{"pageNumber":"790","pageRowStart":"19725","pageSize":"25","recordCount":40759,"records":[{"id":70003387,"text":"70003387 - 2010 - An integrated sampling and analysis approach for improved biodiversity monitoring","interactions":[],"lastModifiedDate":"2021-01-15T13:32:05.463547","indexId":"70003387","displayToPublicDate":"2011-03-17T00:00:00","publicationYear":"2010","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":1547,"text":"Environmental Management","active":true,"publicationSubtype":{"id":10}},"title":"An integrated sampling and analysis approach for improved biodiversity monitoring","docAbstract":"<p><span>Successful biodiversity conservation requires high quality monitoring data and analyses to ensure scientifically defensible policy, legislation, and management. Although monitoring is a critical component in assessing population status and trends, many governmental and non-governmental organizations struggle to develop and implement effective sampling protocols and statistical analyses because of the magnitude and diversity of species in conservation concern. In this article we describe a practical and sophisticated data collection and analysis framework for developing a comprehensive wildlife monitoring program that includes multi-species inventory techniques and community-level hierarchical modeling. Compared to monitoring many species individually, the multi-species approach allows for improved estimates of individual species occurrences, including rare species, and an increased understanding of the aggregated response of a community to landscape and habitat heterogeneity. We demonstrate the benefits and practicality of this approach to address challenges associated with monitoring in the context of US state agencies that are legislatively required to monitor and protect species in greatest conservation need. We believe this approach will be useful to regional, national, and international organizations interested in assessing the status of both common and rare species.</span></p>","language":"English","publisher":"Springer","doi":"10.1007/s00267-010-9457-7","usgsCitation":"DeWan, A.A., and Zipkin, E., 2010, An integrated sampling and analysis approach for improved biodiversity monitoring: Environmental Management, v. 45, no. 5, p. 1223-1230, https://doi.org/10.1007/s00267-010-9457-7.","productDescription":"8 p.","startPage":"1223","endPage":"1230","costCenters":[{"id":531,"text":"Patuxent Wildlife Research Center","active":true,"usgs":true}],"links":[{"id":382193,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"volume":"45","issue":"5","noUsgsAuthors":false,"publicationDate":"2010-03-17","publicationStatus":"PW","scienceBaseUri":"4f4e4ad7e4b07f02db6845aa","contributors":{"authors":[{"text":"DeWan, Amielle A.","contributorId":24486,"corporation":false,"usgs":true,"family":"DeWan","given":"Amielle","email":"","middleInitial":"A.","affiliations":[],"preferred":false,"id":347089,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Zipkin, Elise F.","contributorId":70528,"corporation":false,"usgs":true,"family":"Zipkin","given":"Elise F.","affiliations":[],"preferred":false,"id":347090,"contributorType":{"id":1,"text":"Authors"},"rank":2}]}}
,{"id":99092,"text":"ofr20101266 - 2010 - Computer simulation of reservoir depletion and oil flow from the Macondo well following the Deepwater Horizon blowout","interactions":[],"lastModifiedDate":"2012-02-10T00:11:58","indexId":"ofr20101266","displayToPublicDate":"2011-03-12T00:00:00","publicationYear":"2010","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":"2010-1266","title":"Computer simulation of reservoir depletion and oil flow from the Macondo well following the Deepwater Horizon blowout","docAbstract":"This report describes the application of a computer model to simulate reservoir depletion and oil flow from the Macondo well following the Deepwater Horizon blowout. Reservoir and fluid data used for model development are based on (1) information released in BP's investigation report of the incident, (2) information provided by BP personnel during meetings in Houston, Texas, and (3) calibration by history matching to shut-in pressures measured in the capping stack during the Well Integrity Test. The model is able to closely match the measured shut-in pressures. In the simulation of the 86-day period from the blowout to shut in, the simulated reservoir pressure at the well face declines from the initial reservoir pressure of 11,850 pounds per square inch (psi) to 9,400 psi. After shut in, the simulated reservoir pressure recovers to a final value of 10,300 psi. The pressure does not recover back to the initial pressure owing to reservoir depletion caused by 86 days of oil discharge. The simulated oil flow rate declines from 63,600 stock tank barrels per day just after the Deepwater Horizon blowout to 52,600 stock tank barrels per day just prior to shut in. The simulated total volume of oil discharged is 4.92 million stock tank barrels. The overall uncertainty in the simulated flow rates and total volume of oil discharged is estimated to be + or - 10 percent. \r\n","language":"ENGLISH","publisher":"U.S. Geological Survey","doi":"10.3133/ofr20101266","usgsCitation":"Hsieh, P., 2010, Computer simulation of reservoir depletion and oil flow from the Macondo well following the Deepwater Horizon blowout: U.S. Geological Survey Open-File Report 2010-1266, 18 p., https://doi.org/10.3133/ofr20101266.","productDescription":"18 p.","onlineOnly":"Y","additionalOnlineFiles":"N","costCenters":[{"id":434,"text":"National Research Program","active":false,"usgs":true}],"links":[{"id":116962,"rank":0,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/ofr_2010_1266.gif"},{"id":14542,"rank":100,"type":{"id":15,"text":"Index Page"},"url":"https://pubs.usgs.gov/of/2010/1266/","linkFileType":{"id":5,"text":"html"}}],"geographicExtents":"{ \"type\": \"FeatureCollection\", \"features\": [ { \"type\": \"Feature\", \"properties\": {}, \"geometry\": { \"type\": \"Polygon\", \"coordinates\": [ [ [ -92,28.5 ], [ -92,30 ], [ -88,30 ], [ -88,28.5 ], [ -92,28.5 ] ] ] } } ] }","noUsgsAuthors":false,"publicationStatus":"PW","scienceBaseUri":"4f4e4b16e4b07f02db6a5453","contributors":{"authors":[{"text":"Hsieh, Paul","contributorId":14558,"corporation":false,"usgs":true,"family":"Hsieh","given":"Paul","affiliations":[],"preferred":false,"id":307533,"contributorType":{"id":1,"text":"Authors"},"rank":1}]}}
,{"id":99045,"text":"ofr20101276 - 2010 - An initial SPARROW model of land use and in-stream controls on total organic carbon in streams of the conterminous United States","interactions":[],"lastModifiedDate":"2024-07-17T21:54:29.073692","indexId":"ofr20101276","displayToPublicDate":"2011-02-12T00:00:00","publicationYear":"2010","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":"2010-1276","title":"An initial SPARROW model of land use and in-stream controls on total organic carbon in streams of the conterminous United States","docAbstract":"Watersheds play many important roles in the carbon cycle: (1) they are a site for both terrestrial and aquatic carbon dioxide (CO2) removal through photosynthesis; (2) they transport living and decomposing organic carbon in streams and groundwater; and (3) they store organic carbon for widely varying lengths of time as a function of many biogeochemical factors. Using the U.S. Geological Survey (USGS) Spatially Referenced Regression on Watershed Attributes (SPARROW) model, along with long-term monitoring data on total organic carbon (TOC), this research quantitatively estimates the sources, transport, and fate of the long-term mean annual load of TOC in streams of the conterminous United States. The model simulations use surrogate measures of the major terrestrial and aquatic sources of organic carbon to estimate the long-term mean annual load of TOC in streams. \r\n\r\nThe estimated carbon sources in the model are associated with four land uses (urban, cultivated, forest, and wetlands) and autochthonous fixation of carbon (stream photosynthesis). Stream photosynthesis is determined by reach-level application of an empirical model of stream chlorophyll based on total phosphorus concentration, and a mechanistic model of photosynthetic rate based on chlorophyll, average daily solar irradiance, water column light attenuation, and reach dimensions. It was found that the estimate of in-stream photosynthesis is a major contributor to the mean annual TOC load per unit of drainage area (that is, yield) in large streams, with a median share of about 60 percent of the total mean annual carbon load in streams with mean flows above 500 cubic feet per second. The interquartile range of the model predictions of TOC from in-stream photosynthesis is from 0.1 to 0.4 grams (g) carbon (C) per square meter (m-2) per day (day-1) for the approximately 62,000 stream reaches in the continental United States, which compares favorably with the reported literature range for net carbon fixation by phytoplankton in lakes and streams. The largest contributors per unit of drainage area to the mean annual stream TOC load among the terrestrial sources are, in descending order: wetlands, urban lands, mixed forests, agricultural lands, evergreen forests, and deciduous forests . It was found that the SPARROW model estimates of TOC contributions to streams associated with these land uses are also consistent with literature estimates. SPARROW model calibration results are used to simulate the delivery of TOC loads to the coastal areas of seven major regional drainages. It was found that stream photosynthesis is the largest source of the TOC yields ( about 50 percent) delivered to the coastal waters in two of the seven regional drainages (the Pacific Northwest and Mississippi-Atchafalaya-Red River basins ), whereas terrestrial sources are dominant (greater than 60 percent) in all other regions (North Atlantic, South Atlantic-Gulf, California, Texas-Gulf, and Great Lakes).","language":"English","publisher":"U.S. Geological Survey","doi":"10.3133/ofr20101276","collaboration":"Prepared in cooperation with Resources of the Future and Pennsylvania State University","usgsCitation":"Shih, J., Alexander, R.B., Smith, R.A., Boyer, E.W., Shwarz, G.E., and Chung, S., 2010, An initial SPARROW model of land use and in-stream controls on total organic carbon in streams of the conterminous United States: U.S. Geological Survey Open-File Report 2010-1276, vi, 22 p., https://doi.org/10.3133/ofr20101276.","productDescription":"vi, 22 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Quality","active":true,"usgs":true},{"id":451,"text":"National Water Quality Assessment Program","active":true,"usgs":true}],"preferred":true,"id":307380,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Smith, Richard A. 0000-0003-2117-2269 rsmith1@usgs.gov","orcid":"https://orcid.org/0000-0003-2117-2269","contributorId":580,"corporation":false,"usgs":true,"family":"Smith","given":"Richard","email":"rsmith1@usgs.gov","middleInitial":"A.","affiliations":[{"id":37778,"text":"WMA - Integrated Modeling and Prediction Division","active":true,"usgs":true},{"id":451,"text":"National Water Quality Assessment Program","active":true,"usgs":true},{"id":503,"text":"Office of Water Quality","active":true,"usgs":true}],"preferred":true,"id":307381,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Boyer, Elizabeth W.","contributorId":44659,"corporation":false,"usgs":false,"family":"Boyer","given":"Elizabeth","email":"","middleInitial":"W.","affiliations":[{"id":7260,"text":"Pennsylvania State University","active":true,"usgs":false}],"preferred":false,"id":307383,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Shwarz, Grogory E.","contributorId":89272,"corporation":false,"usgs":true,"family":"Shwarz","given":"Grogory","email":"","middleInitial":"E.","affiliations":[],"preferred":false,"id":307384,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"Chung, Susie","contributorId":90448,"corporation":false,"usgs":true,"family":"Chung","given":"Susie","email":"","affiliations":[],"preferred":false,"id":307385,"contributorType":{"id":1,"text":"Authors"},"rank":6}]}}
,{"id":9000583,"text":"ds565 - 2010 - EAARL coastal topography and imagery-Fire Island National Seashore, New York, 2009","interactions":[],"lastModifiedDate":"2012-02-10T00:10:06","indexId":"ds565","displayToPublicDate":"2011-02-07T00:00:00","publicationYear":"2010","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":"565","title":"EAARL coastal topography and imagery-Fire Island National Seashore, New York, 2009","docAbstract":"These remotely sensed, geographically referenced color-infrared (CIR) imagery and elevation measurements of lidar-derived bare-earth (BE) and first-surface (FS) topography datasets were produced collaboratively by the U.S. Geological Survey (USGS), St. Petersburg Coastal and Marine Science Center, St. Petersburg, FL, and the National Park Service (NPS), Northeast Coastal and Barrier Network, Kingston, RI. This project provides highly detailed and accurate datasets of a portion of the Fire Island National Seashore in New York, acquired on July 9 and August 3, 2009. The datasets are made available for use as a management tool to research scientists and natural-resource managers. An innovative airborne lidar instrument originally developed at the NASA Wallops Flight Facility, and known as the Experimental Advanced Airborne Research Lidar (EAARL), was used during data acquisition. The EAARL system is a raster-scanning, waveform-resolving, green-wavelength (532-nanometer) lidar designed to map near-shore bathymetry, topography, and vegetation structure simultaneously. The EAARL sensor suite includes the raster-scanning, water-penetrating full-waveform adaptive lidar, a down-looking red-green-blue (RGB) digital camera, a high-resolution multispectral CIR camera, two precision dual-frequency kinematic carrier-phase GPS receivers, and an integrated miniature digital inertial measurement unit, which provide for sub-meter georeferencing of each laser sample. The nominal EAARL platform is a twin-engine Cessna 310 aircraft, but the instrument was deployed on a Pilatus PC-6. A single pilot, a lidar operator, and a data analyst constitute the crew for most survey operations. This sensor has the potential to make significant contributions in measuring sub-aerial and submarine coastal topography within cross-environmental surveys. Elevation measurements were collected over the survey area using the EAARL system, and the resulting data were then processed