Using a geology-based assessment methodology, the U.S. Geological Survey estimated undiscovered, technically recoverable continuous mean resources of 96.5 trillion cubic feet of gas in the Middle Devonian Marcellus Shale of the Appalachian Basin Province.
","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston,VA","doi":"10.3133/fs20193050","usgsCitation":"Higley, D.K., Enomoto, C.B., Leathers-Miller, H.M., Ellis, G.S., Mercier, T.J., Schenk, C.J., Trippi, M.H., Le, P.A., Brownfield, M.E., Woodall, C.A., Marra, K.R., and Tennyson, M.E., 2019, Assessment of undiscovered gas resources in the Middle Devonian Marcellus Shale of the Appalachian Basin Province, 2019: U.S. Geological Survey Fact Sheet 2019–3050, 2 p., https://doi.org/10.3133/fs20193050.","productDescription":"Report: 2 p.; Data Release","onlineOnly":"N","ipdsId":"IP-108590","costCenters":[{"id":164,"text":"Central Energy Resources Science Center","active":true,"usgs":true},{"id":241,"text":"Eastern Energy Resources Science Center","active":true,"usgs":true}],"links":[{"id":382921,"rank":5,"type":{"id":22,"text":"Related Work"},"url":"https://pubs.usgs.gov/publication/70207372","text":"Controls on Petroleum Resources for the Devonian Marcellus Shale in the Appalachian Basin Province, Kentucky, West Virginia, Ohio, Pennsylvania, and New York"},{"id":382920,"rank":4,"type":{"id":22,"text":"Related Work"},"url":"https://pubs.usgs.gov/publication/70217792","text":"Burial History Reconstruction of the Appalachian Basin in Kentucky, West Virginia, Ohio, Pennsylvania, and New York, Using 1D Petroleum System Models"},{"id":367867,"rank":2,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/fs/2019/3050/fs20193050.pdf","text":"Report","size":"984 kB","linkFileType":{"id":1,"text":"pdf"},"description":"FS 2019-3050"},{"id":367866,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/fs/2019/3050/coverthb.jpg"},{"id":367907,"rank":3,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/P9S948U5","text":"USGS data release","description":"USGS data release","linkHelpText":"USGS National and Global Oil and Gas Assessment Project—Appalachian Basin Province, Middle Devonian Marcellus Shale Assessment Units and Input Data Forms"}],"country":"United States","state":"Maryland, New York, Ohio, Pennsylvania, Virginia, West Virginia","otherGeospatial":"Middle Devonian Marcellus Shale","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -83.73779296875,\n 36.56260003738545\n ],\n [\n -74.102783203125,\n 36.56260003738545\n ],\n [\n -74.102783203125,\n 43.052833917627936\n ],\n [\n -83.73779296875,\n 43.052833917627936\n ],\n [\n -83.73779296875,\n 36.56260003738545\n ]\n ]\n ]\n }\n }\n ]\n}","contact":"Director, Central Energy Resources Science Center
U.S. Geological Survey
Box 25046, MS-939
Denver, CO 80225-0046
Using a geology-based assessment methodology, the U.S. Geological Survey estimated undiscovered, technically recoverable continuous mean resources of 1.8 billion barrels of oil and 117.2 trillion cubic feet of gas in the Upper Ordovician Point Pleasant Formation and Utica Shale of the Appalachian Basin Province.
","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/fs20193044","usgsCitation":"Enomoto, C.B., Trippi, M.H., Higley, D.K., Drake, R.M., II, Gaswirth, S.B., Mercier, T.J., Brownfield, M.E., Leathers-Miller, H.M., Le, P.A., Marra, K.R., Tennyson, M.E., Woodall, C.A., and Schenk, C.J., 2019, Assessment of undiscovered continuous oil and gas resources in the Upper Ordovician Point Pleasant Formation and Utica Shale of the Appalachian Basin Province, 2019: U.S. Geological Survey Fact Sheet 2019–3044, 2 p., https://doi.org/10.3133/fs20193044.","productDescription":"Report: 2 p.; Data Release","onlineOnly":"N","costCenters":[{"id":164,"text":"Central Energy Resources Science Center","active":true,"usgs":true},{"id":241,"text":"Eastern Energy Resources Science Center","active":true,"usgs":true}],"links":[{"id":437313,"rank":4,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/P93RVVAL","text":"USGS data release","linkHelpText":"USGS National and Global Oil and Gas Assessment Project - Appalachian Basin Province, Point Pleasant Formation and Utica Shale Assessment Unit Boundaries and Assessment Input Data Forms"},{"id":367873,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/fs/2019/3044/coverthb.jpg"},{"id":367874,"rank":2,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/fs/2019/3044/fs20193044.pdf","text":"Report","size":"848 kB","linkFileType":{"id":1,"text":"pdf"},"description":"FS 2019-3044"},{"id":367898,"rank":3,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/P93RVVAL ","text":"USGS data release","description":"USGS data release","linkHelpText":"USGS National and Global Oil and Gas Assessment Project—Appalachian Basin Province, Point Pleasant Formation and Utica Shale Assessment Unit Boundaries and Assessment Input Data Forms"}],"country":"United States","state":"New York, Ohio, Pennsylvania, West Virginia","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -84.407958984375,\n 38.16047628099622\n ],\n [\n -74.981689453125,\n 38.16047628099622\n ],\n [\n -74.981689453125,\n 42.94838139765314\n ],\n [\n -84.407958984375,\n 42.94838139765314\n ],\n [\n -84.407958984375,\n 38.16047628099622\n ]\n ]\n ]\n }\n }\n ]\n}","contact":"Director, Central Energy Resources Science Center
U.S. Geological Survey
Box 25046, MS-939
Denver, CO 80225-0046
The worldwide network of unpaved roads is estimated to include at least 14 million km (8.7 million miles; 1). Although they are vital for local communities, these roads are expensive to maintain and may cause environmental damage through sediment and dust pollution (e.g., 2). Among aggregate-surfaced roads, locally available materials are often used as a surface wearing course, with little or no testing and sometimes no formal specification. The materials vary widely in quality and may deteriorate quickly. As a result, road managers may be forced to increase the frequency of maintenance grading and aggregate replacement to compensate for the poor performance. Improving the quality of surface aggregate on unpaved roads is one strategy for increasing road performance while also reducing environmental impacts. Although higher-quality aggregates require greater up-front investment, they can result in lower overall life-cycle costs by extending road life and reducing maintenance costs.
Driving Surface Aggregate (DSA) is an aggregate specification developed by the Pennsylvania State University Center for Dirt and Gravel Road Studies that is designed to achieve maximum compaction and resist erosion. The gradation of DSA, coupled with recommended optimum moisture and placement guidelines, results in a smoother, more tightly bound surface that preserves fine material rather than allowing it to escape as sediment or dust. In previous studies, DSA has been shown to reduce sediment runoff by 80-90% (3) and dust production by up to 90% (4) compared to existing road surface gradations. Although DSA has been used extensively in the state of Pennsylvania, USA, the specification is almost unknown elsewhere. The objective of this study was to demonstrate the benefits and limitations of DSA when deployed across a wider geographic area. We installed road sections of DSA at two federal lands sites in the eastern United States. Sediment runoff, dust production, and road surface condition on these sections were measured approximately 12 months post-construction. At both sites, DSA reduced sediment runoff by up to 91%, relative to traditional aggregates. At one site in Indiana, DSA also reduced dust production and aggregate loss. At the other site in Vermont, DSA and traditional aggregate sections performed similarly in dust production and road condition. Overall, this study 1) demonstrates that DSA can be an effective and environmentally responsible aggregate choice for unpaved roads, and 2) provides information on site conditions (e.g., roads near headwater streams) under which DSA is likely to be particularly beneficial.
