Restoring dryland ecosystems is a global challenge due to synergistic drivers of disturbance coupled with unpredictable environmental conditions. Dryland plant species have evolved complex life-history strategies to cope with fluctuating resources and climatic extremes. Although rarely quantified, local adaptation is likely widespread among these species and potentially influences restoration outcomes. The common practice of reintroducing propagules to restore dryland ecosystems, often across large spatial scales, compels evaluation of adaptive divergence within these species. Such evaluations are critical to understanding the consequences of large-scale manipulation of gene flow and to predicting success of restoration efforts. However, genetic information for species of interest can be difficult and expensive to obtain through traditional common garden experiments. Recent advances in landscape genetics offer marker-based approaches for identifying environmental drivers of adaptive genetic variability in non-model species, but tools are still needed to link these approaches with practical aspects of ecological restoration. Here, we combine spatially-explicit landscape genetics models with flexible visualization tools to demonstrate how cost-effective evaluations of adaptive genetic divergence can facilitate implementation of different seed sourcing strategies in ecological restoration. We apply these methods to Amplified Fragment Length Polymorphism (AFLP) markers genotyped in two Mojave Desert shrub species of high restoration importance: the long-lived, wind-pollinated gymnosperm Ephedra nevadensis, and the short-lived, insect-pollinated angiosperm Sphaeralcea ambigua. Mean annual temperature was identified as an important driver of adaptive genetic divergence for both species. Ephedra showed stronger adaptive divergence with respect to precipitation variability, while temperature variability and precipitation averages explained a larger fraction of adaptive divergence in Sphaeralcea. We describe multivariate statistical approaches for interpolating spatial patterns of adaptive divergence while accounting for potential bias due to neutral genetic structure. Through a spatial bootstrapping procedure, we also visualize patterns in the magnitude of model uncertainty. Finally, we introduce an interactive, distance-based mapping approach that explicitly links marker-based models of adaptive divergence with local or admixture seed sourcing strategies, promoting effective native plant restoration.
","language":"English","publisher":"Ecological Society of America","publisherLocation":"Washington, D.C.","doi":"10.1002/eap.1447","usgsCitation":"Shryock, D.F., Havrilla, C.A., DeFalco, L.A., Esque, T., Custer, N., and Wood, T.E., 2016, Landscape genetic approaches to guide native plant restoration in the Mojave Desert: Ecological Applications, v. 27, no. 2, p. 429-445, https://doi.org/10.1002/eap.1447.","productDescription":"17 p.","startPage":"429","endPage":"445","ipdsId":"IP-070517","costCenters":[{"id":651,"text":"Western Ecological Research Center","active":true,"usgs":true}],"links":[{"id":470320,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.1002/eap.1447","text":"Publisher Index Page"},{"id":332262,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","otherGeospatial":"Mojave Desert","volume":"27","issue":"2","publishingServiceCenter":{"id":1,"text":"Sacramento PSC"},"noUsgsAuthors":false,"publicationDate":"2017-01-30","publicationStatus":"PW","scienceBaseUri":"58590007e4b03639a6025e27","chorus":{"doi":"10.1002/eap.1447","url":"http://dx.doi.org/10.1002/eap.1447","publisher":"Wiley-Blackwell","authors":"Shryock Daniel F., Havrilla Caroline A., DeFalco Lesley A., Esque Todd C., Custer Nathan A., Wood Troy E.","journalName":"Ecological Applications","publicationDate":"1/30/2017","publiclyAccessibleDate":"1/30/2017"},"contributors":{"authors":[{"text":"Shryock, Daniel F. dshryock@usgs.gov","contributorId":5139,"corporation":false,"usgs":true,"family":"Shryock","given":"Daniel","email":"dshryock@usgs.gov","middleInitial":"F.","affiliations":[{"id":651,"text":"Western Ecological Research Center","active":true,"usgs":true}],"preferred":true,"id":656102,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Havrilla, Caroline A. 0000-0003-3913-0980","orcid":"https://orcid.org/0000-0003-3913-0980","contributorId":146326,"corporation":false,"usgs":true,"family":"Havrilla","given":"Caroline","email":"","middleInitial":"A.","affiliations":[{"id":16669,"text":"U of CO, Boulder","active":true,"usgs":false},{"id":568,"text":"Southwest Biological Science Center","active":true,"usgs":true}],"preferred":false,"id":656104,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"DeFalco, Lesley A. 0000-0002-7542-9261 ldefalco@usgs.gov","orcid":"https://orcid.org/0000-0002-7542-9261","contributorId":177536,"corporation":false,"usgs":true,"family":"DeFalco","given":"Lesley","email":"ldefalco@usgs.gov","middleInitial":"A.","affiliations":[{"id":651,"text":"Western Ecological Research Center","active":true,"usgs":true}],"preferred":true,"id":656105,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Esque, Todd C. 0000-0002-4166-6234 tesque@usgs.gov","orcid":"https://orcid.org/0000-0002-4166-6234","contributorId":168763,"corporation":false,"usgs":true,"family":"Esque","given":"Todd C.","email":"tesque@usgs.gov","affiliations":[{"id":651,"text":"Western Ecological Research Center","active":true,"usgs":true}],"preferred":false,"id":656103,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Custer, Nathan ncuster@usgs.gov","contributorId":5561,"corporation":false,"usgs":true,"family":"Custer","given":"Nathan","email":"ncuster@usgs.gov","affiliations":[{"id":651,"text":"Western Ecological Research Center","active":true,"usgs":true}],"preferred":true,"id":656106,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"Wood, Troy E. 0000-0002-1533-5714 twood@usgs.gov","orcid":"https://orcid.org/0000-0002-1533-5714","contributorId":4023,"corporation":false,"usgs":true,"family":"Wood","given":"Troy","email":"twood@usgs.gov","middleInitial":"E.","affiliations":[{"id":568,"text":"Southwest Biological Science Center","active":true,"usgs":true},{"id":651,"text":"Western Ecological Research Center","active":true,"usgs":true}],"preferred":true,"id":656107,"contributorType":{"id":1,"text":"Authors"},"rank":6}]}} ,{"id":70191292,"text":"70191292 - 2016 - Quarterly wildlife mortality report January 2016 to March 2016","interactions":[],"lastModifiedDate":"2023-10-13T17:02:14.725207","indexId":"70191292","displayToPublicDate":"2016-12-18T00:00:00","publicationYear":"2016","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":3769,"text":"Wildlife Disease Association Newsletter","active":true,"publicationSubtype":{"id":10}},"title":"Quarterly wildlife mortality report January 2016 to March 2016","docAbstract":"No abstract available
","language":"English","publisher":"Wildlife Disease Association","usgsCitation":"Ballmann, A.E., Bodenstein, B.L., Dusek, R., Grear, D.R., and Chipault, J., 2016, Quarterly wildlife mortality report January 2016 to March 2016: Wildlife Disease Association Newsletter, no. July 2016, p. 4-6.","productDescription":"3 p.","startPage":"4","endPage":"6","ipdsId":"IP-076932","costCenters":[{"id":456,"text":"National Wildlife Health Center","active":true,"usgs":true}],"links":[{"id":350045,"rank":2,"type":{"id":15,"text":"Index Page"},"url":"https://www.wildlifedisease.org/PersonifyEbusiness/Resources/Publications/Newsletter/Archive"},{"id":350046,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"issue":"July 2016","noUsgsAuthors":false,"publicationStatus":"PW","scienceBaseUri":"5a60fc73e4b06e28e9c23e9b","contributors":{"authors":[{"text":"Ballmann, Anne E. 0000-0002-0380-056X aballmann@usgs.gov","orcid":"https://orcid.org/0000-0002-0380-056X","contributorId":1153,"corporation":false,"usgs":true,"family":"Ballmann","given":"Anne","email":"aballmann@usgs.gov","middleInitial":"E.","affiliations":[{"id":456,"text":"National Wildlife Health Center","active":true,"usgs":true}],"preferred":false,"id":725160,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Bodenstein, Barbara L. 0000-0001-7946-0103 bbodenstein@usgs.gov","orcid":"https://orcid.org/0000-0001-7946-0103","contributorId":4389,"corporation":false,"usgs":true,"family":"Bodenstein","given":"Barbara","email":"bbodenstein@usgs.gov","middleInitial":"L.","affiliations":[{"id":456,"text":"National Wildlife Health Center","active":true,"usgs":true}],"preferred":true,"id":725161,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Dusek, Robert J. 0000-0001-6177-7479 rdusek@usgs.gov","orcid":"https://orcid.org/0000-0001-6177-7479","contributorId":140066,"corporation":false,"usgs":true,"family":"Dusek","given":"Robert J.","email":"rdusek@usgs.gov","affiliations":[{"id":456,"text":"National Wildlife Health Center","active":true,"usgs":true}],"preferred":false,"id":725162,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Grear, Daniel R. 0000-0002-5478-1549 dgrear@usgs.gov","orcid":"https://orcid.org/0000-0002-5478-1549","contributorId":201066,"corporation":false,"usgs":true,"family":"Grear","given":"Daniel","email":"dgrear@usgs.gov","middleInitial":"R.","affiliations":[],"preferred":false,"id":725163,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Chipault, Jennifer 0000-0002-1368-622X jchipault@usgs.gov","orcid":"https://orcid.org/0000-0002-1368-622X","contributorId":200612,"corporation":false,"usgs":true,"family":"Chipault","given":"Jennifer","email":"jchipault@usgs.gov","affiliations":[],"preferred":false,"id":725164,"contributorType":{"id":1,"text":"Authors"},"rank":5}]}} ,{"id":70175633,"text":"sir20165120 - 2016 - Long Valley Caldera Lake and reincision of Owens River Gorge","interactions":[],"lastModifiedDate":"2016-12-16T20:22:57","indexId":"sir20165120","displayToPublicDate":"2016-12-16T00:00:00","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":"2016-5120","title":"Long Valley Caldera Lake and reincision of Owens River Gorge","docAbstract":"Owens River Gorge, today rimmed exclusively in 767-ka Bishop Tuff, was first cut during the Neogene through a ridge of Triassic granodiorite to a depth as great as its present-day floor and was then filled to its rim by a small basaltic shield at 3.3 Ma. The gorge-filling basalt, 200 m thick, blocked a 5-km-long reach of the upper gorge, diverting the Owens River southward around the shield into Rock Creek where another 200-m-deep gorge was cut through the same basement ridge. Much later, during Marine Isotope Stage (MIS) 22 (~900–866 ka), a piedmont glacier buried the diversion and deposited a thick sheet of Sherwin Till atop the basalt on both sides of the original gorge, showing that the basalt-filled reach had not, by then, been reexcavated. At 767 ka, eruption of the Bishop Tuff blanketed the landscape with welded ignimbrite, deeply covering the till, basalt, and granodiorite and completely filling all additional reaches of both Rock Creek canyon and Owens River Gorge. The ignimbrite rests directly on the basalt and till along the walls of Owens Gorge, but nowhere was it inset against either, showing that the basalt-blocked reach had still not been reexcavated. Subsidence of Long Valley Caldera at 767 ka produced a steep-walled depression at least 700 m deeper than the precaldera floor of Owens Gorge, which was beheaded at the caldera’s southeast rim. Caldera collapse reoriented proximal drainages that had formerly joined east-flowing Owens River, abruptly reversing flow westward into the caldera. It took 600,000 years of sedimentation in the 26-km-long, usually shallow, caldera lake to fill the deep basin and raise lake level to its threshold for overflow. Not until then did reestablishment of Owens River Gorge begin, by incision of the gorge-filling ignimbrite.