using the Airborne Lidar Processing System (ALPS), a custom-built processing system developed in a NASA-USGS collaboration. ALPS supports the exploration and processing of lidar data in an interactive or batch mode. Modules for presurvey flight-line definition, flight-path plotting, lidar raster and waveform investigation, and digital camera image playback have been developed. Processing algorithms have been developed to extract the range to the first and last significant return within each waveform. ALPS is used routinely to create maps that represent submerged or sub-aerial topography. Specialized filtering algorithms have been implemented to determine the 'bare earth' under vegetation from a point cloud of last return elevations. For more information about similar projects, please visit the Decision Support for Coastal Science and Management website.","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/ds565","usgsCitation":"Vivekanandan, S., Klipp, E., Nayegandhi, A., Bonisteel-Cormier, J., Brock, J.C., Wright, C.W., Nagle, D., Fredericks, X., and Stevens, S., 2010, EAARL coastal topography and imagery-Fire Island National Seashore, New York, 2009: U.S. Geological Survey Data Series 565, HTML Page; 1 DVD, https://doi.org/10.3133/ds565.","productDescription":"HTML Page; 1 DVD","additionalOnlineFiles":"Y","costCenters":[{"id":574,"text":"St. Petersburg Coastal and Marine Science Center","active":true,"usgs":true}],"links":[{"id":126200,"rank":0,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/ds_565.bmp"},{"id":19204,"rank":200,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/ds/565/","linkFileType":{"id":5,"text":"html"}}],"country":"United States","geographicExtents":"{ \"type\": \"FeatureCollection\", \"features\": [ { \"type\": \"Feature\", \"properties\": {}, \"geometry\": { \"type\": \"Polygon\", \"coordinates\": [ [ [ -73.3,40.583333333333336 ], [ -73.3,40.833333333333336 ], [ -72.75,40.833333333333336 ], [ -72.75,40.583333333333336 ], [ -73.3,40.583333333333336 ] ] ] } } ] }","noUsgsAuthors":false,"publicationStatus":"PW","scienceBaseUri":"4f4e4a54e4b07f02db62c34d","contributors":{"authors":[{"text":"Vivekanandan, Saisudha","contributorId":84325,"corporation":false,"usgs":true,"family":"Vivekanandan","given":"Saisudha","email":"","affiliations":[],"preferred":false,"id":344320,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Klipp, E.S.","contributorId":100340,"corporation":false,"usgs":true,"family":"Klipp","given":"E.S.","affiliations":[],"preferred":false,"id":344321,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Nayegandhi, Amar","contributorId":37292,"corporation":false,"usgs":true,"family":"Nayegandhi","given":"Amar","affiliations":[],"preferred":false,"id":344317,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Bonisteel-Cormier, J.M.","contributorId":8060,"corporation":false,"usgs":true,"family":"Bonisteel-Cormier","given":"J.M.","affiliations":[],"preferred":false,"id":344314,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Brock, J. C.","contributorId":36095,"corporation":false,"usgs":true,"family":"Brock","given":"J.","email":"","middleInitial":"C.","affiliations":[],"preferred":false,"id":344316,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"Wright, C. W. wwright@usgs.gov","contributorId":49758,"corporation":false,"usgs":true,"family":"Wright","given":"C.","email":"wwright@usgs.gov","middleInitial":"W.","affiliations":[],"preferred":false,"id":344319,"contributorType":{"id":1,"text":"Authors"},"rank":6},{"text":"Nagle, D.B.","contributorId":40568,"corporation":false,"usgs":true,"family":"Nagle","given":"D.B.","email":"","affiliations":[],"preferred":false,"id":344318,"contributorType":{"id":1,"text":"Authors"},"rank":7},{"text":"Fredericks, Xan","contributorId":35704,"corporation":false,"usgs":true,"family":"Fredericks","given":"Xan","affiliations":[],"preferred":false,"id":344315,"contributorType":{"id":1,"text":"Authors"},"rank":8},{"text":"Stevens, Sara","contributorId":104015,"corporation":false,"usgs":true,"family":"Stevens","given":"Sara","affiliations":[],"preferred":false,"id":344322,"contributorType":{"id":1,"text":"Authors"},"rank":9}]}}
,{"id":9000584,"text":"ds564 - 2010 - EAARL coastal topography-Cape Hatteras National Seashore, North Carolina, post-Nor'Ida, 2009: first surface","interactions":[],"lastModifiedDate":"2012-02-10T00:10:06","indexId":"ds564","displayToPublicDate":"2011-02-07T00:00:00","publicationYear":"2010","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":"564","title":"EAARL coastal topography-Cape Hatteras National Seashore, North Carolina, post-Nor'Ida, 2009: first surface","docAbstract":"These remotely sensed, geographically referenced elevation measurements of lidar-derived first-surface (FS) topography datasets were produced collaboratively by the U.S. Geological Survey (USGS), St. Petersburg Coastal and Marine Science Center, St. Petersburg, FL, and the National Park Service (NPS), Northeast Coastal and Barrier Network, Kingston, RI. This project provides highly detailed and accurate datasets of a portion of the National Park Service Southeast Coast Network's Cape Hatteras National Seashore in North Carolina, acquired post-Nor'Ida (November 2009 nor'easter) on November 27 and 29 and December 1, 2009. The datasets are made available for use as a management tool to research scientists and natural-resource managers. An innovative airborne lidar instrument originally developed at the NASA Wallops Flight Facility, and known as the Experimental Advanced Airborne Research Lidar (EAARL), was used during data acquisition. The EAARL system is a raster-scanning, waveform-resolving, green-wavelength (532-nanometer) lidar designed to map near-shore bathymetry, topography, and vegetation structure simultaneously. The EAARL sensor suite includes the raster-scanning, water-penetrating full-waveform adaptive lidar, a down-looking red-green-blue (RGB) digital camera, a high-resolution multispectral color-infrared (CIR) camera, two precision dual-frequency kinematic carrier-phase GPS receivers, and an integrated miniature digital inertial measurement unit, which provide for sub-meter georeferencing of each laser sample. The nominal EAARL platform is a twin-engine aircraft, but the instrument was deployed on a Pilatus PC-6. A single pilot, a lidar operator, and a data analyst constitute the crew for most survey operations. This sensor has the potential to make significant contributions in measuring sub-aerial and submarine coastal topography within cross-environmental surveys. Elevation measurements were collected over the survey area using the EAARL system, and the resulting data were then processed using the Airborne Lidar Processing System (ALPS), a custom-built processing system developed in a NASA-USGS collaboration. ALPS supports the exploration and processing of lidar data in an interactive or batch mode. Modules for presurvey flight-line definition, flight-path plotting, lidar raster and waveform investigation, and digital camera image playback have been developed. Processing algorithms have been developed to extract the range to the first and last significant return within each waveform. ALPS is used routinely to create maps that represent submerged or sub-aerial topography. Specialized filtering algorithms have been implemented to determine the 'bare earth' under vegetation from a point cloud of last return elevations. For more information about similar projects, please visit the Decision Support for Coastal Science and Management website.","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/ds564","usgsCitation":"Bonisteel-Cormier, J., Nayegandhi, A., Brock, J.C., Wright, C.W., Nagle, D., Fredericks, X., and Stevens, S., 2010, EAARL coastal topography-Cape Hatteras National Seashore, North Carolina, post-Nor'Ida, 2009: first surface: U.S. Geological Survey Data Series 564, HTML Page; DVD, https://doi.org/10.3133/ds564.","productDescription":"HTML Page; DVD","additionalOnlineFiles":"Y","costCenters":[{"id":574,"text":"St. Petersburg Coastal and Marine Science Center","active":true,"usgs":true}],"links":[{"id":126204,"rank":0,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/ds_564.bmp"},{"id":19205,"rank":200,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/ds/564/","linkFileType":{"id":5,"text":"html"}}],"country":"United States","geographicExtents":"{ \"type\": \"FeatureCollection\", \"features\": [ { \"type\": \"Feature\", \"properties\": {}, \"geometry\": { \"type\": \"Polygon\", \"coordinates\": [ [ [ -76,34.06666666666667 ], [ -76,36 ], [ -75.46666666666667,36 ], [ -75.46666666666667,34.06666666666667 ], [ -76,34.06666666666667 ] ] ] } } ] }","noUsgsAuthors":false,"publicationStatus":"PW","scienceBaseUri":"4f4e4a54e4b07f02db62c334","contributors":{"authors":[{"text":"Bonisteel-Cormier, J.M.","contributorId":8060,"corporation":false,"usgs":true,"family":"Bonisteel-Cormier","given":"J.M.","affiliations":[],"preferred":false,"id":344323,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Nayegandhi, Amar","contributorId":37292,"corporation":false,"usgs":true,"family":"Nayegandhi","given":"Amar","affiliations":[],"preferred":false,"id":344326,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Brock, J. C.","contributorId":36095,"corporation":false,"usgs":true,"family":"Brock","given":"J.","email":"","middleInitial":"C.","affiliations":[],"preferred":false,"id":344325,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Wright, C. W. wwright@usgs.gov","contributorId":49758,"corporation":false,"usgs":true,"family":"Wright","given":"C.","email":"wwright@usgs.gov","middleInitial":"W.","affiliations":[],"preferred":false,"id":344328,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Nagle, D.B.","contributorId":40568,"corporation":false,"usgs":true,"family":"Nagle","given":"D.B.","email":"","affiliations":[],"preferred":false,"id":344327,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"Fredericks, Xan","contributorId":35704,"corporation":false,"usgs":true,"family":"Fredericks","given":"Xan","affiliations":[],"preferred":false,"id":344324,"contributorType":{"id":1,"text":"Authors"},"rank":6},{"text":"Stevens, Sara","contributorId":104015,"corporation":false,"usgs":true,"family":"Stevens","given":"Sara","affiliations":[],"preferred":false,"id":344329,"contributorType":{"id":1,"text":"Authors"},"rank":7}]}}
,{"id":99027,"text":"sir20105139 - 2010 - Trends in pesticide concentrations in urban streams in the United States, 1992-2008","interactions":[],"lastModifiedDate":"2017-10-14T11:47:15","indexId":"sir20105139","displayToPublicDate":"2011-02-05T00:00:00","publicationYear":"2010","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":"2010-5139","title":"Trends in pesticide concentrations in urban streams in the United States, 1992-2008","docAbstract":"Pesticide concentration trends in streams dominated by urban land use were assessed using data from 27 urban streams sampled as part of the U.S. Geological Survey National Water-Quality Assessment Program. The sites were divided into four regions, Northeast, South, Midwest, and West, to examine possible regional patterns. Three partially overlapping 9-year periods (1992-2000, 1996-2004, and 2000-2008) were examined for eight herbicides and one degradation product (simazine, prometon, atrazine, deethylatrazine, metolachlor, trifluralin, pendimethalin, tebuthiuron, and Dacthal), and five insecticides and two degradation products (chlorpyrifos, malathion, diazinon, fipronil, fipronil sulfide, desulfinylfipronil, and carbaryl). The data were analyzed for trends in concentration using a parametric regression model with seasonality, flow-related variability, and trend, called SEAWAVE-Q. The SEAWAVE-Q model also was used to generate estimated daily concentration percentiles for each analysis period to provide a summary of concentration magnitudes.\r\n\r\nFor herbicides, the largest 90th percentiles of estimated concentrations for simazine were in the South, prometon at some sites in all of the regions, atrazine and deethylatrazine in the South and Midwest, metolachlor in the Midwest and a few sites in the South, pendimethalin at scattered sites in all of the regions, and tebuthiuron in the South and a few sites in the Midwest and West. For insecticides, the largest 90th percentiles of estimated concentrations for diazinon and carbaryl were distributed among various sites in all regions (especially during 1996-2004), and fipronil at isolated sites in all of the regions during 2000-2008.\r\n\r\nTrend analysis results for the herbicides indicated many significant trends, both upward and downward, with varying patterns depending on period, region, and herbicide. Overall, deethylatrazine showed the most consistent pattern of upward trends, especially in the Northeast (2000-2008), South (1996-2004 and 2000-2008), and Midwest (1996-2004 and 2000-2008). Other herbicides showed less consistent upward trends, including simazine in the South (1996-2004), prometon in the Midwest (2000-2008), and atrazine in the South (1996-2004). The most consistent downward trends were for simazine in the Northeast and Midwest (1996-2004), prometon in the Northeast and Midwest (1996-2004) and West (1996-2004 and 2000-2008), and tebuthiuron in the South (1996-2004 and 2000-2008) and West (2000-2008).\r\n\r\nStrong similarity existed between the trends for atrazine and deethylatrazine during 1996-2004. During 2000-2008, however, there were mixed upward and downward trends in atrazine and predominantly upward trends in deethylatrazine. Ten sites with a downward trend in atrazine were paired with an upward trend in deethylatrazine and for three of these sites (1 in the South and 2 in the Midwest) both opposing trends were significant. Opposing trends showing a decrease in atrazine and an increase in deethylatrazine may indicate that decreases in atrazine from surface runoff are being offset in some cases by increases in deethylatrazine from groundwater for the latter analysis period.