The Grizzly Valley fault system (GVFS) strikes northwestward across Sierra Valley, California and is part of a network of active, dextral strike-slip faults in the northern Walker Lane (Figure 1). To investigate Quaternary motion across the GVFS, we analyzed high-resolution (0.25 m) airborne LiDAR data (Figure 2) in combination with six, high-resolution, P-wave, seismic-reflection profiles [Gold and others, 2012]. The 0.5- to 2.0-km-long seismic-reflection profiles were sited orthogonal to suspected tectonic lineaments identified from previous mapping and our analysis of airborne LiDAR data. To image the upper 400–700 m of subsurface stratigraphy of Sierra Valley (Figure 3), we used a 230-kg accelerated weight drop source. Geophone spacing ranged from 2 to 5 m and shots were co-located with the geophones. The profiles reveal a highly reflective, deformed basal marker that we interpret to be the top of Tertiary volcanic rocks, overlain by a 120- to 300-m-thick suite of subhorizontal reflectors we interpret as Plio-Pleistocene lacustrine deposits. Three profiles image the principle active trace of the GVFS, which is a steeply dipping fault zone that offsets the volcanic rocks and the basin fill (Figures 4 & 5).
","largerWorkType":{"id":18,"text":"Report"},"largerWorkTitle":"Guidebook: neotectonics of the Lake Tahoe and Carson and Sierra Valleys, F.O.P. 2012","largerWorkSubtype":{"id":3,"text":"Organization Series"},"language":"English","publisher":"Friends of the Pleistocene","usgsCitation":"Gold, R., Stephenson, W., Odum, J., Briggs, R., Crone, A., and Angster, S., 2012, Grizzly Valley fault system, Sierra Valley, CA, 12 p.","productDescription":"12 p.","startPage":"214","endPage":"225","numberOfPages":"12","onlineOnly":"N","additionalOnlineFiles":"N","ipdsId":"IP-041007","costCenters":[{"id":300,"text":"Geologic Hazards Science Center","active":true,"usgs":true}],"links":[{"id":272340,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","state":"California","city":"Sierra Valley","otherGeospatial":"Grizzly Valley Fault","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -120.50491333007811,\n 39.58134870630412\n ],\n [\n -120.50491333007811,\n 39.8739115680129\n ],\n [\n -120.02014160156249,\n 39.8739115680129\n ],\n [\n -120.02014160156249,\n 39.58134870630412\n ],\n [\n -120.50491333007811,\n 39.58134870630412\n ]\n ]\n ]\n }\n }\n ]\n}","noUsgsAuthors":false,"publicationStatus":"PW","scienceBaseUri":"51975162e4b09a9cb58d5ee9","contributors":{"authors":[{"text":"Gold, Ryan","contributorId":97400,"corporation":false,"usgs":true,"family":"Gold","given":"Ryan","affiliations":[],"preferred":false,"id":476829,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Stephenson, William","contributorId":38804,"corporation":false,"usgs":true,"family":"Stephenson","given":"William","affiliations":[],"preferred":false,"id":476827,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Odum, Jack","contributorId":34798,"corporation":false,"usgs":true,"family":"Odum","given":"Jack","affiliations":[],"preferred":false,"id":476826,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Briggs, Rich","contributorId":62903,"corporation":false,"usgs":true,"family":"Briggs","given":"Rich","email":"","affiliations":[],"preferred":false,"id":476828,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Crone, Anthony","contributorId":20624,"corporation":false,"usgs":true,"family":"Crone","given":"Anthony","affiliations":[],"preferred":false,"id":476825,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"Angster, Steve","contributorId":106779,"corporation":false,"usgs":true,"family":"Angster","given":"Steve","affiliations":[],"preferred":false,"id":476830,"contributorType":{"id":1,"text":"Authors"},"rank":6}]}} ,{"id":70193734,"text":"70193734 - 2012 - Science in support of the Deepwater Horizon response","interactions":[],"lastModifiedDate":"2021-03-25T16:34:19.634986","indexId":"70193734","displayToPublicDate":"2012-01-01T00:00:00","publicationYear":"2012","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":3165,"text":"Proceedings of the National Academy of Sciences of the United States of America","active":true,"publicationSubtype":{"id":10}},"displayTitle":"Science in support of the Deepwater Horizon response","title":"Science in support of the Deepwater Horizon response","docAbstract":"This introduction to the Special Feature presents the context for science during the Deepwater Horizon oil spill response, summarizes how scientific knowledge was integrated across disciplines and statutory responsibilities, identifies areas where scientific information was accurate and where it was not, and considers lessons learned and recommendations for future research and response. Scientific information was integrated within and across federal and state agencies, with input from nongovernmental scientists, across a diverse portfolio of needs—stopping the flow of oil, estimating the amount of oil, capturing and recovering the oil, tracking and forecasting surface oil, protecting coastal and oceanic wildlife and habitat, managing fisheries, and protecting the safety of seafood. Disciplines involved included atmospheric, oceanographic, biogeochemical, ecological, health, biological, and chemical sciences, physics, geology, and mechanical and chemical engineering. Platforms ranged from satellites and planes to ships, buoys, gliders, and remotely operated vehicles to laboratories and computer simulations. The unprecedented response effort depended directly on intense and extensive scientific and engineering data, information, and advice. Many valuable lessons were learned that should be applied to future events.
","language":"English","publisher":"National Academy of Science","doi":"10.1073/pnas.1204729109","usgsCitation":"Lubchenco, J., McNutt, M.K., Dreyfus, G., Murawski, S.A., Kennedy, D.M., Anastas, P.T., Chu, S., and Hunter, T., 2012, Science in support of the Deepwater Horizon response: Proceedings of the National Academy of Sciences of the United States of America, v. 109, no. 50, p. 20212-20221, https://doi.org/10.1073/pnas.1204729109.","productDescription":"10 p.","startPage":"20212","endPage":"20221","ipdsId":"IP-041327","costCenters":[{"id":5066,"text":"Office of the Director USGS","active":true,"usgs":true}],"links":[{"id":474655,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.1073/pnas.1204729109","text":"Publisher Index Page"},{"id":348189,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","state":"Alabama, Florida, Louisiana, Mississippi, Texas","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 -97.36083984375,\n 26.03704188651584\n ],\n [\n -82.0458984375,\n 26.03704188651584\n ],\n [\n -82.0458984375,\n 30.29701788337205\n ],\n [\n -97.36083984375,\n 30.29701788337205\n ],\n [\n -97.36083984375,\n 26.03704188651584\n ]\n ]\n ]\n }\n }\n ]\n}","volume":"109","issue":"50","publishingServiceCenter":{"id":9,"text":"Reston PSC"},"noUsgsAuthors":false,"publicationDate":"2012-12-03","publicationStatus":"PW","scienceBaseUri":"59fedfb5e4b0531197b573ca","contributors":{"authors":[{"text":"Lubchenco, Jane","contributorId":102350,"corporation":false,"usgs":false,"family":"Lubchenco","given":"Jane","affiliations":[{"id":12448,"text":"U.S. National Oceanic and Atmospheric Administration","active":true,"usgs":false}],"preferred":false,"id":720231,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"McNutt, Marcia K. 0000-0003-0117-7716 mcnutt@usgs.gov","orcid":"https://orcid.org/0000-0003-0117-7716","contributorId":327,"corporation":false,"usgs":true,"family":"McNutt","given":"Marcia","email":"mcnutt@usgs.gov","middleInitial":"K.","affiliations":[{"id":5066,"text":"Office of the Director USGS","active":true,"usgs":true}],"preferred":false,"id":720232,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Dreyfus, Gabrielle","contributorId":62479,"corporation":false,"usgs":false,"family":"Dreyfus","given":"Gabrielle","email":"","affiliations":[{"id":34793,"text":"National Oceanic and Atmospheric Administration (NOAA)","active":true,"usgs":false}],"preferred":false,"id":720233,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Murawski, Steven A.","contributorId":46377,"corporation":false,"usgs":false,"family":"Murawski","given":"Steven","email":"","middleInitial":"A.","affiliations":[{"id":34793,"text":"National Oceanic and Atmospheric Administration (NOAA)","active":true,"usgs":false}],"preferred":false,"id":720234,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Kennedy, David M.","contributorId":50421,"corporation":false,"usgs":false,"family":"Kennedy","given":"David","email":"","middleInitial":"M.","affiliations":[{"id":34793,"text":"National Oceanic and Atmospheric Administration (NOAA)","active":true,"usgs":false}],"preferred":false,"id":720235,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"Anastas, Paul T.","contributorId":102760,"corporation":false,"usgs":false,"family":"Anastas","given":"Paul","email":"","middleInitial":"T.","affiliations":[{"id":13226,"text":"U.S. Environmental Protection Agency, Office of Research and Development","active":true,"usgs":false}],"preferred":false,"id":720236,"contributorType":{"id":1,"text":"Authors"},"rank":6},{"text":"Chu, Steven","contributorId":87041,"corporation":false,"usgs":false,"family":"Chu","given":"Steven","email":"","affiliations":[{"id":34152,"text":"US Department of Energy","active":true,"usgs":false}],"preferred":false,"id":720237,"contributorType":{"id":1,"text":"Authors"},"rank":7},{"text":"Hunter, Tom","contributorId":47657,"corporation":false,"usgs":false,"family":"Hunter","given":"Tom","email":"","affiliations":[{"id":34829,"text":"Sandia National Laboratories","active":true,"usgs":false}],"preferred":false,"id":720238,"contributorType":{"id":1,"text":"Authors"},"rank":8}]}} ,{"id":70042454,"text":"70042454 - 2012 - Design of future surveys","interactions":[],"lastModifiedDate":"2022-12-21T17:12:40.458852","indexId":"70042454","displayToPublicDate":"2012-01-01T00:00:00","publicationYear":"2012","noYear":false,"publicationType":{"id":5,"text":"Book chapter"},"publicationSubtype":{"id":24,"text":"Book Chapter"},"chapter":"13","title":"Design of future surveys","docAbstract":"This brief chapter addresses two related issues: how effort should be allocated to different parts of the sampling plan and, given optimal allocation, how large a sample will be required to achieve the PRISM accuracy target. Simulations based on data collected to date showed that 2 plots per cluster on rapid surveys, 2 intensive camps per field crew-year, 2-4 intensive plots per intensive camp, and 2-3 rapid surveys per intensive plot is the most efficient allocation of resources. Using this design, we investigated how crew-years should be allocated to each region in order to meet the PRISM accuracy target most efficiently. The analysis indicated that 40-50 crew-years would achieve the accuracy target for 18-24 of the 26 species breeding widely in the Arctic. This analysis was based on assuming that two rounds of surveys were conducted and that a 50% decline occurred between them. We discuss the complexity of making these estimates and why they should be viewed as first approximations.
