No abstract available.
","language":"English","publisher":"AWRA","usgsCitation":"Blodgett, D.L., and Read, E., 2020, USGS-Water Resources Mission Area progress toward an internet of water, v. 22, no. 2, p. 11-12.","productDescription":"2 p.","startPage":"11","endPage":"12","ipdsId":"IP-116911","costCenters":[{"id":37778,"text":"WMA - Integrated Modeling and Prediction Division","active":true,"usgs":true}],"links":[{"id":373087,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"},{"id":373078,"type":{"id":15,"text":"Index Page"},"url":"https://www.awra.org/Members/Publications/IMPACT.aspx"}],"volume":"22","issue":"2","publishingServiceCenter":{"id":2,"text":"Denver PSC"},"noUsgsAuthors":false,"publicationStatus":"PW","contributors":{"authors":[{"text":"Blodgett, David L. 0000-0001-9489-1710 dblodgett@usgs.gov","orcid":"https://orcid.org/0000-0001-9489-1710","contributorId":3868,"corporation":false,"usgs":true,"family":"Blodgett","given":"David","email":"dblodgett@usgs.gov","middleInitial":"L.","affiliations":[{"id":677,"text":"Wisconsin Water Science Center","active":true,"usgs":true},{"id":37778,"text":"WMA - Integrated Modeling and Prediction Division","active":true,"usgs":true},{"id":5054,"text":"Office of Water Information","active":true,"usgs":true}],"preferred":true,"id":784460,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Read, Emily 0000-0002-9617-9433 eread@usgs.gov","orcid":"https://orcid.org/0000-0002-9617-9433","contributorId":190110,"corporation":false,"usgs":true,"family":"Read","given":"Emily","email":"eread@usgs.gov","affiliations":[{"id":37316,"text":"WMA - Integrated Information Dissemination Division","active":true,"usgs":true},{"id":5054,"text":"Office of Water Information","active":true,"usgs":true}],"preferred":true,"id":784461,"contributorType":{"id":1,"text":"Authors"},"rank":2}]}} ,{"id":70227806,"text":"70227806 - 2020 - Use of multiple temperature logger models can alter conclusions","interactions":[],"lastModifiedDate":"2022-02-01T20:38:40.959549","indexId":"70227806","displayToPublicDate":"2020-03-01T15:37:57","publicationYear":"2020","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":3709,"text":"Water","active":true,"publicationSubtype":{"id":10}},"title":"Use of multiple temperature logger models can alter conclusions","docAbstract":"Remote temperature loggers are often used to measure water temperatures for ecological studies and by regulatory agencies to determine whether water quality standards are being maintained. Equipment specifications are often given a cursory review in the methods; however, the effect of temperature logger model is rarely addressed in the discussion. In a laboratory environment, we compared measurements from three models of temperature loggers at 5 to 40 °C to better understand the utility of these devices. Mean water temperatures recorded by logger models differed statistically even for those with similar accuracy specifications, but were still within manufacturer accuracy specifications. Maximum mean temperature difference between models was 0.4 °C which could have regulatory and ecological implications, such as when a 0.3 °C temperature change triggers a water quality violation or increases species mortality rates. Additionally, precision should be reported as the overall precision (including a consideration of significant digits) for combined model types which in our experiment was 0.7 °C, not the ≤0.4 °C for individual models. Our results affirm that analyzing data collected by different logger models can result in potentially erroneous conclusions when <1 °C difference has regulatory compliance or ecological implications and that combining data from multiple logger models can reduce the overall precision of results.
","language":"English","publisher":"MDPI","doi":"10.3390/w12030668","usgsCitation":"Whittier, J.B., Westhoff, J.T., Paukert, C.P., and Rotman, R.M., 2020, Use of multiple temperature logger models can alter conclusions: Water, v. 12, no. 3, 9 p., https://doi.org/10.3390/w12030668.","productDescription":"9 p.","ipdsId":"IP-092924","costCenters":[{"id":198,"text":"Coop Res Unit Atlanta","active":true,"usgs":true}],"links":[{"id":457535,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.3390/w12030668","text":"Publisher Index Page"},{"id":395243,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"volume":"12","issue":"3","noUsgsAuthors":false,"publicationDate":"2020-03-01","publicationStatus":"PW","contributors":{"authors":[{"text":"Whittier, Joanna B.","contributorId":53151,"corporation":false,"usgs":false,"family":"Whittier","given":"Joanna","email":"","middleInitial":"B.","affiliations":[],"preferred":false,"id":832344,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Westhoff, Jacob T.","contributorId":58106,"corporation":false,"usgs":true,"family":"Westhoff","given":"Jacob","email":"","middleInitial":"T.","affiliations":[],"preferred":false,"id":832345,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Paukert, Craig P. 0000-0002-9369-8545 cpaukert@usgs.gov","orcid":"https://orcid.org/0000-0002-9369-8545","contributorId":879,"corporation":false,"usgs":true,"family":"Paukert","given":"Craig","email":"cpaukert@usgs.gov","middleInitial":"P.","affiliations":[{"id":198,"text":"Coop Res Unit Atlanta","active":true,"usgs":true}],"preferred":true,"id":832346,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Rotman, Robin M.","contributorId":272858,"corporation":false,"usgs":false,"family":"Rotman","given":"Robin","email":"","middleInitial":"M.","affiliations":[{"id":6754,"text":"University of Missouri","active":true,"usgs":false}],"preferred":false,"id":832347,"contributorType":{"id":1,"text":"Authors"},"rank":4}]}} ,{"id":70208889,"text":"70208889 - 2020 - Assessing water-quality changes in agricultural drainages: Examples from oxbow lake tributaries in Mississippi, USA and simulation-based power analyses","interactions":[],"lastModifiedDate":"2020-03-04T15:12:43","indexId":"70208889","displayToPublicDate":"2020-03-01T15:07:50","publicationYear":"2020","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":2456,"text":"Journal of Soil and Water Conservation","active":true,"publicationSubtype":{"id":10}},"title":"Assessing water-quality changes in agricultural drainages: Examples from oxbow lake tributaries in Mississippi, USA and simulation-based power analyses","docAbstract":"Hydrology and water quality (suspended sediment, total nitrogen, ammonia, total Kjeldahl nitrogen, nitrate plus nitrite, and total phosphorus (TP)) were monitored in two small agricultural drainages in northwestern Mississippi to document changes in water quality that coincided with the implementation of BMPs in upstream drainages. Using an event-based dataset and bootstrapping techniques, we tested for difference and equivalence in median event concentration and differences in concentration-discharge (C-Q) relationships between an early and late period at each site, where most of the major BMP implementation occurred during the early period. Results for one site were inconclusive. None of the constituents had statistically different or equivalent event concentrations between the periods, indicating a lack of evidence to tell whether water quality had changed or stayed the same, and only TP had a significantly higher C-Q slope during the late period. At the other site, more than half the constituents had a significantly different median, slope, or intercept between periods, indicating a 35% or more decrease in event concentration following a period of intense BMP implementation. These mixed results could be due to variety of differences between the sites including BMP implementation, production practices, and crops. We also used the monitoring data to generate synthetic data and perform a simulation-based power analysis to explore the ability to detect change under 25 scenarios of sampled event counts and hypothetical percent changes. The simulation-based power analysis indicated that high natural variability in event concentration and flow hindered our ability to detect change. Based on our monitoring, data analysis, and power analysis, we provide recommendations for future monitoring.","language":"English","publisher":"Soil and Water Conservation Society","doi":"10.2489/jswc.75.2.218","usgsCitation":"Murphy, J.C., Hicks, M.B., and Stocks, S.J., 2020, Assessing water-quality changes in agricultural drainages: Examples from oxbow lake tributaries in Mississippi, USA and simulation-based power analyses: Journal of Soil and Water Conservation, v. 75, no. 2, p. 218-230, https://doi.org/10.2489/jswc.75.2.218.","productDescription":"13 p.","startPage":"218","endPage":"230","ipdsId":"IP-091590","costCenters":[{"id":24708,"text":"Lower Mississippi-Gulf Water Science Center","active":true,"usgs":true}],"links":[{"id":457542,"rank":1,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.2489/jswc.75.2.218","text":"Publisher Index Page"},{"id":437076,"rank":0,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/F75H7FJJ","text":"USGS data release","linkHelpText":"Hydrologic event-based water-quality and streamflow data for three oxbow tributaries in northwestern Mississippi, 2007-2016"},{"id":372917,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","state":"Mississippi","otherGeospatial":"Bee Lake, Lake Washington","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -90.889892578125,\n 32.35212281198644\n ],\n [\n -90.186767578125,\n 33.15594830078649\n ],\n [\n -90.098876953125,\n 33.93424531117312\n ],\n [\n -90.208740234375,\n 34.96699890670367\n ],\n [\n -90.54931640625,\n 34.67839374011646\n ],\n [\n -90.802001953125,\n 34.27083595165\n ],\n [\n -91.0546875,\n 33.925129700072\n ],\n [\n -91.1865234375,\n 33.63291573870479\n ],\n [\n -91.153564453125,\n 33.27543541298162\n ],\n [\n -91.131591796875,\n 32.80574473290688\n ],\n [\n -91.043701171875,\n 32.44488496716713\n ],\n [\n -90.889892578125,\n 32.35212281198644\n ]\n ]\n ]\n }\n }\n ]\n}","volume":"75","issue":"2","publishingServiceCenter":{"id":5,"text":"Lafayette PSC"},"noUsgsAuthors":false,"publicationDate":"2020-03-02","publicationStatus":"PW","contributors":{"authors":[{"text":"Murphy, Jennifer C. 0000-0002-0881-0919 jmurphy@usgs.gov","orcid":"https://orcid.org/0000-0002-0881-0919","contributorId":167405,"corporation":false,"usgs":true,"family":"Murphy","given":"Jennifer","email":"jmurphy@usgs.gov","middleInitial":"C.","affiliations":[{"id":37277,"text":"WMA - Earth System Processes Division","active":true,"usgs":true},{"id":24708,"text":"Lower Mississippi-Gulf Water Science Center","active":true,"usgs":true}],"preferred":false,"id":783845,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Hicks, Matthew B. 0000-0001-5516-0296 mhicks@usgs.gov","orcid":"https://orcid.org/0000-0001-5516-0296","contributorId":3778,"corporation":false,"usgs":true,"family":"Hicks","given":"Matthew","email":"mhicks@usgs.gov","middleInitial":"B.","affiliations":[{"id":24708,"text":"Lower Mississippi-Gulf Water Science Center","active":true,"usgs":true}],"preferred":true,"id":783846,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Stocks, Shane J. 0000-0003-1711-3071 sjstocks@usgs.gov","orcid":"https://orcid.org/0000-0003-1711-3071","contributorId":3811,"corporation":false,"usgs":true,"family":"Stocks","given":"Shane","email":"sjstocks@usgs.gov","middleInitial":"J.","affiliations":[{"id":24708,"text":"Lower Mississippi-Gulf Water Science Center","active":true,"usgs":true}],"preferred":true,"id":783898,"contributorType":{"id":1,"text":"Authors"},"rank":3}]}} ,{"id":70227932,"text":"70227932 - 2020 - Assessing the potential to mitigate climate-related expansion of largemouth bass populations using angler harvest","interactions":[],"lastModifiedDate":"2022-02-02T18:19:51.200058","indexId":"70227932","displayToPublicDate":"2020-03-01T11:56:26","publicationYear":"2020","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":1169,"text":"Canadian Journal of Fisheries and Aquatic Sciences","active":true,"publicationSubtype":{"id":10}},"title":"Assessing the potential to mitigate climate-related expansion of largemouth bass populations using angler harvest","docAbstract":"Climate-related changes in fish communities can present new challenges for fishery managers who must address declines in cool- and cold-water sportfish while dealing with increased abundance of warm-water sportfish. We used largemouth bass (Micropterus salmoides) in Wisconsin lakes as model populations to determine whether angler harvest provides a realistic method for reducing abundance of a popular warm-water sportfish that has become more prevalent and has prompted management concerns around the globe. Model results indicate largemouth bass will be resilient to increased fishing mortality. Furthermore, high rates of voluntary catch-and-release occurring in most largemouth bass fisheries likely preclude fishing mortality rates required to reduce bass abundance at meaningful levels (≥25% reductions). Increasing fishing mortality in these scenarios may require more “stimulus” than merely providing anglers with greater harvest opportunities via less stringent harvest regulations. Angler harvest could result in populations dominated by small fish, a scenario that may be undesirable to anglers, but could provide ecological benefits in certain situations.