","conferenceTitle":"12th International Conference on Low-Volume Roads","conferenceDate":"Sep 15-18, 2019","conferenceLocation":"Kalispell, MT","language":"English","publisher":"Transportation Research Board","usgsCitation":"Kunz, B.K., Chase, E.H., Bloser, S.M., Kestler, M.A., and Jutz, B., 2019, Benefits and limitations of installing driving surface aggregate at two federal lands sites, 12th International Conference on Low-Volume Roads, Kalispell, MT, Sep 15-18, 2019, p. 262-268.","productDescription":"7 p.","startPage":"262","endPage":"268","ipdsId":"IP-104494","costCenters":[{"id":192,"text":"Columbia Environmental Research Center","active":true,"usgs":true}],"links":[{"id":378529,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"},{"id":378482,"type":{"id":15,"text":"Index Page"},"url":"https://www.trb.org/Publications/Blurbs/179567.aspx"}],"country":"United States","state":"Indiana, Vermont","otherGeospatial":"Green Mountain National Forest, Muscatatuck National Wildlife Refuge","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -72.8887939453125,\n 42.75104599038353\n ],\n [\n -72.7569580078125,\n 43.113014204188914\n ],\n [\n -72.7899169921875,\n 43.393073720674415\n ],\n [\n -72.916259765625,\n 43.492782808225\n ],\n [\n -73.2623291015625,\n 43.33316939281732\n ],\n [\n -73.2952880859375,\n 42.767178634023345\n ],\n [\n -73.23486328124999,\n 42.74701217318067\n ],\n [\n -72.8887939453125,\n 42.75104599038353\n ]\n ]\n ]\n }\n },\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -85.8193588256836,\n 38.90920161982438\n ],\n [\n -85.77163696289061,\n 38.90920161982438\n ],\n [\n -85.77163696289061,\n 38.97088735291151\n ],\n [\n -85.8193588256836,\n 38.97088735291151\n ],\n [\n -85.8193588256836,\n 38.90920161982438\n ]\n ]\n ]\n }\n }\n ]\n}","noUsgsAuthors":false,"publicationStatus":"PW","contributors":{"authors":[{"text":"Kunz, Bethany K. 0000-0002-7193-9336 bkunz@usgs.gov","orcid":"https://orcid.org/0000-0002-7193-9336","contributorId":3798,"corporation":false,"usgs":true,"family":"Kunz","given":"Bethany","email":"bkunz@usgs.gov","middleInitial":"K.","affiliations":[{"id":192,"text":"Columbia Environmental Research Center","active":true,"usgs":true}],"preferred":true,"id":798945,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Chase, Eric H.","contributorId":240770,"corporation":false,"usgs":false,"family":"Chase","given":"Eric","email":"","middleInitial":"H.","affiliations":[{"id":48138,"text":"Pennsylvania State Center for Dirt and Gravel Road Studies","active":true,"usgs":false}],"preferred":false,"id":798946,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Bloser, Steve M.","contributorId":240771,"corporation":false,"usgs":false,"family":"Bloser","given":"Steve","email":"","middleInitial":"M.","affiliations":[{"id":48138,"text":"Pennsylvania State Center for Dirt and Gravel Road Studies","active":true,"usgs":false}],"preferred":false,"id":798947,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Kestler, Maureen A.","contributorId":240772,"corporation":false,"usgs":false,"family":"Kestler","given":"Maureen","email":"","middleInitial":"A.","affiliations":[{"id":590,"text":"U.S. Army Corps of Engineers","active":false,"usgs":false}],"preferred":false,"id":798948,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Jutz, Brandon","contributorId":240773,"corporation":false,"usgs":false,"family":"Jutz","given":"Brandon","email":"","affiliations":[{"id":36188,"text":"U.S. Fish and Wildlife Service","active":true,"usgs":false}],"preferred":false,"id":798949,"contributorType":{"id":1,"text":"Authors"},"rank":5}]}} ,{"id":70205851,"text":"70205851 - 2019 - A fuzzy logic approach for estimating recovery factors of miscible CO2-EOR projects in the United States","interactions":[],"lastModifiedDate":"2019-10-08T12:35:44","indexId":"70205851","displayToPublicDate":"2019-09-30T12:34:24","publicationYear":"2019","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":2419,"text":"Journal of Petroleum Science and Engineering","active":true,"publicationSubtype":{"id":10}},"title":"A fuzzy logic approach for estimating recovery factors of miscible CO2-EOR projects in the United States","docAbstract":"\"Recovery factor (RF) is one of the most fundamental parameters that define engineering and economical success of any operational phase in oil and gas production. The effectiveness of the operation, e.g. CO2-EOR (enhanced oil recovery with carbon dioxide injection), is usually defined by multiplying the resultant recovery factor by the original oil in place. Moreover, investment decisions for such engineering projects are also performed based on predicted recovery factors. Despite its importance, though, it is not easy to predict recovery factors as they are affected by many factors including the type of the recovery process, reservoir type, fluid properties, reservoir heterogeneity, depth, thickness, to name a few. The usual method of estimating recovery factors is laboratory experiments or numerical modeling, each of which has their own limitations due to data requirements, boundary conditions and scale effects.\nIn this work, a fuzzy inference system approach has been adopted to predict miscible CO2-EOR recovery factors of the major field applications in the United States with the premise that it can be used as a guidance tool for making decisions based on different inputs. The fuzzy system was build using a Mamdani-type fuzzy logic inference engine, and by using reservoir data compiled from different sources as inputs and recovery factors gathered from a literature survey. Due to the limited number of field cases that could be used for this purpose, 24 sets of applications were included in the study. Selected input variables were water saturation after waterflood (Sorw), well spacing, porosity, permeability, depth, net pay thickness, initial pressure, API gravity of oil, hydrocarbon pore volume CO2 injected, and reservoir lithology. The type of membership functions were decided based on the system’s predictive performance. The model showed reasonable predictive capability for the field observations of recovery factor despite the complexity of this parameter. In addition, since the fuzzy solution was multi-dimensional due to multiple inputs, system behavior was used to demonstrate response of miscible CO2-EOR recovery factor to different inputs.\n\"","language":"English","publisher":"Elsevier","doi":"10.1016/j.petrol.2019.106533","usgsCitation":"Karacan, C.O., 2019, A fuzzy logic approach for estimating recovery factors of miscible CO2-EOR projects in the United States: Journal of Petroleum Science and Engineering, v. 184, 106533, https://doi.org/10.1016/j.petrol.2019.106533.","productDescription":"106533","ipdsId":"IP-103343","costCenters":[{"id":241,"text":"Eastern Energy Resources Science Center","active":true,"usgs":true}],"links":[{"id":368100,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"},{"id":368097,"type":{"id":15,"text":"Index Page"},"url":"https://www.sciencedirect.com/science/article/pii/S0920410519309544"}],"volume":"184","publishingServiceCenter":{"id":9,"text":"Reston PSC"},"noUsgsAuthors":false,"publicationStatus":"PW","contributors":{"authors":[{"text":"Karacan, C. Ozgen 0000-0002-0947-8241","orcid":"https://orcid.org/0000-0002-0947-8241","contributorId":201991,"corporation":false,"usgs":true,"family":"Karacan","given":"C.","email":"","middleInitial":"Ozgen","affiliations":[{"id":241,"text":"Eastern Energy Resources Science Center","active":true,"usgs":true}],"preferred":true,"id":772619,"contributorType":{"id":1,"text":"Authors"},"rank":1}]}} ,{"id":70205979,"text":"70205979 - 2019 - Hemidactylus parvimaculatus (Sri Lankan spotted house gecko)","interactions":[],"lastModifiedDate":"2019-10-14T11:34:42","indexId":"70205979","displayToPublicDate":"2019-09-30T11:25:49","publicationYear":"2019","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":1898,"text":"Herpetological Review","active":true,"publicationSubtype":{"id":10}},"title":"Hemidactylus parvimaculatus (Sri Lankan spotted house gecko)","docAbstract":"USA: LOUISIANA: PLAQUEMINES PARISH: 0.15 km S of the intersection of LA-23 and Jump road, Venice (29.266630°N, 89.35570°W; WGS 84). 2 May 2019. V. C. Montross and W. McGighan. Verified by Aaron M. Bauer. Florida Museum of Natural History (UF 189238; photo voucher). New parish record. On 2 May 2019, three Hemidactylus parvimaculatus were observed after lifting an abandoned door left on the side of Jump Basin Road. An adult specimen was photographed. This record extends the known distribution of this species in Louisiana south of all previously recorded parishes and is 105 km SW of the species’ first recorded location in the state at Audubon Zoo, Orleans Parish (Heckard et al. 2013. IRCF Reptil. Amphib. 20:192–196). Four additional parishes in southeastern Louisiana have since been added to its known distribution including Jefferson (Borgardt 2015. Herpetol. Rev. 46:217), St. Tammany (Glorioso 2016. Herpetol. Rev. 47:81), St. John (Borgardt 2016. Herpetol. Rev. 47:258), and Tangipahoa (Erdman 2017. Herpetol. Rev. 48:125), as well as Chambers and Orange counties in east Texas (Davis and LaDuc 2019. Herpetol. Rev. 50:102).
