{"pageNumber":"636","pageRowStart":"15875","pageSize":"25","recordCount":46883,"records":[{"id":70038451,"text":"ofr20121118 - 2012 - Variability of distributions of well-scale estimated ultimate recovery for continuous (unconventional) oil and gas resources in the United States","interactions":[],"lastModifiedDate":"2012-06-03T01:01:45","indexId":"ofr20121118","displayToPublicDate":"2012-06-02T00:00:00","publicationYear":"2012","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":"2012-1118","title":"Variability of distributions of well-scale estimated ultimate recovery for continuous (unconventional) oil and gas resources in the United States","docAbstract":"Since 2000, the U.S. Geological Survey has completed assessments of continuous (unconventional) resources in the United States based on geologic studies and analysis of well-production data. This publication uses those 132 continuous oil and gas assessments to show the variability of well productivity within and among the 132 areas. The production from the most productive wells in an area commonly is more than 100 times larger than that from the poorest productive wells. The 132 assessment units were classified into four categories: shale gas, coalbed gas, tight gas, and continuous oil. For each category, the mean well productivity in the most productive assessment units is considerably greater than that of the least productive assessment units.","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/ofr20121118","usgsCitation":"U.S. Geological Survey Oil and Gas Assessment Team, 2012, Variability of distributions of well-scale estimated ultimate recovery for continuous (unconventional) oil and gas resources in the United States: U.S. Geological Survey Open-File Report 2012-1118, iii, 12 p.; Appendix, https://doi.org/10.3133/ofr20121118.","productDescription":"iii, 12 p.; Appendix","startPage":"i","endPage":"18","numberOfPages":"21","onlineOnly":"Y","additionalOnlineFiles":"N","costCenters":[{"id":164,"text":"Central Energy Resources Science Center","active":true,"usgs":true}],"links":[{"id":257122,"rank":0,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/ofr_2012_1118.gif"},{"id":257120,"rank":100,"type":{"id":15,"text":"Index Page"},"url":"https://pubs.usgs.gov/of/2012/1118/","linkFileType":{"id":5,"text":"html"}},{"id":257121,"rank":300,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/of/2012/1118/OF12-1118.pdf","linkFileType":{"id":1,"text":"pdf"}}],"country":"United States","noUsgsAuthors":false,"publicationStatus":"PW","scienceBaseUri":"505bc137e4b08c986b32a4b0","contributors":{"authors":[{"text":"U.S. Geological Survey Oil and Gas Assessment Team","contributorId":128016,"corporation":true,"usgs":false,"organization":"U.S. Geological Survey Oil and Gas Assessment Team","id":535186,"contributorType":{"id":1,"text":"Authors"},"rank":1}]}}
,{"id":70045092,"text":"70045092 - 2012 - Relative azimuth inversion by way of damped maximum correlation estimates","interactions":[],"lastModifiedDate":"2018-02-08T09:39:10","indexId":"70045092","displayToPublicDate":"2012-06-01T16:26:27","publicationYear":"2012","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":1315,"text":"Computers & Geosciences","printIssn":"0098-3004","active":true,"publicationSubtype":{"id":10}},"title":"Relative azimuth inversion by way of damped maximum correlation estimates","docAbstract":"Horizontal seismic data are utilized in a large number of Earth studies. Such work depends on the published orientations of the sensitive axes of seismic sensors relative to true North. These orientations can be estimated using a number of different techniques: SensOrLoc (Sensitivity, Orientation and Location), comparison to synthetics (Ekstrom and Busby, 2008), or by way of magnetic compass. Current methods for finding relative station azimuths are unable to do so with arbitrary precision quickly because of limitations in the algorithms (e.g. grid search methods). Furthermore, in order to determine instrument orientations during station visits, it is critical that any analysis software be easily run on a large number of different computer platforms and the results be obtained quickly while on site.  We developed a new technique for estimating relative sensor azimuths by inverting for the orientation with the maximum correlation to a reference instrument, using a non-linear parameter estimation routine. By making use of overlapping windows, we are able to make multiple azimuth estimates, which helps to identify the confidence of our azimuth estimate, even when the signal-to-noise ratio (SNR) is low. Finally, our algorithm has been written as a stand-alone, platform independent, Java software package with a graphical user interface for reading and selecting data segments to be analyzed.","language":"English","publisher":"Elsevier","doi":"10.1016/j.cageo.2012.02.025","usgsCitation":"Ringler, A., Edwards, J., Hutt, C., and Shelly, F., 2012, Relative azimuth inversion by way of damped maximum correlation estimates: Computers & Geosciences, v. 43, p. 1-6, https://doi.org/10.1016/j.cageo.2012.02.025.","productDescription":"6 p.","startPage":"1","endPage":"6","ipdsId":"IP-035082","costCenters":[{"id":122,"text":"Albuquerque Seismological Laboratory","active":false,"usgs":true}],"links":[{"id":275028,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"},{"id":275027,"type":{"id":10,"text":"Digital Object Identifier"},"url":"https://dx.doi.org/10.1016/j.cageo.2012.02.025"}],"country":"United States","volume":"43","noUsgsAuthors":false,"publicationStatus":"PW","scienceBaseUri":"51e519efe4b069f8d27ccb2f","contributors":{"authors":[{"text":"Ringler, A. T. 0000-0002-9839-4188","orcid":"https://orcid.org/0000-0002-9839-4188","contributorId":99282,"corporation":false,"usgs":true,"family":"Ringler","given":"A. T.","affiliations":[],"preferred":false,"id":476772,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Edwards, J.D.","contributorId":69622,"corporation":false,"usgs":true,"family":"Edwards","given":"J.D.","email":"","affiliations":[],"preferred":false,"id":476771,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Hutt, C. R. 0000-0001-9033-9195","orcid":"https://orcid.org/0000-0001-9033-9195","contributorId":61910,"corporation":false,"usgs":true,"family":"Hutt","given":"C. R.","affiliations":[],"preferred":false,"id":476770,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Shelly, F.","contributorId":38043,"corporation":false,"usgs":true,"family":"Shelly","given":"F.","email":"","affiliations":[],"preferred":false,"id":476769,"contributorType":{"id":1,"text":"Authors"},"rank":4}]}}
,{"id":70045772,"text":"70045772 - 2012 - Spatially telescoping measurements for improved characterization of groundwater-surface water interactions","interactions":[],"lastModifiedDate":"2013-07-25T15:52:00","indexId":"70045772","displayToPublicDate":"2012-06-01T15:34:00","publicationYear":"2012","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":"Spatially telescoping measurements for improved characterization of groundwater-surface water interactions","docAbstract":"The suite of measurement methods available to characterize fluxes between groundwater and surface water is rapidly growing. However, there are few studies that examine approaches to design of field investigations that include multiple methods. We propose that performing field measurements in a spatially telescoping sequence improves measurement flexibility and accounts for nested heterogeneities while still allowing for parsimonious experimental design. We applied this spatially telescoping approach in a study of ground water-surface water (GW-SW) interaction during baseflow conditions along Lucile Creek, located near Wasilla, Alaska. Catchment-scale data, including channel geomorphic indices and hydrogeologic transects, were used to screen areas of potentially significant GW-SW exchange. Specifically, these data indicated increasing groundwater contribution from a deeper regional aquifer along the middle to lower reaches of the stream. This initial assessment was tested using reach-scale estimates of groundwater contribution during baseflow conditions, including differential discharge measurements and the use of chemical tracers analyzed in a three-component mixing model. The reach-scale measurements indicated a large increase in discharge along the middle reaches of the stream accompanied by a shift in chemical composition towards a regional groundwater end member. Finally, point measurements of vertical water fluxes -- obtained using seepage meters as well as temperature-based methods -- were used to evaluate spatial and temporal variability of GW-SW exchange within representative reaches. The spatial variability of upward fluxes, estimated using streambed temperature mapping at the sub-reach scale, was observed to vary in relation to both streambed composition and the magnitude of groundwater contribution from differential discharge measurements. The spatially telescoping approach improved the efficiency of this field investigation. Beginning our assessment with catchment-scale data allowed us to identify locations of GW-SW exchange, plan measurements at representative field sites and improve our interpretation of reach-scale and point-scale measurements.","largerWorkType":{"id":2,"text":"Article"},"largerWorkTitle":"Journal of Hydrology","largerWorkSubtype":{"id":10,"text":"Journal Article"},"language":"English","publisher":"Elsevier","doi":"10.1016/j.jhydrol.2012.04.002","usgsCitation":"Kikuchi, C., Ferre, T.P., and Welker, J.M., 2012, Spatially telescoping measurements for improved characterization of groundwater-surface water interactions: Journal of Hydrology, v. 446-447, p. 1-12, https://doi.org/10.1016/j.jhydrol.2012.04.002.","productDescription":"13 p.","startPage":"1","endPage":"12","numberOfPages":"13","ipdsId":"IP-030766","costCenters":[{"id":128,"text":"Arizona Water Science Center","active":true,"usgs":true}],"links":[{"id":275411,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"},{"id":275410,"type":{"id":10,"text":"Digital Object Identifier"},"url":"https://dx.doi.org/10.1016/j.jhydrol.2012.04.002"}],"country":"United States","state":"Alaska","otherGeospatial":"Lucile Creek","geographicExtents":"{ \"type\": \"FeatureCollection\", \"features\": [ { \"type\": \"Feature\", \"properties\": {}, \"geometry\": { \"type\": \"Polygon\", \"coordinates\": [ [ [ -150.0,61.466667 ], [ -150.0,61.666667 ], [ -149.416667,61.666667 ], [ -149.416667,61.466667 ], [ -150.0,61.466667 ] ] ] } } ] }","volume":"446-447","noUsgsAuthors":false,"publicationStatus":"PW","scienceBaseUri":"51f25423e4b0279fe2e1c02e","contributors":{"authors":[{"text":"Kikuchi, Colin ckikuchi@usgs.gov","contributorId":3958,"corporation":false,"usgs":true,"family":"Kikuchi","given":"Colin","email":"ckikuchi@usgs.gov","affiliations":[],"preferred":true,"id":478336,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Ferre, Ty P.A.","contributorId":102167,"corporation":false,"usgs":true,"family":"Ferre","given":"Ty","email":"","middleInitial":"P.A.","affiliations":[],"preferred":false,"id":478338,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Welker, Jeffery M.","contributorId":43654,"corporation":false,"usgs":true,"family":"Welker","given":"Jeffery","email":"","middleInitial":"M.","affiliations":[],"preferred":false,"id":478337,"contributorType":{"id":1,"text":"Authors"},"rank":3}]}}
,{"id":70125967,"text":"70125967 - 2012 - Species differentiation on a dynamic landscape: shifts in metapopulation genetic structure using the chronology of the Hawaiian Archipelago","interactions":[],"lastModifiedDate":"2014-09-18T12:53:54","indexId":"70125967","displayToPublicDate":"2012-06-01T12:52:26","publicationYear":"2012","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":1602,"text":"Evolutionary Biology","active":true,"publicationSubtype":{"id":10}},"title":"Species differentiation on a dynamic landscape: shifts in metapopulation genetic structure using the chronology of the Hawaiian Archipelago","docAbstract":"Species formation during adaptive radiation often occurs in the context of a changing environment. The establishment and arrangement of populations, in space and time, sets up ecological and genetic processes that dictate the rate and pattern of differentiation. Here, we focus on how a dynamic habitat can affect genetic structure, and ultimately, differentiation among populations. We make use of the chronology and geographical history provided by the Hawaiian archipelago to examine the initial stages of population establishment and genetic divergence. We use data from a set of 6 spider lineages that differ in habitat affinities, some preferring low elevation habitats with a longer history of connection, others being more specialized for high elevation and/or wet forest, some with more general habitat affinities. We show that habitat preferences associated with lineages are important in ecological and genetic structuring. Lineages that have more restricted habitat preferences are subject to repeated episodes of isolation and fragmentation as a result of lava flows and vegetation succession. The initial dynamic set up by the landscape translates over time into discrete lineages. Further work is needed to understand how genetic changes interact with a changing set of ecological interactions amongst a shifting mosaic of landscapes to achieve species formation.","largerWorkType":{"id":2,"text":"Article"},"largerWorkTitle":"Evolutionary Biology","largerWorkSubtype":{"id":10,"text":"Journal Article"},"language":"English","publisher":"Plenum Press","publisherLocation":"New York, NY","doi":"10.1007/s11692-012-9184-5","usgsCitation":"Roderick, G.K., Croucher, P., Vandergast, A.G., and Gillespie, R.G., 2012, Species differentiation on a dynamic landscape: shifts in metapopulation genetic structure using the chronology of the Hawaiian Archipelago: Evolutionary Biology, v. 39, no. 2, p. 192-206, https://doi.org/10.1007/s11692-012-9184-5.","productDescription":"15 p.","startPage":"192","endPage":"206","numberOfPages":"15","ipdsId":"IP-037509","costCenters":[{"id":651,"text":"Western Ecological Research Center","active":true,"usgs":true}],"links":[{"id":474489,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.1007/s11692-012-9184-5","text":"Publisher Index Page"},{"id":294157,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"},{"id":294152,"type":{"id":10,"text":"Digital Object Identifier"},"url":"https://dx.doi.org/10.1007/s11692-012-9184-5"}],"country":"United States","state":"Hawai'i","geographicExtents":"{ \"type\": \"FeatureCollection\", \"features\": [ { \"type\": \"Feature\", \"properties\": {}, \"geometry\": { \"type\": \"Polygon\", \"coordinates\": [ [ [ -178.31,18.91 ], [ -178.31,28.4 ], [ -154.81,28.4 ], [ -154.81,18.91 ], [ -178.31,18.91 ] ] ] } } ] }","volume":"39","issue":"2","noUsgsAuthors":false,"publicationDate":"2012-05-15","publicationStatus":"PW","scienceBaseUri":"541bf458e4b0e96537ddf890","contributors":{"authors":[{"text":"Roderick, George K.","contributorId":37660,"corporation":false,"usgs":true,"family":"Roderick","given":"George","email":"","middleInitial":"K.","affiliations":[],"preferred":false,"id":501814,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Croucher, Peter","contributorId":107211,"corporation":false,"usgs":true,"family":"Croucher","given":"Peter","email":"","affiliations":[],"preferred":false,"id":501817,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Vandergast, Amy G. 0000-0002-7835-6571","orcid":"https://orcid.org/0000-0002-7835-6571","contributorId":97617,"corporation":false,"usgs":true,"family":"Vandergast","given":"Amy","email":"","middleInitial":"G.","affiliations":[{"id":651,"text":"Western Ecological Research Center","active":true,"usgs":true}],"preferred":true,"id":501816,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Gillespie, Rosemary G.","contributorId":86700,"corporation":false,"usgs":true,"family":"Gillespie","given":"Rosemary","email":"","middleInitial":"G.","affiliations":[],"preferred":false,"id":501815,"contributorType":{"id":1,"text":"Authors"},"rank":4}]}}
