Explosive volcanic super-eruptions of several hundred cubic kilometres or more generate long run-out pyroclastic density currents the dynamics of which are poorly understood and controversial. Deposits of one such event in the southwestern USA, the 18.8 Ma Peach Spring Tuff, were formed by pyroclastic flows that travelled >170 km from the eruptive centre and entrained blocks up to ~70–90 cm diameter from the substrates along the flow paths. Here we combine these data with new experimental results to show that the flow’s base had high-particle concentration and relatively modest speeds of ~5–20 m s−1, fed by an eruption discharging magma at rates up to ~107–108 m3 s−1 for a minimum of 2.5–10 h. We conclude that sustained high-eruption discharge and long-lived high-pore pressure in dense granular dispersion can be more important than large initial velocity and turbulent transport with dilute suspension in promoting long pyroclastic flow distance.
","language":"English","publisher":"Nature Publishing Group","doi":"10.1038/ncomms10890","usgsCitation":"Roche, O., Buesch, D.C., and Valentine, G.A., 2016, Slow-moving and far-travelled dense pyroclastic flows during the Peach Spring super-eruption: Nature Communications, v. 7, p. 1-8, https://doi.org/10.1038/ncomms10890.","productDescription":"Article 10890; 8 p.","startPage":"1","endPage":"8","onlineOnly":"N","additionalOnlineFiles":"N","ipdsId":"IP-064658","costCenters":[{"id":312,"text":"Geology, Minerals, Energy, and Geophysics Science Center","active":true,"usgs":true}],"links":[{"id":471171,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.1038/ncomms10890","text":"Publisher Index Page"},{"id":318678,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","state":"Arizona, California, Nevada","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -117.301025390625,\n 34.15272698011818\n ],\n [\n -117.301025390625,\n 35.6126508187567\n ],\n [\n -113.0712890625,\n 35.6126508187567\n ],\n [\n -113.0712890625,\n 34.15272698011818\n ],\n [\n -117.301025390625,\n 34.15272698011818\n ]\n ]\n ]\n }\n }\n ]\n}","volume":"7","publishingServiceCenter":{"id":14,"text":"Menlo Park PSC"},"noUsgsAuthors":false,"publicationDate":"2016-03-07","publicationStatus":"PW","scienceBaseUri":"56dff7afe4b015c306fcd9fd","contributors":{"authors":[{"text":"Roche, Olivier","contributorId":167382,"corporation":false,"usgs":false,"family":"Roche","given":"Olivier","email":"","affiliations":[{"id":24702,"text":"Laboratoire Magmas et Volcans, Université Blaise Pascal-CNRS-IRD, OPGC, F-63038 6 Clermont-Ferrand, France","active":true,"usgs":false}],"preferred":false,"id":622108,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Buesch, David C. 0000-0002-4978-5027 dbuesch@usgs.gov","orcid":"https://orcid.org/0000-0002-4978-5027","contributorId":1154,"corporation":false,"usgs":true,"family":"Buesch","given":"David","email":"dbuesch@usgs.gov","middleInitial":"C.","affiliations":[{"id":309,"text":"Geology and Geophysics Science Center","active":true,"usgs":true},{"id":312,"text":"Geology, Minerals, Energy, and Geophysics Science Center","active":true,"usgs":true},{"id":234,"text":"Earthquake Hazards Program","active":true,"usgs":true}],"preferred":true,"id":622106,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Valentine, Greg A.","contributorId":167383,"corporation":false,"usgs":false,"family":"Valentine","given":"Greg","email":"","middleInitial":"A.","affiliations":[{"id":24703,"text":"Department of Geology and Center for Geohazards Studies, University at Buffalo, Buffalo, 9 NY 14260, USA","active":true,"usgs":false}],"preferred":false,"id":622109,"contributorType":{"id":1,"text":"Authors"},"rank":3}]}} ,{"id":70162634,"text":"sir20165009 - 2016 - Network global navigation satellite system surveys to harmonize American and Canadian datum for the Lake Champlain Basin","interactions":[],"lastModifiedDate":"2016-04-06T11:51:17","indexId":"sir20165009","displayToPublicDate":"2016-03-08T05:00:00","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":"2016-5009","title":"Network global navigation satellite system surveys to harmonize American and Canadian datum for the Lake Champlain Basin","docAbstract":"Historically high flood levels were observed during flooding in Lake Champlain and the Richelieu River from late April through May 2011. Flooding was caused by record spring precipitation and snowmelt from the third highest cumulative snowfall year on record, which included a warm, saturated late spring snowpack. Flood stage was exceeded for a total of 67 days from April 13 to June 19, 2011. During this flooding, shoreline erosion and lake flood inundation were exacerbated by wind-driven waves associated with local fetch and lake-wide seiche effects. In May 2011, a new water-surface-elevation record was set for Lake Champlain. Peak lake-level water-surface elevations varied at the three U.S. Geological Survey lake-level gages on Lake Champlain in 2011. The May 2011 peak water-surface elevations for Lake Champlain ranged from 103.20 feet above the National Geodetic Vertical Datum of 1929 at the northern end of Lake Champlain (at its outlet into the Richelieu River at Rouses Point, New York) to 103.57 feet above the National Geodetic Vertical Datum of 1929 at the southern end of the Lake in Whitehall, New York. The water-surface elevations for the Richelieu River in Canada are referenced to a different vertical datum than are those in Lake Champlain in the United States, which causes difficulty in assessing real-time flood water-surface elevations and comparing of flood peaks in the Lake Champlain Basin in the United States and Canada.
\nOn March 19, 2012, as a result of the flood event of April and May 2011, the Governments of Canada and the United States asked the International Joint Commission to draft a plan of study to examine the causes and the effects of the spring 2011 flooding on Lake Champlain and the Richelieu River and develop potential mitigation measures. Specific challenges noted by the International Lake Champlain-Richelieu River Technical Working Group (established by the International Joint Commission) included harmonization of vertical datums within the drainage basin. Harmonization of the vertical datum discrepancy is needed for flood assessment and future efforts to model the flow of water through the Lake Champlain Basin in the United States and Canada.
\nIn April 2015, the U.S. Geological Survey and Environment Canada began a joint field effort with the goal of obtaining precise elevations representing a common vertical datum for select reference marks used to determine water-surface elevations throughout Lake Champlain and the Richelieu River. To harmonize the datum difference between the United States and Canada, Global Navigation Satellite System surveys were completed at nine locations in the Lake Champlain Basin to collect simultaneous satellite data. These satellite data were processed to produce elevations for two reference marks associated with dams and seven reference marks associated with active water-level gages (lake gages in Lake Champlain and streamgages in the Richelieu River) to harmonize vertical datums throughout the Lake Champlain Basin. The Global Navigation Satellite System surveys were completed from April 14 to 16, 2015, at locations ranging from southern Lake Champlain near Whitehall, New York, to the northern end of the Richelieu River in Sorel, Quebec, at its confluence with the St. Lawrence River in Canada.
\nLake-gage water-surface elevations determined during the 3 days of surveys were converted to water-surface elevations referenced to the North American Vertical Datum of 1988 by using calculated offsets and historical water-surface elevations. In this report, an “offset” refers to the adjustment that needs to be applied to published data from a particular gage to produce elevation data referenced to the North American Vertical Datum of 1988. Offsets presented in this report can be used in the evaluation of water-surface elevations in a common datum for Lake Champlain and the Richelieu River. In addition, the water-level data referenced to the common datum (as determined from the offsets) may be used to calibrate flow models and support future modeling studies developed for Lake Champlain and the Richelieu River.
","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/sir20165009","collaboration":"Prepared in cooperation with the International Joint Commission","usgsCitation":"Flynn, R.H., Rydlund, P.H., Jr., and Martin, D.J., 2016, Network global navigation satellite system surveys to harmonize American and Canadian datums for the Lake Champlain Basin (ver. 1.1, April 2016): U.S. Geological Survey Scientific Investigations Report 2016–5009, 17 p., https://dx.doi.org/10.3133/sir20165009.","productDescription":"Report: vii, 17 p.; Appendixes: 1-4","onlineOnly":"Y","additionalOnlineFiles":"Y","ipdsId":"IP-069015","costCenters":[{"id":405,"text":"NH/VT office of New England Water Science Center","active":true,"usgs":true}],"links":[{"id":319779,"rank":7,"type":{"id":25,"text":"Version History"},"url":"https://pubs.usgs.gov/sir/2016/5009/versionHist.txt","size":"1 KB","linkFileType":{"id":2,"text":"txt"},"description":"SIR 2016-5009"},{"id":318519,"rank":2,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/sir/2016/5009/sir20165009.pdf","text":"Report","size":"3.07 MB","linkFileType":{"id":1,"text":"pdf"},"description":"SIR 2016-5009"},{"id":318520,"rank":3,"type":{"id":3,"text":"Appendix"},"url":"https://pubs.usgs.gov/sir/2016/5009/downloads/sir20165009_appendix1.zip","text":"Appendix 1","size":"13.1 MB","linkFileType":{"id":6,"text":"zip"},"description":"SIR 2016-5009","linkHelpText":"- Global navigation satellite system data collection information"},{"id":318518,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/sir/2016/5009/coverthb2.jpg"},{"id":318521,"rank":4,"type":{"id":3,"text":"Appendix"},"url":"https://pubs.usgs.gov/sir/2016/5009/downloads/sir20165009_appendix2.txt","text":"Appendix 2","size":"24 KB","linkFileType":{"id":2,"text":"txt"},"description":"SIR 2016-5009","linkHelpText":"- Final coordinates for harmonization of datums"},{"id":318522,"rank":5,"type":{"id":3,"text":"Appendix"},"url":"https://pubs.usgs.gov/sir/2016/5009/downloads/sir20165009_appendix3.zip","text":"Appendix 3","size":"445 KB","linkFileType":{"id":6,"text":"zip"},"description":"SIR 2016-5009","linkHelpText":"- Surveyor leveling information for sites with benchmarks that could not be surveyed directly with global navigation satellite systems"},{"id":318523,"rank":6,"type":{"id":3,"text":"Appendix"},"url":"https://pubs.usgs.gov/sir/2016/5009/downloads/sir20165009_appendix4.xlsx","text":"Appendix 4","size":"19 KB","linkFileType":{"id":3,"text":"xlsx"},"description":"SIR 2016-5009","linkHelpText":"- Elevation offset information for benchmarks surveyed with global navigation satellite systems"}],"country":"Canada, United States","state":"New York, Quebec, Vermont","otherGeospatial":"Lake Champlain Basin","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -74.278564453125,\n 43.37710501700073\n ],\n [\n -74.278564453125,\n 45.96642454131025\n ],\n [\n -72.432861328125,\n 45.96642454131025\n ],\n [\n -72.432861328125,\n 43.37710501700073\n ],\n [\n -74.278564453125,\n 43.37710501700073\n ]\n ]\n ]\n }\n }\n ]\n}","edition":"Version 1.0: Originally posted March 8, 2016; Version 1.1: April 1, 2016","contact":"Director, New England Water Science Center
U.S. Geological Survey
331 Commerce Way, Suite 2
Pembroke, NH 03275
Or visit our Web site at:
http://newengland.water.usgs.gov/
","publishingServiceCenter":{"id":11,"text":"Pembroke PSC"},"publishedDate":"2016-03-08","revisedDate":"2016-04-06","noUsgsAuthors":false,"publicationDate":"2016-03-08","publicationStatus":"PW","scienceBaseUri":"56dff7ade4b015c306fcd9f7","contributors":{"authors":[{"text":"Flynn, Robert H. rflynn@usgs.gov","contributorId":2137,"corporation":false,"usgs":true,"family":"Flynn","given":"Robert","email":"rflynn@usgs.gov","middleInitial":"H.","affiliations":[{"id":405,"text":"NH/VT office of New England Water Science Center","active":true,"usgs":true}],"preferred":true,"id":589992,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Rydlund, Paul H. Jr. 0000-0001-9461-9944 prydlund@usgs.gov","orcid":"https://orcid.org/0000-0001-9461-9944","contributorId":3840,"corporation":false,"usgs":true,"family":"Rydlund","given":"Paul","suffix":"Jr.","email":"prydlund@usgs.gov","middleInitial":"H.","affiliations":[{"id":502,"text":"Office of Surface Water","active":true,"usgs":true},{"id":396,"text":"Missouri Water Science Center","active":true,"usgs":true},{"id":36532,"text":"Central Midwest Water Science Center","active":true,"usgs":true}],"preferred":true,"id":589993,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Martin, Daniel J. dmartin@usgs.gov","contributorId":152244,"corporation":false,"usgs":true,"family":"Martin","given":"Daniel","email":"dmartin@usgs.gov","middleInitial":"J.","affiliations":[{"id":291,"text":"Fort Collins Science Center","active":true,"usgs":true}],"preferred":false,"id":589994,"contributorType":{"id":1,"text":"Authors"},"rank":3}]}} ,{"id":70164436,"text":"tm3A24 - 2016 - Identifying and preserving high-water mark data","interactions":[],"lastModifiedDate":"2018-10-16T11:52:13","indexId":"tm3A24","displayToPublicDate":"2016-03-08T00:00:00","publicationYear":"2016","noYear":false,"publicationType":{"id":18,"text":"Report"},"publicationSubtype":{"id":5,"text":"USGS Numbered Series"},"seriesTitle":{"id":335,"text":"Techniques and Methods","code":"TM","onlineIssn":"2328-7055","printIssn":"2328-7047","active":true,"publicationSubtype":{"id":5}},"seriesNumber":"3-A24","title":"Identifying and preserving high-water mark data","docAbstract":"
High-water marks provide valuable data for understanding recent and historical flood events. The proper collection and recording of high-water mark data from perishable and preserved evidence informs flood assessments, research, and water resource management. Given the high cost of flooding in developed areas, experienced hydrographers, using the best available techniques, can contribute high-quality data toward efforts such as public education of flood risk, flood inundation mapping, flood frequency computations, indirect streamflow measurement, and hazard assessments.
This manual presents guidance for skilled high-water mark identification, including marks left behind in natural and man-made environments by tranquil and rapid flowing water. This manual also presents pitfalls and challenges associated with various types of flood evidence that help hydrographers identify the best high-water marks and assess the uncertainty associated with a given mark. Proficient high-water mark data collection contributes to better understanding of the flooding process and reduces risk through greater ability to estimate flood probability.
The U.S. Geological Survey, operating the Nation’s premier water data collection network, encourages readers of this manual to familiarize themselves with the art and science of high-water mark collection. The U.S. Geological survey maintains a national database at http://water.usgs.gov/floods/FEV/ that includes high-water mark information for many flood events, and local U.S. Geological Survey Water Science Centers can provide information to interested readers about participation in data collection and flood documentation efforts as volunteers or observers.
