Brevibacterium linens AE038-8, isolated from As-contaminated groundwater in Tucumán (Argentina), is highly resistant to arsenic oxyanions, being able to tolerate up to 1 M As(V) and 75 mM As(III) in a complex medium. Strain AE038-8 was also able to reduce As(V) to As(III) when grown in complex medium but paradoxically it could not do this in a defined minimal medium with sodium acetate and ammonium sulfate as carbon and nitrogen sources, respectively. No oxidation of As(III) to As(V) was observed under any conditions. Three copies of the ars operon comprising arsenic resistance genes were found on B. linens AE038-8 genome. In addition to the well known arsC, ACR3 andarsR, two copies of the arsO gene of unknown function were detected.
","language":"English","publisher":"Elsevier Applied Science","publisherLocation":"Barking, Essex, England","doi":"10.1016/j.ibiod.2015.11.022","usgsCitation":"Maizel, D., Blum, J.S., Ferrero, M.A., Utturkar, S.M., Brown, S.D., Rosen, B.P., and Oremland, R.S., 2015, Characterization of the extremely arsenic-resistant Brevibacterium linens strain AE038-8 isolated from contaminated groundwater in Tucumán, Argentina: International Biodeterioration and Biodegradation, v. 107, p. 147-153, https://doi.org/10.1016/j.ibiod.2015.11.022.","productDescription":"7 p.","startPage":"147","endPage":"153","onlineOnly":"N","additionalOnlineFiles":"N","ipdsId":"IP-066757","costCenters":[{"id":438,"text":"National Research Program - Western Branch","active":true,"usgs":true}],"links":[{"id":471567,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.1016/j.ibiod.2015.11.022","text":"Publisher Index Page"},{"id":312248,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"Argentina","state":"Tucumán","city":"Los Pereyra","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -64.9,\n -27\n ],\n [\n -64.9,\n -26.9\n ],\n [\n -64.8,\n -26.9\n ],\n [\n -64.8,\n -27\n ],\n [\n -64.9,\n -27\n ]\n ]\n ]\n }\n }\n ]\n}","volume":"107","publishingServiceCenter":{"id":14,"text":"Menlo Park PSC"},"noUsgsAuthors":false,"publicationStatus":"PW","scienceBaseUri":"566fe829e4b09cfe53ca794f","chorus":{"doi":"10.1016/j.ibiod.2015.11.022","url":"http://dx.doi.org/10.1016/j.ibiod.2015.11.022","publisher":"Elsevier BV","authors":"Maizel Daniela, Blum Jodi Switzer, Ferrero Marcela A., Utturkar Sagar M., Brown Steven D., Rosen Barry P., Oremland Ronald S.","journalName":"International Biodeterioration & Biodegradation","publicationDate":"2/2016"},"contributors":{"authors":[{"text":"Maizel, Daniela","contributorId":150487,"corporation":false,"usgs":false,"family":"Maizel","given":"Daniela","email":"","affiliations":[{"id":18034,"text":"PROIMI-CONICET- Universidad Nacional de Tucuman, Tucuman, 4000 Argentina","active":true,"usgs":false}],"preferred":false,"id":581820,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Blum, Jodi S. jsblum@usgs.gov","contributorId":4263,"corporation":false,"usgs":true,"family":"Blum","given":"Jodi","email":"jsblum@usgs.gov","middleInitial":"S.","affiliations":[{"id":438,"text":"National Research Program - 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With these tensors, in order to isolate the effects of Earth conductivity structure, we perform a synthetic analysis—calculating geoelectric field variations induced by a geomagnetic field that is geographically uniform but varying sinusoidally with a chosen set of oscillation frequencies that are characteristic of magnetic storm variations. For north-south oriented geomagnetic oscillations at a period of T0=100 s, induced geoelectric field vectors show substantial geographically distributed differences in amplitude (approximately a factor of 100), direction (up to 130∘), and phase (over a quarter wavelength). These differences are the result of three-dimensional Earth conductivity structure, and they highlight a shortcoming of one-dimensional conductivity models (and other synthetic models not derived from direct geophysical measurement) that are used in the evaluation of storm time geoelectric hazards for the electric power grid industry. A hypothetical extremely intense magnetic storm having 500 nT amplitude at T0=100 s would induce geoelectric fields with an average amplitude across the midwestern United States of about 2.71 V/km, but with a representative site-to-site range of 0.15 V/km to 16.77 V/km. Significant improvement in the evaluation of such hazards will require detailed knowledge of the Earth's interior three-dimensional conductivity structure.
\n","language":"English","publisher":"American Geophysical Union","doi":"10.1002/2015GL066636","usgsCitation":"Bedrosian, P.A., and Love, J.J., 2015, Mapping geoelectric fields during magnetic storms: Synthetic analysis of empirical United States impedances: Geophysical Research Letters, v. 42, no. 23, p. 10160-10170, https://doi.org/10.1002/2015GL066636.","productDescription":"11 p.","startPage":"10160","endPage":"10170","onlineOnly":"N","additionalOnlineFiles":"N","ipdsId":"IP-070730","costCenters":[{"id":300,"text":"Geologic Hazards Science Center","active":true,"usgs":true}],"links":[{"id":471565,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.1002/2015gl066636","text":"Publisher Index Page"},{"id":316741,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"volume":"42","issue":"23","publishingServiceCenter":{"id":2,"text":"Denver PSC"},"noUsgsAuthors":false,"publicationDate":"2015-12-14","publicationStatus":"PW","scienceBaseUri":"56bb1bc7e4b08d617f654e29","contributors":{"authors":[{"text":"Bedrosian, Paul A. 0000-0002-6786-1038 pbedrosian@usgs.gov","orcid":"https://orcid.org/0000-0002-6786-1038","contributorId":839,"corporation":false,"usgs":true,"family":"Bedrosian","given":"Paul","email":"pbedrosian@usgs.gov","middleInitial":"A.","affiliations":[{"id":312,"text":"Geology, Minerals, Energy, and Geophysics Science Center","active":true,"usgs":true},{"id":211,"text":"Crustal Geophysics and Geochemistry Science Center","active":true,"usgs":true}],"preferred":true,"id":597711,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Love, Jeffrey J. 0000-0002-3324-0348 jlove@usgs.gov","orcid":"https://orcid.org/0000-0002-3324-0348","contributorId":760,"corporation":false,"usgs":true,"family":"Love","given":"Jeffrey","email":"jlove@usgs.gov","middleInitial":"J.","affiliations":[{"id":300,"text":"Geologic Hazards Science Center","active":true,"usgs":true}],"preferred":true,"id":597712,"contributorType":{"id":1,"text":"Authors"},"rank":2}]}} ,{"id":70160545,"text":"70160545 - 2015 - Infecting Pacific Herring with Ichthyophonus sp. in the laboratory","interactions":[],"lastModifiedDate":"2015-12-22T16:09:29","indexId":"70160545","displayToPublicDate":"2015-12-14T00:00:00","publicationYear":"2015","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":2177,"text":"Journal of Aquatic Animal Health","active":true,"publicationSubtype":{"id":10}},"title":"Infecting Pacific Herring with Ichthyophonus sp. in the laboratory","docAbstract":"
The protistan parasite Ichthyophonus sp. occurs in coastal populations of Pacific Herring Clupea pallasii throughout the northeast Pacific region, but the route(s) by which these planktivorous fish become infected is unknown. Several methods for establishing Ichthyophonus infections in laboratory challenges were examined. Infections were most effectively established after intraperitoneal (IP) injections with suspended parasite isolates from culture or after repeated feedings with infected fish tissues. Among groups that were offered the infected tissues, infection prevalence was greater after multiple feedings (65%) than after a single feeding (5%). Additionally, among groups that were exposed to parasite suspensions prepared from culture isolates, infection prevalence was greater after exposure by IP injection (74%) than after exposure via gastric intubation (12%); the flushing of parasite suspensions over the gills did not lead to infections in any of the experimental fish. Although the consumption of infected fish tissues is unlikely to be the primary route of Ichthyophonus sp. transmission in wild populations of Pacific Herring, this route may contribute to abnormally high infection prevalence in areas where juveniles have access to infected offal.
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However, opportunities exist to restore habitat for many imperiled wildlife species. But what is wildlife habitat restoration? We begin this chapter by defining habitat restoration and then provide recommendations on how to maximize success of future habitat restoration efforts for wildlife. Finally, we evaluate whether we have been successful in restoring wildlife habitat and supply recommendations to advance habitat restoration. Successful restoration requires clear and explicit goals that are based on our best understanding of what the habitat was like prior to the disturbing event. Ideally, a restoration project would include: (1) a summary of prerestoration conditions that define the existing status of wildlife populations and their habitat; (2) a description of habitat features required by the focal or indicator species for persistence; (3) an a priori description of measurable, quantitative metrics that define restoration goals and measures of success; (4) a monitoring plan; (5) postrestoration comparisons of habitat features and wildlife populations with adjacent unmodified areas that are similar to the restoration site; and (6) expert review of the entire restoration plan (i.e., the five aforementioned components).
","largerWorkType":{"id":4,"text":"Book"},"largerWorkTitle":"Wildlife habitat conservation : concepts, challenges, and solutions","language":"English","publisher":"Johns Hopkins University Press","publisherLocation":"Baltimore, MD","usgsCitation":"Conway, C.J., and Borgmann, K.L., 2015, Wildlife Habitat Restoration: Chapter 12, chap. of Wildlife habitat conservation : concepts, challenges, and solutions, p. 157-167.","productDescription":"11 p.","startPage":"157","endPage":"167","ipdsId":"IP-056236","costCenters":[{"id":200,"text":"Coop Res Unit Seattle","active":true,"usgs":true}],"links":[{"id":349997,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"},{"id":349996,"rank":1,"type":{"id":15,"text":"Index Page"},"url":"https://jhupbooks.press.jhu.edu/content/wildlife-habitat-conservation"}],"country":"United States","publishingServiceCenter":{"id":12,"text":"Tacoma PSC"},"noUsgsAuthors":false,"publicationStatus":"PW","scienceBaseUri":"5a60fe47e4b06e28e9c252db","contributors":{"editors":[{"text":"Morrison, Michael L.","contributorId":169013,"corporation":false,"usgs":false,"family":"Morrison","given":"Michael","email":"","middleInitial":"L.","affiliations":[],"preferred":false,"id":725069,"contributorType":{"id":2,"text":"Editors"},"rank":1},{"text":"Mathewson, Heather A.","contributorId":70184,"corporation":false,"usgs":false,"family":"Mathewson","given":"Heather","email":"","middleInitial":"A.","affiliations":[],"preferred":false,"id":725070,"contributorType":{"id":2,"text":"Editors"},"rank":2}],"authors":[{"text":"Conway, Courtney J. 0000-0003-0492-2953 cconway@usgs.gov","orcid":"https://orcid.org/0000-0003-0492-2953","contributorId":2951,"corporation":false,"usgs":true,"family":"Conway","given":"Courtney","email":"cconway@usgs.gov","middleInitial":"J.","affiliations":[{"id":200,"text":"Coop Res Unit Seattle","active":true,"usgs":true}],"preferred":true,"id":714089,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Borgmann, Kathi L.","contributorId":171647,"corporation":false,"usgs":false,"family":"Borgmann","given":"Kathi","email":"","middleInitial":"L.","affiliations":[],"preferred":false,"id":725071,"contributorType":{"id":1,"text":"Authors"},"rank":2}]}} ,{"id":70161863,"text":"70161863 - 2015 - The role of dynamic surface water-groundwater exchange on streambed denitrification in a first-order, low-relief agricultural watershed","interactions":[],"lastModifiedDate":"2016-12-16T10:44:50","indexId":"70161863","displayToPublicDate":"2015-12-13T15:00:00","publicationYear":"2015","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":3722,"text":"Water Resources Research","onlineIssn":"1944-7973","printIssn":"0043-1397","active":true,"publicationSubtype":{"id":10}},"title":"The role of dynamic surface water-groundwater exchange on streambed denitrification in a first-order, low-relief agricultural watershed","docAbstract":"The role of temporally varying surface water-groundwater (SW-GW) exchange on nitrate removal by streambed denitrification was examined along a reach of Leary Weber Ditch (LWD), Indiana, a small, first-order, low-relief agricultural watershed within the Upper Mississippi River basin, using data collected in 2004 and 2005. Stream stage, GW heads (H), and temperatures (T) were continuously monitored in streambed piezometers and stream bank wells for two transects across LWD accompanied by synoptic measurements of stream stage, H, T, and nitrate (NO3) concentrations along the reach. The H and T data were used to develop and calibrate vertical two-dimensional, models of streambed water flow and heat transport across and along the axis of the stream. Model-estimated SW-GW exchange varied seasonally and in response to high-streamflow events due to dynamic interactions between SW stage and GW H. Comparison of 2004 and 2005 conditions showed that small changes in precipitation amount and intensity, evapotranspiration, and/or nearby GW levels within a low-relief watershed can readily impact SW-GW interactions. The calibrated LWD flow models and observed stream and streambed NO3 concentrations were used to predict temporal variations in streambed NO3 removal in response to dynamic SW-GW exchange. NO3 removal rates underwent slow seasonal changes, but also underwent rapid changes in response to high-flow events. These findings suggest that increased temporal variability of SW-GW exchange in low-order, low-relief watersheds may be a factor contributing their more efficient removal of NO3.