,{"id":70038823,"text":"ofr20121107 - 2012 - Sulfur dioxide emission rates from Kilauea Volcano, Hawaii, 2007-2010","interactions":[],"lastModifiedDate":"2019-05-30T12:10:57","indexId":"ofr20121107","displayToPublicDate":"2012-06-01T12:42:26","publicationYear":"2012","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":"2012-1107","displayTitle":"Sulfur dioxide emission rates from Kīlauea Volcano, Hawai‘i, 2007–2010","title":"Sulfur dioxide emission rates from Kilauea Volcano, Hawaii, 2007-2010","docAbstract":"K&#299;lauea Volcano has one of the longest running volcanic sulfur dioxide (SO<sub>2</sub>) emission rate databases on record. Sulfur dioxide emission rates from K&#299;lauea Volcano were first measured by Stoiber and Malone (1975) and have been measured on a regular basis since 1979 (Elias and Sutton, 2007, and references within). Compilations of SO<sub>2</sub> emission-rate and wind-vector data from 1979 through 2006 are available on the USGS Web site (Elias and others, 1998; Elias and Sutton, 2002; Elias and Sutton, 2007). This report updates the database, documents the changes in data collection and processing methods, and highlights how SO<sub>2</sub> emissions have varied with eruptive activity at K&#299;lauea Volcano for the interval 2007&ndash;2010.","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/ofr20121107","usgsCitation":"Elias, T., and Sutton, A.J., 2012, Sulfur dioxide emission rates from Kilauea Volcano, Hawaii, 2007-2010: U.S. Geological Survey Open-File Report 2012-1107, iv, 25 p.; Downloads of Spreadsheets 3-7, https://doi.org/10.3133/ofr20121107.","productDescription":"iv, 25 p.; Downloads of Spreadsheets 3-7","startPage":"i","endPage":"25","numberOfPages":"29","onlineOnly":"Y","additionalOnlineFiles":"Y","costCenters":[{"id":336,"text":"Hawaiian Volcano Observatory","active":false,"usgs":true},{"id":615,"text":"Volcano Hazards Program","active":true,"usgs":true},{"id":617,"text":"Volcano Science Center","active":true,"usgs":true}],"links":[{"id":257867,"rank":0,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/ofr_2012_1107.gif"},{"id":257863,"rank":100,"type":{"id":15,"text":"Index Page"},"url":"https://pubs.usgs.gov/of/2012/1107/","linkFileType":{"id":5,"text":"html"}}],"country":"United States","state":"Hawai'i","otherGeospatial":"Kilauea Volcano","noUsgsAuthors":false,"publicationStatus":"PW","scienceBaseUri":"505b9dd4e4b08c986b31dae2","contributors":{"authors":[{"text":"Elias, T. 0000-0002-9592-4518","orcid":"https://orcid.org/0000-0002-9592-4518","contributorId":71195,"corporation":false,"usgs":true,"family":"Elias","given":"T.","affiliations":[],"preferred":false,"id":465021,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Sutton, A. J. 0000-0003-1902-3977","orcid":"https://orcid.org/0000-0003-1902-3977","contributorId":28983,"corporation":false,"usgs":true,"family":"Sutton","given":"A.","email":"","middleInitial":"J.","affiliations":[],"preferred":false,"id":465020,"contributorType":{"id":1,"text":"Authors"},"rank":2}]}}
,{"id":70041952,"text":"70041952 - 2012 - Comparison of stream invertebrate response models for bioassessment metric","interactions":[],"lastModifiedDate":"2017-09-20T13:32:42","indexId":"70041952","displayToPublicDate":"2012-06-01T09:24:00","publicationYear":"2012","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":2529,"text":"Journal of the American Water Resources Association","active":true,"publicationSubtype":{"id":10}},"title":"Comparison of stream invertebrate response models for bioassessment metric","docAbstract":"We aggregated invertebrate data from various sources to assemble data for modeling in two ecoregions in Oregon and one in California. Our goal was to compare the performance of models developed using multiple linear regression (MLR) techniques with models developed using three relatively new techniques: classification and regression trees (CART), random forest (RF), and boosted regression trees (BRT). We used tolerance of taxa based on richness (RICHTOL) and ratio of observed to expected taxa (O/E) as response variables and land use/land cover as explanatory variables. Responses were generally linear; therefore, there was little improvement to the MLR models when compared to models using CART and RF. In general, the four modeling techniques (MLR, CART, RF, and BRT) consistently selected the same primary explanatory variables for each region. However, results from the BRT models showed significant improvement over the MLR models for each region; increases in R<sup>2</sup> from 0.09 to 0.20. The O/E metric that was derived from models specifically calibrated for Oregon consistently had lower R<sup>2</sup> values than RICHTOL for the two regions tested. Modeled O/E R<sup>2</sup> values were between 0.06 and 0.10 lower for each of the four modeling methods applied in the Willamette Valley and were between 0.19 and 0.36 points lower for the Blue Mountains. As a result, BRT models may indeed represent a good alternative to MLR for modeling species distribution relative to environmental variables.","language":"English","publisher":"American Water Resources Association","publisherLocation":"Herndon, VA","doi":"10.1111/j.1752-1688.2011.00632.x","usgsCitation":"Waite, I.R., Kennen, J., May, J., Brown, L.R., Cuffney, T.F., Jones, K.A., and Orlando, J., 2012, Comparison of stream invertebrate response models for bioassessment metric: Journal of the American Water Resources Association, v. 48, no. 3, p. 570-583, https://doi.org/10.1111/j.1752-1688.2011.00632.x.","productDescription":"14 p.","startPage":"570","endPage":"583","ipdsId":"IP-030734","costCenters":[{"id":154,"text":"California Water Science Center","active":true,"usgs":true},{"id":470,"text":"New Jersey Water Science Center","active":true,"usgs":true},{"id":476,"text":"North Carolina Water Science Center","active":true,"usgs":true},{"id":518,"text":"Oregon Water Science Center","active":true,"usgs":true},{"id":610,"text":"Utah Water Science Center","active":true,"usgs":true}],"links":[{"id":281600,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","state":"California, Oregon","otherGeospatial":"Blue Mountains, Willamette Valley","geographicExtents":"{ \"type\": \"FeatureCollection\", \"features\": [ { \"type\": \"Feature\", \"properties\": {}, \"geometry\": { \"type\": \"Polygon\", \"coordinates\": [ [ [ -124.7035,32.53 ], [ -124.7035,46.2991 ], [ -114.13,46.2991 ], [ -114.13,32.53 ], [ -124.7035,32.53 ] ] ] } } ] }","volume":"48","issue":"3","noUsgsAuthors":false,"publicationDate":"2012-02-13","publicationStatus":"PW","scienceBaseUri":"53cd5216e4b0b290850f451a","contributors":{"authors":[{"text":"Waite, Ian R. 0000-0003-1681-6955 iwaite@usgs.gov","orcid":"https://orcid.org/0000-0003-1681-6955","contributorId":616,"corporation":false,"usgs":true,"family":"Waite","given":"Ian","email":"iwaite@usgs.gov","middleInitial":"R.","affiliations":[{"id":518,"text":"Oregon Water Science Center","active":true,"usgs":true}],"preferred":true,"id":470461,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Kennen, Jonathan G. 0000-0002-5426-4445 jgkennen@usgs.gov","orcid":"https://orcid.org/0000-0002-5426-4445","contributorId":574,"corporation":false,"usgs":true,"family":"Kennen","given":"Jonathan G.","email":"jgkennen@usgs.gov","affiliations":[{"id":470,"text":"New Jersey Water Science Center","active":true,"usgs":true}],"preferred":true,"id":470460,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"May, Jason T. 0000-0002-5699-2112","orcid":"https://orcid.org/0000-0002-5699-2112","contributorId":14791,"corporation":false,"usgs":true,"family":"May","given":"Jason T.","affiliations":[],"preferred":false,"id":470464,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Brown, Larry R. 0000-0001-6702-4531 lrbrown@usgs.gov","orcid":"https://orcid.org/0000-0001-6702-4531","contributorId":1717,"corporation":false,"usgs":true,"family":"Brown","given":"Larry","email":"lrbrown@usgs.gov","middleInitial":"R.","affiliations":[{"id":154,"text":"California Water Science Center","active":true,"usgs":true}],"preferred":true,"id":470463,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Cuffney, Thomas F. 0000-0003-1164-5560 tcuffney@usgs.gov","orcid":"https://orcid.org/0000-0003-1164-5560","contributorId":517,"corporation":false,"usgs":true,"family":"Cuffney","given":"Thomas","email":"tcuffney@usgs.gov","middleInitial":"F.","affiliations":[{"id":13634,"text":"South Atlantic Water Science Center","active":true,"usgs":true}],"preferred":true,"id":470459,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"Jones, Kimberly A. kjones@usgs.gov","contributorId":937,"corporation":false,"usgs":true,"family":"Jones","given":"Kimberly","email":"kjones@usgs.gov","middleInitial":"A.","affiliations":[],"preferred":true,"id":470462,"contributorType":{"id":1,"text":"Authors"},"rank":6},{"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":470465,"contributorType":{"id":1,"text":"Authors"},"rank":7}]}}
,{"id":70044047,"text":"70044047 - 2012 - Spatial aspects of building and population exposure data and their implications for global earthquake exposure modeling","interactions":[],"lastModifiedDate":"2013-06-18T15:11:25","indexId":"70044047","displayToPublicDate":"2012-06-01T00:00:00","publicationYear":"2012","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":2822,"text":"Natural Hazards","active":true,"publicationSubtype":{"id":10}},"title":"Spatial aspects of building and population exposure data and their implications for global earthquake exposure modeling","docAbstract":"This paper discusses spatial aspects of the global exposure dataset and mapping needs for earthquake risk assessment. We discuss this in the context of development of a Global Exposure Database for the Global Earthquake Model (GED4GEM), which requires compilation of a multi-scale inventory of assets at risk, for example, buildings, populations, and economic exposure. After defining the relevant spatial and geographic scales of interest, different procedures are proposed to disaggregate coarse-resolution data, to map them, and if necessary to infer missing data by using proxies. We discuss the advantages and limitations of these methodologies and detail the potentials of utilizing remote-sensing data. The latter is used especially to homogenize an existing coarser dataset and, where possible, replace it with detailed information extracted from remote sensing using the built-up indicators for different environments. Present research shows that the spatial aspects of earthquake risk computation are tightly connected with the availability of datasets of the resolution necessary for producing sufficiently detailed exposure. The global exposure database designed by the GED4GEM project is able to manage datasets and queries of multiple spatial scales.","largerWorkType":{"id":2,"text":"Article"},"largerWorkTitle":"Natural Hazards","largerWorkSubtype":{"id":10,"text":"Journal Article"},"language":"English","publisher":"Springer","doi":"10.1007/s11069-012-0241-2","usgsCitation":"Dell’Acqua, F., Gamba, P., and Jaiswal, K., 2012, Spatial aspects of building and population exposure data and their implications for global earthquake exposure modeling: Natural Hazards, 19 p., https://doi.org/10.1007/s11069-012-0241-2.","productDescription":"19 p.","ipdsId":"IP-037493","costCenters":[{"id":300,"text":"Geologic Hazards Science Center","active":true,"usgs":true}],"links":[{"id":273950,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"},{"id":273949,"type":{"id":10,"text":"Digital Object Identifier"},"url":"https://dx.doi.org/10.1007/s11069-012-0241-2"}],"country":"United States","noUsgsAuthors":false,"publicationDate":"2012-06-14","publicationStatus":"PW","scienceBaseUri":"51c1816ce4b0dd0e00d92211","contributors":{"authors":[{"text":"Dell’Acqua, F.","contributorId":91775,"corporation":false,"usgs":true,"family":"Dell’Acqua","given":"F.","email":"","affiliations":[],"preferred":false,"id":474694,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Gamba, P.","contributorId":72281,"corporation":false,"usgs":true,"family":"Gamba","given":"P.","email":"","affiliations":[],"preferred":false,"id":474692,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Jaiswal, K.","contributorId":89260,"corporation":false,"usgs":true,"family":"Jaiswal","given":"K.","affiliations":[],"preferred":false,"id":474693,"contributorType":{"id":1,"text":"Authors"},"rank":3}]}}
,{"id":70044018,"text":"70044018 - 2012 - Reconstruction of past methane availability in an Arctic Alaska wetland indicates climate influenced methane release during the past ~12,000 years","interactions":[],"lastModifiedDate":"2013-06-25T14:28:26","indexId":"70044018","displayToPublicDate":"2012-06-01T00:00:00","publicationYear":"2012","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":2411,"text":"Journal of Paleolimnology","active":true,"publicationSubtype":{"id":10}},"title":"Reconstruction of past methane availability in an Arctic Alaska wetland indicates climate influenced methane release during the past ~12,000 years","docAbstract":"Atmospheric contributions of methane from Arctic wetlands during the Holocene are dynamic and linked to climate oscillations. However, long-term records linking climate variability to methane availability in Arctic wetlands are lacking. We present a multi-proxy ~12,000 year paleoecological reconstruction of intermittent methane availability from a radiocarbon-dated sediment core (LQ-West) taken from a shallow tundra lake (Qalluuraq Lake) in Arctic Alaska. Specifically, stable carbon isotopic values of photosynthetic biomarkers and methane are utilized to estimate the proportional contribution of methane-derived carbon to lake-sediment-preserved benthic (chironomids) and pelagic (cladocerans) components over the last ~12,000 years. These results were compared to temperature, hydrologic, and habitat reconstructions from the same site using chironomid assemblage data, oxygen isotopes of chironomid head capsules, and radiocarbon ages of plant macrofossils. Cladoceran ephippia from ~4,000 cal year BP sediments have δ13C values that range from ~−39 to −31‰, suggesting peak methane carbon assimilation at that time. These low δ13C values coincide with an apparent decrease in effective moisture and development of a wetland that included Sphagnum subsecundum. Incorporation of methane-derived carbon by chironomids and cladocerans decreased from ~2,500 to 1,500 cal year BP, coinciding with a temperature decrease. Live-collected chironomids with a radiocarbon age of 1,640 cal year BP, and fossil chironomids from 1,500 cal year BP in the core illustrate that ‘old’ carbon has also contributed to the development of the aquatic ecosystem since ~1,500 cal year BP. The relatively low δ13C values of aquatic invertebrates (as low as −40.5‰) provide evidence of methane incorporation by lake invertebrates, and suggest intermittent climate-linked methane release from the lake throughout the Holocene.","largerWorkType":{"id":2,"text":"Article"},"largerWorkTitle":"Journal of Paleolimnology","largerWorkSubtype":{"id":10,"text":"Journal Article"},"language":"English","publisher":"Springer","doi":"10.1007/s10933-012-9591-8","usgsCitation":"Wooller, M., Pohlman, J., Gaglioti, B.V., Langdon, P., Jones, M., Anthony, K.M., Becker, K.W., Hinrichs, K., and Elvert, M., 2012, Reconstruction of past methane availability in an Arctic Alaska wetland indicates climate influenced methane release during the past ~12,000 years: Journal of Paleolimnology, v. 48, no. 1, p. 27-42, https://doi.org/10.1007/s10933-012-9591-8.","productDescription":"16 p.","startPage":"27","endPage":"42","ipdsId":"IP-034535","costCenters":[{"id":114,"text":"Alaska Science Center","active":true,"usgs":true},{"id":678,"text":"Woods Hole Coastal and Marine Science Center","active":true,"usgs":true}],"links":[{"id":274188,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"},{"id":274187,"type":{"id":10,"text":"Digital Object Identifier"},"url":"https://dx.doi.org/10.1007/s10933-012-9591-8"}],"country":"United States","state":"Alaska","geographicExtents":"{ \"type\": \"FeatureCollection\", \"features\": [ { \"type\": \"Feature\", \"properties\": {}, \"geometry\": { \"type\": \"Polygon\", \"coordinates\": [ [ [ 172.5,51.2 ], [ 172.5,71.4 ], [ -130,71.4 ], [ -130,51.2 ], [ 172.5,51.2 ] ] ] } } ] }","volume":"48","issue":"1","noUsgsAuthors":false,"publicationDate":"2012-03-31","publicationStatus":"PW","scienceBaseUri":"51cabbe4e4b0d298e5434c68","contributors":{"authors":[{"text":"Wooller, Matthew J.","contributorId":24213,"corporation":false,"usgs":true,"family":"Wooller","given":"Matthew J.","affiliations":[],"preferred":false,"id":474630,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Pohlman, John W.","contributorId":95288,"corporation":false,"usgs":true,"family":"Pohlman","given":"John W.","affiliations":[],"preferred":false,"id":474636,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Gaglioti, Benjamin V. 0000-0003-0591-5253 bgaglioti@usgs.gov","orcid":"https://orcid.org/0000-0003-0591-5253","contributorId":4521,"corporation":false,"usgs":true,"family":"Gaglioti","given":"Benjamin","email":"bgaglioti@usgs.gov","middleInitial":"V.","affiliations":[{"id":118,"text":"Alaska Science Center Geography","active":true,"usgs":true},{"id":114,"text":"Alaska Science Center","active":true,"usgs":true}],"preferred":true,"id":474629,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Langdon, Peter","contributorId":30530,"corporation":false,"usgs":true,"family":"Langdon","given":"Peter","affiliations":[],"preferred":false,"id":474631,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Jones, Miriam","contributorId":56134,"corporation":false,"usgs":true,"family":"Jones","given":"Miriam","affiliations":[],"preferred":false,"id":474633,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"Anthony, Katey M. Walter","contributorId":82603,"corporation":false,"usgs":true,"family":"Anthony","given":"Katey","email":"","middleInitial":"M. Walter","affiliations":[],"preferred":false,"id":474634,"contributorType":{"id":1,"text":"Authors"},"rank":6},{"text":"Becker, Kevin W.","contributorId":54491,"corporation":false,"usgs":true,"family":"Becker","given":"Kevin","email":"","middleInitial":"W.","affiliations":[],"preferred":false,"id":474632,"contributorType":{"id":1,"text":"Authors"},"rank":7},{"text":"Hinrichs, Kai-Uwe","contributorId":89791,"corporation":false,"usgs":true,"family":"Hinrichs","given":"Kai-Uwe","affiliations":[],"preferred":false,"id":474635,"contributorType":{"id":1,"text":"Authors"},"rank":8},{"text":"Elvert, Marcus","contributorId":102362,"corporation":false,"usgs":true,"family":"Elvert","given":"Marcus","affiliations":[],"preferred":false,"id":474637,"contributorType":{"id":1,"text":"Authors"},"rank":9}]}}
