{"pageNumber":"560","pageRowStart":"13975","pageSize":"25","recordCount":46856,"records":[{"id":70047753,"text":"70047753 - 2013 - Data-driven modeling of background and mine-related acidity and metals in river basins","interactions":[],"lastModifiedDate":"2017-05-23T13:32:47","indexId":"70047753","displayToPublicDate":"2013-12-01T09:40:00","publicationYear":"2013","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":1555,"text":"Environmental Pollution","active":true,"publicationSubtype":{"id":10}},"title":"Data-driven modeling of background and mine-related acidity and metals in river basins","docAbstract":"<p><span>A novel application of self-organizing map (SOM) and multivariate statistical techniques is used to model the nonlinear interaction among basin mineral-resources, mining activity, and surface-water quality. First, the SOM is trained using sparse measurements from 228 sample sites in the Animas River Basin, Colorado. The model performance is validated by comparing stochastic predictions of basin-alteration assemblages and mining activity at 104 independent sites. The SOM correctly predicts (&gt;98%) the predominant type of basin hydrothermal alteration and presence (or absence) of mining activity. Second, application of the Davies–Bouldin criteria to k-means clustering of SOM neurons identified ten unique environmental groups. Median statistics of these groups define a nonlinear water-quality response along the spatiotemporal hydrothermal alteration-mining gradient. These results reveal that it is possible to differentiate among the continuum between inputs of background and mine-related acidity and metals, and it provides a basis for future research and empirical model development.</span></p>","language":"English","publisher":"Elsevier","doi":"10.1016/j.envpol.2013.09.036","usgsCitation":"Friedel, M.J., 2013, Data-driven modeling of background and mine-related acidity and metals in river basins: Environmental Pollution, v. 184, p. 530-539, https://doi.org/10.1016/j.envpol.2013.09.036.","productDescription":"10 p.","startPage":"530","endPage":"539","ipdsId":"IP-038503","costCenters":[{"id":211,"text":"Crustal Geophysics and Geochemistry Science Center","active":true,"usgs":true}],"links":[{"id":341590,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"volume":"184","noUsgsAuthors":false,"publicationStatus":"PW","scienceBaseUri":"59254a6ee4b0b7ff9fb361b5","contributors":{"authors":[{"text":"Friedel, Michael J","contributorId":119245,"corporation":false,"usgs":true,"family":"Friedel","given":"Michael","email":"","middleInitial":"J","affiliations":[],"preferred":false,"id":518130,"contributorType":{"id":1,"text":"Authors"},"rank":1}]}}
,{"id":70137270,"text":"70137270 - 2013 - Genetic diversity and mutation of avian paramyxovirus serotype 1 (Newcastle disease virus) in wild birds and evidence for intercontinental spread","interactions":[],"lastModifiedDate":"2018-08-16T21:30:19","indexId":"70137270","displayToPublicDate":"2013-12-01T09:00:00","publicationYear":"2013","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":892,"text":"Archives of Virology","active":true,"publicationSubtype":{"id":10}},"title":"Genetic diversity and mutation of avian paramyxovirus serotype 1 (Newcastle disease virus) in wild birds and evidence for intercontinental spread","docAbstract":"<p>Avian paramyxovirus serotype 1 (APMV-1), or Newcastle disease virus, is the causative agent of Newcastle disease, one of the most economically important diseases for poultry production worldwide and a cause of periodic epizootics in wild birds in North America. In this study, we examined the genetic diversity of APMV-1 isolated from migratory birds sampled in Alaska, Japan, and Russia and assessed the evidence for intercontinental virus spread using phylogenetic methods. Additionally, we predicted viral virulence using deduced amino acid residues for the fusion protein cleavage site and estimated mutation rates for the fusion gene of class I and class II migratory bird isolates. All 73 isolates sequenced as part of this study were most closely related to virus genotypes previously reported for wild birds; however, five class II genotype I isolates formed a monophyletic clade exhibiting previously unreported genetic diversity, which met criteria for the designation of a new sub-genotype. Phylogenetic analysis of wild-bird isolates provided evidence for intercontinental virus spread, specifically viral lineages of APMV-1 class II genotype I sub-genotypes Ib and Ic. This result supports migratory bird movement as a possible mechanism for the redistribution of APMV-1. None of the predicted deduced amino acid motifs for the fusion protein cleavage site of APMV-1 strains isolated from migratory birds in Alaska, Japan, and Russia were consistent with those of previously identified virulent viruses. These data therefore provide no support for these strains contributing to the emergence of avian pathogens. The estimated mutation rates for fusion genes of class I and class II wild-bird isolates were faster than those reported previously for non-virulent APMV-1 strains. Collectively, these findings provide new insight into the diversity, spread, and evolution of APMV-1 in wild birds.</p>","language":"English","publisher":"International Union of Microbiological Societies","publisherLocation":"Wien","doi":"10.1007/s00705-013-1761-0","usgsCitation":"Ramey, A.M., Reeves, A.B., Ogawa, H., Ip, S., Imai, K., Bui, V.N., Yamaguchi, E., Silko, N.Y., and Afonso, C., 2013, Genetic diversity and mutation of avian paramyxovirus serotype 1 (Newcastle disease virus) in wild birds and evidence for intercontinental spread: Archives of Virology, v. 158, no. 12, p. 2495-2503, https://doi.org/10.1007/s00705-013-1761-0.","productDescription":"9 p.","startPage":"2495","endPage":"2503","numberOfPages":"9","onlineOnly":"N","additionalOnlineFiles":"N","ipdsId":"IP-045479","costCenters":[{"id":117,"text":"Alaska Science Center Biology WTEB","active":true,"usgs":true},{"id":456,"text":"National Wildlife Health Center","active":true,"usgs":true}],"links":[{"id":297060,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"volume":"158","issue":"12","publishingServiceCenter":{"id":12,"text":"Tacoma PSC"},"noUsgsAuthors":false,"publicationDate":"2013-06-27","publicationStatus":"PW","scienceBaseUri":"54dd2ba6e4b08de9379b3457","contributors":{"authors":[{"text":"Ramey, Andrew M. 0000-0002-3601-8400 aramey@usgs.gov","orcid":"https://orcid.org/0000-0002-3601-8400","contributorId":1872,"corporation":false,"usgs":true,"family":"Ramey","given":"Andrew","email":"aramey@usgs.gov","middleInitial":"M.","affiliations":[{"id":117,"text":"Alaska Science Center Biology WTEB","active":true,"usgs":true},{"id":114,"text":"Alaska Science Center","active":true,"usgs":true}],"preferred":true,"id":537632,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Reeves, Andrew B. 0000-0002-7526-0726 areeves@usgs.gov","orcid":"https://orcid.org/0000-0002-7526-0726","contributorId":167362,"corporation":false,"usgs":true,"family":"Reeves","given":"Andrew","email":"areeves@usgs.gov","middleInitial":"B.","affiliations":[{"id":114,"text":"Alaska Science Center","active":true,"usgs":true},{"id":117,"text":"Alaska Science Center Biology WTEB","active":true,"usgs":true}],"preferred":true,"id":537633,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Ogawa, Haruko","contributorId":138522,"corporation":false,"usgs":false,"family":"Ogawa","given":"Haruko","email":"","affiliations":[],"preferred":false,"id":537830,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Ip, S. 0000-0003-4844-7533 hip@usgs.gov","orcid":"https://orcid.org/0000-0003-4844-7533","contributorId":727,"corporation":false,"usgs":true,"family":"Ip","given":"S.","email":"hip@usgs.gov","affiliations":[{"id":456,"text":"National Wildlife Health Center","active":true,"usgs":true}],"preferred":true,"id":537634,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Imai, Kunitoshi","contributorId":138523,"corporation":false,"usgs":false,"family":"Imai","given":"Kunitoshi","email":"","affiliations":[],"preferred":false,"id":537831,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"Bui, V. N.","contributorId":138558,"corporation":false,"usgs":false,"family":"Bui","given":"V.","email":"","middleInitial":"N.","affiliations":[],"preferred":false,"id":537832,"contributorType":{"id":1,"text":"Authors"},"rank":6},{"text":"Yamaguchi, Emi","contributorId":138525,"corporation":false,"usgs":false,"family":"Yamaguchi","given":"Emi","email":"","affiliations":[],"preferred":false,"id":537833,"contributorType":{"id":1,"text":"Authors"},"rank":7},{"text":"Silko, N. Y.","contributorId":138559,"corporation":false,"usgs":false,"family":"Silko","given":"N.","email":"","middleInitial":"Y.","affiliations":[],"preferred":false,"id":537834,"contributorType":{"id":1,"text":"Authors"},"rank":8},{"text":"Afonso, C.L.","contributorId":42066,"corporation":false,"usgs":true,"family":"Afonso","given":"C.L.","affiliations":[],"preferred":false,"id":537835,"contributorType":{"id":1,"text":"Authors"},"rank":9}]}}
,{"id":70191673,"text":"70191673 - 2013 - Data management challenges in species distribution modeling","interactions":[],"lastModifiedDate":"2017-10-17T16:48:59","indexId":"70191673","displayToPublicDate":"2013-12-01T00:00:00","publicationYear":"2013","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":5516,"text":"Bulletin of the Technical Committee on Data Engineering","active":true,"publicationSubtype":{"id":10}},"title":"Data management challenges in species distribution modeling","docAbstract":"An important component in the fields of ecology and conservation biology is understanding the environmental\nconditions and geographic areas that are suitable for a given species to inhabit. A common tool\nin determining such areas is species distribution modeling which uses computer algorithms to determine\nthe spatial distribution of organisms. Most commonly the correlative relationships between the organism\nand environmental variables are the primary consideration. The data requirements for this type of\nmodeling consist of known presence and possibly absence locations of the species as well as the values\nof environmental or climatic covariates thought to define the species habitat suitability at these locations.\nThese covariate data are generally extracted from remotely sensed imagery, interpolated/gridded\nhistorical climate data, or downscaled climate model output. Traditionally, ecologists and biologists\nhave constructed species distribution models using workflows and data that reside primarily on their\nlocal workstations or networks. This workflow is becoming challenging as scientists increasingly try to\nuse these modeling techniques to inform management decisions under different climate change scenarios.\nThis challenge stems from the fact that remote sensing products, gridded historical climate, and\ndownscaled climate models are not only increasing in spatial and temporal resolution but proliferating\nas well. Any rigorous assessment of uncertainty requires a computationally intensive sensitivity analysis\naccounting for various sources of uncertainty. The scientists fitting these models generally do not have\nthe background in computer science required to take advantage of recent advances in web-service based\ndata acquisition, remote high-powered data processing, or scientific workflow systems. Ecologists in the\nfield of modeling are in need of a tractable platform that abstracts the inherent computational complexity\nrequired to incorporate the burgeoning field of coupled climate and ecological response modeling.\nIn this paper we describe the computational challenges in species distribution modeling and solutions\nusing scientific workflow systems. We focus on the Software for Assisted Species Modeling (SAHM) a\npackage within VisTrails, an open-source scientific workflow system.","language":"English","publisher":"IEEE","usgsCitation":"Talbert, C., Talbert, M., Morisette, J.T., and Koop, D., 2013, Data management challenges in species distribution modeling: Bulletin of the Technical Committee on Data Engineering, v. 36, no. 4, p. 31-40.","productDescription":"10 p.","startPage":"31","endPage":"40","ipdsId":"IP-053026","costCenters":[{"id":291,"text":"Fort Collins Science Center","active":true,"usgs":true}],"links":[{"id":346766,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"},{"id":346746,"type":{"id":15,"text":"Index Page"},"url":"https://sites.computer.org/debull/A13dec/issue1.htm"}],"volume":"36","issue":"4","publishingServiceCenter":{"id":2,"text":"Denver PSC"},"noUsgsAuthors":false,"publicationStatus":"PW","scienceBaseUri":"59e71695e4b05fe04cd331ed","contributors":{"authors":[{"text":"Talbert, Colin 0000-0002-9505-1876 talbertc@usgs.gov","orcid":"https://orcid.org/0000-0002-9505-1876","contributorId":181913,"corporation":false,"usgs":true,"family":"Talbert","given":"Colin","email":"talbertc@usgs.gov","affiliations":[{"id":291,"text":"Fort Collins Science Center","active":true,"usgs":true}],"preferred":true,"id":713021,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Talbert, Marian 0000-0003-0588-0265 mtalbert@usgs.gov","orcid":"https://orcid.org/0000-0003-0588-0265","contributorId":196740,"corporation":false,"usgs":true,"family":"Talbert","given":"Marian","email":"mtalbert@usgs.gov","affiliations":[{"id":411,"text":"National Climate Change and Wildlife Science Center","active":true,"usgs":true},{"id":477,"text":"North Central Climate Science Center","active":true,"usgs":true}],"preferred":true,"id":713022,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Morisette, Jeffrey T. 0000-0002-0483-0082 morisettej@usgs.gov","orcid":"https://orcid.org/0000-0002-0483-0082","contributorId":307,"corporation":false,"usgs":true,"family":"Morisette","given":"Jeffrey","email":"morisettej@usgs.gov","middleInitial":"T.","affiliations":[{"id":477,"text":"North Central Climate Science Center","active":true,"usgs":true},{"id":569,"text":"Southwest Climate Science Center","active":true,"usgs":true}],"preferred":true,"id":713023,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Koop, David","contributorId":83845,"corporation":false,"usgs":true,"family":"Koop","given":"David","email":"","affiliations":[],"preferred":false,"id":713024,"contributorType":{"id":1,"text":"Authors"},"rank":4}]}}
,{"id":70187704,"text":"70187704 - 2013 - An approach for characterizing the distribution of shrubland ecosystem components as continuous fields as part of NLCD","interactions":[],"lastModifiedDate":"2018-03-08T13:04:32","indexId":"70187704","displayToPublicDate":"2013-12-01T00:00:00","publicationYear":"2013","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":1958,"text":"ISPRS Journal of Photogrammetry and Remote Sensing","active":true,"publicationSubtype":{"id":10}},"title":"An approach for characterizing the distribution of shrubland ecosystem components as continuous fields as part of NLCD","docAbstract":"<p><span>Characterizing and quantifying distributions of shrubland ecosystem components is one of the major challenges for monitoring shrubland vegetation cover change across the United States. A new approach has been developed to quantify shrubland components as fractional products within National Land Cover Database (NLCD). This approach uses remote sensing data and regression tree models to estimate the fractional cover of shrubland ecosystem components. The approach consists of three major steps: field data collection, high resolution estimates of shrubland ecosystem components using WorldView-2 imagery, and coarse resolution estimates of these components across larger areas using Landsat imagery. This research seeks to explore this method to quantify shrubland ecosystem components as continuous fields in regions that contain wide-ranging shrubland ecosystems. Fractional cover of four shrubland ecosystem components, including bare ground, herbaceous, litter, and shrub, as well as shrub heights, were delineated in three ecological regions in Arizona, Florida, and Texas. Results show that estimates for most components have relatively small normalized root mean square errors and significant correlations with validation data in both Arizona and Texas. The distribution patterns of shrub height also show relatively high accuracies in these two areas. The fractional cover estimates of shrubland components, except for litter, are not well represented in the Florida site. The research results suggest that this method provides good potential to effectively characterize shrubland ecosystem conditions over perennial shrubland although it is less effective in transitional shrubland. The fractional cover of shrub components as continuous elements could offer valuable information to quantify biomass and help improve thematic land cover classification in arid and semiarid areas.</span></p>","language":"English","publisher":"Elsevier","doi":"10.1016/j.isprsjprs.2013.09.009","usgsCitation":"Xian, G.Z., Homer, C.G., Meyer, D., and Granneman, B.J., 2013, An approach for characterizing the distribution of shrubland ecosystem components as continuous fields as part of NLCD: ISPRS Journal of Photogrammetry and Remote Sensing, v. 86, p. 136-149, https://doi.org/10.1016/j.isprsjprs.2013.09.009.","productDescription":"14 p.","startPage":"136","endPage":"149","ipdsId":"IP-046020","costCenters":[{"id":222,"text":"Earth Resources Observation and Science (EROS) Center","active":true,"usgs":true}],"links":[{"id":341311,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","state":"Arizona, Florida, Texas","volume":"86","publishingServiceCenter":{"id":4,"text":"Rolla PSC"},"noUsgsAuthors":false,"publicationStatus":"PW","scienceBaseUri":"591abe39e4b0a7fdb43c8c01","contributors":{"authors":[{"text":"Xian, George Z. 0000-0001-5674-2204 xian@usgs.gov","orcid":"https://orcid.org/0000-0001-5674-2204","contributorId":2263,"corporation":false,"usgs":true,"family":"Xian","given":"George","email":"xian@usgs.gov","middleInitial":"Z.","affiliations":[{"id":222,"text":"Earth Resources Observation and Science (EROS) Center","active":true,"usgs":true}],"preferred":true,"id":695183,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Homer, Collin G. 0000-0003-4755-8135 homer@usgs.gov","orcid":"https://orcid.org/0000-0003-4755-8135","contributorId":2262,"corporation":false,"usgs":true,"family":"Homer","given":"Collin","email":"homer@usgs.gov","middleInitial":"G.","affiliations":[{"id":222,"text":"Earth Resources Observation and Science (EROS) Center","active":true,"usgs":true},{"id":223,"text":"Earth Resources Observation and Science (EROS) Center (Geography)","active":false,"usgs":true}],"preferred":true,"id":695182,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Meyer, Debbie 0000-0002-8841-697X debbie.meyer.ctr@usgs.gov","orcid":"https://orcid.org/0000-0002-8841-697X","contributorId":192028,"corporation":false,"usgs":true,"family":"Meyer","given":"Debbie","email":"debbie.meyer.ctr@usgs.gov","affiliations":[],"preferred":false,"id":695180,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Granneman, Brian J. 0000-0002-1910-0955 grann@usgs.gov","orcid":"https://orcid.org/0000-0002-1910-0955","contributorId":4209,"corporation":false,"usgs":true,"family":"Granneman","given":"Brian","email":"grann@usgs.gov","middleInitial":"J.","affiliations":[{"id":223,"text":"Earth Resources Observation and Science (EROS) Center (Geography)","active":false,"usgs":true}],"preferred":true,"id":695181,"contributorType":{"id":1,"text":"Authors"},"rank":4}]}}
