This report provides preliminary estimates of annual agricultural use of 387 pesticide compounds in counties of the conterminous United States in 2013, compiled by means of methods described in Thelin and Stone (2013) and Baker and Stone (2015). U.S. Department of Agriculture county-level data for harvested-crop acreage were used in conjunction with proprietary Crop Reporting District-level pesticide-use data to estimate county-level pesticide use. Where Crop Reporting District data were not available or were incomplete, estimated pesticide use values were calculated with two different methods, resulting in a low and a high estimate based on different assumptions about missing survey data (Thelin and Stone, 2013). Preliminary estimates in this report are expected to be revised upon availability of updated crop acreages in the 2017 Agricultural Census, to be published by the U.S. Department of Agriculture in 2019. Estimates of annual agricultural pesticide use are provided as downloadable, tab-delimited files, which are organized by compound, year, state Federal Information Processing Standard (FIPS) code, county Federal Information Processing Standard code, and amount in kilograms (kg). The files, listed below, are a continuation of the 1992–2009 and 2008–2012 pesticide-use estimates reported by Stone (2013) and Baker and Stone (2015), respectively:
\nHigh estimates of county pesticide use, arranged by pesticide name:
\nTable 1. 1-Methyl Cyclopropene through Chlorantraniliprole
Table 2. Chlorethoxyfos through Diflufenzopyr
Table 3. Dimethenamid through Gibberellic Acid
Table 4. Glufosinate through Metiram
Table 5. Metolachlor through Propazine
Table 6. Propiconazole through Triasulfuron
Table 7. Tribenuron Methyl through Zoxamide
Low estimates of county pesticide use, arranged by pesticide name:
\nTable 8. 1-Methyl Cyclopropene through Chlorantraniliprole
Table 9. Chlorethoxyfos through Diflufenzopyr
Table 10. Dimethenamid through Gibberellic Acid
Table 11. Glufosinate through Metiram
Table 12. Metolachlor through Propazine
Table 13. Propiconazole through Triasulfuron
Table 14. Tribenuron Methyl through Zoxamide
Baker, N.T., and Stone, W.W., 2015, Estimated annual agricultural pesticide use for counties of the conterminous United States, 2008–12: U.S. Geological Survey Data Series 907, 9 p., accessed July, 12, 2015, at http://dx.doi.org/10.3133/ds907.
\nStone, W.W., 2013, Estimated annual agricultural pesticide use for counties of the conterminous United States, 1992–2009: U.S. Geological Survey Data Series 752, 1 p., 14 tables
\nThelin, G.P., and Stone, W.W., 2013, Estimation of annual agricultural pesticide use for counties of the conterminous United States,
1992–2009: U.S. Geological Survey Scientific Investigations Report 2013–5009, 54 p.
Director, Indiana Water Science Center
5957 Lakeside Blvd.
Indianapolis, IN 46278
http://in.water.usgs.gov/
Introduction
\nPesticide-Use Estimates
\nAcknowledgments
\nData from a long-term capture-recapture program were used to assess the status and dynamics of populations of two long-lived, federally endangered catostomids in Upper Klamath Lake, Oregon. Lost River suckers (Deltistes luxatus) and shortnose suckers (Chasmistes brevirostris) have been captured and tagged with passive integrated transponder (PIT) tags during their spawning migrations in each year since 1995. In addition, beginning in 2005, individuals that had been previously PIT-tagged were re-encountered on remote underwater antennas deployed throughout sucker spawning areas. Captures and remote encounters during the spawning season in spring 2014 were incorporated into capture-recapture analyses of population dynamics.
\nCormack-Jolly-Seber (CJS) open population capture-recapture models were used to estimate annual survival probabilities, and a reverse-time analog of the CJS model was used to estimate recruitment of new individuals into the spawning populations. In addition, data on the size composition of captured fish were examined to provide corroborating evidence of recruitment. Model estimates of survival and recruitment were used to derive estimates of changes in population size over time and to determine the status of the populations through 2013. Separate analyses were conducted for each species and also for each subpopulation of Lost River suckers (LRS). Shortnose suckers (SNS) and one subpopulation of LRS migrate into tributary rivers to spawn, whereas the other LRS subpopulation spawns at groundwater upwelling areas along the eastern shoreline of the lake.
\nIn 2014, we captured, tagged, and released 496 LRS at four lakeshore spawning areas and recaptured an additional 970 individuals that had been tagged in previous years. Across all four areas, the remote antennas detected 6,370 individual LRS during the spawning season. Spawning activity peaked in April and most individuals were encountered at Cinder Flats and Sucker Springs. In the Williamson River, we captured, tagged, and released 3,038 LRS and 267 SNS, and recaptured 762 LRS and 156 SNS that had been tagged in previous years. Remote PIT tag antennas in the traps at the weir on the Williamson River and remote antenna systems that spanned the river at three different locations on the Williamson and Sprague Rivers detected a total of 23,446 LRS and 6,259 SNS. Most LRS passed upstream in the first and second weeks of April when water temperatures were increasing and greater than 10 °C. In contrast, upstream passage for SNS occurred in two pulses, one in early April and one in late April to early May, when water temperatures were increasing and near or greater than 12 °C. Finally, an additional 375 LRS and 884 SNS were captured in trammel net sampling at pre-spawn staging areas in the northeastern part of the lake. Of these, 111 of the LRS and 390 of the SNS had been PIT-tagged in previous years. For LRS captured at the staging areas that had encounter histories that were informative about their spawning location, 79 percent of the fish were members of the subpopulation that spawns in the rivers.
\nCapture-recapture analyses for the LRS subpopulation that spawns at the shoreline areas included encounter histories for more than 13,200 individuals, and analyses for the subpopulation that spawns in the rivers included more than 36,400 encounter histories. With a few exceptions, the survival of males and females in both subpopulations was high (greater than 0.88) between 1999 and 2012. Notably lower survival occurred for both sexes from the rivers in 2000, for males from the shoreline areas in 2002, and for males from the rivers in 2006 and 2012. Between 2001 and 2013, the abundance of males in the lakeshore spawning subpopulation decreased by at least 55 percent and the abundance of females decreased by at least 42 percent. Capture-recapture models suggested that the abundance of both sexes in the river spawning subpopulation of LRS had increased substantially since 2006; increases were mostly due to large estimated recruitment events in 2006 and 2008. We know that the estimates in 2006 are substantially biased in favor of recruitment because of a sampling issue. We are skeptical of the magnitude of recruitment indicated by the 2008 estimates as well because (1) few small individuals that would indicate the presence of new recruits were captured in that year, and (2) recapture probabilities in recruitment models based on just physical recaptures of fish were lower than desired for robust inferences from capture-recapture models. If we assume instead that little or no recruitment occurred for this subpopulation, the abundance of both sexes in the river spawning subpopulation likely has decreased at rates similar to the rates for the lakeshore spawning subpopulation between 2002 and 2013.
\nCapture-recapture analyses for SNS included encounter histories for more than 19,200 individuals. Most annual survival estimates between 2001 and 2012 were high (greater than 0.80), but SNS experienced more years of low survival than either LRS subpopulation. Annual survival of both sexes was relatively low in 2004, 2010, and 2012. In addition, male survival was low in 2002. Capture-recapture models and size composition data indicate that recruitment of new individuals into the SNS spawning population was trivial between 2001 and 2005. Models indicate that more than 10 percent of the population was new recruits in a number of more recent years. As a result, capture-recapture modeling suggests that the abundance of adult spawning SNS was relatively stable between 2006 and 2010. We are skeptical of the estimated recruitment in 2006 because of the known sampling issue. We also are skeptical of the estimated recruitment in other recent years because few small individuals that would indicate the presence of new recruits were captured in any of those years, and recapture probabilities in recruitment models were low. The best-case scenario for SNS, based on capture-recapture recruitment modeling, indicates that the abundance of males in the spawning population decreased by 77 percent and the abundance of females decreased by 73 percent between 2001 and 2013. Decreases in abundance for both sexes likely are greater than these estimates indicate.
\nDespite relatively high survival in most years, we conclude that both species have experienced substantial decreases in the abundance of spawning adults because losses from mortality have not been balanced by recruitment of new individuals. Although capture-recapture data indicate substantial recruitment of new individuals into the spawning populations for SNS and river spawning LRS in some years, size data do not corroborate these estimates. As a result, the status of the endangered sucker populations in Upper Klamath Lake remains worrisome, especially for shortnose suckers. Our monitoring program provides a robust platform for estimating vital population parameters, evaluating the status of the populations, and assessing the effectiveness of conservation and recovery efforts.
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Three aeromagnetic surveys were flown to improve understanding of the geology and structure in the Sacramento Valley. The resulting data serve as a basis for geophysical interpretations, and support geological mapping, water and mineral resource investigations, and other topical studies. Local spatial variations in the Earth's magnetic field (evident as anomalies on aeromagnetic maps) reflect the distribution of magnetic minerals, primarily magnetite, in the underlying rocks. In many cases the volume content of magnetic minerals can be related to rock type, and abrupt spatial changes in the amount of magnetic minerals commonly mark lithologic or structural boundaries. Bodies of serpentinite and other mafic and ultramafic rocks tend to produce the most intense positive magnetic anomalies (for example, in the northwest part of the map). These rock types are the inferred sources, concealed beneath weakly magnetic, valley-fill deposits, of the most prominent magnetic features in the map area, the magnetic highs that extend along the valley axis. Cenozoic volcanic rocks are also an important source of magnetic anomalies and coincide with short-wavelength anomalies that can be either positive (strong central positive anomaly flanked by lower-amplitude negative anomalies) or negative (strong central negative anomaly flanked by lower-amplitude positive anomalies), reflecting the contribution of remanent magnetization. Rocks with more felsic compositions or even some sedimentary units also can cause measurable magnetic anomalies. For example, the long, linear, narrow north-trending anomalies (with amplitudes of <50 nanoteslas [nT]) along the western margin of the valley coincide with exposures of the Mesozoic Great Valley sequence. Note that isolated, short-wavelength anomalies, such as those in the city of Sacramento and along some of the major roads, are caused by manmade features.
