Ecosystem science is critical to making informed decisions about natural resources that can sustain our Nation’s economic and environmental well-being. Resource managers and policy-makers are faced with countless decisions each year at local, state, tribal, territorial, and national levels on issues as diverse as renewable and non-renewable energy development, agriculture, forestry, water supply, and resource allocations at the urban-rural interface. The urgency for sound decision-making is increasing dramatically as the world is being transformed at an unprecedented pace and in uncertain directions. Environmental changes are associated with natural hazards, greenhouse gas emissions, and increasing demands for water, land, food, energy, mineral, and living resources. At risk is the Nation’s environmental capital, the goods and services provided by resilient ecosystems that are vital to the health and well-being of human societies. Ecosystem science—the study of systems of organisms interacting with their environment and the consequences of natural and human-induced change on these systems—is necessary to inform decision-makers as they develop policies to adapt to these changes.
This Ecosystems Science Strategy is built on a framework that includes basic and applied science. It highlights the critical roles that USGS scientists and partners can play in building scientific understanding and providing timely information to decision-makers. The strategy underscores the connection between scientific discoveries and the application of new knowledge. The strategy integrates ecosystem science and decision-making, producing new scientific outcomes to assist resource managers and providing public benefits.
The USGS is uniquely positioned to play an important role in ecosystem science. With its wide range of expertise, the agency can bring holistic, cross-scale, interdisciplinary capabilities to the design and conduct of monitoring, research, and modeling and to new technologies for data collection, management, and visualization. Collectively, these capabilities can be used to reveal ecological patterns and processes, explain how and why ecosystems change, and forecast change over different spatial and temporal scales. USGS science can provide managers with options and decision-support tools to use resources sustainably. The USGS has long-standing, collaborative relationships with the DOI and other partners in the natural sciences, in both conducting science and its application. The USGS engages these partners in cooperative investigations that otherwise would lack the necessary support or be too expensive for a single bureau to conduct.
The heart of this strategy is a framework and vision for USGS ecosystems science that focuses on five long-term goals, which are seen as interconnected and reinforcing components:
• Improve understanding of ecosystem structure, function, and processes. The focus for this goal is an understanding of how ecosystems work, including the dynamics of species, their populations, interactions, and genetics, and how they change across spatial and temporal scales.
• Advance understanding of how drivers influence ecosystem change. The challenges here are explaining the drivers of ecosystem change, their spatio-temporal patterns, their uncertainties and interactions, and their influence on ecosystem processes and dynamics.
• Improve understanding of the services that ecosystems provide to society. Here the emphasis is on the measurement of environmental capital and ecosystem services, and the identification of sources and patterns of change in space and time.
• Develop tools, technologies, and capacities to inform decision-making about ecosystems. This includes developing new technologies and approaches for conducting applications-oriented ecosystem science. A principal challenge will be how to quantify uncertainty and incorporate it in decision analysis.
• Apply science to enhance strategies for management, conservation, and restoration of ecosystems. These challenges include development of novel approaches to monitoring, assessment, and restoration of ecosystems; new methods to address species of concern and communities at risk; and innovations in decision analysis and support to address imminent ecosystem changes or those that are underway.
Closely integrated with the five goals are four strategic approaches that provide the path forward for the USGS Ecosystems Mission Area. These approaches cross-cut all of the goals and are seen as essential to the implementation of this strategy:
• Assess information needs for ecosystem science through enhanced partnerships. Work with the DOI and other agencies and institutions to identify, design, and implement priority decision-driven ecological research.
• Promote the use of interdisciplinary ecosystem science. Design and conduct interdisciplinary process-oriented research in ecosystem science.
• Enhance modeling and forecasting. Build models to forecast ecosystem change, assess future management scenarios, and reduce uncertainties through an adaptive learning process.
• Support decision-making. Use quantitative approaches to assess the vulnerabilities of ecosystems, habitats, and species, and evaluate strategies for adaptation, restoration, and sustainable management.
Following the strategic approaches are a set of proposed actions that represent a sampling of specific activities that align with this strategy and that address the Nation’s most pressing environmental needs.
The strategy emphasizes coordination of activities across the USGS mission areas pursuant to these goals. Ecosystem science is inherently interdisciplinary and requires a broad perspective that incorporates the biological and physical sciences, climate science, information technology, and scientific capacity in mission areas across the Bureau. With its emphasis on coordination, this strategy can provide a critical underpinning for integrated science efforts with scientists from multiple mission areas of the USGS working together. Of course, the USGS will continue to conduct both discipline-specific and interdisciplinary investigations, and both will continue to be vital parts of the ecosystem science portfolio.
Finally, the strategy stresses the importance of coordination with other Federal agencies and organizations in the natural resources community. The USGS collaborates with resource agencies in the DOI and other organizations throughout the world to meet societal needs for species and ecosystem management. Working with these agencies and organizations, the USGS will play a key role over the next decade in advancing the scientific foundation for sustaining the natural resources that diverse, productive, resilient ecosystems provide.
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Please see index page for feedback instructions.","usgsCitation":"Williams, B.K., Wingard, G.L., Brewer, G., Cloern, J.E., Gelfenbaum, G.R., Jacobson, R.B., Kershner, J.L., McGuire, A.D., Nichols, J., Shapiro, C.D., van Riper, C., and White, R.P., 2012, The U.S. Geological Survey Ecosystem Science Strategy, 2012-2022 - Advancing discovery and application through collaboration: U.S. Geological Survey Open-File Report 2012-1092, viii, 25 p.; Appendices, https://doi.org/10.3133/ofr20121092.","productDescription":"viii, 25 p.; Appendices","onlineOnly":"Y","costCenters":[],"links":[{"id":257157,"rank":0,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/ofr_2012_1092.gif"},{"id":257138,"rank":100,"type":{"id":15,"text":"Index Page"},"url":"https://pubs.usgs.gov/of/2012/1092/","linkFileType":{"id":5,"text":"html"}}],"noUsgsAuthors":false,"publicationStatus":"PW","scienceBaseUri":"505ba92ce4b08c986b3220c0","contributors":{"authors":[{"text":"Williams, Byron K. 0000-0001-7644-1396","orcid":"https://orcid.org/0000-0001-7644-1396","contributorId":86616,"corporation":false,"usgs":true,"family":"Williams","given":"Byron","email":"","middleInitial":"K.","affiliations":[{"id":554,"text":"Science and Decisions Center","active":true,"usgs":true}],"preferred":false,"id":464220,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Wingard, G. 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2012 - Improved mapping of National Atmospheric Deposition Program wet-deposition in complex terrain using PRISM-gridded data sets","interactions":[],"lastModifiedDate":"2012-06-05T01:01:49","indexId":"70004500","displayToPublicDate":"2012-06-04T00:00:00","publicationYear":"2012","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":1552,"text":"Environmental Monitoring and Assessment","onlineIssn":"1573-2959","printIssn":"0167-6369","active":true,"publicationSubtype":{"id":10}},"title":"Improved mapping of National Atmospheric Deposition Program wet-deposition in complex terrain using PRISM-gridded data sets","docAbstract":"High-elevation regions in the United States lack detailed atmospheric wet-deposition data. The National Atmospheric Deposition Program/National Trends Network (NADP/NTN) measures and reports precipitation amounts and chemical constituent concentration and deposition data for the United States on annual isopleth maps using inverse distance weighted (IDW) interpolation methods. This interpolation for unsampled areas does not account for topographic influences. Therefore, NADP/NTN isopleth maps lack detail and potentially underestimate wet deposition in high-elevation regions. The NADP/NTN wet-deposition maps may be improved using precipitation grids generated by other networks. The Parameter-elevation Regressions on Independent Slopes Model (PRISM) produces digital grids of precipitation estimates from many precipitation-monitoring networks and incorporates influences of topographical and geographical features. Because NADP/NTN ion concentrations do not vary with elevation as much as precipitation depths, PRISM is used with unadjusted NADP/NTN data in this paper to calculate ion wet deposition in complex terrain to yield more accurate and detailed isopleth deposition maps in complex terrain. PRISM precipitation estimates generally exceed NADP/NTN precipitation estimates for coastal and mountainous regions in the western United States. NADP/NTN precipitation estimates generally exceed PRISM precipitation estimates for leeward mountainous regions in Washington, Oregon, and Nevada, where abrupt changes in precipitation depths induced by topography are not depicted by IDW interpolation. PRISM-based deposition estimates for nitrate can exceed NADP/NTN estimates by more than 100% for mountainous regions in the western United States.","largerWorkType":{"id":2,"text":"Article"},"largerWorkTitle":"Environmental Monitoring and Assessment","largerWorkSubtype":{"id":10,"text":"Journal Article"},"language":"English","publisher":"Springer","publisherLocation":"Amsterdam, Netherlands","doi":"10.1007/s10661-011-2009-7","usgsCitation":"Latysh, N.E., and Wetherbee, G.A., 2012, Improved mapping of National Atmospheric Deposition Program wet-deposition in complex terrain using PRISM-gridded data sets: Environmental Monitoring and Assessment, v. 184, no. 2, p. 913-928, https://doi.org/10.1007/s10661-011-2009-7.","productDescription":"16 p.","startPage":"913","endPage":"928","costCenters":[{"id":143,"text":"Branch of Quality Systems","active":true,"usgs":true}],"links":[{"id":257177,"rank":0,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"},{"id":257170,"rank":9999,"type":{"id":10,"text":"Digital Object Identifier"},"url":"https://dx.doi.org/10.1007/s10661-011-2009-7","linkFileType":{"id":5,"text":"html"}}],"country":"United States","volume":"184","issue":"2","noUsgsAuthors":false,"publicationDate":"2011-04-08","publicationStatus":"PW","scienceBaseUri":"505a3959e4b0c8380cd618ba","contributors":{"authors":[{"text":"Latysh, Natalie E.","contributorId":39860,"corporation":false,"usgs":true,"family":"Latysh","given":"Natalie","email":"","middleInitial":"E.","affiliations":[],"preferred":false,"id":350511,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Wetherbee, Gregory Alan","contributorId":36414,"corporation":false,"usgs":true,"family":"Wetherbee","given":"Gregory","email":"","middleInitial":"Alan","affiliations":[],"preferred":false,"id":350510,"contributorType":{"id":1,"text":"Authors"},"rank":2}]}} ,{"id":70004894,"text":"70004894 - 2012 - Modelling rating curves using remotely sensed LiDAR data","interactions":[],"lastModifiedDate":"2018-04-02T15:28:10","indexId":"70004894","displayToPublicDate":"2012-06-04T00:00:00","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":"Modelling rating curves using remotely sensed LiDAR data","docAbstract":"Accurate stream discharge measurements are important for many hydrological studies. In remote locations, however, it is often difficult to obtain stream flow information because of the difficulty in making the discharge measurements necessary to define stage-discharge relationships (rating curves). This study investigates the feasibility of defining rating curves by using a fluid mechanics-based model constrained with topographic data from an airborne LiDAR scanning. The study was carried out for an 8m-wide channel in the boreal landscape of northern Sweden. LiDAR data were used to define channel geometry above a low flow water surface along the 90-m surveyed reach. The channel topography below the water surface was estimated using the simple assumption of a flat streambed. The roughness for the modelled reach was back calculated from a single measurment of discharge. The topographic and roughness information was then used to model a rating curve. To isolate the potential influence of the flat bed assumption, a 'hybrid model' rating curve was developed on the basis of data combined from the LiDAR scan and a detailed ground survey. Whereas this hybrid model rating curve was in agreement with