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The ability of the metal to store and release electrical energy makes it ideally suited for use in certain types of capacitors that are widely used in modern electronics. Approximately 60 percent of global tantalum consumption is in the electronics industry. The ductility and corrosion resistance of the metal lends itself to application in the chemical processing industry, and its high melting point and high strength retention at elevated temperatures make it an important component of super alloys used in aircraft engines.
\nAs a major industrialized nation, the United States is a leading consumer of tantalum and tantalum-containing products. Domestic deposits typically are of low grade, and no tantalum has been recovered from mining activities in the United States since 1959. Consequently, the United States is nearly completely reliant on imports to meet its domestic consumption of tantalum for economic and national security needs. The recovery of tantalum from mine production is economically viable in only a few countries.
\nAlthough developed countries dominated tantalum mine production in the early 2000s, production today is dominated by countries in the Great Lakes Region of Africa. There is concern that the sales of minerals, including columbite-tantalite or “coltan,” a mineral from which tantalum is derived, have helped finance rebel groups accused of violating human rights as part of the continuing armed conflict in the Democratic Republic of the Congo (DRC) and neighboring countries. These accusations have prompted the passage of legislation in the United States to curb the procurement of these mineral commodities, referred to as “conflict minerals,” from the DRC. Specifically, section 1502 of the 2010 Dodd-Frank Wall Street Reform and Consumer Protection Act (Public Law 111–203, 124 Stat. 2213–2218) requires companies that source tantalum, tin, tungsten, and gold (3TG) to perform due diligence on their supply chains to determine if the materials they use originate from the DRC or adjoining countries (defined as sharing a border with the DRC).
\nThe DRC, Rwanda, and surrounding countries are not globally significant sources of tin, tungsten, or gold, accounting for only about 2 percent of the mined world supply for each of these elements. The region has, however, evolved to become the world’s largest producer of mined tantalum.
\nA further complication of the production of tantalum stems from the opacity of the tantalum market. Unlike most base and precious metals, tantalum concentrates are not publicly traded through commodities exchanges but are bought and sold through networks of dealers and on contract between producers and consumers, some of whom may not provide accurate statistical data concerning the amounts, origins, and destination of the concentrates. Some price data can be found in trade journals or in other publications; however, there are no recognized official set exchange prices for either concentrate or tantalum metal. Because price is determined by negotiation between buyer and seller, published prices for concentrate are probably not representative of global prices paid for concentrate. The development of a mine-to-market supply-chain analysis is complicated and difficult because many of the industry participants that produce, trade, and consume tantalum do not publish statistical information, contracts are long term between miners and buyers, and much of the industry is vertically integrated.
\nAs a result of these and other considerations, tantalum is considered by many to be a “critical” commodity. This fact sheet identifies and addresses the major geographic shifts in the source of mine production of tantalum which have occurred over the past 15 years, some of the factors that drove this shift, and some of the related consequences.
\nOne of the activities of the U.S. Geological Survey National Minerals Information Center (USGS-NMIC) is to analyze global supply chains and characterize major components of mineral and material flows from ore extraction through processing to first tier products. These analyses support the core mission of the USGS-NMIC as the Federal entity responsible for the collection, analysis, and dissemination of objective, unbiased, factual information on minerals essential to the U.S. economy and national security.
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Numbered Series"},"seriesTitle":{"id":331,"text":"Professional Paper","code":"PP","onlineIssn":"2330-7102","printIssn":"1044-9612","active":true,"publicationSubtype":{"id":5}},"seriesNumber":"1794","chapter":"C","title":"Status and trends of land change in the Midwest–South Central United States—1973 to 2000","docAbstract":"U.S. Geological Survey (USGS) Professional Paper 1794–C is the third in a four-volume series on the status and trends of the Nation’s land use and land cover, providing an assessment of the rates and causes of land-use and land-cover change in the Midwest–South Central United States between 1973 and 2000. Volumes A, B, and D provide similar analyses for the Western United States, the Great Plains of the United States, and the Eastern United States, respectively. The assessments of land-use and land-cover trends are conducted on an ecoregion-by-ecoregion basis, and each ecoregion assessment is guided by a nationally consistent study design that includes mapping, statistical methods, field studies, and analysis. Individual assessments provide a picture of the characteristics of land change occurring in a given ecoregion; in combination, they provide a framework for understanding the complex national mosaic of change and also the causes and consequences of change. Thus, each volume in this series provides a regional assessment of how (and how fast) land use and land cover are changing, and why. The four volumes together form the first comprehensive picture of land change across the Nation.
Geographic understanding of land-use and land-cover change is directly relevant to a wide variety of stakeholders, including land and resource managers, policymakers, and scientists. The chapters in this volume present brief summaries of the patterns and rates of land change observed in each ecoregion in the Midwest–South Central United States, together with field photographs, statistics, and comparisons with other assessments. In addition, a synthesis chapter summarizes the scope of land change observed across the entire Midwest–South Central United States. The studies provide a way of integrating information across the landscape, and they form a critical component in the efforts to understand how land use and land cover affect important issues such as the provision of ecological goods and services and also the determination of risks to, and vulnerabilities of, human communities. Results from this project also are published in peer-reviewed journals, and they are further used to produce maps of change and other tools for land management, as well as to provide inputs for carbon-cycle modeling and other climate change research.
Contact information, U.S. Geological Survey
Earth Resources Observation and Science (EROS) Center
47914 252nd Street
Sioux Falls, SD 57198-0001
Walker Lake is a threatened and federally protected desert terminal lake in western Nevada. To help protect the desert terminal lake and the surrounding watershed, the Bureau of Reclamation and U.S. Geological Survey have been studying the hydrology of the Walker River Basin in Nevada and California since 2004. Hydrologic data collected for this study during water years 2010 through 2014 included groundwater levels, surface-water discharge, water chemistry, and meteorological data. Groundwater levels were measured in wells, and surface-water discharge was measured in streams, canals, and ditches. Water samples for chemical analyses were collected from wells, streams, springs, and Walker Lake. Chemical analyses included determining physical properties; the concentrations of major ions, nutrients, trace metals, dissolved gases, and radionuclides; and ratios of the stable isotopes of hydrogen and oxygen. Walker Lake water properties and meteorological parameters were monitored from a floating platform on the lake. Data collection methods followed established U.S. Geological Survey guidelines, and all data are stored in the National Water Information System database. All of the data are presented in this report and accessible on the internet, except multiple-depth Walker Lake water-chemistry data, which are available only in this report.
","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/ds967","collaboration":"Prepared in cooperation with the Bureau of Reclamation","usgsCitation":"Pavelko, Michael T., and Orozco, Erin L., 2015, Hydrologic data for the Walker River Basin, Nevada and California, water years 2010–14: U.S. Geological Survey Data Series 967, 17 p., plus appendixes, https://dx.doi.org/10.3133/ds967.","productDescription":"Report: iv, 17 p.; 6 Appendixes","numberOfPages":"26","onlineOnly":"Y","additionalOnlineFiles":"Y","temporalStart":"2009-10-01","temporalEnd":"2014-09-30","ipdsId":"IP-065428","costCenters":[{"id":465,"text":"Nevada Water Science Center","active":true,"usgs":true}],"links":[{"id":311927,"rank":8,"type":{"id":3,"text":"Appendix"},"url":"https://pubs.usgs.gov/ds/0967/ds967_appendix06.xlsx","text":"Appendix 6","size":"3.3 MB","linkFileType":{"id":3,"text":"xlsx"},"description":"DS 967 Appendix 6","linkHelpText":"Lake water-chemistry and meteorological data from sites located on Walker Lake, water years 2011–14."},{"id":311920,"rank":2,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/ds/0967/coverthb.jpg"},{"id":311922,"rank":3,"type":{"id":3,"text":"Appendix"},"url":"https://pubs.usgs.gov/ds/0967/ds967_appendix01.xlsx","text":"Appendix 1","size":"156 KB","linkFileType":{"id":3,"text":"xlsx"},"description":"DS 967 Appendix 1","linkHelpText":"Water-level measurements for the Walker River Basin study, water years 2010–14."},{"id":311919,"rank":1,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/ds/0967/ds967.pdf","text":"Report","size":"2.5 MB","description":"DS 967 PDF"},{"id":311923,"rank":4,"type":{"id":3,"text":"Appendix"},"url":"https://pubs.usgs.gov/ds/0967/ds967_appendix02.xlsx","text":"Appendix 2","size":"20 KB","linkFileType":{"id":3,"text":"xlsx"},"description":"DS 967 Appendix 2","linkHelpText":"Surface-water discharge measurements for the Walker River Basin study, water years 2011–13."},{"id":311924,"rank":5,"type":{"id":3,"text":"Appendix"},"url":"https://pubs.usgs.gov/ds/0967/ds967_appendix03.xlsx","text":"Appendix 3","size":"56 KB","linkFileType":{"id":3,"text":"xlsx"},"description":"DS 967 Appendix 3","linkHelpText":"Groundwater-chemistry data for the Walker River Basin study, water years 2010–12."},{"id":311926,"rank":7,"type":{"id":3,"text":"Appendix"},"url":"https://pubs.usgs.gov/ds/0967/ds967_appendix05.xlsx","text":"Appendix 5","size":"19 KB","linkFileType":{"id":3,"text":"xlsx"},"description":"DS 967 Appendix 5","linkHelpText":"Spring water-chemistry data for the Walker River Basin study, water years 2010–13."},{"id":311925,"rank":6,"type":{"id":3,"text":"Appendix"},"url":"https://pubs.usgs.gov/ds/0967/ds967_appendix04.xlsx","text":"Appendix 4","size":"40 KB","linkFileType":{"id":3,"text":"xlsx"},"description":"DS 967 Appendix 4","linkHelpText":"Stream water-chemistry data for the Walker River Basin study, water years 2011–13."}],"country":"United States","state":"California, Nevada","otherGeospatial":"Walker River Basin","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -119.22912597656249,\n 38.026458711461245\n ],\n [\n -118.98193359375,\n 38.1734326790354\n ],\n [\n -118.8006591796875,\n 38.24249456800328\n ],\n [\n -118.6138916015625,\n 38.29424797320529\n ],\n [\n -118.48205566406251,\n 38.182068998322094\n ],\n [\n -118.55895996093749,\n 38.06539235133249\n ],\n [\n -118.54248046874999,\n 37.97884504049713\n ],\n [\n -118.38317871093749,\n 38.02213147353745\n ],\n [\n -118.26232910156249,\n 38.16479533621134\n ],\n [\n -118.23486328125,\n 38.29424797320529\n ],\n [\n -118.35571289062499,\n 38.638327308061875\n ],\n [\n -118.4381103515625,\n 38.70265930723801\n ],\n [\n -118.5150146484375,\n 38.839707613545144\n ],\n [\n -118.46557617187499,\n 38.950865400919994\n ],\n [\n -118.487548828125,\n 39.02345139405932\n ],\n [\n -118.63037109375,\n 39.027718840211605\n ],\n [\n -118.828125,\n 39.08743603215884\n ],\n [\n -118.9544677734375,\n 39.142842478062505\n ],\n [\n -119.11376953125,\n 39.1854331703021\n ],\n [\n -119.24560546875001,\n 39.1854331703021\n ],\n [\n -119.44335937499999,\n 39.104488809440475\n ],\n [\n -119.54223632812501,\n 38.96795115401593\n ],\n [\n -119.608154296875,\n 38.796908303484294\n ],\n [\n -119.65209960937501,\n 38.732661120482334\n ],\n [\n -119.7344970703125,\n 38.61257832462118\n ],\n [\n -119.6685791015625,\n 38.5008925889646\n ],\n [\n -119.63562011718751,\n 38.40194908237825\n ],\n [\n -119.63012695312499,\n 38.190704293996504\n ],\n [\n -119.60266113281249,\n 38.06971703320484\n ],\n [\n -119.4049072265625,\n 38.0091482264894\n ],\n [\n -119.2950439453125,\n 37.97018468810549\n ],\n [\n -119.22912597656249,\n 38.026458711461245\n ]\n ]\n ]\n }\n }\n ]\n}","contact":"Director, Nevada Water Science Center
