There is little information on the oceanography in the National Park of American Samoa (NPSA). The transport pathways for potentially harmful constituents of land-derived runoff, as well as larvae and other planktonic organisms, are driven by nearshore circulation patterns. To evaluate the processes affecting coral reef ecosystem health, it is first necessary to understand the oceanographic processes driving nearshore circulation, residence times, exposure rates, and transport pathways. Information on how the NPSA’s natural resources may be affected by anthropogenic sources of pollution, sediment runoff, larval transport, or modifications to the marine protected areas is critical to NPSA resource managers for understanding and ultimately managing coastal and marine resources. To address this need, U.S. Geological Survey and U.S. National Park Service researchers conducted a collaborative study in 2015 to determine coastal circulation patterns and water-column properties along north-central Tutuila, American Samoa, in an area focused on NPSA’s Tutuila Unit and its coral reef ecosystem. The continuous measurements of waves, currents, tides, and water-column properties from these instrument deployments over 150 days, coupled with available meteorological measurements of wind and rainfall, provide information on nearshore circulation and the variability in these hydrodynamic properties for NPSA’s Tutuila Unit. In general, circulation was strongly driven by regional winds at longer (greater than day) timescales and by tides at shorter (less than day) timescales. Flows were primarily directed along shore, with current speeds faster offshore to the north and slower closer to shore, especially in embayments. Water-column properties exhibit strong seasonality coupled to the shift from non-trade wind season to trade wind season. During the non-trade wind season that was characterized by variable winds and larger waves in the NPSA, waters were warmer, slightly more saline, relatively less optically clear, and more stratified. When winds shifted to a more consistent trade wind pattern in the austral fall, the waters cooled and became less stratified because of decreased insolation. There are consistent spatial patterns in water column characteristics—Waters were warmer and less saline near the surface and closer to shore, especially in embayments, which tended to be more turbid, less clear, and characterized by higher chlorophyll than waters offshore. Water residence times were shorter farther offshore and longer closer to shore and in embayments, but varied spatially because of different forcing. Warmer, lower salinity, higher chlorophyll, and more turbid waters in embayments tend to reside in those locations for much greater durations, resulting in greater exposure of embayment ecosystems to those waters. This is in contrast with waters farther offshore, where the combination of shorter residence times and cooler, higher salinity water results in less exposure to land runoff. Understanding coastal circulation patterns and water-column properties in NPSA’s waters along north-central Tutuila may help to better understand how meteorological and oceanographic processes, at the regional and local scale, affect coral reef health and sustainability in this region.
","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/ofr20171060","usgsCitation":"Storlazzi, C.D., Cheriton, O.M., Rosenberger, K.J., Logan, J.B., and Clark, T.B., 2017, Coastal circulation and water-column properties in the National Park of American Samoa, February–July 2015: U.S. Geological Survey Open-File Report 2017–1060, 104 p., https://doi.org/10.3133/ofr20171060.","productDescription":"Report: ix, 104 p.; Data Release","numberOfPages":"113","onlineOnly":"Y","ipdsId":"IP-083344","costCenters":[{"id":520,"text":"Pacific Coastal and Marine Science Center","active":true,"usgs":true}],"links":[{"id":341992,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/of/2017/1060/coverthb.jpg"},{"id":342023,"rank":3,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/F7RN362H","text":"USGS data release","description":"USGS data release","linkHelpText":"Data from coastal circulation and water-column properties in the National Park of American Samoa, February–July 2015"},{"id":341994,"rank":2,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/of/2017/1060/ofr20171060.pdf","text":"Report","size":"6.3 MB","linkFileType":{"id":1,"text":"pdf"},"description":"OFR 2017-1060"}],"otherGeospatial":"National Park of American Samoa","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -170.73646545410156,\n -14.295658268883681\n ],\n [\n -170.62454223632812,\n -14.295658268883681\n ],\n [\n -170.62454223632812,\n -14.219791816003891\n ],\n [\n -170.73646545410156,\n -14.219791816003891\n ],\n [\n -170.73646545410156,\n -14.295658268883681\n ]\n ]\n ]\n }\n }\n ]\n}","contact":"Pacific Coastal and Marine Science Center
U.S. Geological Survey
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Intrusive rock sequences in the central and eastern Mojave Desert segment of the Jurassic Cordilleran arc of the western United States record regional and temporal variations in magmas generated during the second prominent pulse of Mesozoic continental arc magmatism. U/Pb zircon ages provide temporal control for describing variations in rock and zircon geochemistry that reflect differences in magma source components. These source signatures are discernible through mixing and fractionation processes associated with magma ascent and emplacement. The oldest well-dated Jurassic rocks defining initiation of the Jurassic pulse are a 183 Ma monzodiorite and a 181 Ma ignimbrite. Early to Middle Jurassic intrusive rocks comprising the main stage of magmatism include two high-K calc-alkalic groups: to the north, the deformed 183–172 Ma Fort Irwin sequence and contemporaneous rocks in the Granite and Clipper Mountains, and to the south, the 167–164 Ma Bullion sequence. A Late Jurassic suite of shoshonitic, alkali-calcic intrusive rocks, the Bristol Mountains sequence, ranges in age from 164 to 161 Ma and was emplaced as the pulse began to wane. Whole-rock and zircon trace-element geochemistry defines a compositionally coherent Jurassic arc with regional and secular variations in melt compositions. The arc evolved through the magma pulse by progressively greater input of old cratonic crust and lithospheric mantle into the arc magma system, synchronous with progressive regional crustal thickening.
","language":"English","publisher":"The Geological Society of America","doi":"10.1130/B31550.1","usgsCitation":"Barth, A., Wooden, J., Miller, D., Howard, K.A., Fox, L., Schermer, E.R., and Jacobson, C., 2017, Regional and temporal variability of melts during a Cordilleran magma pulse: Age and chemical evolution of the jurassic arc, eastern mojave desert, California: Geological Society of America Bulletin, v. 129, no. 3-4, p. 429-448, https://doi.org/10.1130/B31550.1.","productDescription":"20 p.","startPage":"429","endPage":"448","ipdsId":"IP-073981","costCenters":[{"id":312,"text":"Geology, Minerals, Energy, and Geophysics Science Center","active":true,"usgs":true}],"links":[{"id":461519,"rank":0,"type":{"id":41,"text":"Open Access External Repository Page"},"url":"https://hdl.handle.net/1805/14626","text":"External Repository"},{"id":342163,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","state":"California","otherGeospatial":"Mojave Desert, Transverse Ranges","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -119.267578125,\n 34.288991865037524\n ],\n [\n -119.24560546875001,\n 34.19817309627726\n ],\n [\n -119.267578125,\n 33.8521697014074\n ],\n [\n -118.125,\n 33.55970664841198\n ],\n [\n -117.61962890624999,\n 33.578014746143985\n ],\n [\n -117.26806640625,\n 33.706062655101206\n ],\n [\n -116.87255859374999,\n 33.99802726234877\n ],\n [\n -116.08154296875001,\n 34.34343606848294\n ],\n [\n -115.20263671874999,\n 34.994003757575776\n ],\n [\n -114.12597656249999,\n 35.94243575255426\n ],\n [\n -114.06005859375,\n 37.19533058280065\n ],\n [\n -113.818359375,\n 38.976492485539396\n ],\n [\n -113.92822265625,\n 39.027718840211605\n ],\n [\n -114.36767578124999,\n 39.027718840211605\n ],\n [\n -114.76318359375,\n 38.95940879245423\n ],\n [\n -115.11474609375001,\n 38.87392853923629\n ],\n [\n -115.6640625,\n 38.75408327579141\n ],\n [\n -116.30126953125,\n 38.53097889440024\n ],\n [\n -117.00439453125,\n 38.42777351132902\n ],\n [\n -117.6416015625,\n 38.09998264736481\n ],\n [\n -118.2568359375,\n 37.70120736474139\n ],\n [\n -118.95996093749999,\n 36.63316209558658\n ],\n [\n -118.740234375,\n 36.155617833818525\n ],\n [\n -118.65234374999999,\n 35.38904996691167\n ],\n [\n -119.267578125,\n 34.288991865037524\n ]\n ]\n ]\n }\n }\n ]\n}","volume":"129","issue":"3-4","publishingServiceCenter":{"id":14,"text":"Menlo Park PSC"},"noUsgsAuthors":false,"publicationDate":"2016-11-03","publicationStatus":"PW","scienceBaseUri":"5937bf2ce4b0f6c2d0d9c73e","contributors":{"authors":[{"text":"Barth, A.P.","contributorId":192663,"corporation":false,"usgs":false,"family":"Barth","given":"A.P.","email":"","affiliations":[],"preferred":false,"id":697317,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Wooden, J.L.","contributorId":192664,"corporation":false,"usgs":false,"family":"Wooden","given":"J.L.","email":"","affiliations":[],"preferred":false,"id":697318,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Miller, David M. 0000-0003-3711-0441 dmiller@usgs.gov","orcid":"https://orcid.org/0000-0003-3711-0441","contributorId":140769,"corporation":false,"usgs":true,"family":"Miller","given":"David M.","email":"dmiller@usgs.gov","affiliations":[{"id":312,"text":"Geology, Minerals, Energy, and Geophysics Science Center","active":true,"usgs":true},{"id":309,"text":"Geology and Geophysics Science Center","active":true,"usgs":true}],"preferred":true,"id":697316,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Howard, Keith A. 0000-0002-6462-2947 khoward@usgs.gov","orcid":"https://orcid.org/0000-0002-6462-2947","contributorId":3439,"corporation":false,"usgs":true,"family":"Howard","given":"Keith","email":"khoward@usgs.gov","middleInitial":"A.","affiliations":[{"id":312,"text":"Geology, Minerals, Energy, and Geophysics Science Center","active":true,"usgs":true}],"preferred":true,"id":697319,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Fox, Lydia","contributorId":192666,"corporation":false,"usgs":false,"family":"Fox","given":"Lydia","email":"","affiliations":[],"preferred":false,"id":697321,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"Schermer, Elizabeth R.","contributorId":184060,"corporation":false,"usgs":false,"family":"Schermer","given":"Elizabeth","email":"","middleInitial":"R.","affiliations":[],"preferred":false,"id":697322,"contributorType":{"id":1,"text":"Authors"},"rank":6},{"text":"Jacobson, C.E.","contributorId":192665,"corporation":false,"usgs":false,"family":"Jacobson","given":"C.E.","email":"","affiliations":[],"preferred":false,"id":697320,"contributorType":{"id":1,"text":"Authors"},"rank":7}]}} ,{"id":70177812,"text":"sim3367 - 2017 - Geologic map of the Beacon Rock quadrangle, Skamania County, Washington","interactions":[],"lastModifiedDate":"2022-04-19T18:50:25.77411","indexId":"sim3367","displayToPublicDate":"2017-06-06T00:00:00","publicationYear":"2017","noYear":false,"publicationType":{"id":18,"text":"Report"},"publicationSubtype":{"id":5,"text":"USGS Numbered Series"},"seriesTitle":{"id":333,"text":"Scientific Investigations Map","code":"SIM","onlineIssn":"2329-132X","printIssn":"2329-1311","active":true,"publicationSubtype":{"id":5}},"seriesNumber":"3367","title":"Geologic map of the Beacon Rock quadrangle, Skamania County, Washington","docAbstract":"The Beacon Rock 7.5′ quadrangle is located approximately 50 km east of Portland, Oregon, on the north side of the Columbia River Gorge, a scenic canyon carved through the axis of the Cascade Range by the Columbia River. Although approximately 75,000 people live within the gorge, much of the region remains little developed and is encompassed by the 292,500-acre Columbia River Gorge National Scenic Area, managed by a consortium of government agencies “to protect and provide for the enhancement of the scenic, cultural, recreational and natural resources of the Gorge and to protect and support the economy of the Columbia River Gorge area.” As the only low-elevation corridor through the Cascade Range, the gorge is a critical regional transportation and utilities corridor (Wang and Chaker, 2004). Major state and national highways and rail lines run along both shores of the Columbia River, which also provides important water access to ports in the agricultural interior of the Pacific Northwest. Transmission lines carry power from hydroelectric facilities in the gorge and farther east to the growing urban areas of western Oregon and Washington, and natural-gas pipelines transect the corridor (Wang and Chaker, 2004). These lifelines are highly vulnerable to disruption by earthquakes, landslides, and floods. A major purpose of the work described here is to identify and map geologic hazards, such as faults and landslide-prone areas, to provide more accurate assessments of the risks associated with these features.
