{"pageNumber":"618","pageRowStart":"15425","pageSize":"25","recordCount":46883,"records":[{"id":70040674,"text":"ofr20121228 - 2012 - Digital geologic map of the Redding 1° x 2° quadrangle, Shasta, Tehama, Humboldt, and Trinity Counties, California","interactions":[{"subject":{"id":47317,"text":"ofr87257 - 1987 - Geologic map of the Redding 1 x 2 degree quadrangle, Shasta, Tehama, Humboldt, and Trinity Counties, California","indexId":"ofr87257","publicationYear":"1987","noYear":false,"title":"Geologic map of the Redding 1 x 2 degree quadrangle, Shasta, Tehama, Humboldt, and Trinity Counties, California"},"predicate":"SUPERSEDED_BY","object":{"id":70040674,"text":"ofr20121228 - 2012 - Digital geologic map of the Redding 1° x 2° quadrangle, Shasta, Tehama, Humboldt, and Trinity Counties, California","indexId":"ofr20121228","publicationYear":"2012","noYear":false,"title":"Digital geologic map of the Redding 1° x 2° quadrangle, Shasta, Tehama, Humboldt, and Trinity Counties, California"},"id":1}],"lastModifiedDate":"2022-04-15T20:50:57.861912","indexId":"ofr20121228","displayToPublicDate":"2012-11-08T00:00:00","publicationYear":"2012","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":"2012-1228","title":"Digital geologic map of the Redding 1° x 2° quadrangle, Shasta, Tehama, Humboldt, and Trinity Counties, California","docAbstract":"<p>The Redding 1° x 2° quadrangle in northwestern California transects the Franciscan Complex and southern Klamath Mountains province as well as parts of the Great Valley Complex, northern Great Valley, and southernmost Cascades volcanic province. The tectonostratigraphic terranes of the Klamath province represent slices of oceanic crust, island arcs, and overlying sediment that range largely from Paleozoic to Jurassic in age. The Eastern Klamath terrane forms the nucleus to which the other terranes were added westward, primarily during Jurassic time, and that package was probably accreted to North America during earliest Cretaceous time. The younger Franciscan Complex consists of a sequence of westward younging tectonostratigraphic terranes of late Jurassic to Miocene age that were accreted to North America from mid-Cretaceous through Miocene time, with the easternmost being the most strongly metamorphosed. The marine Great Valley sequence, of late Jurassic and Cretaceous age, was deposited unconformably across the southernmost Klamath rocks, but in turn was underthrust at its western margin by Eastern belt Franciscan rocks. Pliocene and Quaternary volcanic rocks and sediment of the Cascades province extend into the southeastern part of the quadrangle, abutting the northernmost part of the great central valley of California. This map and database represent a digital rendition of Open-File Report 87-257, 1987, by L.A. Fraticelli, J.P. Albers, W.P. Irwin, and M.C. Blake, Jr., with various improvements and additions.</p>","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/ofr20121228","usgsCitation":"Fraticelli, L.A., Albers, J., Irwin, W., Blake, M.C., and Wentworth, C.M., 2012, Digital geologic map of the Redding 1° x 2° quadrangle, Shasta, Tehama, Humboldt, and Trinity Counties, California: U.S. Geological Survey Open-File Report 2012-1228, Pamphlet: ii, 19 p.; Plate: 39.9 x 38.8 inches; Readme; Metadata; GIS Data Files, https://doi.org/10.3133/ofr20121228.","productDescription":"Pamphlet: ii, 19 p.; Plate: 39.9 x 38.8 inches; Readme; Metadata; GIS Data Files","numberOfPages":"21","onlineOnly":"Y","additionalOnlineFiles":"Y","costCenters":[{"id":312,"text":"Geology, Minerals, Energy, and Geophysics Science Center","active":true,"usgs":true}],"links":[{"id":263019,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/ofr_2012_1228.gif"},{"id":398872,"rank":10,"type":{"id":36,"text":"NGMDB Index Page"},"url":"https://ngmdb.usgs.gov/Prodesc/proddesc_97674.htm"},{"id":263018,"rank":8,"type":{"id":7,"text":"Companion Files"},"url":"https://pubs.usgs.gov/of/2012/1228/of12-1228-base.zip"},{"id":263017,"type":{"id":7,"text":"Companion Files"},"url":"https://pubs.usgs.gov/of/2012/1228/of12-1228-mapscan.zip"},{"id":263041,"type":{"id":26,"text":"Sheet"},"url":"https://pubs.usgs.gov/of/2012/1228/of2012-1228_map.pdf"},{"id":263014,"type":{"id":20,"text":"Read Me"},"url":"https://pubs.usgs.gov/of/2012/1228/of2012-1228_readme.txt"},{"id":263016,"type":{"id":7,"text":"Companion Files"},"url":"https://pubs.usgs.gov/of/2012/1228/of12-1228-arcpkg.zip"},{"id":263040,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/of/2012/1228/of2012-1228_pamphlet.pdf"},{"id":263023,"type":{"id":15,"text":"Index Page"},"url":"https://pubs.usgs.gov/of/2012/1228/"},{"id":263015,"type":{"id":16,"text":"Metadata"},"url":"https://pubs.usgs.gov/of/2012/1228/of2012-1228_metadata.txt"}],"scale":"250000","projection":"Transverse Mercator projection","datum":"North American Datum 1927","country":"United States","state":"California","county":"Humboldt County, Shasta County, Tehama County, Trinity County","otherGeospatial":"Redding 1° x 2° quadrangle","geographicExtents":"{\n  \"type\": \"FeatureCollection\",\n  \"features\": [\n    {\n      \"type\": \"Feature\",\n      \"properties\": {},\n      \"geometry\": {\n        \"type\": \"Polygon\",\n        \"coordinates\": [\n          [\n            [\n              -124,\n              40\n            ],\n            [\n              -122,\n              40\n            ],\n            [\n              -122,\n              41\n            ],\n            [\n              -124,\n              41\n            ],\n            [\n              -124,\n              40\n            ]\n          ]\n        ]\n      }\n    }\n  ]\n}","noUsgsAuthors":false,"publicationStatus":"PW","scienceBaseUri":"509cf25ae4b0e374086f4665","contributors":{"authors":[{"text":"Fraticelli, Luis A.","contributorId":25917,"corporation":false,"usgs":true,"family":"Fraticelli","given":"Luis","email":"","middleInitial":"A.","affiliations":[],"preferred":false,"id":514771,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Albers, John P.","contributorId":55291,"corporation":false,"usgs":true,"family":"Albers","given":"John P.","affiliations":[],"preferred":false,"id":514772,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Irwin, William P.","contributorId":12889,"corporation":false,"usgs":true,"family":"Irwin","given":"William P.","affiliations":[],"preferred":false,"id":514770,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Blake, Milton C.","contributorId":115862,"corporation":false,"usgs":true,"family":"Blake","given":"Milton","email":"","middleInitial":"C.","affiliations":[],"preferred":false,"id":514773,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Wentworth, Carl M. 0000-0003-2569-569X cwent@usgs.gov","orcid":"https://orcid.org/0000-0003-2569-569X","contributorId":1178,"corporation":false,"usgs":true,"family":"Wentworth","given":"Carl","email":"cwent@usgs.gov","middleInitial":"M.","affiliations":[{"id":312,"text":"Geology, Minerals, Energy, and Geophysics Science Center","active":true,"usgs":true}],"preferred":true,"id":514769,"contributorType":{"id":1,"text":"Authors"},"rank":5}]}}
,{"id":70040649,"text":"cir1379 - 2012 - The United States National Climate Assessment - Alaska Technical Regional Report","interactions":[],"lastModifiedDate":"2012-11-08T08:41:59","indexId":"cir1379","displayToPublicDate":"2012-11-07T00:00:00","publicationYear":"2012","noYear":false,"publicationType":{"id":18,"text":"Report"},"publicationSubtype":{"id":5,"text":"USGS Numbered Series"},"seriesTitle":{"id":307,"text":"Circular","code":"CIR","onlineIssn":"2330-5703","printIssn":"1067-084X","active":true,"publicationSubtype":{"id":5}},"seriesNumber":"1379","title":"The United States National Climate Assessment - Alaska Technical Regional Report","docAbstract":"The Alaskan landscape is changing, both in terms of effects of human activities as a consequence of increased population, social and economic development and their effects on the local and broad landscape; and those effects that accompany naturally occurring hazards such as volcanic eruptions, earthquakes, and tsunamis. Some of the most prevalent changes, however, are those resulting from a changing climate, with both near term and potential upcoming effects expected to continue into the future. Alaska's average annual statewide temperatures have increased by nearly 4&deg;F from 1949 to 2005, with significant spatial variability due to the large latitudinal and longitudinal expanse of the State. Increases in mean annual temperature have been greatest in the interior region, and smallest in the State's southwest coastal regions. In general, however, trends point toward increases in both minimum temperatures, and in fewer extreme cold days. Trends in precipitation are somewhat similar to those in temperature, but with more variability. On the whole, Alaska saw a 10-percent increase in precipitation from 1949 to 2005, with the greatest increases recorded in winter. The National Climate Assessment has designated two well-established scenarios developed by the Intergovernmental Panel on Climate Change (Nakicenovic and others, 2001) as a minimum set that technical and author teams considered as context in preparing portions of this assessment. These two scenarios are referred to as the Special Report on Emissions Scenarios A2 and B1 scenarios, which assume either a continuation of recent trends in fossil fuel use (A2) or a vigorous global effort to reduce fossil fuel use (B1). Temperature increases from 4 to 22&deg;F are predicted (to 2070-2099) depending on which emissions scenario (A2 or B1) is used with the least warming in southeast Alaska and the greatest in the northwest. Concomitant with temperature changes, by the end of the 21st century the growing season is expected to lengthen by 15-25 days in some areas of Alaska, with much of that corresponding with earlier spring snow melt. Future projections of precipitation (30-80 years) over Alaska show an increase across the State, with the largest changes in the northwest and smallest in the southeast. Because of increasing temperatures and growing season length, however, increased precipitation may not correspond with increased water availability, due to temperature related increased evapotranspiration. The extent of snow cover in the Northern Hemisphere has decreased by about 10 percent since the late 1960s, with stronger trends noted since the late 1980s. Alaska has experienced similar trends, with a strong decrease in snow cover extent occurring in May. When averaged across the State, the disappearance of snow in the spring has occurred from 4 to 6 days earlier per decade, and snow return in fall has occurred approximately 2 days later per decade. This change appears to be driven by climate warming rather than a decrease in winter precipitation, with average winter temperatures also increasing by about 2.5&deg;F. The extent of sea ice has been declining, as has been widely published in both national and scientific media outlets, and is projected to continue to decline during this century. The observed decline in annual sea ice minimum extent (September) has occurred more rapidly than was predicted by climate models and has been accompanied by decreases in ice thickness and in the presence of multi-year ice. This decrease was first documented by satellite imagery in the late 1970s for the Bering and Chukchi Seas, and is projected to continue, with the potential for the disappearance of summer sea ice by mid- to late century. A new phenomenon that was not reported in previous assessments is ocean acidification. Uptake of carbon dioxide (CO2) by oceans has a significant effect on marine biogeochemistry by reducing seawater pH. Ocean acidification is of particular concern in Alaska, because cold sea water absorbs CO2 more rapidly than warm water, and a decrease in sea ice extent has allowed increased sea surface exposure and more uptake of CO2 into these northern waters. Ocean acidification will likely affect the ability of organisms to produce and maintain shell material, such as aragonite or calcite (calcium carbonate minerals structured from carbonate ions), required by many shelled organism, from mollusks to corals to microscopic organisms at the base of the food chain. Direct biological effects in Alaska further along the food chain have yet to be studied and may vary among organisms. Some of the potentially most significant changes to Alaska that could result from a changing climate are the effects on the terrestrial cryosphere - particularly glaciers and permafrost. Alaskan glaciers are changing at a rapid rate, the primary driver appearing to be temperature. Statewide, glaciers lost 13 cubic miles of ice annually from the 1950s to the 1990s, and that rate doubled in the 2000s. However, like temperature and precipitation, glacier ice loss is not spatially uniform; most glaciers are losing mass, yet some are growing (for example Hubbard Glacier in southeast Alaska). Alaska glaciers with the most rapid loss are those terminating in sea water or lakes. With this increasing rate of melt, the contribution of surplus fresh water entering into the oceans from Alaska's glaciers, as well as those in neighboring British Columbia, Canada, is approximately 20 percent of that contributed by the Greenland Ice Sheet. Permafrost degradation (that is, the thawing of ice-rich soils) is currently (2012) impacting infrastructure and surface-water availability in areas of both discontinuous and continuous ground ice. Over most of the State, the permafrost is warming, with increasing temperatures broadly consistent with increasing air temperatures. On the Arctic coastal plain of Alaska, permafrost temperatures showed some cooling in the 1950s and 1960s but have been followed by a roughly 5&deg;F increase since the 1980s. Many areas in the continuous permafrost zone have seen increases in temperature in the seasonally active layer and a decrease in re-freezing rates. Changes in the discontinuous permafrost zone are initially much more observable due to the resulting thermokarst terrain (land surface formed as ice rich permafrost thaws), most notable in boreal forested areas. Climate warming in Alaska has potentially broad implications for human health and food security, especially in rural areas, as well as increased risk for injury with changing winter ice conditions. Additionally, such warming poses the potential for increasing damage to existing water and sanitation facilities and challenges for development of new facilities, especially in areas underlain by permafrost. Non-infectious and infectious diseases also are becoming an increasing concern. For example, from 1999 to 2006 there was a statistically significant increase in medical claims for insectbite reactions in five of six regions of Alaska, with the largest percentage increase occurring in the most northern areas. The availability and quality of subsistence foods, normally considered to be very healthy, may change due to changing access, changing habitats, and spoilage of meat in food storage cellars. These and other trends and potential outcomes resulting from a changing climate are further described in this report. In addition, we describe new science leadership activities that have been initiated to address and provide guidance toward conducting research aimed at making available information for policy makers and land management agencies to better understand, address, and plan for changes to the local and regional environment. This report cites data in both metric and standard units due to the contributions by numerous authors and the direct reference of their data.","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/cir1379","usgsCitation":"Markon, C., Trainor, S., and Chapin, F.S., 2012, The United States National Climate Assessment - Alaska Technical Regional Report: U.S. Geological Survey Circular 1379, xiv, 148 p., https://doi.org/10.3133/cir1379.","productDescription":"xiv, 148 p.","numberOfPages":"166","costCenters":[{"id":114,"text":"Alaska Science Center","active":true,"usgs":true}],"links":[{"id":262980,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/cir_1379.jpg"},{"id":262978,"type":{"id":15,"text":"Index Page"},"url":"https://pubs.usgs.gov/circ/1379/"},{"id":262979,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/circ/1379/pdf/circ1379.pdf"}],"country":"United States","state":"Alaska","geographicExtents":"{ \"type\": \"FeatureCollection\", \"features\": [ { \"type\": \"Feature\", \"properties\": {}, \"geometry\": { \"type\": \"Polygon\", \"coordinates\": [ [ [ -172.45,51.21 ], [ -172.45,71.39 ], [ -129.99,71.39 ], [ -129.99,51.21 ], [ -172.45,51.21 ] ] ] } } ] }","noUsgsAuthors":false,"publicationStatus":"PW","scienceBaseUri":"509cf2f2e4b0e374086f46ae","contributors":{"editors":[{"text":"Markon, Carl J.","contributorId":67122,"corporation":false,"usgs":true,"family":"Markon","given":"Carl J.","affiliations":[],"preferred":false,"id":509084,"contributorType":{"id":2,"text":"Editors"},"rank":1},{"text":"Trainor, Sarah F.","contributorId":21396,"corporation":false,"usgs":true,"family":"Trainor","given":"Sarah F.","affiliations":[],"preferred":false,"id":509082,"contributorType":{"id":2,"text":"Editors"},"rank":2},{"text":"Chapin, F. Stuart III","contributorId":65632,"corporation":false,"usgs":false,"family":"Chapin","given":"F.","suffix":"III","email":"","middleInitial":"Stuart","affiliations":[{"id":13117,"text":"Institute of Arctic Biology, University of Alaska Fairbanks","active":true,"usgs":false}],"preferred":false,"id":509083,"contributorType":{"id":2,"text":"Editors"},"rank":3}],"authors":[{"text":"Markon, Carl J.","contributorId":67122,"corporation":false,"usgs":true,"family":"Markon","given":"Carl J.","affiliations":[],"preferred":false,"id":468713,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Trainor, Sarah F.","contributorId":21396,"corporation":false,"usgs":true,"family":"Trainor","given":"Sarah F.","affiliations":[],"preferred":false,"id":468711,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Chapin, F. Stuart III","contributorId":65632,"corporation":false,"usgs":false,"family":"Chapin","given":"F.","suffix":"III","email":"","middleInitial":"Stuart","affiliations":[{"id":13117,"text":"Institute of Arctic Biology, University of Alaska Fairbanks","active":true,"usgs":false}],"preferred":false,"id":468712,"contributorType":{"id":1,"text":"Authors"},"rank":3}]}}
