{"pageNumber":"751","pageRowStart":"18750","pageSize":"25","recordCount":46677,"records":[{"id":98110,"text":"sir20095212 - 2009 - Water Use in Oklahoma 1950-2005","interactions":[],"lastModifiedDate":"2012-03-08T17:16:29","indexId":"sir20095212","displayToPublicDate":"2010-01-16T00:00:00","publicationYear":"2009","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":"2009-5212","title":"Water Use in Oklahoma 1950-2005","docAbstract":"Comprehensive planning for water resources development and use in Oklahoma requires a historical perspective on water resources. The U.S. Geological Survey, in cooperation with the Oklahoma Water Resources Board, summarized the 1950-2005 water-use information for Oklahoma. This report presents 1950-2005 estimates of freshwater withdrawal for water use in Oklahoma by source and category in 5-year intervals. Withdrawal source was either surface water or groundwater. Withdrawal categories include: public supply, irrigation, livestock and aquaculture, thermoelectric-power generation (cooling water), domestic and commercial, and industrial and mining. Withdrawal data were aggregated and tabulated by county, major river basin, and principal aquifer. \r\n\r\nThe purpose of this report is to summarize water-use data in Oklahoma through: (1) presentation of detailed information on freshwater withdrawals by source, county, major river basin, and principal aquifer for 2005; (2) comparison of water use by source, category, major river basin, and principal aquifer at 5-year intervals from 1990-2005; and (3) comparison of water use on a statewide basis by source and category at 5-year intervals from 1950-2005. \r\n\r\nTotal withdrawals from surface-water and groundwater sources during 2005 were 1,559 million gallons per day-989 million gallons a day or 63 percent from surface-water sources and 570 million gallons per day or 37 percent from groundwater sources. The three largest water use categories were: public supply, 646 million gallons per day or 41 percent of total withdrawals; irrigation, 495 million gallons per day or 32 percent of total withdrawals; and livestock and aquaculture, 181 million gallons per day or 12 percent of total withdrawals. All other categories were 237 million gallons per day or 15 percent of total withdrawals.\r\n\r\nThe influence of public supply on the total withdrawals can be seen in the eastern two-thirds of Oklahoma; whereas, the influence of irrigation on total withdrawals can be seen in the western third of Oklahoma. Surface-water sources were dominant in the eastern half of Oklahoma and groundwater sources were dominant in the western half of Oklahoma.\r\n\r\nPublic supply withdrawals increased steadily from 1990-2000 and then decreased slightly in 2005, mainly because of a decrease in surface-water withdrawals. Irrigation withdrawals increased from 1990-1995 and then decreased steadily to 2005. Total livestock and aquaculture withdrawals steadily increased from 1990-2005. The largest increase in the other categories was for thermoelectric power generation that has steadily increased and almost doubled from 1990-2005.\r\n\r\nSurface-water sources have been increasing in importance from 1950-2005, in part because of the increasing percentage of surface-water for public supply as the total population of Oklahoma and population served by surface-water sources increased. Groundwater sources have been generally decreasing in importance as a percentage of total withdrawals in recent years. However, the magnitude of groundwater withdrawals was greatly dependent on irrigation withdrawals and specifically irrigated acreage in the panhandle.\r\n","language":"ENGLISH","publisher":"U.S. Geological Survey","doi":"10.3133/sir20095212","collaboration":"Prepared in cooperation with the Oklahoma Water Resources Board","usgsCitation":"Tortorelli, R.L., 2009, Water Use in Oklahoma 1950-2005: U.S. Geological Survey Scientific Investigations Report 2009-5212, vi, 50 p., https://doi.org/10.3133/sir20095212.","productDescription":"vi, 50 p.","onlineOnly":"N","temporalStart":"1950-01-01","temporalEnd":"2005-12-31","costCenters":[{"id":516,"text":"Oklahoma Water Science Center","active":true,"usgs":true}],"links":[{"id":125634,"rank":0,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/sir_2009_5212.jpg"},{"id":13349,"rank":100,"type":{"id":15,"text":"Index Page"},"url":"https://pubs.usgs.gov/sir/2009/5212/","linkFileType":{"id":5,"text":"html"}}],"geographicExtents":"{ \"type\": \"FeatureCollection\", \"features\": [ { \"type\": \"Feature\", \"properties\": {}, \"geometry\": { \"type\": \"Polygon\", \"coordinates\": [ [ [ -103,33.5 ], [ -103,37 ], [ -94.5,37 ], [ -94.5,33.5 ], [ -103,33.5 ] ] ] } } ] }","noUsgsAuthors":false,"publicationStatus":"PW","scienceBaseUri":"4f4e478fe4b07f02db48a04f","contributors":{"authors":[{"text":"Tortorelli, Robert L.","contributorId":65071,"corporation":false,"usgs":true,"family":"Tortorelli","given":"Robert","email":"","middleInitial":"L.","affiliations":[],"preferred":false,"id":304200,"contributorType":{"id":1,"text":"Authors"},"rank":1}]}}
,{"id":98109,"text":"ofr20091234 - 2009 - Quality-Assurance Data for Routine Water Analyses by the U.S. Geological Survey Laboratory in Troy, New York - July 2005 through June 2007","interactions":[],"lastModifiedDate":"2012-03-08T17:16:30","indexId":"ofr20091234","displayToPublicDate":"2010-01-16T00:00:00","publicationYear":"2009","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":"2009-1234","title":"Quality-Assurance Data for Routine Water Analyses by the U.S. Geological Survey Laboratory in Troy, New York - July 2005 through June 2007","docAbstract":"The laboratory for analysis of low-ionic-strength water at the U.S. Geological Survey (USGS) Water Science Center in Troy, N.Y., analyzes samples collected by USGS projects throughout the Northeast. The laboratory's quality-assurance program is based on internal and interlaboratory quality-assurance samples and quality-control procedures that were developed to ensure proper sample collection, processing, and analysis. The quality-assurance and quality-control data were stored in the laboratory's Lab Master data-management system, which provides efficient review, compilation, and plotting of data. This report presents and discusses results of quality-assurance and quality control samples analyzed from July 2005 through June 2007.\r\n\r\nResults for the quality-control samples for 19 analytical procedures were evaluated for bias and precision. Control charts indicate that data for eight of the analytical procedures were occasionally biased for either high-concentration or low-concentration samples but were within control limits; these procedures were: total aluminum, calcium, magnesium, nitrate (colorimetric method), potassium, silicon, sodium, and sulfate. Eight of the analytical procedures were biased throughout the analysis period for the high-concentration sample, but were within control limits; these procedures were: total aluminum, calcium, dissolved organic carbon, chloride, nitrate (ion chromatograph), potassium, silicon, and sulfate. The magnesium and pH procedures were biased throughout the analysis period for the low-concentration sample, but were within control limits. The acid-neutralizing capacity, total monomeric aluminum, nitrite, and specific conductance procedures were biased for the high-concentration and low-concentration samples, but were within control limits.\r\n\r\nResults from the filter-blank and analytical-blank analyses indicated that the procedures for 16 of 17 analytes were within control limits, although the concentrations for blanks were occasionally outside the control limits. The data-quality objective was not met for dissolved organic carbon.\r\n\r\nSampling and analysis precision are evaluated herein in terms of the coefficient of variation obtained for triplicate samples in the procedures for 18 of the 21 analytes. At least 93 percent of the samples met data-quality objectives for all analytes except acid-neutralizing capacity (85 percent of samples met objectives), total monomeric aluminum (83 percent of samples met objectives), total aluminum (85 percent of samples met objectives), and chloride (85 percent of samples met objectives). The ammonium and total dissolved nitrogen did not meet the data-quality objectives.\r\n\r\nResults of the USGS interlaboratory Standard Reference Sample (SRS) Project met the Troy Laboratory data-quality objectives for 87 percent of the samples analyzed. The P-sample (low-ionic-strength constituents) analysis had two outliers each in two studies. The T-sample (trace constituents) analysis and the N-sample (nutrient constituents) analysis had one outlier each in two studies.\r\n\r\nResults of Environment Canada's National Water Research Institute (NWRI) program indicated that at least 85 percent of the samples met data-quality objectives for 11 of the 14 analytes; the exceptions were acid-neutralizing capacity, total aluminum and ammonium. Data-quality objectives were not met in 41 percent of samples analyzed for acid-neutralizing capacity, 50 percent of samples analyzed for total aluminum, and 44 percent of samples analyzed for ammonium.\r\n\r\nResults from blind reference-sample analyses indicated that data-quality objectives were met by at least 86 percent of the samples analyzed for calcium, magnesium, pH, potassium, and sodium. Data-quality objectives were met by 76 percent of the samples analyzed for chloride, 80 percent of the samples analyzed for specific conductance, and 77 percent of the samples analyzed for sulfate. \r\n","language":"ENGLISH","publisher":"U.S. Geological Survey","doi":"10.3133/ofr20091234","usgsCitation":"Lincoln, T.A., Horan-Ross, D.A., McHale, M.R., and Lawrence, G.B., 2009, Quality-Assurance Data for Routine Water Analyses by the U.S. Geological Survey Laboratory in Troy, New York - July 2005 through June 2007: U.S. Geological Survey Open-File Report 2009-1234, iv, 35 p., https://doi.org/10.3133/ofr20091234.","productDescription":"iv, 35 p.","onlineOnly":"N","temporalStart":"2005-07-01","temporalEnd":"2007-06-30","costCenters":[{"id":474,"text":"New York Water Science Center","active":true,"usgs":true}],"links":[{"id":125580,"rank":0,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/ofr_2009_1234.jpg"},{"id":13347,"rank":100,"type":{"id":15,"text":"Index Page"},"url":"https://pubs.usgs.gov/of/2009/1234/","linkFileType":{"id":5,"text":"html"}}],"noUsgsAuthors":false,"publicationStatus":"PW","scienceBaseUri":"4f4e4a68e4b07f02db63b14e","contributors":{"authors":[{"text":"Lincoln, Tricia A. tarenga@usgs.gov","contributorId":3803,"corporation":false,"usgs":true,"family":"Lincoln","given":"Tricia","email":"tarenga@usgs.gov","middleInitial":"A.","affiliations":[],"preferred":true,"id":304198,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Horan-Ross, Debra A. dhross@usgs.gov","contributorId":3809,"corporation":false,"usgs":true,"family":"Horan-Ross","given":"Debra","email":"dhross@usgs.gov","middleInitial":"A.","affiliations":[],"preferred":true,"id":304199,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"McHale, Michael R. 0000-0003-3780-1816 mmchale@usgs.gov","orcid":"https://orcid.org/0000-0003-3780-1816","contributorId":1735,"corporation":false,"usgs":true,"family":"McHale","given":"Michael","email":"mmchale@usgs.gov","middleInitial":"R.","affiliations":[{"id":474,"text":"New York Water Science Center","active":true,"usgs":true}],"preferred":true,"id":304197,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Lawrence, Gregory B. 0000-0002-8035-2350 glawrenc@usgs.gov","orcid":"https://orcid.org/0000-0002-8035-2350","contributorId":867,"corporation":false,"usgs":true,"family":"Lawrence","given":"Gregory","email":"glawrenc@usgs.gov","middleInitial":"B.","affiliations":[{"id":474,"text":"New York Water Science Center","active":true,"usgs":true}],"preferred":true,"id":304196,"contributorType":{"id":1,"text":"Authors"},"rank":4}]}}
,{"id":98122,"text":"ofr20091233 - 2009 - Quality-assurance data for routine water analyses by the U.S. Geological Survey laboratory in Troy, New York - July 2003 through June 2005","interactions":[],"lastModifiedDate":"2017-10-18T12:41:22","indexId":"ofr20091233","displayToPublicDate":"2010-01-16T00:00:00","publicationYear":"2009","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":"2009-1233","title":"Quality-assurance data for routine water analyses by the U.S. Geological Survey laboratory in Troy, New York - July 2003 through June 2005","docAbstract":"<p>The laboratory for analysis of low-ionic-strength water at the U.S. Geological Survey (USGS) Water Science Center in Troy, N.Y., analyzes samples collected by USGS projects throughout the Northeast. The laboratory's quality-assurance program is based on internal and interlaboratory quality-assurance samples and quality-control procedures that were developed to ensure proper sample collection, processing, and analysis. The quality-assurance and quality-control data were stored in the laboratory's Lab Master data-management system, which provides efficient review, compilation, and plotting of data. This report presents and discusses results of quality-assurance and quality control samples analyzed from July 2003 through June 2005. </p><p>Results for the quality-control samples for 20 analytical procedures were evaluated for bias and precision. Control charts indicate that data for five of the analytical procedures were occasionally biased for either high-concentration or low-concentration samples but were within control limits; these procedures were: acid-neutralizing capacity, total monomeric aluminum, pH, silicon, and sodium. Seven of the analytical procedures were biased throughout the analysis period for the high-concentration sample, but were within control limits; these procedures were: dissolved organic carbon, chloride, nitrate (ion chromatograph), nitrite, silicon, sodium, and sulfate. The calcium and magnesium procedures were biased throughout the analysis period for the low-concentration sample, but were within control limits. The total aluminum and specific conductance procedures were biased for the high-concentration and low-concentration samples, but were within control limits. </p><p>Results from the filter-blank and analytical-blank analyses indicate that the procedures for 17 of 18 analytes were within control limits, although the concentrations for blanks were occasionally outside the control limits. The data-quality objective was not met for dissolved organic carbon. </p><p>Sampling and analysis precision are evaluated herein in terms of the coefficient of variation obtained for triplicate samples in the procedures for 18 of the 22 analytes. At least 85 percent of the samples met data-quality objectives for all analytes except total monomeric aluminum (82 percent of samples met objectives), total aluminum (77 percent of samples met objectives), chloride (80 percent of samples met objectives), fluoride (76 percent of samples met objectives), and nitrate (ion chromatograph) (79 percent of samples met objectives). The ammonium and total dissolved nitrogen did not meet the data-quality objectives. </p><p>Results of the USGS interlaboratory Standard Reference Sample (SRS) Project indicated good data quality over the time period, with ratings for each sample in the satisfactory, good, and excellent ranges or less than 10 percent error. The P-sample (low-ionic-strength constituents) analysis had one marginal and two unsatisfactory ratings for the chloride procedure. The T-sample (trace constituents)analysis had two unsatisfactory ratings and one high range percent error for the aluminum procedure. The N-sample (nutrient constituents) analysis had one marginal rating for the nitrate procedure. </p><p>Results of Environment Canada's National Water Research Institute (NWRI) program indicated that at least 84 percent of the samples met data-quality objectives for 11 of the 14 analytes; the exceptions were ammonium, total aluminum, and acid-neutralizing capacity. The ammonium procedure did not meet data quality objectives in all studies. Data-quality objectives were not met in 23 percent of samples analyzed for total aluminum and 45 percent of samples analyzed acid-neutralizing capacity. </p><p>Results from blind reference-sample analyses indicated that data-quality objectives were met by at least 86 percent of the samples analyzed for calcium, chloride, fluoride, magnesium, pH, potassium, sodium, and sulfate. Data-quality objectives were not met&nbsp;by samples analyzed for fluoride.&nbsp;</p>","language":"English","publisher":"U.S. Geological Survey","doi":"10.3133/ofr20091233","usgsCitation":"Lincoln, T.A., Horan-Ross, D.A., McHale, M.R., and Lawrence, G.B., 2009, Quality-assurance data for routine water analyses by the U.S. Geological Survey laboratory in Troy, New York - July 2003 through June 2005: U.S. Geological Survey Open-File Report 2009-1233, iv, 35 p., https://doi.org/10.3133/ofr20091233.","productDescription":"iv, 35 p.","onlineOnly":"N","additionalOnlineFiles":"N","temporalStart":"2003-07-01","temporalEnd":"2005-06-30","costCenters":[{"id":474,"text":"New York Water Science Center","active":true,"usgs":true}],"links":[{"id":125646,"rank":0,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/ofr_2009_1233.jpg"},{"id":13348,"rank":100,"type":{"id":15,"text":"Index Page"},"url":"https://pubs.usgs.gov/of/2009/1233/","linkFileType":{"id":5,"text":"html"}}],"noUsgsAuthors":false,"publicationStatus":"PW","scienceBaseUri":"4f4e4ac9e4b07f02db67c59c","contributors":{"authors":[{"text":"Lincoln, Tricia A. tarenga@usgs.gov","contributorId":3803,"corporation":false,"usgs":true,"family":"Lincoln","given":"Tricia","email":"tarenga@usgs.gov","middleInitial":"A.","affiliations":[],"preferred":true,"id":304236,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Horan-Ross, Debra A. dhross@usgs.gov","contributorId":3809,"corporation":false,"usgs":true,"family":"Horan-Ross","given":"Debra","email":"dhross@usgs.gov","middleInitial":"A.","affiliations":[],"preferred":true,"id":304237,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"McHale, Michael R. 0000-0003-3780-1816 mmchale@usgs.gov","orcid":"https://orcid.org/0000-0003-3780-1816","contributorId":1735,"corporation":false,"usgs":true,"family":"McHale","given":"Michael","email":"mmchale@usgs.gov","middleInitial":"R.","affiliations":[{"id":474,"text":"New York Water Science Center","active":true,"usgs":true}],"preferred":true,"id":304235,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Lawrence, Gregory B. 0000-0002-8035-2350 glawrenc@usgs.gov","orcid":"https://orcid.org/0000-0002-8035-2350","contributorId":867,"corporation":false,"usgs":true,"family":"Lawrence","given":"Gregory","email":"glawrenc@usgs.gov","middleInitial":"B.","affiliations":[{"id":474,"text":"New York Water Science Center","active":true,"usgs":true}],"preferred":true,"id":304234,"contributorType":{"id":1,"text":"Authors"},"rank":4}]}}