,{"id":70176596,"text":"70176596 - 2013 - Net primary productivity of subalpine meadows in Yosemite National Park in relation to climate variability","interactions":[],"lastModifiedDate":"2017-05-03T13:09:25","indexId":"70176596","displayToPublicDate":"2013-12-01T00:00:00","publicationYear":"2013","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":3746,"text":"Western North American Naturalist","onlineIssn":"1944-8341","printIssn":"1527-0904","active":true,"publicationSubtype":{"id":10}},"title":"Net primary productivity of subalpine meadows in Yosemite National Park in relation to climate variability","docAbstract":"<p><span>Subalpine meadows are some of the most ecologically important components of mountain landscapes, and primary productivity is important to the maintenance of meadow functions. Understanding how changes in primary productivity are associated with variability in moisture and temperature will become increasingly important with current and anticipated changes in climate. Our objective was to describe patterns and variability in aboveground live vascular plant biomass in relation to climatic factors. We harvested aboveground biomass at peak growth from four 64-m</span><sup>2</sup><span> plots each in xeric, mesic, and hydric meadows annually from 1994 to 2000. Data from nearby weather stations provided independent variables of spring snow water content, snow-free date, and thawing degree days for a cumulative index of available energy. We assembled these climatic variables into a set of mixed effects analysis of covariance models to evaluate their relationships with annual aboveground net primary productivity (ANPP), and we used an information theoretic approach to compare the quality of fit among candidate models. ANPP in the xeric meadow was negatively related to snow water content and thawing degree days and in the mesic meadow was negatively related to snow water content. Relationships between ANPP and these 2 covariates in the hydric meadow were not significant. Increasing snow water content may limit ANPP in these meadows if anaerobic conditions delay microbial activity and nutrient availability. Increased thawing degree days may limit ANPP in xeric meadows by prematurely depleting soil moisture. Large within-year variation of ANPP in the hydric meadow limited sensitivity to the climatic variables. These relationships suggest that, under projected warmer and drier conditions, ANPP will increase in mesic meadows but remain unchanged in xeric meadows because declines associated with increased temperatures would offset the increases from decreased snow water content.</span></p>","language":"English","publisher":"Monte L. Bean Life Science Museum, Brigham Young University","doi":"10.3398/064.073.0410","usgsCitation":"Moore, P.E., Van Wagtendonk, J.W., Yee, J.L., McClaran, M.P., Cole, D.N., McDougald, N.K., and Brooks, M.L., 2013, Net primary productivity of subalpine meadows in Yosemite National Park in relation to climate variability: Western North American Naturalist, v. 73, no. 4, p. 409-418, https://doi.org/10.3398/064.073.0410.","productDescription":"10 p.","startPage":"409","endPage":"418","ipdsId":"IP-042537","costCenters":[{"id":651,"text":"Western Ecological Research Center","active":true,"usgs":true}],"links":[{"id":488520,"rank":0,"type":{"id":41,"text":"Open Access External Repository Page"},"url":"https://scholarsarchive.byu.edu/wnan/vol73/iss4/2","text":"External Repository"},{"id":328860,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"volume":"73","issue":"4","noUsgsAuthors":false,"publicationStatus":"PW","scienceBaseUri":"57f7f1a9e4b0bc0bec09feea","contributors":{"authors":[{"text":"Moore, Peggy E. 0000-0002-8481-2617 peggy_moore@usgs.gov","orcid":"https://orcid.org/0000-0002-8481-2617","contributorId":3365,"corporation":false,"usgs":true,"family":"Moore","given":"Peggy","email":"peggy_moore@usgs.gov","middleInitial":"E.","affiliations":[{"id":651,"text":"Western Ecological Research Center","active":true,"usgs":true}],"preferred":true,"id":649322,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Van Wagtendonk, Jan W. jan_van_wagtendonk@usgs.gov","contributorId":2648,"corporation":false,"usgs":true,"family":"Van Wagtendonk","given":"Jan","email":"jan_van_wagtendonk@usgs.gov","middleInitial":"W.","affiliations":[{"id":651,"text":"Western Ecological Research Center","active":true,"usgs":true}],"preferred":true,"id":649323,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Yee, Julie L. 0000-0003-1782-157X julie_yee@usgs.gov","orcid":"https://orcid.org/0000-0003-1782-157X","contributorId":3246,"corporation":false,"usgs":true,"family":"Yee","given":"Julie","email":"julie_yee@usgs.gov","middleInitial":"L.","affiliations":[{"id":651,"text":"Western Ecological Research Center","active":true,"usgs":true}],"preferred":true,"id":649324,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"McClaran, Mitchel P.","contributorId":15453,"corporation":false,"usgs":true,"family":"McClaran","given":"Mitchel","email":"","middleInitial":"P.","affiliations":[],"preferred":false,"id":649325,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Cole, David N.","contributorId":40086,"corporation":false,"usgs":true,"family":"Cole","given":"David","email":"","middleInitial":"N.","affiliations":[],"preferred":false,"id":649326,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"McDougald, Neil K.","contributorId":139339,"corporation":false,"usgs":false,"family":"McDougald","given":"Neil","email":"","middleInitial":"K.","affiliations":[{"id":12739,"text":"UC Cooperative Extension, Madera, CA","active":true,"usgs":false}],"preferred":false,"id":649327,"contributorType":{"id":1,"text":"Authors"},"rank":6},{"text":"Brooks, Matthew L. 0000-0002-3518-6787 mlbrooks@usgs.gov","orcid":"https://orcid.org/0000-0002-3518-6787","contributorId":393,"corporation":false,"usgs":true,"family":"Brooks","given":"Matthew","email":"mlbrooks@usgs.gov","middleInitial":"L.","affiliations":[{"id":651,"text":"Western Ecological Research Center","active":true,"usgs":true}],"preferred":true,"id":649328,"contributorType":{"id":1,"text":"Authors"},"rank":7}]}}
,{"id":70133831,"text":"70133831 - 2013 - The suitability of a simplified isotope-balance approach to quantify transient groundwater-lake interactions over a decade with climatic extremes","interactions":[],"lastModifiedDate":"2014-12-12T15:09:29","indexId":"70133831","displayToPublicDate":"2013-12-01T00:00:00","publicationYear":"2013","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":2342,"text":"Journal of Hydrology","active":true,"publicationSubtype":{"id":10}},"title":"The suitability of a simplified isotope-balance approach to quantify transient groundwater-lake interactions over a decade with climatic extremes","docAbstract":"<p>Groundwater inflow to a subtropical seepage lake was estimated using a transient isotope-balance approach for a decade (2001&ndash;2011) with wet and dry climatic extremes. Lake water &delta;18O ranged from +0.80 to +3.48 &permil;, reflecting the 4 m range in stage. The transient &delta;18O analysis discerned large differences in semiannual groundwater inflow, and the overall patterns of low and high groundwater inflow were consistent with an independent water budget. Despite simplifying assumptions that the isotopic composition of precipitation (&delta;P), groundwater inflow, and atmospheric moisture (&delta;A) were constant, groundwater inflow was within the water-budget error for 12 of the 19 semiannual calculation periods. The magnitude of inflow was over or under predicted during periods of climatic extreme. During periods of high net precipitation from tropical cyclones and El Ni&ntilde;o conditions, &delta;P values were considerably more depleted in 18O than assumed. During an extreme dry period, &delta;A values were likely more enriched in 18O than assumed due to the influence of local lake evaporate. Isotope balance results were most sensitive to uncertainties in relative humidity, evaporation, and &delta;18O of lake water, which can limit precise quantification of groundwater inflow. Nonetheless, the consistency between isotope-balance and water-budget results indicates that this is a viable approach for lakes in similar settings, allowing the magnitude of groundwater inflow to be estimated over less-than-annual time periods. Because lake-water &delta;18O is a good indicator of climatic conditions, these data could be useful in ground-truthing paleoclimatic reconstructions using isotopic data from lake cores in similar settings.</p>","language":"English","publisher":"Elsevier","doi":"10.1016/j.jhydrol.2013.12.012","usgsCitation":"Sacks, L.A., Lee, T.M., and Swancar, A., 2013, The suitability of a simplified isotope-balance approach to quantify transient groundwater-lake interactions over a decade with climatic extremes: Journal of Hydrology, v. 519, no. Part D, p. 3042-3053, https://doi.org/10.1016/j.jhydrol.2013.12.012.","productDescription":"12 p.","startPage":"3042","endPage":"3053","numberOfPages":"12","onlineOnly":"N","additionalOnlineFiles":"N","ipdsId":"IP-038316","costCenters":[{"id":270,"text":"FLWSC-Tampa","active":true,"usgs":true}],"links":[{"id":473426,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.1016/j.jhydrol.2013.12.012","text":"Publisher Index Page"},{"id":296203,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","state":"Florida","otherGeospatial":"Lake Starr","volume":"519","issue":"Part D","noUsgsAuthors":false,"publicationStatus":"PW","scienceBaseUri":"546dbf2de4b0fc7976bf1e64","contributors":{"authors":[{"text":"Sacks, Laura A.","contributorId":19134,"corporation":false,"usgs":true,"family":"Sacks","given":"Laura","email":"","middleInitial":"A.","affiliations":[],"preferred":false,"id":525453,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Lee, Terrie M. tmlee@usgs.gov","contributorId":2461,"corporation":false,"usgs":true,"family":"Lee","given":"Terrie","email":"tmlee@usgs.gov","middleInitial":"M.","affiliations":[],"preferred":true,"id":525452,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Swancar, Amy aswancar@usgs.gov","contributorId":450,"corporation":false,"usgs":true,"family":"Swancar","given":"Amy","email":"aswancar@usgs.gov","affiliations":[{"id":595,"text":"U.S. Geological Survey","active":false,"usgs":true}],"preferred":false,"id":525451,"contributorType":{"id":1,"text":"Authors"},"rank":3}]}}
,{"id":70188341,"text":"70188341 - 2013 - Next generation of global land cover characterization, mapping, and monitoring","interactions":[],"lastModifiedDate":"2017-06-06T14:38:04","indexId":"70188341","displayToPublicDate":"2013-12-01T00:00:00","publicationYear":"2013","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":2027,"text":"International Journal of Applied Earth Observation and Geoinformation","active":true,"publicationSubtype":{"id":10}},"title":"Next generation of global land cover characterization, mapping, and monitoring","docAbstract":"<p><span>Land cover change is increasingly affecting the biophysics, biogeochemistry, and biogeography of the Earth's surface and the atmosphere, with far-reaching consequences to human well-being. However, our scientific understanding of the distribution and dynamics of land cover and land cover change (LCLCC) is limited. Previous global land cover assessments performed using coarse spatial resolution (300&nbsp;m–1&nbsp;km) satellite data did not provide enough thematic detail or change information for global change studies and for resource management. High resolution (∼30&nbsp;m) land cover characterization and monitoring is needed that permits detection of land change at the scale of most human activity and offers the increased flexibility of environmental model parameterization needed for global change studies. However, there are a number of challenges to overcome before producing such data sets including unavailability of consistent global coverage of satellite data, sheer volume of data, unavailability of timely and accurate training and validation data, difficulties in preparing image mosaics, and high performance computing requirements. Integration of remote sensing and information technology is needed for process automation and high-performance computing needs. Recent developments in these areas have created an opportunity for operational high resolution land cover mapping, and monitoring of the world. Here, we report and discuss these advancements and opportunities in producing the next generations of global land cover characterization, mapping, and monitoring at 30-m spatial resolution primarily in the context of United States, Group on Earth Observations Global 30&nbsp;m land cover initiative (UGLC).</span></p>","language":"English","publisher":"Elsevier","doi":"10.1016/j.jag.2013.03.005","usgsCitation":"Giri, C., Pengra, B., Long, J., and Loveland, T.R., 2013, Next generation of global land cover characterization, mapping, and monitoring: International Journal of Applied Earth Observation and Geoinformation, v. 25, p. 30-37, https://doi.org/10.1016/j.jag.2013.03.005.","productDescription":"8 p.","startPage":"30","endPage":"37","ipdsId":"IP-044790","costCenters":[{"id":222,"text":"Earth Resources Observation and Science (EROS) Center","active":true,"usgs":true}],"links":[{"id":342164,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"volume":"25","noUsgsAuthors":false,"publicationStatus":"PW","scienceBaseUri":"5937bf2fe4b0f6c2d0d9c789","contributors":{"authors":[{"text":"Giri, Chandra cgiri@usgs.gov","contributorId":189128,"corporation":false,"usgs":true,"family":"Giri","given":"Chandra","email":"cgiri@usgs.gov","affiliations":[{"id":223,"text":"Earth Resources Observation and Science (EROS) Center (Geography)","active":false,"usgs":true}],"preferred":true,"id":697326,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Pengra, Bruce 0000-0003-2497-8284 bpengra@usgs.gov","orcid":"https://orcid.org/0000-0003-2497-8284","contributorId":5132,"corporation":false,"usgs":true,"family":"Pengra","given":"Bruce","email":"bpengra@usgs.gov","affiliations":[{"id":222,"text":"Earth Resources Observation and Science (EROS) Center","active":true,"usgs":true},{"id":223,"text":"Earth Resources Observation and Science (EROS) Center (Geography)","active":false,"usgs":true}],"preferred":true,"id":697327,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Long, J.","contributorId":41993,"corporation":false,"usgs":true,"family":"Long","given":"J.","affiliations":[],"preferred":false,"id":697328,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Loveland, Thomas R. 0000-0003-3114-6646 loveland@usgs.gov","orcid":"https://orcid.org/0000-0003-3114-6646","contributorId":140256,"corporation":false,"usgs":true,"family":"Loveland","given":"Thomas","email":"loveland@usgs.gov","middleInitial":"R.","affiliations":[{"id":223,"text":"Earth Resources Observation and Science (EROS) Center (Geography)","active":false,"usgs":true}],"preferred":false,"id":697329,"contributorType":{"id":1,"text":"Authors"},"rank":4}]}}