","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/sir20165120","usgsCitation":"Hildreth, Wes, and Fierstein, Judy, 2016, Long Valley Caldera lake and reincision of Owens River\nGorge: U.S. Geological Survey Scientific Investigations Report 2016–5120, 63 p.,\nhttps://doi.org/10.3133/sir20165120.","productDescription":"Report: v, 63 p.; Appendixes: 1-2.","numberOfPages":"74","ipdsId":"IP-068987","costCenters":[{"id":617,"text":"Volcano Science Center","active":true,"usgs":true}],"links":[{"id":332247,"rank":5,"type":{"id":3,"text":"Appendix"},"url":"https://pubs.usgs.gov/sir/2016/5120/sir20165120_appendix2.pdf","text":"Appendix 2","size":"134 KB","linkFileType":{"id":1,"text":"pdf"},"description":"SIR 2016-5120 Appendix 2"},{"id":332186,"rank":3,"type":{"id":3,"text":"Appendix"},"url":"https://pubs.usgs.gov/sir/2016/5120/sir20165120_appendix1.xlsx","text":"Appendix 1","size":"60 KB","linkFileType":{"id":3,"text":"xlsx"},"description":"SIR 2016-5120 Appendix 1 xlsx"},{"id":332185,"rank":2,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/sir/2016/5120/sir20165120.pdf","text":"Report","size":"6.4 MB","linkFileType":{"id":1,"text":"pdf"},"description":"SIR 2016-5120"},{"id":332184,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/sir/2016/5120/coverthb.jpg"},{"id":332187,"rank":4,"type":{"id":3,"text":"Appendix"},"url":"https://pubs.usgs.gov/sir/2016/5120/sir20165120_appendix1.csv","text":"Appendix 1","size":"3 KB","linkFileType":{"id":7,"text":"csv"},"description":"SIR 2016-5120 Appendix 1 csv"}],"country":"United States","state":"California","county":"Mono County","otherGeospatial":"Long Valley Caldera, Owens River Gorge","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -119.20578002929688,\n 37.35924242260126\n ],\n [\n -119.20578002929688,\n 37.97343243999255\n ],\n [\n -118.35159301757811,\n 37.97343243999255\n ],\n [\n -118.35159301757811,\n 37.35924242260126\n ],\n [\n -119.20578002929688,\n 37.35924242260126\n ]\n ]\n ]\n }\n }\n ]\n}","contact":"Contact Information, Volcano Science Center - Menlo Park
U.S. Geological Survey
345 Middlefield Road, MS 910
Menlo Park, CA 94025
http://volcanoes.usgs.gov/
A central goal of observing and modeling the earthquake cycle is to forecast when a particular fault may generate an earthquake: a fault late in its earthquake cycle may be more likely to generate an earthquake than a fault early in its earthquake cycle. Models that can explain geodetic observations throughout the entire earthquake cycle may be required to gain a more complete understanding of relevant physics and phenomenology. Previous efforts to develop unified earthquake models for strike-slip faults have largely focused on explaining both preseismic and postseismic geodetic observations available across a few faults in California, Turkey, and Tibet. An alternative approach leverages the global distribution of geodetic and geologic slip rate estimates on strike-slip faults worldwide. Here we use the Kolmogorov-Smirnov test for similarity of distributions to infer, in a statistically rigorous manner, viscoelastic earthquake cycle models that are inconsistent with 15 sets of observations across major strike-slip faults. We reject a large subset of two-layer models incorporating Burgers rheologies at a significance level of α = 0.05 (those with long-term Maxwell viscosities ηM <~ 4.0 × 1019 Pa s and ηM >~ 4.6 × 1020 Pa s) but cannot reject models on the basis of transient Kelvin viscosity ηK. Finally, we examine the implications of these results for the predicted earthquake cycle timing of the 15 faults considered and compare these predictions to the geologic and historical record.
","language":"English","publisher":"American Geophysical Union","doi":"10.1002/2016GL070681","usgsCitation":"Devries, P.M., and Evans, E., 2016, Statistical tests of simple earthquake cycle models: Geophysical Research Letters, v. 43, no. 23, p. 12,036-12,045, https://doi.org/10.1002/2016GL070681.","productDescription":"10 p.","startPage":"12,036","endPage":"12,045","ipdsId":"IP-077441","costCenters":[{"id":237,"text":"Earthquake Science Center","active":true,"usgs":true}],"links":[{"id":470321,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.1002/2016gl070681","text":"Publisher Index Page"},{"id":341044,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"volume":"43","issue":"23","publishingServiceCenter":{"id":14,"text":"Menlo Park PSC"},"noUsgsAuthors":false,"publicationDate":"2016-12-12","publicationStatus":"PW","scienceBaseUri":"591426bbe4b0e541a03e9602","contributors":{"authors":[{"text":"Devries, Phoebe M. R.","contributorId":191902,"corporation":false,"usgs":false,"family":"Devries","given":"Phoebe","email":"","middleInitial":"M. R.","affiliations":[],"preferred":false,"id":694655,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Evans, Eileen 0000-0002-7290-5269 eevans@usgs.gov","orcid":"https://orcid.org/0000-0002-7290-5269","contributorId":167021,"corporation":false,"usgs":true,"family":"Evans","given":"Eileen","email":"eevans@usgs.gov","affiliations":[{"id":237,"text":"Earthquake Science Center","active":true,"usgs":true}],"preferred":true,"id":694654,"contributorType":{"id":1,"text":"Authors"},"rank":2}]}} ,{"id":70177032,"text":"sir20165148 - 2016 - Mechanisms of aquatic species invasions across the South Atlantic Landscape Conservation Cooperative region","interactions":[],"lastModifiedDate":"2016-12-15T16:03:23","indexId":"sir20165148","displayToPublicDate":"2016-12-15T15:15:00","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":"2016-5148","title":"Mechanisms of aquatic species invasions across the South Atlantic Landscape Conservation Cooperative region","docAbstract":"Invasive species are a global issue, and the southeastern United States is not immune to the problems they present. Therefore, various analyses using modeling and exploratory statistics were performed on the U.S. Geological Survey Nonindigenous Aquatic Species (NAS) Database with the primary objective of determining the most appropriate use of presence-only data as related to invasive species in the South Atlantic Landscape Conservation Cooperative (SALCC) region. A hierarchical model approach showed that a relatively small amount of high-quality data from planned surveys can be used to leverage the information in presence-only observations, having a broad spatial coverage and high biases of observer detection and in site selection. Because a variety of sampling protocols can be used in planned surveys, this approach to the analysis of presence-only data is widely applicable. An important part of the management of natural landscapes is the preservation of designated protected areas. When the hydrologic connection was considered in this analysis, the number of potential invaders that could spread to each protected area within the SALCC region was greatly increased, with a mean exceeding 30 species and the maximum reaching 57 species. Nearly all protected areas are hydrologically connected to at least 20 nonindigenous aquatic species. To examine possible factors which may contribute to nonindigenous aquatic species richness in the SALCC region, a set of exploratory statistics was employed. The best statistical model that included a combination of three anthropogenic variables (densities of housing, roads, and reservoirs) and two environmental variables (elevation range and longitude) explained approximately 62 percent of the variation in introduced species richness. Highest nonindigenous aquatic species richness occurred in the more upland, mountainous regions, where elevation range favored reservoirs and attracted urban centers. Lastly, patterns seen in a diffusion model may reflect less about the diffusion process of the organism and more about the opportunistic nature of the data collection process. These results of the model are considered exploratory in nature.
","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/sir20165148","collaboration":"Prepared in cooperation with the U.S. Fish and Wildlife Service and the South Atlantic Landscape Conservation Cooperative","usgsCitation":"Benson, A.J., Stith, B.M., and Engel, V.C., 2016, Mechanisms of aquatic species invasions across the South Atlantic Landscape Conservation Cooperative region: U.S. Geological Survey Scientific Investigations Report 2016–5148, 68 p., https://doi.org/10.3133/sir20165148.","productDescription":"Report: viii, 68 p.; Data Release","numberOfPages":"80","onlineOnly":"Y","additionalOnlineFiles":"Y","ipdsId":"IP-074281","costCenters":[{"id":17705,"text":"Wetland and Aquatic Research Center","active":true,"usgs":true}],"links":[{"id":332134,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/sir/2016/5148/coverthb.jpg"},{"id":332136,"rank":3,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/F75X2721","text":"USGS data release - 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U.S. Geological Survey
7920 NW 71st Street
Gainesville, FL 32653
https://www.usgs.gov/centers/wetland-and-aquatic-research-center-warc
","tableOfContents":"A multireaction chemical equilibria geothermometry (MEG) model applicable to high-temperature geothermal systems has been developed over the past three decades. Given sufficient data, this model provides more constraint on calculated reservoir temperatures than classical chemical geothermometers that are based on either the concentration of silica (SiO2), or the ratios of cation concentrations. A set of 23 chemical analyses from Ojo Caliente Spring and 22 analyses from other thermal features in the Lower Geyser Basin of Yellowstone National Park are used to examine the sensitivity of calculated reservoir temperatures using the GeoT MEG code (Spycher et al. 2013, 2014) to quantify the effects of solute concentrations, degassing, and mineral assemblages on calculated reservoir temperatures. Results of our analysis demonstrate that the MEG model can resolve reservoir temperatures within approximately ±15°C, and that natural variation in fluid compositions represents a greater source of variance in calculated reservoir temperatures than variations caused by analytical uncertainty (assuming ~5% for major elements). The analysis also suggests that MEG calculations are particularly sensitive to variations in silica concentration, the concentrations of the redox species Fe(II) and H2S, and that the parameters defining steam separation and CO2 degassing from the liquid may be adequately determined by numerical optimization. Results from this study can provide guidance for future applications of MEG models, and thus provide more reliable information on geothermal energy resources during exploration.
","language":"English","publisher":"Elsevier","doi":"10.1016/j.jvolgeores.2016.10.010","usgsCitation":"King, J.M., Hurwitz, S., Lowenstern, J.B., Nordstrom, D.K., and McCleskey, R.B., 2016, Multireaction equilibrium geothermometry: A sensitivity analysis using data from the Lower Geyser Basin, Yellowstone National Park, USA: Journal of Volcanology and Geothermal Research, v. 328, p. 105-114, https://doi.org/10.1016/j.jvolgeores.2016.10.010.","productDescription":"9 p.","startPage":"105","endPage":"114","ipdsId":"IP-080520","costCenters":[{"id":438,"text":"National Research Program - Western Branch","active":true,"usgs":true}],"links":[{"id":335171,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","state":"Idaho, Montana, Wyoming","otherGeospatial":"Yellowstone National Park","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -111.42333984375,\n 43.40903821777055\n ],\n [\n -109.62158203125,\n 43.40903821777055\n ],\n [\n -109.62158203125,\n 45.251688256117646\n ],\n [\n -111.42333984375,\n 45.251688256117646\n ],\n [\n -111.42333984375,\n 43.40903821777055\n ]\n ]\n ]\n }\n }\n ]\n}","volume":"328","publishingServiceCenter":{"id":14,"text":"Menlo Park PSC"},"noUsgsAuthors":false,"publicationStatus":"PW","scienceBaseUri":"589ffefbe4b099f50d3e0447","contributors":{"authors":[{"text":"King, Jonathan M. 0000-0003-0834-2200","orcid":"https://orcid.org/0000-0003-0834-2200","contributorId":179317,"corporation":false,"usgs":false,"family":"King","given":"Jonathan","email":"","middleInitial":"M.","affiliations":[],"preferred":false,"id":663297,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Hurwitz, Shaul 0000-0001-5142-6886 shaulh@usgs.gov","orcid":"https://orcid.org/0000-0001-5142-6886","contributorId":2169,"corporation":false,"usgs":true,"family":"Hurwitz","given":"Shaul","email":"shaulh@usgs.gov","affiliations":[{"id":438,"text":"National Research Program - Western Branch","active":true,"usgs":true},{"id":617,"text":"Volcano Science Center","active":true,"usgs":true}],"preferred":true,"id":663295,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Lowenstern, Jacob B. 0000-0003-0464-7779 jlwnstrn@usgs.gov","orcid":"https://orcid.org/0000-0003-0464-7779","contributorId":2755,"corporation":false,"usgs":true,"family":"Lowenstern","given":"Jacob","email":"jlwnstrn@usgs.gov","middleInitial":"B.","affiliations":[{"id":617,"text":"Volcano Science Center","active":true,"usgs":true}],"preferred":true,"id":663296,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Nordstrom, D. Kirk 0000-0003-3283-5136 dkn@usgs.gov","orcid":"https://orcid.org/0000-0003-3283-5136","contributorId":749,"corporation":false,"usgs":true,"family":"Nordstrom","given":"D.","email":"dkn@usgs.gov","middleInitial":"Kirk","affiliations":[{"id":37277,"text":"WMA - Earth System Processes Division","active":true,"usgs":true},{"id":5044,"text":"National Research Program - Central Branch","active":true,"usgs":true}],"preferred":false,"id":663299,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"McCleskey, R. Blaine 0000-0002-2521-8052 rbmccles@usgs.gov","orcid":"https://orcid.org/0000-0002-2521-8052","contributorId":147399,"corporation":false,"usgs":true,"family":"McCleskey","given":"R.","email":"rbmccles@usgs.gov","middleInitial":"Blaine","affiliations":[{"id":438,"text":"National Research Program - Western Branch","active":true,"usgs":true},{"id":503,"text":"Office of Water Quality","active":true,"usgs":true},{"id":5044,"text":"National Research Program - Central Branch","active":true,"usgs":true}],"preferred":true,"id":663298,"contributorType":{"id":1,"text":"Authors"},"rank":5}]}} ,{"id":70181017,"text":"70181017 - 2016 - The chemistry and isotopic composition of waters in the low-enthalpy geothermal system of Cimino-Vico Volcanic District, Italy","interactions":[],"lastModifiedDate":"2021-08-24T14:13:55.122037","indexId":"70181017","displayToPublicDate":"2016-12-15T00:00:00","publicationYear":"2016","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":2499,"text":"Journal of Volcanology and Geothermal Research","active":true,"publicationSubtype":{"id":10}},"title":"The chemistry and isotopic composition of waters in the low-enthalpy geothermal system of Cimino-Vico Volcanic District, Italy","docAbstract":"Geothermal energy exploration is based in part on interpretation of the chemistry, temperature, and discharge rate of thermal springs. Here we present the major element chemistry and the δD, δ18O, 87Sr/86Sr and δ11B isotopic ratio of groundwater from the low-enthalpy geothermal system near the city of Viterbo in the Cimino-Vico volcanic district of west-Central Italy. The geothermal system hosts many thermal springs and gas vents, but the resource is still unexploited. Water chemistry is controlled by mixing between low salinity,HCO3-rich fresh waters (<24.2°C) flowing in shallow volcanic rocks and SO4-rich thermal waters (25.3°C to 62.2°C) ascending from deep, high permeability Mesozoic limestones. The (equivalent) SO4/Cl (0.01–0.02), Na/Cl (2.82–5.83) and B/Cl ratios (0.02–0.38) of thermal waters differs from the ratios in other geothermal systems from Central Italy, probably implying a lack of hydraulic continuity across the region. The δ18O (−6.6‰ to −5.9‰) and δD (−40.60‰ to −36.30‰) isotopic composition of spring water suggest that the recharge area for the geothermal system is the summit region of Mount Cimino. The strontium isotope ratios (87Sr/86Sr) of thermal waters (0.70797–0.70805) are consistent with dissolution of the Mesozoic evaporite-carbonate units that constitute the reservoir, and the ratios of cold fresh waters mainly reflect shallow circulation through the volcanic cover and some minor admixture (<10%) of thermal water as well. The boron isotopic composition (δ11B) of fresh waters (−5.00 and 6.12‰) is similar to that of the volcanic cover, but the δ11B of thermal waters (−8.37‰ to −4.12‰) is a mismatch for the Mesozoic reservoir rocks and instead reflects dissolution of secondary boron minerals during fluid ascent through flysch units that overlie the reservoir. A slow and tortuous ascent enhances extraction of boron but also promotes conductive cooling, partially masking the heat present in the reservoir. Overall data from this study is consistent with previous studies that concluded that the geothermal system has a large energy potential.