The steep canyon walls of the map area reveal extensive outcrops of Miocene flood-basalt flows of the Columbia River Basalt Group capped by fluvial deposits of the ancestral Columbia River, Pliocene lavas erupted from the axis of the Cascade arc to the east, and volcanic rocks erupted from numerous local vents. The Columbia River Basalt Group unconformably rests on a sequence of late Oligocene and early Miocene rocks of the ancestral Cascade volcanic arc, which underlies most of the map area. The resistant flood-basalt flows form some of the famous landforms in the map area, such as Hamilton Mountain. Extensive landslide complexes have developed where the basalt flows were emplaced on weak volcaniclastic rocks.
","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/sim3367","usgsCitation":"Evarts, R.C., and Fleck, R.J., 2017, Geologic map of the Beacon Rock quadrangle, Skamania County, Washington: U.S. Geological Survey Scientific Investigations Map 3367, pamphlet 61 p., scale 1:24,000, https://doi.org/10.3133/sim3367.","productDescription":"Pamphlet: ii, 61 p.; 2 Sheets: 53.90 x 37.06 inches and 61.75 x 31.64 inches; Basemap: 24.00 x 30.00 inches; Database; Metadata; Read Me","onlineOnly":"Y","ipdsId":"IP-056187","costCenters":[{"id":312,"text":"Geology, Minerals, Energy, and Geophysics Science Center","active":true,"usgs":true}],"links":[{"id":399112,"rank":9,"type":{"id":36,"text":"NGMDB Index Page"},"url":"https://ngmdb.usgs.gov/Prodesc/proddesc_105748.htm"},{"id":342054,"rank":8,"type":{"id":2,"text":"Additional Report Piece"},"url":"https://pubs.usgs.gov/sim/3367/sim3367_base.pdf","text":"Basemap","linkFileType":{"id":1,"text":"pdf"},"description":"SIM 3367 Basemap"},{"id":342053,"rank":7,"type":{"id":16,"text":"Metadata"},"url":"https://pubs.usgs.gov/sim/3367/sim3367_metadata.zip","text":"Metadata","linkFileType":{"id":6,"text":"zip"},"description":"SIM 3367 Metadata"},{"id":342052,"rank":6,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/sim/3367/sim3367_pamphlet.pdf","text":"Pamphlet","linkFileType":{"id":1,"text":"pdf"},"description":"SIM 3367 Pamphlet"},{"id":342047,"rank":4,"type":{"id":26,"text":"Sheet"},"url":"https://pubs.usgs.gov/sim/3367/sim3367_sheet2.pdf","text":"Sheet 2","linkFileType":{"id":1,"text":"pdf"},"description":"SIM 3367 Sheet 2","linkHelpText":"Geologic Map of the Beacon Rock Quadrangle, Skamania County, Washington - Station locations and sample sites in the the Beacon Rock quadrangle, Skamania County, Washington"},{"id":342048,"rank":5,"type":{"id":20,"text":"Read Me"},"url":"https://pubs.usgs.gov/sim/3367/sim3367_readme.txt","text":"Read Me","linkFileType":{"id":2,"text":"txt"},"description":"SIM 3367 Read Me"},{"id":342046,"rank":3,"type":{"id":26,"text":"Sheet"},"url":"https://pubs.usgs.gov/sim/3367/sim3367_sheet1.pdf","text":"Sheet 1","linkFileType":{"id":1,"text":"pdf"},"description":"SIM 3367 Sheet 1","linkHelpText":"Geologic Map of the Beacon Rock Quadrangle, Skamania County, Washington"},{"id":342045,"rank":2,"type":{"id":9,"text":"Database"},"url":"https://pubs.usgs.gov/sim/3367/SIM3367_database.zip","text":"Database","linkFileType":{"id":6,"text":"zip"},"description":"SIM 3367 Database"},{"id":342041,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/sim/3367/coverthb2.jpg"}],"scale":"24000","country":"United States","state":"Washington","county":"Skamania County","otherGeospatial":"Beacon Rock quadrangle","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -122.125,\n 45.625\n ],\n [\n -122,\n 45.625\n ],\n [\n -122,\n 45.75\n ],\n [\n -122.125,\n 45.75\n ],\n [\n -122.125,\n 45.625\n ]\n ]\n ]\n }\n }\n ]\n}","contact":"Geology, Minerals, Energy, & Geophysics Science Center
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Multiple nutrient cycles regulate biological nitrogen (N) fixation in forests, yet long-term feedbacks between N-fixation and coupled element cycles remain largely unexplored. We examined soil nutrients and heterotrophic N-fixation across a gradient of 24 temperate conifer forests shaped by legacies of symbiotic N-fixing trees. We observed positive relationships among mineral soil pools of N, carbon (C), organic molybdenum (Mo), and organic phosphorus (P) across sites, evidence that legacies of symbiotic N-fixing trees can increase the abundance of multiple elements important to heterotrophic N-fixation. Soil N accumulation lowered rates of heterotrophic N-fixation in organic horizons due to both N inhibition of nitrogenase enzymes and declines in soil organic matter quality. Experimental fertilization of organic horizon soil revealed widespread Mo limitation of heterotrophic N-fixation, especially at sites where soil Mo was scarce relative to C. Fertilization also revealed widespread absence of P limitation, consistent with high soil P:Mo ratios. Responses of heterotrophic N-fixation to added Mo (positive) and N (negative) were correlated across sites, evidence that multiple nutrient controls of heterotrophic N-fixation were more common than single-nutrient effects. We propose a conceptual model where symbiotic N-fixation promotes coupled N, C, P, and Mo accumulation in soil, leading to positive feedback that relaxes nutrient limitation of overall N-fixation, though heterotrophic N-fixation is primarily suppressed by strong negative feedback from long-term soil N accumulation.
","language":"English","publisher":"Springer","doi":"10.1007/s10533-017-0341-x","usgsCitation":"Perakis, S.S., Pett-Ridge, J.C., and Catricala, C.E., 2017, Nutrient feedbacks to soil heterotrophic nitrogen fixation in forests: Biogeochemistry, v. 134, no. 1-2, p. 41-55, https://doi.org/10.1007/s10533-017-0341-x.","productDescription":"15 p.","startPage":"41","endPage":"55","ipdsId":"IP-066856","costCenters":[{"id":290,"text":"Forest and Rangeland Ecosystem Science Center","active":false,"usgs":true}],"links":[{"id":342065,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","otherGeospatial":"Oregon Coast Range","volume":"134","issue":"1-2","publishingServiceCenter":{"id":12,"text":"Tacoma PSC"},"noUsgsAuthors":false,"publicationDate":"2017-05-24","publicationStatus":"PW","scienceBaseUri":"59366da7e4b0f6c2d0d7d609","contributors":{"authors":[{"text":"Perakis, Steven S. 0000-0003-0703-9314 sperakis@usgs.gov","orcid":"https://orcid.org/0000-0003-0703-9314","contributorId":145528,"corporation":false,"usgs":true,"family":"Perakis","given":"Steven","email":"sperakis@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":696978,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Pett-Ridge, Julie C.","contributorId":192603,"corporation":false,"usgs":false,"family":"Pett-Ridge","given":"Julie","email":"","middleInitial":"C.","affiliations":[],"preferred":false,"id":696979,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Catricala, Christina E.","contributorId":192604,"corporation":false,"usgs":false,"family":"Catricala","given":"Christina","email":"","middleInitial":"E.","affiliations":[],"preferred":false,"id":696980,"contributorType":{"id":1,"text":"Authors"},"rank":3}]}} ,{"id":70187923,"text":"70187923 - 2017 - Variability of dissolved organic carbon in precipitation during storms at the Shale Hills Critical Zone Observatory","interactions":[],"lastModifiedDate":"2017-08-03T08:39:24","indexId":"70187923","displayToPublicDate":"2017-06-05T00:00:00","publicationYear":"2017","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":1924,"text":"Hydrological Processes","active":true,"publicationSubtype":{"id":10}},"title":"Variability of dissolved organic carbon in precipitation during storms at the Shale Hills Critical Zone Observatory","docAbstract":"Organic compounds are removed from the atmosphere and deposited to the earth's surface via precipitation. In this study, we quantified variations of dissolved organic carbon (DOC) in precipitation during storm events at the Shale Hills Critical Zone Observatory, a forested watershed in central Pennsylvania (USA). Precipitation samples were collected consecutively throughout the storm during 13 events, which spanned a range of seasons and synoptic meteorological conditions, including a hurricane. Further, we explored factors that affect the temporal variability by considering relationships of DOC in precipitation with atmospheric and storm characteristics. Concentrations and chemical composition of DOC changed considerably during storms, with the magnitude of change within individual events being comparable or higher than the range of variation in average event composition among events. While some previous studies observed that concentrations of other elements in precipitation typically decrease over the course of individual storm events, results of this study show that DOC concentrations in precipitation are highly variable. During most storm events concentrations decreased over time, possibly as a result of washing out of the below-cloud atmosphere. However, increasing concentrations that were observed in the later stages of some storm events highlight that DOC removal with precipitation is not merely a dilution response. Increases in DOC during events could result from advection of air masses, local emissions during breaks in precipitation, or chemical transformations in the atmosphere that enhance solubility of organic carbon compounds. This work advances understanding of processes occurring during storms that are relevant to studies of atmospheric chemistry, carbon cycling, and ecosystem responses.
","language":"English","publisher":"Wiley","doi":"10.1002/hyp.11235","usgsCitation":"Iavorivska, L., Boyer, E.W., Grimm, J.W., Miller, M.P., DeWalle, D.R., Davis, K.J., and Kaye, M., 2017, Variability of dissolved organic carbon in precipitation during storms at the Shale Hills Critical Zone Observatory: Hydrological Processes, v. 31, no. 16, p. 2935-2950, https://doi.org/10.1002/hyp.11235.","productDescription":"16 p.","startPage":"2935","endPage":"2950","ipdsId":"IP-079411","costCenters":[{"id":610,"text":"Utah Water Science Center","active":true,"usgs":true}],"links":[{"id":342092,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","state":"Pennsylvania","otherGeospatial":"Stone Valley Forest","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -77.96344757080078,\n 40.60952174235882\n ],\n [\n -77.85358428955078,\n 40.60952174235882\n ],\n [\n -77.85358428955078,\n 40.71655807771725\n ],\n [\n -77.96344757080078,\n 40.71655807771725\n ],\n [\n -77.96344757080078,\n 40.60952174235882\n ]\n ]\n ]\n }\n }\n ]\n}","volume":"31","issue":"16","publishingServiceCenter":{"id":1,"text":"Sacramento PSC"},"noUsgsAuthors":false,"publicationDate":"2017-06-28","publicationStatus":"PW","scienceBaseUri":"59366da7e4b0f6c2d0d7d615","contributors":{"authors":[{"text":"Iavorivska, Lidiia","contributorId":175230,"corporation":false,"usgs":false,"family":"Iavorivska","given":"Lidiia","email":"","affiliations":[],"preferred":false,"id":697077,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Boyer, Elizabeth W.","contributorId":44659,"corporation":false,"usgs":false,"family":"Boyer","given":"Elizabeth","email":"","middleInitial":"W.","affiliations":[{"id":7260,"text":"Pennsylvania State University","active":true,"usgs":false}],"preferred":false,"id":697078,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Grimm, Jeffrey W.","contributorId":192616,"corporation":false,"usgs":false,"family":"Grimm","given":"Jeffrey","email":"","middleInitial":"W.","affiliations":[],"preferred":false,"id":697079,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Miller, Matthew P. 0000-0002-2537-1823 mamiller@usgs.gov","orcid":"https://orcid.org/0000-0002-2537-1823","contributorId":3919,"corporation":false,"usgs":true,"family":"Miller","given":"Matthew","email":"mamiller@usgs.gov","middleInitial":"P.","affiliations":[{"id":610,"text":"Utah Water Science Center","active":true,"usgs":true}],"preferred":true,"id":696000,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"DeWalle, David R.","contributorId":23291,"corporation":false,"usgs":true,"family":"DeWalle","given":"David","email":"","middleInitial":"R.","affiliations":[],"preferred":false,"id":697080,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"Davis, Kenneth J.","contributorId":192617,"corporation":false,"usgs":false,"family":"Davis","given":"Kenneth","email":"","middleInitial":"J.","affiliations":[],"preferred":false,"id":697081,"contributorType":{"id":1,"text":"Authors"},"rank":6},{"text":"Kaye, Margot W.","contributorId":102031,"corporation":false,"usgs":false,"family":"Kaye","given":"Margot W.","affiliations":[],"preferred":false,"id":697082,"contributorType":{"id":1,"text":"Authors"},"rank":7}]}} ,{"id":70188146,"text":"sir20175026 - 2017 - Using high-throughput DNA sequencing, genetic fingerprinting, and quantitative PCR as tools for monitoring bloom-forming and toxigenic cyanobacteria in Upper Klamath Lake, Oregon, 2013 and 2014","interactions":[],"lastModifiedDate":"2018-01-24T16:47:35","indexId":"sir20175026","displayToPublicDate":"2017-06-05T00:00:00","publicationYear":"2017","noYear":false,"publicationType":{"id":18,"text":"Report"},"publicationSubtype":{"id":5,"text":"USGS Numbered Series"},"seriesTitle":{"id":334,"text":"Scientific Investigations Report","code":"SIR","onlineIssn":"2328-0328","printIssn":"2328-031X","active":true,"publicationSubtype":{"id":5}},"seriesNumber":"2017-5026","title":"Using high-throughput DNA sequencing, genetic fingerprinting, and quantitative PCR as tools for monitoring bloom-forming and toxigenic cyanobacteria in Upper Klamath Lake, Oregon, 2013 and 2014","docAbstract":"Monitoring the community structure and metabolic activities of cyanobacterial blooms in Upper Klamath Lake, Oregon, is critical to lake management because these blooms degrade water quality and produce toxic microcystins that are harmful to humans, domestic animals, and wildlife. Genetic tools, such as DNA fingerprinting by terminal restriction fragment length polymorphism (T-RFLP) analysis, high-throughput DNA sequencing (HTS), and real-time, quantitative polymerase chain reaction (qPCR), provide more sensitive and rapid assessments of bloom ecology than traditional techniques. The objectives of this study were (1) to characterize the microbial community at one site in Upper Klamath Lake and determine changes in the cyanobacterial community through time using T-RFLP and HTS in comparison with traditional light microscopy; (2) to determine relative abundances and changes in abundance over time of toxigenic Microcystis using qPCR; and (3) to determine relative abundances and changes in abundance over time of Aphanizomenon, Microcystis, and total cyanobacteria using qPCR. T-RFLP analysis of total cyanobacteria showed a dominance of only one or two distinct genotypes in samples from 2013, but results of HTS in 2013 and 2014 showed more variations in the bloom cycle that fit with the previous understanding of bloom dynamics in Upper Klamath Lake and indicated that potentially toxigenic Microcystis was more prevalent in 2014 than in years prior. The qPCR-estimated copy numbers of all target genes were higher in 2014 than in 2013, when microcystin concentrations also were higher. Total Microcystis density was shown with qPCR to be a better predictor of late-season increases in microcystin concentrations than the relative proportions of potentially toxigenic cells. In addition, qPCR targeting Aphanizomenon at one site in Upper Klamath Lake indicated a moderate bloom of this species (corresponding to chlorophyll a concentrations between approximately 75 and 200 micrograms per liter) from mid-June to mid-August, 2014. After August 18, the Aphanizomenon bloom was overtaken by Microcystis late in the season as microcystin concentrations peaked. Overall, results of this study showed how DNA-based, genetic methods may provide rapid and sensitive diagnoses for the presence of toxigenic cyanobacteria and that they are useful for general monitoring or ecological studies and identification of cyanobacterial community members in complex aquatic habitats. These same methods can also be used to simultaneously address spatial (horizontal and vertical) and temporal variation and under different conditions. Additionally, with some modifications, the same techniques can be applied to different sample types, including water, sediment, and tissue.
