{"pageNumber":"409","pageRowStart":"10200","pageSize":"25","recordCount":46624,"records":[{"id":70176480,"text":"ofr20161162 - 2016 - Genetic diversity and population structure in the threatened Oregon silverspot butterfly (<em>Speyeria zerene hippolyta</em>) in western Oregon and northwestern California— Implications for future translocations and the establishment of new populations","interactions":[],"lastModifiedDate":"2017-11-22T15:35:36","indexId":"ofr20161162","displayToPublicDate":"2016-09-20T00:00:00","publicationYear":"2016","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":"2016-1162","title":"Genetic diversity and population structure in the threatened Oregon silverspot butterfly (<em>Speyeria zerene hippolyta</em>) in western Oregon and northwestern California— Implications for future translocations and the establishment of new populations","docAbstract":"<h1>Executive Summary</h1><p>We present results of population genetic analyses performed on Oregon silverspot butterflies (OSB; <i>Speyeria zerene hippolyta</i>) in western Oregon and northwestern California. We used DNA sequences from a 561-base pair region of the mitochondrial <span class=\"s1\">cytochrome oxidase subunit I </span>(COI) gene for a dataset comprised of 112 <i>S. z. hippolyta </i>and 32 <i>S. z. gloriosa </i>individuals collected at 9 locations in western Oregon and northwestern California. The most pertinent findings thus far are summarized as follows:</p><ul><li>Among OSB populations, genetic diversity is lowest at Mount Hebo and highest at Rock Creek and Bray Point. Of the 32 haplotypes detected in OSB, only 2 were shared among populations (1 shared by Mount Hebo, Cascade Head, Bray Point, and Rock Creek, and 1 shared by Rock Creek and Lake Earl). The remaining 30 haplotypes were identified in individual populations, highlighting the strong differentiation among sites. It is unclear if the shared haplotypes represent widespread, naturally occurring genetic variation or if allele sharing among populations is due to translocation history.</li><li>Using full siblings of individuals that were released at Rock Creek and Bray Point in 2012 as comparison standards, the analyses suggest that 54 percent of the sampled individuals from Bray Point were naturally recruited into the population and were not originating from the 2012 release of captive reared individuals. Likewise, 33 percent of the analyzed individuals from Rock Creek were naturally recruited. Both of these estimates may be underestimates if the shared alleles that we identified among populations are naturally occurring and not a product of the 2012 translocations.</li><li>The results suggest that there are about 12–13 COI haplotypes in the Mount Hebo population. The U.S. Fish and Wildlife Service anticipates using Mount Hebo as the source of individuals when establishing new populations in the future. Nonlinear regression models based on a series of rarefaction analyses suggest that progeny from 12, 37, 109, and 326 female individuals would be required to respectively capture 25, 50, 75, and 90 percent of the allelic diversity from Mount Hebo.</li><li>Phylogenetic analyses identified two different haplotype groups, but the two groups did not correspond to the different subspecies used in the analysis. One group included 22 <i>S. z. hippolyta </i>haplotypes and 7 haplotypes identified in <i>S. z. gloriosa</i>. The second group included eight haplotypes from <i>S. z. hippolyta, </i>three haplotypes from <i>S. z. gloriosa</i>, and one haplotype that was detected in both subspecies.</li></ul>","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/ofr20161162","collaboration":"Prepared in cooperation with the U.S. Fish and Wildlife Service","usgsCitation":"Miller, M.P., Mullins, T.D., and Haig, S.M., 2016, Genetic diversity and population structure in the threatened Oregon silverspot butterfly (<em>Speyeria zerene hippolyta</em>) in western Oregon and northwestern California—Implications for future translocations and the establishment of new populations: U.S. Geological Survey Open-File Report 2016-1162, 23 p., https://dx.doi.org/10.3133/ofr20161162.","productDescription":"Report: iv, 23 p.; Appendix","numberOfPages":"32","onlineOnly":"Y","additionalOnlineFiles":"Y","ipdsId":"IP-073493","costCenters":[{"id":290,"text":"Forest and Rangeland Ecosystem Science Center","active":false,"usgs":true}],"links":[{"id":328770,"rank":3,"type":{"id":3,"text":"Appendix"},"url":"https://pubs.usgs.gov/of/2016/1162/ofr20161162_appendix1.xls","text":"Appendix 1","size":"63 KB","linkFileType":{"id":3,"text":"xlsx"},"description":"OFR 2016-1162 Appendix 1","linkHelpText":"Sample Information for Oregon Silverspot Butterfly Specimens Included in This Analysis"},{"id":328768,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/of/2016/1162/coverthb.jpg"},{"id":328769,"rank":2,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/of/2016/1162/ofr20161162.pdf","text":"Report","size":"539 KB","linkFileType":{"id":1,"text":"pdf"},"description":"OFR 2016-1162"}],"country":"United States","state":"California, Oregon","geographicExtents":"{\n  \"type\": \"FeatureCollection\",\n  \"features\": [\n    {\n      \"type\": \"Feature\",\n      \"properties\": {},\n      \"geometry\": {\n        \"type\": \"Polygon\",\n        \"coordinates\": [\n          [\n            [\n              -124.69482421875,\n              41.66470503009207\n            ],\n            [\n              -124.69482421875,\n              45.321254361171476\n            ],\n            [\n              -123.6181640625,\n              45.321254361171476\n            ],\n            [\n              -123.6181640625,\n              41.66470503009207\n            ],\n            [\n              -124.69482421875,\n              41.66470503009207\n            ]\n          ]\n        ]\n      }\n    }\n  ]\n}","contact":"<p>Director, Forest and Rangeland Ecosystem Science Center<br> U.S. Geological Survey<br> 777 NW 9th St., Suite 400<br> Corvallis, Oregon 97330<br> <a href=\"http://fresc.usgs.gov/\" target=\"blank\" data-mce-href=\"http://fresc.usgs.gov/\">http://fresc.usgs.gov/</a></p>","tableOfContents":"<ul><li>Executive Summary</li><li>Introduction</li><li>Methods</li><li>Results</li><li>Discussion</li><li>Acknowledgments</li><li>Referenced Cited</li><li>Appendix1. Sample Information for Oregon Silverspot Butterfly Specimens Included in This Analysis</li><li>Appendix 2. Haplotype Frequencies Among Populations of Oregon SilverSpot Butterflies (<em>Speyeria zerene hippolyta</em>) and <em>S. z. gloriosa</em></li></ul>","publishingServiceCenter":{"id":12,"text":"Tacoma PSC"},"publishedDate":"2016-09-20","noUsgsAuthors":false,"publicationDate":"2016-09-20","publicationStatus":"PW","scienceBaseUri":"57f7c6e6e4b0bc0bec09cbe5","contributors":{"authors":[{"text":"Miller, Mark P. 0000-0003-1045-1772 mpmiller@usgs.gov","orcid":"https://orcid.org/0000-0003-1045-1772","contributorId":1967,"corporation":false,"usgs":true,"family":"Miller","given":"Mark","email":"mpmiller@usgs.gov","middleInitial":"P.","affiliations":[{"id":38131,"text":"WMA - Office of Planning and Programming","active":true,"usgs":true}],"preferred":true,"id":648898,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Mullins, Thomas D. 0000-0001-8948-9604 tom_mullins@usgs.gov","orcid":"https://orcid.org/0000-0001-8948-9604","contributorId":3615,"corporation":false,"usgs":true,"family":"Mullins","given":"Thomas","email":"tom_mullins@usgs.gov","middleInitial":"D.","affiliations":[{"id":289,"text":"Forest and Rangeland Ecosys Science Center","active":true,"usgs":true}],"preferred":false,"id":648899,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Haig, Susan M. 0000-0002-6616-7589 susan_haig@usgs.gov","orcid":"https://orcid.org/0000-0002-6616-7589","contributorId":719,"corporation":false,"usgs":true,"family":"Haig","given":"Susan","email":"susan_haig@usgs.gov","middleInitial":"M.","affiliations":[{"id":290,"text":"Forest and Rangeland Ecosystem Science Center","active":false,"usgs":true},{"id":289,"text":"Forest and Rangeland Ecosys Science Center","active":true,"usgs":true}],"preferred":true,"id":648900,"contributorType":{"id":1,"text":"Authors"},"rank":3}]}}
,{"id":70175364,"text":"sir20165118 - 2016 - Magnitude, frequency, and trends of floods at gaged and ungaged sites in Washington, based on data through water year 2014","interactions":[],"lastModifiedDate":"2019-03-14T14:54:40","indexId":"sir20165118","displayToPublicDate":"2016-09-20T00:00:00","publicationYear":"2016","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":"2016-5118","title":"Magnitude, frequency, and trends of floods at gaged and ungaged sites in Washington, based on data through water year 2014","docAbstract":"<p class=\"p1\">An investigation into the magnitude and frequency of floods in Washington State computed the annual exceedance probability (AEP) statistics for 648 U.S. Geological Survey unregulated streamgages in and near the borders of Washington using the recorded annual peak flows through water year 2014. This is an updated report from a previous report published in 1998 that used annual peak flows through the water year 1996. New in this report, a regional skew coefficient was developed for the Pacific Northwest region that includes areas in Oregon, Washington, Idaho and western Montana within the Columbia River drainage basin south of the United States-Canada border, the coastal areas of Oregon and western Washington, and watersheds draining into Puget Sound, Washington. The skew coefficient is an important term in the Log Pearson Type III equation used to define the distribution of the log-transformed annual peaks. The Expected Moments Algorithm was used to fit historical and censored peak-flow data to the log Pearson Type III distribution. A Multiple Grubb-Beck test was employed to censor low outliers of annual peak flows to improve on the frequency distribution. This investigation also includes a section on observed trends in annual peak flows that showed significant trends (<i>p</i>-value &lt; 0.05) in 21 of 83 long-term sites, but with small magnitude Kendall <i>tau </i>values suggesting a limited monotonic trend in the time series of annual peaks. Most of the sites with a significant trend in western Washington were positive and all the sites with significant trends (three sites) in eastern Washington were negative.</p><p class=\"p1\">Multivariate regression analysis with measured basin characteristics and the AEP statistics at long-term, unregulated, and un-urbanized (defined as drainage basins with less than 5 percent impervious land cover for this investigation) streamgages within Washington and some in Idaho and Oregon that are near the Washington border was used to develop equations to estimate AEP statistics at ungaged basins. Washington was divided into four regions to improve the accuracy of the regression equations; a set of equations for eight selected AEPs and for each region were constructed. Selected AEP statistics included the annual peak flows that equaled or exceeded 50, 20, 10, 4, 2, 1, 0.5 and 0.2 percent of the time equivalent to peak flows for peaks with a 2-, 5-, 10-, 25-, 50-, 100-, 200-, and 500-year recurrence intervals, respectively. Annual precipitation and drainage area were the significant basin characteristics in the regression equations for all four regression regions in Washington and forest cover was significant for the two regression regions in eastern Washington. Average standard error of prediction for the regional regression equations ranged from 70.19 to 125.72 percent for Regression Regions 1 and 2 on the eastern side of the Cascade Mountains and from 43.22 to 58.04 percent for Regression Regions 3 and 4 on the western side of the Cascade Mountains. The pseudo coefficient of determination (where a value of 100 signifies a perfect regression model) ranged from 68.39 to 90.68 for Regression Regions 1 and 2, and 92.35 to 95.44 for Regions 3 and 4.</p><p class=\"p2\">The calculated AEP statistics for the streamgages and the regional regression equations are expected to be incorporated into StreamStats after the publication of this report. StreamStats is the interactive Web-based map tool created by the U.S. Geological Survey to allow the user to choose a streamgage and obtain published statistics or choose ungaged locations where the program automatically applies the regional regression equations and computes the estimates of the AEP statistics.</p>","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/sir20165118","collaboration":"Prepared in cooperation with the Washington State Department of Transportation and the Washington State Department of Ecology","usgsCitation":"Mastin, M.C., Konrad, C.P., Veilleux, A.G., and Tecca, A.E., 2016, Magnitude, frequency, and trends of floods at gaged and ungaged sites in Washington, based on data through water year 2014 (ver 1.2, November 2017): U.S. Geological Survey Scientific Investigations Report 2016–5118, 70 p., https://dx.doi.org/10.3133/sir20165118.","productDescription":"Report: vi, 69 p.; 3 Tables","numberOfPages":"80","onlineOnly":"Y","additionalOnlineFiles":"Y","ipdsId":"IP-075256","costCenters":[{"id":622,"text":"Washington Water Science Center","active":true,"usgs":true}],"links":[{"id":329272,"rank":7,"type":{"id":25,"text":"Version History"},"url":"https://pubs.usgs.gov/sir/2016/5118/versionHist.txt","size":"3 KB","linkFileType":{"id":2,"text":"txt"},"description":"SIR 2016-5118 Version History"},{"id":328745,"rank":2,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/sir/2016/5118/sir20165118.pdf","text":"Report","size":"17 MB","linkFileType":{"id":1,"text":"pdf"},"description":"SIR 2016-5118"},{"id":328747,"rank":4,"type":{"id":27,"text":"Table"},"url":"https://pubs.usgs.gov/sir/2016/5118/sir20165118_table5.xlsx","text":"Table 5","size":"220 KB","linkFileType":{"id":3,"text":"xlsx"},"description":"SIR 2016-5118 Table 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 \"}}]}","edition":"Version 1.0: Originally post September 20, 2016; Version 1.1: October 4, 2016; Version 1.2: November 2017","contact":"<p><a href=\"mailto:dc_wa@usgs.gov\" data-mce-href=\"mailto:dc_wa@usgs.gov\">Director</a>, <a href=\"http://wa.water.usgs.gov\" target=\"blank\" data-mce-href=\"http://wa.water.usgs.gov\">Washington Water Science Center</a><br> U.S. Geological Survey<br> 934 Broadway, Suite 300<br> Tacoma, Washington 98402</p>","tableOfContents":"<ul><li>Abstract<br></li><li>Introduction<br></li><li>Trends in Peak Flows<br></li><li>Estimating Magnitude and Frequency of Floods<br></li><li>Summary<br></li><li>References Cited<br></li><li>Appendix A. Regional Skewness Regression Analysis for the Pacific Northwest, Including Idaho, Montana, Oregon, and Washington<br></li></ul>","publishingServiceCenter":{"id":12,"text":"Tacoma PSC"},"publishedDate":"2016-09-20","revisedDate":"2017-11-01","noUsgsAuthors":false,"publicationDate":"2016-09-20","publicationStatus":"PW","scienceBaseUri":"57f7c63de4b0bc0bec09c888","contributors":{"authors":[{"text":"Mastin, Mark C. 0000-0003-4018-7861 mcmastin@usgs.gov","orcid":"https://orcid.org/0000-0003-4018-7861","contributorId":1652,"corporation":false,"usgs":true,"family":"Mastin","given":"Mark","email":"mcmastin@usgs.gov","middleInitial":"C.","affiliations":[{"id":622,"text":"Washington Water Science Center","active":true,"usgs":true}],"preferred":true,"id":644904,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Konrad, Christopher P. 0000-0002-7354-547X cpkonrad@usgs.gov","orcid":"https://orcid.org/0000-0002-7354-547X","contributorId":1716,"corporation":false,"usgs":true,"family":"Konrad","given":"Christopher","email":"cpkonrad@usgs.gov","middleInitial":"P.","affiliations":[{"id":622,"text":"Washington Water Science Center","active":true,"usgs":true}],"preferred":true,"id":644906,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Veilleux, Andrea G. aveilleux@usgs.gov","contributorId":4404,"corporation":false,"usgs":true,"family":"Veilleux","given":"Andrea","email":"aveilleux@usgs.gov","middleInitial":"G.","affiliations":[{"id":502,"text":"Office of Surface Water","active":true,"usgs":true}],"preferred":true,"id":644907,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Tecca, Alison E. 0000-0002-1572-0161 atecca@usgs.gov","orcid":"https://orcid.org/0000-0002-1572-0161","contributorId":174699,"corporation":false,"usgs":true,"family":"Tecca","given":"Alison","email":"atecca@usgs.gov","middleInitial":"E.","affiliations":[{"id":622,"text":"Washington Water Science Center","active":true,"usgs":true}],"preferred":false,"id":649048,"contributorType":{"id":1,"text":"Authors"},"rank":4}]}}
,{"id":70176487,"text":"70176487 - 2016 - Evaluation of single photon and Geiger mode Lidar for the 3D Elevation Program","interactions":[],"lastModifiedDate":"2016-09-19T14:47:43","indexId":"70176487","displayToPublicDate":"2016-09-19T14:45:00","publicationYear":"2016","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":3250,"text":"Remote Sensing","active":true,"publicationSubtype":{"id":10}},"title":"Evaluation of single photon and Geiger mode Lidar for the 3D Elevation Program","docAbstract":"<p><span>Data acquired by Harris Corporation’s (Melbourne, FL, USA) Geiger-mode IntelliEarth™ sensor and Sigma Space Corporation’s (Lanham-Seabrook, MD, USA) Single Photon HRQLS sensor were evaluated and compared to accepted 3D Elevation Program (3DEP) data and survey ground control to assess the suitability of these new technologies for the 3DEP. While not able to collect data currently to meet USGS lidar base specification, this is partially due to the fact that the specification was written for linear-mode systems specifically. With little effort on part of the manufacturers of the new lidar systems and the USGS Lidar specifications team, data from these systems could soon serve the 3DEP program and its users. Many of the shortcomings noted in this study have been reported to have been corrected or improved upon in the next generation sensors.</span></p>","language":"English","publisher":"MDPI","doi":"10.3390/rs8090767","usgsCitation":"Stoker, J.M., Abdullah, Q., Nayegandhi, A., and Winehouse, J., 2016, Evaluation of single photon and Geiger mode Lidar for the 3D Elevation Program: Remote Sensing, v. 8, no. 9, Article 767; 16 p., https://doi.org/10.3390/rs8090767.","productDescription":"Article 767; 16 p.","ipdsId":"IP-077259","costCenters":[{"id":423,"text":"National Geospatial Program","active":true,"usgs":true}],"links":[{"id":470564,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.3390/rs8090767","text":"Publisher Index Page"},{"id":328736,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"volume":"8","issue":"9","publishingServiceCenter":{"id":9,"text":"Reston PSC"},"noUsgsAuthors":false,"publicationDate":"2016-09-19","publicationStatus":"PW","scienceBaseUri":"57f7c63de4b0bc0bec09c88e","contributors":{"authors":[{"text":"Stoker, Jason M. 0000-0003-2455-0931 jstoker@usgs.gov","orcid":"https://orcid.org/0000-0003-2455-0931","contributorId":3021,"corporation":false,"usgs":true,"family":"Stoker","given":"Jason","email":"jstoker@usgs.gov","middleInitial":"M.","affiliations":[{"id":223,"text":"Earth Resources Observation and Science (EROS) Center (Geography)","active":false,"usgs":true},{"id":423,"text":"National Geospatial Program","active":true,"usgs":true}],"preferred":true,"id":649023,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Abdullah, Qassim","contributorId":174668,"corporation":false,"usgs":false,"family":"Abdullah","given":"Qassim","email":"","affiliations":[{"id":27496,"text":"Woolpert","active":true,"usgs":false}],"preferred":false,"id":649024,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Nayegandhi, Amar","contributorId":37292,"corporation":false,"usgs":true,"family":"Nayegandhi","given":"Amar","affiliations":[],"preferred":false,"id":649025,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Winehouse, Jayna","contributorId":174696,"corporation":false,"usgs":false,"family":"Winehouse","given":"Jayna","email":"","affiliations":[],"preferred":false,"id":649026,"contributorType":{"id":1,"text":"Authors"},"rank":4}]}}
