Large areas of public land are currently being permitted or evaluated for utility-scale solar energy development (USSED) in the southwestern United States, including areas with high biodiversity and protected species. However, peer-reviewed studies of the effects of USSED on wildlife are lacking. The potential effects of the construction and the eventual decommissioning of solar energy facilities include the direct mortality of wildlife; environmental impacts of fugitive dust and dust suppressants; destruction and modification of habitat, including the impacts of roads; and off-site impacts related to construction material acquisition, processing, and transportation. The potential effects of the operation and maintenance of the facilities include habitat fragmentation and barriers to gene flow, increased noise, electromagnetic field generation, microclimate alteration, pollution, water consumption, and fire. Facility design effects, the efficacy of site-selection criteria, and the cumulative effects of USSED on regional wildlife populations are unknown. Currently available peer-reviewed data are insufficient to allow a rigorous assessment of the impact of USSED on wildlife.
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Center","active":true,"usgs":true}],"preferred":true,"id":355801,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Ennen, Josua R.","contributorId":92799,"corporation":false,"usgs":true,"family":"Ennen","given":"Josua","email":"","middleInitial":"R.","affiliations":[],"preferred":false,"id":355802,"contributorType":{"id":1,"text":"Authors"},"rank":2}]}} ,{"id":70007344,"text":"sim3188 - 2011 - Aeromagnetic and aeromagnetic-based geologic maps of the Coastal Belt, Franciscan Complex, northern California","interactions":[],"lastModifiedDate":"2023-06-22T16:25:48.494421","indexId":"sim3188","displayToPublicDate":"2012-02-09T00:00:00","publicationYear":"2011","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":"3188","title":"Aeromagnetic and aeromagnetic-based geologic maps of the Coastal Belt, Franciscan Complex, northern California","docAbstract":"The Coastal belt of the Franciscan Complex represents a Late Cretaceous to Miocene accretionary prism and overlying slope deposits. Its equivalents may extend from the offshore outer borderland of southern California to north of the Mendocino Triple Junction under the Eel River Basin and in the offshore of Cascadia. The Coastal belt is exposed on land in northern California, yet its structure and stratigraphy are incompletely known because of discontinuous exposure, structural disruption, and lithologically non-distinctive clastic rocks. The intent of this report is to make available, in map form, aeromagnetic data covering the Coastal belt that provide a new dataset to aid in mapping, understanding, and interpreting the incompletely understood geology and structure in northern California.
The newly merged aeromagnetic data over the Coastal belt of the Franciscan Complex reveal long, linear anomalies that indicate remarkably coherent structure within a terrane where mapping at the surface indicates complex deformation and that has been described as \"broken formation\" and, even locally as \"mélange\". The anomalies in the Coastal belt are primarily sourced by volcanic-rich graywackes and exotic blocks of basalt. Some anomalies along the contact of the Coastal belt with the Central belt are likely caused by local interleaving of components of the Coast Ranges ophiolite. These data can be used to map additional exotic blocks within the Coastal belt and to distinguish lithologically indistinct graywackes within the Coastal terrane. Using anomaly asymmetry allows projection of these \"layers\" into the subsurface. This analysis indicates predominant northeast dips consistent with tectonic interleaving of blocks within a subduction zone.
","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/sim3188","usgsCitation":"Langenheim, V., Jachens, R., and McLaughlin, R.J., 2011, Aeromagnetic and aeromagnetic-based geologic maps of the Coastal Belt, Franciscan Complex, northern California (Version 1.0: Originally posted February 3, 2012; Version 1.1: May 15, 2017): U.S. Geological Survey Scientific Investigations Map 3188, Pamphlet: i, 19 p.; 3 Sheets: 29.2 x 48.4 inches or smaller; Readme; Metadata Folder; GIS Database, https://doi.org/10.3133/sim3188.","productDescription":"Pamphlet: i, 19 p.; 3 Sheets: 29.2 x 48.4 inches or smaller; Readme; Metadata Folder; GIS Database","onlineOnly":"Y","additionalOnlineFiles":"Y","costCenters":[{"id":312,"text":"Geology, Minerals, Energy, and Geophysics Science Center","active":true,"usgs":true}],"links":[{"id":341334,"rank":8,"type":{"id":9,"text":"Database"},"url":"https://pubs.usgs.gov/sim/3188/sim3188_database.zip","text":"GIS database","size":"401.3 MB","linkFileType":{"id":6,"text":"zip"}},{"id":341333,"rank":7,"type":{"id":16,"text":"Metadata"},"url":"https://pubs.usgs.gov/sim/3188/sim3188_metadata","text":"Metadata Folder","size":"37 kB"},{"id":341332,"rank":6,"type":{"id":20,"text":"Read Me"},"url":"https://pubs.usgs.gov/sim/3188/sim3188_readme.txt","size":"8 kB","linkFileType":{"id":2,"text":"txt"}},{"id":341331,"rank":5,"type":{"id":26,"text":"Sheet"},"url":"https://pubs.usgs.gov/sim/3188/sim3188_sheet3.pdf","text":"Sheet 3","size":"9.2 MB","linkFileType":{"id":1,"text":"pdf"}},{"id":341330,"rank":4,"type":{"id":26,"text":"Sheet"},"url":"https://pubs.usgs.gov/sim/3188/sim3188_sheet2.pdf","text":"Sheet 2","size":"6 MB","linkFileType":{"id":1,"text":"pdf"}},{"id":341335,"rank":9,"type":{"id":25,"text":"Version History"},"url":"https://pubs.usgs.gov/sim/3188/versionHist.txt"},{"id":341329,"rank":3,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/sim/3188/sim3188_pamphlet.pdf","text":"Pamphlet","size":"6.4 MB","linkFileType":{"id":1,"text":"pdf"}},{"id":115786,"rank":10,"type":{"id":15,"text":"Index Page"},"url":"https://pubs.usgs.gov/sim/3188/","linkFileType":{"id":5,"text":"html"}},{"id":341328,"rank":2,"type":{"id":26,"text":"Sheet"},"url":"https://pubs.usgs.gov/sim/3188/sim3188_sheet1.pdf","text":"Sheet 1","size":"12.8 MB","linkFileType":{"id":1,"text":"pdf"}},{"id":116876,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/sim_3188.gif"}],"scale":"250000","datum":"NAD27","country":"United States","state":"California","geographicExtents":"{ \"type\": \"FeatureCollection\", \"features\": [ { \"type\": \"Feature\", \"properties\": {}, \"geometry\": { \"type\": \"Polygon\", \"coordinates\": [ [ [ -124.5,38.25 ], [ -124.5,40.666666666666664 ], [ -122.75,40.666666666666664 ], [ -122.75,38.25 ], [ -124.5,38.25 ] ] ] } } ] }","edition":"Version 1.0: Originally posted February 3, 2012; Version 1.1: May 15, 2017","noUsgsAuthors":false,"publicationStatus":"PW","scienceBaseUri":"5059e723e4b0c8380cd47887","contributors":{"authors":[{"text":"Langenheim, V.E. 0000-0003-2170-5213","orcid":"https://orcid.org/0000-0003-2170-5213","contributorId":54956,"corporation":false,"usgs":true,"family":"Langenheim","given":"V.E.","affiliations":[{"id":312,"text":"Geology, Minerals, Energy, and Geophysics Science Center","active":true,"usgs":true}],"preferred":false,"id":356291,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Jachens, R.C.","contributorId":55433,"corporation":false,"usgs":true,"family":"Jachens","given":"R.C.","email":"","affiliations":[],"preferred":false,"id":356292,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"McLaughlin, R. J. 0000-0002-4390-2288","orcid":"https://orcid.org/0000-0002-4390-2288","contributorId":107271,"corporation":false,"usgs":true,"family":"McLaughlin","given":"R.","middleInitial":"J.","affiliations":[],"preferred":false,"id":356293,"contributorType":{"id":1,"text":"Authors"},"rank":3}]}} ,{"id":70004671,"text":"70004671 - 2011 - Wastewater dilution index partially explains observed polybrominated diphenyl ether flame retardant concentrations in osprey eggs from Columbia River Basin, 2008-2009","interactions":[],"lastModifiedDate":"2020-01-13T06:24:03","indexId":"70004671","displayToPublicDate":"2012-02-05T10:29:00","publicationYear":"2011","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":1479,"text":"Ecotoxicology","active":true,"publicationSubtype":{"id":10}},"title":"Wastewater dilution index partially explains observed polybrominated diphenyl ether flame retardant concentrations in osprey eggs from Columbia River Basin, 2008-2009","docAbstract":"Several polybrominated biphenyl ether (PBDE) congeners were found in all 175 osprey (Pandion haliaetus) eggs collected from the Columbia River Basin between 2002 and 2009. ΣPBDE concentrations in 2008–2009 were highest in osprey eggs from the two lowest flow rivers studied; however, each river flowed through relatively large and populous metropolitan areas (Boise, Idaho and Spokane, Washington). We used the volume of Wastewater Treatment Plant (WWTP) discharge, a known source of PBDEs, as a measure of human activity at a location, and combined with river flow (both converted to millions of gallons/day) created a novel approach (an approximate Dilution Index) to relate waterborne contaminants to levels of these contaminants that reach avian eggs. This approach provided a useful understanding of the spatial osprey egg concentration patterns observed. Individual osprey egg concentrations along the Upper Willamette River co-varied with the Dilution Index, while combined egg data (geometric means) from rivers or segments of rivers showed a strong, significant relationship to the Dilution Index with one exception, the Boise River. There, we believe osprey egg concentrations were lower than expected because Boise River ospreys foraged perhaps 50–75% of the time off the river at ponds and lakes stocked with fish that contained relatively low ΣPBDE concentrations. Our limited temporal data at specific localities (2004–2009) suggests that ΣPBDE concentrations in osprey eggs peaked between 2005 and 2007, and then decreased, perhaps in response to penta- and octa-PBDE technical mixtures no longer being used in the USA after 2004. Empirical estimates of biomagnification factors (BMFs) from fish to osprey eggs were 3.76–7.52 on a wet weight (ww) basis or 4.37–11.0 lipid weight. Our earlier osprey study suggested that ΣPBDE egg concentrations >1,000 ng/g ww may reduce osprey reproductive success. Only two of the study areas sampled in 2008–2009 contained individual eggs with ΣPBDE concentrations >1,000 ng/g, and non-significant (P > 0.30) negative relationships were found between ΣPBDEs and reproductive success. Additional monitoring is required to confirm not only the apparent decline in PBDE concentrations in osprey eggs that occurred during this study, but also to better understand the relationship between PBDEs in eggs and reproductive success.","language":"English","publisher":"Springer","doi":"10.1007/s10646-011-0608-2","usgsCitation":"Henny, C.J., Grove, R.A., Kaiser, J.L., Johnson, B., Furl, C.V., and