A general lack of consensus about the origin of Himalayan gneiss domes hinders accurate thermomechanical modeling of the orogen. To test whether doming resulted from tectonic contraction (e.g., thrust duplex formation, antiformal bending above a thrust ramp, etc.), channel flow, or via the buoyant rise of anatectic melts, this study investigates the depth and timing of doming processes for Gianbul dome in the western Himalaya. The dome is composed of Greater Himalayan Sequence migmatite, Paleozoic orthogneiss, and metasedimentary rock cut by multiple generations of leucogranite dikes. These rocks record a major penetrative D2 deformational event characterized by a domed foliation and associated NE-SW–trending stretching lineation, and they are flanked by the top-down-to-the-SW (normal-sense) Khanjar shear zone and the top-down-to-the-NE (normal sense) Zanskar shear zone (the western equivalent of the South Tibetan detachment system). Monazite U/Th-Pb geochronology records (1) Paleozoic emplacement of the Kade orthogneiss and associated granite dikes; (2) prograde Barrovian metamorphism from 37 to 33 Ma; (3) doming driven by upper-crustal extension and positive buoyancy of decompression melts between 26 and 22 Ma; and (4) the injection of anatectic melts into the upper levels of the dome—neutralizing the effects of melt buoyancy and potentially adding strength to the host rock—by ca. 22.6 Ma on the southwestern flank and ca. 21 Ma on the northeastern flank. As shown by a northeastward decrease in 40Ar/39Ar muscovite dates from 22.4 to 20.2 Ma, ductile normal-sense displacement within the Zanskar shear zone ended by ca. 22 Ma, after which the Gianbul dome was exhumed as part of a rigid footwall block below the brittle Zanskar normal fault, tilting an estimated 5°–10°SW into its present orientation.
","language":"English","publisher":"Geological Society of America","doi":"10.1130/B31005.1","usgsCitation":"Horton, F., Lee, J., Hacker, B., Bowman-Kamaha’o, M., and Cosca, M.A., 2015, Himalayan gneiss dome formation in the middle crust and exhumation by normal faulting: New geochronology of Gianbul dome, northwestern India: Geological Society of America Bulletin, v. 127, no. 1-2, p. 162-180, https://doi.org/10.1130/B31005.1.","productDescription":"19 p.","startPage":"162","endPage":"180","ipdsId":"IP-056131","costCenters":[{"id":171,"text":"Central Mineral and Environmental Resources Science Center","active":true,"usgs":true}],"links":[{"id":342852,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"India","otherGeospatial":"Gianbul dome","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n 74,\n 30.25\n ],\n [\n 79,\n 30.25\n ],\n [\n 79,\n 36.33333\n ],\n [\n 74,\n 36.33333\n ],\n [\n 74,\n 30.25\n ]\n ]\n ]\n }\n }\n ]\n}","volume":"127","issue":"1-2","publishingServiceCenter":{"id":2,"text":"Denver PSC"},"noUsgsAuthors":false,"publicationDate":"2014-07-16","publicationStatus":"PW","scienceBaseUri":"59521d21e4b062508e3c368d","contributors":{"authors":[{"text":"Horton, Forrest","contributorId":193436,"corporation":false,"usgs":false,"family":"Horton","given":"Forrest","email":"","affiliations":[],"preferred":false,"id":700468,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Lee, Jeffrey","contributorId":193437,"corporation":false,"usgs":false,"family":"Lee","given":"Jeffrey","email":"","affiliations":[],"preferred":false,"id":700469,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Hacker, Bradley","contributorId":193438,"corporation":false,"usgs":false,"family":"Hacker","given":"Bradley","affiliations":[],"preferred":false,"id":700470,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Bowman-Kamaha’o, Meilani","contributorId":193439,"corporation":false,"usgs":false,"family":"Bowman-Kamaha’o","given":"Meilani","email":"","affiliations":[],"preferred":false,"id":700471,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Cosca, Michael A. 0000-0002-0600-7663 mcosca@usgs.gov","orcid":"https://orcid.org/0000-0002-0600-7663","contributorId":1000,"corporation":false,"usgs":true,"family":"Cosca","given":"Michael","email":"mcosca@usgs.gov","middleInitial":"A.","affiliations":[{"id":35995,"text":"Geology, Geophysics, and Geochemistry Science Center","active":true,"usgs":true},{"id":171,"text":"Central Mineral and Environmental Resources Science Center","active":true,"usgs":true}],"preferred":true,"id":700472,"contributorType":{"id":1,"text":"Authors"},"rank":5}]}} ,{"id":70189945,"text":"70189945 - 2015 - Hydrogeochemistry and microbiology of mine drainage: An update","interactions":[],"lastModifiedDate":"2017-11-08T19:26:47","indexId":"70189945","displayToPublicDate":"2015-07-15T00:00:00","publicationYear":"2015","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":835,"text":"Applied Geochemistry","active":true,"publicationSubtype":{"id":10}},"title":"Hydrogeochemistry and microbiology of mine drainage: An update","docAbstract":"The extraction of mineral resources requires access through underground workings, or open pit operations, or through drillholes for solution mining. Additionally, mineral processing can generate large quantities of waste, including mill tailings, waste rock and refinery wastes, heap leach pads, and slag. Thus, through mining and mineral processing activities, large surface areas of sulfide minerals can be exposed to oxygen, water, and microbes, resulting in accelerated oxidation of sulfide and other minerals and the potential for the generation of low-quality drainage. The oxidation of sulfide minerals in mine wastes is accelerated by microbial catalysis of the oxidation of aqueous ferrous iron and sulfide. These reactions, particularly when combined with evaporation, can lead to extremely acidic drainage and very high concentrations of dissolved constituents. Although acid mine drainage is the most prevalent and damaging environmental concern associated with mining activities, generation of saline, basic and neutral drainage containing elevated concentrations of dissolved metals, non-metals, and metalloids has recently been recognized as a potential environmental concern. Acid neutralization reactions through the dissolution of carbonate, hydroxide, and silicate minerals and formation of secondary aluminum and ferric hydroxide phases can moderate the effects of acid generation and enhance the formation of secondary hydrated iron and aluminum minerals which may lessen the concentration of dissolved metals. Numerical models provide powerful tools for assessing impacts of these reactions on water quality.
","language":"English","publisher":"Elsevier","doi":"10.1016/j.apgeochem.2015.02.008","usgsCitation":"Nordstrom, D.K., Blowes, D., and Ptacek, C., 2015, Hydrogeochemistry and microbiology of mine drainage: An update: Applied Geochemistry, v. 57, p. 3-16, https://doi.org/10.1016/j.apgeochem.2015.02.008.","productDescription":"14 p.","startPage":"3","endPage":"16","ipdsId":"IP-063646","costCenters":[{"id":5044,"text":"National Research Program - Central Branch","active":true,"usgs":true}],"links":[{"id":344454,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"volume":"57","publishingServiceCenter":{"id":2,"text":"Denver PSC"},"noUsgsAuthors":false,"publicationStatus":"PW","scienceBaseUri":"5980419ae4b0a38ca278933e","contributors":{"authors":[{"text":"Nordstrom, D. Kirk 0000-0003-3283-5136 dkn@usgs.gov","orcid":"https://orcid.org/0000-0003-3283-5136","contributorId":749,"corporation":false,"usgs":true,"family":"Nordstrom","given":"D.","email":"dkn@usgs.gov","middleInitial":"Kirk","affiliations":[{"id":5044,"text":"National Research Program - Central Branch","active":true,"usgs":true},{"id":37277,"text":"WMA - Earth System Processes Division","active":true,"usgs":true}],"preferred":false,"id":706844,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Blowes, D.W","contributorId":195353,"corporation":false,"usgs":false,"family":"Blowes","given":"D.W","affiliations":[],"preferred":false,"id":706845,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Ptacek, C.J.","contributorId":195354,"corporation":false,"usgs":false,"family":"Ptacek","given":"C.J.","email":"","affiliations":[],"preferred":false,"id":706846,"contributorType":{"id":1,"text":"Authors"},"rank":3}]}} ,{"id":70190125,"text":"70190125 - 2015 - Coevolution of bed surface patchiness and channel morphology: 2. Numerical experiments","interactions":[],"lastModifiedDate":"2017-08-12T08:25:10","indexId":"70190125","displayToPublicDate":"2015-07-15T00:00:00","publicationYear":"2015","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":2318,"text":"Journal of Geophysical Research F: Earth Surface","active":true,"publicationSubtype":{"id":10}},"title":"Coevolution of bed surface patchiness and channel morphology: 2. Numerical experiments","docAbstract":"In gravel bed rivers, bed topography and the bed surface grain size distribution evolve simultaneously, but it is not clear how feedbacks between topography and grain sorting affect channel morphology. In this, the second of a pair of papers examining interactions between bed topography and bed surface sorting in gravel bed rivers, we use a two-dimensional morphodynamic model to perform numerical experiments designed to explore the coevolution of both free and forced bars and bed surface patches. Model runs were carried out on a computational grid simulating a 200 m long, 2.75 m wide, straight, rectangular channel, with an initially flat bed at a slope of 0.0137. Over five numerical experiments, we varied (a) whether an obstruction was present, (b) whether the sediment was a gravel mixture or a single size, and (c) whether the bed surface grain size feeds back on the hydraulic roughness field. Experiments with channel obstructions developed a train of alternate bars that became stationary and were connected to the obstruction. Freely migrating alternate bars formed in the experiments without channel obstructions. Simulations incorporating roughness feedbacks between the bed surface and flow field produced flatter, broader, and longer bars than simulations using constant roughness or uniform sediment. Our findings suggest that patches are not simply a by-product of bed topography, but they interact with the evolving bed and influence morphologic evolution.
","language":"English","publisher":"American Geophysical Union","doi":"10.1002/2014JF003429","usgsCitation":"Nelson, P.A., McDonald, R.R., Nelson, J.M., and Dietrich, W.E., 2015, Coevolution of bed surface patchiness and channel morphology: 2. Numerical experiments: Journal of Geophysical Research F: Earth Surface, v. 120, no. 9, p. 1708-1723, https://doi.org/10.1002/2014JF003429.","productDescription":"16 p.","startPage":"1708","endPage":"1723","ipdsId":"IP-065144","costCenters":[{"id":5044,"text":"National Research Program - Central Branch","active":true,"usgs":true}],"links":[{"id":471941,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.1002/2014jf003429","text":"Publisher Index Page"},{"id":344777,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"volume":"120","issue":"9","publishingServiceCenter":{"id":2,"text":"Denver PSC"},"noUsgsAuthors":false,"publicationDate":"2015-09-07","publicationStatus":"PW","scienceBaseUri":"59901399e4b09fa1cb17892d","contributors":{"authors":[{"text":"Nelson, Peter A.","contributorId":195598,"corporation":false,"usgs":false,"family":"Nelson","given":"Peter","email":"","middleInitial":"A.","affiliations":[],"preferred":false,"id":707583,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"McDonald, Richard R. 0000-0002-0703-0638 rmcd@usgs.gov","orcid":"https://orcid.org/0000-0002-0703-0638","contributorId":2428,"corporation":false,"usgs":true,"family":"McDonald","given":"Richard","email":"rmcd@usgs.gov","middleInitial":"R.","affiliations":[{"id":37778,"text":"WMA - Integrated Modeling and Prediction Division","active":true,"usgs":true},{"id":5044,"text":"National Research Program - Central Branch","active":true,"usgs":true}],"preferred":true,"id":707582,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Nelson, Jonathan M. 0000-0002-7632-8526 jmn@usgs.gov","orcid":"https://orcid.org/0000-0002-7632-8526","contributorId":2812,"corporation":false,"usgs":true,"family":"Nelson","given":"Jonathan","email":"jmn@usgs.gov","middleInitial":"M.","affiliations":[{"id":5044,"text":"National Research Program - Central Branch","active":true,"usgs":true},{"id":37778,"text":"WMA - Integrated Modeling and Prediction Division","active":true,"usgs":true}],"preferred":true,"id":707584,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Dietrich, William E.","contributorId":195599,"corporation":false,"usgs":false,"family":"Dietrich","given":"William","email":"","middleInitial":"E.","affiliations":[],"preferred":false,"id":707585,"contributorType":{"id":1,"text":"Authors"},"rank":4}]}} ,{"id":70186945,"text":"70186945 - 2015 - Volcano warning systems: Chapter 67","interactions":[],"lastModifiedDate":"2017-11-03T18:30:31","indexId":"70186945","displayToPublicDate":"2015-07-15T00:00:00","publicationYear":"2015","noYear":false,"publicationType":{"id":5,"text":"Book chapter"},"publicationSubtype":{"id":24,"text":"Book Chapter"},"title":"Volcano warning systems: Chapter 67","docAbstract":"Messages conveying volcano alert level such as Watches and Warnings are designed to provide people with risk information before, during, and after eruptions. Information is communicated to people from volcano observatories and emergency management agencies and from informal sources and social and environmental cues. Any individual or agency can be both a message sender and a recipient and multiple messages received from multiple sources is the norm in a volcanic crisis. Significant challenges to developing effective warning systems for volcanic hazards stem from the great diversity in unrest, eruption, and post-eruption processes and the rapidly advancing digital technologies that people use to seek real-time risk information. Challenges also involve the need to invest resources before unrest to help people develop shared mental models of important risk factors. Two populations of people are the target of volcano notifications–ground- and aviation-based populations, and volcano warning systems must address both distinctly different populations.