,{"id":70171525,"text":"70171525 - 2013 - Effect of land cover and use on dry season river runoff, runoff efficiency, and peak storm runoff in the seasonal tropics of Central Panama","interactions":[],"lastModifiedDate":"2016-06-02T09:54:21","indexId":"70171525","displayToPublicDate":"2013-12-01T00:00:00","publicationYear":"2013","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":3722,"text":"Water Resources Research","onlineIssn":"1944-7973","printIssn":"0043-1397","active":true,"publicationSubtype":{"id":10}},"title":"Effect of land cover and use on dry season river runoff, runoff efficiency, and peak storm runoff in the seasonal tropics of Central Panama","docAbstract":"<p><span>A paired catchment methodology was used with more than 3 years of data to test whether forests increase base flow in the dry season, despite reduced annual runoff caused by evapotranspiration (the &ldquo;sponge-effect hypothesis&rdquo;), and whether forests reduce maximum runoff rates and totals during storms. The three study catchments were: a 142.3 ha old secondary forest, a 175.6 ha mosaic of mixed age forest, pasture, and subsistence agriculture, and a 35.9 ha actively grazed pasture subcatchment of the mosaic catchment. The two larger catchments are adjacent, with similar morphology, soils, underlying geology, and rainfall. Annual water balances, peak runoff rates, runoff efficiencies, and dry season recessions show significant differences. Dry season runoff from the forested catchment receded more slowly than from the mosaic and pasture catchments. The runoff rate from the forest catchment was 1&ndash;50% greater than that from the similarly sized mosaic catchment at the end of the dry season. This observation supports the sponge-effect hypothesis. The pasture and mosaic catchment median runoff efficiencies were 2.7 and 1.8 times that of the forest catchment, respectively, and increased with total storm rainfall. Peak runoff rates from the pasture and mosaic catchments were 1.7 and 1.4 times those of the forest catchment, respectively. The forest catchment produced 35% less total runoff and smaller peak runoff rates during the flood of record in the Panama Canal Watershed. Flood peak reduction and increased streamflows through dry periods are important benefits relevant to watershed management, payment for ecosystem services, water-quality management, reservoir sedimentation, and fresh water security in the Panama Canal watershed and similar tropical landscapes.</span></p>","language":"English","publisher":"Wiley","doi":"10.1002/2013WR013956","usgsCitation":"Ogden, F.L., Crouch, T.D., Stallard, R.F., and Hall, J.S., 2013, Effect of land cover and use on dry season river runoff, runoff efficiency, and peak storm runoff in the seasonal tropics of Central Panama: Water Resources Research, v. 49, no. 12, p. 8443-8462, https://doi.org/10.1002/2013WR013956.","productDescription":"20 p.","startPage":"8443","endPage":"8462","onlineOnly":"N","additionalOnlineFiles":"N","ipdsId":"IP-045298","costCenters":[{"id":5044,"text":"National Research Program - Central Branch","active":true,"usgs":true}],"links":[{"id":473428,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.1002/2013wr013956","text":"Publisher Index Page"},{"id":322082,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"Panama","geographicExtents":"{\n  \"type\": \"FeatureCollection\",\n  \"features\": [\n    {\n      \"type\": \"Feature\",\n      \"properties\": {},\n      \"geometry\": {\n        \"type\": \"Polygon\",\n        \"coordinates\": [\n          [\n            [\n              -80.15625,\n              8.635334050763124\n            ],\n            [\n              -80.15625,\n              9.546583349757574\n            ],\n            [\n              -79.310302734375,\n              9.546583349757574\n            ],\n            [\n              -79.310302734375,\n              8.635334050763124\n            ],\n            [\n              -80.15625,\n              8.635334050763124\n            ]\n          ]\n        ]\n      }\n    }\n  ]\n}","volume":"49","issue":"12","publishingServiceCenter":{"id":2,"text":"Denver PSC"},"noUsgsAuthors":false,"publicationDate":"2013-12-17","publicationStatus":"PW","scienceBaseUri":"575158b0e4b053f0edd03c38","contributors":{"authors":[{"text":"Ogden, Fred L.","contributorId":169952,"corporation":false,"usgs":false,"family":"Ogden","given":"Fred","email":"","middleInitial":"L.","affiliations":[{"id":590,"text":"U.S. Army Corps of Engineers","active":false,"usgs":false}],"preferred":false,"id":631596,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Crouch, Trey D.","contributorId":169953,"corporation":false,"usgs":false,"family":"Crouch","given":"Trey","email":"","middleInitial":"D.","affiliations":[{"id":590,"text":"U.S. Army Corps of Engineers","active":false,"usgs":false}],"preferred":false,"id":631597,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Stallard, Robert F. 0000-0001-8209-7608 stallard@usgs.gov","orcid":"https://orcid.org/0000-0001-8209-7608","contributorId":1924,"corporation":false,"usgs":true,"family":"Stallard","given":"Robert","email":"stallard@usgs.gov","middleInitial":"F.","affiliations":[{"id":5044,"text":"National Research Program - Central Branch","active":true,"usgs":true}],"preferred":true,"id":631595,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Hall, Jefferson S.","contributorId":169939,"corporation":false,"usgs":false,"family":"Hall","given":"Jefferson","email":"","middleInitial":"S.","affiliations":[{"id":25632,"text":"Smithsonian Tropical Research Institute, Balboa, Panama","active":true,"usgs":false}],"preferred":false,"id":631598,"contributorType":{"id":1,"text":"Authors"},"rank":4}]}}
,{"id":70191975,"text":"70191975 - 2013 - Developing an outcome-based biodiversity metric in support of the field to market project: Final report","interactions":[],"lastModifiedDate":"2018-12-20T11:55:36","indexId":"70191975","displayToPublicDate":"2013-12-01T00:00:00","publicationYear":"2013","noYear":false,"publicationType":{"id":18,"text":"Report"},"publicationSubtype":{"id":9,"text":"Other Report"},"seriesTitle":{"id":5602,"text":"Technical Bulletin","active":true,"publicationSubtype":{"id":9}},"seriesNumber":"334","title":"Developing an outcome-based biodiversity metric in support of the field to market project: Final report","docAbstract":"<p>Our objective was to create a metric that would calculate the relative impact of common commercial agricultural practices on terrestrial vertebrate richness. We sought to define impacts in fields (including field borders) of the southeastern region’s commercial production of corn, wheat, soy, and cotton. The metric is intended to serve as an educational tool, allowing producers to see how operational decisions made at the field level impact overall vertebrate species richness and to explore decision impacts to targeted species groups (e.g. game, pest, or beneficial species). </p><p>Agricultural landscapes are often mistakenly thought to be unsuitable habitat for most species. However, as demonstrated by results reported here, even large-scale, conventional agricultural producers are potentially important partners in biodiversity conservation. Many vertebrate species do inhabit agricultural landscapes, benefitting from the provision of water, food, or shelter within cultivated fields and their immediate borders (e.g., Holland et al. 2012). In the Southeastern US, of the 613 terrestrial vertebrate species modeled by the Southeast Gap Analysis Program (SEGAP) (http://www.basic.ncsu.edu/segap/index.html), 263 utilize row crop and associated agricultural land cover classes as potential habitat (Box 1). While some species may be sensitive to certain operational practices (e.g., tillage, pest management, or field border management practices), others are generally tolerant, and some may benefit either directly or indirectly. For example, field margins and ditches often serve as semi-natural habitats providing foraging resources and shelter for vertebrates and are shown to positively influence species richness and abundance (Billeter et al. 2007; Herzon &amp; Helenius 2008; Marshall &amp; Moonen 2002; Shore et al. 2005; Weibull et al. 2003; Wuczyńskia et al. 2011). Biodiversity responses are, therefore, complex, as an individual species’ responses to agricultural production practices depends on that animal’s resource specialization, mobility, and life history strategies (Jeanneret et al. 2003a, b; Jennings &amp; Pocock 2009). </p><p>The knowledge necessary to define the biodiversity contribution of agricultural lands is specialized, dispersed, and nuanced, and thus not readily accessible. Given access to clearly defined biodiversity tradeoffs between alternative agricultural practices, landowners, land managers and farm operators could collectively enhance the conservation and economic value of agricultural landscapes. Therefore, Field to Market: The Keystone Alliance for Sustainable Agriculture and The Nature Conservancy jointly funded a pilot project to develop a biodiversity metric to integrate into Field to Market’s existing sustainability calculator, The Fieldprint Calculator (http://www. fieldtomarket.org/). Field to Market: The Keystone Alliance for Sustainable Agriculture is an alliance among producers, agribusinesses, food companies, and conservation organizations seeking to create sustainable outcomes for agriculture. The Fieldprint Calculator supports the Keystone Alliance’s vision to achieve safe, accessible, and nutritious food, fiber and fuel in thriving ecosystems to meet the needs of 9 billion people in 2050. In support of this same vision, our project provides proof-of-concept for an outcome-based biodiversity metric for Field to Market to quantify biodiversity impacts of commercial row crop production on terrestrial vertebrate richness. </p><p>Little research exists examining the impacts of alternative commercial agricultural practices on overall terrestrial biodiversity (McLaughlin &amp; Mineau 1995). Instead, most studies compare organic versus conventional practices (e.g. Freemark &amp; Kirk 2001; Wickramasinghe et al. 2004), and most studies focus on flora, avian, or invertebrate communities (Jeanneret et al. 2003a; Maes et al. 2008; Pollard &amp; Relton 1970).&nbsp;Therefore, we used an expert-knowledge-based approach to develop a metric that predicts expected impacts to shelter and forage resources, individual species, and overall biodiversity (species richness). This approach is modeled after an ecosystems services concept (WRI 2005), except that we examine services (i.e., resources) provided to vertebrate wildlife rather than service provided to the human population. SEGAP predicts species that are potentially present in an area given landscape-scale habitat availability, configuration, and context (e.g., patch size, proximity to resources, connectivity, potential for disturbance). Based on the prediction of species that may be potentially present, the impacts of management decisions within fields and around their borders can be analyzed based on the impact of those practices to the availability of species’ resources. The final metric provides an index of a producer’s relative impact, but perhaps even more importantly, the underlying database allows producers to explore details such as which species are most impacted or how alternative decisions would impact their score.&nbsp;</p>","language":"English","publisher":"North Carolina Agricultral Research Service, College of Agriculture and Life Sciences, North Carolina State University","usgsCitation":"Drew, C.A., Alexander-Vaughn, L.B., Collazo, J., McKerrow, A., and Anderson, J., 2013, Developing an outcome-based biodiversity metric in support of the field to market project: Final report: Technical Bulletin 334, 28 p.","productDescription":"28 p.","ipdsId":"IP-046155","costCenters":[{"id":198,"text":"Coop Res Unit Atlanta","active":true,"usgs":true},{"id":37226,"text":"Core Science Analytics, Synthesis, and Libraries","active":true,"usgs":true},{"id":38315,"text":"GAP Analysis Project","active":true,"usgs":true}],"links":[{"id":350596,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"},{"id":350595,"rank":1,"type":{"id":11,"text":"Document"},"url":"https://www.basic.ncsu.edu/eda/downloads/BiodiversityReport_Text.pdf"}],"publishingServiceCenter":{"id":8,"text":"Raleigh PSC"},"noUsgsAuthors":false,"publicationStatus":"PW","scienceBaseUri":"5a6afac9e4b06e28e9c9a91e","contributors":{"authors":[{"text":"Drew, C. Ashton","contributorId":140953,"corporation":false,"usgs":false,"family":"Drew","given":"C.","email":"","middleInitial":"Ashton","affiliations":[],"preferred":false,"id":725790,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Alexander-Vaughn, Louise B.","contributorId":199257,"corporation":false,"usgs":false,"family":"Alexander-Vaughn","given":"Louise","email":"","middleInitial":"B.","affiliations":[],"preferred":false,"id":725791,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"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":713802,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"McKerrow, Alexa 0000-0002-8312-2905 amckerrow@usgs.gov","orcid":"https://orcid.org/0000-0002-8312-2905","contributorId":127753,"corporation":false,"usgs":true,"family":"McKerrow","given":"Alexa","email":"amckerrow@usgs.gov","affiliations":[{"id":208,"text":"Core Science Analytics and Synthesis","active":true,"usgs":true}],"preferred":true,"id":725792,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Anderson, John","contributorId":8763,"corporation":false,"usgs":true,"family":"Anderson","given":"John","affiliations":[],"preferred":false,"id":725793,"contributorType":{"id":1,"text":"Authors"},"rank":5}]}}
,{"id":70156252,"text":"70156252 - 2013 - Protocol for monitoring forest-nesting birds in National Park Service parks","interactions":[],"lastModifiedDate":"2016-09-08T14:38:10","indexId":"70156252","displayToPublicDate":"2013-12-01T00:00:00","publicationYear":"2013","noYear":false,"publicationType":{"id":18,"text":"Report"},"publicationSubtype":{"id":4,"text":"Other Government Series"},"seriesNumber":"NPS/NCRN/NRR—2014/749","title":"Protocol for monitoring forest-nesting birds in National Park Service parks","docAbstract":"<p>These documents detail the protocol for monitoring forest-nesting birds in National Park Service parks in the National Capital Region Network (NCRN). In the first year of sampling, counts of birds should be made at 384 points on the NCRN spatially randomized grid, developed to sample terrestrial resources. Sampling should begin on or about May 20 and continue into early July; on each day the sampling period begins at sunrise and ends five hours later. Each point should be counted twice, once in the first half of the field season and once in the second half, with visits made by different observers, balancing the within-season coverage of points and their spatial coverage by observers, and allowing observer differences to be tested. Three observers, skilled in identifying birds of the region by sight and sound and with previous experience in conducting timed counts of birds, will be needed for this effort. Observers should be randomly assigned to ‘routes’ consisting of eight points, in close proximity and, ideally, in similar habitat, that can be covered in one morning. Counts are 10 minutes in length, subdivided into four 2.5-min intervals. Within each time interval, new birds (i.e., those not already detected) are recorded as within or beyond 50 m of the point, based on where first detected. Binomial distance methods are used to calculate annual estimates of density for species. The data are also amenable to estimation of abundance and detection probability via the removal method. Generalized linear models can be used to assess between-year changes in density estimates or unadjusted count data. This level of sampling is expected to be sufficient to detect a 50% decline in 10 years for approximately 50 bird species, including 14 of 19 species that are priorities for conservation efforts, if analyses are based on unadjusted count data, and for 30 species (6 priority species) if analyses are based on density estimates. The estimates of required sample sizes are based on the mean number of individuals detected per 10 minutes in available data from surveys in three NCRN parks. Once network-wide data from the first year of sampling are available, this and other aspects of the protocol should be re-assessed, and changes made as desired or necessary before the start of the second field season. Thereafter, changes should not be made to the field methods, and sampling should be conducted annually for at least ten years. NCRN staff should keep apprised of new analytical methods developed for analysis of point-count data.</p>","language":"English","publisher":"National Park Service","collaboration":"Murray G. Efford","usgsCitation":"Dawson, D.K., and Efford, M.G., 2013, Protocol for monitoring forest-nesting birds in National Park Service parks, xi, 50 p. .","productDescription":"xi, 50 p. ","ipdsId":"IP-066816","costCenters":[{"id":531,"text":"Patuxent Wildlife Research Center","active":true,"usgs":true}],"links":[{"id":328408,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"},{"id":306801,"type":{"id":15,"text":"Index Page"},"url":"https://irma.nps.gov/App/Reference/Profile/2206488/"}],"publishingServiceCenter":{"id":10,"text":"Baltimore PSC"},"noUsgsAuthors":false,"publicationStatus":"PW","scienceBaseUri":"57d28baee4b0571647d0f93f","contributors":{"authors":[{"text":"Dawson, Deanna K. ddawson@usgs.gov","contributorId":1257,"corporation":false,"usgs":true,"family":"Dawson","given":"Deanna","email":"ddawson@usgs.gov","middleInitial":"K.","affiliations":[{"id":531,"text":"Patuxent Wildlife Research Center","active":true,"usgs":true}],"preferred":true,"id":568250,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Efford, Murray G.","contributorId":91616,"corporation":false,"usgs":true,"family":"Efford","given":"Murray","email":"","middleInitial":"G.","affiliations":[],"preferred":false,"id":568251,"contributorType":{"id":1,"text":"Authors"},"rank":2}]}}