,{"id":70038447,"text":"ofr20121120 - 2012 - A Markov chain analysis of the movements of juvenile salmonids, including sockeye salmon, in the forebay of McNary Dam, Washington and Oregon, 2006-09","interactions":[],"lastModifiedDate":"2012-06-02T01:01:38","indexId":"ofr20121120","displayToPublicDate":"2012-06-01T00:00:00","publicationYear":"2012","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":"2012-1120","title":"A Markov chain analysis of the movements of juvenile salmonids, including sockeye salmon, in the forebay of McNary Dam, Washington and Oregon, 2006-09","docAbstract":"Passage and survival data were collected at McNary Dam between 2006 and 2009. These data have provided critical information for resource managers to implement structural and operational changes designed to improve the survival of juvenile salmonids as they migrate past the dam. Much of the valuable information collected at McNary Dam was in the form of three-dimensional (hereafter referred to as 3-D) tracks of fish movements in the forebay. These data depicted the behavior of multiple species (in three dimensions) during different diel periods, spill conditions, powerhouse operations, and testing of the surface bypass structures (temporary spillway weirs; TSWs). One of the challenges in reporting 3-D results is presenting the information in a manner that allows interested parties to summarize the behavior of many fish over many different conditions across multiple years. To accomplish this, we used a Markov chain analysis to characterize fish movement patterns in the forebay of McNary Dam. The Markov chain analysis allowed us to numerically summarize the behavior of fish in the forebay. This report is the second report published in 2012 that uses this analytical method. The first report included only fish released as part of the annual studies conducted at McNary Dam. This second report includes sockeye salmon that were released as part of studies conducted by the Chelan and Grant County Public Utility Districts at mid-Columbia River dams. The studies conducted in the mid-Columbia used the same transmitters as were used for McNary Dam studies, but transmitter pulse width was different between studies. Additionally, no passive integrated transponder tags were implanted in sockeye salmon. Differences in transmitter pulse width resulted in lower detection probabilities for sockeye salmon at McNary Dam. The absence of passive integrated transponder tags prevented us from determining if fish passed the powerhouse through the juvenile bypass system (JBS) or turbines. To facilitate comparison among species in this report, we combined JBS and turbine passage for yearling Chinook salmon, steelhead, and subyearling Chinook salmon even though we were able to differentiate between passage through the JBS or turbines for these three species. Information on passage proportions through the JBS and turbines can be found in the first report. Numerically summarizing the behavior of juvenile salmonids in the forebay of McNary Dam using the Markov chain analysis allowed us to confirm what had been previously summarized using visualization software. For example, within the powerhouse region, passage proportions among the three powerhouse areas were often greater in the southern and middle areas of the powerhouse compared to the northern area of the powerhouse for yearling and subyearling Chinook salmon. The opposite generally was observed for steelhead. The results of this analysis also allowed us to confirm and quantify the extent of milling behavior that was observed for steelhead. For fish that were first detected in the powerhouse region, less than 0.10 of the steelhead, on average, passed within each of the powerhouse areas. Instead, steelhead transitioned to adjoining areas in the spillway before passing the dam. In comparison, greater than 0.20 of the Chinook salmon passed within each of the powerhouse areas. Less milling behavior was observed for all species for fish that first approached the spillway. Compared to the powerhouse areas, a higher proportion of fish, regardless of species, passed the spillway areas and fewer transitioned to adjoining areas in the powerhouse. In addition to quantifying what had been previously speculated about the behavior of fish in the forebay of McNary Dam, the Markov chain analysis refined our understanding of how fish behavior and passage can be influenced by changes to the operations and structure of McNary Dam. For example, the addition of TSWs to the spillway area clearly influenced the passage of fish. Previous results have been reported showing that TSWs increased passage through non-turbine routes and the fish-track videos indicated, in general, how fish behaved before passing the TSWs. However, the analysis presented in this report allowed us to better understand how fish transitioned across the face of the dam before passing the TSWs and resulted in a quantitative way to measure the effect of moving the location of the TSWs from year to year. Installation of the TSWs in bays 22 and 20 clearly increased passage proportions through the southern one-third of the spillway area for all species, most significantly for steelhead. When the TSWs were moved to bays 19 and 20 in 2008, overall passage through the southern one-third of the spillway remained higher than 2006, but decreased from what was observed in 2007. Shifting the TSWs to the north decreased the proportion of fish passing through the TSWs and increased the number of fish that transitioned to adjoining areas before passing the dam. Perhaps the most interesting new information to come out of the two-step Markov chain analysis relates to how the performance of the TSWs was influenced by their proximity to the powerhouse. During 2007, the highest proportion of fish passing through TSW 22 was for fish that transitioned from the powerhouse area. In contrast, a relatively low proportion of fish passed through TSW 20 after coming from the powerhouse area. Instead, the proportion of fish that passed TSW 20 after coming from the northern part of the spillway was twice as high as the proportion of fish that passed through TSW 20 after coming from the powerhouse. During 2008, the TSW in bay 22 was moved to bay 19, leaving the TSW in bay 20 as the one closest to the powerhouse. As was the case when a TSW was located in bay 22, the proportion of fish passing through TSW 20 after coming from the powerhouse was higher than the proportion of fish passing TSW 20 after coming from the northern part of the spillway. Passage proportions for fish passing through TSW 19, the farthest north of the two TSWs during 2008, was higher for fish that came from the northern part of the spillway compared to the proportion of fish that passed through TSW 19 after coming from the powerhouse. The Markov chain analysis provided a mathematical way to characterize fish behavior in the forebay of McNary Dam and helped refine our understanding of how fish movements were influenced by operational and structural changes at the dam. The numerical information used to quantify the behavior of fish also can be used to construct simulations to examine how proposed fish passage structures might influence passage of juvenile salmonids. To demonstrate this, we used the results of the Markov chain analysis to examine how a virtual fish collector located in the center of the powerhouse might influence passage of juvenile salmonids at McNary Dam.","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/ofr20121120","collaboration":"Prepared in cooperation with the U.S. Army Corps of Engineers","usgsCitation":"Adams, N.S., and Hatton, T., 2012, A Markov chain analysis of the movements of juvenile salmonids, including sockeye salmon, in the forebay of McNary Dam, Washington and Oregon, 2006-09: U.S. Geological Survey Open-File Report 2012-1120, viii, 71 p.; Appendices, https://doi.org/10.3133/ofr20121120.","productDescription":"viii, 71 p.; Appendices","temporalStart":"2006-01-01","temporalEnd":"2009-12-31","costCenters":[{"id":654,"text":"Western Fisheries Research Center","active":true,"usgs":true}],"links":[{"id":257110,"rank":0,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/ofr_2012_1120.jpg"},{"id":257109,"rank":100,"type":{"id":15,"text":"Index Page"},"url":"https://pubs.usgs.gov/of/2012/1120/","linkFileType":{"id":5,"text":"html"}}],"country":"United States","state":"Oregon;Washington","otherGeospatial":"Mcnary Dam;Columbia River;Snake River","geographicExtents":"{ \"type\": \"FeatureCollection\", \"features\": [ { \"type\": \"Feature\", \"properties\": {}, \"geometry\": { \"type\": \"Polygon\", \"coordinates\": [ [ [ -121,45.5 ], [ -121,48.25 ], [ -117.5,48.25 ], [ -117.5,45.5 ], [ -121,45.5 ] ] ] } } ] }","noUsgsAuthors":false,"publicationStatus":"PW","scienceBaseUri":"53cd495be4b0b290850ef173","contributors":{"authors":[{"text":"Adams, Noah S. 0000-0002-8354-0293 nadams@usgs.gov","orcid":"https://orcid.org/0000-0002-8354-0293","contributorId":3521,"corporation":false,"usgs":true,"family":"Adams","given":"Noah","email":"nadams@usgs.gov","middleInitial":"S.","affiliations":[{"id":654,"text":"Western Fisheries Research Center","active":true,"usgs":true}],"preferred":true,"id":464162,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Hatton, Tyson W. 0000-0002-2874-0719","orcid":"https://orcid.org/0000-0002-2874-0719","contributorId":9112,"corporation":false,"usgs":true,"family":"Hatton","given":"Tyson W.","affiliations":[{"id":654,"text":"Western Fisheries Research Center","active":true,"usgs":true}],"preferred":false,"id":464163,"contributorType":{"id":1,"text":"Authors"},"rank":2}]}}
,{"id":70038446,"text":"ofr20121119 - 2012 - A Markov chain analysis of the movements of juvenile salmonids in the forebay of McNary Dam, Washington and Oregon, 2006-09","interactions":[],"lastModifiedDate":"2012-06-02T01:01:38","indexId":"ofr20121119","displayToPublicDate":"2012-06-01T00:00:00","publicationYear":"2012","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":"2012-1119","title":"A Markov chain analysis of the movements of juvenile salmonids in the forebay of McNary Dam, Washington and Oregon, 2006-09","docAbstract":"Passage and survival data for yearling and subyearling Chinook salmon and juvenile steelhead were collected at McNary Dam between 2006 and 2009. These data have provided critical information for resource managers to implement structural and operational changes designed to improve the survival of juvenile salmonids as they migrate past the dam. Much of the information collected at McNary Dam was in the form of three-dimensional tracks of fish movements in the forebay. These data depicted the behavior of multiple species (in three dimensions) during different diel periods, spill conditions, powerhouse operations, and test configurations of the surface bypass structures (temporary spillway weirs; TSWs). One of the challenges in reporting three-dimensional results is presenting the information in a manner that allows interested parties to summarize the behavior of many fish over many different conditions across multiple years. To accomplish this, we investigated the feasibility of using a Markov chain analysis to characterize fish movement patterns in the forebay of McNary Dam. The Markov chain analysis is one way that can be used to summarize numerically the behavior of fish in the forebay. Numerically summarizing the behavior of juvenile salmonids in the forebay of McNary Dam using the Markov chain analysis allowed us to confirm what had been previously summarized using visualization software. For example, proportions of yearling and subyearling Chinook salmon passing the three powerhouse areas was often greater in the southern and middle areas, compared to the northern area. The opposite generally was observed for steelhead. Results of this analysis also allowed us to confirm and quantify the extent of milling behavior that had been observed for steelhead. For fish that were first detected in the powerhouse region, less than 0.10 of the steelhead, on average, passed within each of the powerhouse areas. Instead, steelhead transitioned to adjoining areas in the spillway before passing the dam. In comparison, greater than 0.20 of the Chinook salmon passed within the powerhouse areas. Less milling behavior was observed for all species for fish that first approached the spillway. Compared to the powerhouse areas, a higher proportion of fish, regardless of species, passed the spillway areas and fewer transitioned to adjoining areas in the powerhouse. In addition to quantifying what had been previously speculated about the behavior of fish in the forebay of McNary Dam, the Markov chain analysis refined our understanding of how fish behavior and passage can be influenced by changes to the operations and structure of McNary Dam. For example, the addition of TSWs to the spillway area clearly influenced the passage of fish. Previous results have been reported showing that TSWs increased the number of fish passing through non-turbine routes and the fish-track videos indicated, in general, how fish behaved before passing through the TSWs. However, the analysis presented in this report allowed us to better understand how fish moved across the face of the dam before passing the TSWs and provided a way to quantify the effect of TSW location. Installation of the TSWs in bays 22 and 20 clearly increased passage proportions through the southern one-third of the spillway area for all species, most significantly for steelhead. When the TSWs were moved to bays 19 and 20 in 2008, overall passage through the southern one-third of the spillway remained higher than 2006, but decreased from what was observed in 2007. Shifting the TSWs to the north decreased the proportion of fish passing through the TSWs and increased the number of fish that moved to adjoining areas before passing the dam. Perhaps the most interesting new information to come out of the two-step Markov chain analysis relates to how the performance of the TSWs was influenced by their proximity to the powerhouse. During 2007, the highest proportion of fish passing through TSW22 was for fish that transitioned from the powerhouse area. In contrast, a relatively low proportion of fish passed through TSW20 after coming from the powerhouse area. Instead, the proportion of fish that