,{"id":98105,"text":"ofr20091003 - 2009 - Digital Seismic-Reflection Data from Eastern Rhode Island Sound and Vicinity, 1975-1980","interactions":[],"lastModifiedDate":"2012-02-10T00:11:53","indexId":"ofr20091003","displayToPublicDate":"2010-01-15T00:00:00","publicationYear":"2009","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":"2009-1003","title":"Digital Seismic-Reflection Data from Eastern Rhode Island Sound and Vicinity, 1975-1980","docAbstract":"During 1975 and 1980, the U.S. Geological Survey (USGS) conducted two seismic-reflection surveys in Rhode Island Sound (RIS) aboard the research vessel Asterias: cruise ASTR75-June surveyed eastern RIS in 1975 and cruise AST-80-6B surveyed southern RIS in 1980. Data from these surveys were recorded in analog form and archived at the USGS Woods Hole Coastal and Marine Science Center's Data Library. In response to recent interest in the geology of RIS and in an effort to make the data more readily accessible while preserving the original paper records, the seismic data from these cruises were scanned and converted to black and white Tagged Image File Format and grayscale Portable Network Graphics images and SEG-Y data files. Navigation data were converted from U.S. Coast Guard Long Range Aids to Navigation time delays to latitudes and longitudes that are available in Environmental Systems Research Institute, Inc., shapefile format and as eastings and northings in space-delimited text format. This report complements two others that contain analog seismic-reflection data from RIS (McMullen and others, 2009) and Long Island and Block Island Sounds (Poppe and others, 2002) and were converted into digital form.","language":"ENGLISH","publisher":"U.S. Geological Survey","doi":"10.3133/ofr20091003","usgsCitation":"McMullen, K., Poppe, L., and Soderberg, N., 2009, Digital Seismic-Reflection Data from Eastern Rhode Island Sound and Vicinity, 1975-1980: U.S. Geological Survey Open-File Report 2009-1003, 2 DVD-ROMs, https://doi.org/10.3133/ofr20091003.","productDescription":"2 DVD-ROMs","onlineOnly":"N","additionalOnlineFiles":"Y","temporalStart":"1975-01-01","temporalEnd":"1980-12-31","costCenters":[{"id":680,"text":"Woods Hole Science Center","active":false,"usgs":true}],"links":[{"id":125648,"rank":0,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/ofr_2009_1003.jpg"},{"id":13344,"rank":100,"type":{"id":15,"text":"Index Page"},"url":"https://pubs.usgs.gov/of/2009/1003/","linkFileType":{"id":5,"text":"html"}}],"geographicExtents":"{\"crs\": {\"type\": \"name\", \"properties\": {\"name\": \"urn:ogc:def:crs:OGC:1.3:CRS84\"}}, \"geometry\": {\"type\": \"Polygon\", \"coordinates\": [[[-70.98324394226069, 41.045820236206055], [-71.12874794006342, 41.04593467712412], [-71.42366027832031, 41.086191177368214], [-71.41467101953401, 41.10125099950301], [-71.22406629467042, 41.11094803989816], [-71.32592394990667, 41.19956419995383], [-71.3137010312783, 41.208731388925116], [-71.20063903396607, 41.11807807576481], [-71.1048928380439, 41.13539387715486], [-71.17924892636643, 41.197527046849125], [-71.21897341190856, 41.3992052042169], [-71.20931434631348, 41.44528770446788], [-71.07127654421203, 41.48671911346889], [-70.9614337980106, 41.40059408073864], [-70.86673353219801, 41.41761884762627], [-70.78231906304671, 41.447766872323186], [-70.69754991125188, 41.49848649034265], [-70.65424156188965, 41.49388694763187], [-70.63193362220568, 41.46911876746139], [-70.70499824676523, 41.42833026345966], [-70.77345199695935, 41.35810310004819], [-70.79225017706443, 41.37264508843145], [-70.84616193887541, 41.34568920752594], [-70.80746848475411, 41.279013171038], [-70.85330442961043, 41.23114007307697], [-70.8278400158013, 41.190397010982494], [-70.74635389161229, 41.208731388925116], [-70.69440648744187, 41.300403278637646], [-70.4570781507414, 41.30447758484699], [-70.42549324035645, 41.37813377380376], [-70.42486381530756, 41.32419395446787], [-70.4478530883789, 41.28872108459471], [-70.83382225036621, 41.05370521545409], [-70.98324394226069, 41.045820236206055]]]}, \"properties\": {\"extentType\": \"Custom\", \"code\": \"\", \"name\": \"\", \"notes\": \"\", \"promotedForReuse\": false, \"abbreviation\": \"\", \"shortName\": \"\", \"description\": \"\"}, \"bbox\": [-71.42366027832031, 41.044572830200174, -70.4245300292968, 41.50225257873547], \"type\": \"Feature\", \"id\": \"3091908\"}","noUsgsAuthors":false,"publicationStatus":"PW","scienceBaseUri":"4f4e4b32e4b07f02db6b45f4","contributors":{"authors":[{"text":"McMullen, K.Y.","contributorId":51857,"corporation":false,"usgs":true,"family":"McMullen","given":"K.Y.","email":"","affiliations":[],"preferred":false,"id":304179,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Poppe, L.J.","contributorId":72782,"corporation":false,"usgs":true,"family":"Poppe","given":"L.J.","affiliations":[],"preferred":false,"id":304180,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Soderberg, N.K.","contributorId":34138,"corporation":false,"usgs":true,"family":"Soderberg","given":"N.K.","email":"","affiliations":[],"preferred":false,"id":304178,"contributorType":{"id":1,"text":"Authors"},"rank":3}]}}
,{"id":98103,"text":"fs20093108 - 2009 - US Topo: Topographic Maps for the Nation ","interactions":[],"lastModifiedDate":"2017-07-13T11:06:06","indexId":"fs20093108","displayToPublicDate":"2010-01-15T00:00:00","publicationYear":"2009","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":"2009-3108","title":"US Topo: Topographic Maps for the Nation ","docAbstract":"US Topo is the next generation of topographic maps from the U.S. Geological Survey (USGS). Arranged in the familiar 7.5-minute quadrangle format, digital US Topo maps are designed to look and feel (and perform) like the traditional paper topographic maps for which the USGS is so well known. In contrast to paper-based maps, US Topo maps provide modern technical advantages that support faster, wider public distribution and enable basic, on-screen geographic analysis for all users. \r\n\r\nUS Topo maps are available free on the Web. Each map quadrangle is constructed in GeoPDF? format from key layers of geographic data (orthoimagery, roads, geographic names, topographic contours, and hydrographic features) found in The National Map. \r\n\r\nUS Topo quadrangles can be printed from personal computers or plotters as complete, full-sized, maps or in customized sections, in a user-desired specific format. Paper copies of the maps can also be purchased from the USGS Store. Download links and a users guide are featured on the US Topo Web site.\r\n\r\nUS Topo users can turn geographic data layers on and off as needed; they can zoom in and out to highlight specific features or see a broader area. File size for each digital 7.5-minute quadrangle, about 15-20 megabytes, is suitable for most users. Associated electronic tools for geographic analysis are available free for download.","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/fs20093108","usgsCitation":"Hytes, P.L., 2009, US Topo: Topographic Maps for the Nation : U.S. Geological Survey Fact Sheet 2009-3108, 2 p., https://doi.org/10.3133/fs20093108.","productDescription":"2 p.","onlineOnly":"N","additionalOnlineFiles":"N","costCenters":[{"id":595,"text":"U.S. Geological Survey","active":false,"usgs":true}],"links":[{"id":13365,"rank":100,"type":{"id":15,"text":"Index Page"},"url":"https://pubs.usgs.gov/fs/2009/3108/","linkFileType":{"id":5,"text":"html"}},{"id":343787,"rank":4,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/fs/2009/3108/TopoMapBroch_New5F.pdf","text":"Brochure for Fact Sheet 2009-3108","size":"1773 KB","linkFileType":{"id":1,"text":"pdf"}},{"id":125633,"rank":0,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/fs_2009_3108.jpg"},{"id":13340,"rank":300,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/fs/2009/3108/fs2009-3108.pdf","size":"778 KB","linkFileType":{"id":1,"text":"pdf"}}],"noUsgsAuthors":false,"publicationStatus":"PW","scienceBaseUri":"4f4e4a29e4b07f02db611a40","contributors":{"authors":[{"text":"Hytes, Patricia L. plhytes@usgs.gov","contributorId":1417,"corporation":false,"usgs":true,"family":"Hytes","given":"Patricia","email":"plhytes@usgs.gov","middleInitial":"L.","affiliations":[],"preferred":true,"id":304176,"contributorType":{"id":1,"text":"Authors"},"rank":1}]}}
,{"id":98102,"text":"ofr20091279 - 2009 - Hurricane Gustav: Observations and analysis of coastal change","interactions":[],"lastModifiedDate":"2023-12-07T14:34:51.14829","indexId":"ofr20091279","displayToPublicDate":"2010-01-15T00:00:00","publicationYear":"2009","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":"2009-1279","title":"Hurricane Gustav: Observations and analysis of coastal change","docAbstract":"<p>Understanding storm-induced coastal change and forecasting these changes require knowledge of the physical processes associated with a storm and the geomorphology of the impacted coastline. The primary physical processes of interest are the wind field, storm surge, currents, and wave field. Not only does wind cause direct damage to structures along the coast, but it is ultimately responsible for much of the energy that is transferred to the ocean and expressed as storm surge, mean currents, and surface waves. Waves and currents are the processes most responsible for moving sediments in the coastal zone during extreme storm events. Storm surge, which is the rise in water level due to the wind, barometric pressure, and other factors, allows both waves and currents to attack parts of the coast not normally exposed to these processes.</p><p>Coastal geomorphology, including shapes of the shoreline, beaches, and dunes, is also a significant aspect of the coastal change observed during extreme storms. Relevant geomorphic variables include sand dune elevation, beach width, shoreline position, sediment grain size, and foreshore beach slope. These variables, in addition to hydrodynamic processes, can be used to predict coastal vulnerability to storms.</p><p>The U.S. Geological Survey (USGS) National Assessment of Coastal Change Hazards project (<a href=\"http://coastal.er.usgs.gov/hurricanes/\" data-mce-href=\"http://coastal.er.usgs.gov/hurricanes/\">http://coastal.er.usgs.gov/hurricanes</a>) strives to provide hazard information to those concerned about the Nation’s coastlines, including residents of coastal areas, government agencies responsible for coastal management, and coastal researchers. As part of the National Assessment, observations were collected to measure morphological changes associated with Hurricane Gustav, which made landfall near Cocodrie, Louisiana, on September 1, 2008. Methods of observation included oblique aerial photography, airborne topographic surveys, and ground-based topographic surveys. This report documents these data-collection efforts and presents qualitative and quantitative descriptions of hurricane-induced changes to the shoreline, beaches, dunes, and infrastructure in the region that was heavily impacted by Hurricane Gustav.</p>","language":"English","publisher":"U.S. Geological Survey","doi":"10.3133/ofr20091279","usgsCitation":"Doran, K., Stockdon, H.F., Plant, N.G., Sallenger, A., Guy, K.K., and Serafin, K.A., 2009, Hurricane Gustav: Observations and analysis of coastal change: U.S. Geological Survey Open-File Report 2009-1279, vii, 28 p., https://doi.org/10.3133/ofr20091279.","productDescription":"vii, 28 p.","onlineOnly":"N","costCenters":[{"id":574,"text":"St. Petersburg Coastal and Marine Science Center","active":true,"usgs":true},{"id":595,"text":"U.S. Geological Survey","active":false,"usgs":true}],"links":[{"id":13339,"rank":3,"type":{"id":15,"text":"Index Page"},"url":"https://pubs.usgs.gov/of/2009/1279/","linkFileType":{"id":5,"text":"html"}},{"id":403717,"rank":2,"type":{"id":36,"text":"NGMDB Index Page"},"url":"https://ngmdb.usgs.gov/Prodesc/proddesc_90304.htm","linkFileType":{"id":5,"text":"html"}},{"id":125626,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/ofr_2009_1279.jpg"}],"country":"United States","state":"Alabama, Louisiana, Mississippi","geographicExtents":"{\n  \"type\": \"FeatureCollection\",\n  \"features\": [\n    {\n      \"type\": \"Feature\",\n      \"properties\": {},\n      \"geometry\": {\n        \"type\": \"Polygon\",\n        \"coordinates\": [\n          [\n            [\n              -91,\n              28.9167\n            ],\n            [\n              -88,\n              28.9167\n            ],\n            [\n              -88,\n              30.5\n            ],\n            [\n              -91,\n              30.5\n            ],\n            [\n              -91,\n               28.9167\n            ]\n          ]\n        ]\n      }\n    }\n  ]\n}","noUsgsAuthors":false,"publicationStatus":"PW","scienceBaseUri":"4f4e4affe4b07f02db697bfd","contributors":{"authors":[{"text":"Doran, Kara S. 0000-0001-8050-5727","orcid":"https://orcid.org/0000-0001-8050-5727","contributorId":33010,"corporation":false,"usgs":true,"family":"Doran","given":"Kara S.","affiliations":[],"preferred":false,"id":304173,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Stockdon, Hilary F. 0000-0003-0791-4676 hstockdon@usgs.gov","orcid":"https://orcid.org/0000-0003-0791-4676","contributorId":2153,"corporation":false,"usgs":true,"family":"Stockdon","given":"Hilary","email":"hstockdon@usgs.gov","middleInitial":"F.","affiliations":[{"id":186,"text":"Coastal and Marine Geology Program","active":true,"usgs":true},{"id":574,"text":"St. Petersburg Coastal and Marine Science Center","active":true,"usgs":true}],"preferred":true,"id":304170,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Plant, Nathaniel G. 0000-0002-5703-5672 nplant@usgs.gov","orcid":"https://orcid.org/0000-0002-5703-5672","contributorId":3503,"corporation":false,"usgs":true,"family":"Plant","given":"Nathaniel","email":"nplant@usgs.gov","middleInitial":"G.","affiliations":[{"id":574,"text":"St. Petersburg Coastal and Marine Science Center","active":true,"usgs":true},{"id":508,"text":"Office of the AD Hazards","active":true,"usgs":true}],"preferred":true,"id":304171,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Sallenger, Asbury H. Jr.","contributorId":27458,"corporation":false,"usgs":true,"family":"Sallenger","given":"Asbury H.","suffix":"Jr.","affiliations":[],"preferred":false,"id":304172,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Guy, Kristy K. kguy@usgs.gov","contributorId":45010,"corporation":false,"usgs":true,"family":"Guy","given":"Kristy","email":"kguy@usgs.gov","middleInitial":"K.","affiliations":[],"preferred":false,"id":304174,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"Serafin, Katherine A.","contributorId":84466,"corporation":false,"usgs":true,"family":"Serafin","given":"Katherine","email":"","middleInitial":"A.","affiliations":[],"preferred":false,"id":304175,"contributorType":{"id":1,"text":"Authors"},"rank":6}]}}
