An imaging sonar was used to assess the behavior and abundance of fish sized the same as salmonid smolt and bull trout (Salvelinus confluentus) at the entrance to the juvenile fish floating surface collector (FSC) at North Fork Reservoir, Oregon. The purpose of the FSC is to collect downriver migrating juvenile salmonids (Chinook salmon [Oncorhynchus tshawytscha], Coho salmon [Oncorhynchus kisutch], and steelhead [Oncorhynchus mykiss]) at the North Fork Dam and to safely route them around the hydroelectric projects. The objective of the imaging sonar component of this study was to assess the behaviors of both smolt and predator-size fish (smolt [60–250 millimeter] and predator 350–650 [millimeter]) observed near the FSC and to determine if the presence of predator-size fish influenced the abundance of smolt-size fish. An imaging sonar was deployed near the entrance to the FSC during the spring smolt out-migration period. The imaging sonar technology was an informative tool for assessing abundance and spatial and temporal behaviors of both smolt and predator-size fish near the entrance of the FSC. Both smolt and predator-size fish were regularly observed near the entrance, with greater abundances observed during day than during night. Behavioral differences were also observed between the two fish-size classes, with smolt-size fish traveling straighter with more directed movement, and predator-size fish generally showing more milling behavior. Additionally, the presence of predator-size fish may be effecting the abundance and direction of travel of smolt-size fish, as counts of smolt-size fish were reduced in conjunction with the presence of predator-size fish and a greater proportion of smolt-size fish were observed traveling away from the FSC when predator-size fish were present than when predator-size fish were absent. Results of modeling potential predator-prey interactions and influences indicated that both the number of juvenile fish tracks and photoperiod had the strongest effects on the number of predator fish tracks, with more predator-size fish tracks observed as the number of smolt-size fish tracks increased. Overall, the results indicate that predator-size fish are present near the entrance of the FSC, concomitant with smolt-size fish, and their abundances and behaviors indicate that they may be drawn to the entrance of the FSC because of the abundance of prey-sized fish found there.
","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/ofr20181182","collaboration":"Prepared in cooperation with Portland General Electric","usgsCitation":"Smith, C.D., Plumb J.M., and Adams, N.S. 2018, Fish behavior and abundance monitoring near a floating surface collector in North Fork Reservoir, Clackamas River, Oregon, using multi-beam acoustic imaging sonar: U.S. Geological Survey Open-File Report 2018-1182, 28 p., https://doi.org/10.3133/ofr20181182.","productDescription":"vi, 28 p.","onlineOnly":"Y","ipdsId":"IP-100791","costCenters":[{"id":654,"text":"Western Fisheries Research Center","active":true,"usgs":true}],"links":[{"id":359740,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/of/2018/1182/coverthb2.jpg"},{"id":359741,"rank":2,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/of/2018/1182/ofr20181182.pdf","text":"Report","size":"5 MB","linkFileType":{"id":1,"text":"pdf"},"description":"OFR 2018-1182"}],"country":"United States","state":"Oregon","otherGeospatial":"North Fork Dam","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -122.73376464843749,\n 45.0657615477031\n ],\n [\n -121.73263549804688,\n 45.0657615477031\n ],\n [\n -121.73263549804688,\n 45.45724086262233\n ],\n [\n -122.73376464843749,\n 45.45724086262233\n ],\n [\n -122.73376464843749,\n 45.0657615477031\n ]\n ]\n ]\n }\n }\n ]\n}","contact":"Director, Western Fisheries Research Center
U.S. Geological Survey
6505 NE 65th Street
Seattle, Washington 98115
We related Louisiana Waterthrush (Parkesia motacilla) demographic response and nest survival to benthic macroinvertebrate aquatic prey and to shale gas development parameters using models that accounted for both spatial and non-spatial sources of variability in a Central Appalachian USA watershed. In 2013, aquatic prey density and pollution intolerant genera (i.e., pollution tolerance value <4) decreased statistically with increased waterthrush territory length but not in 2014 when territory densities were lower. In general, most demographic responses to aquatic prey were variable and negatively related to aquatic prey in 2013 but positively related in 2014. Competing aquatic prey covariate models to explain nest survival were not statistically significant but differed annually and in general reversed from negative to positive influence on daily survival rate. Potential hydraulic fracturing runoff decreased nest survival both years and was statistically significant in 2014. The EPA Rapid Bioassessment protocol (EPA) and Habitat Suitability Index (HSI) designed for assessing suitability requirements for waterthrush were positively linked to aquatic prey where higher scores increased aquatic prey metrics, but EPA was more strongly linked than HSI and varied annually. While potential hydraulic fracturing runoff in 2013 may have increased Ephemeroptera, Plecoptera, and Trichoptera (EPT) richness, in 2014 shale gas territory disturbance decreased EPT richness. In 2014, intolerant genera decreased at the territory and nest level with increased shale gas disturbance suggesting the potential for localized negative effects on waterthrush. Loss of food resources does not seem directly or solely responsible for demographic declines where waterthrush likely were able to meet their foraging needs. However collective evidence suggests there may be a shale gas disturbance threshold at which waterthrush respond negatively to aquatic prey community changes. Density-dependent regulation of their ability to adapt to environmental change through acquisition of additional resources may also alter demographic response.
","language":"English","publisher":"Public Library of Science","doi":"10.1371/journal.pone.0206077","usgsCitation":"Frantz, M.W., Wood, P.B., and Merovich, G.T., 2018, Demographic characteristics of an avian predator, Louisiana Waterthrush (Parkesia motacilla), in response to its aquatic prey in a Central Appalachian USA watershed impacted by shale gas development: PLoS ONE, v. 13, no. 11, p. 1-19, https://doi.org/10.1371/journal.pone.0206077.","productDescription":"e0206077, 19 p.","startPage":"1","endPage":"19","ipdsId":"IP-095412","costCenters":[{"id":199,"text":"Coop Res Unit Leetown","active":true,"usgs":true}],"links":[{"id":468230,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.1371/journal.pone.0206077","text":"Publisher Index Page"},{"id":395279,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","state":"West Virginia","otherGeospatial":"Lewis Wetzel Wildlife Management Area","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -80.66818714141846,\n 39.514702147872995\n ],\n [\n -80.64299583435057,\n 39.514702147872995\n ],\n [\n -80.64299583435057,\n 39.530790543485786\n ],\n [\n -80.66818714141846,\n 39.530790543485786\n ],\n [\n -80.66818714141846,\n 39.514702147872995\n ]\n ]\n ]\n }\n }\n ]\n}","volume":"13","issue":"11","noUsgsAuthors":false,"publicationDate":"2018-11-28","publicationStatus":"PW","contributors":{"editors":[{"text":"Lightfoot, David A.","contributorId":273594,"corporation":false,"usgs":false,"family":"Lightfoot","given":"David","email":"","middleInitial":"A.","affiliations":[],"preferred":false,"id":832745,"contributorType":{"id":2,"text":"Editors"},"rank":1}],"authors":[{"text":"Frantz, Mack W.","contributorId":272515,"corporation":false,"usgs":false,"family":"Frantz","given":"Mack","email":"","middleInitial":"W.","affiliations":[{"id":12432,"text":"West Virginia University","active":true,"usgs":false}],"preferred":false,"id":832653,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Wood, Petra B. 0000-0002-8575-1705 pbwood@usgs.gov","orcid":"https://orcid.org/0000-0002-8575-1705","contributorId":199090,"corporation":false,"usgs":true,"family":"Wood","given":"Petra","email":"pbwood@usgs.gov","middleInitial":"B.","affiliations":[{"id":199,"text":"Coop Res Unit Leetown","active":true,"usgs":true}],"preferred":true,"id":832652,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Merovich, George T. Jr.","contributorId":172041,"corporation":false,"usgs":false,"family":"Merovich","given":"George","suffix":"Jr.","email":"","middleInitial":"T.","affiliations":[],"preferred":false,"id":832654,"contributorType":{"id":1,"text":"Authors"},"rank":3}]}} ,{"id":70227756,"text":"70227756 - 2018 - Influence of river discharge on grass carp occupancy dynamics in south-eastern Iowa rivers","interactions":[],"lastModifiedDate":"2022-01-28T14:42:30.747171","indexId":"70227756","displayToPublicDate":"2018-11-28T08:37:39","publicationYear":"2018","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":3301,"text":"River Research and Applications","active":true,"publicationSubtype":{"id":10}},"title":"Influence of river discharge on grass carp occupancy dynamics in south-eastern Iowa rivers","docAbstract":"Despite the longstanding presence of grass carp Ctenopharyngodon idella in the Upper Mississippi River (UMR) watershed, information regarding their populations remains largely unknown, in part because capture is difficult. Occupancy models are a popular wildlife assessment tool to account for imperfect detections but have been slow to be adopted in fisheries. Herein, we used occupancy modelling to evaluate the influence of two environmental covariates (river discharge and water temperature) on grass carp occupancy, extinction, colonization, and detection at nine sites within south-eastern Iowa rivers from April to October 2014 and 2015. Grass carp were detected at least once at all but one site. The most parsimonious model indicated that grass carp colonization probability increased from 0.15 to 0.67 with increases in river discharge. In contrast, occupancy (0.20), extinction (0.29), and detection (0.50) probabilities were temporally constant. Models indicated that water temperatures did not influence grass carp extinction or colonization probabilities relative to river discharge. Cumulative grass carp detection probability approached 1.0, whereas conditional occupancy estimates were less than 0.1 when using five or more sampling transects. The use of a robust design occupancy model allowed us to estimate site occupancy rates of grass carp corrected for imperfect detections, while demonstrating the importance of river discharge for site colonization. These results can be used to assess the distribution of a cryptic fish while helping to guide grass carp sampling and removal efforts.