,{"id":70173504,"text":"70173504 - 2013 - Evidence of Hybridization between Common Gartersnakes (<i>Thamnophis sirtalis</i>) and Butler’s Gartersnakes (<i>Thamnophis butleri</i>) in Wisconsin (USA).","interactions":[],"lastModifiedDate":"2016-06-16T16:43:33","indexId":"70173504","displayToPublicDate":"2013-12-01T00:00:00","publicationYear":"2013","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":2334,"text":"Journal of Herpetology","active":true,"publicationSubtype":{"id":10}},"title":"Evidence of Hybridization between Common Gartersnakes (<i>Thamnophis sirtalis</i>) and Butler’s Gartersnakes (<i>Thamnophis butleri</i>) in Wisconsin (USA).","docAbstract":"<p><span>Snakes within the genus&nbsp;</span><i>Thamnophis</i><span>&nbsp;(Gartersnakes and Ribbonsnakes) are often found in sympatry throughout their geographic distributions. Past work has indicated that some sympatric species within this genus may hybridize, but research of this nature is limited. We attempted to determine whether hybridization occurs between two&nbsp;</span><i>Thamnophis</i><span>&nbsp;species native to the upper midwestern United States: Common Gartersnake (</span><i><i>Thamnophis sirtalis</i></i><span>) and the Butler's Gartersnake (</span><i>Thamnophis butleri</i><span>). We sampled snakes (</span><i>n</i><span>&nbsp;= 411) across 26 locations in Wisconsin, including sites where both species coexist and sites where only Common Gartersnakes are found. We conducted genetic analyses on tissue collected from individuals field-identified as Common Gartersnakes or Butler's Gartersnakes. To verify the results of our field-collected data, we analyzed tissues from juvenile snakes (</span><i>n</i><span>&nbsp;= 4) suspected to be the offspring of a Common Gartersnake and a Butler's Gartersnake that were housed together in a captive situation. Of the field-collected snakes analyzed, eight snakes were consistent with expected Common &times; Butler's Gartersnake hybrids. All four of the captive offspring analyzed resolved as putative hybrids, corresponding with our field-collected samples. Butler's Gartersnake is a globally rare species, endemic only to the upper midwestern United States. Studies involving the potential for hybridization between common and uncommon species are useful from a conservation perspective. The low incidence of hybridization we observed would indicate that hybridization between these species is uncommon. Further research investigating rates of hybridization would help assess any potential threat posed by outbreeding between common and rare gartersnakes in this region of the United States.</span></p>","language":"English","publisher":"Society for the Study of Amphibians and Reptiles","publisherLocation":"Riverside, CA","doi":"10.1670/12-057","usgsCitation":"Joshua M. Kapfer, Sloss, B.L., Gregor W. Schuurman, Paloski, R.A., and Jeffrey M. 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Schuurman","affiliations":[{"id":24833,"text":"Wisconsin DNR, Madison, WI","active":true,"usgs":false}],"preferred":false,"id":637213,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Paloski, Rori A.","contributorId":171368,"corporation":false,"usgs":false,"family":"Paloski","given":"Rori","email":"","middleInitial":"A.","affiliations":[{"id":24833,"text":"Wisconsin DNR, Madison, WI","active":true,"usgs":false}],"preferred":false,"id":637216,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Jeffrey M. Lorch","contributorId":150036,"corporation":false,"usgs":false,"family":"Jeffrey M. Lorch","affiliations":[{"id":17895,"text":"University of Wisconsin-Madison, School of Veterinary Medicine, 2015 Linden Drive, Madison, Wisconsin 53706, USA","active":true,"usgs":false}],"preferred":false,"id":637214,"contributorType":{"id":1,"text":"Authors"},"rank":5}]}}
,{"id":70148143,"text":"70148143 - 2013 - Estimating reef fish discard mortality using surface and bottom tagging: effects of hook injury and barotrauma","interactions":[],"lastModifiedDate":"2015-05-27T13:54:35","indexId":"70148143","displayToPublicDate":"2013-12-01T00:00:00","publicationYear":"2013","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":1169,"text":"Canadian Journal of Fisheries and Aquatic Sciences","active":true,"publicationSubtype":{"id":10}},"title":"Estimating reef fish discard mortality using surface and bottom tagging: effects of hook injury and barotrauma","docAbstract":"<p><span>We estimated survival rates of discarded black sea bass (</span><i>Centropristis striata</i><span>) in various release conditions using tag&ndash;recapture data. Fish were captured with traps and hook and line from waters 29&ndash;34 m deep off coastal North Carolina, USA, marked with internal anchor tags, and observed for release condition. Fish tagged on the bottom using SCUBA served as a control group. Relative return rates for trap-caught fish released at the surface versus bottom provided an estimated survival rate of 0.87 (95% credible interval 0.67&ndash;1.18) for surface-released fish. Adjusted for results from the underwater tagging experiment, fish with evidence of external barotrauma had a median survival rate of 0.91 (0.69&ndash;1.26) compared with 0.36 (0.17&ndash;0.67) for fish with hook trauma and 0.16 (0.08&ndash;0.30) for floating or presumably dead fish. Applying these condition-specific estimates of survival to non-tagging fishery data, we estimated a discard survival rate of 0.81 (0.62&ndash;1.11) for 11 hook and line data sets from waters 20&ndash;35 m deep and 0.86 (0.67&ndash;1.17) for 10 trap data sets from waters 11&ndash;29 m deep. The tag-return approach using a control group with no fishery-associated trauma represents a method to accurately estimate absolute discard survival of physoclistous reef species.</span></p>","language":"English","publisher":"NRC Research Press","doi":"10.1139/cjfas-2013-0337","usgsCitation":"Rudershausen, P.J., Buckel, J.A., and Hightower, J.E., 2013, Estimating reef fish discard mortality using surface and bottom tagging: effects of hook injury and barotrauma: Canadian Journal of Fisheries and Aquatic Sciences, v. 71, no. 4, p. 514-520, https://doi.org/10.1139/cjfas-2013-0337.","productDescription":"7 p.","startPage":"514","endPage":"520","onlineOnly":"N","additionalOnlineFiles":"N","ipdsId":"IP-050975","costCenters":[{"id":198,"text":"Coop Res Unit Atlanta","active":true,"usgs":true}],"links":[{"id":300865,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","state":"North Carolina","otherGeospatial":"Onslow 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,{"id":70057785,"text":"ofr20131278 - 2013 - Hydrologic monitoring and selected hydrologic and environmental studies by the U.S. Geological Survey in Georgia, 2011–2013","interactions":[],"lastModifiedDate":"2016-12-08T16:45:04","indexId":"ofr20131278","displayToPublicDate":"2013-11-27T11:11:04","publicationYear":"2013","noYear":false,"publicationType":{"id":18,"text":"Report"},"publicationSubtype":{"id":5,"text":"USGS Numbered Series"},"seriesTitle":{"id":330,"text":"Open-File Report","code":"OFR","onlineIssn":"2331-1258","printIssn":"0196-1497","active":true,"publicationSubtype":{"id":5}},"seriesNumber":"2013-1278","title":"Hydrologic monitoring and selected hydrologic and environmental studies by the U.S. Geological Survey in Georgia, 2011–2013","docAbstract":"This compendium of papers describes results of hydrologic monitoring and hydrologic and environmental studies completed by the U.S. Geological Survey (USGS) in Georgia during 2011–2013. The USGS addresses a wide variety of water issues in the State of Georgia working with local, State, and Federal partners. As the primary Federal science agency for water resource information, the USGS monitors the quantity and quality of water in the Nation’s rivers and aquifers, assesses the sources and fate of contaminants in aquatic systems, collects and analyzes data on aquatic ecosystems, develops tools to improve the application of hydrologic information, and ensures that its information and tools are available to all potential users. During 2011–2013, the USGS continued a long-term program of monitoring stream and groundwater resources, including flow, water quality, and water use. In addition, a variety of hydrologic and environmental studies were completed to assess water availability, hydrologic hazards, and the impact of development on water resources. Information on USGS activities in Georgia is available online at <a href=\" http://ga.water.usgs.gov/\" target=\"_blank\"> http://ga.water.usgs.gov/</a>.","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/ofr20131278","usgsCitation":"Clarke, J.S., and Dalton, M., 2013, Hydrologic monitoring and selected hydrologic and environmental studies by the U.S. Geological Survey in Georgia, 2011–2013: U.S. Geological Survey Open-File Report 2013-1278, v, 73 p., https://doi.org/10.3133/ofr20131278.","productDescription":"v, 73 p.","numberOfPages":"84","onlineOnly":"Y","costCenters":[{"id":13634,"text":"South Atlantic Water Science Center","active":true,"usgs":true}],"links":[{"id":279865,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/ofr20131278.jpg"},{"id":279864,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/of/2013/1278/pdf/of2013-1278.pdf"},{"id":279863,"type":{"id":15,"text":"Index Page"},"url":"https://pubs.usgs.gov/of/2013/1278/"}],"scale":"150000","country":"United States","state":"Georgia","otherGeospatial":"Savannah River","geographicExtents":"{ \"type\": \"FeatureCollection\", \"features\": [ { \"type\": \"Feature\", \"properties\": {}, \"geometry\": { \"type\": \"Polygon\", \"coordinates\": [ [ [ -81.25,32 ], [ -81.25,32.3 ], [ -80.833,32.3 ], [ -80.833,32 ], [ -81.25,32 ] ] ] } } ] }","noUsgsAuthors":false,"publicationStatus":"PW","scienceBaseUri":"529716d5e4b08e44bf66fb80","contributors":{"authors":[{"text":"Clarke, John S. jsclarke@usgs.gov","contributorId":400,"corporation":false,"usgs":true,"family":"Clarke","given":"John","email":"jsclarke@usgs.gov","middleInitial":"S.","affiliations":[{"id":316,"text":"Georgia Water Science Center","active":true,"usgs":true}],"preferred":true,"id":486871,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Dalton, Melinda J. (compiler)","contributorId":38460,"corporation":false,"usgs":true,"family":"Dalton","given":"Melinda J.","suffix":"(compiler)","affiliations":[],"preferred":false,"id":486872,"contributorType":{"id":1,"text":"Authors"},"rank":2}]}}
,{"id":70048377,"text":"sim3269 - 2013 - Flood-inundation maps for the Elkhart River at Goshen, Indiana","interactions":[],"lastModifiedDate":"2013-11-27T11:05:42","indexId":"sim3269","displayToPublicDate":"2013-11-27T10:43:47","publicationYear":"2013","noYear":false,"publicationType":{"id":18,"text":"Report"},"publicationSubtype":{"id":5,"text":"USGS Numbered Series"},"seriesTitle":{"id":333,"text":"Scientific Investigations Map","code":"SIM","onlineIssn":"2329-132X","printIssn":"2329-1311","active":true,"publicationSubtype":{"id":5}},"seriesNumber":"3269","title":"Flood-inundation maps for the Elkhart River at Goshen, Indiana","docAbstract":"The U.S. Geological Survey (USGS), in cooperation with the Indiana Office of Community and Rural Affairs, created digital flood-inundation maps for an 8.3-mile reach of the Elkhart River at Goshen, Indiana, extending from downstream of the Goshen Dam to downstream from County Road 17. The inundation maps, which can be accessed through the USGS Flood Inundation Mapping Science Web site at <a href=\"http://water.usgs.gov/osw/flood_inundation/\" target=\"_blank\">http://water.usgs.gov/osw/flood_inundation/</a>, depict estimates of the areal extent and depth of flooding corresponding to nine selected water levels (stages) at the USGS streamgage at Elkhart River at Goshen (station number 04100500). Current conditions for the USGS streamgages in Indiana may be obtained on the Internet at <a href=\"http://waterdata.usgs.gov/\" target=\"_blank\">http://waterdata.usgs.gov/</a>. In addition, stream stage data have been provided to the National Weather Service (NWS) for incorporation into their Advanced Hydrologic Prediction Service (AHPS) flood warning system <a href=\"http://water.weather.gov/ahps/\" target=\"_blank\">(http://water.weather.gov/ahps/)</a>. The NWS forecasts flood hydrographs at many places that are often colocated with USGS streamgages. NWS-forecasted peak-stage information may be used in conjunction with the maps developed in this study to show predicted areas of flood inundation. In this study, flood profiles were computed for the stream reach by means of a one-dimensional step-backwater model. The model was calibrated using the most current stage-discharge relation at the Elkhart River at Goshen streamgage. The hydraulic model was then used to compute nine water-surface profiles for flood stages at 1-foot (ft) intervals referenced to the streamgage datum and ranging from approximately bankfull (5 ft) to greater than the highest recorded water level (13 ft). The simulated water-surface profiles were then combined with a geographic information system (GIS) digital-elevation model (DEM), derived from Light Detection and Ranging (LiDAR) data having a 0.37-ft vertical accuracy and 3.9-ft horizontal resolution in order to delineate the area flooded at each water level. The availability of these maps, along with Internet information regarding current stage from USGS streamgages and forecasted stream stages from the NWS, provide emergency management personnel and residents with information that is critical for flood response activities such as evacuations and road closures as well as for postflood recovery efforts.","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/sim3269","collaboration":"Prepared in cooperation with the Indiana Office of Community and Rural Affairs","usgsCitation":"Strauch, K.R., 2013, Flood-inundation maps for the Elkhart River at Goshen, Indiana: U.S. Geological Survey Scientific Investigations Map 3269, Pamphlet: vi, 7 p.; Map sheets JPEG and PDF; Downloads Directory, https://doi.org/10.3133/sim3269.","productDescription":"Pamphlet: vi, 7 p.; Map sheets JPEG and PDF; Downloads Directory","numberOfPages":"18","onlineOnly":"Y","additionalOnlineFiles":"Y","ipdsId":"IP-042153","costCenters":[{"id":346,"text":"Indiana Water Science Center","active":true,"usgs":true}],"links":[{"id":279862,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/sim3269.jpg"},{"id":279860,"type":{"id":17,"text":"Plate"},"url":"https://pubs.usgs.gov/sim/3269/downloads/mapsheets/pdf/"},{"id":279861,"type":{"id":20,"text":"Read Me"},"url":"https://pubs.usgs.gov/sim/3269/downloads/"},{"id":279859,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/sim/3269/pdf/sim3269-pamphlet.pdf"},{"id":279314,"type":{"id":15,"text":"Index Page"},"url":"https://pubs.usgs.gov/sim/3269/"}],"projection":"Indiana State Plane Eastern Zone","datum":"North American Datum of 1983","country":"United States","state":"Indiana","otherGeospatial":"Elkhart River","geographicExtents":"{ \"type\": \"FeatureCollection\", \"features\": [ { \"type\": \"Feature\", \"properties\": {}, \"geometry\": { \"type\": \"Polygon\", \"coordinates\": [ [ [ -85.9,41.5583 ], [ -85.9,41.625 ], [ -85.83,41.625 ], [ -85.83,41.5583 ], [ -85.9,41.5583 ] ] ] } } ] }","noUsgsAuthors":false,"publicationStatus":"PW","scienceBaseUri":"529716b9e4b08e44bf66fb7d","contributors":{"authors":[{"text":"Strauch, Kellan R. 0000-0002-7218-2099 kstrauch@usgs.gov","orcid":"https://orcid.org/0000-0002-7218-2099","contributorId":1006,"corporation":false,"usgs":true,"family":"Strauch","given":"Kellan","email":"kstrauch@usgs.gov","middleInitial":"R.","affiliations":[{"id":464,"text":"Nebraska Water Science Center","active":true,"usgs":true}],"preferred":true,"id":484482,"contributorType":{"id":1,"text":"Authors"},"rank":1}]}}