passed TSW20 after coming from the northern part of the spillway was twice as high as the proportion of fish that passed through TSW20 after coming from the powerhouse. During 2008, the TSW in bay 22 was moved to bay 19, leaving the TSW in bay 20 as the one closest to the powerhouse. As was the case when a TSW was located in bay 22; the proportion of fish passing TSW20 after coming from the powerhouse was greater than the proportion of fish passing through TSW20 after coming from the northern part of the spillway. Passage proportions for fish passing through TSW19, the farthest north of the two TSWs during 2008, was higher for fish that came from the northern part of the spillway compared to the proportion of fish that passed through TSW19 after coming from the powerhouse. The Markov chain analysis provided a mathematical way to characterize fish behavior in the forebay of McNary Dam and helped refine our understanding of how fish movements were influenced by operational and structural changes at McNary Dam. The Markov chain analysis also could be used to examine how future structural and operational changes proposed for McNary Dam might influence the passage of juvenile salmonids.","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/ofr20121119","collaboration":"Prepared in cooperation with the U.S. Army Corps of Engineers","usgsCitation":"Adams, N.S., and Hatton, T., 2012, A Markov chain analysis of the movements of juvenile salmonids in the forebay of McNary Dam, Washington and Oregon, 2006-09: U.S. Geological Survey Open-File Report 2012-1119, viii, 68 p.; Appendices, https://doi.org/10.3133/ofr20121119.","productDescription":"viii, 68 p.; Appendices","temporalStart":"2006-01-01","temporalEnd":"2009-12-31","costCenters":[{"id":654,"text":"Western Fisheries Research Center","active":true,"usgs":true}],"links":[{"id":257111,"rank":0,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/ofr_2012_1119.jpg"},{"id":257108,"rank":100,"type":{"id":15,"text":"Index Page"},"url":"https://pubs.usgs.gov/of/2012/1119/","linkFileType":{"id":5,"text":"html"}}],"country":"United States","state":"Oregon;Washington","otherGeospatial":"Mcnary Dam;Columbia River;Snake River","geographicExtents":"{ \"type\": \"FeatureCollection\", \"features\": [ { \"type\": \"Feature\", \"properties\": {}, \"geometry\": { \"type\": \"Polygon\", \"coordinates\": [ [ [ -121,45.5 ], [ -121,48.25 ], [ -117.5,48.25 ], [ -117.5,45.5 ], [ -121,45.5 ] ] ] } } ] }","noUsgsAuthors":false,"publicationStatus":"PW","scienceBaseUri":"53cd495be4b0b290850ef171","contributors":{"authors":[{"text":"Adams, Noah S. 0000-0002-8354-0293 nadams@usgs.gov","orcid":"https://orcid.org/0000-0002-8354-0293","contributorId":3521,"corporation":false,"usgs":true,"family":"Adams","given":"Noah","email":"nadams@usgs.gov","middleInitial":"S.","affiliations":[{"id":654,"text":"Western Fisheries Research Center","active":true,"usgs":true}],"preferred":true,"id":464160,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Hatton, Tyson W. 0000-0002-2874-0719","orcid":"https://orcid.org/0000-0002-2874-0719","contributorId":9112,"corporation":false,"usgs":true,"family":"Hatton","given":"Tyson W.","affiliations":[{"id":654,"text":"Western Fisheries Research Center","active":true,"usgs":true}],"preferred":false,"id":464161,"contributorType":{"id":1,"text":"Authors"},"rank":2}]}}
,{"id":70176609,"text":"70176609 - 2012 - How to catch a parasite: Parasite Niche Modeler (PaNic) meets Fishbase","interactions":[],"lastModifiedDate":"2016-09-22T15:20:26","indexId":"70176609","displayToPublicDate":"2012-06-01T00:00:00","publicationYear":"2012","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":1445,"text":"Ecography","active":true,"publicationSubtype":{"id":10}},"title":"How to catch a parasite: Parasite Niche Modeler (PaNic) meets Fishbase","docAbstract":"<p><span>Parasite Niche Modeler (PaNic) is a free online software tool that suggests potential hosts for fish parasites. For a particular parasite species from the major helminth groups (Acanthocephala, Cestoda, Monogenea, Nematoda, Trematoda), PaNic takes data from known hosts (maximum body length, growth rate, life span, age at first maturity, trophic level, phylogeny, and biogeography) and hypothesizes similar fish species that might serve as hosts to that parasite. Users can give varying weights to host attributes and create custom models. In addition to suggesting plausible hosts (with varying degrees of confidence), the models indicate known host species that appear to be outliers in comparison to other known hosts. These unique features make PaNic an innovative tool for addressing both theoretical and applied questions in fish parasitology. PaNic can be accessed at &lt;</span><a title=\"Link to external resource: http://purl.oclc.org/fishpest\" href=\"http://purl.oclc.org/fishpest\" target=\"_blank\" data-mce-href=\"http://purl.oclc.org/fishpest\">http://purl.oclc.org/fishpest</a><span>&gt;.</span></p>","language":"English","publisher":"Wiley","doi":"10.1111/j.1600-0587.2012.07439.x","usgsCitation":"Strona, G., and Lafferty, K.D., 2012, How to catch a parasite: Parasite Niche Modeler (PaNic) meets Fishbase: Ecography, v. 35, no. 6, p. 481-486, https://doi.org/10.1111/j.1600-0587.2012.07439.x.","productDescription":"6 p.","startPage":"481","endPage":"486","costCenters":[{"id":651,"text":"Western Ecological Research Center","active":true,"usgs":true}],"links":[{"id":328876,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"volume":"35","issue":"6","noUsgsAuthors":false,"publicationDate":"2012-02-20","publicationStatus":"PW","scienceBaseUri":"57f7f3b1e4b0bc0bec0a0b17","contributors":{"authors":[{"text":"Strona, Giovanni","contributorId":62940,"corporation":false,"usgs":true,"family":"Strona","given":"Giovanni","email":"","affiliations":[],"preferred":false,"id":649373,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Lafferty, Kevin D. 0000-0001-7583-4593 klafferty@usgs.gov","orcid":"https://orcid.org/0000-0001-7583-4593","contributorId":1415,"corporation":false,"usgs":true,"family":"Lafferty","given":"Kevin","email":"klafferty@usgs.gov","middleInitial":"D.","affiliations":[{"id":651,"text":"Western Ecological Research Center","active":true,"usgs":true}],"preferred":true,"id":649374,"contributorType":{"id":1,"text":"Authors"},"rank":2}]}}
,{"id":70039120,"text":"70039120 - 2012 - Knowledge gained from video-monitoring grassland passerine nests","interactions":[],"lastModifiedDate":"2018-01-05T10:00:17","indexId":"70039120","displayToPublicDate":"2012-06-01T00:00:00","publicationYear":"2012","noYear":false,"publicationType":{"id":5,"text":"Book chapter"},"publicationSubtype":{"id":24,"text":"Book Chapter"},"chapter":"1","title":"Knowledge gained from video-monitoring grassland passerine nests","docAbstract":"<p><span id=\"abstract\">In the mid-1990s, researchers began using miniature cameras to videotape activities at cryptic passerine nests in grasslands.In subsequent years, use of these video surveillance systems spread dramatically, leading to major strides in our knowledge of nest predation and nesting ecology of many species.Studies using video nest surveillance have helped overturn or substantiate many long-standing assumptions and have provided insights on a wide range of topics.Using examples from grasslands, we highlight the accumulated knowledge about activities at nests documented with video; we also discuss implications of this knowledge for our understanding of avian ecology.Like all tools, video nest surveillance has potential limitations, and users must take precautions to minimize possible sources of bias in data collection and interpretation.</span></p>","largerWorkType":{"id":4,"text":"Book"},"largerWorkTitle":"Video Surveillance of Nesting Birds","largerWorkSubtype":{"id":15,"text":"Monograph"},"language":"English","publisher":"University of California Press","publisherLocation":"Berkley, CA","isbn":"9780520273139","usgsCitation":"Pietz, P., Granfors, D., and Ribic, C.A., 2012, Knowledge gained from video-monitoring grassland passerine nests, chap. 1 <i>of</i> Video Surveillance of Nesting Birds, p. 1-22.","productDescription":"22 p.","startPage":"1","endPage":"22","onlineOnly":"N","additionalOnlineFiles":"N","ipdsId":"IP-022200","costCenters":[{"id":480,"text":"Northern Prairie 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III","contributorId":12608,"corporation":false,"usgs":true,"family":"Thompson","given":"Frank","suffix":"III","email":"","middleInitial":"R.","affiliations":[],"preferred":false,"id":570123,"contributorType":{"id":2,"text":"Editors"},"rank":2},{"text":"Pietz, Pamela J. ppietz@usgs.gov","contributorId":2382,"corporation":false,"usgs":true,"family":"Pietz","given":"Pamela J.","email":"ppietz@usgs.gov","affiliations":[{"id":595,"text":"U.S. Geological Survey","active":false,"usgs":true}],"preferred":false,"id":570124,"contributorType":{"id":2,"text":"Editors"},"rank":3}],"authors":[{"text":"Pietz, Pamela J. ppietz@usgs.gov","contributorId":2382,"corporation":false,"usgs":true,"family":"Pietz","given":"Pamela J.","email":"ppietz@usgs.gov","affiliations":[{"id":595,"text":"U.S. Geological Survey","active":false,"usgs":true}],"preferred":false,"id":570119,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Granfors, D. A.","contributorId":94256,"corporation":false,"usgs":true,"family":"Granfors","given":"D. A.","affiliations":[],"preferred":false,"id":570120,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Ribic, Christine A. caribic@usgs.gov","contributorId":831,"corporation":false,"usgs":true,"family":"Ribic","given":"Christine","email":"caribic@usgs.gov","middleInitial":"A.","affiliations":[{"id":595,"text":"U.S. Geological Survey","active":false,"usgs":true}],"preferred":false,"id":570121,"contributorType":{"id":1,"text":"Authors"},"rank":3}]}}
,{"id":70192299,"text":"70192299 - 2012 - Guidelines for collecting and maintaining archives for genetic monitoring","interactions":[],"lastModifiedDate":"2017-10-25T13:33:20","indexId":"70192299","displayToPublicDate":"2012-06-01T00:00:00","publicationYear":"2012","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":1325,"text":"Conservation Genetics Resources","active":true,"publicationSubtype":{"id":10}},"title":"Guidelines for collecting and maintaining archives for genetic monitoring","docAbstract":"<p><span>Rapid advances in molecular genetic techniques and the statistical analysis of genetic data have revolutionized the way that populations of animals, plants and microorganisms can be monitored. Genetic monitoring is the practice of using molecular genetic markers to track changes in the abundance, diversity or distribution of populations, species or ecosystems over time, and to follow adaptive and non-adaptive genetic responses to changing external conditions. In recent years, genetic monitoring has become a valuable tool in conservation management of biological diversity and ecological analysis, helping to illuminate and define cryptic and poorly understood species and populations. Many of the detected biodiversity declines, changes in distribution and hybridization events have helped to drive changes in policy and management. Because a time series of samples is necessary to detect trends of change in genetic diversity and species composition, archiving is a critical component of genetic monitoring. Here we discuss the collection, development, maintenance, and use of archives for genetic monitoring. This includes an overview of the genetic markers that facilitate effective monitoring, describes how tissue and DNA can be stored, and provides guidelines for proper practice.</span></p>","language":"English","publisher":"Conservation Genetics Resources","doi":"10.1007/s12686-011-9545-x","usgsCitation":"Jackson, J.A., Laikre, L., Baker, C.S., Kendall, K.C., and The Genetic Monitoring Working Group, 2012, Guidelines for collecting and maintaining archives for genetic monitoring: Conservation Genetics Resources, v. 4, no. 2, p. 527-536, https://doi.org/10.1007/s12686-011-9545-x.","productDescription":"10 p.","startPage":"527","endPage":"536","ipdsId":"IP-027589","costCenters":[{"id":481,"text":"Northern Rocky Mountain Science Center","active":true,"usgs":true}],"links":[{"id":347366,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"volume":"4","issue":"2","publishingServiceCenter":{"id":2,"text":"Denver PSC"},"noUsgsAuthors":false,"publicationDate":"2011-10-16","publicationStatus":"PW","scienceBaseUri":"59f1a2aae4b0220bbd9d9fd2","contributors":{"authors":[{"text":"Jackson, Jennifer A.","contributorId":198138,"corporation":false,"usgs":false,"family":"Jackson","given":"Jennifer","email":"","middleInitial":"A.","affiliations":[],"preferred":false,"id":715726,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Laikre, Linda","contributorId":198139,"corporation":false,"usgs":false,"family":"Laikre","given":"Linda","email":"","affiliations":[],"preferred":false,"id":715727,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Baker, C. Scott","contributorId":198136,"corporation":false,"usgs":false,"family":"Baker","given":"C.","email":"","middleInitial":"Scott","affiliations":[],"preferred":false,"id":715728,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Kendall, Katherine C. 0000-0002-4831-2287 kkendall@usgs.gov","orcid":"https://orcid.org/0000-0002-4831-2287","contributorId":3081,"corporation":false,"usgs":true,"family":"Kendall","given":"Katherine","email":"kkendall@usgs.gov","middleInitial":"C.","affiliations":[{"id":481,"text":"Northern Rocky Mountain Science Center","active":true,"usgs":true}],"preferred":true,"id":715729,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"The Genetic Monitoring Working Group","contributorId":198335,"corporation":true,"usgs":false,"organization":"The Genetic Monitoring Working Group","id":715730,"contributorType":{"id":1,"text":"Authors"},"rank":5}]}}
,{"id":70187335,"text":"70187335 - 2012 - Geophysical study of the San Juan Mountains batholith complex, southwestern Colorado","interactions":[],"lastModifiedDate":"2019-12-17T09:16:06","indexId":"70187335","displayToPublicDate":"2012-06-01T00:00:00","publicationYear":"2012","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":1820,"text":"Geosphere","active":true,"publicationSubtype":{"id":10}},"title":"Geophysical study of the San Juan Mountains batholith complex, southwestern Colorado","docAbstract":"<p><span>One of the largest and most pronounced gravity lows over North America is over the rugged San Juan Mountains of southwestern Colorado (USA). The mountain range is coincident with the San Juan volcanic field (SJVF), the largest erosional remnant of a widespread mid-Cenozoic volcanic field that spanned much of the southern Rocky Mountains. A buried, low-density silicic batholith complex related to the volcanic field has been the accepted interpretation of the source of the gravity low since the 1970s. However, this interpretation was based on gravity data processed with standard techniques that are problematic in the SJVF region. The combination of high-relief topography, topography with low densities, and the use of a common reduction density of 2670 kg/m</span><sup>3</sup><span>produces spurious large-amplitude gravity lows that may distort the geophysical signature of deeper features such as a batholith complex. We applied an unconventional processing procedure that uses geologically appropriate densities for the uppermost crust and digital topography to mostly remove the effect of the low-density units that underlie the topography associated with the SJVF. This approach resulted in a gravity map that provides an improved representation of deeper sources, including reducing the amplitude of the anomaly attributed to a batholith complex. We also reinterpreted vintage seismic refraction data that indicate the presence of low-velocity zones under the SJVF. Assuming that the source of the gravity low on the improved gravity anomaly map is the same as the source of the low seismic velocities, integrated modeling corroborates the interpretation of a batholith complex and then defines the dimensions and overall density contrast of the complex. Models show that the thickness of the batholith complex varies laterally to a significant degree, with the greatest thickness (∼20 km) under the western SJVF, and lesser thicknesses (&lt;10 km) under the eastern SJVF. The largest group of nested calderas on the surface of the SJVF, the central caldera cluster, is not correlated with the thickest part of the batholith complex. This result is consistent with petrologic interpretations from recent studies that the batholith complex continued to be modified after cessation of volcanism and therefore is not necessarily representative of synvolcanic magma chambers. The total volume of the batholith complex is estimated to be 82,000–130,000 km</span><sup>3</sup><span>. The formation of such a large felsic batholith complex would inevitably involve production of a considerably greater volume of residuum, which could be present in the lower crust or uppermost mantle. The interpreted vertically averaged density contrast (–60 to –110 kg/m</span><sup>3</sup><span>), density (2590–2640 kg/m</span><sup>3</sup><span>), and seismic expression of the batholith complex are consistent with results of geophysical studies of other large batholiths in the western United States.