U.S. Geological Survey
2730 N. Deer Run Rd.
Carson City, NV 89701
http://nevada.usgs.gov/water/
The Catahoula aquifer is an important source of fresh groundwater in central Louisiana. In 2010, about 3.96 million gallons per day (Mgal/d) were withdrawn from the Catahoula aquifer in Louisiana.
\nIn 2012, the U.S. Geological Survey in cooperation with the Louisiana Department of Natural Resources began a study to document current water levels in selected aquifers in Louisiana. This report presents water-level data and a map that illustrates the potentiometric surface of the Catahoula aquifer in 2013.
\nThe Catahoula aquifer crops out in a narrow band across north-central Louisiana. This band is broken by alluvial deposits in the valleys of the Red, Little, and Ouachita Rivers that have incised into the aquifer. Saltwater ridges under the Red, Little, and Tensas River Valleys divide the freshwater extents of the Catahoula aquifer and limit the flow of freshwater between these areas. The Catahoula aquifer generally ranges in thickness from about 50 feet (ft) in the outcrop area to about 450 ft in southern Vernon Parish. Sand beds in the aquifer are generally discontinuous, lenticular, and interbedded with silts and clays.
\nThe potentiometric surface of the Catahoula aquifer was constructed by using the altitude of water levels measured at 29 wells during the period May through September 2013. The altitude of water levels ranged from 0.02 ft above the National Geodetic Vertical Datum of 1929 (NGVD 29) in well Co-51 to 238 ft above NGVD 29 in well Na-317. Groundwater movement in the Catahoula aquifer is generally to the southeast and towards discharge areas beneath the Sabine, Red, Little, and Tensas River Valleys.
","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/sim3339","collaboration":"Prepared in cooperation with the Louisiana Department of Natural Resources","usgsCitation":"Fendick, R.B., Jr., and Carter, Kayla, 2015, Potentiometric surface of the Catahoula aquifer in central Louisiana, 2013: U.S. Geological Survey Scientific Investigations Map 3339, 1 sheet, https://dx.doi.org/10.3133/sim3339.","productDescription":"Sheet: 32.0 x 28.0 inches","onlineOnly":"N","additionalOnlineFiles":"N","ipdsId":"IP-064411","costCenters":[{"id":369,"text":"Louisiana Water Science Center","active":true,"usgs":true}],"links":[{"id":310005,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/sim/3339/coverthb.jpg"},{"id":310006,"rank":2,"type":{"id":26,"text":"Sheet"},"url":"https://pubs.usgs.gov/sim/3339/sim3339.pdf","text":"Sheet","size":"788 kB","linkFileType":{"id":1,"text":"pdf"},"description":"SIM 3339"}],"country":"United 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Lower Mississippi-Gulf Water Science Center
U.S. Geological Survey
3535 S. Sherwood Forest Blvd., Suite 120
Baton Rouge, LA 70816
http://la.water.usgs.gov/
This report describes an R package called smwrBase, which consists of a collection of functions to import, transform, manipulate, and manage hydrologic data within the R statistical environment. Functions in the package allow users to import surface-water and groundwater data from the U.S. Geological Survey’s National Water Information System database and other sources. Additional functions are provided to transform, manipulate, and manage hydrologic data in ways necessary for analyzing the data.
","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/ofr20151202","usgsCitation":"Lorenz, D.L., 2015, smwrBase—An R package for managing hydrologic data, version 1.1.1: U.S. Geological\nSurvey Open-File Report 2015–1202, 7 p., https://dx.doi.org/10.3133/ofr20151202.","productDescription":"Report: iii, 5 p.; Appendixes: 1-3","numberOfPages":"16","onlineOnly":"Y","additionalOnlineFiles":"Y","ipdsId":"IP-052705","costCenters":[{"id":392,"text":"Minnesota Water Science Center","active":true,"usgs":true}],"links":[{"id":311723,"rank":2,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/of/2015/1202/ofr20151202.pdf","text":"Report","size":"241 kB","linkFileType":{"id":1,"text":"pdf"},"description":"OFR 2015-1202"},{"id":311724,"rank":3,"type":{"id":3,"text":"Appendix"},"url":"https://pubs.usgs.gov/ofr/2015/1202/downloads/","text":"Appendix","description":"OFR 2015-1202 Appendix","linkHelpText":"Director, Minnesota Water Science Center
U.S. Geological Survey
2280 Woodale Drive
Mounds View, Minnesota 55112
http://mn.water.usgs.gov/
This report summarizes outcomes from a two-day scenario planning workshop for Acadia National Park, Maine. The primary objective of the workshop was to help Acadia senior leadership make management and planning decisions based on up-to-date climate science and assessments of future uncertainty. The workshop was also designed as a training program, helping build participants' capabilities to develop and use scenarios. The details of the workshop are given in later sections. The climate scenarios presented here are based on published global climate model output. The scenario implications for resources and management decisions are based on expert knowledge distilled through scientist-manager interaction during workgroup break-out sessions at the workshop. Thus, the descriptions below are from these small-group discussions in a workshop setting and should not be taken as vetted research statements of responses to the climate scenarios, but rather as insights and examinations of possible futures. Here we provide the main conclusions from the scenario planning workshop.
","language":"English","publisher":"National Park Service","usgsCitation":"Star, J., Fisichelli, N., Bryan, A., Babson, A., Cole-Will, R., and Miller-Rushing, A.J., 2015, Acadia National Park climate change scenario planning workshop summary, 50 p.","productDescription":"50 p.","ipdsId":"IP-069634","costCenters":[{"id":5080,"text":"Northeast Climate Adaptation Science Center","active":true,"usgs":true}],"links":[{"id":381169,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"},{"id":381167,"rank":1,"type":{"id":15,"text":"Index Page"},"url":"https://home.nps.gov/subjects/climatechange/acadiaworkshop.htm"}],"country":"United States","state":"Maine","otherGeospatial":"Acadia National Park","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -68.45100402832031,\n 44.22650439062394\n ],\n [\n -68.13789367675781,\n 44.22650439062394\n ],\n [\n -68.13789367675781,\n 44.42446328709913\n ],\n [\n -68.45100402832031,\n 44.42446328709913\n ],\n [\n -68.45100402832031,\n 44.22650439062394\n ]\n ]\n ]\n }\n }\n ]\n}","noUsgsAuthors":false,"publicationStatus":"PW","contributors":{"authors":[{"text":"Star, Jonathan","contributorId":168823,"corporation":false,"usgs":false,"family":"Star","given":"Jonathan","email":"","affiliations":[{"id":25365,"text":"Scenario Insight","active":true,"usgs":false}],"preferred":false,"id":806566,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Fisichelli, Nicholas","contributorId":168824,"corporation":false,"usgs":false,"family":"Fisichelli","given":"Nicholas","affiliations":[{"id":25366,"text":"National Park Service, Climate Change Response Program","active":true,"usgs":false}],"preferred":false,"id":806567,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Bryan, Alexander 0000-0003-2040-7636 abryan@usgs.gov","orcid":"https://orcid.org/0000-0003-2040-7636","contributorId":168822,"corporation":false,"usgs":true,"family":"Bryan","given":"Alexander","email":"abryan@usgs.gov","affiliations":[{"id":5080,"text":"Northeast Climate Adaptation Science Center","active":true,"usgs":true}],"preferred":true,"id":806568,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Babson, Amanda","contributorId":168825,"corporation":false,"usgs":false,"family":"Babson","given":"Amanda","email":"","affiliations":[{"id":25367,"text":"National Park Service, Northeast Region","active":true,"usgs":false}],"preferred":false,"id":806569,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Cole-Will, Rebecca","contributorId":168826,"corporation":false,"usgs":false,"family":"Cole-Will","given":"Rebecca","email":"","affiliations":[{"id":25368,"text":"National Park Service, Acadia National Park","active":true,"usgs":false}],"preferred":false,"id":806570,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"Miller-Rushing, Abraham J.","contributorId":149650,"corporation":false,"usgs":false,"family":"Miller-Rushing","given":"Abraham","email":"","middleInitial":"J.","affiliations":[{"id":7237,"text":"NPS, Olympic National Park","active":true,"usgs":false}],"preferred":false,"id":806571,"contributorType":{"id":1,"text":"Authors"},"rank":6}]}} ,{"id":70216840,"text":"70216840 - 2015 - Indicators of climate impacts for forests: Recommendations for the U.S. National Climate Assessment Indicators system","interactions":[],"lastModifiedDate":"2020-12-09T14:26:06.364871","indexId":"70216840","displayToPublicDate":"2015-12-09T08:18:32","publicationYear":"2015","noYear":false,"publicationType":{"id":18,"text":"Report"},"publicationSubtype":{"id":4,"text":"Other Government Series"},"title":"Indicators of climate impacts for forests: Recommendations for the U.S. National Climate Assessment Indicators system","docAbstract":"The Third National Climate Assessment (NCA) process for the United States focused in part on developing a system of indicators to communicate key aspects of the physical climate, climate impacts, vulnerabilities, and preparedness to inform decisionmakers and the public. Initially, 13 active teams were formed to recommend indicators in a range of categories, including forest, agriculture, grassland, phenology, mitigation, and physical climate. This publication describes the work of the Forest Indicators Technical Team. We briefly describe the NCA indicator system effort, propose and explain our conceptual model for the forest system, present our methods, and discuss our recommendations. Climate is only one driver of changes in U.S. forests; other drivers include socioeconomic drivers such as population and culture, and other environmental drivers such as nutrients, light, and disturbance. We offer additional details of our work for transparency and to inform an NCA indicator Web portal. We recommend metrics for 11 indicators of climate impacts on forest, spanning the range of important aspects of forest as an ecological type and as a sector. Some indicators can be reported in a Web portal now; others need additional work for reporting in the near future. Indicators such as budburst, which are important to forest but more relevant to other NCA indicator teams, are identified. Potential indicators that need more research are also presented.