","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/sir20175026","usgsCitation":"Eldridge, S.L.C., Driscoll, Connor, and Dreher, T.W., 2017, Using high-throughput DNA sequencing, genetic fingerprinting, and quantitative PCR as tools for monitoring bloom-forming and toxigenic cyanobacteria in Upper Klamath Lake, Oregon, 2013 and 2014: U.S. Geological Survey Scientific Investigations Report 2017–5026, 50 p., https://doi.org/10.3133/sir20175026.","productDescription":"Report: vi, 50 p.; Data Release","onlineOnly":"Y","ipdsId":"IP-075955","costCenters":[{"id":518,"text":"Oregon Water Science Center","active":true,"usgs":true}],"links":[{"id":342012,"rank":3,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/F7C53J3V","text":"USGS data release","description":"USGS data release","linkHelpText":"Datasets of High-throughput DNA Sequencing, Genetic Fingerprinting, and Quantitative PCR from Upper Klamath Lake, Oregon, 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U.S. Geological Survey
2130 SW 5th Avenue
Portland, Oregon 97201
Nest box programs are frequently implemented for the conservation of cavity-nesting birds, but their effectiveness is rarely evaluated in comparison to birds not using nest boxes. In the European Palearctic, Red-footed Falcon Falco vespertinus populations are both of high conservation concern and are strongly associated with nest box programs in heavily managed landscapes. We used a 21-year monitoring dataset collected on 753 nesting attempts by Red-footed Falcons in unmanaged natural or semi-natural habitats to provide basic information on this poorly known species; to evaluate long-term demographic trends; and to evaluate response of demographic parameters of Red-footed Falcons to environmental factors including use of nest boxes. We observed significant differences among years in laying date, offspring loss, and numbers of fledglings produced, but not in egg production. Of these four parameters, offspring loss and, to a lesser extent, number of fledglings exhibited directional trends over time. Variation in laying date and in numbers of eggs were not well explained by any one model, but instead by combinations of models, each with informative terms for nest type. Nevertheless, laying in nest boxes occurred 2.10 ± 0.70 days earlier than in natural nests. In contrast, variation in both offspring loss and numbers of fledglings produced were fairly well explained by a single model including terms for nest type, nest location, and an interaction between the two parameters (65% and 81% model weights respectively), with highest offspring loss in nest boxes on forest edges. Because, for other species, earlier laying dates are associated with more fit individuals, this interaction highlighted a possible ecological trap, whereby birds using nest boxes on forest edges lay eggs earlier but suffer greater offspring loss and produce lower numbers of fledglings than do those in other nesting settings. If nest boxes increase offspring loss for Red-footed Falcons in heavily managed landscapes where populations are at greater risk, or for the many other species of rare or endangered birds supported by nest box programs, these processes could have important demographic and conservation consequences.
","language":"English","publisher":"Wiley","doi":"10.1111/ibi.12503","usgsCitation":"Bragin, E.A., Bragin, A.E., and Katzner, T., 2017, Demographic consequences of nest box use for Red-footed Falcons Falco vespertinus in Central Asia: Ibis, v. 159, no. 4, p. 841-853, https://doi.org/10.1111/ibi.12503.","productDescription":"13 p.","startPage":"841","endPage":"853","ipdsId":"IP-081874","costCenters":[{"id":290,"text":"Forest and Rangeland Ecosystem Science Center","active":false,"usgs":true}],"links":[{"id":342604,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"Kazakhstan","geographicExtents":"{\"type\":\"FeatureCollection\",\"features\":[{\"type\":\"Feature\",\"geometry\":{\"type\":\"Polygon\",\"coordinates\":[[[70.96231,42.26615],[70.38896,42.08131],[69.07003,41.38424],[68.63248,40.66868],[68.2599,40.66232],[67.98586,41.13599],[66.71405,41.16844],[66.51065,41.98764],[66.02339,41.99465],[66.09801,42.99766],[64.90082,43.72808],[63.18579,43.65007],[62.0133,43.50448],[61.05832,44.40582],[60.23997,44.78404],[58.68999,45.50001],[58.50313,45.5868],[55.92892,44.99586],[55.96819,41.30864],[55.45525,41.25986],[54.75535,42.04397],[54.07942,42.32411],[52.94429,42.11603],[52.50246,41.78332],[52.44634,42.02715],[52.69211,42.4439],[52.50143,42.7923],[51.34243,43.13297],[50.89129,44.03103],[50.33913,44.28402],[50.30564,44.60984],[51.2785,44.51485],[51.3169,45.246],[52.16739,45.40839],[53.04088,45.25905],[53.22087,46.23465],[53.04274,46.85301],[52.04202,46.80464],[51.19195,47.0487],[50.03408,46.60899],[49.10116,46.39933],[48.59324,46.56103],[48.69473,47.07563],[48.05725,47.74375],[47.31523,47.71585],[46.46645,48.39415],[47.04367,49.15204],[46.7516,49.35601],[47.54948,50.4547],[48.57784,49.87476],[48.70238,50.60513],[50.76665,51.69276],[52.32872,51.71865],[54.53288,51.02624],[55.71694,50.62172],[56.77796,51.04355],[58.36329,51.06365],[59.64228,50.54544],[59.93281,50.84219],[61.33742,50.79907],[61.588,51.27266],[59.96753,51.96042],[60.92727,52.44755],[60.73999,52.71999],[61.69999,52.98],[60.97807,53.66499],[61.43659,54.00626],[65.17853,54.35423],[65.66688,54.60127],[68.1691,54.97039],[69.06817,55.38525],[70.86527,55.16973],[71.18013,54.13329],[72.22415,54.37666],[73.50852,54.03562],[73.42568,53.48981],[74.38485,53.54686],[76.8911,54.49052],[76.52518,54.177],[77.80092,53.40441],[80.03556,50.86475],[80.56845,51.38834],[81.94599,50.8122],[83.383,51.06918],[83.93511,50.88925],[84.41638,50.3114],[85.11556,50.1173],[85.54127,49.69286],[86.82936,49.82667],[87.35997,49.21498],[86.59878,48.54918],[85.76823,48.45575],[85.72048,47.45297],[85.16429,47.00096],[83.18048,47.33003],[82.45893,45.53965],[81.94707,45.31703],[79.96611,44.91752],[80.86621,43.18036],[80.18015,42.92007],[80.25999,42.35],[79.64365,42.49668],[79.14218,42.85609],[77.65839,42.96069],[76.00035,42.98802],[75.63696,42.8779],[74.21287,43.29834],[73.6453,43.09127],[73.48976,42.50089],[71.84464,42.8454],[71.18628,42.70429],[70.96231,42.26615]]]},\"properties\":{\"name\":\"Kazakhstan\"}}]}","volume":"159","issue":"4","publishingServiceCenter":{"id":12,"text":"Tacoma PSC"},"noUsgsAuthors":false,"publicationDate":"2017-06-23","publicationStatus":"PW","scienceBaseUri":"5944ee13e4b062508e3335e9","contributors":{"authors":[{"text":"Bragin, Evgeny A.","contributorId":194894,"corporation":false,"usgs":false,"family":"Bragin","given":"Evgeny","email":"","middleInitial":"A.","affiliations":[{"id":35656,"text":"Science Department, Naurzum National Nature Reserve, Kostanay Oblast, Naurzumski Raijon, Karamendy, Kazakhstan","active":true,"usgs":false}],"preferred":false,"id":698515,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Bragin, Alexander E.","contributorId":193027,"corporation":false,"usgs":false,"family":"Bragin","given":"Alexander","email":"","middleInitial":"E.","affiliations":[],"preferred":false,"id":698516,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Katzner, Todd E. 0000-0003-4503-8435 tkatzner@usgs.gov","orcid":"https://orcid.org/0000-0003-4503-8435","contributorId":191353,"corporation":false,"usgs":true,"family":"Katzner","given":"Todd E.","email":"tkatzner@usgs.gov","affiliations":[{"id":290,"text":"Forest and Rangeland Ecosystem Science Center","active":false,"usgs":true}],"preferred":true,"id":698514,"contributorType":{"id":1,"text":"Authors"},"rank":3}]}} ,{"id":70188293,"text":"70188293 - 2017 - Spatio-temporal mapping of plate boundary faults in California using geodetic imaging","interactions":[],"lastModifiedDate":"2017-11-13T15:05:50","indexId":"70188293","displayToPublicDate":"2017-06-05T00:00:00","publicationYear":"2017","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":1816,"text":"Geosciences","active":true,"publicationSubtype":{"id":10}},"title":"Spatio-temporal mapping of plate boundary faults in California using geodetic imaging","docAbstract":"The Pacific–North American plate boundary in California is composed of a 400-km-wide network of faults and zones of distributed deformation. Earthquakes, even large ones, can occur along individual or combinations of faults within the larger plate boundary system. While research often focuses on the primary and secondary faults, holistic study of the plate boundary is required to answer several fundamental questions. How do plate boundary motions partition across California faults? How do faults within the plate boundary interact during earthquakes? What fraction of strain accumulation is relieved aseismically and does this provide limits on fault rupture propagation? Geodetic imaging, broadly defined as measurement of crustal deformation and topography of the Earth’s surface, enables assessment of topographic characteristics and the spatio-temporal behavior of the Earth’s crust. We focus here on crustal deformation observed with continuous Global Positioning System (GPS) data and Interferometric Synthetic Aperture Radar (InSAR) from NASA’s airborne UAVSAR platform, and on high-resolution topography acquired from lidar and Structure from Motion (SfM) methods. Combined, these measurements are used to identify active structures, past ruptures, transient motions, and distribution of deformation. The observations inform estimates of the mechanical and geometric properties of faults. We discuss five areas in California as examples of different fault behavior, fault maturity and times within the earthquake cycle: the M6.0 2014 South Napa earthquake rupture, the San Jacinto fault, the creeping and locked Carrizo sections of the San Andreas fault, the Landers rupture in the Eastern California Shear Zone, and the convergence of the Eastern California Shear Zone and San Andreas fault in southern California. These examples indicate that distribution of crustal deformation can be measured using interferometric synthetic aperture radar (InSAR), Global Navigation Satellite System (GNSS), and high-resolution topography and can improve our understanding of tectonic deformation and rupture characteristics within the broad plate boundary zone.
