A series of maps that describe the distribution and texture of sea-floor sediments and physiographic zones of Massachusetts State waters from Nahant to Salisbury, Massachusetts, including western Massachusetts Bay, have been produced by using high-resolution geophysical data (interferometric and multibeam swath bathymetry, lidar bathymetry, backscatter intensity, and seismic reflection profiles), sediment samples, and bottom photographs. These interpretations are intended to aid statewide efforts to inventory and manage coastal and marine resources, link with existing data interpretations, and provide information for research focused on coastal evolution and environmental change. Marine geologic mapping of the inner continental shelf of Massachusetts is a statewide cooperative effort of the U.S. Geological Survey and the Massachusetts Office of Coastal Zone Management.
","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/ofr20151153","collaboration":"Prepared in cooperation with the Massachusetts Office of Coastal Zone Management","usgsCitation":"Pendleton, E.A., Barnhardt, W.A., Baldwin, W.E., Foster, D.S., Schwab, W.C., Andrews, B.D., and Ackerman, S.D., 2015, Sea-floor texture and physiographic zones of the inner continental shelf from Salisbury to Nahant, Massachusetts, including the Merrimack Embayment and Western Massachusetts Bay: U.S. Geological Survey Open-File Report 2015–1153, 36 p., 1 appendix, https://dx.doi.org/10.3133/ofr20151153.","productDescription":"vi, 36 p.; HTML Documet","numberOfPages":"46","onlineOnly":"Y","additionalOnlineFiles":"Y","ipdsId":"IP-057824","costCenters":[{"id":678,"text":"Woods Hole Coastal and Marine Science Center","active":true,"usgs":true}],"links":[{"id":309391,"rank":3,"type":{"id":2,"text":"Additional Report Piece"},"url":"https://pubs.usgs.gov/of/2015/1153/index.html","text":"Report HTML","description":"OFR 2015-1153"},{"id":308684,"rank":2,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/of/2015/1153/ofr20151153.pdf","text":"Report","size":"2.02 MB","linkFileType":{"id":1,"text":"pdf"},"description":"OFR 2015-1153"},{"id":308683,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/of/2015/1153/images/coverthb.jpg"}],"country":"United States","state":"Massachusetts","city":"Nahant, Salisbury","otherGeospatial":"Massachusetts Bay, Merrimack Embayment","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -70.95794677734374,\n 42.407234661551875\n ],\n [\n -70.95794677734374,\n 42.84777884235988\n ],\n [\n -70.56106567382812,\n 42.84777884235988\n ],\n [\n -70.56106567382812,\n 42.407234661551875\n ],\n [\n -70.95794677734374,\n 42.407234661551875\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
Woods Hole, MA 0254
http://woodshole.er.usgs.gov/
The U.S. Geological Survey (USGS) has the critical role of providing scientific information to improve the understanding and management of the Chesapeake Bay ecosystem. The USGS works with Federal, State, and academic science partners to provide research and monitoring, and communicate results of these activities to enhance ecosystem management for both the Chesapeake and other National ecosystems. The USGS Chesapeake Science Strategy was prepared to guide science activities to address the Chesapeake Bay Watershed Agreement (2014–2025), to support the Department of the Interior (DOI) involvement in the Bay restoration efforts, and align with the USGS Mission Area (MA) Science strategies.
\nThe Chesapeake Bay is our Nation’s largest estuary, and provides critical goods and services to the people, fish, and wildlife that use the 64,000-square-mile watershed. The Chesapeake Bay watershed contains over 3,600 species of fish, wildlife, and plants and provides spawning grounds for many ecologically and economically important species including striped bass and blue crabs. The Bay watershed lies in the heart of the Atlantic Flyway and has 29 species of waterfowl, about 1 million of which winter in the region. The size of the Chesapeake seafood harvest is third in the Nation, only behind the Atlantic and Pacific Oceans. Along with agricultural production, tourism, and recreation, the estimated economic value of the services from the Chesapeake Bay watershed is about $100 billion annually. However, the health of the Bay ecosystem began to decline at the beginning of the 20th century due to overfishing and increasing human population and associated land change.
\nThe Chesapeake Bay Program (CBP) is the Federal-State cooperative effort that started in 1983 to restore the Bay and watershed. Given the ecological and economic importance of the Chesapeake ecosystem, President Obama issued an Executive Order (EO) in 2009 for increased Federal leadership in the CBP to enhance the pace of restoration, and the supporting strategy was released in 2010. The EO directed the Federal Government, including the U.S. Department of the Interior (DOI), represented by the National Park Service (NPS), the U.S. Fish & Wildlife Service (FWS), and the USGS, to expand its efforts and increase leadership to restore the Bay and its watershed. The USGS and other Federal agencies expanded their activities in 2011 to meet the President’s Chesapeake EO. Since the EO was released, there have been several important changes in the USGS, DOI, and the CBP including: (1) the Chesapeake Bay Watershed Agreement, (2) increased DOI leadership in the CBP, and (3) the release of the USGS MA science strategies. The EO strategy served as a foundation for the Chesapeake Bay Watershed Agreement that was signed in 2014 by the CBP Partners, and has goals and outcomes to be met by 2025.
\nThe USGS developed the Chesapeake Science Strategy to guide our activities to address the Chesapeake Bay Watershed Agreement, DOI leadership in the CBP, and USGS MA strategies. Improving the understanding of fish and wildlife population and health, and the factors affecting their condition is the emphasis of the Strategy. The science focuses on documenting the critical ecosystem connections in the Chesapeake, and providing implications to enhance decision making for restoration and conservation activities.
\nThe revised Strategy has four themes that address 7 of the 10 goals in the Chesapeake Bay Watershed Agreement:
\nThe structure and function of biological communities of the Bay and its watershed are extremely complex and are affected by a variety of stressors and conditions. To better define the issues being addressed, the USGS has developed cross-cutting questions that define some of the most important scientific challenges where multiple disciplines and collaborators are needed to address an issue. The initial questions include:
\nChesapeake Bay Coordinator
Northeast Region
U.S. Geological Survey
12201 Sunrise Valley Drive, Mail Stop 953
Reston, VA 20192
http://chesapeake.usgs.gov
Heavy rainfall occurred across South Carolina during October 1–5, 2015, as a result of an upper atmospheric low-pressure system that funneled tropical moisture from Hurricane Joaquin into the State. The storm caused major flooding from the central to the coastal areas of South Carolina. Almost 27 inches of rain fell near Mount Pleasant in Charleston County during this period. U.S. Geological Survey streamgages recorded peaks of record at 17 locations, and 15 other locations had peaks that ranked in the top 5 for the period of record. During the October 2015 flood event, U.S. Geological Survey personnel made about 140 streamflow measurements at 86 locations to verify, update, or extend existing rating curves, which are used to compute streamflow from monitored river stage.