","largerWorkType":{"id":18,"text":"Report"},"largerWorkTitle":"Section A: Surface-water techniques in Book 3: Applications of Hydraulics","largerWorkSubtype":{"id":5,"text":"USGS Numbered Series"},"language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/tm3A24","usgsCitation":"Koenig, T.A., Bruce, J.L., O’Connor, J.E., McGee, B.D., Holmes, R.R., Jr., Hollins, Ryan, Forbes, B.T., Kohn, M.S., Schellekens, M.F., Martin, Z.W., and Peppler, M.C., 2016, Identifying and preserving high-water mark data: U.S. Geological Survey Techniques and Methods, book 3, chap. A24, 47 p., https://dx.doi.org/10.3133/tm3A24.","productDescription":"viii, 47 p.","numberOfPages":"60","onlineOnly":"N","additionalOnlineFiles":"Y","ipdsId":"IP-071434","costCenters":[{"id":502,"text":"Office of Surface Water","active":true,"usgs":true}],"links":[{"id":358400,"rank":5,"type":{"id":22,"text":"Related Work"},"url":"https://www.youtube.com/watch?v=uZYRQLMcVOA","text":"Video","description":"YouTube Video","linkHelpText":"A USGS guide for finding and interpreting high-water marks"},{"id":318665,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/tm/03/a24/coverthb.jpg"},{"id":318666,"rank":2,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/tm/03/a24/tm3a24.pdf","text":"Report","size":"12.9 MB","linkFileType":{"id":1,"text":"pdf"},"description":"T&M 3–A24"},{"id":318667,"rank":3,"type":{"id":7,"text":"Companion Files"},"url":"https://pubs.usgs.gov/tm/03/a24/tm3a24_stn_high_water_mark_form.pdf","text":"High-Water Mark Form","size":"283 kB","linkFileType":{"id":1,"text":"pdf"},"description":"High-Water Mark Form"},{"id":346112,"rank":4,"type":{"id":22,"text":"Related Work"},"url":"https://pubs.usgs.gov/publication/ofr20171105","text":"OFR 2017–1105","description":"OFR 2017–1105"}],"publicComments":"This report is Chapter 24 of Section A: Surface-water techniques in Book 3: Applications of Hydraulics.","contact":"Chief, Office of Surface Water
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415 National Center
12201 Sunrise Valley Drive
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http://water.usgs.gov/osw/
Common raven (Corvus corax; hereafter, raven) population abundance in the sagebrush steppe of the American West has increased threefold during the previous four decades, largely as a result of unintended resource subsidies from human land-use practices. This is concerning because ravens frequently depredate nests of species of conservation concern, such as greater sage-grouse (Centrocercus urophasianus; hereafter, sage-grouse). Grazing by livestock in sagebrush ecosystems is common practice on most public lands, but associations between livestock and ravens are poorly understood. The primary objective of this study was to identify the effects of livestock on raven occurrence while accounting for landscape characteristics within human-altered sagebrush steppe habitat, particularly in areas occupied by breeding sage-grouse. Using data from southeastern Idaho collected during spring and summer across 3 yr, we modeled raven occurrence as a function of the presence of livestock while accounting for multiple landscape covariates, including land cover features, topographical features, and proximity to sage-grouse lek sites (breeding grounds), as well as site-level anthropogenic features. While accounting for landscape characteristics, we found that the odds of raven occurrence increased 45.8% in areas where livestock were present. In addition, ravens selected areas near sage-grouse leks, with the odds of occurrence decreasing 8.9% for every 1-km distance, increase away from the lek. We did not find an association between livestock use and distance to lek. We also found that ravens selected sites with relatively lower elevation containing increased amounts of cropland, wet meadow, and urbanization. Limiting raven access to key anthropogenic subsidies and spatially segregating livestock from sage-grouse breeding areas would likely reduce exposure of predatory ravens to sage-grouse nests and chicks.
","language":"English","publisher":"Ecological Society of America","doi":"10.1002/ecs2.1203","usgsCitation":"Coates, P.S., Brussee, B.E., Howe, K., Gustafson, K.B., Casazza, M.L., and Delehanty, D., 2016, Landscape characteristics and livestock presence influence common ravens: Relevance to greater sage-grouse conservation: Ecosphere, v. 7, no. 2, https://doi.org/10.1002/ecs2.1203.","productDescription":"e01203; 20 p.","startPage":"e01203","onlineOnly":"N","additionalOnlineFiles":"N","ipdsId":"IP-052300","costCenters":[{"id":651,"text":"Western Ecological Research Center","active":true,"usgs":true}],"links":[{"id":471173,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.1002/ecs2.1203","text":"Publisher Index Page"},{"id":318663,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","state":"Idaho","county":"Oneida County, Power County","city":"Holbrook","otherGeospatial":"Curlew National Grassland","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -112.71560668945311,\n 42.101788731521644\n ],\n [\n -112.71560668945311,\n 42.25495072629938\n ],\n [\n -112.58720397949219,\n 42.25495072629938\n ],\n [\n -112.58720397949219,\n 42.101788731521644\n ],\n [\n -112.71560668945311,\n 42.101788731521644\n ]\n ]\n ]\n }\n }\n ]\n}","volume":"7","issue":"2","publishingServiceCenter":{"id":1,"text":"Sacramento PSC"},"noUsgsAuthors":false,"publicationDate":"2016-02-26","publicationStatus":"PW","scienceBaseUri":"56dea628e4b015c306fb51d9","contributors":{"authors":[{"text":"Coates, Peter S. 0000-0003-2672-9994 pcoates@usgs.gov","orcid":"https://orcid.org/0000-0003-2672-9994","contributorId":3263,"corporation":false,"usgs":true,"family":"Coates","given":"Peter","email":"pcoates@usgs.gov","middleInitial":"S.","affiliations":[{"id":651,"text":"Western Ecological Research Center","active":true,"usgs":true}],"preferred":true,"id":622094,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Brussee, Brianne E. 0000-0002-2452-7101 bbrussee@usgs.gov","orcid":"https://orcid.org/0000-0002-2452-7101","contributorId":4249,"corporation":false,"usgs":true,"family":"Brussee","given":"Brianne","email":"bbrussee@usgs.gov","middleInitial":"E.","affiliations":[{"id":651,"text":"Western Ecological Research Center","active":true,"usgs":true}],"preferred":true,"id":622095,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Howe, Kristy khowe@usgs.gov","contributorId":167379,"corporation":false,"usgs":true,"family":"Howe","given":"Kristy","email":"khowe@usgs.gov","affiliations":[{"id":651,"text":"Western Ecological Research Center","active":true,"usgs":true}],"preferred":true,"id":622096,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Gustafson, K. Benjamin 0000-0003-3530-0372 kgustafson@usgs.gov","orcid":"https://orcid.org/0000-0003-3530-0372","contributorId":166818,"corporation":false,"usgs":true,"family":"Gustafson","given":"K.","email":"kgustafson@usgs.gov","middleInitial":"Benjamin","affiliations":[{"id":651,"text":"Western Ecological Research Center","active":true,"usgs":true}],"preferred":true,"id":622097,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Casazza, Michael L. 0000-0002-5636-735X mike_casazza@usgs.gov","orcid":"https://orcid.org/0000-0002-5636-735X","contributorId":2091,"corporation":false,"usgs":true,"family":"Casazza","given":"Michael","email":"mike_casazza@usgs.gov","middleInitial":"L.","affiliations":[{"id":651,"text":"Western Ecological Research Center","active":true,"usgs":true}],"preferred":true,"id":622098,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"Delehanty, David J.","contributorId":86683,"corporation":false,"usgs":true,"family":"Delehanty","given":"David J.","affiliations":[],"preferred":false,"id":622099,"contributorType":{"id":1,"text":"Authors"},"rank":6}]}} ,{"id":70170211,"text":"70170211 - 2016 - Supporting diverse data providers in the open water data initiative: Communicating water data quality and fitness of use","interactions":[],"lastModifiedDate":"2016-08-04T15:35:05","indexId":"70170211","displayToPublicDate":"2016-03-06T00:00:00","publicationYear":"2016","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":2529,"text":"Journal of the American Water Resources Association","active":true,"publicationSubtype":{"id":10}},"title":"Supporting diverse data providers in the open water data initiative: Communicating water data quality and fitness of use","docAbstract":"Shared, trusted, timely data are essential elements for the cooperation needed to optimize economic, ecologic, and public safety concerns related to water. The Open Water Data Initiative (OWDI) will provide a fully scalable platform that can support a wide variety of data from many diverse providers. Many of these will be larger, well-established, and trusted agencies with a history of providing well-documented, standardized, and archive-ready products. However, some potential partners may be smaller, distributed, and relatively unknown or untested as data providers. The data these partners will provide are valuable and can be used to fill in many data gaps, but can also be variable in quality or supplied in nonstandardized formats. They may also reflect the smaller partners' variable budgets and missions, be intermittent, or of unknown provenance. A challenge for the OWDI will be to convey the quality and the contextual “fitness” of data from providers other than the most trusted brands. This article reviews past and current methods for documenting data quality. Three case studies are provided that describe processes and pathways for effective data-sharing and publication initiatives. They also illustrate how partners may work together to find a metadata reporting threshold that encourages participation while maintaining high data integrity. And lastly, potential governance is proposed that may assist smaller partners with short- and long-term participation in the OWDI.
","language":"English","publisher":"Wiley","doi":"10.1111/1752-1688.12406","usgsCitation":"Larsen, S., Hamilton, S., Lucido, J., Garner, B.D., and Young, D., 2016, Supporting diverse data providers in the open water data initiative: Communicating water data quality and fitness of use: Journal of the American Water Resources Association, v. 52, no. 4, p. 859-872, https://doi.org/10.1111/1752-1688.12406.","productDescription":"14 p.","startPage":"859","endPage":"872","onlineOnly":"N","additionalOnlineFiles":"N","ipdsId":"IP-069137","costCenters":[{"id":128,"text":"Arizona Water Science Center","active":true,"usgs":true}],"links":[{"id":471179,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.1111/1752-1688.12406","text":"Publisher Index Page"},{"id":320019,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"volume":"52","issue":"4","publishingServiceCenter":{"id":6,"text":"Columbus PSC"},"noUsgsAuthors":false,"publicationDate":"2016-03-06","publicationStatus":"PW","scienceBaseUri":"570f6dbde4b0ef3b7ca356aa","contributors":{"authors":[{"text":"Larsen, Sara","contributorId":168563,"corporation":false,"usgs":false,"family":"Larsen","given":"Sara","email":"","affiliations":[{"id":25336,"text":"Western States Water Council","active":true,"usgs":false}],"preferred":false,"id":626478,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Hamilton, Stuart","contributorId":168564,"corporation":false,"usgs":false,"family":"Hamilton","given":"Stuart","affiliations":[{"id":25337,"text":"Aquatic Informatics","active":true,"usgs":false}],"preferred":false,"id":626479,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Lucido, Jessica M. jlucido@usgs.gov","contributorId":4695,"corporation":false,"usgs":true,"family":"Lucido","given":"Jessica M.","email":"jlucido@usgs.gov","affiliations":[{"id":160,"text":"Center for Integrated Data Analytics","active":false,"usgs":true}],"preferred":true,"id":626477,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Garner, Bradley D. 0000-0002-6912-5093 bdgarner@usgs.gov","orcid":"https://orcid.org/0000-0002-6912-5093","contributorId":2133,"corporation":false,"usgs":true,"family":"Garner","given":"Bradley","email":"bdgarner@usgs.gov","middleInitial":"D.","affiliations":[{"id":5054,"text":"Office of Water Information","active":true,"usgs":true}],"preferred":true,"id":626480,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Young, Dwane","contributorId":168541,"corporation":false,"usgs":false,"family":"Young","given":"Dwane","affiliations":[{"id":25326,"text":"U.S. Environmental Protection Agency, 1200 Pennsylvania Ave., NW, Washington, DC, USA 20460","active":true,"usgs":false}],"preferred":false,"id":626481,"contributorType":{"id":1,"text":"Authors"},"rank":5}]}} ,{"id":70177886,"text":"70177886 - 2016 - Uncertainty analysis of the Operational Simplified Surface Energy Balance (SSEBop) model at multiple flux tower sites","interactions":[],"lastModifiedDate":"2017-01-17T19:17:22","indexId":"70177886","displayToPublicDate":"2016-03-05T00:00:00","publicationYear":"2016","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":2342,"text":"Journal of Hydrology","active":true,"publicationSubtype":{"id":10}},"title":"Uncertainty analysis of the Operational Simplified Surface Energy Balance (SSEBop) model at multiple flux tower sites","docAbstract":"Evapotranspiration (ET) is an important component of the water cycle – ET from the land surface returns approximately 60% of the global precipitation back to the atmosphere. ET also plays an important role in energy transport among the biosphere, atmosphere, and hydrosphere. Current regional to global and daily to annual ET estimation relies mainly on surface energy balance (SEB) ET models or statistical and empirical methods driven by remote sensing data and various climatological databases. These models have uncertainties due to inevitable input errors, poorly defined parameters, and inadequate model structures. The eddy covariance measurements on water, energy, and carbon fluxes at the AmeriFlux tower sites provide an opportunity to assess the ET modeling uncertainties. In this study, we focused on uncertainty analysis of the Operational Simplified Surface Energy Balance (SSEBop) model for ET estimation at multiple AmeriFlux tower sites with diverse land cover characteristics and climatic conditions. The 8-day composite 1-km MODerate resolution Imaging Spectroradiometer (MODIS) land surface temperature (LST) was used as input land surface temperature for the SSEBop algorithms. The other input data were taken from the AmeriFlux database. Results of statistical analysis indicated that the SSEBop model performed well in estimating ET with an R2 of 0.86 between estimated ET and eddy covariance measurements at 42 AmeriFlux tower sites during 2001–2007. It was encouraging to see that the best performance was observed for croplands, where R2 was 0.92 with a root mean square error of 13 mm/month. The uncertainties or random errors from input variables and parameters of the SSEBop model led to monthly ET estimates with relative errors less than 20% across multiple flux tower sites distributed across different biomes. This uncertainty of the SSEBop model lies within the error range of other SEB models, suggesting systematic error or bias of the SSEBop model is within the normal range. This finding implies that the simplified parameterization of the SSEBop model did not significantly affect the accuracy of the ET estimate while increasing the ease of model setup for operational applications. The sensitivity analysis indicated that the SSEBop model is most sensitive to input variables, land surface temperature (LST) and reference ET (ETo); and parameters, differential temperature (dT), and maximum ET scalar (Kmax), particularly during the non-growing season and in dry areas. In summary, the uncertainty assessment verifies that the SSEBop model is a reliable and robust method for large-area ET estimation. The SSEBop model estimates can be further improved by reducing errors in two input variables (ETo and LST) and two key parameters (Kmax and dT).