the direct measurements of discharge, the LiDAR model rating curve was equally in agreement with the medium and high flow measurements based on confidence intervals calculated from the direct measurements. The discrepancy between the LiDAR model rating curve and the low flow measurements was likely due to reduced roughness associated with unresolved submerged bed topography. Scanning during periods of low flow can help minimize this deficiency. These results suggest that combined ground surveys and LiDAR scans or multifrequency LiDAR scans that see 'below' the water surface (bathymetric LiDAR) could be useful in generating data needed to run such a fluid mechanics-based model. This opens a realm of possibility to remotely sense and monitor stream flows in channels in remote locations.","largerWorkType":{"id":2,"text":"Article"},"largerWorkTitle":"Hydrological Processes","largerWorkSubtype":{"id":10,"text":"Journal Article"},"language":"English","publisher":"Wiley","publisherLocation":"Hoboken, NJ","doi":"10.1002/hyp.9225","usgsCitation":"Nathanson, M., Kean, J.W., Grabs, T.J., Seibert, J., Laudon, H., and Lyon, S.W., 2012, Modelling rating curves using remotely sensed LiDAR data: Hydrological Processes, v. 26, no. 9, p. 1427-1434, https://doi.org/10.1002/hyp.9225.","productDescription":"8 p.","startPage":"1427","endPage":"1434","costCenters":[{"id":300,"text":"Geologic Hazards Science Center","active":true,"usgs":true}],"links":[{"id":257151,"rank":0,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"},{"id":257150,"rank":9999,"type":{"id":10,"text":"Digital Object Identifier"},"url":"https://dx.doi.org/10.1002/hyp.9225","linkFileType":{"id":5,"text":"html"}}],"volume":"26","issue":"9","noUsgsAuthors":false,"publicationDate":"2012-03-27","publicationStatus":"PW","scienceBaseUri":"505a5c72e4b0c8380cd6fcd8","contributors":{"authors":[{"text":"Nathanson, Marcus","contributorId":85452,"corporation":false,"usgs":true,"family":"Nathanson","given":"Marcus","affiliations":[],"preferred":false,"id":351621,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Kean, Jason W. 0000-0003-3089-0369 jwkean@usgs.gov","orcid":"https://orcid.org/0000-0003-3089-0369","contributorId":1654,"corporation":false,"usgs":true,"family":"Kean","given":"Jason","email":"jwkean@usgs.gov","middleInitial":"W.","affiliations":[{"id":300,"text":"Geologic Hazards Science Center","active":true,"usgs":true}],"preferred":true,"id":351617,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Grabs, Thomas J.","contributorId":107971,"corporation":false,"usgs":true,"family":"Grabs","given":"Thomas","email":"","middleInitial":"J.","affiliations":[],"preferred":false,"id":351622,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Seibert, Jan","contributorId":176322,"corporation":false,"usgs":false,"family":"Seibert","given":"Jan","email":"","affiliations":[],"preferred":false,"id":351620,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Laudon, Hjalmar","contributorId":46812,"corporation":false,"usgs":true,"family":"Laudon","given":"Hjalmar","affiliations":[],"preferred":false,"id":351619,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"Lyon, Steve W.","contributorId":44780,"corporation":false,"usgs":true,"family":"Lyon","given":"Steve","email":"","middleInitial":"W.","affiliations":[],"preferred":false,"id":351618,"contributorType":{"id":1,"text":"Authors"},"rank":6}]}} ,{"id":70038479,"text":"70038479 - 2012 - Modelling effects of chemical exposure on birds wintering in agricultural landscapes: The western burrowing owl (Athene cunicularia hypugaea) as a case study","interactions":[],"lastModifiedDate":"2017-05-23T16:29:09","indexId":"70038479","displayToPublicDate":"2012-06-02T13:30:00","publicationYear":"2012","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":1458,"text":"Ecological Modelling","active":true,"publicationSubtype":{"id":10}},"title":"Modelling effects of chemical exposure on birds wintering in agricultural landscapes: The western burrowing owl (Athene cunicularia hypugaea) as a case study","docAbstract":"We describe an ecotoxicological model that simulates the sublethal and lethal effects of chronic, low-level, chemical exposure on birds wintering in agricultural landscapes. Previous models estimating the impact on wildlife of chemicals used in agro-ecosystems typically have not included the variety of pathways, including both dermal and oral, by which individuals are exposed. The present model contains four submodels simulating (1) foraging behavior of individual birds, (2) chemical applications to crops, (3) transfers of chemicals among soil, insects, and small mammals, and (4) transfers of chemicals to birds via ingestion and dermal exposure. We demonstrate use of the model by simulating the impacts of a variety of commonly used herbicides, insecticides, growth regulators, and defoliants on western burrowing owls (Athene cunicularia hypugaea) that winter in agricultural landscapes in southern Texas, United States. The model generated reasonable movement patterns for each chemical through soil, water, insects, and rodents, as well as into the owl via consumption and dermal absorption. Sensitivity analysis suggested model predictions were sensitive to uncertainty associated with estimates of chemical half-lives in birds, soil, and prey, sensitive to parameters associated with estimating dermal exposure, and relatively insensitive to uncertainty associated with details of chemical application procedures (timing of application, amount of drift). Nonetheless, the general trends in chemical accumulations and the relative impacts of the various chemicals were robust to these parameter changes. Simulation results suggested that insecticides posed a greater potential risk to owls of both sublethal and lethal effects than do herbicides, defoliants, and growth regulators under crop scenarios typical of southern Texas, and that use of multiple indicators, or endpoints provided a more accurate assessment of risk due to agricultural chemical exposure. The model should prove useful in helping prioritize the chemicals and transfer pathways targeted in future studies and also, as these new data become available, in assessing the relative danger to other birds of exposure to different types of agricultural chemicals.","language":"English","publisher":"Elsevier","publisherLocation":"Amsterdam, Netherlands","doi":"10.1016/j.ecolmodel.2011.10.017","usgsCitation":"Engelman, C.A., Grant, W.E., Mora, M.A., and Woodin, M., 2012, Modelling effects of chemical exposure on birds wintering in agricultural landscapes: The western burrowing owl (Athene cunicularia hypugaea) as a case study: Ecological Modelling, v. 224, no. 1, p. 90-102, https://doi.org/10.1016/j.ecolmodel.2011.10.017.","productDescription":"13 p.","startPage":"90","endPage":"102","costCenters":[{"id":192,"text":"Columbia Environmental Research Center","active":true,"usgs":true}],"links":[{"id":257306,"rank":0,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","state":"Texas","volume":"224","issue":"1","noUsgsAuthors":false,"publicationStatus":"PW","scienceBaseUri":"505a5c66e4b0c8380cd6fc7e","contributors":{"authors":[{"text":"Engelman, Catherine A.","contributorId":33566,"corporation":false,"usgs":true,"family":"Engelman","given":"Catherine","email":"","middleInitial":"A.","affiliations":[],"preferred":false,"id":464340,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Grant, William E.","contributorId":88590,"corporation":false,"usgs":true,"family":"Grant","given":"William","email":"","middleInitial":"E.","affiliations":[],"preferred":false,"id":464343,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Mora, Miguel A. 0000-0002-8393-0216","orcid":"https://orcid.org/0000-0002-8393-0216","contributorId":46643,"corporation":false,"usgs":true,"family":"Mora","given":"Miguel","email":"","middleInitial":"A.","affiliations":[],"preferred":false,"id":464341,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Woodin, Marc","contributorId":84201,"corporation":false,"usgs":true,"family":"Woodin","given":"Marc","affiliations":[],"preferred":false,"id":464342,"contributorType":{"id":1,"text":"Authors"},"rank":4}]}} ,{"id":70038451,"text":"ofr20121118 - 2012 - Variability of distributions of well-scale estimated ultimate recovery for continuous (unconventional) oil and gas resources in the United States","interactions":[],"lastModifiedDate":"2012-06-03T01:01:45","indexId":"ofr20121118","displayToPublicDate":"2012-06-02T00:00:00","publicationYear":"2012","noYear":false,"publicationType":{"id":18,"text":"Report"},"publicationSubtype":{"id":5,"text":"USGS Numbered Series"},"seriesTitle":{"id":330,"text":"Open-File Report","code":"OFR","onlineIssn":"2331-1258","printIssn":"0196-1497","active":true,"publicationSubtype":{"id":5}},"seriesNumber":"2012-1118","title":"Variability of distributions of well-scale estimated ultimate recovery for continuous (unconventional) oil and gas resources in the United States","docAbstract":"Since 2000, the U.S. Geological Survey has completed assessments of continuous (unconventional) resources in the United States based on geologic studies and analysis of well-production data. This publication uses those 132 continuous oil and gas assessments to show the variability of well productivity within and among the 132 areas. The production from the most productive wells in an area commonly is more than 100 times larger than that from the poorest productive wells. The 132 assessment units were classified into four categories: shale gas, coalbed gas, tight gas, and continuous oil. For each category, the mean well productivity in the most productive assessment units is considerably greater than that of the least productive assessment units.","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/ofr20121118","usgsCitation":"U.S. Geological Survey Oil and Gas Assessment Team, 2012, Variability of distributions of well-scale estimated ultimate recovery for continuous (unconventional) oil and gas resources in the United States: U.S. Geological Survey Open-File Report 2012-1118, iii, 12 p.; Appendix, https://doi.org/10.3133/ofr20121118.","productDescription":"iii, 12 p.; Appendix","startPage":"i","endPage":"18","numberOfPages":"21","onlineOnly":"Y","additionalOnlineFiles":"N","costCenters":[{"id":164,"text":"Central Energy Resources Science Center","active":true,"usgs":true}],"links":[{"id":257122,"rank":0,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/ofr_2012_1118.gif"},{"id":257120,"rank":100,"type":{"id":15,"text":"Index Page"},"url":"https://pubs.usgs.gov/of/2012/1118/","linkFileType":{"id":5,"text":"html"}},{"id":257121,"rank":300,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/of/2012/1118/OF12-1118.pdf","linkFileType":{"id":1,"text":"pdf"}}],"country":"United States","noUsgsAuthors":false,"publicationStatus":"PW","scienceBaseUri":"505bc137e4b08c986b32a4b0","contributors":{"authors":[{"text":"U.S. Geological Survey Oil and Gas Assessment Team","contributorId":128016,"corporation":true,"usgs":false,"organization":"U.S. Geological Survey Oil and Gas Assessment Team","id":535186,"contributorType":{"id":1,"text":"Authors"},"rank":1}]}} ,{"id":70038450,"text":"sir20125026 - 2012 - Dam-breach analysis and flood-inundation mapping for Lakes Ellsworth and Lawtonka near Lawton, Oklahoma","interactions":[],"lastModifiedDate":"2020-05-20T12:07:36.292534","indexId":"sir20125026","displayToPublicDate":"2012-06-02T00:00:00","publicationYear":"2012","noYear":false,"publicationType":{"id":18,"text":"Report"},"publicationSubtype":{"id":5,"text":"USGS Numbered Series"},"seriesTitle":{"id":334,"text":"Scientific Investigations Report","code":"SIR","onlineIssn":"2328-0328","printIssn":"2328-031X","active":true,"publicationSubtype":{"id":5}},"seriesNumber":"2012-5026","title":"Dam-breach analysis and flood-inundation mapping for Lakes Ellsworth and Lawtonka near Lawton, Oklahoma","docAbstract":"Dams provide beneficial functions such as flood control, recreation, and reliable water supplies, but they also entail risk: dam breaches and resultant floods can cause substantial property damage and loss of life. The State of Oklahoma requires each owner of a high-hazard dam, which the Federal Emergency Management Agency defines as dams for which failure or misoperation probably will cause loss of human life, to develop an emergency action plan specific to that dam. Components of an emergency action plan are to simulate a flood resulting from a possible dam breach and map the resulting downstream flood-inundation areas. The resulting flood-inundation maps can provide valuable information to city officials, emergency managers, and local residents for planning the emergency response if a dam breach occurs. Accurate topographic data are vital for developing flood-inundation maps. This report presents results of a cooperative study by the city of Lawton, Oklahoma, and the U.S. Geological Survey (USGS) to model dam-breach scenarios at Lakes Ellsworth and Lawtonka near Lawton and to map the potential flood-inundation areas of such dam breaches. To assist the city of Lawton with completion of the emergency action plans for Lakes Ellsworth and Lawtonka Dams, the USGS collected light detection and ranging (lidar) data that were used to develop a high-resolution digital elevation model and a 1-foot contour elevation map for the flood plains downstream from Lakes Ellsworth and Lawtonka. This digital elevation model and field measurements, streamflow-gaging station data (USGS streamflow-gaging station 07311000, East Cache Creek near Walters, Okla.), and hydraulic values were used as inputs for the dynamic (unsteady-flow) model, Hydrologic Engineering Center's River Analysis System (HEC-RAS). The modeled flood elevations were exported to a geographic information system to produce flood-inundation