During 2008–10, the U.S. Geological Survey, in cooperation with the San Antonio Water System, collected geochemical and hydrologic data in Bexar, Comal, and Hays Counties, Texas, to define and characterize the sources of recharge to San Marcos Springs. Precipitation samples were collected for stable isotope analysis at 1 site and water-quality samples were collected at 7 springs, 21 wells, and 9 stream sites in the study area between November 2008 and December 2010. Continuous water-quality monitors were installed in three springs, two wells, and at one stream site. Three continuous stream-gaging stations were installed to measure gage height and a stagedischarge rating was developed at two of the three sites. Depth to water below land surface was continuously measured in two wells.
","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/ds672","collaboration":"Prepared in cooperation with the San Antonio Water System","usgsCitation":"Crow, C.L., 2012, Geochemical and hydrologic data for San Marcos Springs recharge characterization near San Marcos, Texas, November 2008--December 2010: U.S. Geological Survey Data Series 672, Report: vi, 19 p.; Appendixes, https://doi.org/10.3133/ds672.","productDescription":"Report: vi, 19 p.; Appendixes","numberOfPages":"25","onlineOnly":"Y","additionalOnlineFiles":"Y","costCenters":[{"id":583,"text":"Texas Water Science Center","active":true,"usgs":true}],"links":[{"id":254513,"rank":0,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/ds_672.gif"},{"id":254510,"rank":100,"type":{"id":15,"text":"Index Page"},"url":"https://pubs.usgs.gov/ds/672/","linkFileType":{"id":5,"text":"html"}}],"country":"United States","state":"Texas","city":"San Marcos","noUsgsAuthors":false,"publicationStatus":"PW","scienceBaseUri":"505a15d5e4b0c8380cd54f69","contributors":{"authors":[{"text":"Crow, Cassi L. 0000-0002-1279-2485 ccrow@usgs.gov","orcid":"https://orcid.org/0000-0002-1279-2485","contributorId":1666,"corporation":false,"usgs":true,"family":"Crow","given":"Cassi","email":"ccrow@usgs.gov","middleInitial":"L.","affiliations":[{"id":583,"text":"Texas Water Science Center","active":true,"usgs":true}],"preferred":true,"id":463334,"contributorType":{"id":1,"text":"Authors"},"rank":1}]}} ,{"id":70156340,"text":"70156340 - 2012 - Advancing global marine biogeography research with open-source GIS software and cloud-computing","interactions":[],"lastModifiedDate":"2015-08-19T16:45:40","indexId":"70156340","displayToPublicDate":"2012-04-13T00:00:00","publicationYear":"2012","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":3618,"text":"Transactions in GIS","active":true,"publicationSubtype":{"id":10}},"title":"Advancing global marine biogeography research with open-source GIS software and cloud-computing","docAbstract":"Across many scientific domains, the ability to aggregate disparate datasets enables more meaningful global analyses. Within marine biology, the Census of Marine Life served as the catalyst for such a global data aggregation effort. Under the Census framework, the Ocean Biogeographic Information System was established to coordinate an unprecedented aggregation of global marine biogeography data. The OBIS data system now contains 31.3 million observations, freely accessible through a geospatial portal. The challenges of storing, querying, disseminating, and mapping a global data collection of this complexity and magnitude are significant. In the face of declining performance and expanding feature requests, a redevelopment of the OBIS data system was undertaken. Following an Open Source philosophy, the OBIS technology stack was rebuilt using PostgreSQL, PostGIS, GeoServer and OpenLayers. This approach has markedly improved the performance and online user experience while maintaining a standards-compliant and interoperable framework. Due to the distributed nature of the project and increasing needs for storage, scalability and deployment flexibility, the entire hardware and software stack was built on a Cloud Computing environment. The flexibility of the platform, combined with the power of the application stack, enabled rapid re-development of the OBIS infrastructure, and ensured complete standards-compliance.
","language":"English","publisher":"Wiley","doi":"10.1111/j.1467-9671.2012.01310.x","usgsCitation":"Fujioka, E., Vanden Berghe, E., Donnelly, B., Castillo, J., Cleary, J., Holmes, C., McKnight, S., and Halpin, P., 2012, Advancing global marine biogeography research with open-source GIS software and cloud-computing: Transactions in GIS, v. 16, no. 2, p. 143-160, https://doi.org/10.1111/j.1467-9671.2012.01310.x.","productDescription":"17 p.","startPage":"143","endPage":"160","numberOfPages":"17","onlineOnly":"N","additionalOnlineFiles":"N","costCenters":[],"links":[{"id":306977,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"volume":"16","issue":"2","noUsgsAuthors":false,"publicationDate":"2012-04-13","publicationStatus":"PW","scienceBaseUri":"55d5a8ace4b0518e3546a4aa","contributors":{"authors":[{"text":"Fujioka, Ei","contributorId":146701,"corporation":false,"usgs":false,"family":"Fujioka","given":"Ei","email":"","affiliations":[{"id":12643,"text":"Duke University","active":true,"usgs":false}],"preferred":false,"id":568758,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Vanden Berghe, Edward","contributorId":146666,"corporation":false,"usgs":false,"family":"Vanden Berghe","given":"Edward","email":"","affiliations":[{"id":12727,"text":"Rutgers University","active":true,"usgs":false}],"preferred":false,"id":568759,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Donnelly, Ben","contributorId":146702,"corporation":false,"usgs":false,"family":"Donnelly","given":"Ben","email":"","affiliations":[{"id":12643,"text":"Duke University","active":true,"usgs":false}],"preferred":false,"id":568760,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Castillo, Julio","contributorId":146703,"corporation":false,"usgs":false,"family":"Castillo","given":"Julio","email":"","affiliations":[],"preferred":false,"id":568761,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Cleary, Jesse","contributorId":146704,"corporation":false,"usgs":false,"family":"Cleary","given":"Jesse","email":"","affiliations":[{"id":12643,"text":"Duke University","active":true,"usgs":false}],"preferred":false,"id":568762,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"Holmes, Chris","contributorId":146705,"corporation":false,"usgs":false,"family":"Holmes","given":"Chris","email":"","affiliations":[],"preferred":false,"id":568763,"contributorType":{"id":1,"text":"Authors"},"rank":6},{"text":"McKnight, Sean","contributorId":146706,"corporation":false,"usgs":false,"family":"McKnight","given":"Sean","email":"","affiliations":[],"preferred":false,"id":568764,"contributorType":{"id":1,"text":"Authors"},"rank":7},{"text":"Halpin, patrick","contributorId":146707,"corporation":false,"usgs":false,"family":"Halpin","given":"patrick","affiliations":[{"id":12643,"text":"Duke University","active":true,"usgs":false}],"preferred":false,"id":568765,"contributorType":{"id":1,"text":"Authors"},"rank":8}]}} ,{"id":70193784,"text":"70193784 - 2012 - Combining lake and watershed characteristics with Landsat TM data for remote estimation of regional lake clarity","interactions":[],"lastModifiedDate":"2017-11-08T14:35:14","indexId":"70193784","displayToPublicDate":"2012-04-13T00:00:00","publicationYear":"2012","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":3254,"text":"Remote Sensing of Environment","printIssn":"0034-4257","active":true,"publicationSubtype":{"id":10}},"title":"Combining lake and watershed characteristics with Landsat TM data for remote estimation of regional lake clarity","docAbstract":"Water clarity is a reliable indicator of lake productivity and an ideal metric of regional water quality. Clarity is an indicator of other water quality variables including chlorophyll-a, total phosphorus and trophic status; however, unlike these metrics, clarity can be accurately and efficiently estimated remotely on a regional scale. Remote sensing is useful in regions containing a large number of lakes that are cost prohibitive to monitor regularly using traditional field methods. Field-assessed lakes generally are easily accessible and may represent a spatially irregular, non-random sample of a region. We developed a remote monitoring program for Maine lakes >8 ha (1511 lakes) to supplement existing field monitoring programs. We combined Landsat 5 Thematic Mapper (TM) and Landsat 7 Enhanced Thematic Mapper Plus (ETM+) brightness values for TM bands 1 (blue) and 3 (red) to estimate water clarity (secchi disk depth) during 1990–2010. Although similar procedures have been applied to Minnesota and Wisconsin lakes, neither state incorporates physical lake variables or watershed characteristics that potentially affect clarity into their models. Average lake depth consistently improved model fitness, and the proportion of wetland area in lake watersheds also explained variability in clarity in some cases. Nine regression models predicted water clarity (R2 = 0.69–0.90) during 1990–2010, with separate models for eastern (TM path 11; four models) and western Maine (TM path 12; five models that captured differences in topography and landscape disturbance. Average absolute difference between model-estimated and observed secchi depth ranged 0.65–1.03 m. Eutrophic and mesotrophic lakes consistently were estimated more accurately than oligotrophic lakes. Our results show that TM bands 1 and 3 can be used to estimate regional lake water clarity outside the Great Lakes Region and that the accuracy of estimates is improved with additional model variables that reflect physical lake characteristics and watershed conditions.
","language":"English","publisher":"Elsevier ","doi":"10.1016/j.rse.2012.03.006","usgsCitation":"McCullough, I.M., Loftin, C., and Sader, S., 2012, Combining lake and watershed characteristics with Landsat TM data for remote estimation of regional lake clarity: Remote Sensing of Environment, v. 123, p. 109-115, https://doi.org/10.1016/j.rse.2012.03.006.","productDescription":"7 p.","startPage":"109","endPage":"115","ipdsId":"IP-033562","costCenters":[{"id":199,"text":"Coop Res Unit Leetown","active":true,"usgs":true}],"links":[{"id":348474,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","state":"Maine","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -69.32373046875,\n 48.980216985374994\n ],\n [\n -72.94921875,\n 43.56447158721811\n ],\n [\n -69.169921875,\n 42.147114459220994\n ],\n [\n -65.6103515625,\n 47.84265762816538\n ],\n [\n -69.32373046875,\n 48.980216985374994\n ]\n ]\n ]\n }\n }\n ]\n}","volume":"123","publishingServiceCenter":{"id":9,"text":"Reston PSC"},"noUsgsAuthors":false,"publicationStatus":"PW","scienceBaseUri":"5a0425f1e4b0dc0b45b456e5","contributors":{"authors":[{"text":"McCullough, Ian M.","contributorId":149952,"corporation":false,"usgs":false,"family":"McCullough","given":"Ian","email":"","middleInitial":"M.","affiliations":[],"preferred":false,"id":721311,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Loftin, Cyndy 0000-0001-9104-3724 cyndy_loftin@usgs.gov","orcid":"https://orcid.org/0000-0001-9104-3724","contributorId":146427,"corporation":false,"usgs":true,"family":"Loftin","given":"Cyndy","email":"cyndy_loftin@usgs.gov","affiliations":[{"id":199,"text":"Coop Res Unit Leetown","active":true,"usgs":true}],"preferred":true,"id":720505,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Sader, Steven A.","contributorId":112282,"corporation":false,"usgs":true,"family":"Sader","given":"Steven A.","affiliations":[],"preferred":false,"id":721312,"contributorType":{"id":1,"text":"Authors"},"rank":3}]}} ,{"id":70038031,"text":"ofr20121060 - 2012 - Alaska Geochemical Database - Mineral Exploration Tool for the 21st Century - PDF of presentation","interactions":[],"lastModifiedDate":"2018-08-19T21:25:20","indexId":"ofr20121060","displayToPublicDate":"2012-04-12T00:00:00","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":"2012-1060","title":"Alaska Geochemical Database - Mineral Exploration Tool for the 21st Century - PDF of presentation","docAbstract":"The U.S. Geological Survey has created a geochemical database of geologic material samples collected in Alaska. This database is readily accessible to anyone with access to the Internet. Designed as a tool for mineral or environmental assessment, land management, or mineral exploration, the initial version of the Alaska Geochemical Database - U.S. Geological Survey Data Series 637 - contains geochemical, geologic, and geospatial data for 264,158 samples collected from 1962-2009: 108,909 rock samples; 92,701 sediment samples; 48,209 heavy-mineral-concentrate samples; 6,869 soil samples; and 7,470 mineral samples. In addition, the Alaska Geochemical Database contains mineralogic data for 18,138 nonmagnetic-fraction heavy mineral concentrates, making it the first U.S. Geological Survey database of this scope that contains both geochemical and mineralogic data. Examples from the Alaska Range will illustrate potential uses of the Alaska Geochemical Database in mineral exploration. Data from the Alaska Geochemical Database have been extensively checked for accuracy of sample media description, sample site location, and analytical method using U.S. Geological Survey sample-submittal archives and U.S. Geological Survey publications (plus field notebooks and sample site compilation base maps from the Alaska Technical Data Unit in Anchorage, Alaska). The database is also the repository for nearly all previously released U.S. Geological Survey Alaska geochemical datasets. Although the Alaska Geochemical Database is a fully relational database in Microsoft® Access 2003 and 2010 formats, these same data are also provided as a series of spreadsheet files in Microsoft® Excel 2003 and 2010 formats, and as ASCII text files. A DVD version of the Alaska Geochemical Database was released in October 2011, as U.S. Geological Survey Data Series 637, and data downloads are available at http://pubs.usgs.gov/ds/637/. Also, all Alaska Geochemical Database data have been incorporated into the interactive U.S. Geological Survey Mineral Resource Data web portal, available at http://mrdata.usgs.gov/.","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/ofr20121060","usgsCitation":"Granitto, M., Schmidt, J.M., Labay, K., Shew, N.B., and Gamble, B.M., 2012, Alaska Geochemical Database - Mineral Exploration Tool for the 21st Century - PDF of presentation: U.S. Geological Survey Open-File Report 2012-1060, iii, 33 p., https://doi.org/10.3133/ofr20121060.","productDescription":"iii, 33 p.","onlineOnly":"Y","costCenters":[{"id":171,"text":"Central Mineral and Environmental Resources Science Center","active":true,"usgs":true}],"links":[{"id":254505,"rank":100,"type":{"id":15,"text":"Index Page"},"url":"https://pubs.usgs.gov/of/2012/1060/","linkFileType":{"id":5,"text":"html"}},{"id":254506,"rank":0,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/ofr_2012_1060.gif"}],"country":"United States","state":"Alaska","geographicExtents":"{ \"type\": \"FeatureCollection\", \"features\": [ { \"type\": \"Feature\", \"properties\": {}, \"geometry\": { \"type\": \"Polygon\", \"coordinates\": [ [ [ 173,54.666666666666664 ], [ 173,71.83333333333333 ], [ -130,71.83333333333333 ], [ -130,54.666666666666664 ], [ 173,54.666666666666664 ] ] ] } } ] }","noUsgsAuthors":false,"publicationStatus":"PW","scienceBaseUri":"5059e931e4b0c8380cd4814e","contributors":{"authors":[{"text":"Granitto, Matthew 0000-0003-3445-4863 