Populations of migratory birds present unique conservation challenges given the often vast distances separating critical resources throughout the annual cycle. Migration areas close to the breeding grounds represent a link between two key stages of the annual cycle, and understanding migration ecology as birds exit the breeding grounds may be particularly informative for successful conservation. We studied migration phenology and stopover ecology of an endangered subspecies of the Red Knot Calidris canutus rufa at a migration area relatively close to its breeding range. Using mark-recapture/resight data and a Jolly-Seber model for open populations, we described the arrival and departure schedules, stopover duration, and passage population size at the Mingan Archipelago, Quebec, Canada. Red Knots arrived at the study area in two distinct waves of birds separated by approximately 22 days. Nearly 30% of the passage population arrived in the first wave of arrivals during 15–18 July, and approximately 22% arrived in a second wave during 8–11 August. The sex-ratio in the stopover population at the time of the first wave was slightly skewed toward females, whereas the second wave was heavily skewed toward males. Because males remain on the breeding grounds to care for young, this may reflect successful
breeding in the year of our study. The estimated stopover duration (population mean) was 11 days (95% credible interval: 10.3–11.7 days), but stopover persistence was variable throughout the season. We estimated a passage population size of 9,450 birds (8,355–10,710), a minimum estimate for reasons related to the duration of our sampling. Mingan Archipelago is thus an important migration area for this endangered subspecies and could be a priority in conservation planning. Our results also emphasize the advantages of mark-recapture/resight approaches for estimating migration phenology and stopover persistence.
Climate change refugia, areas buffered from climate change relative to their surroundings, are of increasing interest as natural resource managers seek to prioritize climate adaptation actions. However, evidence that refugia buffer the effects of anthropogenic climate change is largely missing.
Focusing on the climate-sensitive Belding’s ground squirrel (Urocitellus beldingi), we predicted that highly connected Sierra Nevada meadows that had warmed less or shown less precipitation change over the last century would have greater population persistence, as measured by short-term occupancy, fewer extirpations over the twentieth century, and long-term persistence measured through genetic diversity.
Across California, U. beldingi were more likely to persist over the last century in meadows with high connectivity that were defined as refugial based on a suite of temperature and precipitation factors. In Yosemite National Park, highly connected refugial meadows were more likely to be occupied by U. beldingi. More broadly, populations inhabiting Sierra Nevada meadows with colder mean winter temperatures had higher values of allelic richness at microsatellite loci, consistent with higher population persistence in temperature-buffered sites. Furthermore, both allelic richness and gene flow were higher in meadows that had higher landscape connectivity, indicating the importance of metapopulation processes. Conversely, anthropogenic refugia, sites where populations appeared to persist due to food or water supplementation, had lower connectivity, genetic diversity, and gene flow, and thus might act as ecological traps. This study provides evidence that validates the climate change refugia concept in a contemporary context and illustrates how to integrate field observations and genetic analyses to test the effectiveness of climate change refugia and connectivity.
Climate change refugia will be important for conserving populations as well as genetic diversity and evolutionary potential. Our study shows that in-depth modeling paired with rigorous fieldwork can identify functioning climate change refugia for conservation.
","language":"English","publisher":"Springer Nature","doi":"10.1186/s40665-017-0036-5","usgsCitation":"Morelli, T.L., Maher, S.P., Lim, M.C., Kastely, C., Eastman, L.M., Flint, L.E., Flint, A.L., Beissinger, S.R., and Moritz, C., 2017, Climate change refugia and habitat connectivity promote species persistence: Climate Change Responses, v. 4, 8, 12 p., https://doi.org/10.1186/s40665-017-0036-5.","productDescription":"8, 12 p.","ipdsId":"IP-091107","costCenters":[{"id":5080,"text":"Northeast Climate Adaptation Science Center","active":true,"usgs":true}],"links":[{"id":469229,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.1186/s40665-017-0036-5","text":"Publisher Index Page"},{"id":376819,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","state":"California","otherGeospatial":"Yosemite National Park","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -119.94873046875,\n 37.51844023887861\n ],\n [\n -119.2236328125,\n 37.51844023887861\n ],\n [\n -119.2236328125,\n 38.199338565983844\n ],\n [\n -119.94873046875,\n 38.199338565983844\n ],\n [\n -119.94873046875,\n 37.51844023887861\n ]\n ]\n ]\n }\n }\n ]\n}","volume":"4","noUsgsAuthors":false,"publicationDate":"2017-12-22","publicationStatus":"PW","contributors":{"authors":[{"text":"Morelli, Toni Lyn 0000-0001-5865-5294 tmorelli@usgs.gov","orcid":"https://orcid.org/0000-0001-5865-5294","contributorId":197458,"corporation":false,"usgs":true,"family":"Morelli","given":"Toni","email":"tmorelli@usgs.gov","middleInitial":"Lyn","affiliations":[{"id":411,"text":"National Climate Change and Wildlife Science Center","active":true,"usgs":true},{"id":5080,"text":"Northeast Climate Adaptation Science Center","active":true,"usgs":true}],"preferred":true,"id":794322,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Maher, Sean P.","contributorId":7998,"corporation":false,"usgs":true,"family":"Maher","given":"Sean","email":"","middleInitial":"P.","affiliations":[],"preferred":false,"id":794323,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Lim, Marisa C. W.","contributorId":236825,"corporation":false,"usgs":false,"family":"Lim","given":"Marisa","email":"","middleInitial":"C. W.","affiliations":[],"preferred":false,"id":794324,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Kastely, Christina","contributorId":236826,"corporation":false,"usgs":false,"family":"Kastely","given":"Christina","email":"","affiliations":[],"preferred":false,"id":794325,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Eastman, Lindsey M.","contributorId":236827,"corporation":false,"usgs":false,"family":"Eastman","given":"Lindsey","email":"","middleInitial":"M.","affiliations":[],"preferred":false,"id":794362,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"Flint, Lorraine E. 0000-0002-7868-441X lflint@usgs.gov","orcid":"https://orcid.org/0000-0002-7868-441X","contributorId":1184,"corporation":false,"usgs":true,"family":"Flint","given":"Lorraine","email":"lflint@usgs.gov","middleInitial":"E.","affiliations":[{"id":154,"text":"California Water Science Center","active":true,"usgs":true}],"preferred":true,"id":794363,"contributorType":{"id":1,"text":"Authors"},"rank":6},{"text":"Flint, Alan L. 0000-0002-5118-751X aflint@usgs.gov","orcid":"https://orcid.org/0000-0002-5118-751X","contributorId":1492,"corporation":false,"usgs":true,"family":"Flint","given":"Alan","email":"aflint@usgs.gov","middleInitial":"L.","affiliations":[{"id":657,"text":"Western Geographic Science Center","active":true,"usgs":true},{"id":154,"text":"California Water Science Center","active":true,"usgs":true}],"preferred":true,"id":794364,"contributorType":{"id":1,"text":"Authors"},"rank":7},{"text":"Beissinger, Steven R.","contributorId":100534,"corporation":false,"usgs":true,"family":"Beissinger","given":"Steven","email":"","middleInitial":"R.","affiliations":[],"preferred":false,"id":794365,"contributorType":{"id":1,"text":"Authors"},"rank":8},{"text":"Moritz, Craig","contributorId":149462,"corporation":false,"usgs":false,"family":"Moritz","given":"Craig","email":"","affiliations":[{"id":17742,"text":"Research School of Biology, The Australian Nat'l U, Acton, Australia","active":true,"usgs":false}],"preferred":false,"id":794366,"contributorType":{"id":1,"text":"Authors"},"rank":9}]}} ,{"id":70193484,"text":"sir20175131 - 2017 - An evaluation of the zooplankton community at the Sheboygan River Area of Concern and non-Area of Concern comparison sites in western Lake Michigan rivers and harbors in 2016","interactions":[],"lastModifiedDate":"2018-01-02T12:58:43","indexId":"sir20175131","displayToPublicDate":"2017-12-22T12:45:00","publicationYear":"2017","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":"2017-5131","title":"An evaluation of the zooplankton community at the Sheboygan River Area of Concern and non-Area of Concern comparison sites in western Lake Michigan rivers and harbors in 2016","docAbstract":"The Great Lakes Areas of Concern (AOCs) are considered to be the most severely degraded areas within the Great Lakes basin, as defined in the Great Lakes Water Quality Agreement and amendments. Among the 43 designated AOCs are four Lake Michigan AOCs in the State of Wisconsin. The smallest of these AOCs is the Sheboygan River AOC, which was designated as an AOC because of sediment contamination from polychlorinated biphenyl compounds (PCBs), polycyclic aromatic hydrocarbons (PAHs), volatile organic compounds (VOCs), and heavy metals. The Sheboygan River AOC has 9 of 14 possible Beneficial Use Impairments (BUIs), which must be addressed to improve overall water-quality, and to ultimately delist the AOC. One of the BUIs associated with this AOC is the “degradation of phytoplankton and zooplankton populations,” which can be removed from the list of impairments when it has been determined that zooplankton community composition and structure at the AOC do not differ significantly from communities at non-AOC comparison sites. In 2012 and 2014, the U.S. Geological Survey collected plankton (phytoplankton and zooplankton) community samples at the Sheboygan River AOC and selected non-AOC sites as part of a larger Great Lakes Restoration Initiative study evaluating both the benthos and plankton communities in all four of Wisconsin’s Lake Michigan AOCs. Although neither richness nor diversity of phytoplankton or zooplankton in the Sheboygan River AOC were found to differ significantly from the non-AOC sites in 2012, results from the 2014 data indicated that zooplankton diversity was significantly lower, and so rated as degraded, when compared to the Manitowoc and Kewaunee Rivers, two non-AOC sites of similar size, land use, and close geographic proximity.
As a follow-up to the 2014 results, zooplankton samples were collected at the same locations in the AOC and non-AOC sites during three sampling trips in spring, summer, and fall 2016. An analysis of similarity indicated no significant difference between the zooplankton community composition and structure in the AOC and non-AOC sites. Zooplankton taxa richness in the AOC was rated as “not degraded” in 2016 because of significantly higher taxa richness values in samples collected from the Sheboygan River AOC, compared with the non-AOC sites as a group (that is, data pooled from both non-AOC sites). Zooplankton diversity in 2016, however, was characterized as “degraded” in the AOC on the basis of significantly lower (p<0.05) values in samples collected from the AOC compared with those collected from the non-AOC sites as a group. Annual variation in zooplankton community composition and structure at the Sheboygan River AOC was significantly different among all 3 years sampled, as indicated by an analysis of similarity test. Zooplankton richness was significantly higher in 2014 than in both 2012 and 2016, and diversity was significantly higher in 2012 than in both 2014 and 2016. Postremediation recovery can often be complicated by non-AOC-related stressors such as nutrients, invasive species, and extremes in flow, which could affect the recovery of zooplankton communities in the Sheboygan River AOC. The effect of the stressors on postremediation recovery underscores the importance of sampling multiple years when assessing the effectiveness of remediation activities. The results from this study will be used by the Wisconsin Department of Natural Resources and the U.S. Environmental Protection Agency to determine if restoration efforts have been effective in removing the plankton BUI and to monitor future conditions in the AOC.
","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/sir20175131","collaboration":"Prepared in cooperation with the Wisconsin Department of Natural Resources and the U.S. Environmental Protection Agency","usgsCitation":"Olds, H.T., Scudder Eikenberry, B.C., Burns, D.J., and Bell, A.H., 2017, An evaluation of the zooplankton community at the Sheboygan River Area of Concern and non-Area of Concern comparison sites in western Lake Michigan rivers and harbors in 2016: U.S. Geological Survey Scientific Investigations Report 2017–5131, 15 p., https://doi.org/10.3133/sir20175131.\n","productDescription":"Report: vii, 15 p.; Data Release","numberOfPages":"28","onlineOnly":"Y","additionalOnlineFiles":"N","ipdsId":"IP-090820","costCenters":[{"id":677,"text":"Wisconsin Water Science Center","active":true,"usgs":true}],"links":[{"id":349967,"rank":2,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/sir/2017/5131/sir20175131.pdf","text":"Report","size":"2.36 MB","linkFileType":{"id":1,"text":"pdf"},"description":"SIR 2017-5131"},{"id":349966,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/sir/2017/5131/coverthb.jpg"},{"id":349968,"rank":3,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/F7QV3KD9","text":"USGS data release","linkHelpText":"Zooplankton Community Data at the Sheboygan River Area of Concern and Non-Areas of Concern Comparison Sites in Western Lake Michigan Rivers and Harbors in 2016"}],"country":"United States","state":"Wisconsin","otherGeospatial":"Lake Michigan","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -87.7313232421875,\n 43.74530493763506\n ],\n [\n -87.4676513671875,\n 43.74530493763506\n ],\n [\n -87.4676513671875,\n 44.484749436619964\n ],\n [\n -87.7313232421875,\n 44.484749436619964\n ],\n [\n -87.7313232421875,\n 43.74530493763506\n ]\n ]\n ]\n }\n }\n ]\n}","contact":"Director, Upper Midwest Water Science Center
U.S. Geological Survey
8505 Research Way
Middleton, WI 53562
The U.S. Geological Survey estimated mean undiscovered, technically recoverable resources of 8.7 billion barrels of oil and 25 trillion cubic feet of natural gas (associated and nonassociated) in conventional accumulations in the Cretaceous Nanushuk and Torok Formations in the National Petroleum Reserve in Alaska, adjacent State and Native lands, and State waters. The estimated undiscovered oil resources in the Nanushuk and Torok Formations are significantly higher than previous estimates, owing primarily to recent, larger than anticipated oil discoveries.