Letcher, R., 2011, Wastewater dilution index partially explains observed polybrominated diphenyl ether flame retardant concentrations in osprey eggs from Columbia River Basin, 2008-2009: Ecotoxicology, v. 20, no. 4, p. 682-697, https://doi.org/10.1007/s10646-011-0608-2.","productDescription":"16 p.","startPage":"682","endPage":"697","numberOfPages":"16","temporalStart":"2002-01-01","temporalEnd":"2009-12-01","costCenters":[{"id":290,"text":"Forest and Rangeland Ecosystem Science Center","active":false,"usgs":true},{"id":34983,"text":"Contaminant Biology Program","active":true,"usgs":true}],"links":[{"id":204689,"rank":0,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","otherGeospatial":"Columbia River Basin","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -124.3212890625,\n 45.089035564831015\n ],\n [\n -119.2236328125,\n 43.389081939117496\n ],\n [\n -113.3349609375,\n 41.86956082699455\n ],\n [\n -110.91796875,\n 44.402391829093915\n ],\n [\n -114.30175781249999,\n 47.45780853075031\n ],\n [\n -118.82812499999997,\n 48.07807894349862\n ],\n [\n -123.61816406249997,\n 47.18971246448421\n ],\n [\n -124.3212890625,\n 45.089035564831015\n ]\n ]\n ]\n }\n }\n ]\n}","volume":"20","issue":"4","noUsgsAuthors":false,"publicationDate":"2011-02-22","publicationStatus":"PW","scienceBaseUri":"505bc3fae4b08c986b32b434","contributors":{"authors":[{"text":"Henny, Charles J. 0000-0001-7474-350X hennyc@usgs.gov","orcid":"https://orcid.org/0000-0001-7474-350X","contributorId":3461,"corporation":false,"usgs":true,"family":"Henny","given":"Charles","email":"hennyc@usgs.gov","middleInitial":"J.","affiliations":[{"id":289,"text":"Forest and Rangeland Ecosys Science Center","active":true,"usgs":true}],"preferred":true,"id":779347,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Grove, Robert A.","contributorId":52134,"corporation":false,"usgs":true,"family":"Grove","given":"Robert","email":"","middleInitial":"A.","affiliations":[],"preferred":false,"id":351072,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Kaiser, James L.","contributorId":57033,"corporation":false,"usgs":true,"family":"Kaiser","given":"James","email":"","middleInitial":"L.","affiliations":[],"preferred":false,"id":351073,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Johnson, Branden L. branden_johnson@usgs.gov","contributorId":4168,"corporation":false,"usgs":true,"family":"Johnson","given":"Branden L.","email":"branden_johnson@usgs.gov","affiliations":[{"id":289,"text":"Forest and Rangeland Ecosys Science Center","active":true,"usgs":true}],"preferred":true,"id":351068,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Furl, Chad V.","contributorId":28365,"corporation":false,"usgs":true,"family":"Furl","given":"Chad","email":"","middleInitial":"V.","affiliations":[],"preferred":false,"id":351071,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"Letcher, Robert J.","contributorId":25292,"corporation":false,"usgs":true,"family":"Letcher","given":"Robert J.","affiliations":[],"preferred":false,"id":351070,"contributorType":{"id":1,"text":"Authors"},"rank":6}]}} ,{"id":70003657,"text":"70003657 - 2011 - Velocity-based movement modeling for individual and population level inference","interactions":[],"lastModifiedDate":"2015-06-10T11:19:11","indexId":"70003657","displayToPublicDate":"2012-01-29T13:32:00","publicationYear":"2011","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":2980,"text":"PLoS ONE","active":true,"publicationSubtype":{"id":10}},"title":"Velocity-based movement modeling for individual and population level inference","docAbstract":"Understanding animal movement and resource selection provides important information about the ecology of the animal, but an animal's movement and behavior are not typically constant in time. We present a velocity-based approach for modeling animal movement in space and time that allows for temporal heterogeneity in an animal's response to the environment, allows for temporal irregularity in telemetry data, and accounts for the uncertainty in the location information. Population-level inference on movement patterns and resource selection can then be made through cluster analysis of the parameters related to movement and behavior. We illustrate this approach through a study of northern fur seal (Callorhinus ursinus) movement in the Bering Sea, Alaska, USA. Results show sex differentiation, with female northern fur seals exhibiting stronger response to environmental variables.
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At least 76 of these vents have erupted, some repeatedly, during the past 10,000 yr. Past volcanic activity has ranged in scale and type from small rhyolitic and basaltic eruptions through large catastrophic rhyolitic eruptions. Sooner or later, volcanoes in California will erupt again, and they could have serious impacts on the health and safety of the State's citizens as well as on its economy. This report describes the nature and probable distribution of potentially hazardous volcanic phenomena and their threat to people and property. It includes hazard-zonation maps that show areas relatively likely to be affected by future eruptions in California. This digital release contains information from maps of potential hazards from future volcanic eruptions in the state of California, published as Plate 1 in U.S. Geological Survey Bulletin 1847. The main component of this digital release is a spatial database prepared using geographic information systems (GIS) applications. This release also contains links to files to view or print the map plate, main report text, and accompanying hazard tables from Bulletin 1847. It should be noted that much has been learned about the ages of eruptive events in the State of California since the publication of Bulletin 1847 in 1989. For the most up to date information on the status of California volcanoes, please refer to the U.S. Geological Survey Volcano Hazards Program website.","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/ds661","usgsCitation":"White, M.N., Ramsey, D.W., and Miller, C.D., 2011, Database for potential hazards from future volcanic eruptions in California: U.S. Geological Survey Data Series 661, HTML Document, https://doi.org/10.3133/ds661.","productDescription":"HTML Document","onlineOnly":"Y","additionalOnlineFiles":"Y","costCenters":[{"id":218,"text":"Denver Federal Center","active":false,"usgs":true}],"links":[{"id":115720,"rank":100,"type":{"id":15,"text":"Index Page"},"url":"https://pubs.usgs.gov/ds/661/index.html","linkFileType":{"id":5,"text":"html"}},{"id":116451,"rank":0,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/ds_661.png"}],"country":"United States","state":"California","geographicExtents":"{ \"type\": \"FeatureCollection\", \"features\": [ { \"type\": \"Feature\", \"properties\": {}, \"geometry\": { \"type\": \"Polygon\", \"coordinates\": [ [ [ -126,32 ], [ -126,42 ], [ -113,42 ], [ -113,32 ], [ -126,32 ] ] ] } } ] }","noUsgsAuthors":false,"publicationStatus":"PW","scienceBaseUri":"5059fdd5e4b0c8380cd4e973","contributors":{"authors":[{"text":"White, Melissa N.","contributorId":84897,"corporation":false,"usgs":true,"family":"White","given":"Melissa","email":"","middleInitial":"N.","affiliations":[],"preferred":false,"id":356153,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Ramsey, David W. 0000-0003-1698-2523 dramsey@usgs.gov","orcid":"https://orcid.org/0000-0003-1698-2523","contributorId":3819,"corporation":false,"usgs":true,"family":"Ramsey","given":"David","email":"dramsey@usgs.gov","middleInitial":"W.","affiliations":[{"id":617,"text":"Volcano Science Center","active":true,"usgs":true}],"preferred":true,"id":356151,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Miller, C. Dan","contributorId":38145,"corporation":false,"usgs":true,"family":"Miller","given":"C.","email":"","middleInitial":"Dan","affiliations":[],"preferred":false,"id":356152,"contributorType":{"id":1,"text":"Authors"},"rank":3}]}} ,{"id":70007221,"text":"sim3176 - 2011 - Bathymetric survey of Carroll Creek Tributary to Lake Tuscaloosa, Tuscaloosa County, Alabama, 2010","interactions":[],"lastModifiedDate":"2012-02-10T00:12:01","indexId":"sim3176","displayToPublicDate":"2012-01-25T00:00:00","publicationYear":"2011","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":"3176","title":"Bathymetric survey of Carroll Creek Tributary to Lake Tuscaloosa, Tuscaloosa County, Alabama, 2010","docAbstract":"The U.S. Geological Survey, in cooperation with the City of Tuscaloosa, conducted a bathymetric survey of Carroll Creek, on May 12-13, 2010. Carroll Creek is one of the major tributaries to Lake Tuscaloosa and contributes about 6 percent of the surface drainage area. A 3.5-mile reach of Carroll Creek was surveyed to prepare a current bathymetric map, determine storage capacities at specified water-surface elevations, and compare current conditions to historical cross sections. Bathymetric data were collected using a high-resolution interferometric mapping system consisting of a phase-differencing bathymetric sonar, navigation and motion-sensing system, and a data acquisition computer. To assess the accuracy of the interferometric mapping system and document depths in shallow areas of the study reach, an electronic total station was used to survey 22 cross sections spaced 50 feet apart. The data were combined and processed and a Triangulated Irregular Network (TIN) and contour map were generated. Cross sections were extracted from the TIN and compared with historical cross sections. Between 2004 and 2010, the area (cross section 1) at the confluence of Carroll Creek and the main run of LakeTuscaloosa showed little to no change in capacity area. Another area (cross section 2) showed a maximum change in elevation of 4 feet and an average change of 3 feet. At the water-surface elevation of 224 feet (National Geodetic Vertical Datum of 1929), the cross-sectional area has changed by 260 square feet for a total loss of 28 percent of cross-sectional storage area. The loss of area may be attributed to sedimentation in Carroll Creek and (or) the difference in accuracy between the two surveys.","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/sim3176","collaboration":"Prepared in cooperation with the City of Tuscaloosa","usgsCitation":"Lee, K., and Kimbrow, D., 2011, Bathymetric survey of Carroll Creek Tributary to Lake Tuscaloosa, Tuscaloosa County, Alabama, 2010: U.S. Geological Survey Scientific Investigations Map 3176, 1 Sheet: 34 inches x 32 inches, https://doi.org/10.3133/sim3176.","productDescription":"1 Sheet: 34 inches x 32 inches","temporalStart":"2010-05-12","temporalEnd":"2010-05-13","costCenters":[{"id":105,"text":"Alabama Water Science Center","active":true,"usgs":true}],"links":[{"id":116379,"rank":0,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/sim_3176.jpg"},{"id":115706,"rank":100,"type":{"id":15,"text":"Index