{"pageNumber":"690","pageRowStart":"17225","pageSize":"25","recordCount":40797,"records":[{"id":70040672,"text":"ofr20121182 - 2012 - Predicting sea-level rise vulnerability of terrestrial habitat and wildlife of the Northwestern Hawaiian Islands","interactions":[],"lastModifiedDate":"2018-04-24T14:23:16","indexId":"ofr20121182","displayToPublicDate":"2012-11-08T00:00:00","publicationYear":"2012","noYear":false,"publicationType":{"id":18,"text":"Report"},"publicationSubtype":{"id":5,"text":"USGS Numbered Series"},"seriesTitle":{"id":330,"text":"Open-File Report","code":"OFR","onlineIssn":"2331-1258","printIssn":"0196-1497","active":true,"publicationSubtype":{"id":5}},"seriesNumber":"2012-1182","title":"Predicting sea-level rise vulnerability of terrestrial habitat and wildlife of the Northwestern Hawaiian Islands","docAbstract":"If current climate change trends continue, rising sea levels may inundate low-lying islands across the globe, placing island biodiversity at risk. Recent models predict a rise of approximately one meter (1 m) in global sea level by 2100, with larger increases possible in areas of the Pacific Ocean. Pacific Islands are unique ecosystems home to many endangered endemic plant and animal species. The Northwestern Hawaiian Islands (NWHI), which extend 1,930 kilometers (km) beyond the main Hawaiian Islands, are a World Heritage Site and part of the Papahanaumokuakea Marine National Monument. These NWHI support the largest tropical seabird rookery in the world, providing breeding habitat for 21 species of seabirds, 4 endemic land bird species and essential foraging, breeding, or haul-out habitat for other resident and migratory wildlife. In recent years, concern has grown about the increasing vulnerability of the NWHI and their wildlife populations to changing climatic patterns, particularly the uncertainty associated with potential impacts from global sea-level rise (SLR) and storms. In response to the need by managers to adapt future resource protection strategies to climate change variability and dynamic island ecosystems, we have synthesized and down scaled analyses for this important region. This report describes a 2-year study of a remote northwestern Pacific atoll ecosystem and identifies wildlife and habitat vulnerable to rising sea levels and changing climate conditions. A lack of high-resolution topographic data for low-lying islands of the NWHI had previously precluded an extensive quantitative model of the potential impacts of SLR on wildlife habitat. The first chapter (chapter 1) describes the vegetation and topography of 20 islands of Papahanaumokuakea Marine National Monument, the distribution and status of wildlife populations, and the predicted impacts for a range of SLR scenarios. Furthermore, this chapter explores the potential effects of SLR on wildlife breeding habitats for each island. The subsequent chapter (chapter 2) details a study of the Laysan Island ecosystem, describing a quantitative model that incorporates SLR, storm wave, and rising groundwater inundation. Wildlife, storm, and oceanographic data allowed for an assessment of the phenological and spatial vulnerability of Laysan Island's breeding bird species to SLR and storms. Using remote sensing and geospatial techniques, we estimated topography, classified vegetation, modeled SLR, and evaluated a range of climate change scenarios. On the basis of high-resolution airborne data collected during 2010-11 (root-mean-squared error = 0.05-0.18 m), we estimated the maximum elevation of 20 individual islands extending from Kure Atoll to French Frigate Shoals (range: 1.8-39.7 m) and computed the mean elevation (1.7 m, standard deviation 1.1 m) across all low-lying islands. We also analyzed general climate models to describe rainfall and temperature scenarios expected to influence adaptation of some plants and animals for this region. Outcomes for the NWHI predicted an increase in temperature of 1.8-2.6 degrees Celsius (&deg;C) and an annual decrease in precipitation of 24.7-76.3 millimeters (mm) across the NWHI by 2100. Our models of passive SLR (excluding wave-driven effects, erosion, and accretion) showed that approximately 4 percent of the total land area in the NWHI will be lost with scenarios of +1.0 m of SLR and 26 percent will be lost with +2.0 m of SLR. Some atolls are especially vulnerable to SLR. For example, at Pearl and Hermes Atoll our analysis indicated substantial habitat losses with 43 percent of the land area inundated at +1.0 m SLR and 92 percent inundated at +2.0 m SLR. Across the NWHI, seven islands will be completely submerged with +2.0 m SLR. The limited global ranges of some tropical nesting birds make them particularly vulnerable to climate change impacts in the NWHI. Climate change scenarios and potential SLR impacts presented here emphasize the need for early climate change adaptation and mitigation planning, especially for species with limited breeding distributions and/or ranges restricted primarily to the low-lying NWHI: <i>Cyperus pennatiformis</i> var. <i>bryanii</i>, Black-footed Albatross (<i>Phoebastria nigripes</i>), Laysan Albatross (<i>P. immutabilis</i>), Bonin Petrel (<i>Pterodroma hypoleuca</i>), Gray-backed Tern (<i>Onychoprion lunatus</i>), Laysan Teal (<i>Anas laysanensis</i>), Laysan Finch (<i>Telespiza cantans</i>), and Hawaiian monk seal (<i>Monachus schauinslandi</i>). Furthermore, SLR scenarios that include the effects of wave dynamics and groundwater rise may indicate amplified vulnerability to climate change driven habitat loss on low-lying islands. In chapter 2, we incorporated the combined effects of SLR, dynamic wave-driven inundation, and rising groundwater in a quantitative study specifically for the Laysan Island ecosystem. This is the first hydrodynamic model to simulate the combined impacts of SLR and wave-driven inundation in the NWHI. We developed a high-resolution digital elevation model (mean vertical accuracy of 0.32 m) for the island. Then using recent satellite imagery, geospatial models, and historical oceanographic, storm, and biological data we estimated potential inundation extent, habitat loss, and wildlife population impacts for a range of potential SLR scenarios (0.00, +0.50, +1.00, +1.50, and +2.00 m) that may occur over the next century. Additionally, we estimated the carrying capacity of Laysan Island for five species based on the available population monitoring data and described how potential losses in nesting habitat could influence population dynamics for Black-footed Albatross, Laysan Albatross, Red-footed Booby (Sula sula), Laysan Teal, and Laysan Finch. For some other seabird populations (Masked Booby, <i>S. dactylatra</i>; Brown Booby, <i>S. leucogaster</i>; Great Frigatebird, <i>Fregata minor</i>; and Sooty Tern, <i>Onychoprion fuscata</i>), we used recent colony distribution data, land cover maps, and nesting behavior to estimate potential losses of nesting habitat from SLR and wave-driven inundation. We observed far greater potential impacts of SLR to wildlife with the dynamic wave-driven modeling approach than with the passive modeling approach. Depending on SLR scenario and coastal orientation, during storms under a +2.00 m SLR scenario, the wave-driven inundation model predicted three times more inundation than the passive model (17.2 percent of total terrestrial area versus 4.6 percent, respectively). Large-wave events generally added 1 m of water height to passive inundation surfaces, therefore our dynamic models (during storm events) forecasted comparable inundation extents earlier than passive models. Although wave-driven water levels were highest in the northwest quadrant of Laysan Island, the greatest extent of inundation occurred in the southeast where coastal dunes less than 3 m above mean sea level provide little protection from wave-driven inundation. When wave-driven inundation was included in the SLR model for Laysan Island greater nesting habitat loss and potential impacts on wildlife population dynamics were predicted. The consequences of habitat loss due to SLR may be worse for species with colonies in the wave-exposed coastal zones (for example, Black-footed Albatross) and for populations already near the island's carrying capacity (for example, Laysan Teal). Species whose peak incubation and chick-rearing periods coincide with seasonally high wave heights also will be increasingly vulnerable, especially those species nesting on the ground in areas vulnerable to inundation, such as Gray-backed Tern and Black-footed Albatross. Other species that have space for population growth, or are not restricted to a narrow range of habitat types on Laysan (for instance, Sooty Terns), may be less sensitive to habitat loss from SLR over the next century. Our assessments of inundation risk, habitat loss, and wildlife species vulnerability synthesize current knowledge about individual islands and contribute to a broader understanding of the impacts of inundation from SLR and storm-induced waves. Yet, most NWHI and their bird populations lack monitoring data to evaluate adaptations to and impacts of climate change. Exceptions include some data sets from long-term monitoring of wildlife populations, tides, or weather at French Frigate Shoals, Laysan Island, and Midway Atoll. These data sets are potentially valuable baselines, which could be informative for adaptive learning (integrating management and science) to predict, adapt, and mitigate the effects of climate change on NWHI wildlife in the future. This study provides the first quantitative vulnerability assessment for all of the low-lying NWHI, and results identify biological communities, locales, and resident endangered species of Papahanaumokuakea Marine National Monument expected to be at risk from SLR. This report is also intended as a reference for managers and conservation planners, a tool to identify and potentially reduce uncertainty, and a starting place for developing climate change monitoring priorities and future scientific studies.","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/ofr20121182","collaboration":"Chapter 1: Climate change vulnerability assessment of the low-lying northwestern Hawaiian Islands; Chapter 2: Sea-level rise and wave-driven inundation models for Laysan Island","usgsCitation":"Reynolds, M.H., Berkowitz, P., Courtot, K., and Krause, C.M., 2012, Predicting sea-level rise vulnerability of terrestrial habitat and wildlife of the Northwestern Hawaiian Islands: U.S. Geological Survey Open-File Report 2012-1182, ix, 139 p., https://doi.org/10.3133/ofr20121182.","productDescription":"ix, 139 p.","numberOfPages":"153","onlineOnly":"Y","costCenters":[{"id":411,"text":"National Climate Change and Wildlife Science Center","active":true,"usgs":true},{"id":521,"text":"Pacific Island Ecosystems Research Center","active":false,"usgs":true},{"id":36940,"text":"National Climate Adaptation Science Center","active":true,"usgs":true}],"links":[{"id":438807,"rank":0,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/P9P5WHVH","text":"USGS data release","linkHelpText":"Northwestern Hawaiian Islands Sea-level Rise Scenarios and Models 2010-2015"},{"id":263022,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/ofr_2012_1182.gif"},{"id":263021,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/of/2012/1182/of2012-1182.pdf"},{"id":263020,"type":{"id":15,"text":"Index Page"},"url":"https://pubs.usgs.gov/of/2012/1182/"}],"country":"United States","state":"Hawai'i","geographicExtents":"{ \"type\": \"FeatureCollection\", \"features\": [ { \"type\": \"Feature\", \"properties\": {}, \"geometry\": { \"type\": \"Polygon\", \"coordinates\": [ [ [ -180.0,10.0 ], [ -180.0,33.0 ], [ -150.0,33.0 ], [ -150.0,10.0 ], [ -180.0,10.0 ] ] ] } } ] }","noUsgsAuthors":false,"publicationStatus":"PW","scienceBaseUri":"509cf2bce4b0e374086f468b","contributors":{"editors":[{"text":"Reynolds, Michelle H. 0000-0001-7253-8158 mreynolds@usgs.gov","orcid":"https://orcid.org/0000-0001-7253-8158","contributorId":3871,"corporation":false,"usgs":true,"family":"Reynolds","given":"Michelle","email":"mreynolds@usgs.gov","middleInitial":"H.","affiliations":[{"id":5049,"text":"Pacific Islands Ecosys Research Center","active":true,"usgs":true},{"id":521,"text":"Pacific Island Ecosystems Research Center","active":false,"usgs":true}],"preferred":true,"id":509100,"contributorType":{"id":2,"text":"Editors"},"rank":1},{"text":"Berkowitz, Paul pberkowitz@usgs.gov","contributorId":4642,"corporation":false,"usgs":true,"family":"Berkowitz","given":"Paul","email":"pberkowitz@usgs.gov","affiliations":[],"preferred":true,"id":509101,"contributorType":{"id":2,"text":"Editors"},"rank":2},{"text":"Courtot, Karen N.","contributorId":26909,"corporation":false,"usgs":true,"family":"Courtot","given":"Karen N.","affiliations":[],"preferred":false,"id":509102,"contributorType":{"id":2,"text":"Editors"},"rank":3},{"text":"Krause, Crystal M.","contributorId":101919,"corporation":false,"usgs":true,"family":"Krause","given":"Crystal","email":"","middleInitial":"M.","affiliations":[],"preferred":false,"id":509103,"contributorType":{"id":2,"text":"Editors"},"rank":4}],"authors":[{"text":"Reynolds, Michelle H. 0000-0001-7253-8158 mreynolds@usgs.gov","orcid":"https://orcid.org/0000-0001-7253-8158","contributorId":3871,"corporation":false,"usgs":true,"family":"Reynolds","given":"Michelle","email":"mreynolds@usgs.gov","middleInitial":"H.","affiliations":[{"id":5049,"text":"Pacific Islands Ecosys Research Center","active":true,"usgs":true},{"id":521,"text":"Pacific Island Ecosystems Research Center","active":false,"usgs":true}],"preferred":true,"id":468766,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Berkowitz, Paul pberkowitz@usgs.gov","contributorId":4642,"corporation":false,"usgs":true,"family":"Berkowitz","given":"Paul","email":"pberkowitz@usgs.gov","affiliations":[],"preferred":true,"id":468767,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Courtot, Karen N.","contributorId":26909,"corporation":false,"usgs":true,"family":"Courtot","given":"Karen N.","affiliations":[],"preferred":false,"id":468768,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Krause, Crystal M.","contributorId":101919,"corporation":false,"usgs":true,"family":"Krause","given":"Crystal","email":"","middleInitial":"M.","affiliations":[],"preferred":false,"id":468769,"contributorType":{"id":1,"text":"Authors"},"rank":4}]}}
,{"id":70040649,"text":"cir1379 - 2012 - The United States National Climate Assessment - Alaska Technical Regional Report","interactions":[],"lastModifiedDate":"2012-11-08T08:41:59","indexId":"cir1379","displayToPublicDate":"2012-11-07T00:00:00","publicationYear":"2012","noYear":false,"publicationType":{"id":18,"text":"Report"},"publicationSubtype":{"id":5,"text":"USGS Numbered Series"},"seriesTitle":{"id":307,"text":"Circular","code":"CIR","onlineIssn":"2330-5703","printIssn":"1067-084X","active":true,"publicationSubtype":{"id":5}},"seriesNumber":"1379","title":"The United States National Climate Assessment - Alaska Technical Regional Report","docAbstract":"The Alaskan landscape is changing, both in terms of effects of human activities as a consequence of increased population, social and economic development and their effects on the local and broad landscape; and those effects that accompany naturally occurring hazards such as volcanic eruptions, earthquakes, and tsunamis. Some of the most prevalent changes, however, are those resulting from a changing climate, with both near term and potential upcoming effects expected to continue into the future. Alaska's average annual statewide temperatures have increased by nearly 4&deg;F from 1949 to 2005, with significant spatial variability due to the large latitudinal and longitudinal expanse of the State. Increases in mean annual temperature have been greatest in the interior region, and smallest in the State's southwest coastal regions. In general, however, trends point toward increases in both minimum temperatures, and in fewer extreme cold days. Trends in precipitation are somewhat similar to those in temperature, but with more variability. On the whole, Alaska saw a 10-percent increase in precipitation from 1949 to 2005, with the greatest increases recorded in winter. The National Climate Assessment has designated two well-established scenarios developed by the Intergovernmental Panel on Climate Change (Nakicenovic and others, 2001) as a minimum set that technical and author teams considered as context in preparing portions of this assessment. These two scenarios are referred to as the Special Report on Emissions Scenarios A2 and B1 scenarios, which assume either a continuation of recent trends in fossil fuel use (A2) or a vigorous global effort to reduce fossil fuel use (B1). Temperature increases from 4 to 22&deg;F are predicted (to 2070-2099) depending on which emissions scenario (A2 or B1) is used with the least warming in southeast Alaska and the greatest in the northwest. Concomitant with temperature changes, by the end of the 21st century the growing season is expected to lengthen by 15-25 days in some areas of Alaska, with much of that corresponding with earlier spring snow melt. Future projections of precipitation (30-80 years) over Alaska show an increase across the State, with the largest changes in the northwest and smallest in the southeast. Because of increasing temperatures and growing season length, however, increased precipitation may not correspond with increased water availability, due to temperature related increased evapotranspiration. The extent of snow cover in the Northern Hemisphere has decreased by about 10 percent since the late 1960s, with stronger trends noted since the late 1980s. Alaska has experienced similar trends, with a strong decrease in snow cover extent occurring in May. When averaged across the State, the disappearance of snow in the spring has occurred from 4 to 6 days earlier per decade, and snow return in fall has occurred approximately 2 days later per decade. This change appears to be driven by climate warming rather than a decrease in winter precipitation, with average winter temperatures also increasing by about 2.5&deg;F. The extent of sea ice has been declining, as has been widely published in both national and scientific media outlets, and is projected to continue to decline during this century. The observed decline in annual sea ice minimum extent (September) has occurred more rapidly than was predicted by climate models and has been accompanied by decreases in ice thickness and in the presence of multi-year ice. This decrease was first documented by satellite imagery in the late 1970s for the Bering and Chukchi Seas, and is projected to continue, with the potential for the disappearance of summer sea ice by mid- to late century. A new phenomenon that was not reported in previous assessments is ocean acidification. Uptake of carbon dioxide (CO2) by oceans has a significant effect on marine biogeochemistry by reducing seawater pH. Ocean acidification is of particular concern in Alaska, because cold sea water absorbs CO2 more rapidly than warm water, and a decrease in sea ice extent has allowed increased sea surface exposure and more uptake of CO2 into these northern waters. Ocean acidification will likely affect the ability of organisms to produce and maintain shell material, such as aragonite or calcite (calcium carbonate minerals structured from carbonate ions), required by many shelled organism, from mollusks to corals to microscopic organisms at the base of the food chain. Direct biological effects in Alaska further along the food chain have yet to be studied and may vary among organisms. Some of the potentially most significant changes to Alaska that could result from a changing climate are the effects on the terrestrial cryosphere - particularly glaciers and permafrost. Alaskan glaciers are changing at a rapid rate, the primary driver appearing to be temperature. Statewide, glaciers lost 13 cubic miles of ice annually from the 1950s to the 1990s, and that rate doubled in the 2000s. However, like temperature and precipitation, glacier ice loss is not spatially uniform; most glaciers are losing mass, yet some are growing (for example Hubbard Glacier in southeast Alaska). Alaska glaciers with the most rapid loss are those terminating in sea water or lakes. With this increasing rate of melt, the contribution of surplus fresh water entering into the oceans from Alaska's glaciers, as well as those in neighboring British Columbia, Canada, is approximately 20 percent of that contributed by the Greenland Ice Sheet. Permafrost degradation (that is, the thawing of ice-rich soils) is currently (2012) impacting infrastructure and surface-water availability in areas of both discontinuous and continuous ground ice. Over most of the State, the permafrost is warming, with increasing temperatures broadly consistent with increasing air temperatures. On the Arctic coastal plain of Alaska, permafrost temperatures showed some cooling in the 1950s and 1960s but have been followed by a