","language":"English","publisher":"Canadian Science Publishing","doi":"10.1139/cjfas-2019-0035","usgsCitation":"Sullivan, C., Isermann, D.A., Whitlock, K.E., and Hansen, J.F., 2020, Assessing the potential to mitigate climate-related expansion of largemouth bass populations using angler harvest: Canadian Journal of Fisheries and Aquatic Sciences, v. 77, no. 3, p. 520-533, https://doi.org/10.1139/cjfas-2019-0035.","productDescription":"14 p.","startPage":"520","endPage":"533","ipdsId":"IP-094995","costCenters":[{"id":199,"text":"Coop Res Unit Leetown","active":true,"usgs":true}],"links":[{"id":395288,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","state":"Wisconain","otherGeospatial":"Big Arbor Vitae Lake, Big Sissabagama Lake, Jungle Lake, Kawaguesaga Lake, Teal Lake, Waupaca Chain Lakes","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": 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Leetown","active":true,"usgs":true}],"preferred":true,"id":832600,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Whitlock, Kaitlin E.","contributorId":273695,"corporation":false,"usgs":false,"family":"Whitlock","given":"Kaitlin","email":"","middleInitial":"E.","affiliations":[{"id":17613,"text":"University of Wisconsin - Stevens Point","active":true,"usgs":false}],"preferred":false,"id":832750,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Hansen, Jonathan F.","contributorId":171519,"corporation":false,"usgs":false,"family":"Hansen","given":"Jonathan","email":"","middleInitial":"F.","affiliations":[],"preferred":false,"id":832602,"contributorType":{"id":1,"text":"Authors"},"rank":4}]}} ,{"id":70220183,"text":"70220183 - 2020 - A primer of fishery studies in Grand Canyon: The nonnative fish removal story","interactions":[],"lastModifiedDate":"2025-03-14T15:13:40.124095","indexId":"70220183","displayToPublicDate":"2020-03-01T11:16:00","publicationYear":"2020","noYear":false,"publicationType":{"id":25,"text":"Newsletter"},"publicationSubtype":{"id":30,"text":"Newsletter"},"seriesTitle":{"id":8569,"text":"Boatman's Quarterly Review","active":true,"publicationSubtype":{"id":30}},"title":"A primer of fishery studies in Grand Canyon: The nonnative fish removal story","docAbstract":"Globally, rivers have become the most altered of ecosystems, chiefly due to pollution, water withdrawals, and dams that have modified their former function, and led to large and unforeseen impacts, particularly for fish populations. Extensive research is directed at studying impacts of dams because they sever migration routes and change the physical template (flow, temperature, and sediment and organic loads), and by extension, influence vital rates of fish populations such as growth, survival, movement and recruitment. Prior to introduction of nonnative fishes and network of dams, the humpback chub (Gila cypha, chub) was broadly distributed throughout the Colorado River (mainstem). Since then, chub have declined over their entire historical range and are now restricted to six populations, a factor that led to it being Federally listed as an endangered species. The largest of these chub populations is found in Grand Canyon and is isolated from other upstream populations by Glen Canyon Dam (Dam). Over 90% of this population resides within the Little Colorado River (LCR) and mainstem in regions adjacent to the confluence. The remainder is broadly distributed in small aggregations throughout the ecosystem. Cold water temperatures from the Dam has largely impeded the growth and spawning of chub in the mainstem. Luckily, chub spawn and rear young successfully in the seasonally warm and saline waters of the LCR, though survival of some juveniles (< 200 mm total length) that disperse into the mainstem varies among years.","language":"English","publisher":"Grand Canyon River Guides","usgsCitation":"Yard, M.D., 2020, A primer of fishery studies in Grand Canyon: The nonnative fish removal story: Boatman's Quarterly Review, v. 33, no. 1, p. 8-10.","productDescription":"3 p.","startPage":"8","endPage":"10","ipdsId":"IP-114117","costCenters":[{"id":568,"text":"Southwest Biological Science Center","active":true,"usgs":true}],"links":[{"id":399159,"rank":2,"type":{"id":15,"text":"Index Page"},"url":"https://www.gcrg.org/bqr"},{"id":399160,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","state":"Arizona","otherGeospatial":"Grand Canyon","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -114.04907226562499,\n 35.49198366469642\n ],\n [\n -111.412353515625,\n 35.49198366469642\n ],\n [\n -111.412353515625,\n 36.97183825093165\n ],\n [\n -114.04907226562499,\n 36.97183825093165\n ],\n [\n -114.04907226562499,\n 35.49198366469642\n ]\n ]\n ]\n }\n }\n ]\n}","volume":"33","issue":"1","noUsgsAuthors":false,"publicationStatus":"PW","contributors":{"authors":[{"text":"Yard, Michael D. 0000-0002-6580-6027 myard@usgs.gov","orcid":"https://orcid.org/0000-0002-6580-6027","contributorId":169281,"corporation":false,"usgs":true,"family":"Yard","given":"Michael","email":"myard@usgs.gov","middleInitial":"D.","affiliations":[{"id":568,"text":"Southwest Biological Science Center","active":true,"usgs":true}],"preferred":true,"id":814658,"contributorType":{"id":1,"text":"Authors"},"rank":1}]}} ,{"id":70204123,"text":"70204123 - 2020 - Quality control and assessment of interpreter consistency of annual land cover reference data in an operational national monitoring program","interactions":[],"lastModifiedDate":"2024-05-17T15:49:38.223294","indexId":"70204123","displayToPublicDate":"2020-03-01T11:07:27","publicationYear":"2020","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":3254,"text":"Remote Sensing of Environment","printIssn":"0034-4257","active":true,"publicationSubtype":{"id":10}},"title":"Quality control and assessment of interpreter consistency of annual land cover reference data in an operational national monitoring program","docAbstract":"The U.S. Geological Survey Land Change Monitoring, Assessment and Projection (USGS LCMAP) initiative is working toward a comprehensive capability to characterize land cover and land cover change using dense Landsat time series data. A suite of products including annual land cover maps and annual land cover change maps will be produced using the Landsat 4-8 data record. LCMAP products will initially be created for the conterminous United States (CONUS) and then extended to include Alaska and Hawaii. A critical component of LCMAP is the collection of reference data using the TimeSync tool, a web-based interface for manually interpreting and recording land cover from Landsat data supplemented with fine resolution imagery and other ancillary data. These reference data will be used for area estimation and validation of the LCMAP annual land cover products. Nearly 12,000 LCMAP reference sample pixels have been interpreted and a simple random subsample of these pixels has been interpreted independently by a second analyst (hereafter referred to as \"duplicate interpretations\"). The annual land cover reference class labels for the 1984-2016 monitoring period obtained from these duplicate interpretations are used to address the following questions: 1) How consistent are the reference class labels among interpreters overall and per class? 2) Does consistency vary by geographic region? 3) Does consistency vary as interpreters gain experience over time; and 4) Does interpreter consistency change with improving availability and quality of imagery from 1984 to 2016? Overall agreement between interpreters was 88%. Class-specific agreement ranged from 46% for Disturbed to 94% for Water, with more prevalent classes (Tree Cover, Grass/Shrub and Cropland) generally having greater agreement than rare classes (Developed, Barren and Wetland). Agreement between interpreters remained approximately the same over the 12-month period during which these interpretations were completed. Increasing availability of Landsat and Google Earth fine resolution data over the 1984 to 2016 monitoring period coincided with increased interpreter consistency for the post-2000 data record. The reference data interpretation and quality assurance protocols implemented for LCMAP demonstrate the technical and practical feasibility of using the Landsat archive and intensive human interpretation to produce national, annual reference land cover data over a 30 year period. Protocols to quantify and enhance interpreter consistency are critical elements to document and ensure quality of these reference data.","language":"English","publisher":"Elsevier","doi":"10.1016/j.rse.2019.111261","usgsCitation":"Pengra, B., Stehman, S.V., Horton, J., Dockter, D., Schroeder, T.A., Yang, Z., Cohen, W.B., Healey, S.P., and Loveland, T., 2020, Quality control and assessment of interpreter consistency of annual land cover reference data in an operational national monitoring program: Remote Sensing of Environment, v. 238, 111261, 10 p., https://doi.org/10.1016/j.rse.2019.111261.","productDescription":"111261, 10 p.","ipdsId":"IP-101422","costCenters":[{"id":222,"text":"Earth Resources Observation and Science (EROS) Center","active":true,"usgs":true}],"links":[{"id":457550,"rank":3,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.1016/j.rse.2019.111261","text":"Publisher Index Page"},{"id":437077,"rank":2,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/P9QA5Q25","text":"USGS data release","linkHelpText":"LCMAP CONUS Intensification Reference Data Product 1984–2019 land cover, land use and change process attributes"},{"id":414788,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"volume":"238","publishingServiceCenter":{"id":4,"text":"Rolla PSC"},"noUsgsAuthors":false,"publicationStatus":"PW","contributors":{"authors":[{"text":"Pengra, Bruce 0000-0003-2497-8284 bpengra@usgs.gov","orcid":"https://orcid.org/0000-0003-2497-8284","contributorId":5132,"corporation":false,"usgs":true,"family":"Pengra","given":"Bruce","email":"bpengra@usgs.gov","affiliations":[{"id":222,"text":"Earth Resources Observation and Science (EROS) Center","active":true,"usgs":true},{"id":223,"text":"Earth Resources Observation and Science (EROS) Center (Geography)","active":false,"usgs":true}],"preferred":true,"id":765622,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Stehman, Stephen V. 0000-0001-5234-2027","orcid":"https://orcid.org/0000-0001-5234-2027","contributorId":216812,"corporation":false,"usgs":false,"family":"Stehman","given":"Stephen","email":"","middleInitial":"V.","affiliations":[{"id":39524,"text":"College of Environmental Science and Forestry, State University of New York, Syracuse, NY 13210, USA","active":true,"usgs":false}],"preferred":false,"id":765623,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Horton, Josephine 0000-0001-8436-4095","orcid":"https://orcid.org/0000-0001-8436-4095","contributorId":216813,"corporation":false,"usgs":true,"family":"Horton","given":"Josephine","email":"","affiliations":[{"id":222,"text":"Earth Resources Observation and Science (EROS) Center","active":true,"usgs":true}],"preferred":true,"id":765624,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Dockter, Daryn 0000-0003-1914-8657","orcid":"https://orcid.org/0000-0003-1914-8657","contributorId":216814,"corporation":false,"usgs":true,"family":"Dockter","given":"Daryn","email":"","affiliations":[{"id":222,"text":"Earth Resources Observation and Science (EROS) Center","active":true,"usgs":true}],"preferred":true,"id":765625,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Schroeder, Todd A. taschroeder@fs.fed.us","contributorId":190802,"corporation":false,"usgs":false,"family":"Schroeder","given":"Todd","email":"taschroeder@fs.fed.us","middleInitial":"A.","affiliations":[],"preferred":false,"id":765626,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"Yang, Zhiqiang","contributorId":189584,"corporation":false,"usgs":false,"family":"Yang","given":"Zhiqiang","email":"","affiliations":[],"preferred":false,"id":765627,"contributorType":{"id":1,"text":"Authors"},"rank":6},{"text":"Cohen, Warren B 0000-0003-3144-9532","orcid":"https://orcid.org/0000-0003-3144-9532","contributorId":216815,"corporation":false,"usgs":false,"family":"Cohen","given":"Warren","email":"","middleInitial":"B","affiliations":[{"id":39525,"text":"USDA Forest Service, Pacific Northwest Research Station, 3200 SW Jefferson Way, Corvallis, OR 97331","active":true,"usgs":false}],"preferred":false,"id":765628,"contributorType":{"id":1,"text":"Authors"},"rank":7},{"text":"Healey, Sean P.","contributorId":216816,"corporation":false,"usgs":false,"family":"Healey","given":"Sean","email":"","middleInitial":"P.","affiliations":[{"id":39526,"text":"USDA Forest Service, Rocky Mountain Research Station, 507 25th Street, Ogden, UT 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Biological Invasions","active":true,"publicationSubtype":{"id":10}},"title":"Predicting suitable habitat for dreissenid mussel invasion in Texas based on climatic and lake physical characteristics","docAbstract":"Eurasian zebra and quagga mussels were likely introduced to the Laurentian Great Lakes via ballast water release in the 1980s, and their range has since expanded across the US, including some of their southernmost occurrences in Texas. Their spread into the state has resulted in a need to revise previous delimitations of suitable dreissenid habitat. We therefore assessed invasion risk in Texas by 1) predicting distribution of suitable habitat of zebra and quagga mussels using Maxent species distribution models based upon global occurrence and climate data; and 2) refining lake-specific predictions via collection and analysis of physicochemical data. Maxent models predicted a lack of suitable habitat for quagga mussels within Texas. However, models did predict the presence of suitable zebra mussel habitat, with hotspots of suitable habitat occurring along the Red and Sabine Rivers of north and east Texas, as well as patches of suitable habitat in central Texas between the Colorado and Brazos Rivers and extending inland along the Gulf Coast. Although predicted suitable habitat extended further west than in previous models, most of the Texas panhandle, west Texas extending toward El Paso, and the Rio Grande valley were predicted to provide poor zebra mussel habitat suitability. Collection of physicochemical data (i.e., dissolved oxygen, pH, specific conductance, and temperature on-site as well as laboratory analysis for Ca, N, and P) from zebra mussel-invaded lakes and a subset of uninvaded but high-risk lakes of North and Central Texas, did not refine model predictions because there was no apparent distinction between invaded and uninvaded lakes. Overall, we demonstrated that while quagga mussels do not appear to represent an invasive threat in Texas, abundant suitable habitat for continuing zebra mussel invasion exists within the state. The threat of continued expansion of this poster-child for negative invasive species impacts warrants further prevention efforts, management, and research.