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\"}}]}","volume":"50","issue":"3","noUsgsAuthors":false,"publicationStatus":"PW","contributors":{"authors":[{"text":"Pellacchia, C. M.","contributorId":219774,"corporation":false,"usgs":false,"family":"Pellacchia","given":"C.","email":"","middleInitial":"M.","affiliations":[],"preferred":false,"id":773153,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Glorioso, Brad M. 0000-0002-5400-7414 gloriosob@usgs.gov","orcid":"https://orcid.org/0000-0002-5400-7414","contributorId":4241,"corporation":false,"usgs":true,"family":"Glorioso","given":"Brad","email":"gloriosob@usgs.gov","middleInitial":"M.","affiliations":[{"id":455,"text":"National Wetlands Research Center","active":true,"usgs":true},{"id":17705,"text":"Wetland and Aquatic Research Center","active":true,"usgs":true}],"preferred":true,"id":773154,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Mendyk, R. W.","contributorId":219775,"corporation":false,"usgs":false,"family":"Mendyk","given":"R.","email":"","middleInitial":"W.","affiliations":[],"preferred":false,"id":773155,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Collen, C. A.","contributorId":219776,"corporation":false,"usgs":false,"family":"Collen","given":"C.","email":"","middleInitial":"A.","affiliations":[],"preferred":false,"id":773156,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Montross, V. C.","contributorId":219777,"corporation":false,"usgs":false,"family":"Montross","given":"V.","email":"","middleInitial":"C.","affiliations":[],"preferred":false,"id":773157,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"McGighan, W.","contributorId":219778,"corporation":false,"usgs":false,"family":"McGighan","given":"W.","email":"","affiliations":[],"preferred":false,"id":773158,"contributorType":{"id":1,"text":"Authors"},"rank":6},{"text":"Macedo, K.","contributorId":219779,"corporation":false,"usgs":false,"family":"Macedo","given":"K.","email":"","affiliations":[],"preferred":false,"id":773159,"contributorType":{"id":1,"text":"Authors"},"rank":7},{"text":"Maldonado, B. R. 0000-0002-9737-6922","orcid":"https://orcid.org/0000-0002-9737-6922","contributorId":219780,"corporation":false,"usgs":false,"family":"Maldonado","given":"B. R.","affiliations":[],"preferred":false,"id":773160,"contributorType":{"id":1,"text":"Authors"},"rank":8},{"text":"Morenc, I. N.","contributorId":219781,"corporation":false,"usgs":false,"family":"Morenc","given":"I.","email":"","middleInitial":"N.","affiliations":[],"preferred":false,"id":773161,"contributorType":{"id":1,"text":"Authors"},"rank":9}]}} ,{"id":70203865,"text":"70203865 - 2019 - Multivariate analysis of hydrochemical data for Jewel Cave, Wind Cave, and surrounding areas","interactions":[],"lastModifiedDate":"2019-12-03T11:08:09","indexId":"70203865","displayToPublicDate":"2019-09-30T11:05:08","publicationYear":"2019","noYear":false,"publicationType":{"id":18,"text":"Report"},"publicationSubtype":{"id":1,"text":"Federal Government Series"},"seriesTitle":{"id":53,"text":"Natural Resource Report","active":false,"publicationSubtype":{"id":1}},"seriesNumber":"NPS/JECA/NRR—2019/1883","title":"Multivariate analysis of hydrochemical data for Jewel Cave, Wind Cave, and surrounding areas","docAbstract":"Jewel Cave National Monument and Wind Cave National Park in South Dakota contain two of the six longest caves worldwide. These caves contain subterranean lakes that are important points of intersection between the water table of the Madison aquifer and the caves. During 2015 to 2017, several subterranean lakes were discovered in Jewel Cave, which previously was thought to be above the regional water table. The objectives of this study were to better understand the hydrology of the recently discovered lakes in Jewel Cave and to evaluate their relation or possible connection to similar subterranean lakes in Wind Cave. Both objectives align with National Park Service resource management purposes. Multivariate analysis, consisting of principal component analysis (PCA), cluster analysis, and end member mixing, was applied to hydrochemical data for 70 sites within and surrounding Jewel Cave and Wind Cave. Hydrochemical data consisted of the contents of major ions (Ca, Mg, Na, HCO3, Cl, Si, SO4), arsenic (As), strontium (Sr), uranium (U), stable isotopes of oxygen and hydrogen (δ18O, δ2H), radiogenic isotope ratios of strontium and uranium (87Sr/86Sr and 234U/238U), pH, and electrical conductivity (EC) in water samples. Five hydrogeologic domains were identified on the basis of PCA and cluster analysis for the area encompassing Jewel Cave and Wind Cave in the southern Black Hills. The Artesian 1 and Artesian 2 domains represent primarily artesian springs and surrounding groundwater, the East and West domains represent areas where Madison and Minnelusa aquifer rocks are at or near the land surface, and the Precambrian domain represents the Precambrian aquifer. Multivariate analysis indicates that the Jewel Cave area is part of the West domain and that Wind Cave is part of the East domain. End member mixing was applied to estimate that groundwater in the Jewel Cave area primarily was derived from the West domain and secondarily from the Precambrian domain. Jewel Cave and Wind Cave contain lakes that are well connected to regional groundwater flow in the Madison aquifer.","language":"English","publisher":"National Park Service","usgsCitation":"Long, A.J., Paces, J.B., and Eldridge, W.G., 2019, Multivariate analysis of hydrochemical data for Jewel Cave, Wind Cave, and surrounding areas: Natural Resource Report NPS/JECA/NRR—2019/1883, ix, 40 p.","productDescription":"ix, 40 p.","ipdsId":"IP-099296","costCenters":[{"id":622,"text":"Washington Water Science Center","active":true,"usgs":true}],"links":[{"id":369865,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"},{"id":364767,"type":{"id":11,"text":"Document"},"url":"https://irma.nps.gov/DataStore/DownloadFile/620542"}],"country":"United States","state":"South Dakota","otherGeospatial":"Jewel Cave, Wind Cave","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -103.95195007324217,\n 43.43995745973526\n ],\n [\n -103.348388671875,\n 43.43995745973526\n ],\n [\n -103.348388671875,\n 43.78844545936668\n ],\n [\n -103.95195007324217,\n 43.78844545936668\n ],\n [\n -103.95195007324217,\n 43.43995745973526\n ]\n ]\n ]\n }\n }\n ]\n}","publishingServiceCenter":{"id":12,"text":"Tacoma PSC"},"noUsgsAuthors":false,"publicationStatus":"PW","contributors":{"authors":[{"text":"Long, Andrew J. 0000-0001-7385-8081 ajlong@usgs.gov","orcid":"https://orcid.org/0000-0001-7385-8081","contributorId":989,"corporation":false,"usgs":true,"family":"Long","given":"Andrew","email":"ajlong@usgs.gov","middleInitial":"J.","affiliations":[{"id":562,"text":"South Dakota Water Science Center","active":true,"usgs":true},{"id":622,"text":"Washington Water Science Center","active":true,"usgs":true}],"preferred":true,"id":764500,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Paces, James B. 0000-0002-9809-8493 jbpaces@usgs.gov","orcid":"https://orcid.org/0000-0002-9809-8493","contributorId":2514,"corporation":false,"usgs":true,"family":"Paces","given":"James","email":"jbpaces@usgs.gov","middleInitial":"B.","affiliations":[{"id":318,"text":"Geosciences and Environmental Change Science Center","active":true,"usgs":true}],"preferred":true,"id":764502,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Eldridge, William G. 0000-0002-3562-728X","orcid":"https://orcid.org/0000-0002-3562-728X","contributorId":208529,"corporation":false,"usgs":true,"family":"Eldridge","given":"William","email":"","middleInitial":"G.","affiliations":[{"id":34685,"text":"Dakota Water Science Center","active":true,"usgs":true}],"preferred":true,"id":764501,"contributorType":{"id":1,"text":"Authors"},"rank":3}]}} ,{"id":70208944,"text":"70208944 - 2019 - Links between tectonics, magmatism, and mineralization in the formation of Late Cretaceous porphyry systems in the Yukon-Tanana upland, eastern Alaska, USA","interactions":[],"lastModifiedDate":"2020-06-04T14:59:53.342442","indexId":"70208944","displayToPublicDate":"2019-09-30T09:55:57","publicationYear":"2019","noYear":false,"publicationType":{"id":24,"text":"Conference