Page"},"url":"https://pubs.usgs.gov/sim/3176/","linkFileType":{"id":5,"text":"html"}}],"state":"Alabama","county":"Tuscaloosa","otherGeospatial":"Lake Tuscaloosa","geographicExtents":"{ \"type\": \"FeatureCollection\", \"features\": [ { \"type\": \"Feature\", \"properties\": {}, \"geometry\": { \"type\": \"Polygon\", \"coordinates\": [ [ [ -87.56666666666666,33.28333333333333 ], [ -87.56666666666666,33.333333333333336 ], [ -87.53333333333333,33.333333333333336 ], [ -87.53333333333333,33.28333333333333 ], [ -87.56666666666666,33.28333333333333 ] ] ] } } ] }","noUsgsAuthors":false,"publicationStatus":"PW","scienceBaseUri":"5059f010e4b0c8380cd4a5a1","contributors":{"authors":[{"text":"Lee, K.G.","contributorId":28319,"corporation":false,"usgs":true,"family":"Lee","given":"K.G.","email":"","affiliations":[],"preferred":false,"id":356130,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Kimbrow, D.R.","contributorId":25702,"corporation":false,"usgs":true,"family":"Kimbrow","given":"D.R.","affiliations":[],"preferred":false,"id":356129,"contributorType":{"id":1,"text":"Authors"},"rank":2}]}} ,{"id":70007214,"text":"ds617 - 2011 - EAARL topography-Potato Creek watershed, Georgia, 2010","interactions":[],"lastModifiedDate":"2012-02-10T00:12:01","indexId":"ds617","displayToPublicDate":"2012-01-25T00:00:00","publicationYear":"2011","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":"617","title":"EAARL topography-Potato Creek watershed, Georgia, 2010","docAbstract":"This DVD contains lidar-derived first-surface (FS) and bare-earth (BE) topography GIS datasets of a portion of the Potato Creek watershed in the Apalachicola-Chattahoochee-Flint River basin, Georgia. These datasets were acquired on February 27, 2010.","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/ds617","usgsCitation":"Bonisteel-Cormier, J., Nayegandhi, A., Fredericks, X., Jones, J.W., Wright, C.W., Brock, J.C., and Nagle, D., 2011, EAARL topography-Potato Creek watershed, Georgia, 2010: U.S. Geological Survey Data Series 617, 1 DVD, https://doi.org/10.3133/ds617.","productDescription":"1 DVD","temporalStart":"2010-02-27","costCenters":[{"id":186,"text":"Coastal and Marine Geology Program","active":true,"usgs":true}],"links":[{"id":116382,"rank":0,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/ds_617.png"},{"id":115700,"rank":100,"type":{"id":15,"text":"Index Page"},"url":"https://pubs.usgs.gov/ds/617/index.html","linkFileType":{"id":5,"text":"html"}}],"state":"Georgia","geographicExtents":"{ \"type\": \"FeatureCollection\", \"features\": [ { \"type\": \"Feature\", \"properties\": {}, \"geometry\": { \"type\": \"Polygon\", \"coordinates\": [ [ [ -84.41666666666667,32.666666666666664 ], [ -84.41666666666667,33.25 ], [ -84.16666666666667,33.25 ], [ -84.16666666666667,32.666666666666664 ], [ -84.41666666666667,32.666666666666664 ] ] ] } } ] }","noUsgsAuthors":false,"publicationStatus":"PW","scienceBaseUri":"505a0450e4b0c8380cd508c9","contributors":{"authors":[{"text":"Bonisteel-Cormier, J.M.","contributorId":8060,"corporation":false,"usgs":true,"family":"Bonisteel-Cormier","given":"J.M.","affiliations":[],"preferred":false,"id":356086,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Nayegandhi, Amar","contributorId":37292,"corporation":false,"usgs":true,"family":"Nayegandhi","given":"Amar","affiliations":[],"preferred":false,"id":356089,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Fredericks, Xan","contributorId":35704,"corporation":false,"usgs":true,"family":"Fredericks","given":"Xan","affiliations":[],"preferred":false,"id":356087,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Jones, J. 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Although these data have been processed successfully on a computer system at the U.S. Geological Survey, no warranty expressed or implied is made regarding the display or utility of the data on any other system, nor shall the act of distribution imply any such warranty. The U.S. Geological Survey shall not be held liable for improper or incorrect use of the data described and (or) contained herein. Reference herein to any specific commercial product, process, or service by trade name, trademark, manufacturer, or otherwise does not constitute or imply its endorsement, recommendation, or favoring by the United States Government or any agency thereof.","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/ds611","collaboration":"Funding and (or) support for this study was provided by the USGS Coastal and Marine Geology Program and MsCIP. 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Available on-line at http://visibleearth.nasa.gov/view_rec.php?id=2364.","usgsCitation":"Forde, A.S., Dadisman, S.V., Flocks, J.G., Wiese, D.S., DeWitt, N.T., Pfeiffer, W.R., Kelso, K.W., and Thompson, P.R., 2011, Archive of digital Chirp subbottom profile data collected during USGS cruises 10CCT01, 10CCT02, and 10CCT03, Mississippi and Alabama Gulf Islands, March and April 2010: U.S. Geological Survey Data Series 611, DVD; 11 DVDs, https://doi.org/10.3133/ds611.","productDescription":"DVD; 11 DVDs","temporalStart":"2010-03-01","temporalEnd":"2010-04-30","costCenters":[{"id":574,"text":"St. Petersburg Coastal and Marine Science Center","active":true,"usgs":true}],"links":[{"id":116377,"rank":0,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/ds_611.png"},{"id":115698,"rank":100,"type":{"id":15,"text":"Index Page"},"url":"https://pubs.usgs.gov/ds/611/","linkFileType":{"id":5,"text":"html"}}],"state":"Mississippi;Alabama","otherGeospatial":"Gulf Islands","noUsgsAuthors":false,"publicationStatus":"PW","scienceBaseUri":"5059ed47e4b0c8380cd496f7","contributors":{"authors":[{"text":"Forde, Arnell S. 0000-0002-5581-2255 aforde@usgs.gov","orcid":"https://orcid.org/0000-0002-5581-2255","contributorId":376,"corporation":false,"usgs":true,"family":"Forde","given":"Arnell","email":"aforde@usgs.gov","middleInitial":"S.","affiliations":[{"id":574,"text":"St. Petersburg Coastal and Marine Science Center","active":true,"usgs":true}],"preferred":true,"id":356069,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Dadisman, Shawn V. sdadisman@usgs.gov","contributorId":2207,"corporation":false,"usgs":true,"family":"Dadisman","given":"Shawn","email":"sdadisman@usgs.gov","middleInitial":"V.","affiliations":[],"preferred":true,"id":356071,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Flocks, James G. 0000-0002-6177-7433 jflocks@usgs.gov","orcid":"https://orcid.org/0000-0002-6177-7433","contributorId":816,"corporation":false,"usgs":true,"family":"Flocks","given":"James","email":"jflocks@usgs.gov","middleInitial":"G.","affiliations":[{"id":574,"text":"St. Petersburg Coastal and Marine Science Center","active":true,"usgs":true}],"preferred":true,"id":356070,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Wiese, Dana S. dwiese@usgs.gov","contributorId":2476,"corporation":false,"usgs":true,"family":"Wiese","given":"Dana","email":"dwiese@usgs.gov","middleInitial":"S.","affiliations":[{"id":574,"text":"St. Petersburg Coastal and Marine Science Center","active":true,"usgs":true}],"preferred":true,"id":356072,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"DeWitt, Nancy T. 0000-0002-2419-4087 ndewitt@usgs.gov","orcid":"https://orcid.org/0000-0002-2419-4087","contributorId":4095,"corporation":false,"usgs":true,"family":"DeWitt","given":"Nancy","email":"ndewitt@usgs.gov","middleInitial":"T.","affiliations":[{"id":186,"text":"Coastal and Marine Geology Program","active":true,"usgs":true},{"id":574,"text":"St. Petersburg Coastal and Marine Science Center","active":true,"usgs":true}],"preferred":true,"id":356074,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"Pfeiffer, William R. wpfeiffer@usgs.gov","contributorId":3725,"corporation":false,"usgs":true,"family":"Pfeiffer","given":"William","email":"wpfeiffer@usgs.gov","middleInitial":"R.","affiliations":[],"preferred":true,"id":356073,"contributorType":{"id":1,"text":"Authors"},"rank":6},{"text":"Kelso, Kyle W. 0000-0003-0615-242X kkelso@usgs.gov","orcid":"https://orcid.org/0000-0003-0615-242X","contributorId":4307,"corporation":false,"usgs":true,"family":"Kelso","given":"Kyle","email":"kkelso@usgs.gov","middleInitial":"W.","affiliations":[{"id":574,"text":"St. Petersburg Coastal and Marine Science Center","active":true,"usgs":true}],"preferred":true,"id":356075,"contributorType":{"id":1,"text":"Authors"},"rank":7},{"text":"Thompson, Phillip R.","contributorId":90023,"corporation":false,"usgs":true,"family":"Thompson","given":"Phillip","email":"","middleInitial":"R.","affiliations":[],"preferred":false,"id":356076,"contributorType":{"id":1,"text":"Authors"},"rank":8}]}} ,{"id":70007210,"text":"ds602 - 2011 - Observations of wave runup, setup, and swash on natural beaches","interactions":[],"lastModifiedDate":"2012-02-02T00:16:01","indexId":"ds602","displayToPublicDate":"2012-01-25T00:00:00","publicationYear":"2011","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":"602","title":"Observations of wave runup, setup, and swash on natural beaches","docAbstract":"Video-based observations of wave runup, setup, and swash from 10 dynamically diverse field experiments are presented. 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Quantiles were used because the management objectives specified proportions of the habitat area that needed to comply with vegetation criteria. The linear model was used to obtain estimates that were averaged across 4 y. The equivalence testing framework allowed us to interpret confidence intervals for estimated proportions with respect to intervals of vegetative criteria (equivalence regions) in either a liberal, benefit-of-doubt or conservative, fail-safe approach associated with minimizing alternative risks. Simple Boolean conditional arguments were used to combine the quantile equivalence results for individual vegetation components into a joint statement for the multivariable management objectives. For example, management objective 2A required at least 809 ha of upland habitat with a shrub composition ≥0.70 sagebrush (Artemisia spp.), 20–30% canopy cover of sagebrush ≥25 cm in height, ≥20% canopy cover of grasses, and ≥10% canopy cover of forbs on average over 4 y. Shrub composition and canopy cover of grass each were readily met on >3,000 ha under either conservative or liberal interpretations of sampling variability. However, there were only 809–1,214 ha (conservative to liberal) with ≥10% forb canopy cover and 405–1,098 ha with 20–30% canopy cover of sagebrush ≥25 cm in height. Only 91–180 ha of uplands simultaneously met criteria for all four components, primarily because canopy cover of sagebrush and forbs was inversely related when considered at the spatial scale (30 m) of a sample transect. We demonstrate how the quantile equivalence analyses also can help refine the numerical specification of habitat objectives and explore specification of spatial scales for objectives with respect to sampling scales used to evaluate those objectives.
