Understanding migratory connectivity for species of concern is of great importance if we are to implement management aimed at conserving them. New methods are improving our understanding of migration; however, banding (ringing) data is by far the most widely available and accessible movement data for researchers. Here, we use band recovery data for American black ducks (Anas rubripes) from 1951–2011 and analyze their movement among seven management regions using a hierarchical Bayesian framework. We showed that black ducks generally exhibit flyway fidelity, and that many black ducks, regardless of breeding region, stopover or overwinter on the Atlantic coast of the United States. We also show that a non-trivial portion of the continental black duck population either does not move at all or moves to the north during the fall migration (they typically move to the south). The results of this analysis will be used in a projection modeling context to evaluate how habitat or harvest management actions in one region would propagate throughout the continental population of black ducks. This analysis may provide a guide for future research and help inform management efforts for black ducks as well as other migratory species.
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U.S. Geological Survey
777 NW 9th St., Suite 400
Corvallis, Oregon 97330
http://fresc.usgs.gov/
The Landscape Fire and Resource Management Planning Tools (LANDFIRE) 2010 data release provides updated and enhanced vegetation, fuel, and fire regime layers consistently across the United States. The data represent landscape conditions from approximately 2010 and are the latest release in a series of planned updates to maintain currency of LANDFIRE data products. Enhancements to the data products included refinement of urban areas by incorporating the National Land Cover Database 2006 land cover product, refinement of agricultural lands by integrating the National Agriculture Statistics Service 2011 cropland data layer, and improved wetlands delineations using the National Land Cover Database 2006 land cover and the U.S. Fish and Wildlife Service National Wetlands Inventory data. Disturbance layers were generated for years 2008 through 2010 using remotely sensed imagery, polygons representing disturbance events submitted by local organizations, and fire mapping program data such as the Monitoring Trends in Burn Severity perimeters produced by the U.S. Geological Survey and the U.S. Forest Service. Existing vegetation data were updated to account for transitions in disturbed areas and to account for vegetation growth and succession in undisturbed areas. Surface and canopy fuel data were computed from the updated vegetation type, cover, and height and occasionally from potential vegetation. Historical fire frequency and succession classes were also updated. Revised topographic layers were created based on updated elevation data from the National Elevation Dataset. The LANDFIRE program also released a new Web site offering updated content, enhanced usability, and more efficient navigation.
","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/ofr20161010","usgsCitation":"Nelson, K.J., Long, D.G., and Connot, J.A., 2016, LANDFIRE 2010—Updates to the national dataset to support improved fire and natural resource management: U.S. Geological Survey Open-File Report, 2016–1010, 48 p, https://dx.doi.org/10.3133/ofr20161010. ","productDescription":"x, 48 p.","onlineOnly":"Y","additionalOnlineFiles":"N","ipdsId":"IP-063923","costCenters":[{"id":222,"text":"Earth Resources Observation and Science (EROS) Center","active":true,"usgs":true}],"links":[{"id":318398,"rank":2,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/of/2016/1010/ofr20161010.pdf","text":"Report","size":"3.54 MB","linkFileType":{"id":1,"text":"pdf"},"description":"OFR 2016-1010"},{"id":318397,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/of/2016/1010/coverthb.jpg"}],"contact":"Director, U.S. Geological Survey
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47914 252nd Street
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The Altoona Water Authority (AWA) obtains all of its water supply from headwater streams that drain western Blair County, an area underlain in part by black shale of the Marcellus Formation. Development of the shale-gas reservoirs will require new access roads, stream crossing, drill-pad construction, and pipeline installation, activities that have the potential to alter existing stream channel morphology, increase runoff and sediment supply, alter streamwater chemistry, and affect aquatic habitat. The U.S. Geological Survey, in cooperation with Altoona Water Authority and Blair County Conservation District, investigated the water quality of 12 headwater streams and biotic health of 10 headwater streams.
\nChannel morphology was characterized at 10 of 12 stream sites using 500-foot (minimum) longitudinal profiles, four cross-sections each, and pebble counts. Channel slopes ranged from 0.008 in Poplar Run near Newry to 0.045 in Mill Run. In general, streams draining watersheds of 5 square miles or less and at higher elevation had the steepest slopes. On the basis of the median particle size, determined during pebble counts, the streambed substrate can be characterized as cobble (Mill Run, Bells Gap Run, Tipton Run, and Sink Run), a mix of gravel and cobble (South Poplar Run, Dry Gap Run, Glenwhite Run, Sugar Run, Blair Gap Run), and gravel (Poplar Run, Newry).
\nDaily mean values of gage height were determined, and continuous (30-minute interval) data consisting of specific conductance and water temperature were collected, at four sites; each site showed typical seasonal fluctuations and the effects of precipitation.
\nStreamflow affected discrete water-quality. Dissolved oxygen always increased with increased streamflow. Most cations (including barium and strontium), along with pH, specific conductance, and total dissolved solids, decreased with greater streamflow, reflecting the dilution effect of moderately acidic surface runoff and precipitation on groundwater discharge (base flow) into the stream channels. Concentrations of trace elements varied by constituent and streamflow.
\nOn the basis of the results of water-quality analyses for the selected constituents, the water quality in 9 of the 12 streams can be considered fair or attaining with no measured constituent exceeding a U.S. Environmental Protection Agency maximum or secondary contaminant level. Abandoned mine drainage (AMD) affects Glenwhite Run, Blair Gap Run, and Sugar Run. For Sugar Run, the AMD is reflected in the elevated iron concentration (greater than 300 micrograms per liter). Manganese concentrations greater than 50 micrograms per liter were measured in Glenwhite Run, Sugar Run, and Blair Gap Run.
\nA mixing curve based upon chloride/bromide ratios for two end points—precipitation and deicing salts—indicate that deicing salt is migrating to the streams. A similar curve representative of late-emerging flowback water from Marcellus gas wells indicated that the surface-water samples had not been influenced by such brines.
\nOn the basis of the concentration of major ions, the streams in the study area generally had mixed cation and anion compositions. Calcium is the dominant cation in one stream. Carbonate and bicarbonate are the dominant anions for two streams, and sulfate is dominant in three streams. The remaining six streams do not have a dominant ion.
\nBiotic health was characterized at 10 of 12 stream sites; the two sites excluded were established late in the study period (May 2013) for refinement of water quality in the headwaters of Poplar Run and the location of Marcellus Formation gas wells. On the basis of the Maryland Index of Biotic Integrity (MdIBI) for fish assemblages, 8 of 10 streams can be considered in fair health. Tipton Run had the highest MdIBI score (3.75) and the greatest number of native species. South Poplar Run had the lowest MdIBI score (1.75); pollution tolerant blacknose dace was dominant. On the basis of the Pennsylvania Department of Environmental Protection macroinvertebrate index of biotic integrity, 9 of 10 streams were characterized as attaining, with scores as high as 88.9 at Tipton Run. Only Sugar Run was characterized as impaired, with a score of 40.4.
","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/ofr20151173","collaboration":"Prepared in cooperation with the Altoona Water Authority and the Blair County Conservation District","usgsCitation":"Low, D.J., Brightbill, R.A., Eggleston, H.L., and Chaplin, J.J., 2016, Physical, chemical, and biological characteristics of selected headwater streams along the Allegheny Front, Blair County, Pennsylvania, July 2011–September 2013: U.S. Geological Survey Open-File Report 2015–1173, 66 p., https://dx.doi.org/10.3133/ofr20151173.","productDescription":"Report: viii, 66 p.; Appendixes 1-3","onlineOnly":"Y","additionalOnlineFiles":"Y","ipdsId":"IP-059743","costCenters":[{"id":532,"text":"Pennsylvania Water Science Center","active":true,"usgs":true}],"links":[{"id":314565,"rank":2,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/of/2015/1173/ofr20151173.pdf","text":"Report","size":"3.07 MB","linkFileType":{"id":1,"text":"pdf"},"description":"OFR 2015-1173"},{"id":314564,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/of/2015/1173/coverthb.jpg"},{"id":314566,"rank":3,"type":{"id":3,"text":"Appendix"},"url":"https://pubs.usgs.gov/of/2015/1173/ofr20151173_appendix1-surfacewaterquality.xlsx","text":"Appendix 1 - Surface Water Quality","size":"145 KB","linkFileType":{"id":3,"text":"xlsx"},"description":"OFR 2015-1173"},{"id":314567,"rank":4,"type":{"id":3,"text":"Appendix"},"url":"https://pubs.usgs.gov/of/2015/1173/ofr20151173_appendix2-fishassemblages.xlsx","text":"Appendix 2 - Fish Assemblages","size":"92.6 KB","linkFileType":{"id":3,"text":"xlsx"},"description":"OFR 2015-1173"},{"id":314568,"rank":5,"type":{"id":3,"text":"Appendix"},"url":"https://pubs.usgs.gov/of/2015/1173/ofr20151173_appendix3-ibiscore.xlsx","text":"Appendix 3 - Index of Biotic Integrity","size":"21.1 KB","linkFileType":{"id":3,"text":"xlsx"},"description":"OFR 2015-1173"}],"country":"United States","state":"Pennsylvania","county":"Blair County","otherGeospatial":"Allegheny Front","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -78.35861206054688,\n 40.73581157695217\n ],\n [\n -78.21578979492188,\n 40.677513627085034\n ],\n [\n -78.45474243164062,\n 40.24913603826261\n ],\n [\n -78.62777709960938,\n 40.32665496008367\n ],\n [\n -78.35861206054688,\n 40.73581157695217\n ]\n ]\n ]\n }\n }\n ]\n}","contact":"Director, Pennsylvania Water Science Center
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Mercury (Hg) emission and deposition can occur to and from soils, and are an important component of the global atmospheric Hg budget. This paper focuses on synthesizing existing surface-air Hg flux data collected throughout the Western North American region and is part of a series of geographically focused Hg synthesis projects. A database of existing Hg flux data collected using the dynamic flux chamber (DFC) approach from almost a thousand locations was created for the Western North America region. Statistical analysis was performed on the data to identify the important variables controlling Hg fluxes and to allow spatiotemporal scaling. The results indicated that most of the variability in soil-air Hg fluxes could be explained by variations in soil-Hg concentrations, solar radiation, and soil moisture. This analysis also identified that variations in DFC methodological approaches were detectable among the field studies, with the chamber material and sampling flushing flow rate influencing the magnitude of calculated emissions. The spatiotemporal scaling of soil-air Hg fluxes identified that the largest emissions occurred from irrigated agricultural landscapes in California. Vegetation was shown to have a large impact on surface-air Hg fluxes due to both a reduction in solar radiation reaching the soil as well as from direct uptake of Hg in foliage. Despite high soil Hg emissions from some forested and other heavily vegetated regions, the net ecosystem flux (soil flux + vegetation uptake) was low. Conversely, sparsely vegetated regions showed larger net ecosystem emissions, which were similar in magnitude to atmospheric Hg deposition (except for the Mediterranean California region where soil emissions were higher). The net ecosystem flux results highlight the important role of landscape characteristics in effecting the balance between Hg sequestration and (re-)emission to the atmosphere.