Paper"},"publicationSubtype":{"id":19,"text":"Conference Paper"},"title":"Links between tectonics, magmatism, and mineralization in the formation of Late Cretaceous porphyry systems in the Yukon-Tanana upland, eastern Alaska, USA","docAbstract":"Cretaceous-Paleocene porphyry Cu(±Mo±Au) occurrences are scattered throughout the Yukon-Tanana upland in eastern Alaska. Known occurrences in eastern Alaska are poorly characterized, despite a resurgence in exploration. Porphyry deposits in the upland are emplaced into structurally complex metamorphic rocks representing a variety of tectonic environments, resulting in diverse alteration and mineralization assemblages. New mapping, drill core logging, petrography, geochemistry, geochronology, and structural analysis allow improved characterization of the parameters of porphyry systems and identify key linkages to regional tectonic and magmatic events. New sericite 40Ar/39Ar and zircon U/Pb dates constrain porphyry systems to the Late Cretaceous-earliest Paleocene (ca. 71-63 Ma). Zircon Hf-isotope ratios and Ce and Eu concentrations indicate that Late Cretaceous-Paleocene intrusions emplaced into basement dominated by Triassic and Jurassic plutons are more isotopically juvenile, reflecting more oxidized conditions. In contrast, those emplaced into basement dominated by mid-Cretaceous plutons are more reduced crustal geochemical-affinity. Diversity in mineral assemblages in contrasting systems may reflect emplacement into crustal domains of varying compositions and oxidation states. Those formed within a domain containing more-oxidized Triassic and Jurassic plutons are molybdenite-rich and apparently lack gold. In contrast, systems formed within domains dominated by more reduced mid-Cretaceous plutons contain lower-sulfidation state mineral assemblages with reported gold.
","largerWorkType":{"id":4,"text":"Book"},"largerWorkTitle":"Proceedings of the 15th biennial meeting for geology applied to mineral deposits","largerWorkSubtype":{"id":12,"text":"Conference publication"},"conferenceTitle":"15th Biennial Meeting of the Society for Geology Applied to Mineral Deposits 27","conferenceDate":"Aug 27-30, 2019","conferenceLocation":"Glasgow, Scotland","language":"English","publisher":"Society for Geology Applied to Mineral Deposits (SGA)","usgsCitation":"Kreiner, D.C., Jones, J.V., Todd, E., Holm-Denoma, C., Caine, J., and Benowitz, J., 2019, Links between tectonics, magmatism, and mineralization in the formation of Late Cretaceous porphyry systems in the Yukon-Tanana upland, eastern Alaska, USA, in Proceedings of the 15th biennial meeting for geology applied to mineral deposits, Glasgow, Scotland, Aug 27-30, 2019, p. 939-942.","productDescription":"4 p.","startPage":"939","endPage":"942","ipdsId":"IP-106263","costCenters":[{"id":119,"text":"Alaska Science Center Geology Minerals","active":true,"usgs":true}],"links":[{"id":375358,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"Canada, United States","state":"Alaska, Yukon","otherGeospatial":"Yukon-Tanana upland","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -133.3740234375,\n 60.58696734225869\n ],\n [\n -129.8583984375,\n 63.450509218001095\n ],\n [\n -150.0732421875,\n 67.20403234340081\n ],\n [\n -153.7646484375,\n 64.8115572502203\n ],\n [\n -133.3740234375,\n 60.58696734225869\n ]\n ]\n ]\n }\n }\n ]\n}","publishingServiceCenter":{"id":12,"text":"Tacoma PSC"},"noUsgsAuthors":false,"publicationStatus":"PW","contributors":{"authors":[{"text":"Kreiner, Douglas C. 0000-0002-4405-1403","orcid":"https://orcid.org/0000-0002-4405-1403","contributorId":220474,"corporation":false,"usgs":true,"family":"Kreiner","given":"Douglas","email":"","middleInitial":"C.","affiliations":[{"id":119,"text":"Alaska Science Center Geology Minerals","active":true,"usgs":true}],"preferred":true,"id":784127,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Jones, James V. III 0000-0002-6602-5935 jvjones@usgs.gov","orcid":"https://orcid.org/0000-0002-6602-5935","contributorId":201245,"corporation":false,"usgs":true,"family":"Jones","given":"James","suffix":"III","email":"jvjones@usgs.gov","middleInitial":"V.","affiliations":[{"id":119,"text":"Alaska Science Center Geology Minerals","active":true,"usgs":true}],"preferred":true,"id":784128,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Todd, Erin 0000-0002-4871-9730 etodd@usgs.gov","orcid":"https://orcid.org/0000-0002-4871-9730","contributorId":202811,"corporation":false,"usgs":true,"family":"Todd","given":"Erin","email":"etodd@usgs.gov","affiliations":[{"id":119,"text":"Alaska Science Center Geology Minerals","active":true,"usgs":true}],"preferred":true,"id":784129,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Holm-Denoma, Christopher S. 0000-0003-3229-5440","orcid":"https://orcid.org/0000-0003-3229-5440","contributorId":219763,"corporation":false,"usgs":true,"family":"Holm-Denoma","given":"Christopher S.","affiliations":[{"id":35995,"text":"Geology, Geophysics, and Geochemistry Science Center","active":true,"usgs":true}],"preferred":true,"id":784130,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Caine, Jonathan Saul 0000-0002-7269-6989 jscaine@usgs.gov","orcid":"https://orcid.org/0000-0002-7269-6989","contributorId":199295,"corporation":false,"usgs":true,"family":"Caine","given":"Jonathan Saul","email":"jscaine@usgs.gov","affiliations":[],"preferred":true,"id":784131,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"Benowitz, Jeff","contributorId":223106,"corporation":false,"usgs":false,"family":"Benowitz","given":"Jeff","affiliations":[{"id":7097,"text":"University of Alaska-Fairbanks","active":true,"usgs":false}],"preferred":false,"id":784132,"contributorType":{"id":1,"text":"Authors"},"rank":6}]}} ,{"id":70223699,"text":"70223699 - 2019 - Migration routes, foraging behavior, and site fidelity of loggerhead sea turtles (Caretta caretta) satellite tracked from a globally important rookery","interactions":[],"lastModifiedDate":"2021-09-02T13:07:37.251796","indexId":"70223699","displayToPublicDate":"2019-09-30T08:04:24","publicationYear":"2019","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":2660,"text":"Marine Biology","active":true,"publicationSubtype":{"id":10}},"title":"Migration routes, foraging behavior, and site fidelity of loggerhead sea turtles (Caretta caretta) satellite tracked from a globally important rookery","docAbstract":"The Archie Carr National Wildlife Refuge, Florida, USA (27.946°N, − 80.494°W) represents one of the largest loggerhead turtle (Caretta caretta) nesting sites in the Western Hemisphere. Surprisingly, little work has been conducted to determine females’ post-nesting migratory behavior and characteristics of their foraging areas. Between 2008 and 2017, satellite telemetry was used to trace the locations and movements of 45 post-nesting loggerhead turtles. A switching state-space model was employed to estimate the behavioral state of each location. Internesting, migrating and foraging activity periods were determined for 38 loggerheads based on the SSSM. Seven environmental variables were extracted from remote sensing imagery for each location to compare values among behaviors. Core primary foraging areas ranged in size from 5.89 to 4572.80 km2. Four foraging types (primary, secondary, seasonal, and loops) were observed. Most turtles resided at a primary foraging area year round. A few individuals conducted foraging loops away from a primary foraging area. Both seasonal and loop movements were associated with changes in sea surface temperature as turtles moved to avoid temperatures that could cause cold-stunning or mortality. Turtle size and nesting beach offshore currents may play a role in foraging area selection, and date of departure from the nesting beach may be linked to foraging destination. By making the connection among oceanic features, foraging areas, and the influence of environmental variables on these areas, it is possible to identify and characterize critically important feeding areas and migration corridors for loggerheads nesting on the east coast of Florida.