","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/ofr20151201","usgsCitation":"Feaster, T.D., Shelton, J.M., and Robbins, J.C., 2015, Preliminary peak stage and streamflow data at selected USGS streamgaging stations for the South Carolina flood of October 2015 (ver. 1.1, November 2015): U.S. Geological Survey Open-File Report 2015–1201, 19 p., https://dx.doi.org/10.3133/ofr20151201.","productDescription":"iv, 19 p.","numberOfPages":"28","onlineOnly":"Y","additionalOnlineFiles":"N","ipdsId":"IP-069972","costCenters":[{"id":13634,"text":"South Atlantic Water Science Center","active":true,"usgs":true}],"links":[{"id":311080,"rank":3,"type":{"id":25,"text":"Version History"},"url":"https://pubs.usgs.gov/of/2015/1201/versionHist.txt","text":"Open-File Report 2015-1201, Version 1.1","size":"1.11 KB","linkFileType":{"id":2,"text":"txt"},"description":"OFR 2015-1201"},{"id":310064,"rank":2,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/of/2015/1201/ofr20151201.pdf","text":"Report","size":"2.69 MB","linkFileType":{"id":1,"text":"pdf"},"description":"OFR 2015-1201"},{"id":310063,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/of/2015/1201/coverthb1.jpg"}],"country":"United States","state":"South Carolina","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -78.50830078125,\n 33.8521697014074\n ],\n [\n -79.683837890625,\n 34.813803317113155\n ],\n [\n -80.67260742187499,\n 34.813803317113155\n ],\n [\n -80.91430664062499,\n 35.10193405724606\n ],\n [\n -81.0791015625,\n 35.18278813800229\n ],\n [\n -82.41943359375,\n 35.24561909420681\n ],\n [\n -83.111572265625,\n 35.038992046780784\n ],\n [\n -83.29833984375,\n 34.90395296559004\n ],\n [\n -83.397216796875,\n 34.74161249883172\n ],\n [\n -83.001708984375,\n 34.46127728843708\n ],\n [\n -82.913818359375,\n 34.488447837809304\n ],\n [\n -82.781982421875,\n 34.31621838080741\n ],\n [\n -82.7490234375,\n 34.098159345215535\n ],\n [\n -82.584228515625,\n 33.90689555128866\n ],\n [\n -82.254638671875,\n 33.75174787568194\n ],\n [\n -82.2216796875,\n 33.58716733904656\n ],\n [\n -82.012939453125,\n 33.486435450999885\n ],\n [\n -81.968994140625,\n 33.348884792201694\n ],\n [\n -81.76025390625,\n 33.119150226768866\n ],\n [\n -81.529541015625,\n 33.03629817885956\n ],\n [\n -81.485595703125,\n 32.7872745269555\n ],\n [\n -81.463623046875,\n 32.59310597426537\n ],\n [\n -81.298828125,\n 32.50049648924482\n ],\n [\n -81.14501953125,\n 32.35212281198644\n ],\n [\n -81.1669921875,\n 32.18491105051798\n ],\n [\n -80.92529296875,\n 31.924192605327708\n ],\n [\n -80.628662109375,\n 32.287132632616355\n ],\n [\n -80.452880859375,\n 32.31499127724556\n ],\n [\n -80.364990234375,\n 32.41706632846282\n ],\n [\n -80.1123046875,\n 32.59310597426537\n ],\n [\n -79.991455078125,\n 32.602361666817515\n ],\n [\n -79.881591796875,\n 32.713355353177555\n ],\n [\n -79.73876953125,\n 32.82421110161336\n ],\n [\n -79.376220703125,\n 32.99945000822839\n ],\n [\n -79.1455078125,\n 33.211116472416855\n ],\n [\n -79.12353515625,\n 33.367237465838315\n ],\n [\n -78.958740234375,\n 33.568861182555565\n ],\n [\n -78.804931640625,\n 33.715201644740844\n ],\n [\n -78.64013671875,\n 33.815666308702774\n ],\n [\n -78.50830078125,\n 33.8521697014074\n ]\n ]\n ]\n }\n }\n ]\n}","edition":"Version 1: Originally posted October 20, 2015; Version 1.1: November 9, 2015","contact":"Director, South Atlantic Water Science Center
U.S. Geological Survey
720 Gracern Road
Columbia, SC 29210
http://sc.water.usgs.gov
Wyoming has led the nation as the producer of uranium ore since 1995 and contains the largest reserves of any state. Approximately one third of Wyoming’s total production came from deposits in, or immediately adjacent to, the Wyoming Landscape Conservation Initiative (WLCI) study area in the southwestern corner of the state including all of Carbon, Lincoln, Sublette, Sweetwater, Uinta, and parts of southern Fremont Counties. Conventional open-pit and underground mining methods were employed in the study area until the early 1990s. Since the early 1990s, all uranium mining has been by in-situ recovery (also called in-situ leach). It is estimated that statewide remaining resources of 141,000 tonnes of uranium are about twice the 84,000 tonnes of uranium that the state has already produced.
\nAn evaluation of the mineral commodities present in the WLCI study area that may have a role in the development of southwest Wyoming includes uranium. The WLCI study area contains five uranium mineralized areas: Ketchum Buttes, Poison Basin, Shirley Basin, the southern part of Crooks Gap–Green Mountain, and most of Great Divide Basin. Mineralized areas described in the report and outlined on an accompanying map are based on the presence of either contiguous claim blocks, continuous mineralization adjacent to prospective uranium properties, suggestions of mineralization based on site entries in the U.S. Geological Survey’s Mineral Resources Data System (MRDS), or extension of geologic host units or structures. Mineralized areas are not the same as mining districts: the latter have defined administrative boundaries.
\nIn the WLCI study area, all uranium areas except Poison Basin and Ketchum Buttes contain roll-front deposits in Eocene (56–34 Ma) sedimentary rocks. Tabular sandstone-hosted uranium deposits are also recognized within the study area.
","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/ofr20141123","usgsCitation":"Wilson, A.B., 2015, Uranium in the Wyoming Landscape Conservation Initiative Study Area, southwestern Wyoming: U.S. Geological Survey Open-File Report 2014–1123, 33 p., 1 plate, https://dx.doi.org/10.3133/ofr20141123.","productDescription":"Report: iv, 33 p.; Plate: 35 x 28 inches; Appendixes A-B","numberOfPages":"37","onlineOnly":"Y","additionalOnlineFiles":"Y","ipdsId":"IP-040674","costCenters":[{"id":171,"text":"Central Mineral and Environmental Resources Science Center","active":true,"usgs":true}],"links":[{"id":309981,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/of/2014/1123/coverthb.jpg"},{"id":309986,"rank":5,"type":{"id":3,"text":"Appendix"},"url":"https://pubs.usgs.gov/of/2014/1123/pdf/AppendixB_MRDS_Database_User_Manual_Explanation.pdf","text":"Appendix B","size":"367 kB","description":"OFR 2014-1123 Appendix B","linkHelpText":"Director, Central Mineral and Environmental Resources Science Center
U.S. Geological Survey
Box 25046, MS–973
Denver, CO 80225
http://minerals.cr.usgs.gov/
Earth materials and structures in the El Casco quadrangle provide considerable information about the late Cenozoic geologic evolution of southern California’s Inland Empire region (fig. 2). Important structural and stratigraphic elements include (1) modern traces of the right-lateral San Jacinto Fault zone, (2) older traces of the San Jacinto Fault zone, and (3) sedimentary materials and geologic structures that formed during the last eight million years or so and that record interactions within the San Andreas Fault system. These materials, and the structures that deform them, provide a geologic context 3 for investigations of groundwater recharge and subsurface flow (Waring, 1919; Burnham and Dutcher, 1960; Bloyd, 1971; Rewis and others, 2006).
\nThis geologic database of the El Casco 7.5′ quadrangle was prepared by the Basins and Landscape Co-Evolution Project (BALANCE), a regional geologic-mapping project sponsored jointly by the U.S. Geological Survey and the California Geological Survey. The database was developed as a contribution to the National Cooperative Geologic Mapping Program’s National Geologic Map Database, and provides a general geologic setting of the El Casco quadrangle. The database and map provide information about earth materials and geologic structures, including faults and folds that have developed in the quadrangle due to complexities in the San Andreas Fault system.
