Arid and semiarid ecosystems (drylands) may dominate the trajectory of biosphere-to-atmosphere carbon (C) exchange, and understanding dryland CO2 efflux is important for C cycling at the global-scale. However, unknowns remain regarding how temperature and moisture interact to regulate dryland soil respiration (R s ), while ‘islands of fertility’ in drylands create spatially heterogeneous R s . At a site in southeastern Utah, USA we added or removed litter (0–650 % of control) in plots associated with either shrubs or biological soil crust-dominated interspaces between vascular plants. We measured R s , soil temperature (Ts), and water content (θ) repeatedly from October 2013 to November 2014. R s was highest following rain in late summer at Ts ~30 °C, and lowest mid-summer at Ts > 40 °C, resulting in apparent negative temperature sensitivity of R s at high temperatures, and positive temperature sensitivity at low-moderate temperatures. We used Bayesian statistical methods to compare models capturing a range of hypothesized relationships between Ts, θ, and R s . The best model indicates that apparent negative temperature sensitivity of R s at high Ts reflects the control of water content, not high temperatures. Modeled Q10 ranged from 2.7 to 1.4 between 5 and 45 °C. Litter addition had no effect on Q10 or reference respiration (R ref = R s at 20 °C and optimum θ) beneath shrubs, and little effect on R ref in interspaces, yet R ref was 1.5 times higher beneath shrubs than in interspaces. Altogether, these results suggest reduced R s often observed at high Ts in drylands is dominated by the control of θ, and, on shorter-timescales, variable litter inputs exert minimal control over R s .
","language":"English","publisher":"Springer","doi":"10.1007/s10533-016-0200-1","usgsCitation":"Tucker, C., and Reed, S.C., 2016, Low soil moisture during hot periods drives apparent negative temperature sensitivity of soil respiration in a dryland ecosystem: A multi-model comparison: Biogeochemistry, v. 128, no. 1, p. 155-169, https://doi.org/10.1007/s10533-016-0200-1.","productDescription":"15 p.","startPage":"155","endPage":"169","onlineOnly":"N","additionalOnlineFiles":"N","ipdsId":"IP-070711","costCenters":[{"id":568,"text":"Southwest Biological Science Center","active":true,"usgs":true}],"links":[{"id":322026,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"volume":"128","issue":"1","publishingServiceCenter":{"id":14,"text":"Menlo Park PSC"},"noUsgsAuthors":false,"publicationDate":"2016-04-02","publicationStatus":"PW","scienceBaseUri":"57500769e4b0ee97d51bb679","contributors":{"authors":[{"text":"Tucker, Colin 0000-0002-4539-7780 ctucker@usgs.gov","orcid":"https://orcid.org/0000-0002-4539-7780","contributorId":167487,"corporation":false,"usgs":true,"family":"Tucker","given":"Colin","email":"ctucker@usgs.gov","affiliations":[{"id":568,"text":"Southwest Biological Science Center","active":true,"usgs":true}],"preferred":true,"id":622468,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Reed, Sasha C. 0000-0002-8597-8619 screed@usgs.gov","orcid":"https://orcid.org/0000-0002-8597-8619","contributorId":462,"corporation":false,"usgs":true,"family":"Reed","given":"Sasha","email":"screed@usgs.gov","middleInitial":"C.","affiliations":[{"id":568,"text":"Southwest Biological Science Center","active":true,"usgs":true}],"preferred":true,"id":622469,"contributorType":{"id":1,"text":"Authors"},"rank":2}]}} ,{"id":70193182,"text":"70193182 - 2016 - NHDPlus as a geospatial framework for SPARROW modeling","interactions":[],"lastModifiedDate":"2018-03-15T10:26:24","indexId":"70193182","displayToPublicDate":"2016-04-01T00:00:00","publicationYear":"2016","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":1975,"text":"Impact Assessment Bulletin","active":true,"publicationSubtype":{"id":10}},"title":"NHDPlus as a geospatial framework for SPARROW modeling","docAbstract":"Successful water-resource management requires thorough knowledge and understanding of the relations among water-quality contaminate sources and the factors that affect the transport throughout a hydrologic system. Surface-water modeling is a valuable tool that can be applied to help advance and achieve the understanding of these dynamic relations. Spatially Referenced Regressions on Watershed Attributes (SPARROW) is one such model that uses nonlinear statistical methods to define conceptual and spatial relations among quantities of contaminant sources, monitored contaminant load, aquatic transport processes. NHDPlus Version 2 (V2) is a digital representation of a hydrologic network of streams and associated catchments. The dataset provides the fundamental spatial framework for SPARROW modeling.","language":"English","publisher":"American Water Resources Association","usgsCitation":"Brakebill, J.W., and Schwarz, G., 2016, NHDPlus as a geospatial framework for SPARROW modeling: Impact Assessment Bulletin, v. 18, no. 3.","ipdsId":"IP-073650","costCenters":[{"id":374,"text":"Maryland Water Science Center","active":true,"usgs":true}],"links":[{"id":348610,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"},{"id":347782,"type":{"id":15,"text":"Index Page"},"url":"https://www.awra.org/impact/"}],"volume":"18","issue":"3","publishingServiceCenter":{"id":10,"text":"Baltimore PSC"},"noUsgsAuthors":false,"publicationStatus":"PW","scienceBaseUri":"5a07ea50e4b09af898c8cc73","contributors":{"authors":[{"text":"Brakebill, John W. 0000-0001-9235-6810 jwbrakeb@usgs.gov","orcid":"https://orcid.org/0000-0001-9235-6810","contributorId":1061,"corporation":false,"usgs":true,"family":"Brakebill","given":"John","email":"jwbrakeb@usgs.gov","middleInitial":"W.","affiliations":[{"id":374,"text":"Maryland Water Science Center","active":true,"usgs":true}],"preferred":true,"id":718136,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Schwarz, Gregory E. 0000-0002-9239-4566 gschwarz@usgs.gov","orcid":"https://orcid.org/0000-0002-9239-4566","contributorId":543,"corporation":false,"usgs":true,"family":"Schwarz","given":"Gregory E.","email":"gschwarz@usgs.gov","affiliations":[{"id":5067,"text":"Northeast Regional Director's Office","active":true,"usgs":true},{"id":451,"text":"National Water Quality Assessment Program","active":true,"usgs":true}],"preferred":false,"id":718137,"contributorType":{"id":1,"text":"Authors"},"rank":2}]}} ,{"id":70179637,"text":"70179637 - 2016 - Basin scale controls on CO2 and CH4 emissions from the Upper Mississippi River","interactions":[],"lastModifiedDate":"2017-01-09T11:30:46","indexId":"70179637","displayToPublicDate":"2016-04-01T00:00:00","publicationYear":"2016","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":1807,"text":"Geophysical Research Letters","active":true,"publicationSubtype":{"id":10}},"title":"Basin scale controls on CO2 and CH4 emissions from the Upper Mississippi River","docAbstract":"The Upper Mississippi River, engineered for river navigation in the 1930s, includes a series of low-head dams and navigation pools receiving elevated sediment and nutrient loads from the mostly agricultural basin. Using high-resolution, spatially resolved water quality sensor measurements along 1385 river kilometers, we show that primary productivity and organic matter accumulation affect river carbon dioxide and methane emissions to the atmosphere. Phytoplankton drive CO2to near or below atmospheric equilibrium during the growing season, while anaerobic carbon oxidation supports a large proportion of the CO2 and CH4 production. Reductions of suspended sediment load, absent of dramatic reductions in nutrients, will likely further reduce net CO2emissions from the river. Large river pools, like Lake Pepin, which removes the majority of upstream sediments, and large agricultural tributaries downstream that deliver significant quantities of sediments and nutrients, are likely to persist as major geographical drivers of greenhouse gas emissions.
","language":"English","publisher":"AGU Publications","doi":"10.1002/2015GL067599","usgsCitation":"Crawford, J.T., Loken, L., Stanley, E.H., Stets, E., Dornblaser, M.M., and Striegl, R.G., 2016, Basin scale controls on CO2 and CH4 emissions from the Upper Mississippi River: Geophysical Research Letters, v. 43, no. 5, p. 1973-1979, https://doi.org/10.1002/2015GL067599.","productDescription":"7 p.","startPage":"1973","endPage":"1979","ipdsId":"IP-071715","costCenters":[{"id":5044,"text":"National Research Program - Central Branch","active":true,"usgs":true}],"links":[{"id":471101,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.1002/2015gl067599","text":"Publisher Index Page"},{"id":332986,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","otherGeospatial":"Upper Mississippi River","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -89.12109375,\n 37.055177106660814\n ],\n [\n -89.5166015625,\n 37.77071473849609\n ],\n [\n -90.263671875,\n 38.272688535980976\n ],\n [\n -89.9560546875,\n 38.87392853923629\n ],\n [\n -90.52734374999999,\n 39.07890809706475\n ],\n [\n -90.81298828125,\n 39.41922073655956\n ],\n [\n -91.40625,\n 39.977120098439634\n ],\n [\n -91.2744140625,\n 40.56389453066509\n ],\n [\n -90.8349609375,\n 40.94671366508002\n ],\n [\n -90.85693359375,\n 41.376808565702355\n ],\n [\n -90.15380859375,\n 41.72213058512578\n ],\n [\n -90.10986328125,\n 42.13082130188811\n ],\n [\n -90.50537109375,\n 42.52069952914966\n ],\n [\n -90.72509765625,\n 42.76314586689492\n ],\n [\n -90.98876953125,\n 42.8115217450979\n ],\n [\n -90.98876953125,\n 43.32517767999296\n ],\n [\n -91.25244140624999,\n 43.94537239244209\n ],\n [\n -91.58203125,\n 44.134913443750726\n ],\n [\n -91.95556640625,\n 44.41808794374846\n ],\n [\n -92.373046875,\n 44.63739123445585\n ],\n [\n -92.724609375,\n 44.793530904744074\n ],\n [\n -92.900390625,\n 44.69989765840318\n ],\n [\n -92.17529296875,\n 44.35527821160296\n ],\n [\n -91.49414062499999,\n 43.8186748554532\n ],\n [\n -91.3623046875,\n 43.30919109985686\n ],\n [\n -91.29638671875,\n 42.89206418807337\n ],\n [\n -90.98876953125,\n 42.601619944327965\n ],\n [\n -90.72509765625,\n 42.391008609205045\n ],\n [\n -90.37353515625,\n 42.114523952464246\n ],\n [\n -90.46142578125,\n 41.705728515237524\n ],\n [\n -90.90087890624999,\n 41.49212083968776\n ],\n [\n -91.1865234375,\n 41.47566020027821\n ],\n [\n -91.2744140625,\n 41.1290213474951\n ],\n [\n -91.0986328125,\n 40.96330795307353\n ],\n [\n -91.42822265625,\n 40.68063802521456\n ],\n [\n -91.69189453125,\n 40.34654412118006\n ],\n [\n -91.69189453125,\n 39.8928799002948\n ],\n [\n -91.29638671875,\n 39.50404070558415\n ],\n [\n -90.85693359375,\n 39.18117526158749\n ],\n [\n -90.46142578125,\n 38.77121637244273\n ],\n [\n -90.3076171875,\n 38.788345355085625\n ],\n [\n -90.50537109375,\n 38.42777351132902\n ],\n [\n -90.52734374999999,\n 38.20365531807149\n ],\n [\n -90.17578124999999,\n 37.90953361677018\n ],\n [\n -89.75830078125,\n 37.50972584293751\n ],\n [\n -89.5166015625,\n 37.03763967977139\n ],\n [\n -89.31884765624999,\n 36.82687474287728\n ],\n [\n -89.12109375,\n 37.055177106660814\n ]\n ]\n ]\n }\n }\n ]\n}","volume":"43","issue":"5","publishingServiceCenter":{"id":2,"text":"Denver PSC"},"noUsgsAuthors":false,"publicationDate":"2016-03-02","publicationStatus":"PW","scienceBaseUri":"5874b0aee4b0a829a320bb6b","contributors":{"authors":[{"text":"Crawford, John T. 0000-0003-4440-6945 jtcrawford@usgs.gov","orcid":"https://orcid.org/0000-0003-4440-6945","contributorId":4081,"corporation":false,"usgs":true,"family":"Crawford","given":"John","email":"jtcrawford@usgs.gov","middleInitial":"T.","affiliations":[{"id":5044,"text":"National Research Program - 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Central Branch","active":true,"usgs":true}],"preferred":false,"id":657999,"contributorType":{"id":1,"text":"Authors"},"rank":6}]}} ,{"id":70177907,"text":"70177907 - 2016 - Temporal, geographic, and host distribution of avian paramyxovirus 1 (Newcastle disease virus)","interactions":[],"lastModifiedDate":"2016-10-26T12:00:06","indexId":"70177907","displayToPublicDate":"2016-04-01T00:00:00","publicationYear":"2016","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":1988,"text":"Infection, Genetics and Evolution","active":true,"publicationSubtype":{"id":10}},"title":"Temporal, geographic, and host distribution of avian paramyxovirus 1 (Newcastle disease virus)","docAbstract":"Newcastle disease is caused by virulent forms of avian paramyxovirus of serotype 1 (APMV-1) and has global economic importance. The disease reached panzootic proportions within two decades after first being identified in 1926 in the United Kingdom and Indonesia and still remains endemic in many countries across the world. Here we review information on the host, temporal, and geographic distribution of APMV-1 genetic diversity based on the evolutionary systematics of the complete coding region of the fusion gene. Strains of APMV-1 are phylogenetically separated into two classes (class I and class II) and further classified into genotypes based on genetic differences. Class I viruses are genetically less diverse, generally present in wild waterfowl, and are of low virulence. Class II viruses are genetically and phenotypically more diverse, frequently isolated from poultry with occasional spillovers into wild birds, and exhibit a wider range of virulence. Waterfowl, cormorants, and pigeons are natural reservoirs of all APMV-1 pathotypes, except viscerotropic velogenic viruses for which natural reservoirs have not been identified. Genotypes I and II within class II include isolates of high and low virulence, the latter often being used as vaccines. Viruses of genotypes III and IX that emerged decades ago are now isolated rarely, but may be found in domestic and wild birds in China. Containing only virulent viruses and responsible for the majority of recent outbreaks in poultry and wild birds, viruses from genotypes V, VI, and VII, are highly mobile and have been isolated on different continents. Conversely, virulent viruses of genotypes XI (Madagascar), XIII (mainly Southwest Asia), XVI (North America) and XIV, XVII and XVIII (Africa) appear to have a more limited geographic distribution and have been isolated predominantly from poultry.
