The Shiprock Disposal Site is the location of the former Navajo Mill (Mill), a uranium ore-processing facility, located on a terrace overlooking the San Juan River in the town of Shiprock, New Mexico. Following the closure of the Mill, all tailings and associated materials were encapsulated in a disposal cell built on top of the former Mill and tailings piles. The milling operations, conducted at the site from 1954 to 1968, created radioactive tailings and process-related wastes that are now found in the groundwater. Elevated concentrations of constituents of concern—ammonium, manganese, nitrate, selenium, strontium, sulfate, and uranium—have also been measured in groundwater seeps in the nearby Many Devils Wash arroyo, leading to the inference that these constituents originated from the Mill. These constituents have also been reported in groundwater that is associated with Mancos Shale, the bedrock that underlies the site. The objective of this report is to increase understanding of the source of water and solutes to the groundwater beneath Many Devils Wash and to establish the background concentrations for groundwater that is in contact with the Mancos Shale at the site. This report presents evidence on three working hypotheses: (1) the water and solutes in Many Devils Wash originated from the operations at the former Mill, (2) groundwater in deep aquifers is upwelling under artesian pressure to recharge the shallow groundwater beneath Many Devils Wash, and (3) the groundwater beneath Many Devils Wash originates as precipitation that infiltrates into the shallow aquifer system and discharges to Many Devils Wash in a series of springs on the east side of the wash. The solute concentrations in the shallow groundwater of Many Devils Wash would result from the interaction of the water and the Mancos Shale if the source of water was upwelling from deep aquifers or precipitation.
In order to compare the groundwater from various wells to groundwater that has been affected by Mill activities, a classification system was developed to determine which wells were most likely to have been affected. Affects to groundwater by the Mill were determined by using the reported uranium alpha activity ratios measured in groundwater samples, along with the concentration of the uranium and the location of the wells relative to the Mill. Activity ratios of 1.2 or less were determined to be the most reliable indicator of Mill-affected groundwater. Wells with samples that had a reported activity ratio of 1.2 or less were classified as Mill affected. To compare groundwater with background water-quality, data from groundwater seeps and springs in the Upper Eagle Nest Arroyo and Salt Creek Wash, located north of the San Juan River, are also presented and analyzed.
Based on groundwater elevations and tritium concentrations measured in wells located between the disposal cell and Many Devils Wash, Mill water is not likely to reach Many Devils Wash. The tritium concentrations also indicate that groundwater from the Mill has not substantially affected Many Devils Wash in the past. Upwelling from deep aquifers was also determined to be an unlikely source, primarily by comparing the composition of the stable isotopes of water in the shallow groundwater with those reported in groundwater samples from the deeper aquifers. The stable-isotope compositions of the shallow groundwater around the site are enriched relative to the San Juan River and local meteoric lines, which suggests that most of the shallow groundwater has been influenced by evaporation and therefore was recharged at the surface. Several observations indicate that focused recharge is the likely source of groundwater in the area of Many Devils Wash. The visible erosional features in Many Devils Wash provide evidence of piping and groundwater sapping, and the distribution and type of vegetation in Many Devils Wash suggest that the focused recharge of precipitation is occurring. The estimated recharge from precipitation was calculated to be 0.0008 inches per year (in/yr) by using the mass-balance approach from reported seep discharge and 0.0011 in/yr using the chloride mass-balance approach.
A conceptual model of groundwater quality beneath Many Devils Wash is presented to explain the source of solutes in the groundwater beneath Many Devils Wash. The major-ion concentrations and geochemical evolution in the groundwater beneath Many Devils Wash and across the study area support the conceptual model that the underlying Mancos Shale is the source of solutes. Differences in the major-ion composition between groundwater samples collected around the site, result from the degree of weathering to the Mancos Shale. The cation distribution appears to be an indicator of effects from the Mill, with samples from the Mill-affected wells largely having a calcium/magnesium-sulfate composition that resembles the reported compositions of more weathered shale; however, that composition could change if the Mill-processed water flowed into areas where the Mancos Shale was less weathered. On the basis of the widespread presence of uranium in the Mancos Shale and the distribution of aqueous uranium in the analog sites and other sites in the region, it appears likely that uranium in the groundwater of Many Devils Wash is naturally sourced from the Mancos Shale.
","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/sir20165031","collaboration":"Prepared in cooperation with the Navajo Nation Environmental Protection Agency","usgsCitation":"Robertson, A.J., Ranalli, A.J., Austin, S.A., and Lawlis, B.R., 2016, The source of groundwater and solutes to Many Devils Wash at a former uranium mill site in Shiprock, New Mexico: U.S. Geological Survey Scientific Investigations Report 2016–5031, 54 p., https://dx.doi.org/10.3133/sir20165031.","productDescription":"x, 54 p.","onlineOnly":"Y","additionalOnlineFiles":"N","ipdsId":"IP-051391","costCenters":[{"id":472,"text":"New Mexico Water Science Center","active":true,"usgs":true}],"links":[{"id":320372,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/sir/2016/5031/coverthb.jpg"},{"id":320373,"rank":2,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/sir/2016/5031/sir20165031.pdf","text":"Report","size":"5.19 MB","linkFileType":{"id":1,"text":"pdf"},"description":"SIR 2016-5031"}],"country":"United States","state":"New Mexico","otherGeospatial":"Shiprock","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -108.74404907226562,\n 36.84775766525783\n ],\n [\n -108.74267578125,\n 36.70806354647625\n ],\n [\n -108.54766845703125,\n 36.71356812817935\n ],\n [\n -108.56552124023438,\n 36.869733528373395\n ],\n [\n -108.74542236328125,\n 36.87742358748459\n ],\n [\n -108.74404907226562,\n 36.84775766525783\n ]\n ]\n ]\n }\n }\n ]\n}","contact":"Director, New Mexico Water Science Center
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http://nm.water.usgs.gov/
Numerous wind energy projects have been constructed in the central and southern Great Plains, USA, the main wintering area for midcontinental Sandhill Cranes (Grus canadensis). In an initial assessment of the potential risks of wind towers to cranes, we estimated spatial overlap, investigated potential avoidance behavior, and determined the habitat associations of cranes. We used data from cranes marked with platform transmitting terminals (PTTs) with and without global positioning system (GPS) capabilities. We estimated the wintering distributions of PTT-marked cranes prior to the construction of wind towers, which we compared with current tower locations. Based on this analysis, we found 7% spatial overlap between the distributions of cranes and towers. When we looked at individually marked cranes, we found that 52% would have occurred within 10 km of a tower at some point during winter. Using data from cranes marked after tower construction, we found a potential indication of avoidance behavior, whereby GPS-marked cranes generally used areas slightly more distant from existing wind towers than would be expected by chance. Results from a habitat selection model suggested that distances between crane locations and towers may have been driven more by habitat selection than by avoidance, as most wind towers were constructed in locations not often selected by wintering cranes. Our findings of modest regional overlap and that few towers have been placed in preferred crane habitat suggest that the current distribution of wind towers may be of low risk to the continued persistence of wintering midcontinental Sandhill Cranes in the central and southern Great Plains.
","language":"English","publisher":"Cooper Ornithological Society","doi":"10.1650/CONDOR-15-99.1","usgsCitation":"Pearse, A.T., Brandt, D.A., and Krapu, G., 2016, Wintering Sandhill Crane exposure to wind energy development in the central and southern Great Plains, USA: The Condor, v. 118, no. 2, p. 391-401, https://doi.org/10.1650/CONDOR-15-99.1.","productDescription":"11 p.","startPage":"391","endPage":"401","onlineOnly":"N","additionalOnlineFiles":"N","ipdsId":"IP-061835","costCenters":[{"id":480,"text":"Northern Prairie Wildlife Research Center","active":true,"usgs":true}],"links":[{"id":471056,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.1650/condor-15-99.1","text":"Publisher Index Page"},{"id":320368,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","state":"Kansas, New Mexico, Oklahoma, 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Center","active":true,"usgs":true}],"preferred":true,"id":627300,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Krapu, Gary 0000-0001-8482-6130 gkrapu@usgs.gov","orcid":"https://orcid.org/0000-0001-8482-6130","contributorId":168791,"corporation":false,"usgs":true,"family":"Krapu","given":"Gary","email":"gkrapu@usgs.gov","affiliations":[{"id":480,"text":"Northern Prairie Wildlife Research Center","active":true,"usgs":true}],"preferred":true,"id":627301,"contributorType":{"id":1,"text":"Authors"},"rank":3}]}} ,{"id":70170094,"text":"ds988 - 2016 - Post-Hurricane Isaac coastal oblique aerial photographs collected along the Alabama, Mississippi, and Louisiana barrier islands, September 2–3, 2012","interactions":[],"lastModifiedDate":"2016-04-25T09:24:56","indexId":"ds988","displayToPublicDate":"2016-04-21T10:45:00","publicationYear":"2016","noYear":false,"publicationType":{"id":18,"text":"Report"},"publicationSubtype":{"id":5,"text":"USGS Numbered Series"},"seriesTitle":{"id":310,"text":"Data Series","code":"DS","onlineIssn":"2327-638X","printIssn":"2327-0271","active":false,"publicationSubtype":{"id":5}},"seriesNumber":"988","title":"Post-Hurricane Isaac coastal oblique aerial photographs collected along the Alabama, Mississippi, and Louisiana barrier islands, September 2–3, 2012","docAbstract":"The U.S. Geological Survey (USGS), as part of the National Assessment of Coastal Change Hazards project, conducts baseline and storm-response photography missions to document and understand the changes in vulnerability of the Nation's coasts to extreme storms (Morgan, 2009). On September 2-3, 2012, the USGS conducted an oblique aerial photographic survey along the Alabama, Mississippi, and Louisiana barrier islands aboard a Cessna 172 (aircraft) at an altitude of 500 feet (ft) and approximately 1,000 ft offshore. This mission was flown to collect post-Hurricane Isaac data for assessing incremental changes in the beach and nearshore area since the last survey, flown in September 2008 (central Louisiana barrier islands) and June 2011 (Dauphin Island, Alabama, to Breton Island, Louisiana), and the data can be used in the assessment of future coastal change.
The photographs provided in this report are Joint Photographic Experts Group (JPEG) images. ExifTool was used to add the following to the header of each photo: time of collection, Global Positioning System (GPS) latitude, GPS longitude, keywords, credit, artist (photographer), caption, copyright, and contact information. The photograph locations are an estimate of the position of the aircraft at the time the photograph was taken and do not indicate the location of any feature in the images (see the Navigation Data page). These photographs document the state of the barrier islands and other coastal features at the time of the survey. Pages containing thumbnail images of the photographs, referred to as contact sheets, were created in 5-minute segments of flight time. These segments can be found on the Photos and Maps page. Photographs can be opened directly with any JPEG-compatible image viewer by clicking on a thumbnail on the contact sheet.
In addition to the photographs, a Google Earth Keyhole Markup Language (KML) file is provided and can be used to view the images by clicking on the marker and then clicking on either the thumbnail or the link above the thumbnail. The KML files were created using the photographic navigation files. These KML file(s) can be found in the kml folder.