,{"id":70176249,"text":"ds1018 - 2016 - California sea otter (<i>Enhydra lutris nereis</i>) census results, Spring 2016","interactions":[],"lastModifiedDate":"2016-09-19T14:55:04","indexId":"ds1018","displayToPublicDate":"2016-09-19T12:00:00","publicationYear":"2016","noYear":false,"publicationType":{"id":18,"text":"Report"},"publicationSubtype":{"id":5,"text":"USGS Numbered Series"},"seriesTitle":{"id":310,"text":"Data Series","code":"DS","onlineIssn":"2327-638X","printIssn":"2327-0271","active":false,"publicationSubtype":{"id":5}},"seriesNumber":"1018","title":"California sea otter (<i>Enhydra lutris nereis</i>) census results, Spring 2016","docAbstract":"<p>The 2016 census of southern sea otters <i>Enhydra lutris nereis</i>&nbsp;was conducted in May along the mainland coast of central California and in April at San Nicolas Island in southern California. The 3-year average of combined counts from the mainland range and San Nicolas Island was 3,272. This is the first year that the official index has exceeded 3,090, the Endangered Species Act delisting threshold suggested by the U.S. Fish and Wildlife Service (the threshold would need to be exceeded for 3 consecutive years before delisting consideration). The 5-year average trend in abundance, including both the mainland range and San Nicolas Island populations, is positive at 3.2 percent per year; however, regional trends vary, with localized declines at the southern and northern peripheries of the mainland range. The lack of population growth in the range peripheries over recent years likely explains the cessation of range expansion, with the range limits remaining almost unchanged from the previous 5 years.</p>","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/ds1018","usgsCitation":"Tinker, M.T., and Hatfield, B.B., 2016, California sea otter (<i>Enhydra lutris nereis</i>) census results, spring 2016: U.S. Geological Survey Data Series 1018, 10 p., https://dx.doi.org/10.3133/ds1018.","productDescription":"Report: iv, 9 p.; Data Release","onlineOnly":"Y","ipdsId":"IP-078738","costCenters":[{"id":651,"text":"Western Ecological Research Center","active":true,"usgs":true}],"links":[{"id":438547,"rank":4,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/F7FJ2DWJ","text":"USGS data release","linkHelpText":"Annual California Sea Otter Census: 2016 Spring Census Summary"},{"id":328735,"rank":3,"type":{"id":30,"text":"Data Release"},"url":"https://dx.doi.org/10.5066/F7FJ2DWJ","text":"USGS data release","linkHelpText":"Annual California Sea Otter Census - 2016 Spring Census Summary"},{"id":328710,"rank":2,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/ds/1018/ds1018.pdf","text":"Report","size":"2.2 MB","linkFileType":{"id":1,"text":"pdf"},"description":"DS 1018 report PDF"},{"id":328709,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/ds/1018/coverthb.jpg"}],"country":"United States","state":"California","geographicExtents":"{\n  \"type\": \"FeatureCollection\",\n  \"features\": [\n    {\n      \"type\": \"Feature\",\n      \"properties\": {},\n      \"geometry\": {\n        \"type\": \"Polygon\",\n        \"coordinates\": [\n          [\n            [\n              -122.431640625,\n              33.128351191631566\n            ],\n            [\n              -122.431640625,\n              37.125286284966805\n            ],\n            [\n              -119.388427734375,\n              37.125286284966805\n            ],\n            [\n              -119.388427734375,\n              33.128351191631566\n            ],\n            [\n              -122.431640625,\n              33.128351191631566\n            ]\n          ]\n        ]\n      }\n    }\n  ]\n}","contact":"<p>Director, Western Ecological Research Center<br>U.S. Geological Survey<br>3020 State University Drive East<br>Sacramento, California 95819<br><a href=\"http://www.werc.usgs.gov/\" data-mce-href=\"http://www.werc.usgs.gov/\">http://www.werc.usgs.gov/</a><br></p>","tableOfContents":"<ul><li>Abstract<br></li><li>Introduction<br></li><li>California Sea Otter Census Results<br></li><li>Acknowledgments<br></li><li>References Cited<br></li></ul>","publishingServiceCenter":{"id":1,"text":"Sacramento PSC"},"publishedDate":"2016-09-19","noUsgsAuthors":false,"publicationDate":"2016-09-19","publicationStatus":"PW","scienceBaseUri":"57f7c63ee4b0bc0bec09c894","contributors":{"authors":[{"text":"Tinker, M. Tim 0000-0002-3314-839X ttinker@usgs.gov","orcid":"https://orcid.org/0000-0002-3314-839X","contributorId":2796,"corporation":false,"usgs":true,"family":"Tinker","given":"M.","email":"ttinker@usgs.gov","middleInitial":"Tim","affiliations":[{"id":651,"text":"Western Ecological Research Center","active":true,"usgs":true}],"preferred":true,"id":648076,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Hatfield, Brian B. 0000-0003-1432-2660 brian_hatfield@usgs.gov","orcid":"https://orcid.org/0000-0003-1432-2660","contributorId":127457,"corporation":false,"usgs":true,"family":"Hatfield","given":"Brian","email":"brian_hatfield@usgs.gov","middleInitial":"B.","affiliations":[{"id":651,"text":"Western Ecological Research Center","active":true,"usgs":true}],"preferred":false,"id":648077,"contributorType":{"id":1,"text":"Authors"},"rank":2}]}}
,{"id":70175461,"text":"sir20165111 - 2016 - Methods for estimating selected spring and fall low-flow frequency statistics for ungaged stream sites in Iowa, based on data through June 2014","interactions":[],"lastModifiedDate":"2016-10-05T11:40:16","indexId":"sir20165111","displayToPublicDate":"2016-09-19T00:00:00","publicationYear":"2016","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":"2016-5111","title":"Methods for estimating selected spring and fall low-flow frequency statistics for ungaged stream sites in Iowa, based on data through June 2014","docAbstract":"<p>A statewide study was led to develop regression equations for estimating three selected spring and three selected fall low-flow frequency statistics for ungaged stream sites in Iowa. The estimation equations developed for the six low-flow frequency statistics include spring (April through June) 1-, 7-, and 30-day mean low flows for a recurrence interval of 10 years and fall (October through December) 1-, 7-, and 30-day mean low flows for a recurrence interval of 10 years. Estimates of the three selected spring statistics are provided for 241 U.S. Geological Survey continuous-record streamgages, and estimates of the three selected fall statistics are provided for 238 of these streamgages, using data through June 2014. Because only 9 years of fall streamflow record were available, three streamgages included in the development of the spring regression equations were not included in the development of the fall regression equations. Because of regulation, diversion, or urbanization, 30 of the 241 streamgages were not included in the development of the regression equations. The study area includes Iowa and adjacent areas within 50 miles of the Iowa border. Because trend analyses indicated statistically significant positive trends when considering the period of record for most of the streamgages, the longest, most recent period of record without a significant trend was determined for each streamgage for use in the study. Geographic information system software was used to measure 63 selected basin characteristics for each of the 211streamgages used to develop the regional regression equations. The study area was divided into three low-flow regions that were defined in a previous study for the development of regional regression equations.</p><p>Because several streamgages included in the development of regional regression equations have estimates of zero flow calculated from observed streamflow for selected spring and fall low-flow frequency statistics, the final equations for the three low-flow regions were developed using two types of regression analyses—left-censored and generalized-least-squares regression analyses. A total of 211 streamgages were included in the development of nine spring regression equations—three equations for each of the three low-flow regions. A total of 208 streamgages were included in the development of nine fall regression equations—three equations for each of the three low-flow regions. A censoring threshold was used to develop 15 left-censored regression equations to estimate the three fall low-flow frequency statistics for each of the three low-flow regions and to estimate the three spring low-flow frequency statistics for the southern and northwest regions. For the northeast region, generalized-least-squares regression was used to develop three equations to estimate the three spring low-flow frequency statistics. For the northeast region, average standard errors of prediction range from 32.4 to 48.4 percent for the spring equations and average standard errors of estimate range from 56.4 to 73.8 percent for the fall equations. For the northwest region, average standard errors of estimate range from 58.9 to 62.1 percent for the spring equations and from 83.2 to 109.4 percent for the fall equations. For the southern region, average standard errors of estimate range from 43.2 to 64.0 percent for the spring equations and from 78.1 to 78.7 percent for the fall equations.</p><p>The regression equations are applicable only to stream sites in Iowa with low flows not substantially affected by regulation, diversion, or urbanization and with basin characteristics within the range of those used to develop the equations. The regression equations will be implemented within the U.S. Geological Survey StreamStats Web-based geographic information system application. StreamStats allows users to click on any ungaged stream site and compute estimates of the six selected spring and fall low-flow statistics; in addition, 90-percent prediction intervals and the measured basin characteristics for the ungaged site are provided. StreamStats also allows users to click on any Iowa streamgage to obtain computed estimates for the six selected spring and fall low-flow statistics.</p>","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/sir20165111","collaboration":"Prepared in cooperation with the Iowa Department of Natural Resources","usgsCitation":"Eash, D.A., Barnes, K.K., and O’Shea, P.S., 2016, Methods for estimating selected spring and fall low-flow frequency statistics for ungaged stream sites in Iowa, based on data through June 2014 (ver. 1.1, October 2016): U.S. Geological Survey Scientific Investigations Report 2016–5111, 32 p., https://dx.doi.org/10.3133/sir20165111.","productDescription":"Report: vi, 32 p.; Appendix Table 1–1","numberOfPages":"42","onlineOnly":"Y","additionalOnlineFiles":"Y","ipdsId":"IP-070749","costCenters":[{"id":351,"text":"Iowa Water Science Center","active":true,"usgs":true}],"links":[{"id":328721,"rank":2,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/sir/2016/5111/sir20165111.pdf","text":"Report","size":"2.56 MB","linkFileType":{"id":1,"text":"pdf"},"description":"SIR 2016–5111"},{"id":328722,"rank":3,"type":{"id":3,"text":"Appendix"},"url":"https://pubs.usgs.gov/sir/2016/5111/sir20165111_appendix_table1-1.xlsx","text":"Appendix Table 1–1","size":"91.9 kB","linkFileType":{"id":3,"text":"xlsx"},"description":"SIR 2016–5111 Appendix Table 1–1"},{"id":329298,"rank":4,"type":{"id":25,"text":"Version History"},"url":"https://pubs.usgs.gov/sir/2016/5111/versionHist.txt","text":"Version History","size":"1 kB","linkFileType":{"id":2,"text":"txt"},"description":"SIR 2016–5111 Version History"},{"id":328720,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/sir/2016/5111/coverthb.jpg"}],"country":"United States","state":"Iowa","geographicExtents":"{\n  \"type\": \"FeatureCollection\",\n  \"features\": [\n    {\n      \"type\": \"Feature\",\n      \"properties\": {},\n      \"geometry\": {\n        \"type\": \"Polygon\",\n        \"coordinates\": [\n          [\n            [\n              -97.218017578125,\n              39.86758762451019\n            ],\n            [\n              -97.218017578125,\n              44.06390660801779\n            ],\n            [\n              -89.461669921875,\n              44.06390660801779\n            ],\n            [\n              -89.461669921875,\n              39.86758762451019\n            ],\n            [\n              -97.218017578125,\n              39.86758762451019\n            ]\n          ]\n        ]\n      }\n    }\n  ]\n}","edition":"Version 1.0: Originally posted September 19, 2016; Version 1.1: October 5, 2016","contact":"<p>Director, Iowa Water Science Center<br>U.S. Geological Survey<br>P.O. Box 1230 <br>Iowa City, IA 52244</p><p><a href=\"http://ia.water.usgs.gov/\" data-mce-href=\"http://ia.water.usgs.gov/\">http://ia.water.usgs.gov/</a></p>","tableOfContents":"<ul><li>Abstract<br></li><li>Introduction<br></li><li>Methods for Dataset Development for Streamgages<br></li><li>Estimating Selected Spring and Fall Low-Flow Frequency Statistics for&nbsp;Ungaged Stream Sites<br></li><li>Weighted Drainage-Area Ratio Method to Estimate Selected Spring and Fall Low-Flow Frequency Statistics for Ungaged Stream Sites on Gaged Streams<br></li><li>StreamStats<br></li><li>Summary<br></li><li>References Cited<br></li><li>Appendix 1. Streamgage Information Included in this Study<br></li></ul>","publishingServiceCenter":{"id":4,"text":"Rolla PSC"},"publishedDate":"2016-09-19","revisedDate":"2016-10-05","noUsgsAuthors":false,"publicationDate":"2016-09-19","publicationStatus":"PW","scienceBaseUri":"57f7c63ee4b0bc0bec09c89f","contributors":{"authors":[{"text":"Eash, David A. 0000-0002-2749-8959 daeash@usgs.gov","orcid":"https://orcid.org/0000-0002-2749-8959","contributorId":1887,"corporation":false,"usgs":true,"family":"Eash","given":"David","email":"daeash@usgs.gov","middleInitial":"A.","affiliations":[{"id":351,"text":"Iowa Water Science Center","active":true,"usgs":true}],"preferred":true,"id":645337,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Barnes, Kimberlee K.","contributorId":41476,"corporation":false,"usgs":true,"family":"Barnes","given":"Kimberlee K.","affiliations":[],"preferred":false,"id":648983,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"O'Shea, Padraic S.","contributorId":150533,"corporation":false,"usgs":true,"family":"O'Shea","given":"Padraic S.","affiliations":[],"preferred":false,"id":648984,"contributorType":{"id":1,"text":"Authors"},"rank":3}]}}
,{"id":70176472,"text":"70176472 - 2016 - The importance of species name synonyms in literature searches","interactions":[],"lastModifiedDate":"2016-09-16T13:31:51","indexId":"70176472","displayToPublicDate":"2016-09-16T00:00:00","publicationYear":"2016","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":2980,"text":"PLoS ONE","active":true,"publicationSubtype":{"id":10}},"title":"The importance of species name synonyms in literature searches","docAbstract":"<p><span>The synonyms of biological species names are shown to be an important component in comprehensive searches of electronic scientific literature databases but they are not well leveraged within the major literature databases examined. For accepted or valid species names in the Integrated Taxonomic Information System (ITIS) which have synonyms in the system, and which are found in citations within PLoS, PMC, PubMed or Scopus, both the percentage of species for which citations will not be found if synonyms are not used, and the percentage increase in number of citations found by including synonyms are very often substantial. However, there is no correlation between the number of synonyms per species and the magnitude of the effect. Further, the number of citations found does not generally increase proportionally to the number of synonyms available. Users looking for literature on specific species across all of the resources investigated here are often missing large numbers of citations if they are not manually augmenting their searches with synonyms. Of course, missing citations can have serious consequences by effectively hiding critical information. Literature searches should include synonym relationships and a new web service in ITIS, with examples of how to apply it to this issue, was developed as a result of this study, and is here announced, to aide in this.</span></p>","language":"English","publisher":"Public Library of Science (PLoS)","publisherLocation":"San Francisco, CA","doi":"10.1371/journal.pone.0162648","usgsCitation":"Guala, G., 2016, The importance of species name synonyms in literature searches: PLoS ONE, v. 11, no. 9, https://doi.org/10.1371/journal.pone.0162648.","productDescription":"7 p.","startPage":"e0162648","ipdsId":"IP-074162","costCenters":[{"id":208,"text":"Core Science Analytics and Synthesis","active":true,"usgs":true}],"links":[{"id":470568,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.1371/journal.pone.0162648","text":"Publisher Index Page"},{"id":328684,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"volume":"11","issue":"9","publishingServiceCenter":{"id":2,"text":"Denver PSC"},"noUsgsAuthors":false,"publicationDate":"2016-09-14","publicationStatus":"PW","scienceBaseUri":"57f7c63fe4b0bc0bec09c8a7","contributors":{"authors":[{"text":"Guala, Gerald gguala@usgs.gov","contributorId":174640,"corporation":false,"usgs":true,"family":"Guala","given":"Gerald","email":"gguala@usgs.gov","affiliations":[{"id":208,"text":"Core Science Analytics and Synthesis","active":true,"usgs":true}],"preferred":true,"id":648859,"contributorType":{"id":1,"text":"Authors"},"rank":1}]}}