","language":"English","publisher":"Multidisciplinary Digital Publishing Institute","doi":"10.3390/geosciences7010015","usgsCitation":"Donnellan, A., Arrowsmith, R., and DeLong, S.B., 2017, Spatio-temporal mapping of plate boundary faults in California using geodetic imaging: Geosciences, v. 7, no. 1, p. 1-26, https://doi.org/10.3390/geosciences7010015.","productDescription":"Article 15; 26 p.","startPage":"1","endPage":"26","ipdsId":"IP-082746","costCenters":[{"id":237,"text":"Earthquake Science Center","active":true,"usgs":true}],"links":[{"id":469772,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.3390/geosciences7010015","text":"Publisher Index Page"},{"id":342133,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United 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to flows and salinity dynamics in the San Francisco Bay-Delta","docAbstract":"A linked modeling approach has been undertaken to understand the impacts of climate and infrastructure on aquatic ecology and water quality in the San Francisco Bay-Delta region. The Delft3D Flexible Mesh modeling suite is used in this effort for its 3D hydrodynamics, salinity, temperature and sediment dynamics, phytoplankton and water-quality coupling infrastructure, and linkage to a habitat suitability model. The hydrodynamic model component of the suite is D-Flow FM, a new 3D unstructured finite-volume model based on the Delft3D model. In this paper, D-Flow FM is applied to the San Francisco Bay-Delta to investigate tidal, seasonal and annual dynamics of water levels, river flows and salinity under historical environmental and infrastructural conditions. The model is driven by historical winds, tides, ocean salinity, and river flows, and includes federal, state, and local freshwater withdrawals, and regional gate and barrier operations. The model is calibrated over a 9-month period, and subsequently validated for water levels, flows, and 3D salinity dynamics over a 2 year period.
Model performance was quantified using several model assessment metrics and visualized through target diagrams. These metrics indicate that the model accurately estimated water levels, flows, and salinity over wide-ranging tidal and fluvial conditions, and the model can be used to investigate detailed circulation and salinity patterns throughout the Bay-Delta. The hydrodynamics produced through this effort will be used to drive affiliated sediment, phytoplankton, and contaminant hindcast efforts and habitat suitability assessments for fish and bivalves. The modeling framework applied here will serve as a baseline to ultimately shed light on potential ecosystem change over the current century.
","language":"English","publisher":"Elsevier","doi":"10.1016/j.ecss.2017.04.024","usgsCitation":"Martyr-Koller, R., Kernkamp, H., Van Dam, A., Mick van der Wegen, Lucas, L., Knowles, N., Jaffe, B., and Fregoso, T., 2017, Application of an unstructured 3D finite volume numerical model to flows and salinity dynamics in the San Francisco Bay-Delta: Estuarine, Coastal and Shelf Science, v. 192, p. 86-107, https://doi.org/10.1016/j.ecss.2017.04.024.","productDescription":"22 p. ","startPage":"86","endPage":"107","ipdsId":"IP-080436","costCenters":[{"id":438,"text":"National Research Program - Western Branch","active":true,"usgs":true}],"links":[{"id":469774,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.1016/j.ecss.2017.04.024","text":"Publisher Index Page"},{"id":342615,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","state":"California","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 -123.101806640625,\n 38.13887716726548\n ],\n [\n -123.101806640625,\n 38.212288054388175\n ],\n [\n -123.0743408203125,\n 37.900865092570065\n ],\n [\n -122.96997070312499,\n 37.98750437106374\n ],\n [\n -122.84362792968749,\n 37.97018468810549\n ],\n [\n -122.70629882812499,\n 37.89219554724437\n ],\n [\n -122.59643554687499,\n 37.844494798834575\n ],\n [\n -122.5250244140625,\n 37.783740105227224\n ],\n [\n -122.5579833984375,\n 37.644684587165884\n ],\n [\n -122.54699707031249,\n 37.53150992479082\n ],\n [\n -122.54150390625,\n 37.339591851359174\n ],\n [\n -122.4920654296875,\n 37.25656608611523\n ],\n [\n -122.431640625,\n 37.16031654673677\n ],\n [\n -122.16796875,\n 36.901587303978474\n ],\n [\n -121.8878173828125,\n 36.88840804313823\n ],\n [\n -121.717529296875,\n 36.94989178681327\n ],\n [\n -121.39343261718749,\n 37.02448395075965\n ],\n [\n -121.2615966796875,\n 37.15156050223665\n ],\n [\n -121.168212890625,\n 37.260938147754544\n ],\n [\n -121.10229492187501,\n 37.4356124041315\n ],\n [\n -121.0748291015625,\n 37.63163475580643\n ],\n [\n -121.08581542968751,\n 37.83148014503288\n ],\n [\n -121.09680175781249,\n 38.013476231041935\n ],\n [\n -121.1572265625,\n 38.25112269630296\n ],\n [\n -121.3385009765625,\n 38.46649284538942\n ],\n [\n -121.431884765625,\n 38.50519140240356\n ],\n [\n -121.7449951171875,\n 38.53097889440024\n ],\n [\n -121.9757080078125,\n 38.53097889440024\n ],\n [\n -122.28332519531249,\n 38.522384090200845\n ],\n [\n -122.56347656249999,\n 38.522384090200845\n ],\n [\n -122.8106689453125,\n 38.50089258896462\n ],\n [\n -122.98095703125,\n 38.47079371120379\n ],\n [\n -123.0743408203125,\n 38.33734763569314\n ],\n [\n -123.101806640625,\n 38.238180119798635\n ],\n [\n -123.101806640625,\n 38.13887716726548\n ]\n ]\n ]\n }\n }\n ]\n}","volume":"192","publishingServiceCenter":{"id":14,"text":"Menlo Park PSC"},"noUsgsAuthors":false,"publicationStatus":"PW","scienceBaseUri":"5944ee12e4b062508e3335e5","contributors":{"authors":[{"text":"Martyr-Koller, R.C.","contributorId":193047,"corporation":false,"usgs":false,"family":"Martyr-Koller","given":"R.C.","affiliations":[],"preferred":false,"id":698577,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Kernkamp, H.W.J.","contributorId":193048,"corporation":false,"usgs":false,"family":"Kernkamp","given":"H.W.J.","email":"","affiliations":[],"preferred":false,"id":698578,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Van Dam, Anne A.","contributorId":68175,"corporation":false,"usgs":true,"family":"Van Dam","given":"Anne A.","affiliations":[],"preferred":false,"id":698579,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Mick van der Wegen","contributorId":191938,"corporation":false,"usgs":false,"family":"Mick van der Wegen","affiliations":[],"preferred":false,"id":698580,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Lucas, Lisa 0000-0001-7797-5517 llucas@usgs.gov","orcid":"https://orcid.org/0000-0001-7797-5517","contributorId":2181,"corporation":false,"usgs":true,"family":"Lucas","given":"Lisa","email":"llucas@usgs.gov","affiliations":[{"id":438,"text":"National Research Program - Western Branch","active":true,"usgs":true}],"preferred":true,"id":698576,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"Knowles, N.","contributorId":61212,"corporation":false,"usgs":true,"family":"Knowles","given":"N.","email":"","affiliations":[],"preferred":false,"id":698581,"contributorType":{"id":1,"text":"Authors"},"rank":6},{"text":"Jaffe, B.","contributorId":78517,"corporation":false,"usgs":true,"family":"Jaffe","given":"B.","affiliations":[],"preferred":false,"id":698582,"contributorType":{"id":1,"text":"Authors"},"rank":7},{"text":"Fregoso, T.A.","contributorId":89371,"corporation":false,"usgs":true,"family":"Fregoso","given":"T.A.","affiliations":[],"preferred":false,"id":698583,"contributorType":{"id":1,"text":"Authors"},"rank":8}]}} ,{"id":70186892,"text":"cir1430 - 2017 - USGS integrated drought science","interactions":[],"lastModifiedDate":"2017-06-05T14:44:21","indexId":"cir1430","displayToPublicDate":"2017-06-05T00:00:00","publicationYear":"2017","noYear":false,"publicationType":{"id":18,"text":"Report"},"publicationSubtype":{"id":5,"text":"USGS Numbered Series"},"seriesTitle":{"id":307,"text":"Circular","code":"CIR","onlineIssn":"2330-5703","printIssn":"1067-084X","active":true,"publicationSubtype":{"id":5}},"seriesNumber":"1430","title":"USGS integrated drought science","docAbstract":"Drought poses a serious threat to the resilience of human communities and ecosystems in the United States (Easterling and others, 2000). Over the past several years, many regions have experienced extreme drought conditions, fueled by prolonged periods of reduced precipitation and exceptionally warm temperatures. Extreme drought has far-reaching impacts on water supplies, ecosystems, agricultural production, critical infrastructure, energy costs, human health, and local economies (Milly and others, 2005; Wihlite, 2005; Vörösmarty and others, 2010; Choat and others, 2012; Ledger and others, 2013). As global temperatures continue to increase, the frequency, severity, extent, and duration of droughts are expected to increase across North America, affecting both humans and natural ecosystems (Parry and others, 2007).
The U.S. Geological Survey (USGS) has a long, proven history of delivering science and tools to help decision-makers manage and mitigate effects of drought. That said, there is substantial capacity for improved integration and coordination in the ways that the USGS provides drought science. A USGS Drought Team was formed in August 2016 to work across USGS Mission Areas to identify current USGS drought-related research and core capabilities. This information has been used to initiate the development of an integrated science effort that will bring the full USGS capacity to bear on this national crisis.
","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/cir1430","collaboration":"USGS Coordinated and Integrated Drought Science","usgsCitation":"Ostroff, A.C., Muhlfeld, C.C., Lambert, P.M., Booth, N.L., Carter, S.L., Stoker, J.M., and Focazio, M.J., 2017, USGS integrated drought science: U.S. Geological Survey Circular 1430, 24 p., https://doi.org/10.3133/cir1430.","productDescription":"iv, 24 p.","numberOfPages":"32","ipdsId":"IP-083129","costCenters":[{"id":506,"text":"Office of the AD Ecosystems","active":true,"usgs":true}],"links":[{"id":341891,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/circ/1430/coverthb.jpg"},{"id":341892,"rank":2,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/circ/1430/cir1430.pdf","text":"Report","size":"2.8 MB","linkFileType":{"id":1,"text":"pdf"},"description":"CIR 1430"}],"contact":"USGS Drought Coordinator
National Center, MS 301
12201 Sunrise Valley Dr., MS 300
Reston, VA 20192
https://www.usgs.gov/special-topic/drought
Hydrologic units provide a convenient but problematic nationwide set of geographic polygons based on subjectively determined subdivisions of land surface areas at several hierarchical levels. The problem is that it is impossible to map watersheds, basins, or catchments of relatively equal size and cover the whole country. The hydrologic unit framework is in fact composed mostly of watersheds and pieces of watersheds. The pieces include units that drain to segments of streams, remnant areas, noncontributing areas, and coastal or frontal units that can include multiple watersheds draining to an ocean or large lake. Hence, half or more of the hydrologic units are not watersheds as the name of the framework “Watershed Boundary Dataset” implies. Nonetheless, hydrologic units and watersheds are commonly treated as synonymous, and this misapplication and misunderstanding can have some serious scientific and management consequences. We discuss some of the strengths and limitations of watersheds and hydrologic units as spatial frameworks. Using examples from the Northwest and Southeast United States, we explain how the misapplication of the hydrologic unit framework has altered the meaning of watersheds and can impair understanding associations between spatial geographic characteristics and surface water conditions.