","language":"English","publisher":"Wiley","doi":"10.1002/rra.3385","usgsCitation":"Sullivan, C.J., Weber, M., Pierce, C., and Camacho, C.A., 2018, Influence of river discharge on grass carp occupancy dynamics in south-eastern Iowa rivers: River Research and Applications, v. 35, no. 1, p. 60-67, https://doi.org/10.1002/rra.3385.","productDescription":"8 p.","startPage":"60","endPage":"67","ipdsId":"IP-090729","costCenters":[{"id":199,"text":"Coop Res Unit Leetown","active":true,"usgs":true}],"links":[{"id":502456,"rank":0,"type":{"id":41,"text":"Open Access External Repository Page"},"url":"https://lib.dr.iastate.edu/nrem_pubs/296","text":"External Repository"},{"id":395045,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","state":"Iowa","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -94.24072265625,\n 40.17887331434696\n ],\n [\n -90.54931640625,\n 40.17887331434696\n ],\n [\n -90.54931640625,\n 42.65012181368022\n ],\n [\n -94.24072265625,\n 42.65012181368022\n ],\n [\n -94.24072265625,\n 40.17887331434696\n ]\n ]\n ]\n }\n }\n ]\n}","volume":"35","issue":"1","noUsgsAuthors":false,"publicationDate":"2018-11-28","publicationStatus":"PW","contributors":{"editors":[{"text":"Weber, Michael J.","contributorId":272530,"corporation":false,"usgs":false,"family":"Weber","given":"Michael J.","affiliations":[{"id":6911,"text":"Iowa State University","active":true,"usgs":false}],"preferred":false,"id":832053,"contributorType":{"id":2,"text":"Editors"},"rank":2}],"authors":[{"text":"Sullivan, Christopher J.","contributorId":272528,"corporation":false,"usgs":false,"family":"Sullivan","given":"Christopher","email":"","middleInitial":"J.","affiliations":[{"id":6911,"text":"Iowa State University","active":true,"usgs":false}],"preferred":false,"id":832051,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Weber, Michael J.","contributorId":272530,"corporation":false,"usgs":false,"family":"Weber","given":"Michael J.","affiliations":[{"id":6911,"text":"Iowa State University","active":true,"usgs":false}],"preferred":false,"id":832102,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Pierce, Clay 0000-0001-5088-5431 cpierce@usgs.gov","orcid":"https://orcid.org/0000-0001-5088-5431","contributorId":150492,"corporation":false,"usgs":true,"family":"Pierce","given":"Clay","email":"cpierce@usgs.gov","affiliations":[{"id":199,"text":"Coop Res Unit Leetown","active":true,"usgs":true}],"preferred":true,"id":832050,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Camacho, Carlos A.","contributorId":272529,"corporation":false,"usgs":false,"family":"Camacho","given":"Carlos","email":"","middleInitial":"A.","affiliations":[{"id":6911,"text":"Iowa State University","active":true,"usgs":false}],"preferred":false,"id":832052,"contributorType":{"id":1,"text":"Authors"},"rank":4}]}} ,{"id":70200972,"text":"tm9A1 - 2018 - Preparations for water sampling","interactions":[{"subject":{"id":4907,"text":"twri09A1 - 2005 - Preparations for water sampling","indexId":"twri09A1","publicationYear":"2005","noYear":false,"displayTitle":"Preparations for Water Sampling","title":"Preparations for water sampling"},"predicate":"SUPERSEDED_BY","object":{"id":70200972,"text":"tm9A1 - 2018 - Preparations for water sampling","indexId":"tm9A1","publicationYear":"2018","noYear":false,"title":"Preparations for water sampling"},"id":1}],"lastModifiedDate":"2019-03-26T13:22:42","indexId":"tm9A1","displayToPublicDate":"2018-11-27T14:30:00","publicationYear":"2018","noYear":false,"publicationType":{"id":18,"text":"Report"},"publicationSubtype":{"id":5,"text":"USGS Numbered Series"},"seriesTitle":{"id":335,"text":"Techniques and Methods","code":"TM","onlineIssn":"2328-7055","printIssn":"2328-7047","active":true,"publicationSubtype":{"id":5}},"seriesNumber":"9-A1","displayTitle":"Chapter A1. Preparations for Water Sampling","title":"Preparations for water sampling","docAbstract":"The “National Field Manual for the Collection of Water-Quality Data” (NFM) provides guidelines and procedures for U.S. Geological Survey (USGS) personnel who collect data used to assess the quality of the Nation’s surface-water and groundwater resources. This chapter, NFM A1, provides an overview of preparations for water sampling, which includes site reconnaissance, project work plans, quality-assurance plans, basic equipment and supplies needed for fieldwork, safety precautions, and planning for data management. It updates and supersedes USGS Techniques of Water-Resources Investigations, book 9, chapter A1, version 2.0, by F.D. Wilde.
Before 2017, the NFM chapters were released in the USGS Techniques of Water-Resources Investigations series. Effective in 2018, new and revised NFM chapters are being released in the USGS Techniques and Methods series; this series change does not affect the content and format of the NFM. More information is in the general introduction to the NFM (USGS Techniques and Methods, book 9, chapter A0) at https://doi.org/10.3133/tm9A0. The authoritative current versions of NFM chapters are available in the USGS Publications Warehouse at https://pubs.er.usgs.gov/. Comments, questions, and suggestions related to the NFM can be addressed to nfm-owq@usgs.gov.
","largerWorkType":{"id":18,"text":"Report"},"largerWorkTitle":"Section A: National field manual for the collection of water-quality data in Book 9: Handbooks for water-resources investigations","largerWorkSubtype":{"id":5,"text":"USGS Numbered Series"},"language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/tm9A1","usgsCitation":"U.S. Geological Survey, 2018, Preparations for water sampling: U.S. Geological Survey Techniques and Methods 9-A1, vii, 42 p., https://doi.org/10.3133/tm9A1.","productDescription":"vii, 42 p.","onlineOnly":"Y","additionalOnlineFiles":"Y","costCenters":[],"links":[{"id":359547,"rank":2,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/tm/09/a1/tm9a1.pdf","text":"Report","size":"2.79 MB","linkFileType":{"id":1,"text":"pdf"},"description":"TM 9-A1"},{"id":359548,"rank":3,"type":{"id":25,"text":"Version History"},"url":"https://pubs.usgs.gov/tm/09/a1/versionHist.txt","text":"Version History","size":"2.74 MB","linkFileType":{"id":2,"text":"txt"}},{"id":359546,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/tm/09/a1/coverthb2.jpg"}],"publicComments":"The 2018 release in the Techniques and Methods series supersedes two earlier editions in the Techniques of Water-Resources Investigations series. Version 1 was released in 1998 and version 2 was released in 2005. More details are in the version history document.","contact":"Chief, Office of Quality Assurance
U.S. Geological Survey
12201 Sunrise Valley Drive, MS 432
Reston, VA 20192
In 2015–16, the U.S. Geological Survey, in cooperation with the Muskingum Watershed Conservancy District, led a study to assess baseline (2015–16) surface-water quality in six lake drainage basins within the Muskingum River watershed that are in the early years of shale-gas development. In 2015, 9 of the 10 most active counties in Ohio for oil and gas development were wholly or partially within the Muskingum River watershed. In addition to shale gas development, the area has a history of conventional oil and gas development and coal mining.
In all, 30 surface-water sites were sampled: 20 in tributaries flowing to the lakes, 4 in lakes themselves, and 6 downstream of the lakes. At each of the 30 sites, 6 samples were collected to characterize surface-water chemistry throughout a range of hydrologic conditions. The sampling generally occurred during low flows (periods of greater groundwater contribution) rather than during runoff events (periods of high stream stage).
Trilinear diagrams of major ion chemistry revealed three main types of water in the study area―sulfate-dominated waters, bicarbonate-dominated waters, and waters with mixed bicarbonate and chloride anions. Most sites produced samples of bicarbonate-dominated water, and 11 sites produced samples with sulfate-type waters. Mixed bicarbonate and chloride waters were found in samples from two of the six lake drainage basins studied.
The baseline (2015–16) assessment of surface-water quality in the study area indicated that few water-chemistry constituents and properties occurred at concentrations or levels that would adversely affect aquatic organisms. Chemical-specific, aquatic life use criteria were not met in only three instances: two were for total dissolved solids at sites likely impacted by coal mining in their drainage basins (hereafter referred to as “mine-impacted sites”), and one was for dissolved oxygen.
Mine drainage from historical coal mining in the region likely affected the quality of about one-third of the streams sampled. To simplify interpretation of water-chemistry results, 11 sites with sulfate-type water were identified as mine-impacted sites based on water-quality criteria established by Ohio Department of Natural Resources, Division of Mineral Resources Management, and separated out for subsequent statistical analysis. Concentrations or levels of bicarbonate, boron, calcium, carbonate, total dissolved solids, fluoride, magnesium, lithium, pH, potassium, sodium, specific conductance, strontium, sulfate, and suspended sediment in water were higher (significance level of 0.05) at mine-impacted stream sites than at non-mine-impacted stream sites.
An accidental release of oil- and gas-related brines could increase salinity (sodium and chloride), the concentration of total dissolved solids in shallow groundwater and streams, and specific conductance. For this study, chloride concentrations in the study area ranged from 2.12 to 76.1 milligrams per liter. Sources of chloride in water samples were evaluated using binary mixing curves and ratios of chloride to bromide. These ratios indicated that 13 samples from 3 sites in the drainage basin that contained the highest density of conventional oil and gas wells in the study, as well as 4 samples collected from other drainage basins, likely contained a component of brine. Concentrations or levels of barium, bromide, chloride, iron, lithium, manganese, and sodium were significantly higher (alpha = 0.05) in samples with a component of brine than in samples without a component of brine.
Benzene, toluene, ethylbenzene and xylene (BTEX), compounds that occur naturally in crude oil, made up 24 of the 45 detections (53 percent) of volatile organic compounds in the study area. The BTEX detections were not associated with sites containing a component of brine. The only volatile organic compound detected in any of the 17 samples that contained a component of brine was acetone, detected in 3 (18 percent) of these samples and in 11 percent of samples not containing a component of brine. Considering that BTEX are gasoline hydrocarbons and that most of the detections occurred during warmer months in and around the lakes, the BTEX detections likely are associated with increases in outdoor activities such as automobile and boating traffic.
Radium-226 and radium-228 were included in the list of analytes for this study because production water from shale-gas drilling can contain these naturally occurring radioactive materials. Concentrations of radium-226 exceeded background levels in only two surface-water samples. Concentrations of radium-228 exceeded background levels in one surface-water sample.
A brine signature potentially indicative of oil and gas contamination was detected in samples collected at two sites that contained active or plugged waste injection wells, or both. Results from the study indicated significant differences in the median concentrations of bromide, chloride, lithium, manganese, sodium, and total dissolved nitrogen between sites with and without injection wells in their drainage areas. Median concentrations of bromide, chloride, lithium, and sodium, which are common oil- and gas-related contaminants, were higher at sites with injection wells in their drainage areas compared to sites without injection wells.
Historical (1960s, 1970s, and 1980s) chloride concentrations and streamflow data at or near five of the six sampling sites downstream from each lake dam were compared to current (2015–16) values. An analysis of covariance was done to test the effects of streamflow, time (decade), and the combined effects (cross product) of streamflow and time on chloride concentrations. Those analyses indicated that streamflow was not significant in explaining the variation in chloride concentration, likely because streamflow in those locations is controlled by dam operations; therefore, association between runoff-generating events and streamflow is less direct than in unregulated streams. From the 1980s to the study period (2015–16), data for three of the five lakes indicated an increase in chloride concentrations. The comparison of historical and current (2015–16) study data from samples collected at another lake indicated that chloride concentrations increased from the 1960s to the 1970s, but concentrations in the 1970s and 2015–16 were similar even though 13 samples from this lake drainage basin were classified as having a component of brine. Median chloride concentrations for the fifth lake, however, seemed to decrease from the 1980s to 2015–16.
","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/sir20185113","collaboration":"Prepared in cooperation with the Muskingum Watershed Conservancy District","usgsCitation":"Covert, S.A., Jagucki, M.L., and Huitger, C., 2018, Baseline water quality of an area undergoing shale-gas development in the Muskingum River watershed, Ohio, 2015–16: U.S. Geological Survey Scientific Investigations Report 2018–5113, 129 p., https://doi.org/10.3133/sir20185113.","productDescription":"Report: ix, 129 p.; Data Release","onlineOnly":"Y","additionalOnlineFiles":"N","ipdsId":"IP-091174","costCenters":[{"id":35860,"text":"Ohio-Kentucky-Indiana Water Science Center","active":true,"usgs":true}],"links":[{"id":359613,"rank":3,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/F7GF0SRT","text":"USGS data release","description":"USGS data release","linkHelpText":"Data from quality-control equipment blanks, field blanks, and field replicates for baseline water quality of an area undergoing shale-gas development in the Muskingum River watershed, Ohio, 2015-16 "},{"id":359612,"rank":2,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/sir/2018/5113/sir20185113.pdf","text":"Report","size":"14.2 MB","linkFileType":{"id":1,"text":"pdf"},"description":"SIR 2018-5113"},{"id":359611,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/sir/2018/5113/coverthb.jpg"}],"country":"United States","state":"Ohio","otherGeospatial":"Muskingum River Watershed","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -81.75,\n 39.75\n ],\n [\n -80.75,\n 39.75\n ],\n [\n -80.75,\n 40.6667\n ],\n [\n -81.75,\n 40.6667\n ],\n [\n -81.75,\n 39.75\n ]\n ]\n ]\n }\n }\n ]\n}","contact":"Director, Ohio-Kentucky-Indiana Water Science Center
U.S. Geological Survey
6460 Busch Blvd
Suite 100
Columbus, OH 43229
Geochronologies derived from sediment cores in coastal locations are often used to infer event bed characteristics such as deposit thicknesses and accumulation rates. Such studies commonly use naturally occurring, short-lived radioisotopes, such as Beryllium-7 (7Be) and Thorium-234 (234Th), to study depositional and post-depositional processes. These radioisotope activities, however, are not generally represented in sediment transport models that characterize coastal flood and storm deposition with grain size patterns and deposit thicknesses. We modified the Community Sediment Transport Modeling System (CSTMS) to account for reactive tracers and used this capability to represent the behavior of these short-lived radioisotopes on the sediment bed. This paper describes the model and presents results from a set of idealized, one-dimensional (vertical) test cases. The model configuration represented fluvial deposition followed by periods of episodic storm resuspension. Sensitivity tests explored the influence on seabed radioisotope profiles by the intensities of bioturbation and wave resuspension and the thickness of fluvial deposits. The intensity of biodiffusion affected the persistence of fluvial event beds as evidenced by 7Be. Both resuspension and biodiffusion increased the modeled seabed inventory of 234Th. A thick fluvial deposit increased the seabed inventory of 7Be and 234Th but mixing over time greatly reduced the difference in inventory of 234Th in fluvial deposits of different thicknesses.
