One of the objectives of the Gulf of Mexico Gas Hydrate Joint Industry Project Leg II (GOM JIP Leg II) was the collection of a comprehensive suite of logging-while-drilling (LWD) data within gas-hydrate-bearing sand reservoirs in order to make accurate estimates of the concentration of gas hydrates under various geologic conditions and to understand the geologic controls on the occurrence of gas hydrate at each of the sites drilled during this expedition. The LWD sensors just above the drill bit provided important information on the nature of the sediments and the occurrence of gas hydrate. There has been significant advancements in the use of downhole well-logging tools to acquire detailed information on the occurrence of gas hydrate in nature: From using electrical resistivity and acoustic logs to identify gas hydrate occurrences in wells to where wireline and advanced logging-while-drilling tools are routinely used to examine the petrophysical nature of gas hydrate reservoirs and the distribution and concentration of gas hydrates within various complex reservoir systems. Recent integrated sediment coring and well-log studies have confirmed that electrical resistivity and acoustic velocity data can yield accurate gas hydrate saturations in sediment grain supported (isotropic) systems such as sand reservoirs, but more advanced log analysis models are required to characterize gas hydrate in fractured (anisotropic) reservoir systems. In support of the GOM JIP Leg II effort, well-log data montages have been compiled and presented in this report which includes downhole logs obtained from all seven wells drilled during this expedition with a focus on identifying and characterizing the potential gas-hydrate-bearing sedimentary section in each of the wells. Also presented and reviewed in this report are the gas-hydrate saturation and sediment porosity logs for each of the wells as calculated from available downhole well logs.
","largerWorkTitle":"Marine and Petroleum Geology","language":"English","publisher":"Elsevier","publisherLocation":"Amsterdam, Netherlands","doi":"10.1016/j.marpetgeo.2011.08.003","usgsCitation":"Collett, T.S., Lee, M.W., Zyrianova, M., Mrozewski, S.A., Guerin, G., Cook, A.E., and Goldberg, D.S., 2012, Gulf of Mexico Gas Hydrate Joint Industry Project Leg II logging-while-drilling data acquisition and analysis: Marine and Petroleum Geology, v. 34, no. 1, p. 41-61, https://doi.org/10.1016/j.marpetgeo.2011.08.003.","productDescription":"21 p.","startPage":"41","endPage":"61","costCenters":[{"id":164,"text":"Central Energy Resources Science Center","active":true,"usgs":true}],"links":[{"id":257165,"rank":0,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","otherGeospatial":"Gulf Of Mexico","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -95.592041015625,\n 26.696545111585152\n ],\n [\n -89.53857421875,\n 26.696545111585152\n ],\n [\n -89.53857421875,\n 30.50548389892728\n ],\n [\n -95.592041015625,\n 30.50548389892728\n ],\n [\n -95.592041015625,\n 26.696545111585152\n ]\n ]\n ]\n }\n }\n ]\n}","volume":"34","issue":"1","noUsgsAuthors":false,"publicationStatus":"PW","scienceBaseUri":"505a2e5ae4b0c8380cd5c49e","contributors":{"authors":[{"text":"Collett, Timothy S. 0000-0002-7598-4708 tcollett@usgs.gov","orcid":"https://orcid.org/0000-0002-7598-4708","contributorId":1698,"corporation":false,"usgs":true,"family":"Collett","given":"Timothy","email":"tcollett@usgs.gov","middleInitial":"S.","affiliations":[{"id":171,"text":"Central Mineral and Environmental Resources Science Center","active":true,"usgs":true},{"id":678,"text":"Woods Hole Coastal and Marine Science Center","active":true,"usgs":true},{"id":164,"text":"Central Energy Resources Science Center","active":true,"usgs":true},{"id":255,"text":"Energy Resources Program","active":true,"usgs":true}],"preferred":true,"id":353428,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Lee, Myung W. mlee@usgs.gov","contributorId":779,"corporation":false,"usgs":true,"family":"Lee","given":"Myung","email":"mlee@usgs.gov","middleInitial":"W.","affiliations":[{"id":164,"text":"Central Energy Resources Science Center","active":true,"usgs":true}],"preferred":true,"id":353427,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Zyrianova, Margarita V. 0000-0002-3669-1320","orcid":"https://orcid.org/0000-0002-3669-1320","contributorId":30665,"corporation":false,"usgs":true,"family":"Zyrianova","given":"Margarita V.","affiliations":[{"id":164,"text":"Central Energy Resources Science Center","active":true,"usgs":true}],"preferred":false,"id":353430,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Mrozewski, Stefan A.","contributorId":75000,"corporation":false,"usgs":true,"family":"Mrozewski","given":"Stefan","email":"","middleInitial":"A.","affiliations":[],"preferred":false,"id":353432,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Guerin, Gilles","contributorId":77783,"corporation":false,"usgs":true,"family":"Guerin","given":"Gilles","email":"","affiliations":[],"preferred":false,"id":353433,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"Cook, Ann E.","contributorId":18218,"corporation":false,"usgs":true,"family":"Cook","given":"Ann","email":"","middleInitial":"E.","affiliations":[],"preferred":false,"id":353429,"contributorType":{"id":1,"text":"Authors"},"rank":6},{"text":"Goldberg, Dave S.","contributorId":42474,"corporation":false,"usgs":true,"family":"Goldberg","given":"Dave","email":"","middleInitial":"S.","affiliations":[],"preferred":false,"id":353431,"contributorType":{"id":1,"text":"Authors"},"rank":7}]}} ,{"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":70004500,"text":"70004500 - 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":70038455,"text":"ofr20121092 - 2012 - The U.S. Geological Survey Ecosystem Science Strategy, 2012-2022 - Advancing discovery and application through collaboration","interactions":[],"lastModifiedDate":"2018-05-24T15:26:19","indexId":"ofr20121092","displayToPublicDate":"2012-06-04T00:00:00","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":"2012-1092","title":"The U.S. Geological Survey Ecosystem Science Strategy, 2012-2022 - Advancing discovery and application through collaboration","docAbstract":"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.