","language":"English","publisher":"Springer","doi":"10.1007/s00267-017-0854-z","usgsCitation":"Omernik, J.M., Griffith, G.E., Hughes, R.M., Glover, J.B., and Weber, M.H., 2017, How misapplication of the hydrologic unit framework diminishes the meaning of watersheds: Environmental Management, v. 60, no. 1, p. 1-11, https://doi.org/10.1007/s00267-017-0854-z.","productDescription":"11 p.","startPage":"1","endPage":"11","ipdsId":"IP-067027","costCenters":[{"id":657,"text":"Western Geographic Science Center","active":true,"usgs":true}],"links":[{"id":469773,"rank":2,"type":{"id":41,"text":"Open Access External Repository Page"},"url":"https://www.ncbi.nlm.nih.gov/pmc/articles/6145848","text":"External Repository"},{"id":342118,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"volume":"60","issue":"1","publishingServiceCenter":{"id":4,"text":"Rolla PSC"},"noUsgsAuthors":false,"publicationDate":"2017-04-04","publicationStatus":"PW","scienceBaseUri":"59366da7e4b0f6c2d0d7d603","contributors":{"authors":[{"text":"Omernik, James M.","contributorId":169740,"corporation":false,"usgs":false,"family":"Omernik","given":"James","email":"","middleInitial":"M.","affiliations":[{"id":25578,"text":"USGS -Volunteer","active":true,"usgs":false}],"preferred":false,"id":697136,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Griffith, Glenn E. 0000-0001-7966-4720 ggriffith@usgs.gov","orcid":"https://orcid.org/0000-0001-7966-4720","contributorId":4053,"corporation":false,"usgs":true,"family":"Griffith","given":"Glenn","email":"ggriffith@usgs.gov","middleInitial":"E.","affiliations":[{"id":657,"text":"Western Geographic Science Center","active":true,"usgs":true}],"preferred":true,"id":697135,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Hughes, Robert M.","contributorId":113579,"corporation":false,"usgs":true,"family":"Hughes","given":"Robert","email":"","middleInitial":"M.","affiliations":[],"preferred":false,"id":697137,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Glover, James B.","contributorId":192631,"corporation":false,"usgs":false,"family":"Glover","given":"James","email":"","middleInitial":"B.","affiliations":[],"preferred":false,"id":697139,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Weber, Marc H.","contributorId":169742,"corporation":false,"usgs":false,"family":"Weber","given":"Marc","email":"","middleInitial":"H.","affiliations":[{"id":13529,"text":"US Environmental Protection Agency","active":true,"usgs":false}],"preferred":false,"id":697138,"contributorType":{"id":1,"text":"Authors"},"rank":5}]}} ,{"id":70188184,"text":"70188184 - 2017 - Snow and ice","interactions":[],"lastModifiedDate":"2020-08-20T19:26:43.019871","indexId":"70188184","displayToPublicDate":"2017-06-05T00:00:00","publicationYear":"2017","noYear":false,"publicationType":{"id":18,"text":"Report"},"publicationSubtype":{"id":1,"text":"Federal Government Series"},"seriesTitle":{"id":32,"text":"General Technical Report","active":false,"publicationSubtype":{"id":1}},"seriesNumber":"PNW-GTR-950","chapter":"3","title":"Snow and ice","docAbstract":"Identification of essential habitat is a fundamental component of amphibian conservation; however, species with complex life histories frequently move among habitats. To better understand dynamic habitat use, we evaluated Wood Frog (Lithobates sylvaticus (LeConte, 1825)) habitat selection and movement patterns during the spring migration and foraging periods and described the spatiotemporal variability of habitats used during all annual life-history periods. We radio-tracked 71 frogs in Maine during 2011–2013 and evaluated spring migration, foraging activity center (FAC), and within-FAC habitat selection. Telemetered frogs spent the greatest percentage of each field season in hibernacula (≥54.4%), followed by FACs (≥25.5%), migration habitat (≥16.9%), and breeding sites (≥4.5%). FACs ranged 49 – 1 335 m2 (568.0 ± 493.4 m2) and annual home ranges spanned 1 413 – 32 165 m2 (11 780.6 ± 12 506.1 m2). During spring migration, Wood Frogs exhibited different movement patterns (e.g., turn angles), selected different habitat features, and selected habitat features less consistently than while occupying FACs, indicating that the migration and foraging periods are ecologically distinct. Habitat-use studies that do not discriminate among annual life-history periods may obscure true ecological relationships and fail to identify essential habitat necessary for sustaining amphibian populations.
","language":"English","publisher":"Canadian Science Publishing","publisherLocation":"Ottawa, ON","doi":"10.1139/cjz-2016-0148","usgsCitation":"Groff, L.A., Calhoun, A.J., and Loftin, C., 2017, Amphibian terrestrial habitat selection and movement patterns vary with annual life-history period: Canadian Journal of Zoology, v. 95, no. 6, p. 433-442, https://doi.org/10.1139/cjz-2016-0148.","productDescription":"10 p.","startPage":"433","endPage":"442","ipdsId":"IP-076126","costCenters":[{"id":199,"text":"Coop Res Unit Leetown","active":true,"usgs":true}],"links":[{"id":348095,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","state":"Maine","volume":"95","issue":"6","publishingServiceCenter":{"id":9,"text":"Reston PSC"},"noUsgsAuthors":false,"publicationStatus":"PW","scienceBaseUri":"59fc2ea4e4b0531197b27f83","contributors":{"authors":[{"text":"Groff, Luke A.","contributorId":95735,"corporation":false,"usgs":true,"family":"Groff","given":"Luke","email":"","middleInitial":"A.","affiliations":[],"preferred":false,"id":719628,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Calhoun, Aram J.K.","contributorId":93829,"corporation":false,"usgs":false,"family":"Calhoun","given":"Aram","email":"","middleInitial":"J.K.","affiliations":[{"id":7063,"text":"University of Maine","active":true,"usgs":false}],"preferred":false,"id":719629,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Loftin, Cynthia S. 0000-0001-9104-3724 cyndy_loftin@usgs.gov","orcid":"https://orcid.org/0000-0001-9104-3724","contributorId":2167,"corporation":false,"usgs":true,"family":"Loftin","given":"Cynthia S.","email":"cyndy_loftin@usgs.gov","affiliations":[],"preferred":true,"id":719627,"contributorType":{"id":1,"text":"Authors"},"rank":3}]}} ,{"id":70193279,"text":"70193279 - 2017 - Indicator-driven conservation planning across terrestrial, freshwater aquatic, and marine ecosystems of the south Atlantic, USA","interactions":[],"lastModifiedDate":"2017-11-17T08:59:32","indexId":"70193279","displayToPublicDate":"2017-06-05T00:00:00","publicationYear":"2017","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":2287,"text":"Journal of Fish and Wildlife Management","active":true,"publicationSubtype":{"id":10}},"title":"Indicator-driven conservation planning across terrestrial, freshwater aquatic, and marine ecosystems of the south Atlantic, USA","docAbstract":"Systematic conservation planning, a widely used approach to identify priority lands and waters, uses efficient, defensible, and transparent methods aimed at conserving biodiversity and ecological systems. Limited financial resources and competing land uses can be major impediments to conservation; therefore, participation of diverse stakeholders in the planning process is advantageous to help address broad-scale threats and challenges of the 21st century. Although a broad extent is needed to identify core areas and corridors for fish and wildlife populations, a fine-scale resolution is needed to manage for multiple, interconnected ecosystems. Here, we developed a conservation plan using a systematic approach to promote landscape-level conservation within the extent of the South Atlantic Landscape Conservation Cooperative. Our objective was to identify the highest-ranked 30% of lands and waters within the South Atlantic deemed necessary to conserve ecological and cultural integrity for the 10 primary ecosystems of the southeastern United States. These environments varied from terrestrial, freshwater aquatic, and marine. The planning process was driven by indicators of ecosystem integrity at a 4-ha resolution. We used the program Zonation and 28 indicators to optimize the identification of lands and waters to meet the stated objective. A novel part of our study was the prioritization of multiple ecosystems, and we discuss the advantages and disadvantages of this approach. The evaluation of indicator representation within prioritizations was a useful method to show where improvements could be made; some indicators dictated hotspots, some had a limited extent and were well represented, and others had a limited effect. Overall, we demonstrate that a broad-scale (408,276 km2 of terrestrial and 411,239 km2 of marine environments) conservation plan can be realized at a fine-scale resolution, which will allow implementation of the regional plan at a local level relevant to decision making.
","language":"English","publisher":"U.S. Fish and Wildlife Service","doi":"10.3996/062016-JFWM-044","usgsCitation":"Pickens, B.A., Mordecai, R.S., Drew, C.A., Alexander-Vaughn, L.B., Keister, A.S., Morris, H.L., and Collazo, J., 2017, Indicator-driven conservation planning across terrestrial, freshwater aquatic, and marine ecosystems of the south Atlantic, USA: Journal of Fish and Wildlife Management, v. 8, no. 1, p. 219-233, https://doi.org/10.3996/062016-JFWM-044.","productDescription":"15 p.","startPage":"219","endPage":"233","ipdsId":"IP-074523","costCenters":[{"id":198,"text":"Coop Res Unit Atlanta","active":true,"usgs":true}],"links":[{"id":469776,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.3996/062016-jfwm-044","text":"Publisher Index Page"},{"id":349000,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -79.40917968749999,\n 37.26530995561875\n ],\n [\n -83.6279296875,\n 34.939985151560435\n ],\n [\n -85.9130859375,\n 33.486435450999885\n ],\n [\n -85.53955078125,\n 30.939924331023445\n ],\n [\n -85.1220703125,\n 29.6880527498568\n ],\n [\n -84.5947265625,\n 29.897805610155874\n ],\n [\n -84.0673828125,\n 30.107117887092357\n ],\n [\n -82.90283203125,\n 29.248063243796576\n ],\n [\n -81.27685546875,\n 29.859701442126756\n ],\n [\n -81.474609375,\n 30.581179257386985\n ],\n [\n -81.18896484375,\n 31.653381399664\n ],\n [\n -80.419921875,\n 32.39851580247402\n ],\n [\n -79.1015625,\n 33.211116472416855\n ],\n [\n -78.81591796875,\n 33.742612777346885\n ],\n [\n -78.02490234375,\n 33.87041555094183\n ],\n [\n -77.431640625,\n 34.542762387234845\n ],\n [\n -76.46484375,\n 34.813803317113155\n ],\n [\n -75.43212890625,\n 35.460669951495305\n ],\n [\n -76.26708984375,\n 37.50972584293751\n ],\n [\n -79.40917968749999,\n 37.26530995561875\n ]\n ]\n ]\n }\n }\n ]\n}","volume":"8","issue":"1","publishingServiceCenter":{"id":9,"text":"Reston PSC"},"noUsgsAuthors":false,"publicationDate":"2017-04-01","publicationStatus":"PW","scienceBaseUri":"5a60fbace4b06e28e9c2345e","contributors":{"authors":[{"text":"Pickens, Bradley A.","contributorId":140926,"corporation":false,"usgs":false,"family":"Pickens","given":"Bradley","email":"","middleInitial":"A.","affiliations":[],"preferred":false,"id":718509,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Mordecai, Rua S.","contributorId":30328,"corporation":false,"usgs":true,"family":"Mordecai","given":"Rua","email":"","middleInitial":"S.","affiliations":[],"preferred":false,"id":718510,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Drew, C. Ashton","contributorId":140953,"corporation":false,"usgs":false,"family":"Drew","given":"C.","email":"","middleInitial":"Ashton","affiliations":[],"preferred":false,"id":718511,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Alexander-Vaughn, Louise B.","contributorId":199257,"corporation":false,"usgs":false,"family":"Alexander-Vaughn","given":"Louise","email":"","middleInitial":"B.","affiliations":[],"preferred":false,"id":718512,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Keister, Amy S.","contributorId":177319,"corporation":false,"usgs":false,"family":"Keister","given":"Amy","email":"","middleInitial":"S.","affiliations":[],"preferred":false,"id":718513,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"Morris, Hilary L.C.","contributorId":199258,"corporation":false,"usgs":false,"family":"Morris","given":"Hilary","email":"","middleInitial":"L.C.","affiliations":[],"preferred":false,"id":718514,"contributorType":{"id":1,"text":"Authors"},"rank":6},{"text":"Collazo, Jaime A. 0000-0002-1816-7744 jaime_collazo@usgs.gov","orcid":"https://orcid.org/0000-0002-1816-7744","contributorId":173448,"corporation":false,"usgs":true,"family":"Collazo","given":"Jaime A.","email":"jaime_collazo@usgs.gov","affiliations":[{"id":199,"text":"Coop Res Unit Leetown","active":true,"usgs":true},{"id":198,"text":"Coop Res Unit Atlanta","active":true,"usgs":true}],"preferred":false,"id":718508,"contributorType":{"id":1,"text":"Authors"},"rank":7}]}} ,{"id":70216818,"text":"70216818 - 2017 - Mapping climate change refugia in the Sierra Nevada","interactions":[],"lastModifiedDate":"2021-10-01T14:08:17.905417","indexId":"70216818","displayToPublicDate":"2017-06-04T08:04:21","publicationYear":"2017","noYear":false,"publicationType":{"id":25,"text":"Newsletter"},"publicationSubtype":{"id":30,"text":"Newsletter"},"seriesTitle":{"id":9365,"text":"Mountain Views","active":true,"publicationSubtype":{"id":30}},"title":"Mapping climate change refugia in the Sierra Nevada","docAbstract":"No abstract available.