","language":"English","publisher":"Elsevier","doi":"10.1016/j.jvolgeores.2016.11.005","usgsCitation":"Battistel, M., Hurwitz, S., Evans, W., and Barbieri, M., 2016, The chemistry and isotopic composition of waters in the low-enthalpy geothermal system of Cimino-Vico Volcanic District, Italy: Journal of Volcanology and Geothermal Research, v. 328, p. 222-229, https://doi.org/10.1016/j.jvolgeores.2016.11.005.","productDescription":"8 p.","startPage":"222","endPage":"229","ipdsId":"IP-081169","costCenters":[{"id":438,"text":"National Research Program - Western Branch","active":true,"usgs":true}],"links":[{"id":470326,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://orbit.dtu.dk/en/publications/48cf1921-ae5b-4f03-9645-430037005165","text":"Publisher Index Page"},{"id":335170,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"Italy","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n 11.77459716796875,\n 42.01869237684385\n ],\n [\n 11.77459716796875,\n 42.76516228327469\n ],\n [\n 12.66998291015625,\n 42.76516228327469\n ],\n [\n 12.66998291015625,\n 42.01869237684385\n ],\n [\n 11.77459716796875,\n 42.01869237684385\n ]\n ]\n ]\n }\n }\n ]\n}","volume":"328","publishingServiceCenter":{"id":14,"text":"Menlo Park PSC"},"noUsgsAuthors":false,"publicationStatus":"PW","scienceBaseUri":"589ffee1e4b099f50d3e043a","contributors":{"authors":[{"text":"Battistel, Maria","contributorId":179320,"corporation":false,"usgs":false,"family":"Battistel","given":"Maria","email":"","affiliations":[],"preferred":false,"id":663302,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Hurwitz, Shaul 0000-0001-5142-6886 shaulh@usgs.gov","orcid":"https://orcid.org/0000-0001-5142-6886","contributorId":2169,"corporation":false,"usgs":true,"family":"Hurwitz","given":"Shaul","email":"shaulh@usgs.gov","affiliations":[{"id":617,"text":"Volcano Science Center","active":true,"usgs":true},{"id":438,"text":"National Research Program - Western Branch","active":true,"usgs":true}],"preferred":true,"id":663300,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Evans, William 0000-0001-5942-3102 wcevans@usgs.gov","orcid":"https://orcid.org/0000-0001-5942-3102","contributorId":179319,"corporation":false,"usgs":true,"family":"Evans","given":"William","email":"wcevans@usgs.gov","affiliations":[{"id":438,"text":"National Research Program - Western Branch","active":true,"usgs":true}],"preferred":true,"id":663301,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Barbieri, Maurizio","contributorId":179321,"corporation":false,"usgs":false,"family":"Barbieri","given":"Maurizio","email":"","affiliations":[],"preferred":false,"id":663303,"contributorType":{"id":1,"text":"Authors"},"rank":4}]}} ,{"id":70178343,"text":"sir20165160 - 2016 - Estimated nitrogen and phosphorus inputs to the Fish Creek watershed, Teton County, Wyoming, 2009–15","interactions":[],"lastModifiedDate":"2017-01-04T09:00:15","indexId":"sir20165160","displayToPublicDate":"2016-12-15T00:00:00","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":"2016-5160","title":"Estimated nitrogen and phosphorus inputs to the Fish Creek watershed, Teton County, Wyoming, 2009–15","docAbstract":"Nutrients, such as nitrogen and phosphorus, are essential for plant and animal growth and nourishment, but the overabundance of bioavailable nitrogen and phosphorus in water can cause adverse health and ecological effects. It is generally accepted that increased primary production of surface-water bodies because of high inputs of nutrients is now the most important polluting effect in surface water in the developed world.
","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/sir20165160","collaboration":"Prepared in cooperation with Teton Conservation District","usgsCitation":"Eddy-Miller, C.A., Sando, Roy, MacDonald, M.J., and Girard, C.E., 2016, Estimated nitrogen and phosphorus inputs to the Fish Creek watershed, Teton County, Wyoming, 2009–15: U.S. Geological Survey Scientific Investigations Report 2016–5160, 29 p., https://doi.org/10.3133/sir20165160.","productDescription":"viii, 29 p.","numberOfPages":"42","onlineOnly":"Y","ipdsId":"IP-077799","costCenters":[{"id":5050,"text":"WY-MT Water Science Center","active":true,"usgs":true}],"links":[{"id":438484,"rank":3,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/F73X84VR","text":"USGS data release","linkHelpText":"Estimated Nitrogen and Phosphorus Input to Fish Creek Watershed, Teton County, Wyoming"},{"id":331477,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/sir/2016/5160/coverthb.jpg"},{"id":331478,"rank":2,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/sir/2016/5160/sir20165160.pdf","text":"Report","size":"3.81 MB","linkFileType":{"id":1,"text":"pdf"},"description":"SIR 2016–5160"}],"country":"Wyoming","state":"Teton County","otherGeospatial":"Fish Creek watershed","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -110.7693099975586,\n 43.61495102209688\n ],\n [\n -110.7696533203125,\n 43.640299091949906\n ],\n [\n -110.76759338378906,\n 43.64651022309985\n ],\n [\n -110.77583312988281,\n 43.652720712083266\n ],\n [\n -110.78201293945311,\n 43.656198305413156\n ],\n [\n -110.7864761352539,\n 43.65594991256823\n ],\n [\n -110.79059600830078,\n 43.66017245119425\n ],\n [\n -110.80123901367188,\n 43.683763524273346\n ],\n [\n -110.81359863281249,\n 43.692204391812545\n ],\n [\n -110.82321166992188,\n 43.700147652647075\n ],\n [\n -110.82801818847655,\n 43.71950494269107\n ],\n [\n -110.82939147949219,\n 43.728436967960704\n ],\n [\n -110.8465576171875,\n 43.75621697418049\n ],\n [\n -110.85205078124999,\n 43.77307711737606\n ],\n [\n -110.86166381835938,\n 43.77753930104478\n ],\n [\n -110.86921691894531,\n 43.78646266947705\n ],\n [\n -110.90698242187499,\n 43.7968715826214\n ],\n [\n -110.91728210449219,\n 43.78943682964683\n ],\n [\n -110.93582153320312,\n 43.78943682964683\n ],\n [\n -110.94749450683594,\n 43.78497553389676\n ],\n [\n -110.95573425292969,\n 43.781009658142914\n ],\n [\n -110.97221374511719,\n 43.78299262890581\n ],\n [\n -110.98388671874999,\n 43.77456454892858\n ],\n [\n -110.994873046875,\n 43.76712702120528\n ],\n [\n -111.016845703125,\n 43.7596885685863\n ],\n [\n -111.02783203125,\n 43.749769193052266\n ],\n [\n -111.02165222167969,\n 43.7368715467591\n ],\n [\n -111.02165222167969,\n 43.72744458647464\n ],\n [\n -111.04156494140625,\n 43.72397112179612\n ],\n [\n -111.05255126953125,\n 43.72049745570917\n ],\n [\n -111.06697082519531,\n 43.70808986126462\n ],\n [\n -111.08413696289062,\n 43.700644071512464\n ],\n [\n -111.0779571533203,\n 43.68773584519811\n ],\n [\n -111.060791015625,\n 43.6768113296983\n ],\n [\n -111.05186462402344,\n 43.6599240747891\n ],\n [\n -111.04499816894531,\n 43.65048501002724\n ],\n [\n -111.02783203125,\n 43.64005063334696\n ],\n [\n -111.01753234863281,\n 43.63309337531845\n ],\n [\n -111.05323791503906,\n 43.62365009386727\n ],\n [\n -111.06285095214842,\n 43.61569670613613\n ],\n [\n -111.06697082519531,\n 43.600781268489776\n ],\n [\n -111.07177734375,\n 43.57044180598566\n ],\n [\n -111.06971740722655,\n 43.55850077671243\n ],\n [\n -111.06697082519531,\n 43.54058479482877\n ],\n [\n -111.07383728027344,\n 43.5281400075293\n ],\n [\n -111.07177734375,\n 43.50623094441342\n ],\n [\n -111.06697082519531,\n 43.49128838597501\n ],\n [\n -111.03263854980469,\n 43.46338571642597\n ],\n [\n -111.00929260253906,\n 43.45590959925724\n ],\n [\n -110.98251342773438,\n 43.450925007583706\n ],\n [\n -110.9564208984375,\n 43.45192195878608\n ],\n [\n -110.93032836914061,\n 43.45192195878608\n ],\n [\n -110.89050292968749,\n 43.448432557680064\n ],\n [\n -110.85273742675781,\n 43.45142348523911\n ],\n [\n -110.85548400878906,\n 43.461890566945534\n ],\n [\n -110.85479736328125,\n 43.47285413777968\n ],\n [\n -110.85067749023438,\n 43.48630671140664\n ],\n [\n -110.84175109863281,\n 43.50423881694708\n ],\n [\n -110.83831787109375,\n 43.51768440153494\n ],\n [\n -110.81977844238281,\n 43.535607188089735\n ],\n [\n -110.80398559570312,\n 43.54854811091286\n ],\n [\n -110.80467224121092,\n 43.562978940066884\n ],\n [\n -110.78956604003905,\n 43.58387263463816\n ],\n [\n -110.7806396484375,\n 43.602272978692746\n ],\n [\n -110.7693099975586,\n 43.61495102209688\n ]\n ]\n ]\n }\n }\n ]\n}","contact":"Director, Wyoming-Montana Water Science Center
U.S. Geological Survey
3162 Bozeman Ave
Helena, MT 59601
Inside soil and saprolite, rock fragments can form weathering clasts (alteration rinds surrounding an unweathered core) and these weathering rinds provide an excellent field system for investigating the initiation of weathering and long term weathering rates. Recently, uranium-series (U-series) disequilibria have shown great potential for determining rind formation rates and quantifying factors controlling weathering advance rates in weathering rinds. To further investigate whether the U-series isotope technique can document differences in long term weathering rates as a function of precipitation, we conducted a new weathering rind study on tropical volcanic Basse-Terre Island in the Lesser Antilles Archipelago. In this study, for the first time we characterized weathering reactions and quantified weathering advance rates in multiple weathering rinds across a steep precipitation gradient. Electron microprobe (EMP) point measurements, bulk major element contents, and U-series isotope compositions were determined in two weathering clasts from the Deshaies watershed with mean annual precipitation (MAP) = 1800 mm and temperature (MAT) = 23 °C. On these clasts, five core-rind transects were measured for locations with different curvature (high, medium, and low) of the rind-core boundary. Results reveal that during rind formation the fraction of elemental loss decreases in the order: Ca ≈ Na > K ≈ Mg > Si ≈ Al > Zr ≈ Ti ≈ Fe. Such observations are consistent with the sequence of reactions after the initiation of weathering: specifically, glass matrix and primary minerals (plagioclase, pyroxene) weather to produce Fe oxyhydroxides, gibbsite and minor kaolinite.