","largerWorkType":{"id":4,"text":"Book"},"largerWorkTitle":"Arctic shorebirds in North America: A decade of monitoring","largerWorkSubtype":{"id":15,"text":"Monograph"},"language":"English","publisher":"University of California Press","publisherLocation":"Berkeley, CA","usgsCitation":"Bart, J., and Smith, P., 2012, Design of future surveys, chap. 13 of Arctic shorebirds in North America: A decade of monitoring, v. 44, p. 201-210.","productDescription":"9 p.","startPage":"201","endPage":"210","numberOfPages":"9","onlineOnly":"N","additionalOnlineFiles":"N","ipdsId":"IP-025859","costCenters":[{"id":290,"text":"Forest and Rangeland Ecosystem Science Center","active":false,"usgs":true}],"links":[{"id":268317,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"},{"id":297351,"type":{"id":15,"text":"Index Page"},"url":"https://www.ucpress.edu/book.php?isbn=9780520273108"}],"volume":"44","noUsgsAuthors":false,"publicationStatus":"PW","scienceBaseUri":"53cd5455e4b0b290850f5ab8","contributors":{"editors":[{"text":"Bart, Jonathan R.","contributorId":74273,"corporation":false,"usgs":true,"family":"Bart","given":"Jonathan","email":"","middleInitial":"R.","affiliations":[],"preferred":false,"id":509162,"contributorType":{"id":2,"text":"Editors"},"rank":1},{"text":"Johnston, Victoria H.","contributorId":70667,"corporation":false,"usgs":true,"family":"Johnston","given":"Victoria","email":"","middleInitial":"H.","affiliations":[],"preferred":false,"id":509161,"contributorType":{"id":2,"text":"Editors"},"rank":2}],"authors":[{"text":"Bart, Jonathan jon_bart@usgs.gov","contributorId":57025,"corporation":false,"usgs":true,"family":"Bart","given":"Jonathan","email":"jon_bart@usgs.gov","affiliations":[{"id":290,"text":"Forest and Rangeland Ecosystem Science Center","active":false,"usgs":true}],"preferred":false,"id":471579,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Smith, Paul A.","contributorId":73477,"corporation":false,"usgs":true,"family":"Smith","given":"Paul A.","affiliations":[],"preferred":false,"id":471580,"contributorType":{"id":1,"text":"Authors"},"rank":2}]}} ,{"id":70042460,"text":"70042460 - 2012 - Associations between forest fragmentation patterns and geneticstructure in Pfrimer’s Parakeet (Pyrrhura pfrimeri), an endangered endemic to central Brazil’s dry forests","interactions":[],"lastModifiedDate":"2013-02-23T08:39:04","indexId":"70042460","displayToPublicDate":"2012-01-01T00:00:00","publicationYear":"2012","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":1324,"text":"Conservation Genetics","active":true,"publicationSubtype":{"id":10}},"title":"Associations between forest fragmentation patterns and geneticstructure in Pfrimer’s Parakeet (Pyrrhura pfrimeri), an endangered endemic to central Brazil’s dry forests","docAbstract":"When habitat becomes fragmented, populations of species may become increasingly isolated. In the absence of habitat corridors, genetic structure may develop and populations risk reductions in genetic diversity from increased genetic drift and inbreeding. Deforestation of the Cerrado biome of Brazil, particularly of the dry forests within the Parana˜ River Basin, has incrementally occurred since the 1970s and increased forest fragmentation within the region. We performed landscape genetic analyses of Pfrimer’s parakeet (Pyrrhura pfrimeri), a globally endangered endemic to the region, to determine if forest fragmentation patterns were associated with genetic structuring in this species. We used previously generated satellite imagery that identified the locations of Parana˜ River Basin forest fragments in 1977, 1993/94, and 2008. Behavioral data quantifying the affinity of Pfrimer’s parakeet for forest habitat was used to parameterize empirically derived landscape conductance surfaces. Though genetic structure was observed among Pfrimer’s parakeet populations, no association between genetic and geographic distance was detected. Likewise, least cost path lengths, circuit theorybased resistance distances, and a new measure of least cost path length complexity could not be conclusively associated with genetic structure patterns. Instead, a new quantity that encapsulated connection redundancy from the 1977 forest fragmentation data provided the clearest associations with pairwise genetic differentiation patterns (Jost’s D: r = 0.72, P = 0.006; FST: r = 0.741, P = 0.001). Our analyses suggest a 35-year or more lag between deforestation and its effect on genetic structure. Because 66 % of the Parana˜ River Basin has been deforested since 1977, we expect that genetic structure will increase substantially among Pfrimer’s Parakeet populations in the future, especially if fragmentation continues at its current pace.","largerWorkType":{"id":2,"text":"Article"},"largerWorkTitle":"Conservation Genetics","largerWorkSubtype":{"id":10,"text":"Journal Article"},"language":"English","publisher":"Springer","doi":"10.1007/s10592-012-0420-4","usgsCitation":"Haig, S.M., Miller, L.F., Bianchi, C., and Mullins, T.D., 2012, Associations between forest fragmentation patterns and geneticstructure in Pfrimer’s Parakeet (Pyrrhura pfrimeri), an endangered endemic to central Brazil’s dry forests: Conservation Genetics, v. 13, no. 6, online, https://doi.org/10.1007/s10592-012-0420-4.","productDescription":"online","ipdsId":"IP-038620","costCenters":[{"id":290,"text":"Forest and Rangeland Ecosystem Science Center","active":false,"usgs":true}],"links":[{"id":267995,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"},{"id":267994,"type":{"id":10,"text":"Digital Object Identifier"},"url":"https://dx.doi.org/10.1007/s10592-012-0420-4"}],"country":"United States","volume":"13","issue":"6","noUsgsAuthors":false,"publicationDate":"2012-12-29","publicationStatus":"PW","scienceBaseUri":"5129f311e4b04edf7e93f859","contributors":{"authors":[{"text":"Haig, Susan M. 0000-0002-6616-7589 susan_haig@usgs.gov","orcid":"https://orcid.org/0000-0002-6616-7589","contributorId":719,"corporation":false,"usgs":true,"family":"Haig","given":"Susan","email":"susan_haig@usgs.gov","middleInitial":"M.","affiliations":[{"id":290,"text":"Forest and Rangeland Ecosystem Science Center","active":false,"usgs":true},{"id":289,"text":"Forest and Rangeland Ecosys Science Center","active":true,"usgs":true}],"preferred":true,"id":471585,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Miller, Leonard F.","contributorId":15898,"corporation":false,"usgs":true,"family":"Miller","given":"Leonard","email":"","middleInitial":"F.","affiliations":[],"preferred":false,"id":471588,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Bianchi, Carlos","contributorId":13880,"corporation":false,"usgs":true,"family":"Bianchi","given":"Carlos","affiliations":[],"preferred":false,"id":471587,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Mullins, Thomas D. 0000-0001-8948-9604 tom_mullins@usgs.gov","orcid":"https://orcid.org/0000-0001-8948-9604","contributorId":3615,"corporation":false,"usgs":true,"family":"Mullins","given":"Thomas","email":"tom_mullins@usgs.gov","middleInitial":"D.","affiliations":[{"id":289,"text":"Forest and Rangeland Ecosys Science Center","active":true,"usgs":true}],"preferred":false,"id":471586,"contributorType":{"id":1,"text":"Authors"},"rank":4}]}} ,{"id":70191928,"text":"70191928 - 2012 - Origins of mineral deposits, Belt-Purcell Basin, United States and Canada: An introduction","interactions":[],"lastModifiedDate":"2020-12-30T16:31:08.376241","indexId":"70191928","displayToPublicDate":"2012-01-01T00:00:00","publicationYear":"2012","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":1472,"text":"Economic Geology","active":true,"publicationSubtype":{"id":10}},"title":"Origins of mineral deposits, Belt-Purcell Basin, United States and Canada: An introduction","docAbstract":"The fill of the Mesoproterozoic Belt-Purcell Basin, which straddles the United States-Canada border within the Rocky Mountains of western North America (Fig. 1), consists of marine and nonmarine clastic and carbonate strata 15 to 20 km thick. Three giant metal-producing ore deposits or districts account for the bulk of the known metal endowment within the bounds of the Belt-Purcell Basin: (1) the syndepositional Sullivan Pb-Zn-Ag deposit in southern British Columbia (total production: Pb, 8.4 million tonnes [Mt]; Zn, 7.9 Mt; Ag, 0.0093 Mt; Lydon, 2000), (2) the mesothermal Pb-Zn-Ag veins of the Coeur d’Alene district in northern Idaho (total production: Pb, 7.5 Mt; Zn, 3.0 Mt; Ag, 0.052 Mt; Long, 1998; post-1997 data from USGS Annual Minerals Yearbooks), and (3) the Cretaceous porphyry copper deposit and associated polymetallic veins in the Butte district in Montana (total resource: Cu, 35 Mt; Zn, 4.6 Mt; Ag, 0.044 Mt; Long et al., 1998). The Sullivan Mine closed in 2001 after more than 92 years of production. Mining of 26 major vein deposits in the Coeur d’Alene district began in the 1880s and peaked about 1950. Production in the Coeur d’Alene district continues today from the Galena and Lucky Friday Mines (the latter closed for 2012 to refurbish the mile-deep vertical access shaft). Mining at Butte began in 1875, with copper production peaking in 1917. Mining continues today in the eastern upfaulted portion of the Butte porphyry copper deposit at the Continental Mine.
