In 2013 to 2015, 833 public supply wells in 15 Principal aquifers in the U.S. were sampled to identify which aquifers contained high methane concentrations (>1 mg/L) and determine the geologic, hydrologic, and geochemical conditions associated with high concentrations. This study represents the first national assessment of methane in aquifers used for public supply in the U.S. and, as such, advances the understanding of the occurrence and distribution of methane in groundwater nationally. Methane concentrations >1 and > 10 mg/L occurred in 6.7 and 1.1% of the samples, respectively. Most high concentrations occurred in aquifers in the Atlantic and Gulf Coastal Plain regions and upper Midwest. High methane concentrations were most commonly associated with Tertiary and younger aquifer sediments, old groundwater (>60 years), and concentrations of oxygen, nitrate-N, and sulfate <0.5 mg/L. Concentrations of methane were also positively correlated (p < 0.05) with dissolved organic carbon and ammonium. Case studies in Florida, Texas, and Iowa were used to explore how regional context from this data set could aid our understanding of local occurrences of methane in groundwater. Regional data for methane, Br/Cl ratios, sulfate, and other parameters helped identify mixing processes involving end members such as wastewater effluent-impacted groundwater, saline formation water, and pore water in glacial till that contributed methane to groundwater in some cases and supported methane oxidation in others.
","language":"English","publisher":"Elsevier","doi":"10.1016/j.apgeochem.2017.07.014","usgsCitation":"McMahon, P.B., Belitz, K., Barlow, J.R., and Jurgens, B.C., 2017, Methane in aquifers used for public supply in the United States: Applied Geochemistry, v. 84, p. 337-347, https://doi.org/10.1016/j.apgeochem.2017.07.014.","productDescription":"11 p.","startPage":"337","endPage":"347","ipdsId":"IP-078350","costCenters":[{"id":191,"text":"Colorado Water Science Center","active":true,"usgs":true}],"links":[{"id":438204,"rank":0,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/F7377766","text":"USGS data release","linkHelpText":"Data from Methane in Aquifers Used for Public Supply in the United States"},{"id":352578,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","volume":"84","publishingServiceCenter":{"id":2,"text":"Denver PSC"},"noUsgsAuthors":false,"publicationStatus":"PW","scienceBaseUri":"5afee7eae4b0da30c1bfc3b1","contributors":{"authors":[{"text":"McMahon, Peter B. 0000-0001-7452-2379 pmcmahon@usgs.gov","orcid":"https://orcid.org/0000-0001-7452-2379","contributorId":724,"corporation":false,"usgs":true,"family":"McMahon","given":"Peter","email":"pmcmahon@usgs.gov","middleInitial":"B.","affiliations":[{"id":191,"text":"Colorado Water Science Center","active":true,"usgs":true}],"preferred":true,"id":707250,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Belitz, Kenneth 0000-0003-4481-2345 kbelitz@usgs.gov","orcid":"https://orcid.org/0000-0003-4481-2345","contributorId":442,"corporation":false,"usgs":true,"family":"Belitz","given":"Kenneth","email":"kbelitz@usgs.gov","affiliations":[{"id":503,"text":"Office of Water Quality","active":true,"usgs":true},{"id":376,"text":"Massachusetts Water Science Center","active":true,"usgs":true},{"id":466,"text":"New England Water Science Center","active":true,"usgs":true},{"id":27111,"text":"National Water Quality Program","active":true,"usgs":true},{"id":451,"text":"National Water Quality Assessment Program","active":true,"usgs":true}],"preferred":true,"id":707251,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Barlow, Jeannie R. B. 0000-0002-0799-4656 jbarlow@usgs.gov","orcid":"https://orcid.org/0000-0002-0799-4656","contributorId":3701,"corporation":false,"usgs":true,"family":"Barlow","given":"Jeannie","email":"jbarlow@usgs.gov","middleInitial":"R. B.","affiliations":[{"id":24708,"text":"Lower Mississippi-Gulf Water Science Center","active":true,"usgs":true},{"id":394,"text":"Mississippi Water Science Center","active":true,"usgs":true},{"id":451,"text":"National Water Quality Assessment Program","active":true,"usgs":true}],"preferred":true,"id":707252,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Jurgens, Bryant C. 0000-0002-1572-113X bjurgens@usgs.gov","orcid":"https://orcid.org/0000-0002-1572-113X","contributorId":127842,"corporation":false,"usgs":true,"family":"Jurgens","given":"Bryant","email":"bjurgens@usgs.gov","middleInitial":"C.","affiliations":[{"id":154,"text":"California Water Science Center","active":true,"usgs":true}],"preferred":true,"id":707253,"contributorType":{"id":1,"text":"Authors"},"rank":4}]}} ,{"id":70195434,"text":"70195434 - 2017 - Degradation of crude 4-MCHM (4-methylcyclohexanemethanol) in sediments from Elk River, West Virginia","interactions":[],"lastModifiedDate":"2018-02-15T10:06:56","indexId":"70195434","displayToPublicDate":"2017-09-30T00:00:00","publicationYear":"2017","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":1565,"text":"Environmental Science & Technology","onlineIssn":"1520-5851","printIssn":"0013-936X","active":true,"publicationSubtype":{"id":10}},"title":"Degradation of crude 4-MCHM (4-methylcyclohexanemethanol) in sediments from Elk River, West Virginia","docAbstract":"In January 2014, approximately 37 800 L of crude 4-methylcyclohexanemethanol (crude MCHM) spilled into the Elk River, West Virginia. To understand the long-term fate of 4-MCHM, we conducted experiments under environmentally relevant conditions to assess the potential for the 2 primary compounds in crude MCHM (1) to undergo biodegradation and (2) for sediments to serve as a long-term source of 4-MCHM. We developed a solid phase microextraction (SPME) method to quantify the cis- and trans-isomers of 4-MCHM. Autoclaved Elk River sediment slurries sorbed 17.5% of cis-4-MCHM and 31% of trans-4-MCHM from water during the 2-week experiment. Sterilized, impacted, spill-site sediment released minor amounts of cis- and up to 35 μg/L of trans-4-MCHM into water, indicating 4-MCHM was present in sediment collected 10 months post spill. In anoxic microcosms, 300 μg/L cis- and 150 μg/L trans-4-MCHM degraded to nondetectable levels in 8–13 days in both impacted and background sediments. Under aerobic conditions, 4-MCHM isomers degraded to nondetectable levels within 4 days. Microbial communities at impacted sites differed in composition compared to background samples, but communities from both sites shifted in response to crude MCHM amendments. Our results indicate that 4-MCHM is readily biodegradable under environmentally relevant conditions.
","language":"English","publisher":"ACS publications","doi":"10.1021/acs.est.7b03142","usgsCitation":"Cozzarelli, I.M., Akob, D.M., Baedecker, M.J., Spencer, T., Jaeschke, J.B., Dunlap, D., Mumford, A.C., Poret-Peterson, A.T., and Chambers, D., 2017, Degradation of crude 4-MCHM (4-methylcyclohexanemethanol) in sediments from Elk River, West Virginia: Environmental Science & Technology, v. 51, no. 21, p. 12139-12145, https://doi.org/10.1021/acs.est.7b03142.","productDescription":"7 p.","startPage":"12139","endPage":"12145","ipdsId":"IP-086118","costCenters":[{"id":436,"text":"National Research Program - Eastern Branch","active":true,"usgs":true}],"links":[{"id":351643,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","state":"West Virginia","otherGeospatial":"Elk River","volume":"51","issue":"21","publishingServiceCenter":{"id":9,"text":"Reston PSC"},"noUsgsAuthors":false,"publicationDate":"2017-10-13","publicationStatus":"PW","scienceBaseUri":"5afee7eae4b0da30c1bfc3af","contributors":{"authors":[{"text":"Cozzarelli, Isabelle M. 0000-0002-5123-1007 icozzare@usgs.gov","orcid":"https://orcid.org/0000-0002-5123-1007","contributorId":1693,"corporation":false,"usgs":true,"family":"Cozzarelli","given":"Isabelle","email":"icozzare@usgs.gov","middleInitial":"M.","affiliations":[{"id":49175,"text":"Geology, Energy & Minerals Science Center","active":true,"usgs":true},{"id":436,"text":"National Research Program - Eastern Branch","active":true,"usgs":true}],"preferred":true,"id":728588,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Akob, Denise M. 0000-0003-1534-3025 dakob@usgs.gov","orcid":"https://orcid.org/0000-0003-1534-3025","contributorId":4980,"corporation":false,"usgs":true,"family":"Akob","given":"Denise","email":"dakob@usgs.gov","middleInitial":"M.","affiliations":[{"id":436,"text":"National Research Program - Eastern Branch","active":true,"usgs":true},{"id":5058,"text":"Office of the Chief Scientist for Water","active":true,"usgs":true}],"preferred":true,"id":728589,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Baedecker, Mary Jo 0000-0002-4865-1043 mjbaedec@usgs.gov","orcid":"https://orcid.org/0000-0002-4865-1043","contributorId":197793,"corporation":false,"usgs":true,"family":"Baedecker","given":"Mary","email":"mjbaedec@usgs.gov","middleInitial":"Jo","affiliations":[{"id":436,"text":"National Research Program - Eastern Branch","active":true,"usgs":true}],"preferred":true,"id":728590,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Spencer, Tracey 0000-0002-9121-2943 tspencer@usgs.gov","orcid":"https://orcid.org/0000-0002-9121-2943","contributorId":197794,"corporation":false,"usgs":true,"family":"Spencer","given":"Tracey","email":"tspencer@usgs.gov","affiliations":[{"id":436,"text":"National Research Program - Eastern Branch","active":true,"usgs":true}],"preferred":true,"id":728591,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Jaeschke, Jeanne B. 0000-0002-6237-6164 jaeschke@usgs.gov","orcid":"https://orcid.org/0000-0002-6237-6164","contributorId":3876,"corporation":false,"usgs":true,"family":"Jaeschke","given":"Jeanne","email":"jaeschke@usgs.gov","middleInitial":"B.","affiliations":[{"id":436,"text":"National Research Program - Eastern Branch","active":true,"usgs":true},{"id":37464,"text":"WMA - Laboratory & Analytical Services Division","active":true,"usgs":true}],"preferred":true,"id":728592,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"Dunlap, Darren S.","contributorId":179297,"corporation":false,"usgs":false,"family":"Dunlap","given":"Darren S.","affiliations":[],"preferred":false,"id":728593,"contributorType":{"id":1,"text":"Authors"},"rank":6},{"text":"Mumford, Adam C. 0000-0002-8082-8910 amumford@usgs.gov","orcid":"https://orcid.org/0000-0002-8082-8910","contributorId":197795,"corporation":false,"usgs":true,"family":"Mumford","given":"Adam","email":"amumford@usgs.gov","middleInitial":"C.","affiliations":[{"id":222,"text":"Earth Resources Observation and Science (EROS) Center","active":true,"usgs":true},{"id":436,"text":"National Research Program - Eastern Branch","active":true,"usgs":true},{"id":37277,"text":"WMA - Earth System Processes Division","active":true,"usgs":true}],"preferred":true,"id":728594,"contributorType":{"id":1,"text":"Authors"},"rank":7},{"text":"Poret-Peterson, Amisha T.","contributorId":179296,"corporation":false,"usgs":false,"family":"Poret-Peterson","given":"Amisha","email":"","middleInitial":"T.","affiliations":[],"preferred":false,"id":728595,"contributorType":{"id":1,"text":"Authors"},"rank":8},{"text":"Chambers, Douglas B. 0000-0002-5275-5427 dbchambe@usgs.gov","orcid":"https://orcid.org/0000-0002-5275-5427","contributorId":2520,"corporation":false,"usgs":true,"family":"Chambers","given":"Douglas B.","email":"dbchambe@usgs.gov","affiliations":[{"id":642,"text":"West Virginia Water Science Center","active":true,"usgs":true}],"preferred":true,"id":728596,"contributorType":{"id":1,"text":"Authors"},"rank":9}]}} ,{"id":70248922,"text":"70248922 - 2017 - Dissolved organic matter compositional change and biolability during two storm runoff events in a small sgricultural watershed","interactions":[],"lastModifiedDate":"2023-09-26T11:59:42.521638","indexId":"70248922","displayToPublicDate":"2017-09-29T06:54:45","publicationYear":"2017","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":2320,"text":"Journal of Geophysical Research: Biogeosciences","active":true,"publicationSubtype":{"id":10}},"title":"Dissolved organic matter compositional change and biolability during two storm runoff events in a small sgricultural watershed","docAbstract":"Agricultural watersheds are globally pervasive, supporting fundamentally different organic matter source, composition, and concentration profiles in comparison to natural systems. Similar to natural systems, agricultural storm runoff exports large amounts of organic carbon from agricultural land into waterways. But intense management of upper soil layers, waterway channelization, wetland and riparian habitat removal, and postharvest vegetation removal promise to uniquely drive organic matter release to waterways. During a winter first flush and a subsequent storm event, this study investigated the influence of a small agricultural watershed on dissolved organic matter (DOM) source, composition, and biolability. Storm water discharge released strongly terrestrial yet biolabile (23 to 32%) dissolved organic carbon (DOC). Following a 21 day bioassay, a parallel factor analysis identified an 80% reduction in a protein-like (phenylpropyl) component (C2) that was previously correlated to lignin phenol concentration, and a 10% reduction in a humic-like, terrestrially sourced component (C4). Storm-driven releases tripled DOC concentration (from 2.8 to 8.7 mg L−1) during the first flush event in comparison to base flow and were terrestrially sourced, with an eightfold increase in vascular plant derived lignin phenols (23.0 to 185 μg L−1). As inferred from system hydrology, lignin composition, and nitrate as a groundwater tracer, an initial pulse of dilute water from the upstream watershed caused a counterclockwise DOC hysteresis loop. DOC concentrations peaked after 3.5 days, with the delay between peak discharge and peak DOC attributed to storm water hydrology and a period of initial water repellency of agricultural soils, which delayed DOM leaching.