","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/ds988","usgsCitation":"Morgan, K.L.M., and Westphal, K.A., 2016, Post-Hurricane Isaac coastal oblique aerial photographs collected along the Alabama, Mississippi, and Louisiana barrier islands, September 2–3, 2012: U.S. Geological Survey Data Series 988, https://dx.doi.org/10.3133/ds988.\n","productDescription":"HTML Document","onlineOnly":"Y","additionalOnlineFiles":"Y","temporalStart":"2012-09-02","ipdsId":"IP-072305","costCenters":[{"id":574,"text":"St. Petersburg Coastal and Marine Science Center","active":true,"usgs":true}],"links":[{"id":320364,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/usgs_thumb.jpg"},{"id":319988,"rank":1,"type":{"id":15,"text":"Index Page"},"url":"https://pubs.usgs.gov/ds/0988/index.html","text":"Report ","linkFileType":{"id":5,"text":"html"},"description":"DS 988"}],"country":"United States","state":"Alabama, Mississippi, Louisiana","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -91.175537109375,\n 29.0273547804184\n ],\n [\n -91.175537109375,\n 30.5717205651999\n ],\n [\n -88.0224609375,\n 30.5717205651999\n ],\n [\n -88.0224609375,\n 29.0273547804184\n ],\n [\n -91.175537109375,\n 29.0273547804184\n ]\n ]\n ]\n }\n }\n ]\n}","contact":"Director, St. Petersburg Coastal and Marine Science Center
U.S. Geological Survey
600 4th Street South
St. Petersburg, FL 33701
(727) 502–8000
http://coastal.er.usgs.gov
Existing models for large-magnitude, right-lateral slip on the San Gregorio–Hosgri fault system imply much more deformation of the onshore block in the Santa Maria basin than is supported by geologic data. This problem is resolved by a model in which dextral slip on this fault system increases gradually from 0–10 km near Point Arguello to ∼150 km at Cape San Martin, but such a model requires abandoning the cross-fault tie between Point Sal and Point Piedras Blancas, which requires 90–100 km of right-lateral slip on the southern Hosgri fault. We collected stratigraphic and detrital zircon data from Miocene clastic rocks overlying Jurassic basement at both localities to determine if either section contained unique characteristics that could establish how far apart they were in the early Miocene. Our data indicate that these basins formed in the early Miocene during a period of widespread transtensional basin formation in the central Coast Ranges, and they filled with sediment derived from nearby pre-Cenozoic basement rocks. Although detrital zircon data do not indicate a unique source component in either section, they establish the maximum depositional age of the previously undated Point Piedras Blancas section to be 18 Ma. We also show that detrital zircon trace-element data can be used to discriminate between zircons of oceanic crust and arc affinity of the same age, a potentially useful tool in future studies of the California Coast Ranges. Overall, we find no characteristics in the stratigraphy and provenance of the Point Sal and Point Piedras Blancas sections that are sufficiently unique to prove whether they were far apart or close together in the early Miocene, making them of questionable utility as piercing points.
","language":"English","publisher":"Geological Society of America","publisherLocation":"Washington, D.C.","doi":"10.1130/GES01289.1","usgsCitation":"Colgan, J.P., and Stanley, R.G., 2016, The Point Sal–Point Piedras Blancas correlation and the problem of slip on the San Gregorio–Hosgri fault, central California Coast Ranges: Geosphere, v. 12, no. 3, p. 971-984, https://doi.org/10.1130/GES01289.1.","productDescription":"14 p.","startPage":"971","endPage":"984","onlineOnly":"N","additionalOnlineFiles":"N","ipdsId":"IP-061433","costCenters":[{"id":318,"text":"Geosciences and Environmental Change Science Center","active":true,"usgs":true}],"links":[{"id":471059,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.1130/ges01289.1","text":"Publisher Index Page"},{"id":321036,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"volume":"12","issue":"3","publishingServiceCenter":{"id":2,"text":"Denver PSC"},"noUsgsAuthors":false,"publicationDate":"2016-04-20","publicationStatus":"PW","scienceBaseUri":"572dc05be4b0dae0d5d8f2d8","contributors":{"authors":[{"text":"Colgan, Joseph P. 0000-0001-6671-1436 jcolgan@usgs.gov","orcid":"https://orcid.org/0000-0001-6671-1436","contributorId":1649,"corporation":false,"usgs":true,"family":"Colgan","given":"Joseph","email":"jcolgan@usgs.gov","middleInitial":"P.","affiliations":[{"id":318,"text":"Geosciences and Environmental Change Science Center","active":true,"usgs":true},{"id":312,"text":"Geology, Minerals, Energy, and Geophysics Science Center","active":true,"usgs":true}],"preferred":true,"id":628909,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Stanley, Richard G. 0000-0001-6192-8783 rstanley@usgs.gov","orcid":"https://orcid.org/0000-0001-6192-8783","contributorId":1832,"corporation":false,"usgs":true,"family":"Stanley","given":"Richard","email":"rstanley@usgs.gov","middleInitial":"G.","affiliations":[{"id":312,"text":"Geology, Minerals, Energy, and Geophysics Science Center","active":true,"usgs":true}],"preferred":true,"id":628910,"contributorType":{"id":1,"text":"Authors"},"rank":2}]}} ,{"id":70170766,"text":"70170766 - 2016 - Biogeographical history and coalescent species delimitation of Pacific island skinks (Squamata: Scincidae: Emoia cyanura species group)","interactions":[],"lastModifiedDate":"2016-09-28T16:26:11","indexId":"70170766","displayToPublicDate":"2016-04-20T16:00:00","publicationYear":"2016","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":2193,"text":"Journal of Biogeography","active":true,"publicationSubtype":{"id":10}},"title":"Biogeographical history and coalescent species delimitation of Pacific island skinks (Squamata: Scincidae: Emoia cyanura species group)","docAbstract":"A prevailing hypothesis for how Pacific islands organisms have obtained their extant distributions is that of a stepping-stone model, in which populations originate from Papua New Guinea in the western Pacific and gradually disperse eastward. Here, we test this model using a spatiotemporal framework for Emoia cyanura and E. impar, two species within the Emoia cyanura species group (ECSG; Family: Scincidae). We further assess species limits within the group, utilizing novel coalescent methods.
\nPacific Islands.
\nWe obtained DNA sequence data from one mitochondrial and three nuclear markers for 117 individuals, representing seven of the nine species within the ECSG. These data were analysed for concordance with the stepping-stone model using estimation of population structure, divergence dates, and historical biogeographical range. To assess hypotheses of independent lineages within each widespread species, we also employed the Bayesian Phylogenetics & Phylogeography (BPP) program to define operational taxonomic units in *BEAST.
\nPopulation structure analyses consistently found individuals from western island groups representing divergent populations, with central and eastern populations demonstrating minimal genetic variation. Phylogenetic hypotheses support a western origin for E. cyanura and E. impar, while biogeographical and divergence time estimations predict a recent and rapid expansion out of the western Pacific. The BPP and *BEAST analyses found evidence for five independent lineages within E. impar and five independent lineages within E. cyanura/E. pseudocyanura.
\nIn contrast to the expectations of a stepping-stone model, E. cyanura and E. impar each exhibit the genetic signature of a rapid radiation during the mid to late Pleistocene, with evidence for newly identified lineages, mainly on western islands. Of these recovered lineages, we propose three to be elevated to species status. These findings expand our understanding of endemic Pacific biota, which are subject to conservation threats from human impacts and climate change.
","language":"English","publisher":"Blackwell Scientific Publications","doi":"10.1111/jbi.12772","usgsCitation":"Klein, E., Harris, R., Fisher, R.N., and Reeder, T., 2016, Biogeographical history and coalescent species delimitation of Pacific island skinks (Squamata: Scincidae: Emoia cyanura species group): Journal of Biogeography, v. 43, no. 10, p. 1917-1929, https://doi.org/10.1111/jbi.12772.","productDescription":"13 p.","startPage":"1917","endPage":"1929","onlineOnly":"N","additionalOnlineFiles":"N","ipdsId":"IP-071494","costCenters":[{"id":651,"text":"Western Ecological Research Center","active":true,"usgs":true}],"links":[{"id":320843,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"otherGeospatial":"Pacific islands","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n 191.7333984375,\n 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PSC"},"noUsgsAuthors":false,"publicationDate":"2016-04-20","publicationStatus":"PW","scienceBaseUri":"57287a2be4b0b13d391865b1","contributors":{"authors":[{"text":"Klein, Elaine","contributorId":169068,"corporation":false,"usgs":false,"family":"Klein","given":"Elaine","email":"","affiliations":[{"id":25403,"text":"U Washington, Seattle, WA","active":true,"usgs":false}],"preferred":false,"id":628335,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Harris, Rebecca","contributorId":169069,"corporation":false,"usgs":false,"family":"Harris","given":"Rebecca","email":"","affiliations":[{"id":25404,"text":"U of Washington, Seattle, WA","active":true,"usgs":false}],"preferred":false,"id":628336,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Fisher, Robert N. 0000-0002-2956-3240 rfisher@usgs.gov","orcid":"https://orcid.org/0000-0002-2956-3240","contributorId":1529,"corporation":false,"usgs":true,"family":"Fisher","given":"Robert","email":"rfisher@usgs.gov","middleInitial":"N.","affiliations":[{"id":651,"text":"Western Ecological Research Center","active":true,"usgs":true}],"preferred":true,"id":628334,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Reeder, Tod","contributorId":169070,"corporation":false,"usgs":false,"family":"Reeder","given":"Tod","affiliations":[{"id":25405,"text":"San Diego State U.","active":true,"usgs":false}],"preferred":false,"id":628337,"contributorType":{"id":1,"text":"Authors"},"rank":4}]}} ,{"id":70162080,"text":"70162080 - 2016 - Effects of lek count protocols on greater sage-grouse population trend estimates","interactions":[],"lastModifiedDate":"2017-12-27T15:01:11","indexId":"70162080","displayToPublicDate":"2016-04-20T16:00:00","publicationYear":"2016","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":2508,"text":"Journal of Wildlife Management","active":true,"publicationSubtype":{"id":10}},"title":"Effects of lek count protocols on greater sage-grouse population trend estimates","docAbstract":"Annual counts of males displaying at lek sites are an important tool for monitoring greater sage-grouse populations (Centrocercus urophasianus), but seasonal and diurnal variation in lek attendance may increase variance and bias of trend analyses. Recommendations for protocols to reduce observation error have called for restricting lek counts to within 30 minutes of sunrise, but this may limit the number of lek counts available for analysis, particularly from years before monitoring was widely standardized. Reducing the temporal window for conducting lek counts also may constrain the ability of agencies to monitor leks efficiently. We used lek count data collected across Wyoming during 1995−2014 to investigate the effect of lek counts conducted between 30 minutes before and 30, 60, or 90 minutes after sunrise on population trend estimates. We also evaluated trends across scales relevant to management, including statewide, within Working Group Areas and Core Areas, and for individual leks. To further evaluate accuracy and precision of trend estimates from lek count protocols, we used simulations based on a lek attendance model and compared simulated and estimated values of annual rate of change in population size (λ) from scenarios of varying numbers of leks, lek count timing, and count frequency (counts/lek/year). We found that restricting analyses to counts conducted within 30 minutes of sunrise generally did not improve precision of population trend estimates, although differences among timings increased as the number of leks and count frequency decreased. Lek attendance declined >30 minutes after sunrise, but simulations indicated that including lek counts conducted up to 90 minutes after sunrise can increase the number of leks monitored compared to trend estimates based on counts conducted within 30 minutes of sunrise. This increase in leks monitored resulted in greater precision of estimates without reducing accuracy. Increasing count frequency also improved precision. These results suggest that the current distribution of count timings available in lek count databases such as that of Wyoming (conducted up to 90 minutes after sunrise) can be used to estimate sage-grouse population trends without reducing precision or accuracy relative to trends from counts conducted within 30 minutes of sunrise. However, only 10% of all Wyoming counts in our sample (1995−2014) were conducted 61−90 minutes after sunrise, and further increasing this percentage may still bias trend estimates because of declining lek attendance.