","largerWorkType":{"id":4,"text":"Book"},"largerWorkTitle":"Encyclopedia of Volcanoes, 2nd Edition","largerWorkSubtype":{"id":15,"text":"Monograph"},"language":"English","publisher":"Academic Press","publisherLocation":"Boston, MA","doi":"10.1016/B978-0-12-385938-9.00067-5","usgsCitation":"Gregg, C., Houghton, B.F., and Ewert, J.W., 2015, Volcano warning systems: Chapter 67, chap. of Encyclopedia of Volcanoes, 2nd Edition, p. 1173-1185, https://doi.org/10.1016/B978-0-12-385938-9.00067-5.","productDescription":"13 p.","startPage":"1173","endPage":"1185","ipdsId":"IP-060628","costCenters":[{"id":617,"text":"Volcano Science Center","active":true,"usgs":true}],"links":[{"id":339779,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"publishingServiceCenter":{"id":14,"text":"Menlo Park PSC"},"noUsgsAuthors":false,"publicationStatus":"PW","scienceBaseUri":"58f5d441e4b0f2e20545e41b","contributors":{"authors":[{"text":"Gregg, Chris E.","contributorId":40397,"corporation":false,"usgs":true,"family":"Gregg","given":"Chris E.","affiliations":[],"preferred":false,"id":691105,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Houghton, Bruce F. 0000-0002-7532-9770","orcid":"https://orcid.org/0000-0002-7532-9770","contributorId":140077,"corporation":false,"usgs":false,"family":"Houghton","given":"Bruce","email":"","middleInitial":"F.","affiliations":[{"id":13351,"text":"University of Hawaii Cooperative Studies Unit","active":true,"usgs":false},{"id":6977,"text":"University of Hawai`i at Hilo","active":true,"usgs":false}],"preferred":false,"id":691106,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Ewert, John W. 0000-0003-2819-4057 jwewert@usgs.gov","orcid":"https://orcid.org/0000-0003-2819-4057","contributorId":642,"corporation":false,"usgs":true,"family":"Ewert","given":"John","email":"jwewert@usgs.gov","middleInitial":"W.","affiliations":[{"id":617,"text":"Volcano Science Center","active":true,"usgs":true}],"preferred":true,"id":691104,"contributorType":{"id":1,"text":"Authors"},"rank":3}]}} ,{"id":70155525,"text":"70155525 - 2015 - Modeled changes in extreme wave climates of the tropical Pacific over the 21st century: Implications for U.S. and U.S.-Affiliated atoll islands","interactions":[],"lastModifiedDate":"2019-12-11T09:30:39","indexId":"70155525","displayToPublicDate":"2015-07-15T00:00:00","publicationYear":"2015","noYear":false,"publicationType":{"id":24,"text":"Conference Paper"},"publicationSubtype":{"id":19,"text":"Conference Paper"},"title":"Modeled changes in extreme wave climates of the tropical Pacific over the 21st century: Implications for U.S. and U.S.-Affiliated atoll islands","docAbstract":"Wave heights, periods, and directions were forecast for 2081–2100 using output from four coupled atmosphere–ocean global climate models for representative concentration pathway scenarios RCP4.5 and RCP8.5. Global climate model wind fields were used to drive the global WAVEWATCH-III wave model to generate hourly time-series of bulk wave parameters for 25 islands in the mid to western tropical Pacific. December–February 95th percentile extreme significant wave heights under both climate scenarios decreased by 2100 compared to 1976–2010 historical values. Trends under both scenarios were similar, with the higher-emission RCP8.5 scenario displaying a greater decrease in extreme significant wave heights than where emissions are reduced in the RCP4.5 scenario. Central equatorial Pacific Islands displayed the greatest departure from historical values; significant wave heights decreased there by as much as 0.32 m during December–February and associated wave directions rotated approximately 30° clockwise during June–August compared to hindcast data.
","conferenceTitle":"Coastal Sediments","conferenceDate":"May 11-15, 2015","conferenceLocation":"San Diego, CA","language":"English","usgsCitation":"Shope, J., Storlazzi, C.D., Erikson, L., and Hegermiller, C., 2015, Modeled changes in extreme wave climates of the tropical Pacific over the 21st century: Implications for U.S. and U.S.-Affiliated atoll islands, Coastal Sediments, San Diego, CA, May 11-15, 2015, 13 p.","productDescription":"13 p.","ipdsId":"IP-063074","costCenters":[{"id":520,"text":"Pacific Coastal and Marine Science Center","active":true,"usgs":true}],"links":[{"id":341825,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","otherGeospatial":"Pacific atoll islands","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n 114.60937499999999,\n 2.1088986592431382\n ],\n [\n 154.68749999999997,\n 2.1088986592431382\n ],\n [\n 154.68749999999997,\n 40.713955826286046\n ],\n [\n 114.60937499999999,\n 40.713955826286046\n ],\n [\n 114.60937499999999,\n 2.1088986592431382\n ]\n ]\n ]\n }\n }\n ]\n}","publishingServiceCenter":{"id":14,"text":"Menlo Park PSC"},"noUsgsAuthors":false,"publicationStatus":"PW","scienceBaseUri":"592e84bbe4b092b266f10d45","contributors":{"authors":[{"text":"Shope, J.B.","contributorId":145942,"corporation":false,"usgs":false,"family":"Shope","given":"J.B.","email":"","affiliations":[{"id":10653,"text":"University of California at Santa Cruz, Earth and Planetary Science Department","active":true,"usgs":false}],"preferred":false,"id":565716,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Storlazzi, Curt D. 0000-0001-8057-4490 cstorlazzi@usgs.gov","orcid":"https://orcid.org/0000-0001-8057-4490","contributorId":140584,"corporation":false,"usgs":true,"family":"Storlazzi","given":"Curt","email":"cstorlazzi@usgs.gov","middleInitial":"D.","affiliations":[{"id":520,"text":"Pacific Coastal and Marine Science Center","active":true,"usgs":true},{"id":186,"text":"Coastal and Marine Geology Program","active":true,"usgs":true}],"preferred":true,"id":565715,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Erikson, Li H. lerikson@usgs.gov","contributorId":145944,"corporation":false,"usgs":true,"family":"Erikson","given":"Li H.","email":"lerikson@usgs.gov","affiliations":[{"id":520,"text":"Pacific Coastal and Marine Science Center","active":true,"usgs":true}],"preferred":false,"id":565718,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Hegermiller, C.A.","contributorId":145943,"corporation":false,"usgs":false,"family":"Hegermiller","given":"C.A.","email":"","affiliations":[{"id":10653,"text":"University of California at Santa Cruz, Earth and Planetary Science Department","active":true,"usgs":false}],"preferred":false,"id":565717,"contributorType":{"id":1,"text":"Authors"},"rank":4}]}} ,{"id":70148646,"text":"ds944 - 2015 - Annual and average estimates of water-budget components based on hydrograph separation and PRISM precipitation for gaged basins in the Appalachian Plateaus Region, 1900-2011","interactions":[],"lastModifiedDate":"2015-07-15T09:26:03","indexId":"ds944","displayToPublicDate":"2015-07-14T17:45:00","publicationYear":"2015","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":"944","title":"Annual and average estimates of water-budget components based on hydrograph separation and PRISM precipitation for gaged basins in the Appalachian Plateaus Region, 1900-2011","docAbstract":"As part of the U.S. Geological Survey’s Groundwater Resources Program study of the Appalachian Plateaus aquifers, annual and average estimates of water-budget components based on hydrograph separation and precipitation data from parameter-elevation regressions on independent slopes model (PRISM) were determined at 849 continuous-record streamflow-gaging stations from Mississippi to New York and covered the period of 1900 to 2011. Only complete calendar years (January to December) of streamflow record at each gage were used to determine estimates of base flow, which is that part of streamflow attributed to groundwater discharge; such estimates can serve as a proxy for annual recharge. For each year, estimates of annual base flow, runoff, and base-flow index were determined using computer programs—PART, HYSEP, and BFI—that have automated the separation procedures. These streamflow-hydrograph analysis methods are provided with version 1.0 of the U.S. Geological Survey Groundwater Toolbox, which is a new program that provides graphing, mapping, and analysis capabilities in a Windows environment. Annual values of precipitation were estimated by calculating the average of cell values intercepted by basin boundaries where previously defined in the GAGES–II dataset. Estimates of annual evapotranspiration were then calculated from the difference between precipitation and streamflow.
","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/ds944","collaboration":"Groundwater Resources Program","usgsCitation":"Nelms, D.L., Messinger, Terence, and McCoy, K.J., 2015, Annual and average estimates of water-budget components based on hydrograph separation and PRISM precipitation for gaged basins in the Appalachian Plateaus Region, 1900–2011: U.S. Geological Survey Data Series 944, 10 p., https://dx.doi.org/10.3133/ds944.","productDescription":"Report: iv, 10 p.; 3 Appendices; Database; Metadata","numberOfPages":"18","onlineOnly":"Y","additionalOnlineFiles":"Y","ipdsId":"IP-060622","costCenters":[{"id":614,"text":"Virginia Water Science 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The primary goal of this research project was to evaluate the response of Moapa dace (Moapa coriacea) to the potential effects of changes in the amount of available habitat due to human influences such as ground water pumping, barriers to movement, and extirpation of Moapa dace from the mainstem Muddy River. To understand how these factors affect Moapa dace populations and to provide a tool to guide recovery actions, we developed a stochastic model to simulate Moapa dace population dynamics. Specifically, we developed an individual based model (IBM) to incorporate the critical components that drive Moapa dace population dynamics. Our model is composed of several interlinked submodels that describe changes in Moapa dace habitat as translated into carrying capacity, the influence of carrying capacity on demographic rates of dace, and the consequent effect on equilibrium population sizes. The model is spatially explicit and represents the stream network as eight discrete stream segments. The model operates at a monthly time step to incorporate seasonally varying reproduction. Growth rates of individuals vary among stream segments, with growth rates increasing along a headwater to mainstem gradient. Movement and survival of individuals are driven by density-dependent relationships that are influenced by the carrying capacity of each stream segment.
\nFirst, we calibrated the model to a historical time series of Moapa dace abundance estimates. The goal of the calibration was to estimate unknown parameters such as larval survival, carrying capacity of the tributary streams harboring the population of Moapa dace upstream of the gabion barrier, and carrying capacity of the mainstem Muddy River and tributaries. Based on historical abundance estimates, we found that the carrying capacity of the mainstem Muddy River was nearly twice the capacity of the tributary streams where Moapa dace have resided for the past 20 years.
\nGiven the calibrated model, we then conducted simulations to assess (1) the effect of altering migration barriers that restrict upstream and downstream movement of dace, and (2) the effect of changes in carrying capacity on equilibrium population sizes. We found that barriers to upstream movement led to extinction of subpopulations upstream of the barriers when initial population sizes were small. The probability of one or more subpopulations going extinct over a 50-year time horizon was >0.80 at initial population sizes of 10 non-larval and 70 larval dace, and was >0.40 at initial population sizes of 50 non-larval and 350 larval dace. The probability of a subpopulation going extinct decreased to zero when the initial population size exceeded 100 non-larval dace. Removal of upstream migration barriers eliminated extinctions of subpopulations, even at low initial population sizes. Compensatory mechanisms such as density-dependent survival and movement acted to buffer against local extinctions because stream segments could be quickly repopulated by dispersal when fish could access all stream segments.
\nProviding access to the mainstem Muddy River through removal of a gabion barrier that restricted upstream and downstream movement increased total population size from about 875 to 3,000 individuals. Additionally, because of higher growth rates of individuals in the mainstem Muddy River, the size structure of the population shifted towards larger individuals with higher fecundity, thereby increasing reproductive capacity of the population.
\nIncreasing or decreasing the total carrying capacity of all stream segments resulted in changes in equilibrium population size that were directly proportional to the change in capacity. However, changes in carrying capacity to some stream segments but not others could result in disproportionate changes in equilibrium population sizes by altering density-dependent movement and survival in the stream network. These simulations show how our IBM can provide a useful management tool for understanding the effect of restoration actions or reintroductions on carrying capacity, and, in turn, how these changes affect Moapa dace abundance. Such tools are critical for devising management strategies to achieve recovery goals.