","language":"English","publisher":"Society of Economic Geologists","doi":"10.2113/econgeo.107.6.1081","usgsCitation":"Box, S.E., Bookstrom, A.A., and Anderson, R.G., 2012, Origins of mineral deposits, Belt-Purcell Basin, United States and Canada: An introduction: Economic Geology, v. 107, no. 6, p. 1081-1088, https://doi.org/10.2113/econgeo.107.6.1081.","productDescription":"8 p.","startPage":"1081","endPage":"1088","ipdsId":"IP-035764","costCenters":[{"id":662,"text":"Western Mineral and Environmental Resources Science Center","active":true,"usgs":true}],"links":[{"id":349517,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"Canada, United States","state":"Alberta, British Columbia, Idaho, Montana","otherGeospatial":"Belt-Purcell Basin","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -117.02636718749999,\n 45.85941212790755\n ],\n [\n -111.4892578125,\n 45.85941212790755\n ],\n [\n -111.4892578125,\n 50.62507306341435\n ],\n [\n -117.02636718749999,\n 50.62507306341435\n ],\n [\n -117.02636718749999,\n 45.85941212790755\n ]\n ]\n ]\n }\n }\n ]\n}","volume":"107","issue":"6","publishingServiceCenter":{"id":14,"text":"Menlo Park PSC"},"noUsgsAuthors":false,"publicationDate":"2012-09-20","publicationStatus":"PW","scienceBaseUri":"5a6105a1e4b06e28e9c25587","contributors":{"authors":[{"text":"Box, Stephen E. 0000-0002-5268-8375 sbox@usgs.gov","orcid":"https://orcid.org/0000-0002-5268-8375","contributorId":1843,"corporation":false,"usgs":true,"family":"Box","given":"Stephen","email":"sbox@usgs.gov","middleInitial":"E.","affiliations":[{"id":312,"text":"Geology, Minerals, Energy, and Geophysics Science Center","active":true,"usgs":true}],"preferred":true,"id":713743,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Bookstrom, Arthur A. 0000-0003-1336-3364 abookstrom@usgs.gov","orcid":"https://orcid.org/0000-0003-1336-3364","contributorId":1542,"corporation":false,"usgs":true,"family":"Bookstrom","given":"Arthur","email":"abookstrom@usgs.gov","middleInitial":"A.","affiliations":[{"id":312,"text":"Geology, Minerals, Energy, and Geophysics Science Center","active":true,"usgs":true},{"id":5056,"text":"Office of the AD Energy and Minerals, and Environmental Health","active":true,"usgs":true}],"preferred":true,"id":713742,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Anderson, Robert G.","contributorId":197569,"corporation":false,"usgs":false,"family":"Anderson","given":"Robert","email":"","middleInitial":"G.","affiliations":[],"preferred":false,"id":713744,"contributorType":{"id":1,"text":"Authors"},"rank":11}]}} ,{"id":70042452,"text":"70042452 - 2012 - Small-scale and reconnaissance surveys","interactions":[],"lastModifiedDate":"2017-11-22T16:17:37","indexId":"70042452","displayToPublicDate":"2012-01-01T00:00:00","publicationYear":"2012","noYear":false,"publicationType":{"id":5,"text":"Book chapter"},"publicationSubtype":{"id":24,"text":"Book Chapter"},"title":"Small-scale and reconnaissance surveys","docAbstract":"This brief chapter addresses two related issues: how effort should be allocated to different parts of the sampling plan and, given optimal allocation, how large a sample will be required to achieve the PRISM accuracy target. Simulations based on data collected to date showed that 2 plots per cluster on rapid surveys, 2 intensive camps per field crew-year, 2-4 intensive plots per intensive camp, and 2-3 rapid surveys per intensive plot is the most efficient allocation of resources. Using this design, we investigated how crew-years should be allocated to each region in order to meet the PRISM accuracy target most efficiently. The analysis indicated that 40-50 crew-years would achieve the accuracy target for 18-24 of the 26 species breeding widely in the Arctic. This analysis was based on assuming that two rounds of surveys were conducted and that a 50% decline occurred between them. We discuss the complexity of making these estimates and why they should be viewed as first approximations.
","largerWorkType":{"id":4,"text":"Book"},"largerWorkTitle":"Arctic shorebirds in North America: a decade of monitoring","largerWorkSubtype":{"id":15,"text":"Monograph"},"language":"English","publisher":"University of California Press","publisherLocation":"Berkeley, CA","usgsCitation":"Bart, J., Andres, B.A., Elliott, K., Francis, C., Johnston, V., Morrison, R.I., Pierce, E.P., and Rausch, J., 2012, Small-scale and reconnaissance surveys, chap. of Arctic shorebirds in North America: a decade of monitoring, p. 141-155.","productDescription":"15 p.","startPage":"141","endPage":"155","onlineOnly":"N","additionalOnlineFiles":"N","ipdsId":"IP-025833","costCenters":[{"id":290,"text":"Forest and Rangeland Ecosystem Science Center","active":false,"usgs":true}],"links":[{"id":268329,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"},{"id":297163,"rank":2,"type":{"id":15,"text":"Index Page"},"url":"https://www.ucpress.edu/book.php?isbn=9780520273108"}],"noUsgsAuthors":false,"publicationStatus":"PW","scienceBaseUri":"53cd7314e4b0b29085108bac","contributors":{"editors":[{"text":"Bart, Jonathan jon_bart@usgs.gov","contributorId":57025,"corporation":false,"usgs":true,"family":"Bart","given":"Jonathan","email":"jon_bart@usgs.gov","affiliations":[{"id":290,"text":"Forest and Rangeland Ecosystem Science Center","active":false,"usgs":true}],"preferred":false,"id":509159,"contributorType":{"id":2,"text":"Editors"},"rank":1},{"text":"Johnston, Victoria","contributorId":90185,"corporation":false,"usgs":true,"family":"Johnston","given":"Victoria","affiliations":[],"preferred":false,"id":509160,"contributorType":{"id":2,"text":"Editors"},"rank":2}],"authors":[{"text":"Bart, Jonathan jon_bart@usgs.gov","contributorId":57025,"corporation":false,"usgs":true,"family":"Bart","given":"Jonathan","email":"jon_bart@usgs.gov","affiliations":[{"id":290,"text":"Forest and Rangeland Ecosystem Science Center","active":false,"usgs":true}],"preferred":false,"id":471569,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Andres, Brad A.","contributorId":68811,"corporation":false,"usgs":true,"family":"Andres","given":"Brad","email":"","middleInitial":"A.","affiliations":[],"preferred":false,"id":471571,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Elliott, Kyle","contributorId":95347,"corporation":false,"usgs":true,"family":"Elliott","given":"Kyle","email":"","affiliations":[],"preferred":false,"id":471573,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Francis, Charles M.","contributorId":14529,"corporation":false,"usgs":true,"family":"Francis","given":"Charles M.","affiliations":[],"preferred":false,"id":471567,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Johnston, Victoria","contributorId":90185,"corporation":false,"usgs":true,"family":"Johnston","given":"Victoria","affiliations":[],"preferred":false,"id":471572,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"Morrison, R. 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G.","affiliations":[],"preferred":false,"id":471570,"contributorType":{"id":1,"text":"Authors"},"rank":6},{"text":"Pierce, Elin P.","contributorId":30110,"corporation":false,"usgs":true,"family":"Pierce","given":"Elin","email":"","middleInitial":"P.","affiliations":[],"preferred":false,"id":471568,"contributorType":{"id":1,"text":"Authors"},"rank":7},{"text":"Rausch, Jennie","contributorId":103938,"corporation":false,"usgs":true,"family":"Rausch","given":"Jennie","affiliations":[],"preferred":false,"id":471574,"contributorType":{"id":1,"text":"Authors"},"rank":8}]}} ,{"id":70159358,"text":"70159358 - 2012 - Maximizing the utility of monitoring to the adaptive management of natural resources","interactions":[],"lastModifiedDate":"2021-10-21T15:36:09.17483","indexId":"70159358","displayToPublicDate":"2012-01-01T00:00:00","publicationYear":"2012","noYear":false,"publicationType":{"id":5,"text":"Book chapter"},"publicationSubtype":{"id":24,"text":"Book Chapter"},"title":"Maximizing the utility of monitoring to the adaptive management of natural resources","docAbstract":"Data collection is an important step in any investigation about the structure or processes related to a natural system. In a purely scientific investigation (experiments, quasi-experiments, observational studies), data collection is part of the scientific method, preceded by the identification of hypotheses and the design of any manipulations of the system to test those hypotheses. Data collection and the manipulations that precede it are ideally designed to maximize the information that is derived from the study. That is, such investigations should be designed for maximum power to evaluate the relative validity of the hypotheses posed. When data collection is intended to inform the management of ecological systems, we call it monitoring. Note that our definition of monitoring encompasses a broader range of data-collection efforts than some alternative definitions – e.g. Chapter 3. The purpose of monitoring as we use the term can vary, from surveillance or “thumb on the pulse” monitoring (see Nichols and Williams 2006), intended to detect changes in a system due to any non-specified source (e.g. the North American Breeding Bird Survey), to very specific and targeted monitoring of the results of specific management actions (e.g. banding and aerial survey efforts related to North American waterfowl harvest management). Although a role of surveillance monitoring is to detect unanticipated changes in a system, the same result is possible from a collection of targeted monitoring programs distributed across the same spatial range (Box 4.1). In the face of limited budgets and many specific management questions, tying monitoring as closely as possible to management needs is warranted (Nichols and Williams 2006). Adaptive resource management (ARM; Walters 1986, Williams 1997, Kendall 2001, Moore and Conroy 2006, McCarthy and Possingham 2007, Conroy et al. 2008a) provides a context and specific purpose for monitoring: to evaluate decisions with respect to achievement of specific management objectives; and to evaluate the relative validity of predictive system models. This latter purpose is analogous to the role of data collection within the scientific method, in a research context.