Two sets of digital flood-inundation map libraries that spanned a combined 16.7-mile reach of the Meramec River that extends upstream from Valley Park, Missouri, to downstream from Fenton, Mo., were created by the U.S. Geological Survey (USGS) in cooperation with the U.S. Army Corps of Engineers, St. Louis Metropolitan Sewer District, Missouri Department of Transportation, Missouri American Water, and Federal Emergency Management Agency Region 7. The flood-inundation maps, which can be accessed through the USGS Flood Inundation Mapping Science website at https://water.usgs.gov/osw/flood_inundation/, depict estimates of the areal extent and depth of flooding corresponding to selected water levels (stages) at the cooperative USGS streamgages on the Meramec River at Valley Park, Mo., (USGS station number 07019130) and the Meramec River at Fenton, Mo. (USGS station number 07019210). Near-real-time stage data at these streamgages may be obtained from the USGS National Water Information System at https://waterdata.usgs.gov/nwis or the National Weather Service (NWS) Advanced Hydrologic Prediction Service at http:/water.weather.gov/ahps/, which also forecasts flood hydrographs at these sites (listed as NWS sites vllm7 and fnnm7, respectively).
Flood profiles were computed for the stream reaches by means of a calibrated one-dimensional step-backwater hydraulic model. The model was calibrated using a stage-discharge relation at the Meramec River near Eureka streamgage (USGS station number 07019000) and documented high-water marks from the flood of December 2015 through January 2016.
The calibrated hydraulic model was used to compute two sets of water-surface profiles: one set for the streamgage at Valley Park, Mo. (USGS station number 07019130), and one set for the USGS streamgage on the Meramec River at Fenton, Mo. (USGS station number 07019210). The water-surface profiles were produced for stages at 1-foot (ft) intervals referenced to the datum from each streamgage and ranging from the NWS action stage, or near bankfull discharge, to the stage corresponding to the estimated 0.2-percent annual exceedance probability (500-year recurrence interval) flood, as determined at the Eureka streamgage (USGS station number 07019000). The simulated water-surface profiles were then combined with a geographic information system digital elevation model (derived from light detection and ranging data having a 0.28-ft vertical accuracy and 3.28-ft horizontal resolution) to delineate the area flooded at each flood stage (water level).
The availability of these maps, along with internet information regarding current stage from the USGS streamgages and forecasted high-flow stages from the NWS, will provide emergency management personnel and residents with information that is critical for flood response activities such as evacuations and road closures and for postflood recovery efforts.
Director, Missouri Water Science Center
U.S. Geological Survey
1400 Independence Road
Rolla, MO 65401
Natural resource management is conducted in the context of multiple anthropogenic stressors and is further challenged owing to changing climate. Experiments to determine the effects of climate change on complex ecological systems are nearly impossible. However, using a simulation model to synthesize current understanding of key ecological processes through the life cycle of a fish population can provide a platform for exploring potential effects of and management responses to changing conditions. Potential climate-change scenarios can be imposed, responses can be observed, and the effectiveness of potential actions can be evaluated. This approach is limited owing to future conditions likely deviating in range and timing from conditions used to create the model so that the model is expected to become obsolete. In the meantime, however, the modeling process explicitly states assumptions, clarifies information gaps, and provides a means to better understand which relationships are robust and which are vulnerable to changing climate by observing whether and why model output diverges from actual observations through time. The purpose of the model described herein is to provide such a decision-support tool regarding coho (Oncorhynchus kisutch) salmon for the Sauk-Suiattle Indian Tribe of Washington State.
The Skagit coho salmon model is implemented in a system dynamics format and has three primary stocks—(1) predicted smolts, (2) realized smolts, and (3) escapement. “Predicted smolts” are the number of smolts expected based on the number of spawners in any year and the Ricker production curve. Pink salmon (Oncorhynchus gorbuscha) return to the Skagit River in odd years, and when they overlap with juvenile rearing coho salmon, coho smolt production is substantially higher than in non-pink years. Therefore, the model uses alternative Ricker equations to predict smolts depending on whether their juvenile year was a pink or non-pink year. The stock “realized smolts” is calculated based on the expected effect of streamflow conditions to alter the productivity predicted by the Ricker curve. Adverse conditions include scouring flow events that occur when redds are present; high-flow events during winter on juveniles, which can cause fish displacement and adverse water turbidity; and extremely low flows in summer. The stock “escapement” represents the fish remaining after accounting for ocean mortality and harvest. Ocean mortality has been linked with indices of ocean conditions, which are related to ocean biological productivity. Ocean survival also may have a density-dependent component such that lower survival is associated with higher numbers of smolts. The model allows the user to change certain model parameters and inputs, and choose among alternative predictors for certain modeled relations.
","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/ofr20171125","collaboration":"Prepared in cooperation with Sauk-Suiattle Indian Tribe","usgsCitation":"Woodward, Andrea, Kirby, Grant, and Morris, Scott, 2017, Skagit River coho salmon life history model—Users’ guide: U.S. Geological Survey Open-File Report 2017–1125, 57 p., https://doi.org/10.3133/ofr20171125.","productDescription":"vi, 57 p.","numberOfPages":"68","onlineOnly":"Y","ipdsId":"IP-081907","costCenters":[{"id":290,"text":"Forest and Rangeland Ecosystem Science Center","active":false,"usgs":true}],"links":[{"id":346300,"rank":2,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/of/2017/1125/ofr20171125.pdf","text":"Report","size":"3.3 MB","linkFileType":{"id":1,"text":"pdf"},"description":"OFR 2017-1125"},{"id":346299,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/of/2017/1125/coverthb.jpg"}],"country":"United States","state":"Washington","otherGeospatial":"Skagit River Basin","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -122.5,\n 47.95314495015594\n ],\n [\n -120.75347900390624,\n 47.95314495015594\n ],\n [\n -120.75347900390624,\n 49\n ],\n [\n -122.5,\n 49\n ],\n [\n -122.5,\n 47.95314495015594\n ]\n ]\n ]\n }\n }\n ]\n}","contact":"Director, Forest and Rangeland Ecosystem Science Center
U.S. Geological Survey
777 NW 9th St., Suite 400
Corvallis, Oregon 97330
Human activities introduce a variety of chemicals to the Laurentian Great Lakes including pesticides, pharmaceuticals, flame retardants, plasticizers, and solvents (collectively referred to as contaminants of emerging concern or CECs) potentially threatening the vitality of these valuable ecosystems. We conducted a basin-wide study to identify the presence of CECs and other chemicals of interest in 12 U.S. tributaries to the Laurentian Great Lakes during 2013 and 2014. A total of 292 surface-water and 80 sediment samples were collected and analyzed for approximately 200 chemicals. A total of 32 and 28 chemicals were detected in at least 30% of water and sediment samples, respectively. Concentrations ranged from 0.0284 (indole) to 72.2 (cholesterol) μg/L in water and 1.75 (diphenhydramine) to 20,800 μg/kg (fluoranthene) in sediment. Cluster analyses revealed chemicals that frequently co-occurred such as pharmaceuticals and flame retardants at sites receiving similar inputs such as wastewater treatment plant effluent. Comparison of environmental concentrations to water and sediment-quality benchmarks revealed that polycyclic aromatic hydrocarbon concentrations often exceeded benchmarks in both water and sediment. Additionally, bis(2-ethylhexyl) phthalate and dichlorvos concentrations exceeded water-quality benchmarks in several rivers. Results from this study can be used to understand organism exposure, prioritize river basins for future management efforts, and guide detailed assessments of factors influencing transport and fate of CECs in the Great Lakes Basin.
