Land-use planning has an important role in local, regional, State, and Federal land management, and planning efforts can benefit from consistent, spatially explicit information that can help guide priorities and decisions. The credibility and relevance of information used to inform planning activities depends on the availability of consistent information about the resources and values of interest or concern within the planning area. To support long-range transportation planning and other regional land-use planning efforts, the U.S. Geological Survey gathered, processed, interpreted, and compiled spatial datasets representing a wide range of information on terrestrial and aquatic ecosystem condition and importance, cultural (historical) features and places, and natural hazards. This report describes the spatial data compiled to represent natural landscape conditions (including social, cultural, and natural attributes) to estimate the potential importance of lands for wildlife, wild habitats, recreation, and conservation based on abundance of species, habitats, land and water conditions, and conservation designations. Abundance of resources, including the potential number of species, presence of important habitats and protected areas, and proximity to particular features or habitats, indicates the potential sensitivity of the natural landscape to land use, especially transportation networks. The source data, derived indices, and the methods for processing these data are described in this final report. The datasets referenced in the report are available from the U.S. Geological Survey (https://www.sciencebase.gov/catalog/ and https://doi.org/10.5066/F7MW2F8W) or the Central Federal Lands Highway Division of the Office of Federal Lands Highway (https://flh.fhwa.dot.gov).
","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/sir20185039","collaboration":"Prepared in cooperation with the Office of Federal Lands Highway, Central Federal Lands Highway Division","usgsCitation":"Manier, D.J., and O’Donnell, M., 2018, Compilation and assessment of resource values and hazards to inform transportation planning and associated land-use planning: U.S. Geological Survey Scientific Investigations Report 2018–5039, 53 p., https://doi.org/10.3133/sir20185039.","productDescription":"Report: ix, 53 p.; Data release","onlineOnly":"Y","ipdsId":"IP-074504","costCenters":[{"id":291,"text":"Fort Collins Science Center","active":true,"usgs":true}],"links":[{"id":437718,"rank":4,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/F7MW2F8W","text":"USGS data release","linkHelpText":"Compilation and Assessment of Resource Values and Hazards to Inform Transportation and Associated Land-use 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\"}}]}\n\n\n","contact":"Director, Fort Collins Science Center
U.S. Geological Survey
2150 Centre Ave., Bldg. C
Fort Collins, CO 80526-8118
The Red Hill Bulk Fuel Storage Facility, operated by the U.S. Navy and located in the Hālawa area, Oʻahu, Hawaiʻi, includes 20 underground storage tanks that can hold a total of 250 million gallons of fuel. In January 2014, the U.S. Navy notified the Hawaiʻi Department of Health and U.S. Environmental Protection Agency of release of an estimated 27,000 gallons of fuel from the Red Hill Bulk Fuel Storage Facility. In response to past and potential future fuel releases, data are needed to evaluate groundwater flow in the surrounding area. During July 2017–February 2018, the U.S. Geological Survey collected groundwater-level data at 24 sites near the Red Hill Bulk Fuel Storage Facility. At 14 of the 24 sites, groundwater-temperature data were also collected, and at 6 of the 24 sites, barometric-pressure data were collected. During the data-collection period, a regional aquifer test was conducted in coordination with the operators of three production wells in the area. The recorded water-level changes in response to planned withdrawal changes during December 2017–February 2018 can be used to improve understanding of the groundwater-flow system. The scope of this report is limited to a non-interpretive presentation of the data and a brief discussion of the factors affecting the water-level data.
Director,
Pacific Islands Water Science Center
U.S. Geological Survey
Inouye Regional Center
1845 Wasp Blvd., B176
Honolulu, HI 96818
In 2017, the U.S. Geological Survey (USGS), in cooperation with the Colorado Water Conservation Board (CWCB), studied floods in the historic record to produce a library of flood-inundation maps for the South Platte River at Fort Morgan, Colorado. Digital flood-inundation maps for a 4.5-mile (7.2-kilometers) reach of the South Platte River at Fort Morgan from Morgan County Road 16 to Morgan County Road 20.5, were created. The flood-inundation maps depict estimates of the areal extent and depth of flooding corresponding to selected water levels (stages) at the U.S. Geological Survey streamgage on the South Platte River at Fort Morgan (streamgage number 06759500).
Water-surface profiles were computed for the stream reach by means of a one-dimensional, step-backwater model. The September 15, 2013, and May 20, 2017, floods were used to calibrate the model, and the June 15, 2015, and May 29, 2017, floods were used to independently validate the model. Nine pressure transducers were deployed to record the stage at nine different locations along the reach and to document the floods of May 20 and 29, 2017, at the South Platte River at Fort Morgan streamgage.
The hydraulic model was then used to compute 16 water-surface profiles for flood stages at 1-foot (ft; 0.3-meter [m]) intervals referenced to the streamgage datum and ranging from 12 ft (3.66 m) or below bankfull to 27 ft (8.23 m), which is 1 ft (0.3 m) greater than the highest recorded water level (25.73 ft [7.84 m] on September 15, 2013) at the South Platte River at Fort Morgan streamgage during its period of record; the 2013 flood exceeds the major flood stage of 21.5 ft (6.55 m) by more than 4 ft (1.2 m), as defined by the National Weather Service.
The simulated water-surface profiles were then combined with a geographic information system digital elevation model (derived from light detection and ranging data having a 0.37-ft [0.11-m] vertical accuracy and 3.28-ft [1.00-m] horizontal resolution) to delineate the area flooded for stages ranging from 12 to 27 ft (3.66 to 8.23 m).
These flood-inundation maps, in conjunction with the real-time stage data from the USGS streamgage on the South Platte River at Fort Morgan, are intended to help guide the general public in taking individual safety precautions and provide emergency management personnel with a tool to efficiently manage emergency flood operations and post flood recovery efforts.
","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/sir20185114","collaboration":"Prepared in cooperation with the Colorado Water Conservation Board","usgsCitation":"Kohn, M.S., and Patton, T.T., 2018, Flood-inundation maps for the South Platte River at Fort Morgan, Colorado, 2018: U.S. Geological Survey Scientific Investigations Report 2018–5114, 14 p., https://doi.org/10.3133/sir20185114.","productDescription":"Report: vi, 14 p.; Data Release","onlineOnly":"Y","ipdsId":"IP-097350","costCenters":[{"id":191,"text":"Colorado Water Science Center","active":true,"usgs":true}],"links":[{"id":358286,"rank":4,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/P9YA4STZ","text":"USGS data release","description":"USGS data release","linkHelpText":"Geospatial Data and Surface-Water Model Archive for a Flood-Inundation Mapping Study of the South Platte River at Fort Morgan, Colorado, 2018"},{"id":358285,"rank":3,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/F7XG9PN1","text":"USGS data release","description":"USGS data release","linkHelpText":"Cross-Section Data and Pressure Transducer Location of the South Platte River near Fort Morgan, Colorado, 2017"},{"id":358279,"rank":2,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/sir/2018/5114/sir20185114.pdf","text":"Report","size":"11.0 MB","linkFileType":{"id":1,"text":"pdf"},"description":"SIR 2018–5114"},{"id":358287,"rank":5,"type":{"id":18,"text":"Project Site"},"url":"https://water.usgs.gov/osw/flood_inundation/","text":"Flood Inundation Mapper —","linkHelpText":"USGS Flood Inundation Mapping (FIM) Program"},{"id":358278,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/sir/2018/5114/coverthb.jpg"}],"country":"United States","state":"Colorado","city":"Fort Morgan","otherGeospatial":"South Platte River","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -103.851040005684,\n 40.244026096225\n ],\n [\n -103.760746121407,\n 40.244026096225\n ],\n [\n -103.760746121407,\n 40.2918348221745\n ],\n [\n -103.851040005684,\n 40.2918348221745\n ],\n [\n -103.851040005684,\n 40.244026096225\n ]\n ]\n ]\n }\n }\n ]\n}","contact":"Director, Colorado Water Science Center
U.S. Geological Survey
Box 25046, MS 415
Denver, CO 80225
Understanding the spatial and temporal dynamics underlying the introduction and spread of nonindigenous aquatic species (NAS) can provide important insights into the historical drivers of biological invasions and aid in forecasting future patterns of nonindigenous species arrival and spread. Increasingly, public databases of species observation records are being used to quantify changes in NAS distributions across space and time, and are becoming an important resource for researchers, managers, and policy-makers. Here we use publicly available data to describe trends in NAS introduction and spread across the conterminous United States over more than two centuries of observation records. Available data on first records of NAS reveal significant shifts in dominance of particular introduction patterns over time, both in terms of recipient regions and likely sources. These spatiotemporal trends at the continental scale may be subject to biases associated with regional variation in sampling effort, reporting, and data curation. We therefore also examined two additional metrics, the number of individual records and the spatial coverage of those records, which are likely to be more closely associated with sampling effort. Our results suggest that broad-scale patterns may mask considerable variation across regions, time periods, and even entities contributing to NAS sampling. In some cases, observed temporal shifts in species discovery may be influenced by dramatic fluctuations in the number and spatial extent of individual observations, reflecting the possibility that shifts in sampling effort may obscure underlying rates of NAS introduction.
","language":"English","publisher":"REABIC","doi":"10.3391/ai.2018.13.3.02","usgsCitation":"Mangiante, M.J., Davis, A.J., Panlasigui, S., Neilson, M.E., Pfingsten, I., Fuller, P., and Darling, J.A., 2018, Trends in nonindigenous aquatic species richness in the United States reveal shifting spatial and temporal patterns of species introductions: Aquatic Invasions, v. 13, no. 3, p. 323-338, https://doi.org/10.3391/ai.2018.13.3.02.","productDescription":"16 p.","startPage":"323","endPage":"338","ipdsId":"IP-091735","costCenters":[{"id":17705,"text":"Wetland and Aquatic Research Center","active":true,"usgs":true}],"links":[{"id":468323,"rank":0,"type":{"id":41,"text":"Open Access External Repository Page"},"url":"https://doi.org/10.3391/ai.2018.13.3.02","text":"External Repository"},{"id":358348,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United 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(2000, 2004) [1,2] databases and presents new liquefaction triggering curves. Compared with these studies from over a decade ago, the resulting new Standard Penetration Test (SPT)-based triggering curves have shifted to slightly higher CSR-levels for a given N1,60,CS for values of N1,60,CS greater than 15 blows/ft, but the correlation curves remain essentially unchanged at N1,60,CS values less than 15 blows/ft. This paper addresses the improved database and the methodologies used for the development of the updated triggering relationships. A companion paper addresses the principal issues that cause differences among three widely used SPT-based liquefaction triggering relationships.