,{"id":70176438,"text":"ofr20161160 - 2016 - Element concentrations in surface soils of the Coconino Plateau, Grand Canyon region, Coconino County, Arizona","interactions":[],"lastModifiedDate":"2016-09-15T15:58:04","indexId":"ofr20161160","displayToPublicDate":"2016-09-15T11:55:00","publicationYear":"2016","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":"2016-1160","title":"Element concentrations in surface soils of the Coconino Plateau, Grand Canyon region, Coconino County, Arizona","docAbstract":"<p>This report provides the geochemical analyses of a large set of background soils collected from the surface of the Coconino Plateau in northern Arizona. More than 700 soil samples were collected at 46 widespread areas, sampled from sites that appear unaffected by mineralization and (or) anthropogenic contamination. The soils were analyzed for 47 elements, thereby providing data on metal concentrations in soils representative of the plateau. These background concentrations can be used, for instance, for comparison to metal concentrations found in soils potentially affected by natural and anthropogenic influences on the Coconino Plateau in the Grand Canyon region of Arizona.</p><p>The soil sampling survey revealed low concentrations for the metals most commonly of environmental concern, such as arsenic, cobalt, chromium, copper, mercury, manganese, molybdenum, lead, uranium, vanadium, and zinc. For example, the median concentrations of the metals in soils of the Coconino Plateau were found to be comparable to the mean values previously reported for soils of the western United States.</p>","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/ofr20161160","usgsCitation":"Van Gosen, B.S., 2016, Element concentrations in surface soils of the Coconino Plateau, Grand Canyon region, Coconino County, Arizona: U.S. Geological Survey Open-File Report 2016–1160, 9 p. https://dx.doi.org/10.3133/ofr20161160.","productDescription":"Report: v, 9 p.; Appendix","numberOfPages":"14","onlineOnly":"Y","ipdsId":"IP-077409","costCenters":[{"id":171,"text":"Central Mineral and Environmental Resources Science Center","active":true,"usgs":true}],"links":[{"id":328663,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/of/2016/1160/coverthb.jpg"},{"id":328665,"rank":3,"type":{"id":3,"text":"Appendix"},"url":"https://pubs.usgs.gov/of/2016/1160/ofr20161160_Appendix-1.xlsx","size":"236 kB","linkFileType":{"id":3,"text":"xlsx"},"description":"SIR 2016-1160 Appendix 1"},{"id":328664,"rank":2,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/of/2016/1160/ofr20161160.pdf","text":"Report","size":"4.55 MB","linkFileType":{"id":1,"text":"pdf"},"description":"SIR 2016-1160"}],"country":"United States","state":"Arizona","county":"Coconino County","otherGeospatial":"Coconino Plateau, Grand Canyon","geographicExtents":"{\n  \"type\": \"FeatureCollection\",\n  \"features\": [\n    {\n      \"type\": \"Feature\",\n      \"properties\": {},\n      \"geometry\": {\n        \"type\": \"Polygon\",\n        \"coordinates\": [\n          [\n            [\n              -113.5,\n              35.5\n            ],\n            [\n              -113.5,\n              36.5\n            ],\n            [\n              -112,\n              36.5\n            ],\n            [\n              -112,\n              35.5\n            ],\n            [\n              -113.5,\n              35.5\n            ]\n          ]\n        ]\n      }\n    }\n  ]\n}","contact":"<p>Center Director<br>USGS Central Mineral and Environmental Resources Science Center<br>U.S. Geological Survey<br>Box 25046, MS 973<br>Denver, CO 80225</p><p><a href=\"http://minerals.cr.usgs.gov/\" target=\"_blank\" data-mce-href=\"http://minerals.cr.usgs.gov/\">http://minerals.cr.usgs.gov/</a></p>","tableOfContents":"<ul><li>Acknowledgments</li><li>Abstract</li><li>Introduction</li><li>Sample Collection</li><li>Summary of Results</li><li>References Cited</li><li>Appendix 1. Geochemical Analyses of Soils Collected on the Surface of the Coconino Plateau Grand Canyon Region, Northern Arizona</li></ul>","publishingServiceCenter":{"id":2,"text":"Denver PSC"},"publishedDate":"2016-09-15","noUsgsAuthors":false,"publicationDate":"2016-09-15","publicationStatus":"PW","scienceBaseUri":"57dbb81ce4b090824ffd106b","contributors":{"authors":[{"text":"Van Gosen, Bradley S. 0000-0003-4214-3811 bvangose@usgs.gov","orcid":"https://orcid.org/0000-0003-4214-3811","contributorId":1174,"corporation":false,"usgs":true,"family":"Van Gosen","given":"Bradley","email":"bvangose@usgs.gov","middleInitial":"S.","affiliations":[{"id":387,"text":"Mineral Resources Program","active":true,"usgs":true},{"id":171,"text":"Central Mineral and Environmental Resources Science Center","active":true,"usgs":true}],"preferred":true,"id":648762,"contributorType":{"id":1,"text":"Authors"},"rank":1}]}}
,{"id":70178661,"text":"70178661 - 2016 - Comparison of methods for quantifying surface sublimation over seasonally snow-covered terrain","interactions":[],"lastModifiedDate":"2017-04-27T10:03:31","indexId":"70178661","displayToPublicDate":"2016-09-15T00:00:00","publicationYear":"2016","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":1924,"text":"Hydrological Processes","active":true,"publicationSubtype":{"id":10}},"title":"Comparison of methods for quantifying surface sublimation over seasonally snow-covered terrain","docAbstract":"<p><span>Snow sublimation can be an important component of the snow-cover mass balance, and there is considerable interest in quantifying the role of this process within the water and energy balance of snow-covered regions. In recent years, robust eddy covariance (EC) instrumentation has been used to quantify snow sublimation over snow-covered surfaces in complex mountainous terrain. However, EC can be challenging for monitoring turbulent fluxes in snow-covered environments because of intensive data, power, and fetch requirements, and alternative methods of estimating snow sublimation are often relied upon. To evaluate the relative merits of methods for quantifying surface sublimation, fluxes calculated by the EC, Bowen ratio–energy balance (BR), bulk aerodynamic flux (BF), and aerodynamic profile (AP) methods and their associated uncertainty were compared at two forested openings in the Colorado Rocky Mountains. Biases between methods are evaluated over a range of environmental conditions, and limitations of each method are discussed. Mean surface sublimation rates from both sites ranged from 0.33 to 0.36 mm day</span><sup>−1</sup><span>, 0.14 to 0.37 mm day</span><sup>−1</sup><span>, 0.10 to 0.17 mm day</span><sup>−1</sup><span>, and 0.03 to 0.10 mm day</span><sup>−1</sup><span> for the EC, BR, BF and AP methods, respectively. The EC and/or BF methods are concluded to be superior for estimating surface sublimation in snow-covered forested openings. The surface sublimation rates quantified in this study are generally smaller in magnitude compared with previously published studies in this region and help to refine sublimation estimates for forested openings in the Colorado Rocky Mountains.</span></p>","language":"English","publisher":"Wiley","doi":"10.1002/hyp.10864","usgsCitation":"Sexstone, G.A., Clow, D.W., Stannard, D.I., and Fassnacht, S.R., 2016, Comparison of methods for quantifying surface sublimation over seasonally snow-covered terrain: Hydrological Processes, v. 30, no. 19, p. 3373-3389, https://doi.org/10.1002/hyp.10864.","productDescription":"17 p.","startPage":"3373","endPage":"3389","ipdsId":"IP-071074","costCenters":[{"id":191,"text":"Colorado Water Science Center","active":true,"usgs":true}],"links":[{"id":331427,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"volume":"30","issue":"19","noUsgsAuthors":false,"publicationDate":"2016-06-06","publicationStatus":"PW","scienceBaseUri":"584144e0e4b04fc80e5073a8","contributors":{"authors":[{"text":"Sexstone, Graham A. 0000-0001-8913-0546 sexstone@usgs.gov","orcid":"https://orcid.org/0000-0001-8913-0546","contributorId":5159,"corporation":false,"usgs":true,"family":"Sexstone","given":"Graham","email":"sexstone@usgs.gov","middleInitial":"A.","affiliations":[{"id":191,"text":"Colorado Water Science Center","active":true,"usgs":true}],"preferred":true,"id":654741,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Clow, David W. 0000-0001-6183-4824 dwclow@usgs.gov","orcid":"https://orcid.org/0000-0001-6183-4824","contributorId":1671,"corporation":false,"usgs":true,"family":"Clow","given":"David","email":"dwclow@usgs.gov","middleInitial":"W.","affiliations":[{"id":191,"text":"Colorado Water Science Center","active":true,"usgs":true}],"preferred":true,"id":654742,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Stannard, David I. distanna@usgs.gov","contributorId":562,"corporation":false,"usgs":true,"family":"Stannard","given":"David","email":"distanna@usgs.gov","middleInitial":"I.","affiliations":[{"id":438,"text":"National Research Program - Western Branch","active":true,"usgs":true}],"preferred":false,"id":654743,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Fassnacht, Steven R.","contributorId":177135,"corporation":false,"usgs":false,"family":"Fassnacht","given":"Steven","email":"","middleInitial":"R.","affiliations":[],"preferred":false,"id":654744,"contributorType":{"id":1,"text":"Authors"},"rank":4}]}}
,{"id":70176435,"text":"70176435 - 2016 - An evaluation of rapid methods for monitoring vegetation characteristics of wetland bird habitat","interactions":[],"lastModifiedDate":"2016-09-14T11:51:56","indexId":"70176435","displayToPublicDate":"2016-09-14T12:50:00","publicationYear":"2016","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":3751,"text":"Wetlands Ecology and Management","active":true,"publicationSubtype":{"id":10}},"title":"An evaluation of rapid methods for monitoring vegetation characteristics of wetland bird habitat","docAbstract":"<p><span>Wetland managers benefit from monitoring data of sufficient precision and accuracy to assess wildlife habitat conditions and to evaluate and learn from past management decisions. For large-scale monitoring programs focused on waterbirds (waterfowl, wading birds, secretive marsh birds, and shorebirds), precision and accuracy of habitat measurements must be balanced with fiscal and logistic constraints. We evaluated a set of protocols for rapid, visual estimates of key waterbird habitat characteristics made from the wetland perimeter against estimates from (1) plots sampled within wetlands, and (2) cover maps made from aerial photographs. Estimated percent cover of annuals and perennials using a perimeter-based protocol fell within 10 percent of plot-based estimates, and percent cover estimates for seven vegetation height classes were within 20&nbsp;% of plot-based estimates. Perimeter-based estimates of total emergent vegetation cover did not differ significantly from cover map estimates. Post-hoc analyses revealed evidence for observer effects in estimates of annual and perennial covers and vegetation height. Median time required to complete perimeter-based methods was less than 7 percent of the time needed for intensive plot-based methods. Our results show that rapid, perimeter-based assessments, which increase sample size and efficiency, provide vegetation estimates comparable to more intensive methods.</span></p>","language":"English","publisher":"Springer","doi":"10.1007/s11273-015-9476-5","usgsCitation":"Tavernia, B.G., Lyons, J., Loges, B.W., Wilson, A., Collazo, J., and Runge, M.C., 2016, An evaluation of rapid methods for monitoring vegetation characteristics of wetland bird habitat: Wetlands Ecology and Management, v. 24, no. 5, p. 495-505, https://doi.org/10.1007/s11273-015-9476-5.","productDescription":"11 p.","startPage":"495","endPage":"505","ipdsId":"IP-067154","costCenters":[{"id":531,"text":"Patuxent Wildlife Research Center","active":true,"usgs":true}],"links":[{"id":328639,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"volume":"24","issue":"5","publishingServiceCenter":{"id":10,"text":"Baltimore PSC"},"noUsgsAuthors":false,"publicationDate":"2015-12-17","publicationStatus":"PW","scienceBaseUri":"57da669ee4b090824ffb1642","contributors":{"authors":[{"text":"Tavernia, Brian G. btavernia@usgs.gov","contributorId":174618,"corporation":false,"usgs":false,"family":"Tavernia","given":"Brian","email":"btavernia@usgs.gov","middleInitial":"G.","affiliations":[{"id":7041,"text":"The Nature Conservancy","active":true,"usgs":false}],"preferred":false,"id":648752,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Lyons, James E.","contributorId":35461,"corporation":false,"usgs":true,"family":"Lyons","given":"James E.","affiliations":[],"preferred":false,"id":648753,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Loges, Brian W.","contributorId":146554,"corporation":false,"usgs":false,"family":"Loges","given":"Brian","email":"","middleInitial":"W.","affiliations":[{"id":6678,"text":"U.S. Fish and Wildlife Service, Alaska Maritime National Wildlife Refuge","active":true,"usgs":false}],"preferred":false,"id":648793,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Wilson, Andrew","contributorId":174620,"corporation":false,"usgs":false,"family":"Wilson","given":"Andrew","affiliations":[],"preferred":false,"id":648794,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Collazo, Jaime A. 0000-0002-1816-7744 jaime_collazo@usgs.gov","orcid":"https://orcid.org/0000-0002-1816-7744","contributorId":173448,"corporation":false,"usgs":true,"family":"Collazo","given":"Jaime A.","email":"jaime_collazo@usgs.gov","affiliations":[{"id":198,"text":"Coop Res Unit Atlanta","active":true,"usgs":true},{"id":199,"text":"Coop Res Unit Leetown","active":true,"usgs":true}],"preferred":false,"id":648754,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"Runge, Michael C. 0000-0002-8081-536X mrunge@usgs.gov","orcid":"https://orcid.org/0000-0002-8081-536X","contributorId":3358,"corporation":false,"usgs":true,"family":"Runge","given":"Michael","email":"mrunge@usgs.gov","middleInitial":"C.","affiliations":[{"id":531,"text":"Patuxent Wildlife Research Center","active":true,"usgs":true}],"preferred":true,"id":648751,"contributorType":{"id":1,"text":"Authors"},"rank":6}]}}
,{"id":70173994,"text":"70173994 - 2016 - Enriching the national map database for multi-scale use: Introducing the visibilityfilter attribution","interactions":[],"lastModifiedDate":"2017-02-28T12:13:17","indexId":"70173994","displayToPublicDate":"2016-09-14T00:00:00","publicationYear":"2016","noYear":false,"publicationType":{"id":24,"text":"Conference Paper"},"publicationSubtype":{"id":19,"text":"Conference Paper"},"title":"Enriching the national map database for multi-scale use: Introducing the visibilityfilter attribution","docAbstract":"The US Geological Survey’s (USGS) National Geospatial Technical Operations Center is prototyping and evaluating the ability to filter data through a range of scales using 1:24,000-scale The National Map (TNM) datasets as the source. A “VisibilityFilter” attribute is under evaluation that can be added to all TNM vector data themes and will permit filtering of data to eight target scales between 1:24,000 and 1:5,000,000, thus defining each feature’s smallest applicable scale-of-use. For a prototype implementation, map specifications for 1:100,000- and 1:250,000-scale USGS Topographic Map Series are being utilized to define feature content appropriate at fixed mapping scales to guide generalization decisions that are documented in a ScaleMaster diagram. This paper defines the VisibilityFilter attribute, the generalization decisions made for each TNM data theme, and how these decisions are embedded into the data to support efficient data filtering.","largerWorkType":{"id":24,"text":"Conference Paper"},"largerWorkTitle":"Proceedings, AutoCarto2016","conferenceTitle":"19th International Research Symposium on Computer-based Cartography","conferenceDate":"September 14-16, 2016","conferenceLocation":"Albuquerque, New Mexico","language":"English","publisher":"Cartography and Geographic Information Center ","usgsCitation":"Stauffer, A.J., Webinger, S., and Roche, B., 2016, Enriching the national map database for multi-scale use: Introducing the visibilityfilter attribution, <i>in</i> Proceedings, AutoCarto2016, Albuquerque, New Mexico, September 14-16, 2016, p. 188-199.","productDescription":"12 p. ","startPage":"188","endPage":"199","ipdsId":"IP-076364","costCenters":[{"id":5047,"text":"NGTOC Denver","active":true,"usgs":true}],"links":[{"id":336336,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"publishingServiceCenter":{"id":2,"text":"Denver PSC"},"noUsgsAuthors":false,"publicationStatus":"PW","scienceBaseUri":"58b69a3fe4b01ccd54ff3f8c","contributors":{"authors":[{"text":"Stauffer, Andrew J. astauffer@usgs.gov","contributorId":5282,"corporation":false,"usgs":true,"family":"Stauffer","given":"Andrew","email":"astauffer@usgs.gov","middleInitial":"J.","affiliations":[{"id":5047,"text":"NGTOC Denver","active":true,"usgs":true}],"preferred":true,"id":640096,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Webinger, Seth swebinger@usgs.gov","contributorId":172264,"corporation":false,"usgs":true,"family":"Webinger","given":"Seth","email":"swebinger@usgs.gov","affiliations":[{"id":5047,"text":"NGTOC Denver","active":true,"usgs":true}],"preferred":true,"id":640097,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Roche, Brittany broche@usgs.gov","contributorId":172265,"corporation":false,"usgs":true,"family":"Roche","given":"Brittany","email":"broche@usgs.gov","affiliations":[{"id":404,"text":"NGTOC Rolla","active":true,"usgs":true}],"preferred":true,"id":640098,"contributorType":{"id":1,"text":"Authors"},"rank":3}]}}