","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/fs20173088","usgsCitation":"Houseknecht, D.W., Lease, R.O., Schenk, C.J., Mercier, T.J., Rouse, W.A., Jarboe, P.J., Whidden, K.J., Garrity, C.P., Lewis, K.A., Heller, S.J., Craddock, W.H., Klett, T.R., Le, P.A., Smith, R.A., Tennyson, M.E., Gaswirth, S.B., Woodall, C.A., Brownfield, M.E., Leathers-Miller, H.M., and Finn, T.M., 2017, Assessment of undiscovered oil and gas resources in the Cretaceous Nanushuk and Torok Formations, Alaska North Slope, and summary of resource potential of the National Petroleum Reserve in Alaska, 2017: U.S. Geological Survey Fact Sheet 2017–3088, 4 p., https://doi.org/10.3133/fs20173088.","productDescription":"4 p.","onlineOnly":"N","ipdsId":"IP-092895","costCenters":[{"id":241,"text":"Eastern Energy Resources Science Center","active":true,"usgs":true}],"links":[{"id":438122,"rank":5,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/P9QSJKNF","text":"USGS data release","linkHelpText":"USGS Alaska Petroleum Systems Project: Northern Alaska Province, Nanushuk and Torok Formations Assessment Units and Assessment Input Forms"},{"id":350008,"rank":3,"type":{"id":22,"text":"Related Work"},"url":"https://pubs.usgs.gov/publication/fs20103102","text":"Fact Sheet 2010–3102: ","linkHelpText":"2010 Updated Assessment of Undiscovered Oil and Gas Resources of the National Petroleum Reserve in Alaska (NPRA)"},{"id":350003,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/fs/2017/3088/coverthb.jpg"},{"id":350004,"rank":2,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/fs/2017/3088/fs20173088.pdf","text":"Report","size":"4.0 MB","linkFileType":{"id":1,"text":"pdf"},"description":"FS 2017-3088"},{"id":350009,"rank":4,"type":{"id":22,"text":"Related Work"},"url":"https://pubs.usgs.gov/publication/fs20053043","text":"Fact Sheet 2005–3043: ","linkHelpText":"Oil and Gas Assessment of Central North Slope, Alaska, 2005"}],"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 -170,\n 67\n ],\n [\n -145,\n 67\n ],\n [\n -145,\n 71.5\n ],\n [\n -170,\n 71.5\n ],\n [\n -170,\n 67\n ]\n ]\n ]\n }\n }\n ]\n}","contact":"Program Coordinator, Energy Resources Program
U.S. Geological Survey
12201 Sunrise Valley Drive
National Center, MS 913
Reston, VA 20192
The distribution and partitioning of elements in igneous rocks is well established for various melt –(fluid) –solid pairs and provides important insights into the petrogenesis of these rocks. Studies of the partitioning behavior of elements under metamorphic conditions are scarce and commonly focus on high-grade metamorphic facies. Little is known about the partitioning behavior of elements under low-grade metamorphic conditions. Greenschist-facies metasedimentary rocks of the North American Belt Supergroup host magnetite that displays equilibrium features with co-existing mineral phases such as quartz and carbonate. Magnetite is an ideal target for geochemical investigations because it can incorporate a large number of cations and is sensitive to changes in temperature, oxygen fugacity, pressure, whole-rock composition, and cooling trends. Whole-rock major and trace element analyses have been undertaken on representative samples from Belt Supergroup metasedimentary rocks using X-ray luorescence. Electron microprobe and laser ablation ICP-MS were used to obtain major and trace element concentrations for magnetite hosted in these rocks. Stable-isotope geothermometry of magnetite–quartz and magnetite–carbonate pairs constrain metamorphic temperatures to ca. 390°C. Partition coefficients (D) for magnetite–matrix pairs presumably reflect equilibrium at these low-grade metamorphic conditions. Except for Mn and Ni, which show comparable partition coefficients, the calculated values are one to two orders of magnitude lower than those for igneous magnetite. Aluminum displays the lowest calculated partition coefficient with a value of 0.006 and Ni and Fe the highest values with 6.3 and 20.9, respectively. Of the elements that commonly occur in spinel-group minerals, two groups can be distinguished: (1) elements that preferentially partition into the host rock (D<1): Al, Mg, Pb, and Ti and (2) elements that show a preference to partition into magnetite (D>1): Zn, Mn, Cr, V, Ni, and Fe.
","language":"English","publisher":"Shweizerbart Science","doi":"10.1127/ejm/2017/0029-2657","usgsCitation":"Nadoll, P., Mauk, J.L., Hayes, T., Koenig, A., and Box, S.E., 2017, Element partitioning in magnetite under low-grade metamorphic conditions – A case study from the Proterozoic Belt Supergroup, USA: European Journal of Mineralogy, v. 29, no. 5, p. 795-805, https://doi.org/10.1127/ejm/2017/0029-2657.","productDescription":"11 p.","startPage":"795","endPage":"805","ipdsId":"IP-079866","costCenters":[{"id":171,"text":"Central Mineral and Environmental Resources Science Center","active":true,"usgs":true},{"id":312,"text":"Geology, Minerals, Energy, and Geophysics Science Center","active":true,"usgs":true}],"links":[{"id":393293,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"volume":"29","issue":"5","noUsgsAuthors":false,"publicationStatus":"PW","contributors":{"authors":[{"text":"Nadoll, Patrick 0000-0002-4061-7203","orcid":"https://orcid.org/0000-0002-4061-7203","contributorId":270292,"corporation":false,"usgs":false,"family":"Nadoll","given":"Patrick","email":"","affiliations":[{"id":56136,"text":"Friedrich-Alexander Universität Erlangen-Nürnberg","active":true,"usgs":false}],"preferred":false,"id":828943,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Mauk, Jeffrey L. 0000-0002-6244-2774 jmauk@usgs.gov","orcid":"https://orcid.org/0000-0002-6244-2774","contributorId":4101,"corporation":false,"usgs":true,"family":"Mauk","given":"Jeffrey","email":"jmauk@usgs.gov","middleInitial":"L.","affiliations":[{"id":171,"text":"Central Mineral and Environmental Resources Science Center","active":true,"usgs":true}],"preferred":true,"id":828944,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Hayes, Timothy 0000-0002-1224-4219","orcid":"https://orcid.org/0000-0002-1224-4219","contributorId":206109,"corporation":false,"usgs":true,"family":"Hayes","given":"Timothy","email":"","affiliations":[{"id":312,"text":"Geology, Minerals, Energy, and Geophysics Science Center","active":true,"usgs":true}],"preferred":true,"id":828945,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Koenig, Alan 0000-0002-5230-0924","orcid":"https://orcid.org/0000-0002-5230-0924","contributorId":206119,"corporation":false,"usgs":true,"family":"Koenig","given":"Alan","affiliations":[{"id":171,"text":"Central Mineral and Environmental Resources Science Center","active":true,"usgs":true}],"preferred":true,"id":828946,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Box, Stephen E. 0000-0002-5268-8375 sbox@usgs.gov","orcid":"https://orcid.org/0000-0002-5268-8375","contributorId":1843,"corporation":false,"usgs":true,"family":"Box","given":"Stephen","email":"sbox@usgs.gov","middleInitial":"E.","affiliations":[{"id":312,"text":"Geology, Minerals, Energy, and Geophysics Science Center","active":true,"usgs":true}],"preferred":true,"id":828947,"contributorType":{"id":1,"text":"Authors"},"rank":5}]}} ,{"id":70191438,"text":"sir20175121 - 2017 - Water quality, sources of nitrate, and chemical loadings in the Geronimo Creek and Plum Creek watersheds, south-central Texas, April 2015–March 2016","interactions":[],"lastModifiedDate":"2018-01-02T10:47:49","indexId":"sir20175121","displayToPublicDate":"2017-12-22T00:00:00","publicationYear":"2017","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":"2017-5121","title":"Water quality, sources of nitrate, and chemical loadings in the Geronimo Creek and Plum Creek watersheds, south-central Texas, April 2015–March 2016","docAbstract":"Located in south-central Texas, the Geronimo Creek and Plum Creek watersheds have long been characterized by elevated nitrate concentrations. From April 2015 through March 2016, an assessment was done by the U.S. Geological Survey, in cooperation with the Guadalupe-Blanco River Authority and the Texas State Soil and Water Conservation Board, to characterize nitrate concentrations and to document possible sources of elevated nitrate in these two watersheds. Water-quality samples were collected from stream, spring, and groundwater sites distributed across the two watersheds, along with precipitation samples and wastewater treatment plant (WWTP) effluent samples from the Plum Creek watershed, to characterize endmember concentrations and isotopic compositions from April 2015 through March 2016. Stream, spring, and groundwater samples from both watersheds were collected during four synoptic sampling events to characterize spatial and temporal variations in water quality and chemical loadings. Water-quality and -quantity data from the WWTPs and stream discharge data also were considered. Samples were analyzed for major ions, selected trace elements, nutrients, and stable isotopes of water and nitrate.
The dominant land use in both watersheds is agriculture (cultivated crops, rangeland, and grassland and pasture). The upper part of the Plum Creek watershed is more highly urbanized and has five major WWTPs; numerous smaller permitted wastewater outfalls are concentrated in the upper and central parts of the Plum Creek watershed. The Geronimo Creek watershed, in contrast, has no WWTPs upstream from or near the sampling sites.
Results indicate that water quality in the Geronimo Creek watershed, which was evaluated only during base-flow conditions, is dominated by groundwater, which discharges to the stream by numerous springs at various locations. Nitrate isotope values for most Geronimo Creek samples were similar, which indicates that they likely have a common source (or sources) of nitrate. Nitrate sources in the Geronimo Creek watershed include a predominance of nitrate from fertilizer applications, as well as a contribution from septic systems. Additional nitrate loading from these sources is ongoing. Chemical loadings of dissolved solids, chloride, and sulfate varied little among sampling events and were low at most sites because of low streamflow.
In contrast to the Geronimo Creek watershed, nitrate sources in the Plum Creek watershed are dominated by effluent discharge from the major WWTPs in the upper and central parts of the watershed. Results indicate that discharge from these WWTPs accounts for the majority of base flow in the watershed. Nitrate concentrations in Plum Creek were dependent on flow conditions, with the highest concentrations measured at lower flows, when flow is dominated by WWTP effluent discharge. In addition to WWTP effluent discharge, the Plum Creek watershed, similar to the Geronimo Creek watershed, also is affected by historical and current loading of nitrate from fertilizer applications and from septic systems in the watershed. Chemical loadings of dissolved solids, chloride, sulfate, and nitrate in Plum Creek at lower flow conditions are highest at the upstream sites and decrease downstream as distance from the WWTPs increases, which is consistent with WWTP effluent as an important control on water quality. Under higher flow conditions, however, nitrate loads to Plum Creek increased by about a factor of three. These higher nitrate loads cannot be accounted for by WWTP effluent discharge from the five major WWTPs in the watershed. This additional loading indicates that nitrate is exported from the northeastern part of the watershed. In the lower part of the Plum Creek watershed, higher concentrations of dissolved solids, chloride, and sulfate occur, which might be affected by produced water associated with oil and gas exploration, or mixing with saline groundwater.
","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/sir20175121","collaboration":"Prepared in cooperation with the Guadalupe-Blanco River Authority and the Texas State Soil and Water Conservation Board","usgsCitation":"Lambert, R.B., Opsahl, S.P., and Musgrove, MaryLynn, 2017, Water quality, sources of nitrate, and chemical loadings in the Geronimo Creek and Plum Creek watersheds, south-central Texas, April 2015–March 2016: U.S. Geological Survey Scientific Investigations Report 2017–5121, 49 p., https://doi.org/10.3133/sir20175121.","productDescription":"Report: x, 49 p.; Data Release","numberOfPages":"64","onlineOnly":"Y","additionalOnlineFiles":"Y","ipdsId":"IP-087883","costCenters":[{"id":583,"text":"Texas Water Science Center","active":true,"usgs":true}],"links":[{"id":350189,"rank":3,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/F7Q23Z5S","text":"USGS data release","description":"USGS Data Release","linkHelpText":"Data release for water-quality and chemical loading data from the Geronimo Creek and Plum Creek watersheds, south-central Texas, April 2015–March 2016"},{"id":350187,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/sir/2017/5121/coverthb.jpg"},{"id":350188,"rank":2,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/sir/2017/5121/sir20175121.pdf","text":"Report","size":"3.27 MB","linkFileType":{"id":1,"text":"pdf"},"description":"SIR 2017–5121"}],"country":"United States","state":"Texas","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -98.1667,\n 29.5\n ],\n [\n -97.4167,\n 29.5\n ],\n [\n -97.4167,\n 30.1667\n ],\n [\n -98.1667,\n 30.1667\n ],\n [\n -98.1667,\n 29.5\n ]\n ]\n ]\n }\n }\n ]\n}","contact":"Director, Texas Water Science Center
U.S. Geological Survey
1505 Ferguson Lane
Austin, Texas 78754–4501
Pavlof Volcano is one of the most frequently active volcanoes in the Aleutian Island arc, having erupted more than 40 times since observations were first recorded in the early 1800s . The volcano is located on the Alaska Peninsula (lat 55.4173° N, long 161.8937° W), near Izembek National Wildlife Refuge. The towns and villages closest to the volcano are Cold Bay, Nelson Lagoon, Sand Point, and King Cove, which are all within 90 kilometers (km) of the volcano (fig. 1). Pavlof is a symmetrically shaped stratocone that is 2,518 meters (m) high, and has about 2,300 m of relief. The volcano supports a cover of glacial ice and perennial snow roughly 2 to 4 cubic kilometers (km3) in volume, which is mantled by variable amounts of tephra fall, rockfall debris, and pyroclastic-flow deposits produced during historical eruptions. Typical Pavlof eruptions are characterized by moderate amounts of ash emission, lava fountaining, spatter-fed lava flows, explosions, and the accumulation of unstable mounds of spatter on the upper flanks of the volcano. The accumulation and subsequent collapse of spatter piles on the upper flanks of the volcano creates hot granular avalanches, which erode and melt snow and ice, and thereby generate watery debris-flow and hyperconcentrated-flow lahars.
Seismic instruments were first installed on Pavlof Volcano in the early 1970s, and since then eruptive episodes have been better characterized and specific processes have been documented with greater certainty. The application of remote sensing techniques, including the use of infrasound data, has also aided the study of more recent eruptions. Although Pavlof Volcano is located in a remote part of Alaska, it is visible from Cold Bay, Sand Point, and Nelson Lagoon, making distal observations of eruptive activity possible, weather permitting. A busy air-travel corridor that is utilized by a numerous transcontinental and regional air carriers passes near Pavlof Volcano. The frequency of air travel across the region results in a relatively large number of airborne observations of eruptive activity. During the 2014 Pavlof eruptions, the Alaska Volcano Observatory received observations and photographs from pilots and local observers, which aided evaluation of the eruptive activity and the areas affected by eruptive products.
This report outlines the chronology of events associated with the 2014 eruptive activity at Pavlof Volcano, provides documentation of the style and character of the eruptive episodes, and reports briefly on the eruptive products and impacts. The principal observations are described and portrayed on maps and photographs, and the 2014 eruptive activity is compared to historical eruptions.
