Arsenic concentrations from 20 450 domestic wells in the U.S. were used to develop a logistic regression model of the probability of having arsenic >10 μg/L (“high arsenic”), which is presented at the county, state, and national scales. Variables representing geologic sources, geochemical, hydrologic, and physical features were among the significant predictors of high arsenic. For U.S. Census blocks, the mean probability of arsenic >10 μg/L was multiplied by the population using domestic wells to estimate the potential high-arsenic domestic-well population. Approximately 44.1 M people in the U.S. use water from domestic wells. The population in the conterminous U.S. using water from domestic wells with predicted arsenic concentration >10 μg/L is 2.1 M people (95% CI is 1.5 to 2.9 M). Although areas of the U.S. were underrepresented with arsenic data, predictive variables available in national data sets were used to estimate high arsenic in unsampled areas. Additionally, by predicting to all of the conterminous U.S., we identify areas of high and low potential exposure in areas of limited arsenic data. These areas may be viewed as potential areas to investigate further or to compare to more detailed local information. Linking predictive modeling to private well use information nationally, despite the uncertainty, is beneficial for broad screening of the population at risk from elevated arsenic in drinking water from private wells.
","language":"English","publisher":"American Chemical Society","doi":"10.1021/acs.est.7b02881","usgsCitation":"Ayotte, J.D., Medalie, L., Qi, S.L., Backer, L.C., and Nolan, B.T., 2017, Estimating the high-arsenic domestic-well population in the conterminous United States: Environmental Science & Technology, v. 51, no. 21, p. 12443-12454, https://doi.org/10.1021/acs.est.7b02881.","productDescription":"12 p.","startPage":"12443","endPage":"12454","ipdsId":"IP-085175","costCenters":[{"id":466,"text":"New England Water Science Center","active":true,"usgs":true}],"links":[{"id":469369,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://www.ncbi.nlm.nih.gov/pmc/articles/8842838","text":"Publisher Index Page"},{"id":349554,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United 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\"}}]}\n","volume":"51","issue":"21","publishingServiceCenter":{"id":11,"text":"Pembroke PSC"},"noUsgsAuthors":false,"publicationDate":"2017-10-18","publicationStatus":"PW","scienceBaseUri":"5a60fb22e4b06e28e9c22d29","contributors":{"authors":[{"text":"Ayotte, Joseph D. 0000-0002-1892-2738 jayotte@usgs.gov","orcid":"https://orcid.org/0000-0002-1892-2738","contributorId":149619,"corporation":false,"usgs":true,"family":"Ayotte","given":"Joseph","email":"jayotte@usgs.gov","middleInitial":"D.","affiliations":[{"id":405,"text":"NH/VT office of New England Water Science Center","active":true,"usgs":true},{"id":466,"text":"New England Water Science Center","active":true,"usgs":true}],"preferred":true,"id":716074,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Medalie, Laura 0000-0002-2440-2149 lmedalie@usgs.gov","orcid":"https://orcid.org/0000-0002-2440-2149","contributorId":3657,"corporation":false,"usgs":true,"family":"Medalie","given":"Laura","email":"lmedalie@usgs.gov","affiliations":[{"id":466,"text":"New England Water Science Center","active":true,"usgs":true}],"preferred":true,"id":716075,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Qi, Sharon L. 0000-0001-7278-4498 slqi@usgs.gov","orcid":"https://orcid.org/0000-0001-7278-4498","contributorId":1130,"corporation":false,"usgs":true,"family":"Qi","given":"Sharon","email":"slqi@usgs.gov","middleInitial":"L.","affiliations":[{"id":622,"text":"Washington Water Science Center","active":true,"usgs":true},{"id":518,"text":"Oregon Water Science Center","active":true,"usgs":true},{"id":191,"text":"Colorado Water Science Center","active":true,"usgs":true}],"preferred":true,"id":716076,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Backer, Lorraine C.","contributorId":198459,"corporation":false,"usgs":false,"family":"Backer","given":"Lorraine","email":"","middleInitial":"C.","affiliations":[{"id":16974,"text":"US Centers for Disease Control and Prevention (CDC)","active":true,"usgs":false}],"preferred":true,"id":716077,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Nolan, Bernard T. 0000-0002-6945-9659 btnolan@usgs.gov","orcid":"https://orcid.org/0000-0002-6945-9659","contributorId":2190,"corporation":false,"usgs":true,"family":"Nolan","given":"Bernard","email":"btnolan@usgs.gov","middleInitial":"T.","affiliations":[{"id":451,"text":"National Water Quality Assessment Program","active":true,"usgs":true},{"id":27111,"text":"National Water Quality Program","active":true,"usgs":true}],"preferred":true,"id":716078,"contributorType":{"id":1,"text":"Authors"},"rank":5}]}} ,{"id":70193277,"text":"70193277 - 2017 - Alternative pathways to landscape transformation: Invasive grasses, burn severity and fire frequency in arid ecosystems","interactions":[],"lastModifiedDate":"2017-11-06T13:06:31","indexId":"70193277","displayToPublicDate":"2017-11-01T00:00:00","publicationYear":"2017","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":2242,"text":"Journal of Ecology","active":true,"publicationSubtype":{"id":10}},"title":"Alternative pathways to landscape transformation: Invasive grasses, burn severity and fire frequency in arid ecosystems","docAbstract":"Vapor intrusion (VI) by volatile organic compounds (VOCs) in the built environment presents a threat to human health. Traditional VI assessments are often time-, cost-, and labor-intensive; whereas traditional subsurface methods sample a relatively small volume in the subsurface and are difficult to collect within and near structures. Trees could provide a similar subsurface sample where roots act as the “sampler’ and are already onsite. Regression models were developed to assess the relation between PCE concentrations in over 500 tree-core samples with PCE concentrations in over 50 groundwater and 1000 soil samples collected from a tetrachloroethylene- (PCE-) contaminated Superfund site and analyzed using gas chromatography. Results indicate that in planta concentrations are significantly and positively related to PCE concentrations in groundwater samples collected at depths less than 20 m (adjusted R2 values greater than 0.80) and in soil samples (adjusted R2 values greater than 0.90). Results indicate that a 30 cm diameter tree characterizes soil concentrations at depths less than 6 m over an area of 700–1600 m2, the volume of a typical basement. These findings indicate that tree sampling may be an appropriate method to detect contamination at shallow depths at sites with VI.
","language":"English","publisher":"ACS Publications","doi":"10.1021/acs.est.7b02667","usgsCitation":"Wilson, J.L., Limmer, M.A., Samaranayake, V., Schumacher, J., and Burken, J.G., 2017, Tree sampling as a method to assess vapor intrusion potential at a site characterized by VOC-contaminated groundwater and soil: Environmental Science & Technology, v. 51, no. 18, p. 10369-10378, https://doi.org/10.1021/acs.est.7b02667.","productDescription":"10 p.","startPage":"10369","endPage":"10378","ipdsId":"IP-083556","costCenters":[{"id":396,"text":"Missouri Water Science Center","active":true,"usgs":true}],"links":[{"id":438169,"rank":0,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/F71835D8","text":"USGS data release","linkHelpText":"Tetrachloroethylene, trichloroethylene, and 1,1,2-Trichloro-1,2,2-trifluoroethane concentrations in tree-core, groundwater, and soil samples at the Vienna Wells Site: Maries County, Missouri, 2011-2016"},{"id":349537,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","state":"Missouri","city":"Vienna","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -91.9442,\n 38.1883\n ],\n [\n -91.9417,\n 38.1883\n ],\n [\n -91.9417,\n 38.19\n ],\n [\n -91.9442,\n 38.19\n ],\n [\n -91.9442,\n 38.1883\n ]\n ]\n ]\n }\n }\n ]\n}","volume":"51","issue":"18","publishingServiceCenter":{"id":4,"text":"Rolla PSC"},"noUsgsAuthors":false,"publicationDate":"2017-08-31","publicationStatus":"PW","scienceBaseUri":"5a60fb21e4b06e28e9c22d02","contributors":{"authors":[{"text":"Wilson, Jordan L. 0000-0003-0490-9062 jlwilson@usgs.gov","orcid":"https://orcid.org/0000-0003-0490-9062","contributorId":5416,"corporation":false,"usgs":true,"family":"Wilson","given":"Jordan","email":"jlwilson@usgs.gov","middleInitial":"L.","affiliations":[{"id":36532,"text":"Central Midwest Water Science Center","active":true,"usgs":true},{"id":396,"text":"Missouri Water Science Center","active":true,"usgs":true}],"preferred":true,"id":723833,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Limmer, Matthew A.","contributorId":200927,"corporation":false,"usgs":false,"family":"Limmer","given":"Matthew","email":"","middleInitial":"A.","affiliations":[],"preferred":false,"id":723834,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Samaranayake, V.A.","contributorId":200928,"corporation":false,"usgs":false,"family":"Samaranayake","given":"V.A.","affiliations":[],"preferred":false,"id":723835,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Schumacher, John G. jschu@usgs.gov","contributorId":2055,"corporation":false,"usgs":true,"family":"Schumacher","given":"John G.","email":"jschu@usgs.gov","affiliations":[{"id":396,"text":"Missouri Water Science Center","active":true,"usgs":true}],"preferred":true,"id":723836,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Burken, Joel G.","contributorId":21218,"corporation":false,"usgs":true,"family":"Burken","given":"Joel","email":"","middleInitial":"G.","affiliations":[],"preferred":false,"id":723837,"contributorType":{"id":1,"text":"Authors"},"rank":5}]}} ,{"id":70193353,"text":"70193353 - 2017 - Characteristics and 40Ar/39Ar geochronology of the Erdenet Cu-Mo deposit, Mongolia","interactions":[],"lastModifiedDate":"2017-11-01T11:26:25","indexId":"70193353","displayToPublicDate":"2017-11-01T00:00:00","publicationYear":"2017","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":1472,"text":"Economic Geology","active":true,"publicationSubtype":{"id":10}},"title":"Characteristics and 40Ar/39Ar geochronology of the Erdenet Cu-Mo deposit, Mongolia","docAbstract":"The Early to Middle Triassic Erdenet porphyry Cu-Mo deposit, in northern Mongolia, developed in a continent-continent arc collision zone, within the Central Asian orogenic belt. The porphyry system is related to multiple intrusions of crystal-crowded biotite granodiorite porphyry, which formed a composite stock about 900 m in diameter, with multiple porphyritic microgranodiorite dikes. Wall rocks are Late Permian to Early Triassic, medium-grained granodiorite, with similar whole-rock geochemistry, mineralogy, and composition to the granodiorite porphyry. Whole-rock analysis of the granodiorite porphyry and wall rocks shows that these rocks cannot be discriminated, but both have depleted middle heavy rare earth elements and Y, typical of fertile porphyry magmatic suites.
At the current pit level (1,250 m elev), early porphyry-style quartz veins (A and B type) are locally infilled by pyrite-chalcopyrite, with subordinate bornite, but most of the chalcopyrite occurs in D veins that constitute more than 50% of the Cu grade (~0.5 wt % Cu). The 0.3 wt % Cu shell resembles a molar tooth, enveloping the granodiorite porphyry, with deeper roots extending down the wal-rock contacts. Molybdenite occurs in monomineralic veins, and in finely laminated to massive quartz-molybdenite veins.
The most important alteration is quartz-muscovite, which occurs as relatively coarse (100–500 μm) alteration selvages (1–5 cm) that envelop D veins. The D veins cut illite ± kaolinite-smectite (or intermediate argillic) alteration. Intermediate argillic alteration, together with abundant pink anhydrite (commonly hydrated to gypsum), extends from at least 1,300- to 900-m elevation in the deepest drill holes, and has overprinted early potassic alteration, or relatively unaltered red granodiorite. Meter-wide zones of kaolinite cut the anhydrite-gypsum at all levels. There is an abrupt transition outward from the intermediate argillic alteration to chlorite-epidote (propylitic) alteration, at 50 to 200 m from the granodiorite porphyry contact, although D veins (and chalcopyrite) extend outward to the propylitic zone.
The Erdenet porphyry system, was overprinted by advanced argillic alteration, which outcrops 2 km northwest of the pit, and forms a lithocap that extends over 10 × 2.5 km. It is characterized by residual quartz, andalusite, Na-Ca and K-alunite, diaspore, pyrophyllite, zunyite, topaz, dickite, and kaolinite. The upper part of the porphyry Cu-Mo deposit (removed by mining), comprised a bornite-chalcocite enriched zone up to 300 m thick with an average grade of 0.7 wt % Cu and up to 5 wt % Cu locally. Based on hypogene bornite-chalcocite mineral textures and high-sulfidation state mineralogy, the enriched zone is inferred to be of hypogene origin, but modified by supergene processes. Consequently, it may be related to formation of the lithocap.
Previous Re-Os dates of 240.4 and 240.7 ± 0.8 Ma for molybdenite in quartz veins are comparable to new 40Ar/39Ar dates of 239.7 ± 1.6 and 240 ± 2 Ma for muscovite that envelops D veins. One 40Ar/39Ar date on K-alunite from the lithocap of 223.5 ± 1.9 Ma suggests that it may be about 16 m.y. younger than Erdenet, but this result needs to be verified by further dating.