maps. Water-surface profiles were developed for a 75-percent probable maximum flood scenario and a sunny-day dam-breach scenario, as well as for maximum flood-inundation elevations and flood-wave arrival times for selected bridge crossings. Some areas of concern near the city of Lawton, if a dam breach occurs at Lakes Ellsworth or Lawtonka, include water treatment plants, wastewater treatment plants, recreational areas, and community-services offices.","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/sir20125026","collaboration":"Prepared in cooperation with the city of Lawton","usgsCitation":"Rendon, S.H., Ashworth, C., and Smith, S.J., 2012, Dam-breach analysis and flood-inundation mapping for Lakes Ellsworth and Lawtonka near Lawton, Oklahoma: U.S. Geological Survey Scientific Investigations Report 2012-5026, iii, 9 p., https://doi.org/10.3133/sir20125026.","productDescription":"iii, 9 p.","costCenters":[{"id":516,"text":"Oklahoma Water Science Center","active":true,"usgs":true}],"links":[{"id":257123,"rank":0,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/sir_2012_5026.bmp"},{"id":257119,"rank":100,"type":{"id":15,"text":"Index Page"},"url":"https://pubs.usgs.gov/sir/2012/5026/","linkFileType":{"id":5,"text":"html"}}],"projection":"Oklahoma State Plane South Projection","datum":"North American Datum, 1983","country":"United States","state":"Oklahoma","county":"Comanche County","city":"Lawton","otherGeospatial":"Ellsworth Lake, Lawtonka Lake","geographicExtents":"{ \"type\": \"FeatureCollection\", \"features\": [ { \"type\": \"Feature\", \"properties\": {}, \"geometry\": { \"type\": \"Polygon\", \"coordinates\": [ [ [ -98.6,34.3 ], [ -98.6,34.93333333333333 ], [ -98.2,34.93333333333333 ], [ -98.2,34.3 ], [ -98.6,34.3 ] ] ] } } ] }","noUsgsAuthors":false,"publicationStatus":"PW","scienceBaseUri":"5059fd5de4b0c8380cd4e7d4","contributors":{"authors":[{"text":"Rendon, Samuel H. 0000-0001-5589-0563 srendon@usgs.gov","orcid":"https://orcid.org/0000-0001-5589-0563","contributorId":3940,"corporation":false,"usgs":true,"family":"Rendon","given":"Samuel","email":"srendon@usgs.gov","middleInitial":"H.","affiliations":[{"id":583,"text":"Texas Water Science Center","active":true,"usgs":true}],"preferred":true,"id":464170,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Ashworth, Chad E.","contributorId":62449,"corporation":false,"usgs":true,"family":"Ashworth","given":"Chad E.","affiliations":[],"preferred":false,"id":464171,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Smith, S. Jerrod 0000-0002-9379-8167 sjsmith@usgs.gov","orcid":"https://orcid.org/0000-0002-9379-8167","contributorId":981,"corporation":false,"usgs":true,"family":"Smith","given":"S.","email":"sjsmith@usgs.gov","middleInitial":"Jerrod","affiliations":[{"id":516,"text":"Oklahoma Water Science Center","active":true,"usgs":true}],"preferred":true,"id":464169,"contributorType":{"id":1,"text":"Authors"},"rank":3}]}} ,{"id":70045092,"text":"70045092 - 2012 - Relative azimuth inversion by way of damped maximum correlation estimates","interactions":[],"lastModifiedDate":"2018-02-08T09:39:10","indexId":"70045092","displayToPublicDate":"2012-06-01T16:26:27","publicationYear":"2012","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":1315,"text":"Computers & Geosciences","printIssn":"0098-3004","active":true,"publicationSubtype":{"id":10}},"title":"Relative azimuth inversion by way of damped maximum correlation estimates","docAbstract":"Horizontal seismic data are utilized in a large number of Earth studies. Such work depends on the published orientations of the sensitive axes of seismic sensors relative to true North. These orientations can be estimated using a number of different techniques: SensOrLoc (Sensitivity, Orientation and Location), comparison to synthetics (Ekstrom and Busby, 2008), or by way of magnetic compass. Current methods for finding relative station azimuths are unable to do so with arbitrary precision quickly because of limitations in the algorithms (e.g. grid search methods). Furthermore, in order to determine instrument orientations during station visits, it is critical that any analysis software be easily run on a large number of different computer platforms and the results be obtained quickly while on site. We developed a new technique for estimating relative sensor azimuths by inverting for the orientation with the maximum correlation to a reference instrument, using a non-linear parameter estimation routine. By making use of overlapping windows, we are able to make multiple azimuth estimates, which helps to identify the confidence of our azimuth estimate, even when the signal-to-noise ratio (SNR) is low. Finally, our algorithm has been written as a stand-alone, platform independent, Java software package with a graphical user interface for reading and selecting data segments to be analyzed.","language":"English","publisher":"Elsevier","doi":"10.1016/j.cageo.2012.02.025","usgsCitation":"Ringler, A., Edwards, J., Hutt, C., and Shelly, F., 2012, Relative azimuth inversion by way of damped maximum correlation estimates: Computers & Geosciences, v. 43, p. 1-6, https://doi.org/10.1016/j.cageo.2012.02.025.","productDescription":"6 p.","startPage":"1","endPage":"6","ipdsId":"IP-035082","costCenters":[{"id":122,"text":"Albuquerque Seismological Laboratory","active":false,"usgs":true}],"links":[{"id":275028,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"},{"id":275027,"type":{"id":10,"text":"Digital Object Identifier"},"url":"https://dx.doi.org/10.1016/j.cageo.2012.02.025"}],"country":"United States","volume":"43","noUsgsAuthors":false,"publicationStatus":"PW","scienceBaseUri":"51e519efe4b069f8d27ccb2f","contributors":{"authors":[{"text":"Ringler, A. T. 0000-0002-9839-4188","orcid":"https://orcid.org/0000-0002-9839-4188","contributorId":99282,"corporation":false,"usgs":true,"family":"Ringler","given":"A. T.","affiliations":[],"preferred":false,"id":476772,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Edwards, J.D.","contributorId":69622,"corporation":false,"usgs":true,"family":"Edwards","given":"J.D.","email":"","affiliations":[],"preferred":false,"id":476771,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Hutt, C. R. 0000-0001-9033-9195","orcid":"https://orcid.org/0000-0001-9033-9195","contributorId":61910,"corporation":false,"usgs":true,"family":"Hutt","given":"C. R.","affiliations":[],"preferred":false,"id":476770,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Shelly, F.","contributorId":38043,"corporation":false,"usgs":true,"family":"Shelly","given":"F.","email":"","affiliations":[],"preferred":false,"id":476769,"contributorType":{"id":1,"text":"Authors"},"rank":4}]}} ,{"id":70045772,"text":"70045772 - 2012 - Spatially telescoping measurements for improved characterization of groundwater-surface water interactions","interactions":[],"lastModifiedDate":"2013-07-25T15:52:00","indexId":"70045772","displayToPublicDate":"2012-06-01T15:34:00","publicationYear":"2012","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":2342,"text":"Journal of Hydrology","active":true,"publicationSubtype":{"id":10}},"title":"Spatially telescoping measurements for improved characterization of groundwater-surface water interactions","docAbstract":"The suite of measurement methods available to characterize fluxes between groundwater and surface water is rapidly growing. However, there are few studies that examine approaches to design of field investigations that include multiple methods. We propose that performing field measurements in a spatially telescoping sequence improves measurement flexibility and accounts for nested heterogeneities while still allowing for parsimonious experimental design. We applied this spatially telescoping approach in a study of ground water-surface water (GW-SW) interaction during baseflow conditions along Lucile Creek, located near Wasilla, Alaska. Catchment-scale data, including channel geomorphic indices and hydrogeologic transects, were used to screen areas of potentially significant GW-SW exchange. Specifically, these data indicated increasing groundwater contribution from a deeper regional aquifer along the middle to lower reaches of the stream. This initial assessment was tested using reach-scale estimates of groundwater contribution during baseflow conditions, including differential discharge measurements and the use of chemical tracers analyzed in a three-component mixing model. The reach-scale measurements indicated a large increase in discharge along the middle reaches of the stream accompanied by a shift in chemical composition towards a regional groundwater end member. Finally, point measurements of vertical water fluxes -- obtained using seepage meters as well as temperature-based methods -- were used to evaluate spatial and temporal variability of GW-SW exchange within representative reaches. The spatial variability of upward fluxes, estimated using streambed temperature mapping at the sub-reach scale, was observed to vary in relation to both streambed composition and the magnitude of groundwater contribution from differential discharge measurements. The spatially telescoping approach improved the efficiency of this field investigation. Beginning our assessment with catchment-scale data allowed us to identify locations of GW-SW exchange, plan measurements at representative field sites and improve our interpretation of reach-scale and point-scale measurements.","largerWorkType":{"id":2,"text":"Article"},"largerWorkTitle":"Journal of Hydrology","largerWorkSubtype":{"id":10,"text":"Journal Article"},"language":"English","publisher":"Elsevier","doi":"10.1016/j.jhydrol.2012.04.002","usgsCitation":"Kikuchi, C., Ferre, T.P., and Welker, J.M., 2012, Spatially telescoping measurements for improved characterization of groundwater-surface water interactions: Journal of Hydrology, v. 446-447, p. 1-12, https://doi.org/10.1016/j.jhydrol.2012.04.002.","productDescription":"13 p.","startPage":"1","endPage":"12","numberOfPages":"13","ipdsId":"IP-030766","costCenters":[{"id":128,"text":"Arizona Water Science Center","active":true,"usgs":true}],"links":[{"id":275411,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"},{"id":275410,"type":{"id":10,"text":"Digital Object Identifier"},"url":"https://dx.doi.org/10.1016/j.jhydrol.2012.04.002"}],"country":"United States","state":"Alaska","otherGeospatial":"Lucile Creek","geographicExtents":"{ \"type\": \"FeatureCollection\", \"features\": [ { \"type\": \"Feature\", \"properties\": {}, \"geometry\": { \"type\": \"Polygon\", \"coordinates\": [ [ [ -150.0,61.466667 ], [ -150.0,61.666667 ], [ -149.416667,61.666667 ], [ -149.416667,61.466667 ], [ -150.0,61.466667 ] ] ] } } ] }","volume":"446-447","noUsgsAuthors":false,"publicationStatus":"PW","scienceBaseUri":"51f25423e4b0279fe2e1c02e","contributors":{"authors":[{"text":"Kikuchi, Colin ckikuchi@usgs.gov","contributorId":3958,"corporation":false,"usgs":true,"family":"Kikuchi","given":"Colin","email":"ckikuchi@usgs.gov","affiliations":[],"preferred":true,"id":478336,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Ferre, Ty P.A.","contributorId":102167,"corporation":false,"usgs":true,"family":"Ferre","given":"Ty","email":"","middleInitial":"P.A.","affiliations":[],"preferred":false,"id":478338,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Welker, Jeffery M.","contributorId":43654,"corporation":false,"usgs":true,"family":"Welker","given":"Jeffery","email":"","middleInitial":"M.","affiliations":[],"preferred":false,"id":478337,"contributorType":{"id":1,"text":"Authors"},"rank":3}]}} ,{"id":70125967,"text":"70125967 - 2012 - Species differentiation on a dynamic landscape: shifts in metapopulation genetic structure using the chronology of the Hawaiian Archipelago","interactions":[],"lastModifiedDate":"2014-09-18T12:53:54","indexId":"70125967","displayToPublicDate":"2012-06-01T12:52:26","publicationYear":"2012","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":1602,"text":"Evolutionary Biology","active":true,"publicationSubtype":{"id":10}},"title":"Species differentiation on a dynamic landscape: shifts in metapopulation genetic structure using the chronology of the Hawaiian Archipelago","docAbstract":"Species formation during adaptive radiation often occurs in the context of a changing environment. The establishment and arrangement of populations, in space and time, sets up ecological and genetic processes that dictate the rate and pattern of differentiation. Here, we focus on how a dynamic habitat can affect genetic structure, and ultimately, differentiation among populations. We make use of the chronology and geographical history provided by the Hawaiian archipelago to examine the initial stages of population establishment and genetic divergence. We use data from a set of 6 spider lineages that differ in habitat affinities, some preferring low elevation habitats with a longer history of connection, others being more specialized for high elevation and/or wet forest, some with more general habitat affinities. We show that habitat preferences associated with lineages are important in ecological and genetic structuring. Lineages that have more restricted habitat preferences are subject to repeated episodes of isolation and fragmentation as a result of lava flows and vegetation succession. The initial dynamic set up by the landscape translates over time into discrete lineages. Further work is needed to understand how genetic changes interact with a changing set of ecological interactions amongst a shifting mosaic of landscapes to achieve species formation.","largerWorkType":{"id":2,"text":"Article"},"largerWorkTitle":"Evolutionary Biology","largerWorkSubtype":{"id":10,"text":"Journal Article"},"language":"English","publisher":"Plenum Press","publisherLocation":"New