","language":"English","publisher":"U.S. Forest Service","usgsCitation":"Morelli, T.L., 2017, Mapping climate change refugia in the Sierra Nevada: Mountain Views, v. 11, no. 2, p. 7-10.","productDescription":"4 p.","startPage":"7","endPage":"10","ipdsId":"IP-108363","costCenters":[{"id":5080,"text":"Northeast Climate Adaptation Science Center","active":true,"usgs":true}],"links":[{"id":386202,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"},{"id":386179,"rank":1,"type":{"id":15,"text":"Index Page"},"url":"https://www.fs.fed.us/psw/cirmount/publications/pdf/Mtn_Views_dec_17.pdf"}],"country":"United States","state":"California","otherGeospatial":"Sierra Nevada","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -120.1904296875,\n 41.77131167976407\n ],\n [\n -121.201171875,\n 41.42625319507269\n ],\n [\n -121.57470703125,\n 40.01078714046552\n ],\n [\n -120.12451171875,\n 37.52715361723378\n ],\n [\n -118.71826171875,\n 35.782170703266075\n ],\n [\n -116.806640625,\n 36.19109202182454\n ],\n [\n -117.66357421875,\n 37.09023980307208\n ],\n [\n -118.67431640625,\n 38.13455657705411\n ],\n [\n -119.81689453125,\n 39.317300373271024\n ],\n [\n -120.1904296875,\n 41.77131167976407\n ]\n ]\n ]\n }\n }\n ]\n}","volume":"11","issue":"2","noUsgsAuthors":false,"publicationStatus":"PW","contributors":{"authors":[{"text":"Morelli, Toni Lyn 0000-0001-5865-5294 tmorelli@usgs.gov","orcid":"https://orcid.org/0000-0001-5865-5294","contributorId":197458,"corporation":false,"usgs":true,"family":"Morelli","given":"Toni","email":"tmorelli@usgs.gov","middleInitial":"Lyn","affiliations":[{"id":5080,"text":"Northeast Climate Adaptation Science Center","active":true,"usgs":true},{"id":411,"text":"National Climate Change and Wildlife Science Center","active":true,"usgs":true}],"preferred":true,"id":806383,"contributorType":{"id":1,"text":"Authors"},"rank":1}]}} ,{"id":70187570,"text":"fs20173035 - 2017 - Magnetic monitoring in Saguaro National Park","interactions":[],"lastModifiedDate":"2020-07-13T14:38:50.422625","indexId":"fs20173035","displayToPublicDate":"2017-06-02T18:00:00","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":"2017-3035","title":"Magnetic monitoring in Saguaro National Park","docAbstract":"On a sandy, arid plain, near the Rincon Mountain Visitor Center of Saguaro National Park, tucked in among brittlebush, creosote, and other hardy desert plants, is an unusual type of observatory—a small unmanned station that is used for monitoring the Earth’s variable magnetic field. Named for the nearby city of Tucson, Arizona, the observatory is 1 of 14 that the Geomagnetism Program of the U.S. Geological Survey operates at various locations across the United States and Territories.
Data from USGS magnetic observatories, including the Tucson observatory, as well as observatories operated by institutions in other countries, record a variety of signals related to a wide diversity of physical phenomena in the Earth’s interior and its surrounding outer-space environment. The data are used for geomagnetic mapping and surveying, for fundamental scientific research, and for assessment of magnetic storms, which can be hazardous for the activities and infrastructure of our modern, technologically based society. The U.S. Geological Survey observatory service is an integral part of a U.S. national project for monitoring and assessing space weather hazards.
","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/fs20173035","usgsCitation":"Love, J.J., Finn, C.A., Gamez Valdez, Y.C., Swann, Don, 2017, Magnetic monitoring in Saguaro National Park: U.S. Geological Survey Fact Sheet 2017–3035, 2 p., https://doi.org/10.3133/fs20173035.","productDescription":"2 p.","onlineOnly":"N","ipdsId":"IP-084937","costCenters":[{"id":300,"text":"Geologic Hazards Science Center","active":true,"usgs":true}],"links":[{"id":342004,"rank":2,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/fs/2017/3035/fs20173035.pdf","text":"Report","size":"14.1 MB","linkFileType":{"id":1,"text":"pdf"},"description":"FS 2017-3035"},{"id":342003,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/fs/2017/3035/coverthb3.jpg"}],"country":"United States","state":"Arizona","otherGeospatial":"Saguaro National Park","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -111.16567611694336,\n 32.24779674710343\n ],\n [\n -111.16567611694336,\n 32.24924855874583\n ],\n [\n -111.10559463500975,\n 32.24924855874583\n ],\n [\n -111.10559463500975,\n 32.265071800242445\n ],\n [\n -111.11383438110352,\n 32.265071800242445\n ],\n [\n -111.11417770385742,\n 32.27218410189552\n ],\n [\n -111.12258911132812,\n 32.272474388080994\n ],\n [\n -111.12224578857422,\n 32.29380787959086\n ],\n [\n -111.10490798950194,\n 32.29453342030047\n ],\n [\n -111.10559463500975,\n 32.30164341194408\n ],\n [\n -111.11434936523438,\n 32.30178850800702\n ],\n [\n -111.11417770385742,\n 32.30483547166609\n ],\n [\n -111.10198974609374,\n 32.30570601389429\n ],\n [\n -111.10370635986328,\n 32.30889793050953\n ],\n [\n -111.10010147094727,\n 32.30904301495666\n ],\n [\n -111.10027313232422,\n 32.31092909162695\n ],\n [\n -111.09563827514648,\n 32.31179957530851\n ],\n [\n -111.09186172485352,\n 32.313540517578666\n ],\n [\n -111.08945846557616,\n 32.31629694107953\n ],\n [\n -111.08963012695312,\n 32.32326016365093\n ],\n [\n -111.10559463500975,\n 32.32311510197487\n ],\n [\n -111.10559463500975,\n 32.319923686309146\n ],\n [\n -111.10937118530273,\n 32.32006875309753\n ],\n [\n -111.10971450805664,\n 32.323405225094625\n ],\n [\n -111.11434936523438,\n 32.32326016365093\n ],\n [\n -111.11400604248047,\n 32.31615186824962\n ],\n [\n -111.12190246582031,\n 32.31658708604226\n ],\n [\n -111.12258911132812,\n 32.33471930167625\n ],\n [\n -111.126708984375,\n 32.33471930167625\n ],\n [\n -111.12653732299805,\n 32.3377651576181\n ],\n [\n -111.13151550292969,\n 32.33791019582042\n ],\n [\n -111.13134384155273,\n 32.35212281198644\n ],\n [\n -111.1402702331543,\n 32.352557856852634\n ],\n [\n -111.14009857177733,\n 32.35560311232581\n ],\n [\n -111.14919662475586,\n 32.355458102485485\n ],\n [\n -111.14953994750977,\n 32.352267827174344\n ],\n [\n -111.18267059326172,\n 32.352267827174344\n ],\n [\n -111.18267059326172,\n 32.33529947261555\n ],\n [\n -111.16722106933594,\n 32.335444514769286\n ],\n [\n -111.16704940795897,\n 32.306431459362386\n ],\n [\n -111.18387222290038,\n 32.306431459362386\n ],\n [\n -111.18370056152344,\n 32.31368559459092\n ],\n [\n -111.23588562011719,\n 32.31368559459092\n ],\n [\n -111.23588562011719,\n 32.306431459362386\n ],\n [\n -111.21820449829102,\n 32.30599619277848\n ],\n [\n -111.21820449829102,\n 32.29148611029876\n ],\n [\n -111.23554229736328,\n 32.29163122262151\n ],\n [\n -111.23537063598633,\n 32.27755424598\n ],\n [\n -111.24395370483398,\n 32.277699380597454\n ],\n [\n -111.24429702758789,\n 32.27029721905168\n ],\n [\n -111.23537063598633,\n 32.26986177897686\n ],\n [\n -111.23537063598633,\n 32.26260413677006\n ],\n [\n -111.21837615966795,\n 32.26274929530214\n ],\n [\n -111.21820449829102,\n 32.24837747454558\n ],\n [\n -111.16567611694336,\n 32.24779674710343\n ]\n ]\n ]\n }\n }\n ]\n}","contact":"Geologic Hazards Science Center
U.S. Geological Survey
Box 25046, MS-966
Denver, CO 80225-0046
An evaluation of trends in hydrologic and water quality conditions and estimation of water budgets through 2013 was done by the U.S. Geological Survey in cooperation with the Chester County Water Resources Authority. Long-term hydrologic, meteorologic, and biologic data collected in Chester County, Pennsylvania, which included streamflow, groundwater levels, surface-water quality, biotic integrity, precipitation, and air temperature were analyzed to determine possible trends or changes in hydrologic conditions. Statistically significant trends were determined by applying the Kendall rank correlation test; the magnitudes of the trends were determined using the Sen slope estimator. Water budgets for eight selected watersheds were updated and a new water budget was developed for the Marsh Creek watershed. An average water budget for Chester County was developed using the eight selected watersheds and the new Marsh Creek water budget.
Annual and monthly mean streamflow, base flow, and runoff were analyzed for trends at 10 streamgages. The periods of record at the 10 streamgages ranged from 1961‒2013 to 1988‒2013. The only statistically significant trend for annual mean streamflow was for West Branch Brandywine Creek near Honey Brook, Pa. (01480300) where annual mean streamflow increased 1.6 cubic feet per second (ft3/s) per decade. The greatest increase in monthly mean streamflow was for Brandywine Creek at Chadds Ford, Pa. (01481000) for December; the increase was 47 ft3/s per decade. No statistically significant trends in annual mean base flow or runoff were determined for the 10 streamgages. The greatest increase in monthly mean base flow was for Brandywine Creek at Chadds Ford, Pa. (01481000) for December; the increase was 26 ft3/s per decade.
The magnitude of peaks greater than a base streamflow was analyzed for trends for 12 streamgages. The period of record at the 12 stream gages ranged from 1912‒2012 to 2004–11. Fifty percent of the streamgages showed a small statistically significant increase in peaks greater than the base streamflow. The greatest increase was for Brandywine Creek at Chadds Ford, Pa. (01481000) during 1962‒2012; the increase was 1.8 ft3/s per decade. There were no statistically significant trends in the number of floods equal to or greater than the 2-year recurrence interval flood flow.
Twenty‒one monitoring wells were evaluated for statistically significant trends in annual mean water level, minimum annual water level, maximum annual water level, and annual range in water-level fluctuations. For four wells, a small statistically significant increase in annual mean water level was determined that ranged from 0.16 to 0.7 feet per decade. There was poor or no correlation between annual mean groundwater levels and annual mean streamflow and base flow. No correlation was determined between annual mean groundwater level and annual precipitation. Despite rapid population growth and land-use change since 1950, there appears to have been little or no detrimental effects on groundwater levels in 21 monitoring wells.
Long-term precipitation and temperature data were available from the West Chester (1893‒2013) and Phoenixville, Pa. (1915‒2013) National Oceanic and Atmospheric Administration (NOAA) weather stations. No statistically significant trends in annual mean precipitation or annual mean temperature were determined for either station. Both weather stations had a significant decrease in the number of days per year with precipitation greater than or equal to 0.1 inch. Annual mean minimum and maximum temperatures from the NOAA Southeastern Piedmont Climate Division increased 0.2 degrees Fahrenheit (F) per decade between 1896 and 2014. The number of days with a maximum temperature equal to or greater than 90 degrees F increased at West Chester and decreased at Phoenixville. No statistically significant trend was determined for annual snowfall amounts.
Data from 1974 to 2013 for three stream water-quality monitors in the Brandywine Creek watershed were evaluated. The monitors are on the West Branch Brandywine Creek at Modena, Pa. (01480617), East Branch Brandywine Creek below Downingtown, Pa. (01480870), and Brandywine Creek at Chadds Ford, Pa. (01481000). Statistically significant upward trends were determined for annual mean specific conductance at all three stations, indicating the total dissolved solids load has been increasing. If the current trend continues, the annual mean specific conductance could almost double from 1974 to 2050. The increase in specific conductance likely is due to increases in chloride concentrations, which have been increasing steadily over time at all three stations. No correlation was found between monthly mean specific conductance and monthly mean streamflow or base flow. Statistically significant upward trends in pH were determined for all three stations. Statistically significant upward trends in stream temperature were determined for East Branch Brandywine Creek below Downingtown, Pa. (01480870) and Brandywine Creek at Chadds Ford, Pa. (01481000). The stream water-quality data indicate substantial increases in the minimum daily dissolved oxygen concentrations in the Brandywine Creek over time.