,{"id":70186544,"text":"70186544 - 2016 - Moving from awareness to action: Advancing climate change vulnerability assessments and adaptation planning for Idaho and Montana National Forests","interactions":[],"lastModifiedDate":"2017-11-22T15:34:56","indexId":"70186544","displayToPublicDate":"2016-09-14T00:00:00","publicationYear":"2016","noYear":false,"publicationType":{"id":18,"text":"Report"},"publicationSubtype":{"id":4,"text":"Other Government Series"},"title":"Moving from awareness to action: Advancing climate change vulnerability assessments and adaptation planning for Idaho and Montana National Forests","docAbstract":"<p style=\"text-align: left;\" data-mce-style=\"text-align: left;\"><span>The rugged landscapes of northern Idaho and western Montana support biodiverse ecosystems, and provide a variety of natural resources and services for human communities. However, the benefits provided by these ecosystems may be at risk as changing climate magnifies existing stressors and allows new stressors to emerge. Preparation for and response to these potential changes can be most effectively addressed through multi-stakeholder partnerships, evaluating vulnerability of important resources to climate change, and developing response and preparation strategies for managing key natural resources in a changing world. This project will support climate-smart conservation and management across forests of northern Idaho and western Montana through three main components: (1) fostering partnerships among scientists, land managers, regional landowners, conservation practitioners, and the public; (2) assessing the vulnerability of a suite of regionally important resources to climate change and other stressors; and (3) creating a portfolio of adaptation strategies and actions to help resource managers prepare for and respond to the likely impacts of climate change. The results of this project will be used to inform the upcoming land management plan revisions for national forests, helping ensure that the most effective and robust conservation and management strategies are implemented to preserve our natural resources.</span></p>","language":"English","publisher":"Northwest Climate Science Center","usgsCitation":"Kershner, J., Woodward, A., and Torregrosa, A.A., 2016, Moving from awareness to action: Advancing climate change vulnerability assessments and adaptation planning for Idaho and Montana National Forests.","ipdsId":"IP-081876","costCenters":[{"id":290,"text":"Forest and Rangeland Ecosystem Science Center","active":false,"usgs":true},{"id":484,"text":"Northwest Climate Science Center","active":true,"usgs":true}],"links":[{"id":339496,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"},{"id":339204,"type":{"id":15,"text":"Index Page"},"url":"https://www.nwclimatescience.org/projects/moving-awareness-action-advancing-climate-change-vulnerability-assessments-and-adaptation"}],"country":"United States","state":"Idaho, Montana","geographicExtents":"{\n  \"type\": \"FeatureCollection\",\n  \"features\": [\n    {\n      \"type\": \"Feature\",\n      \"properties\": {},\n      \"geometry\": {\n        \"type\": \"Polygon\",\n        \"coordinates\": [\n          [\n            [\n              -116.709480285645,\n              48.9080619812012\n            ],\n            [\n              -116.29273223877,\n              45.890007019043\n            ],\n            [\n              -109.875984191895,\n              45.890007019043\n            ],\n            [\n              -110.875984191895,\n              48.9080619812012\n            ],\n            [\n              -116.709480285645,\n              48.9080619812012\n            ]\n          ]\n        ]\n      }\n    }\n  ]\n}","publishingServiceCenter":{"id":12,"text":"Tacoma PSC"},"noUsgsAuthors":false,"publicationStatus":"PW","scienceBaseUri":"58ebadace4b0b4d95d320099","contributors":{"authors":[{"text":"Kershner, Jessi","contributorId":156364,"corporation":false,"usgs":false,"family":"Kershner","given":"Jessi","email":"","affiliations":[{"id":20326,"text":"EcoAdapt","active":true,"usgs":false}],"preferred":false,"id":688676,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Woodward, Andrea 0000-0003-0604-9115 awoodward@usgs.gov","orcid":"https://orcid.org/0000-0003-0604-9115","contributorId":3028,"corporation":false,"usgs":true,"family":"Woodward","given":"Andrea","email":"awoodward@usgs.gov","affiliations":[{"id":289,"text":"Forest and Rangeland Ecosys Science Center","active":true,"usgs":true},{"id":290,"text":"Forest and Rangeland Ecosystem Science Center","active":false,"usgs":true}],"preferred":true,"id":688675,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Torregrosa, Alicia A. 0000-0001-7361-2241 atorregrosa@usgs.gov","orcid":"https://orcid.org/0000-0001-7361-2241","contributorId":3471,"corporation":false,"usgs":true,"family":"Torregrosa","given":"Alicia","email":"atorregrosa@usgs.gov","middleInitial":"A.","affiliations":[{"id":657,"text":"Western Geographic Science Center","active":true,"usgs":true}],"preferred":true,"id":688677,"contributorType":{"id":1,"text":"Authors"},"rank":3}]}}
,{"id":70174822,"text":"sir20165099 - 2016 - Paleoflood investigations to improve peak-streamflow regional-regression equations for natural streamflow in eastern Colorado, 2015","interactions":[],"lastModifiedDate":"2022-11-08T16:51:17.908408","indexId":"sir20165099","displayToPublicDate":"2016-09-13T15:00:00","publicationYear":"2016","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":"2016-5099","title":"Paleoflood investigations to improve peak-streamflow regional-regression equations for natural streamflow in eastern Colorado, 2015","docAbstract":"<p>The U.S. Geological Survey (USGS), in cooperation with the Colorado Department of Transportation, developed regional-regression equations for estimating the 50-, 20-, 10-, 4-, 2-, 1-, 0.5-, 0.2-percent annual exceedance-probability discharge (AEPD) for natural streamflow in eastern Colorado. A total of 188 streamgages, consisting of 6,536 years of record and a mean of approximately 35 years of record per streamgage, were used to develop the peak-streamflow regional-regression equations. The estimated AEPDs for each streamgage were computed using the USGS software program PeakFQ. The AEPDs were determined using systematic data through water year 2013. Based on previous studies conducted in Colorado and neighboring States and on the availability of data, 72 characteristics (57 basin and 15 climatic characteristics) were evaluated as candidate explanatory variables in the regression analysis. Paleoflood and non-exceedance bound ages were established based on reconnaissance-level methods. Multiple lines of evidence were used at each streamgage to arrive at a conclusion (age estimate) to add a higher degree of certainty to reconnaissance-level estimates. Paleoflood or nonexceedance bound evidence was documented at 41 streamgages, and 3 streamgages had previously collected paleoflood data.To determine the peak discharge of a paleoflood or non-exceedanc bound, two different hydraulic models were used.</p><p>The mean standard error of prediction (<i>SEP</i>) for all 8 AEPDs was reduced approximately 25 percent compared to the previous flood-frequency study. For paleoflood data to be effective in reducing the <i>SEP</i> in eastern Colorado, a larger ratio than 44 of 188 (23 percent) streamgages would need paleoflood data and that paleoflood data would need to increase the record length by more than 25 years for the 1-percent AEPD. The greatest reduction in SEP for the peak-streamflow regional-regression equations was observed when additional new basin characteristics were included in the peak-streamflow regional-regression&nbsp;equations and when eastern Colorado was divided into two separate hydrologic regions. To make further reductions in the uncertainties of the peak-streamflow regional-regression equations in the Foothills and Plains hydrologic regions, additional streamgages or crest-stage gages are needed to collect peak-streamflow data on natural streams in eastern Colorado.</p><p>Generalized-Least Squares regression was used to compute the final peak-streamflow regional-regression equations for peak-streamflow. Dividing eastern Colorado into two new individual regions at –104° longitude resulted in peak-streamflow regional-regression equations with the smallest <i>SEP</i>. The new hydrologic region located between –104° longitude and the Kansas-Nebraska State line will be designated the Plains hydrologic region and the hydrologic region comprising the rest of eastern Colorado located west of the –104° longitude and east of the Rocky Mountains and below 7,500 feet in the South Platte River Basin and below 9,000 feet in the Arkansas River Basin will be designated the Foothills hydrologic region.</p>","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/sir20165099","collaboration":"Prepared in cooperation with the Colorado Department of Transportation","usgsCitation":"Kohn, M.S., Stevens, M.R., Harden, T.M., Godaire, J.E., Klinger, R.E., and Mommandi, Amanullah, 2016, Paleoflood investigations to improve peak-streamflow regional-regression equations for natural streamflow in eastern Colorado, 2015: U.S. Geological Survey Scientific Investigations Report 2016–5099, 58 p., https://dx.doi.org/10.3133/sir20165099.","productDescription":"Report: ix, 57 p.; 3 Appendixes","onlineOnly":"Y","ipdsId":"IP-064605","costCenters":[{"id":191,"text":"Colorado Water Science Center","active":true,"usgs":true},{"id":518,"text":"Oregon Water Science Center","active":true,"usgs":true}],"links":[{"id":328604,"rank":5,"type":{"id":3,"text":"Appendix"},"url":"https://pubs.usgs.gov/sir/2016/5099/sir20165099_Appendix6.zip","text":"Appendix 6","size":"10.6 MB","linkFileType":{"id":6,"text":"zip"},"description":"SIR 2016-5099 Appendix 6"},{"id":328603,"rank":4,"type":{"id":3,"text":"Appendix"},"url":"https://pubs.usgs.gov/sir/2016/5099/sir20165099_Appendix5.zip","text":"Appendix 5","size":"540 kB","linkFileType":{"id":6,"text":"zip"},"description":"SIR 2016-5099 Appendix 5"},{"id":328602,"rank":3,"type":{"id":3,"text":"Appendix"},"url":"https://pubs.usgs.gov/sir/2016/5099/sir20165099_Appendix4.zip","text":"Appendix 4","size":"15.4 MB","linkFileType":{"id":6,"text":"zip"},"description":"SIR 2016-5099 Appendix 4"},{"id":328354,"rank":2,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/sir/2016/5099/sir20165099.pdf","text":"Report","size":"127 MB","linkFileType":{"id":1,"text":"pdf"},"description":"SIR 2016-5099"},{"id":328353,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/sir/2016/5099/coverthb.jpg"}],"country":"United States","state":"Colorado","geographicExtents":"{\n  \"type\": \"FeatureCollection\",\n  \"features\": [\n    {\n      \"type\": \"Feature\",\n      \"properties\": {},\n      \"geometry\": {\n        \"type\": \"Polygon\",\n        \"coordinates\": [\n          [\n            [\n              -110.5,\n              42\n            ],\n            [\n              -110.5,\n              36\n            ],\n            [\n              -100.5,\n              36\n            ],\n            [\n              -100.5,\n              42\n            ],\n            [\n              -110.5,\n              42\n            ]\n          ]\n        ]\n      }\n    }\n  ]\n}","contact":"<p>Director, USGS Colorado Water Science Center<br>Box 25046, Mail Stop 415<br>Denver, CO 80225</p><p><a href=\"http://co.water.usgs.gov/\" data-mce-href=\"http://co.water.usgs.gov/\">http://co.water.cr.usgs.gov/</a></p>","tableOfContents":"<ul><li>Acknowledgments</li><li>Abstract</li><li>Introduction</li><li>Methods for Data Development for Streamgages</li><li>Regional-Regression Analyses</li><li>StreamStats</li><li>Summary</li><li>References Cited</li><li>Appendixes</li></ul>","publishingServiceCenter":{"id":2,"text":"Denver PSC"},"publishedDate":"2016-09-09","noUsgsAuthors":false,"publicationDate":"2016-09-09","publicationStatus":"PW","scienceBaseUri":"57d3cf24e4b0571647d15f5b","contributors":{"authors":[{"text":"Kohn, Michael S. 0000-0002-5989-7700 mkohn@usgs.gov","orcid":"https://orcid.org/0000-0002-5989-7700","contributorId":4549,"corporation":false,"usgs":true,"family":"Kohn","given":"Michael","email":"mkohn@usgs.gov","middleInitial":"S.","affiliations":[{"id":191,"text":"Colorado Water Science Center","active":true,"usgs":true}],"preferred":true,"id":642652,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Stevens, Michael R. 0000-0002-9476-6335 mrsteven@usgs.gov","orcid":"https://orcid.org/0000-0002-9476-6335","contributorId":769,"corporation":false,"usgs":true,"family":"Stevens","given":"Michael","email":"mrsteven@usgs.gov","middleInitial":"R.","affiliations":[{"id":191,"text":"Colorado Water Science Center","active":true,"usgs":true}],"preferred":true,"id":642653,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Harden, Tessa M. 0000-0001-9854-1347 tharden@usgs.gov","orcid":"https://orcid.org/0000-0001-9854-1347","contributorId":192153,"corporation":false,"usgs":true,"family":"Harden","given":"Tessa","email":"tharden@usgs.gov","middleInitial":"M.","affiliations":[{"id":518,"text":"Oregon Water Science Center","active":true,"usgs":true}],"preferred":true,"id":642655,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Godaire, Jeanne E. 0000-0001-5103-6888","orcid":"https://orcid.org/0000-0001-5103-6888","contributorId":172928,"corporation":false,"usgs":false,"family":"Godaire","given":"Jeanne","email":"","middleInitial":"E.","affiliations":[{"id":6736,"text":"Bureau of Reclamation","active":true,"usgs":false}],"preferred":false,"id":642654,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Klinger, Ralph E.","contributorId":172929,"corporation":false,"usgs":false,"family":"Klinger","given":"Ralph","email":"","middleInitial":"E.","affiliations":[{"id":6736,"text":"Bureau of Reclamation","active":true,"usgs":false}],"preferred":false,"id":642656,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"Mommandi, Amanullah","contributorId":40874,"corporation":false,"usgs":true,"family":"Mommandi","given":"Amanullah","email":"","affiliations":[],"preferred":false,"id":642657,"contributorType":{"id":1,"text":"Authors"},"rank":6}]}}
,{"id":70176423,"text":"70176423 - 2016 - HESS Opinions: Repeatable research: what hydrologistscan learn from the Duke cancer research scandal","interactions":[],"lastModifiedDate":"2016-09-13T09:55:11","indexId":"70176423","displayToPublicDate":"2016-09-13T10:50:00","publicationYear":"2016","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":1928,"text":"Hydrology and Earth System Sciences","active":true,"publicationSubtype":{"id":10}},"title":"HESS Opinions: Repeatable research: what hydrologistscan learn from the Duke cancer research scandal","docAbstract":"<p><span>In the past decade, difficulties encountered in reproducing the results of a cancer study at Duke University resulted in a scandal and an investigation which concluded that tools used for data management, analysis, and modeling were inappropriate for the documentation of the study, let alone the reproduction of the results. New protocols were developed which require that data analysis and modeling be carried out with scripts that can be used to reproduce the results and are a record of all decisions and interpretations made during an analysis or a modeling effort. In the hydrological sciences, we face similar challenges and need to develop similar standards for transparency and repeatability of results. A promising route is to start making use of open-source languages (such as R&nbsp;and Python) to write scripts and to use collaborative coding environments (such as Git) to share our codes for inspection and use by the hydrological community. An important side-benefit to adopting such protocols is consistency and efficiency among collaborators.</span></p>","language":"English","publisher":"EGU","doi":"10.5194/hess-20-3739-2016","usgsCitation":"Fienen, M., and Bakker, M., 2016, HESS Opinions: Repeatable research: what hydrologistscan learn from the Duke cancer research scandal: Hydrology and Earth System Sciences, v. 20, p. 3739-3743, https://doi.org/10.5194/hess-20-3739-2016.","productDescription":"5 p.","startPage":"3739","endPage":"3743","ipdsId":"IP-075419","costCenters":[{"id":677,"text":"Wisconsin Water Science Center","active":true,"usgs":true}],"links":[{"id":462083,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.5194/hess-20-3739-2016","text":"Publisher Index Page"},{"id":328593,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"volume":"20","publishingServiceCenter":{"id":6,"text":"Columbus PSC"},"noUsgsAuthors":false,"publicationDate":"2016-09-12","publicationStatus":"PW","scienceBaseUri":"57d91527e4b090824ff9fa36","contributors":{"authors":[{"text":"Fienen, Michael 0000-0002-7756-4651 mnfienen@usgs.gov","orcid":"https://orcid.org/0000-0002-7756-4651","contributorId":174604,"corporation":false,"usgs":true,"family":"Fienen","given":"Michael","email":"mnfienen@usgs.gov","affiliations":[{"id":677,"text":"Wisconsin Water Science Center","active":true,"usgs":true}],"preferred":false,"id":648709,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Bakker, Mark","contributorId":56137,"corporation":false,"usgs":true,"family":"Bakker","given":"Mark","email":"","affiliations":[],"preferred":false,"id":648710,"contributorType":{"id":1,"text":"Authors"},"rank":2}]}}