York, NY","doi":"10.1007/s11692-012-9184-5","usgsCitation":"Roderick, G.K., Croucher, P., Vandergast, A.G., and Gillespie, R.G., 2012, Species differentiation on a dynamic landscape: shifts in metapopulation genetic structure using the chronology of the Hawaiian Archipelago: Evolutionary Biology, v. 39, no. 2, p. 192-206, https://doi.org/10.1007/s11692-012-9184-5.","productDescription":"15 p.","startPage":"192","endPage":"206","numberOfPages":"15","ipdsId":"IP-037509","costCenters":[{"id":651,"text":"Western Ecological Research Center","active":true,"usgs":true}],"links":[{"id":474489,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.1007/s11692-012-9184-5","text":"Publisher Index Page"},{"id":294157,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"},{"id":294152,"type":{"id":10,"text":"Digital Object Identifier"},"url":"https://dx.doi.org/10.1007/s11692-012-9184-5"}],"country":"United States","state":"Hawai'i","geographicExtents":"{ \"type\": \"FeatureCollection\", \"features\": [ { \"type\": \"Feature\", \"properties\": {}, \"geometry\": { \"type\": \"Polygon\", \"coordinates\": [ [ [ -178.31,18.91 ], [ -178.31,28.4 ], [ -154.81,28.4 ], [ -154.81,18.91 ], [ -178.31,18.91 ] ] ] } } ] }","volume":"39","issue":"2","noUsgsAuthors":false,"publicationDate":"2012-05-15","publicationStatus":"PW","scienceBaseUri":"541bf458e4b0e96537ddf890","contributors":{"authors":[{"text":"Roderick, George K.","contributorId":37660,"corporation":false,"usgs":true,"family":"Roderick","given":"George","email":"","middleInitial":"K.","affiliations":[],"preferred":false,"id":501814,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Croucher, Peter","contributorId":107211,"corporation":false,"usgs":true,"family":"Croucher","given":"Peter","email":"","affiliations":[],"preferred":false,"id":501817,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Vandergast, Amy G. 0000-0002-7835-6571","orcid":"https://orcid.org/0000-0002-7835-6571","contributorId":97617,"corporation":false,"usgs":true,"family":"Vandergast","given":"Amy","email":"","middleInitial":"G.","affiliations":[{"id":651,"text":"Western Ecological Research Center","active":true,"usgs":true}],"preferred":true,"id":501816,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Gillespie, Rosemary G.","contributorId":86700,"corporation":false,"usgs":true,"family":"Gillespie","given":"Rosemary","email":"","middleInitial":"G.","affiliations":[],"preferred":false,"id":501815,"contributorType":{"id":1,"text":"Authors"},"rank":4}]}} ,{"id":70038823,"text":"ofr20121107 - 2012 - Sulfur dioxide emission rates from Kilauea Volcano, Hawaii, 2007-2010","interactions":[],"lastModifiedDate":"2019-05-30T12:10:57","indexId":"ofr20121107","displayToPublicDate":"2012-06-01T12:42:26","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":"2012-1107","displayTitle":"Sulfur dioxide emission rates from Kīlauea Volcano, Hawai‘i, 2007–2010","title":"Sulfur dioxide emission rates from Kilauea Volcano, Hawaii, 2007-2010","docAbstract":"Kīlauea Volcano has one of the longest running volcanic sulfur dioxide (SO2) emission rate databases on record. Sulfur dioxide emission rates from Kīlauea Volcano were first measured by Stoiber and Malone (1975) and have been measured on a regular basis since 1979 (Elias and Sutton, 2007, and references within). Compilations of SO2 emission-rate and wind-vector data from 1979 through 2006 are available on the USGS Web site (Elias and others, 1998; Elias and Sutton, 2002; Elias and Sutton, 2007). This report updates the database, documents the changes in data collection and processing methods, and highlights how SO2 emissions have varied with eruptive activity at Kīlauea Volcano for the interval 2007–2010.","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/ofr20121107","usgsCitation":"Elias, T., and Sutton, A.J., 2012, Sulfur dioxide emission rates from Kilauea Volcano, Hawaii, 2007-2010: U.S. Geological Survey Open-File Report 2012-1107, iv, 25 p.; Downloads of Spreadsheets 3-7, https://doi.org/10.3133/ofr20121107.","productDescription":"iv, 25 p.; Downloads of Spreadsheets 3-7","startPage":"i","endPage":"25","numberOfPages":"29","onlineOnly":"Y","additionalOnlineFiles":"Y","costCenters":[{"id":336,"text":"Hawaiian Volcano Observatory","active":false,"usgs":true},{"id":615,"text":"Volcano Hazards Program","active":true,"usgs":true},{"id":617,"text":"Volcano Science Center","active":true,"usgs":true}],"links":[{"id":257867,"rank":0,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/ofr_2012_1107.gif"},{"id":257863,"rank":100,"type":{"id":15,"text":"Index Page"},"url":"https://pubs.usgs.gov/of/2012/1107/","linkFileType":{"id":5,"text":"html"}}],"country":"United States","state":"Hawai'i","otherGeospatial":"Kilauea Volcano","noUsgsAuthors":false,"publicationStatus":"PW","scienceBaseUri":"505b9dd4e4b08c986b31dae2","contributors":{"authors":[{"text":"Elias, T. 0000-0002-9592-4518","orcid":"https://orcid.org/0000-0002-9592-4518","contributorId":71195,"corporation":false,"usgs":true,"family":"Elias","given":"T.","affiliations":[],"preferred":false,"id":465021,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Sutton, A. J. 0000-0003-1902-3977","orcid":"https://orcid.org/0000-0003-1902-3977","contributorId":28983,"corporation":false,"usgs":true,"family":"Sutton","given":"A.","email":"","middleInitial":"J.","affiliations":[],"preferred":false,"id":465020,"contributorType":{"id":1,"text":"Authors"},"rank":2}]}} ,{"id":70041952,"text":"70041952 - 2012 - Comparison of stream invertebrate response models for bioassessment metric","interactions":[],"lastModifiedDate":"2017-09-20T13:32:42","indexId":"70041952","displayToPublicDate":"2012-06-01T09:24:00","publicationYear":"2012","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":2529,"text":"Journal of the American Water Resources Association","active":true,"publicationSubtype":{"id":10}},"title":"Comparison of stream invertebrate response models for bioassessment metric","docAbstract":"We aggregated invertebrate data from various sources to assemble data for modeling in two ecoregions in Oregon and one in California. Our goal was to compare the performance of models developed using multiple linear regression (MLR) techniques with models developed using three relatively new techniques: classification and regression trees (CART), random forest (RF), and boosted regression trees (BRT). We used tolerance of taxa based on richness (RICHTOL) and ratio of observed to expected taxa (O/E) as response variables and land use/land cover as explanatory variables. Responses were generally linear; therefore, there was little improvement to the MLR models when compared to models using CART and RF. In general, the four modeling techniques (MLR, CART, RF, and BRT) consistently selected the same primary explanatory variables for each region. However, results from the BRT models showed significant improvement over the MLR models for each region; increases in R2 from 0.09 to 0.20. The O/E metric that was derived from models specifically calibrated for Oregon consistently had lower R2 values than RICHTOL for the two regions tested. Modeled O/E R2 values were between 0.06 and 0.10 lower for each of the four modeling methods applied in the Willamette Valley and were between 0.19 and 0.36 points lower for the Blue Mountains. As a result, BRT models may indeed represent a good alternative to MLR for modeling species distribution relative to environmental variables.","language":"English","publisher":"American Water Resources Association","publisherLocation":"Herndon, VA","doi":"10.1111/j.1752-1688.2011.00632.x","usgsCitation":"Waite, I.R., Kennen, J., May, J., Brown, L.R., Cuffney, T.F., Jones, K.A., and Orlando, J., 2012, Comparison of stream invertebrate response models for bioassessment metric: Journal of the American Water Resources Association, v. 48, no. 3, p. 570-583, https://doi.org/10.1111/j.1752-1688.2011.00632.x.","productDescription":"14 p.","startPage":"570","endPage":"583","ipdsId":"IP-030734","costCenters":[{"id":154,"text":"California Water Science Center","active":true,"usgs":true},{"id":470,"text":"New Jersey Water Science Center","active":true,"usgs":true},{"id":476,"text":"North Carolina Water Science Center","active":true,"usgs":true},{"id":518,"text":"Oregon Water Science Center","active":true,"usgs":true},{"id":610,"text":"Utah Water Science Center","active":true,"usgs":true}],"links":[{"id":281600,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","state":"California, Oregon","otherGeospatial":"Blue Mountains, Willamette Valley","geographicExtents":"{ \"type\": \"FeatureCollection\", \"features\": [ { \"type\": \"Feature\", \"properties\": {}, \"geometry\": { \"type\": \"Polygon\", \"coordinates\": [ [ [ -124.7035,32.53 ], [ -124.7035,46.2991 ], [ -114.13,46.2991 ], [ -114.13,32.53 ], [ -124.7035,32.53 ] ] ] } } ] }","volume":"48","issue":"3","noUsgsAuthors":false,"publicationDate":"2012-02-13","publicationStatus":"PW","scienceBaseUri":"53cd5216e4b0b290850f451a","contributors":{"authors":[{"text":"Waite, Ian R. 0000-0003-1681-6955 iwaite@usgs.gov","orcid":"https://orcid.org/0000-0003-1681-6955","contributorId":616,"corporation":false,"usgs":true,"family":"Waite","given":"Ian","email":"iwaite@usgs.gov","middleInitial":"R.","affiliations":[{"id":518,"text":"Oregon Water Science Center","active":true,"usgs":true}],"preferred":true,"id":470461,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Kennen, Jonathan G. 0000-0002-5426-4445 jgkennen@usgs.gov","orcid":"https://orcid.org/0000-0002-5426-4445","contributorId":574,"corporation":false,"usgs":true,"family":"Kennen","given":"Jonathan G.","email":"jgkennen@usgs.gov","affiliations":[{"id":470,"text":"New Jersey Water Science Center","active":true,"usgs":true}],"preferred":true,"id":470460,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"May, Jason T. 0000-0002-5699-2112","orcid":"https://orcid.org/0000-0002-5699-2112","contributorId":14791,"corporation":false,"usgs":true,"family":"May","given":"Jason T.","affiliations":[],"preferred":false,"id":470464,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Brown, Larry R. 0000-0001-6702-4531 lrbrown@usgs.gov","orcid":"https://orcid.org/0000-0001-6702-4531","contributorId":1717,"corporation":false,"usgs":true,"family":"Brown","given":"Larry","email":"lrbrown@usgs.gov","middleInitial":"R.","affiliations":[{"id":154,"text":"California Water Science Center","active":true,"usgs":true}],"preferred":true,"id":470463,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Cuffney, Thomas F. 0000-0003-1164-5560 tcuffney@usgs.gov","orcid":"https://orcid.org/0000-0003-1164-5560","contributorId":517,"corporation":false,"usgs":true,"family":"Cuffney","given":"Thomas","email":"tcuffney@usgs.gov","middleInitial":"F.","affiliations":[{"id":13634,"text":"South Atlantic Water Science Center","active":true,"usgs":true}],"preferred":true,"id":470459,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"Jones, Kimberly A. kjones@usgs.gov","contributorId":937,"corporation":false,"usgs":true,"family":"Jones","given":"Kimberly","email":"kjones@usgs.gov","middleInitial":"A.","affiliations":[],"preferred":true,"id":470462,"contributorType":{"id":1,"text":"Authors"},"rank":6},{"text":"Orlando, James L. 0000-0002-0099-7221","orcid":"https://orcid.org/0000-0002-0099-7221","contributorId":95954,"corporation":false,"usgs":true,"family":"Orlando","given":"James L.","affiliations":[],"preferred":false,"id":470465,"contributorType":{"id":1,"text":"Authors"},"rank":7}]}} ,{"id":70044047,"text":"70044047 - 2012 - Spatial aspects of building and population exposure data and their implications for global earthquake exposure modeling","interactions":[],"lastModifiedDate":"2013-06-18T15:11:25","indexId":"70044047","displayToPublicDate":"2012-06-01T00:00:00","publicationYear":"2012","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":2822,"text":"Natural Hazards","active":true,"publicationSubtype":{"id":10}},"title":"Spatial aspects of building and population exposure data and their implications for global earthquake exposure modeling","docAbstract":"This paper discusses spatial aspects of the global exposure dataset and mapping needs for earthquake risk assessment. We discuss this in the context of development of a Global Exposure Database for the Global Earthquake Model (GED4GEM), which requires compilation of a multi-scale inventory of assets at risk, for example, buildings, populations, and economic exposure. After defining the relevant spatial and geographic scales of interest, different procedures are proposed to disaggregate coarse-resolution data, to map them, and if necessary to infer missing data by using proxies. We discuss the advantages and limitations of these methodologies and detail the potentials of utilizing remote-sensing data. The latter is used especially to homogenize an existing coarser dataset and, where possible, replace it with detailed information extracted from remote sensing using the built-up indicators for different environments. Present research shows that the spatial aspects of earthquake risk computation are tightly connected with the availability of datasets of the resolution necessary for producing sufficiently detailed exposure. The global exposure database designed by the GED4GEM project is able to manage datasets and queries of multiple spatial scales.","largerWorkType":{"id":2,"text":"Article"},"largerWorkTitle":"Natural Hazards","largerWorkSubtype":{"id":10,"text":"Journal Article"},"language":"English","publisher":"Springer","doi":"10.1007/s11069-012-0241-2","usgsCitation":"Dell’Acqua, F., Gamba, P., and Jaiswal, K., 2012, Spatial aspects of building and population exposure data and their implications for global earthquake exposure modeling: Natural Hazards, 19 p., https://doi.org/10.1007/s11069-012-0241-2.","productDescription":"19 p.","ipdsId":"IP-037493","costCenters":[{"id":300,"text":"Geologic