","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/ofr20151126","collaboration":"Prepared in cooperation with the U.S. Fish and Wildlife Service","usgsCitation":"Perry, R.W., Jones, E.C., and Scoppettone, G.G., 2015, A stochastic population model to evaluate Moapa dace (Moapa coriacea) population growth under alternative management scenarios: U.S. Geological Survey Open-File Report 2015-1126, 46 p., https://dx.doi.org/10.3133/ofr20151126.","productDescription":"iv, 46 p.","numberOfPages":"50","onlineOnly":"Y","additionalOnlineFiles":"N","ipdsId":"IP-062968","costCenters":[{"id":654,"text":"Western Fisheries Research Center","active":true,"usgs":true}],"links":[{"id":305694,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/of/2015/1126/coverthb.jpg"},{"id":305695,"rank":2,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/of/2015/1126/ofr20151126.pdf","text":"Report","size":"2.7 MB","linkFileType":{"id":1,"text":"pdf"},"description":"OFR 2015-1126 Report"}],"country":"United States","state":"Nevada","otherGeospatial":"Muddy River System","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -114.52423095703124,\n 36.44448503928196\n ],\n [\n -114.52423095703124,\n 36.65850456897558\n ],\n [\n -114.31686401367188,\n 36.65850456897558\n ],\n [\n -114.31686401367188,\n 36.44448503928196\n ],\n [\n -114.52423095703124,\n 36.44448503928196\n ]\n ]\n ]\n }\n }\n ]\n}","contact":"Director, Western Fisheries Research Center
U.S. Geological Survey
6505 NE 65th Street
Seattle, Washington 98115
http://wfrc.usgs.gov/
Dust deposition onto mountain snow cover in the Upper Colorado River Basin frequently occurs in the spring when wind speeds and dust emission peaks on the nearby Colorado Plateau. Dust loading has increased since the intensive settlement in the western USA in the mid 1880s. The effects of dust-on-snow have been well studied at Senator Beck Basin Study Area (SBBSA) in the San Juan Mountains, CO, the first high-altitude area of contact for predominantly southwesterly winds transporting dust from the southern Colorado Plateau. To capture variability in dust transport from the broader Colorado Plateau and dust deposition across a larger area of the Colorado River water sources, an additional study plot was established in 2009 on Grand Mesa, 150 km to the north of SBBSA in west central, CO. Here, we compare the 4-year (2010–2013) dust source, deposition, and radiative forcing records at Grand Mesa Study Plot (GMSP) and Swamp Angel Study Plot (SASP), SBBSA's subalpine study plot. The study plots have similar site elevations/environments and differ mainly in the amount of dust deposited and ensuing impacts. At SASP, end of year dust concentrations ranged from 0.83 mg g−1 to 4.80 mg g−1, and daily mean spring dust radiative forcing ranged from 50–65 W m−2, advancing melt by 24–49 days. At GMSP, which received 1.0 mg g−1 less dust per season on average, spring radiative forcings of 32–50 W m−2 advanced melt by 15–30 days. Remote sensing imagery showed that observed dust events were frequently associated with dust emission from the southern Colorado Plateau. Dust from these sources generally passed south of GMSP, and back trajectory footprints modelled for observed dust events were commonly more westerly and northerly for GMSP relative to SASP. These factors suggest that although the southern Colorado Plateau contains important dust sources, dust contributions from other dust sources contribute to dust loading in this region, and likely account for the majority of dust loading at GMSP.
","language":"English","publisher":"John Wiley & Sons","publisherLocation":"Chichester, Sussex, England","doi":"10.1002/hyp.10569","usgsCitation":"Skiles, S.M., Painter, T.H., Belnap, J., Holland, L., Reynolds, R.L., Goldstein, H.L., and Lin, J., 2015, Regional variability in dust-on-snow processes and impacts in the Upper Colorado River Basin: Hydrological Processes, v. 29, no. 26, p. 5397-5413, https://doi.org/10.1002/hyp.10569.","productDescription":"27 p.","startPage":"5397","endPage":"5413","onlineOnly":"N","additionalOnlineFiles":"N","ipdsId":"IP-066323","costCenters":[{"id":568,"text":"Southwest Biological Science Center","active":true,"usgs":true}],"links":[{"id":308422,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"volume":"29","issue":"26","publishingServiceCenter":{"id":14,"text":"Menlo Park PSC"},"noUsgsAuthors":false,"publicationDate":"2015-07-14","publicationStatus":"PW","scienceBaseUri":"5603cd58e4b03bc34f544b37","contributors":{"authors":[{"text":"Skiles, S. McKenzie","contributorId":147878,"corporation":false,"usgs":false,"family":"Skiles","given":"S.","email":"","middleInitial":"McKenzie","affiliations":[{"id":16952,"text":"University of California- Los Angeles, Joint Intitute for Regional Earth System Science and Engineering","active":true,"usgs":false}],"preferred":false,"id":573098,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Painter, Thomas H.","contributorId":12378,"corporation":false,"usgs":true,"family":"Painter","given":"Thomas","email":"","middleInitial":"H.","affiliations":[],"preferred":false,"id":573099,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Belnap, Jayne 0000-0001-7471-2279 jayne_belnap@usgs.gov","orcid":"https://orcid.org/0000-0001-7471-2279","contributorId":1332,"corporation":false,"usgs":true,"family":"Belnap","given":"Jayne","email":"jayne_belnap@usgs.gov","affiliations":[{"id":568,"text":"Southwest Biological Science Center","active":true,"usgs":true}],"preferred":true,"id":573097,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Holland, Lacey","contributorId":147879,"corporation":false,"usgs":false,"family":"Holland","given":"Lacey","email":"","affiliations":[{"id":16953,"text":"University of Utah, Atmospheric Sciences","active":true,"usgs":false}],"preferred":false,"id":573100,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Reynolds, Richard L. 0000-0002-4572-2942 rreynolds@usgs.gov","orcid":"https://orcid.org/0000-0002-4572-2942","contributorId":147880,"corporation":false,"usgs":true,"family":"Reynolds","given":"Richard","email":"rreynolds@usgs.gov","middleInitial":"L.","affiliations":[{"id":318,"text":"Geosciences and Environmental Change Science Center","active":true,"usgs":true},{"id":271,"text":"Federal Center","active":false,"usgs":true}],"preferred":true,"id":573101,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"Goldstein, Harland L. 0000-0002-6092-8818 hgoldstein@usgs.gov","orcid":"https://orcid.org/0000-0002-6092-8818","contributorId":147881,"corporation":false,"usgs":true,"family":"Goldstein","given":"Harland","email":"hgoldstein@usgs.gov","middleInitial":"L.","affiliations":[{"id":318,"text":"Geosciences and Environmental Change Science Center","active":true,"usgs":true}],"preferred":false,"id":573102,"contributorType":{"id":1,"text":"Authors"},"rank":6},{"text":"Lin, J.","contributorId":33065,"corporation":false,"usgs":true,"family":"Lin","given":"J.","email":"","affiliations":[],"preferred":false,"id":573103,"contributorType":{"id":1,"text":"Authors"},"rank":7}]}} ,{"id":70161924,"text":"70161924 - 2015 - The effects of numerical-model complexity and observation type on estimated porosity values","interactions":[],"lastModifiedDate":"2016-01-11T12:54:35","indexId":"70161924","displayToPublicDate":"2015-07-12T00:00:00","publicationYear":"2015","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":1923,"text":"Hydrogeology Journal","active":true,"publicationSubtype":{"id":10}},"title":"The effects of numerical-model complexity and observation type on estimated porosity values","docAbstract":"The relative merits of model complexity and types of observations employed in model calibration are compared. An existing groundwater flow model coupled with an advective transport simulation of the Salt Lake Valley, Utah (USA), is adapted for advective transport, and effective porosity is adjusted until simulated tritium concentrations match concentrations in samples from wells. Two calibration approaches are used: a “complex” highly parameterized porosity field and a “simple” parsimonious model of porosity distribution. The use of an atmospheric tracer (tritium in this case) and apparent ages (from tritium/helium) in model calibration also are discussed. Of the models tested, the complex model (with tritium concentrations and tritium/helium apparent ages) performs best. Although tritium breakthrough curves simulated by complex and simple models are very generally similar, and there is value in the simple model, the complex model is supported by a more realistic porosity distribution and a greater number of estimable parameters. Culling the best quality data did not lead to better calibration, possibly because of processes and aquifer characteristics that are not simulated. Despite many factors that contribute to shortcomings of both the models and the data, useful information is obtained from all the models evaluated. Although any particular prediction of tritium breakthrough may have large errors, overall, the models mimic observed trends.
","language":"English","publisher":"Springer","publisherLocation":"Berlin","doi":"10.1007/s10040-015-1289-3","usgsCitation":"Starn, J., Bagtzoglou, A., and Green, C.T., 2015, The effects of numerical-model complexity and observation type on estimated porosity values: Hydrogeology Journal, v. 23, no. 6, p. 1121-1128, https://doi.org/10.1007/s10040-015-1289-3.","productDescription":"8 p.","startPage":"1121","endPage":"1128","onlineOnly":"N","additionalOnlineFiles":"N","ipdsId":"IP-059357","costCenters":[{"id":196,"text":"Connecticut Water Science Center","active":true,"usgs":true}],"links":[{"id":471943,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.1007/s10040-015-1289-3","text":"Publisher Index Page"},{"id":314146,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","state":"Utah","otherGeospatial":"Salt Lake Valley","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -112.5,\n 40\n ],\n [\n -112.5,\n 41\n ],\n [\n -112,\n 41\n ],\n [\n -112,\n 40\n ],\n [\n -112.5,\n 40\n ]\n ]\n ]\n }\n }\n ]\n}","volume":"23","issue":"6","publishingServiceCenter":{"id":11,"text":"Pembroke PSC"},"noUsgsAuthors":false,"publicationDate":"2015-07-12","publicationStatus":"PW","scienceBaseUri":"5694e066e4b039675d005e9f","contributors":{"authors":[{"text":"Starn, Jeffrey jjstarn@usgs.gov","contributorId":149231,"corporation":false,"usgs":true,"family":"Starn","given":"Jeffrey","email":"jjstarn@usgs.gov","affiliations":[{"id":196,"text":"Connecticut Water Science Center","active":true,"usgs":true}],"preferred":true,"id":588090,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Bagtzoglou, Amvrossios C.","contributorId":30146,"corporation":false,"usgs":true,"family":"Bagtzoglou","given":"Amvrossios C.","affiliations":[],"preferred":false,"id":588092,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Green, Christopher T. 0000-0002-6480-8194 ctgreen@usgs.gov","orcid":"https://orcid.org/0000-0002-6480-8194","contributorId":1343,"corporation":false,"usgs":true,"family":"Green","given":"Christopher","email":"ctgreen@usgs.gov","middleInitial":"T.","affiliations":[{"id":438,"text":"National Research Program - Western Branch","active":true,"usgs":true}],"preferred":true,"id":588091,"contributorType":{"id":1,"text":"Authors"},"rank":3}]}} ,{"id":70154864,"text":"70154864 - 2015 - Coastal vertebrate exposure to predicted habitat changes due to sea level rise","interactions":[],"lastModifiedDate":"2015-10-23T15:04:58","indexId":"70154864","displayToPublicDate":"2015-07-11T16:00:00","publicationYear":"2015","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":1547,"text":"Environmental Management","active":true,"publicationSubtype":{"id":10}},"title":"Coastal vertebrate exposure to predicted habitat changes due to sea level rise","docAbstract":"Sea level rise (SLR) may degrade habitat for coastal vertebrates in the Southeastern United States, but it is unclear which groups or species will be most exposed to habitat changes. We assessed 28 coastal Georgia vertebrate species for their exposure to potential habitat changes due to SLR using output from the Sea Level Affecting Marshes Model and information on the species’ fundamental niches. We assessed forecasted habitat change up to the year 2100 using three structural habitat metrics: total area, patch size, and habitat permanence. Almost all of the species (n = 24) experienced negative habitat changes due to SLR as measured by at least one of the metrics. Salt marsh and ocean beach habitats experienced the most change (out of 16 categorical land cover types) across the three metrics and species that used salt marsh extensively (rails and marsh sparrows) were ranked highest for exposure to habitat changes. Species that nested on ocean beaches (Diamondback Terrapins, shorebirds, and terns) were also ranked highly, but their use of other foraging habitats reduced their overall exposure. Future studies on potential effects of SLR on vertebrates in southeastern coastal ecosystems should focus on the relative importance of different habitat types to these species’ foraging and nesting requirements. Our straightforward prioritization approach is applicable to other coastal systems and can provide insight to managers on which species to focus resources, what components of their habitats need to be protected, and which locations in the study area will provide habitat refuges in the face of SLR.