","language":"English","publisher":"Elsevier","doi":"10.1016/j.scitotenv.2016.02.121","usgsCitation":"Eckley, C.S., Tate, M., Lin, C., Gustin, M., Dent, S., Eagles-Smith, C., Lutz, M.A., Wickland, K., Wang, B., Gray, J.E., Edwards, G., Krabbenhoft, D.P., and Smith, D.B., 2016, Surface-air mercury fluxes across Western North America: A synthesis of spatial trends and controlling variables: Science of the Total Environment, v. 568, p. 651-665, https://doi.org/10.1016/j.scitotenv.2016.02.121.","productDescription":"15 p.","startPage":"651","endPage":"665","onlineOnly":"N","additionalOnlineFiles":"N","ipdsId":"IP-070594","costCenters":[{"id":290,"text":"Forest and Rangeland Ecosystem Science Center","active":false,"usgs":true},{"id":29789,"text":"John Wesley Powell Center for Analysis and Synthesis","active":true,"usgs":true},{"id":34983,"text":"Contaminant Biology 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Central Branch","active":true,"usgs":true}],"preferred":true,"id":621492,"contributorType":{"id":1,"text":"Authors"},"rank":12},{"text":"Smith, David B. 0000-0001-8396-9105 dsmith@usgs.gov","orcid":"https://orcid.org/0000-0001-8396-9105","contributorId":138565,"corporation":false,"usgs":true,"family":"Smith","given":"David","email":"dsmith@usgs.gov","middleInitial":"B.","affiliations":[{"id":211,"text":"Crustal Geophysics and Geochemistry Science Center","active":true,"usgs":true},{"id":171,"text":"Central Mineral and Environmental Resources Science Center","active":true,"usgs":true}],"preferred":true,"id":621493,"contributorType":{"id":1,"text":"Authors"},"rank":13}]}} ,{"id":70177906,"text":"70177906 - 2016 - Influence of vertical and lateral heat transfer on permafrost thaw, peatland landscape transition, and groundwater flow","interactions":[],"lastModifiedDate":"2018-08-10T16:14:39","indexId":"70177906","displayToPublicDate":"2016-02-26T11:45:00","publicationYear":"2016","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":3722,"text":"Water Resources Research","onlineIssn":"1944-7973","printIssn":"0043-1397","active":true,"publicationSubtype":{"id":10}},"title":"Influence of vertical and lateral heat transfer on permafrost thaw, peatland landscape transition, and groundwater flow","docAbstract":"Recent climate change has reduced the spatial extent and thickness of permafrost in many discontinuous permafrost regions. Rapid permafrost thaw is producing distinct landscape changes in the Taiga Plains of the Northwest Territories, Canada. As permafrost bodies underlying forested peat plateaus shrink, the landscape slowly transitions into unforested wetlands. The expansion of wetlands has enhanced the hydrologic connectivity of many watersheds via new surface and near-surface flow paths, and increased streamflow has been observed. Furthermore, the decrease in forested peat plateaus results in a net loss of boreal forest and associated ecosystems. This study investigates fundamental processes that contribute to permafrost thaw by comparing observed and simulated thaw development and landscape transition of a peat plateau-wetland complex in the Northwest Territories, Canada from 1970 to 2012. Measured climate data are first used to drive surface energy balance simulations for the wetland and peat plateau. Near-surface soil temperatures simulated in the surface energy balance model are then applied as the upper boundary condition to a three-dimensional model of subsurface water flow and coupled energy transport with freeze-thaw. Simulation results demonstrate that lateral heat transfer, which is not considered in many permafrost models, can influence permafrost thaw rates. Furthermore, the simulations indicate that landscape evolution arising from permafrost thaw acts as a positive feedback mechanism that increases the energy absorbed at the land surface and produces additional permafrost thaw. The modeling results also demonstrate that flow rates in local groundwater flow systems may be enhanced by the degradation of isolated permafrost bodies.
","language":"English","publisher":"American Geophysical Union","doi":"10.1002/2015WR018057","usgsCitation":"Kurylyk, B.L., Hayashi, M., Quinton, W.L., McKenzie, J.M., and Voss, C.I., 2016, Influence of vertical and lateral heat transfer on permafrost thaw, peatland landscape transition, and groundwater flow: Water Resources Research, v. 52, no. 2, p. 1286-1305, https://doi.org/10.1002/2015WR018057.","productDescription":"20 p.","startPage":"1286","endPage":"1305","onlineOnly":"N","additionalOnlineFiles":"N","ipdsId":"IP-071592","costCenters":[{"id":438,"text":"National Research Program - Western Branch","active":true,"usgs":true}],"links":[{"id":471208,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.1002/2015wr018057","text":"Publisher Index Page"},{"id":330403,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"Canada","state":"Northwest Territories","otherGeospatial":"Scotty Creek","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -123.53027343749999,\n 60.05935761134086\n ],\n [\n -123.53027343749999,\n 62.63376960786813\n ],\n [\n -119.11376953125,\n 62.63376960786813\n ],\n [\n -119.11376953125,\n 60.05935761134086\n ],\n [\n -123.53027343749999,\n 60.05935761134086\n ]\n ]\n ]\n }\n }\n ]\n}","volume":"52","issue":"2","publishingServiceCenter":{"id":14,"text":"Menlo Park PSC"},"noUsgsAuthors":false,"publicationDate":"2016-02-26","publicationStatus":"PW","scienceBaseUri":"5811c0f3e4b0f497e79a5a7d","contributors":{"authors":[{"text":"Kurylyk, Barret L.","contributorId":176296,"corporation":false,"usgs":false,"family":"Kurylyk","given":"Barret","email":"","middleInitial":"L.","affiliations":[],"preferred":false,"id":652148,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Hayashi, Masaki","contributorId":176832,"corporation":false,"usgs":false,"family":"Hayashi","given":"Masaki","affiliations":[],"preferred":false,"id":652149,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Quinton, William L.","contributorId":176298,"corporation":false,"usgs":false,"family":"Quinton","given":"William","email":"","middleInitial":"L.","affiliations":[],"preferred":false,"id":652150,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"McKenzie, Jeffrey M.","contributorId":176299,"corporation":false,"usgs":false,"family":"McKenzie","given":"Jeffrey","email":"","middleInitial":"M.","affiliations":[],"preferred":false,"id":652151,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Voss, Clifford I. 0000-0001-5923-2752 cvoss@usgs.gov","orcid":"https://orcid.org/0000-0001-5923-2752","contributorId":1559,"corporation":false,"usgs":true,"family":"Voss","given":"Clifford","email":"cvoss@usgs.gov","middleInitial":"I.","affiliations":[{"id":438,"text":"National Research Program - Western Branch","active":true,"usgs":true}],"preferred":true,"id":652152,"contributorType":{"id":1,"text":"Authors"},"rank":5}]}} ,{"id":70168795,"text":"70168795 - 2016 - Tolerance to multiple climate stressors: A case study of Douglas-fir drought and cold hardiness","interactions":[],"lastModifiedDate":"2020-10-16T16:18:37.570819","indexId":"70168795","displayToPublicDate":"2016-02-26T11:00:00","publicationYear":"2016","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":1467,"text":"Ecology and Evolution","active":true,"publicationSubtype":{"id":10}},"title":"Tolerance to multiple climate stressors: A case study of Douglas-fir drought and cold hardiness","docAbstract":"Statistical analyses and maps representing mean, high, and low water-level conditions in the surface water and groundwater of Miami-Dade County were made by the U.S. Geological Survey, in cooperation with the Miami-Dade County Department of Regulatory and Economic Resources, to help inform decisions necessary for urban planning and development. Sixteen maps were created that show contours of (1) the mean of daily water levels at each site during October and May for the 2000–2009 water years; (2) the 25th, 50th, and 75th percentiles of the daily water levels at each site during October and May and for all months during 2000–2009; and (3) the differences between mean October and May water levels, as well as the differences in the percentiles of water levels for all months, between 1990–1999 and 2000–2009. The 80th, 90th, and 96th percentiles of the annual maximums of daily groundwater levels during 1974–2009 (a 35-year period) were computed to provide an indication of unusually high groundwater-level conditions. These maps and statistics provide a generalized understanding of the variations of water levels in the aquifer, rather than a survey of concurrent water levels. Water-level measurements from 473 sites in Miami-Dade County and surrounding counties were analyzed to generate statistical analyses. The monitored water levels included surface-water levels in canals and wetland areas and groundwater levels in the Biscayne aquifer.
\nMaps were created by importing site coordinates, summary water-level statistics, and completeness of record statistics into a geographic information system, and by interpolating between water levels at monitoring sites in the canals and water levels along the coastline. Raster surfaces were created from these data by using the triangular irregular network interpolation method. The raster surfaces were contoured by using geographic information system software. These contours were imprecise in some areas because the software could not fully evaluate the hydrology given available information; therefore, contours were manually modified where necessary. The ability to evaluate differences in water levels between 1990–1999 and 2000–2009 is limited in some areas because most of the monitoring sites did not have 80 percent complete records for one or both of these periods. The quality of the analyses was limited by (1) deficiencies in spatial coverage; (2) the combination of pre- and post-construction water levels in areas where canals, levees, retention basins, detention basins, or water-control structures were installed or removed; (3) an inability to address the potential effects of the vertical hydraulic head gradient on water levels in wells of different depths; and (4) an inability to correct for the differences between daily water-level statistics. Contours are dashed in areas where the locations of contours have been approximated because of the uncertainty caused by these limitations. Although the ability of the maps to depict differences in water levels between 1990–1999 and 2000–2009 was limited by missing data, results indicate that near the coast water levels were generally higher in May during 2000–2009 than during 1990–1999; and that inland water levels were generally lower during 2000–2009 than during 1990–1999. Generally, the 25th, 50th, and 75th percentiles of water levels from all months were also higher near the coast and lower inland during 2000–2009 than during 1990–1999. Mean October water levels during 2000–2009 were generally higher than during 1990–1999 in much of western Miami-Dade County, but were lower in a large part of eastern Miami-Dade County.