","language":"English","publisher":"Elsevier","doi":"10.1016/j.jhydrol.2016.02.026","usgsCitation":"Chen, M., Senay, G.B., Singh, R.K., and Verdin, J.P., 2016, Uncertainty analysis of the Operational Simplified Surface Energy Balance (SSEBop) model at multiple flux tower sites: Journal of Hydrology, v. 536, p. 384-399, https://doi.org/10.1016/j.jhydrol.2016.02.026.","productDescription":"16 p.","startPage":"384","endPage":"399","onlineOnly":"N","additionalOnlineFiles":"N","ipdsId":"IP-071555","costCenters":[{"id":222,"text":"Earth Resources Observation and Science (EROS) Center","active":true,"usgs":true}],"links":[{"id":471180,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.1016/j.jhydrol.2016.02.026","text":"Publisher Index Page"},{"id":330417,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"volume":"536","publishingServiceCenter":{"id":4,"text":"Rolla PSC"},"noUsgsAuthors":false,"publicationStatus":"PW","scienceBaseUri":"5811c0f3e4b0f497e79a5a7b","chorus":{"doi":"10.1016/j.jhydrol.2016.02.026","url":"http://dx.doi.org/10.1016/j.jhydrol.2016.02.026","publisher":"Elsevier BV","authors":"Chen Mingshi, Senay Gabriel B., Singh Ramesh K., Verdin James P.","journalName":"Journal of Hydrology","publicationDate":"5/2016","auditedOn":"4/1/2016","publiclyAccessibleDate":"2/23/2016"},"contributors":{"authors":[{"text":"Chen, Mingshi mchen@usgs.gov","contributorId":4204,"corporation":false,"usgs":true,"family":"Chen","given":"Mingshi","email":"mchen@usgs.gov","affiliations":[{"id":223,"text":"Earth Resources Observation and Science (EROS) Center (Geography)","active":false,"usgs":true}],"preferred":true,"id":652025,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Senay, Gabriel B. 0000-0002-8810-8539 senay@usgs.gov","orcid":"https://orcid.org/0000-0002-8810-8539","contributorId":3114,"corporation":false,"usgs":true,"family":"Senay","given":"Gabriel","email":"senay@usgs.gov","middleInitial":"B.","affiliations":[{"id":223,"text":"Earth Resources Observation and Science (EROS) Center (Geography)","active":false,"usgs":true}],"preferred":true,"id":652236,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Singh, Ramesh K. 0000-0002-8164-3483 rsingh@usgs.gov","orcid":"https://orcid.org/0000-0002-8164-3483","contributorId":3895,"corporation":false,"usgs":true,"family":"Singh","given":"Ramesh","email":"rsingh@usgs.gov","middleInitial":"K.","affiliations":[{"id":223,"text":"Earth Resources Observation and Science (EROS) Center (Geography)","active":false,"usgs":true}],"preferred":true,"id":652026,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Verdin, James P. 0000-0003-0238-9657 verdin@usgs.gov","orcid":"https://orcid.org/0000-0003-0238-9657","contributorId":720,"corporation":false,"usgs":true,"family":"Verdin","given":"James","email":"verdin@usgs.gov","middleInitial":"P.","affiliations":[{"id":223,"text":"Earth Resources Observation and Science (EROS) Center (Geography)","active":false,"usgs":true}],"preferred":false,"id":652237,"contributorType":{"id":1,"text":"Authors"},"rank":4}]}} ,{"id":70168397,"text":"ds977 - 2016 - DOI/GTN-P Climate and active-layer data acquired in the National Petroleum Reserve–Alaska and the Arctic National Wildlife Refuge, 1998–2014","interactions":[{"subject":{"id":70135103,"text":"ds892 - 2014 - DOI/GTN-P climate and active-layer data acquired in the National Petroleum Reserve-Alaska and the Arctic National Wildlife Refuge","indexId":"ds892","publicationYear":"2014","noYear":false,"title":"DOI/GTN-P climate and active-layer data acquired in the National Petroleum Reserve-Alaska and the Arctic National Wildlife Refuge"},"predicate":"SUPERSEDED_BY","object":{"id":70168397,"text":"ds977 - 2016 - DOI/GTN-P Climate and active-layer data acquired in the National Petroleum Reserve–Alaska and the Arctic National Wildlife Refuge, 1998–2014","indexId":"ds977","publicationYear":"2016","noYear":false,"title":"DOI/GTN-P Climate and active-layer data acquired in the National Petroleum Reserve–Alaska and the Arctic National Wildlife Refuge, 1998–2014"},"id":1},{"subject":{"id":70168397,"text":"ds977 - 2016 - DOI/GTN-P Climate and active-layer data acquired in the National Petroleum Reserve–Alaska and the Arctic National Wildlife Refuge, 1998–2014","indexId":"ds977","publicationYear":"2016","noYear":false,"title":"DOI/GTN-P Climate and active-layer data acquired in the National Petroleum Reserve–Alaska and the Arctic National Wildlife Refuge, 1998–2014"},"predicate":"SUPERSEDED_BY","object":{"id":70176575,"text":"ds1021 - 2017 - DOI/GTN-P Climate and active-layer data acquired in the National Petroleum Reserve–Alaska and the Arctic National Wildlife Refuge, 1998–2015","indexId":"ds1021","publicationYear":"2017","noYear":false,"title":"DOI/GTN-P Climate and active-layer data acquired in the National Petroleum Reserve–Alaska and the Arctic National Wildlife Refuge, 1998–2015"},"id":2}],"supersededBy":{"id":70176575,"text":"ds1021 - 2017 - DOI/GTN-P Climate and active-layer data acquired in the National Petroleum Reserve–Alaska and the Arctic National Wildlife Refuge, 1998–2015","indexId":"ds1021","publicationYear":"2017","noYear":false,"title":"DOI/GTN-P Climate and active-layer data acquired in the National Petroleum Reserve–Alaska and the Arctic National Wildlife Refuge, 1998–2015"},"lastModifiedDate":"2017-02-09T12:51:41","indexId":"ds977","displayToPublicDate":"2016-03-04T10:00:00","publicationYear":"2016","noYear":false,"publicationType":{"id":18,"text":"Report"},"publicationSubtype":{"id":5,"text":"USGS Numbered Series"},"seriesTitle":{"id":310,"text":"Data Series","code":"DS","onlineIssn":"2327-638X","printIssn":"2327-0271","active":false,"publicationSubtype":{"id":5}},"seriesNumber":"977","title":"DOI/GTN-P Climate and active-layer data acquired in the National Petroleum Reserve–Alaska and the Arctic National Wildlife Refuge, 1998–2014","docAbstract":"This report provides data collected by the climate monitoring array of the U.S. Department of the Interior on Federal lands in Arctic Alaska over the period August 1998 to July 2014; this array is part of the Global Terrestrial Network for Permafrost (DOI/GTN-P). In addition to presenting data, this report also describes monitoring, data collection, and quality-control methods. The array of 16 monitoring stations spans lat 68.5°N. to 70.5°N. and long 142.5°W. to 161°W., an area of approximately 150,000 square kilometers. Climate summaries are presented along with quality-controlled data. Data collection is ongoing and includes the following climate- and permafrost-related variables: air temperature, wind speed and direction, ground temperature, soil moisture, snow depth, rainfall totals, up- and downwelling shortwave radiation, and atmospheric pressure. These data were collected by the U.S. Geological Survey in close collaboration with the Bureau of Land Management and the U.S. Fish and Wildlife Service.
","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/ds977","usgsCitation":"Urban, F.E., and Clow, G.D., 2016, DOI/GTN-P Climate and active-layer data acquired in the National Petroleum Reserve–Alaska and the Arctic National Wildlife Refuge, 1998–2014: U.S. Geological Survey Data Series 977, https://dx.doi.org/10.3133/ds977.","productDescription":"HTML Document; 17 chapters","onlineOnly":"Y","additionalOnlineFiles":"N","ipdsId":"IP-069148","costCenters":[{"id":318,"text":"Geosciences and Environmental Change Science Center","active":true,"usgs":true}],"links":[{"id":318058,"rank":19,"type":{"id":6,"text":"Chapter"},"url":"https://pubs.usgs.gov/ds/0977/Ikpikpuk/Ikpikpuk.html","text":"Ikpikpuk","description":"DS 977 Ikpikpuk"},{"id":318055,"rank":16,"type":{"id":6,"text":"Chapter"},"url":"https://pubs.usgs.gov/ds/0977/DrewPoint/DrewPoint.html","text":"Drew Point","description":"DS 977 Drew Point"},{"id":318045,"rank":7,"type":{"id":6,"text":"Chapter"},"url":"https://pubs.usgs.gov/ds/0977/Lake145/Lake145.html","text":"Lake 145","description":"DS 977 Lake 145"},{"id":318054,"rank":15,"type":{"id":6,"text":"Chapter"},"url":"https://pubs.usgs.gov/ds/0977/CamdenBay/CamdenBay.html","text":"Camden Bay","description":"DS 977 Camden Bay"},{"id":318047,"rank":9,"type":{"id":6,"text":"Chapter"},"url":"https://pubs.usgs.gov/ds/0977/Niguanak/Niguanak.html","text":"Niguanak","description":"DS 977 Niguanak"},{"id":318051,"rank":13,"type":{"id":6,"text":"Chapter"},"url":"https://pubs.usgs.gov/ds/0977/Awuna1/Awuna1.html","text":" Awuna1","description":"DS 977 Awuna1"},{"id":318056,"rank":17,"type":{"id":6,"text":"Chapter"},"url":"https://pubs.usgs.gov/ds/0977/EastTeshekpuk/EastTeshekpuk.html","text":"East Teshekpuk","description":"DS 977 East Teshekpuk"},{"id":318011,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/ds/0977/images/coverthb.jpg"},{"id":318042,"rank":5,"type":{"id":6,"text":"Chapter"},"url":"https://pubs.usgs.gov/ds/0977/Inigok/Inigok.html","text":"Inigok","description":"DS 977 Inigok"},{"id":318046,"rank":8,"type":{"id":6,"text":"Chapter"},"url":"https://pubs.usgs.gov/ds/0977/MarshCreek/MarshCreek.html","text":"Marsh Creek","description":"DS 977 Marsh Creek"},{"id":318048,"rank":10,"type":{"id":6,"text":"Chapter"},"url":"https://pubs.usgs.gov/ds/0977/Piksiksak/Piksiksak.html","text":"Piksiksak","description":"DS 977 Piksiksak"},{"id":318049,"rank":11,"type":{"id":6,"text":"Chapter"},"url":"https://pubs.usgs.gov/ds/0977/RedSheepCreek/RedSheepCreek.html","text":"Red Sheep Creek","description":"DS 977 Red Sheep Creek"},{"id":318043,"rank":6,"type":{"id":6,"text":"Chapter"},"url":"https://pubs.usgs.gov/ds/0977/Koluktak/Koluktak.html","text":"Koluktak","description":"DS 977 Koluktak"},{"id":318053,"rank":14,"type":{"id":6,"text":"Chapter"},"url":"https://pubs.usgs.gov/ds/0977/Awuna2/Awuna2.html","text":" Awuna2","description":"DS 977 Awuna2"},{"id":318057,"rank":18,"type":{"id":6,"text":"Chapter"},"url":"https://pubs.usgs.gov/ds/0977/FishCreek/FishCreek.html","text":"Fish Creek","description":"DS 977 Fish Creek"},{"id":318050,"rank":12,"type":{"id":6,"text":"Chapter"},"url":"https://pubs.usgs.gov/ds/0977/SouthMeade/SouthMeade.html","text":"South Meade","description":"DS 977 South Meade"},{"id":318012,"rank":2,"type":{"id":6,"text":"Chapter"},"url":"https://pubs.usgs.gov/ds/0977/introduction.html","text":"Introduction","description":"DS 977 Introduction"},{"id":318013,"rank":3,"type":{"id":6,"text":"Chapter"},"url":"https://pubs.usgs.gov/ds/0977/Tunalik/Tunalik.html","text":"Tunalik","description":"DS 977 Tunalik"},{"id":318014,"rank":4,"type":{"id":6,"text":"Chapter"},"url":"https://pubs.usgs.gov/ds/0977/Umiat/Umiat.html","text":"Umiat","description":"DS 977 Umiat"}],"country":"Canada, United States","state":"Alaska","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -163.037109375,\n 66.08936427047088\n ],\n [\n -163.037109375,\n 71.66366293141732\n ],\n [\n -140.712890625,\n 71.66366293141732\n ],\n [\n -140.712890625,\n 66.08936427047088\n ],\n [\n -163.037109375,\n 66.08936427047088\n ]\n ]\n ]\n }\n }\n ]\n}","contact":"Director, Geosciences and Environmental Change Science Center
U.S. Geological Survey
Box 25046, Mail Stop 980
Denver, CO 80225
The Upper Black Squirrel Creek Basin is located about 25 kilometers east of Colorado Springs, Colorado. The primary aquifer is a productive section of unconsolidated deposits that overlies bedrock units of the Denver Basin and is a critical resource for local water needs, including irrigation, domestic, and commercial use. The primary aquifer also serves an important regional role by the export of water to nearby communities in the Colorado Springs area. Changes in land use and development over the last decade, which includes substantial growth of subdivisions in the Upper Black Squirrel Creek Basin, have led to uncertainty regarding the potential effects to water quality throughout the basin. In response, the U.S. Geological Survey, in cooperation with Cherokee Metropolitan District, El Paso County, Meridian Service Metropolitan District, Mountain View Electric Association, Upper Black Squirrel Creek Groundwater Management District, Woodmen Hills Metropolitan District, Colorado State Land Board, and Colorado Water Conservation Board, and the stakeholders represented in the Groundwater Quality Study Committee of El Paso County conducted an assessment of groundwater quality and groundwater age with an emphasis on characterizing nitrate in the groundwater.
\nGroundwater-quality samples were collected from 50 randomly selected wells between May and June 2013. The samples were analyzed for major ions, nutrients, dissolved gases, tritium (3H), chlorofluorocarbons (CFC-11, CFC-12, and CFC-113), and fuel products (such as benzene, toluene, ethylbenzene, and xylenes). None of the groundwater samples exceeded the U.S. Environmental Protection Agency (EPA) National Primary Drinking Water Regulations for primary maximum contaminant levels (MCL) for major ions. Secondary maximum contaminant levels, which are not health concerns and affect mainly taste, color, or odor of the water, were observed in rare instances for pH (2 samples), chloride (1 sample), iron (3 samples), and manganese (8 samples). The secondary maximum contaminant level for total dissolved solids was also exceeded for two samples.
\nNitrate (nitrite plus nitrate as nitrogen in groundwater) was elevated above the estimated background concentration of natural recharge waters of 1 milligram per liter (mg/L) in 44 of the 50 wells sampled and showed a median concentration of 5.4 mg/L. Nitrate concentrations were above the MCL of 10 mg/L in 5 of the 50 wells sampled and above half of the EPA MCL (5 mg/L) in 27 of the 50 wells sampled, which included samples above the MCL. Dissolved-oxygen concentrations exceeded 0.5 mg/L in 95 percent of reported values (40 of 42 samples) and exceeded 2.0 mg/L in 90 percent of reported values (38 of 42 samples). The oxidized conditions observed in most areas indicate that nitrate from fertilizers and animal or human waste was geochemically stable and could persist in the groundwater for decades or perhaps longer. A historical analysis of median nitrate concentrations over nearly three decades showed an increase in nitrate of approximately 1 mg/L from 4.3 to 5.4 mg/L, although the increase was not determined to be significantly different using nonparametric statistical methods.
\nMajor-ion data indicate that groundwater representative of the primary aquifer was classified as calcium-sodium bicarbonate type water. Other water samples from wells located mainly along the periphery of the primary aquifer had cation-anion compositions consistent with distinct water sources, including groundwater contributions from the underlying bedrock aquifers. The areas with differentiable water sources were located mainly where alluvial deposits were thin and geologic contacts to the underlying bedrock aquifers were relatively shallow.
\nNitrate concentrations in the groundwater were evaluated for relations to land use. An agricultural region was defined using a sequence of land satellite imagery. Groundwater flow directions interpreted from median water-table elevations measured from 2000 to 2013 were used in conjunction with cropland locations to define the agricultural region boundaries by encompassing potential pathways of nitrate transport in the groundwater from nitrogen-based fertilizers. A statistically significant higher median nitrate concentration was observed for areas inside the agricultural region (6.7 mg/L) compared to areas outside the agricultural region (2.3 mg/L), although median concentrations in both areas were below the MCL (10 mg/L). Median nitrate concentration was also significantly greater in land parcels with septic use (4.9 mg/L) compared to nonseptic parcels (1.7 mg/L). In general, agriculture or septic use was identified as the primary source of nitrate, depending on location, while commercial, county, grazing, and residential land uses were generally secondary sources of nitrate.
\nApparent groundwater ages were estimated from chlorofluorocarbons (CFC-11, CFC-12, and CFC-113) and tritium (3H) data using models that assumed piston flow and binary mixing (dilution of a young component with old, tracer-free water). The mean and median groundwater ages were about 30 years and the standard deviation was 6 years, indicating that most groundwater in the primary aquifer was “young” water that had recharged to the aquifer over the last few decades (post-1950s). The median fraction of young water was about 71 percent, and the standard deviation was 29 percent. The remaining water predated the 1950s, which may have originated from deeper geologic formations or may represent slow moving groundwater within the primary aquifer. Some of the oldest groundwater ages (older than 30 years) were observed in the upper reaches of the aquifer to the northwest where the primary aquifer is thin and intersects bedrock, supporting the hypothesis of geochemically distinct groundwater entering the primary aquifer from below. Groundwater that had reached the central part of the aquifer from upgradient areas of the basin was variable in age because of differences in flow paths and travel velocities. The groundwater age analysis showed that current (2013) land-use practices could affect water quality over decades to come, and that responses to remedial actions could be slow, especially for constituents, such as nitrate, that are stable under oxidized conditions.