","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/sir20175129","usgsCitation":"Waythomas, C.F., Haney, M.M., Wallace, K.L., Cameron, C.E., and Schneider, D.J., 2017, The 2014 eruptions of Pavlof Volcano, Alaska: U.S. Geological Survey Scientific Investigations Report 2017-5129, 27 p., https://doi.org/10.3133/sir20175129. ","productDescription":"vi, 27 p.","numberOfPages":"36","onlineOnly":"Y","ipdsId":"IP-075355","costCenters":[{"id":121,"text":"Alaska Volcano Observatory","active":false,"usgs":true}],"links":[{"id":350485,"rank":3,"type":{"id":3,"text":"Appendix"},"url":"https://pubs.usgs.gov/sir/2017/5129/sir20175129_appendix1.xlsx","text":"Appendix 1","size":"40 KB","linkFileType":{"id":3,"text":"xlsx"},"description":"SIR 2017-5129"},{"id":350486,"rank":4,"type":{"id":3,"text":"Appendix"},"url":"https://pubs.usgs.gov/sir/2017/5129/sir20175129_appendix2a.xlsx","text":"Appendix 2A","size":"40 KB","linkFileType":{"id":3,"text":"xlsx"},"description":"SIR 2017-5129"},{"id":350487,"rank":5,"type":{"id":3,"text":"Appendix"},"url":"https://pubs.usgs.gov/sir/2017/5129/sir20175129_appendix2b.xlsx","text":"Appendix 2B","size":"150 KB","linkFileType":{"id":3,"text":"xlsx"},"description":"SIR 2017-5129"},{"id":350186,"rank":2,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/sir/2017/5129/sir20175129.pdf","text":"Report","size":"5.3 MB","linkFileType":{"id":1,"text":"pdf"},"description":"SIR 2017-5129"},{"id":350185,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/sir/2017/5129/coverthb.jpg"}],"country":"United States","state":"Alaska","otherGeospatial":"Pavlof Volcano","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -162.2,\n 55.25\n ],\n [\n -161.7,\n 55.25\n ],\n [\n -161.7,\n 55.547280698640805\n ],\n [\n -162.2,\n 55.547280698640805\n ],\n [\n -162.2,\n 55.25\n ]\n ]\n ]\n }\n }\n ]\n}","contact":"Alaska Volcano Observatory
U.S. Geological Survey
4210 University Drive
Anchorage, AK 99508
The Chukchi Borderland is both a stand-alone petroleum province and assessment unit (AU) that lies north of the Chukchi Sea. It is a bathymetrically high-standing block of continental crust that was probably rifted from the Canadian continental margin. The sum of our knowledge of this province is based upon geophysical data (seismic, gravity, and magnetic) and a limited number of seafloor core and dredge samples.
As expected from the limited data set, the basin’s petroleum potential is poorly known. A single assessment unit, the Chukchi Borderland AU, was defined and assigned an overall probability of about a 5 percent chance of at least one petroleum accumulation >50 million barrels of oil equivalent (MMBOE). No quantitative assessment of sizes and numbers of petroleum accumulations was conducted for this AU.
Contact Information, Geology, Minerals, Energy, & Geophysics Science Center—Menlo Park
U.S. Geological Survey
345 Middlefield Road
Menlo Park, CA 94025-3591
FAX 650-329-4936
The hydrocarbon potential of the Yukon Flats Basin Province in Central Alaska was assessed in 2004 as part of an update to the National Oil and Gas Assessment. Three assessment units (AUs) were identified and assessed using a methodology somewhat different than that of the 2008 Circum-Arctic Resource Appraisal (CARA). An important difference in the methodology of the two assessments is that the 2004 assessment specified a minimum accumulation size of 0.5 million barrels of oil equivalent (MMBOE), whereas the 2008 CARA assessment specified a minimum size of 50 MMBOE. The 2004 assessment concluded that >95 percent of the estimated mean undiscovered oil and gas resources occur in a single AU, the Tertiary Sandstone AU. This is also the only AU of the three that extends north of the Arctic Circle.
For the CARA project, the number of oil and gas accumulations in the 2004 assessment of the Tertiary Sandstone AU was re-evaluated in terms of the >50-MMBOE minimum accumulation size. By this analysis, and assuming the resource to be evenly distributed across the AU, 0.23 oil fields and 1.20 gas fields larger than 50 MMBOE are expected in the part of the AU north of the Arctic Circle. The geology suggests, however, that the area north of the Arctic Circle has a lower potential for oil and gas accumulations than the area to the south where the sedimentary section is thicker, larger volumes of hydrocarbons may have been generated, and potential structural traps are probably more abundant. Because of the low potential implied for the area of the AU north of the Arctic Circle, the Yukon Flats Tertiary Sandstone AU was not quantitatively assessed for the 2008 CARA.
Contact Information, Geology, Minerals, Energy, & Geophysics Science Center—Menlo Park
U.S. Geological Survey
345 Middlefield Road
Menlo Park, CA 94025-3591
FAX 650-329-4936
The U.S. Geological Survey (USGS) has recently assessed the potential for undiscovered oil and gas resources in the Northwest Laptev Sea Shelf Province as part of the USGS Circum-Arctic Resource Appraisal. The province is in the Russian Arctic, east of Severnaya Zemlya and the Taimyr fold-and-thrust belt. The province is separated from the rest of the Laptev Sea Shelf by the Severnyi transform fault. One assessment unit (AU) was defined for this study: the Northwest Laptev Sea Shelf AU. The estimated mean volumes of undiscovered petroleum resources in the Northwest Laptev Sea Shelf Province are approximately 172 million barrels of crude oil, 4.5 trillion cubic feet of natural gas, and 119 million barrels of natural-gas liquids, north of the Arctic Circle.
","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/pp1824S","usgsCitation":"Klett, T.R. and Pitman J.K., 2017, Geology and assessment of undiscovered oil and gas resources of the Northwest Laptev Sea Shelf Province, 2008, chap. S of Moore, T.E., and Gautier, D.L., eds., The 2008 Circum-Arctic Resource Appraisal: U.S. Geological Survey Professional Paper 1824, 13 p., https://doi.org/10.3133/pp1824S.","productDescription":"Report: vii, 13 p.; Appendix","numberOfPages":"24","ipdsId":"IP-055278","costCenters":[{"id":255,"text":"Energy Resources Program","active":true,"usgs":true}],"links":[{"id":350197,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/pp/1824/s/coverthb.jpg"},{"id":350199,"rank":3,"type":{"id":3,"text":"Appendix"},"url":"https://pubs.usgs.gov/pp/1824/s/pp1824s_appendix1.xls","text":"Appendix 1","size":"55 KB","linkFileType":{"id":3,"text":"xlsx"},"description":"PP 1824 Chapter S"},{"id":350198,"rank":2,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/pp/1824/s/pp1824s.pdf","text":"Report","size":"2.5 MB","linkFileType":{"id":1,"text":"pdf"},"description":"PP 1824 Chapter S"}],"otherGeospatial":"Northwest Laptev Shelf Province","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n 100,\n 74\n ],\n [\n 124,\n 74\n ],\n [\n 124,\n 83\n ],\n [\n 100,\n 83\n ],\n [\n 100,\n 74\n ]\n ]\n ]\n }\n }\n ]\n}","contact":"Contact Information, Geology, Minerals, Energy, & Geophysics Science Center—Menlo Park
U.S. Geological Survey
345 Middlefield Road
Menlo Park, CA 94025-3591
FAX 650-329-4936
The U.S. Geological Survey (USGS) recently assessed the potential for undiscovered oil and gas resources of the Laptev Sea Shelf Province as part of the 2008 Circum-Arctic Resource Appraisal (CARA) program. The province is situated in the Russian Federation and is located between the Taimyr Peninsula and the Novosibirsk (New Siberian) Islands. Three assessment units (AUs) were defined for this study: the West Laptev Grabens AU, the East Laptev Horsts AU, and the Anisin-Novosibirsk AU, two of which were assessed for undiscovered, technically recoverable resources. The East Laptev Horsts AU was not quantitatively assessed. The estimated mean volumes of undiscovered oil and gas for the Laptev Sea Shelf Province are approximately 3 billion barrels of crude oil, 32 trillion cubic feet of natural gas, and <1 billion barrels of natural gas liquids, all north of the Arctic Circle.
","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/pp1824W","usgsCitation":"Klett, T.R. and Pitman J.K., 2017, Geology and assessment of undiscovered oil and gas resources of the Laptev Sea Shelf Province, 2008, chap. W of Moore, T.E., and Gautier, D.L., eds., The 2008 Circum-Arctic Resource Appraisal: U.S. Geological Survey Professional Paper 1824, 13 p., https://doi.org/10.3133/pp1824W.","productDescription":"Report: vii, 13 p.; 4 Appendixes","numberOfPages":"24","onlineOnly":"Y","ipdsId":"IP-050988","costCenters":[{"id":255,"text":"Energy Resources Program","active":true,"usgs":true}],"links":[{"id":350173,"rank":3,"type":{"id":3,"text":"Appendix"},"url":"https://pubs.usgs.gov/pp/1824/w/pp1824w_appendix1a.xls","text":"Appendix 1A","size":"50 KB","linkFileType":{"id":3,"text":"xlsx"},"description":"PP 1824 Chapter W Appendix"},{"id":350170,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/pp/1824/w/coverthb.jpg"},{"id":350171,"rank":2,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/pp/1824/w/pp1824w_.pdf","text":"Report","size":"9.2 MB","linkFileType":{"id":1,"text":"pdf"},"description":"PP 1824 Chapter W"},{"id":350174,"rank":4,"type":{"id":3,"text":"Appendix"},"url":"https://pubs.usgs.gov/pp/1824/w/pp1824w_appendix1b.xls","text":"Appendix 1B","size":"40 KB","linkFileType":{"id":3,"text":"xlsx"},"description":"PP 1824 Chapter W Appendix"},{"id":350176,"rank":6,"type":{"id":3,"text":"Appendix"},"url":"https://pubs.usgs.gov/pp/1824/w/pp1824w_appendix3.xls","text":"Appendix 3","size":"45 KB","linkFileType":{"id":3,"text":"xlsx"},"description":"PP 1824 Chapter W Appendix"},{"id":350175,"rank":5,"type":{"id":3,"text":"Appendix"},"url":"https://pubs.usgs.gov/pp/1824/w/pp1824w_appendix2.xls","text":"Appendix 2","size":"45 KB","linkFileType":{"id":3,"text":"xlsx"},"description":"PP 1824 Chapter W Appendix"}],"otherGeospatial":"Laptev Sea Shelf Province","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n 113,\n 70\n ],\n [\n 148,\n 70\n ],\n [\n 148,\n 78\n ],\n [\n 113,\n 78\n ],\n [\n 113,\n 70\n ]\n ]\n ]\n }\n }\n ]\n}","contact":"Contact Information, Geology, Minerals, Energy, & Geophysics Science Center—Menlo Park
U.S. Geological Survey
345 Middlefield Road
Menlo Park, CA 94025-3591
FAX 650-329-4936
We used geological field studies and diatom biostratigraphy to test a published hypothesis that Neogene marine siliceous strata in the Maricopa and Parkfield areas, located on opposite sides of the San Andreas Fault, were formerly contiguous and then were displaced by about 80–130 kilometers (km) of right-lateral slip along the fault. In the Maricopa area on the northeast side of the San Andreas Fault, the upper Miocene Bitterwater Creek Shale consists of hard, siliceous shale with dolomitic concretions and turbidite sandstone interbeds. Diatom assemblages indicate that the Bitterwater Creek Shale was deposited about 8.0–6.7 million years before present (Ma) at the same time as the uppermost part of the Monterey Formation in parts of coastal California. In the Parkfield area on the southwest side of the San Andreas Fault, the upper Miocene Pancho Rico Formation consists of soft to indurated mudstone and siltstone and fossiliferous, bioturbated sandstone. Diatom assemblages from the Pancho Rico indicate deposition about 6.7–5.7 Ma (latest Miocene), younger than the Bitterwater Creek Shale and at about the same time as parts of the Sisquoc Formation and Purisima Formation in coastal California. Our results show that the Bitterwater Creek Shale and Pancho Rico Formation are lithologically unlike and of different ages and therefore do not constitute a cross-fault tie that can be used to estimate rightlateral displacement along the San Andreas Fault.
In the Maricopa area northeast of the San Andreas Fault, the Bitterwater Creek Shale overlies conglomeratic fan-delta deposits of the upper Miocene Santa Margarita Formation, which in turn overlie siliceous shale of the Miocene Monterey Formation from which we obtained a diatom assemblage dated at about 10.0–9.3 Ma. Previous investigations noted that the Santa Margarita Formation in the Maricopa area contains granitic and metamorphic clasts derived from sources in the northern Gabilan Range, on the opposite side of the San Andreas Fault, that have moved relatively northwestward by 254 ± 5 km of right-lateral displacement along the fault. Our new diatom ages suggest that Santa Margarita deposition and fault displacement began about 10–8 Ma and imply long-term average slip rates along the San Andreas Fault of about 25–32 millimeters per year (mm/yr), Evaluation of Hypotheses for Right-Lateral Displacement of Neogene Strata Along the San Andreas Fault Between Parkfield and Maricopa, California By Richard G. Stanley, John A. Barron, and Charles L. Powell, II about the same as published estimates of Quaternary average slip rates based on geologic and geodetic studies.