","language":"English","publisher":"Springer","publisherLocation":"New York","doi":"10.1007/s00267-015-0580-3","usgsCitation":"Hunter, E., Nibbelink, N.P., Alexander, C.R., Barrett, K., Mengak, L.F., Guy, R., Moore, C.T., and Cooper, R.J., 2015, Coastal vertebrate exposure to predicted habitat changes due to sea level rise: Environmental Management, p. 1-10, https://doi.org/10.1007/s00267-015-0580-3.","productDescription":"10 p.","startPage":"1","endPage":"10","numberOfPages":"10","onlineOnly":"N","additionalOnlineFiles":"N","ipdsId":"IP-055464","costCenters":[{"id":198,"text":"Coop Res Unit Atlanta","active":true,"usgs":true}],"links":[{"id":310611,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","state":"Georgia","otherGeospatial":"Altamaha Estuary","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -81.4306640625,\n 30.704058230919504\n ],\n [\n -81.97448730468749,\n 30.831497881307943\n ],\n [\n -81.89208984375,\n 31.28793989264176\n ],\n [\n -81.507568359375,\n 32.040676557717454\n ],\n [\n -81.1285400390625,\n 32.310348764525806\n ],\n [\n -80.82092285156249,\n 31.994100723260804\n ],\n [\n -81.14501953125,\n 31.70947636001935\n ],\n [\n -81.10107421874999,\n 31.59725256170666\n ],\n [\n -81.2933349609375,\n 31.367708915120826\n ],\n [\n -81.2548828125,\n 31.236288641793006\n ],\n [\n -81.39770507812499,\n 31.1140915948987\n ],\n [\n -81.4306640625,\n 30.704058230919504\n ]\n ]\n ]\n }\n }\n ]\n}","publishingServiceCenter":{"id":8,"text":"Raleigh PSC"},"noUsgsAuthors":false,"publicationDate":"2015-07-11","publicationStatus":"PW","scienceBaseUri":"562b5a29e4b00162522207c0","contributors":{"authors":[{"text":"Hunter, Elizabeth A.","contributorId":149399,"corporation":false,"usgs":false,"family":"Hunter","given":"Elizabeth A.","affiliations":[],"preferred":false,"id":578297,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Nibbelink, Nathan P.","contributorId":141326,"corporation":false,"usgs":false,"family":"Nibbelink","given":"Nathan","email":"","middleInitial":"P.","affiliations":[],"preferred":false,"id":578298,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Alexander, Clark R.","contributorId":149400,"corporation":false,"usgs":false,"family":"Alexander","given":"Clark","email":"","middleInitial":"R.","affiliations":[],"preferred":false,"id":578299,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Barrett, Kyle","contributorId":149401,"corporation":false,"usgs":false,"family":"Barrett","given":"Kyle","email":"","affiliations":[],"preferred":false,"id":578300,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Mengak, Lara F.","contributorId":149402,"corporation":false,"usgs":false,"family":"Mengak","given":"Lara","email":"","middleInitial":"F.","affiliations":[],"preferred":false,"id":578301,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"Guy, Rachel","contributorId":35681,"corporation":false,"usgs":true,"family":"Guy","given":"Rachel","email":"","affiliations":[],"preferred":false,"id":578302,"contributorType":{"id":1,"text":"Authors"},"rank":6},{"text":"Moore, Clinton T. 0000-0002-6053-2880 cmoore@usgs.gov","orcid":"https://orcid.org/0000-0002-6053-2880","contributorId":3643,"corporation":false,"usgs":true,"family":"Moore","given":"Clinton","email":"cmoore@usgs.gov","middleInitial":"T.","affiliations":[{"id":198,"text":"Coop Res Unit Atlanta","active":true,"usgs":true}],"preferred":true,"id":564291,"contributorType":{"id":1,"text":"Authors"},"rank":7},{"text":"Cooper, Robert J.","contributorId":99245,"corporation":false,"usgs":false,"family":"Cooper","given":"Robert","email":"","middleInitial":"J.","affiliations":[{"id":6949,"text":"University of California, Santa Cruz","active":true,"usgs":false}],"preferred":false,"id":578303,"contributorType":{"id":1,"text":"Authors"},"rank":8}]}} ,{"id":70148091,"text":"tm4C4 - 2015 - Design, analysis, and interpretation of field quality-control data for water-sampling projects","interactions":[],"lastModifiedDate":"2021-05-27T13:58:28.962369","indexId":"tm4C4","displayToPublicDate":"2015-07-10T16:15:00","publicationYear":"2015","noYear":false,"publicationType":{"id":18,"text":"Report"},"publicationSubtype":{"id":5,"text":"USGS Numbered Series"},"seriesTitle":{"id":335,"text":"Techniques and Methods","code":"TM","onlineIssn":"2328-7055","printIssn":"2328-7047","active":true,"publicationSubtype":{"id":5}},"seriesNumber":"4-C4","title":"Design, analysis, and interpretation of field quality-control data for water-sampling projects","docAbstract":"The process of obtaining and analyzing water samples from the environment includes a number of steps that can affect the reported result. The equipment used to collect and filter samples, the bottles used for specific subsamples, any added preservatives, sample storage in the field, and shipment to the laboratory have the potential to affect how accurately samples represent the environment from which they were collected. During the early 1990s, the U.S. Geological Survey implemented policies to include the routine collection of quality-control samples in order to evaluate these effects and to ensure that water-quality data were adequately representing environmental conditions. Since that time, the U.S. Geological Survey Office of Water Quality has provided training in how to design effective field quality-control sampling programs and how to evaluate the resultant quality-control data. This report documents that training material and provides a reference for methods used to analyze quality-control data.
\nQuality-control data are those generated from the collection and analysis of quality-control samples, and are used to estimate the magnitude of errors in the process of obtaining environmental data. “Bias” and “variability” are the terms used in this report for the two types of errors in environmental data that are quantified by the data from quality-control samples. Bias is the systematic error inherent in a method or measurement system. Variability is the random error that occurs in independent measurements. The types of field quality-control samples discussed in this report include blanks, spikes, and replicates. Blanks are samples prepared with water that is intended to be free of measurable constituents that will be analyzed by the laboratory; blanks are used to estimate bias caused by contamination. Spiked samples are modified by addition of specific analytes; spikes are used to determine the performance of analytical methods and to estimate the potential bias due to matrix interference or analyte degradation. Replicate samples are two or more samples that are considered to be essentially identical in composition. Replicates are used to evaluate variability in analytical results. Various sub-types of these quality-control samples are defined and discussed in this report, and guidance is provided for incorporating the proper samples into the design for a project. The concept of inference space is introduced to help determine where and when quality-control samples should be collected as well as which environmental samples are related to a set of quality-control samples. The recommended basic quality-control design incorporates project-specific considerations, such as the objectives and scale of the study, and hydrologic and chemical conditions within the study area.
\nThe report provides extensive information about statistical methods used to analyze quality-control data in order to estimate potential bias and variability in environmental data. These methods include construction of confidence intervals on various statistical measures, such as the mean, percentiles and percentages, and standard deviation. The methods are used to compare quality-control results with the larger set of environmental data in order to determine whether the effects of bias and variability might interfere with interpretation of these data. Examples from published reports are presented to illustrate how the methods are applied, how bias and variability are reported, and how the interpretation of environmental data can be qualified based on the quality-control analysis.
","largerWorkType":{"id":18,"text":"Report"},"largerWorkTitle":"Section C in Book 4 Hydrologic analysis and interpretation","largerWorkSubtype":{"id":5,"text":"USGS Numbered Series"},"language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston VA","doi":"10.3133/tm4C4","usgsCitation":"Mueller, D.K., Schertz, T.L., Martin, J.D., and Sandstrom, M.W., 2015, Design, analysis, and interpretation of field quality-control data for water-sampling projects: U.S. Geological Survey Techniques and Methods 4-C4, viii, 54 p., https://doi.org/10.3133/tm4C4.","productDescription":"viii, 54 p.","numberOfPages":"65","onlineOnly":"Y","additionalOnlineFiles":"N","ipdsId":"IP-056948","costCenters":[{"id":452,"text":"National Water Quality Laboratory","active":true,"usgs":true},{"id":503,"text":"Office of Water Quality","active":true,"usgs":true}],"links":[{"id":305661,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/tm4C4.jpg"},{"id":305660,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/tm/04/c04/pdf/tm4c4.pdf","text":"Report","size":"1.72 MB","linkFileType":{"id":1,"text":"pdf"},"description":"Report"},{"id":305622,"type":{"id":15,"text":"Index Page"},"url":"https://pubs.usgs.gov/tm/04/c04/"}],"publicComments":"This report is Chapter 4 of Section C in Book 4 Hydrologic analysis and interpretation","publishingServiceCenter":{"id":9,"text":"Reston PSC"},"noUsgsAuthors":false,"publicationStatus":"PW","scienceBaseUri":"57f7eee2e4b0bc0bec09edae","contributors":{"authors":[{"text":"Mueller, David K. mueller@usgs.gov","contributorId":1585,"corporation":false,"usgs":true,"family":"Mueller","given":"David","email":"mueller@usgs.gov","middleInitial":"K.","affiliations":[{"id":503,"text":"Office of Water Quality","active":true,"usgs":true}],"preferred":true,"id":564508,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Schertz, Terry L. tschertz@usgs.gov","contributorId":188,"corporation":false,"usgs":true,"family":"Schertz","given":"Terry","email":"tschertz@usgs.gov","middleInitial":"L.","affiliations":[{"id":583,"text":"Texas Water Science Center","active":true,"usgs":true}],"preferred":true,"id":564509,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Martin, Jeffrey D. 0000-0003-1994-5285 jdmartin@usgs.gov","orcid":"https://orcid.org/0000-0003-1994-5285","contributorId":1066,"corporation":false,"usgs":true,"family":"Martin","given":"Jeffrey","email":"jdmartin@usgs.gov","middleInitial":"D.","affiliations":[{"id":346,"text":"Indiana Water Science Center","active":true,"usgs":true},{"id":27231,"text":"Indiana-Kentucky Water Science Center","active":true,"usgs":true},{"id":451,"text":"National Water Quality Assessment Program","active":true,"usgs":true}],"preferred":true,"id":564510,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Sandstrom, Mark W. 0000-0003-0006-5675 sandstro@usgs.gov","orcid":"https://orcid.org/0000-0003-0006-5675","contributorId":706,"corporation":false,"usgs":true,"family":"Sandstrom","given":"Mark","email":"sandstro@usgs.gov","middleInitial":"W.","affiliations":[{"id":503,"text":"Office of Water Quality","active":true,"usgs":true},{"id":37464,"text":"WMA - Laboratory & Analytical Services Division","active":true,"usgs":true},{"id":5046,"text":"Branch of Analytical Serv (NWQL)","active":true,"usgs":true},{"id":452,"text":"National Water Quality Laboratory","active":true,"usgs":true}],"preferred":true,"id":564511,"contributorType":{"id":1,"text":"Authors"},"rank":4}]}} ,{"id":70188827,"text":"70188827 - 2015 - Mapping of coal quality using stochastic simulation and isometric logratio transformation with an application to a Texas lignite","interactions":[],"lastModifiedDate":"2017-06-26T12:59:12","indexId":"70188827","displayToPublicDate":"2015-07-09T00:00:00","publicationYear":"2015","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":2033,"text":"International Journal of Coal Geology","active":true,"publicationSubtype":{"id":10}},"title":"Mapping of coal quality using stochastic simulation and isometric logratio transformation with an application to a Texas lignite","docAbstract":"Coal is a chemically complex commodity that often contains most of the natural elements in the periodic table. Coal constituents are conventionally grouped into four components (proximate analysis): fixed carbon, ash, inherent moisture, and volatile matter. These four parts, customarily measured as weight losses and expressed as percentages, share all properties and statistical challenges of compositional data. Consequently, adequate modeling should be done in terms of a logratio transformation, a requirement that is commonly overlooked by modelers. The transformation of choice is the isometric logratio transformation because of its geometrical and statistical advantages. The modeling is done through a series of realizations prepared by applying sequential simulation for the purpose of displaying the parts in maps incorporating uncertainty. The approach makes realistic assumptions and the results honor the data and basic considerations, such as percentages between 0 and 100, all four parts adding to 100% at any location in the study area, and a style of spatial fluctuation in the realizations equal to that of the data. The realizations are used to prepare different results, including probability distributions across a deposit, E-type maps displaying average properties, and probability maps summarizing joint fluctuations of several parts. Application of these maps to a lignite bed clearly delineates the deposit boundary, reveals a channel cutting across, and shows that the most favorable coal quality is to the north and deteriorates toward the southeast.
","language":"English","publisher":"Elsevier","doi":"10.1016/j.coal.2015.10.003","usgsCitation":"Olea, R.A., and Luppens, J.A., 2015, Mapping of coal quality using stochastic simulation and isometric logratio transformation with an application to a Texas lignite: International Journal of Coal Geology, v. 152, no. Part B, p. 80-93, https://doi.org/10.1016/j.coal.2015.10.003.","productDescription":"14 p.","startPage":"80","endPage":"93","ipdsId":"IP-069055","costCenters":[{"id":241,"text":"Eastern Energy Resources Science Center","active":true,"usgs":true}],"links":[{"id":342888,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"volume":"152","issue":"Part B","publishingServiceCenter":{"id":9,"text":"Reston PSC"},"noUsgsAuthors":false,"publicationStatus":"PW","scienceBaseUri":"59521d22e4b062508e3c3691","contributors":{"authors":[{"text":"Olea, Ricardo A. 0000-0003-4308-0808 rolea@usgs.gov","orcid":"https://orcid.org/0000-0003-4308-0808","contributorId":139555,"corporation":false,"usgs":true,"family":"Olea","given":"Ricardo","email":"rolea@usgs.gov","middleInitial":"A.","affiliations":[{"id":241,"text":"Eastern Energy Resources Science Center","active":true,"usgs":true}],"preferred":false,"id":700520,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Luppens, James A. 0000-0001-7607-8750 jluppens@usgs.gov","orcid":"https://orcid.org/0000-0001-7607-8750","contributorId":550,"corporation":false,"usgs":true,"family":"Luppens","given":"James","email":"jluppens@usgs.gov","middleInitial":"A.","affiliations":[{"id":164,"text":"Central Energy Resources Science Center","active":true,"usgs":true}],"preferred":true,"id":700521,"contributorType":{"id":1,"text":"Authors"},"rank":2}]}} ,{"id":70154802,"text":"70154802 - 2015 - Truth, models, model sets, AIC, and multimodel inference: a Bayesian perspective","interactions":[],"lastModifiedDate":"2016-04-13T12:35:45","indexId":"70154802","displayToPublicDate":"2015-07-08T14:45:00","publicationYear":"2015","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":2508,"text":"Journal of Wildlife Management","active":true,"publicationSubtype":{"id":10}},"title":"Truth, models, model sets, AIC, and multimodel inference: a Bayesian perspective","docAbstract":"Statistical inference begins with viewing data as realizations of stochastic processes. Mathematical models provide partial descriptions of these processes; inference is the process of using the data to obtain a more complete description of the stochastic processes. Wildlife and ecological scientists have become increasingly concerned with the conditional nature of model-based inference: what if the model is wrong? Over the last 2 decades, Akaike's Information Criterion (AIC) has been widely and increasingly used in wildlife statistics for 2 related purposes, first for model choice and second to quantify model uncertainty. We argue that for the second of these purposes, the Bayesian paradigm provides the natural framework for describing uncertainty associated with model choice and provides the most easily communicated basis for model weighting. Moreover, Bayesian arguments provide the sole justification for interpreting model weights (including AIC weights) as coherent (mathematically self consistent) model probabilities. This interpretation requires treating the model as an exact description of the data-generating mechanism. We discuss the implications of this assumption, and conclude that more emphasis is needed on model checking to provide confidence in the quality of inference.
