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Lake Tahoe, a large freshwater lake of the eastern Sierra Nevada in California and Nevada, has 63 tributaries that are sources of nutrients and sediment to the lake. The Tahoe watershed is relatively small, and the surface area of the lake occupies about 38% of the watershed area (1313 km2). Only about 6% of the watershed is urbanized or residential land, and as part of a plan to maintain water clarity, wastewater is exported out of the basin. The lake's clarity has been diminishing due to algae and fine sediment, prompting development of management plans. Much of the annual discharge and nutrient load to the lake results from snowmelt in the spring and summer months. To understand the relative importance of land use, climate, forest management, and other factors affecting trends in nutrient stream concentrations and loads, a Weighted Regression on Time Discharge and Season (WRTDS) model simulated these trends over a time frame of >25 years (mid-1970s to 2017). All studied locations generally show nitrate concentration and load trending down. Ammonium concentration and load initially trended down then increased continuously after 2005. Some locations show initially decreasing orthophosphate trends, followed by small significant increases in concentration and loads starting around 2000 to 2005. Total Kjeldahl nitrogen, total phosphorus and suspended sediment mostly trended downward. Overall, the trends in various forms of nitrogen were observed at most sites irrespective of the degree of development and indicate a change in ecological conditions is affecting the nitrogen cycle throughout the watershed, most likely attributable to forest aggradation and fire suppression. Ratios of bioavailable nitrogen in the form of nitrate and ammonium to orthophosphate have also trended downward during the period of record suggesting a shift of these streams from phosphorus limited to nitrogen limited.
","language":"English","publisher":"Elsevier","doi":"10.1016/j.scitotenv.2020.141815","usgsCitation":"Domagalski, J.L., Morway, E.D., Alvarez, N.L., Hutchins, J., Rosen, M.R., and Coats, R., 2021, Trends in nitrogen, phosphorus, and sediment concentrations and loads in streams draining to Lake Tahoe, California, Nevada, USA: Science of the Total Environment (STOTEN), v. 752, 141815, 17 p., https://doi.org/10.1016/j.scitotenv.2020.141815.","productDescription":"141815, 17 p.","ipdsId":"IP-116547","costCenters":[{"id":154,"text":"California Water Science Center","active":true,"usgs":true},{"id":465,"text":"Nevada Water Science Center","active":true,"usgs":true}],"links":[{"id":436671,"rank":0,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/P98T2HNM","text":"USGS data release","linkHelpText":"Discharge, nutrient, and suspended sediment data for selected streams in the Lake Tahoe watershed"},{"id":436670,"rank":0,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/P98T2HNM","text":"USGS data release","linkHelpText":"Discharge, nutrient, and suspended sediment data for selected streams in the Lake Tahoe watershed"},{"id":378607,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/ja/70214032/coverthb.jpg"}],"country":"United States","state":"California, Nevada","otherGeospatial":"Lake Tahoe","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -120.27282714843749,\n 38.807610542357594\n ],\n [\n -119.80865478515625,\n 38.807610542357594\n ],\n [\n -119.80865478515625,\n 39.31942523123949\n ],\n [\n -120.27282714843749,\n 39.31942523123949\n ],\n [\n -120.27282714843749,\n 38.807610542357594\n ]\n ]\n ]\n }\n }\n ]\n}","volume":"752","noUsgsAuthors":false,"publicationStatus":"PW","contributors":{"authors":[{"text":"Domagalski, Joseph L. 0000-0002-6032-757X joed@usgs.gov","orcid":"https://orcid.org/0000-0002-6032-757X","contributorId":1330,"corporation":false,"usgs":true,"family":"Domagalski","given":"Joseph","email":"joed@usgs.gov","middleInitial":"L.","affiliations":[{"id":154,"text":"California Water Science Center","active":true,"usgs":true}],"preferred":true,"id":799282,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Morway, Eric D. 0000-0002-8553-6140 emorway@usgs.gov","orcid":"https://orcid.org/0000-0002-8553-6140","contributorId":4320,"corporation":false,"usgs":true,"family":"Morway","given":"Eric","email":"emorway@usgs.gov","middleInitial":"D.","affiliations":[{"id":465,"text":"Nevada Water Science Center","active":true,"usgs":true}],"preferred":true,"id":799283,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Alvarez, Nancy L. 0000-0001-8037-1001 nalvarez@usgs.gov","orcid":"https://orcid.org/0000-0001-8037-1001","contributorId":206530,"corporation":false,"usgs":true,"family":"Alvarez","given":"Nancy","email":"nalvarez@usgs.gov","middleInitial":"L.","affiliations":[{"id":465,"text":"Nevada Water Science Center","active":true,"usgs":true}],"preferred":true,"id":799284,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Hutchins, Juliet 0000-0001-7385-4160","orcid":"https://orcid.org/0000-0001-7385-4160","contributorId":240999,"corporation":false,"usgs":false,"family":"Hutchins","given":"Juliet","email":"","affiliations":[{"id":37762,"text":"California State University, Sacramento","active":true,"usgs":false}],"preferred":false,"id":799285,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Rosen, Michael R. 0000-0003-3991-0522 mrosen@usgs.gov","orcid":"https://orcid.org/0000-0003-3991-0522","contributorId":495,"corporation":false,"usgs":true,"family":"Rosen","given":"Michael","email":"mrosen@usgs.gov","middleInitial":"R.","affiliations":[{"id":154,"text":"California Water Science Center","active":true,"usgs":true}],"preferred":true,"id":799286,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"Coats, Robert 0000-0002-0402-032X","orcid":"https://orcid.org/0000-0002-0402-032X","contributorId":241000,"corporation":false,"usgs":false,"family":"Coats","given":"Robert","email":"","affiliations":[{"id":48187,"text":"Hydroikos Ltd","active":true,"usgs":false}],"preferred":false,"id":799287,"contributorType":{"id":1,"text":"Authors"},"rank":6}]}} ,{"id":70223100,"text":"70223100 - 2021 - Winter severity, fish community, and availability to traps explain most of the variability in estimates of adult sea lamprey in Lake Superior","interactions":[],"lastModifiedDate":"2022-01-06T17:58:31.982246","indexId":"70223100","displayToPublicDate":"2020-08-20T10:18:21","publicationYear":"2021","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":2330,"text":"Journal of Great Lakes Research","active":true,"publicationSubtype":{"id":10}},"title":"Winter severity, fish community, and availability to traps explain most of the variability in estimates of adult sea lamprey in Lake Superior","docAbstract":"Animal populations are assessed to estimate rates of artificial and natural mortality at ecologically relevant spatial and temporal scales to develop exploitation quotas. But how the population’s natural mortality rate and how the ability to observe the population changes through time are poorly understood in most invasive fishes, despite efforts to control their populations. By investigating a 30-year abundance index of invasive sea lamprey (Petromyzon marinus) in Lake Superior, we found that the index was highly correlated (R2 = 0.75) with biotic and abiotic factors hypothesized to influence sea lamprey natural mortality and their availability to index traps. The index was lowest in years (1) following winters with below average ice cover on Lake Superior, (2) when stream discharge during sea lamprey migration was below average, (3) when adult sea lamprey were smaller than average, and (4) when adult sea lamprey were more likely to be distributed in tributaries on the east side of Lake Superior. These results highlight the need for policy makers to consider invasive species abundance indexes not just in the context of control effort, but also in the context of biotic and abiotic conditions because they could markedly influence natural mortality or the ability to observe highly suppressed populations.
","language":"English","publisher":"Elsevier","doi":"10.1016/j.jglr.2020.08.011","usgsCitation":"Johnson, N.S., Adams, J.V., Bravener, G., Barber, J., Treska, T., and Siefkes, M.J., 2021, Winter severity, fish community, and availability to traps explain most of the variability in estimates of adult sea lamprey in Lake Superior: Journal of Great Lakes Research, v. 47, no. Suppl 1, p. 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and Wildlife Service–Bozeman Fish Technology","active":true,"usgs":false}],"preferred":false,"id":820949,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Treska, Ted","contributorId":264139,"corporation":false,"usgs":false,"family":"Treska","given":"Ted","affiliations":[{"id":36188,"text":"U.S. Fish and Wildlife Service","active":true,"usgs":false}],"preferred":false,"id":820950,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"Siefkes, Michael J","contributorId":150989,"corporation":false,"usgs":false,"family":"Siefkes","given":"Michael","email":"","middleInitial":"J","affiliations":[{"id":7019,"text":"Great Lakes Fishery Commission","active":true,"usgs":false}],"preferred":false,"id":820951,"contributorType":{"id":1,"text":"Authors"},"rank":6}]}} ,{"id":70223476,"text":"70223476 - 2021 - Detrital zircon age spectra of middle and upper Eocene outcrop belts, U.S. Gulf Coast region","interactions":[],"lastModifiedDate":"2021-08-27T13:28:26.45083","indexId":"70223476","displayToPublicDate":"2020-05-09T08:23:12","publicationYear":"2021","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":972,"text":"Basin Research","active":true,"publicationSubtype":{"id":10}},"title":"Detrital zircon age spectra of middle and upper Eocene outcrop belts, U.S. Gulf Coast region","docAbstract":"Recently reported detrital zircon (DZ) data help to associate the Paleogene strata of the Gulf of Mexico region to various provenance areas. By far, recent work has emphasised upper Paleocene-lower Eocene and upper Oligocene strata that were deposited during the two episodes of the highest sediment supply in the Paleogene. The data reveal a dynamic drainage history, including (1) initial routing of western Cordilleran drainages towards the Gulf of Mexico in the Paleocene, (2) an eastward shift of the western continental divide, from the Jura-Cretaceous cordilleran arc to the eastern edge of the Laramide province after the Paleocene and (3) a southward shift, along the eastern Laramide province, of the headwaters of river systems draining to the Mississippi and Houston embayments at some time between the early Eocene and Oligocene. However, DZ characterisation of most (~20 Myr) of the middle Eocene-lower Oligocene section remains limited. We present 60 DZ age spectra, most of which are from the middle or upper Eocene outcrop belts, with 50–200-km spacing. We define six to eight distinct groups of DZ age spectra for middle and upper Eocene strata. Data from this and other studies resolve at least six substantial temporal changes in age spectra at various positions along the continental margin. The evolving age spectra constrain the middle and upper Eocene drainage patterns of large parts of interior North America. The most well-resolved aspects of these drainage patterns include (1) persistent rivers that flowed from erosional landscapes across the Paleozoic Appalachian orogen either into the low-lying Mississippi embayment or directly into the eastern Gulf; (2) at least during marine regressions, a trunk channel that likely flowed southward along the axial part of Mississippi Embayment and integrated tributaries from the east and west; and (3) rivers that flowed to the Houston embayment in the middle Eocene that likely originated in the Laramide province in central Colorado and southern Wyoming, as Precambrian basement highs in those source areas were being unroofed.