","language":"English","publisher":"Society of Economic Geologists","doi":"10.5382/econgeo.2017.4500","usgsCitation":"Kavalieris, I., Khashgerel, B., Morgan, L.E., Undrakhtamir, A., and Borohul, A., 2017, Characteristics and 40Ar/39Ar geochronology of the Erdenet Cu-Mo deposit, Mongolia: Economic Geology, v. 112, no. 5, p. 1033-1053, https://doi.org/10.5382/econgeo.2017.4500.","productDescription":"21 p.","startPage":"1033","endPage":"1053","ipdsId":"IP-080784","costCenters":[{"id":171,"text":"Central Mineral and Environmental Resources Science Center","active":true,"usgs":true}],"links":[{"id":438161,"rank":0,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/F74J0C8H","text":"USGS data release","linkHelpText":"40Ar/39Ar data for: Characteristics and 40Ar/39Ar geochronology of the Erdenet Cu-Mo deposit, Mongolia"},{"id":347960,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"volume":"112","issue":"5","publishingServiceCenter":{"id":2,"text":"Denver PSC"},"noUsgsAuthors":false,"publicationDate":"2017-06-01","publicationStatus":"PW","scienceBaseUri":"59fadd1ce4b0531197b13c59","contributors":{"authors":[{"text":"Kavalieris, Imants","contributorId":199360,"corporation":false,"usgs":false,"family":"Kavalieris","given":"Imants","email":"","affiliations":[],"preferred":false,"id":718798,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Khashgerel, Bat-Erdene","contributorId":199361,"corporation":false,"usgs":false,"family":"Khashgerel","given":"Bat-Erdene","email":"","affiliations":[],"preferred":false,"id":718799,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Morgan, Leah E. 0000-0001-9930-524X lemorgan@usgs.gov","orcid":"https://orcid.org/0000-0001-9930-524X","contributorId":176174,"corporation":false,"usgs":true,"family":"Morgan","given":"Leah","email":"lemorgan@usgs.gov","middleInitial":"E.","affiliations":[{"id":171,"text":"Central Mineral and Environmental Resources Science Center","active":true,"usgs":true}],"preferred":true,"id":718797,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Undrakhtamir, Alexander","contributorId":199362,"corporation":false,"usgs":false,"family":"Undrakhtamir","given":"Alexander","email":"","affiliations":[],"preferred":false,"id":718800,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Borohul, Adiya","contributorId":199363,"corporation":false,"usgs":false,"family":"Borohul","given":"Adiya","email":"","affiliations":[],"preferred":false,"id":718801,"contributorType":{"id":1,"text":"Authors"},"rank":5}]}} ,{"id":70193355,"text":"70193355 - 2017 - Loss of ecosystem services due to chronic pollution of forests and surface waters in the Adirondack region (USA)","interactions":[],"lastModifiedDate":"2017-11-01T10:52:01","indexId":"70193355","displayToPublicDate":"2017-11-01T00:00:00","publicationYear":"2017","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":2258,"text":"Journal of Environmental Management","active":true,"publicationSubtype":{"id":10}},"title":"Loss of ecosystem services due to chronic pollution of forests and surface waters in the Adirondack region (USA)","docAbstract":"Sustaining recent progress in mitigating acid pollution could require lower emissions caps that will give rise to real or perceived tradeoffs between healthy ecosystems and inexpensive energy. Because most impacts of acid rain affect ecosystem functions that are poorly understood by policy-makers and the public, an ecosystem services (ES) framework can help to measure how pollution affects human well-being. Focused on the Adirondack region (USA), a global ‘hot-spot’ of acid pollution, we measured how the chronic acidification of the region's forests, lakes, and streams has affected the potential economic and cultural benefits they provide to society. We estimated that acid-impaired hardwood forests provide roughly half of the potential benefits of forests on moderate to well-buffered soils – an estimated loss of ∼ $10,000 ha−1 in net present value of wood products, maple syrup, carbon sequestration, and visual quality. Acidic deposition has had only nominal impact – relative to the effects of surficial geology and till depth – on the capacity of Adirondack lakes and streams to provide water suitable for drinking. However, as pH declines in lakes, the estimated value of recreational fishing decreases significantly due to loss of desirable fish such as trout. Hatchery stocking programs have partially offset the pollution-mediated losses of fishery value, most effectively in the pH range 4.8–5.5, but are costly and limited in scope. Although any estimates of the monetary ‘damages’ of acid rain have significant uncertainties, our findings highlight some of the more tangible economic and cultural benefits of pollution mitigation efforts, which continue to face litigation and political opposition.
","language":"English","publisher":"Elsevier","doi":"10.1016/j.jenvman.2016.12.069","usgsCitation":"Beier, C.M., Caputo, J., Lawrence, G.B., and Sullivan, T.J., 2017, Loss of ecosystem services due to chronic pollution of forests and surface waters in the Adirondack region (USA): Journal of Environmental Management, v. 191, p. 19-27, https://doi.org/10.1016/j.jenvman.2016.12.069.","productDescription":"9 p.","startPage":"19","endPage":"27","ipdsId":"IP-082679","costCenters":[{"id":474,"text":"New York Water Science Center","active":true,"usgs":true}],"links":[{"id":469355,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.1016/j.jenvman.2016.12.069","text":"Publisher Index Page"},{"id":347959,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","state":"New York","otherGeospatial":"Adirondack region, New England","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -71.4990234375,\n 44.99588261816546\n ],\n [\n -74.92675781249999,\n 45.089035564831036\n ],\n [\n -75.6298828125,\n 44.715513732021336\n ],\n [\n -76.201171875,\n 44.37098696297173\n ],\n [\n -76.728515625,\n 43.96119063892024\n ],\n [\n -76.81640625,\n 43.739352079154706\n ],\n [\n -78.7060546875,\n 43.67581809328341\n ],\n [\n -79.013671875,\n 43.51668853502906\n ],\n [\n -78.9697265625,\n 43.03677585761058\n ],\n [\n -79.8046875,\n 42.50450285299051\n ],\n [\n -79.73876953125,\n 42.01665183556825\n ],\n [\n -75.34423828125,\n 42.01665183556825\n ],\n [\n -75.30029296875,\n 41.88592102814744\n ],\n [\n -75.1025390625,\n 41.78769700539063\n ],\n [\n -75.0146484375,\n 41.590796851056005\n ],\n [\n -74.794921875,\n 41.42625319507269\n ],\n [\n -74.24560546875,\n 41.19518982948959\n ],\n [\n -74.02587890625,\n 41.11246878918088\n ],\n [\n -73.8720703125,\n 40.9964840143779\n ],\n [\n -74.02587890625,\n 40.81380923056958\n ],\n [\n -73.98193359375,\n 40.54720023441049\n ],\n [\n -73.41064453125,\n 40.34654412118006\n ],\n [\n -72.18017578125,\n 40.59727063442024\n ],\n [\n -71.5869140625,\n 40.91351257612758\n ],\n [\n -70.048828125,\n 40.94671366508002\n ],\n [\n -69.43359375,\n 41.60722821271717\n ],\n [\n -70.1806640625,\n 42.19596877629178\n ],\n [\n -70.57617187499999,\n 42.61779143282346\n ],\n [\n -70.0048828125,\n 43.32517767999296\n ],\n [\n -69.521484375,\n 43.739352079154706\n ],\n [\n -68.8623046875,\n 44.15068115978094\n ],\n [\n -66.6650390625,\n 44.59046718130883\n ],\n [\n -66.9287109375,\n 44.96479793033101\n ],\n [\n -67.32421875,\n 45.213003555993964\n ],\n [\n -67.412109375,\n 45.644768217751924\n ],\n [\n -67.8076171875,\n 45.55252525134013\n ],\n [\n -67.8955078125,\n 47.100044694025215\n ],\n [\n -68.4228515625,\n 47.368594345213374\n ],\n [\n -68.8623046875,\n 47.18971246448421\n ],\n [\n -69.169921875,\n 47.45780853075031\n ],\n [\n -69.7412109375,\n 47.15984001304432\n ],\n [\n -70.09277343749999,\n 46.619261036171515\n ],\n [\n -70.2685546875,\n 46.22545288226939\n ],\n [\n -70.4443359375,\n 45.98169518512228\n ],\n [\n -70.7958984375,\n 45.42929873257377\n ],\n [\n -71.015625,\n 45.27488643704891\n ],\n [\n -71.279296875,\n 45.213003555993964\n ],\n [\n -71.4990234375,\n 44.99588261816546\n ]\n ]\n ]\n }\n }\n ]\n}","volume":"191","publishingServiceCenter":{"id":11,"text":"Pembroke PSC"},"noUsgsAuthors":false,"publicationStatus":"PW","scienceBaseUri":"59fadd1be4b0531197b13c55","contributors":{"authors":[{"text":"Beier, Colin M.","contributorId":199364,"corporation":false,"usgs":false,"family":"Beier","given":"Colin","email":"","middleInitial":"M.","affiliations":[],"preferred":false,"id":718804,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Caputo, Jesse","contributorId":168871,"corporation":false,"usgs":false,"family":"Caputo","given":"Jesse","affiliations":[{"id":25371,"text":"Dept of Forest & Natural Resources Mgmt, SUNY College of ESF","active":true,"usgs":false}],"preferred":false,"id":718805,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Lawrence, Gregory B. 0000-0002-8035-2350 glawrenc@usgs.gov","orcid":"https://orcid.org/0000-0002-8035-2350","contributorId":867,"corporation":false,"usgs":true,"family":"Lawrence","given":"Gregory","email":"glawrenc@usgs.gov","middleInitial":"B.","affiliations":[{"id":474,"text":"New York Water Science Center","active":true,"usgs":true}],"preferred":true,"id":718803,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Sullivan, Timothy J.","contributorId":196720,"corporation":false,"usgs":false,"family":"Sullivan","given":"Timothy","email":"","middleInitial":"J.","affiliations":[],"preferred":false,"id":718806,"contributorType":{"id":1,"text":"Authors"},"rank":4}]}} ,{"id":70193363,"text":"70193363 - 2017 - Rapid exhumation of Cretaceous arc-rocks along the Blue Mountains restraining bend of the Enriquillo-Plantain Garden fault, Jamaica, using thermochronometry from multiple closure systems","interactions":[],"lastModifiedDate":"2017-11-29T16:12:53","indexId":"70193363","displayToPublicDate":"2017-11-01T00:00:00","publicationYear":"2017","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":3525,"text":"Tectonophysics","active":true,"publicationSubtype":{"id":10}},"title":"Rapid exhumation of Cretaceous arc-rocks along the Blue Mountains restraining bend of the Enriquillo-Plantain Garden fault, Jamaica, using thermochronometry from multiple closure systems","docAbstract":"The effect of rapid erosion on kinematic partitioning along transpressional plate margins is not well understood, particularly in highly erosive climates. The Blue Mountains restraining bend (BMRB) of eastern Jamaica, bound to the south by the left-lateral Enriquillo-Plantain Garden fault (EPGF), offers an opportunity to test the effects of highly erosive climatic conditions on a 30-km-wide restraining bend system. No previous thermochronometric data exists in Jamaica to describe the spatial or temporal pattern of rock uplift and how oblique (> 20°) plate motion is partitioned into vertical strain. To define the exhumation history, we measured apatite (n = 10) and zircon (n = 6) (U-Th)/He ages, 40Ar/39Ar (n = 2; amphibole and K-spar) ages, and U/Pb zircon (n = 2) crystallization ages. Late Cretaceous U/Pb and 40Ar/39Ar ages (74–68 Ma) indicate rapid cooling following shallow emplacement of plutons during north-south subduction along the Great Caribbean Arc. Early to middle Miocene zircon helium ages (19–14 Ma) along a vertical transect suggest exhumation and island emergence at ~ 0.2 mm/yr. Older zircon ages 10–15 km to the north (44–35 Ma) imply less rock uplift. Apatite helium ages are young (6–1 Ma) across the entire orogen, suggesting rapid exhumation of the BMRB since the late Miocene. These constraints are consistent with previous reports of restraining bend formation and early emergence of eastern Jamaica. An age-elevation relationship from a vertical transect implies an exhumation rate of 0.8 mm/yr, while calculated closure depths and thermal modeling suggests exhumation as rapid as 2 mm/yr. The rapid rock uplift rates in Jamaica are comparable to the most intense transpressive zones worldwide, despite the relatively slow (5–7 mm/yr) strike-slip rate. We hypothesize highly erosive conditions in Jamaica enable a higher fraction of plate motion to be accommodated by vertical deformation. Thus, strike-slip restraining bends may evolve differently depending on erosivity and local climate.","language":"English","publisher":"Elsevier","doi":"10.1016/j.tecto.2017.09.021","usgsCitation":"Cochran, W., Spotila, J.A., Prince, P.S., and McAleer, R., 2017, Rapid exhumation of Cretaceous arc-rocks along the Blue Mountains restraining bend of the Enriquillo-Plantain Garden fault, Jamaica, using thermochronometry from multiple closure systems: Tectonophysics, v. 721, p. 292-309, https://doi.org/10.1016/j.tecto.2017.09.021.","productDescription":"18 p.","startPage":"292","endPage":"309","ipdsId":"IP-087567","costCenters":[{"id":243,"text":"Eastern Geology and Paleoclimate Science Center","active":true,"usgs":true}],"links":[{"id":347957,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"Jamaica","geographicExtents":"{\"type\":\"FeatureCollection\",\"features\":[{\"type\":\"Feature\",\"geometry\":{\"type\":\"Polygon\",\"coordinates\":[[[-77.5696,18.49053],[-76.89662,18.40087],[-76.36536,18.1607],[-76.19966,17.88687],[-76.90256,17.86824],[-77.20634,17.70112],[-77.76602,17.8616],[-78.33772,18.22597],[-78.21773,18.45453],[-77.79736,18.52422],[-77.5696,18.49053]]]},\"properties\":{\"name\":\"Jamaica\"}}]}","volume":"721","publishingServiceCenter":{"id":9,"text":"Reston PSC"},"noUsgsAuthors":false,"publicationStatus":"PW","scienceBaseUri":"59fadd1be4b0531197b13c51","contributors":{"authors":[{"text":"Cochran, William J.","contributorId":199373,"corporation":false,"usgs":false,"family":"Cochran","given":"William J.","affiliations":[],"preferred":false,"id":718849,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Spotila, James A.","contributorId":199374,"corporation":false,"usgs":false,"family":"Spotila","given":"James","email":"","middleInitial":"A.","affiliations":[],"preferred":false,"id":718850,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Prince, Philip S.","contributorId":199375,"corporation":false,"usgs":false,"family":"Prince","given":"Philip","email":"","middleInitial":"S.","affiliations":[],"preferred":false,"id":718851,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"McAleer, Ryan J. 0000-0003-3801-7441 rmcaleer@usgs.gov","orcid":"https://orcid.org/0000-0003-3801-7441","contributorId":5301,"corporation":false,"usgs":true,"family":"McAleer","given":"Ryan J.","email":"rmcaleer@usgs.gov","affiliations":[{"id":243,"text":"Eastern Geology and Paleoclimate Science Center","active":true,"usgs":true},{"id":245,"text":"Eastern Mineral and Environmental Resources Science Center","active":true,"usgs":true}],"preferred":false,"id":718848,"contributorType":{"id":1,"text":"Authors"},"rank":4}]}} ,{"id":70193415,"text":"70193415 - 2017 - Predicting outcomes of restored Everglades high flow: A model system for scientifically managed floodplains","interactions":[],"lastModifiedDate":"2017-11-01T13:09:16","indexId":"70193415","displayToPublicDate":"2017-11-01T00:00:00","publicationYear":"2017","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":3271,"text":"Restoration Ecology","active":true,"publicationSubtype":{"id":10}},"title":"Predicting outcomes of restored Everglades high flow: A model system for scientifically managed floodplains","docAbstract":"Restoration of higher flows through the Everglades is intended to reestablish sheetflow to rebuild a well-functioning ridge and slough landscape that supports a productive and diverse ecosystem. Our objective of the study was to use hydrologic simulations and biophysical analysis to predict restoration outcomes for five major subbasins of the Everglades. Five different scenarios of restoration were examined, and for each we predicted an outcome based on metrics describing the present-day condition of the landscape and additional metrics determined by modeling the hydrologic changes accompanying restoration. Restoration scenarios spanned from a baseline case with average annual flows of about 52% of the predrainage flow to the most aggressive scenario that permits 91% of the predrainage flow. Our predictions indicated that all restoration scenarios could benefit the functionality of the ridge-slough ecosystem. However, the difference between any single restoration scenario and the “no restoration” baseline was far greater than was the difference between any two levels of restoration. Interestingly, our analysis suggested that the most extensive (and highest cost) restoration scenarios are not likely to improve ridge and slough function more than less extensive restoration options. However, the value of more aggressive restoration may lie in factors not considered directly in our analysis. For example, an important reason to implement the more aggressive restoration scenarios could be additional flexibility that permitting greater flow allows for adaptively managing the ecosystem while also serving water needs for southeastern Florida in what could be a drier Everglades in the coming decades.","language":"English","publisher":"Wiley","doi":"10.1111/rec.12479","usgsCitation":"Choi, J., and Harvey, J., 2017, Predicting outcomes of restored Everglades high flow: A model system for scientifically managed floodplains: Restoration Ecology, v. 25, no. S1, p. S39-S47, https://doi.org/10.1111/rec.12479.","productDescription":"9 p.","startPage":"S39","endPage":"S47","ipdsId":"IP-079752","costCenters":[{"id":436,"text":"National Research Program - Eastern Branch","active":true,"usgs":true}],"links":[{"id":348010,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","state":"Florida","otherGeospatial":"Everglades","volume":"25","issue":"S1","publicComments":"Special issue: Synthesis of Everglades Research and Ecosystem Services (SERES) project","publishingServiceCenter":{"id":9,"text":"Reston PSC"},"noUsgsAuthors":false,"publicationDate":"2016-12-22","publicationStatus":"PW","scienceBaseUri":"59fadd1ae4b0531197b13c4d","contributors":{"authors":[{"text":"Choi, Jay jchoi@usgs.gov","contributorId":4731,"corporation":false,"usgs":true,"family":"Choi","given":"Jay","email":"jchoi@usgs.gov","affiliations":[{"id":436,"text":"National Research Program - Eastern Branch","active":true,"usgs":true}],"preferred":true,"id":718966,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Harvey, Judson 0000-0002-2654-9873 jwharvey@usgs.gov","orcid":"https://orcid.org/0000-0002-2654-9873","contributorId":140228,"corporation":false,"usgs":true,"family":"Harvey","given":"Judson","email":"jwharvey@usgs.gov","affiliations":[{"id":436,"text":"National Research Program - Eastern Branch","active":true,"usgs":true}],"preferred":false,"id":718967,"contributorType":{"id":1,"text":"Authors"},"rank":2}]}} ,{"id":70193949,"text":"70193949 - 2017 - Detecting spatial patterns of rivermouth processes using a geostatistical framework for near-real-time analysis","interactions":[],"lastModifiedDate":"2017-11-16T14:50:03","indexId":"70193949","displayToPublicDate":"2017-11-01T00:00:00","publicationYear":"2017","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":1551,"text":"Environmental Modelling and Software","active":true,"publicationSubtype":{"id":10}},"title":"Detecting spatial patterns of rivermouth processes using a geostatistical framework for near-real-time analysis","docAbstract":"This paper proposes a geospatial analysis framework and software to interpret water-quality sampling data from towed undulating vehicles in near-real time. The framework includes data quality assurance and quality control processes, automated kriging interpolation along undulating paths, and local hotspot and cluster analyses. These methods are implemented in an interactive Web application developed using the Shiny package in the R programming environment to support near-real time analysis along with 2- and 3-D visualizations. The approach is demonstrated using historical sampling data from an undulating vehicle deployed at three rivermouth sites in Lake Michigan during 2011. The normalized root-mean-square error (NRMSE) of the interpolation averages approximately 10% in 3-fold cross validation. The results show that the framework can be used to track river plume dynamics and provide insights on mixing, which could be related to wind and seiche events.