","language":"English","publisher":"PLOS","doi":"10.1371/journal.pone.0182868","usgsCitation":"Elliott, S.M., Brigham, M.E., Lee, K., Banda, J.A., Choy, S.J., Gefell, D.J., Minarik, T.A., Moore, J.N., and Jorgenson, Z.G., 2017, Contaminants of emerging concern in tributaries to the Laurentian Great Lakes: I. Patterns of occurrence: PLoS ONE, v. 12, no. 9, p. 1-21, https://doi.org/10.1371/journal.pone.0182868.","productDescription":"e0182868; 21 p.","startPage":"1","endPage":"21","ipdsId":"IP-086440","costCenters":[{"id":392,"text":"Minnesota Water Science Center","active":true,"usgs":true}],"links":[{"id":469497,"rank":4,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.1371/journal.pone.0182868","text":"Publisher Index Page"},{"id":438206,"rank":3,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/F7TH8JS6","text":"USGS data release","linkHelpText":"Chemicals of Emerging Concern in Water and Bottom Sediment in Great Lakes Tributaries, 2014 - Collection Methods, Analytical Methods, Quality Assurance Analyses, and Data"},{"id":438205,"rank":3,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/F7DF6P9D","text":"USGS data release","linkHelpText":"Chemicals of Emerging Concern in Water and Bottom Sediment in Great Lakes Areas of Concern, 2013 - Analytical Methods, Collection Methods, Environmental Data, and Quality Assurance"},{"id":346175,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"},{"id":346174,"rank":2,"type":{"id":30,"text":"Data Release"},"url":"https://dx.doi.org/10.5066/F7DF6P9D","text":"Chemicals of Emerging Concern in Water and Bottom Sediment in Great Lakes Areas of Concern, 2013—Analytical Methods, Collection Methods, Environmental Data, and Quality Assurance"},{"id":346173,"rank":1,"type":{"id":30,"text":"Data Release"},"url":"https://dx.doi.org/10.5066/F7TH8JS6","text":"Chemicals of Emerging Concern in Water and Bottom Sediment in the Great Lakes Basin, 2014 - Analytical Methods, Collection Methods,Quality-Assurance Analyses, and Data"}],"country":"United States","otherGeospatial":"Great Lakes","volume":"12","issue":"9","publishingServiceCenter":{"id":4,"text":"Rolla PSC"},"noUsgsAuthors":false,"publicationDate":"2017-09-27","publicationStatus":"PW","scienceBaseUri":"59ce0a22e4b05fe04cc020e3","contributors":{"authors":[{"text":"Elliott, Sarah M. 0000-0002-1414-3024 selliott@usgs.gov","orcid":"https://orcid.org/0000-0002-1414-3024","contributorId":1472,"corporation":false,"usgs":true,"family":"Elliott","given":"Sarah","email":"selliott@usgs.gov","middleInitial":"M.","affiliations":[{"id":392,"text":"Minnesota Water Science Center","active":true,"usgs":true}],"preferred":true,"id":711446,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Brigham, Mark E. 0000-0001-7412-6800 mbrigham@usgs.gov","orcid":"https://orcid.org/0000-0001-7412-6800","contributorId":1840,"corporation":false,"usgs":true,"family":"Brigham","given":"Mark","email":"mbrigham@usgs.gov","middleInitial":"E.","affiliations":[{"id":392,"text":"Minnesota Water Science Center","active":true,"usgs":true}],"preferred":true,"id":711447,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Lee, Kathy 0000-0002-7683-1367 klee@usgs.gov","orcid":"https://orcid.org/0000-0002-7683-1367","contributorId":2538,"corporation":false,"usgs":true,"family":"Lee","given":"Kathy","email":"klee@usgs.gov","affiliations":[{"id":392,"text":"Minnesota Water Science Center","active":true,"usgs":true},{"id":589,"text":"Toxic Substances Hydrology Program","active":true,"usgs":true}],"preferred":true,"id":711448,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Banda, Jo A.","contributorId":196761,"corporation":false,"usgs":false,"family":"Banda","given":"Jo","email":"","middleInitial":"A.","affiliations":[],"preferred":false,"id":711449,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Choy, Steven J.","contributorId":138668,"corporation":false,"usgs":false,"family":"Choy","given":"Steven","email":"","middleInitial":"J.","affiliations":[{"id":6678,"text":"U.S. Fish and Wildlife Service, Alaska Maritime National Wildlife Refuge","active":true,"usgs":false}],"preferred":false,"id":711450,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"Gefell, Daniel J.","contributorId":138671,"corporation":false,"usgs":false,"family":"Gefell","given":"Daniel","email":"","middleInitial":"J.","affiliations":[{"id":6678,"text":"U.S. Fish and Wildlife Service, Alaska Maritime National Wildlife Refuge","active":true,"usgs":false}],"preferred":false,"id":711451,"contributorType":{"id":1,"text":"Authors"},"rank":6},{"text":"Minarik, Thomas A.","contributorId":139816,"corporation":false,"usgs":false,"family":"Minarik","given":"Thomas","email":"","middleInitial":"A.","affiliations":[{"id":13283,"text":"Metropolitan Water Reclamation District of Greater Chicago","active":true,"usgs":false}],"preferred":false,"id":711454,"contributorType":{"id":1,"text":"Authors"},"rank":7},{"text":"Moore, Jeremy N.","contributorId":138669,"corporation":false,"usgs":false,"family":"Moore","given":"Jeremy","email":"","middleInitial":"N.","affiliations":[{"id":6678,"text":"U.S. Fish and Wildlife Service, Alaska Maritime National Wildlife Refuge","active":true,"usgs":false}],"preferred":false,"id":711452,"contributorType":{"id":1,"text":"Authors"},"rank":8},{"text":"Jorgenson, Zachary G.","contributorId":69476,"corporation":false,"usgs":false,"family":"Jorgenson","given":"Zachary","email":"","middleInitial":"G.","affiliations":[{"id":13317,"text":"Saint Cloud State University","active":true,"usgs":false}],"preferred":false,"id":711453,"contributorType":{"id":1,"text":"Authors"},"rank":9}]}} ,{"id":70190590,"text":"sir20175098 - 2017 - Hydrogeology and simulated groundwater flow and availability in the North Fork Red River aquifer, southwest Oklahoma, 1980–2013","interactions":[],"lastModifiedDate":"2017-09-28T14:29:06","indexId":"sir20175098","displayToPublicDate":"2017-09-28T00:00:00","publicationYear":"2017","noYear":false,"publicationType":{"id":18,"text":"Report"},"publicationSubtype":{"id":5,"text":"USGS Numbered Series"},"seriesTitle":{"id":334,"text":"Scientific Investigations Report","code":"SIR","onlineIssn":"2328-0328","printIssn":"2328-031X","active":true,"publicationSubtype":{"id":5}},"seriesNumber":"2017-5098","displayTitle":"Hydrogeology and simulated groundwater flow and availability in the North Fork Red River aquifer, southwest Oklahoma,On September 8, 1981, the Oklahoma Water Resources Board established regulatory limits on the maximum annual yield of groundwater (343,042 acre-feet per year) and equal-proportionate-share (EPS) pumping rate (1.0 acre-foot per acre per year) for the North Fork Red River aquifer. The maximum annual yield and EPS were based on a hydrologic investigation that used a numerical groundwater-flow model to evaluate the effects of potential groundwater withdrawals on groundwater availability in the North Fork Red River aquifer. The Oklahoma Water Resources Board is statutorily required (every 20 years) to update the hydrologic investigation on which the maximum annual yield and EPS were based. Because 20 years have elapsed since the final order was issued, the U.S. Geological Survey, in cooperation with the Oklahoma Water Resources Board, conducted an updated hydrologic investigation and evaluated the effects of potential groundwater withdrawals on groundwater flow and availability in the North Fork Red River aquifer in Oklahoma. This report describes a hydrologic investigation of the North Fork Red River aquifer that includes an updated summary of the aquifer hydrogeology. As part of this investigation, groundwater flow and availability were simulated by using a numerical groundwater-flow model.
The North Fork Red River aquifer in Beckham, Greer, Jackson, Kiowa, and Roger Mills Counties in Oklahoma is composed of about 777 square miles (497,582 acres) of alluvium and terrace deposits along the North Fork Red River and tributaries, including Sweetwater Creek, Elk Creek, Otter Creek, and Elm Fork Red River. The North Fork Red River is the primary source of surface-water inflow to Lake Altus, which overlies the North Fork Red River aquifer. Lake Altus is a U.S. Bureau of Reclamation reservoir with the primary purpose of supplying irrigation water to the Lugert-Altus Irrigation District.
A hydrogeologic framework was developed for the North Fork Red River aquifer and included a definition of the aquifer extent and potentiometric surface, as well as a description of the textural and hydraulic properties of aquifer materials. The hydrogeologic framework was used in the construction of a numerical groundwater-flow model of the North Fork Red River aquifer described in this report. A conceptual model of aquifer inflows and outflows was developed for the North Fork Red River aquifer to constrain the construction and calibration of a numerical groundwater-flow model that reasonably represented the groundwater-flow system. The conceptual-model water budget estimated mean annual inflows to and outflows from the North Fork Red River aquifer for the period 1980–2013 and included a sub-accounting of mean annual inflows and outflows for the portions of the aquifer that were upgradient and downgradient from Lake Altus. The numerical groundwater-flow model simulated the period 1980–2013 and was calibrated to water-table-altitude observations at selected wells, monthly base flow at selected streamgages, net streambed seepage as estimated for the conceptual model, and Lake Altus stage.
Groundwater-availability scenarios were performed by using the calibrated numerical groundwater-flow model to (1) estimate the EPS pumping rate that guarantees a minimum 20-, 40-, and 50-year life of the aquifer, (2) quantify the potential effects of projected well withdrawals on groundwater storage over a 50-year period, and (3) simulate the potential effects of a hypothetical (10-year) drought on base flow and groundwater storage. The results of the groundwater-availability scenarios could be used by the Oklahoma Water Resources Board to reevaluate the maximum annual yield of groundwater from the North Fork Red River aquifer.
EPS scenarios for the North Fork Red River aquifer were run for periods of 20, 40, and 50 years. The 20-, 40-, and 50-year EPS pumping rates under normal recharge conditions were 0.59, 0.52, and 0.52 acre-foot per acre per year, respectively. Given the 497,582-acre aquifer area, these rates correspond to annual yields of about 294,000, 259,000, and 259,000 acre-feet per year, respectively. Groundwater storage at the end of the 20-year EPS scenario was about 951,000 acre-feet, or about 1,317,000 acre-feet (58 percent) less than the starting EPS scenario storage. This decrease in storage was equivalent to a mean water-level decline of about 22 feet. Most areas of the active alluvium near the North Fork Red River, Elk Creek, and Elm Fork Red River remained partially saturated through the end of the EPS scenario because of streambed seepage. Lake Altus storage was reduced to zero after 6–7 years of EPS pumping in each scenario.
Projected 50-year pumping scenarios were used to simulate the effects of selected well withdrawal rates on groundwater storage of the North Fork Red River aquifer and base flows in the North Fork Red River upstream from Lake Altus. The effects of well withdrawals were evaluated by comparing changes in groundwater storage and base flow between four 50-year scenarios using (1) no groundwater pumping, (2) mean pumping rates for the study period (1980–2013), (3) 2013 pumping rates, and (4) increasing demand pumping rates. The increasing demand pumping rates assumed a 20.4-percent increase in pumping over 50 years based on 2010–60 demand projections for southwest Oklahoma.
Groundwater storage after 50 years with no pumping was about 2,606,000 acre-feet, or 137,000 acre-feet (5.5 percent) greater than the initial groundwater storage; this groundwater storage increase is equivalent to a mean water-level increase of 2.3 feet. Groundwater storage after 50 years with the mean pumping rate for the study period (1980–2013) was about 2,476,000 acre-feet, or about 7,000 acre-feet (0.3 percent) greater than the initial groundwater storage; this groundwater storage increase is equivalent to a mean water-level increase of 0.1 foot. Groundwater storage at the end of the 50-year period with 2013 pumping rates was about 2,398,000 acre-feet, or about 70,000 acre-feet (2.8 percent) less than the initial storage; this groundwater storage decrease is equivalent to a mean water-level decline of 1.2 feet. Groundwater storage at the end of the 50-year period with increasing demand pumping rates was about 2,361,000 acre-feet, or about 107,000 acre-feet (4.3 percent) less than the initial storage; this groundwater storage decrease is equivalent to a mean water-level decline of 1.8 feet. Mean annual base flow simulated at the Carter streamgage (07301500) on North Fork Red River increased by about 4,000 acre-feet (10 percent) after 50 years with no pumping and decreased by about 5,400 acre-feet (13 percent) after 50 years with increasing demand pumping rates. Mean annual base flow simulated at the North Fork Red River inflow to Lake Altus increased by about 7,400 acre-feet (15 percent) after 50 years with no pumping and decreased by about 5,800 acre-feet (12 percent) after 50 years with increasing demand pumping rates.
A hypothetical 10-year drought scenario was used to simulate the effects of a prolonged period of reduced recharge on groundwater storage and Lake Altus stage and storage. Drought effects were quantified by comparing the results of the drought scenario to those of the calibrated numerical model (no drought). To simulate the hypothetical drought, recharge in the calibrated numerical model was reduced by 50 percent during the simulated drought period (1984–1993). Groundwater storage at the end of the drought period was about 2,271,000 acre-feet, or about 426,000 acre-feet (15.8 percent) less than the groundwater storage of the calibrated numerical model. This decrease in groundwater storage is equivalent to a mean water-table-altitude decline of 7.1 feet. At the end of the 10-year hypothetical drought period, base flows at the Sweetwater (07301420), Carter (07301500), Headrick (07305000), and Snyder (07307010) streamgages had decreased by about 37, 61, 44, and 45 percent, respectively. The minimum Lake Altus storage simulated during the drought period was 403 acre-feet, which was a decline of 92 percent from the nondrought storage. Reduced base flows in the North Fork Red River were the primary cause of Lake Altus storage declines.