","largerWorkType":{"id":4,"text":"Book"},"largerWorkTitle":"Design and analysis of long-term ecological monitoring studies","language":"English","publisher":"Cambridge University Press","publisherLocation":"Cambridge; New York","doi":"10.1017/CBO9781139022422.007","usgsCitation":"Kendall, W.L., and Moore, C., 2012, Maximizing the utility of monitoring to the adaptive management of natural resources, chap. of Design and analysis of long-term ecological monitoring studies, p. 74-98, https://doi.org/10.1017/CBO9781139022422.007.","productDescription":"24 p.","startPage":"74","endPage":"98","onlineOnly":"N","additionalOnlineFiles":"N","ipdsId":"IP-028880","costCenters":[{"id":531,"text":"Patuxent Wildlife Research Center","active":true,"usgs":true}],"links":[{"id":310570,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"noUsgsAuthors":false,"publicationStatus":"PW","scienceBaseUri":"562a08d8e4b011227bf1fd8a","contributors":{"editors":[{"text":"Gitzen, Robert A.","contributorId":75498,"corporation":false,"usgs":true,"family":"Gitzen","given":"Robert","email":"","middleInitial":"A.","affiliations":[],"preferred":false,"id":578197,"contributorType":{"id":2,"text":"Editors"},"rank":1},{"text":"Cooper, Andrew B.","contributorId":112048,"corporation":false,"usgs":true,"family":"Cooper","given":"Andrew","email":"","middleInitial":"B.","affiliations":[{"id":606,"text":"Upper Midwest Environmental Sciences Center","active":true,"usgs":true}],"preferred":false,"id":578198,"contributorType":{"id":2,"text":"Editors"},"rank":2},{"text":"Millspaugh, Joshua J.","contributorId":11141,"corporation":false,"usgs":false,"family":"Millspaugh","given":"Joshua J.","affiliations":[],"preferred":false,"id":578199,"contributorType":{"id":2,"text":"Editors"},"rank":3},{"text":"Licht, Daniel S.","contributorId":113213,"corporation":false,"usgs":true,"family":"Licht","given":"Daniel S.","affiliations":[],"preferred":false,"id":578200,"contributorType":{"id":2,"text":"Editors"},"rank":4}],"authors":[{"text":"Kendall, William L. wkendall@usgs.gov","contributorId":406,"corporation":false,"usgs":true,"family":"Kendall","given":"William","email":"wkendall@usgs.gov","middleInitial":"L.","affiliations":[{"id":531,"text":"Patuxent Wildlife Research Center","active":true,"usgs":true}],"preferred":false,"id":578195,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Moore, Clinton T.","contributorId":9767,"corporation":false,"usgs":true,"family":"Moore","given":"Clinton T.","affiliations":[],"preferred":false,"id":578196,"contributorType":{"id":1,"text":"Authors"},"rank":2}]}} ,{"id":70148038,"text":"70148038 - 2012 - Parameter estimation method and updating of regional prediction equations for ungaged sites in the desert region of California","interactions":[],"lastModifiedDate":"2015-11-06T15:07:31","indexId":"70148038","displayToPublicDate":"2012-01-01T00:00:00","publicationYear":"2012","noYear":false,"publicationType":{"id":24,"text":"Conference Paper"},"publicationSubtype":{"id":19,"text":"Conference Paper"},"title":"Parameter estimation method and updating of regional prediction equations for ungaged sites in the desert region of California","docAbstract":"The U.S. Geological Survey (USGS) is currently updating at-site flood frequency estimates for USGS streamflow-gaging stations in the desert region of California. The at-site flood-frequency analysis is complicated by short record lengths (less than 20 years is common) and numerous zero flows/low outliers at many sites. Estimates of the three parameters (mean, standard deviation, and skew) required for fitting the log Pearson Type 3 (LP3) distribution are likely to be highly unreliable based on the limited and heavily censored at-site data. In a generalization of the recommendations in Bulletin 17B, a regional analysis was used to develop regional estimates of all three parameters (mean, standard deviation, and skew) of the LP3 distribution. A regional skew value of zero from a previously published report was used with a new estimated mean squared error (MSE) of 0.20. A weighted least squares (WLS) regression method was used to develop both a regional standard deviation and a mean model based on annual peak-discharge data for 33 USGS stations throughout California’s desert region. At-site standard deviation and mean values were determined by using an expected moments algorithm (EMA) method for fitting the LP3 distribution to the logarithms of annual peak-discharge data. Additionally, a multiple Grubbs-Beck (MGB) test, a generalization of the test recommended in Bulletin 17B, was used for detecting multiple potentially influential low outliers in a flood series. The WLS regression found that no basin characteristics could explain the variability of standard deviation. Consequently, a constant regional standard deviation model was selected, resulting in a log-space value of 0.91 with a MSE of 0.03 log units. Yet drainage area was found to be statistically significant at explaining the site-to-site variability in mean. The linear WLS regional mean model based on drainage area had a Pseudo- 2 R of 51 percent and a MSE of 0.32 log units. The regional parameter estimates were then used to develop a set of equations for estimating flows with 50-, 20-, 10-, 4-, 2-, 1-, 0.5-, and 0.2-percent annual exceedance probabilities for ungaged basins. The final equations are functions of drainage area.Average standard errors of prediction for these regression equations range from 214.2 to 856.2 percent.
","conferenceTitle":"World Environmental and Water Resources Congress 2012","conferenceDate":"Albuquerque, New Mexico, United States","conferenceLocation":"May 20-24, 2012","language":"English","publisher":"American Society of Civil Engineers","doi":"10.1061/9780784412312.238","collaboration":"FEMA","usgsCitation":"Barth, N.A., and Veilleux, A.G., 2012, Parameter estimation method and updating of regional prediction equations for ungaged sites in the desert region of California, World Environmental and Water Resources Congress 2012, May 20-24, 2012, Albuquerque, New Mexico, United States, p. 2356-2366, https://doi.org/10.1061/9780784412312.238.","productDescription":"11 p.","startPage":"2356","endPage":"2366","onlineOnly":"N","additionalOnlineFiles":"N","ipdsId":"IP-034376","costCenters":[{"id":154,"text":"California Water Science Center","active":true,"usgs":true}],"links":[{"id":311099,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","state":"California","otherGeospatial":"Desert region","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -118.45458984375,\n 37.89219554724437\n ],\n [\n -117.70751953125,\n 35.24561909420681\n ],\n [\n -117.83935546874999,\n 34.69646117272349\n ],\n [\n -116.619873046875,\n 33.742612777346885\n ],\n [\n -115.78491210937501,\n 32.63012300670739\n ],\n [\n -114.521484375,\n 32.76880048488168\n ],\n [\n -114.49951171875,\n 33.02708758002874\n ],\n [\n -114.6533203125,\n 33.05471648804276\n ],\n [\n -114.697265625,\n 33.247875947924385\n ],\n [\n -114.730224609375,\n 33.358061612778876\n ],\n [\n -114.6533203125,\n 33.46810795527896\n ],\n [\n -114.5654296875,\n 33.568861182555565\n ],\n [\n -114.510498046875,\n 33.815666308702774\n ],\n [\n -114.521484375,\n 33.916013113401696\n ],\n [\n -114.47753906249999,\n 34.03445260967645\n ],\n [\n -114.345703125,\n 34.161818161230386\n ],\n [\n -114.19189453125,\n 34.261756524459805\n ],\n [\n -114.136962890625,\n 34.334364487026306\n ],\n [\n -114.345703125,\n 34.488447837809304\n ],\n [\n -114.554443359375,\n 34.77771580360469\n ],\n [\n -114.63134765625001,\n 35.02999636902566\n ],\n [\n -118.41064453125,\n 37.883524980871336\n ],\n [\n -118.45458984375,\n 37.89219554724437\n ]\n ]\n ]\n }\n }\n ]\n}","publishingServiceCenter":{"id":1,"text":"Sacramento PSC"},"noUsgsAuthors":false,"publicationDate":"2012-07-13","publicationStatus":"PW","scienceBaseUri":"563ddd42e4b0831b7d6271f3","contributors":{"authors":[{"text":"Barth, Nancy A. nabarth@usgs.gov","contributorId":3276,"corporation":false,"usgs":true,"family":"Barth","given":"Nancy","email":"nabarth@usgs.gov","middleInitial":"A.","affiliations":[{"id":120,"text":"Alaska Science Center Water","active":true,"usgs":true}],"preferred":true,"id":546916,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Veilleux, Andrea G. aveilleux@usgs.gov","contributorId":4404,"corporation":false,"usgs":true,"family":"Veilleux","given":"Andrea","email":"aveilleux@usgs.gov","middleInitial":"G.","affiliations":[{"id":502,"text":"Office of Surface Water","active":true,"usgs":true}],"preferred":true,"id":546915,"contributorType":{"id":1,"text":"Authors"},"rank":2}]}} ,{"id":70139533,"text":"70139533 - 2012 - The use of U.S. Geological Survey digital geospatial data products for science research","interactions":[],"lastModifiedDate":"2017-03-27T12:03:46","indexId":"70139533","displayToPublicDate":"2012-01-01T00:00:00","publicationYear":"2012","noYear":false,"publicationType":{"id":5,"text":"Book chapter"},"publicationSubtype":{"id":24,"text":"Book Chapter"},"title":"The use of U.S. Geological Survey digital geospatial data products for science research","docAbstract":"The development of geographic information system (GIS) transformed the practice of geographic science research. The availability of low-cost, reliable data by the U.S. Geological Survey (USGS) supported the advance of GIS in the early stages of the transition to digital technology. To estimate the extent of the scientific use of USGS digital geospatial data products, a search of science literature databases yielded numbers of articles citing USGS products. Though this method requires careful consideration to avoid false positives, these citation numbers of three types of products (vector, land-use/land-cover, and elevation data) were graphed, and the frequency trends were examined. Trends indicated that the use of several, but not all, products increased with time. The use of some products declined and reasons for these declines are offered. To better understand how these data affected the design and outcomes of research projects, the study begins to build a context for the data by discussing digital cartographic research preceding the production of mass-produced products. The data distribution methods used various media for different system types and were supported by instructional material. The findings are an initial assessment of the affect of USGS products on GIS-enabled science research. A brief examination of the specific papers indicates that USGS data were used for science and GIS conceptual research, advanced education, and problem analysis and solution applications.