\nFuel products (including acetone, benzene, diisopropyl ether, ethylbenzene, methyl acetate, methyl tertiary butyl ether (MTBE), methyl tert-pentyl ether, m- + p-xylene, o-xylene, tert-amyl alcohol, tert-butyl alcohol, tert-butyl ethyl ether, and toluene) were analyzed in groundwater from 49 of the 50 wells. Water from seven sites had detections for fuel compounds; all concentrations were below MCL. The results provided assurance of water quality and a valuable baseline to evaluate future trends of fuel constituents as the region is further developed.
\nProbability maps were developed from logistic regression models to examine the likelihood that nitrate concentrations in groundwater exceeded specified levels. Susceptibility analysis examined relations between mid-level (5.0 mg/L) nitrate concentrations and climatic, hydrologic, and geologic variables; the significant variables were identified as depth to groundwater, soil organic matter, and soil water storage to 25-centimeter (cm) depth. The vulnerability assessments included natural factors driving susceptibility but also human factors related to land use and septic use. Vulnerability to low-level (2.5 mg/L) nitrate was related to depth to groundwater, septic zoning, and soil organic matter. The results highlighted that septic zoning affected low-level nitrate concentrations. Vulnerability to mid-level (5.0 mg/L) nitrate was examined using all 50 samples and also with two data outliers removed, which showed relatively high nitrate concentrations but also anomalous water chemistry or were located beyond the primary study area. Vulnerability to mid-level (5.0 mg/L) nitrate using all 50 samples was related to depth to groundwater, land use, septic use within a 500-meter (m) radius, soil water storage to a 25-cm depth, soil organic matter, and whether a location was within the agricultural region. The mid-level (5.0 mg/L) vulnerability model using 48 samples (two outliers removed) produced the best overall fit and was related to the same variables as when using all samples except septic use. The results for mid-level vulnerability provided additional support that septic use was associated with low levels of nitrate in the groundwater. Soil properties and land use were identified as the main drivers of moderate nitrate concentrations. Probabilities of exceeding low-level nitrate concentrations were high in most areas with the lowest probabilities usually to the northwest along thin geologic deposits in the upper part of the basin.
\nThe results of this investigation offer the foundational information needed for developing best management practices to mitigate nitrate contamination, basic concepts on water quality to aid public education, and information to guide regulatory measures if policy makers determine this is warranted. Science-based decision making will require continued monitoring and analysis of water quality in the future.
","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/sir20165020","collaboration":"Prepared in cooperation with Cherokee Metropolitan District, El Paso County, Meridian Service Metropolitan District, Mountain View Electric Association, Upper Black Squirrel Creek Groundwater Management District, Woodmen Hills Metropolitan District, Colorado State Land Board, Colorado Water Conservation Board, and the stakeholders represented in the Groundwater Quality Study Committee of El Paso County","usgsCitation":"Wellman, T.P., and Rupert, M.G., 2016, Groundwater quality, age, and susceptibility and vulnerability to nitrate contamination with linkages to land use and groundwater flow, Upper Black Squirrel Creek Basin, Colorado, 2013: U.S. Geological Survey Scientific Investigations Report, 2016–5020, 78 p., https://dx.doi.org/10.3133/sir20165020.","productDescription":"viii, 77 p.","onlineOnly":"Y","additionalOnlineFiles":"N","ipdsId":"IP-068864","costCenters":[{"id":191,"text":"Colorado Water Science Center","active":true,"usgs":true}],"links":[{"id":318534,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/sir/2016/5020/coverthb.jpg"},{"id":318535,"rank":2,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/sir/2016/5020/sir20165020.pdf","text":"Report","size":"63.9 MB","linkFileType":{"id":1,"text":"pdf"},"description":"SIR 2016-5020"}],"country":"United States","state":"Colorado","county":"El Paso","otherGeospatial":"Black Squirrel Management District","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -104.67361450195312,\n 38.91668153637508\n ],\n [\n -104.7271728515625,\n 38.971154274048345\n ],\n [\n -104.74090576171875,\n 39.02345139405932\n ],\n [\n -104.75875854492188,\n 39.07784203269269\n ],\n [\n -104.77111816406249,\n 39.12579898118164\n ],\n [\n -104.79034423828125,\n 39.172658670429946\n ],\n [\n -104.73953247070311,\n 39.20459056764483\n ],\n [\n -104.69284057617186,\n 39.218423217141485\n ],\n [\n -104.61868286132812,\n 39.23757161784572\n ],\n [\n -104.53628540039062,\n 39.23863526469436\n ],\n [\n -104.49508666992188,\n 39.198205348894795\n ],\n [\n -104.42642211914062,\n 39.196076813671695\n ],\n [\n -104.34951782226561,\n 39.17052936145295\n ],\n [\n -104.32479858398438,\n 39.135386455771076\n ],\n [\n -104.32342529296875,\n 39.08423817730926\n ],\n [\n -104.32754516601562,\n 39.05651736286005\n ],\n [\n -104.27261352539062,\n 39.04158626095001\n ],\n [\n -104.24102783203124,\n 39.01811672409787\n ],\n [\n -104.23278808593749,\n 38.95940879245423\n ],\n [\n -104.22866821289062,\n 38.9177500315307\n ],\n [\n -104.26437377929686,\n 38.882481197550774\n ],\n [\n -104.33441162109374,\n 38.87179021382536\n ],\n [\n -104.40170288085938,\n 38.87499767781738\n ],\n [\n -104.403076171875,\n 38.72623322072787\n ],\n [\n -104.42092895507812,\n 38.6833657775237\n ],\n [\n -104.45663452148438,\n 38.65227081329621\n ],\n [\n -104.51431274414062,\n 38.65334327823747\n ],\n [\n -104.52804565429688,\n 38.67907761983558\n ],\n [\n -104.53216552734375,\n 38.71873327340352\n ],\n [\n -104.56787109374999,\n 38.749799358878526\n ],\n [\n -104.59945678710936,\n 38.805470223177466\n ],\n [\n -104.62554931640625,\n 38.841846903808985\n ],\n [\n -104.67636108398438,\n 38.922023851268925\n ],\n [\n -104.67361450195312,\n 38.91668153637508\n ]\n ]\n ]\n }\n }\n ]\n}","contact":"Director, USGS Colorado Water Science Center
Box 25046, Mail Stop 415
Denver, CO 80225
The Sauvie Island 7.5' quadrangle is situated in the Puget-Willamette Lowland northwest of downtown Portland, Oreg. This lowland, which extends from Puget Sound to west-central Oregon, is a complex structural and topographic trough between the Coast Range and the Cascade Range. Since late Eocene time, the Cascade Range has been the locus of a discontinuously active volcanic arc associated with underthrusting of oceanic lithosphere beneath the North American continent along the Cascadia Subduction Zone. The Coast Range, which occupies the fore-arc position within the Cascadia arc-trench system, consists of a complex assemblage of Eocene to Miocene volcanic and marine sedimentary rocks.
\nThe Sauvie Island quadrangle lies along the southwest margin of the Portland Basin, a 2,000-km2 topographic and structural depression. The basin boundary is an abrupt topographic break at the base of the Tualatin Mountains, which separates the Portland and Tualatin Basins. The Tualatin Mountains are underlain by lava flows of the Miocene Columbia River Basalt Group that have been folded into an asymmetric anticline. Oligocene marine sedimentary rocks, not exposed at the surface, are inferred to underlie the basalt flows. The abrupt basin boundary marks the location of the northwest-striking Portland Hills Fault Zone, which is probably an active structure.
\nThe Columbia River flows west and north through the Portland Basin at nearly sea level. The Willamette River enters the Columbia near the southeast corner of the map area. Seismic-reflection profiles and lithologic logs of water wells show as much as 550 m of late Miocene and younger sediments in the deepest part of the basin east of the quadrangle. Deposits exposed at the surface consist chiefly of Holocene and late Pleistocene fluvial and eolian sediments and man-made fill.
\nThis map contributes to a U.S. Geological Survey program to improve the geologic database for the Portland region of the Pacific Northwest urban corridor. The map and ancillary data will support assessments of seismic risk, ground-failure hazards, and resource availability.
","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/sim3349","usgsCitation":"Evarts, R.C., O'Connor, J.E., and Cannon, C.M., 2016, Geologic map of the Sauvie Island quadrangle, Multnomah and Columbia Counties, Oregon, and Clark County, Washington: U.S. Geological Survey Scientific Investigations Map 3349, scale 1:24,000, pamphlet 34 p., https://dx.doi.org/10.3133/sim3349.","productDescription":"Pamphlet: iv, 34 p.; 1 Plate: 40.00 x 34.00 inches; Database; Metadata; Read Me; Shape Files","onlineOnly":"Y","additionalOnlineFiles":"Y","ipdsId":"IP-049408","costCenters":[{"id":312,"text":"Geology, Minerals, Energy, and Geophysics Science Center","active":true,"usgs":true},{"id":518,"text":"Oregon Water Science Center","active":true,"usgs":true}],"links":[{"id":318448,"rank":6,"type":{"id":9,"text":"Database"},"url":"https://pubs.usgs.gov/sim/3349/sim3349_db.zip","size":"4.8 MB","linkFileType":{"id":6,"text":"zip"}},{"id":318443,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/sim/3349/coverthb.jpg"},{"id":318449,"rank":7,"type":{"id":16,"text":"Metadata"},"url":"https://pubs.usgs.gov/sim/3349/metadata/"},{"id":318447,"rank":5,"type":{"id":23,"text":"Spatial Data"},"url":"https://pubs.usgs.gov/sim/3349/sim3349_shp.zip","text":"Shape Files","size":"3.3 MB","linkFileType":{"id":6,"text":"zip"}},{"id":318445,"rank":3,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/sim/3349/sim3349_pamphlet.pdf","text":"Pamphlet","size":"1.4 MB","linkFileType":{"id":1,"text":"pdf"},"description":"SIM 3349 Pamphlet PDF"},{"id":318446,"rank":4,"type":{"id":20,"text":"Read Me"},"url":"https://pubs.usgs.gov/sim/3349/sim3349_readme.pdf","size":"177 KB","linkFileType":{"id":2,"text":"txt"}},{"id":318444,"rank":2,"type":{"id":26,"text":"Sheet"},"url":"https://pubs.usgs.gov/sim/3349/sim3349_sheet1.pdf","text":"Sheet 1","size":"74 MB","description":"SIM 3349 Map PDF"},{"id":399013,"rank":8,"type":{"id":36,"text":"NGMDB Index Page"},"url":"https://ngmdb.usgs.gov/Prodesc/proddesc_104040.htm"}],"scale":"24000","country":"United States","state":"Oregon, Washington","county":"Clark County, Columbia County, Multnomah County","otherGeospatial":"Sauvie Island","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -122.875,\n 45.625\n ],\n [\n -122.875,\n 45.75\n ],\n [\n -122.75,\n 45.75\n ],\n [\n -122.75,\n 45.625\n ],\n [\n -122.875,\n 45.625\n ]\n ]\n ]\n }\n }\n ]\n}","contact":"GMEG staff, Geology, Minerals, Energy, & Geophysics Science Center
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U.S. Geological Survey
345 Middlefield Road
Menlo Park, CA 94025-3591
http://geomaps.wr.usgs.gov/gmeg/
Water temperature is a primary driver of stream ecosystems and commonly forms the basis of stream classifications. Robust models of stream temperature are critical as the climate changes, but estimating daily stream temperature poses several important challenges. We developed a statistical model that accounts for many challenges that can make stream temperature estimation difficult. Our model identifies the yearly period when air and water temperature are synchronized, accommodates hysteresis, incorporates time lags, deals with missing data and autocorrelation and can include external drivers. In a small stream network, the model performed well (RMSE = 0.59°C), identified a clear warming trend (0.63 °C decade−1) and a widening of the synchronized period (29 d decade−1). We also carefully evaluated how missing data influenced predictions. Missing data within a year had a small effect on performance (∼0.05% average drop in RMSE with 10% fewer days with data). Missing all data for a year decreased performance (∼0.6 °C jump in RMSE), but this decrease was moderated when data were available from other streams in the network.
","language":"English","publisher":"PeerJ","doi":"10.7717/peerj.1727","usgsCitation":"Letcher, B., Hocking, D., O'Neil, K., Whiteley, A.R., Nislow, K., and O’Donnell, M., 2016, A hierarchical model of daily stream temperature using air-water temperature synchronization, autocorrelation, and time lags: PeerJ, v. 4, e1727: 26 p., https://doi.org/10.7717/peerj.1727.","productDescription":"e1727: 26 p.","onlineOnly":"N","additionalOnlineFiles":"N","ipdsId":"IP-072906","costCenters":[{"id":365,"text":"Leetown Science Center","active":true,"usgs":true}],"links":[{"id":471182,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.7717/peerj.1727","text":"Publisher Index Page"},{"id":318531,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"volume":"4","publishingServiceCenter":{"id":10,"text":"Baltimore PSC"},"noUsgsAuthors":false,"publicationDate":"2016-02-29","publicationStatus":"PW","scienceBaseUri":"56d96027e4b015c306f726ad","contributors":{"authors":[{"text":"Letcher, Benjamin H. 0000-0003-0191-5678 bletcher@usgs.gov","orcid":"https://orcid.org/0000-0003-0191-5678","contributorId":167313,"corporation":false,"usgs":true,"family":"Letcher","given":"Benjamin H.","email":"bletcher@usgs.gov","affiliations":[{"id":365,"text":"Leetown Science Center","active":true,"usgs":true}],"preferred":false,"id":621791,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Hocking, Daniel 0000-0003-1889-9184 dhocking@usgs.gov","orcid":"https://orcid.org/0000-0003-1889-9184","contributorId":149618,"corporation":false,"usgs":true,"family":"Hocking","given":"Daniel","email":"dhocking@usgs.gov","affiliations":[{"id":365,"text":"Leetown Science Center","active":true,"usgs":true}],"preferred":true,"id":621792,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"O'Neil, Kyle","contributorId":82491,"corporation":false,"usgs":true,"family":"O'Neil","given":"Kyle","affiliations":[],"preferred":false,"id":621793,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Whiteley, Andrew R.","contributorId":150155,"corporation":false,"usgs":false,"family":"Whiteley","given":"Andrew","email":"","middleInitial":"R.","affiliations":[{"id":6932,"text":"University of Massachusetts, Amherst","active":true,"usgs":false}],"preferred":false,"id":621794,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Nislow, Keith H.","contributorId":60106,"corporation":false,"usgs":true,"family":"Nislow","given":"Keith H.","affiliations":[],"preferred":false,"id":621795,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"O’Donnell, Matthew 0000-0002-9089-2377 mjodonnell@usgs.gov","orcid":"https://orcid.org/0000-0002-9089-2377","contributorId":167315,"corporation":false,"usgs":true,"family":"O’Donnell","given":"Matthew","email":"mjodonnell@usgs.gov","affiliations":[{"id":365,"text":"Leetown Science Center","active":true,"usgs":true}],"preferred":true,"id":621796,"contributorType":{"id":1,"text":"Authors"},"rank":6}]}} ,{"id":70160400,"text":"ofr20151239 - 2016 - Play-level distributions of estimates of recovery factors for a miscible carbon dioxide enhanced oil recovery method used in oil reservoirs in the conterminous United States","interactions":[],"lastModifiedDate":"2017-08-29T09:39:51","indexId":"ofr20151239","displayToPublicDate":"2016-03-02T15:30:00","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":"2015-1239","title":"Play-level distributions of estimates of recovery factors for a miscible carbon dioxide enhanced oil recovery method used in oil reservoirs in the conterminous United States","docAbstract":"In a U.S. Geological Survey (USGS) study, recovery-factor estimates were calculated by using a publicly available reservoir simulator (CO2 Prophet) to estimate how much oil might be recovered with the application of a miscible carbon dioxide (CO2) enhanced oil recovery (EOR) method to technically screened oil reservoirs located in onshore and State offshore areas in the conterminous United States. A recovery factor represents the percentage of an oil reservoir’s original oil in place estimated to be recoverable by the application of a miscible CO2-EOR method. The USGS estimates were calculated for 2,018 clastic and 1,681 carbonate candidate reservoirs in the “Significant Oil and Gas Fields of the United States Database” prepared by Nehring Associates, Inc. (2012).