,{"id":70040650,"text":"ofr20121173 - 2012 - Upper Clear Creek watershed aquatic chemistry and biota surveys, 2004-5, Whiskeytown National Recreation Area, Shasta County, California","interactions":[],"lastModifiedDate":"2012-11-07T09:37:10","indexId":"ofr20121173","displayToPublicDate":"2012-11-07T00:00:00","publicationYear":"2012","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":"2012-1173","title":"Upper Clear Creek watershed aquatic chemistry and biota surveys, 2004-5, Whiskeytown National Recreation Area, Shasta County, California","docAbstract":"The U.S. Geological Survey, in cooperation with the National Park Service and Whiskeytown National Recreation Area, performed a comprehensive aquatic biota survey of the upper Clear Creek watershed, Shasta County, California, during 2004-5. Data collected in this study can provide resource managers with information regarding aquatic resources, watershed degradation, and regional biodiversity within Whiskeytown National Recreation Area. Surveys of water chemistry, bed-sediment chemistry, algae assemblages, benthic macroinvertebrate assemblages, aquatic vertebrate assemblages, in-stream habitat characteristics, and sediment heterogeneity were conducted at 17 stream sites during both 2004 and 2005, with an additional 4 sites surveyed in 2005. A total of 67 bed-sediment samples were analyzed for major and trace inorganic element concentrations. Forty-six water samples were analyzed for trace metals and nutrients. A total of 224 taxa of invertebrates were collected during these surveys. Eleven fish species, seven of which were native, and two species of larval amphibians, were collected. A total of 24 genera of soft algae and 159 taxa of diatoms were identified. To date, this survey represents the most comprehensive inventory of aquatic resources within Whiskeytown National Recreation Area, and this information can serve as a baseline for future monitoring efforts and to inform management decisions.","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/ofr20121173","collaboration":"Prepared in cooperation with National Park Service, Whiskeytown National Recreation Area","usgsCitation":"Wulff, M.L., May, J., and Brown, L.R., 2012, Upper Clear Creek watershed aquatic chemistry and biota surveys, 2004-5, Whiskeytown National Recreation Area, Shasta County, California: U.S. Geological Survey Open-File Report 2012-1173, Report: vi, 8 p.; Tables 1-19, https://doi.org/10.3133/ofr20121173.","productDescription":"Report: vi, 8 p.; Tables 1-19","numberOfPages":"18","costCenters":[{"id":154,"text":"California Water Science Center","active":true,"usgs":true}],"links":[{"id":262974,"type":{"id":15,"text":"Index Page"},"url":"https://pubs.usgs.gov/of/2012/1173/"},{"id":262975,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/of/2012/1173/pdf/ofr20121173.pdf"},{"id":262976,"type":{"id":7,"text":"Companion Files"},"url":"https://pubs.usgs.gov/of/2012/1173/ofr20121173_tables.xlsx"},{"id":262977,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/ofr_2012_1173.bmp"}],"country":"United States","state":"California","county":"Shasta","otherGeospatial":"Whiskeytown National Recreation Area","geographicExtents":"{ \"type\": \"FeatureCollection\", \"features\": [ { \"type\": \"Feature\", \"properties\": {}, \"geometry\": { \"type\": \"Polygon\", \"coordinates\": [ [ [ -122.716667,40.55 ], [ -122.716667,40.725 ], [ -122.5,40.725 ], [ -122.5,40.55 ], [ -122.716667,40.55 ] ] ] } } ] }","noUsgsAuthors":false,"publicationStatus":"PW","scienceBaseUri":"50e55ee5e4b0a4aa5bb03d78","contributors":{"authors":[{"text":"Wulff, Marissa L. 0000-0003-0121-9066 mwulff@usgs.gov","orcid":"https://orcid.org/0000-0003-0121-9066","contributorId":1719,"corporation":false,"usgs":true,"family":"Wulff","given":"Marissa","email":"mwulff@usgs.gov","middleInitial":"L.","affiliations":[{"id":154,"text":"California Water Science Center","active":true,"usgs":true}],"preferred":true,"id":468715,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"May, Jason T. 0000-0002-5699-2112","orcid":"https://orcid.org/0000-0002-5699-2112","contributorId":14791,"corporation":false,"usgs":true,"family":"May","given":"Jason T.","affiliations":[],"preferred":false,"id":468716,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Brown, Larry R. 0000-0001-6702-4531 lrbrown@usgs.gov","orcid":"https://orcid.org/0000-0001-6702-4531","contributorId":1717,"corporation":false,"usgs":true,"family":"Brown","given":"Larry","email":"lrbrown@usgs.gov","middleInitial":"R.","affiliations":[{"id":154,"text":"California Water Science Center","active":true,"usgs":true}],"preferred":true,"id":468714,"contributorType":{"id":1,"text":"Authors"},"rank":3}]}}
,{"id":70040695,"text":"sir20125168 - 2012 - Construction of estimated flow- and load-duration curves for Kentucky using the <u>W</u>ater <u>A</u>vailability <u>T</u>ool for <u>E</u>nvironmental <u>R</u>esources (WATER)","interactions":[],"lastModifiedDate":"2012-11-09T12:15:41","indexId":"sir20125168","displayToPublicDate":"2012-11-07T00:00:00","publicationYear":"2012","noYear":false,"publicationType":{"id":18,"text":"Report"},"publicationSubtype":{"id":5,"text":"USGS Numbered Series"},"seriesTitle":{"id":334,"text":"Scientific Investigations Report","code":"SIR","onlineIssn":"2328-0328","printIssn":"2328-031X","active":true,"publicationSubtype":{"id":5}},"seriesNumber":"2012-5168","title":"Construction of estimated flow- and load-duration curves for Kentucky using the <u>W</u>ater <u>A</u>vailability <u>T</u>ool for <u>E</u>nvironmental <u>R</u>esources (WATER)","docAbstract":"Flow- and load-duration curves were constructed from the model outputs of the U.S. Geological Survey's Water Availability Tool for Environmental Resources (WATER) application for streams in Kentucky. The WATER application was designed to access multiple geospatial datasets to generate more than 60 years of statistically based streamflow data for Kentucky. The WATER application enables a user to graphically select a site on a stream and generate an estimated hydrograph and flow-duration curve for the watershed upstream of that point. The flow-duration curves are constructed by calculating the exceedance probability of the modeled daily streamflows. User-defined water-quality criteria and (or) sampling results can be loaded into the WATER application to construct load-duration curves that are based on the modeled streamflow results. Estimates of flow and streamflow statistics were derived from TOPographically Based Hydrological MODEL (TOPMODEL) simulations in the WATER application. A modified TOPMODEL code, SDP-TOPMODEL (Sinkhole Drainage Process-TOPMODEL) was used to simulate daily mean discharges over the period of record for 5 karst and 5 non-karst watersheds in Kentucky in order to verify the calibrated model. A statistical evaluation of the model's verification simulations show that calibration criteria, established by previous WATER application reports, were met thus insuring the model's ability to provide acceptably accurate estimates of discharge at gaged and ungaged sites throughout Kentucky. Flow-duration curves are constructed in the WATER application by calculating the exceedence probability of the modeled daily flow values. The flow-duration intervals are expressed as a percentage, with zero corresponding to the highest stream discharge in the streamflow record. Load-duration curves are constructed by applying the loading equation (Load = Flow*Water-quality criterion) at each flow interval.","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/sir20125168","collaboration":"Prepared in cooperation with the Kentucky Division of Water","usgsCitation":"Unthank, M.D., Newson, J.K., Williamson, T., and Nelson, H.L., 2012, Construction of estimated flow- and load-duration curves for Kentucky using the <u>W</u>ater <u>A</u>vailability <u>T</u>ool for <u>E</u>nvironmental <u>R</u>esources (WATER): U.S. Geological Survey Scientific Investigations Report 2012-5168, vi, 14 p., https://doi.org/10.3133/sir20125168.","productDescription":"vi, 14 p.","numberOfPages":"24","onlineOnly":"Y","costCenters":[{"id":354,"text":"Kentucky Water Science Center","active":true,"usgs":true}],"links":[{"id":263069,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/sir_2012_5168.gif"},{"id":263067,"type":{"id":15,"text":"Index Page"},"url":"https://pubs.usgs.gov/sir/2012/5168/"},{"id":263068,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/sir/2012/5168/pdf/sir2012-5168_report_508_rev110612.pdf"}],"country":"United States","state":"Kentucky","geographicExtents":"{ \"type\": \"FeatureCollection\", \"features\": [ { \"type\": \"Feature\", \"properties\": {}, \"geometry\": { \"type\": \"Polygon\", \"coordinates\": [ [ [ -89.5715,36.4972 ], [ -89.5715,39.1475 ], [ -81.965,39.1475 ], [ -81.965,36.4972 ], [ -89.5715,36.4972 ] ] ] } } ] }","noUsgsAuthors":false,"publicationStatus":"PW","scienceBaseUri":"509e3412e4b0cbd9af3af72b","contributors":{"authors":[{"text":"Unthank, Michael D. 0000-0003-2483-0431 munthank@usgs.gov","orcid":"https://orcid.org/0000-0003-2483-0431","contributorId":3902,"corporation":false,"usgs":true,"family":"Unthank","given":"Michael","email":"munthank@usgs.gov","middleInitial":"D.","affiliations":[{"id":27231,"text":"Indiana-Kentucky Water Science Center","active":true,"usgs":true}],"preferred":true,"id":468803,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Newson, Jeremy K. jknewson@usgs.gov","contributorId":4159,"corporation":false,"usgs":true,"family":"Newson","given":"Jeremy","email":"jknewson@usgs.gov","middleInitial":"K.","affiliations":[{"id":677,"text":"Wisconsin Water Science Center","active":true,"usgs":true}],"preferred":false,"id":468805,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Williamson, Tanja N. tnwillia@usgs.gov","contributorId":452,"corporation":false,"usgs":true,"family":"Williamson","given":"Tanja N.","email":"tnwillia@usgs.gov","affiliations":[{"id":354,"text":"Kentucky Water Science Center","active":true,"usgs":true}],"preferred":false,"id":468802,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Nelson, Hugh L. hlnelson@usgs.gov","contributorId":4158,"corporation":false,"usgs":true,"family":"Nelson","given":"Hugh","email":"hlnelson@usgs.gov","middleInitial":"L.","affiliations":[{"id":354,"text":"Kentucky Water Science Center","active":true,"usgs":true}],"preferred":true,"id":468804,"contributorType":{"id":1,"text":"Authors"},"rank":4}]}}
,{"id":70202104,"text":"70202104 - 2012 - Progress on archiving, delivering, and working with planetary data","interactions":[],"lastModifiedDate":"2019-02-11T10:36:34","indexId":"70202104","displayToPublicDate":"2012-11-06T10:35:02","publicationYear":"2012","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":1578,"text":"Eos, Transactions, American Geophysical Union","onlineIssn":"2324-9250","printIssn":"0096-394","active":true,"publicationSubtype":{"id":10}},"title":"Progress on archiving, delivering, and working with planetary data","docAbstract":"<p><strong><i>Planetary Data: A Workshop for Users and Software Developers 2012; Flagstaff, Ariz., 25–29 June 2012</i></strong><span>&nbsp;The recent boom in the volume of data returned by planetary science missions continues to delight and confound users. Recently the NASA Planetary Data System (PDS) has seen an approximately 50‐fold increase in the amount of archived data and now serves nearly half a petabyte. Within 5 years, this volume likely will approach 1 petabyte. While archivists, users, and developers have done a creditable job of providing search and download functions and analysis and visualization tools, the wealth of data necessitates more discussion between users and developers about current limitations and desired improvements.</span></p>","language":"English","publisher":"American Geophysical Union","doi":"10.1029/2012EO450008","usgsCitation":"Gaddis, L.R., Hare, T.M., and Beyer, R., 2012, Progress on archiving, delivering, and working with planetary data: Eos, Transactions, American Geophysical Union, v. 93, no. 45, p. 457-457, https://doi.org/10.1029/2012EO450008.","productDescription":"1 p.","startPage":"457","endPage":"457","costCenters":[{"id":131,"text":"Astrogeology Science Center","active":true,"usgs":true}],"links":[{"id":474274,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.1029/2012eo450008","text":"Publisher Index Page"},{"id":361119,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"volume":"93","issue":"45","noUsgsAuthors":false,"publicationDate":"2012-11-06","publicationStatus":"PW","contributors":{"authors":[{"text":"Gaddis, Lisa R. 0000-0001-9953-5483 lgaddis@usgs.gov","orcid":"https://orcid.org/0000-0001-9953-5483","contributorId":2817,"corporation":false,"usgs":true,"family":"Gaddis","given":"Lisa","email":"lgaddis@usgs.gov","middleInitial":"R.","affiliations":[{"id":131,"text":"Astrogeology Science Center","active":true,"usgs":true}],"preferred":true,"id":756894,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Hare, Trent M. 0000-0001-8842-389X thare@usgs.gov","orcid":"https://orcid.org/0000-0001-8842-389X","contributorId":3188,"corporation":false,"usgs":true,"family":"Hare","given":"Trent","email":"thare@usgs.gov","middleInitial":"M.","affiliations":[{"id":131,"text":"Astrogeology Science Center","active":true,"usgs":true}],"preferred":true,"id":756895,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Beyer, Ross","contributorId":71607,"corporation":false,"usgs":true,"family":"Beyer","given":"Ross","affiliations":[],"preferred":false,"id":756896,"contributorType":{"id":1,"text":"Authors"},"rank":3}]}}
,{"id":70040641,"text":"ds731 - 2012 - Groundwater geochemical and selected volatile organic compound data, Operable Unit 1, Naval Undersea Warfare Center, Division Keyport, Washington, June 2011","interactions":[],"lastModifiedDate":"2012-11-06T15:57:44","indexId":"ds731","displayToPublicDate":"2012-11-06T00:00:00","publicationYear":"2012","noYear":false,"publicationType":{"id":18,"text":"Report"},"publicationSubtype":{"id":5,"text":"USGS Numbered Series"},"seriesTitle":{"id":310,"text":"Data Series","code":"DS","onlineIssn":"2327-638X","printIssn":"2327-0271","active":false,"publicationSubtype":{"id":5}},"seriesNumber":"731","title":"Groundwater geochemical and selected volatile organic compound data, Operable Unit 1, Naval Undersea Warfare Center, Division Keyport, Washington, June 2011","docAbstract":"Previous investigations indicate that concentrations of chlorinated volatile organic compounds are substantial in groundwater beneath the 9-acre former landfill at Operable Unit 1, Naval Undersea Warfare Center, Division Keyport, Washington. Phytoremediation combined with ongoing natural attenuation processes was the preferred remedy selected by the U.S. Navy, as specified in the Record of Decision for the site. The U.S. Navy planted two hybrid poplar plantations on the landfill in spring 1999 to remove and to control the migration of chlorinated volatile organic compounds in shallow groundwater. The U.S. Geological Survey has continued to monitor groundwater geochemistry to ensure that conditions remain favorable for contaminant biodegradation as specified in the Record of Decision. This report presents groundwater geochemical and selected volatile organic compound data collected at Operable Unit 1 by the U.S. Geological Survey during June 20-22, 2011, in support of long-term monitoring for natural attenuation. In 2011, groundwater samples were collected from 13 wells and 9 piezometers. Samples from all wells and piezometers were analyzed for redox sensitive constituents and dissolved gases, and samples from 5 of 13 wells and all piezometers also were analyzed for chlorinated volatile organic compounds. Concentrations of redox sensitive constituents measured in 2011 were consistent with previous years, with dissolved oxygen concentrations all at 0.4 milligram per liter or less; little to no detectable nitrate; abundant dissolved manganese, iron, and methane; and commonly detected sulfide. The reductive declorination byproducts - methane, ethane, and ethene - were either not detected in samples collected from the upgradient wells in the landfill and the upper aquifer beneath the northern phytoremediation plantation or were detected at concentrations less than those measured in 2010. Chlorinated volatile organic compound concentrations in 2011 at most piezometers were similar to or slightly less than chlorinated volatile organic compound concentrations measured in previous years. For the upper aquifer beneath the southern phytoremediation plantation, chlorinated volatile organic compound concentrations in 2011 in groundwater from the piezometers were extremely high and continued to vary considerably over space and between years. At piezometer P1-9, the total chlorinated volatile organic compound concentrations increased from 9,500 micrograms per liter in 2010 to more than 44,000 micrograms per liter in 2011. Total chlorinated volatile organic compound concentrations decreased at piezometers P1-6, P1-7, and P1-10 compared to the concentrations measured in 2010. One or both of the reductive dechlorination byproducts ethane and ethene were detected at all piezometers and three of the four wells in the southern plantation. For the intermediate aquifer, concentrations of redox sensitive constituents and chlorinated volatile organic compounds in 2011 were consistent with concentrations measured in previous years, with the exception of notable decreases in sulfate and chloride concentrations at well MW1-28. Concentrations of the reductive dechlorination byproducts ethane and ethene decreased at wells MW1-25 and MW1-28 compared to previously measured concentrations.","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/ds731","collaboration":"Prepared in cooperation with Department of the Navy, Naval Facilities, Engineering Command, Northwest","usgsCitation":"Huffman, R.L., and Frans, L., 2012, Groundwater geochemical and selected volatile organic compound data, Operable Unit 1, Naval Undersea Warfare Center, Division Keyport, Washington, June 2011: U.S. Geological Survey Data Series 731, iv, 40 p., https://doi.org/10.3133/ds731.","productDescription":"iv, 40 p.","numberOfPages":"48","ipdsId":"IP-040805","costCenters":[{"id":622,"text":"Washington Water Science Center","active":true,"usgs":true}],"links":[{"id":262973,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/ds_731.jpg"},{"id":262971,"type":{"id":15,"text":"Index Page"},"url":"https://pubs.usgs.gov/ds/731/"},{"id":262972,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/ds/731/pdf/ds731.pdf"}],"projection":"Washington State Plane, North Zone","datum":"North American Datum of 1927","country":"United States","state":"Washington","otherGeospatial":"Dogfish Bay;Liberty Bay;Naval Undersea Warfare Center;Division Keyport","geographicExtents":"{ \"type\": \"FeatureCollection\", \"features\": [ { \"type\": \"Feature\", \"properties\": {}, \"geometry\": { \"type\": \"Polygon\", \"coordinates\": [ [ [ -122.633333,47.686111 ], [ -122.633333,47.708333 ], [ -122.608333,47.708333 ], [ -122.608333,47.686111 ], [ -122.633333,47.686111 ] ] ] } } ] }","noUsgsAuthors":false,"publicationStatus":"PW","scienceBaseUri":"509a317be4b04d64aa094c83","contributors":{"authors":[{"text":"Huffman, Raegan L. 0000-0001-8523-5439 rhuffman@usgs.gov","orcid":"https://orcid.org/0000-0001-8523-5439","contributorId":1638,"corporation":false,"usgs":true,"family":"Huffman","given":"Raegan","email":"rhuffman@usgs.gov","middleInitial":"L.","affiliations":[{"id":622,"text":"Washington Water Science Center","active":true,"usgs":true}],"preferred":true,"id":468699,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Frans, L.M.","contributorId":74803,"corporation":false,"usgs":true,"family":"Frans","given":"L.M.","email":"","affiliations":[],"preferred":false,"id":468700,"contributorType":{"id":1,"text":"Authors"},"rank":2}]}}