</span></p>","language":"English","publisher":"Geological Society of America","doi":"10.1130/GES00723.1","usgsCitation":"Drenth, B.J., Keller, G.R., and Thompson, R.A., 2012, Geophysical study of the San Juan Mountains batholith complex, southwestern Colorado: Geosphere, v. 8, no. 3, p. 669-684, https://doi.org/10.1130/GES00723.1.","productDescription":"16 p.","startPage":"669","endPage":"684","ipdsId":"IP-026514","costCenters":[{"id":211,"text":"Crustal Geophysics and Geochemistry Science Center","active":true,"usgs":true}],"links":[{"id":474496,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.1130/ges00723.1","text":"Publisher Index Page"},{"id":340695,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","state":"Colorado","otherGeospatial":"San Juan Mountains","geographicExtents":"{\n  \"type\": \"FeatureCollection\",\n  \"features\": [\n    {\n      \"type\": \"Feature\",\n      \"properties\": {},\n      \"geometry\": {\n        \"type\": \"Polygon\",\n        \"coordinates\": [\n          [\n            [\n              -109.05029296875,\n              36.99377838872517\n            ],\n            [\n              -105.97412109375,\n              36.99377838872517\n            ],\n            [\n              -105.97412109375,\n              38.48369476951686\n            ],\n            [\n              -109.05029296875,\n              38.48369476951686\n            ],\n            [\n              -109.05029296875,\n              36.99377838872517\n            ]\n          ]\n        ]\n      }\n    }\n  ]\n}","volume":"8","issue":"3","publishingServiceCenter":{"id":2,"text":"Denver PSC"},"noUsgsAuthors":false,"publicationStatus":"PW","scienceBaseUri":"59084936e4b0fc4e448ffda2","contributors":{"authors":[{"text":"Drenth, Benjamin J. 0000-0002-3954-8124 bdrenth@usgs.gov","orcid":"https://orcid.org/0000-0002-3954-8124","contributorId":1315,"corporation":false,"usgs":true,"family":"Drenth","given":"Benjamin","email":"bdrenth@usgs.gov","middleInitial":"J.","affiliations":[{"id":211,"text":"Crustal Geophysics and Geochemistry Science Center","active":true,"usgs":true}],"preferred":true,"id":693511,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Keller, G. Randy","contributorId":40602,"corporation":false,"usgs":true,"family":"Keller","given":"G.","email":"","middleInitial":"Randy","affiliations":[],"preferred":false,"id":693513,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Thompson, Ren A. 0000-0002-3044-3043 rathomps@usgs.gov","orcid":"https://orcid.org/0000-0002-3044-3043","contributorId":1265,"corporation":false,"usgs":true,"family":"Thompson","given":"Ren","email":"rathomps@usgs.gov","middleInitial":"A.","affiliations":[{"id":318,"text":"Geosciences and Environmental Change Science Center","active":true,"usgs":true}],"preferred":true,"id":693512,"contributorType":{"id":1,"text":"Authors"},"rank":3}]}}
,{"id":70037976,"text":"70037976 - 2012 - Modeling radium distribution in coastal aquifers during sea level changes: The Dead Sea case","interactions":[],"lastModifiedDate":"2012-06-06T01:01:36","indexId":"70037976","displayToPublicDate":"2012-05-31T12:13:00","publicationYear":"2012","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":1759,"text":"Geochimica et Cosmochimica Acta","active":true,"publicationSubtype":{"id":10}},"title":"Modeling radium distribution in coastal aquifers during sea level changes: The Dead Sea case","docAbstract":"We present a new approach to studying the behavior of radium isotopes in a coastal aquifer. In order to simulate radium isotope distributions in the dynamic flow field of the Dead Sea aquifer, a multi-species density dependent flow model (SUTRA-MS) was used. Field data show that the activity of <sup>226</sup>Ra decreases from 140 to 60 dpm/L upon entering the aquifer from the Dead Sea, and then further decreases linearly due to mixing with Ra-poor fresh water. On the other hand, an increase is observed in the activity of the shorter-lived isotopes (up to 52 dpm/L <sup>224</sup>Ra and 31 dpm/L <sup>223</sup>Ra), which are relatively low in Dead Sea water (up to 2.5 dpm/L <sup>224</sup>Ra and 0.5 dpm/L <sup>223</sup>Ra). The activities of the short lived radium isotopes also decrease with decreasing salinity, which is due to the effect of salinity on the adsorption of radium. The relationship between <sup>224</sup>Ra and salinity suggests that the adsorption partition coefficient (<i>K</i>) is linearly related to salinity. Simulations of the steady-state conditions, show that the distance where equilibrium activity is attained for each radium isotope is affected by the isotope half-life, <i>K</i> and the groundwater velocity, resulting in a longer distance for the long-lived radium isotopes. <i>K</i> affects the radium distribution in transient conditions, especially that of the long-lived radium isotopes. The transient conditions in the Dead Sea system, with a 1 m/yr lake level drop, together with the radium field data, constrains <i>K</i> to be relatively low (<10). Thus, the sharp decrease in <sup>226</sup>Ra cannot be explained by adsorption, and it is better explained by removal via coprecipitation, probably with barite or celestine.","largerWorkType":{"id":2,"text":"Article"},"largerWorkTitle":"Geochimica et Cosmochimica Acta","largerWorkSubtype":{"id":10,"text":"Journal Article"},"language":"English","publisher":"Elsevier","publisherLocation":"Amsterdam, Netherlands","doi":"10.1016/j.gca.2012.03.022","usgsCitation":"Kiro, Y., Yechieli, Y., Voss, C.I., Starinsky, A., and Weinstein, Y., 2012, Modeling radium distribution in coastal aquifers during sea level changes: The Dead Sea case: Geochimica et Cosmochimica Acta, v. 88, p. 237-254, https://doi.org/10.1016/j.gca.2012.03.022.","productDescription":"18 p.","startPage":"237","endPage":"254","costCenters":[{"id":148,"text":"Branch of Regional Research-Western Region","active":false,"usgs":true}],"links":[{"id":257213,"rank":100,"type":{"id":15,"text":"Index Page"},"url":"https://dx.doi.org/10.1016/j.gca.2012.03.022","linkFileType":{"id":5,"text":"html"}},{"id":257226,"rank":0,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"Isreal","otherGeospatial":"Dead Sea","volume":"88","noUsgsAuthors":false,"publicationStatus":"PW","scienceBaseUri":"505a5c20e4b0c8380cd6fa5f","contributors":{"authors":[{"text":"Kiro, Yael","contributorId":88996,"corporation":false,"usgs":true,"family":"Kiro","given":"Yael","email":"","affiliations":[],"preferred":false,"id":463191,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Yechieli, Yoseph","contributorId":95320,"corporation":false,"usgs":true,"family":"Yechieli","given":"Yoseph","email":"","affiliations":[],"preferred":false,"id":463192,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Voss, Clifford I. 0000-0001-5923-2752 cvoss@usgs.gov","orcid":"https://orcid.org/0000-0001-5923-2752","contributorId":1559,"corporation":false,"usgs":true,"family":"Voss","given":"Clifford","email":"cvoss@usgs.gov","middleInitial":"I.","affiliations":[{"id":438,"text":"National Research Program - Western Branch","active":true,"usgs":true}],"preferred":true,"id":463189,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Starinsky, Abraham","contributorId":98988,"corporation":false,"usgs":true,"family":"Starinsky","given":"Abraham","email":"","affiliations":[],"preferred":false,"id":463193,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Weinstein, Yishai","contributorId":44404,"corporation":false,"usgs":true,"family":"Weinstein","given":"Yishai","email":"","affiliations":[],"preferred":false,"id":463190,"contributorType":{"id":1,"text":"Authors"},"rank":5}]}}
,{"id":70007202,"text":"70007202 - 2012 - Mapping socio-environmentally vulnerable populations access and exposure to ecosystem services at the U.S.-Mexico borderlands","interactions":[],"lastModifiedDate":"2012-06-06T01:01:36","indexId":"70007202","displayToPublicDate":"2012-05-31T09:59:00","publicationYear":"2012","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":836,"text":"Applied Geography","active":true,"publicationSubtype":{"id":10}},"title":"Mapping socio-environmentally vulnerable populations access and exposure to ecosystem services at the U.S.-Mexico borderlands","docAbstract":"Socio-environmental vulnerable populations are often unrepresented in land-use planning yet have great potential for loss when exposed to changes in ecosystem services. Administrative boundaries, cultural differences, and language barriers increase the disassociation between land-use management and marginalized populations living in the U.S.&ndash;Mexico borderlands. This paper describes the development of a Modified Socio-Environmental Vulnerability Index (M-SEVI), using determinants from binational census and neighborhood data that describe levels of education, access to resources, migratory status, housing, and number of dependents, to provide a simplified snapshot of the region's populace that can be used in binational planning efforts. We apply this index at the SCW, located on the border between Arizona, USA and Sonora, Mexico. For comparison, the Soil and Water Assessment Tool is concurrently applied to assess the provision of erosion- and flood control services over a 9-year period. We describe how this coupling of data can form the base for an ecosystem services assessment across political boundaries that can be used by land-use planners. Results reveal potential disparities in environmental risks and burdens throughout the binational watershed in residential districts surrounding and between urban centers. The M-SEVI can be used as an important first step in addressing environmental justice for binational decision-making.","largerWorkType":{"id":2,"text":"Article"},"largerWorkTitle":"Applied Geography","largerWorkSubtype":{"id":10,"text":"Journal Article"},"language":"English","publisher":"Elsevier","publisherLocation":"Amsterdam, Netherlands","doi":"10.1016/j.apgeog.2012.01.006","usgsCitation":"Norman, L.M., Villarreal, M., Lara-Valencia, F., Yuan, Y., Nie, W., Wilson, S., Amaya, G., and Sleeter, R., 2012, Mapping socio-environmentally vulnerable populations access and exposure to ecosystem services at the U.S.-Mexico borderlands: Applied Geography, v. 34, p. 413-424, https://doi.org/10.1016/j.apgeog.2012.01.006.","productDescription":"12 p.","startPage":"413","endPage":"424","costCenters":[{"id":583,"text":"Texas Water Science Center","active":true,"usgs":true},{"id":657,"text":"Western Geographic Science Center","active":true,"usgs":true}],"links":[{"id":257198,"rank":100,"type":{"id":15,"text":"Index Page"},"url":"https://dx.doi.org/10.1016/j.apgeog.2012.01.006","linkFileType":{"id":5,"text":"html"}},{"id":257224,"rank":0,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States;Mexico","state":"Arizona","otherGeospatial":"Sonora","volume":"34","noUsgsAuthors":false,"publicationStatus":"PW","scienceBaseUri":"505a5074e4b0c8380cd6b6ce","contributors":{"authors":[{"text":"Norman, Laura M. 0000-0002-3696-8406 lnorman@usgs.gov","orcid":"https://orcid.org/0000-0002-3696-8406","contributorId":967,"corporation":false,"usgs":true,"family":"Norman","given":"Laura","email":"lnorman@usgs.gov","middleInitial":"M.","affiliations":[{"id":657,"text":"Western Geographic Science Center","active":true,"usgs":true}],"preferred":true,"id":356052,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Villarreal, Miguel L.","contributorId":107012,"corporation":false,"usgs":true,"family":"Villarreal","given":"Miguel L.","affiliations":[],"preferred":false,"id":356058,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Lara-Valencia, Francisco","contributorId":77409,"corporation":false,"usgs":true,"family":"Lara-Valencia","given":"Francisco","email":"","affiliations":[],"preferred":false,"id":356055,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Yuan, Yongping","contributorId":75799,"corporation":false,"usgs":true,"family":"Yuan","given":"Yongping","email":"","affiliations":[],"preferred":false,"id":356054,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Nie, Wenming","contributorId":7126,"corporation":false,"usgs":true,"family":"Nie","given":"Wenming","email":"","affiliations":[],"preferred":false,"id":356053,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"Wilson, Sylvia","contributorId":105160,"corporation":false,"usgs":true,"family":"Wilson","given":"Sylvia","email":"","affiliations":[],"preferred":false,"id":356057,"contributorType":{"id":1,"text":"Authors"},"rank":6},{"text":"Amaya, Gladys","contributorId":86212,"corporation":false,"usgs":true,"family":"Amaya","given":"Gladys","email":"","affiliations":[],"preferred":false,"id":356056,"contributorType":{"id":1,"text":"Authors"},"rank":7},{"text":"Sleeter, Rachel 0000-0003-3477-0436 rsleeter@usgs.gov","orcid":"https://orcid.org/0000-0003-3477-0436","contributorId":666,"corporation":false,"usgs":true,"family":"Sleeter","given":"Rachel","email":"rsleeter@usgs.gov","affiliations":[{"id":242,"text":"Eastern Geographic Science Center","active":true,"usgs":true}],"preferred":true,"id":356051,"contributorType":{"id":1,"text":"Authors"},"rank":8}]}}