,{"id":70176392,"text":"ofr20161155 - 2016 - Gravity change from 2014 to 2015, Sierra Vista Subwatershed, Upper San Pedro Basin, Arizona","interactions":[],"lastModifiedDate":"2016-10-03T16:18:23","indexId":"ofr20161155","displayToPublicDate":"2016-09-13T00:00:00","publicationYear":"2016","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":"2016-1155","title":"Gravity change from 2014 to 2015, Sierra Vista Subwatershed, Upper San Pedro Basin, Arizona","docAbstract":"<p>Relative-gravity data and absolute-gravity data were collected at 68 stations in the Sierra Vista Subwatershed, Upper San Pedro Basin, Arizona, in May–June 2015 for the purpose of estimating aquifer-storage change. Similar data from 2014 and a description of the survey network were published in U.S. Geological Survey Open-File Report 2015–1086. Data collection and network adjustment results are presented in this report, which is accompanied by a supporting Web Data Release (<a href=\"http://dx.doi.org/10.5066/F7SQ8XHX\" target=\"blank\" data-mce-href=\"http://dx.doi.org/10.5066/F7SQ8XHX\">http://dx.doi.org/10.5066/F7SQ8XHX</a>). Station positions are presented from a Global Positioning System campaign to determine station elevation.</p>","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/ofr20161155","collaboration":"Prepared in cooperation with The Nature Conservancy","usgsCitation":"Kennedy, J.R., 2016, Gravity change from 2014 to 2015, Sierra Vista Subwatershed, Upper San Pedro Basin, Arizona: U.S. Geological Survey Open-File Report 2016–1155, 15 p., https://dx.doi.org/10.3133/ofr20161155.","productDescription":"Report: v, 15 p.; Datasets","numberOfPages":"20","onlineOnly":"Y","additionalOnlineFiles":"Y","ipdsId":"IP-074089","costCenters":[{"id":128,"text":"Arizona Water Science Center","active":true,"usgs":true}],"links":[{"id":328551,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/of/2016/1155/coverthb.jpg"},{"id":328553,"rank":5,"type":{"id":28,"text":"Dataset"},"url":"https://pubs.usgs.gov/of/2016/1155/ofr20161155_SanPedroGravity2014-2015_AbsoluteGravity.txt","text":"Absolute gravity","size":"237 bytes","linkFileType":{"id":2,"text":"txt"},"description":"OFR 2016-1155 Absolute gravity"},{"id":328552,"rank":2,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/of/2016/1155/ofr20161155.pdf","text":"Report","size":"1.2 MB","linkFileType":{"id":1,"text":"pdf"},"description":"OFR 2016-1155"},{"id":328554,"rank":3,"type":{"id":28,"text":"Dataset"},"url":"https://pubs.usgs.gov/of/2016/1155/ofr20161155_SanPedroGravity2014-2015_AdjustedGravity.csv","text":"Adjusted gravity","size":"5 KB","linkFileType":{"id":7,"text":"csv"},"description":"OFR 2016-1155 Adjusted gravity"},{"id":328756,"rank":6,"type":{"id":30,"text":"Data Release"},"url":"https://dx.doi.org/10.5066/F7SQ8XHX","text":"GIS Data","description":"OFR 2016-1155 GIS Data"},{"id":328555,"rank":4,"type":{"id":28,"text":"Dataset"},"url":"https://pubs.usgs.gov/of/2016/1155/ofr20161155_SanPedroGravity2014-2015_RelativeGravity.txt","text":"Relative gravity","size":"12 KB","linkFileType":{"id":2,"text":"txt"},"description":"OFR 2016-1155 Relative gravity"}],"country":"United States","state":"Arizona","otherGeospatial":"Upper San Pedro Basin, Sierra Vista Subwatershed","geographicExtents":"{\n  \"type\": \"FeatureCollection\",\n  \"features\": [\n    {\n      \"type\": \"Feature\",\n      \"properties\": {},\n      \"geometry\": {\n        \"type\": \"Polygon\",\n        \"coordinates\": [\n          [\n            [\n              -110.33912658691406,\n              31.439794704219466\n            ],\n            [\n              -110.33912658691406,\n              31.633506308954388\n            ],\n            [\n              -110.09880065917969,\n              31.633506308954388\n            ],\n            [\n              -110.09880065917969,\n              31.439794704219466\n            ],\n            [\n              -110.33912658691406,\n              31.439794704219466\n            ]\n          ]\n        ]\n      }\n    }\n  ]\n}","contact":"<p><a href=\"mailto:dc_az@usgs.gov\" target=\"_blank\" data-mce-href=\"mailto:dc_az@usgs.gov\">Director</a>, Arizona Water Science Center<br> U.S. Geological Survey<br> 520 N. Park Avenue<br> Tucson, AZ 85719<br> <a href=\"http://az.water.usgs.gov/\" target=\"_blank\" data-mce-href=\"http://az.water.usgs.gov/\">http://az.water.usgs.gov</a></p>","tableOfContents":"<ul><li>Abstract<br></li><li>Introduction<br></li><li>Gravity Data<br></li><li>Global Positioning System Data<br></li><li>Network Adjustment<br></li><li>Gravity and Storage Change from 2014 to 2015<br></li><li>References Cited<br></li><li>Appendix 1. Data Files<br></li></ul>","publishingServiceCenter":{"id":14,"text":"Menlo Park PSC"},"publishedDate":"2016-09-13","noUsgsAuthors":false,"publicationDate":"2016-09-13","publicationStatus":"PW","scienceBaseUri":"57d91527e4b090824ff9fa34","contributors":{"authors":[{"text":"Kennedy, Jeffrey R. 0000-0002-3365-6589 jkennedy@usgs.gov","orcid":"https://orcid.org/0000-0002-3365-6589","contributorId":2172,"corporation":false,"usgs":true,"family":"Kennedy","given":"Jeffrey","email":"jkennedy@usgs.gov","middleInitial":"R.","affiliations":[{"id":128,"text":"Arizona Water Science Center","active":true,"usgs":true}],"preferred":true,"id":648592,"contributorType":{"id":1,"text":"Authors"},"rank":1}]}}
,{"id":70174261,"text":"sir20165097 - 2016 - Generalized sediment budgets of the Lower Missouri River, 1968–2014","interactions":[],"lastModifiedDate":"2016-09-13T12:16:19","indexId":"sir20165097","displayToPublicDate":"2016-09-13T00:00:00","publicationYear":"2016","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":"2016-5097","title":"Generalized sediment budgets of the Lower Missouri River, 1968–2014","docAbstract":"<p>Sediment budgets of the Lower Missouri River were developed in a study led by the U.S. Geological Survey in cooperation with the U.S. Army Corps of Engineers. The scope of the study included the development of a long-term (post-impoundment, 1968–2014) average annual sediment budget and selected annual, monthly, and daily sediment budgets for a reach and period that adequate data were available. Included in the analyses were 31 main-stem and tributary stations of the Lower Missouri River and two Mississippi River stations—the Mississippi River below Grafton, Illinois, and the Mississippi River at St. Louis, Missouri.</p><p>Long-term average annual suspended-sediment loads of Missouri River main-stem stations ranged from 0.33 million tons at the Missouri River at Yankton, South Dakota, station to 71.2 million tons at Missouri River at Hermann, Mo., station. Gaged tributary gains accounted for 9–36 percent of the local reach budgets and cumulative gaged tributary contributions accounted for 84 percent of the long-term average suspended-sediment load of the Missouri River at Hermann, Mo., station. Although the sediment budgets for seven defined main-stem reaches generally were incomplete—missing bedload, reach storage, and ungaged tributary contributions—the budget residuals (net result of sediment inputs and outputs) for six of the seven reaches ranged from -7.0 to 1.7 million tons, or from -9.2 to 4.0 percent of the reach output suspended-sediment load, and were within the 10 percent reported measurement error of annual suspended-sediment loads for large rivers. The remaining reach, downstream from Gavin’s Point Dam, extended from Yankton, S. Dak., to Sioux City, Iowa, and had a budget residual of -9.8 million tons, which was -88 percent of the suspended-sediment load at Sioux City.</p><p>The Lower Missouri River reach from Omaha, Nebraska, to Nebraska City, Nebr., had periods of concurrent sediment data for each primary budget component with which to analyze and determine a suspended-sediment budget for selected annual, monthly, and daily time increments. The temporal changes in the cumulative annual budget residuals were poorly correlated with the comparatively steady 1968–2011 annual stage trends at the Missouri River at Nebraska City, Nebr., station. An accurate total sediment budget is developed by having concurrent data available for all primary suspended and bedload components for a reach of interest throughout a period. Such a complete budget, with concurrent record for suspended-sediment load and bedload components, is unavailable for any reach and period in the Lower Missouri River. The primary data gaps are in bedload data, and also in suspended-sediment gains and losses including ungaged tributary inputs and sediment storage. Bedload data gaps in the Missouri River Basin are much more prevalent than suspended-sediment data gaps, and the first step in the development of reach bedload budgets is the establishment of a standardized bedload monitoring program at main-stem stations.</p><p>The temporal changes in flow-adjusted suspended-sediment concentrations analyzed at main-stem Missouri River stations indicated an overall downward change in concentrations between 1968 and 2014. Temporary declines in flow-adjusted suspended-sediment concentrations during and following large floods were evident but generally returned to near pre-flood values within about 6 months.</p><p>Data uncertainties associated with the development of a sediment budget include uncertainties associated with the collection of suspended-sediment and bedload data and the computation of suspended-sediment loads. These uncertainties vary depending on the frequency of data collection, the variability of conditions being represented by the discrete samples, and the statistical approach to suspended-sediment load computations. The coefficients of variation of suspended-sediment loads of Missouri River tributary stations for 1968–2014 were greater, 75.0 percent, than the main-stem stations, 47.1 percent. The lower coefficient of variation at main-stem stations compared to tributaries, primarily is the result of the lower variability in streamflow and sediment discharge identified at main-stem stations. To obtain similar accuracy between suspended-sediment loads at main-stem and tributary stations, a longer period of record is required of the tributary stations. During 1968–2014, however, the Missouri River main-stem station record was much more complete (87 percent) than the tributary station record (28 percent).</p>","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/sir20165097","collaboration":"Prepared in cooperation with the U.S. Army Corps of Engineers","usgsCitation":"Heimann, D.C., 2016, Generalized sediment budgets of the Lower Missouri River, 1968–2014: U.S. Geological Survey Scientific Investigations Report 2016–5097, 51 p., https://dx.doi.org/10.3133/sir20165097.","productDescription":"Report: vii, 51 p.; Tables 1-9","numberOfPages":"64","onlineOnly":"Y","additionalOnlineFiles":"Y","ipdsId":"IP-073678","costCenters":[{"id":396,"text":"Missouri Water Science Center","active":true,"usgs":true}],"links":[{"id":328577,"rank":12,"type":{"id":27,"text":"Table"},"url":"https://pubs.usgs.gov/sir/2016/5097/sir20165097_tables.zip","text":"Tables 1–9","size":"8.88 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3"},{"id":328565,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/sir/2016/5097/coverthb.jpg"},{"id":328566,"rank":2,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/sir/2016/5097/sir20165097.pdf","text":"Report","size":"5.29 MB","linkFileType":{"id":1,"text":"pdf"},"description":"SIR 2016–5097"},{"id":328574,"rank":9,"type":{"id":27,"text":"Table"},"url":"https://pubs.usgs.gov/sir/2016/5097/sir20165097_table_7.xlsx","text":"Table 7","size":"496 kB","linkFileType":{"id":3,"text":"xlsx"},"description":"SIR 2016–5097 Table 7"},{"id":328571,"rank":6,"type":{"id":27,"text":"Table"},"url":"https://pubs.usgs.gov/sir/2016/5097/sir20165097_table_4.xlsx","text":"Table 4","size":"496 kB","linkFileType":{"id":3,"text":"xlsx"},"description":"SIR 2016–5097 Table 4"},{"id":328575,"rank":10,"type":{"id":27,"text":"Table"},"url":"https://pubs.usgs.gov/sir/2016/5097/sir20165097_table_8.xlsx","text":"Table 8","size":"46.4 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\"geometry\": {\n        \"type\": \"Polygon\",\n        \"coordinates\": [\n          [\n            [\n              -90.120849609375,\n              38.81617117607388\n            ],\n            [\n              -91.318359375,\n              38.89103282648846\n            ],\n            [\n              -92.933349609375,\n              40.59727063442027\n            ],\n            [\n              -95.361328125,\n              43.51668853502909\n            ],\n            [\n              -97.72338867187499,\n              45.91294412737392\n            ],\n            [\n              -99.54711914062499,\n              47.57652571374621\n            ],\n            [\n              -104.0185546875,\n              48.99463598353408\n            ],\n            [\n              -105.029296875,\n              49.468124067331644\n            ],\n            [\n              -113.203125,\n              49.56797785892715\n            ],\n            [\n              -112.587890625,\n              44.98034238084973\n            ],\n            [\n              -107.09472656249999,\n              40.9964840143779\n            ],\n            [\n              -102.06298828125,\n              38.35888785866677\n            ],\n            [\n              -94.59228515625,\n              37.10776507118514\n            ],\n            [\n              -91.64794921875,\n              37.142803443716836\n            ],\n            [\n              -90.9173583984375,\n              38.36750215395045\n            ],\n            [\n              -90.120849609375,\n              38.81617117607388\n            ]\n          ]\n        ]\n      }\n    }\n  ]\n}","contact":"<p>Director, Missouri Water Science Center<br>U.S. Geological Survey<br>1400 Independence Road<br>Rolla, MO 65401</p><p><a href=\"http://mo.water.usgs.gov/\" data-mce-href=\"http://mo.water.usgs.gov/\">http://mo.water.usgs.gov/</a></p>","tableOfContents":"<ul><li>Acknowledgments<br></li><li>Abstract<br></li><li>Introduction<br></li><li>Study Methods<br></li><li>Sediment Budgets of the Lower Missouri River<br></li><li>Summary<br></li><li>References Cited<br></li></ul><p><br data-mce-bogus=\"1\"></p>","publishingServiceCenter":{"id":4,"text":"Rolla PSC"},"publishedDate":"2016-09-13","noUsgsAuthors":false,"publicationDate":"2016-09-13","publicationStatus":"PW","scienceBaseUri":"57d91525e4b090824ff9fa30","contributors":{"authors":[{"text":"Heimann, David C. 0000-0003-0450-2545 dheimann@usgs.gov","orcid":"https://orcid.org/0000-0003-0450-2545","contributorId":3822,"corporation":false,"usgs":true,"family":"Heimann","given":"David","email":"dheimann@usgs.gov","middleInitial":"C.","affiliations":[{"id":396,"text":"Missouri Water Science Center","active":true,"usgs":true},{"id":36532,"text":"Central Midwest Water Science Center","active":true,"usgs":true}],"preferred":true,"id":648708,"contributorType":{"id":1,"text":"Authors"},"rank":1}]}}
,{"id":70175951,"text":"ofr20161140 - 2016 - Examination of flood characteristics at selected streamgages in the Meramec River Basin, eastern Missouri, December 2015–January 2016","interactions":[],"lastModifiedDate":"2016-09-14T10:32:53","indexId":"ofr20161140","displayToPublicDate":"2016-09-13T00:00:00","publicationYear":"2016","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":"2016-1140","title":"Examination of flood characteristics at selected streamgages in the Meramec River Basin, eastern Missouri, December 2015–January 2016","docAbstract":"<h1>Overview</h1><p>Heavy rainfall resulted in major flooding in the Meramec River Basin in eastern Missouri during late December 2015 through early January 2016. Cumulative rainfall from December 14 to 29, 2015, ranged from 7.6 to 12.3 inches at selected precipitation stations in the basin with flooding driven by the heaviest precipitation (3.9–9.7 inches) between December 27 and 29, 2015. Financial losses from flooding included damage to homes and other structures, damage to roads, and debris removal. Eight of 11 counties in the basin were declared a Federal Disaster Area.</p><p>The U.S. Geological Survey (USGS), in cooperation with the U.S. Army Corps of Engineers and St. Louis Metropolitan Sewer District, operates multiple streamgages along the Meramec River and its primary tributaries including the Bourbeuse River and Big River. The period of record for streamflow at streamgages in the basin included in this report ranges from 24 to 102 years. Instrumentation in a streamgage shelter automatically makes observations of stage using a variety of methods (submersible pressure transducer, non-submersible pressure transducer, or non-contact radar). These observations are recorded autonomously at a predetermined programmed frequency (typically either 15 or 30 minutes) dependent on drainage-area size and concomitant flashiness of the stream. Although stage data are important, streamflow data are equally or more important for streamflow forecasting, water-quality constituent loads computation, flood-frequency analysis, and flood mitigation planning. Streamflows are computed from recorded stage data using an empirically determined relation between stage and streamflow termed a “rating.” Development and verification of the rating requires periodic onsite discrete measurements of streamflow throughout time and over the range of stages to define local hydraulic conditions.</p><p>The purpose of this report is to examine characteristics of flooding that occurred in the Meramec River Basin in December 2015–January 2016 including peak stages, peak streamflows, and the flood-frequency statistics associated with the peak flows. A comparison between the December 2015–January 2016 flood and a similar flood in December 1982 in the Meramec River Basin also is included.</p>","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/ofr20161140","usgsCitation":"Holmes, R.R., Jr., Koenig, T.A., Rydlund, P.H., and Heimann, D.C., 2016, Examination of flood characteristics at selected streamgages in the Meramec River Basin, Eastern Missouri, December 2015–January 2016: U.S. Geological Survey Open-File Report 2016–1140, 7 p., https://dx.doi.org/10.3133/ofr20161140.","productDescription":"Report: 7 p., Tables: 1-3","numberOfPages":"8","onlineOnly":"Y","additionalOnlineFiles":"Y","ipdsId":"IP-077164","costCenters":[{"id":396,"text":"Missouri Water Science Center","active":true,"usgs":true}],"links":[{"id":328622,"rank":2,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/of/2016/1140/ofr20161140.pdf","text":"Report","size":"1.34 MB","linkFileType":{"id":1,"text":"pdf"},"description":"OFR 2016–1140"},{"id":328621,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/of/2016/1140/coverthb.jpg"},{"id":328623,"rank":3,"type":{"id":27,"text":"Table"},"url":"https://pubs.usgs.gov/of/2016/1140/ofr20161140_tables1-3.xlsx","text":"Tables 1–3","size":"333 kB","linkFileType":{"id":3,"text":"xlsx"},"description":"OFR 2016–1140 Tables"}],"country":"United States","state":"Missouri","otherGeospatial":"Meramec River Basin","geographicExtents":"{\n  \"type\": \"FeatureCollection\",\n  \"features\": [\n    {\n      \"type\": \"Feature\",\n      \"properties\": {},\n      \"geometry\": {\n        \"type\": \"Polygon\",\n        \"coordinates\": [\n          [\n            [\n              -91.900634765625,\n              37.3002752813443\n            ],\n            [\n              -91.900634765625,\n              38.18638677411551\n            ],\n            [\n              -90.2911376953125,\n              38.18638677411551\n            ],\n            [\n              -90.2911376953125,\n              37.3002752813443\n            ],\n            [\n              -91.900634765625,\n              37.3002752813443\n            ]\n          ]\n        ]\n      }\n    }\n  ]\n}","contact":"<p>Chief, Office of Surface Water<br>U.S. Geological Survey<br>415 National Center <br>12201 Sunrise Valley Drive <br>Reston, VA 20192</p><p><a href=\"http://water.usgs.gov/osw/\" data-mce-href=\"http://water.usgs.gov/osw/\">http://water.usgs.gov/osw/</a></p>","tableOfContents":"<ul><li>Overview<br></li><li>Characteristics of December 2015–January 2016 Flooding in the Meramec&nbsp;River Basin<br></li><li>References Cited<br></li></ul>","publishingServiceCenter":{"id":4,"text":"Rolla PSC"},"publishedDate":"2016-09-13","noUsgsAuthors":false,"publicationDate":"2016-09-13","publicationStatus":"PW","scienceBaseUri":"57d91521e4b090824ff9fa2c","contributors":{"authors":[{"text":"Holmes, Robert R. Jr. 0000-0002-5060-3999 bholmes@usgs.gov","orcid":"https://orcid.org/0000-0002-5060-3999","contributorId":1624,"corporation":false,"usgs":true,"family":"Holmes","given":"Robert","suffix":"Jr.","email":"bholmes@usgs.gov","middleInitial":"R.","affiliations":[{"id":502,"text":"Office of Surface Water","active":true,"usgs":true}],"preferred":false,"id":646675,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Koenig, Todd A. 0000-0001-5635-0219 tkoenig@usgs.gov","orcid":"https://orcid.org/0000-0001-5635-0219","contributorId":4463,"corporation":false,"usgs":true,"family":"Koenig","given":"Todd","email":"tkoenig@usgs.gov","middleInitial":"A.","affiliations":[{"id":502,"text":"Office of Surface Water","active":true,"usgs":true}],"preferred":true,"id":648755,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Rydlund, Paul H. Jr. 0000-0001-9461-9944 prydlund@usgs.gov","orcid":"https://orcid.org/0000-0001-9461-9944","contributorId":3840,"corporation":false,"usgs":true,"family":"Rydlund","given":"Paul","suffix":"Jr.","email":"prydlund@usgs.gov","middleInitial":"H.","affiliations":[{"id":396,"text":"Missouri Water Science Center","active":true,"usgs":true},{"id":502,"text":"Office of Surface Water","active":true,"usgs":true},{"id":36532,"text":"Central Midwest Water Science Center","active":true,"usgs":true}],"preferred":true,"id":648756,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Heimann, David C. 0000-0003-0450-2545 dheimann@usgs.gov","orcid":"https://orcid.org/0000-0003-0450-2545","contributorId":3822,"corporation":false,"usgs":true,"family":"Heimann","given":"David","email":"dheimann@usgs.gov","middleInitial":"C.","affiliations":[{"id":36532,"text":"Central Midwest Water Science Center","active":true,"usgs":true},{"id":396,"text":"Missouri Water Science Center","active":true,"usgs":true}],"preferred":true,"id":648757,"contributorType":{"id":1,"text":"Authors"},"rank":4}]}}