","language":"English","publisher":"Wiley","doi":"10.1002/jwmg.1050","usgsCitation":"Monroe, A., Edmunds, D.R., and Aldridge, C.L., 2016, Effects of lek count protocols on greater sage-grouse population trend estimates: Journal of Wildlife Management, v. 80, no. 4, p. 667-678, https://doi.org/10.1002/jwmg.1050.","productDescription":"12 p.","startPage":"667","endPage":"678","onlineOnly":"N","additionalOnlineFiles":"N","ipdsId":"IP-068601","costCenters":[{"id":291,"text":"Fort Collins Science Center","active":true,"usgs":true}],"links":[{"id":320336,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"volume":"80","issue":"4","publishingServiceCenter":{"id":2,"text":"Denver PSC"},"noUsgsAuthors":false,"publicationDate":"2016-03-17","publicationStatus":"PW","scienceBaseUri":"57189a1be4b0ef3b7caaf785","contributors":{"authors":[{"text":"Monroe, Adrian P. 0000-0003-0934-8225 amonroe@usgs.gov","orcid":"https://orcid.org/0000-0003-0934-8225","contributorId":152209,"corporation":false,"usgs":true,"family":"Monroe","given":"Adrian P.","email":"amonroe@usgs.gov","affiliations":[{"id":291,"text":"Fort Collins Science Center","active":true,"usgs":true}],"preferred":true,"id":588478,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Edmunds, David R. 0000-0002-5212-8271 dedmunds@usgs.gov","orcid":"https://orcid.org/0000-0002-5212-8271","contributorId":152210,"corporation":false,"usgs":true,"family":"Edmunds","given":"David","email":"dedmunds@usgs.gov","middleInitial":"R.","affiliations":[{"id":291,"text":"Fort Collins Science Center","active":true,"usgs":true}],"preferred":true,"id":588479,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Aldridge, Cameron L. 0000-0003-3926-6941 aldridgec@usgs.gov","orcid":"https://orcid.org/0000-0003-3926-6941","contributorId":191773,"corporation":false,"usgs":true,"family":"Aldridge","given":"Cameron","email":"aldridgec@usgs.gov","middleInitial":"L.","affiliations":[{"id":291,"text":"Fort Collins Science Center","active":true,"usgs":true}],"preferred":false,"id":588480,"contributorType":{"id":1,"text":"Authors"},"rank":3}]}} ,{"id":70175897,"text":"70175897 - 2016 - Using NDVI to measure precipitation in semi-arid landscapes","interactions":[],"lastModifiedDate":"2017-02-08T11:18:50","indexId":"70175897","displayToPublicDate":"2016-04-20T14:30:00","publicationYear":"2016","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":2183,"text":"Journal of Arid Environments","active":true,"publicationSubtype":{"id":10}},"title":"Using NDVI to measure precipitation in semi-arid landscapes","docAbstract":"Measuring precipitation in semi-arid landscapes is important for understanding the processes related to rainfall and run-off; however, measuring precipitation accurately can often be challenging especially within remote regions where precipitation instruments are scarce. Typically, rain-gauges are sparsely distributed and research comparing rain-gauge and RADAR precipitation estimates reveal that RADAR data are often misleading, especially for monsoon season convective storms. This study investigates an alternative way to map the spatial and temporal variation of precipitation inputs along ephemeral stream channels using Normalized Difference Vegetation Index (NDVI) derived from Landsat Thematic Mapper imagery. NDVI values from 26 years of pre- and post-monsoon season Landsat imagery were derived across Yuma Proving Ground (YPG), a region covering 3,367 km2 of semiarid landscapes in southwestern Arizona, USA. The change in NDVI from a pre-to post-monsoon season image along ephemeral stream channels explained 73% of the variance in annual monsoonal precipitation totals from a nearby rain-gauge. In addition, large seasonal changes in NDVI along channels were useful in determining when and where flow events have occurred.
","language":"English","publisher":"Academic Press","doi":"10.1016/j.jaridenv.2016.04.004","usgsCitation":"Birtwhistle, A.N., Laituri, M., Bledsoe, B., and Friedman, J.M., 2016, Using NDVI to measure precipitation in semi-arid landscapes: Journal of Arid Environments, v. 131, p. 15-24, https://doi.org/10.1016/j.jaridenv.2016.04.004.","productDescription":"10 p.","startPage":"15","endPage":"24","onlineOnly":"N","additionalOnlineFiles":"N","ipdsId":"IP-070425","costCenters":[{"id":291,"text":"Fort Collins Science Center","active":true,"usgs":true}],"links":[{"id":471060,"rank":4,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.1016/j.jaridenv.2016.04.004","text":"Publisher Index Page"},{"id":438618,"rank":3,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/F7ZK5DSB","text":"USGS data release","linkHelpText":"Mean of the Top Ten Percent of NDVI Values in the Yuma Proving Ground during Monsoon Season, 1986-2011"},{"id":327106,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"},{"id":334957,"rank":2,"type":{"id":30,"text":"Data Release"},"url":"https://dx.doi.org/10.5066/F7ZK5DSB","text":"Mean of the top ten percent of NDVI values in the Yuma Proving Ground during monsoon season, 1986-2011"}],"country":"United States","state":"Arizona","otherGeospatial":"Sonoran Desert, Yuma Proving Ground","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -114.49676513671875,\n 33.55741786324217\n ],\n [\n -114.23858642578125,\n 33.56199537293026\n ],\n [\n -114.2083740234375,\n 32.95797741405949\n ],\n [\n -113.9666748046875,\n 32.94875863715422\n ],\n [\n -113.9501953125,\n 33.0316929997831\n ],\n [\n -113.74420166015624,\n 33.04320549616557\n ],\n [\n -113.7469482421875,\n 33.33511774753217\n ],\n [\n -113.587646484375,\n 33.33741240611175\n ],\n [\n -113.56842041015625,\n 32.930318199070534\n ],\n [\n -113.90625,\n 32.76880048488168\n ],\n [\n -114.466552734375,\n 32.7295304134847\n ],\n [\n -114.466552734375,\n 32.85421076375023\n ],\n [\n -114.46105957031249,\n 32.877280526855394\n ],\n [\n -114.44183349609375,\n 32.89342578969231\n ],\n [\n -114.46380615234375,\n 32.92109653816924\n ],\n [\n -114.46380615234375,\n 32.94414888814148\n ],\n [\n -114.46105957031249,\n 32.9648908656902\n ],\n [\n -114.46929931640624,\n 32.98562797456918\n ],\n [\n -114.49127197265625,\n 32.98562797456918\n ],\n [\n -114.49676513671875,\n 33.00636021320537\n ],\n [\n -114.52972412109375,\n 33.02939031998959\n ],\n [\n -114.55169677734375,\n 33.038600678138195\n ],\n [\n -114.576416015625,\n 33.04090311724091\n ],\n [\n -114.60113525390625,\n 33.050112271849656\n ],\n [\n -114.5928955078125,\n 33.4955977448657\n ],\n [\n -114.49676513671875,\n 33.55741786324217\n ]\n ]\n ]\n }\n }\n ]\n}","volume":"131","publishingServiceCenter":{"id":2,"text":"Denver PSC"},"noUsgsAuthors":false,"publicationStatus":"PW","scienceBaseUri":"57b82deee4b03fd6b7da3a5e","contributors":{"authors":[{"text":"Birtwhistle, Amy N.","contributorId":173888,"corporation":false,"usgs":false,"family":"Birtwhistle","given":"Amy","email":"","middleInitial":"N.","affiliations":[{"id":6621,"text":"Colorado State University","active":true,"usgs":false}],"preferred":false,"id":646517,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Laituri, Melinda","contributorId":173889,"corporation":false,"usgs":false,"family":"Laituri","given":"Melinda","affiliations":[{"id":6621,"text":"Colorado State University","active":true,"usgs":false}],"preferred":false,"id":646518,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Bledsoe, Brian","contributorId":173890,"corporation":false,"usgs":false,"family":"Bledsoe","given":"Brian","affiliations":[{"id":6621,"text":"Colorado State University","active":true,"usgs":false}],"preferred":false,"id":646519,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Friedman, Jonathan M. 0000-0002-1329-0663 friedmanj@usgs.gov","orcid":"https://orcid.org/0000-0002-1329-0663","contributorId":2473,"corporation":false,"usgs":true,"family":"Friedman","given":"Jonathan","email":"friedmanj@usgs.gov","middleInitial":"M.","affiliations":[{"id":291,"text":"Fort Collins Science Center","active":true,"usgs":true}],"preferred":true,"id":646516,"contributorType":{"id":1,"text":"Authors"},"rank":4}]}} ,{"id":70170412,"text":"70170412 - 2016 - Avian mercury exposure and toxicological risk across western North America: A synthesis","interactions":[],"lastModifiedDate":"2018-08-07T12:31:03","indexId":"70170412","displayToPublicDate":"2016-04-20T11:45:00","publicationYear":"2016","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":3352,"text":"Science of the Total Environment","active":true,"publicationSubtype":{"id":10}},"title":"Avian mercury exposure and toxicological risk across western North America: A synthesis","docAbstract":"Methylmercury contamination of the environment is an important issue globally, and birds are useful bioindicators for mercury monitoring programs. The available data on mercury contamination of birds in western North America were synthesized. Original data from multiple databases were obtained and a literature review was conducted to obtain additional mercury concentrations. In total, 29219 original bird mercury concentrations from 225 species were compiled, and an additional 1712 mean mercury concentrations, representing 19998 individuals and 176 species, from 200 publications were obtained. To make mercury data comparable across bird tissues, published equations of tissue mercury correlations were used to convert all mercury concentrations into blood-equivalent mercury concentrations. Blood-equivalent mercury concentrations differed among species, foraging guilds, habitat types, locations, and ecoregions. Piscivores and carnivores exhibited the greatest mercury concentrations, whereas herbivores and granivores exhibited the lowest mercury concentrations. Bird mercury concentrations were greatest in ocean and salt marsh habitats and lowest in terrestrial habitats. Bird mercury concentrations were above toxicity benchmarks in many areas throughout western North America, and multiple hotspots were identified. Additionally, published toxicity benchmarks established in multiple tissues were summarized and translated into a common blood-equivalent mercury concentration. Overall, 66% of birds sampled in western North American exceeded a blood-equivalent mercury concentration of 0.2 μg/g wet weight (ww; above background levels), which is the lowest-observed effect level, 28% exceeded 1.0 μg/g ww (moderate risk), 8% exceeded 3.0 μg/g ww (high risk), and 4% exceeded 4.0 μg/g ww (severe risk). Mercury monitoring programs should sample bird tissues, such as adult blood and eggs, that are most-easily translated into tissues with well-developed toxicity benchmarks and that are directly relevant to bird reproduction. Results indicate that mercury contamination of birds is prevalent in many areas throughout western North America, and large-scale ecological attributes are important factors influencing bird mercury concentrations.