Uranium shows addition profiles in the rind due to the infiltration of U-containing soil pore water into the rind as dissolved U phases. U is then incorporated into the rind as Fe-Al oxides precipitate. Such processes lead to significant U-series isotope disequilibria in the rinds. This is the first time that multiple weathering clasts from the same watershed were analyzed for U-series isotope disequlibrian and show consistent results. The U-series disequilibria allowed for the determination of rind formation ages and weathering advance rates with a U-series mass balance model. The weathering advance rates generally decreased with decreasing curvature: ∼0.17 ± 0.10 mm/kyr for high curvature, ∼0.12 ± 0.05 mm/kyr for medium curvature, and ∼0.11 ± 0.04, 0.08 ± 0.03, 0.06 ± 0.03 mm/kyr for low curvature locations. The observed positive correlation between the curvature and the weathering rates is well supported by predictions of weathering models, i.e., that the curvature of the rind-core boundary controls the porosity creation and weathering advance rates at the clast scale.
At the watershed scale, the new weathering advance rates derived on the low curvature transects for the relatively dry Deshaies watershed (average rate of 0.08 mm/kyr; MAP = 1800 mm and MAT = 23 °C) are ∼60% slower than the rind formation rates previously determined in the much wetter Bras David watershed (∼0.18 mm/kyr, low curvature transect; MAP = 3400 mm and MAT = 23 °C) also on Basse-Terre Island. Thus, a doubling of MAP roughly correlates with a doubling of weathering advance rate. The new rind study highlights the effect of precipitation on weathering rates over a time scale of ∼100 kyr. Weathering rinds are thus a suitable system for investigating long-term chemical weathering across environmental gradients, complementing short-term riverine solute fluxes.
","language":"English","publisher":"Elsevier ","doi":"10.1016/j.gca.2016.08.040","usgsCitation":"Engel, J.M., May, L., Sak, P.B., Gaillardet, J., Ren, M., Engle, M.A., and Brantley, S.L., 2016, Quantifying chemical weathering rates along a precipitation gradient on Basse-Terre Island, French Guadeloupe: new insight from U-series isotopes in weathering rinds: Geochimica et Cosmochimica Acta, v. 195, p. 29-67, https://doi.org/10.1016/j.gca.2016.08.040.","productDescription":"39 p. 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These basanites of the Hana Volcanics exhibit an enrichment in incompatible trace elements and a more depleted isotopic signature than similarly aged Hawaiian shield lavas from Kilauea and Mauna Loa. Here we posit that as the Pacific lithosphere beneath the active shield volcanoes moves away from the center of the Hawaiian plume, increased incorporation of an intrinsic depleted component with relatively low 206Pb/204Pb produces the source of the basanites of the Hana Volcanics. Haleakala Crater basanites have average (230Th/238U) of 1.23 (n = 13), average age-corrected (226Ra/230Th) of 1.25 (n = 13), and average (231Pa/235U) of 1.67 (n = 4), significantly higher than Kilauea and Mauna Loa tholeiites. U-series modeling shows that solid mantle upwelling velocity for Haleakala Crater basanites ranges from ∼0.7 to 1.0 cm/yr, compared to ∼10 to 20 cm/yr for tholeiites and ∼1 to 2 cm/yr for alkali basalts. These modeling results indicate that solid mantle upwelling rates and porosity of the melting zone are lower for Hana Volcanics basanites than for shield-stage tholeiites from Kilauea and Mauna Loa and alkali basalts from Hualalai. The melting rate, which is directly proportional to both the solid mantle upwelling rate and the degree of melting, is therefore greatest in the center of the Hawaiian plume and lower on its periphery. Our results indicate that solid mantle upwelling velocity is at least 10 times higher at the center of the plume than at its periphery under Haleakala.
","language":"English","publisher":"Elsevier","doi":"10.1016/j.gca.2016.08.017","usgsCitation":"Phillips, E.H., Sims, K., Sherrod, D.R., Salters, V., Blusztajn, J., and Dulaiova, H., 2016, Isotopic constraints on the genesis and evolution of basanitic lavas at Haleakala, Island of Maui, Hawaii: Geochimica et Cosmochimica Acta, v. 195, p. 201-225, https://doi.org/10.1016/j.gca.2016.08.017.","productDescription":"25 p.","startPage":"201","endPage":"225","ipdsId":"IP-068629","costCenters":[{"id":617,"text":"Volcano Science Center","active":true,"usgs":true}],"links":[{"id":470322,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://hdl.handle.net/1912/8691","text":"Publisher Index Page"},{"id":336726,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","state":"Hawaii","otherGeospatial":"Haleakala","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -156.26678466796875,\n 20.915265785641992\n ],\n [\n -156.29150390625,\n 20.83571086093366\n ],\n [\n -156.12121582031247,\n 20.668765746375158\n ],\n [\n -156.104736328125,\n 20.630213817744696\n ],\n [\n -155.99212646484375,\n 20.694461597907797\n ],\n [\n -155.972900390625,\n 20.756113874762082\n ],\n [\n -156.02508544921875,\n 20.82800976296467\n ],\n [\n -156.2200927734375,\n 20.938354479616375\n ],\n [\n -156.26678466796875,\n 20.915265785641992\n ]\n ]\n ]\n }\n }\n ]\n}","volume":"195","publishingServiceCenter":{"id":14,"text":"Menlo Park PSC"},"noUsgsAuthors":false,"publicationStatus":"PW","scienceBaseUri":"58b7eba4e4b01ccd5500bae5","contributors":{"authors":[{"text":"Phillips, Erin H.","contributorId":184202,"corporation":false,"usgs":false,"family":"Phillips","given":"Erin","email":"","middleInitial":"H.","affiliations":[],"preferred":false,"id":673775,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Sims, K.W.W.","contributorId":184203,"corporation":false,"usgs":false,"family":"Sims","given":"K.W.W.","email":"","affiliations":[],"preferred":false,"id":673776,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Sherrod, David R. 0000-0001-9460-0434 dsherrod@usgs.gov","orcid":"https://orcid.org/0000-0001-9460-0434","contributorId":527,"corporation":false,"usgs":true,"family":"Sherrod","given":"David","email":"dsherrod@usgs.gov","middleInitial":"R.","affiliations":[{"id":617,"text":"Volcano Science Center","active":true,"usgs":true}],"preferred":true,"id":673774,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Salters, Vincent","contributorId":184204,"corporation":false,"usgs":false,"family":"Salters","given":"Vincent","email":"","affiliations":[],"preferred":false,"id":673777,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Blusztajn, Jurek","contributorId":184205,"corporation":false,"usgs":false,"family":"Blusztajn","given":"Jurek","email":"","affiliations":[],"preferred":false,"id":673778,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"Dulaiova, Henrieta","contributorId":184206,"corporation":false,"usgs":false,"family":"Dulaiova","given":"Henrieta","email":"","affiliations":[],"preferred":false,"id":673779,"contributorType":{"id":1,"text":"Authors"},"rank":6}]}} ,{"id":70179081,"text":"70179081 - 2016 - Plague cycles in two rodent species from China: Dry years might provide context for epizootics in wet years","interactions":[],"lastModifiedDate":"2016-12-16T09:10:46","indexId":"70179081","displayToPublicDate":"2016-12-15T00:00:00","publicationYear":"2016","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":1475,"text":"Ecosphere","active":true,"publicationSubtype":{"id":10}},"title":"Plague cycles in two rodent species from China: Dry years might provide context for epizootics in wet years","docAbstract":"Plague, a rodent-associated, flea-borne zoonosis, is one of the most notorious diseases in history. Rates of plague transmission can increase when fleas are abundant. Fleas commonly desiccate and die when reared under dry conditions in laboratories, suggesting fleas will be suppressed during droughts in the wild, thus reducing the rate at which plague spreads among hosts. In contrast, fleas might increase in abundance when precipitation is plentiful, producing epizootic outbreaks during wet years. We tested these hypotheses using a 27-yr data set from two rodents in Inner Mongolia, China: Mongolian gerbils (Meriones unguiculatus) and Daurian ground squirrels (Spermophilus dauricus). For both species of rodents, fleas were most abundant during years preceded by dry growing seasons. For gerbils, the prevalence of plague increased during wet years preceded by dry growing seasons. If precipitation is scarce during the primary growing season, succulent plants decline in abundance and, consequently, herbivorous rodents can suffer declines in body condition. Fleas produce more offspring and better survive when parasitizing food-limited hosts, because starving animals tend to exhibit inefficient behavioral and immunological defenses against fleas. Further, rodent burrows might buffer fleas from xeric conditions aboveground during dry years. After a dry year, fleas might be abundant due to the preceding drought, and if precipitation and succulent plants become more plentiful, rodents could increase in density, thereby creating connectivity that facilitates the spread of plague. Moreover, in wet years, mild temperatures might increase the efficiency at which fleas transmit the plague bacterium, while also helping fleas to survive as they quest among hosts. In this way, dry years could provide context for epizootics of plague in wet years.
","language":"English","publisher":"Ecological Society of America","publisherLocation":"Washington, D.C.","doi":"10.1002/ecs2.1495","usgsCitation":"Eads, D.A., Biggins, D.E., Xu, L., and Liu, Q., 2016, Plague cycles in two rodent species from China: Dry years might provide context for epizootics in wet years: Ecosphere, v. 7, no. 10, e01495; 10 p., https://doi.org/10.1002/ecs2.1495.","productDescription":"e01495; 10 p.","ipdsId":"IP-077713","costCenters":[{"id":291,"text":"Fort Collins Science Center","active":true,"usgs":true}],"links":[{"id":470324,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.1002/ecs2.1495","text":"Publisher Index Page"},{"id":332178,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"China","volume":"7","issue":"10","publishingServiceCenter":{"id":2,"text":"Denver PSC"},"noUsgsAuthors":false,"publicationDate":"2016-10-18","publicationStatus":"PW","scienceBaseUri":"5853ba38e4b0e2663625f2ac","chorus":{"doi":"10.1002/ecs2.1495","url":"http://dx.doi.org/10.1002/ecs2.1495","publisher":"Wiley-Blackwell","authors":"Eads David A., Biggins Dean E., Xu Lei, Liu Qiyong","journalName":"Ecosphere","publicationDate":"10/2016","auditedOn":"11/13/2016"},"contributors":{"authors":[{"text":"Eads, David A. 0000-0002-4247-017X deads@usgs.gov","orcid":"https://orcid.org/0000-0002-4247-017X","contributorId":173639,"corporation":false,"usgs":true,"family":"Eads","given":"David","email":"deads@usgs.gov","middleInitial":"A.","affiliations":[{"id":291,"text":"Fort Collins Science Center","active":true,"usgs":true}],"preferred":false,"id":655962,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Biggins, Dean E. 0000-0003-2078-671X bigginsd@usgs.gov","orcid":"https://orcid.org/0000-0003-2078-671X","contributorId":2522,"corporation":false,"usgs":true,"family":"Biggins","given":"Dean","email":"bigginsd@usgs.gov","middleInitial":"E.","affiliations":[{"id":291,"text":"Fort Collins Science Center","active":true,"usgs":true}],"preferred":true,"id":655963,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Xu, Lei","contributorId":177492,"corporation":false,"usgs":false,"family":"Xu","given":"Lei","email":"","affiliations":[],"preferred":false,"id":655964,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Liu, Qiyong","contributorId":177493,"corporation":false,"usgs":false,"family":"Liu","given":"Qiyong","email":"","affiliations":[],"preferred":false,"id":655965,"contributorType":{"id":1,"text":"Authors"},"rank":4}]}} ,{"id":70179086,"text":"70179086 - 2016 - Introduction to “Global tsunami science: Past and future, Volume I”","interactions":[],"lastModifiedDate":"2016-12-15T14:49:34","indexId":"70179086","displayToPublicDate":"2016-12-15T00:00:00","publicationYear":"2016","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":3209,"text":"Pure and Applied Geophysics PAGEOPH","active":true,"publicationSubtype":{"id":10}},"title":"Introduction to “Global tsunami science: Past and future, Volume I”","docAbstract":"Twenty-five papers on the study of tsunamis are included in Volume I of the PAGEOPH topical issue “Global Tsunami Science: Past and Future”. Six papers examine various aspects of tsunami probability and uncertainty analysis related to hazard assessment. Three papers relate to deterministic hazard and risk assessment. Five more papers present new methods for tsunami warning and detection. Six papers describe new methods for modeling tsunami hydrodynamics. Two papers investigate tsunamis generated by non-seismic sources: landslides and meteorological disturbances. The final three papers describe important case studies of recent and historical events. Collectively, this volume highlights contemporary trends in global tsunami research, both fundamental and applied toward hazard assessment and mitigation.