","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/sir20175125","usgsCitation":"Stanley, R.G., Barron, J.A., and Powell, C.L., II, 2017, Evaluation of hypotheses for right-lateral displacement of Neogene strata along the San Andreas Fault between Parkfield and Maricopa, California: U.S. Geological Survey Scientific Investigations Report 2017–5125, 26 p., https://doi.org/10.3133/sir20175125.","productDescription":"Report: v, 26 p.","numberOfPages":"36","onlineOnly":"Y","ipdsId":"IP-066804","costCenters":[{"id":312,"text":"Geology, Minerals, Energy, and Geophysics Science Center","active":true,"usgs":true}],"links":[{"id":350184,"rank":2,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/sir/2017/5125/sir20175125.pdf","text":"Report","size":"2.6 MB","linkFileType":{"id":1,"text":"pdf"},"description":"SIR 2017-5125"},{"id":350183,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/sir/2017/5125/coverthb.jpg"}],"country":"United States","state":"California","city":"Maricopa, Parkfield","otherGeospatial":"San Andreas Fault","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -120.5,\n 34.9167\n ],\n [\n -119.0333,\n 34.9167\n ],\n [\n -119.0333,\n 36\n ],\n [\n -120.5,\n 36\n ],\n [\n -120.5,\n 34.9167\n ]\n ]\n ]\n }\n }\n ]\n}","contact":"The U.S. Geological Survey (USGS), in support of the Massachusetts Estuaries Project (MEP), delineated groundwater-contributing areas to various hydrologic receptors including ponds, streams, and coastal water bodies throughout southeastern Massachusetts, including portions of the Plymouth-Carver aquifer system and all of Cape Cod. These contributing areas were delineated over a 6-year period from 2003 through 2008 by using previously published regional USGS groundwater-flow models for the Plymouth-Carver region (Masterson and others, 2009), the Sagamore (western) and Monomoy (eastern) flow lenses of Cape Cod (Walter and Whealan, 2005), and lower Cape Cod (Masterson, 2004). The original USGS groundwater-contributing areas were subsequently revised in some locations by the MEP to remove modeling artifacts or to make the contributing areas more consistent with site-specific hydrologic conditions without further USGS review. This report describes the process used to create the USGS groundwater-contributing areas and provides these model results in their original format in a single, publicly accessible publication.
","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/ds1074","collaboration":"Prepared in cooperation with the Massachusetts Department of Environmental Protection","usgsCitation":"Carlson, C.S., Masterson, J.P., Walter, D.A., and Barbaro, J.R., 2017, Development of simulated groundwater-contributing areas to selected streams, ponds, coastal water bodies, and production wells in the Plymouth-Carver region and Cape Cod, Massachusetts: U.S. Geological Survey Data Series 1074, 17 p., https://doi.org/10.3133/ds1074.","productDescription":"Report: iv, 17 p.; Data Release","onlineOnly":"Y","additionalOnlineFiles":"N","ipdsId":"IP-087594","costCenters":[{"id":466,"text":"New England Water Science Center","active":true,"usgs":true}],"links":[{"id":350104,"rank":3,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/F7V69H2Z","text":"USGS data release","description":"USGS data release","linkHelpText":"Simulated Groundwater-Contributing Areas to Selected Streams, Ponds, Coastal Water Bodies, and Production Wells, Plymouth-Carver Region and Cape Cod, Massachusetts"},{"id":350102,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/ds/1074/coverthb.jpg"},{"id":350103,"rank":2,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/ds/1074/ds1074.pdf","text":"Report","size":"5.63 MB","linkFileType":{"id":1,"text":"pdf"},"description":"DS 1074"}],"country":"United States","state":"Massachusetts","otherGeospatial":"Cape Cod, Plymouth-Carver Region","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -71.015625,\n 41.50857729743935\n ],\n [\n -69.88128662109375,\n 41.50857729743935\n ],\n [\n -69.88128662109375,\n 42.167475010395336\n ],\n [\n -71.015625,\n 42.167475010395336\n ],\n [\n -71.015625,\n 41.50857729743935\n ]\n ]\n ]\n }\n }\n ]\n}","contact":"Director, New England Water Science Center
U.S. Geological Survey
10 Bearfoot Road
Northborough, MA 01532
Agricultural producers in North Dakota are aware of concerns about degrading water quality, and many of the producers are interested in implementing conservation practices to reduce the export of nutrients from their farms. Producers often implement conservation practices without knowledge of the water quality of the runoff from their farm or if conservation practices they may implement have any effect on water quality. In response to this lack of information, the U.S. Geological Survey, in cooperation with North Dakota State University Extension Service and in coordination with an advisory group consisting of State agencies, agricultural producers, and commodity groups, implemented a monitoring study as part of a Discovery Farms program in North Dakota in 2007. Three data-collection sites were established at each of three farms near Underwood, Embden, and Dazey, North Dakota. The purpose of this report is to describe runoff and water-quality characteristics using data collected at the three Discovery Farms during 2008–16. Runoff and water-quality data were used to help describe the implications of agricultural conservation practices on runoff and water-quality patterns.
Runoff characteristics of monitoring sites at the three farms were determined by measuring flow volume and precipitation. Runoff at the Underwood farm monitoring sites generally was controlled by precipitation in the area, antecedent soil moisture conditions, and, after 2012, possibly by the diversion ditch constructed by the producer. Most of the annual runoff was in March and April each year during spring snowmelt. Runoff characteristics at the Embden farm are complex because of the mix of surface runoff and flow through two separate drainage tile systems. Annual flow volumes for the drainage tiles sites (sites E2 and E3) were several orders of magnitude greater than measured at the surface water site E1. Site E1 generally only had runoff briefly in March and April during spring snowmelt and during only a few large rain events throughout 2009–16. Flow was somewhat continuous at sites E2 and E3 throughout the year during years of increased precipitation, such as in 2010 and 2011. At Dazey farm, annual flow volumes at the most downstream site D3 for 2010–15 ranged from 88 acre-feet (2012) to 12,060 acre-feet (2010). The largest monthly runoff volumes at D1 (most upstream site; combination of data from site D1a [original site] and site D1b [relocated site]) and D3 were in March and April during spring snowmelt runoff and rain events.
At Underwood farm, total ammonia and total phosphorus had the highest concentrations at the most upstream site (U1) and decreased sequentially at sites U2 and U3 downstream. Total ammonia and total phosphorus concentrations at the sites for Underwood farm also generally were higher than measured at sites for the Dazey and Embden farms. At Embden farm, nitrate plus nitrite concentrations were lowest at site E1 (surface-water site) and highest at sites E2 and E3 (drainage tile sites). Nitrate plus nitrite concentrations at sites E2 and E3 also were the highest among all the sites at all three farms. Median total nitrate plus nitrite concentrations for sites E1, E2, and E3 were 0.22, 13, and 10 milligrams per liter as nitrogen, respectively. Nutrient concentrations generally were greater at site D1 (most upstream site) compared to site D3 (most downstream site) at Dazey farm. Higher concentrations at site D1, which is farther upstream and closer to potential sources of nutrients, compared to lower concentrations at site D3, which is farther downstream and receives more runoff, indicates that dilution may be the reason concentrations decrease downstream.
Annual loads for chloride at all three Underwood sites were the greatest in 2011 and the least in 2012, which coincided with years of the greatest and least annual flow volume, respectively. Total ammonia had a similar pattern at the three sites. Nitrate plus nitrite loads displayed a different pattern than chloride and total ammonia, indicating possible different sources. Chloride, total ammonia, total phosphorus, and suspended sediment were transported past site U1 mostly in March and the least from July through October. Monthly nitrate plus nitrite loads had a different pattern than the other constituents, indicating other possible sources such as fertilizer application in the surrounding cropland.
Annual loads for Embden farm were considerably greater at sites E2 and E3 compared to site E1. Annual yields for all constituents also were substantially greater at sites E2 and E3 compared to site E1, mainly because of a combination of higher flow volumes and small contributing drainage areas at sites E2 and E3 compared to site E1.
The greatest annual loads at Dazey farm site D3 for chloride, nitrate plus nitrite, and suspended sediment were in 2010 and 2011, and zero loads were estimated for 2012 because no flow was measured at the site. Mean monthly loads generally were greatest for most constituents in March and April at sites D1 and D3 except for suspended sediment that had the greatest monthly loads in May.
To mitigate runoff and water-quality effects of their operations, the producers implemented various agricultural conservation practices before and during the Discovery Farms monitoring. Even though it was difficult to quantify the effects of the agricultural conservation practices implemented at the farms, the data collected from the Discovery Farms program provided a better understanding of some of the variables that affect runoff and water quality.
","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/sir20175124","collaboration":"Prepared in cooperation with North Dakota State University Extension Service","usgsCitation":"Galloway, J.M., and Nustad, R.A., 2017, Runoff and water-quality characteristics of three Discovery Farms in North Dakota, 2008–16: U.S. Geological Survey Scientific Investigations Report 2017–5124, 68 p.,\nhttps://doi.org/10.3133/sir20175124.","productDescription":"ix, 68 p.","numberOfPages":"82","onlineOnly":"Y","ipdsId":"IP-088875","costCenters":[{"id":478,"text":"North Dakota Water Science Center","active":true,"usgs":true}],"links":[{"id":350167,"rank":2,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/sir/2017/5124/sir20175124.pdf","text":"Report","size":"3.25 MB","linkFileType":{"id":1,"text":"pdf"},"description":"SIR 2017–5124"},{"id":350166,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/sir/2017/5124/coverthb.jpg"}],"country":"United States","state":"North