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","largerWorkType":{"id":4,"text":"Book"},"largerWorkTitle":"Proceedings from the International Telemetering Conference ","largerWorkSubtype":{"id":12,"text":"Conference publication"},"language":"English","publisher":"International Foundation for Telemetering","usgsCitation":"Mah, G.R., O’Brien, M., Garon, H., Mott, C., Ames, A., and Dearth, K., 2012, Design and implementation of the next generation Landsat satellite communications system, in Proceedings from the International Telemetering Conference , 14 p.","productDescription":"14 p.","ipdsId":"IP-038940","costCenters":[{"id":222,"text":"Earth Resources Observation and Science (EROS) Center","active":true,"usgs":true}],"links":[{"id":350124,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"},{"id":347674,"type":{"id":15,"text":"Index Page"},"url":"https://arizona.openrepository.com/arizona/handle/10150/581626"}],"publishingServiceCenter":{"id":4,"text":"Rolla PSC"},"noUsgsAuthors":false,"publicationStatus":"PW","scienceBaseUri":"5a6105a0e4b06e28e9c2557f","contributors":{"authors":[{"text":"Mah, Grant R. 0000-0002-2584-3915 mah@usgs.gov","orcid":"https://orcid.org/0000-0002-2584-3915","contributorId":4087,"corporation":false,"usgs":true,"family":"Mah","given":"Grant","email":"mah@usgs.gov","middleInitial":"R.","affiliations":[{"id":222,"text":"Earth Resources Observation and Science (EROS) Center","active":true,"usgs":true}],"preferred":true,"id":717588,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"O’Brien, Michael mobrien@usgs.gov","contributorId":4333,"corporation":false,"usgs":true,"family":"O’Brien","given":"Michael","email":"mobrien@usgs.gov","affiliations":[],"preferred":true,"id":717589,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Garon, Howard","contributorId":198902,"corporation":false,"usgs":false,"family":"Garon","given":"Howard","email":"","affiliations":[],"preferred":false,"id":717592,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Mott, Claire","contributorId":198901,"corporation":false,"usgs":false,"family":"Mott","given":"Claire","email":"","affiliations":[],"preferred":false,"id":717591,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Ames, Alan","contributorId":198900,"corporation":false,"usgs":false,"family":"Ames","given":"Alan","email":"","affiliations":[],"preferred":false,"id":717590,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"Dearth, Ken","contributorId":198903,"corporation":false,"usgs":false,"family":"Dearth","given":"Ken","email":"","affiliations":[],"preferred":false,"id":717593,"contributorType":{"id":1,"text":"Authors"},"rank":6}]}} ,{"id":70032649,"text":"70032649 - 2012 - Updated determination of stress parameters for nine well-recorded earthquakes in eastern North America","interactions":[],"lastModifiedDate":"2017-10-17T16:51:03","indexId":"70032649","displayToPublicDate":"2012-01-01T00:00:00","publicationYear":"2012","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":3372,"text":"Seismological Research Letters","onlineIssn":"1938-2057","printIssn":"0895-0695","active":true,"publicationSubtype":{"id":10}},"title":"Updated determination of stress parameters for nine well-recorded earthquakes in eastern North America","docAbstract":"Stress parameters (Δσ) are determined for nine relatively well-recorded earthquakes in eastern North America for ten attenuation models. This is an update of a previous study by Boore et al. (2010). New to this paper are observations from the 2010 Val des Bois earthquake, additional observations for the 1988 Saguenay and 2005 Riviere du Loup earthquakes, and consideration of six attenuation models in addition to the four used in the previous study. As in that study, it is clear that Δσ depends strongly on the rate of geometrical spreading (as well as other model parameters). The observations necessary to determine conclusively which attenuation model best fits the data are still lacking. At this time, a simple 1/R model seems to give as good an overall fit to the data as more complex models.
","language":"English","publisher":"Seismological Society of America","doi":"10.1785/gssrl.83.1.190","issn":"08950695","usgsCitation":"Boore, D.M., 2012, Updated determination of stress parameters for nine well-recorded earthquakes in eastern North America: Seismological Research Letters, v. 83, no. 1, p. 190-199, https://doi.org/10.1785/gssrl.83.1.190.","productDescription":"10 p.","startPage":"190","endPage":"199","numberOfPages":"10","ipdsId":"IP-034108","costCenters":[{"id":237,"text":"Earthquake Science Center","active":true,"usgs":true}],"links":[{"id":241355,"rank":0,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"},{"id":213701,"rank":9999,"type":{"id":10,"text":"Digital Object Identifier"},"url":"https://dx.doi.org/10.1785/gssrl.83.1.190"}],"volume":"83","issue":"1","noUsgsAuthors":false,"publicationDate":"2012-01-09","publicationStatus":"PW","scienceBaseUri":"505bbd16e4b08c986b328ebe","contributors":{"authors":[{"text":"Boore, David M. boore@usgs.gov","contributorId":2509,"corporation":false,"usgs":true,"family":"Boore","given":"David","email":"boore@usgs.gov","middleInitial":"M.","affiliations":[{"id":237,"text":"Earthquake Science Center","active":true,"usgs":true}],"preferred":false,"id":437265,"contributorType":{"id":1,"text":"Authors"},"rank":1}]}} ,{"id":70193579,"text":"70193579 - 2012 - The 2010 explosive eruption of Java's Merapi volcano—A ‘100-year’ event","interactions":[],"lastModifiedDate":"2017-11-02T10:54:49","indexId":"70193579","displayToPublicDate":"2012-01-01T00:00:00","publicationYear":"2012","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":2499,"text":"Journal of Volcanology and Geothermal Research","active":true,"publicationSubtype":{"id":10}},"title":"The 2010 explosive eruption of Java's Merapi volcano—A ‘100-year’ event","docAbstract":"Merapi volcano (Indonesia) is one of the most active and hazardous volcanoes in the world. It is known for frequent small to moderate eruptions, pyroclastic flows produced by lava dome collapse, and the large population settled on and around the flanks of the volcano that is at risk. Its usual behavior for the last decades abruptly changed in late October and early November 2010, when the volcano produced its largest and most explosive eruptions in more than a century, displacing at least a third of a million people, and claiming nearly 400 lives. Despite the challenges involved in forecasting this ‘hundred year eruption’, we show that the magnitude of precursory signals (seismicity, ground deformation, gas emissions) was proportional to the large size and intensity of the eruption. In addition and for the first time, near-real-time satellite radar imagery played an equal role with seismic, geodetic, and gas observations in monitoring eruptive activity during a major volcanic crisis. The Indonesian Center of Volcanology and Geological Hazard Mitigation (CVGHM) issued timely forecasts of the magnitude of the eruption phases, saving 10,000–20,000 lives. In addition to reporting on aspects of the crisis management, we report the first synthesis of scientific observations of the eruption. Our monitoring and petrologic data show that the 2010 eruption was fed by rapid ascent of magma from depths ranging from 5 to 30 km. Magma reached the surface with variable gas content resulting in alternating explosive and rapid effusive eruptions, and released a total of ~ 0.44 Tg of SO2. The eruptive behavior seems also related to the seismicity along a tectonic fault more than 40 km from the volcano, highlighting both the complex stress pattern of the Merapi region of Java and the role of magmatic pressurization in activating regional faults. We suggest a dynamic triggering of the main explosions on 3 and 4 November by the passing seismic waves generated by regional earthquakes on these days.
","language":"English","publisher":"Elsevier","doi":"10.1016/j.jvolgeores.2012.06.018","usgsCitation":", S., Jousset, P., Pallister, J.S., Boichu, M., Buongiorno, M.F., Budisantoso, A., Costa, F., Andreastuti, S., Prata, F., Schneider, D.J., Clarisse, L., Humaida, H., Sumarti, S., Bignami, C., Griswold, J.P., Carn, S.A., Oppenheimer, C., and Lavigne, F., 2012, The 2010 explosive eruption of Java's Merapi volcano—A ‘100-year’ event: Journal of Volcanology and Geothermal Research, v. 241-242, p. 121-135, https://doi.org/10.1016/j.jvolgeores.2012.06.018.","productDescription":"15 p.","startPage":"121","endPage":"135","ipdsId":"IP-037583","costCenters":[{"id":617,"text":"Volcano Science Center","active":true,"usgs":true}],"links":[{"id":488719,"rank":0,"type":{"id":41,"text":"Open Access External Repository Page"},"url":"https://gfzpublic.gfz-potsdam.de/pubman/item/item_246296","text":"External Repository"},{"id":348072,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"Indonesia","otherGeospatial":"Merapi volcano","volume":"241-242","publishingServiceCenter":{"id":14,"text":"Menlo Park PSC"},"noUsgsAuthors":false,"publicationStatus":"PW","scienceBaseUri":"59fc2eb1e4b0531197b28026","contributors":{"authors":[{"text":" Surono","contributorId":149582,"corporation":false,"usgs":false,"given":"Surono","email":"","affiliations":[],"preferred":false,"id":719436,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Jousset, Philippe","contributorId":194796,"corporation":false,"usgs":false,"family":"Jousset","given":"Philippe","email":"","affiliations":[],"preferred":false,"id":719437,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Pallister, John S. 0000-0002-2041-2147 jpallist@usgs.gov","orcid":"https://orcid.org/0000-0002-2041-2147","contributorId":2024,"corporation":false,"usgs":true,"family":"Pallister","given":"John","email":"jpallist@usgs.gov","middleInitial":"S.","affiliations":[{"id":617,"text":"Volcano Science Center","active":true,"usgs":true},{"id":114,"text":"Alaska Science