","language":"English","publisher":"MDPI","doi":"10.3390/jmse6040144","usgsCitation":"Birchler, J.J., Harris, C.K., Sherwood, C.R., and Kniskern, T.A., 2018, Sediment transport model including short-lived radioisotopes: Model description and idealized test cases: Journal of Marine Science and Engineering, v. 6, no. 4, p. 1-17, https://doi.org/10.3390/jmse6040144.","productDescription":"144, 17 p.","startPage":"1","endPage":"17","ipdsId":"IP-094563","costCenters":[{"id":574,"text":"St. Petersburg Coastal and Marine Science Center","active":true,"usgs":true}],"links":[{"id":468231,"rank":2,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.3390/jmse6040144","text":"Publisher Index Page"},{"id":422975,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"volume":"6","issue":"4","noUsgsAuthors":false,"publicationDate":"2018-11-27","publicationStatus":"PW","contributors":{"authors":[{"text":"Birchler, Justin J. 0000-0002-0379-2192 jbirchler@usgs.gov","orcid":"https://orcid.org/0000-0002-0379-2192","contributorId":169117,"corporation":false,"usgs":true,"family":"Birchler","given":"Justin","email":"jbirchler@usgs.gov","middleInitial":"J.","affiliations":[{"id":574,"text":"St. Petersburg Coastal and Marine Science Center","active":true,"usgs":true}],"preferred":true,"id":888694,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Harris, Courtney K.","contributorId":19620,"corporation":false,"usgs":false,"family":"Harris","given":"Courtney","email":"","middleInitial":"K.","affiliations":[{"id":6708,"text":"Virginia Institute of Marine Science","active":true,"usgs":false}],"preferred":false,"id":888695,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Sherwood, Christopher R. 0000-0001-6135-3553 csherwood@usgs.gov","orcid":"https://orcid.org/0000-0001-6135-3553","contributorId":2866,"corporation":false,"usgs":true,"family":"Sherwood","given":"Christopher","email":"csherwood@usgs.gov","middleInitial":"R.","affiliations":[{"id":678,"text":"Woods Hole Coastal and Marine Science Center","active":true,"usgs":true}],"preferred":true,"id":888696,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Kniskern, Tara A","contributorId":202170,"corporation":false,"usgs":false,"family":"Kniskern","given":"Tara","email":"","middleInitial":"A","affiliations":[{"id":36356,"text":"Virginia Institute of Marine Science, College of William & Mary","active":true,"usgs":false}],"preferred":false,"id":888697,"contributorType":{"id":1,"text":"Authors"},"rank":4}]}} ,{"id":70201073,"text":"70201073 - 2018 - Tag retention and survival of juvenile bighead carp implanted with a dummy acoustic tag at three temperatures","interactions":[],"lastModifiedDate":"2019-05-29T09:42:15","indexId":"70201073","displayToPublicDate":"2018-11-27T10:11:22","publicationYear":"2018","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":2166,"text":"Journal of Applied Ichthyology","active":true,"publicationSubtype":{"id":10}},"title":"Tag retention and survival of juvenile bighead carp implanted with a dummy acoustic tag at three temperatures","docAbstract":"Bighead carp Hypophthalmichthys nobilis and silver carp Hypophthalmichthys molitrix(together, the bigheaded carps) are invasive fishes in North America that have resulted in substantial negative effects on native fish communities and aquatic ecosystems. Movement and behavior of adult bigheaded carps has been studied previously using telemetry, while similar studies with juvenile bigheaded carps have yet to be attempted. Recent technological advances in telemetry transmitters has increased the availability of tags sufficiently small enough to implant in juvenile carps. However, the effects of surgical implantation of telemetry tags on juvenile bigheaded carps have not been evaluated. We determined tag retention and survival associated with surgical implantation of acoustic telemetry tags into juvenile bighead carp (range 128–152 mm total length) at three temperatures (13, 18, and 23°C). In addition, we assessed the effect of surgically implanted transmitters on the fitness, defined as changes in weight or critical swimming speed, of carp implanted with transmitters. Survival was high among tagged fish (85%) with 47% of tags retained at the conclusion of the 45‐day study. No substantial decline in fitness of the fish was observed in tagged fish compared to untagged fish.
","language":"English","publisher":"Wiley","doi":"10.1111/jai.13841","usgsCitation":"Byrd, C.G., Chapman, D., Pherigo, E.K., and Jolley, J.C., 2018, Tag retention and survival of juvenile bighead carp implanted with a dummy acoustic tag at three temperatures: Journal of Applied Ichthyology, v. 35, no. 3, p. 763-768, https://doi.org/10.1111/jai.13841.","productDescription":"6 p.","startPage":"763","endPage":"768","ipdsId":"IP-098166","costCenters":[{"id":192,"text":"Columbia Environmental Research Center","active":true,"usgs":true}],"links":[{"id":468232,"rank":1,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.1111/jai.13841","text":"Publisher Index Page"},{"id":437672,"rank":0,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/P9T6QGRL","text":"USGS data release","linkHelpText":"Tag retention and survival of juvenile bighead carp implanted with an acoustic tag at three temperatures"},{"id":359699,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"volume":"35","issue":"3","publishingServiceCenter":{"id":4,"text":"Rolla PSC"},"noUsgsAuthors":false,"publicationDate":"2018-11-25","publicationStatus":"PW","scienceBaseUri":"5bfe65dfe4b0815414ca60f0","contributors":{"authors":[{"text":"Byrd, Curtis G. 0000-0002-5124-5652","orcid":"https://orcid.org/0000-0002-5124-5652","contributorId":210798,"corporation":false,"usgs":true,"family":"Byrd","given":"Curtis","email":"","middleInitial":"G.","affiliations":[{"id":192,"text":"Columbia Environmental Research Center","active":true,"usgs":true}],"preferred":true,"id":752261,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Chapman, Duane 0000-0002-1086-8853 dchapman@usgs.gov","orcid":"https://orcid.org/0000-0002-1086-8853","contributorId":1291,"corporation":false,"usgs":true,"family":"Chapman","given":"Duane","email":"dchapman@usgs.gov","affiliations":[{"id":192,"text":"Columbia Environmental Research Center","active":true,"usgs":true},{"id":324,"text":"Great Lakes Science Center","active":true,"usgs":true}],"preferred":true,"id":752262,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Pherigo, Emily K.","contributorId":210799,"corporation":false,"usgs":false,"family":"Pherigo","given":"Emily","email":"","middleInitial":"K.","affiliations":[{"id":36188,"text":"U.S. Fish and Wildlife Service","active":true,"usgs":false}],"preferred":false,"id":752263,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Jolley, Jeffrey C. 0000-0002-4674-5126","orcid":"https://orcid.org/0000-0002-4674-5126","contributorId":210800,"corporation":false,"usgs":true,"family":"Jolley","given":"Jeffrey","email":"","middleInitial":"C.","affiliations":[{"id":192,"text":"Columbia Environmental Research Center","active":true,"usgs":true}],"preferred":true,"id":752264,"contributorType":{"id":1,"text":"Authors"},"rank":4}]}} ,{"id":70199945,"text":"sir20185134 - 2018 - Modeling hydrodynamics, water temperature, and water quality in Klamath Straits Drain, Oregon and California, 2012–15","interactions":[],"lastModifiedDate":"2018-11-27T10:58:23","indexId":"sir20185134","displayToPublicDate":"2018-11-26T15:04:48","publicationYear":"2018","noYear":false,"publicationType":{"id":18,"text":"Report"},"publicationSubtype":{"id":5,"text":"USGS Numbered Series"},"seriesTitle":{"id":334,"text":"Scientific Investigations Report","code":"SIR","onlineIssn":"2328-0328","printIssn":"2328-031X","active":true,"publicationSubtype":{"id":5}},"seriesNumber":"2018-5134","displayTitle":"Modeling Hydrodynamics, Water Temperature, and Water Quality in Klamath Straits Drain, Oregon and California, 2012–15","title":"Modeling hydrodynamics, water temperature, and water quality in Klamath Straits Drain, Oregon and California, 2012–15","docAbstract":"Located southwest of Klamath Falls, Oregon, Klamath Straits Drain is a 10.1-mile-long canal that conveys water uphill and northward through the use of pumps before discharging to the Klamath River. Klamath Straits Drain traverses an area that historically encompassed Lower Klamath Lake. Currently, the Drain receives water from farmland and from parts of the Lower Klamath Lake National Wildlife Refuge. To support water-quality improvement in Klamath Straits Drain, a hydrodynamic and water-temperature model was constructed and calibrated for calendar years 2012–15 with the two-dimensional model CE-QUAL-W2 (version 4.0). Water quality was calibrated for a subset of that time, from April 1, 2012 to March 31, 2015. Flows in calendar year 2012 were within the normal range, while calendar years 2013–15 were dry years. Significant findings from this study include:
This newly constructed model of the Klamath Straits Drain simulates flow, water levels, water temperature, and water quality with acceptable accuracy but with certain data limitations. This model should prove useful in evaluating potential strategies for flow and water-quality management and restoration.