Hazards Science Center","active":true,"usgs":true}],"links":[{"id":273950,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"},{"id":273949,"type":{"id":10,"text":"Digital Object Identifier"},"url":"https://dx.doi.org/10.1007/s11069-012-0241-2"}],"country":"United States","noUsgsAuthors":false,"publicationDate":"2012-06-14","publicationStatus":"PW","scienceBaseUri":"51c1816ce4b0dd0e00d92211","contributors":{"authors":[{"text":"Dell’Acqua, F.","contributorId":91775,"corporation":false,"usgs":true,"family":"Dell’Acqua","given":"F.","email":"","affiliations":[],"preferred":false,"id":474694,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Gamba, P.","contributorId":72281,"corporation":false,"usgs":true,"family":"Gamba","given":"P.","email":"","affiliations":[],"preferred":false,"id":474692,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Jaiswal, K.","contributorId":89260,"corporation":false,"usgs":true,"family":"Jaiswal","given":"K.","affiliations":[],"preferred":false,"id":474693,"contributorType":{"id":1,"text":"Authors"},"rank":3}]}} ,{"id":70044018,"text":"70044018 - 2012 - Reconstruction of past methane availability in an Arctic Alaska wetland indicates climate influenced methane release during the past ~12,000 years","interactions":[],"lastModifiedDate":"2013-06-25T14:28:26","indexId":"70044018","displayToPublicDate":"2012-06-01T00:00:00","publicationYear":"2012","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":2411,"text":"Journal of Paleolimnology","active":true,"publicationSubtype":{"id":10}},"title":"Reconstruction of past methane availability in an Arctic Alaska wetland indicates climate influenced methane release during the past ~12,000 years","docAbstract":"Atmospheric contributions of methane from Arctic wetlands during the Holocene are dynamic and linked to climate oscillations. However, long-term records linking climate variability to methane availability in Arctic wetlands are lacking. We present a multi-proxy ~12,000 year paleoecological reconstruction of intermittent methane availability from a radiocarbon-dated sediment core (LQ-West) taken from a shallow tundra lake (Qalluuraq Lake) in Arctic Alaska. Specifically, stable carbon isotopic values of photosynthetic biomarkers and methane are utilized to estimate the proportional contribution of methane-derived carbon to lake-sediment-preserved benthic (chironomids) and pelagic (cladocerans) components over the last ~12,000 years. These results were compared to temperature, hydrologic, and habitat reconstructions from the same site using chironomid assemblage data, oxygen isotopes of chironomid head capsules, and radiocarbon ages of plant macrofossils. Cladoceran ephippia from ~4,000 cal year BP sediments have δ13C values that range from ~−39 to −31‰, suggesting peak methane carbon assimilation at that time. These low δ13C values coincide with an apparent decrease in effective moisture and development of a wetland that included Sphagnum subsecundum. Incorporation of methane-derived carbon by chironomids and cladocerans decreased from ~2,500 to 1,500 cal year BP, coinciding with a temperature decrease. Live-collected chironomids with a radiocarbon age of 1,640 cal year BP, and fossil chironomids from 1,500 cal year BP in the core illustrate that ‘old’ carbon has also contributed to the development of the aquatic ecosystem since ~1,500 cal year BP. The relatively low δ13C values of aquatic invertebrates (as low as −40.5‰) provide evidence of methane incorporation by lake invertebrates, and suggest intermittent climate-linked methane release from the lake throughout the Holocene.","largerWorkType":{"id":2,"text":"Article"},"largerWorkTitle":"Journal of Paleolimnology","largerWorkSubtype":{"id":10,"text":"Journal Article"},"language":"English","publisher":"Springer","doi":"10.1007/s10933-012-9591-8","usgsCitation":"Wooller, M., Pohlman, J., Gaglioti, B.V., Langdon, P., Jones, M., Anthony, K.M., Becker, K.W., Hinrichs, K., and Elvert, M., 2012, Reconstruction of past methane availability in an Arctic Alaska wetland indicates climate influenced methane release during the past ~12,000 years: Journal of Paleolimnology, v. 48, no. 1, p. 27-42, https://doi.org/10.1007/s10933-012-9591-8.","productDescription":"16 p.","startPage":"27","endPage":"42","ipdsId":"IP-034535","costCenters":[{"id":114,"text":"Alaska Science Center","active":true,"usgs":true},{"id":678,"text":"Woods Hole Coastal and Marine Science Center","active":true,"usgs":true}],"links":[{"id":274188,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"},{"id":274187,"type":{"id":10,"text":"Digital Object Identifier"},"url":"https://dx.doi.org/10.1007/s10933-012-9591-8"}],"country":"United States","state":"Alaska","geographicExtents":"{ \"type\": \"FeatureCollection\", \"features\": [ { \"type\": \"Feature\", \"properties\": {}, \"geometry\": { \"type\": \"Polygon\", \"coordinates\": [ [ [ 172.5,51.2 ], [ 172.5,71.4 ], [ -130,71.4 ], [ -130,51.2 ], [ 172.5,51.2 ] ] ] } } ] }","volume":"48","issue":"1","noUsgsAuthors":false,"publicationDate":"2012-03-31","publicationStatus":"PW","scienceBaseUri":"51cabbe4e4b0d298e5434c68","contributors":{"authors":[{"text":"Wooller, Matthew J.","contributorId":24213,"corporation":false,"usgs":true,"family":"Wooller","given":"Matthew J.","affiliations":[],"preferred":false,"id":474630,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Pohlman, John W.","contributorId":95288,"corporation":false,"usgs":true,"family":"Pohlman","given":"John W.","affiliations":[],"preferred":false,"id":474636,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Gaglioti, Benjamin V. 0000-0003-0591-5253 bgaglioti@usgs.gov","orcid":"https://orcid.org/0000-0003-0591-5253","contributorId":4521,"corporation":false,"usgs":true,"family":"Gaglioti","given":"Benjamin","email":"bgaglioti@usgs.gov","middleInitial":"V.","affiliations":[{"id":114,"text":"Alaska Science Center","active":true,"usgs":true},{"id":118,"text":"Alaska Science Center Geography","active":true,"usgs":true}],"preferred":true,"id":474629,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Langdon, Peter","contributorId":30530,"corporation":false,"usgs":true,"family":"Langdon","given":"Peter","affiliations":[],"preferred":false,"id":474631,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Jones, Miriam","contributorId":56134,"corporation":false,"usgs":true,"family":"Jones","given":"Miriam","affiliations":[],"preferred":false,"id":474633,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"Anthony, Katey M. Walter","contributorId":82603,"corporation":false,"usgs":true,"family":"Anthony","given":"Katey","email":"","middleInitial":"M. Walter","affiliations":[],"preferred":false,"id":474634,"contributorType":{"id":1,"text":"Authors"},"rank":6},{"text":"Becker, Kevin W.","contributorId":54491,"corporation":false,"usgs":true,"family":"Becker","given":"Kevin","email":"","middleInitial":"W.","affiliations":[],"preferred":false,"id":474632,"contributorType":{"id":1,"text":"Authors"},"rank":7},{"text":"Hinrichs, Kai-Uwe","contributorId":89791,"corporation":false,"usgs":true,"family":"Hinrichs","given":"Kai-Uwe","affiliations":[],"preferred":false,"id":474635,"contributorType":{"id":1,"text":"Authors"},"rank":8},{"text":"Elvert, Marcus","contributorId":102362,"corporation":false,"usgs":true,"family":"Elvert","given":"Marcus","affiliations":[],"preferred":false,"id":474637,"contributorType":{"id":1,"text":"Authors"},"rank":9}]}} ,{"id":70038447,"text":"ofr20121120 - 2012 - A Markov chain analysis of the movements of juvenile salmonids, including sockeye salmon, in the forebay of McNary Dam, Washington and Oregon, 2006-09","interactions":[],"lastModifiedDate":"2012-06-02T01:01:38","indexId":"ofr20121120","displayToPublicDate":"2012-06-01T00:00:00","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":"2012-1120","title":"A Markov chain analysis of the movements of juvenile salmonids, including sockeye salmon, in the forebay of McNary Dam, Washington and Oregon, 2006-09","docAbstract":"Passage and survival data were collected at McNary Dam between 2006 and 2009. These data have provided critical information for resource managers to implement structural and operational changes designed to improve the survival of juvenile salmonids as they migrate past the dam. Much of the valuable information collected at McNary Dam was in the form of three-dimensional (hereafter referred to as 3-D) tracks of fish movements in the forebay. These data depicted the behavior of multiple species (in three dimensions) during different diel periods, spill conditions, powerhouse operations, and testing of the surface bypass structures (temporary spillway weirs; TSWs). One of the challenges in reporting 3-D results is presenting the information in a manner that allows interested parties to summarize the behavior of many fish over many different conditions across multiple years. To accomplish this, we used a Markov chain analysis to characterize fish movement patterns in the forebay of McNary Dam. The Markov chain analysis allowed us to numerically summarize the behavior of fish in the forebay. This report is the second report published in 2012 that uses this analytical method. The first report included only fish released as part of the annual studies conducted at McNary Dam. This second report includes sockeye salmon that were released as part of studies conducted by the Chelan and Grant County Public Utility Districts at mid-Columbia River dams. The studies conducted in the mid-Columbia used the same transmitters as were used for McNary Dam studies, but transmitter pulse width was different between studies. Additionally, no passive integrated transponder tags were implanted in sockeye salmon. Differences in transmitter pulse width resulted in lower detection probabilities for sockeye salmon at McNary Dam. The absence of passive integrated transponder tags prevented us from determining if fish passed the powerhouse through the juvenile bypass system (JBS) or turbines. To facilitate comparison among species in this report, we combined JBS and turbine passage for yearling Chinook salmon, steelhead, and subyearling Chinook salmon even though we were able to differentiate between passage through the JBS or turbines for these three species. Information on passage proportions through the JBS and turbines can be found in the first report. Numerically summarizing the behavior of juvenile salmonids in the forebay of McNary Dam using the Markov chain analysis allowed us to confirm what had been previously summarized using visualization software. For example, within the powerhouse region, passage proportions among the three powerhouse areas were often greater in the southern and middle areas of the powerhouse compared to the northern area of the powerhouse for yearling and subyearling Chinook salmon. The opposite generally was observed for steelhead. The results of this analysis also allowed us to confirm and quantify the extent of milling behavior that was observed for steelhead. For fish that were first detected in the powerhouse region, less than 0.10 of the steelhead, on average, passed within each of the powerhouse areas. Instead, steelhead transitioned to adjoining areas in the spillway before passing the dam. In comparison, greater than 0.20 of the Chinook salmon passed within each of the powerhouse areas. Less milling behavior was observed for all species for fish that first approached the spillway. Compared to the powerhouse areas, a higher proportion of fish, regardless of species, passed the spillway areas and fewer transitioned to adjoining areas in the powerhouse. In addition to quantifying what had been previously speculated about the behavior of fish in the forebay of McNary Dam, the Markov chain analysis refined our understanding of how fish behavior and passage can be influenced by changes to the operations and structure of McNary Dam. For example, the addition of TSWs to the spillway area clearly influenced the passage of fish. Previous results have been reported showing that TSWs increased passage through non-turbine routes and the fish-track videos indicated, in general, how fish behaved before passing the TSWs. However, the analysis presented in this report allowed us to better understand how fish transitioned across the face of the dam before passing the TSWs and resulted in a quantitative way to measure the effect of moving the location of the TSWs from year to year. Installation of the TSWs in bays 22 and 20 clearly increased passage proportions through the southern one-third of the spillway area for all species, most significantly for steelhead. When the TSWs were moved to bays 19 and 20 in 2008, overall passage through the southern one-third of the spillway remained higher than 2006, but decreased from what was observed in 2007. Shifting the TSWs to the north decreased the proportion of fish passing through the TSWs and increased the number of fish that transitioned to adjoining areas before passing the dam. Perhaps the most interesting new information to come out of the two-step Markov chain analysis relates to how the performance of the TSWs was influenced by their proximity to the powerhouse. During 2007, the highest proportion of fish passing through TSW 22 was for fish that transitioned from the powerhouse area. In contrast, a relatively low proportion of fish passed through TSW 20 after coming from the powerhouse area. Instead, the proportion of fish that passed TSW 20 after coming from the northern part of the spillway was twice as high as the proportion of fish that passed through TSW 20 after coming from the powerhouse. During 2008, the TSW in bay 22 was moved to bay 19, leaving the TSW in bay 20 as the one closest to the powerhouse. As was the case when a TSW was located in bay 22, the proportion of fish