roughly 5&deg;F increase since the 1980s. Many areas in the continuous permafrost zone have seen increases in temperature in the seasonally active layer and a decrease in re-freezing rates. Changes in the discontinuous permafrost zone are initially much more observable due to the resulting thermokarst terrain (land surface formed as ice rich permafrost thaws), most notable in boreal forested areas. Climate warming in Alaska has potentially broad implications for human health and food security, especially in rural areas, as well as increased risk for injury with changing winter ice conditions. Additionally, such warming poses the potential for increasing damage to existing water and sanitation facilities and challenges for development of new facilities, especially in areas underlain by permafrost. Non-infectious and infectious diseases also are becoming an increasing concern. For example, from 1999 to 2006 there was a statistically significant increase in medical claims for insectbite reactions in five of six regions of Alaska, with the largest percentage increase occurring in the most northern areas. The availability and quality of subsistence foods, normally considered to be very healthy, may change due to changing access, changing habitats, and spoilage of meat in food storage cellars. These and other trends and potential outcomes resulting from a changing climate are further described in this report. In addition, we describe new science leadership activities that have been initiated to address and provide guidance toward conducting research aimed at making available information for policy makers and land management agencies to better understand, address, and plan for changes to the local and regional environment. This report cites data in both metric and standard units due to the contributions by numerous authors and the direct reference of their data.","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/cir1379","usgsCitation":"Markon, C., Trainor, S., and Chapin, F.S., 2012, The United States National Climate Assessment - Alaska Technical Regional Report: U.S. Geological Survey Circular 1379, xiv, 148 p., https://doi.org/10.3133/cir1379.","productDescription":"xiv, 148 p.","numberOfPages":"166","costCenters":[{"id":114,"text":"Alaska Science Center","active":true,"usgs":true}],"links":[{"id":262980,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/cir_1379.jpg"},{"id":262978,"type":{"id":15,"text":"Index Page"},"url":"https://pubs.usgs.gov/circ/1379/"},{"id":262979,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/circ/1379/pdf/circ1379.pdf"}],"country":"United States","state":"Alaska","geographicExtents":"{ \"type\": \"FeatureCollection\", \"features\": [ { \"type\": \"Feature\", \"properties\": {}, \"geometry\": { \"type\": \"Polygon\", \"coordinates\": [ [ [ -172.45,51.21 ], [ -172.45,71.39 ], [ -129.99,71.39 ], [ -129.99,51.21 ], [ -172.45,51.21 ] ] ] } } ] }","noUsgsAuthors":false,"publicationStatus":"PW","scienceBaseUri":"509cf2f2e4b0e374086f46ae","contributors":{"editors":[{"text":"Markon, Carl J.","contributorId":67122,"corporation":false,"usgs":true,"family":"Markon","given":"Carl J.","affiliations":[],"preferred":false,"id":509084,"contributorType":{"id":2,"text":"Editors"},"rank":1},{"text":"Trainor, Sarah F.","contributorId":21396,"corporation":false,"usgs":true,"family":"Trainor","given":"Sarah F.","affiliations":[],"preferred":false,"id":509082,"contributorType":{"id":2,"text":"Editors"},"rank":2},{"text":"Chapin, F. Stuart III","contributorId":65632,"corporation":false,"usgs":false,"family":"Chapin","given":"F.","suffix":"III","email":"","middleInitial":"Stuart","affiliations":[{"id":13117,"text":"Institute of Arctic Biology, University of Alaska Fairbanks","active":true,"usgs":false}],"preferred":false,"id":509083,"contributorType":{"id":2,"text":"Editors"},"rank":3}],"authors":[{"text":"Markon, Carl J.","contributorId":67122,"corporation":false,"usgs":true,"family":"Markon","given":"Carl J.","affiliations":[],"preferred":false,"id":468713,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Trainor, Sarah F.","contributorId":21396,"corporation":false,"usgs":true,"family":"Trainor","given":"Sarah F.","affiliations":[],"preferred":false,"id":468711,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Chapin, F. 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,{"id":70040695,"text":"sir20125168 - 2012 - Construction of estimated flow- and load-duration curves for Kentucky using the <u>W</u>ater <u>A</u>vailability <u>T</u>ool for <u>E</u>nvironmental <u>R</u>esources (WATER)","interactions":[],"lastModifiedDate":"2012-11-09T12:15:41","indexId":"sir20125168","displayToPublicDate":"2012-11-07T00:00:00","publicationYear":"2012","noYear":false,"publicationType":{"id":18,"text":"Report"},"publicationSubtype":{"id":5,"text":"USGS Numbered Series"},"seriesTitle":{"id":334,"text":"Scientific Investigations Report","code":"SIR","onlineIssn":"2328-0328","printIssn":"2328-031X","active":true,"publicationSubtype":{"id":5}},"seriesNumber":"2012-5168","title":"Construction of estimated flow- and load-duration curves for Kentucky using the <u>W</u>ater <u>A</u>vailability <u>T</u>ool for <u>E</u>nvironmental <u>R</u>esources (WATER)","docAbstract":"Flow- and load-duration curves were constructed from the model outputs of the U.S. Geological Survey's Water Availability Tool for Environmental Resources (WATER) application for streams in Kentucky. The WATER application was designed to access multiple geospatial datasets to generate more than 60 years of statistically based streamflow data for Kentucky. The WATER application enables a user to graphically select a site on a stream and generate an estimated hydrograph and flow-duration curve for the watershed upstream of that point. The flow-duration curves are constructed by calculating the exceedance probability of the modeled daily streamflows. User-defined water-quality criteria and (or) sampling results can be loaded into the WATER application to construct load-duration curves that are based on the modeled streamflow results. Estimates of flow and streamflow statistics were derived from TOPographically Based Hydrological MODEL (TOPMODEL) simulations in the WATER application. A modified TOPMODEL code, SDP-TOPMODEL (Sinkhole Drainage Process-TOPMODEL) was used to simulate daily mean discharges over the period of record for 5 karst and 5 non-karst watersheds in Kentucky in order to verify the calibrated model. A statistical evaluation of the model's verification simulations show that calibration criteria, established by previous WATER application reports, were met thus insuring the model's ability to provide acceptably accurate estimates of discharge at gaged and ungaged sites throughout Kentucky. Flow-duration curves are constructed in the WATER application by calculating the exceedence probability of the modeled daily flow values. The flow-duration intervals are expressed as a percentage, with zero corresponding to the highest stream discharge in the streamflow record. Load-duration curves are constructed by applying the loading equation (Load = Flow*Water-quality criterion) at each flow interval.","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/sir20125168","collaboration":"Prepared in cooperation with the Kentucky Division of Water","usgsCitation":"Unthank, M.D., Newson, J.K., Williamson, T., and Nelson, H.L., 2012, Construction of estimated flow- and load-duration curves for Kentucky using the <u>W</u>ater <u>A</u>vailability <u>T</u>ool for <u>E</u>nvironmental <u>R</u>esources (WATER): U.S. Geological Survey Scientific Investigations Report 2012-5168, vi, 14 p., https://doi.org/10.3133/sir20125168.","productDescription":"vi, 14 p.","numberOfPages":"24","onlineOnly":"Y","costCenters":[{"id":354,"text":"Kentucky Water Science Center","active":true,"usgs":true}],"links":[{"id":263069,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/sir_2012_5168.gif"},{"id":263067,"type":{"id":15,"text":"Index Page"},"url":"https://pubs.usgs.gov/sir/2012/5168/"},{"id":263068,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/sir/2012/5168/pdf/sir2012-5168_report_508_rev110612.pdf"}],"country":"United States","state":"Kentucky","geographicExtents":"{ \"type\": \"FeatureCollection\", \"features\": [ { \"type\": \"Feature\", \"properties\": {}, \"geometry\": { \"type\": \"Polygon\", \"coordinates\": [ [ [ -89.5715,36.4972 ], [ -89.5715,39.1475 ], [ -81.965,39.1475 ], [ -81.965,36.4972 ], [ -89.5715,36.4972 ] ] ] } } ] }","noUsgsAuthors":false,"publicationStatus":"PW","scienceBaseUri":"509e3412e4b0cbd9af3af72b","contributors":{"authors":[{"text":"Unthank, Michael D. 0000-0003-2483-0431 munthank@usgs.gov","orcid":"https://orcid.org/0000-0003-2483-0431","contributorId":3902,"corporation":false,"usgs":true,"family":"Unthank","given":"Michael","email":"munthank@usgs.gov","middleInitial":"D.","affiliations":[{"id":27231,"text":"Indiana-Kentucky Water Science Center","active":true,"usgs":true}],"preferred":true,"id":468803,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Newson, Jeremy K. jknewson@usgs.gov","contributorId":4159,"corporation":false,"usgs":true,"family":"Newson","given":"Jeremy","email":"jknewson@usgs.gov","middleInitial":"K.","affiliations":[{"id":677,"text":"Wisconsin Water Science Center","active":true,"usgs":true}],"preferred":false,"id":468805,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Williamson, Tanja N. tnwillia@usgs.gov","contributorId":452,"corporation":false,"usgs":true,"family":"Williamson","given":"Tanja N.","email":"tnwillia@usgs.gov","affiliations":[{"id":354,"text":"Kentucky Water Science Center","active":true,"usgs":true}],"preferred":false,"id":468802,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Nelson, Hugh L. hlnelson@usgs.gov","contributorId":4158,"corporation":false,"usgs":true,"family":"Nelson","given":"Hugh","email":"hlnelson@usgs.gov","middleInitial":"L.","affiliations":[{"id":354,"text":"Kentucky Water Science Center","active":true,"usgs":true}],"preferred":true,"id":468804,"contributorType":{"id":1,"text":"Authors"},"rank":4}]}}
,{"id":70047233,"text":"70047233 - 2012 - Hydrology of the unsaturated zone, Yucca Mountain, Nevada","interactions":[],"lastModifiedDate":"2013-11-05T14:19:10","indexId":"70047233","displayToPublicDate":"2012-11-05T13:50:00","publicationYear":"2012","noYear":false,"publicationType":{"id":5,"text":"Book chapter"},"publicationSubtype":{"id":24,"text":"Book Chapter"},"title":"Hydrology of the unsaturated zone, Yucca Mountain, Nevada","docAbstract":"The unsaturated zone at Yucca Mountain was investigated as a possible site for the nation's first high-level nuclear waste repository. Scientific investigations included infiltration studies, matrix properties testing, borehole testing and monitoring, underground excavation and testing, and the development of conceptual and numerical models of the hydrologic processes at Yucca Mountain. Infiltration estimates by empirical and geochemical methods range from 0.2 to 1.4 mm/yr and 0.2–6.0 mm/yr, respectively. Infiltration estimates from numerical models range from 4.5 mm/yr to 17.6 mm/yr. Rock matrix properties vary vertically and laterally as the result of depositional processes and subsequent postdepositional alteration. Laboratory tests indicate that the average matrix porosity and hydraulic conductivity values for the main level of the proposed repository (Topopah Spring Tuff middle nonlithophysal zone) are 0.08 and 4.7 × 10<sup>−12</sup> m/s, respectively. In situ fracture hydraulic conductivity values are 3–6 orders of magnitude greater. The permeability of fault zones is approximately an order of magnitude greater than that of the surrounding rock unit. Water samples from the fault zones have tritium concentrations that indicate some component of postnuclear testing. Gas and water vapor movement through the unsaturated zone is driven by changes in barometric pressure, temperature-induced density differences, and wind effects. The subsurface pressure response to surface barometric changes is controlled by the distribution and interconnectedness of fractures, the presence of faults and their ability to conduct gas and vapor, and the moisture content and matrix permeability of the rock units. In situ water potential values are generally less than −0.2 MPa (−2 bar), and the water potential gradients in the Topopah Spring Tuff units are very small. Perched-water zones at Yucca Mountain are associated with the basal vitrophyre of the Topopah Spring Tuff or the Calico Hills bedded tuff. Thermal gradients in the unsaturated zone vary with location, and range from ~2.0 °C to 6.0 °C per 100 m; the variability appears to be associated with topography. Large-scale heater testing identified a heat-pipe signature at ~97 °C, and identified thermally induced and excavation-induced changes in the stress field. Elevated gas-phase CO<sub>2</sub> concentrations and a decrease in the pH of water from the condensation zone also were identified. Conceptual and numerical flow and transport models of Yucca Mountain indicate that infiltration is highly variable, both spatially and temporally. Flow in the unsaturated zone is predominately through fractures in the welded units of the Tiva Canyon and Topopah Spring Tuffs and predominately through the matrix in the Paintbrush Tuff nonwelded units and Calico Hills Formation. Isolated, transient, fast-flow paths, such as faults, do exist but probably carry only a small portion of the total liquid-water flux at Yucca Mountain. The Paintbrush Tuff nonwelded units act as a storage buffer for transient infiltration pulses. Faults may act as flow boundaries and/or fast pathways. Below the proposed repository horizon, low-permeability lithostratigraphic units of the Topopah Spring Tuff and/or the Calico Hills Formation may divert flow laterally to faults that act as conduits to the water table. Advective transport pathways are consistent with flow pathways. Matrix diffusion is the major mechanism for mass transfer between fractures and the matrix and may contribute to retardation of radionuclide transport when fracture flow is dominant. Sorption may retard the movement of radionuclides in the unsaturated zone; however, sorption on mobile colloids may enhance radionuclide transport. Dispersion is not expected to be a major transport mechanism in the unsaturated zone at Yucca Mountain. Natural analogue studies support the concepts that percolating water may be diverted around underground openings and that the percentage of infiltration that becomes seepage decreases as infiltration decreases.","largerWorkType":{"id":18,"text":"Report"},"largerWorkTitle":"Hydrology and geochemistry of Yucca Mountain and vicinity, Southern Nevada and California","largerWorkSubtype":{"id":3,"text":"Organization Series"},"language":"English","publisher":"Geological Society of America","publisherLocation":"Boulder, CO","doi":"10.1130/2012.1209(02)","usgsCitation":"LeCain, G.D., and Stuckless, J.S., 2012, Hydrology of the unsaturated zone, Yucca Mountain, Nevada, chap. <i>of</i> Hydrology and geochemistry of Yucca Mountain and vicinity, Southern Nevada and California, v. 209, p. 9-72, https://doi.org/10.1130/2012.1209(02).","productDescription":"64 p.","startPage":"9","endPage":"72","numberOfPages":"64","ipdsId":"IP-009120","costCenters":[],"links":[{"id":278796,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"},{"id":278774,"type":{"id":10,"text":"Digital Object Identifier"},"url":"https://dx.doi.org/10.1130/2012.1209(02)"}],"country":"United States","state":"Nevada","otherGeospatial":"Calico Hills Formation;Paintbrush Tuff;Tiva Canyon;Topopah Spring Tuff;Yucca Mountain","geographicExtents":"{ \"type\": \"FeatureCollection\", \"features\": [ { \"type\": \"Feature\", \"properties\": {}, \"geometry\": { \"type\": \"Polygon\", \"coordinates\": [ [ [ -117.2379,35.4976 ], [ -117.2379,37.501 ], [ -115.4938,37.501 ], [ -115.4938,35.4976 ], [ -117.2379,35.4976 ] ] ] } } ] }","volume":"209","noUsgsAuthors":false,"publicationStatus":"PW","scienceBaseUri":"527a2188e4b051792d019550","contributors":{"authors":[{"text":"LeCain, Gary D.","contributorId":52207,"corporation":false,"usgs":true,"family":"LeCain","given":"Gary","email":"","middleInitial":"D.","affiliations":[],"preferred":false,"id":481465,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Stuckless, John S. 0000-0002-7536-0444 jstuckless@usgs.gov","orcid":"https://orcid.org/0000-0002-7536-0444","contributorId":4974,"corporation":false,"usgs":true,"family":"Stuckless","given":"John","email":"jstuckless@usgs.gov","middleInitial":"S.","affiliations":[{"id":318,"text":"Geosciences and Environmental Change Science Center","active":true,"usgs":true}],"preferred":true,"id":481464,"contributorType":{"id":1,"text":"Authors"},"rank":2}]}}
,{"id":70040616,"text":"sir20125232 - 2012 - Computing daily mean streamflow at ungaged locations in Iowa by using the Flow Anywhere and Flow Duration Curve Transfer statistical methods","interactions":[],"lastModifiedDate":"2012-11-05T15:58:01","indexId":"sir20125232","displayToPublicDate":"2012-11-05T00:00:00","publicationYear":"2012","noYear":false,"publicationType":{"id":18,"text":"Report"},"publicationSubtype":{"id":5,"text":"USGS Numbered Series"},"seriesTitle":{"id":334,"text":"Scientific Investigations Report","code":"SIR","onlineIssn":"2328-0328","printIssn":"2328-031X","active":true,"publicationSubtype":{"id":5}},"seriesNumber":"2012-5232","title":"Computing daily mean streamflow at ungaged locations in Iowa by using the Flow Anywhere and Flow Duration Curve Transfer statistical methods","docAbstract":"The U.S. Geological Survey (USGS) maintains approximately 148 real-time streamgages in Iowa for which daily mean streamflow information is available, but daily mean streamflow data commonly are needed at locations where no streamgages are present. Therefore, the USGS conducted a study as part of a larger project in cooperation with the Iowa Department of Natural Resources to develop methods to estimate daily mean streamflow at locations in ungaged watersheds in Iowa by using two regression-based statistical methods. The regression equations for the statistical methods were developed from historical daily mean streamflow and basin characteristics from streamgages within the study area, which includes the entire State of Iowa and adjacent areas within a 50-mile buffer of Iowa in neighboring states. Results of this study can be used with other techniques to determine the best method for application in Iowa and can be used to produce a Web-based geographic information system tool to compute streamflow estimates automatically. The Flow Anywhere statistical method is a variation of the drainage-area-ratio method, which transfers same-day streamflow information from a reference streamgage to another location by using the daily mean streamflow at the reference streamgage and the drainage-area ratio of the two locations. The Flow Anywhere method modifies the drainage-area-ratio method in order to regionalize the equations for Iowa and determine the best reference streamgage from which to transfer same-day streamflow information to an ungaged location. Data used for the Flow Anywhere method were retrieved for 123 continuous-record streamgages located in Iowa and within a 50-mile buffer of Iowa. The final regression equations were computed by using either left-censored regression techniques with a low limit threshold set at 0.1 cubic feet per second (ft3/s) and the daily mean streamflow for the 15th day of every other month, or by using an ordinary-least-squares multiple linear regression method and the daily mean streamflow for the 15th day of every other month. The Flow Duration Curve Transfer method was used to estimate unregulated daily mean streamflow from the physical and climatic characteristics of gaged basins. For the Flow Duration Curve Transfer method, daily mean streamflow quantiles at the ungaged site were estimated with the parameter-based regression model, which results in a continuous daily flow-duration curve (the relation between exceedance probability and streamflow for each day of observed streamflow) at the ungaged site. By the use of a reference streamgage, the Flow Duration Curve Transfer is converted to a time series. Data used in the Flow Duration Curve Transfer method were retrieved for 113 continuous-record streamgages in Iowa and within a 50-mile buffer of Iowa. The final statewide regression equations for Iowa were computed by using a weighted-least-squares multiple linear regression method and were computed for the 0.01-, 0.05-, 0.10-, 0.15-, 0.20-, 0.30-, 0.40-, 0.50-, 0.60-, 0.70-, 0.80-, 0.85-, 0.90-, and 0.95-exceedance probability statistics determined from the daily mean streamflow with a reporting limit set at 0.1 ft<sup>3</sup>/s. The final statewide regression equation for Iowa computed by using left-censored regression techniques was computed for the 0.99-exceedance probability statistic determined from the daily mean streamflow with a low limit threshold and a reporting