This short course was prepared at the request of Servicio Geológico Colombiano (SGC) as a module for staff training. Prior to the short course, the SGC expressed interest in receiving training in (1) geochemistry and quality of coal; (2) geochemistry of trace elements in coal; (3) mercury and halogens in coal; (4) characterization and cycling of atmospheric mercury; (5) mercury, trace elements, and organic constituents in atmospheric fine particulate matter; (6) mercury in coal and the effect of coal quality on mercury emissions from combustion systems; (7) environmental and health effects related to coal use; and (8) related topics in coal combustion processes. A five-session short course was prepared that addressed all but the engineering aspects of coal use. In the sections that follow, topic overviews are given for the material that was presented. Brief descriptions of each slide are given in appendix 1, and the actual short course material, presented as a series of PowerPoint slides, is included in Portable Document Format (PDF) as appendix 2.
","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/ofr20181145","collaboration":"Prepared in collaboration with Servicio Geológico Colombiano, Bogotá, Colombia","usgsCitation":"Kolker, A., 2018, Topics in coal geochemistry—Short course: U.S. Geological Survey Open-File Report 2018–1145, 31 p., https://doi.org/10.3133/ofr20181145.","productDescription":"Report: x, 31 p.; Appendix","onlineOnly":"Y","additionalOnlineFiles":"Y","ipdsId":"IP-087688","costCenters":[{"id":241,"text":"Eastern Energy Resources Science Center","active":true,"usgs":true}],"links":[{"id":358209,"rank":3,"type":{"id":3,"text":"Appendix"},"url":"https://pubs.usgs.gov/of/2018/1145/ofr20181145_appendix2.pdf","text":"Appendix 2","size":"11.7 MB","linkFileType":{"id":1,"text":"pdf"},"description":"OFR 2018-1145 Appendix 2","linkHelpText":"Appendix 2. Short Course Slides"},{"id":358207,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/of/2018/1145/coverthb.jpg"},{"id":358208,"rank":2,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/of/2018/1145/ofr20181145.pdf","text":"Report","size":"365 KB","linkFileType":{"id":1,"text":"pdf"},"description":"OFR 2018-1145"}],"contact":"Eastern Energy Resources Science Center
U.S. Geological Survey
12201 Sunrise Valley Drive
956 National Center
Reston, VA 20192
https://energy.usgs.gov/
The alluvial diamond deposits of the Central African Republic (CAR) are mined almost exclusively by way of informal artisanal and small-scale mining (ASM) methods. ASM sites range in diameter from a few meters to 30 meters or more, and are typically excavated by crews of diggers using hand tools, sieves, and jigs. CAR’s reported annual production has ranged from 300,000 to 470,000 carats over the past decade. This production is significant for CAR because it accounts for a large portion of the country’s export income and employs an estimated 60,000 to 90,000 miners nationally. Diamond production has also been linked to the violent conflict and political instability which have plagued the country for decades. The most recent conflict began in 2012 and resulted in an international embargo on the export of rough diamonds from CAR. This embargo was followed by a ceasefire and a return of peace in certain zones of the country in 2015; however, political and economic instability continues to afflict many areas of the country. International efforts to restore peace in CAR have included United Nations support as well as international technical assistance in tracking, assessing, and monitoring diamond production. In 2015, the Kimberley Process (KP) developed an operational framework allowing for legitimate exports from five subprefectures in CAR that were deemed to be compliant with KP internal controls and which were also considered to be free from systematic violence or control of armed groups.
The goal of this study was to address information gaps regarding the location and extent of diamond occurrences and mining activity through the integration of geologic research with remote sensing, geographic information systems analysis, and fieldwork. Effective and efficient monitoring of diamond mining activity using satellite imagery requires detailed understanding of the geographic distribution of diamond sources and mining activities.
A two-phase methodology was developed to address the knowledge gaps. The first phase consisted of the creation of a comprehensive geospatial catalogue of diamond mining and occurrence locations from archival records such as historical maps, mining reports, academic publications, and field data. Building upon this locational database, the second phase consisted of the creation of a geospatial dataset cataloguing current mining activity locations through manual interpretation of recently acquired satellite imagery. The accuracy of this second geospatial dataset was then assessed using field observations made between 2016 and 2017 by the U.S. Agency for International Development’s Property Rights and Artisanal Diamond Development II project. This report presents a two-part geodatabase: part 1 contains the locations of diamond mine sites and occurrences from archival sources, and part 2 indicates areas of current or recent mining activity. This geodatabase is unique in its temporal and spatial extent and may be used to analyze the geographic distribution of CAR’s known diamond resources, to assess the effect of recent violent conflicts and KP actions on diamond produc-tion, to provide decision makers with information regarding small-scale diamond mining, and to improve the monitoring of mining in regions of the country prone to conflict.
","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/ofr20181088","collaboration":"Prepared in cooperation with the U.S. Agency for International Development under the auspices of the U.S. Department of State","usgsCitation":"DeWitt, J.D., Chirico, P.G., Bergstresser, S.E., and Clark, I.E., 2018, The Central African Republic Diamond Database—A geodatabase of archival diamond occurrences and areas of recent artisanal and small-scale diamond mining: U.S. Geological Survey Open-File Report 2018–1088, 28 p., 1 pl., https://doi.org/10.3133/ofr20181088.","productDescription":"Report: iv, 28 p.; Plate: 40.0 x 27.0 inches; Databases; Metadata","onlineOnly":"Y","additionalOnlineFiles":"Y","ipdsId":"IP-087222","costCenters":[{"id":243,"text":"Eastern Geology and Paleoclimate Science Center","active":true,"usgs":true}],"links":[{"id":357657,"rank":5,"type":{"id":16,"text":"Metadata"},"url":"https://pubs.usgs.gov/of/2018/1088/ofr20181088_metadata-for-all-gisfiles.zip","size":"2.60 GB","linkFileType":{"id":6,"text":"zip"},"linkHelpText":"- Text formatted metadata for all data release files"},{"id":357991,"rank":6,"type":{"id":9,"text":"Database"},"url":"https://pubs.usgs.gov/of/2018/1088/ofr20181088_hydrologically-corrected-dem.tif.zip","size":"1.31 GB","linkFileType":{"id":6,"text":"zip"},"linkHelpText":"- Data release of hydrologically corrected digital elevation model (DEM) and hillshade model"},{"id":357272,"rank":3,"type":{"id":17,"text":"Plate"},"url":"https://pubs.usgs.gov/of/2018/1088/ofr20181088_plate.pdf","text":"Surficial and Bedrock Geology, Placer Diamond Deposits, and Mineral Occurrences of the Central African Republic","size":"62.5 MB","linkFileType":{"id":1,"text":"pdf"}},{"id":357270,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/of/2018/1088/coverthb1.jpg"},{"id":357271,"rank":2,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/of/2018/1088/ofr20181088.pdf","text":"Report","size":"6.44 MB","linkFileType":{"id":1,"text":"pdf"},"description":"OFR 2018-1088"},{"id":357656,"rank":4,"type":{"id":9,"text":"Database"},"url":"https://pubs.usgs.gov/of/2018/1088/ofr20181088_map-database.zip","size":"17.2 MB","linkFileType":{"id":6,"text":"zip"},"description":"Data Files","linkHelpText":"- Data release of mapped vector data (mining locations, rivers, geology, and geomorphology)"}],"country":"Central African Republic","geographicExtents":"{\"type\":\"FeatureCollection\",\"features\":[{\"type\":\"Feature\",\"geometry\":{\"type\":\"Polygon\",\"coordinates\":[[[15.27946,7.42192],[16.10623,7.49709],[16.29056,7.75431],[16.45618,7.73477],[16.70599,7.50833],[17.96493,7.89091],[18.38955,8.2813],[18.91102,8.63089],[18.81201,8.98291],[19.09401,9.07485],[20.05969,9.01271],[21.00087,9.47599],[21.72382,10.56706],[22.23113,10.97189],[22.86417,11.1424],[22.97754,10.71446],[23.5543,10.08926],[23.55725,9.68122],[23.39478,9.26507],[23.45901,8.95429],[23.80581,8.66632],[24.56737,8.22919],[25.11493,7.8251],[25.12413,7.50009],[25.79665,6.97932],[26.21342,6.5466],[26.46591,5.94672],[27.21341,5.55095],[27.37423,5.23394],[27.04407,5.12785],[26.40276,5.15087],[25.65046,5.25609],[25.2788,5.17041],[25.12883,4.92724],[24.80503,4.89725],[24.41053,5.10878],[23.29721,4.60969],[22.84148,4.71013],[22.70412,4.63305],[22.40512,4.02916],[21.65912,4.22434],[20.92759,4.32279],[20.29068,4.69168],[19.46778,5.03153],[18.93231,4.70951],[18.54298,4.20179],[18.45307,3.50439],[17.8099,3.5602],[17.13304,3.7282],[16.53706,3.19825],[16.01285,2.26764],[15.90738,2.55739],[15.86273,3.01354],[15.4054,3.3353],[15.03622,3.85137],[14.95095,4.21039],[14.47837,4.73261],[14.55894,5.0306],[14.45941,5.45176],[14.53656,6.22696],[14.77655,6.4085],[15.27946,7.42192]]]},\"properties\":{\"name\":\"Central African Republic\"}}]}","contact":"Director, Eastern Geology and Paleoclimate Science Center
U.S. Geological Survey
National Center, MS 926A
12201 Sunrise Valley Drive
Reston, VA 20192
The August 24, 2014, moment magnitude (Mw) 6.0 South Napa earthquake caused an estimated $400 million in structural damage to the City of Napa, California. In 2015, we acquired high-resolution P- and S-wave seismic data near three strong-motion recording stations in Napa and Solano Counties where high peak ground accelerations (PGAs) were recorded during the South Napa earthquake. In this report, we present results from three sites—Lovall Valley Loop Road in Napa County (Northern California Seismic Network station, NCSN N019B) and Broadway Street and Sereno Drive (California Geological Survey station, CGS 68294) and Vallejo Fire Station (National Strong Motion Project station, NSMP 1759) in the City of Vallejo, California. To characterize the recording sites in terms of shallow-depth, shear-wave velocities (VS), we used both surface waves (Rayleigh and Love) and body waves (S-wave) to evaluate the time-averaged VS in the upper 30 meters of the subsurface (VS30). We used two-dimensional (2D) multichannel analysis of surface waves (MASW) to evaluate VS from surface waves, and a refraction tomography inversion algorithm, developed by Hole in 1992, to evaluate VS from the body waves. As determined by the tomography and MASW analysis for Love waves, we found VS30 near the strong-motion recording stations at Lovall Valley Loop Road, Broadway Street and Sereno Drive, and the Vallejo Fire Station to be from 711 meters per second (m/s) to 767 m/s, 455 to 673 m/s, and 490 to 583 m/s, respectively. We found that VS30 determined from Love waves were higher than those determined from Rayleigh waves at the Lovall Valley Loop Road recording site (221 m/s higher) and at the Vallejo Fire Station site (62 and 48 m/s higher); however, VS30 from Love waves was lower than those from Rayleigh waves at the Broadway Street and Sereno Drive site (78 m/s lower). We also found that VS30 varied depending on the number of shot points used in our MASW analysis for both Love and Rayleigh waves. Furthermore, VS30 values determined from S-wave refraction tomography are generally closer to those determined from MASW using Love waves than those determined using Rayleigh waves.