","language":"English","publisher":"Wildlife Society","doi":"10.1002/jwmg.890","usgsCitation":"Barker, R., and Link, W., 2015, Truth, models, model sets, AIC, and multimodel inference: a Bayesian perspective: Journal of Wildlife Management, v. 79, no. 5, p. 730-738, https://doi.org/10.1002/jwmg.890.","productDescription":"9 p.","startPage":"730","endPage":"738","onlineOnly":"N","additionalOnlineFiles":"N","ipdsId":"IP-063229","costCenters":[{"id":531,"text":"Patuxent Wildlife Research Center","active":true,"usgs":true}],"links":[{"id":471945,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.1002/jwmg.890","text":"Publisher Index Page"},{"id":305619,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"volume":"79","issue":"5","publishingServiceCenter":{"id":10,"text":"Baltimore PSC"},"noUsgsAuthors":false,"publicationDate":"2015-05-07","publicationStatus":"PW","scienceBaseUri":"559e3ba6e4b0b94a64018f54","contributors":{"authors":[{"text":"Barker, Richard J.","contributorId":6987,"corporation":false,"usgs":true,"family":"Barker","given":"Richard J.","affiliations":[],"preferred":false,"id":564201,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Link, William A. wlink@usgs.gov","contributorId":145491,"corporation":false,"usgs":true,"family":"Link","given":"William A.","email":"wlink@usgs.gov","affiliations":[{"id":531,"text":"Patuxent Wildlife Research Center","active":true,"usgs":true}],"preferred":false,"id":564200,"contributorType":{"id":1,"text":"Authors"},"rank":2}]}} ,{"id":70154817,"text":"70154817 - 2015 - Comparing spatial capture–recapture modeling and nest count methods to estimate orangutan densities in the Wehea Forest, East Kalimantan, Indonesia","interactions":[],"lastModifiedDate":"2015-07-08T13:35:48","indexId":"70154817","displayToPublicDate":"2015-07-08T14:30:00","publicationYear":"2015","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":1015,"text":"Biological Conservation","active":true,"publicationSubtype":{"id":10}},"title":"Comparing spatial capture–recapture modeling and nest count methods to estimate orangutan densities in the Wehea Forest, East Kalimantan, Indonesia","docAbstract":"Accurate information on the density and abundance of animal populations is essential for understanding species' ecology and for conservation planning, but is difficult to obtain. The endangered orangutan (Pongo spp.) is an example; due to its elusive behavior and low densities, researchers have relied on methods that convert nest counts to orangutan densities and require substantial effort for reliable results. Camera trapping and spatial capture–recapture (SCR) models could provide an alternative but have not been used for primates. We compared density estimates calculated using the two methods for orangutans in the Wehea Forest, East Kalimantan, Indonesia. Camera trapping/SCR modeling produced a density estimate of 0.16 ± 0.09–0.29 indiv/km2, and nest counts produced a density estimate of 1.05 ± 0.18–6.01 indiv/km2. The large confidence interval of the nest count estimate is probably due to high variance in nest encounter rates, indicating the need for larger sample size and the substantial effort required to produce reliable results using this method. The SCR estimate produced a very low density estimate and had a narrower but still fairly wide confidence interval. This was likely due to unmodeled heterogeneity and small sample size, specifically a low number of individual captures and recaptures. We propose methodological fixes that could address these issues and improve precision. A comparison of the overall costs and benefits of the two methods suggests that camera trapping/SCR modeling can potentially be a useful tool for assessing the densities of orangutans and other elusive primates, and warrant further investigation to determine broad applicability and methodological adjustments needed.
\n\n
The Southern Hudson Bay (SH) polar bear subpopulation occurs at the southern extent of the species’ range. Although capture–recapture studies indicate abundance was likely unchanged between 1986 and 2005, declines in body condition and survival occurred during the period, possibly foreshadowing a future decrease in abundance. To obtain a current estimate of abundance, we conducted a comprehensive line transect aerial survey of SH during 2011–2012. We stratified the study site by anticipated densities and flew coastal contour transects and systematically spaced inland transects in Ontario and on Akimiski Island and large offshore islands in 2011. Data were collected with double-observer and distance sampling protocols. We surveyed small islands in James Bay and eastern Hudson Bay and flew a comprehensive transect along the Québec coastline in 2012. We observed 667 bears in Ontario and on Akimiski Island and nearby islands in 2011, and we sighted 80 bears on offshore islands during 2012. Mark–recapture distance sampling and sight–resight models yielded an estimate of 860 (SE = 174) for the 2011 study area. Our estimate of abundance for the entire SH subpopulation (943; SE = 174) suggests that abundance is unlikely to have changed significantly since 1986. However, this result should be interpreted cautiously because of the methodological differences between historical studies (physical capture–recapture) and this survey. A conservative management approach is warranted given previous increases in duration of the ice-free season, which are predicted to continue in the future, and previously documented declines in body condition and vital rates.
","language":"English","publisher":"Springer-Verlag","publisherLocation":"Heidelberg","doi":"10.1007/s00300-015-1737-5","usgsCitation":"Obbard, M.E., Stapleton, S.P., Middel, K.R., Thibault, I., Brodeur, V., and Jutras, C., 2015, Estimating the abundance of the Southern Hudson Bay polar bear subpopulation with aerial surveys: Polar Biology, v. 38, no. 10, p. 1713-1725, https://doi.org/10.1007/s00300-015-1737-5.","productDescription":"13 p.","startPage":"1713","endPage":"1725","onlineOnly":"N","additionalOnlineFiles":"N","ipdsId":"IP-059751","costCenters":[{"id":116,"text":"Alaska Science Center Biology MFEB","active":true,"usgs":true}],"links":[{"id":306319,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"volume":"38","issue":"10","publishingServiceCenter":{"id":12,"text":"Tacoma PSC"},"noUsgsAuthors":false,"publicationDate":"2015-07-07","publicationStatus":"PW","scienceBaseUri":"55c090ade4b033ef52104296","contributors":{"authors":[{"text":"Obbard, Martyn E.","contributorId":108002,"corporation":false,"usgs":false,"family":"Obbard","given":"Martyn","email":"","middleInitial":"E.","affiliations":[{"id":6780,"text":"Ontario Ministry of Natural Resources","active":true,"usgs":false}],"preferred":false,"id":566959,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Stapleton, Seth P. sstapleton@usgs.gov","contributorId":3979,"corporation":false,"usgs":true,"family":"Stapleton","given":"Seth","email":"sstapleton@usgs.gov","middleInitial":"P.","affiliations":[{"id":116,"text":"Alaska Science Center Biology MFEB","active":true,"usgs":true}],"preferred":true,"id":566960,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Middel, Kevin R.","contributorId":141065,"corporation":false,"usgs":false,"family":"Middel","given":"Kevin","email":"","middleInitial":"R.","affiliations":[],"preferred":false,"id":566961,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Thibault, Isabelle","contributorId":141066,"corporation":false,"usgs":false,"family":"Thibault","given":"Isabelle","email":"","affiliations":[],"preferred":false,"id":566962,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Brodeur, Vincent","contributorId":141067,"corporation":false,"usgs":false,"family":"Brodeur","given":"Vincent","email":"","affiliations":[],"preferred":false,"id":566963,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"Jutras, Charles","contributorId":141068,"corporation":false,"usgs":false,"family":"Jutras","given":"Charles","email":"","affiliations":[],"preferred":false,"id":566964,"contributorType":{"id":1,"text":"Authors"},"rank":6}]}} ,{"id":70155521,"text":"70155521 - 2015 - Holocene climate variability in Texas, USA: An integration of existing paleoclimate data and modeling with a new, high-resolution speleothem record","interactions":[],"lastModifiedDate":"2015-10-26T14:00:26","indexId":"70155521","displayToPublicDate":"2015-07-07T12:30:00","publicationYear":"2015","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":3219,"text":"Quaternary Science Reviews","active":true,"publicationSubtype":{"id":10}},"title":"Holocene climate variability in Texas, USA: An integration of existing paleoclimate data and modeling with a new, high-resolution speleothem record","docAbstract":"Delineating the climate processes governing precipitation variability in drought-prone Texas is critical for predicting and mitigating climate change effects, and requires the reconstruction of past climate beyond the instrumental record. We synthesize existing paleoclimate proxy data and climate simulations to provide an overview of climate variability in Texas during the Holocene. Conditions became progressively warmer and drier transitioning from the early to mid Holocene, culminating between 7 and 3 ka (thousand years ago), and were more variable during the late Holocene. The timing and relative magnitude of Holocene climate variability, however, is poorly constrained owing to considerable variability among the different records. To help address this, we present a new speleothem (NBJ) reconstruction from a central Texas cave that comprises the highest resolution proxy record to date, spanning the mid to late Holocene. NBJ trace-element concentrations indicate variable moisture conditions with no clear temporal trend. There is a decoupling between NBJ growth rate, trace-element concentrations, and δ18O values, which indicate that (i) the often direct relation between speleothem growth rate and moisture availability is likely complicated by changes in the overlying ecosystem that affect subsurface CO2 production, and (ii) speleothem δ18O variations likely reflect changes in moisture source (i.e., proportion of Pacific-vs. Gulf of Mexico-derived moisture) that appear not to be linked to moisture amount.