limit set at 0.1 ft<sup>3</sup>/s. For the Flow Anywhere method, results of the validation study conducted by using six streamgages show that differences between the root-mean-square error and the mean absolute error ranged from 1,016 to 138 ft<sup>3</sup>/s, with the larger value signifying a greater occurrence of outliers between observed and estimated streamflows. Root-mean-square-error values ranged from 1,690 to 237 ft<sup>3</sup>/s. Values of the percent root-mean-square error ranged from 115 percent to 26.2 percent. The logarithm (base 10) streamflow percent root-mean-square error ranged from 13.0 to 5.3 percent. Root-mean-square-error observations standard-deviation-ratio values ranged from 0.80 to 0.40. Percent-bias values ranged from 25.4 to 4.0 percent. Untransformed streamflow Nash-Sutcliffe efficiency values ranged from 0.84 to 0.35. The logarithm (base 10) streamflow Nash-Sutcliffe efficiency values ranged from 0.86 to 0.56. For the streamgage with the best agreement between observed and estimated streamflow, higher streamflows appear to be underestimated. For the streamgage with the worst agreement between observed and estimated streamflow, low flows appear to be overestimated whereas higher flows seem to be underestimated. Estimated cumulative streamflows for the period October 1, 2004, to September 30, 2009, are underestimated by -25.8 and -7.4 percent for the closest and poorest comparisons, respectively. For the Flow Duration Curve Transfer method, results of the validation study conducted by using the same six streamgages show that differences between the root-mean-square error and the mean absolute error ranged from 437 to 93.9 ft<sup>3</sup>/s, with the larger value signifying a greater occurrence of outliers between observed and estimated streamflows. Root-mean-square-error values ranged from 906 to 169 ft<sup>3</sup>/s. Values of the percent root-mean-square-error ranged from 67.0 to 25.6 percent. The logarithm (base 10) streamflow percent root-mean-square error ranged from 12.5 to 4.4 percent. Root-mean-square-error observations standard-deviation-ratio values ranged from 0.79 to 0.40. Percent-bias values ranged from 22.7 to 0.94 percent. Untransformed streamflow Nash-Sutcliffe efficiency values ranged from 0.84 to 0.38. The logarithm (base 10) streamflow Nash-Sutcliffe efficiency values ranged from 0.89 to 0.48. For the streamgage with the closest agreement between observed and estimated streamflow, there is relatively good agreement between observed and estimated streamflows. For the streamgage with the poorest agreement between observed and estimated streamflow, streamflows appear to be substantially underestimated for much of the time period. Estimated cumulative streamflow for the period October 1, 2004, to September 30, 2009, are underestimated by -9.3 and -22.7 percent for the closest and poorest comparisons, respectively.","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/sir20125232","collaboration":"Prepared in cooperation with the Iowa Department of Natural Resources","usgsCitation":"Linhart, S., Nania, J.F., Sanders, C.L., and Archfield, S.A., 2012, Computing daily mean streamflow at ungaged locations in Iowa by using the Flow Anywhere and Flow Duration Curve Transfer statistical methods: U.S. Geological Survey Scientific Investigations Report 2012-5232, vi, 50 p., https://doi.org/10.3133/sir20125232.","productDescription":"vi, 50 p.","numberOfPages":"60","onlineOnly":"Y","ipdsId":"IP-033054","costCenters":[{"id":351,"text":"Iowa Water Science Center","active":true,"usgs":true}],"links":[{"id":262965,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/sir_2012_5232.gif"},{"id":262963,"type":{"id":15,"text":"Index Page"},"url":"https://pubs.usgs.gov/sir/2012/5232/"},{"id":262964,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/sir/2012/5232/sir2012-5232.pdf"}],"scale":"24000","projection":"Universal Transverse Mercator projection, Zone 15","country":"United States","state":"Illinois;Iowa;Minnesota;Missouri;Nebraska;Wisconsin","geographicExtents":"{ \"type\": \"FeatureCollection\", \"features\": [ { \"type\": \"Feature\", \"properties\": {}, \"geometry\": { \"type\": \"Polygon\", \"coordinates\": [ [ [ -98.0,39.75 ], [ -98.0,44.15 ], [ -88.5,44.15 ], [ -88.5,39.75 ], [ -98.0,39.75 ] ] ] } } ] }","noUsgsAuthors":false,"publicationStatus":"PW","scienceBaseUri":"5098dfe9e4b0a35ac147a79e","contributors":{"authors":[{"text":"Linhart, S. Mike","contributorId":61073,"corporation":false,"usgs":true,"family":"Linhart","given":"S. Mike","affiliations":[],"preferred":false,"id":468677,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Nania, Jon F. jfnania@usgs.gov","contributorId":4767,"corporation":false,"usgs":true,"family":"Nania","given":"Jon","email":"jfnania@usgs.gov","middleInitial":"F.","affiliations":[{"id":351,"text":"Iowa Water Science Center","active":true,"usgs":true}],"preferred":true,"id":468676,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Sanders, Curtis L. Jr.","contributorId":76391,"corporation":false,"usgs":true,"family":"Sanders","given":"Curtis","suffix":"Jr.","email":"","middleInitial":"L.","affiliations":[],"preferred":false,"id":468678,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Archfield, Stacey A. 0000-0002-9011-3871 sarch@usgs.gov","orcid":"https://orcid.org/0000-0002-9011-3871","contributorId":1874,"corporation":false,"usgs":true,"family":"Archfield","given":"Stacey","email":"sarch@usgs.gov","middleInitial":"A.","affiliations":[{"id":502,"text":"Office of Surface Water","active":true,"usgs":true},{"id":436,"text":"National Research Program - Eastern Branch","active":true,"usgs":true}],"preferred":true,"id":468675,"contributorType":{"id":1,"text":"Authors"},"rank":4}]}}
,{"id":70040609,"text":"sir20105070E - 2012 - Stratiform chromite deposit model","interactions":[],"lastModifiedDate":"2024-04-16T16:35:52.791761","indexId":"sir20105070E","displayToPublicDate":"2012-11-03T00:00:00","publicationYear":"2012","noYear":false,"publicationType":{"id":18,"text":"Report"},"publicationSubtype":{"id":5,"text":"USGS Numbered Series"},"seriesTitle":{"id":334,"text":"Scientific Investigations Report","code":"SIR","onlineIssn":"2328-0328","printIssn":"2328-031X","active":true,"publicationSubtype":{"id":5}},"seriesNumber":"2010-5070","chapter":"E","title":"Stratiform chromite deposit model","docAbstract":"<p>A new descriptive stratiform chromite deposit model was prepared which will provide a framework for understanding the characteristics of stratiform chromite deposits worldwide. Previous stratiform chromite deposit models developed by the U.S. Geological Survey (USGS) have been referred to as Bushveld chromium, because the Bushveld Complex in South Africa is the only stratified, mafic-ultramafic intrusion presently mined for chromite and is the most intensely researched. As part of the on-going effort by the USGS Mineral Resources Program to update existing deposit models for the upcoming national mineral resource assessment, this revised stratiform chromite deposit model includes new data on the geological, mineralogical, geophysical, and geochemical attributes of stratiform chromite deposits worldwide. This model will be a valuable tool in future chromite resource and environmental assessments and supplement previously published models used for mineral resource evaluation.</p>\n<p>Stratiform chromite deposits are found throughout the world, but the chromitite seams of the Bushveld Complex, South Africa, are the largest and most intensely researched. The chromite ore is located primarily in massive chromitite seams and, less abundantly, in disseminated chromite-bearing layers, both of which occur in the ultramafic section of large, layered mafic-ultramafic stratiform complexes. These mafic-ultramafic intrusions mainly formed in stable cratonic settings or during rift-related events during the Archean or early Proterozoic, although exceptions exist. The chromitite seams are cyclic in nature as well as laterally contiguous throughout the entire intrusion. Gangue minerals include olivine, pyroxenes (orthopyroxene and clinopyroxene), plagioclase, sulfides (pyrite, chalcopyrite, pyrrhotite, pentlandite, bornite), platinum group metals (mainly laurite, cooperite, braggite), and alteration minerals. A few deposits also contain rutile and ilmenite. The alteration phases include serpentine, chlorite, talc, magnetite, kaemmererite, uvarovite, hornblende, and carbonate minerals, such as calcite and dolomite.</p>\n<p>Stratiform chromite deposits are primarily hosted by peridotites, harzburgites, dunites, pyroxenites, troctolites, and anorthosites. Although metamorphism may have altered the ultramafic regions of layered intrusions postdeposition, only igneous processes are responsible for formation. From a diagnostic standpoint and for assessment purposes, they have no temporal or spatial relation to sedimentary rocks.</p>\n<p>The exact mechanisms responsible for the development of stratiform chromite deposits and the large, layered mafic-ultramafic intrusions where they are found are highly debated. The leading argument postulates that a parent magma mixed with a more primitive magma during magma chamber recharge. The partially differentiated magma could then be forced into the chromite stability field, resulting in the massive chromitite layers found in stratiform complexes. Contamination of the parent magma by localized assimilation of felsic country rock at the roof of the magma chamber has also been proposed as a mechanism of formation. Others suggest that changes in pressure or oxygen fugacity may be responsible for the occurrence of massive chromitite seams in layered mafic, ultramafic intrusions.</p>\n<p>The massive chromitite layers contain high levels of chromium and strong associations with platinum group elements. Anomalously high magnesium concentrations as well as low sodium, potassium, and phosphorus concentrations are also important geochemical features of stratiform chromite deposits. The presence of orthopyroxenite in many of the deposits suggests high silica and high magnesium concentrations in the parent magma.</p>\n<p>Most environmental concerns associated with the mining and processing of chromite ore focus on the solubility of chromium and its oxidation state. Although trivalent chromium (Cr<sup>3+</sup>) is an essential micronutrient for humans, hexavalent chromium (Cr<sup>6+</sup>) is highly toxic. Chromium-bearing solid phases that occur in the chromite ore-processing residue, for example, can effect the geochemical behavior and oxidation state of chromium in the environment.</p>","largerWorkType":{"id":18,"text":"Report"},"largerWorkTitle":"Mineral deposit models for resource assessment (Scientific Investigations Report 2010-5070)","largerWorkSubtype":{"id":5,"text":"USGS Numbered Series"},"language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/sir20105070E","usgsCitation":"Schulte, R., Taylor, R.D., Piatak, N., and Seal, R., 2012, Stratiform chromite deposit model: U.S. Geological Survey Scientific Investigations Report 2010-5070, xiv, 131 p., https://doi.org/10.3133/sir20105070E.","productDescription":"xiv, 131 p.","numberOfPages":"148","onlineOnly":"Y","additionalOnlineFiles":"Y","ipdsId":"IP-029769","costCenters":[{"id":171,"text":"Central Mineral and Environmental Resources Science Center","active":true,"usgs":true},{"id":245,"text":"Eastern Mineral and Environmental Resources Science Center","active":true,"usgs":true}],"links":[{"id":262962,"rank":3,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/sir_2010_5070_E.gif"},{"id":262960,"rank":2,"type":{"id":15,"text":"Index Page"},"url":"https://pubs.usgs.gov/sir/2010/5070/e/"},{"id":262961,"rank":1,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/sir/2010/5070/e/pdf/sir2010-5070e_LR.pdf","text":"Report Low Resolution","size":"15 MB","linkFileType":{"id":1,"text":"pdf"},"description":"Report"}],"noUsgsAuthors":false,"publicationStatus":"PW","scienceBaseUri":"5098ee18e4b0a35ac147a7b8","contributors":{"authors":[{"text":"Schulte, Ruth F.","contributorId":68604,"corporation":false,"usgs":true,"family":"Schulte","given":"Ruth F.","affiliations":[],"preferred":false,"id":468674,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Taylor, Ryan D. 0000-0002-8845-5290 rtaylor@usgs.gov","orcid":"https://orcid.org/0000-0002-8845-5290","contributorId":3412,"corporation":false,"usgs":true,"family":"Taylor","given":"Ryan","email":"rtaylor@usgs.gov","middleInitial":"D.","affiliations":[{"id":171,"text":"Central Mineral and Environmental Resources Science Center","active":true,"usgs":true}],"preferred":true,"id":468672,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Piatak, Nadine M.","contributorId":23621,"corporation":false,"usgs":true,"family":"Piatak","given":"Nadine M.","affiliations":[],"preferred":false,"id":468673,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Seal, Robert R. II 0000-0003-0901-2529 rseal@usgs.gov","orcid":"https://orcid.org/0000-0003-0901-2529","contributorId":397,"corporation":false,"usgs":true,"family":"Seal","given":"Robert R.","suffix":"II","email":"rseal@usgs.gov","affiliations":[],"preferred":false,"id":468671,"contributorType":{"id":1,"text":"Authors"},"rank":4}]}}
,{"id":70040594,"text":"cir1376 - 2012 - Streamflow depletion by wells--Understanding and managing the effects of groundwater pumping on streamflow","interactions":[],"lastModifiedDate":"2015-12-07T09:13:51","indexId":"cir1376","displayToPublicDate":"2012-11-02T00:00:00","publicationYear":"2012","noYear":false,"publicationType":{"id":18,"text":"Report"},"publicationSubtype":{"id":5,"text":"USGS Numbered Series"},"seriesTitle":{"id":307,"text":"Circular","code":"CIR","onlineIssn":"2330-5703","printIssn":"1067-084X","active":true,"publicationSubtype":{"id":5}},"seriesNumber":"1376","title":"Streamflow depletion by wells--Understanding and managing the effects of groundwater pumping on streamflow","docAbstract":"<p>Groundwater is an important source of water for many human needs, including public supply, agriculture, and industry. With the development of any natural resource, however, adverse consequences may be associated with its use. One of the primary concerns related to the development of groundwater resources is the effect of groundwater pumping on streamflow. Groundwater and surface-water systems are connected, and groundwater discharge is often a substantial component of the total flow of a stream. Groundwater pumping reduces the amount of groundwater that flows to streams and, in some cases, can draw streamflow into the underlying groundwater system. Streamflow reductions (or depletions) caused by pumping have become an important water-resource management issue because of the negative impacts that reduced flows can have on aquatic ecosystems, the availability of surface water, and the quality and aesthetic value of streams and rivers. Scientific research over the past seven decades has made important contributions to the basic understanding of the processes and factors that affect streamflow depletion by wells. Moreover, advances in methods for simulating groundwater systems with computer models provide powerful tools for estimating the rates, locations, and timing of streamflow depletion in response to groundwater pumping and for evaluating alternative approaches for managing streamflow depletion. The primary objective of this report is to summarize these scientific insights and to describe the various field methods and modeling approaches that can be used to understand and manage streamflow depletion. A secondary objective is to highlight several misconceptions concerning streamflow depletion and to explain why these misconceptions are incorrect.</p>","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/cir1376","collaboration":"Groundwater Resources Program","usgsCitation":"Streamflow depletion by wells--Understanding and managing the effects of groundwater pumping on streamflow; 2012; CIR; 1376; Barlow, Paul M.; Leake, Stanley A.","productDescription":"vi, 84 p.; col. ill.; maps (col.)","startPage":"i","endPage":"84","numberOfPages":"95","onlineOnly":"N","additionalOnlineFiles":"N","costCenters":[],"links":[{"id":262918,"type":{"id":15,"text":"Index Page"},"url":"https://pubs.usgs.gov/circ/1376/"},{"id":262919,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/circ/1376/pdf/circ1376_barlow_report_508.pdf"},{"id":262920,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/cir_1376.jpg"}],"publishedDate":"2012-11-02","noUsgsAuthors":false,"publicationDate":"2012-11-02","publicationStatus":"PW","scienceBaseUri":"5094dd8ee4b0e5cfc2acdc8a","contributors":{"authors":[{"text":"Barlow, Paul M. 0000-0003-4247-6456 pbarlow@usgs.gov","orcid":"https://orcid.org/0000-0003-4247-6456","contributorId":1200,"corporation":false,"usgs":true,"family":"Barlow","given":"Paul","email":"pbarlow@usgs.gov","middleInitial":"M.","affiliations":[{"id":493,"text":"Office of Ground Water","active":true,"usgs":true}],"preferred":true,"id":468636,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Leake, Stanley A. 0000-0003-3568-2542 saleake@usgs.gov","orcid":"https://orcid.org/0000-0003-3568-2542","contributorId":1846,"corporation":false,"usgs":true,"family":"Leake","given":"Stanley","email":"saleake@usgs.gov","middleInitial":"A.","affiliations":[{"id":128,"text":"Arizona Water Science Center","active":true,"usgs":true}],"preferred":true,"id":468637,"contributorType":{"id":1,"text":"Authors"},"rank":2}]}}
,{"id":70040370,"text":"ds709 - 2012 - Local-area-enhanced, high-resolution natural-color and color-infrared satellite-image mosaics of mineral districts in Afghanistan","interactions":[{"subject":{"id":70049066,"text":"ds709Z - 2013 - Local-area-enhanced, 2.5-meter resolution natural-color and color-infrared satellite-image mosaics of the Kandahar mineral district in Afghanistan","indexId":"ds709Z","publicationYear":"2013","noYear":false,"chapter":"Z","title":"Local-area-enhanced, 2.5-meter resolution natural-color and color-infrared satellite-image mosaics of the Kandahar mineral district in Afghanistan"},"predicate":"IS_PART_OF","object":{"id":70040370,"text":"ds709 - 2012 - Local-area-enhanced, high-resolution natural-color and color-infrared satellite-image mosaics of mineral districts in Afghanistan","indexId":"ds709","publicationYear":"2012","noYear":false,"title":"Local-area-enhanced, high-resolution natural-color and color-infrared satellite-image mosaics of mineral districts in Afghanistan"},"id":1},{"subject":{"id":70101717,"text":"ds709DD - 2014 - Local-area-enhanced, 2.5-meter resolution natural-color and color-infrared satellite-image mosaics of the Ghazni1 mineral district in Afghanistan","indexId":"ds709DD","publicationYear":"2014","noYear":false,"chapter":"DD","title":"Local-area-enhanced, 2.5-meter resolution natural-color and color-infrared satellite-image mosaics of the Ghazni1 mineral district in Afghanistan"},"predicate":"IS_PART_OF","object":{"id":70040370,"text":"ds709 - 2012 - Local-area-enhanced, high-resolution natural-color and color-infrared satellite-image mosaics of mineral districts in Afghanistan","indexId":"ds709","publicationYear":"2012","noYear":false,"title":"Local-area-enhanced, high-resolution natural-color and color-infrared satellite-image mosaics of mineral districts in Afghanistan"},"id":2},{"subject":{"id":70101718,"text":"ds709EE - 2014 - Local-area-enhanced, 2.5-meter resolution natural-color and color-infrared satellite-image mosaics of the Ghazni2 mineral district in Afghanistan","indexId":"ds709EE","publicationYear":"2014","noYear":false,"chapter":"EE","title":"Local-area-enhanced, 2.5-meter resolution natural-color and color-infrared satellite-image mosaics of the Ghazni2 mineral district in Afghanistan"},"predicate":"IS_PART_OF","object":{"id":70040370,"text":"ds709 - 2012 - Local-area-enhanced, high-resolution natural-color and color-infrared satellite-image mosaics of mineral districts in Afghanistan","indexId":"ds709","publicationYear":"2012","noYear":false,"title":"Local-area-enhanced, high-resolution natural-color and color-infrared satellite-image mosaics of mineral districts in Afghanistan"},"id":3},{"subject":{"id":70101719,"text":"ds709FF - 2014 - Local-area-enhanced, 2.5-meter resolution natural-color and color-infrared satellite-image mosaics of the Farah mineral district in Afghanistan","indexId":"ds709FF","publicationYear":"2014","noYear":false,"chapter":"FF","title":"Local-area-enhanced, 2.5-meter resolution natural-color and color-infrared satellite-image mosaics of the Farah mineral district in Afghanistan"},"predicate":"IS_PART_OF","object":{"id":70040370,"text":"ds709 - 2012 - Local-area-enhanced, high-resolution natural-color and color-infrared satellite-image mosaics of mineral districts in Afghanistan","indexId":"ds709","publicationYear":"2012","noYear":false,"title":"Local-area-enhanced, high-resolution natural-color and color-infrared satellite-image mosaics of mineral districts in