In terrestrial and marine ecosystems, remote sensing has been used to estimate gross primary productivity (GPP) for decades, but few applications exist for shallow freshwater ecosystems.Here we show field-based GPP correlates with satellite and airborne lake color across a range of optically and limnologically diverse lakes in interior Alaska. A strong relationship between in situ GPP derived from stable oxygen isotopes (δ18O) and space-based lake color from satellites (e.g. Landsat-8, Sentinel-2 and CubeSats) and airborne imagery (AVIRIS-NG) demonstrates the potential power of this technique for improving spatial and temporal monitoring of lake GPP when coupled with additional field validation measurements across different systems. In shallow waters clear enough for sunlight to reach lake bottoms, both submerged vegetation (macrophytes and algae) and phytoplankton likely contribute to GPP. The stable isotopes and remotely sensed shallow lake color used here integrate both components. These results demonstrate the utility of lake color as a feasible means for mapping lake GPP from remote sensing. This novel methodology estimates GPP from remote sensing in shallow lakes by combining field measurements of oxygen isotopes with airborne, satellite and CubeSat imagery. This use of lake color for providing insight into ecological processes of shallow lakes is recommended, especially for remote arctic and boreal landscapes.
","language":"English","publisher":"ioP Science","doi":"10.1088/1748-9326/aba46f","usgsCitation":"Kuhn, C.D., Bogard, M.J., Johnston, S.E., John, A., Vermote, E., Spencer, R., Dornblaser, M.M., Wickland, K.P., Striegl, R.G., and Butman, D., 2020, Satellite and airborne remote sensing of gross primary productivity in boreal Alaskan lakes: Environmental Research Letters, v. 10, no. 15, 105001, 13 p., https://doi.org/10.1088/1748-9326/aba46f.","productDescription":"105001, 13 p.","ipdsId":"IP-113821","costCenters":[{"id":37277,"text":"WMA - Earth System Processes Division","active":true,"usgs":true}],"links":[{"id":455274,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.1088/1748-9326/aba46f","text":"Publisher Index Page"},{"id":378668,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","state":"Alaska","otherGeospatial":"Yukon Flats Basin","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -148.68347167968747,\n 65.80727906299441\n ],\n [\n -144.0802001953125,\n 65.80727906299441\n ],\n [\n -144.0802001953125,\n 66.59631225137328\n ],\n [\n -148.68347167968747,\n 66.59631225137328\n ],\n [\n -148.68347167968747,\n 65.80727906299441\n ]\n ]\n ]\n }\n }\n ]\n}","volume":"10","issue":"15","noUsgsAuthors":false,"publicationDate":"2020-09-18","publicationStatus":"PW","contributors":{"authors":[{"text":"Kuhn, Catherine D. 0000-0002-9220-630X","orcid":"https://orcid.org/0000-0002-9220-630X","contributorId":213255,"corporation":false,"usgs":false,"family":"Kuhn","given":"Catherine","email":"","middleInitial":"D.","affiliations":[{"id":6934,"text":"University of Washington","active":true,"usgs":false}],"preferred":false,"id":799398,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Bogard, Matthew J. 0000-0001-9491-0328","orcid":"https://orcid.org/0000-0001-9491-0328","contributorId":213254,"corporation":false,"usgs":false,"family":"Bogard","given":"Matthew","email":"","middleInitial":"J.","affiliations":[{"id":6934,"text":"University of Washington","active":true,"usgs":false}],"preferred":false,"id":799399,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Johnston, Sarah Ellen","contributorId":213256,"corporation":false,"usgs":false,"family":"Johnston","given":"Sarah","email":"","middleInitial":"Ellen","affiliations":[{"id":7092,"text":"Florida State University","active":true,"usgs":false}],"preferred":false,"id":799400,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"John, Aji","contributorId":241042,"corporation":false,"usgs":false,"family":"John","given":"Aji","email":"","affiliations":[{"id":6934,"text":"University of Washington","active":true,"usgs":false}],"preferred":false,"id":799401,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Vermote, Eric","contributorId":198856,"corporation":false,"usgs":false,"family":"Vermote","given":"Eric","email":"","affiliations":[],"preferred":false,"id":799402,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"Spencer, Rob 0000-0003-0860-4717","orcid":"https://orcid.org/0000-0003-0860-4717","contributorId":241050,"corporation":false,"usgs":false,"family":"Spencer","given":"Rob","email":"","affiliations":[{"id":7092,"text":"Florida State University","active":true,"usgs":false}],"preferred":false,"id":799403,"contributorType":{"id":1,"text":"Authors"},"rank":6},{"text":"Dornblaser, Mark M. 0000-0002-6298-3757 mmdornbl@usgs.gov","orcid":"https://orcid.org/0000-0002-6298-3757","contributorId":1636,"corporation":false,"usgs":true,"family":"Dornblaser","given":"Mark","email":"mmdornbl@usgs.gov","middleInitial":"M.","affiliations":[{"id":37277,"text":"WMA - 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For waterfowl, most techniques for determining age class require birds in hand, reducing utility for quickly and efficiently sampling a large portion of the population. As an alternative, we sought to establish an observation‐based methodology, achievable in the field with standard optics, for determining age class of Barrow's goldeneyes (Bucephala islandica). We photographed heads, wings, and bellies of 232 Barrow's goldeneyes captured during late winter (February–April) of 2007–2015 along the north Pacific Coast. From these photographs, we focused on 5 external characteristics for both males and females, with binary states that putatively corresponded to 2 age classes—first‐year birds (<1 yr) and adults (>1 yr). For males, all 5 external traits (belly color, head color, eye color, facial crescent, median secondary coverts color) had binary states that were reliably distinguishable by observers. Moreover, all 5 external traits were highly predictive of age class (≥96% concordance between external vs. bursal‐derived ages), and novice observers, after receiving training, were able to accurately age 96% of first‐year and 99% of adult males. In contrast, patterns were weaker for females; putative external characteristics of female age class (belly color, bill radiance, bill blackness, eye color, median secondary coverts color) had 77–91% concordance with bursal‐derived age, compared with 96–100% for males, and observers misidentified age classes of 15% of females, compared with only 2% of males. Overall, age classes of male Barrow's goldeneyes were accurately and reliably distinguishable during winter based on several external characteristics, whereas those of females were not. Our technique may be used to estimate age composition of male Barrow's goldeneyes during winter, providing a useful metric for monitoring annual changes in adult‐to‐juvenile ratios and other important demographic parameters.
","language":"English","publisher":"The Wildlife Society","doi":"10.1002/wsb.1123","usgsCitation":"Lewis, T.L., Esler, D., Hogan, D.H., Boyd, W., Bowman, T.D., and Thompson, J., 2020, Reliability of external characteristics to age Barrow’s goldeneye: Wildlife Society Bulletin, v. 44, no. 4, p. 654-661, https://doi.org/10.1002/wsb.1123.","productDescription":"8 p.","startPage":"654","endPage":"661","ipdsId":"IP-106102","costCenters":[{"id":116,"text":"Alaska Science Center Biology MFEB","active":true,"usgs":true}],"links":[{"id":436786,"rank":0,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/P92GRT3C","text":"USGS data release","linkHelpText":"Data for Determining Age of Barrow's Goldeneye (Bucephala islandica) from External Characteristics, Alaska and Canada, 2007-2015"},{"id":378606,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"Canada, United States","state":"Alaska, British Columbia","otherGeospatial":"Indian Arm, Prince William Sound, Stephens Passage","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -139.5703125,\n 59.66774058164963\n ],\n [\n -132.890625,\n 53.225768435790194\n ],\n [\n -126.298828125,\n 48.86471476180277\n ],\n [\n -123.04687499999999,\n 49.724479188712984\n ],\n [\n -129.814453125,\n 55.47885346331034\n ],\n [\n -135.35156249999997,\n 59.93300042374631\n ],\n [\n -139.5703125,\n 59.66774058164963\n ]\n ]\n ]\n }\n }\n ]\n}","volume":"44","issue":"4","noUsgsAuthors":false,"publicationDate":"2020-09-18","publicationStatus":"PW","contributors":{"authors":[{"text":"Lewis, Tyler L. 0000-0002-7399-8137 tyler.lewis@alaska.gov","orcid":"https://orcid.org/0000-0002-7399-8137","contributorId":241007,"corporation":false,"usgs":true,"family":"Lewis","given":"Tyler","email":"tyler.lewis@alaska.gov","middleInitial":"L.","affiliations":[{"id":114,"text":"Alaska Science Center","active":true,"usgs":true}],"preferred":true,"id":799288,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Esler, Daniel 0000-0001-5501-4555 desler@usgs.gov","orcid":"https://orcid.org/0000-0001-5501-4555","contributorId":5465,"corporation":false,"usgs":true,"family":"Esler","given":"Daniel","email":"desler@usgs.gov","affiliations":[{"id":12437,"text":"Simon Fraser University, Centre for Wildlife Ecology","active":true,"usgs":false},{"id":114,"text":"Alaska Science Center","active":true,"usgs":true},{"id":116,"text":"Alaska Science Center Biology MFEB","active":true,"usgs":true}],"preferred":true,"id":799289,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Hogan, Danica H.","contributorId":241001,"corporation":false,"usgs":false,"family":"Hogan","given":"Danica","email":"","middleInitial":"H.","affiliations":[{"id":48188,"text":"Environment Canada","active":true,"usgs":false}],"preferred":false,"id":799290,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Boyd, W. 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These costly ecological impacts cascade to human communities, and understanding this changing drought landscape is one of today’s grand challenges. By using a modified horizon-scanning approach that integrated scientists, managers, and decision-makers, we identified the emerging issues in ecological drought that represent key challenges to timely and effective responses. Here we review the themes that most urgently need attention, including novel drought conditions, the potential for transformational drought impacts, and the need for anticipatory drought management. This horizon scan and review provides a roadmap to facilitate the research and management innovations that will support forward-looking, co-developed approaches to reduce the risk of drought to our socio-ecological systems during the 21st century.
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York, 2015–17","title":"Use of time domain electromagnetic soundings and borehole electromagnetic induction logs to delineate the freshwater/saltwater interface on southwestern Long Island, New York, 2015–17","docAbstract":"The U.S. Geological Survey, in cooperation with the New York State Department of Environmental Conservation, used surface and borehole geophysical methods to delineate the freshwater/saltwater interface in coastal plain aquifers along the southwestern part of Long Island, New York. Over pumping of groundwater in the early 20th century combined with freshwater/saltwater interfaces at the coastline created saltwater intrusion in the upper glacial, Jameco, Magothy, and Lloyd aquifers. This study documents, for the first time, extensive saltwater intrusion of the Lloyd aquifer along the southwestern coast of Long Island, N.Y. Several public-supply wells in the southern parts of Nassau, Queens, and Kings Counties have been adversely affected by saltwater intrusion causing supply wells to be shutdown and abandoned. Due to the ongoing groundwater pumping in southern Nassau County, the freshwater/saltwater interface requires delineation and monitoring for any inland movement.
In 2015–17, the U.S. Geological Survey collected time domain electromagnetic soundings at 12 locations and borehole electromagnetic induction conductivity logs at 9 wells within the study area to delineate several saltwater intrusion wedges. The upper glacial, Jameco, and Magothy aquifers were grouped into one aquifer complex within the study area to simplify interpretations. The coastal plain sediments increase in thickness from west to east and north to south because of their regional dip toward the southeast. Three separate wedges, shallow, intermediate, and deep, of saltwater intrusion were delineated in the upper glacial, Jameco, and Magothy aquifer complex. In addition, analysis of geophysical logs collected in an open borehole of a test well in southern Queens County in 1989 revealed the Lloyd aquifer was nearly completely intruded by saltwater with an estimated chloride concentration of 15,000 milligrams per liter. The geophysical logs from this well provides, for the first time, definitive proof of saltwater intrusion of the Lloyd aquifer on Long Island’s south shore, suggesting the freshwater/saltwater interface was at the coastline and not miles offshore as theorized by previous studies.