granitto@usgs.gov","orcid":"https://orcid.org/0000-0003-3445-4863","contributorId":1224,"corporation":false,"usgs":true,"family":"Granitto","given":"Matthew","email":"granitto@usgs.gov","affiliations":[{"id":171,"text":"Central Mineral and Environmental Resources Science Center","active":true,"usgs":true},{"id":387,"text":"Mineral Resources Program","active":true,"usgs":true}],"preferred":true,"id":463306,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"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":463307,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Labay, Keith A. 0000-0002-6763-3190 klabay@usgs.gov","orcid":"https://orcid.org/0000-0002-6763-3190","contributorId":2097,"corporation":false,"usgs":true,"family":"Labay","given":"Keith A.","email":"klabay@usgs.gov","affiliations":[{"id":119,"text":"Alaska Science Center Geology Minerals","active":true,"usgs":true}],"preferred":false,"id":463309,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Shew, Nora B. 0000-0003-0025-7220 nshew@usgs.gov","orcid":"https://orcid.org/0000-0003-0025-7220","contributorId":3382,"corporation":false,"usgs":true,"family":"Shew","given":"Nora","email":"nshew@usgs.gov","middleInitial":"B.","affiliations":[{"id":119,"text":"Alaska Science Center Geology Minerals","active":true,"usgs":true}],"preferred":true,"id":463308,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Gamble, Bruce M. bgamble@usgs.gov","contributorId":560,"corporation":false,"usgs":true,"family":"Gamble","given":"Bruce","email":"bgamble@usgs.gov","middleInitial":"M.","affiliations":[],"preferred":true,"id":463305,"contributorType":{"id":1,"text":"Authors"},"rank":5}]}} ,{"id":70038017,"text":"sir20115216 - 2012 - Status and understanding of groundwater quality in the Tahoe-Martis, Central Sierra, and Southern Sierra study units, 2006-2007--California GAMA Priority Basin Project","interactions":[],"lastModifiedDate":"2012-04-30T16:43:35","indexId":"sir20115216","displayToPublicDate":"2012-04-11T00:00:00","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":"2011-5216","title":"Status and understanding of groundwater quality in the Tahoe-Martis, Central Sierra, and Southern Sierra study units, 2006-2007--California GAMA Priority Basin Project","docAbstract":"Groundwater quality in the Tahoe-Martis, Central Sierra, and Southern Sierra study units was investigated as part of the Priority Basin Project of the California Groundwater Ambient Monitoring and Assessment (GAMA) Program. The three study units are located in the Sierra Nevada region of California in parts of Nevada, Placer, El Dorado, Madera, Tulare, and Kern Counties. The GAMA Priority Basin Project is being conducted by the California State Water Resources Control Board, in collaboration with the U.S. Geological Survey (USGS) and the Lawrence Livermore National Laboratory. The project was designed to provide statistically robust assessments of untreated groundwater quality within the primary aquifer systems used for drinking water. The primary aquifer systems (hereinafter, primary aquifers) for each study unit are defined by the depth of the screened or open intervals of the wells listed in the California Department of Public Health (CDPH) database of wells used for municipal and community drinking-water supply. The quality of groundwater in shallower or deeper water-bearing zones may differ from that in the primary aquifers; shallower groundwater may be more vulnerable to contamination from the surface. The assessments for the Tahoe-Martis, Central Sierra, and Southern Sierra study units were based on water-quality and ancillary data collected by the USGS from 132 wells in the three study units during 2006 and 2007 and water-quality data reported in the CDPH database. Two types of assessments were made: (1) status, assessment of the current quality of the groundwater resource, and (2) understanding, identification of the natural and human factors affecting groundwater quality. The assessments characterize untreated groundwater quality, not the quality of treated drinking water delivered to consumers by water purveyors. Relative-concentrations (sample concentrations divided by benchmark concentrations) were used for evaluating groundwater quality for those constituents that have Federal or California regulatory or non-regulatory benchmarks for drinking-water quality. A relative-concentration (RC) greater than (>) 1.0 indicates a concentration above a benchmark. RCs for organic constituents (volatile organic compounds and pesticides) and special-interest constituents were classified as \"high\" (RC > 1.0), \"moderate\" (1.0 ≥ RC > 0.1), or \"low\" (RC ≤ 0.1). For inorganic constituents (major ions, trace elements, nutrients, and radioactive constituents), the boundary between low and moderate RCs was set at 0.5. A new metric, aquifer-scale proportion, was used in the status assessment as the primary metric for evaluating regional-scale groundwater quality. High aquifer-scale proportion is defined as the percentage of the area of the primary aquifers with RC > 1.0 for a particular constituent or class of constituents; moderate and low aquifer-scale proportions are defined as the percentages of the area of the primary aquifer with moderate and low RCs, respectively. Percentages are based on an areal rather than a volumetric basis. Two statistical approaches—grid-based, which used one value per grid cell, and spatially weighted, which used multiple values per grid cell—were used to calculate aquifer-scale proportions for individual constituents and classes of constituents. The spatially weighted estimates of high aquifer-scale proportions were within the 90-percent (%) confidence intervals of the grid-based estimates in all cases. The status assessment showed that inorganic constituents had greater high and moderate aquifer-scale proportions than did organic constituents in all three study units. In the Tahoe-Martis study unit, RCs for inorganic constituents with health-based benchmarks (primarily arsenic) were high in 20% of the primary aquifer, moderate in 13%, and low in 67%. In the Central Sierra study unit, aquifer-scale proportions for inorganic constituents with health-based benchmarks (primarily arsenic, uranium, fluoride, and molybdenum) were 41% high, 36% moderate, and 23% low. In the Southern Sierra study unit, 32, 34, and 34% of the primary aquifer had high, moderate, and low RCs of inorganic constituents with health-based benchmarks (primarily arsenic, uranium, fluoride, boron, and nitrate). The high aquifer-scale proportions for inorganic constituents with non-health-based benchmarks were 14, 34, and 24% for the Tahoe-Martis, Central Sierra, and Southern Sierra study units, respectively, and the primary constituent was manganese for all three study units. Organic constituents with health-based benchmarks were not present at high RCs in the primary aquifers of the Central Sierra and Southern Sierra study units, and were present at high RCs in only 1% of the Tahoe-Martis study unit. Moderate aquifer-scale proportions for organic constituents were < 5% in all three study units. Of the 173 organic constituents analyzed, 22 were detected, and of those 22, 17 have health-based benchmarks. Organic constituents were detected in 20, 27, and 40% of the primary aquifers in the Tahoe-Martis, Central Sierra, and Southern Sierra study units, respectively. Four organic constituents had study-unit detection frequencies of > 10%: the trihalomethane chloroform in the Tahoe-Martis study unit; chloroform and the herbicide simazine in the Central Sierra study unit; and chloroform, simazine, the herbicide atrazine, and the solvent perchloroethene in the Southern Sierra study unit. The second component of this study, the understanding assessment, identified the natural and human factors that may have affected groundwater quality in the three study units by evaluating statistical correlations between water-quality constituents and potential explanatory factors. The potential explanatory factors evaluated were land use, septic tank density, climate, relative position in the regional flow system, aquifer lithology, geographic location, well depth and depth to the top of the screened or open interval in the well, groundwater age distribution, pH, and dissolved oxygen concentration. Results of the statistical evaluations were used to explain the occurrence and distribution of constituents in the study units. Aquifer lithology (granitic, metamorphic, sedimentary, or volcanic rocks), groundwater age distribution [modern (recharged since 1952), pre-modern (recharged before 1952), or mixed (containing both modern and pre-modern recharge)], geographic location, pH, and dissolved oxygen were the most significant factors explaining the occurrence patterns of most inorganic constituents. High and moderate RCs of arsenic were associated with pre-modern and mixed-age groundwater and two distinct sets of geochemical conditions: (1) oxic, high-pH conditions, particularly in volcanic rocks, and (2) low-oxygen to anoxic conditions and low- to neutral-pH conditions, particularly in granitic rocks. In granitic and metamorphic rocks, high and moderate RCs of uranium were associated with pre-modern and mixed-age groundwater, low-oxygen to anoxic conditions, and location within parts of the Central Sierra and Southern Sierra study units known to have rocks with anomalously high uranium content compared to other parts of the Sierra Nevada. High and moderate RCs of uranium in sedimentary rocks were associated with pre-modern-age groundwater, oxic and high-pH conditions, and location in the Tahoe Valley South subbasin within the Tahoe-Martis study unit. Land use within 500 meters of the well and groundwater age were the most significant factors explaining occurrence patterns of organic constituents. Herbicide detections were most strongly associated with modern- and mixed-age groundwater from wells with agricultural land use. Trihalomethane detections were most strongly associated with modern- and mixed-age groundwater from wells with > 10% urban land use and (or) septic tank density > 7 tanks per square kilometer. Solvent detections were not significantly related to groundwater age. Eighty-three percent of the wells with modern- or mixed-age groundwater, and 86% of wells with detections of herbicides and (or) THMs had depths to the top of the screened or open interval of < 170 feet. These observations suggest that modern groundwater has infiltrated to depths of approximately 170 feet below land surface. Land use and occurrence of herbicides and solvents were the most significant factors explaining the occurrence of nitrate. Wells with > 5% agricultural land use and detection of a herbicide or solvent had the highest nitrate concentrations. Comparison between observed and predicted detection frequencies of perchlorate suggests that the perchlorate detected at concentrations < 1 microgram per liter likely reflects the distribution of perchlorate under natural conditions, and that the perchlorate detected at higher concentrations may reflect redistribution of originally natural perchlorate salts by irrigation in the agricultural areas of the Southern Sierra study unit.","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/sir20115216","collaboration":"A product of the California Groundwater Ambient Monitoring and Assessment (GAMA) Program Prepared in cooperation with the California State Water Resources Control Board","usgsCitation":"Fram, M.S., and Belitz, K., 2012, Status and understanding of groundwater quality in the Tahoe-Martis, Central Sierra, and Southern Sierra study units, 2006-2007--California GAMA Priority Basin Project: U.S. Geological Survey Scientific Investigations Report 2011-5216, xiv, 164 p.; Appendices;, https://doi.org/10.3133/sir20115216.","productDescription":"xiv, 164 p.; Appendices;","startPage":"i","endPage":"222","numberOfPages":"236","additionalOnlineFiles":"N","costCenters":[{"id":154,"text":"California Water Science Center","active":true,"usgs":true}],"links":[{"id":254483,"rank":0,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/sir_2011_5216.jpg"},{"id":254479,"rank":100,"type":{"id":15,"text":"Index Page"},"url":"https://pubs.usgs.gov/sir/2011/5216/","linkFileType":{"id":5,"text":"html"}}],"country":"United States","state":"California","noUsgsAuthors":false,"publicationStatus":"PW","scienceBaseUri":"505b979ce4b08c986b31bb7a","contributors":{"authors":[{"text":"Fram, Miranda S. 0000-0002-6337-059X mfram@usgs.gov","orcid":"https://orcid.org/0000-0002-6337-059X","contributorId":1156,"corporation":false,"usgs":true,"family":"Fram","given":"Miranda","email":"mfram@usgs.gov","middleInitial":"S.","affiliations":[{"id":154,"text":"California Water Science Center","active":true,"usgs":true}],"preferred":true,"id":463257,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Belitz, Kenneth 0000-0003-4481-2345 kbelitz@usgs.gov","orcid":"https://orcid.org/0000-0003-4481-2345","contributorId":442,"corporation":false,"usgs":true,"family":"Belitz","given":"Kenneth","email":"kbelitz@usgs.gov","affiliations":[{"id":466,"text":"New England Water Science Center","active":true,"usgs":true},{"id":27111,"text":"National Water Quality Program","active":true,"usgs":true},{"id":451,"text":"National Water Quality Assessment Program","active":true,"usgs":true},{"id":503,"text":"Office of Water Quality","active":true,"usgs":true},{"id":376,"text":"Massachusetts Water Science Center","active":true,"usgs":true}],"preferred":true,"id":463256,"contributorType":{"id":1,"text":"Authors"},"rank":2}]}} ,{"id":70037956,"text":"70037956 - 2012 - The effect of swab sample choice on the detection of avian influenza in apparently healthy wild ducks","interactions":[],"lastModifiedDate":"2022-11-04T15:22:50.199491","indexId":"70037956","displayToPublicDate":"2012-04-09T16:13:00","publicationYear":"2012","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":948,"text":"Avian Diseases","active":true,"publicationSubtype":{"id":10}},"title":"The effect of swab sample choice on the detection of avian influenza in apparently healthy wild ducks","docAbstract":"Historically, avian influenza viruses have been isolated from cloacal swab specimens, but recent data suggest that the highly pathogenic avian influenza (HPAI) H5N1 virus can be better detected from respiratory tract specimens. To better understand how swab sample type affects the detection ability of low pathogenic avian influenza (LPAI) viruses we collected and tested four swab types: oropharyngeal swabs (OS), cloacal swabs (CS), the two swab types combined in the laboratory (LCS), and the two swab types combined in the field (FCS). A total of 1968 wild waterfowl were sampled by each of these four methods and tested for avian influenza virus using matrix gene reverse-transcription (RT)-PCR. The highest detection rate occurred with the FCS (4.3%) followed by the CS (4.0%). Although this difference did not achieve traditional statistical significance, Bayesian analysis indicated that FCS was superior to CS with an 82% probability. The detection rates for both the LCS (2.4%) and the OS (0.4%) were significantly different from the FCS. In addition, every swab type that was matrix RT-PCR positive was also tested for recovery of viable influenza virus. This protocol reduced the detection rate, but the ordering of swab types remained the same: 1.73% FCS, 1.42% CS, 0.81% LCS, and 0% OS. Our data suggest that the FCS performed at least as well as any other swab type for detecting LPAI viruses in the wild ducks tested. When considering recent studies showing that HPAI H5N1 can be better detected in the respiratory tract, the FCS is the most appropriate sample to collect for HPAI H5N1 surveillance while not compromising LPAI studies.