\nGeologic information contained in the El Casco database is general-purpose data applicable to land-related investigations in the earth and biological sciences. The term “general-purpose” means that all geologic-feature classes have minimal information content adequate to characterize their general geologic characteristics and to interpret their general geologic history. However, no single feature class has enough information to definitively characterize its properties and origin. For this reason the database cannot be used for site-specific geologic evaluations, although it can be used to plan and guide investigations at the site-specific level.
","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/ofr20101274","usgsCitation":"Matti, J.C., Morton, D.M., and Langenheim, V., 2015, Geologic and geophysical maps of the El Casco 7.5′ quadrangle, Riverside County, southern California, with accompanying geologic-map database: U.S. Geological Survey Open-File Report 2010-1274, Report: vi, 141; 3 Sheets: 46.77 x 36.00 inches or smaller; Dataset; Metadata; Read Me, https://doi.org/10.3133/ofr20101274.","productDescription":"Report: vi, 141; 3 Sheets: 46.77 x 36.00 inches or smaller; Dataset; Metadata; Read Me","numberOfPages":"147","onlineOnly":"Y","additionalOnlineFiles":"N","ipdsId":"IP-021187","costCenters":[{"id":312,"text":"Geology, Minerals, Energy, and Geophysics Science Center","active":true,"usgs":true}],"links":[{"id":308467,"rank":2,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/of/2010/1274/ofr20101274_pamphlet.pdf","text":"Pamphlet","linkFileType":{"id":1,"text":"pdf"},"description":"OFR 2010-1274 Pamphlet"},{"id":308466,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/of/2010/1274/coverthb.jpg"},{"id":308468,"rank":3,"type":{"id":26,"text":"Sheet"},"url":"https://pubs.usgs.gov/of/2010/1274/ofr20101274_sheet1.pdf","text":"Sheet 1","linkFileType":{"id":1,"text":"pdf"},"description":"OFR 2010-1274 Sheet 1","linkHelpText":"Plot file of the geologic map of the El Casco 7.5' quadrangle"},{"id":308472,"rank":7,"type":{"id":16,"text":"Metadata"},"url":"https://pubs.usgs.gov/of/2010/1274/ofr20101274_metadata.txt","text":"Metadata","linkFileType":{"id":2,"text":"txt"},"description":"OFR 2010-1274 Metadata"},{"id":308471,"rank":6,"type":{"id":20,"text":"Read Me"},"url":"https://pubs.usgs.gov/of/2010/1274/ofr20101274_readme.txt","text":"Read Me","linkFileType":{"id":2,"text":"txt"},"description":"OFR 2010-1274 Read Me"},{"id":399006,"rank":9,"type":{"id":36,"text":"NGMDB Index Page"},"url":"https://ngmdb.usgs.gov/Prodesc/proddesc_103546.htm"},{"id":308470,"rank":5,"type":{"id":26,"text":"Sheet"},"url":"https://pubs.usgs.gov/of/2010/1274/ofr20101274_sheet3.pdf","text":"Sheet 3","linkFileType":{"id":1,"text":"pdf"},"description":"OFR 2010-1274 Sheet 3","linkHelpText":"Plot file of the gravity map"},{"id":308469,"rank":4,"type":{"id":26,"text":"Sheet"},"url":"https://pubs.usgs.gov/of/2010/1274/ofr20101274_sheet2.pdf","text":"Sheet 2","linkFileType":{"id":1,"text":"pdf"},"description":"OFR 2010-1274 Sheet 2","linkHelpText":"Plot file of observation data for the El Casco 7.5' quadrangle"},{"id":308473,"rank":8,"type":{"id":28,"text":"Dataset"},"url":"https://pubs.usgs.gov/of/2010/1274/ofr20101274_data.zip","text":"Data","linkFileType":{"id":6,"text":"zip"},"description":"OFR 2010-1274 Data"}],"scale":"24000","country":"United States","state":"California","county":"Riverside County","otherGeospatial":"El Casco 7.5' quadrangle","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -117.125,\n 33.875\n ],\n [\n -117,\n 33.875\n ],\n [\n -117,\n 34\n ],\n [\n -117.125,\n 34\n ],\n [\n -117.125,\n 33.875\n ]\n ]\n ]\n }\n }\n ]\n}","contact":"GMEG staff, Geology, Minerals, Energy, & Geophysics Science Center—Tucson
U.S.G.S., c/o University of Arizona
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Tucson, AZ 85719-5035
http://geomaps.wr.usgs.gov/gmeg/
Trace-metal concentrations in sediment and in the clam Macoma petalum (formerly reported as Macoma balthica), clam reproductive activity, and benthic macroinvertebrate community structure were investigated in a mudflat 1 kilometer (km) south of the discharge of the Palo Alto Regional Water Quality Control Plant (PARWQCP) in South San Francisco Bay, Calif. This report includes the data collected by U.S. Geological Survey (USGS) scientists for the period January 2014 to December 2014. These append to long-term datasets extending back to 1974, and serve as the basis for the City of Palo Alto’s Near-Field Receiving Water Monitoring Program, initiated in 1994.
\nFollowing significant reductions in the late 1980s, silver (Ag) and copper (Cu) concentrations in sediment and M. petalum appear to have stabilized. Data for other metals, including chromium (Cr), mercury (Hg), nickel (Ni), selenium (Se), and zinc (Zn), have been collected since 1994. Over this period, concentrations of these elements have remained relatively constant, aside from seasonal variation that is common to all elements. In 2014, concentrations of Ag and Cu in M. petalum varied seasonally in response to a combination of site-specific metal exposures and annual growth and reproduction, as reported previously. Seasonal patterns for other elements, including Cr, Ni, Zn, Hg, and Se, were generally similar in timing and magnitude as those for Ag and Cu. In M. petalum, all observed elements showed annual maxima in January–February and minima in April, except for Zn, which was lowest in December. In sediments, annual maxima also occurred in January–February, and minima were measured in June and September. In 2014, metal concentrations in both sediments and clam tissue were among the lowest on record. This record suggests that regional-scale factors now largely control sedimentary and bioavailable concentrations of Ag and Cu, as well as other elements of regulatory interest, at the Palo Alto site.
\nAnalyses of the benthic community structure of a mudflat in South San Francisco Bay over a 40-year period show that changes in the community have occurred concurrent with reduced concentrations of metals in the sediment and in the tissues of the biosentinel clam, M. petalum, from the same area. Analysis of M. petalum shows increases in reproductive activity concurrent with the decline in metal concentrations in the tissues of this organism. Reproductive activity is presently stable (2014), with almost all animals initiating reproduction in the fall and spawning the following spring. The entire infaunal community has shifted from being dominated by several opportunistic species to a community where the species are more similar in abundance, a pattern that indicates a more stable community that is subjected to fewer stressors. In addition, two of the opportunistic species (Ampelisca abdita and Streblospio benedicti) that brood their young and live on the surface of the sediment in tubes have shown a continual decline in dominance coincident with the decline in metals; both species had short-lived rebounds in abundance in 2008, 2009, and 2010. Heteromastus filiformis (a subsurface polychaete worm that lives in the sediment, consumes sediment and organic particles residing in the sediment, and reproduces by laying its eggs on or in the sediment) showed a concurrent increase in dominance and, in the last several years before 2008, showed a stable population. H. filiformis abundance increased slightly in 2011–2012 and returned to pre-2011 numbers in 2014. An unidentified disturbance occurred on the mudflat in early 2008 that resulted in the loss of the benthic animals, except for deep-dwelling animals like Macoma petalum. However, within two months of this event animals returned to the mudflat. The resilience of the community suggested that the disturbance was not due to a persistent toxin or to anoxia. The reproductive mode of most species present in 2014 is reflective of species that were available either as pelagic larvae or as mobile adults. Although oviparous species were lower in number in this group, the authors hypothesize that these species will return slowly as more species move back into the area. The use of functional ecology was highlighted in the 2014 benthic community data, which showed that the animals that have now returned to the mudflat are those that can respond successfully to a physical, nontoxic disturbance. Today, community data show a mix of species that consume the sediment, or filter feed, have pelagic larvae that must survive landing on the sediment, and those that brood their young. USGS scientists view the 2008 disturbance event as a response by the infaunal community to an episodic natural stressor (possibly sediment accretion or a pulse of freshwater), in contrast to the long-term recovery from metal contamination. We will compare this recovery to the long-term recovery observed after the 1970’s when the decline in sediment pollutants was the dominating factor.