","language":"English","publisher":"Pergamon Press","publisherLocation":"New York, NY","doi":"10.1016/j.quascirev.2015.06.023","usgsCitation":"Wong, C., Banner, J., and Musgrove, M., 2015, Holocene climate variability in Texas, USA: An integration of existing paleoclimate data and modeling with a new, high-resolution speleothem record: Quaternary Science Reviews, v. 127, p. 155-173, https://doi.org/10.1016/j.quascirev.2015.06.023.","productDescription":"19 p.","startPage":"155","endPage":"173","onlineOnly":"N","additionalOnlineFiles":"N","ipdsId":"IP-062965","costCenters":[{"id":583,"text":"Texas Water Science Center","active":true,"usgs":true}],"links":[{"id":306533,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","state":"Texas","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -103.0517578125,\n 36.491973470593685\n ],\n [\n -99.97558593749999,\n 36.50963615733049\n ],\n [\n -99.99755859375,\n 34.66935854524543\n ],\n [\n -99.7119140625,\n 34.415973384481866\n ],\n [\n -99.404296875,\n 34.52466147177172\n ],\n [\n -99.16259765625,\n 34.27083595165\n ],\n [\n -98.525390625,\n 34.10725639663118\n ],\n [\n -98.23974609375,\n 34.21634468843465\n ],\n [\n -96.3720703125,\n 33.96158628979907\n ],\n [\n -95.55908203125,\n 34.052659421375964\n ],\n [\n -94.8779296875,\n 34.03445260967645\n ],\n [\n -94.39453125,\n 33.669496972795535\n ],\n [\n -93.9990234375,\n 33.65120829920497\n ],\n [\n -93.9990234375,\n 31.970803930433096\n ],\n [\n -93.4716796875,\n 31.2221970321032\n ],\n [\n -93.40576171875,\n 30.90222470517144\n ],\n [\n -93.7353515625,\n 30.543338954230222\n ],\n [\n -93.8232421875,\n 29.954934549656144\n ],\n [\n -93.88916015625,\n 29.66896252599253\n ],\n [\n -95.51513671875,\n 28.806173508854776\n ],\n [\n -96.13037109375,\n 28.497660832963472\n ],\n [\n -96.83349609375,\n 28.14950321154457\n ],\n [\n -97.09716796875,\n 27.488781168937997\n ],\n [\n -97.49267578125,\n 27.059125784374068\n ],\n [\n -97.42675781249999,\n 26.509904531413927\n ],\n [\n -97.119140625,\n 25.997549919572112\n ],\n [\n -97.44873046875,\n 25.799891182088334\n ],\n [\n -98.59130859375,\n 26.15543796871355\n ],\n [\n -99.228515625,\n 26.47057302237511\n ],\n [\n -99.84374999999999,\n 27.527758206861886\n ],\n [\n -100.92041015625,\n 29.0945770775118\n ],\n [\n -101.49169921875,\n 29.630771207229\n ],\n [\n -102.3046875,\n 29.783449456820605\n ],\n [\n -102.76611328125,\n 29.516110386062277\n ],\n [\n -103.1396484375,\n 28.844673680771795\n ],\n [\n -104.8974609375,\n 29.726222319395504\n ],\n [\n -104.78759765625,\n 30.088107753367257\n ],\n [\n -105.1171875,\n 30.58117925738696\n ],\n [\n -105.77636718749999,\n 30.996445897426373\n ],\n [\n -106.787109375,\n 31.87755764334002\n ],\n [\n -106.67724609375,\n 32.045332838858506\n ],\n [\n -103.07373046875,\n 31.989441837922904\n ],\n [\n -103.0517578125,\n 36.491973470593685\n ]\n ]\n ]\n }\n }\n ]\n}","volume":"127","publishingServiceCenter":{"id":5,"text":"Lafayette PSC"},"noUsgsAuthors":false,"publicationStatus":"PW","scienceBaseUri":"55c9cb34e4b08400b1fdb70e","contributors":{"authors":[{"text":"Wong, Corinne I.","contributorId":36018,"corporation":false,"usgs":true,"family":"Wong","given":"Corinne I.","affiliations":[],"preferred":false,"id":565675,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Banner, Jay L.","contributorId":58200,"corporation":false,"usgs":true,"family":"Banner","given":"Jay L.","affiliations":[],"preferred":false,"id":565676,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Musgrove, MaryLynn 0000-0003-1607-3864 mmusgrov@usgs.gov","orcid":"https://orcid.org/0000-0003-1607-3864","contributorId":1316,"corporation":false,"usgs":true,"family":"Musgrove","given":"MaryLynn","email":"mmusgrov@usgs.gov","affiliations":[{"id":451,"text":"National Water Quality Assessment Program","active":true,"usgs":true},{"id":583,"text":"Texas Water Science Center","active":true,"usgs":true}],"preferred":false,"id":565674,"contributorType":{"id":1,"text":"Authors"},"rank":3}]}} ,{"id":70160102,"text":"70160102 - 2015 - Renewed inflation of Long Valley Caldera, California (2011 to 2014)","interactions":[],"lastModifiedDate":"2015-12-14T11:17:50","indexId":"70160102","displayToPublicDate":"2015-07-07T00:00:00","publicationYear":"2015","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":1807,"text":"Geophysical Research Letters","active":true,"publicationSubtype":{"id":10}},"title":"Renewed inflation of Long Valley Caldera, California (2011 to 2014)","docAbstract":"Slow inflation began at Long Valley Caldera in late 2011, coinciding with renewed swarm seismicity. Ongoing deformation is concentrated within the caldera. We analyze this deformation using a combination of GPS and InSAR (TerraSAR-X) data processed with a persistent scatterer technique. The extension rate of the dome-crossing baseline during this episode (CA99 to KRAC) is 1 cm/yr, similar to past inflation episodes (1990–1995 and 2002–2003), and about a tenth of the peak rate observed during the 1997 unrest. The current deformation is well modeled by the inflation of a prolate spheroidal magma reservoir ∼7 km beneath the resurgent dome, with a volume change of ∼6 × 106 m3/yr from 2011.7 through the end of 2014. The current data cannot resolve a second source, which was required to model the 1997 episode. This source appears to be in the same region as previous inflation episodes, suggesting a persistent reservoir.
","language":"English","publisher":"American Geophysical Union","publisherLocation":"Washington, DC","doi":"10.1002/2015GL064338","usgsCitation":"Montgomery-Brown, E., Wicks, C.W., Cervelli, P.F., Langbein, J.O., Svarc, J.L., Shelly, D.R., Hill, D.P., and Lisowski, M., 2015, Renewed inflation of Long Valley Caldera, California (2011 to 2014): Geophysical Research Letters, v. 42, no. 13, p. 5250-5257, https://doi.org/10.1002/2015GL064338.","productDescription":"8 p.","startPage":"5250","endPage":"5257","onlineOnly":"N","additionalOnlineFiles":"N","ipdsId":"IP-064951","costCenters":[{"id":617,"text":"Volcano Science Center","active":true,"usgs":true}],"links":[{"id":471949,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.1002/2015gl064338","text":"Publisher Index Page"},{"id":312244,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","state":"California","otherGeospatial":"Long Valley Caldera","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -119.09454345703125,\n 38.348118547988065\n ],\n [\n -118.9105224609375,\n 38.21660403859855\n ],\n [\n -118.65509033203125,\n 38.0545795282119\n ],\n [\n -118.4381103515625,\n 37.846663684549156\n ],\n [\n -118.3172607421875,\n 37.65338320128765\n ],\n [\n -118.27880859375001,\n 37.35487607348372\n ],\n [\n -118.24859619140626,\n 37.07271048132946\n ],\n [\n -119.05059814453125,\n 37.64903402157866\n ],\n [\n -119.22637939453124,\n 37.844494798834596\n ],\n [\n -119.39117431640625,\n 38.1669547678699\n ],\n [\n -119.36920166015624,\n 38.34165619279593\n ],\n [\n -119.21539306640626,\n 38.429925130409366\n ],\n [\n -119.09454345703125,\n 38.348118547988065\n ]\n ]\n ]\n }\n }\n ]\n}","volume":"42","issue":"13","publishingServiceCenter":{"id":14,"text":"Menlo Park PSC"},"noUsgsAuthors":false,"publicationDate":"2015-07-07","publicationStatus":"PW","scienceBaseUri":"566ff656e4b09cfe53ca79c2","chorus":{"doi":"10.1002/2015gl064338","url":"http://dx.doi.org/10.1002/2015gl064338","publisher":"Wiley-Blackwell","authors":"Montgomery-Brown E. K., Wicks C. W., Cervelli P. F., Langbein J. O., Svarc J. L., Shelly D. R., Hill D. P., Lisowski M.","journalName":"Geophysical Research Letters","publicationDate":"7/7/2015","auditedOn":"7/24/2015"},"contributors":{"authors":[{"text":"Montgomery-Brown, Emily emontgomery-brown@usgs.gov","contributorId":150516,"corporation":false,"usgs":true,"family":"Montgomery-Brown","given":"Emily","email":"emontgomery-brown@usgs.gov","affiliations":[{"id":617,"text":"Volcano Science Center","active":true,"usgs":true}],"preferred":false,"id":581996,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Wicks, Charles W. Jr. 0000-0002-0809-1328 cwicks@usgs.gov","orcid":"https://orcid.org/0000-0002-0809-1328","contributorId":127701,"corporation":false,"usgs":true,"family":"Wicks","given":"Charles","suffix":"Jr.","email":"cwicks@usgs.gov","middleInitial":"W.","affiliations":[{"id":237,"text":"Earthquake Science Center","active":true,"usgs":true}],"preferred":true,"id":581997,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Cervelli, Peter F. 0000-0001-6765-1009 pcervelli@usgs.gov","orcid":"https://orcid.org/0000-0001-6765-1009","contributorId":1936,"corporation":false,"usgs":true,"family":"Cervelli","given":"Peter","email":"pcervelli@usgs.gov","middleInitial":"F.","affiliations":[{"id":617,"text":"Volcano Science Center","active":true,"usgs":true}],"preferred":true,"id":581998,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Langbein, John O. 0000-0002-7821-8101 langbein@usgs.gov","orcid":"https://orcid.org/0000-0002-7821-8101","contributorId":3293,"corporation":false,"usgs":true,"family":"Langbein","given":"John","email":"langbein@usgs.gov","middleInitial":"O.","affiliations":[{"id":237,"text":"Earthquake Science Center","active":true,"usgs":true}],"preferred":true,"id":581999,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Svarc, Jerry L. 0000-0002-2802-4528 jsvarc@usgs.gov","orcid":"https://orcid.org/0000-0002-2802-4528","contributorId":2413,"corporation":false,"usgs":true,"family":"Svarc","given":"Jerry","email":"jsvarc@usgs.gov","middleInitial":"L.","affiliations":[{"id":237,"text":"Earthquake Science Center","active":true,"usgs":true}],"preferred":true,"id":582000,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"Shelly, David R. dshelly@usgs.gov","contributorId":2978,"corporation":false,"usgs":true,"family":"Shelly","given":"David","email":"dshelly@usgs.gov","middleInitial":"R.","affiliations":[{"id":617,"text":"Volcano Science Center","active":true,"usgs":true},{"id":114,"text":"Alaska Science Center","active":true,"usgs":true}],"preferred":true,"id":582001,"contributorType":{"id":1,"text":"Authors"},"rank":6},{"text":"Hill, David P. hill@usgs.gov","contributorId":2600,"corporation":false,"usgs":true,"family":"Hill","given":"David","email":"hill@usgs.gov","middleInitial":"P.","affiliations":[{"id":615,"text":"Volcano Hazards Program","active":true,"usgs":true},{"id":617,"text":"Volcano Science Center","active":true,"usgs":true},{"id":114,"text":"Alaska Science Center","active":true,"usgs":true}],"preferred":false,"id":582002,"contributorType":{"id":1,"text":"Authors"},"rank":7},{"text":"Lisowski, Michael 0000-0003-4818-2504 mlisowski@usgs.gov","orcid":"https://orcid.org/0000-0003-4818-2504","contributorId":637,"corporation":false,"usgs":true,"family":"Lisowski","given":"Michael","email":"mlisowski@usgs.gov","affiliations":[{"id":615,"text":"Volcano Hazards Program","active":true,"usgs":true},{"id":617,"text":"Volcano Science Center","active":true,"usgs":true}],"preferred":true,"id":582003,"contributorType":{"id":1,"text":"Authors"},"rank":8}]}} ,{"id":70156128,"text":"70156128 - 2015 - Didymosphenia geminata in the Upper Esopus Creek: current status, variability, and controlling factors","interactions":[],"lastModifiedDate":"2015-08-17T11:37:54","indexId":"70156128","displayToPublicDate":"2015-07-06T12:45:00","publicationYear":"2015","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":2980,"text":"PLoS ONE","active":true,"publicationSubtype":{"id":10}},"title":"Didymosphenia geminata in the Upper Esopus Creek: current status, variability, and controlling factors","docAbstract":"In May of 2009, the bloom-forming diatom Didymosphenia geminata was first identified in the Upper Esopus Creek, a key tributary to the New York City water-supply and a popular recreational stream. The Upper Esopus receives supplemental flows from the Shandaken Portal, an underground aqueduct delivering waters from a nearby basin. The presence of D.geminata is a concern for the local economy, water supply, and aquatic ecosystem because nuisance blooms have been linked to degraded stream condition in other regions. Here we ascertain the extent and severity of the D. geminata invasion, determine the impact of supplemental flows from the Portal on D. geminata, and identify potential factors that may limitD. geminata in the watershed. Stream temperature, discharge, and water quality were characterized at select sites and periphyton samples were collected five times at 6 to 20 study sites between 2009 and 2010 to assess standing crop, diatom community structure, and density of D. geminata and all diatoms. Density of D. geminata ranged from 0–12 cells cm-2 at tributary sites, 0–781 cells cm-2 at sites upstream of the Portal, and 0–2,574 cells cm-2 at sites downstream of the Portal. Survey period and Portal (upstream or downstream) each significantly affected D. geminata cell density. In general, D. geminata was most abundant during the November 2009 and June 2010 surveys and at sites immediately downstream of the Portal. We found that D. geminata did not reach nuisance levels or strongly affect the periphyton community. Similarly, companion studies showed that local macroinvertebrate and fish communities were generally unaffected. A number of abiotic factors including variable flows and moderate levels of phosphorous and suspended sediment may limit blooms of D. geminatain this watershed.