,{"id":70040619,"text":"ofr20121195 - 2012 - A multi-metric assessment of environmental contaminant exposure and effects in an urbanized reach of the Charles River near Watertown, Massachusetts","interactions":[],"lastModifiedDate":"2012-12-26T11:48:28","indexId":"ofr20121195","displayToPublicDate":"2012-11-06T00:00:00","publicationYear":"2012","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":"2012-1195","title":"A multi-metric assessment of environmental contaminant exposure and effects in an urbanized reach of the Charles River near Watertown, Massachusetts","docAbstract":"The Charles River Project provided an opportunity to simultaneously deploy a combination of biomonitoring techniques routinely used by the U.S. Geological Survey National Water Quality Assessment Program, the Biomonitoring of Environmental Status and Trends Project, and the Contaminant Biology Program at an urban site suspected to be contaminated with polycyclic aromatic hydrocarbons. In addition to these standardized methods, additional techniques were used to further elucidate contaminant exposure and potential impacts of exposure on biota. The purpose of the study was to generate a comprehensive, multi-metric data set to support assessment of contaminant exposure and effects at the site. Furthermore, the data set could be assessed to determine the relative performance of the standardized method suites typically used by the National Water Quality Assessment Program and the Biomonitoring of Environmental Status and Trends Project, as well as the additional biomonitoring methods used in the study to demonstrate ecological effects of contaminant exposure. The Contaminant Effects Workgroup, an advisory committee of the U.S. Geological Survey/Contaminant Biology Program, identified polycyclic aromatic hydrocarbons as the contaminant class of greatest concern in urban streams of all sizes. The reach of the Charles River near Watertown, Massachusetts, was selected as the site for this study based on the suspected presence of polycyclic aromatic hydrocarbon contamination and the presence of common carp (<i>Cyprinus carpio</i>), largemouth bass (<i>Micropterus salmoides</i>), and white sucker (<i>Catostomus commersoni</i>). All of these fish have extensive contaminant-exposure profiles related to polycyclic aromatic hydrocarbons and other environmental contaminants. This project represented a collaboration of universities, Department of the Interior bureaus including multiple components of the USGS (Biological Resources Discipline and Water Resources Discipline Science Centers, the Contaminant Biology Program, and the Status and Trends of Biological Resources Program), and the U.S. Fish and Wildlife Service. Samples for analyzing water chemistry, sediment chemistry and toxicity, fish community structure, tissue chemistry, and fish (20 carp, 20 bass, and 40 white sucker) and invertebrate pathology were collected in late August, 2005. This report provides results from the analyses of fish pathology, biomarkers of exposure and effects (reproductive, carcinogenic, genotoxic, and immunologic), sediment chemistry, toxicity, and fish and invertebrate community structure.","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/ofr20121195","usgsCitation":"Smith, S.B., Anderson, P.J., Baumann, P.C., DeWeese, L.R., Goodbred, S.L., Coyle, J.J., and Smith, D.S., 2012, A multi-metric assessment of environmental contaminant exposure and effects in an urbanized reach of the Charles River near Watertown, Massachusetts: U.S. Geological Survey Open-File Report 2012-1195, x; 116 p., https://doi.org/10.3133/ofr20121195.","productDescription":"x; 116 p.","numberOfPages":"128","onlineOnly":"Y","additionalOnlineFiles":"N","temporalStart":"2005-08-01","temporalEnd":"2005-08-31","costCenters":[{"id":291,"text":"Fort Collins Science Center","active":true,"usgs":true}],"links":[{"id":262966,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/ofr_2012_1195.gif"},{"id":264785,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/of/2012/1195/OF12-1195.pdf"},{"id":264783,"type":{"id":15,"text":"Index Page"},"url":"https://pubs.usgs.gov/of/2012/1195/"}],"country":"United States","state":"Massachusetts","otherGeospatial":"Lower Charles River Watershed","geographicExtents":"{ \"type\": \"FeatureCollection\", \"features\": [ { \"type\": \"Feature\", \"properties\": {}, \"geometry\": { \"type\": \"Polygon\", \"coordinates\": [ [ [ -71.250000,42.250000 ], [ -71.250000,42.416667 ], [ -71.000000,42.416667 ], [ -71.000000,42.250000 ], [ -71.250000,42.250000 ] ] ] } } ] }","noUsgsAuthors":false,"publicationStatus":"PW","scienceBaseUri":"509a3167e4b04d64aa094c7b","contributors":{"authors":[{"text":"Smith, Stephen B.","contributorId":14765,"corporation":false,"usgs":true,"family":"Smith","given":"Stephen","email":"","middleInitial":"B.","affiliations":[],"preferred":false,"id":468686,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Anderson, Patrick J. 0000-0003-2281-389X andersonpj@usgs.gov","orcid":"https://orcid.org/0000-0003-2281-389X","contributorId":3590,"corporation":false,"usgs":true,"family":"Anderson","given":"Patrick","email":"andersonpj@usgs.gov","middleInitial":"J.","affiliations":[{"id":291,"text":"Fort Collins Science Center","active":true,"usgs":true}],"preferred":true,"id":468685,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Baumann, Paul C.","contributorId":104455,"corporation":false,"usgs":true,"family":"Baumann","given":"Paul","email":"","middleInitial":"C.","affiliations":[],"preferred":false,"id":468690,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"DeWeese, Lawrence R.","contributorId":72047,"corporation":false,"usgs":true,"family":"DeWeese","given":"Lawrence","email":"","middleInitial":"R.","affiliations":[],"preferred":false,"id":468689,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Goodbred, Steven L. sgoodbred@usgs.gov","contributorId":497,"corporation":false,"usgs":true,"family":"Goodbred","given":"Steven","email":"sgoodbred@usgs.gov","middleInitial":"L.","affiliations":[],"preferred":true,"id":468684,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"Coyle, James J.","contributorId":56741,"corporation":false,"usgs":true,"family":"Coyle","given":"James","email":"","middleInitial":"J.","affiliations":[],"preferred":false,"id":468688,"contributorType":{"id":1,"text":"Authors"},"rank":6},{"text":"Smith, David S.","contributorId":25416,"corporation":false,"usgs":true,"family":"Smith","given":"David","email":"","middleInitial":"S.","affiliations":[],"preferred":false,"id":468687,"contributorType":{"id":1,"text":"Authors"},"rank":7}]}}
,{"id":70100649,"text":"70100649 - 2012 - The effects of wildfire on the sediment yield of a coastal California watershed","interactions":[],"lastModifiedDate":"2014-04-04T10:23:05","indexId":"70100649","displayToPublicDate":"2012-11-05T10:16:02","publicationYear":"2012","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":1786,"text":"Geological Society of America Bulletin","active":true,"publicationSubtype":{"id":10}},"title":"The effects of wildfire on the sediment yield of a coastal California watershed","docAbstract":"The occurrence of two wildfires separated by 31 yr in the chaparral-dominated Arroyo Seco watershed (293 km<sup2</sup>) of California provides a unique opportunity to evaluate the effects of wildfire on suspended-sediment yield. Here, we compile discharge and suspended-sediment sampling data from before and after the fires and show that the effects of the postfire responses differed markedly. The 1977 Marble Cone wildfire was followed by an exceptionally wet winter, which resulted in concentrations and fluxes of both fine and coarse suspended sediment that were ˜35 times greater than average (sediment yield during the 1978 water year was 11,000 t/km<sup>2</sup>/yr). We suggest that the combined 1977–1978 fire and flood had a recurrence interval of greater than 1000 yr. In contrast, the 2008 Basin Complex wildfire was followed by a drier than normal year, and although suspended-sediment fluxes and concentrations were significantly elevated compared to those expected for unburned conditions, the sediment yield during the 2009 water year was less than 1% of the post–Marble Cone wildfire yield. After the first postfire winters, sediment concentrations and yield decreased with time toward prefire relationships and continued to have significant rainfall dependence. We hypothesize that the differences in sediment yield were related to precipitation-enhanced hillslope erosion processes, such as rilling and mass movements. The millennial-scale effects of wildfire on sediment yield were explored further using Monte Carlo simulations, and these analyses suggest that infrequent wildfires followed by floods increase long-term suspended-sediment fluxes markedly. Thus, we suggest that the current approach of estimating sediment yield from sediment rating curves and discharge data—without including periodic perturbations from wildfires—may grossly underestimate actual sediment yields.","largerWorkType":{"id":2,"text":"Article"},"largerWorkTitle":"Geological Society of America Bulletin","largerWorkSubtype":{"id":10,"text":"Journal Article"},"language":"English","publisher":"The Geological Society of America","doi":"10.1130/B30451.1","usgsCitation":"Warrick, J., Hatten, J., Pasternack, G., Gray, A., Goni, M., and Wheatcroft, R.A., 2012, The effects of wildfire on the sediment yield of a coastal California watershed: Geological Society of America Bulletin, https://doi.org/10.1130/B30451.1.","ipdsId":"IP-026419","costCenters":[{"id":520,"text":"Pacific Coastal and Marine Science Center","active":true,"usgs":true}],"links":[{"id":285694,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"},{"id":285689,"type":{"id":10,"text":"Digital Object Identifier"},"url":"https://dx.doi.org/10.1130/B30451.1"}],"country":"United States","state":"California","geographicExtents":"{ \"type\": \"FeatureCollection\", \"features\": [ { \"type\": \"Feature\", \"properties\": {}, \"geometry\": { \"type\": \"Polygon\", \"coordinates\": [ [ [ -124.41,32.53 ], [ -124.41,42.01 ], [ -114.13,42.01 ], [ -114.13,32.53 ], [ -124.41,32.53 ] ] ] } } ] }","noUsgsAuthors":false,"publicationDate":"2012-04-06","publicationStatus":"PW","scienceBaseUri":"5355959fe4b0120853e8c27f","contributors":{"authors":[{"text":"Warrick, J.A.","contributorId":53503,"corporation":false,"usgs":true,"family":"Warrick","given":"J.A.","affiliations":[],"preferred":false,"id":492385,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Hatten, J.A.","contributorId":101493,"corporation":false,"usgs":true,"family":"Hatten","given":"J.A.","email":"","affiliations":[],"preferred":false,"id":492388,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Pasternack, G.B.","contributorId":70566,"corporation":false,"usgs":true,"family":"Pasternack","given":"G.B.","email":"","affiliations":[],"preferred":false,"id":492386,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Gray, A.B.","contributorId":37648,"corporation":false,"usgs":true,"family":"Gray","given":"A.B.","email":"","affiliations":[],"preferred":false,"id":492384,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Goni, M.A.","contributorId":32347,"corporation":false,"usgs":true,"family":"Goni","given":"M.A.","email":"","affiliations":[],"preferred":false,"id":492383,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"Wheatcroft, R. A.","contributorId":76503,"corporation":false,"usgs":false,"family":"Wheatcroft","given":"R.","email":"","middleInitial":"A.","affiliations":[],"preferred":false,"id":492387,"contributorType":{"id":1,"text":"Authors"},"rank":6}]}}
,{"id":70040616,"text":"sir20125232 - 2012 - Computing daily mean streamflow at ungaged locations in Iowa by using the Flow Anywhere and Flow Duration Curve Transfer statistical methods","interactions":[],"lastModifiedDate":"2012-11-05T15:58:01","indexId":"sir20125232","displayToPublicDate":"2012-11-05T00:00:00","publicationYear":"2012","noYear":false,"publicationType":{"id":18,"text":"Report"},"publicationSubtype":{"id":5,"text":"USGS Numbered Series"},"seriesTitle":{"id":334,"text":"Scientific Investigations Report","code":"SIR","onlineIssn":"2328-0328","printIssn":"2328-031X","active":true,"publicationSubtype":{"id":5}},"seriesNumber":"2012-5232","title":"Computing daily mean streamflow at ungaged locations in Iowa by using the Flow Anywhere and Flow Duration Curve Transfer statistical methods","docAbstract":"The U.S. Geological Survey (USGS) maintains approximately 148 real-time streamgages in Iowa for which daily mean streamflow information is available, but daily mean streamflow data commonly are needed at locations where no streamgages are present. Therefore, the USGS conducted a study as part of a larger project in cooperation with the Iowa Department of Natural Resources to develop methods to estimate daily mean streamflow at locations in ungaged watersheds in Iowa by using two regression-based statistical methods. The regression equations for the statistical methods were developed from historical daily mean streamflow and basin characteristics from streamgages within the study area, which includes the entire State of Iowa and adjacent areas within a 50-mile buffer of Iowa in neighboring states. Results of this study can be used with other techniques to determine the best method for application in Iowa and can be used to produce a Web-based geographic information system tool to compute streamflow estimates automatically. The Flow Anywhere statistical method is a variation of the drainage-area-ratio method, which transfers same-day streamflow information from a reference streamgage to another location by using the daily mean streamflow at the reference streamgage and the drainage-area ratio of the two locations. The Flow Anywhere method modifies the drainage-area-ratio method in order to regionalize the equations for Iowa and determine the best reference streamgage from which to transfer same-day streamflow information to an ungaged location. Data used for the Flow Anywhere method were retrieved for 123 continuous-record streamgages located in Iowa and within a 50-mile buffer of Iowa. The final regression equations were computed by using either left-censored regression techniques with a low limit threshold set at 0.1 cubic feet per second (ft3/s) and the daily mean streamflow for the 15th day of every other month, or by using an ordinary-least-squares multiple linear regression method and the daily mean streamflow for the 15th day of every other month. The Flow Duration Curve Transfer method was used to estimate unregulated daily mean streamflow from the physical and climatic characteristics of gaged basins. For the Flow Duration Curve Transfer method, daily mean streamflow quantiles at the ungaged site were estimated with the parameter-based regression model, which results in a continuous daily flow-duration curve (the relation between exceedance probability and streamflow for each day of observed streamflow) at the ungaged site. By the use of a reference streamgage, the Flow Duration Curve Transfer is converted to a time series. Data used in the Flow Duration Curve Transfer method were retrieved for 113 continuous-record streamgages in Iowa and within a 50-mile buffer of Iowa. The final statewide regression equations for Iowa were computed by using a weighted-least-squares multiple linear regression method and were computed for the 0.01-, 0.05-, 0.10-, 0.15-, 0.20-, 0.30-, 0.40-, 0.50-, 0.60-, 0.70-, 0.80-, 0.85-, 0.90-, and 0.95-exceedance probability statistics determined from the daily mean streamflow with a reporting limit set at 0.1 ft<sup>3</sup>/s. The final statewide regression equation for Iowa computed by using left-censored regression techniques was computed for the 0.99-exceedance probability statistic determined from the daily mean streamflow with a low limit threshold and a reporting limit set at 0.1 ft<sup>3</sup>/s. For the Flow Anywhere method, results of the validation study conducted by using six streamgages show that differences between the root-mean-square error and the mean absolute error ranged from 1,016 to 138 ft<sup>3</sup>/s, with the larger value signifying a greater occurrence of outliers between observed and estimated streamflows. Root-mean-square-error values ranged from 1,690 to 237 ft<sup>3</sup>/s. Values of the percent root-mean-square error ranged from 115 percent to 26.2 percent. The logarithm (base 10) streamflow percent root-mean-square error ranged from 13.0 to 5.3 percent. Root-mean-square-error observations standard-deviation-ratio values ranged from 0.80 to 0.40. Percent-bias values ranged from 25.4 to 4.0 percent. Untransformed streamflow Nash-Sutcliffe efficiency values ranged from 0.84 to 0.35. The logarithm (base 10) streamflow Nash-Sutcliffe efficiency values ranged from 0.86 to 0.56. For the streamgage with the best agreement between observed and estimated streamflow, higher streamflows appear to be underestimated. For the streamgage with the worst agreement between observed and estimated streamflow, low flows appear to be overestimated whereas higher flows seem to be underestimated. Estimated cumulative streamflows for the period October 1, 2004, to September 30, 2009, are underestimated by -25.8 and -7.4 percent for the closest and poorest comparisons, respectively. For the Flow Duration Curve Transfer method, results of the validation study conducted by using the same six streamgages show that differences between the root-mean-square error and the mean absolute error ranged from 437 to 93.9 ft<sup>3</sup>/s, with the larger value signifying a greater occurrence of outliers between observed and estimated streamflows. Root-mean-square-error values ranged from 906 to 169 ft<sup>3</sup>/s. Values of the percent root-mean-square-error ranged from 67.0 to 25.6 percent. The logarithm (base 10) streamflow percent root-mean-square error ranged from 12.5 to 4.4 percent. Root-mean-square-error observations standard-deviation-ratio values ranged from 0.79 to 0.40. Percent-bias values ranged from 22.7 to 0.94 percent. Untransformed streamflow Nash-Sutcliffe efficiency values ranged from 0.84 to 0.38. The logarithm (base 10) streamflow Nash-Sutcliffe efficiency values ranged from 0.89 to 0.48. For the streamgage with the closest agreement between observed and estimated streamflow, there is relatively good agreement between observed and estimated streamflows. For the streamgage with the poorest agreement between observed and estimated streamflow, streamflows appear to be substantially underestimated for much of the time period. Estimated cumulative streamflow for the period October 1, 2004, to September 30, 2009, are underestimated by -9.3 and -22.7 percent for the closest and poorest comparisons, respectively.","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/sir20125232","collaboration":"Prepared in cooperation with the Iowa Department of Natural Resources","usgsCitation":"Linhart, S., Nania, J.F., Sanders, C.L., and Archfield, S.A., 2012, Computing daily mean streamflow at ungaged locations in Iowa by using the Flow Anywhere and Flow Duration Curve Transfer statistical methods: U.S. Geological Survey Scientific Investigations Report 2012-5232, vi, 50 p., https://doi.org/10.3133/sir20125232.","productDescription":"vi, 50 p.","numberOfPages":"60","onlineOnly":"Y","ipdsId":"IP-033054","costCenters":[{"id":351,"text":"Iowa Water Science Center","active":true,"usgs":true}],"links":[{"id":262965,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/sir_2012_5232.gif"},{"id":262963,"type":{"id":15,"text":"Index Page"},"url":"https://pubs.usgs.gov/sir/2012/5232/"},{"id":262964,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/sir/2012/5232/sir2012-5232.pdf"}],"scale":"24000","projection":"Universal Transverse Mercator projection, Zone 15","country":"United States","state":"Illinois;Iowa;Minnesota;Missouri;Nebraska;Wisconsin","geographicExtents":"{ \"type\": \"FeatureCollection\", \"features\": [ { \"type\": \"Feature\", \"properties\": {}, \"geometry\": { \"type\": \"Polygon\", \"coordinates\": [ [ [ -98.0,39.75 ], [ -98.0,44.15 ], [ -88.5,44.15 ], [ -88.5,39.75 ], [ -98.0,39.75 ] ] ] } } ] }","noUsgsAuthors":false,"publicationStatus":"PW","scienceBaseUri":"5098dfe9e4b0a35ac147a79e","contributors":{"authors":[{"text":"Linhart, S. Mike","contributorId":61073,"corporation":false,"usgs":true,"family":"Linhart","given":"S. Mike","affiliations":[],"preferred":false,"id":468677,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Nania, Jon F. jfnania@usgs.gov","contributorId":4767,"corporation":false,"usgs":true,"family":"Nania","given":"Jon","email":"jfnania@usgs.gov","middleInitial":"F.","affiliations":[{"id":351,"text":"Iowa Water Science Center","active":true,"usgs":true}],"preferred":true,"id":468676,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Sanders, Curtis L. Jr.","contributorId":76391,"corporation":false,"usgs":true,"family":"Sanders","given":"Curtis","suffix":"Jr.","email":"","middleInitial":"L.","affiliations":[],"preferred":false,"id":468678,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Archfield, Stacey A. 0000-0002-9011-3871 sarch@usgs.gov","orcid":"https://orcid.org/0000-0002-9011-3871","contributorId":1874,"corporation":false,"usgs":true,"family":"Archfield","given":"Stacey","email":"sarch@usgs.gov","middleInitial":"A.","affiliations":[{"id":436,"text":"National Research Program - Eastern Branch","active":true,"usgs":true},{"id":502,"text":"Office of Surface Water","active":true,"usgs":true}],"preferred":true,"id":468675,"contributorType":{"id":1,"text":"Authors"},"rank":4}]}}