","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/sir20185134","collaboration":"Prepared in cooperation with the Bureau of Reclamation","usgsCitation":"Sullivan, A.B., and Rounds, S.A., 2018, Modeling hydrodynamics, water temperature, and water quality in Klamath Straits Drain, Oregon and California, 2012–15: U.S. Geological Survey Scientific Investigations Report 2018-5134, 30 p., https://doi.org/10.3133/sir20185134.","productDescription":"vii, 30 p.","onlineOnly":"Y","ipdsId":"IP-099157","costCenters":[{"id":518,"text":"Oregon Water Science Center","active":true,"usgs":true}],"links":[{"id":359688,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/sir/2018/5134/coverthb.jpg"},{"id":359690,"rank":3,"type":{"id":2,"text":"Additional Report Piece"},"url":"https://or.water.usgs.gov/proj/keno_reach/models.html","text":"Klamath Straits Models —","description":"SIR 2018-5134 Klamath Straits Model","linkHelpText":"Water-Quality Monitoring and Modeling of the Keno Reach of the Klamath River"},{"id":359689,"rank":2,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/sir/2018/5134/sir20185134.pdf","text":"Report","size":"8.1 MB","linkFileType":{"id":1,"text":"pdf"},"description":"SIR 2018-5134"}],"country":"United States","state":"California, Oregon","otherGeospatial":"Klamath Straits Drain","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -122,\n 41.8333\n ],\n [\n -121.5,\n 41.8333\n ],\n [\n -121.5,\n 42.33\n ],\n [\n -122,\n 42.33\n ],\n [\n -122,\n 41.8333\n ]\n ]\n ]\n }\n }\n ]\n}","contact":"Director, Oregon Water Science Center
U.S. Geological Survey
2130 SW 5th Avenue
Portland, Oregon 97201
Terrestrial laser scanners (TLSs) provide a tool to assess and monitor forest structure across forest landscapes. We present TLS methods, suggestions, and mapped guidelines for planning TLS acquisitions at varying scales and forest densities. We examined rates of point‐density decline with distance from two TLS that acquire data at relatively high and low point density and found that the rates were nearly identical between scanners (pvalue <0.01), suggesting that our findings are applicable to a range of TLS types. Using unique, TLS‐adapted processing methods, we determined the relative accuracy of TLS‐derived plot‐scale estimates of tree height, diameter‐at‐breast‐height, height‐to‐canopy, tree counts, as well as treatment‐scale tree density and patch metrics, using both high point density and low point density TLS among thinned and nonthinned forest treatments. The high‐density TLS consistently provides more accurate estimates of plot‐level metrics (R2 = 0.46 to 0.87) than the low‐density TLS (R2 = −0.14 to 0.53). At treatment scales, tree density estimates are similar among scanners (R2 = 0.95 vs. 0.71), as are canopy cover and patch metrics. We develop and present the normalized density‐distance index (NDDI), which can account for up to 59% of the variance in estimate error and can be used to guide TLS‐data acquisition plans. This index indicates whether a given location has generally higher point density (higher NDDI) relative to the distance from the scanner and can be used as a proxy for uncertainty. Using NDDI as a guide for fair comparison between scanners, both plot‐ and treatment‐scale estimates improved.
","language":"English","publisher":"AGU Publications","doi":"10.1029/2018EA000417","usgsCitation":"Donager, J.J., Sankey, T.T., Sankey, J.B., Sanchez Meadorc, A.J., Springer, A., and Bailey, J.D., 2018, Examining forest structure with terrestrial lidar: Suggestions and novel techniques based on comparisons between scanners and forest treatments: Earth and Space Science, v. 5, no. 11, p. 753-776, https://doi.org/10.1029/2018EA000417.","productDescription":"14 p.","startPage":"753","endPage":"776","ipdsId":"IP-081018","costCenters":[{"id":568,"text":"Southwest Biological Science Center","active":true,"usgs":true}],"links":[{"id":468233,"rank":1,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.1029/2018ea000417","text":"Publisher Index Page"},{"id":437673,"rank":0,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/P9F63VYX","text":"USGS data release","linkHelpText":"Northern Arizona Ponderosa Pine Forest Treatment Terrestrial Lidar Data"},{"id":359656,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"volume":"5","issue":"11","publishingServiceCenter":{"id":14,"text":"Menlo Park PSC"},"noUsgsAuthors":false,"publicationDate":"2018-11-12","publicationStatus":"PW","scienceBaseUri":"5bfd146be4b0815414ca38e6","contributors":{"authors":[{"text":"Donager, Jonathon J.","contributorId":210787,"corporation":false,"usgs":false,"family":"Donager","given":"Jonathon","email":"","middleInitial":"J.","affiliations":[{"id":38148,"text":"Northern Arizona University, School of Earth Science and Environmental Sustainability, 1295 S. Knoles Drive, PO Box 5695, Flagstaff, AZ 86011","active":true,"usgs":false}],"preferred":false,"id":751966,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Sankey, Temuulen T.","contributorId":173297,"corporation":false,"usgs":false,"family":"Sankey","given":"Temuulen","email":"","middleInitial":"T.","affiliations":[{"id":7202,"text":"NAU","active":true,"usgs":false}],"preferred":false,"id":751967,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Sankey, Joel B. 0000-0003-3150-4992 jsankey@usgs.gov","orcid":"https://orcid.org/0000-0003-3150-4992","contributorId":3935,"corporation":false,"usgs":true,"family":"Sankey","given":"Joel","email":"jsankey@usgs.gov","middleInitial":"B.","affiliations":[{"id":568,"text":"Southwest Biological Science Center","active":true,"usgs":true}],"preferred":true,"id":751965,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Sanchez Meadorc, Andrew J.","contributorId":210788,"corporation":false,"usgs":false,"family":"Sanchez Meadorc","given":"Andrew","email":"","middleInitial":"J.","affiliations":[{"id":38149,"text":"Northern Arizona University, School of Forestry, 200 East Pine Knoll Drive, PO Box 15018, Flagstaff, AZ 86011","active":true,"usgs":false}],"preferred":false,"id":751968,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Springer, Abraham E.","contributorId":9558,"corporation":false,"usgs":true,"family":"Springer","given":"Abraham E.","affiliations":[],"preferred":false,"id":751969,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"Bailey, John D.","contributorId":42928,"corporation":false,"usgs":true,"family":"Bailey","given":"John","email":"","middleInitial":"D.","affiliations":[],"preferred":false,"id":751970,"contributorType":{"id":1,"text":"Authors"},"rank":6}]}} ,{"id":70199277,"text":"sir20185122 - 2018 - Flood-inundation maps for the North Fork Kentucky River at Hazard, Kentucky","interactions":[],"lastModifiedDate":"2018-11-26T15:06:08","indexId":"sir20185122","displayToPublicDate":"2018-11-26T11:30:00","publicationYear":"2018","noYear":false,"publicationType":{"id":18,"text":"Report"},"publicationSubtype":{"id":5,"text":"USGS Numbered Series"},"seriesTitle":{"id":334,"text":"Scientific Investigations Report","code":"SIR","onlineIssn":"2328-0328","printIssn":"2328-031X","active":true,"publicationSubtype":{"id":5}},"seriesNumber":"2018-5122","displayTitle":"Flood-Inundation Maps for the North Fork Kentucky River at Hazard, Kentucky","title":"Flood-inundation maps for the North Fork Kentucky River at Hazard, Kentucky","docAbstract":"Digital flood-inundation maps for a 7.1-mile reach of the North Fork Kentucky River at Hazard, Kentucky (Ky.), were created by the U.S. Geological Survey (USGS) in cooperation with the Kentucky Silver Jackets and the U.S. Army Corps of Engineers Louisville District. The flood-inundation maps, which can be accessed through the USGS Flood Inundation Mapping Science website at https://water.usgs.gov/osw/flood_inundation/, depict estimates of the areal extent and depth of flooding corresponding to selected water levels (stages) at the USGS streamgage on the North Fork Kentucky River at Hazard, Ky. (USGS station number 03277500). Near-real-time stages at this streamgage may be obtained on the internet from the USGS National Water Information System at https://waterdata.usgs.gov/ or the National Weather Service (NWS) Advanced Hydrologic Prediction Service (AHPS) at https://water.weather.gov/ahps/, which also forecasts flood hydrographs at this site (NWS AHPS site HAZK2). NWS AHPS forecast peak stage information may be used in conjunction with the maps developed in this study to show predicted areas of flood inundation.
Flood profiles were computed for the North Fork Kentucky River reach by means of a one-dimensional, step-backwater model developed by the U.S. Army Corps of Engineers. The hydraulic model was calibrated by using the current stage-discharge relation (USGS rating no. 24.0) at USGS streamgage 03277500, North Fork Kentucky River at Hazard, Ky. The calibrated hydraulic model was then used to compute 26 water-surface profiles for flood stages at 1-foot (ft) intervals referenced to the streamgage datum and ranging from approximately bankfull (14 ft) to the highest even-foot increment stage (39 ft) of the current stage-discharge rating curve. The simulated water-surface profiles were then combined with a geographic information system digital elevation model, derived from light detection and ranging data, to delineate the area flooded at each water level.
The availability of these maps, along with information on the internet regarding current stage from the USGS streamgage at North Fork Kentucky River at Hazard, Ky., and forecasted stream stages from the NWS AHPS, provides emergency management personnel and residents with information that is critical for flood-response activities such as evacuations and road closures, as well as for postflood recovery efforts.
","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/sir20185122","collaboration":"Prepared in cooperation with the Kentucky Silver Jackets and the U.S. Army Corps of Engineers Louisville District","usgsCitation":"Boldt, J.A., Lant, J.G., and Kolarik, N.E., 2018, Flood-inundation maps for the North Fork Kentucky River at Hazard, Kentucky: U.S. Geological Survey Scientific Investigations Report 2018-5122, 12 p., https://doi.org/10.3133/sir20185122.","productDescription":"Report: vi, 12 p.; Data release","onlineOnly":"Y","additionalOnlineFiles":"N","ipdsId":"IP-098752","costCenters":[{"id":35860,"text":"Ohio-Kentucky-Indiana Water Science Center","active":true,"usgs":true}],"links":[{"id":359619,"rank":3,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/P9CNAG9G","text":"USGS data release","description":"USGS data release","linkHelpText":"Geospatial datasets and model for the flood-inundation study of the North Fork Kentucky River at Hazard, Kentucky"},{"id":359617,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/sir/2018/5122/coverthb.jpg"},{"id":359618,"rank":2,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/sir/2018/5122//sir20185122.pdf","text":"Report","size":"5.73 MB","linkFileType":{"id":1,"text":"pdf"},"description":"SIR 2018-5122"}],"country":"United States","state":"Kentucky","city":"Hazard","otherGeospatial":" North Fork Kentucky River","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -83.20315361022949,\n 37.22158045838649\n ],\n [\n -83.15423011779785,\n 37.22158045838649\n ],\n [\n -83.15423011779785,\n 37.274872400526334\n ],\n [\n -83.20315361022949,\n 37.274872400526334\n ],\n [\n -83.20315361022949,\n 37.22158045838649\n ]\n ]\n ]\n }\n }\n ]\n}","contact":"Director, Ohio-Kentucky-Indiana Water Science Center
U.S. Geological Survey
9818 Bluegrass Parkway
Louisville, KY 40299-1906
Groundwater provides nearly 50 percent of the Nation’s drinking water. To help protect this vital resource, the U.S. Geological Survey (USGS) National Water-Quality Assessment (NAWQA) Project assesses groundwater quality in aquifers that are important sources of drinking water (Burow and Belitz, 2014). The Mississippi embayment–Texas coastal uplands aquifer system constitutes one of the important aquifer systems being evaluated.