","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/sir20175098","collaboration":"Prepared in cooperation with the Oklahoma Water Resources Board","usgsCitation":"Smith, S.J., Ellis, J.H., Wagner, D.L., and Peterson, S.M., 2017, Hydrogeology and simulated groundwater flow and availability in the North Fork Red River aquifer, southwest Oklahoma, 1980–2013: U.S. Geological Survey Scientific Investigations Report 2017–5098, 107 p., https://doi.org/10.3133/sir20175098.","productDescription":"Report: xii, 107 p.; Data Release","numberOfPages":"124","onlineOnly":"N","additionalOnlineFiles":"Y","ipdsId":"IP-071702","costCenters":[{"id":516,"text":"Oklahoma Water Science Center","active":true,"usgs":true}],"links":[{"id":346139,"rank":2,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/sir/2017/5098/sir20175098.pdf","text":"Report","size":"29.2 MB","linkFileType":{"id":1,"text":"pdf"},"description":"SIR 2017–5098"},{"id":346138,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/sir/2017/5098/coverthb.jpg"},{"id":346140,"rank":3,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/F7JQ0ZXH","text":"USGS Data Release","description":"USGS Data Release","linkHelpText":"MODFLOW-NWT model used in simulation of groundwater flow and availability in the North Fork Red River aquifer, southwest Oklahoma, 1980–2013"}],"country":"United States","state":"Oklahoma","otherGeospatial":"North Fork Red River Aquifer","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -100,\n 34.5\n ],\n [\n -98.8,\n 34.5\n ],\n [\n -98.8,\n 35.45\n ],\n [\n -100,\n 35.45\n ],\n [\n -100,\n 34.5\n ]\n ]\n ]\n }\n }\n ]\n}","contact":"Director, Oklahoma Water Science Center
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The water we drink, air we breathe, and soil we come into contact with have the potential to adversely affect our health because of contaminants in the environment. Environmental samples can characterize the extent of potential contamination, but traditional methods for collecting water, air, and soil samples below the ground (for example, well drilling or direct-push soil sampling) are expensive and time consuming. Trees are closely connected to the subsurface and sampling tree trunks can indicate subsurface pollutants, a process called phytoforensics. Scientists at the Missouri Water Science Center were among the first to use phytoforensics to screen sites for contamination before using traditional sampling methods, to guide additional sampling, and to show the large cost savings associated with tree sampling compared to traditional methods.
","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/fs20173076","usgsCitation":"Wilson, J.L., 2017, Phytoforensics—Using trees to find contamination: U.S. Geological Survey Fact Sheet 2017–3076, 2 p., https://doi.org/10.3133/fs20173076.","productDescription":"2 p.","numberOfPages":"2","onlineOnly":"N","ipdsId":"IP-087356","costCenters":[{"id":396,"text":"Missouri Water Science Center","active":true,"usgs":true}],"links":[{"id":346125,"rank":2,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/fs/2017/3076/fs20173076.pdf","text":"Report","size":"6.99 MB","linkFileType":{"id":1,"text":"pdf"},"description":"FS 2017–3076"},{"id":346124,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/fs/2017/3076/coverthb.jpg"}],"contact":"Director, Missouri Water Science Center
U.S. Geological Survey
1400 Independence Road
Rolla, MO 65401
The Carson River is a vital water resource for local municipalities and migratory birds travelling the Pacific Flyway. Historic mining practices that used mercury (Hg) to extract gold from Comstock Lode ore has left much of the river system heavily contaminated with Hg, a practice that continues in many parts of the world today. Between 1998 and 2013, the United States Geological Survey (USGS) collected and analyzed Carson River water for Hg and methylmercury (MeHg) concentrations resulting in a sixteen year record of unfiltered total mercury (uf.THg), filtered (dissolved) Hg (f.THg), total methylmercury (uf.MeHg), filtered MeHg (f.MeHg), and particulate-bound THg (p.THg) and MeHg (p.MeHg) concentrations. This represents one of the longest continuous records of Hg speciation data for any riverine system, thereby providing a unique opportunity to evaluate long-term trends in concentrations and annual loads. During the period of analysis, uf.THg concentration and load trended downward at rates of −0.85% and −1.8% per year, respectively. Conversely, the f.THg concentration increased at a rate of 1.7% per year between 1998 and 2005, and 4.9% per year between 2005 and 2013. Trends in flow-normalized partition coefficients for both Hg and MeHg suggest a statistically significant shift from the particulate to the filtered phase. The upwardly accelerating f.THg concentration and observed shift from the solid phase to the aqueous phase among the pools of Hg and MeHg within the river water column signals an increased risk of deteriorating ecological conditions in the lower basin with respect to Hg contamination. More broadly, the 16-year trend analysis, completed 140 years after the commencement of major Hg releases to the Carson River, provides a poignant example of the ongoing legacy left behind by gold and silver mining techniques that relied on Hg amalgamation, and a cautionary tale for regions still pursuing the practice in other countries.
","language":"English","publisher":"Elsevier","doi":"10.1016/j.envpol.2017.07.090","usgsCitation":"Morway, E.D., Thodal, C.E., and Marvin-DiPasquale, M.C., 2017, Long-term trends of surface-water mercury and methylmercury concentrations downstream of historic mining within the Carson River watershed: Environmental Pollution, v. 229, p. 1006-1018, https://doi.org/10.1016/j.envpol.2017.07.090.","productDescription":"13 p.","startPage":"1006","endPage":"1018","ipdsId":"IP-081017","costCenters":[{"id":465,"text":"Nevada Water Science Center","active":true,"usgs":true}],"links":[{"id":469496,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.1016/j.envpol.2017.07.090","text":"Publisher Index Page"},{"id":346163,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","state":"Nevada","otherGeospatial":"Carson River","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -119.72763061523436,\n 39.143907559644944\n ],\n [\n -119.14535522460938,\n 39.143907559644944\n ],\n [\n -119.14535522460938,\n 39.34067026099156\n ],\n [\n -119.72763061523436,\n 39.34067026099156\n ],\n [\n -119.72763061523436,\n 39.143907559644944\n ]\n ]\n ]\n }\n }\n ]\n}","volume":"229","publishingServiceCenter":{"id":1,"text":"Sacramento PSC"},"noUsgsAuthors":false,"publicationStatus":"PW","scienceBaseUri":"59ce0a24e4b05fe04cc020ef","contributors":{"authors":[{"text":"Morway, Eric D. 0000-0002-8553-6140 emorway@usgs.gov","orcid":"https://orcid.org/0000-0002-8553-6140","contributorId":4320,"corporation":false,"usgs":true,"family":"Morway","given":"Eric","email":"emorway@usgs.gov","middleInitial":"D.","affiliations":[{"id":465,"text":"Nevada Water Science Center","active":true,"usgs":true}],"preferred":true,"id":711417,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Thodal, Carl E. 0000-0003-0782-3280 cethodal@usgs.gov","orcid":"https://orcid.org/0000-0003-0782-3280","contributorId":2292,"corporation":false,"usgs":true,"family":"Thodal","given":"Carl","email":"cethodal@usgs.gov","middleInitial":"E.","affiliations":[{"id":465,"text":"Nevada Water Science Center","active":true,"usgs":true}],"preferred":true,"id":711418,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Marvin-DiPasquale, Mark C. 0000-0002-8186-9167 mmarvin@usgs.gov","orcid":"https://orcid.org/0000-0002-8186-9167","contributorId":1485,"corporation":false,"usgs":true,"family":"Marvin-DiPasquale","given":"Mark","email":"mmarvin@usgs.gov","middleInitial":"C.","affiliations":[{"id":438,"text":"National Research Program - Western Branch","active":true,"usgs":true},{"id":37277,"text":"WMA - Earth System Processes Division","active":true,"usgs":true}],"preferred":true,"id":711419,"contributorType":{"id":1,"text":"Authors"},"rank":3}]}} ,{"id":70191135,"text":"ofr20171123 - 2017 - An evaluation of the efficacy of using environmental DNA (eDNA) to detect giant gartersnakes (Thamnophis gigas)","interactions":[],"lastModifiedDate":"2017-10-02T10:20:32","indexId":"ofr20171123","displayToPublicDate":"2017-09-28T00:00:00","publicationYear":"2017","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":"2017-1123","displayTitle":"An evaluation of the efficacy of using environmental DNA (eDNA) to detect giant gartersnakes (Thamnophis gigas)","title":"An evaluation of the efficacy of using environmental DNA (eDNA) to detect giant gartersnakes (Thamnophis gigas)","docAbstract":"Detecting populations of rare or cryptic species is essential for their conservation. For species like giant gartersnakes (Thamnophis gigas), conventional survey methods can be expensive and inefficient. These sampling difficulties might be overcome by modern techniques that detect deoxyribonucleic acid (DNA) shed by organisms into the environment (eDNA). We evaluated the efficacy of detecting giant gartersnake eDNA in water samples from the laboratory and at locations with known giant gartersnake populations in the Sacramento Valley of California, and failed to detect giant gartersnake DNA in most laboratory and all field samples. Aspects of giant gartersnake biology—such as highly keratinized skin and spending extensive time in the terrestrial environment, as well as hot, sunny, and turbid conditions in wetlands and canals of the Sacramento Valley—likely contributed to low detection probabilities. Although detection of eDNA shows promise under many conditions, further development is needed before sampling for eDNA is a viable option for detecting giant gartersnake populations.
","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/ofr20171123","collaboration":"Prepared in cooperation with the Central Valley Project—Bureau of Reclamation and U.S. Fish and Wildlife Service","usgsCitation":"Halstead, B.J., Wood, D.A, Bowen, Lizabeth, Waters, Shannon, Vandergast, A.G., Ersan, J.S.M., Skalos, S.M., and Casazza, M.L., 2017, An evaluation of the efficacy of using environmental DNA (eDNA) to detect giant gartersnakes (Thamnophis gigas): U.S. Geological Survey Open-File Report 2017-1123, 41 p., https://doi.org/10.3133/ofr20171123.","productDescription":"vi, 41 p.","numberOfPages":"52","onlineOnly":"Y","ipdsId":"IP-086792","costCenters":[{"id":651,"text":"Western Ecological Research Center","active":true,"usgs":true}],"links":[{"id":346165,"rank":2,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/of/2017/1123/ofr20171123.pdf","text":"Report","size":"4.2 MB","linkFileType":{"id":1,"text":"pdf"},"description":"OFR 2017-1123"},{"id":346180,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/of/2017/1123/coverthb2.jpg"}],"country":"United States","state":"California","otherGeospatial":"Colusa National Wildlife Refuge, Natomas Basin","contact":"Director, Western Ecological Research Center
U.S. Geological Survey
3020 State University Drive East
Sacramento, California 95819
The U.S. Geological Survey (USGS) recently conducted an assessment of the undiscovered, technically recoverable oil and gas potential of Tertiary strata underlying the onshore areas and State waters of the northern Gulf of Mexico coastal region. The assessment was based on a number of geologic elements including an evaluation of hydrocarbon source rocks, suitable reservoir rocks, and hydrocarbon traps in an Upper Jurassic-Cretaceous-Tertiary Composite Total Petroleum System defined for the region by the USGS. Five conventional assessment units (AUs) were defined for the Midway (Paleocene) and Wilcox (Paleocene-Eocene) Groups, and the Carrizo Sand of the Claiborne Group (Eocene) interval including: (1) the Wilcox Stable Shelf Oil and Gas AU; (2) the Wilcox Expanded Fault Zone Gas and Oil AU; (3) the Wilcox-Lobo Slide Block Gas AU; (4) the Wilcox Slope and Basin Floor Gas AU; and (5) the Wilcox Mississippi Embayment AU (not quantitatively assessed).