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","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/ofr20151184","usgsCitation":"Langseth, M.L., Chang, M.Y., Carlino, Jennifer, Birch, D.D., Bradley, Joshua, Bristol, R.S., Conzelmann, Craig, Diehl, R.H., Earle, Paul, Ellison, L.E., Everette, A.L., Fuller, Pam, Gordon, J.M., Govoni, D.L., Guy, M.R., Henkel, H.S., Hutchison, V.B., Kern, Tim, Lightsom, F.L., Long, J.W., Longhenry, Ryan, Preston, T.M., Smith, Stan, Viger, R.J., Wesenberg, Katherine, and Wood, Eric, 2015, Community for Data Integration 2014 annual report: U.S. Geological Survey Open-File Report 2015–1184, 40 p., https://dx.doi.org/10.3133/ofr20151184.","productDescription":"vi, 40 p.","numberOfPages":"46","onlineOnly":"Y","additionalOnlineFiles":"N","ipdsId":"IP-066330","costCenters":[{"id":222,"text":"Earth Resources Observation and Science (EROS) Center","active":true,"usgs":true},{"id":17705,"text":"Wetland and Aquatic Research Center","active":true,"usgs":true},{"id":37226,"text":"Core Science Analytics, Synthesis, and Libraries","active":true,"usgs":true}],"links":[{"id":309412,"rank":2,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/of/2015/1184/ofr20151184.pdf","text":"Report","size":"7.63 MB","linkFileType":{"id":1,"text":"pdf"},"description":"OFR 2015-1184"},{"id":309411,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/of/2015/1184/coverthb.jpg"}],"contact":"Director, Core Science Analytics and Synthesis
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Reston, VA 20192
http://www.usgs.gov/core_science_systems/
Giant gartersnakes (Thamnophis gigas) comprise a species of rare, semi-aquatic snake precinctive to the Central Valley of California. Because of the loss of more than 90% of their natural habitat, giant gartersnakes are listed as Threatened by the United States and California endangered species acts. Little is known, however, about the distribution of giant gartersnakes in the Sacramento Valley, which is where most extant populations occur. We conducted detection-nondetection surveys for giant gartersnakes throughout the rice-growing regions of the Sacramento Valley, and used occupancy models to examine evidence for the effects of landscape-scale GIS-derived variables, local habitat and vegetation composition, and prey communities on patterns of giant gartersnake occurrence. Although our results are based on a relatively small sample of sites, we found that distance to historic marsh, relative fish count, and an interaction of distance to historic marsh with proportion of habitat composed of submerged vegetation were important variables for explaining occupancy of giant gartersnakes. In particular, giant gartersnakes were more likely to occur closer to historic marsh and where relatively fewer fish were captured in traps. At locations in or near historic marsh, giant gartersnakes were more likely to occur in areas with less submerged vegetation, but this relationship was reversed (and more uncertain) at sites distant from historic marsh. Additional research with a larger sample of sites would further elucidate the distribution of giant gartersnakes in the Sacramento Valley.
","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/ofr20151178","collaboration":"Prepared in cooperation with the California Department of Water Resources","usgsCitation":"Halstead, B.J., Skalos, S.M., Casazza, M.L., and Wylie, G.D., 2015, A preliminary investigation of the variables affecting the distribution of giant gartersnakes (Thamnophis gigas) in the Sacramento Valley, California: U.S. Geological Survey Open-File Report 2015-1178, 34 p., https://dx.doi.org/10.3133/ofr20151178.","productDescription":"vi, 34 p.","numberOfPages":"44","onlineOnly":"Y","additionalOnlineFiles":"N","temporalStart":"2011-01-01","temporalEnd":"2012-12-31","ipdsId":"IP-066320","costCenters":[{"id":651,"text":"Western Ecological Research Center","active":true,"usgs":true}],"links":[{"id":309380,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/of/2015/1178/coverthb.jpg"},{"id":309381,"rank":2,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/of/2015/1178/ofr20151178.pdf","text":"Report","size":"4.3 MB","linkFileType":{"id":1,"text":"pdf"},"description":"OFR 2015-1178 PDF"}],"country":"United States","state":"California","otherGeospatial":"Sacramento Valley","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -122.72277832031251,\n 38.25112269630296\n ],\n [\n -122.72277832031251,\n 40.28371627054261\n ],\n [\n -120.91003417968749,\n 40.28371627054261\n ],\n [\n -120.91003417968749,\n 38.25112269630296\n ],\n [\n -122.72277832031251,\n 38.25112269630296\n ]\n ]\n ]\n }\n }\n ]\n}","contact":"Director, Western Ecological Research Center
U.S. Geological Survey
3020 State University Drive East
Sacramento, California 95819
http://www.werc.usgs.gov/
A secretarial order identified climate adaptation as a critical performance objective for future management of U.S. Department of the Interior (DOI) lands and resources in response to global change. Vulnerability assessments can inform climate adaptation planning by providing insight into what natural resources are most at risk and why. Three components of vulnerability—exposure, sensitivity, and adaptive capacity—were defined by the Intergovernmental Panel on Climate Change (IPCC) as necessary for identifying climate adaptation strategies and actions. In 2011, the DOI requested all internal bureaus report ongoing or completed vulnerability assessments about a defined range of assessment targets or climate-related threats. Assessment targets were defined as freshwater resources, landscapes and wildlife habitat, native and cultural resources, and ocean health. Climate-related threats were defined as invasive species, wildfire risk, sea-level rise, and melting ice and permafrost. Four hundred and three projects were reported, but the original DOI survey did not specify that information be provided on exposure, sensitivity, and adaptive capacity collectively as part of the request, and it was unclear which projects adhered to the framework recommended by the IPCC. Therefore, the U.S. Geological Survey National Climate Change and Wildlife Science Center conducted a supplemental survey to determine how frequently each of the three vulnerability components was assessed. Information was categorized for 124 of the 403 reported projects (30.8 percent) based on the three vulnerability components, and it was discovered that exposure was the most common component assessed (87.9 percent), followed by sensitivity (68.5 percent) and adaptive capacity (33.1 percent). The majority of projects did not fully assess vulnerability; projects focused on landscapes/wildlife habitats and sea-level rise were among the minority that simultaneously addressed all three vulnerability components. To maintain consistency with the IPCC definition of vulnerability, DOI may want to focus initial climate adaptation planning only on the outcomes of studies that comprehensively address vulnerability as inclusive of exposure, sensitivity, and adaptive capacity. Although the present study results are preliminary and used an unstructured survey design, they illustrate the importance of a comprehensive and consistent vulnerability definition and of using information on vulnerability components in DOI surveys to ensure relevant data are used to identify adaptation options.
","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/ofr20151110","usgsCitation":"Thompson, L.M., Staudinger, M.D., and Carter, S.L., 2015, Summarizing components of U.S. Department of the Interior vulnerability assessments to focus climate adaptation planning: U.S. Geological Survey Open-File Report 2015–1110, 14 p., https://dx.doi.org/10.3133/ofr20151110.","productDescription":"iii, 14 p.","numberOfPages":"17","onlineOnly":"Y","additionalOnlineFiles":"N","ipdsId":"IP-053843","costCenters":[{"id":411,"text":"National Climate Change and Wildlife Science Center","active":true,"usgs":true},{"id":36940,"text":"National Climate Adaptation Science Center","active":true,"usgs":true}],"links":[{"id":308292,"rank":2,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/of/2015/1110/ofr20151110.pdf","text":"Report","size":"298 KB","linkFileType":{"id":1,"text":"pdf"},"description":"OFR 2015-1110"},{"id":308291,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/of/2015/1110/coverthb.jpg"}],"contact":"National Climate Change and Wildlife Science Center
U.S. Geological Survey
12201 Sunrise Valley Drive
Reston, VA 20192
https://nccwsc.usgs.gov/
The 3D Elevation Program (3DEP) goal is to acquire, manage, and distribute enhanced three-dimensional elevation data for the Nation and U.S. territories by 2023. This status report covers implementation activities during 2013–2014 to include meeting funding objectives, developing a management structure, modernizing systems, and collecting and producing initial 3DEP data and products. The Nation will not have complete coverage of 3DEP quality data until 2023 assuming that sufficient funding is available. In spite of the overall condition of government budgets, the 3DEP initiative has gained widespread support and had incremental budget success to include supplemental funding resulting from natural disasters. The 3DEP Executive Forum and a wide range of professional organizations are actively working to maintain support for the program. The systems that have been developed to support increasing acquisition and processing levels are largely in place. The first 3DEP quality datasets were released to the public in late 2014. In addition, light detection and ranging (lidar), interferometric synthetic aperture radar (ifsar), and digital elevation models (DEMs) acquired before 2014 are all supported within the new infrastructure and available for download. Research is ongoing to expand the suite of products and services, and to increase overall throughput and data management efficiency. Emerging technologies may result in lower acquisition costs in the future. Elevation data acquired by 3DEP partnerships will be available through The National Map representing one of the largest and most comprehensive databases publicly available for the United States.