","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/ofr20121092","collaboration":"Public Review Release - Feedback on this report will be accepted through August 1, 2012. 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. Lynn 0000-0002-3833-5207 lwingard@usgs.gov","orcid":"https://orcid.org/0000-0002-3833-5207","contributorId":605,"corporation":false,"usgs":true,"family":"Wingard","given":"G.","email":"lwingard@usgs.gov","middleInitial":"Lynn","affiliations":[{"id":40020,"text":"Florence Bascom Geoscience Center","active":true,"usgs":true},{"id":243,"text":"Eastern Geology and Paleoclimate Science Center","active":true,"usgs":true}],"preferred":true,"id":464217,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Brewer, Gary","contributorId":37589,"corporation":false,"usgs":true,"family":"Brewer","given":"Gary","email":"","affiliations":[],"preferred":false,"id":464216,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Cloern, James E. 0000-0002-5880-6862 jecloern@usgs.gov","orcid":"https://orcid.org/0000-0002-5880-6862","contributorId":1488,"corporation":false,"usgs":true,"family":"Cloern","given":"James","email":"jecloern@usgs.gov","middleInitial":"E.","affiliations":[{"id":37277,"text":"WMA - 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2012 - Strategic directions for U.S. Geological Survey water science, 2012-2022 - Observing, understanding, predicting, and delivering water science to the Nation","interactions":[],"lastModifiedDate":"2017-03-29T13:22:13","indexId":"ofr20121066","displayToPublicDate":"2012-06-04T00:00:00","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":"2012-1066","title":"Strategic directions for U.S. Geological Survey water science, 2012-2022 - Observing, understanding, predicting, and delivering water science to the Nation","docAbstract":"This report expands the Water Science Strategy that was begun in the USGS Science Strategy, “Facing Tomorrow’s Challenges—U.S. Geological Survey Science in the Decade 2007–2017” (U.S. Geological Survey, 2007). The report looks at the relevant issues facing society and develops a strategy built around observing, understanding, predicting, and delivering water science for the next 5 to 10 years by building new capabilities, tools, and delivery systems to meet the Nation’s water-resource needs. This report begins by presenting the vision of water science for the USGS and the societal issues that are influenced by, and in turn influence, the water resources of our Nation. The essence of the Water Strategic Science Plan is built on the concept of “water availability,” defined as spatial and temporal distribution of water quantity and quality, as related to human and ecosystem needs, as affected by human and natural influences. The report also describes the core capabilities of the USGS in water science—the strengths, partnerships, and science integrity that the USGS has built over its 130-year history.
\nNine priority actions are presented in the report, which combine and elevate the numerous specific strategic actions listed throughout the report. Priority actions were developed as a means of providing the audience of this report with a list for focused attention, even if resources and time limit the ability of managers to address all of the strategic actions in the report. Priority actions focus on the following:
\nThe body of the report is presented as a hierarchal set of 5 goals, 14 objectives, and 27 strategic actions that the USGS should undertake to advance water science through year 2022.
The goals deal with:
Scientific information produced on water resources would be without value if it were not communicated to society in a fashion that can inform decisions and actions. Therefore, the chapter following the goals describes how the USGS should inform, involve, and educate society about the science it produces. This includes discussions on local outreach and the use of social media for effective communication.
\nThis report concludes with a chapter devoted to the crosscutting science issues of the Water Mission Area with the other USGS Mission Areas: Climate and Land Use Change, Core Science Systems, Ecosystems, Energy and Minerals, Environmental Health Science, and Natural Hazards. Not one of these Mission Areas stands alone—all must work together and integrate their actions to fulfill the USGS science mission for the future. This final chapter identifies the important linkages that must be realized and maintained for this integration to occur.
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Topographic surveys and discharge records from 281 cross sections at 82 gaging stations with drainage areas of 0.52-396 square miles were used to create log-log regressions of region-based relations between bankfull HG metrics and drainage area. The success with which regional models distinguished unique bankfull discharge and HG patterns was assessed by comparing each regional model to those for all other regions and a pooled statewide model. Gages were also stratified (grouped) by mean annual runoff (MAR), Rosgen stream type, and water-surface slope to test if these models were better predictors of HG to drainage area relations. Bankfull discharge models for Regions 4 and 7 were outside the 95% confidence interval bands of the statewide model, and bankfull width, depth, and cross-sectional area models for Region 3 differed significantly (p < 0.05) from those of other regions. This study found that statewide relations between drainage area and HG were strongest when data were stratified by hydrologic region, but that co-variable models could yield more accurate HG estimates in some local regional curve applications.","largerWorkType":{"id":2,"text":"Article"},"largerWorkTitle":"Journal of the American Water Resources Association","largerWorkSubtype":{"id":10,"text":"Journal Article"},"language":"English","publisher":"American Water Resources Association","publisherLocation":"Middleburg, VA","doi":"10.1111/j.1752-1688.2011.00623.x","usgsCitation":"Mulvihill, C., and Baldigo, B.P., 2012, Optimizing bankfull discharge and hydraulic geometry relations for streams in New York state: Journal of the American Water Resources Association, v. 48, no. 3, p. 449-463, https://doi.org/10.1111/j.1752-1688.2011.00623.x.","productDescription":"15 p.","startPage":"449","endPage":"463","costCenters":[{"id":474,"text":"New York Water Science Center","active":true,"usgs":true}],"links":[{"id":474485,"rank":10000,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.1111/j.1752-1688.2011.00623.x","text":"Publisher Index Page"},{"id":257153,"rank":0,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"},{"id":257140,"rank":9999,"type":{"id":10,"text":"Digital Object Identifier"},"url":"https://dx.doi.org/10.1111/j.1752-1688.2011.00623.x"}],"country":"United States","state":"New York","volume":"48","issue":"3","noUsgsAuthors":false,"publicationDate":"2012-01-17","publicationStatus":"PW","scienceBaseUri":"505a6effe4b0c8380cd758e3","contributors":{"authors":[{"text":"Mulvihill, Christiane I.","contributorId":31821,"corporation":false,"usgs":true,"family":"Mulvihill","given":"Christiane I.","affiliations":[],"preferred":false,"id":463120,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Baldigo, Barry P. 0000-0002-9862-9119 bbaldigo@usgs.gov","orcid":"https://orcid.org/0000-0002-9862-9119","contributorId":1234,"corporation":false,"usgs":true,"family":"Baldigo","given":"Barry","email":"bbaldigo@usgs.gov","middleInitial":"P.","affiliations":[{"id":474,"text":"New York Water Science Center","active":true,"usgs":true}],"preferred":true,"id":463119,"contributorType":{"id":1,"text":"Authors"},"rank":2}]}} ,{"id":70038456,"text":"ofr20121093 - 2012 - Science strategy for Core Science Systems in the U.S. Geological Survey, 2013-2023","interactions":[],"lastModifiedDate":"2018-08-10T16:54:09","indexId":"ofr20121093","displayToPublicDate":"2012-06-04T00:00:00","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":"2012-1093","title":"Science strategy for Core Science Systems in the U.S. Geological Survey, 2013-2023","docAbstract":"Core Science Systems is a new mission of the U.S. Geological Survey (USGS) that grew out of the 2007 Science Strategy, “Facing Tomorrow’s Challenges: U.S. Geological Survey Science in the Decade 2007–2017.” This report describes the vision for this USGS mission and outlines a strategy for Core Science Systems to facilitate integrated characterization and understanding of the complex earth system. The vision and suggested actions are bold and far-reaching, describing a conceptual model and framework to enhance the ability of USGS to bring its core strengths to bear on pressing societal problems through data integration and scientific synthesis across the breadth of science.