","language":"English","publisher":"U.S. Forest Service","doi":"10.2737/NRS-GTR-155","collaboration":"USDA Forest Service, Northern Research Station, USGCRP","usgsCitation":"Heath, L.S., Anderson, S., Emery, M.R., Hicke, J., Littell, J.S., Lucier, A., Masek, J.G., Peterson, D.L., Pouyat, R., Potter, K.M., Robertson, G., Sperry, J., Bytnerowicz, A., Jovan, S.E., Mockrin, M.H., Musselman, R., Schulz, B.K., Smith, R.J., and Stewart, S.I., 2015, Indicators of climate impacts for forests: Recommendations for the U.S. National Climate Assessment Indicators system, 143 p., https://doi.org/10.2737/NRS-GTR-155.","productDescription":"143 p.","ipdsId":"IP-065101","costCenters":[{"id":36940,"text":"National Climate Adaptation Science Center","active":true,"usgs":true}],"links":[{"id":471570,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.2737/nrs-gtr-155","text":"Publisher Index 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R.","contributorId":236950,"corporation":false,"usgs":false,"family":"Emery","given":"Marla","email":"","middleInitial":"R.","affiliations":[],"preferred":false,"id":806579,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Hicke, Jeffrey A.","contributorId":245595,"corporation":false,"usgs":false,"family":"Hicke","given":"Jeffrey A.","affiliations":[{"id":49228,"text":"University of Idaho, Department of Geography and Environmental Science Program","active":true,"usgs":false}],"preferred":false,"id":806580,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Littell, Jeremy S. 0000-0002-5302-8280 jlittell@usgs.gov","orcid":"https://orcid.org/0000-0002-5302-8280","contributorId":4428,"corporation":false,"usgs":true,"family":"Littell","given":"Jeremy","email":"jlittell@usgs.gov","middleInitial":"S.","affiliations":[{"id":411,"text":"National Climate Change and Wildlife Science Center","active":true,"usgs":true},{"id":107,"text":"Alaska Climate Science 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Guy","contributorId":245599,"corporation":false,"usgs":false,"family":"Robertson","given":"Guy","email":"","affiliations":[{"id":49231,"text":"national sustainability program leader, USDA Forest Service","active":true,"usgs":false}],"preferred":false,"id":806587,"contributorType":{"id":1,"text":"Authors"},"rank":11},{"text":"Sperry, Jinelle","contributorId":245600,"corporation":false,"usgs":false,"family":"Sperry","given":"Jinelle","affiliations":[{"id":49232,"text":"U.S. Army Corps of Engineers, Research Development Center,","active":true,"usgs":false}],"preferred":false,"id":806588,"contributorType":{"id":1,"text":"Authors"},"rank":12},{"text":"Bytnerowicz, A.","contributorId":30027,"corporation":false,"usgs":true,"family":"Bytnerowicz","given":"A.","email":"","affiliations":[],"preferred":false,"id":806626,"contributorType":{"id":1,"text":"Authors"},"rank":13},{"text":"Jovan, Sarah E.","contributorId":168384,"corporation":false,"usgs":false,"family":"Jovan","given":"Sarah","email":"","middleInitial":"E.","affiliations":[{"id":25277,"text":"US Department of Agriculture Forest Service","active":true,"usgs":false}],"preferred":false,"id":806627,"contributorType":{"id":1,"text":"Authors"},"rank":14},{"text":"Mockrin, Miranda H.","contributorId":211622,"corporation":false,"usgs":false,"family":"Mockrin","given":"Miranda","email":"","middleInitial":"H.","affiliations":[{"id":37389,"text":"U.S. Forest Service","active":true,"usgs":false}],"preferred":false,"id":806628,"contributorType":{"id":1,"text":"Authors"},"rank":15},{"text":"Musselman, Robert","contributorId":245615,"corporation":false,"usgs":false,"family":"Musselman","given":"Robert","email":"","affiliations":[],"preferred":false,"id":806629,"contributorType":{"id":1,"text":"Authors"},"rank":16},{"text":"Schulz, Bethany K.","contributorId":140420,"corporation":false,"usgs":false,"family":"Schulz","given":"Bethany","email":"","middleInitial":"K.","affiliations":[{"id":13487,"text":"USDA Forest Service, Pacific Northwest Research Station, Anchorage, AK 99503, USA (bschulz@fs.fed.us)","active":true,"usgs":false}],"preferred":false,"id":806630,"contributorType":{"id":1,"text":"Authors"},"rank":17},{"text":"Smith, Robert J.","contributorId":36011,"corporation":false,"usgs":true,"family":"Smith","given":"Robert","email":"","middleInitial":"J.","affiliations":[],"preferred":false,"id":806631,"contributorType":{"id":1,"text":"Authors"},"rank":18},{"text":"Stewart, Susan I.","contributorId":78973,"corporation":false,"usgs":true,"family":"Stewart","given":"Susan","email":"","middleInitial":"I.","affiliations":[],"preferred":false,"id":806632,"contributorType":{"id":1,"text":"Authors"},"rank":19}]}} ,{"id":70160009,"text":"70160009 - 2015 - Estimating mercury exposure of piscivorous birds and sport fish using prey fish monitoring","interactions":[],"lastModifiedDate":"2018-09-04T15:36:51","indexId":"70160009","displayToPublicDate":"2015-12-08T15:00:00","publicationYear":"2015","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":1565,"text":"Environmental Science & Technology","onlineIssn":"1520-5851","printIssn":"0013-936X","active":true,"publicationSubtype":{"id":10}},"title":"Estimating mercury exposure of piscivorous birds and sport fish using prey fish monitoring","docAbstract":"Methylmercury is a global pollutant of aquatic ecosystems, and monitoring programs need tools to predict mercury exposure of wildlife. We developed equations to estimate methylmercury exposure of piscivorous birds and sport fish using mercury concentrations in prey fish. We collected original data on western grebes (Aechmophorus occidentalis) and Clark’s grebes (Aechmophorus clarkii) and summarized the published literature to generate predictive equations specific to grebes and a general equation for piscivorous birds. We measured mercury concentrations in 354 grebes (blood averaged 1.06 ± 0.08 μg/g ww), 101 grebe eggs, 230 sport fish (predominantly largemouth bass and rainbow trout), and 505 prey fish (14 species) at 25 lakes throughout California. Mercury concentrations in grebe blood, grebe eggs, and sport fish were strongly related to mercury concentrations in prey fish among lakes. Each 1.0 μg/g dw (∼0.24 μg/g ww) increase in prey fish resulted in an increase in mercury concentrations of 103% in grebe blood, 92% in grebe eggs, and 116% in sport fish. We also found strong correlations between mercury concentrations in grebes and sport fish among lakes. Our results indicate that prey fish monitoring can be used to estimate mercury exposure of piscivorous birds and sport fish when wildlife cannot be directly sampled.
","language":"English","publisher":"ACS Publications","doi":"10.1021/acs.est.5b02691","usgsCitation":"Ackerman, J., Hartman, C.A., Eagles-Smith, C.A., Herzog, M.P., Davis, J., Ichikawa, G., and Bonnema, A., 2015, Estimating mercury exposure of piscivorous birds and sport fish using prey fish monitoring: Environmental Science & Technology, v. 49, no. 22, p. 13596-13604, https://doi.org/10.1021/acs.est.5b02691.","productDescription":"9 p.","startPage":"13596","endPage":"13604","onlineOnly":"N","additionalOnlineFiles":"N","ipdsId":"IP-067013","costCenters":[{"id":290,"text":"Forest and Rangeland Ecosystem Science Center","active":false,"usgs":true},{"id":651,"text":"Western Ecological Research Center","active":true,"usgs":true},{"id":34983,"text":"Contaminant Biology Program","active":true,"usgs":true}],"links":[{"id":312042,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"volume":"49","issue":"22","publishingServiceCenter":{"id":1,"text":"Sacramento PSC"},"noUsgsAuthors":false,"publicationDate":"2015-10-28","publicationStatus":"PW","scienceBaseUri":"5667ff38e4b06a3ea36c8e08","chorus":{"doi":"10.1021/acs.est.5b02691","url":"http://dx.doi.org/10.1021/acs.est.5b02691","publisher":"American Chemical Society (ACS)","authors":"Ackerman Joshua T., Hartman C. 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Alex 0000-0002-7222-1633 chartman@usgs.gov","orcid":"https://orcid.org/0000-0002-7222-1633","contributorId":131109,"corporation":false,"usgs":true,"family":"Hartman","given":"C.","email":"chartman@usgs.gov","middleInitial":"Alex","affiliations":[{"id":651,"text":"Western Ecological Research Center","active":true,"usgs":true}],"preferred":false,"id":581542,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Eagles-Smith, Collin A. 0000-0003-1329-5285 ceagles-smith@usgs.gov","orcid":"https://orcid.org/0000-0003-1329-5285","contributorId":505,"corporation":false,"usgs":true,"family":"Eagles-Smith","given":"Collin","email":"ceagles-smith@usgs.gov","middleInitial":"A.","affiliations":[{"id":290,"text":"Forest and Rangeland Ecosystem Science Center","active":false,"usgs":true},{"id":289,"text":"Forest and Rangeland Ecosys Science Center","active":true,"usgs":true},{"id":518,"text":"Oregon Water Science Center","active":true,"usgs":true}],"preferred":true,"id":581543,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Herzog, Mark P. 0000-0002-5203-2835 mherzog@usgs.gov","orcid":"https://orcid.org/0000-0002-5203-2835","contributorId":131110,"corporation":false,"usgs":true,"family":"Herzog","given":"Mark","email":"mherzog@usgs.gov","middleInitial":"P.","affiliations":[{"id":651,"text":"Western Ecological Research Center","active":true,"usgs":true}],"preferred":false,"id":581544,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Davis, Jay","contributorId":150405,"corporation":false,"usgs":false,"family":"Davis","given":"Jay","affiliations":[{"id":12703,"text":"San Francisco Estuary Institute","active":true,"usgs":false}],"preferred":false,"id":581545,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"Ichikawa, Gary","contributorId":140920,"corporation":false,"usgs":false,"family":"Ichikawa","given":"Gary","email":"","affiliations":[],"preferred":false,"id":581546,"contributorType":{"id":1,"text":"Authors"},"rank":6},{"text":"Bonnema, Autumn","contributorId":140921,"corporation":false,"usgs":false,"family":"Bonnema","given":"Autumn","email":"","affiliations":[],"preferred":false,"id":581547,"contributorType":{"id":1,"text":"Authors"},"rank":7}]}} ,{"id":70157562,"text":"70157562 - 2015 - A statistical learning framework for groundwater nitrate models of the Central Valley, California, USA","interactions":[],"lastModifiedDate":"2015-12-08T13:39:29","indexId":"70157562","displayToPublicDate":"2015-12-08T14:30:00","publicationYear":"2015","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":"A statistical learning framework for groundwater nitrate models of the Central Valley, California, USA","docAbstract":"We used a statistical learning framework to evaluate the ability of three machine-learning methods to predict nitrate concentration in shallow groundwater of the Central Valley, California: boosted regression trees (BRT), artificial neural networks (ANN), and Bayesian networks (BN). Machine learning methods can learn complex patterns in the data but because of overfitting may not generalize well to new data. The statistical learning framework involves cross-validation (CV) training and testing data and a separate hold-out data set for model evaluation, with the goal of optimizing predictive performance by controlling for model overfit. The order of prediction performance according to both CV testing R2 and that for the hold-out data set was BRT > BN > ANN. For each method we identified two models based on CV testing results: that with maximum testing R2 and a version with R2 within one standard error of the maximum (the 1SE model). The former yielded CV training R2 values of 0.94–1.0. Cross-validation testing R2 values indicate predictive performance, and these were 0.22–0.39 for the maximum R2 models and 0.19–0.36 for the 1SE models. Evaluation with hold-out data suggested that the 1SE BRT and ANN models predicted better for an independent data set compared with the maximum R2 versions, which is relevant to extrapolation by mapping. Scatterplots of predicted vs. observed hold-out data obtained for final models helped identify prediction bias, which was fairly pronounced for ANN and BN. Lastly, the models were compared with multiple linear regression (MLR) and a previous random forest regression (RFR) model. Whereas BRT results were comparable to RFR, MLR had low hold-out R2 (0.07) and explained less than half the variation in the training data. Spatial patterns of predictions by the final, 1SE BRT model agreed reasonably well with previously observed patterns of nitrate occurrence in groundwater of the Central Valley.