,{"id":98104,"text":"sir20095261 - 2009 - Travel Times, Streamflow Velocities, and Dispersion Rates in the Yellowstone River, Montana","interactions":[],"lastModifiedDate":"2012-03-08T17:16:29","indexId":"sir20095261","displayToPublicDate":"2010-01-15T00:00:00","publicationYear":"2009","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":"2009-5261","title":"Travel Times, Streamflow Velocities, and Dispersion Rates in the Yellowstone River, Montana","docAbstract":"The Yellowstone River is a vital natural resource to the residents of southeastern Montana and is a primary source of water for irrigation and recreation and the primary source of municipal water for several cities. The Yellowstone River valley is the primary east-west transportation corridor through southern Montana. This complex of infrastructure makes the Yellowstone River especially vulnerable to accidental spills from various sources such as tanker cars and trucks. In 2008, the U.S. Geological Survey (USGS), in cooperation with the Montana Department of Environmental Quality, initiated a dye-tracer study to determine instream travel times, streamflow velocities, and dispersion rates for the Yellowstone River from Lockwood to Glendive, Montana. The purpose of this report is to describe the results of this study and summarize data collected at each of the measurement sites between Lockwood and Glendive. This report also compares the results of this study to estimated travel times from a transport model developed by the USGS for a previous study. For this study, Rhodamine WT dye was injected at four locations in late September and early October 2008 during reasonably steady streamflow conditions. Streamflows ranged from 3,490 to 3,770 cubic feet per second upstream from the confluence of the Bighorn River and ranged from 6,520 to 7,570 cubic feet per second downstream from the confluence of the Bighorn River.\r\n\r\nMean velocities were calculated for each subreach between measurement sites for the leading edge, peak concentration, centroid, and trailing edge at 10 percent of the peak concentration. Calculated velocities for the centroid of the dye plume for subreaches that were completely laterally mixed ranged from 1.83 to 3.18 ft/s within the study reach from Lockwood Bridge to Glendive Bridge. The mean of the completely mixed centroid velocity for the entire study reach, excluding the subreach between Forsyth Bridge and Cartersville Dam, was 2.80 ft/s. Longitudinal dispersion rates of the dye plume for this study ranged from 0.06 ft/s for the subreach upstream from Forsyth Bridge to 2.25 ft/s for the subreach upstream from Calyspo Bridge for subreaches where the dye was completely laterally mixed. A relation was determined between travel time of the peak concentration and time for the dye plume to pass a site (duration). This relation can be used to estimate when the receding concentration of a potential contaminant reaches 10 percent of its peak concentration for accidental spills into the Yellowstone River.\r\n\r\nData from this dye-tracer study were used to evaluate velocity and concentration estimates from a transport model developed as part of an earlier USGS study. Comparison of the estimated and calculated velocities for the study reach indicate that the transport model estimates the velocities of the Yellowstone River between Huntley Bridge and Glendive Bridge with reasonable accuracy. Velocities of the peak concentration of the dye plume calculated for this study averaged 10 percent faster than the most probable velocities and averaged 12 percent slower than the maximum probable velocities estimated from the transport model. Peak Rhodamine WT dye concentrations were consistently lower than the transport model estimates except for the most upstream subreach of each dye injection. The most upstream subreach of each dye injection is expected to have a higher concentration because of incomplete lateral mixing. Lower measured peak concentrations for all other sites were expected because Rhodamine WT dye deteriorates when exposed to sunlight and will sorb onto the streambanks and stream bottom.\r\n\r\nVelocity-streamflow relations developed by using routine streamflow measurements at USGS gaging stations and the transport model can be used to estimate mean streamflow velocities throughout a range of streamflows. The variation in these velocity-streamflow relations emphasizes the uncertainty in estimating the mean streamflow veloc","language":"ENGLISH","publisher":"U.S. Geological Survey","doi":"10.3133/sir20095261","isbn":"9781411326606","collaboration":"Prepared in cooperation with the Montana Department of Environmental Quality","usgsCitation":"McCarthy, P., 2009, Travel Times, Streamflow Velocities, and Dispersion Rates in the Yellowstone River, Montana: U.S. Geological Survey Scientific Investigations Report 2009-5261, Report: vi, 26 p.; 4 Appendixes (xls), https://doi.org/10.3133/sir20095261.","productDescription":"Report: vi, 26 p.; 4 Appendixes (xls)","onlineOnly":"N","additionalOnlineFiles":"Y","temporalStart":"2008-09-15","temporalEnd":"2008-10-15","costCenters":[{"id":400,"text":"Montana Water Science Center","active":false,"usgs":true}],"links":[{"id":125630,"rank":0,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/sir_2009_5261.jpg"},{"id":13341,"rank":100,"type":{"id":15,"text":"Index Page"},"url":"https://pubs.usgs.gov/sir/2009/5261/","linkFileType":{"id":5,"text":"html"}}],"geographicExtents":"{ \"type\": \"FeatureCollection\", \"features\": [ { \"type\": \"Feature\", \"properties\": {}, \"geometry\": { \"type\": \"Polygon\", \"coordinates\": [ [ [ -109,45 ], [ -109,47.5 ], [ -104,47.5 ], [ -104,45 ], [ -109,45 ] ] ] } } ] }","noUsgsAuthors":false,"publicationStatus":"PW","scienceBaseUri":"4f4e4a4ce4b07f02db626af7","contributors":{"authors":[{"text":"McCarthy, Peter 0000-0002-2396-7463 pmccarth@usgs.gov","orcid":"https://orcid.org/0000-0002-2396-7463","contributorId":2504,"corporation":false,"usgs":true,"family":"McCarthy","given":"Peter","email":"pmccarth@usgs.gov","affiliations":[{"id":5050,"text":"WY-MT Water Science Center","active":true,"usgs":true}],"preferred":true,"id":304177,"contributorType":{"id":1,"text":"Authors"},"rank":1}]}}
,{"id":98107,"text":"sir20095233 - 2009 - Evaluation of catchment delineation methods for the medium-resolution National Hydrography Dataset","interactions":[],"lastModifiedDate":"2017-03-29T14:23:27","indexId":"sir20095233","displayToPublicDate":"2010-01-15T00:00:00","publicationYear":"2009","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":"2009-5233","title":"Evaluation of catchment delineation methods for the medium-resolution National Hydrography Dataset","docAbstract":"Different methods for determining catchments (incremental drainage areas) for stream segments of the medium-resolution (1:100,000-scale) National Hydrography Dataset (NHD) were evaluated by the U.S. Geological Survey (USGS), in cooperation with the U.S. Environmental Protection Agency (USEPA). The NHD is a comprehensive set of digital spatial data that contains information about surface-water features (such as lakes, ponds, streams, and rivers) of the United States. The need for NHD catchments was driven primarily by the goal to estimate NHD streamflow and velocity to support water-quality modeling. The application of catchments for this purpose also demonstrates the broader value of NHD catchments for supporting landscape characterization and analysis.\n\nFive catchment delineation methods were evaluated. Four of the methods use topographic information for the delineation of the NHD catchments. These methods include the Raster Seeding Method; two variants of a method first used in a USGS New England study-one used the Watershed Boundary Dataset (WBD) and the other did not-termed the 'New England Methods'; and the Outlet Matching Method. For these topographically based methods, the elevation data source was the 30-meter (m) resolution National Elevation Dataset (NED), as this was the highest resolution available for the conterminous United States and Hawaii. The fifth method evaluated, the Thiessen Polygon Method, uses distance to the nearest NHD stream segments to determine catchment boundaries.\n\nCatchments were generated using each method for NHD stream segments within six hydrologically and geographically distinct Subbasins to evaluate the applicability of the method across the United States. The five methods were evaluated by comparing the resulting catchments with the boundaries and the computed area measurements available from several verification datasets that were developed independently using manual methods.\n\nThe results of the evaluation indicated that the two New England Methods provided the most accurate catchment boundaries. The New England Method with the WBD provided the most accurate results. The time and cost to implement and apply these automated methods were also considered in ultimately selecting the methods used to produce NHD catchments for the conterminous United States and Hawaii.\n\nThis study was conducted by a joint USGS-USEPA team during the 2-year period that ended in September 2004. During the following 2-year period ending in the fall of 2006, the New England Methods were used to produce NHD catchments as part of a multiagency effort to generate the NHD streamflow and velocity estimates for a suite of integrated geospatial products known as 'NHDPlus.'","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/sir20095233","collaboration":"Prepared in cooperation with the U.S. Environmental Protection Agency","usgsCitation":"Johnston, C.M., Dewald, T.G., Bondelid, T., Worstell, B.B., McKay, L., Rea, A., Moore, R.B., and Goodall, J.L., 2009, Evaluation of catchment delineation methods for the medium-resolution National Hydrography Dataset: U.S. Geological Survey Scientific Investigations Report 2009-5233, x, 89 p., https://doi.org/10.3133/sir20095233.","productDescription":"x, 89 p.","onlineOnly":"N","additionalOnlineFiles":"N","costCenters":[{"id":222,"text":"Earth Resources Observation and Science (EROS) Center","active":true,"usgs":true},{"id":468,"text":"New Hampshire-Vermont Water Science Center","active":false,"usgs":true}],"links":[{"id":125649,"rank":0,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/sir_2009_5233.jpg"},{"id":13346,"rank":100,"type":{"id":15,"text":"Index Page"},"url":"https://pubs.usgs.gov/sir/2009/5233/","linkFileType":{"id":5,"text":"html"}},{"id":338662,"rank":3,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/sir/2009/5233/pdf/sir2009-5233.pdf"}],"country":"United States","otherGeospatial":"New England","geographicExtents":"{ \"type\": \"FeatureCollection\", \"features\": [ { \"type\": \"Feature\", \"properties\": {}, \"geometry\": { \"type\": \"Polygon\", \"coordinates\": [ [ [ -125,23 ], [ -125,50 ], [ -65,50 ], [ -65,23 ], [ -125,23 ] ] ] } } ] }","noUsgsAuthors":false,"publicationStatus":"PW","scienceBaseUri":"4f4e4a09e4b07f02db5fb018","contributors":{"authors":[{"text":"Johnston, Craig M. cmjohnst@usgs.gov","contributorId":1814,"corporation":false,"usgs":true,"family":"Johnston","given":"Craig","email":"cmjohnst@usgs.gov","middleInitial":"M.","affiliations":[{"id":405,"text":"NH/VT office of New England Water Science Center","active":true,"usgs":true}],"preferred":true,"id":304185,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Dewald, Thomas G.","contributorId":97600,"corporation":false,"usgs":true,"family":"Dewald","given":"Thomas","email":"","middleInitial":"G.","affiliations":[],"preferred":false,"id":304191,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Bondelid, Timothy R.","contributorId":90420,"corporation":false,"usgs":true,"family":"Bondelid","given":"Timothy R.","affiliations":[],"preferred":false,"id":304190,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Worstell, Bruce B. 0000-0001-8927-3336 worstell@usgs.gov","orcid":"https://orcid.org/0000-0001-8927-3336","contributorId":1815,"corporation":false,"usgs":true,"family":"Worstell","given":"Bruce","email":"worstell@usgs.gov","middleInitial":"B.","affiliations":[{"id":223,"text":"Earth Resources Observation and Science (EROS) Center (Geography)","active":false,"usgs":true}],"preferred":true,"id":304186,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"McKay, Lucinda D.","contributorId":90010,"corporation":false,"usgs":true,"family":"McKay","given":"Lucinda D.","affiliations":[],"preferred":false,"id":304189,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"Rea, Alan","contributorId":41018,"corporation":false,"usgs":true,"family":"Rea","given":"Alan","affiliations":[],"preferred":false,"id":304187,"contributorType":{"id":1,"text":"Authors"},"rank":6},{"text":"Moore, Richard B. rmoore@usgs.gov","contributorId":1464,"corporation":false,"usgs":true,"family":"Moore","given":"Richard","email":"rmoore@usgs.gov","middleInitial":"B.","affiliations":[{"id":405,"text":"NH/VT office of New England Water Science Center","active":true,"usgs":true}],"preferred":true,"id":304184,"contributorType":{"id":1,"text":"Authors"},"rank":7},{"text":"Goodall, Jonathan L.","contributorId":59535,"corporation":false,"usgs":true,"family":"Goodall","given":"Jonathan","email":"","middleInitial":"L.","affiliations":[],"preferred":false,"id":304188,"contributorType":{"id":1,"text":"Authors"},"rank":8}]}}
,{"id":98101,"text":"ofr20091225 - 2009 - Natural offshore oil seepage and related tarball accumulation on the California coastline — Santa Barbara Channel and the southern Santa Maria Basin; source identification and inventory","interactions":[],"lastModifiedDate":"2022-06-09T20:25:10.852413","indexId":"ofr20091225","displayToPublicDate":"2010-01-14T00:00:00","publicationYear":"2009","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":"2009-1225","title":"Natural offshore oil seepage and related tarball accumulation on the California coastline — Santa Barbara Channel and the southern Santa Maria Basin; source identification and inventory","docAbstract":"<p>Oil spillage from natural sources is very common in the waters of southern California. Active oil extraction and shipping is occurring concurrently within the region and it is of great interest to resource managers to be able to distinguish between natural seepage and anthropogenic oil spillage.</p><p>The major goal of this study was to establish the geologic setting, sources, and ultimate dispersal of natural oil seeps in the offshore southern Santa Maria Basin and Santa Barbara Basins. Our surveys focused on likely areas of hydrocarbon seepage that are known to occur between Point Arguello and Ventura, California.</p><p>Our approach was to 1) document the locations and geochemically fingerprint natural seep oils or tar; 2) geochemically fingerprint coastal tar residues and potential tar sources in this region, both onshore and offshore; 3) establish chemical correlations between offshore active seeps and coastal residues thus linking seep sources to oil residues; 4) measure the rate of natural seepage of individual seeps and attempt to assess regional natural oil and gas seepage rates; and 5) interpret the petroleum system history for the natural seeps.</p><p>To document the location of sub-sea oil seeps, we first looked into previous studies within and near our survey area. We measured the concentration of methane gas in the water column in areas of reported seepage and found numerous gas plumes and measured high concentrations of methane in the water column. The result of this work showed that the seeps were widely distributed between Point Conception east to the vicinity of Coal Oil Point, and that they by in large occur within the 3-mile limit of California State waters. Subsequent cruises used sidescan and high resolution seismic to map the seafloor, from just south of Point Arguello, east to near Gaviota, California. The results of the methane survey guided the exploration of the area west of Point Conception east to Gaviota using a combination of seismic instruments. The seafloor was mapped by sidescan sonar, and numerous lines of high -resolution seismic surveys were conducted over areas of interest.</p><p>Biomarker and stable carbon isotope ratios were used to infer the age, lithology, organic matter input, and depositional environment of the source rocks for 388 samples of produced crude oil, seep oil, and tarballs mainly from coastal California. These samples were used to construct a chemometric fingerprint (multivariate statistics) decision tree to classify 288 additional samples, including tarballs of unknown origin collected from Monterey and San Mateo County beaches after a storm in early 2007. A subset of 9 of 23 active offshore platform oils and one inactive platform oil representing a few oil reservoirs from the western Santa Barbara Channel were used in this analysis, and thus this model is not comprehensive and the findings are not conclusive. The platform oils included in this study are from west to east: Irene, Hildago, Harvest, Hermosa, Heritage, Harmony, Hondo, Holly, Platform A, and Hilda (now removed).</p><p>The results identify three “tribes” of<span>&nbsp;</span><sup>13</sup>C-rich oil samples inferred to originate from thermally mature equivalents of the clayey-siliceous, carbonaceous marl, and lower calcareous-siliceous members of the Monterey Formation. Tribe 1 contains four oil families having geochemical traits of clay-rich marine shale source rock deposited under suboxic conditions with substantial higher-plant input. Tribe 2 contains four oil families with intermediate traits, except for abundant 28,30-bisnorhopane, indicating suboxic to anoxic marine marl source rock with hemipelagic input. Tribe 3 contains five oil families with traits of distal marine carbonate source rock deposited under anoxic conditions with pelagic but little or no higher-plant input. Tribes 1 and 2 occur mainly south of Point Conception in paleogeographic settings where deep burial of the Monterey Formation source rock favored generation from all three members or their equivalents. In this area, oil from the clayey-siliceous and carbonaceous marl members (Tribes 1 and 2) may overwhelm that from the lower calcareous-siliceous member (Tribe 3) because the latter is thinner and less oil-prone than the overlying members. Tribe 3 occurs mainly north of Point Conception, where shallow burial caused preferential generation from the underlying lower calcareous-siliceous member or another unit with similar characteristics.</p><p>It is very desirable to be able to clearly distinguish the naturally occurring seep oils from the anthropogenically derived platform oils. Within the “training set” of oils and tars (388 samples), the biomarker parameters are sometimes sufficient to allow unique discrimination of individual platform oils. More often however, platform samples and seep samples with sources geographically close to each other are too similar to each other, with respect to the biomarker parameters, to definitively differentiate them on that basis alone. In some cases other parameters can be helpful. These other parameters are related to the degree of biogeochemical degradation or weathering that the oils or tars have experienced. These components include the typical oil distribution of n-alkane hydrocarbons and isoprenoids pristane and phytane. All of the platform oils in our sample set contain these components. On the other hand, the seep oils or tars have been exposed to significant biodegradation while in the near subsurface. The majority, but not all of seep oils or tars have been biodegraded up to or beyond the loss of n-alkanes and isoprenoids. Seep oils found in the vicinity of Coal Oil Point or Arroyo Burro are apparently the least weathered and are particularly likely to retain significant n-alkanes and isoprenoids. Therefore the combination of chemometric fingerprinting and the presence or absence of n-alkanes and isoprenoids help to differentiate anthropogenic production oils versus natural seeps oils and tars. The differentiation is not always definitive because of the close chemical similarity of some samples and the variability in the biodegradation progression. This is the case near Coal Oil Point, and near Platform A (Dos Cuadros Field) where seep oils and Platform Holly and Platform A oils are genetically very similar and cannot be definitively distinguished after a period of a few days of weathering. In contrast, oils from the Point Conception platforms can be distinguished on the basis of chemometric fingerprinting alone. In the middle of this spectrum are oils from Platforms Harmony, Heritage, and Hondo, where it is expected that oil weathering would take on the order of two weeks to a month to produce tarballs similar to those seen near Point Conception. In this case there is a much greater degree of weathering needed to proceed from produced oil to the biodegraded tar characteristic of tarball stranded on the beach.