\nThe U.S. Geological Survey, in cooperation with the City of Lubbock and the Texas State Soil and Water Conservation Board, developed and calibrated a Soil and Water Assessment Tool watershed model of the Double Mountain Fork Brazos River watershed in western Texas to simulate monthly mean streamflow and to evaluate the effects of brush management on water yields in the watershed, particularly to Lake Alan Henry, for calendar years 1994–2013. Model simulations were done to quantify the possible change in water yield of individual subbasins in the Double Mountain Fork Brazos River watershed as a result of the replacement of shrubland (brush) with grassland. The simulation results will serve as a tool for resource managers to guide brush-management efforts.
\nThe model was calibrated from 1994 through 2008 and validated from 2009 through 2013 with streamflow data collected at the U.S. Geological Survey streamflow-gaging station 08079600 Double Mountain Fork Brazos River at Justiceburg, Texas (hereinafter referred to as the “Justiceburg gage”). Simulated monthly mean streamflow showed agreement with measured monthly mean streamflow for the 1994–2013 study period: the percentage bias was +6, the coefficient of determination was 0.73, and the Nash–Sutcliffe coefficient of model efficiency was 0.71.
\nThe calibrated watershed model was used to perform brush-management simulations. The National Land Cover Database 2006, which was the land-cover data used to develop the watershed model, was modified to simulate shrubland replacement with grassland in each of the 35 model subbasins. After replacement of shrubland with grassland in areas with land slope less than 20 percent and excluding riparian areas, the modeled 20-year (1994 through 2013) water yields to Lake Alan Henry increased by 114,000 acre-feet or about 5,700 acre-feet per year. In terms of the increase in water yield per acre of shrubland replaced with grassland, the average annual increase in water yield was 17,300 gallons per acre. Within the modeled subbasins, the increase in average annual water yield ranged from 5,850 to 34,400 gallons per acre of shrubland replaced with grassland. Subbasins downstream from the Justiceburg gage had a higher average annual increase in water yield (21,700 gallons per acre) than subbasins upstream from the streamflow-gaging station (16,800 gallons per acre).
","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/sir20165032","collaboration":"Prepared in cooperation with the City of Lubbock and the Texas State Soil and Water Conservation Board","usgsCitation":"Harwell, G.R., Stengel, V.G., and Bumgarner, J.R., 2016, Simulation of streamflow and the effects of brush management on water yields in the Double Mountain Fork Brazos River watershed, western Texas 1994–2013: U.S. Geological Survey Scientific Investigations Report 2016–5032, 39 p., https://dx.doi.org/10.3133/sir20165032.","productDescription":"Report: viii, 39 p.; Precipitation and Temperature Data","numberOfPages":"50","onlineOnly":"Y","additionalOnlineFiles":"Y","ipdsId":"IP-072426","costCenters":[{"id":583,"text":"Texas Water Science Center","active":true,"usgs":true}],"links":[{"id":320033,"rank":3,"type":{"id":7,"text":"Companion Files"},"url":"https://pubs.usgs.gov/sir/2016/5032/sir20165032_data.zip","text":"Precipitation and Temperature Data","linkFileType":{"id":6,"text":"zip"},"description":"SIR 2016–5032 Precipitation and Temperature Data"},{"id":319869,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/sir/2016/5032/coverthb.jpg"},{"id":319870,"rank":2,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/sir/2016/5032/sir20165032.pdf","text":"Report","size":"4.36 MB","linkFileType":{"id":1,"text":"pdf"},"description":"SIR 2016–5032"}],"country":"United States","state":"Texas","otherGeospatial":"Double Mountain Fork Brazos River watershed","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -101.7,\n 33.165\n ],\n [\n -101.7,\n 32.835\n ],\n [\n -100.9,\n 32.835\n ],\n [\n -100.9,\n 33.165\n ],\n [\n -101.7,\n 33.165\n ]\n ]\n ]\n }\n }\n ]\n}","contact":"Director, Texas Water Science Center
U.S. Geological Survey
1505 Ferguson Lane
Austin, TX 78754–4733
This report describes a method for estimating ecologically relevant low-flow metrics that quantify late‑season streamflow regime for ungaged sites in the upper Grande Ronde River Basin, Oregon. The analysis presented here focuses on sites sampled by the Columbia River Inter‑Tribal Fish Commission as part of their efforts to monitor habitat restoration to benefit spring Chinook salmon recovery in the basin. Streamflow data were provided by the U.S. Geological Survey and the Oregon Water Resources Department. Specific guidance was provided for selection of streamgages, development of probabilistic frequency distributions for annual 7-day low-flow events, and regionalization of the frequency curves based on multivariate analysis of watershed characteristics. Evaluation of the uncertainty associated with the various components of this protocol indicates that the results are reliable for the intended purpose of hydrologic classification to support ecological analysis of factors contributing to juvenile salmon success. They should not be considered suitable for more standard water-resource evaluations that require greater precision, especially those focused on management and forecasting of extreme low-flow conditions.
","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston VA","doi":"10.3133/sir20165041","collaboration":"Prepared in cooperation with Columbia River Inter-Tribal Fish Commission","usgsCitation":"Kelly, V.J., and White, Seth, 2016, A method for characterizing late-season low-flow regime in the upper Grand Ronde River Basin, Oregon: U.S. Geological Survey Scientific Investigations Report 2016–5041, 41 p.,\nhttps://dx.doi.org/10.3133/sir20165041.","productDescription":"Report: vi, 41 p.; Appendixes A-F","onlineOnly":"Y","additionalOnlineFiles":"Y","ipdsId":"IP-059110","costCenters":[{"id":518,"text":"Oregon Water Science Center","active":true,"usgs":true}],"links":[{"id":320198,"rank":2,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/sir/2016/5041/sir20165041.pdf","text":"Report","size":"12 MB","linkFileType":{"id":1,"text":"pdf"},"description":"SIR 2016-5041 Report PDF"},{"id":320199,"rank":3,"type":{"id":3,"text":"Appendix"},"url":"https://pubs.usgs.gov/sir/2016/5041/sir20165041_appendixes.xlsx","text":"Appendixes A-F","size":"75 KB","linkFileType":{"id":3,"text":"xlsx"},"description":"SIR 2016-5041 Appendixes"},{"id":320197,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/sir/2016/5041/coverthb.jpg"}],"country":"United States","state":"Oregon","otherGeospatial":"Grande Ronde River","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -115,\n 44\n ],\n [\n -115,\n 47\n ],\n [\n -119,\n 47\n ],\n [\n -119,\n 44\n ],\n [\n -115,\n 44\n ]\n ]\n ]\n }\n }\n ]\n}","contact":"Director, Oregon Water Science Center
U.S. Geological Survey
2130 SW 5th Avenue
Portland, Oregon 97201
http://or.water.usgs.gov
Many barrier islands in the United States are eroding and losing elevation substantively because of storm surge, waves, and sea-level changes. This is particularly true for the deltaic barrier system in Louisiana. Breton Island is near the mouth of the Mississippi River at the southern end of the Chandeleur barrier island chain in southeast Louisiana. This report expands on previous geomorphic studies of Breton Island by incorporating additional historic and recent datasets. Multiple analyses focus on longand short-term shoreline change, as well as episodic events and anthropogenic modification. Analyses periods include long term (1869–2014), long-term historic (1869–1950), post-Mississippi River-Gulf Outlet (1950–2014), pre/post-Hurricane Katrina (2004–5), and recent (2005–14). In addition to shoreline change, barrier island geomorphology is evaluated using island area, elevation, and sediment volume change. In the long term (1869–2014), Breton Island was affected by landward transgression, island narrowing, and elevation loss. Major storm events exacerbated the long-term trends. In the recent period (2005–14), Breton Island eroded at a slower rate than in the long-term and gained area and total sediment volume. The recent accretion is likely because of the lack of major storms since Hurricane Katrina in 2005.
","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/ofr20161039","usgsCitation":"Terrano, J.F., Flocks, J.G., and Smith, K.E.L., 2016, Analysis of shoreline and geomorphic change for Breton Island, Louisiana, from 1869 to 2014: U.S. Geological Survey Open-File Report 2016–1039, 34 p.,\nhttps://dx.doi.org/10.3133/ofr20161039.","productDescription":"Report: viii, 34 p.; Data Releases","numberOfPages":"43","onlineOnly":"Y","additionalOnlineFiles":"Y","ipdsId":"IP-070444","costCenters":[{"id":574,"text":"St. Petersburg Coastal and Marine Science Center","active":true,"usgs":true}],"links":[{"id":320056,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/of/2016/1039/coverthb.jpg"},{"id":320057,"rank":2,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/of/2016/1039/ofr20161039.pdf","text":"Report","size":"3.69 MB","linkFileType":{"id":1,"text":"pdf"},"description":"OFR 2016–1039"},{"id":324797,"rank":3,"type":{"id":30,"text":"Data Release"},"url":"https://dx.doi.org/10.5066/F70G3H6G","text":"USGS data release - Topobathymetric Lidar Survey of Breton and Gosier Islands, Louisiana, January 16 and 18, 2014"},{"id":324798,"rank":4,"type":{"id":30,"text":"Data Release"},"url":"https://dx.doi.org/10.5066/F7XS5SGM","text":"USGS data release - A GIS Compilation of Vector Shorelines and Associated Shoreline Change Data for Breton Island, Louisiana: 1869–2014"}],"country":"United States","state":"Louisiana","otherGeospatial":"Breton Island, Breton National Wildlife Refuge, Chandeleur barrier island chain","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -89.17087554931639,\n 29.50729642400116\n ],\n [\n -89.17757034301758,\n 29.506698839472033\n ],\n [\n -89.18306350708008,\n 29.503262659979747\n ],\n [\n -89.18684005737305,\n 29.49579230227246\n ],\n [\n -89.18478012084961,\n 29.493849919009545\n ],\n [\n -89.1840934753418,\n 29.492206334848714\n ],\n [\n -89.17928695678711,\n 29.492804004901785\n ],\n [\n -89.17671203613281,\n 29.49235575269256\n ],\n [\n -89.17722702026367,\n 29.48862024048175\n ],\n [\n -89.18169021606444,\n 29.483390291999466\n ],\n [\n -89.18254852294922,\n 29.477861195816843\n ],\n [\n -89.1789436340332,\n 29.473676814427723\n ],\n [\n -89.1734504699707,\n 29.473527369040777\n ],\n [\n -89.16847229003906,\n 29.478907264175373\n ],\n [\n -89.1650390625,\n 29.48742484748479\n ],\n [\n -89.16435241699219,\n 29.497585238377603\n ],\n [\n -89.16521072387695,\n 29.501918036260868\n ],\n [\n -89.1653823852539,\n 29.506101251415647\n ],\n [\n -89.16778564453125,\n 29.508043399702284\n ],\n [\n -89.17087554931639,\n 29.50729642400116\n ]\n ]\n ]\n }\n }\n ]\n}","contact":"Director, St. Petersburg Coastal and Marine Science Center
U.S. Geological Survey
600 4th Street South
St. Petersburg, FL 33701
Concentrations of H2O and CO2 in olivine-hosted melt inclusions can be used to estimate crystallization depths for the olivine host. However, the original dissolved CO2concentration of melt inclusions at the time of trapping can be difficult to measure directly because in many cases substantial CO2 is transferred to shrinkage bubbles that form during post-entrapment cooling and crystallization. To investigate this problem, we heated olivine from the 1959 Kīlauea Iki and 1960 Kapoho (Hawai‘i) eruptions in a 1-atm furnace to temperatures above the melt inclusion trapping temperature to redissolve the CO2 in shrinkage bubbles. The measured CO2 concentrations of the experimentally rehomogenized inclusions (⩽590 ppm for Kīlauea Iki [n=10]; ⩽880 ppm for Kapoho, with one inclusion at 1863 ppm [n=38]) overlap with values for naturally quenched inclusions from the same samples, but experimentally rehomogenized inclusions have higher within-sample median CO2 values than naturally quenched inclusions, indicating at least partial dissolution of CO2 from the vapor bubble during heating. Comparison of our data with predictions from modeling of vapor bubble formation and published Raman data on the density of CO2 in the vapor bubbles suggests that 55-85% of the dissolved CO2 in the melt inclusions at the time of trapping was lost to post-entrapment shrinkage bubbles. Our results combined with the Raman data demonstrate that olivine from the early part of the Kīlauea Iki eruption crystallized at <6 km depth, with the majority of olivine in the 1-3 km depth range. These depths are consistent with the interpretation that the Kīlauea Iki magma was supplied from Kīlauea’s summit magma reservoir (∼2-5 km depth). In contrast, olivine from Kapoho, which was the rift zone extension of the Kīlauea Iki eruption, crystallized over a much wider range of depths (∼1-16 km). The wider depth range requires magma transport during the Kapoho eruption from deep beneath the summit region and/or from deep beneath Kīlauea’s east rift zone. The deeply derived olivine crystals and their host magma mixed with stored, more evolved magma in the rift zone, and the mixture was later erupted at Kapoho.