,{"id":70048921,"text":"ds797 - 2013 - Occurrence of fungicides and other pesticides in surface water, groundwater, and sediment from three targeted-use areas in the United States, 2009","interactions":[],"lastModifiedDate":"2026-05-28T20:59:17.474267","indexId":"ds797","displayToPublicDate":"2013-11-26T14:30:00","publicationYear":"2013","noYear":false,"publicationType":{"id":18,"text":"Report"},"publicationSubtype":{"id":5,"text":"USGS Numbered Series"},"seriesTitle":{"id":310,"text":"Data Series","code":"DS","onlineIssn":"2327-638X","printIssn":"2327-0271","active":false,"publicationSubtype":{"id":5}},"seriesNumber":"797","title":"Occurrence of fungicides and other pesticides in surface water, groundwater, and sediment from three targeted-use areas in the United States, 2009","docAbstract":"<p>Surface-water, groundwater, and suspended- and bedsediment samples were collected in three targeted-use areas in the United States where potatoes were grown during 2009 and analyzed for an extensive suite of fungicides and other pesticides by gas chromatograph/mass spectrometry and liquid chromatography with tandem mass spectrometry. Fungicides were detected in all environmental matrices sampled during the study. The most frequently detected fungicides were azoxystrobin, boscalid, chlorothalonil, and pyraclostrobin. Other pesticides that were detected frequently included amino phosphonic acid (AMPA), atrazine, metolaclor, and the organochlorine insecticide p,p&rsquo;-DDT and its degradates p,p&rsquo;-DDD and p,p&rsquo;-DDE. A greater number of pesticides were detected in surface water relative to the other environmental matrices sampled, and at least one pesticide was detected in 62 of the 63 surfacewater samples. The greatest numbers of pesticides and the maximum observed concentrations for most pesticides were measured in surface-water samples from Idaho. In eight surface- water samples (six from Idaho and two from Wisconsin), concentrations of bifenthrin, metolachlor, or malathion exceeded U.S. Environmental Protection Agency freshwater aquatic-life benchmarks for chronic toxicity to invertebrates. Thirteen pesticides, including seven fungicides, were detected in groundwater samples. Shallow groundwater samples collected beneath recently harvested potato fields contained more pesticides and had higher concentrations of pesticides than samples collected from other groundwater sources sampled during the study. Generally, pesticide concentrations were lower in groundwater samples than in surfacewater or sediment samples, with the exception of the fungicide boscalid, which was found to have its highest concentration in a shallow groundwater sample collected in Wisconsin. Thirteen pesticides, including four fungicides, were detected in suspended-sediment samples. The most frequently detected compounds were the fungicides boscalid, pyraclostrobin, and zoxamide, and the degradates p,p&rsquo;-DDD and p,p&rsquo;-DDE. Twenty pesticides, including six fungicides, were detected in bed-sediment samples. The most frequently detected compounds were pyraclostrobin, p,p&rsquo;-DDT, p,p&rsquo;-DDD, and p,p&rsquo;-DDE.</p>","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/ds797","issn":"2327-698X","usgsCitation":"Orlando, J., Smalling, K., Reilly, T.J., Boehlke, A., Meyer, M.T., and Kuivila, K., 2013, Occurrence of fungicides and other pesticides in surface water, groundwater, and sediment from three targeted-use areas in the United States, 2009: U.S. Geological Survey Data Series 797, viii, 73 p., https://doi.org/10.3133/ds797.","productDescription":"viii, 73 p.","numberOfPages":"85","onlineOnly":"N","additionalOnlineFiles":"N","temporalStart":"2009-01-01","temporalEnd":"2009-12-31","ipdsId":"IP-023568","costCenters":[{"id":154,"text":"California Water Science Center","active":true,"usgs":true}],"links":[{"id":504823,"rank":6,"type":{"id":36,"text":"NGMDB Index Page"},"url":"https://ngmdb.usgs.gov/Prodesc/proddesc_99344.htm","text":"central Wisconsin","linkFileType":{"id":5,"text":"html"}},{"id":504822,"rank":5,"type":{"id":36,"text":"NGMDB Index Page"},"url":"https://ngmdb.usgs.gov/Prodesc/proddesc_99343.htm","text":"near Presque Isle, Maine","linkFileType":{"id":5,"text":"html"}},{"id":504821,"rank":4,"type":{"id":36,"text":"NGMDB Index Page"},"url":"https://ngmdb.usgs.gov/Prodesc/proddesc_99342.htm","text":"Snake and Boise Rivers, Idaho","linkFileType":{"id":5,"text":"html"}},{"id":279850,"rank":1,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/ds/797/pdf/ds797.pdf"},{"id":279851,"rank":3,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.er.usgs.gov/thumbnails/ds797.jpg"},{"id":279839,"rank":2,"type":{"id":15,"text":"Index Page"},"url":"https://pubs.usgs.gov/ds/797/"}],"country":"United States","state":"Idaho, Maine, Wisconsin","noUsgsAuthors":false,"publicationStatus":"PW","scienceBaseUri":"5295c300e4b0becc369c7cff","contributors":{"authors":[{"text":"Orlando, James L. 0000-0002-0099-7221","orcid":"https://orcid.org/0000-0002-0099-7221","contributorId":95954,"corporation":false,"usgs":true,"family":"Orlando","given":"James L.","affiliations":[],"preferred":false,"id":485820,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Smalling, Kelly L.","contributorId":16105,"corporation":false,"usgs":true,"family":"Smalling","given":"Kelly L.","affiliations":[],"preferred":false,"id":485819,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Reilly, Timothy J. 0000-0002-2939-3050 tjreilly@usgs.gov","orcid":"https://orcid.org/0000-0002-2939-3050","contributorId":1858,"corporation":false,"usgs":true,"family":"Reilly","given":"Timothy","email":"tjreilly@usgs.gov","middleInitial":"J.","affiliations":[{"id":34983,"text":"Contaminant Biology Program","active":true,"usgs":true},{"id":470,"text":"New Jersey Water Science Center","active":true,"usgs":true}],"preferred":true,"id":485817,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Boehlke, Adam 0000-0003-4980-431X aboehlke@usgs.gov","orcid":"https://orcid.org/0000-0003-4980-431X","contributorId":3470,"corporation":false,"usgs":true,"family":"Boehlke","given":"Adam","email":"aboehlke@usgs.gov","affiliations":[{"id":164,"text":"Central Energy Resources Science Center","active":true,"usgs":true}],"preferred":true,"id":485818,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Meyer, Michael T. 0000-0001-6006-7985 mmeyer@usgs.gov","orcid":"https://orcid.org/0000-0001-6006-7985","contributorId":866,"corporation":false,"usgs":true,"family":"Meyer","given":"Michael","email":"mmeyer@usgs.gov","middleInitial":"T.","affiliations":[{"id":353,"text":"Kansas Water Science Center","active":false,"usgs":true}],"preferred":true,"id":485815,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"Kuivila, Kathryn  0000-0001-7940-489X kkuivila@usgs.gov","orcid":"https://orcid.org/0000-0001-7940-489X","contributorId":1367,"corporation":false,"usgs":true,"family":"Kuivila","given":"Kathryn ","email":"kkuivila@usgs.gov","affiliations":[{"id":518,"text":"Oregon Water Science Center","active":true,"usgs":true}],"preferred":false,"id":485816,"contributorType":{"id":1,"text":"Authors"},"rank":6}]}}
,{"id":70056026,"text":"ofr20131060 - 2013 - Sea-floor geology and topography offshore in northeastern Long Island Sound","interactions":[],"lastModifiedDate":"2013-11-26T10:11:38","indexId":"ofr20131060","displayToPublicDate":"2013-11-26T10:00:00","publicationYear":"2013","noYear":false,"publicationType":{"id":18,"text":"Report"},"publicationSubtype":{"id":5,"text":"USGS Numbered Series"},"seriesTitle":{"id":330,"text":"Open-File Report","code":"OFR","onlineIssn":"2331-1258","printIssn":"0196-1497","active":true,"publicationSubtype":{"id":5}},"seriesNumber":"2013-1060","title":"Sea-floor geology and topography offshore in northeastern Long Island Sound","docAbstract":"Datasets of gridded multibeam bathymetry, covering approximately 52.9 square kilometers, were used to interpret character and geology of the sea floor in northeastern Long Island Sound. Although originally collected for charting purposes during National Oceanic and Atmospheric Administration hydrographic survey H12012, these acoustic data and the sea-floor sampling and photography stations subsequently occupied to verify the acoustic data are interpreted (1) to define the composition and terrain of the seabed, (2) to provide information on sediment transport and benthic habitat, and (3) as part of an expanding series of studies that provide a fundamental framework for research and resource management (for example, cables, pipelines, and dredging) activities in this major east coast estuary.","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/ofr20131060","collaboration":"Also available in DVD-ROM format; see <a href=\"http://pubs.usgs.gov/of/2013/1060/title_page.html\" target=\"_blank\">Open-File Report 2013-1060</a> for ordering information.","usgsCitation":"Poppe, L., McMullen, K., Ackerman, S., and Glomb, K., 2013, Sea-floor geology and topography offshore in northeastern Long Island Sound: U.S. Geological Survey Open-File Report 2013-1060, HTML Document, https://doi.org/10.3133/ofr20131060.","productDescription":"HTML Document","ipdsId":"IP-044630","costCenters":[{"id":678,"text":"Woods Hole Coastal and Marine Science Center","active":true,"usgs":true}],"links":[{"id":279784,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/ofr20131060.GIF"},{"id":279782,"type":{"id":15,"text":"Index Page"},"url":"https://pubs.usgs.gov/of/2013/1060/"},{"id":279783,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/of/2013/1060/title_page.html"}],"country":"United States","state":"Connecticut;New York","otherGeospatial":"Long Island Sound","geographicExtents":"{\"crs\": {\"type\": \"name\", \"properties\": {\"name\": \"urn:ogc:def:crs:OGC:1.3:CRS84\"}}, \"geometry\": {\"type\": \"Polygon\", \"coordinates\": [[[-72.40061964236243, 41.221039699120766], [-72.24182293597879, 41.25788919031744], [-72.15439394393304, 41.260297737976906], [-72.1465243042542, 41.25951206200242], [-72.14514615131536, 41.257180793946965], [-72.14803125653313, 41.24904067565398], [-72.40118635945878, 41.19922109091135], [-72.40061964236243, 41.221039699120766]]]}, \"properties\": {\"extentType\": \"Custom\", \"code\": \"\", \"name\": \"\", \"notes\": \"\", \"promotedForReuse\": false, \"abbreviation\": \"\", \"shortName\": \"\", \"description\": \"\"}, \"bbox\": [-72.4021781143774, 41.19922109091135, -72.14495295230523, 41.260297737976906], \"type\": \"Feature\", \"id\": \"3091986\"}","noUsgsAuthors":false,"publicationStatus":"PW","scienceBaseUri":"5295c301e4b0becc369c7d05","contributors":{"authors":[{"text":"Poppe, L. J.","contributorId":72782,"corporation":false,"usgs":true,"family":"Poppe","given":"L.","middleInitial":"J.","affiliations":[],"preferred":false,"id":486297,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"McMullen, K. Y.","contributorId":51857,"corporation":false,"usgs":true,"family":"McMullen","given":"K.","middleInitial":"Y.","affiliations":[],"preferred":false,"id":486295,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Ackerman, S. D.","contributorId":88843,"corporation":false,"usgs":true,"family":"Ackerman","given":"S.","middleInitial":"D.","affiliations":[],"preferred":false,"id":486298,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Glomb, K.A.","contributorId":67996,"corporation":false,"usgs":true,"family":"Glomb","given":"K.A.","email":"","affiliations":[],"preferred":false,"id":486296,"contributorType":{"id":1,"text":"Authors"},"rank":4}]}}
,{"id":70057473,"text":"ofr20131254 - 2013 - Petrologic and isotopic data from the Cretaceous (Campanian) Blackhawk Formation and Star Point Sandstone (Mesaverde Group), Wasatch Plateau, Utah","interactions":[],"lastModifiedDate":"2013-11-25T11:41:26","indexId":"ofr20131254","displayToPublicDate":"2013-11-25T11:03:00","publicationYear":"2013","noYear":false,"publicationType":{"id":18,"text":"Report"},"publicationSubtype":{"id":5,"text":"USGS Numbered Series"},"seriesTitle":{"id":330,"text":"Open-File Report","code":"OFR","onlineIssn":"2331-1258","printIssn":"0196-1497","active":true,"publicationSubtype":{"id":5}},"seriesNumber":"2013-1254","title":"Petrologic and isotopic data from the Cretaceous (Campanian) Blackhawk Formation and Star Point Sandstone (Mesaverde Group), Wasatch Plateau, Utah","docAbstract":"The presence of discrete minerals associated with coal—whether (1) detrital or authigenic constituents of the coals or in thin mudstone or siltstone units interbedded with coals, or (2) authigenic phases that formed along cleats—might influence its utilization as an energy resource. The build-up of sintered ash deposits on the surfaces of heat exchangers in coal-fired power plants, due to the alteration of minerals during combustion of the coal, can seriously affect the functioning of the boiler and enhance corrosion of combustion equipment. In particular, the presence of sodium in coals has been considered a key factor in the fouling of boilers; however, other elements (such as calcium or magnesium) and the amount of discrete minerals burned with coal can also play a significant role in the inefficiency of and damage to boilers. \n\nPrevious studies of the quality of coals in the Cretaceous (Campanian) Blackhawk Formation of the Wasatch Plateau, Utah, revealed that the sodium content of the coals varied across the region. To better understand the origin and distribution of sodium in these coals, petrologic studies were undertaken within a sedimentological framework to evaluate the timing and geochemical constraints on the emplacement of sodium-bearing minerals, particularly analcime, which previously had been identified in coals in the Blackhawk Formation. Further, the study was broadened to include not just coals in the Blackhawk Formation from various localities across the Wasatch Plateau, but also sandstones interbedded with the coals as well as sandstones in the underlying Star Point Sandstone. The alteration history of the sandstones in both formations was considered a key component of this study because it records the nature and timing of fluids passing through them and the associated precipitation of sodium-bearing minerals; thus, the alteration history could place constraints on the distribution and timing of sodium mineralization in the interbedded or overlying Blackhawk coals. Although some preliminary results were previously presented at scientific meetings, the petrologic and geochemical data have not been fully compiled and reported. The purpose of this report is to present the methods of data acquisition and the results of petrologic and isotopic analyses on coal and sandstone samples from the Blackhawk Formation as well as sandstones of the underlying Star Point Sandstone.","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/ofr20131254","usgsCitation":"Fishman, N.S., Turner, C., and Peterson, F., 2013, Petrologic and isotopic data from the Cretaceous (Campanian) Blackhawk Formation and Star Point Sandstone (Mesaverde Group), Wasatch Plateau, Utah: U.S. Geological Survey Open-File Report 2013-1254, Report: iii, 15 p.; Plate: 47.38 inches x 28.21 inches, https://doi.org/10.3133/ofr20131254.","productDescription":"Report: iii, 15 p.; Plate: 47.38 inches x 28.21 inches","onlineOnly":"Y","additionalOnlineFiles":"Y","ipdsId":"IP-042917","costCenters":[{"id":164,"text":"Central Energy Resources Science Center","active":true,"usgs":true}],"links":[{"id":279628,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/ofr20131254.jpg"},{"id":279627,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/of/2013/1254/pdf/of2013-1254.pdf"},{"id":279626,"type":{"id":17,"text":"Plate"},"url":"https://pubs.usgs.gov/of/2013/1254/pdf/of2013-1254_plate1.pdf"},{"id":279617,"type":{"id":15,"text":"Index Page"},"url":"https://pubs.usgs.gov/of/2013/1254/"}],"country":"United States","state":"Utah","geographicExtents":"{ \"type\": \"FeatureCollection\", \"features\": [ { \"type\": \"Feature\", \"properties\": {}, \"geometry\": { \"type\": \"Polygon\", \"coordinates\": [ [ [ -112.2083,39.3641 ], [ -112.2083,41.5524 ], [ -111.1022,41.5524 ], [ -111.1022,39.3641 ], [ -112.2083,39.3641 ] ] ] } } ] }","noUsgsAuthors":false,"publicationStatus":"PW","scienceBaseUri":"52947165e4b01cca2b1128ec","contributors":{"authors":[{"text":"Fishman, Neil S.","contributorId":106464,"corporation":false,"usgs":true,"family":"Fishman","given":"Neil","email":"","middleInitial":"S.","affiliations":[],"preferred":false,"id":486782,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Turner, Christine E.","contributorId":27164,"corporation":false,"usgs":true,"family":"Turner","given":"Christine E.","affiliations":[],"preferred":false,"id":486781,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Peterson, Fred fpeterson@usgs.gov","contributorId":1309,"corporation":false,"usgs":true,"family":"Peterson","given":"Fred","email":"fpeterson@usgs.gov","affiliations":[],"preferred":true,"id":486780,"contributorType":{"id":1,"text":"Authors"},"rank":3}]}}