","language":"English","publisher":"Elsevier","doi":"10.1016/j.soildyn.2018.09.012","usgsCitation":"Cetin, K., Seed, R., Der Kiureghian, A., Kayen, R., Moss, R., Bilge, H.T., Ilgac, M., Chowdhury, S., and Tokimatsu, K., 2018, SPT-based probabilistic and deterministic assessment of seismic soil liquefaction triggering hazard: Soil Dynamics and Earthquake Engineering, v. 115, p. 698-709, https://doi.org/10.1016/j.soildyn.2018.09.012.","productDescription":"12 p.","startPage":"698","endPage":"709","ipdsId":"IP-072847","costCenters":[{"id":520,"text":"Pacific Coastal and Marine Science Center","active":true,"usgs":true}],"links":[{"id":460831,"rank":0,"type":{"id":41,"text":"Open Access External Repository Page"},"url":"https://hdl.handle.net/11511/38287","text":"External Repository"},{"id":385412,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"volume":"115","noUsgsAuthors":false,"publicationStatus":"PW","contributors":{"authors":[{"text":"Cetin, K. 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S.","contributorId":146591,"corporation":false,"usgs":false,"family":"Moss","given":"Robb E. S.","affiliations":[{"id":16725,"text":"California Polytechnic State University, San Luis Obispo","active":true,"usgs":false}],"preferred":false,"id":814990,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"Bilge, H. 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The study area, which straddles the state boundary between southeastern California and southern Nevada, encompasses Mountain Pass, which is host to one of the world’s largest rare earth element carbonatite deposits.
The deposit is found along a north-northwest-trending, fault-bounded block that extends along the eastern parts of the Clark Mountain Range, Mescal Range, and Ivanpah Mountains. This Paleoproterozoic block is composed of a 1.7-Ga metamorphic complex of gneiss and schist that underwent widespread metamorphism and associated plutonism during the Ivanpah orogeny. The Paleoproterozoic rocks were intruded by a Mesoproterozoic (1.4 Ga) ultrapotassic alkaline intrusive suite and carbonatite body. The intrusive rocks include, from oldest to youngest, shonkinite, mesosyenite, syenite, quartz syenite, potassic granite, carbonatite, carbonatite dikes, and late shonkinite dikes.
Generally speaking, gravity anomalies can be used to infer subsurface geologic structure, revealing variations in lithology and delineating features such as faults, plutons, volcanic centers, calderas, and deep sedimentary basins.
As part of this study, gravity data from more than 2,400 stations were collected and processed to identify lateral changes in subsurface density. Gravity stations were distributed across parts of Shadow Valley, Clark Mountain Range, Mescal Range, Ivanpah Mountains, and Ivanpah Valley. The new gravity data were combined with preexisting gravity data from the surrounding areas in California and Nevada. All gravity data were gridded using a minimum curvature algorithm at an interval of 200 m, and the result is displayed as a color-contour isostatic gravity map.
","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/sim3412A","usgsCitation":"Ponce, D.A., and Denton, K.M. (D.A. Ponce, ed.), 2018, Isostatic gravity map of Mountain Pass and vicinity, California and Nevada: U.S. Geological Survey Scientific Investigations Map 3412–A, scale 1:62,500, https://doi.org/10.3133/sim3412A.","productDescription":"44.06 x 30.24 inches","onlineOnly":"Y","ipdsId":"IP-095950","costCenters":[{"id":312,"text":"Geology, Minerals, Energy, and Geophysics Science Center","active":true,"usgs":true}],"links":[{"id":357499,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/sim/3412/a/coverthb.jpg"},{"id":357500,"rank":2,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/sim/3412/a/sim3412a.pdf","text":"Report","size":"23 MB","linkFileType":{"id":1,"text":"pdf"},"description":"SIM 3412-A"}],"country":"United States","state":"California, Nevada","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -115.75,\n 35.2833\n ],\n [\n -115.25,\n 35.2833\n ],\n [\n -115.25,\n 35.6167\n ],\n [\n -115.75,\n 35.6167\n ],\n [\n -115.75,\n 35.2833\n ]\n ]\n ]\n }\n }\n ]\n}","contact":"Director,
Geology, Minerals, Energy, & Geophysics Science Center
Menlo Park, California
U.S. Geological Survey
345 Middlefield Road
Menlo Park, CA 94025-3591
Oil producers have been using enhanced oil recovery methods, including (1) thermal recovery for heavy oil and (2) carbon dioxide enhanced oil recovery (CO2-EOR) for medium or light oil, to maximize oil recovery from existing reservoirs. The CO2-EOR method is widely used for recovering additional oil after waterflood, which leaves behind a large volume of oil in the reservoir. Completing a CO2-EOR feasibility study requires values of various geologic, petrophysical, and reservoir properties, as well as production data. Most of the required data are available except for two critical parameters: (1) the oil saturation at the start of CO2-EOR and (2) the oil recovery factor. Several methods, including core analysis, open-hole and cased-hole well logging, well-to-well tracer tests, and material balance, have been deployed to determine the residual oil saturation after waterflood (at which the relative permeability to oil nears zero) or remaining oil saturation after waterflood, equal to the oil saturation at the start of CO2-EOR. This report presents the material balance approach, which is less expensive than other approaches and provides reasonably accurate values of oil saturation at the start of CO2-EOR, and therefore is more useful when assessing a large number of reservoirs.
","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/ofr20181146","usgsCitation":"Verma, M.K., 2018, Material balance approach for determining oil saturation at the start of carbon dioxide enhanced oil recovery: U.S. Geological Survey Open-File Report 2018–1146, 14 p., https://doi.org/10.3133/ofr20181146.","productDescription":"v, 14 p.","onlineOnly":"Y","additionalOnlineFiles":"N","ipdsId":"IP-091063","costCenters":[{"id":241,"text":"Eastern Energy Resources Science Center","active":true,"usgs":true}],"links":[{"id":358130,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/of/2018/1146/coverthb.jpg"},{"id":358131,"rank":2,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/of/2018/1146/ofr20181146.pdf","text":"Report","size":"527 KB","linkFileType":{"id":1,"text":"pdf"},"description":"OFR 2018-1146"}],"contact":"Energy Resources Program
U.S. Geological Survey
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Email: gd-energyprogram@usgs.gov
For nature reserves in urban settings, wildlife and wildlife habitats may be affected by recreational activities and intensive, adjacent development. Sustaining biodiversity in such reserves is a challenge for land and natural resource managers, but identification of core areas and key resources for wildlife species may help in planning for current and emerging threats. To help identify core areas and resources, we conducted spatial analyses and predictive modeling of vertebrate distributions for a network of nature reserves in densely populated Orange County, California. We primarily focused on bobcats (Lynx rufus), a species with a strong association with natural habitat. Bobcat space use has been correlated with broad, simple land-use categories, but relatively little is known about the influence of greater landscape complexity on habitat suitability for bobcats. To examine habitat selection by bobcats, we developed spatial data layers representing environmental factors that might influence this species, and we used previously collected Global Positioning System tracking data for 30 male and 21 female bobcats to indicate bobcat response to complex landscape factors. We examined these inputs using Resource Selection Function (RSF) modeling and developed spatially explicit models of the probability of bobcat use (selection or avoidance) of landscape characteristics. RSF models highlighted the general importance of reserve habitat for bobcats, but suggested that female bobcats were more dependent that male bobcats on habitat within designated reserves. Male bobcats, which range more widely than female bobcats, were associated with undeveloped areas both within and outside reserves. Small areas were present outside reserves that seemed to provide additional suitable habitat or movement areas for bobcats, potentially through restoration, connectivity, or reduced edge effects.
Although bobcat RSFs suggested areas of high value to this species and potentially other species, taxa can differ greatly in their resource-selection and spatial requirements. Thus, for several species of reptiles, amphibians, and birds, we adapted species distribution models based on occurrence data to examine the response of other vertebrates to the landscape. To identify potential High-Value Areas (HVAs) for single or multiple species, we then developed a step-wise filtering process that can be applied to a series of spatial data layers. We provide examples of alternative decision models for HVAs that capture different elements of biodiversity and a range of management considerations. As landscape and management challenges change, these spatial layers and decision rules can be adjusted based on new information. Our approach thus establishes a general framework for identifying high-value habitat that can be used for current management decisions and refined in the future, depending on management interests and goals and the availability of suitable quality data or adequate surrogate information.
","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/ofr20181095","collaboration":"Prepared in cooperation with Natural Communities Coalition","usgsCitation":"Boydston, E.E., and Tracey, J.A., 2018, Modeling resource selection of bobcats (Lynx rufus) and vertebrate species distributions in Orange County, southern California, with a section on Modeling for reptile, amphibian, and bird distributions by Tracey, J.A., Preston, K.L., Rochester, C.J., Boydston, E.E., and Fisher, R.N.: U.S. Geological Survey Open-File Report 2018–1095, 65 p., https://doi.org/10.3133/ofr20181095.","productDescription":"vi, 65 p.","onlineOnly":"Y","ipdsId":"IP-086435","costCenters":[{"id":651,"text":"Western Ecological Research Center","active":true,"usgs":true}],"links":[{"id":358295,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/of/2018/1095/coverthb.jpg"},{"id":358296,"rank":2,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/of/2018/1095/ofr20181095.pdf","text":"Report","size":"13.2 MB","linkFileType":{"id":1,"text":"pdf"},"description":"OFR 2018-1095"}],"country":"United States","state":"California","county":"Orange County","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -117.95,\n 33.5\n ],\n [\n -117.5667,\n 33.5\n ],\n [\n -117.5667,\n 34\n ],\n [\n -117.95,\n 34\n ],\n [\n -117.95,\n 33.5\n ]\n ]\n ]\n }\n }\n ]\n}","contact":"Director, Western Ecological Research Center
U.S. Geological Survey
3020 State University Drive East
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Eroding permafrost coasts are indicators and integrators of changes in the Arctic System as they are susceptible to the combined effects of declining sea ice extent, increases in open water duration, more frequent and impactful storms, sea-level rise, and warming permafrost. However, few observation sites in the Arctic have yet to link decadal-scale erosion rates with changing environmental conditions due to temporal data gaps. This study increases the temporal fidelity of coastal permafrost bluff observations using near-annual high spatial resolution (<1 m) satellite imagery acquired between 2008 and 2017 for a 9-km segment of coastline at Drew Point, Beaufort Sea coast, Alaska. Our results show that mean annual erosion for the 2007 to 2016 decade was 17.2 m yr<sup>-1</sup>, which is 2.5 times faster than historic rates, indicating that bluff erosion at this site is likely responding to changes in the Arctic System. In spite of a sustained increase in decadal-scale mean annual erosion rates, mean open water season erosion varied from 6.7 m yr<sup>-1</sup> in 2010 to more than 22.0 m yr<sup>-1</sup> in 2007, 2012, and 2016. This variability provided a range of coastal responses through which we explored the different roles of potential environmental drivers. The lack of significant correlations between mean open water season erosion and the environmental variables compiled in this study indicates that we may not be adequately capturing the environmental forcing factors, that the system is conditioned by long-term transient effects or extreme weather events rather than annual variability, or that other not yet considered factors may be responsible for the increased erosion occurring at Drew Point. Our results highlight an increase in erosion at Drew Point in the 21st century as well as the complexities associated with unraveling the factors responsible for changing coastal permafrost bluffs in the Arctic.