passing through TSW 20 after coming from the powerhouse was higher than the proportion of fish passing TSW 20 after coming from the northern part of the spillway. Passage proportions for fish passing through TSW 19, the farthest north of the two TSWs during 2008, was higher for fish that came from the northern part of the spillway compared to the proportion of fish that passed through TSW 19 after coming from the powerhouse. The Markov chain analysis provided a mathematical way to characterize fish behavior in the forebay of McNary Dam and helped refine our understanding of how fish movements were influenced by operational and structural changes at the dam. The numerical information used to quantify the behavior of fish also can be used to construct simulations to examine how proposed fish passage structures might influence passage of juvenile salmonids. To demonstrate this, we used the results of the Markov chain analysis to examine how a virtual fish collector located in the center of the powerhouse might influence passage of juvenile salmonids at McNary Dam.","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/ofr20121120","collaboration":"Prepared in cooperation with the U.S. Army Corps of Engineers","usgsCitation":"Adams, N.S., and Hatton, T., 2012, A Markov chain analysis of the movements of juvenile salmonids, including sockeye salmon, in the forebay of McNary Dam, Washington and Oregon, 2006-09: U.S. Geological Survey Open-File Report 2012-1120, viii, 71 p.; Appendices, https://doi.org/10.3133/ofr20121120.","productDescription":"viii, 71 p.; Appendices","temporalStart":"2006-01-01","temporalEnd":"2009-12-31","costCenters":[{"id":654,"text":"Western Fisheries Research Center","active":true,"usgs":true}],"links":[{"id":257110,"rank":0,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/ofr_2012_1120.jpg"},{"id":257109,"rank":100,"type":{"id":15,"text":"Index Page"},"url":"https://pubs.usgs.gov/of/2012/1120/","linkFileType":{"id":5,"text":"html"}}],"country":"United States","state":"Oregon;Washington","otherGeospatial":"Mcnary Dam;Columbia River;Snake River","geographicExtents":"{ \"type\": \"FeatureCollection\", \"features\": [ { \"type\": \"Feature\", \"properties\": {}, \"geometry\": { \"type\": \"Polygon\", \"coordinates\": [ [ [ -121,45.5 ], [ -121,48.25 ], [ -117.5,48.25 ], [ -117.5,45.5 ], [ -121,45.5 ] ] ] } } ] }","noUsgsAuthors":false,"publicationStatus":"PW","scienceBaseUri":"53cd495be4b0b290850ef173","contributors":{"authors":[{"text":"Adams, Noah S. 0000-0002-8354-0293 nadams@usgs.gov","orcid":"https://orcid.org/0000-0002-8354-0293","contributorId":3521,"corporation":false,"usgs":true,"family":"Adams","given":"Noah","email":"nadams@usgs.gov","middleInitial":"S.","affiliations":[{"id":654,"text":"Western Fisheries Research Center","active":true,"usgs":true}],"preferred":true,"id":464162,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Hatton, Tyson W. 0000-0002-2874-0719","orcid":"https://orcid.org/0000-0002-2874-0719","contributorId":9112,"corporation":false,"usgs":true,"family":"Hatton","given":"Tyson W.","affiliations":[{"id":654,"text":"Western Fisheries Research Center","active":true,"usgs":true}],"preferred":false,"id":464163,"contributorType":{"id":1,"text":"Authors"},"rank":2}]}} ,{"id":70038446,"text":"ofr20121119 - 2012 - A Markov chain analysis of the movements of juvenile salmonids in the forebay of McNary Dam, Washington and Oregon, 2006-09","interactions":[],"lastModifiedDate":"2012-06-02T01:01:38","indexId":"ofr20121119","displayToPublicDate":"2012-06-01T00:00:00","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":"2012-1119","title":"A Markov chain analysis of the movements of juvenile salmonids in the forebay of McNary Dam, Washington and Oregon, 2006-09","docAbstract":"Passage and survival data for yearling and subyearling Chinook salmon and juvenile steelhead were collected at McNary Dam between 2006 and 2009. These data have provided critical information for resource managers to implement structural and operational changes designed to improve the survival of juvenile salmonids as they migrate past the dam. Much of the information collected at McNary Dam was in the form of three-dimensional tracks of fish movements in the forebay. These data depicted the behavior of multiple species (in three dimensions) during different diel periods, spill conditions, powerhouse operations, and test configurations of the surface bypass structures (temporary spillway weirs; TSWs). One of the challenges in reporting three-dimensional results is presenting the information in a manner that allows interested parties to summarize the behavior of many fish over many different conditions across multiple years. To accomplish this, we investigated the feasibility of using a Markov chain analysis to characterize fish movement patterns in the forebay of McNary Dam. The Markov chain analysis is one way that can be used to summarize numerically the behavior of fish in the forebay. Numerically summarizing the behavior of juvenile salmonids in the forebay of McNary Dam using the Markov chain analysis allowed us to confirm what had been previously summarized using visualization software. For example, proportions of yearling and subyearling Chinook salmon passing the three powerhouse areas was often greater in the southern and middle areas, compared to the northern area. The opposite generally was observed for steelhead. Results of this analysis also allowed us to confirm and quantify the extent of milling behavior that had been observed for steelhead. For fish that were first detected in the powerhouse region, less than 0.10 of the steelhead, on average, passed within each of the powerhouse areas. Instead, steelhead transitioned to adjoining areas in the spillway before passing the dam. In comparison, greater than 0.20 of the Chinook salmon passed within the powerhouse areas. Less milling behavior was observed for all species for fish that first approached the spillway. Compared to the powerhouse areas, a higher proportion of fish, regardless of species, passed the spillway areas and fewer transitioned to adjoining areas in the powerhouse. In addition to quantifying what had been previously speculated about the behavior of fish in the forebay of McNary Dam, the Markov chain analysis refined our understanding of how fish behavior and passage can be influenced by changes to the operations and structure of McNary Dam. For example, the addition of TSWs to the spillway area clearly influenced the passage of fish. Previous results have been reported showing that TSWs increased the number of fish passing through non-turbine routes and the fish-track videos indicated, in general, how fish behaved before passing through the TSWs. However, the analysis presented in this report allowed us to better understand how fish moved across the face of the dam before passing the TSWs and provided a way to quantify the effect of TSW location. Installation of the TSWs in bays 22 and 20 clearly increased passage proportions through the southern one-third of the spillway area for all species, most significantly for steelhead. When the TSWs were moved to bays 19 and 20 in 2008, overall passage through the southern one-third of the spillway remained higher than 2006, but decreased from what was observed in 2007. Shifting the TSWs to the north decreased the proportion of fish passing through the TSWs and increased the number of fish that moved to adjoining areas before passing the dam. Perhaps the most interesting new information to come out of the two-step Markov chain analysis relates to how the performance of the TSWs was influenced by their proximity to the powerhouse. During 2007, the highest proportion of fish passing through TSW22 was for fish that transitioned from the powerhouse area. In contrast, a relatively low proportion of fish passed through TSW20 after coming from the powerhouse area. Instead, the proportion of fish that passed TSW20 after coming from the northern part of the spillway was twice as high as the proportion of fish that passed through TSW20 after coming from the powerhouse. During 2008, the TSW in bay 22 was moved to bay 19, leaving the TSW in bay 20 as the one closest to the powerhouse. As was the case when a TSW was located in bay 22; the proportion of fish passing TSW20 after coming from the powerhouse was greater than the proportion of fish passing through TSW20 after coming from the northern part of the spillway. Passage proportions for fish passing through TSW19, the farthest north of the two TSWs during 2008, was higher for fish that came from the northern part of the spillway compared to the proportion of fish that passed through TSW19 after coming from the powerhouse. The Markov chain analysis provided a mathematical way to characterize fish behavior in the forebay of McNary Dam and helped refine our understanding of how fish movements were influenced by operational and structural changes at McNary Dam. The Markov chain analysis also could be used to examine how future structural and operational changes proposed for McNary Dam might influence the passage of juvenile salmonids.","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/ofr20121119","collaboration":"Prepared in cooperation with the U.S. Army Corps of Engineers","usgsCitation":"Adams, N.S., and Hatton, T., 2012, A Markov chain analysis of the movements of juvenile salmonids in the forebay of McNary Dam, Washington and Oregon, 2006-09: U.S. Geological Survey Open-File Report 2012-1119, viii, 68 p.; Appendices, https://doi.org/10.3133/ofr20121119.","productDescription":"viii, 68 p.; Appendices","temporalStart":"2006-01-01","temporalEnd":"2009-12-31","costCenters":[{"id":654,"text":"Western Fisheries Research Center","active":true,"usgs":true}],"links":[{"id":257111,"rank":0,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/ofr_2012_1119.jpg"},{"id":257108,"rank":100,"type":{"id":15,"text":"Index Page"},"url":"https://pubs.usgs.gov/of/2012/1119/","linkFileType":{"id":5,"text":"html"}}],"country":"United States","state":"Oregon;Washington","otherGeospatial":"Mcnary Dam;Columbia River;Snake River","geographicExtents":"{ \"type\": \"FeatureCollection\", \"features\": [ { \"type\": \"Feature\", \"properties\": {}, \"geometry\": { \"type\": \"Polygon\", \"coordinates\": [ [ [ -121,45.5 ], [ -121,48.25 ], [ -117.5,48.25 ], [ -117.5,45.5 ], [ -121,45.5 ] ] ] } } ] }","noUsgsAuthors":false,"publicationStatus":"PW","scienceBaseUri":"53cd495be4b0b290850ef171","contributors":{"authors":[{"text":"Adams, Noah S. 0000-0002-8354-0293 nadams@usgs.gov","orcid":"https://orcid.org/0000-0002-8354-0293","contributorId":3521,"corporation":false,"usgs":true,"family":"Adams","given":"Noah","email":"nadams@usgs.gov","middleInitial":"S.","affiliations":[{"id":654,"text":"Western Fisheries Research Center","active":true,"usgs":true}],"preferred":true,"id":464160,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Hatton, Tyson W. 0000-0002-2874-0719","orcid":"https://orcid.org/0000-0002-2874-0719","contributorId":9112,"corporation":false,"usgs":true,"family":"Hatton","given":"Tyson W.","affiliations":[{"id":654,"text":"Western Fisheries Research Center","active":true,"usgs":true}],"preferred":false,"id":464161,"contributorType":{"id":1,"text":"Authors"},"rank":2}]}} ,{"id":70176609,"text":"70176609 - 2012 - How to catch a parasite: Parasite Niche Modeler (PaNic) meets Fishbase","interactions":[],"lastModifiedDate":"2016-09-22T15:20:26","indexId":"70176609","displayToPublicDate":"2012-06-01T00:00:00","publicationYear":"2012","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":1445,"text":"Ecography","active":true,"publicationSubtype":{"id":10}},"title":"How to catch a parasite: Parasite Niche Modeler (PaNic) meets Fishbase","docAbstract":"Parasite Niche Modeler (PaNic) is a free online software tool that suggests potential hosts for fish parasites. For a particular parasite species from the major helminth groups (Acanthocephala, Cestoda, Monogenea, Nematoda, Trematoda), PaNic takes data from known hosts (maximum body length, growth rate, life span, age at first maturity, trophic level, phylogeny, and biogeography) and hypothesizes similar fish species that might serve as hosts to that parasite. Users can give varying weights to host attributes and create custom models. In addition to suggesting plausible hosts (with varying degrees of confidence), the models indicate known host species that appear to be outliers in comparison to other known hosts. These unique features make PaNic an innovative tool for addressing both theoretical and applied questions in fish parasitology. PaNic can be accessed at <http://purl.oclc.org/fishpest>.
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Genetic monitoring is the practice of using molecular genetic markers to track changes in the abundance, diversity or distribution of populations, species or ecosystems over time, and to follow adaptive and non-adaptive genetic responses to changing external conditions. In recent years, genetic monitoring has become a valuable tool in conservation management of biological diversity and ecological analysis, helping to illuminate and define cryptic and poorly understood species and populations. Many of the detected biodiversity declines, changes in distribution and hybridization events have helped to drive changes in policy and management. Because a time series of samples is necessary to detect trends of change in genetic diversity and species composition, archiving is a critical component of genetic monitoring. Here we discuss the collection, development, maintenance, and use of archives for genetic monitoring. This includes an overview of the genetic markers that facilitate effective monitoring, describes how tissue and DNA can be stored, and provides guidelines for proper practice.