The Chester County Index of Biotic Integrity (CC-IBI) determined for 1998‒2013 was evaluated for the five biological sampling sites collocated with streamgages. CC-IBI scores are based on a 0‒100 scale with higher scores indicating better stream quality. Statistically significant upward trends in the CC-IBI were determined for West Branch Brandywine Creek at Modena, Pa. (01480617) and East Branch Brandywine Creek below Downingtown, Pa. (01480870). No correlation was found between the CC-IBI and streamflow, precipitation, or stream specific conductance, pH, temperature, or dissolved oxygen concentration.
A Chester County average water budget was developed using the nine estimated watershed water budgets. Average precipitation was 48.4 inches, and average streamflow was 21.4 inches. Average runoff and base flow were 8.3 and 13.1 inches, respectively, and average evapotranspiration and estimation of errors was 27.2 inches.\"
","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/sir20175025","collaboration":"Prepared in cooperation with the Chester County Water Resources Authority","usgsCitation":"Sloto, R.A., and Reif, A.G., 2017, Evaluation of long-term trends in hydrologic and water-quality conditions, and estimation of water budgets through 2013, Chester County, Pennsylvania (ver.1.1, July 2017): U.S. Geological Survey Scientific Investigations Report 2017–5025, 59 p., https://doi.org/10.3133/sir20175025.","productDescription":"vii, 59 p.","numberOfPages":"71","onlineOnly":"Y","additionalOnlineFiles":"Y","ipdsId":"IP-064731","costCenters":[{"id":532,"text":"Pennsylvania Water Science Center","active":true,"usgs":true}],"links":[{"id":341981,"rank":2,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/sir/2017/5025/sir20175025.pdf","text":"Report","size":"8.49 MB","linkFileType":{"id":1,"text":"pdf"},"description":"SIR 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1.0: Originally posted June 2,2017; Version 1.1: July 10, 2017","contact":"Director, Pennsylvania Water Science Center
U.S. Geological Survey
215 Limekiln Road
New Cumberland, PA 17070
Habitat and species conservation plans usually rely on monitoring to assess progress towards conservation goals. Southern California, USA, is a hotspot of biodiversity and home to many federally endangered and threatened species. Here, several regional multi-species conservation plans have been implemented to balance development and conservation goals, including in San Diego County. In the San Diego County Management Strategic Plan Area (MSPA), a monitoring framework for the preserve system has been developed with a focus on species monitoring, vegetation monitoring, threats monitoring and abiotic monitoring. Genetic sampling over time (genetic monitoring) has proven useful in gathering species presence and abundance data and detecting population trends, particularly related to species and threats monitoring objectives. This report reviews genetic concepts and techniques of genetics that relate to monitoring goals and outlines components of a genetic monitoring scheme that could be applied in San Diego or in other monitoring frameworks throughout the Nation.
","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/ofr20171061","collaboration":"Prepared for San Diego Association of Governments","usgsCitation":"Vandergast, A.G., 2017, Incorporating genetic sampling in long-term monitoring and adaptive management in the San Diego County Management Strategic Plan Area, southern California: U.S. Geological Survey Open-File Report 2017-1061, 32 p., https://doi.org/10.3133/ofr20171061.","productDescription":"iv, 34 p.","onlineOnly":"Y","ipdsId":"IP-085941","costCenters":[{"id":651,"text":"Western Ecological Research Center","active":true,"usgs":true}],"links":[{"id":342051,"rank":2,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/of/2017/1061/ofr20171061.pdf","text":"Report","size":"690 KB","linkFileType":{"id":1,"text":"pdf"},"description":"OFR 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U.S. Geological Survey
3020 State University Drive East
Sacramento, California 95819
We present a detailed example of how a subbasin develops adjacent to a transfer zone in the Rio Grande rift. The Embudo transfer zone in the Rio Grande rift is considered one of the classic examples and has been used as the inspiration for several theoretical models. Despite this attention, the history of its development into a major rift structure is poorly known along its northern extent near Taos, New Mexico. Geologic evidence for all but its young rift history is concealed under Quaternary cover. We focus on understanding the pre-Quaternary evidence that is in the subsurface by integrating diverse pieces of geologic and geophysical information. As a result, we present a substantively new understanding of the tectonic configuration and evolution of the northern extent of the Embudo fault and its adjacent subbasin.
We integrate geophysical, borehole, and geologic information to interpret the subsurface configuration of the rift margins formed by the Embudo and Sangre de Cristo faults and the geometry of the subbasin within the Taos embayment. Key features interpreted include (1) an imperfect D-shaped subbasin that slopes to the east and southeast, with the deepest point ∼2 km below the valley floor located northwest of Taos at ∼36° 26′N latitude and 105° 37′W longitude; (2) a concealed Embudo fault system that extends as much as 7 km wider than is mapped at the surface, wherein fault strands disrupt or truncate flows of Pliocene Servilleta Basalt and step down into the subbasin with a minimum of 1.8 km of vertical displacement; and (3) a similar, wider than expected (5–7 km) zone of stepped, west-down normal faults associated with the Sangre de Cristo range front fault.
From the geophysical interpretations and subsurface models, we infer relations between faulting and flows of Pliocene Servilleta Basalt and older, buried basaltic rocks that, combined with geologic mapping, suggest a revised rift history involving shifts in the locus of fault activity as the Taos subbasin developed. We speculate that faults related to north-striking grabens at the end of Laramide time formed the first west-down master faults. The Embudo fault may have initiated in early Miocene southwest of the Taos region. Normal-oblique slip on these early fault strands likely transitioned in space and time to dominantly left-lateral slip as the Embudo fault propagated to the northeast. During and shortly after eruption of Servilleta Basalt, proto-Embudo fault strands were active along and parallel to the modern, NE-aligned Rio Pueblo de Taos, ∼4–7 km basinward of the modern, mapped Embudo fault zone. Faults along the northeastern subbasin margin had northwest strikes for most of the period of subbasin formation and were located ∼5–7 km basinward of the modern Sangre de Cristo fault. The locus of fault activity shifted to more northerly striking faults within 2 km of the modern range front sometime after Servilleta volcanism had ceased. The northerly faults may have linked with the northeasterly proto-Embudo faults at this time, concurrent with the development of N-striking Los Cordovas normal faults within the interior of the subbasin. By middle Pleistocene(?) time, the Los Cordovas faults had become inactive, and the linked Embudo–Sangre de Cristo fault system migrated to the south, to the modern range front.
","language":"English","publisher":"Geological Society of America","doi":"10.1130/GES01425.1","usgsCitation":"Grauch, V.J., Bauer, P.W., Drenth, B.J., and Kelson, K.I., 2017, A shifting rift—Geophysical insights into the evolution of Rio Grande rift margins and the Embudo transfer zone near Taos, New Mexico: Geosphere, v. 13, no. 3, p. 870-910, https://doi.org/10.1130/GES01425.1.","productDescription":"41 p.","startPage":"870","endPage":"910","ipdsId":"IP-076788","costCenters":[{"id":211,"text":"Crustal Geophysics and Geochemistry Science Center","active":true,"usgs":true}],"links":[{"id":469777,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.1130/ges01425.1","text":"Publisher Index Page"},{"id":342032,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","state":"New Mexico","city":"Taos","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -105.71456909179686,\n 36.32950909247666\n ],\n [\n -105.53466796874999,\n 36.32950909247666\n ],\n [\n -105.53466796874999,\n 36.474306755095235\n ],\n [\n -105.71456909179686,\n 36.474306755095235\n ],\n [\n -105.71456909179686,\n 36.32950909247666\n ]\n ]\n ]\n }\n }\n ]\n}","volume":"13","issue":"3","publishingServiceCenter":{"id":2,"text":"Denver PSC"},"noUsgsAuthors":false,"publicationDate":"2017-04-07","publicationStatus":"PW","scienceBaseUri":"59327922e4b0e9bd0eab54ed","contributors":{"authors":[{"text":"Grauch, V. J. S. 0000-0002-0761-3489 tien@usgs.gov","orcid":"https://orcid.org/0000-0002-0761-3489","contributorId":886,"corporation":false,"usgs":true,"family":"Grauch","given":"V.","email":"tien@usgs.gov","middleInitial":"J. S.","affiliations":[{"id":211,"text":"Crustal Geophysics and Geochemistry Science Center","active":true,"usgs":true}],"preferred":true,"id":696930,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Bauer, Paul W.","contributorId":145562,"corporation":false,"usgs":false,"family":"Bauer","given":"Paul","email":"","middleInitial":"W.","affiliations":[{"id":16150,"text":"New Mexico Bureau of Geology and Mineral Resources","active":true,"usgs":false}],"preferred":false,"id":696931,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Drenth, Benjamin J. 0000-0002-3954-8124 bdrenth@usgs.gov","orcid":"https://orcid.org/0000-0002-3954-8124","contributorId":1315,"corporation":false,"usgs":true,"family":"Drenth","given":"Benjamin","email":"bdrenth@usgs.gov","middleInitial":"J.","affiliations":[{"id":211,"text":"Crustal Geophysics and Geochemistry Science Center","active":true,"usgs":true}],"preferred":true,"id":696932,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Kelson, Keith I.","contributorId":192585,"corporation":false,"usgs":false,"family":"Kelson","given":"Keith","email":"","middleInitial":"I.","affiliations":[],"preferred":false,"id":696933,"contributorType":{"id":1,"text":"Authors"},"rank":4}]}} ,{"id":70188152,"text":"70188152 - 2017 - Monitoring the cooling of the 1959 Kīlauea Iki lava lake using surface magnetic measurements","interactions":[],"lastModifiedDate":"2017-06-02T11:00:03","indexId":"70188152","displayToPublicDate":"2017-06-02T00:00:00","publicationYear":"2017","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":1109,"text":"Bulletin of Volcanology","active":true,"publicationSubtype":{"id":10}},"title":"Monitoring the cooling of the 1959 Kīlauea Iki lava lake using surface magnetic measurements","docAbstract":"Lava lakes can be considered as proxies for small magma chambers, offering a unique opportunity to investigate magma evolution and solidification. Repeated magnetic ground surveys over more than 50 years each show a large vertical magnetic intensity anomaly associated with Kīlauea Iki Crater, partly filled with a lava lake during the 1959 eruption of Kīlauea Volcano (Island of Hawai’i). The magnetic field values recorded across the Kīlauea Iki crater floor and the cooling lava lake below result from three simple effects: the static remnant magnetization of the rocks forming the steep crater walls, the solidifying lava lake crust, and the hot, but shrinking, paramagnetic non-magnetic lens (>540 °C). We calculate 2D magnetic models to reconstruct the temporal evolution of the geometry of this non-magnetic body, its depth below the surface, and its thickness. Our results are in good agreement with the theoretical increase in thickness of the solidifying crust with time. Using the 2D magnetic models and the theoretical curve for crustal growth over a lava lake, we estimate that the former lava lake will be totally cooled below the Curie temperature in about 20 years. This study shows the potential of magnetic methods for detecting and monitoring magmatic intrusions at various scales.