The USGS assessment of undiscovered oil and gas resources for the Midway-Wilcox-Carrizo interval resulted in estimated mean values of 110 million barrels of oil (MMBO), 36.9 trillion cubic feet of gas (TCFG), and 639 million barrels of natural gas liquids (MMBNGL) in the four assessed units. The undiscovered oil resources are almost evenly divided between fluvial-deltaic sandstone reservoirs within the Wilcox Stable Shelf (54 MMBO) AU and deltaic sandstone reservoirs of the Wilcox Expanded Fault Zone (52 MMBO) AU. Greater than 70 percent of the undiscovered gas and 66 percent of the natural gas liquids (NGL) are estimated to be in deep (13,000 to 30,000 feet), untested distal deltaic and slope sandstone reservoirs within the Wilcox Slope and Basin Floor Gas AU.
","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/ofr20171111","usgsCitation":"Warwick, P.D., 2017, Geologic assessment of undiscovered conventional oil and gas resources in the lower Paleogene Midway and Wilcox Groups, and the Carrizo Sand of the Claiborne Group, of the northern Gulf Coast region: U.S. Geological Survey Open-File Report 2017–1111, 67 p., https://doi.org/10.3133/ofr20171111.","productDescription":"Report: vi, 60 p.; Appendixes 1-4","numberOfPages":"78","onlineOnly":"Y","additionalOnlineFiles":"Y","ipdsId":"IP-063993","costCenters":[{"id":241,"text":"Eastern Energy Resources Science Center","active":true,"usgs":true}],"links":[{"id":410834,"rank":7,"type":{"id":22,"text":"Related Work"},"url":"https://pubs.usgs.gov/publication/ofr20171167","text":"Open-File Report 2017–1167","linkHelpText":"- Geologic Assessment of Undiscovered Gas Resources in Cretaceous–Tertiary Coal Beds of the U.S. Gulf of Mexico Coastal Plain"},{"id":346065,"rank":4,"type":{"id":3,"text":"Appendix"},"url":"https://pubs.usgs.gov/of/2017/1111/ofr20171111_appendix2.pdf","text":"Appendix 2","size":"490 KB","linkHelpText":"- Input Data Form for the Wilcox Expanded Fault Zone Gas and Oil Assessment Unit (50470117)"},{"id":346063,"rank":2,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/of/2017/1111/ofr20171111.pdf","text":"Report","size":"14.2 MB","linkFileType":{"id":1,"text":"pdf"},"description":"OFR 2017-1111"},{"id":346062,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/of/2017/1111/coverthb.jpg"},{"id":346066,"rank":5,"type":{"id":3,"text":"Appendix"},"url":"https://pubs.usgs.gov/of/2017/1111/ofr20171111_appendix3.pdf","text":"Appendix 3","size":"510 KB","linkHelpText":"- Input Data Form for the Wilcox-Lobo Slide Block Gas Assessment Unit (50470119)"},{"id":346067,"rank":6,"type":{"id":3,"text":"Appendix"},"url":"https://pubs.usgs.gov/of/2017/1111/ofr20171111_appendix4.pdf","text":"Appendix 4","size":"396 KB","linkHelpText":"- Input Data Form for the Wilcox Slope and Basin Floor Gas Assessment Unit (50470118)"},{"id":346064,"rank":3,"type":{"id":3,"text":"Appendix"},"url":"https://pubs.usgs.gov/of/2017/1111/ofr20171111_appendix1.pdf","text":"Appendix 1","size":"389 KB","linkHelpText":"- Input Data Form for the Wilcox Stable Shelf Oil and Gas Assessment Unit (50470116)"}],"country":"United States","otherGeospatial":"Gulf of Mexico","contact":"Director, Eastern Energy Resources Science Center
U.S. Geological Survey
Mail Stop 956
12201 Sunrise Valley Drive
Reston, VA 20192
http://energy.usgs.gov/
The Laurentian Great Lakes contain one fifth of the world’s surface freshwater and have been impacted by human activity since the Industrial Revolution. In addition to legacy contaminants, nitrification and invasive species, this aquatic ecosystem is also the recipient of Contaminants of Emerging Concern (CECs) with poorly understood biological consequences. In the current study, we documented the presence, concentrations, and biological effects of CECs across 27 field sites in six Great Lakes tributaries by examining over 2250 resident and caged sunfish (Lepomis ssp.) for a variety of morphological and physiological endpoints and related these results to CEC occurrence. CEC were ubiquitous across studies sites and their presence and concentrations in water and sediment were highest in effluent dominated rivers and downstream of municipal wastewater treatment plant discharges. However, even putative upstream reference sites were not free of CEC presence and fish at these sites exhibited biological effects consistent with CEC exposure. Only the Fox River exhibited consistent adverse biological effects, including increased relative liver size, greater prominence of hepatocyte vacuoles and increased plasma glucose concentrations. Canonical Redundancy Analysis revealed consistent patterns of biological consequences of CEC exposure across all six tributaries. Increasing plasma glucose concentrations, likely as a result of pollutant-induced metabolic stress, were associated with increased relative liver size and greater prominence of hepatocyte vacuoles. These indicators of pollutant exposure were inversely correlated with indicators of reproductive potential including smaller gonad size and less mature gametes. The current study highlights the need for greater integration of chemical and biological studies and suggests that CECs in the Laurentian Great Lakes Basin may adversely affect the reproductive potential of exposed fish populations.
","language":"English","publisher":"Public Library of Science","doi":"10.1371/journal.pone.0184725","usgsCitation":"Thomas, L., Jorgenson, Z.G., Brigham, M.E., Choy, S.J., Moore, J.N., Banda, J.A., Gefell, D., Minarik, T.A., and Schoenfuss, H.L., 2017, Contaminants of emerging concern in tributaries to the Laurentian Great Lakes: II. Biological consequences of exposure: PLoS ONE, v. 12, no. 9, Article e0184725; 36 p., https://doi.org/10.1371/journal.pone.0184725.","productDescription":"Article e0184725; 36 p.","ipdsId":"IP-087970","costCenters":[{"id":392,"text":"Minnesota Water Science Center","active":true,"usgs":true}],"links":[{"id":469499,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.1371/journal.pone.0184725","text":"Publisher Index Page"},{"id":348862,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"otherGeospatial":"Great Lakes","volume":"12","issue":"9","publishingServiceCenter":{"id":4,"text":"Rolla PSC"},"noUsgsAuthors":false,"publicationDate":"2017-09-27","publicationStatus":"PW","scienceBaseUri":"5a60fb44e4b06e28e9c22ea3","contributors":{"authors":[{"text":"Thomas, Linnea M.","contributorId":146311,"corporation":false,"usgs":false,"family":"Thomas","given":"Linnea M.","affiliations":[],"preferred":false,"id":721922,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Jorgenson, Zachary G.","contributorId":69476,"corporation":false,"usgs":false,"family":"Jorgenson","given":"Zachary","email":"","middleInitial":"G.","affiliations":[{"id":13317,"text":"Saint Cloud State University","active":true,"usgs":false}],"preferred":false,"id":721923,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Brigham, Mark E. 0000-0001-7412-6800 mbrigham@usgs.gov","orcid":"https://orcid.org/0000-0001-7412-6800","contributorId":1840,"corporation":false,"usgs":true,"family":"Brigham","given":"Mark","email":"mbrigham@usgs.gov","middleInitial":"E.","affiliations":[{"id":392,"text":"Minnesota Water Science Center","active":true,"usgs":true}],"preferred":true,"id":721921,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Choy, Steven J.","contributorId":138668,"corporation":false,"usgs":false,"family":"Choy","given":"Steven","email":"","middleInitial":"J.","affiliations":[{"id":6678,"text":"U.S. Fish and Wildlife Service, Alaska Maritime National Wildlife Refuge","active":true,"usgs":false}],"preferred":false,"id":721924,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Moore, Jeremy N.","contributorId":138669,"corporation":false,"usgs":false,"family":"Moore","given":"Jeremy","email":"","middleInitial":"N.","affiliations":[{"id":6678,"text":"U.S. Fish and Wildlife Service, Alaska Maritime National Wildlife Refuge","active":true,"usgs":false}],"preferred":false,"id":721925,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"Banda, Jo A.","contributorId":196761,"corporation":false,"usgs":false,"family":"Banda","given":"Jo","email":"","middleInitial":"A.","affiliations":[],"preferred":false,"id":721926,"contributorType":{"id":1,"text":"Authors"},"rank":6},{"text":"Gefell, D.J.","contributorId":200326,"corporation":false,"usgs":false,"family":"Gefell","given":"D.J.","affiliations":[],"preferred":false,"id":721927,"contributorType":{"id":1,"text":"Authors"},"rank":7},{"text":"Minarik, Thomas A.","contributorId":139816,"corporation":false,"usgs":false,"family":"Minarik","given":"Thomas","email":"","middleInitial":"A.","affiliations":[{"id":13283,"text":"Metropolitan Water Reclamation District of Greater Chicago","active":true,"usgs":false}],"preferred":false,"id":721928,"contributorType":{"id":1,"text":"Authors"},"rank":8},{"text":"Schoenfuss, Heiko L.","contributorId":76409,"corporation":false,"usgs":false,"family":"Schoenfuss","given":"Heiko","email":"","middleInitial":"L.","affiliations":[{"id":13317,"text":"Saint Cloud State University","active":true,"usgs":false}],"preferred":false,"id":721929,"contributorType":{"id":1,"text":"Authors"},"rank":9}]}} ,{"id":70190500,"text":"ofr20171115 - 2017 - Groundwater quality in the Yuba River and Bear River Watersheds, Sierra Nevada, California","interactions":[],"lastModifiedDate":"2017-09-28T11:03:52","indexId":"ofr20171115","displayToPublicDate":"2017-09-27T00:00:00","publicationYear":"2017","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":"2017-1115","title":"Groundwater quality in the Yuba River and Bear River Watersheds, Sierra Nevada, California","docAbstract":"Groundwater provides more than 40 percent of California’s drinking water. To protect this vital resource, the State of California created the Groundwater Ambient Monitoring and Assessment (GAMA) Program. The GAMA Program’s Priority Basin Project assesses the quality of groundwater resources used for drinking water supply and increases public access to groundwater-quality information. In the Yuba River and Bear River Watersheds of the Sierra Nevada, many rural households rely on private wells for their drinking water supplies.