,{"id":70176388,"text":"70176388 - 2016 - Projected shifts in fish species dominance in Wisconsin lakes under climate change","interactions":[],"lastModifiedDate":"2017-03-14T08:44:32","indexId":"70176388","displayToPublicDate":"2016-09-12T12:15:00","publicationYear":"2016","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":1837,"text":"Global Change Biology","active":true,"publicationSubtype":{"id":10}},"title":"Projected shifts in fish species dominance in Wisconsin lakes under climate change","docAbstract":"<p><span>Temperate lakes may contain both coolwater fish species such as walleye (</span><i>Sander vitreus</i><span>) and warmwater fish species such as largemouth bass (</span><i>Micropterus salmoides</i><span>). Recent declining walleye and increasing largemouth bass populations have raised questions regarding the future trajectories and management actions for these species. We developed a thermodynamic model of water temperatures driven by downscaled climate data and lake-specific characteristics to estimate daily water temperature profiles for 2148 lakes in Wisconsin, US, under contemporary (1989–2014) and future (2040–2064 and 2065–2089) conditions. We correlated contemporary walleye recruitment and largemouth bass relative abundance to modeled water temperature, lake morphometry, and lake productivity, and projected lake-specific changes in each species under future climate conditions. Walleye recruitment success was negatively related and largemouth bass abundance was positively related to water temperature degree days. Both species exhibited a threshold response at the same degree day value, albeit in opposite directions. Degree days were predicted to increase in the future, although the magnitude of increase varied among lakes, time periods, and global circulation models (GCMs). Under future conditions, we predicted a loss of walleye recruitment in 33–75% of lakes where recruitment is currently supported and a 27–60% increase in the number of lakes suitable for high largemouth bass abundance. The percentage of lakes capable of supporting abundant largemouth bass but failed walleye recruitment was predicted to increase from 58% in contemporary conditions to 86% by mid-century and to 91% of lakes by late century, based on median projections across GCMs. Conversely, the percentage of lakes with successful walleye recruitment and low largemouth bass abundance was predicted to decline from 9% of lakes in contemporary conditions to only 1% of lakes in both future periods. Importantly, we identify up to 85 resilient lakes predicted to continue to support natural walleye recruitment. Management resources could target preserving these resilient walleye populations.</span></p>","language":"English","publisher":"Wiley","doi":"10.1111/gcb.13462","usgsCitation":"Hansen, G., Read, J.S., Hansen, J.F., and Winslow, L., 2016, Projected shifts in fish species dominance in Wisconsin lakes under climate change: Global Change Biology, v. 23, no. 4, p. 1463-1476, https://doi.org/10.1111/gcb.13462.","productDescription":"14 p.","startPage":"1463","endPage":"1476","ipdsId":"IP-073795","costCenters":[{"id":160,"text":"Center for Integrated Data Analytics","active":false,"usgs":true}],"links":[{"id":470572,"rank":1,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.1111/gcb.13462","text":"Publisher Index Page"},{"id":438550,"rank":0,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/F7X0655K","text":"USGS data release","linkHelpText":"Projected shifts in fish species dominance in Wisconsin lakes under climate 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A.","contributorId":174557,"corporation":false,"usgs":false,"family":"Hansen","given":"Gretchen J. A.","affiliations":[{"id":27469,"text":"Wisconsin Department of Natural Resources, Madison, Wisconsin","active":true,"usgs":false}],"preferred":false,"id":648580,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Read, Jordan S. 0000-0002-3888-6631 jread@usgs.gov","orcid":"https://orcid.org/0000-0002-3888-6631","contributorId":4453,"corporation":false,"usgs":true,"family":"Read","given":"Jordan","email":"jread@usgs.gov","middleInitial":"S.","affiliations":[{"id":677,"text":"Wisconsin Water Science Center","active":true,"usgs":true},{"id":5054,"text":"Office of Water Information","active":true,"usgs":true},{"id":160,"text":"Center for Integrated Data Analytics","active":false,"usgs":true}],"preferred":true,"id":648578,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Hansen, Jonathan F.","contributorId":171519,"corporation":false,"usgs":false,"family":"Hansen","given":"Jonathan","email":"","middleInitial":"F.","affiliations":[],"preferred":false,"id":648581,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Winslow, Luke 0000-0002-8602-5510 lwinslow@usgs.gov","orcid":"https://orcid.org/0000-0002-8602-5510","contributorId":168947,"corporation":false,"usgs":true,"family":"Winslow","given":"Luke","email":"lwinslow@usgs.gov","affiliations":[{"id":5054,"text":"Office of Water Information","active":true,"usgs":true},{"id":677,"text":"Wisconsin Water Science Center","active":true,"usgs":true},{"id":160,"text":"Center for Integrated Data Analytics","active":false,"usgs":true}],"preferred":true,"id":648579,"contributorType":{"id":1,"text":"Authors"},"rank":4}]}}
,{"id":70176389,"text":"70176389 - 2016 - Emerging tools for continuous nutrient monitoring networks: Sensors advancing science and water resources protection","interactions":[],"lastModifiedDate":"2016-09-12T11:35:59","indexId":"70176389","displayToPublicDate":"2016-09-12T12:00:00","publicationYear":"2016","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":2529,"text":"Journal of the American Water Resources Association","active":true,"publicationSubtype":{"id":10}},"title":"Emerging tools for continuous nutrient monitoring networks: Sensors advancing science and water resources protection","docAbstract":"Sensors and enabling technologies are becoming increasingly important tools for water quality monitoring and associated water resource management decisions.  In particular, nutrient sensors are of interest because of the well-known adverse effects of nutrient enrichment on coastal hypoxia, harmful algal blooms, and impacts to human health.  Accurate and timely information on nutrient concentrations and loads is integral to strategies designed to minimize risk to humans and manage the underlying drivers of water quality impairment.  Using nitrate sensors as an example, we highlight the types of applications in freshwater and coastal environments that are likely to benefit from continuous, real-time nutrient data.  The concurrent emergence of new tools to integrate, manage and share large data sets is critical to the successful use of nutrient sensors and has made it possible for the field of continuous nutrient monitoring to rapidly move forward.  We highlight several near-term opportunities for  Federal agencies, as well as the broader scientific and management community, that will help accelerate sensor development, build and leverage sites within a national network, and develop open data standards and data management protocols that are key to realizing the benefits of a large-scale, integrated monitoring network.  Investing in these opportunities will provide new information to guide management and policies designed to protect and restore our nation’s water resources.","language":"English","publisher":"American Water Resources Association","doi":"10.1111/1752-1688.12386","usgsCitation":"Pellerin, B., Stauffer, B.A., Young, D.A., Sullivan, D.J., Bricker, S.B., Walbridge, M.R., Clyde, G.A., and Shaw, D.M., 2016, Emerging tools for continuous nutrient monitoring networks: Sensors advancing science and water resources protection: Journal of the American Water Resources Association, v. 52, no. 4, p. 993-1008, https://doi.org/10.1111/1752-1688.12386.","productDescription":"16 p.","startPage":"993","endPage":"1008","ipdsId":"IP-068633","costCenters":[{"id":154,"text":"California Water Science Center","active":true,"usgs":true}],"links":[{"id":470573,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.1111/1752-1688.12386","text":"Publisher Index Page"},{"id":328501,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"volume":"52","issue":"4","publishingServiceCenter":{"id":1,"text":"Sacramento PSC"},"noUsgsAuthors":false,"publicationDate":"2016-02-07","publicationStatus":"PW","scienceBaseUri":"57d7c39ae4b090824ff8b8e3","contributors":{"authors":[{"text":"Pellerin, Brian A. 0000-0003-3712-7884 bpeller@usgs.gov","orcid":"https://orcid.org/0000-0003-3712-7884","contributorId":147077,"corporation":false,"usgs":true,"family":"Pellerin","given":"Brian","email":"bpeller@usgs.gov","middleInitial":"A.","affiliations":[{"id":503,"text":"Office of Water Quality","active":true,"usgs":true},{"id":154,"text":"California Water Science Center","active":true,"usgs":true}],"preferred":true,"id":648582,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Stauffer, Beth A","contributorId":174558,"corporation":false,"usgs":false,"family":"Stauffer","given":"Beth","email":"","middleInitial":"A","affiliations":[{"id":7155,"text":"University of Louisiana at Lafayette","active":true,"usgs":false}],"preferred":false,"id":648583,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Young, Dwane A","contributorId":174559,"corporation":false,"usgs":false,"family":"Young","given":"Dwane","email":"","middleInitial":"A","affiliations":[{"id":27470,"text":"US EPA, Office of Research and Development","active":true,"usgs":false}],"preferred":false,"id":648584,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Sullivan, Daniel J. 0000-0003-2705-3738 djsulliv@usgs.gov","orcid":"https://orcid.org/0000-0003-2705-3738","contributorId":1703,"corporation":false,"usgs":true,"family":"Sullivan","given":"Daniel","email":"djsulliv@usgs.gov","middleInitial":"J.","affiliations":[{"id":677,"text":"Wisconsin Water Science Center","active":true,"usgs":true}],"preferred":false,"id":648585,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Bricker, Suzanne B.","contributorId":64555,"corporation":false,"usgs":false,"family":"Bricker","given":"Suzanne","email":"","middleInitial":"B.","affiliations":[{"id":12448,"text":"U.S. National Oceanic and Atmospheric Administration","active":true,"usgs":false}],"preferred":false,"id":648586,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"Walbridge, Mark R","contributorId":174560,"corporation":false,"usgs":false,"family":"Walbridge","given":"Mark","email":"","middleInitial":"R","affiliations":[{"id":25505,"text":"USDA Agricultural Research Service, Ft. Collins, CO","active":true,"usgs":false}],"preferred":false,"id":648587,"contributorType":{"id":1,"text":"Authors"},"rank":6},{"text":"Clyde, Gerard A","contributorId":174561,"corporation":false,"usgs":false,"family":"Clyde","given":"Gerard","email":"","middleInitial":"A","affiliations":[{"id":27471,"text":"US Army Corp of Engineers","active":true,"usgs":false}],"preferred":false,"id":648588,"contributorType":{"id":1,"text":"Authors"},"rank":7},{"text":"Shaw, Denice M","contributorId":174562,"corporation":false,"usgs":false,"family":"Shaw","given":"Denice","email":"","middleInitial":"M","affiliations":[{"id":27470,"text":"US EPA, Office of Research and Development","active":true,"usgs":false}],"preferred":false,"id":648589,"contributorType":{"id":1,"text":"Authors"},"rank":8}]}}
,{"id":70176246,"text":"ofr20161150 - 2016 - Marine magnetic survey and onshore gravity and magnetic survey, San Pablo Bay, northern California","interactions":[],"lastModifiedDate":"2022-01-21T16:41:13.609605","indexId":"ofr20161150","displayToPublicDate":"2016-09-12T00:00:00","publicationYear":"2016","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":"2016-1150","title":"Marine magnetic survey and onshore gravity and magnetic survey, San Pablo Bay, northern California","docAbstract":"<h1>Introduction</h1><p>From November 2011 to August 2015, the U.S. Geological Survey (USGS) collected more than 1,000 line-kilometers (length of lines surveyed in kilometers) of marine magnetic data on San Pablo Bay, 98 onshore gravity stations, and over 27 line-kilometers of ground magnetic data in northern California. Combined magnetic and gravity investigations were undertaken to study subsurface geologic structures as an aid in understanding the geologic framework and earthquake hazard potential in the San Francisco Bay Area. Furthermore, marine magnetic data illuminate local subsurface geologic features in the shallow crust beneath San Pablo Bay where geologic exposure is absent.</p><p>Magnetic and gravity methods, which reflect contrasting physical properties of the subsurface, are ideal for studying San Pablo Bay. Exposed rock units surrounding San Pablo Bay consist mainly of Jurassic Coast Range ophiolite, Great Valley sequence, Franciscan Complex rocks, Miocene sedimentary rocks, and unconsolidated alluvium (Graymer and others, 2006). The contrasting magnetic and density properties of these rocks enable us to map their subsurface extent.</p>","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/ofr20161150","usgsCitation":"Ponce, D.A., Denton, K.M., and Watt, J.T., 2016, Marine magnetic survey and onshore gravity and magnetic survey, San Pablo Bay, northern California: U.S. Geological Survey Open-File Report 2016–1150, 14 p., https://dx.doi.org/10.3133/ofr20161150.","productDescription":"Report: iv, 14 p.; 3 Tables; Metadata","numberOfPages":"20","onlineOnly":"Y","additionalOnlineFiles":"Y","ipdsId":"IP-077167","costCenters":[{"id":312,"text":"Geology, Minerals, Energy, and Geophysics Science Center","active":true,"usgs":true}],"links":[{"id":328401,"rank":7,"type":{"id":16,"text":"Metadata"},"url":"https://pubs.usgs.gov/of/2016/1150/metadata_magnetic.txt","text":"Magnetic","size":"7 KB","linkFileType":{"id":2,"text":"txt"},"description":"OFR 2016-1150 Magnetic Metadata"},{"id":328400,"rank":6,"type":{"id":16,"text":"Metadata"},"url":"https://pubs.usgs.gov/of/2016/1150/metadata_gravity.txt","text":"Gravity","size":"7 KB","linkFileType":{"id":2,"text":"txt"},"description":"OFR 2016-1150 Gravity Metadata"},{"id":328397,"rank":3,"type":{"id":27,"text":"Table"},"url":"https://pubs.usgs.gov/of/2016/1150/ofr20161150_table1.csv","text":"Table 1","size":"69.2 MB","linkFileType":{"id":7,"text":"csv"},"description":"OFR 2016-1150 Table 1"},{"id":328398,"rank":4,"type":{"id":27,"text":"Table"},"url":"https://pubs.usgs.gov/of/2016/1150/ofr20161150_table2.csv","text":"Table 2","size":"796 KB","linkFileType":{"id":7,"text":"csv"},"description":"OFR 2016-1150 Table 2"},{"id":328399,"rank":5,"type":{"id":27,"text":"Table"},"url":"https://pubs.usgs.gov/of/2016/1150/ofr20161150_table4.csv","text":"Table 4","size":"19 KB","linkFileType":{"id":7,"text":"csv"},"description":"OFR 2016-1150 Table 4"},{"id":328396,"rank":2,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/of/2016/1150/ofr20161150.pdf","text":"Report","size":"5.9 MB","linkFileType":{"id":1,"text":"pdf"},"description":"OFR 2016-1150"},{"id":328395,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/of/2016/1150/coverthb.jpg"}],"country":"United States","state":"California","otherGeospatial":"San Pablo Bay","geographicExtents":"{\n  \"type\": \"FeatureCollection\",\n  \"features\": [\n    {\n      \"type\": \"Feature\",\n      \"properties\": {},\n      \"geometry\": {\n        \"type\": \"Polygon\",\n        \"coordinates\": [\n          [\n            [\n              -122.48245239257812,\n              37.966395462637834\n            ],\n            [\n              -122.48245239257812,\n              38.134556577054134\n            ],\n            [\n              -122.28332519531249,\n              38.134556577054134\n            ],\n            [\n              -122.28332519531249,\n              37.966395462637834\n            ],\n            [\n              -122.48245239257812,\n              37.966395462637834\n            ]\n          ]\n        ]\n      }\n    }\n  ]\n}","contact":"<p><a href=\"http://geomaps.wr.usgs.gov/gmeg/staff.htm\" target=\"_blank\" data-mce-href=\"http://geomaps.wr.usgs.gov/gmeg/staff.htm\">Contact Information</a>, Geology, Minerals, Energy, &amp; Geophysics Science Center—Menlo Park, California<br> U.S. Geological Survey<br> 345 Middlefield Road<br> Menlo Park, CA 94025-3591<br> <a href=\"http://geomaps.wr.usgs.gov/\" target=\"_blank\" data-mce-href=\"http://geomaps.wr.usgs.gov/\">http://geomaps.wr.usgs.gov/</a></p>","tableOfContents":"<ul><li>Introduction<br></li><li>Magnetic Data<br></li><li>Gravity Data<br></li><li>Discussion<br></li><li>Acknowledgments<br></li><li>References Cited<br></li></ul>","publishingServiceCenter":{"id":14,"text":"Menlo Park PSC"},"publishedDate":"2016-09-12","noUsgsAuthors":false,"publicationDate":"2016-09-12","publicationStatus":"PW","scienceBaseUri":"57d7c39be4b090824ff8b8ee","contributors":{"authors":[{"text":"Ponce, David A. 0000-0003-4785-7354 ponce@usgs.gov","orcid":"https://orcid.org/0000-0003-4785-7354","contributorId":1049,"corporation":false,"usgs":true,"family":"Ponce","given":"David","email":"ponce@usgs.gov","middleInitial":"A.","affiliations":[{"id":312,"text":"Geology, Minerals, Energy, and Geophysics Science Center","active":true,"usgs":true},{"id":300,"text":"Geologic Hazards Science Center","active":true,"usgs":true}],"preferred":true,"id":648063,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Denton, Kevin M. 0000-0001-9604-4021 kmdenton@usgs.gov","orcid":"https://orcid.org/0000-0001-9604-4021","contributorId":5303,"corporation":false,"usgs":true,"family":"Denton","given":"Kevin","email":"kmdenton@usgs.gov","middleInitial":"M.","affiliations":[{"id":312,"text":"Geology, Minerals, Energy, and Geophysics Science Center","active":true,"usgs":true}],"preferred":true,"id":648064,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Watt, Janet Tilden 0000-0002-4759-3814 jwatt@usgs.gov","orcid":"https://orcid.org/0000-0002-4759-3814","contributorId":1754,"corporation":false,"usgs":true,"family":"Watt","given":"Janet","email":"jwatt@usgs.gov","middleInitial":"Tilden","affiliations":[{"id":520,"text":"Pacific Coastal and Marine Science Center","active":true,"usgs":true}],"preferred":false,"id":648065,"contributorType":{"id":1,"text":"Authors"},"rank":3}]}}