Dakota","geographicExtents":"{\"type\":\"FeatureCollection\",\"features\":[{\"type\":\"Feature\",\"geometry\":{\"type\":\"MultiPolygon\",\"coordinates\":[[[[-97.961,47.241],[-97.7061,47.2402],[-97.4504,47.2393],[-96.8375,47.2382],[-96.8361,47.2384],[-96.8326,47.2371],[-96.831,47.2353],[-96.8305,47.2344],[-96.8312,47.2341],[-96.8338,47.2341],[-96.8365,47.2347],[-96.8399,47.2351],[-96.8413,47.2337],[-96.8425,47.2329],[-96.8423,47.2314],[-96.8409,47.23],[-96.8396,47.2296],[-96.8387,47.2296],[-96.8349,47.2288],[-96.8335,47.2284],[-96.8328,47.2275],[-96.8335,47.2266],[-96.8363,47.2253],[-96.838,47.2255],[-96.842,47.2245],[-96.8439,47.2235],[-96.8468,47.2206],[-96.8457,47.2189],[-96.8444,47.2185],[-96.8412,47.2199],[-96.8404,47.2202],[-96.8372,47.2212],[-96.8365,47.2209],[-96.8352,47.22],[-96.8347,47.2179],[-96.8354,47.2166],[-96.8351,47.2158],[-96.8339,47.2141],[-96.833,47.2123],[-96.833,47.211],[-96.8336,47.2104],[-96.8344,47.2101],[-96.835,47.2089],[-96.8339,47.2069],[-96.8327,47.2059],[-96.8314,47.205],[-96.8294,47.203],[-96.8297,47.2015],[-96.8322,47.2016],[-96.8361,47.2015],[-96.8368,47.2013],[-96.8374,47.2008],[-96.842,47.1966],[-96.8426,47.1961],[-96.8428,47.195],[-96.8421,47.1924],[-96.8397,47.1933],[-96.8378,47.1943],[-96.8365,47.1948],[-96.8351,47.1943],[-96.8327,47.194],[-96.8307,47.193],[-96.8289,47.1913],[-96.8282,47.1899],[-96.8282,47.189],[-96.8294,47.188],[-96.8314,47.188],[-96.8327,47.187],[-96.8332,47.1859],[-96.8332,47.1848],[-96.8305,47.1842],[-96.8286,47.1844],[-96.8266,47.1841],[-96.8252,47.1846],[-96.8245,47.1845],[-96.8237,47.1839],[-96.824,47.1827],[-96.8257,47.1808],[-96.827,47.1794],[-96.8283,47.1784],[-96.8296,47.1779],[-96.8302,47.177],[-96.8301,47.1751],[-96.8288,47.1747],[-96.8281,47.1743],[-96.826,47.1734],[-96.8245,47.1721],[-96.8227,47.1702],[-96.8217,47.168],[-96.8203,47.1657],[-96.821,47.1633],[-96.8224,47.1629],[-96.8237,47.1629],[-96.8264,47.163],[-96.8275,47.1628],[-96.8284,47.1625],[-96.8295,47.1623],[-96.8301,47.1618],[-96.8293,47.1603],[-96.8279,47.159],[-96.8253,47.1584],[-96.8248,47.1565],[-96.8254,47.1561],[-96.8281,47.1557],[-96.8291,47.1559],[-96.8315,47.1562],[-96.8341,47.1563],[-96.8361,47.1559],[-96.8377,47.1538],[-96.837,47.1525],[-96.8364,47.1519],[-96.8335,47.1498],[-96.8319,47.1484],[-96.8307,47.1471],[-96.8299,47.1453],[-96.8298,47.143],[-96.8291,47.1409],[-96.8288,47.1352],[-96.8286,47.1329],[-96.8279,47.1311],[-96.8272,47.1306],[-96.8256,47.1303],[-96.8242,47.1303],[-96.823,47.1289],[-96.8219,47.1274],[-96.823,47.1261],[-96.8243,47.1256],[-96.8272,47.1253],[-96.8292,47.1249],[-96.8316,47.125],[-96.8343,47.1254],[-96.8369,47.1244],[-96.8375,47.1235],[-96.8368,47.1226],[-96.8354,47.1217],[-96.8314,47.1209],[-96.83,47.1205],[-96.8273,47.1196],[-96.8249,47.1184],[-96.8237,47.1165],[-96.8218,47.1137],[-96.8196,47.1115],[-96.8175,47.1102],[-96.817,47.1084],[-96.8169,47.1062],[-96.8179,47.1051],[-96.8192,47.1046],[-96.8198,47.1032],[-96.8197,47.101],[-96.8189,47.0991],[-96.8195,47.0962],[-96.8202,47.0948],[-96.8209,47.0944],[-96.8222,47.0942],[-96.8238,47.0936],[-96.8257,47.0922],[-96.8271,47.0909],[-96.8265,47.0895],[-96.8252,47.089],[-96.8239,47.0889],[-96.8224,47.0889],[-96.8204,47.089],[-96.8191,47.089],[-96.8177,47.0886],[-96.8177,47.0872],[-96.8183,47.0858],[-96.8192,47.0847],[-96.8188,47.083],[-96.8196,47.0819],[-96.82,47.0807],[-96.8217,47.0801],[-96.8226,47.0797],[-96.8252,47.0788],[-96.8281,47.0765],[-96.8293,47.0753],[-96.8289,47.0746],[-96.8267,47.0747],[-96.8241,47.0745],[-96.8234,47.0742],[-96.8223,47.0729],[-96.8222,47.071],[-96.8227,47.0692],[-96.822,47.0683],[-96.8206,47.0669],[-96.8205,47.0654],[-96.8205,47.0642],[-96.8224,47.0627],[-96.8247,47.0628],[-96.8261,47.0619],[-96.8261,47.0605],[-96.8255,47.0596],[-96.8236,47.058],[-96.8224,47.0567],[-96.8207,47.055],[-96.8205,47.0535],[-96.8212,47.053],[-96.8232,47.0522],[-96.8251,47.0507],[-96.8269,47.0481],[-96.8262,47.0476],[-96.8229,47.0471],[-96.8196,47.0463],[-96.819,47.0451],[-96.8181,47.0435],[-96.8194,47.0426],[-96.8206,47.0403],[-96.8199,47.0398],[-96.8192,47.0396],[-96.8173,47.04],[-96.8166,47.0404],[-96.8153,47.041],[-96.8127,47.0414],[-96.8111,47.0407],[-96.8112,47.0391],[-96.8119,47.0382],[-96.8145,47.0372],[-96.8158,47.0363],[-96.8178,47.0355],[-96.8203,47.0353],[-96.821,47.0352],[-96.8236,47.0347],[-96.8249,47.0347],[-96.8263,47.0338],[-96.8257,47.0325],[-96.8244,47.0315],[-96.8218,47.031],[-96.8188,47.0302],[-96.8181,47.0298],[-96.818,47.0288],[-96.8184,47.0282],[-96.8201,47.0272],[-96.8207,47.0271],[-96.8252,47.0255],[-96.8282,47.0238],[-96.8317,47.0202],[-96.8328,47.0179],[-96.8333,47.0156],[-96.8345,47.0128],[-96.8364,47.011],[-96.8383,47.0095],[-96.8383,47.0082],[-96.8369,47.0063],[-96.8348,47.0055],[-96.8322,47.0055],[-96.83,47.0056],[-96.8272,47.0058],[-96.8246,47.0058],[-96.8237,47.005],[-96.8213,47.0012],[-96.8205,46.9989],[-96.8211,46.9971],[-96.8223,46.9952],[-96.8229,46.9929],[-96.8227,46.9897],[-96.8219,46.9874],[-96.8207,46.9853],[-96.8197,46.9833],[-96.8196,46.9815],[-96.8202,46.9787],[-96.8213,46.975],[-96.8204,46.9709],[-96.8206,46.9694],[-96.8183,46.9682],[-96.8156,46.9669],[-96.8135,46.9662],[-96.8122,46.9661],[-96.8108,46.9668],[-96.8103,46.9679],[-96.8076,46.9671],[-96.8069,46.9662],[-96.8042,46.9658],[-96.8028,46.9671],[-96.8013,46.9693],[-96.8,46.9693],[-96.7987,46.9692],[-96.7974,46.9683],[-96.7975,46.9655],[-96.7983,46.9637],[-96.8005,46.9615],[-96.8022,46.9604],[-96.8029,46.9591],[-96.8027,46.9585],[-96.799,46.954],[-96.8003,46.9514],[-96.8024,46.9501],[-96.8035,46.9484],[-96.8026,46.947],[-96.7999,46.9462],[-96.7953,46.9472],[-96.7933,46.9472],[-96.7919,46.9468],[-96.7912,46.9445],[-96.7911,46.9427],[-96.792,46.9351],[-96.7918,46.9303],[-96.7895,46.9295],[-96.7869,46.9283],[-96.7843,46.9272],[-96.783,46.9268],[-96.781,46.9268],[-96.7784,46.9274],[-96.7765,46.9284],[-96.7729,46.9313],[-96.7712,46.9325],[-96.7674,46.9344],[-96.7641,46.9355],[-96.7615,46.936],[-96.7595,46.9356],[-96.7588,46.9343],[-96.7587,46.9324],[-96.76,46.9315],[-96.7639,46.93],[-96.7642,46.9293],[-96.7624,46.9283],[-96.7611,46.9282],[-96.7584,46.9274],[-96.7564,46.927],[-96.7546,46.9253],[-96.756,46.9249],[-96.7583,46.9246],[-96.7602,46.9241],[-96.7615,46.9235],[-96.7628,46.9222],[-96.7615,46.9194],[-96.7596,46.9192],[-96.7569,46.9184],[-96.756,46.9183],[-96.7554,46.9167],[-96.7565,46.9155],[-96.7592,46.9154],[-96.7605,46.9154],[-96.7625,46.9149],[-96.7637,46.9135],[-96.7636,46.9112],[-96.7639,46.9104],[-96.7649,46.9091],[-96.7667,46.9074],[-96.768,46.9065],[-96.7706,46.9055],[-96.7718,46.9047],[-96.7718,46.9032],[-96.7732,46.902],[-96.7754,46.8997],[-96.7756,46.899],[-96.7756,46.8981],[-96.7746,46.8969],[-96.7734,46.8953],[-96.7723,46.8955],[-96.771,46.895],[-96.7704,46.8941],[-96.7698,46.8927],[-96.7712,46.8918],[-96.7718,46.8913],[-96.7732,46.8908],[-96.7745,46.8898],[-96.7744,46.8889],[-96.7737,46.8875],[-96.7716,46.8857],[-96.7709,46.8849],[-96.7683,46.8834],[-96.7674,46.8817],[-96.7673,46.8794],[-96.7686,46.8785],[-96.77,46.878],[-96.7719,46.8784],[-96.7732,46.879],[-96.7773,46.8796],[-96.7786,46.8792],[-96.7792,46.8782],[-96.7792,46.8773],[-96.7784,46.8755],[-96.7783,46.8736],[-96.7791,46.8728],[-96.7797,46.8725],[-96.7809,46.8717],[-96.7822,46.8717],[-96.7834,46.8699],[-96.784,46.8685],[-96.7839,46.866],[-96.781,46.8612],[-96.7789,46.8585],[-96.7775,46.8571],[-96.7774,46.8558],[-96.7781,46.8553],[-96.7794,46.8548],[-96.7821,46.8552],[-96.7832,46.8548],[-96.784,46.8547],[-96.7844,46.854],[-96.7832,46.8524],[-96.7825,46.8522],[-96.7801,46.8521],[-96.7794,46.8518],[-96.7781,46.8517],[-96.7774,46.8513],[-96.7766,46.8512],[-96.7758,46.8494],[-96.7764,46.8488],[-96.777,46.8486],[-96.7784,46.848],[-96.7811,46.8461],[-96.7806,46.8456],[-96.7771,46.8439],[-96.7761,46.8429],[-96.7755,46.8413],[-96.7767,46.8402],[-96.7786,46.8397],[-96.7813,46.8396],[-96.782,46.84],[-96.7834,46.8414],[-96.7854,46.8418],[-96.7866,46.8417],[-96.7873,46.8403],[-96.7865,46.8381],[-96.7858,46.8377],[-96.7851,46.8358],[-96.7854,46.8352],[-96.7864,46.835],[-96.7897,46.8353],[-96.791,46.8357],[-96.7923,46.8352],[-96.793,46.8345],[-96.7925,46.8334],[-96.7898,46.8317],[-96.7872,46.8307],[-96.7848,46.8294],[-96.7834,46.8281],[-96.7828,46.8269],[-96.7832,46.8258],[-96.7859,46.8253],[-96.7875,46.8261],[-96.7888,46.8266],[-96.7908,46.8257],[-96.7915,46.8248],[-96.7943,46.8218],[-96.7979,46.8181],[-96.8008,46.8155],[-96.801,46.8149],[-96.799,46.8108],[-96.7977,46.8098],[-96.7974,46.809],[-96.7989,46.807],[-96.8012,46.8058],[-96.8007,46.8042],[-96.7987,46.8025],[-96.7973,46.8011],[-96.797,46.7997],[-96.7975,46.7985],[-96.7977,46.7961],[-96.7976,46.794],[-96.7971,46.7931],[-96.7953,46.7906],[-96.7933,46.7888],[-96.7906,46.788],[-96.7884,46.7871],[-96.787,46.7858],[-96.7879,46.7839],[-96.7884,46.7825],[-96.7913,46.7817],[-96.7934,46.7804],[-96.7939,46.7794],[-96.7941,46.7778],[-96.7929,46.7771],[-96.7922,46.7776],[-96.7889,46.7784],[-96.7882,46.7784],[-96.7858,46.7781],[-96.7848,46.7775],[-96.7848,46.7766],[-96.786,46.7757],[-96.788,46.7752],[-96.7899,46.7742],[-96.7899,46.7725],[-96.7886,46.7715],[-96.7844,46.7693],[-96.7817,46.768],[-96.7823,46.7666],[-96.786,46.7661],[-96.7869,46.7658],[-96.7862,46.7641],[-96.7844,46.764],[-96.7818,46.7631],[-96.7814,46.7616],[-96.782,46.7602],[-96.7839,46.7588],[-96.7854,46.7576],[-96.7857,46.7555],[-96.7862,46.7532],[-96.7862,46.7518],[-96.7854,46.75],[-96.7818,46.7436],[-96.7816,46.7395],[-96.784,46.734],[-96.7855,46.7329],[-96.7852,46.7321],[-96.7851,46.7312],[-96.7838,46.7303],[-96.7823,46.7282],[-96.7844,46.7269],[-96.7861,46.7243],[-96.7854,46.724],[-96.784,46.7236],[-96.7821,46.7225],[-96.7827,46.7211],[-96.7846,46.7206],[-96.7885,46.7192],[-96.7889,46.7183],[-96.787,46.7168],[-96.7866,46.7163],[-96.7856,46.7146],[-96.7855,46.7114],[-96.7874,46.7091],[-96.7866,46.7064],[-96.7898,46.7049],[-96.7917,46.7039],[-96.7905,46.7032],[-96.7878,46.7026],[-96.7863,46.7009],[-96.7875,46.6981],[-96.7883,46.6964],[-96.7882,46.6953],[-96.7906,46.6934],[-96.7917,46.6936],[-96.7913,46.6918],[-96.791,46.6911],[-96.7893,46.6884],[-96.7887,46.687],[-96.7878,46.686],[-96.7888,46.6843],[-96.79,46.6829],[-96.7936,46.6821],[-96.7964,46.6803],[-96.7958,46.6791],[-96.7943,46.6784],[-96.7924,46.6782],[-96.7924,46.6768],[-96.793,46.6764],[-96.7956,46.6763],[-96.7976,46.6763],[-96.7989,46.6758],[-96.7988,46.6744],[-96.7975,46.6735],[-96.7955,46.6731],[-96.7929,46.6716],[-96.7934,46.6709],[-96.7958,46.6704],[-96.7973,46.6698],[-96.7992,46.6684],[-96.7992,46.667],[-96.799,46.6648],[-96.7989,46.6625],[-96.7999,46.6608],[-96.8006,46.6595],[-96.8008,46.6589],[-96.8017,46.6582],[-96.802,46.6575],[-96.8026,46.6564],[-96.8016,46.6557],[-96.7999,46.6551],[-96.7979,46.6547],[-96.7958,46.6534],[-96.7953,46.6528],[-96.7937,46.6507],[-96.7936,46.6487],[-96.795,46.6464],[-96.7961,46.646],[-96.7967,46.6451],[-96.7954,46.6442],[-96.7947,46.6442],[-96.7907,46.6434],[-96.7896,46.6419],[-96.7898,46.6398],[-96.7911,46.6388],[-96.7924,46.6374],[-96.7936,46.636],[-96.7955,46.6341],[-96.7973,46.6312],[-96.7954,46.6309],[-96.7938,46.6312],[-96.7919,46.6311],[-96.7892,46.6315],[-96.7872,46.6315],[-96.7859,46.6314],[-96.7846,46.6305],[-96.7841,46.6286],[-96.9071,46.6287],[-97.0308,46.6292],[-97.052,46.6292],[-97.179,46.6296],[-97.2822,46.6293],[-97.429,46.6295],[-97.5329,46.6295],[-97.5567,46.6295],[-97.683,46.6294],[-97.81,46.6297],[-97.9059,46.6293],[-97.9357,46.6294],[-98.0349,46.6293],[-98.1889,46.6297],[-98.2868,46.63],[-98.3152,46.63],[-98.4396,46.6296],[-98.4412,46.9789],[-98.4685,46.9788],[-98.4677,47.2402],[-97.9958,47.2411],[-97.9764,47.2412],[-97.961,47.241]]],[[[-102.3839,47.7593],[-102.386,47.8076],[-102.3865,47.8495],[-102.2888,47.8494],[-101.8711,47.8486],[-101.8193,47.8482],[-101.4858,47.8468],[-101.2279,47.8479],[-101.0008,47.8471],[-100.9685,47.8472],[-100.5846,47.847],[-100.5851,47.7094],[-100.5851,47.702],[-100.585,47.6726],[-100.6737,47.6725],[-100.6728,47.3272],[-100.7483,47.3261],[-100.7503,47.158],[-100.9596,47.1579],[-100.9683,47.1625],[-100.975,47.167],[-100.981,47.1744],[-100.9844,47.178],[-100.9864,47.1826],[-100.9885,47.1858],[-100.9912,47.1941],[-100.9906,47.1996],[-100.9906,47.2024],[-100.9906,47.207],[-100.988,47.2125],[-100.9867,47.2161],[-100.9834,47.2203],[-100.9821,47.2231],[-100.9794,47.2258],[-100.9768,47.2295],[-100.9761,47.2323],[-100.9768,47.2355],[-100.9782,47.2369],[-100.9789,47.2414],[-100.9796,47.2437],[-100.9809,47.2488],[-100.9836,47.2547],[-100.9857,47.258],[-100.9924,47.2635],[-100.9951,47.2648],[-101.0004,47.2676],[-101.0064,47.2694],[-101.0132,47.2726],[-101.0185,47.2753],[-101.0225,47.2785],[-101.0259,47.2803],[-101.0333,47.2854],[-101.0407,47.2885],[-101.048,47.2904],[-101.058,47.2912],[-101.0714,47.2912],[-101.0781,47.2953],[-101.0935,47.2948],[-101.1062,47.2947],[-101.1248,47.2928],[-101.1368,47.2904],[-101.1428,47.2886],[-101.1555,47.2862],[-101.1675,47.2829],[-101.1748,47.2806],[-101.1781,47.2801],[-101.1848,47.2783],[-101.1894,47.2746],[-101.192,47.2695],[-101.1919,47.2566],[-101.1912,47.2534],[-101.1932,47.2497],[-101.1958,47.2479],[-101.2011,47.2465],[-101.2118,47.2464],[-101.2219,47.251],[-101.2353,47.255],[-101.2467,47.2609],[-101.2501,47.2632],[-101.2568,47.2682],[-101.2675,47.2705],[-101.2735,47.2723],[-101.2796,47.2736],[-101.2856,47.2791],[-101.2897,47.2841],[-101.2937,47.2869],[-101.2978,47.2882],[-101.3051,47.2886],[-101.3238,47.289],[-101.3318,47.2889],[-101.3398,47.2889],[-101.3446,47.2916],[-101.3459,47.2953],[-101.348,47.2994],[-101.3487,47.304],[-101.3494,47.3072],[-101.3508,47.31],[-101.3515,47.3109],[-101.3542,47.3145],[-101.3549,47.315],[-101.3569,47.3191],[-101.3583,47.3228],[-101.3604,47.3278],[-101.3605,47.3338],[-101.3619,47.3434],[-101.3627,47.3535],[-101.3629,47.3669],[-101.3663,47.371],[-101.371,47.3737],[-101.3771,47.3