","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/ofr20171115","collaboration":"Prepared in collaboration with California State Water Resources Control Board","usgsCitation":"Fram, M.S., Jasper, Monica, and Taylor, K.A., 2017, Groundwater Quality in the Yuba River and Bear River watersheds, Sierra Nevada, California: U.S. Geological Survey Open-File Report 2017–1115, 4 p., https://doi.org/10.3133/ofr20171115.","productDescription":"4 p.","numberOfPages":"4","ipdsId":"IP-087403","costCenters":[{"id":154,"text":"California Water Science Center","active":true,"usgs":true}],"links":[{"id":438208,"rank":3,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/F73F4MS9","text":"USGS data release","linkHelpText":"Groundwater-Quality Data in the Yuba and Bear Watersheds Shallow Aquifer Study Unit, 2015-2016: Results from the California GAMA Priority Basin Project"},{"id":346137,"rank":2,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/of/2017/1115/ofr20171115.pdf","text":"Report","size":"3.75 MB","linkFileType":{"id":1,"text":"pdf"},"description":"OFR 2017-1115"},{"id":346136,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/of/2017/1115/coverthb.jpg"}],"country":"United States","state":"California","otherGeospatial":"Bear River Watershed, Yuba River Watershed","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -121.5,\n 39\n ],\n [\n -120.4167,\n 39\n ],\n [\n -120.4167,\n 39.75\n ],\n [\n -121.5,\n 39.75\n ],\n [\n -121.5,\n 39\n ]\n ]\n ]\n }\n }\n ]\n}","contact":"California Water Science Center
California GAMA
U.S. Geological Survey
6000 J Street, Placer Hall
Sacramento, California 95819
Many approaches have been developed for measuring or estimating actual evapotranspiration (ETa), and research over many years has led to the development of remote sensing methods that are reliably reproducible and effective in estimating ETa. Several remote sensing methods can be used to estimate ETa at the high spatial resolution of agricultural fields and the large extent of river basins. More complex remote sensing methods apply an analytical approach to ETa estimation using physically based models of varied complexity that require a combination of ground-based and remote sensing data, and are grounded in the theory behind the surface energy balance model. This report, funded through cooperation with the International Joint Commission, provides an overview of selected remote sensing methods used for estimating water consumed through ETa and focuses on Mapping Evapotranspiration at High Resolution with Internalized Calibration (METRIC) and Operational Simplified Surface Energy Balance (SSEBop), two energy balance models for estimating ETa that are currently applied successfully in the United States. The METRIC model can produce maps of ETa at high spatial resolution (30 meters using Landsat data) for specific areas smaller than several hundred square kilometers in extent, an improvement in practice over methods used more generally at larger scales. Many studies validating METRIC estimates of ETa against measurements from lysimeters have shown model accuracies on daily to seasonal time scales ranging from 85 to 95 percent. The METRIC model is accurate, but the greater complexity of METRIC results in greater data requirements, and the internalized calibration of METRIC leads to greater skill required for implementation. In contrast, SSEBop is a simpler model, having reduced data requirements and greater ease of implementation without a substantial loss of accuracy in estimating ETa. The SSEBop model has been used to produce maps of ETa over very large extents (the conterminous United States) using lower spatial resolution (1 kilometer) Moderate Resolution Imaging Spectroradiometer (MODIS) data. Model accuracies ranging from 80 to 95 percent on daily to annual time scales have been shown in numerous studies that validated ETa estimates from SSEBop against eddy covariance measurements. The METRIC and SSEBop models can incorporate low and high spatial resolution data from MODIS and Landsat, but the high spatiotemporal resolution of ETa estimates using Landsat data over large extents takes immense computing power. Cloud computing is providing an opportunity for processing an increasing amount of geospatial “big data” in a decreasing period of time. For example, Google Earth EngineTM has been used to implement METRIC with automated calibration for regional-scale estimates of ETa using Landsat data. The U.S. Geological Survey also is using Google Earth EngineTM to implement SSEBop for estimating ETa in the United States at a continental scale using Landsat data.
","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/sir20175087","collaboration":"Prepared in cooperation with the International Joint Commission","usgsCitation":"McShane, R.R., Driscoll, K.P., and Sando, Roy, 2017, A review of surface energy balance models for estimating actual evapotranspiration with remote sensing at high spatiotemporal resolution over large extents: U.S. Geological Survey Scientific Investigations Report 2017–5087, 19 p., https://doi.org/10.3133/sir20175087.","productDescription":"vi, 19 p.","numberOfPages":"30","onlineOnly":"Y","ipdsId":"IP-083112","costCenters":[{"id":5050,"text":"WY-MT Water Science Center","active":true,"usgs":true}],"links":[{"id":346073,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/sir/2017/5087/coverthb.jpg"},{"id":346074,"rank":2,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/sir/2017/5087/sir20175087.pdf","text":"Report","size":"678 kB","linkFileType":{"id":1,"text":"pdf"},"description":"SIR 2017–5087"}],"contact":"Director, Wyoming-Montana Water Science Center
U.S. Geological Survey
3162 Bozeman Avenue
Helena, MT 59601
Microbiomes are the communities of microorganisms (for example, bacteria, viruses, and fungi) that live on, in, and around people, plants, animals, soil, water, and the atmosphere. Microbiomes are active in the functioning of diverse ecosystems, for instance, by influencing water quality, nutrient acquisition and stress tolerance in plants, and stability of soil and aquatic environments. Microbiome research conducted by the U.S. Geological Survey spans many of our mission areas. Key research areas include water quality, understanding climate effects on soil and permafrost, ecosystem and wildlife health, invasive species, contaminated environments to improve bioremediation, and enhancing energy production. Microbiome research will fundamentally strengthen the ability to address the global challenges of maintaining clean water, ensuring adequate food supply, meeting energy needs, and preserving human and ecosystem health.
","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston VA","doi":"10.3133/fs20173074","usgsCitation":"Kellogg, C.A., and Hopkins, M.C., 2017, USGS microbiome research: U.S. Geological Survey Fact Sheet 2017–3074, 4 p., https://doi.org/10.3133/fs20173074.","productDescription":"4 p.","onlineOnly":"Y","additionalOnlineFiles":"N","ipdsId":"IP-087448","costCenters":[{"id":506,"text":"Office of the AD Ecosystems","active":true,"usgs":true}],"links":[{"id":346030,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/fs/2017/3074/coverthb.jpg"},{"id":346031,"rank":2,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/fs/2017/3074/fs20173074l.pdf","text":"Report","size":"2.61 MB","linkFileType":{"id":1,"text":"pdf"},"description":"FS 2017-3074","linkHelpText":"- Low resolution"},{"id":346032,"rank":3,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/fs/2017/3074/fs20173074h.pdf","text":"Report","size":"18.9 MB","linkFileType":{"id":1,"text":"pdf"},"description":"FS 2017-3074","linkHelpText":"- High resolution"}],"contact":"Ecosystems Mission Area
U.S. Geological Survey
12201 Sunrise Valley Drive
Reston, VA 20192
https://answers.usgs.gov/
Surface-water supplies are important sources of drinking water for residents in the Triangle area of North Carolina, which is located within the upper Cape Fear and Neuse River Basins. Since 1988, the U.S. Geological Survey and a consortium of local governments have tracked water-quality conditions and trends in several of the area’s water-supply lakes and streams. This report summarizes data collected through this cooperative effort, known as the Triangle Area Water Supply Monitoring Project, during October 2013 through September 2014 (water year 2014) and October 2014 through September 2015 (water year 2015). Major findings for this period include:
Director, South Atlantic Water Science Center
U.S. Geological Survey
720 Gracern Road
Stephenson Center, Suite 129
Columbia, SC 29210
Dauphin Island is a 26-km-long barrier island located southwest of Mobile Bay, Alabama, in the north-central Gulf of Mexico. The island contains sandy beaches, dunes, maritime forests, freshwater ponds and intertidal wetlands, providing habitat for many endangered and threatened species. Dauphin Island also provides protection for and maintains estuarine conditions within Mississippi Sound, supporting oyster habitat and seagrasses. Wetland marshes along the Alabama mainland are protected by the island from wave-induced erosion during storms approaching from the Gulf of Mexico. Over the years, the island has been eroded by storms, most recently by Hurricane Ivan (2004) and Hurricane Katrina (2005) (Ivan/Katrina), which breached the island along its narrowest extent and caused damage to infrastructure. Along with storms producing significant episodic change, long-term beach erosion has exposed numerous pine tree stumps in the shoreface. The stumps are remnants of past maritime forests and reflect the consistent landward retreat of the island.
Island change has prompted the State of Alabama to evaluate restoration alternatives to increase island resilience and sustainability by protecting and preserving the natural habitat, and by understanding the processes that influence shoreline change. Under a grant from the National Fish and Wildlife Foundation, restoration alternatives are being developed that will allow the State to make decisions on engineering and ecological restoration designs based on scientific analysis of likely outcomes and tradeoffs between impacts to stakeholder interests. Science-based assessment of the coastal zone requires accurate and up-to-date baseline data to provide a valid image of present conditions and to support modeling of coastal processes. Bathymetric elevation measurements are essential to this requirement. In August 2015, the U.S. Army Corps of Engineers and the U.S. Geological Survey conducted single beam and multibeam bathymetric surveys around Dauphin Island using a variety of shallow draft vessels and equipment. More than 95 square kilometers of seafloor was imaged. The data were integrated into a seamless digital elevation model to provide a high-resolution bathymetric map of the seafloor extending 9.5 kilometers seaward from the island’s eastern end and approximately 2 km along the rest of the island on the gulf and sound sides. Water depths range from 0.3 to 15.0 meters (m), with depths greater than 10.0 m constrained to the Mobile ship channel on the extreme eastern flank of the coverage.
To measure seafloor change, two periods of historic hydrographic survey data were acquired from the National Oceanic and Atmospheric Administration National Centers for Environmental Information data archive. The two timeframes (1987–1988 and 2005–2007) were selected for their completeness of spatial coverage and because they encompass a period of significant storm impacts to the island. These timeframes were compared to each other and with the 2015 dataset to monitor elevation gain (sediment accretion) and elevation loss (sediment erosion) over time. Sediment dynamics is by far the most significant driver of nearshore elevation change in this area. The Mississippi-Alabama inner shelf is a passive margin, and other influences on elevation change (for example, tectonic adjustment, Holocene subsidence, and eustatic sea-level rise) are neither significant nor variable enough over this time period to have an imprint.
","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/ofr20171112","usgsCitation":"Flocks, J.G., DeWitt, N.T., and Stalk, C.A., 2018, Analysis of seafloor change around Dauphin Island, Alabama, 1987–2015 (ver. 1.1, February 2018):St. Petersburg Science Center
U.S. Geological Survey
600 4th Street, South
St Petersburg, FL 33701
Introduced American Bullfrogs (Lithobates catesbeianus) have been present in Grand Teton National Park since approximately the 1950s, but little is known about their distribution and potential impacts. In this study, we surveyed the current bullfrog distribution and spatial overlap with sympatric native amphibians in the park, and characterized post-metamorphic bullfrog diets from July – September 2015. Despite surveys in multiple large rivers and floodplain habitats, we only documented bullfrogs in a geothermal pond and 5 km of stream channel immediately downstream of this pond. In these waters, bullfrogs overlapped with native amphibians at the downstream end of their distribution, and we did not document native amphibians in bullfrog stomach contents. Larger bullfrogs (SVL ≥ 96 mm) primarily consumed native rodents (especially meadow voles, Microtus pennsylvanicus), while smaller bullfrogs frequently consumed native invertebrates and less frequently consumed non-native invertebrates and fish. Taken together, these data indicate that the distribution and implications of the bullfrog invasion in Grand Teton National Park are currently localized to a small area, so these bullfrogs should therefore be vulnerable to eradication.