","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/ofr20201093","collaboration":"Prepared in cooperation with the New York State Department of Environmental Conservation","usgsCitation":"Stumm, F., Como, M.D., and Zuck, M.A., 2020, Use of time domain electromagnetic soundings and borehole electromagnetic induction logs to delineate the freshwater/saltwater interface on southwestern Long Island, New York, 2015–17: U.S. Geological Survey Open-File Report 2020–1093, 27 p., https://doi.org/10.3133/ofr20201093.","productDescription":"Report: vi, 27 p.; Data Release; Database","onlineOnly":"Y","additionalOnlineFiles":"Y","ipdsId":"IP-118303","costCenters":[{"id":474,"text":"New York Water Science Center","active":true,"usgs":true}],"links":[{"id":378439,"rank":4,"type":{"id":9,"text":"Database"},"url":"https://doi.org/10.5066/F7X63KT0","linkFileType":{"id":5,"text":"html"},"linkHelpText":"- USGS GeoLog Locator"},{"id":378438,"rank":3,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/P90B6OTX","text":"USGS data release","linkFileType":{"id":5,"text":"html"},"linkHelpText":"Time domain electromagnetic surveys collected to estimate the extent of saltwater intrusion in Nassau and Queens County, New York, October–November 2017"},{"id":378437,"rank":2,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/of/2020/1093/ofr20201093.pdf","text":"Report","size":"3.02 MB","linkFileType":{"id":1,"text":"pdf"},"description":"OFR 2020-1093"},{"id":378436,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/of/2020/1093/coverthb.jpg"}],"country":"United States","state":"New York","otherGeospatial":"Long Island","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -74.03961181640625,\n 40.54406959045767\n ],\n [\n -73.32687377929688,\n 40.54406959045767\n ],\n [\n -73.32687377929688,\n 40.77638178482896\n ],\n [\n -74.03961181640625,\n 40.77638178482896\n ],\n [\n -74.03961181640625,\n 40.54406959045767\n ]\n ]\n ]\n }\n }\n ]\n}","contact":"Director, New York Water Science Center
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Water-quality conditions (including temperature) in the Willamette River and many of its adjacent off-channel features, such as alcoves and side channels, were monitored between river miles 67 (near Salem, Oregon) and 168 (near Eugene, Oregon) during the summers of 2015 and 2016. One or more parameters (water temperature, dissolved oxygen, pH, specific conductance, and [or] water depth) were continuously measured at sites in the main channel (9 sites in 2015; 5 sites in 2016) and select off-channel features (20 features in 2015; 22 features in 2016). This study was initiated in reaction to the unusually warm, dry weather and resulting low streamflows that occurred in the Pacific Northwest in 2015 and the need for flow managers to understand the effects of streamflow on water-quality conditions in off-channel features of the Willamette River. Field monitoring was focused on documenting water-quality conditions during low summer streamflows and during fluctuations in streamflow, including when side channels became alcoves and reconnected to become side channels again.
Water in the main channel of the Willamette River upstream from river mile 50 near Newberg typically is well mixed during summer, with warm water temperatures (greater than 18 degrees Celsius) and high dissolved-oxygen concentrations (often greater than 7.7 milligrams per liter). During low summer flows, a diverse suite of off-channel features exists adjacent to the main channel of the Willamette River. Despite temporal and spatial variability within individual features, comparison of continuous water-temperature data between the main channel and off-channel features indicated that some off-channel features were consistently cooler than the main channel, some were consistently warmer than the main channel, and others frequently fluctuated between warmer or cooler than the main channel. Site-specific characteristics including upstream connection, depth, and presence or absence of aquatic or riparian vegetation were factors that seemed to affect the water quality of a feature.
Results from this study showed a relation between the geomorphology, hydrology, ecology, and water quality of an off-channel feature. Data confirmed that many features that can be classified as cold-water refuges based on water-temperature standards also contained low concentrations of dissolved oxygen that may not be suitable for sensitive fish species. A simplified site classification scheme is proposed that links water-quality conditions in measured off-channel features with site-specific characteristics and summer streamflows. The site classification scheme was extended to create a theoretical process matrix that relates measured water-quality conditions to a list of the processes and site-specific characteristics that could create those conditions.
","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/sir20205068","collaboration":"Prepared in cooperation with the U.S. Army Corps of Engineers, Portland District","usgsCitation":"Smith, C.D., Mangano, J.F., and Rounds, S.A., 2020, Temperature and water-quality diversity and the effects of surface-water connection in off-channel features of the Willamette River, Oregon, 2015–16: U.S. Geological Survey Scientific Investigations Report 2020–5068, 70 p., https://doi.org/10.3133/sir20205068.","productDescription":"Report: viii, 70 p.; 3 Data Releases","onlineOnly":"Y","ipdsId":"IP-102289","costCenters":[{"id":518,"text":"Oregon Water Science Center","active":true,"usgs":true}],"links":[{"id":378475,"rank":5,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/F73T9FPK","text":"USGS data release","description":"USGS Data Release","linkHelpText":"Continuous temperature measurements to assess upstream connection of off-channel features of the middle and upper Willamette River, Oregon, summer, 2016"},{"id":378473,"rank":3,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/F7VQ315D","text":"USGS data release","description":"USGS Data Release","linkHelpText":"Point measurements of temperature and water quality in main-channel and off-channel features of the Willamette River, 2015 -16"},{"id":378472,"rank":2,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/sir/2020/5068/sir20205068.pdf","text":"Report","size":"11.6 MB","linkFileType":{"id":1,"text":"pdf"},"description":"SIR 2020-5068"},{"id":378471,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/sir/2020/5068/coverthb.jpg"},{"id":378474,"rank":4,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/F77M06DV","text":"USGS data release","description":"USGS Data Release","linkHelpText":"Water surface elevations recorded by submerged water level loggers in off-channel features of the middle and upper Willamette River, Oregon, summer, 2016"}],"country":"United States","state":"Oregon","otherGeospatial":"Willamette River basin","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -123.46435546875,\n 44.133333\n ],\n [\n -122.43713378906249,\n 44.133333\n ],\n [\n -122.43713378906249,\n 45.216667\n ],\n [\n -123.46435546875,\n 45.216667\n ],\n [\n -123.46435546875,\n 44.133333\n ]\n ]\n ]\n }\n }\n ]\n}","contact":"Director, Oregon Water Science Center
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2130 SW 5th Avenue
Portland, Oregon 97201
Mercury (Hg) is a naturally occurring element that bonds with organic matter and, when converted to methylmercury, is a potent neurotoxicant. Here we estimate potential future releases of Hg from thawing permafrost for low and high greenhouse gas emissions scenarios using a mechanistic model. By 2200, the high emissions scenario shows annual permafrost Hg emissions to the atmosphere comparable to current global anthropogenic emissions. By 2100, simulated Hg concentrations in the Yukon River increase by 14% for the low emissions scenario, but double for the high emissions scenario. Fish Hg concentrations do not exceed United States Environmental Protection Agency guidelines for the low emissions scenario by 2300, but for the high emissions scenario, fish in the Yukon River exceed EPA guidelines by 2050. Our results indicate minimal impacts to Hg concentrations in water and fish for the low emissions scenario and high impacts for the high emissions scenario.
","language":"English","publisher":"Nature","doi":"10.1038/s41467-020-18398-5","usgsCitation":"Schaefer, K., Elshorbany, Y., Jafarov, E., Schuster, P.F., Striegl, R.G., Wickland, K.P., and Sunderland, E.M., 2020, Potential impacts of mercury released from thawing permafrost: Nature-Communications, v. 11, 4650, 6 p., https://doi.org/10.1038/s41467-020-18398-5.","productDescription":"4650, 6 p.","ipdsId":"IP-115389","costCenters":[{"id":37277,"text":"WMA - Earth System Processes Division","active":true,"usgs":true}],"links":[{"id":455300,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.1038/s41467-020-18398-5","text":"Publisher Index Page"},{"id":378509,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","state":"Alaska","otherGeospatial":"Yukon River basin","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -163.564453125,\n 60.19615576604439\n ],\n [\n -158.466796875,\n 61.52269494598361\n ],\n [\n -153.544921875,\n 63.89873081524394\n ],\n [\n -142.294921875,\n 61.56457388515458\n ],\n [\n -139.658203125,\n 59.88893689676585\n ],\n [\n -137.900390625,\n 60.1524422143808\n ],\n [\n -136.7578125,\n 61.52269494598361\n ],\n [\n -136.0986328125,\n 64.47279382008166\n ],\n [\n -145.3271484375,\n 67.90861918215302\n ],\n [\n -147.744140625,\n 67.97463396204759\n ],\n [\n -153.72070312499997,\n 67.45808150845772\n ],\n [\n -156.88476562499997,\n 66.7745857647255\n ],\n [\n -160.400390625,\n 64.92354174306496\n ],\n [\n -160.83984375,\n 63.64625919492172\n ],\n [\n -163.16894531249997,\n 62.85514553774182\n ],\n [\n -163.916015625,\n 63.37183226679281\n ],\n [\n -165.6298828125,\n 62.28836509824845\n ],\n [\n -163.564453125,\n 60.19615576604439\n ]\n ]\n ]\n }\n }\n ]\n}","volume":"11","noUsgsAuthors":false,"publicationDate":"2020-09-16","publicationStatus":"PW","contributors":{"authors":[{"text":"Schaefer, Kevin 0000-0002-5444-9917","orcid":"https://orcid.org/0000-0002-5444-9917","contributorId":202096,"corporation":false,"usgs":false,"family":"Schaefer","given":"Kevin","email":"","affiliations":[{"id":36340,"text":"National Snow and National Snow and Ice Data Center, Cooperative Institute for Research, Environmental Sciences, University of Colorado at Boulder, Boulder, Colorado, USA","active":true,"usgs":false}],"preferred":false,"id":799032,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Elshorbany, Yasin","contributorId":240870,"corporation":false,"usgs":false,"family":"Elshorbany","given":"Yasin","email":"","affiliations":[{"id":7163,"text":"University of South Florida","active":true,"usgs":false}],"preferred":false,"id":799033,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Jafarov, Elchin","contributorId":195182,"corporation":false,"usgs":false,"family":"Jafarov","given":"Elchin","affiliations":[],"preferred":false,"id":799034,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Schuster, Paul F. 0000-0002-8314-1372 pschuste@usgs.gov","orcid":"https://orcid.org/0000-0002-8314-1372","contributorId":1360,"corporation":false,"usgs":true,"family":"Schuster","given":"Paul","email":"pschuste@usgs.gov","middleInitial":"F.","affiliations":[{"id":5044,"text":"National Research Program - Central Branch","active":true,"usgs":true},{"id":438,"text":"National Research Program - Western Branch","active":true,"usgs":true}],"preferred":true,"id":799035,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Striegl, Robert G. 0000-0002-8251-4659 rstriegl@usgs.gov","orcid":"https://orcid.org/0000-0002-8251-4659","contributorId":1630,"corporation":false,"usgs":true,"family":"Striegl","given":"Robert","email":"rstriegl@usgs.gov","middleInitial":"G.","affiliations":[{"id":37277,"text":"WMA - Earth System Processes Division","active":true,"usgs":true},{"id":5044,"text":"National Research Program - Central Branch","active":true,"usgs":true},{"id":36183,"text":"Hydro-Ecological Interactions Branch","active":true,"usgs":true},{"id":200,"text":"Coop Res Unit Seattle","active":true,"usgs":true}],"preferred":false,"id":799036,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"Wickland, Kimberly P. 0000-0002-6400-0590 kpwick@usgs.gov","orcid":"https://orcid.org/0000-0002-6400-0590","contributorId":1835,"corporation":false,"usgs":true,"family":"Wickland","given":"Kimberly","email":"kpwick@usgs.gov","middleInitial":"P.","affiliations":[{"id":36183,"text":"Hydro-Ecological Interactions Branch","active":true,"usgs":true},{"id":37277,"text":"WMA - Earth System Processes Division","active":true,"usgs":true},{"id":5044,"text":"National Research Program - Central Branch","active":true,"usgs":true}],"preferred":true,"id":799037,"contributorType":{"id":1,"text":"Authors"},"rank":6},{"text":"Sunderland, Elsie M.","contributorId":65376,"corporation":false,"usgs":true,"family":"Sunderland","given":"Elsie","email":"","middleInitial":"M.","affiliations":[],"preferred":false,"id":799090,"contributorType":{"id":1,"text":"Authors"},"rank":7}]}} ,{"id":70214561,"text":"70214561 - 2020 - Wildfire risk and hazardous fuel reduction treatments along the US-Mexico border: A review of the science (1985-2019)","interactions":[],"lastModifiedDate":"2020-09-30T14:30:26.308002","indexId":"70214561","displayToPublicDate":"2020-09-16T09:25:28","publicationYear":"2020","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":686,"text":"Air, Soil and Water Research","active":true,"publicationSubtype":{"id":10}},"title":"Wildfire risk and hazardous fuel reduction treatments along the US-Mexico border: A review of the science (1985-2019)","docAbstract":"The ecosystems along the border between the United States and Mexico are at increasing risk to wildfire due to interactions among climate, land-use, and fuel loads. A wide range of fuel treatments have been implemented to mitigate wildfire and its threats to valued resources, yet we have little information about treatment effectiveness. To fill critical knowledge gaps, we reviewed wildfire risk and fuel treatment studies that were conducted near the US-Mexico border and published in the peer-reviewed literature between 1986 and 2019. The number of studies has grown during this time in warm desert to forest ecosystems on primarily federal lands. The most common study topics included fire effects on native species, the role of invasive species and woody encroachment on wildfire risk, historical fire regimes, and remote sensing and modeling to study wildfire risk across the landscape. A majority of fuel treatment studies focused on prescribed burns, and fuel treatments collectively had mixed effects on mitigating future wildfire risk and threats to ecosystems depending on vegetation and fire characteristics. The diversity of ecosystems and land ownership along the US-Mexico border present unique challenges for understanding and managing wildfire risk, and also create opportunities for collaboration and cross-site studies to promote knowledge across broad environmental gradients.
Power consumption coefficients (PCCs) and dedicated flowmeter records for irrigation wells in three Utah groundwater basins were analyzed to develop a method to better characterize the accuracy of annual groundwater withdrawal estimates. The PCC method has been used by the U.S. Geological Survey in Utah since 1963 as a way to estimate groundwater withdrawal. As a result, most irrigation wells in Utah have historic records consisting of multiple PCCs. Over time, numerous wells have been retrofitted with dedicated flowmeters to more accurately describe groundwater use for irrigation. The combination of historical PCCs and flowmeter data was examined to classify wells as simple, complex, or borderline. The PCCs for each well were statistically analyzed for each period of record to determine the PCC coefficient of variation (CV). Variance, standard deviation, and CV also were calculated for each well, yielding similar results. The CV was selected as the best statistical method for classifying wells. Through field verification and examination of records, CV thresholds were established, allowing wells to be classified as simple, complex, or borderline. This well classification provides information on the uncertainty and best methods for quantifying annual groundwater withdrawals from irrigation wells in a basin.