","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/ofr20151161","usgsCitation":"Lukas, Vicki, Eldridge, D.F., Jason, A.L., Saghy, D.L., Steigerwald, P.R., Stoker, J.M., Sugarbaker, L.J., and Thunen, D.R., 2015, Status report for the 3D Elevation Program, 2013–2014: U.S. Geological Survey Open-File Report 2015–1161, 17 p., https://dx.doi.org/10.3133/ofr20151161.","productDescription":"iv, 17 p.","numberOfPages":"22","onlineOnly":"Y","additionalOnlineFiles":"Y","ipdsId":"IP-066538","costCenters":[{"id":423,"text":"National Geospatial Program","active":true,"usgs":true}],"links":[{"id":333334,"rank":3,"type":{"id":22,"text":"Related Work"},"url":"https://pubs.usgs.gov/publication/ofr20161196","text":"Open-File Report 2016–1196 - ","linkHelpText":"Status Report for the 3D Elevation Program, 2015"},{"id":308532,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/of/2015/1161/coverthb.jpg"},{"id":308533,"rank":2,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/of/2015/1161/ofr20151161.pdf","text":"Report","size":"1.49 MB","linkFileType":{"id":1,"text":"pdf"},"description":"OFR 2015-1161"}],"contact":"Director, National Geospatial Program
U.S. Geological Survey
12201 Sunrise Valley Drive
511 National Center
Reston, VA 20192
Email: 3dep@usgs.gov
http://www.usgs.gov/ngpo/
http://nationalmap.gov/3DEP/
Evaporation from April 2005 through October 2007 at two central Florida lakes, one close to the Gulf of Mexico and one in the center of the peninsula, was 4.043 and 4.111 meters (m), respectively; evaporation for 2006 was 1.534 and 1.538 m, respectively. Although annual evaporation rates at the two lakes were similar, there were monthly differences between the two lakes because of changes in stored heat; the shallower Lake Calm (mean depth 3 m) stored less heat and exchanged heat more rapidly than the deeper Lake Starr (mean depth 5 m).
\nBoth lakes are seepage lakes (no surface-water inflow or outflows) that are dependent on groundwater inflow from their basins to offset an atmospheric deficit, because long-term rainfall in this area is less than evaporation. The Lake Starr basin, where sandy, well-drained ridges surround the lake, has a greater capacity to store infiltrating rain than the Lake Calm basin, which is flat and has poorly drained soils. The storage capacities of the basins affect groundwater exchange with the lakes. Rainfall and net groundwater exchange, which is related to basin characteristics, varied more between these two lakes than did evaporation during this study.
","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/ofr20151075","collaboration":"Prepared in cooperation with the Southwest Florida Water Management District","usgsCitation":"Swancar, Amy, 2015, Comparison of evaporation at two central Florida lakes, April 2005–November 2007: U.S. Geological Survey Open-File ReportDirector, Caribbean-Florida Water Science Center
4446 Pet Lane, Suite 108
Lutz, FL 33559
(813) 498-5000
Or visit the Caribbean-Florida Water Science Center
fl.water.usgs.gov
Whooping cranes (Grus americana) of the Aransas-Wood Buffalo population migrate twice each year through the Great Plains in North America. Recovery activities for this endangered species include providing adequate places to stop and rest during migration, which are generally referred to as stopover sites. To assist in recovery efforts, initial estimates of stopover site use intensity are presented, which provide opportunity to identify areas across the migration range used more intensively by whooping cranes. We used location data acquired from 58 unique individuals fitted with platform transmitting terminals that collected global position system locations. Radio-tagged birds provided 2,158 stopover sites over 10 migrations and 5 years (2010–14). Using a grid-based approach, we identified 1,095 20-square-kilometer grid cells that contained stopover sites. We categorized occupied grid cells based on density of stopover sites and the amount of time cranes spent in the area. This assessment resulted in four categories of stopover site use: unoccupied, low intensity, core intensity, and extended-use core intensity. Although provisional, this evaluation of stopover site use intensity offers the U.S. Fish and Wildlife Service and partners a tool to identify landscapes that may be of greater conservation significance to migrating whooping cranes. Initially, the tool will be used by the U.S. Fish and Wildlife Service and other interested parties in evaluating the Great Plains Wind Energy Habitat Conservation Plan.
","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/ofr20151166","collaboration":"Prepared in collaboration with the Canadian Wildlife Service, Crane Trust, Platte River Recovery Implementation Program, and U.S. Fish and Wildlife Service","usgsCitation":"Pearse, A.T., Brandt, D.A., Harrell, W.C., Metzger, K.L., Baasch, D.M., and Hefley, T.J., 2015, Whooping crane stopover site use intensity within the Great Plains: U.S. Geological Survey Open-File Report 2015–1166, 12 p., https://dx.doi.org/10.3133/ofr20151166.","productDescription":"v, 12 p.","onlineOnly":"Y","additionalOnlineFiles":"N","temporalStart":"2010-01-01","temporalEnd":"2014-12-31","ipdsId":"IP-066665","costCenters":[{"id":480,"text":"Northern Prairie Wildlife Research Center","active":true,"usgs":true}],"links":[{"id":310152,"rank":3,"type":{"id":28,"text":"Dataset"},"url":"https://www.sciencebase.gov/catalog/item/56253ce5e4b0fb9a11dd3d2b"},{"id":308397,"rank":2,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/of/2015/1166/ofr2015-1166.pdf","text":"Report","size":"2.48 MB","linkFileType":{"id":1,"text":"pdf"},"description":"OF 2015-1166"},{"id":308396,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/of/2015/1166/coverthb.jpg"}],"country":"Canada, United States","state":"Alberta, Iowa, Kansas, Manitoba, Minnesota, Missouri, Montana, Nebraska, North Dakota, Northwest Territories, Oklahoma, Saskatchewan, South Dakota, Texas","otherGeospatial":"Great Plains","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {\n \"stroke\": \"#555555\",\n \"stroke-width\": 2,\n \"stroke-opacity\": 1,\n \"fill\": \"#555555\",\n \"fill-opacity\": 0.5\n },\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -110.478515625,\n 61.4597705702975\n ],\n [\n -119.15771484375,\n 61.44927080076419\n ],\n [\n -118.564453125,\n 58.263287052486035\n ],\n [\n -114.521484375,\n 55.19141243527063\n ],\n [\n -109.9951171875,\n 49.023461463214126\n ],\n [\n -104.04052734375,\n 45.9511496866914\n ],\n [\n -98.997802734375,\n 26.49024045886963\n ],\n [\n -95.6689453125,\n 28.57487404744697\n ],\n [\n -94.9658203125,\n 29.248063243796576\n ],\n [\n -94.5703125,\n 33.687781758439364\n ],\n [\n -94.41650390625,\n 36.491973470593685\n ],\n [\n -93.33984375,\n 40.613952441166596\n ],\n [\n -93.42773437499999,\n 43.48481212891603\n ],\n [\n -96.591796875,\n 45.99696161820381\n ],\n [\n -99.31640625,\n 49.023461463214126\n ],\n [\n -101.0302734375,\n 52.24125614966341\n ],\n [\n -105.22705078125,\n 56.30434864830834\n ],\n [\n -110.478515625,\n 61.4597705702975\n ]\n ]\n ]\n }\n }\n ]\n}","contact":"Director, Northern Prairie Wildlife Research Center
U.S. Geological Survey
8711 37th Street Southeast
Jamestown, North Dakota 58401
http://www.npwrc.usgs.gov/
Beaches serve as a natural buffer between the ocean and inland communities, ecosystems, and natural resources. However, these dynamic environments move and change in response to winds, waves, and currents. During extreme storms, changes to beaches can be great, and the results are sometimes catastrophic. Lives may be lost, communities destroyed, and millions of dollars spent on rebuilding.
\nDuring storms, large waves may erode beaches, and high storm surge may shift the erosive force of the waves higher on the beach. In some cases, the combined effects of waves and surge may cause overwash (when waves and surge overtop the dune, transporting sediment inland) or flooding. Buildings and infrastructure on or near a dune can be undermined during wave attack and subsequent erosion. A number of strong northeast storms—storms with winds tending to blow from the northeast direction—referred to as nor’easters, have hit the mid- and northeast Atlantic coast of the United States in recent years (February 2013 and January 2015). Waves from these storms caused severe erosion, flooding, and undermining of roads in many areas along the northeast Atlantic coast.
\nWaves overtopping a dune can transport water and sand inland, covering roads and blocking evacuation routes or impeding emergency relief. If storm surge inundates barrier island dunes, currents flowing across the island can create a breach, or a new inlet, completely severing evacuation routes.
\nExtreme coastal changes caused by hurricanes or nor’easters may increase the vulnerability of communities both during a storm and to future storms. For example, when sand dunes are substantially eroded, inland structures are exposed to storm surge and waves. On barrier islands, absent or low dunes allow water to flow inland across the island.
","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/ofr20151154","usgsCitation":"Birchler, J.J., Dalyander, P.S., Stockdon, H.F., and Doran, K.S., 2015, National assessment of nor’easter-induced coastal erosion hazards—Mid- and northeast Atlantic coast: U.S. Geological Survey Open-File Report 2015–1154, 34 p., https://dx.doi.org/10.3133/ofr20151154.","productDescription":"Report: vi, 34 p.; Metadata; Spatial Data","numberOfPages":"41","onlineOnly":"Y","additionalOnlineFiles":"Y","ipdsId":"IP-064838","costCenters":[{"id":574,"text":"St. Petersburg Coastal and Marine Science Center","active":true,"usgs":true}],"links":[{"id":308309,"rank":4,"type":{"id":16,"text":"Metadata"},"url":"https://olga.er.usgs.gov/data/NACCH/Noreasters_erosion_hazards_metadata.html","linkFileType":{"id":5,"text":"html"},"description":"OFR 2015-1154","linkHelpText":"Probability Model Outputs: National Assessment of Nor'easter-Induced Coastal Erosion Hazards: Mid- and Northeast Atlantic Coast (Polyline Shapefile)"},{"id":308195,"rank":2,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/of/2015/1154/ofr20151154.pdf","text":"Report","size":"10.4 MB","linkFileType":{"id":1,"text":"pdf"},"description":"OFR 2015-1154"},{"id":308308,"rank":3,"type":{"id":23,"text":"Spatial Data"},"url":"https://olga.er.usgs.gov/data/NACCH/Noreasters_erosion_hazards.zip","text":"Nor'easter Erosion Hazards Data Download","linkFileType":{"id":6,"text":"zip"},"description":"OFR 2015-1154"},{"id":308187,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/of/2015/1154/coverthb.jpg"}],"country":"United States","state":"Delaware, Maine, Maryland, Massachusetts, New Hampshire, New Jersey, New York, North Carolina, Rhode Island, Virginia","otherGeospatial":"Mid-Atlantic Coast, Northeast Atlantic Coast","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -78.99169921875,\n 33.65120829920497\n ],\n [\n -78.99169921875,\n 44.66865287227321\n ],\n [\n -68.18115234375,\n 44.66865287227321\n ],\n [\n -68.18115234375,\n 33.65120829920497\n ],\n [\n -78.99169921875,\n 33.65120829920497\n ]\n ]\n ]\n }\n }\n ]\n}","contact":"Director, St. Petersburg Coastal and Marine Science Center
U.S. Geological Survey
600 Fourth Street South
St. Petersburg, FL 33701
http://marine.usgs.gov/coastalchangehazards/
A detailed inventory of irrigated crop acreage is not available at the level of resolution needed to accurately estimate water use or to project future water demands in many Florida counties. This report provides a detailed digital map and summary of irrigated areas for 2014 within Jackson, Calhoun, and Gadsden Counties in Florida, and Houston County in Alabama. The irrigated areas were delineated using land-use data and orthoimagery that were then field verified between June and November 2014. Selected attribute data were collected for the irrigated areas, including crop type, primary water source, and type of irrigation system. Results of the 2014 study indicate that an estimated 31,608 acres were irrigated in Jackson County during 2014. This estimate includes 25,733 acres of field crops, 1,534 acres of ornamentals and grasses (including pasture), and 420 acres of orchards. Specific irrigated crops include cotton (11,759 acres), peanuts (9,909 acres), field corn (2,444 acres), and 3,235 acres of various vegetable (row) crops. The vegetable acreage includes 1,714 acres of which 857 acres were planted with both a spring and fall crop on the same field (double cropped). Overall, groundwater was used to irrigate 98.6 percent of the total irrigated acreage in Jackson County during 2014, whereas surface water and wastewater were used to irrigate the remaining 1.4 percent.