The context of this report is inspired by a direction set forth in the 2007 Science Strategy. Specifically, ecosystem-based approaches provide the underpinnings for essentially all science themes that define the USGS. Every point on earth falls within a specific ecosystem where data, other information assets, and the expertise of USGS and its many partners can be employed to quantitatively understand how that ecosystem functions and how it responds to natural and anthropogenic disturbances. Every benefit society obtains from the planet—food, water, raw materials to build infrastructure, homes and automobiles, fuel to heat homes and cities, and many others, are derived from or effect ecosystems.
The vision for Core Science Systems builds on core strengths of the USGS in characterizing and understanding complex earth and biological systems through research, modeling, mapping, and the production of high quality data on the nation’s natural resource infrastructure. Together, these research activities provide a foundation for ecosystem-based approaches through geologic mapping, topographic mapping, and biodiversity mapping. The vision describes a framework founded on these core mapping strengths that makes it easier for USGS scientists to discover critical information, share and publish results, and identify potential collaborations that transcend all USGS missions. The framework is designed to improve the efficiency of scientific work within USGS by establishing a means to preserve and recall data for future applications, organizing existing scientific knowledge and data to facilitate new use of older information, and establishing a future workflow that naturally integrates new data, applications, and other science products to make it easier and more efficient to conduct interdisciplinary research over time. Given the increasing need for integrated data and interdisciplinary approaches to solve modern problems, leadership by the Core Science Systems mission will facilitate problem solving by all USGS missions in ways not formerly possible.
The report lays out a strategy to achieve this vision through three goals with accompanying objectives and actions. The first goal builds on and enhances the strengths of the Core Science Systems mission in characterizing and understanding the earth system from the geologic framework to the topographic characteristics of the land surface and biodiversity across the nation. The second goal enhances and develops new strengths in computer and information science to make it easier for USGS scientists to discover data and models, share and publish results, and discover connections between scientific information and knowledge. The third goal brings additional focus to research and development methods to address complex issues affecting society that require integration of knowledge and new methods for synthesizing scientific information. Collectively, the report lays out a strategy to create a seamless connection between all USGS activities to accelerate and make USGS science more efficient by fully integrating disciplinary expertise within a new and evolving science paradigm for a changing world in the 21st century.
","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/ofr20121093","usgsCitation":"Bristol, R., Euliss, N.H., Booth, N., Burkardt, N., Diffendorfer, J.E., Gesch, D.B., McCallum, B.E., Miller, D., Morman, S.A., Poore, B.S., Signell, R.P., and Viger, R., 2012, Science strategy for Core Science Systems in the U.S. Geological Survey, 2013-2023: U.S. Geological Survey Open-File Report 2012-1093, vi, 29 p., https://doi.org/10.3133/ofr20121093.","productDescription":"vi, 29 p.","onlineOnly":"Y","costCenters":[{"id":222,"text":"Earth Resources Observation and Science (EROS) Center","active":true,"usgs":true},{"id":37226,"text":"Core Science Analytics, Synthesis, and Libraries","active":true,"usgs":true}],"links":[{"id":257158,"rank":0,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/ofr_2012_1093.gif"},{"id":338619,"rank":3,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/of/2012/1093/of2012-1093.pdf"},{"id":257139,"rank":100,"type":{"id":15,"text":"Index Page"},"url":"https://pubs.usgs.gov/of/2012/1093/","linkFileType":{"id":5,"text":"html"}}],"noUsgsAuthors":false,"publicationStatus":"PW","scienceBaseUri":"505b8774e4b08c986b3164be","contributors":{"authors":[{"text":"Bristol, R. 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2012 - U.S. Geological Survey energy and minerals science strategy","interactions":[],"lastModifiedDate":"2012-06-05T01:01:48","indexId":"ofr20121072","displayToPublicDate":"2012-06-04T00:00:00","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":"2012-1072","title":"U.S. Geological Survey energy and minerals science strategy","docAbstract":"The economy, national security, and standard of living of the United States depend heavily on adequate and reliable supplies of energy and mineral resources. Based on current population and consumption trends, the Nation's use of energy and minerals can be expected to grow, driving the demand for ever broader scientific understanding of resource formation, location, and availability. In addition, the increasing importance of environmental stewardship, human health, and sustainable growth place further emphasis on energy and mineral resources research and understanding. Collectively, these trends in resource demand and the interconnectedness among resources will lead to new challenges and, in turn, require cutting-edge science for the next generation of societal decisions. The contributions of the U.S. Geological Survey to energy and minerals research are well established. Based on five interrelated goals, this plan establishes a comprehensive science strategy. It provides a structure that identifies the most critical aspects of energy and mineral resources for the coming decade. * Goal 1. - Understand fundamental Earth processes that form energy and mineral resources. * Goal 2. - Understand the environmental behavior of energy and mineral resources and their waste products. * Goal 3. - Provide inventories and assessments of energy and mineral resources. * Goal 4. - Understand the effects of energy and mineral development on natural resources. * Goal 5. - Understand the availability and reliability of energy and mineral resource supplies. Within each goal, multiple, scalable actions are identified. The level of specificity and complexity of these actions varies, consistent with the reality that even a modest refocus can yield large payoffs in the near term whereas more ambitious plans may take years to reach fruition. As such, prioritization of actions is largely dependent on policy direction, available resources, and the sequencing of prerequisite steps that will lead up to the most visionary directions. The science strategy stresses early planning and places an emphasis on interdisciplinary collaboration and leveraging