Annual irrigation groundwater withdrawals in Tooele, Parowan, and Goshen Valleys were calculated by using various combinations of historical PCC records and data from dedicated flowmeters. Differences between annual groundwater withdrawal using the most recent measurements, and historic minimum, maximum, mean, and median PCCs were compared. The smallest percent difference between annual groundwater withdrawal calculated using the most recently measured PCCs, which is the current method for calculating withdrawal in most basins, in Tooele and Parowan Valleys, was 7 and 9 percent respectively, using historical median and mean.
In Goshen Valley, most wells have dedicated flowmeters, and there is a subset of wells that have 2016 power usage data, historical PCC records, and 2016 reported dedicated flowmeter withdrawal. Using this subset of irrigation wells, the smallest percent different between withdrawal from dedicated flowmeters and withdrawal calculated by using other methods was 5 percent (using withdrawal calculated with historical mean PCCs for each well). Annual groundwater withdrawal calculated using the most recently measured PCCs was 9-percent less than dedicated flowmeter reported withdrawal. So, if withdrawal from dedicated flowmeters is as close to reality as possible, then in the case of Goshen Valley, using historical mean PCCs to calculate withdrawal is closer to reality than using the most recently measured PCCs to calculate withdrawal.
","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/ofr20201106","collaboration":"Water Availability and Use Science ProgramDirector, Utah Water Science Center
U.S. Geological Survey
2329 West Orton Circle
Salt Lake City, Utah 84119-2047
Retention, processing, and transport of solutes and particulates in stream corridors are influenced by the travel time of streamflow through stream channels, which varies dynamically with discharge. The effects of streamflow variability across sites and over time cannot be addressed by time-averaged models if parameters are based solely on the characteristics of mean streamflow. We develop methods to account for the effects of streamflow variability on travel time and compare our estimates to flow-weighted (“effective”) travel time at 100 streams in the southeastern United States. Velocity time series were generated for each stream from multiple-year (median 15.5 years), high-frequency (15 min interval) records of instantaneous streamflow and field measurements of velocity and inverted to produce time series of specific travel time [T/L]. The effective travel times for streams are 60–90% of the specific travel time of mean streamflow because a large fraction of the total streamflow volume is discharged during higher flows with higher velocities. We find that adjusting the specific travel time of mean streamflow at a site by a factor of 0.81 generally accounts for the effect of a skewed streamflow distribution, but at-site estimates of the coefficient of variation of streamflow are necessary to resolve differences in streamflow variability between streams or changes in variability over time. For example, the effective travel time of urban streams is less than the effective travel of forested streams in the southeastern United States as a result of increased streamflow variability in urban streams. Effective travel time accounts for both the variation in velocity with streamflow and the large fraction of streamflow discharged during high flows in most streams and provides time-averaged models with limited capability to account for effects of streamflow variability that otherwise they lack. This capability is needed for continental-scale modeling where streamflow variability is not uniform because of heterogeneous surficial geology, hydro-climatology, and vegetation and for applications where streamflow variability is not stationary as a response to climate change or hydrologic alteration.
Manipulative experiments provide stronger evidence for identifying cause-and-effect relationships than correlative studies, but protocols for implementing temperature manipulations are lacking for large species in remote settings. We developed an experimental protocol for holding adult Chinook salmon (Oncorhynchus tshawytscha) and exposing them to elevated temperature treatments. The goal of the experimental protocol was to validate heat stress biomarkers by increasing river water temperature from ambient (~14°C) to a treatment temperature of 18°C or 21°C and then maintain the treatment temperature over 4 hours within a range of ±1.0°C. Our protocol resulted in a mean rate of temperature rise of 3.71°C h-1 (SD = 1.31) to treatment temperatures and mean holding temperatures of 18.0°C (SD = 0.2) and 21.0°C (SD = 0.2) in the low- and high-heat treatments, respectively. Our work demonstrated that manipulative experiments with large, mobile study species can be successfully developed in remote locations to examine thermal stress.
","language":"English","publisher":"Oxford Academic","doi":"10.1093/conphys/coaa074","usgsCitation":"Donnelly, D., von Biela, V.R., McCormick, S.D., Laske, S.M., Carey, M.P., Waters-Dynes, S.C., Bowen, L., Brown, R., Larson, S., and Zimmerman, C.E., 2020, A manipulative thermal challenge protocol for adult salmonids in remote field settings: Conservation Physiology, v. 1, no. 8, coaa074, 11 p., https://doi.org/10.1093/conphys/coaa074.","productDescription":"coaa074, 11 p.","ipdsId":"IP-111875","costCenters":[{"id":114,"text":"Alaska Science Center","active":true,"usgs":true},{"id":365,"text":"Leetown Science Center","active":true,"usgs":true},{"id":651,"text":"Western Ecological Research Center","active":true,"usgs":true}],"links":[{"id":455326,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.1093/conphys/coaa074","text":"Publisher Index Page"},{"id":379683,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"volume":"1","issue":"8","noUsgsAuthors":false,"publicationDate":"2020-09-14","publicationStatus":"PW","contributors":{"authors":[{"text":"Donnelly, Daniel S. 0000-0001-9456-885X","orcid":"https://orcid.org/0000-0001-9456-885X","contributorId":243180,"corporation":false,"usgs":false,"family":"Donnelly","given":"Daniel S.","affiliations":[{"id":48651,"text":"Formally USGS Alaska Science Center","active":true,"usgs":false}],"preferred":false,"id":802824,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"von Biela, Vanessa R. 0000-0002-7139-5981 vvonbiela@usgs.gov","orcid":"https://orcid.org/0000-0002-7139-5981","contributorId":3104,"corporation":false,"usgs":true,"family":"von Biela","given":"Vanessa","email":"vvonbiela@usgs.gov","middleInitial":"R.","affiliations":[{"id":116,"text":"Alaska Science Center Biology 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Center","active":true,"usgs":true},{"id":116,"text":"Alaska Science Center Biology MFEB","active":true,"usgs":true},{"id":120,"text":"Alaska Science Center Water","active":true,"usgs":true}],"preferred":true,"id":802827,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Carey, Michael P. 0000-0002-3327-8995 mcarey@usgs.gov","orcid":"https://orcid.org/0000-0002-3327-8995","contributorId":5397,"corporation":false,"usgs":true,"family":"Carey","given":"Michael","email":"mcarey@usgs.gov","middleInitial":"P.","affiliations":[{"id":120,"text":"Alaska Science Center Water","active":true,"usgs":true},{"id":114,"text":"Alaska Science Center","active":true,"usgs":true},{"id":116,"text":"Alaska Science Center Biology MFEB","active":true,"usgs":true},{"id":117,"text":"Alaska Science Center Biology WTEB","active":true,"usgs":true}],"preferred":true,"id":802828,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"Waters-Dynes, Shannon C. 0000-0002-9707-4684 swaters@usgs.gov","orcid":"https://orcid.org/0000-0002-9707-4684","contributorId":5826,"corporation":false,"usgs":true,"family":"Waters-Dynes","given":"Shannon","email":"swaters@usgs.gov","middleInitial":"C.","affiliations":[{"id":651,"text":"Western Ecological Research Center","active":true,"usgs":true}],"preferred":true,"id":802829,"contributorType":{"id":1,"text":"Authors"},"rank":6},{"text":"Bowen, Lizabeth 0000-0001-9115-4336 lbowen@usgs.gov","orcid":"https://orcid.org/0000-0001-9115-4336","contributorId":4539,"corporation":false,"usgs":true,"family":"Bowen","given":"Lizabeth","email":"lbowen@usgs.gov","affiliations":[{"id":651,"text":"Western Ecological Research Center","active":true,"usgs":true}],"preferred":true,"id":802830,"contributorType":{"id":1,"text":"Authors"},"rank":7},{"text":"Brown, Randy J","contributorId":243248,"corporation":false,"usgs":false,"family":"Brown","given":"Randy J","affiliations":[{"id":48666,"text":"USFWS, Fairbanks, Alaska","active":true,"usgs":false}],"preferred":false,"id":802831,"contributorType":{"id":1,"text":"Authors"},"rank":8},{"text":"Larson, Sean","contributorId":243250,"corporation":false,"usgs":false,"family":"Larson","given":"Sean","email":"","affiliations":[{"id":7058,"text":"Alaska Department of Fish and Game","active":true,"usgs":false}],"preferred":false,"id":802832,"contributorType":{"id":1,"text":"Authors"},"rank":9},{"text":"Zimmerman, Christian E. 0000-0002-3646-0688 czimmerman@usgs.gov","orcid":"https://orcid.org/0000-0002-3646-0688","contributorId":410,"corporation":false,"usgs":true,"family":"Zimmerman","given":"Christian","email":"czimmerman@usgs.gov","middleInitial":"E.","affiliations":[{"id":114,"text":"Alaska Science Center","active":true,"usgs":true},{"id":120,"text":"Alaska Science Center Water","active":true,"usgs":true},{"id":116,"text":"Alaska Science Center Biology MFEB","active":true,"usgs":true}],"preferred":true,"id":802833,"contributorType":{"id":1,"text":"Authors"},"rank":10}]}} ,{"id":70213199,"text":"70213199 - 2020 - Land-use change and future water demand in California’s central coast","interactions":[],"lastModifiedDate":"2020-09-15T12:17:33.92982","indexId":"70213199","displayToPublicDate":"2020-09-14T07:07:05","publicationYear":"2020","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":2596,"text":"Land","active":true,"publicationSubtype":{"id":10}},"title":"Land-use change and future water demand in California’s central coast","docAbstract":"Understanding future land-use related water demand is important for planners and resource managers in identifying potential shortages and crafting mitigation strategies. This is especially the case for regions dependent on limited local groundwater supplies. For the groundwater dependent Central Coast of California, we developed two scenarios of future land use and water demand based on sampling from a historical land change record: a business-as-usual scenario (BAU; 1992–2016) and a recent-modern scenario (RM; 2002–2016). We modeled the scenarios in the stochastic, empirically based, spatially explicit LUCAS state-and-transition simulation model at a high resolution (270-m) for the years 2001–2100 across 10 Monte Carlo simulations, applying current land zoning restrictions. Under the BAU scenario, regional water demand increased by an estimated ~222.7 Mm3 by 2100, driven by the continuation of perennial cropland expansion as well as higher than modern urbanization rates. Since 2000, mandates have been in place restricting new development unless adequate water resources could be identified. Despite these restrictions, water demand dramatically increased in the RM scenario by 310.6 Mm3 by century’s end, driven by the projected continuation of dramatic orchard and vineyard expansion trends. Overall, increased perennial cropland leads to a near doubling to tripling perennial water demand by 2100. Our scenario projections can provide water managers and policy makers with information on diverging land use and water use futures based on observed land change and water use trends, helping to better inform land and resource management decisions.
","language":"English","publisher":"MDPI","doi":"10.3390/land9090322","usgsCitation":"Wilson, T., Van Schmidt, N.D., and Langridge, R., 2020, Land-use change and future water demand in California’s central coast: Land, v. 9, no. 322, p. 322-343, https://doi.org/10.3390/land9090322.","productDescription":"21 p.","startPage":"322","endPage":"343","ipdsId":"IP-112033","costCenters":[{"id":657,"text":"Western Geographic Science Center","active":true,"usgs":true}],"links":[{"id":455329,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.3390/land9090322","text":"Publisher Index Page"},{"id":378385,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","state":"California","county":"Santa Cruz, San Benito, Monterey, San Luis Obispo, & Santa Barbara","otherGeospatial":"central California coast","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -120.838623046875,\n 35.585851593232356\n ],\n [\n -121.06109619140625,\n 35.505400093441324\n ],\n [\n -120.89904785156251,\n 35.22094130403182\n ],\n [\n -120.66558837890626,\n 34.89043681762452\n ],\n [\n -120.63812255859375,\n 34.76417891445512\n ],\n [\n -120.58319091796874,\n 34.646766246519114\n ],\n [\n -120.22613525390624,\n 34.80252766591687\n ],\n [\n -120.0640869140625,\n 34.872411827691025\n ],\n [\n -120.838623046875,\n 35.585851593232356\n ]\n ]\n ]\n }\n }\n ]\n}","volume":"9","issue":"322","noUsgsAuthors":false,"publicationDate":"2020-09-14","publicationStatus":"PW","contributors":{"authors":[{"text":"Wilson, Tamara 0000-0001-7399-7532 tswilson@usgs.gov","orcid":"https://orcid.org/0000-0001-7399-7532","contributorId":2975,"corporation":false,"usgs":true,"family":"Wilson","given":"Tamara","email":"tswilson@usgs.gov","affiliations":[{"id":657,"text":"Western Geographic Science Center","active":true,"usgs":true}],"preferred":true,"id":798599,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Van Schmidt, Nathan D. 0000-0002-5973-7934","orcid":"https://orcid.org/0000-0002-5973-7934","contributorId":240648,"corporation":false,"usgs":false,"family":"Van Schmidt","given":"Nathan","middleInitial":"D.","affiliations":[{"id":32898,"text":"U.C. Santa Cruz","active":true,"usgs":false}],"preferred":false,"id":798600,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Langridge, Ruth 0000-0001-5320-8882","orcid":"https://orcid.org/0000-0001-5320-8882","contributorId":240649,"corporation":false,"usgs":false,"family":"Langridge","given":"Ruth","email":"","affiliations":[{"id":32898,"text":"U.C. Santa Cruz","active":true,"usgs":false}],"preferred":false,"id":798601,"contributorType":{"id":1,"text":"Authors"},"rank":3}]}} ,{"id":70212635,"text":"70212635 - 2020 - Using boosted regression tree models to predict salinity in Mississippi embayment aquifers, central United States","interactions":[],"lastModifiedDate":"2023-11-08T16:13:16.263836","indexId":"70212635","displayToPublicDate":"2020-09-13T13:38:03","publicationYear":"2020","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":6465,"text":"Journal of American Water Resources Association","active":true,"publicationSubtype":{"id":10}},"title":"Using boosted regression tree models to predict salinity in Mississippi embayment aquifers, central United States","docAbstract":"High salinity limits groundwater use in parts of the Mississippi embayment. Machine learning was used to create spatially continuous and three‐dimensional predictions of salinity across drinking‐water aquifers in the embayment. Boosted regression tree (BRT) models, a type of machine learning, were used to predict specific conductance (SC) and chloride (Cl), and total dissolved solids (TDS) was calculated from a correlation with SC. Explanatory variables for BRT models included well location and construction, surficial variables (e.g., soils and land use), and variables extracted from a groundwater‐flow model, including simulated groundwater ages. BRT model fits (r2) were 0.74 (SC and Cl) and 0.62 (TDS). BRT models provided spatially continuous salinity predictions across surficial and deeper aquifers where discrete water‐quality samples were missing. Uncertainty was smaller where salinity was lower, and models tended to underpredict in areas of highest salinity. Despite this, BRT models were able to capture areas of documented high salinity that exceed the TDS secondary maximum contaminant level for drinking water of 500 mg/L. Variables that served as surrogates for position along groundwater flowpaths were the most important predictors, indicating that much of the control on dissolved solids is related to rock‐water interaction as residence time increases. BRT models additionally support hypotheses of both surficial and deep sources of salinity.