","language":"English","publisher":"REABIC","usgsCitation":"Barnes, M., and Patino, R., 2020, Predicting suitable habitat for dreissenid mussel invasion in Texas based on climatic and lake physical characteristics: Management of Biological Invasions, v. 11, no. 1, p. 63-79.","productDescription":"17 p.","startPage":"63","endPage":"79","ipdsId":"IP-107295","costCenters":[{"id":198,"text":"Coop Res Unit Atlanta","active":true,"usgs":true}],"links":[{"id":393733,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"},{"id":393746,"rank":2,"type":{"id":15,"text":"Index Page"},"url":"https://www.reabic.net/journals/mbi/2020/Issue1.aspx"}],"country":"United States","state":"Texas","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -99.30541992187499,\n 29.334298230315675\n ],\n [\n -95.361328125,\n 29.334298230315675\n ],\n [\n -95.361328125,\n 33.925129700072\n ],\n [\n -99.30541992187499,\n 33.925129700072\n ],\n [\n -99.30541992187499,\n 29.334298230315675\n ]\n ]\n ]\n }\n }\n ]\n}","volume":"11","issue":"1","noUsgsAuthors":false,"publicationStatus":"PW","contributors":{"authors":[{"text":"Barnes, M. A.","contributorId":270689,"corporation":false,"usgs":false,"family":"Barnes","given":"M. A.","affiliations":[{"id":36331,"text":"Texas Tech University","active":true,"usgs":false}],"preferred":false,"id":829770,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Patino, Reynaldo 0000-0002-4831-8400 r.patino@usgs.gov","orcid":"https://orcid.org/0000-0002-4831-8400","contributorId":2311,"corporation":false,"usgs":true,"family":"Patino","given":"Reynaldo","email":"r.patino@usgs.gov","affiliations":[{"id":198,"text":"Coop Res Unit Atlanta","active":true,"usgs":true}],"preferred":true,"id":829769,"contributorType":{"id":1,"text":"Authors"},"rank":2}]}} ,{"id":70209967,"text":"70209967 - 2020 - Acute and chronic toxicity of sodium nitrate and sodium sulfate to several freshwater organisms in water-only exposures","interactions":[],"lastModifiedDate":"2020-05-07T12:38:54.176536","indexId":"70209967","displayToPublicDate":"2020-02-29T07:32:54","publicationYear":"2020","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":1571,"text":"Environmental Toxicology and Chemistry","active":true,"publicationSubtype":{"id":10}},"title":"Acute and chronic toxicity of sodium nitrate and sodium sulfate to several freshwater organisms in water-only exposures","docAbstract":"Elevated nitrate (NO3) and sulfate (SO4) in surface water are of global concern, and studies are needed to generate toxicity data to develop environmental guideline values for NO3 and SO4. The present study was designed to fill existing gaps in toxicity databases by determining the acute and/or chronic toxicity of NO3 (tested as NaNO3) to a unionid mussel (Lampsilis siliquoidea), a midge (Chironomus dilutus), a fish (rainbow trout, Oncorhynchus mykiss), and 2 amphibians (Hyla versicolor and Lithobates sylvaticus), and to determine the acute and/or chronic toxicity of SO4 (tested as Na2SO4) to 2 unionid mussels (L. siliquoidea and Villosa iris), an amphipod (Hyalella azteca), and 2 fish species (fathead minnow, Pimephales promelas and O. mykiss). Among the different test species, acute NO3 median effect concentrations (EC50s) ranged from 189 to >883 mg NO3‐N/L, and chronic NO3 20% effect concentrations (EC20s) based on the most sensitive endpoint ranged from 9.6 to 47 mg NO3‐N/L. The midge was the most sensitive species, and the trout was the least sensitive species in both acute and chronic NO3 exposures. Acute SO4 EC50s for the 2 mussel species (2071 and 2064 mg SO4/L) were similar to the EC50 for the amphipod (2689 mg SO4/L), whereas chronic EC20s for the 2 mussels (438 and 384 mg SO4/L) were >2‐fold lower than the EC20 of the amphipod (1111 mg SO4/L), indicating the high sensitivity of mussels in chronic SO4 exposures. However, the fathead minnow, with an EC20 of 374 mg SO4/L, was the most sensitive species in chronic SO4 exposures whereas the rainbow trout was the least sensitive species (EC20 > 3240 mg SO4/L). The high sensitivity of fathead minnow was consistent with the finding in a previous chronic Na2SO4 study. However, the EC20 values from the present study conducted in test water containing a higher potassium concentration (3 mg K/L) were >2‐fold greater than those in the previous study at a lower potassium concentration (1 mg K/L), which confirmed the influence of potassium on chronic Na2SO4 toxicity to the minnow.","language":"English","publisher":"SETAC","doi":"10.1002/etc.4701","collaboration":"","usgsCitation":"Wang, N., Dorman, R.A., Ivey, C.D., Soucek, D.J., Dickinson, A., Kunz, B.K., Steevens, J.A., Hammer, E.J., and Bauer, C.R., 2020, Acute and chronic toxicity of sodium nitrate and sodium sulfate to several freshwater organisms in water-only exposures: Environmental Toxicology and Chemistry, v. 39, no. 5, p. 1071-1085, https://doi.org/10.1002/etc.4701.","productDescription":"15 p.","startPage":"1071","endPage":"1085","ipdsId":"IP-114888","costCenters":[{"id":192,"text":"Columbia Environmental Research Center","active":true,"usgs":true}],"links":[{"id":437081,"rank":0,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/P9V2O84R","text":"USGS data release","linkHelpText":"Chemical and biological data from acute and chronic exposure to sodium nitrate and sodium sulfate for several freshwater organisms in water-only bioassays"},{"id":374531,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"volume":"39","issue":"5","noUsgsAuthors":false,"publicationDate":"2020-02-29","publicationStatus":"PW","contributors":{"authors":[{"text":"Wang, Ning 0000-0002-2846-3352 nwang@usgs.gov","orcid":"https://orcid.org/0000-0002-2846-3352","contributorId":2818,"corporation":false,"usgs":true,"family":"Wang","given":"Ning","email":"nwang@usgs.gov","affiliations":[{"id":192,"text":"Columbia Environmental Research Center","active":true,"usgs":true}],"preferred":true,"id":788623,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Dorman, Rebecca A. 0000-0002-5748-7046","orcid":"https://orcid.org/0000-0002-5748-7046","contributorId":28522,"corporation":false,"usgs":true,"family":"Dorman","given":"Rebecca","email":"","middleInitial":"A.","affiliations":[{"id":192,"text":"Columbia Environmental Research Center","active":true,"usgs":true}],"preferred":true,"id":788624,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Ivey, Chris D. 0000-0002-0485-7242 civey@usgs.gov","orcid":"https://orcid.org/0000-0002-0485-7242","contributorId":3308,"corporation":false,"usgs":true,"family":"Ivey","given":"Chris","email":"civey@usgs.gov","middleInitial":"D.","affiliations":[{"id":192,"text":"Columbia Environmental Research Center","active":true,"usgs":true}],"preferred":true,"id":788625,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Soucek, David J. 0000-0002-7741-0193","orcid":"https://orcid.org/0000-0002-7741-0193","contributorId":224591,"corporation":false,"usgs":false,"family":"Soucek","given":"David","email":"","middleInitial":"J.","affiliations":[{"id":40897,"text":"Illinois Natural History Survey, University of Illinois, Urbana-Champaign, IL","active":true,"usgs":false}],"preferred":false,"id":788626,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Dickinson, Amy","contributorId":224592,"corporation":false,"usgs":false,"family":"Dickinson","given":"Amy","email":"","affiliations":[{"id":40897,"text":"Illinois Natural History Survey, University of Illinois, Urbana-Champaign, IL","active":true,"usgs":false}],"preferred":false,"id":788627,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"Kunz, Bethany K. 0000-0002-7193-9336 bkunz@usgs.gov","orcid":"https://orcid.org/0000-0002-7193-9336","contributorId":3798,"corporation":false,"usgs":true,"family":"Kunz","given":"Bethany","email":"bkunz@usgs.gov","middleInitial":"K.","affiliations":[{"id":192,"text":"Columbia Environmental Research Center","active":true,"usgs":true}],"preferred":true,"id":788628,"contributorType":{"id":1,"text":"Authors"},"rank":6},{"text":"Steevens, Jeffery A. 0000-0003-3946-1229","orcid":"https://orcid.org/0000-0003-3946-1229","contributorId":207511,"corporation":false,"usgs":true,"family":"Steevens","given":"Jeffery","middleInitial":"A.","affiliations":[{"id":192,"text":"Columbia Environmental Research Center","active":true,"usgs":true}],"preferred":true,"id":788629,"contributorType":{"id":1,"text":"Authors"},"rank":7},{"text":"Hammer, Edward J.","contributorId":150723,"corporation":false,"usgs":false,"family":"Hammer","given":"Edward","email":"","middleInitial":"J.","affiliations":[{"id":18077,"text":"U. S. Environmental Protection Agency, Region 5, Water Quality Branch, Chicago, Illinois","active":true,"usgs":false}],"preferred":false,"id":788630,"contributorType":{"id":1,"text":"Authors"},"rank":8},{"text":"Bauer, Candice R.","contributorId":150724,"corporation":false,"usgs":false,"family":"Bauer","given":"Candice","email":"","middleInitial":"R.","affiliations":[{"id":18077,"text":"U. S. Environmental Protection Agency, Region 5, Water Quality Branch, Chicago, Illinois","active":true,"usgs":false}],"preferred":false,"id":788631,"contributorType":{"id":1,"text":"Authors"},"rank":9}]}} ,{"id":70208312,"text":"ofr20201011 - 2020 - Development of a process-based littoral sediment transport model for Dauphin Island, Alabama","interactions":[],"lastModifiedDate":"2022-04-21T20:39:46.098727","indexId":"ofr20201011","displayToPublicDate":"2020-02-28T14:45:00","publicationYear":"2020","noYear":false,"publicationType":{"id":18,"text":"Report"},"publicationSubtype":{"id":5,"text":"USGS Numbered Series"},"seriesTitle":{"id":330,"text":"Open-File Report","code":"OFR","onlineIssn":"2331-1258","printIssn":"0196-1497","active":true,"publicationSubtype":{"id":5}},"seriesNumber":"2020-1011","displayTitle":"Development of a Process-Based Littoral Sediment Transport Model for Dauphin Island, Alabama","title":"Development of a process-based littoral sediment transport model for Dauphin Island, Alabama","docAbstract":"Dauphin Island, Alabama, located in the Northern Gulf of Mexico just outside of Mobile Bay, is Alabama’s only barrier island and provides an array of historical, natural, and economic resources. The dynamic island shoreline of Dauphin Island evolved across time scales while constantly acted upon by waves and currents during both storms and calm periods. Reductions in the vulnerability and enhancements to the resiliency of Dauphin Island—through offshore sand placement, breach closure, berm construction, and other means—have been used to protect the island and its vital resources. Planning for a resilient Dauphin Island requires predicting the long-term evolution of the barrier island system and the dominant, temporally varying processes that influence it, including littoral alongshore sediment transport under typical wave conditions, beach and dune erosion, the island overwash and breaching that occur rapidly during storm events, and the recovery of primary sand dunes through Aeolian transport over decadal time scales. Littoral sediment transport within the Dauphin Island decadal-scale framework was simulated using the Delft-3D modeling software suite. The influences of wind, waves, water levels, and sediment transport are incorporated into the model. Model skill in the prediction of waves, water levels, currents, volumetric flow rates through inlets, and shoreline position was assessed by using a set of deterministic and statistical hindcast simulations. The Delft-3D modeling application described here can be coupled with validated models of storm-response and dune recovery to predict the evolution of Dauphin Island on decadal time scales.
","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/ofr20201011","usgsCitation":"Jenkins, R.L., III, Long, J.W., Dalyander, P.S., Thompson, D.M., and Mickey, R.C., 2020, Development of a process-based littoral sediment transport model for Dauphin Island, Alabama: U.S. Geological Survey Open-File Report 2020–1011, 43 p., https://doi.org/10.3133/ofr20201011.","productDescription":"vii, 43 p.","numberOfPages":"51","onlineOnly":"Y","additionalOnlineFiles":"N","ipdsId":"IP-109477","costCenters":[{"id":574,"text":"St. Petersburg Coastal and Marine Science Center","active":true,"usgs":true}],"links":[{"id":399455,"rank":3,"type":{"id":36,"text":"NGMDB Index Page"},"url":"https://ngmdb.usgs.gov/Prodesc/proddesc_109731.htm"},{"id":372743,"rank":2,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/of/2020/1011/ofr20201011.pdf","text":"Report","linkFileType":{"id":1,"text":"pdf"},"description":"OFR 2020-1011"},{"id":372742,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/of/2020/1011/coverthb.jpg"}],"country":"United States","state":"Alabama","otherGeospatial":"Dauphin Island","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -88.36715698242186,\n 30.210421455819937\n ],\n [\n -88.06640625,\n 30.210421455819937\n ],\n [\n -88.06640625,\n 30.26974231529823\n ],\n [\n -88.36715698242186,\n 30.26974231529823\n ],\n [\n -88.36715698242186,\n 30.210421455819937\n ]\n ]\n ]\n }\n }\n ]\n}","contact":"Director, St. Petersburg Coastal and Marine Science Center
U.S. Geological Survey
600 4th Street South
St. Petersburg, FL 33701
A study was conducted by the U.S. Geological Survey (USGS) in cooperation with the Washington, D.C., Department of Energy & Environment to estimate the loads of suspended-sediment-bound chemical compounds in five gaged tributaries and four ungaged tributaries of the Anacostia River (known locally as “Lower Anacostia River”) in Washington, D.C. Tributaries whose discharge is measured by the USGS are the Northeast and Northwest Branches of the Anacostia River, referred to in this report as “Northeast Branch” (NEB) and “Northwest Branch” (NWB), respectively; Watts Branch (WB); and Hickey Run (HR). A USGS streamflow-gaging station was established in 2016 on Beaverdam Creek (known locally as “Lower Beaverdam Creek” [LBDC]) to support this study. The ungaged streams studied include Nash Run; Pope Branch; an unnamed stream at Fort DuPont, referred to in this report as “Fort DuPont Creek”; and an unnamed stream at Fort Stanton, referred to in this report as “Fort Stanton Creek.” The gaged streams were sampled during four to five storms and two low-flow events during January, March, May, and July 2017. The ungaged streams were sampled during one storm and one low-flow event during July 2017. Storm sampling involved collecting large-volume (60- to 70-liter) composite samples, then removing sediment by filtration in the laboratory. Low-flow samples were obtained by filtering streamwater directly in the field. Continuously recording data sondes were deployed throughout the study to measure turbidity and other water-quality characteristics. During sampling, multiple discrete samples of streamwater were collected to determine suspended-sediment concentration (SSC) and particulate organic carbon (POC) concentration. Shortly after each storm, bed sediment was collected for chemical analysis.