</p><p>Tar deposition on beaches was monitored as part of cooperative with the County of Santa Barbara Energy Division and the U.S. Geological Survey during 2001-2003. We found tar deposition varies on a seasonal basis. In general, tarballs accumulate at a faster rate or remain longer on all beaches during the summer and fall months. The reasons for this are unclear based on our limited observations, however we speculate that factors such as prevailing winds and currents combined with more quiescent wave conditions favors the accumulation and preservation of tarballs on the beach during the summer and fall months. In contrast, winter storms, with much greater wave action remove beach sand and other materials, and stormy seas tend to break up oil that might weather into tarballs. Natural seepage is affected by the spring/neap tidal cycle; however, the link to tar deposition is unclear. Longer periods of monitoring are needed to address the variability in the data and provide a more robust statistical analysis.</p>","language":"English","publisher":"U.S. Geological Survey","doi":"10.3133/ofr20091225","collaboration":"A study in cooperation with the Minerals Management Service and the Energy Division, County of Santa Barbara, California; Also released as MMS report 2009-030; This study was funded in part by the U. S. Department of the Interior, Minerals Management Service (MMS), through an Interagency Agreement No. 18985 with the U.S. Geological Survey, Western Coastal and Marine Geology Team, as part of the MMS Environmental Studies Program.","usgsCitation":"Lorenson, T., Hostettler, F.D., Rosenbauer, R.J., Peters, K., Dougherty, J.A., Kvenvolden, K.A., Gutmacher, C.E., Wong, F.L., and Normark, W.R., 2009, Natural offshore oil seepage and related tarball accumulation on the California coastline — Santa Barbara Channel and the southern Santa Maria Basin; source identification and inventory: U.S. Geological Survey Open-File Report 2009-1225, Report: iii, 116 p.; Appendixes, https://doi.org/10.3133/ofr20091225.","productDescription":"Report: iii, 116 p.; Appendixes","onlineOnly":"N","additionalOnlineFiles":"Y","costCenters":[{"id":520,"text":"Pacific Coastal and Marine Science Center","active":true,"usgs":true},{"id":645,"text":"Western Coastal and Marine Geology","active":false,"usgs":true}],"links":[{"id":125640,"rank":0,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/ofr_2009_1225.jpg"},{"id":402029,"rank":3,"type":{"id":36,"text":"NGMDB Index Page"},"url":"https://ngmdb.usgs.gov/Prodesc/proddesc_90302.htm"},{"id":13337,"rank":100,"type":{"id":15,"text":"Index Page"},"url":"https://pubs.usgs.gov/of/2009/1225/","linkFileType":{"id":5,"text":"html"}}],"country":"United States","state":"California","otherGeospatial":"Santa Barbara Channel, Santa Maria Basin","geographicExtents":"{\n  \"type\": \"FeatureCollection\",\n  \"features\": [\n    {\n      \"type\": \"Feature\",\n      \"properties\": {},\n      \"geometry\": {\n        \"type\": \"Polygon\",\n        \"coordinates\": [\n          [\n            [\n              -121.014404296875,\n              34.134541681937364\n            ],\n            [\n              -119.32250976562499,\n              34.134541681937364\n            ],\n            [\n      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fdhostet@usgs.gov","contributorId":3383,"corporation":false,"usgs":true,"family":"Hostettler","given":"Frances","email":"fdhostet@usgs.gov","middleInitial":"D.","affiliations":[],"preferred":true,"id":304164,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Rosenbauer, Robert J. brosenbauer@usgs.gov","contributorId":204,"corporation":false,"usgs":true,"family":"Rosenbauer","given":"Robert","email":"brosenbauer@usgs.gov","middleInitial":"J.","affiliations":[{"id":520,"text":"Pacific Coastal and Marine Science Center","active":true,"usgs":true}],"preferred":true,"id":304161,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Peters, Kenneth E.","contributorId":10897,"corporation":false,"usgs":true,"family":"Peters","given":"Kenneth E.","affiliations":[],"preferred":false,"id":304167,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Dougherty, Jennifer A.","contributorId":6114,"corporation":false,"usgs":true,"family":"Dougherty","given":"Jennifer","email":"","middleInitial":"A.","affiliations":[],"preferred":false,"id":304166,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"Kvenvolden, Keith A. kkvenvolden@usgs.gov","contributorId":3384,"corporation":false,"usgs":true,"family":"Kvenvolden","given":"Keith","email":"kkvenvolden@usgs.gov","middleInitial":"A.","affiliations":[{"id":520,"text":"Pacific Coastal and Marine Science Center","active":true,"usgs":true}],"preferred":true,"id":304165,"contributorType":{"id":1,"text":"Authors"},"rank":6},{"text":"Gutmacher, Christina E.","contributorId":28272,"corporation":false,"usgs":true,"family":"Gutmacher","given":"Christina","email":"","middleInitial":"E.","affiliations":[],"preferred":false,"id":304168,"contributorType":{"id":1,"text":"Authors"},"rank":7},{"text":"Wong, Florence L. 0000-0002-3918-5896 fwong@usgs.gov","orcid":"https://orcid.org/0000-0002-3918-5896","contributorId":1990,"corporation":false,"usgs":true,"family":"Wong","given":"Florence","email":"fwong@usgs.gov","middleInitial":"L.","affiliations":[{"id":520,"text":"Pacific Coastal and Marine Science Center","active":true,"usgs":true}],"preferred":true,"id":304162,"contributorType":{"id":1,"text":"Authors"},"rank":8},{"text":"Normark, William R.","contributorId":69570,"corporation":false,"usgs":true,"family":"Normark","given":"William","email":"","middleInitial":"R.","affiliations":[],"preferred":false,"id":304169,"contributorType":{"id":1,"text":"Authors"},"rank":9}]}}
,{"id":98095,"text":"ofr20091232 - 2009 - Quality-Assurance Data for Routine Water Analyses by the U.S. Geological Survey Laboratory in Troy, New York - July 2001 Through June 2003","interactions":[],"lastModifiedDate":"2012-03-08T17:16:29","indexId":"ofr20091232","displayToPublicDate":"2010-01-07T00:00:00","publicationYear":"2009","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":"2009-1232","title":"Quality-Assurance Data for Routine Water Analyses by the U.S. Geological Survey Laboratory in Troy, New York - July 2001 Through June 2003","docAbstract":"The laboratory for analysis of low-ionic-strength water at the U.S. Geological Survey (USGS) Water Science Center in Troy, N.Y., analyzes samples collected by USGS projects throughout the Northeast. The laboratory's quality-assurance program is based on internal and interlaboratory quality-assurance samples and quality-control procedures that were developed to ensure proper sample collection, processing, and analysis. The quality-assurance and quality-control data were stored in the laboratory's Lab Master data-management system, which provides efficient review, compilation, and plotting of data. This report presents and discusses results of quality-assurance and quality control samples analyzed from July 2001 through June 2003.\r\n\r\nResults for the quality-control samples for 19 analytical procedures were evaluated for bias and precision. Control charts indicate that data for six of the analytical procedures were occasionally biased for either high-concentration or low-concentration samples but were within control limits; these procedures were: acid-neutralizing capacity, chloride, magnesium, nitrate (ion chromatography), potassium, and sodium. The calcium procedure was biased throughout the analysis period for the high-concentration sample, but was within control limits. The total monomeric aluminum and fluoride procedures were biased throughout the analysis period for the low-concentration sample, but were within control limits. The total aluminum, pH, specific conductance, and sulfate procedures were biased for the high-concentration and low-concentration samples, but were within control limits.\r\n\r\nResults from the filter-blank and analytical-blank analyses indicate that the procedures for 16 of 18 analytes were within control limits, although the concentrations for blanks were occasionally outside the control limits. The data-quality objective was not met for the dissolved organic carbon or specific conductance procedures.\r\nSampling and analysis precision are evaluated herein in terms of the coefficient of variation obtained for triplicate samples in the procedures for 18 of the 21 analytes. At least 90 percent of the samples met data-quality objectives for all procedures except total monomeric aluminum (83 percent of samples met objectives), total aluminum (76 percent of samples met objectives), ammonium (73 percent of samples met objectives), dissolved organic carbon (86 percent of samples met objectives), and nitrate (81 percent of samples met objectives). The data-quality objective was not met for the nitrite procedure.\r\n\r\nResults of the USGS interlaboratory Standard Reference Sample (SRS) Project indicated satisfactory or above data quality over the time period, with most performance ratings for each sample in the good-to-excellent range. The N-sample (nutrient constituents) analysis had one unsatisfactory rating for the ammonium procedure in one study. The T-sample (trace constituents) analysis had one unsatisfactory rating for the magnesium procedure and one marginal rating for the potassium procedure in one study and one unsatisfactory rating for the sodium procedure in another.\r\n\r\nResults of Environment Canada's National Water Research Institute (NWRI) program indicated that at least 90 percent of the samples met data-quality objectives for 10 of the 14 analytes; the exceptions were acid-neutralizing capacity, ammonium, dissolved organic carbon, and sodium. Data-quality objectives were not met in 37 percent of samples analyzed for acid-neutralizing capacity, 28 percent of samples analyzed for dissolved organic carbon, and 30 percent of samples analyzed for sodium. Results indicate a positive bias for the ammonium procedure in one study and a negative bias in another.\r\nResults from blind reference-sample analyses indicated that data-quality objectives were met by at least 90 percent of the samples analyzed for calcium, chloride, magnesium, pH, potassium, and sodium. Data-quality objectives were met by 78 percent of ","language":"ENGLISH","publisher":"U.S. Geological Survey","doi":"10.3133/ofr20091232","usgsCitation":"Lincoln, T.A., Horan-Ross, D.A., McHale, M.R., and Lawrence, G.B., 2009, Quality-Assurance Data for Routine Water Analyses by the U.S. Geological Survey Laboratory in Troy, New York - July 2001 Through June 2003: U.S. Geological Survey Open-File Report 2009-1232, iv, 33 p., https://doi.org/10.3133/ofr20091232.","productDescription":"iv, 33 p.","onlineOnly":"Y","additionalOnlineFiles":"N","temporalStart":"2001-07-01","temporalEnd":"2003-06-30","costCenters":[{"id":474,"text":"New York Water Science Center","active":true,"usgs":true}],"links":[{"id":125861,"rank":0,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/ofr_2009_1232.jpg"},{"id":13331,"rank":100,"type":{"id":15,"text":"Index Page"},"url":"https://pubs.usgs.gov/of/2009/1232/","linkFileType":{"id":5,"text":"html"}}],"noUsgsAuthors":false,"publicationStatus":"PW","scienceBaseUri":"4f4e4a69e4b07f02db63bfd2","contributors":{"authors":[{"text":"Lincoln, Tricia A. tarenga@usgs.gov","contributorId":3803,"corporation":false,"usgs":true,"family":"Lincoln","given":"Tricia","email":"tarenga@usgs.gov","middleInitial":"A.","affiliations":[],"preferred":true,"id":304134,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Horan-Ross, Debra A. dhross@usgs.gov","contributorId":3809,"corporation":false,"usgs":true,"family":"Horan-Ross","given":"Debra","email":"dhross@usgs.gov","middleInitial":"A.","affiliations":[],"preferred":true,"id":304135,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"McHale, Michael R. 0000-0003-3780-1816 mmchale@usgs.gov","orcid":"https://orcid.org/0000-0003-3780-1816","contributorId":1735,"corporation":false,"usgs":true,"family":"McHale","given":"Michael","email":"mmchale@usgs.gov","middleInitial":"R.","affiliations":[{"id":474,"text":"New York Water Science Center","active":true,"usgs":true}],"preferred":true,"id":304133,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Lawrence, Gregory B. 0000-0002-8035-2350 glawrenc@usgs.gov","orcid":"https://orcid.org/0000-0002-8035-2350","contributorId":867,"corporation":false,"usgs":true,"family":"Lawrence","given":"Gregory","email":"glawrenc@usgs.gov","middleInitial":"B.","affiliations":[{"id":474,"text":"New York Water Science Center","active":true,"usgs":true}],"preferred":true,"id":304132,"contributorType":{"id":1,"text":"Authors"},"rank":4}]}}
,{"id":98092,"text":"sir20095216 - 2009 - Analysis of Water-Quality Trends for Selected Streams in the Water Chemistry Monitoring Program, Michigan, 1998-2005 ","interactions":[],"lastModifiedDate":"2012-03-08T17:16:29","indexId":"sir20095216","displayToPublicDate":"2010-01-06T00:00:00","publicationYear":"2009","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":"2009-5216","title":"Analysis of Water-Quality Trends for Selected Streams in the Water Chemistry Monitoring Program, Michigan, 1998-2005 ","docAbstract":"In 1998, the Michigan Department of Environmental Quality and the U.S. Geological Survey began a long-term monitoring program to evaluate the water quality of most watersheds in Michigan. Major goals of this Water-Chemistry Monitoring Program were to identify streams exceeding or not meeting State or Federal water-quality standards and to assess if constituent concentrations reflecting water quality in these streams were increasing or decreasing over time. As part of this program, water-quality data collected from 1998 to 2005 were analyzed to identify potential trends. Sixteen water-quality constituents were analyzed at 31 sites across Michigan, 28 of which had sufficient data to analyze for trends. Trend analysis on the various water-quality data was done using the uncensored Seasonal Kendall test within the computer program ESTREND. The most prevalent trend detected throughout the state was for chloride. Chloride trends were detected at 8 of the 28 sites; trends at 7 sites were increasing and the trend at 1 site was decreasing. Although no trends were detected for various nitrogen species or phosphorus, these constituents were detected at levels greater than the U.S. Environmental Protection Agency recommendations for nutrients in water. The results of the trend analysis will help to establish a baseline to evaluate future changes in water quality in Michigan streams.","language":"ENGLISH","publisher":"U.S. Geological Survey","doi":"10.3133/sir20095216","collaboration":"Prepared in cooperation with the Michigan Department of Environmental Quality","usgsCitation":"Hoard, C.J., Fuller, L.M., and Fogarty, L., 2009, Analysis of Water-Quality Trends for Selected Streams in the Water Chemistry Monitoring Program, Michigan, 1998-2005 : U.S. Geological Survey Scientific Investigations Report 2009-5216, viii, 48 p., https://doi.org/10.3133/sir20095216.","productDescription":"viii, 48 p.","onlineOnly":"N","additionalOnlineFiles":"N","temporalStart":"1998-01-01","temporalEnd":"2005-12-31","costCenters":[{"id":382,"text":"Michigan Water Science Center","active":true,"usgs":true}],"links":[{"id":125875,"rank":0,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/sir_2009_5216.jpg"},{"id":13328,"rank":100,"type":{"id":15,"text":"Index Page"},"url":"https://pubs.usgs.gov/sir/2009/5216/","linkFileType":{"id":5,"text":"html"}}],"geographicExtents":"{ \"type\": \"FeatureCollection\", \"features\": [ { \"type\": \"Feature\", \"properties\": {}, \"geometry\": { \"type\": \"Polygon\", \"coordinates\": [ [ [ -91,41 ], [ -91,48 ], [ -82,48 ], [ -82,41 ], [ -91,41 ] ] ] } } ] }","noUsgsAuthors":false,"publicationStatus":"PW","scienceBaseUri":"4f4e4ad0e4b07f02db680ae5","contributors":{"authors":[{"text":"Hoard, C. J.","contributorId":37436,"corporation":false,"usgs":true,"family":"Hoard","given":"C.","email":"","middleInitial":"J.","affiliations":[],"preferred":false,"id":304125,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Fuller, Lori M. lmfuller@usgs.gov","contributorId":2100,"corporation":false,"usgs":true,"family":"Fuller","given":"Lori","email":"lmfuller@usgs.gov","middleInitial":"M.","affiliations":[{"id":382,"text":"Michigan Water Science Center","active":true,"usgs":true}],"preferred":false,"id":304124,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Fogarty, Lisa R.","contributorId":74074,"corporation":false,"usgs":true,"family":"Fogarty","given":"Lisa R.","affiliations":[],"preferred":false,"id":304126,"contributorType":{"id":1,"text":"Authors"},"rank":3}]}}