","language":"English","publisher":"IOP Publishing","doi":"10.1088/1748-9326/aae471","usgsCitation":"Jones, B., Farquharson, L.M., Baughman, C., Buzard, R., Arp, C.D., Grosse, G., Bull, D.L., Gunther, F., Nitze, I., Urban, F., Kasper, J., Frederick, J.M., Thomas, M.A., Jones, C., Mota, A., Dallimore, S., Tweedie, C.E., Maio, C.V., Mann, D.H., Richmond, B.M., Gibbs, A.E., Xiao, M., Sachs, T., Iwahana, G., Kanevskiy, M.Z., and Romanovsky, V.E., 2018, A decade of remotely sensed observations highlight complex processes linked to coastal permafrost bluff erosion in the Arctic: Environmental Research Letters, v. 13, no. 11, 13 p., https://doi.org/10.1088/1748-9326/aae471.","productDescription":"13 p.","ipdsId":"IP-100635","costCenters":[{"id":118,"text":"Alaska Science Center Geography","active":true,"usgs":true}],"links":[{"id":468327,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.1088/1748-9326/aae471","text":"Publisher Index 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for Polar and Marine Research, Potsdam, Germany","active":true,"usgs":false}],"preferred":false,"id":747898,"contributorType":{"id":1,"text":"Authors"},"rank":6},{"text":"Bull, Diana L.","contributorId":208628,"corporation":false,"usgs":false,"family":"Bull","given":"Diana","email":"","middleInitial":"L.","affiliations":[{"id":37851,"text":"Sandia National Laboratories, Albuquerque, New Mexico, UNITED STATES","active":true,"usgs":false}],"preferred":false,"id":747899,"contributorType":{"id":1,"text":"Authors"},"rank":7},{"text":"Gunther, Frank 0000-0001-8298-8937","orcid":"https://orcid.org/0000-0001-8298-8937","contributorId":208629,"corporation":false,"usgs":false,"family":"Gunther","given":"Frank","email":"","affiliations":[{"id":37852,"text":"Periglacial Research, Alfred-Wegener-Institut fur Polar und Meeresforschung, Potsdam, GERMANY","active":true,"usgs":false}],"preferred":false,"id":747900,"contributorType":{"id":1,"text":"Authors"},"rank":8},{"text":"Nitze, 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STATES","active":true,"usgs":false}],"preferred":false,"id":747903,"contributorType":{"id":1,"text":"Authors"},"rank":11},{"text":"Frederick, Jennifer M. 0000-0003-2414-778X","orcid":"https://orcid.org/0000-0003-2414-778X","contributorId":208631,"corporation":false,"usgs":false,"family":"Frederick","given":"Jennifer","email":"","middleInitial":"M.","affiliations":[{"id":37851,"text":"Sandia National Laboratories, Albuquerque, New Mexico, UNITED STATES","active":true,"usgs":false}],"preferred":false,"id":747904,"contributorType":{"id":1,"text":"Authors"},"rank":12},{"text":"Thomas, Matthew A. 0000-0002-9828-5539 matthewthomas@usgs.gov","orcid":"https://orcid.org/0000-0002-9828-5539","contributorId":200616,"corporation":false,"usgs":true,"family":"Thomas","given":"Matthew","email":"matthewthomas@usgs.gov","middleInitial":"A.","affiliations":[{"id":300,"text":"Geologic Hazards Science Center","active":true,"usgs":true}],"preferred":true,"id":747905,"contributorType":{"id":1,"text":"Authors"},"rank":13},{"text":"Jones, Craig","contributorId":208632,"corporation":false,"usgs":false,"family":"Jones","given":"Craig","affiliations":[{"id":37853,"text":"Integral Constulting Inc., Santa Cruz, California, UNITED STATES","active":true,"usgs":false}],"preferred":false,"id":747906,"contributorType":{"id":1,"text":"Authors"},"rank":14},{"text":"Mota, Alejandro","contributorId":208633,"corporation":false,"usgs":false,"family":"Mota","given":"Alejandro","email":"","affiliations":[{"id":37854,"text":"Sandia National Laboratories California, Livermore, California, UNITED STATES","active":true,"usgs":false}],"preferred":false,"id":747907,"contributorType":{"id":1,"text":"Authors"},"rank":15},{"text":"Dallimore, Scott","contributorId":208634,"corporation":false,"usgs":false,"family":"Dallimore","given":"Scott","email":"","affiliations":[{"id":37855,"text":"Geological Survey of Canada Pacific Vancouver, Vancouver, British Columbia, CANADA","active":true,"usgs":false}],"preferred":false,"id":747908,"contributorType":{"id":1,"text":"Authors"},"rank":16},{"text":"Tweedie, Craig E.","contributorId":200176,"corporation":false,"usgs":false,"family":"Tweedie","given":"Craig","email":"","middleInitial":"E.","affiliations":[],"preferred":false,"id":747909,"contributorType":{"id":1,"text":"Authors"},"rank":17},{"text":"Maio, Christopher V.","contributorId":208635,"corporation":false,"usgs":false,"family":"Maio","given":"Christopher","email":"","middleInitial":"V.","affiliations":[{"id":37850,"text":"University of Alaska Fairbanks, Fairbanks, Alaska, UNITED STATES","active":true,"usgs":false}],"preferred":false,"id":747910,"contributorType":{"id":1,"text":"Authors"},"rank":18},{"text":"Mann, Daniel H.","contributorId":193130,"corporation":false,"usgs":false,"family":"Mann","given":"Daniel","email":"","middleInitial":"H.","affiliations":[],"preferred":false,"id":747911,"contributorType":{"id":1,"text":"Authors"},"rank":19},{"text":"Richmond, Bruce M. 0000-0002-0056-5832 brichmond@usgs.gov","orcid":"https://orcid.org/0000-0002-0056-5832","contributorId":2459,"corporation":false,"usgs":true,"family":"Richmond","given":"Bruce","email":"brichmond@usgs.gov","middleInitial":"M.","affiliations":[{"id":520,"text":"Pacific Coastal and Marine Science Center","active":true,"usgs":true}],"preferred":true,"id":747912,"contributorType":{"id":1,"text":"Authors"},"rank":20},{"text":"Gibbs, Ann E. 0000-0002-0883-3774 agibbs@usgs.gov","orcid":"https://orcid.org/0000-0002-0883-3774","contributorId":2644,"corporation":false,"usgs":true,"family":"Gibbs","given":"Ann","email":"agibbs@usgs.gov","middleInitial":"E.","affiliations":[{"id":520,"text":"Pacific Coastal and Marine Science Center","active":true,"usgs":true}],"preferred":true,"id":747913,"contributorType":{"id":1,"text":"Authors"},"rank":21},{"text":"Xiao, Ming","contributorId":208636,"corporation":false,"usgs":false,"family":"Xiao","given":"Ming","email":"","affiliations":[{"id":37856,"text":"Penn State, University Park, Pennsylvania, UNITED STATES","active":true,"usgs":false}],"preferred":false,"id":747914,"contributorType":{"id":1,"text":"Authors"},"rank":22},{"text":"Sachs, Torsten 0000-0002-9959-4771","orcid":"https://orcid.org/0000-0002-9959-4771","contributorId":208637,"corporation":false,"usgs":false,"family":"Sachs","given":"Torsten","email":"","affiliations":[{"id":34716,"text":"GFZ German Research Centre for Geosciences, Potsdam, Germany","active":true,"usgs":false}],"preferred":false,"id":747915,"contributorType":{"id":1,"text":"Authors"},"rank":23},{"text":"Iwahana, Go 0000-0003-4628-1074","orcid":"https://orcid.org/0000-0003-4628-1074","contributorId":208638,"corporation":false,"usgs":false,"family":"Iwahana","given":"Go","email":"","affiliations":[{"id":37850,"text":"University of Alaska Fairbanks, Fairbanks, Alaska, UNITED STATES","active":true,"usgs":false}],"preferred":false,"id":747916,"contributorType":{"id":1,"text":"Authors"},"rank":24},{"text":"Kanevskiy, Mikhail Z.","contributorId":199153,"corporation":false,"usgs":false,"family":"Kanevskiy","given":"Mikhail","email":"","middleInitial":"Z.","affiliations":[],"preferred":false,"id":747917,"contributorType":{"id":1,"text":"Authors"},"rank":25},{"text":"Romanovsky, Vladimir E.","contributorId":169658,"corporation":false,"usgs":false,"family":"Romanovsky","given":"Vladimir","email":"","middleInitial":"E.","affiliations":[{"id":6695,"text":"UAF","active":true,"usgs":false}],"preferred":false,"id":747918,"contributorType":{"id":1,"text":"Authors"},"rank":26}]}} ,{"id":70200024,"text":"70200024 - 2018 - California Gull population growth and ecological impacts in the San Francisco Bay estuary, 1980–2016","interactions":[],"lastModifiedDate":"2019-01-28T09:03:47","indexId":"70200024","displayToPublicDate":"2018-10-11T11:06:00","publicationYear":"2018","noYear":false,"publicationType":{"id":5,"text":"Book chapter"},"publicationSubtype":{"id":24,"text":"Book Chapter"},"title":"California Gull population growth and ecological impacts in the San Francisco Bay estuary, 1980–2016","docAbstract":"The breeding population of California Gulls (Larus californicus) in the San Francisco\nBay estuary increased from 24 individuals in 1980 to a peak of over 53,000 in 2014, then\ndeclined to 38,040 in 2016. The expansion of the breeding population may be related to\nthe availability of suitable nesting sites in close proximity to anthropogenic food subsidies\nat landfills. Telemetry data indicate that California Gull movements are largely dictated by\nthe two primary landfills in South San Francisco Bay. The large population of California\nGulls has had negative effects on locally breeding shorebirds and terns, especially the Forster’s\nTern (Sterna forsteri), American Avocet (Recurvirostra americana), and Western Snowy Plover\n(Charadrius nivosus nivosus). In South San Francisco Bay, California Gulls were responsible\nfor 13% and 38% of egg predation events at nests of American Avocets and Snowy Plovers,\nrespectively, and 55% and 54% of chick predation events of American Avocets and Forster’s\nTerns, respectively. The forced relocation of the largest gull colony (~24,000) at Pond A6 in\n2010 resulted in increased survival of Forster’s Tern chicks at the adjacent colony at Pond A7 in\n2011. The California Gull population and its effects on locally breeding shorebirds and terns\nare among the most pressing concerns for wetland managers within the San Francisco Bay\nestuary, especially for the South Bay Salt Pond Restoration Project. Further research is needed\nto evaluate the gull’s reproductive rates, habitat use, and annual movements and so to clarify\nits demographics and to quantify its effects on other breeding birds.","largerWorkType":{"id":4,"text":"Book"},"largerWorkTitle":"Trends and traditions: Avifaunal change in western North America, Studies of Western Birds No. 3","language":"English","publisher":"Western Field Ornithologists","doi":"10.21199/SWB3.9","usgsCitation":"Burns, C.E., Ackerman, J., Washburn, N.B., Bluso-Demers, J., Robinson-Nilsen, C., and Strong, C., 2018, California Gull population growth and ecological impacts in the San Francisco Bay estuary, 1980–2016, chap. of Trends and traditions: Avifaunal change in