","language":"English","publisher":"Elsevier","doi":"10.1016/j.meegid.2016.01.008","usgsCitation":"Dimitrov, K.M., Ramey, A.M., Qiu, X., Bahl, J., and Afonso, C.L., 2016, Temporal, geographic, and host distribution of avian paramyxovirus 1 (Newcastle disease virus): Infection, Genetics and Evolution, v. 39, p. 22-34, https://doi.org/10.1016/j.meegid.2016.01.008.","productDescription":"13 p.","startPage":"22","endPage":"34","ipdsId":"IP-069077","costCenters":[{"id":117,"text":"Alaska Science Center Biology WTEB","active":true,"usgs":true}],"links":[{"id":471099,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.1016/j.meegid.2016.01.008","text":"Publisher Index Page"},{"id":330406,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"volume":"39","publishingServiceCenter":{"id":12,"text":"Tacoma PSC"},"noUsgsAuthors":false,"publicationStatus":"PW","scienceBaseUri":"5811c0f2e4b0f497e79a5a73","chorus":{"doi":"10.1016/j.meegid.2016.01.008","url":"http://dx.doi.org/10.1016/j.meegid.2016.01.008","publisher":"Elsevier BV","authors":"Dimitrov Kiril M., Ramey Andrew M., Qiu Xueting, Bahl Justin, Afonso Claudio L.","journalName":"Infection, Genetics and Evolution","publicationDate":"4/2016"},"contributors":{"authors":[{"text":"Dimitrov, Kiril M.","contributorId":176311,"corporation":false,"usgs":false,"family":"Dimitrov","given":"Kiril","email":"","middleInitial":"M.","affiliations":[],"preferred":false,"id":652158,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Ramey, Andrew M. 0000-0002-3601-8400 aramey@usgs.gov","orcid":"https://orcid.org/0000-0002-3601-8400","contributorId":1872,"corporation":false,"usgs":true,"family":"Ramey","given":"Andrew","email":"aramey@usgs.gov","middleInitial":"M.","affiliations":[{"id":117,"text":"Alaska Science Center Biology WTEB","active":true,"usgs":true},{"id":114,"text":"Alaska Science Center","active":true,"usgs":true}],"preferred":true,"id":652108,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Qiu, Xueting","contributorId":176312,"corporation":false,"usgs":false,"family":"Qiu","given":"Xueting","email":"","affiliations":[],"preferred":false,"id":652159,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Bahl, Justin","contributorId":171803,"corporation":false,"usgs":false,"family":"Bahl","given":"Justin","affiliations":[{"id":26950,"text":"University of Texas School of Public Health, 1200 Pressler Street, Houston, TX 77030, USA","active":true,"usgs":false}],"preferred":false,"id":652160,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Afonso, Claudio L.","contributorId":171954,"corporation":false,"usgs":false,"family":"Afonso","given":"Claudio","email":"","middleInitial":"L.","affiliations":[],"preferred":false,"id":652161,"contributorType":{"id":1,"text":"Authors"},"rank":5}]}} ,{"id":70177963,"text":"70177963 - 2016 - Municipal solid waste landfills harbor distinct microbiomes","interactions":[],"lastModifiedDate":"2018-08-07T11:52:05","indexId":"70177963","displayToPublicDate":"2016-04-01T00:00:00","publicationYear":"2016","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":1702,"text":"Frontiers in Microbiology","onlineIssn":"1664-302X","active":true,"publicationSubtype":{"id":10}},"title":"Municipal solid waste landfills harbor distinct microbiomes","docAbstract":"Landfills are the final repository for most of the discarded material from human society and its “built environments.” Microorganisms subsequently degrade this discarded material in the landfill, releasing gases (largely CH4 and CO2) and a complex mixture of soluble chemical compounds in leachate. Characterization of “landfill microbiomes” and their comparison across several landfills should allow the identification of environmental or operational properties that influence the composition of these microbiomes and potentially their biodegradation capabilities. To this end, the composition of landfill microbiomes was characterized as part of an ongoing USGS national survey studying the chemical composition of leachates from 19 non-hazardous landfills across 16 states in the continental U.S. The landfills varied in parameters such as size, waste composition, management strategy, geography, and climate zone. The diversity and composition of bacterial and archaeal populations in leachate samples were characterized by 16S rRNA gene sequence analysis, and compared against a variety of physical and chemical parameters in an attempt to identify their impact on selection. Members of the Epsilonproteobacteria, Gammaproteobacteria, Clostridia, and candidate division OP3 were the most abundant. The distribution of the observed phylogenetic diversity could best be explained by a combination of variables and was correlated most strongly with the concentrations of chloride and barium, rate of evapotranspiration, age of waste, and the number of detected household chemicals. This study illustrates how leachate microbiomes are distinct from those of other natural or built environments, and sheds light on the major selective forces responsible for this microbial diversity.
","language":"English","publisher":"Frontiers","doi":"10.3389/fmicb.2016.00534","usgsCitation":"Stamps, B.W., Lyles, C.N., Suflita, J.M., Masoner, J.R., Cozzarelli, I.M., Kolpin, D.W., and Stevenson, B.S., 2016, Municipal solid waste landfills harbor distinct microbiomes: Frontiers in Microbiology, v. 7, p. 1-11, https://doi.org/10.3389/fmicb.2016.00534.","productDescription":"Article 534; 11 p.","startPage":"1","endPage":"11","ipdsId":"IP-062988","costCenters":[{"id":516,"text":"Oklahoma Water Science Center","active":true,"usgs":true},{"id":589,"text":"Toxic Substances Hydrology Program","active":true,"usgs":true}],"links":[{"id":471095,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.3389/fmicb.2016.00534","text":"Publisher Index Page"},{"id":330574,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"volume":"7","publishingServiceCenter":{"id":5,"text":"Lafayette PSC"},"noUsgsAuthors":false,"publicationDate":"2016-04-20","publicationStatus":"PW","scienceBaseUri":"5818582fe4b0bb36a4c6fa13","contributors":{"authors":[{"text":"Stamps, Blake W.","contributorId":176485,"corporation":false,"usgs":false,"family":"Stamps","given":"Blake","email":"","middleInitial":"W.","affiliations":[],"preferred":false,"id":652503,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Lyles, Christopher N.","contributorId":176484,"corporation":false,"usgs":false,"family":"Lyles","given":"Christopher","email":"","middleInitial":"N.","affiliations":[],"preferred":false,"id":652504,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Suflita, Joseph M.","contributorId":82997,"corporation":false,"usgs":true,"family":"Suflita","given":"Joseph","email":"","middleInitial":"M.","affiliations":[],"preferred":false,"id":652505,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Masoner, Jason R. 0000-0002-4829-6379 jmasoner@usgs.gov","orcid":"https://orcid.org/0000-0002-4829-6379","contributorId":3193,"corporation":false,"usgs":true,"family":"Masoner","given":"Jason","email":"jmasoner@usgs.gov","middleInitial":"R.","affiliations":[{"id":436,"text":"National Research Program - Eastern Branch","active":true,"usgs":true},{"id":516,"text":"Oklahoma Water Science Center","active":true,"usgs":true}],"preferred":true,"id":652506,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Cozzarelli, Isabelle M. 0000-0002-5123-1007 icozzare@usgs.gov","orcid":"https://orcid.org/0000-0002-5123-1007","contributorId":1693,"corporation":false,"usgs":true,"family":"Cozzarelli","given":"Isabelle","email":"icozzare@usgs.gov","middleInitial":"M.","affiliations":[{"id":49175,"text":"Geology, Energy & Minerals Science Center","active":true,"usgs":true},{"id":436,"text":"National Research Program - Eastern Branch","active":true,"usgs":true}],"preferred":true,"id":652507,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"Kolpin, Dana W. 0000-0002-3529-6505 dwkolpin@usgs.gov","orcid":"https://orcid.org/0000-0002-3529-6505","contributorId":1239,"corporation":false,"usgs":true,"family":"Kolpin","given":"Dana","email":"dwkolpin@usgs.gov","middleInitial":"W.","affiliations":[{"id":351,"text":"Iowa Water Science Center","active":true,"usgs":true}],"preferred":true,"id":652508,"contributorType":{"id":1,"text":"Authors"},"rank":6},{"text":"Stevenson, Bradley S.","contributorId":176491,"corporation":false,"usgs":false,"family":"Stevenson","given":"Bradley","email":"","middleInitial":"S.","affiliations":[],"preferred":false,"id":652509,"contributorType":{"id":1,"text":"Authors"},"rank":7}]}} ,{"id":70178035,"text":"70178035 - 2016 - Airborne pathogens from dairy manure aerial irrigation and the human health risk","interactions":[],"lastModifiedDate":"2016-12-19T17:44:05","indexId":"70178035","displayToPublicDate":"2016-04-01T00:00:00","publicationYear":"2016","noYear":false,"publicationType":{"id":18,"text":"Report"},"publicationSubtype":{"id":3,"text":"Organization Series"},"title":"Airborne pathogens from dairy manure aerial irrigation and the human health risk","docAbstract":"Dairy manure, like the fecal excrement from any domesticated or wild animal, can contain pathogens capable of infecting humans and causing illness or even death. Pathogens in dairy manure can be broadly divided into categories of taxonomy or infectiousness. Dividing by taxonomy there are three pathogen groups in dairy manure: viruses (e.g., bovine rotavirus), bacteria (e.g., Salmonella species), and protozoa (e.g., Cryptosporidium parvum). There are two categories of infectiousness for pathogens found in animals: those that are zoonotic and those that are not. A zoonotic pathogen is one that can infect both human and animal hosts. Some zoonotic pathogens found in dairy manure cause illness in both hosts (e.g., Salmonella) while other zoonotic pathogens, like Escherichia coli O157:H7, (enterohemorrhagic E. coli (EHEC)) cause illness only in humans. As a general rule, the gastrointestinal viruses found in dairy manure are not zoonotic. While there are exceptions (e.g., rare reports of bovine rotavirus infecting children), for the most part the viruses in dairy manure are not a human health concern. The primary concerns are the zoonotic bacteria and protozoa in dairy manure.