","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/ofr20151199","usgsCitation":"Cain, D.J., Thompson, J.K., Crauder, J., Parcheso, F., Stewart, A.R., Kleckner, A.E., Dyke, J., Hornberger, M.I., and Luoma, S.N., 2015, Near-field receiving water monitoring of trace metals and a benthic community near the Palo Alto Regional Water Quality Control Plant in south San Francisco Bay, California: 2014: U.S. Geological Survey Open-File Report 2015-1199, viii, 79 p., https://doi.org/10.3133/ofr20151199.","productDescription":"viii, 79 p.","numberOfPages":"89","onlineOnly":"Y","additionalOnlineFiles":"N","temporalStart":"2014-01-01","temporalEnd":"2014-12-31","ipdsId":"IP-068498","costCenters":[{"id":438,"text":"National Research Program - Western Branch","active":true,"usgs":true}],"links":[{"id":310004,"rank":2,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/of/2015/1199/ofr20151199.pdf","text":"Report","size":"9.9 MB","linkFileType":{"id":1,"text":"pdf"},"description":"OFR 2015-1199"},{"id":310003,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/of/2015/1199/coverthb.jpg"}],"country":"United States","state":"California","otherGeospatial":"San Francisco Bay","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -122.11578369140626,\n 37.43493087364719\n ],\n [\n -122.11578369140626,\n 37.46123344639866\n ],\n [\n -122.09020614624023,\n 37.46123344639866\n ],\n [\n -122.09020614624023,\n 37.43493087364719\n ],\n [\n -122.11578369140626,\n 37.43493087364719\n ]\n ]\n ]\n }\n }\n ]\n}","contact":"NRP staff
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U.S. Geological Survey
345 Middlefield Road, MS-435
Menlo Park, CA 94025
http://water.usgs.gov/nrp/
This study provides viable estimates of historical storm-induced water levels in the coastal communities of Gambell and Savoonga situated on St. Lawrence Island in the Bering Sea, as well as Unalakleet located at the head of Norton Sound on the western coast of Alaska. Gambell, Savoonga, and Unalakleet are small Native Villages that are regularly impacted by coastal storms but where little quantitative information about these storms exists. The closest continuous water-level gauge is at Nome, located more than 200 kilometers from both St. Lawrence Island and Unalakleet. In this study, storms are identified and quantified using historical atmospheric and sea-ice data and then used as boundary conditions for a suite of numerical models. The work includes storm-surge (temporary rise in water levels due to persistent strong winds and low atmospheric pressures) modeling in the Bering Strait region, as well as modeling of wave runup along specified sections of the coast in Gambell and Unalakleet. Modeled historical water levels are used to develop return periods of storm surge and storm surge plus wave runup at key locations in each community. It is anticipated that the results will fill some of the data void regarding coastal flood data in western Alaska and be used for production of coastal vulnerability maps and community planning efforts.
","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/ofr20151193","usgsCitation":"Erikson, L., McCall, R.T., van Rooijen, A., and Norris, B., 2015, Hindcast storm events in the Bering Sea for the St. Lawrence Island and Unalakleet Regions, Alaska: U.S. Geological Survey Open-File Report 2015-1193, vii, 47 p., https://doi.org/10.3133/ofr20151193.","productDescription":"vii, 47 p.","numberOfPages":"57","onlineOnly":"Y","additionalOnlineFiles":"N","ipdsId":"IP-059633","costCenters":[{"id":520,"text":"Pacific Coastal and Marine Science Center","active":true,"usgs":true}],"links":[{"id":309820,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/of/2015/1193/coverthb.jpg"},{"id":309821,"rank":2,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/of/2015/1193/ofr20151193.pdf","text":"Report","size":"3.8 MB","linkFileType":{"id":1,"text":"pdf"},"description":"OFR 2015-1192"}],"country":"United States","state":"Alaska","otherGeospatial":"Bering Sea, St. Lawrence Island, Unalakleet Regions","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -171.87011718749997,\n 63.84066844285508\n ],\n [\n -171.36474609375,\n 63.75334975181205\n ],\n [\n -171.01318359374997,\n 63.6949869286095\n ],\n [\n -170.595703125,\n 63.80189351770543\n ],\n [\n -169.892578125,\n 63.66576033778838\n ],\n [\n -169.56298828124997,\n 63.470144746565445\n ],\n [\n -168.64013671875,\n 63.37183226679281\n ],\n [\n -168.55224609375,\n 63.05495931065107\n ],\n [\n -169.21142578125,\n 63.06491420208559\n ],\n [\n -169.56298828124997,\n 62.865168668923125\n ],\n [\n -170.22216796875,\n 62.94523066452288\n ],\n [\n -170.52978515624997,\n 63.22373025329546\n ],\n [\n -171.2109375,\n 63.28306240110864\n ],\n [\n -171.54052734375,\n 63.22373025329546\n ],\n [\n -172.06787109375,\n 63.361982464431236\n ],\n [\n -171.87011718749997,\n 63.84066844285508\n ]\n ]\n ]\n }\n },\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -161.69677734375,\n 64.74601725111455\n ],\n [\n -161.38916015625,\n 64.830253743883\n ],\n [\n -160.7080078125,\n 64.830253743883\n ],\n [\n -160.55419921875,\n 64.67091929440798\n ],\n [\n -160.59814453125,\n 64.37794095121995\n ],\n [\n -160.75195312499997,\n 64.20637724320852\n ],\n [\n -161.25732421875,\n 64.07219957867284\n ],\n [\n -162.09228515625,\n 64.19681461100495\n ],\n [\n -161.982421875,\n 64.58618480339979\n ],\n [\n -161.806640625,\n 64.74601725111455\n ],\n [\n -161.69677734375,\n 64.74601725111455\n ]\n ]\n ]\n }\n }\n ]\n}","contact":"Pacific Coastal and Marine Science Center
U.S. Geological Survey
Pacific Science Center
2885 Mission St.
Santa Cruz, CA 95060
http://walrus.wr.usgs.gov/
The use of acoustic and other parameters as surrogates for suspended-sediment concentrations (SSC) in rivers has been successful in multiple applications across the Nation. Tools to process and evaluate the data are critical to advancing the operational use of surrogates along with the subsequent development of regression models from which real-time sediment concentrations can be made available to the public. Recent developments in both areas are having an immediate impact on surrogate research and on surrogate monitoring sites currently (2015) in operation.