Increasing urbanization and suburban growth in cities globally has highlighted the importance of land planning using detailed geomorphologic maps that depict anthropogenic landform changes. Such mapping provides information crucial for land management, hazard identification, and the management of the challenges arising from urbanization. The development and use of quantitative and repeatable methods to map anthropogenic and natural processes are required to advance the science of urban geomorphological mapping. This study investigated the application of geospatial modeling, structure-from-motion (SfM) photogrammetric methods and DEM differencing as means of quantifying anthropogenic landform changes from archival aerial imagery. Anthropogenic landforms were incorporated into a detailed geomorphologic map in an urbanizing watershed located in the Washington, D.C. metropolitan suburb of Vienna, Virginia.
The long-standing ore-genesis model for world-class deposits of the Butte mining district, Montana, is of deep pre-Main Stage porphyry Cu-Mo and overlying Main Stage Ag-Zn-Cu-zoned lode veinsformed from discrete hydrothermal systems related to rhyolite dikes. The lode-specific model describes metals zones that formed in the lode veins as hydrothermal processes diminished in intensity (changing temperature and chemical characteristics) outward from the district center. New geologic and multi-method geochronologic studies pro- vide new timing constraints on the lode veins and reevaluation of geologic relations (Lund and others, 2018), leading to a new model for formation of the lode veins and their relations to stockwork Cu-Mo deposits and igneous events.
","largerWorkType":{"id":4,"text":"Book"},"largerWorkTitle":"Proceedings of the Montana Mining and Mineral Symposium 2019","largerWorkSubtype":{"id":12,"text":"Conference publication"},"conferenceTitle":"Montana Mining and Mineral Symposium 2019","conferenceDate":"October 9-11, 2019","language":"English","publisher":"Montana Bureau of Mines and Geology","usgsCitation":"Lund, K., McAleer, R.J., Aleinikoff, J.N., and Cosca, M., 2021, Two-event genesis of Butte lode veins: Geologic and geochronologic evidence from ore veins, dikes, and host plutons, in Proceedings of the Montana Mining and Mineral Symposium 2019, October 9-11, 2019, p. 71-73.","productDescription":"3 p.","startPage":"71","endPage":"73","ipdsId":"IP-112266","costCenters":[{"id":171,"text":"Central Mineral and Environmental Resources Science Center","active":true,"usgs":true},{"id":245,"text":"Eastern Mineral and Environmental Resources Science Center","active":true,"usgs":true},{"id":35995,"text":"Geology, Geophysics, and Geochemistry Science Center","active":true,"usgs":true}],"links":[{"id":386095,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"},{"id":385671,"type":{"id":15,"text":"Index Page"},"url":"https://www.mbmg.mtech.edu/mbmgcat/public/ListCitation.asp?pub_id=32264&"}],"country":"United States","state":"Montana","otherGeospatial":"Butte mining district","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -112.76916503906249,\n 45.882360730184025\n ],\n [\n -112.37640380859375,\n 45.882360730184025\n ],\n [\n -112.37640380859375,\n 46.086566879725034\n ],\n [\n -112.76916503906249,\n 46.086566879725034\n ],\n [\n -112.76916503906249,\n 45.882360730184025\n ]\n ]\n ]\n }\n }\n ]\n}","noUsgsAuthors":false,"publicationStatus":"PW","contributors":{"authors":[{"text":"Lund, Karen 0000-0002-4249-3582 klund@usgs.gov","orcid":"https://orcid.org/0000-0002-4249-3582","contributorId":1235,"corporation":false,"usgs":true,"family":"Lund","given":"Karen","email":"klund@usgs.gov","affiliations":[{"id":171,"text":"Central Mineral and Environmental Resources Science Center","active":true,"usgs":true},{"id":387,"text":"Mineral Resources Program","active":true,"usgs":true}],"preferred":true,"id":815738,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"McAleer, Ryan J. 0000-0003-3801-7441 rmcaleer@usgs.gov","orcid":"https://orcid.org/0000-0003-3801-7441","contributorId":215498,"corporation":false,"usgs":true,"family":"McAleer","given":"Ryan","email":"rmcaleer@usgs.gov","middleInitial":"J.","affiliations":[{"id":243,"text":"Eastern Geology and Paleoclimate Science Center","active":true,"usgs":true},{"id":40020,"text":"Florence Bascom Geoscience Center","active":true,"usgs":true}],"preferred":true,"id":815739,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Aleinikoff, John N. 0000-0003-3494-6841 jaleinikoff@usgs.gov","orcid":"https://orcid.org/0000-0003-3494-6841","contributorId":1478,"corporation":false,"usgs":true,"family":"Aleinikoff","given":"John","email":"jaleinikoff@usgs.gov","middleInitial":"N.","affiliations":[{"id":171,"text":"Central Mineral and Environmental Resources Science Center","active":true,"usgs":true}],"preferred":true,"id":815740,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Cosca, Michael 0000-0002-0600-7663","orcid":"https://orcid.org/0000-0002-0600-7663","contributorId":33043,"corporation":false,"usgs":true,"family":"Cosca","given":"Michael","affiliations":[{"id":35995,"text":"Geology, Geophysics, and Geochemistry Science Center","active":true,"usgs":true}],"preferred":true,"id":815741,"contributorType":{"id":1,"text":"Authors"},"rank":4}]}} ,{"id":70204951,"text":"70204951 - 2021 - Lake Andrei: A pliocene pluvial lake in Eureka Valley, Eastern California","interactions":[{"subject":{"id":70204951,"text":"70204951 - 2021 - Lake Andrei: A pliocene pluvial lake in Eureka Valley, Eastern California","indexId":"70204951","publicationYear":"2021","noYear":false,"chapter":"8","title":"Lake Andrei: A pliocene pluvial lake in Eureka Valley, Eastern California"},"predicate":"IS_PART_OF","object":{"id":70225733,"text":"70225733 - 2021 - From saline to freshwater: The diversity of western lakes in space and time","indexId":"70225733","publicationYear":"2021","noYear":false,"title":"From saline to freshwater: The diversity of western lakes in space and time"},"id":1}],"isPartOf":{"id":70225733,"text":"70225733 - 2021 - From saline to freshwater: The diversity of western lakes in space and time","indexId":"70225733","publicationYear":"2021","noYear":false,"title":"From saline to freshwater: The diversity of western lakes in space and time"},"lastModifiedDate":"2021-11-08T18:06:42.694758","indexId":"70204951","displayToPublicDate":"2019-08-27T09:14:28","publicationYear":"2021","noYear":false,"publicationType":{"id":5,"text":"Book chapter"},"publicationSubtype":{"id":24,"text":"Book Chapter"},"chapter":"8","title":"Lake Andrei: A pliocene pluvial lake in Eureka Valley, Eastern California","docAbstract":"We used geologic mapping, tephrochronology and 40Ar/39Ar dating to describe evidence of a ca. 3.5 Ma pluvial lake in Eureka Valley, eastern California, that we informally name herein Lake Andrei. We identified six different tuffs in the Eureka Valley drainage basin including two previously undescribed tuffs: the 3.509 ± 0.009 Ma tuff of Hanging Rock Canyon and the 3.506 ± 0.010 Ma tuff of Last Chance (informal names). We focused on four Pliocene stratigraphic sequences. Three sequences are composed of fluvial sandstone and conglomerate with basalt flows in two of these sequences. The fourth sequence, located about 1.5 km south of the Death Valley/Big Pine Road along the western piedmont of the Last Chance Range, included green, fine-grained, gypsiferous lacustrine deposits interbedded with the 3.506 Ma tuff of Last Chance that we interpret as evidence of a pluvial lake. Pluvial Lake Andrei is similar in age pluvial lakes in Searles Valley, Amargosa Valley, Fish Lake Valley and Death Valley of the western Great Basin. We interpret these simultaneous lakes in the region as indirect evidence of a significant glacial climate in western North America during Marine Isotope Stages MG5/M2 and a persistent Pacific jet stream south of 37°N.","largerWorkType":{"id":4,"text":"Book"},"largerWorkTitle":"From saline to freshwater: The diversity of western lakes in space and time","largerWorkSubtype":{"id":15,"text":"Monograph"},"language":"English","publisher":"Geological Society of America","doi":"10.1130/2018.2536(08)","usgsCitation":"Knott, J.R., Wan, E., Deino, A.L., Casteel, M., Reheis, M.C., Phillips, F., Walkup, L., McCarty, K., Manoukian, D.N., and Nunez, E., 2021, Lake Andrei: A pliocene pluvial lake in Eureka Valley, Eastern California, chap. 8 of From saline to freshwater: The diversity of western lakes in space and time, p. 125-142, https://doi.org/10.1130/2018.2536(08).","productDescription":"18 p.","startPage":"125","endPage":"142","ipdsId":"IP-082661","costCenters":[{"id":312,"text":"Geology, 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niobium, platinum-group elements, rare earth elements, tantalum, tin, titanium, and tungsten","interactions":[],"lastModifiedDate":"2026-03-25T16:54:19.281618","indexId":"ofr20191023B","displayToPublicDate":"2022-07-14T10:31:00","publicationYear":"2020","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":"2019-1023","chapter":"B","displayTitle":"Focus Areas for Data Acquisition for Potential Domestic Resources of 11 Critical Minerals in the Conterminous United States, Hawaii, and Puerto Rico—Aluminum, Cobalt, Graphite, Lithium, Niobium, Platinum-Group Elements, Rare Earth Elements, Tantalum, Tin, Titanium, and Tungsten","title":"Focus areas for data acquisition for potential domestic resources of 11 critical minerals in the conterminous United States, Hawaii, and Puerto Rico—Aluminum, cobalt, graphite, lithium, niobium, platinum-group elements, rare earth elements, tantalum, tin, titanium, and tungsten","docAbstract":"In response to a need for information on potential domestic sources of critical minerals, the Earth Mapping Resources Initiative (Earth MRI) was established to identify and prioritize areas for acquisition of new geologic mapping, geophysical data, and elevation data to improve our knowledge of the geologic framework of the United States. Phase 1 of Earth MRI concentrated on those geologic terranes favorable for hosting the rare earth elements (REEs). Phase 2 continued to address the REEs and also identified focus areas for potential domestic sources of 10 more of the 35 critical minerals on the U.S. critical minerals list (aluminum, cobalt, graphite, lithium, niobium, platinum-group elements, tantalum, tin, titanium, tungsten). This report describes the methodology, data sources, and summary results for mineral systems that host these 11 critical minerals in the conterminous United States, Hawaii, and Puerto Rico; Alaska is covered in a separate report. The mineral systems framework adopted for this study links critical mineral commodities to families of genetically related mineral deposit types. The mineral systems approach is an efficient approach, providing a simultaneous evaluation of geologic terranes through aggregation of genetically related mineral deposit types that are much larger than individual ore deposits. Geologic, geochemical, topographic, and geophysical mapping provided by Earth MRI will document geologic features that reflect the extent of individual mineral systems and provide information about critical mineral deposits that may not have been recognized previously.