","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/sir20165005","collaboration":"Prepared in cooperation with the Miami-Dade County Department of Regulatory and Economic Resources","usgsCitation":"Prinos, S.T., and Dixon, J.F., 2016, Statistical analysis and mapping of water levels in the Biscayne aquifer, water conservation areas, and Everglades National Park, Miami-Dade County, Florida, 2000–2009: U.S. Geological Survey Scientific Investigations Report 2016–5005, 42 p., https://dx.doi.org/10.3133/sir20165005.","productDescription":"Report: vi, 42 p.; 16 Plates: 23.00 x 30.00 inches or smaller; Appendix; Companion File","onlineOnly":"Y","additionalOnlineFiles":"Y","ipdsId":"IP-053912","costCenters":[{"id":269,"text":"FLWSC-Ft. Lauderdale","active":true,"usgs":true}],"links":[{"id":318341,"rank":20,"type":{"id":3,"text":"Appendix"},"url":"https://pubs.usgs.gov/sir/2016/5005/sir20165005_appendix8.pdf","text":"Figure 8-1 - (11x17)","size":"1.35 MB","linkFileType":{"id":1,"text":"pdf"},"description":"SIR 2016-5005","linkHelpText":"Locations of all sites used to map water levels in the Biscayne aquifer, water conservation areas, and Everglades National Park, in Miami-Dade County, Florida, during the 2000-2009 water years. The same index number may be used for adjacent sites."},{"id":318331,"rank":10,"type":{"id":17,"text":"Plate"},"url":"https://pubs.usgs.gov/sir/2016/5005/plates/sir20165005_plate7.pdf","size":"5.04 MB","linkFileType":{"id":1,"text":"pdf"},"description":"SIR 2016-5005","linkHelpText":"50th Percentile of October Water Levels During the 2000–2009 Water Years, Miami-Dade County, Florida"},{"id":318329,"rank":8,"type":{"id":17,"text":"Plate"},"url":"https://pubs.usgs.gov/sir/2016/5005/plates/sir20165005_plate5.pdf","size":"4.90 MB","linkFileType":{"id":1,"text":"pdf"},"description":"SIR 2016-5005","linkHelpText":"75th Percentile of May Water Levels During the 2000–2009 Water Years, Miami-Dade County, Florida"},{"id":318330,"rank":9,"type":{"id":17,"text":"Plate"},"url":"https://pubs.usgs.gov/sir/2016/5005/plates/sir20165005_plate6.pdf","size":"5.05 MB","linkFileType":{"id":1,"text":"pdf"},"description":"SIR 2016-5005","linkHelpText":"25th Percentile of October Water Levels During the 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the 75th Percentiles of all Water Levels for Water-year Periods 1990–99 and 2000–2009, Miami-Dade County, Florida"},{"id":318172,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/sir/2016/5005/coverthb.jpg"},{"id":318326,"rank":5,"type":{"id":17,"text":"Plate"},"url":"https://pubs.usgs.gov/sir/2016/5005/plates/sir20165005_plate2.pdf","size":"5.05 MB","linkFileType":{"id":1,"text":"pdf"},"description":"SIR 2016-5005","linkHelpText":"Mean of October Water Levels During the 2000–2009 Water Years, Miami-Dade County, Florida"},{"id":318173,"rank":2,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/sir/2016/5005/sir20165005.pdf","text":"Report","size":"3.48 MB","linkFileType":{"id":1,"text":"pdf"},"description":"SIR 2016-5005"},{"id":318334,"rank":13,"type":{"id":17,"text":"Plate"},"url":"https://pubs.usgs.gov/sir/2016/5005/plates/sir20165005_plate10.pdf","size":"5.02 MB","linkFileType":{"id":1,"text":"pdf"},"description":"SIR 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MB","linkFileType":{"id":1,"text":"pdf"},"description":"SIR 2016-5005","linkHelpText":"25th Percentile of May Water Levels During the 2000–2009 Water Years, Miami-Dade County, Florida"},{"id":318339,"rank":18,"type":{"id":17,"text":"Plate"},"url":"https://pubs.usgs.gov/sir/2016/5005/plates/sir20165005_plate15.pdf","size":"4.95 MB","linkFileType":{"id":1,"text":"pdf"},"description":"SIR 2016-5005","linkHelpText":"Difference Between the 50th Percentiles of all Water Levels for Water-year Periods 1990–99 and 2000–2009, Miami-Dade County, Florida"},{"id":318328,"rank":7,"type":{"id":17,"text":"Plate"},"url":"https://pubs.usgs.gov/sir/2016/5005/plates/sir20165005_plate4.pdf","size":"4.88 MB","linkFileType":{"id":1,"text":"pdf"},"description":"SIR 2016-5005","linkHelpText":"50th Percentile of May Water Levels During the 2000–2009 Water Years, Miami-Dade County, Florida"},{"id":318333,"rank":12,"type":{"id":17,"text":"Plate"},"url":"https://pubs.usgs.gov/sir/2016/5005/plates/sir20165005_plate9.pdf","size":"4.99 MB","linkFileType":{"id":1,"text":"pdf"},"description":"SIR 2016-5005","linkHelpText":"50th Percentile of Water Levels From All Months During the 2000–2009 Water Years, Miami-Dade County, Florida"},{"id":318276,"rank":3,"type":{"id":7,"text":"Companion Files"},"url":"https://dx.doi.org/10.5066/F7M61H9W","text":"Data, Statistics, and Geographic Information System Files,","description":"SIR 2016-5005","linkHelpText":"Pertaining to Mapping of Water Levels in the Biscayne Aquifer, Water Conservation Areas, and Everglades National Park, Miami-Dade County, Florida, 2000-2009 - Scientific data associated with USGS SIR 2015-5005"},{"id":318336,"rank":15,"type":{"id":17,"text":"Plate"},"url":"https://pubs.usgs.gov/sir/2016/5005/plates/sir20165005_plate12.pdf","size":"4.86 MB","linkFileType":{"id":1,"text":"pdf"},"description":"SIR 2016-5005","linkHelpText":"Difference in May Mean Water Levels From the Water-year Periods 1990–99 and 2000–2009, Miami-Dade County, Florida"},{"id":318325,"rank":4,"type":{"id":17,"text":"Plate"},"url":"https://pubs.usgs.gov/sir/2016/5005/plates/sir20165005_plate1.pdf","size":"5.26 MB","linkFileType":{"id":1,"text":"pdf"},"description":"SIR 2016-5005","linkHelpText":"Mean of May Water Levels During the 2000–2009 Water Years, Miami-Dade County, Florida"}],"country":"United States","state":"Florida","county":"Miami-Dade","geographicExtents":"{\"type\":\"FeatureCollection\",\"features\":[{\"type\":\"Feature\",\"geometry\":{\"type\":\"MultiPolygon\",\"coordinates\":[[[[-80.7769,25.9793],[-80.1236,25.9748],[-80.4387,25.1799],[-80.8621,25.2431],[-80.873,25.9795],[-80.7769,25.9793]]]]},\"properties\":{\"name\":\"Miami-Dade\",\"state\":\"FL\"}}]}","contact":"Director, Florida Water Science Center
U.S. Geological Survey
4446 Pet Lane, Suite 108
Lutz, FL 3355
http://fl.water.usgs.gov/
The Apalachicola-Chattahoochee-Flint (ACF) River Basin encompasses about 20,230 square miles in parts of Alabama, Florida, and Georgia. Increasing population growth and agricultural production from the 1970s to 2010 has prompted increases in water-resources development and substantially increased water demand in the basin. Since the 1980s, Alabama, Florida, Georgia, and the U.S. Army Corps of Engineers are parties to litigation concerning water management in the ACF River Basin.
\nEstimating the 2010 water use in the ACF River Basin is one aspect of a multipart water resources study on the ACF River Basin that began in 2011. This ACF River Basin study is one focus area of the U.S. Geological Survey’s National Water Census program. The 2010 water-use estimates for the ACF River Basin are presented in this report. These estimates include an inventory of the quantity and sources of water withdrawn by category of use and location (State and river basin), and the surface-water returns in the ACF River Basin during 2010. Water-use trends from 1985 to 2010 in the basin also are presented. Offstream water-withdrawal data in the ACF River Basin are presented for each of the following categories: public supply, self-supplied domestic, self-supplied commercial, industrial, mining, agricultural (including crop irrigation, livestock, and aquaculture uses), and thermoelectric-power generation. Water-use data are compiled for the 14 subbasins in the ACF River Basin. For the counties in Alabama, Florida, and Georgia that are partially within the ACF River Basin, data are presented for only that part of the county that lies within the basin. A variety of Federal, State, local, private, and online sources in Alabama, Florida, and Georgia were used to gather surface-water and groundwater withdrawal, surface-water discharges (return flows), and water-use data for the ACF River Basin in 2010.
\nThe population in the ACF River Basin was 3.835 million in 2010, a 45-percent increase from the 1990 population of nearly 2.636 million. About 92 percent of the 2010 ACF population resided in Georgia with nearly 75 percent living in the Atlanta metropolitan area. In 2010, 1,645 million gallons per day (Mgal/d) of water were withdrawn from groundwater (576 Mgal/d) and surface-water (1,069 Mgal/d) sources in the ACF River Basin. About 89 percent of the groundwater and 83 percent of the surface-water withdrawals were from Georgia. About 5.6 percent of the total groundwater and nearly 4 percent of the total surface-water withdrawals in the ACF River Basin were from Florida, whereas about 5.3 percent of groundwater and nearly 16 percent of surface water were withdrawn in Alabama. Total water use (withdrawals plus public-supplied deliveries) in the ACF River Basin was 1,593 Mgal/d in 2010. About 56 Mgal/d of water withdrawn in the ACF River Basin was delivered (interbasin transfer) to basins beyond the ACF River Basin. About 564 Mgal/d of water was returned to surface-water bodies in the ACF River Basin. Most of that amount, 63 percent, was treated wastewater discharged by public wastewater-treatment facilities. Water used for once-through cooling by thermoelectric-power facilities accounted for nearly 24 percent of the surface-water returns in the basin.
\nAbout 70 percent of all water withdrawals in the ACF River Basin were by self-supplied agricultural water users and public water suppliers. Agricultural withdrawals were greatest in the Flint River Basin (501 Mgal/d) with ground-water representing 84 percent of the withdrawals from that basin. Within the Flint River Basin, agricultural withdrawals were greatest in the Lower Flint River and Spring Creek subbasins. About 3.52 million people were served by public water suppliers in the ACF River Basin during 2010, and 88 percent of that population used surface water. Georgia had the largest public-supplied population, representing nearly 93 percent (3.17 million) of the public-supplied population in the ACF River Basin. Public water suppliers served 193,700 people (5.7 percent) in Alabama and 31,880 people in Florida (1.3 percent). Public-supply losses were estimated at 101 Mgal/d.
\nWithdrawals for public supply (483 Mgal/d) and self-supplied industry (141 Mgal/d) were greatest in the Chattahoochee River Basin. Surface water accounted for 96 percent of all withdrawals in the Chattahoochee River Basin. Withdrawals for public supply were greatest in the Upper Chattahoochee River subbasin (366 Mgal/d), whereas self-supplied industrial withdrawals were greatest in the Lower Chattahoochee River subbasin (110 Mgal/d).
\nWater-use trends in the ACF River Basin have varied during the 25 years between 1985 and 2010. Surface-water withdrawals declined between 1985 and 2000, sharply increased in 2000, and declined again between 2000 and 2010. In contrast, groundwater withdrawals increased between 1985 and 2000, declined in 2005, and increased between 2005 and 2010.
","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/sir20165007","collaboration":"Prepared in cooperation with the National Water Census Program","usgsCitation":"Lawrence, S.J., 2016, Water use in the Apalachicola-Chattahoochee-Flint River Basin, Alabama, Florida, and Georgia, 2010, and water-use trends,Director, South Atlantic Water Science Center
U.S. Geological Survey
720 Gracern Road
Columbia, SC 29210
http://www.usgs.gov/water/southatlantic/
American black ducks (Anas rubripes) are a harvested, international migratory waterfowl species in eastern North America. Despite an extended period of restrictive harvest regulations, the black duck population is still below the population goal identified in the North American Waterfowl Management Plan (NAWMP). It has been hypothesized that density-dependent factors restrict population growth in the black duck population and that habitat management (increases, improvements, etc.) may be a key component of growing black duck populations and reaching the prescribed NAWMP population goal. Using banding data from 1951 to 2011 and breeding population survey data from 1990 to 2014, we developed a full annual cycle population model for the American black duck. This model uses the seven management units as set by the Black Duck Joint Venture, allows movement into and out of each unit during each season, and models survival and fecundity for each region separately. We compare model population trajectories with observed population data and abundance estimates from the breeding season counts to show the accuracy of this full annual cycle model. With this model, we then show how to simulate the effects of habitat management on the continental black duck population.