\nThe Surrogate Analysis and Index Developer (SAID) standalone tool, under development by the U.S. Geological Survey (USGS), assists in the creation of linear regression models that relate constituent and surrogate parameters by providing visual and quantitative diagnostics to the user. SAID also processes acoustic parameters to be used as explanatory variables for SSC. The sediment acoustic method utilizes acoustic parameters from fixed-mount stationary equipment. The theory and method used by the SAID tool have been described in recent publications. The tool also serves to support sediment-acoustic-index methods and other surrogate guidelines such as turbidity and SSC (Rasmussen and others, 2009).
\nThe regression models created in SAID can be used in utilities that have been developed to work with the USGS National Water Information System (NWIS) and for the USGS National Real-Time Water Quality (NRTWQ) Web site. The real-time dissemination of predicted SSC and prediction intervals for each time step has substantial potential to improve understanding of sediment-related water quality and associated engineering and ecological management decisions.
","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/ofr20151177","collaboration":"Federal Interagency Sedimentation Project and Midwest Region River Sediments and Nutrients Investigations Initiative","usgsCitation":"Domanski, M.M., Straub, T.D., and Landers, M.N., 2015, Surrogate Analysis and Index Developer (SAID) tool (version 1.0, September 2015): U.S. Geological Survey Open-File Report 2015–1177, 38 p., https://doi.org/10.3133/ofr20151177.","productDescription":"Report: vi, 36 p.; HTML Document","onlineOnly":"Y","additionalOnlineFiles":"Y","ipdsId":"IP-066947","costCenters":[{"id":344,"text":"Illinois Water Science Center","active":true,"usgs":true}],"links":[{"id":309366,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/of/2015/1177/coverthb.jpg"},{"id":309367,"rank":2,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/of/2015/1177/ofr20151177.pdf","text":"Report","size":"1.18 MB","linkFileType":{"id":1,"text":"pdf"},"description":"OFR 2015-1177"},{"id":309845,"rank":3,"type":{"id":7,"text":"Companion Files"},"url":"https://water.usgs.gov/osw/SALT/SAID/","text":"The Surrogate Analysis and Index Developer (SAID) Tool","linkFileType":{"id":5,"text":"html"},"description":"OFR 2015-1177"}],"contact":"Director, llinois Water Science Center
405 N Goodwin
Urbana, IL 61801
http://il.water.usgs.gov/
Rigorous analysis and management of animal populations requires that observers account for limitations inherent to the detection of those populations and the individuals within them. Researchers are usually unable to see every individual of a population or to even detect some entire populations. Ignoring this imperfect detectability can bias estimates of population characteristics, such as probability of occurrence, abundance, survival, recruitment, and population growth rate. Furthermore, the precision with which these population characteristics are estimated is dependent on detection probabilities (the probability that at least one individual of a species is detected during a survey, given that the species occurs where the survey is conducted) and capture probabilities (the probability that a given individual is observed or captured during a single survey); greater detection and capture probabilities result in less uncertainty about the values of population characteristics and a greater ability to evaluate the effects of variables or experimental treatments on the population characteristic(s) of interest.
\nDetection and capture probabilities for giant gartersnakes (Thamnophis gigas) are very low, and successfully evaluating the effects of variables or experimental treatments on giant gartersnake populations will require greater detection and capture probabilities than those that had been achieved with standard trap designs. Previous research identified important trap modifications that can increase the probability of snakes entering traps and help prevent the escape of captured snakes. The purpose of this study was to quantify detection and capture probabilities obtained using the most successful modification to commercially available traps to date (2015), and examine the ability of realized detection and capture probabilities to achieve benchmark levels of precision in occupancy and capture-mark-recapture studies.
","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/ofr20151200","collaboration":"Prepared in cooperation with the California Department of Water Resources","usgsCitation":"Halstead, B., Skalos, S., Casazza, M.L., and Wylie, G.D., 2015, Realized detection and capture probabilities for giant gartersnakes (Thamnophis gigas) using modified floating aquatic funnel traps: U.S. Geological Survey Open-File Report 2015-1200, iv, 36 p., https://doi.org/10.3133/ofr20151200.","productDescription":"iv, 36 p.","numberOfPages":"44","onlineOnly":"Y","additionalOnlineFiles":"N","temporalStart":"2013-06-14","temporalEnd":"2013-11-30","ipdsId":"IP-067831","costCenters":[{"id":651,"text":"Western Ecological Research Center","active":true,"usgs":true}],"links":[{"id":309850,"rank":2,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/of/2015/1200/ofr20151200.pdf","text":"Report","size":"2.2 MB","linkFileType":{"id":1,"text":"pdf"},"description":"OFR 2015-1200"},{"id":309849,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/of/2015/1200/coverthb.jpg"}],"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 -122.33551025390625,\n 38.50519140240354\n ],\n [\n -122.33551025390625,\n 39.61626788999701\n ],\n [\n -121.4923095703125,\n 39.61626788999701\n ],\n [\n -121.4923095703125,\n 38.50519140240354\n ],\n [\n -122.33551025390625,\n 38.50519140240354\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/
Changes to barrier islands occur at time scales that vary from the few hours it takes an individual storm to pass (Morton, 2008) to the millennia it takes for coastal systems to undergo geologic evolution. Developing an understanding of how barrier islands will respond to climate change, sea level rise, and major storms over a range of time scales is relevant to studies of physical, geological, ecological, and societal processes and will help to guide and improve management of our coastal resources (Sallenger and others, 1987). Observations of coastal processes made over a range of spatial and temporal scales and from a variety of instrument platforms (for example, in situ and remote remote sensing) are required to understand and eventually predict the evolution of coastal systems.
\nThe deployment of Landsat and other earth-observing satellites within the last few decades has provided an opportunity to observe barrier islands at frequent intervals, often many times a year. This sample frequency is much higher and the spatial coverage much greater than most routine high-resolution topographic surveys (Guy and others, 2014). In addition, the historical record of these datasets have become long enough to document shorter- (that is, annual) and longer-term (that is, decadal) changes from a single data source. Certain aspects of barrier island morphology, such as island size, shape, and position, can be determined from these images and can indicate erosion, land loss, and island breakup (McBride and others, 1989; Plant and Guy, 2013).
\nThe shoreline is a common variable used as a metric for coastal erosion or change (Himmelstoss and others, 2010). Although shorelines are often extracted from topographic data (for example, ground-based surveys and light detection and ranging [lidar]), image-based shorelines, corrected for their inherent uncertainties (Moore and others, 2006), have provided much of our understanding of long-term shoreline change because they pre-date routine lidar elevation survey methods. Image-based shorelines continue to be valuable because of their higher temporal resolution compared to costly airborne lidar surveys. A method for extracting sandy shorelines from 30-meter (m) resolution Landsat imagery is presented here.