Most geothermal resources in the Great Basin region of the western USA are blind, and thus the discovery of new commercial-grade systems requires synthesis of favorable characteristics for geothermal activity. The geothermal play fairway concept involves integration of multiple parameters indicative of geothermal activity to identify promising areas for new development. This project integrated multiple datasets to apply the play fairway concept and assess geothermal potential in a large region of the Great Basin in Nevada. It is therefore referred to as the Nevada play fairway project. This project was a strong collaborative effort between several organizations, led by the Nevada Bureau of Mines and Geology at the University of Nevada, Reno, but with key support from the U.S. Geological Survey, ATLAS Geosciences, Inc,, Hi-Q Geophysical, Inc., Lawrence Berkeley National Laboratory, Utah Geological Survey, and Innovative Geothermal Ltd. In Budget Period 1 of this project, available data for nine geologic, geochemical, and geophysical parameters were initially synthesized to produce a new detailed geothermal potential map of 96,000 km2 from west-central to eastern Nevada (Figure 1). These parameters were grouped into subsets and individually weighted (Figure 2) to delineate rankings for local permeability, intermediate permeability, regional permeability, and thermal potential, which collectively defined geothermal play fairways (i.e., most likely locations for significant geothermal fluid flow). This initial work was aimed at reducing the risks in regional exploration and therefore facilitating discovery of new commercial-grade systems in blind settings, as well as in areas with surface expressions of geothermal activity. Budget Period 2 of the project involved detailed analysis of some of the most promising areas identified in Phase 1. Twenty-four highly prospective areas, including both known undeveloped systems and previously undiscovered potential blind systems, were identified for further analysis (Figures 3 and 4). After reconnaissance of these areas, five of the most promising sites were selected for detailed studies. Multiple techniques were employed in the detailed studies, including geologic mapping, shallow temperature surveys, gravity surveys, Lidar, geochemical studies, seismic reflection analysis, and 3D modeling. The goal of the detailed studies was to identify specific areas with the highest likelihood for high permeability and thermal fluids, such that drill sites could be targeted. Three main sets of predictive maps were generated for each detailed study area: 1) play fairway maps, 2) play fairway error maps, and 3) direct evidence maps. Local- and intermediate-scale permeability models were revised to reflect results of the detailed geologic, geophysical, and geochemical analyses. Budget Period 3 of the project involved more detailed geophysical analyses and temperature-gradient (TG) drilling in southeastern Gabbs Valley and northern Granite Springs Valley (Figure 4), deemed the two most promising sites, with the goal of providing preliminary validation of the play fairway methodology. In southeastern Gabbs Valley, the collocation of a favorable structural setting (displacement transfer zone and fault intersections), Quaternary faults, intersecting and terminating gravity gradients, magnetic low, shallow (2 m) temperature anomaly, low resistivity anomaly, and promising geothermometry from nearby water wells provided evidence for a blind system. Drilling of six TG holes defines an apparent geothermal system at this locality with temperatures as high as 124°C at 152 m. This system is blind, with no surface hot springs, fumaroles, or paleo-geothermal deposits. For northern Granite Springs Valley, a favorable structural setting (termination of a major Quaternary normal fault), terminating gravity gradient, magnetic gradient, newly discovered sinter deposits, nearby warm water wells, previously drilled TG holes in the vicinity, and promising geothermometry suggest a hidden system. Drilling of six new TG holes yields temperatures of ~96°C at ~250 m, suggesting the presence of a geothermal system. Major lessons learned in the course of this project include: 1) initially identified sites commonly include multiple favorable structural settings at a finer scale; 2) promising sites in Cenozoic basins cannot be recognized without detailed geophysical surveys; and 3) play fairway analysis should be refined as the exploration program vectors into the most promising sites and finer-scale data are acquired. In addition to producing copious amounts of data, this project resulted in 16 published papers, 10 abstracts, more than 40 presentations across the U.S. and abroad (including several keynote addresses), 2 Masters theses, and 7 media reports.
Radial and linear dike swarms in the eroded roots of volcanoes and along rift zones are sensitive structural indicators of conduit and eruption geometry that can record regional paleostress orientations. Compositionally diverse dikes and larger intrusions that radiate westward from the polycyclic Platoro caldera complex in the Southern Rocky Mountain volcanic field (southwestern United States) merge in structural trend, composition, and age with the enormous but little-studied Dulce swarm of trachybasaltic dikes that continue southwest and south for ∼125 km along the eastern margin of the Colorado Plateau from southern Colorado into northern New Mexico. Some Dulce dikes, though only 1–2 m thick, are traceable for 20 km. More than 200 dikes of the Platoro-Dulce swarm are depicted on regional maps, but only a few compositions and ages have been published previously, and relations to Platoro caldera have not been evaluated. Despite complications from deuteric alteration, bulk compositions of Platoro-Dulce dikes (105 new X-ray fluorescence and inductively coupled plasma mass spectrometry analyses) become more mafic and alkalic with distance from the caldera. Fifty-eight (58) new 40Ar/39Ar ages provide insight into the timing of dike emplacement in relation to evolution of Platoro caldera (source of six regional ignimbrites between 30.3 and 28.8 Ma). The majority of Dulce dikes were emplaced during a brief period (26.5–25.0 Ma) of postcaldera magmatism. Some northeast-trending dikes yield ages as old as 27.5 Ma, and the northernmost north-trending dikes have younger ages (20.1–18.6 Ma). In contrast to high-K lamprophyres farther west on the Colorado Plateau, the Dulce dikes are trachybasalts that contain only anhydrous phenocrysts (clinopyroxene, olivine). Dikes radial to Platoro caldera range from pyroxene- and hornblende-bearing andesite to sanidine dacite, mostly more silicic than trachybasalts of the Dulce swarm. Some distal andesite dikes have ages (31.2–30.4 Ma) similar to those of late precaldera lavas; ages of other proximal dikes (29.2–27.5 Ma) are akin to those of caldera-filling lavas and the oldest Dulce dikes. The largest radial dikes are dacites that have yet younger sanidine 40Ar/39Ar ages (26.5–26.4 Ma), similar to those of the main Dulce swarm.