","language":"English","publisher":"Wiley","doi":"10.1111/nrm.12088","usgsCitation":"Robinson, O.J., McGowan, C.P., Devers, P.K., Brook, R.W., Huang, M., Jones, M., McAuley, D.G., and Zimmerman, G.S., 2016, A full annual cycle modeling framework for American black ducks: Natural Resource Modeling, v. 29, no. 1, p. 159-174, https://doi.org/10.1111/nrm.12088.","productDescription":"16 p.","startPage":"159","endPage":"174","onlineOnly":"N","additionalOnlineFiles":"N","ipdsId":"IP-068504","costCenters":[{"id":198,"text":"Coop Res Unit Atlanta","active":true,"usgs":true}],"links":[{"id":318368,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"volume":"29","issue":"1","publishingServiceCenter":{"id":8,"text":"Raleigh PSC"},"noUsgsAuthors":false,"publicationDate":"2016-01-28","publicationStatus":"PW","scienceBaseUri":"56ced42de4b015c306ec2fdc","contributors":{"authors":[{"text":"Robinson, Orin J.","contributorId":167172,"corporation":false,"usgs":false,"family":"Robinson","given":"Orin","email":"","middleInitial":"J.","affiliations":[{"id":33694,"text":"School of Forestry and Wildlife Sciences, Auburn University","active":true,"usgs":false}],"preferred":false,"id":621307,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"McGowan, Conor P. 0000-0002-7330-9581 cmcgowan@usgs.gov","orcid":"https://orcid.org/0000-0002-7330-9581","contributorId":167162,"corporation":false,"usgs":true,"family":"McGowan","given":"Conor","email":"cmcgowan@usgs.gov","middleInitial":"P.","affiliations":[{"id":198,"text":"Coop Res Unit Atlanta","active":true,"usgs":true},{"id":17705,"text":"Wetland and Aquatic Research Center","active":true,"usgs":true}],"preferred":false,"id":621264,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Devers, Patrick K.","contributorId":167173,"corporation":false,"usgs":false,"family":"Devers","given":"Patrick","email":"","middleInitial":"K.","affiliations":[],"preferred":false,"id":621308,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Brook, Rodney W.","contributorId":92083,"corporation":false,"usgs":false,"family":"Brook","given":"Rodney","email":"","middleInitial":"W.","affiliations":[],"preferred":false,"id":621309,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Huang, Min","contributorId":167174,"corporation":false,"usgs":false,"family":"Huang","given":"Min","email":"","affiliations":[],"preferred":false,"id":621310,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"Jones, Malcom","contributorId":167175,"corporation":false,"usgs":false,"family":"Jones","given":"Malcom","email":"","affiliations":[],"preferred":false,"id":621311,"contributorType":{"id":1,"text":"Authors"},"rank":6},{"text":"McAuley, Daniel G. dmcauley@usgs.gov","contributorId":5377,"corporation":false,"usgs":true,"family":"McAuley","given":"Daniel","email":"dmcauley@usgs.gov","middleInitial":"G.","affiliations":[],"preferred":true,"id":621312,"contributorType":{"id":1,"text":"Authors"},"rank":7},{"text":"Zimmerman, Guthrie S.","contributorId":42473,"corporation":false,"usgs":false,"family":"Zimmerman","given":"Guthrie","email":"","middleInitial":"S.","affiliations":[{"id":6661,"text":"US Fish and Wildlife Service","active":true,"usgs":false}],"preferred":false,"id":621313,"contributorType":{"id":1,"text":"Authors"},"rank":8}]}} ,{"id":70161867,"text":"sir20155185 - 2016 - Stochastic model for simulating Souris River Basin precipitation, evapotranspiration, and natural streamflow","interactions":[],"lastModifiedDate":"2017-10-12T19:59:21","indexId":"sir20155185","displayToPublicDate":"2016-02-24T13:00:00","publicationYear":"2016","noYear":false,"publicationType":{"id":18,"text":"Report"},"publicationSubtype":{"id":5,"text":"USGS Numbered Series"},"seriesTitle":{"id":334,"text":"Scientific Investigations Report","code":"SIR","onlineIssn":"2328-0328","printIssn":"2328-031X","active":true,"publicationSubtype":{"id":5}},"seriesNumber":"2015-5185","title":"Stochastic model for simulating Souris River Basin precipitation, evapotranspiration, and natural streamflow","docAbstract":"The Souris River Basin is a 61,000-square-kilometer basin in the Provinces of Saskatchewan and Manitoba and the State of North Dakota. In May and June of 2011, record-setting rains were seen in the headwater areas of the basin. Emergency spillways of major reservoirs were discharging at full or nearly full capacity, and extensive flooding was seen in numerous downstream communities. To determine the probability of future extreme floods and droughts, the U.S. Geological Survey, in cooperation with the North Dakota State Water Commission, developed a stochastic model for simulating Souris River Basin precipitation, evapotranspiration, and natural (unregulated) streamflow. Simulations from the model can be used in future studies to simulate regulated streamflow, design levees, and other structures; and to complete economic cost/benefit analyses.
Long-term climatic variability was analyzed using tree-ring chronologies to hindcast precipitation to the early 1700s and compare recent wet and dry conditions to earlier extreme conditions. The extended precipitation record was consistent with findings from the Devils Lake and Red River of the North Basins (southeast of the Souris River Basin), supporting the idea that regional climatic patterns for many centuries have consisted of alternating wet and dry climate states.
A stochastic climate simulation model for precipitation, temperature, and potential evapotranspiration for the Souris River Basin was developed using recorded meteorological data and extended precipitation records provided through tree-ring analysis. A significant climate transition was seen around1970, with 1912–69 representing a dry climate state and 1970–2011 representing a wet climate state. Although there were some distinct subpatterns within the basin, the predominant differences between the two states were higher spring through early fall precipitation and higher spring potential evapotranspiration for the wet compared to the dry state.
A water-balance model was developed for simulating monthly natural (unregulated) mean streamflow based on precipitation, temperature, and potential evapotranspiration at select streamflow-gaging stations. The model was calibrated using streamflow data from the U.S. Geological Survey and Environment Canada, along with natural (unregulated) streamflow data from the U.S. Army Corps of Engineers. Correlation coefficients between simulated and natural (unregulated) flows generally were high (greater than 0.8), and the seasonal means and standard deviations of the simulated flows closely matched the means and standard deviations of the natural (unregulated) flows. After calibrating the model for a monthly time step, monthly streamflow for each subbasin was disaggregated into three values per month, or an approximately 10-day time step, and a separate routing model was developed for simulating 10-day streamflow for downstream gages.
The stochastic climate simulation model for precipitation, temperature, and potential evapotranspiration was combined with the water-balance model to simulate potential future sequences of 10-day mean streamflow for each of the streamflow-gaging station locations. Flood risk, as determined by equilibrium flow-frequency distributions for the dry (1912–69) and wet (1970–2011) climate states, was considerably higher for the wet state compared to the dry state. Future flood risk will remain high until the wet climate state ends, and for several years after that, because there may be a long lag-time between the return of drier conditions and the onset of a lower soil-moisture storage equilibrium.
","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/sir20155185","collaboration":"Prepared in cooperation with the North Dakota State Water Commission","usgsCitation":"Kolars, K.A., Vecchia, A.V., and Ryberg, K.R., 2016, Stochastic model for simulating Souris River Basin precipitation, evapotranspiration, and natural streamflow: U.S. Geological Survey Scientific Investigations Report 2015–5185, 55 p., https://dx.doi.org/10.3133/sir20155185.","productDescription":"viii, 55 p.","onlineOnly":"Y","additionalOnlineFiles":"N","ipdsId":"IP-068149","costCenters":[{"id":478,"text":"North Dakota Water Science Center","active":true,"usgs":true},{"id":34685,"text":"Dakota Water Science Center","active":true,"usgs":true}],"links":[{"id":318270,"rank":2,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/sir/2015/5185/sir20155185.pdf","text":"Report","size":"12.1 MB","linkFileType":{"id":1,"text":"pdf"},"description":"SIR 2015-5185"},{"id":318269,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/sir/2015/5185/coverthb.jpg"}],"country":"Canada, United States","state":"Manitoba, North Dakota, Saskatchewan","otherGeospatial":"Souris River Basin","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -104.04052734375,\n 48.99824008113872\n ],\n [\n -104.74365234375,\n 49.42884000063522\n ],\n [\n -104.7930908203125,\n 50.004208515595614\n ],\n [\n -103.480224609375,\n 50.52041218671901\n ],\n [\n -102.0245361328125,\n 50.604159488561\n ],\n [\n -101.195068359375,\n 50.25071752130677\n ],\n [\n -100.65673828125,\n 49.745781306155735\n ],\n [\n -99.60891723632812,\n 49.648069803718805\n ],\n [\n -99.18594360351562,\n 49.577773933420914\n ],\n [\n -99.2340087890625,\n 49.39131220507362\n ],\n [\n -99.76547241210936,\n 49.413653634531116\n ],\n [\n -99.4482421875,\n 48.100094697973795\n ],\n [\n -101.502685546875,\n 47.99727386804474\n ],\n [\n -103.568115234375,\n 48.52388120259336\n ],\n [\n -104.04052734375,\n 48.99824008113872\n ]\n ]\n ]\n }\n }\n ]\n}","contact":"Director, USGS North Dakota Water Science Center
821 East Interstate Avenue
Bismarck, North Dakota 58503
Weather and climate affect many ecological processes, making spatially continuous yet fine-resolution weather data desirable for ecological research and predictions. Numerous downscaled weather data sets exist, but little attempt has been made to evaluate them systematically. Here we address this shortcoming by focusing on four major questions: (1) How accurate are downscaled, gridded climate data sets in terms of temperature and precipitation estimates?, (2) Are there significant regional differences in accuracy among data sets?, (3) How accurate are their mean values compared with extremes?, and (4) Does their accuracy depend on spatial resolution? We compared eight widely used downscaled data sets that provide gridded daily weather data for recent decades across the United States. We found considerable differences among data sets and between downscaled and weather station data. Temperature is represented more accurately than precipitation, and climate averages are more accurate than weather extremes. The data set exhibiting the best agreement with station data varies among ecoregions. Surprisingly, the accuracy of the data sets does not depend on spatial resolution. Although some inherent differences among data sets and weather station data are to be expected, our findings highlight how much different interpolation methods affect downscaled weather data, even for local comparisons with nearby weather stations located inside a grid cell. More broadly, our results highlight the need for careful consideration among different available data sets in terms of which variables they describe best, where they perform best, and their resolution, when selecting a downscaled weather data set for a given ecological application.
","language":"English","publisher":"Ecological Society of America","publisherLocation":"Tempe, AZ","doi":"10.1002/15-1061","usgsCitation":"Behnke, R.J., Stephen J. 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Vavrus","affiliations":[{"id":16925,"text":"University of Wisconsin-Madison","active":true,"usgs":false}],"preferred":false,"id":622127,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Allstadt, Andrew","contributorId":167392,"corporation":false,"usgs":false,"family":"Allstadt","given":"Andrew","email":"","affiliations":[{"id":16925,"text":"University of Wisconsin-Madison","active":true,"usgs":false}],"preferred":false,"id":622128,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Albright, Thomas P.","contributorId":167393,"corporation":false,"usgs":false,"family":"Albright","given":"Thomas","email":"","middleInitial":"P.","affiliations":[{"id":24706,"text":"University of Nevada-Reno","active":true,"usgs":false}],"preferred":false,"id":622129,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Thogmartin, Wayne E. 0000-0002-2384-4279 wthogmartin@usgs.gov","orcid":"https://orcid.org/0000-0002-2384-4279","contributorId":2545,"corporation":false,"usgs":true,"family":"Thogmartin","given":"Wayne","email":"wthogmartin@usgs.gov","middleInitial":"E.","affiliations":[{"id":114,"text":"Alaska Science Center","active":true,"usgs":true},{"id":606,"text":"Upper Midwest Environmental Sciences Center","active":true,"usgs":true}],"preferred":true,"id":622125,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"Radeloff, Volker C.","contributorId":149494,"corporation":false,"usgs":false,"family":"Radeloff","given":"Volker","email":"","middleInitial":"C.","affiliations":[{"id":13679,"text":"SILVIS Lab, Department of Forest and Wildlife Ecology, University of Wisconsin-Madison","active":true,"usgs":false}],"preferred":false,"id":622130,"contributorType":{"id":1,"text":"Authors"},"rank":6}]}} ,{"id":70175344,"text":"70175344 - 2016 - Are brown trout replacing or displacing bull trout populations in a changing climate?","interactions":[],"lastModifiedDate":"2016-09-06T13:36:12","indexId":"70175344","displayToPublicDate":"2016-02-23T17:15:00","publicationYear":"2016","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":1169,"text":"Canadian Journal of Fisheries and Aquatic Sciences","active":true,"publicationSubtype":{"id":10}},"title":"Are brown trout replacing or displacing bull trout populations in a changing climate?","docAbstract":"Understanding how climate change may facilitate species turnover is an important step in identifying potential conservation strategies. We used data from 33 sites in western Montana to quantify climate associations with native bull trout (Salvelinus confluentus) and non-native brown trout (Salmo trutta) abundance and population growth rates (λ). We estimated λ using exponential growth state space models and delineated study sites based on bull trout use for either Spawning and Rearing (SR) or Foraging, Migrating, and Overwintering (FMO) habitat. Bull trout abundance was negatively associated with mean August stream temperatures within SR habitat (r = -0.75). Brown trout abundance was generally highest at temperatures between 12 and 14°C. We found bull trout λ were generally stable at sites with mean August temperature below 10°C but significantly decreasing, rare, or extirpated at 58% of the sites with temperatures exceeding 10°C. Brown trout λ were highest in SR and sites with temperatures exceeding 12°C. Declining bull trout λs at sites where brown trout were absent suggests brown trout are likely replacing bull trout in a warming climate.