Afghanistan"},"id":4}],"lastModifiedDate":"2013-02-01T11:10:22","indexId":"ds709","displayToPublicDate":"2012-11-02T00:00:00","publicationYear":"2012","noYear":false,"publicationType":{"id":18,"text":"Report"},"publicationSubtype":{"id":5,"text":"USGS Numbered Series"},"seriesTitle":{"id":310,"text":"Data Series","code":"DS","onlineIssn":"2327-638X","printIssn":"2327-0271","active":false,"publicationSubtype":{"id":5}},"seriesNumber":"709","title":"Local-area-enhanced, high-resolution natural-color and color-infrared satellite-image mosaics of mineral districts in Afghanistan","docAbstract":"The U.S. Geological Survey (USGS), in cooperation with the U.S. Department of Defense Task Force for Business and Stability Operations, prepared databases for mineral-resource target areas in Afghanistan. The purpose of the databases is to (1) provide useful data to ground-survey crews for use in performing detailed assessments of the areas and (2) provide useful information to private investors who are considering investment in a particular area for development of its natural resources. The set of satellite-image mosaics provided in this Data Series (DS) is one such database. Although airborne digital color-infrared imagery was acquired for parts of Afghanistan in 2006, the image data have radiometric variations that preclude their use in creating a consistent image mosaic for geologic analysis. Consequently, image mosaics were created using ALOS (Advanced Land Observation Satellite; renamed Daichi) satellite images, whose radiometry has been well determined (Saunier, 2007a,b). This DS consists of the locally enhanced ALOS image mosaics for each of the 24 mineral project areas (referred to herein as areas of interest), whose locality names, locations, and main mineral occurrences are shown on the index map of Afghanistan (fig. 1). ALOS was launched on January 24, 2006, and provides multispectral images from the AVNIR (Advanced Visible and Near-Infrared Radiometer) sensor in blue (420-500 nanometer, nm), green (520-600 nm), red (610-690 nm), and near-infrared (760-890 nm) wavelength bands with an 8-bit dynamic range and a 10-meter (m) ground resolution. The satellite also provides a panchromatic band image from the PRISM (Panchromatic Remote-sensing Instrument for Stereo Mapping) sensor (520-770 nm) with the same dynamic range but a 2.5-m ground resolution. The image products in this DS incorporate copyrighted data provided by the Japan Aerospace Exploration Agency, but the image processing has altered the original pixel structure and all image values of the JAXA ALOS data, such that original image values cannot be recreated from this DS. As such, the DS products match JAXA criteria for value added products, which are not copyrighted, according to the ALOS end-user license agreement. The selection criteria for the satellite imagery used in our mosaics were images having (1) the highest solar-elevation angles (near summer solstice) and (2) the least cloud, cloud-shadow, and snow cover. The multispectral and panchromatic data were orthorectified with ALOS satellite ephemeris data, a process which is not as accurate as orthorectification using digital elevation models (DEMs); however, the ALOS processing center did not have a precise DEM. As a result, the multispectral and panchromatic image pairs were generally not well registered to the surface and not coregistered well enough to perform resolution enhancement on the multispectral data. Therefore, it was necessary to (1) register the 10-m AVNIR multispectral imagery to a well-controlled Landsat image base, (2) mosaic the individual multispectral images into a single image of the entire area of interest, (3) register each panchromatic image to the registered multispectral image base, and (4) mosaic the individual panchromatic images into a single image of the entire area of interest. The two image-registration steps were facilitated using an automated control-point algorithm developed by the USGS that allows image coregistration to within one picture element. PRISM image orthorectification for one-half of the target areas was performed by the Alaska Satellite Facility, applying its photogrammetric software to PRISM stereo images with vertical control points obtained from the digital elevation database produced by the Shuttle Radar Topography Mission (Farr and others, 2007) and horizontal adjustments based on a controlled Landsat image base (Davis, 2006). Before rectification, the multispectral and panchromatic images were converted to radiance values and then to relative-reflectance values using the methods described in Davis (2006). Mosaicking the multispectral or panchromatic images started with the image with the highest sun-elevation angle and the least atmospheric scattering, which was treated as the standard image. The band-reflectance values of all other multispectral or panchromatic images within the area were sequentially adjusted to that of the standard image by determining band-reflectance correspondence between overlapping images using linear least-squares analysis. The resolution of the multispectral image mosaic was then increased to that of the panchromatic image mosaic using SPARKLE logic, which is described in Davis (2006). Each of the four-band images within each resolution-enhanced image mosaic was individually subjected to a local-area histogram stretch algorithm (described in Davis, 2007), which stretches each band's picture element based on the digital values of all picture elements within a specified radius that was usually 500 m. The final databases, which are provided in this DS, are three-band, color-composite images of the local-area-enhanced, natural-color data (the blue, green, and red wavelength bands) and color-infrared data (the green, red, and near-infrared wavelength bands). All image data were initially projected and maintained in Universal Transverse Mercator (UTM) map projection using the target area's local zone (either 41 or 42) and the WGS84 datum. Most final image mosaics were subdivided into overlapping tiles or quadrants because of the large size of the target areas. The image tiles (or quadrants) for each area of interest are provided as embedded geotiff images, which can be read and used by most geographic information system (GIS) and image-processing software. The tiff world files (tfw) are provided, even though they are generally not needed for most software to read an embedded geotiff image. Approximately one-half of the study areas have at least one subarea designated for detailed field investigations; the subareas were extracted from the area's image mosaic and are provided as separate embedded geotiff images.","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/ds709","collaboration":"Prepared in cooperation with the U.S. Department of Defense <a href=\"http://tfbso.defense.gov/www/\" target=\"_blank\">Task Force for Business and Stability Operations</a> and the <a href=\"http://www.bgs.ac.uk/AfghanMinerals/\" target=\"_blank\">Afghanistan Geological Survey</a>.  This report is composed of 24 chapters.  Please visit <a href=\"http://pubs.er.usgs.gov/publication/ds709\" target=\"_blank\">DS 709</a> to view available chapters.","usgsCitation":"Davis, P.A., 2012, Local-area-enhanced, high-resolution natural-color and color-infrared satellite-image mosaics of mineral districts in Afghanistan: U.S. Geological Survey Data Series 709, 24  Chapters, https://doi.org/10.3133/ds709.","productDescription":"24  Chapters","onlineOnly":"Y","additionalOnlineFiles":"Y","costCenters":[{"id":387,"text":"Mineral Resources Program","active":true,"usgs":true}],"links":[{"id":262620,"rank":0,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/ds_709.jpg"},{"id":262613,"rank":100,"type":{"id":15,"text":"Index Page"},"url":"https://pubs.usgs.gov/ds/709/","linkFileType":{"id":5,"text":"html"}}],"country":"Afghanistan","geographicExtents":"{ \"type\": \"FeatureCollection\", \"features\": [ { \"type\": \"Feature\", \"properties\": {}, \"geometry\": { \"type\": \"Polygon\", \"coordinates\": [ [ [ 60.52,29.38 ], [ 60.52,38.49 ], [ 74.89,38.49 ], [ 74.89,29.38 ], [ 60.52,29.38 ] ] ] } } ] }","noUsgsAuthors":false,"publicationStatus":"PW","scienceBaseUri":"507ee041e4b022001d87bb82","contributors":{"authors":[{"text":"Davis, Philip A. pdavis@usgs.gov","contributorId":692,"corporation":false,"usgs":true,"family":"Davis","given":"Philip","email":"pdavis@usgs.gov","middleInitial":"A.","affiliations":[],"preferred":true,"id":468184,"contributorType":{"id":1,"text":"Authors"},"rank":1}]}}
,{"id":70040582,"text":"70040582 - 2012 - Critique on the use of the standardized avian acute oral toxicity test for first generation anticoagulant rodenticides","interactions":[],"lastModifiedDate":"2017-01-03T13:27:00","indexId":"70040582","displayToPublicDate":"2012-11-02T00:00:00","publicationYear":"2012","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":1913,"text":"Human and Ecological Risk Assessment","active":true,"publicationSubtype":{"id":10}},"title":"Critique on the use of the standardized avian acute oral toxicity test for first generation anticoagulant rodenticides","docAbstract":"Avian risk assessments for rodenticides are often driven by the results of standardized acute oral toxicity tests without regards to a toxicant's mode of action and time course of adverse effects. First generation anticoagulant rodenticides (FGARs) generally require multiple feedings over several days to achieve a threshold concentration in tissue and cause adverse effects. This exposure regimen is much different than that used in the standardized acute oral toxicity test methodology. Median lethal dose values derived from standardized acute oral toxicity tests underestimate the environmental hazard and risk of FGARs. Caution is warranted when FGAR toxicity, physiological effects, and pharmacokinetics derived from standardized acute oral toxicity testing are used for forensic confirmation of the cause of death in avian mortality incidents and when characterizing FGARs' risks to free-ranging birds.","language":"English","publisher":"Taylor & Francis","publisherLocation":"Philadelphia, PA","doi":"10.1080/10807039.2012.707934","usgsCitation":"Vyas, N.B., and Rattner, B.A., 2012, Critique on the use of the standardized avian acute oral toxicity test for first generation anticoagulant rodenticides: Human and Ecological Risk Assessment, v. 18, no. 5, p. 1069-1077, https://doi.org/10.1080/10807039.2012.707934.","productDescription":"9 p.","startPage":"1069","endPage":"1077","numberOfPages":"9","ipdsId":"IP-029392","costCenters":[{"id":531,"text":"Patuxent Wildlife Research Center","active":true,"usgs":true}],"links":[{"id":262946,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"},{"id":262945,"type":{"id":10,"text":"Digital Object Identifier"},"url":"https://dx.doi.org/10.1080/10807039.2012.707934"}],"volume":"18","issue":"5","noUsgsAuthors":false,"publicationStatus":"PW","scienceBaseUri":"5094dd6ae4b0e5cfc2acdc6a","contributors":{"authors":[{"text":"Vyas, Nimish B. 0000-0003-0191-1319 nvyas@usgs.gov","orcid":"https://orcid.org/0000-0003-0191-1319","contributorId":4494,"corporation":false,"usgs":true,"family":"Vyas","given":"Nimish","email":"nvyas@usgs.gov","middleInitial":"B.","affiliations":[{"id":531,"text":"Patuxent Wildlife Research Center","active":true,"usgs":true}],"preferred":true,"id":468601,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Rattner, Barnett A. 0000-0003-3676-2843 brattner@usgs.gov","orcid":"https://orcid.org/0000-0003-3676-2843","contributorId":4142,"corporation":false,"usgs":true,"family":"Rattner","given":"Barnett","email":"brattner@usgs.gov","middleInitial":"A.","affiliations":[{"id":531,"text":"Patuxent Wildlife Research Center","active":true,"usgs":true}],"preferred":true,"id":468600,"contributorType":{"id":1,"text":"Authors"},"rank":2}]}}
,{"id":70040593,"text":"sir20105090H - 2012 - Economic filters for evaluating porphyry copper deposit resource assessments using grade-tonnage deposit models, with examples from the U.S. Geological Survey global mineral resource assessment: Chapter H in <i>Global mineral resource assessment</i>","interactions":[{"subject":{"id":70040593,"text":"sir20105090H - 2012 - Economic filters for evaluating porphyry copper deposit resource assessments using grade-tonnage deposit models, with examples from the U.S. Geological Survey global mineral resource assessment: Chapter H in <i>Global mineral resource assessment</i>","indexId":"sir20105090H","publicationYear":"2012","noYear":false,"chapter":"H","title":"Economic filters for evaluating porphyry copper deposit resource assessments using grade-tonnage deposit models, with examples from the U.S. Geological Survey global mineral resource assessment: Chapter H in <i>Global mineral resource assessment</i>"},"predicate":"IS_PART_OF","object":{"id":70040436,"text":"sir20105090 - 2010 - Global mineral resource assessment","indexId":"sir20105090","publicationYear":"2010","noYear":false,"title":"Global mineral resource assessment"},"id":1}],"isPartOf":{"id":70040436,"text":"sir20105090 - 2010 - Global mineral resource assessment","indexId":"sir20105090","publicationYear":"2010","noYear":false,"title":"Global mineral resource assessment"},"lastModifiedDate":"2018-11-19T10:28:53","indexId":"sir20105090H","displayToPublicDate":"2012-11-02T00:00:00","publicationYear":"2012","noYear":false,"publicationType":{"id":18,"text":"Report"},"publicationSubtype":{"id":5,"text":"USGS Numbered Series"},"seriesTitle":{"id":334,"text":"Scientific Investigations Report","code":"SIR","onlineIssn":"2328-0328","printIssn":"2328-031X","active":true,"publicationSubtype":{"id":5}},"seriesNumber":"2010-5090","chapter":"H","title":"Economic filters for evaluating porphyry copper deposit resource assessments using grade-tonnage deposit models, with examples from the U.S. Geological Survey global mineral resource assessment: Chapter H in <i>Global mineral resource assessment</i>","docAbstract":"<p>An analysis of the amount and location of undiscovered mineral resources that are likely to be economically recoverable is important for assessing the long-term adequacy and availability of mineral supplies. This requires an economic evaluation of estimates of undiscovered resources generated by traditional resource assessments (Singer and Menzie, 2010). In this study, simplified engineering cost models were used to estimate the economic fraction of resources contained in undiscovered porphyry copper deposits, predicted in a global assessment of copper resources. The cost models of Camm (1991) were updated with a cost index to reflect increases in mining and milling costs since 1989. The updated cost models were used to perform an economic analysis of undiscovered resources estimated in porphyry copper deposits in six tracts located in North America. The assessment estimated undiscovered porphyry copper deposits within 1 kilometer of the land surface in three depth intervals.</p>\n<p>The results of the updated engineering cost model analysis for open-pit porphyry copper deposits are in agreement with the grade-tonnage boundary defining positive economic returns for copper deposits developed between 1989 and 2008. This correspondence demonstrates that the updated engineering cost equations are performing well and appear to be appropriate to evaluate the economic status of open-pit porphyry copper mines under current, and potentially future, economic conditions. Economic filters based on these simplified engineering cost models provide a method for estimating potential tonnages of undiscovered metals that may be economic in individual assessment areas.</p>\n<p>One implication of the economic filter results for undiscovered copper resources is that global copper supply will continue to be dominated by production from a small number of giant deposits. This domination of resource supply by a small number of producers may increase in the future, because an increasing proportion of new deposit discoveries are likely to occur in remote areas and be concealed deep beneath covering rock and sediments. Extensive mineral exploration activity will be required to meet future resource demand, because these deposits will be harder to find and more costly to mine than near-surface deposits located in more accessible areas. Relatively few of the new deposit discoveries in these high-cost settings will have sufficient tonnage and grade characteristics to assure positive economic returns on development and exploration costs.</p>","largerWorkType":{"id":18,"text":"Report"},"largerWorkTitle":"Global mineral resource assessment (Scientific Investigations Report 2010-5090)","largerWorkSubtype":{"id":5,"text":"USGS Numbered Series"},"language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/sir20105090H","issn":"2328-0328","usgsCitation":"Robinson, G.R., and Menzie, W.D., 2012, Economic filters for evaluating porphyry copper deposit resource assessments using grade-tonnage deposit models, with examples from the U.S. Geological Survey global mineral resource assessment: Chapter H in <i>Global mineral resource assessment</i> (Originally posted November 2, 2012; Revised May 14, 2013, ver. 1.1; Revised March 31, 2014, ver. 1.2): U.S. Geological Survey Scientific 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1.2","noUsgsAuthors":false,"publicationStatus":"PW","scienceBaseUri":"5094dd74e4b0e5cfc2acdc72","contributors":{"authors":[{"text":"Robinson, Gilpin R. Jr. grobinso@usgs.gov","contributorId":3083,"corporation":false,"usgs":true,"family":"Robinson","given":"Gilpin","suffix":"Jr.","email":"grobinso@usgs.gov","middleInitial":"R.","affiliations":[{"id":245,"text":"Eastern Mineral and Environmental Resources Science Center","active":true,"usgs":true}],"preferred":false,"id":468634,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Menzie, W. David","contributorId":15645,"corporation":false,"usgs":true,"family":"Menzie","given":"W.","email":"","middleInitial":"David","affiliations":[{"id":432,"text":"National Minerals Information Center","active":true,"usgs":true}],"preferred":false,"id":468635,"contributorType":{"id":1,"text":"Authors"},"rank":2}]}}
,{"id":70040585,"text":"70040585 - 2012 - Occupancy in continuous habitat","interactions":[],"lastModifiedDate":"2012-11-02T10:52:44","indexId":"70040585","displayToPublicDate":"2012-11-02T00:00:00","publicationYear":"2012","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":1475,"text":"Ecosphere","active":true,"publicationSubtype":{"id":10}},"title":"Occupancy in continuous habitat","docAbstract":"The probability that a site has at least one individual of a species ('occupancy') has come to be widely used as a state variable for animal population monitoring. The available statistical theory for estimation when detection is imperfect applies particularly to habitat patches or islands, although it is also used for arbitrary plots in continuous habitat. The probability that such a plot is occupied depends on plot size and home-range characteristics (size, shape and dispersion) as well as population density. Plot size is critical to the definition of occupancy as a state variable, but clear advice on plot size is missing from the literature on the design of occupancy studies. We describe models for the effects of varying plot size and home-range size on expected occupancy. Temporal, spatial, and species variation in average home-range size is to be expected, but information on home ranges is difficult to retrieve from species presence/absence data collected in occupancy studies. The effect of variable home-range size is negligible when plots are very large (>100 x area of home range), but large plots pose practical problems. At the other extreme, sampling of 'point' plots with cameras or other passive detectors allows the true 'proportion of area occupied' to be estimated. However, this measure equally reflects home-range size and density, and is of doubtful value for population monitoring or cross-species comparisons. Plot size is ill-defined and variable in occupancy studies that detect animals at unknown distances, the commonest example being unlimited-radius point counts of song birds. We also find that plot size is ill-defined in recent treatments of \"multi-scale\" occupancy; the respective scales are better interpreted as temporal (instantaneous and asymptotic) rather than spatial. Occupancy is an inadequate metric for population monitoring when it is confounded with home-range size or detection distance.","largerWorkType":{"id":2,"text":"Article"},"largerWorkTitle":"Ecosphere","largerWorkSubtype":{"id":10,"text":"Journal Article"},"language":"English","publisher":"Ecological Society of America","publisherLocation":"Ithaca, NY","doi":"10.1890/ES11-00308.1","usgsCitation":"Efford, M.G., and Dawson, D.K., 2012, Occupancy in continuous habitat: Ecosphere, v. 3, no. 4, p. 1-15, https://doi.org/10.1890/ES11-00308.1.","productDescription":"Article 32: 15 p.","startPage":"1","endPage":"15","ipdsId":"IP-027918","costCenters":[{"id":531,"text":"Patuxent Wildlife Research Center","active":true,"usgs":true}],"links":[{"id":474282,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.1890/es11-00308.1","text":"Publisher Index Page"},{"id":262929,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"},{"id":262928,"type":{"id":10,"text":"Digital Object Identifier"},"url":"https://dx.doi.org/10.1890/ES11-00308.1"}],"volume":"3","issue":"4","noUsgsAuthors":false,"publicationDate":"2012-04-25","publicationStatus":"PW","scienceBaseUri":"5094dd89e4b0e5cfc2acdc86","contributors":{"authors":[{"text":"Efford, Murray G.","contributorId":91616,"corporation":false,"usgs":true,"family":"Efford","given":"Murray","email":"","middleInitial":"G.","affiliations":[],"preferred":false,"id":468611,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Dawson, Deanna K. ddawson@usgs.gov","contributorId":1257,"corporation":false,"usgs":true,"family":"Dawson","given":"Deanna","email":"ddawson@usgs.gov","middleInitial":"K.","affiliations":[{"id":531,"text":"Patuxent Wildlife Research Center","active":true,"usgs":true}],"preferred":true,"id":468610,"contributorType":{"id":1,"text":"Authors"},"rank":2}]}}