","largerWorkType":{"id":18,"text":"Report"},"largerWorkTitle":"Considerations for the use of manure irrigation practices","language":"English","publisher":"Wisconsin Manure Irrigation Workgroup","collaboration":"Wisconsin Department of Natural Resources","usgsCitation":"Borchardt, M.A., and Burch, T.R., 2016, Airborne pathogens from dairy manure aerial irrigation and the human health risk, C-1-C-24.","productDescription":"C-1-C-24","ipdsId":"IP-069593","costCenters":[{"id":677,"text":"Wisconsin Water Science Center","active":true,"usgs":true}],"links":[{"id":332310,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"},{"id":330596,"type":{"id":15,"text":"Index Page"},"url":"https://fyi.uwex.edu/manureirrigation/files/2016/04/Manure-Irrigation-Workgroup-Report-2016.pdf"}],"publishingServiceCenter":{"id":6,"text":"Columbus PSC"},"noUsgsAuthors":false,"publicationStatus":"PW","scienceBaseUri":"58590009e4b03639a6025e2d","contributors":{"authors":[{"text":"Borchardt, Mark A. 0000-0002-6471-2627","orcid":"https://orcid.org/0000-0002-6471-2627","contributorId":151033,"corporation":false,"usgs":false,"family":"Borchardt","given":"Mark","email":"","middleInitial":"A.","affiliations":[{"id":6684,"text":"USDA Forest Service, Southern Research Station, Aiken, SC","active":true,"usgs":false}],"preferred":false,"id":652579,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Burch, Tucker R tburch@usgs.gov","contributorId":5689,"corporation":false,"usgs":true,"family":"Burch","given":"Tucker","email":"tburch@usgs.gov","middleInitial":"R","affiliations":[{"id":677,"text":"Wisconsin Water Science Center","active":true,"usgs":true}],"preferred":true,"id":652578,"contributorType":{"id":1,"text":"Authors"},"rank":2}]}} ,{"id":70178868,"text":"70178868 - 2016 - Increased temperatures combined with lowered salinities differentially impact oyster size class growth and mortality","interactions":[],"lastModifiedDate":"2016-12-09T15:46:38","indexId":"70178868","displayToPublicDate":"2016-04-01T00:00:00","publicationYear":"2016","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":2455,"text":"Journal of Shellfish Research","active":true,"publicationSubtype":{"id":10}},"title":"Increased temperatures combined with lowered salinities differentially impact oyster size class growth and mortality","docAbstract":"Changes in the timing and interaction of seasonal high temperatures and low salinities as predicted by climate change models could dramatically alter oyster population dynamics. Little is known explicitly about how low salinity and high temperature combinations affect spat (<25mm), seed (25–75mm), andmarket (>75mm) oyster growth and mortality. Using field and laboratory studies, this project quantified the combined effects of extremely low salinities (<5) and high temperatures (>30°C) on growth and survival of spat, seed, andmarket-sized oysters. In 2012 and 2013, hatchery-produced oysters were placed in open and closed cages at three sites in Breton Sound, LA, along a salinity gradient that typically ranged from 5 to 20. Growth and mortality were recorded monthly. Regardless of size class, oysters at the lowest salinity site (annualmean = 4.8) experienced significantly highermortality and lower growth than oysters located in higher salinity sites (annual means = 11.1 and 13.0, respectively); furthermore, all oysters in open cages at the two higher salinity sites experienced higher mortality than in closed cages, likely due to predation. To explicitly examine oyster responses to extreme low salinity and high temperature combinations, a series of laboratory studies were conducted. Oysters were placed in 18 tanks in a fully crossed temperature (25°C, 32°C) by salinity (1, 5, and 15) study with three replicates, and repeated at least twice for each oyster size class. Regardless of temperature, seed and market oysters held in low salinity tanks (salinity 1) experienced 100% mortality within 7 days. In contrast, at salinity 5, temperature significantly affected mortality; oysters in all size classes experienced greater than 50%mortality at 32°C and less than 40%mortality at 25°C. At the highest salinity tested (15), only market-sized oysters held at 32°C experienced significant mortality (>60%). These studies demonstrate that high water temperatures (>30°C) and low salinities (<5) negatively impact oyster growth and survival differentially and that high temperatures alone may negatively impact market-sized oysters. It is critical to understand the potential impacts of climate and anthropogenic changes on oyster resources to better adapt and manage for long-term sustainability.
","language":"English","publisher":"National Shellfisheries Association","doi":"10.2983/035.035.0112","usgsCitation":"LaPeyre, M.K., Rybovich, M., Hall, S., and La Peyre, J.F., 2016, Increased temperatures combined with lowered salinities differentially impact oyster size class growth and mortality: Journal of Shellfish Research, v. 35, no. 1, p. 101-113, https://doi.org/10.2983/035.035.0112.","productDescription":"13 p.","startPage":"101","endPage":"113","ipdsId":"IP-070396","costCenters":[{"id":198,"text":"Coop Res Unit Atlanta","active":true,"usgs":true}],"links":[{"id":331826,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","state":"Louisiana","city":"Breton Sound","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -89.9945068359375,\n 29.501768632523262\n ],\n [\n -89.9945068359375,\n 29.878755346037977\n ],\n [\n -89.39849853515625,\n 29.878755346037977\n ],\n [\n -89.39849853515625,\n 29.501768632523262\n ],\n [\n -89.9945068359375,\n 29.501768632523262\n ]\n ]\n ]\n }\n }\n ]\n}","volume":"35","issue":"1","publishingServiceCenter":{"id":8,"text":"Raleigh PSC"},"noUsgsAuthors":false,"publicationStatus":"PW","scienceBaseUri":"584bd0dfe4b077fc20250e12","contributors":{"authors":[{"text":"LaPeyre, Megan K. 0000-0001-9936-2252 mlapeyre@usgs.gov","orcid":"https://orcid.org/0000-0001-9936-2252","contributorId":585,"corporation":false,"usgs":true,"family":"LaPeyre","given":"Megan","email":"mlapeyre@usgs.gov","middleInitial":"K.","affiliations":[{"id":198,"text":"Coop Res Unit Atlanta","active":true,"usgs":true},{"id":17705,"text":"Wetland and Aquatic Research Center","active":true,"usgs":true}],"preferred":true,"id":655385,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Rybovich, Molly","contributorId":177344,"corporation":false,"usgs":false,"family":"Rybovich","given":"Molly","email":"","affiliations":[],"preferred":false,"id":655401,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Hall, Steven G.","contributorId":177345,"corporation":false,"usgs":false,"family":"Hall","given":"Steven G.","affiliations":[],"preferred":false,"id":655402,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"La Peyre, Jerome F.","contributorId":34697,"corporation":false,"usgs":true,"family":"La Peyre","given":"Jerome","email":"","middleInitial":"F.","affiliations":[],"preferred":false,"id":655403,"contributorType":{"id":1,"text":"Authors"},"rank":4}]}} ,{"id":70179445,"text":"70179445 - 2016 - Slow climate velocities of mountain streams portend their role as refugia for cold-water biodiversity","interactions":[],"lastModifiedDate":"2017-01-03T11:21:55","indexId":"70179445","displayToPublicDate":"2016-04-01T00:00:00","publicationYear":"2016","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":3165,"text":"Proceedings of the National Academy of Sciences of the United States of America","active":true,"publicationSubtype":{"id":10}},"title":"Slow climate velocities of mountain streams portend their role as refugia for cold-water biodiversity","docAbstract":"The imminent demise of montane species is a recurrent theme in the climate change literature, particularly for aquatic species that are constrained to networks and elevational rather than latitudinal retreat as temperatures increase. Predictions of widespread species losses, however, have yet to be fulfilled despite decades of climate change, suggesting that trends are much weaker than anticipated and may be too subtle for detection given the widespread use of sparse water temperature datasets or imprecise surrogates like elevation and air temperature. Through application of large water-temperature databases evaluated for sensitivity to historical air-temperature variability and computationally interpolated to provide high-resolution thermal habitat information for a 222,000-km network, we estimate a less dire thermal plight for cold-water species within mountains of the northwestern United States. Stream warming rates and climate velocities were both relatively low for 1968–2011 (average warming rate = 0.101 °C/decade; median velocity = 1.07 km/decade) when air temperatures warmed at 0.21 °C/decade. Many cold-water vertebrate species occurred in a subset of the network characterized by low climate velocities, and three native species of conservation concern occurred in extremely cold, slow velocity environments (0.33–0.48 km/decade). Examination of aggressive warming scenarios indicated that although network climate velocities could increase, they remain low in headwaters because of strong local temperature gradients associated with topographic controls. Better information about changing hydrology and disturbance regimes is needed to complement these results, but rather than being climatic cul-de-sacs, many mountain streams appear poised to be redoubts for cold-water biodiversity this century.