","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/ofr20181162","usgsCitation":"Chan, J.H., Catchings, R.D., Goldman, M.R., and Criley, C.J., 2018, VS30 at three strong-motion recording stations in Napa and Solano Counties, California — Lovall Valley Road, Broadway Street and Sereno Drive in Vallejo, and Vallejo Fire Station — Calculations determined from S-wave refraction tomography and multichannel analysis of surface waves (Rayleigh and Love): U.S. Geological Survey Open-File Report 2018–1162, 62 p., https://doi.org/10.3133/ofr20181162.","productDescription":"Report: viii, 62 p.; Data Release","onlineOnly":"Y","ipdsId":"IP-096908","costCenters":[{"id":237,"text":"Earthquake Science Center","active":true,"usgs":true}],"links":[{"id":358151,"rank":4,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/P9F4IAAL","text":"USGS data release","description":"USGS data release","linkHelpText":"2015 high resolution seismic data recorded at six strong motion seismograph sites in Napa and Solano counties, California"},{"id":358150,"rank":3,"type":{"id":22,"text":"Related Work"},"url":"https://doi.org/10.3133/ofr20181161","text":"Open-File Report 2018-1161","linkHelpText":"- VS30 at Three Strong-Motion Recording Stations in Napa and Napa County, California—Main Street in Downtown Napa, Napa Fire Station Number 3, and Kreuzer Lane—Calculations Determined From S-wave Refraction Tomography and Multichannel Analysis of Surface Waves (Rayleigh and Love)"},{"id":358149,"rank":2,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/of/2018/1162/ofr20181162.pdf","text":"Report","size":"18 MB","linkFileType":{"id":1,"text":"pdf"},"description":"Open-File Report 2018-1162"},{"id":358148,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/of/2018/1162/coverthb.jpg"}],"country":"United States","state":"California","county":"Napa County, Solano County","city":"Vallejo","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"coordinates\": [\n [\n [\n -122.41713722595051,\n 38.31876308218918\n ],\n [\n -122.41713722595051,\n 38.293376084062544\n ],\n [\n -122.3791235695102,\n 38.293376084062544\n ],\n [\n -122.3791235695102,\n 38.31876308218918\n ],\n [\n -122.41713722595051,\n 38.31876308218918\n ]\n ]\n ],\n \"type\": \"Polygon\"\n }\n },\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"coordinates\": [\n [\n [\n -122.27260163802902,\n 38.14045873726971\n ],\n [\n -122.27260163802902,\n 38.09368883459186\n ],\n [\n -122.23175717738528,\n 38.09368883459186\n ],\n [\n -122.23175717738528,\n 38.14045873726971\n ],\n [\n -122.27260163802902,\n 38.14045873726971\n ]\n ]\n ],\n \"type\": \"Polygon\"\n }\n }\n ]\n}","contact":"Contact Information,
Earthquake Science Center
U.S. Geological Survey
345 Middlefield Road, MS 977
Menlo Park, CA 94025
The August 24, 2014, moment magnitude (Mw) 6.0 South Napa earthquake caused an estimated $400 million in structural damage to the City of Napa, California. In 2015, we acquired high-resolution P- and S-wave seismic data near three strong-motion recording stations in Napa County where high peak ground accelerations (PGAs) were recorded during the South Napa earthquake. In this report, we present results from three sites—Main Street in Downtown Napa (Northern California Seismic Network station, NCSN N016), Napa Fire Station Number 3 (National Strong Motion Project station, NSMP 1765), and Kreuzer Lane (station KRE, temporary deployment). To characterize the recording sites in terms of shallow-depth shear-wave velocities (VS), we used both surface waves (Rayleigh and Love) and body waves (S-wave) to evaluate the time-averaged VS in the upper 30 meters of the subsurface (VS30). We used two-dimensional multichannel analysis of surface waves (MASW) to evaluate VS from the surface waves, and a refraction tomography inversion algorithm, developed by Hole in 1992, to evaluate VS from the body waves. As determined by the various methods, we found VS30 near the strong-motion recording stations on Main Street in Downtown Napa, Napa Fire Station Number 3, and on Kreuzer Lane to be from 281 meters per second (m/s) to 286 m/s, 297 to 371 m/s, and 885 to 916 m/s, respectively. The VS30 calculated from Love waves were slightly lower (10 m/s) than those calculated from Rayleigh waves at the Downtown Napa location and at Napa Fire Station Number 3 (4 m/s); however, VS30 calculated from Love waves was higher (190 m/s) than those calculated from Rayleigh waves at Kreuzer Lane. We also found that VS30 determined from MASW for both Love and Rayleigh waves varied depending on the number of shots along the profiles, and VS30 was not systematic based on the number of shots used in the analysis. Furthermore, VS30 calculated from S-wave refraction tomography are closer to those determined from MASW calculated from Love waves than from using Rayleigh waves.
","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/ofr20181161","usgsCitation":"Chan, J.H., Catchings, R.D., Goldman, M.R., and Criley, C.J., 2018, VS30 at three strong-motion recording stations in Napa and Napa County, California — Main Street in downtown Napa, Napa fire station number 3, and Kreuzer Lane — Calculations determined from s-wave refraction tomography and multichannel analysis of surface waves (Rayleigh and Love): U.S. Geological Survey Open-File Report 2018–1161, 47 p., https://doi.org/10.3133/ofr20181161.","productDescription":"Report: vii, 47 p.; Data Release","onlineOnly":"Y","ipdsId":"IP-091224","costCenters":[{"id":237,"text":"Earthquake Science Center","active":true,"usgs":true}],"links":[{"id":358146,"rank":4,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/P9F4IAAL","text":"USGS data release","description":"USGS data release","linkHelpText":"2015 high resolution seismic data recorded at six strong motion seismograph sites in Napa and Solano counties, California"},{"id":358144,"rank":2,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/of/2018/1161/ofr20181161.pdf","text":"Report","size":"13 MB","linkFileType":{"id":1,"text":"pdf"},"description":"Open-File Report 2018-1161"},{"id":358143,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/of/2018/1161/coverthb.jpg"},{"id":358145,"rank":3,"type":{"id":22,"text":"Related Work"},"url":"https://doi.org/10.3133/ofr20181162","text":"Open-File Report 2018-1162","linkHelpText":"- VS30 at three strong-motion recording stations in Napa and Solano Counties, California—Lovall Valley Road, Broadway Street and Sereno Drive in Vallejo, and Vallejo Fire Station—Calculations determined from S-wave refraction tomography and multichannel analysis of surface waves (Rayleigh and Love)"}],"country":"United States","state":"California","county":"Napa County","city":"Napa","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"coordinates\": [\n [\n [\n -122.33231191016716,\n 38.340761964883995\n ],\n [\n -122.33231191016716,\n 38.28175275852939\n ],\n [\n -122.22277030151793,\n 38.28175275852939\n ],\n [\n -122.22277030151793,\n 38.340761964883995\n ],\n [\n -122.33231191016716,\n 38.340761964883995\n ]\n ]\n ],\n \"type\": \"Polygon\"\n }\n }\n ]\n}","contact":"Contact Information,
Earthquake Science Center
U.S. Geological Survey
345 Middlefield Road, MS 977
Menlo Park, CA 94025
The U.S. Geological Survey (USGS), along with partner organizations, has developed an earthquake early warning (EEW) system called ShakeAlert for the highest risk areas of the United States: namely, California, Oregon, and Washington. The purpose of the system is to reduce the impact of earthquakes and save lives and property by providing alerts to institutional users and the public. Using networks of ground-motion sensors and sophisticated computer algorithms, ShakeAlert can detect an earthquake seconds after it begins, calculate its location and magnitude, and estimate the resulting intensity of shaking. Alerts can then be sent to people and systems that may experience damaging shaking, allowing them to take appropriate protective actions. Depending on the user’s distance from the earthquake, alerts may be delivered before, during, or after the arrival of strong shaking.
ShakeAlert is built on the foundation of the sensor networks and data processing infrastructure of the USGS-led Advanced National Seismic System. However, these networks were not originally designed for EEW; old equipment needs to be updated and new stations must be added to construct EEW-capable networks. The ShakeAlert data-processing infrastructure includes redundant servers that are geographically distributed at monitoring centers in Seattle, Washington, as well as Menlo Park, Berkeley, and Pasadena in California. Three data-processing layers collect raw ground-motion data from field stations (data layer), analyze these data to estimate the area and intensity of the resulting shaking (production layer), and publish alert products as appropriate for end users (alert layer). The alert layer can support thousands of institutional users and alert redistributors, but the USGS does not have the mission, infrastructure, or expertise to perform public notifications and is therefore recruiting technology enablers from the private sector. Additionally, ShakeAlert will coordinate with both public and private partners to accomplish consistent and ongoing public communication, education, and outreach.