","language":"English","publisher":"American Association of Avian Pathologists","publisherLocation":"Jacksonville, FL","doi":"10.1637/9832-061311-Reg.1","usgsCitation":"Ip, S., Dusek, R., and Heisey, D.M., 2012, The effect of swab sample choice on the detection of avian influenza in apparently healthy wild ducks: Avian Diseases, v. 56, no. 1, p. 114-119, https://doi.org/10.1637/9832-061311-Reg.1.","productDescription":"6 p.","startPage":"114","endPage":"119","onlineOnly":"N","additionalOnlineFiles":"N","costCenters":[{"id":456,"text":"National Wildlife Health Center","active":true,"usgs":true}],"links":[{"id":254472,"rank":0,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","state":"California, Colorado, North Dakota","otherGeospatial":"Delevan National Wildlife Refuge, J. Clark Salyer National Wildlife Refuge, Monte Vista National Wildlife Refuge","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -122.11921691894531,\n 39.34412196864768\n ],\n [\n -122.11887359619139,\n 39.33469574877604\n ],\n [\n -122.12865829467772,\n 39.334828521261365\n ],\n [\n -122.12900161743164,\n 39.321682822112805\n ],\n [\n -122.12900161743164,\n 39.31769878905631\n ],\n [\n -122.11801528930664,\n 39.31716756750504\n ],\n [\n -122.11801528930664,\n 39.27372656321117\n ],\n [\n -122.08110809326172,\n 39.27319500791644\n ],\n [\n -122.07733154296875,\n 39.27704869244068\n ],\n [\n -122.07767486572266,\n 39.284489690283785\n ],\n [\n -122.07286834716795,\n 39.285154026653785\n ],\n [\n -122.07286834716795,\n 39.33150913348649\n ],\n [\n -122.08110809326172,\n 39.33217302364838\n ],\n [\n -122.08145141601561,\n 39.34359094779544\n ],\n [\n -122.11921691894531,\n 39.34412196864768\n ]\n ]\n ]\n }\n },\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"coordinates\": [\n [\n [\n -106.05468776960139,\n 37.480597666970866\n ],\n [\n -106.05468776960139,\n 37.47295956804537\n ],\n [\n -106.04243825242341,\n 37.47295956804537\n ],\n [\n -106.04243825242341,\n 37.480597666970866\n ],\n [\n -106.05468776960139,\n 37.480597666970866\n ]\n ]\n ],\n \"type\": \"Polygon\"\n }\n },\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"coordinates\": [\n [\n [\n -100.91689643902382,\n 48.807075742439\n ],\n [\n -100.91689643902382,\n 48.78301305457373\n ],\n [\n -100.87075637724375,\n 48.78301305457373\n ],\n [\n -100.87075637724375,\n 48.807075742439\n ],\n [\n -100.91689643902382,\n 48.807075742439\n ]\n ]\n ],\n \"type\": \"Polygon\"\n }\n }\n ]\n}","volume":"56","issue":"1","noUsgsAuthors":false,"publicationStatus":"PW","scienceBaseUri":"505bab53e4b08c986b322d81","contributors":{"authors":[{"text":"Ip, S. 0000-0003-4844-7533 hip@usgs.gov","orcid":"https://orcid.org/0000-0003-4844-7533","contributorId":727,"corporation":false,"usgs":true,"family":"Ip","given":"S.","email":"hip@usgs.gov","affiliations":[{"id":456,"text":"National Wildlife Health Center","active":true,"usgs":true}],"preferred":true,"id":463146,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Dusek, Robert J. 0000-0001-6177-7479 rdusek@usgs.gov","orcid":"https://orcid.org/0000-0001-6177-7479","contributorId":2397,"corporation":false,"usgs":true,"family":"Dusek","given":"Robert J.","email":"rdusek@usgs.gov","affiliations":[{"id":456,"text":"National Wildlife Health Center","active":true,"usgs":true}],"preferred":false,"id":463147,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Heisey, Dennis M. dheisey@usgs.gov","contributorId":2455,"corporation":false,"usgs":true,"family":"Heisey","given":"Dennis","email":"dheisey@usgs.gov","middleInitial":"M.","affiliations":[{"id":456,"text":"National Wildlife Health Center","active":true,"usgs":true}],"preferred":true,"id":463148,"contributorType":{"id":1,"text":"Authors"},"rank":3}]}} ,{"id":70037966,"text":"70037966 - 2012 - Bayesian shared frailty models for regional inference about wildlife survival","interactions":[],"lastModifiedDate":"2023-10-13T11:01:50.321221","indexId":"70037966","displayToPublicDate":"2012-04-09T16:01:00","publicationYear":"2012","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":774,"text":"Animal Conservation","active":true,"publicationSubtype":{"id":10}},"title":"Bayesian shared frailty models for regional inference about wildlife survival","docAbstract":"One can joke that 'exciting statistics' is an oxymoron, but it is neither a joke nor an exaggeration to say that these are exciting times to be involved in statistical ecology. As Halstead et al.'s (2012) paper nicely exemplifies, recently developed Bayesian analyses can now be used to extract insights from data using techniques that would have been unavailable to the ecological researcher just a decade ago. Some object to this, implying that the subjective priors of the Bayesian approach is the pathway to perdition (e.g. Lele & Dennis, 2009). It is reasonable to ask whether these new approaches are really giving us anything that we could not obtain with traditional tried-and-true frequentist approaches. I believe the answer is a clear yes.","language":"English","publisher":"The Zoological Society of London","publisherLocation":"London, England","doi":"10.1111/j.1469-1795.2012.00532.x","usgsCitation":"Heisey, D., 2012, Bayesian shared frailty models for regional inference about wildlife survival: Animal Conservation, v. 15, no. 2, p. 127-128, https://doi.org/10.1111/j.1469-1795.2012.00532.x.","productDescription":"2 p.","startPage":"127","endPage":"128","numberOfPages":"8","ipdsId":"IP-036681","costCenters":[{"id":456,"text":"National Wildlife Health Center","active":true,"usgs":true}],"links":[{"id":474523,"rank":2,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.1111/j.1469-1795.2012.00532.x","text":"Publisher Index Page"},{"id":254471,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"volume":"15","issue":"2","noUsgsAuthors":false,"publicationDate":"2012-03-19","publicationStatus":"PW","scienceBaseUri":"5059f02de4b0c8380cd4a61a","contributors":{"authors":[{"text":"Heisey, D.M.","contributorId":77496,"corporation":false,"usgs":true,"family":"Heisey","given":"D.M.","email":"","affiliations":[],"preferred":false,"id":463181,"contributorType":{"id":1,"text":"Authors"},"rank":1}]}} ,{"id":70044111,"text":"70044111 - 2012 - Seasonal use of shallow water habitat in the Lower Snake River reservoirs by juvenile fall Chinook salmon","interactions":[],"lastModifiedDate":"2016-05-04T12:39:00","indexId":"70044111","displayToPublicDate":"2012-04-06T03:45:00","publicationYear":"2012","noYear":false,"publicationType":{"id":18,"text":"Report"},"publicationSubtype":{"id":4,"text":"Other Government Series"},"title":"Seasonal use of shallow water habitat in the Lower Snake River reservoirs by juvenile fall Chinook salmon","docAbstract":"The U.S. Army Corps of Engineers (COE) is preparing a long term management plan for sediments that affect the authorized project purposes of the Lower Granite, Little Goose, Lower Monumental, and Ice Harbor reservoirs (hereafter, the lower Snake River reservoirs), and the area from the mouth of the Snake River to Ice Harbor Dam. We conducted a study from spring 2010 through winter 2011 to describe the habitat use by juvenile Chinook salmon within a selected group of shallow water habitat complexes (< 6 m deep) in the lower Snake River reservoirs to help inform the long-term plan. Natural fry and parr were present within all four shallow water habitat complexes that we studied from early spring through early summer, and parr ( = 40,345 ± 18,800 [error bound]) were more abundant than fry ( = 24,615 ± 5,701). Water < 2 m deep was highly used for rearing by natural fall Chinook salmon subyearlings (fry and parr combined; hereafter natural subyearlings) based on duration of use and relative group abundances during spring and summer, whereas the 2–6 m depth interval was more highly used by migratory hatchery fall Chinook salmon subyearlings and spring, summer, and fall Chinook salmon yearlings. Overall mean spring-summer apparent density of natural subyearlings was 15.5 times higher within the < 2 m depth interval than within the 2–6 m depth interval. Density of natural subyearlings also decreased as the distance a given shallow water habitat complex was located from the riverine spawning areas increased. Reservoir-type juveniles (or fish likely destined to become reservoir-type juveniles) were present in the lower Snake River reservoirs from fall 2010 through winter 2011; however, use of shallow water habitat by reservoir-type juveniles was limited during our study. We only collected 38 reservoir-type juveniles in shallow water habitat sites in beach and lampara seines during the fall. Radiotelemetry data revealed that though many tagged fish passed shallow water habitat sites, relatively few fish entered them and the median time fish spent within a given site was less than 1.4 h. Fish located by mobile tracking away from study sites were pelagically oriented, and generally not found over shallow water or close to shore. The findings in this report: (1) support the selection of natural fall Chinook subyearlings as the indicator group for determining the potential benefits of using dredge spoils to create shallow water habitat, (2) provide evidence for shallow water habitat use by natural subyearlings, (3) provide evidence against large-scale use of shallow water habitat by reservoir-type juveniles, (4) suggest that the depth criterion for defining shallow water habitat (i.e., < 6 m deep) warrants reconsideration, and (5) provide guidance for when to dredge and create shallow water habitat. Future research on habitat preference, feeding ecology, the food web, and intra-specific competition would help to better inform the long-term management plan.
","language":"English","publisher":"U.S. Army Corps of Engineers","publisherLocation":"Walla Walla District, Walla Walla, WA","usgsCitation":"Tiffan, K.F., and Connor, W.P., 2012, Seasonal use of shallow water habitat in the Lower Snake River reservoirs by juvenile fall Chinook salmon, 74 p.","productDescription":"74 p.","numberOfPages":"74","onlineOnly":"N","additionalOnlineFiles":"N","ipdsId":"IP-033601","costCenters":[{"id":654,"text":"Western Fisheries Research Center","active":true,"usgs":true}],"links":[{"id":320972,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"noUsgsAuthors":false,"publicationStatus":"PW","scienceBaseUri":"572b1d3ae4b0b13d391b44fe","contributors":{"authors":[{"text":"Tiffan, Kenneth F. 0000-0002-5831-2846 ktiffan@usgs.gov","orcid":"https://orcid.org/0000-0002-5831-2846","contributorId":3200,"corporation":false,"usgs":true,"family":"Tiffan","given":"Kenneth","email":"ktiffan@usgs.gov","middleInitial":"F.","affiliations":[{"id":654,"text":"Western Fisheries Research Center","active":true,"usgs":true}],"preferred":true,"id":628801,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Connor, William P.","contributorId":107589,"corporation":false,"usgs":false,"family":"Connor","given":"William","email":"","middleInitial":"P.","affiliations":[{"id":16677,"text":"U.S. Fish and Wildlife Service, Idaho Fishery Resource Office, 276 Dworshak Complex Drive, Orofino, ID 83544","active":true,"usgs":false}],"preferred":false,"id":517162,"contributorType":{"id":1,"text":"Authors"},"rank":2}]}} ,{"id":70037959,"text":"ds614 - 2012 - Geospatial database for regional environmental assessment of central Colorado.","interactions":[],"lastModifiedDate":"2012-04-30T16:43:34","indexId":"ds614","displayToPublicDate":"2012-04-05T00:00:00","publicationYear":"2012","noYear":false,"publicationType":{"id":18,"text":"Report"},"publicationSubtype":{"id":5,"text":"USGS Numbered Series"},"seriesTitle":{"id":310,"text":"Data Series","code":"DS","onlineIssn":"2327-638X","printIssn":"2327-0271","active":false,"publicationSubtype":{"id":5}},"seriesNumber":"614","title":"Geospatial database for regional environmental assessment of central Colorado.","docAbstract":"In conjunction with the future planning needs of the U.S. Department of Agriculture, Forest Service, the U.S. Geological Survey conducted a detailed environmental assessment of the effects of historical mining on Forest Service lands in central Colorado. Stream sediment, macroinvertebrate, and various filtered and unfiltered water quality samples were collected during low-flow over a four-year period from 2004–2007. This report summarizes the sampling strategy, data collection, and analyses performed on these samples. The data are presented in Geographic Information System, Microsoft Excel, and comma-delimited formats. Reports on data interpretation are being prepared separately.","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/ds614","collaboration":"Prepared in cooperation with U.S. Department of Agriculture—Forest Service; U.S. Department of Interior—Bureau of Land Management; U.S. Department of Interior—National Park Service; Colorado Geological Survey","usgsCitation":"Church, S.E., San Juan, C.A., Fey, D.L., Schmidt, T., Klein, T.L., DeWitt, E.H., Wanty, R.B., Verplanck, P.L., Mitchell, K.A., Adams, M., Choate, L.M., Todorov, T., Rockwell, B.W., McEachron, L., and Anthony, M.W., 2012, Geospatial database for regional environmental assessment of central Colorado.: U.S. Geological Survey Data Series 614, vii, 64 p.; Appendix, https://doi.org/10.3133/ds614.","productDescription":"vii, 64 p.; Appendix","startPage":"i","endPage":"76","numberOfPages":"83","onlineOnly":"Y","additionalOnlineFiles":"Y","costCenters":[{"id":171,"text":"Central Mineral and Environmental Resources Science Center","active":true,"usgs":true}],"links":[{"id":254435,"rank":0,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/ds_614.png"},{"id":254433,"rank":100,"type":{"id":15,"text":"Index Page"},"url":"https://pubs.usgs.gov/ds/614/","linkFileType":{"id":5,"text":"html"}}],"projection":"Universal Transverse Mercator Projection","country":"United States","state":"Colorado","geographicExtents":"{ \"type\": \"FeatureCollection\", \"features\": [ { \"type\": \"Feature\", \"properties\": {}, \"geometry\": { \"type\": \"Polygon\", \"coordinates\": [ [ [ -107.5,36.25 ], [ -107.5,41.25 ], [ -103.75,41.25 ], [ -103.75,36.25 ], [ -107.5,36.25 ] ] ] } } ] }","noUsgsAuthors":false,"publicationStatus":"PW","scienceBaseUri":"505a28abe4b0c8380cd5a2da","contributors":{"authors":[{"text":"Church, Stan E. schurch@usgs.gov","contributorId":803,"corporation":false,"usgs":true,"family":"Church","given":"Stan","email":"schurch@usgs.gov","middleInitial":"E.","affiliations":[],"preferred":false,"id":463157,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"San Juan, Carma A. 0000-0002-9151-1919 csanjuan@usgs.gov","orcid":"https://orcid.org/0000-0002-9151-1919","contributorId":1146,"corporation":false,"usgs":true,"family":"San Juan","given":"Carma","email":"csanjuan@usgs.gov","middleInitial":"A.","affiliations":[{"id":35995,"text":"Geology, Geophysics, and Geochemistry Science Center","active":true,"usgs":true},{"id":171,"text":"Central Mineral and Environmental Resources Science Center","active":true,"usgs":true}],"preferred":true,"id":463158,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Fey, David L. dfey@usgs.gov","contributorId":713,"corporation":false,"usgs":true,"family":"Fey","given":"David","email":"dfey@usgs.gov","middleInitial":"L.","affiliations":[{"id":35995,"text":"Geology, Geophysics, and Geochemistry Science Center","active":true,"usgs":true}],"preferred":true,"id":463155,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Schmidt, Travis S. 0000-0003-1400-0637 tschmidt@usgs.gov","orcid":"https://orcid.org/0000-0003-1400-0637","contributorId":1300,"corporation":false,"usgs":true,"family":"Schmidt","given":"Travis S.","email":"tschmidt@usgs.gov","affiliations":[{"id":37277,"text":"WMA - 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2012 - Determination of streamflow of the Arkansas River near Bentley in south-central