Each critical mineral commodity is discussed in terms of importance to the Nation’s economy, modes of occurrence, mineral systems, and deposit types along with maps and tables listing examples of focus areas for each critical mineral. Important mineral systems for these critical minerals include chemical weathering systems for aluminum (bauxite); placer systems for titanium and REEs; metamorphic systems for graphite; mafic magmatic systems for platinum-group elements and cobalt; lacustrine evaporite and porphyry tin systems for lithium; and copper-molybdenum-gold (Cu-Mo-Au) systems for tungsten. REEs occur in many different mineral systems. Focus areas were developed by scientists from the U.S. Geological Survey in collaboration with scientists from State geological surveys and other institutions. This first national-scale compilation of focus areas represents an initial step in addressing the Nation’s critical mineral needs by screening areas for acquisition of new data to provide the geologic framework necessary for identifying domestic sources of critical minerals.
","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/ofr20191023B","collaboration":"Prepared in cooperation with American Association of State Geologists","usgsCitation":"Hammarstrom, J., Dicken, C., Day, W., Hofstra, A., Drenth, B., Shah, A., McCafferty, A., Woodruff, L., Foley, N., Ponce, D., Frost, T., and Stillings, L., 2020, Focus areas for data acquisition for potential domestic resources of 11 critical minerals in the conterminous United States, Hawaii, and Puerto Rico—Aluminum, cobalt, graphite, lithium, niobium, platinum-group elements, rare earth elements, tantalum, tin, titanium, and tungsten (ver. 1.1, July 2022), chap. B of U.S. Geological Survey, Focus areas for data acquisition for potential domestic sources of critical minerals: U.S. Geological Survey Open-File Report 2019–1023, 67 p., https://doi.org/10.3133/ofr20191023B.","productDescription":"xiii, 67 p.","numberOfPages":"67","onlineOnly":"Y","additionalOnlineFiles":"N","ipdsId":"IP-119187","costCenters":[{"id":171,"text":"Central Mineral and Environmental Resources Science Center","active":true,"usgs":true},{"id":211,"text":"Crustal Geophysics and Geochemistry Science Center","active":true,"usgs":true},{"id":245,"text":"Eastern Mineral and Environmental Resources Science Center","active":true,"usgs":true},{"id":312,"text":"Geology, Minerals, Energy, and Geophysics Science Center","active":true,"usgs":true}],"links":[{"id":436687,"rank":9,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/P9U6SODG","text":"USGS data release","linkHelpText":"GIS for focus areas of potential domestic resources of 11 critical minerals-aluminum, 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Resources Program
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913 National Center
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In 2019, total daily tight oil and gas production increased in the United States month over month, with annualized growth of 14% for oil and 12% for gas. Those gains leveled off in the first quarter of 2020 due to aggressive price competition and increases in international production. Then came the pandemic with a substantially larger dose of economic turmoil, driving down demand due in part to shelter in place orders and safety concerns around travel. Between March and May, tight oil and gas production dropped by nearly 2 million bpd and almost 5 Bcf/day before beginning to recover. Production has continued to increase for the most part through the second half of 2020, but drilling remains subdued throughout most of the U.S. and uncertainty around long term demand along with the current price environment and general state of the economy has contributed to layoffs throughout the industry.
Some shale-gas production has declined recently, but a few areas have seen expansion due to construction of LNG facilities along the East Coast of the U.S. (e.g., the Haynesville Formation). Current U.S. shale-gas production is still higher now than in 2019, with daily production of almost 71 Bcf as of October 2020 driven in large part by increased production from the Marcellus Shale in the Appalachian Basin and shales within the Permian Basin. Shale liquids production is down by around a million bpd to approximately 7.1 million (September 2020; U.S. EIA) from pre-pandemic production levels at the end of 2019 and beginning of 2020. Tight oil production remains dominated by plays in the Permian Basin as well as the Bakken and Eagle Ford Formations.
On the development and production front, new enhanced oil recovery approaches for tight shale reservoirs are being more widely implemented. Natural gas or CO2 injection is currently being utilized in the Bakken Formation, Eagle Ford Formation, Anadarko Basin, and the Permian Basin to optimize injection sequences and boost recovery. Refracturing of existing wells to reduce drilling costs, improve production, and prolong well productive life has also begun to occur more widely in developed plays.
International interest in exploiting hydrocarbons from unconventional reservoirs continues to develop, with active exploration projects on most continents. Europe remains relatively underexplored as compared to North America, although a total of 141 exploration and appraisal wells with a possible shale-gas exploration component have been spudded, including horizontal legs from vertical wells. Shale exploration has made a breakthrough in China with shale gas output in 2019 of 10 billion cubic meters (35.3 Bcf), 60% of which was produced from Sinopec’s Fuling Shale Gas field. Lacustrine shale oil exploration has also been successful in the Sichuan and Ordos Basins in central China, Junggar and Tarim Basins in northwest China, and Songliao Basin in north China, and Bohai Bay Basins in northeast China as of 2018.
South America’s potential as an unconventional shale gas and oil province is mainly in Argentina and Brazil, where the production from Neuquen Basin’s tight shale of the Vaca Muerta Formation has been steadily increasing since 2016, but only 4% of the shale resource has been developed thus far. According to International Energy Agency’s report in 2013, Brazil holds the 9th largest unconventional gas reserves. Brazil has shale oil and gas potential in the Parana, Solimoes and Amazon Basins and is actively producing from the oil shale unit of the Irati Formation. In 2019, the Brazil energy ministry launched REATE 2020 to boost onshore investments that include the expectation of drilling an experimental unconventional well in the northeast region.
For this inaugural report, the new AAPG EMD Tight Oil and Gas Committee (TO&G; formerly the Shale Gas & Liquids and Tight Gas Sands committees) has developed new commodity report requirements for contributors. This includes shorter annual reports focused on new developments, play concepts, along with the typical updates on production and new drilling in the play areas they cover. We are also asking contributors to collect background geologic and production related information into a document that summarizes important features of the plays they cover that will be stored on the TO&G webpage along with our commodity reports.
TO&G is currently working to expand the number of contributors to cover more play areas and replace committee and advisory board members that have recently stepped down. Changes to committee leadership occurred in October as recent chairs transition to EMD elected positions.
This report presents potentiometric-surface maps of the Aquia and Magothy aquifers and the Upper Patapsco, Lower Patapsco, and Patuxent aquifer systems using water levels measured during the fall season of 2019. The potentiometric surface maps show water levels ranging from 56 feet above sea level to 163 feet below sea level in the Aquia aquifer, from 87 feet above sea level to 119 feet below sea level in the Magothy aquifer, from 114 feet above sea level to 120 feet below sea level in the Upper Patapsco aquifer system, from 136 feet above sea level to 174 feet below sea level in the Lower Patapsco aquifer system, and from 168 feet above sea level to 184 feet below sea level in the Patuxent aquifer system.
Cones of depression have formed around locations with significant aquifer withdrawals. The Aquia aquifer has depressed water levels around well fields at Lexington Park, Solomons Island, and central Talbot County. Cones of depression have formed in the Magothy aquifer around well fields at Waldorf, Arnold, and Easton. The Upper Patapsco aquifer system has depressed water levels around well fields in the Annapolis-Arnold area, Waldorf, the Lexington Park-Leonardtown area, and at Easton. The Lower Patapsco aquifer system has depressed water levels around well fields at Severndale, Broad Creek, Arnold, and Crofton Meadows as well as in central and western Charles County. Cones of depression have formed in the Patuxent aquifer system around well fields at Dorsey Road, Crofton, Arnold, northwestern Charles County, and at the Chalk Point power plant.
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The National Environmental Policy Act and the Endangered Species Act requires Department of Defense land managers to prioritize identification, monitoring, and conservation of Indiana bat day-roost areas, foraging habitat during the maternity season, and pre-hibernation swarming sites during autumn.
","largerWorkType":{"id":4,"text":"Book"},"largerWorkTitle":"Bat echolocation research: A handbook for planning and conducting acoustic studies","largerWorkSubtype":{"id":15,"text":"Monograph"},"language":"English","publisher":"Bat Conservation International","usgsCitation":"Ford, W., Dobony, C., Jachowski, D., Coleman, L.S., Nocera, T., and Britzke, E.R., 2020, Case study 1: Acoustic Surveys at Fort Drum Military Installation – the Value of Long-term Monitoring, chap. of Bat echolocation research: A handbook for planning and conducting acoustic studies, p. 82-85.","productDescription":"4 p.","startPage":"82","endPage":"85","ipdsId":"IP-090899","costCenters":[{"id":199,"text":"Coop Res Unit Leetown","active":true,"usgs":true}],"links":[{"id":394588,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"},{"id":393639,"type":{"id":15,"text":"Index Page"},"url":"https://batsurveysolutions.com/pages/batss-covid-19-response"}],"country":"United States","state":"New York","otherGeospatial":"Fort Drum Military Installation","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -75.59349060058594,\n 44.00615076564076\n ],\n [\n -75.40054321289062,\n 44.146246915303635\n ],\n [\n -75.3826904296875,\n 44.19303602884215\n ],\n [\n -75.52894592285156,\n 44.25897020133368\n ],\n [\n -75.64498901367188,\n 44.189097324204134\n ],\n [\n -75.76309204101562,\n 44.086598616569646\n ],\n [\n -75.79124450683594,\n 44.07081379264681\n ],\n [\n -75.80291748046875,\n 44.07969327425713\n ],\n [\n -75.82763671875,\n 44.07525370000069\n ],\n [\n -75.84548950195312,\n 44.02837121279199\n ],\n [\n -75.78849792480469,\n 44.01306468595375\n ],\n [\n -75.69717407226562,\n 44.040712307588414\n ],\n [\n -75.59349060058594,\n 44.00615076564076\n ]\n ]\n ]\n }\n }\n ]\n}","noUsgsAuthors":false,"publicationStatus":"PW","contributors":{"authors":[{"text":"Ford, W. 