The estimated cost of completing the ShakeAlert infrastructure and sensor networks is \\$39.4 million and has an estimated annual operation and maintenance cost of \\$28.6 million per year. Building a highly reliable data telemetry infrastructure would cost another \\$20.5 million and operating this telemetry system would add \\$49.8 million per year; however, these costs could be reduced if project partners provide bandwidth on existing systems.
","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/ofr20181155","usgsCitation":"Given, D.D., Allen, R.M., Baltay, A.S., Bodin, P., Cochran, E.S., Creager, K., de Groot, R.M., Gee, L.S., Hauksson, E., Heaton, T.H., Hellweg, M., Murray, J.R., Thomas, V.I., Toomey, D., and Yelin, T.S., 2018, Revised technical implementation plan for the ShakeAlert system—An earthquake early warning system for the West Coast of the United States: U.S. Geological Survey Open-File Report 2018–1155, 42 p., https://doi.org/10.3133/ofr20181155. 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\"}}]}","contact":"Earthquake Science Center-Pasadena Field Office
U.S. Geological Survey
525 South Wilson Ave.
Pasadena, CA 91106-3212
The Allegheny Portage Railroad National Historic Site (ALPO) in Blair and Cambria Counties, Pennsylvania, protects historic features of the first railroad portage over the Allegheny Front and the first railroad tunnel in the United States. This report, which was completed by the U.S. Geological Survey in cooperation with the National Park Service, summarizes water resources in the headwaters of the Blair Gap Run and Bradley Run watersheds at the ALPO Summit area during 2014–16. These new baseline data fill an existing gap in knowledge and may be helpful to evaluate potential changes in the hydrologic characteristics of streams and associated wetlands at the Summit area.
Results of synoptic water-quality surveys and continuous stage records at two streamgages near the headwaters of Blair Gap Run and Bradley Run indicate that the headwater streams of the ALPO Summit area are perennial but have different water-quality characteristics. The water sampled in the headwaters of Blair Gap Run had pH that ranged from acidic to near neutral, combined with elevated concentrations of dissolved solids, mainly sulfate, chloride, and sodium. These characteristics can be attributed to drainage from legacy coal mines and runoff from nearby roads treated with deicing salt. More than once during the study, the chloride and associated contaminant concentrations in tributaries of Blair Gap Run exceeded chronic thresholds for protection of freshwater aquatic organisms. In contrast, the water quality at tributaries of Bradley Run in the Summit area was characterized by near-neutral pH and relatively low concentrations of dissolved constituents, which met criteria for protection of freshwater aquatic life. By comparison, the deep groundwater discharged as abandoned mine drainage to Sugar Run from the Argyle Stone Bridge Mine, which underlies the Summit area, had acidic pH and elevated concentrations of sulfate and metals, which exceeded chronic and acute thresholds for aquatic life.
Data on shallow groundwater levels in piezometers at two wetlands in the Summit area, which were monitored during spring through fall of 2016, indicate downward hydraulic gradients (higher water level in shallow piezometer than in deeper piezometer) and potential for local groundwater recharge during rainfall events, particularly in the summer and fall seasons. The wetlands in the upland area (wetland 3, at altitude 2,370 feet NAVD 88) near the divide between Blair Gap Run and Bradley Run between the Lemon House and Picnic Area, exhibited a consistent downward gradient from spring through fall of 2016. The associated surface seepage at wetland 3 dried up in the summer of 2016. In contrast, the wetlands in the adjoining valley (wetland 6, at altitude 2,198 feet NAVD 88) in the northwestern Summit area exhibited upward hydraulic gradients in the spring and produced continuous seepage. Despite downward gradients during summer and fall, the seepage associated with wetland 6 sustained perennial conditions in the Bradley Run drainage through the summer of 2016.
Differences in groundwater altitudes and associated water quality among the surface water, shallow groundwater, and deep groundwater in the Summit area imply that the surface water and shallow groundwater in the Summit area could recharge the groundwater of the underlying coal mines. Seasonally upward and downward vertical gradients in the near-surface soil and bedrock at wetland 6, and unimpaired water quality in the Bradley Run headwaters, are consistent with a perched water table and local hydrology that is influenced by local recharge. Persistent downward gradients and impaired water quality at wetland 3 and the adjacent headwaters seeps and tributaries of Blair Gap Run could be attributed to subsidence and drainage from shallow coalbeds (Upper Freeport, seam E) and associated mine workings in that area; however, the underlying deep coal mine pool (Lower Kittanning, seam B), which is hundreds of feet below the surface, does not appear to affect the hydrologic characteristics of the headwater streams and wetlands in the Summit area.
","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/ofr20181125","collaboration":"Prepared in cooperation with the National Park Service","usgsCitation":"Cravotta, C.A., III, Galeone, D.G., and Penrod, K.A., 2018, Hydrologic characteristics and water quality of headwater streams and wetlands at the Allegheny Portage Railroad National Historic Site, Summit area, Blair and Cambria Counties, Pennsylvania, 2014–16 (ver. 1.1, December 2018): U.S. Geological Survey Open-File Report 2018–1125, 21 p., https://doi.org/10.3133/ofr20181125.","productDescription":"Report: vi, 21 p.; Table; Appendix; Data release","onlineOnly":"Y","additionalOnlineFiles":"Y","ipdsId":"IP-098767","costCenters":[{"id":532,"text":"Pennsylvania Water Science Center","active":true,"usgs":true}],"links":[{"id":357980,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/of/2018/1125/coverthb2.jpg"},{"id":357981,"rank":2,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/of/2018/1125/ofr20181125.pdf","text":"Report","size":"5.19 MB","linkFileType":{"id":1,"text":"pdf"},"description":"OFR 2018-1125"},{"id":357982,"rank":3,"type":{"id":3,"text":"Appendix"},"url":"https://pubs.usgs.gov/of/2018/1125/ofr20181125_appendixes.xlsx","size":"1.65 MB","linkFileType":{"id":3,"text":"xlsx"}},{"id":357984,"rank":5,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/P9YWMMHG","text":"USGS data release","description":"USGS data release","linkHelpText":"Hydrologic data collected by the U.S. Geological Survey and National Park Service at the Allegheny Portage Railroad National Historic Site, Summit Area, Blair and Cambria Counties, Pennsylvania, April 2014-December 2016"},{"id":357983,"rank":4,"type":{"id":27,"text":"Table"},"url":"https://pubs.usgs.gov/of/2018/1125/ofr20181125_tables.xlsx","size":"1.12 MB","linkFileType":{"id":3,"text":"xlsx"}},{"id":360331,"rank":6,"type":{"id":25,"text":"Version History"},"url":"https://pubs.usgs.gov/of/2018/1125/versionHist.txt","text":"Version History","size":"1.27 KB","linkFileType":{"id":2,"text":"txt"}}],"country":"United States","state":"Pennsylvania","county":"Blair County, Cambria County","otherGeospatial":"Allegheny Portage Railroad National Historic Site","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -78.56268,\n 40.45209\n ],\n [\n -78.5237,\n 40.45209\n ],\n [\n -78.5237,\n 40.47688\n ],\n [\n -78.56268,\n 40.47688\n ],\n [\n -78.56268,\n 40.45209\n ]\n ]\n ]\n }\n }\n ]\n}","edition":"Version 1.1: December 2018; Version 1.0: October 2018","contact":"Director, Pennsylvania Water Science Center
U.S. Geological Survey
215 Limekiln Road
New Cumberland, PA 17070
As part of local wellhead protection area programs, areas
contributing water to production wells need to be periodically
updated because groundwater-flow paths depend in part on
the stresses to the groundwater-flow system. A steady-state
groundwater-flow model that was constructed in 2009 was
updated to reflect recent (2017) pumping conditions in the
Lansing and East Lansing area in the Tri-County region, Michigan.
For this current (2017) study, withdrawals from selected
production wells were updated, and the existing model calibration
under the new pumping conditions was checked. Results
of flow simulations indicate that 10-year time-of-travel areas
cover approximately 25 square miles and 40-year time-oftravel
areas cover approximately 51 square miles.
Director, Upper Midwest Water Science Center
U.S. Geological Survey
6520 Mercantile Way Suite 5
Lansing, MI 48911
We present numeric grids containing estimates of the thickness of unconsolidated sediments and depth to the pre-Cenozoic
basement for the western United States. Values for these grids were combined and integrated from previous studies or derived
directly from gravity analyses. The grids are provided with 1-kilometer grid-node spacing in ScienceBase (https://www.sciencebase.gov).
These layers may be updated as results from new studies become available.
Director, Geology, Geophysics, and Geochemistry Science Center
U.S. Geological Survey
Box 25046, MS 973
Denver, CO 80225
The U.S. Geological Survey completed a geology-based assessment of undiscovered, technically recoverable oil and gas resources in the Haynesville and Bossier Formations of the onshore and State waters portion of the U.S. Gulf Coast region. Haynesville Formation conventional oil and gas production began in the late 1930s, whereas Bossier Formation production began in the early 1970s. Production of continuous gas resources from both formations began in 2006–7. Most of the current activity is focused on natural gas production from Haynesville and Bossier shales using horizontal wells and hydraulic fracturing. In 2016, the U.S. Geological Survey assessed technically recoverable mean resources of 4 billion barrels of oil and 304.4 trillion cubic feet of gas in the Haynesville and Bossier Formations of the onshore and State waters portion of the U.S. Gulf Coast region.