","language":"English","publisher":"Elevier","doi":"10.1016/j.envsoft.2017.06.049","usgsCitation":"Collingsworth, P.D., Xu, W., Bailey, B., Carlson Mazur, M.L., Schaeffer, J., and Minsker, B., 2017, Detecting spatial patterns of rivermouth processes using a geostatistical framework for near-real-time analysis: Environmental Modelling and Software, v. 97, p. 72-85, https://doi.org/10.1016/j.envsoft.2017.06.049.","productDescription":"14 p.","startPage":"72","endPage":"85","ipdsId":"IP-071315","costCenters":[{"id":324,"text":"Great Lakes Science Center","active":true,"usgs":true}],"links":[{"id":469375,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.1016/j.envsoft.2017.06.049","text":"Publisher Index Page"},{"id":349016,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","otherGeospatial":"Lake Michigan","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -87.67364501953124,\n 43.058854606434494\n ],\n [\n -86.27288818359375,\n 43.058854606434494\n ],\n [\n -86.27288818359375,\n 44.10533762552548\n ],\n [\n -87.67364501953124,\n 44.10533762552548\n ],\n [\n -87.67364501953124,\n 43.058854606434494\n ]\n ]\n ]\n }\n }\n ]\n}","volume":"97","publishingServiceCenter":{"id":6,"text":"Columbus PSC"},"noUsgsAuthors":false,"publicationStatus":"PW","scienceBaseUri":"5a60fb22e4b06e28e9c22d13","contributors":{"authors":[{"text":"Xu, Wenzhao","contributorId":200526,"corporation":false,"usgs":false,"family":"Xu","given":"Wenzhao","email":"","affiliations":[],"preferred":false,"id":722554,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Collingsworth, Paris D.","contributorId":145526,"corporation":false,"usgs":false,"family":"Collingsworth","given":"Paris","email":"","middleInitial":"D.","affiliations":[],"preferred":false,"id":722555,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Bailey, Barbara","contributorId":200527,"corporation":false,"usgs":false,"family":"Bailey","given":"Barbara","email":"","affiliations":[],"preferred":false,"id":722556,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Carlson Mazur, Martha L.","contributorId":95377,"corporation":false,"usgs":true,"family":"Carlson Mazur","given":"Martha","email":"","middleInitial":"L.","affiliations":[{"id":324,"text":"Great Lakes Science Center","active":true,"usgs":true}],"preferred":false,"id":722557,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Schaeffer, Jeff 0000-0003-3430-0872 jschaeffer@usgs.gov","orcid":"https://orcid.org/0000-0003-3430-0872","contributorId":2041,"corporation":false,"usgs":true,"family":"Schaeffer","given":"Jeff","email":"jschaeffer@usgs.gov","affiliations":[{"id":324,"text":"Great Lakes Science Center","active":true,"usgs":true}],"preferred":true,"id":722558,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"Minsker, Barbara","contributorId":200528,"corporation":false,"usgs":false,"family":"Minsker","given":"Barbara","email":"","affiliations":[],"preferred":false,"id":722559,"contributorType":{"id":1,"text":"Authors"},"rank":6}]}} ,{"id":70192245,"text":"sir20175128 - 2017 - Simulation of groundwater flow and pumping scenarios for 1900–2050 near Mount Pleasant, South Carolina","interactions":[],"lastModifiedDate":"2020-08-25T16:37:11.720369","indexId":"sir20175128","displayToPublicDate":"2017-10-31T12:15:00","publicationYear":"2017","noYear":false,"publicationType":{"id":18,"text":"Report"},"publicationSubtype":{"id":5,"text":"USGS Numbered Series"},"seriesTitle":{"id":334,"text":"Scientific Investigations Report","code":"SIR","onlineIssn":"2328-0328","printIssn":"2328-031X","active":true,"publicationSubtype":{"id":5}},"seriesNumber":"2017-5128","title":"Simulation of groundwater flow and pumping scenarios for 1900–2050 near Mount Pleasant, South Carolina","docAbstract":"Groundwater withdrawals from the Upper Cretaceous-age Middendorf aquifer in South Carolina have created a large, regional cone of depression in the potentiometric surface of the Middendorf aquifer in Charleston and Berkeley Counties, South Carolina. Groundwater-level declines of as much as 249 feet have been observed in wells over the past 125 years and are a result of groundwater use for public water supply, irrigation, and private industry. To address the concerns of users of the Middendorf aquifer, the U.S. Geological Survey, in cooperation with Mount Pleasant Waterworks (MPW), recalibrated an existing groundwater-flow model to incorporate additional groundwater-use and water-level data since 2008. This recalibration process consisted of a technique of parameter estimation that uses regularized inversion and employs “pilot points” for spatial hydraulic property characterization. The groundwater-flow system of the Coastal Plain physiographic province of South Carolina and parts of Georgia and North Carolina was simulated using the U.S. Geological Survey finite-difference computer code MODFLOW-2000.
After the model recalibration, the following six predictive water-management scenarios were created to simulate potential changes in groundwater flow and groundwater-level conditions in the Mount Pleasant, South Carolina, area: Scenario 1—maximize MPW reverse-osmosis plant capacity by increasing groundwater withdrawals from the Middendorf aquifer from 3.9 million gallons per day (Mgal/d), which was the amount withdrawn in 2015, to 8.58 Mgal/d; Scenario 2—same as Scenario 1, but with the addition of a 0.5 Mgal/d supply well in the Middendorf aquifer near Moncks Corner, South Carolina; Scenario 3—same as Scenario 1, but with the addition of a 1.5 Mgal/d supply well in the Middendorf aquifer near Moncks Corner, South Carolina; Scenario 4—maximize MPW well capacity by increasing withdrawals from the Middendorf aquifer from 3.9 Mgal/d (in 2015) to 10.16 Mgal/d; Scenario 5—minimize MPW surface-water purchase from the Charleston Water System by adding supply wells and increasing withdrawals from the Middendorf aquifer from 3.9 Mgal/d (in 2015) to 12.16 Mgal/d; and Scenario 6—same as Scenario 1, but with he addition of quarterly model stress periods to simulate seasonal variations in the groundwater withdrawals. Results from the simulations indicated further decline of groundwater levels creating cones of depressions near pumping wells in the Middendorf aquifer in the Mount Pleasant, South Carolina, area between 2015 and 2050 for all six scenarios.
Simulation results from Scenario 1 showed an average decline of about 150 feet in the groundwater levels of the MPW production wells. Simulated hydrographs for two area observation wells illustrate the gradual decline in groundwater levels with overall changes in water-level altitudes of –92 and –33 feet, respectively. Simulated groundwater altitudes at a hypothetical observation well located in the MPW well field declined 121 feet between 2015 and 2050.
Scenarios 2 and 3 have the same pumping rates as Scenario 1 for the MPW production wells; however, a single hypothetical pumping well was added in the Middendorf aquifer near the town of Moncks Corner, South Carolina. This hypothetical pumping well has a withdrawal rate of 0.5 Mgal/d for Scenario 2 and 1.5 Mgal/d for Scenario 3. A comparison to the 2050 Scenario 1 simulation indicates groundwater altitudes for Scenarios 2 and Scenario 3 are 3 feet and 8 feet lower, respectively, at the MPW production wells.
Scenario 4 simulates the maximum pumping capacity of 10.16 Mgal/d for the MPW network of production wells. Simulated 2050 groundwater altitudes for this simulation declined to –359 feet. Simulated hydrographs for two observation wells show groundwater-level declines of 116 and 41 feet, respectively. Simulated differences in groundwater altitudes at a hypothetical observation well located in the MPW well field indicate a water-level decline of 164 feet between 2015 and 2050.
Scenario 5 is a modification of Scenario 4 with the addition of two new MPW production wells. For this scenario, the MPW network of production wells were simulated the same as in Scenario 4, but withdrawals from the two new production wells were added in 2020. Simulated 2050 groundwater altitudes for this simulation declined to – 405 feet. Simulated hydrographs for two observation wells show groundwater-level declines of 143 and 51 feet, respectively. Simulated groundwater altitudes at a hypothetical observation well located in the MPW well field declined 199 feet between 2015 and 2050.
Scenario 6 is a modification of Scenario 1, in which 140 additional quarterly stress periods were added to simulate MPW seasonal demands. Simulated groundwater altitudes for Scenario 6 declined to –353 feet during 2050. For Scenario 6, simulated hydrographs for two observation wells and the hypothetical observation well show similar groundwater-level declines as seen in Scenario 1, but with seasonal fluctuations of as much as 56 feet in the hypothetical observation well.
Water budgets for the model area immediately surrounding Mount Pleasant, South Carolina, were calculated for 2015 and for 2050. The water budget for 2015 is equal for all of the scenarios because it represents the year prior to the hypothetical pumping beginning in 2016. The largest flow component in the 2015 water budget for the Mount Pleasant area is discharge to wells at a rate of 4.17 Mgal/d. Additionally, 0.23 Mgal/d flows laterally out of the Middendorf aquifer in this area of the model due to the regional horizontal hydraulic gradient. Flow into this zone consists predominantly of lateral flow within the Middendorf aquifer at 4.08 Mgal/d. Additionally, 0.02 Mgal/d is released into this zone from aquifer storage. Vertically, 0.06 Mgal/d flows down from the Middendorf confining unit located above the Middendorf aquifer, and 0.25 Mgal/d flows up from the Cape Fear confining unit below.
The largest flow component in the 2050 water budget for all six scenarios is discharge to wells in the Mount Pleasant area at rates between 8.89 and 12.47 Mgal/d. Flow into this zone consists mostly of lateral flow between 8.47 and 11.77 Mgal/d within the Middendorf aquifer. Between 0.003 and 0.46 Mgal/d is released into this zone from aquifer storage. Between 0.004 and 0.15 Mgal/d flows laterally out of this zone into adjacent areas of the Middendorf aquifer due to the regional horizontal hydraulic gradient. Finally, between 0.15 and 0.22 Mgal/d flows vertically into this zone from confining units above and below the Middendorf aquifer.