","language":"English","publisher":"Wiley","doi":"10.1111/1752-1688.12879","usgsCitation":"Knierim, K.J., Kingsbury, J.A., Haugh, C., and Ransom, K.M., 2020, Using boosted regression tree models to predict salinity in Mississippi embayment aquifers, central United States: Journal of American Water Resources Association, v. 56, no. 6, https://doi.org/10.1111/1752-1688.12879.","productDescription":"20 p.","startPage":"1029","ipdsId":"IP-111775","costCenters":[{"id":154,"text":"California Water Science Center","active":true,"usgs":true},{"id":24708,"text":"Lower Mississippi-Gulf Water Science Center","active":true,"usgs":true},{"id":37273,"text":"Advanced Research Computing (ARC)","active":true,"usgs":true}],"links":[{"id":455333,"rank":3,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.1111/1752-1688.12879","text":"Publisher Index Page"},{"id":436791,"rank":2,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/P9WBFR1T","text":"USGS data release","linkHelpText":"Machine-learning model predictions and groundwater-quality rasters of specific conductance, total dissolved solids, and chloride in aquifers of the Mississippi embayment"},{"id":382516,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","state":"Alabama, Arkansas, Kentucky, Louisiana, Mississippi, Missouri, Tennessee","otherGeospatial":"Mississippi Embayment","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -89.3408203125,\n 36.98500309285596\n ],\n [\n -90.52734374999999,\n 36.73888412439431\n ],\n [\n -92.3291015625,\n 34.66935854524543\n ],\n [\n -93.779296875,\n 32.32427558887655\n ],\n [\n -92.548828125,\n 31.240985378021307\n ],\n [\n -90.52734374999999,\n 32.509761735919426\n ],\n [\n -88.857421875,\n 32.10118973232094\n ],\n [\n -87.2314453125,\n 30.789036751261136\n ],\n [\n -86.923828125,\n 31.690781806136822\n ],\n [\n -87.275390625,\n 32.879587173066305\n ],\n [\n -88.9453125,\n 33.87041555094183\n ],\n [\n -89.2529296875,\n 35.17380831799959\n ],\n [\n -88.6376953125,\n 36.59788913307022\n ],\n [\n -88.76953125,\n 36.914764288955936\n ],\n [\n -89.3408203125,\n 36.98500309285596\n ]\n ]\n ]\n }\n }\n ]\n}","volume":"56","issue":"6","edition":"1010","noUsgsAuthors":false,"publicationDate":"2020-09-13","publicationStatus":"PW","contributors":{"authors":[{"text":"Knierim, Katherine J. 0000-0002-5361-4132 kknierim@usgs.gov","orcid":"https://orcid.org/0000-0002-5361-4132","contributorId":191788,"corporation":false,"usgs":true,"family":"Knierim","given":"Katherine","email":"kknierim@usgs.gov","middleInitial":"J.","affiliations":[{"id":24708,"text":"Lower Mississippi-Gulf Water Science Center","active":true,"usgs":true}],"preferred":true,"id":797182,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Kingsbury, James A. 0000-0003-4985-275X jakingsb@usgs.gov","orcid":"https://orcid.org/0000-0003-4985-275X","contributorId":883,"corporation":false,"usgs":true,"family":"Kingsbury","given":"James","email":"jakingsb@usgs.gov","middleInitial":"A.","affiliations":[{"id":37277,"text":"WMA - Earth System Processes Division","active":true,"usgs":true},{"id":451,"text":"National Water Quality Assessment Program","active":true,"usgs":true},{"id":581,"text":"Tennessee Water Science Center","active":true,"usgs":true},{"id":24708,"text":"Lower Mississippi-Gulf Water Science Center","active":true,"usgs":true}],"preferred":true,"id":797183,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Haugh, Connor J. 0000-0002-5204-8271","orcid":"https://orcid.org/0000-0002-5204-8271","contributorId":219945,"corporation":false,"usgs":true,"family":"Haugh","given":"Connor J.","affiliations":[{"id":24708,"text":"Lower Mississippi-Gulf Water Science Center","active":true,"usgs":true}],"preferred":true,"id":797184,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Ransom, Katherine Marie 0000-0001-6195-7699","orcid":"https://orcid.org/0000-0001-6195-7699","contributorId":239552,"corporation":false,"usgs":true,"family":"Ransom","given":"Katherine","email":"","middleInitial":"Marie","affiliations":[{"id":154,"text":"California Water Science Center","active":true,"usgs":true}],"preferred":true,"id":797185,"contributorType":{"id":1,"text":"Authors"},"rank":4}]}} ,{"id":70218454,"text":"70218454 - 2020 - Hydro-climatic drought in the Delaware River Basin","interactions":[],"lastModifiedDate":"2021-02-26T13:54:09.110536","indexId":"70218454","displayToPublicDate":"2020-09-13T07:50:23","publicationYear":"2020","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":2529,"text":"Journal of the American Water Resources Association","active":true,"publicationSubtype":{"id":10}},"title":"Hydro-climatic drought in the Delaware River Basin","docAbstract":"The Delaware River Basin (DRB) supplies water to approximately 15 million people and is essential to agriculture and industry. In this study, a monthly water balance model is used to compute monthly water balance components (i.e., potential evapotranspiration, actual evapotranspiration, and runoff [R]) for the DRB for the 1901 through 2015 period. Water‐year R is used to identify drought periods in the basin and seven drought periods were identified. All but one of the drought periods occurred before about 1970; after this date, precipitation increased in the DRB and droughts were infrequent. The seven droughts were largely driven by precipitation deficits, rather than by unusually warm temperatures. For six of the seven droughts, the precipitation deficits were associated with atmospheric pressure patterns that resulted in northerly wind anomalies (i.e., conditions that deviate from the long‐term mean) over the basin that indicate an anomalous flow of dry air from the North American continent into the DRB. An examination of drought events estimated from a tree ring–based reconstruction of the Palmer Drought Severity Index for the 490 through 2005 time period indicates that although there were some DRB droughts that were longer and more severe during previous centuries, the DRB droughts during 1901 through 2015 were comparable in duration and severity to most drought events during previous centuries.
Chinook salmon (Oncorhynchus tshawytscha) declines are widespread and may be attributed, at least in part, to warming river temperatures. Water temperatures in the Yukon River and tributaries often exceed 18°C, a threshold commonly associated with heat stress and elevated mortality in Pacific salmon. Untangling the complex web of direct and indirect physiological effects of heat stress on salmon is difficult in a natural setting with innumerable system challenges but is necessary to increase our understanding of both lethal and sublethal impacts of heat stress on populations. The goal of this study was to characterize the cellular stress response in multiple Chinook salmon tissues after acute elevated temperature challenges. We conducted a controlled 4-hour temperature exposure (control, 18°C and 21°C) experiment on the bank of the Yukon River followed by gene expression (GE) profiling using a 3′-Tag-RNA-Seq protocol. The full transcriptome was analysed for 22 Chinook salmon in muscle, gill and liver tissue. Both the 21°C and 18°C treatments induced greater activity in genes associated with protein folding (e.g. HSP70, HSP90 mRNA) processes in all tissues. Global GE patterns indicate that transcriptomic responses to heat stress were highly tissue-specific, underscoring the importance of analyzing multiple tissues for determination of physiological effect. Primary superclusters (i.e. groupings of loosely related terms) of altered biological processes were identified in each tissue type, including regulation of DNA damage response (gill), regulation by host of viral transcription (liver) and regulation of the force of heart contraction (muscle) in the 21°C treatment. This study provides insight into mechanisms potentially affecting adult Chinook salmon as they encounter warm water during their spawning migration in the Yukon River and suggests that both basic and more specialized cellular functions may be disrupted.
","language":"English","publisher":"Oxford Academic","doi":"10.1093/conphys/coaa084","usgsCitation":"Bowen, L., von Biela, V.R., McCormick, S.D., Regish, A.M., Waters-Dynes, S.C., Durbin-Johnson, B., Britton, M., Settles, M., Donnelly, D., Laske, S.M., Carey, M.P., Brown, R., and Zimmerman, C.E., 2020, Transcriptomic response to elevated water temperatures in adult migrating Yukon River Chinook salmon (Oncorhynchus tshawytscha): Conservation Physiology, v. 8, no. 1, coaa084, 7 p., https://doi.org/10.1093/conphys/coaa084.","productDescription":"coaa084, 7 p.","ipdsId":"IP-112827","costCenters":[{"id":114,"text":"Alaska Science Center","active":true,"usgs":true},{"id":365,"text":"Leetown Science Center","active":true,"usgs":true},{"id":651,"text":"Western Ecological Research Center","active":true,"usgs":true}],"links":[{"id":455343,"rank":1,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.1093/conphys/coaa084","text":"Publisher Index Page"},{"id":436792,"rank":0,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/P9ECW77M","text":"USGS data release","linkHelpText":"Water Temperature and Dissolved Oxygen Measured During a Manipulative Thermal Challenge Experiment for Adult Salmonids, Yukon River, Alaska, 2018"},{"id":379459,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","state":"Alaska","otherGeospatial":"Yukon River","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -163.564453125,\n 60.19615576604439\n ],\n [\n -158.466796875,\n 61.52269494598361\n ],\n [\n -153.544921875,\n 63.89873081524394\n ],\n [\n -142.294921875,\n 61.56457388515458\n ],\n [\n -139.658203125,\n 59.88893689676585\n ],\n [\n -137.900390625,\n 60.1524422143808\n ],\n [\n -136.7578125,\n 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Davis","active":true,"usgs":false}],"preferred":false,"id":801873,"contributorType":{"id":1,"text":"Authors"},"rank":7},{"text":"Settles, Matt","contributorId":243243,"corporation":false,"usgs":false,"family":"Settles","given":"Matt","email":"","affiliations":[{"id":7214,"text":"University of California, Davis","active":true,"usgs":false}],"preferred":false,"id":801874,"contributorType":{"id":1,"text":"Authors"},"rank":8},{"text":"Donnelly, Daniel S. 0000-0001-9456-885X","orcid":"https://orcid.org/0000-0001-9456-885X","contributorId":243180,"corporation":false,"usgs":false,"family":"Donnelly","given":"Daniel S.","affiliations":[{"id":48651,"text":"Formally USGS Alaska Science Center","active":true,"usgs":false}],"preferred":false,"id":801875,"contributorType":{"id":1,"text":"Authors"},"rank":9},{"text":"Laske, Sarah M. 0000-0002-6096-0420 slaske@usgs.gov","orcid":"https://orcid.org/0000-0002-6096-0420","contributorId":204872,"corporation":false,"usgs":true,"family":"Laske","given":"Sarah","email":"slaske@usgs.gov","middleInitial":"M.","affiliations":[{"id":116,"text":"Alaska Science Center Biology MFEB","active":true,"usgs":true},{"id":120,"text":"Alaska Science Center Water","active":true,"usgs":true},{"id":114,"text":"Alaska Science Center","active":true,"usgs":true}],"preferred":true,"id":801876,"contributorType":{"id":1,"text":"Authors"},"rank":10},{"text":"Carey, Michael P. 0000-0002-3327-8995 mcarey@usgs.gov","orcid":"https://orcid.org/0000-0002-3327-8995","contributorId":5397,"corporation":false,"usgs":true,"family":"Carey","given":"Michael","email":"mcarey@usgs.gov","middleInitial":"P.","affiliations":[{"id":114,"text":"Alaska Science Center","active":true,"usgs":true},{"id":116,"text":"Alaska Science Center Biology MFEB","active":true,"usgs":true},{"id":120,"text":"Alaska Science Center 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Center","active":true,"usgs":true}],"preferred":true,"id":801880,"contributorType":{"id":1,"text":"Authors"},"rank":13}]}} ,{"id":70213107,"text":"ofr20201086 - 2020 - Impacts of periodic dredging on macroinvertebrate prey availability for benthic foraging fishes in central San Francisco Bay, California","interactions":[],"lastModifiedDate":"2020-09-14T12:29:00.575115","indexId":"ofr20201086","displayToPublicDate":"2020-09-11T07:59:47","publicationYear":"2020","noYear":false,"publicationType":{"id":18,"text":"Report"},"publicationSubtype":{"id":5,"text":"USGS Numbered Series"},"seriesTitle":{"id":330,"text":"Open-File Report","code":"OFR","onlineIssn":"2331-1258","printIssn":"0196-1497","active":true,"publicationSubtype":{"id":5}},"seriesNumber":"2020-1086","displayTitle":"Impacts of Periodic Dredging on Macroinvertebrate Prey Availability for Benthic Foraging Fishes in Central San Francisco Bay, California","title":"Impacts of periodic dredging on macroinvertebrate prey availability for benthic foraging fishes in central San Francisco Bay, California","docAbstract":"Because of its importance for species covered under Federal Fishery Management Plans (FMPs), the San Francisco Bay (SFB) estuary has been designated as Essential Fish Habitat (EFH) under the Magnuson-Stevens Fishery Conservation and Management Act (MSA; 16 United States Code §18559b). Within this estuary, benthic macroinvertebrate communities provide important prey resources for many economically significant fish species that rely on EFH. Periodic maintenance dredging can impact benthic communities; however, there is a lack of scientific information specific to SFB regarding dredging effects on macroinvertebrates in fish foraging areas. In addition, rates of benthic community recolonization and recovery following dredging and subsequent effects on foraging fish are unknown. For this reason, it is difficult for regulatory and resource agencies to determine the impacts of maintenance dredging. Thus, the National Marine Fisheries Service (NMFS) and the consortium of agencies (U.S. Environmental Protection Agency [EPA], U.S. Army Corp of Engineers [USACE], San Francisco Regional Water Quality Control Board [SFRWQCB], and San Francisco Bay Conservation and Development Commission [BCDC]) that make up the San Francisco Bay Long Term Management Strategy for Dredging (LTMS) identified a study of dredging impacts on SFB fish foraging habitat as one of their highest priorities in their 2011 Programmatic EFH Agreement (U.S. Army Corp of Engineers and U.S. Environmental Protection Agency, 2011).