\r\n\r\nTrend results for insecticides indicated widespread significant downward trends for chlorpyrifos (especially 1996-2004), diazinon (1996-2004 and 2000-2008), and malathion (especially 1996-2004); widespread significant upward trends for fipronil and its degradation products (2000-2008); and mostly nonsignificant trends for carbaryl (1996-2004 and 2000-2008). The downward trends for chlorpyrifos and diazinon were consistent with the regulatory phaseout of residential uses of these insecticides and the upward trends for fipronil and its degradation products were consistent with its introduction in 1996 and subsequent increasing use as a possible substitute for chlorpyrifos and diazinon. The downward trends in malathion may be caused by voluntary substitution of pyrethroids or fipronil for malathio","language":"English","publisher":"U.S. Geological Survey","doi":"10.3133/sir20105139","usgsCitation":"Ryberg, K.R., Vecchia, A.V., Martin, J.D., and Gilliom, R.J., 2010, Trends in pesticide concentrations in urban streams in the United States, 1992-2008: U.S. Geological Survey Scientific Investigations Report 2010-5139, viii, 42 p., https://doi.org/10.3133/sir20105139.","productDescription":"viii, 42 p.","additionalOnlineFiles":"Y","temporalStart":"1992-01-01","temporalEnd":"2011-12-31","costCenters":[{"id":478,"text":"North Dakota Water Science Center","active":true,"usgs":true},{"id":34685,"text":"Dakota Water Science Center","active":true,"usgs":true}],"links":[{"id":126227,"rank":0,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/sir_2010_5139.jpg"},{"id":14467,"rank":100,"type":{"id":15,"text":"Index Page"},"url":"https://pubs.usgs.gov/sir/2010/5139/","linkFileType":{"id":5,"text":"html"}}],"country":"United States","publicComments":"National Water-Quality Assessment Program","noUsgsAuthors":false,"publicationStatus":"PW","scienceBaseUri":"4f4e4a4ce4b07f02db6264fe","contributors":{"authors":[{"text":"Ryberg, Karen R. 0000-0002-9834-2046 kryberg@usgs.gov","orcid":"https://orcid.org/0000-0002-9834-2046","contributorId":1172,"corporation":false,"usgs":true,"family":"Ryberg","given":"Karen","email":"kryberg@usgs.gov","middleInitial":"R.","affiliations":[{"id":34685,"text":"Dakota Water Science Center","active":true,"usgs":true}],"preferred":true,"id":307313,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Vecchia, Aldo V. 0000-0002-2661-4401","orcid":"https://orcid.org/0000-0002-2661-4401","contributorId":41810,"corporation":false,"usgs":true,"family":"Vecchia","given":"Aldo","email":"","middleInitial":"V.","affiliations":[],"preferred":false,"id":307314,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Martin, Jeffrey D. 0000-0003-1994-5285 jdmartin@usgs.gov","orcid":"https://orcid.org/0000-0003-1994-5285","contributorId":1066,"corporation":false,"usgs":true,"family":"Martin","given":"Jeffrey","email":"jdmartin@usgs.gov","middleInitial":"D.","affiliations":[{"id":27231,"text":"Indiana-Kentucky Water Science Center","active":true,"usgs":true},{"id":451,"text":"National Water Quality Assessment Program","active":true,"usgs":true},{"id":346,"text":"Indiana Water Science Center","active":true,"usgs":true}],"preferred":true,"id":307312,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Gilliom, Robert J. rgilliom@usgs.gov","contributorId":488,"corporation":false,"usgs":true,"family":"Gilliom","given":"Robert","email":"rgilliom@usgs.gov","middleInitial":"J.","affiliations":[{"id":451,"text":"National Water Quality Assessment Program","active":true,"usgs":true}],"preferred":true,"id":307311,"contributorType":{"id":1,"text":"Authors"},"rank":4}]}}
,{"id":9000578,"text":"ds557 - 2010 - EAARL Coastal Topography-Sandy Hook Unit, Gateway National Recreation Area, New Jersey, Post-Nor'Ida, 2009","interactions":[],"lastModifiedDate":"2012-02-10T00:10:06","indexId":"ds557","displayToPublicDate":"2011-02-04T00:00:00","publicationYear":"2010","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":"557","title":"EAARL Coastal Topography-Sandy Hook Unit, Gateway National Recreation Area, New Jersey, Post-Nor'Ida, 2009","docAbstract":"These remotely sensed, geographically referenced elevation measurements of lidar-derived bare-earth (BE) and first-surface (FS) topography datasets were produced collaboratively by the U.S. Geological Survey (USGS), St. Petersburg Coastal and Marine Science Center, St. Petersburg, FL, and the National Park Service (NPS), Northeast Coastal and Barrier Network, Kingston, RI. This project provides highly detailed and accurate datasets of a portion of the Sandy Hook Unit of Gateway National Recreation Area in New Jersey, acquired post-Nor'Ida (November 2009 nor'easter) on December 4, 2009. The datasets are made available for use as a management tool to research scientists and natural-resource managers. An innovative airborne lidar instrument originally developed at the NASA Wallops Flight Facility, and known as the Experimental Advanced Airborne Research Lidar (EAARL), was used during data acquisition. The EAARL system is a raster-scanning, waveform-resolving, green-wavelength (532-nanometer) lidar designed to map near-shore bathymetry, topography, and vegetation structure simultaneously. The EAARL sensor suite includes the raster-scanning, water-penetrating full-waveform adaptive lidar, a down-looking red-green-blue (RGB) digital camera, a high-resolution multispectral color infrared (CIR) camera, two precision dual-frequency kinematic carrier-phase GPS receivers, and an integrated miniature digital inertial measurement unit, which provide for sub-meter georeferencing of each laser sample. The nominal EAARL platform is a twin-engine aircraft, but the instrument was deployed on a Pilatus PC-6. A single pilot, a lidar operator, and a data analyst constitute the crew for most survey operations. This sensor has the potential to make significant contributions in measuring sub-aerial and submarine coastal topography within cross-environmental surveys. Elevation measurements were collected over the survey area using the EAARL system, and the resulting data were then processed using the Airborne Lidar Processing System (ALPS), a custom-built processing system developed in a NASA-USGS collaboration. ALPS supports the exploration and processing of lidar data in an interactive or batch mode. Modules for presurvey flight-line definition, flight-path plotting, lidar raster and waveform investigation, and digital camera image playback have been developed. Processing algorithms have been developed to extract the range to the first and last significant return within each waveform. ALPS is used routinely to create maps that represent submerged or sub-aerial topography. Specialized filtering algorithms have been implemented to determine the 'bare earth' under vegetation from a point cloud of last return elevations. 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,{"id":9000580,"text":"ds556 - 2010 - EAARL Coastal Topography-Mississippi and Alabama Barrier Islands, Post-Hurricane Gustav, 2008","interactions":[],"lastModifiedDate":"2012-02-10T00:10:07","indexId":"ds556","displayToPublicDate":"2011-02-04T00:00:00","publicationYear":"2010","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":"556","title":"EAARL Coastal Topography-Mississippi and Alabama Barrier Islands, Post-Hurricane Gustav, 2008","docAbstract":"These remotely sensed, geographically referenced elevation measurements of lidar-derived bare-earth (BE) and first-surface (FS) topography datasets were produced collaboratively by the U.S. Geological Survey (USGS), St. Petersburg Coastal and Marine Science Center, St. Petersburg, FL; the National Park Service (NPS), Gulf Coast Network, Lafayette, LA; and the National Aeronautics and Space Administration (NASA), Wallops Flight Facility, VA. This project provides highly detailed and accurate datasets of a portion of the Mississippi and Alabama barrier islands, acquired post-Hurricane Gustav (September 2008 hurricane) on September 8, 2008. The datasets are made available for use as a management tool to research scientists and natural-resource managers. An innovative airborne lidar instrument originally developed at the NASA Wallops Flight Facility, and known as the Experimental Advanced Airborne Research Lidar (EAARL), was used during data acquisition. The EAARL system is a raster-scanning, waveform-resolving, green-wavelength (532-nanometer) lidar designed to map near-shore bathymetry, topography, and vegetation structure simultaneously. The EAARL sensor suite includes the raster-scanning, water-penetrating full-waveform adaptive lidar, a down-looking red-green-blue (RGB) digital camera, a high-resolution multispectral color infrared (CIR) camera, two precision dual-frequency kinematic carrier-phase GPS receivers, and an integrated miniature digital inertial measurement unit, which provide for sub-meter georeferencing of each laser sample. The nominal EAARL platform is a twin-engine Cessna 310 aircraft, but the instrument may be deployed on a range of light aircraft. A single pilot, a lidar operator, and a data analyst constitute the crew for most survey operations. This sensor has the potential to make significant contributions in measuring sub-aerial and submarine coastal topography within cross-environmental surveys. Elevation measurements were collected over the survey area using the EAARL system, and the resulting data were then processed using the Airborne Lidar Processing System (ALPS), a custom-built processing system developed in a NASA-USGS collaboration. ALPS supports the exploration and processing of lidar data in an interactive or batch mode. Modules for presurvey flight-line definition, flight-path plotting, lidar raster and waveform investigation, and digital camera image playback have been developed. Processing algorithms have been developed to extract the range to the first and last significant return within each waveform. ALPS is used routinely to create maps that represent submerged or sub-aerial topography. Specialized filtering algorithms have been implemented to determine the 'bare earth' under vegetation from a point cloud of last return elevations. 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,{"id":9000579,"text":"ds558 - 2010 - EAARL Coastal Topography-Fire Island National Seashore, New York, Post-Nor'Ida, 2009","interactions":[],"lastModifiedDate":"2012-02-10T00:10:06","indexId":"ds558","displayToPublicDate":"2011-02-04T00:00:00","publicationYear":"2010","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":"558","title":"EAARL Coastal Topography-Fire Island National Seashore, New York, Post-Nor'Ida, 2009","docAbstract":"These remotely sensed, geographically referenced elevation measurements of lidar-derived bare-earth (BE) and first-surface (FS) topography datasets were produced collaboratively by the U.S. Geological Survey (USGS), St. Petersburg Coastal and Marine Science Center, St. Petersburg, FL, and the National Park Service (NPS), Northeast Coastal and Barrier Network, Kingston, RI. This project provides highly detailed and accurate datasets of a portion of the Fire Island National Seashore in New York, acquired post-Nor'Ida (November 2009 nor'easter) on December 4, 2009. The datasets are made available for use as a management tool to research scientists and natural-resource managers. An innovative airborne lidar instrument originally developed at the NASA Wallops Flight Facility, and known as the Experimental Advanced Airborne Research Lidar (EAARL), was used during data acquisition. The EAARL system is a raster-scanning, waveform-resolving, green-wavelength (532-nanometer) lidar designed to map near-shore bathymetry, topography, and vegetation structure simultaneously. 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Elevation measurements were collected over the survey area using the EAARL system, and the resulting data were then processed using the Airborne Lidar Processing System (ALPS), a custom-built processing system developed in a NASA-USGS collaboration. ALPS supports the exploration and processing of lidar data in an interactive or batch mode. Modules for presurvey flight-line definition, flight-path plotting, lidar raster and waveform investigation, and digital camera image playback have been developed. Processing algorithms have been developed to extract the range to the first and last significant return within each waveform. ALPS is used routinely to create maps that represent submerged or sub-aerial topography. Specialized filtering algorithms have been implemented to determine the 'bare earth' under vegetation from a point cloud of last return elevations. 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,{"id":9000575,"text":"ds562 - 2010 - EAARL Coastal Topography-Maryland and Delaware, Post-Nor'Ida, 2009","interactions":[],"lastModifiedDate":"2012-02-10T00:10:08","indexId":"ds562","displayToPublicDate":"2011-02-04T00:00:00","publicationYear":"2010","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":"562","title":"EAARL Coastal Topography-Maryland and Delaware, Post-Nor'Ida, 2009","docAbstract":"These remotely sensed, geographically referenced elevation measurements of lidar-derived bare-earth (BE) and first-surface (FS) topography datasets were produced by the U.S. Geological Survey (USGS), St. Petersburg Coastal and Marine Science Center, St. Petersburg, FL. This project provides highly detailed and accurate datasets of a portion of the eastern Maryland and Delaware coastline beachface, acquired post-Nor'Ida (November 2009 nor'easter) on November 28 and 30, 2009. The datasets are made available for use as a management tool to research scientists and natural-resource managers. An innovative airborne lidar instrument originally developed at the NASA Wallops Flight Facility, and known as the Experimental Advanced Airborne Research Lidar (EAARL), was used during data acquisition. The EAARL system is a raster-scanning, waveform-resolving, green-wavelength (532-nanometer) lidar designed to map near-shore bathymetry, topography, and vegetation structure simultaneously. The EAARL sensor suite includes the raster-scanning, water-penetrating full-waveform adaptive lidar, a down-looking red-green-blue (RGB) digital camera, a high-resolution multispectral color-infrared (CIR) camera, two precision dual-frequency kinematic carrier-phase GPS receivers, and an integrated miniature digital inertial measurement unit, which provide for sub-meter georeferencing of each laser sample. The nominal EAARL platform is a twin-engine aircraft, but the instrument was deployed on a Pilatus PC-6. A single pilot, a lidar operator, and a data analyst constitute the crew for most survey operations. This sensor has the potential to make significant contributions in measuring sub-aerial and submarine coastal topography within cross-environmental surveys. Elevation measurements were collected over the survey area using the EAARL system, and the resulting data were then processed using the Airborne Lidar Processing System (ALPS), a custom-built processing system developed in a NASA-USGS collaboration. ALPS supports the exploration and processing of lidar data in an interactive or batch mode. Modules for presurvey flight-line definition, flight-path plotting, lidar raster and waveform investigation, and digital camera image playback have been developed. Processing algorithms have been developed to extract the range to the first and last significant return within each waveform. ALPS is used routinely to create maps that represent submerged or sub-aerial topography. Specialized filtering algorithms have been implemented to determine the 'bare earth' under vegetation from a point cloud of last return elevations. 