\nIrrigated cropland totaled 3,060 acres in Calhoun County; 4,547 acres in Gadsden County; and 10,333 acres in Houston County. In Calhoun County, sod accounted for the largest irrigated acreage (1,145 acres) followed by peanuts (886 acres). In Gadsden County, ornamentals accounted for the largest irrigated acreage (1,104 acres) followed by cotton (977 acres). In Houston County, cotton accounted for the largest irrigated acreage (4,310 acres) followed by peanuts (2,493 acres). Overall, an estimated 49,548 acres of land were irrigated during 2014 in the four counties inventoried. About 45,052 acres were irrigated by a center pivot, permanent or solid overhead fixtures, or a portable or traveling gun. In all, 650 center pivot irrigation systems were identified, and the calculated acreage under these pivots totaled 43,070 acres. There were 405 center pivot irrigation systems counted in Jackson County during the 2014 field verification followed by Houston with 197, Gadsden with 48, and Calhoun with 10. An estimated 35,087 acres of field corn, cotton, peanuts, and sorghum were irrigated by center pivot systems during 2014 in these four counties combined. Vegetable acreage for the four counties combined totaled 6,699 acres, with 54 percent being irrigated by a drip irrigation system and the remaining 46 percent irrigated by a center pivot or traveling gun.
\nThe irrigated acreage estimated for Jackson County in 2014 (31,608) is about 47 percent higher than the 2012 estimated acreage published by the USDA (21,508 acres). The estimates of irrigated acreage field verified during 2014 for Calhoun and Gadsden Counties are also higher than those published by the USDA for 2012 (86 percent and 71 percent, respectively). In Calhoun County the USDA reported 1,647 irrigated acres while the current study estimated 3,060 acres, and in Gadsden County the USDA reported 2,650 acres while the current study estimated 4,547 acres. For Houston County the USDA-reported value of 9,138 acres in 2012 was 13 percent below the 10,333 acres field verified in the current study. Differences between the USDA 2012 values and 2014 field verified estimates in these two datasets may occur because (1) irrigated acreage for some specific crops increased or decreased substantially during the 2-year interval due to commodity prices or economic changes, (2) irrigated acreage calculated for the current study may be estimated high because irrigation was assumed if an irrigation system was present and therefore the acreage was counted as irrigated, when in fact that may not have been the case as some farmers may not have used their irrigation systems during this growing period even if they had a crop in the field, or (3) the amount of irrigated acreages published by the USDA for selected crops may be underestimated in some cases.
","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/ofr20151170","collaboration":"Prepared in cooperation with the Florida Department of Agriculture and Consumer Services","usgsCitation":"Marella, R.L., and Dixon, J.F., 2015, Agricultural irrigated land-use inventory for Jackson, Calhoun, and Gadsden Counties in Florida, and Houston County in Alabama, 2014: U.S. Geological Survey Open-File Report 2015–1170, 14 p., https://dx.doi.org/10.3133/ofr20151170.","productDescription":"Report: 14 p.; Appendix; GIS Shape Files","onlineOnly":"Y","additionalOnlineFiles":"Y","ipdsId":"IP-062523","costCenters":[{"id":5051,"text":"FLWSC-Orlando","active":true,"usgs":true}],"links":[{"id":308269,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/of/2015/1170/coverthb.jpg"},{"id":308270,"rank":2,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/of/2015/1170/ofr20151170.pdf","text":"Report","size":"2.78 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4446 Pet Lane, Suite 108
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http://fl.water.usgs.gov
The database in this Open-File Report describes the geology of Joshua Tree National Park and was completed in support of the National Cooperative Geologic Mapping Program of the U.S. Geological Survey (USGS) and in cooperation with the National Park Service (NPS). The geologic observations and interpretations represented in the database are relevant to both the ongoing scientific interests of the USGS in southern California and the management requirements of NPS, specifically of Joshua Tree National Park (JOTR).
Joshua Tree National Park is situated within the eastern part of California’s Transverse Ranges province and straddles the transition between the Mojave and Sonoran deserts. The geologically diverse terrain that underlies JOTR reveals a rich and varied geologic evolution, one that spans nearly two billion years of Earth history. The Park’s landscape is the current expression of this evolution, its varied landforms reflecting the differing origins of underlying rock types and their differing responses to subsequent geologic events. Crystalline basement in the Park consists of Proterozoic plutonic and metamorphic rocks intruded by a composite Mesozoic batholith of Triassic through Late Cretaceous plutons arrayed in northwest-trending lithodemic belts. The basement was exhumed during the Cenozoic and underwent differential deep weathering beneath a low-relief erosion surface, with the deepest weathering profiles forming on quartz-rich, biotite-bearing granitoid rocks. Disruption of the basement terrain by faults of the San Andreas system began ca. 20 Ma and the JOTR sinistral domain, preceded by basalt eruptions, began perhaps as early as ca. 7 Ma, but no later than 5 Ma. Uplift of the mountain blocks during this interval led to erosional stripping of the thick zones of weathered quartz-rich granitoid rocks to form etchplains dotted by bouldery tors—the iconic landscape of the Park. The stripped debris filled basins along the fault zones.
Mountain ranges and basins in the Park exhibit an east-west physiographic grain controlled by left-lateral fault zones that form a sinistral domain within the broad zone of dextral shear along the transform boundary between the North American and Pacific plates. Geologic and geophysical evidence reveal that movement on the sinistral faults zones has resulted in left steps along the zones, resulting in the development of sub-basins beneath Pinto Basin and Shavers and Chuckwalla Valleys. The sinistral fault zones connect the Mojave Desert dextral faults of the Eastern California Shear Zone to the north and east with the Coachella Valley strands of the southern San Andreas Fault Zone to the west.
Quaternary surficial deposits accumulated in alluvial washes and playas and lakes along the valley floors; in alluvial fans, washes, and sheet wash aprons along piedmonts flanking the mountain ranges; and in eolian dunes and sand sheets that span the transition from valley floor to piedmont slope. Sequences of Quaternary pediments are planed into piedmonts flanking valley-floor and upland basins, each pediment in turn overlain by successively younger residual and alluvial surficial deposits.
GMEG staff, Geology, Minerals, Energy, & Geophysics Science Center—Tucson, Arizona
U.S. Geological Survey, c/o University of Arizona
ENRB Bldg, 520 N. Park Ave, Rm 355
Tucson, Arizona 85719-5035
http://geomaps.wr.usgs.gov/
The effect of tropical and extratropical cyclones on coastal wetlands and marshes is highly variable and depends on a number of climatic, geologic, and physical variables. The impacts of storms can be either positive or negative with respect to the wetland and marsh ecosystems. Small to moderate amounts of inorganic sediment added to the marsh surface during storms or other events help to abate pressure from sea-level rise. However, if the volume of sediment is large and the resulting deposits are thick, the organic substrate may compact causing submergence and a loss in elevation. Similarly, thick deposits of coarse inorganic sediment may also alter the hydrology of the site and impede vegetative processes. Alternative impacts associated with storms include shoreline erosion at the marsh edge as well as potential emergence. Evaluating the outcome of these various responses and potential long-term implications is possible from a systematic assessment of both historical and recent event deposits. A study was conducted by the U.S. Geological Survey to assess the sedimentological and radiochemical characteristics of marsh deposits from Assateague Island and areas around Chincoteague Bay, Maryland and Virginia, following Hurricane Sandy in 2012. The objectives of this study were to (1) characterize the surficial sediment of the relict to recent washover fans and back-barrier marshes in the study area, and (2) characterize the sediment of six marsh cores from the back-barrier marshes and a single marsh island core near the mainland. These geologic data will be integrated with other remote sensing data collected along Assateague Island in Maryland and Virginia and assimilated into an assessment of coastal wetland response to storms.
\nThis report serves as an archive for sedimentological and radiochemical data derived from the surface sediments and marsh cores collected March 26–April 4, 2014. Select surficial data are available for the additional sampling periods October 21–30, 2014. Downloadable data are available as Excel spreadsheets and as JPEG files. Additional files include: Field documentation, x-radiographs, photographs, detailed results of sediment grain size analyses, and formal Federal Geographic Data Committee metadata (data downloads).