Kansas","interactions":[],"lastModifiedDate":"2012-04-30T16:43:34","indexId":"sir20125059","displayToPublicDate":"2012-04-04T00:00:00","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":"2012-5059","title":"Determination of streamflow of the Arkansas River near Bentley in south-central Kansas","docAbstract":"The Kansas Department of Agriculture, Division of Water Resources, requires that the streamflow of the Arkansas River just upstream from Bentley in south-central Kansas be measured or calculated before groundwater can be pumped from the well field. When the daily streamflow of the Arkansas River near Bentley is less than 165 cubic feet per second (ft3/s), pumping must be curtailed. Daily streamflow near Bentley was calculated by determining the relations between streamflow data from two reference streamgages with a concurrent record of 24 years, one located 17.2 miles (mi) upstream and one located 10.9 mi downstream, and streamflow at a temporary gage located just upstream from Bentley (Arkansas River near Bentley, Kansas). Flow-duration curves for the two reference streamgages indicate that during 1988?2011, the mean daily streamflow was less than 165 ft3/s 30 to 35 percent of the time. During extreme low-flow (drought) conditions, the reach of the Arkansas River between Hutchinson and Maize can lose flow to the adjacent alluvial aquifer, with streamflow losses as much as 1.6 cubic feet per second per mile. Three models were developed to calculate the streamflow of the Arkansas River near Bentley, Kansas. The model chosen depends on the data available and on whether the reach of the Arkansas River between Hutchinson and Maize is gaining or losing groundwater from or to the adjacent alluvial aquifer. The first model was a pair of equations developed from linear regressions of the relation between daily streamflow data from the Bentley streamgage and daily streamflow data from either the Arkansas River near Hutchinson, Kansas, station (station number 07143330) or the Arkansas River near Maize, Kansas, station (station number 07143375). The standard error of the Hutchinson-only equation was 22.8 ft3/s, and the standard error of the Maize-only equation was 22.3 ft3/s. The single-station model would be used if only one streamgage was available. In the second model, the flow gradient between the streamflow near Hutchinson and the streamflow near Maize was used to calculate the streamflow at the Bentley streamgage. This equation resulted in a standard error of 26.7 ft3/s. In the third model, a multiple regression analysis between both the daily streamflow of the Arkansas River near Hutchinson, Kansas, and the daily streamflow of the Arkansas River near Maize, Kansas, was used to calculate the streamflow at the Bentley streamgage. The multiple regression equation had a standard error of 21.2 ft3/s, which was the smallest of the standard errors for all the models. An analysis of the number of low-flow days and the number of days when the reach between Hutchinson and Maize loses flow to the adjacent alluvial aquifer indicates that the long-term trend is toward fewer days of losing conditions. This trend may indicate a long-term increase in water levels in the alluvial aquifer, which could be caused by one or more of several conditions, including an increase in rainfall, a decrease in pumping, a decrease in temperature, and an increase in streamflow upstream from the Hutchinson-to-Maize reach of the Arkansas River.","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/sir20125059","collaboration":"Prepared in cooperation with the City of Wichita, Kansas","usgsCitation":"Perry, C.A., 2012, Determination of streamflow of the Arkansas River near Bentley in south-central Kansas: U.S. Geological Survey Scientific Investigations Report 2012-5059, vi, 7 p.; National Water Information System : Web Interface, https://doi.org/10.3133/sir20125059.","productDescription":"vi, 7 p.; National Water Information System : Web Interface","onlineOnly":"Y","costCenters":[{"id":353,"text":"Kansas Water Science Center","active":false,"usgs":true}],"links":[{"id":254429,"rank":0,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/sir_2012_5059.gif"},{"id":254428,"rank":100,"type":{"id":15,"text":"Index Page"},"url":"https://pubs.usgs.gov/sir/2012/5059/","linkFileType":{"id":5,"text":"html"}}],"scale":"100000","country":"United States","state":"Kansas","county":"Harvey;Kingman;Reno;Sedgwick","city":"Bentley","otherGeospatial":"Arkansas River;Bentley Well Field","geographicExtents":"{ \"type\": \"FeatureCollection\", \"features\": [ { \"type\": \"Feature\", \"properties\": {}, \"geometry\": { \"type\": \"Polygon\", \"coordinates\": [ [ [ -97.83333333333333,37.666666666666664 ], [ -97.83333333333333,38 ], [ -97.33333333333333,38 ], [ -97.33333333333333,37.666666666666664 ], [ -97.83333333333333,37.666666666666664 ] ] ] } } ] }","noUsgsAuthors":false,"publicationStatus":"PW","scienceBaseUri":"5059ffcae4b0c8380cd4f3d0","contributors":{"authors":[{"text":"Perry, Charles A. cperry@usgs.gov","contributorId":2093,"corporation":false,"usgs":true,"family":"Perry","given":"Charles","email":"cperry@usgs.gov","middleInitial":"A.","affiliations":[],"preferred":true,"id":463136,"contributorType":{"id":1,"text":"Authors"},"rank":1}]}} ,{"id":70146287,"text":"70146287 - 2012 - Advances in the simulation and automated measurement of well-sorted granular material: 1. Simulation","interactions":[],"lastModifiedDate":"2015-05-04T13:28:33","indexId":"70146287","displayToPublicDate":"2012-04-03T14:30:00","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":"Advances in the simulation and automated measurement of well-sorted granular material: 1. Simulation","docAbstract":"1. In this, the first of a pair of papers which address the simulation and automated measurement of well-sorted natural granular material, a method is presented for simulation of two-phase (solid, void) assemblages of discrete non-cohesive particles. The purpose is to have a flexible, yet computationally and theoretically simple, suite of tools with well constrained and well known statistical properties, in order to simulate realistic granular material as a discrete element model with realistic size and shape distributions, for a variety of purposes. The stochastic modeling framework is based on three-dimensional tessellations with variable degrees of order in particle-packing arrangement. Examples of sediments with a variety of particle size distributions and spatial variability in grain size are presented. The relationship between particle shape and porosity conforms to published data. The immediate application is testing new algorithms for automated measurements of particle properties (mean and standard deviation of particle sizes, and apparent porosity) from images of natural sediment, as detailed in the second of this pair of papers. The model could also prove useful for simulating specific depositional structures found in natural sediments, the result of physical alterations to packing and grain fabric, using discrete particle flow models. While the principal focus here is on naturally occurring sediment and sedimentary rock, the methods presented might also be useful for simulations of similar granular or cellular material encountered in engineering, industrial and life sciences.
","language":"English","publisher":"American Geophysical Union","publisherLocation":"Richmond, VA","doi":"10.1029/2011JF001974","usgsCitation":"Buscombe, D., and Rubin, D.M., 2012, Advances in the simulation and automated measurement of well-sorted granular material: 1. Simulation: Journal of Geophysical Research F: Earth Surface, v. 117, no. F2, 17 p., https://doi.org/10.1029/2011JF001974.","productDescription":"17 p.","numberOfPages":"17","onlineOnly":"N","additionalOnlineFiles":"N","ipdsId":"IP-026993","costCenters":[{"id":520,"text":"Pacific Coastal and Marine Science Center","active":true,"usgs":true}],"links":[{"id":300044,"rank":1,"type":{"id":22,"text":"Related Work"},"url":"https://onlinelibrary.wiley.com/doi/10.1029/2011JF001975/abstract"},{"id":300045,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"volume":"117","issue":"F2","publishingServiceCenter":{"id":14,"text":"Menlo Park PSC"},"noUsgsAuthors":false,"publicationDate":"2012-04-03","publicationStatus":"PW","scienceBaseUri":"55489833e4b0a658d7960d3a","contributors":{"authors":[{"text":"Buscombe, Daniel","contributorId":140252,"corporation":false,"usgs":false,"family":"Buscombe","given":"Daniel","email":"","affiliations":[{"id":590,"text":"U.S. Army Corps of Engineers","active":false,"usgs":false}],"preferred":false,"id":544941,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Rubin, David M. 0000-0003-1169-1452 drubin@usgs.gov","orcid":"https://orcid.org/0000-0003-1169-1452","contributorId":3159,"corporation":false,"usgs":true,"family":"Rubin","given":"David","email":"drubin@usgs.gov","middleInitial":"M.","affiliations":[{"id":520,"text":"Pacific Coastal and Marine Science Center","active":true,"usgs":true}],"preferred":true,"id":544940,"contributorType":{"id":1,"text":"Authors"},"rank":2}]}} ,{"id":70177029,"text":"70177029 - 2012 - Assessing time-integrated dissolved concentrations and predicting toxicity of metals during diel cycling in streams","interactions":[],"lastModifiedDate":"2016-10-19T14:52:56","indexId":"70177029","displayToPublicDate":"2012-04-03T05:15:00","publicationYear":"2012","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":3352,"text":"Science of the Total Environment","active":true,"publicationSubtype":{"id":10}},"title":"Assessing time-integrated dissolved concentrations and predicting toxicity of metals during diel cycling in streams","docAbstract":"Evaluating water quality and the health of aquatic organisms is challenging in systems with systematic diel (24 hour) or less predictable runoff-induced changes in water composition. To advance our understanding of how to evaluate environmental health in these dynamic systems, field studies of diel cycling were conducted in two streams (Silver Bow Creek and High Ore Creek) affected by historical mining activities in southwestern Montana. A combination of sampling and modeling tools were used to assess the toxicity of metals in these systems. Diffusive Gradients in Thin Films (DGT) samplers were deployed at multiple time intervals during diel sampling to confirm that DGT integrates time-varying concentrations of dissolved metals. Thermodynamic speciation calculations using site specific water compositions, including time-integrated dissolved metal concentrations determined from DGT, and a competitive, multiple-metal biotic ligand model incorporated into the Windemere Humic Aqueous Model Version 6.0 (WHAM VI) were used to determine the chemical speciation of dissolved metals and biotic ligands. The model results were combined with previously collected toxicity data on cutthroat trout to derive a relationship that predicts the relative survivability of these fish at a given site. This integrative approach may prove useful for assessing water quality and toxicity of metals to aquatic organisms in dynamic systems and evaluating whether potential changes in environmental health of aquatic systems are due to anthropogenic activities or natural variability.","language":"English","publisher":"Elsevier Science Direct","doi":"10.1016/j.scitotenv.2012.03.008","usgsCitation":"Balistrieri, L.S., Nimick, D.A., and Mebane, C.A., 2012, Assessing time-integrated dissolved concentrations and predicting toxicity of metals during diel cycling in streams: Science of the Total Environment, v. 425, p. 155-168, https://doi.org/10.1016/j.scitotenv.2012.03.008.","productDescription":"14 p.","startPage":"155","endPage":"168","onlineOnly":"N","additionalOnlineFiles":"N","ipdsId":"IP-038447","costCenters":[{"id":662,"text":"Western Mineral and Environmental Resources Science Center","active":true,"usgs":true}],"links":[{"id":329763,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"volume":"425","publishingServiceCenter":{"id":14,"text":"Menlo Park PSC"},"noUsgsAuthors":false,"publicationStatus":"PW","scienceBaseUri":"58088688e4b0f497e78e24d9","contributors":{"authors":[{"text":"Balistrieri, Laurie S. 0000-0002-6359-3849 balistri@usgs.gov","orcid":"https://orcid.org/0000-0002-6359-3849","contributorId":1406,"corporation":false,"usgs":true,"family":"Balistrieri","given":"Laurie","email":"balistri@usgs.gov","middleInitial":"S.","affiliations":[{"id":200,"text":"Coop Res Unit Seattle","active":true,"usgs":true},{"id":191,"text":"Colorado Water Science Center","active":true,"usgs":true},{"id":312,"text":"Geology, Minerals, Energy, and Geophysics Science Center","active":true,"usgs":true},{"id":662,"text":"Western Mineral and Environmental Resources Science Center","active":true,"usgs":true}],"preferred":true,"id":651046,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Nimick, David A. dnimick@usgs.gov","contributorId":421,"corporation":false,"usgs":true,"family":"Nimick","given":"David","email":"dnimick@usgs.gov","middleInitial":"A.","affiliations":[{"id":5050,"text":"WY-MT Water Science Center","active":true,"usgs":true},{"id":573,"text":"Special Applications Science Center","active":true,"usgs":true}],"preferred":true,"id":651048,"contributorType":{"id":1,"text":"Authors"},"rank":11},{"text":"Mebane, Christopher A. 0000-0002-9089-0267 cmebane@usgs.gov","orcid":"https://orcid.org/0000-0002-9089-0267","contributorId":110,"corporation":false,"usgs":true,"family":"Mebane","given":"Christopher","email":"cmebane@usgs.gov","middleInitial":"A.","affiliations":[{"id":343,"text":"Idaho Water Science Center","active":true,"usgs":true}],"preferred":true,"id":651047,"contributorType":{"id":1,"text":"Authors"},"rank":12}]}} ,{"id":70037942,"text":"sir20125016 - 2012 - Dispersal of larval suckers at the Williamson River Delta, Upper Klamath Lake, Oregon, 2006-09","interactions":[],"lastModifiedDate":"2012-04-30T16:43:34","indexId":"sir20125016","displayToPublicDate":"2012-04-03T00:00:00","publicationYear":"2012","noYear":false,"publicationType":{"id":18,"text":"Report"},"publicationSubtype":{"id":5,"text":"USGS Numbered Series"},"seriesTitle":{"id":334,"text":"Scientific Investigations Report","code":"SIR","onlineIssn":"2328-0328","printIssn":"2328-031X","active":true,"publicationSubtype":{"id":5}},"seriesNumber":"2012-5016","title":"Dispersal of larval suckers at the Williamson River Delta, Upper Klamath Lake, Oregon, 2006-09","docAbstract":"An advection/diffusion modeling approach was used to simulate the transport of larval suckers from spawning areas in the Williamson River, through the newly restored Williamson River Delta, to Upper Klamath Lake. The density simulations spanned the years of phased restoration, from 2006/2007 prior to any levee breaching, to 2008 when the northern part of the delta was reconnected to the lake, and 2009 when levees on both sides of the delta had been breached. Model simulation results from all four years were compared to field data using rank correlation. Spearman ρ correlation coefficients were usually significant and in the range 0.30 to 0.60, providing moderately strong validation of the model. The correlation coefficients varied with fish size class in a way that suggested that the model best described the distribution of smaller fish near the Williamson River channel, and larger fish away from the channel. When Lost River and shortnose/Klamath largescale suckers were simulated independently, the correlation results suggested that the model better described the transport and dispersal of the latter species. The incorporation of night-time-only drift behavior in the Williamson River channel neither improved nor degraded correlations with field data. The model showed that advection by currents is an important factor in larval dispersal.","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/sir20125016","collaboration":"Prepared in cooperation with the Bureau of