Sediment samples were analyzed for 209 polychlorinated biphenyl (PCB) congeners; 35 polyaromatic hydrocarbon (PAH) compounds, including 20 nonalkylated and 15 alkylated species; and 20 organochlorine pesticide (OP) compounds. Sediment from one storm was analyzed for 23 metals.
Relations were developed among turbidity, discharge, and measured SSC by using multiple linear regression of log-transformed data. These relations were used to estimate SSC from continuous records of discharge and turbidity and were subsequently used to estimate sediment loads for the 2017 calendar year. USGS continuous records of turbidity in NEB, NWB, Watts Branch, and Hickey Run were available for 2013–17, which allowed sediment loads to be calculated for these years. Sediment loads for the ungaged streams were estimated by using loads measured in Watts Branch adjusted on the basis of stream-basin areas.
Sediment loads for 2017 total 3.10×107 kilograms (kg), with 1.02×107 kg (33 percent of total) from the NEB, 1.55×107 kg (50 percent) from the NWB, 4.45×106 kg (14 percent) from LBDC, 5.62×105 kg (2 percent) from Watts Branch, and 2.82×105 kg (1 percent) from Hickey Run. Sediment yields were highest from NWB and LBDC (3.13×105 kilograms per year per square mile [kg/yr/mi2] and 3.01 kg/yr/mi2, respectively). As a result of gaps in turbidity and discharge data, the load for LBDC reported here was calculated from measurements representing only 88 percent of the year (2017), and thus underestimates the actual load. All other gaged tributaries had datasets covering 100 percent of the year and are considered to fully represent actual loads. Estimated sediment loads for the ungaged streams during 2017 total 3.5×105 kg, with 1.2×105 kg from Nash Run, 6.2×104 kg from Pope Branch, 1.1×105 kg from Fort DuPont Creek, and 5.6×104 kg from Fort Stanton Creek.
Concentrations of PCBs, PAHs, and chlorinated pesticides in streamwater are presented for stormflow and low-flow conditions. Average concentrations (in stormflow and low-flow samples) of total PCBs (sum of all congeners, including coelutions) are 5.9 micrograms per kilogram (µg/kg) for NEB, 6.6 µg/kg for NWB, 130 µg/kg for LBDC, 34 µg/kg for Watts Branch, and 69 µg/kg for Hickey Run. Average concentrations of total PAHs (tPAH) (total of nonalkylated and alkylated species) are 2,000 µg/kg for NEB, 3,300 µg/kg for NWB, 2,200 µg/kg for LBDC, 2,400 µg/kg for Watts Branch, and 18,000 µg/kg for Hickey Run. tPAH concentrations among the ungaged streams were highest in Nash Run (5,500 µg/kg); concentrations in the other ungaged streams were less than (<) 700 µg/kg.
The general magnitude of tPCB and tPAH concentrations in streamwater samples was low-flow samples greater than (>) stormflow samples greater than or equal to (≥) bed-sediment samples. PCB congener profiles in the three types of samples were nearly identical in each stream and were similar in all streams except for LBDC, where the dominant PCBs shifted to the lighter di- through tetra- homologs. LBDC showed higher tPCB concentrations and a distinct congener profile from the other streams. The similarity in congener makeup supported that averaging PCB concentrations in stormflow and low-flow samples was appropriate for calculating chemical loads.
Loads of tPCB, tPAH (total of alkylated and nonalkylated forms), and pesticides were estimated for each stream by multiplying average contaminant concentrations by the respective sediment loads. Total PCB loads for 2017 were estimated to be 820 grams (g) with 8 percent (60 g) from NEB, 12 percent (95 g) from NWB, 75 percent (590 g) from LBDC, 3 percent (25 g) from Watts Branch, and 2.5 percent (19 g) from Hickey Run. PCB toxicity totaled 3.8×10−3 µg/kg, with the largest contribution (47 percent) derived from LBDC. Total PAH loads (sum of alkylated and nonalkylated forms) for 2017 were estimated to be 89,000 g, with 23 percent (20,000 g) from NEB, 59 percent (52,000 g) from NWB, 11 percent (9,800 g) from LBDC, 2 percent (1,400 g) from Watts Branch, and 6 percent (5,200 g) from Hickey Run. These results indicate that the largest contributor (75 percent) of PCBs to the Anacostia River is LBDC, although it contributes only 15 percent of the sediment and its basin area represents only 10 percent of the area of the Anacostia River watershed. The majority of the PAH load originates from NWB (59 percent of total) and NEB (22 percent). The ungaged tributaries contribute extremely small loads of PCBs and PAHs, totaling 8.1 g and 765 kg, respectively. More than 94 percent of the total load from the ungaged tributaries is derived from the Nash Run Basin.
Various organochlorine pesticides were present in suspended and bed sediment from all gaged and ungaged tributaries; however, elevated detection levels associated with the analytical methods resulted in numerous unquantifiable concentrations in the suspended-sediment samples. Only the pesticide chlordane was found in measurable concentrations in all gaged tributaries. As a result, in this report, a combination of analytical data from suspended-sediment and bed-sediment samples was used to estimate the maximum pesticide loading for each tributary. Chlordane was the principal compound present in the gaged tributaries; the highest average concentration (average of stormflow and low-flow samples from each stream) was 62 µg/kg in sediment from Watts Branch. Chlordane loads for 2017 totaled 1,100 g, of which 7 percent (430 g) was from NEB, 28 percent (320 g) was from NWB, 28 percent (310 g) was from LBDC, 5 percent (56 g) was from Watts Branch, and 1 percent (11 g) was from Hickey Run. Chlordane was not present in suspended or bed sediment from any of the ungaged tributaries. Loads of the other pesticides were estimated by using the highest concentration measured in the combined suspended-sediment and bed-sediment data for each stream. Notable loads include dieldrin (860 g from NWB), methoxychlor (205 g from LBDC), endrin aldehyde (150 g from NWB), and 4,4-DDT (79 g from Watts Branch). Compared with pesticide loads from the gaged streams, those from the ungaged streams were minimal, with only the Pope Branch contribution exceeding 1 gram per year for 4,4-DDE (1.05 g) and 4,4’-DDT (1.3 g).
The results of this study show that the dominant source of PCBs and chlordane is LBDC, despite its relatively small basin area. PAHs are ubiquitous throughout the study area, with the largest sources being NEB and NWB; this finding is a result of the large sediment load originating from these basins. The small, ungaged streams supply only minimal PCB and PAH loads, with Nash Run being the largest contributor.
","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/sir20195092","collaboration":"Prepared in cooperation with the Washington, D.C., Department of Energy & Environment","usgsCitation":"Wilson, T.P., 2019, Sediment and chemical contaminant loads in tributaries to the Anacostia River, Washington, District of Columbia, 2016–17: U.S. Geological Survey Scientific Investigations Report 2019–5092, 146 p., https://doi.org/10.3133/sir20195092.","productDescription":"Report: x, 146 p.; Data Release","numberOfPages":"160","onlineOnly":"Y","additionalOnlineFiles":"N","ipdsId":"IP-099743","costCenters":[{"id":374,"text":"Maryland Water Science Center","active":true,"usgs":true}],"links":[{"id":399540,"rank":4,"type":{"id":36,"text":"NGMDB Index Page"},"url":"https://ngmdb.usgs.gov/Prodesc/proddesc_109730.htm"},{"id":372690,"rank":3,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/P9RUZSMV","text":"USGS data release","linkHelpText":"Discharge and sediment data for selected tributaries to the Anacostia River, Washington, District of Columbia, 2003–18"},{"id":372692,"rank":2,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/sir/2019/5092/sir20195092.pdf","text":"Report","size":"5.33 MB","linkFileType":{"id":1,"text":"pdf"},"description":"SIR 2019-5092"},{"id":372691,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/sir/2019/5092/coverthb.jpg"}],"country":"United States","state":"District of Columbia","county":"Washington","otherGeospatial":"Anacostia River","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -77.0797,\n 38.8447\n ],\n [\n -76.7689,\n 38.8447\n ],\n [\n -76.7689,\n 39.1611\n ],\n [\n -77.0797,\n 39.1611\n ],\n [\n -77.0797,\n 38.8447\n ]\n ]\n ]\n }\n }\n ]\n}","contact":"Director, MD-DE-DC Water Science Center
U.S. Geological Survey
5522 Research Park Drive
Baltimore, MD 21228
A fully worked example of decision-support-scale uncertainty quantification (UQ) and parameter estimation (PE) is presented. The analyses are implemented for an existing groundwater flow model of the Edwards aquifer, Texas, USA, and are completed in a script-based workflow that strives to be transparent and reproducible. High-dimensional PE is used to history-match simulated outputs to corresponding state observations of spring flow and groundwater level. Then a hindcast of a historical drought is made. Using available state observations recorded during drought conditions, the combined UQ and PE analyses are shown to yield an ensemble of model results that bracket the observed hydrologic responses. All files and scripts used for the analyses are placed in the public domain to serve as a template for other practitioners who are interested in undertaking these types of analyses.