","language":"English","publisher":"Geological Society of America","doi":"10.1130/GES02068.1","usgsCitation":"Lipman, P.W., and Zimmerer, M.J., 2019, Magmato-tectonic links: Ignimbrite calderas, regional dike swarms, and the transition from arc to rift in the Southern Rocky Mountains: Geosphere, v. 15, no. 6, p. 1893-1926, https://doi.org/10.1130/GES02068.1.","productDescription":"34 p.","startPage":"1893","endPage":"1926","ipdsId":"IP-099412","costCenters":[{"id":617,"text":"Volcano Science Center","active":true,"usgs":true}],"links":[{"id":467321,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.1130/ges02068.1","text":"Publisher Index Page"},{"id":463051,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","state":"Colorado","otherGeospatial":"Southern Rocky Mountains","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"coordinates\": [\n [\n [\n -108.72050712008375,\n 40.432731172362224\n ],\n [\n -108.72050712008375,\n 36.565245969445655\n ],\n [\n -104.15019462008387,\n 36.565245969445655\n ],\n [\n -104.15019462008387,\n 40.432731172362224\n ],\n [\n -108.72050712008375,\n 40.432731172362224\n ]\n ]\n ],\n \"type\": \"Polygon\"\n }\n }\n ]\n}","volume":"15","issue":"6","noUsgsAuthors":false,"publicationDate":"2019-09-30","publicationStatus":"PW","contributors":{"authors":[{"text":"Lipman, Peter W. 0000-0001-9175-6118","orcid":"https://orcid.org/0000-0001-9175-6118","contributorId":203612,"corporation":false,"usgs":true,"family":"Lipman","given":"Peter","email":"","middleInitial":"W.","affiliations":[{"id":114,"text":"Alaska Science Center","active":true,"usgs":true},{"id":617,"text":"Volcano Science Center","active":true,"usgs":true}],"preferred":true,"id":916166,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Zimmerer, Matthew J.","contributorId":191162,"corporation":false,"usgs":false,"family":"Zimmerer","given":"Matthew","email":"","middleInitial":"J.","affiliations":[],"preferred":false,"id":916167,"contributorType":{"id":1,"text":"Authors"},"rank":2}]}} ,{"id":70200886,"text":"ofr20181178 - 2019 - Preliminary GIS representation of deep coal areas for carbon dioxide storage in the contiguous United States and Alaska","interactions":[],"lastModifiedDate":"2019-09-27T16:27:11","indexId":"ofr20181178","displayToPublicDate":"2019-09-27T14:35:00","publicationYear":"2019","noYear":false,"publicationType":{"id":18,"text":"Report"},"publicationSubtype":{"id":5,"text":"USGS Numbered Series"},"seriesTitle":{"id":330,"text":"Open-File Report","code":"OFR","onlineIssn":"2331-1258","printIssn":"0196-1497","active":true,"publicationSubtype":{"id":5}},"seriesNumber":"2018-1178","displayTitle":"Preliminary GIS Representation of Deep Coal Areas for Carbon Dioxide Storage in the Contiguous United States and Alaska","title":"Preliminary GIS representation of deep coal areas for carbon dioxide storage in the contiguous United States and Alaska","docAbstract":"This report and its accompanying geospatial data outline many areas of coal in the United States beneath more than 3,000 ft of overburden. Based on depth, these areas may be targets for injection and storage of supercritical carbon dioxide. Additional areas where coal exists beneath more than 1,000 ft of overburden are also outlined; these may be targets for geologic storage of carbon dioxide in conjunction with enhanced coalbed methane production. These areas of deep coal were compiled as polygons into a shapefile for use in a geographic information system (GIS). The coal-bearing formation names, coal basin or field names, geographic provinces, coal ranks, coal geologic ages, and estimated individual coalbed thicknesses (if known) of the coal-bearing formations were included. An additional point shapefile, coal_co2_projects.shp, contains the locations of pilot projects for carbon dioxide injection into coalbeds. This report is not a comprehensive study of deep coal in the United States. Some areas of deep coal were excluded based on geologic or data-quality criteria, while others may be absent from the literature and still others may have been overlooked by the authors.","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/ofr20181178","usgsCitation":"Jones, K.B., Barnhart, L.E., Warwick, P.D., and Corum, M.D., 2019, Preliminary GIS representation of deep coal areas for carbon dioxide storage in the contiguous United States and Alaska: U.S. Geological Survey Open-File Report 2018–1178, 21 p., https://doi.org/10.3133/ofr20181178.","productDescription":"iv, 21 p.","numberOfPages":"30","onlineOnly":"Y","additionalOnlineFiles":"Y","ipdsId":"IP-079955","costCenters":[{"id":241,"text":"Eastern Energy Resources Science 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12201 Sunrise Valley Drive
956 National Center
Reston, VA 20192
The Biscayne and Southern Everglades Coastal Transport (BISECT) model was developed by the U.S. Geological Survey under the Greater Everglades Priority Ecosystem Studies Initiative to evaluate, both separately and in conjunction, the likely effects on surface-water stages and flows, hydroperiod, and groundwater levels and salinity in south Florida of (1) a vertical Biscayne aquifer barrier to maintain higher wetland levels, (2) possible future changes to current water-management practices, and (3) sea-level rise. The BISECT model is a combination of the Tides and Inflows to the Mangrove Everglades (TIME) and Biscayne models of the western and eastern parts of south Florida including Everglades National Park, the southern Miami-Dade urban area, and the Biscayne Bay coast and simulates hydrodynamic surface-water flow and three-dimensional groundwater conditions dynamically for the period 1996–2004 by using the Flow and Transport in a Linked Overland/Aquifer Density-Dependent System (FTLOADDS) simulator. BISECT includes a number of parameter and algorithmic refinements that improve simulation results relative to the TIME and Biscayne models and represents the hydrologic system more explicitly, including (1) improved topographic representations, (2) refined Manning’s friction coefficients, (3) improved evapotranspiration computation through spatially variable albedo, (4) increased vertical aquifer discretization, and (5) extension of the western boundary farther offshore.
Sensitivity analyses demonstrate that simulated flows into Long Sound have a different pattern of response to tidal amplitude, wind, and frictional resistance changes than do other coastal streams in the model; flows at Broad River and Lostmans River are most sensitive to tidal amplitude, wind, and frictional resistance changes; and flow to the Everglades coastal streams is substantially affected by surface-water/groundwater interactions in the eastern urban areas. Insight into the hydrologic system came from scenario simulations that represent proposed management actions, such as grouting of the aquifer to prevent seepage from the wetlands and changes to water deliveries proposed by the Comprehensive Everglades Restoration Plan (CERP), and projected sea-level rise. These scenario management changes are considered separately to isolate their specific effects and also in conjunction with sea-level rise. Scenario simulations show that (1) attempts to prevent seepage from the wetlands by grouting the aquifer along the L 31N levee produce minimal effects on surface-water levels; (2) the increased water deliveries proposed in the CERP redistribute flow to the northwestern coastal part of the study area with a minimal reduction to the southeast and a more substantial reduction in flows in the intervening coastal zones, mitigating some sea-level rise effects; (3) sea-level rise has a larger effect on the hydrology (water levels, flow, and salinity) than does CERP restoration; and (4) support for ecological models and hydrologic studies can be provided by applying BISECT to scenarios influenced by climatic and anthropogenic changes or by meteorological variability, such as extreme wet or dry periods.