","language":"English","publisher":"PNAS","doi":"10.1073/pnas.1522429113","usgsCitation":"Isaak, D.J., Young, M.K., Luce, C.H., Hostetler, S.W., Wenger, S., Peterson, E.E., Ver Hoef, J., Groce, M.C., Horan, D.L., and Nagel, D.E., 2016, Slow climate velocities of mountain streams portend their role as refugia for cold-water biodiversity: Proceedings of the National Academy of Sciences of the United States of America, v. 113, no. 16, p. 4374-4379, https://doi.org/10.1073/pnas.1522429113.","productDescription":"6 p.","startPage":"4374","endPage":"4379","ipdsId":"IP-072844","costCenters":[{"id":438,"text":"National Research Program - Western Branch","active":true,"usgs":true}],"links":[{"id":471098,"rank":0,"type":{"id":41,"text":"Open Access External Repository Page"},"url":"https://www.ncbi.nlm.nih.gov/pmc/articles/4843441","text":"External Repository"},{"id":332732,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"volume":"113","issue":"16","publishingServiceCenter":{"id":14,"text":"Menlo Park PSC"},"noUsgsAuthors":false,"publicationDate":"2016-04-04","publicationStatus":"PW","scienceBaseUri":"586cc697e4b0f5ce109fa957","contributors":{"authors":[{"text":"Isaak, Daniel J.","contributorId":177835,"corporation":false,"usgs":false,"family":"Isaak","given":"Daniel","email":"","middleInitial":"J.","affiliations":[],"preferred":false,"id":657245,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Young, Michael K.","contributorId":177836,"corporation":false,"usgs":false,"family":"Young","given":"Michael","email":"","middleInitial":"K.","affiliations":[],"preferred":false,"id":657246,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Luce, Charles H.","contributorId":177837,"corporation":false,"usgs":false,"family":"Luce","given":"Charles","email":"","middleInitial":"H.","affiliations":[{"id":37389,"text":"U.S. Forest Service","active":true,"usgs":false}],"preferred":false,"id":657247,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Hostetler, Steven W. 0000-0003-2272-8302 swhostet@usgs.gov","orcid":"https://orcid.org/0000-0003-2272-8302","contributorId":3249,"corporation":false,"usgs":true,"family":"Hostetler","given":"Steven","email":"swhostet@usgs.gov","middleInitial":"W.","affiliations":[{"id":438,"text":"National Research Program - Western Branch","active":true,"usgs":true},{"id":200,"text":"Coop Res Unit Seattle","active":true,"usgs":true}],"preferred":true,"id":657244,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Wenger, Seth J.","contributorId":177838,"corporation":false,"usgs":false,"family":"Wenger","given":"Seth J.","affiliations":[],"preferred":false,"id":657248,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"Peterson, Erin E.","contributorId":177839,"corporation":false,"usgs":false,"family":"Peterson","given":"Erin","email":"","middleInitial":"E.","affiliations":[],"preferred":false,"id":657249,"contributorType":{"id":1,"text":"Authors"},"rank":6},{"text":"Ver Hoef, Jay","contributorId":177840,"corporation":false,"usgs":false,"family":"Ver Hoef","given":"Jay","affiliations":[],"preferred":false,"id":657250,"contributorType":{"id":1,"text":"Authors"},"rank":7},{"text":"Groce, Matthew C.","contributorId":177841,"corporation":false,"usgs":false,"family":"Groce","given":"Matthew","email":"","middleInitial":"C.","affiliations":[],"preferred":false,"id":657251,"contributorType":{"id":1,"text":"Authors"},"rank":8},{"text":"Horan, Dona L.","contributorId":36672,"corporation":false,"usgs":true,"family":"Horan","given":"Dona","email":"","middleInitial":"L.","affiliations":[],"preferred":false,"id":657252,"contributorType":{"id":1,"text":"Authors"},"rank":9},{"text":"Nagel, David E.","contributorId":86887,"corporation":false,"usgs":true,"family":"Nagel","given":"David","email":"","middleInitial":"E.","affiliations":[],"preferred":false,"id":657253,"contributorType":{"id":1,"text":"Authors"},"rank":10}]}} ,{"id":70188066,"text":"70188066 - 2016 - The power of remote sensing: Global monitoring of weather, water, and crops with satellites and data integration","interactions":[],"lastModifiedDate":"2020-12-21T16:00:29.132398","indexId":"70188066","displayToPublicDate":"2016-04-01T00:00:00","publicationYear":"2016","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":5406,"text":"Resource: Engineering and Technology for a Sustainable World","printIssn":"1076-3333","active":true,"publicationSubtype":{"id":10}},"title":"The power of remote sensing: Global monitoring of weather, water, and crops with satellites and data integration","docAbstract":"Imagine a family of six whose livelihood is based on subsistence farming on a small, maybe one hectare, parcel of land somewhere in Africa. The seasonal rainfall varies greatly, from 500 to 800 mm per year, and the land is degraded. Thus, the parcel’s total productivity is not more than 1.5 tonnes in a good year, hardly meeting the family’s food requirements. The lack of surplus grain eliminates the need for grain storage structures, and due to the high rainfall variability, drought hazard is always looming, with an average recurrence interval of five to ten years.
","language":"English","publisher":"American Society of Agricultural and Biological Engineers","usgsCitation":"Senay, G., 2016, The power of remote sensing: Global monitoring of weather, water, and crops with satellites and data integration: Resource: Engineering and Technology for a Sustainable World, v. 23, no. 2, p. 6-9.","productDescription":"4 p.","startPage":"6","endPage":"9","ipdsId":"IP-069936","costCenters":[{"id":222,"text":"Earth Resources Observation and Science (EROS) Center","active":true,"usgs":true}],"links":[{"id":341855,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"},{"id":341853,"rank":1,"type":{"id":15,"text":"Index Page"},"url":"https://elibrary.asabe.org/abstract.asp?aid=46628"}],"volume":"23","issue":"2","publishingServiceCenter":{"id":4,"text":"Rolla PSC"},"noUsgsAuthors":false,"publicationStatus":"PW","scienceBaseUri":"592e84b9e4b092b266f10d32","contributors":{"authors":[{"text":"Senay, Gabriel B. 0000-0002-8810-8539 senay@usgs.gov","orcid":"https://orcid.org/0000-0002-8810-8539","contributorId":166812,"corporation":false,"usgs":true,"family":"Senay","given":"Gabriel","email":"senay@usgs.gov","middleInitial":"B.","affiliations":[{"id":223,"text":"Earth Resources Observation and Science (EROS) Center (Geography)","active":false,"usgs":true},{"id":222,"text":"Earth Resources Observation and Science (EROS) Center","active":true,"usgs":true}],"preferred":true,"id":696381,"contributorType":{"id":1,"text":"Authors"},"rank":1}]}} ,{"id":70185203,"text":"70185203 - 2016 - Fines classification based on sensitivity to pore-fluid chemistry","interactions":[],"lastModifiedDate":"2017-03-16T12:40:30","indexId":"70185203","displayToPublicDate":"2016-04-01T00:00:00","publicationYear":"2016","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":2327,"text":"Journal of Geotechnical and Geoenvironmental Engineering","active":true,"publicationSubtype":{"id":10}},"title":"Fines classification based on sensitivity to pore-fluid chemistry","docAbstract":"The 75-μm particle size is used to discriminate between fine and coarse grains. Further analysis of fine grains is typically based on the plasticity chart. Whereas pore-fluid-chemistry-dependent soil response is a salient and distinguishing characteristic of fine grains, pore-fluid chemistry is not addressed in current classification systems. Liquid limits obtained with electrically contrasting pore fluids (deionized water, 2-M NaCl brine, and kerosene) are combined to define the soil “electrical sensitivity.” Liquid limit and electrical sensitivity can be effectively used to classify fine grains according to their fluid-soil response into no-, low-, intermediate-, or high-plasticity fine grains of low, intermediate, or high electrical sensitivity. The proposed methodology benefits from the accumulated experience with liquid limit in the field and addresses the needs of a broader range of geotechnical engineering problems.
Methylmercury (MeHg) levels in dragonfly larvae and water were measured over two years in aquatic systems impacted to varying degrees by sulfate releases related to iron mining activity. This study examined the impact of elevated sulfate loads on MeHg concentrations and tested the use of MeHg in dragonfly larvae as an indicator of MeHg levels in a range of aquatic systems including 16 river/stream sites and two lakes. MeHg concentrations in aeshnid dragonfly larvae were positively correlated (R2 = 0.46, p < 0.01) to peak MeHg concentrations in the dissolved phase for the combined years of 2012 and 2013. This relation was strong in 2012 (R2 = 0.85, p < 0.01), but showed no correlation in 2013 (R2 = 0.02, p > 0.05). MeHg in dragonfly larvae were not elevated at the highest sulfate sites, but rather the reverse was generally observed. Record rainfall events in 2012 and above average rainfall in 2013 likely delivered the majority of Hg and MeHg to these systems via interflow and activated groundwater flow through reduced sediments. As a result, the impacts of elevated sulfate releases due to mining activities were not apparent in these systems where little of the sulfate is reduced. Lower bioaccumulation factors for MeHg in aeshnid dragonfly larvae were observed with increasing dissolved organic carbon (DOC) concentrations. This finding is consistent with previous studies showing that MeHg in high DOC systems is less bioavailable; an equilibrium model shows that more MeHg being associated with DOC rather than algae at the base of the food chain readily explains the lower bioaccumulation factors.
","language":"English","publisher":"Springer","doi":"10.1007/s10646-015-1603-9","usgsCitation":"Jeremiason, J.D., Reiser, T.K., Weitz, R.A., Berndt, M., and Aiken, G.R., 2016, Aeshnid dragonfly larvae as bioindicators of methylmercury contamination in aquatic systems impacted by elevated sulfate loading: Ecotoxicology, v. 25, no. 3, p. 456-468, https://doi.org/10.1007/s10646-015-1603-9.","productDescription":"13 p.","startPage":"456","endPage":"468","ipdsId":"IP-071234","costCenters":[{"id":5044,"text":"National Research Program - Central Branch","active":true,"usgs":true}],"links":[{"id":343854,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"volume":"25","issue":"3","noUsgsAuthors":false,"publicationDate":"2016-01-06","publicationStatus":"PW","scienceBaseUri":"5969d82be4b0d1f9f060a18c","contributors":{"authors":[{"text":"Jeremiason, Jeffrey D.","contributorId":7146,"corporation":false,"usgs":true,"family":"Jeremiason","given":"Jeffrey","email":"","middleInitial":"D.","affiliations":[],"preferred":false,"id":704977,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Reiser, T. K.","contributorId":194673,"corporation":false,"usgs":false,"family":"Reiser","given":"T.","email":"","middleInitial":"K.","affiliations":[],"preferred":false,"id":704978,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Weitz, R. A.","contributorId":194674,"corporation":false,"usgs":false,"family":"Weitz","given":"R.","email":"","middleInitial":"A.","affiliations":[],"preferred":false,"id":704979,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Berndt, M.E.","contributorId":78487,"corporation":false,"usgs":true,"family":"Berndt","given":"M.E.","email":"","affiliations":[],"preferred":false,"id":704980,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Aiken, George R. 0000-0001-8454-0984 graiken@usgs.gov","orcid":"https://orcid.org/0000-0001-8454-0984","contributorId":1322,"corporation":false,"usgs":true,"family":"Aiken","given":"George","email":"graiken@usgs.gov","middleInitial":"R.","affiliations":[{"id":5044,"text":"National Research Program - Central Branch","active":true,"usgs":true},{"id":191,"text":"Colorado Water Science Center","active":true,"usgs":true}],"preferred":true,"id":704981,"contributorType":{"id":1,"text":"Authors"},"rank":5}]}} ,{"id":70190336,"text":"70190336 - 2016 - Seasonal habitat use of brook trout and juvenile steelhead in a Lake Ontario tributary","interactions":[],"lastModifiedDate":"2017-08-26T13:47:28","indexId":"70190336","displayToPublicDate":"2016-04-01T00:00:00","publicationYear":"2016","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":2299,"text":"Journal of Freshwater Ecology","active":true,"publicationSubtype":{"id":10}},"title":"Seasonal habitat use of brook trout and juvenile steelhead in a Lake Ontario tributary","docAbstract":"Brook trout (Salvelinus fontinalis) are generally restricted to headwaters in New York tributaries of Lake Ontario. In only a few streams are brook trout abundant in lower stream reaches that are accessible to adult Pacific salmonids migrating from the lake. Consequently, because of the rarity of native brook trout populations in these lower stream reaches it is important to understand how they use stream habitat in sympatry with juvenile Pacific salmonids which are now naturalized in several Lake Ontario tributaries. In this study, we examined the seasonal (spring, summer, and fall) habitat use of brook trout and juvenile steelhead (Oncorhynchus mykiss) in Hart Brook, a tributary of eastern Lake Ontario. We found interspecific, intraspecific, and seasonal variation in habitat use. Subyearling steelhead were associated with faster water velocities than subyearling brook trout and, overall, had the least habitat similarity to the other salmonid groups examined. Overyearling brook trout and yearling steelhead exhibited the greatest degree of habitat selection and habitat selection by all four salmonid groups was greatest in summer. The availability of pool habitat for overyearling salmonids may pose the largest impediment to these species in Hart Brook.