,{"id":70040609,"text":"sir20105070E - 2012 - Stratiform chromite deposit model","interactions":[],"lastModifiedDate":"2024-04-16T16:35:52.791761","indexId":"sir20105070E","displayToPublicDate":"2012-11-03T00:00:00","publicationYear":"2012","noYear":false,"publicationType":{"id":18,"text":"Report"},"publicationSubtype":{"id":5,"text":"USGS Numbered Series"},"seriesTitle":{"id":334,"text":"Scientific Investigations Report","code":"SIR","onlineIssn":"2328-0328","printIssn":"2328-031X","active":true,"publicationSubtype":{"id":5}},"seriesNumber":"2010-5070","chapter":"E","title":"Stratiform chromite deposit model","docAbstract":"<p>A new descriptive stratiform chromite deposit model was prepared which will provide a framework for understanding the characteristics of stratiform chromite deposits worldwide. Previous stratiform chromite deposit models developed by the U.S. Geological Survey (USGS) have been referred to as Bushveld chromium, because the Bushveld Complex in South Africa is the only stratified, mafic-ultramafic intrusion presently mined for chromite and is the most intensely researched. As part of the on-going effort by the USGS Mineral Resources Program to update existing deposit models for the upcoming national mineral resource assessment, this revised stratiform chromite deposit model includes new data on the geological, mineralogical, geophysical, and geochemical attributes of stratiform chromite deposits worldwide. This model will be a valuable tool in future chromite resource and environmental assessments and supplement previously published models used for mineral resource evaluation.</p>\n<p>Stratiform chromite deposits are found throughout the world, but the chromitite seams of the Bushveld Complex, South Africa, are the largest and most intensely researched. The chromite ore is located primarily in massive chromitite seams and, less abundantly, in disseminated chromite-bearing layers, both of which occur in the ultramafic section of large, layered mafic-ultramafic stratiform complexes. These mafic-ultramafic intrusions mainly formed in stable cratonic settings or during rift-related events during the Archean or early Proterozoic, although exceptions exist. The chromitite seams are cyclic in nature as well as laterally contiguous throughout the entire intrusion. Gangue minerals include olivine, pyroxenes (orthopyroxene and clinopyroxene), plagioclase, sulfides (pyrite, chalcopyrite, pyrrhotite, pentlandite, bornite), platinum group metals (mainly laurite, cooperite, braggite), and alteration minerals. A few deposits also contain rutile and ilmenite. The alteration phases include serpentine, chlorite, talc, magnetite, kaemmererite, uvarovite, hornblende, and carbonate minerals, such as calcite and dolomite.</p>\n<p>Stratiform chromite deposits are primarily hosted by peridotites, harzburgites, dunites, pyroxenites, troctolites, and anorthosites. Although metamorphism may have altered the ultramafic regions of layered intrusions postdeposition, only igneous processes are responsible for formation. From a diagnostic standpoint and for assessment purposes, they have no temporal or spatial relation to sedimentary rocks.</p>\n<p>The exact mechanisms responsible for the development of stratiform chromite deposits and the large, layered mafic-ultramafic intrusions where they are found are highly debated. The leading argument postulates that a parent magma mixed with a more primitive magma during magma chamber recharge. The partially differentiated magma could then be forced into the chromite stability field, resulting in the massive chromitite layers found in stratiform complexes. Contamination of the parent magma by localized assimilation of felsic country rock at the roof of the magma chamber has also been proposed as a mechanism of formation. Others suggest that changes in pressure or oxygen fugacity may be responsible for the occurrence of massive chromitite seams in layered mafic, ultramafic intrusions.</p>\n<p>The massive chromitite layers contain high levels of chromium and strong associations with platinum group elements. Anomalously high magnesium concentrations as well as low sodium, potassium, and phosphorus concentrations are also important geochemical features of stratiform chromite deposits. The presence of orthopyroxenite in many of the deposits suggests high silica and high magnesium concentrations in the parent magma.</p>\n<p>Most environmental concerns associated with the mining and processing of chromite ore focus on the solubility of chromium and its oxidation state. Although trivalent chromium (Cr<sup>3+</sup>) is an essential micronutrient for humans, hexavalent chromium (Cr<sup>6+</sup>) is highly toxic. Chromium-bearing solid phases that occur in the chromite ore-processing residue, for example, can effect the geochemical behavior and oxidation state of chromium in the environment.</p>","largerWorkType":{"id":18,"text":"Report"},"largerWorkTitle":"Mineral deposit models for resource assessment (Scientific Investigations Report 2010-5070)","largerWorkSubtype":{"id":5,"text":"USGS Numbered Series"},"language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/sir20105070E","usgsCitation":"Schulte, R., Taylor, R.D., Piatak, N., and Seal, R., 2012, Stratiform chromite deposit model: U.S. Geological Survey Scientific Investigations Report 2010-5070, xiv, 131 p., https://doi.org/10.3133/sir20105070E.","productDescription":"xiv, 131 p.","numberOfPages":"148","onlineOnly":"Y","additionalOnlineFiles":"Y","ipdsId":"IP-029769","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}],"links":[{"id":262962,"rank":3,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/sir_2010_5070_E.gif"},{"id":262960,"rank":2,"type":{"id":15,"text":"Index Page"},"url":"https://pubs.usgs.gov/sir/2010/5070/e/"},{"id":262961,"rank":1,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/sir/2010/5070/e/pdf/sir2010-5070e_LR.pdf","text":"Report Low Resolution","size":"15 MB","linkFileType":{"id":1,"text":"pdf"},"description":"Report"}],"noUsgsAuthors":false,"publicationStatus":"PW","scienceBaseUri":"5098ee18e4b0a35ac147a7b8","contributors":{"authors":[{"text":"Schulte, Ruth F.","contributorId":68604,"corporation":false,"usgs":true,"family":"Schulte","given":"Ruth F.","affiliations":[],"preferred":false,"id":468674,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Taylor, Ryan D. 0000-0002-8845-5290 rtaylor@usgs.gov","orcid":"https://orcid.org/0000-0002-8845-5290","contributorId":3412,"corporation":false,"usgs":true,"family":"Taylor","given":"Ryan","email":"rtaylor@usgs.gov","middleInitial":"D.","affiliations":[{"id":171,"text":"Central Mineral and Environmental Resources Science Center","active":true,"usgs":true}],"preferred":true,"id":468672,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Piatak, Nadine M.","contributorId":23621,"corporation":false,"usgs":true,"family":"Piatak","given":"Nadine M.","affiliations":[],"preferred":false,"id":468673,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Seal, Robert R. II 0000-0003-0901-2529 rseal@usgs.gov","orcid":"https://orcid.org/0000-0003-0901-2529","contributorId":397,"corporation":false,"usgs":true,"family":"Seal","given":"Robert R.","suffix":"II","email":"rseal@usgs.gov","affiliations":[],"preferred":false,"id":468671,"contributorType":{"id":1,"text":"Authors"},"rank":4}]}}
,{"id":70040585,"text":"70040585 - 2012 - Occupancy in continuous habitat","interactions":[],"lastModifiedDate":"2012-11-02T10:52:44","indexId":"70040585","displayToPublicDate":"2012-11-02T00:00:00","publicationYear":"2012","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":1475,"text":"Ecosphere","active":true,"publicationSubtype":{"id":10}},"title":"Occupancy in continuous habitat","docAbstract":"The probability that a site has at least one individual of a species ('occupancy') has come to be widely used as a state variable for animal population monitoring. The available statistical theory for estimation when detection is imperfect applies particularly to habitat patches or islands, although it is also used for arbitrary plots in continuous habitat. The probability that such a plot is occupied depends on plot size and home-range characteristics (size, shape and dispersion) as well as population density. Plot size is critical to the definition of occupancy as a state variable, but clear advice on plot size is missing from the literature on the design of occupancy studies. We describe models for the effects of varying plot size and home-range size on expected occupancy. Temporal, spatial, and species variation in average home-range size is to be expected, but information on home ranges is difficult to retrieve from species presence/absence data collected in occupancy studies. The effect of variable home-range size is negligible when plots are very large (>100 x area of home range), but large plots pose practical problems. At the other extreme, sampling of 'point' plots with cameras or other passive detectors allows the true 'proportion of area occupied' to be estimated. However, this measure equally reflects home-range size and density, and is of doubtful value for population monitoring or cross-species comparisons. Plot size is ill-defined and variable in occupancy studies that detect animals at unknown distances, the commonest example being unlimited-radius point counts of song birds. We also find that plot size is ill-defined in recent treatments of \"multi-scale\" occupancy; the respective scales are better interpreted as temporal (instantaneous and asymptotic) rather than spatial. Occupancy is an inadequate metric for population monitoring when it is confounded with home-range size or detection distance.","largerWorkType":{"id":2,"text":"Article"},"largerWorkTitle":"Ecosphere","largerWorkSubtype":{"id":10,"text":"Journal Article"},"language":"English","publisher":"Ecological Society of America","publisherLocation":"Ithaca, NY","doi":"10.1890/ES11-00308.1","usgsCitation":"Efford, M.G., and Dawson, D.K., 2012, Occupancy in continuous habitat: Ecosphere, v. 3, no. 4, p. 1-15, https://doi.org/10.1890/ES11-00308.1.","productDescription":"Article 32: 15 p.","startPage":"1","endPage":"15","ipdsId":"IP-027918","costCenters":[{"id":531,"text":"Patuxent Wildlife Research Center","active":true,"usgs":true}],"links":[{"id":474282,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.1890/es11-00308.1","text":"Publisher Index Page"},{"id":262929,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"},{"id":262928,"type":{"id":10,"text":"Digital Object Identifier"},"url":"https://dx.doi.org/10.1890/ES11-00308.1"}],"volume":"3","issue":"4","noUsgsAuthors":false,"publicationDate":"2012-04-25","publicationStatus":"PW","scienceBaseUri":"5094dd89e4b0e5cfc2acdc86","contributors":{"authors":[{"text":"Efford, Murray G.","contributorId":91616,"corporation":false,"usgs":true,"family":"Efford","given":"Murray","email":"","middleInitial":"G.","affiliations":[],"preferred":false,"id":468611,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Dawson, Deanna K. ddawson@usgs.gov","contributorId":1257,"corporation":false,"usgs":true,"family":"Dawson","given":"Deanna","email":"ddawson@usgs.gov","middleInitial":"K.","affiliations":[{"id":531,"text":"Patuxent Wildlife Research Center","active":true,"usgs":true}],"preferred":true,"id":468610,"contributorType":{"id":1,"text":"Authors"},"rank":2}]}}
,{"id":70040592,"text":"sir20125230 - 2012 - Completion summary for borehole USGS 136 near the Advanced Test Reactor Complex, Idaho National Laboratory, Idaho","interactions":[],"lastModifiedDate":"2017-09-19T18:31:20","indexId":"sir20125230","displayToPublicDate":"2012-11-02T00:00:00","publicationYear":"2012","noYear":false,"publicationType":{"id":18,"text":"Report"},"publicationSubtype":{"id":5,"text":"USGS Numbered Series"},"seriesTitle":{"id":334,"text":"Scientific Investigations Report","code":"SIR","onlineIssn":"2328-0328","printIssn":"2328-031X","active":true,"publicationSubtype":{"id":5}},"seriesNumber":"2012-5230","title":"Completion summary for borehole USGS 136 near the Advanced Test Reactor Complex, Idaho National Laboratory, Idaho","docAbstract":"<p>In 2011, the U.S. Geological Survey, in cooperation with the U.S. Department of Energy, cored and completed borehole USGS 136 for stratigraphic framework analyses and long-term groundwater monitoring of the eastern Snake River Plain aquifer at the Idaho National Laboratory. The borehole was initially cored to a depth of 1,048 feet (ft) below land surface (BLS) to collect core, open-borehole water samples, and geophysical data. After these data were collected, borehole USGS 136 was cemented and backfilled between 560 and 1,048 ft BLS. The final construction of borehole USGS 136 required that the borehole be reamed to allow for installation of 6-inch (in.) diameter carbon-steel casing and 5-in. diameter stainless-steel screen; the screened monitoring interval was completed between 500 and 551 ft BLS. A dedicated pump and water-level access line were placed to allow for aquifer testing, for collecting periodic water samples, and for measuring water levels.</p><p>Geophysical and borehole video logs were collected after coring and after the completion of the monitor well. Geophysical logs were examined in conjunction with the borehole core to describe borehole lithology and to identify primary flow paths for groundwater, which occur in intervals of fractured and vesicular basalt.</p><p>A single-well aquifer test was used to define hydraulic characteristics for borehole USGS 136 in the eastern Snake River Plain aquifer. Specific-capacity, transmissivity, and hydraulic conductivity from the aquifer test were at least 975 gallons per minute per foot, 1.4 × 10<sup>5</sup><span>&nbsp;</span>feet squared per day (ft<sup>2</sup>/d), and 254 feet per day, respectively. The amount of measureable drawdown during the aquifer test was about 0.02&nbsp;ft. The transmissivity for borehole USGS 136 was in the range of values determined from previous aquifer tests conducted in other wells near the Advanced Test Reactor Complex: 9.5 × 10<sup>3</sup><span>&nbsp;</span>to 1.9 × 10<sup>5</sup><span>&nbsp;</span>ft<sup>2</sup>/d.</p><p>Water samples were analyzed for cations, anions, metals, nutrients, total organic carbon, volatile organic compounds, stable isotopes, and radionuclides. Water samples from borehole USGS 136 indicated that concentrations of tritium, sulfate, and chromium were affected by wastewater disposal practices at the Advanced Test Reactor Complex. Depth-discrete groundwater samples were collected in the open borehole USGS 136 near 965, 710, and 573 ft BLS using a thief sampler; on the basis of selected constituents, deeper groundwater samples showed no influence from wastewater disposal at the Advanced Test Reactor Complex.</p>","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/sir20125230","collaboration":"Prepared in cooperation with the U.S. Department of Energy","usgsCitation":"Twining, B.V., Bartholomay, R.C., and Hodges, M., 2012, Completion summary for borehole USGS 136 near the Advanced Test Reactor Complex, Idaho National Laboratory, Idaho: U.S. Geological Survey Scientific Investigations Report 2012-5230, vi; 32 p.; Appendixes A-D, https://doi.org/10.3133/sir20125230.","productDescription":"vi; 32 p.; Appendixes A-D","numberOfPages":"42","additionalOnlineFiles":"Y","costCenters":[{"id":343,"text":"Idaho Water Science Center","active":true,"usgs":true},{"id":610,"text":"Utah Water Science Center","active":true,"usgs":true}],"links":[{"id":262907,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/sir_2012_5230.jpg"},{"id":262905,"type":{"id":15,"text":"Index Page"},"url":"https://pubs.usgs.gov/sir/2012/5230/"},{"id":262906,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/sir/2012/5230/pdf/sir20125230.pdf"}],"country":"United States","state":"Idaho","noUsgsAuthors":false,"publicationStatus":"PW","scienceBaseUri":"5059ee44e4b0c8380cd49c75","contributors":{"authors":[{"text":"Twining, Brian V. 0000-0003-1321-4721 btwining@usgs.gov","orcid":"https://orcid.org/0000-0003-1321-4721","contributorId":2387,"corporation":false,"usgs":true,"family":"Twining","given":"Brian","email":"btwining@usgs.gov","middleInitial":"V.","affiliations":[{"id":343,"text":"Idaho Water Science Center","active":true,"usgs":true}],"preferred":true,"id":468632,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Bartholomay, Roy C. 0000-0002-4809-9287 rcbarth@usgs.gov","orcid":"https://orcid.org/0000-0002-4809-9287","contributorId":1131,"corporation":false,"usgs":true,"family":"Bartholomay","given":"Roy","email":"rcbarth@usgs.gov","middleInitial":"C.","affiliations":[{"id":343,"text":"Idaho Water Science Center","active":true,"usgs":true}],"preferred":true,"id":468631,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Hodges, Mary K.V.","contributorId":66848,"corporation":false,"usgs":true,"family":"Hodges","given":"Mary K.V.","affiliations":[],"preferred":false,"id":468633,"contributorType":{"id":1,"text":"Authors"},"rank":3}]}}