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,{"id":9000574,"text":"ds561 - 2010 - EAARL Coastal Topography-Eastern Florida, Post-Hurricane Jeanne, 2004: First Surface","interactions":[],"lastModifiedDate":"2012-02-10T00:10:07","indexId":"ds561","displayToPublicDate":"2011-02-04T00:00:00","publicationYear":"2010","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":"561","title":"EAARL Coastal Topography-Eastern Florida, Post-Hurricane Jeanne, 2004: First Surface","docAbstract":"These remotely sensed, geographically referenced elevation measurements of lidar-derived first-surface (FS) topography datasets were produced collaboratively by the U.S. Geological Survey (USGS), St. Petersburg Coastal and Marine Science Center, St. Petersburg, FL, and the National Aeronautics and Space Administration (NASA), Wallops Flight Facility, VA. This project provides highly detailed and accurate datasets of a portion of the eastern Florida coastline beachface, acquired post-Hurricane Jeanne (September 2004 hurricane) on October 1, 2004. The datasets are made available for use as a management tool to research scientists and natural-resource managers. An innovative airborne lidar instrument originally developed at the NASA Wallops Flight Facility, and known as the Experimental Advanced Airborne Research Lidar (EAARL), was used during data acquisition. The EAARL system is a raster-scanning, waveform-resolving, green-wavelength (532-nanometer) lidar designed to map near-shore bathymetry, topography, and vegetation structure simultaneously. The EAARL sensor suite includes the raster-scanning, water-penetrating full-waveform adaptive lidar, a down-looking red-green-blue (RGB) digital camera, a high-resolution multispectral color-infrared (CIR) camera, two precision dual-frequency kinematic carrier-phase GPS receivers, and an integrated miniature digital inertial measurement unit, which provide for sub-meter georeferencing of each laser sample. The nominal EAARL platform is a twin-engine Cessna 310 aircraft, but the instrument may be deployed on a range of light aircraft. A single pilot, a lidar operator, and a data analyst constitute the crew for most survey operations. This sensor has the potential to make significant contributions in measuring sub-aerial and submarine coastal topography within cross-environmental surveys. Elevation measurements were collected over the survey area using the EAARL system, and the resulting data were then processed using the Airborne Lidar Processing System (ALPS), a custom-built processing system developed in a NASA-USGS collaboration. ALPS supports the exploration and processing of lidar data in an interactive or batch mode. Modules for presurvey flight-line definition, flight-path plotting, lidar raster and waveform investigation, and digital camera image playback have been developed. Processing algorithms have been developed to extract the range to the first and last significant return within each waveform. ALPS is used routinely to create maps that represent submerged or sub-aerial topography. Specialized filtering algorithms have been implemented to determine the 'bare earth' under vegetation from a point cloud of last return elevations. For more information about similar projects, please visit the Decision Support for Coastal Science and Management website.","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/ds561","usgsCitation":"Fredericks, X., Nayegandhi, A., Bonisteel-Cormier, J., Wright, C.W., Sallenger, A., Brock, J.C., Klipp, E., and Nagle, D., 2010, EAARL Coastal Topography-Eastern Florida, Post-Hurricane Jeanne, 2004: First Surface: U.S. Geological Survey Data Series 561, 1 DVD; HTML Page; Home; Purpose; Metadata; Colaborators; Acronyms, https://doi.org/10.3133/ds561.","productDescription":"1 DVD; HTML Page; Home; Purpose; Metadata; Colaborators; Acronyms","additionalOnlineFiles":"Y","costCenters":[{"id":574,"text":"St. Petersburg Coastal and Marine Science Center","active":true,"usgs":true}],"links":[{"id":126222,"rank":0,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/ds_561.jpg"},{"id":19196,"rank":200,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/ds/561/","linkFileType":{"id":5,"text":"html"}}],"country":"United States","geographicExtents":"{ \"type\": \"FeatureCollection\", \"features\": [ { \"type\": \"Feature\", \"properties\": {}, \"geometry\": { \"type\": \"Polygon\", \"coordinates\": [ [ [ -80.66666666666667,26.666666666666668 ], [ -80.66666666666667,28.333333333333332 ], [ -80,28.333333333333332 ], [ -80,26.666666666666668 ], [ -80.66666666666667,26.666666666666668 ] ] ] } } ] }","noUsgsAuthors":false,"publicationStatus":"PW","scienceBaseUri":"4f4e4a57e4b07f02db62e4fd","contributors":{"authors":[{"text":"Fredericks, Xan","contributorId":35704,"corporation":false,"usgs":true,"family":"Fredericks","given":"Xan","affiliations":[],"preferred":false,"id":344252,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Nayegandhi, Amar","contributorId":37292,"corporation":false,"usgs":true,"family":"Nayegandhi","given":"Amar","affiliations":[],"preferred":false,"id":344254,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Bonisteel-Cormier, J.M.","contributorId":8060,"corporation":false,"usgs":true,"family":"Bonisteel-Cormier","given":"J.M.","affiliations":[],"preferred":false,"id":344251,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Wright, C. 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C.","contributorId":36095,"corporation":false,"usgs":true,"family":"Brock","given":"J.","email":"","middleInitial":"C.","affiliations":[],"preferred":false,"id":344253,"contributorType":{"id":1,"text":"Authors"},"rank":6},{"text":"Klipp, E.S.","contributorId":100340,"corporation":false,"usgs":true,"family":"Klipp","given":"E.S.","affiliations":[],"preferred":false,"id":344258,"contributorType":{"id":1,"text":"Authors"},"rank":7},{"text":"Nagle, D.B.","contributorId":40568,"corporation":false,"usgs":true,"family":"Nagle","given":"D.B.","email":"","affiliations":[],"preferred":false,"id":344255,"contributorType":{"id":1,"text":"Authors"},"rank":8}]}}
,{"id":9000577,"text":"ds560 - 2010 - EAARL Coastal Topography-Eastern Louisiana Barrier Islands, Post-Hurricane Gustav, 2008: First Surface","interactions":[],"lastModifiedDate":"2012-02-10T00:10:05","indexId":"ds560","displayToPublicDate":"2011-02-04T00:00:00","publicationYear":"2010","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":"560","title":"EAARL Coastal Topography-Eastern Louisiana Barrier Islands, Post-Hurricane Gustav, 2008: First Surface","docAbstract":"These remotely sensed, geographically referenced elevation measurements of lidar-derived first-surface (FS) topography datasets were produced collaboratively by the U.S. Geological Survey (USGS), St. Petersburg Coastal and Marine Science Center, St. Petersburg, FL, and the National Aeronautics and Space Administration (NASA), Wallops Flight Facility, VA. This project provides highly detailed and accurate datasets of a portion of the eastern Louisiana barrier islands, acquired post-Hurricane Gustav (September 2008 hurricane) on September 6 and 7, 2008. The datasets are made available for use as a management tool to research scientists and natural-resource managers. An innovative airborne lidar instrument originally developed at the NASA Wallops Flight Facility, and known as the Experimental Advanced Airborne Research Lidar (EAARL), was used during data acquisition. The EAARL system is a raster-scanning, waveform-resolving, green-wavelength (532-nanometer) lidar designed to map near-shore bathymetry, topography, and vegetation structure simultaneously. The EAARL sensor suite includes the raster-scanning, water-penetrating full-waveform adaptive lidar, a down-looking red-green-blue (RGB) digital camera, a high-resolution multispectral color infrared (CIR) camera, two precision dual-frequency kinematic carrier-phase GPS receivers, and an integrated miniature digital inertial measurement unit, which provide for sub-meter georeferencing of each laser sample. The nominal EAARL platform is a twin-engine Cessna 310 aircraft, but the instrument may be deployed on a range of light aircraft. A single pilot, a lidar operator, and a data analyst constitute the crew for most survey operations. This sensor has the potential to make significant contributions in measuring sub-aerial and submarine coastal topography within cross-environmental surveys. Elevation measurements were collected over the survey area using the EAARL system, and the resulting data were then processed using the Airborne Lidar Processing System (ALPS), a custom-built processing system developed in a NASA-USGS collaboration. ALPS supports the exploration and processing of lidar data in an interactive or batch mode. Modules for presurvey flight-line definition, flight-path plotting, lidar raster and waveform investigation, and digital camera image playback have been developed. Processing algorithms have been developed to extract the range to the first and last significant return within each waveform. ALPS is used routinely to create maps that represent submerged or sub-aerial topography. Specialized filtering algorithms have been implemented to determine the 'bare earth' under vegetation from a point cloud of last return elevations. For more information about similar projects, please visit the Decision Support for Coastal Science and Management website.","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/ds560","usgsCitation":"Bonisteel-Cormier, J., Nayegandhi, A., Wright, C.W., Sallenger, A., Brock, J.C., Nagle, D., Vivekanandan, S., and Fredericks, X., 2010, EAARL Coastal Topography-Eastern Louisiana Barrier Islands, Post-Hurricane Gustav, 2008: First Surface: U.S. Geological Survey Data Series 560, 1 DVD; HTML Page; Home; Purpose; Metadata; Collaborators; Acronyms, https://doi.org/10.3133/ds560.","productDescription":"1 DVD; HTML Page; Home; Purpose; Metadata; Collaborators; Acronyms","additionalOnlineFiles":"Y","costCenters":[{"id":574,"text":"St. Petersburg Coastal and Marine Science Center","active":true,"usgs":true}],"links":[{"id":126218,"rank":0,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/ds_560.jpg"},{"id":19199,"rank":200,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/ds/560/","linkFileType":{"id":5,"text":"html"}}],"country":"United States","geographicExtents":"{ \"type\": \"FeatureCollection\", \"features\": [ { \"type\": \"Feature\", \"properties\": {}, \"geometry\": { \"type\": \"Polygon\", \"coordinates\": [ [ [ -90.93333333333334,29.166666666666668 ], [ -90.93333333333334,29.333333333333332 ], [ -89.43333333333334,29.333333333333332 ], [ -89.43333333333334,29.166666666666668 ], [ -90.93333333333334,29.166666666666668 ] ] ] } } ] }","noUsgsAuthors":false,"publicationStatus":"PW","scienceBaseUri":"4f4e4a58e4b07f02db62efbf","contributors":{"authors":[{"text":"Bonisteel-Cormier, J.M.","contributorId":8060,"corporation":false,"usgs":true,"family":"Bonisteel-Cormier","given":"J.M.","affiliations":[],"preferred":false,"id":344276,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Nayegandhi, Amar","contributorId":37292,"corporation":false,"usgs":true,"family":"Nayegandhi","given":"Amar","affiliations":[],"preferred":false,"id":344279,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Wright, C. W. wwright@usgs.gov","contributorId":49758,"corporation":false,"usgs":true,"family":"Wright","given":"C.","email":"wwright@usgs.gov","middleInitial":"W.","affiliations":[],"preferred":false,"id":344281,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Sallenger, A. H.","contributorId":78290,"corporation":false,"usgs":true,"family":"Sallenger","given":"A. H.","affiliations":[],"preferred":false,"id":344282,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Brock, J. C.","contributorId":36095,"corporation":false,"usgs":true,"family":"Brock","given":"J.","email":"","middleInitial":"C.","affiliations":[],"preferred":false,"id":344278,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"Nagle, D.B.","contributorId":40568,"corporation":false,"usgs":true,"family":"Nagle","given":"D.B.","email":"","affiliations":[],"preferred":false,"id":344280,"contributorType":{"id":1,"text":"Authors"},"rank":6},{"text":"Vivekanandan, Saisudha","contributorId":84325,"corporation":false,"usgs":true,"family":"Vivekanandan","given":"Saisudha","email":"","affiliations":[],"preferred":false,"id":344283,"contributorType":{"id":1,"text":"Authors"},"rank":7},{"text":"Fredericks, Xan","contributorId":35704,"corporation":false,"usgs":true,"family":"Fredericks","given":"Xan","affiliations":[],"preferred":false,"id":344277,"contributorType":{"id":1,"text":"Authors"},"rank":8}]}}