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Catch per unit effort (CPUE) from minnow traps embedded in drift fences was treated as an encounter rate and used to estimate speed, when combined with an independent estimate of density obtained by use of throw traps that enclose 1 m2 of marsh habitat. Underwater video was used to evaluate capture efficiency and species-specific bias of minnow traps and two sampling studies were used to estimate trap saturation and diel-movement patterns; these results were used to optimize sampling and derive correction factors to adjust species-specific encounter rates for bias and capture efficiency. Sailfin mollies Poecilia latipinna displayed a high frequency of escape from traps, whereas eastern mosquitofish Gambusia holbrooki were most likely to avoid a trap once they encountered it; dollar sunfish Lepomis marginatus were least likely to avoid the trap once they encountered it or to escape once they were captured. Length of sampling and time of day affected CPUE; fishes generally had a very low retention rate over a 24 h sample time and only the Everglades pygmy sunfish Elassoma evergladei were commonly captured at night. Dispersal speed of fishes in the Florida Everglades, U.S.A., was shown to vary seasonally and among species, ranging from 0.05 to 0.15 m s-1 for small poeciliids and fundulids to 0.1 to 1.8 m s-1 for L. marginatus. Speed was generally highest late in the wet season and lowest in the dry season, possibly tied to dispersal behaviours linked to finding and remaining in dry-season refuges. These speed estimates can be used to estimate the diffusive movement rate, which is commonly employed in spatial ecological models.","largerWorkType":{"id":2,"text":"Article"},"largerWorkTitle":"Journal of Fish Biology","largerWorkSubtype":{"id":10,"text":"Journal Article"},"language":"English","publisher":"The Fisheries Society of the British Isles","doi":"10.1111/j.1095-8649.2010.02867.x","usgsCitation":"Obaza, A., DeAngelis, D., and Trexler, J., 2011, Using data from an encounter sampler to model fish dispersal: Journal of Fish Biology, v. 78, no. 2, p. 495-513, https://doi.org/10.1111/j.1095-8649.2010.02867.x.","productDescription":"18 p.","startPage":"495","endPage":"513","costCenters":[{"id":275,"text":"Florida Integrated Science Center","active":false,"usgs":true}],"links":[{"id":115757,"rank":100,"type":{"id":15,"text":"Index Page"},"url":"https://dx.doi.org/10.1111/j.1095-8649.2010.02867.x","linkFileType":{"id":5,"text":"html"}},{"id":204582,"rank":0,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","state":"Florida","otherGeospatial":"Everglades","volume":"78","issue":"2","noUsgsAuthors":false,"publicationDate":"2011-01-07","publicationStatus":"PW","scienceBaseUri":"505bc042e4b08c986b32a00d","contributors":{"authors":[{"text":"Obaza, A.","contributorId":14109,"corporation":false,"usgs":true,"family":"Obaza","given":"A.","affiliations":[],"preferred":false,"id":349783,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"DeAngelis, D.L. 0000-0002-1570-4057","orcid":"https://orcid.org/0000-0002-1570-4057","contributorId":32470,"corporation":false,"usgs":true,"family":"DeAngelis","given":"D.L.","affiliations":[],"preferred":false,"id":349785,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Trexler, J.C.","contributorId":23108,"corporation":false,"usgs":true,"family":"Trexler","given":"J.C.","email":"","affiliations":[],"preferred":false,"id":349784,"contributorType":{"id":1,"text":"Authors"},"rank":3}]}} ,{"id":70005703,"text":"70005703 - 2011 - Using avian radar to examine relationships among avian activity, bird strikes, and meteorological factors","interactions":[],"lastModifiedDate":"2021-02-26T15:09:53.84988","indexId":"70005703","displayToPublicDate":"2012-01-24T10:11:00","publicationYear":"2011","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":1914,"text":"Human-Wildlife Interactions","active":true,"publicationSubtype":{"id":10}},"title":"Using avian radar to examine relationships among avian activity, bird strikes, and meteorological factors","docAbstract":"Radar systems designed to detect avian activity at airfields are useful in understanding factors that influence the risk of bird and aircraft collisions (bird strikes). We used an avian radar system to measure avian activity at Beale Air Force Base, California, USA, during 2008 and 2009. We conducted a 2-part analysis to examine relationships among avian activity, bird strikes, and meteorological and time-dependent factors. We found that avian activity around the airfield was greater at times when bird strikes occurred than on average using a permutation resampling technique. Second, we developed generalized linear mixed models of an avian activity index (AAI). Variation in AAI was first explained by seasons that were based on average migration dates of birds at the study area. We then modeled AAI by those seasons to further explain variation by meteorological factors and daily light levels within a 24- hour period. In general, avian activity increased with decreased temperature, wind, visibility, precipitation, and increased humidity and cloud cover. These effects differed by season. For example, during the spring bird migration period, most avian activity occurred before sunrise at twilight hours on clear days with low winds, whereas during fall migration, substantial activity occurred after sunrise, and birds generally were more active at lower temperatures. We report parameter estimates (i.e., constants and coefficients) averaged across models and a relatively simple calculation for safety officers and wildlife managers to predict AAI and the relative risk of bird strike based on time, date, and meteorological values. We validated model predictability and assessed model fi t. These analyses will be useful for general inference of avian activity and risk assessment efforts. Further investigation and ongoing data collection will refine these inference models and improve our understanding of factors that influence avian activity, which is necessary to inform management decisions aimed at reducing risk of bird strikes.