","language":"English","publisher":"Springer","doi":"10.1007/s00445-017-1119-7","usgsCitation":"Gailler, L., and Kauahikaua, J.P., 2017, Monitoring the cooling of the 1959 Kīlauea Iki lava lake using surface magnetic measurements: Bulletin of Volcanology, v. 79, p. 1-7, https://doi.org/10.1007/s00445-017-1119-7.","productDescription":"Article 40; 7 p.","startPage":"1","endPage":"7","ipdsId":"IP-080299","costCenters":[{"id":617,"text":"Volcano Science Center","active":true,"usgs":true}],"links":[{"id":342034,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","state":"Hawaii","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -155.2613639831543,\n 19.40443049681278\n ],\n [\n -155.23492813110352,\n 19.40443049681278\n ],\n [\n -155.23492813110352,\n 19.421430209692744\n ],\n [\n -155.2613639831543,\n 19.421430209692744\n ],\n [\n -155.2613639831543,\n 19.40443049681278\n ]\n ]\n ]\n }\n }\n ]\n}","volume":"79","publishingServiceCenter":{"id":14,"text":"Menlo Park PSC"},"noUsgsAuthors":false,"publicationDate":"2017-05-06","publicationStatus":"PW","scienceBaseUri":"59327922e4b0e9bd0eab54f4","contributors":{"authors":[{"text":"Gailler, Lydie 0000-0002-8132-2428","orcid":"https://orcid.org/0000-0002-8132-2428","contributorId":192584,"corporation":false,"usgs":false,"family":"Gailler","given":"Lydie","email":"","affiliations":[],"preferred":false,"id":696928,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Kauahikaua, James P. 0000-0003-3777-503X jimk@usgs.gov","orcid":"https://orcid.org/0000-0003-3777-503X","contributorId":2146,"corporation":false,"usgs":true,"family":"Kauahikaua","given":"James","email":"jimk@usgs.gov","middleInitial":"P.","affiliations":[{"id":617,"text":"Volcano Science Center","active":true,"usgs":true}],"preferred":true,"id":696927,"contributorType":{"id":1,"text":"Authors"},"rank":2}]}} ,{"id":70188150,"text":"70188150 - 2017 - Methane and benzene in drinking-water wells overlying the Eagle Ford, Fayetteville, and Haynesville Shale hydrocarbon production areas","interactions":[],"lastModifiedDate":"2018-09-25T09:37:10","indexId":"70188150","displayToPublicDate":"2017-06-02T00:00:00","publicationYear":"2017","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":1565,"text":"Environmental Science & Technology","onlineIssn":"1520-5851","printIssn":"0013-936X","active":true,"publicationSubtype":{"id":10}},"title":"Methane and benzene in drinking-water wells overlying the Eagle Ford, Fayetteville, and Haynesville Shale hydrocarbon production areas","docAbstract":"Water wells (n = 116) overlying the Eagle Ford, Fayetteville, and Haynesville Shale hydrocarbon production areas were sampled for chemical, isotopic, and groundwater-age tracers to investigate the occurrence and sources of selected hydrocarbons in groundwater. Methane isotopes and hydrocarbon gas compositions indicate most of the methane in the wells was biogenic and produced by the CO2 reduction pathway, not from thermogenic shale gas. Two samples contained methane from the fermentation pathway that could be associated with hydrocarbon degradation based on their co-occurrence with hydrocarbons such as ethylbenzene and butane. Benzene was detected at low concentrations (<0.15 μg/L), but relatively high frequencies (2.4–13.3% of samples), in the study areas. Eight of nine samples containing benzene had groundwater ages >2500 years, indicating the benzene was from subsurface sources such as natural hydrocarbon migration or leaking hydrocarbon wells. One sample contained benzene that could be from a surface release associated with hydrocarbon production activities based on its age (10 ± 2.4 years) and proximity to hydrocarbon wells. Groundwater travel times inferred from the age-data indicate decades or longer may be needed to fully assess the effects of potential subsurface and surface releases of hydrocarbons on the wells.
","language":"English","publisher":"ACS Publications","doi":"10.1021/acs.est.7b00746","usgsCitation":"McMahon, P.B., Barlow, J.R., Engle, M.A., Belitz, K., Ging, P.B., Hunt, A.G., Jurgens, B.C., Kharaka, Y.K., Tollett, R.W., and Kresse, T.M., 2017, Methane and benzene in drinking-water wells overlying the Eagle Ford, Fayetteville, and Haynesville Shale hydrocarbon production areas: Environmental Science & Technology, v. 51, no. 12, p. 6727-6734, https://doi.org/10.1021/acs.est.7b00746.","productDescription":"8 p.","startPage":"6727","endPage":"6734","ipdsId":"IP-084127","costCenters":[{"id":154,"text":"California Water Science Center","active":true,"usgs":true},{"id":191,"text":"Colorado Water Science Center","active":true,"usgs":true}],"links":[{"id":438307,"rank":0,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/F77D2SC4","text":"USGS data release","linkHelpText":"Methane and benzene in drinking-water wells overlying the Eagle Ford, Fayetteville, and Haynesville Shale hydrocarbon production areas"},{"id":342035,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -100.92041015625,\n 27.6251403350933\n ],\n [\n -91.64794921875,\n 27.6251403350933\n ],\n [\n -91.64794921875,\n 35.92464453144099\n ],\n [\n -100.92041015625,\n 35.92464453144099\n ],\n [\n -100.92041015625,\n 27.6251403350933\n ]\n ]\n ]\n }\n }\n ]\n}","volume":"51","issue":"12","publishingServiceCenter":{"id":2,"text":"Denver PSC"},"noUsgsAuthors":false,"publicationDate":"2017-05-31","publicationStatus":"PW","scienceBaseUri":"59327923e4b0e9bd0eab54f9","contributors":{"authors":[{"text":"McMahon, Peter B. 0000-0001-7452-2379 pmcmahon@usgs.gov","orcid":"https://orcid.org/0000-0001-7452-2379","contributorId":724,"corporation":false,"usgs":true,"family":"McMahon","given":"Peter","email":"pmcmahon@usgs.gov","middleInitial":"B.","affiliations":[{"id":191,"text":"Colorado Water Science Center","active":true,"usgs":true}],"preferred":true,"id":696915,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Barlow, Jeannie R. B. 0000-0002-0799-4656 jbarlow@usgs.gov","orcid":"https://orcid.org/0000-0002-0799-4656","contributorId":3701,"corporation":false,"usgs":true,"family":"Barlow","given":"Jeannie","email":"jbarlow@usgs.gov","middleInitial":"R. 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Western Branch","active":true,"usgs":true}],"preferred":true,"id":696953,"contributorType":{"id":1,"text":"Authors"},"rank":8},{"text":"Tollett, Roland W. 0000-0002-4726-5845 rtollett@usgs.gov","orcid":"https://orcid.org/0000-0002-4726-5845","contributorId":1896,"corporation":false,"usgs":true,"family":"Tollett","given":"Roland","email":"rtollett@usgs.gov","middleInitial":"W.","affiliations":[{"id":24708,"text":"Lower Mississippi-Gulf Water Science Center","active":true,"usgs":true}],"preferred":true,"id":696921,"contributorType":{"id":1,"text":"Authors"},"rank":9},{"text":"Kresse, Timothy M. 0000-0003-1035-0672 tkresse@usgs.gov","orcid":"https://orcid.org/0000-0003-1035-0672","contributorId":2758,"corporation":false,"usgs":true,"family":"Kresse","given":"Timothy","email":"tkresse@usgs.gov","middleInitial":"M.","affiliations":[{"id":129,"text":"Arkansas Water Science Center","active":true,"usgs":true},{"id":24708,"text":"Lower Mississippi-Gulf Water Science Center","active":true,"usgs":true}],"preferred":true,"id":696922,"contributorType":{"id":1,"text":"Authors"},"rank":10}]}} ,{"id":70198379,"text":"70198379 - 2017 - Seasonal and spatial variabilities in northern Gulf of Alaska surface water iron concentrations driven by shelf sediment resuspension, glacial meltwater, a Yakutat eddy, and dust","interactions":[],"lastModifiedDate":"2018-08-02T11:57:27","indexId":"70198379","displayToPublicDate":"2017-06-01T11:57:20","publicationYear":"2017","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":1836,"text":"Global Biogeochemical Cycles","active":true,"publicationSubtype":{"id":10}},"title":"Seasonal and spatial variabilities in northern Gulf of Alaska surface water iron concentrations driven by shelf sediment resuspension, glacial meltwater, a Yakutat eddy, and dust","docAbstract":"Phytoplankton growth in the Gulf of Alaska (GoA) is limited by iron (Fe), yet Fe sources are poorly constrained. We examine the temporal and spatial distributions of Fe, and its sources in the GoA, based on data from three cruises carried out in 2010 from the Copper River (AK) mouth to beyond the shelf break. April data are the first to describe late winter Fe behavior before surface water nitrate depletion began. Sediment resuspension during winter and spring storms generated high “total dissolvable Fe” (TDFe) concentrations of ~1000 nmol kg−1 along the entire continental shelf, which decreased beyond the shelf break. In July, high TDFe concentrations were similar on the shelf, but more spatially variable, and driven by low‐salinity glacial meltwater. Conversely, dissolved Fe (DFe) concentrations in surface waters were far lower and more seasonally consistent, ranging from ~4 nmol kg−1 in nearshore waters to ~0.6–1.5 nmol kg−1seaward of the shelf break during April and July, despite dramatic depletion of nitrate over that period. The reasonably constant DFe concentrations are likely maintained during the year across the shelf by complexation by strong organic ligands, coupled with ample supply of labile particulate Fe. The April DFe data can be simulated using a simple numerical model that assumes a DFe flux from shelf sediments, horizontal transport by eddy diffusion, and removal by scavenging. Given how global change is altering many processes impacting the Fe cycle, additional studies are needed to examine controls on DFe in the Gulf of Alaska.
","language":"English","publisher":"Ameican Geophysical Union","doi":"10.1002/2016GB005493","usgsCitation":"Crusius, J., Schroth, A.W., Resing, J.A., Cullen, J., and Campbell, R.W., 2017, Seasonal and spatial variabilities in northern Gulf of Alaska surface water iron concentrations driven by shelf sediment resuspension, glacial meltwater, a Yakutat eddy, and dust: Global Biogeochemical Cycles, v. 31, no. 6, p. 942-960, https://doi.org/10.1002/2016GB005493.","productDescription":"19 p.","startPage":"942","endPage":"960","ipdsId":"IP-078971","costCenters":[{"id":119,"text":"Alaska Science Center Geology Minerals","active":true,"usgs":true}],"links":[{"id":469779,"rank":1,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.1002/2016gb005493","text":"Publisher Index Page"},{"id":438308,"rank":0,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/F7222S06","text":"USGS data release","linkHelpText":"Gulf of Alaska Shelf and Slope Iron and Nitrate data, Copper River Region, 2010"},{"id":356112,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","state":"Alaska","otherGeospatial":"Gulf of Alaska","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -147.5,\n 58\n ],\n [\n -143,\n 58\n ],\n [\n -143,\n 60.359564131824236\n ],\n [\n -147.5,\n 60.359564131824236\n ],\n [\n -147.5,\n 58\n ]\n ]\n ]\n }\n }\n ]\n}","volume":"31","issue":"6","publishingServiceCenter":{"id":12,"text":"Tacoma PSC"},"noUsgsAuthors":false,"publicationDate":"2017-06-03","publicationStatus":"PW","scienceBaseUri":"5b6fc67ce4b0f5d57878eb84","contributors":{"authors":[{"text":"Crusius, John 0000-0003-2554-0831 jcrusius@usgs.gov","orcid":"https://orcid.org/0000-0003-2554-0831","contributorId":2155,"corporation":false,"usgs":true,"family":"Crusius","given":"John","email":"jcrusius@usgs.gov","affiliations":[{"id":119,"text":"Alaska Science Center Geology Minerals","active":true,"usgs":true},{"id":114,"text":"Alaska Science Center","active":true,"usgs":true}],"preferred":true,"id":741299,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Schroth, Andrew W.","contributorId":192042,"corporation":false,"usgs":false,"family":"Schroth","given":"Andrew","email":"","middleInitial":"W.","affiliations":[{"id":17809,"text":"University of Vermont, Burlington","active":true,"usgs":false}],"preferred":false,"id":741300,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Resing, Joseph A.","contributorId":206619,"corporation":false,"usgs":false,"family":"Resing","given":"Joseph","email":"","middleInitial":"A.","affiliations":[{"id":37351,"text":"University of Washington; Joint Institute for the Study of the Atmosphere and the Ocean","active":true,"usgs":false}],"preferred":false,"id":741301,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Cullen, Jay","contributorId":206620,"corporation":false,"usgs":false,"family":"Cullen","given":"Jay","email":"","affiliations":[{"id":37352,"text":"University of Victoria; School of Earth and Ocean Sciences Victoria, B.C.","active":true,"usgs":false}],"preferred":false,"id":741302,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Campbell, Robert W.","contributorId":206621,"corporation":false,"usgs":false,"family":"Campbell","given":"Robert","email":"","middleInitial":"W.","affiliations":[{"id":37353,"text":"Prince William Sound Science Center, Cordova, AK","active":true,"usgs":false}],"preferred":false,"id":741303,"contributorType":{"id":1,"text":"Authors"},"rank":5}]}} ,{"id":70202279,"text":"70202279 - 2017 - Thermal and petrologic constraints on lower crustal melt accumulation under the Salton Sea Geothermal Field","interactions":[],"lastModifiedDate":"2019-02-20T10:52:59","indexId":"70202279","displayToPublicDate":"2017-06-01T10:52:53","publicationYear":"2017","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":1427,"text":"Earth and Planetary Science Letters","active":true,"publicationSubtype":{"id":10}},"title":"Thermal and petrologic constraints on lower crustal melt accumulation under the Salton Sea Geothermal Field","docAbstract":"In the Salton Sea region of southern California (USA), concurrent magmatism, extension, subsidence, and sedimentation over the past 0.5 to 1.0 Ma have led to the creation of the Salton Sea Geothermal Field (SSGF)—the second largest and hottest geothermal system in the continental United States—and the small-volume rhyolite eruptions that created the Salton Buttes. In this study, we determine the flux of mantle-derived basaltic magma that would be required to produce the elevated average heat flow and sustain the magmatic roots of rhyolite volcanism observed at the surface of the Salton Sea region. We use a 2D thermal model to show that a lower-crustal, partially molten mush containing