,{"id":70040586,"text":"70040586 - 2012 - Hybridization among Arctic white-headed gulls (Larus spp.) obscures the genetic legacy of the Pleistocene","interactions":[],"lastModifiedDate":"2012-11-05T11:08:42","indexId":"70040586","displayToPublicDate":"2012-11-02T00:00:00","publicationYear":"2012","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":1467,"text":"Ecology and Evolution","active":true,"publicationSubtype":{"id":10}},"title":"Hybridization among Arctic white-headed gulls (Larus spp.) obscures the genetic legacy of the Pleistocene","docAbstract":"We studied the influence of glacial oscillations on the genetic structure of seven species of white-headed gull that breed at high latitudes (<i>Larus argentatus, L. canus, L. glaucescens, L. glaucoides, L. hyperboreus, L. schistisagus,</i> and <i>L. thayeri</i>). We evaluated localities hypothesized as ice-free areas or glacial refugia in other Arctic vertebrates using molecular data from 11 microsatellite loci, mitochondrial DNA (mtDNA) control region, and six nuclear introns for 32 populations across the Holarctic. Moderate levels of genetic structure were observed for microsatellites (<i>F<sub>ST</sub></i>= 0.129), introns (<i>&#934;<sub>ST</sub></i>= 0.185), and mtDNA control region (<i>&#934;<sub>ST</sub></i>= 0.461), with among-group variation maximized when populations were grouped based on subspecific classification. Two haplotype and at least two allele groups were observed across all loci. However, no haplotype/allele group was composed solely of individuals of a single species, a pattern consistent with recent divergence. Furthermore, northernmost populations were not well differentiated and among-group variation was maximized when <i>L. argentatus</i> and <i>L. hyberboreus</i> populations were grouped by locality rather than species, indicating recent hybridization. Four populations are located in putative Pleistocene glacial refugia and had larger t estimates than the other 28 populations. However, we were unable to substantiate these putative refugia using coalescent theory, as all populations had genetic signatures of stability based on mtDNA. The extent of haplotype and allele sharing among Arctic white-headed gull species is noteworthy. Studies of other Arctic taxa have generally revealed species-specific clusters as well as genetic structure within species, usually correlated with geography. Aspects of white-headed gull behavioral biology, such as colonization ability and propensity to hybridize, as well as their recent evolutionary history, have likely played a large role in the limited genetic structure observed.","largerWorkType":{"id":2,"text":"Article"},"largerWorkTitle":"Ecology and Evolution","largerWorkSubtype":{"id":10,"text":"Journal Article"},"language":"English","publisher":"Blackwell Publishing Ltd.","publisherLocation":"Oxford, U.K.","doi":"10.1002/ece3.240","usgsCitation":"Sonsthagen, S.A., Chesser, R., Bell, D., and Dove, C.J., 2012, Hybridization among Arctic white-headed gulls (Larus spp.) obscures the genetic legacy of the Pleistocene: Ecology and Evolution, v. 2, no. 6, p. 1278-1295, https://doi.org/10.1002/ece3.240.","productDescription":"18 p.","startPage":"1278","endPage":"1295","numberOfPages":"18","ipdsId":"IP-022392","costCenters":[{"id":531,"text":"Patuxent Wildlife Research Center","active":true,"usgs":true}],"links":[{"id":474281,"rank":0,"type":{"id":41,"text":"Open Access External Repository Page"},"url":"https://doi.org/10.1002/ece3.240","text":"External Repository"},{"id":262927,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"},{"id":262924,"type":{"id":10,"text":"Digital Object Identifier"},"url":"https://dx.doi.org/10.1002/ece3.240"},{"id":262932,"type":{"id":11,"text":"Document"},"url":"https://onlinelibrary.wiley.com/doi/10.1002/ece3.240/pdf"}],"otherGeospatial":"Arctic","geographicExtents":"{ \"type\": \"FeatureCollection\", \"features\": [ { \"type\": \"Feature\", \"properties\": {}, \"geometry\": { \"type\": \"Polygon\", \"coordinates\": [ [ [ -180.0,41.0 ], [ -180.0,90.0 ], [ 180.0,90.0 ], [ 180.0,41.0 ], [ -180.0,41.0 ] ] ] } } ] }","volume":"2","issue":"6","noUsgsAuthors":false,"publicationDate":"2012-05-24","publicationStatus":"PW","scienceBaseUri":"5094dd85e4b0e5cfc2acdc82","contributors":{"authors":[{"text":"Sonsthagen, Sarah A. 0000-0001-6215-5874 ssonsthagen@usgs.gov","orcid":"https://orcid.org/0000-0001-6215-5874","contributorId":3711,"corporation":false,"usgs":true,"family":"Sonsthagen","given":"Sarah","email":"ssonsthagen@usgs.gov","middleInitial":"A.","affiliations":[{"id":114,"text":"Alaska Science Center","active":true,"usgs":true},{"id":117,"text":"Alaska Science Center Biology WTEB","active":true,"usgs":true}],"preferred":true,"id":468612,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Chesser, R. Terry 0000-0003-4389-7092","orcid":"https://orcid.org/0000-0003-4389-7092","contributorId":87669,"corporation":false,"usgs":true,"family":"Chesser","given":"R. Terry","affiliations":[{"id":531,"text":"Patuxent Wildlife Research Center","active":true,"usgs":true}],"preferred":true,"id":468614,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Bell, Douglas A.","contributorId":44427,"corporation":false,"usgs":true,"family":"Bell","given":"Douglas A.","affiliations":[],"preferred":false,"id":468613,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Dove, Carla J.","contributorId":98577,"corporation":false,"usgs":true,"family":"Dove","given":"Carla","email":"","middleInitial":"J.","affiliations":[],"preferred":false,"id":468615,"contributorType":{"id":1,"text":"Authors"},"rank":4}]}}
,{"id":70101174,"text":"70101174 - 2012 - Population ecology of breeding Pacific common eiders on the Yukon-Kuskokwim Delta, Alaska","interactions":[],"lastModifiedDate":"2014-04-10T11:45:15","indexId":"70101174","displayToPublicDate":"2012-11-01T11:40:00","publicationYear":"2012","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":3773,"text":"Wildlife Monographs","active":true,"publicationSubtype":{"id":10}},"title":"Population ecology of breeding Pacific common eiders on the Yukon-Kuskokwim Delta, Alaska","docAbstract":"Populations of Pacific common eiders (Somateria mollissima v-nigrum) on the Yukon-Kuskokwim Delta (YKD) in western Alaska declined by 50–90% from 1957 to 1992 and then stabilized at reduced numbers from the early 1990s to the present. We investigated the underlying processes affecting their population dynamics by collection and analysis of demographic data from Pacific common eiders at 3 sites on the YKD (1991–2004) for 29 site-years. We examined variation in components of reproduction, tested hypotheses about the influence of specific ecological factors on life-history variables, and investigated their relative contributions to local population dynamics. Reproductive output was low and variable, both within and among individuals, whereas apparent survival of adult females was high and relatively invariant (0.89 ± 0.005). All reproductive parameters varied across study sites and years. Clutch initiation dates ranged from 4 May to 28 June, with peak (modal) initiation occurring on 26 May. Females at an island study site consistently initiated clutches 3–5 days earlier in each year than those on 2 mainland sites. Population variance in nest initiation date was negatively related to the peak, suggesting increased synchrony in years of delayed initiation. On average, total clutch size (laid) ranged from 4.8 to 6.6 eggs, and declined with date of nest initiation. After accounting for partial predation and non-viability of eggs, average clutch size at hatch ranged from 2.0 to 5.8 eggs. Within seasons, daily survival probability (DSP) of nests was lowest during egg-laying and late-initiation dates. Estimated nest survival varied considerably across sites and years (mean = 0.55, range: 0.06–0.92), but process variance in nest survival was relatively low (0.02, CI: 0.01–0.05), indicating that most variance was likely attributed to sampling error. We found evidence that observer effects may have reduced overall nest survival by 0.0–0.36 across site-years. Study sites with lower sample sizes and more frequent visitations appeared to experience greater observer effects. In general, Pacific common eiders exhibited high spatio-temporal variance in reproductive components. Larger clutch sizes and high nest survival at early initiation dates suggested directional selection favoring early nesting. However, stochastic environmental effects may have precluded response to this apparent selection pressure. Our results suggest that females breeding early in the season have the greatest reproductive value, as these birds lay the largest clutches and have the highest probability of successfully hatching. We developed stochastic, stage-based, matrix population models that incorporated observed spatio-temporal (process) variance and co-variation in vital rates, and projected the stable stage distribution () and population growth rate (λ). We used perturbation analyses to examine the relative influence of changes in vital rates on λ and variance decomposition to assess the proportion of variation in λ explained by process variation in each vital rate. In addition to matrix-based λ, we estimated λ using capture–recapture approaches, and log-linear regression. We found the stable age distribution for Pacific common eiders was weighted heavily towards experienced adult females (≥4 yr of age), and all calculations of λ indicated that the YKD population was stable to slightly increasing (λmatrix = 1.02, CI: 1.00–1.04); λreverse-capture–recapture = 1.05, CI: 0.99–1.11; λlog-linear = 1.04, CI: 0.98–1.10). Perturbation analyses suggested the population would respond most dramatically to changes in adult female survival (relative influence of adult survival was 1.5 times that of fecundity), whereas retrospective variation in λ was primarily explained by fecundity parameters (60%), particularly duckling survival (42%). Among components of fecundity, sensitivities were highest for duckling survival, suggesti","largerWorkType":{"id":2,"text":"Article"},"largerWorkTitle":"Wildlife Monographs","largerWorkSubtype":{"id":10,"text":"Journal Article"},"publisher":"Wildlife Monographs","doi":"10.1002/wmon.8","usgsCitation":"Wilson, H.M., Flint, P.L., Powell, A., Grand, J., and Moral, C.L., 2012, Population ecology of breeding Pacific common eiders on the Yukon-Kuskokwim Delta, Alaska: Wildlife Monographs, v. 182, no. 1, 28 p., https://doi.org/10.1002/wmon.8.","productDescription":"28 p.","ipdsId":"IP-028472","costCenters":[{"id":117,"text":"Alaska Science Center Biology WTEB","active":true,"usgs":true}],"links":[{"id":438809,"rank":0,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/P9G6G0AV","text":"USGS data release","linkHelpText":"Pacific common eider (Somateria mollissima v-nigrum) nest records, Yukon-Kuskokwim Delta, Alaska, 1991-2004"},{"id":286181,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"},{"id":286079,"type":{"id":10,"text":"Digital Object Identifier"},"url":"https://dx.doi.org/10.1002/wmon.8"},{"id":286080,"type":{"id":15,"text":"Index Page"},"url":"https://onlinelibrary.wiley.com/doi/10.1002/wmon.8/full"}],"country":"United States","state":"Alaska","otherGeospatial":"Yukon-kushokwim Delta","geographicExtents":"{ \"type\": \"FeatureCollection\", \"features\": [ { \"type\": \"Feature\", \"properties\": {}, \"geometry\": { \"type\": \"Polygon\", \"coordinates\": [ [ [ -165.1094,62.3668 ], [ -165.1094,63.2645 ], [ -162.7789,63.2645 ], [ -162.7789,62.3668 ], [ -165.1094,62.3668 ] ] ] } } ] }","volume":"182","issue":"1","noUsgsAuthors":false,"publicationDate":"2012-10-24","publicationStatus":"PW","scienceBaseUri":"535594f7e4b0120853e8c109","contributors":{"authors":[{"text":"Wilson, Heather M.","contributorId":37056,"corporation":false,"usgs":false,"family":"Wilson","given":"Heather","email":"","middleInitial":"M.","affiliations":[{"id":13236,"text":"U.S. Fish and Wildlife Service, Migratory Bird Management","active":true,"usgs":false}],"preferred":false,"id":492634,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Flint, Paul L. 0000-0002-8758-6993 pflint@usgs.gov","orcid":"https://orcid.org/0000-0002-8758-6993","contributorId":3284,"corporation":false,"usgs":true,"family":"Flint","given":"Paul","email":"pflint@usgs.gov","middleInitial":"L.","affiliations":[{"id":114,"text":"Alaska Science Center","active":true,"usgs":true},{"id":117,"text":"Alaska Science Center Biology WTEB","active":true,"usgs":true}],"preferred":true,"id":492633,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Powell, Abby N. abby_powell@usgs.gov","contributorId":2534,"corporation":false,"usgs":false,"family":"Powell","given":"Abby N.","email":"abby_powell@usgs.gov","affiliations":[{"id":13117,"text":"Institute of Arctic Biology, University of Alaska Fairbanks","active":true,"usgs":false}],"preferred":false,"id":492632,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Grand, J. Barry","contributorId":61950,"corporation":false,"usgs":true,"family":"Grand","given":"J. Barry","affiliations":[],"preferred":false,"id":492636,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Moral, Christine L.","contributorId":57765,"corporation":false,"usgs":true,"family":"Moral","given":"Christine","email":"","middleInitial":"L.","affiliations":[],"preferred":false,"id":492635,"contributorType":{"id":1,"text":"Authors"},"rank":5}]}}
,{"id":70169895,"text":"70169895 - 2012 - Free tropospheric transport of microorganisms from Asia to North America","interactions":[],"lastModifiedDate":"2016-03-29T10:27:36","indexId":"70169895","displayToPublicDate":"2012-11-01T11:30:00","publicationYear":"2012","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":2729,"text":"Microbial Ecology","active":true,"publicationSubtype":{"id":10}},"title":"Free tropospheric transport of microorganisms from Asia to North America","docAbstract":"<p><span>Microorganisms are abundant in the troposphere and can be transported vast distances on prevailing winds. This study measures the abundance and diversity of airborne bacteria and fungi sampled at the Mt. Bachelor Observatory (located 2.7 km above sea level in North America) where incoming free tropospheric air routinely arrives from distant sources across the Pacific Ocean, including Asia. Overall deoxyribonucleic acid (DNA) concentrations for microorganisms in the free troposphere, derived from quantitative polymerase chain reaction assays, averaged 4.94&thinsp;&times;&thinsp;10(-5) ng DNA m(-3) for bacteria and 4.77&thinsp;&times;&thinsp;10(-3) ng DNA m(-3) for fungi. Aerosols occasionally corresponded with microbial abundance, most often in the springtime. Viable cells were recovered from 27.4 % of bacterial and 47.6 % of fungal samples (N&thinsp;=&thinsp;124), with 49 different species identified by ribosomal DNA gene sequencing. The number of microbial isolates rose significantly above baseline values on 22-23 April 2011 and 13-15 May 2011. Both events were analyzed in detail, revealing distinct free tropospheric chemistries (e.g., low water vapor, high aerosols, carbon monoxide, and ozone) useful for ruling out boundary layer contamination. Kinematic back trajectory modeling suggested air from these events probably originated near China or Japan. Even after traveling for 10 days across the Pacific Ocean in the free troposphere, diverse and viable microbial populations, including presumptive plant pathogens Alternaria infectoria and Chaetomium globosum, were detected in Asian air samples. Establishing a connection between the intercontinental transport of microorganisms and specific diseases in North America will require follow-up investigations on both sides of the Pacific Ocean.</span></p>","language":"English","publisher":"International Society for Microbial Ecology","publisherLocation":"New York, NY","doi":"10.1007/s00248-012-0088-9","usgsCitation":"D. Smith, Jaffe, D., Birmele, M., Griffin, D.W., Andrew Schuerger, Hee, J., and Roberts, M., 2012, Free tropospheric transport of microorganisms from Asia to North America: Microbial Ecology, v. 64, no. 4, p. 973-985, https://doi.org/10.1007/s00248-012-0088-9.","productDescription":"13 p.","startPage":"973","endPage":"985","numberOfPages":"13","onlineOnly":"N","additionalOnlineFiles":"N","ipdsId":"IP-036127","costCenters":[{"id":574,"text":"St. Petersburg Coastal and Marine Science Center","active":true,"usgs":true}],"links":[{"id":319573,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"volume":"64","issue":"4","publishingServiceCenter":{"id":8,"text":"Raleigh PSC"},"noUsgsAuthors":false,"publicationDate":"2012-07-04","publicationStatus":"PW","scienceBaseUri":"56fba7a7e4b0a6037df1a148","contributors":{"authors":[{"text":"D. Smith","contributorId":168340,"corporation":false,"usgs":false,"family":"D. Smith","affiliations":[{"id":25260,"text":"University of Washington, Department of Biology, Seattle, WA, U","active":true,"usgs":false}],"preferred":false,"id":625512,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Jaffe, Dan","contributorId":168345,"corporation":false,"usgs":false,"family":"Jaffe","given":"Dan","email":"","affiliations":[{"id":25263,"text":"University of Washington-Bothell, Department of Atmospheric Scie","active":true,"usgs":false}],"preferred":false,"id":625511,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Birmele, Michele","contributorId":168347,"corporation":false,"usgs":false,"family":"Birmele","given":"Michele","email":"","affiliations":[{"id":25261,"text":"NASA Kennedy Space Center, ESC Team QNA, Kennedy Space Center,","active":true,"usgs":false}],"preferred":false,"id":625514,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Griffin, Dale W. 0000-0003-1719-5812 dgriffin@usgs.gov","orcid":"https://orcid.org/0000-0003-1719-5812","contributorId":2178,"corporation":false,"usgs":true,"family":"Griffin","given":"Dale","email":"dgriffin@usgs.gov","middleInitial":"W.","affiliations":[{"id":574,"text":"St. Petersburg Coastal and Marine Science Center","active":true,"usgs":true}],"preferred":true,"id":625509,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Andrew Schuerger","contributorId":168344,"corporation":false,"usgs":false,"family":"Andrew Schuerger","affiliations":[{"id":25262,"text":"University of Florida, Department of Plant Pathology","active":true,"usgs":false}],"preferred":false,"id":625510,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"Hee, J.","contributorId":168350,"corporation":false,"usgs":false,"family":"Hee","given":"J.","email":"","affiliations":[],"preferred":false,"id":625532,"contributorType":{"id":1,"text":"Authors"},"rank":6},{"text":"Roberts, Michael","contributorId":168346,"corporation":false,"usgs":false,"family":"Roberts","given":"Michael","email":"","affiliations":[{"id":25262,"text":"University of Florida, Department of Plant Pathology","active":true,"usgs":false}],"preferred":false,"id":625513,"contributorType":{"id":1,"text":"Authors"},"rank":7}]}}