","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/fs20183067","usgsCitation":"Kingsbury, J.A., 2018, Groundwater quality in the Mississippi embayment–Texas coastal uplands aquifer system, south-central United States (ver. 1.1, September 2020): U.S. Geological Survey Fact Sheet 2018–3067, 4 p., https://doi.org/10.3133/fs20183067.","productDescription":"4 p.","ipdsId":"IP-097552","costCenters":[{"id":37277,"text":"WMA - Earth System Processes Division","active":true,"usgs":true}],"links":[{"id":358167,"rank":2,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/fs/2018/3067/fs20183067_v1.1.pdf","text":"Report","size":"3.1 MB","linkFileType":{"id":1,"text":"pdf"},"description":"Fact Sheet 2018-3067"},{"id":378457,"rank":3,"type":{"id":25,"text":"Version History"},"url":"https://pubs.usgs.gov/fs/2018/3067/versionHist.txt","size":"2 KB","linkFileType":{"id":2,"text":"txt"}},{"id":358166,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/fs/2018/3067/coverthb.jpg"}],"country":"United States","otherGeospatial":"Mississippi Embayment–Texas Coastal Uplands Aquifer System","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -100,\n 26\n ],\n [\n -87,\n 26\n ],\n [\n -87,\n 37.16031654673677\n ],\n [\n -100,\n 37.16031654673677\n ],\n [\n -100,\n 26\n ]\n ]\n ]\n }\n }\n ]\n}","edition":"Version 1.0: November 26, 2018; Version 1.1: September 16, 2020","contact":"National Water-Quality Assessment (NAWQA) Program
U.S. Geological Survey
413 National Center
12201 Sunrise Valley Drive
Reston, Virginia 20192
Groundwater provides nearly 50 percent of the Nation’s drinking water. To help protect this vital resource, the U.S. Geological Survey (USGS) National Water-Quality Assessment (NAWQA) Project assesses groundwater quality in aquifers that are important sources of drinking water (Burow and Belitz, 2014). The Floridan aquifer system constitutes one of the important aquifer systems being evaluated.
","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/fs20183066","usgsCitation":"Kingsbury, J.A., 2018, Groundwater quality in the Floridan aquifer system, Southeastern United States: U.S. Geological Survey Fact Sheet 2018–3066, 4 p., https://doi.org/10.3133/fs20183066.","productDescription":"4 p.","ipdsId":"IP-097551","costCenters":[{"id":37277,"text":"WMA - Earth System Processes Division","active":true,"usgs":true}],"links":[{"id":358164,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/fs/2018/3066/coverthb.jpg"},{"id":358165,"rank":2,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/fs/2018/3066/fs20183066.pdf","text":"Report","size":"3.1 MB","linkFileType":{"id":1,"text":"pdf"},"description":"Fact Sheet 2018-3066"}],"country":"United States","state":"Alabama, Florida, Georgia, South Carolina","otherGeospatial":"Floridan Aquifer System","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -87.978515625,\n 25.64152637306577\n ],\n [\n -79.07958984375,\n 25.64152637306577\n ],\n [\n -79.07958984375,\n 33.33970700424026\n ],\n [\n -87.978515625,\n 33.33970700424026\n ],\n [\n -87.978515625,\n 25.64152637306577\n ]\n ]\n ]\n }\n }\n ]\n}","contact":"National Water-Quality Assessment (NAWQA) Program
U.S. Geological Survey
413 National Center
12201 Sunrise Valley Drive
Reston, Virginia 20192
Background: The isolated population of desert bighorn sheep in the Silver Bell Mountains of southern Arizona underwent an unprecedented expansion in merely four years. We hypothesized that immigration from neighboring bighorn sheep populations could have caused the increase in numbers as detected by Arizona Game and Fish Department annual aerial counts.
Methods: We applied a multilocus genetic approach using mitochondrial DNA and nuclear microsatellite markers for genetic analyses to find evidence of immigration. We sampled the Silver Bell Mountains bighorn sheep before (2003) and during (2015) the population expansion, and a small number of available samples from the Gila Mountains (southwestern Arizona) and the Morenci Mine (Rocky Mountain bighorn) in an attempt to identify the source of putative immigrants and, more importantly, to serve as comparisons for genetic diversity metrics.
Results: We did not find evidence of substantial gene flow into the Silver Bell Mountains population. We did not detect any new mitochondrial haplotypes in the 2015 bighorn sheep samples. The microsatellite analyses detected only one new allele, in one individual from the 2015 population that was not detected in the 2003 samples. Overall, the genetic diversity of the Silver Bell Mountains population was lower than that seen in either the Gila population or the Morenci Mine population.
Discussion: Even though the results of this study did not help elucidate the precise reason for the recent population expansion, continued monitoring and genetic sampling could provide more clarity on the genetic demographics of this population.
Keywords: Bighorn sheep; Microsatellites; Migration; Mitochondrial DNA; Ovis canadensis; Population growth; Silver Bell Mountains.
","language":"English","publisher":"PeerJ","doi":"10.7717/peerj.5978","usgsCitation":"Erwin, J.A., Vargasc, K., Blaisc, B.R., Bennettc, K., Muldoond, J., Findysz, S., Christiec, C., Heffelfingere, J.R., and Culver, M., 2018, Genetic assessment of a bighorn sheep population expansion in the Silver Bell Mountains, Arizona: PeerJ, v. 6, e5978, https://doi.org/10.7717/peerj.5978.","productDescription":"e5978","ipdsId":"IP-098318","costCenters":[{"id":200,"text":"Coop Res Unit Seattle","active":true,"usgs":true}],"links":[{"id":468234,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.7717/peerj.5978","text":"Publisher Index Page"},{"id":395751,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","state":"Arizona","otherGeospatial":"Silver Bell 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Series"},"seriesTitle":{"id":334,"text":"Scientific Investigations Report","code":"SIR","onlineIssn":"2328-0328","printIssn":"2328-031X","active":true,"publicationSubtype":{"id":5}},"seriesNumber":"2018-5131","displayTitle":"Federal Lands Greenhouse Gas Emissions and Sequestration in the United States: Estimates for 2005–14","title":"Federal lands greenhouse emissions and sequestration in the United States—Estimates for 2005–14","docAbstract":"In January 2016, the Secretary of the U.S. Department of the Interior tasked the U.S. Geological Survey (USGS) with producing a publicly available and annually updated database of estimated greenhouse gas emissions associated with the extraction and use (predominantly some form of combustion) of fossil fuels from Federal lands. In response, the USGS has produced estimates of the greenhouse gas emissions resulting from the extraction and end-use combustion of fossil fuels produced on Federal lands in the United States, as well as estimates of ecosystem carbon emissions and sequestration on those lands. American Indian and Tribal lands were not included in this analysis. The emissions estimates span a 10-year period (2005–14) and are reported for 28 States and two offshore areas. Nationwide emissions from fossil fuels produced on Federal lands in 2014 were 1,279.0 million metric tons of carbon dioxide equivalent (MMT CO2 Eq.) for carbon dioxide (CO2), 47.6 MMT CO2 Eq. for methane (CH4), and 5.5 MMT CO2 Eq. for nitrous oxide (N2O). Compared to 2005, the 2014 totals represent decreases in emissions for all three greenhouse gases (decreases of 6.1 percent for CO2, 10.5 percent for CH4, and 20.3 percent for N2O). Emissions from fossil fuels produced on Federal lands represent, on average, 23.7 percent of national emissions for CO2, 7.3 percent for CH4, and 1.5 percent for N2O over the 10 years included in this estimate.
In 2005, Federal lands of the conterminous United States stored 82,289 MMT CO2 Eq. in terrestrial ecosystems. By 2014, carbon storage, or sequestration, was estimated at 83,600 MMT CO2 Eq., representing an increase of 1.6 percent, or 1,311 MMT CO2 Eq. Soils stored most of the ecosystem carbon (63 percent), followed by live vegetation (26 percent) and dead organic matter (11 percent). The rate of net carbon uptake in ecosystems ranged from a sink (sequestration) of 475 million metric tons of carbon dioxide per year (MMT CO2 Eq./yr) to a source (emission) of 51 MMT CO2 Eq./yr because of annual variability in climate and weather, rates of land-use and land-cover change, and wildfire frequency, among other factors. At the national level, the USGS estimates that terrestrial ecosystems (forests, grasslands, and shrublands) on Federal lands sequestered an average of 195 MMT CO2 Eq./yr between 2005 and 2014, offsetting approximately 15 percent of the CO2 emissions resulting from the extraction of fossil fuels on Federal lands and their end-use combustion.
The USGS estimates presented in this report represent a first-of-its-kind accounting for the emissions resulting from fossil fuel extraction on Federal lands and the end-use combustion of those fuels, as well as for the sequestration of carbon in terrestrial ecosystems on Federal lands. The net CO2 emissions estimate, which is the difference between the emitted and sequestered CO2, provides an informative combined result describing the emissions (fossil fuel extraction and end-use combustion) associated with a State’s Federal lands and sequestration on those same lands. The estimates included in this report can provide context for future energy decisions, as well as a basis to track change in the future.
","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/sir20185131","usgsCitation":"Merrill, M.D., Sleeter, B.M., Freeman, P.A., Liu, J., Warwick, P.D., and Reed, B.C., 2018, Federal lands greenhouse emissions and sequestration in the United States—Estimates for 2005–14: U.S. Geological Survey Scientific Investigations Report 2018–5131, 31 p., https://doi.org/10.3133/sir20185131.","productDescription":"Report: viii, 31 p.; Data Release","onlineOnly":"Y","additionalOnlineFiles":"Y","ipdsId":"IP-095255","costCenters":[{"id":241,"text":"Eastern Energy Resources Science Center","active":true,"usgs":true}],"links":[{"id":437674,"rank":5,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/F7KH0MK4","text":"USGS data release","linkHelpText":"Federal Lands Greenhouse Gas Emissions and Sequestration in the United States: Estimates 2005-14 - Data Release"},{"id":359542,"rank":4,"type":{"id":18,"text":"Project 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States\"}}]}","contact":"Eastern Energy Resources Science Center
U.S. Geological Survey
956 National Center
12201 Sunrise Valley Drive
Reston, VA 20192
Animal migrations act to couple ecosystems and are undertaken by some of the world’s most endangered taxa. Predators often exploit migrant prey, but the movements taken by these consumers are rarely studied or understood. We define such movements, where migrant prey induce large-scale movements of predators, as migratory coupling. Migratory coupling can have ecological consequences for the participating prey, predators and the communities they traverse across the landscape. We review examples of migratory coupling in the literature and provide hypotheses regarding conditions favourable for their occurrence. We also provide a framework for interactions induced by migratory coupling and demonstrate their potential community-level impacts by examining other forms of spatial shifts in predators. Migratory coupling integrates the fields of landscape, movement, food web and community ecologies, and represents an understudied frontier in ecology.