Reclamation?","usgsCitation":"Wood, T.M., Hendrixson, H.A., Markle, D.F., Erdman, C.S., Burdick, S.M., Ellsworth, C.M., and Buccola, N., 2012, Dispersal of larval suckers at the Williamson River Delta, Upper Klamath Lake, Oregon, 2006-09: U.S. Geological Survey Scientific Investigations Report 2012-5016, vi, 28 p.; Animation Downloads 2006-2009, https://doi.org/10.3133/sir20125016.","productDescription":"vi, 28 p.; Animation Downloads 2006-2009","temporalStart":"2006-01-01","temporalEnd":"2009-12-31","costCenters":[{"id":518,"text":"Oregon Water Science Center","active":true,"usgs":true}],"links":[{"id":251619,"rank":0,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/sir_2012_5016.jpg"},{"id":251617,"rank":100,"type":{"id":15,"text":"Index Page"},"url":"https://pubs.usgs.gov/sir/2012/5016/","linkFileType":{"id":5,"text":"html"}}],"projection":"UTM, Zone 10N","datum":"North American Datum of 1927","country":"United States","state":"Oregon","otherGeospatial":"Upper Klamath Lake;Agency Lake;Williamson River Delta","geographicExtents":"{ \"type\": \"FeatureCollection\", \"features\": [ { \"type\": \"Feature\", \"properties\": {}, \"geometry\": { \"type\": \"Polygon\", \"coordinates\": [ [ [ -122.16666666666667,42.05 ], [ -122.16666666666667,42.666666666666664 ], [ -121.58333333333333,42.666666666666664 ], [ -121.58333333333333,42.05 ], [ -122.16666666666667,42.05 ] ] ] } } ] }","noUsgsAuthors":false,"publicationStatus":"PW","scienceBaseUri":"505a021ae4b0c8380cd4feb0","contributors":{"authors":[{"text":"Wood, Tamara M. 0000-0001-6057-8080 tmwood@usgs.gov","orcid":"https://orcid.org/0000-0001-6057-8080","contributorId":1164,"corporation":false,"usgs":true,"family":"Wood","given":"Tamara","email":"tmwood@usgs.gov","middleInitial":"M.","affiliations":[{"id":518,"text":"Oregon Water Science Center","active":true,"usgs":true}],"preferred":true,"id":463123,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Hendrixson, Heather A.","contributorId":43602,"corporation":false,"usgs":true,"family":"Hendrixson","given":"Heather","email":"","middleInitial":"A.","affiliations":[],"preferred":false,"id":463128,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Markle, Douglas F.","contributorId":14530,"corporation":false,"usgs":true,"family":"Markle","given":"Douglas","email":"","middleInitial":"F.","affiliations":[],"preferred":false,"id":463126,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Erdman, Charles S.","contributorId":66102,"corporation":false,"usgs":true,"family":"Erdman","given":"Charles","email":"","middleInitial":"S.","affiliations":[],"preferred":false,"id":463129,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Burdick, Summer M. 0000-0002-3480-5793 sburdick@usgs.gov","orcid":"https://orcid.org/0000-0002-3480-5793","contributorId":3448,"corporation":false,"usgs":true,"family":"Burdick","given":"Summer","email":"sburdick@usgs.gov","middleInitial":"M.","affiliations":[{"id":654,"text":"Western Fisheries Research Center","active":true,"usgs":true}],"preferred":true,"id":463124,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"Ellsworth, Craig M.","contributorId":14913,"corporation":false,"usgs":true,"family":"Ellsworth","given":"Craig","email":"","middleInitial":"M.","affiliations":[],"preferred":false,"id":463127,"contributorType":{"id":1,"text":"Authors"},"rank":6},{"text":"Buccola, Norman L. nbuccola@usgs.gov","contributorId":4295,"corporation":false,"usgs":true,"family":"Buccola","given":"Norman L.","email":"nbuccola@usgs.gov","affiliations":[{"id":518,"text":"Oregon Water Science Center","active":true,"usgs":true}],"preferred":false,"id":463125,"contributorType":{"id":1,"text":"Authors"},"rank":7}]}} ,{"id":70037935,"text":"70037935 - 2012 - Spatially explicit land-use and land-cover scenarios for the Great Plains of the United States","interactions":[],"lastModifiedDate":"2018-03-08T12:54:26","indexId":"70037935","displayToPublicDate":"2012-04-02T11:18:00","publicationYear":"2012","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":682,"text":"Agriculture, Ecosystems and Environment","active":true,"publicationSubtype":{"id":10}},"title":"Spatially explicit land-use and land-cover scenarios for the Great Plains of the United States","docAbstract":"The Great Plains of the United States has undergone extensive land-use and land-cover change in the past 150 years, with much of the once vast native grasslands and wetlands converted to agricultural crops, and much of the unbroken prairie now heavily grazed. Future land-use change in the region could have dramatic impacts on ecological resources and processes. A scenario-based modeling framework is needed to support the analysis of potential land-use change in an uncertain future, and to mitigate potentially negative future impacts on ecosystem processes. We developed a scenario-based modeling framework to analyze potential future land-use change in the Great Plains. A unique scenario construction process, using an integrated modeling framework, historical data, workshops, and expert knowledge, was used to develop quantitative demand for future land-use change for four IPCC scenarios at the ecoregion level. The FORE-SCE model ingested the scenario information and produced spatially explicit land-use maps for the region at relatively fine spatial and thematic resolutions. Spatial modeling of the four scenarios provided spatial patterns of land-use change consistent with underlying assumptions and processes associated with each scenario. Economically oriented scenarios were characterized by significant loss of natural land covers and expansion of agricultural and urban land uses. Environmentally oriented scenarios experienced modest declines in natural land covers to slight increases. Model results were assessed for quantity and allocation disagreement between each scenario pair. In conjunction with the U.S. Geological Survey's Biological Carbon Sequestration project, the scenario-based modeling framework used for the Great Plains is now being applied to the entire United States.","largerWorkType":{"id":2,"text":"Article"},"largerWorkTitle":"Agriculture, Ecosystems and Environment","largerWorkSubtype":{"id":10,"text":"Journal Article"},"language":"English","publisher":"Elsevier","publisherLocation":"Amsterdam, Netherlands","doi":"10.1016/j.agee.2012.02.019","usgsCitation":"Sohl, T.L., Sleeter, B.M., Sayler, K., Bouchard, M., Reker, R.R., Bennett, S.L., Sleeter, R., Kanengieter, R.L., and Zhu, Z., 2012, Spatially explicit land-use and land-cover scenarios for the Great Plains of the United States: Agriculture, Ecosystems and Environment, v. 153, p. 1-15, https://doi.org/10.1016/j.agee.2012.02.019.","productDescription":"15 p.","startPage":"1","endPage":"15","costCenters":[{"id":222,"text":"Earth Resources Observation and Science (EROS) 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An approach was developed for quantifying the effects of these processes on aquatic biota, and then linking those effects to endpoints that can be used for environmental management. These complex, interacting systems are challenging to model from a scientific standpoint. A desirable model clearly shows the system, simulates the interactions, and ultimately predicts results of management actions. Traditional regression techniques that calculate empirical relations between pairs of environmental factors do not capture the interconnected web of multiple stressors, but urban development effects are not yet understood at the detailed scales required to make mechanistic modeling approaches feasible. Therefore, in contrast to a fully deterministic or fully statistical modeling approach, a Bayesian network model provides a hybrid approach that can be used to represent known general associations between variables while acknowledging uncertainty in predicted outcomes. It does so by quantifying an expert-elicited network of probabilistic relations between variables. Advantages of this modeling approach include (1) flexibility in accommodating many model specifications and information types; (2) efficiency in storing and manipulating complex information, and to parameterize; and (3) transparency in describing the relations using nodes and arrows and in describing uncertainties with discrete probability distributions for each variable.
\nIn realization of the aforementioned advantages, a Bayesian network model was constructed to characterize the effect of urban development on aquatic macroinvertebrate stream communities through three simultaneous, interacting ecological pathways affecting stream hydrology, habitat, and water quality across watersheds in the Northeastern United States. This model incorporates both empirical data and expert knowledge to calculate the probabilities of attaining desired aquatic ecosystem conditions under different urban stress levels, environmental conditions, and management options. Ecosystem conditions are characterized in terms of standardized Biological Condition Gradient (BCG) management endpoints. This approach to evaluating urban development-induced perturbations in watersheds integrates statistical and mechanistic perspectives, different information sources, and several ecological processes into a comprehensive description of the system that can be used to support decision making. The completed model can be used to infer which management actions would lead to the highest likelihood of desired BCG tier achievement. For example, if best management practices (BMP) were implemented in a highly urbanized watershed to reduce flashiness to medium levels and specific conductance to low levels, the stream would have a 70-percent chance of achieving BCG Tier 3 or better, relative to a 24-percent achievement likelihood for unmanaged high urban land cover. Results are reported probabilistically to account for modeling uncertainty that is inherent in sources such as natural variability and model simplification error.
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2009","interactions":[],"lastModifiedDate":"2012-09-06T17:16:18","indexId":"ds675","displayToPublicDate":"2012-04-02T09:18:00","publicationYear":"2012","noYear":false,"publicationType":{"id":18,"text":"Report"},"publicationSubtype":{"id":5,"text":"USGS Numbered Series"},"seriesTitle":{"id":310,"text":"Data Series","code":"DS","onlineIssn":"2327-638X","printIssn":"2327-0271","active":false,"publicationSubtype":{"id":5}},"seriesNumber":"675","title":"Archive of single beam and swath bathymetry data collected nearshore of the Gulf Islands National Seashore, Mississippi, from West Ship Island, Mississippi, to Dauphin Island, Alabama: Methods and data report for USGS Cruises 08CCT01 and 08CCT02, July 2008, and 09CCT03 and 09CCT04, June 2009","docAbstract":"During the summers of 2008 and 2009 the USGS conducted bathymetric surveys from West Ship Island, Miss., to Dauphin Island, Ala., as part of the Northern Gulf of Mexico (NGOM) Ecosystem Change and Hazard Susceptibility project. The survey area extended from the shoreline out to approximately 2 kilometers and included the adjacent passes (fig. 1). The bathymetry was primarily used to create a topo-bathymetric map and provide a base-level assessment of the seafloor following the 2005 hurricane season. Additionally, these data will be used in conjunction with other geophysical data (chirp and side scan sonar) to construct a comprehensive geological framework of the Mississippi Barrier Island Complex. The culmination of the geophysical surveys will provide baseline bathymetry necessary for scientists to define and interpret seafloor habitat for this area and for scientists to predict future geomorpholocial changes of the islands with respect to climate change, storm impact, and sea-level rise. Furthermore, these data provide information for feasibility of barrier island restoration, particularly in Camille Cut, and for the preservation of historical Fort Massachusetts. For more information refer to http://ngom.usgs.gov/gomsc/mscip/index.html.
\nSince bathymetric surveys have often been conducted for navigational purposes, soundings have traditionally been referenced to a water level datum using tide gages and tide models. Bathymetric measurements referenced to a Global Positioning System (GPS) is a more accurate way of representing water depth and has been implemented in the acquisition and processing procedures for these datasets. Previous single-beam bathymetric studies performed at the USGS Center for Coastal and Marine Science have successfully referenced bathymetric measurements to GPS (DeWitt and others, 2007; Hansen 2008 and 2009). The 2008-2009 bathymetry surveys were conducted as a test to (1) develop acquisition and processing technology utilizing both single beam and swath bathymetry survey methods together, (2) reference both types of measurements to GPS rather than water level, and (3) compare the differences between methods in acquisition and processing. Results of the survey are explained in greater detail within this report.
\nTo acquire suitable coverage of the study area in a limited time frame, the seafloor-elevation survey was conducted using three techniques: single-beam bathymetry, interferometric swath bathymetry, and a walking kinematic survey of the island shorelines. All three techniques utilized GPS measurements. Implementation of these techniques was executed concurrently yet independently aboard two research vessels: the RV Survey Cat, a 26-foot (ft) shallow-draft Glacier Bay Coastal Runner, and the 50-ft RV G.K. Gilbert. A portable push buggy with a rigid antenna mount served as the platform for the kinematic shoreline survey. Data from each survey technique was post-processed and edited independently with proper inclusion of the differentially processed external navigation files. The x,y,z components from each method were then combined and the two survey years (2008 and 2009) were merged into one dataset. The 2008 bathymetry data were processed at the USGS Center for Coastal and Marine Science in St. Petersburg, Fla., and the 2009 bathymetry data were processed at the USGS Coastal and Marine Science Center located in Woods Hole, Mass.
\nThis report serves as an archive of the processed single beam and interferometric swath bathymetry, outlines the methodology, and reports the results. Data products herein include gridded and interpolated digital depth surfaces, and x,y,z data products for both single beam and interferometric swath bathymetry. Additional files include trackline maps, navigation files, geographic information system (GIS) files, Field Activity Collection System (FACS) logs, and formal Federal Geographic Data Committee (FGDC) metadata. Scanned images of the handwritten FACS logs and digital FACS logs are also provided as PDF files. Refer to the Acronyms page for description of acronyms and abbreviations used in this report or hold the cursor over an acronym for a pop-up explanation.
\nThe USGS St. Petersburg Coastal and Marine Science Center assigns a unique identifier to each cruise or field activity. For example, 08CCT01 indicates that the data were collected in 2008 for the Coastal Change and Transport (CCT) study and the data were collected during the first (01) field activity for that project in that calendar year. Refer to http://walrus.wr.usgs.gov/infobank/programs/html/definition/activity.html for a detailed description of the method used to assign the field activity ID.
\nSee the digital FACS equipment log for details about the acquisition equipment used. Raw datasets are stored digitally at the USGS St. Petersburg Coastal and Marine Science Center and processed systematically using Novatel's GrafNav version 7.6, SANDS version 3.7, SEA SWATHplus version 3.06.04.03, CARIS HIPS AND SIPS version 3.6, and ESRI ArcGIS version 9.3.1. For more information on processing refer to the Equipment and Processing page. Chirp seismic data were also collected during these surveys and are archived separately.