","language":"English","publisher":"Springer","doi":"10.1007/s00024-016-1427-4","usgsCitation":"Geist, E.L., Fritz, H., Rabinovich, A.B., and Tanioka, Y., 2016, Introduction to “Global tsunami science: Past and future, Volume I”: Pure and Applied Geophysics PAGEOPH, v. 173, no. 12, p. 3663-3669, https://doi.org/10.1007/s00024-016-1427-4.","productDescription":"7 p.","startPage":"3663","endPage":"3669","ipdsId":"IP-081118","costCenters":[{"id":520,"text":"Pacific Coastal and Marine Science Center","active":true,"usgs":true}],"links":[{"id":461997,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.1007/s00024-016-1427-4","text":"Publisher Index Page"},{"id":332179,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"volume":"173","issue":"12","publishingServiceCenter":{"id":14,"text":"Menlo Park PSC"},"noUsgsAuthors":false,"publicationDate":"2016-11-16","publicationStatus":"PW","scienceBaseUri":"5853ba37e4b0e2663625f2a8","contributors":{"authors":[{"text":"Geist, Eric L. 0000-0003-0611-1150 egeist@usgs.gov","orcid":"https://orcid.org/0000-0003-0611-1150","contributorId":1956,"corporation":false,"usgs":true,"family":"Geist","given":"Eric","email":"egeist@usgs.gov","middleInitial":"L.","affiliations":[{"id":186,"text":"Coastal and Marine Geology Program","active":true,"usgs":true},{"id":520,"text":"Pacific Coastal and Marine Science Center","active":true,"usgs":true}],"preferred":true,"id":655996,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Fritz, Hermann","contributorId":106040,"corporation":false,"usgs":true,"family":"Fritz","given":"Hermann","affiliations":[],"preferred":false,"id":655997,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Rabinovich, Alexander B.","contributorId":177506,"corporation":false,"usgs":false,"family":"Rabinovich","given":"Alexander","email":"","middleInitial":"B.","affiliations":[],"preferred":false,"id":655998,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Tanioka, Yuichiro","contributorId":177507,"corporation":false,"usgs":false,"family":"Tanioka","given":"Yuichiro","email":"","affiliations":[],"preferred":false,"id":655999,"contributorType":{"id":1,"text":"Authors"},"rank":4}]}} ,{"id":70179087,"text":"70179087 - 2016 - Impacts of the Deepwater Horizon oil spill on deep-sea coral-associated sediment communities","interactions":[],"lastModifiedDate":"2016-12-15T13:29:18","indexId":"70179087","displayToPublicDate":"2016-12-15T00:00:00","publicationYear":"2016","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":2663,"text":"Marine Ecology Progress Series","active":true,"publicationSubtype":{"id":10}},"title":"Impacts of the Deepwater Horizon oil spill on deep-sea coral-associated sediment communities","docAbstract":"Cold-water corals support distinct populations of infauna within surrounding sediments that provide vital ecosystem functions and services in the deep sea. Yet due to their sedentary existence, infauna are vulnerable to perturbation and contaminant exposure because they are unable to escape disturbance events. While multiple deep-sea coral habitats were injured by the 2010 Deepwater Horizon (DWH) oil spill, the extent of adverse effects on coral-associated sediment communities is unknown. In 2011, sediments were collected adjacent to several coral habitats located 6 to 183 km from the wellhead in order to quantify the extent of impact of the DWH spill on infaunal communities. Higher variance in macrofaunal abundance and diversity, and different community structure (higher multivariate dispersion) were associated with elevated hydrocarbon concentrations and contaminants at sites closest to the wellhead (MC294, MC297, and MC344), consistent with impacts from the spill. In contrast, variance in meiofaunal diversity was not significantly related to distance from the wellhead and no other community metric (e.g. density or multivariate dispersion) was correlated with contaminants or hydrocarbon concentrations. Concentrations of polycyclic aromatic hydrocarbons (PAH) provided the best statistical explanation for observed macrofaunal community structure, while depth and presence of fine-grained mud best explained meiofaunal community patterns. Impacts associated with contaminants from the DWH spill resulted in a patchwork pattern of infaunal community composition, diversity, and abundance, highlighting the role of variability as an indicator of disturbance. These data represent a useful baseline for tracking post-spill recovery of these deep-sea communities.
","language":"English","publisher":"Inter-Research","doi":"10.3354/meps11905","usgsCitation":"Demopoulos, A.W., Bourque, J.R., Cordes, E.E., and Stamler, K., 2016, Impacts of the Deepwater Horizon oil spill on deep-sea coral-associated sediment communities: Marine Ecology Progress Series, v. 561, p. 51-68, https://doi.org/10.3354/meps11905.","productDescription":"18 p.","startPage":"51","endPage":"68","ipdsId":"IP-073329","costCenters":[{"id":17705,"text":"Wetland and Aquatic Research Center","active":true,"usgs":true}],"links":[{"id":332167,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"volume":"561","publishingServiceCenter":{"id":5,"text":"Lafayette PSC"},"noUsgsAuthors":false,"publicationStatus":"PW","scienceBaseUri":"5853ba36e4b0e2663625f2a6","contributors":{"authors":[{"text":"Demopoulos, Amanda W.J. 0000-0003-2096-4694 ademopoulos@usgs.gov","orcid":"https://orcid.org/0000-0003-2096-4694","contributorId":145681,"corporation":false,"usgs":true,"family":"Demopoulos","given":"Amanda","email":"ademopoulos@usgs.gov","middleInitial":"W.J.","affiliations":[{"id":520,"text":"Pacific Coastal and Marine Science Center","active":true,"usgs":true},{"id":17705,"text":"Wetland and Aquatic Research Center","active":true,"usgs":true},{"id":566,"text":"Southeast Ecological Science Center","active":true,"usgs":true}],"preferred":true,"id":656000,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Bourque, Jill R. 0000-0003-3809-2601 jbourque@usgs.gov","orcid":"https://orcid.org/0000-0003-3809-2601","contributorId":5452,"corporation":false,"usgs":true,"family":"Bourque","given":"Jill","email":"jbourque@usgs.gov","middleInitial":"R.","affiliations":[{"id":17705,"text":"Wetland and Aquatic Research Center","active":true,"usgs":true}],"preferred":true,"id":656001,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Cordes, Erik E.","contributorId":37623,"corporation":false,"usgs":false,"family":"Cordes","given":"Erik","email":"","middleInitial":"E.","affiliations":[{"id":16710,"text":"Temple University, Department of Biology","active":true,"usgs":false}],"preferred":false,"id":656002,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Stamler, Katherine kstamler@usgs.gov","contributorId":177508,"corporation":false,"usgs":true,"family":"Stamler","given":"Katherine","email":"kstamler@usgs.gov","affiliations":[],"preferred":true,"id":656003,"contributorType":{"id":1,"text":"Authors"},"rank":4}]}} ,{"id":70179048,"text":"70179048 - 2016 - Combined exposure of diesel exhaust particles and respirable Soufrière Hills volcanic ash causes a (pro-)inflammatory response in an in vitro multicellular epithelial tissue barrier model","interactions":[],"lastModifiedDate":"2016-12-15T16:16:03","indexId":"70179048","displayToPublicDate":"2016-12-15T00:00:00","publicationYear":"2016","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":5238,"text":"Particle and Fibre Toxicology","active":true,"publicationSubtype":{"id":10}},"title":"Combined exposure of diesel exhaust particles and respirable Soufrière Hills volcanic ash causes a (pro-)inflammatory response in an in vitro multicellular epithelial tissue barrier model","docAbstract":"There are justifiable health concerns regarding the potential adverse effects associated with human exposure to volcanic ash (VA) particles, especially when considering communities living in urban areas already exposed to heightened air pollution. The aim of this study was, therefore, to gain an imperative, first understanding of the biological impacts of respirable VA when exposed concomitantly with diesel particles.
A sophisticated in vitro 3D triple cell co-culture model of the human alveolar epithelial tissue barrier was exposed to either a single or repeated dose of dry respirable VA (deposited dose of 0.26 ± 0.09 or 0.89 ± 0.29 μg/cm2, respectively) from Soufrière Hills volcano, Montserrat for a period of 24 h at the air-liquid interface (ALI). Subsequently, co-cultures were exposed to co-exposures of single or repeated VA and diesel exhaust particles (DEP; NIST SRM 2975; 0.02 mg/mL), a model urban pollutant, at the pseudo-ALI. The biological impact of each individual particle type was also analysed under these precise scenarios. The cytotoxic (LDH release), oxidative stress (depletion of intracellular GSH) and (pro-)inflammatory (TNF-α, IL-8 and IL-1β) responses were assessed after the particulate exposures. The impact of VA exposure upon cell morphology, as well as its interaction with the multicellular model, was visualised via confocal laser scanning microscopy (LSM) and scanning electron microscopy (SEM), respectively.
The combination of respirable VA and DEP, in all scenarios, incited an heightened release of TNF-α and IL-8 as well as significant increases in IL-1β, when applied at sub-lethal doses to the co-culture compared to VA exposure alone. Notably, the augmented (pro-)inflammatory responses observed were not mediated by oxidative stress. LSM supported the quantitative assessment of cytotoxicity, with no changes in cell morphology within the barrier model evident. A direct interaction of the VA with all three cell types of the multicellular system was observed by SEM.
Combined exposure of respirable Soufrière Hills VA with DEP causes a (pro-)inflammatory effect in an advanced in vitro multicellular model of the epithelial airway barrier. This finding suggests that the combined exposure to volcanic and urban particulate matter should be further investigated in order to deduce the potential human health hazard, especially how it may influence the respiratory function of susceptible individuals (i.e. with pre-existing lung diseases) in the population.
In May 2013, the U.S. Geological Survey’s Grand Canyon Monitoring and Research Center acquired airborne multispectral high-resolution data for the Colorado River in the Grand Canyon, Arizona. The image data, which consist of four color bands (blue, green, red, and near-infrared) with a ground resolution of 20 centimeters, are available to the public as 16-bit geotiff files at http://dx.doi.org/10.5066/F7TX3CHS. The images are projected in the State Plane map projection, using the central Arizona zone (202) and the North American Datum of 1983. The assessed accuracy for these data is based on 91 ground-control points and is reported at the 95-percent confidence level as 0.64 meter (m) and a root mean square error of 0.36 m. The primary intended uses of this dataset are for maps to support field data collection and simple river navigation; high-spatial-resolution change detection of sandbars, other geomorphic landforms, riparian vegetation, and backwater and nearshore habitats; and other ecosystem-wide mapping.