","language":"English","publisher":"Taylor & Francis","doi":"10.1080/02705060.2015.1123655","usgsCitation":"Johnson, J.H., Abbett, R., Chalupnicki, M.A., and Verdoliva, F., 2016, Seasonal habitat use of brook trout and juvenile steelhead in a Lake Ontario tributary: Journal of Freshwater Ecology, v. 31, no. 2, p. 239-249, https://doi.org/10.1080/02705060.2015.1123655.","productDescription":"11 p.","startPage":"239","endPage":"249","ipdsId":"IP-070095","costCenters":[{"id":324,"text":"Great Lakes Science Center","active":true,"usgs":true}],"links":[{"id":471366,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.1080/02705060.2015.1123655","text":"Publisher Index Page"},{"id":345159,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"volume":"31","issue":"2","noUsgsAuthors":false,"publicationDate":"2016-01-27","publicationStatus":"PW","scienceBaseUri":"59a288c9e4b077f0056692af","contributors":{"authors":[{"text":"Johnson, James H. 0000-0002-5619-3871 jhjohnson@usgs.gov","orcid":"https://orcid.org/0000-0002-5619-3871","contributorId":389,"corporation":false,"usgs":true,"family":"Johnson","given":"James","email":"jhjohnson@usgs.gov","middleInitial":"H.","affiliations":[{"id":324,"text":"Great Lakes Science Center","active":true,"usgs":true}],"preferred":true,"id":708508,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Abbett, Ross 0000-0001-6276-5541 rabbett@usgs.gov","orcid":"https://orcid.org/0000-0001-6276-5541","contributorId":4359,"corporation":false,"usgs":true,"family":"Abbett","given":"Ross","email":"rabbett@usgs.gov","affiliations":[{"id":324,"text":"Great Lakes Science Center","active":true,"usgs":true}],"preferred":true,"id":708509,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Chalupnicki, Marc A. mchalupnicki@usgs.gov","contributorId":3236,"corporation":false,"usgs":true,"family":"Chalupnicki","given":"Marc","email":"mchalupnicki@usgs.gov","middleInitial":"A.","affiliations":[{"id":324,"text":"Great Lakes Science Center","active":true,"usgs":true}],"preferred":false,"id":708510,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Verdoliva, Francis","contributorId":150381,"corporation":false,"usgs":false,"family":"Verdoliva","given":"Francis","email":"","affiliations":[{"id":13678,"text":"New York State Department of Environmental Conservation","active":true,"usgs":false}],"preferred":false,"id":708511,"contributorType":{"id":1,"text":"Authors"},"rank":4}]}} ,{"id":70177908,"text":"70177908 - 2016 - Effect of phytoremediation on concentrations of benzene, toluene, naphthalene, and dissolved oxygen in groundwater at a former manufactured gas plant site, Charleston, South Carolina, USA, 1998–2014","interactions":[],"lastModifiedDate":"2016-10-26T15:04:14","indexId":"70177908","displayToPublicDate":"2016-03-31T22:30:00","publicationYear":"2016","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":1534,"text":"Environmental Earth Sciences","active":true,"publicationSubtype":{"id":10}},"title":"Effect of phytoremediation on concentrations of benzene, toluene, naphthalene, and dissolved oxygen in groundwater at a former manufactured gas plant site, Charleston, South Carolina, USA, 1998–2014","docAbstract":"Concentrations of benzene, toluene, naphthalene, and dissolved oxygen in groundwater at a former manufactured gas plant site near Charleston, South Carolina, USA, have been monitored since the installation of a phytoremediation system of hybrid poplar trees in 1998. Between 2000 and 2014, the concentrations of benzene, toluene, and naphthalene (BT&N) in groundwater in the planted area have decreased. For example, in the monitoring well containing the highest concentrations of BT&N, benzene concentrations decreased from 10,200 µg/L to less than 4000 µg/L, toluene concentrations decreased from 2420 µg/L to less than 20 µg/L, and naphthalene concentrations decreased from 6840 µg/L to less than 3000 µg/L. Concentrations of BT&N in groundwater in all wells were observed to be lower during the summer months relative to the winter months of a particular year during the first few years after installing the phytoremediation system, most likely due to increased transpiration and contaminant uptake by the hybrid poplar trees during the warm summer months; this pathway of uptake by trees was confirmed by the detection of benzene, toluene, and naphthalene in trees during sampling events in 2002, and later in the study in 2012. These data suggest that the phytoremediation system affects the groundwater contaminants on a seasonal basis and, over multiple years, has resulted in a cumulative decrease in dissolved-phase contaminant concentrations in groundwater. The removal of dissolved organic contaminants from the aquifer has resulted in a lower demand on dissolved oxygen supplied by recharge and, as a result, the redox status of the groundwater has changed from anoxic to oxic conditions. This study provides much needed information for water managers and other scientists on the viability of the long-term effectiveness of phytoremediation in decreasing groundwater contaminants and increasing dissolved oxygen at sites contaminated by benzene, toluene, and naphthalene.
","language":"English","publisher":"Springer-Verlag","doi":"10.1007/s12665-016-5408-9","usgsCitation":"Landmeyer, J., and Effinger, T.N., 2016, Effect of phytoremediation on concentrations of benzene, toluene, naphthalene, and dissolved oxygen in groundwater at a former manufactured gas plant site, Charleston, South Carolina, USA, 1998–2014: Environmental Earth Sciences, v. 75, 605; 13 p., https://doi.org/10.1007/s12665-016-5408-9.","productDescription":"605; 13 p.","onlineOnly":"N","additionalOnlineFiles":"N","ipdsId":"IP-070997","costCenters":[{"id":13634,"text":"South Atlantic Water Science Center","active":true,"usgs":true}],"links":[{"id":330430,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","state":"South Carolina","city":"Charleston","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -79.92934584617615,\n 32.7883749103601\n ],\n [\n -79.92934584617615,\n 32.791134828514295\n ],\n [\n -79.92491483688354,\n 32.791134828514295\n ],\n [\n -79.92491483688354,\n 32.7883749103601\n ],\n [\n -79.92934584617615,\n 32.7883749103601\n ]\n ]\n ]\n }\n }\n ]\n}","volume":"75","publishingServiceCenter":{"id":8,"text":"Raleigh PSC"},"noUsgsAuthors":false,"publicationDate":"2016-03-31","publicationStatus":"PW","scienceBaseUri":"5811c0f2e4b0f497e79a5a77","chorus":{"doi":"10.1007/s12665-016-5408-9","url":"http://dx.doi.org/10.1007/s12665-016-5408-9","publisher":"Springer Nature","authors":"Landmeyer James E., Effinger Thomas N.","journalName":"Environmental Earth Sciences","publicationDate":"3/31/2016","auditedOn":"8/1/2016","publiclyAccessibleDate":"3/31/2016"},"contributors":{"authors":[{"text":"Landmeyer, James 0000-0002-5640-3816 jlandmey@usgs.gov","orcid":"https://orcid.org/0000-0002-5640-3816","contributorId":3257,"corporation":false,"usgs":true,"family":"Landmeyer","given":"James","email":"jlandmey@usgs.gov","affiliations":[{"id":13634,"text":"South Atlantic Water Science Center","active":true,"usgs":true}],"preferred":true,"id":652109,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Effinger, Thomas N.","contributorId":176300,"corporation":false,"usgs":false,"family":"Effinger","given":"Thomas","email":"","middleInitial":"N.","affiliations":[],"preferred":false,"id":652110,"contributorType":{"id":1,"text":"Authors"},"rank":2}]}} ,{"id":70169948,"text":"ofr20161056 - 2016 - Construction, calibration, and validation of the RBM10 water temperature model for the Trinity River, northern California","interactions":[],"lastModifiedDate":"2016-04-01T08:35:51","indexId":"ofr20161056","displayToPublicDate":"2016-03-31T17:00:00","publicationYear":"2016","noYear":false,"publicationType":{"id":18,"text":"Report"},"publicationSubtype":{"id":5,"text":"USGS Numbered Series"},"seriesTitle":{"id":330,"text":"Open-File Report","code":"OFR","onlineIssn":"2331-1258","printIssn":"0196-1497","active":true,"publicationSubtype":{"id":5}},"seriesNumber":"2016-1056","title":"Construction, calibration, and validation of the RBM10 water temperature model for the Trinity River, northern California","docAbstract":"We constructed a one-dimensional daily averaged water-temperature model to simulate Trinity River temperatures for 1980–2013. The purpose of this model is to assess effects of water-management actions on water temperature and to provide water temperature inputs for a salmon population dynamics model. Simulated meteorological data, observed streamflow data, and observed water temperatures were used as model inputs to simulate a continuous 34-year time series of historical daily mean water temperature at eight locations along 112.2 river miles from Lewiston Dam near Weaverville, California, downstream to the Klamath River confluence. To demonstrate the utility of the model to inform management actions, we simulated three management alternatives to assess the effects of bypass flow augmentation in a drought year, 1994, and compared those results to the simulated historical baseline, referred to as the “No Action” alternative scenario. Augmentation flows from the Lewiston Dam bypass consist of temperature-controlled releases capable of cooling downstream water temperatures in hot times of the year, which can reduce the probability of disease outbreaks in fish populations. Outputs from the Trinity River water-temperature model were then used as inputs to an existing water-temperature model of the Klamath River to evaluate the effect of augmentation flow releases on water temperatures in the lower Klamath River.
\nWe structured the Trinity River water-temperature model in River Basin Model-10 (RBM10), which uses a simple equilibrium flow model, assuming discharge in each river segment on each day is transmitted downstream instantaneously. The model uses a heat-budget formulation to quantify heat flux at the air-water interface. Inputs for the heat budget are calculated from daily mean meteorological data, including net shortwave solar radiation, net longwave atmospheric radiation, air temperature, wind speed, vapor pressure, and a psychrometric constant needed to calculate the Bowen ratio. The modeling domain was divided into eight reaches ranging in length from 8.8 to 20.6 miles, which were calibrated and validated separately with observed water temperature data collected irregularly from 1980 to 2013. Root mean square errors of observed and simulated water temperatures for the eight reaches ranged from 0.25 to 1.12 degrees Celsius (°C). Mean absolute errors ranged from 0.18 to 0.89 °C. For model validation, a k-fold cross-validation technique was used. Validation root mean square error and mean absolute error for the eight reaches ranged from 0.24 to 1.11 °C and from 0.18 to 0.89 °C, respectively.
\nAugmentation scenarios were based on historical hydrological and meteorological data, combined with prescribed flow and temperature releases from Lewiston Dam provided by the Bureau of Reclamation. Water releases were scheduled to achieve targeted flows of 2,500, 2,800, and 3,200 cubic feet per second in the lower Klamath River from mid-August through late September, coinciding with the upstream migration of adult fall-run Chinook salmon (Oncorhynchus tshawytscha). Water temperatures simulated at river mile 5.7 on the Klamath River showed a 5 °C decrease from the No Action historical baseline, which was near or greater than 23 °C when augmentation began in mid-August. Thereafter, an approximate 1 °C difference among augmentation scenarios emerged, with the decrease in water temperature commensurate to the level of augmentation. All augmentation scenarios simulated water temperatures equal to or less than 21 °C from mid-August through late September. Water temperatures equal to or greater than 23 °C are of particular interest because of a thermal threshold known to inhibit upstream migration of salmon. When temperatures exceed this approximate 23 °C threshold, Chinook salmon are known to congregate in high densities in thermal refugias and show extended residence times, which can potentially trigger epizootic outbreaks such as of Ichthyophthirius multifiliis (“Ich”) and Flavobacterium columnare (“Columnaris”) that were the causative factors of the Klamath River fish kill in 2002. A model with the ability to simulate water temperatures in response to management actions at the basin scale is a valuable asset for water managers who must make decisions about how best to use limited water resources, which directly affect the state of fisheries in the Klamath Basin.
","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/ofr20161056","collaboration":"Prepared in cooperation with the U.S. Fish and Wildlife Service and the Bureau of Reclamation","usgsCitation":"Jones, E.C., Perry, R.W., Risley, J.C., Som, N.A., and Hetrick, N.J., 2016, Construction, calibration, and validation of the RBM10 water temperature model for the Trinity River, northern California: U.S. Geological Survey Open-File Report 2016–1056, 46 p., https://dx.doi.org/10.3133/ofr20161056.","productDescription":"vi, 46 p.","numberOfPages":"56","onlineOnly":"Y","additionalOnlineFiles":"N","ipdsId":"IP-070848","costCenters":[{"id":654,"text":"Western Fisheries Research 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U.S. Geological Survey
6505 NE 65th Street
Seattle, Washington 98115
http://wfrc.usgs.gov/
In 2015, a major outbreak of highly pathogenic avian influenza virus (HPAIV) infection devastated poultry facilities in Minnesota, USA. To clarify the role of wild birds, we tested 3,139 waterfowl fecal samples and 104 sick and dead birds during March 9–June 4, 2015. HPAIV was isolated from a Cooper’s hawk but not from waterfowl.