","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/sir20175128","collaboration":"Prepared in cooperation with Mount Pleasant Waterworks","usgsCitation":"Fine, J.M., Petkewich, M.D., and Campbell, B.G., 2017, Simulation of groundwater flow and pumping scenarios for 1900–2050 near Mount Pleasant, South Carolina (ver. 1.1, November 6, 2017): Scientific Investigations Report 2017–5128, 36 p., https://doi.org/10.3133/sir20175128.","productDescription":"Report: vi, 36 p.; 3 Data Releases","numberOfPages":"46","onlineOnly":"Y","additionalOnlineFiles":"Y","ipdsId":"IP-088974","costCenters":[{"id":13634,"text":"South Atlantic Water Science Center","active":true,"usgs":true}],"links":[{"id":347690,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/sir/2017/5128/coverthb2.jpg"},{"id":377650,"rank":6,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/P9FA07XD","text":"USGS data release","description":"USGS data release","linkHelpText":"2020 scenarios archive--MODFLOW-2000 data sets used in two predictive scenarios of groundwater flow and pumping (1900-2050) near Mount Pleasant, South Carolina"},{"id":347691,"rank":2,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/sir/2017/5128/sir20175128.pdf","text":"Report","size":"16.8 MB","linkFileType":{"id":1,"text":"pdf"},"description":"SIR 2017-5128"},{"id":348296,"rank":3,"type":{"id":25,"text":"Version History"},"url":"https://pubs.usgs.gov/sir/2017/5128/versionHist.txt","size":"1.02","linkFileType":{"id":2,"text":"txt"}},{"id":348298,"rank":4,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/F7S181FC","text":"USGS data release","description":"USGS data release","linkHelpText":"Original model archive--MODFLOW-2000 model data sets used in the simulation of Groundwater Flow and Pumping Scenarios for 1900-2050 near Mount Pleasant, South Carolina"},{"id":377837,"rank":5,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/P9GZEE4E","text":"USGS data release","description":"USGS data release","linkHelpText":"2018 scenarios archive--MODFLOW-2000 and MODPATH model data sets used in scenarios of groundwater flow and pumping (1900-2500) near Mount Pleasant, South Carolina"}],"country":"United States","state":"South Carolina","city":"Mount Pleasant","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -80.892333984375,\n 31.914867503276223\n ],\n [\n -79.134521484375,\n 33.18813395605041\n ],\n [\n -78.5357666015625,\n 33.85673152928873\n ],\n [\n -79.6783447265625,\n 34.80929324176267\n ],\n [\n -80.694580078125,\n 34.82282272723702\n ],\n [\n -82.2052001953125,\n 33.61919376817004\n ],\n [\n -80.892333984375,\n 31.914867503276223\n ]\n ]\n ]\n }\n }\n ]\n}","edition":"Version 1.0: Originally posted October 31, 2017; Version 1.1: November 6, 2017","contact":"Director, South Atlantic Water Science Center
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720 Gracern Road
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Deglacial (12.8–10.7 ka) sea level history on the East Siberian continental shelf and upper continental slope was reconstructed using new geophysical records and sediment cores taken during Leg 2 of the 2014 SWERUS-C3 expedition. The focus of this study is two cores from Herald Canyon, piston core SWERUS-L2-4-PC1 (4-PC1) and multicore SWERUS-L2-4-MC1 (4-MC1), and a gravity core from an East Siberian Sea transect, SWERUS-L2-20-GC1 (20-GC1). Cores 4-PC1 and 20-GC were taken at 120 and 115 m of modern water depth, respectively, only a few meters above the global last glacial maximum (LGM; ∼ 24 kiloannum or ka) minimum sea level of ∼ 125–130 meters below sea level (m b.s.l.). Using calibrated radiocarbon ages mainly on molluscs for chronology and the ecology of benthic foraminifera and ostracode species to estimate paleodepths, the data reveal a dominance of river-proximal species during the early part of the Younger Dryas event (YD, Greenland Stadial GS-1) followed by a rise in river-intermediate species in the late Younger Dryas or the early Holocene (Preboreal) period. A rapid relative sea level rise beginning at roughly 11.4 to 10.8 ka ( ∼ 400 cm of core depth) is indicated by a sharp faunal change and unconformity or condensed zone of sedimentation. Regional sea level at this time was about 108 m b.s.l. at the 4-PC1 site and 102 m b.s.l. at 20-GC1. Regional sea level near the end of the YD was up to 42–47 m lower than predicted by geophysical models corrected for glacio-isostatic adjustment. This discrepancy could be explained by delayed isostatic adjustment caused by a greater volume and/or geographical extent of glacial-age land ice and/or ice shelves in the western Arctic Ocean and adjacent Siberian land areas.
","language":"English","publisher":"European Geosciences Union","doi":"10.5194/cp-13-1097-2017","usgsCitation":"Cronin, T.M., O’Regan, M., Pearce, C., Gemery, L., Toomey, M., and Semiletov, I., 2017, Deglacial sea level history of the East Siberian Sea and Chukchi Sea margins: Climate of the Past, v. 13, no. 9, p. 1097-1110, https://doi.org/10.5194/cp-13-1097-2017.","productDescription":"14 p.","startPage":"1097","endPage":"1110","ipdsId":"IP-083404","costCenters":[{"id":243,"text":"Eastern Geology and Paleoclimate Science Center","active":true,"usgs":true}],"links":[{"id":461367,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.5194/cp-13-1097-2017","text":"Publisher Index Page"},{"id":347913,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"Russia, United States","otherGeospatial":"Chukchi Sea, East Siberian Sea","volume":"13","issue":"9","publishingServiceCenter":{"id":9,"text":"Reston PSC"},"noUsgsAuthors":false,"publicationDate":"2017-09-05","publicationStatus":"PW","scienceBaseUri":"59f98ba4e4b0531197af9f8d","contributors":{"authors":[{"text":"Cronin, Thomas M. 0000-0002-2643-0979 tcronin@usgs.gov","orcid":"https://orcid.org/0000-0002-2643-0979","contributorId":2579,"corporation":false,"usgs":true,"family":"Cronin","given":"Thomas","email":"tcronin@usgs.gov","middleInitial":"M.","affiliations":[{"id":243,"text":"Eastern Geology and Paleoclimate Science Center","active":true,"usgs":true},{"id":40020,"text":"Florence Bascom Geoscience Center","active":true,"usgs":true}],"preferred":true,"id":718700,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"O’Regan, Matt","contributorId":197135,"corporation":false,"usgs":false,"family":"O’Regan","given":"Matt","email":"","affiliations":[{"id":25421,"text":"Department of Geological Sciences, Stockholm University, Sweden","active":true,"usgs":false}],"preferred":false,"id":718702,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Pearce, Christof","contributorId":197126,"corporation":false,"usgs":false,"family":"Pearce","given":"Christof","email":"","affiliations":[{"id":25421,"text":"Department of Geological Sciences, Stockholm University, Sweden","active":true,"usgs":false}],"preferred":false,"id":718703,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Gemery, Laura 0000-0003-1966-8732 lgemery@usgs.gov","orcid":"https://orcid.org/0000-0003-1966-8732","contributorId":5402,"corporation":false,"usgs":true,"family":"Gemery","given":"Laura","email":"lgemery@usgs.gov","affiliations":[{"id":243,"text":"Eastern Geology and Paleoclimate Science Center","active":true,"usgs":true},{"id":40020,"text":"Florence Bascom Geoscience Center","active":true,"usgs":true}],"preferred":true,"id":718707,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Toomey, Michael 0000-0003-0167-9273 mtoomey@usgs.gov","orcid":"https://orcid.org/0000-0003-0167-9273","contributorId":184097,"corporation":false,"usgs":true,"family":"Toomey","given":"Michael","email":"mtoomey@usgs.gov","affiliations":[{"id":40020,"text":"Florence Bascom Geoscience Center","active":true,"usgs":true},{"id":243,"text":"Eastern Geology and Paleoclimate Science Center","active":true,"usgs":true}],"preferred":true,"id":718704,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"Semiletov, Igor","contributorId":197134,"corporation":false,"usgs":false,"family":"Semiletov","given":"Igor","email":"","affiliations":[{"id":35519,"text":"Russian Academy Sciences, Vladivostok, Russia","active":true,"usgs":false},{"id":24563,"text":"Tomsk Polytechnic University","active":true,"usgs":false}],"preferred":false,"id":718706,"contributorType":{"id":1,"text":"Authors"},"rank":6}]}} ,{"id":70195397,"text":"70195397 - 2017 - Flow and residence times of dynamic river bank storage and sinuosity-driven hyporheic exchange","interactions":[],"lastModifiedDate":"2018-02-14T10:11:16","indexId":"70195397","displayToPublicDate":"2017-10-31T00:00:00","publicationYear":"2017","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":3722,"text":"Water Resources Research","onlineIssn":"1944-7973","printIssn":"0043-1397","active":true,"publicationSubtype":{"id":10}},"title":"Flow and residence times of dynamic river bank storage and sinuosity-driven hyporheic exchange","docAbstract":"Hydrologic exchange fluxes (HEFs) vary significantly along river corridors due to spatiotemporal changes in discharge and geomorphology. This variability results in the emergence of biogeochemical hot-spots and hot-moments that ultimately control solute and energy transport and ecosystem services from the local to the watershed scales. In this work, we use a reduced-order model to gain mechanistic understanding of river bank storage and sinuosity-driven hyporheic exchange induced by transient river discharge. This is the first time that a systematic analysis of both processes is presented and serves as an initial step to propose parsimonious, physics-based models for better predictions of water quality at the large watershed scale. The effects of channel sinuosity, alluvial valley slope, hydraulic conductivity, and river stage forcing intensity and duration are encapsulated in dimensionless variables that can be easily estimated or constrained. We find that the importance of perturbations in the hyporheic zone's flux, residence times, and geometry is mainly explained by two-dimensionless variables representing the ratio of the hydraulic time constant of the aquifer and the duration of the event (Γd) and the importance of the ambient groundwater flow ( ![\"math](\"http://binarystore.wiley.com/store/10.1002/2017WR021362/asset/equation/wrcr22904-math-0001.png?v=1&s=be103418b21131f03172a44a3017f7dbb804f190\")
). Our model additionally shows that even systems with small sensitivity, resulting in small changes in the hyporheic zone extent, are characterized by highly variable exchange fluxes and residence times. These findings highlight the importance of including dynamic changes in hyporheic zones for typical HEF models such as the transient storage model.
Fresh groundwater discharge to coastal environments contributes to the physical and chemical conditions of coastal waters, but the role of coastal groundwater at regional to continental scales remains poorly defined due to diverse hydrologic conditions and the difficulty of tracking coastal groundwater flow paths through heterogeneous subsurface materials. We use three-dimensional groundwater flow models for the first time to calculate the magnitude and source areas of groundwater discharge from unconfined aquifers to coastal waterbodies along the entire eastern U.S. We find that 27.1 km3/yr (22.8–30.5 km3/yr) of groundwater directly enters eastern U.S. and Gulf of Mexico coastal waters. The contributing recharge areas comprised ~175,000 km2 of U.S. land area, extending several kilometers inland. This result provides new information on the land area that can supply natural and anthropogenic constituents to coastal waters via groundwater discharge, thereby defining the subterranean domain potentially affecting coastal chemical budgets and ecosystem processes.
","language":"English","publisher":"AGU","doi":"10.1002/2017GL075238","usgsCitation":"Befus, K., Kroeger, K.D., Smith, C.G., and Swarzenski, P.W., 2017, The magnitude and origin of groundwater discharge to eastern U.S. and Gulf of Mexico coastal waters: Geophysical Research Letters, v. 44, no. 20, p. 10396-10406, https://doi.org/10.1002/2017GL075238.","productDescription":"11 p.","startPage":"10396","endPage":"10406","ipdsId":"IP-088608","costCenters":[{"id":678,"text":"Woods Hole Coastal and Marine Science Center","active":true,"usgs":true}],"links":[{"id":469383,"rank":1,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.1002/2017gl075238","text":"Publisher Index Page"},{"id":349917,"rank":2,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","otherGeospatial":"Gulf of Mexico","volume":"44","issue":"20","publishingServiceCenter":{"id":11,"text":"Pembroke PSC"},"noUsgsAuthors":false,"publicationDate":"2017-10-28","publicationStatus":"PW","scienceBaseUri":"5a60fb23e4b06e28e9c22d2e","contributors":{"authors":[{"text":"Befus, Kevin 0000-0001-7553-4195 kbefus@usgs.gov","orcid":"https://orcid.org/0000-0001-7553-4195","contributorId":190617,"corporation":false,"usgs":true,"family":"Befus","given":"Kevin","email":"kbefus@usgs.gov","affiliations":[],"preferred":true,"id":724822,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Kroeger, Kevin D. 0000-0002-4272-2349 kkroeger@usgs.gov","orcid":"https://orcid.org/0000-0002-4272-2349","contributorId":1603,"corporation":false,"usgs":true,"family":"Kroeger","given":"Kevin","email":"kkroeger@usgs.gov","middleInitial":"D.","affiliations":[{"id":41100,"text":"Coastal and Marine Hazards and Resources Program","active":true,"usgs":true}],"preferred":true,"id":724821,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Smith, Christopher G. 0000-0002-8075-4763 cgsmith@usgs.gov","orcid":"https://orcid.org/0000-0002-8075-4763","contributorId":3410,"corporation":false,"usgs":true,"family":"Smith","given":"Christopher","email":"cgsmith@usgs.gov","middleInitial":"G.","affiliations":[{"id":574,"text":"St. Petersburg Coastal and Marine Science Center","active":true,"usgs":true},{"id":24708,"text":"Lower Mississippi-Gulf Water Science Center","active":true,"usgs":true},{"id":186,"text":"Coastal and Marine Geology Program","active":true,"usgs":true}],"preferred":true,"id":724824,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Swarzenski, Peter W. 0000-0003-0116-0578 pswarzen@usgs.gov","orcid":"https://orcid.org/0000-0003-0116-0578","contributorId":1070,"corporation":false,"usgs":true,"family":"Swarzenski","given":"Peter","email":"pswarzen@usgs.gov","middleInitial":"W.","affiliations":[{"id":520,"text":"Pacific Coastal and Marine Science Center","active":true,"usgs":true}],"preferred":true,"id":724823,"contributorType":{"id":1,"text":"Authors"},"rank":4}]}} ,{"id":70193123,"text":"70193123 - 2017 - Relative influences of climate change and human activity on the onshore distribution of polar bears","interactions":[],"lastModifiedDate":"2017-10-31T10:08:45","indexId":"70193123","displayToPublicDate":"2017-10-31T00:00:00","publicationYear":"2017","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":1015,"text":"Biological Conservation","active":true,"publicationSubtype":{"id":10}},"title":"Relative influences of climate change and human activity on the onshore distribution of polar bears","docAbstract":"Climate change is altering habitat for many species, leading to shifts in distributions that can increase levels of