Center","active":true,"usgs":true}],"preferred":true,"id":719438,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Boichu, Marie","contributorId":199559,"corporation":false,"usgs":false,"family":"Boichu","given":"Marie","email":"","affiliations":[],"preferred":false,"id":719439,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Buongiorno, M. Fabrizia","contributorId":102698,"corporation":false,"usgs":true,"family":"Buongiorno","given":"M.","email":"","middleInitial":"Fabrizia","affiliations":[],"preferred":false,"id":719440,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"Budisantoso, Agus","contributorId":199556,"corporation":false,"usgs":false,"family":"Budisantoso","given":"Agus","email":"","affiliations":[],"preferred":false,"id":719441,"contributorType":{"id":1,"text":"Authors"},"rank":6},{"text":"Costa, Fidel","contributorId":184169,"corporation":false,"usgs":false,"family":"Costa","given":"Fidel","email":"","affiliations":[],"preferred":false,"id":719442,"contributorType":{"id":1,"text":"Authors"},"rank":7},{"text":"Andreastuti, Supriyati","contributorId":82087,"corporation":false,"usgs":true,"family":"Andreastuti","given":"Supriyati","email":"","affiliations":[],"preferred":false,"id":719443,"contributorType":{"id":1,"text":"Authors"},"rank":8},{"text":"Prata, Fred","contributorId":148068,"corporation":false,"usgs":false,"family":"Prata","given":"Fred","email":"","affiliations":[{"id":16991,"text":"Norwegian Institute for Air Research","active":true,"usgs":false}],"preferred":false,"id":719444,"contributorType":{"id":1,"text":"Authors"},"rank":9},{"text":"Schneider, David J. 0000-0001-9092-1054 djschneider@usgs.gov","orcid":"https://orcid.org/0000-0001-9092-1054","contributorId":198601,"corporation":false,"usgs":true,"family":"Schneider","given":"David","email":"djschneider@usgs.gov","middleInitial":"J.","affiliations":[],"preferred":true,"id":719445,"contributorType":{"id":1,"text":"Authors"},"rank":10},{"text":"Clarisse, Lieven","contributorId":199561,"corporation":false,"usgs":false,"family":"Clarisse","given":"Lieven","email":"","affiliations":[],"preferred":false,"id":719446,"contributorType":{"id":1,"text":"Authors"},"rank":11},{"text":"Humaida, Hanik","contributorId":199562,"corporation":false,"usgs":false,"family":"Humaida","given":"Hanik","email":"","affiliations":[],"preferred":false,"id":719447,"contributorType":{"id":1,"text":"Authors"},"rank":12},{"text":"Sumarti, Sri","contributorId":149584,"corporation":false,"usgs":false,"family":"Sumarti","given":"Sri","email":"","affiliations":[],"preferred":false,"id":719448,"contributorType":{"id":1,"text":"Authors"},"rank":13},{"text":"Bignami, Christian","contributorId":199563,"corporation":false,"usgs":false,"family":"Bignami","given":"Christian","email":"","affiliations":[],"preferred":false,"id":719449,"contributorType":{"id":1,"text":"Authors"},"rank":14},{"text":"Griswold, Julia P. griswold@usgs.gov","contributorId":4148,"corporation":false,"usgs":true,"family":"Griswold","given":"Julia","email":"griswold@usgs.gov","middleInitial":"P.","affiliations":[],"preferred":true,"id":719450,"contributorType":{"id":1,"text":"Authors"},"rank":15},{"text":"Carn, Simon A.","contributorId":28092,"corporation":false,"usgs":true,"family":"Carn","given":"Simon","email":"","middleInitial":"A.","affiliations":[],"preferred":false,"id":719451,"contributorType":{"id":1,"text":"Authors"},"rank":16},{"text":"Oppenheimer, Clive","contributorId":174445,"corporation":false,"usgs":false,"family":"Oppenheimer","given":"Clive","email":"","affiliations":[{"id":27136,"text":"University of Cambridge","active":true,"usgs":false}],"preferred":false,"id":719452,"contributorType":{"id":1,"text":"Authors"},"rank":17},{"text":"Lavigne, Franck","contributorId":66030,"corporation":false,"usgs":true,"family":"Lavigne","given":"Franck","email":"","affiliations":[],"preferred":false,"id":719453,"contributorType":{"id":1,"text":"Authors"},"rank":18}]}} ,{"id":70193286,"text":"70193286 - 2012 - Displacement fields from point cloud data: Application of particle imaging velocimetry to landslide geodesy","interactions":[],"lastModifiedDate":"2019-05-30T10:00:16","indexId":"70193286","displayToPublicDate":"2012-01-01T00:00:00","publicationYear":"2012","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":2318,"text":"Journal of Geophysical Research F: Earth Surface","active":true,"publicationSubtype":{"id":10}},"title":"Displacement fields from point cloud data: Application of particle imaging velocimetry to landslide geodesy","docAbstract":"Acquiring spatially continuous ground-surface displacement fields from Terrestrial Laser Scanners (TLS) will allow better understanding of the physical processes governing landslide motion at detailed spatial and temporal scales. Problems arise, however, when estimating continuous displacement fields from TLS point-clouds because reflecting points from sequential scans of moving ground are not defined uniquely, thus repeat TLS surveys typically do not track individual reflectors. Here, we implemented the cross-correlation-based Particle Image Velocimetry (PIV) method to derive a surface deformation field using TLS point-cloud data. We estimated associated errors using the shape of the cross-correlation function and tested the method's performance with synthetic displacements applied to a TLS point cloud. We applied the method to the toe of the episodically active Cleveland Corral Landslide in northern California using TLS data acquired in June 2005–January 2007 and January–May 2010. Estimated displacements ranged from decimeters to several meters and they agreed well with independent measurements at better than 9% root mean squared (RMS) error. For each of the time periods, the method provided a smooth, nearly continuous displacement field that coincides with independently mapped boundaries of the slide and permits further kinematic and mechanical inference. For the 2010 data set, for instance, the PIV-derived displacement field identified a diffuse zone of displacement that preceded by over a month the development of a new lateral shear zone. Additionally, the upslope and downslope displacement gradients delineated by the dense PIV field elucidated the non-rigid behavior of the slide.
","language":"English","publisher":"AGU","doi":"10.1029/2011JF002161","usgsCitation":"Aryal, A., Brooks, B.A., Reid, M.E., Bawden, G.W., and Pawlak, G., 2012, Displacement fields from point cloud data: Application of particle imaging velocimetry to landslide geodesy: Journal of Geophysical Research F: Earth Surface, v. 117, no. F1, p. 1-15, https://doi.org/10.1029/2011JF002161.","productDescription":"F01029; 15 p.","startPage":"1","endPage":"15","ipdsId":"IP-034573","costCenters":[{"id":615,"text":"Volcano Hazards Program","active":true,"usgs":true},{"id":617,"text":"Volcano Science Center","active":true,"usgs":true}],"links":[{"id":474690,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.1029/2011jf002161","text":"Publisher Index Page"},{"id":347929,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","state":"California","otherGeospatial":"Sierra Nevada Mountains","volume":"117","issue":"F1","publishingServiceCenter":{"id":14,"text":"Menlo Park PSC"},"noUsgsAuthors":false,"publicationDate":"2012-03-21","publicationStatus":"PW","scienceBaseUri":"59f98bc1e4b0531197afa068","contributors":{"authors":[{"text":"Aryal, Arjun","contributorId":199281,"corporation":false,"usgs":false,"family":"Aryal","given":"Arjun","affiliations":[],"preferred":false,"id":718548,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Brooks, Benjamin A. 0000-0001-7954-6281 bbrooks@usgs.gov","orcid":"https://orcid.org/0000-0001-7954-6281","contributorId":5237,"corporation":false,"usgs":true,"family":"Brooks","given":"Benjamin","email":"bbrooks@usgs.gov","middleInitial":"A.","affiliations":[{"id":237,"text":"Earthquake Science Center","active":true,"usgs":true}],"preferred":true,"id":718549,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Reid, Mark E. 0000-0002-5595-1503 mreid@usgs.gov","orcid":"https://orcid.org/0000-0002-5595-1503","contributorId":1167,"corporation":false,"usgs":true,"family":"Reid","given":"Mark","email":"mreid@usgs.gov","middleInitial":"E.","affiliations":[{"id":312,"text":"Geology, Minerals, Energy, and Geophysics Science Center","active":true,"usgs":true},{"id":186,"text":"Coastal and Marine Geology Program","active":true,"usgs":true},{"id":617,"text":"Volcano Science Center","active":true,"usgs":true}],"preferred":true,"id":718547,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Bawden, Gerald W. gbawden@usgs.gov","contributorId":1071,"corporation":false,"usgs":true,"family":"Bawden","given":"Gerald","email":"gbawden@usgs.gov","middleInitial":"W.","affiliations":[{"id":154,"text":"California Water Science Center","active":true,"usgs":true}],"preferred":true,"id":718546,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Pawlak, Geno","contributorId":66178,"corporation":false,"usgs":true,"family":"Pawlak","given":"Geno","email":"","affiliations":[],"preferred":false,"id":718550,"contributorType":{"id":1,"text":"Authors"},"rank":13}]}} ,{"id":70192540,"text":"70192540 - 2012 - Morphometric-based sexual determination of Bananaquits (Coereba flaveola)","interactions":[],"lastModifiedDate":"2017-11-28T12:47:13","indexId":"70192540","displayToPublicDate":"2012-01-01T00:00:00","publicationYear":"2012","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":2967,"text":"Ornitologia Neotropical","active":true,"publicationSubtype":{"id":10}},"displayTitle":"Morphometric-based sexual determination of Bananaquits (Coereba flaveola)","title":"Morphometric-based sexual determination of Bananaquits (Coereba flaveola)","docAbstract":"The Bananaquit (Coereba flaveola) is a common passerine throughout the tropics and has been a convenient species for ecological studies. This species has sexually monomorphic plumage and cannot be reliably sexed unless in breeding condition. This is problematic for demographic and comparative studies, which are contingent upon accurately aging and sexing individuals. Although male Bananaquits are larger than females, there is overlap in both wing chord and mass. We used morphometric data collected over eight years to develop a predictive model based on logistic regression to assign adult Bananaquits to sex. Our model classified 96% of validation individuals to the correct sex. We suggest that this approach may enhance ecological studies of the species by facilitating correct sex determination independent of breeding status. We believe our modeling approach is applicable elsewhere but, because there may be geographical variation across the species distribution, models will need to be customized to local populations.