western North America, Studies of Western Birds No. 3, p. 180-189, https://doi.org/10.21199/SWB3.9.","productDescription":"10 p.","startPage":"180","endPage":"189","ipdsId":"IP-061943","costCenters":[{"id":651,"text":"Western Ecological Research Center","active":true,"usgs":true}],"links":[{"id":358276,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"publishingServiceCenter":{"id":1,"text":"Sacramento PSC"},"noUsgsAuthors":false,"publicationDate":"2017-09-01","publicationStatus":"PW","scienceBaseUri":"5bc02f71e4b0fc368eb53821","contributors":{"authors":[{"text":"Burns, Catherine E.","contributorId":208622,"corporation":false,"usgs":false,"family":"Burns","given":"Catherine","email":"","middleInitial":"E.","affiliations":[{"id":37846,"text":"SFBay Bird Observatory","active":true,"usgs":false}],"preferred":false,"id":747888,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Ackerman, Joshua T. 0000-0002-3074-8322 jackerman@usgs.gov","orcid":"https://orcid.org/0000-0002-3074-8322","contributorId":147078,"corporation":false,"usgs":true,"family":"Ackerman","given":"Joshua T.","email":"jackerman@usgs.gov","affiliations":[{"id":651,"text":"Western Ecological Research Center","active":true,"usgs":true}],"preferred":false,"id":747887,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Washburn, Natalie B.","contributorId":208623,"corporation":false,"usgs":false,"family":"Washburn","given":"Natalie","email":"","middleInitial":"B.","affiliations":[{"id":37847,"text":"San Francisco Bay Bird Observatory, Ducks Unlimited","active":true,"usgs":false}],"preferred":false,"id":747889,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Bluso-Demers, Jill","contributorId":177613,"corporation":false,"usgs":false,"family":"Bluso-Demers","given":"Jill","affiliations":[],"preferred":false,"id":747890,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Robinson-Nilsen, Caitlin","contributorId":208624,"corporation":false,"usgs":false,"family":"Robinson-Nilsen","given":"Caitlin","email":"","affiliations":[{"id":17738,"text":"San Francisco Bay Bird Observatory","active":true,"usgs":false}],"preferred":false,"id":747891,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"Strong, Cheryl","contributorId":149428,"corporation":false,"usgs":false,"family":"Strong","given":"Cheryl","email":"","affiliations":[{"id":6927,"text":"USFWS, National Wildlife Refuge System","active":true,"usgs":false}],"preferred":false,"id":747892,"contributorType":{"id":1,"text":"Authors"},"rank":6}]}} ,{"id":70199161,"text":"sir20185118 - 2018 - Completion summary for borehole TAN-2312 at Test Area North, Idaho National Laboratory, Idaho","interactions":[],"lastModifiedDate":"2018-10-12T11:03:03","indexId":"sir20185118","displayToPublicDate":"2018-10-11T10:47:02","publicationYear":"2018","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":"2018-5118","title":"Completion summary for borehole TAN-2312 at Test Area North, Idaho National Laboratory, Idaho","docAbstract":"In 2017, the U.S. Geological Survey, in cooperation with the U.S. Department of Energy, drilled and constructed borehole TAN-2312 for stratigraphic framework analyses and long-term groundwater monitoring of the eastern Snake River Plain aquifer at the Idaho National Laboratory in southeast Idaho. The location of borehole TAN-2312 was selected because it was downgradient from TAN and believed to be the outer extent of waste plumes originating from the TAN facility. Borehole TAN-2312 initially was cored to collect continuous geologic data, and then re-drilled to complete construction as a monitor well. The final construction for borehole TAN-2312 required 16- and 10-inch (in.) diameter carbon-steel well casing to 37 and 228 feet below land surface (ft BLS), respectively, and 9.9-in. diameter open-hole completion below the casing to 522 ft BLS. Depth to water is measured near 244 ft BLS. Following construction and data collection, a temporary submersible pump and water-level access line were placed near 340 ft BLS to allow for aquifer testing, for collecting periodic water samples, and for measuring water levels.
Borehole TAN-2312 was cored continuously, starting at the first basalt contact (about 37 ft BLS) to a depth of 568 ft BLS. Not including surface sediment (0–37 ft), recovery of basalt and sediment core at borehole TAN-2312 was about 93 percent; however, core recovery from 170 to 568 ft BLS was 100 percent. Based on visual inspection of core and geophysical data, basalt examined from 37 to 568 ft BLS consists of about 32 basalt flows that range from approximately 3 to 87 ft in thickness and 4 sediment layers with a combined thickness of approximately 76 ft. About 2 ft of total sediment was described for the saturated zone, observed from 244 to 568 ft BLS, near 296 and 481 ft BLS. Sediment described for the saturated zone were composed of fine-grained sand and silt with a lesser amount of clay. Basalt texture for borehole TAN-2312 generally was described as aphanitic, phaneritic, and porphyritic. Basalt flows varied from highly fractured to dense with high to low vesiculation.
Geophysical and borehole video logs were collected after core drilling and after final construction at borehole TAN-2312. Geophysical logs were examined synergistically with available core material to suggest zones where groundwater flow was anticipated. Natural gamma log measurements were used to assess sediment layer thickness and location. Neutron and gamma-gamma source logs were used to identify fractured areas for aquifer testing. Acoustic televiewer logs, fluid logs, and electromagnetic flow meter results were used to identify fractures and assess groundwater movement when compared against neutron measurements. Furthermore, gyroscopic deviation measurements were used to measure horizontal and vertical displacement for borehole TAN-2312.
After construction of borehole TAN-2312, a single-well aquifer test was completed September 27, 2017, to provide estimates of transmissivity and hydraulic conductivity. Estimates for transmissivity and hydraulic conductivity were 1.51×102 feet squared per day and 0.23 feet per day, respectively. During the 220-minute aquifer test, well TAN-2312 had about 23 ft of measured drawdown at sustained pumping rate of 27.2 gallons per minute. The transmissivity and hydraulic conductivity estimates for well TAN-2312 were lower than the values determined from previous aquifer tests in other wells near Test Area North.
Water samples were analyzed for cations, anions, metals, nutrients, volatile organic compounds, stable isotopes, and radionuclides. Water samples for most of the inorganic constituents showed concentrations near background levels for eastern regional groundwater. Water samples for stable isotopes of oxygen, hydrogen, and sulfur indicated some possible influence of irrigation on the water quality. The volatile organic compound data indicated that this well had some minor influence by wastewater disposal practices at Test Area North.
","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/sir20185118","collaboration":"Prepared in cooperation with the U.S. Department of Energy","usgsCitation":"Twining, B.V., Bartholomay, R.C., and Hodges, M.K.V., 2018, Completion summary for borehole TAN-2312 at Test Area North, Idaho National Laboratory, Idaho: U.S. Geological Survey Scientific Investigations Report 2018-5118, DOE/ID-22247, 29 p., plus appendixes, https://doi.org/10.3133/sir20185118.","productDescription":"Report: vi, 29 p.; Appendixes","onlineOnly":"Y","ipdsId":"IP-090126","costCenters":[{"id":343,"text":"Idaho Water Science Center","active":true,"usgs":true}],"links":[{"id":358288,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/sir/2018/5118/coverthb.jpg"},{"id":358289,"rank":2,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/sir/2018/5118/sir20185118.pdf","text":"Report","size":"1.7 MB","linkFileType":{"id":1,"text":"pdf"},"description":"SIR 2018-5118"},{"id":358290,"rank":3,"type":{"id":3,"text":"Appendix"},"url":"https://pubs.usgs.gov/sir/2018/5118/sir20185118_appendix01.pdf","text":"Appendix 1","size":"85 KB","linkFileType":{"id":1,"text":"pdf"},"description":"SIR 2018-5118 Appendix 1"},{"id":358291,"rank":4,"type":{"id":3,"text":"Appendix"},"url":"https://pubs.usgs.gov/sir/2018/5118/sir20185118_appendix02.pdf","text":"Appendix 2","size":"27 KB","linkFileType":{"id":1,"text":"pdf"},"description":"SIR 2018-5118 Appendix 2"},{"id":358292,"rank":5,"type":{"id":3,"text":"Appendix"},"url":"https://pubs.usgs.gov/sir/2018/5118/sir20185118_appendix03.pdf","text":"Appendix 3","size":"2.9 MB","linkFileType":{"id":1,"text":"pdf"},"description":"SIR 2018-5118 Appendix 3"},{"id":358293,"rank":6,"type":{"id":3,"text":"Appendix"},"url":"https://pubs.usgs.gov/sir/2018/5118/sir20185118_appendix04.pdf","text":"Appendix 4","size":"138 KB","linkFileType":{"id":1,"text":"pdf"},"description":"SIR 2018-5118 Appendix 4"}],"country":"United States","state":"Idaho","otherGeospatial":"Idaho National Laboratory","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -112.75,\n 43.8167\n ],\n [\n -112.6833,\n 43.8167\n ],\n [\n -112.6833,\n 43.8667\n ],\n [\n -112.75,\n 43.8667\n ],\n [\n -112.75,\n 43.8167\n ]\n ]\n ]\n }\n }\n ]\n}","contact":"Director, Idaho Water Science Center
U.S. Geological Survey
230 Collins Road
Boise, Idaho 83702
Dauphin Island, Alabama, is a barrier island located in the northern Gulf of Mexico that supports local residences, tourism, commercial infrastructure, and historic Fort Gaines. During the past decade, Dauphin Island was affected by several major hurricanes—Hurricanes Ivan (2004), Katrina (2005), and Isaac (2012)—and storms, along with sea-level rise, continue to present a threat to island stability. State and Federal managers are using a scientific approach to identify, formulate, and implement a long-term plan to provide restoration options for Dauphin Island, thereby helping increase its resilience against future storms and sea-level rise. Island morphology, including current bathymetry and shoreline data, is one scientific domain being investigated in an effort to produce a comprehensive restoration plan funded by an interagency grant from the National Fish and Wildlife Foundation Gulf Environmental Benefit Fund.