","language":"English","publisher":"Centers for Disease Control and Prevention","doi":"10.3201/eid2207.152032","usgsCitation":"Jennelle, C., Carstensen, M., Hildebrand, E.C., Cornicelli, L., Wolf, P.C., Grear, D.A., Ip, S., VanDalen, K.K., and Minicucci, L.A., 2016, Surveillance for highly pathogenic avian influenza virus in wild birds during outbreaks in domestic poultry, Minnesota, 2015: Emerging Infectious Diseases, v. 22, no. 7, p. 1278-1282, https://doi.org/10.3201/eid2207.152032.","productDescription":"5 p.","startPage":"1278","endPage":"1282","onlineOnly":"N","additionalOnlineFiles":"N","ipdsId":"IP-071667","costCenters":[{"id":456,"text":"National Wildlife Health Center","active":true,"usgs":true}],"links":[{"id":471108,"rank":2,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.3201/eid2207.152032","text":"Publisher Index 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C.","contributorId":168399,"corporation":false,"usgs":false,"family":"Hildebrand","given":"Erik","email":"","middleInitial":"C.","affiliations":[{"id":6964,"text":"Minnesota Department of Natural Resources","active":true,"usgs":false}],"preferred":false,"id":625805,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Cornicelli, Louis","contributorId":168400,"corporation":false,"usgs":false,"family":"Cornicelli","given":"Louis","affiliations":[{"id":6964,"text":"Minnesota Department of Natural Resources","active":true,"usgs":false}],"preferred":false,"id":625806,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Wolf, Paul C.","contributorId":127725,"corporation":false,"usgs":false,"family":"Wolf","given":"Paul","email":"","middleInitial":"C.","affiliations":[{"id":7124,"text":"United States Department of Agriculture, Animal and Plant Health Inspection Service, Wildlife Services, 644 Bayfield Street, Suite 215, St Paul, Minnesota, 55107, USA","active":true,"usgs":false}],"preferred":false,"id":625807,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"Grear, Daniel A. 0000-0002-5478-1549 dgrear@usgs.gov","orcid":"https://orcid.org/0000-0002-5478-1549","contributorId":149047,"corporation":false,"usgs":true,"family":"Grear","given":"Daniel","email":"dgrear@usgs.gov","middleInitial":"A.","affiliations":[{"id":456,"text":"National Wildlife Health Center","active":true,"usgs":true},{"id":5068,"text":"Midwest Regional Director's Office","active":true,"usgs":true}],"preferred":false,"id":625802,"contributorType":{"id":1,"text":"Authors"},"rank":6},{"text":"Ip, S. 0000-0003-4844-7533 hip@usgs.gov","orcid":"https://orcid.org/0000-0003-4844-7533","contributorId":727,"corporation":false,"usgs":true,"family":"Ip","given":"S.","email":"hip@usgs.gov","affiliations":[{"id":456,"text":"National Wildlife Health Center","active":true,"usgs":true}],"preferred":true,"id":625803,"contributorType":{"id":1,"text":"Authors"},"rank":7},{"text":"VanDalen, Kaci K.","contributorId":86984,"corporation":false,"usgs":true,"family":"VanDalen","given":"Kaci","email":"","middleInitial":"K.","affiliations":[],"preferred":false,"id":625812,"contributorType":{"id":1,"text":"Authors"},"rank":8},{"text":"Minicucci, Larissa A.","contributorId":168401,"corporation":false,"usgs":false,"family":"Minicucci","given":"Larissa","email":"","middleInitial":"A.","affiliations":[{"id":25280,"text":"University of Minnesota Veterinary Population Medicine","active":true,"usgs":false}],"preferred":false,"id":625808,"contributorType":{"id":1,"text":"Authors"},"rank":9}]}} ,{"id":70169977,"text":"70169977 - 2016 - Temporal and spatial patterns of wetland extent influence variability of surface water connectivity in the Prairie Pothole Region, United States","interactions":[],"lastModifiedDate":"2016-03-31T12:39:04","indexId":"70169977","displayToPublicDate":"2016-03-31T13:30:00","publicationYear":"2016","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":2602,"text":"Landscape Ecology","active":true,"publicationSubtype":{"id":10}},"title":"Temporal and spatial patterns of wetland extent influence variability of surface water connectivity in the Prairie Pothole Region, United States","docAbstract":"Context. Quantifying variability in landscape-scale surface water connectivity can help improve our understanding of the multiple effects of wetlands on downstream waterways. Objectives. We examined how wetland merging and the coalescence of wetlands with streams varied both spatially (among ecoregions) and interannually (from drought to deluge) across parts of the Prairie Pothole Region. Methods. Wetland extent was derived over a time series (1990-2011) using Landsat imagery. Changes in landscape-scale connectivity, generated by the physical coalescence of wetlands with other surface water features, were quantified by fusing static wetland and stream datasets with Landsat-derived wetland extent maps, and related to multiple wetness indices. The usage of Landsat allows for decadal-scale analysis, but limits the types of surface water connections that can be detected. Results. Wetland extent correlated positively with the merging of wetlands and wetlands with streams. Wetness conditions, as defined by drought indices and runoff, were positively correlated with wetland extent, but less consistently correlated with measures of surface water connectivity. The degree of wetland-wetland merging was found to depend less on total wetland area or density, and more on climate conditions, as well as the threshold for how wetland/upland was defined. In contrast, the merging of wetlands with streams was positively correlated with stream density, and inversely related to wetland density. Conclusions. Characterizing the degree of surface water connectivity within the Prairie Pothole Region in North America requires consideration of 1) climate-driven variation in wetness conditions and 2) within-region variation in wetland and stream spatial arrangements.
","language":"English","publisher":"Springer","doi":"10.1007/s10980-015-0290-5","usgsCitation":"Vanderhoof, M.K., Alexander, L., and Todd, J., 2016, Temporal and spatial patterns of wetland extent influence variability of surface water connectivity in the Prairie Pothole Region, United States: Landscape Ecology, v. 31, no. 4, p. 805-824, https://doi.org/10.1007/s10980-015-0290-5.","productDescription":"20 p.","startPage":"805","endPage":"824","onlineOnly":"N","additionalOnlineFiles":"N","ipdsId":"IP-069152","costCenters":[{"id":318,"text":"Geosciences and Environmental Change Science Center","active":true,"usgs":true}],"links":[{"id":471109,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.1007/s10980-015-0290-5","text":"Publisher Index Page"},{"id":319678,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","state":"Minnesota, North Dakota, South Dakota","otherGeospatial":"Prairie Pothole Region","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -83.1005859375,\n 24.462150693715266\n ],\n [\n -83.1005859375,\n 24.77177232822881\n ],\n [\n -82.6171875,\n 24.77177232822881\n ],\n [\n -82.6171875,\n 24.462150693715266\n ],\n [\n -83.1005859375,\n 24.462150693715266\n ]\n ]\n ]\n }\n },\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -100.1513671875,\n 46.46813299215554\n ],\n [\n -100.1513671875,\n 48.545705491847464\n ],\n [\n -97.20703125,\n 48.545705491847464\n ],\n [\n -97.20703125,\n 46.46813299215554\n ],\n [\n -100.1513671875,\n 46.46813299215554\n ]\n ]\n ]\n }\n },\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -98.06396484375,\n 43.77109381775651\n ],\n [\n -98.06396484375,\n 45.55252525134013\n ],\n [\n -95.38330078125,\n 45.55252525134013\n ],\n [\n -95.38330078125,\n 43.77109381775651\n ],\n [\n -98.06396484375,\n 43.77109381775651\n ]\n ]\n ]\n }\n }\n ]\n}","volume":"31","issue":"4","publishingServiceCenter":{"id":2,"text":"Denver PSC"},"noUsgsAuthors":false,"publicationDate":"2015-10-06","publicationStatus":"PW","scienceBaseUri":"56fe3c48e4b075ab2b2aa0d8","contributors":{"authors":[{"text":"Vanderhoof, Melanie K. 0000-0002-0101-5533 mvanderhoof@usgs.gov","orcid":"https://orcid.org/0000-0002-0101-5533","contributorId":168395,"corporation":false,"usgs":true,"family":"Vanderhoof","given":"Melanie","email":"mvanderhoof@usgs.gov","middleInitial":"K.","affiliations":[{"id":318,"text":"Geosciences and Environmental Change Science Center","active":true,"usgs":true},{"id":5044,"text":"National Research Program - Central Branch","active":true,"usgs":true}],"preferred":true,"id":625787,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Alexander, Laurie C.","contributorId":138989,"corporation":false,"usgs":false,"family":"Alexander","given":"Laurie C.","affiliations":[{"id":6914,"text":"U.S. Environmental Protection Agency","active":true,"usgs":false}],"preferred":false,"id":625788,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Todd, Jason","contributorId":168396,"corporation":false,"usgs":false,"family":"Todd","given":"Jason","email":"","affiliations":[{"id":25279,"text":"U.S. EPA NCEA","active":true,"usgs":false}],"preferred":false,"id":625789,"contributorType":{"id":1,"text":"Authors"},"rank":3}]}} ,{"id":70169971,"text":"70169971 - 2016 - Habitat selection by green turtles in a spatially heterogeneous benthic landscape in Dry Tortugas National Park, Florida","interactions":[],"lastModifiedDate":"2016-07-11T15:47:17","indexId":"70169971","displayToPublicDate":"2016-03-31T12:15:00","publicationYear":"2016","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":860,"text":"Aquatic Biology","active":true,"publicationSubtype":{"id":10}},"title":"Habitat selection by green turtles in a spatially heterogeneous benthic landscape in Dry Tortugas National Park, Florida","docAbstract":"We examined habitat selection by green turtles Chelonia mydas at Dry Tortugas National Park, Florida, USA. We tracked 15 turtles (6 females and 9 males) using platform transmitter terminals (PTTs); 13 of these turtles were equipped with additional acoustic transmitters. Location data by PTTs comprised periods of 40 to 226 d in varying months from 2009 to 2012. Core areas were concentrated in shallow water (mean bathymetry depth of 7.7 m) with a comparably dense coverage of seagrass; however, the utilization distribution overlap index indicated a low degree of habitat sharing. The probability of detecting a turtle on an acoustic receiver was inversely associated with the distance from the receiver to turtle capture sites and was lower in shallower water. The estimated daily detection probability of a single turtle at a given acoustic station throughout the acoustic array was small (<0.1 in any year), and that of multiple turtle detections was even smaller. However, the conditional probability of multiple turtle detections, given at least one turtle detection at a receiver, was much higher despite the small number of tagged turtles in each year (n = 1 to 5). Also, multiple detections of different turtles at a receiver frequently occurred within a few minutes (40%, or 164 of 415, occurred within 1 min). Our numerical estimates of core area overlap, co-occupancy probabilities, and habitat characterization for green turtles could be used to guide conservation of the area to sustain the population of this species.
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Streamflow and groundwater data are verified throughout the year by USGS hydrographers and made available to water-resource managers, recreationists, and Federal, State, and local agencies. Hydrologic conditions are determined by comparing the statistical analyses of data collected during the current water year to historical data. Changing hydrologic conditions underscore the need for accurate, timely data to allow informed decisions about the management and conservation of Georgia’s water resources for agricultural, recreational, ecological, and water-supply needs and in protecting life and property.
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U.S. Geological Survey
720 Gracern Road
Columbia, SC 29210
http://www.usgs.gov/water/southatlantic/
Poor understanding of the water cycle in tropical ecosystems has the potential to exacerbate water shortages and water crises in the region. We suggest that the Galápagos Islands provide an excellent proxy to regions across the tropics as a result of sharp hydroclimatic, anthropogenic, and pedohydrologic gradients across the archipelago. Hydroclimatic and pedohydrologic gradients are found across different elevations on single islands, as well as across the archipelago, whereas anthropogenic gradients reflect land use and land cover change across islands as population and growth in tourism have affected individual islands differently. This article highlights specific opportunities to further examine our understanding of the interactions between water and critical zone processes in tropical ecosystems, making connections between the Galápagos archipelago and much of the understudied tropics. The Galápagos archipelago offers a natural laboratory through which we can examine current threats to freshwater security as well as the dynamics of coupled natural and human systems.