","language":"English","publisher":"Jack H. Berryman Institute","publisherLocation":"Logan, UT","doi":"10.26077/nbzd-kn35","usgsCitation":"Coates, P.S., Casazza, M.L., Halstead, B., Fleskes, J.P., and Laughlin, J.A., 2011, Using avian radar to examine relationships among avian activity, bird strikes, and meteorological factors: Human-Wildlife Interactions, v. 5, no. 2, p. 249-268, https://doi.org/10.26077/nbzd-kn35.","productDescription":"20 p.","startPage":"249","endPage":"268","numberOfPages":"20","onlineOnly":"N","additionalOnlineFiles":"N","temporalStart":"2008-01-01","temporalEnd":"2009-12-31","costCenters":[{"id":651,"text":"Western Ecological Research Center","active":true,"usgs":true}],"links":[{"id":204585,"rank":0,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","state":"California","county":"Yuba County","otherGeospatial":"Beale Air Force Base","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -121.48887634277342,\n 39.0591832989046\n ],\n [\n -121.31309509277344,\n 39.0591832989046\n ],\n [\n -121.31309509277344,\n 39.17957847932612\n ],\n [\n -121.48887634277342,\n 39.17957847932612\n ],\n [\n -121.48887634277342,\n 39.0591832989046\n ]\n ]\n ]\n }\n }\n ]\n}","volume":"5","issue":"2","noUsgsAuthors":false,"publicationStatus":"PW","scienceBaseUri":"505bc034e4b08c986b329fb1","contributors":{"authors":[{"text":"Coates, Peter S. 0000-0003-2672-9994 pcoates@usgs.gov","orcid":"https://orcid.org/0000-0003-2672-9994","contributorId":3263,"corporation":false,"usgs":true,"family":"Coates","given":"Peter","email":"pcoates@usgs.gov","middleInitial":"S.","affiliations":[{"id":651,"text":"Western Ecological Research Center","active":true,"usgs":true}],"preferred":true,"id":353089,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Casazza, Michael L. 0000-0002-5636-735X mike_casazza@usgs.gov","orcid":"https://orcid.org/0000-0002-5636-735X","contributorId":2091,"corporation":false,"usgs":true,"family":"Casazza","given":"Michael","email":"mike_casazza@usgs.gov","middleInitial":"L.","affiliations":[{"id":651,"text":"Western Ecological Research Center","active":true,"usgs":true}],"preferred":true,"id":353087,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Halstead, Brian J. 0000-0002-5535-6528 bhalstead@usgs.gov","orcid":"https://orcid.org/0000-0002-5535-6528","contributorId":3051,"corporation":false,"usgs":true,"family":"Halstead","given":"Brian J.","email":"bhalstead@usgs.gov","affiliations":[{"id":651,"text":"Western Ecological Research Center","active":true,"usgs":true},{"id":200,"text":"Coop Res Unit Seattle","active":true,"usgs":true}],"preferred":true,"id":353088,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Fleskes, Joseph P. 0000-0001-5388-6675 joe_fleskes@usgs.gov","orcid":"https://orcid.org/0000-0001-5388-6675","contributorId":1889,"corporation":false,"usgs":true,"family":"Fleskes","given":"Joseph","email":"joe_fleskes@usgs.gov","middleInitial":"P.","affiliations":[{"id":651,"text":"Western Ecological Research Center","active":true,"usgs":true}],"preferred":false,"id":353086,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Laughlin, James A.","contributorId":100529,"corporation":false,"usgs":true,"family":"Laughlin","given":"James","email":"","middleInitial":"A.","affiliations":[],"preferred":false,"id":353090,"contributorType":{"id":1,"text":"Authors"},"rank":5}]}} ,{"id":70004016,"text":"70004016 - 2011 - Use of spatial capture-recapture modeling and DNA data to estimate densities of elusive animals","interactions":[],"lastModifiedDate":"2012-02-02T00:16:02","indexId":"70004016","displayToPublicDate":"2012-01-24T09:10:00","publicationYear":"2011","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":1321,"text":"Conservation Biology","active":true,"publicationSubtype":{"id":10}},"title":"Use of spatial capture-recapture modeling and DNA data to estimate densities of elusive animals","docAbstract":"Assessment of abundance, survival, recruitment rates, and density (i.e., population assessment) is especially challenging for elusive species most in need of protection (e.g., rare carnivores). Individual identification methods, such as DNA sampling, provide ways of studying such species efficiently and noninvasively. Additionally, statistical methods that correct for undetected animals and account for locations where animals are captured are available to efficiently estimate density and other demographic parameters. We collected hair samples of European wildcat (Felis silvestris) from cheek-rub lure sticks, extracted DNA from the samples, and identified each animals' genotype. To estimate the density of wildcats, we used Bayesian inference in a spatial capture-recapture model. We used WinBUGS to fit a model that accounted for differences in detection probability among individuals and seasons and between two lure arrays. We detected 21 individual wildcats (including possible hybrids) 47 times. Wildcat density was estimated at 0.29/km2 (SE 0.06), and 95% of the activity of wildcats was estimated to occur within 1.83 km from their home-range center. Lures located systematically were associated with a greater number of detections than lures placed in a cell on the basis of expert opinion. Detection probability of individual cats was greatest in late March. Our model is a generalized linear mixed model; hence, it can be easily extended, for instance, to incorporate trap- and individual-level covariates. We believe that the combined use of noninvasive sampling techniques and spatial capture-recapture models will improve population assessments, especially for rare and elusive animals.","largerWorkType":{"id":2,"text":"Article"},"largerWorkTitle":"Conservation Biology","largerWorkSubtype":{"id":10,"text":"Journal Article"},"language":"English","publisher":"Society for Conservation Biology","publisherLocation":"Washington, D.C.","doi":"10.1111/j.1523-1739.2010.01616.x","usgsCitation":"Kery, M., Gardner, B., Stoeckle, T., Weber, D., and Royle, J., 2011, Use of spatial capture-recapture modeling and DNA data to estimate densities of elusive animals: Conservation Biology, v. 25, no. 2, p. 356-364, https://doi.org/10.1111/j.1523-1739.2010.01616.x.","productDescription":"9 p.","startPage":"356","endPage":"364","costCenters":[{"id":531,"text":"Patuxent Wildlife Research Center","active":true,"usgs":true}],"links":[{"id":204583,"rank":0,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"},{"id":21768,"rank":100,"type":{"id":15,"text":"Index Page"},"url":"https://dx.doi.org/10.1111/j.1523-1739.2010.01616.x","linkFileType":{"id":5,"text":"html"}}],"volume":"25","issue":"2","noUsgsAuthors":false,"publicationDate":"2010-12-16","publicationStatus":"PW","scienceBaseUri":"505bbf81e4b08c986b329bcf","contributors":{"authors":[{"text":"Kery, Marc","contributorId":38680,"corporation":false,"usgs":true,"family":"Kery","given":"Marc","affiliations":[],"preferred":false,"id":350159,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Gardner, Beth","contributorId":91612,"corporation":false,"usgs":false,"family":"Gardner","given":"Beth","affiliations":[{"id":13553,"text":"University of Washington-Seattle","active":true,"usgs":false}],"preferred":false,"id":350163,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Stoeckle, Tabea","contributorId":81632,"corporation":false,"usgs":true,"family":"Stoeckle","given":"Tabea","email":"","affiliations":[],"preferred":false,"id":350162,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Weber, Darius","contributorId":41586,"corporation":false,"usgs":true,"family":"Weber","given":"Darius","email":"","affiliations":[],"preferred":false,"id":350160,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Royle, J. Andrew 0000-0003-3135-2167","orcid":"https://orcid.org/0000-0003-3135-2167","contributorId":80808,"corporation":false,"usgs":true,"family":"Royle","given":"J. Andrew","affiliations":[{"id":531,"text":"Patuxent Wildlife Research Center","active":true,"usgs":true}],"preferred":true,"id":350161,"contributorType":{"id":1,"text":"Authors"},"rank":5}]}} ,{"id":70007204,"text":"ofr20111265 - 2011 - Impact of mine and natural sources of mercury on water, sediment, and biota in Harley Gulch adjacent to the Abbott-Turkey Run mine, Lake County, California","interactions":[],"lastModifiedDate":"2022-01-19T15:08:05.776269","indexId":"ofr20111265","displayToPublicDate":"2012-01-24T00:00:00","publicationYear":"2011","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":"2011-1265","title":"Impact of mine and natural sources of mercury on water, sediment, and biota in Harley Gulch adjacent to the Abbott-Turkey Run mine, Lake County, California","docAbstract":"Executive Summary
Stable-isotope data indicate that there are three sources of water that effect the composition and Hg concentration of waters in Harley Gulch: (1) meteoric water that dominates water chemistry during the wet season; (2) thermal water effluent from the Turkey Run mine that effects the chemistry at sample site HG1; and (3) cold connate groundwater that dominates water chemistry during the dry season as it upwells and reaches the surface. The results from sampling executed for this study suggest four distinct areas in Harley Gulch: (1) the contaminated West Fork of Harley Gulch, consisting of the stream immediately downstream from the mine area and the wetlands upstream from Harley Gulch canyon (sample sites HG1-HG2, (2) the East Fork of Harley Gulch, where no mining has occurred (sample site HG3), (3) sample sites HG4-HG7, where a seasonal influx of saline groundwater alters stream chemistry, and (4) sample sites HG7-HG10, downstream in Harley Gulch towards the confluence with Cache Creek.
West Fork: Mine Area and Wetlands
The concentration of Hg in both storm sediment and active channel sediment was highest at sample site HG1, immediately downstream from the mine. The highest concentrations of total Hg (HgT) in water also occurred at site HG1, and they decreased systematically downstream from the mine. The high concentration of HgT at site HG1 reflects input of thermal-water effluent from the Turkey Run mine which comprises most of the flow at this site during the dry season. During the May 2011 low-flow sampling, HgT concentration was very high at site HG1, but the maximum in HgT concentration occurred at sample site HG1.5 in the middle of the wetland area. The high concentration of HgT and isotopic chemistry at this site indicates that a significant input of connate groundwater into the creek at this location contributes to the high Hg concentration in water. At site HG1, just downstream from the thermal water input from the Turkey Run mine, water sampled in June 2010 was almost entirely composed of thermal-water effluent. During the storm sampling in March 2011, which resulted in the highest flows of the winter, thermal effluent was virtually undetectable at site HG1, and the water was all meteoric. During the May 2011 sampling event, the input of connate groundwater in the middle of the wetland area at site HG1.5 was dominant. Discharge from the adit and runoff from the mine contributes to the high Hg concentration at site HG1 under both high and low-flow conditions.
East Fork: Background
Hg levels in waters collected from the East Fork of Harley Gulch, where no mining has occurred, were as high as 32.8 parts per trillion (pptr). These levels of Hg in water are significantly higher than regional background Hg concentrations, which range from 4-7 pptr. These anomalous Hg concentrations are partially explained by the abundance of Hg-enriched groundwater in Harley Gulch.
Sites HG4-HG7
Downstream from the wetland, the aqueous concentration of HgT decreased, but remained above background levels as another input of connate groundwater occurs in the creek segment between sample sites HG4 and HG7. The input of connate groundwater in this segment of the creek is reflected in the increase in dissolved constituents characteristic of the connate groundwater, such as sulfate (SO4), chloride (Cl) and magnesium (Mg). Stable-isotope data for heavy isotopes d18O and d2D also confirm two areas of input of connate groundwater into Harley Gulch: the creek segment in the West Fork near sample site HG1.5 and the segment between sample sites HG4 and HG7. Downstream from the second area of input of connate groundwater, both HgF and HgT concentrations decrease similarly, but the percentage of Hg in the filtered fraction increases. The decreases in HgT and HgF between sample sites HG5 and HG7 suggests that this second source of connate groundwater to Harley Gulch is distinct from the Hg-enriched source that enters the middle of the wetlands at sample site HG1.5. During low-flow conditions in June 2010, input of connate groundwater increased from sample site HG4 and reached a maximum near sample site HG7, where it dominated creek water chemistry. Waters collected from sample site HG7 during the June 2010 sampling event were the heaviest isotopically and contained high concentrations of Cl and SO4, constituents that are characteristically high in the connate groundwater. Both above and below sample site HG7, the amount of connate groundwater in the creek water decreased.