","language":"English","publisher":"NRC Research Press","doi":"10.1139/cjfas-2015-0293","usgsCitation":"Al-Chokhachy, R.K., Schmetterling, D.A., Clancy, C., Saffel, P., Kovach, R., Nyce, L., Liermann, B., Fredenberg, W.A., and Pierce, R., 2016, Are brown trout replacing or displacing bull trout populations in a changing climate?: Canadian Journal of Fisheries and Aquatic Sciences, v. 73, no. 9, p. 1395-1404, https://doi.org/10.1139/cjfas-2015-0293.","productDescription":"10 p.","startPage":"1395","endPage":"1404","onlineOnly":"N","additionalOnlineFiles":"N","ipdsId":"IP-070449","costCenters":[{"id":481,"text":"Northern Rocky Mountain Science Center","active":true,"usgs":true}],"links":[{"id":326148,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","state":"Idaho, Montana","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -116.1474609375,\n 47.60616304386874\n ],\n [\n -109.2919921875,\n 47.45780853075031\n ],\n [\n -109.599609375,\n 45.521743896993634\n ],\n [\n -116.19140625,\n 45.30580259943578\n ],\n [\n -116.23535156249999,\n 47.30903424774781\n ],\n [\n -116.1474609375,\n 47.60616304386874\n ]\n ]\n ]\n }\n }\n ]\n}","volume":"73","issue":"9","publishingServiceCenter":{"id":2,"text":"Denver PSC"},"noUsgsAuthors":false,"publicationStatus":"PW","scienceBaseUri":"57a5b8b4e4b0ebae89b7884e","contributors":{"authors":[{"text":"Al-Chokhachy, Robert K. 0000-0002-2136-5098 ral-chokhachy@usgs.gov","orcid":"https://orcid.org/0000-0002-2136-5098","contributorId":1674,"corporation":false,"usgs":true,"family":"Al-Chokhachy","given":"Robert","email":"ral-chokhachy@usgs.gov","middleInitial":"K.","affiliations":[{"id":481,"text":"Northern Rocky Mountain Science Center","active":true,"usgs":true}],"preferred":true,"id":644795,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Schmetterling, David A.","contributorId":20223,"corporation":false,"usgs":true,"family":"Schmetterling","given":"David","email":"","middleInitial":"A.","affiliations":[],"preferred":false,"id":644796,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Clancy, Chris","contributorId":173465,"corporation":false,"usgs":false,"family":"Clancy","given":"Chris","email":"","affiliations":[{"id":6581,"text":"Montana Fish, Wildlife and Parks, Kalispell, Montana 59901, USA","active":true,"usgs":false}],"preferred":false,"id":644797,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Saffel, Pat","contributorId":173466,"corporation":false,"usgs":false,"family":"Saffel","given":"Pat","email":"","affiliations":[{"id":6581,"text":"Montana Fish, Wildlife and Parks, Kalispell, Montana 59901, USA","active":true,"usgs":false}],"preferred":false,"id":644798,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Kovach, Ryan 0000-0001-5402-2123 rkovach@usgs.gov","orcid":"https://orcid.org/0000-0001-5402-2123","contributorId":145914,"corporation":false,"usgs":true,"family":"Kovach","given":"Ryan","email":"rkovach@usgs.gov","affiliations":[{"id":481,"text":"Northern Rocky Mountain Science Center","active":true,"usgs":true}],"preferred":true,"id":644799,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"Nyce, Leslie","contributorId":173467,"corporation":false,"usgs":false,"family":"Nyce","given":"Leslie","email":"","affiliations":[{"id":6581,"text":"Montana Fish, Wildlife and Parks, Kalispell, Montana 59901, USA","active":true,"usgs":false}],"preferred":false,"id":644800,"contributorType":{"id":1,"text":"Authors"},"rank":6},{"text":"Liermann, Brad","contributorId":173468,"corporation":false,"usgs":false,"family":"Liermann","given":"Brad","email":"","affiliations":[{"id":6581,"text":"Montana Fish, Wildlife and Parks, Kalispell, Montana 59901, USA","active":true,"usgs":false}],"preferred":false,"id":644801,"contributorType":{"id":1,"text":"Authors"},"rank":7},{"text":"Fredenberg, Wade A.","contributorId":78860,"corporation":false,"usgs":true,"family":"Fredenberg","given":"Wade","email":"","middleInitial":"A.","affiliations":[],"preferred":false,"id":644802,"contributorType":{"id":1,"text":"Authors"},"rank":8},{"text":"Pierce, Ron","contributorId":171578,"corporation":false,"usgs":false,"family":"Pierce","given":"Ron","email":"","affiliations":[{"id":6581,"text":"Montana Fish, Wildlife and Parks, Kalispell, Montana 59901, USA","active":true,"usgs":false}],"preferred":false,"id":644803,"contributorType":{"id":1,"text":"Authors"},"rank":9}]}} ,{"id":70161955,"text":"sir20155163 - 2016 - Groundwater ages from the freshwater zone of the Edwards aquifer, Uvalde County, Texas—Insights into groundwater flow and recharge","interactions":[],"lastModifiedDate":"2016-02-24T09:17:23","indexId":"sir20155163","displayToPublicDate":"2016-02-23T13:00:00","publicationYear":"2016","noYear":false,"publicationType":{"id":18,"text":"Report"},"publicationSubtype":{"id":5,"text":"USGS Numbered Series"},"seriesTitle":{"id":334,"text":"Scientific Investigations Report","code":"SIR","onlineIssn":"2328-0328","printIssn":"2328-031X","active":true,"publicationSubtype":{"id":5}},"seriesNumber":"2015-5163","title":"Groundwater ages from the freshwater zone of the Edwards aquifer, Uvalde County, Texas—Insights into groundwater flow and recharge","docAbstract":"Tritium–helium-3 groundwater ages of the Edwards aquifer in south-central Texas were determined as part of a long-term study of groundwater flow and recharge in the Edwards and Trinity aquifers. These ages help to define groundwater residence times and to provide constraints for calibration of groundwater flow models. A suite of 17 samples from public and private supply wells within Uvalde County were collected for active and noble gases, and for tritium–helium-3 analyses from the confined and unconfined parts of the Edwards aquifer. Samples were collected from monitoring wells at discrete depths in open boreholes as well as from integrated pumped well-head samples. The data indicate a fairly uniform groundwater flow system within an otherwise structurally complex geologic environment comprised of regionally and locally faulted rock units, igneous intrusions, and karst features within carbonate rocks. Apparent ages show moderate, downward average, linear velocities in the Uvalde area with increasing age to the east along a regional groundwater flow path. Though the apparent age data show a fairly consistent distribution across the study area, many apparent ages indicate mixing of both modern (less than 60 years) and premodern (greater than 60 years) waters. This mixing is most evident along the “bad water” line, an arbitrary delineation of 1,000 milligrams per liter dissolved solids that separates the freshwater zone of the Edwards aquifer from the downdip saline water zone. Mixing of modern and premodern waters also is indicated within the unconfined zone of the aquifer by high excess helium concentrations in young waters. Excess helium anomalies in the unconfined aquifer are consistent with possible subsurface discharge of premodern groundwater from the underlying Trinity aquifer into the younger groundwater of the Edwards aquifer.
","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/sir20155163","usgsCitation":"Hunt, A.G., Landis, G.P., and Faith, J.R., 2016, Groundwater ages from the freshwater zone of the Edwards Aquifer, Uvalde County, Texas—Insights into groundwater flow and recharge: U.S. Geological Survey Scientific Investigations Report 2015–5163, 28 p., https://dx.doi.org/10.3133/sir20155163.","productDescription":"viii, 28 p.","onlineOnly":"Y","additionalOnlineFiles":"N","ipdsId":"IP-065915","costCenters":[{"id":211,"text":"Crustal Geophysics and Geochemistry Science Center","active":true,"usgs":true}],"links":[{"id":318180,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/sir/2015/5163/coverthb.jpg"},{"id":318181,"rank":2,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/sir/2015/5163/sir20155163.pdf","text":"Report","size":"3.50 MB","linkFileType":{"id":1,"text":"pdf"},"description":"SIR 2015-5163"}],"country":"United States","state":"Texas","county":"Uvalde County","otherGeospatial":"Edwards Aquifer","geographicExtents":"{\"type\":\"FeatureCollection\",\"features\":[{\"type\":\"Feature\",\"geometry\":{\"type\":\"Polygon\",\"coordinates\":[[[-99.4132,29.6253],[-99.4107,29.087],[-99.6813,29.0872],[-100.1119,29.0844],[-100.1112,29.3486],[-100.111,29.6236],[-100.0145,29.6237],[-99.6173,29.6257],[-99.6033,29.6257],[-99.4132,29.6253]]]},\"properties\":{\"name\":\"Uvalde\",\"state\":\"TX\"}}]}","contact":"Center Director, USGS Crustal Geophysics and Geochemistry Science Center
Box 25046, Mail Stop 964
Denver, CO 80225
In 2013, the U.S. Geological Survey and the Miami Conservancy District investigated the effectiveness of methods used to remove arsenic from drinking water at 11 homes in southwestern and central Ohio. The untreated (raw) ground-water had arsenic concentrations of 7.7–382 micrograms per liter (µg/L), and the median concentration was 30 µg/L. The pH was neutral to slightly alkaline, and redox conditions were strongly reducing, as indicated by high concentrations of iron. The predominant arsenic species was arsenite (As3+), which is difficult to treat because it exists in water as an uncharged compound (H3AsO3).
The water-treatment systems included (1) seven single-tap reverse-osmosis systems, (2) two whole-house oxidation/filtration systems, and (3) two systems that included wholehouse anion exchange and single-tap reverse osmosis. All but one system included pretreatment by a water softener, and two systems included preoxidation to convert arsenite (As3+) to arsenate (As5+) before treatment by anion exchange.
None of the treatment systems removed all of the arsenic from the drinking water. About one-half of the systems decreased the arsenic concentration to less than the maximum contamination level of 10 µg/L. The effectiveness of the systems varied widely; the percentage of arsenic removed ranged from 2 to 90 percent, and the median was 65 percent.
At some sites, the low effectiveness of arsenic removal may have been related to system maintenance and(or) operation issues. At two sites, homeowners acknowledged that the treatment systems had not been maintained for several years. At two other sites, the treatment systems were being maintained, but the water-quality data indicated that one of the components was not working, unbeknownst to the homeowner. EPA research at a small number of sites in Ohio indicated that operation and maintenance of some arsenic-treatment systems was not always simple.