","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/ofr20181135","usgsCitation":"Paxton, S.T., 2018, Assessment of oil and gas resources in the Upper Jurassic Haynesville and Bossier Formations, U.S. Gulf Coast, 2016: U.S. Geological Survey Open-File Report 2018–1135, 13 p., https://doi.org/10.3133/ofr20181135.","productDescription":"ii, 13 p.","numberOfPages":"18","onlineOnly":"Y","ipdsId":"IP-098785","costCenters":[{"id":164,"text":"Central Energy Resources Science Center","active":true,"usgs":true}],"links":[{"id":357757,"rank":2,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/of/2018/1135/ofr20181135.pdf","text":"Report","size":"9.66 MB","linkFileType":{"id":1,"text":"pdf"},"description":"OFR 2018-1135"},{"id":357756,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/of/2018/1135/coverthb.jpg"}],"otherGeospatial":"Upper Jurassic Haynesville and Bossier Formations","contact":"Director, Central Energy Resources Science Center
U.S. Geological Survey
Box 25046, MS-939
Denver, CO 80225-0046
In 2016, the U.S. Geological Survey (USGS) completed an updated assessment of undiscovered, technically recoverable oil and gas resources in the Upper Jurassic Bossier Formation of the onshore U.S. Gulf Coast Province (Paxton and others, 2017). The Bossier Formation was assessed using both the standard continuous (unconventional) and conventional methodologies established by the USGS for three assessment units (AUs): (1) Bossier Eastern Shelf Sandstone Gas and Oil AU, (2) Bossier Western Shelf Sandstone Gas AU, and (3) Bossier Shale Continuous Gas AU. A fourth assessment unit, the Upper Jurassic Downdip Continuous Gas AU, was also defined but was not quantitatively assessed because of limited well data within the extent of the AU. The revised assessment resulted in total estimated mean resources of 2.9 billion barrels of oil, 108.6 trillion cubic feet of gas, and 1.1 billion barrels of natural gas liquids. The purpose of this report is to provide supplemental documentation of the input parameters used in the USGS 2016 Bossier Formation assessment.
","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/ofr20181134","usgsCitation":"Paxton, S.T., Pitman, J.K., Kinney, S.A., Gianoutsos, N.J., Pearson, O.N., Whidden, K.J., Dubiel, R.F., Schenk, C.J., Burke, L.A., Klett, T.R., Leathers-Miller, H.M., Mercier, T.J., Haines, S.S., Varela, B.A., Le, P.A., Finn, T.M., Gaswirth, S.B., Hawkins, S.J., Marra, K.R., and Tennyson, M.E., 2018, U.S. Geological Survey input-data forms for the assessment of the Upper Jurassic Bossier Formation, U.S. Gulf Coast, 2016: U.S. Geological Survey Open-File Report 2018–1134, 48 p., https://doi.org/10.3133/ofr20181134.","productDescription":"iii, 48 p.","onlineOnly":"Y","ipdsId":"IP-098783","costCenters":[{"id":164,"text":"Central Energy Resources Science Center","active":true,"usgs":true}],"links":[{"id":357711,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/of/2018/1134/coverthb.jpg"},{"id":357712,"rank":2,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/of/2018/1134/ofr20181134.pdf","text":"Report","size":"960 kB","linkFileType":{"id":1,"text":"pdf"},"description":"OFR 2018-1134"}],"otherGeospatial":"Upper Jurassic Bossier Formation","contact":"Director, Central Energy Resources Science Center
U.S. Geological Survey
Box 25046, MS-939
Denver, CO 80225-0046
In 2016, the U.S. Geological Survey (USGS) completed an updated assessment of undiscovered, technically recoverable oil and gas resources in the Upper Jurassic Haynesville Formation of the onshore U.S. Gulf Coast Province (Paxton and others, 2017). The Haynesville Formation was assessed using both the standard continuous (unconventional) and conventional methodologies established by the USGS for four assessment units (AUs): (1) Haynesville Western Shelf Carbonate Gas and Oil AU, (2) Haynesville Eastern Shelf Sandstone and Carbonate Oil and Gas AU, (3) Haynesville Shale Continuous Gas AU, and (4) Haynesville Shale Peripheral Continuous Gas AU. The revised assessment resulted in total estimated mean resources of 1.1 billion barrels of oil, 195.8 trillion cubic feet of gas, and 866 million barrels of natural gas liquids. The purpose of this report is to provide supplemental documentation of the input parameters used in the USGS 2016 Haynesville Formation assessment.
","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/ofr20181130","usgsCitation":"Paxton, S.T., Pitman, J.K., Kinney, S.A., Gianoutsos, N.J., Pearson, O.N., Whidden, K.J., Dubiel, R.F., Schenk, C.J., Burke, L.A., Klett, T.R., Leathers-Miller, H.M., Mercier, T.J., Haines, S.S., Varela, B.A., Le, P.A., Finn, T.M., Gaswirth, S.B., Hawkins, S.J., Marra, K.R., and Tennyson, M.E., 2018, U.S. Geological Survey input-data forms for the assessment of the Upper Jurassic Haynesville Formation, U.S. Gulf Coast, 2016: U.S. Geological Survey Open-File Report 2018–1130, 62 p., https://doi.org/10.3133/ofr20181130.","productDescription":"iii, 62 p.","onlineOnly":"Y","ipdsId":"IP-098784","costCenters":[{"id":164,"text":"Central Energy Resources Science Center","active":true,"usgs":true}],"links":[{"id":357710,"rank":2,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/of/2018/1130/ofr20181130.pdf","text":"Report","size":"1.03 MB","linkFileType":{"id":1,"text":"pdf"},"description":"OFR 2018-1130"},{"id":357709,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/of/2018/1130/coverthb.jpg"}],"otherGeospatial":"Upper Jurassic Haynesville Formation","contact":"Director, Central Energy Resources Science Center
U.S. Geological Survey
Box 25046, MS-939
Denver, CO 80225-0046
On March 22, 2018, the Seattle Department of Construction and Inspections (SDCI) and the U.S. Geological Survey (USGS) convened a workshop of engineers and seismologists to provide guidance on incorporating sedimentary basin response into the design of tall buildings in Seattle. This workshop provided recommendations that build on those from a March 2013 workshop (Chang and others, 2014), primarily based on new results from 3-D simulations of magnitude (M) 9 Cascadia earthquakes (The M9 Project). Susan Chang, a geotechnical engineer with the Seattle Department of Construction and Inspections, organized and led the workshop; Art Frankel (USGS) assisted in constructing the agenda.
The workshop agenda and attendees are provided in the appendix. The attendees represented a wide range of expertise, including seismologists with expertise in ground motions and basin response, geotechnical engineers, and structural engineers. Their professional experience included working on local projects related to the design of long-period structures; peer reviewing ground motions for performance-based design of high-rises in Seattle; researching basin response in academic, government and industry settings; developing ground motion models; and representing local and national structural engineering organizations. In this report, we summarize the technical presentations, key discussion points, and recommendations from the workshop.
","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/ofr20181149","usgsCitation":"Wirth, E.A., Chang, S.W., and Frankel, A.D., 2018, 2018 report on incorporating sedimentary basin response into the design of tall buildings in Seattle, Washington: U.S. Geological Survey Open-File Report 2018–1149, 19 p., https://doi.org/10.3133/ofr20181149.","productDescription":"iv, 19 p.","numberOfPages":"23","onlineOnly":"Y","ipdsId":"IP-099788","costCenters":[{"id":237,"text":"Earthquake Science Center","active":true,"usgs":true}],"links":[{"id":357679,"rank":2,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/of/2018/1149/ofr20181149.pdf","text":"Report","size":"3.3 MB","linkFileType":{"id":1,"text":"pdf"},"description":"Open-File Report 2018-1149"},{"id":357678,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/of/2018/1149/coverthb.jpg"}],"country":"United States","state":"Washington","city":"Seattle","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -122.5,\n 47.5\n ],\n [\n -122.2,\n 47.5\n ],\n [\n -122.2,\n 47.8\n ],\n [\n -122.5,\n 47.8\n ],\n [\n -122.5,\n 47.5\n ]\n ]\n ]\n }\n }\n ]\n}","contact":"Earthquake Science Center, Seattle Field Office
U.S. Geological Survey
University of Washington, Dept. of Earth and Space Sciences
Box 351310
Seattle, WA 98195
For more than three decades, the U.S. Geological Survey (USGS) Pliocene Research, Interpretation and Synoptic Mapping (PRISM) Project has compiled paleoenvironmental data with the goal of reconstructing global conditions during the warm interval in the middle of the Piacenzian Age of the Pliocene Epoch (about 3.3 to 3.0 million years ago). Because this is the most recent interval of time in which climatic conditions were similar to those expected in the near future, a global reconstruction of conditions from this interval offers an imperfect yet useful representation of near future conditions. PRISM reconstructions have been used extensively as boundary conditions in general circulation model experiments aimed at better understanding Pliocene climate. They have also served as hindcasting targets when testing the ability of climate models to simulate real climates of the past, an exercise in estimating a model’s ability to accurately predict future climate. As data coverage has grown and model precision has improved, PRISM datasets have become important validation tools for pinpointing discrete areas of data-model disagreement and model-model disagreement. The Pliocene sea surface temperature (SST) dataset is the best developed component of the PRISM reconstructions and is the keystone of Pliocene paleoclimate research. For the first time, we compile all data related to PRISM SST estimation. This discussion chronicles the history of PRISM SST research as it evolved, responding to advances in paleochronology and paleotemperature estimation. Paleoclimatic considerations unique to each location are illustrated, as are any new developments since the initial publication of the data.
","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/ofr20181148","usgsCitation":"Robinson, M.M., Dowsett, H.J., Foley, K.M., and Riesselman, C.R., 2018, PRISM marine sites—The history of PRISM sea surface temperature estimation: U.S. Geological Survey Open-File Report 2018–1148, 49 p., https://doi.org/10.3133/ofr20181148.","productDescription":"vi, 49 p.","onlineOnly":"Y","additionalOnlineFiles":"N","ipdsId":"IP-087999","costCenters":[{"id":243,"text":"Eastern Geology and Paleoclimate Science Center","active":true,"usgs":true}],"links":[{"id":357306,"rank":2,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/of/2018/1148/ofr20181148.pdf","text":"Report","size":"1 MB","description":"OFR 2018-1148"},{"id":357305,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/of/2018/1148/coverthb3.jpg"}],"contact":"Eastern Geology and Paleoclimate Science Center
U.S. Geological Survey
12201 Sunrise Valley Drive
926A National Center
Reston, VA 20192
The U.S. Army Corps of Engineers, Jacksonville District, plans to deepen the St. Johns River channel in Jacksonville, Florida, from 40 to 47 feet along 13 miles of the river channel, beginning at the mouth of the river at the Atlantic Ocean, to accommodate larger, fully loaded cargo vessels. The U.S. Geological Survey installed continuous data-collection stations to monitor discharge, salinity, and associated parameters at 22 sites prior to the commencement of dredging. The U.S. Geological Survey monitored stage and discharge at 13 sites, and water temperature, specific conductance, and salinity at 15 sites; some sites included all parameters.