755],[-101.3811,47.3783],[-101.3845,47.381],[-101.3885,47.3833],[-101.3913,47.3869],[-101.3933,47.3883],[-101.3947,47.3901],[-101.3967,47.3929],[-101.398,47.3942],[-101.4008,47.3984],[-101.4022,47.4025],[-101.4056,47.4103],[-101.4057,47.4172],[-101.4078,47.4241],[-101.4174,47.441],[-101.4229,47.4474],[-101.429,47.452],[-101.4364,47.4565],[-101.4391,47.4588],[-101.4398,47.4602],[-101.4412,47.4629],[-101.4412,47.4666],[-101.4413,47.4712],[-101.44,47.4772],[-101.4361,47.4823],[-101.4281,47.4878],[-101.4175,47.4934],[-101.4135,47.4953],[-101.4082,47.4981],[-101.3995,47.5023],[-101.3935,47.5051],[-101.3842,47.5111],[-101.3803,47.5139],[-101.3763,47.5171],[-101.375,47.5199],[-101.3757,47.5217],[-101.3791,47.5249],[-101.3811,47.5272],[-101.3872,47.5309],[-101.3967,47.5368],[-101.4048,47.5432],[-101.4251,47.5587],[-101.4299,47.5619],[-101.4353,47.5632],[-101.44,47.5636],[-101.458,47.5626],[-101.4701,47.5593],[-101.4814,47.5569],[-101.4895,47.555],[-101.5022,47.5522],[-101.5088,47.5512],[-101.5175,47.5502],[-101.5335,47.545],[-101.5489,47.5426],[-101.5563,47.5407],[-101.5756,47.5337],[-101.5943,47.5299],[-101.6117,47.5279],[-101.6331,47.5259],[-101.6425,47.5276],[-101.6506,47.5299],[-101.6573,47.5307],[-101.664,47.532],[-101.6674,47.5334],[-101.6782,47.5361],[-101.6883,47.5373],[-101.6963,47.5373],[-101.703,47.5363],[-101.709,47.5353],[-101.7124,47.5344],[-101.7177,47.5339],[-101.7371,47.5305],[-101.7458,47.5276],[-101.7524,47.5248],[-101.7583,47.5197],[-101.7643,47.5164],[-101.7723,47.5136],[-101.7849,47.5084],[-101.7922,47.5051],[-101.7975,47.5028],[-101.8035,47.4995],[-101.8095,47.4976],[-101.8175,47.4953],[-101.8235,47.4957],[-101.8289,47.497],[-101.8363,47.4992],[-101.8417,47.5005],[-101.8471,47.5009],[-101.8525,47.5023],[-101.8599,47.5036],[-101.8666,47.5035],[-101.8733,47.5034],[-101.8847,47.5033],[-101.8887,47.5038],[-101.8995,47.5087],[-101.905,47.5119],[-101.9097,47.5141],[-101.9137,47.5155],[-101.9151,47.5177],[-101.9192,47.5205],[-101.9246,47.5241],[-101.9301,47.5277],[-101.9341,47.529],[-101.9395,47.5299],[-101.9429,47.5308],[-101.9476,47.5321],[-101.9517,47.533],[-101.9584,47.5339],[-101.9624,47.5352],[-101.9692,47.5365],[-101.9759,47.5373],[-101.9813,47.5382],[-101.9866,47.5382],[-101.9906,47.5381],[-101.996,47.5381],[-102.0027,47.5371],[-102.01,47.5342],[-102.018,47.5332],[-102.0228,47.5345],[-102.0282,47.5372],[-102.0309,47.5404],[-102.029,47.5446],[-102.0258,47.5483],[-102.0238,47.5511],[-102.0226,47.5538],[-102.0179,47.5606],[-102.0243,47.5689],[-102.0295,47.5729],[-102.0377,47.5742],[-102.0535,47.5741],[-102.0545,47.5741],[-102.0885,47.5753],[-102.0948,47.5742],[-102.1262,47.5693],[-102.1442,47.5663],[-102.1583,47.5647],[-102.169,47.5655],[-102.1831,47.5667],[-102.1879,47.5681],[-102.1939,47.5703],[-102.2,47.572],[-102.2034,47.5734],[-102.2089,47.5765],[-102.2137,47.5811],[-102.2179,47.5888],[-102.2213,47.5911],[-102.2384,47.6024],[-102.2516,47.617],[-102.2586,47.6274],[-102.2649,47.6398],[-102.2666,47.6526],[-102.2671,47.6692],[-102.2653,47.6798],[-102.2657,47.6913],[-102.2613,47.7038],[-102.2609,47.7139],[-102.2604,47.7222],[-102.2619,47.7263],[-102.2646,47.7286],[-102.2735,47.7336],[-102.2783,47.7353],[-102.2845,47.7408],[-102.2879,47.744],[-102.2894,47.7481],[-102.2889,47.7536],[-102.2876,47.7564],[-102.2809,47.7583],[-102.2736,47.7593],[-102.2581,47.7618],[-102.2577,47.7683],[-102.2571,47.7734],[-102.258,47.7812],[-102.2621,47.7825],[-102.2702,47.7856],[-102.2763,47.7869],[-102.2844,47.7882],[-102.2898,47.7886],[-102.298,47.7908],[-102.3048,47.7935],[-102.3122,47.7952],[-102.3217,47.7979],[-102.3353,47.8],[-102.3467,47.8003],[-102.3547,47.7979],[-102.3599,47.7918],[-102.3652,47.7876],[-102.3703,47.7779],[-102.3774,47.7667],[-102.3839,47.7593]]]]},\"properties\":{\"name\":\"Barnes\",\"state\":\"ND\"}}]}","contact":"Director, Dakota Water Science Center, North Dakota Office
U.S. Geological Survey
821 East Interstate Avenue
Bismarck, ND 58503
Wastewater discharged to wells and ponds and wastes buried in shallow pits and trenches at facilities at the Idaho National Laboratory (INL) have contributed contaminants to the eastern Snake River Plain (ESRP) aquifer in the southwestern part of the INL. This report describes the correlation between subsurface stratigraphy in the southwestern part of the INL with information on the presence or absence of wastewater constituents to better understand how flow pathways in the aquifer control the movement of wastewater discharged at INL facilities. Paleomagnetic inclination was used to identify subsurface basalt flows based on similar inclination measurements, polarity, and stratigraphic position. Tritium concentrations, along with other chemical information for wells where tritium concentrations were lacking, were used as an indicator of which wells were influenced by wastewater disposal.
The basalt lava flows in the upper 150 feet of the ESRP aquifer where wastewater was discharged at the Idaho Nuclear Technology and Engineering Center (INTEC) consisted of the Central Facilities Area (CFA) Buried Vent flow and the AEC Butte flow. At the Advanced Test Reactor (ATR) Complex, where wastewater would presumably pond on the surface of the water table, the CFA Buried Vent flow probably occurs as the primary stratigraphic unit present; however, AEC Butte flow also could be present at some of the locations. At the Radioactive Waste Management Complex (RWMC), where contamination from buried wastes would presumably move down through the unsaturated zone and pond on the surface of the water table, the CFA Buried Vent; Late Basal Brunhes; or Early Basal Brunhes basalt flows are the flow unit at or near the water table in different cores.
In the wells closer to where wastewater disposal occurred at INTEC and the ATR-Complex, almost all the wells show wastewater influence in the upper part of the ESRP aquifer and wastewater is present in both the CFA Buried Vent flow and AEC Butte flow. The CFA Buried Vent flow and AEC Butte flow are also present in wells at and north of CFA and are all influenced by wastewater contamination. All wells with the AEC Butte flow present have wastewater influence and 83 percent of the wells with the more prevalent CFA Buried Vent flow have wastewater influence. South and southeast of CFA, most wells are not influenced by wastewater disposal and are completed in the Big Lost Flow and the CFA Buried Vent flow. Wells southwest of CFA are influenced by wastewater disposal and are completed in the Big Lost flow and CFA Buried Vent flow at the top of the aquifer. Basalt stratigraphy indicates that the CFA Buried Vent flow is the predominant flow in the upper part of the ESRP aquifer at and near the RWMC as it is present in all the wells in this area. The Late Basal Brunhes flow, Middle Basal Brunhes flow, Early Basal Brunhes flow, South Late Matuyama flow, and Matuyama flow are also present in various wells influenced by waste disposal.
Some wells south of RWMC do not show wastewater influence, and the lack of wastewater influence could be due to low hydraulic conductivities. Several wells south and southeast of CFA also do not show wastewater influence. Low hydraulic conductivities or ESRP subsidence are possible causes for lack of wastewater south of CFA.
Multilevel monitoring wells completed much deeper in the aquifer show influence of wastewater in numerous basalt flows. Well Middle 2051 (northwest of RWMC) does not show wastewater influence in its upper three basalt flows (CFA Buried Vent, Late Basal Brunhes, and Middle Basal Brunhes); however, wastewater is present in two deeper flows (the Matuyama and Jaramillo flows). Well USGS 131A (southwest of CFA) and USGS132 (south of RWMC) both show wastewater influence in all the basalt flows sampled in the upper 600 feet of the aquifer. Wells USGS 137A, 105, 108, and 103 completed along the southern boundary of the INL all show wastewater influence in several basalt flows including the G flow, Middle and Early Basal Brunhes flows, the South Late Matuyama flow and the Matuyama flow; however, the strongest wastewater influence appears to be in the South Late Matuyama flow. The concentrations of wastewater constituents in deeper parts of these wells support the concept of groundwater flow deepening in the southwestern part of the INL.
","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/sir20175148","collaboration":"DOE/ID-22245Director, Idaho Water Science Center
U.S. Geological Survey
230 Collins Road
Boise, Idaho 83702
Earthquake-triggered ground failure, such as landsliding and liquefaction, can contribute significantly to losses, but our current ability to accurately include them in earthquake-hazard analyses is limited. The development of robust and widely applicable models requires access to numerous inventories of ground failures triggered by earthquakes that span a broad range of terrains, shaking characteristics, and climates. We present an openly accessible, centralized earthquake-triggered groundfailure inventory repository in the form of a ScienceBase Community to provide open access to these data with the goal of accelerating research progress. The ScienceBase Community hosts digital inventories created by both U.S. Geological Survey (USGS) and non-USGS authors. We present the original digital inventory files (when available) as well as an integrated database with uniform attributes. We also summarize the mapping methodology and level of completeness as reported by the original author(s) for each inventory. This document describes the steps taken to collect, process, and compile the inventories and the process for adding additional ground-failure inventories to the ScienceBase Community in the future.
","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/ds1064","usgsCitation":"Schmitt, R.G., Tanyas, Hakan, Nowicki Jessee, M.A., Zhu, Jing, Biegel, K.M., Allstadt, K.E., Jibson, R.W., Thompson, E.M., van Westen, C.J., Sato, H.P., Wald, D.J., Godt, J.W., Gorum, Tolga, Xu, Chong, Rathje, E.M., Knudsen, K.L., 2017, An open repository of earthquake-triggered ground-failure inventories: U.S. Geological Survey Data Series 1064, 17 p., https://doi.org/10.3133/ds1064.","productDescription":"Report: iii, 17 p.; Data Release","numberOfPages":"26","onlineOnly":"Y","ipdsId":"IP-088662","costCenters":[{"id":300,"text":"Geologic Hazards Science Center","active":true,"usgs":true}],"links":[{"id":438123,"rank":4,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/F7DN43Z6","text":"USGS data release","linkHelpText":"landslides-metadata"},{"id":350095,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/ds/1064/coverthb.jpg"},{"id":350096,"rank":2,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/ds/1064/ds1064.pdf","text":"Report","size":"2.81 MB","linkFileType":{"id":1,"text":"pdf"},"description":"DS 1064"},{"id":350097,"rank":3,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/F7H70DB4","text":"USGS data release","description":"USGS data release","linkHelpText":"An Open Repository of Earthquake-Triggered Ground-Failure Inventories"}],"contact":"Director, Geologic Hazards Science Center
U.S. Geological Survey
Box 25046, MS–966
Denver, CO 80225–0046
In support of earthquake hazard studies and ground motion simulations in the Pacific Northwest, three-dimensional P- and S-wave velocity (VP and VS, respectively) models incorporating the Cascadia subduction zone were previously developed for the region encompassed from about 40.2°N. to 50°N. latitude, and from about 122°W. to 129°W. longitude. This report describes updates to the Cascadia velocity property volumes of model version 1.3 (V1.3), herein called model version 1.6 (V1.6). As in model V1.3, the updated V1.6 model volume includes depths from 0 kilometers (mean sea level) to 60 kilometers, and it is intended to be a reference for researchers who have used, or are planning to use, this model in their Earth science investigations. To this end, it is intended that the VP and VS property volumes of model V1.6 will be considered a template for a community velocity model of the Cascadia region as additional results become available. With the recent and ongoing development of the National Crustal Model, we envision any future versions of this model will be directly integrated with that effort.