","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/sir20195045","collaboration":"USGS Greater Everglades Priority Ecosystem Studies Initiative","usgsCitation":"Swain, E.D., Lohmann, M.A., and Goodwin, C.R., 2019, The hydrologic system of the south Florida peninsula—Development and application of the Biscayne and Southern Everglades Coastal Transport (BISECT) model: U.S. Geological Survey Scientific Investigations Report 2019–5045, 114 p., https://doi.org/10.3133/sir20195045.","productDescription":"Report: viii, 114 p.; Data Release","numberOfPages":"126","onlineOnly":"Y","ipdsId":"IP-062750","costCenters":[{"id":27821,"text":"Caribbean-Florida Water Science Center","active":true,"usgs":true}],"links":[{"id":367710,"rank":3,"type":{"id":30,"text":"Data Release"},"url":"https://dx.doi.org/10.5066/P9MDUQPK","text":"USGS data release ","description":"USGS Data Release","linkHelpText":"FTLOADDS (combined SWIFT2D surface-water model and SEAWAT groundwater model) simulator used to assess proposed sea-level rise response and water-resource management plans for the hydrologic system of the south Florida peninsula for the Biscayne and Southern Everglades Coastal Transport (BISECT) model"},{"id":367709,"rank":2,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/sir/2019/5045/sir20195045.pdf","text":"Report","size":"24.0 MB","linkFileType":{"id":1,"text":"pdf"},"description":"SIR 2019–5045"},{"id":367708,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/sir/2019/5045/coverthb2.jpg"}],"country":"United States","state":"Florida","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -81.49795532226562,\n 25.11544539706194\n ],\n [\n -80.15213012695312,\n 25.11544539706194\n ],\n [\n -80.15213012695312,\n 25.856751966503136\n ],\n [\n -81.49795532226562,\n 25.856751966503136\n ],\n [\n -81.49795532226562,\n 25.11544539706194\n ]\n ]\n ]\n }\n }\n ]\n}","contact":"Director, Caribbean-Florida Water Science Center
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4446 Pet Lane, Suite 108
Lutz, FL 33559
The inland extent of saltwater at the base of the Biscayne aquifer in eastern Miami-Dade County, Florida, was mapped in 2011, and it was mapped in the Model Land Area in 2016. The saltwater interface has continued to move inland in some areas and is now near several active well fields. An updated approximation of the inland extent of saltwater has been created by using data collected during March 8–December 13, 2018, from 111 monitoring wells open to the Biscayne aquifer near its base. Chloride concentrations in water samples from the monitoring wells and bulk conductivity from geophysical logs and measurements of the specific conductance of groundwater were used to approximate the position of the isochlor representing a chloride concentration of 1,000 milligrams per liter (mg/L) at the base of the Biscayne aquifer.
An average rate of saltwater interface movement of about 102 meters per year in the Model Land Area along SW 360 Street was estimated from the approximated dates of arrival of the 250-, 500-, and 1,000-mg/L isochlors at wells TPGW-7L (2013–2014) and ACI-MW-05-FS (2017–2018). This estimate assumes that the interface is traveling in a path parallel to an imaginary line connecting the two monitoring wells.
Of the 111 wells from which data were used, 80 wells have open intervals of ≤ 4 meters, 20 of the wells have open intervals that range from 4.3 to 39.6 meters, and the lengths of the open intervals could not be determined in 11 wells. Studies have shown that long open intervals might allow water from various depths to mix under ambient or pumped conditions, which in turn could alter the maximum chloride concentration sampled in the well, or it might change the depth at which the maximum specific conductance is measured within a well, relative to its depth in the aquifer. The approximation of the inland extent of the saltwater interface and the estimated rate of movement of the interface are dependent on the quality of existing data. Improved estimates could be obtained by installing uniformly designed monitoring wells in systematic transects extending landward of the advancing saltwater interface. To achieve this goal, Miami-Dade County and some other organizations are routinely adding new monitoring wells with short open intervals and replacing poorly designed or positioned monitoring wells to improve spatial coverage of the network.
","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/sim3438","collaboration":"Prepared in cooperation with Miami-Dade County","usgsCitation":"Prinos, S.T., 2019, Map of the approximate inland extent of saltwater at the base of the Biscayne aquifer in Miami-Dade County, Florida, 2018: U.S. Geological Survey Scientific Investigations Map 3438, 10-p. pamphlet, 1 sheet, https://doi.org/10.3133/sim3438.","productDescription":"Pamphlet: vii, 10 p.; 1 Plate: 35.8 x 46.0 inches; Data Release","onlineOnly":"Y","additionalOnlineFiles":"Y","ipdsId":"IP-107371","costCenters":[{"id":27821,"text":"Caribbean-Florida Water Science Center","active":true,"usgs":true}],"links":[{"id":367675,"rank":2,"type":{"id":26,"text":"Sheet"},"url":"https://pubs.usgs.gov/sim/3438/sim3438.pdf","text":"Sheet","size":"898 kB","linkFileType":{"id":1,"text":"pdf"},"description":"SIM 3438"},{"id":367674,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/sim/3438/coverthb3.jpg"},{"id":367676,"rank":3,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/sim/3438/sim3438_pamphlet.pdf","text":"Pamphlet","size":"857 kB","linkFileType":{"id":1,"text":"pdf"},"description":"SIM 3438 Pamphlet"},{"id":367677,"rank":4,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/P9ZIC1O4","text":"USGS data release","description":"USGS Data Release","linkHelpText":"Data Pertaining to Mapping the Approximate Inland Extent of Saltwater at the Base of the Biscayne Aquifer in Miami-Dade County, Florida, 2018"}],"country":"United States","state":"Florida","county":"Miami-Dade County","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -80.8538818359375,\n 25.095548539604252\n ],\n [\n -79.9969482421875,\n 25.095548539604252\n ],\n [\n -79.9969482421875,\n 26.892679095908164\n ],\n [\n -80.8538818359375,\n 26.892679095908164\n ],\n [\n -80.8538818359375,\n 25.095548539604252\n ]\n ]\n ]\n }\n }\n ]\n}","contact":"Director, Caribbean-Florida Water Science Center
U.S. Geological Survey
4446 Pet Lane, Suite 108
Lutz, FL 33559
Geologic cross section A–A′ is the fifth in a series of cross sections constructed by the U.S. Geological Survey (USGS) to document and improve understanding of the geologic framework and petroleum systems of the Appalachian basin. Cross section A–A′ provides a regional view of the structural and stratigraphic framework of the Appalachian basin from the southern margin of the Ontario Lowlands province in western New York, across the Allegheny Plateau province of central New York and north-central Pennsylvania, to the Valley and Ridge province in north-central Pennsylvania, a distance of approximately 176 miles. This cross section is a companion to cross sections E–E′, D–D′, C–C′, and I–I′ that are located approximately 100 to 500 miles to the southwest. Cross section A–A′ complements earlier geologic or stratigraphic cross sections through the central New York and north-central Pennsylvania part of the Appalachian basin. Although some of these other cross sections show more structural and stratigraphic detail, they are of more limited extent geographically and stratigraphically.
Cross section A–A′ contains much information that is useful for evaluating energy resources in the Appalachian basin. Although the Appalachian basin petroleum systems are not shown on the cross section, many of their key elements (such as source rocks, reservoir rocks, seals, and traps) can be inferred from lithologic units, unconformities, and geologic structures shown on the cross section. Important oil- and gas-bearing formations like the Oriskany Sandstone, Medina Group sandstones, Tuscarora Sandstone, and the Marcellus and Utica Shales are present on cross-section A–A′.