,{"id":70040597,"text":"ofr20121024C - 2012 - Geologic framework for the national assessment of carbon dioxide storage resources: Hanna, Laramie, and Shirley Basins, Wyoming: Chapter C in <i>Geologic framework for the national assessment of carbon dioxide storage resources</i>","interactions":[{"subject":{"id":70040597,"text":"ofr20121024C - 2012 - Geologic framework for the national assessment of carbon dioxide storage resources: Hanna, Laramie, and Shirley Basins, Wyoming: Chapter C in <i>Geologic framework for the national assessment of carbon dioxide storage resources</i>","indexId":"ofr20121024C","publicationYear":"2012","noYear":false,"chapter":"C","title":"Geologic framework for the national assessment of carbon dioxide storage resources: Hanna, Laramie, and Shirley Basins, Wyoming: Chapter C in <i>Geologic framework for the national assessment of carbon dioxide storage resources</i>"},"predicate":"IS_PART_OF","object":{"id":70093199,"text":"ofr20121024 - 2012 - Geologic framework for the national assessment of carbon dioxide storage resources","indexId":"ofr20121024","publicationYear":"2012","noYear":false,"title":"Geologic framework for the national assessment of carbon dioxide storage resources"},"id":1}],"isPartOf":{"id":70093199,"text":"ofr20121024 - 2012 - Geologic framework for the national assessment of carbon dioxide storage resources","indexId":"ofr20121024","publicationYear":"2012","noYear":false,"title":"Geologic framework for the national assessment of carbon dioxide storage resources"},"lastModifiedDate":"2019-03-19T13:16:32","indexId":"ofr20121024C","displayToPublicDate":"2012-11-02T00:00:00","publicationYear":"2012","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":"2012-1024","chapter":"C","title":"Geologic framework for the national assessment of carbon dioxide storage resources: Hanna, Laramie, and Shirley Basins, Wyoming: Chapter C in <i>Geologic framework for the national assessment of carbon dioxide storage resources</i>","docAbstract":"<p>The 2007 Energy Independence and Security Act (Public Law 110-140) directs the U.S. Geological Survey (USGS) to conduct a national assessment of potential geologic storage resources for carbon dioxide (CO<sup>2</sup>). The methodology used for the national CO<sup>2</sup> assessment is non-economic and intended to be used at regional to subbasinal scales. This report identifies and contains geologic descriptions of twelve storage assessment units (SAUs) in six separate packages of sedimentary rock within the Hanna, Laramie, and Shirley Basins of Wyoming. It focuses on the particular characteristics, specified in the methodology, that influence the potential CO<sup>2</sup> storage resource in those SAUs. Specific descriptions of SAU boundaries as well as their sealing and reservoir units are included. Properties for each SAU, such as depth to top, gross thickness, net porous thickness, porosity, permeability, groundwater quality, and structural reservoir traps are provided to illustrate geologic factors critical to the assessment. Although assessment results are not contained in this report, the geologic information included herein will be employed, as specified in the methodology, to calculate a statistical Monte Carlo-based distribution of potential storage space in the various SAUs. Figures in this report show SAU boundaries and cell maps of well penetrations through the sealing unit into the top of the storage formation. Cell maps show the number of penetrating wells within one square mile and are derived from interpretations of incompletely attributed well data in a digital compilation that is known not to include all drilling. The USGS does not expect to know the location of all wells and cannot guarantee the amount of drilling through specific formations in any given cell shown on cell maps.</p>","largerWorkType":{"id":18,"text":"Report"},"largerWorkTitle":"Geologic framework for the national assessment of carbon dioxide storage resources (Open-File Report 2012-1024)","largerWorkSubtype":{"id":9,"text":"Other Report"},"language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/ofr20121024C","collaboration":"This report is Chapter C in <i>Geologic framework for the national assessment of carbon dioxide storage resources</i>. For more information, see <a href=\"http://pubs.er.usgs.gov/publication/ofr20121024\" target=\"_blank\">Open-File Report 2012-1024</a>.","usgsCitation":"Merrill, M., Covault, J.A., Craddock, W.H., Slucher, E.R., Warwick, P.D., Blondes, M., Gosai, M.A., Freeman, P., Cahan, S.M., and Lohr, C., 2012, Geologic framework for the national assessment of carbon dioxide storage resources: Hanna, Laramie, and Shirley Basins, Wyoming: Chapter C in <i>Geologic framework for the national assessment of carbon dioxide storage resources</i>: U.S. Geological Survey Open-File Report 2012-1024, Report: v, 24 p.; col. ill.; maps (col.); Downloads of Compressed Files, https://doi.org/10.3133/ofr20121024C.","productDescription":"Report: v, 24 p.; col. ill.; maps (col.); Downloads of Compressed Files","startPage":"i","endPage":"24","numberOfPages":"29","onlineOnly":"Y","additionalOnlineFiles":"Y","costCenters":[{"id":164,"text":"Central Energy Resources Science 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A.","contributorId":48451,"corporation":false,"usgs":true,"family":"Gosai","given":"Mayur","email":"","middleInitial":"A.","affiliations":[],"preferred":false,"id":468650,"contributorType":{"id":1,"text":"Authors"},"rank":7},{"text":"Freeman, P.A. 0000-0002-0863-7431 pfreeman@usgs.gov","orcid":"https://orcid.org/0000-0002-0863-7431","contributorId":3154,"corporation":false,"usgs":true,"family":"Freeman","given":"P.A.","email":"pfreeman@usgs.gov","affiliations":[{"id":241,"text":"Eastern Energy Resources Science Center","active":true,"usgs":true}],"preferred":false,"id":468642,"contributorType":{"id":1,"text":"Authors"},"rank":8},{"text":"Cahan, Steven M. 0000-0002-4776-3668 scahan@usgs.gov","orcid":"https://orcid.org/0000-0002-4776-3668","contributorId":4529,"corporation":false,"usgs":true,"family":"Cahan","given":"Steven","email":"scahan@usgs.gov","middleInitial":"M.","affiliations":[{"id":241,"text":"Eastern Energy Resources Science 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,{"id":70040586,"text":"70040586 - 2012 - Hybridization among Arctic white-headed gulls (Larus spp.) obscures the genetic legacy of the Pleistocene","interactions":[],"lastModifiedDate":"2012-11-05T11:08:42","indexId":"70040586","displayToPublicDate":"2012-11-02T00:00:00","publicationYear":"2012","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":1467,"text":"Ecology and Evolution","active":true,"publicationSubtype":{"id":10}},"title":"Hybridization among Arctic white-headed gulls (Larus spp.) obscures the genetic legacy of the Pleistocene","docAbstract":"We studied the influence of glacial oscillations on the genetic structure of seven species of white-headed gull that breed at high latitudes (<i>Larus argentatus, L. canus, L. glaucescens, L. glaucoides, L. hyperboreus, L. schistisagus,</i> and <i>L. thayeri</i>). We evaluated localities hypothesized as ice-free areas or glacial refugia in other Arctic vertebrates using molecular data from 11 microsatellite loci, mitochondrial DNA (mtDNA) control region, and six nuclear introns for 32 populations across the Holarctic. Moderate levels of genetic structure were observed for microsatellites (<i>F<sub>ST</sub></i>= 0.129), introns (<i>&#934;<sub>ST</sub></i>= 0.185), and mtDNA control region (<i>&#934;<sub>ST</sub></i>= 0.461), with among-group variation maximized when populations were grouped based on subspecific classification. Two haplotype and at least two allele groups were observed across all loci. However, no haplotype/allele group was composed solely of individuals of a single species, a pattern consistent with recent divergence. Furthermore, northernmost populations were not well differentiated and among-group variation was maximized when <i>L. argentatus</i> and <i>L. hyberboreus</i> populations were grouped by locality rather than species, indicating recent hybridization. Four populations are located in putative Pleistocene glacial refugia and had larger t estimates than the other 28 populations. However, we were unable to substantiate these putative refugia using coalescent theory, as all populations had genetic signatures of stability based on mtDNA. The extent of haplotype and allele sharing among Arctic white-headed gull species is noteworthy. Studies of other Arctic taxa have generally revealed species-specific clusters as well as genetic structure within species, usually correlated with geography. Aspects of white-headed gull behavioral biology, such as colonization ability and propensity to hybridize, as well as their recent evolutionary history, have likely played a large role in the limited genetic structure observed.","largerWorkType":{"id":2,"text":"Article"},"largerWorkTitle":"Ecology and Evolution","largerWorkSubtype":{"id":10,"text":"Journal Article"},"language":"English","publisher":"Blackwell Publishing Ltd.","publisherLocation":"Oxford, U.K.","doi":"10.1002/ece3.240","usgsCitation":"Sonsthagen, S.A., Chesser, R., Bell, D., and Dove, C.J., 2012, Hybridization among Arctic white-headed gulls (Larus spp.) obscures the genetic legacy of the Pleistocene: Ecology and Evolution, v. 2, no. 6, p. 1278-1295, https://doi.org/10.1002/ece3.240.","productDescription":"18 p.","startPage":"1278","endPage":"1295","numberOfPages":"18","ipdsId":"IP-022392","costCenters":[{"id":531,"text":"Patuxent Wildlife Research Center","active":true,"usgs":true}],"links":[{"id":474281,"rank":0,"type":{"id":41,"text":"Open Access External Repository Page"},"url":"https://doi.org/10.1002/ece3.240","text":"External Repository"},{"id":262927,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"},{"id":262924,"type":{"id":10,"text":"Digital Object Identifier"},"url":"https://dx.doi.org/10.1002/ece3.240"},{"id":262932,"type":{"id":11,"text":"Document"},"url":"https://onlinelibrary.wiley.com/doi/10.1002/ece3.240/pdf"}],"otherGeospatial":"Arctic","geographicExtents":"{ \"type\": \"FeatureCollection\", \"features\": [ { \"type\": \"Feature\", \"properties\": {}, \"geometry\": { \"type\": \"Polygon\", \"coordinates\": [ [ [ -180.0,41.0 ], [ -180.0,90.0 ], [ 180.0,90.0 ], [ 180.0,41.0 ], [ -180.0,41.0 ] ] ] } } ] }","volume":"2","issue":"6","noUsgsAuthors":false,"publicationDate":"2012-05-24","publicationStatus":"PW","scienceBaseUri":"5094dd85e4b0e5cfc2acdc82","contributors":{"authors":[{"text":"Sonsthagen, Sarah A. 0000-0001-6215-5874 ssonsthagen@usgs.gov","orcid":"https://orcid.org/0000-0001-6215-5874","contributorId":3711,"corporation":false,"usgs":true,"family":"Sonsthagen","given":"Sarah","email":"ssonsthagen@usgs.gov","middleInitial":"A.","affiliations":[{"id":114,"text":"Alaska Science Center","active":true,"usgs":true},{"id":117,"text":"Alaska Science Center Biology WTEB","active":true,"usgs":true}],"preferred":true,"id":468612,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Chesser, R. Terry 0000-0003-4389-7092","orcid":"https://orcid.org/0000-0003-4389-7092","contributorId":87669,"corporation":false,"usgs":true,"family":"Chesser","given":"R. Terry","affiliations":[{"id":531,"text":"Patuxent Wildlife Research Center","active":true,"usgs":true}],"preferred":true,"id":468614,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Bell, Douglas A.","contributorId":44427,"corporation":false,"usgs":true,"family":"Bell","given":"Douglas A.","affiliations":[],"preferred":false,"id":468613,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Dove, Carla J.","contributorId":98577,"corporation":false,"usgs":true,"family":"Dove","given":"Carla","email":"","middleInitial":"J.","affiliations":[],"preferred":false,"id":468615,"contributorType":{"id":1,"text":"Authors"},"rank":4}]}}
,{"id":70040598,"text":"sim3153 - 2012 - Geologic map of the Cook Inlet region, Alaska, including parts of the Talkeetna, Talkeetna Mountains, Tyonek, Anchorage, Lake Clark, Kenai, Seward, Iliamna, Seldovia, Mount Katmai, and Afognak 1:250,000-scale quadrangles","interactions":[],"lastModifiedDate":"2017-06-07T16:39:33","indexId":"sim3153","displayToPublicDate":"2012-11-02T00:00:00","publicationYear":"2012","noYear":false,"publicationType":{"id":18,"text":"Report"},"publicationSubtype":{"id":5,"text":"USGS Numbered Series"},"seriesTitle":{"id":333,"text":"Scientific Investigations Map","code":"SIM","onlineIssn":"2329-132X","printIssn":"2329-1311","active":true,"publicationSubtype":{"id":5}},"seriesNumber":"3153","title":"Geologic map of the Cook Inlet region, Alaska, including parts of the Talkeetna, Talkeetna Mountains, Tyonek, Anchorage, Lake Clark, Kenai, Seward, Iliamna, Seldovia, Mount Katmai, and Afognak 1:250,000-scale quadrangles","docAbstract":"In 1976, L.B. Magoon, W.L. Adkinson, and R.M. Egbert published a major geologic map of the Cook Inlet region, which has served well as a compilation of existing information and a guide for future research and mapping. The map in this report updates Magoon and others (1976) and incorporates new and additional mapping and interpretation. This map is also a revision of areas of overlap with the geologic map completed for central Alaska (Wilson and others, 1998). Text from that compilation remains appropriate and is summarized here; many compromises have been made in strongly held beliefs to allow construction of this compilation. Yet our willingness to make interpretations and compromises does not allow resolution of all mapping conflicts. Nonetheless, we hope that geologists who have mapped in this region will recognize that, in incorporating their work, our regional correlations may have required some generalization or lumping of map units. Many sources were used to produce this geologic map and, in most cases, data from available maps were combined, without generalization, and new data were added where available. A preliminary version of this map was published as U.S. Geological Survey Open-File Report 2009&ndash;1108. The main differences between the versions concern revised mapping of surfical deposits in the northern and eastern parts of the map area. Minor error corrections have been made also.","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/sim3153","collaboration":"Prepared in cooperation with the Alaska Department of Natural Resources Division of Oil and Gas","usgsCitation":"Wilson, F.H., Hults, C.P., Schmoll, H.R., Haeussler, P.J., Schmidt, J.M., Yehle, L.A., and Labay, K., 2012, Geologic map of the Cook Inlet region, Alaska, including parts of the Talkeetna, Talkeetna Mountains, Tyonek, Anchorage, Lake Clark, Kenai, Seward, Iliamna, Seldovia, Mount Katmai, and Afognak 1:250,000-scale quadrangles: U.S. Geological Survey Scientific Investigations Map 3153, Pamphlet: ii, 71 p.; 2 Sheets: 58 x 48 inches and 68 x 48 inches; Database Site, https://doi.org/10.3133/sim3153.","productDescription":"Pamphlet: ii, 71 p.; 2 Sheets: 58 x 48 inches and 68 x 48 inches; Database Site","startPage":"i","endPage":"71","numberOfPages":"75","onlineOnly":"N","additionalOnlineFiles":"Y","costCenters":[{"id":114,"text":"Alaska Science Center","active":true,"usgs":true}],"links":[{"id":262944,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/sim_3153.jpg"},{"id":262941,"type":{"id":17,"text":"Plate"},"url":"https://pubs.usgs.gov/sim/3153/sim3153_sheet2.pdf"},{"id":262938,"type":{"id":15,"text":"Index Page"},"url":"https://pubs.usgs.gov/sim/3153/"},{"id":262940,"type":{"id":17,"text":"Plate"},"url":"https://pubs.usgs.gov/sim/3153/sim3153_sheet1.pdf"},{"id":262939,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/sim/3153/sim3153_pamphlet.pdf"}],"scale":"250000","projection":"Alaska Albers Equal Area","datum":"North American Datum 1927","country":"United States","state":"Alaska","otherGeospatial":"Cook Inlet","geographicExtents":"{ \"type\": \"FeatureCollection\", \"features\": [ { \"type\": \"Feature\", \"properties\": {}, \"geometry\": { \"type\": \"Polygon\", \"coordinates\": [ [ [ -155.0,58.0 ], [ -155.0,63.0 ], [ -148.0,63.0 ], [ -148.0,58.0 ], [ -155.0,58.0 ] ] ] } } ] }","noUsgsAuthors":false,"publicationStatus":"PW","scienceBaseUri":"5094dd81e4b0e5cfc2acdc7e","contributors":{"authors":[{"text":"Wilson, Frederic H. 0000-0003-1761-6437 fwilson@usgs.gov","orcid":"https://orcid.org/0000-0003-1761-6437","contributorId":67174,"corporation":false,"usgs":true,"family":"Wilson","given":"Frederic","email":"fwilson@usgs.gov","middleInitial":"H.","affiliations":[{"id":114,"text":"Alaska Science Center","active":true,"usgs":true},{"id":119,"text":"Alaska Science Center Geology Minerals","active":true,"usgs":true}],"preferred":true,"id":468652,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Hults, Chad P. chults@usgs.gov","contributorId":1930,"corporation":false,"usgs":true,"family":"Hults","given":"Chad","email":"chults@usgs.gov","middleInitial":"P.","affiliations":[],"preferred":false,"id":468657,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Schmoll, Henry R. schmoll@usgs.gov","contributorId":3793,"corporation":false,"usgs":true,"family":"Schmoll","given":"Henry","email":"schmoll@usgs.gov","middleInitial":"R.","affiliations":[],"preferred":true,"id":468654,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Haeussler, Peter J. 0000-0002-1503-6247 pheuslr@usgs.gov","orcid":"https://orcid.org/0000-0002-1503-6247","contributorId":503,"corporation":false,"usgs":true,"family":"Haeussler","given":"Peter","email":"pheuslr@usgs.gov","middleInitial":"J.","affiliations":[{"id":119,"text":"Alaska Science Center Geology Minerals","active":true,"usgs":true},{"id":114,"text":"Alaska Science Center","active":true,"usgs":true}],"preferred":true,"id":468651,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Schmidt, Jeanine M. jschmidt@usgs.gov","contributorId":3138,"corporation":false,"usgs":true,"family":"Schmidt","given":"Jeanine","email":"jschmidt@usgs.gov","middleInitial":"M.","affiliations":[{"id":119,"text":"Alaska Science Center Geology Minerals","active":true,"usgs":true}],"preferred":true,"id":468656,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"Yehle, Lynn A. yehle@usgs.gov","contributorId":3794,"corporation":false,"usgs":true,"family":"Yehle","given":"Lynn","email":"yehle@usgs.gov","middleInitial":"A.","affiliations":[],"preferred":true,"id":468655,"contributorType":{"id":1,"text":"Authors"},"rank":6},{"text":"Labay, Keith A. 0000-0002-6763-3190 klabay@usgs.gov","orcid":"https://orcid.org/0000-0002-6763-3190","contributorId":2097,"corporation":false,"usgs":true,"family":"Labay","given":"Keith A.","email":"klabay@usgs.gov","affiliations":[{"id":119,"text":"Alaska Science Center Geology Minerals","active":true,"usgs":true}],"preferred":false,"id":468653,"contributorType":{"id":1,"text":"Authors"},"rank":7}]}}
,{"id":70040595,"text":"sir20125055 - 2012 - Development of invertebrate community indexes of stream quality for the islands of Maui and Oahu, Hawaii","interactions":[],"lastModifiedDate":"2016-08-31T17:09:58","indexId":"sir20125055","displayToPublicDate":"2012-11-02T00:00:00","publicationYear":"2012","noYear":false,"publicationType":{"id":18,"text":"Report"},"publicationSubtype":{"id":5,"text":"USGS Numbered Series"},"seriesTitle":{"id":334,"text":"Scientific Investigations Report","code":"SIR","onlineIssn":"2328-0328","printIssn":"2328-031X","active":true,"publicationSubtype":{"id":5}},"seriesNumber":"2012-5055","title":"Development of invertebrate community indexes of stream quality for the islands of Maui and Oahu, Hawaii","docAbstract":"<p>In 2009-10 the U.S. Geological Survey (USGS) collected physical habitat information and benthic macroinvertebrates at 40 wadeable sites on 25 perennial streams on the Island of Maui, Hawaiʻi, to evaluate the relations between the macroinvertebrate assemblages and environmental characteristics and to develop a multimetric invertebrate community index (ICI) that could be used as an indicator of stream quality. The macroinvertebrate community data were used to identify metrics that could best differentiate among sites according to disturbance gradients such as embeddedness, percent fines (silt and sand areal coverage), or percent agricultural land in the contributing basin area. Environmental assessments were conducted using land-use/land-cover data and reach-level physical habitat data. The Maui data were first evaluated using the previously developed Preliminary-Hawaiian Benthic Index of Biotic Integrity (P-HBIBI) to determine if existing metrics would successfully differentiate stream quality among the sites. Secondly, a number of candidate invertebrate metrics were screened and tested and the individual metrics that proved the best at discerning among the sites along one or more disturbance gradients were combined into a multimetric invertebrate community index (ICI) of stream quality. These metrics were: total invertebrate abundance, Class Insecta relative abundance, the ratio of Trichoptera abundance to nonnative Diptera abundance, native snail (hihiwai) presence or absence, native mountain shrimp (&prime;&delta;pae) presence or absence, native torrent midge (<i>Telmatogeton</i> spp.) presence or absence, and native <i>Megalagrion</i> damselfly presence or absence. The Maui ICI classified 15 of the 40 sites (37.5 percent) as having \"good\" quality communities, 17 of the sites (42.5 percent) as having \"fair\" quality communities, and 8 sites (20 percent) as having \"poor\" quality communities, a classification that may be used to initiate further investigation into the causes of the poor rating. Additionally, quantitative macroinvertebrate samples collected from 31 randomly selected sites on Oʻahu in 2006-07 as part of the U.S. Environmental Protection Agency's Wadeable Stream Assessment (WSA) were used to refine and develop an ICI of stream quality for Oʻahu. The set of metrics that were included in the revised index were: total invertebrate abundance, Class Insecta relative abundance, the ratio of Trichoptera abundance to nonnative Diptera abundance, turbellarian relative abundance, amphipod relative abundance, nonnative mollusk abundance, and nonnative crayfish (<i>Procambarus clarkii</i>) and/or red cherry shrimp (<i>Neocaridina denticulata sinensis</i>) presence or absence. The Oʻahu ICI classified 10 of the 31 sites (32.3 percent) as \"good\" quality communities, 16 of the sites (51.6 percent) as \"fair\" quality communities, and 5 of the sites (16.1 percent) as \"poor\" quality communities. A reanalysis of 18 of the Oʻahu macroinvertebrate sites used to develop the P-HBIBI resulted in the reclassification of 3 samples. The beginning of a statewide ICI was developed on the basis of a combination of metrics from the Maui and Oʻahu ICIs. This combined ICI is intended to help identify broad problem areas so that the Hawaii State Department of Health (HIDOH) can prioritize their efforts on a statewide scale. Once these problem areas are identified, the island-wide ICIs can be used to more accurately assess the quality of individual stream reaches so that the HIDOH can prioritize their efforts on the most impaired streams. By using the combined ICI, 70 percent of the Maui sites and 10 percent of the Oʻahu WSA sites were designated as \"good\" quality sites; 25 percent of the Maui sites and 45 percent of the Oʻahu WSA sites were designated as \"fair\" quality sites; and 5 percent of the Maui sites and 45 percent of the Oʻahu WSA sites were designated as \"poor\" quality sites.