,{"id":70038445,"text":"ofr20121108 - 2012 - Monitoring of stream restoration habitat on the main stem of the Methow River, Washington, during the pre-treatment phase (October 2008-May 2012) with a progress report for activities from March 2011 to November 2011","interactions":[],"lastModifiedDate":"2016-05-04T12:00:42","indexId":"ofr20121108","displayToPublicDate":"2012-05-31T00:00:00","publicationYear":"2012","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":"2012-1108","title":"Monitoring of stream restoration habitat on the main stem of the Methow River, Washington, during the pre-treatment phase (October 2008-May 2012) with a progress report for activities from March 2011 to November 2011","docAbstract":"<h1 data-canvas-width=\"127.4938\">Introduction</h1>\n<div data-canvas-width=\"127.4938\"><br />The U.S. Geological Survey (USGS) received a request from the Bureau of Reclamation (Reclamation) to provide monitoring and an evaluation of the effectiveness of habitat actions that Reclamation plans to implement in the Upper Columbia River basin, which includes the Methow River. This monitoring and evaluation program is to partially fulfill Reclamations part of the 2008 Biological Opinion for the Federal Columbia River Power System that includes a Reasonable and Prudent Alternative (RPA) to protect listed salmon and steelhead across their life cycle. The target species in the Methow River for this monitoring and restoration effort include Upper Columbia River (UCR) spring Chinook salmon (<i>Oncorhynchus tshawytscha</i>), UCR steelhead (<i>O. mykiss</i>), and bull trout (<i>Salvelinus confluentus</i>), which are listed as threatened or endangered under the Endangered Species Act.</div>\n<div data-canvas-width=\"127.4938\"><br />This report covers UCR activities performed by USGS personnel from March 2011 to November 2011. It involves collecting and analyzing data collected during pre-implementation (2008&ndash;2012) there will be a follow-up after actions are completed (2012&ndash;2014). The goal of Reclamation is to maximize the potential of habitat and improve proposed limiting factors affecting the middle Methow River subwatershed (Reclamation, 2010). The Middle Methow (M2) reach (river kilometer mile [rkm] 843.065 to 843.080) of the Methow River was selected as the treatment reach for this study based on possible stream restoration plans by Reclamation (fig. 1). The upper Methow River (rkm 843.094 and 843.080), Chewuch River, and the Methow River downstream of the Twisp River (rkm 843.065) are being sampled as reference and control reaches in this study (fig. 2). This report covers the third year in the pre-evaluation of the M2 reach and its side channels. Restoration of the M2 reach is scheduled for 2012, which is planned to be followed by a multi-year intensive post-evaluation period.</div>\n<div data-canvas-width=\"127.4938\"><br />The intent of the summary of information provided in this report is to fulfill the objectives and tasks submitted in a statement of work to Reclamation in November 2010 (Connolly and Martens, 2011). The study design provides data by which to assess potential fish response to a Reclamation habitat restoration effort focused to improve juvenile salmonid rearing habitat in the M2, which runs between the towns of Winthrop and Twisp, Washington (fig. 1). The pre-treatment phase of the study is designed so that specific questions about the response of target fish species (spring Chinook salmon, steelhead, and bull trout) to the restoration actions can be addressed. Effectiveness is being determined by measuring fish productivity and habitat connectivity of the restoration reach and adjoining reaches, and their tributaries. The study includes sampling efforts directed to understand the relationships between stream habitat and the abundance of various fish species and to assess the response of the fish community. To complement these measurements, we will use models to predict response to treatment, and we will update the model&nbsp;with empirically derived data as these data become available. This modeling effort is expected to inform us of data gaps, sensitivity of key variables, and ability to detect response based on variability of the data.</div>\n<div data-canvas-width=\"127.4938\"><br />The approach and actions taken or planned by Reclamation to modify off-channel habitat are largely untested as to their effectiveness to improve target fish species&rsquo; productivity and survival needs. Those documented strategies that identify both physical parameters and biological relationships and benefits have been identified (Reclamation, 2008). To assess biological performance, we plan to compare age structure, growth, and age at smolting between those fish that stay in natal areas versus those fish that move. To assess retention in, and movement from or into, the restoration reach, we have used a combination of within-reach and out-of-reach sampling. We are using passive integrated transponder (PIT) tags, a network of instream PIT tag interrogation systems, and smolt traps to assess differences in biological performance and the magnitude of retention in, and movement from and into, the restoration reach.</div>","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/ofr20121108","usgsCitation":"Tibbits, W.T., Martens, K.D., and Connolly, P., 2012, Monitoring of stream restoration habitat on the main stem of the Methow River, Washington, during the pre-treatment phase (October 2008-May 2012) with a progress report for activities from March 2011 to November 2011: U.S. Geological Survey Open-File Report 2012-1108, Report: iv, 15 p.; 4 Excel Table Downloads, https://doi.org/10.3133/ofr20121108.","productDescription":"Report: iv, 15 p.; 4 Excel Table Downloads","onlineOnly":"N","additionalOnlineFiles":"N","temporalStart":"2008-10-01","temporalEnd":"2012-05-31","costCenters":[{"id":193,"text":"Columbia River Fisheries Program","active":false,"usgs":true},{"id":654,"text":"Western Fisheries Research Center","active":true,"usgs":true}],"links":[{"id":257103,"rank":0,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/ofr_2012_1108.JPG"},{"id":320963,"rank":101,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/of/2012/1108/pdf/ofr20121108.pdf","text":"Report","size":"225 KB","linkFileType":{"id":1,"text":"pdf"},"description":"Report"},{"id":320964,"rank":102,"type":{"id":7,"text":"Companion Files"},"url":"https://pubs.usgs.gov/of/2012/1108/data/ofr20121108_table01.xlsx","text":"Table 1","size":"15 KB","linkFileType":{"id":3,"text":"xlsx"},"description":"Table 1"},{"id":320965,"rank":103,"type":{"id":7,"text":"Companion 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D.","contributorId":12740,"corporation":false,"usgs":true,"family":"Martens","given":"Kyle","email":"","middleInitial":"D.","affiliations":[],"preferred":false,"id":464159,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Connolly, Patrick J. 0000-0001-7365-7618 pconnolly@usgs.gov","orcid":"https://orcid.org/0000-0001-7365-7618","contributorId":2920,"corporation":false,"usgs":true,"family":"Connolly","given":"Patrick J.","email":"pconnolly@usgs.gov","affiliations":[{"id":654,"text":"Western Fisheries Research Center","active":true,"usgs":true}],"preferred":true,"id":464157,"contributorType":{"id":1,"text":"Authors"},"rank":3}]}}
,{"id":70038442,"text":"ofr20121101 - 2012 - Dissolved oxygen analysis, TMDL model comparison, and particulate matter shunting&mdash;Preliminary results from three model scenarios for the Klamath River upstream of Keno Dam, Oregon","interactions":[],"lastModifiedDate":"2012-06-01T01:01:40","indexId":"ofr20121101","displayToPublicDate":"2012-05-31T00:00:00","publicationYear":"2012","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":"2012-1101","title":"Dissolved oxygen analysis, TMDL model comparison, and particulate matter shunting&mdash;Preliminary results from three model scenarios for the Klamath River upstream of Keno Dam, Oregon","docAbstract":"Efforts are underway to identify actions that would improve water quality in the Link River to Keno Dam reach of the Upper Klamath River in south-central Oregon. To provide further insight into water-quality improvement options, three scenarios were developed, run, and analyzed using previously calibrated CE-QUAL-W2 hydrodynamic and water-quality models. Additional scenarios are under development as part of this ongoing study. Most of these scenarios evaluate changes relative to a \"current conditions\" model, but in some cases a \"natural conditions\" model was used that simulated the reach without the effect of point and nonpoint sources and set Upper Klamath Lake at its Total Maximum Daily Load (TMDL) targets. These scenarios were simulated using a model developed by the U.S. Geological Survey (USGS) and Watercourse Engineering, Inc. for the years 2006&ndash;09, referred to here as the \"USGS model.\" Another model of the reach was developed by Tetra Tech, Inc. for years 2000 and 2002 to support the Klamath River TMDL process; that model is referred to here as the \"TMDL model.\" The three scenarios described in this report included (1) an analysis of whether this reach of the Upper Klamath River would be in compliance with dissolved oxygen standards if sources met TMDL allocations, (2) an application of more recent datasets to the TMDL model with comparison to results from the USGS model, and (3) an examination of the effect on dissolved oxygen in the Klamath River if particulate material were stopped from entering Klamath Project diversion canals. Updates and modifications to the USGS model are in progress, so in the future these scenarios will be reanalyzed with the updated model and the interim results presented here will be superseded. Significant findings from this phase of the investigation include: * The TMDL analysis used depth-averaged dissolved oxygen concentrations from model output for comparison with dissolved oxygen standards. The Oregon dissolved oxygen standards do not specify whether the numeric criteria are based on depth-averaged dissolved oxygen concentration; this was an interpretation of the standards rule by the Oregon Department of Environmental Quality (ODEQ). In this study, both depth-averaged and volume-averaged dissolved oxygen concentrations were calculated from model output. Results showed that modeled depth-averaged concentrations typically were lower than volume-averaged dissolved oxygen concentrations because depth-averaging gives a higher weight to small volume areas near the channel bottom that often have lower dissolved oxygen concentrations. Results from model scenarios in this study are reported using volume-averaged dissolved oxygen concentrations. * Under all scenarios analyzed, violations of the dissolved oxygen standard occurred most often in summer. Of the three dissolved oxygen criteria that must be met, the 30-day standard was violated most frequently. Under the base case (current conditions), fewer violations occurred in the upstream part of the reach. More violations occurred in the down-stream direction, due in part to oxygen demand from the decay of algae and organic matter from Link River and other inflows. * A condition in which Upper Klamath Lake and its Link River outflow achieved Upper Klamath Lake TMDL water-quality targets was most effective in reducing the number of violations of the dissolved oxygen standard in the Link River to Keno Dam reach of the Klamath River. The condition in which point and nonpoint sources within the Link River to Keno Dam reach met Klamath River TMDL allocations had no effect on dissolved oxygen compliance in some locations and a small effect in others under current conditions. On the other hand, meeting TMDL allocations for nonpoint and point sources was predicted to be important in meeting dissolved oxygen criteria when Upper Klamath Lake and Link River also met Upper Klamath TMDL water-quality targets. * The location of greatest dissolved oxygen improvement from nutrient and organic matter reductions was downstream from point and nonpoint source inflows because time and distance are required for decay to occur and for oxygen demand to be exerted. * After assessing compliance with dissolved oxygen standards at all 102 model segments in the Link River to Keno Dam reach, it was determined that the seven locations used by ODEQ appear to be a representative subset of the reach for dissolved oxygen analysis. * The USGS and TMDL models were qualitatively compared by running both models for the 2006&ndash;09 period but preserving the essential characteristics of each, such as organic matter partitioning, bathymetric representation, and parameter rates. The analysis revealed that some constituents were not greatly affected by the differing algorithms, rates, and assumptions in the two models. Conversely, other constituents, especially organic matter, were simulated differently by the two models. Organic matter in this river system is best represented by a mixture of relatively labile particulate material and a substantial concentration of refractory dissolved material. In addition, the use of a first-order sediment oxygen demand, as in the USGS model, helps to capture the seasonal and dynamic effect of settled organic and algal material. * Simulation of shunting (diverting) particulate material away from the intake of four Klamath Project diversion canals, so that the material stayed in the river and out of the Project area, caused higher concentrations of particulate material to occur in the river. In all cases modeled, the increase in in-river particulate material also produced decreased dissolved oxygen concentrations and an increase in the number of days when dissolved oxygen standards were violated. * If particulate material were shunted back into the river at the Klamath Project diversion canals, less organic matter and nutrients would be taken into the Klamath Project area and the Lost River basin, resulting in return flows to the Klamath River via Lost River Diversion Channel that may have reduced nutrient concentrations. Model scenarios bracketing potential end-member nutrient concentrations showed that the composition of the return flows had little to no effect on dissolved oxygen compliance under simulated conditions.","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/ofr20121101","collaboration":"Prepared in cooperation with the Bureau of Reclamation","usgsCitation":"Sullivan, A.B., Rounds, S.A., Deas, M., and Sogutlugil, I.E., 2012, Dissolved oxygen analysis, TMDL model comparison, and particulate matter shunting&mdash;Preliminary results from three model scenarios for the Klamath River upstream of Keno Dam, Oregon: U.S. Geological Survey Open-File Report 2012-1101, v, 28; Appendix, https://doi.org/10.3133/ofr20121101.","productDescription":"v, 28; Appendix","startPage":"i","endPage":"30","numberOfPages":"35","additionalOnlineFiles":"N","costCenters":[{"id":518,"text":"Oregon Water Science Center","active":true,"usgs":true}],"links":[{"id":257075,"rank":0,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/ofr_2012_1101.bmp"},{"id":257060,"rank":100,"type":{"id":15,"text":"Index Page"},"url":"https://pubs.usgs.gov/of/2012/1101/","linkFileType":{"id":5,"text":"html"}},{"id":257061,"rank":300,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/of/2012/1101/pdf/ofr20121101.pdf","linkFileType":{"id":1,"text":"pdf"}}],"country":"United States","state":"Oregon","otherGeospatial":"Klamath River;Keno Dam","noUsgsAuthors":false,"publicationStatus":"PW","scienceBaseUri":"505a023be4b0c8380cd4ff67","contributors":{"authors":[{"text":"Sullivan, Annett B. 0000-0001-7783-3906 annett@usgs.gov","orcid":"https://orcid.org/0000-0001-7783-3906","contributorId":56317,"corporation":false,"usgs":true,"family":"Sullivan","given":"Annett","email":"annett@usgs.gov","middleInitial":"B.","affiliations":[],"preferred":false,"id":464149,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Rounds, Stewart A. 0000-0002-8540-2206 sarounds@usgs.gov","orcid":"https://orcid.org/0000-0002-8540-2206","contributorId":905,"corporation":false,"usgs":true,"family":"Rounds","given":"Stewart","email":"sarounds@usgs.gov","middleInitial":"A.","affiliations":[{"id":518,"text":"Oregon Water Science Center","active":true,"usgs":true}],"preferred":true,"id":464147,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Deas, Michael L.","contributorId":98830,"corporation":false,"usgs":true,"family":"Deas","given":"Michael L.","affiliations":[],"preferred":false,"id":464150,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Sogutlugil, I. Ertugrul","contributorId":50277,"corporation":false,"usgs":true,"family":"Sogutlugil","given":"I.","email":"","middleInitial":"Ertugrul","affiliations":[],"preferred":false,"id":464148,"contributorType":{"id":1,"text":"Authors"},"rank":4}]}}