\nThis report presents distributions of estimated recovery factors organized by plays in seven U.S. regions. The distributional parameters for plays containing at least three candidate reservoirs are presented in tables, and parameters for plays containing at least six candidate reservoirs are presented in boxplots. Over all the reservoirs evaluated, 90 percent of the recovery-factor estimates for clastic reservoirs fell within the range from 8.7 to 16.2 percent, and the median value of the distribution was 9.5 percent. Similarly, 90 percent of the recovery-factor estimates for carbonate reservoirs were within the range from 11.8 to 27.5 percent, and the median value of the distribution was 13.6 percent. Both distributions were right skewed.
\nThe retention factor is the percentage of injected CO2 that is naturally retained in the reservoir. Retention factors were also estimated in this study. For clastic reservoirs, 90 percent of the estimated retention factors were between 21.7 and 32.1 percent, and for carbonate reservoirs, 90 percent were between 23.7 and 38.2 percent. The respective median values were 22.9 for clastic reservoirs and 26.1 for carbonate reservoirs. Both distributions were right skewed. The recovery and retention factors that were calculated are consistent with the corresponding factors reported in the literature.
","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/ofr20151239","usgsCitation":"Attanasi, E.D., and Freeman, P.A., 2016, Play-level distributions of estimates of recovery factors for a miscible carbon dioxide enhanced oil recovery method used in oil reservoirs in the conterminous United States: U.S. Geological Survey Open-File Report 2015–1239, 36 p., https://dx.doi.org/10.3133/ofr20151239.","productDescription":"vii, 36 p.","onlineOnly":"Y","additionalOnlineFiles":"Y","ipdsId":"IP-067608","costCenters":[{"id":241,"text":"Eastern Energy Resources Science Center","active":true,"usgs":true}],"links":[{"id":318493,"rank":2,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/of/2015/1239/ofr20151239.pdf","text":"Report","size":"2.13 MB","linkFileType":{"id":1,"text":"pdf"},"description":"OFR 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\"}}]}\n","contact":"Eastern Energy Resources Science Center
U.S. Geological Survey
MS 956 National Center
12201 Sunrise Valley Drive
Reston, VA 20192
http://energy.usgs.gov/GeneralInfo/
ScienceCenters/Eastern.aspx
The Navajo (N) aquifer is an extensive aquifer and the primary source of groundwater in the 5,400-square-mile Black Mesa area in northeastern Arizona. Availability of water is an important issue in northeastern Arizona because of continued water requirements for industrial and municipal use by a growing population and because of low precipitation in the arid climate of the Black Mesa area. Precipitation in the area typically is between 6 and 14 inches per year.
The U.S. Geological Survey water-monitoring program in the Black Mesa area began in 1971 and provides information about the long-term effects of groundwater withdrawals from the N aquifer for industrial and municipal uses. This report presents results of data collected as part of the monitoring program in the Black Mesa area from January 2012 to September 2013. The monitoring program includes measurements of (1) groundwater withdrawals, (2) groundwater levels, (3) spring discharge, (4) surface-water discharge, and (5) groundwater chemistry.
In calendar year 2012, total groundwater withdrawals were 4,010 acre-ft, industrial withdrawals were 1,370 acre-ft, and municipal withdrawals were 2,640 acre-ft. Total withdrawals during 2012 were about 45 percent less than total withdrawals in 2005 because of Peabody Western Coal Company’s discontinued use of water to transport coal in a coal slurry pipeline. From 2011 to 2012 total withdrawals decreased by 10 percent; industrial withdrawals decreased by approximately 1 percent, and total municipal withdrawals decreased by 15 percent.
From 2012 to 2013, annually measured water levels in the Black Mesa area declined in 6 of 16 wells that were available for comparison in the unconfined areas of the N aquifer, and the median change was 0.8 feet. Water levels declined in 5 of 16 wells measured in the confined area of the aquifer. The median change for the confined area of the aquifer was 0.3 feet. From the prestress period (prior to 1965) to 2013, the median water-level change for 34 wells in both the confined and unconfined areas was -13.5 feet; the median water-level changes were -0.8 feet for 16 wells measured in the unconfined areas and -51.0 feet for 16 wells measured in the confined area.
Spring flow was measured at four springs in 2013; Burro, Unnamed Spring near Dennehotso, Moenkopi School, and Pasture Canyon Springs. Flow fluctuated during the period of record for Burro and Unnamed Springs near Dennehotso, but a decreasing trend was apparent at Moenkopi School Spring and Pasture Canyon Spring. Discharge at Burro Spring has remained relatively constant since it was first measured in the 1980s and discharge at Unnamed Spring near Dennehotso has fluctuated for the period of record at each spring. Trend analysis for discharge at Moenkopi School and Pasture Canyon Springs showed a decreasing trend.
Continuous records of surface-water discharge in the Black Mesa area were collected from streamflow-gaging stations at the following sites: Moenkopi Wash at Moenkopi 09401260 (1976 to 2013), Dinnebito Wash near Sand Springs 09401110 (1993 to 2013), Polacca Wash near Second Mesa 09400568 (1994 to 2013), and Pasture Canyon Springs 09401265 (2004 to 2013). Median winter flows (November through February) from these sites for each water year were used as an index of the amount of groundwater discharge. For the period of record of each streamflow-gaging station, the median winter flows have generally remained constant, which suggests no change in groundwater discharge.
In 2013, water samples collected from 12 wells and 4 springs in the Black Mesa area were analyzed for selected chemical constituents, and the results were compared with previous analyses. Concentrations of dissolved solids, chloride, and sulfate have varied at all 12 wells for the period of record, but neither increasing nor decreasing trends over time were found. Dissolved solids, chloride, and sulfate concentrations increased at Moenkopi School Spring during the more than 13 years of record at that site. Concentrations of dissolved solids, chloride, and sulfate at Pasture Canyon Spring have not varied significantly since the early 1980s. Concentrations of dissolved solids, chloride, and sulfate at Burro Spring and Unnamed Spring near Dennehotso have varied for the period of record with no increasing or decreasing trend in the data.
","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/ofr20151221","collaboration":"Prepared in cooperation with the Bureau of Indian Affairs and the Arizona Department of Water Resources","usgsCitation":"Macy, J.P., and Truini, Margot, 2016, Groundwater, surface-water, and water-chemistry data, Black Mesa area, northeastern Arizona—2012–2013: U.S. Geological Survey Open-File Report 2015–1221, 43 p., https://dx.doi.org/10.3133/ofr20151221.","productDescription":"vi, 43 p.","onlineOnly":"Y","additionalOnlineFiles":"N","temporalStart":"2012-01-01","ipdsId":"IP-059312","costCenters":[{"id":128,"text":"Arizona Water Science Center","active":true,"usgs":true}],"links":[{"id":318423,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/of/2015/1221/coverthb.jpg"},{"id":318424,"rank":2,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/of/2015/1221/ofr20151221.pdf","text":"Report","size":"5.8 MB","linkFileType":{"id":1,"text":"pdf"},"description":"OFR 2015-1221 PDF"}],"country":"United States","state":"Arizona","otherGeospatial":"Black Mesa Area","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -111.3,\n 35.3\n ],\n [\n -111.3,\n 37\n ],\n [\n -109.3,\n 37\n ],\n [\n -109.3,\n 35.3\n ],\n [\n -111.3,\n 35.3\n ]\n ]\n ]\n }\n }\n ]\n}","contact":"Director, Arizona Water Science Center
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Thermokarst processes characterize a variety of ice-rich permafrost terrains and often lead to lake formation. The long-term evolution of thermokarst landscapes and the stability and longevity of lakes depend upon climate, vegetation and ground conditions, including the volume of excess ground ice and its distribution. The current lake status of thermokarst-lake landscapes and their future trajectories under climate warming are better understood in the light of their long-term development. We studied the lake-rich southern marginal upland of the Yukon Flats (northern interior Alaska) using dated lake-sediment cores, observations of river-cut exposures, and remotely-sensed data. The region features thick (up to 40 m) Quaternary deposits (mainly loess) that contain massive ground ice. Two of three studied lakes formed ~ 11,000–12,000 cal yr BP through inferred thermokarst processes, and fire may have played a role in initiating thermokarst development. From ~ 9000 cal yr BP, all lakes exhibited steady sedimentation, and pollen stratigraphies are consistent with regional patterns. The current lake expansion rates are low (0 to < 7 cm yr− 1 shoreline retreat) compared with other regions (~ 30 cm yr− 1 or more). This thermokarst lake-rich region does not show evidence of extensive landscape lowering by lake drainage, nor of multiple lake generations within a basin. However, LiDAR images reveal linear “corrugations” (> 5 m amplitude), deep thermo-erosional gullies, and features resembling lake drainage channels, suggesting that highly dynamic surface processes have previously shaped the landscape. Evidently, widespread early Holocene permafrost degradation and thermokarst lake initiation were followed by lake longevity and landscape stabilization, the latter possibly related to establishment of dense forest cover. Partial or complete drainage of three lakes in 2013 reveals that there is some contemporary landscape dynamism. Holocene landscape evolution in the study area differs from that described from other thermokarst-affected regions; regional responses to future environmental change may be equally individualistic.
","language":"English","publisher":"Elsevier","doi":"10.1016/j.sedgeo.2016.01.018","usgsCitation":"Edwards, M., Grosse, G., Jones, B.M., and McDowell, P.F., 2016, The evolution of a thermokarst-lake landscape: Late Quaternary permafrost degradation and stabilization in interior Alaska: Sedimentary Geology, v. 340, p. 3-14, https://doi.org/10.1016/j.sedgeo.2016.01.018.","productDescription":"12 p.","startPage":"3","endPage":"14","onlineOnly":"N","additionalOnlineFiles":"N","ipdsId":"IP-068641","costCenters":[{"id":118,"text":"Alaska Science Center Geography","active":true,"usgs":true}],"links":[{"id":471185,"rank":0,"type":{"id":41,"text":"Open Access External Repository Page"},"url":"https://epic.awi.de/id/eprint/41740/","text":"External Repository"},{"id":318496,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","state":"Alaska","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -150.084228515625,\n 65.0025821781929\n ],\n [\n -150.084228515625,\n 67.51277075847912\n ],\n [\n -141.207275390625,\n 67.51277075847912\n ],\n [\n -141.207275390625,\n 65.0025821781929\n ],\n [\n -150.084228515625,\n 65.0025821781929\n ]\n ]\n ]\n }\n }\n ]\n}","volume":"340","publishingServiceCenter":{"id":12,"text":"Tacoma PSC"},"noUsgsAuthors":false,"publicationStatus":"PW","scienceBaseUri":"56d80eb2e4b015c306f5ea18","contributors":{"authors":[{"text":"Edwards, Mary E.","contributorId":103490,"corporation":false,"usgs":true,"family":"Edwards","given":"Mary E.","affiliations":[],"preferred":false,"id":621678,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Grosse, Guido","contributorId":146182,"corporation":false,"usgs":false,"family":"Grosse","given":"Guido","email":"","affiliations":[{"id":12916,"text":"Alfred Wegener Institute, Helmholtz Centre for Polar and Marine Research, Potsdam, Germany","active":true,"usgs":false}],"preferred":false,"id":621679,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Jones, Benjamin M. 0000-0002-1517-4711 bjones@usgs.gov","orcid":"https://orcid.org/0000-0002-1517-4711","contributorId":2286,"corporation":false,"usgs":true,"family":"Jones","given":"Benjamin","email":"bjones@usgs.gov","middleInitial":"M.","affiliations":[{"id":114,"text":"Alaska Science Center","active":true,"usgs":true},{"id":118,"text":"Alaska Science Center Geography","active":true,"usgs":true}],"preferred":true,"id":621677,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"McDowell, Patricia F.","contributorId":116892,"corporation":false,"usgs":false,"family":"McDowell","given":"Patricia","email":"","middleInitial":"F.","affiliations":[{"id":6604,"text":"University of Oregon","active":true,"usgs":false}],"preferred":false,"id":621680,"contributorType":{"id":1,"text":"Authors"},"rank":4}]}} ,{"id":70168783,"text":"70168783 - 2016 - Comparative evaluation of statistical and mechanistic models of Escherichia coli at beaches in southern Lake Michigan","interactions":[],"lastModifiedDate":"2021-08-24T15:54:40.292443","indexId":"70168783","displayToPublicDate":"2016-03-02T12:00:00","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}},"displayTitle":"Comparative evaluation of statistical and mechanistic models of Escherichia coli at beaches in southern Lake Michigan","title":"Comparative evaluation of statistical and mechanistic models of Escherichia coli at beaches in southern Lake Michigan","docAbstract":"Statistical and mechanistic models are popular tools for predicting the levels of indicator bacteria at recreational beaches. Researchers tend to use one class of model or the other, and it is difficult to generalize statements about their relative performance due to differences in how the models are developed, tested, and used. We describe a cooperative modeling approach for freshwater beaches impacted by point sources in which insights derived from mechanistic modeling were used to further improve the statistical models and vice versa. The statistical models provided a basis for assessing the mechanistic models which were further improved using probability distributions to generate high-resolution time series data at the source, long-term “tracer” transport modeling based on observed electrical conductivity, better assimilation of meteorological data, and the use of unstructured-grids to better resolve nearshore features. This approach resulted in improved models of comparable performance for both classes including a parsimonious statistical model suitable for real-time predictions based on an easily measurable environmental variable (turbidity). The modeling approach outlined here can be used at other sites impacted by point sources and has the potential to improve water quality predictions resulting in more accurate estimates of beach closures.