,{"id":9000576,"text":"ds559 - 2010 - EAARL Coastal Topography and Imagery-Assateague Island National Seashore, Maryland and Virginia, Post-Nor'Ida, 2009","interactions":[],"lastModifiedDate":"2012-02-10T00:10:08","indexId":"ds559","displayToPublicDate":"2011-02-04T00:00:00","publicationYear":"2010","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":"559","title":"EAARL Coastal Topography and Imagery-Assateague Island National Seashore, Maryland and Virginia, Post-Nor'Ida, 2009","docAbstract":"These remotely sensed, geographically referenced color-infrared (CIR) imagery and elevation measurements of lidar-derived bare-earth (BE) and first-surface (FS) topography datasets were produced collaboratively by the U.S. Geological Survey (USGS), St. Petersburg Coastal and Marine Science Center, St. Petersburg, FL, and the National Park Service (NPS), Northeast Coastal and Barrier Network, Kingston, RI. This project provides highly detailed and accurate datasets of a portion of the Assateague Island National Seashore in Maryland and Virginia, acquired post-Nor'Ida (November 2009 nor'easter) on November 28 and 30, 2009. The datasets are made available for use as a management tool to research scientists and natural-resource managers. An innovative airborne lidar instrument originally developed at the NASA Wallops Flight Facility, and known as the Experimental Advanced Airborne Research Lidar(EAARL), was used during data acquisition. The EAARL system is a raster-scanning, waveform-resolving, green-wavelength (532-nanometer) lidar designed to map near-shore bathymetry, topography, and vegetation structure simultaneously. The EAARL sensor suite includes the raster-scanning, water-penetrating full-waveform adaptive lidar, a down-looking red-green-blue (RGB) digital camera, a high-resolution multispectral color-infrared (CIR) camera, two precision dual-frequency kinematic carrier-phase GPS receivers, and an integrated miniature digital inertial measurement unit, which provide for sub-meter georeferencing of each laser sample. The nominal EAARL platform is a twin-engine aircraft, but the instrument was deployed on a Pilatus PC-6. A single pilot, a lidar operator, and a data analyst constitute the crew for most survey operations. This sensor has the potential to make significant contributions in measuring sub-aerial and submarine coastal topography within cross-environmental surveys. Elevation measurements were collected over the survey area using the EAARL system, and the resulting data were then processed using the Airborne Lidar Processing System (ALPS), a custom-built processing system developed in a NASA-USGS collaboration. ALPS supports the exploration and processing of lidar data in an interactive or batch mode. Modules for presurvey flight-line definition, flight-path plotting, lidar raster and waveform investigation, and digital camera image playback have been developed. Processing algorithms have been developed to extract the range to the first and last significant return within each waveform. ALPS is used routinely to create maps that represent submerged or sub-aerial topography. Specialized filtering algorithms have been implemented to determine the 'bare earth' under vegetation from a point cloud of last return elevations. For more information about similar projects, please visit the Decision Support for Coastal Science and Management website.","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/ds559","usgsCitation":"Bonisteel-Cormier, J., Nayegandhi, A., Brock, J.C., Wright, C.W., Nagle, D., Klipp, E., Vivekanandan, S., Fredericks, X., and Stevens, S., 2010, EAARL Coastal Topography and Imagery-Assateague Island National Seashore, Maryland and Virginia, Post-Nor'Ida, 2009: U.S. Geological Survey Data Series 559, 1 DVD; HTML Page; Home; Purpose; Metadata; Collaborators; Acronyms, https://doi.org/10.3133/ds559.","productDescription":"1 DVD; HTML Page; Home; Purpose; Metadata; Collaborators; Acronyms","additionalOnlineFiles":"Y","costCenters":[{"id":574,"text":"St. Petersburg Coastal and Marine Science Center","active":true,"usgs":true}],"links":[{"id":126228,"rank":0,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/ds_559.jpg"},{"id":19198,"rank":200,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/ds/559/","linkFileType":{"id":5,"text":"html"}}],"country":"United States","geographicExtents":"{ \"type\": \"FeatureCollection\", \"features\": [ { \"type\": \"Feature\", \"properties\": {}, \"geometry\": { \"type\": \"Polygon\", \"coordinates\": [ [ [ -75.41666666666667,37.86666666666667 ], [ -75.41666666666667,38.333333333333336 ], [ -75.05,38.333333333333336 ], [ -75.05,37.86666666666667 ], [ -75.41666666666667,37.86666666666667 ] ] ] } } ] }","noUsgsAuthors":false,"publicationStatus":"PW","scienceBaseUri":"4f4e4a59e4b07f02db62f687","contributors":{"authors":[{"text":"Bonisteel-Cormier, J.M.","contributorId":8060,"corporation":false,"usgs":true,"family":"Bonisteel-Cormier","given":"J.M.","affiliations":[],"preferred":false,"id":344267,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Nayegandhi, Amar","contributorId":37292,"corporation":false,"usgs":true,"family":"Nayegandhi","given":"Amar","affiliations":[],"preferred":false,"id":344270,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Brock, J. C.","contributorId":36095,"corporation":false,"usgs":true,"family":"Brock","given":"J.","email":"","middleInitial":"C.","affiliations":[],"preferred":false,"id":344269,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Wright, C. W. wwright@usgs.gov","contributorId":49758,"corporation":false,"usgs":true,"family":"Wright","given":"C.","email":"wwright@usgs.gov","middleInitial":"W.","affiliations":[],"preferred":false,"id":344272,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Nagle, D.B.","contributorId":40568,"corporation":false,"usgs":true,"family":"Nagle","given":"D.B.","email":"","affiliations":[],"preferred":false,"id":344271,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"Klipp, E.S.","contributorId":100340,"corporation":false,"usgs":true,"family":"Klipp","given":"E.S.","affiliations":[],"preferred":false,"id":344274,"contributorType":{"id":1,"text":"Authors"},"rank":6},{"text":"Vivekanandan, Saisudha","contributorId":84325,"corporation":false,"usgs":true,"family":"Vivekanandan","given":"Saisudha","email":"","affiliations":[],"preferred":false,"id":344273,"contributorType":{"id":1,"text":"Authors"},"rank":7},{"text":"Fredericks, Xan","contributorId":35704,"corporation":false,"usgs":true,"family":"Fredericks","given":"Xan","affiliations":[],"preferred":false,"id":344268,"contributorType":{"id":1,"text":"Authors"},"rank":8},{"text":"Stevens, Sara","contributorId":104015,"corporation":false,"usgs":true,"family":"Stevens","given":"Sara","affiliations":[],"preferred":false,"id":344275,"contributorType":{"id":1,"text":"Authors"},"rank":9}]}}
,{"id":9000571,"text":"ofr20101264 - 2010 - A Servicewide Benthic Mapping Program for National Parks","interactions":[],"lastModifiedDate":"2023-05-19T13:39:53.612492","indexId":"ofr20101264","displayToPublicDate":"2011-01-26T00:00:00","publicationYear":"2010","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":"2010-1264","title":"A Servicewide Benthic Mapping Program for National Parks","docAbstract":"In 2007, the National Park Service (NPS) Inventory and Monitoring Program directed the initiation of a benthic habitat mapping program in ocean and coastal parks in alignment with the NPS Ocean Park Stewardship 2007-2008 Action Plan. With 74 ocean and Great Lakes parks stretching over more than 5,000 miles of coastline across 26 States and territories, this Servicewide Benthic Mapping Program (SBMP) is essential. This program will deliver benthic habitat maps and their associated inventory reports to NPS managers in a consistent, servicewide format to support informed management and protection of 3 million acres of submerged National Park System natural and cultural resources. The NPS and the U.S. Geological Survey (USGS) convened a workshop June 3-5, 2008, in Lakewood, Colo., to discuss the goals and develop the design of the NPS SBMP with an assembly of experts (Moses and others, 2010) who identified park needs and suggested best practices for inventory and mapping of bathymetry, benthic cover, geology, geomorphology, and some water-column properties. The recommended SBMP protocols include servicewide standards (such as gap analysis, minimum accuracy, final products) as well as standards that can be adapted to fit network and park unit needs (for example, minimum mapping unit, mapping priorities). SBMP Mapping Process. The SBMP calls for a multi-step mapping process for each park, beginning with a gap assessment and data mining to determine data resources and needs. An interagency announcement of intent to acquire new data will provide opportunities to leverage partnerships. Prior to new data acquisition, all involved parties should be included in a scoping meeting held at network scale. Data collection will be followed by processing and interpretation, and finally expert review and publication. After publication, all digital materials will be archived in a common format. SBMP Classification Scheme. The SBMP will map using the Coastal and Marine Ecological Classification Standard (CMECS) that is being modified to include all NPS needs, such as lacustrine ecosystems and submerged cultural resources. CMECS Version III (Madden and others, 2010) includes components for water column, biotic cover, surface geology, sub-benthic, and geoform. SBMP Data Archiving. The SBMP calls for the storage of all raw data and final products in common-use data formats. The concept of 'collect once, use often' is essential to efficient use of mapping resources. Data should also be shared with other agencies and the public through various digital clearing houses, such as Geospatial One-Stop (http://gos2.geodata.gov/wps/portal/gos). To be most useful for managing submerged resources, the SBMP advocates the inventory and mapping of the five components of marine ecosystems: surface geology, biotic cover, geoform, sub-benthic, and water column. A complete benthic inventory of a park would include maps of bathymetry and the five components of CMECS. The completion of mapping for any set of components, such as bathymetry and surface geology, or a particular theme (for example, submerged aquatic vegetation) should also include a printed report.","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/ofr20101264","collaboration":"Prepared in cooperation with the National Park Service","usgsCitation":"Moses, C.S., Nayegandhi, A., Beavers, R., and Brock, J., 2010, A Servicewide Benthic Mapping Program for National Parks: U.S. Geological Survey Open-File Report 2010-1264, vi, 88 p., https://doi.org/10.3133/ofr20101264.","productDescription":"vi, 88 p.","additionalOnlineFiles":"N","costCenters":[{"id":574,"text":"St. Petersburg Coastal and Marine Science 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,{"id":9000570,"text":"sim3141 - 2010 - Land area change and fractional water maps in the Chenier Plain, Louisiana, following Hurricane Rita (2005)","interactions":[],"lastModifiedDate":"2019-12-30T16:11:05","indexId":"sim3141","displayToPublicDate":"2011-01-25T00:00:00","publicationYear":"2010","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":"3141","title":"Land area change and fractional water maps in the Chenier Plain, Louisiana, following Hurricane Rita (2005)","docAbstract":"In this study, we estimated the changes in land and water coverage of a 1,961-square-kilometer (km2) area in Louisiana's Chenier Plain. The study area is roughly centered on the Sabine National Wildlife Refuge, which was impacted by Hurricane Rita on September 24, 2005. The objective of this study is twofold: (1) to provide pre- and post-Hurricane Rita moderate-resolution (30-meter (m)) fractional water maps based upon multiple source images, and (2) to quantify land and water coverage changes due to Hurricane Rita.","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/sim3141","usgsCitation":"Palaseanu-Lovejoy, M., Kranenburg, C., and Brock, J., 2010, Land area change and fractional water maps in the Chenier Plain, Louisiana, following Hurricane Rita (2005): U.S. Geological Survey Scientific Investigations Map 3141, i, 6 p., https://doi.org/10.3133/sim3141.","productDescription":"i, 6 p.","additionalOnlineFiles":"Y","costCenters":[{"id":574,"text":"St. Petersburg Coastal and Marine Science Center","active":true,"usgs":true}],"links":[{"id":116279,"rank":0,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/sim_3141.bmp"},{"id":19193,"rank":200,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/sim/3141/","linkFileType":{"id":5,"text":"html"}}],"country":"United States","state":"Louisiana","geographicExtents":"{\n  \"type\": \"FeatureCollection\",\n  \"features\": [\n    {\n      \"type\": \"Feature\",\n      \"properties\": {},\n      \"geometry\": {\n        \"type\": \"Polygon\",\n        \"coordinates\": [\n          [\n            [\n              -93.8232421875,\n              28.8831596093235\n            ],\n            [\n              -88.9453125,\n              28.8831596093235\n            ],\n            [\n              -88.9453125,\n              30.883369321692268\n            ],\n            [\n              -93.8232421875,\n              30.883369321692268\n            ],\n            [\n              -93.8232421875,\n              28.8831596093235\n            ]\n          ]\n        ]\n      }\n    }\n  ]\n}","noUsgsAuthors":false,"publicationStatus":"PW","scienceBaseUri":"4f4e4b32e4b07f02db6b422e","contributors":{"authors":[{"text":"Palaseanu-Lovejoy, Monica 0000-0002-3786-5118 mpal@usgs.gov","orcid":"https://orcid.org/0000-0002-3786-5118","contributorId":3639,"corporation":false,"usgs":true,"family":"Palaseanu-Lovejoy","given":"Monica","email":"mpal@usgs.gov","affiliations":[{"id":242,"text":"Eastern Geographic Science Center","active":true,"usgs":true},{"id":5061,"text":"National Cooperative Geologic Mapping and Landslide Hazards","active":true,"usgs":true},{"id":312,"text":"Geology, Minerals, Energy, and Geophysics Science Center","active":true,"usgs":true}],"preferred":true,"id":344242,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Kranenburg, Christine J. ckranenburg@usgs.gov","contributorId":3924,"corporation":false,"usgs":true,"family":"Kranenburg","given":"Christine J.","email":"ckranenburg@usgs.gov","affiliations":[{"id":574,"text":"St. Petersburg Coastal and Marine Science Center","active":true,"usgs":true}],"preferred":false,"id":344243,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Brock, John 0000-0002-5289-9332 jbrock@usgs.gov","orcid":"https://orcid.org/0000-0002-5289-9332","contributorId":2261,"corporation":false,"usgs":true,"family":"Brock","given":"John","email":"jbrock@usgs.gov","affiliations":[{"id":5061,"text":"National Cooperative Geologic Mapping and Landslide Hazards","active":true,"usgs":true}],"preferred":true,"id":344241,"contributorType":{"id":1,"text":"Authors"},"rank":3}]}}