","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/ds675","usgsCitation":"DeWitt, N.T., Flocks, J.G., Pendleton, E., Hansen, M., Reynolds, B., Kelso, K.W., Wiese, D.S., and Worley, C.R., 2012, Archive of single beam and swath bathymetry data collected nearshore of the Gulf Islands National Seashore, Mississippi, from West Ship Island, Mississippi, to Dauphin Island, Alabama: Methods and data report for USGS Cruises 08CCT01 and 08CCT02, July 2008, and 09CCT03 and 09CCT04, June 2009: U.S. Geological Survey Data Series 675, HTML Document; GIS Download, https://doi.org/10.3133/ds675.","productDescription":"HTML Document; GIS Download","costCenters":[],"links":[{"id":246891,"rank":100,"type":{"id":15,"text":"Index Page"},"url":"https://pubs.usgs.gov/ds/675/","linkFileType":{"id":5,"text":"html"}},{"id":246894,"rank":0,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/ds_675.jpg"}],"country":"United States","state":"Mississippi;Alabama","otherGeospatial":"Gulf Islands National Seashore;West Ship Island;Dauphin Island","geographicExtents":"{ \"type\": \"FeatureCollection\", \"features\": [ { \"type\": \"Feature\", \"properties\": {}, \"geometry\": { \"type\": \"Polygon\", \"coordinates\": [ [ [ -89.21666666666667,30.183333333333334 ], [ -89.21666666666667,30.45 ], [ -88.18333333333334,30.45 ], [ -88.18333333333334,30.183333333333334 ], [ -89.21666666666667,30.183333333333334 ] ] ] } } ] }","noUsgsAuthors":false,"publicationStatus":"PW","scienceBaseUri":"5059ed4de4b0c8380cd49719","contributors":{"authors":[{"text":"DeWitt, Nancy T. 0000-0002-2419-4087 ndewitt@usgs.gov","orcid":"https://orcid.org/0000-0002-2419-4087","contributorId":4095,"corporation":false,"usgs":true,"family":"DeWitt","given":"Nancy","email":"ndewitt@usgs.gov","middleInitial":"T.","affiliations":[{"id":574,"text":"St. Petersburg Coastal and Marine Science Center","active":true,"usgs":true},{"id":186,"text":"Coastal and Marine Geology Program","active":true,"usgs":true}],"preferred":true,"id":463088,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Flocks, James G. 0000-0002-6177-7433 jflocks@usgs.gov","orcid":"https://orcid.org/0000-0002-6177-7433","contributorId":816,"corporation":false,"usgs":true,"family":"Flocks","given":"James","email":"jflocks@usgs.gov","middleInitial":"G.","affiliations":[{"id":574,"text":"St. Petersburg Coastal and Marine Science Center","active":true,"usgs":true}],"preferred":true,"id":463085,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Pendleton, Elizabeth A.","contributorId":101312,"corporation":false,"usgs":true,"family":"Pendleton","given":"Elizabeth A.","affiliations":[],"preferred":false,"id":463092,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Hansen, Mark E.","contributorId":49943,"corporation":false,"usgs":true,"family":"Hansen","given":"Mark E.","affiliations":[],"preferred":false,"id":463091,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Reynolds, B.J.","contributorId":47874,"corporation":false,"usgs":true,"family":"Reynolds","given":"B.J.","email":"","affiliations":[],"preferred":false,"id":463090,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"Kelso, Kyle W. 0000-0003-0615-242X kkelso@usgs.gov","orcid":"https://orcid.org/0000-0003-0615-242X","contributorId":4307,"corporation":false,"usgs":true,"family":"Kelso","given":"Kyle","email":"kkelso@usgs.gov","middleInitial":"W.","affiliations":[{"id":574,"text":"St. Petersburg Coastal and Marine Science Center","active":true,"usgs":true}],"preferred":true,"id":463089,"contributorType":{"id":1,"text":"Authors"},"rank":6},{"text":"Wiese, Dana S. dwiese@usgs.gov","contributorId":2476,"corporation":false,"usgs":true,"family":"Wiese","given":"Dana","email":"dwiese@usgs.gov","middleInitial":"S.","affiliations":[{"id":574,"text":"St. Petersburg Coastal and Marine Science Center","active":true,"usgs":true}],"preferred":true,"id":463086,"contributorType":{"id":1,"text":"Authors"},"rank":7},{"text":"Worley, Charles R. cworley@usgs.gov","contributorId":3063,"corporation":false,"usgs":true,"family":"Worley","given":"Charles","email":"cworley@usgs.gov","middleInitial":"R.","affiliations":[{"id":678,"text":"Woods Hole Coastal and Marine Science Center","active":true,"usgs":true}],"preferred":true,"id":463087,"contributorType":{"id":1,"text":"Authors"},"rank":8}]}} ,{"id":70037933,"text":"sir20125031 - 2012 - Simulation of streamflows and basin-wide hydrologic variables over several climate-change scenarios, Methow River basin, Washington","interactions":[],"lastModifiedDate":"2012-04-30T16:43:35","indexId":"sir20125031","displayToPublicDate":"2012-04-02T08:54:00","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":"2012-5031","title":"Simulation of streamflows and basin-wide hydrologic variables over several climate-change scenarios, Methow River basin, Washington","docAbstract":"The purpose of this project was to demonstrate the capabilities of an existing watershed model and downscaling procedures to provide simulated hydrological data over various greenhouse gas emission scenarios for use in the Methow River framework prototype. An existing watershed model was used to simulate daily time series of streamflow and basin-wide hydrologic variables for baseline conditions (1990–2000), and then for all combinations of three greenhouse gas emission scenarios and five general circulation models for future conditions (2008–2095). Input data for 18 precipitation and 17 temperature model input sites were generated using statistical techniques to downscale general circulation model data. The simulated results were averaged using an 11-year moving window to characterize the central year of the window to provide simulated data for water years 2008–2095.
\nSimulation results indicate that substantial changes of monthly mean streamflows will occur. For all greenhouse gas emission scenarios, the future streamflows are greater in the winter than baseline conditions because a greater percentage of future precipitation is projected to fall as rain rather than as snow. The simulated future spring streamflows are less than baseline conditions because the spring snowpacks are smaller; therefore, flow contributions from snowmelt are less.
\nA database was developed to automate model execution and to provide users with Internet access to voluminous data products ranging from summary figures to model output timeseries. Database-enabled Internet tools were developed to allow users to create interactive graphs of output results based on their analysis needs. For example, users were able to create graphs by selecting time intervals, greenhouse gas emission scenarios, general circulation models, and specific hydrologic variables.
","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/sir20125031","usgsCitation":"Voss, F.D., and Mastin, M.C., 2012, Simulation of streamflows and basin-wide hydrologic variables over several climate-change scenarios, Methow River basin, Washington: U.S. Geological Survey Scientific Investigations Report 2012-5031, vi, 18 p.; Web tools link, https://doi.org/10.3133/sir20125031.","productDescription":"vi, 18 p.; Web tools link","onlineOnly":"Y","costCenters":[{"id":622,"text":"Washington Water Science Center","active":true,"usgs":true}],"links":[{"id":246895,"rank":0,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/sir_2012_5031.jpg"},{"id":246890,"rank":100,"type":{"id":15,"text":"Index Page"},"url":"https://pubs.usgs.gov/sir/2012/5031/","linkFileType":{"id":5,"text":"html"}}],"country":"United States","state":"Washington","otherGeospatial":"Methow River Basin","geographicExtents":"{ \"type\": \"FeatureCollection\", \"features\": [ { \"type\": \"Feature\", \"properties\": {}, \"geometry\": { \"type\": \"Polygon\", \"coordinates\": [ [ [ -120.83333333333333,48 ], [ -120.83333333333333,49 ], [ -119.75,49 ], [ -119.75,48 ], [ -120.83333333333333,48 ] ] ] } } ] }","noUsgsAuthors":false,"publicationStatus":"PW","scienceBaseUri":"505b9097e4b08c986b3195be","contributors":{"authors":[{"text":"Voss, Frank D. fdvoss@usgs.gov","contributorId":1651,"corporation":false,"usgs":true,"family":"Voss","given":"Frank","email":"fdvoss@usgs.gov","middleInitial":"D.","affiliations":[{"id":677,"text":"Wisconsin Water Science Center","active":true,"usgs":true}],"preferred":true,"id":463083,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Mastin, Mark C. 0000-0003-4018-7861 mcmastin@usgs.gov","orcid":"https://orcid.org/0000-0003-4018-7861","contributorId":1652,"corporation":false,"usgs":true,"family":"Mastin","given":"Mark","email":"mcmastin@usgs.gov","middleInitial":"C.","affiliations":[{"id":622,"text":"Washington Water Science Center","active":true,"usgs":true}],"preferred":true,"id":463084,"contributorType":{"id":1,"text":"Authors"},"rank":2}]}} ,{"id":70037931,"text":"ofr20121024A - 2012 - Geologic framework for the national assessment of carbon dioxide storage resources: Bighorn Basin, Wyoming and Montana: Chapter A in Geologic framework for the national assessment of carbon dioxide storage resources","interactions":[{"subject":{"id":70037931,"text":"ofr20121024A - 2012 - Geologic framework for the national assessment of carbon dioxide storage resources: Bighorn Basin, Wyoming and Montana: Chapter A in Geologic framework for the national assessment of carbon dioxide storage resources","indexId":"ofr20121024A","publicationYear":"2012","noYear":false,"chapter":"A","title":"Geologic framework for the national assessment of carbon dioxide storage resources: Bighorn Basin, Wyoming and Montana: Chapter A in Geologic framework for the national assessment of carbon dioxide storage resources"},"predicate":"IS_PART_OF","object":{"id":70093199,"text":"ofr20121024 - 2012 - Geologic framework for the national assessment of carbon dioxide storage resources","indexId":"ofr20121024","publicationYear":"2012","noYear":false,"title":"Geologic framework for the national assessment of carbon dioxide storage resources"},"id":1}],"isPartOf":{"id":70093199,"text":"ofr20121024 - 2012 - Geologic framework for the national assessment of carbon dioxide storage resources","indexId":"ofr20121024","publicationYear":"2012","noYear":false,"title":"Geologic framework for the national assessment of carbon dioxide storage resources"},"lastModifiedDate":"2023-06-16T16:10:07.723253","indexId":"ofr20121024A","displayToPublicDate":"2012-04-02T00:00:00","publicationYear":"2012","noYear":false,"publicationType":{"id":18,"text":"Report"},"publicationSubtype":{"id":5,"text":"USGS Numbered Series"},"seriesTitle":{"id":330,"text":"Open-File Report","code":"OFR","onlineIssn":"2331-1258","printIssn":"0196-1497","active":true,"publicationSubtype":{"id":5}},"seriesNumber":"2012-1024","chapter":"A","title":"Geologic framework for the national assessment of carbon dioxide storage resources: Bighorn Basin, Wyoming and Montana: Chapter A in Geologic framework for the national assessment of carbon dioxide storage resources","docAbstract":"The 2007 Energy Independence and Security Act (Public Law 110–140) directs the U.S. Geological Survey (USGS) to conduct a national assessment of potential geologic storage resources for carbon dioxide (CO2). The methodology used for the national CO2 assessment follows that of previous USGS work. The methodology is non-economic and intended to be used at regional to subbasinal scales.
\nThis report identifies and contains geologic descriptions of twelve storage assessment units (SAUs) in six separate packages of sedimentary rocks within the Bighorn Basin of Wyoming and Montana and focuses on the particular characteristics, specified in the methodology, that influence the potential CO2 storage resource in those SAUs. Specific descriptions of the SAU boundaries as well as their sealing and reservoir units are included. Properties for each SAU such as depth to top, gross thickness, net porous thickness, porosity, permeability, groundwater quality, and structural reservoir traps are provided to illustrate geologic factors critical to the assessment. Although assessment results are not contained in this report, the geologic information included here will be employed, as specified in the methodology of earlier work, to calculate a statistical Monte Carlo-based distribution of potential storage space in the various SAUs. Figures in this report show SAU boundaries and cell maps of well penetrations through the sealing unit into the top of the storage formation. Wells sharing the same well borehole are treated as a single penetration. Cell maps show the number of penetrating wells within one square mile and are derived from interpretations of incompletely attributed well data, a digital compilation that is known not to include all drilling. The USGS does not expect to know the location of all wells and cannot guarantee the amount of drilling through specific formations in any given cell shown on cell maps.