\nIn 2013, Landsat 8 began adding high quality, global, moderate-resolution imagery to the more than 40-year archive of Landsat imagery. To assess the potential effects of the availability of Landsat 8 imagery on users and their work, the U.S. Geological Survey (USGS) Land Remote Sensing Program (LRS) initiated a survey of Landsat users. The objectives of the survey were to
\n1. Characterize various Landsat user groups, such as United States (U.S.) and international users and Landsat 8 and non-Landsat 8 users;
2. Identify any differences among user groups in uses and preferences;
3. Measure the importance of and satisfaction with Landsat 8 attributes;
4. Assess the importance to users of the frequency of usable imagery; and
5. Determine any challenges in using Landsat 8.
The online survey was sent to 51,617 Landsat users registered with USGS in May 2014. Almost 13,000 people responded to the survey for a response rate of 25 percent (n = 12,966). Current Landsat users (users who had used Landsat in their work in the year prior to the survey) composed 89 percent of the sample (n = 11,549) and past Landsat users composed 11 percent (n = 1,417). The results reported here apply to current Landsat users registered with the USGS Earth Resources Observation and Science (EROS) Center.
\nUsers from 161 countries responded to the survey. Of those, 19 percent were citizens or permanent residents of the United States and 81 percent resided in other countries. More than 70 percent of current users had used Landsat 8 in the year prior to the survey. The majority of Landsat 8 users (65 percent) were established users who used Landsat imagery regularly both before and after Landsat 8 imagery became available. The average current Landsat user was male, 36 years old, and highly educated, with 9 years of experience using satellite imagery or geographic information system (GIS) software. Landsat 8 users had, on average, two more years of experience than non-Landsat 8 users. Users were employed predominantly by academic institutions (65 percent), followed by private businesses (13 percent), Federal governments (10 percent), State and local governments (6 percent), and nonprofit organizations (6 percent).
\nOf the Landsat imagery obtained in the past year by current users, on average 31 percent came from a Landsat 8 sensor. An equivalent amount came from the Landsat 7 ETM+ sensor (33 percent); slightly less came from Landsats 4 and 5 TM sensors (27 percent). Much less came from Landsats 1 through 5 MSS sensors (5 percent). Overall, more than a third of users’ work used Landsat imagery (38 percent). Of this work, on average, 37 percent of the work was operational. Landsat 8 users considered a greater proportion of their work operational than non-Landsat 8 users (39 percent compared with 29 percent). Environmental sciences and management were the most commonly selected primary applications (selected by 42 percent of users). Land use/land cover (23 percent) was the second most commonly selected primary application, followed by education (12 percent), agriculture (9 percent), and planning and development (6 percent).
\nLandsat 8 users were asked to rank the importance of certain attributes in determining whether to use Landsat 8 imagery in their work. The archive was ranked most important, followed by cost, spatial resolution, extent of coverage, data quality, and frequency of revisit. Users were asked how satisfied they were with these same attributes as they currently apply to Landsat 8 imagery. On average, users were most satisfied with lack of cost, extent of coverage, data quality, and the archive, but they were satisfied with all attributes.
\nUsers were asked how often they needed Landsat imagery to meet various requirements for their primary application. The survey question specifically asked how often users needed usable imagery, which differs from how often they would like the Landsat satellites to acquire an image. Users were asked to identify their needed frequency of usable imagery for the following levels:
\n1. Threshold level—the minimum frequency of usable imagery needed to be of any value to their primary application.
2. Breakthrough level—the frequency of usable imagery that would result in a significant improvement for their primary application of the imagery.
3. Target level—the frequency of usable imagery that would only provide a limited additional increase in the expected performance for their primary application.
To meet the threshold level, three-quarters of users needed usable imagery every 17 days or less frequently. At the breakthrough level, two-thirds of users (64 percent) needed a usable image every 5–16 days. The current constellation of two satellites (Landsat 7 and 8) is capable of meeting the threshold and breakthrough needs of most users at least some of the time, but a single satellite would be highly unlikely to do so. Two-fifths of users (40 percent) felt that usable imagery provided every 4 days or more frequently would meet their target level which the current Landsat constellation cannot provide. Landsat 8 users were significantly more likely than non-Landsat 8 users to need usable imagery more frequently to meet their target levels. Additionally, U.S. Landsat 8 users were significantly more likely than other Landsat users to need usable imagery more frequently in order meet both their breakthrough and target levels.
\nTo explore the effect of the availability of Landsat 8 imagery on Landsat imagery use in general, established users (those who had consistently used Landsat imagery both before and after Landsat 8 imagery became available) using Landsat 8 imagery were asked about changes in the amount of Landsat imagery they used. The majority of established users using Landsat 8 imagery (60 percent) reported an average increase of 51 percent in the number of scenes obtained after Landsat 8 imagery became available. Landsat 8 users were asked if they had encountered challenges in using Landsat 8 whereas non-Landsat 8 users were asked if such challenges had played a role in why they were not using Landsat 8 imagery. Although many users did not encounter challenges when using or trying to use Landsat 8 data, slightly less than 30 percent did encounter issues with processing the data to a usable point. The most common issue reported was not being able to create or have access to a surface reflectance corrected product. Other challenges were related to the file sizes of images being too large to download, store, or analyze. There were no statistically significant differences between Landsat 8 and non-Landsat 8 users in terms of challenges encountered when using or trying to use the imagery, which indicates that users were not unduly discouraged by the challenges they may have encountered. When asked about potential consequences of not using Landsat 8, more than half of the non-Landsat 8 users did not report detrimental effects on their work from not using the imagery. Of those who did report detrimental effects, decreased quality of work, decreased scope of work, and increased time spent on work were the most common.
","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/ofr20161032","usgsCitation":"Miller, H.M., 2016, Users and uses of Landsat 8 satellite imagery—2014 survey results: U.S. Geological Survey Open-File Report 2016–1032, 27 p., https://dx.doi/org/10.3133/ofr20161032.","productDescription":"vi, 27 p.","numberOfPages":"33","onlineOnly":"Y","additionalOnlineFiles":"N","ipdsId":"IP-069774","costCenters":[{"id":291,"text":"Fort Collins Science Center","active":true,"usgs":true}],"links":[{"id":320059,"rank":2,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/of/2016/1032/ofr20161032.pdf","text":"Report","size":"2.37 MB","linkFileType":{"id":1,"text":"pdf"},"description":"OFR 2016-1032"},{"id":320058,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/of/2016/1032/coverthb.jpg"}],"contact":"Center Director, USGS Fort Collins Science Center
2150 Centre Ave., Bldg. C
Box 25046, MS-939
Fort Collins, CO 80526-8118
The North Polar Layered Deposits (NPLD) of Mars contain a complex stratigraphy that has been suggested to retain a record of past eccentricity- and obliquity-forced climate changes. The surface accumulation rate in the current climate can be constrained by the crater retention age. We scale NPLD crater diameters to account for icy target strength and compare surface age using a new production function for recent small impacts on Mars to the previously used model of Hartmann (2005). Our results indicate that ice is accumulating in these craters several times faster than previously thought, with a 100 m diameter crater being completely infilled within centuries. Craters appear to have a diameter-dependent lifetime, but the data also permit a complete resurfacing of the NPLD at ~1.5 ka.