human-wildlife conflict. To develop effective strategies for minimizing human-wildlife conflict, we must understand the relative influences that climate change and other factors have on wildlife distributions. Polar bears (Ursus maritimus) are increasingly using land during summer and autumn due to sea ice loss, leading to higher incidents of conflict and concerns for human safety. We sought to understand the relative influence of sea ice conditions, onshore habitat characteristics, and human-provisioned food attractants on the distribution and abundance of polar bears while on shore. We also wanted to determine how mitigation measures might reduce human-polar bear conflict associated with an anthropogenic food source. We built a Bayesian hierarchical model based on 14 years of aerial survey data to estimate the weekly number and distribution of polar bears on the coast of northern Alaska in autumn. We then used the model to predict how effective two management options for handling subsistence-harvested whale remains in the community of Kaktovik, Alaska might be. The distribution of bears on shore was most strongly influenced by the presence of whale carcasses and to a lesser extent sea ice and onshore habitat conditions. The numbers of bears on shore were related to sea ice conditions. The two management strategies for handling the whale carcasses reduced the estimated number of bears near Kaktovik by > 75%. By considering multiple factors associated with the onshore distribution and abundance of polar bears we discerned what role human activities played in where bears occur and how successful efforts to manage the whale carcasses might be for reducing human-polar bear conflict.","language":"English","publisher":"Elsevier","doi":"10.1016/j.biocon.2017.08.005","usgsCitation":"Wilson, R.H., Regehr, E.V., St. Martin, M., Atwood, T.C., Peacock, E.L., Miller, S., and Divoky, G.J., 2017, Relative influences of climate change and human activity on the onshore distribution of polar bears: Biological Conservation, v. 214, p. 288-294, https://doi.org/10.1016/j.biocon.2017.08.005.","productDescription":"7 p.","startPage":"288","endPage":"294","ipdsId":"IP-081468","costCenters":[{"id":116,"text":"Alaska Science Center Biology MFEB","active":true,"usgs":true}],"links":[{"id":469378,"rank":1,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.1016/j.biocon.2017.08.005","text":"Publisher Index Page"},{"id":438172,"rank":0,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/F74Q7S6Z","text":"USGS data release","linkHelpText":"Polar Bear Fall Coastal Survey Data from the Southern Beaufort Sea of Alaska, 2010-2013"},{"id":347803,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","state":"Alaska","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -157.8955078125,\n 69.51914693717981\n ],\n [\n -147.205810546875,\n 69.51914693717981\n ],\n [\n -147.205810546875,\n 71.54926391392517\n ],\n [\n -157.8955078125,\n 71.54926391392517\n ],\n [\n -157.8955078125,\n 69.51914693717981\n ]\n ]\n ]\n }\n }\n ]\n}","volume":"214","publishingServiceCenter":{"id":12,"text":"Tacoma PSC"},"noUsgsAuthors":false,"publicationStatus":"PW","scienceBaseUri":"59f98baee4b0531197af9fc1","contributors":{"authors":[{"text":"Wilson, Ryan H. 0000-0001-7740-7771","orcid":"https://orcid.org/0000-0001-7740-7771","contributorId":130989,"corporation":false,"usgs":false,"family":"Wilson","given":"Ryan","email":"","middleInitial":"H.","affiliations":[{"id":6987,"text":"U.S. Fish and Wildlife Sevice","active":true,"usgs":false}],"preferred":false,"id":718060,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Regehr, Eric V. 0000-0003-4487-3105","orcid":"https://orcid.org/0000-0003-4487-3105","contributorId":66364,"corporation":false,"usgs":false,"family":"Regehr","given":"Eric","email":"","middleInitial":"V.","affiliations":[{"id":12428,"text":"U. 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Fish and Wildlife Service","active":true,"usgs":false}],"preferred":false,"id":718061,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"St. Martin, Michelle","contributorId":150114,"corporation":false,"usgs":false,"family":"St. Martin","given":"Michelle","email":"","affiliations":[{"id":6661,"text":"US Fish and Wildlife Service","active":true,"usgs":false}],"preferred":false,"id":718062,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Atwood, Todd C. 0000-0002-1971-3110 tatwood@usgs.gov","orcid":"https://orcid.org/0000-0002-1971-3110","contributorId":4368,"corporation":false,"usgs":true,"family":"Atwood","given":"Todd","email":"tatwood@usgs.gov","middleInitial":"C.","affiliations":[{"id":116,"text":"Alaska Science Center Biology MFEB","active":true,"usgs":true},{"id":114,"text":"Alaska Science Center","active":true,"usgs":true}],"preferred":true,"id":718059,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Peacock, Elizabeth L. 0000-0001-7279-0329 lpeacock@usgs.gov","orcid":"https://orcid.org/0000-0001-7279-0329","contributorId":3361,"corporation":false,"usgs":true,"family":"Peacock","given":"Elizabeth","email":"lpeacock@usgs.gov","middleInitial":"L.","affiliations":[{"id":116,"text":"Alaska Science Center Biology MFEB","active":true,"usgs":true}],"preferred":false,"id":718063,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"Miller, Susanne","contributorId":50955,"corporation":false,"usgs":false,"family":"Miller","given":"Susanne","email":"","affiliations":[{"id":13235,"text":"U.S. Fish and Wildlife Service, Marine Mammals Management","active":true,"usgs":false}],"preferred":false,"id":718064,"contributorType":{"id":1,"text":"Authors"},"rank":6},{"text":"Divoky, George J.","contributorId":100912,"corporation":false,"usgs":false,"family":"Divoky","given":"George","email":"","middleInitial":"J.","affiliations":[{"id":13117,"text":"Institute of Arctic Biology, University of Alaska Fairbanks","active":true,"usgs":false}],"preferred":false,"id":718065,"contributorType":{"id":1,"text":"Authors"},"rank":7}]}} ,{"id":70193120,"text":"70193120 - 2017 - Environmental and behavioral changes may influence the exposure of an Arctic apex predator to pathogens and contaminants","interactions":[],"lastModifiedDate":"2017-10-31T11:28:39","indexId":"70193120","displayToPublicDate":"2017-10-31T00:00:00","publicationYear":"2017","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":3358,"text":"Scientific Reports","active":true,"publicationSubtype":{"id":10}},"title":"Environmental and behavioral changes may influence the exposure of an Arctic apex predator to pathogens and contaminants","docAbstract":"Recent decline of sea ice habitat has coincided with increased use of land by polar bears (Ursus maritimus) from the southern Beaufort Sea (SB), which may alter the risks of exposure to pathogens and contaminants. We assayed blood samples from SB polar bears to assess prior exposure to the pathogens Brucella spp., Toxoplasma gondii, Coxiella burnetii, Francisella tularensis, and Neospora caninum, estimate concentrations of persistent organic pollutants (POPs), and evaluate risk factors associated with exposure to pathogens and POPs. We found that seroprevalence of Brucella spp. and T. gondii antibodies likely increased through time, and provide the first evidence of exposure of polar bears to C. burnetii, N. caninum, and F. tularensis. Additionally, the odds of exposure to T. gondii were greater for bears that used land than for bears that remained on the sea ice during summer and fall, while mean concentrations of the POP chlordane (ΣCHL) were lower for land-based bears. Changes in polar bear behavior brought about by climate-induced modifications to the Arctic marine ecosystem may increase exposure risk to certain pathogens and alter contaminant exposure pathways.
","language":"English","publisher":"Macmillan Publishers","doi":"10.1038/s41598-017-13496-9","usgsCitation":"Atwood, T.C., Duncan, C.G., Patyk, K.A., Nol, P., Rhyan, J., McCollum, M., McKinney, M.A., Ramey, A.M., Cerqueira-Cezar, C., Kwok, O., Dubey, J.P., and Hennager, S., 2017, Environmental and behavioral changes may influence the exposure of an Arctic apex predator to pathogens and contaminants: Scientific Reports, v. 7, 13193; 12 p., https://doi.org/10.1038/s41598-017-13496-9.","productDescription":"13193; 12 p.","ipdsId":"IP-086497","costCenters":[{"id":116,"text":"Alaska Science Center Biology MFEB","active":true,"usgs":true}],"links":[{"id":469377,"rank":1,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.1038/s41598-017-13496-9","text":"Publisher Index Page"},{"id":438171,"rank":0,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/F78P5Z0G","text":"USGS data release","linkHelpText":"Pathogen and Contaminant Exposure Data from Southern Beaufort Sea Polar Bears, 2007-2014"},{"id":347841,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","state":"Alaska","otherGeospatial":"Beaufort Sea","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -156.763916015625,\n 69.79034074260014\n ],\n [\n -148.38134765625,\n 69.79034074260014\n ],\n [\n -148.38134765625,\n 71.61867863505971\n ],\n [\n -156.763916015625,\n 71.61867863505971\n ],\n [\n -156.763916015625,\n 69.79034074260014\n ]\n ]\n ]\n }\n }\n ]\n}","volume":"7","publishingServiceCenter":{"id":12,"text":"Tacoma PSC"},"noUsgsAuthors":false,"publicationDate":"2017-10-16","publicationStatus":"PW","scienceBaseUri":"59f98bb0e4b0531197af9fca","contributors":{"authors":[{"text":"Atwood, Todd C. 0000-0002-1971-3110 tatwood@usgs.gov","orcid":"https://orcid.org/0000-0002-1971-3110","contributorId":4368,"corporation":false,"usgs":true,"family":"Atwood","given":"Todd","email":"tatwood@usgs.gov","middleInitial":"C.","affiliations":[{"id":116,"text":"Alaska Science Center Biology MFEB","active":true,"usgs":true},{"id":114,"text":"Alaska Science Center","active":true,"usgs":true}],"preferred":true,"id":718034,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Duncan, Colleen G.","contributorId":15512,"corporation":false,"usgs":false,"family":"Duncan","given":"Colleen","email":"","middleInitial":"G.","affiliations":[{"id":6621,"text":"Colorado State University","active":true,"usgs":false}],"preferred":false,"id":718035,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Patyk, Kelly A.","contributorId":139696,"corporation":false,"usgs":false,"family":"Patyk","given":"Kelly","email":"","middleInitial":"A.","affiliations":[{"id":6622,"text":"US Department of Agriculture","active":true,"usgs":false}],"preferred":false,"id":718036,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Nol, Pauline","contributorId":34053,"corporation":false,"usgs":false,"family":"Nol","given":"Pauline","email":"","affiliations":[{"id":6622,"text":"US Department of Agriculture","active":true,"usgs":false}],"preferred":false,"id":718037,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Rhyan, Jack","contributorId":199054,"corporation":false,"usgs":false,"family":"Rhyan","given":"Jack","affiliations":[],"preferred":false,"id":718038,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"McCollum, Matthew","contributorId":199055,"corporation":false,"usgs":false,"family":"McCollum","given":"Matthew","email":"","affiliations":[],"preferred":false,"id":718039,"contributorType":{"id":1,"text":"Authors"},"rank":6},{"text":"McKinney, Melissa A.","contributorId":11496,"corporation":false,"usgs":false,"family":"McKinney","given":"Melissa","email":"","middleInitial":"A.","affiliations":[{"id":6619,"text":"University of Connecticutt","active":true,"usgs":false}],"preferred":false,"id":718040,"contributorType":{"id":1,"text":"Authors"},"rank":7},{"text":"Ramey, Andrew M. 0000-0002-3601-8400 aramey@usgs.gov","orcid":"https://orcid.org/0000-0002-3601-8400","contributorId":1872,"corporation":false,"usgs":true,"family":"Ramey","given":"Andrew","email":"aramey@usgs.gov","middleInitial":"M.","affiliations":[{"id":117,"text":"Alaska Science Center Biology WTEB","active":true,"usgs":true},{"id":114,"text":"Alaska Science Center","active":true,"usgs":true}],"preferred":true,"id":718041,"contributorType":{"id":1,"text":"Authors"},"rank":8},{"text":"Cerqueira-Cezar, Camila","contributorId":199056,"corporation":false,"usgs":false,"family":"Cerqueira-Cezar","given":"Camila","affiliations":[],"preferred":false,"id":718042,"contributorType":{"id":1,"text":"Authors"},"rank":9},{"text":"Kwok, Oliver C H","contributorId":199057,"corporation":false,"usgs":false,"family":"Kwok","given":"Oliver C H","affiliations":[],"preferred":false,"id":718043,"contributorType":{"id":1,"text":"Authors"},"rank":10},{"text":"Dubey, Jitender P","contributorId":199058,"corporation":false,"usgs":false,"family":"Dubey","given":"Jitender","email":"","middleInitial":"P","affiliations":[],"preferred":false,"id":718044,"contributorType":{"id":1,"text":"Authors"},"rank":11},{"text":"Hennager, S.G.","contributorId":38309,"corporation":false,"usgs":true,"family":"Hennager","given":"S.G.","email":"","affiliations":[],"preferred":false,"id":718045,"contributorType":{"id":1,"text":"Authors"},"rank":12}]}} ,{"id":70192467,"text":"70192467 - 2017 - Compositional variations in sands of the Bagnold Dunes, Gale Crater, Mars, from visible-shortwave infrared spectroscopy and comparison with ground truth from the Curiosity Rover","interactions":[],"lastModifiedDate":"2018-01-24T15:52:15","indexId":"70192467","displayToPublicDate":"2017-10-31T00:00:00","publicationYear":"2017","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":2317,"text":"Journal of Geophysical Research E: Planets","active":true,"publicationSubtype":{"id":10}},"title":"Compositional variations in sands of the Bagnold Dunes, Gale Crater, Mars, from visible-shortwave infrared spectroscopy and comparison with ground truth from the Curiosity Rover","docAbstract":"During its ascent up Mount Sharp, the Mars Science Laboratory Curiosity rover traversed the Bagnold Dune Field. We model sand modal mineralogy and grain size at four locations near the rover traverse, using orbital shortwave infrared single scattering albedo spectra and a Markov-Chain Monte Carlo implementation of Hapke's radiative transfer theory to fully constrain uncertainties and permitted solutions. These predictions, evaluated against in situ measurements at one site from the Curiosity rover, show that XRD-measured mineralogy of the basaltic sands is within the 95% confidence interval of model predictions. However, predictions are relatively insensitive to grain size and are non-unique, especially when modeling the composition of minerals with solid solutions. We find an overall basaltic mineralogy and show subtle spatial variations in composition in and around the Bagnold dunes, consistent with a mafic enrichment of sands with cumulative transport distance by sorting of olivine, pyroxene, and plagioclase grains during aeolian saltation. Furthermore, the large variations in Fe and Mg abundances (~20 wt%) at the Bagnold Dunes suggest that compositional variability induced by wind sorting may be enhanced by local mixing with proximal sand sources. Our estimates demonstrate a method for orbital quantification of composition with rigorous uncertainty determination and provide key constraints for interpreting in situ measurements of compositional variability within martian aeolian sandstones.