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Mark","email":"wford@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":829730,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Dobony, Christopher A.","contributorId":264897,"corporation":false,"usgs":false,"family":"Dobony","given":"Christopher A.","affiliations":[{"id":54576,"text":"DoD","active":true,"usgs":false}],"preferred":false,"id":831296,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Jachowski, David S.","contributorId":228814,"corporation":false,"usgs":false,"family":"Jachowski","given":"David S.","affiliations":[{"id":7084,"text":"Clemson University","active":true,"usgs":false}],"preferred":false,"id":831297,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Coleman, Laci S.","contributorId":171672,"corporation":false,"usgs":false,"family":"Coleman","given":"Laci","email":"","middleInitial":"S.","affiliations":[],"preferred":false,"id":831298,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Nocera, Tomas","contributorId":270948,"corporation":false,"usgs":false,"family":"Nocera","given":"Tomas","email":"","affiliations":[],"preferred":false,"id":831299,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"Britzke, Eric R.","contributorId":8327,"corporation":false,"usgs":true,"family":"Britzke","given":"Eric","email":"","middleInitial":"R.","affiliations":[],"preferred":false,"id":831300,"contributorType":{"id":1,"text":"Authors"},"rank":6}]}} ,{"id":70215354,"text":"70215354 - 2020 - Smallmouth buffalo (Ictiobus bubalus) growth across a 1200km human use and ecological disturbance gradient in the Upper Mississippi River System","interactions":[],"lastModifiedDate":"2021-10-01T15:35:34.106947","indexId":"70215354","displayToPublicDate":"2020-12-31T10:25:19","publicationYear":"2020","noYear":false,"publicationType":{"id":18,"text":"Report"},"publicationSubtype":{"id":1,"text":"Federal Government Series"},"seriesTitle":{"id":9371,"text":"Mississippi River Restoration Program","active":true,"publicationSubtype":{"id":1}},"displayTitle":"Smallmouth buffalo (Ictiobus bubalus) growth across a 1200km human use and ecological disturbance gradient in the Upper Mississippi River System","title":"Smallmouth buffalo (Ictiobus bubalus) growth across a 1200km human use and ecological disturbance gradient in the Upper Mississippi River System","docAbstract":"Smallmouth buffalo (Ictiobus bubalus) is a common and widely distributed large-bodied species of the family Catostomidae. It inhabits large rivers and reservoirs of the eastern continental United States (east of the continental Divide) and is most abundant and common in the large rivers of the Midwest and Central Plains, though it does occur as far north and east as the Hudson Bay drainage and as far south and west as Arizona (Edwards and Twoney 1982).\n\nHistorically, smallmouth buffalo were an important component of commercial fisheries on both the Mississippi and Illinois Rivers. However, following the introduction of common carp (Cyprinus carpio) in the mid-1800s (Carlander 1954), the construction of a system of navigation dams on Upper Mississippi and Illinois River in the 1930s (USGS 1999), and water quality/pollution issues through the 1980s (Weiner 2010), the role of smallmouth buffalo in the overall UMRS fish community and commercial fishery has generally diminished relative to historical standards. Still, smallmouth buffalo remains an important and valued component of the UMRS commercial fishery.\n\nThe study area is represented by three study reaches on the Illinois River and three study reaches on the Upper Mississippi River (Figure 1). Collectively, these study reaches represent nearly 1200 river km and exist across strong and pronounced ecological and disturbance gradients. For example, habitat composition, water quality, commercial navigation intensity, aquatic plant prominence, and the number and abundance of nonnative fish species vary strongly across the study domain, with northern Mississippi River reaches exhibiting less navigation traffic, better water quality, markedly greater aquatic plant prominence, more diverse habitat composition, and comparably much smaller numbers of nonnative species than the lower Mississippi River study reach and those on the Illinois River (USGS 1999; Johnson and Hagerty [eds] 2008; Irons et al. 2009).\n\nLong term monitoring efforts conducted under the auspices of the Upper Mississippi River Restoration program over the past 27 years have provided tremendous insights into shifts and changes of the overall UMRS fish community (Ickes et al. 2005; Garvey et al. 2010; Schramm and Ickes 2016). However, these monitoring efforts observe only the most basic aspects of the UMRS fish community (i.e., catch, length, weight, distribution, and occurrence). To gain a greater understanding of forces driving community level shifts and changes, more directed study is needed on the functional attributes of fish populations (i.e., growth, mortality, recruitment). Collectively, these functional attributes of populations are termed population dynamics and/or vital rates.\n\nIt is important to note, the population dynamics of fishes in large rivers is generally poorly understood, especially for non-game species (Ickes 2018). The prevailing view is that abiotic factors largely govern inter-annual population dynamics, typically based upon rather short-term observations and correlations with assorted abiotic river attributes that vary on a seasonal or annual basis (for example, Risotto and Turner 1985). However, the role that longer-term abiotic factors play in regulating population abundance, or that biotic factors internal to the population (e.g., spawner-recruit dynamics, growth dynamics) or external to the population (e.g., predator-prey dynamics, sympatric competitors, disease) remain poorly understood. Achieving a greater understanding of these dynamics is important for stock, game, and invasive species management.\n\nIn 2017, as part of a larger study designed to gain vital population rate information for smallmouth buffalo in the Upper Mississippi and Illinois Rivers (“Smallmouth Buffalo population demographics of the Upper Mississippi River System”; UMRR LTRM 2018SOW project items 2018MMBF1-2018MMBF6) annual growth patterns in smallmouth buffalo were determined and evaluated. This was accomplished by measuring growth histories recorded in annual growth increments on hard bony parts (here otoliths), a method known generically as biochronology, and somewhat analogous to dendrochronology practiced by foresters. These methods allow one to generate time-series of annual growth histories that depend upon age, year class (i.e., cohort), and annual environmental conditions experienced by the population over time (Weisberg, 1993).\n\nBiochronology methods were used to develop a 36-year time series of smallmouth buffalo growth in the Upper Mississippi and Illinois Rivers across a 1200 km ecological and human use disturbance gradient. Annual growth intervals were identified and measured from otoliths to determine fish age and growth history. A mixed model that parses the growth increment into age and year effects was fit to these data.\n\nGiven the pronounced ecological and disturbance gradients inherent to the UMRS and the study domain, an a priori expectation of differing patterns in growth is accepted as a null hypothesis to test.\n\nThe goal of this study was to model smallmouth buffalo growth as a function of the age of the fish and the growth year in which the growth was gained. The primary modeling objective was to parse growth observed on each annulus into a portion attributable to the age of the fish and the portion attributable to the year in which the growth was gained. In effect, this modeling approach removes the somewhat trivial age effects on growth so that a non-confounded growth year effect can be gained. Results attributable to growth year provide a time series of growth information that is of the same duration as the oldest fish observed and solely reflects environmental influences on growth. These model responses can then be investigated relative to environmental covariate time-series suspected of influencing growth of smallmouth buffalo in the Upper Mississippi and Illinois Rivers (e.g., temperature, discharge, population density, population mortality, forage availability, sympatric competition, habitat composition, navigation intensity, nonnative fish prominence, etc.). Thus, the primary scientific objective was to investigate if and how smallmouth buffalo growth varies in accordance with innate ecological and disturbance gradients across the study domain.","language":"English","publisher":"US Army Corps of Engineers","usgsCitation":"Ickes, B., 2020, Smallmouth buffalo (Ictiobus bubalus) growth across a 1200km human use and ecological disturbance gradient in the Upper Mississippi River System: Mississippi River Restoration Program, 16 p.","productDescription":"16 p.","ipdsId":"IP-111767","costCenters":[{"id":606,"text":"Upper Midwest Environmental Sciences Center","active":true,"usgs":true}],"links":[{"id":390126,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"},{"id":390125,"rank":1,"type":{"id":15,"text":"Index Page"},"url":"https://umesc.usgs.gov/reports_publications/ltrmp_rep_list.html"}],"country":"United States","state":"Illinois, Iowa, Minnesota, Missouri, Wisconsin","otherGeospatial":"Illinois river, upper Mississippi River system","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -91.285400390625,\n 40.329795743702064\n ],\n [\n -90.802001953125,\n 41.1290213474951\n ],\n [\n -90.06591796875,\n 41.88592102814744\n ],\n [\n -90.04394531249999,\n 42.13082130188811\n ],\n [\n -90.889892578125,\n 43.052833917627936\n ],\n [\n -91.14257812499999,\n 43.97700467496408\n ],\n [\n -91.900634765625,\n 44.47299117260252\n ],\n [\n -92.757568359375,\n 44.809121700077355\n ],\n [\n -93.71337890625,\n 45.390735154248894\n ],\n [\n -94.141845703125,\n 45.69083283645816\n ],\n [\n -94.15283203125,\n 46.37725420510028\n ],\n [\n -94.7021484375,\n 46.38483322349276\n ],\n [\n -94.39453125,\n 45.583289756006316\n ],\n [\n -93.44970703125,\n 44.92591837128866\n ],\n [\n -92.186279296875,\n 44.37098696297173\n ],\n [\n -91.64794921875,\n 43.98491011404692\n ],\n [\n -91.38427734374999,\n 43.74728909225908\n ],\n [\n -91.34033203125,\n 43.33316939281732\n ],\n [\n -91.2744140625,\n 42.64204079304426\n ],\n [\n -90.582275390625,\n 42.285437007491545\n ],\n [\n -90.3515625,\n 42.00032514831621\n ],\n [\n -91.219482421875,\n 41.36856413680967\n ],\n [\n -91.669921875,\n 40.622291783092706\n ],\n [\n -91.7138671875,\n 39.93501296038254\n ],\n [\n -90.8349609375,\n 38.75408327579141\n ],\n [\n -90.384521484375,\n 38.762650338334154\n ],\n [\n -90.494384765625,\n 38.298559092254344\n ],\n [\n -89.857177734375,\n 37.60552821745789\n ],\n [\n -89.58251953125,\n 37.02886944696474\n ],\n [\n -89.53857421875,\n 36.82687474287728\n ],\n [\n -89.80224609374999,\n 36.30627216957992\n ],\n [\n -89.384765625,\n 36.32397712011264\n ],\n [\n -89.02221679687499,\n 36.76529191711624\n ],\n [\n -89.110107421875,\n 37.28279464911045\n ],\n [\n -89.49462890625,\n 37.92686760148135\n ],\n [\n -90.263671875,\n 38.35027253825765\n ],\n [\n -89.912109375,\n 39.01064750994083\n ],\n [\n -90.50537109375,\n 39.18969082109678\n ],\n [\n -90.362548828125,\n 39.85915479295669\n ],\n [\n -89.593505859375,\n 40.51379915504413\n ],\n [\n -89.154052734375,\n 41.16211393939692\n ],\n [\n -88.4619140625,\n 41.253032440653186\n ],\n [\n -88.05541992187499,\n 41.3850519497068\n ],\n [\n -88.2861328125,\n 41.590796851056005\n ],\n [\n -89.549560546875,\n 41.42625319507269\n ],\n [\n -90.208740234375,\n 40.613952441166596\n ],\n [\n -90.867919921875,\n 39.90130858574735\n ],\n [\n -91.219482421875,\n 39.8928799002948\n ],\n [\n -91.285400390625,\n 40.329795743702064\n ]\n ]\n ]\n }\n }\n ]\n}","noUsgsAuthors":false,"publicationStatus":"PW","contributors":{"authors":[{"text":"Ickes, Brian 0000-0001-5622-3842 bickes@usgs.gov","orcid":"https://orcid.org/0000-0001-5622-3842","contributorId":2925,"corporation":false,"usgs":true,"family":"Ickes","given":"Brian","email":"bickes@usgs.gov","affiliations":[{"id":198,"text":"Coop Res Unit Atlanta","active":true,"usgs":true},{"id":606,"text":"Upper Midwest Environmental Sciences Center","active":true,"usgs":true}],"preferred":true,"id":801849,"contributorType":{"id":1,"text":"Authors"},"rank":1}]}} ,{"id":70218021,"text":"70218021 - 2020 - Geologic map of the Dog River and northern part of the Badger Lake 7.5′ quadrangles, Hood River County, Oregon","interactions":[],"lastModifiedDate":"2021-04-14T14:37:25.364879","indexId":"70218021","displayToPublicDate":"2020-12-31T09:33:36","publicationYear":"2020","noYear":false,"publicationType":{"id":18,"text":"Report"},"publicationSubtype":{"id":2,"text":"State or Local Government Series"},"seriesTitle":{"id":8123,"text":"Geological Map","active":true,"publicationSubtype":{"id":2}},"seriesNumber":"126","title":"Geologic map of the Dog River and northern part of the Badger Lake 7.5′ quadrangles, Hood River County, Oregon","docAbstract":"The Dog River and northern part of the Badger Lake 7.5' quadrangles encompasses an area of ~201 km2 (77.6 mi2) of the High Cascades of north-central Oregon, lying across the eastern slopes of Mount Hood volcano (Figure 1-1; Plate 1; referred to herein as Dog River–Badger Lake area). Mount Hood, known as Wy’east to Native Americans, is Oregon’s tallest peak (3,427 m [11,241 ft]). The volcano has erupted episodically for the past 500,000 years, experiencing two major eruptive periods during the last 1,500 years (Scott and others, 1997a; Scott and others, 2003; Scott and Gardner, 2017). Cascade Range volcanism and structural development in the area dates back longer, with eruptive activity dating from latest Miocene to recent time; part of that volcano-tectonic record is detailed by new high-resolution geologic mapping presented here.