,{"id":70048932,"text":"sir20135173 - 2013 - Streamflow statistics for unregulated and regulated conditions for selected locations on the Yellowstone, Tongue, and Powder Rivers, Montana, 1928-2002","interactions":[],"lastModifiedDate":"2014-07-11T11:22:50","indexId":"sir20135173","displayToPublicDate":"2013-11-25T10:29:00","publicationYear":"2013","noYear":false,"publicationType":{"id":18,"text":"Report"},"publicationSubtype":{"id":5,"text":"USGS Numbered Series"},"seriesTitle":{"id":334,"text":"Scientific Investigations Report","code":"SIR","onlineIssn":"2328-0328","printIssn":"2328-031X","active":true,"publicationSubtype":{"id":5}},"seriesNumber":"2013-5173","title":"Streamflow statistics for unregulated and regulated conditions for selected locations on the Yellowstone, Tongue, and Powder Rivers, Montana, 1928-2002","docAbstract":"<p>Major floods in 1996 and 1997 on the Yellowstone River in Montana intensified public debate over the effects of human activities on the Yellowstone River. In 1999, the Yellowstone River Conservation District Council was formed to address conservation issues on the river. The Yellowstone River Conservation District Council partnered with the U.S. Army Corps of Engineers to conduct a cumulative-effects study on the main stem of the Yellowstone River. The cumulative-effects study is intended to provide a basis for future management decisions in the watershed. Streamflow statistics, such as flow-frequency and flow-duration data calculated for unregulated and regulated streamflow conditions, are a necessary component of the cumulative effects study.</p>\n<br/>\n<p>The U.S. Geological Survey, in cooperation with the Yellowstone River Conservation District Council and the U.S. Army Corps of Engineers, calculated streamflow statistics for unregulated and regulated conditions for the Yellowstone, Tongue, and Powder Rivers for the 1928–2002 study period. Unregulated streamflow represents flow conditions that might have occurred during the 1928–2002 study period if there had been no water-resources development in the Yellowstone River Basin. Regulated streamflow represents estimates of flow conditions during the 1928–2002 study period if the level of water-resources development existing in 2002 was in place during the entire study period. Peak-flow frequency estimates for regulated and unregulated streamflow were developed using methods described in Bulletin 17B. High-flow frequency and low-flow frequency data were developed for regulated and unregulated streamflows from the annual series of highest and lowest (respectively) mean flows for specified n-day consecutive periods within the calendar year. Flow-duration data, and monthly and annual streamflow characteristics, also were calculated for the unregulated and regulated streamflows.</p>","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/sir20135173","collaboration":"Prepared in cooperation with the Yellowstone River Conservation District Council and the U.S. Army Corps of Engineers","usgsCitation":"Chase, K.J., 2013, Streamflow statistics for unregulated and regulated conditions for selected locations on the Yellowstone, Tongue, and Powder Rivers, Montana, 1928-2002 (Originally posted November 22, 2013; Version 1.1: June 23, 2014): U.S. Geological Survey Scientific Investigations Report 2013-5173, Report: vii, 183 p.; Appendixes 1, 3-6, https://doi.org/10.3133/sir20135173.","productDescription":"Report: vii, 183 p.; Appendixes 1, 3-6","numberOfPages":"194","onlineOnly":"Y","additionalOnlineFiles":"Y","temporalStart":"1928-01-01","temporalEnd":"2002-12-31","ipdsId":"IP-028451","costCenters":[{"id":685,"text":"Wyoming-Montana Water Science Center","active":false,"usgs":true}],"links":[{"id":279625,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/sir20135173.jpg"},{"id":279619,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/sir/2013/5173/pdf/sir2013-5173.pdf"},{"id":279621,"type":{"id":3,"text":"Appendix"},"url":"https://pubs.usgs.gov/sir/2013/5173/downloads/sir2013-5173_APP_3_peakflow.xlsx"},{"id":279620,"type":{"id":3,"text":"Appendix"},"url":"https://pubs.usgs.gov/sir/2013/5173/downloads/sir2013-5173_APP_1_depletions.xlsx"},{"id":279622,"type":{"id":3,"text":"Appendix"},"url":"https://pubs.usgs.gov/sir/2013/5173/downloads/sir2013-5173_APP_4_highflowfreq.xlsx"},{"id":279623,"type":{"id":3,"text":"Appendix"},"url":"https://pubs.usgs.gov/sir/2013/5173/downloads/sir2013-5173_APP_5_lowflowfreq.xlsx"},{"id":279624,"type":{"id":3,"text":"Appendix"},"url":"https://pubs.usgs.gov/sir/2013/5173/downloads/sir2013-5173_APP_6_Flowduration.xlsm"},{"id":279618,"type":{"id":15,"text":"Index Page"},"url":"https://pubs.usgs.gov/sir/2013/5173/"}],"scale":"2000000","projection":"Lambert Conformal Conic","datum":"North American Datum of 1983","country":"United States","state":"Montana;North Dakota;Wyoming","otherGeospatial":"Powder River;Tongue River;Yellowstone River","geographicExtents":"{ \"type\": \"FeatureCollection\", \"features\": [ { \"type\": \"Feature\", \"properties\": {}, \"geometry\": { \"type\": \"Polygon\", \"coordinates\": [ [ [ -111.2915,42.7389 ], [ -111.2915,47.9752 ], [ -103.3374,47.9752 ], [ -103.3374,42.7389 ], [ -111.2915,42.7389 ] ] ] } } ] }","edition":"Originally posted November 22, 2013; Version 1.1: June 23, 2014","noUsgsAuthors":false,"publicationStatus":"PW","scienceBaseUri":"52947166e4b01cca2b1128f2","contributors":{"authors":[{"text":"Chase, Katherine J. 0000-0002-5796-4148 kchase@usgs.gov","orcid":"https://orcid.org/0000-0002-5796-4148","contributorId":454,"corporation":false,"usgs":true,"family":"Chase","given":"Katherine","email":"kchase@usgs.gov","middleInitial":"J.","affiliations":[{"id":685,"text":"Wyoming-Montana Water Science Center","active":false,"usgs":true}],"preferred":true,"id":485822,"contributorType":{"id":1,"text":"Authors"},"rank":1}]}}
,{"id":70048906,"text":"sir20135017 - 2013 - Hydrogeology, distribution, and volume of saline groundwater in the southern midcontinent and adjacent areas of the United States","interactions":[],"lastModifiedDate":"2013-11-22T14:30:00","indexId":"sir20135017","displayToPublicDate":"2013-11-22T14:13:24","publicationYear":"2013","noYear":false,"publicationType":{"id":18,"text":"Report"},"publicationSubtype":{"id":5,"text":"USGS Numbered Series"},"seriesTitle":{"id":334,"text":"Scientific Investigations Report","code":"SIR","onlineIssn":"2328-0328","printIssn":"2328-031X","active":true,"publicationSubtype":{"id":5}},"seriesNumber":"2013-5017","title":"Hydrogeology, distribution, and volume of saline groundwater in the southern midcontinent and adjacent areas of the United States","docAbstract":"The hydrogeology, distribution, and volume of saline water in 22 aquifers in the southern midcontinent of the United States were evaluated to provide information about saline groundwater resources that may be used to reduce dependency on freshwater resources. Those aquifers underlie six States in the southern midcontinent—Arkansas, Kansas, Louisiana, Missouri, Oklahoma, and Texas—and adjacent areas including all or parts of Alabama, Colorado, Florida, Illinois, Kentucky, Mississippi, Nebraska, New Mexico, South Dakota, Tennessee, and Wyoming and some offshore areas of the Gulf of Mexico. Saline waters of the aquifers were evaluated by defining salinity zones; digitizing data, primarily from the Regional Aquifer-System Analysis Program of the U.S. Geological Survey; and computing the volume of saline water in storage.  The distribution of saline groundwater in the southern midcontinent is substantially affected by the hydrogeology and groundwater-flow systems of the aquifers. Many of the aquifers in the southern midcontinent are underlain by one or more aquifers, resulting in vertically stacked aquifers containing groundwaters of varying salinity. Saline groundwater is affected by past and present hydrogeologic conditions. Spatial variation of groundwater salinity in the southern midcontinent is controlled primarily by locations of recharge and discharge areas, groundwater-flow paths and residence time, mixing of freshwater and saline water, and interactions with aquifer rocks and sediments.  The volume calculations made for the evaluated aquifers in the southern midcontinent indicate that about 39,900 million acre-feet (acre-ft) of saline water is in storage. About 21,600 million acre-ft of the water in storage is slightly to moderately saline (1,000–10,000 milligrams per liter [mg/L] dissolved solids), and about 18,300 million acre-ft is very saline (10,000–35,000 mg/L dissolved solids). The largest volumes of saline water are in the coastal lowlands (about 16,300 million acre-ft), Mississippi embayment and Texas coastal uplands (about 12,000 million acre-ft), and Great Plains (about 8,170 million acre-ft) aquifer systems. Of the 22 aquifers evaluated in this report, the Maha aquifer in the Great Plains aquifer system contains both the largest total volume of saline water (about 6,280 million acre-ft) and the largest volume of slightly to moderately saline water (about 5,150 million acre-ft).","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/sir20135017","collaboration":"Groundwater Resources Program","usgsCitation":"Osborn, N.I., Smith, S.J., and Seger, C.H., 2013, Hydrogeology, distribution, and volume of saline groundwater in the southern midcontinent and adjacent areas of the United States: U.S. Geological Survey Scientific Investigations Report 2013-5017, vi, 58 p., https://doi.org/10.3133/sir20135017.","productDescription":"vi, 58 p.","numberOfPages":"67","onlineOnly":"Y","ipdsId":"IP-043576","costCenters":[{"id":516,"text":"Oklahoma Water Science Center","active":true,"usgs":true}],"links":[{"id":279613,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/sir20135017.jpg"},{"id":279612,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/sir/2013/5017/pdf/sir2013-5017.pdf"},{"id":279611,"type":{"id":15,"text":"Index Page"},"url":"https://pubs.usgs.gov/sir/2013/5017/"}],"country":"United States","state":"Alabama;Arkansas;Colorado;Florida;Illinois;Kansas;Kentucky;Louisiana;Mississippi;Missouri;Nebraska;New Mexico;Oklahoma;South Dakota;Tennessee;Texas;Wyoming","geographicExtents":"{ \"type\": \"FeatureCollection\", \"features\": [ { \"type\": \"Feature\", \"properties\": {}, \"geometry\": { \"type\": \"Polygon\", \"coordinates\": [ [ [ -0.01638888888888889,5.555555555555556E-4 ], [ -0.01638888888888889,0.0011111111111111111 ], [ -83,0.0011111111111111111 ], [ -83,5.555555555555556E-4 ], [ -0.01638888888888889,5.555555555555556E-4 ] ] ] } } ] }","noUsgsAuthors":false,"publicationStatus":"PW","scienceBaseUri":"52907d0ce4b0bbdcf23ed30a","contributors":{"authors":[{"text":"Osborn, Noel I.","contributorId":75844,"corporation":false,"usgs":true,"family":"Osborn","given":"Noel","email":"","middleInitial":"I.","affiliations":[],"preferred":false,"id":485795,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Smith, S. Jerrod 0000-0002-9379-8167 sjsmith@usgs.gov","orcid":"https://orcid.org/0000-0002-9379-8167","contributorId":981,"corporation":false,"usgs":true,"family":"Smith","given":"S.","email":"sjsmith@usgs.gov","middleInitial":"Jerrod","affiliations":[{"id":516,"text":"Oklahoma Water Science Center","active":true,"usgs":true}],"preferred":true,"id":485793,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Seger, Christian H.","contributorId":34799,"corporation":false,"usgs":true,"family":"Seger","given":"Christian","email":"","middleInitial":"H.","affiliations":[],"preferred":false,"id":485794,"contributorType":{"id":1,"text":"Authors"},"rank":3}]}}
,{"id":70056145,"text":"sir20135171 - 2013 - Estimated nitrogen loads from selected tributaries in Connecticut draining to Long Island Sound, 1999–2009","interactions":[],"lastModifiedDate":"2015-03-03T08:17:53","indexId":"sir20135171","displayToPublicDate":"2013-11-22T14:00:08","publicationYear":"2013","noYear":false,"publicationType":{"id":18,"text":"Report"},"publicationSubtype":{"id":5,"text":"USGS Numbered Series"},"seriesTitle":{"id":334,"text":"Scientific Investigations Report","code":"SIR","onlineIssn":"2328-0328","printIssn":"2328-031X","active":true,"publicationSubtype":{"id":5}},"seriesNumber":"2013-5171","title":"Estimated nitrogen loads from selected tributaries in Connecticut draining to Long Island Sound, 1999–2009","docAbstract":"<p>The total nitrogen load to Long Island Sound from Connecticut and contributing areas to the north was estimated for October 1998 to September 2009. Discrete measurements of total nitrogen concentrations and continuous flow data from 37 water-quality monitoring stations in the Long Island Sound watershed were used to compute total annual nitrogen yields and loads. Total annual computed yields and basin characteristics were used to develop a generalized-least squares regression model for use in estimating the total nitrogen yields from unmonitored areas in coastal and central Connecticut. Significant variables in the regression included the percentage of developed land, percentage of row crops, point-source nitrogen yields from wastewater-treatment facilities, and annual mean streamflow. Computed annual median total nitrogen yields at individual monitoring stations ranged from less than 2,000 pounds per square mile in mostly forested basins (typically less than 10 percent developed land) to more than 13,000 pounds per square mile in urban basins (greater than 40 percent developed) with wastewater-treatment facilities and in one agricultural basin. Medians of computed total annual nitrogen yields for water years 1999&ndash;2009 at most stations were similar to those previously computed for water years 1988&ndash;98. However, computed medians of annual yields at several stations, including the Naugatuck River, Quinnipiac River, and Hockanum River, were lower than during 1988&ndash;98. Nitrogen yields estimated for 26 unmonitored areas downstream from monitoring stations ranged from less than 2,000 pounds per square mile to 34,000 pounds per square mile. Computed annual total nitrogen loads at the farthest downstream monitoring stations were combined with the corresponding estimates for the downstream unmonitored areas for a combined estimate of the total nitrogen load from the entire study area. Resulting combined total nitrogen loads ranged from 38 to 68 million pounds per year during water years 1999&ndash;2009. Total annual loads from the monitored basins represent 63 to 74 percent of the total load. Computed annual nitrogen loads from four stations near the Massachusetts border with Connecticut represent 52 to 54 percent of the total nitrogen load during water years 2008&ndash;9, the only years with data for all the border sites. During the latter part of the 1999&ndash;2009 study period, total nitrogen loads to Long Island Sound from the study area appeared to increase slightly. The apparent increase in loads may be due to higher than normal streamflows, which consequently increased nonpoint nitrogen loads during the study, offsetting major reductions of nitrogen from wastewater-treatment facilities. Nitrogen loads from wastewater treatment facilities declined as much as 2.3 million pounds per year in areas of Connecticut upstream from the monitoring stations and as much as 5.8 million pounds per year in unmonitored areas downstream in coastal and central Connecticut.