","language":"English","publisher":"Public Library of Science","publisherLocation":"San Francisco, CA","doi":"10.1371/journal.pone.0130558","collaboration":"New York State Dept of Environmental Conservation; USGS","usgsCitation":"George, S.D., and Baldigo, B.P., 2015, Didymosphenia geminata in the Upper Esopus Creek: current status, variability, and controlling factors: PLoS ONE, v. 10, no. 8, p. 1-20, https://doi.org/10.1371/journal.pone.0130558.","productDescription":"20 p.","startPage":"1","endPage":"20","numberOfPages":"20","onlineOnly":"N","additionalOnlineFiles":"N","ipdsId":"IP-043086","costCenters":[{"id":474,"text":"New York Water Science Center","active":true,"usgs":true}],"links":[{"id":471950,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.1371/journal.pone.0130558","text":"Publisher Index Page"},{"id":306799,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"volume":"10","issue":"8","publishingServiceCenter":{"id":11,"text":"Pembroke PSC"},"noUsgsAuthors":false,"publicationDate":"2015-07-06","publicationStatus":"PW","scienceBaseUri":"55d305a9e4b0518e35468ccc","contributors":{"authors":[{"text":"George, Scott D. 0000-0002-8197-1866 sgeorge@usgs.gov","orcid":"https://orcid.org/0000-0002-8197-1866","contributorId":3014,"corporation":false,"usgs":true,"family":"George","given":"Scott","email":"sgeorge@usgs.gov","middleInitial":"D.","affiliations":[{"id":474,"text":"New York Water Science Center","active":true,"usgs":true}],"preferred":true,"id":567894,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Baldigo, Barry P. 0000-0002-9862-9119 bbaldigo@usgs.gov","orcid":"https://orcid.org/0000-0002-9862-9119","contributorId":1234,"corporation":false,"usgs":true,"family":"Baldigo","given":"Barry","email":"bbaldigo@usgs.gov","middleInitial":"P.","affiliations":[{"id":474,"text":"New York Water Science Center","active":true,"usgs":true}],"preferred":true,"id":567893,"contributorType":{"id":1,"text":"Authors"},"rank":2}]}} ,{"id":70154748,"text":"ofr20151124 - 2015 - An evaluation of fish behavior upstream of the water temperature control tower at Cougar Dam, Oregon, using acoustic cameras, 2013","interactions":[],"lastModifiedDate":"2016-01-08T14:45:29","indexId":"ofr20151124","displayToPublicDate":"2015-07-06T12:00:00","publicationYear":"2015","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":"2015-1124","title":"An evaluation of fish behavior upstream of the water temperature control tower at Cougar Dam, Oregon, using acoustic cameras, 2013","docAbstract":"This report describes the initial year of a 2-year study to determine the feasibility of using acoustic cameras to monitor fish movements to help inform decisions about fish passage at Cougar Dam near Springfield, Oregon. Specifically, we used acoustic cameras to measure fish presence, travel speed, and direction adjacent to the water temperature control tower in the forebay of Cougar Dam during the spring (May, June, and July) and fall (September, October, and November) of 2013. Cougar Dam is a high-head flood-control dam, and the water temperature control tower enables depth-specific water withdrawals to facilitate adjustment of water temperatures released downstream of the dam. The acoustic cameras were positioned at the upstream entrance of the tower to monitor free-ranging subyearling and yearling-size juvenile Chinook salmon (Oncorhynchus tshawytscha). Because of the large size discrepancy, we could distinguish juvenile Chinook salmon from their predators, which enabled us to measure predators and prey in areas adjacent to the entrance of the tower. We used linear models to quantify and assess operational and environmental factors—such as time of day, discharge, and water temperature—that may influence juvenile Chinook salmon movements within the beam of the acoustic cameras. Although extensive milling behavior of fish near the structure may have masked directed movement of fish and added unpredictability to fish movement models, the acoustic-camera technology enabled us to ascertain the general behavior of discrete size classes of fish. Fish travel speed, direction of travel, and counts of fish moving toward the water temperature control tower primarily were influenced by the amount of water being discharged through the dam.
","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/ofr20151124","collaboration":"Prepared in cooperation with the U.S. Army Corps of Engineers","usgsCitation":"Adams, N.S., Smith, C.D., Plumb, J.M., Hansen, G.S., and Beeman, J.W., 2015, An evaluation of fish behavior upstream of the water temperature control tower at Cougar Dam, Oregon, using acoustic cameras, 2013: U.S. Geological Survey Open-File Report 2015-1124, 62 p., https://dx.doi.org/10.3133/ofr20151124.","productDescription":"x, 62 p.","numberOfPages":"76","onlineOnly":"Y","additionalOnlineFiles":"N","ipdsId":"IP-063666","costCenters":[{"id":654,"text":"Western Fisheries Research Center","active":true,"usgs":true}],"links":[{"id":305440,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/of/2015/1124/coverthb.jpg"},{"id":305441,"rank":2,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/of/2015/1124/ofr20151124.pdf","text":"Report","size":"4 MB","linkFileType":{"id":1,"text":"pdf"},"description":"Report"}],"country":"United States","state":"Oregon","otherGeospatial":"Cougar Dam","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -122.25345611572266,\n 44.122345529999656\n ],\n [\n -122.25345611572266,\n 44.132942183139654\n ],\n [\n -122.23114013671875,\n 44.132942183139654\n ],\n [\n -122.23114013671875,\n 44.122345529999656\n ],\n [\n -122.25345611572266,\n 44.122345529999656\n ]\n ]\n ]\n }\n }\n ]\n}","contact":"Director, Western Fisheries Research Center
U.S. Geological Survey
6505 NE 65th Street
Seattle, Washington 98115
http://wfrc.usgs.gov
This study identifies areas prone to lahars from hydrothermally altered volcanic edifices on a global scale, using visible and near infrared (VNIR) and short wavelength infrared (SWIR) reflectance data from the Advanced Spaceborne Thermal Emission and Reflection Radiometer (ASTER) and digital elevation data from the ASTER Global Digital Elevation Model (GDEM) dataset. This is the first study to create a global database of hydrothermally altered volcanoes showing quantitatively compiled alteration maps and potentially affected drainages, as well as drainage-specific maps illustrating modeled lahars and their potential inundation zones. We (1) identified and prioritized 720 volcanoes based on population density surrounding the volcanoes using the Smithsonian Institution Global Volcanism Program database (GVP) and LandScan™ digital population dataset; (2) validated ASTER hydrothermal alteration mapping techniques using Airborne Visible and Infrared Imaging Spectrometer (AVIRIS) and ASTER data for Mount Shasta, California, and Pico de Orizaba (Citlaltépetl), Mexico; (3) mapped and slope-classified hydrothermal alteration using ASTER VNIR-SWIR reflectance data on 100 of the most densely populated volcanoes; (4) delineated drainages using ASTER GDEM data that show potential flow paths of possible lahars for the 100 mapped volcanoes; (5) produced potential alteration-related lahar inundation maps using the LAHARZ GIS code for Iztaccíhuatl, Mexico, and Mount Hood and Mount Shasta in the United States that illustrate areas likely to be affected based on DEM-derived volume estimates of hydrothermally altered rocks and the ~2x uncertainty factor inherent within a statistically-based lahar model; and (6) saved all image and vector data for 3D and 2D display in Google Earth™, ArcGIS® and other graphics display programs. In addition, these data are available from the ASTER Volcano Archive (AVA) for distribution (available at http://ava.jpl.nasa.gov/recent_alteration_zones.php).
\nUsing the GVP and the LandScan™ digital population dataset, 350 of the most densely populated stratovolcanoes were assessed for study. Of the 350 volcanoes, 250 volcanoes were not mapped due to excessive snow, ice, and (or) vegetation. Results from mapping the remaining 100 stratovolcanoes show that 87 contain slopes with hydrothermal alteration, and 49 have hydrothermally altered rocks on steep slopes situated above areas with populations >100 people per km2. Of these, 17 stratovolcanoes exhibit laterally extensive hydrothermal alteration on slopes >35° and cover an area >0.25 km2, which may pose a significant possibility of generating debris flows.
\nThis study was undertaken during 2012–2013 in cooperation with the National Aeronautics and Space Administration (NASA). Since completion of this study, a new lahar modeling program (LAHAR_pz) has been released, which may produce slightly different modeling results from the LAHARZ model used in this study. The maps and data from this study should not be used in place of existing volcano hazard maps published by local authorities. For volcanoes without hazard maps and (or) published lahar-related hazard studies, this work will provide a starting point from which more accurate hazard maps can be produced. This is the first dataset to provide digital maps of altered volcanoes and adjacent watersheds that can be used for assessing volcanic hazards, hydrothermal alteration, and other volcanic processes in future studies.
","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/sir20155035","usgsCitation":"Mars, J., Hubbard, B.E., Pieri, D., and Linick, J., 2015, Alteration, slope-classified alteration, and potential lahar inundation maps of volcanoes for the Advanced Spaceborne Thermal Emission and Reflection Radiometer (ASTER) Volcano Archive: U.S. Geological Survey Scientific Investigations Report 2015-5035, https://doi.org/10.3133/sir20155035.","onlineOnly":"Y","additionalOnlineFiles":"N","ipdsId":"IP-054579","costCenters":[{"id":245,"text":"Eastern Mineral and Environmental Resources Science Center","active":true,"usgs":true}],"links":[{"id":305571,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/sir20155035.gif"},{"id":305570,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/sir/2015/5035/pdf/sir2015-5035.pdf","text":"Report","size":"13 MB","linkFileType":{"id":1,"text":"pdf"},"description":"Report"},{"id":305557,"type":{"id":15,"text":"Index Page"},"url":"https://pubs.usgs.gov/sir/2015/5035/"}],"publishingServiceCenter":{"id":9,"text":"Reston PSC"},"noUsgsAuthors":false,"publicationStatus":"PW","scienceBaseUri":"57f7eef3e4b0bc0bec09ee12","contributors":{"authors":[{"text":"Mars, John C. jmars@usgs.gov","contributorId":127493,"corporation":false,"usgs":true,"family":"Mars","given":"John C.","email":"jmars@usgs.gov","affiliations":[{"id":245,"text":"Eastern Mineral and Environmental Resources Science Center","active":true,"usgs":true}],"preferred":false,"id":564125,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Hubbard, Bernard E. 0000-0002-9315-2032 bhubbard@usgs.gov","orcid":"https://orcid.org/0000-0002-9315-2032","contributorId":2342,"corporation":false,"usgs":true,"family":"Hubbard","given":"Bernard","email":"bhubbard@usgs.gov","middleInitial":"E.","affiliations":[{"id":245,"text":"Eastern Mineral and Environmental Resources Science Center","active":true,"usgs":true}],"preferred":true,"id":564126,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Pieri, David","contributorId":139492,"corporation":false,"usgs":false,"family":"Pieri","given":"David","affiliations":[{"id":7023,"text":"Jet Propulsion Laboratory, California Institute of Technology","active":true,"usgs":false}],"preferred":false,"id":564127,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Linick, Justin","contributorId":139493,"corporation":false,"usgs":false,"family":"Linick","given":"Justin","email":"","affiliations":[{"id":7023,"text":"Jet Propulsion Laboratory, California Institute of Technology","active":true,"usgs":false}],"preferred":false,"id":564128,"contributorType":{"id":1,"text":"Authors"},"rank":4}]}} ,{"id":70115013,"text":"70115013 - 2015 - Primative components, crustal assimilation, and magmatic degassing of the 2008 Kilauea summit eruption","interactions":[],"lastModifiedDate":"2015-11-16T16:11:12","indexId":"70115013","displayToPublicDate":"2015-07-02T14:09:00","publicationYear":"2015","noYear":false,"publicationType":{"id":5,"text":"Book chapter"},"publicationSubtype":{"id":24,"text":"Book Chapter"},"title":"Primative components, crustal assimilation, and magmatic degassing of the 2008 Kilauea summit eruption","docAbstract":"Simultaneous summit and rift zone eruptions at Kīlauea starting in 2008 reflect a shallow eruptive plumbing system inundated by a bourgeoning supply of new magma from depth. Olivine-hosted melt inclusions, host glass, and bulk lava compositions of magma erupted at both the summit and east rift zone demonstrate chemical continuity at both ends of a well-worn summit-to-rift pipeline. Analysis of glass within dense-cored lapilli erupted from the summit in March – August 2008 show these are not samplings of compositionally distinct magmas stored in the shallow summit magma reservoir, but instead result from remelting and assimilation of fragments from conduit wall and vent blocks. Summit pyroclasts show the predominant and most primitive component erupted to be a homogenous, relatively trace-element-depleted melt that is a compositionally indistinguishable from east rift lava. Based on a “top-down” model for the geochemical variation in east rift zone lava over the past 30 years, we suggest that the apparent absence of a 1982 enriched component in melt inclusions, as well as the proposed summit-rift zone connectivity based on sulfur and mineral chemistry, indicate that the last of the pre-1983 magma has been flushed out of the summit reservoir during the surge of mantle-derived magma from 2003-2007.