","language":"English","publisher":"The Neotropical Ornithological Society","usgsCitation":"Bibles, B.D., and Boal, C.W., 2012, Morphometric-based sexual determination of Bananaquits (Coereba flaveola): Ornitologia Neotropical, v. 23, p. 507-515.","productDescription":"9 p.","startPage":"507","endPage":"515","ipdsId":"IP-030771","costCenters":[{"id":198,"text":"Coop Res Unit Atlanta","active":true,"usgs":true}],"links":[{"id":349456,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"},{"id":349455,"rank":1,"type":{"id":15,"text":"Index Page"},"url":"https://sora.unm.edu/node/133335"}],"country":"British Virgin Islands","otherGeospatial":"Guana Island","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -64.58527565002441,\n 18.46235033603078\n ],\n [\n -64.55703735351562,\n 18.46235033603078\n ],\n [\n -64.55703735351562,\n 18.49112747057403\n ],\n [\n -64.58527565002441,\n 18.49112747057403\n ],\n [\n -64.58527565002441,\n 18.46235033603078\n ]\n ]\n ]\n }\n }\n ]\n}","volume":"23","publishingServiceCenter":{"id":8,"text":"Raleigh PSC"},"noUsgsAuthors":false,"publicationStatus":"PW","scienceBaseUri":"5a6105a0e4b06e28e9c25583","contributors":{"authors":[{"text":"Bibles, Brent D.","contributorId":77720,"corporation":false,"usgs":true,"family":"Bibles","given":"Brent","email":"","middleInitial":"D.","affiliations":[],"preferred":false,"id":723845,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Boal, Clint W. 0000-0001-6008-8911 cboal@usgs.gov","orcid":"https://orcid.org/0000-0001-6008-8911","contributorId":1909,"corporation":false,"usgs":true,"family":"Boal","given":"Clint","email":"cboal@usgs.gov","middleInitial":"W.","affiliations":[{"id":198,"text":"Coop Res Unit Atlanta","active":true,"usgs":true},{"id":200,"text":"Coop Res Unit Seattle","active":true,"usgs":true}],"preferred":true,"id":716154,"contributorType":{"id":1,"text":"Authors"},"rank":2}]}} ,{"id":70193298,"text":"70193298 - 2012 - A robust method to forecast volcanic ash clouds","interactions":[],"lastModifiedDate":"2017-10-31T15:39:48","indexId":"70193298","displayToPublicDate":"2012-01-01T00:00:00","publicationYear":"2012","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":2316,"text":"Journal of Geophysical Research D: Atmospheres","active":true,"publicationSubtype":{"id":10}},"title":"A robust method to forecast volcanic ash clouds","docAbstract":"Ash clouds emanating from volcanic eruption columns often form trails of ash extending thousands of kilometers through the Earth's atmosphere, disrupting air traffic and posing a significant hazard to air travel. To mitigate such hazards, the community charged with reducing flight risk must accurately assess risk of ash ingestion for any flight path and provide robust forecasts of volcanic ash dispersal. In response to this need, a number of different transport models have been developed for this purpose and applied to recent eruptions, providing a means to assess uncertainty in forecasts. Here we provide a framework for optimal forecasts and their uncertainties given any model and any observational data. This involves random sampling of the probability distributions of input (source) parameters to a transport model and iteratively running the model with different inputs, each time assessing the predictions that the model makes about ash dispersal by direct comparison with satellite data. The results of these comparisons are embodied in a likelihood function whose maximum corresponds to the minimum misfit between model output and observations. Bayes theorem is then used to determine a normalized posterior probability distribution and from that a forecast of future uncertainty in ash dispersal. The nature of ash clouds in heterogeneous wind fields creates a strong maximum likelihood estimate in which most of the probability is localized to narrow ranges of model source parameters. This property is used here to accelerate probability assessment, producing a method to rapidly generate a prediction of future ash concentrations and their distribution based upon assimilation of satellite data as well as model and data uncertainties. Applying this method to the recent eruption of Eyjafjallajökull in Iceland, we show that the 3 and 6 h forecasts of ash cloud location probability encompassed the location of observed satellite-determined ash cloud loads, providing an efficient means to assess all of the hazards associated with these ash clouds.
","language":"English","publisher":"AGU","doi":"10.1029/2012JD017732","usgsCitation":"Denlinger, R.P., Pavolonis, M.J., and Sieglaff, J., 2012, A robust method to forecast volcanic ash clouds: Journal of Geophysical Research D: Atmospheres, v. 117, no. D13, p. 1-10, https://doi.org/10.1029/2012JD017732.","productDescription":"D13208; 10 p.","startPage":"1","endPage":"10","ipdsId":"IP-035253","costCenters":[{"id":617,"text":"Volcano Science Center","active":true,"usgs":true}],"links":[{"id":474632,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.1029/2012jd017732","text":"Publisher Index Page"},{"id":347920,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"volume":"117","issue":"D13","publishingServiceCenter":{"id":14,"text":"Menlo Park PSC"},"noUsgsAuthors":false,"publicationDate":"2012-07-13","publicationStatus":"PW","scienceBaseUri":"59f98bc0e4b0531197afa05e","contributors":{"authors":[{"text":"Denlinger, Roger P. 0000-0003-0930-0635 roger@usgs.gov","orcid":"https://orcid.org/0000-0003-0930-0635","contributorId":2679,"corporation":false,"usgs":true,"family":"Denlinger","given":"Roger","email":"roger@usgs.gov","middleInitial":"P.","affiliations":[{"id":617,"text":"Volcano Science Center","active":true,"usgs":true},{"id":615,"text":"Volcano Hazards Program","active":true,"usgs":true}],"preferred":true,"id":718582,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Pavolonis, Michael J.","contributorId":199297,"corporation":false,"usgs":false,"family":"Pavolonis","given":"Michael","email":"","middleInitial":"J.","affiliations":[],"preferred":false,"id":718584,"contributorType":{"id":1,"text":"Authors"},"rank":11},{"text":"Sieglaff, Justin","contributorId":199296,"corporation":false,"usgs":false,"family":"Sieglaff","given":"Justin","email":"","affiliations":[],"preferred":false,"id":718583,"contributorType":{"id":1,"text":"Authors"},"rank":12}]}} ,{"id":70032503,"text":"70032503 - 2012 - Assessment of pingo distribution and morphometry using an IfSAR derived digital surface model, western Arctic Coastal Plain, Northern Alaska","interactions":[],"lastModifiedDate":"2018-08-07T12:20:33","indexId":"70032503","displayToPublicDate":"2012-01-01T00:00:00","publicationYear":"2012","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":1801,"text":"Geomorphology","active":true,"publicationSubtype":{"id":10}},"title":"Assessment of pingo distribution and morphometry using an IfSAR derived digital surface model, western Arctic Coastal Plain, Northern Alaska","docAbstract":"Pingos are circular to elongate ice-cored mounds that form by injection and freezing of pressurized water in near-surface permafrost. Here we use a digital surface model (DSM) derived from an airborne Interferometric Synthetic Aperture Radar (IfSAR) system to assess the distribution and morphometry of pingos within a 40,000 km2 area on the western Arctic Coastal Plain of northern Alaska. We have identified 1247 pingo forms in the study region, ranging in height from 2 to 21 m, with a mean height of 4.6 m. Pingos in this region are of hydrostatic origin, with 98% located within 995 drained lake basins, most of which are underlain by thick eolian sand deposits. The highest pingo density (0.18 km− 2) occurs where streams have reworked these deposits. Morphometric analyses indicate that most pingos are small to medium in size (< 200 m diameter), gently to moderately sloping (< 30°), circular to slightly elongate (mean circularity index of 0.88), and of relatively low height (2 to 5 m). However, 57 pingos stand higher than 10 m, 26 have a maximum slope greater than 30°, and 42 are larger than 200 m in diameter. Comparison with a legacy pingo dataset based on 1950s stereo-pair photography indicates that 66 may have partially or completely collapsed over the last half-century. However, we mapped over 400 pingos not identified in the legacy dataset, and identified only three higher than 2 m to have formed between ca. 1955 and ca. 2005, indicating that caution should be taken when comparing contemporary and legacy datasets derived by different techniques. This comprehensive database of pingo location and morphometry based on an IfSAR DSM may prove useful for land and resource managers as well as aid in the identification of pingo-like features on Mars.
","language":"English","publisher":"Elsevier","doi":"10.1016/j.geomorph.2011.08.007","issn":"0169555X","usgsCitation":"Jones, B.M., Grosse, G., Hinkel, K.M., Arp, C., Walker, S., Beck, R., and Galloway, J., 2012, Assessment of pingo distribution and morphometry using an IfSAR derived digital surface model, western Arctic Coastal Plain, Northern Alaska: Geomorphology, v. 138, no. 1, p. 1-14, https://doi.org/10.1016/j.geomorph.2011.08.007.","productDescription":"14 p.","startPage":"1","endPage":"14","costCenters":[{"id":114,"text":"Alaska Science Center","active":true,"usgs":true}],"links":[{"id":241755,"rank":0,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"},{"id":214067,"rank":9999,"type":{"id":10,"text":"Digital Object Identifier"},"url":"https://dx.doi.org/10.1016/j.geomorph.2011.08.007"}],"volume":"138","issue":"1","noUsgsAuthors":false,"publicationStatus":"PW","scienceBaseUri":"5059ee48e4b0c8380cd49c89","contributors":{"authors":[{"text":"Jones, Benjamin M. 0000-0002-1517-4711 bjones@usgs.gov","orcid":"https://orcid.org/0000-0002-1517-4711","contributorId":2286,"corporation":false,"usgs":true,"family":"Jones","given":"Benjamin","email":"bjones@usgs.gov","middleInitial":"M.","affiliations":[{"id":118,"text":"Alaska Science Center Geography","active":true,"usgs":true},{"id":114,"text":"Alaska Science Center","active":true,"usgs":true}],"preferred":true,"id":436511,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Grosse, G.","contributorId":82140,"corporation":false,"usgs":true,"family":"Grosse","given":"G.","affiliations":[],"preferred":false,"id":436514,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Hinkel, Kenneth M.","contributorId":15405,"corporation":false,"usgs":true,"family":"Hinkel","given":"Kenneth","email":"","middleInitial":"M.","affiliations":[],"preferred":false,"id":436508,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Arp, C.D.","contributorId":54715,"corporation":false,"usgs":true,"family":"Arp","given":"C.D.","email":"","affiliations":[],"preferred":false,"id":436512,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Walker, S.","contributorId":71777,"corporation":false,"usgs":true,"family":"Walker","given":"S.","email":"","affiliations":[],"preferred":false,"id":436513,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"Beck, R.A.","contributorId":44246,"corporation":false,"usgs":true,"family":"Beck","given":"R.A.","email":"","affiliations":[],"preferred":false,"id":436510,"contributorType":{"id":1,"text":"Authors"},"rank":6},{"text":"Galloway, J. P.","contributorId":19142,"corporation":false,"usgs":true,"family":"Galloway","given":"J. P.","affiliations":[],"preferred":false,"id":436509,"contributorType":{"id":1,"text":"Authors"},"rank":7}]}} ,{"id":70032283,"text":"70032283 - 2012 - The impact of biotic/abiotic interfaces in mineral nutrient cycling: A study of soils of the Santa Cruz chronosequence, California","interactions":[],"lastModifiedDate":"2020-12-03T17:49:49.83412","indexId":"70032283","displayToPublicDate":"2012-01-01T00:00:00","publicationYear":"2012","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":1759,"text":"Geochimica et Cosmochimica Acta","active":true,"publicationSubtype":{"id":10}},"title":"The impact of biotic/abiotic interfaces in mineral nutrient cycling: A study of soils of the Santa Cruz chronosequence, California","docAbstract":"Biotic/abiotic interactions between soil mineral nutrients and annual grassland vegetation are characterized for five soils in a marine terrace chronosequence near Santa Cruz, California. A Mediterranean climate, with wet winters and dry summers, controls the annual cycle of plant growth and litter decomposition, resulting in net above-ground productivities of 280–600 g m−2 yr−1. The biotic/abiotic (A/B) interface separates seasonally reversible nutrient gradients, reflecting biological cycling in the shallower soils, from downward chemical weathering gradients in the deeper soils. The A/B interface is pedologically defined by argillic clay horizons centered at soil depths of about one meter which intensify with soil age. Below these horizons, elevated solute Na/Ca, Mg/Ca and Sr/Ca ratios reflect plagioclase and smectite weathering along pore water flow paths. Above the A/B interface, lower cation ratios denote temporal variability due to seasonal plant nutrient uptake and litter leaching. Potassium and Ca exhibit no seasonal variability beneath the A/B interface, indicating closed nutrient cycling within the root zone, whereas Mg variability below the A/B interface denotes downward leakage resulting from higher inputs of marine aerosols and lower plant nutrient requirements.