</p>","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/sir20125055","collaboration":"Prepared in cooperation with the State of Hawaiʻi Department of Health","usgsCitation":"Wolff, R.H., 2012, Development of invertebrate community indexes of stream quality for the islands of Maui and Oahu, Hawaii: U.S. Geological Survey Scientific Investigations Report 2012-5055, Report: viii; 199 p.; Appendixes: A-D, https://doi.org/10.3133/sir20125055.","productDescription":"Report: viii; 199 p.; Appendixes: 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The purpose of the databases is to (1) provide useful data to ground-survey crews for use in performing detailed assessments of the areas and (2) provide useful information to private investors who are considering investment in a particular area for development of its natural resources. The set of satellite-image mosaics provided in this Data Series (DS) is one such database. Although airborne digital color-infrared imagery was acquired for parts of Afghanistan in 2006, the image data have radiometric variations that preclude their use in creating a consistent image mosaic for geologic analysis. Consequently, image mosaics were created using ALOS (Advanced Land Observation Satellite; renamed Daichi) satellite images, whose radiometry has been well determined (Saunier, 2007a,b). This DS consists of the locally enhanced ALOS image mosaics for each of the 24 mineral project areas (referred to herein as areas of interest), whose locality names, locations, and main mineral occurrences are shown on the index map of Afghanistan (fig. 1). ALOS was launched on January 24, 2006, and provides multispectral images from the AVNIR (Advanced Visible and Near-Infrared Radiometer) sensor in blue (420-500 nanometer, nm), green (520-600 nm), red (610-690 nm), and near-infrared (760-890 nm) wavelength bands with an 8-bit dynamic range and a 10-meter (m) ground resolution. The satellite also provides a panchromatic band image from the PRISM (Panchromatic Remote-sensing Instrument for Stereo Mapping) sensor (520-770 nm) with the same dynamic range but a 2.5-m ground resolution. The image products in this DS incorporate copyrighted data provided by the Japan Aerospace Exploration Agency, but the image processing has altered the original pixel structure and all image values of the JAXA ALOS data, such that original image values cannot be recreated from this DS. As such, the DS products match JAXA criteria for value added products, which are not copyrighted, according to the ALOS end-user license agreement. The selection criteria for the satellite imagery used in our mosaics were images having (1) the highest solar-elevation angles (near summer solstice) and (2) the least cloud, cloud-shadow, and snow cover. The multispectral and panchromatic data were orthorectified with ALOS satellite ephemeris data, a process which is not as accurate as orthorectification using digital elevation models (DEMs); however, the ALOS processing center did not have a precise DEM. As a result, the multispectral and panchromatic image pairs were generally not well registered to the surface and not coregistered well enough to perform resolution enhancement on the multispectral data. Therefore, it was necessary to (1) register the 10-m AVNIR multispectral imagery to a well-controlled Landsat image base, (2) mosaic the individual multispectral images into a single image of the entire area of interest, (3) register each panchromatic image to the registered multispectral image base, and (4) mosaic the individual panchromatic images into a single image of the entire area of interest. The two image-registration steps were facilitated using an automated control-point algorithm developed by the USGS that allows image coregistration to within one picture element. PRISM image orthorectification for one-half of the target areas was performed by the Alaska Satellite Facility, applying its photogrammetric software to PRISM stereo images with vertical control points obtained from the digital elevation database produced by the Shuttle Radar Topography Mission (Farr and others, 2007) and horizontal adjustments based on a controlled Landsat image base (Davis, 2006). Before rectification, the multispectral and panchromatic images were converted to radiance values and then to relative-reflectance values using the methods described in Davis (2006). Mosaicking the multispectral or panchromatic images started with the image with the highest sun-elevation angle and the least atmospheric scattering, which was treated as the standard image. The band-reflectance values of all other multispectral or panchromatic images within the area were sequentially adjusted to that of the standard image by determining band-reflectance correspondence between overlapping images using linear least-squares analysis. The resolution of the multispectral image mosaic was then increased to that of the panchromatic image mosaic using SPARKLE logic, which is described in Davis (2006). Each of the four-band images within each resolution-enhanced image mosaic was individually subjected to a local-area histogram stretch algorithm (described in Davis, 2007), which stretches each band's picture element based on the digital values of all picture elements within a specified radius that was usually 500 m. The final databases, which are provided in this DS, are three-band, color-composite images of the local-area-enhanced, natural-color data (the blue, green, and red wavelength bands) and color-infrared data (the green, red, and near-infrared wavelength bands). All image data were initially projected and maintained in Universal Transverse Mercator (UTM) map projection using the target area's local zone (either 41 or 42) and the WGS84 datum. Most final image mosaics were subdivided into overlapping tiles or quadrants because of the large size of the target areas. The image tiles (or quadrants) for each area of interest are provided as embedded geotiff images, which can be read and used by most geographic information system (GIS) and image-processing software. The tiff world files (tfw) are provided, even though they are generally not needed for most software to read an embedded geotiff image. Approximately one-half of the study areas have at least one subarea designated for detailed field investigations; the subareas were extracted from the area's image mosaic and are provided as separate embedded geotiff images.","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/ds709","collaboration":"Prepared in cooperation with the U.S. Department of Defense <a href=\"http://tfbso.defense.gov/www/\" target=\"_blank\">Task Force for Business and Stability Operations</a> and the <a href=\"http://www.bgs.ac.uk/AfghanMinerals/\" target=\"_blank\">Afghanistan Geological Survey</a>.  This report is composed of 24 chapters.  Please visit <a href=\"http://pubs.er.usgs.gov/publication/ds709\" target=\"_blank\">DS 709</a> to view available chapters.","usgsCitation":"Davis, P.A., 2012, Local-area-enhanced, high-resolution natural-color and color-infrared satellite-image mosaics of mineral districts in Afghanistan: U.S. Geological Survey Data Series 709, 24  Chapters, https://doi.org/10.3133/ds709.","productDescription":"24  Chapters","onlineOnly":"Y","additionalOnlineFiles":"Y","costCenters":[{"id":387,"text":"Mineral Resources Program","active":true,"usgs":true}],"links":[{"id":262620,"rank":0,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/ds_709.jpg"},{"id":262613,"rank":100,"type":{"id":15,"text":"Index Page"},"url":"https://pubs.usgs.gov/ds/709/","linkFileType":{"id":5,"text":"html"}}],"country":"Afghanistan","geographicExtents":"{ \"type\": \"FeatureCollection\", \"features\": [ { \"type\": \"Feature\", \"properties\": {}, \"geometry\": { \"type\": \"Polygon\", \"coordinates\": [ [ [ 60.52,29.38 ], [ 60.52,38.49 ], [ 74.89,38.49 ], [ 74.89,29.38 ], [ 60.52,29.38 ] ] ] } } ] }","noUsgsAuthors":false,"publicationStatus":"PW","scienceBaseUri":"507ee041e4b022001d87bb82","contributors":{"authors":[{"text":"Davis, Philip A. pdavis@usgs.gov","contributorId":692,"corporation":false,"usgs":true,"family":"Davis","given":"Philip","email":"pdavis@usgs.gov","middleInitial":"A.","affiliations":[],"preferred":true,"id":468184,"contributorType":{"id":1,"text":"Authors"},"rank":1}]}}
,{"id":70249837,"text":"70249837 - 2012 - Mapped versus actual burned area within wildfire perimeters: Characterizing the unburned","interactions":[],"lastModifiedDate":"2023-11-01T21:03:50.201344","indexId":"70249837","displayToPublicDate":"2012-11-01T15:58:22","publicationYear":"2012","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":1687,"text":"Forest Ecology and Management","active":true,"publicationSubtype":{"id":10}},"title":"Mapped versus actual burned area within wildfire perimeters: Characterizing the unburned","docAbstract":"<div id=\"aep-abstract-id19\" class=\"abstract author\" lang=\"en\"><div id=\"aep-abstract-sec-id20\"><p id=\"sp0010\">For decades, wildfire studies have utilized fire occurrence as the primary data source for investigating the causes and effects of wildfire on the landscape. Fire occurrence data fall primarily into two categories: ignition points and perimeter polygons which are used to calculate a ‘burned area’ for a fire. However, understanding the relationships between climate and fire or between fire and its ecological effects requires an understanding of the burn heterogeneity across the landscape and the area within fire perimeters that remains unburned. This research characterizes unburned areas within fire perimeters, which provide ecological refugia and seed source for post-fire regeneration. We utilized differenced Normalized Burn Ratio (dNBR) data to examine the frequency, extent, and spatial patterns of unburned area in three national parks across the western US (Glacier, Yosemite, and Yukon-Charley Rivers). We characterized unburned area within fire perimeters by fire size and severity, characterized distance to an unburned area across the burned portion of the fire, and investigated patch dynamics of unburned patches within the fire perimeter. From 1984 through 2009, the total area within the fire perimeters that was classified as unburned from dNBR was 37% for Yosemite, 17% for Glacier, and 14% for Yukon-Charley. Variation in unburned area between fires was highest in Yosemite and lowest in Yukon-Charley. The unburned proportion significantly decreased with increasing fire size and severity across all three parks. Unburned patch size increased with size of fire perimeter, but patches decreased in density. There were no temporal trends in unburned area found. These results raise questions about the validity of relationships found between external forcing agents, such as climate, and ‘burned area’ values derived solely from polygon fire perimeters.</p></div></div>","language":"English","publisher":"Elsevier","doi":"10.1016/j.foreco.2012.08.020","usgsCitation":"Key, C.H., James Lutz, Key, C.H., Jonathan Kane, and Van Wagtendonk, J.W., 2012, Mapped versus actual burned area within wildfire perimeters: Characterizing the unburned: Forest Ecology and Management, v. 286, p. 38-47, https://doi.org/10.1016/j.foreco.2012.08.020.","productDescription":"10 p.","startPage":"38","endPage":"47","ipdsId":"IP-039364","costCenters":[{"id":481,"text":"Northern Rocky Mountain Science Center","active":true,"usgs":true}],"links":[{"id":422316,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","state":"Alaska, Montana, Wyoming","otherGeospatial":"Yosemite National Park, Glacier National Park, Yukon-Charley Rivers National Preserve","geographicExtents":"{\n  \"type\": \"FeatureCollection\",\n  \"features\": [\n    {\n      \"type\": \"Feature\",\n      \"properties\": {},\n      \"geometry\": {\n        \"coordinates\": [\n          [\n            [\n              -111.2239315959776,\n              45.14819375625538\n            ],\n            [\n              -111.2239315959776,\n              43.41806384026984\n            ],\n            [\n              -108.73003511160249,\n              43.41806384026984\n            ],\n            [\n              -108.73003511160249,\n              45.14819375625538\n            ],\n            [\n              -111.2239315959776,\n              45.14819375625538\n            ]\n          ]\n        ],\n        \"type\": \"Polygon\"\n      }\n    },\n    {\n      \"type\": \"Feature\",\n      \"properties\": {},\n      \"geometry\": {\n        \"coordinates\": [\n          [\n            [\n              -114.90435217871973,\n              49.04942005803713\n            ],\n            [\n              -114.90435217871973,\n              48.12286489452612\n            ],\n            [\n              -112.71257971778198,\n              48.12286489452612\n            ],\n            [\n              -112.71257971778198,\n              49.04942005803713\n            ],\n            [\n              -114.90435217871973,\n              49.04942005803713\n            ]\n          ]\n        ],\n        \"type\": \"Polygon\"\n      }\n    },\n    {\n      \"type\": \"Feature\",\n      \"properties\": {},\n      \"geometry\": {\n        \"coordinates\": [\n          [\n            [\n              -143.54242128938068,\n              65.82194598460669\n            ],\n            [\n              -143.54242128938068,\n              64.41714830535625\n            ],\n            [\n              -140.55414003938063,\n              64.41714830535625\n            ],\n            [\n              -140.55414003938063,\n              65.82194598460669\n            ],\n            [\n              -143.54242128938068,\n              65.82194598460669\n            ]\n          ]\n        ],\n        \"type\": \"Polygon\"\n      }\n    }\n  ]\n}","volume":"286","noUsgsAuthors":false,"publicationStatus":"PW","contributors":{"authors":[{"text":"Key, Carl H carl_key@usgs.gov","contributorId":331312,"corporation":false,"usgs":true,"family":"Key","given":"Carl","email":"carl_key@usgs.gov","middleInitial":"H","affiliations":[],"preferred":true,"id":887307,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"James Lutz","contributorId":331315,"corporation":false,"usgs":false,"family":"James Lutz","affiliations":[{"id":6934,"text":"University of Washington","active":true,"usgs":false}],"preferred":false,"id":887310,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Key, Carl H. carl_key@usgs.gov","contributorId":4138,"corporation":false,"usgs":true,"family":"Key","given":"Carl","email":"carl_key@usgs.gov","middleInitial":"H.","affiliations":[],"preferred":true,"id":887317,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Jonathan Kane","contributorId":331316,"corporation":false,"usgs":false,"family":"Jonathan Kane","affiliations":[{"id":6934,"text":"University of Washington","active":true,"usgs":false}],"preferred":false,"id":887311,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Van Wagtendonk, Jan W jan_van_wagtendonk@usgs.gov","contributorId":331313,"corporation":false,"usgs":true,"family":"Van Wagtendonk","given":"Jan","email":"jan_van_wagtendonk@usgs.gov","middleInitial":"W","affiliations":[],"preferred":true,"id":887308,"contributorType":{"id":1,"text":"Authors"},"rank":5}]}}
,{"id":70101174,"text":"70101174 - 2012 - Population ecology of breeding Pacific common eiders on the Yukon-Kuskokwim Delta, Alaska","interactions":[],"lastModifiedDate":"2014-04-10T11:45:15","indexId":"70101174","displayToPublicDate":"2012-11-01T11:40:00","publicationYear":"2012","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":3773,"text":"Wildlife Monographs","active":true,"publicationSubtype":{"id":10}},"title":"Population ecology of breeding Pacific common eiders on the Yukon-Kuskokwim Delta, Alaska","docAbstract":"Populations of Pacific common eiders (Somateria mollissima v-nigrum) on the Yukon-Kuskokwim Delta (YKD) in western Alaska declined by 50–90% from 1957 to 1992 and then stabilized at reduced numbers from the early 1990s to the present. We investigated the underlying processes affecting their population dynamics by collection and analysis of demographic data from Pacific common eiders at 3 sites on the YKD (1991–2004) for 29 site-years. We examined variation in components of reproduction, tested hypotheses about the influence of specific ecological factors on life-history variables, and investigated their relative contributions to local population dynamics. Reproductive output was low and variable, both within and among individuals, whereas apparent survival of adult females was high and relatively invariant (0.89 ± 0.005). All reproductive parameters varied across study sites and years. Clutch initiation dates ranged from 4 May to 28 June, with peak (modal) initiation occurring on 26 May. Females at an island study site consistently initiated clutches 3–5 days earlier in each year than those on 2 mainland sites. Population variance in nest initiation date was negatively related to the peak, suggesting increased synchrony in years of delayed initiation. On average, total clutch size (laid) ranged from 4.8 to 6.6 eggs, and declined with date of nest initiation. After accounting for partial predation and non-viability of eggs, average clutch size at hatch ranged from 2.0 to 5.8 eggs. Within seasons, daily survival probability (DSP) of nests was lowest during egg-laying and late-initiation dates. Estimated nest survival varied considerably across sites and years (mean = 0.55, range: 0.06–0.92), but process variance in nest survival was relatively low (0.02, CI: 0.01–0.05), indicating that most variance was likely attributed to sampling error. We found evidence that observer effects may have reduced overall nest survival by 0.0–0.36 across site-years. Study sites with lower sample sizes and more frequent visitations appeared to experience greater observer effects. In general, Pacific common eiders exhibited high spatio-temporal variance in reproductive components. Larger clutch sizes and high nest survival at early initiation dates suggested directional selection favoring early nesting. However, stochastic environmental effects may have precluded response to this apparent selection pressure. Our results suggest that females breeding early in the season have the greatest reproductive value, as these birds lay the largest clutches and have the highest probability of successfully hatching. We developed stochastic, stage-based, matrix population models that incorporated observed spatio-temporal (process) variance and co-variation in vital rates, and projected the stable stage distribution () and population growth rate (λ). We used perturbation analyses to examine the relative influence of changes in vital rates on λ and variance decomposition to assess the proportion of variation in λ explained by process variation in each vital rate. In addition to matrix-based λ, we estimated λ using capture–recapture approaches, and log-linear regression. We found the stable age distribution for Pacific common