This report contains information pertinent to the data collection sites from their installation date to September 2016, with additional information and data from Hurricane Matthew in October 2016. Site installations began in October 2015; all sites were installed and began collecting data by January 2016. All data available for each site after October 2015 are included in this report.
Discharge and salinity ranged widely during the data collection period, which included the effects of Hurricane Hermine in September 2016 and Hurricane Matthew in October 2016. Of the tributaries, annual mean discharge was greatest at Ortega River, followed by Cedar River, Julington Creek, Durbin Creek, and Clapboard Creek. Annual mean salinity for the main-stem sites indicates that salinity decreases with distance upstream, which is expected. The closest tributary site to the Atlantic Ocean (Clapboard Creek) produced the highest annual mean salinity of the tributaries, and Durbin Creek salinity was the lowest of all monitoring locations.
","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/ofr20181108","collaboration":"Prepared in cooperation with the U.S. Army Corps of Engineers","usgsCitation":"Ryan, P.J., 2018, Continuous stream discharge, salinity, and associated data collected in the lower St. Johns River and its tributaries, Florida, 2016: U.S. Geological Survey Open-File Report 2018–1108, 28 p., https://doi.org/10.3133/ofr20181108.","productDescription":"viii, 28 p.","numberOfPages":"40","onlineOnly":"Y","ipdsId":"IP-086635","costCenters":[{"id":5051,"text":"FLWSC-Orlando","active":true,"usgs":true}],"links":[{"id":357273,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/of/2018/1108/coverthb.jpg"},{"id":357274,"rank":2,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/of/2018/1108/ofr20181108.pdf","text":"Report","size":"7.31 MB","linkFileType":{"id":1,"text":"pdf"},"description":"OFR 2018–1108"}],"country":"United States","state":"Florida","otherGeospatial":"St. Johns River","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -82,\n 29\n ],\n [\n -81,\n 29\n ],\n [\n -81,\n 30.5\n ],\n [\n -82,\n 30.5\n ],\n [\n -82,\n 29\n ]\n ]\n ]\n }\n }\n ]\n}","contact":"Director, Caribbean-Florida Water Science Center
U.S. Geological Survey
4446 Pet Lane, Suite 108
Lutz, FL 33559
The 2014 update of the U.S. Geological Survey (USGS) National Seismic Hazard Model (NSHM) for the conterminous United States (2014 NSHM; Petersen and others, 2014, 2015) included probabilistic ground motion maps for 2 percent and 10 percent probabilities of exceedance in 50 years, derived from seismic hazard curves for peak ground acceleration (PGA) and 0.2 and 1.0 second spectral accelerations (SAs) with 5 percent damping for the National Earthquake Hazards Reduction Program (NEHRP) site class boundary B/C (time-averaged shear wave velocity in the upper 30 meters [VS30]=760 meters per second [m/s]). We now provide uniform NEHRP site class maps for 2, 5, and 10 percent probabilities of exceedance in 50 years derived from hazard curves for additional spectral periods. For the central and eastern United States (CEUS) and western United States (WUS), hazard curves and maps for PGA, 0.1, 0.2, 0.3, 0.5, 1.0, and 2.0 second SAs are now available. The WUS additionally includes hazard curves and maps for 0.75, 3.0, 4.0, and 5.0 second SAs. The use of region-specific suites of weighted ground motion models (GMMs) in the 2014 NSHM precluded the calculation of ground motions for a uniform set of periods and site classes for the conterminous United States. At the time of the development of the 2014 NSHM, there was no consensus in the CEUS on an appropriate site-amplification model to use; therefore, we calculated hazard curves and maps for NEHRP site class A, for which most stable continental GMMs were originally developed, based on simulations for hard rock site conditions (VS30=2,000 m/s). In the WUS, however, the active crustal Next Generation Attenuation Relationships for the WUS (NGA-West2 GMMs) and subduction GMMs allow amplification of ground motions based on site class (defined by VS30); so we calculated hazard curves and maps for NEHRP site classes B (VS30=1,080 m/s), C (VS30=530 m/s), D (VS30=260 m/s), and E (VS30=150 m/s) and site class boundaries A/B (VS30=1,500 m/s), B/C (VS30=760 m/s), C/D (VS30=365 m/s), and D/E (VS30=185 m/s). The 2014 NSHM introduced a set of criteria for selecting GMMs for use in the NSHMs. When calculating additional period and site class maps, we verified whether the 2014 NSHM original suites of GMMs satisfied these ground motion selection criteria at all additional periods and site classes using GMM magnitude-distance scaling relation plots. Results of our analysis show that certain GMMs give unrealistic results at longer periods, distances, and softer soils in the WUS. In these rare instances, the GMM was removed from the original suite of GMMs (for all periods and site classes) and the weights of the remaining GMMs in the suite were renormalized. Ratio maps show these updated suites of weighted GMMs result in probabilistic ground motion changes of less than 10 percent in the WUS at PGA, as well as 0.2 and 1.0 second SAs, except in the Pacific Northwest, where differences as much as 20 percent are seen. Hazard curves and uniform hazard response spectra at test sites across the conterminous United States were produced to verify that results were reasonable. The additional period and site class maps, and the hazard curves from which they were derived, are available for download from the USGS ScienceBase Catalog.
","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/ofr20181111","usgsCitation":"Shumway, A.M., Petersen, M.D., Powers, P.M., and Rezaeian, S., 2018, Additional period and site class maps for the 2014 National Seismic Hazard Model for the conterminous United States: U.S. Geological Survey Open-File Report 2018–1111, 46 p., https://doi.org/10.3133/ofr20181111.","productDescription":"Report: v, 46 p.; Data release","onlineOnly":"Y","ipdsId":"IP-098308","costCenters":[{"id":300,"text":"Geologic Hazards Science Center","active":true,"usgs":true}],"links":[{"id":357217,"rank":3,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/P9I6BPX5","text":"USGS data release","linkHelpText":"Data Release for Additional Period and Site Class Maps for the 2014 National Seismic Hazard Model for the Conterminous United 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\"}}]}\n\n\n","contact":"Director, Geologic Hazards Science Center
U.S. Geological Survey
Box 25046, MS 966
Denver, CO 80225
The ability of populations to persist and adapt to abiotic and biotic changes is reliant on genetic diversity. When connectivity across a species landscape is disrupted, the levels and distribution of genetic diversity can rapidly deteriorate as a result of genetic drift, leading to increased inbreeding and reduced adaptive potential. Therefore, understanding the distribution and degree of genetic variation within imperiled populations provides important information for conservation management and recovery strategies, especially when paired with translocation and repatriation programs. Here, we used genome-wide nuclear markers to study the population structure and genetic diversity from tissue samples collected between 2010 and 2016 of two threatened species of gartersnakes inhabiting the lower Colorado River Basin in the United States: Mogollon Narrow-headed gartersnake (Thamnophis rufipunctatus) and Northern Mexican gartersnake (Thamnophis eques megalops). Our specific objectives were to determine how populations inhabiting the lower Colorado River Basin were related to sister species and southern populations along the Sierra Madre Occidental in Mexico, to determine how genetic variation is partitioned among drainage basins in the lower Colorado River Basin, and to provide estimates of genetic diversity and effective sizes of sampled sites to aide species-specific conservation management of these threatened gartersnakes. For both species, we found that populations along the lower Colorado River Basin are highly differentiated from sister species and southern populations located further south in Mexico, and exhibit reduced genetic diversity relative to populations along the Sierra Madre Occidental. Within the lower Colorado River Basin, genetic analyses revealed highly structured genetic groups for both species of gartersnakes that point to shared contemporary and historical drivers of differentiation. We found that most sites throughout the lower Colorado River Basin have low genetic diversity and effective population sizes below the threshold required to retain adaptive potential. However, these trends were especially pronounced for T. rufipunctatus. If genetic management and translocation strategies are adopted in the future, these population genetic results can be used to highlight sites of particular concern and locate the most genetically similar sites for translocation or re-establishment efforts. Such measures could help curb further population genetic change, alleviate problems associated with low genetic diversity, and strengthen the adaptive potential across the range of these two gartersnake species.
","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/ofr20181141","collaboration":"Prepared for the U.S. Fish and Wildlife Service","usgsCitation":"Wood, D.A., Emmons, I.D., Nowak, E.M., Christman, B.L., Holycross, A.T., Jennings, R.D., and Vandergast, A.G., 2018, Conservation genomics of the Mogollon Narrow-headed gartersnake (Thamnophis rufipunctatus) and Northern Mexican gartersnake (Thamnophis eques megalops): U.S. Geological Survey Open-File Report 2018-1141, 47 p., https://doi.org/10.3133/ofr20181141.","productDescription":"Report: viii, 47 p.","onlineOnly":"Y","ipdsId":"IP-096783","costCenters":[{"id":651,"text":"Western Ecological Research Center","active":true,"usgs":true}],"links":[{"id":356979,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/of/2018/1141/coverthb_.jpg"},{"id":357008,"rank":2,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/of/2018/1141/ofr20181141_.pdf","text":"Report","size":"10 MB","linkFileType":{"id":1,"text":"pdf"},"description":"OFR 2018-1141"}],"contact":"Director,
Western Ecological Research Center
U.S. Geological Survey
3020 State University Drive East
Sacramento, California 95819
Changes in land-use practices and the extirpation (local extinction) of beaver populations in the early 20th century during European settlement are believed to have resulted in many changes in how streams in the Western United States function. Some of the negative changes that have resulted include stream channelization, soil erosion, changing vegetation, water turbidity, and a loss of overland flow. Efforts to restore streams and reduce soil erosion by water have included reintroductions of beaver, incorporating Native American traditional knowledge of dry-land farming techniques, and the installation of rigid check-dams. Many of these efforts have been successful in improving both intermittent and perennial stream function. Therefore, stakeholders in the Southern Rockies Landscape Conservation Cooperative (SRLCC) have identified a need to prioritize streams within their region of interest for the installation of check-dams to continue restoration and conservation efforts and to improve sediment catchment.