Sites HG8-HG10
Sediment with high Hg concentration is present throughout the West Fork of Harley Gulch below the mine and in the upper part of the Harley Gulch main stem to just above sample site HG10. At the sample site furthest downstream, HG10, Hg concentration is at background levels, as are cobalt (Co), nickel (Ni), and tungsten (W), indicating that the sediment is not significantly contaminated with Hg from the mine.
","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/ofr20111265","collaboration":"Prepared in cooperation with the Bureau of Land Management","usgsCitation":"Rytuba, J.J., Hothem, R.L., Brussee, B.E., and Goldstein, D., 2011, Impact of mine and natural sources of mercury on water, sediment, and biota in Harley Gulch adjacent to the Abbott-Turkey Run mine, Lake County, California: U.S. Geological Survey Open-File Report 2011-1265, ix, 105 p., https://doi.org/10.3133/ofr20111265.","productDescription":"ix, 105 p.","onlineOnly":"Y","costCenters":[{"id":663,"text":"Western Mineral and Environmental Resources Science Center-Menlo Park Office","active":false,"usgs":true},{"id":34983,"text":"Contaminant Biology Program","active":true,"usgs":true}],"links":[{"id":116447,"rank":0,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/ofr_2011_1265.gif"},{"id":115690,"rank":100,"type":{"id":15,"text":"Index Page"},"url":"https://pubs.usgs.gov/of/2011/1265/","linkFileType":{"id":5,"text":"html"}},{"id":394515,"rank":3,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/of/2011/1265/of2011-1265.pdf","text":"Report","size":"9 MB","linkFileType":{"id":1,"text":"pdf"},"description":"Report"}],"country":"United States","state":"California","county":"Lake County","otherGeospatial":"Harley Gulch","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -122.47550010681154,\n 38.98630040014555\n ],\n [\n -122.46953487396239,\n 38.98913576852135\n ],\n [\n -122.46584415435791,\n 38.99210444428017\n ],\n [\n -122.46232509613036,\n 38.99610695603966\n ],\n [\n -122.45932102203369,\n 38.99847500223872\n ],\n [\n -122.45584487915039,\n 38.99994248405357\n ],\n [\n -122.45090961456299,\n 39.000642862372096\n ],\n [\n -122.44996547698975,\n 39.00231040189239\n ],\n [\n -122.44528770446779,\n 39.003977902109774\n ],\n [\n -122.4415111541748,\n 39.0037444544454\n ],\n [\n -122.44013786315918,\n 39.004478144510884\n ],\n [\n -122.43631839752197,\n 39.003777804158915\n ],\n [\n -122.43498802185059,\n 39.00371110471618\n ],\n [\n -122.42915153503418,\n 39.00727943658698\n ],\n [\n -122.4305248260498,\n 39.00941367990123\n ],\n [\n -122.43348598480225,\n 39.00948037396715\n ],\n [\n -122.43614673614502,\n 39.00954706797022\n ],\n [\n -122.44155406951904,\n 39.00797974227288\n ],\n [\n -122.4439573287964,\n 39.00864669362303\n ],\n [\n -122.44949340820312,\n 39.0077463078146\n ],\n [\n -122.451810836792,\n 39.0061455936338\n ],\n [\n -122.45803356170654,\n 39.004344746883135\n ],\n [\n -122.46138095855713,\n 39.00524517598894\n ],\n [\n -122.46442794799805,\n 39.003177506910475\n ],\n [\n -122.46743202209471,\n 39.00117647929471\n ],\n [\n -122.46923446655273,\n 38.99854170661785\n ],\n [\n -122.47047901153564,\n 38.997441076321095\n ],\n [\n -122.47116565704346,\n 38.996307075685365\n ],\n [\n -122.47318267822266,\n 38.99410572845627\n ],\n [\n -122.47464179992674,\n 38.992304575244454\n ],\n [\n -122.47610092163085,\n 38.99003639117867\n ],\n [\n -122.47871875762938,\n 38.98860206079838\n ],\n [\n -122.47550010681154,\n 38.98630040014555\n ]\n ]\n ]\n }\n }\n ]\n}","noUsgsAuthors":false,"publicationStatus":"PW","scienceBaseUri":"505a38c1e4b0c8380cd616a2","contributors":{"authors":[{"text":"Rytuba, James J. jrytuba@usgs.gov","contributorId":3043,"corporation":false,"usgs":true,"family":"Rytuba","given":"James","email":"jrytuba@usgs.gov","middleInitial":"J.","affiliations":[{"id":312,"text":"Geology, Minerals, Energy, and Geophysics Science Center","active":true,"usgs":true}],"preferred":true,"id":356060,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Hothem, Roger L. roger_hothem@usgs.gov","contributorId":1721,"corporation":false,"usgs":true,"family":"Hothem","given":"Roger","email":"roger_hothem@usgs.gov","middleInitial":"L.","affiliations":[{"id":651,"text":"Western Ecological Research Center","active":true,"usgs":true}],"preferred":true,"id":356059,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Brussee, Brianne E. 0000-0002-2452-7101 bbrussee@usgs.gov","orcid":"https://orcid.org/0000-0002-2452-7101","contributorId":4249,"corporation":false,"usgs":true,"family":"Brussee","given":"Brianne","email":"bbrussee@usgs.gov","middleInitial":"E.","affiliations":[{"id":651,"text":"Western Ecological Research Center","active":true,"usgs":true}],"preferred":true,"id":356061,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Goldstein, Daniel N.","contributorId":87671,"corporation":false,"usgs":true,"family":"Goldstein","given":"Daniel N.","affiliations":[],"preferred":false,"id":356062,"contributorType":{"id":1,"text":"Authors"},"rank":4}]}} ,{"id":70007191,"text":"ofr20111315 - 2011 - Bathymetry and digital elevation models of Coyote Creek and Alviso Slough, South San Francisco Bay, California","interactions":[],"lastModifiedDate":"2020-07-09T18:09:19.490137","indexId":"ofr20111315","displayToPublicDate":"2012-01-23T13:04:00","publicationYear":"2011","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":"2011-1315","title":"Bathymetry and digital elevation models of Coyote Creek and Alviso Slough, South San Francisco Bay, California","docAbstract":"In 2010, the U.S. Geological Survey (USGS), Pacific Coastal and Marine Science Center completed three cruises to map the bathymetry of the main channel and shallow intertidal mudflats in the southernmost part of south San Francisco Bay. The three surveys were merged to generate comprehensive maps of Coyote Creek (from Calaveras Point east to the railroad bridge) and Alviso Slough (from the bay to the town of Alviso) to establish baseline bathymetry prior to the breaching of levees adjacent to Alviso and Guadalupe Sloughs as part of the South Bay Salt Pond Restoration Project (http://www.southbayrestoration.org). Since 2010, the USGS has conducted fourteen additional surveys to monitor bathymetric change in this region as restoration progresses.
The bathymetric surveys were conducted using the state-of-the-art research vessel R/V Parke Snavely outfitted with an interferometric sidescan sonar for swath mapping in extremely shallow water. This publication provides high-resolution bathymetric data collected by the USGS. For the 2010 baseline survey we have merged the bathymetry with aerial lidar data that were collected for the USGS during the same time period to create a seamless, high-resolution digital elevation model (DEM) of the study area. The series of bathymetric datasets are provided at 1 m resolution and the 2010 bathymetric/topographic DEM at 2 m resolution. The data are formatted as both X, Y, Z text files and ESRI Arc ASCII files that are accompanied by Federal Geographic Data Committee (FGDC) compliant metadata.
Pacific Coastal and Marine Science Center
U.S. Geological Survey
2885 Mission Street
Santa Cruz, CA 95060
Many Federal, State, and local agencies use low-flow data to establish water-use policy and help determine the total maximum daily loads and effluent limits of point and nonpoint sources of contamination of surface water during periods of decreased streamflow. Low-flow magnitude and frequency are used often by water-supply planners, reservoir managers, and hydroelectric facilities to manage water availability for supply and power generation.
\nLow-flow statistics for eight selected U.S. Geological Survey streamgages in New York State were calculated for the period from 1976 through 2006 and for the entire period of continuous streamflow record. The 7-day, 2-year and 10-year low flows were computed and compared with those low flows published in the1979 U.S. Geological Survey report, Low-flow frequency analysis of streams in New York, Bulletin 74. Observed changes in low-flow frequency at each gage were then examined and compared to changes in precipitation and land use to determine whether a relation between similar patterns could be identified.
\nA statewide U.S. Geological Survey study has not been done to develop equations for estimating low flows on rural unregulated streams in New York. Currently (2010) only one regional study developed for parts of the lower Hudson River Basin in 1986 is available to assist in estimating low flows on rural streams with unregulated streamflow in New York. Low-flow statistics published in the 1979 report need to be updated by using additional data collected since 1976 to determine current low-flow conditions across New York State.
\nAt-site low-flow statistics were updated for eight streamgages in New York by using continuous daily streamflow data through 2006 for the future development of a statewide research study. Selection of the eight streamgages used in this study identified a major deficiency in the number of available unregulated long-term U.S. Geological Survey streamgages needed for the development of regional low-flow equations in New York. A limited analysis of the changes in land use for the contributing drainage areas for each streamgage, changes in precipitation, and trends in the annual 7-day minimum flow also are presented. The 7-day, 2-year low flow showed increases of 14 to 35 percent and the 7-day 10-year low flow showed zero to 19 percent increases at rural streamgages with unregulated streamflows when statistics were computed by using data from 1976 through 2006 and compared with published data in Bulletin 74. When the entire period of record was used to compute low flow frequencies, the 7-day, 2-year low flows increased from about 6 to 15 percent whereas the 7-day 10-year low flows showed zero to 5 percent increases. Streamgages affected by urbanization and regulation for water supply showed the most significant changes in the 7-day, 2-year and 10-year low-flow frequencies. These streamgages are included to help identify the effects of urbanization and regulation on streamflow at these locations. The 7-day 10-year low flow increased by 65 percent at the U.S. Geological Survey streamgage Hackensack River at West Nyack, N.Y., and increased 120 percent at the U.S. Geological Survey streamgage Neversink River at Godeffroy, N.Y., when statistics were computed by using data from 1976 through 2006 and compared with the statistics for the regulated period computed in Bulletin 74.