,{"id":98089,"text":"sir20095224 - 2009 - Riparian and Associated Habitat Characteristics Related to Nutrient Concentrations and Biological Responses of Small Streams in Selected Agricultural Areas, United States, 2003-04","interactions":[],"lastModifiedDate":"2012-03-08T17:16:29","indexId":"sir20095224","displayToPublicDate":"2010-01-06T00:00:00","publicationYear":"2009","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":"2009-5224","title":"Riparian and Associated Habitat Characteristics Related to Nutrient Concentrations and Biological Responses of Small Streams in Selected Agricultural Areas, United States, 2003-04","docAbstract":"Physical factors, including both in-stream and riparian habitat characteristics that limit biomass or otherwise regulate aquatic biological condition, have been identified by previous studies. However, linking the ecological significance of nutrient enrichment to habitat or landscape factors that could allow for improved management of streams has proved to be a challenge in many regions, including agricultural landscapes, where many ecological stressors are strong and the variability among watersheds typically is large. Riparian and associated habitat characteristics were sampled once during 2003-04 for an intensive ecological and nutrients study of small perennial streams in five contrasting agricultural landscapes across the United States to determine how biological communities and ecosystem processes respond to varying levels of nutrient enrichment. Nutrient concentrations were determined in stream water at two different sampling times per site and biological samples were collected once per site near the time of habitat characterization. Data for 141 sampling sites were compiled, representing five study areas, located in parts of the Delmarva Peninsula (Delaware and Maryland), Georgia, Indiana, Ohio, Nebraska, and Washington. This report examines the available data for riparian and associated habitat characteristics to address questions related to study-unit contrasts, spatial scale-related differences, multivariate correlation structure, and bivariate relations between selected habitat characteristics and either stream nutrient conditions or biological responses.\r\n\r\nRiparian and associated habitat characteristics were summarized and categorized into 22 groups of habitat variables, with 11 groups representing land-use and land-cover characteristics and 11 groups representing other riparian or in-stream habitat characteristics. Principal components analysis was used to identify a reduced set of habitat variables that describe most of the variability among the sampled sites. The habitat characteristics sampled within the five study units were compared statistically. Bivariate correlations between riparian habitat variables and either nutrient-chemistry or biological-response variables were examined for all sites combined, and for sites within each study area.\r\n\r\nNutrient concentrations were correlated with the extent of riparian cropland. For nitrogen species, these correlations were more frequently at the basin scale, whereas for phosphorus, they were about equally frequent at the segment and basin scales. Basin-level extents of riparian cropland and reach-level bank vegetative cover were correlated strongly with both total nitrogen and dissolved inorganic nitrogen (DIN) among multiple study areas, reflecting the importance of agricultural land-management and conservation practices for reducing nitrogen delivery from near-stream sources. When sites lacking segment-level wetlands were excluded, the negative correlation of riparian wetland extent with DIN among 49 sites was strong at the reach and segment levels. Riparian wetland vegetation thus may be removing dissolved nutrients from soil water and shallow groundwater passing through riparian zones. Other habitat variables that correlated strongly with nitrogen and phosphorus species included suspended sediment, light availability, and antecedent water temperature.\r\n\r\nChlorophyll concentrations in seston were positively correlated with phosphorus concentrations for all sites combined. Benthic chlorophyll was correlated strongly with nutrient concentrations in only the Delmarva study area and only in fine-grained habitats. Current velocity or hydraulic scour could explain correlation patterns for benthic chlorophyll among Georgia sites, whereas chlorophyll in seston was correlated with antecedent water temperature among Washington and Delmarva sites. The lack of any consistent correlation pattern between habitat characteristics and organic material density (ash-free dry mass)","language":"ENGLISH","publisher":"U.S. Geological Survey","doi":"10.3133/sir20095224","usgsCitation":"Zelt, R.B., and Munn, M.D., 2009, Riparian and Associated Habitat Characteristics Related to Nutrient Concentrations and Biological Responses of Small Streams in Selected Agricultural Areas, United States, 2003-04: U.S. Geological Survey Scientific Investigations Report 2009-5224, xiv, 79 p., https://doi.org/10.3133/sir20095224.","productDescription":"xiv, 79 p.","temporalStart":"2003-01-01","temporalEnd":"2004-12-31","costCenters":[{"id":464,"text":"Nebraska Water Science Center","active":true,"usgs":true}],"links":[{"id":125779,"rank":0,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/sir_2009_5224.jpg"},{"id":13323,"rank":100,"type":{"id":15,"text":"Index Page"},"url":"https://pubs.usgs.gov/sir/2009/5224/","linkFileType":{"id":5,"text":"html"}}],"geographicExtents":"{ \"type\": \"FeatureCollection\", \"features\": [ { \"type\": \"Feature\", \"properties\": {}, \"geometry\": { \"type\": \"Polygon\", \"coordinates\": [ [ [ -130,20 ], [ -130,50 ], [ -65,50 ], [ -65,20 ], [ -130,20 ] ] ] } } ] }","noUsgsAuthors":false,"publicationStatus":"PW","scienceBaseUri":"4f4e4a13e4b07f02db6020ef","contributors":{"authors":[{"text":"Zelt, Ronald B. 0000-0001-9024-855X rbzelt@usgs.gov","orcid":"https://orcid.org/0000-0001-9024-855X","contributorId":300,"corporation":false,"usgs":true,"family":"Zelt","given":"Ronald","email":"rbzelt@usgs.gov","middleInitial":"B.","affiliations":[{"id":464,"text":"Nebraska Water Science Center","active":true,"usgs":true},{"id":154,"text":"California Water Science Center","active":true,"usgs":true}],"preferred":true,"id":304114,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Munn, Mark D. 0000-0002-7154-7252 mdmunn@usgs.gov","orcid":"https://orcid.org/0000-0002-7154-7252","contributorId":976,"corporation":false,"usgs":true,"family":"Munn","given":"Mark","email":"mdmunn@usgs.gov","middleInitial":"D.","affiliations":[{"id":622,"text":"Washington Water Science Center","active":true,"usgs":true}],"preferred":true,"id":304115,"contributorType":{"id":1,"text":"Authors"},"rank":2}]}}
,{"id":98090,"text":"ofr20091297 - 2009 - Compilation of Mineral Resource Data for Mississippi Valley-Type and Clastic-Dominated Sediment-Hosted Lead-Zinc Deposits","interactions":[],"lastModifiedDate":"2012-02-02T00:14:47","indexId":"ofr20091297","displayToPublicDate":"2010-01-06T00:00:00","publicationYear":"2009","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":"2009-1297","title":"Compilation of Mineral Resource Data for Mississippi Valley-Type and Clastic-Dominated Sediment-Hosted Lead-Zinc Deposits","docAbstract":"This report contains a global compilation of the mineral resource data for sediment-hosted lead-zinc (SH Pb-Zn) deposits. Sediment-hosted lead-zinc deposits are historically the most significant sources of lead and zinc, and are mined throughout the world. The most important SH Pb-Zn deposits are hosted in clastic-dominated sedimentary rock sequences (CD Pb-Zn) that are traditionally called sedimentary exhalative (SEDEX) deposits, and those in carbonate-dominated sequences that are known as Mississippi Valley-type (MVT) Pb-Zn deposits. In this report, we do not include sandstone-Pb, sandstone-hosted Pb, or Pb-Zn vein districts such as those in Freiberg, Germany, or Coeur d'Alene, Idaho, because these deposits probably represent different deposit types (Leach and others, 2005). We do not include fracture-controlled deposits in which fluorite is dominant and barite typically abundant (for example, Central Kentucky; Hansonburg, N. Mex.) or the stratabound fluorite-rich, but also lead- and zinc-bearing deposits, such as those in southern Illinois, which are considered a genetic variant of carbonate-hosted Pb-Zn deposits (Leach and Sangster, 1993).\r\n\r\nThis report updates the Pb, Zn, copper (Cu), and silver (Ag) grade and tonnage data in Leach and others (2005), which itself was based on efforts in the Canadian Geological Survey World Minerals Geoscience Database Project (contributions of D.F. Sangster to Sinclair and others, 1999). New geological or geochronological data, classifications of the tectonic environment in which the deposits formed, and key references to the geology of the deposits are presented in our report. Data for 121 CD deposits, 113 MVT deposits, and 6 unclassified deposits that were previously classified as either SEDEX or MVT in the Leach and others (2005) compilation, are given in appendix table A1. In some cases, mineral resource data were available only for total district resources, but not for individual mines within the district. For these districts, the resource data are presented in appendix table A2. In addition, numerous figures (appendix figures B1-B9) displaying important grade-tonnage and geologic features are included.\r\n\r\nThese mineral deposit resource data are important for exploration targeting and mineral resource assessments. There is significant variability in the resource data for these deposit types, and ore controls vary from one region to another. Therefore, grade-tonnage estimations are best evaluated as subsets of the data in appendix table A1 where local mineralization styles and ore controls characterize the region being evaluated for grade-tonnage relations. Furthermore, consideration should also be given to the tendency for MVT resources to occur in large mineralized regions.","language":"ENGLISH","publisher":"U.S. Geological Survey","doi":"10.3133/ofr20091297","usgsCitation":"Taylor, R.D., Leach, D.L., Bradley, D., and Pisarevsky, S.A., 2009, Compilation of Mineral Resource Data for Mississippi Valley-Type and Clastic-Dominated Sediment-Hosted Lead-Zinc Deposits: U.S. Geological Survey Open-File Report 2009-1297, iii, 42 p., https://doi.org/10.3133/ofr20091297.","productDescription":"iii, 42 p.","onlineOnly":"Y","additionalOnlineFiles":"N","costCenters":[{"id":177,"text":"Central Region Mineral Resources Science Center","active":false,"usgs":true}],"links":[{"id":125938,"rank":0,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/ofr_2009_1297.jpg"},{"id":13324,"rank":100,"type":{"id":15,"text":"Index Page"},"url":"https://pubs.usgs.gov/of/2009/1297/","linkFileType":{"id":5,"text":"html"}}],"noUsgsAuthors":false,"publicationStatus":"PW","scienceBaseUri":"4f4e4b1ee4b07f02db6aa045","contributors":{"authors":[{"text":"Taylor, Ryan D. 0000-0002-8845-5290 rtaylor@usgs.gov","orcid":"https://orcid.org/0000-0002-8845-5290","contributorId":3412,"corporation":false,"usgs":true,"family":"Taylor","given":"Ryan","email":"rtaylor@usgs.gov","middleInitial":"D.","affiliations":[{"id":171,"text":"Central Mineral and Environmental Resources Science Center","active":true,"usgs":true}],"preferred":true,"id":304117,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Leach, David L.","contributorId":83902,"corporation":false,"usgs":true,"family":"Leach","given":"David","email":"","middleInitial":"L.","affiliations":[],"preferred":false,"id":304119,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Bradley, Dwight 0000-0001-9116-5289 bradleyorchard2@gmail.com","orcid":"https://orcid.org/0000-0001-9116-5289","contributorId":2358,"corporation":false,"usgs":true,"family":"Bradley","given":"Dwight","email":"bradleyorchard2@gmail.com","affiliations":[{"id":171,"text":"Central Mineral and Environmental Resources Science Center","active":true,"usgs":true},{"id":119,"text":"Alaska Science Center Geology Minerals","active":true,"usgs":true},{"id":114,"text":"Alaska Science Center","active":true,"usgs":true}],"preferred":true,"id":304116,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Pisarevsky, Sergei A.","contributorId":62315,"corporation":false,"usgs":true,"family":"Pisarevsky","given":"Sergei","email":"","middleInitial":"A.","affiliations":[],"preferred":false,"id":304118,"contributorType":{"id":1,"text":"Authors"},"rank":4}]}}
,{"id":98087,"text":"sir20095248 - 2009 - The Water Availability Tool for Environmental Resources (WATER):  A water-budget modeling approach for managing water-supply resources in non-karst areas of Kentucky (phase I) —  Data processing and model structure documentstion","interactions":[],"lastModifiedDate":"2021-12-15T21:02:47.973093","indexId":"sir20095248","displayToPublicDate":"2010-01-05T00:00:00","publicationYear":"2009","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":"2009-5248","title":"The Water Availability Tool for Environmental Resources (WATER):  A water-budget modeling approach for managing water-supply resources in non-karst areas of Kentucky (phase I) —  Data processing and model structure documentstion","docAbstract":"The Water Availability Tool for Environmental Resources (WATER) was developed in cooperation with the Kentucky Division of Water to provide a consistent and defensible method of estimating streamflow and water availability in ungaged basins. WATER is process oriented; it is based on the TOPMODEL code and incorporates historical water-use data together with physiographic data that quantitatively describe topography and soil-water storage. The result is a user-friendly decision tool that can estimate water availability in non-karst areas of Kentucky without additional data or processing. The model runs on a daily time step, and critical source data include a historical record of daily temperature and precipitation, digital elevation models (DEMs), the Soil Survey Geographic Database (SSURGO), and historical records of water discharges and withdrawals. The model was calibrated and statistically evaluated for 12 basins by comparing the estimated discharge to that observed at U.S. Geological Survey streamflow-gaging stations. When statistically evaluated over a 2,119-day time period, the discharge estimates showed a bias of -0.29 to 0.42, a root mean square error of 1.66 to 5.06, a correlation of 0.54 to 0.85, and a Nash-Sutcliffe Efficiency of 0.26 to 0.72. The parameter and input modifications that most significantly improved the accuracy and precision of streamflow-discharge estimates were the addition of Next Generation radar (NEXRAD) precipitation data, a rooting depth of 30 centimeters, and a TOPMODEL scaling parameter (m) derived directly from SSURGO data that was multiplied by an adjustment factor of 0.10. No site-specific optimization was used.","language":"English","publisher":"U.S. Geological Survey","doi":"10.3133/sir20095248","collaboration":"Prepared in cooperation with the Kentucky Division of Water","usgsCitation":"Williamson, T., Odom, K.R., Newson, J.K., Downs, A.C., Nelson, H.L., Cinotto, P.J., and Ayers, M.A., 2009, The Water Availability Tool for Environmental Resources (WATER):  A water-budget modeling approach for managing water-supply resources in non-karst areas of Kentucky (phase I) —  Data processing and model structure documentstion: U.S. Geological Survey Scientific Investigations Report 2009-5248, vi, 34 p., https://doi.org/10.3133/sir20095248.","productDescription":"vi, 34 p.","costCenters":[{"id":354,"text":"Kentucky Water Science Center","active":true,"usgs":true}],"links":[{"id":125858,"rank":0,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/sir_2009_5248.jpg"},{"id":392968,"rank":3,"type":{"id":36,"text":"NGMDB Index 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,{"id":98088,"text":"sir20095231 - 2009 - Regression models to estimate real-time concentrations of selected constituents in two tributaries to Lake Houston near Houston, Texas, 2005-07","interactions":[],"lastModifiedDate":"2016-08-15T11:00:28","indexId":"sir20095231","displayToPublicDate":"2010-01-05T00:00:00","publicationYear":"2009","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":"2009-5231","title":"Regression models to estimate real-time concentrations of selected constituents in two tributaries to Lake Houston near Houston, Texas, 2005-07","docAbstract":"<p>In December 2005, the U.S. Geological Survey in cooperation with the City of Houston, Texas, began collecting discrete water-quality samples for nutrients, total organic carbon, bacteria (total coliform and Escherichia coli), atrazine, and suspended sediment at two U.S. Geological Survey streamflow-gaging stations upstream from Lake Houston near Houston (08068500 Spring Creek near Spring, Texas, and 08070200 East Fork San Jacinto River near New Caney, Texas). The data from the discrete water-quality samples collected during 2005-07, in conjunction with monitored real-time data already being collected - physical properties (specific conductance, pH, water temperature, turbidity, and dissolved oxygen), streamflow, and rainfall - were used to develop regression models for predicting water-quality constituent concentrations for inflows to Lake Houston. Rainfall data were obtained from a rain gage monitored by Harris County Homeland Security and Emergency Management and colocated with the Spring Creek station. The leaps and bounds algorithm was used to find the best subsets of possible regression models (minimum residual sum of squares for a given number of variables). The potential explanatory or predictive variables included discharge (streamflow), specific conductance, pH, water temperature, turbidity, dissolved oxygen, rainfall, and time (to account for seasonal variations inherent in some water-quality data). The response variables at each site were nitrite plus nitrate nitrogen, total phosphorus, organic carbon, Escherichia coli, atrazine, and suspended sediment. The explanatory variables provide easily measured quantities as a means to estimate concentrations of the various constituents under investigation, with accompanying estimates of measurement uncertainty. Each regression equation can be used to estimate concentrations of a given constituent in real time. In conjunction with estimated concentrations, constituent loads were estimated by multiplying the estimated concentration by the corresponding streamflow and applying the appropriate conversion factor. By computing loads from estimated constituent concentrations, a continuous record of estimated loads can be available for comparison to total maximum daily loads. The regression equations presented in this report are site specific to the Spring Creek and East Fork San Jacinto River streamflow-gaging stations; however, the methods that were developed and documented could be applied to other tributaries to Lake Houston for estimating real-time water-quality data for streams entering Lake Houston.</p>","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/sir20095231","isbn":"9781411326286","collaboration":"Prepared in cooperation with the City of Houston","usgsCitation":"Oden, T., Asquith, W.H., and Milburn, M.S., 2009, Regression models to estimate real-time concentrations of selected constituents in two tributaries to Lake Houston near Houston, Texas, 2005-07: U.S. Geological Survey Scientific Investigations Report 2009-5231, vi, 44 p., https://doi.org/10.3133/sir20095231.","productDescription":"vi, 44 p.","onlineOnly":"N","additionalOnlineFiles":"N","temporalStart":"2005-01-01","temporalEnd":"2007-12-31","costCenters":[{"id":583,"text":"Texas Water Science Center","active":true,"usgs":true}],"links":[{"id":125870,"rank":0,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/sir_2009_5231.jpg"},{"id":326480,"rank":101,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/sir/2009/5231/pdf/sir2009-5231.pdf","text":"Report","size":"8.0 MB","linkFileType":{"id":1,"text":"pdf"}},{"id":13322,"rank":100,"type":{"id":15,"text":"Index Page"},"url":"https://pubs.usgs.gov/sir/2009/5231/","linkFileType":{"id":5,"text":"html"}}],"geographicExtents":"{ \"type\": \"FeatureCollection\", \"features\": [ { \"type\": \"Feature\", \"properties\": {}, \"geometry\": { \"type\": \"Polygon\", \"coordinates\": [ [ [ -96.25,29.75 ], [ -96.25,31 ], [ -94.75,31 ], [ -94.75,29.75 ], [ -96.25,29.75 ] ] ] } } ] }","noUsgsAuthors":false,"publicationStatus":"PW","scienceBaseUri":"4f4e4a60e4b07f02db634da9","contributors":{"authors":[{"text":"Oden, Timothy D. toden@usgs.gov","contributorId":1284,"corporation":false,"usgs":true,"family":"Oden","given":"Timothy D.","email":"toden@usgs.gov","affiliations":[],"preferred":true,"id":304112,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Asquith, William H. 0000-0002-7400-1861 wasquith@usgs.gov","orcid":"https://orcid.org/0000-0002-7400-1861","contributorId":1007,"corporation":false,"usgs":true,"family":"Asquith","given":"William","email":"wasquith@usgs.gov","middleInitial":"H.","affiliations":[{"id":48595,"text":"Oklahoma-Texas Water Science Center","active":true,"usgs":true}],"preferred":true,"id":304111,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Milburn, Matthew S.","contributorId":53896,"corporation":false,"usgs":true,"family":"Milburn","given":"Matthew","email":"","middleInitial":"S.","affiliations":[],"preferred":false,"id":304113,"contributorType":{"id":1,"text":"Authors"},"rank":3}]}}