","language":"English","publisher":"Elsevier","doi":"10.1016/j.jhydrol.2015.10.025","usgsCitation":"Nolan, B.T., Fienen, M., and Lorenz, D.L., 2015, A statistical learning framework for groundwater nitrate models of the Central Valley, California, USA: Journal of Hydrology, v. 531, no. 3, p. 902-911, https://doi.org/10.1016/j.jhydrol.2015.10.025.","productDescription":"10 p.","startPage":"902","endPage":"911","onlineOnly":"N","additionalOnlineFiles":"N","ipdsId":"IP-065964","costCenters":[{"id":451,"text":"National Water Quality Assessment Program","active":true,"usgs":true}],"links":[{"id":471571,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.1016/j.jhydrol.2015.10.025","text":"Publisher Index Page"},{"id":312041,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","state":"California","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -122.025146484375,\n 40.73893324113603\n ],\n [\n -122.93701171874999,\n 40.38002840251183\n ],\n [\n -122.16796875,\n 38.048091067457236\n ],\n [\n -119.39941406249999,\n 34.95799531086792\n ],\n [\n -118.67431640625,\n 34.97600151317591\n ],\n [\n -118.795166015625,\n 36.19995805932895\n ],\n [\n -120.92651367187499,\n 38.38472766885085\n ],\n [\n -122.025146484375,\n 40.73893324113603\n ]\n ]\n ]\n }\n }\n ]\n}","volume":"531","issue":"3","publishingServiceCenter":{"id":9,"text":"Reston PSC"},"noUsgsAuthors":false,"publicationStatus":"PW","scienceBaseUri":"5667ff32e4b06a3ea36c8e04","contributors":{"authors":[{"text":"Nolan, Bernard T. 0000-0002-6945-9659 btnolan@usgs.gov","orcid":"https://orcid.org/0000-0002-6945-9659","contributorId":2190,"corporation":false,"usgs":true,"family":"Nolan","given":"Bernard","email":"btnolan@usgs.gov","middleInitial":"T.","affiliations":[{"id":27111,"text":"National Water Quality Program","active":true,"usgs":true},{"id":451,"text":"National Water Quality Assessment Program","active":true,"usgs":true}],"preferred":true,"id":573640,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Fienen, Michael N. 0000-0002-7756-4651 mnfienen@usgs.gov","orcid":"https://orcid.org/0000-0002-7756-4651","contributorId":893,"corporation":false,"usgs":true,"family":"Fienen","given":"Michael N.","email":"mnfienen@usgs.gov","affiliations":[{"id":677,"text":"Wisconsin Water Science Center","active":true,"usgs":true}],"preferred":false,"id":573641,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Lorenz, David L. 0000-0003-3392-4034 lorenz@usgs.gov","orcid":"https://orcid.org/0000-0003-3392-4034","contributorId":1384,"corporation":false,"usgs":true,"family":"Lorenz","given":"David","email":"lorenz@usgs.gov","middleInitial":"L.","affiliations":[{"id":392,"text":"Minnesota Water Science Center","active":true,"usgs":true}],"preferred":true,"id":573642,"contributorType":{"id":1,"text":"Authors"},"rank":3}]}} ,{"id":70160008,"text":"70160008 - 2015 - Surprise and opportunity for learning in Grand Canyon: the Glen Canyon Dam Adaptive Management Program","interactions":[],"lastModifiedDate":"2015-12-08T12:25:42","indexId":"70160008","displayToPublicDate":"2015-12-08T13:15:00","publicationYear":"2015","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":1468,"text":"Ecology and Society","active":true,"publicationSubtype":{"id":10}},"title":"Surprise and opportunity for learning in Grand Canyon: the Glen Canyon Dam Adaptive Management Program","docAbstract":"With a focus on resources of the Colorado River ecosystem below Glen Canyon Dam, the Glen Canyon Dam Adaptive Management Program has included a variety of experimental policy tests, ranging from manipulation of water releases from the dam to removal of non-native fish within Grand Canyon National Park. None of these field-scale experiments has yet produced unambiguous results in terms of management prescriptions. But there has been adaptive learning, mostly from unanticipated or surprising resource responses relative to predictions from ecosystem modeling. Surprise learning opportunities may often be viewed with dismay by some stakeholders who might not be clear about the purpose of science and modeling in adaptive management. However, the experimental results from the Glen Canyon Dam program actually represent scientific successes in terms of revealing new opportunities for developing better river management policies. A new long-term experimental management planning process for Glen Canyon Dam operations, started in 2011 by the U.S. Department of the Interior, provides an opportunity to refocus management objectives, identify and evaluate key uncertainties about the influence of dam releases, and refine monitoring for learning over the next several decades. Adaptive learning since 1995 is critical input to this long-term planning effort. Embracing uncertainty and surprise outcomes revealed by monitoring and ecosystem modeling will likely continue the advancement of resource objectives below the dam, and may also promote efficient learning in other complex programs.
","language":"English","publisher":"The Resilience Alliance","doi":"10.5751/ES-07621-200322","usgsCitation":"Melis, T., Walters, C., and Korman, J., 2015, Surprise and opportunity for learning in Grand Canyon: the Glen Canyon Dam Adaptive Management Program: Ecology and Society, v. 20, no. 3, Art22; 33 p., https://doi.org/10.5751/ES-07621-200322.","productDescription":"Art22; 33 p.","numberOfPages":"33","onlineOnly":"N","additionalOnlineFiles":"N","ipdsId":"IP-022401","costCenters":[{"id":568,"text":"Southwest Biological Science Center","active":true,"usgs":true}],"links":[{"id":471572,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.5751/es-07621-200322","text":"Publisher Index Page"},{"id":312037,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","state":"Arizona, Nevada, Utah","otherGeospatial":"Colorado River, Glen Canyon Dam, Grand Canyon National Park, Lake Mead","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -114.60937499999999,\n 35.33977430038646\n ],\n [\n -114.60937499999999,\n 37.37015718405753\n ],\n [\n -110.76416015625,\n 37.37015718405753\n ],\n [\n -110.76416015625,\n 35.33977430038646\n ],\n [\n -114.60937499999999,\n 35.33977430038646\n ]\n ]\n ]\n }\n }\n ]\n}","volume":"20","issue":"3","publishingServiceCenter":{"id":14,"text":"Menlo Park PSC"},"noUsgsAuthors":false,"publicationStatus":"PW","scienceBaseUri":"5667ff3ce4b06a3ea36c8e12","contributors":{"authors":[{"text":"Melis, Theodore S. 0000-0003-0473-3968 tmelis@usgs.gov","orcid":"https://orcid.org/0000-0003-0473-3968","contributorId":1829,"corporation":false,"usgs":true,"family":"Melis","given":"Theodore S.","email":"tmelis@usgs.gov","affiliations":[{"id":568,"text":"Southwest Biological Science Center","active":true,"usgs":true}],"preferred":true,"id":581538,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Walters, Carl","contributorId":66156,"corporation":false,"usgs":true,"family":"Walters","given":"Carl","affiliations":[],"preferred":false,"id":581539,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Korman, Josh","contributorId":29922,"corporation":false,"usgs":true,"family":"Korman","given":"Josh","affiliations":[],"preferred":false,"id":581540,"contributorType":{"id":1,"text":"Authors"},"rank":3}]}} ,{"id":70159406,"text":"sir20155153 - 2015 - Simulation of the effects of different inflows on hydrologic conditions in Lake Houston with a three-dimensional hydrodynamic model, Houston, Texas, 2009–10","interactions":[],"lastModifiedDate":"2020-05-19T18:00:59.387807","indexId":"sir20155153","displayToPublicDate":"2015-12-08T12:00:00","publicationYear":"2015","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":"2015-5153","title":"Simulation of the effects of different inflows on hydrologic conditions in Lake Houston with a three-dimensional hydrodynamic model, Houston, Texas, 2009–10","docAbstract":"Lake Houston, an important water resource for the Houston, Texas, area, receives inflows from seven major tributaries that compose the San Jacinto River Basin upstream from the reservoir. The effects of different inflows from the watersheds drained by these tributaries on the residence time of water in Lake Houston and closely associated physical and chemical properties including lake elevation, salinity, and water temperature are not well known. Accordingly, the U.S. Geological Survey (USGS), in cooperation with the City of Houston, developed a three-dimensional hydrodynamic model of Lake Houston as a tool for evaluating the effects of different inflows on residence time of water in the lake and associated physical and chemical properties. The Environmental Fluid Dynamics Code (EFDC), a grid-based, surface-water modeling package for simulating three-dimensional circulation, mass transport, sediments, and biogeochemical processes, was used to develop the model of Lake Houston. The Lake Houston EFDC model was developed and calibrated by using 2009 data and verified by using 2010 data. Three statistics (mean error, root mean square error, and the Nash-Sutcliffe model efficiency coefficient) were used to evaluate how well the Lake Houston EFDC model simulated lake elevation, salinity, and water temperature. The residence time of water in reservoirs is associated with various physical and chemical properties (including lake elevation, salinity, and water temperature). Simulated and measured lake-elevation values were compared at USGS reservoir station 08072000 Lake Houston near Sheldon, Tex. The accuracy of simulated salinity and water temperature values was assessed by using the salinity (computed from measured specific conductance) and water temperature at two USGS monitoring stations: 295826095082200 Lake Houston south Union Pacific Railroad Bridge near Houston, Tex., and 295554095093401 Lake Houston at mouth of Jack’s Ditch near Houston, Tex. Specific conductance and water temperature were measured at as many as four different depths at each of the two monitoring stations during 2009 and then used for assessing the accuracy of simulated values of salinity and water temperature during 2010. The performance evaluation statistics indicate that the model performed satisfactorily. The calibrated model was used to simulate two possible inflow scenarios to evaluate the changes in the residence time of water in Lake Houston. The two scenarios tested were an increased inflow of approximately 300 cubic feet per second for 1 month (May 2010) from two watersheds: the West Fork San Jacinto River and Luce Bayou. These scenarios were chosen to mimic the effects of possible small releases or diversions of water from outside the San Jacinto River Basin into the basin (or directly into the lake) on the residence time of water in Lake Houston. During the time of increased inflow for the two scenarios tested, maximum residence time decreased slightly from approximately 106 to 97 days.