of expertise across the U.S. Geological Survey.","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/ofr20121072","collaboration":"Public Review Release - Feedback on this report will be accepted through August 1, 2012. Please see index page for feedback instructions.","usgsCitation":"Ferrero, R.C., Kolak, J.J., Bills, D., Bowen, Z.H., Cordier, D.J., Gallegos, T.J., Hein, J.R., Kelley, K., Nelson, P.H., Nuccio, V.F., Schmidt, J.M., and Seal, R., 2012, U.S. Geological Survey energy and minerals science strategy: U.S. Geological Survey Open-File Report 2012-1072, vi, 35 p., https://doi.org/10.3133/ofr20121072.","productDescription":"vi, 35 p.","onlineOnly":"Y","costCenters":[],"links":[{"id":257135,"rank":0,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/ofr_2012_1072.gif"},{"id":257129,"rank":100,"type":{"id":15,"text":"Index Page"},"url":"https://pubs.usgs.gov/of/2012/1072/","linkFileType":{"id":5,"text":"html"}}],"noUsgsAuthors":false,"publicationStatus":"PW","scienceBaseUri":"505bbaa7e4b08c986b3282ab","contributors":{"authors":[{"text":"Ferrero, Richard C. rferrero@usgs.gov","contributorId":473,"corporation":false,"usgs":true,"family":"Ferrero","given":"Richard","email":"rferrero@usgs.gov","middleInitial":"C.","affiliations":[],"preferred":true,"id":464186,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Kolak, Jonathan J.","contributorId":59100,"corporation":false,"usgs":true,"family":"Kolak","given":"Jonathan","email":"","middleInitial":"J.","affiliations":[],"preferred":false,"id":464196,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Bills, Donald J. djbills@usgs.gov","contributorId":4180,"corporation":false,"usgs":true,"family":"Bills","given":"Donald J.","email":"djbills@usgs.gov","affiliations":[{"id":128,"text":"Arizona Water Science Center","active":true,"usgs":true}],"preferred":false,"id":464193,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Bowen, Zachary H. 0000-0002-8656-1831 bowenz@usgs.gov","orcid":"https://orcid.org/0000-0002-8656-1831","contributorId":821,"corporation":false,"usgs":true,"family":"Bowen","given":"Zachary","email":"bowenz@usgs.gov","middleInitial":"H.","affiliations":[{"id":291,"text":"Fort Collins Science Center","active":true,"usgs":true}],"preferred":true,"id":464187,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Cordier, Daniel J.","contributorId":14678,"corporation":false,"usgs":true,"family":"Cordier","given":"Daniel","email":"","middleInitial":"J.","affiliations":[],"preferred":false,"id":464194,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"Gallegos, Tanya J. 0000-0003-3350-6473 tgallegos@usgs.gov","orcid":"https://orcid.org/0000-0003-3350-6473","contributorId":2206,"corporation":false,"usgs":true,"family":"Gallegos","given":"Tanya","email":"tgallegos@usgs.gov","middleInitial":"J.","affiliations":[{"id":241,"text":"Eastern Energy Resources Science Center","active":true,"usgs":true},{"id":436,"text":"National Research Program - Eastern Branch","active":true,"usgs":true}],"preferred":true,"id":464190,"contributorType":{"id":1,"text":"Authors"},"rank":6},{"text":"Hein, James R. 0000-0002-5321-899X jhein@usgs.gov","orcid":"https://orcid.org/0000-0002-5321-899X","contributorId":2828,"corporation":false,"usgs":true,"family":"Hein","given":"James","email":"jhein@usgs.gov","middleInitial":"R.","affiliations":[{"id":520,"text":"Pacific Coastal and Marine Science Center","active":true,"usgs":true}],"preferred":false,"id":464191,"contributorType":{"id":1,"text":"Authors"},"rank":7},{"text":"Kelley, Karen D. 0000-0002-3232-5809","orcid":"https://orcid.org/0000-0002-3232-5809","contributorId":57817,"corporation":false,"usgs":true,"family":"Kelley","given":"Karen D.","affiliations":[],"preferred":false,"id":464195,"contributorType":{"id":1,"text":"Authors"},"rank":8},{"text":"Nelson, Philip H. pnelson@usgs.gov","contributorId":862,"corporation":false,"usgs":true,"family":"Nelson","given":"Philip","email":"pnelson@usgs.gov","middleInitial":"H.","affiliations":[{"id":164,"text":"Central Energy Resources Science Center","active":true,"usgs":true}],"preferred":true,"id":464189,"contributorType":{"id":1,"text":"Authors"},"rank":9},{"text":"Nuccio, Vito F. vnuccio@usgs.gov","contributorId":853,"corporation":false,"usgs":true,"family":"Nuccio","given":"Vito","email":"vnuccio@usgs.gov","middleInitial":"F.","affiliations":[],"preferred":true,"id":464188,"contributorType":{"id":1,"text":"Authors"},"rank":10},{"text":"Schmidt, Jeanine M. jschmidt@usgs.gov","contributorId":3138,"corporation":false,"usgs":true,"family":"Schmidt","given":"Jeanine","email":"jschmidt@usgs.gov","middleInitial":"M.","affiliations":[{"id":119,"text":"Alaska Science Center Geology Minerals","active":true,"usgs":true}],"preferred":true,"id":464192,"contributorType":{"id":1,"text":"Authors"},"rank":11},{"text":"Seal, Robert R. II 0000-0003-0901-2529 rseal@usgs.gov","orcid":"https://orcid.org/0000-0003-0901-2529","contributorId":397,"corporation":false,"usgs":true,"family":"Seal","given":"Robert R.","suffix":"II","email":"rseal@usgs.gov","affiliations":[],"preferred":false,"id":464185,"contributorType":{"id":1,"text":"Authors"},"rank":12}]}} ,{"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":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":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":70175264,"text":"70175264 - 2012 - Assessing the status and trend of bat populations across broad geographic regions with dynamic distribution models","interactions":[],"lastModifiedDate":"2016-08-03T14:24:11","indexId":"70175264","displayToPublicDate":"2012-06-01T15:30:00","publicationYear":"2012","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":1450,"text":"Ecological Applications","active":true,"publicationSubtype":{"id":10}},"title":"Assessing the status and trend of bat populations across broad geographic regions with dynamic distribution models","docAbstract":"Bats face unprecedented threats from habitat loss, climate change, disease, and wind power development, and populations of many species are in decline. A better ability to quantify bat population status and trend is urgently needed in order to develop effective conservation strategies. We used a Bayesian autoregressive approach to develop dynamic distribution models for Myotis lucifugus, the little brown bat, across a large portion of northwestern USA, using a four-year detection history matrix obtained from a regional monitoring program. This widespread and abundant species has experienced precipitous local population declines in northeastern USA resulting from the novel disease white-nose syndrome, and is facing likely range-wide declines. Our models were temporally dynamic and accounted for imperfect detection. Drawing on species–energy theory, we included measures of net primary productivity (NPP) and forest cover in models, predicting that M. lucifugus occurrence probabilities would covary positively along those gradients.