","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/ofr20151169","usgsCitation":"Smith, C.G., Marot, M.E., Ellis, A.M., Wheaton, C.J., Bernier, J.C., and Adams, C.S., 2015, Sedimentological and radiochemical characteristics of marsh deposits from Assateague Island and the adjacent vicinity, Maryland and Virginia, following Hurricane Sandy: U.S. Geological Survey Open-File Report 2015–1169, https://dx.doi.org/10.3133/ofr20151169.","productDescription":"HTML Document","onlineOnly":"Y","additionalOnlineFiles":"N","temporalStart":"2014-03-26","temporalEnd":"2014-10-30","ipdsId":"IP-065781","costCenters":[{"id":574,"text":"St. Petersburg Coastal and Marine Science Center","active":true,"usgs":true}],"links":[{"id":308090,"rank":2,"type":{"id":15,"text":"Index Page"},"url":"https://pubs.usgs.gov/of/2015/1169/index.html","text":"Report (HTML)","description":"OFR 2015-1169"},{"id":308089,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/of/2015/1169/images/coverthb.jpg"}],"country":"United States","state":"Maryland, Virginia","otherGeospatial":"Assateague Island","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -75.4815673828125,\n 37.82931081282506\n ],\n [\n -75.4815673828125,\n 38.447135775082444\n ],\n [\n -75.02838134765625,\n 38.447135775082444\n ],\n [\n -75.02838134765625,\n 37.82931081282506\n ],\n [\n -75.4815673828125,\n 37.82931081282506\n ]\n ]\n ]\n }\n }\n ]\n}","contact":"Director, St. Petersburg Coastal and Marine Science Center
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600 Fourth Street South
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(727) 502-8000
http://coastal.er.usgs.gov/
This report summarizes and interprets results of the work in the Ahankashan Area of Interest in northwestern Afghanistan and four study areas—the Ahankashan Prospect Area, Syahsang-Kushkak, Taghab-Soni, and Zakak-e ‘Olya—delineated for their potential undiscovered mineral occurrences with specific emphasis on porphyry copper and related occurrence types. The Area of Interest is underlain by rocks of three different geologic domains that cross from east to west—the Band-e-Bayan Block/Central Pamirs Domain in the south, the Hindu Kush Domain in the Paropamisus Mountains, and the Afghan Turkestan Domain in the north. The domains are sutured remnants of Tethyan tectonic elements. Interpretation of the geologic maps indicates the presence of thrust faults, strike-slip faults, and granitic intrusions emplaced in ground prepared by faulting. Thrust faulting was followed by strike-slip faulting and then followed by magmatic intrusions. Advanced Spaceborne Thermal Emission and Reflection Radiometer data were used to map minerals that have been altered by hydrothermal fluids typically associated with mineralization to delineate new potential occurrences of copper, gold, and silver. Propylitic-, argillic-, and phyllic-altered intrusive rocks are found in the area, as well as very minor amounts of hydrothermal silica-rich rocks. This area of interest is vastly underexplored and contains only seven known mineral occurrences, of which the Ahankashan copper (gold) skarn occurrence is the best known. Gold has been found in stream sediments near the Ahankashan skarn, in the Taghab-Soni study area, and possibly other parts of the Area of Interest, suggesting potential for at least small-scale placer occurrences.
","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/ofr20151040","usgsCitation":"Drew, L.J., Sutphin, D.M., Mars, J.C., and Bogdanow, A.K., 2015, Summary of the Ahankashan area of interest: U.S. Geological Survey Open-File Report 2015–1040, 26 p., https://dx.doi.org/10.3133/ofr20151040.","productDescription":"iv, 26 p.","numberOfPages":"31","onlineOnly":"Y","additionalOnlineFiles":"N","ipdsId":"IP-051081","costCenters":[{"id":245,"text":"Eastern Mineral and Environmental Resources Science Center","active":true,"usgs":true}],"links":[{"id":308094,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/of/2015/1040/ofr20151040.pdf","text":"Report","size":"4.40 MB","linkFileType":{"id":1,"text":"pdf"},"description":"OFR 2015-1040"},{"id":308092,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/of/2015/1040/coverthb.jpg"}],"country":"Afghanistan","otherGeospatial":"Ahankashan Area of Interest","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n 63.19335937499999,\n 34.20725938207231\n ],\n [\n 63.19335937499999,\n 34.71452466170392\n ],\n [\n 64.83032226562499,\n 34.71452466170392\n ],\n [\n 64.83032226562499,\n 34.20725938207231\n ],\n [\n 63.19335937499999,\n 34.20725938207231\n ]\n ]\n ]\n }\n }\n ]\n}","contact":"Mineral Resources Program
U.S. Geological Survey
12201 Sunrise Valley Dr.
Reston, VA 20192
http://minerals.usgs.gov
This document provides an overview of the Advanced Spaceborne Thermal Emission and Reflection Radiometer (ASTER) supplemental algorithms in conjunction with the reuse of Landsat geometric algorithms modified by the National Aeronautics and Space Administration (NASA Land Processes Distributed Active Archive Center (LP DAAC) to create an ASTER Level 1 Precision Terrain Corrected Registered At-Sensor Radiance (AST_L1T) product. Implementation of these algorithms occurs within the AST_L1T product generation executable (PGE) as part of the open source Simple, Scalable, Script-based Science Processor for Missions (S4PM) processing software subsystem.
The AST_L1T algorithms include the following:
Earth Resources Observation and Science (EROS) Center
U.S. Geological Survey
47914 252nd Street
Sioux Falls, South Dakota 57198
http://eros.usgs.gov/
This crosswalk is based on the premise that there is a connection between the way mineral commodities are used and how this use is reflected in the economy. Raw mineral commodities are the basic materials from which goods, finished products, or intermediate materials are manufactured or made. Mineral commodities are vital to the development of the U.S. economy and they impact nearly every industrial segment of the economy, representing 12.2 percent of the U.S. gross domestic product (GDP) in 2010 (U.S. Bureau of Economic Analysis, 2014). In an effort to better understand the distribution of mineral commodities in the economy, the U.S. Geological Survey (USGS) attempts to link the end uses of mineral commodities to the corresponding North American Industry Classification System (NAICS) codes.
\nThe links between the end uses of mineral commodities and the NAICS codes provide an instrument for analyzing the use of mineral commodities in the economy. The crosswalk is also a guide, highlighting those industrial sectors in the economy that rely heavily on mineral commodities. The distribution of mineral commodities across the economy is dynamic and does differ from year to year. This report reflects a snapshot of the state of the economy and mineral commodities in 2010.
","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/ofr20151163","usgsCitation":"Barry, J.J., Matos, G.R., and Menzie, W.D., 2015, A crosswalk of mineral commodity end uses and North American Industry Classification System (NAICS) codes: U.S. Geological Survey Open-File Report 2015–1163, 4 p., plus 77 tables in 1 Excel file, https://dx.doi.org/10.3133/ofr20151163.","productDescription":"Report: v, 4 p.; Excel File","numberOfPages":"10","onlineOnly":"Y","additionalOnlineFiles":"Y","ipdsId":"IP-057770","costCenters":[{"id":432,"text":"National Minerals Information Center","active":true,"usgs":true}],"links":[{"id":308045,"rank":3,"type":{"id":27,"text":"Table"},"url":"https://pubs.usgs.gov/of/2015/1163/ofr20151163_tables1-77.xlsx","text":"Excel File","size":"651 KB","linkFileType":{"id":3,"text":"xlsx"},"description":"OFR 2015-1163"},{"id":308044,"rank":2,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/of/2015/1163/ofr20151163.pdf","text":"Report","size":"172 KB","linkFileType":{"id":1,"text":"pdf"},"description":"OFR 2015-1163"},{"id":308043,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/ofr/2015/1163/coverthbr.jpg"}],"contact":"Director, National Minerals Information Center
U.S. Geological Survey
12201 Sunrise Valley Dr.
Reston, VA 20192
Email: nmicrecordsmgt@usgs.gov
http://minerals.usgs.gov/minerals/
Scientists from the U.S. Geological Survey, St. Petersburg Coastal and Marine Science Center collected shallow sediment cores and surface samples from a coastal salt marsh environment next to Vermilion Bay in southwest Louisiana in January 2013. The sampling was part of a larger USGS study to gather data for assessing environmental changes over the past 150 years. The objective of the study was to expand upon the historical context of sea level and storms affecting coastal systems and how these systems might change under persistent or varying conditions. The data from this report add to a regional environmental change database that aids with the continuing effort to understand the evolution of coastal systems.
\nThis report serves as an archive for sedimentological, radiochemical, and microbiological data derived from the sediment cores. Data are available for January 2013. Downloadable data are available as Excel spreadsheets and as JPEG files. Additional files include ArcGIS shapefiles of the sampling sites, detailed results of sediment analyses, and formal Federal Geographic Data Committee metadata.
\nThe authors thank Nancy DeWitt, B.J. Reynolds, Christopher Reich (USGS, St. Petersburg Coastal and Marine Science Center), for help with sample collection and processing; Michael Ball, Sarai Piazza, and Gregory Steyer (USGS, Coastal Restoration Assessment Branch) for assistance accessing CRMS Site 541; and Darrell Anders and Phillip Turnipseed (USGS National Wetlands Research Center) for technical support while in the field. We would also like to thank Caitlyn Reynolds and Nicholas Zaremba for their pre-release commentary and peer review.