","language":"English","publisher":"John Wiley & Sons","publisherLocation":"Hoboken, NJ","doi":"10.1002/wat2.1145","usgsCitation":"Percy, M., Schmitt, S., Riveros-Iregui, D., and Mirus, B.B., 2016, The Galapagos archipelago: A natural laboratory to examine sharp hydroclimatic, geologic and anthropogenic gradients: WIREs Water, v. 3, no. 4, p. 587-600, https://doi.org/10.1002/wat2.1145.","productDescription":"14 p.","startPage":"587","endPage":"600","numberOfPages":"14","onlineOnly":"N","additionalOnlineFiles":"N","ipdsId":"IP-069817","costCenters":[{"id":300,"text":"Geologic Hazards Science Center","active":true,"usgs":true}],"links":[{"id":319746,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"Ecuador","state":"Galapagos Islands","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n 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Landslide Hazards","active":true,"usgs":true},{"id":5077,"text":"Northwest Regional Director's Office","active":true,"usgs":true}],"preferred":true,"id":578277,"contributorType":{"id":1,"text":"Authors"},"rank":4}]}} ,{"id":70171122,"text":"70171122 - 2016 - Young of the year bluefish (Pomatomus saltatrix) as a bioindicator of estuarine health: Establishing a new baseline for persistent organic pollutants after Hurricane Sandy for selected estuaries in New Jersey and New York","interactions":[],"lastModifiedDate":"2018-08-07T12:33:06","indexId":"70171122","displayToPublicDate":"2016-03-31T06:30:00","publicationYear":"2016","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":2676,"text":"Marine Pollution Bulletin","active":true,"publicationSubtype":{"id":10}},"title":"Young of the year bluefish (Pomatomus saltatrix) as a bioindicator of estuarine health: Establishing a new baseline for persistent organic pollutants after Hurricane Sandy for selected estuaries in New Jersey and New York","docAbstract":"Atlantic coastal bays of the US are essential habitat for young of year bluefish (Pomatomus saltatrix). Their residence in these estuaries during critical life stages, high lipid content, and piscivory make bluefish an ideal bioindicator species for evaluating estuarine health. Individual whole fish from four estuaries impacted by Hurricane Sandy were collected in August 2013, analyzed for a suite of persistent organic pollutants (POPs) including polychlorinated biphenyls, polybrominated diphenyl ethers and organochlorine pesticides and evaluated using health metrics. Concentrations in whole bluefish differed by estuary; however, concentrations for many POPs decreased or were similar to those observed prior to the hurricane. Prevalence of the ectoparasitic gill isopod (Lironeca ovalis) varied by estuary and no relationships between contaminants and lesions were observed. Bluefish should be considered for monitoring programs and, if sampled frequently, could be an effective bioindicator of incremental and episodic changes in contaminants within aquatic food webs.
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In 2015, the network included about 200 real-time streamgages (hereafter referred to as “gages”), 12 real-time reservoir-level monitoring stations, and 30 groundwater-level monitoring wells. These data and associated analyses provide a unique overview of hydrologic conditions and help improve the understanding of Kansas’s water resources.
Real-time data are verified by the USGS throughout the year with regular measurements of streamflow, lake levels, and groundwater levels. These data are used in protecting life and property; and managing water resources for agricultural, industrial, public supply, ecological, and recreational purposes. Yearly hydrologic conditions are characterized by comparing statistical analyses of current and historical water year (WY) data for the period of record. A WY is the 12-month period from October 1 through September 30 and is designated by the year in which it ends.
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U.S. Geological Survey
4821 Quail Crest Place
Lawrence, Kansas 66049
Stand-replacing wildfires are a keystone disturbance in the boreal forest, and they are becoming more common as the climate warms. Paleo-fire archives from the wildland–urban interface can quantify the prehistoric fire regime and assess how both human land-use and climate change impact ecosystem dynamics. Here, we use a combination of a sedimentary charcoal record preserved in varved lake sediments (annually layered) and fire scars in living trees to document changes in local fire return intervals (FRIs) and regional fire activity over the last 500 years. Ace Lake is within the boreal forest, located near the town of Fairbanks in interior Alaska, which was settled by gold miners in AD 1902. In the 400 years before settlement, fires occurred near the lake on average every 58 years. After settlement, fires became much more frequent (average every 18 years), and background charcoal flux rates rose to four times their preindustrial levels, indicating a region-wide increase in burning. Despite this surge in burning, the preindustrial boreal forest ecosystem and permafrost in the watershed have remained intact. Although fire suppression has reduced charcoal influx since the 1950s, an aging fuel load experiencing increasingly warm summers may pose management problems for this and other boreal sites that have similar land-use and fire histories. The large human-caused fire events that we identify can be used to test how increasingly common megafires may alter ecosystem dynamics in the future.
","language":"English","publisher":"SAGE Publications","doi":"10.1177/0959683616632893","usgsCitation":"Gaglioti, B.V., Mann, D.H., Jones, B.M., Wooller, M.J., and Finney, B., 2016, High-resolution records detect human-caused changes to the boreal forest wildfire regime in interior Alaska: The Holocene, v. 26, no. 7, p. 1064-1074, https://doi.org/10.1177/0959683616632893.","productDescription":"11 p.","startPage":"1064","endPage":"1074","onlineOnly":"N","additionalOnlineFiles":"N","ipdsId":"IP-069309","costCenters":[{"id":118,"text":"Alaska Science Center Geography","active":true,"usgs":true}],"links":[{"id":319614,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","state":"Alaska","otherGeospatial":"Ace Lake","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -148.00918579101562,\n 64.84572695664546\n ],\n [\n -148.00918579101562,\n 64.8674620028723\n ],\n [\n -147.92352676391602,\n 64.8674620028723\n ],\n [\n -147.92352676391602,\n 64.84572695664546\n ],\n [\n -148.00918579101562,\n 64.84572695664546\n ]\n ]\n ]\n }\n }\n ]\n}","volume":"26","issue":"7","publishingServiceCenter":{"id":12,"text":"Tacoma PSC"},"noUsgsAuthors":false,"publicationDate":"2016-03-24","publicationStatus":"PW","scienceBaseUri":"56fceb6de4b0a6037df29e37","contributors":{"authors":[{"text":"Gaglioti, Benjamin V. 0000-0003-0591-5253 bgaglioti@usgs.gov","orcid":"https://orcid.org/0000-0003-0591-5253","contributorId":4521,"corporation":false,"usgs":true,"family":"Gaglioti","given":"Benjamin","email":"bgaglioti@usgs.gov","middleInitial":"V.","affiliations":[{"id":114,"text":"Alaska Science Center","active":true,"usgs":true},{"id":118,"text":"Alaska Science Center Geography","active":true,"usgs":true}],"preferred":true,"id":625560,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Mann, Daniel H.","contributorId":67010,"corporation":false,"usgs":true,"family":"Mann","given":"Daniel","email":"","middleInitial":"H.","affiliations":[],"preferred":false,"id":625561,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Jones, Benjamin M. 0000-0002-1517-4711 bjones@usgs.gov","orcid":"https://orcid.org/0000-0002-1517-4711","contributorId":2286,"corporation":false,"usgs":true,"family":"Jones","given":"Benjamin","email":"bjones@usgs.gov","middleInitial":"M.","affiliations":[{"id":114,"text":"Alaska Science Center","active":true,"usgs":true},{"id":118,"text":"Alaska Science Center Geography","active":true,"usgs":true}],"preferred":true,"id":625559,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Wooller, Matthew J.","contributorId":81039,"corporation":false,"usgs":true,"family":"Wooller","given":"Matthew","email":"","middleInitial":"J.","affiliations":[],"preferred":false,"id":625562,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Finney, Bruce P.","contributorId":88074,"corporation":false,"usgs":true,"family":"Finney","given":"Bruce P.","affiliations":[],"preferred":false,"id":625563,"contributorType":{"id":1,"text":"Authors"},"rank":5}]}} ,{"id":70170433,"text":"70170433 - 2016 - Scripting MODFLOW model development using Python and FloPy","interactions":[],"lastModifiedDate":"2016-10-21T13:43:28","indexId":"70170433","displayToPublicDate":"2016-03-30T07:30:00","publicationYear":"2016","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":3825,"text":"Groundwater","active":true,"publicationSubtype":{"id":10}},"title":"Scripting MODFLOW model development using Python and FloPy","docAbstract":"Graphical user interfaces (GUIs) are commonly used to construct and postprocess numerical groundwater flow and transport models. Scripting model development with the programming language Python is presented here as an alternative approach. One advantage of Python is that there are many packages available to facilitate the model development process, including packages for plotting, array manipulation, optimization, and data analysis. For MODFLOW-based models, the FloPy package was developed by the authors to construct model input files, run the model, and read and plot simulation results. Use of Python with the available scientific packages and FloPy facilitates data exploration, alternative model evaluations, and model analyses that can be difficult to perform with GUIs. Furthermore, Python scripts are a complete, transparent, and repeatable record of the modeling process. The approach is introduced with a simple FloPy example to create and postprocess a MODFLOW model. A more complicated capture-fraction analysis with a real-world model is presented to demonstrate the types of analyses that can be performed using Python and FloPy.
","language":"English","publisher":"Wiley","doi":"10.1111/gwat.12413","usgsCitation":"Bakker, M., Post, V., Langevin, C.D., Hughes, J.D., White, J.T., Starn, J., and Fienen, M., 2016, Scripting MODFLOW model development using Python and FloPy: Groundwater, v. 54, no. 5, p. 733-739, https://doi.org/10.1111/gwat.12413.","productDescription":"7 p.","startPage":"733","endPage":"739","onlineOnly":"N","additionalOnlineFiles":"N","ipdsId":"IP-069750","costCenters":[{"id":493,"text":"Office of Ground Water","active":true,"usgs":true}],"links":[{"id":320582,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"volume":"54","issue":"5","publishingServiceCenter":{"id":9,"text":"Reston PSC"},"noUsgsAuthors":false,"publicationDate":"2016-03-30","publicationStatus":"PW","scienceBaseUri":"57209138e4b071321fe65690","contributors":{"authors":[{"text":"Bakker, Mark","contributorId":56137,"corporation":false,"usgs":true,"family":"Bakker","given":"Mark","email":"","affiliations":[],"preferred":false,"id":627194,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Post, Vincent E. A.","contributorId":166764,"corporation":false,"usgs":false,"family":"Post","given":"Vincent E. A.","affiliations":[{"id":24501,"text":"National Centre for Groundwater Reserach and Training, Flinders Univ.","active":true,"usgs":false}],"preferred":false,"id":627195,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Langevin, Christian D. 0000-0001-5610-9759 langevin@usgs.gov","orcid":"https://orcid.org/0000-0001-5610-9759","contributorId":1030,"corporation":false,"usgs":true,"family":"Langevin","given":"Christian","email":"langevin@usgs.gov","middleInitial":"D.","affiliations":[{"id":37778,"text":"WMA - Integrated Modeling and Prediction Division","active":true,"usgs":true}],"preferred":true,"id":627193,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Hughes, Joseph D. 0000-0003-1311-2354 jdhughes@usgs.gov","orcid":"https://orcid.org/0000-0003-1311-2354","contributorId":2492,"corporation":false,"usgs":true,"family":"Hughes","given":"Joseph","email":"jdhughes@usgs.gov","middleInitial":"D.","affiliations":[{"id":37778,"text":"WMA - Integrated Modeling and Prediction Division","active":true,"usgs":true}],"preferred":true,"id":627196,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"White, Jeremy T. 0000-0002-4950-1469 jwhite@usgs.gov","orcid":"https://orcid.org/0000-0002-4950-1469","contributorId":167708,"corporation":false,"usgs":true,"family":"White","given":"Jeremy","email":"jwhite@usgs.gov","middleInitial":"T.","affiliations":[{"id":583,"text":"Texas Water Science Center","active":true,"usgs":true}],"preferred":true,"id":627197,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"Starn, Jeffrey jjstarn@usgs.gov","contributorId":149231,"corporation":false,"usgs":true,"family":"Starn","given":"Jeffrey","email":"jjstarn@usgs.gov","affiliations":[{"id":196,"text":"Connecticut Water Science Center","active":true,"usgs":true}],"preferred":true,"id":627198,"contributorType":{"id":1,"text":"Authors"},"rank":6},{"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":627199,"contributorType":{"id":1,"text":"Authors"},"rank":7}]}} ,{"id":70174175,"text":"70174175 - 2016 - Salinity and temperature tolerance of an emergent alien species, the Amazon fish Astronotus ocellatus","interactions":[],"lastModifiedDate":"2016-06-28T13:56:06","indexId":"70174175","displayToPublicDate":"2016-03-30T00:00:00","publicationYear":"2016","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":1919,"text":"Hydrobiologia","onlineIssn":"1573-5117","printIssn":"0018-8158","active":true,"publicationSubtype":{"id":10}},"title":"Salinity and temperature tolerance of an emergent alien species, the Amazon fish Astronotus ocellatus","docAbstract":"Astronotus ocellatus (oscar), is native to the Amazon basin and, although it has been introduced to many countries, little is known regarding its tolerances for salinity and temperature. In this report, we provide data on the tolerance of A. ocellatus to abrupt and gradual changes in salinity, its high and low temperature tolerance, and information on how salinity, temperature, and fish size interact to affect survival. Fish were able to survive abrupt transfer to salinities as high as 16 ppt with no mortality. When salinity change was gradual (2 ppt/day), fish in the warm-temperature experiment (28°C) survived longer than fish in the cool-temperature experiment (18°C). Larger fish survived longer than smaller ones at the higher salinities when the temperature was warm, but when the temperature was cool fish size had little effect on survival. In the temperature-tolerance experiments, fish survived from 9 to 41°C for short periods of time. Overall, the species showed a wide range of temperature and salinity tolerance. Thus, in spite of the tropical freshwater origin of this species, physiological stress is not likely to hinder its dispersal to brackish waters, especially when temperatures are warm.