","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/ds1027","usgsCitation":"Durning, L.E., Sankey, J.B., Davis, P.A., and Sankey, T.T., 2016, Four-band image mosaic of the Colorado River corridor downstream of Glen Canyon Dam in Arizona, derived from the May 2013 airborne image acquisition: U.S. Geological Survey Data Series 1027, https://doi.org/10.3133/ds1027.","productDescription":"HTML Document; Data Release","onlineOnly":"Y","additionalOnlineFiles":"Y","ipdsId":"IP-076889","costCenters":[{"id":568,"text":"Southwest Biological Science Center","active":true,"usgs":true}],"links":[{"id":438486,"rank":4,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/F7TX3CHS","text":"USGS data release","linkHelpText":"Four Band Multispectral High Resolution Image Mosaic of the Colorado River Corridor, Arizona - 2013"},{"id":332009,"rank":3,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/ds/1027/coverthb.jpg"},{"id":332008,"rank":2,"type":{"id":30,"text":"Data Release"},"url":"https://dx.doi.org/10.5066/F7TX3CHS","text":"USGS Data Release","description":"DS 1027 Data Release","linkHelpText":"Four Band Multispectral High Resolution Image Mosaic of the Colorado River Corridor, Arizona"},{"id":332007,"rank":1,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/ds/1027/index.html","text":"Report HTML","linkFileType":{"id":5,"text":"html"},"description":"DS 1027 HTML"}],"country":"United States","state":"Arizona","otherGeospatial":"Glen Canyon Dam","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -114.04907226562499,\n 35.48751102385376\n ],\n [\n -114.04907226562499,\n 37.00255267215955\n ],\n [\n -111.302490234375,\n 37.00255267215955\n ],\n [\n -111.302490234375,\n 35.48751102385376\n ],\n [\n -114.04907226562499,\n 35.48751102385376\n ]\n ]\n ]\n }\n }\n ]\n}","contact":"GCMRC Staff, Southwest Biological Science Center
U.S. Geological Survey
Grand Canyon Monitoring and Research Center
2255 N. Gemini Drive
Flagstaff, AZ 86001
http://www.gcmrc.gov/
Lead and zinc were mined in the Tri-State Mining District (TSMD) of southwest Missouri, northeast Oklahoma, and southeast Kansas for more than 100 years. The effects of mining on the landscape are still evident, nearly 50 years after the last mine ceased operation. The legacies of mining are the mine waste and discharge of groundwater from underground mines. The mine-waste piles and underground mines are continuous sources of trace metals (primarily lead, zinc, and cadmium) to the streams that drain the TSMD. Many previous studies characterized the horizontal extent of mine-waste contamination in streams but little information exists on the depth of mine-waste contamination in these streams. Characterizing the vertical extent of contamination is difficult because of the large amount of coarse-grained material, ranging from coarse gravel to boulders, within channel sediment. The U.S. Geological Survey, in cooperation with U.S. Fish and Wildlife service, collected channel-sediment samples at depth for subsequent analyses that would allow attainment of the following goals: (1) determination of the relation between concentration and depth for lead, zinc and cadmium in channel sediments and flood-plain sediments, and (2) determination of the volume of gravel-bar sediment from the surface to the maximum depth with concentrations of these metals that exceeded sediment-quality guidelines. For the purpose of this report, volume of gravel-bar sediment is considered to be distributed in two forms, gravel bars and the wetted channel, and this study focused on gravel bars. Concentrations of lead, zinc, and cadmium in samples were compared to the consensus probable effects concentration (CPEC) and Tri-State Mining District specific probable effects concentration (TPEC) sediment-quality guidelines.
During the study, more than 700 sediment samples were collected from borings at multiple sites, including gravel bars and flood plains, along Center Creek, Turkey Creek, Shoal Creek, Tar Creek, and Spring River in order to characterize the vertical extent of mine waste in select streams in the TSMD. The largest concentrations of lead, zinc, and cadmium in gravel bar-sediment samples generally were detected in Turkey Creek and Tar Creek and the smallest concentrations were detected in Shoal Creek followed by the Spring River. Gravel bar-sediment samples from Turkey Creek exceeded the CPEC for cadmium (minimum of 70 percent of samples), lead (94 percent), and zinc (99 percent) at a slightly higher frequency than similar samples from Tar Creek (69 percent, 88 percent, and 96 percent, respectively). Gravel bar-sediment samples from Turkey Creek also contained the largest concentrations of cadmium (174 milligrams per kilogram [mg/kg]) and lead (7,520 mg/kg) detected; however, the largest zinc concentration (46,600 mg/kg) was detected in a gravel bar-sediment sample from Tar Creek. In contrast, none of the 65 gravel bar-sediment samples from Shoal Creek contained cadmium above the x-ray fluorescence reporting level of 12 mg/kg, and lead and zinc exceeded the CPEC in only 12 percent and 74 percent of samples, respectively. In most cases, concentrations of lead and zinc above the CPEC or TPEC were present at the maximum depth of boring, which indicated that nearly the entire thickness of sediment in the stream has been contaminated by mine wastes. Approximately 284,000 cubic yards of channel sediment from land surface to the maximum depth that exceeded the CPEC and approximately 236,000 cubic yards of channel sediment from land surface to the maximum depth that exceeded the TPEC were estimated along 37.6 of the 55.1 miles of Center Creek, Turkey Creek, Shoal Creek, and Tar Creek examined in this study. Mine-waste contamination reported along additional reaches of these streams is beyond the scope of this study. Flood-plain cores collected in the TSMD generally only had exceedances of the CPEC and TPEC for lead and zinc in the top 1 or 2 feet of soil with a few exceptions, such as cores in low areas near the stream or cores in areas disturbed by past mining.
Director, Missouri Water Science Center
U.S. Geological Survey
1400 Independence Road
Rolla, MO 65401
An effort to build a unified collection of geospatial data for use in land-change modeling (LCM) led to new insights into the requirements and challenges of building an LCM data infrastructure. A case study of data compilation and unification for the Richmond, Va., Metropolitan Statistical Area (MSA) delineated the problems of combining and unifying heterogeneous data from many independent localities such as counties and cities. The study also produced conclusions and recommendations for use by the national LCM community, emphasizing the critical need for simple, practical data standards and conventions for use by localities. This report contributes an uncopyrighted core glossary and a much needed operational definition of data unification.
","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/ofr20161176","usgsCitation":"Donato, D.I., and Shapiro, J.L., 2016, Building unified geospatial data for land-change modeling—A case study in the area of Richmond, Virginia: U.S. Geological Survey Open-File Report 2016‒1176, 84 p., https://doi.org/10.3133/ofr20161176.","productDescription":"v, 84 p.","numberOfPages":"92","onlineOnly":"Y","ipdsId":"IP-069929","costCenters":[{"id":242,"text":"Eastern Geographic Science Center","active":true,"usgs":true},{"id":245,"text":"Eastern Mineral and Environmental Resources Science Center","active":true,"usgs":true}],"links":[{"id":331929,"rank":2,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/of/2016/1176/ofr20161176.pdf","text":"Report","size":"2.48 MB","linkFileType":{"id":1,"text":"pdf"},"description":"OFR 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521 National Center
12201 Sunrise Valley Drive
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Telephone: 703–648–4230
http://egsc.usgs.gov/
The High Plains aquifer is a nationally important water resource underlying about 175,000 square miles in parts of eight states: Colorado, Kansas, Oklahoma, Nebraska, New Mexico, South Dakota, Texas, and Wyoming. Droughts across much of the Northern High Plains from 2001 to 2007 have combined with recent (2004) legislative mandates to elevate concerns regarding future availability of groundwater and the need for additional information to support science-based water-resource management. To address these needs, the U.S. Geological Survey began the High Plains Groundwater Availability Study to provide a tool for water-resource managers and other stakeholders to assess the status and availability of groundwater resources.
A transient groundwater-flow model was constructed using the U.S. Geological Survey modular three-dimensional finite-difference groundwater-flow model with Newton-Rhapson solver (MODFLOW–NWT). The model uses an orthogonal grid of 565 rows and 795 columns, and each grid cell measures 3,281 feet per side, with one variably thick vertical layer, simulated as unconfined. Groundwater flow was simulated for two distinct periods: (1) the period before substantial groundwater withdrawals, or before about 1940, and (2) the period of increasing groundwater withdrawals from May 1940 through April 2009. A soil-water-balance model was used to estimate recharge from precipitation and groundwater withdrawals for irrigation. The soil-water-balance model uses spatially distributed soil and landscape properties with daily weather data and estimated historical land-cover maps to calculate spatial and temporal variations in potential recharge. Mean annual recharge estimated for 1940–49, early in the history of groundwater development, and 2000–2009, late in the history of groundwater development, was 3.3 and 3.5 inches per year, respectively.
Primary model calibration was completed using statistical techniques through parameter estimation using the parameter estimation suite of software with Tikhonov regularization. Calibration targets for the groundwater model included 343,067 groundwater levels measured in wells and 10,820 estimated monthly stream base flows at streamgages. A total of 1,312 parameters were adjusted during calibration to improve the match between calibration targets and simulated equivalents. Comparison of calibration targets to simulated equivalents indicated that, at the regional scale, the model correctly reproduced groundwater levels and stream base flows for 1940–2009. This comparison indicates that the model can be used to examine the likely response of the aquifer system to potential future stresses.
Mean calibrated recharge for 1940–49 and 2000–2009 was smaller than that estimated with the soil-water-balance model. This indicated that although the general spatial patterns of recharge estimated with the soil-water-balance model were approximately correct at the regional scale of the Northern High Plains aquifer, the soil-water-balance model had overestimated recharge, and adjustments were needed to decrease recharge to improve the match of the groundwater model to calibration targets. The largest components of the simulated groundwater budgets were recharge from precipitation, recharge from canal seepage, outflows to evapotranspiration, and outflows to stream base flow. Simulated outflows to irrigation wells increased from 7 percent of total outflows in 1940–49 to 38 percent of 1970–79 total outflows and 49 percent of 2000–2009 total outflows.
","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/sir20165153","usgsCitation":"Peterson, S.M., Flynn, A.T., and Traylor, J.P., 2016, Groundwater-flow model of the Northern High Plains aquifer in Colorado, Kansas, Nebraska, South Dakota, and Wyoming: U.S. Geological Survey Scientific Investigations Report 2016–5153, 88 p., https://doi.org/10.3133/sir20165153.","productDescription":"Report: x, 88 p.; 2 Figures: 11.00 x 8.50 inches; 2 Data Releases","numberOfPages":"102","onlineOnly":"Y","additionalOnlineFiles":"Y","ipdsId":"IP-070028","costCenters":[{"id":464,"text":"Nebraska Water Science Center","active":true,"usgs":true}],"links":[{"id":331684,"rank":4,"type":{"id":29,"text":"Figure"},"url":"https://pubs.usgs.gov/sir/2016/5153/sir20165153_fig15.pdf","text":"Figure 15","size":"9.23 MB","linkFileType":{"id":1,"text":"pdf"},"description":"SIR 2016–5153 Figure 15"},{"id":331685,"rank":5,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/F7K072C9","text":"USGS data release - Base of aquifer contours for the Northern High Plains aquifer","description":"USGS Data Release"},{"id":331683,"rank":3,"type":{"id":29,"text":"Figure"},"url":"https://pubs.usgs.gov/sir/2016/5153/sir20165153_fig14.pdf","text":"Figure 14","size":"988 kB","linkFileType":{"id":1,"text":"pdf"},"description":"SIR 2016–5153 Figure 14"},{"id":331686,"rank":6,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/F7JS9NKD","text":"USGS data release - MODFLOW-NWT groundwater flow model used to evaluate conditions in the Northern High Plains Aquifer in Colorado, Kansas, Nebraska, South Dakota, and Wyoming","description":"USGS Data Release"},{"id":331678,"rank":2,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/sir/2016/5153/sir20165153.pdf","text":"Report","size":"36.2 MB","linkFileType":{"id":1,"text":"pdf"},"description":"SIR 2016–5153"},{"id":331677,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/sir/2016/5153/coverthb.jpg"}],"country":"United States","state":"Colorado, Kansas, Nebraska, South Dakota, Wyoming","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -106,\n 38\n ],\n [\n -106,\n 44\n ],\n [\n -96,\n 44\n ],\n [\n -96,\n 38\n ],\n [\n -106,\n 38\n ]\n ]\n ]\n }\n }\n ]\n}","publicComments":"Water Availability and Use Science Program","contact":"Director, Nebraska Water Science Center
U.S. Geological Survey
5231 South 19th Street
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Environmental DNA (eDNA) analysis is rapidly evolving as a tool for monitoring the distributions of aquatic species. Detection of species’ populations in streams may be challenging because the persistence time for intact DNA fragments is unknown and because eDNA is diluted and dispersed by dynamic hydrological processes. During 2015, the DNA of Brook Trout Salvelinus fontinalis was analyzed from water samples collected at 40 streams across the Adirondack region of upstate New York, where Brook Trout populations were recently quantified. Study objectives were to evaluate different sampling methods and the ability of eDNA to accurately predict the presence and abundance of resident Brook Trout populations. Results from three-pass electrofishing surveys indicated that Brook Trout were absent from 10 sites and were present in low (<100 fish/0.1 ha), moderate (100–300 fish/0.1 ha), and high (>300 fish/0.1 ha) densities at 9, 11, and 10 sites, respectively. The eDNA results correctly predicted the presence and confirmed the absence of Brook Trout at 85.0–92.5% of the study sites; eDNA also explained 44% of the variability in Brook Trout population density and 24% of the variability in biomass. These findings indicate that eDNA surveys will enable researchers to effectively characterize the presence and abundance of Brook Trout and other species’ populations in headwater streams across the Adirondack region and elsewhere.