Another factor that affected system effectiveness was the quality of the raw water. In general, the treatment systems were less effective at treating higher concentrations of arsenic. For five sites with raw-water arsenic concentrations of 10–30 µg/L, the systems removed 65–81 percent of the arsenic, and the final concentrations were less than the maximum contamination level. For three sites with higher raw-water arsenic concentrations (50–75 µg/L), the systems removed 22–34 percent of the arsenic; and the final concentrations were 4–5 times more than the maximum contamination level. Other characteristics of the raw water may have affected the performance of treatment systems; in general, raw water with the higher arsenic concentrations also had higher pH, higher concentrations of organic carbon and ammonia, and more reducing (methanogenic) redox conditions.
For sites with raw-water arsenic concentrations of 10–30 µg/L, two types of systems (reverse osmosis and oxidation/filtration) removed similar amounts of arsenic, but the quality of the treated water differed in other respects. Reverse osmosis caused substantial decreases in pH, alkalinity, and concentrations of most ions. On the other hand, oxidation/filtration using manganese-based media caused a large increase of manganese concentrations, from less than 50 µg/L in raw water to more than 700 µg/L in outflow from the oxidation filtration units.
It is not known if the results of this study are widely applicable; the number of systems sampled was relatively small, and each system was sampled only once. Further study may be warranted to investigate whether available methods of arsenic removal are effective/practical for residential use in areas like Ohio, were groundwater with elevated arsenic concentrations is strongly reducing, and the predominant arsenic species is arsenite (As3+).
","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/sir20155156","isbn":"978-1-4113-4006-0","collaboration":"Prepared in cooperation with Miami Conservancy District","usgsCitation":"Thomas, M.A., and Ekberg, Mike, 2016, The effectiveness of water-treatment systems for arsenic used in 11 homes in Southwestern and Central Ohio, 2013: U.S. Geological Survey Scientific Investigations Report 2015–5156, 26 p., https://dx.doi.org/10.3133/sir20155156.","productDescription":"Report: vi, 26 p.; Table","numberOfPages":"37","onlineOnly":"N","additionalOnlineFiles":"Y","ipdsId":"IP-065490","costCenters":[{"id":513,"text":"Ohio Water Science Center","active":true,"usgs":true}],"links":[{"id":318264,"rank":3,"type":{"id":27,"text":"Table"},"url":"https://pubs.usgs.gov/sir/2015/5156/sir20155156_table2.xlsx","text":"Table 2. Water-quality results from 11 domestic wells in southwestern and central Ohio, 2013","size":"36.0 kb","linkFileType":{"id":3,"text":"xlsx"},"description":"SIR 2015-5156 Table 2"},{"id":318263,"rank":2,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/sir/2015/5156/sir20155156.pdf","text":"Report","size":"1.72 MB","linkFileType":{"id":1,"text":"pdf"},"description":"SIR 2015-5156"},{"id":318262,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/sir/2015/5156/coverthb.jpg"}],"country":"United States","state":"Ohio","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -84.825439453125,\n 39.24927084622338\n ],\n [\n -84.825439453125,\n 40.43858586704328\n ],\n [\n -81.815185546875,\n 40.43858586704328\n ],\n [\n -81.815185546875,\n 39.24927084622338\n ],\n [\n -84.825439453125,\n 39.24927084622338\n ]\n ]\n ]\n }\n }\n ]\n}","contact":"Director, USGS Ohio Water Science Center
6480 Doubletree Ave
Columbus, OH 43229-1111
","tableOfContents":"
The U.S. Geological Survey (USGS) assessed the undiscovered, technically recoverable oil and gas resources of Armenia in 2013. A Paleozoic and a Cenozoic total petroleum system (TPS) were identified within the country of Armenia. The postulated petroleum system elements are uncertain, resulting in low geologic probabilities for significant oil an gas resources. Two assessment units (AU) were delineated in each TPS—a Paleozoic-Sourced Conventional Reservoirs AU and a Permian Shale Gas AU in the Paleozoic Composite TPS and a Paleogene-Sourced Conventional Reservoirs AU and a Cenozoic Coalbed Gas AU in the Cenozoic Composite TPS. The TPS elements are largely uncertain and risked, and so only the Paleogene-Sourced Conventional Reservoirs AU was quantitatively assessed because the geologic probability is more than the threshold of 10 percent (that is, the probability of at least one conventional oil or gas accumulation of 5 million barrels of oil equivalent or greater based on postulated petroleum-system elements). The USGS estimated fully risked mean volumes of about 1 million barrels of oil (MMBO), about 6 billion cubic feet of natural gas (BCFG), and less than 1 million barrels of natural gas liquids (MMBNGL).
","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/ds69PP","usgsCitation":"Klett, T.R., 2016, Geology and assessment of undiscovered, technically recoverable petroleum resources of Armenia, 2013: U.S. Geological Survey Digital Data Series 69–PP, 21 p., https://dx.doi.org/10.3133/ds69PP.","productDescription":"Report: vi, 21 p.; Figure","onlineOnly":"Y","additionalOnlineFiles":"N","ipdsId":"IP-063804","costCenters":[{"id":164,"text":"Central Energy Resources Science Center","active":true,"usgs":true}],"links":[{"id":318114,"rank":2,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/dds/dds-069/dds-069-pp/dds69pp.pdf","text":"Report","size":"1.16 MB","linkFileType":{"id":1,"text":"pdf"},"description":"DDS-69-PP Report"},{"id":318113,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/dds/dds-069/dds-069-pp/coverthb.jpg"},{"id":318154,"rank":3,"type":{"id":29,"text":"Figure"},"url":"https://pubs.usgs.gov/dds/dds-069/dds-069-pp/figure09.pdf","text":"Figure 9, lithostratigraphic column","size":"424 kb","linkFileType":{"id":1,"text":"pdf"},"description":"DDS-69-PP Figure 9"}],"country":"Armenia","geographicExtents":"{\"type\":\"FeatureCollection\",\"features\":[{\"type\":\"Feature\",\"geometry\":{\"type\":\"Polygon\",\"coordinates\":[[[43.58275,41.09214],[44.97248,41.24813],[45.1795,40.98535],[45.56035,40.81229],[45.35917,40.5615],[45.89191,40.21848],[45.61001,39.89999],[46.03453,39.62802],[46.4835,39.46415],[46.50572,38.77061],[46.14362,38.7412],[45.73538,39.31972],[45.73998,39.474],[45.29814,39.47175],[45.00199,39.74],[44.79399,39.713],[44.40001,40.005],[43.65644,40.25356],[43.75266,40.7402],[43.58275,41.09214]]]},\"properties\":{\"name\":\"Armenia\"}}]}","contact":"Center Director, USGS Central Energy Resources Science Center
Box 25046, Mail Stop 939
Denver, CO 80225
This study analyzes the active tectonics within the northwestern and southeastern extensions of the Pambak-Sevan-Syunik fault (PSSF), a major right-lateral strike-slip fault cutting through Armenia. Quantifying the deformations in terms of geometry, kinematics, slip rates and earthquake activity, using cosmogenic 3He, OSL/IRSL and radiocarbon dating techniques, reveal different behaviors between the two regions. Within the northwestern extension, in the region of Amasia, the PSSF bends to the west and splits into two main WNW–ESE trending reverse faults defining a compressional pop-up structure. We estimate an uplift rate and a shortening rate of 0.5 ± 0.1 mm/y and 1.4 ± 0.6 mm/y, respectively. This suggests that most of the ∼2 mm/y right lateral movement of the PSSF seems to be absorbed within the Amasia pop-structure. Within the southeastern extension, the PSSF shows signs of dying out within the Tsghuk Volcano region at the southernmost tip of the Syunik graben. There, the tectonic activity is characterized by a very slow NS trending normal faulting associated with a slight right-lateral movement. Slip rates analyses (i.e. vertical slip rate, EW stretching rate at 90° to the fault, and right-lateral slip rate of ∼0.2 mm/y, ∼0.1 mm/y and ∼0.05 mm/y, respectively) lead to the conclusion that the right lateral movement observed further north along the PSSF is mainly transferred within other active faults further west within the Karabagh (Hagari fault or other structures further northwestwards). Comparing our slip rates with those estimated from GPS data suggests that most of the deformation is localized and seismic, at least within the Tsghuk region. The geometrical and kinematic pattern observed within the two terminations of the PSSF suggests that the fault and its surrounding crustal blocks are presently rotating anticlockwise, as also observed within the GPS velocity field. This is consistent with the recent kinematic models proposed for the Caucasus-Kura-South Caspian region and brings a new insight into the present geodynamics of Armenia.
\n","largerWorkType":{"id":2,"text":"Article"},"largerWorkTitle":"Quaternary International","largerWorkSubtype":{"id":10,"text":"Journal Article"},"language":"English","publisher":"Pergamon","publisherLocation":"Oxford","doi":"10.1016/j.quaint.2015.05.021","collaboration":"Geosciences Montpellier, UMR CNRS 5243, University of Montpellier II, France; Institute of Geological Sciences, National Academy of Sciences of Armenia, Armenia; Research Institute for Earth Sciences, Geological Survey of Iran, Iran; CRPG, UMR 7358, CNRS, Université de Lorraine, Vandoeuvre-lès-Nancy, France; Laboratoire Préhistoire et Quaternaire, Université de Lille 1, France; US Geological Survey, Denver Federal Center, Denver, Colorado, USA; Department of Earth and Atmospheric Sciences, University of Québec, Montréal, Canada","usgsCitation":"Ritz, J., Avagyan, A., Mkrtchyan, M., Nazari, H., Blard, P., Karakhanian, A., Philip, H., Balescu, S., Mahan, S.A., Huot, S., Munch, P., and Lamothe, M., 2016, Active tectonics within the NW and SE extensions of the Pambak-Sevan-Syunik fault: Implications for the present geodynamics of Armenia: Quaternary International, v. 395, p. 61-78, https://doi.org/10.1016/j.quaint.2015.05.021.","productDescription":"18 p.","startPage":"61","endPage":"78","numberOfPages":"18","onlineOnly":"N","additionalOnlineFiles":"N","ipdsId":"IP-065629","costCenters":[{"id":318,"text":"Geosciences and Environmental Change Science Center","active":true,"usgs":true}],"links":[{"id":318395,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"Armenia","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n 45,\n 41.31082388091818\n ],\n [\n 44.769287109375,\n 41.29431726315258\n ],\n [\n 44.6923828125,\n 41.244772343082076\n ],\n [\n 44.439697265625,\n 41.21172151054787\n ],\n [\n 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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 in the central and coastal parts of South Carolina. Almost 27 inches of rain fell near Mount Pleasant in Charleston County during this period. U.S. Geological Survey (USGS) 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, USGS 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). Immediately after the storm event, USGS personnel documented 602 high-water marks, noting the location and height of the water above land surface. Later in October, 50 additional high-water marks were documented near bridges for South Carolina Department of Transportation. Using a subset of these high-water marks, 20 flood-inundation maps of 12 communities were created. Digital datasets of the inundation area, modeling boundary, and water depth rasters are all available for download.