In August 2015, the U.S. Geological Survey (USGS) St. Petersburg Coastal and Marine Science Center (SPCMSC), in cooperation with the U.S. Army Corps of Engineers at the U.S. Army Engineer Research and Development Center, Mobile District, and the State of Alabama, conducted bathymetric surveys of the nearshore waters surrounding Dauphin Island. This report provides a detailed methodology for the data acquisition and post-processing of 1,165-line kilometers (km) of single-beam bathymetry data collected under the USGS–SPCMSC Alabama Barrier Island Restoration Study. These data were acquired and processed under USGS field activity number 2015–326–FA. Data are provided in three datums: (1) the International Terrestrial Reference Frame of 2000, ellipsoid height (from –47.04 meters (m) to –29.36 m); (2) the North American Datum of 1983, CORS96 realization (NAD83 (CORS96)) horizontal, and the North American Vertical Datum 1988 GEOID12A vertical (from –0.24 m to –17.33 m); and (3) NAD83 (CORS96) horizontal, and mean lower low water vertical (from –0.12 m to –17.93 m). The x,y,z point datasets, trackline shapefiles, digital and handwritten Field Activity Collection Systems logs, one 50-m digital elevation model, and formal Federal Geographic Data Committee metadata are obtainable from the Data Downloads page or the associated USGS data release.
","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/ds1095","usgsCitation":"DeWitt, N.T., Stalk, C.A., Flocks, J.G., Bernier, J.C., Kelso, K.W., Fredericks, J.J., and Tuten, T.M., 2018, Nearshore single-beam bathymetry data collected in 2015, Dauphin Island, Alabama: U.S. Geological Survey Data Series 1095, https://doi.org/10.3133/ds1095.","productDescription":"Report: HTML; Data release","onlineOnly":"Y","additionalOnlineFiles":"Y","ipdsId":"IP-090788","costCenters":[{"id":574,"text":"St. Petersburg Coastal and Marine Science Center","active":true,"usgs":true}],"links":[{"id":358117,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/ds/1095/coverthb.jpg"},{"id":358118,"rank":2,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/ds/1095/index.html","text":"Report HTML","linkFileType":{"id":5,"text":"html"},"description":"DS 1095"},{"id":358119,"rank":3,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/F7BZ648W","text":"USGS data release","description":"USGS data release","linkHelpText":"Single-beam bathymetry data collected in 2015 nearshore Dauphin Island, Alabama"}],"country":"United States","state":"Alabama","otherGeospatial":"Dauphin Island","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -88.38706970214844,\n 30.194992169502903\n ],\n [\n -88.05061340332031,\n 30.194992169502903\n ],\n [\n -88.05061340332031,\n 30.292274851024256\n ],\n [\n -88.38706970214844,\n 30.292274851024256\n ],\n [\n -88.38706970214844,\n 30.194992169502903\n ]\n ]\n ]\n }\n }\n ]\n}","contact":"St. Petersburg Coastal and Marine Science Center
U.S. Geological Survey
600 4th St. S.
St. Petersburg, FL 33701
Lead mining first began in the Big River watershed during the 1700s. Lead was the primary metal mined throughout most of the 1700s and early 1800s and it continued to be mined until the mid-1900s. Barite mining began in the middle part of the watershed in the mid- to late 1800s. Although considerable attention has been given to concentrations of miningrelated trace elements (mostly cadmium, lead, and zinc) in the Big River and its tributaries draining the Old Lead Belt, there is less information regarding concentrations of mining-related trace elements in tributaries draining the Barite District in southeast Missouri, which is downstream from the Old Lead Belt, and the contribution of sediment transported from this district to trace elements in lower reaches of the Big River. The purpose of this report is to present results of an investigation of the distribution of mining-related trace elements in sediments in the middle reach of the Big River downstream from the Old Lead Belt and the Big River tributaries that drain a large part of the Barite District.
In general, concentrations of cadmium and lead in streambed sediment were largest in samples from the Big River and smallest in Barite District tributary samples. Concentrations of zinc were somewhat similar in the Big River and Barite District tributaries; however, higher concentrations were present in upstream Big River site samples, as well as in samples from one site on Maddin Creek and at another site on Old Mines Creek that drains the Barite District. Barium concentrations were considerably larger in samples from Barite District tributaries compared to samples collected on the Big River. Samples collected downstream from the Barite District on the Big River had considerably larger barium concentrations than samples collected upstream from the Barite District.
Flood-plain core samples were collected from 26 cores at 5 transect locations along tributaries in the Barite District. Of the individual 693 bulk (unsieved) samples from these cores analyzed by x-ray fluorescence, the probable effects concentration (PEC) values were exceeded for cadmium (PEC of 4.98 milligrams per kilogram [mg/kg], 218 samples), lead (PEC of 128 mg/kg, 91 samples), nickel (PEC of 48.6 mg/kg, 45 samples), and zinc (PEC of 459 mg/kg, 77 samples). Of the 693 samples, 21 exceeded the U.S. Environmental Protection Agency residential yard cleanup level of 400 mg/kg for lead; 19 of these were samples from a single transect near the mouth of Mineral Fork Creek where its flood plain joins the Big River flood plain.
Shortly after the December 2015 flood on the Big River (the third largest flood along the river since the 1950s), 23 samples of fine sediment deposited from the flood were collected from the Big River flood plain upstream and downstream from the Barite District and several tributaries. Overall, the general pattern of barium, lead, and zinc concentrations in the 2015 flood sediment samples was similar to that observed in the streambed-sediment samples.
Overall concentrations of barium were larger at Big River sites downstream from the Barite District, and cadmium, lead, and zinc concentrations were generally similar or smaller at sites downstream from the Barite District when compared to sites upstream from the Barite District. These data indicate a substantial influx of barium from the Barite District into the Big River but only a minimal influx of cadmium, lead, and zinc.
","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/sir20185103","collaboration":"Prepared in cooperation with the U.S. Environmental Protection Agency","usgsCitation":"Smith, D.C., and Schumacher, J.G., 2018, Distribution of mining-related trace elements in streambed and flood-plain sediment along the middle Big River and tributaries in the Southeast Missouri Barite District, 2012–15: U.S. Geological Survey Scientific Investigations Report 2018–5103, 89 p., https://doi.org/10.3133/sir20185103.","productDescription":"Report: vii, 89 p.; Data Release","numberOfPages":"102","onlineOnly":"Y","ipdsId":"IP-090502","costCenters":[{"id":396,"text":"Missouri Water Science Center","active":true,"usgs":true},{"id":36532,"text":"Central Midwest Water Science Center","active":true,"usgs":true}],"links":[{"id":357852,"rank":2,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/sir/2018/5103/sir20185103.pdf","text":"Report","size":"4.85 MB","linkFileType":{"id":1,"text":"pdf"},"description":"SIR 2018–5103"},{"id":357851,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/sir/2018/5103/coverthb2.jpg"},{"id":357853,"rank":3,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/P9OFYN3C","text":"USGS data release","description":"USGS Data Release","linkHelpText":"Concentrations of Major and Trace Elements in Streambed and Floodplain Sediment along the Middle Big River and Tributaries in the Southeast Missouri Barite District and in Quality-Assurance Samples, 2012–15"}],"country":"United States","state":"Missouri","otherGeospatial":"Middle Big River","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -91,\n 37.5\n ],\n [\n -90,\n 37.5\n ],\n [\n -90,\n 38.5\n ],\n [\n -91,\n 38.5\n ],\n [\n -91,\n 37.5\n ]\n ]\n ]\n }\n }\n ]\n}","contact":"Director, Central Midwest Water Science Center
U.S. Geological Survey
1400 Independence Road
Rolla, MO 65401
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
Using Dual-Frequency Identification Sonar (DIDSON), fishery acoustic observation data was collected from the Ocqueoc River, a tributary of Lake Huron in northern Michigan, USA. Data were collected March through July 2013 and 2016 and included the identification, via technology or expert analysis, of eight fish species as they passed through the DIDSON’s field of view. A set of short DIDSON clips containing identified fish was curated. Additionally, two other datasets were created that include visualizations of the acoustic data and longer DIDSON clips. These datasets could complement future research characterizing the abundance and behavior of valued fishes such as walleye (Sander vitreus) or white sucker (Catostomus commersonii) or invasive fishes such as sea lamprey (Petromyzon marinus) or European carp (Cyprinus carpio). Given the abundance of DIDSON data and the fact that a portion of it is labeled, these data could aid in the creation of machine learning tools from DIDSON data, particularly for invasive sea lamprey which are amply represented and a destructive invader of the Laurentian Great Lakes.