","language":"English","publisher":"Springer","publisherLocation":"New York, New York","doi":"10.1007/s10750-016-2740-8","usgsCitation":"Gutierrel, S.M., Schofield, P.J., and Prodocimo, V., 2016, Salinity and temperature tolerance of an emergent alien species, the Amazon fish Astronotus ocellatus: Hydrobiologia, v. 777, no. 1, p. 21-31, https://doi.org/10.1007/s10750-016-2740-8.","productDescription":"11 p.","startPage":"21","endPage":"31","numberOfPages":"11","onlineOnly":"Y","additionalOnlineFiles":"N","ipdsId":"IP-068532","costCenters":[{"id":17705,"text":"Wetland and Aquatic Research Center","active":true,"usgs":true}],"links":[{"id":324520,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"},{"id":324519,"type":{"id":15,"text":"Index Page"},"url":"https://dx.doi.org/10.1007/s10750-016-2740-8"}],"country":"Bolivia, Brazil, Columbia, Ecuador, Guyana, Venezuela,","otherGeospatial":"Northern South America","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -49.92187499999999,\n -0.17578097424708533\n ],\n [\n -49.92187499999999,\n -2.1967272417616583\n ],\n [\n -50.361328125,\n -4.653079918274038\n ],\n [\n -52.470703125,\n -9.188870084473393\n ],\n [\n -55.01953125,\n -13.068776734357682\n ],\n [\n -55.54687499999999,\n -15.284185114076433\n ],\n [\n -55.634765625,\n -16.720385051693988\n ],\n [\n -60.64453125000001,\n -18.312810846425432\n ],\n [\n -64.248046875,\n -18.312810846425432\n ],\n [\n -67.32421875,\n -17.72775860985227\n ],\n [\n -69.60937499999999,\n -16.63619187839765\n ],\n [\n -71.103515625,\n -15.114552871944102\n ],\n [\n -74.1796875,\n -14.519780046326085\n ],\n [\n -75.673828125,\n -13.068776734357682\n ],\n [\n -76.46484375,\n -10.833305983642491\n ],\n [\n -77.6953125,\n -9.015302333420586\n ],\n [\n -78.046875,\n -6.751896464843375\n ],\n [\n -78.046875,\n -5.0909441750333855\n ],\n [\n -77.431640625,\n -3.2502085616531686\n ],\n [\n -75.849609375,\n -0.8788717828324148\n ],\n [\n -72.50976562499999,\n 1.142502403706165\n ],\n [\n -69.08203125,\n 1.845383988573187\n ],\n [\n -63.54492187500001,\n 1.6696855009865839\n ],\n [\n -61.083984375,\n 0.4394488164139768\n ],\n [\n -57.65624999999999,\n 0.17578097424708533\n ],\n [\n -49.92187499999999,\n -0.17578097424708533\n ]\n ]\n ]\n }\n }\n ]\n}","volume":"777","issue":"1","publishingServiceCenter":{"id":8,"text":"Raleigh PSC"},"noUsgsAuthors":false,"publicationDate":"2016-03-29","publicationStatus":"PW","scienceBaseUri":"57739fb6e4b07657d1a90d47","contributors":{"authors":[{"text":"Gutierrel, Silvia M M","contributorId":172503,"corporation":false,"usgs":false,"family":"Gutierrel","given":"Silvia","email":"","middleInitial":"M M","affiliations":[{"id":27057,"text":"Setor de Ciencias Biologicas, Universidade Federal do Parana, Brazil","active":true,"usgs":false}],"preferred":false,"id":641045,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Schofield, Pamela J. 0000-0002-8752-2797 pschofield@usgs.gov","orcid":"https://orcid.org/0000-0002-8752-2797","contributorId":168659,"corporation":false,"usgs":true,"family":"Schofield","given":"Pamela","email":"pschofield@usgs.gov","middleInitial":"J.","affiliations":[{"id":566,"text":"Southeast Ecological Science Center","active":true,"usgs":true},{"id":17705,"text":"Wetland and Aquatic Research Center","active":true,"usgs":true}],"preferred":true,"id":641046,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Prodocimo, Viviane","contributorId":172504,"corporation":false,"usgs":false,"family":"Prodocimo","given":"Viviane","email":"","affiliations":[{"id":27057,"text":"Setor de Ciencias Biologicas, Universidade Federal do Parana, Brazil","active":true,"usgs":false}],"preferred":false,"id":641047,"contributorType":{"id":1,"text":"Authors"},"rank":3}]}} ,{"id":70168800,"text":"sir20165026 - 2016 - Simulation of groundwater storage changes in the eastern Pasco Basin, Washington","interactions":[],"lastModifiedDate":"2019-07-22T14:07:29","indexId":"sir20165026","displayToPublicDate":"2016-03-29T18:00:00","publicationYear":"2016","noYear":false,"publicationType":{"id":18,"text":"Report"},"publicationSubtype":{"id":5,"text":"USGS Numbered Series"},"seriesTitle":{"id":334,"text":"Scientific Investigations Report","code":"SIR","onlineIssn":"2328-0328","printIssn":"2328-031X","active":true,"publicationSubtype":{"id":5}},"seriesNumber":"2016-5026","title":"Simulation of groundwater storage changes in the eastern Pasco Basin, Washington","docAbstract":"The Miocene Columbia River Basalt Group and younger sedimentary deposits of lacustrine, fluvial, eolian, and cataclysmic-flood origins compose the aquifer system of the Pasco Basin in eastern Washington. Irrigation return flow and canal leakage from the Columbia Basin Project have caused groundwater levels to rise substantially in some areas, contributing to landslides along the Columbia River. Water resource managers are considering extraction of additional stored groundwater to supply increasing demand and possibly mitigate problems caused by the increased water levels. To help address these concerns, the transient groundwater model of the Pasco Basin documented in this report was developed to quantify the changes in groundwater flow and storage. The MODFLOW model uses a 1-kilometer finite-difference grid and is constrained by logs and water levels from 846 wells in the study area. Eight model layers represent five sedimentary hydrogeologic units and underlying basalt formations. Head‑dependent flux boundaries represent the Columbia and Snake Rivers to the west and south, respectively, underflow to and (or) from adjacent areas to the northeast, and discharge to agricultural drains, springs, and groundwater withdrawal wells. Specified flux boundaries represent recharge from infiltrated precipitation and anthropogenic sources, including irrigation return flow and leakage from water-distribution canals. The model was calibrated with the parameter‑estimation code PEST++ to groundwater levels measured from 1907 through 2013 and measured discharge to springs and estimated discharge to agricultural drains. Increased recharge since pre-development resulted in a 6.8 million acre-feet increase in storage in the 508-14 administrative area of the Pasco Basin. Four groundwater-management scenarios simulate the 7-year drawdown resulting from withdrawals in different locations. Withdrawals of 2 million gallons per day (Mgal/d) from a hypothetical well field in the upper Ringold Formation along the Columbia River could generate 30–70 feet of drawdown, which may reduce landslide susceptibility along the White Bluffs. Drawdowns resulting from a 1 Mgal/d withdrawal from wells screened in either Pasco gravels, upper Ringold Formation, or both Ringold Formation and underlying basalt are simulated in the other three scenarios, and differ because of the contrasting hydraulic conductivities within the screened intervals.
","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/sir20165026","collaboration":"Prepared in cooperation with the Bureau of Reclamation","usgsCitation":"Heywood, C.E., Kahle, S.C., Olsen, T.D., Patterson, J.D., and Burns, Erick, 2016, Simulation of groundwater storage changes in the eastern Pasco Basin, Washington: U.S. Geological Survey Scientific Investigations Report 2016–5026, 44 p., 1 pl., https://dx.doi.org/10.3133/sir20165026.","productDescription":"Report: viii, 44 p.; Plate; Table","onlineOnly":"Y","additionalOnlineFiles":"Y","ipdsId":"IP-069891","costCenters":[{"id":312,"text":"Geology, Minerals, Energy, and Geophysics Science Center","active":true,"usgs":true},{"id":622,"text":"Washington Water Science Center","active":true,"usgs":true}],"links":[{"id":319591,"rank":2,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/sir/2016/5026/sir20165026.pdf","text":"Report","size":"13.2 MB","linkFileType":{"id":1,"text":"pdf"},"description":"SIR 2016-5026"},{"id":319590,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/sir/2016/5026/coverthb.jpg"},{"id":319592,"rank":3,"type":{"id":17,"text":"Plate"},"url":"https://pubs.usgs.gov/sir/2016/5026/sir20165026_plate01.pdf","text":"Plate 1","size":"10.9 MB","linkFileType":{"id":1,"text":"pdf"},"description":"SIR 2016-5026 Plate 1"},{"id":319593,"rank":4,"type":{"id":27,"text":"Table"},"url":"https://pubs.usgs.gov/sir/2016/5026/sir20165026_table07.xlsx","text":"Table 7","size":"96 KB","linkFileType":{"id":3,"text":"xlsx"},"description":"SIR 2016-5026 Table 7"}],"country":"United States","state":"Washington","county":"Adams County, Franklin County, Grant County","otherGeospatial":"Pasco Basin","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -119.9981689453125,\n 47.00273390667881\n ],\n [\n -118.49304199218749,\n 46.998987638154624\n ],\n [\n -118.50952148437499,\n 46.195042108660154\n ],\n [\n -118.7347412109375,\n 46.09228143052649\n ],\n [\n -118.9434814453125,\n 46.00459325574482\n ],\n [\n -119.344482421875,\n 46.00840867976965\n ],\n [\n -119.59716796875,\n 46.038922598236\n ],\n [\n -119.73999023437499,\n 46.33555079758302\n ],\n [\n -119.9871826171875,\n 46.49082901981415\n ],\n [\n -119.9981689453125,\n 47.00273390667881\n ]\n ]\n ]\n }\n }\n ]\n}","contact":"Director, Washington Water Science Center
U.S. Geological Survey
934 Broadway, Suite 300
Tacoma, Washington 98402
http://wa.water.usgs.gov