,{"id":70046818,"text":"70046818 - 2009 - USGS standard quadrangle maps for emergency response","interactions":[],"lastModifiedDate":"2014-07-02T13:43:40","indexId":"70046818","displayToPublicDate":"2010-01-01T13:32:00","publicationYear":"2009","noYear":false,"publicationType":{"id":4,"text":"Book"},"publicationSubtype":{"id":12,"text":"Conference publication"},"title":"USGS standard quadrangle maps for emergency response","docAbstract":"<p>The 1:24,000-scale topographic quadrangle was the primary product of the U.S. Geological Survey's (USGS) National Mapping Program from 1947-1992.  This map series includes about 54,000 map sheets for the conterminous United States, and is the only uniform map series ever produced that covers this area at such a large scale.</p>\n<br/>\n<p>This map series partially was revised under several programs, starting as early as 1968, but these programs were not adequate to keep the series current.  Through the 1990s the emphasis of the USGS mapping program shifted away from topographic maps and toward more specialized digital data products.  Topographic map revision dropped off rapidly after 1999, and stopped completely by 2004.</p>\n<br/>\n<p>Since 2001, emergency-response and homeland security requirement have revived the question of whether a standard national topographic series is needed.  Emergencies such as Hurricane Katrina in 2005 and California wildfires in 2007-08 demonstrated that familiar maps are important to first responders.  Maps that have a standard scale, extent, and grids help reduce confusion and save time in emergencies.</p>\n<br/>\n<p>Traditional maps are designed to allow the human brain to quickly process large amounts of information, and depend on artistic layout and design that cannot be fully automated.  In spite of technical advances, creating a traditional, general-purpose topographic map is still expensive.</p>\n<br/>\n<p>Although the content and layout of traditional topographic maps probably is still desirable, the preferred packaging and delivery of maps has changed.  Digital image files are now desired by most users, but to be useful to the emergency-response community, these files must be easy to view and easy to print without specialized geographic information system expertise or software.</p>","largerWorkTitle":"Annual Association of American Geographers Meeting","conferenceTitle":"Association of American Geographers Annual Meeting","conferenceDate":"2009-03-22T00:00:00","conferenceLocation":"Las Vegas, NV","language":"English","publisher":"Annual Association of American Geographers Meeting","publisherLocation":"Washington, D.C.","usgsCitation":"Moore, L.R., 2009, USGS standard quadrangle maps for emergency response, p. 35-39.","productDescription":"p. 35-39","numberOfPages":"5","ipdsId":"IP-009495","costCenters":[],"links":[{"id":289390,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"noUsgsAuthors":false,"publicationStatus":"PW","scienceBaseUri":"53b7b277e4b0388651d9196d","contributors":{"authors":[{"text":"Moore, Laurence R. 0000-0001-9678-7183 lmoore@usgs.gov","orcid":"https://orcid.org/0000-0001-9678-7183","contributorId":2057,"corporation":false,"usgs":true,"family":"Moore","given":"Laurence","email":"lmoore@usgs.gov","middleInitial":"R.","affiliations":[{"id":5047,"text":"NGTOC Denver","active":true,"usgs":true}],"preferred":true,"id":480360,"contributorType":{"id":1,"text":"Authors"},"rank":1}]}}
,{"id":70046864,"text":"70046864 - 2009 - Using a coupled groundwater/surface-water model to predict climate-change impacts to lakes in the Trout Lake Watershed, northern Wisconsin","interactions":[{"subject":{"id":70046864,"text":"70046864 - 2009 - Using a coupled groundwater/surface-water model to predict climate-change impacts to lakes in the Trout Lake Watershed, northern Wisconsin","indexId":"70046864","publicationYear":"2009","noYear":false,"title":"Using a coupled groundwater/surface-water model to predict climate-change impacts to lakes in the Trout Lake Watershed, northern Wisconsin"},"predicate":"IS_PART_OF","object":{"id":97928,"text":"sir20095049 - 2009 - Planning for an uncertain future - Monitoring, integration, and adaptation","indexId":"sir20095049","publicationYear":"2009","noYear":false,"title":"Planning for an uncertain future - Monitoring, integration, and adaptation"},"id":1}],"isPartOf":{"id":97928,"text":"sir20095049 - 2009 - Planning for an uncertain future - Monitoring, integration, and adaptation","indexId":"sir20095049","publicationYear":"2009","noYear":false,"title":"Planning for an uncertain future - Monitoring, integration, and adaptation"},"lastModifiedDate":"2016-08-18T16:10:55","indexId":"70046864","displayToPublicDate":"2010-01-01T11:49:00","publicationYear":"2009","noYear":false,"publicationType":{"id":24,"text":"Conference Paper"},"publicationSubtype":{"id":19,"text":"Conference Paper"},"seriesNumber":"2009-5049","title":"Using a coupled groundwater/surface-water model to predict climate-change impacts to lakes in the Trout Lake Watershed, northern Wisconsin","docAbstract":"<p>A major focus of the U.S. Geological Survey&rsquo;s Trout Lake Water, Energy, and Biogeochemical Budgets (WEBB) project is the development of a watershed model to allow predictions of hydrologic response to future conditions including land-use and climate change. The coupled groundwater/surface-water model GSFLOW was chosen for this purpose because it could easily incorporate an existing groundwater flow model and it provides for simulation of surface-water processes.</p>\n<p>&nbsp;</p>\n<p>The Trout Lake watershed in northern Wisconsin is underlain by a highly conductive outwash sand aquifer. In this area, streamflow is dominated by groudwater contributions, however, surface runoff occurs during intense rainfall periods and spring snowmelt. Surface runoff also occurs locally near stream/lake areas where the unsaturated zone is thin. A diverse data set, collected from 1992 to 2007 for the Trout Lake WEBB project and the co-located and NSF-funded North Temperate Lake LTER project, includes snowpack, solar radiation, potential evapotranspiration, lake levels, groundwater levels, and streamflow. The time-series processing software TSPROC (Doherty 2001)was used to distill the large time series data set to a smaller set of observations and summary statistics that captured the salient hydrologic information. The time-series processing reduced hundreds of thousands of observations to less than 5,000. Model calibration included specific predictions for several lakes in the study area using the PEST parameter estimation suit of software (Doherty 2007). The calibrated model was used to simulate the hydrologic response in the study lakes to a variety of climate change scenarios culled from the IPCC Fourth Assessment Report of the Intergovernmental Panel of Climate Change (Solomon et al. 2007). Results from the simulations indicate climate change could result in substantial changes to the lake levels and components of the hydrologic budget of a seepage lake in the flow system. For a drainage lake lower in the flow system, the impacts of climate change are diminished.</p>","largerWorkType":{"id":18,"text":"Report"},"largerWorkTitle":"Planning for an uncertain future - monitoring, integration, and adaptation (SIR 2009-5049)","largerWorkSubtype":{"id":5,"text":"USGS Numbered Series"},"conferenceTitle":"3rd interagency conference on research in the watersheds: planning for an uncertain future: monitoring, integration, and adaptation","conferenceDate":"8-11 September, 2008","conferenceLocation":"Estes Park, CO","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","usgsCitation":"Hunt, R., Walker, J.F., Markstrom, S., Hay, L.E., and Doherty, J., 2009, Using a coupled groundwater/surface-water model to predict climate-change impacts to lakes in the Trout Lake Watershed, northern Wisconsin, <i>in</i> Planning for an uncertain future - monitoring, integration, and adaptation (SIR 2009-5049), Estes Park, CO, 8-11 September, 2008, p. 155-161.","productDescription":"7 p.","startPage":"155","endPage":"161","numberOfPages":"7","onlineOnly":"N","additionalOnlineFiles":"N","ipdsId":"IP-009788","costCenters":[{"id":677,"text":"Wisconsin Water Science Center","active":true,"usgs":true}],"links":[{"id":289370,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/usgs_thumb.jpg"},{"id":326855,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/sir/2009/5049/pdf/SIR09-5049.pdf"}],"country":"United States","state":"Wisconsin","otherGeospatial":"Trout Lake","geographicExtents":"{ \"type\": \"FeatureCollection\", \"features\": [ { \"type\": \"Feature\", \"properties\": {}, \"geometry\": { \"type\": \"Polygon\", \"coordinates\": [ [ [ -89.703926,46.012934 ], [ -89.703926,46.079112 ], [ -89.646771,46.079112 ], [ -89.646771,46.012934 ], [ -89.703926,46.012934 ] ] ] } } ] }","noUsgsAuthors":false,"publicationStatus":"PW","scienceBaseUri":"53b7b27ee4b0388651d9198c","contributors":{"editors":[{"text":"Webb, Richard M. T. 0000-0001-9531-2207","orcid":"https://orcid.org/0000-0001-9531-2207","contributorId":35772,"corporation":false,"usgs":true,"family":"Webb","given":"Richard M. T.","affiliations":[],"preferred":false,"id":646256,"contributorType":{"id":2,"text":"Editors"},"rank":1},{"text":"Semmens, Darius J. 0000-0001-7924-6529 dsemmens@usgs.gov","orcid":"https://orcid.org/0000-0001-7924-6529","contributorId":1714,"corporation":false,"usgs":true,"family":"Semmens","given":"Darius","email":"dsemmens@usgs.gov","middleInitial":"J.","affiliations":[{"id":318,"text":"Geosciences and Environmental Change Science Center","active":true,"usgs":true}],"preferred":true,"id":646257,"contributorType":{"id":2,"text":"Editors"},"rank":2}],"authors":[{"text":"Hunt, Randall J. 0000-0001-6465-9304","orcid":"https://orcid.org/0000-0001-6465-9304","contributorId":16118,"corporation":false,"usgs":true,"family":"Hunt","given":"Randall J.","affiliations":[{"id":677,"text":"Wisconsin Water Science Center","active":true,"usgs":true},{"id":37947,"text":"Upper Midwest Water Science Center","active":true,"usgs":true}],"preferred":true,"id":480496,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Walker, John F. jfwalker@usgs.gov","contributorId":1081,"corporation":false,"usgs":true,"family":"Walker","given":"John","email":"jfwalker@usgs.gov","middleInitial":"F.","affiliations":[{"id":677,"text":"Wisconsin Water Science Center","active":true,"usgs":true}],"preferred":true,"id":480493,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Markstrom, Steven L. 0000-0001-7630-9547 markstro@usgs.gov","orcid":"https://orcid.org/0000-0001-7630-9547","contributorId":1986,"corporation":false,"usgs":true,"family":"Markstrom","given":"Steven L.","email":"markstro@usgs.gov","affiliations":[{"id":5044,"text":"National Research Program - Central Branch","active":true,"usgs":true}],"preferred":false,"id":480495,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Hay, Lauren E. 0000-0003-3763-4595 lhay@usgs.gov","orcid":"https://orcid.org/0000-0003-3763-4595","contributorId":1287,"corporation":false,"usgs":true,"family":"Hay","given":"Lauren","email":"lhay@usgs.gov","middleInitial":"E.","affiliations":[{"id":5044,"text":"National Research Program - Central Branch","active":true,"usgs":true}],"preferred":true,"id":480494,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Doherty, John","contributorId":43843,"corporation":false,"usgs":true,"family":"Doherty","given":"John","affiliations":[],"preferred":false,"id":480497,"contributorType":{"id":1,"text":"Authors"},"rank":5}]}}
,{"id":70104212,"text":"70104212 - 2009 - Hindcasting of decadal‐timescale estuarine bathymetric change with a tidal‐timescale model","interactions":[],"lastModifiedDate":"2014-05-13T09:07:41","indexId":"70104212","displayToPublicDate":"2010-01-01T09:00:28","publicationYear":"2009","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":2318,"text":"Journal of Geophysical Research F: Earth Surface","active":true,"publicationSubtype":{"id":10}},"title":"Hindcasting of decadal‐timescale estuarine bathymetric change with a tidal‐timescale model","docAbstract":"Hindcasting decadal-timescale bathymetric change in estuaries is prone to error due to limited data for initial conditions, boundary forcing, and calibration; computational limitations further hinder efforts. We developed and calibrated a tidal-timescale model to bathymetric change in Suisun Bay, California, over the 1867–1887 period. A general, multiple-timescale calibration ensured robustness over all timescales; two input reduction methods, the morphological hydrograph and the morphological acceleration factor, were applied at the decadal timescale. The model was calibrated to net bathymetric change in the entire basin; average error for bathymetric change over individual depth ranges was 37%. On a model cell-by-cell basis, performance for spatial amplitude correlation was poor over the majority of the domain, though spatial phase correlation was better, with 61% of the domain correctly indicated as erosional or depositional. Poor agreement was likely caused by the specification of initial bed composition, which was unknown during the 1867–1887 period. Cross-sectional bathymetric change between channels and flats, driven primarily by wind wave resuspension, was modeled with higher skill than longitudinal change, which is driven in part by gravitational circulation. The accelerated response of depth may have prevented gravitational circulation from being represented properly. As performance criteria became more stringent in a spatial sense, the error of the model increased. While these methods are useful for estimating basin-scale sedimentation changes, they may not be suitable for predicting specific locations of erosion or deposition. They do, however, provide a foundation for realistic estuarine geomorphic modeling applications.","largerWorkType":{"id":2,"text":"Article"},"largerWorkTitle":"Journal of Geophysical Research F: Earth Surface","largerWorkSubtype":{"id":10,"text":"Journal Article"},"language":"English","publisher":"Amercan Geophysical Union","doi":"10.1029/2008JF001191","usgsCitation":"Ganju, N., Schoellhamer, D., and Jaffe, B.E., 2009, Hindcasting of decadal‐timescale estuarine bathymetric change with a tidal‐timescale model: Journal of Geophysical Research F: Earth Surface, v. 114, no. F4, 15 p., https://doi.org/10.1029/2008JF001191.","productDescription":"15 p.","numberOfPages":"15","temporalStart":"1866-12-31","temporalEnd":"1887-12-31","ipdsId":"IP-003086","costCenters":[{"id":154,"text":"California Water Science Center","active":true,"usgs":true}],"links":[{"id":476012,"rank":0,"type":{"id":41,"text":"Open Access External Repository Page"},"url":"https://hdl.handle.net/1912/3105","text":"External Repository"},{"id":287069,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"},{"id":287063,"type":{"id":10,"text":"Digital Object Identifier"},"url":"https://dx.doi.org/10.1029/2008JF001191"}],"country":"United States","state":"California","otherGeospatial":"Suisun Bay","geographicExtents":"{ \"type\": \"FeatureCollection\", \"features\": [ { \"type\": \"Feature\", \"properties\": {}, \"geometry\": { \"type\": \"Polygon\", \"coordinates\": [ [ [ -122.75,37.75 ], [ -122.75,38.25 ], [ -121.75,38.25 ], [ -121.75,37.75 ], [ -122.75,37.75 ] ] ] } } ] }","volume":"114","issue":"F4","noUsgsAuthors":false,"publicationDate":"2009-12-02","publicationStatus":"PW","scienceBaseUri":"53733efae4b0497061278906","contributors":{"authors":[{"text":"Ganju, Neil K. 0000-0002-1096-0465","orcid":"https://orcid.org/0000-0002-1096-0465","contributorId":93543,"corporation":false,"usgs":true,"family":"Ganju","given":"Neil K.","affiliations":[],"preferred":false,"id":493638,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Schoellhamer, David H. 0000-0001-9488-7340 dschoell@usgs.gov","orcid":"https://orcid.org/0000-0001-9488-7340","contributorId":631,"corporation":false,"usgs":true,"family":"Schoellhamer","given":"David H.","email":"dschoell@usgs.gov","affiliations":[{"id":154,"text":"California Water Science Center","active":true,"usgs":true}],"preferred":true,"id":493636,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Jaffe, Bruce E. 0000-0002-8816-5920 bjaffe@usgs.gov","orcid":"https://orcid.org/0000-0002-8816-5920","contributorId":2049,"corporation":false,"usgs":true,"family":"Jaffe","given":"Bruce","email":"bjaffe@usgs.gov","middleInitial":"E.","affiliations":[{"id":520,"text":"Pacific Coastal and Marine Science Center","active":true,"usgs":true},{"id":186,"text":"Coastal and Marine Geology Program","active":true,"usgs":true}],"preferred":true,"id":493637,"contributorType":{"id":1,"text":"Authors"},"rank":3}]}}