","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/sir20155153","collaboration":"Prepared in cooperation with the City of Houston","usgsCitation":"Rendon, S.H., and Lee, M.T., 2015, Simulation of the effects of different inflows on hydrologic conditions in Lake Houston with a three-dimensional hydrodynamic model, Houston, Texas, 2009–10: U.S. Geological Survey Scientific Investigations Report 2015–5153, 42 p., https://dx.doi.org/10.3133/sir20155153.","productDescription":"vi, 42 p.","numberOfPages":"52","onlineOnly":"Y","additionalOnlineFiles":"N","ipdsId":"IP-060635","costCenters":[{"id":583,"text":"Texas Water Science Center","active":true,"usgs":true}],"links":[{"id":311980,"rank":2,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/sir/2015/5153/sir20155153.pdf","text":"Report","size":"13.1 MB","linkFileType":{"id":1,"text":"pdf"},"description":"SIR 2015-5153"},{"id":311979,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/sir/2015/5153/coverthb.jpg"}],"country":"United States","state":"Texas","otherGeospatial":"Lake Houston","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -95.99578857421875,\n 29.90256760730233\n ],\n [\n -95.99578857421875,\n 30.62845887475364\n ],\n [\n -95.10040283203125,\n 30.62845887475364\n ],\n [\n -95.10040283203125,\n 29.90256760730233\n ],\n [\n -95.99578857421875,\n 29.90256760730233\n ]\n ]\n ]\n }\n }\n ]\n}","contact":"Director, Texas Water Science Center
U.S. Geological Survey
1505 Ferguson Lane
Austin, Texas 78754–4501
http://tx.usgs.gov/
Geologic cross section I‒I’ is the fourth in a series of cross sections constructed by the U.S. Geological Survey to document and improve understanding of the geologic framework and petroleum systems of the Appalachian basin. Cross section I‒I’ provides a regional view of the structural and stratigraphic framework of the Appalachian basin from the eastern margin of the Illinois basin in central Kentucky, across the Cincinnati arch (Lexington dome), to the Valley and Ridge province in southwestern Virginia, a distance of approximately 280 miles. This cross section is a companion to cross sections E‒E’, D‒D’, and C‒C’ that are located about 200 to 300 miles to the northeast. Cross section I‒I’ either updates or complements earlier geologic cross sections through the central Kentucky and southwestern Virginia part of the Appalachian basin. Although other published cross sections through parts of the basin show more structural and stratigraphic detail, these other cross sections are of more limited extent geographically and (or) stratigraphically.
\nCross section I‒I ’ contains much information that is useful for evaluating energy resources in the Appalachian basin. Many of the key elements of the Appalachian basin petroleum systems (such as source rocks, reservoir rocks, seals, and traps) can be inferred from lithologic units, unconformities, and geologic structures shown on the cross section. Other aspects of petroleum systems (such as the timing of petroleum generation and petroleum migration pathways) may be evaluated by burial history, thermal history, and fluid flow models on the basis of what is shown on the cross section. Cross section I‒I’ also provides a stratigraphic and structural framework for the Pennsylvanian coal-bearing section. In addition, geologists and engineers could use cross section I‒I’ as a reconnaissance tool to identify plausible geologic structures and strata for the subsurface storage of liquid waste or for the sequestration of carbon dioxide.
","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/sim3343","usgsCitation":"Ryder, R.T., Trippi, M.H., and Swezey, C.S., 2015, Geologic cross section I–I′ through the Appalachian basin from the eastern margin of the Illinois basin, Jefferson County, Kentucky, to the Valley and Ridge province, Scott County, Virginia: U.S. Geological Survey Scientific Investigations Map 3343, 2 sheets and pamphlet A, 41 p.; pamphlet B, 102 p., https://dx.doi.org/10.3133/sim3343.","productDescription":"Pamphlet A: iii, 41 p.; Pamphlet B: Appendix; 2 Sheets: 51.0 x 41.0 inches and 47.31 x 41.00 inches","onlineOnly":"N","additionalOnlineFiles":"Y","ipdsId":"IP-021269","costCenters":[{"id":241,"text":"Eastern Energy Resources Science Center","active":true,"usgs":true}],"links":[{"id":311693,"rank":5,"type":{"id":26,"text":"Sheet"},"url":"https://pubs.usgs.gov/sim/3343/sim3343_sheet2.pdf","text":"SIM 3343 - Sheet 2","size":"4.98 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Energy Resources Science Center
U.S. Geological Survey
954 National Center
12201 Sunrise Valley Drive
Reston, Virginia 20192
http://energy.usgs.gov/GeneralInfo/
ScienceCenters/Eastern.aspx
Or
Michael H. Trippi
U.S. Geological Survey
12201 Sunrise Valley Drive
Reston, Virginia 20192
The Mississippi River Delta (MRD) has undergone tremendous land loss over the past century due to natural and anthropogenic influences, a fate shared by many river deltas globally. A globally unprecedented effort to restore and sustain the remaining subaerial portions of the delta is now underway, an endeavor that is expected to cost $50–100B over the next 50 yr. Success of this effort requires a thorough understanding of natural and anthropogenic controls on sediment supply and delta geomorphology. In the MRD, hurricanes have been paradoxically identified as both substantial agents of widespread land loss, and vertical marsh sediment accretion. We present the first multi-decadal chronostratigraphic assessment of sediment supply for a major coastal basin of the MRD that assesses both fluvial and hurricane-induced contributions to sediment accumulation in deltaic wetlands. Our findings indicate that over multidecadal timescales, hurricane-induced sediment delivery may be an important contributor for deltaic wetland vertical accretion, but the contribution from hurricanes to long-term sediment accumulation is substantially less than sediment delivery supplied by existing and planned river-sediment diversions at present-day river-sediment loads.
","language":"English","publisher":"Nature Publishing Group","doi":"10.1038/srep17582","usgsCitation":"Smith, J.E., Bentley, S., Snedden, G., and White, C., 2015, What role do hurricanes play in sediment delivery to subsiding river deltas?: Scientific Reports, v. 5, 17582; 8 p., https://doi.org/10.1038/srep17582.","productDescription":"17582; 8 p.","onlineOnly":"N","additionalOnlineFiles":"N","ipdsId":"IP-064329","costCenters":[{"id":17705,"text":"Wetland and Aquatic Research Center","active":true,"usgs":true}],"links":[{"id":471573,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.1038/srep17582","text":"Publisher Index Page"},{"id":312036,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","state":"Mississippi","otherGeospatial":"Mississippi River Delta","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -90.15380859375,\n 29.42524498547202\n ],\n [\n -90.15380859375,\n 29.938275329718987\n ],\n [\n -89.6044921875,\n 29.938275329718987\n ],\n [\n -89.6044921875,\n 29.42524498547202\n ],\n [\n -90.15380859375,\n 29.42524498547202\n ]\n ]\n ]\n }\n }\n ]\n}","volume":"5","publishingServiceCenter":{"id":5,"text":"Lafayette PSC"},"noUsgsAuthors":false,"publicationDate":"2015-12-02","publicationStatus":"PW","scienceBaseUri":"5667ff3de4b06a3ea36c8e14","contributors":{"authors":[{"text":"Smith, James E.","contributorId":150401,"corporation":false,"usgs":false,"family":"Smith","given":"James","email":"","middleInitial":"E.","affiliations":[],"preferred":false,"id":581533,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Bentley, Samuel J.","contributorId":150402,"corporation":false,"usgs":false,"family":"Bentley","given":"Samuel J.","affiliations":[],"preferred":false,"id":581534,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Snedden, Gregg 0000-0001-7821-3709 sneddeng@usgs.gov","orcid":"https://orcid.org/0000-0001-7821-3709","contributorId":140235,"corporation":false,"usgs":true,"family":"Snedden","given":"Gregg","email":"sneddeng@usgs.gov","affiliations":[{"id":455,"text":"National Wetlands Research Center","active":true,"usgs":true},{"id":17705,"text":"Wetland and Aquatic Research Center","active":true,"usgs":true}],"preferred":true,"id":581492,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"White, Crawford","contributorId":150403,"corporation":false,"usgs":false,"family":"White","given":"Crawford","email":"","affiliations":[],"preferred":false,"id":581535,"contributorType":{"id":1,"text":"Authors"},"rank":4}]}} ,{"id":70160625,"text":"70160625 - 2015 - Taxonomic revision of Phascogale tapoatafa (Meyer, 1793) (Dasyuridae; Marsupialia), including descriptions of two new subspecies and confirmation of P. pirata Thomas, 1904 as a ‘Top End’ endemic","interactions":[],"lastModifiedDate":"2019-12-13T06:26:09","indexId":"70160625","displayToPublicDate":"2015-12-08T11:30:00","publicationYear":"2015","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":3814,"text":"Zootaxa","onlineIssn":"1175-5334","printIssn":"1175-5326","active":true,"publicationSubtype":{"id":10}},"displayTitle":"Taxonomic revision of Phascogale tapoatafa (Meyer, 1793) (Dasyuridae; Marsupialia), including descriptions of two new subspecies and confirmation of P. pirata Thomas, 1904 as a ‘Top End’ endemic","title":"Taxonomic revision of Phascogale tapoatafa (Meyer, 1793) (Dasyuridae; Marsupialia), including descriptions of two new subspecies and confirmation of P. pirata Thomas, 1904 as a ‘Top End’ endemic","docAbstract":"The Australian Brush-tailed Phascogale (Phascogale tapoatafa sensu lato) has a broad but highly fragmented distribution around the periphery of the Australian continent and all populations are under significant ongoing threat to survival. A new appraisal of morphological and molecular diversity within the group reveals that the population in the ‘Top End’ of the Northern Territory is specifically distinct from all others, including those in the Kimberley region of Western Australia to the west and on Cape York of Queensland to the east. The name P. pirata Thomas, 1904 is available for the ‘Top End’ taxon. Three geographically disjunct populations are distinguished at subspecies level within P. tapoatafa on a suite of external and cranio-dental features; these are found in southeast Australia from South Australia to mid-coastal Queensland (nominotypical tapoatafa), southwest Western Australia (wambenger subsp. nov.), and the Kimberley region of Western Australia (kimberleyensis subsp. nov.). A potential fourth subspecies occurs on Cape York but remains too poorly represented in collections for adequate characterization. Molecular divergence estimates based on partial sequences of the mitochondrial cytochrome b gene indicate that the range disjunction across southern Australia probably dates from the Late Pliocene, with the multiple disjunctions across northern Australia being more recent though almost certainly exceeding 400,000 years. An argument is made for the continued use of the subspecies rank in Australian mammalogy, despite a general lack of consistency in its current application.