The LTMS agencies identified the region of interest as shallow (<13 feet [<4 meters (m)] mean lower low water [MLLW]), soft-bottom (silt/clay soil texture) areas in the Central Bay of SFB that were periodically dredged (every 1–3 years). Fish species of interest were compiled by NMFS and included those managed by the Pacific Groundfish, Pacific Salmon, and Coastal Pelagic FMPs (pursuant to the MSA) as well as those listed under the California State or Federal Endangered Species Act (ESA; 16 U.S.C. §1531–1544) as threatened or endangered. Target species included leopard shark (Triakis semifasciata), big skate (Raja binoculata), English sole (Parophrys vetulus), starry flounder (Platichthys stellatus), brown rockfish (Sebastes auriculatus), green sturgeon (Acipenser medirostris; threatened species under Federal ESA), northern anchovy (Engraulis mordax), longfin smelt (Spirinchus thaleichthys, threatened under California ESA), and Pacific sardine (Sardinops sagax). In addition, Dungeness crab (Cancer magister), California halibut (Paralichthys californicus), and white sturgeon (Acipenser transmontanus) also were included because they are substantial contributors to the California State fishery.
To address LTMS priorities, U.S. Geological Survey, Western Ecological Research Center, San Francisco Bay Estuary Field Station (hereafter USGS) conducted a multi-phased project including an initial literature review, study design, pilot study, and implementation of a full study. The overarching goal was to assess the effects of periodic dredge operations (every 1–3 years) on benthic habitat for foraging fish in the Central Bay, with emphasis on the foraging requirements of target fish species and analyses of benthic macroinvertebrates in dredged areas compared to adjacent undredged reference areas. The USGS partnered with University of California, Davis, fisheries expert James Hobbs to synthesize existing knowledge of fish foraging ecology and review benthic infauna community composition in SFB with a focus on the Central Bay. The literature review (Phase I; De La Cruz and others, 2016) addressed key questions identified by the LTMS on benthic foraging fish in the study area, including the following: (1) What are target fish eating? (2) What are the seasonal differences in prey items and macroinvertebrate assemblages? (3) What are the annual differences in prey items and macroinvertebrate assemblages? (4) What are the predominant macroinvertebrate functional groups from the perspective of fish foraging? Phase II consisted of creating a framework for a functional assessment of maintenance dredging effects on foraging fish and drafting a full study design (De La Cruz and others, 2017), which was then tested in the Phase III pilot study. The Phase IV full study incorporated lessons learned from the pilot study. Here we focus on the results of the full study and implications for benthic foraging fishes.
","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/ofr20201086","usgsCitation":"De La Cruz, S.E.W., Woo, I., Hall, L., Flanagan, A., and Mittelstaedt, H., 2020, Impacts of periodic dredging on macroinvertebrate prey availability for benthic foraging fishes in central San Francisco Bay, California: U.S. Geological Survey Open-File Report 2020–1086, 96 p., https://doi.org/10.3133/ofr20201086.","productDescription":"x, 96 p.","onlineOnly":"Y","ipdsId":"IP-112237","costCenters":[{"id":651,"text":"Western Ecological Research Center","active":true,"usgs":true}],"links":[{"id":378273,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/of/2020/1086/coverthb.jpg"},{"id":378274,"rank":2,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/of/2020/1086/ofr20201086.pdf","text":"Report","size":"13.1 MB","linkFileType":{"id":1,"text":"pdf"},"description":"OFR 2020-1086"}],"country":"United States","state":"California","otherGeospatial":"Central San Francisco Bay","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -122.65411376953125,\n 37.75334401310656\n ],\n [\n -122.17346191406249,\n 37.75334401310656\n ],\n [\n -122.17346191406249,\n 37.98317483351337\n ],\n [\n -122.65411376953125,\n 37.98317483351337\n ],\n [\n -122.65411376953125,\n 37.75334401310656\n ]\n ]\n ]\n }\n }\n ]\n}","contact":"Director, Western Ecological Research Center
U.S. Geological Survey
3020 State University Drive East
Sacramento, California 95819
The Southeast Missouri Lead District is among the most productive lead deposits exploited in modern times. Intensive mining conducted prior to regulations resulted in a legacy of lead contaminated soil, large piles of mine tailings and elevated childhood blood lead levels. This study seeks to identify the source of the lead contamination in the Big River and inform risk to the public. Isotopic analysis indicated the mine tailing piles at the head of the Big River are the primary source of the lead contamination. The isotopic signature of the lead in these mine tailings matched the lead over 100 km downstream. All of the other potential lead sources investigated had different isotopic signatures. Lead concentrations in soils and sediments decrease with distance downstream of the mine tailings piles. Additionally, the speciation of the lead changes from predominantly mineralized forms, such as galena, to adsorbed lead. This is reflected in the in-vitro bioaccessibility assay (IVBA) analysis which shows higher bioaccessibility further downstream, demonstrating the importance of speciation in risk evaluation.
","language":"English","publisher":"Elsevier","doi":"10.1016/j.apgeochem.2020.104757","usgsCitation":"Noerpel, M., Pribil, M., Rutherford, D., Law, P., Bradham, K., Nelson, C., Weber, R., Gunn, G., and Scheckel, K.G., 2020, Lead speciation, bioaccessibility and source attribution in Missouri's Big River watershed: Applied Geochemistry, v. 123, 104757, 11 p., https://doi.org/10.1016/j.apgeochem.2020.104757.","productDescription":"104757, 11 p.","ipdsId":"IP-115151","costCenters":[{"id":35995,"text":"Geology, Geophysics, and Geochemistry Science Center","active":true,"usgs":true}],"links":[{"id":455349,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://www.ncbi.nlm.nih.gov/pmc/articles/7787989","text":"Publisher Index Page"},{"id":385438,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","state":"Missouri","otherGeospatial":"Big River 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Development","active":true,"usgs":false}],"preferred":false,"id":815156,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"Nelson, Clay","contributorId":257858,"corporation":false,"usgs":false,"family":"Nelson","given":"Clay","email":"","affiliations":[{"id":52137,"text":"United States Environmental Protection Agency, Office of Research and Development","active":true,"usgs":false}],"preferred":false,"id":815157,"contributorType":{"id":1,"text":"Authors"},"rank":6},{"text":"Weber, Rob","contributorId":257859,"corporation":false,"usgs":false,"family":"Weber","given":"Rob","email":"","affiliations":[{"id":52138,"text":"United States Environmental Protection Agency, Region 7","active":true,"usgs":false}],"preferred":false,"id":815158,"contributorType":{"id":1,"text":"Authors"},"rank":7},{"text":"Gunn, Gene","contributorId":257860,"corporation":false,"usgs":false,"family":"Gunn","given":"Gene","email":"","affiliations":[{"id":52138,"text":"United States Environmental Protection Agency, Region 7","active":true,"usgs":false}],"preferred":false,"id":815159,"contributorType":{"id":1,"text":"Authors"},"rank":8},{"text":"Scheckel, Kirk G.","contributorId":167121,"corporation":false,"usgs":false,"family":"Scheckel","given":"Kirk","email":"","middleInitial":"G.","affiliations":[{"id":6914,"text":"U.S. Environmental Protection Agency","active":true,"usgs":false}],"preferred":false,"id":815160,"contributorType":{"id":1,"text":"Authors"},"rank":9}]}} ,{"id":70228423,"text":"70228423 - 2020 - Effect of water velocity and temperature on energy use, behaviour and mortality of pallid sturgeon Scaphirhynchus albus larvae","interactions":[],"lastModifiedDate":"2022-02-10T15:47:38.190457","indexId":"70228423","displayToPublicDate":"2020-09-10T09:44:45","publicationYear":"2020","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":2285,"text":"Journal of Fish Biology","active":true,"publicationSubtype":{"id":10}},"displayTitle":"Effect of water velocity and temperature on energy use, behaviour and mortality of pallid sturgeon Scaphirhynchus albus larvae","title":"Effect of water velocity and temperature on energy use, behaviour and mortality of pallid sturgeon Scaphirhynchus albus larvae","docAbstract":"Natural reproduction of pallid sturgeon Scaphirhynchus albus has been limited for decades and a recruitment bottleneck is hypothesized to occur during the larval stage of development. In this study, we evaluated the effects of water velocity and temperature on the swimming activity, energy use, settling behaviour and mortality of endogenously feeding larvae. The swimming activity of drifting sturgeon larvae (i.e., fish exhibiting negative rheotaxis) increased at low water velocity. In subsequent experiments, we observed greater energy depletion and resultant mortality of larvae in no-flow environments (0 cm s−1) compared to tanks with water velocity ranging from 3.5 to 8.3 cm s−1. The growth rate of drifting larvae was positively related to water temperature (18.7–23.3°C), but reduced growth rate at low water temperature (18.7°C) resulted in protracted development that extended average drift duration by ~4 days compared to larvae reared at 23.3°C. This study provides evidence that cooler summer water temperatures, characteristic of present-day conditions in the upper Missouri River, can reduce larval development and extend both the drift duration and distance requirements of S. albus. Moreover, if dispersed into low velocity environments, such as in reservoir headwaters, larvae may experience increased mortality owing to a mismatch between early life stage drift requirements and habitat conditions in the river. Manipulation of water releases to increase seasonal water temperature below dams may aid survival of S. albus larvae by shortening the time and distance spent drifting.