","language":"English","publisher":"Nature","doi":"10.1038/sdata.2018.190","usgsCitation":"McCann, E.L., Li, L., Pangle, K., Johnson, N., and Eickholt, J., 2018, An underwater observation dataset for fish classification and fishery assessment: Scientific Data, v. 5, p. 1-8, https://doi.org/10.1038/sdata.2018.190.","productDescription":"Article number: 180190; 8 p.","startPage":"1","endPage":"8","ipdsId":"IP-098788","costCenters":[{"id":324,"text":"Great Lakes Science Center","active":true,"usgs":true}],"links":[{"id":468330,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.1038/sdata.2018.190","text":"Publisher Index Page"},{"id":359920,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"volume":"5","publishingServiceCenter":{"id":15,"text":"Madison PSC"},"noUsgsAuthors":false,"publicationDate":"2018-10-09","publicationStatus":"PW","scienceBaseUri":"5c07a064e4b0815414cee783","contributors":{"authors":[{"text":"McCann, Erin L.","contributorId":195636,"corporation":false,"usgs":false,"family":"McCann","given":"Erin","email":"","middleInitial":"L.","affiliations":[],"preferred":false,"id":753030,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Li, Liling","contributorId":205580,"corporation":false,"usgs":false,"family":"Li","given":"Liling","email":"","affiliations":[{"id":13588,"text":"Central Michigan University","active":true,"usgs":false}],"preferred":false,"id":753031,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Pangle, Kevin","contributorId":195637,"corporation":false,"usgs":false,"family":"Pangle","given":"Kevin","affiliations":[],"preferred":false,"id":753032,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Johnson, Nicholas S. 0000-0002-7419-6013 njohnson@usgs.gov","orcid":"https://orcid.org/0000-0002-7419-6013","contributorId":150983,"corporation":false,"usgs":true,"family":"Johnson","given":"Nicholas S.","email":"njohnson@usgs.gov","affiliations":[{"id":324,"text":"Great Lakes Science Center","active":true,"usgs":true}],"preferred":true,"id":753029,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Eickholt, Jesse","contributorId":205582,"corporation":false,"usgs":false,"family":"Eickholt","given":"Jesse","email":"","affiliations":[{"id":13588,"text":"Central Michigan University","active":true,"usgs":false}],"preferred":false,"id":753033,"contributorType":{"id":1,"text":"Authors"},"rank":5}]}} ,{"id":70199996,"text":"70199996 - 2018 - Downhole log evidence for the coexistence of structure II gas hydrate and free gas below the bottom simulating reflector in the South China Sea","interactions":[],"lastModifiedDate":"2018-10-10T10:06:27","indexId":"70199996","displayToPublicDate":"2018-10-09T10:03:02","publicationYear":"2018","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":2682,"text":"Marine and Petroleum Geology","active":true,"publicationSubtype":{"id":10}},"title":"Downhole log evidence for the coexistence of structure II gas hydrate and free gas below the bottom simulating reflector in the South China Sea","docAbstract":"Stratigraphic layered pore-filling gas hydrates are identified above the bottom simulating reflector (BSR) using the well log and core data acquired at Sites W11 and W17 during the third gas hydrate drilling expedition conducted by China's Geological Survey/Guangzhou Marine Geological Survey (GMGS3) in the South China Sea. A seismic profile near Site W17, reveal the presence of two BSRs (i.e., double BSR), which we show to relate to zones of structure I gas hydrate (I-BSR) and structure II gas hydrate (II-BSR). Well log data from Site W17 between the “I-BSR” (projected depth of 250 mbsf) and “II-BSR” (projected depth of 330 mbsf) showed anomalous responses for gas hydrate-bearing sediments with high resistivity, high S-wave velocity, and alternating high and low P-wave velocities. Pressure core data support the interpretation that structure II gas hydrate occurs at a depth of 263 mbsf at Site W17. The cross-plot between log-derived neutron and density porosities reveals a free gas-bearing layer at a depth of 258–270 mbsf, suggesting gas hydrate coexists with free gas between the “I-BSR” and the “II-BSR.” Synthetic seismograms generated from the P-wave velocity and density logs further support the presence of free gas in this section. Based on the coexistence of hydrate, free gas and water, the simplified three-phase equation (STPE) was modified to simultaneously estimate free gas and hydrate saturations beneath the “I-BSR” from P-wave and S-wave velocity logs, assuming uniform or patchy distributions of free gas. The estimated free gas and hydrate saturations, together with gas compositions from pressure core samples, collectively indicate that structure II gas hydrate and free gas are interbedded and coexist below the “I-BSR” at Site W17. Our study of the coexistence of gas hydrate and free gas between the double BSR at Site W17 provides new insights into gas hydrate systems in nature that contain more complex gas chemistries.
","language":"English","publisher":"Elsevier","doi":"10.1016/j.marpetgeo.2018.09.024","usgsCitation":"Qian, J., Wang, X., Collett, T.S., Guo, Y., Kang, D., and Jin, J., 2018, Downhole log evidence for the coexistence of structure II gas hydrate and free gas below the bottom simulating reflector in the South China Sea: Marine and Petroleum Geology, v. 98, p. 662-674, https://doi.org/10.1016/j.marpetgeo.2018.09.024.","productDescription":"13 p.","startPage":"662","endPage":"674","costCenters":[{"id":164,"text":"Central Energy Resources Science Center","active":true,"usgs":true}],"links":[{"id":358236,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"China","otherGeospatial":"South China Sea","volume":"98","noUsgsAuthors":false,"publicationStatus":"PW","scienceBaseUri":"5bc02f78e4b0fc368eb5383f","contributors":{"authors":[{"text":"Qian, Jin","contributorId":208554,"corporation":false,"usgs":false,"family":"Qian","given":"Jin","email":"","affiliations":[],"preferred":false,"id":747680,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Wang, Xiujuan","contributorId":87071,"corporation":false,"usgs":true,"family":"Wang","given":"Xiujuan","affiliations":[],"preferred":false,"id":747681,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Collett, Timothy S. 0000-0002-7598-4708 tcollett@usgs.gov","orcid":"https://orcid.org/0000-0002-7598-4708","contributorId":1698,"corporation":false,"usgs":true,"family":"Collett","given":"Timothy","email":"tcollett@usgs.gov","middleInitial":"S.","affiliations":[{"id":255,"text":"Energy Resources Program","active":true,"usgs":true},{"id":164,"text":"Central Energy Resources Science Center","active":true,"usgs":true},{"id":678,"text":"Woods Hole Coastal and Marine Science Center","active":true,"usgs":true},{"id":171,"text":"Central Mineral and Environmental Resources Science Center","active":true,"usgs":true}],"preferred":true,"id":747682,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Guo, Yiqun","contributorId":195860,"corporation":false,"usgs":false,"family":"Guo","given":"Yiqun","email":"","affiliations":[],"preferred":false,"id":747683,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Kang, Dongju","contributorId":208555,"corporation":false,"usgs":false,"family":"Kang","given":"Dongju","email":"","affiliations":[],"preferred":false,"id":747684,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"Jin, Jiapeng","contributorId":208556,"corporation":false,"usgs":false,"family":"Jin","given":"Jiapeng","email":"","affiliations":[],"preferred":false,"id":747685,"contributorType":{"id":1,"text":"Authors"},"rank":6}]}} ,{"id":70199950,"text":"70199950 - 2018 - Machine learning for ecosystem services","interactions":[],"lastModifiedDate":"2018-10-05T14:40:59","indexId":"70199950","displayToPublicDate":"2018-10-05T14:40:55","publicationYear":"2018","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":1477,"text":"Ecosystem Services","active":true,"publicationSubtype":{"id":10}},"title":"Machine learning for ecosystem services","docAbstract":"Recent developments in machine learning have expanded data-driven modelling (DDM) capabilities, allowing artificial intelligence to infer the behaviour of a system by computing and exploiting correlations between observed variables within it. Machine learning algorithms may enable the use of increasingly available ‘big data’ and assist applying ecosystem service models across scales, analysing and predicting the flows of these services to disaggregated beneficiaries. We use the Weka and ARIES software to produce two examples of DDM: firewood use in South Africa and biodiversity value in Sicily, respectively. Our South African example demonstrates that DDM (64–91% accuracy) can identify the areas where firewood use is within the top quartile with comparable accuracy as conventional modelling techniques (54–77% accuracy). The Sicilian example highlights how DDM can be made more accessible to decision makers, who show both capacity and willingness to engage with uncertainty information. Uncertainty estimates, produced as part of the DDM process, allow decision makers to determine what level of uncertainty is acceptable to them and to use their own expertise for potentially contentious decisions. We conclude that DDM has a clear role to play when modelling ecosystem services, helping produce interdisciplinary models and holistic solutions to complex socio-ecological issues.
","language":"English","publisher":"Elsevier","doi":"10.1016/j.ecoser.2018.04.004","usgsCitation":"Willcock, S., Martinez-Lopez, J., Hooftman, D.A., Bagstad, K.J., Balbi, S., Marzo, A., Prato, C., Sciandrello, S., Signorello, G., Voigt, B., Villa, F., Bullock, J.M., and Athanasiadis, I., 2018, Machine learning for ecosystem services: Ecosystem Services, v. 33, no. Part B, p. 165-174, https://doi.org/10.1016/j.ecoser.2018.04.004.","productDescription":"10 p.","startPage":"165","endPage":"174","ipdsId":"IP-091205","costCenters":[{"id":318,"text":"Geosciences and Environmental Change Science Center","active":true,"usgs":true}],"links":[{"id":468333,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.1016/j.ecoser.2018.04.004","text":"Publisher Index Page"},{"id":358188,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"volume":"33","issue":"Part B","publishingServiceCenter":{"id":2,"text":"Denver PSC"},"noUsgsAuthors":false,"publicationStatus":"PW","scienceBaseUri":"5bc02f79e4b0fc368eb53845","contributors":{"authors":[{"text":"Willcock, Simon 0000-0001-9534-9114","orcid":"https://orcid.org/0000-0001-9534-9114","contributorId":201576,"corporation":false,"usgs":false,"family":"Willcock","given":"Simon","email":"","affiliations":[{"id":36207,"text":"Bangor University","active":true,"usgs":false}],"preferred":false,"id":747437,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Martinez-Lopez, Javier 0000-0003-4857-3396","orcid":"https://orcid.org/0000-0003-4857-3396","contributorId":208480,"corporation":false,"usgs":false,"family":"Martinez-Lopez","given":"Javier","email":"","affiliations":[{"id":32916,"text":"Basque Centre for Climate Change","active":true,"usgs":false}],"preferred":false,"id":747438,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Hooftman, Danny A.P.","contributorId":208490,"corporation":false,"usgs":false,"family":"Hooftman","given":"Danny","email":"","middleInitial":"A.P.","affiliations":[{"id":37805,"text":"NERC Centre for Ecology and Hydrology","active":true,"usgs":false}],"preferred":false,"id":747439,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Bagstad, Kenneth J. 0000-0001-8857-5615 kjbagstad@usgs.gov","orcid":"https://orcid.org/0000-0001-8857-5615","contributorId":3680,"corporation":false,"usgs":true,"family":"Bagstad","given":"Kenneth","email":"kjbagstad@usgs.gov","middleInitial":"J.","affiliations":[{"id":318,"text":"Geosciences and Environmental Change Science Center","active":true,"usgs":true}],"preferred":true,"id":747436,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Balbi, Stefano 0000-0001-8190-5968","orcid":"https://orcid.org/0000-0001-8190-5968","contributorId":208481,"corporation":false,"usgs":false,"family":"Balbi","given":"Stefano","email":"","affiliations":[{"id":32916,"text":"Basque Centre for Climate Change","active":true,"usgs":false}],"preferred":false,"id":747440,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"Marzo, Alessia","contributorId":208491,"corporation":false,"usgs":false,"family":"Marzo","given":"Alessia","email":"","affiliations":[{"id":37806,"text":"University of Catania","active":true,"usgs":false}],"preferred":false,"id":747441,"contributorType":{"id":1,"text":"Authors"},"rank":6},{"text":"Prato, Carlo","contributorId":208492,"corporation":false,"usgs":false,"family":"Prato","given":"Carlo","email":"","affiliations":[{"id":37806,"text":"University of Catania","active":true,"usgs":false}],"preferred":false,"id":747442,"contributorType":{"id":1,"text":"Authors"},"rank":7},{"text":"Sciandrello, Saverio 0000-0003-1132-5698","orcid":"https://orcid.org/0000-0003-1132-5698","contributorId":208493,"corporation":false,"usgs":false,"family":"Sciandrello","given":"Saverio","email":"","affiliations":[{"id":37806,"text":"University of Catania","active":true,"usgs":false}],"preferred":false,"id":747443,"contributorType":{"id":1,"text":"Authors"},"rank":8},{"text":"Signorello, Giovanni 0000-0002-5140-4975","orcid":"https://orcid.org/0000-0002-5140-4975","contributorId":208494,"corporation":false,"usgs":false,"family":"Signorello","given":"Giovanni","email":"","affiliations":[{"id":37807,"text":"University of Cataria","active":true,"usgs":false}],"preferred":false,"id":747444,"contributorType":{"id":1,"text":"Authors"},"rank":9},{"text":"Voigt, Brian","contributorId":208483,"corporation":false,"usgs":false,"family":"Voigt","given":"Brian","email":"","affiliations":[{"id":13253,"text":"University of Vermont","active":true,"usgs":false}],"preferred":false,"id":747445,"contributorType":{"id":1,"text":"Authors"},"rank":10},{"text":"Villa, Ferdinando 0000-0002-5114-3007","orcid":"https://orcid.org/0000-0002-5114-3007","contributorId":208486,"corporation":false,"usgs":false,"family":"Villa","given":"Ferdinando","email":"","affiliations":[{"id":32916,"text":"Basque Centre for Climate Change","active":true,"usgs":false}],"preferred":false,"id":747446,"contributorType":{"id":1,"text":"Authors"},"rank":11},{"text":"Bullock, James M.","contributorId":208495,"corporation":false,"usgs":false,"family":"Bullock","given":"James","email":"","middleInitial":"M.","affiliations":[{"id":37805,"text":"NERC Centre for Ecology and Hydrology","active":true,"usgs":false}],"preferred":false,"id":747447,"contributorType":{"id":1,"text":"Authors"},"rank":12},{"text":"Athanasiadis, Ioannis 0000-0003-2764-0078","orcid":"https://orcid.org/0000-0003-2764-0078","contributorId":208484,"corporation":false,"usgs":false,"family":"Athanasiadis","given":"Ioannis","email":"","affiliations":[{"id":37803,"text":"Wageningen University","active":true,"usgs":false}],"preferred":false,"id":747448,"contributorType":{"id":1,"text":"Authors"},"rank":13}]}} ,{"id":70197900,"text":"sir20185085 - 2018 - Historical eruptions and hazards at Bogoslof volcano, Alaska","interactions":[],"lastModifiedDate":"2018-10-09T11:20:05","indexId":"sir20185085","displayToPublicDate":"2018-10-05T12:05:38","publicationYear":"2018","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":"2018-5085","title":"Historical eruptions and hazards at Bogoslof volcano, Alaska","docAbstract":"Bogoslof volcano is a submarine volcano in the southern
Bering Sea (53.9272°N, 168.0344°W), located 100 kilometers
(km) west of Dutch Harbor/Unalaska, and 40 km north
of Umnak Island. The volcano has a relatively long history of
scientific investigation and several of its historical eruptions
have been documented during brief visits to the area since the
late 1700s. The purpose of this report is to provide a modern
volcanological perspective on past eruptions of Bogoslof and
to readdress some of the eruptive phenomena described in
historical documents and reports. We also present for the first
time a brief analysis of the hazards posed by Bogoslof eruptions.