\nDespite its common status, M. lucifugus was only detected during ∼50% of the surveys in occupied sample units. The overall naïve estimate for the proportion of the study region occupied by the species was 0.69, but after accounting for imperfect detection, this increased to ∼0.90. Our models provide evidence of an association between NPP and forest cover and M. lucifugus distribution, with implications for the projected effects of accelerated climate change in the region, which include net aridification as snowpack and stream flows decline. Annual turnover, the probability that an occupied sample unit was a newly occupied one, was estimated to be low (∼0.04–0.14), resulting in flat trend estimated with relatively high precision (SD = 0.04). We mapped the variation in predicted occurrence probabilities and corresponding prediction uncertainty along the productivity gradient. Our results provide a much needed baseline against which future anticipated declines in M. lucifugus occurrence can be measured. The dynamic distribution modeling approach has broad applicability to regional bat monitoring efforts now underway in several countries and we suggest ways to improve and expand our grid-based monitoring program to gain robust insights into bat population status and trend across large portions of North America.
","language":"English","publisher":"Ecological Society of America","publisherLocation":"Tempe, AZ","doi":"10.1890/11-1662.1","usgsCitation":"Rodhouse, T., Ormsbee, P., Irvine, K.M., Vierling, L.A., Szewczak, J.M., and Vierling, K.T., 2012, Assessing the status and trend of bat populations across broad geographic regions with dynamic distribution models: Ecological Applications, v. 22, no. 4, p. 1098-1113, https://doi.org/10.1890/11-1662.1.","startPage":"1098","endPage":"1113","numberOfPages":"16","onlineOnly":"N","additionalOnlineFiles":"N","ipdsId":"IP-032671","costCenters":[{"id":481,"text":"Northern Rocky Mountain Science Center","active":true,"usgs":true}],"links":[{"id":326061,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"volume":"22","issue":"4","publishingServiceCenter":{"id":2,"text":"Denver PSC"},"noUsgsAuthors":false,"publicationStatus":"PW","scienceBaseUri":"57a315bbe4b006cb45558a2f","contributors":{"authors":[{"text":"Rodhouse, Thomas J.","contributorId":127378,"corporation":false,"usgs":false,"family":"Rodhouse","given":"Thomas J.","affiliations":[{"id":6924,"text":"National Park Service, Upper Columbia Basin Network","active":true,"usgs":false}],"preferred":false,"id":644614,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Ormsbee, Patricia C.","contributorId":127379,"corporation":false,"usgs":false,"family":"Ormsbee","given":"Patricia C.","affiliations":[{"id":6925,"text":"US Forest Service, retired","active":true,"usgs":false}],"preferred":false,"id":644613,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Irvine, Kathryn M. 0000-0002-6426-940X kirvine@usgs.gov","orcid":"https://orcid.org/0000-0002-6426-940X","contributorId":2218,"corporation":false,"usgs":true,"family":"Irvine","given":"Kathryn","email":"kirvine@usgs.gov","middleInitial":"M.","affiliations":[{"id":481,"text":"Northern Rocky Mountain Science Center","active":true,"usgs":true}],"preferred":true,"id":644609,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Vierling, Lee A.","contributorId":169443,"corporation":false,"usgs":false,"family":"Vierling","given":"Lee","email":"","middleInitial":"A.","affiliations":[{"id":6711,"text":"University of Idaho, Moscow ID","active":true,"usgs":false}],"preferred":false,"id":644612,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Szewczak, Joseph M.","contributorId":30127,"corporation":false,"usgs":false,"family":"Szewczak","given":"Joseph","email":"","middleInitial":"M.","affiliations":[{"id":6958,"text":"Department of Biological Sciences, Humboldt State University","active":true,"usgs":false}],"preferred":false,"id":644610,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"Vierling, Kerri T.","contributorId":140099,"corporation":false,"usgs":false,"family":"Vierling","given":"Kerri","email":"","middleInitial":"T.","affiliations":[{"id":13384,"text":"Department of Fish and Wildlife Sciences, University of Idaho,","active":true,"usgs":false}],"preferred":false,"id":644611,"contributorType":{"id":1,"text":"Authors"},"rank":6}]}} ,{"id":70148651,"text":"70148651 - 2012 - Habitat use of woodpeckers in the Big Woods of eastern Arkansas","interactions":[],"lastModifiedDate":"2015-07-13T11:42:26","indexId":"70148651","displayToPublicDate":"2012-06-01T12:45:00","publicationYear":"2012","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":2287,"text":"Journal of Fish and Wildlife Management","active":true,"publicationSubtype":{"id":10}},"title":"Habitat use of woodpeckers in the Big Woods of eastern Arkansas","docAbstract":"The Big Woods of eastern Arkansas contain some of the highest densities of woodpeckers recorded within bottomland hardwood forests of the southeastern United States. A better understanding of habitat use patterns by these woodpeckers is a priority for conservationists seeking to maintain these high densities in the Big Woods and the Lower Mississippi Alluvial Valley as a whole. Hence, we used linear mixed-effects and linear models to estimate the importance of habitat characteristics to woodpecker density in the Big Woods during the breeding seasons of 2006 and 2007 and the winter of 2007. Northern flicker Colaptes auratus density was negatively related to tree density both for moderate (. 25 cm diameter at breast height) and larger trees (>61 cm diameter at breast height). Red-headed woodpeckers Melanerpes erythrocephalus also had a negative relationship with density of large (. 61 cm diameter at breast height) trees. Bark disfiguration (an index of tree health) was negatively related to red-bellied woodpecker Melanerpes carolinus and yellow-bellied sapsucker Sphyrapicus varius densities. No measured habitat variables explained pileated woodpecker Dryocopus pileatus density. Overall, the high densities of woodpeckers observed in our study suggest that the current forest management of the Big Woods of Arkansas is meeting the nesting, roosting, and foraging requirements for these birds.