","language":"English","publisher":"Frontiers","doi":"10.3389/feart.2020.00050","usgsCitation":"White, J.T., Foster, L.K., Fienen, M., Knowling, M.J., Hemmings, B., and Winterle, J.R., 2020, Towards reproducible environmental modeling for decision support: A worked example: Frontiers in Earth Science, Hydrosphere, v. 28, 50, 11 p., https://doi.org/10.3389/feart.2020.00050.","productDescription":"50, 11 p.","ipdsId":"IP-115342","costCenters":[{"id":48595,"text":"Oklahoma-Texas Water Science Center","active":true,"usgs":true}],"links":[{"id":457567,"rank":1,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.3389/feart.2020.00050","text":"Publisher Index Page"},{"id":437084,"rank":0,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/P9AUZMI7","text":"USGS data release","linkHelpText":"Towards reproducible environmental modeling for decision support: a worked example"},{"id":386114,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","state":"Texas","otherGeospatial":"southern-central Texas","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -100.96435546875,\n 28.76765910569123\n ],\n [\n -96.83349609375,\n 28.76765910569123\n ],\n [\n -96.83349609375,\n 30.14512718337613\n ],\n [\n -100.96435546875,\n 30.14512718337613\n ],\n [\n -100.96435546875,\n 28.76765910569123\n ]\n ]\n ]\n }\n }\n ]\n}","volume":"28","noUsgsAuthors":false,"publicationDate":"2020-02-28","publicationStatus":"PW","contributors":{"authors":[{"text":"White, Jeremy T. 0000-0002-4950-1469 jwhite@usgs.gov","orcid":"https://orcid.org/0000-0002-4950-1469","contributorId":167708,"corporation":false,"usgs":true,"family":"White","given":"Jeremy","email":"jwhite@usgs.gov","middleInitial":"T.","affiliations":[{"id":583,"text":"Texas Water Science Center","active":true,"usgs":true}],"preferred":true,"id":816772,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Foster, Linzy K. 0000-0002-7373-7017","orcid":"https://orcid.org/0000-0002-7373-7017","contributorId":259186,"corporation":false,"usgs":true,"family":"Foster","given":"Linzy","email":"","middleInitial":"K.","affiliations":[{"id":583,"text":"Texas Water Science Center","active":true,"usgs":true}],"preferred":true,"id":816773,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Fienen, Michael N. 0000-0002-7756-4651","orcid":"https://orcid.org/0000-0002-7756-4651","contributorId":245632,"corporation":false,"usgs":true,"family":"Fienen","given":"Michael N.","affiliations":[{"id":474,"text":"New York Water Science Center","active":true,"usgs":true}],"preferred":true,"id":816774,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Knowling, Matthew J.","contributorId":251909,"corporation":false,"usgs":false,"family":"Knowling","given":"Matthew","email":"","middleInitial":"J.","affiliations":[{"id":36277,"text":"GNS Science","active":true,"usgs":false}],"preferred":false,"id":816775,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Hemmings, Brioch","contributorId":259187,"corporation":false,"usgs":false,"family":"Hemmings","given":"Brioch","affiliations":[{"id":36277,"text":"GNS Science","active":true,"usgs":false}],"preferred":false,"id":816776,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"Winterle, James R.","contributorId":259189,"corporation":false,"usgs":false,"family":"Winterle","given":"James","email":"","middleInitial":"R.","affiliations":[{"id":52328,"text":"Edwards Aquifer Authority","active":true,"usgs":false}],"preferred":false,"id":816777,"contributorType":{"id":1,"text":"Authors"},"rank":6}]}} ,{"id":70208792,"text":"70208792 - 2020 - Causal factors for pesticide trends in streams of the United States: Atrazine and deethylatrazine","interactions":[],"lastModifiedDate":"2020-03-02T06:42:23","indexId":"70208792","displayToPublicDate":"2020-02-28T06:41:05","publicationYear":"2020","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":2262,"text":"Journal of Environmental Quality","active":true,"publicationSubtype":{"id":10}},"title":"Causal factors for pesticide trends in streams of the United States: Atrazine and deethylatrazine","docAbstract":"Pesticides are important for agriculture in the United States, and atrazine is one of the most widely used and widely detected pesticides in surface water. A better understanding of the mechanisms by which atrazine and its degradation product, deethylatrazine, increase and decrease in surface waters can help inform future decisions for water-quality improvement. This study considers causal factors for trends in pesticide concentration in streams in the United States and models the causal factors, other than use, in structural equation models. The structural equation models use a concomitant trend in corn and a latent variable model indicating moisture supply and management. The moisture supply and management latent variable incorporates long-term moisture conditions in the individual watersheds by using the Palmer Hydrologic Drought Index; human influence on the hydrologic cycle through the percent of the watershed drained by tile drains in 2012; and the base-flow contribution to streamflow, using the base-flow index. The structural equation models explain 77% and 38% of the variability in atrazine and deethylatrazine trends, respectively, across the conterminous United States. The models highlight future water-quality challenges, particularly in tile-drained settings where fall precipitation and heavy precipitation are increasing.","language":"English","publisher":"ACSESS","doi":"10.1002/jeq2.20045","usgsCitation":"Ryberg, K.R., Stone, W.W., and Baker, N.T., 2020, Causal factors for pesticide trends in streams of the United States: Atrazine and deethylatrazine: Journal of Environmental Quality, v. 49, no. 1, p. 152-162, https://doi.org/10.1002/jeq2.20045.","productDescription":"11 p.","startPage":"152","endPage":"162","ipdsId":"IP-102928","costCenters":[{"id":37277,"text":"WMA - Earth System Processes Division","active":true,"usgs":true}],"links":[{"id":457571,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.1002/jeq2.20045","text":"Publisher Index Page"},{"id":372755,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -126.73828125,\n 24.686952411999155\n ],\n [\n -66.181640625,\n 24.686952411999155\n ],\n [\n -66.181640625,\n 49.095452162534826\n ],\n [\n -126.73828125,\n 49.095452162534826\n ],\n [\n -126.73828125,\n 24.686952411999155\n ]\n ]\n ]\n }\n }\n ]\n}","volume":"49","issue":"1","publishingServiceCenter":{"id":4,"text":"Rolla PSC"},"noUsgsAuthors":false,"publicationDate":"2020-02-28","publicationStatus":"PW","contributors":{"authors":[{"text":"Ryberg, Karen R. 0000-0002-9834-2046 kryberg@usgs.gov","orcid":"https://orcid.org/0000-0002-9834-2046","contributorId":1172,"corporation":false,"usgs":true,"family":"Ryberg","given":"Karen","email":"kryberg@usgs.gov","middleInitial":"R.","affiliations":[{"id":34685,"text":"Dakota Water Science Center","active":true,"usgs":true}],"preferred":true,"id":783394,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Stone, Wesley W. 0000-0003-0239-2063 wwstone@usgs.gov","orcid":"https://orcid.org/0000-0003-0239-2063","contributorId":1496,"corporation":false,"usgs":true,"family":"Stone","given":"Wesley","email":"wwstone@usgs.gov","middleInitial":"W.","affiliations":[{"id":27231,"text":"Indiana-Kentucky Water Science Center","active":true,"usgs":true},{"id":346,"text":"Indiana Water Science Center","active":true,"usgs":true},{"id":451,"text":"National Water Quality Assessment Program","active":true,"usgs":true}],"preferred":true,"id":783395,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Baker, Nancy T. 0000-0002-7979-5744 ntbaker@usgs.gov","orcid":"https://orcid.org/0000-0002-7979-5744","contributorId":1955,"corporation":false,"usgs":true,"family":"Baker","given":"Nancy","email":"ntbaker@usgs.gov","middleInitial":"T.","affiliations":[{"id":451,"text":"National Water Quality Assessment Program","active":true,"usgs":true},{"id":27231,"text":"Indiana-Kentucky Water Science Center","active":true,"usgs":true},{"id":346,"text":"Indiana Water Science Center","active":true,"usgs":true},{"id":27111,"text":"National Water Quality Program","active":true,"usgs":true}],"preferred":true,"id":783396,"contributorType":{"id":1,"text":"Authors"},"rank":3}]}} ,{"id":70208777,"text":"70208777 - 2020 - Borehole‐scale testing of matrix diffusion for contaminated‐rock aquifers","interactions":[],"lastModifiedDate":"2020-03-02T06:40:01","indexId":"70208777","displayToPublicDate":"2020-02-28T06:37:04","publicationYear":"2020","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":3249,"text":"Remediation Journal","active":true,"publicationSubtype":{"id":10}},"title":"Borehole‐scale testing of matrix diffusion for contaminated‐rock aquifers","docAbstract":"A new method was developed to assess the effect of matrix diffusion on contaminant transport and remediation of groundwater in fractured rock. This method utilizes monitoring wells constructed of open boreholes in fractured rock to conduct backward diffusion experiments on chlorinated volatile organic compounds (CVOCs) in groundwater. The experiments are performed on relatively unfractured zones (called test zones) of the open boreholes over short intervals (approximately 1 meter) by physical isolation using straddle packers. The test zones were identified with a combination of borehole geophysical logging and chemical profiling of CVOCs with passive samplers in the open boreholes. To confirm the test zones are within inactive flow zones, they are subjected to a series of hydraulic tests. Afterwards, the test zones are air sparged with argon to volatilize the CVOCs from aqueous to air phase. Backward diffusion is then measured by periodic passive-sampling of water in the test zone to identify rebound. The passive (non-hydraulically stressed) sampling negates the need to extract water and potentially dewater the test zone. We also monitor active flowing zones of the borehole to assess trends in concentrations in other parts of the fractured rock by purge and passive sampling methods.","language":"English","publisher":"Wiley","doi":"10.1002/rem.21637","usgsCitation":"Harte, P., and Brandon, W.C., 2020, Borehole‐scale testing of matrix diffusion for contaminated‐rock aquifers: Remediation Journal, v. 30, no. 2, p. 37-53, https://doi.org/10.1002/rem.21637.","productDescription":"17 p.","startPage":"37","endPage":"53","ipdsId":"IP-080063","costCenters":[{"id":466,"text":"New England Water Science Center","active":true,"usgs":true}],"links":[{"id":372754,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"volume":"30","issue":"2","publishingServiceCenter":{"id":11,"text":"Pembroke PSC"},"noUsgsAuthors":false,"publicationDate":"2020-02-28","publicationStatus":"PW","contributors":{"authors":[{"text":"Harte, Philip 0000-0002-7718-1204","orcid":"https://orcid.org/0000-0002-7718-1204","contributorId":222856,"corporation":false,"usgs":true,"family":"Harte","given":"Philip","affiliations":[{"id":466,"text":"New England Water Science Center","active":true,"usgs":true}],"preferred":true,"id":783359,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Brandon, William C.","contributorId":199890,"corporation":false,"usgs":false,"family":"Brandon","given":"William","email":"","middleInitial":"C.","affiliations":[],"preferred":false,"id":783360,"contributorType":{"id":1,"text":"Authors"},"rank":2}]}} ,{"id":70208828,"text":"70208828 - 2020 - Egg counts of Southern Leopard Frog, Lithobates sphenocephalus, egg masses from southern Louisiana, USA","interactions":[],"lastModifiedDate":"2020-03-03T09:00:25","indexId":"70208828","displayToPublicDate":"2020-02-27T08:58:34","publicationYear":"2020","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":1899,"text":"Herpetology Notes","active":true,"publicationSubtype":{"id":10}},"title":"Egg counts of Southern Leopard Frog, Lithobates sphenocephalus, egg masses from southern Louisiana, USA","docAbstract":"Southern Leopard Frogs, Lithobates sphenocephalus (Cope, 1889), lay eggs year-round in their southern range, including Louisiana, but their peak breeding season is the cooler months from late fall through early spring (Mount, 1975; Caldwell, 1986; Dundee and Rossman, 1989). Double-enveloped eggs in globular masses are typically deposited in shallow water, but deeper waters are used when temperatures are warmer (Dodd, 2013). Egg masses are often attached to vegetation when present but may also lie free on the substrate (Dundee and Rossman, 1989; Dodd, 2013). Egg masses may be deposited singly, but are often found communally, with hundreds of egg masses in a small area (Dundee and Rossman, 1989; Trauth, 1989). Communal laying in Southern Leopard Frogs may be an adaptative response to cold temperatures (Caldwell, 1986), as has been noted in congeners (Waldman and Ryan, 1983).","language":"English","publisher":"Societas Europaea Herpetologica","usgsCitation":"Glorioso, B.M., Muse, L.J., and Waddle, J.H., 2020, Egg counts of Southern Leopard Frog, Lithobates sphenocephalus, egg masses from southern Louisiana, USA: Herpetology Notes, v. 13, p. 187-189.","productDescription":"3 p.","startPage":"187","endPage":"189","ipdsId":"IP-111582","costCenters":[{"id":17705,"text":"Wetland and Aquatic Research Center","active":true,"usgs":true}],"links":[{"id":372793,"type":{"id":15,"text":"Index Page"},"url":"https://www.biotaxa.org/hn/article/view/57036/60009"},{"id":372837,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","state":"Louisiana","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -93.71337890625,\n 29.621221113784504\n ],\n [\n -92.6806640625,\n 29.420460341013133\n ],\n [\n -90.615234375,\n 28.786918085420226\n ],\n [\n -88.846435546875,\n 28.748396571187406\n ],\n [\n -88.53881835937499,\n 29.5830116903775\n ],\n [\n -89.09912109375,\n 30.088107753367257\n ],\n [\n -89.571533203125,\n 30.211608223816906\n ],\n [\n -89.84619140625,\n 30.65681556429287\n ],\n [\n -89.725341796875,\n 31.005862904624205\n ],\n [\n -91.64794921875,\n 30.996445897426373\n ],\n [\n -93.58154296875,\n 30.817346256492073\n ],\n [\n -93.80126953124999,\n 30.315987718557867\n ],\n [\n -93.944091796875,\n 29.81205076752506\n ],\n [\n -93.71337890625,\n 29.621221113784504\n ]\n ]\n ]\n }\n }\n ]\n}","volume":"13","publishingServiceCenter":{"id":5,"text":"Lafayette PSC"},"noUsgsAuthors":false,"publicationStatus":"PW","contributors":{"authors":[{"text":"Glorioso, Brad M. 0000-0002-5400-7414 gloriosob@usgs.gov","orcid":"https://orcid.org/0000-0002-5400-7414","contributorId":4241,"corporation":false,"usgs":true,"family":"Glorioso","given":"Brad","email":"gloriosob@usgs.gov","middleInitial":"M.","affiliations":[{"id":455,"text":"National Wetlands Research Center","active":true,"usgs":true},{"id":17705,"text":"Wetland and Aquatic Research Center","active":true,"usgs":true}],"preferred":true,"id":783512,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Muse, Lindy J.","contributorId":172438,"corporation":false,"usgs":false,"family":"Muse","given":"Lindy","email":"","middleInitial":"J.","affiliations":[{"id":27041,"text":"Cherokee at USGS-WARC Lafayette","active":true,"usgs":false}],"preferred":false,"id":783513,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Waddle, J. Hardin 0000-0003-1940-2133 waddleh@usgs.gov","orcid":"https://orcid.org/0000-0003-1940-2133","contributorId":138953,"corporation":false,"usgs":true,"family":"Waddle","given":"J.","email":"waddleh@usgs.gov","middleInitial":"Hardin","affiliations":[{"id":17705,"text":"Wetland and Aquatic Research Center","active":true,"usgs":true}],"preferred":true,"id":783514,"contributorType":{"id":1,"text":"Authors"},"rank":3}]}} ,{"id":70210990,"text":"70210990 - 2020 - Evaluation of soil zone processes and a novel radiocarbon correction approach for groundwater with mixed sources","interactions":[],"lastModifiedDate":"2020-07-10T13:54:41.871444","indexId":"70210990","displayToPublicDate":"2020-02-27T08:48:36","publicationYear":"2020","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":2342,"text":"Journal of Hydrology","active":true,"publicationSubtype":{"id":10}},"title":"Evaluation of soil zone processes and a novel radiocarbon correction approach for groundwater with mixed sources","docAbstract":"Estimates of groundwater age based on 14C is often limited by the uncertainty in geochemical processes that alter the 14C concentration measured in water and the composition (δ13C and 14C) of carbon sources needed to appropriately parametrize 14C adjustment models. Estimated ages for samples that contain a mixture of young and old groundwater will be particularly sensitive to model parametrization as relatively small additions of modern 14C from recent recharge can mask the presence and amount of old groundwater. A novel multi-model approach based on inverse geochemical modeling and lumped parameter modeling of age tracers (3H, 3Hetrit, and SF6) was used to better constrain 14C dilution caused by dissolution of carbonates in the unsaturated zone or shallow parts of the Glacial aquifer, which extends over 2000 miles across the northern contiguous United States. Calibration of 14C inverse geochemical models to LPM computed 14C concentrations in modern water indicated that 14C of soil zone and shallow aquifer carbonates were not 14C-dead (0 pmC), as is typically assumed for 14C correction models. 