","language":"English","publisher":"ACS Publications","doi":"10.1021/acs.est.5b05378","usgsCitation":"Safaie, A., Wendzel, A., Ge, Z., Nevers, M., Whitman, R.L., Corsi, S., and Phanikumar, M., 2016, Comparative evaluation of statistical and mechanistic models of Escherichia coli at beaches in southern Lake Michigan: Environmental Science & Technology, v. 50, no. 5, p. 2442-2449, https://doi.org/10.1021/acs.est.5b05378.","productDescription":"8 p.","startPage":"2442","endPage":"2449","onlineOnly":"N","additionalOnlineFiles":"N","ipdsId":"IP-069953","costCenters":[{"id":324,"text":"Great Lakes Science Center","active":true,"usgs":true},{"id":677,"text":"Wisconsin Water Science Center","active":true,"usgs":true}],"links":[{"id":318495,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","otherGeospatial":"Lake Michigan, Ogden Dunes","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -87.1,\n 41.5\n ],\n [\n -87.1,\n 41.75\n ],\n [\n -87.25,\n 41.75\n ],\n [\n -87.25,\n 41.5\n ],\n [\n -87.1,\n 41.5\n ]\n ]\n ]\n }\n }\n ]\n}","volume":"50","issue":"5","publishingServiceCenter":{"id":6,"text":"Columbus PSC"},"noUsgsAuthors":false,"publicationDate":"2016-02-15","publicationStatus":"PW","scienceBaseUri":"56d80ea9e4b015c306f5e9ec","chorus":{"doi":"10.1021/acs.est.5b05378","url":"http://dx.doi.org/10.1021/acs.est.5b05378","publisher":"American Chemical Society (ACS)","authors":"Safaie Ammar, Wendzel Aaron, Ge Zhongfu, Nevers Meredith B., Whitman Richard L., Corsi Steven R., Phanikumar Mantha S.","journalName":"Environmental Science & Technology","publicationDate":"3/2016"},"contributors":{"authors":[{"text":"Safaie, Ammar","contributorId":167285,"corporation":false,"usgs":false,"family":"Safaie","given":"Ammar","email":"","affiliations":[{"id":6601,"text":"Michigan State University","active":true,"usgs":false}],"preferred":false,"id":621744,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Wendzel, Aaron","contributorId":167286,"corporation":false,"usgs":false,"family":"Wendzel","given":"Aaron","email":"","affiliations":[{"id":6601,"text":"Michigan State University","active":true,"usgs":false}],"preferred":false,"id":621745,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Ge, Zhongfu","contributorId":139463,"corporation":false,"usgs":false,"family":"Ge","given":"Zhongfu","email":"","affiliations":[{"id":12773,"text":"American Bureau of Shipping, Corporate Marine Technology","active":true,"usgs":false}],"preferred":false,"id":621746,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Nevers, Meredith 0000-0001-6963-6734 mnevers@usgs.gov","orcid":"https://orcid.org/0000-0001-6963-6734","contributorId":2013,"corporation":false,"usgs":true,"family":"Nevers","given":"Meredith","email":"mnevers@usgs.gov","affiliations":[{"id":324,"text":"Great Lakes Science Center","active":true,"usgs":true}],"preferred":true,"id":621743,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Whitman, Richard L. rwhitman@usgs.gov","contributorId":542,"corporation":false,"usgs":true,"family":"Whitman","given":"Richard","email":"rwhitman@usgs.gov","middleInitial":"L.","affiliations":[{"id":324,"text":"Great Lakes Science Center","active":true,"usgs":true}],"preferred":true,"id":621747,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"Corsi, Steven R. srcorsi@usgs.gov","contributorId":150657,"corporation":false,"usgs":true,"family":"Corsi","given":"Steven R.","email":"srcorsi@usgs.gov","affiliations":[{"id":677,"text":"Wisconsin Water Science Center","active":true,"usgs":true}],"preferred":false,"id":621749,"contributorType":{"id":1,"text":"Authors"},"rank":6},{"text":"Phanikumar, Mantha S.","contributorId":17888,"corporation":false,"usgs":true,"family":"Phanikumar","given":"Mantha S.","affiliations":[],"preferred":false,"id":621748,"contributorType":{"id":1,"text":"Authors"},"rank":7}]}} ,{"id":70159457,"text":"sir20155159 - 2016 - Application of hydrogeology and groundwater-age estimates to assess the travel time of groundwater at the site of a landfill to the Mahomet Aquifer, near Clinton, Illinois","interactions":[],"lastModifiedDate":"2016-03-02T13:44:50","indexId":"sir20155159","displayToPublicDate":"2016-03-02T10:30:00","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":"2015-5159","title":"Application of hydrogeology and groundwater-age estimates to assess the travel time of groundwater at the site of a landfill to the Mahomet Aquifer, near Clinton, Illinois","docAbstract":"The U.S. Geological Survey used interpretations of hydrogeologic conditions and tritium-based groundwater age estimates to assess the travel time of groundwater at a landfill site near Clinton, Illinois (the “Clinton site”) where a chemical waste unit (CWU) was proposed to be within the Clinton landfill unit #3 (CLU#3). Glacial deposits beneath the CWU consist predominantly of low-permeability silt- and clay-rich till interspersed with thin (typically less than 2 feet in thickness) layers of more permeable deposits, including the Upper and Lower Radnor Till Sands and the Organic Soil unit. These glacial deposits are about 170 feet thick and overlie the Mahomet Sand Member of the Banner Formation. The Mahomet aquifer is composed of the Mahomet Sand Member and is used for water supply in much of east-central Illinois.
Eight tritium analyses of water from seven wells were used to evaluate the overall age of recharge to aquifers beneath the Clinton site. Groundwater samples were collected from six monitoring wells on or adjacent to the CLU#3 that were open to glacial deposits above the Mahomet aquifer (the upper and lower parts of the Radnor Till Member and the Organic Soil unit) and one proximal production well (approximately 0.5 miles from the CLU#3) that is screened in the Mahomet aquifer. The tritium-based age estimates were computed with a simplifying, piston-flow assumption: that groundwater moves in discrete packets to the sampled interval by advection, without hydrodynamic dispersion or mixing.
Tritium concentrations indicate a recharge age of at least 59 years (pre-1953 recharge) for water sampled from deposits below the upper part of the Radnor Till Member at the CLU#3, with older water expected at progressively greater depth in the tills. The largest tritium concentration from a well sampled by this study (well G53S; 0.32 ± 0.10 tritium units) was in groundwater from a sand deposit in the upper part of the Radnor Till Member; the shallowest permeable unit sampled by this study. That result indicated that nearly all groundwater sampled from well G53S entered the aquifer as recharge before 1953. Tritium was detected in a trace concentration in one sample from a second monitoring well open to the upper part of the Radnor Till Member (well G07S; 0.11 ± 0.09 tritium units), and not detected in samples collected from two monitoring wells open to a sand deposit in the lower part of the Radnor Till Member, from two samples collected from two monitoring wells open to the Organic Soil unit, and in two samples collected from a production well screened in the middle of the Mahomet aquifer (a groundwater sample and a sequential replicate sample). The lack of tritium in five of the six groundwater samples collected from the shallow permeable units beneath CLU#3 site and the two samples from the one Mahomet aquifer well indicates an absence of post-1952 recharge. Groundwater-flow paths that could contribute post-1952 recharge to the lower part of the Radnor Till Member, the Organic Soil unit, or the Mahomet aquifer at the CLU#3 are not indicated by these data.
Hypothetical two-part mixtures of tritium-dead, pre-1953 recharge water and decay-corrected tritium concentrations in post-1952 recharge were computed and compared with tritium analyses in groundwater sampled from monitoring wells at the CLU#3 site to evaluate whether tritium concentrations in groundwater could be represented by mixtures involving some post-1952 recharge. Results from the hypothetical two-part mixtures indicate that groundwater from monitoring well (G53S) was predominantly composed of pre-1953 recharge and that if present, younger, post-1955 recharge, contributed less than 2.5 percent to that sample. The hypothetical two-part mixing results also indicated that very small amounts of post-1952 recharge composing less than about 2.5 percent of the sample volume could not be distinguished in groundwater samples with tritium concentrations less than about 0.15 TU.
The piston-flow based age of recharge determined from the tritium concentration in the groundwater sample from monitoring well G53S yielded an estimated maximum vertical velocity from the land surface to the upper part of the Radnor Till Member of 0.85 feet per year or less. This velocity, ifassumed to apply to the remaining glacial till deposits above the Mahomet aquifer, indicates that recharge flows through the 170 feet of glacial deposits between the base of the proposed chemical waste unit and the top of the Mahomet aquifer in a minimum of 200 years or longer. Analysis of hydraulic data from the site, constrained by a tritium-age based maximum groundwater velocity estimate, computed minimum estimates of effective porosity that range from about 0.021 to 0.024 for the predominantly till deposits above the Mahomet aquifer.
Estimated rates of transport of recharge from land surface to the Mahomet aquifer for the CLU#3 site computed using the Darcy velocity equation with site-specific data were about 260 years or longer. The Darcy velocity-based estimates were computed using values that were based on tritium data, estimates of vertical velocity and effective porosity and available site-specific data. Solution of the Darcy velocity equation indicated that maximum vertical groundwater velocities through the deposits above the aquifer were 0.41 or 0.61 feet per year, depending on the site-specific values of vertical hydraulic conductivity (laboratory triaxial test values) and effective porosity used for the computation. The resulting calculated minimum travel times for groundwater to flow from the top of the Berry Clay Member (at the base of the proposed chemical waste unit) to the top of the Mahomet aquifer ranged from about 260 to 370 years, depending on the velocity value used in the calculation. In comparison, plausible travel times calculated using vertical hydraulic conductivity values from a previously published regional groundwater flow model were either slightly less than or longer than those calculated using site data and ranged from 230 to 580 years.
Tritium data from 1996 to 2011 USGS regional sampling of groundwater from domestic wells in the confined part of the Mahomet aquifer—which are 2.5 to about 40 miles from the Clinton site—were compared with site-specific data from a production well at the Clinton site. Tritium-based groundwater-age estimates indicated predominantly pre- 1953 recharge dates for USGS and other prior regional samples of groundwater from domestic wells in the Mahomet aquifer. These results agreed with the tritium-based, pre-1953 recharge age estimated for a groundwater sample and a sequential replicate sample from a production well in the confined part of the Mahomet aquifer beneath the Clinton site.
The regional tritium-based groundwater age estimates also were compared with pesticide detections in samples from distal domestic wells in the USGS regional network that are about 2.5 to 40 miles from the Clinton site to identify whether very small amounts of post-1952 recharge have in places reached confined parts of the Mahomet aquifer at locations other than the Clinton site in an approximately 2,000 square mile area of the Mahomet aquifer. Very small amounts of post-1952 recharge were defined in this analysis as less than about 2.5 percent of the total recharge contributing to a groundwater sample, based on results from the two-part mixing analysis of tritium data from the Clinton site. Pesticide-based groundwater-age estimates based on 22 detections of pesticides (13 of these detections were estimated concentrations), including atrazine, deethylatrazine (2-Chloro-4-isopropylamino-6-amino- s-triazine), cyanazine, diazinon, metolachlor, molinate, prometon, and trifluralin in groundwater samples from 10 domestic wells 2.5 to about 40 miles distant from the Clinton site indicate that very small amounts of post-1956 to post-1992 recharge can in places reach the confined part of the Mahomet aquifer in other parts of central Illinois. The relative lack of tritium in these samples indicate that the amounts of post-1956 to post-1992 recharge contributing to the 10 domestic wells were a very small part of the overall older groundwater sampled from those wells.
The flow process by which very small amounts of pesticide-bearing groundwater reached the screened intervals of the 10 domestic wells could not be distinguished between well-integrity related infiltration and natural hydrogeologic features. Potential explanations include: (1) infiltration through man-made avenues in or along the well, (2) flow of very small amounts of post-1956 to post-1992 recharge through sparsely distributed natural permeable aspects of the glacial till and diluted by mixing with older groundwater, or (3) a combination of both processes.
Presuming the domestic wells sampled by the USGS in 1996–2011 in the regional study of the confined part of the Mahomet aquifer are adequately sealed and produce groundwater that is representative of aquifer conditions, the regional tritium and pesticide-based groundwater-age results indicate substantial heterogeneity in the glacial stratigraphy above the Mahomet aquifer. The pesticide-based groundwater-age estimates from the domestic wells distant from the Clinton site also indicate that parts of the Mahomet aquifer with the pesticide detections can be susceptible to contaminant sources at the land surface. The regional pesticide and tritium results from the domestic wells further indicate that a potential exists for possible contaminants from land surface to be transported through the glacial drift deposits that confine the Mahomet aquifer in other parts of central Illinois at faster rates than those computed for recharge at the Clinton site, including CLU#3. This analysis indicates the potential value of sub-microgram-per-liter level concentrations of land-use derived indicators of modern recharge to indicate the presence of very small amounts of modern, post-1952 age recharge in overall older, pre-1953 age groundwater.
","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/sir20155159","usgsCitation":"Kay, R.T., and Buszka, P.M., 2016, Application of hydrogeology and groundwater-age estimates to assess the travel time of groundwater at the site of a landfill to the Mahomet Aquifer, near Clinton, Illinois, with a section on Regional Indications of Recharge to the Mahomet Aquifer from Previously Collected Tritium and Pesticide Data, by Buszka, P.M. and Morrow, W.S.: U.S. Geological Survey Scientific Investigations Report 2015–5159, 54 p., https://dx.doi.org/10.3133/sir20155159.\n","productDescription":"vii, 54 p.","numberOfPages":"68","onlineOnly":"Y","additionalOnlineFiles":"N","ipdsId":"IP-038616","costCenters":[{"id":344,"text":"Illinois Water Science Center","active":true,"usgs":true}],"links":[{"id":314192,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/sir/2015/5159/coverthb.jpg"},{"id":314193,"rank":2,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/sir/2015/5159/sir20155159.pdf","text":"Report","size":"1.68 MB","linkFileType":{"id":1,"text":"pdf"},"description":"SIR 2015-5159"}],"country":"United States","state":"Illinois","city":"Clinton","otherGeospatial":"Mahomet Aquifer","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -88.96428108215332,\n 40.107618711896095\n ],\n [\n -88.96428108215332,\n 40.117793139514546\n ],\n [\n -88.94694328308105,\n 40.117793139514546\n ],\n [\n -88.94694328308105,\n 40.107618711896095\n ],\n [\n -88.96428108215332,\n 40.107618711896095\n ]\n ]\n ]\n }\n }\n ]\n}","contact":"Director, Illinois Water Science Center
U.S. Geological Survey
405 N. Goodwin Avenue
Urbana, IL 61801
http://il.water.usgs.gov/
We describe high-resolution gravity and seismic refraction surveys acquired to determine the thickness of valley-fill deposits and to delineate geologic structures that might influence groundwater flow beneath the Smoke Tree Wash area in Joshua Tree National Park. These surveys identified a sedimentary basin that is fault-controlled. A profile across the Smoke Tree Wash fault zone reveals low gravity values and seismic velocities that coincide with a mapped strand of the Smoke Tree Wash fault. Modeling of the gravity data reveals a basin about 2–2.5 km long and 1 km wide that is roughly centered on this mapped strand, and bounded by inferred faults. According to the gravity model the deepest part of the basin is about 270 m, but this area coincides with low velocities that are not characteristic of typical basement complex rocks. Most likely, the density contrast assumed in the inversion is too high or the uncharacteristically low velocities represent highly fractured or weathered basement rocks, or both. A longer seismic profile extending onto basement outcrops would help differentiate which scenario is more accurate. The seismic velocities also determine the depth to water table along the profile to be about 40–60 m, consistent with water levels measured in water wells near the northern end of the profile.