","language":"English","publisher":"AGU Publications","doi":"10.1002/2016GL068434","usgsCitation":"Landis, M., Byrne, S., Daubar, I., Herkenhoff, K.E., and Dundas, C.M., 2016, A revised surface age for the North Polar Layered Deposits of Mars : Geophysical Research Letters, v. 43, no. 7, p. 3060-3068, https://doi.org/10.1002/2016GL068434.","productDescription":"9 p.","startPage":"3060","endPage":"3068","numberOfPages":"9","ipdsId":"IP-070849","costCenters":[{"id":131,"text":"Astrogeology Science Center","active":true,"usgs":true}],"links":[{"id":471067,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.1002/2016gl068434","text":"Publisher Index Page"},{"id":330832,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"otherGeospatial":"Mars","volume":"43","issue":"7","publishingServiceCenter":{"id":14,"text":"Menlo Park PSC"},"noUsgsAuthors":false,"publicationDate":"2016-04-05","publicationStatus":"PW","scienceBaseUri":"5821a0dde4b02f1a881de974","contributors":{"authors":[{"text":"Landis, Margaret E.","contributorId":176713,"corporation":false,"usgs":false,"family":"Landis","given":"Margaret E.","affiliations":[{"id":25655,"text":"Lunar and Planetary Laboratory, 1629 E. University Blvd., The University of Arizona, Tucson, AZ 85721, United States","active":true,"usgs":false}],"preferred":false,"id":653227,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Byrne, Shane","contributorId":53513,"corporation":false,"usgs":false,"family":"Byrne","given":"Shane","affiliations":[{"id":7042,"text":"University of Arizona","active":true,"usgs":false}],"preferred":false,"id":653228,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Daubar, Ingrid J.","contributorId":34431,"corporation":false,"usgs":true,"family":"Daubar","given":"Ingrid J.","affiliations":[],"preferred":false,"id":653229,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Herkenhoff, Kenneth E. 0000-0002-3153-6663 kherkenhoff@usgs.gov","orcid":"https://orcid.org/0000-0002-3153-6663","contributorId":2275,"corporation":false,"usgs":true,"family":"Herkenhoff","given":"Kenneth","email":"kherkenhoff@usgs.gov","middleInitial":"E.","affiliations":[{"id":131,"text":"Astrogeology Science Center","active":true,"usgs":true}],"preferred":true,"id":653164,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Dundas, Colin M. 0000-0003-2343-7224 cdundas@usgs.gov","orcid":"https://orcid.org/0000-0003-2343-7224","contributorId":2937,"corporation":false,"usgs":true,"family":"Dundas","given":"Colin","email":"cdundas@usgs.gov","middleInitial":"M.","affiliations":[{"id":131,"text":"Astrogeology Science Center","active":true,"usgs":true}],"preferred":true,"id":653165,"contributorType":{"id":1,"text":"Authors"},"rank":5}]}} ,{"id":70170268,"text":"ofr20151212 - 2016 - Total dissolved gas and water temperature in the lower Columbia River, Oregon and Washington, water year 2015","interactions":[],"lastModifiedDate":"2016-04-18T08:33:49","indexId":"ofr20151212","displayToPublicDate":"2016-04-15T19:00:00","publicationYear":"2016","noYear":false,"publicationType":{"id":18,"text":"Report"},"publicationSubtype":{"id":5,"text":"USGS Numbered Series"},"seriesTitle":{"id":330,"text":"Open-File Report","code":"OFR","onlineIssn":"2331-1258","printIssn":"0196-1497","active":true,"publicationSubtype":{"id":5}},"seriesNumber":"2015-1212","title":"Total dissolved gas and water temperature in the lower Columbia River, Oregon and Washington, water year 2015","docAbstract":"An analysis of total-dissolved-gas (TDG) and water-temperature data collected at eight fixed monitoring stations on the lower Columbia River in Oregon and Washington in water year 2015 indicated the following:
\nDirector, Oregon Water Science Center
U.S. Geological Survey
2130 SW 5th Avenue
Portland, Oregon 97201
http://or.water.usgs.gov
This paper represents the first attempt to show, by means other than just petrographic ones, that one type of macrinite, herein designated copromacrinite, may result from macrofauna feces. For that purpose a combination of coal petrography, X-ray photoelectron spectroscopy, and elemental-analysis continuous-flow isotope ratio mass spectrometry methods were used to determine nitrogen functionalities and δ13C andδ15N compositions in 1) vitrinite-rich, 2) fusinite + semifusinite-rich, and 3) macrinite-rich (with a possible coprolitic origin) samples of the high volatile A bituminous Peach Orchard coal (Bolsovian; Middle Pennsylvanian) from Magoffin County, Kentucky. There were no significant differences between pyridinic-N and quaternary-N abundance in the three samples, however, pyrrolic-N was higher (~ 54%) in the macrinite-rich sample than in the other two samples (~ 38%). The data suggest that pyridinic-N and quaternary-N are independent of maceral group composition and that pyrrolic-N is dependent on maceral composition (fusinite + semifusinite versus macrinite). δ13C values obtained for bulk and demineralized coal of the vitrinite- and fusinite + semifusinite-rich samples are similar with δ13C values of − 24.80 ± 0.01‰ VPDB and − 24.61 ± 0.09‰ VPDB for bulk samples and − 24.81 ± 0.07‰ VPDB and − 24.52 ± 0.04‰ VPDB for demineralized samples. These values are within the expected range for vitrinite-rich samples and the slightly higher δ13C value of the fusinite + semifusinite-rich sample is expected as δ13C values for inertinite are higher than for vitrinite. However, there was a significant shift to a lower δ13C value (− 26.80 ± 0.01‰ VPDB for the bulk sample value) for the macrinite-rich sample. Because the samples are basically isorank, and δ13C (and δ15N) shifts do not occur during maturation until anthracite rank, the difference may be related to the presence or composition of the macrinite within the sample which lacks heat-effect indicators, such as devolatilization vacuoles and distorted pores. δ15N values are also similar for bulk and demineralized coal of the vitrinite- and fusinite + semifusinite-rich samples, and the bulk values were heavier in this samples (3.07 ± 0.03‰ Air and 2.92 ± 0.10‰ Air, respectively), and much lighter (− 2.83 ± 0.09‰ Air) for the macrinite-rich sample.
\nThe study of Peach Orchard coal samples using reflected-light microscopy, isotopic composition, and nitrogen-forms analyses revealed that the macrinite-rich sample contains macrinite with coprolitic features (e.g. oxidation rind, mix of undigested palynomorphs, frequent and randomly located funginite, agglutination pulp of semifusinite reflectance, internal lack of bedding fabric, and suggestion of structures resulting from intestines and stomach walls), more pyrrolic-N (~ 16%), and lower δ13C (~ 2‰ VPDB) and δ15N (~ 4‰ Air) values than the vitrinite and semifusinite + fusinite rich samples. These findings suggest that the maceral macrinite has multiple origins based on petrography and measurable chemical differences between the macrinite, vitrinite, and semifusinite + fusinite fractions within the coal. Assuming that copromacrinite observed is an excretion then the anomalies observed may result from the symbiotic relations between the macrofauna (e.g. cockroaches) and microbiota during the digestive processes, and the nitrogen balance mechanisms inside macrofauna body.
","language":"English","publisher":"Elsevier","doi":"10.1016/j.coal.2016.05.004","usgsCitation":"Valentim, B., Algarra, M., Guedes, A., Ruppert, L.F., and Hower, J., 2016, Notes on the origin of copromacrinite based on nitrogen functionalities and δ13C and δ15N determined on samples from the Peach Orchard coal bed, southern Magoffin County, Kentucky: International Journal of Coal Geology, v. 160-161, p. 63-72, https://doi.org/10.1016/j.coal.2016.05.004.","productDescription":"10 p.","startPage":"63","endPage":"72","onlineOnly":"N","additionalOnlineFiles":"N","ipdsId":"IP-062704","costCenters":[{"id":241,"text":"Eastern Energy Resources Science Center","active":true,"usgs":true}],"links":[{"id":324653,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","state":"Kentucky","county":"Magoffin 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chelonians","interactions":[],"lastModifiedDate":"2016-06-29T16:43:17","indexId":"70174180","displayToPublicDate":"2016-04-14T02:30:00","publicationYear":"2016","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":1176,"text":"Canadian Journal of Zoology","active":true,"publicationSubtype":{"id":10}},"title":"Using climate, energy, and spatial-based hypotheses to interpret macroecological patterns of North America chelonians","docAbstract":"Our study investigates how factors, such as latitude, productivity, and several environmental variables, influence contemporary patterns of the species richness in North American turtles. In particular, we test several hypotheses explaining broad-scale species richness patterns on several species richness data sets: (i) total turtles, (ii) freshwater turtles only, (iii) aquatic turtles, (iv) terrestrial turtles only, (v) Emydidae, and (vi) Kinosternidae. In addition to spatial data, we used a combination of 25 abiotic variables in spatial regression models to predict species richness patterns. Our results provide support for multiple hypotheses related to broad-scale patterns of species richness, and in particular, hypotheses related to climate, productivity, water availability, topography, and latitude. In general, species richness patterns were positively associated with temperature, precipitation, diversity of streams, coefficient of variation of elevation, and net primary productivity. We also found that North America turtles follow the general latitudinal diversity gradient pattern (i.e., increasing species richness towards equator) by exhibiting a negative association with latitude. Because of the incongruent results among our six data sets, our study highlights the importance of considering phylogenetic constraints and guilds when interpreting species richness patterns, especially for taxonomic groups that occupy a myriad of habitats.
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part of a cooperative effort among the U.S. Geological Survey (USGS), the U.S. Department of Energy, and the U.S. Department of the Interior Bureau of Ocean Energy Management, two grids of two-dimensional multichannel seismic reflection data were acquired in the Gulf of Mexico over lease blocks Green Canyon 955 and Walker Ridge 313 between April 18 and May 3, 2013. The purpose of the data acquisition was to fill knowledge gaps in an ongoing study of known gas hydrate accumulations in the area. These data were initially processed onboard the recording ship R/V Pelican for more quality control during the recording. The data were subsequently processed in detail by the U.S. Geological Survey in Denver, Colorado, in two phases. The first phase was to create a “kinematic” dataset that removed extensive noise present in the data but did not preserve relative amplitudes. The second phase was to create a true relative amplitude dataset that included noise removal and “wavelet” deconvolution that preserved the amplitude information. This report describes the processing techniques used to create both datasets.
","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/ofr20161037","collaboration":"Performed in cooperation with the U.S. Department of Energy and the U.S. Department of the Interior Bureau of Ocean Energy Management","usgsCitation":"Miller, J.J., Agena, W.F., Haines, S.S., and Hart, P.E., 2016, Processing of multichannel seismic reflection data acquired in 2013 for seismic investigations of gas hydrates in the Gulf of Mexico: U.S. Geological Survey Open-File Report 2016‒1037, 32 p., https://dx.doi.org/10.3133/ofr20161037.","productDescription":"vi, 32 p.","onlineOnly":"Y","additionalOnlineFiles":"N","ipdsId":"IP-067340","costCenters":[{"id":164,"text":"Central Energy Resources Science Center","active":true,"usgs":true}],"links":[{"id":319934,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/of/2016/1037/coverthb.jpg"},{"id":319936,"rank":2,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/of/2016/1037/ofr20161037.pdf","text":"Report","size":"30.0 MB","linkFileType":{"id":1,"text":"pdf"},"description":"OFR 2016-1037"}],"otherGeospatial":"Gulf of Mexico","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -96,\n 26\n ],\n [\n -96,\n 30.5\n ],\n [\n -89.7,\n 30.5\n ],\n [\n -89.7,\n 26\n ],\n [\n -96,\n 26\n ]\n ]\n ]\n }\n }\n ]\n}","contact":"Director, Central Energy Resources Science Center
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Denver, CO 80225-0046
This report presents the annual and monthly minimum 1- and 7-day average streamflows with the 10-year recurrence interval (1Q10 and 7Q10) for 197 continuous-record streamgages in Georgia. Streamgages used in the study included active and discontinued stations having a minimum of 10 complete climatic years of record as of September 30, 2013. The 1Q10 and 7Q10 flow statistics were computed for 85 streamgages on unregulated streams with minimal diversions upstream, 43 streamgages on regulated streams, and 69 streamgages known, or considered, to be affected by varying degrees of diversions upstream. Descriptive information for each of these streamgages, including the U.S. Geological Survey (USGS) station number, station name, latitude, longitude, county, drainage area, and period of record analyzed also is presented.
Kendall’s tau nonparametric test was used to determine the statistical significance of trends in annual and monthly minimum 1-day and 7-day average flows for the 197 streamgages. Significant negative trends in the minimum annual 1-day and 7-day average streamflow were indicated for 77 of the 197 streamgages. Many of these significant negative trends are due to the period of record ending during one of the recent droughts in Georgia, particularly those streamgages with record through the 2013 water year. Long-term unregulated streamgages with 70 or more years of record indicate significant negative trends in the annual minimum 7-day average flow for central and southern Georgia. Watersheds for some of these streamgages have experienced minimal human impact, thus indicating that the significant negative trends observed in flows at the long-term streamgages may be influenced by changing climatological conditions. A Kendall-tau trend analysis of the annual air temperature and precipitation totals for Georgia indicated no significant trends. A comprehensive analysis of causes of the trends in annual and monthly minimum 1-day and 7-day average flows in central and southern Georgia is outside the scope of this study. Further study is needed to determine some of the causes, including both climatological and human impacts, of the significant negative trends in annual minimum 1-day and 7-day average flows in central and southern Georgia.