","language":"English","publisher":"Taylor & Francis","doi":"10.1080/00028487.2016.1243578","usgsCitation":"Baldigo, B.P., Sporn, L., George, S.D., and Ball, J., 2016, Efficacy of environmental DNA to detect and quantify Brook Trout populations in headwater streams of the Adirondack Mountains, New York: Transactions of the American Fisheries Society, v. 146, no. 1, p. 99-111, https://doi.org/10.1080/00028487.2016.1243578.","productDescription":"13 p.","startPage":"99","endPage":"111","ipdsId":"IP-071778","costCenters":[{"id":474,"text":"New York Water Science Center","active":true,"usgs":true}],"links":[{"id":470327,"rank":1,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.1080/00028487.2016.1243578","text":"Publisher Index Page"},{"id":438489,"rank":0,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/F78913ZC","text":"USGS data release","linkHelpText":"Community composition data for assessing fish populations in headwater streams of the Adirondack Mountains, New York, USA"},{"id":332019,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","state":"New York","otherGeospatial":"Adirondack Mountains","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -75.4376220703125,\n 43.40305202432616\n ],\n [\n -75.4376220703125,\n 44.3002644115815\n ],\n [\n -73.751220703125,\n 44.3002644115815\n ],\n [\n -73.751220703125,\n 43.40305202432616\n ],\n [\n -75.4376220703125,\n 43.40305202432616\n ]\n ]\n ]\n }\n }\n ]\n}","volume":"146","issue":"1","publishingServiceCenter":{"id":11,"text":"Pembroke PSC"},"noUsgsAuthors":false,"publicationDate":"2016-12-06","publicationStatus":"PW","scienceBaseUri":"585116bae4b08138bf1abd4a","contributors":{"authors":[{"text":"Baldigo, Barry P. 0000-0002-9862-9119 bbaldigo@usgs.gov","orcid":"https://orcid.org/0000-0002-9862-9119","contributorId":1234,"corporation":false,"usgs":true,"family":"Baldigo","given":"Barry","email":"bbaldigo@usgs.gov","middleInitial":"P.","affiliations":[{"id":474,"text":"New York Water Science Center","active":true,"usgs":true}],"preferred":true,"id":655603,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Sporn, Lee Ann","contributorId":177388,"corporation":false,"usgs":false,"family":"Sporn","given":"Lee Ann","affiliations":[],"preferred":false,"id":655604,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"George, Scott D. 0000-0002-8197-1866 sgeorge@usgs.gov","orcid":"https://orcid.org/0000-0002-8197-1866","contributorId":3014,"corporation":false,"usgs":true,"family":"George","given":"Scott","email":"sgeorge@usgs.gov","middleInitial":"D.","affiliations":[{"id":474,"text":"New York Water Science Center","active":true,"usgs":true}],"preferred":true,"id":655605,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Ball, Jacob","contributorId":177389,"corporation":false,"usgs":false,"family":"Ball","given":"Jacob","email":"","affiliations":[],"preferred":false,"id":655606,"contributorType":{"id":1,"text":"Authors"},"rank":4}]}} ,{"id":70179023,"text":"70179023 - 2016 - Changing agricultural practices: Potential consequences to aquatic organisms","interactions":[],"lastModifiedDate":"2018-08-09T12:05:12","indexId":"70179023","displayToPublicDate":"2016-12-13T00:00:00","publicationYear":"2016","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":1552,"text":"Environmental Monitoring and Assessment","onlineIssn":"1573-2959","printIssn":"0167-6369","active":true,"publicationSubtype":{"id":10}},"title":"Changing agricultural practices: Potential consequences to aquatic organisms","docAbstract":"Agricultural practices pose threats to biotic diversity in freshwater systems with increasing use of glyphosate-based herbicides for weed control and animal waste for soil amendment becoming common in many regions. Over the past two decades, these particular agricultural trends have corresponded with marked declines in populations of fish and mussel species in the Upper Conasauga River watershed in Georgia/Tennessee, USA. To investigate the potential role of agriculture in the population declines, surface waters and sediments throughout the basin were tested for toxicity and analyzed for glyphosate, metals, nutrients, and steroid hormones. Assessments of chronic toxicity with Ceriodaphnia dubia and Hyalella azteca indicated that few water or sediment samples were harmful and metal concentrations were generally below impairment levels. Glyphosate was not observed in surface waters, although its primary degradation product, aminomethyl phosphonic acid (AMPA), was detected in 77% of the samples (mean = 509 μg/L, n = 99) and one or both compounds were measured in most sediment samples. Waterborne AMPA concentrations supported an inference that surfactants associated with glyphosate may be present at levels sufficient to affect early life stages of mussels. Nutrient enrichment of surface waters was widespread with nitrate (mean = 0.7 mg NO3-N/L, n = 179) and phosphorus (mean = 275 μg/L, n = 179) exceeding levels associated with eutrophication. Hormone concentrations in sediments were often above those shown to cause endocrine disruption in fish and appear to reflect the widespread application of poultry litter and manure. Observed species declines may be at least partially due to hormones, although excess nutrients and herbicide surfactants may also be implicated.
","language":"English","publisher":"Springer","doi":"10.1007/s10661-016-5691-7","usgsCitation":"Lasier, P.J., Urich, M.L., Hassan, S.M., Jacobs, W.N., Bringolf, R.B., and Owens, K.M., 2016, Changing agricultural practices: Potential consequences to aquatic organisms: Environmental Monitoring and Assessment, v. 188, p. 1-17, https://doi.org/10.1007/s10661-016-5691-7.","productDescription":"Article 672; 17 p.","startPage":"1","endPage":"17","ipdsId":"IP-070145","costCenters":[{"id":531,"text":"Patuxent Wildlife Research Center","active":true,"usgs":true},{"id":34983,"text":"Contaminant Biology Program","active":true,"usgs":true}],"links":[{"id":332084,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"volume":"188","publishingServiceCenter":{"id":10,"text":"Baltimore PSC"},"noUsgsAuthors":false,"publicationDate":"2016-11-15","publicationStatus":"PW","scienceBaseUri":"585116b8e4b08138bf1abd46","contributors":{"authors":[{"text":"Lasier, Peter J.","contributorId":6178,"corporation":false,"usgs":true,"family":"Lasier","given":"Peter","email":"","middleInitial":"J.","affiliations":[],"preferred":false,"id":655832,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Urich, Matthew L.","contributorId":127367,"corporation":false,"usgs":false,"family":"Urich","given":"Matthew","email":"","middleInitial":"L.","affiliations":[{"id":6918,"text":"Georgia","active":true,"usgs":false}],"preferred":false,"id":655833,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Hassan, Sayed M.","contributorId":90027,"corporation":false,"usgs":true,"family":"Hassan","given":"Sayed","email":"","middleInitial":"M.","affiliations":[],"preferred":false,"id":655834,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Jacobs, Whitney N.","contributorId":177444,"corporation":false,"usgs":false,"family":"Jacobs","given":"Whitney","email":"","middleInitial":"N.","affiliations":[],"preferred":false,"id":655835,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Bringolf, Robert B.","contributorId":139241,"corporation":false,"usgs":true,"family":"Bringolf","given":"Robert","email":"","middleInitial":"B.","affiliations":[],"preferred":false,"id":655836,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"Owens, Kathleen M.","contributorId":177445,"corporation":false,"usgs":false,"family":"Owens","given":"Kathleen","email":"","middleInitial":"M.","affiliations":[],"preferred":false,"id":655837,"contributorType":{"id":1,"text":"Authors"},"rank":6}]}} ,{"id":70179019,"text":"70179019 - 2016 - The estimated six-year mercury dry deposition across North America","interactions":[],"lastModifiedDate":"2017-05-11T15:18:47","indexId":"70179019","displayToPublicDate":"2016-12-13T00:00:00","publicationYear":"2016","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":1565,"text":"Environmental Science & Technology","onlineIssn":"1520-5851","printIssn":"0013-936X","active":true,"publicationSubtype":{"id":10}},"title":"The estimated six-year mercury dry deposition across North America","docAbstract":"Dry deposition of atmospheric mercury (Hg) to various land covers surrounding 24 sites in North America was estimated for the years 2009 to 2014. Depending on location, multiyear mean annual Hg dry deposition was estimated to range from 5.1 to 23.8 μg m–2 yr–1 to forested canopies, 2.6 to 20.8 μg m–2 yr–1 to nonforest vegetated canopies, 2.4 to 11.2 μg m–2 yr–1 to urban and built up land covers, and 1.0 to 3.2 μg m–2 yr–1 to water surfaces. In the rural or remote environment in North America, annual Hg dry deposition to vegetated surfaces is dominated by leaf uptake of gaseous elemental mercury (GEM), contrary to what was commonly assumed in earlier studies which frequently omitted GEM dry deposition as an important process. Dry deposition exceeded wet deposition by a large margin in all of the seasons except in the summer at the majority of the sites. GEM dry deposition over vegetated surfaces will not decrease at the same pace, and sometimes may even increase with decreasing anthropogenic emissions, suggesting that Hg emission reductions should be a long-term policy sustained by global cooperation.
","language":"English","publisher":"ACS Publications","doi":"10.1021/acs.est.6b04276","usgsCitation":"Zhang, L., Wu, Z., Cheng, I., Wright, L.P., Olson, M.L., Gay, D., Risch, M.R., Brooks, S., Castro, M.S., Conley, G.D., Edgerton, E.S., Holsen, T.M., Luke, W., Tordon, R., and Weiss-Penzias, P., 2016, The estimated six-year mercury dry deposition across North America: Environmental Science & Technology, v. 50, no. 23, p. 12864-12873, https://doi.org/10.1021/acs.est.6b04276.","productDescription":"10 p.","startPage":"12864","endPage":"12873","ipdsId":"IP-078907","costCenters":[{"id":27231,"text":"Indiana-Kentucky Water Science Center","active":true,"usgs":true}],"links":[{"id":470328,"rank":0,"type":{"id":41,"text":"Open Access External Repository Page"},"url":"https://repository.library.noaa.gov/view/noaa/66096","text":"External Repository"},{"id":332050,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"volume":"50","issue":"23","publishingServiceCenter":{"id":6,"text":"Columbus PSC"},"noUsgsAuthors":false,"publicationDate":"2016-11-22","publicationStatus":"PW","scienceBaseUri":"585116b9e4b08138bf1abd48","contributors":{"authors":[{"text":"Zhang, Leiming","contributorId":72516,"corporation":false,"usgs":true,"family":"Zhang","given":"Leiming","affiliations":[],"preferred":false,"id":655788,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Wu, Zhiyong","contributorId":177431,"corporation":false,"usgs":false,"family":"Wu","given":"Zhiyong","email":"","affiliations":[],"preferred":false,"id":655789,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Cheng, Irene","contributorId":177432,"corporation":false,"usgs":false,"family":"Cheng","given":"Irene","email":"","affiliations":[],"preferred":false,"id":655790,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Wright, L. Paige","contributorId":177433,"corporation":false,"usgs":false,"family":"Wright","given":"L.","email":"","middleInitial":"Paige","affiliations":[],"preferred":false,"id":655791,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Olson, Mark L.","contributorId":101693,"corporation":false,"usgs":true,"family":"Olson","given":"Mark","email":"","middleInitial":"L.","affiliations":[],"preferred":false,"id":655792,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"Gay, David A.","contributorId":68022,"corporation":false,"usgs":true,"family":"Gay","given":"David A.","affiliations":[],"preferred":false,"id":655793,"contributorType":{"id":1,"text":"Authors"},"rank":6},{"text":"Risch, Martin R. 0000-0002-7908-7887 mrrisch@usgs.gov","orcid":"https://orcid.org/0000-0002-7908-7887","contributorId":2118,"corporation":false,"usgs":true,"family":"Risch","given":"Martin","email":"mrrisch@usgs.gov","middleInitial":"R.","affiliations":[{"id":35860,"text":"Ohio-Kentucky-Indiana Water Science Center","active":true,"usgs":true},{"id":27231,"text":"Indiana-Kentucky Water Science Center","active":true,"usgs":true},{"id":346,"text":"Indiana Water Science Center","active":true,"usgs":true}],"preferred":true,"id":655794,"contributorType":{"id":1,"text":"Authors"},"rank":7},{"text":"Brooks, Steven","contributorId":177434,"corporation":false,"usgs":false,"family":"Brooks","given":"Steven","email":"","affiliations":[],"preferred":false,"id":655795,"contributorType":{"id":1,"text":"Authors"},"rank":8},{"text":"Castro, Mark S.","contributorId":172723,"corporation":false,"usgs":false,"family":"Castro","given":"Mark","email":"","middleInitial":"S.","affiliations":[],"preferred":false,"id":655796,"contributorType":{"id":1,"text":"Authors"},"rank":9},{"text":"Conley, Gary D.","contributorId":177435,"corporation":false,"usgs":false,"family":"Conley","given":"Gary","email":"","middleInitial":"D.","affiliations":[],"preferred":false,"id":655797,"contributorType":{"id":1,"text":"Authors"},"rank":10},{"text":"Edgerton, Eric S.","contributorId":177436,"corporation":false,"usgs":false,"family":"Edgerton","given":"Eric","email":"","middleInitial":"S.","affiliations":[],"preferred":false,"id":655798,"contributorType":{"id":1,"text":"Authors"},"rank":11},{"text":"Holsen, Thomas M.","contributorId":150058,"corporation":false,"usgs":false,"family":"Holsen","given":"Thomas","email":"","middleInitial":"M.","affiliations":[{"id":17897,"text":"Department of Civil and Environmental Engineering, Clarkson