,{"id":70039495,"text":"70039495 - 2012 - Incorporating movement patterns to improve survival estimates for juvenile bull trout","interactions":[],"lastModifiedDate":"2014-01-15T10:50:54","indexId":"70039495","displayToPublicDate":"2012-11-01T10:42:34","publicationYear":"2012","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":2886,"text":"North American Journal of Fisheries Management","active":true,"publicationSubtype":{"id":10}},"title":"Incorporating movement patterns to improve survival estimates for juvenile bull trout","docAbstract":"Populations of many fish species are sensitive to changes in vital rates during early life stages, but our understanding of the factors affecting growth, survival, and movement patterns is often extremely limited for juvenile fish. These critical information gaps are particularly evident for bull trout <i>Salvelinus confluentus</i>, a threatened Pacific Northwest char. We combined several active and passive mark–recapture and resight techniques to assess migration rates and estimate survival for juvenile bull trout (70–170 mm total length). We evaluated the relative performance of multiple survival estimation techniques by comparing results from a common Cormack–Jolly–Seber (CJS) model, the less widely used Barker model, and a simple return rate (an index of survival). Juvenile bull trout of all sizes emigrated from their natal habitat throughout the year, and thereafter migrated up to 50 km downstream. With the CJS model, high emigration rates led to an extreme underestimate of apparent survival, a combined estimate of site fidelity and survival. In contrast, the Barker model, which allows survival and emigration to be modeled as separate parameters, produced estimates of survival that were much less biased than the return rate. Estimates of age-class-specific annual survival from the Barker model based on all available data were 0.218±0.028 (estimate±SE) for age-1 bull trout and 0.231±0.065 for age-2 bull trout. This research demonstrates the importance of incorporating movement patterns into survival analyses, and we provide one of the first field-based estimates of juvenile bull trout annual survival in relatively pristine rearing conditions. These estimates can provide a baseline for comparison with future studies in more impacted systems and will help managers develop reliable stage-structured population models to evaluate future recovery strategies.","largerWorkType":{"id":2,"text":"Article"},"largerWorkTitle":"North American Journal of Fisheries Management","largerWorkSubtype":{"id":10,"text":"Journal Article"},"language":"English","publisher":"American Fisheries Society","publisherLocation":"Lawrence, KS","doi":"10.1080/02755947.2012.720644","usgsCitation":"Bowerman, T., and Budy, P., 2012, Incorporating movement patterns to improve survival estimates for juvenile bull trout: North American Journal of Fisheries Management, v. 32, no. 6, p. 1123-1136, https://doi.org/10.1080/02755947.2012.720644.","productDescription":"14 p.","startPage":"1123","endPage":"1136","numberOfPages":"14","ipdsId":"IP-036702","costCenters":[{"id":609,"text":"Utah Cooperative Fish and Wildlife Research Unit","active":false,"usgs":true}],"links":[{"id":498970,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.1080/02755947.2012.720644","text":"Publisher Index Page"},{"id":281076,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"},{"id":281075,"type":{"id":10,"text":"Digital Object Identifier"},"url":"https://dx.doi.org/10.1080/02755947.2012.720644"}],"country":"United States","state":"Oregon","otherGeospatial":"Blue Mountains;Skiphorton Creek;South Fork Walla Walla River;Walla Walla River","geographicExtents":"{ \"type\": \"FeatureCollection\", \"features\": [ { \"type\": \"Feature\", \"properties\": {}, \"geometry\": { \"type\": \"Polygon\", \"coordinates\": [ [ [ -119.07,45.58 ], [ -119.07,46.21 ], [ -117.39,46.21 ], [ -117.39,45.58 ], [ -119.07,45.58 ] ] ] } } ] }","volume":"32","issue":"6","noUsgsAuthors":false,"publicationDate":"2012-11-01","publicationStatus":"PW","scienceBaseUri":"53cd6240e4b0b290850fe112","contributors":{"authors":[{"text":"Bowerman, Tracy","contributorId":95796,"corporation":false,"usgs":true,"family":"Bowerman","given":"Tracy","email":"","affiliations":[],"preferred":false,"id":466366,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Budy, Phaedra","contributorId":24215,"corporation":false,"usgs":true,"family":"Budy","given":"Phaedra","affiliations":[{"id":609,"text":"Utah Cooperative Fish and Wildlife Research Unit","active":false,"usgs":true}],"preferred":false,"id":466365,"contributorType":{"id":1,"text":"Authors"},"rank":2}]}}
,{"id":70191248,"text":"70191248 - 2012 - Morphological and chemical evidence of stromatolitic deposits in the 2.75 Ga Carajás banded iron formation, Brazil","interactions":[],"lastModifiedDate":"2017-10-02T14:59:23","indexId":"70191248","displayToPublicDate":"2012-11-01T00:00:00","publicationYear":"2012","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":1427,"text":"Earth and Planetary Science Letters","active":true,"publicationSubtype":{"id":10}},"title":"Morphological and chemical evidence of stromatolitic deposits in the 2.75 Ga Carajás banded iron formation, Brazil","docAbstract":"<p id=\"sp0055\">We describe evidence of biogenicity in the morphology and carbon content of well-preserved, Neoarchean samples of banded iron formation (BIF) from Carajás, Brazil. Silica-rich BIF layers contain translucent ellipsoidal or trapezoidal structures (∼5–10&nbsp;μm diameter) composed of silica, hematite, and kerogen, which are arranged in larger ring-like forms (rosettes). Stable carbon isotope analysis yields a δ<sup>13</sup>C value of −24.5‰ indicating that the contained carbon is likely biogenic. Raman and SEM analyses, as well as wavelength-dispersive X-ray elemental maps, show kerogen inside the rosette forms. Within the iron-rich BIF layers, tubular structures (0.5–5&nbsp;μm) were observed between hematite granules and blades. Kerogen and kaolinite are present in these structures. Both the rosettes and the tubular structures resemble morphologies that are characteristic of some bacterial species.</p><p id=\"sp0060\">We hypothesize that the Carajás BIFs originated as biomats formed by one or more species that over time produced large stromatolitic structures. The rosettes and the tubular structures, associated with chert-rich and iron-rich BIF layers, respectively, may represent two different species, or perhaps, two phases of a bacterium life cycle. For example, some modern myxobacteria exhibit similar morphologies in their resting and vegetative stages.</p><p id=\"sp0065\">Fe(III) precipitation may have occurred by contact of Fe(II) with bacterial slime, leading to oxidation by chemical reactions with exposed polysaccharide hydroxyl and carboxyl groups. The Fe(III) would then have been available for use as a source of energy in a dissimilatory iron reduction type of metabolism. Organic carbon input presumably came from primary producers (not necessarily aerobic) within the local water column, perhaps in shallow-water communities. Alternatively, the carbon may have originated by Fischer–Tropsch synthesis at ocean hydrothermal vents. The observed lateral continuity of BIF layers may perhaps be explained by chemical signaling by the bacteria of favorable or unfavorable environmental conditions, leading to nearly synchronous cell morphogenesis from a vegetative to resting phase and vice versa.</p>","language":"English","publisher":"Elsevier","doi":"10.1016/j.epsl.2012.08.028","usgsCitation":"Ribeiro da Luz, B., and Crowley, J.K., 2012, Morphological and chemical evidence of stromatolitic deposits in the 2.75 Ga Carajás banded iron formation, Brazil: Earth and Planetary Science Letters, v. 355-356, p. 60-72, https://doi.org/10.1016/j.epsl.2012.08.028.","productDescription":"13 p.","startPage":"60","endPage":"72","ipdsId":"IP-026854","costCenters":[{"id":245,"text":"Eastern Mineral and Environmental Resources Science Center","active":true,"usgs":true}],"links":[{"id":346328,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"Brazil","volume":"355-356","publishingServiceCenter":{"id":9,"text":"Reston PSC"},"noUsgsAuthors":false,"publicationStatus":"PW","scienceBaseUri":"59d3502be4b05fe04cc34d80","contributors":{"authors":[{"text":"Ribeiro da Luz, Beatriz bribeirodaluz@usgs.gov","contributorId":3260,"corporation":false,"usgs":true,"family":"Ribeiro da Luz","given":"Beatriz","email":"bribeirodaluz@usgs.gov","affiliations":[],"preferred":true,"id":711677,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Crowley, James K.","contributorId":10928,"corporation":false,"usgs":true,"family":"Crowley","given":"James","email":"","middleInitial":"K.","affiliations":[],"preferred":false,"id":711678,"contributorType":{"id":1,"text":"Authors"},"rank":2}]}}
,{"id":70040570,"text":"70040570 - 2012 - Modeling the mesozoic-cenozoic structural evolution of east texas","interactions":[],"lastModifiedDate":"2012-11-02T10:07:47","indexId":"70040570","displayToPublicDate":"2012-11-01T00:00:00","publicationYear":"2012","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":1870,"text":"Gulf Coast Assoc of Geological Societies Journal","active":true,"publicationSubtype":{"id":10}},"title":"Modeling the mesozoic-cenozoic structural evolution of east texas","docAbstract":"The U.S. Geological Survey (USGS) recently assessed the undiscovered technically recoverable oil and gas resources within Jurassic and Cretaceous strata of the onshore coastal plain and State waters of the U.S. Gulf Coast. Regional 2D seismic lines for key parts of the Gulf Coast basin were interpreted in order to examine the evolution of structural traps and the burial history of petroleum source rocks. Interpretation and structural modeling of seismic lines from eastern Texas provide insights into the structural evolution of this part of the Gulf of Mexico basin. Since completing the assessment, the USGS has acquired additional regional seismic lines in east Texas; interpretation of these new lines, which extend from the Texas-Oklahoma state line to the Gulf Coast shoreline, show how some of the region's prominent structural elements (e.g., the Talco and Mount Enterprise fault zones, the East Texas salt basin, and the Houston diapir province) vary along strike. The interpretations also indicate that unexplored structures may lie beneath the current drilling floor. Structural restorations based upon interpretation of these lines illustrate the evolution of key structures and show the genetic relation between structural growth and movement of the Jurassic Louann Salt. 1D thermal models that integrate kinetics and burial histories were also created for the region's two primary petroleum source rocks, the Oxfordian Smackover Formation and the Cenomanian-Turonian Eagle Ford Shale. Integrating results from the thermal models with the structural restorations provides insights into the distribution and timing of petroleum expulsion from the Smackover Formation and Eagle Ford Shale in eastern Texas.","largerWorkType":{"id":2,"text":"Article"},"largerWorkTitle":"Gulf Coast Assoc of Geological Societies Journal","largerWorkSubtype":{"id":10,"text":"Journal Article"},"language":"English","publisher":"Gulf Coast Association of Geological Societies","publisherLocation":"Austin, TX","usgsCitation":"Pearson, O.N., Rowan, E.L., and Miller, J.J., 2012, Modeling the mesozoic-cenozoic structural evolution of east texas: Gulf Coast Assoc of Geological Societies Journal, v. 1, p. 118-128.","productDescription":"11 p.","startPage":"118","endPage":"128","numberOfPages":"31","ipdsId":"IP-036590","costCenters":[{"id":164,"text":"Central Energy Resources Science Center","active":true,"usgs":true}],"links":[{"id":262895,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"},{"id":262894,"type":{"id":15,"text":"Index Page"},"url":"https://www.gcags.org/"}],"country":"United States","state":"Texas","otherGeospatial":"Gulf Of Mexico Basin","geographicExtents":"{ \"type\": \"FeatureCollection\", \"features\": [ { \"type\": \"Feature\", \"properties\": {}, \"geometry\": { \"type\": \"Polygon\", \"coordinates\": [ [ [ -106.65,25.84 ], [ -106.65,36.5 ], [ -93.51,36.5 ], [ -93.51,25.84 ], [ -106.65,25.84 ] ] ] } } ] }","volume":"1","noUsgsAuthors":false,"publicationStatus":"PW","scienceBaseUri":"5094ebe8e4b0e5cfc2acdce9","contributors":{"authors":[{"text":"Pearson, Ofori N. 0000-0002-9550-1128 opearson@usgs.gov","orcid":"https://orcid.org/0000-0002-9550-1128","contributorId":1680,"corporation":false,"usgs":true,"family":"Pearson","given":"Ofori","email":"opearson@usgs.gov","middleInitial":"N.","affiliations":[{"id":164,"text":"Central Energy Resources Science Center","active":true,"usgs":true}],"preferred":true,"id":468562,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Rowan, Elisabeth L. 0000-0001-5753-6189 erowan@usgs.gov","orcid":"https://orcid.org/0000-0001-5753-6189","contributorId":2075,"corporation":false,"usgs":true,"family":"Rowan","given":"Elisabeth","email":"erowan@usgs.gov","middleInitial":"L.","affiliations":[{"id":241,"text":"Eastern Energy Resources Science Center","active":true,"usgs":true}],"preferred":true,"id":468563,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Miller, John J. 0000-0002-9098-0967 jmiller@usgs.gov","orcid":"https://orcid.org/0000-0002-9098-0967","contributorId":3785,"corporation":false,"usgs":true,"family":"Miller","given":"John","email":"jmiller@usgs.gov","middleInitial":"J.","affiliations":[{"id":164,"text":"Central Energy Resources Science Center","active":true,"usgs":true}],"preferred":true,"id":468564,"contributorType":{"id":1,"text":"Authors"},"rank":3}]}}
,{"id":70040556,"text":"70040556 - 2012 - Space-time models for a panzootic in bats, with a focus on the endangered Indiana bat","interactions":[],"lastModifiedDate":"2012-11-02T10:08:02","indexId":"70040556","displayToPublicDate":"2012-11-01T00:00:00","publicationYear":"2012","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":2507,"text":"Journal of Wildlife Diseases","active":true,"publicationSubtype":{"id":10}},"title":"Space-time models for a panzootic in bats, with a focus on the endangered Indiana bat","docAbstract":"Knowledge of current trends of quickly spreading infectious wildlife diseases is vital to efficient and effective management. We developed space-time mixed-effects logistic regressions to characterize a disease, white-nose syndrome (WNS), quickly spreading among endangered Indiana bats (<i>Myotis sodalis</i>) in eastern North America. Our goal was to calculate and map the risk probability faced by uninfected colonies of hibernating Indiana bats. Model covariates included annual distance from and direction to nearest sources of infection, geolocational information, size of the Indiana bat populations within each wintering population, and total annual size of populations known or suspected to be affected by WNS. We considered temporal, spatial, and spatiotemporal formulae through the use of random effects for year, complex (a collection of interacting hibernacula), and yearxcomplex. Since first documented in 2006, WNS has spread across much of the range of the Indiana bat. No sizeable wintering population now occurs outside of the migrational distance of an infected source. Annual rates of newly affected wintering Indiana bat populations between winter 2007 to 2008 and 2010 to 2011 were 4, 6, 8, and 12%; this rate increased each year at a rate of 3%. If this increasing rate of newly affected populations continues, all wintering populations may be affected by 2016. Our models indicated the probability of a wintering population exhibiting infection was a linear function of proximity to affected Indiana bat populations and size of the at-risk population. Geographic location was also important, suggesting broad-scale influences. For every 50-km increase in distance from a WNS-affected population, risk of disease declined by 6% (95% CI=5.2-5.7%); for every increase of 1,000 Indiana bats, there was an 8% (95% CI = 1-21%) increase in disease risk. The increasing rate of infection seems to be associated with the movement of this disease into the core of the Indiana bat range. Our spatially explicit estimates of disease risk may aid managers in prioritizing surveillance and management for wintering populations of Indiana bats and help understand the risk faced by other hibernating bat species.","largerWorkType":{"id":2,"text":"Article"},"largerWorkTitle":"Journal of Wildlife Diseases","largerWorkSubtype":{"id":10,"text":"Journal Article"},"language":"English","publisher":"Wildlife Disease Association","publisherLocation":"Lawrence, KS","doi":"10.7589/2011-06-176","usgsCitation":"Thogmartin, W.E., King, R.A., Szymanski, J.A., and Pruitt, L., 2012, Space-time models for a panzootic in bats, with a focus on the endangered Indiana bat: Journal of Wildlife Diseases, v. 48, no. 4, p. 876-887, https://doi.org/10.7589/2011-06-176.","productDescription":"12 p.","startPage":"876","endPage":"887","ipdsId":"IP-030496","costCenters":[{"id":606,"text":"Upper Midwest Environmental Sciences Center","active":true,"usgs":true}],"links":[{"id":262869,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"},{"id":262868,"type":{"id":10,"text":"Digital Object Identifier"},"url":"https://dx.doi.org/10.7589/2011-06-176"}],"country":"Canada;United States","volume":"48","issue":"4","noUsgsAuthors":false,"publicationStatus":"PW","scienceBaseUri":"5094ec0ae4b0e5cfc2acdd01","contributors":{"authors":[{"text":"Thogmartin, Wayne E. 0000-0002-2384-4279 wthogmartin@usgs.gov","orcid":"https://orcid.org/0000-0002-2384-4279","contributorId":2545,"corporation":false,"usgs":true,"family":"Thogmartin","given":"Wayne","email":"wthogmartin@usgs.gov","middleInitial":"E.","affiliations":[{"id":606,"text":"Upper Midwest Environmental Sciences Center","active":true,"usgs":true},{"id":114,"text":"Alaska Science Center","active":true,"usgs":true}],"preferred":true,"id":468507,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"King, R. Andrew","contributorId":40839,"corporation":false,"usgs":true,"family":"King","given":"R.","email":"","middleInitial":"Andrew","affiliations":[],"preferred":false,"id":468509,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Szymanski, Jennifer A.","contributorId":51593,"corporation":false,"usgs":true,"family":"Szymanski","given":"Jennifer","email":"","middleInitial":"A.","affiliations":[],"preferred":false,"id":468510,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Pruitt, Lori","contributorId":17468,"corporation":false,"usgs":true,"family":"Pruitt","given":"Lori","email":"","affiliations":[],"preferred":false,"id":468508,"contributorType":{"id":1,"text":"Authors"},"rank":4}]}}
,{"id":70192291,"text":"70192291 - 2012 - A comparison among observations and earthquake simulator results for the allcal2 California fault model","interactions":[],"lastModifiedDate":"2017-10-31T14:43:16","indexId":"70192291","displayToPublicDate":"2012-11-01T00:00:00","publicationYear":"2012","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":3372,"text":"Seismological Research Letters","onlineIssn":"1938-2057","printIssn":"0895-0695","active":true,"publicationSubtype":{"id":10}},"title":"A comparison among observations and earthquake simulator results for the allcal2 California fault model","docAbstract":"<p id=\"p-3\">In order to understand earthquake hazards we would ideally have a statistical description of earthquakes for tens of thousands of years. Unfortunately the ∼100‐year instrumental, several 100‐year historical, and few 1000‐year paleoseismological records are woefully inadequate to provide a statistically significant record. Physics‐based earthquake simulators can generate arbitrarily long histories of earthquakes; thus they can provide a statistically meaningful history of simulated earthquakes. The question is, how realistic are these simulated histories? This purpose of this paper is to begin to answer that question. We compare the results between different simulators and with information that is known from the limited instrumental, historic, and paleoseismological data.</p><p id=\"p-4\">As expected, the results from all the simulators show that the observational record is too short to properly represent the system behavior; therefore, although tests of the simulators against the limited observations are necessary, they are not a sufficient test of the simulators’ realism. The simulators appear to pass this necessary test. In addition, the physics‐based simulators show similar behavior even though there are large differences in the methodology. This suggests that they represent realistic behavior. Different assumptions concerning the constitutive properties of the faults do result in enhanced capabilities of some simulators. However, it appears that the similar behavior of the different simulators may result from the fault‐system geometry, slip rates, and assumed strength drops, along with the shared physics of stress transfer.</p><p id=\"p-5\">This paper describes the results of running four earthquake simulators that are described elsewhere in this issue of Seismological Research Letters. The simulators ALLCAL (Ward, 2012), VIRTCAL (Sachs et al., 2012), RSQSim (Richards‐Dinger and Dieterich, 2012), and ViscoSim (Pollitz, 2012) were run on our most recent all‐California fault model, allcal2. With the exception of ViscoSim, which ran for 10,000 years, all the simulators ran for 30,000 years. Presentations containing content similar to this paper can be found at http://scec.usc.edu/research/eqsims/.