","language":"English","publisher":"Wiley","doi":"10.1111/jfb.14532","usgsCitation":"Mrnak, J.T., Heironimus, L., James, D., and Chipps, S.R., 2020, Effect of water velocity and temperature on energy use, behaviour and mortality of pallid sturgeon Scaphirhynchus albus larvae: Journal of Fish Biology, v. 97, no. 6, p. 1690-1700, https://doi.org/10.1111/jfb.14532.","productDescription":"11 p.","startPage":"1690","endPage":"1700","ipdsId":"IP-115757","costCenters":[{"id":199,"text":"Coop Res Unit Leetown","active":true,"usgs":true}],"links":[{"id":455351,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://www.osti.gov/biblio/1804961","text":"Publisher Index Page"},{"id":395772,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"volume":"97","issue":"6","noUsgsAuthors":false,"publicationDate":"2020-10-05","publicationStatus":"PW","contributors":{"authors":[{"text":"Mrnak, Joseph T.","contributorId":275764,"corporation":false,"usgs":false,"family":"Mrnak","given":"Joseph","email":"","middleInitial":"T.","affiliations":[{"id":7122,"text":"University of Wisconsin","active":true,"usgs":false}],"preferred":false,"id":834270,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Heironimus, Laura B.","contributorId":275765,"corporation":false,"usgs":false,"family":"Heironimus","given":"Laura B.","affiliations":[{"id":12438,"text":"Washington Department of Fish and Wildlife","active":true,"usgs":false}],"preferred":false,"id":834271,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"James, Daniel A.","contributorId":275768,"corporation":false,"usgs":false,"family":"James","given":"Daniel A.","affiliations":[{"id":12428,"text":"U. S. Fish and Wildlife Service","active":true,"usgs":false}],"preferred":false,"id":834272,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Chipps, Steven R. 0000-0001-6511-7582 steve_chipps@usgs.gov","orcid":"https://orcid.org/0000-0001-6511-7582","contributorId":2243,"corporation":false,"usgs":true,"family":"Chipps","given":"Steven","email":"steve_chipps@usgs.gov","middleInitial":"R.","affiliations":[{"id":199,"text":"Coop Res Unit Leetown","active":true,"usgs":true}],"preferred":true,"id":834269,"contributorType":{"id":1,"text":"Authors"},"rank":4}]}} ,{"id":70213260,"text":"70213260 - 2020 - Spatial and vertical bias in down-looking ship-based acoustic estimates of fish density in Lake Superior: Lessons learned from multi-directional acoustics","interactions":[],"lastModifiedDate":"2025-02-07T15:20:59.464877","indexId":"70213260","displayToPublicDate":"2020-09-10T09:20:09","publicationYear":"2020","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":2330,"text":"Journal of Great Lakes Research","active":true,"publicationSubtype":{"id":10}},"title":"Spatial and vertical bias in down-looking ship-based acoustic estimates of fish density in Lake Superior: Lessons learned from multi-directional acoustics","docAbstract":"Hydroacoustic surveys using hull-mounted down-looking transducers are useful for estimating pelagic fish densities; however, this method may miss shallow fish owing to the acoustic surface dead zone and vessel avoidance. Our objective was to compare pelagic fish density estimates acquired by a traditional down-looking acoustic survey to estimates obtained by a new multi-directional-towed sled capable of sampling the entire water column using upward-, sideways-, and downward-aimed transducers simultaneously. We deployed both systems concurrently in the western arm of Lake Superior during a period of stable stratification. We found the two survey approaches provided significantly different estimates of fish density in the upper water column layer (~4–9 m below the lake surface) with the sled up-looking transducer providing 56 times higher densities compared to the traditional ship down-looking method. Densities also varied significantly in the 9–14 m layer where densities were 6.2 times higher in the sled survey. Midwater trawl sampling indicated that cisco (Coregonus artedi) and rainbow smelt (Osmerus mordax) were the predominant species occupying the uppermost 14 m of the water column. The two acoustic approaches provided similar results at water column depths >14 m where rainbow smelt and kiyi (Coregonus kiyi) were predominant. Overall, the sled-based method estimates were, on average, 2.5 times higher for the whole water column. Our findings show that the new sled can reduce bias by better sampling the surface dead zone leading to more accurate estimation of pelagic fish densities for both management and research.
","language":"English","publisher":"Elsevier","doi":"10.1016/j.jglr.2020.08.010","usgsCitation":"Grow, R.C., Hrabik, T.R., Yule, D., Matthias, B.G., Myers, J., and Abel, C., 2020, Spatial and vertical bias in down-looking ship-based acoustic estimates of fish density in Lake Superior: Lessons learned from multi-directional acoustics: Journal of Great Lakes Research, v. 46, no. 6, p. 1639-1649, https://doi.org/10.1016/j.jglr.2020.08.010.","productDescription":"11 p.","startPage":"1639","endPage":"1649","ipdsId":"IP-114549","costCenters":[{"id":324,"text":"Great Lakes Science Center","active":true,"usgs":true}],"links":[{"id":378452,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"Canada, United States","otherGeospatial":"Lake Superior","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -92.0654296875,\n 46.758679967095574\n ],\n [\n -91.95556640625,\n 46.702202151643455\n ],\n [\n -91.62597656249999,\n 46.773730730079386\n ],\n [\n -91.1590576171875,\n 46.9052455464292\n ],\n [\n -90.85693359375,\n 46.97275640318636\n ],\n [\n -90.7745361328125,\n 46.95401192579361\n ],\n [\n -90.703125,\n 46.852678248531106\n ],\n [\n -90.8184814453125,\n 46.73609593770121\n ],\n [\n -90.87890625,\n 46.61548796222358\n ],\n [\n -90.692138671875,\n 46.694667307773116\n ],\n [\n -90.46142578125,\n 46.60039303734547\n ],\n [\n -90.164794921875,\n 46.67582559793001\n ],\n [\n -89.8077392578125,\n 46.84140712700584\n ],\n [\n -89.4342041015625,\n 46.87145819560722\n ],\n [\n -89.14306640625,\n 47.017716353979225\n ],\n [\n -88.9892578125,\n 47.025206001585396\n ],\n [\n -88.78051757812499,\n 47.19344533938292\n ],\n [\n -88.5498046875,\n 47.30903424774781\n ],\n [\n -88.4014892578125,\n 47.409502941311075\n ],\n [\n -88.165283203125,\n 47.491224888201955\n ],\n [\n -87.791748046875,\n 47.51349065484327\n ],\n [\n -87.681884765625,\n 47.4355191531953\n ],\n [\n -87.703857421875,\n 47.364873807434094\n ],\n [\n -87.9180908203125,\n 47.36115300722623\n ],\n [\n -87.91259765625,\n 47.327653995607115\n ],\n [\n -88.1927490234375,\n 47.15984001304432\n ],\n [\n -88.4124755859375,\n 46.93526088057719\n ],\n [\n -88.4564208984375,\n 46.837649560937464\n ],\n [\n -88.17626953125,\n 47.00647991252098\n ],\n [\n -87.659912109375,\n 46.89398546092549\n ],\n [\n -87.34130859375,\n 46.581518465658014\n ],\n [\n -87.2314453125,\n 46.51351558059737\n ],\n [\n -86.923828125,\n 46.55130547880643\n ],\n [\n -86.824951171875,\n 46.43407119942979\n ],\n [\n -86.693115234375,\n 46.57019056757178\n ],\n [\n -86.5997314453125,\n 46.57774276255591\n ],\n [\n -86.55029296875,\n 46.5172957536981\n ],\n [\n -86.1163330078125,\n 46.73609593770121\n ],\n [\n -85.5230712890625,\n 46.721034661295676\n ],\n [\n -84.9847412109375,\n 46.807579571992385\n ],\n [\n -84.9188232421875,\n 46.781254534638606\n ],\n [\n -84.990234375,\n 46.69843486113957\n ],\n [\n -85.001220703125,\n 46.521075663842865\n ],\n [\n -84.8089599609375,\n 46.475699386607516\n ],\n [\n -84.693603515625,\n 46.51351558059737\n ],\n [\n -84.6112060546875,\n 46.51351558059737\n ],\n [\n -84.6112060546875,\n 46.57019056757178\n ],\n [\n -84.5123291015625,\n 46.66828707388311\n ],\n [\n -84.6441650390625,\n 46.68336307047754\n ],\n [\n -84.605712890625,\n 46.837649560937464\n ],\n [\n -84.7210693359375,\n 46.93526088057719\n ],\n [\n -84.8638916015625,\n 46.99524110694593\n ],\n [\n -84.66064453125,\n 47.30903424774781\n ],\n [\n -85.089111328125,\n 47.58023129789275\n ],\n [\n -84.91333007812499,\n 47.931066347509784\n ],\n [\n -85.3857421875,\n 47.90529605906089\n ],\n [\n -85.97351074218749,\n 47.98624517426206\n ],\n [\n -86.46240234375,\n 48.68733411186308\n ],\n [\n -87.022705078125,\n 48.7453232421382\n ],\n [\n -87.703857421875,\n 48.70908786918211\n ],\n [\n -88.17626953125,\n 48.58932584966975\n ],\n [\n -88.890380859375,\n 48.27953734226008\n ],\n [\n -89.285888671875,\n 48.122101028190805\n ],\n [\n -89.5660400390625,\n 47.94946583788702\n ],\n [\n -90.2197265625,\n 47.724544549099676\n ],\n [\n -90.6317138671875,\n 47.635783590864854\n ],\n [\n -91.109619140625,\n 47.33510005753559\n ],\n [\n -91.5765380859375,\n 47.025206001585396\n ],\n [\n -92.0654296875,\n 46.758679967095574\n ]\n ]\n ]\n }\n }\n ]\n}","volume":"46","issue":"6","noUsgsAuthors":false,"publicationStatus":"PW","contributors":{"authors":[{"text":"Grow, Ryan C","contributorId":240742,"corporation":false,"usgs":false,"family":"Grow","given":"Ryan","email":"","middleInitial":"C","affiliations":[{"id":6915,"text":"University of Minnesota - Duluth","active":true,"usgs":false}],"preferred":false,"id":798909,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Hrabik, Thomas R.","contributorId":35614,"corporation":false,"usgs":false,"family":"Hrabik","given":"Thomas","email":"","middleInitial":"R.","affiliations":[{"id":6915,"text":"University of Minnesota - Duluth","active":true,"usgs":false}],"preferred":false,"id":798910,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Yule, Daniel 0000-0002-0117-5115 dyule@usgs.gov","orcid":"https://orcid.org/0000-0002-0117-5115","contributorId":139532,"corporation":false,"usgs":true,"family":"Yule","given":"Daniel","email":"dyule@usgs.gov","affiliations":[{"id":324,"text":"Great Lakes Science Center","active":true,"usgs":true}],"preferred":true,"id":798911,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Matthias, Bryan G.","contributorId":240763,"corporation":false,"usgs":false,"family":"Matthias","given":"Bryan","email":"","middleInitial":"G.","affiliations":[],"preferred":false,"id":798912,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Myers, Jared T. 0009-0004-9362-8792","orcid":"https://orcid.org/0009-0004-9362-8792","contributorId":44055,"corporation":false,"usgs":false,"family":"Myers","given":"Jared T.","affiliations":[{"id":6596,"text":"Quantitative Fisheries Center, Department of Fisheries and Wildlife Michigan State University","active":true,"usgs":false}],"preferred":false,"id":798913,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"Abel, Chad","contributorId":240745,"corporation":false,"usgs":false,"family":"Abel","given":"Chad","email":"","affiliations":[{"id":48137,"text":"Red Cliff Band of Lake Superior Chippewa","active":true,"usgs":false}],"preferred":false,"id":798914,"contributorType":{"id":1,"text":"Authors"},"rank":6}]}} ,{"id":70213048,"text":"70213048 - 2020 - Integrated borehole, radar, and seismic velocity analysis reveals dynamic spatial variations within a firn aquifer in southeast Greenland","interactions":[],"lastModifiedDate":"2021-01-22T18:25:16.363054","indexId":"70213048","displayToPublicDate":"2020-09-09T12:14:28","publicationYear":"2020","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":1807,"text":"Geophysical Research Letters","active":true,"publicationSubtype":{"id":10}},"title":"Integrated borehole, radar, and seismic velocity analysis reveals dynamic spatial variations within a firn aquifer in southeast Greenland","docAbstract":"Perennial water storage in firn aquifers has been observed within the lower percolation zone of the southeast Greenland ice sheet. Spatially distributed seismic and radar observations, made ~50 km upstream of the Helheim Glacier terminus, reveal spatial variations of seismic velocity within a firn aquifer. From 1.65 to 1.8 km elevation, shear‐wave velocity (Vs) is 1,290 ± 180 m/s in the unsaturated firn, decreasing below the water table (~15 m depth) to 1,130 ± 250 m/s. Below 1.65 km elevation, Vs in the saturated firn is 1,270 ± 220 m/s. The compressional‐to‐shear velocity ratio decreases in the downstream saturated zone, from 2.30 ± 0.54 to 2.01 ± 0.46, closer to its value for pure ice (2.00). Consistent with colocated firn cores, these results imply an increasing concentration of ice in the downstream sites, reducing the porosity and storage potential of the firn likely caused by episodic melt and freeze during the evolution of the aquifer.