","language":"English","publisher":"Springer Nature","doi":"10.1038/s41559-018-0711-3","usgsCitation":"Furey, N.B., Armstrong, J., Beauchamp, D.A., and Hinch, S.G., 2018, Migratory coupling between predators and prey: Nature Ecology & Evolution, v. 2, p. 1846-1853, https://doi.org/10.1038/s41559-018-0711-3.","productDescription":"8 p.","startPage":"1846","endPage":"1853","ipdsId":"IP-100906","costCenters":[{"id":654,"text":"Western Fisheries Research Center","active":true,"usgs":true}],"links":[{"id":361378,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"volume":"2","publishingServiceCenter":{"id":12,"text":"Tacoma PSC"},"noUsgsAuthors":false,"publicationDate":"2018-11-22","publicationStatus":"PW","contributors":{"authors":[{"text":"Furey, Nathan B.","contributorId":174497,"corporation":false,"usgs":false,"family":"Furey","given":"Nathan","email":"","middleInitial":"B.","affiliations":[],"preferred":false,"id":757612,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Armstrong, Jonathan B.","contributorId":213380,"corporation":false,"usgs":false,"family":"Armstrong","given":"Jonathan B.","affiliations":[{"id":38742,"text":"Department of Fisheries and Wildlife, Oregon State University, Corvallis, OR 97331, USA","active":true,"usgs":false}],"preferred":false,"id":757613,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Beauchamp, David A. 0000-0002-3592-8381 fadave@usgs.gov","orcid":"https://orcid.org/0000-0002-3592-8381","contributorId":4205,"corporation":false,"usgs":true,"family":"Beauchamp","given":"David","email":"fadave@usgs.gov","middleInitial":"A.","affiliations":[{"id":654,"text":"Western Fisheries Research Center","active":true,"usgs":true}],"preferred":true,"id":757614,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Hinch, Scott G.","contributorId":174498,"corporation":false,"usgs":false,"family":"Hinch","given":"Scott","email":"","middleInitial":"G.","affiliations":[],"preferred":false,"id":757615,"contributorType":{"id":1,"text":"Authors"},"rank":4}]}} ,{"id":70198861,"text":"70198861 - 2018 - Disparate perspectives on evidence from the Cerutti Mastodon site: A reply to Braje et al.","interactions":[],"lastModifiedDate":"2018-08-27T12:16:18","indexId":"70198861","displayToPublicDate":"2018-11-22T08:07:51","publicationYear":"2018","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":5732,"text":"PaleoAmerica","active":true,"publicationSubtype":{"id":10}},"title":"Disparate perspectives on evidence from the Cerutti Mastodon site: A reply to Braje et al.","docAbstract":"The Perspective editorial by Braje, T., T. D. Dillehay, J. M. Erlandson, S. M. Fitzpatrick, D. K. Grayson, V. T. Holliday, R. L. Kelly, R. G. Klein, D. J. Meltzer, and T. C. Rick (2017. “Were Hominins in California ∼130,000 Years Ago?” PaleoAmerica 3 (3): 200–202) takes issue with our argument [Holen, S. R., T. A. Deméré, D. C. Fisher, R. Fullagar, J. B. Paces, G. T. Jefferson, J. M. Beeton, et al. (2017. “A 130,000-Year-Old Archaeological Site in Southern California, USA.” Nature 544 (7651): 479–483) that the assemblage of bones and stones at the Cerutti Mastodon (CM) site implicates hominin activity in site formation 130,000 years ago. Braje et al. propose instead that features of the CM site can be better explained by geological or other causes unrelated to ancient human activity. However, we contend that their conclusion reflects an incomplete assessment of our evidence. They further propose a standard of evidence at odds with current practice in the philosophy of science, and misuse a commonly quoted aphorism that “extraordinary claims require extraordinary evidence.”
","language":"English","publisher":"Taylor & Francis ","doi":"10.1080/20555563.2017.1396836","usgsCitation":"Holen, S., Demere, T.A., Fisher, D., Fullagar, R., Paces, J.B., Jefferson, G.T., Beeton, J., Rountrey, A., and Holen, K., 2018, Disparate perspectives on evidence from the Cerutti Mastodon site: A reply to Braje et al.: PaleoAmerica, v. 4, no. 1, p. 12-15, https://doi.org/10.1080/20555563.2017.1396836.","productDescription":"4 p.","startPage":"12","endPage":"15","ipdsId":"IP-090034","costCenters":[{"id":318,"text":"Geosciences and Environmental Change Science Center","active":true,"usgs":true}],"links":[{"id":356781,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","state":"California","otherGeospatial":"Cerutti Mastodon site ","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -117.29690551757814,\n 32.52249989111295\n ],\n [\n -116.94671630859375,\n 32.52249989111295\n ],\n [\n -116.94671630859375,\n 32.83228893100241\n ],\n [\n -117.29690551757814,\n 32.83228893100241\n ],\n [\n -117.29690551757814,\n 32.52249989111295\n ]\n ]\n ]\n }\n }\n ]\n}","volume":"4","issue":"1","publishingServiceCenter":{"id":2,"text":"Denver PSC"},"noUsgsAuthors":false,"publicationDate":"2017-11-22","publicationStatus":"PW","scienceBaseUri":"5c10a8e5e4b034bf6a7e4de0","contributors":{"authors":[{"text":"Holen, Steven R.","contributorId":198785,"corporation":false,"usgs":false,"family":"Holen","given":"Steven R.","affiliations":[{"id":16175,"text":"San Diego Natural History Museum","active":true,"usgs":false},{"id":35320,"text":"Center for American Paleolithic Research","active":true,"usgs":false}],"preferred":false,"id":743138,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Demere, Thomas A.","contributorId":207194,"corporation":false,"usgs":false,"family":"Demere","given":"Thomas","email":"","middleInitial":"A.","affiliations":[{"id":16175,"text":"San Diego Natural History Museum","active":true,"usgs":false}],"preferred":false,"id":743139,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Fisher, Daniel C.","contributorId":127409,"corporation":false,"usgs":false,"family":"Fisher","given":"Daniel C.","affiliations":[{"id":33091,"text":"University of Michigan, Ann Arbor, Michigan","active":true,"usgs":false}],"preferred":false,"id":743140,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Fullagar, Richard","contributorId":198789,"corporation":false,"usgs":false,"family":"Fullagar","given":"Richard","affiliations":[{"id":16754,"text":"University of Wollongong, Australia","active":true,"usgs":false}],"preferred":false,"id":743141,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Paces, James B. 0000-0002-9809-8493 jbpaces@usgs.gov","orcid":"https://orcid.org/0000-0002-9809-8493","contributorId":2514,"corporation":false,"usgs":true,"family":"Paces","given":"James","email":"jbpaces@usgs.gov","middleInitial":"B.","affiliations":[{"id":318,"text":"Geosciences and Environmental Change Science Center","active":true,"usgs":true}],"preferred":true,"id":743137,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"Jefferson, George T.","contributorId":198787,"corporation":false,"usgs":false,"family":"Jefferson","given":"George","email":"","middleInitial":"T.","affiliations":[{"id":35321,"text":"California Department of Parks and Recreation","active":true,"usgs":false}],"preferred":false,"id":743142,"contributorType":{"id":1,"text":"Authors"},"rank":6},{"text":"Beeton, Jared M.","contributorId":198788,"corporation":false,"usgs":false,"family":"Beeton","given":"Jared M.","affiliations":[{"id":35737,"text":"Adams State University, Alamosa, Colorado","active":true,"usgs":false}],"preferred":false,"id":743144,"contributorType":{"id":1,"text":"Authors"},"rank":7},{"text":"Rountrey, Adam N.","contributorId":127421,"corporation":false,"usgs":false,"family":"Rountrey","given":"Adam N.","affiliations":[{"id":33091,"text":"University of Michigan, Ann Arbor, Michigan","active":true,"usgs":false}],"preferred":false,"id":743145,"contributorType":{"id":1,"text":"Authors"},"rank":8},{"text":"Holen, Kathleen A.","contributorId":198791,"corporation":false,"usgs":false,"family":"Holen","given":"Kathleen A.","affiliations":[{"id":16175,"text":"San Diego Natural History Museum","active":true,"usgs":false},{"id":35320,"text":"Center for American Paleolithic Research","active":true,"usgs":false}],"preferred":false,"id":743146,"contributorType":{"id":1,"text":"Authors"},"rank":9}]}} ,{"id":70208201,"text":"70208201 - 2018 - Modeling water quality in the Anthropocene: Directions for the next-generation aquatic ecosystem models","interactions":[],"lastModifiedDate":"2020-01-31T07:00:33","indexId":"70208201","displayToPublicDate":"2018-11-22T06:58:16","publicationYear":"2018","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":5061,"text":"Current Opinion in Environmental Sustainability","active":true,"publicationSubtype":{"id":10}},"title":"Modeling water quality in the Anthropocene: Directions for the next-generation aquatic ecosystem models","docAbstract":"“Everything changes and nothing stands still” (Heraclitus). Here we review three major improvements to freshwater aquatic ecosystem models — and ecological models in general — as water quality scenario analysis tools towards a sustainable future. To tackle the rapid and deeply connected dynamics characteristic of the Anthropocene, we argue for the inclusion of eco-evolutionary, novel ecosystem and social-ecological dynamics. These dynamics arise from adaptive responses in organisms and ecosystems to global environmental change and act at different integration levels and different time scales. We provide reasons and means to incorporate each improvement into aquatic ecosystem models. Throughout this study we refer to Lake Victoria as a microcosm of the evolving novel social-ecological systems of the Anthropocene. The Lake Victoria case clearly shows how interlinked eco-evolutionary, novel ecosystem and social-ecological dynamics are, and demonstrates the need for transdisciplinary research approaches towards global sustainability.","language":"English","publisher":"Elsevier","doi":"10.1016/j.cosust.2018.10.012","usgsCitation":"Mooij, W.M., van Wijk, D., Beusen, A.H., Brederveld, R.J., Chang, M., Cobben, M., DeAngelis, D.L., Downing, A.S., Green, P., Gsell, A., Huttunen, I., Janse, J.H., Janssen, A.B., Hengeveld, G.M., Kong, X., Kramer, L., Kuiper, J.J., Langan, S.J., Nolet, B.A., Nuijten, R.J., Strokal, M., Troost, T.A., van Dam, A.A., and Teurlincx, S., 2018, Modeling water quality in the Anthropocene: Directions for the next-generation aquatic ecosystem models: Current Opinion in Environmental Sustainability, v. 36, p. 85-95, https://doi.org/10.1016/j.cosust.2018.10.012.","productDescription":"11 p.","startPage":"85","endPage":"95","ipdsId":"IP-098790","costCenters":[{"id":17705,"text":"Wetland and Aquatic Research Center","active":true,"usgs":true}],"links":[{"id":468235,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.1016/j.cosust.2018.10.012","text":"Publisher Index Page"},{"id":371785,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"volume":"36","publishingServiceCenter":{"id":5,"text":"Lafayette PSC"},"noUsgsAuthors":false,"publicationStatus":"PW","contributors":{"authors":[{"text":"Mooij, Wolf M.","contributorId":215556,"corporation":false,"usgs":false,"family":"Mooij","given":"Wolf","email":"","middleInitial":"M.","affiliations":[{"id":39277,"text":"Dept. of Aquatic Ecology, Netherlands Institute of Ecology, the Netherlands","active":true,"usgs":false}],"preferred":false,"id":780918,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"van Wijk, Dianneke","contributorId":215557,"corporation":false,"usgs":false,"family":"van Wijk","given":"Dianneke","email":"","affiliations":[{"id":39277,"text":"Dept. of Aquatic Ecology, Netherlands Institute of Ecology, the Netherlands","active":true,"usgs":false}],"preferred":false,"id":780919,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Beusen, Arthur H.W.","contributorId":222005,"corporation":false,"usgs":false,"family":"Beusen","given":"Arthur","email":"","middleInitial":"H.W.","affiliations":[{"id":36496,"text":"PBL Netherlands Environmental Assessment