The geology of the Dog River–Badger Lake area was mapped by the Oregon Department of Geology and Mineral Industries (DOGAMI) between 2017 and 2020, in collaboration with geoscientists from the U. S. Geological Survey Cascade Volcano Observatory (USGS CVO) and Hamilton College, New York. The primary objective of this investigation is to provide an updated and spatially accurate geologic framework for the Dog River–Badger Lake area as part of a multi-year study of the geology of the larger Middle Columbia Basin (Figure 1-1, Figure 1-2). Additional key objectives of this project are to: 1) determine the geologic history of volcanic rocks in this part of the northern Oregon Cascade Range, including lava flows and volcaniclastic deposits erupted from Middle Pleistocene to Holocene Mount Hood volcano; 2) provide significant new details about the structure and fault history along the northern segment of the High Cascades intra-arc graben (Hood River graben); and 3) better understand geologic hazards in the region, related to earthquakes, volcanoes, and landslides. New detailed geologic data presented here also provides a basis for future geologic, geohydrologic, and geohazard studies in the greater Middle Columbia Basin. Detailed geologic mapping in this part of the Middle Columbia Basin is a high priority of the Oregon Geologic Map Advisory Committee (OGMAC), supported in part by grants from the STATEMAP component of the USGS National Cooperative Geologic Mapping Program (G17AC00210, G19AC00160). Additional funds were provided by the State of Oregon.
The core products of this study are this report, an accompanying geologic map and cross sections (Plate 1), an Esri ArcGIS™ geodatabase, and Microsoft Excel® spreadsheets tabulating point data for geochemistry, geochronology, magnetic polarity, orientation points, and well data. The geodatabase presents the new geologic mapping in a digital format consistent with the USGS National Cooperative Geologic Mapping Program Geologic Map Schema (GeMS) (U.S. Geological Survey National Cooperative Geologic Mapping Program, 2020). This geodatabase contains spatial information, including geologic polygons, contacts, structures, geochemistry, geochronology, magnetic observation, orientation points, and well data, as well as data about each geologic unit such as age, lithology, mineralogy, and structure. Digitization at scales of 1:8,000 or better was accomplished using a combination of high-resolution lidar topography and imagery. Surficial and bedrock geologic units contained in the geodatabase are depicted on the Plate 1 at a scale of 1:24,000. Both the geodatabase and geologic map are supported by this report describing the geology in detail.
","language":"English","publisher":"Oregon Department of Geology and Mineral Industries","usgsCitation":"McClaughry, J.D., Scott, W., Duda, C.J., and Conrey, R.M., 2020, Geologic map of the Dog River and northern part of the Badger Lake 7.5′ quadrangles, Hood River County, Oregon: Geological Map 126, Report: 154 p.; 1 Plate 48 x 52 inches; Database; Metadata.","productDescription":"Report: 154 p.; 1 Plate 48 x 52 inches; Database; Metadata","ipdsId":"IP-126371","costCenters":[{"id":617,"text":"Volcano Science Center","active":true,"usgs":true}],"links":[{"id":385093,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"},{"id":383245,"type":{"id":15,"text":"Index Page"},"url":"https://www.oregongeology.org/pubs/gms/p-GMS-126.htm"}],"scale":"24000","country":"United States","state":"Oregon","county":"Hood River County","otherGeospatial":"Dog River and Northern Part of the Badger Lake 7.5' Quadrangles","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -121.87683105468749,\n 44.97839955494438\n ],\n [\n -120.5914306640625,\n 44.97839955494438\n ],\n [\n -120.5914306640625,\n 45.73494252455993\n ],\n [\n -121.87683105468749,\n 45.73494252455993\n ],\n [\n -121.87683105468749,\n 44.97839955494438\n ]\n ]\n ]\n }\n }\n ]\n}","noUsgsAuthors":false,"publicationStatus":"PW","contributors":{"authors":[{"text":"McClaughry, Jason D.","contributorId":194544,"corporation":false,"usgs":false,"family":"McClaughry","given":"Jason","email":"","middleInitial":"D.","affiliations":[],"preferred":false,"id":810242,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Scott, William E. 0000-0001-8156-979X","orcid":"https://orcid.org/0000-0001-8156-979X","contributorId":250706,"corporation":false,"usgs":true,"family":"Scott","given":"William E.","affiliations":[{"id":617,"text":"Volcano Science Center","active":true,"usgs":true}],"preferred":true,"id":810243,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Duda, Carlie J. M.","contributorId":250707,"corporation":false,"usgs":false,"family":"Duda","given":"Carlie","email":"","middleInitial":"J. M.","affiliations":[{"id":32397,"text":"Oregon Department of Geology and Mineral Industries","active":true,"usgs":false}],"preferred":false,"id":810244,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Conrey, Richard M.","contributorId":194345,"corporation":false,"usgs":false,"family":"Conrey","given":"Richard","email":"","middleInitial":"M.","affiliations":[{"id":13203,"text":"School of the Environment, Washington State University","active":true,"usgs":false}],"preferred":false,"id":810245,"contributorType":{"id":1,"text":"Authors"},"rank":4}]}} ,{"id":70229205,"text":"70229205 - 2020 - Reconnaissance map of the Cenozoic geology in the Carlin basin area, Elko and Eureka counties, Nevada","interactions":[],"lastModifiedDate":"2022-03-03T14:38:55.904902","indexId":"70229205","displayToPublicDate":"2020-12-31T08:27:42","publicationYear":"2020","noYear":false,"publicationType":{"id":18,"text":"Report"},"publicationSubtype":{"id":2,"text":"State or Local Government Series"},"seriesTitle":{"id":10147,"text":"Nevada Bureau of Mines and Geology Open File Report","active":true,"publicationSubtype":{"id":2}},"seriesNumber":"2020-02","title":"Reconnaissance map of the Cenozoic geology in the Carlin basin area, Elko and Eureka counties, Nevada","docAbstract":"The current map publication was supported by the USGS National Cooperative Geologic Mapping Program under STATEMAP award number G19AC00383.
Arey Lagoon, located in eastern Arctic Alaska, supports a highly productive ecosystem, where soft substrate and coastal wet sedge fringing the shores are feeding grounds and nurseries for a variety of marine fish and waterfowl. The lagoon is partially protected from the direct onslaught of Arctic Ocean waves by a barrier island chain (Arey Island) which in itself provides important habitat for migratory shorebirds and waterfowl. In this work, numerically modeled waves and water levels are computed under the provision of sea-level rise and changing conditions brought about by 21st century climate variability. Model results, supported by observations, are used to assess the stability of the barrier chain and spatiotemporal changes in flood patterns across fringing coastal wet sedge areas. The results aim to support studies that investigate the possibility of new biological succession trajectories and loss or increase of habitat areas.
","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/ofr20201142","collaboration":"Prepared in cooperation with and funded in part by the Arctic Landscape Conservation Cooperation (ALCC)","usgsCitation":"Erikson, L.H., Gibbs, A.E., Richmond, B.M., Storlazzi, C.D., Jones, B.M., and Ohman, K.A., 2020, Changing storm conditions in response to projected 21st century climate change and the potential impact on an arctic barrier island–lagoon system—A pilot study for Arey Island and Lagoon, eastern Arctic Alaska: U.S. Geological Survey Open-File Report 2020–1142, 68, p., https://doi.org/10.3133/ofr20201142.","productDescription":"Report: x, 68 p.; Data Release","onlineOnly":"Y","ipdsId":"IP-079323","costCenters":[{"id":114,"text":"Alaska Science Center","active":true,"usgs":true},{"id":520,"text":"Pacific Coastal and Marine Science Center","active":true,"usgs":true}],"links":[{"id":381735,"rank":3,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/P9LGYO2Q","text":"USGS data release","linkHelpText":"Modeled 21st century storm surge, waves, and coastal flood hazards and supporting oceanographic and geological field data (2010 and 2011) for Arey and Barter Islands, Alaska and vicinity"},{"id":381739,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/of/2020/1142/coverthb.jpg"},{"id":381740,"rank":2,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/of/2020/1142/ofr20201142.pdf","text":"Report","size":"8.98 MB","linkFileType":{"id":1,"text":"pdf"},"description":"OFR 2020-1142"}],"country":"United States","state":"Alaska","otherGeospatial":"Arey Island and Lagoon","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -144.09805297851562,\n 70.03559723423488\n ],\n [\n -143.6407470703125,\n 70.03559723423488\n ],\n [\n -143.6407470703125,\n 70.13476515043729\n ],\n [\n -144.09805297851562,\n 70.13476515043729\n ],\n [\n -144.09805297851562,\n 70.03559723423488\n ]\n ]\n ]\n }\n }\n ]\n}","contact":"Pacific Coastal and Marine Science Center
U.S. Geological Survey
Pacific Science Center
2885 Mission St.
Santa Cruz, CA 95060
Structured decision making is a systematic, transparent process for improving the quality of complex decisions by identifying measurable management objectives and feasible management actions; predicting the potential consequences of management actions relative to the stated objectives; and selecting a course of action that maximizes the total benefit achieved and balances tradeoffs among objectives. The U.S. Geological Survey, in cooperation with the U.S. Fish and Wildlife Service, applied an existing, regional framework for structured decision making to develop a prototype tool for optimizing tidal marsh management decisions at the Stewart B. McKinney National Wildlife Refuge in Connecticut. Refuge biologists, refuge managers, and research scientists identified multiple potential management actions to improve the ecological integrity of two marsh management units within the refuge and estimated the outcomes of each action in terms of performance metrics associated with each management objective. Value functions previously developed at the regional level were used to transform metric scores to a common utility scale, and utilities were summed to produce a single score representing the total management benefit that would be accrued from each potential management action. Constrained optimization was used to identify the set of management actions, one per marsh management unit, that would maximize total management benefits at different cost constraints at the refuge scale. Results indicated that, for the objectives and actions considered here, total management benefits may increase consistently up to approximately $1,190,000, but that further expenditures may yield diminishing return on investment. Management actions in optimal portfolios at total costs less than $1,190,000 included controlling avian predators in both management units, managing stormwater on lands adjacent to one marsh management unit, and removing a tide gate and breaching a dike to improve tidal flow in the other marsh management unit. The management benefits were derived from expected increases in the numbers of spiders (as an indicator of trophic health) and tidal marsh obligate birds, and an expected decrease in the use of herbicides to control invasive vegetation. The prototype presented here provides a framework for decision making at the Stewart B. McKinney National Wildlife Refuge that can be updated as new data and information become available. Insights from this process may also be useful to inform future habitat management planning at the refuges.