","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/sir20115112","collaboration":"Prepared in cooperation with the New York State Department of Environmental Conservation","usgsCitation":"Suro, T.P., and Gazoorian, C.L., 2011, Changes in low-flow frequency from 1976-2006 at selected streamgages in New York, excluding Long Island: U.S. Geological Survey Scientific Investigations Report 2011-5112, vi, 21 p., https://doi.org/10.3133/sir20115112.","productDescription":"vi, 21 p.","onlineOnly":"Y","temporalStart":"1976-01-01","temporalEnd":"2006-12-31","costCenters":[{"id":474,"text":"New York Water Science Center","active":true,"usgs":true}],"links":[{"id":116367,"rank":0,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/sir_2011_5112.gif"},{"id":115677,"rank":100,"type":{"id":15,"text":"Index Page"},"url":"https://pubs.usgs.gov/sir/2011/5112/","linkFileType":{"id":5,"text":"html"}}],"country":"United States","state":"New York","geographicExtents":"{ \"type\": \"FeatureCollection\", \"features\": [ { \"type\": \"Feature\", \"properties\": {}, \"geometry\": { \"type\": \"Polygon\", \"coordinates\": [ [ [ -81,40 ], [ -81,45 ], [ -72,45 ], [ -72,40 ], [ -81,40 ] ] ] } } ] }","noUsgsAuthors":false,"publicationStatus":"PW","scienceBaseUri":"5059f41be4b0c8380cd4bb43","contributors":{"authors":[{"text":"Suro, Thomas P. 0000-0002-9476-6829 tsuro@usgs.gov","orcid":"https://orcid.org/0000-0002-9476-6829","contributorId":2841,"corporation":false,"usgs":true,"family":"Suro","given":"Thomas","email":"tsuro@usgs.gov","middleInitial":"P.","affiliations":[{"id":502,"text":"Office of Surface Water","active":true,"usgs":true},{"id":470,"text":"New Jersey Water Science Center","active":true,"usgs":true}],"preferred":true,"id":356021,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Gazoorian, Christopher L. 0000-0002-5408-6212 cgazoori@usgs.gov","orcid":"https://orcid.org/0000-0002-5408-6212","contributorId":2929,"corporation":false,"usgs":true,"family":"Gazoorian","given":"Christopher","email":"cgazoori@usgs.gov","middleInitial":"L.","affiliations":[{"id":474,"text":"New York Water Science Center","active":true,"usgs":true}],"preferred":true,"id":356022,"contributorType":{"id":1,"text":"Authors"},"rank":2}]}} ,{"id":70005838,"text":"70005838 - 2011 - Use of airborne hyperspectral imagery to map soil parameters in tilled agricultural fields","interactions":[],"lastModifiedDate":"2012-02-02T00:16:02","indexId":"70005838","displayToPublicDate":"2012-01-22T16:11:00","publicationYear":"2011","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":851,"text":"Applied and Environmental Soil Science","active":true,"publicationSubtype":{"id":10}},"title":"Use of airborne hyperspectral imagery to map soil parameters in tilled agricultural fields","docAbstract":"Soil hyperspectral reflectance imagery was obtained for six tilled (soil) agricultural fields using an airborne imaging spectrometer (400–2450 nm, ~10 nm resolution, 2.5 m spatial resolution). Surface soil samples (n = 315) were analyzed for carbon content, particle size distribution, and 15 agronomically important elements (Mehlich-III extraction). When partial least squares (PLS) regression of imagery-derived reflectance spectra was used to predict analyte concentrations, 13 of the 19 analytes were predicted with R2 > 0.50, including carbon (0.65), aluminum (0.76), iron (0.75), and silt content (0.79). Comparison of 15 spectral math preprocessing treatments showed that a simple first derivative worked well for nearly all analytes. The resulting PLS factors were exported as a vector of coefficients and used to calculate predicted maps of soil properties for each field. Image smoothing with a 3 × 3 low-pass filter prior to spectral data extraction improved prediction accuracy. The resulting raster maps showed variation associated with topographic factors, indicating the effect of soil redistribution and moisture regime on in-field spatial variability. High-resolution maps of soil analyte concentrations can be used to improve precision environmental management of farmlands.","largerWorkType":{"id":2,"text":"Article"},"largerWorkTitle":"Applied and Environmental Soil Science","largerWorkSubtype":{"id":10,"text":"Journal Article"},"language":"English","publisher":"Hindawi Publishing Corporation","publisherLocation":"Cairo, Egypt","doi":"10.1155/2011/358193","usgsCitation":"Hively, W., McCarty, G.W., Reeves, J.B., Lang, M., Oesterling, R.A., and Delwiche, S.R., 2011, Use of airborne hyperspectral imagery to map soil parameters in tilled agricultural fields: Applied and Environmental Soil Science, v. 2011, no. 358193, 13 p., https://doi.org/10.1155/2011/358193.","productDescription":"13 p.","numberOfPages":"13","costCenters":[{"id":242,"text":"Eastern Geographic Science Center","active":true,"usgs":true}],"links":[{"id":474777,"rank":101,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.1155/2011/358193","text":"Publisher Index Page"},{"id":204685,"rank":0,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"},{"id":115749,"rank":100,"type":{"id":15,"text":"Index Page"},"url":"https://dx.doi.org/10.1155/2011/358193","linkFileType":{"id":5,"text":"html"}}],"volume":"2011","issue":"358193","noUsgsAuthors":false,"publicationStatus":"PW","scienceBaseUri":"505bbeaee4b08c986b32970e","contributors":{"authors":[{"text":"Hively, W. Dean 0000-0002-5383-8064","orcid":"https://orcid.org/0000-0002-5383-8064","contributorId":9391,"corporation":false,"usgs":true,"family":"Hively","given":"W. Dean","affiliations":[],"preferred":false,"id":353347,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"McCarty, Gregory W.","contributorId":78861,"corporation":false,"usgs":true,"family":"McCarty","given":"Gregory","email":"","middleInitial":"W.","affiliations":[],"preferred":false,"id":353352,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Reeves, James B. III","contributorId":40693,"corporation":false,"usgs":true,"family":"Reeves","given":"James","suffix":"III","email":"","middleInitial":"B.","affiliations":[],"preferred":false,"id":353349,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Lang, Megan W.","contributorId":58014,"corporation":false,"usgs":true,"family":"Lang","given":"Megan W.","affiliations":[],"preferred":false,"id":353350,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Oesterling, Robert A.","contributorId":25703,"corporation":false,"usgs":true,"family":"Oesterling","given":"Robert","email":"","middleInitial":"A.","affiliations":[],"preferred":false,"id":353348,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"Delwiche, Stephen R.","contributorId":68036,"corporation":false,"usgs":true,"family":"Delwiche","given":"Stephen","email":"","middleInitial":"R.","affiliations":[],"preferred":false,"id":353351,"contributorType":{"id":1,"text":"Authors"},"rank":6}]}} ,{"id":70005507,"text":"70005507 - 2011 - Use of upscaled elevation and surface roughness data in two-dimensional surface water models","interactions":[],"lastModifiedDate":"2012-03-08T17:16:42","indexId":"70005507","displayToPublicDate":"2012-01-22T15:41:00","publicationYear":"2011","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":664,"text":"Advances in Water Resources","active":true,"publicationSubtype":{"id":10}},"title":"Use of upscaled elevation and surface roughness data in two-dimensional surface water models","docAbstract":"In this paper, we present an approach that uses a combination of cell-block- and cell-face-averaging of high-resolution cell elevation and roughness data to upscale hydraulic parameters and accurately simulate surface water flow in relatively low-resolution numerical models. The method developed allows channelized features that preferentially connect large-scale grid cells at cell interfaces to be represented in models where these features are significantly smaller than the selected grid size. The developed upscaling approach has been implemented in a two-dimensional finite difference model that solves a diffusive wave approximation of the depth-integrated shallow surface water equations using preconditioned Newton–Krylov methods. Computational results are presented to show the effectiveness of the mixed cell-block and cell-face averaging upscaling approach in maintaining model accuracy, reducing model run-times, and how decreased grid resolution affects errors. Application examples demonstrate that sub-grid roughness coefficient variations have a larger effect on simulated error than sub-grid elevation variations.","largerWorkType":{"id":2,"text":"Article"},"largerWorkTitle":"Advances in Water Resources","largerWorkSubtype":{"id":10,"text":"Journal Article"},"language":"English","publisher":"Elsevier","publisherLocation":"Amsterdam, The Netherlands","doi":"10.1016/j.advwatres.2011.02.004","usgsCitation":"Hughes, J., Decker, J., and Langevin, C., 2011, Use of upscaled elevation and surface roughness data in two-dimensional surface water models: Advances in Water Resources, v. 34, no. 9, p. 1151-1164, https://doi.org/10.1016/j.advwatres.2011.02.004.","productDescription":"14 p.","startPage":"1151","endPage":"1164","costCenters":[{"id":285,"text":"Florida Water Science Center","active":false,"usgs":true}],"links":[{"id":204682,"rank":0,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"},{"id":115748,"rank":100,"type":{"id":15,"text":"Index Page"},"url":"https://dx.doi.org/10.1016/j.advwatres.2011.02.004","linkFileType":{"id":5,"text":"html"}}],"volume":"34","issue":"9","noUsgsAuthors":false,"publicationStatus":"PW","scienceBaseUri":"505bbfabe4b08c986b329cce","contributors":{"authors":[{"text":"Hughes, J.D.","contributorId":25539,"corporation":false,"usgs":true,"family":"Hughes","given":"J.D.","email":"","affiliations":[],"preferred":false,"id":352679,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Decker, J.D.","contributorId":66418,"corporation":false,"usgs":true,"family":"Decker","given":"J.D.","email":"","affiliations":[],"preferred":false,"id":352681,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Langevin, C.D.","contributorId":25976,"corporation":false,"usgs":true,"family":"Langevin","given":"C.D.","email":"","affiliations":[],"preferred":false,"id":352680,"contributorType":{"id":1,"text":"Authors"},"rank":3}]}} ,{"id":70007134,"text":"sir20115203 - 2011 - Improvement in precipitation-runoff model simulations by recalibration with basin-specific