\nThis publication was prepared by an agency of the United States Government. Although these data were processed successfully on a computer system at the U.S. Geological Survey, no warranty expressed or implied is made regarding the display or utility of the data on any other system, or for general or scientific purposes, nor shall the act of distribution imply any such warranty. The U.S. Geological Survey shall not be held liable for improper or incorrect use of the data described and (or) contained herein. Reference herein to any specific commercial product, process, or service by trade name, trademark, manufacturer, or otherwise does not constitute or imply its endorsement, recommendation, or favoring by the United States Government or any agency
","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/ofr20151143","usgsCitation":"Richwine, K.A., Marot, M.E., Smith, C.G., Osterman, L.E., and Adams, C.S., 2015, Biological and geochemical data along Indian Point, Vermilion Bay, Louisiana: U.S. Geological Survey Open-File Report 2015-1143, https://dx.doi.org/10.3133/ofr20151143.","productDescription":"Report: HTML Document; Downloads Directory","onlineOnly":"Y","additionalOnlineFiles":"Y","ipdsId":"IP-059007","costCenters":[{"id":574,"text":"St. Petersburg Coastal and Marine Science Center","active":true,"usgs":true}],"links":[{"id":307119,"rank":3,"type":{"id":7,"text":"Companion Files"},"url":"https://pubs.usgs.gov/of/2015/1143/downloads","text":"Biological and Geochemical Data","description":"OFR 2015-1143"},{"id":307094,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/of/2015/1143/coverthb.jpg"},{"id":307095,"rank":2,"type":{"id":15,"text":"Index Page"},"url":"https://pubs.usgs.gov/of/2015/1143/index.html","text":"Report","linkFileType":{"id":5,"text":"html"},"description":"OFR 2015-1143"}],"country":"United States","state":"Louisiana","otherGeospatial":"Indian Point, Vermilion Bay","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -92.05787658691406,\n 29.608088257406806\n ],\n [\n -92.05787658691406,\n 29.6361427369564\n ],\n [\n -92.00363159179688,\n 29.6361427369564\n ],\n [\n -92.00363159179688,\n 29.608088257406806\n ],\n [\n -92.05787658691406,\n 29.608088257406806\n ]\n ]\n ]\n }\n }\n ]\n}","contact":"Director, St. Petersburg Coastal and Marine Science Center
U.S. Geological Survey
600 4th Street South St.
Petersburg, FL 33701
(727) 502-8000
http://coastal.er.usgs.gov/
Cyanobacteria can produce toxins and form harmful algal blooms. The Native American and Alaska Native communities that are dependent on subsistence fishing have an increased risk of exposure to these cyanotoxins. It is important to recognize the presence of an algal bloom in a waterbody and to distinguish a potentially toxic harmful algal bloom from a non-toxic bloom. This guide provides field images that show cyanobacteria blooms, some of which can be toxin producers, as well as other non-toxic algae blooms and floating plants that might be confused with algae. After recognition of a potential toxin-producing cyanobacterial bloom in the field, the type(s) of cyanobacteria present needs to be identified. Species identification, which requires microscopic examination, may help distinguish a toxin-producer from a non-toxin producer. This guide also provides microscopic images of the common cyanobacteria that are known to produce toxins, as well as images of algae that form blooms but do not produce toxins.
","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/ofr20151164","usgsCitation":"Rosen, B.H., and St. Amand, Ann, Field and laboratory guide to freshwater cyanobacteria harmful algal blooms for Native American and Alaska Native Communities: U.S. Geological Survey Open-File Report 2015–1164, 44 p., https://dx.doi.org/10.3133/ofr20151164.","productDescription":"vi, 44 p.","numberOfPages":"54","onlineOnly":"N","additionalOnlineFiles":"N","ipdsId":"IP-065834","costCenters":[{"id":566,"text":"Southeast Ecological Science Center","active":true,"usgs":true}],"links":[{"id":308071,"rank":2,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/of/2015/1164/ofr20151164.pdf","text":"Report","size":"7.88 MB","linkFileType":{"id":1,"text":"pdf"},"description":"OFR 2015-1164"},{"id":308070,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/of/2015/1164/coverthb.jpg"}],"contact":"National Tribal Liaison
USGS Office of Tribal Relations
12201 Sunrise Valley Drive, Mail Stop 911
Reston, VA 20192
(703) 648-4437
Geographic information system (GIS) information may facilitate energy studies, which in turn provide input for energy policy decisions. The U.S. Geological Survey (USGS) has compiled GIS data representing coal mines, deposits (including those with and without coal mines), occurrences, areas, basins, and provinces of Mongolia as of 2009. These data are now available for download, and may be used in a GIS for a variety of energy resource and environmental studies of Mongolia. Chemical data for 37 coal samples from a previous USGS study of Mongolia (Tewalt and others, 2010) are included in a downloadable GIS point shapefile and shown on the map of Mongolia. A brief report summarizes the methodology used for creation of the shapefiles and the chemical analyses run on the samples.
","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/ofr20151144","usgsCitation":"Trippi, M.H., and Belkin, H.E., comps., 2015, USGS compilation of geographic information system (GIS) data representing coal mines and coal-bearing areas of Mongolia: U.S. Geological Survey Open-File Report 2015–1144, 18 p., https://dx.doi.org/10.3133/ofr20151144.","productDescription":"Report: v, 18 p.; Metadata; Spatial data","numberOfPages":"24","onlineOnly":"Y","additionalOnlineFiles":"Y","ipdsId":"IP-051260","costCenters":[{"id":241,"text":"Eastern Energy Resources Science Center","active":true,"usgs":true}],"links":[{"id":307735,"rank":7,"type":{"id":16,"text":"Metadata"},"url":"https://pubs.usgs.gov/of/2015/1144/downloads/ofr20151144_mongolia-coal-deposits-metadata.html","text":"Mongolia Coal Deposits Metadata","size":"35.4 KB","linkFileType":{"id":5,"text":"html"},"description":"OFR 2015-1144"},{"id":307731,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/of/2015/1144/coverthb.jpg"},{"id":307740,"rank":4,"type":{"id":23,"text":"Spatial Data"},"url":"https://pubs.usgs.gov/of/2015/1144/downloads/ofr20151144_mongolia-coal-areas-basins-provinces.zip","text":"Mongolia Coal Areas, Basins, and Provinces Shape Files","size":"203 KB","linkFileType":{"id":6,"text":"zip"},"description":"OFR 2015-1144"},{"id":307732,"rank":2,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/of/2015/1144/ofr20151144.pdf","text":"Report","size":"4.07 MB","linkFileType":{"id":1,"text":"pdf"},"description":"OFR 2015-1144"},{"id":307733,"rank":3,"type":{"id":23,"text":"Spatial Data"},"url":"https://pubs.usgs.gov/of/2015/1144/downloads/ofr20151144_mongolia-allshapefiles-metadata.zip","text":"All Mongolia Shape Files and Metadata","size":"294 KB","linkFileType":{"id":6,"text":"zip"},"description":"OFR 2015-1144"},{"id":307734,"rank":5,"type":{"id":16,"text":"Metadata"},"url":"https://pubs.usgs.gov/of/2015/1144/downloads/ofr20151144_mongolia-coal-areas-basins-provinces-metadata.html","text":"Mongolia Coal Areas, Basins, and Provinces Metadata","size":"20.9 KB","linkFileType":{"id":5,"text":"html"},"description":"OFR 2015-1144"},{"id":307737,"rank":9,"type":{"id":16,"text":"Metadata"},"url":"https://pubs.usgs.gov/of/2015/1144/downloads/ofr20151144_mongolia-analytical-data-metadata.html","text":"Mongolia Anaytical Data Metadata","size":"100 KB","linkFileType":{"id":5,"text":"html"},"description":"OFR 2015-1144"},{"id":307738,"rank":6,"type":{"id":23,"text":"Spatial Data"},"url":"https://pubs.usgs.gov/of/2015/1144/downloads/ofr20151144_mongolia-coal-deposits.zip","text":"Mongolia Coal Deposit Shape File","size":"24.6 KB","linkFileType":{"id":6,"text":"zip"},"description":"OFR 2015-1144"},{"id":307739,"rank":8,"type":{"id":23,"text":"Spatial Data"},"url":"https://pubs.usgs.gov/of/2015/1144/downloads/ofr20151144_mongolia-analytical-data.zip","text":"Mongolia Analytical Data Shape File","size":"43.6 KB","linkFileType":{"id":6,"text":"zip"},"description":"OFR 2015-1144"}],"country":"Mongolia","geographicExtents":"{\"type\":\"FeatureCollection\",\"features\":[{\"type\":\"Feature\",\"geometry\":{\"type\":\"Polygon\",\"coordinates\":[[[87.75126,49.2972],[88.80557,49.47052],[90.71367,50.33181],[92.23471,50.80217],[93.10422,50.49529],[94.14757,50.48054],[94.81595,50.01343],[95.81403,49.97747],[97.25973,49.72606],[98.23176,50.4224],[97.82574,51.011],[98.86149,52.04737],[99.98173,51.63401],[100.88948,51.51686],[102.06522,51.25992],[102.25591,50.51056],[103.67655,50.08997],[104.62155,50.27533],[105.88659,50.40602],[106.8888,50.2743],[107.86818,49.79371],[108.47517,49.28255],[109.40245,49.29296],[110.66201,49.13013],[111.58123,49.37797],[112.89774,49.54357],[114.36246,50.2483],[114.96211,50.14025],[115.4857,49.80518],[116.6788,49.88853],[116.1918,49.1346],[115.48528,48.13538],[115.74284,47.72654],[116.30895,47.85341],[117.29551,47.69771],[118.06414,48.06673],[118.86657,47.74706],[119.77282,47.04806],[119.66327,46.69268],[118.87433,46.80541],[117.4217,46.67273],[116.71787,46.3882],[115.9851,45.72724],[114.46033,45.33982],[113.46391,44.80889],[112.43606,45.01165],[111.87331,45.10208],[111.34838,44.45744],[111.66774,44.07318],[111.82959,43.74312],[111.12968,43.40683],[110.4121,42.87123],[109.2436,42.51945],[107.74477,42.48152],[106.12932,42.13433],[104.96499,41.59741],[104.52228,41.90835],[103.31228,41.90747],[101.83304,42.51487],[100.84587,42.6638],[99.51582,42.52469],[97.45176,42.74889],[96.3494,42.72564],[95.76245,43.31945],[95.30688,44.24133],[94.68893,44.35233],[93.48073,44.97547],[92.13389,45.11508],[90.94554,45.28607],[90.58577,45.71972],[90.97081,46.88815],[90.28083,47.69355],[88.8543,48.06908],[88.01383,48.59946],[87.75126,49.2972]]]},\"properties\":{\"name\":\"Mongolia\"}}]}","contact":"Eastern Energy Resources Science Center
U.S. Geological Survey
954 National Center
12201 Sunrise Valley Drive
Reston, Virginia 20192
http://energy.usgs.gov/GeneralInfo/
ScienceCenters/Eastern.aspx
Greater sage-grouse (Centrocercus urophasianus; hereinafter, sage-grouse) are a sagebrush obligate species that has declined concomitantly with the loss and fragmentation of sagebrush ecosystems across most of its geographical range. The species currently is listed as a candidate for federal protection under the Endangered Species Act (ESA). Increasing wildfire frequency and changing climate frequently are identified as two environmental drivers that contribute to the decline of sage-grouse populations, yet few studies have rigorously quantified their effects on sage-grouse populations across broad spatial scales and long time periods. To help inform a threat assessment within the Great Basin for listing sage-grouse in 2015 under the ESA, we conducted an extensive analysis of wildfire and climatic effects on sage-grouse population growth derived from 30 years of lek-count data collected across the hydrographic Great Basin of Western North America. Annual (1984–2013) patterns of wildfire were derived from an extensive dataset of remotely sensed 30-meter imagery and precipitation derived from locally downscaled spatially explicit data. In the sagebrush ecosystem, underlying soil conditions also contribute strongly to variation in resilience to disturbance and resistance to plant community changes (R&R). Thus, we developed predictions from models of post-wildfire recovery and chronic effects of wildfire based on three spatially explicit R&R classes derived from soil moisture and temperature regimes. We found evidence of an interaction between the effects of wildfire (chronically affected burned area within 5 kilometers of a lek) and climatic conditions (spring through fall precipitation) after accounting for a consistent density-dependent effect. Specifically, burned areas near leks nullifies population growth that normally follows years with relatively high precipitation. In models, this effect results in long-term population declines for sage-grouse despite cyclic periods of high precipitation. Based on 30-year projections of burn and recovery rates, our population model predicted steady and substantial long-term declines in population size across the Great Basin. Further, example management scenarios that may help offset adverse wildfire effects are provided by models of varying levels of fire suppression and post-wildfire restoration that focus on areas especially important to sage-grouse populations. These models illustrate how sage-grouse population persistence likely will be compromised as sagebrush ecosystems and sage-grouse habitat are degraded by wildfire, especially in a warmer and drier climate, and by invasion of annual grasses that can increase wildfire frequency and size in the Great Basin.