","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/ofr20161019","collaboration":"Prepared in cooperation with the Federal Emergency Management Agency","usgsCitation":"Musser, J.W., Watson, K.M., Painter, J.A., and Gotvald, A.J., 2016, Flood-inundation maps of selected areas affected by the flood of October 2015 in central and coastal South Carolina: U.S. Geological Survey Open-File Report 2016–1019, 81 p., https://dx.doi.org/10.3133/ofr20161019.","productDescription":"Report: v, 81 p.; Raw Data","onlineOnly":"Y","additionalOnlineFiles":"Y","ipdsId":"IP-072657","costCenters":[{"id":13634,"text":"South Atlantic Water Science Center","active":true,"usgs":true}],"links":[{"id":318176,"rank":2,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/of/2016/1019/ofr20161019.pdf","text":"Report","size":"47.4 MB","linkFileType":{"id":1,"text":"pdf"},"description":"OFR 2016-1019"},{"id":318175,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/of/2016/1019/coverthb.jpg"},{"id":318184,"rank":3,"type":{"id":19,"text":"Raw Data"},"url":"https://water.usgs.gov/floods/events/2015/Joaquin/data_ofr20161019","text":"USGS Flood Information (Data Download)"}],"country":"United States","state":"South Carolina","otherGeospatial":"Salkehatchie River Basin, Savannah River Basin","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -78.541259765625,\n 33.86129311351553\n ],\n [\n -78.64013671875,\n 33.76088200086917\n ],\n [\n -78.94775390625,\n 33.61461929233378\n ],\n [\n -79.1015625,\n 33.348884792201694\n ],\n [\n -79.1455078125,\n 33.22030778968541\n ],\n [\n -79.34326171875,\n 32.99945000822837\n ],\n [\n -79.530029296875,\n 32.96258644191747\n ],\n [\n -79.60693359375,\n 32.85190345738802\n ],\n [\n -79.815673828125,\n 32.74108223150125\n ],\n [\n -80.057373046875,\n 32.55607364492029\n ],\n [\n -80.2880859375,\n 32.491230287947594\n ],\n [\n -80.386962890625,\n 32.41706632846282\n ],\n [\n -80.474853515625,\n 32.287132632616384\n ],\n [\n -80.628662109375,\n 32.175612478499346\n ],\n [\n -80.7275390625,\n 32.06395559466043\n ],\n [\n -80.85937499999999,\n 31.942839972853083\n ],\n [\n -80.936279296875,\n 31.99875937194732\n ],\n [\n -81.123046875,\n 32.12910537866886\n ],\n [\n -81.14501953125,\n 32.25926542645936\n ],\n [\n -81.14501953125,\n 32.36140331527543\n ],\n [\n -81.2548828125,\n 32.44488496716713\n ],\n [\n -81.298828125,\n 32.537551746769\n ],\n [\n -81.375732421875,\n 32.58384932565662\n ],\n [\n -81.4306640625,\n 32.685619853722\n ],\n [\n -81.474609375,\n 32.879587173066305\n ],\n [\n -81.507568359375,\n 33.00866349457558\n ],\n [\n -81.58447265624999,\n 33.073130945006625\n ],\n [\n -81.71630859375,\n 33.146750228776455\n ],\n [\n -81.815185546875,\n 33.23868752757414\n ],\n [\n -81.88110351562499,\n 33.32134852669881\n ],\n [\n -81.990966796875,\n 33.37641235124676\n ],\n [\n -81.947021484375,\n 33.458942753687644\n ],\n [\n -82.078857421875,\n 33.568861182555544\n ],\n [\n -82.166748046875,\n 33.642062504753675\n ],\n [\n -82.19970703125,\n 33.715201644740844\n ],\n [\n -82.28759765625,\n 33.80653802509606\n ],\n [\n -82.430419921875,\n 33.897777013859475\n ],\n [\n -82.55126953124999,\n 34.00713506435885\n ],\n [\n -82.496337890625,\n 34.17999758688084\n ],\n [\n -82.254638671875,\n 34.75966612466248\n ],\n [\n -81.947021484375,\n 35.11990857099681\n ],\n [\n -81.837158203125,\n 35.191766965947394\n ],\n [\n -81.39770507812499,\n 35.16482750605027\n ],\n [\n -81.046142578125,\n 35.146862906756304\n ],\n [\n -81.024169921875,\n 35.074964853989556\n ],\n [\n -80.91430664062499,\n 35.110921809704756\n ],\n [\n -80.771484375,\n 34.95799531086792\n ],\n [\n -80.782470703125,\n 34.84987503195418\n ],\n [\n -80.5517578125,\n 34.831841149828676\n ],\n [\n -80.255126953125,\n 34.804782919572425\n ],\n [\n -79.716796875,\n 34.82282272723702\n ],\n [\n -78.541259765625,\n 33.86129311351553\n ]\n ]\n ]\n }\n }\n ]\n}","contact":"Director, South Atlantic Water Science Center
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720 Gracern Road
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http://www.usgs.gov/water/southatlantic/
The successful development of a geothermal electric power generation facility relies on (1) the identification of sufficiently high temperatures at an economically viable depth and (2) the existence of or potential to create and maintain a permeable zone (permeability >10-14 m2) of sufficient size to allow efficient long-term extraction of heat from the reservoir host rock. If both occur at depth under the Columbia Plateau, development of geothermal resources there has the potential to expand both the magnitude and spatial extent of geothermal energy production. However, a number of scientific and technical issues must be resolved in order to evaluate the likelihood that the Columbia River Basalts, or deeper geologic units under the Columbia Plateau, are viable geothermal targets.
Recent research has demonstrated that heat flow beneath the Columbia Plateau Regional Aquifer System may be higher than previously measured in relatively shallow (<600 m depth) wells, indicating that sufficient temperatures for electricity generation occur at depths 5 km. The remaining consideration is evaluating the likelihood that naturally high permeability exists, or that it is possible to replicate the high average permeability (approximately 10-14 to 10-12 m2) characteristic of natural hydrothermal reservoirs. From a hydraulic perspective, Columbia River Basalts are typically divided into dense, impermeable flow interiors and interflow zones comprising the top of one flow, the bottom of the overlying flow, and any sedimentary interbed. Interflow zones are highly variable in texture but, at depths <600 m, some of them form highly permeable regional aquifers with connectivity over many tens of kilometers. Below depths of ~600 m, permeability reduction occurs in many interflow zones, caused by the formation of low-temperature hydrothermal alteration minerals (corresponding to temperatures above ~35 °C). However, some high permeability (>10-14 m2) interflows are documented at depths up to ~1,400 m. If the elevated permeability in these zones persists to greater depths, they may provide natural permeability of sufficient magnitude to allow their exploitation as conventional geothermal reservoirs. Alternatively, if the permeability in these interflow zones is less than 10-14 m2 at depth, it may be possible to use hydraulic and thermal stimulation to enhance the permeability of both the interflow zones and the natural jointing within the low-permeability interior portions of individual basalt flows in order to develop Enhanced/Engineered Geothermal System (EGS) reservoirs. The key challenge for an improved Columbia Plateau geothermal assessment is acquiring and interpreting comprehensive field data that can provide quantitative constraints on the recovery of heat from the Columbia River Basalts at depths greater than those currently tested by deep boreholes.
","largerWorkType":{"id":24,"text":"Conference Paper"},"largerWorkTitle":"Proceedings, 41st Workshop on Geothermal Reservoir Engineering","largerWorkSubtype":{"id":19,"text":"Conference Paper"},"conferenceTitle":"41st Workshop on Geothermal Reservoir Engineering","conferenceDate":"February 22-24, 2016","conferenceLocation":"Stanford, CA","language":"English","publisher":"Stanford University","publisherLocation":"Stanford, CA","usgsCitation":"Burns, E.R., Williams, C.F., Tolan, T., and Kaven, J.O., 2016, Are the Columbia River Basalts, Columbia Plateau, Idaho, Oregon, and Washington, USA, a viable geothermal target? A preliminary analysis, in Proceedings, 41st Workshop on Geothermal Reservoir Engineering, Stanford, CA, February 22-24, 2016, 11 p.","productDescription":"11 p.","ipdsId":"IP-071284","costCenters":[{"id":518,"text":"Oregon Water Science Center","active":true,"usgs":true}],"links":[{"id":340099,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","state":"Idaho, Oregon, Washington","otherGeospatial":"Columbia Plateau","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -122.25,\n 44.25\n ],\n [\n -115.25,\n 44.25\n ],\n [\n -115.25,\n 48.5\n ],\n [\n -122.25,\n 48.5\n ],\n [\n -122.25,\n 44.25\n ]\n ]\n ]\n }\n }\n ]\n}","publishingServiceCenter":{"id":12,"text":"Tacoma PSC"},"noUsgsAuthors":false,"publicationStatus":"PW","scienceBaseUri":"58fb1a4ee4b0c3010a8087c5","contributors":{"authors":[{"text":"Burns, Erick R. 0000-0002-1747-0506 eburns@usgs.gov","orcid":"https://orcid.org/0000-0002-1747-0506","contributorId":3094,"corporation":false,"usgs":true,"family":"Burns","given":"Erick","email":"eburns@usgs.gov","middleInitial":"R.","affiliations":[{"id":518,"text":"Oregon Water Science Center","active":true,"usgs":true}],"preferred":false,"id":623483,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Williams, Colin F. 0000-0003-2196-5496 colin@usgs.gov","orcid":"https://orcid.org/0000-0003-2196-5496","contributorId":274,"corporation":false,"usgs":true,"family":"Williams","given":"Colin","email":"colin@usgs.gov","middleInitial":"F.","affiliations":[{"id":312,"text":"Geology, Minerals, Energy, and Geophysics Science Center","active":true,"usgs":true}],"preferred":true,"id":623484,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Tolan, Terry","contributorId":55489,"corporation":false,"usgs":true,"family":"Tolan","given":"Terry","affiliations":[],"preferred":false,"id":623485,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Kaven, Joern Ole","contributorId":148002,"corporation":false,"usgs":false,"family":"Kaven","given":"Joern","email":"","middleInitial":"Ole","affiliations":[],"preferred":false,"id":623486,"contributorType":{"id":1,"text":"Authors"},"rank":4}]}} ,{"id":70177889,"text":"70177889 - 2016 - Toward a quantitative and empirical dissolved organic carbon budget for the Gulf of Maine, a semienclosed shelf sea","interactions":[],"lastModifiedDate":"2016-10-26T14:12:02","indexId":"70177889","displayToPublicDate":"2016-02-20T00:00:00","publicationYear":"2016","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":1836,"text":"Global Biogeochemical Cycles","active":true,"publicationSubtype":{"id":10}},"title":"Toward a quantitative and empirical dissolved organic carbon budget for the Gulf of Maine, a semienclosed shelf sea","docAbstract":"A time series of organic carbon export from Gulf of Maine (GoM) watersheds was compared to a time series of biological, chemical, bio-optical, and hydrographic properties, measured across the GoM between Yarmouth, NS, Canada, and Portland, ME, U.S. Optical proxies were used to quantify the dissolved organic carbon (DOC) and particulate organic carbon in the GoM. The Load Estimator regression model applied to river discharge data demonstrated that riverine DOC export (and its decadal variance) has increased over the last 80 years. Several extraordinarily wet years (2006–2010) resulted in a massive pulse of chromophoric dissolved organic matter (CDOM; proxy for DOC) into the western GoM along with unidentified optically scattering material (<0.2 μm diameter). A survey of DOC in the GoM and Scotian Shelf showed the strong influence of the Gulf of Saint Lawrence on the DOC that enters the GoM. A deep plume of CDOM-rich water was observed near the coast of Maine which decreased in concentration eastward. The Forel-Ule color scale was derived and compared to the same measurements made in 1912–1913 by Henry Bigelow. Results show that the GoM has yellowed in the last century, particularly in the region of the extension of the Eastern Maine Coastal Current. Time lags between DOC discharge and its appearance in the GoM increased with distance from the river mouths. Algae were also a significant source of DOC but not CDOM. Gulf-wide algal primary production has decreased. Increases in precipitation and DOC discharge to the GoM are predicted over the next century.","language":"English","publisher":"American Geophysical Union","doi":"10.1002/2015GB005332","usgsCitation":"Balch, W., Huntington, T.G., Aiken, G.R., Drapeau, D., Bowler, B., Lubelczyk, L., and Butler, K.D., 2016, Toward a quantitative and empirical dissolved organic carbon budget for the Gulf of Maine, a semienclosed shelf sea: Global Biogeochemical Cycles, v. 