","language":"English","publisher":"American Geophysical Union","doi":"10.1002/2016JE005133","usgsCitation":"Lapotre, M.G., Ehlmann, B.L., Minson, S.E., Arvidson, R., Ayoub, F., Fraeman, A.A., Ewing, R.C., and Bridges, N.T., 2017, Compositional variations in sands of the Bagnold Dunes, Gale Crater, Mars, from visible-shortwave infrared spectroscopy and comparison with ground truth from the Curiosity Rover: Journal of Geophysical Research E: Planets, v. 122, no. 12, p. 2489-2509, https://doi.org/10.1002/2016JE005133.","productDescription":"21 p.","startPage":"2489","endPage":"2509","ipdsId":"IP-081365","costCenters":[{"id":237,"text":"Earthquake Science Center","active":true,"usgs":true}],"links":[{"id":469382,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.1002/2016je005133","text":"Publisher Index Page"},{"id":347892,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"volume":"122","issue":"12","publishingServiceCenter":{"id":14,"text":"Menlo Park PSC"},"noUsgsAuthors":false,"publicationDate":"2017-12-07","publicationStatus":"PW","scienceBaseUri":"59f98bb2e4b0531197af9fd8","contributors":{"authors":[{"text":"Lapotre, Mathieu G.A.","contributorId":198421,"corporation":false,"usgs":false,"family":"Lapotre","given":"Mathieu","email":"","middleInitial":"G.A.","affiliations":[{"id":16811,"text":"Harvard University","active":true,"usgs":false}],"preferred":false,"id":715994,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Ehlmann, B. L.","contributorId":198422,"corporation":false,"usgs":false,"family":"Ehlmann","given":"B.","email":"","middleInitial":"L.","affiliations":[],"preferred":false,"id":715995,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Minson, Sarah E. 0000-0001-5869-3477 sminson@usgs.gov","orcid":"https://orcid.org/0000-0001-5869-3477","contributorId":5357,"corporation":false,"usgs":true,"family":"Minson","given":"Sarah","email":"sminson@usgs.gov","middleInitial":"E.","affiliations":[{"id":237,"text":"Earthquake Science Center","active":true,"usgs":true}],"preferred":true,"id":715993,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Arvidson, R. E.","contributorId":198423,"corporation":false,"usgs":false,"family":"Arvidson","given":"R. E.","affiliations":[],"preferred":false,"id":715996,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Ayoub, F.","contributorId":198424,"corporation":false,"usgs":false,"family":"Ayoub","given":"F.","email":"","affiliations":[],"preferred":false,"id":715997,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"Fraeman, A. A.","contributorId":198425,"corporation":false,"usgs":false,"family":"Fraeman","given":"A.","email":"","middleInitial":"A.","affiliations":[],"preferred":false,"id":715998,"contributorType":{"id":1,"text":"Authors"},"rank":6},{"text":"Ewing, R. C.","contributorId":198426,"corporation":false,"usgs":false,"family":"Ewing","given":"R.","email":"","middleInitial":"C.","affiliations":[],"preferred":false,"id":715999,"contributorType":{"id":1,"text":"Authors"},"rank":7},{"text":"Bridges, N. T.","contributorId":198427,"corporation":false,"usgs":false,"family":"Bridges","given":"N.","email":"","middleInitial":"T.","affiliations":[],"preferred":false,"id":716000,"contributorType":{"id":1,"text":"Authors"},"rank":8}]}} ,{"id":70193218,"text":"70193218 - 2017 - Origin of discrepancies between crater size-frequency distributions of coeval lunar geologic units via target property contrasts","interactions":[],"lastModifiedDate":"2018-11-01T14:41:00","indexId":"70193218","displayToPublicDate":"2017-10-31T00:00:00","publicationYear":"2017","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":1963,"text":"Icarus","active":true,"publicationSubtype":{"id":10}},"title":"Origin of discrepancies between crater size-frequency distributions of coeval lunar geologic units via target property contrasts","docAbstract":"Recent work on dating Copernican-aged craters, using Lunar Reconnaissance Orbiter (LRO) Camera data, re-encountered a curious discrepancy in crater size-frequency distribution (CSFD) measurements that was observed, but not understood, during the Apollo era. For example, at Tycho, Copernicus, and Aristarchus craters, CSFDs of impact melt deposits give significantly younger relative and absolute model ages (AMAs) than impact ejecta blankets, although these two units formed during one impact event, and would ideally yield coeval ages at the resolution of the CSFD technique. We investigated the effects of contrasting target properties on CSFDs and their resultant relative and absolute model ages for coeval lunar impact melt and ejecta units. We counted craters with diameters through the transition from strength- to gravity-scaling on two large impact melt deposits at Tycho and King craters, and we used pi-group scaling calculations to model the effects of differing target properties on final crater diameters for five different theoretical lunar targets. The new CSFD for the large King Crater melt pond bridges the gap between the discrepant CSFDs within a single geologic unit. Thus, the observed trends in the impact melt CSFDs support the occurrence of target property effects, rather than self-secondary and/or field secondary contamination. The CSFDs generated from the pi-group scaling calculations show that targets with higher density and effective strength yield smaller crater diameters than weaker targets, such that the relative ages of the former are lower relative to the latter. Consequently, coeval impact melt and ejecta units will have discrepant apparent ages. Target property differences also affect the resulting slope of the CSFD, with stronger targets exhibiting shallower slopes, so that the final crater diameters may differ more greatly at smaller diameters. Besides their application to age dating, the CSFDs may provide additional information about the characteristics of the target. For example, the transition diameter from strength- to gravity-scaling could provide a tool for investigating the relative strengths of different geologic units. The magnitude of the offset between the impact melt and ejecta isochrons may also provide information about the relative target properties and/or exposure/degradation ages of the two units. Robotic or human sampling of coeval units on the Moon could provide a direct test of the importance and magnitude of target property effects on CSFDs.
","language":"English","publisher":"Elsevier","doi":"10.1016/j.icarus.2016.11.040","usgsCitation":"Van der Bogert, C.H., Hiesinger, H., Dundas, C.M., Kruger, T., McEwen, A.S., Zanetti, M., and Robinson, M.S., 2017, Origin of discrepancies between crater size-frequency distributions of coeval lunar geologic units via target property contrasts: Icarus, v. 298, p. 49-63, https://doi.org/10.1016/j.icarus.2016.11.040.","productDescription":"14 p.","startPage":"49","endPage":"63","ipdsId":"IP-067339","costCenters":[{"id":131,"text":"Astrogeology Science Center","active":true,"usgs":true}],"links":[{"id":347822,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"volume":"298","publishingServiceCenter":{"id":14,"text":"Menlo Park PSC"},"noUsgsAuthors":false,"publicationStatus":"PW","scienceBaseUri":"59f98babe4b0531197af9fb0","contributors":{"authors":[{"text":"Van der Bogert, Carolyn H.","contributorId":199120,"corporation":false,"usgs":false,"family":"Van der Bogert","given":"Carolyn","email":"","middleInitial":"H.","affiliations":[],"preferred":false,"id":718237,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Hiesinger, Harald","contributorId":172686,"corporation":false,"usgs":false,"family":"Hiesinger","given":"Harald","email":"","affiliations":[{"id":27080,"text":"Institut für Planetologie, Westfälische Wilhelms-Universität, Münster","active":true,"usgs":false}],"preferred":false,"id":718238,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"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":718236,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Kruger, T.","contributorId":199121,"corporation":false,"usgs":false,"family":"Kruger","given":"T.","email":"","affiliations":[],"preferred":false,"id":718239,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"McEwen, Alfred S.","contributorId":61657,"corporation":false,"usgs":false,"family":"McEwen","given":"Alfred","email":"","middleInitial":"S.","affiliations":[{"id":7042,"text":"University of Arizona","active":true,"usgs":false}],"preferred":false,"id":718241,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"Zanetti, Michael","contributorId":199122,"corporation":false,"usgs":false,"family":"Zanetti","given":"Michael","email":"","affiliations":[],"preferred":false,"id":718240,"contributorType":{"id":1,"text":"Authors"},"rank":6},{"text":"Robinson, Mark S.","contributorId":167665,"corporation":false,"usgs":false,"family":"Robinson","given":"Mark","email":"","middleInitial":"S.","affiliations":[{"id":6607,"text":"Arizona State University","active":true,"usgs":false}],"preferred":false,"id":718242,"contributorType":{"id":1,"text":"Authors"},"rank":7}]}} ,{"id":70193244,"text":"70193244 - 2017 - Lidar aboveground vegetation biomass estimates in shrublands: Prediction, uncertainties and application to coarser scales","interactions":[],"lastModifiedDate":"2017-11-22T16:40:44","indexId":"70193244","displayToPublicDate":"2017-10-31T00:00:00","publicationYear":"2017","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":3250,"text":"Remote Sensing","active":true,"publicationSubtype":{"id":10}},"title":"Lidar aboveground vegetation biomass estimates in shrublands: Prediction, uncertainties and application to coarser scales","docAbstract":"Our study objectives were to model the aboveground biomass in a xeric shrub-steppe landscape with airborne light detection and ranging (Lidar) and explore the uncertainty associated with the models we created. We incorporated vegetation vertical structure information obtained from Lidar with ground-measured biomass data, allowing us to scale shrub biomass from small field sites (1 m subplots and 1 ha plots) to a larger landscape. A series of airborne Lidar-derived vegetation metrics were trained and linked with the field-measured biomass in Random Forests (RF) regression models. A Stepwise Multiple Regression (SMR) model was also explored as a comparison. Our results demonstrated that the important predictors from Lidar-derived metrics had a strong correlation with field-measured biomass in the RF regression models with a pseudo R2 of 0.76 and RMSE of 125 g/m2 for shrub biomass and a pseudo R2 of 0.74 and RMSE of 141 g/m2 for total biomass, and a weak correlation with field-measured herbaceous biomass. The SMR results were similar but slightly better than RF, explaining 77–79% of the variance, with RMSE ranging from 120 to 129 g/m2 for shrub and total biomass, respectively. We further explored the computational efficiency and relative accuracies of using point cloud and raster Lidar metrics at different resolutions (1 m to 1 ha). Metrics derived from the Lidar point cloud processing led to improved biomass estimates at nearly all resolutions in comparison to raster-derived Lidar metrics. Only at 1 m were the results from the point cloud and raster products nearly equivalent. The best Lidar prediction models of biomass at the plot-level (1 ha) were achieved when Lidar metrics were derived from an average of fine resolution (1 m) metrics to minimize boundary effects and to smooth variability. Overall, both RF and SMR methods explained more than 74% of the variance in biomass, with the most important Lidar variables being associated with vegetation structure and statistical measures of this structure (e.g., standard deviation of height was a strong predictor of biomass). Using our model results, we developed spatially-explicit Lidar estimates of total and shrub biomass across our study site in the Great Basin, U.S.A., for monitoring and planning in this imperiled ecosystem.
","language":"English","publisher":" MDPI AG","doi":"10.3390/rs9090903","usgsCitation":"Li, A., Dhakal, S., Glenn, N.F., Spaete, L.P., Shinneman, D.J., Pilliod, D.S., Arkle, R., and McIlroy, S., 2017, Lidar aboveground vegetation biomass estimates in shrublands: Prediction, uncertainties and application to coarser scales: Remote Sensing, v. 9, 903; 19 p., https://doi.org/10.3390/rs9090903.","productDescription":"903; 19 p.","ipdsId":"IP-087544","costCenters":[{"id":290,"text":"Forest and Rangeland Ecosystem Science Center","active":false,"usgs":true}],"links":[{"id":469380,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.3390/rs9090903","text":"Publisher Index Page"},{"id":347848,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","state":"Idaho","otherGeospatial":"Morley Nelson Snake River Birds of Prey National Conservation Area","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -116.66,\n 42.71473218539458\n ],\n [\n -115,\n 42.71473218539458\n ],\n [\n -115,\n 43.929549935614595\n ],\n [\n -116.66,\n 43.929549935614595\n ],\n [\n -116.66,\n 42.71473218539458\n ]\n ]\n ]\n }\n }\n ]\n}","volume":"9","publishingServiceCenter":{"id":12,"text":"Tacoma PSC"},"noUsgsAuthors":false,"publicationDate":"2017-08-31","publicationStatus":"PW","scienceBaseUri":"59f98ba8e4b0531197af9fa0","contributors":{"authors":[{"text":"Li, Aihua","contributorId":169445,"corporation":false,"usgs":false,"family":"Li","given":"Aihua","email":"","affiliations":[{"id":16201,"text":"Boise State University","active":true,"usgs":false}],"preferred":false,"id":718350,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Dhakal, Shital","contributorId":199163,"corporation":false,"usgs":false,"family":"Dhakal","given":"Shital","email":"","affiliations":[],"preferred":false,"id":718352,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Glenn, Nancy F.","contributorId":195241,"corporation":false,"usgs":false,"family":"Glenn","given":"Nancy","email":"","middleInitial":"F.","affiliations":[],"preferred":false,"id":718351,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Spaete, Luke P.","contributorId":199164,"corporation":false,"usgs":false,"family":"Spaete","given":"Luke","email":"","middleInitial":"P.","affiliations":[],"preferred":false,"id":718353,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Shinneman, Douglas J. 0000-0002-4909-5181 dshinneman@usgs.gov","orcid":"https://orcid.org/0000-0002-4909-5181","contributorId":147745,"corporation":false,"usgs":true,"family":"Shinneman","given":"Douglas","email":"dshinneman@usgs.gov","middleInitial":"J.","affiliations":[{"id":289,"text":"Forest and Rangeland Ecosys Science Center","active":true,"usgs":true},{"id":290,"text":"Forest and Rangeland Ecosystem Science Center","active":false,"usgs":true}],"preferred":true,"id":718349,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"Pilliod, David S. 0000-0003-4207-3518 dpilliod@usgs.gov","orcid":"https://orcid.org/0000-0003-4207-3518","contributorId":149254,"corporation":false,"usgs":true,"family":"Pilliod","given":"David","email":"dpilliod@usgs.gov","middleInitial":"S.","affiliations":[{"id":289,"text":"Forest and Rangeland Ecosys Science Center","active":true,"usgs":true},{"id":290,"text":"Forest and Rangeland Ecosystem Science Center","active":false,"usgs":true}],"preferred":true,"id":718354,"contributorType":{"id":1,"text":"Authors"},"rank":6},{"text":"Arkle, Robert 0000-0003-3021-1389 rarkle@usgs.gov","orcid":"https://orcid.org/0000-0003-3021-1389","contributorId":149893,"corporation":false,"usgs":true,"family":"Arkle","given":"Robert","email":"rarkle@usgs.gov","affiliations":[{"id":290,"text":"Forest and Rangeland Ecosystem Science Center","active":false,"usgs":true},{"id":289,"text":"Forest and Rangeland Ecosys Science Center","active":true,"usgs":true}],"preferred":true,"id":718355,"contributorType":{"id":1,"text":"Authors"},"rank":7},{"text":"McIlroy, Susan K. 0000-0001-5088-3700 smcilroy@usgs.gov","orcid":"https://orcid.org/0000-0001-5088-3700","contributorId":169446,"corporation":false,"usgs":true,"family":"McIlroy","given":"Susan","email":"smcilroy@usgs.gov","middleInitial":"K.","affiliations":[{"id":290,"text":"Forest and Rangeland Ecosystem Science Center","active":false,"usgs":true},{"id":289,"text":"Forest and Rangeland Ecosys Science Center","active":true,"usgs":true}],"preferred":true,"id":718356,"contributorType":{"id":1,"text":"Authors"},"rank":8}]}} ,{"id":70192201,"text":"fs20173072 - 2017 - FEQinput—An editor for the full equations (FEQ) hydraulic modeling system","interactions":[],"lastModifiedDate":"2017-10-30T13:18:34","indexId":"fs20173072","displayToPublicDate":"2017-10-30T11:15:00","publicationYear":"2017","noYear":false,"publicationType":{"id":18,"text":"Report"},"publicationSubtype":{"id":5,"text":"USGS Numbered Series"},"seriesTitle":{"id":313,"text":"Fact Sheet","code":"FS","onlineIssn":"2327-6932","printIssn":"2327-6916","active":true,"publicationSubtype":{"id":5}},"seriesNumber":"2017-3072","title":"FEQinput—An editor for the full equations (FEQ) hydraulic modeling system","docAbstract":"The Full Equations Model (FEQ) is a computer program that solves the full, dynamic equations of motion for one-dimensional unsteady hydraulic flow in open channels and through control structures. As a result, hydrologists have used FEQ to design and operate flood-control structures, delineate inundation maps, and analyze peak-flow impacts. To aid in fighting floods, hydrologists are using the software to develop a system that uses flood-plain models to simulate real-time streamflow.
Input files for FEQ are composed of text files that contain large amounts of parameters, data, and instructions that are written in a format exclusive to FEQ. Although documentation exists that can aid in the creation and editing of these input files, new users face a steep learning curve in order to understand the specific format and language of the files.
FEQinput provides a set of tools to help a new user overcome the steep learning curve associated with creating and modifying input files for the FEQ hydraulic model and the related utility tool, Full Equations Utilities (FEQUTL).