,{"id":98996,"text":"ofr20101294 - 2010 - Assessment of coal geology, resources, and reserves in the northern Wyoming Powder River Basin","interactions":[],"lastModifiedDate":"2012-02-10T00:11:57","indexId":"ofr20101294","displayToPublicDate":"2011-01-13T00:00:00","publicationYear":"2010","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":"2010-1294","title":"Assessment of coal geology, resources, and reserves in the northern Wyoming Powder River Basin","docAbstract":"The abundance of new borehole data from recent coal bed natural gas development in the Powder River Basin was utilized by the U.S. Geological Survey for the most comprehensive evaluation to date of coal resources and reserves in the Northern Wyoming Powder River Basin assessment area. It is the second area within the Powder River Basin to be assessed as part of a regional coal assessment program; the first was an evaluation of coal resources and reserves in the Gillette coal field, adjacent to and south of the Northern Wyoming Powder River Basin assessment area. There are no active coal mines in the Northern Wyoming Powder River Basin assessment area at present. However, more than 100 million short tons of coal were produced from the Sheridan coal field between the years 1887 and 2000, which represents most of the coal production within the northwestern part of the Northern Wyoming Powder River Basin assessment area.\r\n\r\nA total of 33 coal beds were identified during the present study, 24 of which were modeled and evaluated to determine in-place coal resources. Given current technology, economic factors, and restrictions to mining, seven of the beds were evaluated for potential reserves. The restrictions included railroads, a Federal interstate highway, urban areas, and alluvial valley floors. Other restrictions, such as depth, thickness of coal beds, mined-out areas, and areas of burned coal, were also considered.\r\n\r\nThe total original coal resource in the Northern Wyoming Powder River Basin assessment area for all 24 coal beds assessed, with no restrictions applied, was calculated to be 285 billion short tons. Available coal resources, which are part of the original coal resource that is accessible for potential mine development after subtracting all restrictions, are about 263 billion short tons (92.3 percent of the original coal resource). Recoverable coal, which is that portion of available coal remaining after subtracting mining and processing losses, was determined for seven coal beds with a stripping ratio of 10:1 or less. After mining and processing losses were subtracted, a total of 50 billion short tons of recoverable coal was calculated.\r\n\r\nCoal reserves are the portion of the recoverable coal that can be mined, processed, and marketed at a profit at the time of the economic evaluation. With a discounted cash flow at 8 percent rate of return, the coal reserves estimate for the Northern Wyoming Powder River Basin assessment area is 1.5 billion short tons of coal (1 percent of the original resource total) for the seven coal beds evaluated.\r\n","language":"ENGLISH","publisher":"U.S. Geological Survey","doi":"10.3133/ofr20101294","usgsCitation":"Scott, D.C., Haacke, J., Osmonson, L.M., Luppens, J.A., Pierce, P.E., and Rohrbacher, T.J., 2010, Assessment of coal geology, resources, and reserves in the northern Wyoming Powder River Basin: U.S. Geological Survey Open-File Report 2010-1294, ix, 136 p. , https://doi.org/10.3133/ofr20101294.","productDescription":"ix, 136 p. ","onlineOnly":"N","additionalOnlineFiles":"N","costCenters":[{"id":171,"text":"Central Mineral and Environmental Resources Science Center","active":true,"usgs":true}],"links":[{"id":126135,"rank":0,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/ofr_2010_1294.bmp"},{"id":14433,"rank":100,"type":{"id":15,"text":"Index Page"},"url":"https://pubs.usgs.gov/of/2010/1294/","linkFileType":{"id":5,"text":"html"}}],"geographicExtents":"{ \"type\": \"FeatureCollection\", \"features\": [ { \"type\": \"Feature\", \"properties\": {}, \"geometry\": { \"type\": \"Polygon\", \"coordinates\": [ [ [ -108,42.5 ], [ -108,46.5 ], [ -104,46.5 ], [ -104,42.5 ], [ -108,42.5 ] ] ] } } ] }","noUsgsAuthors":false,"publicationStatus":"PW","scienceBaseUri":"4f4e4822e4b07f02db4e1fe1","contributors":{"authors":[{"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":307169,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Haacke, Jon E.","contributorId":86054,"corporation":false,"usgs":true,"family":"Haacke","given":"Jon E.","affiliations":[],"preferred":false,"id":307173,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Osmonson, Lee M.","contributorId":33322,"corporation":false,"usgs":false,"family":"Osmonson","given":"Lee","email":"","middleInitial":"M.","affiliations":[{"id":6676,"text":"USGS (retired)","active":true,"usgs":false}],"preferred":false,"id":307172,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Luppens, James A. 0000-0001-7607-8750 jluppens@usgs.gov","orcid":"https://orcid.org/0000-0001-7607-8750","contributorId":550,"corporation":false,"usgs":true,"family":"Luppens","given":"James","email":"jluppens@usgs.gov","middleInitial":"A.","affiliations":[{"id":164,"text":"Central Energy Resources Science Center","active":true,"usgs":true}],"preferred":true,"id":307168,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Pierce, Paul E. 0000-0001-9675-7320 ppierce@usgs.gov","orcid":"https://orcid.org/0000-0001-9675-7320","contributorId":3732,"corporation":false,"usgs":true,"family":"Pierce","given":"Paul","email":"ppierce@usgs.gov","middleInitial":"E.","affiliations":[],"preferred":true,"id":307170,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"Rohrbacher, Timothy J.","contributorId":20355,"corporation":false,"usgs":true,"family":"Rohrbacher","given":"Timothy","email":"","middleInitial":"J.","affiliations":[],"preferred":false,"id":307171,"contributorType":{"id":1,"text":"Authors"},"rank":6}]}}
,{"id":98986,"text":"fs20103110 - 2010 - Streamflow characteristics and benthic invertebrate assemblages in streams across the western United States","interactions":[],"lastModifiedDate":"2017-02-03T14:49:52","indexId":"fs20103110","displayToPublicDate":"2011-01-07T00:00:00","publicationYear":"2010","noYear":false,"publicationType":{"id":18,"text":"Report"},"publicationSubtype":{"id":5,"text":"USGS Numbered Series"},"seriesTitle":{"id":313,"text":"Fact Sheet","code":"FS","onlineIssn":"2327-6932","printIssn":"2327-6916","active":true,"publicationSubtype":{"id":5}},"seriesNumber":"2010-3110","title":"Streamflow characteristics and benthic invertebrate assemblages in streams across the western United States","docAbstract":"<p>Hydrographic characteristics of streamflow, such as high-flow pulses, base flow (background discharge between floods), extreme low flows, and floods, significantly influence aquatic organisms. Streamflow can be described in terms of magnitude, timing, duration, frequency, and variation (hydrologic regime). These characteristics have broad effects on ecosystem productivity, habitat structure, and ultimately on resident fish, invertebrate, and algae communities. Increasing human use of limited water resources has modified hydrologic regimes worldwide. Identifying the most ecologically significant hydrographic characteristics would facilitate the development of water-management strategies.</p><p>Benthic invertebrates include insects, mollusks (snails and clams), worms, and crustaceans (shrimp) that live on the streambed. Invertebrates play an important role in the food web, consuming other invertebrates and algae and being consumed by fish and birds. Hydrologic alteration associated with land and water use can change the natural hydrologic regime and may affect benthic invertebrate assemblage composition and structure through changes in density of invertebrates or taxa richness (number of different species).</p><p>This study examined associations between the hydrologic regime and characteristics of benthic invertebrate assemblages across the western United States and developed tools to identify streamflow characteristics that are likely to affect benthic invertebrate assemblages.</p>","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/fs20103110","usgsCitation":"Brasher, A., Konrad, C.P., May, J., Edmiston, C.S., and Close, R.N., 2010, Streamflow characteristics and benthic invertebrate assemblages in streams across the western United States: U.S. Geological Survey Fact Sheet 2010-3110, 4 p., https://doi.org/10.3133/fs20103110.","productDescription":"4 p.","numberOfPages":"4","onlineOnly":"N","additionalOnlineFiles":"N","costCenters":[{"id":610,"text":"Utah Water Science Center","active":true,"usgs":true},{"id":622,"text":"Washington Water Science Center","active":true,"usgs":true}],"links":[{"id":116278,"rank":0,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/fs_2010_3110.bmp"},{"id":14422,"rank":100,"type":{"id":15,"text":"Index Page"},"url":"https://pubs.usgs.gov/fs/2010/3110/","linkFileType":{"id":5,"text":"html"}},{"id":334726,"rank":3,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/fs/2010/3110/pdf/fs20103110.pdf"}],"country":"United States","publicComments":"National Water-Quality Assessment Program","noUsgsAuthors":false,"publicationStatus":"PW","scienceBaseUri":"4f4e4b15e4b07f02db6a4e59","contributors":{"authors":[{"text":"Brasher, Anne M.D.","contributorId":33686,"corporation":false,"usgs":true,"family":"Brasher","given":"Anne M.D.","affiliations":[],"preferred":false,"id":307153,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Konrad, Chris P.","contributorId":26666,"corporation":false,"usgs":true,"family":"Konrad","given":"Chris","email":"","middleInitial":"P.","affiliations":[],"preferred":false,"id":307151,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"May, Jason T. 0000-0002-5699-2112","orcid":"https://orcid.org/0000-0002-5699-2112","contributorId":14791,"corporation":false,"usgs":true,"family":"May","given":"Jason T.","affiliations":[],"preferred":false,"id":307149,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Edmiston, C. Scott","contributorId":30595,"corporation":false,"usgs":true,"family":"Edmiston","given":"C.","email":"","middleInitial":"Scott","affiliations":[],"preferred":false,"id":307152,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Close, Rebecca N.","contributorId":16803,"corporation":false,"usgs":true,"family":"Close","given":"Rebecca","email":"","middleInitial":"N.","affiliations":[],"preferred":false,"id":307150,"contributorType":{"id":1,"text":"Authors"},"rank":5}]}}
,{"id":9000557,"text":"sir20105108 - 2010 - Assessment of nonpoint source chemical loading potential to watersheds containing uranium waste dumps and human health hazards associated with uranium exploration and mining, Red, White, and Fry Canyons, southeastern Utah, 2007","interactions":[],"lastModifiedDate":"2017-01-25T10:32:40","indexId":"sir20105108","displayToPublicDate":"2011-01-07T00:00:00","publicationYear":"2010","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":"2010-5108","title":"Assessment of nonpoint source chemical loading potential to watersheds containing uranium waste dumps and human health hazards associated with uranium exploration and mining, Red, White, and Fry Canyons, southeastern Utah, 2007","docAbstract":"During May, June, and July 2007, 58 solid-phase samples were collected from abandoned uranium mine waste dumps, background sites, and adjacent streambeds in Red, White, and Fry Canyons in southeastern Utah. The objectives of this sampling program were to (1) assess the nonpoint-source chemical loading potential to ephemeral and perennial drainage basins from uranium waste dumps and (2) assess potential effects on human health due to recreational activities on and around uranium waste dumps on Bureau of Land Management property. Uranium waste-dump samples were collected using solid-phase sampling protocols. After collection, solid-phase samples were homogenized and extracted in the laboratory using a leaching procedure. Filtered (0.45 micron) water samples were obtained from the field leaching procedure and were analyzed for major and trace elements at the Inductively Coupled Plasma-Mass Spectrometry Metals Analysis Laboratory at the University of Utah. A subset of the solid-phase samples also were digested with strong acids and analyzed for major ions and trace elements at the U.S. Geological Survey Geologic Division Laboratory in Denver, Colorado. For the initial ranking of chemical loading potential for uranium waste dumps, results of leachate analyses were compared with existing aquatic-life and drinking-water-quality standards. To assess potential effects on human health, solid-phase digestion values for uranium were compared to soil screening levels (SSL) computed using the computer model RESRAD 6.5 for a probable concentration of radium. One or more chemical constituents exceeded aquatic life and drinking-water-quality standards in approximately 64 percent (29/45) of the leachate samples extracted from uranium waste dumps. Most of the uranium waste dump sites with elevated trace-element concentrations in leachates were located in Red Canyon. Approximately 69 percent (31/45) of the strong acid digestible soil concentration values were greater than a calculated SSL. Uranium waste dump sites with elevated leachate and total digestible concentrations may need to be further investigated to determine the most appropriate remediation method.","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/sir20105108","collaboration":"Prepared in cooperation with the Bureau of Land Management","usgsCitation":"Beisner, K.R., Marston, T.M., Naftz, D.L., Snyder, T., and Freeman, M.L., 2010, Assessment of nonpoint source chemical loading potential to watersheds containing uranium waste dumps and human health hazards associated with uranium exploration and mining, Red, White, and Fry Canyons, southeastern Utah, 2007: U.S. Geological Survey Scientific Investigations Report 2010-5108, Report: vi, 29 p.; Appendix; XLS Download of Appendix A, https://doi.org/10.3133/sir20105108.","productDescription":"Report: vi, 29 p.; Appendix; XLS Download of Appendix A","numberOfPages":"30","onlineOnly":"N","additionalOnlineFiles":"Y","costCenters":[{"id":128,"text":"Arizona Water Science Center","active":true,"usgs":true},{"id":610,"text":"Utah Water Science Center","active":true,"usgs":true},{"id":5050,"text":"WY-MT Water Science Center","active":true,"usgs":true}],"links":[{"id":203747,"rank":0,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/usgs_thumb.jpg"},{"id":19184,"rank":200,"type":{"id":15,"text":"Index Page"},"url":"https://pubs.usgs.gov/sir/2010/5108/","linkFileType":{"id":5,"text":"html"}}],"projection":"Universal Transverse Mercator projection","country":"United States","state":"Utah","geographicExtents":"{ \"type\": \"FeatureCollection\", \"features\": [ { \"type\": \"Feature\", \"properties\": {}, \"geometry\": { \"type\": \"Polygon\", \"coordinates\": [ [ [ -110.45083333333334,37.516666666666666 ], [ -110.45083333333334,37.78333333333333 ], [ -110.05083333333333,37.78333333333333 ], [ -110.05083333333333,37.516666666666666 ], [ -110.45083333333334,37.516666666666666 ] ] ] } } ] }","noUsgsAuthors":false,"publicationStatus":"PW","scienceBaseUri":"4f4e4abae4b07f02db671ee7","contributors":{"authors":[{"text":"Beisner, Kimberly R. 0000-0002-2077-6899 kbeisner@usgs.gov","orcid":"https://orcid.org/0000-0002-2077-6899","contributorId":2733,"corporation":false,"usgs":true,"family":"Beisner","given":"Kimberly","email":"kbeisner@usgs.gov","middleInitial":"R.","affiliations":[{"id":128,"text":"Arizona Water Science Center","active":true,"usgs":true},{"id":472,"text":"New Mexico Water Science Center","active":true,"usgs":true}],"preferred":true,"id":344212,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Marston, Thomas M. 0000-0003-1053-4172 tmarston@usgs.gov","orcid":"https://orcid.org/0000-0003-1053-4172","contributorId":3272,"corporation":false,"usgs":true,"family":"Marston","given":"Thomas","email":"tmarston@usgs.gov","middleInitial":"M.","affiliations":[{"id":610,"text":"Utah Water Science Center","active":true,"usgs":true}],"preferred":true,"id":344213,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Naftz, David L. 0000-0003-1130-6892 dlnaftz@usgs.gov","orcid":"https://orcid.org/0000-0003-1130-6892","contributorId":1041,"corporation":false,"usgs":true,"family":"Naftz","given":"David","email":"dlnaftz@usgs.gov","middleInitial":"L.","affiliations":[{"id":5050,"text":"WY-MT Water Science Center","active":true,"usgs":true},{"id":610,"text":"Utah Water Science Center","active":true,"usgs":true}],"preferred":true,"id":344210,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Snyder, Terry","contributorId":30708,"corporation":false,"usgs":true,"family":"Snyder","given":"Terry","email":"","affiliations":[],"preferred":false,"id":344214,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Freeman, Michael L. mfreeman@usgs.gov","contributorId":1042,"corporation":false,"usgs":true,"family":"Freeman","given":"Michael","email":"mfreeman@usgs.gov","middleInitial":"L.","affiliations":[],"preferred":true,"id":344211,"contributorType":{"id":1,"text":"Authors"},"rank":5}]}}