","language":"English","publisher":"Magnolia Press","publisherLocation":"Auckland, NZ","doi":"10.11646/zootaxa.4055.1.1","usgsCitation":"Aplin, K.P., Rhind, S.G., Ten Have, J., and Chesser, R., 2015, Taxonomic revision of Phascogale tapoatafa (Meyer, 1793) (Dasyuridae; Marsupialia), including descriptions of two new subspecies and confirmation of P. pirata Thomas, 1904 as a ‘Top End’ endemic: Zootaxa, v. 4055, no. 1, p. 1-73, https://doi.org/10.11646/zootaxa.4055.1.1.","productDescription":"73 p.","startPage":"1","endPage":"73","onlineOnly":"N","additionalOnlineFiles":"N","ipdsId":"IP-052756","costCenters":[{"id":531,"text":"Patuxent Wildlife Research Center","active":true,"usgs":true}],"links":[{"id":312883,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"Australia","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n 142.646484375,\n -10.660607953624762\n ],\n [\n 141.50390625,\n -11.867350911459294\n ],\n [\n 141.240234375,\n -15.792253570362446\n ],\n [\n 140.18554687499997,\n -17.644022027872712\n ],\n [\n 135.703125,\n -14.519780046326085\n ],\n [\n 137.021484375,\n -12.039320557540572\n ],\n [\n 131.30859375,\n -10.919617760254685\n ],\n [\n 129.814453125,\n -11.5230875068685\n ],\n [\n 129.19921875,\n -14.179186142354169\n ],\n [\n 126.65039062499999,\n -13.496472765758952\n ],\n [\n 121.728515625,\n -16.63619187839765\n ],\n [\n 121.728515625,\n -18.47960905583197\n ],\n [\n 116.54296874999999,\n -20.632784250388013\n ],\n [\n 113.291015625,\n -21.943045533438166\n ],\n [\n 112.939453125,\n -26.431228064506424\n ],\n [\n 114.697265625,\n -31.05293398570514\n ],\n [\n 115.31249999999999,\n -32.62087018318112\n ],\n [\n 114.873046875,\n -33.87041555094182\n ],\n [\n 116.98242187499999,\n -35.532226227703376\n ],\n [\n 119.61914062499999,\n -35.24561909420681\n ],\n [\n 119.88281249999999,\n -34.234512362369856\n ],\n [\n 123.662109375,\n -34.089061315849946\n ],\n [\n 126.03515625,\n -32.175612478499325\n ],\n [\n 127.35351562499999,\n -32.472695022061494\n ],\n [\n 129.7265625,\n -31.728167146023935\n ],\n [\n 132.01171875,\n -32.175612478499325\n ],\n [\n 135.966796875,\n -34.95799531086791\n ],\n [\n 136.669921875,\n -36.3151251474805\n ],\n [\n 140.537109375,\n -37.78808138412045\n ],\n [\n 141.943359375,\n -38.61687046392973\n ],\n [\n 145.546875,\n -39.09596293630548\n ],\n [\n 148.359375,\n -38.8225909761771\n ],\n [\n 149.94140625,\n -37.5097258429375\n ],\n [\n 152.9296875,\n -31.653381399663985\n ],\n [\n 153.896484375,\n -27.215556209029675\n ],\n [\n 153.28125,\n -24.686952411999144\n ],\n [\n 148.359375,\n -19.72534224805787\n ],\n [\n 142.646484375,\n -10.660607953624762\n ]\n ]\n ]\n }\n }\n ]\n}","volume":"4055","issue":"1","publishingServiceCenter":{"id":10,"text":"Baltimore PSC"},"noUsgsAuthors":false,"publicationDate":"2015-12-08","publicationStatus":"PW","scienceBaseUri":"56826b48e4b0a04ef4925ba1","contributors":{"authors":[{"text":"Aplin, K. P.","contributorId":150872,"corporation":false,"usgs":false,"family":"Aplin","given":"K.","email":"","middleInitial":"P.","affiliations":[{"id":12519,"text":"Smithsonian Institution Research Assoicate","active":true,"usgs":false}],"preferred":false,"id":583369,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Rhind, S. G.","contributorId":150873,"corporation":false,"usgs":false,"family":"Rhind","given":"S.","email":"","middleInitial":"G.","affiliations":[{"id":18128,"text":"University of Woolongong, Australia","active":true,"usgs":false}],"preferred":false,"id":583370,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Ten Have, J.","contributorId":150874,"corporation":false,"usgs":false,"family":"Ten Have","given":"J.","affiliations":[{"id":18129,"text":"Department of Agriculture, Fisheries and Forestry, Canberra, Australia","active":true,"usgs":false}],"preferred":false,"id":583371,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Chesser, R. Terry 0000-0003-4389-7092 tchesser@usgs.gov","orcid":"https://orcid.org/0000-0003-4389-7092","contributorId":894,"corporation":false,"usgs":true,"family":"Chesser","given":"R. Terry","email":"tchesser@usgs.gov","affiliations":[{"id":531,"text":"Patuxent Wildlife Research Center","active":true,"usgs":true}],"preferred":false,"id":583368,"contributorType":{"id":1,"text":"Authors"},"rank":4}]}} ,{"id":70171004,"text":"70171004 - 2015 - Reviews and syntheses: Effects of permafrost thaw on Arctic aquatic ecosystems","interactions":[],"lastModifiedDate":"2016-05-17T10:15:51","indexId":"70171004","displayToPublicDate":"2015-12-08T05:15:00","publicationYear":"2015","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":1011,"text":"Biogeosciences","active":true,"publicationSubtype":{"id":10}},"title":"Reviews and syntheses: Effects of permafrost thaw on Arctic aquatic ecosystems","docAbstract":"The Arctic is a water-rich region, with freshwater systems covering about 16 % of the northern permafrost landscape. Permafrost thaw creates new freshwater ecosystems, while at the same time modifying the existing lakes, streams, and rivers that are impacted by thaw. Here, we describe the current state of knowledge regarding how permafrost thaw affects lentic (still) and lotic (moving) systems, exploring the effects of both thermokarst (thawing and collapse of ice-rich permafrost) and deepening of the active layer (the surface soil layer that thaws and refreezes each year). Within thermokarst, we further differentiate between the effects of thermokarst in lowland areas vs. that on hillslopes. For almost all of the processes that we explore, the effects of thaw vary regionally, and between lake and stream systems. Much of this regional variation is caused by differences in ground ice content, topography, soil type, and permafrost coverage. Together, these modifying factors determine (i) the degree to which permafrost thaw manifests as thermokarst, (ii) whether thermokarst leads to slumping or the formation of thermokarst lakes, and (iii) the manner in which constituent delivery to freshwater systems is altered by thaw. Differences in thaw-enabled constituent delivery can be considerable, with these modifying factors determining, for example, the balance between delivery of particulate vs. dissolved constituents, and inorganic vs. organic materials. Changes in the composition of thaw-impacted waters, coupled with changes in lake morphology, can strongly affect the physical and optical properties of thermokarst lakes. The ecology of thaw-impacted lakes and streams is also likely to change; these systems have unique microbiological communities, and show differences in respiration, primary production, and food web structure that are largely driven by differences in sediment, dissolved organic matter, and nutrient delivery. The degree to which thaw enables the delivery of dissolved vs. particulate organic matter, coupled with the composition of that organic matter and the morphology and stratification characteristics of recipient systems will play an important role in determining the balance between the release of organic matter as greenhouse gases (CO2 and CH4), its burial in sediments, and its loss downstream. The magnitude of thaw impacts on northern aquatic ecosystems is increasing, as is the prevalence of thaw-impacted lakes and streams. There is therefore an urgent need to quantify how permafrost thaw is affecting aquatic ecosystems across diverse Arctic landscapes, and the implications of this change for further climate warming.
\n","language":"English","publisher":"European Geosciences Union","doi":"10.5194/bg-12-7129-2015","usgsCitation":"Vonk, J., Tank, S., Bowden, W., Laurion, I., Vincent, W., Alekseychik, P., Amyot, Y., Billet, M., Canario, J., Cory, R., Deshpande, B., Helbig, M., Jammet, M., Karlsson, J., Larouche, J., MacMillan, G., Rautio, M., Walter Anthony, K., and Wickland, K.P., 2015, Reviews and syntheses: Effects of permafrost thaw on Arctic aquatic ecosystems: Biogeosciences, v. 12, p. 7129-7167, https://doi.org/10.5194/bg-12-7129-2015.","productDescription":"39 p.","startPage":"7129","endPage":"7167","onlineOnly":"N","additionalOnlineFiles":"N","ipdsId":"IP-066263","costCenters":[{"id":5044,"text":"National Research Program - Central Branch","active":true,"usgs":true}],"links":[{"id":471575,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.5194/bg-12-7129-2015","text":"Publisher Index 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Fairbanks","active":true,"usgs":false}],"preferred":false,"id":629475,"contributorType":{"id":1,"text":"Authors"},"rank":18},{"text":"Wickland, Kimberly P. 0000-0002-6400-0590 kpwick@usgs.gov","orcid":"https://orcid.org/0000-0002-6400-0590","contributorId":1835,"corporation":false,"usgs":true,"family":"Wickland","given":"Kimberly","email":"kpwick@usgs.gov","middleInitial":"P.","affiliations":[{"id":36183,"text":"Hydro-Ecological Interactions Branch","active":true,"usgs":true},{"id":5044,"text":"National Research Program - Central Branch","active":true,"usgs":true},{"id":37277,"text":"WMA - Earth System Processes Division","active":true,"usgs":true}],"preferred":true,"id":629457,"contributorType":{"id":1,"text":"Authors"},"rank":19}]}} ,{"id":70188064,"text":"70188064 - 2015 - The climate hazards infrared precipitation with stations—a new environmental record for monitoring extremes","interactions":[],"lastModifiedDate":"2017-05-31T16:06:56","indexId":"70188064","displayToPublicDate":"2015-12-08T00:00:00","publicationYear":"2015","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":3907,"text":"Scientific Data","active":true,"publicationSubtype":{"id":10}},"title":"The climate hazards infrared precipitation with stations—a new environmental record for monitoring extremes","docAbstract":"
The Climate Hazards group Infrared Precipitation with Stations (CHIRPS) dataset builds on previous approaches to ‘smart’ interpolation techniques and high resolution, long period of record precipitation estimates based on infrared Cold Cloud Duration (CCD) observations. The algorithm i) is built around a 0.05° climatology that incorporates satellite information to represent sparsely gauged locations, ii) incorporates daily, pentadal, and monthly 1981-present 0.05° CCD-based precipitation estimates, iii) blends station data to produce a preliminary information product with a latency of about 2 days and a final product with an average latency of about 3 weeks, and iv) uses a novel blending procedure incorporating the spatial correlation structure of CCD-estimates to assign interpolation weights. We present the CHIRPS algorithm, global and regional validation results, and show how CHIRPS can be used to quantify the hydrologic impacts of decreasing precipitation and rising air temperatures in the Greater Horn of Africa. Using the Variable Infiltration Capacity model, we show that CHIRPS can support effective hydrologic forecasts and trend analyses in southeastern Ethiopia.