Using Natural Resource Conservation Service soil databases, topographic features derived from digital elevation models, stream networks, and regional climatic patterns, I developed a ranking system for watershed potential erosion rates and suitability for check-dam placement across the SRLCC. This ranking system serves as a first step for land managers to prioritize areas for check-dam installation based on relatively static factors (soil properties, topography, and hydrology) that can contribute to rates of soil erosion by water and the stability of check-dams. Many other relatively dynamic factors over time can contribute to rates of soil erosion by water, such as recent wildfire events, changes in weather patterns and extreme climate events, and changing land-use such as grazing, logging, mining, development, and cultivation. These factors that influence vegetative and biological soil crusts cover are also important elements to the potential erosion of soil by water. Because of this, SRLCC stakeholders might consider further evaluation of the watersheds identified here as high ranking. Final watershed prioritization among the high-ranking watersheds identified here should include current knowledge of land-use and land-cover estimates to identify areas at risk for soil erosion or degree of existing erosion problems.
","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/ofr20181127","usgsCitation":"Ironside, K.E., 2018, Southern Rockies Landscape Conservation Cooperative unit watershed erosion potential prioritization for check-dam installation: U.S. Geological Survey Open-File Report 2018–1127, 15 p., https://doi.org/10.3133/ofr20181127.","productDescription":"Report: v, 15 p.; Data Release","onlineOnly":"Y","ipdsId":"IP-096570","costCenters":[{"id":568,"text":"Southwest Biological Science Center","active":true,"usgs":true}],"links":[{"id":356855,"rank":3,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/P9SEUC93","text":"Data release","description":"USGS Data Release","linkHelpText":"Watershed potential erosion rate ranking system and check-dam placement suitability data within the Southern Rockies Landscape Conservation Cooperative (SRLCC)"},{"id":356853,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/of/2018/1127/coverthb.jpg"},{"id":356854,"rank":2,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/of/2018/1127/ofr20181127.pdf","text":"Report","size":"8 MB","linkFileType":{"id":1,"text":"pdf"},"description":"OFR 2018-1127"}],"country":"United States","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -116.173197,\n 32.416412\n ],\n [\n -103.499364,\n 32.416412\n ],\n [\n -103.499364,\n 43.335375\n ],\n [\n -116.173197,\n 43.335375\n ],\n [\n -116.173197,\n 32.416412\n ]\n ]\n ]\n }\n }\n ]\n}","contact":"SBSC Staff,
Southwest Biological Science Center
U.S. Geological Survey
2255 N. Gemini Drive
Flagstaff, AZ 86001
Earthquake early warning (EEW) is the rapid detection of an earthquake and issuance of an alert or notification to people and vulnerable systems likely to experience potentially damaging ground shaking. The level of ground shaking that is considered damaging is defined by the specific application; for example, manufacturing equipment may experience damage at a lower intensity ground shaking than would cause damage to a building. Along the West Coast of the United States, the warning times for ground shaking could range as high as tens of seconds for moderate levels of ground shaking, or potentially longer, if a lower ground-shaking threshold is used to issue alerts. However, it is not always possible to provide advance warning of ground shaking, particularly for locations close to an earthquake that are most likely to experience very strong ground shaking. EEW alerts may be useful to individuals who can use a few seconds to move to a safe zone and to electromechanical systems that can take automatic actions to reduce damage and injuries. An EEW system, ShakeAlert, has been under development in the United States since 2006. Federal and State governments, as well as the private sector, are now investing in the ShakeAlert prototype system that will, when completed, become an operational public system for the West Coast of the United States.
While the current prototype is delivering alerts to test users, improvements to the accuracy, timeliness, and utility of the alerts are needed. For this reason, it is essential that the ShakeAlert system be continuously improved through targeted research, involving not only the current ShakeAlert partner organizations, but also the broader scientific, engineering, and emergencyresponse communities. To this end, this report describes the opportunities for improvement that can be addressed through research and development over the next 5 years.
Our recommendations are organized into four areas: (1) understand EEW capabilities and user needs, (2) make alerts as fast and accurate as possible, (3) ensure reliability when it counts, and (4) explore the use of new instrumentation.
The first challenge is to understand EEW capabilities and user needs. EEW must deliver actionable information to people and to automated systems to mitigate short- and longterm impacts of damaging ground shaking, so development of EEW must be motivated by the needs of users. Within this challenge, we must study the technical capabilities and limitations of EEW in general, and the ShakeAlert system specifically. This includes development of performance metrics that assess the timeliness and accuracy of alerts to understand the value and utility of the ShakeAlert EEW product(s) for various user groups, including different industry sectors, emergency-management agencies, and the public. Research is needed to define the alerting choices that maximize the utility of the system for users and to determine what the available communication pathways are for providing timely alert information. Additionally, we engage users to assess how alerts will be used by different sectors to mitigate losses and to inform EEW product design. Further, social-science research is needed to develop alert messaging, including what relevant prior and follow-up information are required, to ensure effective use of alerts.
The second challenge is to make alerts as fast and as accurate as possible. The timeliness and accuracy of an EEW alert is important because it will set in motion a series of actions and downstream products. An EEW alert will trigger notification across emergency-alert systems and across multiple communication channels to populations in impacted regions. The EEW alert region may grow as the earthquake fault-rupture length increases, and the EEW system’s characterization of it, evolves. We must continue research into new or improved seismic and geodetic waveform-processing methods necessary to rapidly characterize the expected ground shaking and associated uncertainties. It is important to thoroughly evaluate whether new methods improve alerts through more accurate ground-motion estimates and (or) reduced latencies (that is, longer warning times). New methods could include tracking the extent of a large rupture in real time (known as finite-fault algorithms) and ground-motionbased EEW algorithms. Additionally, ground motion predictions could be optimized for each earthquake as the earthquake fault rupture progresses by using, for example, event terms to shift ground-motion curves for more (or less) energetic ruptures.
The third challenge is to ensure reliability when it counts. This challenge requires us to explore approaches that assess the expected performance of ShakeAlert across the range of earthquake magnitudes, locations, and depths that may occur within the alerting region. Large, damaging earthquakes and their associated aftershock sequences matter most for hazard and for EEW, but these large-earthquake sequences occur infrequently. We expect ShakeAlert to respond robustly to these large-earthquake sequences despite potentially long periods of relative seismic quiescence in the intervening years, and in spite of inevitable communication challenges that arise during and after a large earthquake. We must develop methods to utilize the broadest available datasets to test EEW performance, including ground-motion data recorded in other parts of the world. The observational period for large, damaging earthquakes in any particular region has been short in comparison to estimated large-earthquake recurrence times. Ground-motion records for very large, damaging western United States events and major aftershock sequences do not yet exist, nor do data exist for all potential sources of noise and spurious signals that ShakeAlert must be “tuned” to reject. In addition, robust synthetic data could provide the flexibility to test a wider range of earthquake magnitude, tectonic-setting, and noise scenarios than are covered by existing observational data. Synthetic ground-motion data must be thoroughly vetted against records of smaller magnitude earthquakes to ensure that they accurately capture both the onset and the amplitude of the ground shaking.
The final challenge is to explore the use of new instrumentation. The development of EEW around the world to date has focused on the use of high-quality, scientific-grade seismic and geodetic instrumentation. The use of additional types of instrumentation or information may also improve EEW products by filling gaps in sensor coverage in countries that already have dense seismic networks or enable EEW in countries without such networks. We must keep up with these developments and continuously assess their value in supplementing existing EEW systems, such as ShakeAlert, or enabling EEW where such systems do not exist. Such developments include low-cost instrumentation with microelectromechanical system (MEMS) sensors and global positioning system (GPS)/global navigation satellite system (GNSS) antennas embedded in low-cost consumer electronics, sea-floor seismometers, geodetic instrumentation deployed along the Cascadia and Alaska megathrust margins of western North America, and borehole strainmeters that are already deployed across the region.
","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/ofr20181131","usgsCitation":"Cochran, E.S., Aagaard, B.T., Allen, R.M., Andrews, J., Baltay, A.S., Barbour, A.J., Bodin, P., Brooks, B.A., Chung, A., Crowell, B.W., Given, D.D., Hanks, T.C., Hartog, J.R., Hauksson, E., Heaton, T.H., McBride, S., Meier, M-A., Melgar, D., Minson, S.E., Murray, J.R., Strauss, J.A., and Toomey, D., 2018, Research to improve ShakeAlert earthquake early warning products and their utility: U.S. Geological Survey Open-File Report 2018–1131, 17 p., https://doi.org/10.3133/ofr20181131.","productDescription":"iv, 17 p.","onlineOnly":"Y","ipdsId":"IP-098968","costCenters":[{"id":237,"text":"Earthquake Science Center","active":true,"usgs":true}],"links":[{"id":356971,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/of/2018/1131/coverthb.jpg"},{"id":356972,"rank":2,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/of/2018/1131/ofr20181131.pdf","text":"Report","size":"600 KB","linkFileType":{"id":1,"text":"pdf"},"description":"Open-File Report 2018-1131"}],"contact":"Earthquake Science Center-Pasadena Field Office
U.S. Geological Survey
525 South Wilson Ave.
Pasadena, CA 91106-3212
This chapter presents information pertinent to the geologic carbon dioxide (CO2) sequestration potential within saline aquifers located in the Atlantic Coastal Plain and Eastern Mesozoic Rift Basins of the Eastern United States. The Atlantic Coastal Plain is underlain by a Jurassic to Quaternary succession of sedimentary strata that onlap westward onto strata of the Appalachian Piedmont physiographic province and generally thicken eastward toward the present-day Atlantic coastline and onto the present-day continental shelf. Although no significant petroleum discoveries have been made on the coastal plain, the deep saline aquifers of the region appear to contain porous strata (potential reservoirs, or “storage formations”) that are overlain by fine-grained, laterally continuous strata (potential seals), which are prospective CO2 sequestration targets. For the Atlantic Coastal Plain, we identify two storage assessment units (SAUs), both of which consist of Cretaceous strata. The two SAUs are the Lower Cretaceous Composite SAU C50700101 and the Upper Cretaceous Composite SAU C50700102.