14C of such carbonates was on average about 53 pmC (ranged 0-110 pmC, n = 72). This information was used to correct 14C concentrations for water recharged entirely before 1950 and water that is a mixture of pre- and post-1950 water. The multi-model approach developed here was compared to an analytical 14C-adjustment model (Revised Fontes and Garnier) that assumed solid carbonates were 14C-dead. 14C corrections using the analytical adjustment model tended to over-correct final 14C concentrations by 21 pmC and underestimates mean ages by 40% for groundwater mixtures. In fact, 14C corrections based on analytical model yielded negative ages (14C > 120 pmC) in nearly 36% of mixed samples. This work presents a new approach to constraining 14C corrections and age estimates of mixtures of young and old groundwater. The new method is applied to three well networks distributed across the spatially expansive Glacial aquifer.","language":"English","publisher":"Elsevier","doi":"10.1016/j.jhydrol.2020.124766","usgsCitation":"Solder, J.E., and Jurgens, B., 2020, Evaluation of soil zone processes and a novel radiocarbon correction approach for groundwater with mixed sources: Journal of Hydrology, v. 588, 124766, 14 p., https://doi.org/10.1016/j.jhydrol.2020.124766.","productDescription":"124766, 14 p.","ipdsId":"IP-098920","costCenters":[{"id":154,"text":"California Water Science Center","active":true,"usgs":true},{"id":610,"text":"Utah Water Science Center","active":true,"usgs":true}],"links":[{"id":376259,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","otherGeospatial":"Glacier aquifer system","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -123.3544921875,\n 49.15296965617039\n ],\n [\n -123.79394531249999,\n 47.517200697839414\n ],\n [\n -122.16796875,\n 46.800059446787316\n ],\n [\n -120.234375,\n 48.019324184801185\n ],\n [\n -114.3896484375,\n 47.249406957888446\n ],\n [\n -108.984375,\n 47.931066347509756\n ],\n [\n -107.75390625,\n 48.268569112964336\n ],\n [\n -105.64453125,\n 48.28319289548347\n ],\n [\n -102.1728515625,\n 46.830133640447386\n ],\n [\n -101.3818359375,\n 43.739352079154706\n ],\n [\n -99.66796875,\n 42.87596410238256\n ],\n [\n -98.5693359375,\n 40.83043687764923\n ],\n [\n -96.9873046875,\n 38.99357205820944\n ],\n [\n -94.76806640625,\n 38.27268853598095\n ],\n [\n -92.2412109375,\n 38.5825261593533\n ],\n [\n -90,\n 38.41055825094609\n ],\n [\n -86.39648437499999,\n 38.65119833229951\n ],\n [\n -82.705078125,\n 39.36827914916011\n ],\n [\n -82.001953125,\n 40.54720023441049\n ],\n [\n -81.07910156249999,\n 41.11246878918086\n ],\n [\n -80.2001953125,\n 41.40977583200954\n ],\n [\n -77.87109375,\n 41.343824581185686\n ],\n [\n -74.7509765625,\n 40.68063802521456\n ],\n [\n -73.5205078125,\n 41.07935114946896\n ],\n [\n -71.1474609375,\n 41.54147766679026\n ],\n [\n -69.9169921875,\n 41.83682786072712\n ],\n [\n -70.9716796875,\n 42.35854391749705\n ],\n [\n -70.8837890625,\n 43.67581809328341\n ],\n [\n -67.03857421875,\n 44.88701247981298\n ],\n [\n -67.82958984375,\n 45.75219336063106\n ],\n [\n -68.4228515625,\n 47.398349200359235\n ],\n [\n -69.4775390625,\n 47.487513008956554\n ],\n [\n -70.9716796875,\n 45.38301927899063\n ],\n [\n -75.322265625,\n 45.10454630976873\n ],\n [\n -76.4208984375,\n 44.18220395771563\n ],\n [\n -76.376953125,\n 43.64402584769947\n ],\n [\n -79.453125,\n 43.389081939117496\n ],\n [\n -79.0576171875,\n 42.84375132629018\n ],\n [\n -83.4521484375,\n 42.00032514831621\n ],\n [\n -82.265625,\n 44.3709869629717\n ],\n [\n -84.6826171875,\n 46.649436163350245\n ],\n [\n -88.1982421875,\n 47.487513008956554\n ],\n [\n -91.9775390625,\n 46.89023157359399\n ],\n [\n -91.318359375,\n 48.166085419012504\n ],\n [\n -94.6142578125,\n 48.777912755501845\n ],\n [\n -95.3173828125,\n 49.468124067331615\n ],\n [\n -95.44921875,\n 49.03786794532641\n ],\n [\n -123.3544921875,\n 49.15296965617039\n ]\n ]\n ]\n }\n }\n ]\n}","volume":"588","noUsgsAuthors":false,"publicationStatus":"PW","contributors":{"authors":[{"text":"Solder, John E. 0000-0002-0660-3326","orcid":"https://orcid.org/0000-0002-0660-3326","contributorId":201953,"corporation":false,"usgs":true,"family":"Solder","given":"John","email":"","middleInitial":"E.","affiliations":[{"id":610,"text":"Utah Water Science Center","active":true,"usgs":true}],"preferred":true,"id":792356,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Jurgens, Bryant 0000-0002-1572-113X","orcid":"https://orcid.org/0000-0002-1572-113X","contributorId":203430,"corporation":false,"usgs":true,"family":"Jurgens","given":"Bryant","affiliations":[{"id":154,"text":"California Water Science Center","active":true,"usgs":true}],"preferred":true,"id":792357,"contributorType":{"id":1,"text":"Authors"},"rank":2}]}} ,{"id":70208763,"text":"70208763 - 2020 - Increased prespawning mortality threatens an integrated natural- and hatchery-origin sockeye salmon population in the Lake Washington Basin","interactions":[],"lastModifiedDate":"2020-03-02T06:23:12","indexId":"70208763","displayToPublicDate":"2020-02-27T06:44:44","publicationYear":"2020","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":1661,"text":"Fisheries Research","active":true,"publicationSubtype":{"id":10}},"title":"Increased prespawning mortality threatens an integrated natural- and hatchery-origin sockeye salmon population in the Lake Washington Basin","docAbstract":"The life cycle of diadromous fishes such as salmonids involves natural mortality in a series of distinct life history stages, occurring sequentially in different habitats. Decades of research have emphasized mortality at the embryo, juvenile, and sub-adult stages but it is increasingly clear that some adults that survive and return to freshwater habitats die during the final homeward migration or after they reach the spawning grounds, prior to breeding. These are termed “en route” and “prespawning” mortality, respectively, and can threaten populations depleted by mortality at previous stages. In this study, we present evidence that the sockeye salmon, Oncorhynchus nerka, population that returns to the Lake Washington Basin, in Washington State, USA, is experiencing both forms of adult mortality. Counts of the salmon entering the basin on their return migration in June and July were compared to counts in the major spawning grounds in September through November for 1995–2018. The disparity has increased markedly in recent years. The counts on the spawning grounds have decreased as a proportion of the number entering the system with an average 49 % of sockeye unaccounted for, consistent with increased en route mortality. In addition, prespawning mortality rates have increased in salmon that reach the Cedar River, the main spawning tributary, both at a hatchery holding adult fish in 1995–2018, and in the naturally spawning populations when monitored in the last five years. Hatchery records indicated <10 % prespawning mortality for 1995–2010, increasing to an average 43 % for 2014 – 2018. Recent carcass surveys in the Cedar River documented that 33.6% (2014), 22.3% (2015), 30.3% (2016) and 50.0% (2018) of female sockeye died before completing spawning. These recent increases in prespawning mortality have been associated with warm water during entry to freshwater, but comparably warm water in past decades had no such effect. Steady warming of river temperatures around the median run completion date from < 8.0 °C to > 13.0 °C was correlated with increased prespawning mortality rates at the hatchery from 1995–2018. We conclude that warming conditions during migration and spawning, in concert with other factors such as infections with pathogens, are responsible for the increased prespawning mortality of adult sockeye salmon that are high enough to threaten the population’s viability.","language":"English","publisher":"Elsevier","doi":"10.1016/j.fishres.2020.105527","usgsCitation":"Barnett, H.K., Quinn, T.P., Bhuthimethee, M., and Winton, J., 2020, Increased prespawning mortality threatens an integrated natural- and hatchery-origin sockeye salmon population in the Lake Washington Basin: Fisheries Research, v. 227, 105527, 10 p., https://doi.org/10.1016/j.fishres.2020.105527.","productDescription":"105527, 10 p.","ipdsId":"IP-115029","costCenters":[{"id":654,"text":"Western Fisheries Research Center","active":true,"usgs":true}],"links":[{"id":372723,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","state":"Washington","otherGeospatial":"Lake Washington Basin","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -122.29980468749999,\n 47.49772004565105\n ],\n [\n -122.18238830566406,\n 47.49772004565105\n ],\n [\n -122.18238830566406,\n 47.758714187846294\n ],\n [\n -122.29980468749999,\n 47.758714187846294\n ],\n [\n -122.29980468749999,\n 47.49772004565105\n ]\n ]\n ]\n }\n }\n ]\n}","volume":"227","publishingServiceCenter":{"id":12,"text":"Tacoma PSC"},"noUsgsAuthors":false,"publicationStatus":"PW","contributors":{"authors":[{"text":"Barnett, Heidy K","contributorId":222835,"corporation":false,"usgs":false,"family":"Barnett","given":"Heidy","email":"","middleInitial":"K","affiliations":[{"id":40608,"text":"West Fork Environmental, Tumwater, WA","active":true,"usgs":false}],"preferred":false,"id":783313,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Quinn, Thomas P.","contributorId":167272,"corporation":false,"usgs":false,"family":"Quinn","given":"Thomas","email":"","middleInitial":"P.","affiliations":[{"id":24671,"text":"School of Aquatic and Fsiery Sciences, UW, Box 355020, Seattle, WA","active":true,"usgs":false}],"preferred":false,"id":783314,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Bhuthimethee, Mary","contributorId":222836,"corporation":false,"usgs":false,"family":"Bhuthimethee","given":"Mary","email":"","affiliations":[{"id":40609,"text":"Seattle Public Utilities, Seattle, WA","active":true,"usgs":false}],"preferred":false,"id":783315,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Winton, James 0000-0002-3505-5509 jwinton@usgs.gov","orcid":"https://orcid.org/0000-0002-3505-5509","contributorId":179330,"corporation":false,"usgs":true,"family":"Winton","given":"James","email":"jwinton@usgs.gov","affiliations":[{"id":654,"text":"Western Fisheries Research Center","active":true,"usgs":true}],"preferred":true,"id":783316,"contributorType":{"id":1,"text":"Authors"},"rank":4}]}} ,{"id":70209424,"text":"70209424 - 2020 - Geology of the Trout Rock caves (Hamilton Cave, Trout Cave, New Trout Cave) in Pendleton County, West Virginia (USA), and implications regarding the origin of maze caves","interactions":[],"lastModifiedDate":"2020-04-09T17:51:25.200543","indexId":"70209424","displayToPublicDate":"2020-02-26T12:26:03","publicationYear":"2020","noYear":false,"publicationType":{"id":5,"text":"Book chapter"},"publicationSubtype":{"id":24,"text":"Book Chapter"},"title":"Geology of the Trout Rock caves (Hamilton Cave, Trout Cave, New Trout Cave) in Pendleton County, West Virginia (USA), and implications regarding the origin of maze caves","docAbstract":"The Trout Rock caves (Hamilton Cave, Trout Cave, New Trout Cave) are located in a hill named Cave Knob that overlooks the South Branch of the Potomac River in Pendleton County, West Virginia (U.S.A). The geologic structure of this hill is a northeasttrending anticline, and the caves are located at different elevations primarily along the contact between the Devonian New Creek Limestone (Helderberg Group) and the overlying Devonian Corriganville Limestone (Helderberg Group). The entrance to New Trout Cave (Stop 1) is located on the east flank of Cave Knob anticline at an elevation of 585 m (1,920 ft) relative to sea level, or 39 m (128 ft) above the modern river. Much of the cave consists of passages that extend to the northeast along strike, and many of these passages have developed along primary joints that trend N40E or secondary joints that trend N40W. Sediments in New Trout Cave include mud and sand (some of which was mined for nitrate during the American Civil War), as well as large boulders in the front part of the cave. Gypsum crusts are present in a maze section of the cave ~213 to 305 m (700 to 1,000 ft) from the cave entrance. Excavations in New Trout Cave have produced vertebrate fossils of Rancholabrean age, ~300,000 to 10,000 years Before Present (BP). The entrance to Trout Cave (Stop 2) is located on the east flank of Cave Knob anticline ~100 m (328 ft) northwest of the New Trout Cave entrance at an elevation of 622 m (2,040 ft) relative to sea level, or 76 m (249 ft) above the modern river. Much of the cave consists of passages that extend to the northeast along strike, although a small area of network maze passages is present in the western portion of Trout Cave that is closest to Hamilton Cave. Many of the passages of Trout Cave have developed along primary joints that trend N40E or secondary joints that trend N40W. Sediments in Trout Cave include mud (also mined for nitrate during the American Civil War), as well as large boulders in the front part of the cave. Excavations in the upper levels of Trout Cave have produced vertebrate fossils of Rancholabrean age (~300,000 to 10,000 years BP), whereas excavations in the lower levels of the cave have produced vertebrate fossils of Irvingtonian age (~1,810,000 to 300,000 years BP). The entrance to Hamilton Cave (Stop 3) is located along the axis of Cave Knob anticline ~165 m (540 ft) northwest of the Trout Cave entrance at an elevation of 640 m (2,100 ft) relative to sea level, or 94 m (308 ft) above the modern river. The front (upper) part of Hamilton Cave has a classic network maze pattern that is an angular grid of relatively horizontal passages, most of which follow vertical or near-vertical primary joints that trend N40W and N50W and secondary joints that trend N60W and N80E. This part of the cave lies along the axis of Cave Knob anticline. In contrast, the passages in the back (lower) part of Hamilton Cave lie along the west flank of Cave Knob anticline at ~58 to 85 m (190 to 279 ft) above the modern river. These passages do not display a classic maze pattern, and instead they may be divided into the following two categories: (1) longer northeast-trending passages that are relatively horizontal and follow the strike of the beds; and (2) shorter northwest-trending passages that descend steeply to the west and follow the dip of the beds. Sediments in Hamilton Cave include mud (which was apparently not mined for nitrate during the American Civil War), as well as large boulders in the front part of the cave. Gypsum crusts are present along passage walls of the New Creek Limestone from the Slab Room to the Airblower. Excavations in the front part of Hamilton Cave (maze section) have produced vertebrate fossils of Irvingtonian age (~1,810,000 to 300,000 years BP). The network maze portions of Hamilton Cave are interpreted as having developed at or near the water table where water did not have a free surface in contact with air and where the following conditions were present: (1) Location on or near the axis of an anticline (the location of the greatest amount of flexure); (2) Abundant vertical or near vertical joints, which are favored by location in the area of greatest flexure and by a lithologic unit (chert-rich limestone) that is more likely to experience brittle rather than ductile deformation; (3) Widening of joints to enhance ease of water infiltration, favored by location in area of greatest amount of flexure; and (4) Dissolution along nearly all major joints to produce cave passages of approximately the same size (which would most likely occur via water without a free surface in contact with air). The cave passages that are located along anticline axes and along strike at the New Creek-Corriganville contact are interpreted as having formed initially during times of base level stillstand at or near the water table where water did not have a free surface in contact with air and where the water flowed along the hydraulic gradient at gentle slopes. Under such conditions, dissolution occurred in all directions to produce cave passages with relatively linear wall morphologies. In the lower portions of some of the along-strike passages, the cave walls have a more sinuous (meandering) morphology, which is interpreted as having formed during subsequent initial base level fall as cave development continued under vadose conditions where the water had a free surface in contact with air, and where water flow was governed primarily by gravitational processes. Steeply inclined cave passages that are located along dip at the New Creek-Corriganville contact are interpreted as having formed during subsequent true vadose conditions (after base level fall). This chronology of base level stasis (with cave development in the phreatic zone a short distance below top of water table) followed by base level fall (with cave development in the vadose or epiphreatic zone) has repeated multiple times at Cave Knob during the past ~4 to 3 million years, resulting in multiple cave passages at different elevations, with different passage morphologies, and at different passage locations with respect to strike and dip.