To assess the changes in the annual 1Q10 and 7Q10 statistics over time for long-term continuous streamgages with significant trends in record, the annual 1Q10 and 7Q10 statistics were computed on a decadal accumulated basis for 39 streamgages having 40 or more years of record that indicated a significant trend. Records from most of the streamgages showed a decline in 7Q10 statistics for the decades of 1980–89, 1990–99, and 2000–09 because of the recent droughts in Georgia. Twenty four of the 39 streamgages had complete records from 1980 to 2010, and records from 23 of these gages exhibited a decline in the 7Q10 statistics during this period, ranging from –6.3 to –76.2 percent with a mean of –27.3 percent. No attempts were made during this study to adjust streamflow records or statistical analyses on the basis of trends.
The monthly and annual 1Q10 and 7Q10 flow statistics for the entire period of record analyzed in the study are incorporated into the USGS StreamStatsDB, which is a database accessible to users through the recently released USGS StreamStats application for Georgia. StreamStats is a Web-based geographic information system that provides users with access to an assortment of analytical tools that are useful for water-resources planning and management, and for engineering design applications, such as the design of bridges. StreamStats allows users to easily obtain streamflow statistics, basin characteristics, and other information for user-selected streamgages.
","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/sir20165037","collaboration":"Prepared in cooperation with the Georgia Environmental Protection Division","usgsCitation":"Gotvald, A.J., 2016, Selected low-flow frequency statistics for continuous-record streamgages in Georgia, 2013: U.S. Geological Survey Scientific Investigations Report 2016–5037, 20 p., https://dx.doi.org/10.3133/sir20165037. 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\"}}]}","contact":"Director, South Atlantic Water Science Center
U.S. Geological Survey
720 Gracern Road
Columbia, SC 29210
http://www.usgs.gov/water/southatlantic/
Loess is a widespread Quaternary deposit in Alaska and loess accretion occurs today in some regions, such as the Matanuska Valley. The source of loess in the Matanuska Valley has been debated for more than seven decades, with the Knik River and the Matanuska River, both to the east, being the leading candidates and the Susitna River, to the west, as a less favorable source. We report here new stratigraphic, mineralogic, and geochemical data that test the competing hypotheses of these river sources. Loess thickness data are consistent with previous studies that show that a source or sources lay to the east, which rules out the Susitna River as a source. Knik and Matanuska River silts can be distinguished using Sc–Th–La, LaN/YbN vs. Eu/Eu∗, Cr/Sc, and As/Sb. Matanuska Valley loess falls clearly within the range of values for these ratios found in Matanuska River silt. Dust storms from the Matanuska River are most common in autumn, when river discharge is at a minimum and silt-rich point bars are exposed, wind speed from the north is beginning to increase after a low-velocity period in summer, snow depth is still minimal, and soil temperatures are still above freezing. Thus, seasonal changes in climate and hydrology emerge as critical factors in the timing of aeolian silt transport in southern Alaska. These findings could be applicable to understanding seasonal controls on Pleistocene loess accretion in Europe, New Zealand, South America, and elsewhere in North America.
","language":"English","publisher":"Elsevier Science","doi":"10.1016/j.aeolia.2016.02.005","usgsCitation":"Muhs, D., Budahn, J.R., Skipp, G.L., and McGeehin, J., 2016, Geochemical evidence for seasonal controls on the transportation of Holocene loess, Matanuska Valley, southern Alaska, USA: Aeolian Research, v. 21, p. 61-73, https://doi.org/10.1016/j.aeolia.2016.02.005.","productDescription":"13 p.","startPage":"61","endPage":"73","ipdsId":"IP-068983","costCenters":[{"id":318,"text":"Geosciences and Environmental Change Science Center","active":true,"usgs":true}],"links":[{"id":471077,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.1016/j.aeolia.2016.02.005","text":"Publisher Index Page"},{"id":320018,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United 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mcgeehin@usgs.gov","contributorId":167455,"corporation":false,"usgs":true,"family":"McGeehin","given":"John","email":"mcgeehin@usgs.gov","affiliations":[{"id":242,"text":"Eastern Geographic Science Center","active":true,"usgs":true},{"id":243,"text":"Eastern Geology and Paleoclimate Science Center","active":true,"usgs":true}],"preferred":true,"id":626547,"contributorType":{"id":1,"text":"Authors"},"rank":4}]}} ,{"id":70164456,"text":"sir20165012 - 2016 - Variability in rainfall at monitoring stations and derivation of a long-term rainfall intensity record in the Grand Canyon Region, Arizona, USA","interactions":[],"lastModifiedDate":"2016-04-12T09:26:18","indexId":"sir20165012","displayToPublicDate":"2016-04-11T15:00:00","publicationYear":"2016","noYear":false,"publicationType":{"id":18,"text":"Report"},"publicationSubtype":{"id":5,"text":"USGS Numbered Series"},"seriesTitle":{"id":334,"text":"Scientific Investigations Report","code":"SIR","onlineIssn":"2328-0328","printIssn":"2328-031X","active":true,"publicationSubtype":{"id":5}},"seriesNumber":"2016-5012","title":"Variability in rainfall at monitoring stations and derivation of a long-term rainfall intensity record in the Grand Canyon Region, Arizona, USA","docAbstract":"In this study, we examine rainfall datasets of varying temporal length, resolution, and spatial distribution to characterize rainfall depth, intensity, and seasonality for monitoring stations along the Colorado River within Marble and Grand Canyons. We identify maximum separation distances between stations at which rainfall measurements might be most useful for inferring rainfall characteristics at other locations. We demonstrate a method for applying relations between daily rainfall depth and intensity, from short-term high-resolution data to lower-resolution longer-term data, to synthesize a long-term record of daily rainfall intensity from 1950–2012. We consider the implications of our spatio-temporal characterization of rainfall for understanding local landscape change in sedimentary deposits and archaeological sites, and for better characterizing past and present rainfall and its potential role in overland flow erosion within the canyons. We find that rainfall measured at stations within the river corridor is spatially correlated at separation distances of tens of kilometers, and is not correlated at the large elevation differences that separate stations along the Colorado River from stations above the canyon rim. These results provide guidance for reasonable separation distances at which rainfall measurements at stations within the Grand Canyon region might be used to infer rainfall at other nearby locations along the river. Like other rugged landscapes, spatial variability between rainfall measured at monitoring stations appears to be influenced by canyon and rim physiography and elevation, with preliminary results suggesting the highest elevation landform in the region, the Kaibab Plateau, may function as an important orographic influence. Stations at specific locations within the canyons and along the river, such as in southern (lower) Marble Canyon and eastern (upper) Grand Canyon, appear to have strong potential to receive high-intensity rainfall that can generate runoff which may erode alluvium. The characterization of past and present rainfall variability in this study will be useful for future studies that evaluate more spatially continuous datasets in order to better understand the rainfall dynamics within this, and potentially other, deep canyons.
","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/sir20165012","usgsCitation":"Caster, Joshua, and Sankey, J.B., 2016, Variability in rainfall at monitoring stations and derivation of a long-term rainfall intensity record in the Grand Canyon region, Arizona, USA: U.S. Geological Survey Scientific Investigations Report 2016-5012, 38 p., https://dx.doi.org/10.3133/sir20165012.","productDescription":"iv, 38 p.","onlineOnly":"Y","additionalOnlineFiles":"Y","ipdsId":"IP-065524","costCenters":[{"id":568,"text":"Southwest Biological Science Center","active":true,"usgs":true}],"links":[{"id":319918,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/sir/2016/5012/cover.jpg"},{"id":319915,"rank":2,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/sir/2016/5012/sir20165012.pdf","text":"Report","size":"5.7 MB","linkFileType":{"id":1,"text":"pdf"},"description":"SIR 2016-5012 Report"},{"id":319916,"rank":3,"type":{"id":3,"text":"Appendix"},"url":"https://pubs.usgs.gov/sir/2016/5012/sir20165012_appendix4.csv","text":"Appendix 4","size":"400 KB","linkFileType":{"id":7,"text":"csv"},"description":"SIR 2016-5012 Appendix 4"}],"country":"United States","state":"Arizona","otherGeospatial":"Grand Canyon","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -114.0655517578125,\n 35.68407153314097\n ],\n [\n -114.0655517578125,\n 37.02448395075965\n ],\n [\n -111.3629150390625,\n 37.02448395075965\n ],\n [\n -111.3629150390625,\n 35.68407153314097\n ],\n [\n -114.0655517578125,\n 35.68407153314097\n ]\n ]\n ]\n }\n }\n ]\n}","contact":"SBSC Staff, Southwest Biological Science Center
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Historically unprecedented flooding occurred in the Souris River Basin of Saskatchewan, North Dakota and Manitoba in 2011, during a longer term period of wet conditions in the basin. In order to develop a model of future flows, there is a need to evaluate effects of past multidecadal climate variability and/or possible climate change on precipitation. In this study, tree-ring chronologies and historical precipitation data in a four-degree buffer around the Souris River Basin were analyzed to develop regression models that can be used for predicting long-term variations of precipitation. To focus on longer term variability, 12-year moving average precipitation was modeled in five subregions (determined through cluster analysis of measures of precipitation) of the study area over three seasons (November–February, March–June and July–October). The models used multiresolution decomposition (an additive decomposition based on powers of two using a discrete wavelet transform) of tree-ring chronologies from Canada and the US and seasonal 12-year moving average precipitation based on Adjusted and Homogenized Canadian Climate Data and US Historical Climatology Network data. Results show that precipitation varies on long-term (multidecadal) time scales of 16, 32 and 64 years. Past extended pluvial and drought events, which can vary greatly with season and subregion, were highlighted by the models. Results suggest that the recent wet period may be a part of natural variability on a very long time scale.