","language":"English","publisher":"American Geophysical Union","doi":"10.1029/2020GL089335","usgsCitation":"Killingbeck, S., Schmerr, N.C., Montgomery, L.N., Booth, A.D., Livermore, P.W., Guandique, J., Miller, O.L., Burdick, S., Forster, R.R., Koenig, L.S., Legchenko, A., Ligtenberg, S., Miege, C., Solomon, D.K., and West, L.J., 2020, Integrated borehole, radar, and seismic velocity analysis reveals dynamic spatial variations within a firn aquifer in southeast Greenland: Geophysical Research Letters, v. 47, no. 18, e2020GL089335, 10 p., https://doi.org/10.1029/2020GL089335.","productDescription":"e2020GL089335, 10 p.","ipdsId":"IP-119458","costCenters":[{"id":610,"text":"Utah Water Science Center","active":true,"usgs":true}],"links":[{"id":455364,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.1029/2020gl089335","text":"Publisher Index Page"},{"id":382507,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"Greenland","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -44.351806640625,\n 65.79827293622165\n ],\n [\n -37.562255859375,\n 65.79827293622165\n ],\n [\n -37.562255859375,\n 67.22105296735408\n ],\n [\n -44.351806640625,\n 67.22105296735408\n ],\n [\n -44.351806640625,\n 65.79827293622165\n ]\n ]\n ]\n }\n }\n ]\n}","volume":"47","issue":"18","noUsgsAuthors":false,"publicationDate":"2020-09-15","publicationStatus":"PW","contributors":{"authors":[{"text":"Killingbeck, Siobhan","contributorId":239900,"corporation":false,"usgs":false,"family":"Killingbeck","given":"Siobhan","email":"","affiliations":[{"id":13344,"text":"University of Leeds","active":true,"usgs":false}],"preferred":false,"id":798074,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Schmerr, N. C.","contributorId":248294,"corporation":false,"usgs":false,"family":"Schmerr","given":"N.","email":"","middleInitial":"C.","affiliations":[],"preferred":false,"id":808824,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Montgomery, L. N.","contributorId":248295,"corporation":false,"usgs":false,"family":"Montgomery","given":"L.","email":"","middleInitial":"N.","affiliations":[],"preferred":false,"id":808825,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Booth, A. D.","contributorId":248296,"corporation":false,"usgs":false,"family":"Booth","given":"A.","email":"","middleInitial":"D.","affiliations":[],"preferred":false,"id":808826,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"Livermore, P. W.","contributorId":248297,"corporation":false,"usgs":false,"family":"Livermore","given":"P.","email":"","middleInitial":"W.","affiliations":[],"preferred":false,"id":808827,"contributorType":{"id":1,"text":"Authors"},"rank":6},{"text":"Miller, Olivia L. 0000-0002-8846-7048","orcid":"https://orcid.org/0000-0002-8846-7048","contributorId":219231,"corporation":false,"usgs":true,"family":"Miller","given":"Olivia","email":"","middleInitial":"L.","affiliations":[{"id":610,"text":"Utah Water Science Center","active":true,"usgs":true}],"preferred":true,"id":798075,"contributorType":{"id":1,"text":"Authors"},"rank":7},{"text":"Guandique, J.","contributorId":248298,"corporation":false,"usgs":false,"family":"Guandique","given":"J.","email":"","affiliations":[],"preferred":false,"id":808828,"contributorType":{"id":1,"text":"Authors"},"rank":7},{"text":"Burdick, S.","contributorId":248299,"corporation":false,"usgs":false,"family":"Burdick","given":"S.","affiliations":[],"preferred":false,"id":808829,"contributorType":{"id":1,"text":"Authors"},"rank":8},{"text":"Forster, R. R.","contributorId":248300,"corporation":false,"usgs":false,"family":"Forster","given":"R.","email":"","middleInitial":"R.","affiliations":[],"preferred":false,"id":808830,"contributorType":{"id":1,"text":"Authors"},"rank":9},{"text":"Koenig, L. S.","contributorId":248301,"corporation":false,"usgs":false,"family":"Koenig","given":"L.","email":"","middleInitial":"S.","affiliations":[],"preferred":false,"id":808831,"contributorType":{"id":1,"text":"Authors"},"rank":10},{"text":"Legchenko, Anatoly","contributorId":61107,"corporation":false,"usgs":true,"family":"Legchenko","given":"Anatoly","email":"","affiliations":[],"preferred":false,"id":808832,"contributorType":{"id":1,"text":"Authors"},"rank":11},{"text":"Ligtenberg, S. R. M.","contributorId":248302,"corporation":false,"usgs":false,"family":"Ligtenberg","given":"S. R. M.","affiliations":[],"preferred":false,"id":808833,"contributorType":{"id":1,"text":"Authors"},"rank":12},{"text":"Miege, C.","contributorId":248303,"corporation":false,"usgs":false,"family":"Miege","given":"C.","email":"","affiliations":[],"preferred":false,"id":808834,"contributorType":{"id":1,"text":"Authors"},"rank":13},{"text":"Solomon, D. K.","contributorId":98324,"corporation":false,"usgs":false,"family":"Solomon","given":"D.","email":"","middleInitial":"K.","affiliations":[],"preferred":false,"id":808835,"contributorType":{"id":1,"text":"Authors"},"rank":14},{"text":"West, L. J.","contributorId":248304,"corporation":false,"usgs":false,"family":"West","given":"L.","email":"","middleInitial":"J.","affiliations":[],"preferred":false,"id":808836,"contributorType":{"id":1,"text":"Authors"},"rank":15}]}} ,{"id":70213132,"text":"70213132 - 2020 - Influenza A viruses remain infectious for more than seven months in northern wetlands of North America","interactions":[],"lastModifiedDate":"2020-09-10T14:26:32.153618","indexId":"70213132","displayToPublicDate":"2020-09-09T09:21:24","publicationYear":"2020","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":3174,"text":"Proceedings of the Royal Society B: Biological Sciences","active":true,"publicationSubtype":{"id":10}},"title":"Influenza A viruses remain infectious for more than seven months in northern wetlands of North America","docAbstract":"In this investigation, we used a combination of field- and laboratory-based approaches to assess if influenza A viruses (IAVs) shed by ducks could remain viable for extended periods in surface water within three wetland complexes of North America. In a field experiment, replicate filtered surface water samples inoculated with duck swabs were tested for IAVs upon collection and again after an overwintering period of approximately 6–7 months. Numerous IAVs were molecularly detected and isolated from these samples, including replicates maintained at wetland field sites in Alaska and Minnesota for 181–229 days. In a parallel laboratory experiment, we attempted to culture IAVs from filtered surface water samples inoculated with duck swabs from Minnesota each month during September 2018–April 2019 and found monthly declines in viral viability. In an experimental challenge study, we found that IAVs maintained in filtered surface water within wetlands of Alaska and Minnesota for 214 and 226 days, respectively, were infectious in a mallard model. Collectively, our results support surface waters of northern wetlands as a biologically important medium in which IAVs may be both transmitted and maintained, potentially serving as an environmental reservoir for infectious IAVs during the overwintering period of migratory birds.
We present results from 30 quantitative degassing experiments of pressure core sections collected during The University of Texas-Gulf of Mexico 2-1 (UT-GOM2-1) Hydrate Pressure Coring Expedition at Green Canyon Block 955 in the deep-water Gulf of Mexico as part of The University of Texas at Austin–US Department of Energy Deepwater Methane Hydrate Characterization and Scientific Assessment. The hydrate saturation (Sh), the volume fraction of the pore space occupied by hydrate, is 79% to 93% within sandy silt beds (centimeters to meters in thickness) between 413 and 442 m below seafloor in 2032 m water depth. Sandy silt intervals are characterized by high compressional wave velocity (Vp) (2515–3012 m s−1) and are interbedded with clayey silt sections that have lower Sh (2%–35%) and lower Vp (1684–2023 m s−1). Clayey silt intervals are composed of thin laminae of silts with high Sh within clay-rich intervals containing little to no hydrate. Degassing of single-lithofacies sections reveals higher-resolution variation in Sh than is possible to observe in well logs; however, the average Sh of 64% through the reservoir is similar to well log estimates. Gas recovered from the hydrates during these experiments is composed almost entirely of methane (99.99% CH4, <100 ppm C2H6 on average), with an isotopic composition (δ13C: −60.4‰ and −63.6‰ Vienna Peedee belemnite and δ2H: −178.2‰ and −179.0‰ Vienna standard mean ocean water) that suggests the methane is primarily from a microbial source. A subset of six degassing experiments performed using very small pressure decrements indicates that the salinity within these samples is close to the average seawater concentration, suggesting that hydrate either formed slowly or formed during a rapid event at least tens of thousands of years before present.
","language":"English","publisher":"American Association of Petroleum Geologists (AAPG) Bulletin","doi":"10.1306/01062018280","usgsCitation":"Philips, S., Flemings, P., Holland, M., Schultheiss, P., Waite, W., Jang, J., Petrou, E., and Hammon, H., 2020, High concentration methane hydrate in a silt reservoir from the deep-water Gulf of Mexico: AAPG Bulletin, v. 104, no. 9, p. 1971-1995, https://doi.org/10.1306/01062018280.","productDescription":"25 p.","startPage":"1971","endPage":"1995","ipdsId":"IP-104475","costCenters":[{"id":678,"text":"Woods Hole Coastal and Marine Science Center","active":true,"usgs":true}],"links":[{"id":378271,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","state":"Texas, Louisiana","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -93.1201171875,\n 30.183121842195515\n ],\n [\n -95.361328125,\n 29.84064389983441\n ],\n [\n -95.185546875,\n 29.267232865200878\n ],\n [\n -91.5380859375,\n 29.305561325527698\n ],\n [\n -93.1201171875,\n 30.183121842195515\n ]\n ]\n ]\n }\n }\n ]\n}","volume":"104","issue":"9","noUsgsAuthors":false,"publicationStatus":"PW","contributors":{"authors":[{"text":"Philips, Stephen","contributorId":239916,"corporation":false,"usgs":false,"family":"Philips","given":"Stephen","email":"","affiliations":[{"id":48044,"text":"Institute for Geophysics, Jackson School of Geosciences, University of Texas at Austin","active":true,"usgs":false}],"preferred":false,"id":798128,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Flemings, Peter","contributorId":198205,"corporation":false,"usgs":false,"family":"Flemings","given":"Peter","affiliations":[{"id":13127,"text":"Jackson School of Geosciences, University of Texas, Austin","active":true,"usgs":false}],"preferred":false,"id":798129,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Holland, Melanie","contributorId":239904,"corporation":false,"usgs":false,"family":"Holland","given":"Melanie","email":"","affiliations":[{"id":48040,"text":"Geotek Ltd","active":true,"usgs":false}],"preferred":false,"id":798130,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Schultheiss, Peter","contributorId":239913,"corporation":false,"usgs":false,"family":"Schultheiss","given":"Peter","email":"","affiliations":[{"id":48040,"text":"Geotek Ltd","active":true,"usgs":false}],"preferred":false,"id":798131,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Waite, William F. 0000-0002-9436-4109 wwaite@usgs.gov","orcid":"https://orcid.org/0000-0002-9436-4109","contributorId":625,"corporation":false,"usgs":true,"family":"Waite","given":"William F.","email":"wwaite@usgs.gov","affiliations":[{"id":678,"text":"Woods Hole Coastal and Marine Science Center","active":true,"usgs":true},{"id":186,"text":"Coastal and Marine Geology Program","active":true,"usgs":true}],"preferred":true,"id":798132,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"Jang, Junbong 0000-0001-5500-7558 jjang@usgs.gov","orcid":"https://orcid.org/0000-0001-5500-7558","contributorId":189400,"corporation":false,"usgs":true,"family":"Jang","given":"Junbong","email":"jjang@usgs.gov","affiliations":[{"id":678,"text":"Woods Hole Coastal and Marine Science Center","active":true,"usgs":true}],"preferred":true,"id":798133,"contributorType":{"id":1,"text":"Authors"},"rank":6},{"text":"Petrou, Ethan","contributorId":239909,"corporation":false,"usgs":false,"family":"Petrou","given":"Ethan","email":"","affiliations":[{"id":48038,"text":"Institute for Geophysics and Department of Geological Sciences, Jackson School of Geosciences, University of Texas","active":true,"usgs":false}],"preferred":false,"id":798134,"contributorType":{"id":1,"text":"Authors"},"rank":7},{"text":"Hammon, Helen","contributorId":239917,"corporation":false,"usgs":false,"family":"Hammon","given":"Helen","email":"","affiliations":[{"id":48044,"text":"Institute for Geophysics, Jackson School of Geosciences, University of Texas at Austin","active":true,"usgs":false}],"preferred":false,"id":798135,"contributorType":{"id":1,"text":"Authors"},"rank":8}]}} ,{"id":70213072,"text":"70213072 - 2020 - Pressure coring a Gulf of Mexico deep-water turbidite gas hydrate reservoir: Initial results from The University of Texas–Gulf of Mexico 2-1 (UT-GOM2-1) Hydrate Pressure Coring Expedition","interactions":[],"lastModifiedDate":"2020-09-09T12:59:51.010478","indexId":"70213072","displayToPublicDate":"2020-09-09T07:49:08","publicationYear":"2020","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":605,"text":"AAPG Bulletin","printIssn":"0149-1423","active":true,"publicationSubtype":{"id":10}},"title":"Pressure coring a Gulf of Mexico deep-water turbidite gas hydrate reservoir: Initial results from The University of Texas–Gulf of Mexico 2-1 (UT-GOM2-1) Hydrate Pressure Coring Expedition","docAbstract":"The University of Texas Hydrate Pressure Coring Expedition (UT-GOM2-1) recovered cores at near in situ formation pressures from a gas hydrate reservoir composed of sandy silt and clayey silt beds in Green Canyon Block 955 in the deep-water Gulf of Mexico. The expedition results are synthesized and linked to other detailed analyses presented in this volume. Millimeter- to meter-scale beds of sandy silt and clayey silt are interbedded on the levee of a turbidite channel. The hydrate saturation (the volume fraction of the pore space occupied by hydrate) in the sandy silts ranges from 79% to 93%, and there is little to no hydrate in the clayey silt. Gas from the hydrates is composed of nearly pure methane (99.99%) with less than 400 ppm of ethane or heavier hydrocarbons. The δ13C values from the methane are depleted (−60‰ to −65‰ Vienna Peedee belemnite), and it is interpreted that the gases were largely generated by primary microbial methanogenesis but that low concentrations of propane or heavier hydrocarbons record at least trace thermogenic components. The in situ pore-water salinity is very close to that of seawater. This suggests that the excess salinity generated during hydrate formation diffused away because the hydrate formed slowly or because it formed long ago. Because the sandy silt deposits have high hydrate concentration and high intrinsic permeability, they may represent a class of reservoir that can be economically developed. Results from this expedition will inform a new generation of reservoir simulation models that will illuminate how these reservoirs might be best produced.