","language":"English","publisher":"U.S. Fish and Wildlife Service","publisherLocation":"Washington, D.C.","doi":"10.3996/112011-JFWM-065","collaboration":"U.S. Fish and Wildlife Service","usgsCitation":"Krementz, D.G., Lehnen, S.E., and Luscier, J., 2012, Habitat use of woodpeckers in the Big Woods of eastern Arkansas: Journal of Fish and Wildlife Management, v. 3, no. 1, p. 89-97, https://doi.org/10.3996/112011-JFWM-065.","productDescription":"9 p.","startPage":"89","endPage":"97","numberOfPages":"9","onlineOnly":"N","additionalOnlineFiles":"N","ipdsId":"IP-034124","costCenters":[{"id":198,"text":"Coop Res Unit Atlanta","active":true,"usgs":true}],"links":[{"id":474490,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.3996/112011-jfwm-065","text":"Publisher Index Page"},{"id":305681,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"volume":"3","issue":"1","publishingServiceCenter":{"id":8,"text":"Raleigh PSC"},"noUsgsAuthors":false,"publicationStatus":"PW","scienceBaseUri":"55a4e141e4b0183d66e45398","contributors":{"authors":[{"text":"Krementz, David G. 0000-0002-5661-4541 dkrementz@usgs.gov","orcid":"https://orcid.org/0000-0002-5661-4541","contributorId":2827,"corporation":false,"usgs":true,"family":"Krementz","given":"David","email":"dkrementz@usgs.gov","middleInitial":"G.","affiliations":[{"id":198,"text":"Coop Res Unit Atlanta","active":true,"usgs":true}],"preferred":true,"id":548950,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Lehnen, Sarah E.","contributorId":145588,"corporation":false,"usgs":false,"family":"Lehnen","given":"Sarah","email":"","middleInitial":"E.","affiliations":[],"preferred":false,"id":564713,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Luscier, J.D.","contributorId":20961,"corporation":false,"usgs":true,"family":"Luscier","given":"J.D.","email":"","affiliations":[],"preferred":false,"id":564714,"contributorType":{"id":1,"text":"Authors"},"rank":3}]}} ,{"id":70118290,"text":"70118290 - 2012 - Toward an understanding of disequilibrium dihedral angles in mafic rocks","interactions":[],"lastModifiedDate":"2014-07-28T11:42:26","indexId":"70118290","displayToPublicDate":"2012-06-01T11:39:13","publicationYear":"2012","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":2312,"text":"Journal of Geophysical Research","active":true,"publicationSubtype":{"id":10}},"title":"Toward an understanding of disequilibrium dihedral angles in mafic rocks","docAbstract":"The median dihedral angle at clinopyroxene-plagioclase-plagioclase junctions in mafic rocks, Θcpp, is generally lower than equilibrium (109˚ {plus minus} 2˚). Observation of a wide range of mafic bodies demonstrates that previous work on systematic variations of Θcpp is incorrect in several important respects. Firstly, the spatial distribution of plagioclase compositional zoning demonstrates that the final geometry of three-grain junctions, and hence Θcpp, is formed during solidification (the igneous process): sub-solidus textural modification in most dolerites and gabbros, previously thought to be the dominant control on Θcpp, is insignificant. Θcpp is governed by mass transport constraints, the inhibiting effects of small pore size on crystallization, and variation in relative growth rates of pyroxene and plagioclase. During rapid cooling, pyroxene preferentially fills wider pores while the narrower pores remain melt-filled, resulting in an initial value of Θcpp of 78˚, rather than 60˚ which would be expected if all melt-filled pores were filled with pyroxene. Lower cooling rates create a higher initial Θcpp due to changes in relative growth rates of the two minerals at the nascent three-grain junction. Low Θcpp (associated with cuspate clinopyroxene grains at triple junctions) can also be diagnostic of infiltration of previously melt-free rocks by late-stage evolved liquids (the metasomatic process). Modification of Θcpp by sub-solidus textural equilibration (the metamorphic process) is only important for fine-grained mafic rocks such as chilled margins and intra-plutonic chill zones. In coarse-grained gabbros from shallow crustal intrusions the metamorphic process occurs only in the centres of oikocrysts, associated with rounding of chadacrysts.","largerWorkType":{"id":2,"text":"Article"},"largerWorkTitle":"Journal of Geophysical Research","largerWorkSubtype":{"id":10,"text":"Journal Article"},"language":"English","publisher":"American Geophysical Union","publisherLocation":"Richmond, VA","doi":"10.1029/2011JB008902","usgsCitation":"Holness, M.B., Humphreys, M.C., Sides, R., Helz, R., and Tegner, C., 2012, Toward an understanding of disequilibrium dihedral angles in mafic rocks: Journal of Geophysical Research, v. 117, no. 6, 31 p., https://doi.org/10.1029/2011JB008902.","productDescription":"31 p.","numberOfPages":"31","costCenters":[],"links":[{"id":474492,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.1029/2011jb008902","text":"Publisher Index Page"},{"id":291150,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"},{"id":291149,"type":{"id":10,"text":"Digital Object Identifier"},"url":"https://dx.doi.org/10.1029/2011JB008902"}],"volume":"117","issue":"6","noUsgsAuthors":false,"publicationDate":"2012-06-26","publicationStatus":"PW","scienceBaseUri":"57f7f4ede4b0bc0bec0a12ca","contributors":{"authors":[{"text":"Holness, Marian B.","contributorId":17541,"corporation":false,"usgs":true,"family":"Holness","given":"Marian","email":"","middleInitial":"B.","affiliations":[],"preferred":false,"id":496707,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Humphreys, Madeleine C.S.","contributorId":103199,"corporation":false,"usgs":true,"family":"Humphreys","given":"Madeleine","email":"","middleInitial":"C.S.","affiliations":[],"preferred":false,"id":496711,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Sides, Rachel","contributorId":58956,"corporation":false,"usgs":true,"family":"Sides","given":"Rachel","email":"","affiliations":[],"preferred":false,"id":496709,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Helz, Rosalind T. 0000-0003-1550-0684","orcid":"https://orcid.org/0000-0003-1550-0684","contributorId":66181,"corporation":false,"usgs":true,"family":"Helz","given":"Rosalind T.","affiliations":[],"preferred":false,"id":496710,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Tegner, Christian","contributorId":44477,"corporation":false,"usgs":true,"family":"Tegner","given":"Christian","email":"","affiliations":[],"preferred":false,"id":496708,"contributorType":{"id":1,"text":"Authors"},"rank":5}]}} ,{"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":70110901,"text":"70110901 - 2012 - Uncertainty","interactions":[],"lastModifiedDate":"2014-07-07T09:25:03","indexId":"70110901","displayToPublicDate":"2012-06-01T09:21:04","publicationYear":"2012","noYear":false,"publicationType":{"id":5,"text":"Book chapter"},"publicationSubtype":{"id":24,"text":"Book Chapter"},"title":"Uncertainty","docAbstract":"Management decisions will often be directly informed by model predictions. However, we now know there can be no expectation of a single ‘true’ model; thus, model results are uncertain. Understandable reporting of underlying uncertainty provides necessary context to decision-makers, as model results are used for management decisions. This, in turn, forms a mechanism by which groundwater models inform a risk-management framework because uncertainty around a prediction provides the basis for estimating the probability or likelihood of some event occurring. Given that the consequences of management decisions vary, it follows that the extent of and resources devoted to an uncertainty analysis may depend on the consequences. For events with low impact, a qualitative, limited uncertainty analysis may be sufficient for informing a decision. For events with a high impact, on the other hand, the risks might be better assessed and associated decisions made using a more robust and comprehensive uncertainty analysis.