","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/ofr20161027","usgsCitation":"Langenheim, V.E., Rymer, M.J., Catchings, R.D., Goldman, M.R., Watt, J.T., Powell, R.E., and Matti, J.C., 2016, High-resolution gravity and seismic-refraction surveys of the Smoke Tree Wash Area, Joshua Tree National Park, California: U.S. Geological Survey Open-File Report 2016–1027, 15 p., https://dx.doi.org/10.3133/ofr20161027.","productDescription":"Report: iii, 15 p.; Dataset; Metadata; Read Me","onlineOnly":"Y","additionalOnlineFiles":"Y","ipdsId":"IP-070548","costCenters":[{"id":312,"text":"Geology, Minerals, Energy, and Geophysics Science Center","active":true,"usgs":true}],"links":[{"id":318441,"rank":4,"type":{"id":20,"text":"Read Me"},"url":"https://pubs.usgs.gov/of/2016/1027/ofr20161027_readme.txt","size":"4 KB","linkFileType":{"id":2,"text":"txt"}},{"id":318440,"rank":3,"type":{"id":16,"text":"Metadata"},"url":"https://pubs.usgs.gov/of/2016/1027/ofr20161027_metadata.txt","size":"10 KB","linkFileType":{"id":2,"text":"txt"}},{"id":318439,"rank":2,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/of/2016/1027/ofr20161027.pdf","text":"Report","size":"700 KB","linkFileType":{"id":1,"text":"pdf"},"description":"OFR 2016-1027 Report PDF"},{"id":318438,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/of/2016/1027/coverthb.jpg"},{"id":318442,"rank":5,"type":{"id":28,"text":"Dataset"},"url":"https://pubs.usgs.gov/of/2016/1027/ofr20161027_iso_all.txt","text":"Gravity Data","size":"11 KB","linkFileType":{"id":2,"text":"txt"}}],"country":"United States","state":"California","otherGeospatial":"Joshua Tree National Park","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -115.8745,\n 33.7498\n ],\n [\n -115.8745,\n 33.8402\n ],\n [\n -115.7667,\n 33.8402\n ],\n [\n -115.7667,\n 33.7498\n ],\n [\n -115.8745,\n 33.7498\n ]\n ]\n ]\n }\n }\n ]\n}","contact":"GMEG staff, Geology, Minerals, Energy, & Geophysics Science Center
Menlo Park, California
U.S. Geological Survey
345 Middlefield Road
Menlo Park, CA 94025-3591
http://geomaps.wr.usgs.gov/gmeg/
Sediment-toxicity benchmarks are needed to interpret the biological significance of currently used pesticides detected in whole sediments. Two types of freshwater sediment benchmarks for pesticides were developed using spiked-sediment bioassay (SSB) data from the literature. These benchmarks can be used to interpret sediment-toxicity data or to assess the potential toxicity of pesticides in whole sediment. The Likely Effect Benchmark (LEB) defines a pesticide concentration in whole sediment above which there is a high probability of adverse effects on benthic invertebrates, and the Threshold Effect Benchmark (TEB) defines a concentration below which adverse effects are unlikely. For compounds without available SSBs, benchmarks were estimated using equilibrium partitioning (EqP). When a sediment sample contains a pesticide mixture, benchmark quotients can be summed for all detected pesticides to produce an indicator of potential toxicity for that mixture. Benchmarks were developed for 48 pesticide compounds using SSB data and 81 compounds using the EqP approach. In an example application, data for pesticides measured in sediment from 197 streams across the United States were evaluated using these benchmarks, and compared to measured toxicity from whole-sediment toxicity tests conducted with the amphipod Hyalella azteca (28-d exposures) and the midge Chironomus dilutus (10-d exposures). Amphipod survival, weight, and biomass were significantly and inversely related to summed benchmark quotients, whereas midge survival, weight, and biomass showed no relationship to benchmarks. Samples with LEB exceedances were rare (n = 3), but all were toxic to amphipods (i.e., significantly different from control). Significant toxicity to amphipods was observed for 72% of samples exceeding one or more TEBs, compared to 18% of samples below all TEBs. Factors affecting toxicity below TEBs may include the presence of contaminants other than pesticides, physical/chemical characteristics of sediment, and uncertainty in TEB values. Additional evaluations of benchmarks in relation to sediment chemistry and toxicity are ongoing.
","language":"English","publisher":"Elsevier","doi":"10.1016/j.scitotenv.2016.01.081","usgsCitation":"Nowell, L.H., Norman, J.E., Ingersoll, C.G., and Moran, P.W., 2016, Development and application of freshwater sediment-toxicity benchmarks for currently used pesticides: Science of the Total Environment, v. 550, p. 835-850, https://doi.org/10.1016/j.scitotenv.2016.01.081.","productDescription":"16 p.","startPage":"835","endPage":"850","numberOfPages":"16","onlineOnly":"N","additionalOnlineFiles":"N","ipdsId":"IP-069668","costCenters":[{"id":154,"text":"California Water Science Center","active":true,"usgs":true},{"id":34983,"text":"Contaminant Biology Program","active":true,"usgs":true}],"links":[{"id":318863,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United 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jnorman@usgs.gov","orcid":"https://orcid.org/0000-0002-2820-6225","contributorId":3832,"corporation":false,"usgs":true,"family":"Norman","given":"Julia","email":"jnorman@usgs.gov","middleInitial":"E.","affiliations":[{"id":518,"text":"Oregon Water Science Center","active":true,"usgs":true},{"id":154,"text":"California Water Science Center","active":true,"usgs":true},{"id":37277,"text":"WMA - Earth System Processes Division","active":true,"usgs":true}],"preferred":true,"id":622726,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Ingersoll, Christopher G. 0000-0003-4531-5949 cingersoll@usgs.gov","orcid":"https://orcid.org/0000-0003-4531-5949","contributorId":2071,"corporation":false,"usgs":true,"family":"Ingersoll","given":"Christopher","email":"cingersoll@usgs.gov","middleInitial":"G.","affiliations":[{"id":192,"text":"Columbia Environmental Research Center","active":true,"usgs":true}],"preferred":true,"id":622727,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Moran, Patrick W. 0000-0002-2002-3539 pwmoran@usgs.gov","orcid":"https://orcid.org/0000-0002-2002-3539","contributorId":489,"corporation":false,"usgs":true,"family":"Moran","given":"Patrick","email":"pwmoran@usgs.gov","middleInitial":"W.","affiliations":[{"id":622,"text":"Washington Water Science Center","active":true,"usgs":true}],"preferred":true,"id":622728,"contributorType":{"id":1,"text":"Authors"},"rank":4}]}} ,{"id":70171539,"text":"70171539 - 2016 - 1984–2010 trends in fire burn severity and area for the conterminous US","interactions":[],"lastModifiedDate":"2017-01-18T09:16:44","indexId":"70171539","displayToPublicDate":"2016-03-01T14:30:00","publicationYear":"2016","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":2083,"text":"International Journal of Wildland Fire","active":true,"publicationSubtype":{"id":10}},"title":"1984–2010 trends in fire burn severity and area for the conterminous US","docAbstract":"Burn severity products created by the Monitoring Trends in Burn Severity (MTBS) project were used to analyse historical trends in burn severity. Using a severity metric calculated by modelling the cumulative distribution of differenced Normalized Burn Ratio (dNBR) and Relativized dNBR (RdNBR) data, we examined burn area and burn severity of 4893 historical fires (1984–2010) distributed across the conterminous US (CONUS) and mapped by MTBS. Yearly mean burn severity values (weighted by area), maximum burn severity metric values, mean area of burn, maximum burn area and total burn area were evaluated within 27 US National Vegetation Classification macrogroups. Time series assessments of burned area and severity were performed using Mann–Kendall tests. Burned area and severity varied by vegetation classification, but most vegetation groups showed no detectable change during the 1984–2010 period. Of the 27 analysed vegetation groups, trend analysis revealed burned area increased in eight, and burn severity has increased in seven. This study suggests that burned area and severity, as measured by the severity metric based on dNBR or RdNBR, have not changed substantially for most vegetation groups evaluated within CONUS.
","language":"English","publisher":"Fire Research Institute","publisherLocation":"Rosyn, WA","doi":"10.1071/WF15039","usgsCitation":"Picotte, J.J., Peterson, B.E., Meier, G., and Howard, S.M., 2016, 1984–2010 trends in fire burn severity and area for the conterminous US: International Journal of Wildland Fire, v. 25, no. 4, p. 413-420, https://doi.org/10.1071/WF15039.","productDescription":"9 p.","startPage":"413","endPage":"420","onlineOnly":"N","additionalOnlineFiles":"N","ipdsId":"IP-056002","costCenters":[{"id":222,"text":"Earth Resources Observation and Science (EROS) Center","active":true,"usgs":true}],"links":[{"id":322099,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"volume":"25","issue":"4","publishingServiceCenter":{"id":4,"text":"Rolla PSC"},"noUsgsAuthors":false,"publicationStatus":"PW","scienceBaseUri":"575158abe4b053f0edd03c13","contributors":{"authors":[{"text":"Picotte, Joshua J. 0000-0002-4021-4623 jpicotte@usgs.gov","orcid":"https://orcid.org/0000-0002-4021-4623","contributorId":4626,"corporation":false,"usgs":true,"family":"Picotte","given":"Joshua","email":"jpicotte@usgs.gov","middleInitial":"J.","affiliations":[{"id":222,"text":"Earth Resources Observation and Science (EROS) Center","active":true,"usgs":true},{"id":223,"text":"Earth Resources Observation and Science (EROS) Center (Geography)","active":false,"usgs":true}],"preferred":true,"id":631701,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Peterson, Birgit E. 0000-0002-4356-1540 bpeterson@usgs.gov","orcid":"https://orcid.org/0000-0002-4356-1540","contributorId":3599,"corporation":false,"usgs":true,"family":"Peterson","given":"Birgit","email":"bpeterson@usgs.gov","middleInitial":"E.","affiliations":[{"id":223,"text":"Earth Resources Observation and Science (EROS) Center (Geography)","active":false,"usgs":true}],"preferred":false,"id":631702,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Meier, Gretchen gmeier@usgs.gov","contributorId":3124,"corporation":false,"usgs":true,"family":"Meier","given":"Gretchen","email":"gmeier@usgs.gov","affiliations":[],"preferred":true,"id":631703,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Howard, Stephen M. 0000-0001-5255-5882 smhoward@usgs.gov","orcid":"https://orcid.org/0000-0001-5255-5882","contributorId":3483,"corporation":false,"usgs":true,"family":"Howard","given":"Stephen","email":"smhoward@usgs.gov","middleInitial":"M.","affiliations":[{"id":223,"text":"Earth Resources Observation and Science (EROS) Center (Geography)","active":false,"usgs":true}],"preferred":true,"id":631700,"contributorType":{"id":1,"text":"Authors"},"rank":4}]}} ,{"id":70169863,"text":"70169863 - 2016 - Determining the 95% limit of detection for waterborne pathogen analyses from primary concentration to qPCR","interactions":[],"lastModifiedDate":"2016-03-28T11:39:18","indexId":"70169863","displayToPublicDate":"2016-03-01T12:45:00","publicationYear":"2016","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":3716,"text":"Water Research","onlineIssn":"1879-2448","printIssn":"0043-1354","active":true,"publicationSubtype":{"id":10}},"title":"Determining the 95% limit of detection for waterborne pathogen analyses from primary concentration to qPCR","docAbstract":"The limit of detection (LOD) for qPCR-based analyses is not consistently defined or determined in studies on waterborne pathogens. Moreover, the LODs reported often reflect the qPCR assay alone rather than the entire sample process. Our objective was to develop an approach to determine the 95% LOD (lowest concentration at which 95% of positive samples are detected) for the entire process of waterborne pathogen detection. We began by spiking the lowest concentration that was consistently positive at the qPCR step (based on its standard curve) into each procedural step working backwards (i.e., extraction, secondary concentration, primary concentration), which established a concentration that was detectable following losses of the pathogen from processing. Using the fraction of positive replicates (n = 10) at this concentration, we selected and analyzed a second, and then third, concentration. If the fraction of positive replicates equaled 1 or 0 for two concentrations, we selected another. We calculated the LOD using probit analysis. To demonstrate our approach we determined the 95% LOD for Salmonella enterica serovar Typhimurium, adenovirus 41, and vaccine-derived poliovirus Sabin 3, which were 11, 12, and 6 genomic copies (gc) per reaction (rxn), respectively (equivalent to 1.3, 1.5, and 4.0 gc L−1 assuming the 1500 L tap-water sample volume prescribed in EPA Method 1615). This approach limited the number of analyses required and was amenable to testing multiple genetic targets simultaneously (i.e., spiking a single sample with multiple microorganisms). An LOD determined this way can facilitate study design, guide the number of required technical replicates, aid method evaluation, and inform data interpretation.
","language":"English","publisher":"Elsevier","publisherLocation":"Amsterdam","doi":"10.1016/j.watres.2016.03.026","usgsCitation":"Stokdyk, J., Firnstahl, A.D., Spencer, S., Burch, T.R., and Borchardt, M.A., 2016, Determining the 95% limit of detection for waterborne pathogen analyses from primary concentration to qPCR: Water Research, v. 96, p. 105-113, https://doi.org/10.1016/j.watres.2016.03.026.","productDescription":"9 p.","startPage":"105","endPage":"113","numberOfPages":"9","onlineOnly":"N","additionalOnlineFiles":"N","ipdsId":"IP-069379","costCenters":[{"id":677,"text":"Wisconsin Water Science Center","active":true,"usgs":true}],"links":[{"id":319550,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"volume":"96","publishingServiceCenter":{"id":6,"text":"Columbus PSC"},"noUsgsAuthors":false,"publicationStatus":"PW","scienceBaseUri":"56fa55bce4b0a6037df0aaa6","contributors":{"authors":[{"text":"Stokdyk, Joel P. jstokdyk@usgs.gov","contributorId":168295,"corporation":false,"usgs":true,"family":"Stokdyk","given":"Joel P.","email":"jstokdyk@usgs.gov","affiliations":[{"id":677,"text":"Wisconsin Water Science Center","active":true,"usgs":true}],"preferred":false,"id":625370,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Firnstahl, Aaron D. 0000-0003-2686-7596 afirnstahl@usgs.gov","orcid":"https://orcid.org/0000-0003-2686-7596","contributorId":168296,"corporation":false,"usgs":true,"family":"Firnstahl","given":"Aaron","email":"afirnstahl@usgs.gov","middleInitial":"D.","affiliations":[{"id":677,"text":"Wisconsin Water Science Center","active":true,"usgs":true},{"id":37947,"text":"Upper Midwest Water Science Center","active":true,"usgs":true}],"preferred":true,"id":625371,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Spencer, Susan K.","contributorId":39511,"corporation":false,"usgs":true,"family":"Spencer","given":"Susan K.","affiliations":[],"preferred":false,"id":625372,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Burch, Tucker R tburch@usgs.gov","contributorId":5689,"corporation":false,"usgs":true,"family":"Burch","given":"Tucker","email":"tburch@usgs.gov","middleInitial":"R","affiliations":[{"id":677,"text":"Wisconsin Water Science Center","active":true,"usgs":true}],"preferred":true,"id":625373,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Borchardt, Mark A. 0000-0002-6471-2627","orcid":"https://orcid.org/0000-0002-6471-2627","contributorId":151033,"corporation":false,"usgs":false,"family":"Borchardt","given":"Mark","email":"","middleInitial":"A.","affiliations":[{"id":6684,"text":"USDA Forest Service, Southern Research Station, Aiken, SC","active":true,"usgs":false}],"preferred":false,"id":625374,"contributorType":{"id":1,"text":"Authors"},"rank":5}]}} ,{"id":70169121,"text":"70169121 - 2016 - Tarangire revisited: Consequences of declining connectivity in a tropical ungulate population","interactions":[],"lastModifiedDate":"2016-03-21T11:34:10","indexId":"70169121","displayToPublicDate":"2016-03-01T12:30:00","publicationYear":"2016","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":1015,"text":"Biological Conservation","active":true,"publicationSubtype":{"id":10}},"title":"Tarangire revisited: Consequences of declining connectivity in a tropical ungulate population","docAbstract":"The hyper-abundance of migratory wildlife in many ecosystems depends on maintaining access to seasonally available resources. In Eastern and Southern Africa, land-use change and a loss of connectivity have coincided with widespread declines in the abundance and geographic range of ungulate populations. Using photographic capture-mark-recapture, we examine the historical pattern of loss of connectivity and its impact on population trends in a partially migratory wildebeest population in northern Tanzania. To estimate abundance, we use a novel modeling approach that overcomes bias associated with photo misidentifications. Our data indicate (1) diminished connectivity within and between seasonal areas as a result of human activities, (2) a reduction in the overall population size compared to historical numbers, with high variability over time, (3) the continued use of highly constrained movement corridors between the three main seasonal ranges, (4) higher recruitment in the non-migratory subpopulation (Lake Manyara National Park) than in other areas of the ecosystem, and (5) an increase in the relative abundance of resident to migrant wildebeest. Recent conservation efforts to protect seasonal habitat and to enforce anti-poaching policies outside protected areas have likely helped stabilize the population, at least temporarily, but we caution that several key vulnerabilities remain.