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York\",\"nation\":\"USA \"}}]}","contact":"Eastern Energy Resources Science Center
U.S. Geological Survey
954 National Center
12201 Sunrise Valley Drive
Reston, VA 20192
This report presents a 1:24,000-scale geologic map, cross sections, and descriptive and interpretative text for the Poncha Pass area in central Colorado. The map area is irregular in shape, covering all of one 7 ½' quadrangle (Poncha Pass) and parts of five others (Mount Ouray, Maysville, Salida West, Salida East, and Wellsville). The map boundaries were drawn to cover all of the “Poncha mountain block,” our designation for the approximately 15-kilometer-long northwestern end of the Sangre de Cristo Mountains. The map conveys the areal distribution of (1) Proterozoic basement rocks forming the core of the Poncha mountain block, (2) overlying Eocene and Oligocene volcanic rocks, (3) Miocene and younger basin-fill deposits, (4) Quaternary surficial glacial and alluvial deposits, and (5) faults and folds affecting all of the above units. The Poncha mountain block, which lies within the Rio Grande rift, is topographically and geologically distinctive. Generally, the Rio Grande rift is internally characterized by subsided structural basins or grabens and subdued, low-relief topography rather than elevated mountain blocks. The intrarift, topographically high Poncha mountain block spans the axial part of the rift and separates the low-lying basins of the west-tilted upper Arkansas River half graben and east-northeast-tilted San Luis half graben. These distinctive aspects of the Poncha mountain block were the primary motivations to conduct geologic mapping in the area. Important questions addressed by geologic mapping and related studies in the Poncha Pass area include (1) what were the structural controls and tectonic mechanism(s) that resulted in development of the Poncha mountain block in an intrarift environment; (2) did surface uplift of the Poncha block occur during rift development in the Neogene and Quaternary, and at what rate(s); (3) how was extensional strain accommodated and relayed across the Poncha block between the opposite-polarity rift basins and flanking mountain blocks; (4) is there a clear Laramide deformational signal in rocks of the map area; and (5) have earlier Laramide contractional structures, if they exist, influenced later rift-related extensional deformation through reactivation or strain localization. Prior to our mapping, the geology of much of the Poncha Pass area had only been mapped in reconnaissance fashion, reflecting the area’s poor bedrock exposures, poor access due to the rugged terrain, and geologic complexity. The map presented here provides new details of the geology of this difficult area and helps elucidate the development of the Poncha block and improves understanding of the geologic framework and geologic history of the area.
","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/sim3436","usgsCitation":"Minor, S.A., Caine, J.S., Ruleman, C.A., Fridrich, C.J., Chan, C.F., Brandt, T.R., Holm-Denoma, C.S., Morgan, L.E., Cosca, M.A., and Grauch, V.J.S., 2019, Geologic map of the Poncha Pass area, Chaffee, Fremont, and Saguache Counties, Colorado: U.S. Geological Survey Scientific Investigations Map 3436, 4 sheets, scale 1:24,000, https://doi.org/10.3133/sim3436.","productDescription":"4 Sheets: 60.5 x 36 inches or smaller; Data Release; Read Me","onlineOnly":"Y","costCenters":[{"id":211,"text":"Crustal Geophysics and Geochemistry Science Center","active":true,"usgs":true},{"id":318,"text":"Geosciences and Environmental Change Science Center","active":true,"usgs":true}],"links":[{"id":437342,"rank":8,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/F7PV6J8X","text":"USGS data release","linkHelpText":"Argon data for Poncha Pass Geologic Map"},{"id":367261,"rank":3,"type":{"id":26,"text":"Sheet"},"url":"https://pubs.usgs.gov/sim/3436/sim3436_sheet1_georeferenced.pdf","text":"Sheet 1—Georeferenced Geologic Map of the Poncha Pass Area, Chaffee, Fremont, and Saguache Counties, Colorado","size":"135 MB","linkFileType":{"id":1,"text":"pdf"},"description":"Georeferenced SIM 3436 Sheet 1"},{"id":367301,"rank":7,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/P9GYYF4F","text":"USGS data release","description":"USGS data release","linkHelpText":"Data release for Geologic Map of the Poncha Pass Area, Chaffee, Fremont, and Saguache Counties, Colorado"},{"id":367260,"rank":2,"type":{"id":26,"text":"Sheet"},"url":"https://pubs.usgs.gov/sim/3436/sim3436_sheet1.pdf","text":"Sheet 1—Geologic Map of the Poncha Pass Area, Chaffee, Fremont, and Saguache Counties, Colorado","size":"35.4 MB","linkFileType":{"id":1,"text":"pdf"},"description":"SIM 3436 Sheet 1"},{"id":367263,"rank":5,"type":{"id":26,"text":"Sheet"},"url":"https://pubs.usgs.gov/sim/3436/sim3436_sheet3.pdf","text":"Sheet 3—Explanation of Map Units","size":"9.37 MB","linkFileType":{"id":1,"text":"pdf"},"description":"SIM 3436 Explanation of Map Units"},{"id":367259,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/sim/3436/coverthb_sheet1.jpg"},{"id":367298,"rank":6,"type":{"id":20,"text":"Read Me"},"url":"https://pubs.usgs.gov/sim/3436/sim3436_ReadMe.txt","text":"Read Me","size":"16.0 kB","linkFileType":{"id":2,"text":"txt"},"description":"SIM 3436 Read Me"},{"id":367262,"rank":4,"type":{"id":26,"text":"Sheet"},"url":"https://pubs.usgs.gov/sim/3436/sim3436_sheet2.pdf","text":"Sheet 2—Cross Sections, Geologic Map of the Poncha Pass Area, Chaffee, Fremont, and Saguache Counties, Colorado","size":"2.99 MB","linkFileType":{"id":1,"text":"pdf"},"description":"SIM 3436 Cross Sections"}],"country":"United States","state":"Colorado","county":"Chaffee County, Fremont County, Saguache County","otherGeospatial":"Poncha Pass","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -107.20733642578125,\n 37.51626173528878\n ],\n [\n -105.02655029296875,\n 37.51626173528878\n ],\n [\n -105.02655029296875,\n 39.07464374293251\n ],\n [\n -107.20733642578125,\n 39.07464374293251\n ],\n [\n -107.20733642578125,\n 37.51626173528878\n ]\n ]\n ]\n }\n }\n ]\n}","contact":"Director, Geosciences and Environmental Change Science Center
U.S. Geological Survey
Box 25046, MS-980
Denver, CO 80225-0046
The rare earth elements (REEs) are a group of 17 elements sharing similar chemical properties. They include yttrium (Y, atomic number 39), scandium (Sc, atomic number 21), and the 15 elements of the lanthanide series, atomic numbers 57 (lanthanum, La) to 71 (lutetium, Lu). Because promethium (Pm, atomic number 61) does not occur in the Earth’s crust and scandium typically has different geological occurrences from other REEs, they are not discussed further herein.
REEs are, on average, more abundant than precious metals (for example, gold, silver, and platinum), but because of their unique geochemical properties, they do not commonly form economically viable ore deposits. Nevertheless, REEs are increasingly required for a range of modern applications in defense and renewable energy technologies and in commercial products, primarily as magnets, batteries, and catalysts. The United States currently (2018) produces REEs from a single mine in California, accounting for just 9 percent of global production, whereas 70 percent of global REE production comes from China. For these reasons, REEs are considered a critical resource, and the U.S. Geological Survey (USGS) has an interest in helping to identify new sources of REEs for domestic production.
In 2017, coal use accounted for about 30 percent of the electric power generated in the United States. Fly ash, produced during the burning of coal, is a fine-grained solid derived from noncombustible constituents of coal, such as clay minerals and quartz. When coal is burned, REEs are retained and enriched in the fly ash and, as a result, fly ash has long been considered a potential resource for REEs.
The United States has the world’s largest coal reserves and, even though gas-fired power generation has increased significantly in the last decade, the United States continues to produce vast quantities of fly ash, about half of which is beneficially reused, primarily in construction materials. The remainder is stored, mostly in landfills and impoundments. Thus, annual fly ash production, combined with fly ash already in storage, constitutes a large potential resource.
Research into how to utilize coal and coal fly ash as sources of REEs is ongoing. Viable recovery of REEs from coal and coal ash requires identification of coals and ashes with the highest REE concentrations and development of workable methods for REE extraction and recovery. Understanding how REEs occur within fly ash, described in this fact sheet, is one of the keys to developing possible methods for their recovery.
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