to 4-meter-high scarp that cuts across the western end of the Boylston Mountains, perpendicular to the mapped anticline. The scarp continues to the northeast from the ridge on the southern side of Park Creek and across the low ridges for a total length of about 3 kilometers. A small stream deeply incises its flood plain where it projects across Johnson Canyon. The scarp is inferred to be late Quaternary in age based on its presence on the modern landscape and the incised flood-plain sediments in Johnson Canyon. Two trenches were excavated across this scarp. The most informative of the two, the Horned Lizard trench, exposed shallow, 15.5-Ma Grande Ronde Basalt, which is split by a deep, wide crack that is coincident with the base of the scarp and filled with wedges of silty gravels that are interpreted to represent at least two generations of fault colluvium that offset a buried soil.","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/sim3212","usgsCitation":"Barnett, E., Sherrod, B.L., Norris, R., and Gibbons, D., 2013, Paleoseismology of a newly discovered scarp in the Yakima fold-and-thrust belt, Kittitas County, Washington: U.S. Geological Survey Scientific Investigations Map 3212, 1 Sheet: 48 x 36 inches, https://doi.org/10.3133/sim3212.","productDescription":"1 Sheet: 48 x 36 inches","numberOfPages":"1","onlineOnly":"Y","costCenters":[{"id":237,"text":"Earthquake Science Center","active":true,"usgs":true}],"links":[{"id":267521,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/sim_3212.gif"},{"id":267519,"type":{"id":15,"text":"Index Page"},"url":"https://pubs.usgs.gov/sim/3212/"},{"id":267520,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/sim/3212/sim3212_sheet.pdf"}],"country":"United States","state":"Washington","county":"Kittitas County","geographicExtents":"{ \"type\": \"FeatureCollection\", \"features\": [ { \"type\": \"Feature\", \"properties\": {}, \"geometry\": { \"type\": \"Polygon\", \"coordinates\": [ [ [ -121.46,46.73 ], [ -121.46,47.6 ], [ -119.92,47.6 ], [ -119.92,46.73 ], [ -121.46,46.73 ] ] ] } } ] }","noUsgsAuthors":false,"publicationStatus":"PW","scienceBaseUri":"511f5908e4b03b29402c5d52","contributors":{"authors":[{"text":"Barnett, Elizabeth A.","contributorId":41550,"corporation":false,"usgs":true,"family":"Barnett","given":"Elizabeth A.","affiliations":[],"preferred":false,"id":473962,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Sherrod, Brian L.","contributorId":16874,"corporation":false,"usgs":true,"family":"Sherrod","given":"Brian","email":"","middleInitial":"L.","affiliations":[],"preferred":false,"id":473960,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Norris, Robert","contributorId":75943,"corporation":false,"usgs":true,"family":"Norris","given":"Robert","affiliations":[],"preferred":false,"id":473963,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Gibbons, Douglas","contributorId":18246,"corporation":false,"usgs":true,"family":"Gibbons","given":"Douglas","email":"","affiliations":[],"preferred":false,"id":473961,"contributorType":{"id":1,"text":"Authors"},"rank":4}]}}
]}