","language":"English","publisher":"American Association of Petroleum Geologists (AAPG) Bulletin","doi":"10.1306/05212019052","usgsCitation":"Flemings, P., Phillips, S., Boswell, R., Collett, T., Cook, A., Dong, T., Frye, M., Goldberg, D., Guerin, G., Holland, M., Jang, J., Meazell, K., Morrison, J., O’Connell, J., Petrou, E., Pettigrew, T., Polito, P., Portnov, A., Santra, M., Schultheiss, P., Seol, Y., Shedd, W., Solomon, E.S., Thomas, C., Waite, W., and You, K., 2020, Pressure coring a Gulf of Mexico deep-water turbidite gas hydrate reservoir: Initial results from The University of Texas–Gulf of Mexico 2-1 (UT-GOM2-1) Hydrate Pressure Coring Expedition: AAPG Bulletin, v. 104, no. 9, p. 1847-1876, https://doi.org/10.1306/05212019052.","productDescription":"30 p.","startPage":"1847","endPage":"1876","ipdsId":"IP-105681","costCenters":[{"id":678,"text":"Woods Hole Coastal and Marine Science 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Carla","contributorId":239914,"corporation":false,"usgs":false,"family":"Thomas","given":"Carla","email":"","affiliations":[{"id":48038,"text":"Institute for Geophysics and Department of Geological Sciences, Jackson School of Geosciences, University of Texas","active":true,"usgs":false}],"preferred":false,"id":798125,"contributorType":{"id":1,"text":"Authors"},"rank":24},{"text":"Waite, William F. 0000-0002-9436-4109 wwaite@usgs.gov","orcid":"https://orcid.org/0000-0002-9436-4109","contributorId":625,"corporation":false,"usgs":true,"family":"Waite","given":"William F.","email":"wwaite@usgs.gov","affiliations":[{"id":678,"text":"Woods Hole Coastal and Marine Science Center","active":true,"usgs":true},{"id":186,"text":"Coastal and Marine Geology Program","active":true,"usgs":true}],"preferred":true,"id":798126,"contributorType":{"id":1,"text":"Authors"},"rank":25},{"text":"You, Kehua","contributorId":239915,"corporation":false,"usgs":false,"family":"You","given":"Kehua","email":"","affiliations":[{"id":48038,"text":"Institute for Geophysics and Department of Geological Sciences, Jackson School of Geosciences, University of Texas","active":true,"usgs":false}],"preferred":false,"id":798127,"contributorType":{"id":1,"text":"Authors"},"rank":26}]}} ,{"id":70216070,"text":"70216070 - 2020 - Rethinking a groundwater flow system using a multiple-tracer geochemical approach: A case study in Moab-Spanish Valley, Utah","interactions":[],"lastModifiedDate":"2020-11-04T13:23:52.632484","indexId":"70216070","displayToPublicDate":"2020-09-09T07:18:47","publicationYear":"2020","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":2342,"text":"Journal of Hydrology","active":true,"publicationSubtype":{"id":10}},"title":"Rethinking a groundwater flow system using a multiple-tracer geochemical approach: A case study in Moab-Spanish Valley, Utah","docAbstract":"The Glen Canyon Group Aquifer (GCGA) is the sole source of public water supply for the city of Moab, Utah, a domestic and international tourist destination. Population and tourism growth are likely to target the GCGA for future water resources, but our analysis indicates that additional withdrawals would likely be sourced from groundwater storage and not be sustained by recharge. A quantitative estimate of groundwater discharge from the GCGA is problematic because the downgradient aquifer boundary is the Colorado River, and groundwater discharge to the river is very small compared to the river flow. A water budget based on a conceptual model of GCGA discharging into an adjacent alluvial Valley-Fill Aquifer (VFA) was reported by Sumsion (1971) and numerous subsequent studies have repeated and utilized this water budget. The GCGA contains stable isotopes, tritium, 3He/4He ratios, dissolved solids, and sulfate concentrations that contrast with the VFA, indicating it is instead recharged by local streams rather than from the GCGA. Water-budget calculations, based on: (1) measured spring discharge and streamflow gains, (2) horizontal gradients in VFA groundwater age, and (3) GCGA outcrop area vadose-zone pore waters are all less than previously thought. Using a lumped parameter model and 14C groundwater ages, we estimate recharge to the deeper GCGA (DGCGA) to be 4.2 ± 2.3 × 106 m3/yr, which is approximately equal to the measured discharge from wells and springs.
The U.S. Geological Survey, in cooperation with the Village of East Hampton, New York, conducted a 1-year study from August 2017 to August 2018 to provide data necessary to improve understanding of the sources of nutrients and pathogens to Hook Pond watershed to allow for possible mitigation or reduction of loads. Chronic eutrophication and recent concern over harmful cyanobacteria in Hook Pond are the result of past and present land uses and a changing climate that have prompted the Village of East Hampton and local businesses to study and remediate factors contributing to the persistent loading of nutrients, organic contaminants, and pathogens. This assessment of Hook Pond, Hook Pond Dreen, and shallow groundwater provides the most comprehensive set of water-quality data to date. Interpretations presented in this study and the data on which they are based can be used to support management decisions, inform and contribute to modeling, and serve as a baseline for future assessments.
Results from continuous monitoring of water temperature, specific conductance, and elevation at Hook Pond site 10 (Maidstone Club golf cart bridge), as well as ancillary weather and tidal data from nearby stations, were used to help explain seasonal and storm-related concentration variation of nitrogen, phosphorus, wastewater-indicator compounds, and pathogens. Data collected were also compared to existing historical data. Physicochemical constituents measured on a routine basis throughout the pond and along the tributary showed the spatial variability in water temperature, specific conductance, dissolved oxygen, pH, turbidity, and chlorophyll a and phycocyanin fluorescence. A lakebed survey was compiled based on the year-round sampling throughout the pond for future comparisons. Water-quality data from shallow groundwater at points around Hook Pond and adjacent to Hook Pond Dreen were interpreted and quantified to estimate relative contributions and species of nutrients, wastewater-indicator compounds, and microbial source tracking (MST) markers to base flow. To supplement the continuous water-surface elevation data, a single set of discharge measurements was collected under normal (nonstorm) conditions to better understand the relative contributions and dilution of surface waters by contaminated groundwater.
The nutrient and physicochemical data from this study can be used in conjunction with current and future models and decision support tools to guide planned and ongoing restoration efforts, such as dredging to reduce sediment accumulation, opening a pathway to the ocean (which would change the salinity and flow dynamics of the pond and adjacent groundwater), and addressing growing concerns over cyanobacterial blooms, while serving as a baseline for measuring changes resulting from sea-level rise, climate change, and changes in nutrient loading. The microbial source tracking and indicator bacteria results can help direct efforts to reduce runoff and direct contributions of fecal contamination from dogs and waterfowl along Hook Pond Dreen. The results can also be used to assess the current state of wastewater infrastructure surrounding and contributing to Hook Pond Dreen, based on detection of human markers throughout the year and with both Bacteroides and coliphage methods.
","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/sir20205071","collaboration":"Prepared in cooperation with the Village of East Hampton","usgsCitation":"Fisher, S.C., McCarthy, B.A., Kephart, C.M., and Griffin, D.W., 2020, Assessment of water quality and fecal contamination sources at Hook Pond, East Hampton, New York: U.S. Geological Survey Scientific Investigations Report 2020–5071, 58 p., https://doi.org/10.3133/sir20205071.","productDescription":"Report: viii, 58 p.; Dataset","numberOfPages":"58","onlineOnly":"Y","additionalOnlineFiles":"N","ipdsId":"IP-103528","costCenters":[{"id":474,"text":"New York Water Science Center","active":true,"usgs":true}],"links":[{"id":378179,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/sir/2020/5071/coverthb.jpg"},{"id":378180,"rank":2,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/sir/2020/5071/sir20205071.pdf","text":"Report","size":"3.74 MB","linkFileType":{"id":1,"text":"pdf"},"description":"SIR 2020-5071"},{"id":378181,"rank":3,"type":{"id":28,"text":"Dataset"},"url":"https://doi.org/10.5066/F7P55KJN","linkFileType":{"id":5,"text":"html"},"linkHelpText":"- U.S. Geological Survey National Water Information System database"}],"country":"United States","state":"New York","otherGeospatial":"Hook Pond","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -72.20489501953125,\n 40.94360177170972\n ],\n [\n -72.17124938964844,\n 40.94360177170972\n ],\n [\n -72.17124938964844,\n 40.95656702665609\n ],\n [\n -72.20489501953125,\n 40.95656702665609\n ],\n [\n -72.20489501953125,\n 40.94360177170972\n ]\n ]\n ]\n }\n }\n ]\n}","contact":"Director, New York Water Science Center
U.S. Geological Survey
425 Jordan Road
Troy, NY 12180–8349
Groundwater provides nearly half of the Nation’s drinking water. As the Nation’s population grows, the importance of (and need for) high-quality drinking-water supplies increases. As part of a national-scale effort to assess groundwater quality in principal aquifers (PAs) that supply most of the groundwater used for public supply, the U.S. Geological Survey National Water-Quality Assessment (NAWQA) Project staff sampled six principal aquifers in the western United States between 2013 and 2017: (1) the Basin and Range carbonate-rock aquifers, (2) Basin and Range basin-fill aquifers, (3) Rio Grande aquifer system, (4) High Plains aquifer, (5) Colorado Plateaus aquifers, and (6) Columbia Plateau basaltic-rock aquifers. These six PAs supply a large part of the Nation’s drinking water and cover a large geographic extent of the western conterminous United States. Groundwater samples were analyzed for a large suite of water-quality constituents including major ions, nutrients, trace elements, volatile organic compounds (VOCs), pesticide compounds, radioactive constituents, age tracers, and, in selected PAs, perchlorate. Two types of assessments were made: (1) a status assessment that describes the quality of the groundwater resource at time of collection and (2) an understanding assessment that evaluates relations between groundwater quality and potential explanatory factors that represent characteristics of the aquifer system. The assessments characterize untreated groundwater quality, which might be different than the quality of drinking water delivered to consumers. The assessments are based on water-quality data collected from 352 wells and 6 springs using an equal-area grid sampling design. This sampling approach allows for the estimation of the proportion of high, moderate, or low concentrations relative to federal water-quality benchmarks of selected constituents in the area of each PA. Results were compared to established benchmarks for drinking-water quality to provide context for evaluating the quality of untreated groundwater: Federal regulatory benchmarks for protecting human health, non-regulatory human-health benchmarks, and non-regulatory benchmarks for nuisance chemicals. Not all constituents that were analyzed have benchmarks and thus were not considered for assessments. Concentrations are characterized as high if they are greater than their benchmark. Concentrations are considered moderate if they are greater than one-half their benchmark (for inorganic constituents), or greater than one-tenth their benchmark (for organic constituents). Concentrations are considered low if they are less than moderate or the constituent was not detected.
Status assessment results indicated that inorganic constituents more commonly occurred at high and moderate concentrations in the six PAs than organic constituents, and organic constituents predominately occurred at low concentrations. Inorganic constituents that exceeded health-based benchmarks (high concentrations) were present in all six PAs; aquifer-scale proportion were 30 percent in the Rio Grande aquifer system, 22 percent in the Basin and Range basin-fill aquifers, 20 percent in the Basin and Range carbonate-rock aquifers, 19 percent in the High Plains aquifer, 16 percent in the Colorado Plateaus aquifers, and 8 percent in the Columbia Plateau basaltic-rock aquifers. Arsenic, fluoride, manganese, and total dissolved solids were the constituents most commonly present at high concentrations. Organic constituents with human-health benchmarks (pesticide compounds and VOCs) did not occur at high concentrations and moderate concentrations were infrequent; aquifer-scale proportions ranged from 0 to 5 percent. Detections of organic compounds at low concentrations, however, occurred in all six PAs, with detection frequencies ranging from 10 to 26 percent for pesticide compounds and from 10 to 46 percent for VOCs. Specific organic constituents with detection frequencies greater than 10 percent were four herbicides (atrazine, didealkylatrazine, bromoform, and propazine), one insecticide (propoxur), and two VOCs (the trihalomethanes chloroform and bromodichloromethane). Where collected—in the Rio Grande aquifer system and High Plains aquifer—perchlorate did not occur at high concentrations; moderate aquifer-scale proportions were 3 and 11 percent, respectively.
The understanding assessment included statistical tests to evaluate relations between constituent concentrations and potential explanatory factors to identify natural and human factors that affect groundwater quality. Potential explanatory factors included depth to bottom of well perforation, groundwater age category, land use, aquifer lithology, hydrologic conditions, and geochemical conditions. Higher concentrations of trace elements, radioactive constituents, and constituents with non-health-based benchmarks generally were associated with unconsolidated sand and gravel aquifer lithologies, premodern groundwater age, greater aridity, and more alkaline pH. Organic constituents with detection frequencies greater than 10 percent generally were associated with urban land use, shallower well depths, and higher total dissolved solids concentrations. The results for the six western PAs provide important insights into the quality of groundwater that is used for drinking water in the western United States, as well as natural and human factors that affect groundwater quality in this region.
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U.S. Geological Survey
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Sacramento, California 95819