Agency","active":true,"usgs":false}],"preferred":false,"id":780920,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Brederveld, Robert J.","contributorId":215554,"corporation":false,"usgs":false,"family":"Brederveld","given":"Robert","email":"","middleInitial":"J.","affiliations":[{"id":39278,"text":"Witteveen+Bos, Consulting Engineers, the Netherlands","active":true,"usgs":false}],"preferred":false,"id":780921,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Chang, Manqi","contributorId":218274,"corporation":false,"usgs":false,"family":"Chang","given":"Manqi","email":"","affiliations":[],"preferred":false,"id":780922,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"Cobben, Marleen","contributorId":222006,"corporation":false,"usgs":false,"family":"Cobben","given":"Marleen","email":"","affiliations":[{"id":40464,"text":"Department of Terrestrial Ecology, Netherlands Institute for Ecology","active":true,"usgs":false}],"preferred":false,"id":780923,"contributorType":{"id":1,"text":"Authors"},"rank":6},{"text":"DeAngelis, Donald L. 0000-0002-1570-4057 don_deangelis@usgs.gov","orcid":"https://orcid.org/0000-0002-1570-4057","contributorId":148065,"corporation":false,"usgs":true,"family":"DeAngelis","given":"Donald","email":"don_deangelis@usgs.gov","middleInitial":"L.","affiliations":[{"id":17705,"text":"Wetland and Aquatic Research Center","active":true,"usgs":true},{"id":566,"text":"Southeast Ecological Science Center","active":true,"usgs":true}],"preferred":true,"id":780917,"contributorType":{"id":1,"text":"Authors"},"rank":7},{"text":"Downing, Andrea S.","contributorId":222007,"corporation":false,"usgs":false,"family":"Downing","given":"Andrea","email":"","middleInitial":"S.","affiliations":[{"id":40465,"text":"Stockholm Resilience Centre, Stockholm University","active":true,"usgs":false}],"preferred":false,"id":780924,"contributorType":{"id":1,"text":"Authors"},"rank":8},{"text":"Green, Pamela","contributorId":222008,"corporation":false,"usgs":false,"family":"Green","given":"Pamela","email":"","affiliations":[{"id":40466,"text":"Environmental Sciences Initiative, Advanced Science Research Center at the Graduate Center, City University of New York (CUNY)","active":true,"usgs":false}],"preferred":false,"id":780925,"contributorType":{"id":1,"text":"Authors"},"rank":9},{"text":"Gsell, Alena S.","contributorId":222009,"corporation":false,"usgs":false,"family":"Gsell","given":"Alena S.","affiliations":[{"id":40467,"text":"Department of Aquatic Ecology, Netherlands Institute for Ecology","active":true,"usgs":false}],"preferred":false,"id":780926,"contributorType":{"id":1,"text":"Authors"},"rank":10},{"text":"Huttunen, Inese","contributorId":222010,"corporation":false,"usgs":false,"family":"Huttunen","given":"Inese","email":"","affiliations":[{"id":40468,"text":"Fresh Water Centre, Finnish Environment Institute","active":true,"usgs":false}],"preferred":false,"id":780927,"contributorType":{"id":1,"text":"Authors"},"rank":11},{"text":"Janse, Jan H.","contributorId":215555,"corporation":false,"usgs":false,"family":"Janse","given":"Jan","email":"","middleInitial":"H.","affiliations":[{"id":39277,"text":"Dept. of Aquatic Ecology, Netherlands Institute of Ecology, the Netherlands","active":true,"usgs":false}],"preferred":false,"id":780928,"contributorType":{"id":1,"text":"Authors"},"rank":12},{"text":"Janssen, Annette B. G.","contributorId":215552,"corporation":false,"usgs":false,"family":"Janssen","given":"Annette","email":"","middleInitial":"B. G.","affiliations":[{"id":39277,"text":"Dept. of Aquatic Ecology, Netherlands Institute of Ecology, the Netherlands","active":true,"usgs":false}],"preferred":false,"id":780929,"contributorType":{"id":1,"text":"Authors"},"rank":13},{"text":"Hengeveld, Geerten M.","contributorId":189334,"corporation":false,"usgs":false,"family":"Hengeveld","given":"Geerten","email":"","middleInitial":"M.","affiliations":[],"preferred":false,"id":780930,"contributorType":{"id":1,"text":"Authors"},"rank":14},{"text":"Kong, Xiangzhen","contributorId":222011,"corporation":false,"usgs":false,"family":"Kong","given":"Xiangzhen","email":"","affiliations":[{"id":40469,"text":"Department of Lake Research, Helmholtz Centre for Environmental Research","active":true,"usgs":false}],"preferred":false,"id":780931,"contributorType":{"id":1,"text":"Authors"},"rank":15},{"text":"Kramer, Lilith","contributorId":222012,"corporation":false,"usgs":false,"family":"Kramer","given":"Lilith","email":"","affiliations":[{"id":36257,"text":"Deltares","active":true,"usgs":false}],"preferred":false,"id":780932,"contributorType":{"id":1,"text":"Authors"},"rank":16},{"text":"Kuiper, Jan J.","contributorId":222013,"corporation":false,"usgs":false,"family":"Kuiper","given":"Jan","email":"","middleInitial":"J.","affiliations":[{"id":40465,"text":"Stockholm Resilience Centre, Stockholm University","active":true,"usgs":false}],"preferred":false,"id":780933,"contributorType":{"id":1,"text":"Authors"},"rank":17},{"text":"Langan, Simon J.","contributorId":222014,"corporation":false,"usgs":false,"family":"Langan","given":"Simon","email":"","middleInitial":"J.","affiliations":[{"id":40470,"text":"International Institute for Applied Systems Analysis","active":true,"usgs":false}],"preferred":false,"id":780934,"contributorType":{"id":1,"text":"Authors"},"rank":18},{"text":"Nolet, Bart A.","contributorId":222015,"corporation":false,"usgs":false,"family":"Nolet","given":"Bart","email":"","middleInitial":"A.","affiliations":[{"id":40471,"text":"Department of Animal Ecology, Netherlands Institute for Ecology","active":true,"usgs":false}],"preferred":false,"id":780935,"contributorType":{"id":1,"text":"Authors"},"rank":19},{"text":"Nuijten, Rascha J. M.","contributorId":222016,"corporation":false,"usgs":false,"family":"Nuijten","given":"Rascha","email":"","middleInitial":"J. M.","affiliations":[{"id":40471,"text":"Department of Animal Ecology, Netherlands Institute for Ecology","active":true,"usgs":false}],"preferred":false,"id":780936,"contributorType":{"id":1,"text":"Authors"},"rank":20},{"text":"Strokal, Maryna","contributorId":222017,"corporation":false,"usgs":false,"family":"Strokal","given":"Maryna","email":"","affiliations":[{"id":40472,"text":"Water Systems and Global Change group, Wageningen University","active":true,"usgs":false}],"preferred":false,"id":780937,"contributorType":{"id":1,"text":"Authors"},"rank":21},{"text":"Troost, Tineke A.","contributorId":218276,"corporation":false,"usgs":false,"family":"Troost","given":"Tineke","email":"","middleInitial":"A.","affiliations":[],"preferred":false,"id":780938,"contributorType":{"id":1,"text":"Authors"},"rank":22},{"text":"van Dam, Anne A.","contributorId":222018,"corporation":false,"usgs":false,"family":"van Dam","given":"Anne","email":"","middleInitial":"A.","affiliations":[{"id":40473,"text":"Aquatic Ecosystems Group, IHE Delft Institute for Water Education","active":true,"usgs":false}],"preferred":false,"id":780939,"contributorType":{"id":1,"text":"Authors"},"rank":23},{"text":"Teurlincx, Sven","contributorId":218275,"corporation":false,"usgs":false,"family":"Teurlincx","given":"Sven","email":"","affiliations":[],"preferred":false,"id":780940,"contributorType":{"id":1,"text":"Authors"},"rank":24}]}} ,{"id":70200433,"text":"sir20185142 - 2018 - Groundwater chemistry and water-level elevations in bedrock aquifers of the Piceance and Yellow Creek watersheds, Rio Blanco County, Colorado, 2013–16","interactions":[],"lastModifiedDate":"2018-11-26T10:01:42","indexId":"sir20185142","displayToPublicDate":"2018-11-21T14:45:00","publicationYear":"2018","noYear":false,"publicationType":{"id":18,"text":"Report"},"publicationSubtype":{"id":5,"text":"USGS Numbered Series"},"seriesTitle":{"id":334,"text":"Scientific Investigations Report","code":"SIR","onlineIssn":"2328-0328","printIssn":"2328-031X","active":true,"publicationSubtype":{"id":5}},"seriesNumber":"2018-5142","title":"Groundwater chemistry and water-level elevations in bedrock aquifers of the Piceance and Yellow Creek watersheds, Rio Blanco County, Colorado, 2013–16","docAbstract":"The Piceance and Yellow Creek watersheds in Rio Blanco County, Colorado, are known to contain important energy resources (oil shale and natural gas) and mineral resources (nahcolite). The primary sources of fresh groundwater in the Piceance and Yellow Creek watersheds are bedrock aquifers in the Uinta and Green River Formations. The aquifers are divided into an upper and lower aquifer separated by a regionally extensive semiconfining layer. These aquifers provide water to streams and springs in the watersheds and are an important resource to people living and working in the area. Development of energy and mineral resources has the potential to affect the quality of groundwater in several ways. The Bureau of Land Management and the U.S. Geological Survey began groundwater monitoring in 2010 to characterize the groundwater quality and water-level elevations of shallow bedrock aquifers in the Piceance and Yellow Creek watersheds. The purpose of this report is to present ground-water chemistry and water-level elevations collected during 2013–16. Comparisons are made to data that were collected from the bedrock aquifers from 2010 to 2012 to identify the potential for changes in water quality and water-level elevations.
Appreciable changes in water-level elevations and hydraulic gradient were observed in early April 2015 in two wells completed in the upper and lower aquifers. The hydraulic gradient between the two wells was consistently downward from the upper aquifer to the lower aquifer during 2010–15; however, in early April 2015, the gradient changed from downward to upward between the two aquifers. Overall, water-level elevations declined by about 14 and 11 feet in the upper and lower aquifers, respectively, from 2013 to 2016. Previously published data estimated groundwater ages at 1,200 years old in the upper aquifer and 9,600 years old in the lower aquifer. These groundwater ages indicate that ground-water was recharged over thousands of years. With such long periods of time for aquifer recharge, declines in water-level elevation over short time steps (a few months) have important implications for sustainable management of this resource. Solution mining activities or drilling for oil and natural gas in the area could be related to the changes observed in water-level elevations in these wells; however, further investigation would be needed to evaluate causation.
Changes in major-ion chemistry were evaluated in the bedrock aquifer using time series plots of select major-ion data from 2010 to 2016. Major-ion chemistry was variable for a single well from 2010 to 2016 where alkalinity and sulfate were the most variable constituents. One possible explanation for the observed changes in major-ion chemistry may be that the sample depth for that well no longer represents the most appreciable flow in the borehole. On a larger scale, potential changes in flow within the borehole may indicate changes in the regional flow system. Methane and volatile organic compound concentrations were evaluated using a similar approach to that of major ions and had similar findings. Methane concentrations in wells sampled from 2010 to 2016 were generally constant. The only exception was observed at a single well where the range of methane concentrations was from 57.4 (2010) to 4.02 milligrams per liter (2013). This is the same well where changes in water-level elevation, hydraulic gradient, and major-ion chemistry were observed, providing multiple lines of evidence to indicate change in the bedrock aquifers. Sampling of a well located in an area with little energy development but where faults or fractures could provide a path for the migration of fluids indicate mixing of groundwater between the upper and lower aquifers.