</p>","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/sir20135171","collaboration":"Prepared in cooperation with the Connecticut Department of Energy and Environmental Protection","usgsCitation":"Mullaney, J.R., and Schwarz, G., 2013, Estimated nitrogen loads from selected tributaries in Connecticut draining to Long Island Sound, 1999–2009: U.S. Geological Survey Scientific Investigations Report 2013-5171, vii, 65 p., https://doi.org/10.3133/sir20135171.","productDescription":"vii, 65 p.","numberOfPages":"78","onlineOnly":"Y","additionalOnlineFiles":"N","ipdsId":"IP-050762","costCenters":[{"id":466,"text":"New England Water Science Center","active":true,"usgs":true}],"links":[{"id":279610,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/sir20135171.jpg"},{"id":279608,"type":{"id":15,"text":"Index Page"},"url":"https://pubs.usgs.gov/sir/2013/5171/"},{"id":279609,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/sir/2013/5171/pdf/sir2013-5171.pdf"}],"country":"United States","state":"Connecticut;Massachusetts;New Hampshire;Vermont","geographicExtents":"{\n  \"type\": \"FeatureCollection\",\n  \"features\": [\n    {\n      \"type\": \"Feature\",\n      \"properties\": {},\n      \"geometry\": {\n        \"type\": \"Polygon\",\n        \"coordinates\": [\n          [\n            [\n              -73.62487792968749,\n              40.925964939514294\n            ],\n            [\n              -73.7896728515625,\n              41.11660732012896\n            ],\n            [\n              -73.6138916015625,\n              41.24890252240322\n            ],\n            [\n    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,{"id":70160619,"text":"70160619 - 2013 - Broad-scale patterns of Brook Trout responses to introduced Brown Trout in New York","interactions":[],"lastModifiedDate":"2019-12-13T06:39:07","indexId":"70160619","displayToPublicDate":"2013-11-22T12:45:00","publicationYear":"2013","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":2886,"text":"North American Journal of Fisheries Management","active":true,"publicationSubtype":{"id":10}},"title":"Broad-scale patterns of Brook Trout responses to introduced Brown Trout in New York","docAbstract":"<p>Brook Trout <i>Salvelinus fontinalis</i> and Brown Trout <i>Salmo trutta</i> are valuable sport fish that coexist in many parts of the world due to stocking introductions. Causes for the decline of Brook Trout within their native range are not clear but include competition with Brown Trout, habitat alteration, and repetitive stocking practices. New York State contains a large portion of the Brook Trout's native range, where both species are maintained by stocking and other management actions. We used artificial neural network models, regression, principal components analysis, and simulation to evaluate the effects of Brown Trout, environmental conditions, and stocking on the distribution of Brook Trout in the center of their native range. We found evidence for the decline of Brook Trout in the presence of Brown Trout across many watersheds; 22% of sampled reaches where both species were expected to occur contained only Brown Trout. However, a model of the direct relationship between Brook Trout and Brown Trout abundance explained less than 1% of data variation. Ordination showed extensive overlap of Brook Trout and Brown Trout habitat conditions, with only small components of the hypervolume (multidimensional space) being distinctive. Subsequent analysis indicated higher abundances of Brook Trout in highly forested areas, while Brown Trout were more abundant in areas with relatively high proportions of agriculture. Simulation results indicated that direct interactions and habitat conditions were relatively minor factors compared with the effects of repeated stocking of Brown Trout into Brook Trout habitat. Intensive annual stocking of Brown Trout could eliminate resident Brook Trout in less than a decade. Ecological differences, harvest behavior, and other habitat changes can exacerbate Brook Trout losses. Custom stocking scenarios with Brown Trout introductions at relatively low proportions of resident Brook Trout populations may be able to sustain healthy populations of both species within their present range.</p>","language":"English","publisher":"American Fisheries Society","publisherLocation":"Lawrence, KS","doi":"10.1080/02755947.2013.830998","usgsCitation":"McKenna, J., Slattery, M.T., and Clifford, K.M., 2013, Broad-scale patterns of Brook Trout responses to introduced Brown Trout in New York: North American Journal of Fisheries Management, v. 33, no. 6, p. 1221-1235, https://doi.org/10.1080/02755947.2013.830998.","productDescription":"15 p.","startPage":"1221","endPage":"1235","numberOfPages":"15","onlineOnly":"N","additionalOnlineFiles":"N","ipdsId":"IP-049402","costCenters":[{"id":324,"text":"Great Lakes Science Center","active":true,"usgs":true}],"links":[{"id":473436,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index 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Jr. 0000-0002-1428-7597 jemckenna@usgs.gov","orcid":"https://orcid.org/0000-0002-1428-7597","contributorId":627,"corporation":false,"usgs":true,"family":"McKenna","given":"James E.","suffix":"Jr.","email":"jemckenna@usgs.gov","affiliations":[{"id":324,"text":"Great Lakes Science Center","active":true,"usgs":true}],"preferred":false,"id":583357,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Slattery, Michael T. mslattery@usgs.gov","contributorId":5470,"corporation":false,"usgs":true,"family":"Slattery","given":"Michael","email":"mslattery@usgs.gov","middleInitial":"T.","affiliations":[{"id":324,"text":"Great Lakes Science Center","active":true,"usgs":true}],"preferred":true,"id":583358,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Clifford, Kean M.","contributorId":150867,"corporation":false,"usgs":false,"family":"Clifford","given":"Kean","email":"","middleInitial":"M.","affiliations":[{"id":18127,"text":"State University of New York, College of Environmental Science and Foresty","active":true,"usgs":false}],"preferred":false,"id":583359,"contributorType":{"id":1,"text":"Authors"},"rank":3}]}}
,{"id":70049000,"text":"sir20135188 - 2013 - Methods for estimating water consumption for thermoelectric power plants in the United States","interactions":[],"lastModifiedDate":"2014-11-24T14:26:19","indexId":"sir20135188","displayToPublicDate":"2013-11-22T08:26:00","publicationYear":"2013","noYear":false,"publicationType":{"id":18,"text":"Report"},"publicationSubtype":{"id":5,"text":"USGS Numbered Series"},"seriesTitle":{"id":334,"text":"Scientific Investigations Report","code":"SIR","onlineIssn":"2328-0328","printIssn":"2328-031X","active":true,"publicationSubtype":{"id":5}},"seriesNumber":"2013-5188","title":"Methods for estimating water consumption for thermoelectric power plants in the United States","docAbstract":"<p>Water consumption at thermoelectric power plants represents a small but substantial share of total water consumption in the U.S. However, currently available thermoelectric water consumption data are inconsistent and incomplete, and coefficients used to estimate consumption are contradictory. The U.S. Geological Survey (USGS) has resumed the estimation of thermoelectric water consumption, last done in 1995, based on the use of linked heat and water budgets to complement reported water consumption. This report presents the methods used to estimate freshwater consumption at a study set of 1,284 power plants based on 2010 plant characteristics and operations data.</p>\n<p>Power plants were categorized for estimation of water consumption in two tiers. First, generating units were assigned to categories based on the technology used to generate electricity. These generation-type categories are combustion steam, combined-cycle, nuclear, geothermal, and solar thermal. Second, cooling systems were separately categorized as either wet cooling towers or surface-water cooling systems, and the surface-water cooling systems were subcategorized as cooling ponds, lakes, and rivers.</p>\n<p>Heat budgets were constructed for the first four generation-type categories; data at solar thermal plants were insufficient for heat budgets. These heat budgets yielded estimates of the amount of heat transferred to the condenser. The ratio of evaporation to the heat discharged through the condenser was estimated using existing heat balance models that are sensitive to environmental data; this feature allows estimation of consumption under different climatic conditions. These two estimates were multiplied to yield an estimate of consumption at each power plant.</p>","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/sir20135188","collaboration":"USGS National Water Census and National Streamflow Information Program","usgsCitation":"Diehl, T.H., Harris, M., Murphy, J.C., Hutson, S.S., and Ladd, D.E., 2013, Methods for estimating water consumption for thermoelectric power plants in the United States: U.S. Geological Survey Scientific Investigations Report 2013-5188, Report: vii, 78 p.; Appendix 4, https://doi.org/10.3133/sir20135188.","productDescription":"Report: vii, 78 p.; Appendix 4","numberOfPages":"90","onlineOnly":"N","additionalOnlineFiles":"Y","ipdsId":"IP-045152","costCenters":[{"id":581,"text":"Tennessee Water Science Center","active":true,"usgs":true}],"links":[{"id":279513,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/sir20135188.jpg"},{"id":279511,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/sir/2013/5188/pdf/sir2013-5188.pdf"},{"id":279512,"type":{"id":3,"text":"Appendix"},"url":"https://pubs.usgs.gov/sir/2013/5188/appendix/sir2013-5188_appendix4_fews_version_3.104_edit_20141106.xlsx","description":"Appendix 4"},{"id":279502,"type":{"id":15,"text":"Index Page"},"url":"https://pubs.usgs.gov/sir/2013/5188/"}],"country":"United States","geographicExtents":"{ \"type\": \"FeatureCollection\", \"features\": [ { \"type\": \"Feature\", \"properties\": {}, \"geometry\": { \"type\": \"Polygon\", \"coordinates\": [ [ [ 173,16.916667 ], [ 173,71.833333 ], [ -66.95,71.833333 ], [ -66.95,16.916667 ], [ 173,16.916667 ] ] ] } } ] }","noUsgsAuthors":false,"publicationStatus":"PW","scienceBaseUri":"52907d0ee4b0bbdcf23ed313","contributors":{"authors":[{"text":"Diehl, Timothy H. 0000-0001-9691-2212 thdiehl@usgs.gov","orcid":"https://orcid.org/0000-0001-9691-2212","contributorId":546,"corporation":false,"usgs":true,"family":"Diehl","given":"Timothy","email":"thdiehl@usgs.gov","middleInitial":"H.","affiliations":[{"id":24708,"text":"Lower Mississippi-Gulf Water Science Center","active":true,"usgs":true},{"id":581,"text":"Tennessee Water Science Center","active":true,"usgs":true}],"preferred":true,"id":485968,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Harris, Melissa","contributorId":17519,"corporation":false,"usgs":true,"family":"Harris","given":"Melissa","affiliations":[],"preferred":false,"id":485972,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Murphy, Jennifer C. 0000-0002-0881-0919 jmurphy@usgs.gov","orcid":"https://orcid.org/0000-0002-0881-0919","contributorId":4281,"corporation":false,"usgs":true,"family":"Murphy","given":"Jennifer","email":"jmurphy@usgs.gov","middleInitial":"C.","affiliations":[{"id":36532,"text":"Central Midwest Water Science Center","active":true,"usgs":true}],"preferred":true,"id":485971,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Hutson, Susan S. sshutson@usgs.gov","contributorId":2040,"corporation":false,"usgs":true,"family":"Hutson","given":"Susan","email":"sshutson@usgs.gov","middleInitial":"S.","affiliations":[{"id":374,"text":"Maryland Water Science Center","active":true,"usgs":true}],"preferred":true,"id":485970,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Ladd, David E. 0000-0002-9247-7839 deladd@usgs.gov","orcid":"https://orcid.org/0000-0002-9247-7839","contributorId":1646,"corporation":false,"usgs":true,"family":"Ladd","given":"David","email":"deladd@usgs.gov","middleInitial":"E.","affiliations":[{"id":581,"text":"Tennessee Water Science Center","active":true,"usgs":true}],"preferred":true,"id":485969,"contributorType":{"id":1,"text":"Authors"},"rank":5}]}}
,{"id":70055684,"text":"sir20135142 - 2013 - Land subsidence along the Delta-Mendota Canal in the northern part of the San Joaquin Valley, California, 2003-10","interactions":[],"lastModifiedDate":"2013-11-21T12:47:43","indexId":"sir20135142","displayToPublicDate":"2013-11-21T12:40:00","publicationYear":"2013","noYear":false,"publicationType":{"id":18,"text":"Report"},"publicationSubtype":{"id":5,"text":"USGS Numbered Series"},"seriesTitle":{"id":334,"text":"Scientific Investigations Report","code":"SIR","onlineIssn":"2328-0328","printIssn":"2328-031X","active":true,"publicationSubtype":{"id":5}},"seriesNumber":"2013-5142","title":"Land subsidence along the Delta-Mendota Canal in the northern part of the San Joaquin Valley, California, 2003-10","docAbstract":"Extensive groundwater withdrawal from the unconsolidated deposits in the San Joaquin Valley caused widespread aquifer-system compaction and resultant land subsidence from 1926 to 1970—locally exceeding 8.5 meters. The importation of surface water beginning in the early 1950s through the Delta-Mendota Canal and in the early 1970s through the California Aqueduct resulted in decreased pumping, initiation of water-level recovery, and a reduced rate of compaction in some areas of the San Joaquin Valley. However, drought conditions during 1976–77 and 1987–92, and drought conditions and regulatory reductions in surface-water deliveries during 2007–10, decreased surface-water availability, causing pumping to increase, water levels to decline, and renewed compaction. Land subsidence from this compaction has reduced freeboard and flow capacity of the Delta-Mendota Canal, the California Aqueduct, and other canals that deliver irrigation water and transport floodwater.\n\nThe U.S. Geological Survey, in cooperation with the U.S. Bureau of Reclamation and the San Luis and Delta-Mendota Water Authority, assessed land subsidence in the vicinity of the Delta-Mendota Canal as part of an effort to minimize future subsidence-related damages to the canal. The location, magnitude, and stress regime of land-surface deformation during 2003–10 were determined by using extensometer, Global Positioning System (GPS), Interferometric Synthetic Aperture Radar (InSAR), spirit leveling, and groundwater-level data. Comparison of continuous GPS, shallow extensometer, and groundwater-level data, combined with results from a one-dimensional model, indicated the vast majority of the compaction took place beneath the Corcoran Clay, the primary regional confining unit.\n\nLand-surface deformation measurements indicated that much of the northern portion of the Delta-Mendota Canal (Clifton Court Forebay to Check 14) was fairly stable or minimally subsiding on an annual basis; some areas showed seasonal periods of subsidence and of uplift that resulted in little or no longer-term elevation loss. Many groundwater levels in this northern area did not reach historical lows during 2003–10, indicating that deformation in this region was primarily elastic.\n\nAlthough the northern portion of the Delta-Mendota Canal was relatively stable, land-surface deformation measurements indicated the southern portion of the Delta-Mendota Canal (Checks 15–21) subsided as part of a large subsidence feature centered about 15 kilometers northeast of the Delta-Mendota Canal, south of the town of El Nido. Results of InSAR analysis indicated at least 540 millimeters of subsidence near the San Joaquin River and the Eastside Bypass during 2008–10, which is part of a 3,200 square-kilometer area—including the southern part of the Delta-Mendota Canal—affected by 20 millimeters or more of subsidence during the same period. Calculations indicated that the subsidence rate doubled in 2008 in some areas. The GPS surveys done in 2008 and 2010 confirmed the high subsidence rate measured by using InSAR for the same period. Water levels in many shallow and deep wells in this area declined during 2007–10; water levels in many deep wells reached historical lows, indicating that subsidence measured during this period was largely inelastic. InSAR-derived subsidence maps for various periods during 2003–10 showed that the area of maximum active subsidence (that is, the largest rates of subsidence) shifted from its historical (1926–70) location southwest of Mendota to south of El Nido.\n\nContinued groundwater-level and land-subsidence monitoring in the San Joaquin Valley is important because (1) regulatory- and drought-related reductions in surface-water deliveries since 1976 have resulted in increased groundwater pumping and associated land subsidence, and (2) land use and associated groundwater pumping continue to change throughout the valley. The availability of surface water remains uncertain; even during record-setting precipitation years, such as 2010–11, water deliveries have fallen short of requests and groundwater pumping was required to meet the irrigation demand. Due to the expected continued demand for irrigation supply water and the limitations and uncertainty of surface-water supplies, groundwater pumping and associated land subsidence is likely to continue in the future. Spatially detailed information on land subsidence is needed to facilitate minimization of future subsidence-related damages to the Delta-Mendota Canal and other infrastructure in the San Joaquin Valley. The integration of subsidence, deformation, and water-level measurements—particularly continuous measurements—enables the analysis of aquifer-system response to increased groundwater pumping, which in turn, enables identification of the preconsolidation head and calculation of aquifer-system storage properties. This information can be used to improve numerical model simulations of groundwater flow and aquifer-system compaction and allow for consideration of land subsidence in the evaluation of water-resource management alternatives.","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/sir20135142","issn":"2328-0328","collaboration":"Prepared in cooperation with U.S. Bureau of Reclamation and the San Luis and Delta-Mendota Water Authority","usgsCitation":"Sneed, M., Brandt, J.T., and Solt, M., 2013, Land subsidence along the Delta-Mendota Canal in the northern part of the San Joaquin Valley, California, 2003-10: U.S. Geological Survey Scientific Investigations Report 2013-5142, Report: x, 86 p.; 2 Appendices, https://doi.org/10.3133/sir20135142.","productDescription":"Report: x, 86 p.; 2 Appendices","numberOfPages":"100","onlineOnly":"Y","temporalStart":"2003-01-01","temporalEnd":"2010-12-31","ipdsId":"IP-037140","costCenters":[{"id":154,"text":"California Water Science Center","active":true,"usgs":true}],"links":[{"id":279412,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/sir20135142.jpg"},{"id":279407,"type":{"id":3,"text":"Appendix"},"url":"https://pubs.usgs.gov/sir/2013/5142/pdf/sir2013-5142_appendixE.pdf"},{"id":279408,"type":{"id":3,"text":"Appendix"},"url":"https://pubs.usgs.gov/sir/2013/5142/sir2013-5142_appendixE_tables.xlsx"},{"id":279405,"type":{"id":15,"text":"Index Page"},"url":"https://pubs.usgs.gov/sir/2013/5142/"},{"id":279406,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/sir/2013/5142/pdf/sir2013-5142.pdf"}],"country":"United States","state":"California","otherGeospatial":"Delta-mendota Canal;San Joaquin Valley","geographicExtents":"{ \"type\": \"FeatureCollection\", \"features\": [ { \"type\": \"Feature\", \"properties\": {}, \"geometry\": { \"type\": \"Polygon\", \"coordinates\": [ [ [ -122.5,35.5 ], [ -122.5,38.0 ], [ -119.5,38.0 ], [ -119.5,35.5 ], [ -122.5,35.5 ] ] ] } } ] }","noUsgsAuthors":false,"publicationStatus":"PW","scienceBaseUri":"528f53c9e4b0660d392bed6f","contributors":{"authors":[{"text":"Sneed, Michelle 0000-0002-8180-382X micsneed@usgs.gov","orcid":"https://orcid.org/0000-0002-8180-382X","contributorId":155,"corporation":false,"usgs":true,"family":"Sneed","given":"Michelle","email":"micsneed@usgs.gov","affiliations":[{"id":154,"text":"California Water Science Center","active":true,"usgs":true}],"preferred":true,"id":486200,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Brandt, Justin T. 0000-0002-9397-6824 jbrandt@usgs.gov","orcid":"https://orcid.org/0000-0002-9397-6824","contributorId":157,"corporation":false,"usgs":true,"family":"Brandt","given":"Justin","email":"jbrandt@usgs.gov","middleInitial":"T.","affiliations":[{"id":154,"text":"California Water Science Center","active":true,"usgs":true}],"preferred":true,"id":486201,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Solt, Mike","contributorId":88258,"corporation":false,"usgs":true,"family":"Solt","given":"Mike","email":"","affiliations":[],"preferred":false,"id":486202,"contributorType":{"id":1,"text":"Authors"},"rank":3}]}}