30, no. 2, p. 268-292, https://doi.org/10.1002/2015GB005332.","productDescription":"25 p.","startPage":"268","endPage":"292","ipdsId":"IP-072221","costCenters":[{"id":5044,"text":"National Research Program - Central Branch","active":true,"usgs":true}],"links":[{"id":471216,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.1002/2015gb005332","text":"Publisher Index Page"},{"id":330416,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"Canada, United States","otherGeospatial":"Gulf of Maine","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -72,\n 42\n ],\n [\n -72,\n 47\n ],\n [\n -65,\n 47\n ],\n [\n -65,\n 42\n ],\n [\n -72,\n 42\n ]\n ]\n ]\n }\n }\n ]\n}","volume":"30","issue":"2","publishingServiceCenter":{"id":2,"text":"Denver PSC"},"noUsgsAuthors":false,"publicationDate":"2016-02-20","publicationStatus":"PW","scienceBaseUri":"5811c0f3e4b0f497e79a5a7f","contributors":{"authors":[{"text":"Balch, William","contributorId":176267,"corporation":false,"usgs":false,"family":"Balch","given":"William","affiliations":[],"preferred":false,"id":652037,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Huntington, Thomas G. 0000-0002-9427-3530 thunting@usgs.gov","orcid":"https://orcid.org/0000-0002-9427-3530","contributorId":1884,"corporation":false,"usgs":true,"family":"Huntington","given":"Thomas","email":"thunting@usgs.gov","middleInitial":"G.","affiliations":[{"id":371,"text":"Maine Water Science Center","active":true,"usgs":true},{"id":466,"text":"New England Water Science Center","active":true,"usgs":true}],"preferred":true,"id":652038,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Aiken, George R. 0000-0001-8454-0984 graiken@usgs.gov","orcid":"https://orcid.org/0000-0001-8454-0984","contributorId":1322,"corporation":false,"usgs":true,"family":"Aiken","given":"George","email":"graiken@usgs.gov","middleInitial":"R.","affiliations":[{"id":5044,"text":"National Research Program - Central Branch","active":true,"usgs":true},{"id":191,"text":"Colorado Water Science Center","active":true,"usgs":true}],"preferred":true,"id":652036,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Drapeau, David","contributorId":176268,"corporation":false,"usgs":false,"family":"Drapeau","given":"David","email":"","affiliations":[],"preferred":false,"id":652039,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Bowler, Bruce","contributorId":176269,"corporation":false,"usgs":false,"family":"Bowler","given":"Bruce","email":"","affiliations":[],"preferred":false,"id":652040,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"Lubelczyk, Laura","contributorId":176270,"corporation":false,"usgs":false,"family":"Lubelczyk","given":"Laura","email":"","affiliations":[],"preferred":false,"id":652041,"contributorType":{"id":1,"text":"Authors"},"rank":6},{"text":"Butler, Kenna D. kebutler@usgs.gov","contributorId":3283,"corporation":false,"usgs":true,"family":"Butler","given":"Kenna","email":"kebutler@usgs.gov","middleInitial":"D.","affiliations":[{"id":5044,"text":"National Research Program - Central Branch","active":true,"usgs":true}],"preferred":false,"id":652042,"contributorType":{"id":1,"text":"Authors"},"rank":7}]}} ,{"id":70164420,"text":"ds980 - 2016 - Terrestrial-based lidar beach topography of Fire Island, New York, June 2014","interactions":[],"lastModifiedDate":"2016-08-03T08:45:50","indexId":"ds980","displayToPublicDate":"2016-02-19T12:30:00","publicationYear":"2016","noYear":false,"publicationType":{"id":18,"text":"Report"},"publicationSubtype":{"id":5,"text":"USGS Numbered Series"},"seriesTitle":{"id":310,"text":"Data Series","code":"DS","onlineIssn":"2327-638X","printIssn":"2327-0271","active":false,"publicationSubtype":{"id":5}},"seriesNumber":"980","title":"Terrestrial-based lidar beach topography of Fire Island, New York, June 2014","docAbstract":"The U.S. Geological Survey (USGS) St. Petersburg Coastal and Marine Science Center (SPCMSC) in Florida and the USGS Lower Mississippi-Gulf Water Science Center (LMG WSC) in Montgomery, Alabama, collaborated to gather alongshore terrestrial-based lidar beach elevation data at Fire Island, New York. 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Seasonal mean daily flow data from 119 U.S. Geological Survey streamflow-gaging stations in North Dakota; the surrounding states of Montana, Minnesota, and South Dakota; and the Canadian provinces of Manitoba and Saskatchewan with 10 or more years of unregulated flow record were used to develop regression equations for flow duration, n-day high flow and n-day low flow using ordinary least-squares and Tobit regression techniques. Regression equations were developed for seasonal flow durations at the 10th, 25th, 50th, 75th, and 90th percent exceedances; the 1-, 7-, and 30-day seasonal mean high flows for the 10-, 25-, and 50-year recurrence intervals; and the 1-, 7-, and 30-day seasonal mean low flows for the 2-, 5-, and 10-year recurrence intervals. Basin and climatic characteristics determined to be significant explanatory variables in one or more regression equations included drainage area, percentage of basin drainage area that drains to isolated lakes and ponds, ruggedness number, stream length, basin compactness ratio, minimum basin elevation, precipitation, slope ratio, stream slope, and soil permeability. The adjusted coefficient of determination for the n-day high-flow regression equations ranged from 55.87 to 94.53 percent. The Chi2 values for the duration regression equations ranged from 13.49 to 117.94, whereas the Chi2 values for the n-day low-flow regression equations ranged from 4.20 to 49.68.
","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/sir20155184","collaboration":"Prepared in cooperation with the North Dakota State Water Commission, North Dakota Department of Transportation, North Dakota Department of Health, Red River Joint Water Resources Board, and Devils Lake Basin Joint Water Resource Board","usgsCitation":"Williams-Sether, Tara, and Gross, T.A., 2016, Regression equations to estimate seasonal flow duration, n-day high-flow frequency, and n-day low-flow frequency at sites in North Dakota using data through water year 2009: U.S. Geological Survey Scientific Investigations Report 2015–5184, 12 p., https://dx.doi.org/10.3133/sir20155184.","productDescription":"Report: iv, 12 p.; 1 Table","onlineOnly":"Y","additionalOnlineFiles":"Y","ipdsId":"IP-069878","costCenters":[{"id":478,"text":"North Dakota Water Science Center","active":true,"usgs":true},{"id":34685,"text":"Dakota Water Science Center","active":true,"usgs":true}],"links":[{"id":316691,"rank":3,"type":{"id":27,"text":"Table"},"url":"https://pubs.usgs.gov/sir/2015/5184/sir20155184_table1.xlsx","text":"Table 1","size":"72.0 kb","linkFileType":{"id":3,"text":"xlsx"},"description":"SIR 2015-5184 Table 1"},{"id":316670,"rank":2,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/sir/2015/5184/sir20155184.pdf","text":"Report","size":"3.85 MB","linkFileType":{"id":1,"text":"pdf"},"description":"SIR 2015-5184"},{"id":316669,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/sir/2015/5184/coverthb.jpg"}],"country":"Canada, United States","state":"Manitoba, Minnesota, Montana, North Dakota, Saskatchewan, South Dakota, Wyoming","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -101.31591796875,\n 49.63917719651036\n ],\n [\n -101.854248046875,\n 49.94415035164548\n ],\n [\n -103.128662109375,\n 50.21206446065373\n ],\n [\n -104.359130859375,\n 50.240178884797025\n ],\n [\n -105.13916015625,\n 49.90171121726089\n ],\n [\n -105.3369140625,\n 48.64016871811908\n ],\n [\n -105.567626953125,\n 47.39834920035926\n ],\n [\n -105.27099609375,\n 46.59661864884465\n ],\n [\n -105.128173828125,\n 45.71385093029221\n ],\n [\n -105.46875,\n 44.574817404670306\n ],\n [\n -103.9306640625,\n 44.62175409623324\n ],\n [\n -103.524169921875,\n 45.54483149242463\n ],\n [\n -102.513427734375,\n 45.43700828867389\n ],\n [\n -101.019287109375,\n 45.205263456162385\n ],\n [\n -99.635009765625,\n 44.98811302615805\n ],\n [\n -98.668212890625,\n 45.0502402697946\n ],\n [\n -97.85522460937499,\n 44.89479576469787\n ],\n [\n -96.339111328125,\n 44.56699093657141\n ],\n [\n -95.185546875,\n 44.91813929958515\n ],\n [\n -95.987548828125,\n 45.95878764035642\n ],\n [\n -95.701904296875,\n 47.03269459852135\n ],\n [\n -95.767822265625,\n 47.82053186746053\n ],\n [\n -96.0205078125,\n 48.62564740882851\n ],\n [\n -96.427001953125,\n 49.24629332459796\n ],\n [\n -96.99829101562499,\n 49.986552130506176\n ],\n [\n -97.5146484375,\n 49.66762782262192\n ],\n [\n -97.97607421875,\n 49.50380954152213\n ],\n [\n -98.6572265625,\n 49.75997752330658\n ],\n [\n -99.29443359375,\n 49.61070993807422\n ],\n [\n -100.008544921875,\n 49.489538473066474\n ],\n [\n -100.34912109375,\n 49.36806633482156\n ],\n [\n -101.31591796875,\n 49.63917719651036\n ]\n ]\n ]\n }\n }\n ]\n}","contact":"Director, USGS North Dakota Water Science Center
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Spatial capture–recapture (SCR) is a relatively recent development in ecological statistics that provides a spatial context for estimating abundance and space use patterns, and improves inference about absolute population density. SCR has been applied to individual encounter data collected noninvasively using methods such as camera traps, hair snares, and scat surveys. Despite the widespread use of capture-based surveys to monitor amphibians and reptiles, there are few applications of SCR in the herpetological literature. We demonstrate the utility of the application of SCR for studies of reptiles and amphibians by analyzing capture–recapture data from Red-Backed Salamanders, Plethodon cinereus, collected using artificial cover boards. Using SCR to analyze spatial encounter histories of marked individuals, we found evidence that density differed little among four sites within the same forest (on average, 1.59 salamanders/m2) and that salamander detection probability peaked in early October (Julian day 278) reflecting expected surface activity patterns of the species. The spatial scale of detectability, a measure of space use, indicates that the home range size for this population of Red-Backed Salamanders in autumn was 16.89 m2. Surveying reptiles and amphibians using artificial cover boards regularly generates spatial encounter history data of known individuals, which can readily be analyzed using SCR methods, providing estimates of absolute density and inference about the spatial scale of habitat use.
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A novel method is presented for evaluating assumed relations between K and geologic interpretations for regional-scale groundwater modeling. The approach relies on simultaneous interpretation of multiple aquifer tests using alternative geologic frameworks of variable complexity, where each framework is incorporated as prior information that assumes homogeneous K within each model unit. This approach is tested at Pahute Mesa within the Nevada National Security Site (USA), where observed drawdowns from eight aquifer tests in complex, highly faulted volcanic rocks provide the necessary hydraulic constraints. The investigated volume encompasses 40 mi3 (167 km3) where drawdowns traversed major fault structures and were detected more than 2 mi (3.2 km) from pumping wells. Complexity of the five frameworks assessed ranges from an undifferentiated mass of rock with a single unit to 14 distinct geologic units. Results show that only four geologic units can be justified as hydraulically unique for this location. The approach qualitatively evaluates the consistency of hydraulic property estimates within extents of investigation and effects of geologic frameworks on extrapolation. Distributions of transmissivity are similar within the investigated extents irrespective of the geologic framework. In contrast, the extrapolation of hydraulic properties beyond the volume investigated with interfering aquifer tests is strongly affected by the complexity of a given framework. Testing at Pahute Mesa illustrates how this method can be employed to determine the appropriate level of geologic complexity for large-scale groundwater modeling.
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