","language":"English","publisher":"Northwest Science Association","doi":"10.3955/046.091.0305","collaboration":"Grand Teton National Park; Greater Yellowstone Inventory & Monitoring Network","usgsCitation":"Flynn, L.M., Kreofsky, T.M., and Sepulveda, A.J., 2017, Introduced American Bullfrog distribution and diets in Grand Teton National Park: Northwest Science, v. 3, no. 91, p. 244-256, https://doi.org/10.3955/046.091.0305.","productDescription":"13 p.","startPage":"244","endPage":"256","ipdsId":"IP-074245","costCenters":[{"id":481,"text":"Northern Rocky Mountain Science Center","active":true,"usgs":true}],"links":[{"id":346086,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","state":"Wyoming","otherGeospatial":"Grand Teton National 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\"}}]}","volume":"3","issue":"91","publishingServiceCenter":{"id":2,"text":"Denver PSC"},"noUsgsAuthors":false,"publicationStatus":"PW","scienceBaseUri":"59cb672be4b017cf3141c671","contributors":{"authors":[{"text":"Flynn, Lauren M","contributorId":196692,"corporation":false,"usgs":false,"family":"Flynn","given":"Lauren","email":"","middleInitial":"M","affiliations":[],"preferred":false,"id":711144,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Kreofsky, Tess Marie","contributorId":196693,"corporation":false,"usgs":false,"family":"Kreofsky","given":"Tess","email":"","middleInitial":"Marie","affiliations":[],"preferred":false,"id":711145,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Sepulveda, Adam J. 0000-0001-7621-7028 asepulveda@usgs.gov","orcid":"https://orcid.org/0000-0001-7621-7028","contributorId":150628,"corporation":false,"usgs":true,"family":"Sepulveda","given":"Adam","email":"asepulveda@usgs.gov","middleInitial":"J.","affiliations":[{"id":481,"text":"Northern Rocky Mountain Science Center","active":true,"usgs":true}],"preferred":true,"id":711143,"contributorType":{"id":1,"text":"Authors"},"rank":3}]}} ,{"id":70190591,"text":"sir20175101 - 2017 - Bathymetric surveys of the Neosho River, Spring River, and Elk River, northeastern Oklahoma and southwestern Missouri, 2016–17","interactions":[],"lastModifiedDate":"2017-11-02T17:06:23","indexId":"sir20175101","displayToPublicDate":"2017-09-26T00:00:00","publicationYear":"2017","noYear":false,"publicationType":{"id":18,"text":"Report"},"publicationSubtype":{"id":5,"text":"USGS Numbered Series"},"seriesTitle":{"id":334,"text":"Scientific Investigations Report","code":"SIR","onlineIssn":"2328-0328","printIssn":"2328-031X","active":true,"publicationSubtype":{"id":5}},"seriesNumber":"2017-5101","title":"Bathymetric surveys of the Neosho River, Spring River, and Elk River, northeastern Oklahoma and southwestern Missouri, 2016–17","docAbstract":"In February 2017, the Grand River Dam Authority filed to relicense the Pensacola Hydroelectric Project with the Federal Energy Regulatory Commission. The predominant feature of the Pensacola Hydroelectric Project is Pensacola Dam, which impounds Grand Lake O’ the Cherokees (locally called Grand Lake) in northeastern Oklahoma. Identification of information gaps and assessment of project effects on stakeholders are central aspects of the Federal Energy Regulatory Commission relicensing process. Some upstream stakeholders have expressed concerns about the dynamics of sedimentation and flood flows in the transition zone between major rivers and Grand Lake O’ the Cherokees. To relicense the Pensacola Hydroelectric Project with the Federal Energy Regulatory Commission, the hydraulic models for these rivers require high-resolution bathymetric data along the river channels. In support of the Federal Energy Regulatory Commission relicensing process, the U.S. Geological Survey, in cooperation with the Grand River Dam Authority, performed bathymetric surveys of (1) the Neosho River from the Oklahoma border to the U.S. Highway 60 bridge at Twin Bridges State Park, (2) the Spring River from the Oklahoma border to the U.S. Highway 60 bridge at Twin Bridges State Park, and (3) the Elk River from Noel, Missouri, to the Oklahoma State Highway 10 bridge near Grove, Oklahoma. The Neosho River and Spring River bathymetric surveys were performed from October 26 to December 14, 2016; the Elk River bathymetric survey was performed from February 27 to March 21, 2017. Only areas inundated during those periods were surveyed.
The bathymetric surveys covered a total distance of about 76 river miles and a total area of about 5 square miles. Greater than 1.4 million bathymetric-survey data points were used in the computation and interpolation of bathymetric-survey digital elevation models and derived contours at 1-foot (ft) intervals. The minimum bathymetric-survey elevation of the Neosho River was 709.18 ft above North American Vertical Datum of 1988, which corresponds to a maximum depth of 34.22 ft. The minimum bathymetric-survey elevation of the Spring River was 714.18 ft above North American Vertical Datum of 1988, which corresponds to a maximum depth of 29.22 ft. The minimum bathymetric-survey elevation of the Elk River was 715.62 ft above North American Vertical Datum of 1988, which corresponds to a maximum depth of 27.78 ft.
","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/sir20175101","collaboration":"Prepared in cooperation with the Grand River Dam Authority","usgsCitation":"Hunter, S.L., Ashworth, C.E., and Smith, S.J., 2017, Bathymetric surveys of the Neosho River, Spring River, and Elk River, northeastern Oklahoma and southwestern Missouri, 2016–17 (ver. 1.1, October 2017): U.S. Geological Survey Scientific Investigations Report 2017–5101, 59 p., https://doi.org/10.3133/sir20175101.","productDescription":"Report: 59 p.; Data Release","numberOfPages":"68","onlineOnly":"Y","additionalOnlineFiles":"Y","ipdsId":"IP-087073","costCenters":[{"id":516,"text":"Oklahoma Water Science Center","active":true,"usgs":true}],"links":[{"id":346091,"rank":1,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/F71N7ZMS","text":"USGS Data Release","description":"USGS Data Release","linkHelpText":"Bathymetric surveys of the Neosho River, Spring River, and Elk River, northeastern Oklahoma and southwestern Missouri, 2016–17"},{"id":348038,"rank":2,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/sir/2017/5101/coverthb2.jpg"},{"id":348039,"rank":3,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/sir/2017/5101/sir20175101_ver1.1.pdf","text":"Report","size":"75.2 MB","linkFileType":{"id":1,"text":"pdf"},"description":"SIR 2017–5101"},{"id":348040,"rank":4,"type":{"id":25,"text":"Version History"},"url":"https://pubs.usgs.gov/sir/2017/5101/versionHist.txt","text":"Version History","size":"1.68 kB","linkFileType":{"id":2,"text":"txt"},"description":"Version History"}],"country":"United States","state":"Missouri, Oklahoma","otherGeospatial":"Neosho River, Spring River","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -95.1,\n 36.8\n ],\n [\n -94.5667,\n 36.8\n ],\n [\n -94.5667,\n 37.0333\n ],\n [\n -95.1,\n 37.0333\n ],\n [\n -95.1,\n 36.8\n ]\n ]\n ]\n }\n },\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -94.7,\n 36.6667\n ],\n [\n -94.4667,\n 36.6667\n ],\n [\n -94.4667,\n 36.5333\n ],\n [\n -94.7,\n 36.5333\n ],\n [\n -94.7,\n 36.6667\n ]\n ]\n ]\n }\n }\n ]\n}","edition":"Version 1.0: Originally posted September 26, 2017; Version 1.1: October 25, 2017","contact":"Director, Oklahoma Water Science Center
U.S. Geological Survey
202 NW 66th, Bldg 7
Oklahoma City, OK 73116
Bathymetric and velocimetric data were collected by the U.S. Geological Survey, in cooperation with the Missouri Department of Transportation, near 13 bridges at 8 highway crossings of the Missouri and Mississippi Rivers in the greater St. Louis, Missouri, area from May 23 to 27, 2016. A multibeam echosounder mapping system was used to obtain channel-bed elevations for river reaches ranging from 1,640 to 1,970 feet longitudinally and extending laterally across the active channel from bank to bank during low to moderate flood flow conditions. These bathymetric surveys indicate the channel conditions at the time of the surveys and provide characteristics of scour holes that may be useful in the development of predictive guidelines or equations for scour holes. These data also may be useful to the Missouri Department of Transportation as a low to moderate flood flow comparison to help assess the bridges for stability and integrity issues with respect to bridge scour during floods.
Bathymetric data were collected around every pier that was in water, except those at the edge of water, and scour holes were observed at most surveyed piers. The observed scour holes at the surveyed bridges were examined with respect to shape and depth.
The frontal slope values determined for scour holes observed in the current (2016) study generally are similar to recommended values in the literature and to values determined for scour holes in previous bathymetric surveys. Several of the structures had piers that were skewed to primary approach flow, as indicated by the scour hole being longer on the side of the pier with impinging flow, and some amount of deposition on the leeward side, as typically has been observed at piers skewed to approach flow; however, at most skewed piers in the current (2016) study, the scour hole was deeper on the leeward side of the pier. At most of these piers, the angled approach flow was the result of a deflection or contraction of flow caused by a spur dike near the pier, which may affect flow differently than for a simple skew. At structure A6500 (site 33), the wide face of the pier footing and seal course would behave as a complex foundation, for which scour is computed differently.
Previous bathymetric surveys exist for all the sites examined in this study. A previous survey in October 2010 at most of the sites had similar flow conditions and similar results to the 2016 surveys. A survey during flood conditions in August 2011 at the sites on the Missouri River and in May 2009 at structures A4936 and A1850 (site 35) on the Mississippi River did not always indicate more substantial scour during flood conditions. At structure A6500 (site 33) on the Mississippi River, a previous survey in 2009 was part of a habitat assessment before construction of the bridge and provides unique insight into the effects of the construction of that bridge on the channel in this reach. Substantial scour was observed near the right pier, and the riprap blanket surrounding the left pier seems to limit scour near that pier. Multiple additional surveys have been completed at structures A4936 and A1850 (site 35) on the Mississippi River, and the results of these surveys also are presented.
","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/sir20175076","collaboration":"Prepared in cooperation with the Missouri Department of Transportation","usgsCitation":"Huizinga, R.J., 2017, Bathymetric and velocimetric surveys at highway bridges crossing the Missouri and Mississippi Rivers near St. Louis, Missouri, May 23–27, 2016: U.S. Geological Survey Scientific Investigations Report 2017–5076, 102 p., https://doi.org/10.3133/sir20175076.","productDescription":"Report: x, 102 p.; Data Release","numberOfPages":"116","onlineOnly":"Y","additionalOnlineFiles":"Y","ipdsId":"IP-086447","costCenters":[{"id":396,"text":"Missouri Water Science Center","active":true,"usgs":true}],"links":[{"id":346078,"rank":3,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/F71C1VCC","text":"USGS Data Release","linkHelpText":"Bathymetry and Velocity Data from Surveys at Highway Bridges crossing the Missouri and Mississippi Rivers near St. Louis, Missouri, October 2008 through May 2016"},{"id":346076,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/sir/2017/5076/coverthb.jpg"},{"id":346077,"rank":2,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/sir/2017/5076/sir20175076.pdf","text":"Report","size":"17.2 MB","linkFileType":{"id":1,"text":"pdf"},"description":"SIR 2017–5076"}],"country":"United States","state":"Missouri","city":"St. Louis","otherGeospatial":"Mississippi River, Missouri River","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -90.69419860839844,\n 38.42293213401053\n ],\n [\n -90.06935119628906,\n 38.42293213401053\n ],\n [\n -90.06935119628906,\n 38.92843409820933\n ],\n [\n -90.69419860839844,\n 38.92843409820933\n ],\n [\n -90.69419860839844,\n 38.42293213401053\n ]\n ]\n ]\n }\n }\n ]\n}","contact":"Director, Missouri Water Science Center
U.S. Geological Survey
1400 Independence Road
Rolla, MO 65401
The maps and graphs in this summary describe national streamflow conditions for water year 2016 (October 1, 2015, to September 30, 2016) in the context of streamflow ranks relative to the 87-year period of 1930–2016, unless otherwise noted. The illustrations are based on observed data from the U.S. Geological Survey’s (USGS) National Streamflow Network. The period of 1930–2016 was used because the number of streamgages before 1930 was too small to provide representative data for computing statistics for most regions of the country.
In the summary, reference is made to the term “runoff,” which is the depth to which a river basin, State, or other geographic area would be covered with water if all the streamflow within the area during a specified period was uniformly distributed on it. Runoff quantifies the magnitude of water flowing through the Nation’s rivers and streams in measurement units that can be compared from one area to another.
In all the graphics, a rank of 1 indicates the highest flow of all years analyzed and 87 indicates the lowest flow of all years. Rankings of streamflow are grouped into much below normal, below normal, normal, above normal, and much above normal based on percentiles of flow (less than 10 percent, 10–24 percent, 25–75 percent, 76–90 percent, and greater than 90 percent, respectively). Some of the data used to produce the maps and graphs are provisional and subject to change.