","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/ofr20171152","collaboration":"Earthquake Hazards Ground Motion Investigations","usgsCitation":"Stephenson, W.J., Reitman, N.G., and Angster, S.J., 2017, P- and S-wave velocity models incorporating the Cascadia subduction zone for 3D earthquake ground motion simulations, version 1.6—Update for Open-File Report 2007–1348 (ver. 1.1, Sept. 10, 2019): U.S. Geological Survey Open-File Report 2017–1152, 17 p., https://doi.org/10.3133/ofr20171152. [Supersedes USGS Open-File Report 2007–1348.]","productDescription":"Report: vi, 17 p.; Read Me; Data Release","numberOfPages":"28","onlineOnly":"Y","additionalOnlineFiles":"Y","ipdsId":"IP-088666","costCenters":[{"id":300,"text":"Geologic Hazards Science Center","active":true,"usgs":true}],"links":[{"id":350108,"rank":4,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/F7NS0SWM","text":"USGS data release","description":"USGS Data Release","linkHelpText":"Data for P- and S-wave Seismic Velocity Models Incorporating the Cascadia Subduction Zone for 3D Earthquake Ground Motion simulations-Update for Open-File Report 2007-1348"},{"id":350107,"rank":3,"type":{"id":20,"text":"Read Me"},"url":"https://pubs.usgs.gov/of/2017/1152/Readme.txt","text":"Read Me","size":"3.82 kB","linkFileType":{"id":2,"text":"txt"},"description":"OFR 2017–1152 Read Me"},{"id":350105,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/of/2017/1152/coverthb2.jpg"},{"id":350106,"rank":2,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/of/2017/1152/ofr20171152.pdf","text":"Report","size":"9.72 MB","linkFileType":{"id":1,"text":"pdf"},"description":"OFR 2017–1152"},{"id":367615,"rank":5,"type":{"id":25,"text":"Version History"},"url":"https://pubs.usgs.gov/of/2017/1152/versionHist.txt","text":"Version History","size":"4.0 kB","linkFileType":{"id":2,"text":"txt"},"description":"OFR 2017–1152 Version History"}],"country":"Canada, United States","state":"Oregon, Vancouver, Washington","otherGeospatial":"Cascadia Subduction Zone","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -128.583984375,\n 40.36328834091583\n ],\n [\n -121.61865234375,\n 40.36328834091583\n ],\n [\n -121.61865234375,\n 49.92293545449574\n ],\n [\n -128.583984375,\n 49.92293545449574\n ],\n [\n -128.583984375,\n 40.36328834091583\n ]\n ]\n ]\n }\n }\n ]\n}","edition":"Version 1.0: December 20, 2017; Version 1.1: September 11, 2019","contact":"Center Director, Geologic Hazards Science Center
U.S. Geological Survey
Box 25046 Mail Stop 966
Denver, CO 80225
The U.S. Geological Survey estimates a mean of 40 million pounds of in-place uranium oxide (U3O8) remaining as potential undiscovered resources in the Southern High Plains region of Texas, New Mexico, and Oklahoma. This estimate used a geology-based assessment method specific to calcrete uranium deposits.
","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/fs20173078","usgsCitation":"Hall, S.M., Mihalasky, M.J., and Van Gosen, B.S., 2017, Assessment of undiscovered resources in calcrete uranium deposits, Southern High Plains region of Texas, New Mexico, and Oklahoma, 2017: U.S. Geological Survey Fact Sheet 2017–3078,Central Energy Resources Science Center
U.S. Geological Survey
Box 25046, MS-939
Denver, CO 80225-0046
Recent developments of community abundance models (CAMs) enable us to analyze communities subject to imperfect detection. However, existing CAMs assume spatial closure, that is, that individuals are always present in the sampling plots, which is often violated in field surveys. Violation of this assumption, such as in the presence of spatial temporary emigration, can lead to the underestimates of detection probability and overestimates of population densities and diversity metrics. Here, we propose a model that simultaneously accommodates both temporary emigration and imperfect detection by integrating CAMs and a form of hierarchical distance sampling for open populations. Expected values of species richness are obtained via the summation of occupancy (or incidence) probabilities, based on species‐level densities, across all species of the community. Simulations were used to examine the effects of spatial temporary emigration on the estimation of biological communities. We also applied the proposed model to empirical data and constructed area‐based rarefaction curves accounting for temporary emigration. Simulation experiments showed that temporary emigration can decrease the local species richness (α diversity) based on densities and increase the species turnover (β diversity). Raw species counts can overestimate or underestimate α diversity in the presence of temporary emigration, but the specific biases depend on the values of detection and emigration probabilities. Our newly proposed model yielded unbiased estimates of α, β, and γ diversity in the presence of temporary emigration. The application to empirical data suggested that accounting for temporary emigration lowered area‐based rarefaction curves because availability probabilities of individual species were estimated to be <1. Temporary emigration prevails in field surveys and has broad significance for understanding the ecology and function of biological communities and separation of imperfect detection and temporary emigration resolves long‐standing issues in the use of count data. We therefore suggest that the consideration of temporary emigration would contribute to understanding the nature and role of biological communities.
","language":"English","publisher":"Ecological Society of America","doi":"10.1002/ecs2.2028","usgsCitation":"Yamaura, Y., and Royle, A., 2017, Community distance sampling models allowing for imperfect detection and temporary emigration: Ecosphere, v. 8, no. 12, p. 1-15, https://doi.org/10.1002/ecs2.2028.","productDescription":"e02028, 15 p.","startPage":"1","endPage":"15","ipdsId":"IP-089901","costCenters":[{"id":531,"text":"Patuxent Wildlife Research Center","active":true,"usgs":true}],"links":[{"id":469230,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.1002/ecs2.2028","text":"Publisher Index Page"},{"id":363415,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"volume":"8","issue":"12","publishingServiceCenter":{"id":10,"text":"Baltimore PSC"},"noUsgsAuthors":false,"publicationDate":"2017-12-20","publicationStatus":"PW","contributors":{"authors":[{"text":"Yamaura, Yuichi","contributorId":173122,"corporation":false,"usgs":false,"family":"Yamaura","given":"Yuichi","email":"","affiliations":[{"id":16855,"text":"Hokkaido University","active":true,"usgs":false}],"preferred":false,"id":761807,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Royle, J. Andrew 0000-0003-3135-2167 aroyle@usgs.gov","orcid":"https://orcid.org/0000-0003-3135-2167","contributorId":146229,"corporation":false,"usgs":true,"family":"Royle","given":"J. Andrew","email":"aroyle@usgs.gov","affiliations":[{"id":531,"text":"Patuxent Wildlife Research Center","active":true,"usgs":true}],"preferred":true,"id":761806,"contributorType":{"id":1,"text":"Authors"},"rank":2}]}} ,{"id":70193498,"text":"70193498 - 2017 - Alaska and Yukon magnetic compilation, residual total magnetic field","interactions":[],"lastModifiedDate":"2017-12-21T10:06:37","indexId":"70193498","displayToPublicDate":"2017-12-20T00:00:00","publicationYear":"2017","noYear":false,"publicationType":{"id":18,"text":"Report"},"publicationSubtype":{"id":4,"text":"Other Government Series"},"seriesTitle":{"id":5589,"text":"Open File","active":true,"publicationSubtype":{"id":4}},"seriesNumber":"7862","title":"Alaska and Yukon magnetic compilation, residual total magnetic field","docAbstract":"This map is a compilation of aeromagnetic surveys over Yukon and eastern Alaska. Aeromagnetic surveys measure the total intensity of the earth's magnetic field. The field was measured by a magnetometer aboard an aircraft flown in parallel lines spaced at 200 m to 10000 m across the map area. The magnetic field reflects magnetic properties of bedrock and provides qualitative and quantitative information used in geological mapping. Understanding the geology will help geologists map the area, assist mineral/hydrocarbon exploration activities, and provide useful information necessary for communities, aboriginal associations, and government to make land use decisions. This survey was flown to improve our knowledge of the area. It will support ongoing geological mapping and resource assessment.
","language":"English, French","publisher":"Geological Survey of Canada","doi":"10.4095/301695","usgsCitation":"Miles, W., Saltus, R.W., Hayward, N., and Oneschuk, D., 2017, Alaska and Yukon magnetic compilation, residual total magnetic field: Open File 7862, 52.79 x 35.26 inches, https://doi.org/10.4095/301695.","productDescription":"52.79 x 35.26 inches","ipdsId":"IP-062639","costCenters":[{"id":211,"text":"Crustal Geophysics and Geochemistry Science Center","active":true,"usgs":true}],"links":[{"id":469231,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.4095/301695","text":"Publisher Index Page"},{"id":350146,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"projection":"Lambert Conformal Conic Projection, zone 7N (NAD83)","country":"Canada, United States","state":"Alaska, Yukon","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -150,\n 68\n ],\n [\n -124,\n 68\n ],\n [\n -124,\n 60\n ],\n [\n -150,\n 60\n ],\n [\n -150,\n 68\n ]\n ]\n ]\n }\n }\n ]\n}","publishingServiceCenter":{"id":2,"text":"Denver PSC"},"noUsgsAuthors":false,"publicationStatus":"PW","scienceBaseUri":"5a60fae3e4b06e28e9c228f4","contributors":{"authors":[{"text":"Miles, W.","contributorId":201441,"corporation":false,"usgs":false,"family":"Miles","given":"W.","email":"","affiliations":[],"preferred":false,"id":725283,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Saltus, Richard W. saltus@usgs.gov","contributorId":777,"corporation":false,"usgs":true,"family":"Saltus","given":"Richard","email":"saltus@usgs.gov","middleInitial":"W.","affiliations":[{"id":211,"text":"Crustal Geophysics and Geochemistry Science Center","active":true,"usgs":true}],"preferred":true,"id":719259,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Hayward, Nathan","contributorId":201439,"corporation":false,"usgs":false,"family":"Hayward","given":"Nathan","affiliations":[],"preferred":false,"id":725284,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Oneschuk, D.","contributorId":201440,"corporation":false,"usgs":false,"family":"Oneschuk","given":"D.","email":"","affiliations":[],"preferred":false,"id":725285,"contributorType":{"id":1,"text":"Authors"},"rank":4}]}} ,{"id":70192576,"text":"ofr20171137 - 2017 - Visualization of groundwater withdrawals","interactions":[],"lastModifiedDate":"2018-02-21T11:46:21","indexId":"ofr20171137","displayToPublicDate":"2017-12-19T13:30:00","publicationYear":"2017","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":"2017-1137","title":"Visualization of groundwater withdrawals","docAbstract":"Generating an informative display of groundwater withdrawals can sometimes be difficult because the symbols for closely spaced wells can overlap. An alternative method for displaying groundwater withdrawals is to generate a “footprint” of the withdrawals. WellFootprint version 1.0 implements the Footprint algorithm with two optional variations that can speed up the footprint calculation. ModelMuse has been modified in order to generate the input for WellFootprint and to read and graphically display the output from WellFootprint.
","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/ofr20171137","usgsCitation":"Winston, R.B., and Goode, D.J., 2017, Visualization of groundwater withdrawals: U.S. Geological Survey Open-File Report 2017–1137, 8 p., https://doi.org/10.3133/ofr20171137.","productDescription":"Report: vi, 8 p.; Application Site","numberOfPages":"18","onlineOnly":"Y","additionalOnlineFiles":"N","ipdsId":"IP-089907","costCenters":[{"id":436,"text":"National Research Program - Eastern Branch","active":true,"usgs":true}],"links":[{"id":438124,"rank":4,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/F70C4TQ8","text":"USGS data release","linkHelpText":"WellFootprint Software Release"},{"id":350110,"rank":2,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/of/2017/1137/ofr20171137.pdf","text":"Report","size":"1 MB","linkFileType":{"id":1,"text":"pdf"},"description":"OFR 2017-1137"},{"id":350113,"rank":3,"type":{"id":4,"text":"Application Site"},"url":"https://doi.org/10.5066/F70C4TQ8","linkHelpText":"- WellFootprint source code and examples: U.S. Geological Survey software release"},{"id":350109,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/of/2017/1137/coverthb.jpg"}],"contact":"Director, Integrated Modeling and Prediction Division
U.S. Geological Survey
MS 415 National Center
12201 Sunrise Valley Drive
Reston, VA 20192
Tin (Sn) is one of the first metals to be used by humans. Almost without exception, tin is used as an alloy. Because of its hardening effect on copper, tin was used in bronze implements as early as 3500 B.C. The major uses of tin today are for cans and containers, construction materials, transportation materials, and solder. The predominant ore mineral of tin, by far, is cassiterite (SnO2).
In 2015, the world’s total estimated mine production of tin was 289,000 metric tons of contained tin. Total world reserves at the end of 2016 were estimated to be 4,700,000 metric tons. China held about 24 percent of the world’s tin reserves and accounted for 38 percent of the world’s 2015 production of tin.
The proportion of scrap used in tin production is between 10 and 25 percent. Unlike many metals, tin recycling is relatively efficient, and the fraction of tin in discarded products that get recycled is greater than 50 percent.
Only about 20 percent of the world’s identified tin resources occur as primary hydrothermal hard-rock veins, or lodes. These lodes contain predominantly high-temperature minerals and almost invariably occur in close association with silicic, peraluminous granites. About 80 percent of the world’s identified tin resources occur as unconsolidated secondary or placer deposits in riverbeds and valleys or on the sea floor. The largest concentration of both onshore and offshore placers is in the extensive tin belt of Southeast Asia, which stretches from China in the north, through Thailand, Burma (also referred to as Myanmar), and Malaysia, to the islands of Indonesia in the south. Furthermore, tin placers are almost always found closely allied to the granites from which they originate. Other countries with significant tin resources are Australia, Bolivia, and Brazil.
Most hydrothermal tin deposits belong to what can be thought of as a superclass of porphyry-greisen deposits. The hydrothermal tin deposits are all characterized by a close spatial, temporal, and genetic association with highly differentiated, peraluminous porphyritic granite intrusions. The intrusions form pegmatites; disseminated ore; parallel or subparallel, greisen-bordered sheeted veins that either cross-cut the intrusion or are peripheral to it; skarns; and (or) limestone replacements that contain different amounts of cassiterite, molybdenite, and wolframite.
The tectonic settings of tin-bearing granites are relatively well understood and of limited variety. Tin and tungsten deposits and their associated igneous rocks are found mainly in continental settings.
Historically, prospecting for tin has been carried out by the time-honored methods of panning, drilling, trenching, and assaying. Geophysical and geochemical surveys have been employed to cover large areas more rapidly, isolating areas of possible tin deposits so that drilling can be more effective and less costly. Elemental concentrations and relationships of the lithophile elements, especially barium, lithium, niobium, potassium, rubidium, and zirconium, are the most reliable chemical indicators of ore-forming processes and tin-bearing potential.
The average human diet includes an intake of about 10 milligrams per day of tin. Ingestion of tin in significantly greater amounts than 10 milligrams per day may lead to a stomach ache, anemia, and liver and kidney problems. Exposure to some organo-tin compounds can interfere with brain and nervous system function and, in severe cases, can cause death. Extended inhalation of tin oxide—an issue mainly for those people who work in the tin industry—results in a higher potential to develop stannosis, which is a mild disease of the lungs caused by the inhalation of tin-bearing dust. Inorganic tin is poorly absorbed by the body, and no evidence exists for the carcinogenicity of metallic tin and tin compounds in humans.