</p>","language":"English","publisher":"Seismological Society of America","doi":"10.1785/0220120094","usgsCitation":"Tullis, T.E., Richards-Dinger, K.B., Barall, M., Dieterich, J.H., Field, E.H., Heien, E.M., Kellogg, L., Pollitz, F., Rundle, J.B., Sachs, M.K., Turcotte, D.L., Ward, S.N., and Yikilmaz, M.B., 2012, A comparison among observations and earthquake simulator results for the allcal2 California fault model: Seismological Research Letters, v. 83, no. 6, p. 994-1006, https://doi.org/10.1785/0220120094.","productDescription":"13 p.","startPage":"994","endPage":"1006","ipdsId":"IP-040844","costCenters":[{"id":237,"text":"Earthquake Science Center","active":true,"usgs":true}],"links":[{"id":347899,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"volume":"83","issue":"6","publishingServiceCenter":{"id":14,"text":"Menlo Park PSC"},"noUsgsAuthors":false,"publicationDate":"2012-11-08","publicationStatus":"PW","scienceBaseUri":"59f98bbfe4b0531197afa050","contributors":{"authors":[{"text":"Tullis, Terry. E.","contributorId":198122,"corporation":false,"usgs":false,"family":"Tullis","given":"Terry.","email":"","middleInitial":"E.","affiliations":[],"preferred":false,"id":718674,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Richards-Dinger, Keith B.","contributorId":198155,"corporation":false,"usgs":false,"family":"Richards-Dinger","given":"Keith","email":"","middleInitial":"B.","affiliations":[],"preferred":false,"id":718675,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Barall, Michael mbarall@usgs.gov","contributorId":2595,"corporation":false,"usgs":true,"family":"Barall","given":"Michael","email":"mbarall@usgs.gov","affiliations":[{"id":237,"text":"Earthquake Science Center","active":true,"usgs":true}],"preferred":false,"id":718676,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Dieterich, James H.","contributorId":198156,"corporation":false,"usgs":false,"family":"Dieterich","given":"James","email":"","middleInitial":"H.","affiliations":[],"preferred":false,"id":718677,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Field, Edward H. 0000-0001-8172-7882 field@usgs.gov","orcid":"https://orcid.org/0000-0001-8172-7882","contributorId":52242,"corporation":false,"usgs":true,"family":"Field","given":"Edward","email":"field@usgs.gov","middleInitial":"H.","affiliations":[{"id":300,"text":"Geologic Hazards Science Center","active":true,"usgs":true}],"preferred":true,"id":718678,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"Heien, Eric M.","contributorId":199330,"corporation":false,"usgs":false,"family":"Heien","given":"Eric","email":"","middleInitial":"M.","affiliations":[],"preferred":false,"id":718679,"contributorType":{"id":1,"text":"Authors"},"rank":6},{"text":"Kellogg, Louise","contributorId":191823,"corporation":false,"usgs":false,"family":"Kellogg","given":"Louise","email":"","affiliations":[],"preferred":false,"id":718680,"contributorType":{"id":1,"text":"Authors"},"rank":7},{"text":"Pollitz, Fred F. fpollitz@usgs.gov","contributorId":2408,"corporation":false,"usgs":true,"family":"Pollitz","given":"Fred F.","email":"fpollitz@usgs.gov","affiliations":[{"id":237,"text":"Earthquake Science Center","active":true,"usgs":true}],"preferred":false,"id":718681,"contributorType":{"id":1,"text":"Authors"},"rank":8},{"text":"Rundle, John B.","contributorId":113221,"corporation":false,"usgs":true,"family":"Rundle","given":"John","email":"","middleInitial":"B.","affiliations":[],"preferred":false,"id":718682,"contributorType":{"id":1,"text":"Authors"},"rank":9},{"text":"Sachs, Michael K.","contributorId":199331,"corporation":false,"usgs":false,"family":"Sachs","given":"Michael","email":"","middleInitial":"K.","affiliations":[],"preferred":false,"id":718683,"contributorType":{"id":1,"text":"Authors"},"rank":10},{"text":"Turcotte, Donald L.","contributorId":111578,"corporation":false,"usgs":true,"family":"Turcotte","given":"Donald","email":"","middleInitial":"L.","affiliations":[],"preferred":false,"id":718684,"contributorType":{"id":1,"text":"Authors"},"rank":11},{"text":"Ward, Steven N.","contributorId":9164,"corporation":false,"usgs":true,"family":"Ward","given":"Steven","email":"","middleInitial":"N.","affiliations":[],"preferred":false,"id":718685,"contributorType":{"id":1,"text":"Authors"},"rank":12},{"text":"Yikilmaz, M. Burak","contributorId":191805,"corporation":false,"usgs":false,"family":"Yikilmaz","given":"M.","email":"","middleInitial":"Burak","affiliations":[],"preferred":false,"id":718686,"contributorType":{"id":1,"text":"Authors"},"rank":13}]}}
,{"id":70040571,"text":"sir20125231 - 2012 - Simulating potential structural and operational changes for Detroit Dam on the North Santiam River, Oregon, for downstream temperature management","interactions":[{"subject":{"id":70005682,"text":"ofr20111268 - 2011 - Simulating potential structural and operational changes for Detroit Dam on the North Santiam River, Oregon-Interim Results","indexId":"ofr20111268","publicationYear":"2011","noYear":false,"title":"Simulating potential structural and operational changes for Detroit Dam on the North Santiam River, Oregon-Interim Results"},"predicate":"SUPERSEDED_BY","object":{"id":70040571,"text":"sir20125231 - 2012 - Simulating potential structural and operational changes for Detroit Dam on the North Santiam River, Oregon, for downstream temperature management","indexId":"sir20125231","publicationYear":"2012","noYear":false,"title":"Simulating potential structural and operational changes for Detroit Dam on the North Santiam River, Oregon, for downstream temperature management"},"id":1}],"lastModifiedDate":"2013-06-12T10:07:25","indexId":"sir20125231","displayToPublicDate":"2012-11-01T00:00:00","publicationYear":"2012","noYear":false,"publicationType":{"id":18,"text":"Report"},"publicationSubtype":{"id":5,"text":"USGS Numbered Series"},"seriesTitle":{"id":334,"text":"Scientific Investigations Report","code":"SIR","onlineIssn":"2328-0328","printIssn":"2328-031X","active":true,"publicationSubtype":{"id":5}},"seriesNumber":"2012-5231","title":"Simulating potential structural and operational changes for Detroit Dam on the North Santiam River, Oregon, for downstream temperature management","docAbstract":"Detroit Dam was constructed in 1953 on the North Santiam River in western Oregon and resulted in the formation of Detroit Lake. With a full-pool storage volume of 455,100 acre-feet and a dam height of 463 feet, Detroit Lake is one of the largest and most important reservoirs in the Willamette River basin in terms of power generation, recreation, and water storage and releases. The U.S. Army Corps of Engineers operates Detroit Dam as part of a system of 13 reservoirs in the Willamette Project to meet multiple goals, which include flood-damage protection, power generation, downstream navigation, recreation, and irrigation. A distinct cycle in water temperature occurs in Detroit Lake as spring and summer heating through solar radiation creates a warm layer of water near the surface and isolates cold water below. Controlling the temperature of releases from Detroit Dam, therefore, is highly dependent on the location, characteristics, and usage of the dam's outlet structures. Prior to operational changes in 2007, Detroit Dam had a well-documented effect on downstream water temperature that was problematic for endangered salmonid fish species, releasing water that was too cold in midsummer and too warm in autumn. This unnatural seasonal temperature pattern caused problems in the timing of fish migration, spawning, and emergence.  In this study, an existing calibrated 2-dimensional hydrodynamic water-quality model [CE-QUAL-W2] of Detroit Lake was used to determine how changes in dam operation or changes to the structural release points of Detroit Dam might affect downstream water temperatures under a range of historical hydrologic and meteorological conditions. The results from a subset of the Detroit Lake model scenarios then were used as forcing conditions for downstream CE-QUAL-W2 models of Big Cliff Reservoir (the small reregulating reservoir just downstream of Detroit Dam) and the North Santiam and Santiam Rivers. Many combinations of environmental, operational, and structural options were explored with the model scenarios. Multiple downstream temperature targets were used along with three sets of environmental forcing conditions representing <i>cool/wet, normal, and hot/dry</i> conditions. Five structural options at Detroit Dam were modeled, including the use of existing outlets, one hypothetical variable-elevation outlet such as a sliding gate, a hypothetical combination of a floating outlet and a fixed-elevation outlet, and a hypothetical combination of a floating outlet and a sliding gate. Finally, 14 sets of operational guidelines for Detroit Dam were explored to gain an understanding of the effects of imposing different downstream minimum streamflows, imposing minimum outflow rules to specific outlets, and managing the level of the lake with different timelines through the year. Selected subsets of these combinations of operational and structural scenarios were run through the downstream models of Big Cliff Reservoir and the North Santiam and Santiam Rivers to explore how hypothetical changes at Detroit Dam might provide improved temperatures for endangered salmonids downstream of the Detroit-Big Cliff Dam complex.\r\nConclusions that can be drawn from these model scenarios include: \r\n*The water-temperature targets set by the U.S. Army Corps of Engineers for releases from Detroit Dam can be met through a combination of new dam outlets or a delayed drawdown of the lake in autumn.\r\n*Spring and summer dam operations greatly affect the available release temperatures and operational flexibility later in the autumn. Releasing warm water during midsummer tends to keep more cool water available for release in autumn. \r\n*The ability to meet downstream temperature targets during spring depends on the characteristics of the available outlets. Under existing conditions, although warm water sometimes is present at the lake surface in spring and early summer, such water may not be available for release if the lake level is either well below or well above the spillway crest. \r\n*Managing lake releases to meet downstream temperature targets depends on having outlet structures that can access both (warm) lake surface water and (cold) deeper lake water throughout the year. The existing outlets at Detroit Dam do not allow near-surface waters to be released during times when the lake surface level is below the spillway (spring and autumn). \r\n*Using the existing outlets at Detroit Dam, lake level management is important to the water temperature of releases because it controls the availability and depth of water at the spillway. When lake level is lowered below the spillway crest in late summer, the loss of access to warm water at the lake surface can result in abrupt changes to release temperatures. \r\n*Because the power-generation intakes (penstocks) are 166 feet below the full-pool lake level, imposing minimum power production requirements at Detroit Dam limits the amount of warm surface water that can be expelled from the lake in midsummer, thereby postponing and amplifying warm outflows from Detroit Lake into the autumn spawning season. \r\n*Likewise, imposing minimum power production requirements at Detroit Dam in autumn can limit the amount of cool hypolimnetic water that is released from the lake, thereby limiting cool outflows from Detroit Lake during the autumn spawning season. \r\n*Model simulations indicate that a delayed drawdown of Detroit Lake in autumn would result in better control over release temperatures in the immediate downstream vicinity of Big Cliff Dam, but the reduced outflows necessary to retain more water in the lake in late summer are more susceptible to rapid heating downstream. \r\n*Compared to the existing outlets at Detroit Dam, floating or sliding-gate outlet structures can provide greater control over release temperatures because they provide better access to warm water at the lake surface and cooler water at depth. These conclusions can be grouped into several common themes. First, optimal and flexible management and achievement of downstream temperature goals requires that releases of warm water near the surface of the lake and cold water below the thermocline are both possible with the available dam outlets during spring, summer, and autumn. This constraint can be met to some extent with existing outlets, but only if access to the spillway is extended into autumn by keeping the lake level higher than called for by the current rule curve (the typical target water-surface elevation throughout the year). If new outlets are considered, a variable-elevation outlet such as a sliding gate structure, or a floating outlet in combination with a fixed-elevation outlet at sufficient depth to access cold water, is likely to work well in terms of accessing a range of water temperatures and achieving downstream temperature targets. Furthermore, model results indicate that it is important to release warm water from near the lake surface during midsummer. If not released downstream, the warm water will build up at the top of the lake as a result of solar energy inputs and the thermocline will deepen, potentially causing warm water to reach the depth of deeper fixed-elevation outlets in autumn, particularly when the lake level is drawn down to make room for flood storage. Delaying the drawdown in autumn can help to keep the thermocline above such outlets and preserve access to cold water. Although it is important to generate hydropower at Detroit Dam, minimum power-production requirements limit the ability of dam operators to meet downstream temperature targets with existing outlet structures. The location of the power penstocks below the thermocline in spring and most of summer causes the release of more cool water during summer than is optimal. Reducing the power-production constraint allows the temperature target to be met more frequently, but at the cost of less power generation.\r\nFinally, running the Detroit Dam, Big Cliff Dam, and North Santiam and Santiam River models in series allows dam operators to evaluate how different operational strategies or combinations of new dam outlets might affect downstream temperatures for many miles of critical endangered salmonid habitat. Temperatures can change quickly in these downstream reaches as the river exchanges heat with its surroundings, and heating or cooling of 6 degrees Celsius is not unusual in the 40–50 miles downstream of Big Cliff Dam.  The results published in this report supersede preliminary results published in U.S. Geological Survey Open-File Report 2011-1268 (Buccola and Rounds, 2011). Those preliminary results are still valid, but the results in this report are more current and comprehensive.","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/sir20125231","collaboration":"Prepared in cooperation with the U.S. Army Corps of Engineers; Version 1.1, June 2013","usgsCitation":"Buccola, N., Rounds, S.A., Sullivan, A.B., and Risley, J.C., 2012, Simulating potential structural and operational changes for Detroit Dam on the North Santiam River, Oregon, for downstream temperature management (Originally posted October 30, 2012; Revised June 11, 2013): U.S. Geological Survey Scientific Investigations Report 2012-5231, viii; 68 p., https://doi.org/10.3133/sir20125231.","productDescription":"viii; 68 p.","numberOfPages":"80","costCenters":[{"id":518,"text":"Oregon Water Science Center","active":true,"usgs":true}],"links":[{"id":262864,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/sir_2012_5231.jpg"},{"id":262862,"type":{"id":15,"text":"Index Page"},"url":"https://pubs.usgs.gov/sir/2012/5231/"},{"id":262863,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/sir/2012/5231/pdf/sir20125231.pdf"}],"scale":"24000","projection":"UTM, Zone 10","datum":"North American Datum of 1927","country":"United States","state":"Oregon","otherGeospatial":"Big Cliff Dam;Detroit Dam;Detroit Lake;North Santiam River Basin","geographicExtents":"{ \"type\": \"FeatureCollection\", \"features\": [ { \"type\": \"Feature\", \"properties\": {}, \"geometry\": { \"type\": \"Polygon\", \"coordinates\": [ [ [ -122.333333,44.583333 ], [ -122.333333,44.833333 ], [ -121.750000,44.833333 ], [ -121.750000,44.583333 ], [ -122.333333,44.583333 ] ] ] } } ] }","edition":"Originally posted October 30, 2012; Revised June 11, 2013","noUsgsAuthors":false,"publicationStatus":"PW","scienceBaseUri":"5094ec06e4b0e5cfc2acdcfd","contributors":{"authors":[{"text":"Buccola, Norman L. nbuccola@usgs.gov","contributorId":4295,"corporation":false,"usgs":true,"family":"Buccola","given":"Norman L.","email":"nbuccola@usgs.gov","affiliations":[{"id":518,"text":"Oregon Water Science Center","active":true,"usgs":true}],"preferred":false,"id":468567,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Rounds, Stewart A. 0000-0002-8540-2206 sarounds@usgs.gov","orcid":"https://orcid.org/0000-0002-8540-2206","contributorId":905,"corporation":false,"usgs":true,"family":"Rounds","given":"Stewart","email":"sarounds@usgs.gov","middleInitial":"A.","affiliations":[{"id":518,"text":"Oregon Water Science Center","active":true,"usgs":true}],"preferred":true,"id":468565,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Sullivan, Annett B. 0000-0001-7783-3906 annett@usgs.gov","orcid":"https://orcid.org/0000-0001-7783-3906","contributorId":56317,"corporation":false,"usgs":true,"family":"Sullivan","given":"Annett","email":"annett@usgs.gov","middleInitial":"B.","affiliations":[],"preferred":false,"id":468568,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Risley, John C. 0000-0002-8206-5443 jrisley@usgs.gov","orcid":"https://orcid.org/0000-0002-8206-5443","contributorId":2698,"corporation":false,"usgs":true,"family":"Risley","given":"John","email":"jrisley@usgs.gov","middleInitial":"C.","affiliations":[{"id":518,"text":"Oregon Water Science Center","active":true,"usgs":true}],"preferred":true,"id":468566,"contributorType":{"id":1,"text":"Authors"},"rank":4}]}}
,{"id":70189352,"text":"70189352 - 2012 - Difference infiltrometer: a method to measure temporally variable infiltration rates during rainstorms","interactions":[],"lastModifiedDate":"2017-07-11T15:52:36","indexId":"70189352","displayToPublicDate":"2012-11-01T00:00:00","publicationYear":"2012","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":1924,"text":"Hydrological Processes","active":true,"publicationSubtype":{"id":10}},"title":"Difference infiltrometer: a method to measure temporally variable infiltration rates during rainstorms","docAbstract":"<p><span>We developed a difference infiltrometer to measure time series of non-steady infiltration rates during rainstorms at the point scale. The infiltrometer uses two, tipping bucket rain gages. One gage measures rainfall onto, and the other measures runoff from, a small circular plot about 0.5-m in diameter. The small size allows the infiltration rate to be computed as the difference of the cumulative rainfall and cumulative runoff without having to route water through a large plot. Difference infiltrometers were deployed in an area burned by the 2010 Fourmile Canyon Fire near Boulder, Colorado, USA, and data were collected during the summer of 2011. The difference infiltrometer demonstrated the capability to capture different magnitudes of infiltration rates and temporal variability associated with convective (high intensity, short duration) and cyclonic (low intensity, long duration) rainstorms. Data from the difference infiltrometer were used to estimate saturated hydraulic conductivity of soil affected by the heat from a wildfire. The difference infiltrometer is portable and can be deployed in rugged, steep terrain and does not require the transport of water, as many rainfall simulators require, because it uses natural rainfall. It can be used to assess infiltration models, determine runoff coefficients, identify rainfall depth or rainfall intensity thresholds to initiate runoff, estimate parameters for infiltration models, and compare remediation treatments on disturbed landscapes. The difference infiltrometer can be linked with other types of soil monitoring equipment in long-term studies for detecting temporal and spatial variability at multiple time scales and in nested designs where it can be linked to hillslope and basin-scale runoff responses.</span></p>","language":"English","publisher":"Wiley","doi":"10.1002/hyp.9424","usgsCitation":"Moody, J.A., and Ebel, B.A., 2012, Difference infiltrometer: a method to measure temporally variable infiltration rates during rainstorms: Hydrological Processes, v. 26, no. 21, p. 3312-3318, https://doi.org/10.1002/hyp.9424.","productDescription":"7 p.","startPage":"3312","endPage":"3318","ipdsId":"IP-034604","costCenters":[{"id":5044,"text":"National Research Program - Central Branch","active":true,"usgs":true}],"links":[{"id":343604,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"volume":"26","issue":"21","publishingServiceCenter":{"id":2,"text":"Denver PSC"},"noUsgsAuthors":false,"publicationDate":"2012-07-03","publicationStatus":"PW","scienceBaseUri":"5965bacfe4b0d1f9f05b38d9","contributors":{"authors":[{"text":"Moody, John A. 0000-0003-2609-364X jamoody@usgs.gov","orcid":"https://orcid.org/0000-0003-2609-364X","contributorId":771,"corporation":false,"usgs":true,"family":"Moody","given":"John","email":"jamoody@usgs.gov","middleInitial":"A.","affiliations":[{"id":37277,"text":"WMA - Earth System Processes Division","active":true,"usgs":true},{"id":5044,"text":"National Research Program - Central Branch","active":true,"usgs":true}],"preferred":true,"id":704334,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Ebel, Brian A. 0000-0002-5413-3963 bebel@usgs.gov","orcid":"https://orcid.org/0000-0002-5413-3963","contributorId":2557,"corporation":false,"usgs":true,"family":"Ebel","given":"Brian","email":"bebel@usgs.gov","middleInitial":"A.","affiliations":[{"id":5044,"text":"National Research Program - Central Branch","active":true,"usgs":true},{"id":438,"text":"National Research Program - Western Branch","active":true,"usgs":true}],"preferred":true,"id":704333,"contributorType":{"id":1,"text":"Authors"},"rank":2}]}}