While this report was being prepared, Bogoslof volcano
was in an ongoing state of eruptive activity that began in
mid-December 2016. Detectable eruptive activity ended in late
August 2017 and the volcano has remained quiet since then.
Because we have not yet visited Bogoslof Island and have
only a few distal tephra samples from two eruptive events,
we will not discuss in detail the 2016–17 eruptive sequence,
but will provide some information for comparative purposes.
When more detailed data has been collected, a more extensive
review of the 2016–17 Bogoslof eruption should be the subject
of future reports.
Alaska Volcano Observatory
U.S. Geological Survey
4210 University Drive
Anchorage, AK 99508
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
Studies were conducted to develop methods to assess the effects of a complex mixture of polychlorinated biphenyls (PCBs) in the domestic chicken (Gallus domesticus). Treatments were administered by egg injection to compare embryonic effects of an environmentally relevant PCB congener mixture in the domestic chicken over a range of doses. Chicken eggs were injected with the PCB mixture with a profile similar to that found in avian eggs collected on the upper Hudson River, New York, USA, at doses that spanned 0 to 98 μg/g egg. Eggs were hatched in the laboratory to ascertain hatching success. In the domestic chicken, the median lethal dose was 0.3 μg/g. These data demonstrate adverse effects of an environmentally relevant PCB mixture and provide the basis for further work using in vitro and other models to characterize the potential risk to avian populations.
","language":"English","publisher":"SETAC","doi":"10.1002/etc.4218","usgsCitation":"Ottinger, M.A., Lavoie, E.T., Bohannon, M.E., Marcel, A.M., Tschiffely, A.E., Duffy, K.B., McKernan, M.A., Thompson, N., Whitehouse, H.K., Davani, K., Strauss, M., Tillitt, D.E., Lipton, J., and Dean, K.M., 2018, Embryonic effects of an environmentally relevant PCB mixture in the domestic chicken: Environmental Toxicology and Chemistry, v. 37, no. 10, p. 2513-2522, https://doi.org/10.1002/etc.4218.","productDescription":"10 p.","startPage":"2513","endPage":"2522","ipdsId":"IP-090575","costCenters":[{"id":192,"text":"Columbia Environmental Research Center","active":true,"usgs":true}],"links":[{"id":358133,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"volume":"37","issue":"10","publishingServiceCenter":{"id":4,"text":"Rolla PSC"},"noUsgsAuthors":false,"publicationDate":"2018-06-27","publicationStatus":"PW","scienceBaseUri":"5bc02f7be4b0fc368eb53853","contributors":{"authors":[{"text":"Ottinger, Mary Ann","contributorId":26422,"corporation":false,"usgs":false,"family":"Ottinger","given":"Mary","email":"","middleInitial":"Ann","affiliations":[{"id":7083,"text":"University of Maryland","active":true,"usgs":false}],"preferred":false,"id":747297,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Lavoie, Emma T.","contributorId":208444,"corporation":false,"usgs":false,"family":"Lavoie","given":"Emma","email":"","middleInitial":"T.","affiliations":[{"id":37802,"text":"Environmental Protection Agency, Washington, DC","active":true,"usgs":false}],"preferred":false,"id":747298,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Bohannon, Mary E. B.","contributorId":208445,"corporation":false,"usgs":false,"family":"Bohannon","given":"Mary","email":"","middleInitial":"E. B.","affiliations":[{"id":37802,"text":"Environmental Protection Agency, Washington, DC","active":true,"usgs":false}],"preferred":false,"id":747299,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Marcel, Allegra M.","contributorId":208446,"corporation":false,"usgs":false,"family":"Marcel","given":"Allegra","email":"","middleInitial":"M.","affiliations":[],"preferred":false,"id":747300,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Tschiffely, Anna E.","contributorId":208447,"corporation":false,"usgs":false,"family":"Tschiffely","given":"Anna","email":"","middleInitial":"E.","affiliations":[],"preferred":false,"id":747301,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"Duffy, Kara B.","contributorId":208448,"corporation":false,"usgs":false,"family":"Duffy","given":"Kara","email":"","middleInitial":"B.","affiliations":[],"preferred":false,"id":747302,"contributorType":{"id":1,"text":"Authors"},"rank":6},{"text":"McKernan, Moira A.","contributorId":33038,"corporation":false,"usgs":true,"family":"McKernan","given":"Moira","email":"","middleInitial":"A.","affiliations":[],"preferred":false,"id":747303,"contributorType":{"id":1,"text":"Authors"},"rank":7},{"text":"Thompson, Nichola","contributorId":208478,"corporation":false,"usgs":false,"family":"Thompson","given":"Nichola","email":"","affiliations":[],"preferred":false,"id":747304,"contributorType":{"id":1,"text":"Authors"},"rank":8},{"text":"Whitehouse, H. Kasen","contributorId":208450,"corporation":false,"usgs":false,"family":"Whitehouse","given":"H.","email":"","middleInitial":"Kasen","affiliations":[],"preferred":false,"id":747305,"contributorType":{"id":1,"text":"Authors"},"rank":9},{"text":"Davani, Kimya","contributorId":208451,"corporation":false,"usgs":false,"family":"Davani","given":"Kimya","email":"","affiliations":[],"preferred":false,"id":747306,"contributorType":{"id":1,"text":"Authors"},"rank":10},{"text":"Strauss, Marci","contributorId":208452,"corporation":false,"usgs":false,"family":"Strauss","given":"Marci","email":"","affiliations":[],"preferred":false,"id":747307,"contributorType":{"id":1,"text":"Authors"},"rank":11},{"text":"Tillitt, Donald E. 0000-0002-8278-3955 dtillitt@usgs.gov","orcid":"https://orcid.org/0000-0002-8278-3955","contributorId":1875,"corporation":false,"usgs":true,"family":"Tillitt","given":"Donald","email":"dtillitt@usgs.gov","middleInitial":"E.","affiliations":[{"id":192,"text":"Columbia Environmental Research Center","active":true,"usgs":true}],"preferred":true,"id":747296,"contributorType":{"id":1,"text":"Authors"},"rank":12},{"text":"Lipton, Joshua","contributorId":172780,"corporation":false,"usgs":false,"family":"Lipton","given":"Joshua","email":"","affiliations":[],"preferred":false,"id":747308,"contributorType":{"id":1,"text":"Authors"},"rank":13},{"text":"Dean, Karen M.","contributorId":201896,"corporation":false,"usgs":false,"family":"Dean","given":"Karen","email":"","middleInitial":"M.","affiliations":[{"id":36281,"text":"Abt Associates, Boulder, CO","active":true,"usgs":false}],"preferred":false,"id":747309,"contributorType":{"id":1,"text":"Authors"},"rank":14}]}} ,{"id":70199929,"text":"70199929 - 2018 - Wrangling distributed computing for high-throughput environmental science: An introduction to HTCondor","interactions":[],"lastModifiedDate":"2018-10-04T10:35:36","indexId":"70199929","displayToPublicDate":"2018-10-04T10:35:26","publicationYear":"2018","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":5727,"text":"PLOS Computational Biology","active":true,"publicationSubtype":{"id":10}},"title":"Wrangling distributed computing for high-throughput environmental science: An introduction to HTCondor","docAbstract":"Biologists and environmental scientists now routinely solve computational problems that were unimaginable a generation ago. Examples include processing geospatial data, analyzing -omics data, and running large-scale simulations. Conventional desktop computing cannot handle these tasks when they are large, and high-performance computing is not always available nor the most appropriate solution for all computationally intense problems. High-throughput computing (HTC) is one method for handling computationally intense research. In contrast to high-performance computing, which uses a single \"supercomputer,\" HTC can distribute tasks over many computers (e.g., idle desktop computers, dedicated servers, or cloud-based resources). HTC facilities exist at many academic and government institutes and are relatively easy to create from commodity hardware. Additionally, consortia such as Open Science Grid facilitate HTC, and commercial entities sell cloud-based solutions for researchers who lack HTC at their institution. We provide an introduction to HTC for biologists and environmental scientists. Our examples from biology and the environmental sciences use HTCondor, an open source HTC system.