The Eastern Mesozoic Rift Basins are a chain of generally southwest- to northeast-trending, elongate sedimentary basins that either underlie the Atlantic Coastal Plain or crop out within adjacent geologic provinces to the west. Similar to the Atlantic Coastal Plain, there has been no significant oil and gas production from any of the basins, although there is a proven petroleum system in several of them. At least three of these basins appear to contain potential storage formations overlain by potential seal units. Most of the other basins were not assessed because a storage and (or) seal formation could not be established in the timeframe of the assessment, often because of the paucity of subsurface data for these basins in comparison to other petroliferous basins of the United States. Thus, we present information supporting one quantitative assessment in the Newark basin, as well as information supporting two nonquantitative assessments, one for strata in the Gettysburg basin and the other for strata in the Culpeper basin. We briefly discuss six other basins within the Eastern Mesozoic Rift Basins that were not assessed.
For all SAUs, we discuss the areal distribution of suitable CO2 reservoir rock. We also describe the overlying sealing unit and the geologic characteristics that influence the potential CO2 storage volume and reservoir characteristics. These characteristics include storage formation depth, gross thickness, net thickness, porosity, permeability, and groundwater salinity. Case-by-case strategies for estimating the pore volume existing within structurally and (or) stratigraphically closed traps are presented. Although assessment results are not contained in this chapter, the geologic information included herein was used to calculate the potential storage space in the SAUs.
","largerWorkType":{"id":18,"text":"Report"},"largerWorkTitle":"Geologic framework for the national assessment of carbon dioxide storage resources","largerWorkSubtype":{"id":5,"text":"USGS Numbered Series"},"language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/ofr20121024N","usgsCitation":"Craddock, W.H., Merrill, M.D., Roberts-Ashby, T.L., Brennan, S.T., Buursink, M.L., Drake, R.M., II, Warwick, P.D., Cahan, S.M., DeVera, C.A., Freeman, P.A., Gosai, M.A., and Lohr, C.D., 2018, Geologic framework for the national assessment of carbon dioxide storage resources—Atlantic Coastal Plain and Eastern Mesozoic Rift Basins, chap. N of Warwick, P.D., and Corum, M.D., eds., Geologic framework for the national assessment of carbon dioxide storage resources: U.S. Geological Survey Open-File Report 2012–1024, 32 p., https://doi.org/10.3133/ofr20121024N.","productDescription":"Report: vi, 32 p.; Spatial Data","onlineOnly":"Y","additionalOnlineFiles":"Y","ipdsId":"IP-082323","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":356831,"rank":4,"type":{"id":23,"text":"Spatial Data"},"url":"https://pubs.usgs.gov/of/2012/1024/n/ofr20121024n_acp-cell-c5070.zip","text":"Atlantic Coastal Plain Well Density","size":"1.77 GB","linkFileType":{"id":6,"text":"zip"}},{"id":356832,"rank":5,"type":{"id":23,"text":"Spatial Data"},"url":"https://pubs.usgs.gov/of/2012/1024/n/ofr20121024n_emrb_sau-c5068.zip","text":"Eastern Mesozoic Rift Basins Storage Assessment Units","size":"1.77 GB","linkFileType":{"id":6,"text":"zip"}},{"id":356833,"rank":6,"type":{"id":23,"text":"Spatial Data"},"url":"https://pubs.usgs.gov/of/2012/1024/n/ofr20121024n_emrb-cell-c5068.zip","text":"Eastern Mesozoic Rift Basins Well Density","size":"1.77 GB","linkFileType":{"id":6,"text":"zip"}},{"id":356830,"rank":3,"type":{"id":23,"text":"Spatial Data"},"url":"https://pubs.usgs.gov/of/2012/1024/n/ofr20121024n_acp-sau-c5070.zip","text":"Atlantic Coastal Plain Storage Assessment Units","size":"1.77 GB","linkFileType":{"id":6,"text":"zip"}},{"id":356407,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/of/2012/1024/n/coverthb.jpg"},{"id":356408,"rank":2,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/of/2012/1024/n/ofr20121024n.pdf","text":"Report","size":"13 MB","linkFileType":{"id":1,"text":"pdf"},"description":"OFR 2012 1024 N"}],"country":"United States","otherGeospatial":"Atlantic Coastal Plain and Eastern Mesozoic Rift Basins","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -86.72607421875,\n 27.235094607795503\n ],\n [\n -71.34521484375,\n 27.235094607795503\n ],\n [\n -71.34521484375,\n 42.71473218539458\n ],\n [\n -86.72607421875,\n 42.71473218539458\n ],\n [\n -86.72607421875,\n 27.235094607795503\n ]\n ]\n ]\n }\n }\n ]\n}","contact":"Energy Resources Program
12201 Sunrise Valley Drive
913 National Center
Reston, VA 20192
Email: gd-energyprogram@usgs.gov
Zebra mussels (Dreissena polymorpha, Pallas, 1771) are an aquatic invasive species in the
United States, and new infestations of zebra mussels can rapidly expand into dense colonies. Zebra
mussels were first reported in Marion Lake, Dakota County, Minnesota, in September 2017, and
surveys indicated the infestation was likely isolated near a public boat access. A 2.4-hectare area
containing the known zebra mussel infestation was enclosed and treated by area resource managers for
9 days with EarthTec QZ (target concentration: 0.5 milligrams per liter as copper), a copper-based
molluscicide, to eradicate the zebra mussels. Researchers led an onsite bioassay to provide an estimate
of the treatment efficacy within the enclosure. The bioassay was conducted in a mobile assay trailer that
received a continuous flow of treated lake water. Bioassay tanks (n=9; 350 liters) within the trailer were
stocked with zebra mussels (25 mussels per containment bag; 7 bags per tank) collected from White
Bear Lake, Ramsey County, Minn. Mortality in the treated bioassay tanks reached a mean of 99 percent
(95-percent confidence interval: 98–100 percent), there were no mortalities in the control tanks.
However, a predictive model produced for timely delivery to area resource managers indicated zebra
mussel mortality within the treated enclosure may have been as low as 85 percent. Onsite bioassays are
a viable and important tool for treatment evaluation particularly in newly infested waterbodies with low
zebra mussel densities.
Director, Upper Midwest Environmental Science Center
U.S. Geological Survey
2630 Fanta Reed Road
La Crosse, WI 54602
Mercury (Hg) analyses were conducted on samples of water and sport fish collected from selected sampling sites in the Boise and Snake Rivers and Brownlee Reservoir, in Idaho and Oregon, to meet National Pollution Discharge and Elimination System permit requirements for the City of Boise, Idaho, from 2013 to 2017. City of Boise personnel collected water samples from six sites in October and November of 2013, 2015 and 2017, and sampled one site in 2014 and 2016. Total Hg concentrations in unfiltered water samples ranged from 0.41 to 8.78 nanograms per liter (ng/L), with the highest value (8.78 ng/L) observed in Brownlee Reservoir in 2013. All samples were less than the U.S. Environmental Protection Agency aquatic life criterion of 12 ng/L.
Individual fillets of mountain whitefish (Prosopium williamsoni), rainbow trout (Oncorhynchus mykiss), smallmouth bass (Micropterus dolomieu), and channel catfish (Ictalurus punctatus) were collected and analyzed for Hg. The tissue Hg concentrations were compared with regulatory or advisory values for wet-weight methylmercury in fish tissue. In this report, methylmercury concentrations in fish tissue are considered similar to total Hg in fish muscle tissue and are simply referred to as Hg. The 2013 average Hg concentration for smallmouth bass (0.32 mg/kg) collected at Brownlee Reservoir and for channel catfish (0.33 mg/kg) collected at the Boise River mouth, exceeded the Idaho water quality criterion (>0.3 mg/kg). The 2017 Hg concentrations in smallmouth bass from Brownlee Reservoir (geometric mean of 0.22 mg/kg) was at the Idaho Fish Consumption Advisory Program action level.
Selenium (Se) interacts with Hg to reduce the health risks of Hg, such that tissues with Se-to-Hg molar ratios greater than 1 are considered to present less potential health risks for a given Hg concentration than are tissues with lower Se-to-Hg ratios. One composite fish tissue sample per site was analyzed for Se. Selenium-to-Hg molar ratios in the fish tissue samples ranged from 0.99 to 24.7.
","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/ofr20181122","collaboration":"Prepared in cooperation with the City of Boise, Idaho","usgsCitation":"MacCoy, D.E., and Mebane, C.A., 2018, Mercury concentrations in water and mercury and selenium concentrations in fish from Brownlee Reservoir and selected sites in the Boise and Snake Rivers, Idaho and Oregon, 2013-17: U.S. Geological Survey Open-File Report 2018-1122, 37 p., https://doi.org/10.3133/ofr20181122.","productDescription":"iv, 37 p.","onlineOnly":"Y","ipdsId":"IP-091972","costCenters":[{"id":343,"text":"Idaho Water Science Center","active":true,"usgs":true}],"links":[{"id":356923,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/of/2018/1122/coverthb.jpg"},{"id":356924,"rank":2,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/of/2018/1122/ofr20181122.pdf","text":"Report","size":"7.5 MB","linkFileType":{"id":1,"text":"pdf"},"description":"OFR 2018-1122"}],"country":"United States","state":"Idaho, Oregon","otherGeospatial":"Boise River, Brownlee Reservoir, Snake River","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -117.27081298828125,\n 43.241201214257885\n ],\n [\n -116.0540771484375,\n 43.241201214257885\n ],\n [\n -116.0540771484375,\n 44.40827836571936\n ],\n [\n -117.27081298828125,\n 44.40827836571936\n ],\n [\n -117.27081298828125,\n 43.241201214257885\n ]\n ]\n ]\n }\n }\n ]\n}","contact":"Director, Idaho Water Science Center
U.S. Geological Survey
230 Collins Road
Boise, Idaho 83702
A Delft3D model was developed to evaluate the potential effects of proposed navigation
channel deepening and widening in Mobile Harbor, Alabama. The model performance was
assessed through comparisons of modeled and observed data of water levels, velocities, and bed
level changes; the model captured hydrodynamic and sediment transport patterns in the study
area with skill. The validated model was used to simulate changes in sediment transport for existing
conditions and with the proposed modifications to the navigational channel (with-project),
with and without accounting for 0.5 meter (m) of sea level rise (SLR). Each scenario was simulated
for 1 year with a wave climatology representative of the year 2010 as well as for 10 years
with a longer-term wave climatology spanning from 1988 to 2016. Bed level differences for the
existing and with-project 2010 simulations were minimal, ranging from −0.11 to 0.11 m offshore
of Pelican Island and −0.81 to 0.22 m offshore of the Fort Morgan Peninsula. For the simulations
accounting for 0.5 m of SLR, differences in bed levels from −0.20 to 0.32 m near Pelican
Island and −0.38 to 0.34 m offshore of the Fort Morgan Peninsula. The proposed modifications
reduced the channel shoaling volume by 4.77 and 8.09 percent for the 2010 simulations without
and with 0.5 m of SLR, respectively. For the 10-year simulations, bed level differences for the
existing and with-project simulations ranged from −3.17 to 3.94 m for the simulation without
SLR and −1.92 to 1.47 m for the simulation with 0.5 m of SLR. The with-project condition reduced
the entrance channel shoaling volume by 5.54 percent for the simulation without SLR and
14.98 percent for the simulation with 0.5 m of SLR.