","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/fs20173072","usgsCitation":"Ancalle, D.S., Ancalle, P.J., and Domanski, M.M., 2017, FEQinput—An editor for the full equations (FEQ) hydraulic modeling system: U.S. Geological Survey Fact Sheet 2017–3072, 4 p., https://doi.org/10.3133/fs20173072.","productDescription":"Report: 4 p.; Project Site","onlineOnly":"N","additionalOnlineFiles":"N","ipdsId":"IP-082519","costCenters":[{"id":344,"text":"Illinois Water Science Center","active":true,"usgs":true}],"links":[{"id":347141,"rank":2,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/fs/2017/3072/fs20173072.pdf","text":"Report","size":"770 KB","linkFileType":{"id":1,"text":"pdf"},"description":"FS 2017-3072"},{"id":347345,"rank":3,"type":{"id":18,"text":"Project Site"},"url":"https://il.water.usgs.gov/proj/feq/software/feqinput/","text":"Software"},{"id":347140,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/fs/2017/3072/coverthb2.jpg"}],"contact":"Director, Illinois-Iowa Water Science Center
U.S. Geological Survey
405 North Goodwin Avenue
Urbana, IL 61801
Water temperature is an important factor determining estuarine species habitat conditions. Water temperature is mainly governed by advection (e.g., from rivers) and atmospheric exchange processes varying strongly over time (day-night, seasonally) and the spatial domain. On a long time scale, climate change will impact water temperature in estuarine systems due to changes in river flow regimes, air temperature, and sea level rise. To determine which factors govern estuarine water temperature and its sensitivity to changes in its forcing, we developed a process-based numerical model (Delft3D Flexible Mesh) and applied it to a well-monitored estuarine system (the San Francisco Estuary) for validation. The process-based approach allows for detailed process description and a physics-based analysis of governing processes. The model was calibrated for water year 2011 and incorporated 3-D hydrodynamics, salinity intrusion, water temperature dynamics, and atmospheric coupling. Results show significant skill in reproducing temperature observations on daily, seasonal, and yearly time scales. In North San Francisco Bay, thermal stratification is present, enhanced by salinity stratification. The temperature of the upstream, fresh water Delta area is captured well in 2-D mode, although locally—on a small scale—vertical processes (e.g., stratification) may be important. The impact of upstream river temperature and discharge and atmospheric forcing on water temperatures differs throughout the Delta, possibly depending on dispersion and residence times. Our modeling effort provides a sound basis for future modeling studies including climate change impact on water temperature and associated ecological modeling, e.g., clam and fish habitat and phytoplankton dynamics.
","language":"English","publisher":"American Geophysical Union","doi":"10.1002/2016WR020062","usgsCitation":"Vroom, J., Van der Wegen, M., Martyr-Koller, R.C., and Lucas, L., 2017, What determines water temperature dynamics in the San Francisco Bay-Delta system?: Water Resources Research, v. 53, no. 11, p. 9901-9921, https://doi.org/10.1002/2016WR020062.","productDescription":"21 p.","startPage":"9901","endPage":"9921","ipdsId":"IP-081741","costCenters":[{"id":37778,"text":"WMA - Integrated Modeling and Prediction Division","active":true,"usgs":true}],"links":[{"id":469384,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.1002/2016wr020062","text":"Publisher Index Page"},{"id":386639,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","state":"California","city":"San Francisco","otherGeospatial":"San Francisco Bay Delta","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -122.73925781250001,\n 37.23470197166817\n ],\n [\n -121.67907714843751,\n 37.23470197166817\n ],\n [\n -121.67907714843751,\n 38.302869955150044\n ],\n [\n -122.73925781250001,\n 38.302869955150044\n ],\n [\n -122.73925781250001,\n 37.23470197166817\n ]\n ]\n ]\n }\n }\n ]\n}","volume":"53","issue":"11","noUsgsAuthors":false,"publicationDate":"2017-11-30","publicationStatus":"PW","contributors":{"authors":[{"text":"Vroom, Julia 0000-0001-5354-8780","orcid":"https://orcid.org/0000-0001-5354-8780","contributorId":260502,"corporation":false,"usgs":false,"family":"Vroom","given":"Julia","email":"","affiliations":[{"id":36257,"text":"Deltares","active":true,"usgs":false}],"preferred":false,"id":818034,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Van der Wegen, Mick","contributorId":191095,"corporation":false,"usgs":false,"family":"Van der Wegen","given":"Mick","email":"","affiliations":[],"preferred":false,"id":818035,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Martyr-Koller, Rosanne C. 0000-0002-0506-667X","orcid":"https://orcid.org/0000-0002-0506-667X","contributorId":260505,"corporation":false,"usgs":false,"family":"Martyr-Koller","given":"Rosanne","email":"","middleInitial":"C.","affiliations":[{"id":13243,"text":"University of California Berkeley","active":true,"usgs":false}],"preferred":false,"id":818036,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Lucas, Lisa 0000-0001-7797-5517 llucas@usgs.gov","orcid":"https://orcid.org/0000-0001-7797-5517","contributorId":260498,"corporation":false,"usgs":true,"family":"Lucas","given":"Lisa","email":"llucas@usgs.gov","affiliations":[{"id":37778,"text":"WMA - Integrated Modeling and Prediction Division","active":true,"usgs":true}],"preferred":true,"id":818037,"contributorType":{"id":1,"text":"Authors"},"rank":4}]}} ,{"id":70192830,"text":"70192830 - 2017 - Was the Mw 7.5 1952 Kern County, California, earthquake induced (or triggered)?","interactions":[],"lastModifiedDate":"2017-11-29T16:13:55","indexId":"70192830","displayToPublicDate":"2017-10-30T00:00:00","publicationYear":"2017","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":2453,"text":"Journal of Seismology","active":true,"publicationSubtype":{"id":10}},"title":"Was the Mw 7.5 1952 Kern County, California, earthquake induced (or triggered)?","docAbstract":"Several recent studies have presented evidence that significant induced earthquakes occurred in a number of oil-producing regions during the early and mid-twentieth century related to either production or wastewater injection. We consider whether the 21 July 1952 Mw 7.5 Kern County earthquake might have been induced by production in the Wheeler Ridge oil field. The mainshock, which was not preceded by any significant foreshocks, occurred 98 days after the initial production of oil in Eocene strata at depths reaching 3 km, within ~1 km of the White Wolf fault (WWF). Based on this spatial and temporal proximity, we explore a potential causal relationship between the earthquake and oil production. While production would have normally be expected to have reduced pore pressure, inhibiting failure on the WWF, we present an analytical model based on industry stratigraphic data and best estimates of parameters whereby an impermeable splay fault adjacent to the main WWF could plausibly have blocked direct pore pressure effects, allowing the poroelastic stress change associated with production to destabilize the WWF, promoting initial failure. This proof-of-concept model can also account for the 98-day delay between the onset of production and the earthquake. While the earthquake clearly released stored tectonic stress, any initial perturbation on or near a major fault system can trigger a larger rupture. Our proposed mechanism provides an explanation for why significant earthquakes are not commonly induced by production in proximity to major faults.
","language":"English","publisher":"Springer","doi":"10.1007/s10950-017-9685-x","usgsCitation":"Hough, S.E., Tsai, V., Walker, R., and Aminzadeh, F., 2017, Was the Mw 7.5 1952 Kern County, California, earthquake induced (or triggered)?: Journal of Seismology, v. 21, no. 6, p. 1613-1621, https://doi.org/10.1007/s10950-017-9685-x.","productDescription":"9 p.","startPage":"1613","endPage":"1621","ipdsId":"IP-081014","costCenters":[{"id":237,"text":"Earthquake Science Center","active":true,"usgs":true}],"links":[{"id":461369,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.1007/s10950-017-9685-x","text":"Publisher Index Page"},{"id":347754,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","state":"California","county":"Kern County","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -119.1667,\n 34.8333\n ],\n [\n -118.3333,\n 34.8333\n ],\n [\n -118.3333,\n 35.3333\n ],\n [\n -119.1667,\n 35.3333\n ],\n [\n -119.1667,\n 34.8333\n ]\n ]\n ]\n }\n }\n ]\n}","volume":"21","issue":"6","publishingServiceCenter":{"id":14,"text":"Menlo Park PSC"},"noUsgsAuthors":false,"publicationDate":"2017-10-02","publicationStatus":"PW","scienceBaseUri":"59f83a2de4b063d5d309808e","contributors":{"authors":[{"text":"Hough, Susan E. 0000-0002-5980-2986 hough@usgs.gov","orcid":"https://orcid.org/0000-0002-5980-2986","contributorId":587,"corporation":false,"usgs":true,"family":"Hough","given":"Susan","email":"hough@usgs.gov","middleInitial":"E.","affiliations":[{"id":237,"text":"Earthquake Science Center","active":true,"usgs":true}],"preferred":true,"id":717094,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Tsai, Victor C. 0000-0003-1809-6672","orcid":"https://orcid.org/0000-0003-1809-6672","contributorId":87675,"corporation":false,"usgs":true,"family":"Tsai","given":"Victor C.","affiliations":[],"preferred":false,"id":717095,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Walker, Robert","contributorId":198764,"corporation":false,"usgs":false,"family":"Walker","given":"Robert","affiliations":[],"preferred":false,"id":717096,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Aminzadeh, Fred","contributorId":198765,"corporation":false,"usgs":false,"family":"Aminzadeh","given":"Fred","email":"","affiliations":[],"preferred":false,"id":717097,"contributorType":{"id":1,"text":"Authors"},"rank":4}]}} ,{"id":70192766,"text":"70192766 - 2017 - Examining the value of global seasonal reference evapotranspiration forecasts to support FEWS NET’s food insecurity outlooks","interactions":[],"lastModifiedDate":"2018-02-21T14:09:57","indexId":"70192766","displayToPublicDate":"2017-10-30T00:00:00","publicationYear":"2017","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":5202,"text":"Journal of Applied Meteorology and Climatology","onlineIssn":"1558-8432","printIssn":"1558-8424","active":true,"publicationSubtype":{"id":10}},"title":"Examining the value of global seasonal reference evapotranspiration forecasts to support FEWS NET’s food insecurity outlooks","docAbstract":"The Famine Early Warning Systems Network (FEWS NET) team provides food insecurity outlooks for several developing countries in Africa, Central Asia, and Central America. This study describes development of a new global reference evapotranspiration (ETo) seasonal reforecast and skill evaluation with a particular emphasis on the potential use of this dataset by the FEWS NET to support food insecurity early warning. The ETo reforecasts span the 1982-2009 period and are calculated following ASCE’s formulation of Penman-Monteith method driven by seasonal climate forecasts of monthly mean temperature, humidity, wind speed, and solar radiation from NCEP’s CFSv2 and NASA’s GEOS-5 models. The skill evaluation using deterministic and probabilistic scores, focuses on the December-February (DJF), March-May (MAM), June-August (JJA) and September-November (SON) seasons. The results indicate that ETo forecasts are a promising tool for early warning of drought and food insecurity. Globally, the regions where forecasts are most skillful (correlation >0.35 at lead-2) include Western U.S., northern parts of South America, parts of Sahel region and Southern Africa. The FEWS NET regions where forecasts are most skillful (correlation >0.35 at lead-3) include Northern Sub-Saharan Africa (DJF, dry season), Central America (DJF, dry season), parts of East Africa (JJA, wet Season), Southern Africa (JJA, dry season), and Central Asia (MAM, wet season). A case study over parts of East Africa for the JJA season shows that ETo forecasts in combination with the precipitation forecasts could have provided early warning of recent severe drought events (e.g., 2002, 2004, 2009) that contributed to substantial food insecurity in the region.
","language":"English","publisher":"American Meteorological Society","doi":"10.1175/jamc-d-17-0104.1","usgsCitation":"Shukla, S., McEvoy, D., Hobbins, M., Husak, G., Huntington, J., Funk, C., Macharia, D., and Verdin, J.P., 2017, Examining the value of global seasonal reference evapotranspiration forecasts to support FEWS NET’s food insecurity outlooks: Journal of Applied Meteorology and Climatology, v. 56, p. 2941-2949, https://doi.org/10.1175/jamc-d-17-0104.1.","productDescription":"9 p.","startPage":"2941","endPage":"2949","ipdsId":"IP-090049","costCenters":[{"id":222,"text":"Earth Resources Observation and Science (EROS) Center","active":true,"usgs":true}],"links":[{"id":461371,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.1175/jamc-d-17-0104.1","text":"Publisher Index Page"},{"id":347733,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"volume":"56","publishingServiceCenter":{"id":4,"text":"Rolla PSC"},"noUsgsAuthors":false,"publicationStatus":"PW","scienceBaseUri":"59f83a2ee4b063d5d3098096","contributors":{"authors":[{"text":"Shukla, Shraddhanand","contributorId":145841,"corporation":false,"usgs":false,"family":"Shukla","given":"Shraddhanand","affiliations":[{"id":16255,"text":"Climate Hazards Group University of California Santa Barbara","active":true,"usgs":false}],"preferred":false,"id":716858,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"McEvoy, Daniel 0000-0003-3800-718X","orcid":"https://orcid.org/0000-0003-3800-718X","contributorId":198696,"corporation":false,"usgs":false,"family":"McEvoy","given":"Daniel","email":"","affiliations":[],"preferred":false,"id":716859,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Hobbins, Michael","contributorId":127605,"corporation":false,"usgs":false,"family":"Hobbins","given":"Michael","email":"","affiliations":[{"id":7075,"text":"National Integrated Drought Information System, Boulder, CO","active":true,"usgs":false}],"preferred":false,"id":716860,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Husak, Gregory","contributorId":145811,"corporation":false,"usgs":false,"family":"Husak","given":"Gregory","affiliations":[{"id":16236,"text":"UCSB Climate Hazards Group","active":true,"usgs":false}],"preferred":false,"id":716861,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Huntington, Justin 0000-0002-2699-0108","orcid":"https://orcid.org/0000-0002-2699-0108","contributorId":178785,"corporation":false,"usgs":false,"family":"Huntington","given":"Justin","affiliations":[],"preferred":false,"id":716862,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"Funk, Chris 0000-0002-9254-6718 cfunk@usgs.gov","orcid":"https://orcid.org/0000-0002-9254-6718","contributorId":167070,"corporation":false,"usgs":true,"family":"Funk","given":"Chris","email":"cfunk@usgs.gov","affiliations":[{"id":223,"text":"Earth Resources Observation and Science (EROS) Center (Geography)","active":false,"usgs":true},{"id":222,"text":"Earth Resources Observation and Science (EROS) Center","active":true,"usgs":true}],"preferred":true,"id":716857,"contributorType":{"id":1,"text":"Authors"},"rank":6},{"text":"Macharia, Denis","contributorId":195985,"corporation":false,"usgs":false,"family":"Macharia","given":"Denis","email":"","affiliations":[],"preferred":false,"id":717864,"contributorType":{"id":1,"text":"Authors"},"rank":7},{"text":"Verdin, James P. 0000-0003-0238-9657 verdin@usgs.gov","orcid":"https://orcid.org/0000-0003-0238-9657","contributorId":720,"corporation":false,"usgs":true,"family":"Verdin","given":"James","email":"verdin@usgs.gov","middleInitial":"P.","affiliations":[{"id":223,"text":"Earth Resources Observation and Science (EROS) Center (Geography)","active":false,"usgs":true}],"preferred":false,"id":717865,"contributorType":{"id":1,"text":"Authors"},"rank":8}]}} ,{"id":70193051,"text":"ofr20171135 - 2017 - Near-field receiving water monitoring of trace metals and a benthic community near the Palo Alto Regional Water Quality Control Plant in south San Francisco Bay, California; 2016","interactions":[],"lastModifiedDate":"2023-04-24T21:14:41.275526","indexId":"ofr20171135","displayToPublicDate":"2017-10-30T00:00:00","publicationYear":"2017","noYear":false,"publicationType":{"id":18,"text":"Report"},"publicationSubtype":{"id":5,"text":"USGS Numbered Series"},"seriesTitle":{"id":330,"text":"Open-File Report","code":"OFR","onlineIssn":"2331-1258","printIssn":"0196-1497","active":true,"publicationSubtype":{"id":5}},"seriesNumber":"2017-1135","title":"Near-field receiving water monitoring of trace metals and a benthic community near the Palo Alto Regional Water Quality Control Plant in south San Francisco Bay, California; 2016","docAbstract":"Trace-metal concentrations in sediment and in the clam Macoma petalum (formerly reported as Macoma balthica), clam reproductive activity, and benthic macroinvertebrate community structure were investigated in a mudflat 1 kilometer south of the discharge of the Palo Alto Regional Water Quality Control Plant (PARWQCP) in south San Francisco Bay, Calif. This report includes the data collected by U.S. Geological Survey (USGS) scientists for the period January 2014 to December 2016. These append to long-term datasets extending back to 1974. A major focus of the report is an integrated description of the 2016 data within the context of the longer, multi-decadal dataset. This dataset supports the City of Palo Alto’s Near-Field Receiving Water Monitoring Program, initiated in 1994.