","language":"English","publisher":"PLOS","doi":"10.1371/journal.pcbi.1006468","usgsCitation":"Erickson, R.A., Fienen, M.N., McCalla, S.G., Weiser, E.L., Bower, M.L., Knudson, J.M., and Thain, G., 2018, Wrangling distributed computing for high-throughput environmental science: An introduction to HTCondor: PLOS Computational Biology, v. 14, no. 10, p. 1-8, https://doi.org/10.1371/journal.pcbi.1006468.","productDescription":"e1006468; 8 p.","startPage":"1","endPage":"8","ipdsId":"IP-087169","costCenters":[{"id":606,"text":"Upper Midwest Environmental Sciences Center","active":true,"usgs":true}],"links":[{"id":468338,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.1371/journal.pcbi.1006468","text":"Publisher Index Page"},{"id":358128,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"volume":"14","issue":"10","publishingServiceCenter":{"id":15,"text":"Madison PSC"},"noUsgsAuthors":false,"publicationDate":"2018-10-03","publicationStatus":"PW","scienceBaseUri":"5bc02f7ce4b0fc368eb53859","contributors":{"authors":[{"text":"Erickson, Richard A. 0000-0003-4649-482X rerickson@usgs.gov","orcid":"https://orcid.org/0000-0003-4649-482X","contributorId":5455,"corporation":false,"usgs":true,"family":"Erickson","given":"Richard","email":"rerickson@usgs.gov","middleInitial":"A.","affiliations":[{"id":606,"text":"Upper Midwest Environmental Sciences Center","active":true,"usgs":true}],"preferred":true,"id":747336,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Fienen, Michael N. 0000-0002-7756-4651 mnfienen@usgs.gov","orcid":"https://orcid.org/0000-0002-7756-4651","contributorId":171511,"corporation":false,"usgs":true,"family":"Fienen","given":"Michael","email":"mnfienen@usgs.gov","middleInitial":"N.","affiliations":[{"id":37947,"text":"Upper Midwest Water Science Center","active":true,"usgs":true}],"preferred":true,"id":747337,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"McCalla, S. Grace 0000-0003-4292-8694 smccalla@usgs.gov","orcid":"https://orcid.org/0000-0003-4292-8694","contributorId":168436,"corporation":false,"usgs":true,"family":"McCalla","given":"S.","email":"smccalla@usgs.gov","middleInitial":"Grace","affiliations":[{"id":606,"text":"Upper Midwest Environmental Sciences Center","active":true,"usgs":true}],"preferred":true,"id":747338,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Weiser, Emily L. 0000-0003-1598-659X","orcid":"https://orcid.org/0000-0003-1598-659X","contributorId":206605,"corporation":false,"usgs":true,"family":"Weiser","given":"Emily","email":"","middleInitial":"L.","affiliations":[{"id":65299,"text":"Alaska Science Center Ecosystems","active":true,"usgs":true}],"preferred":true,"id":747335,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Bower, Melvin L. 0000-0002-4408-3771","orcid":"https://orcid.org/0000-0002-4408-3771","contributorId":208457,"corporation":false,"usgs":true,"family":"Bower","given":"Melvin","email":"","middleInitial":"L.","affiliations":[{"id":606,"text":"Upper Midwest Environmental Sciences Center","active":true,"usgs":true}],"preferred":true,"id":747339,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"Knudson, Jonathan M. 0000-0003-4985-988X","orcid":"https://orcid.org/0000-0003-4985-988X","contributorId":208458,"corporation":false,"usgs":true,"family":"Knudson","given":"Jonathan","email":"","middleInitial":"M.","affiliations":[{"id":606,"text":"Upper Midwest Environmental Sciences Center","active":true,"usgs":true}],"preferred":true,"id":747340,"contributorType":{"id":1,"text":"Authors"},"rank":6},{"text":"Thain, Greg","contributorId":208459,"corporation":false,"usgs":false,"family":"Thain","given":"Greg","email":"","affiliations":[{"id":16925,"text":"University of Wisconsin-Madison","active":true,"usgs":false}],"preferred":false,"id":747341,"contributorType":{"id":1,"text":"Authors"},"rank":7}]}} ,{"id":70199930,"text":"70199930 - 2018 - Regional patterns in the geochemistry of oil-field water, southern San Joaquin Valley, California, USA","interactions":[],"lastModifiedDate":"2018-10-04T10:31:11","indexId":"70199930","displayToPublicDate":"2018-10-04T10:31:04","publicationYear":"2018","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":835,"text":"Applied Geochemistry","active":true,"publicationSubtype":{"id":10}},"title":"Regional patterns in the geochemistry of oil-field water, southern San Joaquin Valley, California, USA","docAbstract":"Chemical and isotopic data for water co-extracted with hydrocarbons in oil and gas fields are commonly used to examine the source of the formation water and possible impacts on groundwater in areas of oil and gas development. Understanding the geochemical variability of oil-field water could help to evaluate its origin and delineate possible contamination of shallow aquifers in cases where oil-field water is released to the environment. Here we report geochemical and multiple isotope (H, C, O, Sr, Ra) data from 22 oil wells, three sources of produced water that are disposed of in injection wells, and two surface disposal ponds in four oil fields in the southern San Joaquin Valley, California (Fruitvale, Lost Hills, North and South Belridge). Correlations between Cl and δ18O, as well as other ions, and gradual increases in salinity with depth, indicate dilution of one or more saline end-members by meteoric water. The saline end-members, represented by deep samples (610 m–2621 m) in three oil-bearing zones, are characterized by NaCl composition, near-seawater Cl concentrations (median 20,000 mg/L), enriched δ18O
H2O (median 3.4‰), high ammonium(up to 460 mg-N/L), and relatively high radium activity (226Ra+228Ra = 12.3 Bq/L). The deepest sample has low Na/Cl (0.74), high Ca/Mg (5.0), and low 87Sr/86Sr (0.7063), whereas the shallower samples have higher Na/Cl (0.86–1.2), Ca/Mg near 1, and higher 87Sr/86Sr (∼0.7083). The data are consistent with an original seawater source being modified by various depth and lithology dependent diagenetic processes. Dilution by meteoric water occurs naturally on the east side of the valley, and in association with water-injectionactivities on the west side. Meteoric-water flushing, particularly on the east side, results in lower solute concentrations (minimum total dissolved solids 2730 mg/L) and total radium (minimum 0.27 Bq/L) in oil-field water, and promotes biodegradation of dissolved organic carbon and hydrocarbon gases like propane. Acetate concentrations and δ13C of dissolved inorganic carbon indicate biogenic methane production occurs in some shallow oil zones. Natural and human processes produce substantial variability in the geochemistry of oil-field water that should be considered when evaluating mixing between oil-field waters and groundwater. The variability could result in uncertainty as to detecting the potential source and impact of oil-field water on groundwater.
The Fuels Guide and Database (FGD) is intended to provide fuel loading and vegetation information for big sagebrush (Artemisia tridentata) ecological sites in the Morley Nelson Snake River Birds of Prey National Conservation Area (hereinafter the NCA) in southern Idaho. Sagebrush ecosystems in the NCA and throughout much of the Great Basin are highly influenced by non-native plants that alter successional trajectories and promote frequent wildfires, especially due to fine-fuel loadings that are highly variable over time and space. These dynamic fuel conditions can increase uncertainty when attempting to project fire risk and fire behavior. The FGD was developed to help quantify and assess these dynamic fuel loadings, and it provides access to fuels data across a range of conditions, from relatively intact sagebrush-bunchgrass communities to degraded communities dominated by nonnative annual grasses and forbs. The FGD can be queried for a variety of environmental conditions, and it provides tabular data, reports, and photographic records of fuels based on user queries. This report describes the FGD, including overall data content and data-collection methods, as well as instructions for installing and using the database.
","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/ds1048","collaboration":"Prepared in cooperation with the Joint Fire Science Program","usgsCitation":"Shinneman, D.J., Welty, J.L., Arkle, R.S., Pilliod, D.S., Glenn, N.F., McIlroy, S.K., and Halford, A.S., 2018, Fuels guide and database for intact and invaded big sagebrush (Artemisia tridentata) ecological sites—User manual: U.S. Geological Survey Data Series 1048, 9 p., https://doi.org/10.3133/ds1048.","productDescription":"Report: iv, 9 p.; Data release","onlineOnly":"Y","ipdsId":"IP-083880","costCenters":[{"id":290,"text":"Forest and Rangeland Ecosystem Science Center","active":false,"usgs":true}],"links":[{"id":358027,"rank":2,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/ds/1048/ds1048.pdf","text":"Report","size":"2.1 MB","linkFileType":{"id":1,"text":"pdf"},"description":"DS 1048"},{"id":358026,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/ds/1048/coverthb.jpg"},{"id":358028,"rank":3,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/F7PC31P4","text":"USGS data release","description":"USGS Data Release","linkHelpText":"Fuels database for intact and invaded big sagebrush (Artemisia tridentata) ecological sites"}],"contact":"Director, Forest and Rangeland Ecosystem Science Center
U.S. Geological Survey
777 NW 9th St., Suite 400
Corvallis, Oregon 97330
Floods in the Mississippi basin can have large negative societal, natural, and economic impacts. Understanding the drivers of floods, now and in the future, is relevant for risk management and infrastructure-planning purposes. We investigate the drivers of 100-yr-return lower Mississippi River floods using a global coupled climate model with an integrated surface water module. The model provides 3400 years of physically consistent data from a static climate, in contrast to available observational data (relatively short records, incomplete land surface data, transient climate). In the months preceding the model’s 100-yr floods, as indicated by extreme monthly discharge, above-average rain and snowfall lead to moist subsurface conditions and the buildup of snowpack, making the river system prone to these major flooding events. The meltwater from snowpack in the northern Missouri and upper Mississippi catchments primes the river system, sensitizing it to subsequent above-average precipitation in the Ohio and Tennessee catchments. An ensemble of transient forcing experiments is used to investigate the impacts of past and projected anthropogenic climate change on extreme floods. There is no statistically significant projected trend in the occurrence of 100-yr floods in the model ensemble, despite significant increases in extreme precipitation, significant decreases in extreme snowmelt, and significant decreases in less extreme floods. The results emphasize the importance of considering the fully coupled land–atmosphere system for extreme floods. This initial analysis provides avenues for further investigation, including comparison to characteristics of less extreme floods, the sensitivity to model configuration, the role of human water management, and implications for future flood-risk management.
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