,{"id":9000556,"text":"fs20103091 - 2010 - Effects of ocean acidification and sea-level rise on coral reefs","interactions":[],"lastModifiedDate":"2014-06-02T13:45:58","indexId":"fs20103091","displayToPublicDate":"2011-01-06T00:00:00","publicationYear":"2010","noYear":false,"publicationType":{"id":18,"text":"Report"},"publicationSubtype":{"id":5,"text":"USGS Numbered Series"},"seriesTitle":{"id":313,"text":"Fact Sheet","code":"FS","onlineIssn":"2327-6932","printIssn":"2327-6916","active":true,"publicationSubtype":{"id":5}},"seriesNumber":"2010-3091","title":"Effects of ocean acidification and sea-level rise on coral reefs","docAbstract":"U.S. Geological Survey (USGS) scientists are developing comprehensive records of historical and modern coral reef growth and calcification rates relative to changing seawater chemistry resulting from increasing atmospheric CO2 from the pre-industrial period to the present. These records will provide the scientific foundation for predicting future impacts of ocean acidification and sea-level rise on coral reef growth. Changes in coral growth rates in response to past changes in seawater pH are being examined by using cores from coral colonies.","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/fs20103091","usgsCitation":"Yates, K.K., and Moyer, R., 2010, Effects of ocean acidification and sea-level rise on coral reefs: U.S. Geological Survey Fact Sheet 2010-3091, 2 p., https://doi.org/10.3133/fs20103091.","productDescription":"2 p.","onlineOnly":"N","additionalOnlineFiles":"N","costCenters":[{"id":574,"text":"St. Petersburg Coastal and Marine Science Center","active":true,"usgs":true}],"links":[{"id":116238,"rank":0,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/fs_2010_3091.bmp"},{"id":14461,"rank":100,"type":{"id":15,"text":"Index Page"},"url":"https://pubs.usgs.gov/fs/2010/3091/","linkFileType":{"id":5,"text":"html"}},{"id":287962,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/fs/2010/3091/pdf/FS_2010-3091.pdf"}],"country":"United States","noUsgsAuthors":false,"publicationStatus":"PW","scienceBaseUri":"4f4e4acce4b07f02db67ebd8","contributors":{"authors":[{"text":"Yates, K. K.","contributorId":108056,"corporation":false,"usgs":true,"family":"Yates","given":"K.","email":"","middleInitial":"K.","affiliations":[],"preferred":false,"id":344209,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Moyer, R.P.","contributorId":95839,"corporation":false,"usgs":true,"family":"Moyer","given":"R.P.","email":"","affiliations":[],"preferred":false,"id":344208,"contributorType":{"id":1,"text":"Authors"},"rank":2}]}}
,{"id":98984,"text":"ofr20101328 - 2010 - Documentation for assessment of modal pushover-based scaling procedure for nonlinear response history analysis of \"ordinary standard\" bridges","interactions":[],"lastModifiedDate":"2012-02-02T00:04:54","indexId":"ofr20101328","displayToPublicDate":"2011-01-06T00:00:00","publicationYear":"2010","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":"2010-1328","title":"Documentation for assessment of modal pushover-based scaling procedure for nonlinear response history analysis of \"ordinary standard\" bridges","docAbstract":"The earthquake engineering profession is increasingly utilizing nonlinear response history analyses (RHA) to evaluate seismic performance of existing structures and proposed designs of new structures. One of the main ingredients of nonlinear RHA is a set of ground-motion records representing the expected hazard environment for the structure. When recorded motions do not exist (as is the case for the central United States), or when high-intensity records are needed (as is the case for San Francisco and Los Angeles), ground motions from other tectonically similar regions need to be selected and scaled. The modal-pushover-based scaling (MPS) procedure recently was developed to determine scale factors for a small number of records, such that the scaled records provide accurate and efficient estimates of 'true' median structural responses. The adjective 'accurate' refers to the discrepancy between the benchmark responses and those computed from the MPS procedure. The adjective 'efficient' refers to the record-to-record variability of responses. Herein, the accuracy and efficiency of the MPS procedure are evaluated by applying it to four types of existing 'ordinary standard' bridges typical of reinforced-concrete bridge construction in California. These bridges are the single-bent overpass, multi span bridge, curved-bridge, and skew-bridge. As compared to benchmark analyses of unscaled records using a larger catalog of ground motions, it is demonstrated that the MPS procedure provided an accurate estimate of the engineering demand parameters (EDPs) accompanied by significantly reduced record-to-record variability of the responses. Thus, the MPS procedure is a useful tool for scaling ground motions as input to nonlinear RHAs of 'ordinary standard' bridges.","language":"ENGLISH","publisher":"U.S. Geological Survey","doi":"10.3133/ofr20101328","collaboration":"In Cooperation with the Pacific Earthquake Engineering Research Center, University of California, Berkeley","usgsCitation":"Kalkan, E., and Kwong, N.S., 2010, Documentation for assessment of modal pushover-based scaling procedure for nonlinear response history analysis of \"ordinary standard\" bridges: U.S. Geological Survey Open-File Report 2010-1328, iii, 58 p.; Tables, https://doi.org/10.3133/ofr20101328.","productDescription":"iii, 58 p.; Tables","onlineOnly":"Y","additionalOnlineFiles":"N","costCenters":[{"id":237,"text":"Earthquake Science Center","active":true,"usgs":true},{"id":379,"text":"Menlo Park Science Center","active":false,"usgs":true}],"links":[{"id":115905,"rank":0,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/ofr_2010_1328.gif"},{"id":14418,"rank":100,"type":{"id":15,"text":"Index Page"},"url":"https://pubs.usgs.gov/of/2010/1328/","linkFileType":{"id":5,"text":"html"}}],"noUsgsAuthors":false,"publicationStatus":"PW","scienceBaseUri":"4f4e4a6ae4b07f02db63d0d4","contributors":{"authors":[{"text":"Kalkan, Erol 0000-0002-9138-9407 ekalkan@usgs.gov","orcid":"https://orcid.org/0000-0002-9138-9407","contributorId":1218,"corporation":false,"usgs":true,"family":"Kalkan","given":"Erol","email":"ekalkan@usgs.gov","affiliations":[{"id":237,"text":"Earthquake Science Center","active":true,"usgs":true}],"preferred":true,"id":307143,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Kwong, Neal S.","contributorId":26279,"corporation":false,"usgs":true,"family":"Kwong","given":"Neal","email":"","middleInitial":"S.","affiliations":[],"preferred":false,"id":307144,"contributorType":{"id":1,"text":"Authors"},"rank":2}]}}
,{"id":70040460,"text":"70040460 - 2010 - Local sensitivity analysis for inverse problems solved by singular value decomposition","interactions":[],"lastModifiedDate":"2014-03-04T16:07:51","indexId":"70040460","displayToPublicDate":"2011-01-01T16:03:41","publicationYear":"2010","noYear":false,"publicationType":{"id":4,"text":"Book"},"publicationSubtype":{"id":12,"text":"Conference publication"},"title":"Local sensitivity analysis for inverse problems solved by singular value decomposition","docAbstract":"Local sensitivity analysis provides computationally frugal ways to evaluate models commonly used for resource management, risk assessment, and so on. This includes diagnosing inverse model convergence problems caused by parameter insensitivity and(or) parameter interdependence (correlation), understanding what aspects of the model and data contribute to measures of uncertainty, and identifying new data likely to reduce model uncertainty. Here, we consider sensitivity statistics relevant to models in which the process model parameters are transformed using singular value decomposition (SVD) to create SVD parameters for model calibration. The statistics considered include the PEST identifiability statistic, and combined use of the process-model parameter statistics composite scaled sensitivities and parameter correlation coefficients (CSS and PCC). The statistics are complimentary in that the identifiability statistic integrates the effects of parameter sensitivity and interdependence, while CSS and PCC provide individual measures of sensitivity and interdependence. PCC quantifies correlations between pairs or larger sets of parameters; when a set of parameters is intercorrelated, the absolute value of PCC is close to 1.00 for all pairs in the set. The number of singular vectors to include in the calculation of the identifiability statistic is somewhat subjective and influences the statistic. To demonstrate the statistics, we use the USDA’s Root Zone Water Quality Model to simulate nitrogen fate and transport in the unsaturated zone of the Merced River Basin, CA. There are 16 log-transformed process-model parameters, including water content at field capacity (WFC) and bulk density (BD) for each of five soil layers. Calibration data consisted of 1,670 observations comprising soil moisture, soil water tension, aqueous nitrate and bromide concentrations, soil nitrate concentration, and organic matter content. All 16 of the SVD parameters could be estimated by regression based on the range of singular values. Identifiability statistic results varied based on the number of SVD parameters included. Identifiability statistics calculated for four SVD parameters indicate the same three most important process-model parameters as CSS/PCC (WFC1, WFC2, and BD2), but the order differed. Additionally, the identifiability statistic showed that BD1 was almost as dominant as WFC1. The CSS/PCC analysis showed that this results from its high correlation with WCF1 (-0.94), and not its individual sensitivity. Such distinctions, combined with analysis of how high correlations and(or) sensitivities result from the constructed model, can produce important insights into, for example, the use of sensitivity analysis to design monitoring networks. In conclusion, the statistics considered identified similar important parameters. They differ because (1) with CSS/PCC can be more awkward because sensitivity and interdependence are considered separately and (2) identifiability requires consideration of how many SVD parameters to include. A continuing challenge is to understand how these computationally efficient methods compare with computationally demanding global methods like Markov-Chain Monte Carlo given common nonlinear processes and the often even more nonlinear models.","largerWorkTitle":"American Geophysical Union, Fall Meeting 2010","language":"English","publisher":"American Geophysical Union","usgsCitation":"Hill, M.C., and Nolan, B.T., 2010, Local sensitivity analysis for inverse problems solved by singular value decomposition.","ipdsId":"IP-037059","costCenters":[{"id":451,"text":"National Water Quality Assessment Program","active":true,"usgs":true}],"links":[{"id":283359,"type":{"id":15,"text":"Index Page"},"url":"https://adsabs.harvard.edu/abs/2010AGUFM.H11E0857H"},{"id":283360,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"noUsgsAuthors":false,"publicationStatus":"PW","scienceBaseUri":"53cd64d6e4b0b290850ffb77","contributors":{"authors":[{"text":"Hill, M. C.","contributorId":48993,"corporation":false,"usgs":true,"family":"Hill","given":"M.","email":"","middleInitial":"C.","affiliations":[],"preferred":false,"id":468379,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Nolan, B. T.","contributorId":21565,"corporation":false,"usgs":true,"family":"Nolan","given":"B.","email":"","middleInitial":"T.","affiliations":[{"id":595,"text":"U.S. Geological Survey","active":false,"usgs":true}],"preferred":false,"id":468378,"contributorType":{"id":1,"text":"Authors"},"rank":2}]}}