,{"id":70048958,"text":"sir20135166 - 2013 - Developing and implementing the use of predictive models for estimating water quality at Great Lakes beaches","interactions":[],"lastModifiedDate":"2013-11-21T12:41:47","indexId":"sir20135166","displayToPublicDate":"2013-11-21T11:40:00","publicationYear":"2013","noYear":false,"publicationType":{"id":18,"text":"Report"},"publicationSubtype":{"id":5,"text":"USGS Numbered Series"},"seriesTitle":{"id":334,"text":"Scientific Investigations Report","code":"SIR","onlineIssn":"2328-0328","printIssn":"2328-031X","active":true,"publicationSubtype":{"id":5}},"seriesNumber":"2013-5166","title":"Developing and implementing the use of predictive models for estimating water quality at Great Lakes beaches","docAbstract":"Predictive models have been used at beaches to improve the timeliness and accuracy of recreational water-quality assessments over the most common current approach to water-quality monitoring, which relies on culturing fecal-indicator bacteria such as <i>Escherichia coli (E. coli.)</i>. Beach-specific predictive models use environmental and water-quality variables that are easily and quickly measured as surrogates to estimate concentrations of fecal-indicator bacteria or to provide the probability that a State recreational water-quality standard will be exceeded. When predictive models are used for beach closure or advisory decisions, they are referred to as “nowcasts.” During the recreational seasons of 2010-12, the U.S. Geological Survey (USGS), in cooperation with 23 local and State agencies, worked to improve existing nowcasts at 4 beaches, validate predictive models at another 38 beaches, and collect data for predictive-model development at 7 beaches throughout the Great Lakes. This report summarizes efforts to collect data and develop predictive models by multiple agencies and to compile existing information on the beaches and beach-monitoring programs into one comprehensive report.\n\n<p>Local agencies measured <i>E. coli</i> concentrations and variables expected to affect <i>E. coli</i> concentrations such as wave height, turbidity, water temperature, and numbers of birds at the time of sampling. In addition to these field measurements, equipment was installed by the USGS or local agencies at or near several beaches to collect water-quality and metrological measurements in near real time, including nearshore buoys, weather stations, and tributary staff gages and monitors. The USGS worked with local agencies to retrieve data from existing sources either manually or by use of tools designed specifically to compile and process data for predictive-model development.</p>\n\n<p>Predictive models were developed by use of linear regression and (or) partial least squares techniques for 42 beaches that had at least 2 years of data (2010-11 and sometimes earlier) and for 1 beach that had 1 year of data. For most models, software designed for model development by the U.S. Environmental Protection Agency (Virtual Beach) was used. The selected model for each beach was based on a combination of explanatory variables including, most commonly, turbidity, day of the year, change in lake level over 24 hours, wave height, wind direction and speed, and antecedent rainfall for various time periods. Forty-two predictive models were validated against data collected during an independent year (2012) and compared to the current method for assessing recreational water quality-using the previous day’s <i>E. coli</i> concentration (persistence model). Goals for good predictive-model performance were responses that were at least 5 percent greater than the persistence model and overall correct responses greater than or equal to 80 percent, sensitivities (percentage of exceedances of the bathing-water standard that were correctly predicted by the model) greater than or equal to 50 percent, and specificities (percentage of nonexceedances correctly predicted by the model) greater than or equal to 85 percent. Out of 42 predictive models, 24 models yielded over-all correct responses that were at least 5 percent greater than the use of the persistence model. Predictive-model responses met the performance goals more often than the persistence-model responses in terms of overall correctness (28 versus 17 models, respectively), sensitivity (17 versus 4 models), and specificity (34 versus 25 models). Gaining knowledge of each beach and the factors that affect <i>E. coli</i> concentrations is important for developing good predictive models. Collection of additional years of data with a wide range of environmental conditions may also help to improve future model performance. The USGS will continue to work with local agencies in 2013 and beyond to develop and validate predictive models at beaches and improve existing nowcasts, restructuring monitoring activities to accommodate future uncertainties in funding and resources.</P>","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/sir20135166","collaboration":"Coastal and Marine Geology Program Prepared in cooperation with the U.S. Environmental Protection Agency, Great Lakes Restoration Initiative","usgsCitation":"Francy, D.S., Brady, A., Carvin, R.B., Corsi, S., Fuller, L.M., Harrison, J.H., Hayhurst, B.A., Lant, J., Nevers, M.B., Terrio, P.J., and Zimmerman, T.M., 2013, Developing and implementing the use of predictive models for estimating water quality at Great Lakes beaches: U.S. Geological Survey Scientific Investigations Report 2013-5166, Report: vii, 68 p.; 3 Appendices, Downloads Directory, https://doi.org/10.3133/sir20135166.","productDescription":"Report: vii, 68 p.; 3 Appendices, Downloads Directory","ipdsId":"IP-037603","costCenters":[{"id":513,"text":"Ohio Water Science Center","active":true,"usgs":true}],"links":[{"id":279395,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/sir20135166.jpg"},{"id":279349,"type":{"id":15,"text":"Index Page"},"url":"https://pubs.usgs.gov/sir/2013/5166/"},{"id":279400,"type":{"id":3,"text":"Appendix"},"url":"https://pubs.usgs.gov/sir/2013/5166/pdf/sir2013-5166_appendix2.pdf"},{"id":279401,"type":{"id":3,"text":"Appendix"},"url":"https://pubs.usgs.gov/sir/2013/5166/table/sir2013-5166_appendix3.xlsx"},{"id":279398,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/sir/2013/5166/pdf/sir2013-5166.pdf"},{"id":279399,"type":{"id":3,"text":"Appendix"},"url":"https://pubs.usgs.gov/sir/2013/5166/table/sir2013-5166_appendix1.xls"},{"id":279402,"type":{"id":7,"text":"Companion Files"},"url":"https://pubs.usgs.gov/sir/2013/5166/Downloads"}],"country":"Canada;United States","otherGeospatial":"Great Lakes","geographicExtents":"{ \"type\": \"FeatureCollection\", \"features\": [ { \"type\": \"Feature\", \"properties\": {}, \"geometry\": { \"type\": \"Polygon\", \"coordinates\": [ [ [ -92.33,41.25 ], [ -92.33,49.12 ], [ -75.73,49.12 ], [ -75.73,41.25 ], [ -92.33,41.25 ] ] ] } } ] }","noUsgsAuthors":false,"publicationStatus":"PW","scienceBaseUri":"528f53c8e4b0660d392bed69","contributors":{"authors":[{"text":"Francy, Donna S. 0000-0001-9229-3557 dsfrancy@usgs.gov","orcid":"https://orcid.org/0000-0001-9229-3557","contributorId":1853,"corporation":false,"usgs":true,"family":"Francy","given":"Donna","email":"dsfrancy@usgs.gov","middleInitial":"S.","affiliations":[{"id":513,"text":"Ohio Water Science Center","active":true,"usgs":true},{"id":35860,"text":"Ohio-Kentucky-Indiana Water Science Center","active":true,"usgs":true}],"preferred":true,"id":485870,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Brady, Amie M. G.","contributorId":29774,"corporation":false,"usgs":true,"family":"Brady","given":"Amie M. G.","affiliations":[],"preferred":false,"id":485876,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Carvin, Rebecca B. 0000-0001-7778-4841 rbcarvin@usgs.gov","orcid":"https://orcid.org/0000-0001-7778-4841","contributorId":4456,"corporation":false,"usgs":true,"family":"Carvin","given":"Rebecca","email":"rbcarvin@usgs.gov","middleInitial":"B.","affiliations":[{"id":677,"text":"Wisconsin Water Science Center","active":true,"usgs":true}],"preferred":true,"id":485875,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Corsi, Steven R. srcorsi@usgs.gov","contributorId":511,"corporation":false,"usgs":true,"family":"Corsi","given":"Steven R.","email":"srcorsi@usgs.gov","affiliations":[{"id":677,"text":"Wisconsin Water Science Center","active":true,"usgs":true}],"preferred":false,"id":485869,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Fuller, Lori M. lmfuller@usgs.gov","contributorId":2100,"corporation":false,"usgs":true,"family":"Fuller","given":"Lori","email":"lmfuller@usgs.gov","middleInitial":"M.","affiliations":[{"id":382,"text":"Michigan Water Science Center","active":true,"usgs":true}],"preferred":false,"id":485871,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"Harrison, John H.","contributorId":34011,"corporation":false,"usgs":true,"family":"Harrison","given":"John","email":"","middleInitial":"H.","affiliations":[],"preferred":false,"id":485878,"contributorType":{"id":1,"text":"Authors"},"rank":6},{"text":"Hayhurst, Brett A. 0000-0002-1717-2015 bhayhurs@usgs.gov","orcid":"https://orcid.org/0000-0002-1717-2015","contributorId":3398,"corporation":false,"usgs":true,"family":"Hayhurst","given":"Brett","email":"bhayhurs@usgs.gov","middleInitial":"A.","affiliations":[{"id":474,"text":"New York Water Science Center","active":true,"usgs":true}],"preferred":true,"id":485874,"contributorType":{"id":1,"text":"Authors"},"rank":7},{"text":"Lant, Jeremiah","contributorId":30904,"corporation":false,"usgs":true,"family":"Lant","given":"Jeremiah","affiliations":[],"preferred":false,"id":485877,"contributorType":{"id":1,"text":"Authors"},"rank":8},{"text":"Nevers, Meredith B.","contributorId":91803,"corporation":false,"usgs":true,"family":"Nevers","given":"Meredith","email":"","middleInitial":"B.","affiliations":[],"preferred":false,"id":485879,"contributorType":{"id":1,"text":"Authors"},"rank":9},{"text":"Terrio, Paul J. 0000-0002-1515-9570 pjterrio@usgs.gov","orcid":"https://orcid.org/0000-0002-1515-9570","contributorId":3313,"corporation":false,"usgs":true,"family":"Terrio","given":"Paul","email":"pjterrio@usgs.gov","middleInitial":"J.","affiliations":[{"id":36532,"text":"Central Midwest Water Science Center","active":true,"usgs":true}],"preferred":true,"id":485873,"contributorType":{"id":1,"text":"Authors"},"rank":10},{"text":"Zimmerman, Tammy M. 0000-0003-0842-6981 tmzimmer@usgs.gov","orcid":"https://orcid.org/0000-0003-0842-6981","contributorId":2359,"corporation":false,"usgs":true,"family":"Zimmerman","given":"Tammy","email":"tmzimmer@usgs.gov","middleInitial":"M.","affiliations":[{"id":532,"text":"Pennsylvania Water Science Center","active":true,"usgs":true}],"preferred":false,"id":485872,"contributorType":{"id":1,"text":"Authors"},"rank":11}]}}
,{"id":70048949,"text":"ds802 - 2013 - Water quality, sediment characteristics, aquatic habitat, geomorphology, and mussel population status of the Clinch River, Virginia and Tennessee, 2009-2011","interactions":[],"lastModifiedDate":"2026-05-28T21:06:33.300445","indexId":"ds802","displayToPublicDate":"2013-11-21T10:40:00","publicationYear":"2013","noYear":false,"publicationType":{"id":18,"text":"Report"},"publicationSubtype":{"id":5,"text":"USGS Numbered Series"},"seriesTitle":{"id":310,"text":"Data Series","code":"DS","onlineIssn":"2327-638X","printIssn":"2327-0271","active":false,"publicationSubtype":{"id":5}},"seriesNumber":"802","title":"Water quality, sediment characteristics, aquatic habitat, geomorphology, and mussel population status of the Clinch River, Virginia and Tennessee, 2009-2011","docAbstract":"<p>Chemical, physical, and biological data were collected during 2009-2011 as part of a study of the Clinch River in Virginia and Tennessee. The data from this study, data-collection methods, and laboratory analytical methods used in the study are documented in this report. The study was conducted to describe the conditions of the Clinch River and to determine if there are measurable differences in chemical, physical, or biological characteristics in a segment of the river where freshwater mussel populations are in decline, have low density, richness, little to no recruitment, and lack endangered species (low-quality reach) compared to a segment of the river where mussel assemblages have relatively high density, richness, evidence of recruitment, and support endangered species (high-quality reach). Five continuous water-quality monitors were installed and operated on the mainstem of the Clinch River and two tributaries. Discrete water-quality sample sets were collected during base-flow and stormflow conditions two sites on the Clinch River and on the Guest River, a tributary to the Clinch River predominantly in the Appalachian Plateaus Physiographic Province. Base-flow water-quality samples were collected in July and August 2011 at 15 sites along the mainstem of the Clinch River. Other analyses included longitudinal sampling along the mainstem of the Clinch River at 10 sites to evaluate bed-sediment chemistry, habitat condition, and mollusk community status. In situ freshwater mussel growth and mortality experiments were conducted with hatchery propogated <i>Villosa iris</i> (rainbow mussels). Tissue from the <i>V. iris</i> as well as tissue from 16 <i>Actinonaias pectorosa</i> mussels were analyzed for trace metals, and <i>V. iris</i> mussel tissue was analyzed for organic compounds. Data collected during this investigation were analyzed by various U.S. Geological Survey or U.S. Fish and Wildlife Service laboratories.</p>","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/ds802","collaboration":"Prepared in cooperation with the Tennessee Wildlife Resources Agency and the Tennessee Department of Environment and Conservation","usgsCitation":"Krstolic, J.L., Johnson, G.C., and Ostby, B.J., 2013, Water quality, sediment characteristics, aquatic habitat, geomorphology, and mussel population status of the Clinch River, Virginia and Tennessee, 2009-2011: U.S. Geological Survey Data Series 802, Report: vi, 14 p.; 2 Appendices; 1 Figure: 17 inches x 11 inches, https://doi.org/10.3133/ds802.","productDescription":"Report: vi, 14 p.; 2 Appendices; 1 Figure: 17 inches x 11 inches","onlineOnly":"Y","additionalOnlineFiles":"N","ipdsId":"IP-034903","costCenters":[{"id":614,"text":"Virginia Water Science Center","active":true,"usgs":true}],"links":[{"id":279329,"rank":6,"type":{"id":29,"text":"Figure"},"url":"https://pubs.usgs.gov/ds/0802/pdf/ds802_figure1_clinch_river_11x17.pdf","text":"Figure 1","size":"984.67 KB","linkFileType":{"id":1,"text":"pdf"},"description":"Figure 1"},{"id":279318,"rank":2,"type":{"id":15,"text":"Index Page"},"url":"https://pubs.usgs.gov/ds/0802/"},{"id":279330,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/ds802.gif"},{"id":279328,"rank":5,"type":{"id":3,"text":"Appendix"},"url":"https://pubs.usgs.gov/ds/0802/table/ds802_appendix_table3.xlsx","text":"Table 3","size":"11.4 MB","linkFileType":{"id":3,"text":"xlsx"},"description":"Table 3"},{"id":279327,"rank":4,"type":{"id":3,"text":"Appendix"},"url":"https://pubs.usgs.gov/ds/0802/table/ds802_appendix_tables_1-2_4-25.xlsx","text":"Tables 1-2, 4-25","size":"542 KB","linkFileType":{"id":3,"text":"xlsx"},"description":"Tables 1-2, 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