University, Potsdam, New York","active":true,"usgs":false}],"preferred":false,"id":655799,"contributorType":{"id":1,"text":"Authors"},"rank":12},{"text":"Luke, Winston","contributorId":177437,"corporation":false,"usgs":false,"family":"Luke","given":"Winston","email":"","affiliations":[],"preferred":false,"id":655800,"contributorType":{"id":1,"text":"Authors"},"rank":13},{"text":"Tordon, Robert","contributorId":177438,"corporation":false,"usgs":false,"family":"Tordon","given":"Robert","email":"","affiliations":[],"preferred":false,"id":655801,"contributorType":{"id":1,"text":"Authors"},"rank":14},{"text":"Weiss-Penzias, Peter","contributorId":177440,"corporation":false,"usgs":false,"family":"Weiss-Penzias","given":"Peter","affiliations":[],"preferred":false,"id":655802,"contributorType":{"id":1,"text":"Authors"},"rank":15}]}} ,{"id":70178248,"text":"ofr20161194 - 2016 - Collection, processing, and quality assurance of time-series electromagnetic-induction log datasets, 1995–2016, south Florida","interactions":[],"lastModifiedDate":"2016-12-13T16:19:43","indexId":"ofr20161194","displayToPublicDate":"2016-12-13T00:00:00","publicationYear":"2016","noYear":false,"publicationType":{"id":18,"text":"Report"},"publicationSubtype":{"id":5,"text":"USGS Numbered Series"},"seriesTitle":{"id":330,"text":"Open-File Report","code":"OFR","onlineIssn":"2331-1258","printIssn":"0196-1497","active":true,"publicationSubtype":{"id":5}},"seriesNumber":"2016-1194","title":"Collection, processing, and quality assurance of time-series electromagnetic-induction log datasets, 1995–2016, south Florida","docAbstract":"Time-series electromagnetic-induction log (TSEMIL) datasets are collected from polyvinyl-chloride cased or uncased monitoring wells to evaluate changes in water conductivity over time. TSEMIL datasets consist of a series of individual electromagnetic-induction logs, generally collected at a frequency of once per month or once per year that have been compiled into a dataset by eliminating small uniform offsets in bulk conductivity between logs probably caused by minor variations in calibration. These offsets are removed by selecting a depth at which no changes are apparent from year to year, and by adjusting individual logs to the median of all logs at the selected depth. Generally, the selected depths are within the freshwater saturated part of the aquifer, well below the water table. TSEMIL datasets can be used to monitor changes in water conductivity throughout the full thickness of an aquifer, without the need for long open-interval wells which have, in some instances, allowed vertical water flow within the well bore that has biased water conductivity profiles. The TSEMIL dataset compilation process enhances the ability to identify small differences between logs that were otherwise obscured by the offsets. As a result of TSEMIL dataset compilation, the root mean squared error of the linear regression between bulk conductivity of the electromagnetic-induction log measurements and the chloride concentration of water samples decreased from 17.4 to 1.7 millisiemens per meter in well G–3611 and from 3.7 to 2.2 millisiemens per meter in well G–3609. The primary use of the TSEMIL datasets in south Florida is to detect temporal changes in bulk conductivity associated with saltwater intrusion in the aquifer; however, other commonly observed changes include (1) variations in bulk conductivity near the water table where water saturation of pore spaces might vary and water temperature might be more variable, and (2) dissipation of conductive water in high-porosity rock layers, which might have entered these layers during drilling. Although TSEMIL dataset processing of even a few logs improves evaluations of the differences between the logs that are related to changes in the salinity, about 16 logs are needed to estimate the bulk conductivity within ±2 millisiemens per meter. Unlike many other types of data published by the U.S. Geological Survey, the median of TSEMIL datasets should not be considered final until 16 logs are collected and the median of the dataset is stable.
","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/ofr20161194","usgsCitation":"Prinos, S.T., and Valderrama, Robert, 2016, Collection, processing, and quality assurance of time-series electromagnetic-induction log datasets, 1995–2016, south Florida: U.S. Geological Survey Open-File Report 2016–1194, 24 p., https://doi.org/10.3133/ofr20161194.","productDescription":"Report: vii, 24 p.; Data Release","numberOfPages":"36","onlineOnly":"Y","ipdsId":"IP-069121","costCenters":[{"id":27821,"text":"Caribbean-Florida Water Science Center","active":true,"usgs":true}],"links":[{"id":438492,"rank":4,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/P92Y62KV","text":"USGS data release","linkHelpText":"Time Series Electromagnetic Induction-Log Datasets, Including Logs Collected through the 2018 Water Year in South Florida"},{"id":438491,"rank":4,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/F70R9NPF","text":"USGS data release","linkHelpText":"Time Series Electromagnetic Induction-Log Datasets, Including Logs Collected through the 2017 Water Year in South Florida"},{"id":438490,"rank":4,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/F7BG2MWD","text":"USGS data release","linkHelpText":"Time Series Electromagnetic Induction-Log Datasets, Including LogsCollected through the 2016 Water Year in South Florida"},{"id":332010,"rank":3,"type":{"id":30,"text":"Data Release"},"url":"https://dx.doi.org/10.5066/F78W3BF5","text":"USGS data release - Time Series Electromagnetic Induction Log datasets","description":"USGS Data Release"},{"id":331983,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/of/2016/1194/coverthb.jpg"},{"id":331984,"rank":2,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/of/2016/1194/ofr20161194.pdf","text":"Report","size":"7.64 MB","linkFileType":{"id":1,"text":"pdf"},"description":"OFR 2016–1194"}],"country":"United States","state":"Florida","county":"Broward County, Glades County, Hendry County, Martin County, Miami-Dade County, Palm Beach County","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -80.584716796875,\n 28.19792655722615\n ],\n [\n -80.2001953125,\n 27.430289738862594\n ],\n [\n -79.95849609375,\n 26.362342068998764\n ],\n [\n -80.04638671875,\n 25.97779895546436\n ],\n [\n -80.167236328125,\n 25.53252846853444\n ],\n [\n -80.474853515625,\n 25.11544539706194\n ],\n [\n -80.6396484375,\n 25.60190226111573\n ],\n [\n -80.96923828125,\n 26.49024045886963\n ],\n [\n -81.4306640625,\n 26.37218544169559\n ],\n [\n -81.661376953125,\n 27.059125784374068\n ],\n [\n -81.84814453125,\n 27.49852672279832\n ],\n [\n -80.584716796875,\n 28.19792655722615\n ]\n ]\n ]\n }\n }\n ]\n}","contact":"Director, Caribbean-Florida Water Science Center
U.S. Geological Survey
4446 Pet Lane, Suite 108
Lutz, FL 33559
Freshwater flow to the Ten Thousand Islands estuary has been altered by the construction of the Tamiami Trail and the Southern Golden Gate Estates. The Picayune Strand Restoration Project, which is associated with the Comprehensive Everglades Restoration Plan, has been implemented to improve freshwater delivery to the Ten Thousand Islands estuary by removing hundreds of miles of roads, emplacing hundreds of canal plugs, removing exotic vegetation, and constructing three pump stations. Quantifying the tributary flows and salinity patterns prior to, during, and after the restoration is essential to assessing the effectiveness of upstream restoration efforts.
Tributary flow and salinity patterns during preliminary restoration efforts and prior to the installation of pump stations were analyzed to provide baseline data and preliminary analysis of changes due to restoration efforts. The study assessed streamflow and salinity data for water years1 2007–2014 for the Faka Union River (canal flow included), East River, Little Wood River, Pumpkin River, and Blackwater River. Salinity data from the Palm River and Faka Union Boundary water-quality stations were also assessed.
Faka Union River was the dominant contributor of freshwater during water years 2007–14 to the Ten Thousand Islands estuary, followed by Little Wood and East Rivers. Pumpkin River and Blackwater River were the least substantial contributors of freshwater flow. The lowest annual flow volumes, the highest annual mean salinities, and the highest percentage of salinity values greater than 35 parts per thousand (ppt) occurred in water year 2011 at all sites with available data, corresponding with the lowest annual rainfall during the study. The highest annual flow volumes and the lowest percentage of salinities greater than 35 ppt occurred in water year 2013 for all sites with available data, corresponding with the highest rainfall during the study.
In water year 2014, the percentage of monitored annual flow contributed by East River increased and the percentage of flow contributed by Faka Union River decreased, compared to the earlier years. No changes in annual flow occurred at any sites west of Faka Union River. No changes in the relative flow contributions were observed during the wet season; however, the relative amounts of streamflow increased during the dry season at East River in 2014. East River had only 1 month of negative flow in 2014 compared to 6 months in 2011 and 7 months in 2008. Higher dry season flows in East River may be in response to restoration efforts. The sites to the west of Faka Union River had higher salinities on average than Faka Union River and East River. Faka Union River had the highest range in salinities, and Faka Union Boundary had the lowest range in salinities. Pumpkin River was the tributary with the lowest range in salinities.
1Water year is defined as the 12-month period from October 1, for any given year, through September 30 of the following year.
","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/sir20165158","collaboration":"Prepared in cooperation with the U.S. Army Corps of Engineers as part of the U.S. Geological Survey Greater Everglades Priority Ecosystem Science","usgsCitation":"Booth, A.C., and Soderqvist, L.E., 2016, Flow characteristics and salinity patterns of tidal rivers within the northern Ten Thousand Islands, southwest Florida, water years 2007–14: U.S. Geological Survey Scientific\nInvestigations Report 2016–5158, 22 p., https://doi.org/10.3133/sir20165158.","productDescription":"vi, 22 p.","numberOfPages":"32","onlineOnly":"Y","ipdsId":"IP-072364","costCenters":[{"id":269,"text":"FLWSC-Ft. Lauderdale","active":true,"usgs":true}],"links":[{"id":331582,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/sir/2016/5158/coverthb.jpg"},{"id":331583,"rank":2,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/sir/2016/5158/sir20165158.pdf","text":"Report","size":"4.51 MB","linkFileType":{"id":1,"text":"pdf"},"description":"SIR 2016–5158"}],"country":"United States","state":"Florida","otherGeospatial":"Ten Thousand Islands","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -81.73553466796875,\n 25.79370901679868\n ],\n [\n -81.73553466796875,\n 26.16776399795339\n ],\n [\n -81.34002685546875,\n 26.16776399795339\n ],\n [\n -81.34002685546875,\n 25.79370901679868\n ],\n [\n -81.73553466796875,\n 25.79370901679868\n ]\n ]\n ]\n }\n }\n ]\n}","contact":"Director, Caribbean-Florida Water Science Center
U.S. Geological Survey
4446 Pet Lane, Suite 108
Lutz, FL 33559
Because natural patterns of streamflow are a fundamental property of the health of streams, there is a critical need to quantify the degree to which human activities have modified natural streamflows. A requirement for assessing streamflow modification in a given stream is a reliable estimate of flows expected in the absence of human influences. Although there are many techniques to predict streamflows in specific river basins, there is a lack of approaches for making predictions of natural conditions across large regions and over many decades. In this study conducted by the U.S. Geological Survey, in cooperation with The Nature Conservancy and Trout Unlimited, the primary objective was to develop empirical models that predict natural (that is, unaffected by land use or water management) monthly streamflows from 1950 to 2012 for all stream segments in California. Models were developed using measured streamflow data from the existing network of streams where daily flow monitoring occurs, but where the drainage basins have minimal human influences. Widely available data on monthly weather conditions and the physical attributes of river basins were used as predictor variables. Performance of regional-scale models was comparable to that of published mechanistic models for specific river basins, indicating the models can be reliably used to estimate natural monthly flows in most California streams. A second objective was to develop a model that predicts the likelihood that streams experience modified hydrology. New models were developed to predict modified streamflows at 558 streamflow monitoring sites in California where human activities affect the hydrology, using basin-scale geospatial indicators of land use and water management. Performance of these models was less reliable than that for the natural-flow models, but results indicate the models could be used to provide a simple screening tool for identifying, across the State of California, which streams may be experiencing anthropogenic flow modification.
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\"}}]}","contact":"Chief, National Water-Quality Assessment Program
U.S. Geological Survey
413 National Center
12201 Sunrise Valley Drive
Reston, VA 20192