","largerWorkTitle":"Hawaiian volcanoes, from source to surface","language":"English","publisher":"American Geophysical Union","usgsCitation":"Rowe, M.C., Thornber, C.R., and Orr, T., 2015, Primative components, crustal assimilation, and magmatic degassing of the 2008 Kilauea summit eruption, chap. of Hawaiian volcanoes, from source to surface, p. 439-457.","productDescription":"18 p.","startPage":"439","endPage":"457","onlineOnly":"N","additionalOnlineFiles":"N","ipdsId":"IP-057405","costCenters":[{"id":617,"text":"Volcano Science Center","active":true,"usgs":true}],"links":[{"id":311401,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","state":"Hawaii","otherGeospatial":"Kīlauea volcano","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -155.2756118774414,\n 19.43162918399349\n ],\n [\n -155.25157928466797,\n 19.425153718960157\n ],\n [\n -155.23990631103513,\n 19.413821034154534\n ],\n [\n -155.2639389038086,\n 19.40443049681278\n ],\n [\n -155.2910614013672,\n 19.399896939902558\n ],\n [\n -155.29483795166016,\n 19.409935360334085\n ],\n [\n -155.28350830078125,\n 19.427743935948932\n ],\n [\n -155.2756118774414,\n 19.43162918399349\n ]\n ]\n ]\n }\n }\n ]\n}","noUsgsAuthors":false,"publicationStatus":"PW","scienceBaseUri":"564b0c57e4b0ebfbef0d3179","contributors":{"authors":[{"text":"Rowe, Michael C.","contributorId":79191,"corporation":false,"usgs":true,"family":"Rowe","given":"Michael","email":"","middleInitial":"C.","affiliations":[],"preferred":false,"id":519011,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Thornber, Carl R. cthornber@usgs.gov","contributorId":2016,"corporation":false,"usgs":true,"family":"Thornber","given":"Carl","email":"cthornber@usgs.gov","middleInitial":"R.","affiliations":[{"id":157,"text":"Cascades Volcano Observatory","active":false,"usgs":true}],"preferred":false,"id":519009,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Orr, Tim R. torr@usgs.gov","contributorId":3766,"corporation":false,"usgs":true,"family":"Orr","given":"Tim R.","email":"torr@usgs.gov","affiliations":[],"preferred":false,"id":519010,"contributorType":{"id":1,"text":"Authors"},"rank":3}]}} ,{"id":70154794,"text":"70154794 - 2015 - A collision risk model to predict avian fatalities at wind facilities: an example using golden eagles, Aquila chrysaetos","interactions":[],"lastModifiedDate":"2015-07-06T11:41:25","indexId":"70154794","displayToPublicDate":"2015-07-02T12:45:00","publicationYear":"2015","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":2980,"text":"PLoS ONE","active":true,"publicationSubtype":{"id":10}},"title":"A collision risk model to predict avian fatalities at wind facilities: an example using golden eagles, Aquila chrysaetos","docAbstract":"Wind power is a major candidate in the search for clean, renewable energy. Beyond the technical and economic challenges of wind energy development are environmental issues that may restrict its growth. Avian fatalities due to collisions with rotating turbine blades are a leading concern and there is considerable uncertainty surrounding avian collision risk at wind facilities. This uncertainty is not reflected in many models currently used to predict the avian fatalities that would result from proposed wind developments. We introduce a method to predict fatalities at wind facilities, based on pre-construction monitoring. Our method can directly incorporate uncertainty into the estimates of avian fatalities and can be updated if information on the true number of fatalities becomes available from post-construction carcass monitoring. Our model considers only three parameters: hazardous footprint, bird exposure to turbines and collision probability. By using a Bayesian analytical framework we account for uncertainties in these values, which are then reflected in our predictions and can be reduced through subsequent data collection. The simplicity of our approach makes it accessible to ecologists concerned with the impact of wind development, as well as to managers, policy makers and industry interested in its implementation in real-world decision contexts. We demonstrate the utility of our method by predicting golden eagle (Aquila chrysaetos) fatalities at a wind installation in the United States. Using pre-construction data, we predicted 7.48 eagle fatalities year-1 (95% CI: (1.1, 19.81)). The U.S. Fish and Wildlife Service uses the 80th quantile (11.0 eagle fatalities year-1) in their permitting process to ensure there is only a 20% chance a wind facility exceeds the authorized fatalities. Once data were available from two-years of post-construction monitoring, we updated the fatality estimate to 4.8 eagle fatalities year-1 (95% CI: (1.76, 9.4); 80th quantile, 6.3). In this case, the increased precision in the fatality prediction lowered the level of authorized take, and thus lowered the required amount of compensatory mitigation.
","language":"English","publisher":"Public Library of Science","publisherLocation":"San Francisco, CA","doi":"10.1371/journal.pone.0130978","usgsCitation":"New, L., Bjerre, E., Millsap, B.A., Otto, M.C., and Runge, M.C., 2015, A collision risk model to predict avian fatalities at wind facilities: an example using golden eagles, Aquila chrysaetos: PLoS ONE, v. 10, no. 7, p. 1-12, https://doi.org/10.1371/journal.pone.0130978.","productDescription":"12 p.","startPage":"1","endPage":"12","numberOfPages":"12","onlineOnly":"N","additionalOnlineFiles":"N","ipdsId":"IP-049300","costCenters":[{"id":531,"text":"Patuxent Wildlife Research Center","active":true,"usgs":true}],"links":[{"id":471954,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.1371/journal.pone.0130978","text":"Publisher Index Page"},{"id":305581,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"volume":"10","issue":"7","publishingServiceCenter":{"id":10,"text":"Baltimore PSC"},"noUsgsAuthors":false,"publicationDate":"2015-07-02","publicationStatus":"PW","scienceBaseUri":"559ba6a8e4b0b94a640170c5","contributors":{"authors":[{"text":"New, Leslie lnew@usgs.gov","contributorId":145484,"corporation":false,"usgs":true,"family":"New","given":"Leslie","email":"lnew@usgs.gov","affiliations":[{"id":531,"text":"Patuxent Wildlife Research Center","active":true,"usgs":true}],"preferred":true,"id":564175,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Bjerre, Emily","contributorId":44451,"corporation":false,"usgs":true,"family":"Bjerre","given":"Emily","affiliations":[],"preferred":false,"id":564176,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Millsap, Brian A.","contributorId":75841,"corporation":false,"usgs":true,"family":"Millsap","given":"Brian","email":"","middleInitial":"A.","affiliations":[],"preferred":false,"id":564177,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Otto, Mark C.","contributorId":6307,"corporation":false,"usgs":true,"family":"Otto","given":"Mark","email":"","middleInitial":"C.","affiliations":[],"preferred":false,"id":564178,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Runge, Michael C. 0000-0002-8081-536X mrunge@usgs.gov","orcid":"https://orcid.org/0000-0002-8081-536X","contributorId":3358,"corporation":false,"usgs":true,"family":"Runge","given":"Michael","email":"mrunge@usgs.gov","middleInitial":"C.","affiliations":[{"id":531,"text":"Patuxent Wildlife Research Center","active":true,"usgs":true}],"preferred":true,"id":564174,"contributorType":{"id":1,"text":"Authors"},"rank":5}]}} ,{"id":70154807,"text":"70154807 - 2015 - Testing the thermal-niche oxygen-squeeze hypothesis for estuarine striped bass","interactions":[],"lastModifiedDate":"2015-09-10T15:17:54","indexId":"70154807","displayToPublicDate":"2015-07-02T11:45:00","publicationYear":"2015","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":1528,"text":"Environmental Biology of Fishes","active":true,"publicationSubtype":{"id":10}},"title":"Testing the thermal-niche oxygen-squeeze hypothesis for estuarine striped bass","docAbstract":"In many stratified coastal ecosystems, conceptual and bioenergetics models predict seasonal reduction in quality and quantity of fish habitat due to high temperatures and hypoxia. We tested these predictions using acoustic telemetry of 2 to 4 kg striped bass (Morone saxatilis Walbaum) and high-resolution spatial water quality sampling in the Patuxent River, a sub-estuary of the Chesapeake Bay, during 2008 and 2009. Striped bass avoided hypoxic (dissolved oxygen ≤2 mg·l−1) subpycnocline waters, but frequently occupied habitats with high temperatures (>25 °C) in the summer months, as cooler habitats were typically not available. Using traditional concepts of the seasonal thermal-niche oxygen-squeeze, most of the Patuxent estuary would beconsidered unsuitable habitat for adult striped bass during summer. Application of a bioenergetics model revealed that habitats selected by striped bass during summer would support positive growth rates assuming fish could feed at one-half ofmaximum consumption. Occupancy of the estuary during summer by striped bass in this study was likely facilitated by sufficient prey and innate tolerance of high temperatures by sub-adult fish of the size range that we tagged. Our results help extend the thermalniche oxygen-squeeze hypothesis to native populations of striped bass in semi-enclosed coastal systems. Tolerance of for supraoptimal temperatures in our study supports recent suggestions by others that the thermal-niche concept for striped bass should be revised to include warmer temperatures.
","language":"English","publisher":"Kluwer Academic Publishers","publisherLocation":"Dordrecht","doi":"10.1007/s10641-015-0431-3","usgsCitation":"Kraus, R.T., Secor, D., and Wingate, R.L., 2015, Testing the thermal-niche oxygen-squeeze hypothesis for estuarine striped bass: Environmental Biology of Fishes, v. 98, no. 10, p. 2083-2092, https://doi.org/10.1007/s10641-015-0431-3.","productDescription":"10 p.","startPage":"2083","endPage":"2092","onlineOnly":"N","additionalOnlineFiles":"N","ipdsId":"IP-049336","costCenters":[{"id":324,"text":"Great Lakes Science Center","active":true,"usgs":true}],"links":[{"id":305673,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"volume":"98","issue":"10","publishingServiceCenter":{"id":6,"text":"Columbus PSC"},"noUsgsAuthors":false,"publicationDate":"2015-07-02","publicationStatus":"PW","scienceBaseUri":"55a4e143e4b0183d66e453a8","contributors":{"authors":[{"text":"Kraus, Richard T. 0000-0003-4494-1841 rkraus@usgs.gov","orcid":"https://orcid.org/0000-0003-4494-1841","contributorId":2609,"corporation":false,"usgs":true,"family":"Kraus","given":"Richard","email":"rkraus@usgs.gov","middleInitial":"T.","affiliations":[{"id":324,"text":"Great Lakes Science Center","active":true,"usgs":true}],"preferred":true,"id":564215,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Secor, D.H.","contributorId":99495,"corporation":false,"usgs":true,"family":"Secor","given":"D.H.","email":"","affiliations":[],"preferred":false,"id":564699,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Wingate, Rebecca L.","contributorId":145585,"corporation":false,"usgs":false,"family":"Wingate","given":"Rebecca","email":"","middleInitial":"L.","affiliations":[],"preferred":false,"id":564700,"contributorType":{"id":1,"text":"Authors"},"rank":3}]}} ,{"id":70154742,"text":"70154742 - 2015 - Southern San Andreas Fault seismicity is consistent with the Gutenberg-Richter magnitude-frequency distribution","interactions":[],"lastModifiedDate":"2015-08-03T10:26:16","indexId":"70154742","displayToPublicDate":"2015-07-01T13:00:00","publicationYear":"2015","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":1135,"text":"Bulletin of the Seismological Society of America","onlineIssn":"1943-3573","printIssn":"0037-1106","active":true,"publicationSubtype":{"id":10}},"title":"Southern San Andreas Fault seismicity is consistent with the Gutenberg-Richter magnitude-frequency distribution","docAbstract":"The magnitudes of any collection of earthquakes nucleating in a region are generally observed to follow the Gutenberg-Richter (G-R) distribution. On some major faults, however, paleoseismic rates are higher than a G-R extrapolation from the modern rate of small earthquakes would predict. This, along with other observations, led to formulation of the characteristic earthquake hypothesis, which holds that the rate of small to moderate earthquakes is permanently low on large faults relative to the large-earthquake rate (Wesnousky et al., 1983; Schwartz and Coppersmith, 1984). We examine the rate difference between recent small to moderate earthquakes on the southern San Andreas fault (SSAF) and the paleoseismic record, hypothesizing that the discrepancy can be explained as a rate change in time rather than a deviation from G-R statistics. We find that with reasonable assumptions, the rate changes necessary to bring the small and large earthquake rates into alignment agree with the size of rate changes seen in epidemic-type aftershock sequence (ETAS) modeling, where aftershock triggering of large earthquakes drives strong fluctuations in the seismicity rates for earthquakes of all magnitudes. The necessary rate changes are also comparable to rate changes observed for other faults worldwide. These results are consistent with paleoseismic observations of temporally clustered bursts of large earthquakes on the SSAF and the absence of M greater than or equal to 7 earthquakes on the SSAF since 1857.
","language":"English","publisher":"Seismological Society of Amercia","doi":"10.1785/0120140340","usgsCitation":"Page, M.T., and Felzer, K., 2015, Southern San Andreas Fault seismicity is consistent with the Gutenberg-Richter magnitude-frequency distribution: Bulletin of the Seismological Society of America, v. 105, no. 4, p. 2070-2080, https://doi.org/10.1785/0120140340.","productDescription":"11 p.","startPage":"2070","endPage":"2080","onlineOnly":"N","additionalOnlineFiles":"N","ipdsId":"IP-060995","costCenters":[{"id":237,"text":"Earthquake Science Center","active":true,"usgs":true}],"links":[{"id":305534,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","state":"California","otherGeospatial":"Southern San Andreas Fault","volume":"105","issue":"4","publishingServiceCenter":{"id":14,"text":"Menlo Park PSC"},"noUsgsAuthors":false,"publicationDate":"2015-06-16","publicationStatus":"PW","scienceBaseUri":"55950123e4b0b6d21dd6cbc0","contributors":{"authors":[{"text":"Page, Morgan T. 0000-0001-9321-2990 mpage@usgs.gov","orcid":"https://orcid.org/0000-0001-9321-2990","contributorId":3762,"corporation":false,"usgs":true,"family":"Page","given":"Morgan","email":"mpage@usgs.gov","middleInitial":"T.","affiliations":[{"id":237,"text":"Earthquake Science Center","active":true,"usgs":true},{"id":234,"text":"Earthquake Hazards Program","active":true,"usgs":true}],"preferred":true,"id":563889,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Felzer, Karen 0000-0003-0828-5525 kfelzer@usgs.gov","orcid":"https://orcid.org/0000-0003-0828-5525","contributorId":145408,"corporation":false,"usgs":true,"family":"Felzer","given":"Karen","email":"kfelzer@usgs.gov","affiliations":[{"id":237,"text":"Earthquake Science Center","active":true,"usgs":true}],"preferred":true,"id":563890,"contributorType":{"id":1,"text":"Authors"},"rank":2}]}} ]}