","language":"English","publisher":"Nature Publishing Group","doi":"10.1038/sdata.2015.66","usgsCitation":"Funk, C., Peterson, P., Landsfeld, M., Pedreros, D., Verdin, J., Shukla, S., Husak, G., Rowland, J., Harrison, L., Hoell, A., and Michaelsen, J., 2015, The climate hazards infrared precipitation with stations—a new environmental record for monitoring extremes: Scientific Data, v. 2, Article 150066: 21 p., https://doi.org/10.1038/sdata.2015.66.","productDescription":"Article 150066: 21 p.","ipdsId":"IP-066224","costCenters":[{"id":222,"text":"Earth Resources Observation and Science (EROS) Center","active":true,"usgs":true}],"links":[{"id":471576,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.1038/sdata.2015.66","text":"Publisher Index Page"},{"id":341859,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"volume":"2","publishingServiceCenter":{"id":4,"text":"Rolla PSC"},"noUsgsAuthors":false,"publicationDate":"2015-12-08","publicationStatus":"PW","scienceBaseUri":"592e84bbe4b092b266f10d3d","contributors":{"authors":[{"text":"Funk, Chris 0000-0002-9254-6718 cfunk@usgs.gov","orcid":"https://orcid.org/0000-0002-9254-6718","contributorId":167070,"corporation":false,"usgs":true,"family":"Funk","given":"Chris","email":"cfunk@usgs.gov","affiliations":[{"id":222,"text":"Earth Resources Observation and Science (EROS) Center","active":true,"usgs":true},{"id":223,"text":"Earth Resources Observation and Science (EROS) Center (Geography)","active":false,"usgs":true}],"preferred":true,"id":696368,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Peterson, Pete","contributorId":192379,"corporation":false,"usgs":false,"family":"Peterson","given":"Pete","affiliations":[],"preferred":false,"id":696432,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Landsfeld, Martin","contributorId":192380,"corporation":false,"usgs":false,"family":"Landsfeld","given":"Martin","affiliations":[],"preferred":false,"id":696433,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Pedreros, Diego 0000-0001-9943-7373 pedreros@usgs.gov","orcid":"https://orcid.org/0000-0001-9943-7373","contributorId":4195,"corporation":false,"usgs":true,"family":"Pedreros","given":"Diego","email":"pedreros@usgs.gov","affiliations":[{"id":223,"text":"Earth Resources Observation and Science (EROS) Center (Geography)","active":false,"usgs":true}],"preferred":true,"id":696371,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Verdin, James 0000-0003-0238-9657 verdin@usgs.gov","orcid":"https://orcid.org/0000-0003-0238-9657","contributorId":145830,"corporation":false,"usgs":true,"family":"Verdin","given":"James","email":"verdin@usgs.gov","affiliations":[{"id":223,"text":"Earth Resources Observation and Science (EROS) Center (Geography)","active":false,"usgs":true}],"preferred":true,"id":696376,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"Shukla, Shraddhanand","contributorId":145802,"corporation":false,"usgs":false,"family":"Shukla","given":"Shraddhanand","affiliations":[{"id":16236,"text":"UCSB Climate Hazards Group","active":true,"usgs":false}],"preferred":false,"id":696372,"contributorType":{"id":1,"text":"Authors"},"rank":6},{"text":"Husak, Gregory","contributorId":145811,"corporation":false,"usgs":false,"family":"Husak","given":"Gregory","affiliations":[{"id":16236,"text":"UCSB Climate Hazards Group","active":true,"usgs":false}],"preferred":false,"id":696373,"contributorType":{"id":1,"text":"Authors"},"rank":7},{"text":"Rowland, James 0000-0003-4837-3511 rowland@usgs.gov","orcid":"https://orcid.org/0000-0003-4837-3511","contributorId":145846,"corporation":false,"usgs":true,"family":"Rowland","given":"James","email":"rowland@usgs.gov","affiliations":[{"id":222,"text":"Earth Resources Observation and Science (EROS) Center","active":true,"usgs":true},{"id":223,"text":"Earth Resources Observation and Science (EROS) Center (Geography)","active":false,"usgs":true}],"preferred":true,"id":696375,"contributorType":{"id":1,"text":"Authors"},"rank":8},{"text":"Harrison, Laura","contributorId":78859,"corporation":false,"usgs":true,"family":"Harrison","given":"Laura","affiliations":[],"preferred":false,"id":696374,"contributorType":{"id":1,"text":"Authors"},"rank":9},{"text":"Hoell, Andrew","contributorId":145803,"corporation":false,"usgs":false,"family":"Hoell","given":"Andrew","affiliations":[{"id":16236,"text":"UCSB Climate Hazards Group","active":true,"usgs":false}],"preferred":false,"id":696434,"contributorType":{"id":1,"text":"Authors"},"rank":10},{"text":"Michaelsen, Joel","contributorId":149202,"corporation":false,"usgs":false,"family":"Michaelsen","given":"Joel","affiliations":[],"preferred":false,"id":696435,"contributorType":{"id":1,"text":"Authors"},"rank":11}]}} ,{"id":70159636,"text":"sir20155166 - 2015 - Occurrence and transport of selected constituents in streams near the Stibnite mining area, Central Idaho, 2012–14","interactions":[],"lastModifiedDate":"2016-01-05T08:28:48","indexId":"sir20155166","displayToPublicDate":"2015-12-07T17:45:00","publicationYear":"2015","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":"2015-5166","title":"Occurrence and transport of selected constituents in streams near the Stibnite mining area, Central Idaho, 2012–14","docAbstract":"Mining of stibnite (antimony sulfide), tungsten, gold, silver, and mercury near the town of Stibnite in central Idaho has left a legacy of trace element contamination in local streams. Water-quality and streamflow monitoring data from a network of five streamflow-gaging stations were used to estimate trace-element and suspended-sediment loads and flow-weighted concentrations in the Stibnite mining area between 2012 and 2014. Measured concentrations of arsenic exceeded human health-based water-quality criteria at each streamflow-gaging station, except for Meadow Creek (site 2), which was selected to represent background conditions in the study area. Measured concentrations of antimony exceeded human health-based water-quality criteria at sites 3, 4, and 5.
\nRegression models developed using the U.S. Geological Survey LOAD ESTimation (LOADEST) program showed that concentrated sources of arsenic and antimony are present in specific reaches along Meadow Creek and the East Fork of South Fork of the Salmon River (EFSFSR) between the EFSFSR at Stibnite (site 3) and the EFSFSR above Sugar Creek (site 4). Eighty percent of the arsenic and antimony loads were attributable to discrete reaches that accounted for 25 percent of the total streamflow in the study area. Streamflow was negatively correlated with arsenic and antimony concentrations, indicating groundwater sources. Continuously monitored specific conductance, alone or combined with continuously computed streamflow, was more significant than streamflow alone as a surrogate measure of dissolved arsenic and antimony concentrations. Surrogate regression models (with coefficients of determination ranging from 0.96 to 0.65) can be used to estimate arsenic and antimony concentrations in real time at all five streamflow-gaging stations.
\nLOADEST model simulation results indicated hysteresis in transport of suspended sediment and sediment-associated constituents. Predictor variables that account for streamflow variability reduced model bias and root mean square error when included in regression models used to estimate concentrations and loads of suspended sediment, total aluminum, total lead, and total mercury.
\nNinety-eight percent of the estimated total mercury load transported downstream of the study area is attributable to Sugar Creek. A maximum concentration of 26 micrograms per liter was measured in Sugar Creek during May 2013 when snowmelt runoff occurred during a single peak in the hydrograph. Monitoring and modeling results indicate sediment and sediment-associated constituent concentrations and loads increase along Meadow Creek, likely because of the inflow of the East Fork of Meadow Creek, and decrease between sites 3 and 4 because the Glory Hole is trapping sediments. Sugar Creek (site 5) accounted for most of the sediment and sediment-associated constituent loading leaving the study area because loads from the East Fork of Meadow Creek remained trapped in the Glory Hole. Additionally, total mercury was detected at all five streamflow-gaging stations, and sampled mercury concentrations exceeded Idaho ambient water-quality criteria at all five streamflow-gaging stations.
","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/sir20155166","collaboration":"Prepared in cooperation with the Idaho Department of Lands and the Midas Gold Corporation","usgsCitation":"Etheridge, A.B., 2015, Occurrence and transport of selected constituents in streams near the Stibnite mining area, central Idaho, 2012–14: U.S. Geological Survey Scientific Investigations Report 2015–5166, 47 p., https://dx.doi.org/10.3133/sir20155166.","productDescription":"Report: vii, 47 p.; Appendix B","numberOfPages":"59","onlineOnly":"Y","additionalOnlineFiles":"Y","temporalStart":"2011-10-01","temporalEnd":"2014-09-30","ipdsId":"IP-060615","costCenters":[{"id":343,"text":"Idaho Water Science Center","active":true,"usgs":true}],"links":[{"id":311962,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/sir/2015/5166/coverthb.jpg"},{"id":311964,"rank":3,"type":{"id":3,"text":"Appendix"},"url":"https://pubs.usgs.gov/sir/2015/5166/sir20155166_appendixb.xlsx","text":"Appendix B","size":"40 KB","linkFileType":{"id":3,"text":"xlsx"},"description":"SIR 2015-5166 Appendix B"},{"id":311963,"rank":2,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/sir/2015/5166/sir20155166.pdf","text":"Report","size":"4.3 MB","linkFileType":{"id":1,"text":"pdf"},"description":"SIR 2015-5166 PDF"}],"country":"United States","state":"Idaho","otherGeospatial":"Stibnite Mining Area","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {\n \"stroke\": \"#555555\",\n \"stroke-width\": 2,\n \"stroke-opacity\": 1,\n \"fill\": \"#555555\",\n \"fill-opacity\": 0.5\n },\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -115.36502838134766,\n 44.82397775537488\n ],\n [\n -115.36502838134766,\n 44.98034238084973\n ],\n [\n -115.20195007324217,\n 44.98034238084973\n ],\n [\n -115.20195007324217,\n 44.82397775537488\n ],\n [\n -115.36502838134766,\n 44.82397775537488\n ]\n ]\n ]\n }\n }\n ]\n}","contact":"Director, Idaho Water Science Center
U.S. Geological Survey
230 Collins Road
Boise, Idaho 83702
http://id.water.usgs.gov
Geomorphic and habitat data were collected along Underwood Creek as part of a larger study of stream water quality conditions in the greater Milwaukee, Wisconsin, area. The data were collected to characterize baseline physical conditions in Underwood Creek prior to a potential discharge of wastewater return flow to the stream from the city of Waukesha, Wis. Geomorphic and habitat assessments were conducted in the summer and fall of 2012 by the U.S. Geological Survey (USGS) in cooperation with the Milwaukee Metropolitan Sewerage District. The assessments used a transect based, reach scale assessment at a total of eight reaches—six reaches along Underwood Creek and two reaches along the Menomonee River upstream and downstream of its confluence with Underwood Creek. The reach scale assessment was an updated version of the USGS National Water Quality Assessment Program habitat assessment integrated with an intensive geomorphic assessment. Channel cross sections and longitudinal profiles were surveyed along each of the eight reaches, and discharge and water temperature were measured. Additionally, a geomorphic river walk-through was completed along a 10 kilometer reach that spanned the individual assessment reaches and the sections of channel between them. The assessments and river walk-through described channel and bank stability, channel shape and size, sediment and riparian conditions along these areas of Underwood Creek and the Menomonee River. Since the time of the data collection, focus has turned to other Lake Michigan tributary watersheds for possible Waukesha return-flow discharges; however, the data collected for this effort remains a valuable asset for the baseline characterization, design, and prioritization of planned stream rehabilitation activities in Underwood Creek. The data files presented in this report include a variety of formats including geographic information system files, spreadsheets, photos, and scans of field forms.
\nA subset of habitat-specific data collected during the baseline study can be retrieved through USGS BioData https://aquatic.biodata.usgs.gov/landing.action.
","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/ds947","collaboration":"Prepared in cooperation with Milwaukee Metropolitan Sewerage District","usgsCitation":"Young, B.M., Fitzpatrick, F.A., and Blount, J.D., 2015, Stream geomorphic and habitat data from a baseline study of Underwood Creek, Wisconsin, 2012: U.S. Geological Survey Data Series 947, 14 p., plus data files, https://dx.doi.org/10.3133/ds947.","productDescription":"Report: v, 14 p.; 3 Tables; Figures; ReadMe; Spatial Data; Photos; Downloads Directory","numberOfPages":"24","onlineOnly":"Y","additionalOnlineFiles":"Y","ipdsId":"IP-053458","costCenters":[{"id":677,"text":"Wisconsin Water Science Center","active":true,"usgs":true}],"links":[{"id":311608,"rank":8,"type":{"id":27,"text":"Table"},"url":"https://pubs.usgs.gov/ds/0947/downloads/ds947_USGS-Underwood-Creek-Site-Surveys-Cross-Sections.xlsx","text":"Underwood Creek Site Survey - Cross Section","size":"239 KB","linkFileType":{"id":3,"text":"xlsx"},"description":"DS 947"},{"id":311606,"rank":6,"type":{"id":7,"text":"Companion Files"},"url":"https://pubs.usgs.gov/ds/0947/downloads/photos","text":"Photos","description":"DS 947","linkHelpText":"Geomorphic and Habit Assessment Site Photos (108 files, 270 MB), River Walk-Through PhotosDirector, Wisconsin Water Science Center
U.S. Geological Survey
8505 Research Way
Middleton, Wisconsin 53562-3586
http://wi.water.usgs.gov/