","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/sir20185142","collaboration":"Prepared in cooperation with the Bureau of Land Management, White River Field Office","usgsCitation":"Thomas, J.C., and McMahon, P.B., 2018, Groundwater chemistry and water-level elevations in bedrock aquifers of the Piceance and Yellow Creek watersheds, Rio Blanco County, Colorado, 2013–16: U.S. Geological Survey Scientific Investigations Report 2018–5142, 26 p., https://doi.org/10.3133/sir20185142.","productDescription":"v, 26 p.","onlineOnly":"Y","ipdsId":"IP-093390","costCenters":[{"id":191,"text":"Colorado Water Science Center","active":true,"usgs":true}],"links":[{"id":359632,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/sir/2018/5142/coverthb.jpg"},{"id":359633,"rank":2,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/sir/2018/5142/sir20185142.pdf","text":"Report","size":"13.6 MB","linkFileType":{"id":1,"text":"pdf"},"description":"SIR 2018–5142"}],"country":"United States","state":"Colorado","county":"Rio Blanco County","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -108.75,\n 39.5\n ],\n [\n -107.75,\n 39.5\n ],\n [\n -107.75,\n 40.25\n ],\n [\n -108.75,\n 40.25\n ],\n [\n -108.75,\n 39.5\n ]\n ]\n ]\n }\n }\n ]\n}","contact":"Director, Colorado Water Science Center
U.S. Geological Survey
Box 25046, MS 415
Denver, CO 80225
Safe drinking water at the point-of-use (tapwater, TW) is a United States public health priority. Multiple lines of evidence were used to evaluate potential human health concerns of 482 organics and 19 inorganics in TW from 13 (7 public supply, 6 private well self-supply) home and 12 (public supply) workplace locations in 11 states. Only uranium (61.9 μg L–1, private well) exceeded a National Primary Drinking Water Regulation maximum contaminant level (MCL: 30 μg L–1). Lead was detected in 23 samples (MCL goal: zero). Seventy-five organics were detected at least once, with median detections of 5 and 17 compounds in self-supply and public supply samples, respectively (corresponding maxima: 12 and 29). Disinfection byproducts predominated in public supply samples, comprising 21% of all detected and 6 of the 10 most frequently detected. Chemicals designed to be bioactive (26 pesticides, 10 pharmaceuticals) comprised 48% of detected organics. Site-specific cumulative exposure–activity ratios (∑EAR) were calculated for the 36 detected organics with ToxCast data. Because these detections are fractional indicators of a largely uncharacterized contaminant space, ∑EAR in excess of 0.001 and 0.01 in 74 and 26% of public supply samples, respectively, provide an argument for prioritized assessment of cumulative effects to vulnerable populations from trace-level TW exposures.
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The Chesapeake Bay Program partnership has developed criteria guidance supporting the definition of state water quality standards and associated assessment procedures for DO and other parameters, which provides a binary classification of attainment or impairment. Evaluating time series of these two outcomes alone, however, provides limited information on water quality change over time or space. Here we introduce an extension of the existing Chesapeake Bay water quality criterion assessment framework to quantify the amount of impairment shown by space-time exceedance of DO criterion (“attainment deficit”) for a specific tidal management unit (i.e., segment). We demonstrate the usefulness of this extended framework by applying it to Bay segments for each 3-year assessment period between 1985 and 2016. In general, the attainment deficit for the most recent period assessed (i.e., 2014–2016) is considerably worse for deep channel (DC; n = 10) segments than open water (OW; n = 92) and deep water (DW; n = 18) segments. Most subgroups – classified by designated uses, salinity zones, or tidal systems – show better (or similar) attainment status in 2014–2016 than their initial status (1985–1987). Some significant temporal trends (p < 0.1) were detected, presenting evidence on the recovery for portions of Chesapeake Bay with respect to DO criterion attainment. Significant, improving trends were observed in seven OW segments, four DW segments, and one DC segment over the 30 3-year assessment periods (1985–2016). Likewise, significant, improving trends were observed in 15 OW, five DW, and four DC segments over the recent 15 assessment periods (2000–2016). Subgroups showed mixed trends, with the Patuxent, Nanticoke, and Choptank Rivers experiencing significant, improving short-term (2000–2016) trends while Elizabeth experiencing a significant, degrading short-term trend. The general lack of significantly improving trends across the Bay suggests that further actions will be necessary to achieve full attainment of DO criterion. Insights revealed in this work are critical for understanding the dynamics of the Bay ecosystem and for further assessing the effectiveness of management initiatives aimed toward Bay restoration.
The Long Strait Basin is both a stand alone petroleum province and an assessment unit (AU) that lies offshore in the East Siberian Sea north of Chukotka and south of Wrangel Island. This basin is known only on the basis of gravity data and a single proprietary seismic line. In the absence of more specific data, its position and regional setting suggest that it may have petroleum geologic characteristics similar to the nearby Hope Basin.
Because the geology and petroleum potential of the Long Strait Basin are so poorly known, only a single AU was defined for this study area. An overall probability of ~0.08 (8 percent) of at least one petroleum accumulation larger than 50 million barrels of oil equivalent was determined on the basis of estimated probabilities of the occurrence of petroleum source, adequate reservoir, trap and seal, and favorable timing. Because this probability falls below the 10 percent probability cutoff used in the U.S. Geological Survey’s Circum-Arctic Resource Appraisal, no quantitative assessment of sizes and numbers of petroleum accumulations was conducted for this AU.
","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/pp1824AA","usgsCitation":"Bird, K.J., Houseknecht, D.W., and Pitman, J.K., 2018, Geology and assessment of undiscovered oil and gas resources of the Long Strait Basin Province, 2008, chap. AA of Moore, T.E., and Gautier, D.L., eds., The 2008 Circum-Arctic Resource Appraisal: U.S. Geological Survey Professional Paper 1824, 7 p., https://doi.org/10.3133/pp1824AA.","productDescription":"Report: vi, 7 p.; Appendix","onlineOnly":"Y","ipdsId":"IP-050996","costCenters":[{"id":255,"text":"Energy Resources Program","active":true,"usgs":true}],"links":[{"id":359574,"rank":3,"type":{"id":3,"text":"Appendix"},"url":"https://pubs.usgs.gov/pp/1824/aa/pp1824aa_appendix1.xls","text":"Appendix 1","size":"45 KB","linkFileType":{"id":3,"text":"xlsx"},"description":"PP 1824 Chapter AA Appendix 1","linkHelpText":"- Input Data for the Long Strait Basin Assessment Unit"},{"id":359571,"rank":2,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/pp/1824/aa/pp1824aa.pdf","text":"Report","size":"1.5 MB","linkFileType":{"id":1,"text":"pdf"},"description":"PP 1824 Chapter AA"},{"id":359570,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/pp/1824/aa/coverthb.jpg"}],"otherGeospatial":"Long Strait Basin Province","contact":"Contact Information, Geology, Minerals, Energy, & Geophysics Science Center—Menlo Park
U.S. Geological Survey
345 Middlefield Road
Menlo Park, CA 94025-3591
FAX 650-329-4936
The U.S. Geological Survey collected continuous water-temperature data in select tributaries of the lowermost 80 kilometers (50 miles) of the Willamette River in northwestern Oregon, during summers 2016 and 2017. Point measurements of water temperature and water quality (dissolved oxygen, specific conductance, and pH) also were collected at multiple locations and depths within the river and in the lower reaches of three major tributaries (Clackamas and Molalla Rivers, and Johnson Creek). These datasets were collected to identify potential locations of cold-water refuges for sensitive fish species, and to characterize daily, seasonal, and spatial variability in water conditions. These datasets may be useful for local municipalities that are required to identify cold-water refuges (as defined in State of Oregon water-quality standards) and determine approaches for protecting and enhancing these features as part of their Willamette River water-temperature Total Maximum Daily Load implementation plans. This report documents the data collection methods, provides summary graphs and maps of the water-temperature data, and outlines steps for accessing the data.
","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/ofr20181184","collaboration":"Prepared in cooperation with the City of Lake Oswego, City of Wilsonville, Meyer Memorial Trust, and Benton Soil and Water Conservation District","usgsCitation":"Mangano, J.F., Piatt, D.R., Jones, K.L, and Rounds, S.A., 2018, Water temperature in tributaries, off-channel features, and main channel of the lower Willamette River, northwestern Oregon, summers 2016 and 2017: U.S. Geological Survey Open-File Report 2018-1184, 33 p., https://doi.org/10.3133/ofr20181184.","productDescription":"iv, 33 p.","onlineOnly":"Y","ipdsId":"IP-099746","costCenters":[{"id":518,"text":"Oregon Water Science Center","active":true,"usgs":true}],"links":[{"id":359616,"rank":2,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/of/2018/1184/ofr20181184.pdf","text":"Report","size":"11.1 MB","linkFileType":{"id":1,"text":"pdf"},"description":"OFR 2018-1184"},{"id":359615,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/of/2018/1184/coverthb.jpg"}],"country":"United States","state":"Oregon","otherGeospatial":"Lower Willamette River","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -122.85736083984375,\n 45.268121280142886\n ],\n [\n -122.57995605468749,\n 45.268121280142886\n ],\n [\n -122.57995605468749,\n 45.66108710567762\n ],\n [\n -122.85736083984375,\n 45.66108710567762\n ],\n [\n -122.85736083984375,\n 45.268121280142886\n ]\n ]\n ]\n }\n }\n ]\n}","contact":"Director, Oregon Water Science Center
U.S. Geological Survey
2130 SW 5th Avenue
Portland, Oregon 97201
Remote-sensing-based maps of tidal marshes, both of their extents and carbon stocks, will play a key role in conducting greenhouse gas (GHG) inventories.
The U.N. Environment Programme World Conservation Monitoring Centre has produced a new Global Distribution of Salt Marsh dataset that estimates global salt marsh area at 5.5 Mha.
A Tier 1–2 GHG Inventory of U.S. Coastal Wetlands has been developed using the NOAA Coastal-Change Analysis Program Landsat-based land cover maps as a primary dataset.
180Successful mapping of tidal marsh biomass with optical satellite images provides opportunity to improve GHG Inventories.
Further work is needed to map tidal marsh salinity gradients, the extent of tidal vs. non-tidal marshes, methane emissions, and high-resolution elevation.
","largerWorkType":{"id":4,"text":"Book"},"largerWorkTitle":"A blue carbon primer: The state of coastal wetland carbon science, practice and policy","language":"English","publisher":"CRC Press","doi":"10.1201/9780429435362-14","usgsCitation":"Byrd, K.B., Mcowen, C., Weatherdon, L., Holmquist, J., and Crooks, S., 2018, Status of tidal marsh mapping for blue carbon inventories, chap. of A blue carbon primer: The state of coastal wetland carbon science, practice and policy, 17 p., https://doi.org/10.1201/9780429435362-14.","productDescription":"17 p.","ipdsId":"IP-079453","costCenters":[{"id":657,"text":"Western Geographic Science Center","active":true,"usgs":true}],"links":[{"id":359982,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"publishingServiceCenter":{"id":14,"text":"Menlo Park PSC"},"noUsgsAuthors":false,"publicationStatus":"PW","scienceBaseUri":"5c0a4357e4b0815414d2812e","contributors":{"authors":[{"text":"Byrd, Kristin B. 0000-0002-5725-7486 kbyrd@usgs.gov","orcid":"https://orcid.org/0000-0002-5725-7486","contributorId":3814,"corporation":false,"usgs":true,"family":"Byrd","given":"Kristin","email":"kbyrd@usgs.gov","middleInitial":"B.","affiliations":[{"id":657,"text":"Western Geographic Science Center","active":true,"usgs":true}],"preferred":true,"id":753197,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Mcowen, Chris","contributorId":211096,"corporation":false,"usgs":false,"family":"Mcowen","given":"Chris","email":"","affiliations":[{"id":38181,"text":"U.N. Environment Programme World Conservation Monitoring Programme","active":true,"usgs":false}],"preferred":false,"id":753198,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Weatherdon, Lauren","contributorId":211097,"corporation":false,"usgs":false,"family":"Weatherdon","given":"Lauren","affiliations":[{"id":38181,"text":"U.N. Environment Programme World Conservation Monitoring Programme","active":true,"usgs":false}],"preferred":false,"id":753199,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Holmquist, James","contributorId":204126,"corporation":false,"usgs":false,"family":"Holmquist","given":"James","affiliations":[{"id":36858,"text":"Smithsonian","active":true,"usgs":false}],"preferred":false,"id":753200,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Crooks, Stephen","contributorId":211098,"corporation":false,"usgs":false,"family":"Crooks","given":"Stephen","affiliations":[{"id":38182,"text":"Silvestrum Climate Associates","active":true,"usgs":false}],"preferred":false,"id":753201,"contributorType":{"id":1,"text":"Authors"},"rank":5}]}} ]}