","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/ofr20151165","collaboration":"Prepared in cooperation with the U.S. Fish and Wildlife Service","usgsCitation":"Coates, P.S., Ricca, M.A., Prochazka, B.G., Doherty, K.E., Brooks, M.L., and Casazza, M.L., 2015, Long-term effects of wildfire on greater sage-grouse—Integrating population and ecosystem concepts for management in the Great Basin: U.S. Geological Survey Open-File Report 2015–1165, 42 p., https://dx.doi.org/10.3133/ofr20151165.","productDescription":"Report: vi, 42 p.; Dataset","numberOfPages":"52","onlineOnly":"Y","additionalOnlineFiles":"N","ipdsId":"IP-067577","costCenters":[{"id":651,"text":"Western Ecological Research Center","active":true,"usgs":true}],"links":[{"id":438684,"rank":4,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/F7K35RRS","text":"USGS data release","linkHelpText":"Long-term effects of wildfire on greater sage-grouse - integrating population and ecosystem concepts for management in the Great Basin"},{"id":307537,"rank":1,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/of/2015/1165/ofr20151165.pdf","text":"Report","size":"6 MB","linkFileType":{"id":1,"text":"pdf"},"description":"OFR 2015-1165 PDF"},{"id":307539,"rank":2,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/of/2015/1165/coverthb.jpg"},{"id":321005,"rank":3,"type":{"id":28,"text":"Dataset"},"url":"https://dx.doi.org/10.5066/F7K35RRS","text":"Data release"}],"country":"United States","state":"California, Idaho, Nevada, Oregon, Utah","otherGeospatial":"Great Basin","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -111.03881835937499,\n 44.879228141635274\n ],\n [\n -113.97216796875,\n 45.72152152227954\n ],\n [\n -121.5087890625,\n 45.706179285330855\n ],\n [\n -122.49755859375,\n 40.713955826286046\n ],\n [\n -118.69628906249999,\n 35.53222622770337\n ],\n [\n -114.5654296875,\n 34.88593094075317\n ],\n [\n -112.30224609374999,\n 37.020098201368114\n ],\n [\n -110.54443359375,\n 40.9964840143779\n ],\n [\n -111.03881835937499,\n 44.879228141635274\n ]\n ]\n ]\n }\n }\n ]\n}","contact":"Director, Western Ecological Research Center
U.S. Geological Survey
3020 State University Drive East
Sacramento, California 95819
http://werc.usgs.gov/
Fire ranks among the top three threats to the greater sage-grouse (Centrocercus urophasianus) throughout its range, and among the top two threats in the western part of its range. The national research strategy for this species and the recent U.S. Department of the Interior Secretarial Order 3336 call for science-based threats assessment of fire to inform conservation planning and fire management efforts. The cornerstone of such assessments is a clear understanding of where fires are occurring and what aspects of fire regimes may be shifting outside of their historical range of variation. This report fulfills this need by describing patterns of fire area, fire size, fire rotation, and fire season length and timing from 1984 to 2013 across the range of the greater sage-grouse. This information need is further addressed by evaluating the ecological and management implications of these fire patterns. Analyses are stratified by major vegetation types and the seven greater sage-grouse management zones, delineated regionally as four western and three eastern management zones. Soil temperature and moisture indicators of resilience to fire and resistance to cheatgrass invasion, and the potential for establishment of a grass/fire cycle, are used as unifying concepts in developing fire threat assessments for each analysis strata.
\nThe results indicate that fire threats are higher in the four western than in the three eastern management zones. Among the four western management zones, the Snake River Plain and the Columbia Basin ranked somewhat higher than the Southern Great Basin and Northern Great Basin in terms of fire effects on sage-grouse habitat. These results support the previous high ranking of fire as a threat to the greater sage-grouse in the western region. In contrast, considering the low rankings for fire threats in the eastern region, it may be useful to reconsider the relative importance of wildfire as a threat to greater sage-grouse in those three management zones.
","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/ofr20151167","usgsCitation":"Brooks, M.L., Matchett, J.R., Shinneman, D.J., and Coates, P.S., 2015, Fire patterns in the range of greater sage-grouse, 1984–2013—Implications for conservation and management: U.S. Geological Survey Open-File Report 2015-1167, 66 p., https://dx.doi.org/10.3133/ofr20151167.","productDescription":"Report: vi, 66 p.; Dataset","onlineOnly":"Y","additionalOnlineFiles":"N","ipdsId":"IP-067763","costCenters":[{"id":651,"text":"Western Ecological Research Center","active":true,"usgs":true}],"links":[{"id":438683,"rank":4,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/F76971N5","text":"USGS data release","linkHelpText":"Fire Patterns in the Range of the Greater Sage-Grouse, 1984-2013-Implications for Conservation and Management"},{"id":307546,"rank":1,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/of/2015/1167/ofr20151167.pdf","text":"Report","size":"6 MB","linkFileType":{"id":1,"text":"pdf"},"description":"OFR 2015-1167 PDF"},{"id":307547,"rank":2,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/of/2015/1167/coverthb.jpg"},{"id":312278,"rank":3,"type":{"id":28,"text":"Dataset"},"url":"https://dx.doi.org/10.5066/F76971N5","text":"Data Release"}],"country":"United States","state":"California, Colorado, Idaho, Montana, Nevada, Oregon, Utah, Washington, Wyoming","otherGeospatial":"Colorado Plateau, Columbia Basin, Great Plains, Northern Great Basin, Snake River Plain, Southern Great Basin","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -125.5078125,\n 35.42486791930558\n ],\n [\n -102.3046875,\n 35.42486791930558\n ],\n [\n -102.3046875,\n 49.03786794532644\n ],\n [\n -125.5078125,\n 49.03786794532644\n ],\n [\n -125.5078125,\n 35.42486791930558\n ]\n ]\n ]\n }\n }\n ]\n}","contact":"Director, Western Ecological Research Center
U.S. Geological Survey
3020 State University Drive East
Sacramento, California 95819
http://werc.usgs.gov/
Abstract
Introduction
Methods
Fire Pattern Results
Discussion of Fire Patterns
Fire Threats Assessment for Greater Sage-Grouse Habitat
Acknowledgments
References Cited
Appendixes 1-13
Multibeam echosounder data collected by the National Oceanic and Atmospheric Administration along with sediment samples and still and video photography of the sea floor collected by the U.S. Geological Survey were used to interpret sea-floor features and sedimentary environments in southern Narragansett Bay, Rhode Island, as part of a long-term effort to map the sea floor along the northeastern coast of the United States. Sea-floor features include rocky areas and scour depressions in high-energy environments characterized by erosion or nondeposition, and sand waves and megaripples in environments characterized by coarse-grained bedload transport. Two shipwrecks are also located in the study area. Much of the sea floor is relatively featureless within the resolution of the multibeam data; sedimentary environments in these areas are characterized by processes associated with sorting and reworking. This report releases bathymetric data from the multibeam echosounder, grain-size analyses of sediment samples, and photographs of the sea floor and interpretations of the sea-floor features and sedimentary environments. It provides base maps that can be used for resource management and studies of topics such as benthic ecology, contaminant inventories, and sediment transport.