","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/ofr20201139","collaboration":"Prepared in cooperation with U.S. Fish and Wildlife Service","usgsCitation":"Low, L.E. , Neckles, H.A., Lyons, J.E., Nagel, J.L., Adamowicz, S.C., Mikula, T., Vagos, K., and Potvin, R., 2020, Optimization of salt marsh management at the Stewart B. McKinney National Wildlife Refuge, Connecticut, through use of structured decision making: U.S. Geological Survey Open-File Report 2020–1139, 28 p., https://doi.org/10.3133/ofr20201139.","productDescription":"vi, 28 p.","numberOfPages":"28","onlineOnly":"Y","additionalOnlineFiles":"N","ipdsId":"IP-120812","costCenters":[{"id":531,"text":"Patuxent Wildlife Research Center","active":true,"usgs":true},{"id":50464,"text":"Eastern Ecological Science Center","active":true,"usgs":true}],"links":[{"id":381645,"rank":2,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/of/2020/1139/ofr20201139.pdf","text":"Report","size":"2.73 MB","linkFileType":{"id":1,"text":"pdf"},"description":"OFR 2020-1139"},{"id":381644,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/of/2020/1139/coverthb.jpg"}],"country":"United States","state":"Connecticut","otherGeospatial":"Stewart B. McKinney 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 -73.17169189453125,\n 41.15022321163024\n ],\n [\n -73.13186645507812,\n 41.13677892209895\n ],\n [\n -73.10028076171875,\n 41.14867208811923\n ],\n [\n -73.15177917480469,\n 41.18537216794189\n ],\n [\n -73.18113327026366,\n 41.17090135180691\n ],\n [\n -73.17169189453125,\n 41.15022321163024\n ]\n ]\n ]\n }\n }\n ]\n}","contact":"Director, Eastern Ecological Science Center
U.S. Geological Survey
12100 Beech Forest Road
Laurel, MD 20708–4039
The bedrock geology of the 7.5- by 15-minute Springfield quadrangle consists of highly deformed and metamorphosed Mesoproterozoic through Devonian metasedimentary and meta-igneous rocks. In the west, Mesoproterozoic gneisses of the Mount Holly Complex are the oldest rocks and form the eastern side of the Chester dome. The Moretown slice structurally overlies the Chester dome along the Keyes Mountain thrust fault which represents the Ordovician Taconic suture (Red Indian Line) between Laurentian and Ganderian crust. The allochthonous Cambrian through Ordovician Moretown slice includes the Moretown and Cram Hill Formations and the North River Igneous Suite. Silurian and Devonian metasedimentary and metavolcanic rocks of the Connecticut Valley trough (CVT) unconformably overlie the Moretown slice. Ordovician to Silurian and Devonian metasedimentary and meta-igneous rocks of the New Hampshire sequence structurally overlie the CVT along the Devonian, Acadian Monroe thrust fault. The oldest part of the New Hampshire sequence consists of Ordovician metamorphosed volcanic, plutonic, and sedimentary rocks of the Bronson Hill arc including the Ammonoosuc Volcanics, the Partridge Formation, and the Oliverian Plutonic Suite. The Ammonoosuc Volcanics are the base of the exposed arc section in the map area. The Bronson Hill arc rocks are exposed in fault-bounded structural belts, including the Monroe thrust sheet, the Claremont belt, the Sugar River and Unity domes, and the footwall of the Brennan Hill thrust fault. Silurian to Devonian metasedimentary rocks of the Clough Quartzite, and Fitch and Littleton Formations unconformably overlie the Bronson Hill arc rocks. Devonian granitic and pegmatitic dikes and sills of the New Hampshire Plutonic Suite intruded previously deformed rocks.
Devonian, Acadian F1 fold nappes have a sheath fold geometry and are truncated by multiple generations of faults. The Bronson Hill arc structurally overlies the CVT along the Acadian Monroe fault with preserved tectonic mélange in the footwall. Upright dome-stage F2 folds post-date amphibolite facies metamorphism and locally developed into sheath folds in high-strain zones. F3 folds exhibit sinistral rotation associated with Alleghanian lower-greenschist facies faults. Late Paleozoic Alleghanian to Mesozoic shear zones transpose stratigraphy, early structures, and peak metamorphic isograds. 40Ar/39Ar white-mica growth ages (300–250 million years before present [Ma]) indicate that retrograde deformation continued into the latest Paleozoic and earliest Mesozoic. Apatite fission track data show that brittle faults were active prior to about 100 Ma and experienced Late Cretaceous and even Paleocene re-activation.
The bedrock geology was mapped to study the tectonic history of the area and to provide a framework for ongoing characterization of the bedrock of Vermont and New Hampshire. This Scientific Investigations Map of the Springfield 7.5- x 15-minute quadrangle consists of sheets 1 and 2 as well as a geographic information system (GIS) database that includes bedrock geologic units, faults, outcrops, and structural geologic information. Sheet 1 of the report includes a bedrock geologic map, a correlation of map units, and a description of map units. Sheet 2 includes a discussion of the geology, references cited, two cross sections from the geologic map on sheet 1, a tectonic map showing major structural features, and a structural domain map showing the orientation of brittle features.
","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/sim3462","collaboration":"Prepared in cooperation with the State of Vermont, Vermont Agency of Natural Resources, Vermont Geological Survey; and the State of New Hampshire, Department of Environmental Services, New Hampshire Geological Survey","usgsCitation":"Walsh, G.J., Valley, P.M., Armstrong, T.R., Ratcliffe, N.M., Merschat, A.J., and Gentry, B.J., 2020, Bedrock geologic map of the Springfield 7.5- x 15-minute quadrangle, Windsor County, Vermont, and Sullivan County, New Hampshire (ver. 1.1, June 2024): U.S. Geological Survey Scientific Investigations Map 3462, 2 sheets, scale 1:24,000, https://doi.org/10.3133/sim3462.","productDescription":"2 Sheets: 62.78 x 40.79 inches and 48.00 x 41.00 inches; Base Map; Metadata; Database; Read Me; Companion File","onlineOnly":"Y","additionalOnlineFiles":"Y","ipdsId":"IP-091203","costCenters":[{"id":40020,"text":"Florence Bascom Geoscience Center","active":true,"usgs":true}],"links":[{"id":499273,"rank":10,"type":{"id":36,"text":"NGMDB Index Page"},"url":"https://ngmdb.usgs.gov/Prodesc/proddesc_110835.htm","linkFileType":{"id":5,"text":"html"}},{"id":429409,"rank":9,"type":{"id":25,"text":"Version History"},"url":"https://pubs.usgs.gov/sim/3462/versionHist.txt","size":"715 B","linkFileType":{"id":2,"text":"txt"}},{"id":381115,"rank":8,"type":{"id":7,"text":"Companion Files"},"url":"https://pubs.usgs.gov/sim/3462/sim3462_openaccess.zip","text":"Open Access","size":"5.06 MB","linkFileType":{"id":6,"text":"zip"}},{"id":381114,"rank":7,"type":{"id":23,"text":"Spatial Data"},"url":"https://pubs.usgs.gov/sim/3462/sim3462_basemap.zip","text":"Base Map","size":"84.1 MB","linkFileType":{"id":6,"text":"zip"}},{"id":381111,"rank":4,"type":{"id":20,"text":"Read Me"},"url":"https://pubs.usgs.gov/sim/3462/sim3462_readme.txt","size":"11.8 KB","linkFileType":{"id":2,"text":"txt"}},{"id":381110,"rank":3,"type":{"id":26,"text":"Sheet"},"url":"https://pubs.usgs.gov/sim/3462/sim3462_sheet2.pdf","text":"Sheet 2","size":"7.61 MB","linkFileType":{"id":1,"text":"pdf"},"description":"SIM 3462"},{"id":381108,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/sim/3462/coverthb.jpg"},{"id":381113,"rank":6,"type":{"id":16,"text":"Metadata"},"url":"https://pubs.usgs.gov/sim/3462/sim3462_metadata.zip","size":"445 KB","linkFileType":{"id":6,"text":"zip"}},{"id":381109,"rank":2,"type":{"id":26,"text":"Sheet"},"url":"https://pubs.usgs.gov/sim/3462/sim3462_sheet1.pdf","text":"Sheet 1","size":"23.2 MB","linkFileType":{"id":1,"text":"pdf"},"description":"SIM 3462"},{"id":381112,"rank":5,"type":{"id":9,"text":"Database"},"url":"https://pubs.usgs.gov/sim/3462/sim3462_database.zip","size":"5.38 MB","linkFileType":{"id":6,"text":"zip"}}],"country":"United States","state":"New Hampshire, Vermont","county":"Sullivan County, Windsor County","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -72.5,\n 43.25\n ],\n [\n -72.25,\n 43.25\n ],\n [\n -72.25,\n 43.375\n ],\n [\n -72.5,\n 43.375\n ],\n [\n -72.5,\n 43.25\n ]\n ]\n ]\n }\n }\n ]\n}","edition":"Version 1.0: December 22, 2020; Version 1.1: June 4, 2024","contact":"Florence Bascom Geoscience Center
U.S. Geological Survey
926A National Center
12201 Sunrise Valley Drive
Reston, VA 20192
Hanford, Washington (USA) is the construction site of a multi-billion-dollar high-level nuclear waste treatment facility. This site lies 200 kilometers (km) east of Mount St. Helens (MSH), the most active volcano in the contiguous United States. Tephra from a future MSH eruption could pose a hazard to the air intake and filtration systems at this plant. In this report, we present a probabilistic estimate of the amount of tephra that could fall, and the concentrations of airborne ash that could occur at the Hanford Site during a future eruption. Mount St. Helens has produced four large explosive eruptions in approximately the past 500 years, suggesting that its annual probability of eruption (P1) is roughly 4/500=0.008. Assuming that a large eruption occurs, we calculate the probability (P3|1) of a given fall deposit thickness or airborne concentration at Hanford by running about 10,000 simulations of ash-producing eruptions using the atmospheric transport model Ash3d. In each simulation, we calculate the pattern of tephra dispersal, deposit thickness at Hanford, and airborne ash concentration at ground level. As input for each simulation, we choose meteorological conditions from a randomly chosen time in the historical record between 1980 and 2010, using data from the European Centre for Medium-Range Weather Forecasting (ECMWF) Reanalysis (ERA) Interim model. The volume (dense-rock equivalent) of each simulated eruption is randomly chosen from a uniform probability distribution on a log scale from the range of magma volumes (0.008–2.3 cubic kilometers [km3]) estimated for late Holocene eruptions at MSH. Plume heights and durations of each eruption are chosen using empirical correlations between volume, height, and eruption rate, which account for the fact that larger eruptions have higher plumes and last longer. We construct summary tables of final deposit thickness (T), maximum ground-level airborne concentration (Cmax), and average ground-level airborne concentration (Cavg) during tephra-fall for each run. Each table is sorted and ranked by decreasing value of T, Cmax, or Cavg. Conditional probabilities (P3|1) are derived by dividing rank by n+1, where n is the total number of successful runs. For example, a deposit thickness of 5.10 centimeters (cm) from run 446 is ranked 123 of 9,785 successful runs, yielding P3|1=123/9,786=0.01257. Its annual probability is P=P1·P3|1=0.008×0.01257=0.000101. By interpolation, the deposit thickness (T10k) having an annual probability of 1 in 10,000 (P= 0.0001) is 5.11 cm. Analogous concentration values are Cmax,10k=3,819 and Cavg,10k=1,513 milligrams per cubic meter (mg/m3), respectively. Independent calculations using the known mass accumulation rate of the deposit (=0.001–0.006 kilograms per square meter per second [kg/m2/s]), aggregate fall velocities (u=0.3–0.8 meters per second [m/s]), and the simple formula , yield similar results, although highly variable fall velocities add significant uncertainty. This formula implies that deposit accumulation rates of millimeters (mm) to greater than 1 cm per hour, which are not uncommon during heavy ash fall, are associated with airborne concentrations of 102–103 milligrams per cubic meter (mg/m3). These concentrations are much higher than published measurements (10-3–101 mg/m3), which record only suspended particles sampled in sheltered areas. During heavy ashfall, most fine ash falls as aggregates. Whether such aggregates will be ingested into air ducts will depend on the aggregate size and fall rate, the fragility of the aggregates, the air duct geometry, intake velocity, and other factors.