data, and subsequent model applications, Onondaga Lake Basin, Onondaga County, New York","interactions":[],"lastModifiedDate":"2012-03-08T17:16:43","indexId":"sir20115203","displayToPublicDate":"2012-01-18T00:00:00","publicationYear":"2011","noYear":false,"publicationType":{"id":18,"text":"Report"},"publicationSubtype":{"id":5,"text":"USGS Numbered Series"},"seriesTitle":{"id":334,"text":"Scientific Investigations Report","code":"SIR","onlineIssn":"2328-0328","printIssn":"2328-031X","active":true,"publicationSubtype":{"id":5}},"seriesNumber":"2011-5203","title":"Improvement in precipitation-runoff model simulations by recalibration with basin-specific data, and subsequent model applications, Onondaga Lake Basin, Onondaga County, New York","docAbstract":"Water-resource managers in Onondaga County, New York, are faced with the challenge of improving the water quality of Onondaga Lake, which has the distinction of being one of the most contaminated lakes in the United States. To assist in this endeavor, during 2003-07 the U.S. Geological Survey (USGS), in cooperation with the Onondaga Lake Partnership, developed a precipitation-runoff model of the 285-square-mile Onondaga Lake Basin with the computer program Hydrological Simulation Program-Fortran (HSPF). The model was intended to provide a tool whereby the processes responsible for the generation of loads of sediment and nutrients that are transported to Onondaga Lake could be better understood. This objective was only partly attained because data for calibration of the model were available from monitoring sites only at or near the mouths of the major tributaries to Onondaga Lake; no calibration data from headwater subbasins, where the loads originated, were available. To address this limitation and thereby decrease the uncertainty in the simulated results that were associated with headwater processes, the USGS conducted a 3-year (2005-08) basinwide study to assess the quality of surface water in the Onondaga Lake Basin. The study quantified the relative contributions of nonpoint sources associated with the major land uses and land covers in the basin and also monitored known sources and presumed sinks of sediment and nutrient loads, which previously had not been evaluated. The use of the newly acquired data to recalibrate the HSPF model resulted in improvements in the simulation of processes in the headwater subbasins, including suspended-sediment, orthophosphate, and phosphorus generation and transport.\nSimulation of streamflows in small subbasins was improved by adjusting model parameter values to match base flows, storm peaks, and storm recessions more precisely than had been done with the original model. Simulated recessional and low flows were either increased or decreased as appropriate for a given stream, and simulated peak flows generally were lowered in the revised model. The use of suspended-sediment concentrations rather than concentrations of the surrogate constituent, total suspended solids, resulted in increases in the simulated low-flow sediment concentrations and, in most cases, decreases in the simulated peak-flow sediment concentrations. Simulated orthophosphate concentrations in base flows generally increased but decreased for peak flows in selected headwater subbasins in the revised model. Compared with the original model, phosphorus concentrations simulated by the revised model were comparable in forested subbasins, generally decreased in developed and wetland-dominated subbasins, and increased in agricultural subbasins. A final revision to the model was made by the addition of the simulation of chloride (salt) concentrations in the Onondaga Creek Basin to help water-resource managers better understand the relative contributions of salt from multiple sources in this particular tributary. The calibrated revised model was used to (1) compute loading rates for the various land types that were simulated in the model, (2) conduct a watershed-management analysis that estimated the portion of the total load that was likely to be transported to Onondaga Lake from each of the modeled subbasins, (3) compute and assess chloride loads to Onondaga Lake from the Onondaga Creek Basin, and (4) simulate precolonization (forested) conditions in the basin to estimate the probable minimum phosphorus loads to the lake.","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/sir20115203","collaboration":"Prepared in cooperation with the Onondaga Lake Partnership","usgsCitation":"Coon, W.F., 2011, Improvement in precipitation-runoff model simulations by recalibration with basin-specific data, and subsequent model applications, Onondaga Lake Basin, Onondaga County, New York: U.S. Geological Survey Scientific Investigations Report 2011-5203, x, 37 p., https://doi.org/10.3133/sir20115203.","productDescription":"x, 37 p.","onlineOnly":"Y","costCenters":[{"id":474,"text":"New York Water Science Center","active":true,"usgs":true}],"links":[{"id":116441,"rank":0,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/sir_2011_5203.gif"},{"id":112501,"rank":100,"type":{"id":15,"text":"Index Page"},"url":"https://pubs.usgs.gov/sir/2011/5203/","linkFileType":{"id":5,"text":"html"}}],"state":"New York","county":"Onondaga","geographicExtents":"{ \"type\": \"FeatureCollection\", \"features\": [ { \"type\": \"Feature\", \"properties\": {}, \"geometry\": { \"type\": \"Polygon\", \"coordinates\": [ [ [ -76.5,42.7 ], [ -76.5,43.166666666666664 ], [ -75.96666666666667,43.166666666666664 ], [ -75.96666666666667,42.7 ], [ -76.5,42.7 ] ] ] } } ] }","noUsgsAuthors":false,"publicationStatus":"PW","scienceBaseUri":"505a3969e4b0c8380cd618f8","contributors":{"authors":[{"text":"Coon, William F. 0000-0002-7007-7797 wcoon@usgs.gov","orcid":"https://orcid.org/0000-0002-7007-7797","contributorId":1765,"corporation":false,"usgs":true,"family":"Coon","given":"William","email":"wcoon@usgs.gov","middleInitial":"F.","affiliations":[{"id":474,"text":"New York Water Science Center","active":true,"usgs":true}],"preferred":true,"id":355918,"contributorType":{"id":1,"text":"Authors"},"rank":1}]}} ,{"id":70006356,"text":"70006356 - 2011 - Twitter earthquake detection: Earthquake monitoring in a social world","interactions":[],"lastModifiedDate":"2012-02-02T00:16:01","indexId":"70006356","displayToPublicDate":"2012-01-17T10:09:00","publicationYear":"2011","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":793,"text":"Annals of Geophysics","active":true,"publicationSubtype":{"id":10}},"title":"Twitter earthquake detection: Earthquake monitoring in a social world","docAbstract":"The U.S. Geological Survey (USGS) is investigating how the social networking site Twitter, a popular service for sending and receiving short, public text messages, can augment USGS earthquake response products and the delivery of hazard information. Rapid detection and qualitative assessment of shaking events are possible because people begin sending public Twitter messages (tweets) with in tens of seconds after feeling shaking. Here we present and evaluate an earthquake detection procedure that relies solely on Twitter data. A tweet-frequency time series constructed from tweets containing the word \"earthquake\" clearly shows large peaks correlated with the origin times of widely felt events. To identify possible earthquakes, we use a short-term-average, long-term-average algorithm. When tuned to a moderate sensitivity, the detector finds 48 globally-distributed earthquakes with only two false triggers in five months of data. The number of detections is small compared to the 5,175 earthquakes in the USGS global earthquake catalog for the same five-month time period, and no accurate location or magnitude can be assigned based on tweet data alone. However, Twitter earthquake detections are not without merit. The detections are generally caused by widely felt events that are of more immediate interest than those with no human impact. The detections are also fast; about 75% occur within two minutes of the origin time. This is considerably faster than seismographic detections in poorly instrumented regions of the world. The tweets triggering the detections also provided very short first-impression narratives from people who experienced the shaking.","largerWorkType":{"id":2,"text":"Article"},"largerWorkTitle":"Annals of Geophysics","largerWorkSubtype":{"id":10,"text":"Journal Article"},"language":"English","publisher":"Istituto Nazionale di Geofisica e Vulcanologia","publisherLocation":"Rome, Italy","doi":"10.4401/ag-5364","usgsCitation":"Earle, P.S., Bowden, D.C., and Guy, M., 2011, Twitter earthquake detection: Earthquake monitoring in a social world: Annals of Geophysics, v. 54, no. 6, p. 708-715, https://doi.org/10.4401/ag-5364.","productDescription":"8 p.","startPage":"708","endPage":"715","costCenters":[{"id":300,"text":"Geologic Hazards Science Center","active":true,"usgs":true}],"links":[{"id":474780,"rank":101,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.4401/ag-5364","text":"Publisher Index Page"},{"id":204580,"rank":0,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"},{"id":115694,"rank":100,"type":{"id":15,"text":"Index Page"},"url":"https://dx.doi.org/10.4401/ag-5364","linkFileType":{"id":5,"text":"html"}}],"volume":"54","issue":"6","noUsgsAuthors":false,"publicationDate":"2012-01-14","publicationStatus":"PW","scienceBaseUri":"505bb947e4b08c986b327ba5","contributors":{"authors":[{"text":"Earle, Paul S. pearle@usgs.gov","contributorId":840,"corporation":false,"usgs":true,"family":"Earle","given":"Paul","email":"pearle@usgs.gov","middleInitial":"S.","affiliations":[{"id":300,"text":"Geologic Hazards Science Center","active":true,"usgs":true}],"preferred":false,"id":354370,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Bowden, Daniel C.","contributorId":70918,"corporation":false,"usgs":true,"family":"Bowden","given":"Daniel","email":"","middleInitial":"C.","affiliations":[],"preferred":false,"id":354372,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Guy, Michelle R. mguy@usgs.gov","contributorId":4235,"corporation":false,"usgs":true,"family":"Guy","given":"Michelle R.","email":"mguy@usgs.gov","affiliations":[{"id":300,"text":"Geologic Hazards Science Center","active":true,"usgs":true}],"preferred":false,"id":354371,"contributorType":{"id":1,"text":"Authors"},"rank":3}]}} ]}