","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/ofr20151149","isbn":"978-1-4113-3933-0","collaboration":"Prepared in cooperation with the National Oceanic and Atmospheric Administration","usgsCitation":"McMullen, K.Y., Poppe, L.J., Blackwood, D.S., Nardi, M.J., and Andring, M.A., 2015, Sea-floor morphology and sedimentary environments in southern Narragansett Bay, Rhode Island: U.S. Geological Survey Open-File Report 2015–1149, 1 DVD-ROM, https://dx.doi.org/10.3133/ofr20151149.","productDescription":"HMTL Document","onlineOnly":"Y","additionalOnlineFiles":"N","temporalStart":"2011-06-01","temporalEnd":"2011-09-30","ipdsId":"IP-065057","costCenters":[{"id":678,"text":"Woods Hole Coastal and Marine Science Center","active":true,"usgs":true}],"links":[{"id":307926,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/of/2015/1149/images/coverthb.jpg"},{"id":307927,"rank":2,"type":{"id":15,"text":"Index Page"},"url":"https://pubs.usgs.gov/of/2015/1149/index.html","text":"Report","linkFileType":{"id":5,"text":"html"},"description":"OFR 2015-1149"}],"country":"United States","state":"Rhode Island","otherGeospatial":"Narragansett Bay","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -71.455078125,\n 41.396384896536276\n ],\n [\n -71.455078125,\n 41.748775021355044\n ],\n [\n -71.26419067382812,\n 41.748775021355044\n ],\n [\n -71.26419067382812,\n 41.396384896536276\n ],\n [\n -71.455078125,\n 41.396384896536276\n ]\n ]\n ]\n }\n }\n ]\n}","contact":"Director, Woods Hole Coastal and Marine Science Center
U.S. Geological Survey
384 Woods Hole Road
Quissett Campus
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(508) 548-8700 or (508) 457-2200
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The Mediterranean region is seismically active due to the convergence of the Africa Plate with the Eurasia plate. Present day Africa-Eurasia motion ranges from ~4 millimeters per year (mm/yr) in a northwest-southeast direction in the western Mediterranean to ~10 mm/yr (north-south) in the eastern Mediterranean. The Africa-Eurasia plate boundary is complex, and includes extensional and translational zones in addition to the dominant convergent regimes characterized by subduction and continental collision. This convergence began at approximately 50 million years ago and was associated with the closure of the Tethys Sea; the Mediterranean Sea is all that remains of the Tethys. The highest rates of seismicity in the Mediterranean region are found along the Hellenic subduction zone of southern Greece and the North Anatolian Fault Zone of northwestern Turkey, but significant rates of current seismicity and large historical earthquakes have occurred throughout the region spanning the Mediterranean Sea.
","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/ofr20101083Q","usgsCitation":"Herman, M.W., Hayes, G.P., Smoczyk, G.M., Turner, Rebecca, Turner, Bethan, Jenkins, Jennifer, Davies, Sian, Parker, Amy, Sinclair, Allison, Benz, H.M., Furlong, K.P., and Villaseñor, Antonio, 2015, Seismicity of the Earth 1900–2013, Mediterranean Sea and vicinity: U.S. Geological Survey Open-File Report 2010–1083-Q, scale 1:10,000,000, https://dx.doi.org/10.3133/ofr20101083Q.","productDescription":"1 p.","numberOfPages":"1","onlineOnly":"Y","additionalOnlineFiles":"N","ipdsId":"IP-065794","costCenters":[{"id":300,"text":"Geologic Hazards Science Center","active":true,"usgs":true}],"links":[{"id":307744,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/of/2010/1083/q/coverthb.jpg"},{"id":307745,"rank":2,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/of/2010/1083/q/ofr20101083q.pdf","text":"Report","size":"78.1 MB","linkFileType":{"id":1,"text":"pdf"},"description":"OF 2010-1083-Q"}],"geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -18.45703125,\n 22.431340156360594\n ],\n [\n -18.45703125,\n 57.040729838360875\n ],\n [\n 51.328125,\n 57.040729838360875\n ],\n [\n 51.328125,\n 22.431340156360594\n ],\n [\n -18.45703125,\n 22.431340156360594\n ]\n ]\n ]\n }\n }\n ]\n}","contact":"Director, Geologic Hazards Science Center
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Denver, CO 80225
http://geohazards.cr.usgs.gov/
The network of areas delineated in 11 Western States for prioritizing management of greater sage-grouse (Centrocercus urophasianus) represents a grand experiment in conservation biology and reserve design. We used centrality metrics from social network theory to gain insights into how this priority area network might function. The network was highly centralized. Twenty of 188 priority areas accounted for 80 percent of the total centrality scores. These priority areas, characterized by large size and a central location in the range-wide distribution, are strongholds for greater sage-grouse populations and also might function as sources. Mid-ranking priority areas may serve as stepping stones because of their location between large central and smaller peripheral priority areas. The current network design and conservation strategy has risks. The contribution of almost one-half (n = 93) of the priority areas combined for less than 1 percent of the cumulative centrality scores for the network. These priority areas individually are likely too small to support viable sage-grouse populations within their boundary. Without habitat corridors to connect small priority areas either to larger priority areas or as a clustered group within the network, their isolation could lead to loss of sage-grouse within these regions of the network.
","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/ofr20151158","collaboration":"Prepared in cooperation with the U.S. Fish and Wildlife Service","usgsCitation":"Crist, M.R., Knick, S.T., and Hanser, S.E., 2015, Range-wide network of priority areas for greater sage-grouse—A design for conserving connected distributions or isolating individual zoos?: U.S. Geological Survey Open-File Report 2015-1158, 34 p., https://dx.doi.org/10.3133/20151158.","productDescription":"iv, 34 p.","numberOfPages":"42","onlineOnly":"Y","additionalOnlineFiles":"N","ipdsId":"IP-067179","costCenters":[{"id":290,"text":"Forest and Rangeland Ecosystem Science Center","active":false,"usgs":true}],"links":[{"id":307589,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/usgs_thumb.jpg"},{"id":307943,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/of/2015/1158/ofr20151158.pdf","text":"Report","size":"3.4 MB","linkFileType":{"id":1,"text":"pdf"},"description":"OFR 2015-1158"}],"country":"United States","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -120.84960937499999,\n 48.980216985374994\n ],\n [\n -122.36572265625,\n 42.032974332441405\n ],\n [\n -119.267578125,\n 36.049098959065645\n ],\n [\n -109.0283203125,\n 33.815666308702774\n ],\n [\n -104.17236328125,\n 33.76088200086917\n ],\n [\n -104.04052734375,\n 41.062786068733026\n ],\n [\n -101.162109375,\n 43.56447158721811\n ],\n [\n -101.29394531249999,\n 48.99463598353408\n ],\n [\n -120.84960937499999,\n 48.980216985374994\n ]\n ]\n ]\n }\n }\n ]\n}","contact":"Director, Forest and Rangeland Ecosystem Science Center
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Chloride exists as a major ion in most natural waters, but many anthropogenic sources are increasing concentrations of chloride in many receiving waters. Although natural concentrations in continental waters can be as high as 200,000 milligrams per liter, chloride concentrations that are suitable for freshwater ecology, human consumption, and agricultural and industrial water uses commonly are on the order of 10 to 1,000 milligrams per liter. “Road salt” frequently is identified as the sole source of anthropogenic chloride, but only about 30 percent of the salt consumed and released to the environment is used for deicing. Furthermore, several studies in Southern States where the use of deicing salt is minimal also show anthropogenic chloride in rising concentrations and in strong correlation to imperviousness and road density. This is because imperviousness and road density also are strongly correlated to population density. The term “road salt” is a misnomer because deicers applied to parking lots, sidewalks, and driveways can be a substantial source of chloride in some catchments because these land covers are comparable to roadways as a percentage of the total impervious area and commonly receive higher salt application rates than some roadways. Other sources of anthropogenic chloride include wastewater, dust control on unpaved roads, fertilizer, animal waste, irrigation, aquaculture, energy production wastes, and landfill leachates. The assumption that rising chloride concentrations in surface water or groundwater is indicative of contamination by deicing chemicals rather than one or more other potential sources may preclude the identification of toxic, carcinogenic, mutagenic, or endocrine-disrupting contaminants that are associated with many sources of elevated chloride concentrations. Once the sources of anthropogenic chloride in an area of interest have been identified and measured, water and solute budgets can be estimated to guide decisionmakers to identify and apply potential mitigation measures that can reduce the problem.
\nScientists, engineers, regulators, and decisionmakers need information about potential sources of chloride, water and solute budgets, and methods for collecting water-quality data to help identify potential sources. This information is needed to evaluate potential sources of chloride in areas where chloride may have adverse ecological effects or may degrade water supplies used for drinking water, agriculture, or industry. Knowledge of potential sources will help decisionmakers identify the best mitigation measures to reduce the total background chloride load, thereby reducing the potential for water-quality exceedances that occur because of superposition on rising background concentrations. Also, knowledge of potential sources may help decisionmakers identify the potential for the presence of contaminants that have toxic, carcinogenic, mutagenic, or endocrine-disrupting effects at concentrations that are lower by orders of magnitude than the chloride concentrations in the source water. This report is a comprehensive synthesis of relevant information, but it is not the result of an exhaustive search for literature on each topic. The potential adverse effects of chloride on infrastructure and the environment are not discussed in this report because these issues have been extensively documented elsewhere.
","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/ofr20151080","collaboration":"Prepared in cooperation with the U.S. Department of Transportation Federal Highway Administration Office of Project Development and Environmental Review","usgsCitation":"Granato, G.E., DeSimone, L.A., Barbaro, J.R., and Jeznach, L.C., 2015, Methods for evaluating potential sources of chloride in surface waters and groundwaters of the conterminous United States: U.S. Geological Survey Open-File Report 2015–1080, 89 p., https://dx.doi.org/10.3133/ofr20151080.","productDescription":"ix, 89 p.","numberOfPages":"104","onlineOnly":"Y","additionalOnlineFiles":"N","ipdsId":"IP-063136","costCenters":[{"id":376,"text":"Massachusetts Water Science 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U.S. Geological Survey
10 Bearfoot Road
Northborough, MA 01532
Or visit our Web site at:
http://newengland.water.usgs.gov