","language":"English","publisher":"Conservation Genetics Resources","doi":"10.1007/s12686-011-9545-x","usgsCitation":"Jackson, J.A., Laikre, L., Baker, C.S., Kendall, K.C., and The Genetic Monitoring Working Group, 2012, Guidelines for collecting and maintaining archives for genetic monitoring: Conservation Genetics Resources, v. 4, no. 2, p. 527-536, https://doi.org/10.1007/s12686-011-9545-x.","productDescription":"10 p.","startPage":"527","endPage":"536","ipdsId":"IP-027589","costCenters":[{"id":481,"text":"Northern Rocky Mountain Science Center","active":true,"usgs":true}],"links":[{"id":347366,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"volume":"4","issue":"2","publishingServiceCenter":{"id":2,"text":"Denver PSC"},"noUsgsAuthors":false,"publicationDate":"2011-10-16","publicationStatus":"PW","scienceBaseUri":"59f1a2aae4b0220bbd9d9fd2","contributors":{"authors":[{"text":"Jackson, Jennifer A.","contributorId":198138,"corporation":false,"usgs":false,"family":"Jackson","given":"Jennifer","email":"","middleInitial":"A.","affiliations":[],"preferred":false,"id":715726,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Laikre, Linda","contributorId":198139,"corporation":false,"usgs":false,"family":"Laikre","given":"Linda","email":"","affiliations":[],"preferred":false,"id":715727,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Baker, C. Scott","contributorId":198136,"corporation":false,"usgs":false,"family":"Baker","given":"C.","email":"","middleInitial":"Scott","affiliations":[],"preferred":false,"id":715728,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Kendall, Katherine C. 0000-0002-4831-2287 kkendall@usgs.gov","orcid":"https://orcid.org/0000-0002-4831-2287","contributorId":3081,"corporation":false,"usgs":true,"family":"Kendall","given":"Katherine","email":"kkendall@usgs.gov","middleInitial":"C.","affiliations":[{"id":481,"text":"Northern Rocky Mountain Science Center","active":true,"usgs":true}],"preferred":true,"id":715729,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"The Genetic Monitoring Working Group","contributorId":198335,"corporation":true,"usgs":false,"organization":"The Genetic Monitoring Working Group","id":715730,"contributorType":{"id":1,"text":"Authors"},"rank":5}]}} ,{"id":70187335,"text":"70187335 - 2012 - Geophysical study of the San Juan Mountains batholith complex, southwestern Colorado","interactions":[],"lastModifiedDate":"2019-12-17T09:16:06","indexId":"70187335","displayToPublicDate":"2012-06-01T00:00:00","publicationYear":"2012","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":1820,"text":"Geosphere","active":true,"publicationSubtype":{"id":10}},"title":"Geophysical study of the San Juan Mountains batholith complex, southwestern Colorado","docAbstract":"One of the largest and most pronounced gravity lows over North America is over the rugged San Juan Mountains of southwestern Colorado (USA). The mountain range is coincident with the San Juan volcanic field (SJVF), the largest erosional remnant of a widespread mid-Cenozoic volcanic field that spanned much of the southern Rocky Mountains. A buried, low-density silicic batholith complex related to the volcanic field has been the accepted interpretation of the source of the gravity low since the 1970s. However, this interpretation was based on gravity data processed with standard techniques that are problematic in the SJVF region. The combination of high-relief topography, topography with low densities, and the use of a common reduction density of 2670 kg/m3produces spurious large-amplitude gravity lows that may distort the geophysical signature of deeper features such as a batholith complex. We applied an unconventional processing procedure that uses geologically appropriate densities for the uppermost crust and digital topography to mostly remove the effect of the low-density units that underlie the topography associated with the SJVF. This approach resulted in a gravity map that provides an improved representation of deeper sources, including reducing the amplitude of the anomaly attributed to a batholith complex. We also reinterpreted vintage seismic refraction data that indicate the presence of low-velocity zones under the SJVF. Assuming that the source of the gravity low on the improved gravity anomaly map is the same as the source of the low seismic velocities, integrated modeling corroborates the interpretation of a batholith complex and then defines the dimensions and overall density contrast of the complex. Models show that the thickness of the batholith complex varies laterally to a significant degree, with the greatest thickness (∼20 km) under the western SJVF, and lesser thicknesses (<10 km) under the eastern SJVF. The largest group of nested calderas on the surface of the SJVF, the central caldera cluster, is not correlated with the thickest part of the batholith complex. This result is consistent with petrologic interpretations from recent studies that the batholith complex continued to be modified after cessation of volcanism and therefore is not necessarily representative of synvolcanic magma chambers. The total volume of the batholith complex is estimated to be 82,000–130,000 km3. The formation of such a large felsic batholith complex would inevitably involve production of a considerably greater volume of residuum, which could be present in the lower crust or uppermost mantle. The interpreted vertically averaged density contrast (–60 to –110 kg/m3), density (2590–2640 kg/m3), and seismic expression of the batholith complex are consistent with results of geophysical studies of other large batholiths in the western United States.
","language":"English","publisher":"Geological Society of America","doi":"10.1130/GES00723.1","usgsCitation":"Drenth, B.J., Keller, G.R., and Thompson, R.A., 2012, Geophysical study of the San Juan Mountains batholith complex, southwestern Colorado: Geosphere, v. 8, no. 3, p. 669-684, https://doi.org/10.1130/GES00723.1.","productDescription":"16 p.","startPage":"669","endPage":"684","ipdsId":"IP-026514","costCenters":[{"id":211,"text":"Crustal Geophysics and Geochemistry Science Center","active":true,"usgs":true}],"links":[{"id":474496,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.1130/ges00723.1","text":"Publisher Index Page"},{"id":340695,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","state":"Colorado","otherGeospatial":"San Juan Mountains","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -109.05029296875,\n 36.99377838872517\n ],\n [\n -105.97412109375,\n 36.99377838872517\n ],\n [\n -105.97412109375,\n 38.48369476951686\n ],\n [\n -109.05029296875,\n 38.48369476951686\n ],\n [\n -109.05029296875,\n 36.99377838872517\n ]\n ]\n ]\n }\n }\n ]\n}","volume":"8","issue":"3","publishingServiceCenter":{"id":2,"text":"Denver PSC"},"noUsgsAuthors":false,"publicationStatus":"PW","scienceBaseUri":"59084936e4b0fc4e448ffda2","contributors":{"authors":[{"text":"Drenth, Benjamin J. 0000-0002-3954-8124 bdrenth@usgs.gov","orcid":"https://orcid.org/0000-0002-3954-8124","contributorId":1315,"corporation":false,"usgs":true,"family":"Drenth","given":"Benjamin","email":"bdrenth@usgs.gov","middleInitial":"J.","affiliations":[{"id":211,"text":"Crustal Geophysics and Geochemistry Science Center","active":true,"usgs":true}],"preferred":true,"id":693511,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Keller, G. Randy","contributorId":40602,"corporation":false,"usgs":true,"family":"Keller","given":"G.","email":"","middleInitial":"Randy","affiliations":[],"preferred":false,"id":693513,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Thompson, Ren A. 0000-0002-3044-3043 rathomps@usgs.gov","orcid":"https://orcid.org/0000-0002-3044-3043","contributorId":1265,"corporation":false,"usgs":true,"family":"Thompson","given":"Ren","email":"rathomps@usgs.gov","middleInitial":"A.","affiliations":[{"id":318,"text":"Geosciences and Environmental Change Science Center","active":true,"usgs":true}],"preferred":true,"id":693512,"contributorType":{"id":1,"text":"Authors"},"rank":3}]}} ,{"id":70037976,"text":"70037976 - 2012 - Modeling radium distribution in coastal aquifers during sea level changes: The Dead Sea case","interactions":[],"lastModifiedDate":"2012-06-06T01:01:36","indexId":"70037976","displayToPublicDate":"2012-05-31T12:13:00","publicationYear":"2012","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":1759,"text":"Geochimica et Cosmochimica Acta","active":true,"publicationSubtype":{"id":10}},"title":"Modeling radium distribution in coastal aquifers during sea level changes: The Dead Sea case","docAbstract":"We present a new approach to studying the behavior of radium isotopes in a coastal aquifer. In order to simulate radium isotope distributions in the dynamic flow field of the Dead Sea aquifer, a multi-species density dependent flow model (SUTRA-MS) was used. Field data show that the activity of 226Ra decreases from 140 to 60 dpm/L upon entering the aquifer from the Dead Sea, and then further decreases linearly due to mixing with Ra-poor fresh water. On the other hand, an increase is observed in the activity of the shorter-lived isotopes (up to 52 dpm/L 224Ra and 31 dpm/L 223Ra), which are relatively low in Dead Sea water (up to 2.5 dpm/L 224Ra and 0.5 dpm/L 223Ra). The activities of the short lived radium isotopes also decrease with decreasing salinity, which is due to the effect of salinity on the adsorption of radium. The relationship between 224Ra and salinity suggests that the adsorption partition coefficient (K) is linearly related to salinity. Simulations of the steady-state conditions, show that the distance where equilibrium activity is attained for each radium isotope is affected by the isotope half-life, K and the groundwater velocity, resulting in a longer distance for the long-lived radium isotopes. K affects the radium distribution in transient conditions, especially that of the long-lived radium isotopes. The transient conditions in the Dead Sea system, with a 1 m/yr lake level drop, together with the radium field data, constrains K to be relatively low (<10). Thus, the sharp decrease in 226Ra cannot be explained by adsorption, and it is better explained by removal via coprecipitation, probably with barite or celestine.","largerWorkType":{"id":2,"text":"Article"},"largerWorkTitle":"Geochimica et Cosmochimica Acta","largerWorkSubtype":{"id":10,"text":"Journal Article"},"language":"English","publisher":"Elsevier","publisherLocation":"Amsterdam, Netherlands","doi":"10.1016/j.gca.2012.03.022","usgsCitation":"Kiro, Y., Yechieli, Y., Voss, C.I., Starinsky, A., and Weinstein, Y., 2012, Modeling radium distribution in coastal aquifers during sea level changes: The Dead Sea case: Geochimica et Cosmochimica Acta, v. 88, p. 237-254, https://doi.org/10.1016/j.gca.2012.03.022.","productDescription":"18 p.","startPage":"237","endPage":"254","costCenters":[{"id":148,"text":"Branch of Regional Research-Western Region","active":false,"usgs":true}],"links":[{"id":257213,"rank":100,"type":{"id":15,"text":"Index Page"},"url":"https://dx.doi.org/10.1016/j.gca.2012.03.022","linkFileType":{"id":5,"text":"html"}},{"id":257226,"rank":0,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"Isreal","otherGeospatial":"Dead Sea","volume":"88","noUsgsAuthors":false,"publicationStatus":"PW","scienceBaseUri":"505a5c20e4b0c8380cd6fa5f","contributors":{"authors":[{"text":"Kiro, Yael","contributorId":88996,"corporation":false,"usgs":true,"family":"Kiro","given":"Yael","email":"","affiliations":[],"preferred":false,"id":463191,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Yechieli, Yoseph","contributorId":95320,"corporation":false,"usgs":true,"family":"Yechieli","given":"Yoseph","email":"","affiliations":[],"preferred":false,"id":463192,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Voss, Clifford I. 0000-0001-5923-2752 cvoss@usgs.gov","orcid":"https://orcid.org/0000-0001-5923-2752","contributorId":1559,"corporation":false,"usgs":true,"family":"Voss","given":"Clifford","email":"cvoss@usgs.gov","middleInitial":"I.","affiliations":[{"id":438,"text":"National Research Program - Western Branch","active":true,"usgs":true}],"preferred":true,"id":463189,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Starinsky, Abraham","contributorId":98988,"corporation":false,"usgs":true,"family":"Starinsky","given":"Abraham","email":"","affiliations":[],"preferred":false,"id":463193,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Weinstein, Yishai","contributorId":44404,"corporation":false,"usgs":true,"family":"Weinstein","given":"Yishai","email":"","affiliations":[],"preferred":false,"id":463190,"contributorType":{"id":1,"text":"Authors"},"rank":5}]}} ,{"id":70007202,"text":"70007202 - 2012 - Mapping socio-environmentally vulnerable populations access and exposure to ecosystem services at the U.S.-Mexico borderlands","interactions":[],"lastModifiedDate":"2012-06-06T01:01:36","indexId":"70007202","displayToPublicDate":"2012-05-31T09:59:00","publicationYear":"2012","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":836,"text":"Applied Geography","active":true,"publicationSubtype":{"id":10}},"title":"Mapping socio-environmentally vulnerable populations access and exposure to ecosystem services at the U.S.-Mexico borderlands","docAbstract":"Socio-environmental vulnerable populations are often unrepresented in land-use planning yet have great potential for loss when exposed to changes in ecosystem services. Administrative boundaries, cultural differences, and language barriers increase the disassociation between land-use management and marginalized populations living in the U.S.