","largerWorkType":{"id":18,"text":"Report"},"largerWorkTitle":"Geologic framework for the national assessment of carbon dioxide storage resources (Open-File Report 2012-1024)","largerWorkSubtype":{"id":9,"text":"Other Report"},"language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/ofr20121024A","collaboration":"This report is Chapter A in Geologic framework for the national assessment of carbon dioxide storage resources. For more information, see Open-File Report 2012-1024.","usgsCitation":"Covault, J.A., Buursink, M.L., Craddock, W.H., Merrill, M., Blondes, M., Gosai, M.A., and Freeman, P., 2012, Geologic framework for the national assessment of carbon dioxide storage resources: Bighorn Basin, Wyoming and Montana: Chapter A in Geologic framework for the national assessment of carbon dioxide storage resources: U.S. Geological Survey Open-File Report 2012-1024, Report: vii, 23 p.; Data Downloads, https://doi.org/10.3133/ofr20121024A.","productDescription":"Report: vii, 23 p.; Data Downloads","onlineOnly":"Y","additionalOnlineFiles":"N","costCenters":[{"id":164,"text":"Central Energy Resources Science Center","active":true,"usgs":true},{"id":241,"text":"Eastern Energy Resources Science Center","active":true,"usgs":true},{"id":255,"text":"Energy Resources Program","active":true,"usgs":true}],"links":[{"id":246893,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/ofr_2012_1024_a.png"},{"id":246892,"rank":5,"type":{"id":15,"text":"Index Page"},"url":"https://pubs.usgs.gov/of/2012/1024/a/","linkFileType":{"id":5,"text":"html"}},{"id":282242,"rank":4,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/of/2012/1024/a/contents/OF12-1024-A.pdf"},{"id":282243,"rank":3,"type":{"id":7,"text":"Companion Files"},"url":"https://pubs.usgs.gov/of/2012/1024/a/contents/cell_C5034.zip"},{"id":282244,"rank":2,"type":{"id":7,"text":"Companion Files"},"url":"https://pubs.usgs.gov/of/2012/1024/a/contents/sau_C5034.zip"}],"country":"United States","state":"Wyoming, Montana","otherGeospatial":"Bighorn Basin","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -110,\n 43\n ],\n [\n -107,\n 43\n ],\n [\n -107,\n 45.5\n ],\n [\n -110,\n 45.5\n ],\n [\n -110,\n 43\n ]\n ]\n ]\n }\n }\n ]\n}","noUsgsAuthors":false,"publicationStatus":"PW","scienceBaseUri":"505a196ae4b0c8380cd5599b","contributors":{"editors":[{"text":"Warwick, Peter D. 0000-0002-3152-7783 pwarwick@usgs.gov","orcid":"https://orcid.org/0000-0002-3152-7783","contributorId":762,"corporation":false,"usgs":true,"family":"Warwick","given":"Peter","email":"pwarwick@usgs.gov","middleInitial":"D.","affiliations":[{"id":241,"text":"Eastern Energy Resources Science Center","active":true,"usgs":true}],"preferred":false,"id":508955,"contributorType":{"id":2,"text":"Editors"},"rank":1},{"text":"Corum, M.D. 0000-0002-9038-3935 mcorum@usgs.gov","orcid":"https://orcid.org/0000-0002-9038-3935","contributorId":2249,"corporation":false,"usgs":true,"family":"Corum","given":"M.D.","email":"mcorum@usgs.gov","affiliations":[{"id":241,"text":"Eastern Energy Resources Science 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wcraddock@usgs.gov","orcid":"https://orcid.org/0000-0002-4181-4735","contributorId":3411,"corporation":false,"usgs":true,"family":"Craddock","given":"William","email":"wcraddock@usgs.gov","middleInitial":"H.","affiliations":[{"id":241,"text":"Eastern Energy Resources Science Center","active":true,"usgs":true}],"preferred":true,"id":463077,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Merrill, Matthew D. 0000-0003-3766-847X","orcid":"https://orcid.org/0000-0003-3766-847X","contributorId":48256,"corporation":false,"usgs":true,"family":"Merrill","given":"Matthew D.","affiliations":[],"preferred":false,"id":463080,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Blondes, Madalyn S. 0000-0003-0320-0107 mblondes@usgs.gov","orcid":"https://orcid.org/0000-0003-0320-0107","contributorId":3598,"corporation":false,"usgs":true,"family":"Blondes","given":"Madalyn S.","email":"mblondes@usgs.gov","affiliations":[{"id":241,"text":"Eastern Energy Resources Science Center","active":true,"usgs":true}],"preferred":true,"id":463078,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"Gosai, Mayur A.","contributorId":48451,"corporation":false,"usgs":true,"family":"Gosai","given":"Mayur","email":"","middleInitial":"A.","affiliations":[],"preferred":false,"id":463081,"contributorType":{"id":1,"text":"Authors"},"rank":6},{"text":"Freeman, P.A. 0000-0002-0863-7431 pfreeman@usgs.gov","orcid":"https://orcid.org/0000-0002-0863-7431","contributorId":3154,"corporation":false,"usgs":true,"family":"Freeman","given":"P.A.","email":"pfreeman@usgs.gov","affiliations":[{"id":241,"text":"Eastern Energy Resources Science Center","active":true,"usgs":true}],"preferred":false,"id":463076,"contributorType":{"id":1,"text":"Authors"},"rank":7}]}} ,{"id":70038139,"text":"tm10C18 - 2012 - Determination of the δ13C of dissolved inorganic carbon in water; RSIL lab code 1710","interactions":[],"lastModifiedDate":"2012-04-30T16:43:36","indexId":"tm10C18","displayToPublicDate":"2012-04-01T20:21:00","publicationYear":"2012","noYear":false,"publicationType":{"id":18,"text":"Report"},"publicationSubtype":{"id":5,"text":"USGS Numbered Series"},"seriesTitle":{"id":335,"text":"Techniques and Methods","code":"TM","onlineIssn":"2328-7055","printIssn":"2328-7047","active":true,"publicationSubtype":{"id":5}},"seriesNumber":"10-c18","title":"Determination of the δ13C of dissolved inorganic carbon in water; RSIL lab code 1710","docAbstract":"The purpose of the Reston Stable Isotope Laboratory (RSIL) lab code 1710 is to present a method to determine the δ13C of dissolved inorganic carbon (DIC) of water. The DIC of water is precipitated using ammoniacal strontium chloride (SrCl2) solution to form strontium carbonate (SrCO3). The δ13C is analyzed by reacting SrCO3 with 100-percent phosphoric acid (H3PO4) to liberate carbon quantitatively as carbon dioxide (CO2), which is collected, purified by vacuum sublimation, and analyzed by dual inlet isotope-ratio mass spectrometry (DI-IRMS). The DI-IRMS is a DuPont double-focusing mass spectrometer. One ion beam passes through a slit in a forward collector and is collected in the rear collector. The other measurable ion beams are collected in the front collector. By changing the ion-accelerating voltage under computer control, the instrument is capable of measuring mass/charge (m/z) 45 or 46 in the rear collector and m/z 44 and 46 or 44 and 45, respectively, in the front collector. The ion beams from these m/z values are as follows: m/z 44 = CO2 = 12C16O16O, m/z 45 = CO2 = 13C16O16O primarily, and m/z 46 = CO2 = 12C16O18O primarily. The data acquisition and control software calculates δ13C values.","largerWorkType":{"id":4,"text":"Book"},"largerWorkTitle":"Chapter 18 of Section C, Stable Isotope-Ratio Methods, Book 10, Methods of the Reston Stable Isotope Laboratory","largerWorkSubtype":{"id":4,"text":"Other Government Series"},"language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/tm10C18","usgsCitation":"Singleton, G.L., Revesz, K., and Coplen, T.B., 2012, Determination of the δ13C of dissolved inorganic carbon in water; RSIL lab code 1710: U.S. Geological Survey Techniques and Methods 10-c18, viii, 12 p.; Appendices; Chapter 18, Section C, https://doi.org/10.3133/tm10C18.","productDescription":"viii, 12 p.; Appendices; Chapter 18, Section C","startPage":"i","endPage":"28","numberOfPages":"36","additionalOnlineFiles":"N","costCenters":[{"id":543,"text":"Reston Stable Isotope Laboratory","active":false,"usgs":true}],"links":[{"id":254558,"rank":0,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/tm_10c18.gif"},{"id":254557,"rank":100,"type":{"id":15,"text":"Index Page"},"url":"https://pubs.usgs.gov/tm/10c18/","linkFileType":{"id":5,"text":"html"}}],"noUsgsAuthors":false,"publicationStatus":"PW","scienceBaseUri":"5059ffcee4b0c8380cd4f3e5","contributors":{"authors":[{"text":"Singleton, Glenda L.","contributorId":77430,"corporation":false,"usgs":true,"family":"Singleton","given":"Glenda","email":"","middleInitial":"L.","affiliations":[],"preferred":false,"id":463503,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Revesz, Kinga","contributorId":64285,"corporation":false,"usgs":true,"family":"Revesz","given":"Kinga","affiliations":[],"preferred":false,"id":463502,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Coplen, Tyler B. 0000-0003-4884-6008 tbcoplen@usgs.gov","orcid":"https://orcid.org/0000-0003-4884-6008","contributorId":508,"corporation":false,"usgs":true,"family":"Coplen","given":"Tyler","email":"tbcoplen@usgs.gov","middleInitial":"B.","affiliations":[{"id":27111,"text":"National Water Quality Program","active":true,"usgs":true},{"id":436,"text":"National Research Program - Eastern Branch","active":true,"usgs":true},{"id":37277,"text":"WMA - Earth System Processes Division","active":true,"usgs":true},{"id":37464,"text":"WMA - Laboratory & Analytical Services Division","active":true,"usgs":true}],"preferred":true,"id":463501,"contributorType":{"id":1,"text":"Authors"},"rank":3}]}} ,{"id":70199539,"text":"70199539 - 2012 - VARBOOT: A spatial bootstrap program for semivariogram uncertainty assessment","interactions":[],"lastModifiedDate":"2018-09-20T15:30:49","indexId":"70199539","displayToPublicDate":"2012-04-01T15:30:37","publicationYear":"2012","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":1315,"text":"Computers & Geosciences","printIssn":"0098-3004","active":true,"publicationSubtype":{"id":10}},"title":"VARBOOT: A spatial bootstrap program for semivariogram uncertainty assessment","docAbstract":"In applied geostatistics, the semivariogram is commonly estimated from experimental data, producing an empirical semivariogram for a specified number of discrete lags. In a second stage, a model defined by a few parameters is fitted to the empirical semivariogram. As the experimental data are usually few and sparsely located, there is considerable uncertainty about the calculated semivariogram values (uncertainty of the empirical semivariogram) and about the parameters of any model fitted to them (uncertainty of the estimated model parameters). In this paper, the uncertainty in the modeling of the empirical semivariogram is numerically assessed by the generalized bootstrap, which is an extension of the classic bootstrap procedure modified for spatially correlated data. A computer program is described and provided for the assessment of those uncertainties. In particular, the program provides for the empirical semivariogram: the standard errors, the bootstrap percentile confidence intervals, the complete variance–covariance matrix, standard deviation correlation matrix. A public domain, natural dataset is used to illustrate the performance of the program. A promising result is that, for any distance, the median of the bootstrap distribution for the empirical semivariogram approximates more closely the underlying semivariogram than the estimate derived from the empirical sample.
","language":"English","publisher":"Elsevier","doi":"10.1016/j.cageo.2011.09.002","usgsCitation":"Pardo-Iguzquiza, E., and Olea, R., 2012, VARBOOT: A spatial bootstrap program for semivariogram uncertainty assessment: Computers & Geosciences, v. 41, p. 188-198, https://doi.org/10.1016/j.cageo.2011.09.002.","productDescription":"11 p.","startPage":"188","endPage":"198","ipdsId":"IP-021577","costCenters":[{"id":241,"text":"Eastern Energy Resources Science Center","active":true,"usgs":true}],"links":[{"id":474530,"rank":0,"type":{"id":41,"text":"Open Access External Repository Page"},"url":"http://hdl.handle.net/20.500.12468/527","text":"External Repository"},{"id":357566,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"volume":"41","publishingServiceCenter":{"id":9,"text":"Reston PSC"},"noUsgsAuthors":false,"publicationStatus":"PW","scienceBaseUri":"5c10beb5e4b034bf6a7f08fc","contributors":{"authors":[{"text":"Pardo-Iguzquiza, Eulogio","contributorId":208073,"corporation":false,"usgs":false,"family":"Pardo-Iguzquiza","given":"Eulogio","email":"","affiliations":[{"id":40847,"text":"Instituto Geologico y Minero de Espana","active":true,"usgs":false}],"preferred":false,"id":745816,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Olea, Ricardo A. 0000-0003-4308-0808","orcid":"https://orcid.org/0000-0003-4308-0808","contributorId":26436,"corporation":false,"usgs":true,"family":"Olea","given":"Ricardo A.","affiliations":[{"id":241,"text":"Eastern Energy Resources Science Center","active":true,"usgs":true}],"preferred":false,"id":745815,"contributorType":{"id":1,"text":"Authors"},"rank":2}]}} ,{"id":70136251,"text":"70136251 - 2012 - Estimating survival rates with time series of standing age‐structure data","interactions":[],"lastModifiedDate":"2018-03-30T09:24:33","indexId":"70136251","displayToPublicDate":"2012-04-01T11:30:00","publicationYear":"2012","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":1465,"text":"Ecology","active":true,"publicationSubtype":{"id":10}},"title":"Estimating survival rates with time series of standing age‐structure data","docAbstract":"It has long been recognized that age‐structure data contain useful information for assessing the status and dynamics of wildlife populations. For example, age‐specific survival rates can be estimated with just a single sample from the age distribution of a stable, stationary population. For a population that is not stable, age‐specific survival rates can be estimated using techniques such as inverse methods that combine time series of age‐structure data with other demographic data. However, estimation of survival rates using these methods typically requires numerical optimization, a relatively long time series of data, and smoothing or other constraints to provide useful estimates. We developed general models for possibly unstable populations that combine time series of age‐structure data with other demographic data to provide explicit maximum likelihood estimators of age‐specific survival rates with as few as two years of data. As an example, we applied these methods to estimate survival rates for female bison (Bison bison) in Yellowstone National Park, USA. This approach provides a simple tool for monitoring survival rates based on age‐structure data.
Northern Goshawks occupying the Alexander Archipelago, Alaska, and coastal British Columbia nest primarily in old-growth and mature forest, which results in spatial heterogeneity in the distribution of individuals across the landscape. We used microsatellite and mitochondrial data to infer genetic structure, gene flow, and fluctuations in population demography through evolutionary time. Patterns in the genetic signatures were used to assess predictions associated with the three population models: panmixia, metapopulation, and isolated populations. Population genetic structure was observed along with asymmetry in gene flow estimates that changed directionality at different temporal scales, consistent with metapopulation model predictions. Therefore, Northern Goshawk assemblages located in the Alexander Archipelago and coastal British Columbia interact through a metapopulation framework, though they may not fit the classic model of a metapopulation. Long-term population sources (coastal mainland British Columbia) and sinks (Revillagigedo and Vancouver islands) were identified. However, there was no trend through evolutionary time in the directionality of dispersal among the remaining assemblages, suggestive of a rescue-effect dynamic. Admiralty, Douglas, and Chichagof island complex appears to be an evolutionarily recent source population in the Alexander Archipelago. In addition, Kupreanof island complex and Kispiox Forest District populations have high dispersal rates to populations in close geographic proximity and potentially serve as local source populations. Metapopulation dynamics occurring in the Alexander Archipelago and coastal British Columbia by Northern Goshawks highlight the importance of both occupied and unoccupied habitats to long-term population persistence of goshawks in this region.