,{"id":70056364,"text":"70056364 - 2010 - A comparison between SWI and SEAWAT: the importance of dispersion, inversion and vertical anisotropy","interactions":[],"lastModifiedDate":"2014-05-27T11:01:17","indexId":"70056364","displayToPublicDate":"2011-01-01T10:51:00","publicationYear":"2010","noYear":false,"publicationType":{"id":24,"text":"Conference Paper"},"publicationSubtype":{"id":19,"text":"Conference Paper"},"title":"A comparison between SWI and SEAWAT: the importance of dispersion, inversion and vertical anisotropy","docAbstract":"SWI and SEAWAT are both computer codes designed to model variable-density systems. One of the options in SWI is to model Dupuit interface flow, where freshwater and seawater are separated by an interface. In this paper we compare seawater intrusion model results of SWI to model results of SEAWAT, which simulates full variable-density flow and transport. Results indicate that SWI is valid for many variable-density systems. For the case considered in this paper, SWI results are accurate when the simulated width of the transition zone between seawater to freshwater is 15% or less of the scale of the problem, density inversion (saltwater over freshwater) occurs over only a small part of the model domain, and the ratio of vertical to horizontal hydraulic conductivity is larger than 0.01. Results also show that the simulated interface moves further inland using SWI than for the same conditions using SEAWAT. SWI is preferable to be used in systems where run times for a fully-coupled variable-density flow and transport model would be prohibitive; for the case considered here, SWI run times were a few seconds and SEAWAT run times were almost three hours.","largerWorkType":{"id":4,"text":"Book"},"largerWorkTitle":"Proceedings of the 21st Salt Water Intrusion Meeting, Azores, Portugal, 2010","largerWorkSubtype":{"id":12,"text":"Conference publication"},"language":"English","usgsCitation":"Dausman, A., Langevin, C.D., Bakker, M., and Schaars, F., 2010, A comparison between SWI and SEAWAT: the importance of dispersion, inversion and vertical anisotropy, <i>in</i> Proceedings of the 21st Salt Water Intrusion Meeting, Azores, Portugal, 2010, p. 271-274.","productDescription":"4 p.","startPage":"271","endPage":"274","numberOfPages":"4","ipdsId":"IP-021042","costCenters":[{"id":286,"text":"Florida Water Science Center-Ft. Lauderdale","active":false,"usgs":true}],"links":[{"id":287587,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"},{"id":279204,"type":{"id":15,"text":"Index Page"},"url":"https://www.swim-site.nl/pdf/swim21.html"}],"noUsgsAuthors":false,"publicationStatus":"PW","scienceBaseUri":"5385b3e4e4b09e18fc023a0c","contributors":{"authors":[{"text":"Dausman, Alyssa M.","contributorId":64337,"corporation":false,"usgs":true,"family":"Dausman","given":"Alyssa M.","affiliations":[],"preferred":false,"id":486540,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Langevin, Christian D. 0000-0001-5610-9759 langevin@usgs.gov","orcid":"https://orcid.org/0000-0001-5610-9759","contributorId":1030,"corporation":false,"usgs":true,"family":"Langevin","given":"Christian","email":"langevin@usgs.gov","middleInitial":"D.","affiliations":[{"id":37778,"text":"WMA - Integrated Modeling and Prediction Division","active":true,"usgs":true}],"preferred":true,"id":486537,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Bakker, Mark","contributorId":56137,"corporation":false,"usgs":true,"family":"Bakker","given":"Mark","email":"","affiliations":[],"preferred":false,"id":486539,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Schaars, Frans","contributorId":15920,"corporation":false,"usgs":true,"family":"Schaars","given":"Frans","email":"","affiliations":[],"preferred":false,"id":486538,"contributorType":{"id":1,"text":"Authors"},"rank":4}]}}
,{"id":70056542,"text":"70056542 - 2010 - Effect of sea-level rise on future coastal groundwater resources in southern Florida, USA","interactions":[],"lastModifiedDate":"2014-05-28T09:17:51","indexId":"70056542","displayToPublicDate":"2011-01-01T09:07:18","publicationYear":"2010","noYear":false,"publicationType":{"id":24,"text":"Conference Paper"},"publicationSubtype":{"id":19,"text":"Conference Paper"},"title":"Effect of sea-level rise on future coastal groundwater resources in southern Florida, USA","docAbstract":"An existing variable‐density groundwater flow and solute transport model, developed for the northern part of Broward County, Florida, was used to predict the effect of sealevel rise on future coastal groundwater resources. Using average annual conditions from 2005, simulations were performed for 100 years into the future using four different rates of sea‐level rise: 0, 24, 48, and 88 centimeters per century. Results from these predictive analyses suggest that the average concentration of groundwater withdrawn at the municipal well field will exceed the potable limit after 70, 60, 55, and 49 years, respectively, for the four simulations.","largerWorkType":{"id":4,"text":"Book"},"largerWorkTitle":"Proceedings of the 21st Salt Water Intrusion Meeting, Azores, Portugal, 2010","largerWorkSubtype":{"id":12,"text":"Conference publication"},"language":"English","usgsCitation":"Langevin, C.D., Zygnerski, M.R., White, J., and Hughes, J.D., 2010, Effect of sea-level rise on future coastal groundwater resources in southern Florida, USA, <i>in</i> Proceedings of the 21st Salt Water Intrusion Meeting, Azores, Portugal, 2010, p. 125-128.","productDescription":"4 p.","startPage":"125","endPage":"128","numberOfPages":"4","ipdsId":"IP-020760","costCenters":[{"id":286,"text":"Florida Water Science Center-Ft. Lauderdale","active":false,"usgs":true}],"links":[{"id":287646,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"},{"id":279251,"type":{"id":15,"text":"Index Page"},"url":"https://www.swim-site.nl/pdf/swim21.html"}],"projection":"Universal Transverse Mercator, Zone 17","country":"United States","state":"Florida","county":"Broward County","geographicExtents":"{ \"type\": \"FeatureCollection\", \"features\": [ { \"type\": \"Feature\", \"properties\": {}, \"geometry\": { \"type\": \"Polygon\", \"coordinates\": [ [ [ -80.505,25.9567 ], [ -80.505,26.3347 ], [ -80.0747,26.3347 ], [ -80.0747,25.9567 ], [ -80.505,25.9567 ] ] ] } } ] }","noUsgsAuthors":false,"publicationStatus":"PW","scienceBaseUri":"53870565e4b0aa26cd7b5396","contributors":{"authors":[{"text":"Langevin, Christian D. 0000-0001-5610-9759 langevin@usgs.gov","orcid":"https://orcid.org/0000-0001-5610-9759","contributorId":1030,"corporation":false,"usgs":true,"family":"Langevin","given":"Christian","email":"langevin@usgs.gov","middleInitial":"D.","affiliations":[{"id":37778,"text":"WMA - Integrated Modeling and Prediction Division","active":true,"usgs":true}],"preferred":true,"id":486593,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Zygnerski, Michael R.","contributorId":25469,"corporation":false,"usgs":true,"family":"Zygnerski","given":"Michael","email":"","middleInitial":"R.","affiliations":[],"preferred":false,"id":486596,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"White, Jeremy T. jwhite@usgs.gov","contributorId":3930,"corporation":false,"usgs":true,"family":"White","given":"Jeremy T.","email":"jwhite@usgs.gov","affiliations":[{"id":270,"text":"FLWSC-Tampa","active":true,"usgs":true}],"preferred":false,"id":486595,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Hughes, Joseph D. 0000-0003-1311-2354 jdhughes@usgs.gov","orcid":"https://orcid.org/0000-0003-1311-2354","contributorId":2492,"corporation":false,"usgs":true,"family":"Hughes","given":"Joseph","email":"jdhughes@usgs.gov","middleInitial":"D.","affiliations":[{"id":37778,"text":"WMA - Integrated Modeling and Prediction Division","active":true,"usgs":true}],"preferred":true,"id":486594,"contributorType":{"id":1,"text":"Authors"},"rank":4}]}}
,{"id":70110897,"text":"70110897 - 2010 - Using a cloud to replenish parched groundwater modeling efforts","interactions":[],"lastModifiedDate":"2014-06-02T08:57:00","indexId":"70110897","displayToPublicDate":"2011-01-01T08:51:38","publicationYear":"2010","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":1861,"text":"Ground Water","active":true,"publicationSubtype":{"id":10}},"title":"Using a cloud to replenish parched groundwater modeling efforts","docAbstract":"Groundwater models can be improved by introduction of additional parameter flexibility and simultaneous use of soft-knowledge. However, these sophisticated approaches have high computational requirements. Cloud computing provides unprecedented access to computing power via the Internet to facilitate the use of these techniques. A modeler can create, launch, and terminate “virtual” computers as needed, paying by the hour, and save machine images for future use. Such cost-effective and flexible computing power empowers groundwater modelers to routinely perform model calibration and uncertainty analysis in ways not previously possible.","largerWorkType":{"id":2,"text":"Article"},"largerWorkTitle":"Ground Water","largerWorkSubtype":{"id":10,"text":"Journal Article"},"language":"English","publisher":"Wiley Online Library","doi":"10.1111/j.1745-6584.2010.00699.x","usgsCitation":"Hunt, R.J., Luchette, J., Schreuder, W.A., Rumbaugh, J.O., Doherty, J., Tonkin, M.J., and Rumbaugh, D.B., 2010, Using a cloud to replenish parched groundwater modeling efforts: Ground Water, v. 48, no. 3, p. 360-365, https://doi.org/10.1111/j.1745-6584.2010.00699.x.","productDescription":"6 p.","startPage":"360","endPage":"365","ipdsId":"IP-018384","costCenters":[{"id":677,"text":"Wisconsin Water Science Center","active":true,"usgs":true}],"links":[{"id":475606,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.1111/j.1745-6584.2010.00699.x","text":"Publisher Index Page"},{"id":287938,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"},{"id":287937,"type":{"id":10,"text":"Digital Object Identifier"},"url":"https://dx.doi.org/10.1111/j.1745-6584.2010.00699.x"}],"volume":"48","issue":"3","noUsgsAuthors":false,"publicationDate":"2010-04-28","publicationStatus":"PW","scienceBaseUri":"53ae789ce4b0abf75cf2da99","contributors":{"authors":[{"text":"Hunt, Randall J. 0000-0001-6465-9304 rjhunt@usgs.gov","orcid":"https://orcid.org/0000-0001-6465-9304","contributorId":1129,"corporation":false,"usgs":true,"family":"Hunt","given":"Randall","email":"rjhunt@usgs.gov","middleInitial":"J.","affiliations":[{"id":677,"text":"Wisconsin Water Science Center","active":true,"usgs":true}],"preferred":true,"id":494176,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Luchette, Joseph","contributorId":58569,"corporation":false,"usgs":true,"family":"Luchette","given":"Joseph","email":"","affiliations":[],"preferred":false,"id":494181,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Schreuder, Willem A.","contributorId":47213,"corporation":false,"usgs":true,"family":"Schreuder","given":"Willem","email":"","middleInitial":"A.","affiliations":[],"preferred":false,"id":494180,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Rumbaugh, James O.","contributorId":87458,"corporation":false,"usgs":true,"family":"Rumbaugh","given":"James","email":"","middleInitial":"O.","affiliations":[],"preferred":false,"id":494182,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Doherty, John","contributorId":43843,"corporation":false,"usgs":true,"family":"Doherty","given":"John","affiliations":[],"preferred":false,"id":494179,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"Tonkin, Matthew J.","contributorId":26376,"corporation":false,"usgs":true,"family":"Tonkin","given":"Matthew","email":"","middleInitial":"J.","affiliations":[],"preferred":false,"id":494177,"contributorType":{"id":1,"text":"Authors"},"rank":6},{"text":"Rumbaugh, Douglas B.","contributorId":42879,"corporation":false,"usgs":true,"family":"Rumbaugh","given":"Douglas","email":"","middleInitial":"B.","affiliations":[],"preferred":false,"id":494178,"contributorType":{"id":1,"text":"Authors"},"rank":7}]}}
,{"id":98977,"text":"fs20103116 - 2010 - Airborne volcanic ash; a global threat to aviation","interactions":[],"lastModifiedDate":"2013-10-22T09:38:33","indexId":"fs20103116","displayToPublicDate":"2011-01-01T00:00:00","publicationYear":"2010","noYear":false,"publicationType":{"id":18,"text":"Report"},"publicationSubtype":{"id":5,"text":"USGS Numbered Series"},"seriesTitle":{"id":313,"text":"Fact Sheet","code":"FS","onlineIssn":"2327-6932","printIssn":"2327-6916","active":true,"publicationSubtype":{"id":5}},"seriesNumber":"2010-3116","title":"Airborne volcanic ash; a global threat to aviation","docAbstract":"The world's busy air traffic corridors pass over or downwind of hundreds of volcanoes capable of hazardous explosive eruptions. The risk to aviation from volcanic activity is significant - in the United States alone, aircraft carry about 300,000 passengers and hundreds of millions of dollars of cargo near active volcanoes each day. Costly disruption of flight operations in Europe and North America in 2010 in the wake of a moderate-size eruption in Iceland clearly demonstrates how eruptions can have global impacts on the aviation industry. Airborne volcanic ash can be a serious hazard to aviation even hundreds of miles from an eruption. Encounters with high-concentration ash clouds can diminish visibility, damage flight control systems, and cause jet engines to fail. Encounters with low-concentration clouds of volcanic ash and aerosols can accelerate wear on engine and aircraft components, resulting in premature replacement. The U.S. Geological Survey (USGS), in cooperation with national and international partners, is playing a leading role in the international effort to reduce the risk posed to aircraft by volcanic eruptions.","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/fs20103116","usgsCitation":"Neal, C., and Guffanti, M.C., 2010, Airborne volcanic ash; a global threat to aviation: U.S. Geological Survey Fact Sheet 2010-3116, 6 p., https://doi.org/10.3133/fs20103116.","productDescription":"6 p.","onlineOnly":"N","additionalOnlineFiles":"N","costCenters":[],"links":[{"id":115900,"rank":0,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/fs_2010_3116.gif"},{"id":14410,"rank":100,"type":{"id":15,"text":"Index Page"},"url":"https://pubs.usgs.gov/fs/2010/3116/","linkFileType":{"id":5,"text":"html"}},{"id":278312,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/fs/2010/3116/fs2010-3116.pdf"}],"noUsgsAuthors":false,"publicationStatus":"PW","scienceBaseUri":"4f4e4aefe4b07f02db691503","contributors":{"authors":[{"text":"Neal, Christina A. 0000-0002-7697-7825","orcid":"https://orcid.org/0000-0002-7697-7825","contributorId":82660,"corporation":false,"usgs":true,"family":"Neal","given":"Christina A.","affiliations":[],"preferred":false,"id":307128,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Guffanti, Marianne C. guffanti@usgs.gov","contributorId":641,"corporation":false,"usgs":true,"family":"Guffanti","given":"Marianne","email":"guffanti@usgs.gov","middleInitial":"C.","affiliations":[{"id":617,"text":"Volcano Science Center","active":true,"usgs":true},{"id":243,"text":"Eastern Geology and Paleoclimate Science Center","active":true,"usgs":true}],"preferred":true,"id":307127,"contributorType":{"id":1,"text":"Authors"},"rank":2}]}}
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