\nThe purpose of this chapter is to provide guidance on uncertainty analysis through discussion of concepts and approaches, which can vary from heuristic (i.e. the modeller’s assessment of prediction uncertainty based on trial and error and experience) to a comprehensive, sophisticated, statistics-based uncertainty analysis. Most of the material presented here is taken from Doherty et al. (2010) if not otherwise cited. Although the treatment here is necessarily brief, the reader can find citations for the source material and additional references within this chapter.
","largerWorkType":{"id":18,"text":"Report"},"largerWorkTitle":"Australian groundwater modelling guidelines","largerWorkSubtype":{"id":9,"text":"Other Report"},"language":"English","publisher":"National Water Commission","publisherLocation":"Canberra, Australia","usgsCitation":"Hunt, R.J., 2012, Uncertainty, chap. of Australian groundwater modelling guidelines, p. 92-105.","productDescription":"p. 92-105","numberOfPages":"14","ipdsId":"IP-036106","costCenters":[{"id":677,"text":"Wisconsin Water Science Center","active":true,"usgs":true}],"links":[{"id":289446,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"noUsgsAuthors":false,"publicationStatus":"PW","scienceBaseUri":"53bbc186e4b084059e8bff06","contributors":{"authors":[{"text":"Hunt, Randall J. 0000-0001-6465-9304 rjhunt@usgs.gov","orcid":"https://orcid.org/0000-0001-6465-9304","contributorId":1129,"corporation":false,"usgs":true,"family":"Hunt","given":"Randall","email":"rjhunt@usgs.gov","middleInitial":"J.","affiliations":[{"id":677,"text":"Wisconsin Water Science Center","active":true,"usgs":true}],"preferred":true,"id":494187,"contributorType":{"id":1,"text":"Authors"},"rank":1}]}} ,{"id":70038548,"text":"70038548 - 2012 - Extending a prototype knowledge- and object-based image analysis model to coarser spatial resolution imagery: an example from the Missouri River","interactions":[],"lastModifiedDate":"2016-08-25T14:55:34","indexId":"70038548","displayToPublicDate":"2012-06-01T00:00:00","publicationYear":"2012","noYear":false,"publicationType":{"id":24,"text":"Conference Paper"},"publicationSubtype":{"id":19,"text":"Conference Paper"},"title":"Extending a prototype knowledge- and object-based image analysis model to coarser spatial resolution imagery: an example from the Missouri River","docAbstract":"A prototype knowledge- and object-based image analysis model was developed to inventory and map least tern and piping plover habitat on the Missouri River, USA. The model has been used to inventory the state of sandbars annually for 4 segments of the Missouri River since 2006 using QuickBird imagery. Interpretation of the state of sandbars is difficult when images for the segment are acquired at different river stages and different states of vegetation phenology and canopy cover. Concurrent QuickBird and RapidEye images were classified using the model and the spatial correspondence of classes in the land cover and sandbar maps were analysed for the spatial extent of the images and at nest locations for both bird species. Omission and commission errors were low for unvegetated land cover classes used for nesting by both bird species and for land cover types with continuous vegetation cover and water. Errors were larger for land cover classes characterized by a mixture of sand and vegetation. Sandbar classification decisions are made using information on land cover class proportions and disagreement between sandbar classes was resolved using fuzzy membership possibilities. Regression analysis of area for a paired sample of 47 sandbars indicated an average positive bias, 1.15 ha, for RapidEye that did not vary with sandbar size. RapidEye has potential to reduce temporal uncertainty about least tern and piping plover habitat but would not be suitable for mapping sandbar erosion, and characterization of sandbar shapes or vegetation patches at fine spatial resolution.
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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>.