","language":"English","publisher":"Kluwer Academic Publishers","publisherLocation":"Dordrecht","doi":"10.1007/s10592-012-0352-z","usgsCitation":"Sonsthagen, S.A., McClaren, E.L., Doyle, F.I., Titus, K., Sage, G.K., Wilson, R.E., Gust, J.R., and Talbot, S.L., 2012, Identification of metapopulation dynamics among Northern Goshawks of the Alexander Archipelago, Alaska, and Coastal British Columbia: Conservation Genetics, v. 13, no. 4, p. 1045-1057, https://doi.org/10.1007/s10592-012-0352-z.","productDescription":"13 p.","startPage":"1045","endPage":"1057","numberOfPages":"13","onlineOnly":"N","additionalOnlineFiles":"N","ipdsId":"IP-023923","costCenters":[{"id":117,"text":"Alaska Science Center Biology WTEB","active":true,"usgs":true}],"links":[{"id":296608,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"Canada, United States","state":"Alaska, British Columbia","otherGeospatial":"Alexander Archipelago","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -140.9765625,\n 60.413852350464936\n ],\n [\n -131.220703125,\n 60.108670463036\n ],\n [\n -120.14648437499999,\n 49.210420445650286\n ],\n [\n -127.35351562499999,\n 46.13417004624326\n ],\n [\n -140.9765625,\n 60.413852350464936\n ]\n ]\n ]\n }\n }\n ]\n}","volume":"13","issue":"4","noUsgsAuthors":false,"publicationDate":"2012-04-19","publicationStatus":"PW","scienceBaseUri":"548ace3de4b00f366bee37b8","contributors":{"authors":[{"text":"Sonsthagen, Sarah A. 0000-0001-6215-5874 ssonsthagen@usgs.gov","orcid":"https://orcid.org/0000-0001-6215-5874","contributorId":3711,"corporation":false,"usgs":true,"family":"Sonsthagen","given":"Sarah","email":"ssonsthagen@usgs.gov","middleInitial":"A.","affiliations":[{"id":117,"text":"Alaska Science Center Biology WTEB","active":true,"usgs":true},{"id":114,"text":"Alaska Science Center","active":true,"usgs":true}],"preferred":true,"id":526836,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"McClaren, Erica L.","contributorId":127825,"corporation":false,"usgs":false,"family":"McClaren","given":"Erica","email":"","middleInitial":"L.","affiliations":[],"preferred":false,"id":526969,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Doyle, Frank I.","contributorId":127826,"corporation":false,"usgs":false,"family":"Doyle","given":"Frank","email":"","middleInitial":"I.","affiliations":[],"preferred":false,"id":526970,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Titus, K.","contributorId":93865,"corporation":false,"usgs":true,"family":"Titus","given":"K.","email":"","affiliations":[],"preferred":false,"id":526971,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Sage, George K. 0000-0003-1431-2286 ksage@usgs.gov","orcid":"https://orcid.org/0000-0003-1431-2286","contributorId":87833,"corporation":false,"usgs":true,"family":"Sage","given":"George","email":"ksage@usgs.gov","middleInitial":"K.","affiliations":[{"id":117,"text":"Alaska Science Center Biology WTEB","active":true,"usgs":true}],"preferred":false,"id":526972,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"Wilson, Robert E. 0000-0003-1800-0183 rewilson@usgs.gov","orcid":"https://orcid.org/0000-0003-1800-0183","contributorId":5718,"corporation":false,"usgs":true,"family":"Wilson","given":"Robert","email":"rewilson@usgs.gov","middleInitial":"E.","affiliations":[{"id":114,"text":"Alaska Science Center","active":true,"usgs":true},{"id":117,"text":"Alaska Science Center Biology WTEB","active":true,"usgs":true}],"preferred":true,"id":526973,"contributorType":{"id":1,"text":"Authors"},"rank":6},{"text":"Gust, Judy R.","contributorId":62458,"corporation":false,"usgs":false,"family":"Gust","given":"Judy","email":"","middleInitial":"R.","affiliations":[],"preferred":false,"id":526974,"contributorType":{"id":1,"text":"Authors"},"rank":7},{"text":"Talbot, Sandra L. 0000-0002-3312-7214 stalbot@usgs.gov","orcid":"https://orcid.org/0000-0002-3312-7214","contributorId":140512,"corporation":false,"usgs":true,"family":"Talbot","given":"Sandra","email":"stalbot@usgs.gov","middleInitial":"L.","affiliations":[{"id":117,"text":"Alaska Science Center Biology WTEB","active":true,"usgs":true},{"id":114,"text":"Alaska Science Center","active":true,"usgs":true}],"preferred":true,"id":526975,"contributorType":{"id":1,"text":"Authors"},"rank":8}]}} ,{"id":70148132,"text":"70148132 - 2012 - Demographic population model for American shad: will access to additional habitat upstream of dams increase population sizes?","interactions":[],"lastModifiedDate":"2015-06-03T10:06:14","indexId":"70148132","displayToPublicDate":"2012-04-01T00:00:00","publicationYear":"2012","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":2680,"text":"Marine and Coastal Fisheries: Dynamics, Management, and Ecosystem Science","active":true,"publicationSubtype":{"id":10}},"title":"Demographic population model for American shad: will access to additional habitat upstream of dams increase population sizes?","docAbstract":"American shad Alosa sapidissima are in decline in their native range, and modeling possible management scenarios could help guide their restoration. We developed a density-dependent, deterministic, stage-based matrix model to predict the population-level results of transporting American shad to suitable spawning habitat upstream of dams on the Roanoke River, North Carolina and Virginia. We used data on sonic-tagged adult American shad and oxytetracycline-marked American shad fry both above and below dams on the Roanoke River with information from other systems to estimate a starting population size and vital rates. We modeled the adult female population over 30 years under plausible scenarios of adult transport, effective fecundity (egg production), and survival of adults (i.e., to return to spawn the next year) and juveniles (from spawned egg to age 1). We also evaluated the potential effects of increased survival for adults and juveniles. The adult female population size in the Roanoke River was estimated to be 5,224. With no transport, the model predicted a slow population increase over the next 30 years. Predicted population increases were highest when survival was improved during the first year of life. Transport was predicted to benefit the population only if high rates of effective fecundity and juvenile survival could be achieved. Currently, transported adults and young are less likely to successfully out-migrate than individuals below the dams, and the estimated adult population size is much smaller than either of two assumed values of carrying capacity for the lower river; therefore, transport is not predicted to help restore the stock under present conditions. Research on survival rates, density-dependent processes, and the impacts of structures to increase out-migration success would improve evaluation of the potential benefits of access to additional spawning habitat for American shad.
","language":"English","publisher":"American Fisheries Society","doi":"10.1080/19425120.2012.675969","usgsCitation":"Harris, J., and Hightower, J.E., 2012, Demographic population model for American shad: will access to additional habitat upstream of dams increase population sizes?: Marine and Coastal Fisheries: Dynamics, Management, and Ecosystem Science, v. 4, no. 1, p. 262-283, https://doi.org/10.1080/19425120.2012.675969.","productDescription":"22 p.","startPage":"262","endPage":"283","onlineOnly":"N","additionalOnlineFiles":"N","ipdsId":"IP-028285","costCenters":[{"id":198,"text":"Coop Res Unit Atlanta","active":true,"usgs":true}],"links":[{"id":474538,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.1080/19425120.2012.675969","text":"Publisher Index Page"},{"id":301002,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","state":"North Carolina, Virginia","otherGeospatial":"Roanoke River","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -79.18121337890625,\n 36.38149043210595\n ],\n [\n -79.18121337890625,\n 37.084762325442966\n ],\n [\n -77.57720947265624,\n 37.084762325442966\n ],\n [\n -77.57720947265624,\n 36.38149043210595\n ],\n [\n -79.18121337890625,\n 36.38149043210595\n ]\n ]\n ]\n }\n }\n ]\n}","volume":"4","issue":"1","publishingServiceCenter":{"id":8,"text":"Raleigh PSC"},"noUsgsAuthors":false,"publicationDate":"2012-06-18","publicationStatus":"PW","scienceBaseUri":"55702532e4b0d9246a9fd18d","contributors":{"authors":[{"text":"Harris, Julianne E.","contributorId":57687,"corporation":false,"usgs":true,"family":"Harris","given":"Julianne E.","affiliations":[],"preferred":false,"id":548124,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Hightower, Joseph E. jhightower@usgs.gov","contributorId":835,"corporation":false,"usgs":true,"family":"Hightower","given":"Joseph","email":"jhightower@usgs.gov","middleInitial":"E.","affiliations":[{"id":198,"text":"Coop Res Unit Atlanta","active":true,"usgs":true}],"preferred":true,"id":547461,"contributorType":{"id":1,"text":"Authors"},"rank":2}]}} ,{"id":70190680,"text":"70190680 - 2012 - Progressive failure of sheeted rock slopes: the 2009–2010 Rhombus Wall rock falls in Yosemite Valley, California, USA","interactions":[],"lastModifiedDate":"2017-09-12T11:33:24","indexId":"70190680","displayToPublicDate":"2012-04-01T00:00:00","publicationYear":"2012","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":1425,"text":"Earth Surface Processes and Landforms","active":true,"publicationSubtype":{"id":10}},"title":"Progressive failure of sheeted rock slopes: the 2009–2010 Rhombus Wall rock falls in Yosemite Valley, California, USA","docAbstract":"Progressive rock-fall failures in natural rock slopes are common in many environments, but often elude detailed quantitative documentation and analysis. Here we present high-resolution photography, video, and laser scanning data that document spatial and temporal patterns of a 15-month-long sequence of at least 14 rock falls from the Rhombus Wall, a sheeted granitic cliff in Yosemite Valley, California. The rock-fall sequence began on 26 August 2009 with a small failure at the tip of an overhanging rock slab. Several hours later, a series of five rock falls totaling 736 m3progressed upward along a sheeting joint behind the overhanging slab. Over the next 3 weeks, audible cracking occurred on the Rhombus Wall, suggesting crack propagation, while visual monitoring revealed opening of a sheeting joint adjacent to the previous failure surface. On 14 September 2009 a 110 m3 slab detached along this sheeting joint. Additional rock falls between 30 August and 20 November 2010, totaling 187 m3, radiated outward from the initial failure area along cliff (sub)parallel sheeting joints. We suggest that these progressive failures might have been related to stress redistributions accompanying propagation of sheeting joints behind the cliff face. Mechanical analyses indicate that tensile stresses should occur perpendicular to the cliff face and open sheeting joints, and that sheeting joints should propagate parallel to a cliff face from areas of stress concentrations. The analyses also account for how sheeting joints can propagate to lengths many times greater than their depths behind cliff faces. We posit that as a region of failure spreads across a cliff face, stress concentrations along its margin will spread with it, promoting further crack propagation and rock falls.
","language":"English","publisher":"Wiley","doi":"10.1002/esp.3192","usgsCitation":"Stock, G.M., Martel, S.J., Collins, B.D., and Harp, E.L., 2012, Progressive failure of sheeted rock slopes: the 2009–2010 Rhombus Wall rock falls in Yosemite Valley, California, USA: Earth Surface Processes and Landforms, v. 37, no. 5, p. 546-561, https://doi.org/10.1002/esp.3192.","productDescription":"16 p.","startPage":"546","endPage":"561","ipdsId":"IP-034187","costCenters":[{"id":312,"text":"Geology, Minerals, Energy, and Geophysics Science Center","active":true,"usgs":true}],"links":[{"id":345644,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","state":"California","otherGeospatial":"Yosemite Valley","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -119.62051391601564,\n 37.72863798965106\n ],\n [\n -119.57056045532227,\n 37.72863798965106\n ],\n [\n -119.57056045532227,\n 37.76135133865817\n ],\n [\n -119.62051391601564,\n 37.76135133865817\n ],\n [\n -119.62051391601564,\n 37.72863798965106\n ]\n ]\n ]\n }\n }\n ]\n}","volume":"37","issue":"5","noUsgsAuthors":false,"publicationDate":"2012-01-31","publicationStatus":"PW","scienceBaseUri":"59b8f221e4b08b1644e0aefb","contributors":{"authors":[{"text":"Stock, Greg M.","contributorId":88593,"corporation":false,"usgs":true,"family":"Stock","given":"Greg","email":"","middleInitial":"M.","affiliations":[],"preferred":false,"id":710149,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Martel, Stephen J.","contributorId":196359,"corporation":false,"usgs":false,"family":"Martel","given":"Stephen","email":"","middleInitial":"J.","affiliations":[],"preferred":false,"id":710150,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Collins, Brian D. bcollins@usgs.gov","contributorId":2406,"corporation":false,"usgs":true,"family":"Collins","given":"Brian","email":"bcollins@usgs.gov","middleInitial":"D.","affiliations":[{"id":312,"text":"Geology, Minerals, Energy, and Geophysics Science Center","active":true,"usgs":true}],"preferred":false,"id":710151,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Harp, Edwin L. harp@usgs.gov","contributorId":1290,"corporation":false,"usgs":true,"family":"Harp","given":"Edwin","email":"harp@usgs.gov","middleInitial":"L.","affiliations":[{"id":218,"text":"Denver Federal Center","active":false,"usgs":true}],"preferred":false,"id":710152,"contributorType":{"id":1,"text":"Authors"},"rank":4}]}} ,{"id":70191695,"text":"70191695 - 2012 - Using pad‐stripped acausally filtered strong‐motion data","interactions":[],"lastModifiedDate":"2017-10-17T17:02:41","indexId":"70191695","displayToPublicDate":"2012-04-01T00:00:00","publicationYear":"2012","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":1135,"text":"Bulletin of the Seismological Society of America","onlineIssn":"1943-3573","printIssn":"0037-1106","active":true,"publicationSubtype":{"id":10}},"title":"Using pad‐stripped acausally filtered strong‐motion data","docAbstract":"Most strong‐motion data processing involves acausal low‐cut filtering, which requires the addition of sometimes lengthy zero pads to the data. These padded sections are commonly removed by organizations supplying data, but this can lead to incompatibilities in measures of ground motion derived in the usual way from the padded and the pad‐stripped data. One way around this is to use the correct initial conditions in the pad‐stripped time series when computing displacements, velocities, and linear oscillator response. Another way of ensuring compatibility is to use postprocessing of the pad‐stripped acceleration time series. Using 4071 horizontal and vertical acceleration time series from the Turkish strong‐motion database, we show that the procedures used by two organizations—ITACA (ITalian ACcelerometric Archive) and PEER NGA (Pacific Earthquake Engineering Research Center–Next Generation Attenuation)—lead to little bias and distortion of derived seismic‐intensity measures.
","language":"English","publisher":"Seismological Society of America","doi":"10.1785/0120110222","usgsCitation":"Boore, D., Sisi, A.A., and Akkar, S., 2012, Using pad‐stripped acausally filtered strong‐motion data: Bulletin of the Seismological Society of America, v. 102, no. 2, p. 751-760, https://doi.org/10.1785/0120110222.","productDescription":"10 p.","startPage":"751","endPage":"760","ipdsId":"IP-034111","costCenters":[{"id":237,"text":"Earthquake Science Center","active":true,"usgs":true}],"links":[{"id":487191,"rank":0,"type":{"id":41,"text":"Open Access External Repository Page"},"url":"https://zenodo.org/record/3440334","text":"External Repository"},{"id":346769,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"volume":"102","issue":"2","publishingServiceCenter":{"id":14,"text":"Menlo Park PSC"},"noUsgsAuthors":false,"publicationDate":"2012-03-29","publicationStatus":"PW","scienceBaseUri":"59e71695e4b05fe04cd331f8","contributors":{"authors":[{"text":"Boore, David 0000-0002-8605-9673 boore@usgs.gov","orcid":"https://orcid.org/0000-0002-8605-9673","contributorId":140502,"corporation":false,"usgs":true,"family":"Boore","given":"David","email":"boore@usgs.gov","affiliations":[{"id":237,"text":"Earthquake Science Center","active":true,"usgs":true}],"preferred":true,"id":713088,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Sisi, Aida Azari","contributorId":197265,"corporation":false,"usgs":false,"family":"Sisi","given":"Aida","email":"","middleInitial":"Azari","affiliations":[],"preferred":false,"id":713089,"contributorType":{"id":1,"text":"Authors"},"rank":11},{"text":"Akkar, Sinan","contributorId":39175,"corporation":false,"usgs":true,"family":"Akkar","given":"Sinan","email":"","affiliations":[],"preferred":false,"id":713090,"contributorType":{"id":1,"text":"Authors"},"rank":12}]}} ]}