,{"id":70038440,"text":"sir20125091 - 2012 - Reconnaissance of land-use sources of pesticides in drinking water, McKenzie River, Oregon","interactions":[],"lastModifiedDate":"2012-06-05T01:01:48","indexId":"sir20125091","displayToPublicDate":"2012-05-31T00:00:00","publicationYear":"2012","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":"2012-5091","title":"Reconnaissance of land-use sources of pesticides in drinking water, McKenzie River, Oregon","docAbstract":"The Eugene Water and Electric Board (EWEB) provides water and electricity to the City of Eugene, Oregon, from the McKenzie River. In the spring of 2002, EWEB initiated a pesticide monitoring program in cooperation with the U.S. Geological Survey as part of their Drinking Water Source Protection Plan. Approximately twice yearly pesticide samples were collected from 2002 to 2010 at a suite of sampling sites representing varying land uses in the lower McKenzie River basin. A total of 117 ambient samples were collected from 28 tributary and mainstem sites, including those dominated by forestry, urban, and agricultural activities, as well as the mouths of major tributaries characterized by a mixture of upstream land use. Constituents tested included 175 compounds in filtered water (72 herbicides, 43 insecticides, 10 fungicides, and 36 of their degradation products, as well as 14 pharmaceutical compounds). No attempt was made to sample different site types equivalently; sampling was instead designed primarily to characterize representative storm events during spring and fall runoff conditions in order to assess or confirm the perceived importance of the different site types as sources for pesticides. Sampling was especially limited for agricultural sites, which were only sampled during two spring storm surveys. A total of 43 compounds were detected at least once, with many of these detected only at low concentrations (<0.1 micrograms per liter). Nine compounds were detected at the drinking- water intake, and most of these were reported as estimates less than the laboratory reporting level. Human-health benchmark concentrations were consistently several orders of magnitude higher than detected concentrations at the intake, indicating that pesticide concentrations present a negligible threat to human health. The largest number of pesticide detections occurred during spring storm surveys and primarily were associated with urban stormwater drains. Urban sites also were associated with the highest concentrations, occasionally exceeding 1 microgram per liter. Many of the compounds detected at urban sites were relatively hydrophobic (do not mix easily with water), persistent, and suspected of endocrine disruption. In contrast, forestry compounds were rarely detectable in the McKenzie River, even though forest land predominates in the basin and forestry pesticide use was detected in small tributaries draining forested lands following application. Agricultural pesticide runoff was not well characterized by the limited data available, although a large number of compounds was estimated to be used in the basin and concentrations were moderately high in the few samples collected from small tributaries draining agricultural lands. Results from this analysis indicate that urban pesticide use is potentially an important source for pesticides of concern for drinking water, not limited exclusively to storm conditions. Forestry pesticide use is not considered a likely threat to drinking water quality at the present time (2012). A more complete understanding of agricultural chemicals in runoff in the McKenzie River basin requires further investigation. In addition to evaluating the data collected in this study, a conceptual model describing pesticide contamination in the McKenzie River basin is provided, based on current scientific understanding that is consistent with the data analysis presented in this report. This model is intended to provide a foundation for future monitoring in the basin.","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/sir20125091","collaboration":"Prepared in cooperation with Eugene Water and Electric Board","usgsCitation":"Kelly, V.J., Anderson, C., and Morgenstern, K., 2012, Reconnaissance of land-use sources of pesticides in drinking water, McKenzie River, Oregon: U.S. Geological Survey Scientific Investigations Report 2012-5091, vi, 38 p.; Appendices; PDF Download of Appendix B, https://doi.org/10.3133/sir20125091.","productDescription":"vi, 38 p.; Appendices; PDF Download of Appendix B","startPage":"i","endPage":"46","numberOfPages":"52","additionalOnlineFiles":"N","costCenters":[{"id":518,"text":"Oregon Water Science Center","active":true,"usgs":true}],"links":[{"id":257054,"rank":0,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/sir_2012_5091.jpg"},{"id":257049,"rank":100,"type":{"id":15,"text":"Index Page"},"url":"https://pubs.usgs.gov/sir/2012/5091/","linkFileType":{"id":5,"text":"html"}},{"id":257050,"rank":300,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/sir/2012/5091/pdf/sir20125091.pdf","linkFileType":{"id":1,"text":"pdf"}}],"country":"United States","state":"Oregon","otherGeospatial":"Mckenzie River Basin","noUsgsAuthors":false,"publicationStatus":"PW","scienceBaseUri":"505a98ade4b0c8380cd82b45","contributors":{"authors":[{"text":"Kelly, Valerie J. vjkelly@usgs.gov","contributorId":4161,"corporation":false,"usgs":true,"family":"Kelly","given":"Valerie","email":"vjkelly@usgs.gov","middleInitial":"J.","affiliations":[{"id":518,"text":"Oregon Water Science Center","active":true,"usgs":true}],"preferred":true,"id":464142,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Anderson, Chauncey W. 0000-0002-1016-3781 chauncey@usgs.gov","orcid":"https://orcid.org/0000-0002-1016-3781","contributorId":1151,"corporation":false,"usgs":true,"family":"Anderson","given":"Chauncey W.","email":"chauncey@usgs.gov","affiliations":[{"id":518,"text":"Oregon Water Science Center","active":true,"usgs":true}],"preferred":false,"id":464141,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Morgenstern, Karl","contributorId":57716,"corporation":false,"usgs":true,"family":"Morgenstern","given":"Karl","email":"","affiliations":[],"preferred":false,"id":464143,"contributorType":{"id":1,"text":"Authors"},"rank":3}]}}
,{"id":70038435,"text":"ofr20121109 - 2012 - Introduction to geospatial semantics and technology workshop handbook","interactions":[],"lastModifiedDate":"2012-06-01T01:01:40","indexId":"ofr20121109","displayToPublicDate":"2012-05-31T00:00:00","publicationYear":"2012","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":"2012-1109","title":"Introduction to geospatial semantics and technology workshop handbook","docAbstract":"The workshop is a tutorial on introductory geospatial semantics with hands-on exercises using standard Web browsers. The workshop is divided into two sections, general semantics on the Web and specific examples of geospatial semantics using data from The National Map of the U.S. Geological Survey and the Open Ontology Repository. The general semantics section includes information and access to publicly available semantic archives. The specific session includes information on geospatial semantics with access to semantically enhanced data for hydrography, transportation, boundaries, and names. The Open Ontology Repository offers open-source ontologies for public use.","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/ofr20121109","usgsCitation":"Varanka, D.E., 2012, Introduction to geospatial semantics and technology workshop handbook: U.S. Geological Survey Open-File Report 2012-1109, iii, 107 p., https://doi.org/10.3133/ofr20121109.","productDescription":"iii, 107 p.","startPage":"i","endPage":"107","numberOfPages":"110","costCenters":[{"id":161,"text":"Center of Excellence for Geospatial Information Science (CEGIS)","active":false,"usgs":true}],"links":[{"id":257055,"rank":0,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/ofr_2012_1109.gif"},{"id":257040,"rank":100,"type":{"id":15,"text":"Index Page"},"url":"https://pubs.usgs.gov/of/2012/1109/","linkFileType":{"id":5,"text":"html"}}],"noUsgsAuthors":false,"publicationStatus":"PW","scienceBaseUri":"505a3dece4b0c8380cd6395d","contributors":{"authors":[{"text":"Varanka, Dalia E. 0000-0003-2857-9600 dvaranka@usgs.gov","orcid":"https://orcid.org/0000-0003-2857-9600","contributorId":1296,"corporation":false,"usgs":true,"family":"Varanka","given":"Dalia","email":"dvaranka@usgs.gov","middleInitial":"E.","affiliations":[{"id":5074,"text":"Center for Geospatial Information Science (CEGIS)","active":true,"usgs":true},{"id":404,"text":"NGTOC Rolla","active":true,"usgs":true}],"preferred":true,"id":464128,"contributorType":{"id":1,"text":"Authors"},"rank":1}]}}
,{"id":70038438,"text":"sir20125035 - 2012 - Invertebrate response to changes in streamflow hydraulics in two urban areas in the United States","interactions":[],"lastModifiedDate":"2012-06-01T01:01:40","indexId":"sir20125035","displayToPublicDate":"2012-05-31T00:00:00","publicationYear":"2012","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":"2012-5035","title":"Invertebrate response to changes in streamflow hydraulics in two urban areas in the United States","docAbstract":"Stream hydrology is foundational to aquatic ecosystems and has been shown to be a structuring element for fish and invertebrates. The relations among urbanization, hydraulics, and invertebrate communities were investigated by the U.S. Geological Survey, National Water-Quality Assessment Program by using measures of stream hydraulics in two areas of the United States. Specifically, the hypothesis that the effects of urbanization on streamflow and aquatic biota are transferable across geographic regions was tested. Data from sites in Raleigh, North Carolina, and Milwaukee&ndash;Green Bay, Wisconsin, were compared and indicate that increasing urbanization has an effect on hydraulic characteristics (Reynolds number, shear stress, and stream power for example) in each metropolitan area, though limited commonality of significant correlations was noted between areas. Correspondence of significant correlations between invertebrate and hydraulic metrics between study areas also was limited. The links between urbanization, hydraulics, and invertebrates could be seen only in the Raleigh data. Connections among these three elements in the Milwaukee&ndash;Green Bay data were not clear and likely were obscured by antecedent land cover. Observed biotic differences due to hydrology and urbanization characteristics are not similar between geographic regions.","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/sir20125035","collaboration":"National Water-Quality Assessment Program","usgsCitation":"Knight, R., and Cuffney, T.F., 2012, Invertebrate response to changes in streamflow hydraulics in two urban areas in the United States: U.S. Geological Survey Scientific Investigations Report 2012-5035, vi, 19 p., https://doi.org/10.3133/sir20125035.","productDescription":"vi, 19 p.","startPage":"i","endPage":"19","numberOfPages":"25","onlineOnly":"Y","additionalOnlineFiles":"N","costCenters":[{"id":581,"text":"Tennessee Water Science Center","active":true,"usgs":true}],"links":[{"id":257056,"rank":0,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/sir_2012_5035.jpg"},{"id":257045,"rank":100,"type":{"id":15,"text":"Index Page"},"url":"https://pubs.usgs.gov/sir/2012/5035/","linkFileType":{"id":5,"text":"html"}},{"id":257046,"rank":300,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/sir/2012/5035/pdf/2012-5035.pdf","linkFileType":{"id":1,"text":"pdf"}}],"country":"United States","noUsgsAuthors":false,"publicationStatus":"PW","scienceBaseUri":"505a3e60e4b0c8380cd63d16","contributors":{"authors":[{"text":"Knight, Rodney R. rrknight@usgs.gov","contributorId":2272,"corporation":false,"usgs":true,"family":"Knight","given":"Rodney R.","email":"rrknight@usgs.gov","affiliations":[{"id":581,"text":"Tennessee Water Science Center","active":true,"usgs":true}],"preferred":false,"id":464137,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Cuffney, Thomas F. 0000-0003-1164-5560 tcuffney@usgs.gov","orcid":"https://orcid.org/0000-0003-1164-5560","contributorId":517,"corporation":false,"usgs":true,"family":"Cuffney","given":"Thomas","email":"tcuffney@usgs.gov","middleInitial":"F.","affiliations":[{"id":13634,"text":"South Atlantic Water Science Center","active":true,"usgs":true}],"preferred":true,"id":464136,"contributorType":{"id":1,"text":"Authors"},"rank":2}]}}
,{"id":70038437,"text":"ofr20121082 - 2012 - Assessment of soil-gas contamination at three former fuel-dispensing sites, Fort Gordon, Georgia, 2010&mdash;2011","interactions":[],"lastModifiedDate":"2012-06-01T01:01:41","indexId":"ofr20121082","displayToPublicDate":"2012-05-31T00:00:00","publicationYear":"2012","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":"2012-1082","title":"Assessment of soil-gas contamination at three former fuel-dispensing sites, Fort Gordon, Georgia, 2010&mdash;2011","docAbstract":"Soil gas was assessed for contaminants at three former fuel-dispensing sites at Fort Gordon, Georgia, from October 2010 to September 2011. The assessment included delineation of organic contaminants using soil-gas samplers collected from the former fuel-dispensing sites at 8th Street, Chamberlain Avenue, and 12th Street. This assessment was conducted to provide environmental contamination data to Fort Gordon personnel pursuant to requirements for the Resource Conservation and Recovery Act Part B Hazardous Waste Permit process. Soil-gas samplers installed and retrieved during June and August 2011 at the 8th Street site had detections above the method detection level (MDL) for the mass of total petroleum hydrocarbons (TPH), benzene, toluene, ortho-xylene, undecane, tridecane, pentadecane, and chloroform. Total petroleum hydrocarbons soil-gas mass exceeded the MDL of 0.02 microgram in 54 of the 55 soil-gas samplers. The highest detection of TPH soil-gas mass was 146.10 micrograms, located in the central part of the site. Benzene mass exceeded the MDL of 0.01 microgram in 23 soil-gas samplers, whereas toluene was detected in only 10 soil-gas samplers. Ortho-xylene was detected above the MDL in only one soil-gas sampler. The highest soil-gas mass detected for undecane, tridecane, and pentadecane was located in the northeastern corner of the 8th Street site. Chloroform mass greater than the MDL of 0.01 microgram was detected in less than one-third of the soil-gas samplers. Soil-gas masses above the MDL were identified for TPH, gasoline-related compounds, diesel-range alkanes, trimethylbenzenes, naphthalene, 2-methyl-napthalene, octane, and tetrachloroethylene for the July 2011 soil-gas survey at the Chamberlain Avenue site. All 30 of the soil-gas samplers contained TPH mass above the MDL. The highest detection of TPH mass, 426.36 micrograms, was for a soil-gas sampler located near the northern boundary of the site. Gasoline-related compounds and diesel-range alkanes were detected in multiple soil-gas samplers, and the highest detections of these compounds were located near the central part of the site near existing, nonoperational, fuel-dispensing pumps. Trimethylbenzenes were detected in less than half of the soil-gas samplers. Naphthalene soil-gas mass was detected above the MDL in 10 soil-gas samplers, whereas 2-methyl-napthalene was detected above the MDL in half of the soil-gas samplers. Octane mass was detected above the MDL in one soil-gas sampler located near the central part of the site. Tetrachloroethylene soil-gas mass was detected above the MDL in more than half of the soil-gas samplers, and the highest tetrachloroethylene soil-gas mass of 0.90 microgram was located in the northeastern part of the site. Soil-gas samplers collected at the 12th Street site during July 2011 contained soil-gas mass above the MDL for TPH, toluene, undecane, tridecane, and pentadecane (diesel-range alkanes), trichloroethylene, 1,4-dichlorobenzene, chloroform, and 1,2,4-trimethylbenzene. The highest detected TPH mass was 24.37 micrograms in a soil-gas sampler located in the northern part of the site. The highest detection of toluene soil-gas mass was from a soil-gas sampler located near the southern boundary of the site. The diesel-range alkanes were detected above the MDL in five soil-gas samplers; the highest detection of soil-gas diesel mass, 0.65 microgram, was located in the southern part of the site. Trichloroethylene and 1,4-dichlorobenzene were detected above the MDL in the northern part of the site in one soil-gas sampler that also had one of the highest detections of TPH. Chloroform was detected above the MDL in three soil-gas samplers, whereas 1,2,4-trimethylbenzene soil-gas mass was detected above the MDL in two soil-gas samplers.","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/ofr20121082","collaboration":"Prepared in cooperation with the U.S. Department of the Army Environmental and Natural Resources Management Office of the U.S. Army Signal Center and Fort Gordon","usgsCitation":"Caldwell, A.W., Falls, W.F., Guimaraes, W.B., Ratliff, W.H., Wellborn, J.B., and Landmeyer, J., 2012, Assessment of soil-gas contamination at three former fuel-dispensing sites, Fort Gordon, Georgia, 2010&mdash;2011: U.S. Geological Survey Open-File Report 2012-1082, v, 7 p.; Figures; Tables, https://doi.org/10.3133/ofr20121082.","productDescription":"v, 7 p.; Figures; Tables","startPage":"i","endPage":"37","numberOfPages":"42","onlineOnly":"Y","additionalOnlineFiles":"N","temporalStart":"2010-10-01","temporalEnd":"2011-09-30","costCenters":[{"id":559,"text":"South Carolina Water Science Center","active":true,"usgs":true}],"links":[{"id":257053,"rank":0,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/ofr_2012_1082.jpg"},{"id":257044,"rank":300,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/of/2012/1082/pdf/2012-1082.pdf","linkFileType":{"id":1,"text":"pdf"}},{"id":257043,"rank":100,"type":{"id":15,"text":"Index Page"},"url":"https://pubs.usgs.gov/of/2012/1082/","linkFileType":{"id":5,"text":"html"}}],"country":"United States","state":"Georgia","otherGeospatial":"Fort Gordon","noUsgsAuthors":false,"publicationStatus":"PW","scienceBaseUri":"5059ee59e4b0c8380cd49cf5","contributors":{"authors":[{"text":"Caldwell, Andral W. 0000-0003-1269-5463 acaldwel@usgs.gov","orcid":"https://orcid.org/0000-0003-1269-5463","contributorId":3228,"corporation":false,"usgs":true,"family":"Caldwell","given":"Andral","email":"acaldwel@usgs.gov","middleInitial":"W.","affiliations":[{"id":559,"text":"South Carolina Water Science Center","active":true,"usgs":true}],"preferred":true,"id":464130,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Falls, W. Fred 0000-0003-2928-9795 wffalls@usgs.gov","orcid":"https://orcid.org/0000-0003-2928-9795","contributorId":107754,"corporation":false,"usgs":true,"family":"Falls","given":"W.","email":"wffalls@usgs.gov","middleInitial":"Fred","affiliations":[],"preferred":false,"id":464135,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Guimaraes, Wladmir B. wbguimar@usgs.gov","contributorId":3818,"corporation":false,"usgs":true,"family":"Guimaraes","given":"Wladmir","email":"wbguimar@usgs.gov","middleInitial":"B.","affiliations":[{"id":559,"text":"South Carolina Water Science Center","active":true,"usgs":true}],"preferred":true,"id":464132,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Ratliff, W. Hagan","contributorId":60347,"corporation":false,"usgs":true,"family":"Ratliff","given":"W.","email":"","middleInitial":"Hagan","affiliations":[],"preferred":false,"id":464134,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Wellborn, John B.","contributorId":24822,"corporation":false,"usgs":true,"family":"Wellborn","given":"John","email":"","middleInitial":"B.","affiliations":[],"preferred":false,"id":464133,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"Landmeyer, James 0000-0002-5640-3816 jlandmey@usgs.gov","orcid":"https://orcid.org/0000-0002-5640-3816","contributorId":3257,"corporation":false,"usgs":true,"family":"Landmeyer","given":"James","email":"jlandmey@usgs.gov","affiliations":[{"id":13634,"text":"South Atlantic Water Science Center","active":true,"usgs":true}],"preferred":true,"id":464131,"contributorType":{"id":1,"text":"Authors"},"rank":6}]}}