,{"id":70176381,"text":"70176381 - 2016 - Assessing groundwater depletion and dynamics using GRACE and InSAR: Potential and limitations","interactions":[],"lastModifiedDate":"2019-09-06T10:31:31","indexId":"70176381","displayToPublicDate":"2016-09-12T00:00:00","publicationYear":"2016","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":3825,"text":"Groundwater","active":true,"publicationSubtype":{"id":10}},"title":"Assessing groundwater depletion and dynamics using GRACE and InSAR: Potential and limitations","docAbstract":"<p><span>In the last decade, remote sensing of the temporal variation of ground level and gravity has improved our understanding of groundwater dynamics and storage. Mass changes are measured by GRACE (Gravity Recovery and Climate Experiment) satellites, whereas ground deformation is measured by processing synthetic aperture radar satellites data using the InSAR (Interferometry of Synthetic Aperture Radar) techniques. Both methods are complementary and offer different sensitivities to aquifer system processes. GRACE is sensitive to mass changes over large spatial scales (more than 100,000 km</span><sup>2</sup><span>). As such, it fails in providing groundwater storage change estimates at local or regional scales relevant to most aquifer systems, and at which most groundwater management schemes are applied. However, InSAR measures ground displacement due to aquifer response to fluid-pressure changes. InSAR applications to groundwater depletion assessments are limited to aquifer systems susceptible to measurable deformation. Furthermore, the inversion of InSAR-derived displacement maps into volume of depleted groundwater storage (both reversible and largely irreversible) is confounded by vertical and horizontal variability of sediment compressibility. During the last decade, both techniques have shown increasing interest in the scientific community to complement available in situ observations where they are insufficient. In this review, we present the theoretical and conceptual bases of each method, and present idealized scenarios to highlight the potential benefits and challenges of combining these techniques to remotely assess groundwater storage changes and other aspects of the dynamics of aquifer systems.</span></p>","language":"English","publisher":"Wiley","doi":"10.1111/gwat.12453","usgsCitation":"Castellazzi, P., Martel, R., Galloway, D.L., Longuevergne, L., and Rivera, A., 2016, Assessing groundwater depletion and dynamics using GRACE and InSAR: Potential and limitations: Groundwater, v. 54, no. 6, p. 768-780, https://doi.org/10.1111/gwat.12453.","productDescription":"13 p.","startPage":"768","endPage":"780","ipdsId":"IP-065886","costCenters":[{"id":5058,"text":"Office of the Chief Scientist for Water","active":true,"usgs":true},{"id":35860,"text":"Ohio-Kentucky-Indiana Water Science Center","active":true,"usgs":true}],"links":[{"id":470574,"rank":0,"type":{"id":41,"text":"Open Access External Repository Page"},"url":"https://doi.org/10.1111/gwat.12453","text":"External Repository"},{"id":328496,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"volume":"54","issue":"6","publishingServiceCenter":{"id":9,"text":"Reston PSC"},"noUsgsAuthors":false,"publicationDate":"2016-08-30","publicationStatus":"PW","scienceBaseUri":"57d7c39ae4b090824ff8b8de","contributors":{"authors":[{"text":"Castellazzi, Pascal","contributorId":174551,"corporation":false,"usgs":false,"family":"Castellazzi","given":"Pascal","email":"","affiliations":[{"id":27468,"text":"Univ. of Quebec","active":true,"usgs":false}],"preferred":false,"id":648567,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Martel, Richard","contributorId":174550,"corporation":false,"usgs":false,"family":"Martel","given":"Richard","email":"","affiliations":[{"id":27468,"text":"Univ. of Quebec","active":true,"usgs":false}],"preferred":false,"id":648566,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Galloway, Devin L. 0000-0003-0904-5355 dlgallow@usgs.gov","orcid":"https://orcid.org/0000-0003-0904-5355","contributorId":679,"corporation":false,"usgs":true,"family":"Galloway","given":"Devin","email":"dlgallow@usgs.gov","middleInitial":"L.","affiliations":[{"id":37277,"text":"WMA - Earth System Processes Division","active":true,"usgs":true},{"id":509,"text":"Office of the Associate Director for Water","active":true,"usgs":true},{"id":35860,"text":"Ohio-Kentucky-Indiana Water Science Center","active":true,"usgs":true},{"id":5058,"text":"Office of the Chief Scientist for Water","active":true,"usgs":true},{"id":5078,"text":"Southwest Regional Director's Office","active":true,"usgs":true}],"preferred":true,"id":648565,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Longuevergne, Laurent","contributorId":83014,"corporation":false,"usgs":true,"family":"Longuevergne","given":"Laurent","email":"","affiliations":[],"preferred":false,"id":648577,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Rivera, Alfonso","contributorId":174549,"corporation":false,"usgs":false,"family":"Rivera","given":"Alfonso","email":"","affiliations":[{"id":13092,"text":"Geological Survey of Canada","active":true,"usgs":false}],"preferred":false,"id":648564,"contributorType":{"id":1,"text":"Authors"},"rank":5}]}}
,{"id":70176345,"text":"70176345 - 2016 - Projected wetland densities under climate change: Habitat loss but little geographic shift in conservation strategy","interactions":[],"lastModifiedDate":"2016-09-09T09:40:48","indexId":"70176345","displayToPublicDate":"2016-09-09T00:00:00","publicationYear":"2016","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":1450,"text":"Ecological Applications","active":true,"publicationSubtype":{"id":10}},"title":"Projected wetland densities under climate change: Habitat loss but little geographic shift in conservation strategy","docAbstract":"<p><span>Climate change poses major challenges for conservation and management because it alters the area, quality, and spatial distribution of habitat for natural populations. To assess species’ vulnerability to climate change and target ongoing conservation investments, researchers and managers often consider the effects of projected changes in climate and land use on future habitat availability and quality and the uncertainty associated with these projections. Here, we draw on tools from hydrology and climate science to project the impact of climate change on the density of wetlands in the Prairie Pothole Region of the USA, a critical area for breeding waterfowl and other wetland-dependent species. We evaluate the potential for a trade-off in the value of conservation investments under current and future climatic conditions and consider the joint effects of climate and land use. We use an integrated set of hydrological and climatological projections that provide physically based measures of water balance under historical and projected future climatic conditions. In addition, we use historical projections derived from ten general circulation models (GCMs) as a baseline from which to assess climate change impacts, rather than historical climate data. This method isolates the impact of greenhouse gas emissions and ensures that modeling errors are incorporated into the baseline rather than attributed to climate change. Our work shows that, on average, densities of wetlands (here defined as wetland basins holding water) are projected to decline across the U.S. Prairie Pothole Region, but that GCMs differ in both the magnitude and the direction of projected impacts. However, we found little evidence for a shift in the locations expected to provide the highest wetland densities under current vs. projected climatic conditions. This result was robust to the inclusion of projected changes in land use under climate change. We suggest that targeting conservation towards wetland complexes containing both small and relatively large wetland basins, which is an ongoing conservation strategy, may also act to hedge against uncertainty in the effects of climate change.</span></p>","language":"English","publisher":"Ecological Society of America","publisherLocation":"Tempe, AZ","doi":"10.1890/15-0750.1","usgsCitation":"Sofaer, H., Skagen, S., Barsugli, J.J., Rashford, B.S., Reese, G., Hoeting, J.A., Wood, A.W., and Noon, B.R., 2016, Projected wetland densities under climate change: Habitat loss but little geographic shift in conservation strategy: Ecological Applications, v. 26, no. 6, p. 1677-1692, https://doi.org/10.1890/15-0750.1.","startPage":"1677","endPage":"1692","numberOfPages":"16","ipdsId":"IP-068693","costCenters":[{"id":291,"text":"Fort Collins Science Center","active":true,"usgs":true}],"links":[{"id":470579,"rank":1,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.1890/15-0750.1","text":"Publisher Index Page"},{"id":438551,"rank":0,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/F7VX0DMQ","text":"USGS data release","linkHelpText":"Data used to estimate and project the effects of climate and land use change on wetland densities in the Prairie Pothole Region"},{"id":328429,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","state":"Montana, North Dakota, South Dakota","otherGeospatial":"Prairie Pothole 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R.","contributorId":119751,"corporation":false,"usgs":true,"family":"Noon","given":"Barry","email":"","middleInitial":"R.","affiliations":[],"preferred":false,"id":648439,"contributorType":{"id":1,"text":"Authors"},"rank":8}]}}
,{"id":70176101,"text":"ofr20161142 - 2016 - Quaternary geologic map of the Wolf Point 1° × 2° quadrangle, Montana and North Dakota","interactions":[],"lastModifiedDate":"2016-09-08T16:06:28","indexId":"ofr20161142","displayToPublicDate":"2016-09-08T15:00:00","publicationYear":"2016","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":"2016-1142","title":"Quaternary geologic map of the Wolf Point 1° × 2° quadrangle, Montana and North Dakota","docAbstract":"<p>The Wolf Point quadrangle encompasses approximately 16,084 km<sup>2</sup> (6,210 mi<sup>2</sup>). The northern boundary is the Montana/Saskatchewan (U.S.-Canada) boundary. The quadrangle is in the Northern Plains physiographic province and it includes the Peerless Plateau and Flaxville Plain. The primary river is the Missouri River.</p><p>The map units are surficial deposits and materials, not landforms. Deposits that comprise some constructional landforms (for example, ground-moraine deposits, end-moraine deposits, and stagnation-moraine deposits, all composed of till) are distinguished for purposes of reconstruction of glacial history. Surficial deposits and materials are assigned to 23 map units on the basis of genesis, age, lithology or composition, texture or particle size, and other physical, chemical, and engineering characteristics. It is not a map of soils that are recognized in pedology or agronomy.&nbsp; Rather, it is a generalized map of soils recognized in engineering geology, or of substrata or parent materials in which pedologic or agronomic soils are formed.&nbsp; Glaciotectonic (ice-thrust) structures and deposits are mapped separately, represented by a symbol. The surficial deposits are glacial, ice-contact, glaciofluvial, alluvial, lacustrine, eolian, colluvial, and mass-movement deposits.</p><p>Till of late Wisconsin age is represented by three map units. Till of Illinoian age also is mapped.&nbsp; Till deposited during pre-Illinoian glaciations is not mapped, but is widespread in the subsurface.&nbsp; Linear ice-molded landforms (primarily drumlins), shown by symbol, indicate directions of ice flow during late Wisconsin and Illinoian glaciations. The Quaternary geologic map of the Wolf Point quadrangle, northeastern Montana and North Dakota, was prepared to provide a database for compilation of a Quaternary geologic map of the Regina 4° × 6° quadrangle, United States and Canada, at scale 1:1,000,000, for the U.S. Geological Survey Quaternary Geologic Atlas of the United States map series.&nbsp; This map was compiled from data from many sources, at several different map scales.&nbsp; That information was generalized and simplified, and then transferred to a base map at 1:250,000 scale to serve as the base for final reduction to 1:1,000,000, the nominal reading scale of maps in the Quaternary Geologic Atlas of the United States map series.&nbsp; This map is the generalized and simplified 1:250,000 scale compilation.&nbsp; Letter symbols for the map units are those used for the same units in the Quaternary Geologic Atlas of the United States map series. The map summarizes new, and selected published and unpublished, geologic information for public use and for use by Federal, State, and local governmental agencies for land use planning, including assessment of natural resources, natural hazards, recreation potential, and land use management.&nbsp; It also is a base from which a variety of maps relating to earth surface processes and Quaternary geologic history can be derived.</p><p><br data-mce-bogus=\"1\"></p>","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/ofr20161142","collaboration":"Prepared in cooperation with the Montana Bureau of Mines and Geology","usgsCitation":"Fullerton, D.S., Colton, R.B., and Bush, C.A., 2016, Quaternary geologic map of the Wolf Point 1˚ × 2˚ quadrangle, Montana and North Dakota: U.S. Geological Survey Open-File Report 2016–1142, scale 1:250,000, https://dx.doi.org/10.3133/ofr20161142.","productDescription":"Map: 56.00 x 35.00 inches; Geodatabase; Metadata; Read Me; Shape Files","onlineOnly":"Y","ipdsId":"IP-059222","costCenters":[{"id":318,"text":"Geosciences and Environmental Change Science Center","active":true,"usgs":true}],"links":[{"id":328086,"rank":6,"type":{"id":9,"text":"Database"},"url":"https://pubs.usgs.gov/of/2016/1142/ofr20161142_wolfpoint_gdb.zip","text":"Geodatabase","size":"3.52 MB","linkFileType":{"id":6,"text":"zip"},"description":"OFR 2016-1142 Geodatabase"},{"id":328082,"rank":2,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/of/2016/1142/ofr20161142_map.pdf","text":"Map","size":"2.71 MB","linkFileType":{"id":1,"text":"pdf"},"description":"OFR 2016-1142 Map"},{"id":328081,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/of/2016/1142/coverthb.jpg"},{"id":328083,"rank":3,"type":{"id":16,"text":"Metadata"},"url":"https://pubs.usgs.gov/of/2016/1142/ofr20161142_metadata.zip","text":"Metadata","size":"16.0 kB","linkFileType":{"id":6,"text":"zip"},"description":"OFR 2016-1142 Metadata"},{"id":328084,"rank":4,"type":{"id":20,"text":"Read Me"},"url":"https://pubs.usgs.gov/of/2016/1142/ofr20161142_Readme.txt","text":"Read Me","size":"8.0 kB","linkFileType":{"id":2,"text":"txt"},"description":"OFR 2016-1142 Read Me"},{"id":328085,"rank":5,"type":{"id":23,"text":"Spatial Data"},"url":"https://pubs.usgs.gov/of/2016/1142/ofr20161142_shapefiles.zip","text":"Shape Files","size":"6.22 MB","linkFileType":{"id":6,"text":"zip"},"description":"OFR 2016-1142 Shape Files"}],"country":"United States","state":"Montana, North Dakota ","geographicExtents":"{\n  \"type\": \"FeatureCollection\",\n  \"features\": [\n    {\n      \"type\": \"Feature\",\n      \"properties\": {},\n      \"geometry\": {\n        \"type\": \"Polygon\",\n        \"coordinates\": [\n          [\n            [\n              -106,\n              49\n            ],\n            [\n              -106,\n              48\n            ],\n            [\n              -104,\n              48\n            ],\n            [\n              -104,\n              49\n            ],\n            [\n              -106,\n              49\n            ]\n          ]\n        ]\n      }\n    }\n  ]\n}","contact":"<p>Center Director, USGS Geosciences and Environmental Change Science Center<br>Box 25046, Mail Stop 980<br>Denver, CO 80225</p><p><a href=\"http://gec.cr.usgs.gov/\" data-mce-href=\"http://gec.cr.usgs.gov/\">http://gec.cr.usgs.gov/</a></p>","publishingServiceCenter":{"id":2,"text":"Denver PSC"},"publishedDate":"2016-09-08","noUsgsAuthors":false,"publicationDate":"2016-09-08","publicationStatus":"PW","scienceBaseUri":"57d27d9fe4b0571647d0d901","contributors":{"authors":[{"text":"Fullerton, David S. fullerton@usgs.gov","contributorId":448,"corporation":false,"usgs":true,"family":"Fullerton","given":"David","email":"fullerton@usgs.gov","middleInitial":"S.","affiliations":[{"id":318,"text":"Geosciences and Environmental Change Science Center","active":true,"usgs":true}],"preferred":true,"id":647104,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Colton, Roger B.","contributorId":17967,"corporation":false,"usgs":true,"family":"Colton","given":"Roger","email":"","middleInitial":"B.","affiliations":[],"preferred":false,"id":647105,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Bush, Charles A. cbush@usgs.gov","contributorId":1258,"corporation":false,"usgs":true,"family":"Bush","given":"Charles","email":"cbush@usgs.gov","middleInitial":"A.","affiliations":[{"id":318,"text":"Geosciences and Environmental Change Science Center","active":true,"usgs":true}],"preferred":true,"id":647106,"contributorType":{"id":1,"text":"Authors"},"rank":3}]}}