","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/ofr20151179","usgsCitation":"Guy, K.K., 2015, Barrier island shorelines extracted from Landsat imagery: U.S. Geological Survey Open-File Report 2015–1179, 3 p., https://dx.doi.org/10.3133/ofr20151179.","productDescription":"Report: iv, 3 p.; Spatial Data","numberOfPages":"8","onlineOnly":"Y","additionalOnlineFiles":"Y","ipdsId":"IP-067042","costCenters":[{"id":574,"text":"St. Petersburg Coastal and Marine Science Center","active":true,"usgs":true}],"links":[{"id":438679,"rank":8,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/F7028PMP","text":"USGS data release","linkHelpText":"Shorelines Extracted from Landsat Imagery: Dauphin Island, Alabama"},{"id":312288,"rank":4,"type":{"id":23,"text":"Spatial Data"},"url":"https://dx.doi.org/10.5066/F7DZ06CD","text":"Shorelines Extracted from Landsat Imagery: Ship Island, Mississippi","description":"OFR 2015-1179"},{"id":312289,"rank":5,"type":{"id":23,"text":"Spatial Data"},"url":"https://dx.doi.org/10.5066/F7JQ0Z3R","text":"Shorelines Extracted from Landsat Imagery: Cat Island, Mississippi","description":"OFR 2015-1179"},{"id":312290,"rank":6,"type":{"id":23,"text":"Spatial Data"},"url":"https://dx.doi.org/10.5066/F7XW4GVG","text":"Shorelines Extracted from Landsat Imagery: Horn Island, Mississippi","description":"OFR 2015-1179"},{"id":312291,"rank":7,"type":{"id":23,"text":"Spatial Data"},"url":"https://dx.doi.org/10.5066/F72N509Q","text":"Shorelines Extracted from Landsat Imagery: Petit Bois Island, Mississippi","description":"OFR 2015-1179"},{"id":309457,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/of/2015/1179/coverthb.jpg"},{"id":309459,"rank":3,"type":{"id":23,"text":"Spatial Data"},"url":"https://dx.doi.org/10.5066/F7028PMP","text":"Shorelines Extracted from Landsat Imagery: Dauphin Island, Alabama","description":"OFR 2015-1179"},{"id":309458,"rank":2,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/of/2015/1179/ofr20151179.pdf","text":"Report","size":"242 KB","linkFileType":{"id":1,"text":"pdf"},"description":"OFR 2015-1179"}],"country":"United States","contact":"St. Petersburg Coastal and Marine Science Center
U.S. Geological Survey
600 4th Street South
St. Petersburg, FL 33701
http://coastal.er.usgs.gov/
The accuracy of groundwater-level tapes was investigated by developing a tape calibration method and device and testing the accuracy of a sample of groundwater-level tapes with the calibration method and device. The sample of tapes included in-service U.S. Geological Survey (USGS) Water Science Center steel and electric groundwater-level tapes.
\nThe tape calibration method developed during the study is based on a National Institute for Standards and Technology (NIST) protocol, and compares the tape under calibration (test tape) with a NIST-traceable reference tape. The calibration method can be used to determine tape accuracy and tape corrections. The tape calibration device consists of an anchor and tensioner. The device allows individual tensioning of the test and reference tapes. The Vernier scale on the tensioner allows measurement of the difference (or displacement) between the test and reference tapes graduations with a resolution of 0.0005 foot (ft). The calibration method used with the calibration device has a repeatability (standard deviation) of 0.0011 ft.
\nThe calibration device and proposed method were used to calibrate a sample of in-service USGS steel and electric groundwater tapes. The sample of in-service groundwater steel tapes were in relatively good condition. All steel tapes, except one, were accurate to ±0.01 ft per 100 ft over their entire length. One steel tape, which had obvious damage in the first hundred feet, was marginally outside the accuracy of ±0.01 ft per 100 ft by 0.001 ft. The sample of in-service groundwater-level electric tapes were in a range of conditions—from like new, with cosmetic damage, to nonfunctional. The in-service electric tapes did not meet the USGS accuracy recommendation of ±0.01 ft. In-service electric tapes, except for the nonfunctional tape, were accurate to about ±0.03 ft per 100 ft. A comparison of new with in-service electric tapes found that steel-core electric tapes maintained their length and accuracy better than electric tapes without a steel core. The in-service steel tapes could be used as is and achieve USGS accuracy recommendations for groundwater-level measurements. The in-service electric tapes require tape corrections to achieve USGS accuracy recommendations for groundwater-level measurement.
","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/ofr20151137","usgsCitation":"Fulford, J.M., and Clayton, C.S., 2015, Accuracy testing of steel and electric groundwater-level measuring tapes—Test method and in-service tape accuracy: U.S. Geological Survey Open-File Report 2015–1137, 31 p., https://dx.doi.org/10.3133/ofr20151137.","productDescription":"v, 31 p.","numberOfPages":"42","onlineOnly":"Y","additionalOnlineFiles":"N","ipdsId":"IP-064514","costCenters":[{"id":502,"text":"Office of Surface Water","active":true,"usgs":true}],"links":[{"id":309731,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/of/2015/1137/coverthb.jpg"},{"id":309732,"rank":2,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/of/2015/1137/ofr20151137.pdf","text":"Report","size":"15.3 MB","linkFileType":{"id":1,"text":"pdf"},"description":"OFR 2015-1137"}],"contact":"Hydrologic Instrumentation Facility
U.S. Geological Survey
Building 2101
Stennis Space Center, MS 39529
http://water.usgs.gov/hif/
The locations and sizes of potential cold-water refuges for trout were examined in 2005 along a 27-kilometer segment of the Indian and Hudson Rivers in northern New York to evaluate the extent of refuges, the effects of routine flow releases from an impoundment, and how these refuges and releases might influence trout survival in reaches that otherwise would be thermally stressed. This river segment supports small populations of brook trout (Salvelinus fontinalis), brown trout (Salmo trutta), and rainbow trout (Oncorhynchus mykiss) and also receives regular releases of reservoir-surface waters to support rafting during the summer, when water temperatures in both the reservoir and the river frequently exceed thermal thresholds for trout survival. Airborne thermal infrared imaging was supplemented with continuous, in-stream temperature loggers to identify potential refuges that may be associated with tributary inflows or groundwater seeps and to define the extent to which the release flows decrease the size of existing refuges. In general, the release flows overwhelmed the refuge areas and greatly decreased the size and number of the areas. Mean water temperatures were unaffected by the releases, but small-scale heterogeneity was diminished. At a larger scale, water temperatures in the upper and lower segments of the reach were consistently warmer than in the middle segment, even during passage of release waters. The inability of remote thermal infrared images to consistently distinguish land from water (in shaded areas) and to detect groundwater seeps (away from the shallow edges of the stream) limited data analysis and the ability to identify potential thermal refuge areas.
","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/ofr20151078","collaboration":"Prepared in cooperation with New York State Department of Environmental Conservation and Rochester Institute of Technology","usgsCitation":"Ernst, A.G., Baldigo, B.P., Calef, F.J., Freehafer, D.A., and Kremens, R.L., 2015, Identifying trout refuges in the Indian and Hudson Rivers in northern New York through airborne thermal infrared remote sensing: U.S. Geological Survey Open-File Report 2015–1078, 17 p., https://dx.doi.org/10.3133/ofr20151078.","productDescription":"vii, 17 p.","numberOfPages":"30","onlineOnly":"Y","additionalOnlineFiles":"N","ipdsId":"IP-054790","costCenters":[{"id":474,"text":"New York Water Science Center","active":true,"usgs":true}],"links":[{"id":308601,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/of/2015/1078/coverthb.jpg"},{"id":308602,"rank":2,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/of/2015/1078/ofr20151078.pdf","text":"Report","size":"3.78 MB","linkFileType":{"id":1,"text":"pdf"},"description":"OFR 2015-1078"}],"country":"United States","state":"New York","otherGeospatial":"Hudson River and Indian River, Adirondack Park","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -74.5751953125,\n 43.60326743161359\n ],\n [\n -74.5751953125,\n 43.98886243884903\n ],\n [\n -73.90777587890625,\n 43.98886243884903\n ],\n [\n -73.90777587890625,\n 43.60326743161359\n ],\n [\n -74.5751953125,\n 43.60326743161359\n ]\n ]\n ]\n }\n }\n ]\n}","contact":"Director, New York Water Science Center
U.S. Geological Survey
425 Jordan Road
Troy, NY 12180-8349
http://ny.water.usgs.gov
Cape Canaveral, Florida, is a prominent feature along the Southeast U.S. coastline. The region includes Merritt Island National Wildlife Refuge, Cape Canaveral Air Force Station, NASA’s Kennedy Space Center, and a large portion of Canaveral National Seashore. The actual promontory of the modern Cape falls within the jurisdictional boundaries of Cape Canaveral Air Force Station. Erosion hazards result from winter and tropical storms, changes in sand resources, sediment budgets, and sea-level rise. Previous work by the USGS has focused on the vulnerability of the dunes to storms, where updated bathymetry and topography have been used for modeling efforts. Existing research indicates that submerged shoals, ridges, and sandbars affect patterns of wave refraction and height, coastal currents, and control sediment transport. These seabed anomalies indicate the availability and movement of sand within the nearshore environment, which may be directly related to the stability of the Cape Canaveral shoreline. Understanding the complex dynamics of the offshore bathymetry and associated sediment pathways can help identify current and future erosion vulnerabilities due to short-term (for example, hurricane and other extreme storms) and long-term (for example, sea-level rise) hazards.