","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/ofr20201133","collaboration":"Prepared in cooperation with the U.S. Department of Energy, Office of River Protection","usgsCitation":"Mastin, L.G., Van Eaton, A., and Schwaiger, H.F., 2020, A probabilistic assessment of tephra-fall hazards at Hanford, Washington, from a future eruption of Mount St. Helens: U.S. Geological Survey Open-File Report 2020–1133, 54 p., https://doi.org/10.3133/ofr20201133.","productDescription":"Report: ix, 54 p.; Data Release","numberOfPages":"54","onlineOnly":"Y","ipdsId":"IP-112179","costCenters":[{"id":617,"text":"Volcano Science Center","active":true,"usgs":true}],"links":[{"id":381546,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/of/2020/1133/covrthb.jpg"},{"id":381547,"rank":2,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/of/2020/1133/ofr20201133.pdf","text":"Report","size":"9.5 MB","linkFileType":{"id":1,"text":"pdf"}},{"id":381548,"rank":3,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/P9VPFXQR","linkHelpText":"Data Used to Develop A Probabilistic Assessment of Tephra-Fall Hazards at Hanford, Washington"}],"country":"United States","state":"Washington","otherGeospatial":"Hanford","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -119.88281249999999,\n 46.33175800051563\n ],\n [\n -119.2950439453125,\n 46.33175800051563\n ],\n [\n -119.2950439453125,\n 46.81509864599243\n ],\n [\n -119.88281249999999,\n 46.81509864599243\n ],\n [\n -119.88281249999999,\n 46.33175800051563\n ]\n ]\n ]\n }\n }\n ]\n}","contact":"Volcano Science Center
Cascades Volcano Observatory
U.S. Geological Survey
1300 SE Cardinal Court
Vancouver, WA, 98683
In the U.S., 525,000 horseshoe crabs (Limulus polyphemus) per year have been captured during 2013–2017, brought to biomedical facilities, and bled to produce Limulus amebocyte lysate (LAL), then mostly released to the area of capture. The Atlantic States Marine Fisheries Commission estimates short-term bleeding-induced mortality to be 15% (4% to 30%), resulting in mortality of approximately 78,750 horseshoe crabs annually in recent years comprising a minor portion (<13%) of the up to one million annual coastwide landings dominated by harvest for bait. However, the long-term effect of bleeding for LAL on annual survival and spawning behavior is unknown; thus, results from short-term studies alone might underestimate bleeding effects at the population level. To address this knowledge gap, we analyzed data from the U.S. Fish and Wildlife horseshoe crab tagging database to estimate the differences in survival and recapture rates of bled and not bled horseshoe crabs tagged in the same years and geographic area. Contrary to expectation, survival was not lower for bled crabs compared to unbled crabs. Differences varied, but survival estimates tended to be higher for bled crabs than for unbled crabs. However, biomedical culling and selection for younger or healthier animals could have resulted in biomedically tagged individuals representing a healthier subset of the overall population with subsequent higher survival. Furthermore, the tagging analysis revealed a post-bleeding reduction in capture probability, which could indicate decreased spawning activity, evident in males more than females. Continued tagging of bled and unbled crabs in the same geographic area while recording age class and sex will contribute to the further resolution of LAL production’s effect on horseshoe crab populations.
","language":"English","publisher":"Frontiers Media","doi":"10.3389/fmars.2020.607668","usgsCitation":"Smith, D.R., Newhard, J., McGowan, C.P., and Butler, C.A., 2020, The Long-term effect of bleeding for Limulus amebocyte lysate on annual survival and recapture of tagged horseshoe crabs: Frontiers in Marine Science, v. 7, 607668, 13 p., https://doi.org/10.3389/fmars.2020.607668.","productDescription":"607668, 13 p.","ipdsId":"IP-115044","costCenters":[{"id":50464,"text":"Eastern Ecological Science Center","active":true,"usgs":true}],"links":[{"id":454627,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.3389/fmars.2020.607668","text":"Publisher Index Page"},{"id":388481,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","state":"Delaware, Maryland, New Jersey, Virginia","otherGeospatial":"Delaware Bay","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -75.69580078125,\n 37.09900294387622\n ],\n [\n -75.08056640625,\n 37.97018468810549\n ],\n [\n -74.6630859375,\n 38.496593518947584\n ],\n [\n -74.92675781249999,\n 38.950865400919994\n ],\n [\n -74.8828125,\n 39.172658670429946\n ],\n [\n -75.509033203125,\n 39.46164364205549\n ],\n [\n -75.421142578125,\n 39.257778150283364\n ],\n [\n -75.30029296875,\n 38.89958342598271\n ],\n [\n -75.12451171875,\n 38.685509760012\n ],\n [\n -75.146484375,\n 38.53097889440024\n ],\n [\n -75.135498046875,\n 38.272688535980976\n ],\n [\n -75.30029296875,\n 38.151837403006766\n ],\n [\n -75.7177734375,\n 37.55328764595765\n ],\n [\n -75.89355468749999,\n 37.274052809979054\n ],\n [\n -75.69580078125,\n 37.09900294387622\n ]\n ]\n ]\n }\n }\n ]\n}","volume":"7","noUsgsAuthors":false,"publicationDate":"2020-12-21","publicationStatus":"PW","contributors":{"authors":[{"text":"Smith, David R. 0000-0001-6074-9257 drsmith@usgs.gov","orcid":"https://orcid.org/0000-0001-6074-9257","contributorId":168442,"corporation":false,"usgs":true,"family":"Smith","given":"David","email":"drsmith@usgs.gov","middleInitial":"R.","affiliations":[{"id":365,"text":"Leetown Science Center","active":true,"usgs":true}],"preferred":true,"id":821862,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Newhard, Joshua","contributorId":264675,"corporation":false,"usgs":false,"family":"Newhard","given":"Joshua","email":"","affiliations":[{"id":6661,"text":"US Fish and Wildlife Service","active":true,"usgs":false}],"preferred":false,"id":821863,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"McGowan, Conor P. 0000-0002-7330-9581 cmcgowan@usgs.gov","orcid":"https://orcid.org/0000-0002-7330-9581","contributorId":167162,"corporation":false,"usgs":true,"family":"McGowan","given":"Conor","email":"cmcgowan@usgs.gov","middleInitial":"P.","affiliations":[{"id":198,"text":"Coop Res Unit Atlanta","active":true,"usgs":true},{"id":17705,"text":"Wetland and Aquatic Research Center","active":true,"usgs":true}],"preferred":false,"id":821864,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Butler, C. Alyssa","contributorId":264748,"corporation":false,"usgs":false,"family":"Butler","given":"C.","email":"","middleInitial":"Alyssa","affiliations":[],"preferred":false,"id":821933,"contributorType":{"id":1,"text":"Authors"},"rank":4}]}} ,{"id":70223486,"text":"70223486 - 2020 - Estimating the invasion extent of Asian swamp eel (Monopterus: Synbranchidae) in an altered river of the south-eastern United States","interactions":[],"lastModifiedDate":"2021-08-30T13:25:27.947909","indexId":"70223486","displayToPublicDate":"2020-12-18T08:21:38","publicationYear":"2020","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":2681,"text":"Marine and Freshwater Research","active":true,"publicationSubtype":{"id":10}},"title":"Estimating the invasion extent of Asian swamp eel (Monopterus: Synbranchidae) in an altered river of the south-eastern United States","docAbstract":"The first reported invasion of Asian swamp eels (Monopterus albus, ASE) in the continental United States was in the state of Georgia in 1994. This population was first discovered within several ponds on a private nature centre, but the ponds drained via an outflow pipe into marsh habitats along the Chattahoochee River. Our objective was to delineate the current invasion extent of ASE in the Chattahoochee River, Georgia, by sampling juvenile ASE within an occupancy modelling framework. We sampled 111 and 100 sites in 2015 and 2016 respectively, on 10 occasions, each within a 2-km radius of the purported invasion point to estimate the spatial extent of their invasion in this system. Leaf-litter traps (LLTs) were effective at documenting an increase in the invasion extent of ASE, from within 100 m of the Chattahoochee Nature Center pond outflow to 1.6 km away. Documenting the extent of invasion of this population has proven elusive in the past, but the use of LLTs to target juvenile eels has documented a larger invasion extent than previously known in the study system. The results of this research can be used to develop effective control and management strategies, such as locating potential breeding areas for targeted removal sampling.
Hinkley Valley, in the central to western Mojave Desert of southeastern California, has a long historical record owing to its position as a crossroads for rail and road traffic and its position adjacent to the Mojave River. Subflow in the Mojave River provided groundwater recharge that maintained water consumption and demand by way of shallow wells for local agriculture in the valley. Its crossroads position led to construction of several power-transmission lines, pipeline, and communications cable routes that transect Hinkley Valley. One of these, a natural gas pipeline and its associated compressor station, was the locus of hexavalent chromium, Cr(VI), released into, and consequent contamination of, groundwater. Understanding the movement and fate of the contaminants is a complex hydrologic and geochemical problem. Geologic mapping of the Hinkley Valley area provides framework elements for use in resolving this problem. This report provides new information on surface and subsurface geology to better constrain the origin and geometry of hydrologically important deposits in the Hinkley Valley area and describes youthful faults that may control sediment distribution and groundwater flow. The geologic map (sheet 1) presents substantial new information on surficial geology, including Pliocene deposits, but does not contain significant new work on bedrock. Bedrock investigations were specific to identifying youthful faults and representative outcrops for rocks that were penetrated by boreholes in the valley. Special attention was placed on locating and describing youthful faults. In addition, we analyzed gravity data to (1) map horizontal gradients that we interpret to reflect long-term fault traces and to (2) estimate the depth to bedrock, which is defined as Miocene and older intrusive and metamorphic rocks for the purposes of this report. The subsurface geology of Hinkley Valley was investigated by examining borehole sediment cores and rock encountered at the base of the sediment section. We analyzed the core to determine depositional environments, provenance, and age of the sediment that infilled the valley. Valleys, mountains, and basins in the Hinkley Valley area are topographically complex and incompletely named. The nearly flat floored Hinkley Valley slopes gently northward. It is framed by Mount General and the informally named “Hinkley hills” (southeast of Mount General) on the northeast and by Iron Mountain and Lynx Cat Mountain on the southwest, although breaks in the western mountains allow stream connections between Hinkley Valley and another valley to the west that is herein referred to as Hawes valley. At its south end, Hinkley Valley is traversed by the entrenched Mojave River, which passes east out of the valley past Barstow. North of Hinkley Valley, a few low hills (including Red Hill) separate the valley from a broad west-sloping piedmont that is part of the physiographic Harper Basin (of which the Harper Lake playa is the center). The lower part of this piedmont, however, is referred to as Water Valley, although it is not a distinct valley. The name derives from groundwater sourced from subflow in the Mojave River, which caused shallow water and even artesian flow in Water Valley but not in other parts of the Harper Basin. When water filled the Harper Basin to form Pleistocene Lake Harper it not only submerged Water Valley but also northern Hinkley Valley.