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2016 - Establishing a pre-mining geochemical baseline at a uranium mine near Grand Canyon National Park, USA","interactions":[],"lastModifiedDate":"2018-08-08T10:31:11","indexId":"70168806","displayToPublicDate":"2016-03-01T11:45:00","publicationYear":"2016","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":1760,"text":"Geoderma","active":true,"publicationSubtype":{"id":10}},"title":"Establishing a pre-mining geochemical baseline at a uranium mine near Grand Canyon National Park, USA","docAbstract":"During 2012, approximately 404,000 ha of Federal Land in northern Arizona was withdrawn from consideration of mineral extraction for a 20-year period to protect the Grand Canyon watershed from potentially adverse effects of U mineral exploration and development. The development, operation, and reclamation of the Canyon Mine during the withdrawal period provide an excellent field site to understand and document off-site migration of radionuclides within the withdrawal area. As part of the Department of Interior's (DOI's) study plan for the exclusion area, the objective of our study is to utilize pre-defined decision units (DUs) in areas within and surrounding the Canyon Mine to demonstrate how newly established incremental sampling methodologies (ISM) combined with multivariate statistical methods can be used to document a repeatable and statistically defensible measure of pre-mining baseline conditions in surface soils and stream sediment samples prior to ore extraction. During the survey in June 2013, the highest pre-mining 95% upper confidence level (UCL) concentrations with respect to As, Mo, U, and V were found in the triplicate samples collected from surface soils in the mine site DU designated as M1. Gamma activities were slightly elevated in soils within the M1 DU (up to 28 μR/h); however, off-site gamma activities in soil and stream-sediment samples were lower (< 6 to 12 μR/h). Hierarchical cluster analysis (HCA) was applied to 33 chemical constituents contained in the multivariate data generated from the analysis of triplicate samples collected in the soil and stream sediment DUs within and surrounding Canyon Mine. Most of the triplicate samples from individual DUs were grouped in the same dendrogram cluster when using a similarity value (SV) of 0.70 (unitless). Different group membership of triplicate samples from two of the four haul road DUs was likely the result of heterogeneity induced by non-native soil material introduced from the gravel road base or from vehicular traffic. Application of HCA and ISM will provide critical metrics to meet DOI's long-term goals for assessing off-site migration of radionuclides resulting from mining and reclamation in the current (2015) exclusion area associated within the Grand Canyon watershed and the associated national park.
","language":"English","publisher":"Elsevier","doi":"10.1016/j.geodrs.2016.01.004","usgsCitation":"Naftz, D.L., and Walton-Day, K., 2016, Establishing a pre-mining geochemical baseline at a uranium mine near Grand Canyon National Park, USA: Geoderma, v. 7, no. 1, p. 76-92, https://doi.org/10.1016/j.geodrs.2016.01.004.","productDescription":"17 p.","startPage":"76","endPage":"92","numberOfPages":"17","onlineOnly":"N","additionalOnlineFiles":"N","ipdsId":"IP-062046","costCenters":[{"id":589,"text":"Toxic Substances Hydrology Program","active":true,"usgs":true},{"id":5050,"text":"WY-MT Water Science Center","active":true,"usgs":true}],"links":[{"id":471188,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.1016/j.geodrs.2016.01.004","text":"Publisher Index Page"},{"id":318556,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","state":"Arizona","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -114.0380859375,\n 35.44724605551148\n ],\n [\n -114.0380859375,\n 36.99377838872517\n ],\n [\n -111.566162109375,\n 36.99377838872517\n ],\n [\n -111.566162109375,\n 35.44724605551148\n ],\n [\n -114.0380859375,\n 35.44724605551148\n ]\n ]\n ]\n }\n }\n ]\n}","volume":"7","issue":"1","publishingServiceCenter":{"id":4,"text":"Rolla PSC"},"noUsgsAuthors":false,"publicationStatus":"PW","scienceBaseUri":"56dabfdbe4b015c306f84c84","chorus":{"doi":"10.1016/j.geodrs.2016.01.004","url":"http://dx.doi.org/10.1016/j.geodrs.2016.01.004","publisher":"Elsevier BV","authors":"Naftz David, Walton-Day Katie","journalName":"Geoderma Regional","publicationDate":"3/2016"},"contributors":{"authors":[{"text":"Naftz, David L. 0000-0003-1130-6892 dlnaftz@usgs.gov","orcid":"https://orcid.org/0000-0003-1130-6892","contributorId":1041,"corporation":false,"usgs":true,"family":"Naftz","given":"David","email":"dlnaftz@usgs.gov","middleInitial":"L.","affiliations":[{"id":610,"text":"Utah Water Science Center","active":true,"usgs":true},{"id":5050,"text":"WY-MT Water Science Center","active":true,"usgs":true}],"preferred":true,"id":621831,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Walton-Day, Katherine 0000-0002-9146-6193 kwaltond@usgs.gov","orcid":"https://orcid.org/0000-0002-9146-6193","contributorId":1245,"corporation":false,"usgs":true,"family":"Walton-Day","given":"Katherine","email":"kwaltond@usgs.gov","affiliations":[{"id":191,"text":"Colorado Water Science Center","active":true,"usgs":true}],"preferred":false,"id":621832,"contributorType":{"id":1,"text":"Authors"},"rank":2}]}} ,{"id":70169141,"text":"70169141 - 2016 - Available data support protection of the Southwestern Willow Flycatcher under the Endangered Species Act","interactions":[],"lastModifiedDate":"2016-03-22T09:27:28","indexId":"70169141","displayToPublicDate":"2016-03-01T10:30:00","publicationYear":"2016","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":3551,"text":"The Condor","active":true,"publicationSubtype":{"id":10}},"title":"Available data support protection of the Southwestern Willow Flycatcher under the Endangered Species Act","docAbstract":"Zink (2015) argued there was no evidence for genetic, morphological, or ecological differentiation between the federally endangered Southwestern Willow Flycatcher (Empidonax traillii extimus) and other Willow Flycatcher subspecies. Using the same data, we show there is a step-cline in both the frequency of a mtDNA haplotype and in plumage variation roughly concordant with the currently recognized boundary between E. t. extimus and E. t adastus, the subspecies with which it shares the longest common boundary. The geographical pattern of plumage variation is also concordant with previous song analyses differentiating those 2 subspecies and identified birds in one low-latitude, high-elevation site in Arizona as the northern subspecies. We also demonstrate that the ecological niche modeling approach used by Zink yields the same result whether applied to the 2 flycatcher subspecies or to 2 unrelated species, E. t. extimus and Yellow Warbler (Setophaga petechia). As a result, any interpretation of those results as evidence for lack of ecological niche differentiation among Willow Flycatcher subspecies would also indicate no differentiation among recognized species and would therefore be an inappropriate standard for delineating subspecies. We agree that many analytical techniques now available to examine genetic, morphological, and ecological differentiation would improve our understanding of the distinctness (or lack thereof) of Willow Flycatcher subspecies, but we argue that currently available evidence supports protection of the Southwestern Willow Flycatcher under the Endangered Species Act.
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\nThis is the culmination of three years of work to compile information on seabirds, pinnipeds, and cetaceans, and advance a modeling framework that can integrate data sets and develop accurate predictions of relative density for important species off the Pacific Coast of Washington. Previous reports, which evaluated existing datasets of at-sea observations (Menza et al., 2014; Kracker and Menza, 2015) and presented superseded versions of seabird models (Menza et al., 2015), provided base information for this report. In addition to the maps in this published report, all new seabird, pinniped and cetacean predictions will be made publicly available as digital geospatial data through the National Centers for Environmental Information.
\nThis research supports the National Oceanic and Atmospheric Administration (NOAA) Coastal Zone Management Program, a voluntary partnership between the federal government and U.S. coastal and Great Lakes states and territories authorized by the Coastal Zone Management Act (CZMA) of 1972 to address national coastal issues. The act provides the basis for protecting, restoring, and responsibly developing our nation’s diverse coastal communities and resources. To meet the goals of the CZMA, the national program takes a comprehensive approach to coastal resource management – balancing the often competing and occasionally conflicting demands of coastal resource use, economic development, and conservation. A wide range of issues are addressed through the program, including coastal development, water quality, public access, habitat protection, energy facility siting, ocean governance and planning, coastal hazards, and climate change. Accurate maps of seabird and marine mammal distributions are an important tool for making informed management decisions that affect all of these issues.
","language":"English","publisher":"NOAA NCCOS Center of Coastal Monitoring and Assessment","doi":"10.7289/V5NV9G7Z","collaboration":"A collaborative investigation by NOAA's National Ocean Service and National Marine Fisheries Service, U.S. Geological Survey, Bureau of Ocean Energy Management, Washington State Department of Fish and Wildlife, Cascadia Research Collective","usgsCitation":"Menza, C., Leirness, J.B., White, T., Winship, A., Kinlan, B.P., Kracker, L., Zamon, J.E., Ballance, L., Becker, E., Forney, K.A., Barlow, J., Adams, J., Pereksta, D., Pearson, S., Pierce, J., Jeffries, S.J., Calambokidis, J., Douglas, A., Hanson, B.C., Benson, S.R., and Antrim, L., 2016, Predictive mapping of seabirds, pinnipeds and cetaceans off the Pacific Coast of Washington, i. 96 p., https://doi.org/10.7289/V5NV9G7Z.","productDescription":"i. 96 p.","onlineOnly":"N","additionalOnlineFiles":"N","ipdsId":"IP-073210","costCenters":[{"id":651,"text":"Western Ecological Research 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Since the late 1970’s, Lake Ontario benthic prey fish status was primarily assessed using bottom trawl observations confined to the lake’s south shore, in waters from 8 – 150 m (26 – 492 ft). In 2015, the Benthic Prey Fish Survey was cooperatively adjusted and expanded to address resource management information needs including lake-wide benthic prey fish population dynamics. Effort increased from 55 bottom trawl sites to 135 trawl sites collected in depths from 8 - 225m (26 – 738 ft). The spatial coverage of sampling was also expanded and occurred in all major lake basins. The resulting distribution of tow depths more closely matched the available lake depth distribution. The additional effort illustrated how previous surveys were underestimating lake-wide Deepwater Sculpin, Myoxocephalus thompsonii, abundance by not sampling in areas of highest density. We also found species richness was greater in the new sampling sites relative to the historic sites with 11 new fish species caught in the new sites including juvenile Round Whitefish, Prosopium cylindraceum, and Mottled sculpin, Cottus bairdii. Species-specific assessments found Slimy Sculpin, Cottus cognatus abundance increased slightly in 2015 relative to 2014, while Deepwater Sculpin and Round Goby, Neogobius melanostomus, dramatically increased in 2015, relative to 2014. The cooperative, lake-wide Benthic Prey Fish Survey expanded our understanding of benthic fish population dynamics and habitat use in Lake Ontario. This survey’s data and interpretations influence international resource management decision making, such as informing the Deepwater Sculpin conservation status and assessing the balance between sport fish consumption and prey fish populations. Additionally a significant Lake Ontario event occurred in May 2015 when a single juvenile Bloater Coregonus hoyi, was captured during the spring bottom trawl survey at 95m (312 ft) near Oswego, NY. This native, deep-water prey fish, last captured in Lake Ontario survey trawls in 1983, is part of an international, collaborative coregonid restoration effort in the Great Lakes.
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However, few surveys of mussel populations have been conducted over areas large enough and at resolutions fine enough to quantify spatial patterns in their distribution. We used global and local indicators of spatial autocorrelation (i.e., Moran’s I) to quantify spatial patterns of adult and juvenile (≤5 y of age) freshwater mussels across multiple scales based on survey data from 4 reaches (navigation pools 3, 5, 6, and 18) of the Upper Mississippi River, USA. Native mussel densities were sampled at a resolution of ∼300 m and across distances ranging from 21 to 37 km, making these some of the most spatially extensive surveys conducted in a large river. Patch density and the degree and scale of patchiness varied by river reach, age group, and the scale of analysis. In all 4 pools, some patches of adults overlapped patches of juveniles, suggesting spatial and temporal persistence of adequate habitat. In pools 3 and 5, patches of juveniles were found where there were few adults, suggesting recent emergence of positive structuring mechanisms. Last, in pools 3, 5, and 6, some patches of adults were found where there were few juveniles, suggesting that negative structuring mechanisms may have replaced positive ones, leading to a lack of localized recruitment. Our results suggest that: 1) the detection of patches of freshwater mussels requires a multiscaled approach, 2) insights into the spatial and temporal dynamics of structuring mechanisms can be gained by conducting independent analyses of adults and juveniles, and 3) maps of patch distributions can be used to guide restoration and management actions and identify areas where mussels are most likely to influence ecosystem function.
","language":"English","publisher":"University of Chicago Press","doi":"10.1086/686670","usgsCitation":"Ries, P.R., De Jager, N.R., Zigler, S.J., and Newton, T., 2016, Spatial patterns of native freshwater mussels in the Upper Mississippi River: Freshwater Science, v. 35, no. 3, p. 934-947, https://doi.org/10.1086/686670.","productDescription":"14 p.","startPage":"934","endPage":"947","onlineOnly":"N","additionalOnlineFiles":"N","ipdsId":"IP-065443","costCenters":[{"id":606,"text":"Upper Midwest Environmental Sciences Center","active":true,"usgs":true}],"links":[{"id":320634,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","otherGeospatial":"Upper Misssissippi River","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -93.251953125,\n 40.83043687764923\n ],\n [\n -93.251953125,\n 44.84029065139799\n ],\n [\n -89.82421875,\n 44.84029065139799\n ],\n [\n -89.82421875,\n 40.83043687764923\n ],\n [\n -93.251953125,\n 40.83043687764923\n ]\n ]\n ]\n }\n }\n ]\n}","volume":"35","issue":"3","publishingServiceCenter":{"id":6,"text":"Columbus PSC"},"noUsgsAuthors":false,"publicationStatus":"PW","scienceBaseUri":"57233434e4b0b13d39148cfb","contributors":{"authors":[{"text":"Ries, Patricia R. pries@usgs.gov","contributorId":5954,"corporation":false,"usgs":true,"family":"Ries","given":"Patricia","email":"pries@usgs.gov","middleInitial":"R.","affiliations":[{"id":606,"text":"Upper Midwest Environmental Sciences Center","active":true,"usgs":true}],"preferred":true,"id":627660,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"De Jager, Nathan R. 0000-0002-6649-4125 ndejager@usgs.gov","orcid":"https://orcid.org/0000-0002-6649-4125","contributorId":3717,"corporation":false,"usgs":true,"family":"De Jager","given":"Nathan","email":"ndejager@usgs.gov","middleInitial":"R.","affiliations":[{"id":606,"text":"Upper Midwest Environmental Sciences Center","active":true,"usgs":true}],"preferred":true,"id":627661,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Zigler, Steven J. 0000-0002-4153-0652 szigler@usgs.gov","orcid":"https://orcid.org/0000-0002-4153-0652","contributorId":2410,"corporation":false,"usgs":true,"family":"Zigler","given":"Steven","email":"szigler@usgs.gov","middleInitial":"J.","affiliations":[{"id":606,"text":"Upper Midwest Environmental Sciences Center","active":true,"usgs":true}],"preferred":true,"id":627662,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Newton, Teresa 0000-0001-9351-5852 tnewton@usgs.gov","orcid":"https://orcid.org/0000-0001-9351-5852","contributorId":150098,"corporation":false,"usgs":true,"family":"Newton","given":"Teresa","email":"tnewton@usgs.gov","affiliations":[{"id":606,"text":"Upper Midwest Environmental Sciences Center","active":true,"usgs":true}],"preferred":true,"id":627663,"contributorType":{"id":1,"text":"Authors"},"rank":4}]}} ]}