Significant reductions in silver and copper concentrations in sediment and M. petalum occurred at the site in the 1980s following the implementation by PARWQCP of advanced wastewater treatment and source control measures. Since the 1990s, concentrations of these elements appear to have stabilized at concentrations somewhat above (silver) or near (copper) regional background concentrations Data for other metals, including chromium (Cr), mercury (Hg), nickel (Ni), selenium (Se), and zinc (Zn), have been collected since 1994. Over this period, concentrations of these elements have remained relatively constant, aside from seasonal variation that is common to all elements. In 2016, concentrations of silver and copper in M. petalum varied seasonally in response to a combination of site-specific metal exposures and annual growth and reproduction, as reported previously. Seasonal patterns for other elements, including Cr, Ni, Zn, Hg, and Se, were generally similar in timing and magnitude as those for Ag and Cu. This record suggests that legacy contamination and regional-scale factors now largely control sedimentary and bioavailable concentrations of silver and copper, as well as other elements of regulatory interest, at the Palo Alto site.
Analyses of the benthic community structure of a mudflat in south San Francisco Bay over a 40-year period show that changes in the community have occurred concurrent with reduced concentrations of metals in the sediment and in the tissues of the biosentinel clam, M. petalum, from the same area. Analysis of M. petalum shows increases in reproductive activity concurrent with the decline in metal concentrations in the tissues of this organism. Reproductive activity is presently stable (2016), with almost all animals initiating reproduction in the fall and spawning the following spring. The entire infaunal community has shifted from being dominated by several opportunistic species to a community where the species are more similar in abundance, a pattern that indicates a more stable community that is subjected to fewer stressors. In addition, two of the opportunistic species (Ampelisca abdita and Streblospio benedicti) that brood their young and live on the surface of the sediment in tubes have shown a continual decline in dominance coincident with the decline in metals; both species had short-lived rebounds in abundance in 2008, 2009, and 2010 and showed signs of increasing abundance in 2016. Heteromastus filiformis (a subsurface polychaete worm that lives in the sediment, consumes sediment and organic particles residing in the sediment, and reproduces by laying its eggs on or in the sediment) showed a concurrent increase in dominance and, in the last several years before 2008, showed a stable population. H. filiformis abundance increased slightly in 2011–2012 and returned to pre-2011 numbers in 2016. An unidentified disturbance occurred on the mudflat in early 2008 that resulted in the loss of the benthic animals, except for deep-dwelling animals like Macoma petalum. However, within two months of this event animals returned to the mudflat. The resilience of the community suggested that the disturbance was not due to a persistent toxin or anoxia. The reproductive mode of most species present in 2016 is reflective of species that were available either as pelagic larvae or as mobile adults. Although oviparous species were lower in number in this group, the authors hypothesize that these species will return slowly as more species move back into the area. The use of functional ecology was highlighted in the 2016 benthic community data, which showed that the animals that have now returned to the mudflat are those that can respond successfully to a physical, nontoxic disturbance. Today, community data show a mix of species that consume the sediment, or filter feed, have pelagic larvae that must survive landing on the sediment, and those that brood their young. USGS scientists view the 2008 disturbance event as a response by the infaunal community to an episodic natural stressor (possibly sediment accretion or a pulse of freshwater), in contrast to the long-term recovery from metal contamination. We will compare this recovery to the long-term recovery observed after the 1970s when the decline in sediment pollutants was the dominating factor.
","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/ofr20171135","collaboration":"Prepared in cooperation with the City of Palo Alto, California","usgsCitation":"Cain, D.J., Thompson, J.K., Parchaso, F., Pearson, S., Stewart, R., Turner, M., Barasch, D., and Luoma, S.N., 2017, Near-field receiving water monitoring of trace metals and a benthic community near the Palo Alto Regional Water Quality Control Plant in south San Francisco Bay, California; 2016: U.S. Geological Survey Open-File Report 2017–1135, 75 p., https://doi.org/10.3133/ofr20171135.","productDescription":"vi, 75 p.","numberOfPages":"82","onlineOnly":"Y","ipdsId":"IP-088104","costCenters":[{"id":438,"text":"National Research Program - Western Branch","active":true,"usgs":true}],"links":[{"id":416202,"rank":7,"type":{"id":22,"text":"Related Work"},"url":"https://doi.org/10.3133/ofr20231017","text":"Open-File Report 2023-1017","linkHelpText":"- Near-Field Receiving-Water Monitoring of Trace Metals and a Benthic Community Near the Palo Alto Regional Water Quality Control Plant in South San Francisco Bay, California—2020"},{"id":416201,"rank":6,"type":{"id":22,"text":"Related Work"},"url":"https://doi.org/10.3133/ofr20211079","text":"Open-File Report 2021-1079","linkHelpText":"- Near-Field Receiving-Water Monitoring of Trace Metals and a Benthic Community Near the Palo Alto Regional Water Quality Control Plant in South San Francisco Bay, California—2019"},{"id":416200,"rank":5,"type":{"id":22,"text":"Related Work"},"url":"https://doi.org/10.3133/ofr20191084","text":"Open-File Report 2019-1084","linkHelpText":"- Near-Field Receiving-Water Monitoring of Trace Metals and a Benthic Community Near the Palo Alto Regional Water Quality Control Plant in South San Francisco Bay, California—2018"},{"id":416199,"rank":4,"type":{"id":22,"text":"Related Work"},"url":"https://doi.org/10.3133/ofr20181107","text":"Open-File Report 2018-1107","linkHelpText":"- Near-field receiving-water monitoring of trace metals and a benthic community near the Palo Alto Regional Water Quality Control Plant in south San Francisco Bay, California—2017"},{"id":416198,"rank":3,"type":{"id":22,"text":"Related Work"},"url":"https://doi.org/10.3133/ofr20161118","text":"Open-File Report 2016-1118","linkHelpText":"- Near-field receiving water monitoring of trace metals and a benthic community near the Palo Alto Regional Water Quality Control Plant in south San Francisco Bay, California; 2015"},{"id":347750,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/of/2017/1135/coverthb_.jpg"},{"id":347751,"rank":2,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/of/2017/1135/ofr.20171135.pdf","text":"Report","size":"4.1 MB","linkFileType":{"id":1,"text":"pdf"},"description":"OFR 2017-1135"}],"country":"United States","state":"California","city":"Palo Alto","otherGeospatial":"south San Francisco bay","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -122.16590881347656,\n 37.398528132728615\n ],\n [\n -121.91184997558595,\n 37.398528132728615\n ],\n [\n -121.91184997558595,\n 37.54566616715801\n ],\n [\n -122.16590881347656,\n 37.54566616715801\n ],\n [\n -122.16590881347656,\n 37.398528132728615\n ]\n ]\n ]\n }\n }\n ]\n}","contact":"NRP staff
National Research Program
U.S. Geological Survey
345 Middlefield Road, MS-435
Menlo Park, CA 94025
Bed stability is an important stream habitat attribute because it affects geomorphology and biotic communities. Natural resource managers desire indices of bed stability that can be used under a wide range of geomorphic conditions, are biologically meaningful, and are easily incorporated into sampling protocols. To eliminate potential bias due to presence of instream wood and increase precision of stability values, we modified a stream bed instability index (ISI) to include measurements of bankfull depth (dbf) and median particle diameter (D50) only in riffles and increased the pebble count to decrease variability (i.e., increase precision) in D50.The new riffle-based instability index (RISI) was compared to two established indices: ISI and the riffle stability index (RSI). RISI and ISI were strongly associated with each other but neither was closely associated with RSI. RISI and ISI were closely associated with both a diatom- and two macrovertebrate-based stream health indices, but RSI was only weakly associated with the macroinvertebrate indices. Unexpectedly, precision of D50 did not differ between RISI and ISI. Results suggest that RISI is a viable alternative to both ISI and RSI for evaluating bed stability in multiple stream types. With few data requirements and a simple protocol, RISI may also better conform to riffle-based sampling methods used by some water quality practitioners.
","language":"English","publisher":"Springer","doi":"10.1007/s10661-017-6291-x","usgsCitation":"Kusnierz, P., and Holbrook, C., 2017, Relative performance of three stream bed stability indices as indicators of stream health: Environmental Monitoring and Assessment, v. 189, p. 1-10, https://doi.org/10.1007/s10661-017-6291-x.","productDescription":"Article 563; 10 p.","startPage":"1","endPage":"10","ipdsId":"IP-090619","costCenters":[{"id":324,"text":"Great Lakes Science Center","active":true,"usgs":true}],"links":[{"id":347717,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","state":"Montana","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -114.71923828124999,\n 44.4808302785626\n ],\n [\n -110.61035156249999,\n 44.4808302785626\n ],\n [\n -110.61035156249999,\n 49.001843917978526\n ],\n [\n -114.71923828124999,\n 49.001843917978526\n ],\n [\n -114.71923828124999,\n 44.4808302785626\n ]\n ]\n ]\n }\n }\n ]\n}","volume":"189","publishingServiceCenter":{"id":6,"text":"Columbus PSC"},"noUsgsAuthors":false,"publicationDate":"2017-10-16","publicationStatus":"PW","scienceBaseUri":"59f83a2ce4b063d5d3098085","contributors":{"authors":[{"text":"Kusnierz, Paul C","contributorId":198849,"corporation":false,"usgs":false,"family":"Kusnierz","given":"Paul C","affiliations":[],"preferred":false,"id":717401,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Holbrook, Christopher M. 0000-0001-8203-6856 cholbrook@usgs.gov","orcid":"https://orcid.org/0000-0001-8203-6856","contributorId":139681,"corporation":false,"usgs":true,"family":"Holbrook","given":"Christopher","email":"cholbrook@usgs.gov","middleInitial":"M.","affiliations":[{"id":324,"text":"Great Lakes Science Center","active":true,"usgs":true}],"preferred":true,"id":717400,"contributorType":{"id":1,"text":"Authors"},"rank":2}]}} ,{"id":70191544,"text":"ofr20171119 - 2017 - Methods for converting continuous shrubland ecosystem component values to thematic National Land Cover Database classes","interactions":[],"lastModifiedDate":"2017-10-26T15:42:15","indexId":"ofr20171119","displayToPublicDate":"2017-10-26T00:00:00","publicationYear":"2017","noYear":false,"publicationType":{"id":18,"text":"Report"},"publicationSubtype":{"id":5,"text":"USGS Numbered Series"},"seriesTitle":{"id":330,"text":"Open-File Report","code":"OFR","onlineIssn":"2331-1258","printIssn":"0196-1497","active":true,"publicationSubtype":{"id":5}},"seriesNumber":"2017-1119","title":"Methods for converting continuous shrubland ecosystem component values to thematic National Land Cover Database classes","docAbstract":"The National Land Cover Database (NLCD) provides thematic land cover and land cover change data at 30-meter spatial resolution for the United States. Although the NLCD is considered to be the leading thematic land cover/land use product and overall classification accuracy across the NLCD is high, performance and consistency in the vast shrub and grasslands of the Western United States is lower than desired. To address these issues and fulfill the needs of stakeholders requiring more accurate rangeland data, the USGS has developed a method to quantify these areas in terms of the continuous cover of several cover components. These components include the cover of shrub, sagebrush (Artemisia spp), big sagebrush (Artemisia tridentata spp.), herbaceous, annual herbaceous, litter, and bare ground, and shrub and sagebrush height. To produce maps of component cover, we collected field data that were then associated with spectral values in WorldView-2 and Landsat imagery using regression tree models. The current report outlines the procedures and results of converting these continuous cover components to three thematic NLCD classes: barren, shrubland, and grassland. To accomplish this, we developed a series of indices and conditional models using continuous cover of shrub, bare ground, herbaceous, and litter as inputs. The continuous cover data are currently available for two large regions in the Western United States. Accuracy of the “cross-walked” product was assessed relative to that of NLCD 2011 at independent validation points (n=787) across these two regions. Overall thematic accuracy of the “cross-walked” product was 0.70, compared to 0.63 for NLCD 2011. The kappa value was considerably higher for the “cross-walked” product at 0.41 compared to 0.28 for NLCD 2011. Accuracy was also evaluated relative to the values of training points (n=75,000) used in the development of the continuous cover components. Again, the “cross-walked” product outperformed NLCD 2011, with an overall accuracy of 0.81, compared to 0.66 for NLCD 2011. These results demonstrated that our continuous cover predictions and models were successful in increasing thematic classification accuracy in Western United States shrublands. We plan to directly use the “cross-walked” product, where available, in the NLCD 2016 product.
","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/ofr20171119","usgsCitation":"Rigge, M.B., Gass, Leila, Homer, C.G., and Xian, G.Z., 2017, Methods for converting continuous shrubland ecosystem component values to thematic National Land Cover Database classes: U.S. Geological Survey Open-File Report 2017–1119, 10 p., https://doi.org/10.3133/ofr20171119.","productDescription":"iv, 10 p.","numberOfPages":"18","onlineOnly":"Y","ipdsId":"IP-089077","costCenters":[{"id":222,"text":"Earth Resources Observation and Science (EROS) Center","active":true,"usgs":true}],"links":[{"id":347404,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/of/2017/1119/coverthb.jpg"},{"id":347405,"rank":2,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/of/2017/1119/ofr20171119.pdf","text":"Report","size":"2.43 MB","linkFileType":{"id":1,"text":"pdf"},"description":"OFR 2017–1119"}],"contact":"Director, Earth Resources Observation and Science (EROS) Center
U.S. Geological Survey
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Sioux Falls, SD 57198