,{"id":70040580,"text":"sir20125140 - 2012 - Demonstration optimization analyses of pumping from selected Arapahoe aquifer municipal wells in the west-central Denver Basin, Colorado, 2010–2109","interactions":[],"lastModifiedDate":"2012-11-01T15:56:06","indexId":"sir20125140","displayToPublicDate":"2012-11-01T00:00:00","publicationYear":"2012","noYear":false,"publicationType":{"id":18,"text":"Report"},"publicationSubtype":{"id":5,"text":"USGS Numbered Series"},"seriesTitle":{"id":334,"text":"Scientific Investigations Report","code":"SIR","onlineIssn":"2328-0328","printIssn":"2328-031X","active":true,"publicationSubtype":{"id":5}},"seriesNumber":"2012-5140","title":"Demonstration optimization analyses of pumping from selected Arapahoe aquifer municipal wells in the west-central Denver Basin, Colorado, 2010–2109","docAbstract":"Declining water levels caused by withdrawals of water from wells in the west-central part of the Denver Basin bedrock-aquifer system have raised concerns with respect to the ability of the aquifer system to sustain production. The Arapahoe aquifer in particular is heavily used in this area. Two optimization analyses were conducted to demonstrate approaches that could be used to evaluate possible future pumping scenarios intended to prolong the productivity of the aquifer and to delay excessive loss of saturated thickness. These analyses were designed as demonstrations only, and were not intended as a comprehensive optimization study. Optimization analyses were based on a groundwater-flow model of the Denver Basin developed as part of a recently published U.S. Geological Survey groundwater-availability study. For each analysis an optimization problem was set up to maximize total withdrawal rate, subject to withdrawal-rate and hydraulic-head constraints, for 119 selected municipal water-supply wells located in 96 model cells. The optimization analyses were based on 50- and 100-year simulations of groundwater withdrawals. The optimized total withdrawal rate for all selected wells for a 50-year simulation time was about 58.8 cubic feet per second. For an analysis in which the simulation time and head-constraint time were extended to 100 years, the optimized total withdrawal rate for all selected wells was about 53.0 cubic feet per second, demonstrating that a reduction in withdrawal rate of about 10 percent may extend the time before the hydraulic-head constraints are violated by 50 years, provided that pumping rates are optimally distributed. Analysis of simulation results showed that initially, the pumping produces water primarily by release of water from storage in the Arapahoe aquifer. However, because confining layers between the Denver and Arapahoe aquifers are thin, in less than 5 years, most of the water removed by managed-flows pumping likely would be supplied by depleting overlying hydrogeologic units, substantially increasing the rate of decline of hydraulic heads in parts of the overlying Denver aquifer.","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/sir20125140","collaboration":"Prepared in cooperation with the Colorado Water Conservation Board","usgsCitation":"Banta, E., and Paschke, S.S., 2012, Demonstration optimization analyses of pumping from selected Arapahoe aquifer municipal wells in the west-central Denver Basin, Colorado, 2010–2109: U.S. Geological Survey Scientific Investigations Report 2012-5140, v; 37 p., https://doi.org/10.3133/sir20125140.","productDescription":"v; 37 p.","numberOfPages":"46","onlineOnly":"Y","costCenters":[{"id":191,"text":"Colorado Water Science Center","active":true,"usgs":true}],"links":[{"id":262898,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/sir_2012_5140.gif"},{"id":262896,"type":{"id":15,"text":"Index Page"},"url":"https://pubs.usgs.gov/sir/2012/5140/"},{"id":262897,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/sir/2012/5140/SIR12-5140.pdf"}],"scale":"100000","projection":"Lambert Conformal Conic projection","country":"United States","state":"Colorado","city":"Denver","otherGeospatial":"Denver Basin","geographicExtents":"{ \"type\": \"FeatureCollection\", \"features\": [ { \"type\": \"Feature\", \"properties\": {}, \"geometry\": { \"type\": \"Polygon\", \"coordinates\": [ [ [ -105.500000,38.500000 ], [ -105.500000,40.500000 ], [ -103.500000,40.500000 ], [ -103.500000,38.500000 ], [ -105.500000,38.500000 ] ] ] } } ] }","noUsgsAuthors":false,"publicationStatus":"PW","scienceBaseUri":"50da02e2e4b07a5aecdf05bc","contributors":{"authors":[{"text":"Banta, Edward R.","contributorId":49820,"corporation":false,"usgs":true,"family":"Banta","given":"Edward R.","affiliations":[],"preferred":false,"id":468599,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Paschke, Suzanne S.","contributorId":14072,"corporation":false,"usgs":true,"family":"Paschke","given":"Suzanne","email":"","middleInitial":"S.","affiliations":[],"preferred":false,"id":468598,"contributorType":{"id":1,"text":"Authors"},"rank":2}]}}
,{"id":70189182,"text":"70189182 - 2012 - Uncertainty quantification for environmental models","interactions":[],"lastModifiedDate":"2017-07-07T09:52:05","indexId":"70189182","displayToPublicDate":"2012-11-01T00:00:00","publicationYear":"2012","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":5454,"text":"SIAM News","active":true,"publicationSubtype":{"id":10}},"title":"Uncertainty quantification for environmental models","docAbstract":"Environmental models are used to evaluate the fate of fertilizers in agricultural settings (including soil denitrification), the degradation of hydrocarbons at spill sites, and water supply for people and ecosystems in small to large basins and cities—to mention but a few applications of these models. They also play a role in understanding and diagnosing potential environmental impacts of global climate change. The models are typically mildly to extremely nonlinear. The persistent demand for enhanced dynamics and resolution to improve model realism [17] means that lengthy individual model execution times will remain common, notwithstanding continued enhancements in computer power. In addition, high-dimensional parameter spaces are often defined, which increases the number of model runs required to quantify uncertainty [2]. Some environmental modeling projects have access to extensive funding and computational resources; many do not. The many recent studies of uncertainty quantification in environmental model predictions have focused on uncertainties related to data error and sparsity of data, expert judgment expressed mathematically through prior information, poorly known parameter values, and model structure (see, for example, [1,7,9,10,13,18]). Approaches for quantifying uncertainty include frequentist (potentially with prior information [7,9]), Bayesian [13,18,19], and likelihood-based. A few of the numerous methods, including some sensitivity and inverse methods with consequences for understanding and quantifying uncertainty, are as follows: Bayesian hierarchical modeling and Bayesian model averaging; single-objective optimization with error-based weighting [7] and multi-objective optimization [3]; methods based on local derivatives [2,7,10]; screening methods like OAT (one at a time) and the method of Morris [14]; FAST (Fourier amplitude sensitivity testing) [14]; the Sobol' method [14]; randomized maximum likelihood [10]; Markov chain Monte Carlo (MCMC) [10]. There are also bootstrapping and cross-validation approaches.Sometimes analyses are conducted using surrogate models [12]. The availability of so many options can be confusing. Categorizing methods based on fundamental questions assists in communicating the essential results of uncertainty analyses to stakeholders. Such questions can focus on model adequacy (e.g., How well does the model reproduce observed system characteristics and dynamics?) and sensitivity analysis (e.g., What parameters can be estimated with available data? What observations are important to parameters and predictions? What parameters are important to predictions?), as well as on the uncertainty quantification (e.g., How accurate and precise are the predictions?). The methods can also be classified by the number of model runs required: few (10s to 1000s) or many (10,000s to 1,000,000s). Of the methods listed above, the most computationally frugal are generally those based on local derivatives; MCMC methods tend to be among the most computationally demanding. Surrogate models (emulators)do not necessarily produce computational frugality because many runs of the full model are generally needed to create a meaningful surrogate model. With this categorization, we can, in general, address all the fundamental questions mentioned above using either computationally frugal or demanding methods. Model development and analysis can thus be conducted consistently using either computation-ally frugal or demanding methods; alternatively, different fundamental questions can be addressed using methods that require different levels of effort. Based on this perspective, we pose the question: Can computationally frugal methods be useful companions to computationally demanding meth-ods? The reliability of computationally frugal methods generally depends on the model being reasonably linear, which usually means smooth nonlin-earities and the assumption of Gaussian errors; both tend to be more valid with more linear","language":"English","publisher":"Society for Industrial and Applied Mathematics","usgsCitation":"Hill, M.C., Lu, D., Kavetski, D., Clark, M.P., and Ye, M., 2012, Uncertainty quantification for environmental models: SIAM News, v. 45, no. 9, HTML Document.","productDescription":"HTML Document","ipdsId":"IP-041596","costCenters":[{"id":5044,"text":"National Research Program - Central Branch","active":true,"usgs":true}],"links":[{"id":343434,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"},{"id":343432,"rank":1,"type":{"id":15,"text":"Index Page"},"url":"https://sinews.siam.org/Current-Issue/Issue-Archives/Issue-Archives-ListView/PID/2282/mcat/2279/evl/0/TagID/206?TagName=Volume-45-|-Number-9-|-November-2012"}],"volume":"45","issue":"9","publishingServiceCenter":{"id":2,"text":"Denver PSC"},"noUsgsAuthors":false,"publicationStatus":"PW","scienceBaseUri":"595f4c45e4b0d1f9f057e372","contributors":{"authors":[{"text":"Hill, Mary C. mchill@usgs.gov","contributorId":974,"corporation":false,"usgs":true,"family":"Hill","given":"Mary","email":"mchill@usgs.gov","middleInitial":"C.","affiliations":[{"id":5044,"text":"National Research Program - Central Branch","active":true,"usgs":true}],"preferred":true,"id":703388,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Lu, Dan","contributorId":58176,"corporation":false,"usgs":true,"family":"Lu","given":"Dan","affiliations":[],"preferred":false,"id":703762,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"Kavetski, Dmitri","contributorId":194182,"corporation":false,"usgs":false,"family":"Kavetski","given":"Dmitri","email":"","affiliations":[],"preferred":false,"id":703389,"contributorType":{"id":1,"text":"Authors"},"rank":11},{"text":"Clark, Martyn P.","contributorId":194183,"corporation":false,"usgs":false,"family":"Clark","given":"Martyn","email":"","middleInitial":"P.","affiliations":[],"preferred":false,"id":703390,"contributorType":{"id":1,"text":"Authors"},"rank":12},{"text":"Ye, Ming","contributorId":194184,"corporation":false,"usgs":false,"family":"Ye","given":"Ming","email":"","affiliations":[],"preferred":false,"id":703391,"contributorType":{"id":1,"text":"Authors"},"rank":13}]}}
,{"id":70173870,"text":"70173870 - 2012 - Effect of Feeding-Fasting Cycles on Oxygen Consumption and Bioenergetics of Yellow Perch","interactions":[],"lastModifiedDate":"2016-06-15T15:24:01","indexId":"70173870","displayToPublicDate":"2012-11-01T00:00:00","publicationYear":"2012","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":3624,"text":"Transactions of the American Fisheries Society","active":true,"publicationSubtype":{"id":10}},"title":"Effect of Feeding-Fasting Cycles on Oxygen Consumption and Bioenergetics of Yellow Perch","docAbstract":"<div class=\"paragraph\">We measured growth and oxygen consumption of age-1 yellow perch&nbsp;<i>Perca flavescens</i>subjected to ad libitum (control) or variable feeding cycles of 2 (i.e., 2 d of feed, 2 d of deprivation), 6, or 12 d for a 72-d period. Individual, female yellow perch (initial weight = 51.9 &plusmn; 0.9&nbsp;g [mean &plusmn; SE]) were stocked in 110-L aquaria to provide six replicates per treatment and fed measured rations of live fathead minnow&nbsp;<i>Pimephales promelas</i>. Consumption, absolute growth rate, growth efficiency, and oxygen consumption were similar among feeding regimens. However, growth trajectories for fish on the 2-d cycle were significantly lower than other feed&ndash;fast cycles. Hyperphagia occurred in all treatments. Bioenergetics model simulations indicated that consumption was significantly underestimated (<i>t</i>&nbsp;= 5.4, df = 4,&nbsp;<i>P</i>&nbsp;= 0.006), while growth was overestimated (<i>t</i>&nbsp;= &minus;5.5, df = 4,&nbsp;<i>P</i>&nbsp;= 0.005) for fish on the 12-d cycle. However, model errors detected between observed and predicted values were low, ranging from &minus;10.1% to +7.8%. We found that juvenile yellow perch exhibited compensatory growth (CG), but none of the feed&ndash;fast treatments resulted in growth overcompensation. Likewise, we found no evidence that respiration rates varied with CG, implying that yellow perch bioenergetics models could be used to predict the effects of feeding history and CG response on food consumption and fish growth.</div>","language":"English","publisher":"Taylor & Francis Online","publisherLocation":"Abingdon, United Kindgom","doi":"10.1080/00028487.2012.703155","usgsCitation":"Chipps, S.R., Travis W. Schaeffer, Daniel E. Spengler, Casey W. Schoenebeck, and Brown, M.L., 2012, Effect of Feeding-Fasting Cycles on Oxygen Consumption and Bioenergetics of Yellow Perch: Transactions of the American Fisheries Society, v. 141, no. 6, p. 1480-1491, https://doi.org/10.1080/00028487.2012.703155.","productDescription":"12 p.","startPage":"1480","endPage":"1491","numberOfPages":"12","onlineOnly":"N","additionalOnlineFiles":"N","ipdsId":"IP-034018","costCenters":[{"id":199,"text":"Coop Res Unit Leetown","active":true,"usgs":true}],"links":[{"id":323713,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"volume":"141","issue":"6","publishingServiceCenter":{"id":9,"text":"Reston PSC"},"noUsgsAuthors":false,"publicationDate":"2012-10-09","publicationStatus":"PW","scienceBaseUri":"57627c30e4b07657d19a69da","contributors":{"authors":[{"text":"Chipps, Steven R. 0000-0001-6511-7582 steve_chipps@usgs.gov","orcid":"https://orcid.org/0000-0001-6511-7582","contributorId":2243,"corporation":false,"usgs":true,"family":"Chipps","given":"Steven","email":"steve_chipps@usgs.gov","middleInitial":"R.","affiliations":[{"id":199,"text":"Coop Res Unit Leetown","active":true,"usgs":true}],"preferred":true,"id":638868,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Travis W. Schaeffer","contributorId":171857,"corporation":false,"usgs":false,"family":"Travis W. Schaeffer","affiliations":[{"id":26958,"text":"South Dakota State University, Brookings, SD","active":true,"usgs":false}],"preferred":false,"id":638872,"contributorType":{"id":1,"text":"Authors"},"rank":11},{"text":"Daniel E. Spengler","contributorId":171855,"corporation":false,"usgs":false,"family":"Daniel E. Spengler","affiliations":[{"id":26958,"text":"South Dakota State University, Brookings, SD","active":true,"usgs":false}],"preferred":false,"id":638870,"contributorType":{"id":1,"text":"Authors"},"rank":12},{"text":"Casey W. Schoenebeck","contributorId":171854,"corporation":false,"usgs":false,"family":"Casey W. Schoenebeck","affiliations":[{"id":26958,"text":"South Dakota State University, Brookings, SD","active":true,"usgs":false}],"preferred":false,"id":638869,"contributorType":{"id":1,"text":"Authors"},"rank":13},{"text":"Brown, Michael L.","contributorId":171856,"corporation":false,"usgs":false,"family":"Brown","given":"Michael","email":"","middleInitial":"L.","affiliations":[{"id":26958,"text":"South Dakota State University, Brookings, SD","active":true,"usgs":false}],"preferred":false,"id":638871,"contributorType":{"id":1,"text":"Authors"},"rank":14}]}}
,{"id":70192878,"text":"70192878 - 2012 - Regional regression models of watershed suspended-sediment discharge for the eastern United States","interactions":[],"lastModifiedDate":"2017-11-21T15:23:06","indexId":"70192878","displayToPublicDate":"2012-11-01T00:00:00","publicationYear":"2012","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":2341,"text":"Journal of Hydrologic Engineering","active":true,"publicationSubtype":{"id":10}},"title":"Regional regression models of watershed suspended-sediment discharge for the eastern United States","docAbstract":"<p><span>Estimates of mean annual watershed sediment discharge, derived from long-term measurements of suspended-sediment concentration and streamflow, often are not available at locations of interest. The goal of this study was to develop multivariate regression models to enable prediction of mean annual suspended-sediment discharge from available basin characteristics useful for most ungaged river locations in the eastern United States. The models are based on long-term mean sediment discharge estimates and explanatory variables obtained from a combined dataset of 1201 US Geological Survey (USGS) stations derived from a SPAtially Referenced Regression on Watershed attributes (SPARROW) study and the Geospatial Attributes of Gages for Evaluating Streamflow (GAGES) database. The resulting regional regression models summarized for major US water resources regions 1–8, exhibited prediction&nbsp;</span><i>R</i><sup>2</sup><span><span>&nbsp;</span>values ranging from 76.9% to 92.7% and corresponding average model prediction errors ranging from 56.5% to 124.3%. Results from cross-validation experiments suggest that a majority of the models will perform similarly to calibration runs. The 36-parameter regional regression models also outperformed a 16-parameter national SPARROW model of suspended-sediment discharge and indicate that mean annual sediment loads in the eastern United States generally correlates with a combination of basin area, land use patterns, seasonal precipitation, soil composition, hydrologic modification, and to a lesser extent, topography.</span></p>","language":"English","publisher":"Elsevier","doi":"10.1016/j.jhydrol.2012.09.011","usgsCitation":"Roman, D.C., Vogel, R.M., and Schwarz, G., 2012, Regional regression models of watershed suspended-sediment discharge for the eastern United States: Journal of Hydrologic Engineering, v. 472-4723, p. 53-62, https://doi.org/10.1016/j.jhydrol.2012.09.011.","productDescription":"10 p.","startPage":"53","endPage":"62","ipdsId":"IP-023743","costCenters":[{"id":451,"text":"National Water Quality Assessment Program","active":true,"usgs":true}],"links":[{"id":349232,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"volume":"472-4723","publishingServiceCenter":{"id":9,"text":"Reston PSC"},"noUsgsAuthors":false,"publicationStatus":"PW","scienceBaseUri":"5a610553e4b06e28e9c2553a","contributors":{"authors":[{"text":"Roman, David C.","contributorId":198831,"corporation":false,"usgs":false,"family":"Roman","given":"David","email":"","middleInitial":"C.","affiliations":[],"preferred":false,"id":717278,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Vogel, Richard M.","contributorId":66811,"corporation":false,"usgs":true,"family":"Vogel","given":"Richard","email":"","middleInitial":"M.","affiliations":[],"preferred":false,"id":723121,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Schwarz, Gregory E. 0000-0002-9239-4566 gschwarz@usgs.gov","orcid":"https://orcid.org/0000-0002-9239-4566","contributorId":543,"corporation":false,"usgs":true,"family":"Schwarz","given":"Gregory E.","email":"gschwarz@usgs.gov","affiliations":[{"id":5067,"text":"Northeast Regional Director's Office","active":true,"usgs":true},{"id":451,"text":"National Water Quality Assessment Program","active":true,"usgs":true}],"preferred":false,"id":723122,"contributorType":{"id":1,"text":"Authors"},"rank":3}]}}
]}