","language":"English","publisher":"Taylor & Francis Online","doi":"10.1080/07011784.2016.1164627","usgsCitation":"Ryberg, K.R., Vecchia, A.V., Akyuz, F.A., and Lin, W., 2016, Tree-ring-based estimates of long-term seasonal precipitation in the Souris River Region of Saskatchewan, North Dakota and Manitoba: Canadian Water Resources Journal, v. 41, no. 3, p. 412-428, https://doi.org/10.1080/07011784.2016.1164627.","productDescription":"17 p.","startPage":"412","endPage":"428","onlineOnly":"N","additionalOnlineFiles":"N","ipdsId":"IP-061513","costCenters":[{"id":478,"text":"North Dakota Water Science Center","active":true,"usgs":true},{"id":34685,"text":"Dakota Water Science Center","active":true,"usgs":true}],"links":[{"id":319955,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"Canada, United States","state":"Manitoba, North Dakota, Saskatchewan","otherGeospatial":"Souris River","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -103.798828125,\n 47.5913464767971\n ],\n [\n -103.798828125,\n 50.45750402042058\n ],\n [\n -98.89892578125,\n 50.45750402042058\n ],\n [\n -98.89892578125,\n 47.5913464767971\n ],\n [\n -103.798828125,\n 47.5913464767971\n ]\n ]\n ]\n }\n }\n ]\n}","volume":"41","issue":"3","publishingServiceCenter":{"id":4,"text":"Rolla PSC"},"noUsgsAuthors":false,"publicationDate":"2016-04-05","publicationStatus":"PW","scienceBaseUri":"570cbc9ce4b0ef3b7ca0dbe5","chorus":{"doi":"10.1080/07011784.2016.1164627","url":"http://dx.doi.org/10.1080/07011784.2016.1164627","publisher":"Informa UK Limited","authors":"Ryberg Karen R., Vecchia Aldo V., Akyüz F. Adnan, Lin Wei","journalName":"Canadian Water Resources Journal / Revue canadienne des ressources hydriques","publicationDate":"4/5/2016"},"contributors":{"authors":[{"text":"Ryberg, Karen R. 0000-0002-9834-2046 kryberg@usgs.gov","orcid":"https://orcid.org/0000-0002-9834-2046","contributorId":1172,"corporation":false,"usgs":true,"family":"Ryberg","given":"Karen","email":"kryberg@usgs.gov","middleInitial":"R.","affiliations":[{"id":34685,"text":"Dakota Water Science Center","active":true,"usgs":true}],"preferred":true,"id":626227,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Vecchia, Aldo V. 0000-0002-2661-4401 avecchia@usgs.gov","orcid":"https://orcid.org/0000-0002-2661-4401","contributorId":1173,"corporation":false,"usgs":true,"family":"Vecchia","given":"Aldo","email":"avecchia@usgs.gov","middleInitial":"V.","affiliations":[{"id":478,"text":"North Dakota Water Science Center","active":true,"usgs":true},{"id":34685,"text":"Dakota Water Science Center","active":true,"usgs":true}],"preferred":true,"id":626228,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Akyuz, F. Adnan","contributorId":140760,"corporation":false,"usgs":false,"family":"Akyuz","given":"F.","email":"","middleInitial":"Adnan","affiliations":[{"id":13555,"text":"North Dakota Climate Office","active":true,"usgs":false}],"preferred":false,"id":626229,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Lin, Wei","contributorId":93805,"corporation":false,"usgs":true,"family":"Lin","given":"Wei","email":"","affiliations":[],"preferred":false,"id":626230,"contributorType":{"id":1,"text":"Authors"},"rank":4}]}} ,{"id":70169018,"text":"tm7C13 - 2016 - User’s guide for GcClust—An R package for clustering of regional geochemical data","interactions":[],"lastModifiedDate":"2025-05-14T19:06:32.9755","indexId":"tm7C13","displayToPublicDate":"2016-04-08T13:00:00","publicationYear":"2016","noYear":false,"publicationType":{"id":18,"text":"Report"},"publicationSubtype":{"id":5,"text":"USGS Numbered Series"},"seriesTitle":{"id":335,"text":"Techniques and Methods","code":"TM","onlineIssn":"2328-7055","printIssn":"2328-7047","active":true,"publicationSubtype":{"id":5}},"seriesNumber":"7-C13","title":"User’s guide for GcClust—An R package for clustering of regional geochemical data","docAbstract":"GcClust is a software package developed by the U.S. Geological Survey for statistical clustering of regional geochemical data, and similar data such as regional mineralogical data. Functions within the software package are written in the R statistical programming language. These functions, their documentation, and a copy of the user’s guide are bundled together in R’s unit of sharable code, which is called a “package.” The user’s guide includes step-by-step instructions showing how the functions are used to cluster data and to evaluate the clustering results. These functions are demonstrated in this report using test data, which are included in the package.
","largerWorkType":{"id":18,"text":"Report"},"largerWorkTitle":"Section C: Computer Programs in Book 7: Automated Data Processing and Computations","largerWorkSubtype":{"id":5,"text":"USGS Numbered Series"},"language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/tm7C13","usgsCitation":"Ellefsen, K.J, and Smith, D.B., 2016, User's guide for GcClust—An R package for clustering of regional geochemical data: U.S. Geological Survey report Techniques and Methods 7–C13, 21 p., https://dx.doi.org/10.3133/tm7c13.","productDescription":"Report: iv, 21 p.; Installation Instructions; Example R Scripts; GcClust; Read Me File","onlineOnly":"Y","additionalOnlineFiles":"N","ipdsId":"IP-072334","costCenters":[{"id":211,"text":"Crustal Geophysics and Geochemistry Science Center","active":true,"usgs":true}],"links":[{"id":320329,"rank":7,"type":{"id":7,"text":"Companion Files"},"url":"https://github.com/USGS-R/GcClust","text":"GcClust Source Code","description":"TM 7-C13GcClust Source Code"},{"id":319810,"rank":6,"type":{"id":7,"text":"Companion Files"},"url":"https://pubs.usgs.gov/tm/07/c13/GcClust_1.0.tar.gz","text":"GcClust","size":"1.74 MB","linkFileType":{"id":6,"text":"zip"},"description":"TM 7-C13 GcClust"},{"id":319809,"rank":5,"type":{"id":7,"text":"Companion Files"},"url":"https://pubs.usgs.gov/tm/07/c13/ProcessScripts.R","text":"Example R scripts","size":"6.0 kB","linkFileType":{"id":2,"text":"txt"},"description":"TM 7-C13 Example R scripts"},{"id":319808,"rank":4,"type":{"id":7,"text":"Companion Files"},"url":"https://pubs.usgs.gov/tm/07/c13/InstallationInstructions.txt","text":"Installation Instructions","size":"2.0 kB","linkFileType":{"id":2,"text":"txt"},"description":"TM 7-C13 Installation Instructions"},{"id":319807,"rank":3,"type":{"id":20,"text":"Read Me"},"url":"https://pubs.usgs.gov/tm/07/c13/00ReadMe.txt","text":"Read Me File","size":"4.0 kB","linkFileType":{"id":2,"text":"txt"},"description":"TM 7-C13 Read Me"},{"id":319806,"rank":2,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/tm/07/c13/tm7c13.pdf","text":"Report","size":"2.84 MB","linkFileType":{"id":1,"text":"pdf"},"description":"TM 7-C13"},{"id":319805,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/tm/07/c13/coverthb.jpg"}],"publicComments":"This report is Chapter 13 of Section C: Computer Programs in Book 7: Automated Data Processing and Computations.","contact":"Center Director, USGS Central Mineral and Environmental Resources
Science Center
Box 25046, Mail Stop 973
Denver, CO 80225
A one-dimensional step-backwater model was developed by the U.S. Geological Survey (USGS) in cooperation with the U.S. Fish and Wildlife Service, Pee Dee National Wildlife Refuge, North Carolina, to provide a means for predicting flood-plain inundation. The model was developed for selected reaches of the Pee Dee River, Brown Creek, and Rocky River, using the U.S. Army Corps of Engineers Hydrologic Engineering Center River Analysis System (HEC-RAS) software. Multiple cross sections were defined on each modeled stream, and hydrologic data were collected between August 2011 and August 2013 at selected locations on the Pee Dee River and on its tributaries Brown Creek, Rocky River, and Thoroughfare Creek. Cross-section, stage, and flow data were used to develop the model and simulate water-surface profiles at 1.0-foot increments at the USGS streamgage Pee Dee River at Pee Dee Refuge near Ansonville, N.C. The profiles were produced for 31 selected water levels that ranged from approximately 193.0 feet to 223.0 feet in elevation at the Pee Dee River at Pee Dee Refuge streamgage.
\nA series of digital flood-inundation maps were developed on the basis of the water-surface profiles produced by the model. The inundation maps, which can be accessed through the USGS Flood Inundation Mapping Program Web site at http://water.usgs.gov/osw/flood_inundation, depict estimates of the areal extent and depth of flooding corresponding to selected water levels at the USGS streamgage Pee Dee River at Pee Dee Refuge near Ansonville, N.C. These maps, when combined with real-time water-level information from USGS streamgages, provide managers with critical information to help plan flood-response activities and resource protection efforts.
","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/sir20155137","collaboration":"Prepared in cooperation with the U.S. Fish and Wildlife Service","usgsCitation":"Smith, D.G., and Wagner, C.R., 2016, Hydraulic model and flood-inundation maps developed for the Pee Dee National Wildlife Refuge, North Carolina: U.S. Geological Survey Scientific Investigations Report 2015–5137, 14 p., https://dx.doi.org/10.3133/sir20155137.","productDescription":"Document: vi, 14 p.; Metadata: 3 downloadable files","onlineOnly":"Y","additionalOnlineFiles":"Y","ipdsId":"IP-069288","costCenters":[{"id":13634,"text":"South Atlantic Water Science Center","active":true,"usgs":true}],"links":[{"id":319758,"rank":2,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/sir/2015/5137/sir20155137.pdf","size":"1.83 MB","linkFileType":{"id":1,"text":"pdf"},"description":"SIR 2015-5137"},{"id":319789,"rank":5,"type":{"id":16,"text":"Metadata"},"url":"https://pubs.usgs.gov/sir/2015/5137/sir20155137_peedee.xml","size":"15.6 KB xml","description":"SIR 2015-5137"},{"id":319787,"rank":3,"type":{"id":16,"text":"Metadata"},"url":"https://pubs.usgs.gov/sir/2015/5137/sir20155137_peedee-grids.zip","size":"302 MB grids","linkFileType":{"id":6,"text":"zip"},"description":"SIR 2015-5137"},{"id":319788,"rank":4,"type":{"id":16,"text":"Metadata"},"url":"https://pubs.usgs.gov/sir/2015/5137/sir20155137_peedee-shapefiles.zip","size":"18.8 MB","linkFileType":{"id":4,"text":"shapefile"},"description":"SIR 2015-5137"},{"id":319757,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/sir/2015/5137/coverthb.jpg"}],"country":"United States","state":"North Carolina","otherGeospatial":"Pee Dee National Wildlife Refuge","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -80.266667,\n 35.233333\n ],\n [\n -80.266667,\n 35\n ],\n [\n -79.85,\n 35\n ],\n [\n -79.85,\n 35.233333\n ],\n [\n -80.266667,\n 35.233333\n ]\n ]\n ]\n }\n }\n ]\n}","contact":"Director, South Atlantic Water Science Center
U.S. Geological Survey
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http://www.usgs.gov/water/southatlantic/
This report is the earliest Government publication to furnish estimates covering 2015 nonfuel mineral industry data. Data sheets contain information on the domestic industry structure, Government programs, tariffs, and 5-year salient statistics for more than 90 individual minerals and materials
","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/70170140","usgsCitation":"U.S. Geological Survey, 2016, Mineral commodity summaries 2016: U.S. Geological Survey, 202 p, https://dx.doi.org/10.3133/70140094.","productDescription":"202 p.","numberOfPages":"205","onlineOnly":"N","additionalOnlineFiles":"N","ipdsId":"IP-072363","costCenters":[{"id":432,"text":"National Minerals Information Center","active":true,"usgs":true}],"links":[{"id":319895,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/70170140.JPG"},{"id":319942,"rank":2,"type":{"id":15,"text":"Index Page"},"url":"https://minerals.usgs.gov/minerals/pubs/mcs/index.html"}],"publishingServiceCenter":{"id":9,"text":"Reston PSC"},"noUsgsAuthors":false,"publicationStatus":"PW","scienceBaseUri":"572477ace4b0b13d3914e0ab","contributors":{"authors":[{"text":"Ober, Joyce A. 0000-0003-1608-5611 jober@usgs.gov","orcid":"https://orcid.org/0000-0003-1608-5611","contributorId":394,"corporation":false,"usgs":true,"family":"Ober","given":"Joyce","email":"jober@usgs.gov","middleInitial":"A.","affiliations":[{"id":432,"text":"National Minerals Information Center","active":true,"usgs":true}],"preferred":true,"id":626267,"contributorType":{"id":1,"text":"Authors"},"rank":1}]}} ]}