,{"id":70042305,"text":"70042305 - 2009 - Importance of light, temperature, zooplankton, and fish in predicting the nighttime vertical distribution of Mysis diluviana","interactions":[],"lastModifiedDate":"2022-09-05T17:02:28.814924","indexId":"70042305","displayToPublicDate":"2010-01-01T00:00:00","publicationYear":"2009","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":860,"text":"Aquatic Biology","active":true,"publicationSubtype":{"id":10}},"title":"Importance of light, temperature, zooplankton, and fish in predicting the nighttime vertical distribution of Mysis diluviana","docAbstract":"<p><span>The opossum shrimp&nbsp;</span><i>Mysis diluviana<span>&nbsp;</span></i><span>(formerly&nbsp;</span><i>M. relicta</i><span>) performs large amplitude diel vertical migrations in Lake Ontario and its nighttime distribution is influenced by temperature, light and the distribution of its predators and prey. At one location in southeastern Lake Ontario, we measured the vertical distribution of mysids, mysid predators (i.e. planktivorous fishes) and mysid prey (i.e. zooplankton), in addition to light and temperature, on 8 occasions from May to September, 2004 and 2005. We use these data to test 3 different predictive models of mysid habitat selection, based on: (1) laboratory-derived responses of mysids to different light and temperature gradients in the absence of predator or prey cues; (2) growth rate of mysids, as estimated with a mysid bioenergetics model, given known prey densities and temperatures at different depths in the water column; (3) ratio of growth rates (</span><i>g</i><span>) and mortality risk (μ) associated with the distribution of predatory fishes. The model based on light and temperature preferences was a better predictor of mysid vertical distribution than the models based on growth rate and&nbsp;</span><i>g</i><span>:μ on all 8 occasions. Although mysid temperature and light preferences probably evolved as mechanisms to reduce predation while increasing foraging intake, the response to temperature and light alone predicts mysid vertical distribution across seasons in Lake Ontario.</span></p>","language":"English","publisher":"Inter-Research Science Publisher","doi":"10.3354/ab00161","usgsCitation":"Boscarino, B., Rudstam, L.G., Ellenberger, S., and O’Gorman, R., 2009, Importance of light, temperature, zooplankton, and fish in predicting the nighttime vertical distribution of Mysis diluviana: Aquatic Biology, v. 5, p. 263-279, https://doi.org/10.3354/ab00161.","productDescription":"17 p.","startPage":"263","endPage":"279","ipdsId":"IP-011081","costCenters":[{"id":324,"text":"Great Lakes Science Center","active":true,"usgs":true}],"links":[{"id":476014,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.3354/ab00161","text":"Publisher Index 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-79.749755859375,\n              43.393073720674415\n            ],\n            [\n              -79.98046875,\n              43.297198404646366\n            ],\n            [\n              -79.9420166015625,\n              43.25320494908846\n            ],\n            [\n              -79.8046875,\n              43.24520272203356\n            ],\n            [\n              -79.310302734375,\n              43.141078106345866\n            ]\n          ]\n        ]\n      }\n    }\n  ]\n}","volume":"5","noUsgsAuthors":false,"publicationStatus":"PW","scienceBaseUri":"512b44a7e4b0523e997a8154","contributors":{"authors":[{"text":"Boscarino, Brent","contributorId":9883,"corporation":false,"usgs":true,"family":"Boscarino","given":"Brent","email":"","affiliations":[],"preferred":false,"id":813100,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Rudstam, Lars G.","contributorId":56609,"corporation":false,"usgs":false,"family":"Rudstam","given":"Lars","email":"","middleInitial":"G.","affiliations":[{"id":12722,"text":"Cornell University","active":true,"usgs":false}],"preferred":false,"id":471237,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Ellenberger, S.A.","contributorId":221950,"corporation":false,"usgs":false,"family":"Ellenberger","given":"S.A.","email":"","affiliations":[],"preferred":false,"id":813101,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"O’Gorman, Robert rogorman@usgs.gov","contributorId":3451,"corporation":false,"usgs":true,"family":"O’Gorman","given":"Robert","email":"rogorman@usgs.gov","affiliations":[],"preferred":true,"id":813102,"contributorType":{"id":1,"text":"Authors"},"rank":4}]}}
,{"id":70042388,"text":"70042388 - 2009 - Multi-scale measurements and modeling of denitrification in streams with varying flow and nitrate concentration in the upper Mississippi River basin, USA","interactions":[],"lastModifiedDate":"2018-10-05T09:50:25","indexId":"70042388","displayToPublicDate":"2010-01-01T00:00:00","publicationYear":"2009","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":1007,"text":"Biogeochemistry","active":true,"publicationSubtype":{"id":10}},"title":"Multi-scale measurements and modeling of denitrification in streams with varying flow and nitrate concentration in the upper Mississippi River basin, USA","docAbstract":"<p><span>Denitrification is an important net sink for NO</span><sub>3</sub><sup>−</sup><span> in streams, but direct measurements are limited and in situ controlling factors are not well known. We measured denitrification at multiple scales over a range of flow conditions and NO</span><sub>3</sub><sup>−</sup><span> concentrations in streams draining agricultural land in the upper Mississippi River basin. Comparisons of reach-scale measurements (in-stream mass transport and tracer tests) with local-scale in situ measurements (pore-water profiles, benthic chambers) and laboratory data (sediment core microcosms) gave evidence for heterogeneity in factors affecting benthic denitrification both temporally (e.g., seasonal variation in NO</span><sub>3</sub><sup>−</sup><span> concentrations and loads, flood-related disruption and re-growth of benthic communities and organic deposits) and spatially (e.g., local stream morphology and sediment characteristics). When expressed as vertical denitrification flux per unit area of streambed (</span><i class=\"EmphasisTypeItalic \">U</i><sub>denit</sub><span>, in μmol&nbsp;N&nbsp;m</span><sup>−2</sup><span>&nbsp;h</span><sup>−1</sup><span>), results of different methods for a given set of conditions commonly were in agreement within a factor of 2–3. At approximately constant temperature (~20&nbsp;±&nbsp;4°C) and with minimal benthic disturbance, our aggregated data indicated an overall positive relation between </span><i class=\"EmphasisTypeItalic \">U</i><sub>denit</sub><span> (~0–4,000&nbsp;μmol&nbsp;N&nbsp;m</span><sup>−2</sup><span>&nbsp;h</span><sup>−1</sup><span>) and stream NO</span><sub>3</sub><sup>−</sup><span>concentration (~20–1,100&nbsp;μmol&nbsp;L</span><sup>−1</sup><span>) representing seasonal variation from spring high flow (high NO</span><sub>3</sub><sup>−</sup><span>) to late summer low flow (low NO</span><sub>3</sub><sup>−</sup><span>). The temporal dependence of </span><i class=\"EmphasisTypeItalic \">U</i><sub>denit</sub><span> on NO</span><sub>3</sub><sup>−</sup><span>was less than first-order and could be described about equally well with power-law or saturation equations (e.g., for the unweighted dataset, </span><i class=\"EmphasisTypeItalic \">U</i><sub>denit</sub><span>&nbsp;≈26&nbsp;*&nbsp;[NO</span><sub>3</sub><sup>−</sup><span>]</span><sup>0.44</sup><span> or </span><i class=\"EmphasisTypeItalic \">U</i><sub>denit</sub><span>≈640&nbsp;*&nbsp;[NO</span><sub>3</sub><sup>−</sup><span>]/[180&nbsp;+&nbsp;NO</span><sub>3</sub><sup>−</sup><span>]; for a partially weighted dataset, </span><i class=\"EmphasisTypeItalic \">U</i><sub>denit</sub><span>&nbsp;≈14&nbsp;*&nbsp;[NO</span><sub>3</sub><sup>−</sup><span>]</span><sup>0.54</sup><span> or </span><i class=\"EmphasisTypeItalic \">U</i><sub>denit</sub><span>&nbsp;≈700&nbsp;*&nbsp;[NO</span><sub>3</sub><sup>−</sup><span>]/[320&nbsp;+&nbsp;NO</span><sub>3</sub><sup>−</sup><span>]). Similar parameters were derived from a recent spatial comparison of stream denitrification extending to lower NO</span><sub>3</sub><sup>−</sup><span> concentrations (LINX2), and from the combined dataset from both studies over 3 orders of magnitude in NO</span><sub>3</sub><sup>−</sup><span>concentration. Hypothetical models based on our results illustrate: (1) </span><i class=\"EmphasisTypeItalic \">U</i><sub>denit</sub><span> was inversely related to denitrification rate constant (</span><i class=\"EmphasisTypeItalic \">k</i><span>1</span><sub>denit</sub><span>, in day</span><sup>−1</sup><span>) and vertical transfer velocity (</span><i class=\"EmphasisTypeItalic \">v</i><sub>f,denit</sub><span>, in m day</span><sup>−1</sup><span>) at seasonal and possibly event time scales; (2) although </span><i class=\"EmphasisTypeItalic \">k</i><span>1</span><sub>denit</sub><span> was relatively large at low flow (low NO</span><sub>3</sub><sup>−</sup><span>), its impact on annual loads was relatively small because higher concentrations and loads at high flow were not fully compensated by increases in </span><i class=\"EmphasisTypeItalic \">U</i><sub>denit</sub><span>; and (3) although NO</span><sub>3</sub><sup>−</sup><span> assimilation and denitrification were linked through production of organic reactants, rates of NO</span><sub>3</sub><sup>−</sup><span> loss by these processes may have been partially decoupled by changes in flow and sediment transport. Whereas </span><i class=\"EmphasisTypeItalic \">k</i><span>1</span><sub>denit</sub><span> and </span><i class=\"EmphasisTypeItalic \">v</i><sub>f,denit</sub><span> are linked implicitly with stream depth, NO</span><sub>3</sub><sup>−</sup><span> concentration, and(or) NO</span><sub>3</sub><sup>−</sup><span> load, estimates of </span><i class=\"EmphasisTypeItalic \">U</i><sub>denit</sub><span> may be related more directly to field factors (including NO</span><sub>3</sub><sup>−</sup><span> concentration) affecting denitrification rates in benthic sediments. Regional regressions and simulations of benthic denitrification in stream networks might be improved by including a non-linear relation between </span><i class=\"EmphasisTypeItalic \">U</i><sub>denit</sub><span> and stream NO</span><sub>3</sub><sup>−</sup><span>concentration and accounting for temporal variation.</span></p>","language":"English","publisher":"Springer","doi":"10.1007/s10533-008-9282-8","usgsCitation":"Bohlke, J., Antweiler, R.C., Harvey, J.W., Laursen, A.E., Smith, L.K., Smith, R.L., and Voytek, M.A., 2009, Multi-scale measurements and modeling of denitrification in streams with varying flow and nitrate concentration in the upper Mississippi River basin, USA: Biogeochemistry, v. 93, no. 1, p. 117-141, https://doi.org/10.1007/s10533-008-9282-8.","productDescription":"24 p.","startPage":"117","endPage":"141","ipdsId":"IP-008428","costCenters":[{"id":146,"text":"Branch of Regional Research-Eastern Region","active":false,"usgs":true},{"id":589,"text":"Toxic Substances Hydrology Program","active":true,"usgs":true}],"links":[{"id":476016,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.1007/s10533-008-9282-8","text":"Publisher Index Page"},{"id":270742,"type":{"id":10,"text":"Digital Object Identifier"},"url":"https://dx.doi.org/10.1007/s10533-008-9282-8"},{"id":270743,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"volume":"93","issue":"1","noUsgsAuthors":false,"publicationDate":"2009-01-13","publicationStatus":"PW","scienceBaseUri":"5165386ce4b077fa94dadfc3","contributors":{"authors":[{"text":"Bohlke, J.K. 0000-0001-5693-6455 jkbohlke@usgs.gov","orcid":"https://orcid.org/0000-0001-5693-6455","contributorId":191103,"corporation":false,"usgs":true,"family":"Bohlke","given":"J.K.","email":"jkbohlke@usgs.gov","affiliations":[{"id":37277,"text":"WMA - Earth System Processes Division","active":true,"usgs":true},{"id":36183,"text":"Hydro-Ecological Interactions Branch","active":true,"usgs":true},{"id":436,"text":"National Research Program - Eastern Branch","active":true,"usgs":true}],"preferred":true,"id":471448,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Antweiler, Ronald C. 0000-0001-5652-6034 antweil@usgs.gov","orcid":"https://orcid.org/0000-0001-5652-6034","contributorId":1481,"corporation":false,"usgs":true,"family":"Antweiler","given":"Ronald","email":"antweil@usgs.gov","middleInitial":"C.","affiliations":[{"id":5044,"text":"National Research Program - Central Branch","active":true,"usgs":true}],"preferred":true,"id":471444,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Harvey, Judson W. 0000-0002-2654-9873 jwharvey@usgs.gov","orcid":"https://orcid.org/0000-0002-2654-9873","contributorId":1796,"corporation":false,"usgs":true,"family":"Harvey","given":"Judson","email":"jwharvey@usgs.gov","middleInitial":"W.","affiliations":[{"id":436,"text":"National Research Program - Eastern Branch","active":true,"usgs":true}],"preferred":true,"id":471446,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Laursen, Andrew E.","contributorId":99783,"corporation":false,"usgs":true,"family":"Laursen","given":"Andrew","email":"","middleInitial":"E.","affiliations":[],"preferred":false,"id":471450,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Smith, Lesley K.","contributorId":82657,"corporation":false,"usgs":true,"family":"Smith","given":"Lesley","email":"","middleInitial":"K.","affiliations":[],"preferred":false,"id":471447,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"Smith, Richard L. 0000-0002-3829-0125 rlsmith@usgs.gov","orcid":"https://orcid.org/0000-0002-3829-0125","contributorId":1592,"corporation":false,"usgs":true,"family":"Smith","given":"Richard","email":"rlsmith@usgs.gov","middleInitial":"L.","affiliations":[{"id":37277,"text":"WMA - Earth System Processes Division","active":true,"usgs":true},{"id":5044,"text":"National Research Program - Central Branch","active":true,"usgs":true},{"id":36183,"text":"Hydro-Ecological Interactions Branch","active":true,"usgs":true},{"id":38175,"text":"Toxics Substances Hydrology Program","active":true,"usgs":true}],"preferred":true,"id":471445,"contributorType":{"id":1,"text":"Authors"},"rank":6},{"text":"Voytek, Mary A.","contributorId":91943,"corporation":false,"usgs":true,"family":"Voytek","given":"Mary","email":"","middleInitial":"A.","affiliations":[],"preferred":false,"id":471449,"contributorType":{"id":1,"text":"Authors"},"rank":7}]}}
,{"id":70161856,"text":"70161856 - 2009 - 2008 Spawning Cisco Investigations in the Canadian Waters of Lake Superior","interactions":[],"lastModifiedDate":"2016-06-23T14:48:40","indexId":"70161856","displayToPublicDate":"2010-01-01T00:00:00","publicationYear":"2009","noYear":false,"publicationType":{"id":18,"text":"Report"},"publicationSubtype":{"id":6,"text":"USGS Unnumbered Series"},"title":"2008 Spawning Cisco Investigations in the Canadian Waters of Lake Superior","docAbstract":"<p>The Great Lakes Science Center of the United States Geological Survey (USGS) is working cooperatively with the Ontario Ministry of Natural Resources (OMNR) on a threeyear study to develop standard procedures for acoustic and midwater trawl (AC-MT) assessments of spawning cisco Coregonus artedi that the OMNR can carry forward as a management activity. In year two (2008), we conducted an AC-MT survey of the northern shore from Nipigon Bay to Thunder Bay. Spawning-cisco (&gt; 250 mm total length) densities were lowest near Nipigon Bay (&lt;10/ha), moderate in and around Black Bay (15- 30/ha), and highest in Thunder Bay (118/ha). Rainbow smelt Osmerus mordax densities were highest in Nipigon (2,179/ha) and Black (3,219/ha) bays, and lowest in Thunder Bay (961/ha). We combined our AC-MT survey results with commercial catch records to estimate exploitation fractions of female cisco in Thunder Bay during the 2008 fishery at 4% for ages 1-5, 8.7% for ages 6-12, and 4.4% for ages &ge; 13. Lake Superior fishery managers recently recommended that annual exploitation of adult female lake cisco be kept below 10-15%. Recruitment of cisco since 2003 has been low and there is a strong probability the Thunder Bay stock will decline into the future. Using a simple population dynamics approach we estimated that if the current total allowable catch (TAC) quota is held constant, exploitation fractions could exceed 10% by 2010 and 15% by 2011. Our 2008 collections suggested the survey of Black Bay was likely conducted before all spawners had returned there to spawn. Our data also suggested that cisco collected in Black Bay and east of this site in mid-November may be from the same stock. During November 2009 we will attempt to get better definition of the area occupied by cisco around Black Bay and also determine when surveys should be conducted at this location.</p>","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/70161856","usgsCitation":"Yule, D., Addison, P.A., Evrard, L.M., Cullis, K.I., and Cholwek, G.A., 2009, 2008 Spawning Cisco Investigations in the Canadian Waters of Lake Superior, 47 p., https://doi.org/10.3133/70161856.","productDescription":"47 p.","numberOfPages":"47","onlineOnly":"N","additionalOnlineFiles":"N","ipdsId":"IP-014726","costCenters":[{"id":324,"text":"Great Lakes Science Center","active":true,"usgs":true}],"links":[{"id":324306,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/usgs_thumb.jpg"},{"id":313995,"type":{"id":11,"text":"Document"},"url":"https://www.glsc.usgs.gov/products/reports/1970178148"}],"publishingServiceCenter":{"id":6,"text":"Columbus PSC"},"noUsgsAuthors":false,"publicationStatus":"PW","scienceBaseUri":"576d082ce4b07657d1a37543","contributors":{"authors":[{"text":"Yule, Daniel 0000-0002-0117-5115 dyule@usgs.gov","orcid":"https://orcid.org/0000-0002-0117-5115","contributorId":139532,"corporation":false,"usgs":true,"family":"Yule","given":"Daniel","email":"dyule@usgs.gov","affiliations":[{"id":324,"text":"Great Lakes Science Center","active":true,"usgs":true}],"preferred":true,"id":587943,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Addison, Peter A.","contributorId":105987,"corporation":false,"usgs":true,"family":"Addison","given":"Peter","email":"","middleInitial":"A.","affiliations":[],"preferred":false,"id":587947,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Evrard, Lori M. 0000-0001-8582-5818 levrard@usgs.gov","orcid":"https://orcid.org/0000-0001-8582-5818","contributorId":2720,"corporation":false,"usgs":true,"family":"Evrard","given":"Lori","email":"levrard@usgs.gov","middleInitial":"M.","affiliations":[{"id":324,"text":"Great Lakes Science Center","active":true,"usgs":true}],"preferred":true,"id":587945,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Cullis, Ken I.","contributorId":150786,"corporation":false,"usgs":false,"family":"Cullis","given":"Ken","email":"","middleInitial":"I.","affiliations":[{"id":13173,"text":"Ontario Ministry of Natural Resources, Upper Great Lakes Management Unit","active":true,"usgs":false}],"preferred":false,"id":587948,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Cholwek, Gary A. gcholwek@usgs.gov","contributorId":2719,"corporation":false,"usgs":true,"family":"Cholwek","given":"Gary","email":"gcholwek@usgs.gov","middleInitial":"A.","affiliations":[{"id":324,"text":"Great Lakes Science Center","active":true,"usgs":true}],"preferred":true,"id":587944,"contributorType":{"id":1,"text":"Authors"},"rank":5}]}}