","language":"English","publisher":"Wiley","doi":"10.1111/j.1600-0587.2012.07439.x","usgsCitation":"Strona, G., and Lafferty, K.D., 2012, How to catch a parasite: Parasite Niche Modeler (PaNic) meets Fishbase: Ecography, v. 35, no. 6, p. 481-486, https://doi.org/10.1111/j.1600-0587.2012.07439.x.","productDescription":"6 p.","startPage":"481","endPage":"486","costCenters":[{"id":651,"text":"Western Ecological Research Center","active":true,"usgs":true}],"links":[{"id":328876,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"volume":"35","issue":"6","noUsgsAuthors":false,"publicationDate":"2012-02-20","publicationStatus":"PW","scienceBaseUri":"57f7f3b1e4b0bc0bec0a0b17","contributors":{"authors":[{"text":"Strona, Giovanni","contributorId":62940,"corporation":false,"usgs":true,"family":"Strona","given":"Giovanni","email":"","affiliations":[],"preferred":false,"id":649373,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Lafferty, Kevin D. 0000-0001-7583-4593 klafferty@usgs.gov","orcid":"https://orcid.org/0000-0001-7583-4593","contributorId":1415,"corporation":false,"usgs":true,"family":"Lafferty","given":"Kevin","email":"klafferty@usgs.gov","middleInitial":"D.","affiliations":[{"id":651,"text":"Western Ecological Research Center","active":true,"usgs":true}],"preferred":true,"id":649374,"contributorType":{"id":1,"text":"Authors"},"rank":2}]}} ,{"id":70187353,"text":"70187353 - 2012 - Uplift history of the Sila Massif, southern Italy, deciphered from cosmogenic 10Be erosion rates and river longitudinal profile analysis","interactions":[],"lastModifiedDate":"2017-05-01T13:19:13","indexId":"70187353","displayToPublicDate":"2012-06-01T00:00:00","publicationYear":"2012","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":3524,"text":"Tectonics","active":true,"publicationSubtype":{"id":10}},"title":"Uplift history of the Sila Massif, southern Italy, deciphered from cosmogenic 10Be erosion rates and river longitudinal profile analysis","docAbstract":"The Sila Massif in the Calabrian Arc (southern Italy) is a key site to study the response of a landscape to rock uplift. Here an uplift rate of ∼1 mm/yr has imparted a deep imprint on the Sila landscape recorded by a high-standing low-relief surface on top of the massif, deeply incised fluvial valleys along its flanks, and flights of marine terraces in the coastal belt. In this framework, we combined river longitudinal profile analysis with hillslope erosion rates calculated by 10Be content in modern fluvial sediments to reconstruct the long-term uplift history of the massif. Cosmogenic data show a large variation in erosion rates, marking two main domains. The samples collected in the high-standing low-relief surface atop Sila provide low erosion rates (from 0.09 ± 0.01 to 0.13 ± 0.01 mm/yr). Conversely, high values of erosion rate (up to 0.92 ± 0.08 mm/yr) characterize the incised fluvial valleys on the massif flanks. The analyzed river profiles exhibit a wide range of shapes diverging from the commonly accepted equilibrium concave-up form. Generally, the studied river profiles show two or, more frequently, three concave-up segments bounded by knickpoints and characterized by different values of concavity and steepness indices. The wide variation in cosmogenic erosion rates and the non-equilibrated river profiles indicate that the Sila landscape is in a transient state of disequilibrium in response to a strong and unsteady uplift not yet counterbalanced by erosion.
","language":"English","publisher":"American Geophysical Union","doi":"10.1029/2011TC003037","usgsCitation":"Olivetti, V., Cyr, A.J., Molin, P., Faccenna, C., and Granger, D.E., 2012, Uplift history of the Sila Massif, southern Italy, deciphered from cosmogenic 10Be erosion rates and river longitudinal profile analysis: Tectonics, v. 31, no. 3, Article TC3007; 19 p., https://doi.org/10.1029/2011TC003037.","productDescription":"Article TC3007; 19 p.","ipdsId":"IP-033323","costCenters":[{"id":312,"text":"Geology, Minerals, Energy, and Geophysics Science Center","active":true,"usgs":true}],"links":[{"id":474500,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.1029/2011tc003037","text":"Publisher Index Page"},{"id":340681,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"Italy","otherGeospatial":"Sila Massif","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n 15.957641601562498,\n 38.85682013474361\n ],\n [\n 17.341918945312496,\n 38.85682013474361\n ],\n [\n 17.341918945312496,\n 39.73676229957947\n ],\n [\n 15.957641601562498,\n 39.73676229957947\n ],\n [\n 15.957641601562498,\n 38.85682013474361\n ]\n ]\n ]\n }\n }\n ]\n}","volume":"31","issue":"3","publishingServiceCenter":{"id":14,"text":"Menlo Park PSC"},"noUsgsAuthors":false,"publicationDate":"2012-05-17","publicationStatus":"PW","scienceBaseUri":"59084936e4b0fc4e448ffda0","contributors":{"authors":[{"text":"Olivetti, Valerio","contributorId":191611,"corporation":false,"usgs":false,"family":"Olivetti","given":"Valerio","email":"","affiliations":[],"preferred":false,"id":693597,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Cyr, Andrew J. 0000-0003-2293-5395 acyr@usgs.gov","orcid":"https://orcid.org/0000-0003-2293-5395","contributorId":3539,"corporation":false,"usgs":true,"family":"Cyr","given":"Andrew","email":"acyr@usgs.gov","middleInitial":"J.","affiliations":[{"id":312,"text":"Geology, Minerals, Energy, and Geophysics Science Center","active":true,"usgs":true}],"preferred":true,"id":693594,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Molin, Paola","contributorId":191612,"corporation":false,"usgs":false,"family":"Molin","given":"Paola","affiliations":[],"preferred":false,"id":693598,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Faccenna, Claudio","contributorId":191609,"corporation":false,"usgs":false,"family":"Faccenna","given":"Claudio","email":"","affiliations":[],"preferred":false,"id":693595,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Granger, Darryl E.","contributorId":191610,"corporation":false,"usgs":false,"family":"Granger","given":"Darryl","email":"","middleInitial":"E.","affiliations":[],"preferred":false,"id":693596,"contributorType":{"id":1,"text":"Authors"},"rank":5}]}} ,{"id":70192582,"text":"70192582 - 2012 - Linear complementarity formulation for 3D frictional sliding problems","interactions":[],"lastModifiedDate":"2017-10-26T14:52:04","indexId":"70192582","displayToPublicDate":"2012-06-01T00:00:00","publicationYear":"2012","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":1308,"text":"Computational Geosciences","active":true,"publicationSubtype":{"id":10}},"title":"Linear complementarity formulation for 3D frictional sliding problems","docAbstract":"Frictional sliding on quasi-statically deforming faults and fractures can be modeled efficiently using a linear complementarity formulation. We review the formulation in two dimensions and expand the formulation to three-dimensional problems including problems of orthotropic friction. This formulation accurately reproduces analytical solutions to static Coulomb friction sliding problems. The formulation accounts for opening displacements that can occur near regions of non-planarity even under large confining pressures. Such problems are difficult to solve owing to the coupling of relative displacements and tractions; thus, many geomechanical problems tend to neglect these effects. Simple test cases highlight the importance of including friction and allowing for opening when solving quasi-static fault mechanics models. These results also underscore the importance of considering the effects of non-planarity in modeling processes associated with crustal faulting.
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