–Mexico borderlands. This paper describes the development of a Modified Socio-Environmental Vulnerability Index (M-SEVI), using determinants from binational census and neighborhood data that describe levels of education, access to resources, migratory status, housing, and number of dependents, to provide a simplified snapshot of the region's populace that can be used in binational planning efforts. We apply this index at the SCW, located on the border between Arizona, USA and Sonora, Mexico. For comparison, the Soil and Water Assessment Tool is concurrently applied to assess the provision of erosion- and flood control services over a 9-year period. We describe how this coupling of data can form the base for an ecosystem services assessment across political boundaries that can be used by land-use planners. Results reveal potential disparities in environmental risks and burdens throughout the binational watershed in residential districts surrounding and between urban centers. The M-SEVI can be used as an important first step in addressing environmental justice for binational decision-making.","largerWorkType":{"id":2,"text":"Article"},"largerWorkTitle":"Applied Geography","largerWorkSubtype":{"id":10,"text":"Journal Article"},"language":"English","publisher":"Elsevier","publisherLocation":"Amsterdam, Netherlands","doi":"10.1016/j.apgeog.2012.01.006","usgsCitation":"Norman, L.M., Villarreal, M., Lara-Valencia, F., Yuan, Y., Nie, W., Wilson, S., Amaya, G., and Sleeter, R., 2012, Mapping socio-environmentally vulnerable populations access and exposure to ecosystem services at the U.S.-Mexico borderlands: Applied Geography, v. 34, p. 413-424, https://doi.org/10.1016/j.apgeog.2012.01.006.","productDescription":"12 p.","startPage":"413","endPage":"424","costCenters":[{"id":583,"text":"Texas Water Science Center","active":true,"usgs":true},{"id":657,"text":"Western Geographic Science Center","active":true,"usgs":true}],"links":[{"id":257198,"rank":100,"type":{"id":15,"text":"Index Page"},"url":"https://dx.doi.org/10.1016/j.apgeog.2012.01.006","linkFileType":{"id":5,"text":"html"}},{"id":257224,"rank":0,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States;Mexico","state":"Arizona","otherGeospatial":"Sonora","volume":"34","noUsgsAuthors":false,"publicationStatus":"PW","scienceBaseUri":"505a5074e4b0c8380cd6b6ce","contributors":{"authors":[{"text":"Norman, Laura M. 0000-0002-3696-8406 lnorman@usgs.gov","orcid":"https://orcid.org/0000-0002-3696-8406","contributorId":967,"corporation":false,"usgs":true,"family":"Norman","given":"Laura","email":"lnorman@usgs.gov","middleInitial":"M.","affiliations":[{"id":657,"text":"Western Geographic Science Center","active":true,"usgs":true}],"preferred":true,"id":356052,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Villarreal, Miguel L.","contributorId":107012,"corporation":false,"usgs":true,"family":"Villarreal","given":"Miguel L.","affiliations":[],"preferred":false,"id":356058,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Lara-Valencia, Francisco","contributorId":77409,"corporation":false,"usgs":true,"family":"Lara-Valencia","given":"Francisco","email":"","affiliations":[],"preferred":false,"id":356055,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Yuan, Yongping","contributorId":75799,"corporation":false,"usgs":true,"family":"Yuan","given":"Yongping","email":"","affiliations":[],"preferred":false,"id":356054,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Nie, Wenming","contributorId":7126,"corporation":false,"usgs":true,"family":"Nie","given":"Wenming","email":"","affiliations":[],"preferred":false,"id":356053,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"Wilson, Sylvia","contributorId":105160,"corporation":false,"usgs":true,"family":"Wilson","given":"Sylvia","email":"","affiliations":[],"preferred":false,"id":356057,"contributorType":{"id":1,"text":"Authors"},"rank":6},{"text":"Amaya, Gladys","contributorId":86212,"corporation":false,"usgs":true,"family":"Amaya","given":"Gladys","email":"","affiliations":[],"preferred":false,"id":356056,"contributorType":{"id":1,"text":"Authors"},"rank":7},{"text":"Sleeter, Rachel 0000-0003-3477-0436 rsleeter@usgs.gov","orcid":"https://orcid.org/0000-0003-3477-0436","contributorId":666,"corporation":false,"usgs":true,"family":"Sleeter","given":"Rachel","email":"rsleeter@usgs.gov","affiliations":[{"id":242,"text":"Eastern Geographic Science Center","active":true,"usgs":true}],"preferred":true,"id":356051,"contributorType":{"id":1,"text":"Authors"},"rank":8}]}} ,{"id":70038439,"text":"sir20125070 - 2012 - Representation of regional urban development conditions using a watershed-based gradient study design","interactions":[],"lastModifiedDate":"2018-04-02T16:30:50","indexId":"sir20125070","displayToPublicDate":"2012-05-31T00:00:00","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":"2012-5070","title":"Representation of regional urban development conditions using a watershed-based gradient study design","docAbstract":"As part of the U.S. Geological Survey National Water-Quality Assessment Program, the effects of urbanization on stream ecosystems (EUSE) have been intensively investigated in nine metropolitan areas in the United States, including Boston, Massachusetts; Atlanta, Georgia; Birmingham, Alabama; Raleigh, North Carolina; Salt Lake City, Utah; Denver, Colorado; Dallas–Fort Worth, Texas; Portland, Oregon; and Milwaukee–Green Bay, Wisconsin. Each of the EUSE study area watersheds was associated with one ecological region of the United States. This report evaluates whether each metropolitan area can be generalized across the ecological regions (ecoregions) within which the EUSE study watersheds are located. Seven characteristics of the EUSE watersheds that affect stream ecosystems were examined to determine the similarities in the same seven characteristics of the watersheds in the entire ecoregion. Land cover (percentage developed, forest and shrubland, and herbaceous and cultivated classes), average annual temperature, average annual precipitation, average surface elevation, and average percentage slope were selected as human-influenced, climate, and topography characteristics. Three findings emerged from this comparison that have implications for the use of EUSE data in models used to predict stream ecosystem condition. One is that the predominant or \"background\" land-cover type (either forested or agricultural land) in each ecoregion also is the predominant land-cover type within the associated EUSE study watersheds. The second finding is that in all EUSE study areas, the watersheds account for the range of developed land conditions that exist in the corresponding ecoregion watersheds. However, six of the nine EUSE study area watersheds have significantly different distributions of developed land from the ecoregion watersheds. Finally, in seven of the nine EUSE/ecoregion comparisons, the distributions of the values of climate variables in the EUSE watersheds are different from the distributions for watersheds in the corresponding ecoregions.","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/sir20125070","collaboration":"National Water-Quality Assessment Program","usgsCitation":"Terziotti, S., McMahon, G., and Bell, A.H., 2012, Representation of regional urban development conditions using a watershed-based gradient study design: U.S. Geological Survey Scientific Investigations Report 2012-5070, viii, 91 p.; Appendix, https://doi.org/10.3133/sir20125070.","productDescription":"viii, 91 p.; Appendix","startPage":"i","endPage":"109","numberOfPages":"117","onlineOnly":"Y","additionalOnlineFiles":"N","costCenters":[{"id":13634,"text":"South Atlantic Water Science 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Series"},"seriesTitle":{"id":330,"text":"Open-File Report","code":"OFR","onlineIssn":"2331-1258","printIssn":"0196-1497","active":true,"publicationSubtype":{"id":5}},"seriesNumber":"2012-1082","title":"Assessment of soil-gas contamination at three former fuel-dispensing sites, Fort Gordon, Georgia, 2010—2011","docAbstract":"Soil gas was assessed for contaminants at three former fuel-dispensing sites at Fort Gordon, Georgia, from October 2010 to September 2011. The assessment included delineation of organic contaminants using soil-gas samplers collected from the former fuel-dispensing sites at 8th Street, Chamberlain Avenue, and 12th Street. This assessment was conducted to provide environmental contamination data to Fort Gordon personnel pursuant to requirements for the Resource Conservation and Recovery Act Part B Hazardous Waste Permit process. Soil-gas samplers installed and retrieved during June and August 2011 at the 8th Street site had detections above the method detection level (MDL) for the mass of total petroleum hydrocarbons (TPH), benzene, toluene, ortho-xylene, undecane, tridecane, pentadecane, and chloroform. Total petroleum hydrocarbons soil-gas mass exceeded the MDL of 0.02 microgram in 54 of the 55 soil-gas samplers. The highest detection of TPH soil-gas mass was 146.10 micrograms, located in the central part of the site. Benzene mass exceeded the MDL of 0.01 microgram in 23 soil-gas samplers, whereas toluene was detected in only 10 soil-gas samplers. Ortho-xylene was detected above the MDL in only one soil-gas sampler. The highest soil-gas mass detected for undecane, tridecane, and pentadecane was located in the northeastern corner of the 8th Street site. Chloroform mass greater than the MDL of 0.01 microgram was detected in less than one-third of the soil-gas samplers. Soil-gas masses above the MDL were identified for TPH, gasoline-related compounds, diesel-range alkanes, trimethylbenzenes, naphthalene, 2-methyl-napthalene, octane, and tetrachloroethylene for the July 2011 soil-gas survey at the Chamberlain Avenue site. All 30 of the soil-gas samplers contained TPH mass above the MDL. The highest detection of TPH mass, 426.36 micrograms, was for a soil-gas sampler located near the northern boundary of the site. Gasoline-related compounds and diesel-range alkanes were detected in multiple soil-gas samplers, and the highest detections of these compounds were located near the central part of the site near existing, nonoperational, fuel-dispensing pumps. Trimethylbenzenes were detected in less than half of the soil-gas samplers. Naphthalene soil-gas mass was detected above the MDL in 10 soil-gas samplers, whereas 2-methyl-napthalene was detected above the MDL in half of the soil-gas samplers. Octane mass was detected above the MDL in one soil-gas sampler located near the central part of the site. Tetrachloroethylene soil-gas mass was detected above the MDL in more than half of the soil-gas samplers, and the highest tetrachloroethylene soil-gas mass of 0.90 microgram was located in the northeastern part of the site. Soil-gas samplers collected at the 12th Street site during July 2011 contained soil-gas mass above the MDL for TPH, toluene, undecane, tridecane, and pentadecane (diesel-range alkanes), trichloroethylene, 1,4-dichlorobenzene, chloroform, and 1,2,4-trimethylbenzene. The highest detected TPH mass was 24.37 micrograms in a soil-gas sampler located in the northern part of the site. The highest detection of toluene soil-gas mass was from a soil-gas sampler located near the southern boundary of the site. The diesel-range alkanes were detected above the MDL in five soil-gas samplers; the highest detection of soil-gas diesel mass, 0.65 microgram, was located in the southern part of the site. Trichloroethylene and 1,4-dichlorobenzene were detected above the MDL in the northern part of the site in one soil-gas sampler that also had one of the highest detections of TPH. Chloroform was detected above the MDL in three soil-gas samplers, whereas 1,2,4-trimethylbenzene soil-gas mass was detected above the MDL in two soil-gas samplers.","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/ofr20121082","collaboration":"Prepared in cooperation with the U.S. Department of the Army Environmental and Natural Resources Management Office of the U.S. Army Signal Center and Fort Gordon","usgsCitation":"Caldwell, A.W., Falls, W.F., Guimaraes, W.B., Ratliff, W.H., Wellborn, J.B., and Landmeyer, J., 2012, Assessment of soil-gas contamination at three former fuel-dispensing sites, Fort Gordon, Georgia, 2010—2011: U.S. Geological Survey Open-File Report 2012-1082, v, 7 p.; Figures; Tables, https://doi.org/10.3133/ofr20121082.","productDescription":"v, 7 p.; Figures; Tables","startPage":"i","endPage":"37","numberOfPages":"42","onlineOnly":"Y","additionalOnlineFiles":"N","temporalStart":"2010-10-01","temporalEnd":"2011-09-30","costCenters":[{"id":559,"text":"South Carolina Water Science Center","active":true,"usgs":true}],"links":[{"id":257053,"rank":0,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/ofr_2012_1082.jpg"},{"id":257044,"rank":300,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/of/2012/1082/pdf/2012-1082.pdf","linkFileType":{"id":1,"text":"pdf"}},{"id":257043,"rank":100,"type":{"id":15,"text":"Index Page"},"url":"https://pubs.usgs.gov/of/2012/1082/","linkFileType":{"id":5,"text":"html"}}],"country":"United States","state":"Georgia","otherGeospatial":"Fort Gordon","noUsgsAuthors":false,"publicationStatus":"PW","scienceBaseUri":"5059ee59e4b0c8380cd49cf5","contributors":{"authors":[{"text":"Caldwell, Andral W. 0000-0003-1269-5463 acaldwel@usgs.gov","orcid":"https://orcid.org/0000-0003-1269-5463","contributorId":3228,"corporation":false,"usgs":true,"family":"Caldwell","given":"Andral","email":"acaldwel@usgs.gov","middleInitial":"W.","affiliations":[{"id":559,"text":"South Carolina Water Science Center","active":true,"usgs":true}],"preferred":true,"id":464130,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Falls, W. 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