The fraction of a mineral nutrient annually cycled through the plants, compared to that lost from pore water discharge, is defined their respective fluxes Fj,plants = qj,plants/(qj,plants + qj,discharge) with average values for K and Ca (FK,plants = 0.99; FCa,plants = 0.93) much higher than for Mg and Na (FMg,plants 0.64; FNa,plants = 0.28). The discrimination against Rb and Sr by plants is described by fractionation factors (KSr/Ca = 0.86; KRb/K = 0.83) which are used in Rayleigh fractionation-mixing calculations to fit seasonal patterns in solute K and Ca cycling. KRb/K and
The coupled ocean–atmosphere–wave–sediment transport (COAWST) modeling system was used to investigate atmosphere–ocean–wave interactions in November 2009 during Hurricane Ida and its subsequent evolution to Nor’Ida, which was one of the most costly storm systems of the past two decades. One interesting aspect of this event is that it included two unique atmospheric extreme conditions, a hurricane and a nor’easter storm, which developed in regions with different oceanographic characteristics. Our modeled results were compared with several data sources, including GOES satellite infrared data, JASON-1 and JASON-2 altimeter data, CODAR measurements, and wave and tidal information from the National Data Buoy Center (NDBC) and the National Tidal Database. By performing a series of numerical runs, we were able to isolate the effect of the interaction terms between the atmosphere (modeled with Weather Research and Forecasting, the WRF model), the ocean (modeled with Regional Ocean Modeling System (ROMS)), and the wave propagation and generation model (modeled with Simulating Waves Nearshore (SWAN)). Special attention was given to the role of the ocean surface roughness. Three different ocean roughness closure models were analyzed: DGHQ (which is based on wave age), TY2001 (which is based on wave steepness), and OOST (which considers both the effects of wave age and steepness). Including the ocean roughness in the atmospheric module improved the wind intensity estimation and therefore also the wind waves, surface currents, and storm surge amplitude. For example, during the passage of Hurricane Ida through the Gulf of Mexico, the wind speeds were reduced due to wave-induced ocean roughness, resulting in better agreement with the measured winds. During Nor’Ida, including the wave-induced surface roughness changed the form and dimension of the main low pressure cell, affecting the intensity and direction of the winds. The combined wave age- and wave steepness-based parameterization (OOST) provided the best results for wind and wave growth prediction. However, the best agreement between the measured (CODAR) and computed surface currents and storm surge values was obtained with the wave steepness-based roughness parameterization (TY2001), although the differences obtained with respect to DGHQ were not significant. The influence of sea surface temperature (SST) fields on the atmospheric boundary layer dynamics was examined; in particular, we evaluated how the SST affects wind wave generation, surface currents and storm surges. The integrated hydrograph and integrated wave height, parameters that are highly correlated with the storm damage potential, were found to be highly sensitive to the ocean surface roughness parameterization.
As part of the government response to the Deepwater Horizon blowout, a Well Integrity Team evaluated the geologic hazards of shutting in the Macondo Well at the seafloor and determined the conditions under which it could safely be undertaken. Of particular concern was the possibility that, under the anticipated high shut-in pressures, oil could leak out of the well casing below the seafloor. Such a leak could lead to new geologic pathways for hydrocarbon release to the Gulf of Mexico. Evaluating this hazard required analyses of 2D and 3D seismic surveys, seafloor bathymetry, sediment properties, geophysical well logs, and drilling data to assess the geological, hydrological, and geomechanical conditions around the Macondo Well. After the well was successfully capped and shut in on July 15, 2010, a variety of monitoring activities were used to assess subsurface well integrity. These activities included acquisition of wellhead pressure data, marine multichannel seismic profiles, seafloor and water-column sonar surveys, and wellhead visual/acoustic monitoring. These data showed that the Macondo Well was not leaking after shut in, and therefore, it could remain safely shut until reservoir pressures were suppressed (killed) with heavy drilling mud and the well was sealed with cement.
","language":"English","publisher":"National Academy of Sciences","doi":"10.1073/pnas.1115847109","usgsCitation":"Hickman, S.H., Hsieh, P.A., Mooney, W.D., Enomoto, C.B., Nelson, P.H., Weber, T.S., Mayer, L., Moran, K., Flemings, P., and McNutt, M.K., 2012, Scientific basis for safely shutting in the Macondo Well after the April 20, 2010 Deepwater Horizon blowout : PNAS, v. 109, no. 50, p. 20268-20273, https://doi.org/10.1073/pnas.1115847109.","productDescription":"6 p.","startPage":"20268","endPage":"20273","ipdsId":"IP-036940","costCenters":[{"id":237,"text":"Earthquake Science Center","active":true,"usgs":true}],"links":[{"id":490048,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.1073/pnas.1115847109","text":"Publisher Index Page"},{"id":347348,"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 -98.349609375,\n 22.350075806124867\n ],\n [\n -81.5625,\n 22.350075806124867\n ],\n [\n -81.5625,\n 31.353636941500987\n ],\n [\n -98.349609375,\n 31.353636941500987\n ],\n [\n -98.349609375,\n 22.350075806124867\n ]\n ]\n ]\n }\n }\n ]\n}","volume":"109","issue":"50","publishingServiceCenter":{"id":14,"text":"Menlo Park PSC"},"noUsgsAuthors":false,"publicationDate":"2012-12-03","publicationStatus":"PW","scienceBaseUri":"59f1a2aae4b0220bbd9d9fd4","contributors":{"editors":[{"text":"Rice, James R.","contributorId":62601,"corporation":false,"usgs":false,"family":"Rice","given":"James","email":"","middleInitial":"R.","affiliations":[{"id":16811,"text":"Harvard University","active":true,"usgs":false}],"preferred":false,"id":715630,"contributorType":{"id":2,"text":"Editors"},"rank":1}],"authors":[{"text":"Hickman, Stephen H. 0000-0003-2075-9615 hickman@usgs.gov","orcid":"https://orcid.org/0000-0003-2075-9615","contributorId":2705,"corporation":false,"usgs":true,"family":"Hickman","given":"Stephen","email":"hickman@usgs.gov","middleInitial":"H.","affiliations":[{"id":312,"text":"Geology, Minerals, Energy, and Geophysics Science Center","active":true,"usgs":true},{"id":237,"text":"Earthquake Science Center","active":true,"usgs":true}],"preferred":true,"id":715357,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Hsieh, Paul A. 0000-0003-4873-4874 pahsieh@usgs.gov","orcid":"https://orcid.org/0000-0003-4873-4874","contributorId":1634,"corporation":false,"usgs":true,"family":"Hsieh","given":"Paul","email":"pahsieh@usgs.gov","middleInitial":"A.","affiliations":[{"id":39113,"text":"WMA - 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2012 - Empirical methods for detecting regional trends and other spatial expressions in antrim shale gas productivity, with implications for improving resource projections using local nonparametric estimation techniques","interactions":[],"lastModifiedDate":"2020-11-24T17:35:13.36174","indexId":"70032688","displayToPublicDate":"2012-01-01T00:00:00","publicationYear":"2012","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":2832,"text":"Natural Resources Research","onlineIssn":"1573-8981","printIssn":"1520-7439","active":true,"publicationSubtype":{"id":10}},"title":"Empirical methods for detecting regional trends and other spatial expressions in antrim shale gas productivity, with implications for improving resource projections using local nonparametric estimation techniques","docAbstract":"The primary objectives of this research were to (1) investigate empirical methods for establishing regional trends in unconventional gas resources as exhibited by historical production data and (2) determine whether or not incorporating additional knowledge of a regional trend in a suite of previously established local nonparametric resource prediction algorithms influences assessment results. Three different trend detection methods were applied to publicly available production data (well EUR aggregated to 80-acre cells) from the Devonian Antrim Shale gas play in the Michigan Basin. This effort led to the identification of a southeast–northwest trend in cell EUR values across the play that, in a very general sense, conforms to the primary fracture and structural orientations of the province. However, including this trend in the resource prediction algorithms did not lead to improved results. Further analysis indicated the existence of clustering among cell EUR values that likely dampens the contribution of the regional trend. The reason for the clustering, a somewhat unexpected result, is not completely understood, although the geological literature provides some possible explanations. With appropriate data, a better understanding of this clustering phenomenon may lead to important information about the factors and their interactions that control Antrim Shale gas production, which may, in turn, help establish a more general protocol for better estimating resources in this and other shale gas plays.
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The susceptibility meter used measures about 30 cm3 of rock and measures variations in the modal distribution of magnetic minerals that form a minor component volumetrically in these coarsely crystalline granitic to granodioritic rocks. Recent measurements include 50–150 measurements on each outcrop, and show that the distribution of magnetic susceptibilities is highly variable, multimodal and strongly non-Gaussian. Although the distribution of magnetic susceptibility is well known to be multifractal, the small number of data points at an outcrop precludes calculation of the multifractal spectrum by conventional methods. Instead, a brute force approach was adopted using multiplicative cascade models to fit the outcrop scale variability of magnetic minerals. Model segment proportion and length parameters resulted in 26 676 models to span parameter space. Distributions at each outcrop were normalized to unity magnetic susceptibility and added to compare all data for a rock body accounting for variations in petrology and alteration. Once the best-fitting model was found, the equation relating the segment proportion and length parameters was solved numerically to yield the multifractal spectrum estimate. For the best fits, the relative density (the proportion divided by the segment length) of one segment tends to be dominant and the other two densities are smaller and nearly equal. No other consistent relationships between the best fit parameters were identified. The multifractal spectrum estimates appear to distinguish between metamorphic gneiss sites and sites on plutons, even if the plutons have been metamorphosed. In particular, rocks that have undergone multiple tectonic events tend to have a larger range of scaling exponents.
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