eiders was weighted heavily towards experienced adult females (≥4 yr of age), and all calculations of λ indicated that the YKD population was stable to slightly increasing (λmatrix = 1.02, CI: 1.00–1.04); λreverse-capture–recapture = 1.05, CI: 0.99–1.11; λlog-linear = 1.04, CI: 0.98–1.10). Perturbation analyses suggested the population would respond most dramatically to changes in adult female survival (relative influence of adult survival was 1.5 times that of fecundity), whereas retrospective variation in λ was primarily explained by fecundity parameters (60%), particularly duckling survival (42%). Among components of fecundity, sensitivities were highest for duckling survival, suggesti","largerWorkType":{"id":2,"text":"Article"},"largerWorkTitle":"Wildlife Monographs","largerWorkSubtype":{"id":10,"text":"Journal Article"},"publisher":"Wildlife Monographs","doi":"10.1002/wmon.8","usgsCitation":"Wilson, H.M., Flint, P.L., Powell, A., Grand, J., and Moral, C.L., 2012, Population ecology of breeding Pacific common eiders on the Yukon-Kuskokwim Delta, Alaska: Wildlife Monographs, v. 182, no. 1, 28 p., https://doi.org/10.1002/wmon.8.","productDescription":"28 p.","ipdsId":"IP-028472","costCenters":[{"id":117,"text":"Alaska Science Center Biology WTEB","active":true,"usgs":true}],"links":[{"id":438809,"rank":0,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/P9G6G0AV","text":"USGS data release","linkHelpText":"Pacific common eider (Somateria mollissima v-nigrum) nest records, Yukon-Kuskokwim Delta, Alaska, 1991-2004"},{"id":286181,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"},{"id":286079,"type":{"id":10,"text":"Digital Object Identifier"},"url":"https://dx.doi.org/10.1002/wmon.8"},{"id":286080,"type":{"id":15,"text":"Index Page"},"url":"https://onlinelibrary.wiley.com/doi/10.1002/wmon.8/full"}],"country":"United States","state":"Alaska","otherGeospatial":"Yukon-kushokwim Delta","geographicExtents":"{ \"type\": \"FeatureCollection\", \"features\": [ { \"type\": \"Feature\", \"properties\": {}, \"geometry\": { \"type\": \"Polygon\", \"coordinates\": [ [ [ -165.1094,62.3668 ], [ -165.1094,63.2645 ], [ -162.7789,63.2645 ], [ -162.7789,62.3668 ], [ -165.1094,62.3668 ] ] ] } } ] }","volume":"182","issue":"1","noUsgsAuthors":false,"publicationDate":"2012-10-24","publicationStatus":"PW","scienceBaseUri":"535594f7e4b0120853e8c109","contributors":{"authors":[{"text":"Wilson, Heather M.","contributorId":37056,"corporation":false,"usgs":false,"family":"Wilson","given":"Heather","email":"","middleInitial":"M.","affiliations":[{"id":13236,"text":"U.S. Fish and Wildlife Service, Migratory Bird Management","active":true,"usgs":false}],"preferred":false,"id":492634,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Flint, Paul L. 0000-0002-8758-6993 pflint@usgs.gov","orcid":"https://orcid.org/0000-0002-8758-6993","contributorId":3284,"corporation":false,"usgs":true,"family":"Flint","given":"Paul","email":"pflint@usgs.gov","middleInitial":"L.","affiliations":[{"id":114,"text":"Alaska Science Center","active":true,"usgs":true},{"id":117,"text":"Alaska Science Center Biology WTEB","active":true,"usgs":true}],"preferred":true,"id":492633,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Powell, Abby N. abby_powell@usgs.gov","contributorId":2534,"corporation":false,"usgs":false,"family":"Powell","given":"Abby N.","email":"abby_powell@usgs.gov","affiliations":[{"id":13117,"text":"Institute of Arctic Biology, University of Alaska Fairbanks","active":true,"usgs":false}],"preferred":false,"id":492632,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Grand, J. Barry","contributorId":61950,"corporation":false,"usgs":true,"family":"Grand","given":"J. Barry","affiliations":[],"preferred":false,"id":492636,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Moral, Christine L.","contributorId":57765,"corporation":false,"usgs":true,"family":"Moral","given":"Christine","email":"","middleInitial":"L.","affiliations":[],"preferred":false,"id":492635,"contributorType":{"id":1,"text":"Authors"},"rank":5}]}}
,{"id":70047415,"text":"70047415 - 2012 - Mapping temperature and radiant geothermal heat flux anomalies in the Yellowstone geothermal system using ASTER thermal infrared data","interactions":[],"lastModifiedDate":"2019-06-04T08:58:05","indexId":"70047415","displayToPublicDate":"2012-11-01T11:10:31","publicationYear":"2012","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":1827,"text":"Geothermal Resources Council Transactions","active":true,"publicationSubtype":{"id":10}},"title":"Mapping temperature and radiant geothermal heat flux anomalies in the Yellowstone geothermal system using ASTER thermal infrared data","docAbstract":"<p>The purpose of this work was to use satellite-based thermal infrared (TIR) remote sensing data to measure, map, and monitor geothermal activity within the Yellowstone geothermal area to help meet the missions of both the U.S. Geological Survey Yellowstone Volcano Observatory and the Yellowstone National Park Geology Program. Specifically, the goals were to: 1) address the challenges of remotely characterizing the spatially and temporally dynamic thermal features in Yellowstone by using nighttime TIR data from the Advanced Spaceborne Thermal Emission and Reflection Radiometer (ASTER) and 2) estimate the temperature, geothermal radiant emittance, and radiant geothermal heat flux (GHF) for Yellowstone’s thermal areas (both Park wide and for individual thermal areas). </p>\n<br/>\n<p>ASTER TIR data (90-m pixels) acquired at night during January and February, 2010, were used to estimate surface temperature, radiant emittance, and radiant GHF from all of Yellowstone’s thermal features, produce thermal anomaly maps, and update field-based maps of thermal areas. A background subtraction technique was used to isolate the geothermal component of TIR radiance from thermal radiance due to insolation. A lower limit for the Yellowstone’s total radiant GHF was established at ~2.0 GW, which is ~30-45% of the heat flux estimated through geochemical (Cl-flux) methods. Additionally, about 5 km<sup>2</sup> was added to the geodatabase of mapped thermal areas. </p>\n<br/>\n<p>This work provides a framework for future satellite-based thermal monitoring at Yellowstone as well as exploration of other volcanic / geothermal systems on a global scale.</p>","language":"English","publisher":"Geothermal Resources Council","publisherLocation":"Davis, CA","usgsCitation":"Vaughan, R.G., Lowenstern, J.B., Keszthelyi, L., Jaworowski, C., and Heasler, H., 2012, Mapping temperature and radiant geothermal heat flux anomalies in the Yellowstone geothermal system using ASTER thermal infrared data: Geothermal Resources Council Transactions, v. 36, p. 1403-1409.","productDescription":"7 p.","startPage":"1403","endPage":"1409","numberOfPages":"7","ipdsId":"IP-039085","costCenters":[{"id":131,"text":"Astrogeology Science Center","active":true,"usgs":true},{"id":615,"text":"Volcano Hazards Program","active":true,"usgs":true}],"links":[{"id":287664,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"},{"id":287663,"type":{"id":15,"text":"Index Page"},"url":"https://www.geothermal-library.org/index.php?mode=pubs&action=view&record=1030414"}],"country":"United States","state":"Wyoming","otherGeospatial":"Yellowstone National Park","geographicExtents":"{ \"type\": \"FeatureCollection\", \"features\": [ { \"type\": \"Feature\", \"properties\": {}, \"geometry\": { \"type\": \"Polygon\", \"coordinates\": [ [ [ -111.156,44.1324 ], [ -111.156,45.109 ], [ -109.8242,45.109 ], [ -109.8242,44.1324 ], [ -111.156,44.1324 ] ] ] } } ] }","volume":"36","noUsgsAuthors":false,"publicationStatus":"PW","scienceBaseUri":"5387056de4b0aa26cd7b53c9","contributors":{"authors":[{"text":"Vaughan, R. Greg 0000-0002-0850-6669","orcid":"https://orcid.org/0000-0002-0850-6669","contributorId":69030,"corporation":false,"usgs":true,"family":"Vaughan","given":"R.","email":"","middleInitial":"Greg","affiliations":[{"id":131,"text":"Astrogeology Science Center","active":true,"usgs":true}],"preferred":true,"id":481983,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Lowenstern, Jacob B. 0000-0003-0464-7779 jlwnstrn@usgs.gov","orcid":"https://orcid.org/0000-0003-0464-7779","contributorId":2755,"corporation":false,"usgs":true,"family":"Lowenstern","given":"Jacob","email":"jlwnstrn@usgs.gov","middleInitial":"B.","affiliations":[{"id":617,"text":"Volcano Science Center","active":true,"usgs":true}],"preferred":true,"id":481979,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Keszthelyi, Laszlo P. 0000-0003-1879-4331 laz@usgs.gov","orcid":"https://orcid.org/0000-0003-1879-4331","contributorId":52802,"corporation":false,"usgs":true,"family":"Keszthelyi","given":"Laszlo P.","email":"laz@usgs.gov","affiliations":[{"id":131,"text":"Astrogeology Science Center","active":true,"usgs":true}],"preferred":true,"id":481981,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Jaworowski, Cheryl","contributorId":25989,"corporation":false,"usgs":true,"family":"Jaworowski","given":"Cheryl","affiliations":[],"preferred":false,"id":481980,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Heasler, Henry","contributorId":62683,"corporation":false,"usgs":true,"family":"Heasler","given":"Henry","affiliations":[],"preferred":false,"id":481982,"contributorType":{"id":1,"text":"Authors"},"rank":5}]}}
,{"id":70039495,"text":"70039495 - 2012 - Incorporating movement patterns to improve survival estimates for juvenile bull trout","interactions":[],"lastModifiedDate":"2014-01-15T10:50:54","indexId":"70039495","displayToPublicDate":"2012-11-01T10:42:34","publicationYear":"2012","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":2886,"text":"North American Journal of Fisheries Management","active":true,"publicationSubtype":{"id":10}},"title":"Incorporating movement patterns to improve survival estimates for juvenile bull trout","docAbstract":"Populations of many fish species are sensitive to changes in vital rates during early life stages, but our understanding of the factors affecting growth, survival, and movement patterns is often extremely limited for juvenile fish. These critical information gaps are particularly evident for bull trout <i>Salvelinus confluentus</i>, a threatened Pacific Northwest char. We combined several active and passive mark–recapture and resight techniques to assess migration rates and estimate survival for juvenile bull trout (70–170 mm total length). We evaluated the relative performance of multiple survival estimation techniques by comparing results from a common Cormack–Jolly–Seber (CJS) model, the less widely used Barker model, and a simple return rate (an index of survival). Juvenile bull trout of all sizes emigrated from their natal habitat throughout the year, and thereafter migrated up to 50 km downstream. With the CJS model, high emigration rates led to an extreme underestimate of apparent survival, a combined estimate of site fidelity and survival. In contrast, the Barker model, which allows survival and emigration to be modeled as separate parameters, produced estimates of survival that were much less biased than the return rate. Estimates of age-class-specific annual survival from the Barker model based on all available data were 0.218±0.028 (estimate±SE) for age-1 bull trout and 0.231±0.065 for age-2 bull trout. This research demonstrates the importance of incorporating movement patterns into survival analyses, and we provide one of the first field-based estimates of juvenile bull trout annual survival in relatively pristine rearing conditions. These estimates can provide a baseline for comparison with future studies in more impacted systems and will help managers develop reliable stage-structured population models to evaluate future recovery strategies.","largerWorkType":{"id":2,"text":"Article"},"largerWorkTitle":"North American Journal of Fisheries Management","largerWorkSubtype":{"id":10,"text":"Journal Article"},"language":"English","publisher":"American Fisheries Society","publisherLocation":"Lawrence, KS","doi":"10.1080/02755947.2012.720644","usgsCitation":"Bowerman, T., and Budy, P., 2012, Incorporating movement patterns to improve survival estimates for juvenile bull trout: North American Journal of Fisheries Management, v. 32, no. 6, p. 1123-1136, https://doi.org/10.1080/02755947.2012.720644.","productDescription":"14 p.","startPage":"1123","endPage":"1136","numberOfPages":"14","ipdsId":"IP-036702","costCenters":[{"id":609,"text":"Utah Cooperative Fish and Wildlife Research Unit","active":false,"usgs":true}],"links":[{"id":498970,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.1080/02755947.2012.720644","text":"Publisher Index Page"},{"id":281076,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"},{"id":281075,"type":{"id":10,"text":"Digital Object Identifier"},"url":"https://dx.doi.org/10.1080/02755947.2012.720644"}],"country":"United States","state":"Oregon","otherGeospatial":"Blue Mountains;Skiphorton Creek;South Fork Walla Walla River;Walla Walla River","geographicExtents":"{ \"type\": \"FeatureCollection\", \"features\": [ { \"type\": \"Feature\", \"properties\": {}, \"geometry\": { \"type\": \"Polygon\", \"coordinates\": [ [ [ -119.07,45.58 ], [ -119.07,46.21 ], [ -117.39,46.21 ], [ -117.39,45.58 ], [ -119.07,45.58 ] ] ] } } ] }","volume":"32","issue":"6","noUsgsAuthors":false,"publicationDate":"2012-11-01","publicationStatus":"PW","scienceBaseUri":"53cd6240e4b0b290850fe112","contributors":{"authors":[{"text":"Bowerman, Tracy","contributorId":95796,"corporation":false,"usgs":true,"family":"Bowerman","given":"Tracy","email":"","affiliations":[],"preferred":false,"id":466366,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Budy, Phaedra","contributorId":24215,"corporation":false,"usgs":true,"family":"Budy","given":"Phaedra","affiliations":[{"id":609,"text":"Utah Cooperative Fish and Wildlife Research Unit","active":false,"usgs":true}],"preferred":false,"id":466365,"contributorType":{"id":1,"text":"Authors"},"rank":2}]}}
,{"id":70048307,"text":"70048307 - 2012 - Using geochemistry to identify the source of groundwater to Montezuma Well, a natural spring in Central Arizona, USA: Part 2","interactions":[],"lastModifiedDate":"2013-09-20T09:36:05","indexId":"70048307","displayToPublicDate":"2012-11-01T09:24:44","publicationYear":"2012","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":1534,"text":"Environmental Earth Sciences","active":true,"publicationSubtype":{"id":10}},"title":"Using geochemistry to identify the source of groundwater to Montezuma Well, a natural spring in Central Arizona, USA: Part 2","docAbstract":"Montezuma Well is a unique natural spring located in a sinkhole surrounded by travertine. Montezuma Well is managed by the National Park Service, and groundwater development in the area is a potential threat to the water source for Montezuma Well. This research was undertaken to better understand the sources of groundwater to Montezuma Well. Strontium isotopes (<sup>87</sup>Sr/<sup>86</sup>Sr) indicate that groundwater in the recharge area has flowed through surficial basalts with subsequent contact with the underlying Permian aged sandstones and the deeper, karstic, Mississippian Redwall Limestone. The distinctive geochemistry in Montezuma Well and nearby Soda Springs (higher concentrations of alkalinity, As, B, Cl, and Li) is coincident with added carbon dioxide and mantle-sourced He. The geochemistry and isotopic data from Montezuma Well and Soda Springs allow for the separation of groundwater samples into four categories: (1) upgradient, (2) deep groundwater with carbon dioxide, (3) shallow Verde Formation, and (4) mixing zone. δ<sup>18</sup>O and δD values, along with noble gas recharge elevation data, indicate that the higher elevation areas to the north and east of Montezuma Well are the groundwater recharge zones for Montezuma Well and most of the groundwater in this portion of the Verde Valley. Adjusted groundwater age dating using likely <sup>14</sup>C and δ<sup>13</sup>C sources indicate an age for Montezuma Well and Soda Springs groundwaters at 5,400–13,300 years, while shallow groundwater in the Verde Formation appears to be older (18,900). Based on water chemistry and isotopic evidence, groundwater flow to Montezuma Well is consistent with a hydrogeologic framework that indicates groundwater flow by (1) recharge in higher elevation basalts to the north and east of Montezuma Well, (2) movement through the upgradient Permian and Mississippian units, especially the Redwall Limestone, and (3) contact with a basalt dike/fracture system that provides a mechanism for groundwater to flow to the surface. While the exact nature of the groundwater flow connections is still uncertain, the available data indicate that flow to Montezuma Well may be more susceptible to future groundwater development in the Redwall Limestone than from any other geologic unit. Overall, the shallow groundwater in the surrounding Verde Formation appears to be largely disconnected from deeper groundwater flowing to Montezuma Well.","largerWorkType":{"id":2,"text":"Article"},"largerWorkTitle":"Environmental Earth Sciences","largerWorkSubtype":{"id":10,"text":"Journal Article"},"language":"English","publisher":"Springer","doi":"10.1007/s12665-012-1844-3","usgsCitation":"Johnson, R.H., DeWitt, E., Wirt, L., Manning, A.H., and Hunt, A.G., 2012, Using geochemistry to identify the source of groundwater to Montezuma Well, a natural spring in Central Arizona, USA: Part 2: Environmental Earth Sciences, v. 67, no. 6, p. 1837-1853, https://doi.org/10.1007/s12665-012-1844-3.","productDescription":"17 p.","startPage":"1837","endPage":"1853","ipdsId":"IP-027796","costCenters":[{"id":211,"text":"Crustal Geophysics and Geochemistry Science Center","active":true,"usgs":true}],"links":[{"id":277954,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"},{"id":277952,"type":{"id":10,"text":"Digital Object Identifier"},"url":"https://dx.doi.org/10.1007/s12665-012-1844-3"}],"country":"United States","state":"Arizona","otherGeospatial":"Montezuma Well","geographicExtents":"{ \"type\": \"FeatureCollection\", \"features\": [ { \"type\": \"Feature\", \"properties\": {}, \"geometry\": { \"type\": \"Polygon\", \"coordinates\": [ [ [ -111.83,34.5 ], [ -111.83,34.83 ], [ -111.45,34.83 ], [ -111.45,34.5 ], [ -111.83,34.5 ] ] ] } } ] }","volume":"67","issue":"6","noUsgsAuthors":false,"publicationDate":"2012-08-29","publicationStatus":"PW","scienceBaseUri":"523d6e6ae4b097188d6c771b","contributors":{"authors":[{"text":"Johnson, Raymond H. rhjohnso@usgs.gov","contributorId":707,"corporation":false,"usgs":true,"family":"Johnson","given":"Raymond","email":"rhjohnso@usgs.gov","middleInitial":"H.","affiliations":[],"preferred":true,"id":484278,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"DeWitt, Ed","contributorId":65081,"corporation":false,"usgs":true,"family":"DeWitt","given":"Ed","affiliations":[],"preferred":false,"id":484282,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Wirt, Laurie","contributorId":13204,"corporation":false,"usgs":true,"family":"Wirt","given":"Laurie","affiliations":[],"preferred":false,"id":484281,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Manning, Andrew H. 0000-0002-6404-1237 amanning@usgs.gov","orcid":"https://orcid.org/0000-0002-6404-1237","contributorId":1305,"corporation":false,"usgs":true,"family":"Manning","given":"Andrew","email":"amanning@usgs.gov","middleInitial":"H.","affiliations":[{"id":171,"text":"Central Mineral and Environmental Resources Science Center","active":true,"usgs":true}],"preferred":true,"id":484279,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Hunt, Andrew G. 0000-0002-3810-8610 ahunt@usgs.gov","orcid":"https://orcid.org/0000-0002-3810-8610","contributorId":1582,"corporation":false,"usgs":true,"family":"Hunt","given":"Andrew","email":"ahunt@usgs.gov","middleInitial":"G.","affiliations":[{"id":211,"text":"Crustal Geophysics and Geochemistry Science Center","active":true,"usgs":true}],"preferred":true,"id":484280,"contributorType":{"id":1,"text":"Authors"},"rank":5}]}}