,{"id":70038436,"text":"fs20113038 - 2012 - Sound data management as a foundation for natural resources management and science","interactions":[],"lastModifiedDate":"2016-08-08T08:58:55","indexId":"fs20113038","displayToPublicDate":"2012-05-31T00:00:00","publicationYear":"2012","noYear":false,"publicationType":{"id":18,"text":"Report"},"publicationSubtype":{"id":5,"text":"USGS Numbered Series"},"seriesTitle":{"id":313,"text":"Fact Sheet","code":"FS","onlineIssn":"2327-6932","printIssn":"2327-6916","active":true,"publicationSubtype":{"id":5}},"seriesNumber":"2011-3038","title":"Sound data management as a foundation for natural resources management and science","docAbstract":"<p>Effective decision making is closely related to the quality and completeness of available data and information. Data management helps to ensure data quality in any discipline and supports decision making. Managing data as a long-term scientific asset helps to ensure that data will be usable beyond the original intended application. Emerging issues in water-resources management and climate variability require the ability to analyze change in the conditions of natural resources over time. The availability of quality, well-managed, and documented data from the past and present helps support this requirement.</p>","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/fs20113038","usgsCitation":"Burley, T.E., 2012, Sound data management as a foundation for natural resources management and science: U.S. Geological Survey Fact Sheet 2011-3038, 2 p., https://doi.org/10.3133/fs20113038.","productDescription":"2 p.","onlineOnly":"N","additionalOnlineFiles":"N","costCenters":[{"id":583,"text":"Texas Water Science Center","active":true,"usgs":true}],"links":[{"id":257052,"rank":0,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/fs_2011_3038.gif"},{"id":257041,"rank":100,"type":{"id":15,"text":"Index Page"},"url":"https://pubs.usgs.gov/fs/2011/3038","linkFileType":{"id":5,"text":"html"}},{"id":257042,"rank":300,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/fs/2011/3038/pdf/fs2011-3038.pdf","linkFileType":{"id":1,"text":"pdf"}}],"noUsgsAuthors":false,"publicationStatus":"PW","scienceBaseUri":"505b9315e4b08c986b31a2a7","contributors":{"authors":[{"text":"Burley, Thomas E. 0000-0002-2235-8092 teburley@usgs.gov","orcid":"https://orcid.org/0000-0002-2235-8092","contributorId":3499,"corporation":false,"usgs":true,"family":"Burley","given":"Thomas","email":"teburley@usgs.gov","middleInitial":"E.","affiliations":[{"id":583,"text":"Texas Water Science Center","active":true,"usgs":true}],"preferred":true,"id":464129,"contributorType":{"id":1,"text":"Authors"},"rank":1}]}}
,{"id":70038439,"text":"sir20125070 - 2012 - Representation of regional urban development conditions using a watershed-based gradient study design","interactions":[],"lastModifiedDate":"2018-04-02T16:30:50","indexId":"sir20125070","displayToPublicDate":"2012-05-31T00:00:00","publicationYear":"2012","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":"2012-5070","title":"Representation of regional urban development conditions using a watershed-based gradient study design","docAbstract":"As part of the U.S. Geological Survey National Water-Quality Assessment Program, the effects of urbanization on stream ecosystems (EUSE) have been intensively investigated in nine metropolitan areas in the United States, including Boston, Massachusetts; Atlanta, Georgia; Birmingham, Alabama; Raleigh, North Carolina; Salt Lake City, Utah; Denver, Colorado; Dallas&ndash;Fort Worth, Texas; Portland, Oregon; and Milwaukee&ndash;Green Bay, Wisconsin. Each of the EUSE study area watersheds was associated with one ecological region of the United States. This report evaluates whether each metropolitan area can be generalized across the ecological regions (ecoregions) within which the EUSE study watersheds are located. Seven characteristics of the EUSE watersheds that affect stream ecosystems were examined to determine the similarities in the same seven characteristics of the watersheds in the entire ecoregion. Land cover (percentage developed, forest and shrubland, and herbaceous and cultivated classes), average annual temperature, average annual precipitation, average surface elevation, and average percentage slope were selected as human-influenced, climate, and topography characteristics. Three findings emerged from this comparison that have implications for the use of EUSE data in models used to predict stream ecosystem condition. One is that the predominant or \"background\" land-cover type (either forested or agricultural land) in each ecoregion also is the predominant land-cover type within the associated EUSE study watersheds. The second finding is that in all EUSE study areas, the watersheds account for the range of developed land conditions that exist in the corresponding ecoregion watersheds. However, six of the nine EUSE study area watersheds have significantly different distributions of developed land from the ecoregion watersheds. Finally, in seven of the nine EUSE/ecoregion comparisons, the distributions of the values of climate variables in the EUSE watersheds are different from the distributions for watersheds in the corresponding ecoregions.","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/sir20125070","collaboration":"National Water-Quality Assessment Program","usgsCitation":"Terziotti, S., McMahon, G., and Bell, A.H., 2012, Representation of regional urban development conditions using a watershed-based gradient study design: U.S. Geological Survey Scientific Investigations Report 2012-5070, viii, 91 p.; Appendix, https://doi.org/10.3133/sir20125070.","productDescription":"viii, 91 p.; Appendix","startPage":"i","endPage":"109","numberOfPages":"117","onlineOnly":"Y","additionalOnlineFiles":"N","costCenters":[{"id":13634,"text":"South Atlantic Water Science 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,{"id":70038430,"text":"sir20125045 - 2012 - Prioritizing pesticide compounds for analytical methods development","interactions":[],"lastModifiedDate":"2012-05-31T01:01:41","indexId":"sir20125045","displayToPublicDate":"2012-05-30T00:00:00","publicationYear":"2012","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":"2012-5045","title":"Prioritizing pesticide compounds for analytical methods development","docAbstract":"The U.S. Geological Survey (USGS) has a periodic need to re-evaluate pesticide compounds in terms of priorities for inclusion in monitoring and studies and, thus, must also assess the current analytical capabilities for pesticide detection. To meet this need, a strategy has been developed to prioritize pesticides and degradates for analytical methods development. Screening procedures were developed to separately prioritize pesticide compounds in water and sediment. The procedures evaluate pesticide compounds in existing USGS analytical methods for water and sediment and compounds for which recent agricultural-use information was available. Measured occurrence (detection frequency and concentrations) in water and sediment, predicted concentrations in water and predicted likelihood of occurrence in sediment, potential toxicity to aquatic life or humans, and priorities of other agencies or organizations, regulatory or otherwise, were considered. Several existing strategies for prioritizing chemicals for various purposes were reviewed, including those that identify and prioritize persistent, bioaccumulative, and toxic compounds, and those that determine candidates for future regulation of drinking-water contaminants. The systematic procedures developed and used in this study rely on concepts common to many previously established strategies. The evaluation of pesticide compounds resulted in the classification of compounds into three groups: Tier 1 for high priority compounds, Tier 2 for moderate priority compounds, and Tier 3 for low priority compounds. For water, a total of 247 pesticide compounds were classified as Tier 1 and, thus, are high priority for inclusion in analytical methods for monitoring and studies. Of these, about three-quarters are included in some USGS analytical method; however, many of these compounds are included on research methods that are expensive and for which there are few data on environmental samples. The remaining quarter of Tier 1 compounds are high priority as new analytes. The objective for analytical methods development is to design an integrated analytical strategy that includes as many of the Tier 1 pesticide compounds as possible in a relatively few, cost-effective methods. More than 60 percent of the Tier 1 compounds are high priority because they are anticipated to be present at concentrations approaching levels that could be of concern to human health or aquatic life in surface water or groundwater. An additional 17 percent of Tier 1 compounds were frequently detected in monitoring studies, but either were not measured at levels potentially relevant to humans or aquatic organisms, or do not have benchmarks available with which to compare concentrations. The remaining 21 percent are pesticide degradates that were included because their parent pesticides were in Tier 1. Tier 1 pesticide compounds for water span all major pesticide use groups and a diverse range of chemical classes, with herbicides and their degradates composing half of compounds. Many of the high priority pesticide compounds also are in several national regulatory programs for water, including those that are regulated in drinking water by the U.S. Environmental Protection Agency under the Safe Drinking Water Act and those that are on the latest Contaminant Candidate List. For sediment, a total of 175 pesticide compounds were classified as Tier 1 and, thus, are high priority for inclusion in analytical methods available for monitoring and studies. More than 60 percent of these compounds are included in some USGS analytical method; however, some are spread across several research methods that are expensive to perform, and monitoring data are not extensive for many compounds. The remaining Tier 1 compounds for sediment are high priority as new analytes. The objective for analytical methods development for sediment is to enhance an existing analytical method that currently includes nearly half of the pesticide compounds in Tier 1 by adding as many additional Tier 1 compounds as are analytically compatible. About 35 percent of the Tier 1 compounds for sediment are high priority on the basis of measured occurrence. A total of 74 compounds, or 42 percent, are high priority on the basis of predicted likelihood of occurrence according to physical-chemical properties, and either have potential toxicity to aquatic life, high pesticide useage, or both. The remaining 22 percent of Tier 1 pesticide compounds were either degradates of Tier 1 parent compounds or included for other reasons. As with water, the Tier 1 pesticide compounds for sediment are distributed across the major pesticide-use groups; insecticides and their degradates are the largest fraction, making up 45 percent of Tier 1. In contrast to water, organochlorines, at 17 percent, are the largest chemical class for Tier 1 in sediment, which is to be expected because there is continued widespread detection in sediments of persistent organochlorine pesticides and their degradates at concentrations high enough for potential effects on aquatic life. Compared to water, there are fewer available benchmarks with which to compare contaminant concentrations in sediment, but a total of 19 Tier 1 compounds have at least one sediment benchmark or screening value for aquatic organisms. Of the 175 compounds in Tier 1, 77 percent have high aquatic-life toxicity, as defined for this process. This evaluation of pesticides and degradates resulted in two lists of compounds that are priorities for USGS analytical methods development, one for water and one for sediment. These lists will be used as the basis for redesigning and enhancing USGS analytical capabilities for pesticides in order to capture as many high-priority pesticide compounds as possible using an economically feasible approach.","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/sir20125045","collaboration":"Prepared in cooperation with the National Water-Quality Assessment Program","usgsCitation":"Norman, J.E., Kuivila, K., and Nowell, L.H., 2012, Prioritizing pesticide compounds for analytical methods development: U.S. Geological Survey Scientific Investigations Report 2012-5045, xi, 74 p.; Appendices; Appendix 1 Excel Download, https://doi.org/10.3133/sir20125045.","productDescription":"xi, 74 p.; Appendices; Appendix 1 Excel Download","costCenters":[{"id":154,"text":"California Water Science Center","active":true,"usgs":true}],"links":[{"id":257039,"rank":0,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/sir_2012_5045.jpg"},{"id":257028,"rank":100,"type":{"id":15,"text":"Index Page"},"url":"https://pubs.usgs.gov/sir/2012/5045/","linkFileType":{"id":5,"text":"html"}}],"noUsgsAuthors":false,"publicationStatus":"PW","scienceBaseUri":"505a8c74e4b0c8380cd7e6ce","contributors":{"authors":[{"text":"Norman, Julia E. 0000-0002-2820-6225 jnorman@usgs.gov","orcid":"https://orcid.org/0000-0002-2820-6225","contributorId":3832,"corporation":false,"usgs":true,"family":"Norman","given":"Julia","email":"jnorman@usgs.gov","middleInitial":"E.","affiliations":[{"id":154,"text":"California Water Science Center","active":true,"usgs":true},{"id":37277,"text":"WMA - Earth System Processes Division","active":true,"usgs":true},{"id":518,"text":"Oregon Water Science Center","active":true,"usgs":true}],"preferred":true,"id":464109,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"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":464108,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Nowell, Lisa H. 0000-0001-5417-7264 lhnowell@usgs.gov","orcid":"https://orcid.org/0000-0001-5417-7264","contributorId":490,"corporation":false,"usgs":true,"family":"Nowell","given":"Lisa","email":"lhnowell@usgs.gov","middleInitial":"H.","affiliations":[{"id":37277,"text":"WMA - Earth System Processes Division","active":true,"usgs":true},{"id":154,"text":"California Water Science Center","active":true,"usgs":true},{"id":451,"text":"National Water Quality Assessment Program","active":true,"usgs":true}],"preferred":true,"id":464107,"contributorType":{"id":1,"text":"Authors"},"rank":3}]}}
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