Most placer tin deposits are mined by open pit and (or) dredging methods. Mining of alluvial placers in modern streambeds and riverbeds is likely to increase the amount of sediment delivered downstream. This, combined with potential diversion of rivers and streams, may negatively affect downstream ecosystems. Many of the placer deposits located in Burma, Indonesia, Malaysia, and Thailand are located offshore. Most offshore placer tin deposits are mined by dredging methods, which have the potential to negatively affect benthic, midwater, and pelagic ecosystems.
In a congressionally mandated U.S. Department of Defense study of strategic minerals published in 2013, tin has the greatest shortfall amount (insufficient supply to meet demand) at \\$416 million; this amount is more than twice that of antimony ($182 million), which is the strategic mineral with the next largest shortfall amount (U.S. Department of Defense, 2013). The United States imported 75 percent of its tin supply in 2015. During the period 2012–15, these imports were from, in descending order of amount imported, Peru, Indonesia, Malaysia, and Bolivia.
A promising advancement concerning research into the origin of tin deposits is the recent development of a reliable method of analyzing tin isotopes in cassiterite. Although the mechanism of transport and deposition of tin is fairly well understood, the means by which tin is incorporated into the parent magma at the points of magma generation and ascent needs further investigation.
Tin metallogenic provinces worldwide are well known. Consequently, any undiscovered tin deposits will likely be spatially close to known deposits or extensions of the same.
","largerWorkType":{"id":18,"text":"Report"},"largerWorkTitle":"Critical mineral resources of the United States—Economic and environmental geology and prospects for future supply","largerWorkSubtype":{"id":5,"text":"USGS Numbered Series"},"language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/pp1802S","isbn":"978-1-4113-3991-0","usgsCitation":"Kamilli, R.J., Kimball, B.E., and Carlin, J.F., Jr., 2017, Tin, chap. S of Schulz, K.J., DeYoung, J.H., Jr., Seal, R.R., II, and Bradley, D.C., eds., Critical mineral resources of the United States—Economic and environmental geology and prospects for future supply: U.S. Geological Survey Professional Paper 1802, p. S1–S53, https://doi.org/10.3133/ pp1802S.","productDescription":"ix, 53 p.","numberOfPages":"68","onlineOnly":"N","additionalOnlineFiles":"N","ipdsId":"IP-058673","costCenters":[{"id":312,"text":"Geology, Minerals, Energy, and Geophysics Science Center","active":true,"usgs":true}],"links":[{"id":334846,"rank":2,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/pp/1802/s/pp1802s.pdf","text":"Report","size":"4.63 MB","linkFileType":{"id":1,"text":"pdf"},"description":"PP 1802 S"},{"id":334845,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/pp/1802/s/coverthb1.jpg"}],"contact":"Mineral Resources Program Coordinator
U.S. Geological Survey
913 National Center
Reston, VA 20192
Email: minerals@usgs.gov
https://minerals.usgs.gov
Mineral commodities are vital for economic growth, improving the quality of life, providing for national defense, and the overall functioning of modern society. Minerals are being used in larger quantities than ever before and in an increasingly diverse range of applications. With the increasing demand for a considerably more diverse suite of mineral commodities has come renewed recognition that competition and conflict over mineral resources can pose significant risks to the manufacturing industries that depend on them. In addition, production of many mineral commodities has become concentrated in relatively few countries (for example, tungsten, rare-earth elements, and antimony in China; niobium in Brazil; and platinum-group elements in South Africa and Russia), thus increasing the risk for supply disruption owing to political, social, or other factors. At the same time, an increasing awareness of and sensitivity to potential environmental and health issues caused by the mining and processing of many mineral commodities may place additional restrictions on mineral supplies. These factors have led a number of Governments, including the Government of the United States, to attempt to identify those mineral commodities that are viewed as most “critical” to the national economy and (or) security if supplies should be curtailed.
This book presents resource and geologic information on the following 23 mineral commodities currently among those viewed as important to the national economy and national security of the United States: antimony (Sb), barite (barium, Ba), beryllium (Be), cobalt (Co), fluorite or fluorspar (fluorine, F), gallium (Ga), germanium (Ge), graphite (carbon, C), hafnium (Hf), indium (In), lithium (Li), manganese (Mn), niobium (Nb), platinum-group elements (PGE), rare-earth elements (REE), rhenium (Re), selenium (Se), tantalum (Ta), tellurium (Te), tin (Sn), titanium (Ti), vanadium (V), and zirconium (Zr). For a number of these commodities—for example, graphite, manganese, niobium, and tantalum—the United States is currently wholly dependent on imports to meet its needs. The first two chapters (A and B) deal with general information pertinent to the study of mineral resources. Chapters C through V describe individual mineral commodities and include an overview of current uses of the commodity, identified resources and their distribution nationally and globally, the state of current geologic knowledge, the potential for finding additional deposits nationally and globally, and geoenvironmental issues that may be related to the production and uses of the commodity. These chapters are updates of the commodity chapters published in 1973 in U.S. Geological Survey Professional Paper 820, “United States Mineral Resources.”
","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/pp1802","isbn":"978-1-4113-3991-0","usgsCitation":"Schulz, K.J., DeYoung, J.H., Jr., Seal, R.R., II, and Bradley, D.C., eds., 2017, Critical mineral resources of the United States—Economic and environmental geology and prospects for future supply: U.S. Geological Survey Professional Paper 1802, 797 p., https://doi.org/10.3133/pp1802.","productDescription":"Report: 862 p.; Data Release","numberOfPages":"862","onlineOnly":"N","additionalOnlineFiles":"N","ipdsId":"IP-069563","costCenters":[{"id":245,"text":"Eastern Mineral and Environmental Resources Science Center","active":true,"usgs":true}],"links":[{"id":350071,"rank":5,"type":{"id":22,"text":"Related Work"},"url":"https://mrdata.usgs.gov/pp1802/ ","text":"- Global Distribution of Selected Mines, Deposits, and Districts of Critical Minerals","linkFileType":{"id":5,"text":"html"},"description":"Global distribution of selected mines, deposits, and districts of critical minerals"},{"id":336929,"rank":2,"type":{"id":8,"text":"Cover"},"url":"https://pubs.usgs.gov/pp/1802/pp1802_frontbackcovers.pdf","text":"Front and Back Covers","size":"1.23 MB","linkFileType":{"id":1,"text":"pdf"},"description":"PP 1802"},{"id":352473,"rank":7,"type":{"id":12,"text":"Errata"},"url":"https://pubs.usgs.gov/pp/1802/pp1802_erratum-march132018.txt","text":"Erratum","size":"1 KB","linkFileType":{"id":2,"text":"txt"}},{"id":336933,"rank":3,"type":{"id":2,"text":"Additional Report Piece"},"url":"https://pubs.usgs.gov/pp/1802/cover/pp1802frontmatter.pdf","text":"Professional Paper 1802 - Front Matter","size":"326 KB","linkFileType":{"id":1,"text":"pdf"},"description":"PP 1802","linkHelpText":" - Front Matter"},{"id":336928,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/pp/1802/coverthb.jpg"},{"id":350094,"rank":6,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/pp/1802/pp1802_entirebook.pdf","text":"Report (Entire Book)","size":"148 MB","linkFileType":{"id":1,"text":"pdf"}},{"id":349464,"rank":4,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/F7GH9GQR","text":"USGS data release","description":"USGS data release","linkHelpText":"Global Distribution of Selected Mines, Deposits, and Districts of Critical Minerals"}],"contact":"Mineral Resources Program Coordinator
U.S. Geological Survey
913 National Center
Reston, VA 20192
Email: minerals@usgs.gov
http://minerals.usgs.gov
Titanium is a mineral commodity that is essential to the smooth functioning of modern industrial economies. Most of the titanium produced is refined into titanium dioxide, which has a high refractive index and is thus able to impart a durable white color to paint, paper, plastic, rubber, and wallboard. Because of their high strength-to-weight ratio and corrosion resistance, titanium metal and titanium metal alloys are used in the aerospace industry as well as for welding rod coatings, biological implants, and consumer goods.
Ilmenite and rutile are currently the principal titanium-bearing ore minerals, although other minerals, including anatase, perovskite, and titanomagnetite, could have economic importance in the future. Ilmenite is currently being mined from two large magmatic deposits hosted in rocks of Proterozoic-age anorthosite plutonic suites. Most rutile and nearly one-half of the ilmenite produced are from heavy-mineral alluvial, fluvial, and eolian deposits. Titanium-bearing minerals occur in diverse geologic settings, but many of the known deposits are currently subeconomic for titanium because of complications related to the mineralogy or because of the presence of trace contaminants that can compromise the pigment production process.
Global production of titanium minerals is currently dominated by Australia, Canada, Norway, and South Africa; additional amounts are produced in Brazil, India, Madagascar, Mozambique, Sierra Leone, and Sri Lanka. The United States accounts for about 4 percent of the total world production of titanium minerals and is heavily dependent on imports of titanium mineral concentrates to meet its domestic needs.
Titanium occurs only in silicate or oxide minerals and never in sulfide minerals. Environmental considerations for titanium mining are related to waste rock disposal and the impact of trace constituents on water quality. Because titanium is generally inert in the environment, human health risks from titanium and titanium mining are minimal; however, the processes required to extract titanium from titanium feedstock can produce industrial waste.
","largerWorkType":{"id":18,"text":"Report"},"largerWorkTitle":"Critical mineral resources of the United States—Economic and environmental geology and prospects for future supply","largerWorkSubtype":{"id":5,"text":"USGS Numbered Series"},"language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/pp1802T","isbn":"978-1-4113-3991-0","usgsCitation":"Woodruff, L.G., Bedinger, G.M., and Piatak, N.M., 2017, Titanium, chap. T of Schulz, K.J., DeYoung, J.H., Jr., Seal, R.R., II, and Bradley, D.C., eds., Critical mineral resources of the United States—Economic and environmental geology and prospects for future supply: U.S. Geological Survey Professional Paper 1802, p. T1–T23, https://doi.org/10.3133/pp1802T.","productDescription":"viii, 23 p.","numberOfPages":"36","onlineOnly":"N","additionalOnlineFiles":"N","ipdsId":"IP-045879","costCenters":[{"id":245,"text":"Eastern Mineral and Environmental Resources Science Center","active":true,"usgs":true}],"links":[{"id":334850,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/pp/1802/t/coverthb1.jpg"},{"id":334851,"rank":2,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/pp/1802/t/pp1802t.pdf","text":"Report","size":"10.7 MB","linkFileType":{"id":1,"text":"pdf"},"description":"PP 1802 T"}],"contact":"Mineral Resources Program Coordinator
U.S. Geological Survey
913 National Center
Reston, VA 20192
Email: minerals@usgs.gov
https://minerals.usgs.gov
Rhenium is one of the rarest elements in Earth’s continental crust; its estimated average crustal abundance is less than 1 part per billion. Rhenium is a metal that has an extremely high melting point and a heat-stable crystalline structure. More than 80 percent of the rhenium consumed in the world is used in high-temperature superalloys, especially those used to make turbine blades for jet aircraft engines. Rhenium’s other major application is in platinum-rhenium catalysts used in petroleum refining.
Rhenium rarely occurs as a native element or as its own sulfide mineral; most rhenium is present as a substitute for molybdenum in molybdenite. Annual world mine production of rhenium is about 50 metric tons. Nearly all primary rhenium production (that is, rhenium produced by mining rather than through recycling) is as a byproduct of copper mining, and about 80 percent of the rhenium obtained through mining is recovered from the flue dust produced during the roasting of molybdenite concentrates from porphyry copper deposits. Molybdenite in porphyry copper deposits can contain hundreds to several thousand grams per metric ton of rhenium, although the estimated rhenium grades of these deposits range from less than 0.1 gram per metric ton to about 0.6 gram per metric ton.
Continental-arc porphyry copper-(molybdenum-gold) deposits supply most of the world’s rhenium production and have large inferred rhenium resources. Porphyry copper mines in Chile account for about 55 percent of the world’s mine production of rhenium; rhenium is also recovered from porphyry copper deposits in the United States, Armenia, Kazakhstan, Mexico, Peru, Russia, and Uzbekistan. Sediment-hosted strata-bound copper deposits in Kazakhstan (of the sandstone type) and in Poland (of the reduced-facies, or Kupferschiefer, type) account for most other rhenium produced by mining. These types of deposits also have large amounts of identified rhenium resources. The future supply of rhenium is likely to depend largely on the capacity of the specialized processing facilities needed to recover rhenium from molybdenite concentrates.
The environmental consequences of rhenium recovery are closely linked to the consequences of mining large porphyry copper and strata-bound copper deposits; no additional environmental impact from recovery of rhenium from these deposits has been identified. No information is available regarding the potential toxic effects of rhenium on humans, partly because of the low natural abundance of rhenium.
","largerWorkType":{"id":18,"text":"Report"},"largerWorkTitle":"Critical mineral resources of the United States—Economic and environmental geology and prospects for future supply","largerWorkSubtype":{"id":5,"text":"USGS Numbered Series"},"language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/pp1802P","isbn":"978-1-4113-3991-0","usgsCitation":"John, D.A., Seal, R.R., II, and Polyak, D.E., 2017, Rhenium, chap. P of Schulz, K.J., DeYoung, J.H., Jr., Seal, R.R., II, and Bradley, D.C., eds., Critical mineral resources of the United States—Economic and environmental geology and prospects for future supply: U.S. Geological Survey Professional Paper 1802, p. P1–P49, https:/doi.org/10.3133/pp1802P.","productDescription":"viii, 49 p.","numberOfPages":"62","onlineOnly":"N","additionalOnlineFiles":"N","ipdsId":"IP-052034","costCenters":[{"id":312,"text":"Geology, Minerals, Energy, and Geophysics Science Center","active":true,"usgs":true}],"links":[{"id":334220,"rank":2,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/pp/1802/p/pp1802p.pdf","text":"Report","size":"10.9 MB","linkFileType":{"id":1,"text":"pdf"},"description":"PP 1802 P"},{"id":334219,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/pp/1802/p/coverthb2.jpg"}],"contact":"Mineral Resources Program Coordinator
U.S. Geological Survey
913 National Center
Reston, VA 20192
Email: minerals@usgs.gov
https://minerals.usgs.gov