Director, St. Petersburg Coastal and Marine Science Center
U.S. Geological Survey
600 4th Street South
St. Petersburg, FL 33701
Sea-Bird Scientific’s HydroCycle-PO4 phosphate sensor is a single-analyte wet-chemistry sensor designed for in situ environmental monitoring. The unit was evaluated at the U.S. Geological Survey Hydrologic Instrumentation Facility to assess the accuracy of the sensor in solutions with known phosphorous concentration and to test the effects of chromophoric (colored) dissolved organic matter (CDOM) and natural water matrixes on sensor accuracy. Accuracy was tested with three standards: 0.110, 0.174, and 0.260 milligram per liter, as phosphorous (mg/L as P). The 0.110- and 0.260-mg/L standards were made from a dilution of a National Institute of Standards and Technology-traceable phosphate-phosphorous standard with Type I deionized water (DIW). Average measured phosphate concentrations of the tested standards (0.110, 0.174, and 0.260 mg/L as P) in DIW were 0.132, 0.181, and 0.310 mg/L as P, for differences of 20, 4, and 19 percent, respectively.
Measured phosphate concentration of a tested standard was biased by the addition of tea water filtered through a 0.45-micrometer pore size filter (filtered tea water [FTW]) simulating the effect of CDOM. An aliquot of the filtered tea solution was sent to a certified environmental laboratory, which reported a less than reporting level (<0.004 mg/L as P) phosphate concentration. True color of the FTW was measured at 380 platinum-cobalt units by using Standard Methods 8025. For this FTW test, the measured phosphate concentration for the clear 0.260 mg/L as P standard averaged 0.366 mg/L as P. This concentration increased to an average of 0.653 mg/L as P with the addition of 10 percent FTW, and to an average of 0.859 mg/L as P with the addition of 20 percent FTW. These test results indicate a positive bias of up to 40 percent of the concentrations of the measured phosphate concentrations when CDOM is present and indicate a proportional increase in an apparent concentration of phosphorus instrument response as CDOM concentration increases.
","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/ofr20181120","usgsCitation":"Snazelle, T.T., 2018, Laboratory evaluation of the Sea-Bird Scientific HydroCycle-PO4 phosphate sensor: U.S. Geological Survey Open-File Report 2018–1120, 10 p., https://doi.org/ofr20181120.","productDescription":"vi, 10 p.","numberOfPages":"20","onlineOnly":"Y","ipdsId":"IP-090916","costCenters":[{"id":502,"text":"Office of Surface Water","active":true,"usgs":true}],"links":[{"id":356807,"rank":2,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/of/2018/1120/coverthb5.jpg"},{"id":356711,"rank":1,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/of/2018/1120/ofr20181120.pdf","text":"Report","size":"1.16 MB","linkFileType":{"id":1,"text":"pdf"},"description":"OFR 2018–1120"}],"contact":"Chief, Hydrologic Instrumentation Facility
U.S. Geological Survey
Building 2101
Stennis Space Center, MS 39529
Since the early 1990s, substantial effort and funding have been expended to conduct research to guide development of a 20-year Elk and Vegetation Management Plan for Rocky Mountain National Park (RMNP) in Colorado. One goal of the plan is to maintain the elk (Cervus elaphus) population size at the lower end of the natural range of variation. To implement management actions called for in the plan, accurate and reliable population estimates are needed, as well as a better understanding of the spatial and temporal distribution of elk. The previous aerial survey protocol and population estimation model used by the park had not been updated since the model’s initial calibration more than 15 years ago and the model was developed with an insufficient number (n=44) of observations. Thus we initiated research to reevaluate, update, and improve elk population estimation protocols for RMNP.
We considered several alternative survey and analysis methods, and concluded that a hybrid population-estimation model using a simultaneous double-observer technique with sighting covariates was the most appropriate and effective aerial survey methodology for this population and the environmental conditions in RMNP. Instructional protocols for conducting these surveys in the future, along with datasheets, are provided in this report’s appendixes.
To develop an improved method for aerial elk surveys, we used elk radio-collar location data from our study and other studies, and applied geographic information system analyses to define the survey area and develop effective, repeatable survey transect lines. We used telemetry data from radio-collared elk to inform our understanding of the temporal and spatial scale of elk movements across the park boundary, elk use of tree cover during potential survey hours, and elk use of different elevations within their range. Determining where elk were during surveys helped to fine-tune a survey design that improved spatial cover-age and decreased costs where possible, while standardizing the method to make it repeatable from year to year. We conducted aerial helicopter surveys to test our methodology in an adaptive, iterative process during three winters: 2007–2008, 2008–2009, and 2009–2010. We gained new information on each survey and used results to refine subsequent surveys.
Our results confirm that elk movements were highly dynamic with respect to park boundary crossings; an average of six round trips from the park to Estes Park, Colorado, and back per month were taken by global positioning system-collared bull elk. We observed a strong diurnal temporal pattern of bull elk use of trees; elk were found in dense tree cover from 15:00−22:00 but not as often during morning and early afternoon hours when surveys were conducted. We used information on elk use of different altitudes to refine and establish a more efficient survey area.
During the time of our study, a concurrent study in the park deployed 120 very high frequency radio collars on elk cows. We used those collar locations during our flights to increase sample size and to evaluate the level of precision we would gain by using “known fates analysis” (in which elk were known to be in the survey area, out of the survey area, or deceased based on radio-collar locations collected simultaneously during aerial surveys). Using radio-collar locations reduced bias by 1.1–8.8 percent and increased precision (that is, reduced the width of confidence intervals). This report provides final population estimates analyzed with and without radio-collar data to demonstrate what is gained by using marked individuals.
","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/ofr20181085","collaboration":"Prepared in cooperation with Colorado State University and the National Park Service","usgsCitation":"Schoenecker, K.A., Lubow, B.C., and Johnson, T.L., 2018, Development of an aerial population survey method for elk (Cervus elaphus) in Rocky Mountain National Park, Colorado: U.S. Geological Survey Open–File Report 2018–1085, 45 p., https://doi.org/10.3133/ofr20181085.","productDescription":"vii, 45 p.","onlineOnly":"Y","ipdsId":"IP-053382","costCenters":[{"id":291,"text":"Fort Collins Science Center","active":true,"usgs":true}],"links":[{"id":356646,"rank":2,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/of/2018/1085/ofr20181085.pdf","text":"Report","size":"10.1 MB","linkFileType":{"id":1,"text":"pdf"},"description":"OFR 2018-1085"},{"id":356645,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/of/2018/1085/coverthb.jpg"}],"country":"United States","state":"Colorado","otherGeospatial":"Estes Valley, Rocky Mountain National Park","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -106,\n 40\n ],\n [\n -105.1667,\n 40\n ],\n [\n -105.1667,\n 40.5833\n ],\n [\n -106,\n 40.5833\n ],\n [\n -106,\n 40\n ]\n ]\n ]\n }\n }\n ]\n}","contact":"Director, Fort Collins Science Center
U.S. Geological Survey
2150 Centre Ave., Building C
Fort Collins, CO 80526-8118
The U.S. Geological Survey Coastal and Marine Geology Program uses three methods to derive a datum-based, mean high water shoreline on open-ocean coasts from light detection and ranging (lidar) elevation surveys. This work compared the shorelines produced by the three methods for two different surveys: one survey with simple beach morphology, and one survey with complex beach morphology. For the survey with simple beach morphology, the three methods gave very similar results. The mean differences were less than 0.1 meter, and the root mean square differences were all less than 1.0 meter. For the survey of a beach with complex morphology, the quality control used in the Profile method and Smoothed Contour/Manual Hybrid method produced cleaner shorelines than the Grid method. Only the Profile method can extrapolate if there is no data around mean high water. The Grid and Profile methods produce a point by point estimate of uncertainty which is needed for some applications. Only the Contour method can be easily transferred to external users.
","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/ofr20181121","usgsCitation":"Farris, A.S., Weber, K.M., Doran, K.S., and List, J.H., 2018, Comparing methods used by the U.S. Geological Survey Coastal and Marine Geology Program for deriving shoreline position from lidar data: U.S. Geological Survey Open-File Report 2018–1121, 13 p., https://doi.org/10.3133/ofr20181121.","productDescription":"iv, 13 p.","onlineOnly":"Y","additionalOnlineFiles":"N","ipdsId":"IP-097675","costCenters":[{"id":678,"text":"Woods Hole Coastal and Marine Science Center","active":true,"usgs":true}],"links":[{"id":356712,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/of/2018/1121/coverthb.jpg"},{"id":356713,"rank":2,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/of/2018/1121/ofr20181121.pdf","text":"Report","size":"977 KB","linkFileType":{"id":1,"text":"pdf"},"description":"OFR 2018-1121"}],"contact":"Director, Woods Hole Coastal and Marine Science Center
U.S. Geological Survey
384 Woods Hole Road
Quissett Campus
Woods Hole, MA 02543