,{"id":70176333,"text":"70176333 - 2016 - Uncertainty in tsunami sediment transport modeling","interactions":[],"lastModifiedDate":"2016-09-14T14:34:28","indexId":"70176333","displayToPublicDate":"2016-09-08T00:00:00","publicationYear":"2016","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":5206,"text":"Journal of Disaster Research","active":true,"publicationSubtype":{"id":10}},"title":"Uncertainty in tsunami sediment transport modeling","docAbstract":"Erosion and deposition from tsunamis record information about tsunami hydrodynamics and size that can be interpreted to improve tsunami hazard assessment. We explore sources and methods for quantifying uncertainty in tsunami sediment transport modeling. Uncertainty varies with tsunami, study site, available input data, sediment grain size, and model. Although uncertainty has the potential to be large, published case studies indicate that both forward and inverse tsunami sediment transport models perform well enough to be useful for deciphering tsunami characteristics, including size, from deposits. New techniques for quantifying uncertainty, such as Ensemble Kalman Filtering inversion, and more rigorous reporting of uncertainties will advance the science of tsunami sediment transport modeling. Uncertainty may be decreased with additional laboratory studies that increase our understanding of the semi-empirical parameters and physics of tsunami sediment transport, standardized benchmark tests to assess model performance, and development of hybrid modeling approaches to exploit the strengths of forward and inverse models.","language":"English","publisher":"Fuji Technology Press, Ltd.","publisherLocation":"Tokyo, Japan","doi":"10.20965/jdr.2016.p0647","usgsCitation":"Jaffe, B.E., Goto, K., Sugawara, D., Gelfenbaum, G.R., and La Selle, S., 2016, Uncertainty in tsunami sediment transport modeling: Journal of Disaster Research, v. 11, no. 4, p. 647-661, https://doi.org/10.20965/jdr.2016.p0647.","productDescription":"15 p.","startPage":"647","endPage":"661","numberOfPages":"15","ipdsId":"IP-075827","costCenters":[{"id":520,"text":"Pacific Coastal and Marine Science Center","active":true,"usgs":true}],"links":[{"id":470582,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.20965/jdr.2016.p0647","text":"Publisher Index Page"},{"id":328364,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"volume":"11","issue":"4","publishingServiceCenter":{"id":14,"text":"Menlo Park PSC"},"noUsgsAuthors":false,"publicationDate":"2016-08-01","publicationStatus":"PW","scienceBaseUri":"57d27d9fe4b0571647d0d904","contributors":{"authors":[{"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":186,"text":"Coastal and Marine Geology Program","active":true,"usgs":true},{"id":520,"text":"Pacific Coastal and Marine Science Center","active":true,"usgs":true}],"preferred":true,"id":648384,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Goto, Kazuhisa","contributorId":174495,"corporation":false,"usgs":false,"family":"Goto","given":"Kazuhisa","email":"","affiliations":[{"id":27458,"text":"International Research Institute of Disaster Research, Tohoku University, Sendai, Japan","active":true,"usgs":false}],"preferred":false,"id":648385,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Sugawara, Daisuke","contributorId":174496,"corporation":false,"usgs":false,"family":"Sugawara","given":"Daisuke","email":"","affiliations":[{"id":27459,"text":"Museaum of Natural and Environmental History, Shizouka, Japan","active":true,"usgs":false}],"preferred":false,"id":648386,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Gelfenbaum, Guy R. 0000-0003-1291-6107 ggelfenbaum@usgs.gov","orcid":"https://orcid.org/0000-0003-1291-6107","contributorId":742,"corporation":false,"usgs":true,"family":"Gelfenbaum","given":"Guy","email":"ggelfenbaum@usgs.gov","middleInitial":"R.","affiliations":[{"id":186,"text":"Coastal and Marine Geology Program","active":true,"usgs":true},{"id":520,"text":"Pacific Coastal and Marine Science Center","active":true,"usgs":true}],"preferred":true,"id":648387,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"La Selle, SeanPaul M. slaselle@usgs.gov","contributorId":5317,"corporation":false,"usgs":true,"family":"La Selle","given":"SeanPaul M.","email":"slaselle@usgs.gov","affiliations":[{"id":520,"text":"Pacific Coastal and Marine Science Center","active":true,"usgs":true}],"preferred":false,"id":648388,"contributorType":{"id":1,"text":"Authors"},"rank":5}]}}
,{"id":70176265,"text":"70176265 - 2016 - Estimating indices of range shifts in birds using dynamic models when detection is imperfect","interactions":[],"lastModifiedDate":"2016-09-07T10:54:03","indexId":"70176265","displayToPublicDate":"2016-09-07T11:45:00","publicationYear":"2016","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":1837,"text":"Global Change Biology","active":true,"publicationSubtype":{"id":10}},"title":"Estimating indices of range shifts in birds using dynamic models when detection is imperfect","docAbstract":"<p><span>There is intense interest in basic and applied ecology about the effect of global change on current and future species distributions. Projections based on widely used static modeling methods implicitly assume that species are in equilibrium with the environment and that detection during surveys is perfect. We used multiseason correlated detection occupancy models, which avoid these assumptions, to relate climate data to distributional shifts of Louisiana Waterthrush in the North American Breeding Bird Survey (BBS) data. We summarized these shifts with indices of range size and position and compared them to the same indices obtained using more basic modeling approaches. Detection rates during point counts in BBS surveys were low, and models that ignored imperfect detection severely underestimated the proportion of area occupied and slightly overestimated mean latitude. Static models indicated Louisiana Waterthrush distribution was most closely associated with moderate temperatures, while dynamic occupancy models indicated that initial occupancy was associated with diurnal temperature ranges and colonization of sites was associated with moderate precipitation. Overall, the proportion of area occupied and mean latitude changed little during the 1997–2013 study period. Near-term forecasts of species distribution generated by dynamic models were more similar to subsequently observed distributions than forecasts from static models. Occupancy models incorporating a finite mixture model on detection – a new extension to correlated detection occupancy models – were better supported and may reduce bias associated with detection heterogeneity. We argue that replacing phenomenological static models with more mechanistic dynamic models can improve projections of future species distributions. In turn, better projections can improve biodiversity forecasts, management decisions, and understanding of global change biology.</span></p>","language":"English","publisher":"Wiley","doi":"10.1111/gcb.13283","usgsCitation":"Clement, M.J., Hines, J., Nichols, J., Pardieck, K.L., and Ziolkowski, D., 2016, Estimating indices of range shifts in birds using dynamic models when detection is imperfect: Global Change Biology, v. 22, no. 10, p. 3273-3285, https://doi.org/10.1111/gcb.13283.","productDescription":"13 p.","startPage":"3273","endPage":"3285","ipdsId":"IP-069235","costCenters":[{"id":531,"text":"Patuxent Wildlife Research Center","active":true,"usgs":true}],"links":[{"id":328305,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"volume":"22","issue":"10","publishingServiceCenter":{"id":10,"text":"Baltimore PSC"},"noUsgsAuthors":false,"publicationDate":"2016-05-12","publicationStatus":"PW","scienceBaseUri":"57d12c1fe4b0571647cec225","contributors":{"authors":[{"text":"Clement, Matthew J. mclement@usgs.gov","contributorId":5278,"corporation":false,"usgs":true,"family":"Clement","given":"Matthew","email":"mclement@usgs.gov","middleInitial":"J.","affiliations":[{"id":531,"text":"Patuxent Wildlife Research Center","active":true,"usgs":true}],"preferred":false,"id":648130,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Hines, James E. jhines@usgs.gov","contributorId":3506,"corporation":false,"usgs":true,"family":"Hines","given":"James E.","email":"jhines@usgs.gov","affiliations":[{"id":531,"text":"Patuxent Wildlife Research Center","active":true,"usgs":true}],"preferred":false,"id":648131,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Nichols, James D. 0000-0002-7631-2890 jnichols@usgs.gov","orcid":"https://orcid.org/0000-0002-7631-2890","contributorId":405,"corporation":false,"usgs":true,"family":"Nichols","given":"James D.","email":"jnichols@usgs.gov","affiliations":[{"id":531,"text":"Patuxent Wildlife Research Center","active":true,"usgs":true}],"preferred":false,"id":648132,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Pardieck, Keith L. 0000-0003-2779-4392 kpardieck@usgs.gov","orcid":"https://orcid.org/0000-0003-2779-4392","contributorId":4104,"corporation":false,"usgs":true,"family":"Pardieck","given":"Keith","email":"kpardieck@usgs.gov","middleInitial":"L.","affiliations":[{"id":531,"text":"Patuxent Wildlife Research Center","active":true,"usgs":true}],"preferred":true,"id":648133,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Ziolkowski, David J. Jr. 0000-0002-2500-4417 dziolkowski@usgs.gov","orcid":"https://orcid.org/0000-0002-2500-4417","contributorId":4103,"corporation":false,"usgs":true,"family":"Ziolkowski","given":"David J.","suffix":"Jr.","email":"dziolkowski@usgs.gov","affiliations":[{"id":531,"text":"Patuxent Wildlife Research Center","active":true,"usgs":true}],"preferred":false,"id":648134,"contributorType":{"id":1,"text":"Authors"},"rank":5}]}}
,{"id":70175660,"text":"ofr20161137 - 2016 - Quality of surface-water supplies in the Triangle Area of North Carolina, water years 2012–13","interactions":[],"lastModifiedDate":"2016-12-09T10:01:39","indexId":"ofr20161137","displayToPublicDate":"2016-09-07T09:30:00","publicationYear":"2016","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":"2016-1137","title":"Quality of surface-water supplies in the Triangle Area of North Carolina, water years 2012–13","docAbstract":"<p>Surface-water supplies are important sources of drinking water for residents in the Triangle area of North Carolina, which is located within the upper Cape Fear and Neuse River Basins. Since 1988, the U.S. Geological Survey and a consortium of local governments have tracked water-quality conditions and trends in several of the area’s water-supply lakes and streams. This report summarizes data collected through this cooperative effort, known as the Triangle Area Water Supply Monitoring Project, during October 2011 through September 2012 (water year 2012) and October 2012 through September 2013 (water year 2013). Major findings for this period include:</p><ul><li>Annual precipitation was approximately 2 percent above the long-term mean (average) annual precipitation in 2012 and approximately 3 percent below the long-term mean in 2013.</li><li>In water year 2012, streamflow was generally below the long-term mean during most of the period for the 10 project streamflow gaging stations. Streamflow was near or above the long-term mean at the same streamflow gaging stations during the 2013 water year.</li><li>More than 7,000 individual measurements of water quality were made at a total of 17 sites—6 in the Neuse River Basin and 11 in the Cape Fear River Basin. Forty-three water-quality properties or constituents were measured; State water-quality standards exist for 23 of these.</li><li>All observations met State water-quality standards for pH, temperature, hardness, chloride, fluoride, sulfate, nitrate, arsenic, cadmium, chromium, lead, nickel, and selenium.</li><li>North Carolina water-quality standards were exceeded one or more times for dissolved oxygen, dissolved-oxygen percent saturation, turbidity, chlorophyll <i>a</i>, copper, iron, manganese, mercury, silver, and zinc. Exceedances occurred at all 17 sites.</li><li>Stream samples collected during storm events contained elevated concentrations of 19 water-quality constituents relative to non-storm events.</li></ul>","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/ofr20161137","collaboration":"Prepared in cooperation with the Triangle Area Water Supply Monitoring Project Steering Committee","usgsCitation":"Pfeifle, C.A., Cain, J.L., and Rasmussen, R.B., 2016, Quality of surface-water supplies in the Triangle area of North Carolina, water years 2012–13: U.S. Geological Survey Open-File Report 2016–1137, 18 p., https://dx.doi.org/10.3133/ofr20161137.","productDescription":"Report: vi, 18 p.; Table; 4 Appendixes; Data Release","numberOfPages":"28","onlineOnly":"Y","additionalOnlineFiles":"Y","ipdsId":"IP-064972","costCenters":[{"id":13634,"text":"South Atlantic Water Science Center","active":true,"usgs":true}],"links":[{"id":438552,"rank":9,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/F77M061X","text":"USGS data release","linkHelpText":"Associated data for Surface-Water Supplies in the Triangle Area of North Carolina, Water Years 2012-13"},{"id":328190,"rank":6,"type":{"id":3,"text":"Appendix"},"url":"https://pubs.usgs.gov/of/2016/1137/ofr20161137_appendix4.xlsx","text":"Appendix 4 - ","size":"24 KB","linkFileType":{"id":3,"text":"xlsx"},"description":"OFR 2016-1137","linkHelpText":"Chemical constituents and physical properties analyzed by the U.S. Geological Survey National Water Quality Laboratory for the Triangle Area Water Supply Monitoring Project, and the methods used for sample analyses, October 2011 through September 2013"},{"id":328189,"rank":5,"type":{"id":3,"text":"Appendix"},"url":"https://pubs.usgs.gov/of/2016/1137/ofr20161137_appendix3.xlsx","text":"Appendix 3 - ","size":"35.6 KB","linkFileType":{"id":3,"text":"xlsx"},"description":"OFR 2016-1137","linkHelpText":"Analytical results for the environmental and replicate sample sets collected from October 2011 through September 2013 at the Triangle Area Water Supply Monitoring Project study sites, North Carolina"},{"id":328192,"rank":8,"type":{"id":30,"text":"Data Release"},"url":"https://dx.doi.org/10.5066/F77M061X","text":"USGS data release","description":"Data Release","linkHelpText":"Associated data for Surface-Water Supplies in the Triangle Area of North Carolina, Water Years 2012-13"},{"id":328191,"rank":7,"type":{"id":27,"text":"Table"},"url":"https://pubs.usgs.gov/of/2016/1137/ofr20161137_table3.xlsx","text":"Table 3 - ","linkFileType":{"id":3,"text":"xlsx"},"description":"OFR 2016-1137","linkHelpText":"Summary of water-quality results for sampled sites in the Triangle Area Water Supply Monitoring Project, October 2011 through September 2013"},{"id":328187,"rank":3,"type":{"id":3,"text":"Appendix"},"url":"https://pubs.usgs.gov/of/2016/1137/ofr20161137_appendix1.xlsx","text":"Appendix 1 - ","size":"83.7 KB","linkFileType":{"id":3,"text":"xlsx"},"description":"OFR 2016-1137","linkHelpText":"Water-quality data for surface-water samples collected from October 2011 through September 2013 at the Triangle Area Water Supply Monitoring Project study sites, North Carolina"},{"id":328185,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/of/2016/1137/coverthb.jpg"},{"id":328186,"rank":2,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/of/2016/1137/ofr20161137.pdf","text":"Report ","size":"4.97 MB","linkFileType":{"id":1,"text":"pdf"},"description":"OFR 2016-1137"},{"id":328188,"rank":4,"type":{"id":3,"text":"Appendix"},"url":"https://pubs.usgs.gov/of/2016/1137/ofr20161137_appendix2.xlsx","text":"Appendix 2 - ","size":"27.7 KB","linkFileType":{"id":3,"text":"xlsx"},"description":"OFR 2016-1137","linkHelpText":"Analytical results for blanks collected from October 2011 through September 2013 during surface-water sampling at the Triangle Area Supply Monitoring Project study sites, North Carolina"}],"country":"United States","state":"North Carolina","otherGeospatial":"Cape Fear Basin, Neuse River Basin","geographicExtents":"{\n  \"type\": \"FeatureCollection\",\n  \"features\": [\n    {\n      \"type\": \"Feature\",\n      \"properties\": {},\n      \"geometry\": {\n        \"type\": \"Polygon\",\n        \"coordinates\": [\n          [\n            [\n              -79.29656982421875,\n              35.536696378395035\n            ],\n            [\n              -79.29656982421875,\n              36.372644996081206\n            ],\n            [\n              -78.31878662109375,\n              36.372644996081206\n            ],\n            [\n              -78.31878662109375,\n              35.536696378395035\n            ],\n            [\n              -79.29656982421875,\n              35.536696378395035\n            ]\n          ]\n        ]\n      }\n    }\n  ]\n}","contact":"<p><a href=\"mailto:dc_sc@usgs.gov\">Director</a> South Atlantic Water Science Center <br /> U.S. Geological Survey <br /> 720 Gracern Road <br /> Columbia, SC 29210 <br /> <a href=\"http://www.usgs.gov/water/southatlantic/\">http://www.usgs.gov/water/southatlantic/</a></p>","tableOfContents":"<ul><li>Abstract</li><li>Introduction</li><li>Monitoring Network</li><li>Quality Assurance</li><li>Precipitation and Streamflow</li><li>Water Quality</li><li>References Cited</li></ul>","publishingServiceCenter":{"id":8,"text":"Raleigh PSC"},"publishedDate":"2016-09-07","noUsgsAuthors":false,"publicationDate":"2016-09-07","publicationStatus":"PW","scienceBaseUri":"57d12c21e4b0571647cec244","contributors":{"authors":[{"text":"Pfeifle, C.A. cmendoza@usgs.gov","contributorId":173786,"corporation":false,"usgs":true,"family":"Pfeifle","given":"C.A.","email":"cmendoza@usgs.gov","affiliations":[{"id":13634,"text":"South Atlantic Water Science Center","active":true,"usgs":true}],"preferred":false,"id":645940,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Cain, J.L. jcain@usgs.gov","contributorId":173787,"corporation":false,"usgs":true,"family":"Cain","given":"J.L.","email":"jcain@usgs.gov","affiliations":[{"id":13634,"text":"South Atlantic Water Science Center","active":true,"usgs":true}],"preferred":false,"id":645941,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Rasmussen, R.B. rbrasmus@usgs.gov","contributorId":173788,"corporation":false,"usgs":true,"family":"Rasmussen","given":"R.B.","email":"rbrasmus@usgs.gov","affiliations":[{"id":13634,"text":"South Atlantic Water Science Center","active":true,"usgs":true}],"preferred":false,"id":645942,"contributorType":{"id":1,"text":"Authors"},"rank":3}]}}
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