","largerWorkType":{"id":4,"text":"Book"},"largerWorkTitle":"Geological Society of America Field Guide","largerWorkSubtype":{"id":15,"text":"Monograph"},"language":"English","publisher":"Geological Society of America","doi":"10.1130/2020.0057(03)","collaboration":"","usgsCitation":"Swezey, C.S., and Brent, E.L., 2020, Geology of the Trout Rock caves (Hamilton Cave, Trout Cave, New Trout Cave) in Pendleton County, West Virginia (USA), and implications regarding the origin of maze caves, chap. of Geological Society of America Field Guide, v. 57, p. 43-77, https://doi.org/10.1130/2020.0057(03).","productDescription":"35 p.","startPage":"43","endPage":"77","ipdsId":"IP-113405","costCenters":[{"id":40020,"text":"Florence Bascom Geoscience Center","active":true,"usgs":true}],"links":[{"id":457583,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.1130/2020.0057(03)","text":"Publisher Index Page"},{"id":373863,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","state":"West Virginia","county":"Pendleton County","otherGeospatial":"Trout Rock Caves","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -79.06036376953125,\n 38.768004230175954\n ],\n [\n -79.37759399414062,\n 38.975424875431436\n ],\n [\n -79.4586181640625,\n 38.932707274379595\n ],\n [\n -79.53826904296875,\n 38.839707613545144\n ],\n [\n -79.66323852539062,\n 38.59970036588819\n ],\n [\n -79.53414916992186,\n 38.543869175876154\n ],\n [\n -79.47509765625,\n 38.460041065720446\n ],\n [\n -79.33364868164062,\n 38.415938460513274\n ],\n [\n -79.27322387695312,\n 38.41486245064945\n ],\n [\n -79.20867919921875,\n 38.50304202775689\n ],\n [\n -79.21005249023438,\n 38.515937313413474\n ],\n [\n -79.12216186523438,\n 38.66299474019031\n ],\n [\n -79.1015625,\n 38.659777730712534\n ],\n [\n -79.08233642578124,\n 38.6897975322717\n ],\n [\n -79.06036376953125,\n 38.768004230175954\n ]\n ]\n ]\n }\n }\n ]\n}","volume":"57","noUsgsAuthors":false,"publicationStatus":"PW","contributors":{"authors":[{"text":"Swezey, Christopher S. 0000-0003-4019-9264 cswezey@usgs.gov","orcid":"https://orcid.org/0000-0003-4019-9264","contributorId":173033,"corporation":false,"usgs":true,"family":"Swezey","given":"Christopher","email":"cswezey@usgs.gov","middleInitial":"S.","affiliations":[{"id":40020,"text":"Florence Bascom Geoscience Center","active":true,"usgs":true},{"id":243,"text":"Eastern Geology and Paleoclimate Science Center","active":true,"usgs":true},{"id":241,"text":"Eastern Energy Resources Science Center","active":true,"usgs":true}],"preferred":true,"id":786454,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Brent, Emily L","contributorId":223860,"corporation":false,"usgs":false,"family":"Brent","given":"Emily","email":"","middleInitial":"L","affiliations":[],"preferred":false,"id":786455,"contributorType":{"id":1,"text":"Authors"},"rank":2}]}} ,{"id":70211287,"text":"70211287 - 2020 - The role of Northeast Pacific meltwater events in deglacial climate change","interactions":[],"lastModifiedDate":"2020-07-22T15:13:57.928397","indexId":"70211287","displayToPublicDate":"2020-02-26T10:11:20","publicationYear":"2020","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":5010,"text":"Science Advances","active":true,"publicationSubtype":{"id":10}},"title":"The role of Northeast Pacific meltwater events in deglacial climate change","docAbstract":"Columbia River megafloods occurred repeatedly during the last deglaciation, but the impacts of this fresh water on Pacific hydrography are largely unknown. To reconstruct changes in ocean circulation during this period, we used a numerical model to simulate the flow trajectory of Columbia River megafloods and compiled records of sea surface temperature, paleo-salinity, and deep-water radiocarbon from marine sediment cores in the Northeast Pacific. The North Pacific sea surface cooled and freshened during the early deglacial (19.0-16.5 ka) and Younger Dryas (12.9-11.7 ka) intervals, coincident with the appearance of subsurface water masses depleted in radiocarbon relative to the sea surface. We infer that Pacific meltwater fluxes contributed to net Northern Hemisphere cooling prior to North Atlantic Heinrich Events, and again during the Younger Dryas stadial. Abrupt warming in the Northeast Pacific similarly contributed to hemispheric warming during the Bølling and Holocene transitions. These findings underscore the importance of changes in North Pacific freshwater fluxes and circulation in deglacial climate events.","language":"English","publisher":"AAAS","doi":"10.1126/sciadv.aay2915","usgsCitation":"Praetorius, S.K., Condron, A., Mix, A., Walczak, M., McKay, J., and Du, J., 2020, The role of Northeast Pacific meltwater events in deglacial climate change: Science Advances, v. 6, no. 9, eaay2915, 18 p., https://doi.org/10.1126/sciadv.aay2915.","productDescription":"eaay2915, 18 p.","ipdsId":"IP-093675","costCenters":[{"id":312,"text":"Geology, Minerals, Energy, and Geophysics Science Center","active":true,"usgs":true},{"id":678,"text":"Woods Hole Coastal and Marine Science Center","active":true,"usgs":true}],"links":[{"id":457590,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.1126/sciadv.aay2915","text":"Publisher Index Page"},{"id":376636,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"volume":"6","issue":"9","noUsgsAuthors":false,"publicationStatus":"PW","contributors":{"authors":[{"text":"Praetorius, Summer K. 0000-0003-2683-3652","orcid":"https://orcid.org/0000-0003-2683-3652","contributorId":206966,"corporation":false,"usgs":true,"family":"Praetorius","given":"Summer","email":"","middleInitial":"K.","affiliations":[{"id":312,"text":"Geology, Minerals, Energy, and Geophysics Science Center","active":true,"usgs":true}],"preferred":true,"id":793519,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Condron, Alan 0000-0002-7337-1713","orcid":"https://orcid.org/0000-0002-7337-1713","contributorId":229547,"corporation":false,"usgs":false,"family":"Condron","given":"Alan","email":"","affiliations":[{"id":36711,"text":"Woods Hole Oceanographic Institution","active":true,"usgs":false}],"preferred":false,"id":793520,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Mix, Alan","contributorId":184163,"corporation":false,"usgs":false,"family":"Mix","given":"Alan","affiliations":[],"preferred":false,"id":793521,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Walczak, Maureen 0000-0002-4123-6998","orcid":"https://orcid.org/0000-0002-4123-6998","contributorId":206972,"corporation":false,"usgs":false,"family":"Walczak","given":"Maureen","email":"","affiliations":[{"id":6680,"text":"Oregon State University","active":true,"usgs":false}],"preferred":false,"id":793522,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"McKay, Jennifer","contributorId":229548,"corporation":false,"usgs":false,"family":"McKay","given":"Jennifer","affiliations":[{"id":6680,"text":"Oregon State University","active":true,"usgs":false}],"preferred":false,"id":793523,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"Du, Jianghui 0000-0002-3386-9314","orcid":"https://orcid.org/0000-0002-3386-9314","contributorId":206970,"corporation":false,"usgs":false,"family":"Du","given":"Jianghui","email":"","affiliations":[{"id":6680,"text":"Oregon State University","active":true,"usgs":false}],"preferred":false,"id":793524,"contributorType":{"id":1,"text":"Authors"},"rank":6}]}} ,{"id":70223337,"text":"70223337 - 2020 - Trends in cheetah Acinonyx jubatus density in north-central Namibia","interactions":[],"lastModifiedDate":"2021-08-24T13:13:40.57478","indexId":"70223337","displayToPublicDate":"2020-02-26T08:09:04","publicationYear":"2020","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":3103,"text":"Population Ecology","active":true,"publicationSubtype":{"id":10}},"displayTitle":"Trends in cheetah Acinonyx jubatus density in north-central Namibia","title":"Trends in cheetah Acinonyx jubatus density in north-central Namibia","docAbstract":"Assessing trends in abundance and density of species of conservation concern is vital to inform conservation and management strategies. The remaining population of the cheetah (Acinonyx jubatus) largely exists outside of protected areas, where they are often in conflict with humans. Despite this, the population status and dynamics of cheetah outside of protected areas have received relatively limited attention across its range. We analyzed remote camera trapping data of nine surveys conducted from 2005 to 2014 in the Waterberg Conservancy, north-central Namibia, which included detections of 74 individuals (52 adult males, 7 adult females and 15 dependents). Using spatial capture–recapture methods, we assessed annual and seasonal trends in cheetah density. We found evidence of a stable trend in cheetah density over the study period, with an average density of 1.94/100 km2 (95% confidence interval 1.33–2.84). This apparent stability of cheetah density is likely the result of stable and abundant prey availability, a high tolerance to carnivores by farmers and low turnover rates in home range tenure. This study highlights the importance of promoting long-term surveys that capture a broad range of environmental variation that may influence species density and the importance of nonprotected areas for cheetah conservation.
In 2015, the total amount of water withdrawn in Florida was estimated to be 15,319 million gallons per day (Mgal/d). Saline water accounted for 9,598 Mgal/d (63 percent) and freshwater accounted for 5,721 Mgal/d (37 percent) of the total. Groundwater accounted for 3,604 Mgal/d (63 percent) of freshwater withdrawals and surface water accounted for the remaining 2,117 Mgal/d (37 percent). Surface-water sources accounted for 9,401 Mgal/d (98 percent) of the saline-water withdrawals, and groundwater sources accounted for the remaining 198 Mgal/d (2 percent). The majority of groundwater withdrawals (almost 62 percent) in 2015 were from the Floridan aquifer system, which is used throughout most of the State while the majority of fresh surface-water withdrawals (52 percent) occurred in the Southern Florida Subregion, a hydrologic unit that includes Lake Okeechobee and canals in the Everglades Agricultural Area. Groundwater provided drinking water (public supplied and self-supplied) for 18.324 million people (92 percent of Florida’s population), and fresh surface water provided drinking water for 1.491 million people (8 percent).