,{"id":70043493,"text":"70043493 - 2009 - Lysimetric Evaluation of Simplified Surface Energy Balance Approach in the Texas High Plains","interactions":[],"lastModifiedDate":"2013-04-07T21:40:33","indexId":"70043493","displayToPublicDate":"2010-01-01T00:00:00","publicationYear":"2009","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":833,"text":"Applied Engineering in Agriculture","active":true,"publicationSubtype":{"id":10}},"title":"Lysimetric Evaluation of Simplified Surface Energy Balance Approach in the Texas High Plains","docAbstract":"Numerous energy balance (EB) algorithms have been developed to make use of remote sensing data to estimate evapotranspiration (ET) regionally. However, most EB models are complex to use and efforts are being made to simplify procedures mainly through the scaling of reference ET. The Simplified Surface Energy Balance (SSEB) is one such method. This approach has never been evaluated using measured ET data. In this study, the SSEB approach was applied to fourteen Landsat TM images covering a major portion of the Southern High Plains that were acquired during 2006 and 2007 cropping seasons. Performance of the SSEB was evaluated by comparing estimated ET with measured daily ET from four large monolithic lysimeters at the USDA-ARS Conservation and Production Research Laboratory, Bushland, Texas. Statistical evaluation of results indicated that the SSEB accounted for 84% of the variability in the measured ET values with a slope and intercept of 0.75 and 1.1 mm d-1, respectively. Considering the minimal amount of ancillary data required and excellent performance in predicting daily ET, the SSEB approach is a promising tool for mapping ET in the semiarid Texas High Plains and in other parts of the world with similar hydro-climatic conditions.","largerWorkType":{"id":2,"text":"Article"},"largerWorkTitle":"Applied Engineering in Agriculture","largerWorkSubtype":{"id":10,"text":"Journal Article"},"language":"English","publisherLocation":"Reston, VA","usgsCitation":"Senay, G.B., Gowda, P., Howell, T., and Marek, T., 2009, Lysimetric Evaluation of Simplified Surface Energy Balance Approach in the Texas High Plains: Applied Engineering in Agriculture, v. 25, no. 5, p. 665-669.","startPage":"665","endPage":"669","ipdsId":"IP-021547","costCenters":[{"id":222,"text":"Earth Resources Observation and Science (EROS) Center","active":true,"usgs":true}],"links":[{"id":270644,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"},{"id":270643,"type":{"id":11,"text":"Document"},"url":"https://naldc.nal.usda.gov/download/37867/PDF"}],"country":"United States","volume":"25","issue":"5","noUsgsAuthors":false,"publicationStatus":"PW","scienceBaseUri":"5162956ee4b0c25842758cff","contributors":{"authors":[{"text":"Senay, Gabriel B. 0000-0002-8810-8539 senay@usgs.gov","orcid":"https://orcid.org/0000-0002-8810-8539","contributorId":3114,"corporation":false,"usgs":true,"family":"Senay","given":"Gabriel","email":"senay@usgs.gov","middleInitial":"B.","affiliations":[{"id":223,"text":"Earth Resources Observation and Science (EROS) Center (Geography)","active":false,"usgs":true}],"preferred":true,"id":473705,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Gowda, P.H.","contributorId":63652,"corporation":false,"usgs":true,"family":"Gowda","given":"P.H.","email":"","affiliations":[],"preferred":false,"id":473708,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Howell, T.A.","contributorId":57694,"corporation":false,"usgs":true,"family":"Howell","given":"T.A.","affiliations":[],"preferred":false,"id":473707,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Marek, T.H.","contributorId":38815,"corporation":false,"usgs":true,"family":"Marek","given":"T.H.","email":"","affiliations":[],"preferred":false,"id":473706,"contributorType":{"id":1,"text":"Authors"},"rank":4}]}}
,{"id":70034027,"text":"70034027 - 2009 - Predicting the natural flow regime: Models for assessing hydrological alteration in streams","interactions":[],"lastModifiedDate":"2017-10-25T12:51:49","indexId":"70034027","displayToPublicDate":"2010-01-01T00:00:00","publicationYear":"2009","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":3301,"text":"River Research and Applications","active":true,"publicationSubtype":{"id":10}},"title":"Predicting the natural flow regime: Models for assessing hydrological alteration in streams","docAbstract":"Understanding the extent to which natural streamflow characteristics have been altered is an important consideration for ecological assessments of streams. Assessing hydrologic condition requires that we quantify the attributes of the flow regime that would be expected in the absence of anthropogenic modifications. The objective of this study was to evaluate whether selected streamflow characteristics could be predicted at regional and national scales using geospatial data. Long-term, gaged river basins distributed throughout the contiguous US that had streamflow characteristics representing least disturbed or near pristine conditions were identified. Thirteen metrics of the magnitude, frequency, duration, timing and rate of change of streamflow were calculated using a 20-50 year period of record for each site. We used random forests (RF), a robust statistical modelling approach, to develop models that predicted the value for each streamflow metric using natural watershed characteristics. We compared the performance (i.e. bias and precision) of national- and regional-scale predictive models to that of models based on landscape classifications, including major river basins, ecoregions and hydrologic landscape regions (HLR). For all hydrologic metrics, landscape stratification models produced estimates that were less biased and more precise than a null model that accounted for no natural variability. Predictive models at the national and regional scale performed equally well, and substantially improved predictions of all hydrologic metrics relative to landscape stratification models. Prediction error rates ranged from 15 to 40%, but were 25% for most metrics. We selected three gaged, non-reference sites to illustrate how predictive models could be used to assess hydrologic condition. These examples show how the models accurately estimate predisturbance conditions and are sensitive to changes in streamflow variability associated with long-term land-use change. We also demonstrate how the models can be applied to predict expected natural flow characteristics at ungaged sites. ?? 2009 John Wiley & Sons, Ltd.","language":"English","publisher":"Wiley","doi":"10.1002/rra.1247","issn":"15351459","usgsCitation":"Carlisle, D., Falcone, J., Wolock, D., Meador, M.R., and Norris, R., 2009, Predicting the natural flow regime: Models for assessing hydrological alteration in streams: River Research and Applications, v. 26, no. 2, p. 118-136, https://doi.org/10.1002/rra.1247.","productDescription":"19 p.","startPage":"118","endPage":"136","ipdsId":"IP-004184","costCenters":[{"id":451,"text":"National Water Quality Assessment Program","active":true,"usgs":true}],"links":[{"id":244636,"rank":0,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United 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,{"id":70206925,"text":"70206925 - 2009 - Efficacy of automatic vocalization recognition software for anuran monitoring","interactions":[],"lastModifiedDate":"2019-11-27T16:22:42","indexId":"70206925","displayToPublicDate":"2009-12-31T16:13:55","publicationYear":"2009","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":1894,"text":"Herpetological Conservation and Biology","onlineIssn":"2151-0733","printIssn":"1931-7603","active":true,"publicationSubtype":{"id":10}},"title":"Efficacy of automatic vocalization recognition software for anuran monitoring","docAbstract":"<p><span id=\"_mce_caret\" data-mce-bogus=\"1\" data-mce-type=\"format-caret\">Surveys of vocalizations are a widely used method for monitoring anurans, but it can be difficult to coordinate standardized data collection across a large geographic area. Digital automated recording systems (ARS) offer a low-cost method for obtaining samples of anuran vocalizations, but the number of recordings can easily overwhelm human listeners. We tested Song Scope, an automatic vocalization recognition software program for personal computers to determine if this type of machine learning approach is currently a viable solution for anuran monitoring. For three species, Song Scope scanned more than 200 h of recordings in 3-20 h at the settings we chose. The software misidentified true calls (false positive) at rates of 2.7%-15.8% per species and failed to detect calls (false negative) in 45%-51% of recordings. There exists a tradeoff between false positive and false negative errors, which can be adjusted by setting the minimum criteria for the recognition software. Users of this approach should carefully consider their reasons for monitoring and how they intend to use the data before creating a large monitoring network.&nbsp;</span><br data-mce-bogus=\"1\"></p>","language":"English","publisher":"Herpetological Conservation and Biology","usgsCitation":"Waddle, J.H., Thigpen, T.F., and Glorioso, B.M., 2009, Efficacy of automatic vocalization recognition software for anuran monitoring: Herpetological Conservation and Biology, v. 4, no. 3, p. 384-388.","productDescription":"5 p.","startPage":"384","endPage":"388","costCenters":[{"id":455,"text":"National Wetlands Research Center","active":true,"usgs":true},{"id":17705,"text":"Wetland and Aquatic Research Center","active":true,"usgs":true}],"links":[{"id":369756,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"},{"id":369755,"rank":1,"type":{"id":15,"text":"Index Page"},"url":"https://www.herpconbio.org/contents_vol4_issue3.html"}],"country":"United States","state":"Louisiana","otherGeospatial":"Atchafalaya River Basin","geographicExtents":"{\n  \"type\": \"FeatureCollection\",\n  \"features\": [\n    {\n      \"type\": \"Feature\",\n      \"properties\": {},\n      \"geometry\": {\n        \"type\": \"Polygon\",\n        \"coordinates\": [\n          [\n            [\n              -91.22222900390625,\n              29.437205610623263\n            ],\n            [\n              -91.0601806640625,\n              29.735762444449076\n            ],\n            [\n              -91.42822265625,\n              30.259067203213018\n            ],\n            [\n              -91.71936035156249,\n              30.95641324406724\n            ],\n            [\n              -91.6754150390625,\n              31.0741083805733\n            ],\n            [\n              -91.845703125,\n              31.123496964067325\n            ],\n            [\n              -92.021484375,\n              30.8951543714447\n            ],\n            [\n              -91.88140869140625,\n              30.37761431777479\n            ],\n            [\n              -91.527099609375,\n              29.91923280484215\n            ],\n            [\n              -91.47491455078125,\n              29.742916942840562\n            ],\n            [\n              -91.53259277343749,\n              29.54717721331581\n            ],\n            [\n              -91.22222900390625,\n              29.437205610623263\n            ]\n          ]\n        ]\n      }\n    }\n  ]\n}","volume":"4","issue":"3","noUsgsAuthors":false,"publicationStatus":"PW","contributors":{"authors":[{"text":"Waddle, J. Hardin 0000-0003-1940-2133 waddleh@usgs.gov","orcid":"https://orcid.org/0000-0003-1940-2133","contributorId":138953,"corporation":false,"usgs":true,"family":"Waddle","given":"J.","email":"waddleh@usgs.gov","middleInitial":"Hardin","affiliations":[{"id":17705,"text":"Wetland and Aquatic Research Center","active":true,"usgs":true}],"preferred":true,"id":776274,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Thigpen, Tyler F.","contributorId":97412,"corporation":false,"usgs":true,"family":"Thigpen","given":"Tyler","email":"","middleInitial":"F.","affiliations":[],"preferred":false,"id":776275,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Glorioso, Brad M. 0000-0002-5400-7414 gloriosob@usgs.gov","orcid":"https://orcid.org/0000-0002-5400-7414","contributorId":4241,"corporation":false,"usgs":true,"family":"Glorioso","given":"Brad","email":"gloriosob@usgs.gov","middleInitial":"M.","affiliations":[{"id":455,"text":"National Wetlands Research Center","active":true,"usgs":true},{"id":17705,"text":"Wetland and Aquatic Research Center","active":true,"usgs":true}],"preferred":true,"id":776276,"contributorType":{"id":1,"text":"Authors"},"rank":3}]}}
,{"id":70201988,"text":"70201988 - 2009 - Coregistration of Mars Orbiter Laser Altimeter (MOLA) topography with high-resolution Mars images","interactions":[],"lastModifiedDate":"2019-02-04T13:37:55","indexId":"70201988","displayToPublicDate":"2009-12-31T13:36:32","publicationYear":"2009","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":1315,"text":"Computers & Geosciences","printIssn":"0098-3004","active":true,"publicationSubtype":{"id":10}},"title":"Coregistration of Mars Orbiter Laser Altimeter (MOLA) topography with high-resolution Mars images","docAbstract":"<p><span>Spacecraft continue to send back extraordinary amounts of data from Mars leaving scientists with the considerable task of analyzing an ever-increasing wealth of information. There are abundant uses for coregistered topography and images, but coregistering various datasets can be time-consuming and labor-intensive. We have developed a set of C-shell Unix scripts and Interactive Data Language (IDL) programs that overlay individual Mars Orbiter Laser Altimeter topography footprints on high-resolution Mars images including those from the Mars Orbiter Camera (MOC), the High Resolution Imaging Science Experiment (HiRISE), and the Context Camera (CTX). After all programs are installed, initial file customizations are made, and raw data are obtained, a user enters only three commands to overlay color-coded, scaled, and labeled topographic data on a high-resolution image. The codes have a variety of options including incremental labeling, zooming in on the topography, and shifting large-scale features to align with topography. They can be used as a group or individually. Our codes minimize human interaction in the coregistration process and produce usable, effective products.</span></p>","language":"English","publisher":"Elsevier","doi":"10.1016/j.cageo.2009.04.008","usgsCitation":"Kolb, K.J., and Okubo, C.H., 2009, Coregistration of Mars Orbiter Laser Altimeter (MOLA) topography with high-resolution Mars images: Computers & Geosciences, v. 35, no. 12, p. 2304-2313, https://doi.org/10.1016/j.cageo.2009.04.008.","productDescription":"10 p.","startPage":"2304","endPage":"2313","costCenters":[{"id":131,"text":"Astrogeology Science Center","active":true,"usgs":true}],"links":[{"id":360978,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"otherGeospatial":"Mars","volume":"35","issue":"12","noUsgsAuthors":false,"publicationStatus":"PW","contributors":{"authors":[{"text":"Kolb, Kelly J.","contributorId":210546,"corporation":false,"usgs":false,"family":"Kolb","given":"Kelly","email":"","middleInitial":"J.","affiliations":[],"preferred":false,"id":756438,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Okubo, Chris H. 0000-0001-9776-8128 cokubo@usgs.gov","orcid":"https://orcid.org/0000-0001-9776-8128","contributorId":140482,"corporation":false,"usgs":true,"family":"Okubo","given":"Chris","email":"cokubo@usgs.gov","middleInitial":"H.","affiliations":[{"id":131,"text":"Astrogeology Science Center","active":true,"usgs":true}],"preferred":false,"id":756439,"contributorType":{"id":1,"text":"Authors"},"rank":2}]}}
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