,{"id":70040666,"text":"pp1789D - 2012 - Atmospheric inputs to watersheds of the Luquillo Mountains in eastern Puerto Rico: Chapter D in <i>Water quality and landscape processes of four watersheds in eastern Puerto Rico</i>","interactions":[],"lastModifiedDate":"2015-06-01T08:44:21","indexId":"pp1789D","displayToPublicDate":"2012-11-01T00:00:00","publicationYear":"2012","noYear":false,"publicationType":{"id":18,"text":"Report"},"publicationSubtype":{"id":5,"text":"USGS Numbered Series"},"seriesTitle":{"id":331,"text":"Professional Paper","code":"PP","onlineIssn":"2330-7102","printIssn":"1044-9612","active":true,"publicationSubtype":{"id":5}},"seriesNumber":"1789","chapter":"D","title":"Atmospheric inputs to watersheds of the Luquillo Mountains in eastern Puerto Rico: Chapter D in <i>Water quality and landscape processes of four watersheds in eastern Puerto Rico</i>","docAbstract":"Twenty years of precipitation-chemistry data from the National Atmospheric Deposition Program site at El Verde, Puerto Rico, demonstrate that three major sources control the composition of solutes in rain in eastern Puerto Rico. In order of importance, these sources are marine salts, temperate contamination from the Northern Hemisphere, and Sahara Desert dust. Marine salts are a source of roughly 82 percent of the ionic charge in precipitation; marine salt inputs are greatest in January. Evaluation of 15 years of U.S. Geological Survey data for four watersheds in eastern Puerto Rico suggests that large storms, including hurricanes, are associated with exceptionally high chloride concentrations in stream waters. Some of these storms were missed in sampling by the National Atmospheric Deposition Program, and therefore its data on the marine contribution likely underestimate chloride. The marine contribution is a weak source of acidity. Temperate contamination contributes about 10 percent of the ionic charge in precipitation; contaminants are primarily nitrate, ammonia, and sulfate derived from various manmade and natural sources. Peak deposition of temperate contaminants is during January, April, and May, months in which strong weather fronts arrive from the north. Temperate contamination, a strong source of acidity, is the only component that is increasing through time. Sahara Desert dust provides 5 percent of the ionic charge in precipitation; it is strongly seasonal, peaking in June and July during times of maximum dust transport from the Sahara and sub-Saharan regions. This dust contributes, on average, enough alkalinity to neutralize the acidity in June and July rains.","largerWorkType":{"id":18,"text":"Report"},"largerWorkTitle":"Water quality and landscape processes of four watersheds in eastern Puerto Rico (Professional Paper 1789)","largerWorkSubtype":{"id":5,"text":"USGS Numbered Series"},"language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/pp1789D","collaboration":"This report is Chapter D in <i>Water quality and landscape processes of four watersheds in eastern Puerto Rico</i>.  For more information, see: <a href=\"http://pubs.er.usgs.gov/publication/pp1789\" target=\"_blank\">Professional Paper 1789</a>.","usgsCitation":"Stallard, R.F., 2012, Atmospheric inputs to watersheds of the Luquillo Mountains in eastern Puerto Rico: Chapter D in <i>Water quality and landscape processes of four watersheds in eastern Puerto Rico</i>: U.S. Geological Survey Professional Paper 1789, 28 p., https://doi.org/10.3133/pp1789D.","productDescription":"28 p.","startPage":"85","endPage":"112","onlineOnly":"N","additionalOnlineFiles":"N","costCenters":[{"id":145,"text":"Branch of Regional Research-Central Region","active":false,"usgs":true}],"links":[{"id":263010,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/pp_1789_D.jpg"},{"id":263008,"type":{"id":15,"text":"Index Page"},"url":"https://pubs.usgs.gov/pp/1789/"},{"id":263009,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/pp/1789/pdfs/ChapterD.pdf"}],"country":"Puerto Rico","geographicExtents":"{ \"type\": \"FeatureCollection\", \"features\": [ { \"type\": \"Feature\", \"properties\": {}, \"geometry\": { \"type\": \"Polygon\", \"coordinates\": [ [ [ -67.9455,17.8814 ], [ -67.9455,18.516 ], [ -65.2211,18.516 ], [ -65.2211,17.8814 ], [ -67.9455,17.8814 ] ] ] } } ] }","noUsgsAuthors":false,"publicationStatus":"PW","scienceBaseUri":"50d85caae4b0064e695a14d6","contributors":{"editors":[{"text":"Murphy, Sheila F. 0000-0002-5481-3635 sfmurphy@usgs.gov","orcid":"https://orcid.org/0000-0002-5481-3635","contributorId":1854,"corporation":false,"usgs":true,"family":"Murphy","given":"Sheila","email":"sfmurphy@usgs.gov","middleInitial":"F.","affiliations":[{"id":37277,"text":"WMA - Earth System Processes Division","active":true,"usgs":true},{"id":5044,"text":"National Research Program - Central Branch","active":true,"usgs":true}],"preferred":true,"id":509096,"contributorType":{"id":2,"text":"Editors"},"rank":1},{"text":"Stallard, Robert F. 0000-0001-8209-7608 stallard@usgs.gov","orcid":"https://orcid.org/0000-0001-8209-7608","contributorId":1924,"corporation":false,"usgs":true,"family":"Stallard","given":"Robert","email":"stallard@usgs.gov","middleInitial":"F.","affiliations":[{"id":5044,"text":"National Research Program - Central Branch","active":true,"usgs":true}],"preferred":true,"id":509097,"contributorType":{"id":2,"text":"Editors"},"rank":2}],"authors":[{"text":"Stallard, Robert F. 0000-0001-8209-7608 stallard@usgs.gov","orcid":"https://orcid.org/0000-0001-8209-7608","contributorId":1924,"corporation":false,"usgs":true,"family":"Stallard","given":"Robert","email":"stallard@usgs.gov","middleInitial":"F.","affiliations":[{"id":5044,"text":"National Research Program - Central Branch","active":true,"usgs":true}],"preferred":true,"id":468743,"contributorType":{"id":1,"text":"Authors"},"rank":1}]}}
,{"id":70040658,"text":"pp1789F - 2012 - Landslides and sediment budgets in four watersheds in eastern Puerto Rico: Chapter F in <i>Water quality and landscape processes of four watersheds in eastern Puerto Rico</i>","interactions":[],"lastModifiedDate":"2013-02-01T14:18:05","indexId":"pp1789F","displayToPublicDate":"2012-11-01T00:00:00","publicationYear":"2012","noYear":false,"publicationType":{"id":18,"text":"Report"},"publicationSubtype":{"id":5,"text":"USGS Numbered Series"},"seriesTitle":{"id":331,"text":"Professional Paper","code":"PP","onlineIssn":"2330-7102","printIssn":"1044-9612","active":true,"publicationSubtype":{"id":5}},"seriesNumber":"1789","chapter":"F","title":"Landslides and sediment budgets in four watersheds in eastern Puerto Rico: Chapter F in <i>Water quality and landscape processes of four watersheds in eastern Puerto Rico</i>","docAbstract":"The low-latitude regions of the Earth are undergoing profound, rapid landscape change as forests are converted to agriculture to support growing population. Understanding the effects of these land-use changes requires analysis of watershed-scale geomorphic processes to better inform and manage this usually disorganized process. The investigation of hillslope erosion and the development of sediment budgets provides essential information for resource managers. Four small, montane, humid-tropical watersheds in the Luquillo Experimental Forest and nearby R&iacute;o Grande de Lo&iacute;za watershed, Puerto Rico (18&deg; 20' N., 65&deg; 45' W.), were selected to compare and contrast the geomorphic effects of land use and bedrock geology. Two of the watersheds are underlain largely by resistant Cretaceous volcaniclastic rocks but differ in land use and mean annual runoff: the Mameyes watershed, with predominantly primary forest cover and runoff of 2,750 millimeters per year, and the Can&oacute;vanas watershed, with mixed secondary forest and pasture and runoff of 970 millimeters per year. The additional two watersheds are underlain by relatively erodible granitic bedrock: the forested Icacos watershed, with runoff of 3,760 millimeters per year and the agriculturally developed Cayagu&aacute;s watershed, with a mean annual runoff of 1,620 millimeters per year. Annual sediment budgets were estimated for each watershed using landslide, slopewash, soil creep, treethrow, suspended sediment, and streamflow data. The budgets also included estimates of sediment storage in channel beds, bars, floodplains, and in colluvial deposits. In the two watersheds underlain by volcaniclastic rocks, the forested Mameyes and the developed Can&oacute;vanas watersheds, landslide frequency (0.21 and 0.04 landslides per square kilometer per year, respectively), slopewash (5 and 30 metric tons per square kilometer per year), and suspended sediment yield (325 and 424 metric tons per square kilometer per year), were lower than in the two watersheds underlain by granitic bedrock. In these granitic watersheds, landslide frequency, slopewash, and suspended sediment yield were 0.43 landslides per square kilometer per year, 20 metric tons per square kilometer per year, and 2,140 metric tons per square kilometer per year, respectively, in the forested Icacos watershed and 0.8 landslides per square kilometer per year, 105 metric tons per square kilometer per year, and 2,110 metric tons per square kilometer per year, respectively, in the agriculturally developed Cayagu&aacute;s watershed. Comparison of sediment budgets from the forested and developed watersheds indicates that human activities increase landslide frequency by as much as factor of 5 and slopewash by as much as a factor of 6. When the difference in annual runoff is considered, the effect of land use on suspended sediment yields is also notable. Sediment concentration, calculated as sediment yield normalized by runoff, was about 2.3 to 3.7 times as great in the two watersheds in secondary forest and pasture compared with sediment concentration in the watersheds in primary forest. Even in the two watersheds with primary forest cover, the Mameyes and Icacos, located in the Luquillo Experimental Forest, the effects of anthropogenic disturbance were marked: 43 to 63 percent of landslide-related erosion was associated with road construction and maintenance.","largerWorkType":{"id":18,"text":"Report"},"largerWorkTitle":"Water quality and landscape processes of four watersheds in eastern Puerto Rico (Professional Paper 1789)","largerWorkSubtype":{"id":1,"text":"Federal Government Series"},"language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/pp1789F","collaboration":"This report is Chapter F in <i>Water quality and landscape processes of four watersheds in eastern Puerto Rico</i>. For more information, see: <a href=\"http://pubs.er.usgs.gov/publication/pp1789\" target=\"_blank\">Professional Paper 1789</a>.","usgsCitation":"Larsen, M.C., 2012, Landslides and sediment budgets in four watersheds in eastern Puerto Rico: Chapter F in <i>Water quality and landscape processes of four watersheds in eastern Puerto Rico</i>: U.S. Geological Survey Professional Paper 1789, 26 p., https://doi.org/10.3133/pp1789F.","productDescription":"26 p.","startPage":"153","endPage":"178","costCenters":[{"id":145,"text":"Branch of Regional Research-Central Region","active":false,"usgs":true}],"links":[{"id":262998,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/pp_1789_F.jpg"},{"id":262997,"type":{"id":15,"text":"Index Page"},"url":"https://pubs.usgs.gov/pp/1789/"},{"id":262996,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/pp/1789/pdfs/ChapterF.pdf"}],"country":"Puerto Rico","geographicExtents":"{ \"type\": \"FeatureCollection\", \"features\": [ { \"type\": \"Feature\", \"properties\": {}, \"geometry\": { \"type\": \"Polygon\", \"coordinates\": [ [ [ -67.9455,17.8814 ], [ -67.9455,18.516 ], [ -65.2211,18.516 ], [ -65.2211,17.8814 ], [ -67.9455,17.8814 ] ] ] } } ] }","noUsgsAuthors":false,"publicationStatus":"PW","scienceBaseUri":"50df70b0e4b0dfbe79e6c651","contributors":{"editors":[{"text":"Murphy, Sheila F. 0000-0002-5481-3635 sfmurphy@usgs.gov","orcid":"https://orcid.org/0000-0002-5481-3635","contributorId":1854,"corporation":false,"usgs":true,"family":"Murphy","given":"Sheila","email":"sfmurphy@usgs.gov","middleInitial":"F.","affiliations":[{"id":37277,"text":"WMA - Earth System Processes Division","active":true,"usgs":true},{"id":5044,"text":"National Research Program - Central Branch","active":true,"usgs":true}],"preferred":true,"id":509092,"contributorType":{"id":2,"text":"Editors"},"rank":1},{"text":"Stallard, Robert F. 0000-0001-8209-7608 stallard@usgs.gov","orcid":"https://orcid.org/0000-0001-8209-7608","contributorId":1924,"corporation":false,"usgs":true,"family":"Stallard","given":"Robert","email":"stallard@usgs.gov","middleInitial":"F.","affiliations":[{"id":5044,"text":"National Research Program - Central Branch","active":true,"usgs":true}],"preferred":true,"id":509093,"contributorType":{"id":2,"text":"Editors"},"rank":2}],"authors":[{"text":"Larsen, Matthew C. mclarsen@usgs.gov","contributorId":1568,"corporation":false,"usgs":true,"family":"Larsen","given":"Matthew","email":"mclarsen@usgs.gov","middleInitial":"C.","affiliations":[],"preferred":true,"id":468739,"contributorType":{"id":1,"text":"Authors"},"rank":1}]}}
,{"id":70189182,"text":"70189182 - 2012 - Uncertainty quantification for environmental models","interactions":[],"lastModifiedDate":"2017-07-07T09:52:05","indexId":"70189182","displayToPublicDate":"2012-11-01T00:00:00","publicationYear":"2012","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":5454,"text":"SIAM News","active":true,"publicationSubtype":{"id":10}},"title":"Uncertainty quantification for environmental models","docAbstract":"Environmental models are used to evaluate the fate of fertilizers in agricultural settings (including soil denitrification), the degradation of hydrocarbons at spill sites, and water supply for people and ecosystems in small to large basins and cities—to mention but a few applications of these models. They also play a role in understanding and diagnosing potential environmental impacts of global climate change. The models are typically mildly to extremely nonlinear. The persistent demand for enhanced dynamics and resolution to improve model realism [17] means that lengthy individual model execution times will remain common, notwithstanding continued enhancements in computer power. In addition, high-dimensional parameter spaces are often defined, which increases the number of model runs required to quantify uncertainty [2]. Some environmental modeling projects have access to extensive funding and computational resources; many do not. The many recent studies of uncertainty quantification in environmental model predictions have focused on uncertainties related to data error and sparsity of data, expert judgment expressed mathematically through prior information, poorly known parameter values, and model structure (see, for example, [1,7,9,10,13,18]). Approaches for quantifying uncertainty include frequentist (potentially with prior information [7,9]), Bayesian [13,18,19], and likelihood-based. A few of the numerous methods, including some sensitivity and inverse methods with consequences for understanding and quantifying uncertainty, are as follows: Bayesian hierarchical modeling and Bayesian model averaging; single-objective optimization with error-based weighting [7] and multi-objective optimization [3]; methods based on local derivatives [2,7,10]; screening methods like OAT (one at a time) and the method of Morris [14]; FAST (Fourier amplitude sensitivity testing) [14]; the Sobol' method [14]; randomized maximum likelihood [10]; Markov chain Monte Carlo (MCMC) [10]. There are also bootstrapping and cross-validation approaches.Sometimes analyses are conducted using surrogate models [12]. The availability of so many options can be confusing. Categorizing methods based on fundamental questions assists in communicating the essential results of uncertainty analyses to stakeholders. Such questions can focus on model adequacy (e.g., How well does the model reproduce observed system characteristics and dynamics?) and sensitivity analysis (e.g., What parameters can be estimated with available data? What observations are important to parameters and predictions? What parameters are important to predictions?), as well as on the uncertainty quantification (e.g., How accurate and precise are the predictions?). The methods can also be classified by the number of model runs required: few (10s to 1000s) or many (10,000s to 1,000,000s). Of the methods listed above, the most computationally frugal are generally those based on local derivatives; MCMC methods tend to be among the most computationally demanding. Surrogate models (emulators)do not necessarily produce computational frugality because many runs of the full model are generally needed to create a meaningful surrogate model. With this categorization, we can, in general, address all the fundamental questions mentioned above using either computationally frugal or demanding methods. Model development and analysis can thus be conducted consistently using either computation-ally frugal or demanding methods; alternatively, different fundamental questions can be addressed using methods that require different levels of effort. Based on this perspective, we pose the question: Can computationally frugal methods be useful companions to computationally demanding meth-ods? The reliability of computationally frugal methods generally depends on the model being reasonably linear, which usually means smooth nonlin-earities and the assumption of Gaussian errors; both tend to be more valid with more linear","language":"English","publisher":"Society for Industrial and Applied Mathematics","usgsCitation":"Hill, M.C., Lu, D., Kavetski, D., Clark, M.P., and Ye, M., 2012, Uncertainty quantification for environmental models: SIAM News, v. 45, no. 9, HTML Document.","productDescription":"HTML Document","ipdsId":"IP-041596","costCenters":[{"id":5044,"text":"National Research Program - Central Branch","active":true,"usgs":true}],"links":[{"id":343434,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"},{"id":343432,"rank":1,"type":{"id":15,"text":"Index Page"},"url":"https://sinews.siam.org/Current-Issue/Issue-Archives/Issue-Archives-ListView/PID/2282/mcat/2279/evl/0/TagID/206?TagName=Volume-45-|-Number-9-|-November-2012"}],"volume":"45","issue":"9","publishingServiceCenter":{"id":2,"text":"Denver PSC"},"noUsgsAuthors":false,"publicationStatus":"PW","scienceBaseUri":"595f4c45e4b0d1f9f057e372","contributors":{"authors":[{"text":"Hill, Mary C. mchill@usgs.gov","contributorId":974,"corporation":false,"usgs":true,"family":"Hill","given":"Mary","email":"mchill@usgs.gov","middleInitial":"C.","affiliations":[{"id":5044,"text":"National Research Program - Central Branch","active":true,"usgs":true}],"preferred":true,"id":703388,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Lu, Dan","contributorId":58176,"corporation":false,"usgs":true,"family":"Lu","given":"Dan","affiliations":[],"preferred":false,"id":703762,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"Kavetski, Dmitri","contributorId":194182,"corporation":false,"usgs":false,"family":"Kavetski","given":"Dmitri","email":"","affiliations":[],"preferred":false,"id":703389,"contributorType":{"id":1,"text":"Authors"},"rank":11},{"text":"Clark, Martyn P.","contributorId":194183,"corporation":false,"usgs":false,"family":"Clark","given":"Martyn","email":"","middleInitial":"P.","affiliations":[],"preferred":false,"id":703390,"contributorType":{"id":1,"text":"Authors"},"rank":12},{"text":"Ye, Ming","contributorId":194184,"corporation":false,"usgs":false,"family":"Ye","given":"Ming","email":"","affiliations":[],"preferred":false,"id":703391,"contributorType":{"id":1,"text":"Authors"},"rank":13}]}}
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