Overall, public supply accounted for 39 percent of the total freshwater withdrawals (ground and surface) and 53 percent of groundwater withdrawals, followed by agricultural self-supplied uses, which accounted for 37 percent of the total freshwater withdrawals and 28 percent of groundwater withdrawals. Other self-supplied groundwater withdrawals include commercial-industrial-mining self-supplied (8 percent), recreational-landscape irrigation and domestic self-supplied (5 percent each), and power generation (less than 1 percent). Agricultural self-supplied withdrawals accounted for 51 percent of fresh surface-water withdrawals, followed by power generation (19 percent), public supply (15 percent), recreational-landscape irrigation (10 percent), and commercial-industrial-mining self-supplied (5 percent).
In 1975, agricultural water withdrawals accounted for 43 percent of the total freshwater withdrawals, followed by power generation (24 percent) and public supply (17 percent). By 2000, agricultural withdrawals increased to 48 percent of the total freshwater withdrawals, followed by public supply (30 percent). For 2015, agricultural self-supplied decreased to 37 percent of total freshwater withdrawals, and was surpassed by public supply at 39 percent. Over the 40-year period between 1975 and 2015, increases in freshwater withdrawals caused by large gains in population and the expansion of irrigated acreage were offset by decreases in water used for power generation and commercial-industrial-mining withdrawals. Since 2000, however, irrigated acreage has decreased statewide because of crop disease, storm damage, and urbanization. This decline, coupled with large gains in water conservation measures in the farming industry, has led to agricultural withdrawals in Florida being less than public-supply withdrawals for the first time since water-use data were first reported in 1965.
","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/sir20195147","collaboration":"Prepared in cooperation with the Florida Department of Agricultural and Consumer Services","usgsCitation":"Marella, R.L., 2020, Water withdrawals, uses, and trends in Florida, 2015: U.S. Geological Survey Scientific Investigations Report 2019–5147, 52 p., https://doi.org/10.3133/sir20195147.","productDescription":"Report: vii, 52 p.; Data Release","numberOfPages":"64","onlineOnly":"Y","ipdsId":"IP-093230","costCenters":[{"id":27821,"text":"Caribbean-Florida Water Science Center","active":true,"usgs":true}],"links":[{"id":372545,"rank":2,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/sir/2019/5147/sir20195147.pdf","text":"Report","size":"2.34 MB","linkFileType":{"id":1,"text":"pdf"},"description":"SIR 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\"}}]}","contact":"Director, Caribbean-Florida Water Science Center
U.S. Geological Survey
4446 Pet Lane, Suite 108
Lutz, FL 33559
The U.S. Geological Survey’s National Water-Quality Assessment (NAWQA) Project assessed the quality of groundwater in aquifers that are important sources of drinking water in the United States. One major aquifer in Texas that was assessed by NAWQA in 2013 is the coastal lowlands aquifer system, which is often referred to in Texas as the “Gulf Coast aquifer system.” The coastal lowlands aquifer system supplies water for millions of people; self-supplied (private) well withdrawals in 2005 from this aquifer system were the sixth largest among all major aquifer systems in the Nation. A major aquifer in Texas that was assessed by NAWQA in 2015 is the Texas coastal uplands aquifer system; the Carrizo-Wilcox aquifer is one of several aquifers that compose this aquifer system in Texas. The rocks composing the Texas coastal uplands aquifer system extend east from Texas as part of the Mississippi embayment aquifer system and underlie areas of several States. The Texas coastal uplands aquifer system and Mississippi embayment aquifer system are often collectively referred to as the “Mississippi embayment-Texas coastal uplands aquifer system.” Self-supplied withdrawals from the Mississippi embayment-Texas coastal uplands aquifer system in 2005 were the eighth largest among all major aquifer systems in the Nation. The coastal lowlands aquifer system and Mississippi embayment-Texas coastal uplands aquifer system were assessed as part of the NAWQA Principal Aquifer Surveys (PAS), which were designed to evaluate constituent concentrations in water samples obtained from domestic and public-supply wells prior to any treatment. PAS assessments like these allow for the comparison of water-quality concentrations in untreated groundwater using preestablished benchmarks for the protection of human health and for aesthetic qualities such as taste, color, and odor. The use of preestablished benchmarks can provide a basis for comparison of groundwater quality among principal aquifers.
","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/fs20203009","collaboration":"U.S. Geological Survey National Water-Quality Assessment","usgsCitation":"Ging, P.B., 2020, Water-quality comparison of the Gulf Coast aquifer system and Carrizo-Wilcox aquifer in Texas from National Water-Quality Assessment Project Principal Aquifer Surveys, 2013 and 2015: U.S. Geological Survey Fact Sheet 2020–3009, 4 p., https://doi.org/10.3133/fs20203009.","productDescription":"4 p.","numberOfPages":"4","onlineOnly":"N","ipdsId":"IP-111986","costCenters":[{"id":583,"text":"Texas Water Science Center","active":true,"usgs":true}],"links":[{"id":399199,"rank":3,"type":{"id":36,"text":"NGMDB Index Page"},"url":"https://ngmdb.usgs.gov/Prodesc/proddesc_109727.htm"},{"id":372560,"rank":2,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/fs/2020/3009/fs20203009.pdf","text":"Report","size":"1.01 MB","linkFileType":{"id":1,"text":"pdf"},"description":"FS 20220–3009"},{"id":372559,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/fs/2020/3009/coverthb.jpg"}],"country":"United States","state":"Texas","otherGeospatial":"Gulf Coast aquifer system, Carrizo-Wilcox aquifer","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -100.5,\n 25.8378\n ],\n [\n -93.5069,\n 25.8378\n ],\n [\n -93.5069,\n 33.5433\n ],\n [\n -100.5,\n 33.5433\n ],\n [\n -100.5,\n 25.8378\n ]\n ]\n ]\n }\n }\n ]\n}","contact":"Director, Oklahoma-Texas Water Science Center
U.S. Geological Survey
1505 Ferguson Lane
Austin, TX 78754–4501
Phytoplankton play key roles in the oceans by regulating global biogeochemical cycles and production in marine food webs. Global warming is thought to affect phytoplankton production both directly, by impacting their photosynthetic metabolism, and indirectly by modifying the physical environment in which they grow. In this respect, the Bermuda Atlantic Time-series Study (BATS) in the Sargasso Sea (North Atlantic gyre) provides a unique opportunity to explore effects of warming on phytoplankton production across the vast oligotrophic ocean regions because it is one of the few multidecadal records of measured net primary productivity (NPP). We analysed the time series of phytoplankton primary productivity at BATS site using machine learning techniques (ML) to show that increased water temperature over a 27-year period (1990–2016), and the consequent weakening of vertical mixing in the upper ocean, induced a negative feedback on phytoplankton productivity by reducing the availability of essential resources, nitrogen and light. The unbalanced availability of these resources with warming, coupled with ecological changes at the community level, is expected to intensify the oligotrophic state of open-ocean regions that are far from land-based nutrient sources.
","language":"English","publisher":"Nature Publications","doi":"10.1038/s41598-020-59989-y","usgsCitation":"D’Alelio, D., Rampone, S., Cusano, L.M., Morfino, V., Russo, L., Sanseverino, N., Cloern, J.E., and Lomas, M.W., 2020, Machine learning identifies a strong association between warming and reduced primary productivity in an oligotrophic ocean gyre: Scientific Reports, v. 10, 3287, 12 p., https://doi.org/10.1038/s41598-020-59989-y.","productDescription":"3287, 12 p.","ipdsId":"IP-111898","costCenters":[{"id":37277,"text":"WMA - Earth System Processes Division","active":true,"usgs":true}],"links":[{"id":457603,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.1038/s41598-020-59989-y","text":"Publisher Index Page"},{"id":387061,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"otherGeospatial":"North Atlantic Gyre","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -64.3359375,\n 23.885837699862005\n ],\n [\n -38.84765625,\n 28.613459424004414\n ],\n [\n -19.51171875,\n 34.016241889667015\n ],\n [\n -17.75390625,\n 41.11246878918088\n ],\n [\n -26.3671875,\n 47.754097979680026\n ],\n [\n -41.66015625,\n 46.6795944656402\n ],\n [\n -61.17187499999999,\n 39.639537564366684\n ],\n [\n -69.78515625,\n 35.31736632923788\n ],\n [\n -76.9921875,\n 31.203404950917395\n ],\n [\n -75.41015624999999,\n 26.902476886279832\n ],\n [\n -71.54296874999999,\n 23.563987128451217\n ],\n [\n -64.3359375,\n 23.885837699862005\n ]\n ]\n ]\n }\n }\n ]\n}","volume":"10","noUsgsAuthors":false,"publicationDate":"2020-02-25","publicationStatus":"PW","contributors":{"authors":[{"text":"D’Alelio, Domenico","contributorId":260813,"corporation":false,"usgs":false,"family":"D’Alelio","given":"Domenico","email":"","affiliations":[{"id":27945,"text":"Stazione Zoologica Anton Dohrn","active":true,"usgs":false}],"preferred":false,"id":818883,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Rampone, Salvatore","contributorId":260814,"corporation":false,"usgs":false,"family":"Rampone","given":"Salvatore","email":"","affiliations":[{"id":52676,"text":"Università degli Studi del Sannio","active":true,"usgs":false}],"preferred":false,"id":818884,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Cusano, Luigi Maria","contributorId":260815,"corporation":false,"usgs":false,"family":"Cusano","given":"Luigi","email":"","middleInitial":"Maria","affiliations":[{"id":52676,"text":"Università degli Studi del Sannio","active":true,"usgs":false}],"preferred":false,"id":818885,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Morfino, Valerio","contributorId":260816,"corporation":false,"usgs":false,"family":"Morfino","given":"Valerio","email":"","affiliations":[{"id":52676,"text":"Università degli Studi del Sannio","active":true,"usgs":false}],"preferred":false,"id":818886,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Russo, Luca","contributorId":260817,"corporation":false,"usgs":false,"family":"Russo","given":"Luca","email":"","affiliations":[{"id":27945,"text":"Stazione Zoologica Anton Dohrn","active":true,"usgs":false}],"preferred":false,"id":818887,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"Sanseverino, Nadia","contributorId":260818,"corporation":false,"usgs":false,"family":"Sanseverino","given":"Nadia","email":"","affiliations":[{"id":52676,"text":"Università degli Studi del Sannio","active":true,"usgs":false}],"preferred":false,"id":818888,"contributorType":{"id":1,"text":"Authors"},"rank":6},{"text":"Cloern, James E. 0000-0002-5880-6862 jecloern@usgs.gov","orcid":"https://orcid.org/0000-0002-5880-6862","contributorId":1488,"corporation":false,"usgs":true,"family":"Cloern","given":"James","email":"jecloern@usgs.gov","middleInitial":"E.","affiliations":[{"id":438,"text":"National Research Program - 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Artificial containers and wetspots provide major sources of mosquito larval habitat in residential areas. Mosquito abatement and control strategies remain the most effective public health interventions for minimizing the impact of these vector borne diseases. Understanding how water insecurity is conducive to the establishment and elimination of endemic mosquito populations, particularly in arid or semi-arid regions, is a vital component in shaping these intervention strategies.","language":"English","publisher":"AGU","doi":"10.1029/2019gh000201","usgsCitation":"Akanda, A.S., Johnson, K.D., Ginsberg, H., and Couret, J., 2020, Prioritizing water security in the management of vector borne diseases: Lessons from Oaxaca, Mexico: Geohealth News, v. 4, no. 3, e2019GH000201, 5 p., https://doi.org/10.1029/2019gh000201.","productDescription":"e2019GH000201, 5 p.","ipdsId":"IP-112940","costCenters":[{"id":531,"text":"Patuxent Wildlife Research Center","active":true,"usgs":true}],"links":[{"id":457606,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.1029/2019gh000201","text":"Publisher Index Page"},{"id":373277,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"Mexico","city":"Oaxaca","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -97.18505859374999,\n 16.74142754700361\n ],\n [\n -95.877685546875,\n 16.74142754700361\n ],\n [\n -95.877685546875,\n 17.403062993328923\n ],\n [\n -97.18505859374999,\n 17.403062993328923\n ],\n [\n -97.18505859374999,\n 16.74142754700361\n ]\n ]\n ]\n }\n }\n ]\n}","volume":"4","issue":"3","publishingServiceCenter":{"id":10,"text":"Baltimore PSC"},"noUsgsAuthors":false,"publicationDate":"2020-03-13","publicationStatus":"PW","contributors":{"authors":[{"text":"Akanda, Ali S","contributorId":223365,"corporation":false,"usgs":false,"family":"Akanda","given":"Ali","email":"","middleInitial":"S","affiliations":[{"id":6922,"text":"University of Rhode Island","active":true,"usgs":false}],"preferred":false,"id":784850,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Johnson, Kristine D.","contributorId":168716,"corporation":false,"usgs":false,"family":"Johnson","given":"Kristine","email":"","middleInitial":"D.","affiliations":[],"preferred":false,"id":784851,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Ginsberg, Howard S. 0000-0002-4933-2466 hginsberg@usgs.gov","orcid":"https://orcid.org/0000-0002-4933-2466","contributorId":147665,"corporation":false,"usgs":true,"family":"Ginsberg","given":"Howard S.","email":"hginsberg@usgs.gov","affiliations":[{"id":531,"text":"Patuxent Wildlife Research Center","active":true,"usgs":true}],"preferred":true,"id":784849,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Couret, Janelle","contributorId":194159,"corporation":false,"usgs":false,"family":"Couret","given":"Janelle","affiliations":[],"preferred":false,"id":784852,"contributorType":{"id":1,"text":"Authors"},"rank":4}]}} ]}