\nThe purpose of this work is to describe an updated bathymetric dataset collected in 2014 and compare it to previous datasets. The updated data focus on the bathymetric features and sediment transport pathways that connect the offshore regions to the shoreline and, therefore, are related to the protection of other portions of the coastal environment, such as dunes, that support infrastructure and ecosystems. Previous survey data include National Oceanic and Atmospheric Administration’s (NOAA) National Ocean Service (NOS) hydrographic survey from 1956 and a USGS survey from 2010 that is augmented with NOS surveys from 2006 and 2007. The primary result of this analysis is documentation and quantification of the nature and rates of bathymetric changes that are near (within about 2.5 km) the current Cape Canaveral shoreline and interpretation of the impact of these changes on future erosion vulnerability.
","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/ofr20151180","usgsCitation":"Thompson, D.M., Plant, N.G., and Hansen, M.E., 2015, Analysis of bathymetric surveys to identify coastal vulnerabilities at Cape Canaveral, Florida: U.S. Geological Survey Open–File Report 2015–1180, 24 p., https://dx.doi.org/10.3133/ofr20151180.","productDescription":"vi, 24 p.","numberOfPages":"31","onlineOnly":"Y","additionalOnlineFiles":"Y","ipdsId":"IP-064347","costCenters":[{"id":574,"text":"St. Petersburg Coastal and Marine Science Center","active":true,"usgs":true}],"links":[{"id":309576,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/of/2015/1180/coverthb.jpg"},{"id":309577,"rank":2,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/of/2015/1180/ofr20151180.pdf","size":"6.74 MB","linkFileType":{"id":1,"text":"pdf"},"description":"OFR 2015-1180"},{"id":309798,"rank":3,"type":{"id":7,"text":"Companion Files"},"url":"https://dx.doi.org/10.5066/F75Q4T4N","text":"Single-Beam Bathymetry Data, Cape Canaveral, Florida, 2010","linkFileType":{"id":5,"text":"html"},"description":"OFR 2015-1180"}],"country":"United States","state":"Florida","city":"Cape Canaveral","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -80.63003540039062,\n 28.655645954019544\n ],\n [\n -80.57098388671874,\n 28.585727616133926\n ],\n [\n -80.56549072265625,\n 28.54954469910389\n ],\n [\n -80.55450439453125,\n 28.50369515241441\n ],\n [\n -80.53390502929688,\n 28.481969987679054\n ],\n [\n -80.52429199218749,\n 28.452996148032607\n ],\n [\n -80.53939819335938,\n 28.4433364363651\n ],\n [\n -80.5572509765625,\n 28.43971381702788\n ],\n [\n -80.57235717773438,\n 28.425222099239033\n ],\n [\n -80.58059692382812,\n 28.409520499193246\n ],\n [\n -80.4913330078125,\n 28.382943257980948\n ],\n [\n -80.44876098632812,\n 28.44454394857482\n ],\n [\n -80.45150756835938,\n 28.509729126769052\n ],\n [\n -80.48171997070312,\n 28.555576049185973\n ],\n [\n -80.48858642578125,\n 28.611048252984677\n ],\n [\n -80.52703857421875,\n 28.662876227341822\n ],\n [\n -80.54351806640625,\n 28.689382962051912\n ],\n [\n -80.63003540039062,\n 28.655645954019544\n ]\n ]\n ]\n }\n }\n ]\n}","contact":"St. Petersburg Coastal and Marine Science Center
U.S. Geological Survey
600 4th Street South
St. Petersburg, FL 33701
http://coastal.er.usgs.gov/
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.
","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/ofr20151189","collaboration":"Prepared in cooperation with the Bureau of Reclamation","usgsCitation":"Hewitt, D.A., Janney, E.C., Hayes, B.S., and Harris, A.C., 2015, Status and trends of adult Lost River (Deltistes luxatus) and shortnose (Chasmistes brevirostris) sucker populations in Upper Klamath Lake, Oregon, 2014: U.S. Geological Survey Open-File Report 2015-1189, 36 p., https://dx.doi.org/10.3133/ofr20151189.","productDescription":"iv, 36 p.","numberOfPages":"44","onlineOnly":"Y","additionalOnlineFiles":"N","ipdsId":"IP-065787","costCenters":[{"id":654,"text":"Western Fisheries Research Center","active":true,"usgs":true}],"links":[{"id":309534,"rank":2,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/of/2015/1189/ofr20151189.pdf","text":"Report","size":"1.7 MB","linkFileType":{"id":1,"text":"pdf"},"description":"OFR 2015-1189 PDF"},{"id":309533,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/of/2015/1189/coverthb.jpg"}],"country":"United States","state":"Oregon","otherGeospatial":"Upper Klamath Lake","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -121.80610656738281,\n 42.23512673690766\n ],\n [\n -121.81297302246092,\n 42.26511445833756\n ],\n [\n -121.82876586914061,\n 42.279848959767385\n ],\n [\n -121.82258605957031,\n 42.29711950573175\n ],\n [\n -121.81365966796874,\n 42.30879983710443\n ],\n [\n -121.82052612304688,\n 42.34382782918463\n ],\n [\n -121.82327270507812,\n 42.35296235855687\n ],\n [\n -121.80679321289062,\n 42.383401202818526\n ],\n [\n -121.83563232421875,\n 42.42142901536395\n ],\n [\n -121.86447143554686,\n 42.44372793752476\n ],\n [\n -121.87339782714844,\n 42.453354564875475\n ],\n [\n -121.88987731933594,\n 42.449301428414955\n ],\n [\n -121.90086364746094,\n 42.461460050936715\n ],\n [\n -121.91665649414062,\n 42.47361631282951\n ],\n [\n -121.93244934082031,\n 42.47007098029904\n ],\n [\n -121.95716857910155,\n 42.46855149059522\n ],\n [\n -121.97776794433594,\n 42.48323834594139\n ],\n [\n -122.00592041015626,\n 42.49792175437361\n ],\n [\n -122.04711914062499,\n 42.50197174319114\n ],\n [\n -122.05810546875,\n 42.488808320425846\n ],\n [\n -122.06565856933592,\n 42.46753847696729\n ],\n [\n -122.07252502441406,\n 42.46652544694582\n ],\n [\n -122.07870483398436,\n 42.48627657532141\n ],\n [\n -122.08831787109375,\n 42.482731960032936\n ],\n [\n -122.09449768066405,\n 42.46804498583046\n ],\n [\n -122.08145141601561,\n 42.455381034748896\n ],\n [\n -122.04299926757812,\n 42.42548395494743\n ],\n [\n -122.04505920410156,\n 42.416359972082866\n ],\n [\n -122.03201293945311,\n 42.39810802339276\n ],\n [\n -122.00523376464842,\n 42.39151573685182\n ],\n [\n -121.99356079101562,\n 42.40368557113506\n ],\n [\n -122.00042724609374,\n 42.41179748277328\n ],\n [\n -121.98257446289062,\n 42.40622065620649\n ],\n [\n -121.981201171875,\n 42.39405131362432\n ],\n [\n -121.97708129882812,\n 42.37934354245872\n ],\n [\n -121.95854187011717,\n 42.381879610913195\n ],\n [\n -121.95167541503906,\n 42.39455841668649\n ],\n [\n -121.95716857910155,\n 42.42092212947584\n ],\n [\n -121.94618225097656,\n 42.39912215986002\n ],\n [\n -121.94000244140624,\n 42.38035798213233\n ],\n [\n -121.91940307617188,\n 42.370720143531955\n ],\n [\n -121.90086364746094,\n 42.354992073710456\n ],\n [\n -121.92008972167969,\n 42.34941019930749\n ],\n [\n -121.94549560546875,\n 42.34433533786168\n ],\n [\n -121.94961547851562,\n 42.33519955487233\n ],\n [\n -121.94686889648438,\n 42.31590854308647\n ],\n [\n -121.93588256835938,\n 42.28950073090457\n ],\n [\n -121.92283630371092,\n 42.28035698458569\n ],\n [\n -121.92558288574217,\n 42.29711950573175\n ],\n [\n -121.9207763671875,\n 42.30676863078423\n ],\n [\n -121.904296875,\n 42.31083097788356\n ],\n [\n -121.89193725585936,\n 42.31540080499991\n ],\n [\n -121.88987731933594,\n 42.30321386201705\n ],\n [\n -121.87889099121092,\n 42.293056273848215\n ],\n [\n -121.86927795410156,\n 42.277816819534955\n ],\n [\n -121.8548583984375,\n 42.266638876842244\n ],\n [\n -121.84730529785155,\n 42.25495072629938\n ],\n [\n -121.84730529785155,\n 42.245801966774025\n ],\n [\n -121.83769226074219,\n 42.23766862211923\n ],\n [\n -121.8109130859375,\n 42.23105950761338\n ],\n [\n -121.80610656738281,\n 42.23512673690766\n ]\n ]\n ]\n }\n }\n ]\n}","contact":"Director, Western Fisheries Research Center