","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/fs20173063","usgsCitation":"Jian, Xiaodong, Wolock, D.M., Lins, H.F., and Brady, S.J., 2017, Streamflow of 2016—Water year summary: U.S. Geological Survey Fact Sheet 2017–3063, 6 p., https://doi.org/10.3133/fs20173063.","productDescription":"6 p.","numberOfPages":"6","onlineOnly":"Y","ipdsId":"IP-088111","costCenters":[{"id":353,"text":"Kansas Water Science Center","active":false,"usgs":true},{"id":502,"text":"Office of Surface Water","active":true,"usgs":true}],"links":[{"id":346071,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/fs/2017/3063/coverthb.jpg"},{"id":346072,"rank":2,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/fs/2017/3063/fs20173063.pdf","text":"Fact Sheet","size":"524 kB","linkFileType":{"id":1,"text":"pdf"},"description":"FS 2017–3063"}],"country":"United 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415 National Center
Reston, VA 20192
This field manual provides general guidance for identifying and collecting high-water marks and is meant to be used by field personnel as a quick reference. The field manual describes purposes for collecting and documenting high-water marks along with the most common types of high-water marks. The manual provides a list of suggested field equipment, describes rules of thumb and best practices for finding high-water marks, and describes the importance of evaluating each high-water mark and assigning a numeric uncertainty value as part of the flagging process. The manual also includes an appendix of photographs of a variety of high-water marks obtained from various U.S. Geological Survey field investigations along with general comments about the logic for the assigned uncertainty values.
","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/ofr20171105","collaboration":"Prepared in cooperation with the South Carolina Department of Transportation","usgsCitation":"Feaster, T.D., and Koenig, T.A, 2017, Field manual for identifying and preserving high-water mark data: U.S. Geological Survey Open-File Report 2017–1105, 67 p., https://doi.org/10.3133/ofr20171105.","productDescription":"x, 67 p.","numberOfPages":"80","onlineOnly":"N","additionalOnlineFiles":"Y","ipdsId":"IP-087515","costCenters":[{"id":13634,"text":"South Atlantic Water Science Center","active":true,"usgs":true}],"links":[{"id":346094,"rank":2,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/of/2017/1105/ofr20171105.pdf","text":"Report","size":"4.88 MB","linkFileType":{"id":1,"text":"pdf"},"description":"OFR 2017–1105"},{"id":346095,"rank":3,"type":{"id":21,"text":"Referenced Work"},"url":"https://doi.org/10.3133/tm3A24","text":"T & M 3–A24","description":"T & M 3–A24"},{"id":346093,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/of/2017/1105/coverthb2.jpg"}],"contact":"Director, South Atlantic Water Science Center
U.S. Geological Survey
720 Gracern Road
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Lago Loíza reservoir in east-central Puerto Rico is one of the primary sources of public water supply for the San Juan metropolitan area. To evaluate and predict the Lago Loíza water budget, an artificial neural network (ANN) technique is trained to predict river inflows. A method is developed to combine ANN-predicted daily flows with ANN-predicted 30-day cumulative flows to improve flow estimates. The ANN application trains well for representing 2007–2012 and the drier 1994–1997 periods. Rainfall data downscaled from global circulation model (GCM) simulations are used to predict 2050–2055 conditions. Evapotranspiration is estimated with the Hargreaves equation using minimum and maximum air temperatures from the downscaled GCM data. These simulated 2050–2055 river flows are input to a water budget formulation for the Lago Loíza reservoir for comparison with 2007–2012. The ANN scenarios require far less computational effort than a numerical model application, yet produce results with sufficient accuracy to evaluate and compare hydrologic scenarios. This hydrologic tool will be useful for future evaluations of the Lago Loíza reservoir and water supply to the San Juan metropolitan area.
Attribution of the causes of trends in nutrient loading is often limited to correlation, qualitative reasoning, or references to the work of others. This paper represents efforts to improve causal attribution of water-quality changes. The Red River of the North basin provides a regional test case because of international interest in the reduction of total phosphorus loads and the availability of long-term total phosphorus data and ancillary geospatial data with the potential to explain changes in water quality over time. The objectives of the study are to investigate structural equation modeling methods for application to water-quality problems and to test causal hypotheses related to the drivers of total phosphorus loads over the period 1970 to 2012. Multiple working hypotheses that explain total phosphorus loads and methods for estimating missing ancillary data were developed, and water-quality related challenges to structural equation modeling (including skewed data and scaling issues) were addressed. The model indicates that increased precipitation in season 1 (November–February) or season 2 (March–June) would increase total phosphorus loads in the basin. The effect of agricultural practices on total phosphorus loads was significant, although the effect is about one-third of the effect of season 1 precipitation. The structural equation model representing loads at six sites in the basin shows that climate and agricultural practices explain almost 60% of the annual total phosphorus load in the Red River of the North basin. The modeling process and the unexplained variance highlight the need for better ancillary long-term data for causal assessments.
","language":"English","publisher":"ACSESS","doi":"10.2134/jeq2017.04.0131","usgsCitation":"Ryberg, K.R., 2017, Structural equation model of total phosphorus loads in the Red River of the North Basin, USA and Canada: Journal of Environmental Quality, v. 46, no. 5, p. 1072-1080, https://doi.org/10.2134/jeq2017.04.0131.","productDescription":"9 p.","startPage":"1072","endPage":"1080","ipdsId":"IP-075962","costCenters":[{"id":478,"text":"North Dakota Water Science Center","active":true,"usgs":true},{"id":34685,"text":"Dakota Water Science Center","active":true,"usgs":true}],"links":[{"id":469503,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.2134/jeq2017.04.0131","text":"Publisher Index Page"},{"id":346042,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"Canada, United States","otherGeospatial":"Red River of the North Basin","volume":"46","issue":"5","publishingServiceCenter":{"id":4,"text":"Rolla PSC"},"noUsgsAuthors":false,"publicationStatus":"PW","scienceBaseUri":"59ca15a7e4b017cf314041a4","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":711096,"contributorType":{"id":1,"text":"Authors"},"rank":1}]}} ,{"id":70191048,"text":"70191048 - 2017 - Local adaptation in Trinidadian guppies alters stream ecosystem structure at landscape scales despite high environmental variability","interactions":[],"lastModifiedDate":"2017-09-25T12:01:36","indexId":"70191048","displayToPublicDate":"2017-09-25T00:00:00","publicationYear":"2017","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":1337,"text":"Copeia","active":true,"publicationSubtype":{"id":10}},"title":"Local adaptation in Trinidadian guppies alters stream ecosystem structure at landscape scales despite high environmental variability","docAbstract":"While previous studies have shown that evolutionary divergence alters ecological processes in small-scale experiments, a major challenge is to assess whether such evolutionary effects are important in natural ecosystems at larger spatial scales. At the landscape scale, across eight streams in the Caroni drainage, we found that the presence of locally adapted populations of guppies (Poecilia reticulata) is associated with reduced algal biomass and increased invertebrate biomass, while the opposite trends were true in streams with experimentally introduced populations of non-locally adapted guppies. Exclusion experiments conducted in two separate reaches of a single stream showed that guppies with locally adapted phenotypes significantly reduced algae with no effect on invertebrates, while non-adapted guppies had no effect on algae but significantly reduced invertebrates. These divergent effects of phenotype on stream ecosystems are comparable in strength to the effects of abiotic factors (e.g., light) known to be important drivers of ecosystem condition. They also corroborate the results of previous experiments conducted in artificial streams. Our results demonstrate that local adaptation can produce phenotypes with significantly different effects in natural ecosystems at a landscape scale, within a tropical watershed, despite high variability in abiotic factors: five of the seven physical and chemical parameters measured across the eight study streams varied by more than one order of magnitude. Our findings suggest that ecosystem structure is, in part, an evolutionary product and not simply an ecological pattern.
","language":"English","publisher":"The American Society of Ichthyologists and Herpetologists","doi":"10.1643/CE-16-517","usgsCitation":"Simon, T.N., Bassar, R.D., Binderup, A.J., Flecker, A.S., Freeman, M., Gill, J.F., Marshall, M.C., Thomas, S.A., Travis, J., Reznick, D.N., and Pringle, C.M., 2017, Local adaptation in Trinidadian guppies alters stream ecosystem structure at landscape scales despite high environmental variability: Copeia, v. 105, no. 3, p. 504-513, https://doi.org/10.1643/CE-16-517.","productDescription":"10 p.","startPage":"504","endPage":"513","ipdsId":"IP-084421","costCenters":[{"id":531,"text":"Patuxent Wildlife Research Center","active":true,"usgs":true}],"links":[{"id":346050,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"Trinidad","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -61.59484863281249,\n 10.510766951875397\n ],\n [\n -60.99472045898437,\n 10.510766951875397\n ],\n [\n -60.99472045898437,\n 10.873767739353797\n ],\n [\n -61.59484863281249,\n 10.873767739353797\n ],\n [\n -61.59484863281249,\n 10.510766951875397\n ]\n ]\n ]\n }\n }\n ]\n}","volume":"105","issue":"3","publishingServiceCenter":{"id":10,"text":"Baltimore PSC"},"noUsgsAuthors":false,"publicationDate":"2017-09-12","publicationStatus":"PW","scienceBaseUri":"59ca15abe4b017cf314041b7","contributors":{"authors":[{"text":"Simon, Troy N.","contributorId":196661,"corporation":false,"usgs":false,"family":"Simon","given":"Troy","email":"","middleInitial":"N.","affiliations":[],"preferred":false,"id":711047,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Bassar, Ronald D.","contributorId":150154,"corporation":false,"usgs":false,"family":"Bassar","given":"Ronald","email":"","middleInitial":"D.","affiliations":[{"id":6932,"text":"University of Massachusetts, Amherst","active":true,"usgs":false}],"preferred":false,"id":711048,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Binderup, Andrew J.","contributorId":196662,"corporation":false,"usgs":false,"family":"Binderup","given":"Andrew","email":"","middleInitial":"J.","affiliations":[],"preferred":false,"id":711049,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Flecker, Alex S.","contributorId":196663,"corporation":false,"usgs":false,"family":"Flecker","given":"Alex","email":"","middleInitial":"S.","affiliations":[],"preferred":false,"id":711050,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Freeman, Mary 0000-0001-7615-6923 mcfreeman@usgs.gov","orcid":"https://orcid.org/0000-0001-7615-6923","contributorId":3528,"corporation":false,"usgs":true,"family":"Freeman","given":"Mary","email":"mcfreeman@usgs.gov","affiliations":[{"id":531,"text":"Patuxent Wildlife Research Center","active":true,"usgs":true}],"preferred":true,"id":711046,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"Gill, James F.","contributorId":196664,"corporation":false,"usgs":false,"family":"Gill","given":"James","email":"","middleInitial":"F.","affiliations":[],"preferred":false,"id":711051,"contributorType":{"id":1,"text":"Authors"},"rank":6},{"text":"Marshall, Michael C.","contributorId":196665,"corporation":false,"usgs":false,"family":"Marshall","given":"Michael","email":"","middleInitial":"C.","affiliations":[],"preferred":false,"id":711052,"contributorType":{"id":1,"text":"Authors"},"rank":7},{"text":"Thomas, Steve A.","contributorId":196666,"corporation":false,"usgs":false,"family":"Thomas","given":"Steve","email":"","middleInitial":"A.","affiliations":[],"preferred":false,"id":711053,"contributorType":{"id":1,"text":"Authors"},"rank":8},{"text":"Travis, Joseph","contributorId":196667,"corporation":false,"usgs":false,"family":"Travis","given":"Joseph","email":"","affiliations":[],"preferred":false,"id":711054,"contributorType":{"id":1,"text":"Authors"},"rank":9},{"text":"Reznick, David N.","contributorId":196668,"corporation":false,"usgs":false,"family":"Reznick","given":"David","email":"","middleInitial":"N.","affiliations":[],"preferred":false,"id":711055,"contributorType":{"id":1,"text":"Authors"},"rank":10},{"text":"Pringle, Catherine M.","contributorId":176292,"corporation":false,"usgs":false,"family":"Pringle","given":"Catherine","email":"","middleInitial":"M.","affiliations":[],"preferred":false,"id":711056,"contributorType":{"id":1,"text":"Authors"},"rank":11}]}} ]}