","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/sim3458","collaboration":"Prepared in cooperation with the Lahontan Regional Water Quality Control Board and the State Water Resources Control Board","usgsCitation":"Miller, D.M., Langenheim, V.E., and Haddon, E.K., 2020, Geologic map and borehole stratigraphy of Hinkley Valley and vicinity, San Bernardino County, California: U.S. Geological Survey Scientific Investigations Map 3458, pamphlet 23 p., 2 sheets, scale 1:24,000, https://doi.org/10.3133/sim3458.","productDescription":"Pamphlet,: iv, 23 p.; 2 Sheets ; 2 Tables; Database; Data Release; Metadata","numberOfPages":"23","onlineOnly":"Y","additionalOnlineFiles":"Y","ipdsId":"IP-102109","costCenters":[{"id":312,"text":"Geology, Minerals, Energy, and Geophysics Science Center","active":true,"usgs":true}],"links":[{"id":381271,"rank":7,"type":{"id":26,"text":"Sheet"},"url":"https://pubs.usgs.gov/sim/3458/sim3458_sheet2.pdf","text":"Sheet 2","size":"32 MB","linkFileType":{"id":1,"text":"pdf"}},{"id":381270,"rank":6,"type":{"id":26,"text":"Sheet"},"url":"https://pubs.usgs.gov/sim/3458/sim3458_sheet1.pdf","text":"Sheet 1","size":"40 MB","linkFileType":{"id":1,"text":"pdf"}},{"id":381269,"rank":5,"type":{"id":9,"text":"Database"},"url":"https://pubs.usgs.gov/sim/3458/sim3458_database.zip","text":"Database","size":"7.5 MB","linkFileType":{"id":6,"text":"zip"}},{"id":381268,"rank":4,"type":{"id":9,"text":"Database"},"url":"https://pubs.usgs.gov/sim/3458/sim3458_base.zip","text":"Base","size":"1.25 GB","linkFileType":{"id":6,"text":"zip"}},{"id":381267,"rank":3,"type":{"id":16,"text":"Metadata"},"url":"https://pubs.usgs.gov/sim/3458/sim3458_metadata.txt","size":"10 KB","linkFileType":{"id":2,"text":"txt"}},{"id":381266,"rank":2,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/sim/3458/sim3458_pamphlet.pdf","text":"Pamphlet","size":"8 MB","linkFileType":{"id":1,"text":"pdf"}},{"id":381451,"rank":10,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/P9FV5LG5","linkHelpText":"Gravity data of the Hinkley area, southern California"},{"id":381273,"rank":9,"type":{"id":27,"text":"Table"},"url":"https://pubs.usgs.gov/sim/3458/sim3458_table_7.xlsx","text":"Table 7","size":"60 KB","linkFileType":{"id":3,"text":"xlsx"}},{"id":381265,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/sim/3458/covrthb.jpg"},{"id":381272,"rank":8,"type":{"id":27,"text":"Table"},"url":"https://pubs.usgs.gov/sim/3458/sim3458_table_3.xlsx","text":"Table 3","size":"20 KB","linkFileType":{"id":3,"text":"xlsx"}}],"country":"United States","state":"California","county":"San Bernadino County","otherGeospatial":"Hinkley Valley","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -117.26257324218749,\n 34.80647431931937\n ],\n [\n -117.06619262695312,\n 34.80647431931937\n ],\n [\n -117.06619262695312,\n 35.060352812431496\n ],\n [\n -117.26257324218749,\n 35.060352812431496\n ],\n [\n -117.26257324218749,\n 34.80647431931937\n ]\n ]\n ]\n }\n }\n ]\n}","contact":"Director,
Geology, Minerals, Energy, & Geophysics Science Center
Menlo Park, California
U.S. Geological Survey
345 Middlefield Road
Menlo Park, CA 94025-3591
A three-dimensional groundwater-flow model was developed for the aquifer system of Long Island, New York, to evaluate (1) responses of the hydrologic system to changes in natural and anthropogenic hydraulic stresses, (2) the subsurface distribution of groundwater age, and (3) the regional-scale distribution of groundwater travel times and the source of water to fresh surface waters and coastal receiving waters. The model also provides the groundwater flow components used to define model boundaries for possible inset models used for local-scale analyses.
The three-dimensional, groundwater flow model developed for this investigation uses the numerical code MODFLOW–NWT to represent steady-state conditions for average groundwater pumping and aquifer recharge for 2005–15. The particle-tracking algorithm MODPATH, which simulates advective transport in the aquifer, was used to estimate groundwater age, delineate the areas at the water table that contribute recharge to coastal and freshwater bodies, and estimate total travel times of water from the water table to discharge locations.
A three-dimensional, 1-meter (3.3-foot) topobathymetric model was used to determine land-surface altitudes for the island and seabed altitudes for the surrounding coastal waters. The mapped extents and surface altitudes of major geologic units were compiled and used to develop a three-dimensional hydrogeologic framework of the aquifer system, including aquifers and confining units. Lithologic data from deep boreholes and previous aquifer-test results were used to estimate the three-dimensional distribution of hydraulic conductivity in principal aquifers. Natural recharge from precipitation was estimated for 2005–15 using a modified Thornthwaite-Mather methodology as implemented in a soil-water balance model. Components of anthropogenic recharge—wastewater return flow, storm water inflow, and inflow from leaky infrastructure—also were estimated for 2005–15. Groundwater withdrawals for various sources, including public water supply, industrial, remediation, and agricultural, were compiled or estimated for the same period.
These data were incorporated into the model development to represent the aquifer system geometry, boundaries, and initial hydraulic properties of the regional aquifers and confining units within the Long Island aquifer system. Average hydraulic conditions—water levels and streamflows—for 2005–15 were estimated using existing data from the U.S. Geological Survey National Water Information System database. Model inputs were adjusted to best match average hydrologic conditions using inverse methods as implemented in the parameter-estimating software PEST. The calibrated model was used to simulate average hydrologic conditions in the aquifer system for 2005–15.
About 656 cubic feet per second of water was withdrawn on average annually for 2005–15 for water supply and an average of about 349 cubic feet per second of water recharged the aquifer annually from return flow and leaky infrastructure. Parts of New York City have drawdowns exceeding 25 feet, mostly because of urbanization and associated large decreases in recharge rates. Large areas in the western part of the island have drawdowns exceeding 10 feet, mostly from large groundwater withdrawals and sewering, which largely eliminates wastewater return flow. Water-table altitudes in eastern parts of the island increased by more than 2 feet in some areas as a result of wastewater return flow in unsewered areas and changes in land use. Changes in streamflows show a similar pattern as water-table altitudes. Streamflows generally decrease in western parts of the island where there are large drawdowns and increase in eastern parts of the island where water-table altitudes increase.
","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/sir20205091","collaboration":"Prepared in cooperation with the New York State Department of Environmental Conservation","usgsCitation":"Walter, D.A., Masterson, J.P., Finkelstein, J.S., Monti, J., Jr., Misut, P.E., and Fienen, M.N., 2020, Simulation of groundwater flow in the regional aquifer system on Long Island, New York, for pumping and recharge conditions in 2005–15: U.S. Geological Survey Scientific Investigations Report 2020–5091, 75 p., https://doi.org/10.3133/sir20205091.","productDescription":"Report: ix, 75 p.; 3 Data Releases","numberOfPages":"75","onlineOnly":"Y","additionalOnlineFiles":"Y","ipdsId":"IP-112206","costCenters":[{"id":466,"text":"New England Water Science Center","active":true,"usgs":true},{"id":474,"text":"New York Water Science Center","active":true,"usgs":true}],"links":[{"id":381521,"rank":7,"type":{"id":34,"text":"Image Folder"},"url":"https://pubs.usgs.gov/sir/2020/5091/images/"},{"id":381195,"rank":5,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/sir/2020/5091/sir20205091.pdf","text":"Report","size":"35 MB","linkFileType":{"id":1,"text":"pdf"},"description":"SIR 2020-5091"},{"id":381194,"rank":4,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/sir/2020/5091/coverthb2.jpg"},{"id":381192,"rank":3,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/P954DLLC","text":"USGS data release","linkHelpText":"Aquifer texture data describing the Long Island aquifer system"},{"id":381191,"rank":2,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/P9KWQSEJ","text":"USGS data release","linkHelpText":"MODFLOW–NWT and MODPATH6 used to simulate groundwater flow in the regional aquifer system on Long Island, New York, for pumping and recharge conditions in 2005–15"},{"id":381190,"rank":1,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/P90B6OTX","text":"USGS data release","linkHelpText":"Time domain electromagnetic surveys collected to estimate the extent of saltwater intrusion in Nassau and Queens Counties, New York, October-November 2017"},{"id":381520,"rank":6,"type":{"id":31,"text":"Publication XML"},"url":"https://pubs.usgs.gov/sir/2020/5091/sir20205091.XML"}],"country":"United States","state":"New York","otherGeospatial":"Long Island","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -74.102783203125,\n 40.55554790286311\n ],\n [\n -73.7017822265625,\n 40.49709237269567\n ],\n [\n -72.8778076171875,\n 40.65147128144057\n ],\n [\n -72.037353515625,\n 40.91351257612758\n ],\n [\n -71.6143798828125,\n 41.16211393939692\n ],\n [\n -71.9384765625,\n 41.178653972331674\n ],\n [\n -72.191162109375,\n 41.236511201246216\n ],\n [\n -72.65808105468749,\n 41.11660732012896\n ],\n [\n -73.4490966796875,\n 40.967455873296714\n ],\n [\n -73.751220703125,\n 40.89275342420696\n ],\n [\n -74.0203857421875,\n 40.718119379753446\n ],\n [\n -74.102783203125,\n 40.55554790286311\n ]\n ]\n ]\n }\n }\n ]\n}","contact":"Director, New England Water Science Center
U.S. Geological Survey
10 Bearfoot Road
Northborough, MA 01532