U.S. Geological Survey
6505 NE 65th Street
Seattle, Washington 98115
http://wfrc.usgs.gov/
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.
","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/ofr20151186","usgsCitation":"Langenheim, V.E., 2015, Aeromagnetic survey map of Sacramento Valley, California: U.S. Geological Survey Open-File Report 2015-1186, Map: 32.40 x 44.91 inches; Datasets; Metadata; Read Me, https://doi.org/10.3133/ofr20151186.","productDescription":"Map: 32.40 x 44.91 inches; Datasets; Metadata; Read Me","onlineOnly":"Y","additionalOnlineFiles":"Y","ipdsId":"IP-065763","costCenters":[{"id":312,"text":"Geology, Minerals, Energy, and Geophysics Science Center","active":true,"usgs":true}],"links":[{"id":309022,"rank":11,"type":{"id":16,"text":"Metadata"},"url":"https://pubs.usgs.gov/of/2015/1186/ofr20151186_metadata_sacramento_new.txt","text":"Sacramento","size":"13 KB","linkFileType":{"id":2,"text":"txt"},"description":"OFR 2015-1186 Sacramento 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zulanger@usgs.gov","orcid":"https://orcid.org/0000-0003-2170-5213","contributorId":148146,"corporation":false,"usgs":true,"family":"Langenheim","given":"Victoria","email":"zulanger@usgs.gov","middleInitial":"E.","affiliations":[{"id":312,"text":"Geology, Minerals, Energy, and Geophysics Science Center","active":true,"usgs":true}],"preferred":false,"id":574063,"contributorType":{"id":1,"text":"Authors"},"rank":1}]}} ,{"id":70157349,"text":"ofr20151184 - 2015 - Community for Data Integration 2014 annual report","interactions":[],"lastModifiedDate":"2018-08-10T16:28:07","indexId":"ofr20151184","displayToPublicDate":"2015-10-02T11:30:00","publicationYear":"2015","noYear":false,"publicationType":{"id":18,"text":"Report"},"publicationSubtype":{"id":5,"text":"USGS Numbered Series"},"seriesTitle":{"id":330,"text":"Open-File Report","code":"OFR","onlineIssn":"2331-1258","printIssn":"0196-1497","active":true,"publicationSubtype":{"id":5}},"seriesNumber":"2015-1184","title":"Community for Data Integration 2014 annual report","docAbstract":"The U.S. Geological Survey (USGS) researches Earth science to help address complex issues affecting society and the environment. In 2006, the USGS held the first Scientific Information Management Workshop to bring together staff from across the organization to discuss the data and information management issues affecting the integration and delivery of Earth science research and investigate the use of “communities of practice” as mechanisms to share expertise about these issues. Out of this effort emerged the Council for Data Integration, which was conceived as an official organizational function that would help guide data integration activities and formalize communities of practice into working groups; however, by 2009 it became evident that many members of the Council for Data Integration had an interest in developing data integration solutions and sharing expertise in a less formal, grassroots manner, which transformed the Council into a Community for Data Integration (CDI). As of 2014, the CDI represents a dynamic community of practice focused on advancing science data and information management and integration capabilities across the USGS and the CDI community.
\nThe CDI fosters an environment for collaboration and sharing by bringing together expertise from external partners and representatives across the USGS who are involved in research, data management, and information technology. Membership is voluntary and open to USGS employees and other individuals and organizations willing to contribute to the community (if interested, contact cdi@usgs.gov). The purpose of the CDI is to do the following:
• advance understanding of Earth systems through enhanced use of data and information including associated tools and techniques,
• provide a forum for people doing work with data integration to come together to share ideas and learn new skills and techniques, and
• grow overall USGS capabilities with data and information by increasing visibility of the work of many people throughout the USGS and the CDI community.
To achieve these goals, the CDI operates within four applied areas: monthly forums, annual workshop/webinar series, working groups, and projects. The monthly forums, also known as the Opportunity/Challenge of the Month, provide an open dialogue to share and learn about data integration efforts or to present problems that invite the community to offer solutions, advice, and support. Since 2010, the CDI has also sponsored annual workshops/webinar series to encourage the exchange of ideas, sharing of activities, presentations of current projects, and networking among members. Stemming from common interests, the working groups are focused on efforts to address data management and technical challenges including the development of standards and tools, improving interoperability and information infrastructure, and data preservation within USGS and its partners. The growing support for the activities of the working groups led to the CDI’s first formal request for proposals (RFP) process in 2013 to fund projects that produced tangible products. As of 2014, the CDI continues to hold an annual RFP that creates data management tools and practices, collaboration tools, and training in support of data integration and delivery.
","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
U.S. Geological Survey
108 National Center
12201 Sunrise Valley Drive
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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Water Science Center
U.S. Geological Survey
4446 Pet Lane, Suite 108
Lutz, FL 33559
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/